diff --git a/.github/ISSUE_TEMPLATE.md b/.github/ISSUE_TEMPLATE.md index 22726d5..5072075 100644 --- a/.github/ISSUE_TEMPLATE.md +++ b/.github/ISSUE_TEMPLATE.md @@ -1,15 +1,16 @@ # STOP! +**Please don't waste my time!** + Most of the problems people are having are already described in the [installation instructions](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/README.md). You should first make a serious attempt to solve your problem. If you ask a question that has already been answered elsewhere, or if you do not give enough details about your problem, then your issue may be closed immediately. -Please don't waste my time! -## Python 3.5 +## Python 3 -These tutorials were developed in **Python 3.5** and may give strange errors in Python 2.7 +These tutorials were developed in **Python 3.5** (and higher) and may give strange errors in Python 2.7 ## Missing Files @@ -19,6 +20,12 @@ You need to **download the whole repository**, either using `git clone` or as a General questions about TensorFlow should either be asked on [StackOverflow](http://stackoverflow.com/questions/tagged/tensorflow) or the [official TensorFlow repository](https://github.com/tensorflow/tensorflow/issues). +## Modifications + +Questions about modifications or how to use these tutorials on your own data-set should also be asked on [StackOverflow](http://stackoverflow.com/questions/tagged/tensorflow). + +Thousands of people are using these tutorials. It is impossible for me to give individual support for your project. + ## Suggestions for Changes The tutorials cannot change too much because it would make the [YouTube videos](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ) too different from the source-code. diff --git a/01_Simple_Linear_Model.ipynb b/01_Simple_Linear_Model.ipynb index a5cf611..6df8479 100644 --- a/01_Simple_Linear_Model.ipynb +++ b/01_Simple_Linear_Model.ipynb @@ -22,6 +22,15 @@ "You should be familiar with basic linear algebra, Python and the Jupyter Notebook editor. It also helps if you have a basic understanding of Machine Learning and classification." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## TensorFlow 2\n", + "\n", + "This tutorial was developed using TensorFlow v.1 back in the year 2016. There have been significant API changes in TensorFlow v.2. This tutorial uses TF2 in \"v.1 compatibility mode\", which is still useful for learning how TensorFlow works, but you would have to implement it slightly differently in TF2 (see Tutorial 03C on the Keras API). It would be too big a job for me to keep updating these tutorials every time Google's engineers update the TensorFlow API, so this tutorial may eventually stop working." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -33,43 +42,55 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from sklearn.metrics import confusion_matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/importlib/_bootstrap.py:205: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n", - " return f(*args, **kwds)\n" + "WARNING:tensorflow:From /home/magnus/anaconda3/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/compat/v2_compat.py:88: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "non-resource variables are not supported in the long term\n" ] } ], "source": [ - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "import tensorflow as tf\n", - "import numpy as np\n", - "from sklearn.metrics import confusion_matrix" + "# Use TensorFlow v.2 with this old v.1 code.\n", + "# E.g. placeholder variables and sessions have changed in TF2.\n", + "import tensorflow.compat.v1 as tf\n", + "tf.disable_v2_behavior()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "This was developed using Python 3.6.1 (Anaconda) and TensorFlow version:" + "This was developed using Python 3.6 (Anaconda) and TensorFlow version:" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'1.4.0'" + "'2.1.0'" ] }, - "execution_count": 2, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -94,35 +115,24 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Extracting data/MNIST/train-images-idx3-ubyte.gz\n", - "Extracting data/MNIST/train-labels-idx1-ubyte.gz\n", - "Extracting data/MNIST/t10k-images-idx3-ubyte.gz\n", - "Extracting data/MNIST/t10k-labels-idx1-ubyte.gz\n" - ] - } - ], + "outputs": [], "source": [ - "from tensorflow.examples.tutorials.mnist import input_data\n", - "data = input_data.read_data_sets(\"data/MNIST/\", one_hot=True)" + "from mnist import MNIST\n", + "data = MNIST(data_dir=\"data/MNIST/\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The MNIST data-set has now been loaded and consists of 70.000 images and associated labels (i.e. classifications of the images). The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial." + "The MNIST data-set has now been loaded and consists of 70.000 images and class-numbers for the images. The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -131,77 +141,53 @@ "text": [ "Size of:\n", "- Training-set:\t\t55000\n", - "- Test-set:\t\t10000\n", - "- Validation-set:\t5000\n" + "- Validation-set:\t5000\n", + "- Test-set:\t\t10000\n" ] } ], "source": [ "print(\"Size of:\")\n", - "print(\"- Training-set:\\t\\t{}\".format(len(data.train.labels)))\n", - "print(\"- Test-set:\\t\\t{}\".format(len(data.test.labels)))\n", - "print(\"- Validation-set:\\t{}\".format(len(data.validation.labels)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### One-Hot Encoding" + "print(\"- Training-set:\\t\\t{}\".format(data.num_train))\n", + "print(\"- Validation-set:\\t{}\".format(data.num_val))\n", + "print(\"- Test-set:\\t\\t{}\".format(data.num_test))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The data-set has been loaded as so-called One-Hot encoding. This means the labels have been converted from a single number to a vector whose length equals the number of possible classes. All elements of the vector are zero except for the $i$'th element which is one and means the class is $i$. For example, the One-Hot encoded labels for the first 5 images in the test-set are:" + "Copy some of the data-dimensions for convenience." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.],\n", - " [ 0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],\n", - " [ 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n", - " [ 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", - " [ 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.]])" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "data.test.labels[0:5, :]" + "# The images are stored in one-dimensional arrays of this length.\n", + "img_size_flat = data.img_size_flat\n", + "\n", + "# Tuple with height and width of images used to reshape arrays.\n", + "img_shape = data.img_shape\n", + "\n", + "# Number of classes, one class for each of 10 digits.\n", + "num_classes = data.num_classes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We also need the classes as single numbers for various comparisons and performance measures, so we convert the One-Hot encoded vectors to a single number by taking the index of the highest element. Note that the word 'class' is a keyword used in Python so we need to use the name 'cls' instead." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "data.test.cls = np.array([label.argmax() for label in data.test.labels])" + "### One-Hot Encoding" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We can now see the class for the first five images in the test-set. Compare these to the One-Hot encoded vectors above. For example, the class for the first image is 7, which corresponds to a One-Hot encoded vector where all elements are zero except for the element with index 7." + "The output-data is loaded as both integer class-numbers and so-called One-Hot encoded arrays. This means the class-numbers have been converted from a single integer to a vector whose length equals the number of possible classes. All elements of the vector are zero except for the $i$'th element which is 1 and means the class is $i$. For example, the One-Hot encoded labels for the first 5 images in the test-set are:" ] }, { @@ -212,7 +198,11 @@ { "data": { "text/plain": [ - "array([7, 2, 1, 0, 4])" + "array([[0., 0., 0., 0., 0., 0., 0., 1., 0., 0.],\n", + " [0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],\n", + " [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n", + " [1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", + " [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.]])" ] }, "execution_count": 7, @@ -221,40 +211,34 @@ } ], "source": [ - "data.test.cls[0:5]" + "data.y_test[0:5, :]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Data dimensions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The data dimensions are used in several places in the source-code below. In computer programming it is generally best to use variables and constants rather than having to hard-code specific numbers every time that number is used. This means the numbers only have to be changed in one single place. Ideally these would be inferred from the data that has been read, but here we just write the numbers." + "We also need the classes as integers for various comparisons and performance measures. These can be found from the One-Hot encoded arrays by taking the index of the highest element using the `np.argmax()` function. But this has already been done for us when the data-set was loaded, so we can see the class-number for the first five images in the test-set. Compare these to the One-Hot encoded arrays above." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([7, 2, 1, 0, 4])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# We know that MNIST images are 28 pixels in each dimension.\n", - "img_size = 28\n", - "\n", - "# Images are stored in one-dimensional arrays of this length.\n", - "img_size_flat = img_size * img_size\n", - "\n", - "# Tuple with height and width of images used to reshape arrays.\n", - "img_shape = (img_size, img_size)\n", - "\n", - "# Number of classes, one class for each of 10 digits.\n", - "num_classes = 10" + "data.y_test_cls[0:5]" ] }, { @@ -319,9 +303,9 @@ "outputs": [ { "data": { - "image/png": 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\n", 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" ] }, "metadata": {}, @@ -330,10 +314,10 @@ ], "source": [ "# Get the first images from the test-set.\n", - "images = data.test.images[0:9]\n", + "images = data.x_test[0:9]\n", "\n", "# Get the true classes for those images.\n", - "cls_true = data.test.cls[0:9]\n", + "cls_true = data.y_test_cls[0:9]\n", "\n", "# Plot the images and labels using our helper-function above.\n", "plot_images(images=images, cls_true=cls_true)" @@ -349,11 +333,11 @@ "\n", "TensorFlow can also automatically calculate the gradients that are needed to optimize the variables of the graph so as to make the model perform better. This is because the graph is a combination of simple mathematical expressions so the gradient of the entire graph can be calculated using the chain-rule for derivatives.\n", "\n", - "TensorFlow can also take advantage of multi-core CPUs as well as GPUs - and Google has even built special chips just for TensorFlow which are called TPUs (Tensor Processing Units) and are even faster than GPUs.\n", + "TensorFlow can also take advantage of multi-core CPUs as well as GPUs - and Google has even built special chips just for TensorFlow which are called TPUs (Tensor Processing Units) that are even faster than GPUs.\n", "\n", "A TensorFlow graph consists of the following parts which will be detailed below:\n", "\n", - "* Placeholder variables used to change the input to the graph.\n", + "* Placeholder variables used to feed input into the graph.\n", "* Model variables that are going to be optimized so as to make the model perform better.\n", "* The model which is essentially just a mathematical function that calculates some output given the input in the placeholder variables and the model variables.\n", "* A cost measure that can be used to guide the optimization of the variables.\n", @@ -545,8 +529,8 @@ "metadata": {}, "outputs": [], "source": [ - "cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=logits,\n", - " labels=y_true)" + "cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits,\n", + " labels=y_true)" ] }, { @@ -685,7 +669,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "There are 50.000 images in the training-set. It takes a long time to calculate the gradient of the model using all these images. We therefore use Stochastic Gradient Descent which only uses a small batch of images in each iteration of the optimizer." + "There are 55.000 images in the training-set. It takes a long time to calculate the gradient of the model using all these images. We therefore use Stochastic Gradient Descent which only uses a small batch of images in each iteration of the optimizer." ] }, { @@ -715,7 +699,7 @@ " # Get a batch of training examples.\n", " # x_batch now holds a batch of images and\n", " # y_true_batch are the true labels for those images.\n", - " x_batch, y_true_batch = data.train.next_batch(batch_size)\n", + " x_batch, y_true_batch, _ = data.random_batch(batch_size=batch_size)\n", " \n", " # Put the batch into a dict with the proper names\n", " # for placeholder variables in the TensorFlow graph.\n", @@ -750,9 +734,9 @@ "metadata": {}, "outputs": [], "source": [ - "feed_dict_test = {x: data.test.images,\n", - " y_true: data.test.labels,\n", - " y_true_cls: data.test.cls}" + "feed_dict_test = {x: data.x_test,\n", + " y_true: data.y_test,\n", + " y_true_cls: data.y_test_cls}" ] }, { @@ -791,7 +775,7 @@ "source": [ "def print_confusion_matrix():\n", " # Get the true classifications for the test-set.\n", - " cls_true = data.test.cls\n", + " cls_true = data.y_test_cls\n", " \n", " # Get the predicted classifications for the test-set.\n", " cls_pred = session.run(y_pred_cls, feed_dict=feed_dict_test)\n", @@ -845,13 +829,13 @@ " \n", " # Get the images from the test-set that have been\n", " # incorrectly classified.\n", - " images = data.test.images[incorrect]\n", + " images = data.x_test[incorrect]\n", " \n", " # Get the predicted classes for those images.\n", " cls_pred = cls_pred[incorrect]\n", "\n", " # Get the true classes for those images.\n", - " cls_true = data.test.cls[incorrect]\n", + " cls_true = data.y_test_cls[incorrect]\n", " \n", " # Plot the first 9 images.\n", " plot_images(images=images[0:9],\n", @@ -949,9 +933,9 @@ "outputs": [ { "data": { - "image/png": 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0u7Vqxaodn61WpnyGGX4L7IQTTgDg3nvvBaBp06aRYk+XMk0RkQhUaYqIRGBR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\nQmCC\n", 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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -968,7 +952,7 @@ "source": [ "## Performance after 1 optimization iteration\n", "\n", - "Already after a single optimization iteration, the model has increased its accuracy on the test-set to 40.7% up from 9.8%. This means that it mis-classifies the images about 6 out of 10 times, as demonstrated on a few examples below." + "Already after a single optimization iteration, the model has increased its accuracy on the test-set significantly." ] }, { @@ -989,7 +973,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Accuracy on test-set: 21.4%\n" + "Accuracy on test-set: 22.2%\n" ] } ], @@ -1004,9 +988,9 @@ "outputs": [ { "data": { - "image/png": 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v3x+Ap59OzErRqlUrAFauXAnAl19+WWffF154IeN12GfttddOvnfzzTcDcPjh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\nb8i0bzekTQAAAABJRU5ErkJggg==\n", 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\n", 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" ] }, "metadata": {}, @@ -1037,9 +1021,9 @@ "outputs": [ { "data": { - "image/png": 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IrtiYPloq39Xqqkxq+fvvf9vvOSw4s/4wixZRFqsz+MwztaypSfek34w+4KE+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\nLdkIYGsiDlljuBqQ+noZBcjzUREvC/L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\n", 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" ] }, "metadata": {}, @@ -1076,7 +1060,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Accuracy on test-set: 79.3%\n" + "Accuracy on test-set: 77.6%\n" ] } ], @@ -1091,9 +1075,9 @@ "outputs": [ { "data": { - "image/png": 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SS5m5Xy+88MLstrvuuguAp59+GoABAwaUP2EiEZVGIiIpVGWk+etf/xqAZZdd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\nmiIiKajQFBFJ4f8B5DaVvKWqiOwAAAAASUVORK5CYII=\n", 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\n", 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XZnWtbe1+duzQ8qWXsnWaVbXxuFMlxGvAe5XXgsu3RLF7xcpM1VYMyxtw48b8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\n", 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\n", 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" ] }, "metadata": {}, @@ -1152,7 +1136,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Accuracy on test-set: 91.8%\n" + "Accuracy on test-set: 91.6%\n" ] } ], @@ -1167,9 +1151,9 @@ "outputs": [ { "data": { - "image/png": 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piohEkNaMoFz55ZfYsnt+D49tttkmn9kRka2YIk0RkQgKOtIcPXo0AL///e8B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\nAAAASUVORK5CYII=\n", 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\n", 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2ADllyQXe+zcA7HTOLdLohz8D8P/245xFoCJlXGGoWBk7574N4GgA147WrwxQ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\nMBSE/x+WxwZbwb8SZwAAAABJRU5ErkJggg==\n", 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\n", 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" ] }, "metadata": {}, @@ -1211,7 +1195,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can also print and plot the so-called confusion matrix which lets us see more details about the mis-classifications. For example, it shows that images actually depicting a 5 have sometimes been mis-classified as all other possible digits, but mostly either 3, 6 or 8." + "We can also print and plot the so-called confusion matrix which lets us see more details about the mis-classifications. For example, it shows that images actually depicting a 5 have sometimes been mis-classified as all other possible digits, but mostly as 6 or 8." ] }, { @@ -1223,26 +1207,28 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[ 952 0 0 1 0 10 13 2 2 0]\n", - " [ 0 1109 2 2 1 2 4 2 13 0]\n", - " [ 6 11 889 16 16 7 17 18 46 6]\n", - " [ 3 1 14 901 1 36 5 15 19 15]\n", - " [ 1 1 2 1 918 0 16 2 9 32]\n", - " [ 8 3 1 27 7 784 20 8 26 8]\n", - " [ 7 3 2 2 9 12 920 2 1 0]\n", - " [ 2 10 19 8 6 1 0 952 2 28]\n", - " [ 5 6 4 17 9 37 13 13 859 11]\n", - " [ 10 6 1 9 42 8 1 31 7 894]]\n" + "[[ 958 0 1 4 0 7 5 2 3 0]\n", + " [ 0 1103 3 5 1 1 3 2 17 0]\n", + " [ 7 6 917 25 12 3 8 11 38 5]\n", + " [ 1 0 12 943 0 19 1 11 17 6]\n", + " [ 2 3 4 2 921 1 8 2 8 31]\n", + " [ 10 2 4 53 8 754 10 8 35 8]\n", + " [ 15 3 5 2 23 17 885 2 6 0]\n", + " [ 3 8 20 8 7 1 0 946 4 31]\n", + " [ 7 4 6 40 9 25 9 12 858 4]\n", + " [ 11 5 2 12 49 8 0 31 11 880]]\n" ] }, { "data": { - "image/png": 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1323,7 +1309,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.1" + "version": "3.6.10" } }, "nbformat": 4, diff --git a/02_Convolutional_Neural_Network.ipynb b/02_Convolutional_Neural_Network.ipynb index 78218b3..c4f3446 100644 --- a/02_Convolutional_Neural_Network.ipynb +++ b/02_Convolutional_Neural_Network.ipynb @@ -94,6 +94,15 @@ "Note that the second convolutional layer is more complicated because it takes 16 input channels. We want a separate filter for each input channel, so we need 16 filters instead of just one. Furthermore, we want 36 output channels from the second convolutional layer, so in total we need 16 x 36 = 576 filters for the second convolutional layer. It can be a bit challenging to understand how this works." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## TensorFlow 2\n", + "\n", + "This tutorial was developed using TensorFlow v.1 back in the year 2016. There have been significant API changes in TensorFlow v.2. This tutorial uses TF2 in \"v.1 compatibility mode\", which is still useful for learning how TensorFlow works, but you would have to implement it slightly differently in TF2 (see Tutorial 03C on the Keras API). It would be too big a job for me to keep updating these tutorials every time Google's engineers update the TensorFlow API, so this tutorial may eventually stop working." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -105,20 +114,10 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/importlib/_bootstrap.py:205: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n", - " return f(*args, **kwds)\n" - ] - } - ], + "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", - "import tensorflow as tf\n", "import numpy as np\n", "from sklearn.metrics import confusion_matrix\n", "import time\n", @@ -126,25 +125,47 @@ "import math" ] }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /home/magnus/anaconda3/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/compat/v2_compat.py:88: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "non-resource variables are not supported in the long term\n" + ] + } + ], + "source": [ + "# Use TensorFlow v.2 with this old v.1 code.\n", + "# E.g. placeholder variables and sessions have changed in TF2.\n", + "import tensorflow.compat.v1 as tf\n", + "tf.disable_v2_behavior()" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "This was developed using Python 3.6.1 (Anaconda) and TensorFlow version:" + "This was developed using Python 3.6 (Anaconda) and TensorFlow version:" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'1.4.0'" + "'2.1.0'" ] }, - "execution_count": 2, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -164,7 +185,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -196,36 +217,27 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Extracting data/MNIST/train-images-idx3-ubyte.gz\n", - "Extracting data/MNIST/train-labels-idx1-ubyte.gz\n", - "Extracting data/MNIST/t10k-images-idx3-ubyte.gz\n", - "Extracting data/MNIST/t10k-labels-idx1-ubyte.gz\n" - ] - } - ], + "outputs": [], "source": [ - "from tensorflow.examples.tutorials.mnist import input_data\n", - "data = input_data.read_data_sets('data/MNIST/', one_hot=True)\n" + "from mnist import MNIST\n", + "data = MNIST(data_dir=\"data/MNIST/\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The MNIST data-set has now been loaded and consists of 70,000 images and associated labels (i.e. classifications of the images). The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial." + "The MNIST data-set has now been loaded and consists of 70.000 images and class-numbers for the images. The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial." ] }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, + "execution_count": 6, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", @@ -233,46 +245,23 @@ "text": [ "Size of:\n", "- Training-set:\t\t55000\n", - "- Test-set:\t\t10000\n", - "- Validation-set:\t5000\n" + "- Validation-set:\t5000\n", + "- Test-set:\t\t10000\n" ] } ], "source": [ "print(\"Size of:\")\n", - "print(\"- Training-set:\\t\\t{}\".format(len(data.train.labels)))\n", - "print(\"- Test-set:\\t\\t{}\".format(len(data.test.labels)))\n", - "print(\"- Validation-set:\\t{}\".format(len(data.validation.labels)))" + "print(\"- Training-set:\\t\\t{}\".format(data.num_train))\n", + "print(\"- Validation-set:\\t{}\".format(data.num_val))\n", + "print(\"- Test-set:\\t\\t{}\".format(data.num_test))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The class-labels are One-Hot encoded, which means that each label is a vector with 10 elements, all of which are zero except for one element. The index of this one element is the class-number, that is, the digit shown in the associated image. We also need the class-numbers as integers for the test-set, so we calculate it now." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "data.test.cls = np.argmax(data.test.labels, axis=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Dimensions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The data dimensions are used in several places in the source-code below. They are defined once so we can use these variables instead of numbers throughout the source-code below." + "Copy some of the data-dimensions for convenience." ] }, { @@ -281,20 +270,20 @@ "metadata": {}, "outputs": [], "source": [ - "# We know that MNIST images are 28 pixels in each dimension.\n", - "img_size = 28\n", + "# The number of pixels in each dimension of an image.\n", + "img_size = data.img_size\n", "\n", - "# Images are stored in one-dimensional arrays of this length.\n", - "img_size_flat = img_size * img_size\n", + "# The images are stored in one-dimensional arrays of this length.\n", + "img_size_flat = data.img_size_flat\n", "\n", "# Tuple with height and width of images used to reshape arrays.\n", - "img_shape = (img_size, img_size)\n", - "\n", - "# Number of colour channels for the images: 1 channel for gray-scale.\n", - "num_channels = 1\n", + "img_shape = data.img_shape\n", "\n", "# Number of classes, one class for each of 10 digits.\n", - "num_classes = 10" + "num_classes = data.num_classes\n", + "\n", + "# Number of colour channels for the images: 1 channel for gray-scale.\n", + "num_channels = data.num_channels" ] }, { @@ -360,9 +349,9 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": 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OgwcPysT5PqOqKpcvX+bs2bO89957LC4u0mw2abfbOJ1ODh8+zMjICL/3e7/H7OwsgUCg30OW7AHNZpPz58+zsrJCLBajVCoxOjrKzMwMoVBoYG56j4QAdrtdOp0OhUKB1dVVksmkuKN4PB6CwaDwJ0n6i9bSYHV1lVQqRbVaFUtfk8lEMBgkEokwMjIi0hQljx9a0VutF1Cr1UKn0+FwOAaqwMUjIYDJZJL19XXeffddXnvtNYrFInBzN+k3fuM3mJ6eljF/A4Kqqly9epUf//jH5HK5Hd3dhoaG+PrXv87U1BRjY2OyQMVjTKfTIZ/P76j4o5W/MplM0gK8H0qlEolEgtXVVRYWFlBVVVQOjkajTE9Py14fA0Qmk2F5efm21pZWq5WZmRkOHDiA0+nc9c0qLen+Xh3ldDrdwFx8jzOqqtJoNHYUP9UquA+C709joAWw0WjQbrc5f/48r7/+Ojdu3KDT6WC325mZmSEajTI1NSUS6CWDTbfbpVarUalUMBqNd630DTcvFs1S0KxErY3Cp/sGt9tt0Wz78uXLIuB2O1r3wMnJSY4ePSo61kn2hna7TSwWY35+nnw+D0AoFOLIkSNEIpGBuQkN9Deg1WpRr9dZWlrinXfe2VHsdHR0lLGxMUZGRqQj/RGh2+3SaDREmMy9mp9rrQ50Op0QwO39Y7a3SW21WlQqFRKJBO+///5dOwhqbVPn5uYwmUxSAPeQdrvNxsYGKysrlEol4a+PRqP4fD4pgPdCVVXa7TYffvghCwsLnD17lkKhgKqquN1uIpEIzz33HOPj43Lp+wiRyWT4y7/8S3w+32cGPzscDsbHx0VlH51OJzZUtKKaGlq3sUKhwNWrV0Wu8XY0ETUajUQiEQKBALOzs7t/kvucVqtFNptlY2NDhL/c60bXbwZWAJvNJu+88w6vv/466+vrZLNZXC4XPp+PaDTKyy+/TCgUwul09nu4ks/J5uYmf/Znf/a5nhsIBHj++edxOBwiu6dQKFCv1zl37hw3bty44+u05kp3o91u4/V6OXjwINPT0/d9DpJ702w2icfjrK2tkclkdvgAB5GBE0CtcqyWSK9to8PNXcSnnnqK2dlZUfxA7iIOHi6Xi+HhYYrFovD/aHzei6FWqxGPx7FYLNjtdhRFoVarCUtve1qdhrZc1uv1eL1eTCYTNpttR9jF1NQUgUAAl8s1MMuwx4lms0kikSCRSNBsNkX6m9FoFJ3+nE7nwHz2AyeA3W6XfD5PLpcjHo8Ti8XErt7k5CSvvvoqkUiE0dFRLBbLwHyQkpsoisLIyAhHjx5leXlZuC7ul2KxyIULF8T8Koqyo4+09jct73v798BqtTI3N4ff72dsbGxHma1wOMzMzAxDQ0PSB7gHVKtVrl69ytLSkghX09qcTk9Pc/DgwYHK1R+Yb4Dm96vVaiwvL5NIJEQCtcfjET0jhoeHxZd3UD5EyU6Gh4d54oknMBgMlMvlu4amaDu62rxrlX22P2YwGHA6nSJ8YrvFb7VasdvtGAyGHVaexWJhbm4Oj8fDyMjIDjeJ3+8Xqwf5/dldtA2qbDZLLpej2WwCN8tfaZbfoFX7HhgBbLfbZLNZtra2+Pf//t9z6dIl0eHt6NGjfPGLX+Spp57i+PHjcgdvgNHpdLz00kt84Qtf4Ny5c7z99tuiiMWnUVWVra0tisUiCwsLLCws3PYcm83GiRMnhBWxXegmJyeZnZ3F6XQSDAaFOGohNFqoy3bR1Ov1Qkhl0Yzdo9vt7ghFWlpaolKpoNfrmZ6e5ujRo4yOjg6cy2pgVKTT6VAsFslmsySTSeLxuPD9OZ1OwuEwfr8fm80mxW/AcTgcOBwOwuEw0WiUTqdzR99ft9vFZrNRLBZFeMyncblcRKNRIYDb6wZGo1Gi0ehtAijpH5oQNptNDAYDdrsdn89HKBQayCLFA6MkpVKJ9957j9XVVWKxGNlsllarhaIoBAIBDh06RDgcll/yR4ipqSl8Pp9Y/t5pGdxsNkXTpO1LYA2tebbRaLzNmrNaraKDnPxe9BfNH2s0GrFarXg8HgKBAGazmZdffpmTJ08yNDTU72HexkAIoFbkNJFIiPaWmjWgKApmsxm32y0bHD1i2O12Gae5j9BE0Ol04na7cTqdYiUQDocHKgVOo+8CWC6XSSQSLC4u8vbbbxOPx28LnZBIJIONJn6RSIQ//MM/pFarCat9amoKk8k0kFZ63wVQa3GZSCRYWloiHo/fMZdTIpEMNoqi4HK5OHnyZL+H8rnpuwDWajXW1tZYX1+nWq3SarWEr8jhcGCxWHA6nXLnVyKR7Dp9V5Rarcb6+rroG6HFDm1vnKPVEJMCKJFIdpOBUxSdTofT6cRisXDy5EkOHDjAsWPH8Hg8sn+ERCLZVQZOAPV6PT6fD7/fz9/4G3+DV155BYfDIcVPIpHsOn0XQLvdTjQaxWw2c+rUKarVKoFAALfbzdjYmEikluInkUh2m74LYCgU4rd+67fodrt861vfEu0TFUXBarUOVP8AiUTyeNF3AdTr9aKXrwyalUgkvUS5n1JFiqKkgJW9G87AMaGq6r6qty/n+PFHzvEn3JcASiQSyePE4OWmSCQSSY+QAiiRSPYtUgAlEsm+Zc93gRVF8QFv3vp1GOgAqVu/f0FV1eYuv9+/AV6+9asNCKqq6tnN95DspA9z/MfA3wfat97n76qqup+c+j2nD3P8ZeD/Bo4C31ZV9b/s5vHF+/RyE0RRlH8BlFVV/Vfb/mZQVfX2eum7837/E/C0qqp/dy+OL7mdXsyxoigvAx+oqlpVFOV/AF5SVfVbu3V8yb3p0RxHARfwT4DX9koA+xIHqCjKfwDqwNPALxVFKbLtA1UU5RLwu6qqxhRF+VvAPwJMwAfAH6mq+nkbjf53wP+22+OXfDZ7Oceqqr617df3gb+1N2chuRd7PMexW8e4d6Pnh6SfPsBR4KSqqn98tycoinII+BbwRVVVj3HT7P6btx77rqIox+/x2glgEvj5ro5acj/s6Rzf4u8Br+/SeCX3Ty/meM/oZybI9z+HJffrwLPAR7fS4azAFoCqqn//M177beC/3Ie1KNl99nSOb1kVx4EXH36okgdkr6/jPaWfAljZ9v82O61RrfWXAvxHVVX/6QMc/9vA//iAY5PsDns2x4qi/AbwvwIvqqp6ezs5Sa/Y6+t4TxmUMJgY8AyAoijPcHPpCjd3nb6pKErw1mNDt5a290RRlDnAC7y3J6OVPAgxdmmOFUV5Gvi3wO+pqrq1ZyOW3C8xdvE67gWDIoB/CQwpinIZ+IfAAoCqqleAfwb8VFGUi8AbwAh8pu/g28D/q8o8v0FiN+f4/wQcwPcVRTmvKMprvTgByWeya3OsKMoJRVHiwKvAv711zF1H5gJLJJJ9y6BYgBKJRNJzpABKJJJ9ixRAiUSyb5ECKJFI9i33FQfo9/vVaDS6R0MZPGKxGOl0el81JJFz/Pgj5/gT7ksAo9Eop0+f3p1RPQIcP963DJ2+Ief48UfO8SfIJbBEItm3SAGUSCT7FimAEolk3yIFUCKR7FukAEokkn1LP8th3ZVut0un02F1dZVsNksmkyGbzWI2m7Hb7Xg8Hubm5jCbzZjNZnQ6qeMSieT+GTgBVFWVVqtFvV7nl7/8JWfPnuXcuXOcO3cOn8/H6OgoR44c4Y/+6I/w+/34/X4pgBKJ5IEYOAEEhABubW2xsrLC1tYW5XIZvV6P2Wxma2uLTCaDwWDA4/FgNBr7PWTJPeh0OtTrdWq1Guvr6wBMTU3hdDof6Hi1Wo1Go4HBYMBoNKLX6zEYBvKrLLkH7XabcrlMrVYjHo/TarUIhULYbDZcLhd2u33PxzBw35put0upVCKXy3H+/HneeustGo0GiqJQqVSIxWKYzWbOnz/P2NgYoVAIq9Xa72FL7kG1WiUej7O4uMh3v/tdut0u//Jf/kuefvrp+z6Wqqqsr6+zsbGB1+vF5/NhtVrxeDzcKrcueUQolUp8/PHHrKys8Kd/+qdks1m+8Y1vcOjQIY4fP87s7CyKouzpvA6UAHa7XVqtFul0mq2tLbLZLJXKzYrbOp2OTqdDu92m0WgIK0DWMxx8Wq0WxWKRfD5PIpEAoNF48Cr2lUqFTCZDu92m0+ng8Xhwu91SAB8hut0u1WqV9fV11tbWiMfjpNNpYrEYVquVgwcPimt7Xwhgp9OhUqmQzWb5r//1vzI/P8/169f7PSzJLlAsFrl06RKxWIxKpYLRaHzgG5eqqqyurnLmzBlxQzx69Ci///u/j9ls3uWRS/aCVqtFrVZjcXGR//yf/zPr6+tkMhkajQY/+9nP+OCDDxgdHeXYsWPo9fo99fH3XQC73S7tdptWqyWWvrFYjMXFRYrF4h1fo6oqnU6HVqtFs9mk2Wyi0+lQFEX8KxkcGo0G2WyWQqFAu91+KJ+tqqrU63VKpRKlUolyuczIyAidjmz+96ig+YSLxSKrq6skk0larRaqqpLJZCiXy1QqlZ6s7vougKlUiosXL5LNZrl69SqZTIYzZ86QSCTE8vfTaHePYrGI2WzG4/EQCASw2WwMDw/j9Xp7fBaSO6GqKqqqUigUuHz5MpubmzQajYfy2SqKgs/nIxqNsrCwwMLCAul0mnq9jtFolBtijwCFQoHr16+zvLxMOp2mWCzSbrcxGAzMzs4SDAYJh8MYjcY9j/DouwBWKhUWFxdJJpOcPn2aTCZDPB6nVCrd9TXNZpNMJkO322VpaQmXy0Wz2cTtduNyuaQADgiqqgpfz+bmJul0mm63+9DHtdlseL1eFEWhWCxSqVSEP9BgMMgVwIBTr9dJp9NkMhnhy4ebfv5AIMDY2Bgulwu9Xr/nY+m7ACaTSV5//XUymQybm5vU63Xq9fo9X1OpVJifn8disRCLxbBYLAwPD+NyuXjllVdQVRWn04nb7e7RWUjuRKFQYGtri6WlJWKxGN1ul7m5OcLh8AOHwMDNG2C1WqVYLLK1tUUul6PZbNJqtTCZTFIABxTthri2tsZbb71FLBbbsRmm1+uZmpriySefJBAI9GRMfRfAdDrNu+++Sy6XEz68z6JSqbC0tCSeq9Pp8Pl8OJ1OwuEwkUgERVGkAPaZcrnM2toa6+vrJBIJbDYbU1NTTExMPFSMlxYnWiqVyGQyFItFms2m9AMOOJq/P5VK8dFHH5HJZGi1WuJxg8HAxMQEhw4d6tkqrm8CuLy8zOXLl/nwww+FAxS45xJJr9eLH4PBgKqq1Go1sczqdru899575PN5XnzxRcLhsLQG+kC1WqVWq3HlyhXefPNNEdoQCoU4evQo0Wj0gS1AzVG+vLxMNpvd5ZFL9pJqtUqpVCKVSpFKpSgUCjtuWnq9nmAwyPj4+EOtEO6HvgngpUuX+N73vsf6+rpY8n7Wro9er8fhcKDX67FYLCImsNlsUiwWKRaLvP766/z4xz9Gp9Pxm7/5mz3xI0h2UiwWSafTfPDBB/yn//SfUBQFh8PB2NgYL7zwAtFo9IE3QlRVZWNjg0uXLpFMJnfFpyjpDaVSiY2NDRH7t335q9PpMJlMjI+PMzc31zPDpecCqFkHWljE9u3uu520x+PB4/Hgcrnw+XyYTCYsFgutVot4PE61WiWZTFIul8UdJZfLsbKygtPpxO/3S0uwR6iqytbWFjdu3CCZTNJoNAgEAhw5coSZmRmcTidGo/GB5kMLfdIEtlwu78EZSPaKUqlEIpEgn8/vuHHpdDqGhobw+XyYzeaeXqs9F8DNzU0SiQTLy8skEgkR63e3lBedTseBAwd46qmnCIfDzM7OYjKZsNlsYpmVTqd5/fXXWVxcFK9bXV3l5z//OdPT03zpS1+S4RE9QlVVLl++zE9+8hMWFhao1+tEIhH+zt/5O4yMjBAIBB4oYFmL/9PS6q5evUq325WZQI8QGxsbnD59mlgstmPezGYzBw4ceOjNsQehZwKohSlsbm5y/fp1kskktVqNVqt1291AC2Ww2+0YjUZGR0eZmpoiFAoRDocxGAxYrVZqtRqFQgGj0Sieq10U+XyepaUljEYjm5ub2O32nm2t70dUVaVardJoNEQqY7VaFSXM/H4/Xq/3gYsWdLtdstksuVyOcrlMu9JY5wsAACAASURBVN0WKwGr1YrBYJBVgQacWq0m5m/7NW82mxkZGWFsbKznef09EUBVVSkWi5TLZX784x/zgx/8gFwux9bW1m13cZ1Oh9vtxmq1cvjwYSKRCC+88AJf/vKXMZvNOBwOYS22Wi2CwSBbW1v86le/Yn19XVyE8/PzbGxs8MQTTwgr5Etf+hJOp1OK4B7Q6XRYWFggkUhw+vRpzp49i9lsJhgMEolEmJqaeqjKPc1mk1/96ldcu3aNpaUlAHw+HxMTE0SjURwOB1arVYrggKKqKqlUivn5eRKJhLjmtYpOX/nKV5idnSUcDvd0XD0TQC3dLZfLsbGxQb1ep91uizuBoiiYTCbMZjOBQACn08nY2BhjY2NEIhECgYAof6RhNBrxeDy0222REN9qtXYUS/B6vaysrKDT6UQWgkyX2320m1wqlSKfz1OpVESWjtPpxGq1YjKZHujYWpGMVCrF+vo6lUpFbKwEAgE8Ho+0AAcYLWe7Wq1SKBSo1WpCAHU6nbjmg8Fgz/O5e7YE1mKAarUaxWLxtt07q9XK5OQkgUCAb33rW0xPTxMIBHC5XLjd7js6R/V6vbAqXnjhBfx+P7/4xS/4+OOP6XQ6dDodlpeX+f73v8/hw4d5+umnUVWVoaGhB74YJXem3W6zsLDAuXPnxA5fOBzm5Zdf5oknnnjgpW+n0yGfz5PNZrlw4QLvv/8+m5ub6PV6Dh48yNe+9jUmJyexWCzSsh9AVFUlmUySzWZF6uL2zA+z2YzNZiMSiTA2NobFYunp+HomgFpeaLvdpt1ui7/rdDq63S5GoxGfz8fw8DCHDx/miSeewG633/OOoFmNqqoyMjJCvV7n/PnzKIoiCiYUi0VKpRIul4tKpUKz2ZSO8z2g2+2Sz+dJJpNUKhU6nY4IfQkGgw8sTlqMZ6lUIp1Oi2whnU6Hx+NhcnKSUCgkxW9AUVWVSqVCPp8nn89TKBTEY3q9HqPRKPzEvSiA+mn6nglitVrx+XyMjY3xzW9+k7GxMSYnJ8WmxudBS6FxOp1cunSJtbU18vk8mUxmj0cvUVVVpKZtbGwQi8XElzwSifDiiy+KJeqDUK1WOXv2rMgoKZfLOBwOPB4P4+PjzM7OYrPZ5PJ3QFFVVdT5y+fzOx7zeDw899xzTE1N9UX8YAAE0Gg0MjQ0RCQS4cSJE4yNjYl4oM+LTqfD7/djsVgIhUL4fD5RMEFDs/o0S1SyO2g9XBqNBvl8XlRmAfB6vRw8ePChytW3Wi1WVlZYXl4mn8/TaDQYGhrC7Xbj9/sZHh6W4jfAqKoqUhZrtdqOx+x2O9PT00xOTvatlmNfBHD7zu/ExAR/8Ad/wOjoKKOjozidzge+YDRx2/6jUSqVeO+990gkEpw6darnvobHGc2/W6lUKJVKeL1eotEoIyMjD73ZpLkx8vk8rVYLnU6H0+kkEAjgcDh26Qwke4UWkra+vi5ifnU6nTB8nn76aUZHR7HZbH0ZX18EcLswTUxM8J3vfGfXkp/vJH5aTboPPviAZDLJs88+SzAY3JX32+9ovlZtl69cLnPgwAFmZ2f3RAABHA6HaJ4jGWw0AUwkEiJzx2AwYLFY8Pv9PPXUUwwPD/etr09PBLDb7YpGNrlcbk+Ov7W1JZzk6XSaarUqHteCqg8dOiT8i5LdQVEUDAYDJpMJl8vF0NAQnU6HbDZLqVSiXq9jMpnu26rXQl/K5TLxeJyVlRVRINfpdBIKhXC5XDKcaUDpdrsUCgXK5TIrKyssLi6Ka1/L+9V2gPvpw+2JALbbbebn5/n444/Z2NjY8aXdjS9wp9MhFouxvLzM4uIi8Xhc5ARrQdNer5df//VfZ3JykqGhoYd+T8lNtJ14q9UqKvlqBQsymYyo0mO32+9rrjudDuVymUwmw+XLl7ly5YooeBoIBJienpbzOMC0223W19fZ2tri4sWLnD59WlyTWjETu90ucvz7Rc8CoXO5HJubm1Sr1YdWe23Z1el0aDQaVCoV4vE4S0tL5HK5HcUVtNJZJpMJk8n0wIn4kjuj1XDc/qXWenXE43EuXLiAx+MhHA7fM1RF6+/S6XREn5d8Pk8sFqNare4om7S9/4tkcNE2x1qt1o7QN5PJxNDQ0ECkpvbMAlxaWuLs2bNsbW099PE6nY5YXiUSCTKZDG+88Qbnz5+/rUacwWDA6XSKsBq9Xi8FcJfRYrmGhoYIBoNkMhnW19f52c9+xurqKpOTk5w6deqeO33pdFpUeMlms1SrVVHsNJ1Oi+dpcyezeQabbrdLrVajXC7vKHoKN6MDjhw5wtTUVN8b2vfs3ZvNprgb7MaxtO5RsViMVColmqtox9eEzul0EolECIVCWCwW2TNij9ACk0OhEBsbG6JeYzKZxGQysby8fM/sm1wuJxLlc7kc9XqdTCZDpVKh1WrtmDOr1YrL5ZI7+QOMFgCtVevW0PzxWgpjv634vscBPgipVIof/ehHbGxs8Itf/IJMJkM6nRbVoeFm4xyHw8HRo0f5xje+QTgcFknz/Ta7H0eMRiMnT57kwIED+Hw+3G43m5ubrK6ukkgkuHjx4me+Xq/XC/eGVuS23W7fdgEdPHiQU6dOicIYksGj2Wxy5coVUa4OEBsf0WiUU6dOMTw83Pdezo+UAGq9IHK5nKgqe7fS6Hq9HqvVisfjYWpqikAgIMomSXYfrail0WgkEomwsbFBq9Vic3NT5PPeKwDd6XQKQTMYDKJ8Wrvd3vE6zYIYGhqSczmgaCmv2WyWVColAuO1UncOh4NgMIjX65UW4P1w+fJlfvjDH7K1tcXHH38sKkvcCa1BUjQa5eDBg7hcLlkUdQ/RBNDlcvFbv/VbPP/88yQSCVZWVkQduHs1LZqamiIajQqLbmVlhddee43NzU2uXLmyo/qzXq+X3d8GFC1uc2triwsXLnD69GlhoGhpr8PDwyIcrd+rsb5lgmzn04HL2oXyaYshkUjw0UcfiaT7OxVU1dBKMXk8HoaGhvoWaLmf0HxyWoaGVsqsUqmI2o9344knnmBubk78Pj8/z4ULF1AUhYWFhR3PlTvAg0u326Ver1Mul9nc3GR9fV08poVLORwOUeGp3/Q9E6RQKHDt2jXRwrJarfLLX/6Szc1NIXAaa2trfPzxx6LenxYKc6el1dGjR/nbf/tvE4lEpOXXJxwOB5FIhHa7zcjIyD2XwJ/OBDIajXi9Xkql0o7507J6NjY2cDgcfY0hk9xOo9EgHo+ztrZ2W3/vUCjE4cOHGR0dHZgbWE8F8E6FCLQeD1qliHw+zw9+8AOuXr1KsVjckdHx6dfeawk0NTXFb//2b/fdxN7PWCyWB96p1ev1O/yC26lUKmQyGXQ6nRTAAaPVaomWCNu7vgHCHz9ITcp6IoB6vZ7JyUmOHTsmfHcayWSSH/7wh8IcrtfrxONxarXajuBJQNT50/7/aRRFYXR0FJ/PJ3sCPyZsr+KjoVWClimNg4fWvKperwuXh9FoFE3Pn332WSYmJvaXBajX65mdnUVRFNLpNNevXxePxeNx/vzP/xz4RNQ+vet3J+70d4PBwNTUFIcPH2ZiYkIK4CPO3ZbMLpeL4eHhHo9G8nnQAqC3h6SZzWasVivT09N88YtfHKjeLT0RQG2HcHR0FI/Hg9VqFWEO20tj3cm5vf137QPVKslq3eFMJhPRaFREmE9OTjIyMtKLU5NIJNvQlsCpVErEb2ptbC0WC2azeaDCl3pmAR44cIBgMMh7772Hw+GgXq+L6h4PcjyHw4HFYiEcDjM0NMR3vvMdDh8+LJrwWCwWaQE+4tzNzSHndXCp1Wpcv36dxcVFcX07HA68Xi8ulwur1TpQfvmeCKCiKFgsFrrdLuFwmIMHD1IsFkW1kM8Kkv00ZrNZNFGemZnB5/MRiUTw+XzY7faB+5AlD8b2TTNtdSDFb3DRsngqlYrw4WvzpmX6DNoc9kwAXS4Xdrud3/zN32R6epqVlRWuXr3KysoKf/VXf3XbjtG98Pv9fOUrXyESiXDq1Cl8Ph9OpxOTySQ+4EH6kCX3j1Zqf/tGmBYALW9ug4cmfo1GQ9Tl1ELYDAaD+Bm0YiQ9W4zrdDrhCxwfH0dVVRHLFwqFREPzbrdLs9kUneK0XsDbE+lDoRAjIyOEw2FGRkZ2rZq0ZHBot9uUSiUqlQqqqoqqPg6HYyACaCV3RyuPpgnd9hvXIIkf9CEQOhQK4fF4iEajnDhxgkQiwTPPPEMqleLMmTNkMhmuXbtGpVIhGo0SCoWYm5tjdnYWuHmn8Xq9HD58WDTcljx+5PN53n//fRKJBJ1OB4/Hw4svvkg0GuXAgQP9Hp7kDmiurvHxcdrttqj/6Xa7GR4eHsgeLj0XQLPZjNlsFgGsdrudZrNJMplka2sLk8nE5uYmiqIQDAaJRCIcOHCAJ598UviDbDYbgUBANsN+jGk2m2SzWQqFggijGB0dZXp6WgY/DyCa28lgMIj0U4vFItLfbDbbQGZk9X0/2uVyMTc3RzQaZXJyUlR7aTabImTG5/PtWObq9XpsNpuo9ix5/LBaraKp+rFjx/D7/Tz33HOEw2HZ0GpA0el0eL1eXnnlFTY3N4Gbcb5zc3OMjIzg8Xj6PMLb6bt6mM1mQqEQcLNDnEQCn/SLVhSF48ePEw6HOXToED6fr99Dk9wDm83G7OwswWCQq1evYrFYGBkZwefzDWQXv74LoERyJ0ZGRnj11VcBOHDgAA6HYyAvIMlOtBhdg8HAqVOnKBQKOJ1ObDYb4XC438O7DSmAkoEkEAjwu7/7u/0ehuQ+0el0otXlyZMn+z2cz2QwEvIkEomkD0gBlEgk+xYpgBKJZN8iBVAikexbpABKJJJ9i3I/VVgURUkBK3s3nIFjQlXVQL8H0UvkHD/+yDn+hPsSQIlEInmckEtgiUSyb5ECKJFI9i1SACUSyb5lzwVQURSfoijnb/0kFUVZ3/a76bOPcN/vZ1YU5S8URbmhKMoHiqJEd/s9JDvp9Rxve99vKIqiKopyfK/eQ3KTPlzHX1YU5ayiKG1FUb6528fX2PNcYFVVM8AxAEVR/gVQVlX1X2mPK4piUFW1fZeXPwh/D8ipqnpAUZRvA/8H8K1dPL7kU/RhjlEUxQn8z8AHu3lcyZ3pwxyvAv898E928Zi30ZdiCIqi/AegDjwN/FJRlCLbPlBFUS4Bv6uqakxRlL8F/CPAxM0v+x+pqtq5x+G/CvyLW///L8D/oyiKosrt7p6yx3MM8L9z8+b2v+zRKUg+g72cY1VVY7eO0d3Lc+inD3AUOKmq6h/f7QmKohzipvX2RVVVjwEd4G/eeuy7d1n6RIA1gFt3pAIgi8j1hz2ZY0VRngHGVFX9//Zm2JL7YK+u457Qz3JY3/8cd/lfB54FPrrVTMUKbAGoqvr393Z4kl1g1+dYURQd8H9xc3kk6T+P9HXcTwHc3hW9zU5r1HLrXwX4j6qq/tP7OO46MAbEFUUxAG4g8zADlTwwezHHTuAI8ItbF9Mw8JqiKL+nqurphxyv5P7Zq+u4JwxKGEwMeAbE8mby1t/fBL6pKErw1mNDiqJ8Vt3814A/uPX/bwI/l/6/gSDGLsyxqqoFVVX9qqpGVVWNAu8DUvwGgxi7dx33hEERwL8EhhRFuQz8Q2ABQFXVK8A/A36qKMpF4A1gBO7pO/ge4FMU5Qbwx8Cf9GD8ks9mN+dYMpjs2hwrinJCUZQ48Crwb28dc9eRucASiWTfMigWoEQikfQcKYASiWTfIgVQIpHsW6QASiSSfct9xQH6/X41Go3u0VAGj1gsRjqdVvo9jl4i5/jxR87xJ9yXAEajUU6f3j/hVseP778IDDnHjz9yjj9BLoElEsm+RQqgRCLZt0gBlEgk+xYpgBKJZN8iBVAikexbpABKJJJ9Sz/rAUokEgkA7XabbrdLo9Gg07m9vqper8dsNlOv19nY2KDdbmO32zEYDFgsFoxGI1arFbPZfF/vKwVQIpH0lW63S61Wo9Vqkc1mqdVqtz3HZrMxNDREKpXijTfeoFarEQ6HcTqdBINBnE4noVCIQCBwX+/dNwHsdrt0Oh3K5TLr6+vAzZM0Go14PB5MJhNGoxGd7uFW6aqqiveqVqsA4s5xq6KwRCLpIc1mk0qlQqvVolwu02w2SafTNBoN0un0HQXQbrfj9/vJZrPcuHGDer1OsVjEYrEwNDSE3W7nmWeeeXQEUPsQLly4wJ/92Z8BMDU1hdfr5eTJkwwPD+P1erHZbA/1Pp1Oh3q9TqlU4saNGyiKwqFDh3C73ej1eimCEkmPyWQyXLt2jXQ6zccff0w+n+fatWsUi0U2NzeFobIdzcJrNpusr6/TarXQapnqdDoMBgN/8id/wpNPPnlfY+m5AKqqiqqqFAoF1tfXWVlZYXV1FUVRcDgcqKpKu93eFWHqdrtUKhU2NjYoFousrKyg1+sZHx/HbrejKAp6vX4XzkryedEs8nK5TKvVEr4fp9OJ0+ns9/Aku0in09mxvG00GjQaDRKJBMvLy6TTaWKxGIVCgXg8TqlUIpPJ3FEAte9Lp9Mhn8/Tbrd3iKBer6dSqdz2us+i5wLYbDZpNpu8+eabfO973yObzRKPx3E4HMIqs9ls+P1+DIYHH16r1aLZbHL69Gn+3b/7d+TzeVKpFD6fD5/Ph06nY2ho6KEtTMnnR3Nyl0olfvrTnxKPx0mlUlQqFX7nd36Hr3zlK+h0uod2e0j6T7fbJZfLUalUOHv2LLFYjPn5ea5du0atVqNUKtFsNqlWq7TbbWq1Gp1Oh3b7zr3V6/U6yWQS+GTDZDeq2fdUAFVVpVarUalUiMfjXLp0iWazSavVwmKxoNfr0ev1mEwmTCbTQ71Xq9WiUqmwtbXF5cuXKRaLNJtN8WFvv3tI9hbNqm+32xSLRXK5HLFYjKWlJZLJJMVikePHj9PtdqVL4jFBVVUajQbVapX19XWuX7/Oxx9/zIULF+h2u5/7+lMUBZ1Oh6IoO6y9T6/cHnQ11zMBrNVqNJtN3nrrLT766CPOnTtHsVjE7/fzxBNPMD4+zje+8Q2Gh4cJh8MP/X6Li4vifZLJJA6Hg6985SuMjY0xMzNDMBi87y1zyYORzWZZWFhgc3OTt99+m1Qqxfz8PLlcjnq9TqfTIZlMUqvVMJlMWK3Wfg9Z8pB0u12xpD179izvvvsu+XyeVqt1X9abw+HA5XJht9vxer13vUHq9XqGh4fve5w9EUBVVWm1WtTrdRYXF3n//feJx+M0m01MJhPRaJSZmRmeffbZ+97FuRuZTIb5+XlWVlYol8t4PB7m5uaYmJjA7/djt9t35X0kn021WmV1dZVYLMYvfvELUqkUmUyGRqMB3Lx7a7uBcvn7aPFpIdMEarsFmEgkuHHjxh1fv13Qts+99ner1YrH48HtdhMOh+/6/dDpdLhcrvsef88EMJvNks1mWVlZYXFxkVKphKqqBINBXnrpJSKRyK7444rFItVqlStXrvDOO+/QarU4ePAgU1NTHD16lEgkIsWvRzSbTWq1GktLS/zoRz8imUySTCapVCo7gl1VVeXq1av8t//235ienubEiROYTCZpoQ8wmutqfX2dpaUlrFYrgUAAm81GJBIBboqS5tKyWCwYDAb0er1YAtvtdkZGRrBYLHg8HsxmMz6fD5vNhtvtxm63Y7fbcTgcWK1W3G73PV0khw4duu/z6IkAdrtd8vk8m5ubrK+vs7a2Jh7zer2cOHECv9+PxWK5x1E+H5VKhUwmw/LyMqdPn2ZkZIRnnnmGmZkZZmdnCQaDconVI5rNJuVymbW1Nf7qr/6KXC5HsVik2+0CO+/+y8vLvPXWW9RqNQ4fPgyAyWSSPsEBpdVqUavVWF1d5Ve/+hVer5eZmRmGhoYIBoMizlav12MwGDCbzcK332q1hMU2PT2Ny+VibGwMh8PB9PQ0Xq+X0dFR/H4/RqMRg8EgjrHb7KkAqqpKs9mkXq8Tj8fFljfA+Pg4MzMzPPPMMzidTsxm8wMvf1RVpVwu02g0+OCDD7h06RKXLl1CVVUcDgeTk5OMjo5itVoxGo3youoRqVSKy5cvc+PGDarVKo1G466+Hy02rNPp0Gq18Pv9zM3N4XQ6mZiYwGq1yuXxANDpdOh0Oly4cIH5+XmuX7/OxYsXiUajTExMiJubXq/H7/djNpt5+eWXCYVCWK1W7Ha7iARwuVxMTU1htVrxer2YzWYCgYBY9mpzrtfr92zu91wAq9Uq5XKZxcVFrly5QjqdBmBmZoZXX32VqakpYf4+KNqWez6f5yc/+Qk//OEPKZfLqKqK2+3m8OHD4g7zsLvLks9PPB7n7bff5vr16xQKhXsK4MbGBolEgvn5eX7+858TjUb56le/yujoKF6vV1iD8ubVX9rtNs1mk3fffZfvf//7pFIpNjY2ePbZZ/nSl74kNji0TYlgMMirr74q/PBut5tKpUIul8NkMuHz+XYInDa/vZrnPRXAZrPJ0tISqVSKGzduEIvFaDabuN1uAoEAY2Nj+P3+hw5GVlWVYrFIJpOhUChQqVSwWCy43W5GR0cZGxsjFAo9VFyh5POjBThnMhlisRibm5viotDu6polXiqVxGaIFi5Tr9fJ5/MsLi6KcCa32y2sAUnv0eZmcXGRVCrF8vIymUwGo9HI5OQk0WiUYDCIx+PZMUc6nQ6r1YqiKMIPaLFYcDgcYnmrhbn0gz1VhFKpxE9+8hOuX7/Ou+++y9raGsFgkImJCQ4dOsSJEyewWq0P/aXudDpsbGywvLxMMpmkUCgwNzfHsWPH+MIXvsDJkydF/q9k76lUKpRKJRYWFnj33XepVqu0Wi3MZjPDw8NYrVZcLhd6vZ5r166xubkpXqvFC66vr/Pmm28yNTXFK6+8gtvtFk50Se/Rculff/11Tp8+zYULF4jH4zz11FO88MILHDp0iCeffPKOqyyn04nD4RAip/kDoXeW3t3Y8yVwvV4X/p9GoyHuCBaLBbPZ/MA+Oe2OVCgUKJVKrK2tsba2JnaXh4aGmJmZIRKJiDuPpDdoed7VapVarUa328Vut+N2u5mdncXpdIpUxGazidlsplKpUC6XxQ5hp9OhVCqRz+dZXV3FZDIxPT2Nx+Ppq8Ww3+h2u7TbbbLZLPl8nvX1dTY2NkS6mtPpZHx8nFAoJMpSfXpu7uS6GJT523NV0E5e+9EcnppF9iDOTS2uMJfL8dOf/lRYC7f6fwJw7Ngx/vAP/xC73S79fj1EC3mKxWJsbW1Rq9Ww2+1MTEwwOzvLP/7H/5iRkRGR9nT69GlisRgfffQRZ8+epVQqkcvlRNZOLBbju9/9LpFIhH/wD/4BTz31FFarVc5pD9BSFwuFAm+88QZra2u8++67XL16Fb1ej9Pp5IknnuCv//W/Lqy8R+3m1FOzSEtnabfbVKtVstms8ANoBQ/v9eFpdyMtnS2dTgvLL5lMkk6n6XQ6Yok1PDwsdw77gObH06L+TSYTfr+fYDDI2NgY4XCYVqtFq9UilUoBsLKygtVqFf5A+OQCTKfT6PV66vX6ruWASj4brZBBqVRifX2d1dVVCoUC9Xodv98vQl6GhoZEKuujRs9zgdfX10XFh5WVFYxGIzabDY/Hw/Hjx3E4HHd9vVY4oVAosLy8TD6f5/z58xQKBWE1TE5OMjIywsTERA/PTLIdzbrTgp1DoRCvvPIKExMTIghdu+k98cQTTE5Okk6nuXbtGgBbW1tC5BRFETFgWjyYvKn1hlKpxMWLF1lbW+P1119nZWWFZrOJ3+/n1Vdf5dSpU0xOTuJwOB5J8YM9FsDtgZDal7ZSqVCpVIjFYiiKgtFoxG63EwgEGB4exu123/V4m5ub3Lhxg2w2y9WrVykWiywtLQl/hMFgwOVyEQ6Hcblcj5Qp/jihxfJpMWEWi4Xh4WECgYC4UDSXiBYaoYVCaUVwtddqO8aaYD5qS6xHmVarRTqdZnNzU6y0AoEATqeTyclJYbAYjcZ+D/WB2VMBtFqtnDhxgpGREeLxOPl8nnq9TqPRIJ/PiwKl2tb40tLSPX07tVqNYrEo/BJaaS1FUXC5XFitVp5//nleeOEFZmdn9/LUJHdBVVVWV1f56KOPWF1dBW6mJ165coV6vc6xY8fu+Lp8Ps/Kygr5fF6IH4DRaCQSiTA+Po7b7cZsNj+y1sajRr1eF/U6m80mer2eQCBAIBAgGAzi9XofafGDPRZALUbI4XDg8/mw2+10Oh2RJK1Zbtpy5/r16595d9++NNLQ4svcbjdTU1M888wzeDweaSn0iWw2K+LEut2uaGRjt9vvWO9NC5jPZDLU6/UdPj6dTofH48Hn82G1WuVufg9ptVpkMhnS6TStVksULfZ6vTidzseiluaefpsMBgN+vx+r1crXv/51nn32WdLpNPl8nnw+TzqdJp1Oc+PGDTqdDmazGbPZTDQavaMvcGhoiEgkQiKR4Gc/+5moAKvT6QgGg+LH6/XuSl6x5P5RFIWRkRGefPJJ6vW6sPwSiQQOh4OtrS0AkRmSSCTI5/N8/PHH1Go1VFXFYrGIDZBGo8H8/Dz5fJ7nn38en8+Hw+GQ89sDqtUq165dIx6Piw2oRCJBtVrlzJkzOJ1OvF4vwWBQFDR41IyOPRVALR/Q5/Pxta99jWazycbGBqlUilgsxrVr11hYWGB5eRlA7N4ePXr0jrW9pqenee655zhz5gy//OUvdwigz+djdHRUCKCkf4yMjHDkyBHW1tZQFEVU83U4HCJMaW1tjUKhwPnz51ldXeXy5cvU63XhDul0OqKazNWrV0kmk6ytrTE6Ooper5cC2AOq1SqLi4usrq6Kis2JRIJ0Os2ZM2dQFIWpqSkOHz6M1+sVwe2PEj1ZT2ibHYqi4PV6MRgMGI1GnE4no6OjBAIBut2uCJA+ePDgHTdDtJxQ7UPW6/U4HA6cTidHjhxhdnaWUCjUi1OS3AVFUUTtEH+Y1AAADspJREFUNm2+tGpAKysr/PjHP8bhcIj2hysrK+RyOVqtFiMjIwwPDzM7O0smk+HMmTOiT2y9XicWi4nNEnmT23s090Wz2RRuCS2MLR6PYzAYRAZWMBjkyJEjwojR6XQ7LHqj0YjFYsFisQgrsdVqUa1WRWUYo9G44/ruBT1zqGjLW5vNhqqqzMzMiAZJWriE9sHcbaevUqmQzWbFB2QwGAiFQgSDQU6dOsUXvvCFe+4iS3qD1qc1EolgtVqFBZhMJrly5cqO8ubahsfExAQzMzM899xzfOtb32J+fp5MJsPW1pZIbzx37hyFQoFAIMDU1FQ/T3Ff0Ol0RNQG3Lw+NR/upUuXuHLlirhWo9EoL730Ej6fj+npaQwGA9lsVlT2cTqdYgPl/2/vXGLbutI7/ju85JX4EimKpPjUw5ZkNzGVxB3EcBQkjqUsDCiTV4Ms2gIpUKBAEAyQQbsYoIsCRRctii6CLgdNs+piUqBJO4s0mdSOE6d1Dbt2RplkLMeyRIoiadEURYl6UacL655IjhM/RhQp6/wAw6RJ33suD/m/5/F9/89iYWGBTCaDw+Ggr68Pj8dDIBB4MAXQ4nZpMXe7k2TFDuZyOWq1GqZp0tXVRTweVwWO9CJ547Hi9jo7Ozl06JBa511bW9sS6GzNCDweD/39/Tz88MMMDAzQ3t6ugmzX1tbUInypVCKfzzM3N0e1WlXhMZr60NLSQiwWo1arUSwWWVlZUa9Ztlhwsx/L5TKTk5Mq0sNutzM3N8fa2hq5XE7F+m4eoCwtLXH9+nXsdjulUgm3200kEsHj8RCJRAgEAnW/xl317bl27RrvvfceExMTLC8v097ezokTJ+jv76evr0/H/jUJVrL7kSNHcLlcnDt3jrfffpv5+XlqtZoa/dntdlKpFP39/Rw/fpxjx47hdDrxeDxUq1UOHz5MMBhkamqKxcVFtbM8NDREPp+nra1NT4XrSCAQYHh4mMnJST788EOVtXMrUkry+TwnT57c4thjzfCsUeKtvn7WdNput9Pe3o7T6WRgYIBgMMirr77K008/Xfdr3BUCuLKywvLyMsVikenpabWQbrfb8Xq9+Hw+7R7cZAgh8Hq9xGIxkskkiUSCUqlEsVikVqshhFAjjO7ubnXHtwLnXS4X0WiU1dVVfD4fy8vLW1Ioc7kcNptNC2AdaWlpIZlMYrPZ6Onpwel0qhEefFvj2cr8+b6SlnfCMAzlFuRyuVhaWiKXyzE7O4vT6axruM2uEMCpqSm++eYbPv30U06dOsXy8rLyF/N4PHg8Hj0VakKskBVrmlsoFDh37hzValVteI2OjjI4OEggENhyEwuFQoyOjpJOp8lkMly9epXLly+Ty+U4c+YMlUqFp556ikQioW98dSIcDvPiiy9SqVQ4cuQIhUKBd999l7GxMZWTvdnF535ztK21xsXFRcbGxmhpaSEYDDI3N8fg4CCPP/543fp4V6jG4uIihUKBYrHIjRs3kFLidru3WGrp/NDmw+Fw4HA4CAaDqvbD9evXWVhYwOVy0draSjweJxqNfmcEb5omoVCI1dVVwuEw5XKZ8fFxVlZWVE64VV9kt4Ve7BZM0yQcDuPz+ajVairUzDIdWV9fVwYm1nR3fX2dlZUVZVxyt1jvtWqN5PN5MpkMPT09dbq6m+wKAZyZmeHSpUtMTk6yvr6uwl56e3vZv3+/qhmhaU68Xi8HDx6kt7eX/v5+arWaWg+yfORuvYFZOcBWXKjP51PmqTMzM6yurvLoo49SqVRoaWnRcYF1wmaz0dLSQjweJxQK8frrrzM3N6cEb2ZmhmvXrqmpcLFY5LPPPmN2dpZcLqeyve4FKaVyE7+fSm/3wq4QwEqlQi6XY35+HkDtMEYiEZWWo2leTNNUO3rRaPSu/5/NZsM0TTo7O1UZRZvNpuq9WHnh1o9UT4Xrg81mU5lZt+7MptNp5e9oObNfvnyZWq3G7OzslvcKIdSGyK2jxs3TZ8uGy7LeklLu3SmwlJJCoaDiwux2O6FQiKGhIbq6un7QPkuz+2ltbeXgwYMEg0Eee+wx4KYrUKlU4vLly5w+fZpkMsnhw4f1OnAD8Pv9W2J6/X4/qVQKv99PNpulXC4r4bPq84RCIfr6+pibm2NsbIxyucyVK1fua7T4u7IrvjFzc3PKi8wwDPx+P4cOHSIWi+mpzwOOw+EgmUzi9/vp6+ujXC5TLpdJp9NMT0/zxRdfIKXkkUce0QLYAKxNSAvTNOnt7VXu73BzBGktdzz00EMMDAzw5JNPMj09jZSSbDarQp12mqb9xkgpmZqaolgsbvlwLAPVUChER0fHrrfj0dwZK5XywIEDGIZBNpvl6tWrTE9P8/nnnyOl5IknnlC1RvSGWOMwTVMFT1sCaGX7ZLNZFTS9vr6u6kZvDq2xsCI86r200bQCWKvVuHLlijJLKJfLtLa20tbWhtvtJhqNEg6HG91MzQ5hmqYa9V+4cIGzZ8+SyWSYmJjAMAxeeOEF9aPTAtg4LDcnu92uBNBKd52amiKdTpNOp5mZmWF+fp6xsbHv1Iu22WyqrG29Z3hN+02RUioHaGsx1eVyEYlEVDFl+NZ+fWlpiWq1et/BmJrmxgqsDgQC9PT0kEqlCIfDSCkplUqMjY0xPj6uqtBpGoNVLMnv9xONRkkkEqoMAqBiBwuFArOzs1sygwzDoKOjg1gsxsDAAIODg/e0aXY/NPUI8KuvvuLkyZNq6BwIBEilUirZWkqp3EIWFxdZW1vD7/frtaAHEJvNRigUor29naGhIVwuF6dPn1aB0u+//z4HDhygv78f0zR1ZlCDcDgchMNhDMNgcHAQwzC4cOGCMlSAm2v61k6+lU9sGAYOh4P9+/cTiUR49tlnGR4e3uIeUw+aWinW1tZYWVlRH5LNZsNut7O+vs6NGzdYXFwkl8tteY9hGFvuOJoHByuf1DLG7ejoUN6BuVyOQCDAwsICXq9XF1FvEFYftba2kkgkqFarZLNZZYywOUjaSoe02+34fD7cbjcDAwMkEgk6Ozt3xNykqQXwVqzt9HK5zNmzZ6lWq3z88ceUSiU6OzvxeDy89NJLtzVT1TwY2Gw2+vr6CIfDZLNZzpw5Q7Va5fz58ywvL5NOpzEMA9M0dXB8AxBCYJomPp+PEydOcPToUUzTxOVykc/nyefzLC0tUalUME2Tjo4O2traOHLkCJFIhOeee05ZY+1EbOeuEsDV1VUWFha4ceMGk5OTLC0tUSgUqFQq+P1+HRO4R3A6nQghCIVCJJNJ8vk8169fp1KpUCwWaWtr08a4DcYKVzNNk3g8Tnd3N4ZhqOUqm82G0+kkHo/T3t6uDDFisdiObm7uKgHMZrOcOnVKuYU4nU727dtHJBJhZGSE3t5eent7G91MTZ1pbW3FNE2OHTtGPB7nk08+4a233qJYLPLRRx/R09OjTFk1jcHKHrHqAQ0PD/Pll1+qhIZ0Ok0sFmNkZERtmDidTjo6Ona0nU0tgJaNtmV8WavVKJVKKpbI5/ORSqUIBoMkEgm6urr0l34PYAXWhsNhWlpamJycxOFwUKvVyGQyGIahdoN1SEzjMAwDwzCIx+PE43FVL9rv92Oz2eju7iaVSuHz+RpWT6RpBdBut3P06FF8Ph/nz5/n/PnztLW1qQLbqVQKr9er0uG6urpUXQHN3sDpdGIYBrFYjIMHD1KpVBgfH1fuxJZhql4LbA4SiQRer5elpSUWFhZwu92EQqGGujk1rQBuNmGcn58nk8nQ2dlJf38/yWSSkZERPB4Pbrcbu92O3W7XYQ97DKvffT4f0WiUbDbL119/zerqKsVikXK5rKZhmsbj9/vx+/2NbsYWmlYAhRCqpvAzzzxDX18fbrdbld+zKo5Zdw8tfnuXrq4uXnnlFcbHxymVShiGoTZEdnpNSbO7aGoBDAQCBAIBkslko5ujaWLi8Tijo6NcvHiRDz74QFUyK5VKrK6uNrp5miamaQVQo7lbLN/AaDTK888/z/LyskqVq2c9Cc3uRwugZtdjGaLu27ePN998EyklhmHctgSrRrMZLYCaBwYhhM4D19wTOkhKo9HsWbQAajSaPYu4l1qeQogCcK1+zWk6uqWUoUY3YifRffzgo/v4W+5JADUajeZBQk+BNRrNnkULoEaj2bNoAdRoNHuWugugEKJDCPF/G39mhBCZTc/NOp73ZSGEFEL8qF7n0Nxkp/tYCNEthPiVEOKSEOKkECKx3efQbKUBffyaEKKw6Rx/ut3ngB3eBBFC/BVQkVL+/aZ/s0spt7WUmxDCC/wSMIE3pJTntvP4mu9nJ/pYCPEL4D+klO8IIY4DfyKl/OPtOr7mh9mhPn4N+JGU8o3tOubtaEjYvBDin4El4DHgMyFEmU0fqBDi18ColHJCCPFHwE+4KWb/A7wupazd4RR/Dfwt8Bd1ugTNHahzHz8E/HTj8X8B/1afq9D8EDvwO647jVwDTABPSCl/+n1vEEL8HvAqMCSlfBSoAX+48drPbze9FUIcBpJSyl/Wp9mae6AufQxcBF7aePwi4BVCaN+rxlCvPgZ4eWOZ410hRF0soRqZOPmLu7gDDAO/D/zvRlK7E8gDSCm/syYghLAB/wC8tq0t1dwv297HG/w58I8b06RPgAw3f1SanadeffzvwL9IKZeFEH8GvAMc354mf0sjBXBh0+M1to5GWzf+FsA7Usqf3eUxvcAh4OTGBx0B3hdC/FivAzaEevQxUsppNkaAQggP8LKUsvQ7tlVzf9Srj2c3Pf058Hf33cIfoFnCYCaAw6CmsFZpt18BfyCECG+8FhBCdH/fQaSUc1LKoJSyR0rZA/w3oMWvOZhgG/p44z3BjdE+wM+Af6pLizX3ygTb18fRTU9/DPxm21tL8wjgvwIBIcQY8AbwWwAp5ZfAXwL/KYS4BHwIROGOawea5mM7+/gY8LUQ4rdAJ/A39W++5i7Yzj7+iRBiTAhxkZubJ6/Vo8E6F1ij0exZmmUEqNFoNDuOFkCNRrNn0QKo0Wj2LFoANRrNnkULoEaj2bNoAdRoNHsWLYAajWbP8v+wh+/4o3CBiwAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": {}, @@ -371,10 +360,10 @@ ], "source": [ "# Get the first images from the test-set.\n", - "images = data.test.images[0:9]\n", + "images = data.x_test[0:9]\n", "\n", "# Get the true classes for those images.\n", - "cls_true = data.test.cls[0:9]\n", + "cls_true = data.y_test_cls[0:9]\n", "\n", "# Plot the images and labels using our helper-function above.\n", "plot_images(images=images, cls_true=cls_true)" @@ -1002,7 +991,22 @@ "cell_type": "code", "execution_count": 32, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From :2: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "\n", + "Future major versions of TensorFlow will allow gradients to flow\n", + "into the labels input on backprop by default.\n", + "\n", + "See `tf.nn.softmax_cross_entropy_with_logits_v2`.\n", + "\n" + ] + } + ], "source": [ "cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=layer_fc2,\n", " labels=y_true)" @@ -1187,7 +1191,7 @@ " # Get a batch of training examples.\n", " # x_batch now holds a batch of images and\n", " # y_true_batch are the true labels for those images.\n", - " x_batch, y_true_batch = data.train.next_batch(train_batch_size)\n", + " x_batch, y_true_batch, _ = data.random_batch(batch_size=train_batch_size)\n", "\n", " # Put the batch into a dict with the proper names\n", " # for placeholder variables in the TensorFlow graph.\n", @@ -1257,13 +1261,13 @@ " \n", " # Get the images from the test-set that have been\n", " # incorrectly classified.\n", - " images = data.test.images[incorrect]\n", + " images = data.x_test[incorrect]\n", " \n", " # Get the predicted classes for those images.\n", " cls_pred = cls_pred[incorrect]\n", "\n", " # Get the true classes for those images.\n", - " cls_true = data.test.cls[incorrect]\n", + " cls_true = data.y_test_cls[incorrect]\n", " \n", " # Plot the first 9 images.\n", " plot_images(images=images[0:9],\n", @@ -1291,7 +1295,7 @@ " # all images in the test-set.\n", "\n", " # Get the true classifications for the test-set.\n", - " cls_true = data.test.cls\n", + " cls_true = data.y_test_cls\n", " \n", " # Get the confusion matrix using sklearn.\n", " cm = confusion_matrix(y_true=cls_true,\n", @@ -1347,7 +1351,7 @@ " show_confusion_matrix=False):\n", "\n", " # Number of images in the test-set.\n", - " num_test = len(data.test.images)\n", + " num_test = data.num_test\n", "\n", " # Allocate an array for the predicted classes which\n", " # will be calculated in batches and filled into this array.\n", @@ -1365,10 +1369,10 @@ " j = min(i + test_batch_size, num_test)\n", "\n", " # Get the images from the test-set between index i and j.\n", - " images = data.test.images[i:j, :]\n", + " images = data.x_test[i:j, :]\n", "\n", " # Get the associated labels.\n", - " labels = data.test.labels[i:j, :]\n", + " labels = data.y_test[i:j, :]\n", "\n", " # Create a feed-dict with these images and labels.\n", " feed_dict = {x: images,\n", @@ -1382,7 +1386,7 @@ " i = j\n", "\n", " # Convenience variable for the true class-numbers of the test-set.\n", - " cls_true = data.test.cls\n", + " cls_true = data.y_test_cls\n", "\n", " # Create a boolean array whether each image is correctly classified.\n", " correct = (cls_true == cls_pred)\n", @@ -1428,7 +1432,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Accuracy on Test-Set: 10.4% (1036 / 10000)\n" + "Accuracy on Test-Set: 9.2% (915 / 10000)\n" ] } ], @@ -1454,7 +1458,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Optimization Iteration: 1, Training Accuracy: 10.9%\n", + "Optimization Iteration: 1, Training Accuracy: 4.7%\n", "Time usage: 0:00:00\n" ] } @@ -1474,7 +1478,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Accuracy on Test-Set: 10.9% (1090 / 10000)\n" + "Accuracy on Test-Set: 8.7% (873 / 10000)\n" ] } ], @@ -1502,7 +1506,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Time usage: 0:00:00\n" + "Time usage: 0:00:02\n" ] } ], @@ -1519,15 +1523,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "Accuracy on Test-Set: 66.3% (6634 / 10000)\n", + "Accuracy on Test-Set: 61.2% (6117 / 10000)\n", "Example errors:\n" ] }, { "data": { - "image/png": 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\n", 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" ] }, "metadata": {}, @@ -1559,15 +1563,15 @@ "output_type": "stream", "text": [ "Optimization Iteration: 101, Training Accuracy: 62.5%\n", - "Optimization Iteration: 201, Training Accuracy: 85.9%\n", - "Optimization Iteration: 301, Training Accuracy: 89.1%\n", - "Optimization Iteration: 401, Training Accuracy: 89.1%\n", + "Optimization Iteration: 201, Training Accuracy: 68.8%\n", + "Optimization Iteration: 301, Training Accuracy: 85.9%\n", + "Optimization Iteration: 401, Training Accuracy: 81.2%\n", "Optimization Iteration: 501, Training Accuracy: 89.1%\n", - "Optimization Iteration: 601, Training Accuracy: 89.1%\n", - "Optimization Iteration: 701, Training Accuracy: 82.8%\n", - "Optimization Iteration: 801, Training Accuracy: 87.5%\n", - "Optimization Iteration: 901, Training Accuracy: 96.9%\n", - "Time usage: 0:00:03\n" + "Optimization Iteration: 601, Training Accuracy: 90.6%\n", + "Optimization Iteration: 701, Training Accuracy: 92.2%\n", + "Optimization Iteration: 801, Training Accuracy: 90.6%\n", + "Optimization Iteration: 901, Training Accuracy: 92.2%\n", + "Time usage: 0:00:17\n" ] } ], @@ -1586,15 +1590,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "Accuracy on Test-Set: 93.3% (9329 / 10000)\n", + "Accuracy on Test-Set: 94.1% (9409 / 10000)\n", "Example errors:\n" ] }, { "data": { - "image/png": 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\n", 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Training Accuracy: 96.9%\n", "Optimization Iteration: 1901, Training Accuracy: 98.4%\n", - "Optimization Iteration: 2001, Training Accuracy: 96.9%\n", - "Optimization Iteration: 2101, Training Accuracy: 100.0%\n", - "Optimization Iteration: 2201, Training Accuracy: 100.0%\n", - "Optimization Iteration: 2301, Training Accuracy: 100.0%\n", - "Optimization Iteration: 2401, Training Accuracy: 96.9%\n", - "Optimization Iteration: 2501, Training Accuracy: 98.4%\n", - "Optimization Iteration: 2601, Training Accuracy: 95.3%\n", + "Optimization Iteration: 2001, Training Accuracy: 95.3%\n", + "Optimization Iteration: 2101, Training Accuracy: 95.3%\n", + "Optimization Iteration: 2201, Training Accuracy: 96.9%\n", + "Optimization Iteration: 2301, Training Accuracy: 98.4%\n", + "Optimization Iteration: 2401, Training Accuracy: 98.4%\n", + "Optimization Iteration: 2501, Training Accuracy: 96.9%\n", + "Optimization Iteration: 2601, Training Accuracy: 92.2%\n", "Optimization Iteration: 2701, Training Accuracy: 96.9%\n", "Optimization Iteration: 2801, Training Accuracy: 98.4%\n", "Optimization Iteration: 2901, Training Accuracy: 98.4%\n", - "Optimization Iteration: 3001, Training Accuracy: 95.3%\n", - "Optimization Iteration: 3101, Training Accuracy: 98.4%\n", + "Optimization Iteration: 3001, Training Accuracy: 98.4%\n", + "Optimization Iteration: 3101, Training Accuracy: 92.2%\n", "Optimization Iteration: 3201, Training Accuracy: 96.9%\n", - "Optimization Iteration: 3301, Training Accuracy: 98.4%\n", - "Optimization Iteration: 3401, Training Accuracy: 93.8%\n", - "Optimization Iteration: 3501, Training Accuracy: 95.3%\n", - "Optimization Iteration: 3601, Training Accuracy: 100.0%\n", - "Optimization Iteration: 3701, Training Accuracy: 95.3%\n", - "Optimization Iteration: 3801, Training Accuracy: 98.4%\n", - "Optimization Iteration: 3901, Training Accuracy: 96.9%\n", - "Optimization Iteration: 4001, Training Accuracy: 98.4%\n", - "Optimization Iteration: 4101, Training Accuracy: 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"Optimization Iteration: 7501, Training Accuracy: 98.4%\n", - "Optimization Iteration: 7601, Training Accuracy: 100.0%\n", + "Optimization Iteration: 7301, Training Accuracy: 98.4%\n", + "Optimization Iteration: 7401, Training Accuracy: 98.4%\n", + "Optimization Iteration: 7501, Training Accuracy: 100.0%\n", + "Optimization Iteration: 7601, Training Accuracy: 96.9%\n", "Optimization Iteration: 7701, Training Accuracy: 98.4%\n", - "Optimization Iteration: 7801, Training Accuracy: 96.9%\n", - "Optimization Iteration: 7901, Training Accuracy: 98.4%\n", + "Optimization Iteration: 7801, Training Accuracy: 100.0%\n", + "Optimization Iteration: 7901, Training Accuracy: 100.0%\n", "Optimization Iteration: 8001, Training Accuracy: 98.4%\n", - "Optimization Iteration: 8101, Training Accuracy: 100.0%\n", + "Optimization Iteration: 8101, Training Accuracy: 95.3%\n", "Optimization Iteration: 8201, Training Accuracy: 100.0%\n", - "Optimization Iteration: 8301, Training Accuracy: 96.9%\n", + "Optimization Iteration: 8301, Training Accuracy: 100.0%\n", "Optimization Iteration: 8401, Training Accuracy: 98.4%\n", - "Optimization Iteration: 8501, Training Accuracy: 95.3%\n", + "Optimization Iteration: 8501, Training Accuracy: 100.0%\n", "Optimization Iteration: 8601, Training Accuracy: 100.0%\n", "Optimization Iteration: 8701, Training Accuracy: 100.0%\n", - "Optimization Iteration: 8801, Training Accuracy: 93.8%\n", - "Optimization Iteration: 8901, Training Accuracy: 100.0%\n", - "Optimization Iteration: 9001, Training Accuracy: 100.0%\n", - "Optimization Iteration: 9101, Training Accuracy: 100.0%\n", + "Optimization Iteration: 8801, Training Accuracy: 100.0%\n", + "Optimization Iteration: 8901, Training Accuracy: 98.4%\n", + "Optimization Iteration: 9001, Training Accuracy: 98.4%\n", + "Optimization Iteration: 9101, Training Accuracy: 95.3%\n", "Optimization Iteration: 9201, Training Accuracy: 100.0%\n", - "Optimization Iteration: 9301, Training Accuracy: 100.0%\n", - "Optimization Iteration: 9401, Training Accuracy: 98.4%\n", + "Optimization Iteration: 9301, Training Accuracy: 98.4%\n", + "Optimization Iteration: 9401, Training Accuracy: 100.0%\n", "Optimization Iteration: 9501, Training Accuracy: 100.0%\n", - "Optimization Iteration: 9601, Training Accuracy: 98.4%\n", - "Optimization Iteration: 9701, Training Accuracy: 98.4%\n", - "Optimization Iteration: 9801, Training Accuracy: 96.9%\n", - "Optimization Iteration: 9901, Training Accuracy: 95.3%\n", - "Time usage: 0:00:27\n" + "Optimization Iteration: 9601, Training Accuracy: 100.0%\n", + "Optimization Iteration: 9701, Training Accuracy: 100.0%\n", + "Optimization Iteration: 9801, Training Accuracy: 100.0%\n", + "Optimization Iteration: 9901, Training Accuracy: 96.9%\n", + "Time usage: 0:02:51\n" ] } ], @@ -1734,15 +1738,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "Accuracy on Test-Set: 98.5% (9852 / 10000)\n", + "Accuracy on Test-Set: 98.7% (9867 / 10000)\n", "Example errors:\n" ] }, { "data": { - "image/png": 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\n", 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" ] }, "metadata": {}, @@ -1753,26 +1757,28 @@ "output_type": "stream", "text": [ "Confusion Matrix:\n", - "[[ 970 0 1 0 0 2 2 1 4 0]\n", - " [ 0 1127 3 0 2 0 1 1 1 0]\n", - " [ 0 2 1022 1 2 0 0 4 1 0]\n", - " [ 0 0 2 999 0 3 0 4 2 0]\n", - " [ 0 0 0 0 982 0 0 0 0 0]\n", - " [ 1 0 1 7 1 879 1 1 0 1]\n", - " [ 4 2 1 0 12 8 931 0 0 0]\n", - " [ 0 1 5 0 1 0 0 1018 1 2]\n", - " [ 3 1 3 3 4 3 0 3 950 4]\n", - " [ 1 4 0 1 18 3 0 6 2 974]]\n" + "[[ 975 0 0 0 0 0 1 1 3 0]\n", + " [ 0 1128 3 0 0 0 2 1 1 0]\n", + " [ 5 2 1015 1 0 0 0 6 3 0]\n", + " [ 1 0 0 1003 0 3 0 1 2 0]\n", + " [ 0 0 1 1 975 0 1 0 1 3]\n", + " [ 2 0 0 8 0 879 2 0 1 0]\n", + " [ 6 2 0 0 2 3 942 0 3 0]\n", + " [ 1 2 5 2 0 0 0 1012 1 5]\n", + " [ 5 0 3 1 0 0 0 2 960 3]\n", + " [ 4 5 0 6 8 2 0 4 2 978]]\n" ] }, { "data": { - "image/png": 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s4EHg7eXcrwMnA2uofsfP6rXSOnMkX6OKmwLmUU3o3Ae8pNdKI6JDg4HE9ipgsnmUpZOca+CcJuqdNpDYfmnnflm08p+bqHzWUDNLoJtISvTi25u5K2L1K7c0Uk6Mh65/qmzfIenYNhoTMRs1fw1l5tWZI3lfx+5OVLPAj7TWoojZZpZkSNu94/UWqjmTa9ppTsQsY+pe2h1qUwaSshBtd9sfmKH2RMw6Yz20kTTX9hZJx81kgyJmnXEOJMD3qeZDVklaAXwZeGriTdvXtty2iNlhzAPJhHnAz4AT+e16EgPTBpJyw96TwFZgyzT3CUTMOl2mCBhaUwWS/csVm7v4bQCZ0M1Xf63tjb00LmJWGPOrNnOA5zF5rvwxiKERQ2IMfpumCiTrbX+4z/IN3FhuW/7fti/Z/oSSU2EZwDzm91ldxOjRmF/+baK/9Qe210naH7hJ0r22b+k8oQSXSwCer73HIDZHdGFM5kimSiPwr27y6ZbtdeXPDVQ5Termeo2YPcY5i7ztx/spWNICSbtPvAbeQDVxGxGdxiCQtPmArAOA61Td+ToX+ILt61usL2IkjcPQprVAYvsBqnT4ETHm8uzfiEFLjyQi+uLxv/w7GE1kE/OQhfghak9Tmc1OuXtTI+V89SV7NVLOSBueH4+eDV8giZhFRCZbI6IJCSQR0ZcxWdmaQBIxaAkkEdGvXLWJiP6NQY+kzkPEeyZpT0nLJd0rabWkV7VZX8TIqXufzZAHm7Z7JBcD19t+W3kWaRKORGwnk61TkLQHcDzwRwC2nwWebau+iJE1BoGkzaHNIcBjwGcl/VDSpSWdwHNIWibpNkm3beaZFpsTMZwmEkBPtw2zNgPJXKrHWXzG9iuoHmVx/vYn2b7E9mLbi3dm1xabEzGkxmCOpM1AshZYa3tl2V9OFVgioqjbG+mmRyJpThkFfLXsHyJppaQ1kr5U5iuRtGvZX1PeP7jX79FaILH9KPCwpMPLoaXAPW3VFzGymu+RnAes7tj/GHCR7UOBTcDZ5fjZwKZy/KJyXk9avfwLnAtcKelO4CjgL1uuL2LkNNkjkbQIeDNwadkX1cPtlpdTrgBOK69PLfuU95eW87vW6uVf26uAPF0vYirNzn98EvggsHvZ3wf4ue2J/BFrgYXl9ULgYYDynO8nyvldP9Cu7R5JREyn/tBm34krnGVb1lmMpFOADbZvn8HWA1kiHzFY3U2kbpzm+dnHAW+RdDLVM7ufT7UodE9Jc0uvZBGwrpy/DjgIWCtpLrAH1XO+uzZ8gWSIsok1kq0Nhus7NaSpzGYfvP9HjZTz17/70kbKaSZDX8vn76gY+wLgAgBJJwAfsP0fJH0ZeBvwReBM4CvlIyvK/nfL+9+0e/thzdAmYsC0rd7Whw8B75O0hmoO5LJy/DJgn3L8fUyyzquu4euRRMwybaxatf1t4Nvl9QNM8pRL278G/rCJ+hJIIgZpBFat1pFAEjFoCSQR0Y9xySLf2mSrpMMlrerYfiHpPW3VFzGyxuCmvTaf/Xsf1bJ4JM2humZ9XVv1RYwqjcHygJka2iwF7rf94AzVFzEa8sjOrpwOXDVDdUWMltHvkLS/IK3kPngL8OUdvJ8MaTGrJUNaPScBd9j+6WRvJkNazHqZbK3lDDKsiZjcCPQ26mj7uTYLgNcD17ZZT8RIS49karaforpJKCImMS4L0rKyNWLAtG30I0kCScQgjcCwpY4EkogBy4K0NjSSoaqxlFPNlBM71FRms7evfrSRcq5+8e80Uk5XxuDHbPgCScQsk8nWiOiPGYuebwJJxIBljiQi+pJ1JBHRP3sshjZtL5F/r6S7Jd0l6SpJ89qsL2IU5e7fKUhaCLwbWGz7SGAOVV6SiOiUe21qlb+bpM3AfOCRluuLGDnD3tuoo7Ueie11wMeBh4D1wBO2b2yrvoiRZGCb621DrM2hzV7AqcAhwAuABZLeMcl5yZAWs9oMPLKzdW1Otr4O+LHtx2xvpspJ8urtT0qGtJj1Jq7cTLcNsTbnSB4ClkiaD/yKKpP8bS3WFzGSMkcyBdsrgeXAHcCPSl2XtFVfxEiqe8VmyINN2xnSLgQubLOOiFFWrWwd8ihRQ1a2RgzakE+k1jETj6OIiCnIrrVNW450kKRvSbqnrCg/rxzfW9JNkv6l/LlXOS5Jn5K0RtKdko7u9TskkEQMkmuuIam3jmQL8H7bRwBLgHMkHQGcD9xs+zDg5rIP1TOnDivbMuAzvX6N4RvaNDFebCLLGjR3ya2p9jRhDMbjk2kqs9lZ9/X/eOr739rdeqimrtrYXk+1+BPbT0paDSykWs91QjntCuDbwIfK8c/bNvA9SXtKOrCU05XhCyQRs0394L6vpM4lFJfYnvRKqKSDgVcAK4EDOoLDo8AB5fVC4OGOj60txxJIIkaKu1q1utH24ulOkvQ84BrgPbZ/oY4esW1Lza9cyRxJxKA1uLJV0s5UQeRK2xNPuPyppAPL+wcCG8rxdcBBHR9fVI51LYEkYtAaWpCmqutxGbDa9ic63loBnFlenwl8peP4u8rVmyVUN9Z2PayBDG0iBq7BBWnHAe8EfiRpVTn258BHgaslnQ08CLy9vPd14GRgDfA0cFavFbcaSMp17D+mWsD3t7Y/2WZ9ESPHwNZmAontW6l+1yazdJLzDZzTRN1tphE4kiqIHAO8HDhF0qFt1RcxikS9xWjDvoy+zTmSFwMrbT9tewvwD8BbW6wvYjSNQRqBNgPJXcBrJO1TUgmczHNniCMCxiKQtDZHYnu1pI8BNwJPAauArdufJ2kZ1fJc5jG/reZEDCeTm/amY/sy26+0fTywCfjnSc5JhrSY1cZhjqTtqzb7294g6YVU8yNL2qwvYiQNeZCoo+11JNdI2gfYDJxj++ct1xcxWmzYNvpjm7YzpL2mzfIjxsLox5GsbI0YtGGf/6gjgSRi0BJIIqIvE0/aG3FDFUieZNPGb3j5dCmq9gU2TnnG9P8u05dRT71yhq09o1XOjLblG7/XSDkvqtckgOFfbFbHUAUS2/tNd46k2+okd2m7jJQzM+UMU1uaLOc5Ekgioi8Gto7+ZZsEkoiBMjiBZBCaeOxnU48OTTntlzNMbWmynN8ag6GNPAZfYtxI2kr1vOS5wGrgTNtP91jW54Cv2l4u6VLgE7bv2cG5JwDP2v7HLuv4CbDYdhOTorPKHrsc4Ff/zhm1zr3+4Ytvb3x+piHJ2TqcfmX7KNtHAs8Cf9r5pqSeepK2/+OOgkhxAvDqXsqOPoxBGoEEkuH3HeBQSSdI+o6kFcA9kuZI+h+SflAet/gn8JvHMP6NpPskfQPYf6IgSd+WtLi8fpOkOyT9k6Sby3NQ/hR4r6RVkl4jaT9J15Q6fiDpuPLZfSTdWB4LeSk7Tu8XdYxBIBnFOZJZo/Q8TgKuL4eOBo60/eOSx+UJ2/9W0q7A/5N0I9VDkQ4HjqB6ENI9wOXblbsf8LfA8aWsvW0/Lul/Ab+0/fFy3heAi2zfWu7gvoEq892FwK22PyzpzcDZrf5FjDMbtv6rND0jJ4FkOO3WkQX8O1SPGHg18H3bPy7H3wC8TNLbyv4eVM9wPR64yvZW4BFJ35yk/CXALRNl2X58B+14HXBExwOWnl8evnQ8JW2m7a9J2tTj9wwY+t5GHQkkw+lXto/qPFB+mZ/qPASca/uG7c47ucF27AQssf3rSdoSTRmDQJI5ktF1A/CfypPVkPR7khYAtwD/rsyhHAi8dpLPfg84XtIh5bN7l+NPArt3nHcjcO7EjqSJ4HYL8O/LsZOAvRr7VrOOq3tt6mxDLD2S0XUpcDBwR3nC2mPAacB1wIlUcyMPAd/d/oO2HytzLNdK2onqEY6vB/4eWC7pVKoA8m7g05LupPpZuYVqQvYvgKsk3Q38Y6knemHwGCxIyzqSiAHaY+5+ftXzT6t17g2bLh3adSTpkUQM2hj8Z55AEjFIufwbEU1wkj9HRH+Gf9VqHQkkEYM0JqkWs44kYtC8rd5WQ7mH6j5JaySd33LLfyM9kogBMuCGeiSS5gCfploTtBb4gaQV09zx3Yj0SCIGyW6yR3IMsMb2A7afBb4InNpq+4v0SCIGzM1d/l0IPNyxvxY4tqnCp5JAEjFAT7Lphm94+b41T58n6baO/UtsN5/6sQcJJBEDZPtNDRa3DjioY39ROda6zJFEjI8fAIdJOkTSLsDpwIqZqDg9kogxYXuLpD+jSjExB7jc9t0zUXfu/o2IvmVoExF9SyCJiL4lkERE3xJIIqJvCSQR0bcEkojoWwJJRPQtgSQi+vb/AchepZcwOkSoAAAAAElFTkSuQmCC\n", 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"text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "image1 = data.test.images[0]\n", + "image1 = data.x_test[0]\n", "plot_image(image1)" ] }, @@ -1977,17 +1985,19 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "image2 = data.test.images[13]\n", + "image2 = data.x_test[13]\n", "plot_image(image2)" ] }, @@ -2016,9 +2026,9 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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RR0Q+7MfTzBpr4Z6tW+3j9Fm9UmQlDz4YdXwA/8wnInKCzZSIyAE2UyIiB2K6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\n+G+SXKUoMYZfOgAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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5scE8C5jMpaHBBDR33nmnsWnXrp3SuFAsYg/h4MJa2MMJfDIlhBAP0JkSQogH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\nMyWEEA/QmRJCiAfoTAkhxAP/DxkSP5EwP1q5AAAAAElFTkSuQmCC\n", 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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -2328,7 +2338,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.1" + "version": "3.6.10" } }, "nbformat": 4, diff --git a/03B_Layers_API.ipynb b/03B_Layers_API.ipynb index dcc8f72..58d8fbc 100644 --- a/03B_Layers_API.ipynb +++ b/03B_Layers_API.ipynb @@ -2,10 +2,7 @@ "cells": [ { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "# TensorFlow Tutorial #03-B\n", "# Layers API\n", @@ -16,10 +13,16 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, + "source": [ + "## WARNING!\n", + "\n", + "**The Layers API was intended to be a basic builder API for creating Neural Networks in TensorFlow, but the Layers API was never fully completed. Although it still works in TensorFlow v. 1.9, it seems quite possible that it may be deprecated in the future. It is recommended that you use the more complete _Keras API_ instead, see Tutorial #03-C.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, "source": [ "## Introduction\n", "\n", @@ -34,40 +37,28 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Flowchart" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The following chart shows roughly how the data flows in the Convolutional Neural Network that is implemented below. See Tutorial #02 for a more detailed description of convolution." ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "![Flowchart](images/02_network_flowchart.png)" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The input image is processed in the first convolutional layer using the filter-weights. This results in 16 new images, one for each filter in the convolutional layer. The images are also down-sampled using max-pooling so the image resolution is decreased from 28x28 to 14x14.\n", "\n", @@ -84,10 +75,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Imports" ] @@ -96,9 +84,7 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -112,10 +98,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This was developed using Python 3.6 (Anaconda) and TensorFlow version:" ] @@ -123,11 +106,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -146,20 +125,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Load Data" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The MNIST data-set is about 12 MB and will be downloaded automatically if it is not located in the given path." ] @@ -167,11 +140,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -191,10 +160,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The MNIST data-set has now been loaded and consists of 70,000 images and associated labels (i.e. classifications of the images). The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial." ] @@ -202,11 +168,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -228,10 +190,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The class-labels are One-Hot encoded, which means that each label is a vector with 10 elements, all of which are zero except for one element. The index of this one element is the class-number, that is, the digit shown in the associated image. We also need the class-numbers as integers for the test-set, so we calculate it now." ] @@ -240,9 +199,7 @@ "cell_type": "code", "execution_count": 5, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -251,20 +208,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Data Dimensions" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The data dimensions are used in several places in the source-code below. They are defined once so we can use these variables instead of numbers throughout the source-code below." ] @@ -273,9 +224,7 @@ "cell_type": "code", "execution_count": 6, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -297,20 +246,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Helper-function for plotting images" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Function used to plot 9 images in a 3x3 grid, and writing the true and predicted classes below each image." ] @@ -319,9 +262,7 @@ "cell_type": "code", "execution_count": 7, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -356,10 +297,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Plot a few images to see if data is correct" ] @@ -367,11 +305,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -397,10 +331,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## TensorFlow Graph\n", "\n", @@ -423,20 +354,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Placeholder variables" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Placeholder variables serve as the input to the TensorFlow computational graph that we may change each time we execute the graph. We call this feeding the placeholder variables and it is demonstrated further below.\n", "\n", @@ -447,9 +372,7 @@ "cell_type": "code", "execution_count": 9, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -458,10 +381,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The convolutional layers expect `x` to be encoded as a 4-dim tensor so we have to reshape it so its shape is instead `[num_images, img_height, img_width, num_channels]`. Note that `img_height == img_width == img_size` and `num_images` can be inferred automatically by using -1 for the size of the first dimension. So the reshape operation is:" ] @@ -470,9 +390,7 @@ "cell_type": "code", "execution_count": 10, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -481,10 +399,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Next we have the placeholder variable for the true labels associated with the images that were input in the placeholder variable `x`. The shape of this placeholder variable is `[None, num_classes]` which means it may hold an arbitrary number of labels and each label is a vector of length `num_classes` which is 10 in this case." ] @@ -492,11 +407,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "y_true = tf.placeholder(tf.float32, shape=[None, num_classes], name='y_true')" @@ -504,10 +415,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We could also have a placeholder variable for the class-number, but we will instead calculate it using argmax. Note that this is a TensorFlow operator so nothing is calculated at this point." ] @@ -516,9 +424,7 @@ "cell_type": "code", "execution_count": 12, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -527,10 +433,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## PrettyTensor Implementation\n", "\n", @@ -543,9 +446,6 @@ "cell_type": "code", "execution_count": 13, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [], @@ -566,10 +466,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Layers Implementation\n", "\n", @@ -582,9 +479,7 @@ "cell_type": "code", "execution_count": 14, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -593,10 +488,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The input image is then input to the first convolutional layer, which has 16 filters each of size 5x5 pixels. The activation-function is the Rectified Linear Unit (ReLU) described in more detail in Tutorial #02." ] @@ -605,9 +497,7 @@ "cell_type": "code", "execution_count": 15, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -617,10 +507,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "One of the advantages of constructing neural networks in this fashion, is that we can now easily pull out a reference to a layer. This was more complicated in PrettyTensor.\n", "\n", @@ -631,9 +518,7 @@ "cell_type": "code", "execution_count": 16, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -642,10 +527,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We now do the max-pooling on the output of the convolutional layer. This was also described in more detail in Tutorial #02." ] @@ -654,9 +536,7 @@ "cell_type": "code", "execution_count": 17, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -665,10 +545,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We now add the second convolutional layer which has 36 filters each with 5x5 pixels, and a ReLU activation function again." ] @@ -677,9 +554,7 @@ "cell_type": "code", "execution_count": 18, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -689,10 +564,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We also want to plot the output of this convolutional layer, so we keep a reference for later use." ] @@ -701,9 +573,7 @@ "cell_type": "code", "execution_count": 19, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -712,10 +582,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The output of the second convolutional layer is also max-pooled for down-sampling the images." ] @@ -723,11 +590,7 @@ { "cell_type": "code", "execution_count": 20, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "net = tf.layers.max_pooling2d(inputs=net, pool_size=2, strides=2)" @@ -735,10 +598,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The tensors that are being output by this max-pooling are 4-rank, as can be seen from this:" ] @@ -746,11 +606,7 @@ { "cell_type": "code", "execution_count": 21, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -769,10 +625,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Next we want to add fully-connected layers to the Neural Network, but these require 2-rank tensors as input, so we must first flatten the tensors.\n", "\n", @@ -783,9 +636,7 @@ "cell_type": "code", "execution_count": 22, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -797,10 +648,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This has now flattened the data to a 2-rank tensor, as can be seen from this:" ] @@ -808,11 +656,7 @@ { "cell_type": "code", "execution_count": 23, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -831,10 +675,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We can now add fully-connected layers to the neural network. These are called *dense* layers in the Layers API." ] @@ -843,9 +684,7 @@ "cell_type": "code", "execution_count": 24, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -855,10 +694,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We need the neural network to classify the input images into 10 different classes. So the final fully-connected layer has `num_classes=10` output neurons." ] @@ -867,9 +703,7 @@ "cell_type": "code", "execution_count": 25, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -879,10 +713,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The output of the final fully-connected layer are sometimes called logits, so we have a convenience variable with that name." ] @@ -891,9 +722,7 @@ "cell_type": "code", "execution_count": 26, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -902,10 +731,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We use the softmax function to 'squash' the outputs so they are between zero and one, and so they sum to one." ] @@ -914,9 +740,7 @@ "cell_type": "code", "execution_count": 27, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -925,10 +749,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This tells us how likely the neural network thinks the input image is of each possible class. The one that has the highest value is considered the most likely so its index is taken to be the class-number." ] @@ -937,9 +758,7 @@ "cell_type": "code", "execution_count": 28, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -948,10 +767,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We have now created the exact same Convolutional Neural Network in a few lines of code that required many complex lines of code in the direct TensorFlow implementation.\n", "\n", @@ -960,20 +776,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Loss-Function to be Optimized" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "To make the model better at classifying the input images, we must somehow change the variables of the Convolutional Neural Network.\n", "\n", @@ -985,11 +795,7 @@ { "cell_type": "code", "execution_count": 29, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_true, logits=logits)" @@ -997,10 +803,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We have now calculated the cross-entropy for each of the image classifications so we have a measure of how well the model performs on each image individually. But in order to use the cross-entropy to guide the optimization of the model's variables we need a single scalar value, so we simply take the average of the cross-entropy for all the image classifications." ] @@ -1009,9 +812,7 @@ "cell_type": "code", "execution_count": 30, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -1020,10 +821,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Optimization Method\n", "\n", @@ -1035,11 +833,7 @@ { "cell_type": "code", "execution_count": 31, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(loss)" @@ -1047,10 +841,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Classification Accuracy\n", "\n", @@ -1063,9 +854,7 @@ "cell_type": "code", "execution_count": 32, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -1074,10 +863,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The classification accuracy is calculated by first type-casting the vector of booleans to floats, so that False becomes 0 and True becomes 1, and then taking the average of these numbers." ] @@ -1086,9 +872,7 @@ "cell_type": "code", "execution_count": 33, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -1097,10 +881,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Getting the Weights\n", "\n", @@ -1112,11 +893,7 @@ { "cell_type": "code", "execution_count": 34, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1158,10 +935,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Each of the convolutional layers has two variables. For the first convolutional layer they are named `layer_conv1/kernel:0` and `layer_conv1/bias:0`. The `kernel` variables are the ones we want to plot further below.\n", "\n", @@ -1172,9 +946,7 @@ "cell_type": "code", "execution_count": 35, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -1192,10 +964,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Using this helper-function we can retrieve the variables. These are TensorFlow objects. In order to get the contents of the variables, you must do something like: `contents = session.run(weights_conv1)` as demonstrated further below." ] @@ -1204,9 +973,6 @@ "cell_type": "code", "execution_count": 36, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [], @@ -1217,20 +983,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## TensorFlow Run" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Create TensorFlow session\n", "\n", @@ -1241,9 +1001,7 @@ "cell_type": "code", "execution_count": 37, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -1252,10 +1010,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Initialize variables\n", "\n", @@ -1265,11 +1020,7 @@ { "cell_type": "code", "execution_count": 38, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "session.run(tf.global_variables_initializer())" @@ -1277,20 +1028,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Helper-function to perform optimization iterations" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "There are 55,000 images in the training-set. It takes a long time to calculate the gradient of the model using all these images. We therefore only use a small batch of images in each iteration of the optimizer.\n", "\n", @@ -1301,9 +1046,7 @@ "cell_type": "code", "execution_count": 39, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -1312,10 +1055,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This function performs a number of optimization iterations so as to gradually improve the variables of the neural network layers. In each iteration, a new batch of data is selected from the training-set and then TensorFlow executes the optimizer using those training samples. The progress is printed every 100 iterations." ] @@ -1323,11 +1063,7 @@ { "cell_type": "code", "execution_count": 40, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# Counter for total number of iterations performed so far.\n", @@ -1372,20 +1108,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Helper-function to plot example errors" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Function for plotting examples of images from the test-set that have been mis-classified." ] @@ -1394,9 +1124,7 @@ "cell_type": "code", "execution_count": 41, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -1430,10 +1158,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Helper-function to plot confusion matrix" ] @@ -1442,9 +1167,7 @@ "cell_type": "code", "execution_count": 42, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -1482,20 +1205,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Helper-function for showing the performance" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Below is a function for printing the classification accuracy on the test-set.\n", "\n", @@ -1507,11 +1224,7 @@ { "cell_type": "code", "execution_count": 43, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# Split the test-set into smaller batches of this size.\n", @@ -1586,10 +1299,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Performance before any optimization\n", "\n", @@ -1599,11 +1309,7 @@ { "cell_type": "code", "execution_count": 44, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1619,10 +1325,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Performance after 1 optimization iteration\n", "\n", @@ -1632,11 +1335,7 @@ { "cell_type": "code", "execution_count": 45, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1654,9 +1353,6 @@ "cell_type": "code", "execution_count": 46, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -1674,10 +1370,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Performance after 100 optimization iterations\n", "\n", @@ -1688,9 +1381,6 @@ "cell_type": "code", "execution_count": 47, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -1711,11 +1401,7 @@ { "cell_type": "code", "execution_count": 48, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1742,10 +1428,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Performance after 1000 optimization iterations\n", "\n", @@ -1756,9 +1439,6 @@ "cell_type": "code", "execution_count": 49, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": false }, "outputs": [ @@ -1789,9 +1469,6 @@ "cell_type": "code", "execution_count": 50, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -1820,10 +1497,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Performance after 10,000 optimization iterations\n", "\n", @@ -1834,9 +1508,6 @@ "cell_type": "code", "execution_count": 51, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -1948,9 +1619,6 @@ "cell_type": "code", "execution_count": 52, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -2007,20 +1675,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Visualization of Weights and Layers" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Helper-function for plotting convolutional weights" ] @@ -2029,9 +1691,7 @@ "cell_type": "code", "execution_count": 53, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -2083,10 +1743,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Helper-function for plotting the output of a convolutional layer" ] @@ -2095,9 +1752,7 @@ "cell_type": "code", "execution_count": 54, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -2146,10 +1801,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Input Images\n", "\n", @@ -2160,9 +1812,7 @@ "cell_type": "code", "execution_count": 55, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -2176,10 +1826,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Plot an image from the test-set which will be used as an example below." ] @@ -2187,11 +1834,7 @@ { "cell_type": "code", "execution_count": 56, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -2211,10 +1854,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Plot another example image from the test-set." ] @@ -2222,11 +1862,7 @@ { "cell_type": "code", "execution_count": 57, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -2246,20 +1882,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Convolution Layer 1" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Now plot the filter-weights for the first convolutional layer.\n", "\n", @@ -2270,9 +1900,6 @@ "cell_type": "code", "execution_count": 58, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": false }, "outputs": [ @@ -2293,10 +1920,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Applying each of these convolutional filters to the first input image gives the following output images, which are then used as input to the second convolutional layer." ] @@ -2304,11 +1928,7 @@ { "cell_type": "code", "execution_count": 59, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -2327,10 +1947,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The following images are the results of applying the convolutional filters to the second image." ] @@ -2338,11 +1955,7 @@ { "cell_type": "code", "execution_count": 60, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -2361,20 +1974,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Convolution Layer 2" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Now plot the filter-weights for the second convolutional layer.\n", "\n", @@ -2387,9 +1994,6 @@ "cell_type": "code", "execution_count": 61, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": false }, "outputs": [ @@ -2410,10 +2014,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "There are 16 input channels to the second convolutional layer, so we can make another 15 plots of filter-weights like this. We just make one more with the filter-weights for the second channel. " ] @@ -2421,11 +2022,7 @@ { "cell_type": "code", "execution_count": 62, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -2444,10 +2041,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "It can be difficult to understand and keep track of how these filters are applied because of the high dimensionality.\n", "\n", @@ -2459,11 +2053,7 @@ { "cell_type": "code", "execution_count": 63, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -2482,10 +2072,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "And these are the results of applying the filter-weights to the second image." ] @@ -2493,11 +2080,7 @@ { "cell_type": "code", "execution_count": 64, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -2516,20 +2099,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Close TensorFlow Session" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We are now done using TensorFlow, so we close the session to release its resources." ] @@ -2537,11 +2114,7 @@ { "cell_type": "code", "execution_count": 65, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# This has been commented out in case you want to modify and experiment\n", @@ -2551,10 +2124,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Conclusion\n", "\n", @@ -2565,10 +2135,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Exercises\n", "\n", @@ -2591,10 +2158,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## License (MIT)\n", "\n", @@ -2629,5 +2193,5 @@ } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/03C_Keras_API.ipynb b/03C_Keras_API.ipynb index a87cf6f..b439d13 100644 --- a/03C_Keras_API.ipynb +++ b/03C_Keras_API.ipynb @@ -2,10 +2,7 @@ "cells": [ { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "# TensorFlow Tutorial #03-C\n", "# Keras API\n", @@ -16,10 +13,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Introduction\n", "\n", @@ -34,20 +28,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Flowchart" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The following chart shows roughly how the data flows in the Convolutional Neural Network that is implemented below. See Tutorial #02 for a more detailed description of convolution.\n", "\n", @@ -56,20 +44,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "![Flowchart](images/02_network_flowchart.png)" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Imports" ] @@ -77,21 +59,8 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/importlib/_bootstrap.py:205: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n", - " return f(*args, **kwds)\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", @@ -102,37 +71,26 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ - "We need to import several things from Keras. Note the long import-statements. This might be a bug. Hopefully it will be possible to write shorter and more elegant lines in the future." + "We need to import several things from Keras." ] }, { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ - "# from tf.keras.models import Sequential # This does not work!\n", - "from tensorflow.python.keras.models import Sequential\n", - "from tensorflow.python.keras.layers import InputLayer, Input\n", - "from tensorflow.python.keras.layers import Reshape, MaxPooling2D\n", - "from tensorflow.python.keras.layers import Conv2D, Dense, Flatten" + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import InputLayer, Input\n", + "from tensorflow.keras.layers import Reshape, MaxPooling2D\n", + "from tensorflow.keras.layers import Conv2D, Dense, Flatten" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This was developed using Python 3.6 (Anaconda) and TensorFlow version:" ] @@ -141,16 +99,13 @@ "cell_type": "code", "execution_count": 3, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ - "'1.4.0'" + "'2.1.0'" ] }, "execution_count": 3, @@ -162,93 +117,41 @@ "tf.__version__" ] }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'2.0.8-tf'" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tf.keras.__version__" - ] - }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Load Data" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The MNIST data-set is about 12 MB and will be downloaded automatically if it is not located in the given path." ] }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Extracting data/MNIST/train-images-idx3-ubyte.gz\n", - "Extracting data/MNIST/train-labels-idx1-ubyte.gz\n", - "Extracting data/MNIST/t10k-images-idx3-ubyte.gz\n", - "Extracting data/MNIST/t10k-labels-idx1-ubyte.gz\n" - ] - } - ], + "execution_count": 4, + "metadata": {}, + "outputs": [], "source": [ - "from tensorflow.examples.tutorials.mnist import input_data\n", - "data = input_data.read_data_sets('data/MNIST/', one_hot=True)" + "from mnist import MNIST\n", + "data = MNIST(data_dir=\"data/MNIST/\")" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ - "The MNIST data-set has now been loaded and consists of 70,000 images and associated labels (i.e. classifications of the images). The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial." + "The MNIST data-set has now been loaded and consists of 70.000 images and class-numbers for the images. The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial." ] }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 5, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -256,120 +159,69 @@ "text": [ "Size of:\n", "- Training-set:\t\t55000\n", - "- Test-set:\t\t10000\n", - "- Validation-set:\t5000\n" + "- Validation-set:\t5000\n", + "- Test-set:\t\t10000\n" ] } ], "source": [ "print(\"Size of:\")\n", - "print(\"- Training-set:\\t\\t{}\".format(len(data.train.labels)))\n", - "print(\"- Test-set:\\t\\t{}\".format(len(data.test.labels)))\n", - "print(\"- Validation-set:\\t{}\".format(len(data.validation.labels)))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, - "source": [ - "The class-labels are One-Hot encoded, which means that each label is a vector with 10 elements, all of which are zero except for one element. The index of this one element is the class-number, that is, the digit shown in the associated image. We also need the class-numbers as integers for the test-set, so we calculate it now." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, - "outputs": [], - "source": [ - "data.test.cls = np.argmax(data.test.labels, axis=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, - "source": [ - "## Data Dimensions" + "print(\"- Training-set:\\t\\t{}\".format(data.num_train))\n", + "print(\"- Validation-set:\\t{}\".format(data.num_val))\n", + "print(\"- Test-set:\\t\\t{}\".format(data.num_test))" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ - "The data dimensions are used in several places in the source-code below. They are defined once so we can use these variables instead of numbers throughout the source-code below." + "Copy some of the data-dimensions for convenience." ] }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 6, + "metadata": {}, "outputs": [], "source": [ - "# We know that MNIST images are 28 pixels in each dimension.\n", - "img_size = 28\n", + "# The number of pixels in each dimension of an image.\n", + "img_size = data.img_size\n", "\n", - "# Images are stored in one-dimensional arrays of this length.\n", - "img_size_flat = img_size * img_size\n", + "# The images are stored in one-dimensional arrays of this length.\n", + "img_size_flat = data.img_size_flat\n", "\n", "# Tuple with height and width of images used to reshape arrays.\n", - "# This is used for plotting the images.\n", - "img_shape = (img_size, img_size)\n", + "img_shape = data.img_shape\n", "\n", "# Tuple with height, width and depth used to reshape arrays.\n", "# This is used for reshaping in Keras.\n", - "img_shape_full = (img_size, img_size, 1)\n", - "\n", - "# Number of colour channels for the images: 1 channel for gray-scale.\n", - "num_channels = 1\n", + "img_shape_full = data.img_shape_full\n", "\n", "# Number of classes, one class for each of 10 digits.\n", - "num_classes = 10" + "num_classes = data.num_classes\n", + "\n", + "# Number of colour channels for the images: 1 channel for gray-scale.\n", + "num_channels = data.num_channels" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Helper-function for plotting images" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Function used to plot 9 images in a 3x3 grid, and writing the true and predicted classes below each image." ] }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 7, + "metadata": {}, "outputs": [], "source": [ "def plot_images(images, cls_true, cls_pred=None):\n", @@ -403,28 +255,21 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Plot a few images to see if data is correct" ] }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 8, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" ] }, "metadata": {}, @@ -433,10 +278,10 @@ ], "source": [ "# Get the first images from the test-set.\n", - "images = data.test.images[0:9]\n", + "images = data.x_test[0:9]\n", "\n", "# Get the true classes for those images.\n", - "cls_true = data.test.cls[0:9]\n", + "cls_true = data.y_test_cls[0:9]\n", "\n", "# Plot the images and labels using our helper-function above.\n", "plot_images(images=images, cls_true=cls_true)" @@ -444,10 +289,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Helper-function to plot example errors\n", "\n", @@ -456,12 +298,8 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 9, + "metadata": {}, "outputs": [], "source": [ "def plot_example_errors(cls_pred):\n", @@ -469,17 +307,17 @@ " # all images in the test-set.\n", "\n", " # Boolean array whether the predicted class is incorrect.\n", - " incorrect = (cls_pred != data.test.cls)\n", + " incorrect = (cls_pred != data.y_test_cls)\n", "\n", " # Get the images from the test-set that have been\n", " # incorrectly classified.\n", - " images = data.test.images[incorrect]\n", + " images = data.x_test[incorrect]\n", " \n", " # Get the predicted classes for those images.\n", " cls_pred = cls_pred[incorrect]\n", "\n", " # Get the true classes for those images.\n", - " cls_true = data.test.cls[incorrect]\n", + " cls_true = data.y_test_cls[incorrect]\n", " \n", " # Plot the first 9 images.\n", " plot_images(images=images[0:9],\n", @@ -489,10 +327,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## PrettyTensor API\n", "\n", @@ -501,12 +336,8 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 10, + "metadata": {}, "outputs": [], "source": [ "if False:\n", @@ -525,10 +356,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Sequential Model\n", "\n", @@ -537,11 +365,8 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [], @@ -581,10 +406,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Model Compilation\n", "\n", @@ -595,37 +417,26 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 12, + "metadata": {}, "outputs": [], "source": [ - "from tensorflow.python.keras.optimizers import Adam\n", + "from tensorflow.keras.optimizers import Adam\n", "\n", "optimizer = Adam(lr=1e-3)" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "For a classification-problem such as MNIST which has 10 possible classes, we need to use the loss-function called `categorical_crossentropy`. The performance metric we are interested in is the classification accuracy." ] }, { "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 13, + "metadata": {}, "outputs": [], "source": [ "model.compile(optimizer=optimizer,\n", @@ -635,10 +446,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Training\n", "\n", @@ -647,44 +455,37 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 14, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1/1\n", - "55000/55000 [==============================] - 5s - loss: 0.2261 - acc: 0.9335 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b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+ "Train on 55000 samples\n", + "55000/55000 [==============================] - 21s 375us/sample - loss: 0.2251 - accuracy: 0.9335\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 16, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "model.fit(x=data.train.images,\n", - " y=data.train.labels,\n", + "model.fit(x=data.x_train,\n", + " y=data.y_train,\n", " epochs=1, batch_size=128)" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Evaluation\n", "\n", @@ -693,51 +494,40 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 15, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " 9152/10000 [==========================>...] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + "10000/10000 [==============================] - 2s 187us/sample - loss: 0.0771 - accuracy: 0.9756\n" ] } ], "source": [ - "result = model.evaluate(x=data.test.images,\n", - " y=data.test.labels)" + "result = model.evaluate(x=data.x_test,\n", + " y=data.y_test)" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We can print all the performance metrics for the test-set." ] }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 16, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "loss 0.0618685603024\n", - "acc 0.9801\n" + "loss 0.07707656768076122\n", + "accuracy 0.9756\n" ] } ], @@ -748,28 +538,21 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Or we can just print the classification accuracy." ] }, { "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 17, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "acc: 98.01%\n" + "accuracy: 97.56%\n" ] } ], @@ -779,10 +562,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Prediction\n", "\n", @@ -791,58 +571,40 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 18, + "metadata": {}, "outputs": [], "source": [ - "images = data.test.images[0:9]" + "images = data.x_test[0:9]" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "These are the true class-number for those images. This is only used when plotting the images." ] }, { "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 19, + "metadata": {}, "outputs": [], "source": [ - "cls_true = data.test.cls[0:9]" + "cls_true = data.y_test_cls[0:9]" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Get the predicted classes as One-Hot encoded arrays." ] }, { "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 20, + "metadata": {}, "outputs": [], "source": [ "y_pred = model.predict(x=images)" @@ -850,41 +612,30 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Get the predicted classes as integers." ] }, { "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 21, + "metadata": {}, "outputs": [], "source": [ - "cls_pred = np.argmax(y_pred,axis=1)" + "cls_pred = np.argmax(y_pred, axis=1)" ] }, { "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 22, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" ] }, "metadata": {}, @@ -899,10 +650,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Examples of Mis-Classified Images\n", "\n", @@ -913,64 +661,46 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 23, + "metadata": {}, "outputs": [], "source": [ - "y_pred = model.predict(x=data.test.images)" + "y_pred = model.predict(x=data.x_test)" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Then we convert the predicted class-numbers from One-Hot encoded arrays to integers." ] }, { "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 24, + "metadata": {}, "outputs": [], "source": [ - "cls_pred = np.argmax(y_pred,axis=1)" + "cls_pred = np.argmax(y_pred, axis=1)" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Plot some of the mis-classified images." ] }, { "cell_type": "code", - "execution_count": 27, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 25, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -983,10 +713,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Functional Model\n", "\n", @@ -995,12 +722,8 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 26, + "metadata": {}, "outputs": [], "source": [ "# Create an input layer which is similar to a feed_dict in TensorFlow.\n", @@ -1040,10 +763,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Model Compilation\n", "\n", @@ -1052,12 +772,8 @@ }, { "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 27, + "metadata": {}, "outputs": [], "source": [ "from tensorflow.python.keras.models import Model" @@ -1065,22 +781,15 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Create a new instance of the Keras Functional Model. We give it the inputs and outputs of the Convolutional Neural Network that we constructed above." ] }, { "cell_type": "code", - "execution_count": 30, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 28, + "metadata": {}, "outputs": [], "source": [ "model2 = Model(inputs=inputs, outputs=outputs)" @@ -1088,22 +797,15 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ - "Compile the Keras model using the `rmsprop` optimizer and with a loss-function for multiple categories. The only performance metric we are interested in is the classification accuracy, but you could use a list of metrics here." + "Compile the Keras model using the RMSprop optimizer and with a loss-function for multiple categories. The only performance metric we are interested in is the classification accuracy, but you could use a list of metrics here." ] }, { "cell_type": "code", - "execution_count": 31, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 29, + "metadata": {}, "outputs": [], "source": [ "model2.compile(optimizer='rmsprop',\n", @@ -1113,10 +815,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Training\n", "\n", @@ -1125,44 +824,37 @@ }, { "cell_type": "code", - "execution_count": 32, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 30, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1/1\n", - "55000/55000 [==============================] - 2s - loss: 0.1924 - acc: 0.9409 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+ "Train on 55000 samples\n", + "55000/55000 [==============================] - 16s 298us/sample - loss: 0.1977 - accuracy: 0.9389\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 32, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "model2.fit(x=data.train.images,\n", - " y=data.train.labels,\n", + "model2.fit(x=data.x_train,\n", + " y=data.y_train,\n", " epochs=1, batch_size=128)" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Evaluation\n", "\n", @@ -1171,43 +863,33 @@ }, { "cell_type": "code", - "execution_count": 33, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 31, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " 8992/10000 [=========================>....] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b" + "10000/10000 [==============================] - 2s 169us/sample - loss: 0.0563 - accuracy: 0.9809\n" ] } ], "source": [ - "result = model2.evaluate(x=data.test.images,\n", - " y=data.test.labels)" + "result = model2.evaluate(x=data.x_test,\n", + " y=data.y_test)" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The result is a list of values, containing the loss-value and all the metrics we defined when we compiled the model. Note that 'accuracy' is now called 'acc' which is a small inconsistency." ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 32, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -1215,53 +897,43 @@ "name": "stdout", "output_type": "stream", "text": [ - "loss 0.0654281976447\n", - "acc 0.9786\n" + "loss 0.05628199413705152\n", + "accuracy 0.9809\n" ] } ], "source": [ - "for name, value in zip(model.metrics_names, result):\n", + "for name, value in zip(model2.metrics_names, result):\n", " print(name, value)" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We can also print the classification accuracy as a percentage:" ] }, { "cell_type": "code", - "execution_count": 35, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 33, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "acc: 97.86%\n" + "accuracy: 98.09%\n" ] } ], "source": [ - "print(\"{0}: {1:.2%}\".format(model.metrics_names[1], result[1]))" + "print(\"{0}: {1:.2%}\".format(model2.metrics_names[1], result[1]))" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Examples of Mis-Classified Images\n", "\n", @@ -1272,35 +944,24 @@ }, { "cell_type": "code", - "execution_count": 36, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 34, + "metadata": {}, "outputs": [], "source": [ - "y_pred = model2.predict(x=data.test.images)" + "y_pred = model2.predict(x=data.x_test)" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Then we convert the predicted class-numbers from One-Hot encoded arrays to integers." ] }, { "cell_type": "code", - "execution_count": 37, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 35, + "metadata": {}, "outputs": [], "source": [ "cls_pred = np.argmax(y_pred, axis=1)" @@ -1308,28 +969,21 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Plot some of the mis-classified images." ] }, { "cell_type": "code", - "execution_count": 38, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 36, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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0Br5wzq3MILails0yBpYCvzazuhbUZl2BReG2xfQ5bhJ52IOgbbdK2eyneSXB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\nQmCC\n", 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\n", 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" ] }, "metadata": {}, @@ -1342,10 +996,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Save & Load Model\n", "\n", @@ -1358,12 +1009,8 @@ }, { "cell_type": "code", - "execution_count": 39, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 37, + "metadata": {}, "outputs": [], "source": [ "path_model = 'model.keras'" @@ -1371,21 +1018,15 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Saving a Keras model with the trained weights is then just a single function call, as it should be." ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 38, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [], @@ -1395,22 +1036,15 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Delete the model from memory so we are sure it is no longer used." ] }, { "cell_type": "code", - "execution_count": 41, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 39, + "metadata": {}, "outputs": [], "source": [ "del model2" @@ -1418,22 +1052,15 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We need to import this Keras function for loading the model." ] }, { "cell_type": "code", - "execution_count": 42, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 40, + "metadata": {}, "outputs": [], "source": [ "from tensorflow.python.keras.models import load_model" @@ -1441,22 +1068,15 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Loading the model is then just a single function-call, as it should be." ] }, { "cell_type": "code", - "execution_count": 43, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 41, + "metadata": {}, "outputs": [], "source": [ "model3 = load_model(path_model)" @@ -1464,58 +1084,40 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We can then use the model again e.g. to make predictions. We get the first 9 images from the test-set and their true class-numbers." ] }, { "cell_type": "code", - "execution_count": 44, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 42, + "metadata": {}, "outputs": [], "source": [ - "images = data.test.images[0:9]" + "images = data.x_test[0:9]" ] }, { "cell_type": "code", - "execution_count": 45, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 43, + "metadata": {}, "outputs": [], "source": [ - "cls_true = data.test.cls[0:9]" + "cls_true = data.y_test_cls[0:9]" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We then use the restored model to predict the class-numbers for those images." ] }, { "cell_type": "code", - "execution_count": 46, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 44, + "metadata": {}, "outputs": [], "source": [ "y_pred = model3.predict(x=images)" @@ -1523,22 +1125,15 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Get the class-numbers as integers." ] }, { "cell_type": "code", - "execution_count": 47, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 45, + "metadata": {}, "outputs": [], "source": [ "cls_pred = np.argmax(y_pred, axis=1)" @@ -1546,28 +1141,21 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Plot the images with their true and predicted class-numbers." ] }, { "cell_type": "code", - "execution_count": 48, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 46, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -1582,32 +1170,22 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Visualization of Layer Weights and Outputs" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Helper-function for plotting convolutional weights" ] }, { "cell_type": "code", - "execution_count": 49, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 47, + "metadata": {}, "outputs": [], "source": [ "def plot_conv_weights(weights, input_channel=0):\n", @@ -1651,10 +1229,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Get Layers\n", "\n", @@ -1663,37 +1238,34 @@ }, { "cell_type": "code", - "execution_count": 50, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 48, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "Model: \"model\"\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", - "input_2 (InputLayer) (None, 784) 0 \n", + "input_2 (InputLayer) [(None, 784)] 0 \n", "_________________________________________________________________\n", - "reshape_2 (Reshape) (None, 28, 28, 1) 0 \n", + "reshape_1 (Reshape) (None, 28, 28, 1) 0 \n", "_________________________________________________________________\n", "layer_conv1 (Conv2D) (None, 28, 28, 16) 416 \n", "_________________________________________________________________\n", - "max_pooling2d_3 (MaxPooling2 (None, 14, 14, 16) 0 \n", + "max_pooling2d_2 (MaxPooling2 (None, 14, 14, 16) 0 \n", "_________________________________________________________________\n", "layer_conv2 (Conv2D) (None, 14, 14, 36) 14436 \n", "_________________________________________________________________\n", - "max_pooling2d_4 (MaxPooling2 (None, 7, 7, 36) 0 \n", + "max_pooling2d_3 (MaxPooling2 (None, 7, 7, 36) 0 \n", "_________________________________________________________________\n", - "flatten_2 (Flatten) (None, 1764) 0 \n", + "flatten_1 (Flatten) (None, 1764) 0 \n", "_________________________________________________________________\n", - "dense_3 (Dense) (None, 128) 225920 \n", + "dense_2 (Dense) (None, 128) 225920 \n", "_________________________________________________________________\n", - "dense_4 (Dense) (None, 10) 1290 \n", + "dense_3 (Dense) (None, 10) 1290 \n", "=================================================================\n", "Total params: 242,062\n", "Trainable params: 242,062\n", @@ -1708,10 +1280,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We count the indices to get the layers we want.\n", "\n", @@ -1720,12 +1289,8 @@ }, { "cell_type": "code", - "execution_count": 51, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 49, + "metadata": {}, "outputs": [], "source": [ "layer_input = model3.layers[0]" @@ -1733,31 +1298,25 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The first convolutional layer has index 2." ] }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 50, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 52, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" } @@ -1769,22 +1328,15 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The second convolutional layer has index 4." ] }, { "cell_type": "code", - "execution_count": 53, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 51, + "metadata": {}, "outputs": [], "source": [ "layer_conv2 = model3.layers[4]" @@ -1792,10 +1344,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Convolutional Weights\n", "\n", @@ -1804,12 +1353,8 @@ }, { "cell_type": "code", - "execution_count": 54, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 52, + "metadata": {}, "outputs": [], "source": [ "weights_conv1 = layer_conv1.get_weights()[0]" @@ -1817,21 +1362,15 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This gives us a 4-rank tensor." ] }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 53, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -1841,7 +1380,7 @@ "(5, 5, 1, 16)" ] }, - "execution_count": 55, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } @@ -1852,29 +1391,23 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Plot the weights using the helper-function from above." ] }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 54, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -1887,22 +1420,15 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We can also get the weights for the second convolutional layer and plot them." ] }, { "cell_type": "code", - "execution_count": 57, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 55, + "metadata": {}, "outputs": [], "source": [ "weights_conv2 = layer_conv2.get_weights()[0]" @@ -1910,18 +1436,14 @@ }, { "cell_type": "code", - "execution_count": 58, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 56, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" ] }, "metadata": {}, @@ -1934,22 +1456,15 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Helper-function for plotting the output of a convolutional layer" ] }, { "cell_type": "code", - "execution_count": 59, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 57, + "metadata": {}, "outputs": [], "source": [ "def plot_conv_output(values):\n", @@ -1984,10 +1499,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Input Image\n", "\n", @@ -1996,12 +1508,8 @@ }, { "cell_type": "code", - "execution_count": 60, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 58, + "metadata": {}, "outputs": [], "source": [ "def plot_image(image):\n", @@ -2014,168 +1522,49 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Plot an image from the test-set which will be used as an example below." ] }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 59, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "
" ] }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "image1 = data.test.images[0]\n", - "plot_image(image1)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, - "source": [ - "### Output of Convolutional Layer - Method 1\n", - "\n", - "There are different ways of getting the output of a layer in a Keras model. This method uses a so-called K-function which turns a part of the Keras model into a function." - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], - "source": [ - "from tensorflow.python.keras import backend as K" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], - "source": [ - "output_conv1 = K.function(inputs=[layer_input.input],\n", - " outputs=[layer_conv1.output])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, - "source": [ - "We can then call this function with the input image. Note that the image is wrapped in two lists because the function expects an array of that dimensionality. Likewise, the function returns an array with one more dimensionality than we want so we just take the first element." - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(1, 28, 28, 16)" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "layer_output1 = output_conv1([[image1]])[0]\n", - "layer_output1.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, - "source": [ - "We can then plot the output of all 16 channels of the convolutional layer." - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] + "metadata": { + "needs_background": "light" }, - "metadata": {}, "output_type": "display_data" } ], "source": [ - "plot_conv_output(values=layer_output1)" + "image1 = data.x_test[0]\n", + "plot_image(image1)" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ - "### Output of Convolutional Layer - Method 2\n", + "### Output of Convolutional Layer\n", "\n", - "Keras also has another method for getting the output of a layer inside the model. This creates another Functional Model using the same input as the original model, but the output is now taken from the convolutional layer that we are interested in." + "In order to show the output of a convolutional layer, we can create another Functional Model using the same input as the original model, but the output is now taken from the convolutional layer that we are interested in." ] }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 60, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [], @@ -2186,22 +1575,15 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This creates a new model-object where we can call the typical Keras functions. To get the output of the convoloutional layer we call the `predict()` function with the input image." ] }, { "cell_type": "code", - "execution_count": 67, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 61, + "metadata": {}, "outputs": [ { "data": { @@ -2209,7 +1591,7 @@ "(1, 14, 14, 36)" ] }, - "execution_count": 67, + "execution_count": 61, "metadata": {}, "output_type": "execute_result" } @@ -2221,28 +1603,21 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We can then plot the images for all 36 channels." ] }, { "cell_type": "code", - "execution_count": 68, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 62, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -2255,10 +1630,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Conclusion\n", "\n", @@ -2271,10 +1643,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Exercises\n", "\n", @@ -2298,10 +1667,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## License (MIT)\n", "\n", @@ -2332,9 +1698,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.1" + "version": "3.6.10" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/03_PrettyTensor.ipynb b/03_PrettyTensor.ipynb index bb3fbb8..a23fe03 100644 --- a/03_PrettyTensor.ipynb +++ b/03_PrettyTensor.ipynb @@ -2,10 +2,7 @@ "cells": [ { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "# TensorFlow Tutorial #03\n", "# PrettyTensor\n", @@ -16,10 +13,16 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, + "source": [ + "## WARNING!\n", + "\n", + "**This tutorial does not work with TensorFlow v. 1.9 due to the PrettyTensor builder API apparently no longer being updated and supported by the Google Developers. It is recommended that you use the _Keras API_ instead, see Tutorial #03-C.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, "source": [ "## Introduction\n", "\n", @@ -34,20 +37,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Flowchart" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The following chart shows roughly how the data flows in the Convolutional Neural Network that is implemented below. See the previous tutorial for a more detailed description of convolution." ] @@ -56,9 +53,6 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": false }, "outputs": [ @@ -81,10 +75,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The input image is processed in the first convolutional layer using the filter-weights. This results in 16 new images, one for each filter in the convolutional layer. The images are also down-sampled so the image resolution is decreased from 28x28 to 14x14.\n", "\n", @@ -101,10 +92,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Imports" ] @@ -113,9 +101,7 @@ "cell_type": "code", "execution_count": 2, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -134,10 +120,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This was developed using Python 3.5.2 (Anaconda) and TensorFlow version:" ] @@ -145,11 +128,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -168,10 +147,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "PrettyTensor version:" ] @@ -179,11 +155,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -202,20 +174,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Load Data" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The MNIST data-set is about 12 MB and will be downloaded automatically if it is not located in the given path." ] @@ -223,11 +189,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -247,10 +209,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The MNIST data-set has now been loaded and consists of 70,000 images and associated labels (i.e. classifications of the images). The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial." ] @@ -258,11 +217,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -284,10 +239,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The class-labels are One-Hot encoded, which means that each label is a vector with 10 elements, all of which are zero except for one element. The index of this one element is the class-number, that is, the digit shown in the associated image. We also need the class-numbers as integers for the test-set, so we calculate it now." ] @@ -296,9 +248,7 @@ "cell_type": "code", "execution_count": 7, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -307,20 +257,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Data Dimensions" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The data dimensions are used in several places in the source-code below. They are defined once so we can use these variables instead of numbers throughout the source-code below." ] @@ -329,9 +273,7 @@ "cell_type": "code", "execution_count": 8, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -353,20 +295,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Helper-function for plotting images" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Function used to plot 9 images in a 3x3 grid, and writing the true and predicted classes below each image." ] @@ -375,9 +311,7 @@ "cell_type": "code", "execution_count": 9, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -412,10 +346,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Plot a few images to see if data is correct" ] @@ -423,11 +354,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -453,10 +380,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## TensorFlow Graph\n", "\n", @@ -479,20 +403,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Placeholder variables" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Placeholder variables serve as the input to the TensorFlow computational graph that we may change each time we execute the graph. We call this feeding the placeholder variables and it is demonstrated further below.\n", "\n", @@ -503,9 +421,7 @@ "cell_type": "code", "execution_count": 11, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -514,10 +430,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The convolutional layers expect `x` to be encoded as a 4-dim tensor so we have to reshape it so its shape is instead `[num_images, img_height, img_width, num_channels]`. Note that `img_height == img_width == img_size` and `num_images` can be inferred automatically by using -1 for the size of the first dimension. So the reshape operation is:" ] @@ -526,9 +439,7 @@ "cell_type": "code", "execution_count": 12, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -537,10 +448,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Next we have the placeholder variable for the true labels associated with the images that were input in the placeholder variable `x`. The shape of this placeholder variable is `[None, num_classes]` which means it may hold an arbitrary number of labels and each label is a vector of length `num_classes` which is 10 in this case." ] @@ -549,9 +457,7 @@ "cell_type": "code", "execution_count": 13, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -560,10 +466,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We could also have a placeholder variable for the class-number, but we will instead calculate it using argmax. Note that this is a TensorFlow operator so nothing is calculated at this point." ] @@ -572,9 +475,7 @@ "cell_type": "code", "execution_count": 14, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -583,20 +484,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## TensorFlow Implementation" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This section shows the original source-code from Tutorial #02 which implements the Convolutional Neural Network directly in TensorFlow. The code is not actually used in this Notebook and is only meant for easy comparison to the PrettyTensor implementation below.\n", "\n", @@ -605,20 +500,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Helper-functions" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "In the direct TensorFlow implementation, we first make some helper-functions which will be used several times in the graph construction.\n", "\n", @@ -629,9 +518,7 @@ "cell_type": "code", "execution_count": 15, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -643,9 +530,7 @@ "cell_type": "code", "execution_count": 16, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -655,10 +540,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The following helper-function creates a new convolutional network. The input and output are 4-dimensional tensors (aka. 4-rank tensors). Note the low-level details of the TensorFlow API, such as the shape of the weights-variable. It is easy to make a mistake somewhere which may result in strange error-messages that are difficult to debug." ] @@ -667,9 +549,7 @@ "cell_type": "code", "execution_count": 17, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -734,10 +614,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The following helper-function flattens a 4-dim tensor to 2-dim so we can add fully-connected layers after the convolutional layers." ] @@ -746,9 +623,7 @@ "cell_type": "code", "execution_count": 18, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -779,10 +654,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The following helper-function creates a fully-connected layer." ] @@ -791,9 +663,7 @@ "cell_type": "code", "execution_count": 19, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -819,10 +689,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Graph Construction\n", "\n", @@ -837,9 +704,7 @@ "cell_type": "code", "execution_count": 20, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -890,20 +755,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## PrettyTensor Implementation" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This section shows how to make the exact same implementation of a Convolutional Neural Network using PrettyTensor.\n", "\n", @@ -914,9 +773,7 @@ "cell_type": "code", "execution_count": 21, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -925,10 +782,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Now that we have wrapped the input image in a PrettyTensor object, we can add the convolutional and fully-connected layers in just a few lines of source-code.\n", "\n", @@ -939,9 +793,6 @@ "cell_type": "code", "execution_count": 22, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [], @@ -959,10 +810,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "That's it! We have now created the exact same Convolutional Neural Network in a few simple lines of code that required many complex lines of code in the direct TensorFlow implementation.\n", "\n", @@ -971,20 +819,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Getting the Weights" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Unfortunately, not everything is pretty when using PrettyTensor.\n", "\n", @@ -999,9 +841,7 @@ "cell_type": "code", "execution_count": 23, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -1019,10 +859,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Using this helper-function we can retrieve the variables. These are TensorFlow objects. In order to get the contents of the variables, you must do something like: `contents = session.run(weights_conv1)` as demonstrated further below." ] @@ -1031,9 +868,7 @@ "cell_type": "code", "execution_count": 24, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -1043,20 +878,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Optimization Method" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "PrettyTensor gave us the predicted class-label (`y_pred`) as well as a loss-measure that must be minimized, so as to improve the ability of the Neural Network to classify the input images.\n", "\n", @@ -1068,11 +897,7 @@ { "cell_type": "code", "execution_count": 25, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(loss)" @@ -1080,10 +905,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Performance Measures\n", "\n", @@ -1096,9 +918,7 @@ "cell_type": "code", "execution_count": 26, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -1107,10 +927,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Then we create a vector of booleans telling us whether the predicted class equals the true class of each image." ] @@ -1119,9 +936,7 @@ "cell_type": "code", "execution_count": 27, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -1130,10 +945,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The classification accuracy is calculated by first type-casting the vector of booleans to floats, so that False becomes 0 and True becomes 1, and then taking the average of these numbers." ] @@ -1142,9 +954,7 @@ "cell_type": "code", "execution_count": 28, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -1153,20 +963,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## TensorFlow Run" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Create TensorFlow session\n", "\n", @@ -1177,9 +981,7 @@ "cell_type": "code", "execution_count": 29, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -1188,10 +990,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Initialize variables\n", "\n", @@ -1201,11 +1000,7 @@ { "cell_type": "code", "execution_count": 30, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "session.run(tf.global_variables_initializer())" @@ -1213,20 +1008,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Helper-function to perform optimization iterations" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "There are 55,000 images in the training-set. It takes a long time to calculate the gradient of the model using all these images. We therefore only use a small batch of images in each iteration of the optimizer.\n", "\n", @@ -1237,9 +1026,7 @@ "cell_type": "code", "execution_count": 31, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -1248,10 +1035,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Function for performing a number of optimization iterations so as to gradually improve the variables of the network layers. In each iteration, a new batch of data is selected from the training-set and then TensorFlow executes the optimizer using those training samples. The progress is printed every 100 iterations." ] @@ -1259,11 +1043,7 @@ { "cell_type": "code", "execution_count": 32, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# Counter for total number of iterations performed so far.\n", @@ -1320,20 +1100,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Helper-function to plot example errors" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Function for plotting examples of images from the test-set that have been mis-classified." ] @@ -1342,9 +1116,7 @@ "cell_type": "code", "execution_count": 33, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -1378,10 +1150,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Helper-function to plot confusion matrix" ] @@ -1390,9 +1159,7 @@ "cell_type": "code", "execution_count": 34, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -1430,20 +1197,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Helper-function for showing the performance" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Function for printing the classification accuracy on the test-set.\n", "\n", @@ -1455,11 +1216,7 @@ { "cell_type": "code", "execution_count": 35, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# Split the test-set into smaller batches of this size.\n", @@ -1534,10 +1291,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Performance before any optimization\n", "\n", @@ -1547,11 +1301,7 @@ { "cell_type": "code", "execution_count": 36, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1567,10 +1317,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Performance after 1 optimization iteration\n", "\n", @@ -1580,11 +1327,7 @@ { "cell_type": "code", "execution_count": 37, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1603,9 +1346,6 @@ "cell_type": "code", "execution_count": 38, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -1623,10 +1363,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Performance after 100 optimization iterations\n", "\n", @@ -1637,9 +1374,6 @@ "cell_type": "code", "execution_count": 39, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -1658,11 +1392,7 @@ { "cell_type": "code", "execution_count": 40, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1689,10 +1419,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Performance after 1000 optimization iterations\n", "\n", @@ -1703,9 +1430,6 @@ "cell_type": "code", "execution_count": 41, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": false }, "outputs": [ @@ -1734,9 +1458,6 @@ "cell_type": "code", "execution_count": 42, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -1765,10 +1486,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Performance after 10,000 optimization iterations\n", "\n", @@ -1779,9 +1497,6 @@ "cell_type": "code", "execution_count": 43, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -1891,9 +1606,6 @@ "cell_type": "code", "execution_count": 44, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -1950,10 +1662,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Visualization of Weights and Layers\n", "\n", @@ -1962,10 +1671,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Helper-function for plotting convolutional weights" ] @@ -1974,9 +1680,7 @@ "cell_type": "code", "execution_count": 45, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -2028,20 +1732,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Convolution Layer 1" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Now plot the filter-weights for the first convolutional layer.\n", "\n", @@ -2052,9 +1750,6 @@ "cell_type": "code", "execution_count": 46, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": false }, "outputs": [ @@ -2075,20 +1770,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Convolution Layer 2" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Now plot the filter-weights for the second convolutional layer.\n", "\n", @@ -2101,9 +1790,6 @@ "cell_type": "code", "execution_count": 47, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": false }, "outputs": [ @@ -2124,10 +1810,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "There are 16 input channels to the second convolutional layer, so we can make another 15 plots of filter-weights like this. We just make one more with the filter-weights for the second channel. " ] @@ -2135,11 +1818,7 @@ { "cell_type": "code", "execution_count": 48, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -2158,20 +1837,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Close TensorFlow Session" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We are now done using TensorFlow, so we close the session to release its resources." ] @@ -2179,11 +1852,7 @@ { "cell_type": "code", "execution_count": 49, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# This has been commented out in case you want to modify and experiment\n", @@ -2193,10 +1862,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Conclusion\n", "\n", @@ -2209,10 +1875,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Exercises\n", "\n", @@ -2236,10 +1899,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## License (MIT)\n", "\n", @@ -2274,5 +1934,5 @@ } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/04_Save_Restore.ipynb b/04_Save_Restore.ipynb index 523cdba..4491afd 100644 --- a/04_Save_Restore.ipynb +++ b/04_Save_Restore.ipynb @@ -11,6 +11,15 @@ "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## WARNING!\n", + "\n", + "**This tutorial does not work with TensorFlow v. 1.9 due to the PrettyTensor builder API apparently no longer being updated and supported by the Google Developers. It is recommended that you use the _Keras API_ instead, which also makes it much easier to save and load a model, see Tutorial #03-C.**" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -44,7 +53,6 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -104,9 +112,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -133,9 +139,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -169,9 +173,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -199,9 +201,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -231,9 +231,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "data.test.cls = np.argmax(data.test.labels, axis=1)\n", @@ -339,9 +337,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -572,9 +568,7 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "weights_conv1 = get_weights_variable(layer_name='layer_conv1')\n", @@ -602,9 +596,7 @@ { "cell_type": "code", "execution_count": 19, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(loss)" @@ -737,9 +729,7 @@ { "cell_type": "code", "execution_count": 26, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "save_path = os.path.join(save_dir, 'best_validation')" @@ -803,9 +793,7 @@ { "cell_type": "code", "execution_count": 29, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "init_variables()" @@ -873,9 +861,7 @@ { "cell_type": "code", "execution_count": 32, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Counter for total number of iterations performed so far.\n", @@ -1234,9 +1220,7 @@ { "cell_type": "code", "execution_count": 40, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "def print_test_accuracy(show_example_errors=False,\n", @@ -1344,7 +1328,6 @@ "cell_type": "code", "execution_count": 42, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1371,7 +1354,6 @@ "cell_type": "code", "execution_count": 43, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -1412,7 +1394,6 @@ "cell_type": "code", "execution_count": 44, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -1491,7 +1472,6 @@ "cell_type": "code", "execution_count": 45, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1560,9 +1540,7 @@ { "cell_type": "code", "execution_count": 46, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1598,9 +1576,7 @@ { "cell_type": "code", "execution_count": 47, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "init_variables()" @@ -1616,9 +1592,7 @@ { "cell_type": "code", "execution_count": 48, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1643,7 +1617,6 @@ "cell_type": "code", "execution_count": 49, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -1702,7 +1675,6 @@ "cell_type": "code", "execution_count": 51, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1768,7 +1740,6 @@ "cell_type": "code", "execution_count": 52, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1811,9 +1782,7 @@ { "cell_type": "code", "execution_count": 53, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# This has been commented out in case you want to modify and experiment\n", @@ -1875,9 +1844,9 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [conda env:tf-gpu]", + "display_name": "Python 3", "language": "python", - "name": "conda-env-tf-gpu-py" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -1889,9 +1858,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.1" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/05_Ensemble_Learning.ipynb b/05_Ensemble_Learning.ipynb index 60d1ccf..6afc6cf 100644 --- a/05_Ensemble_Learning.ipynb +++ b/05_Ensemble_Learning.ipynb @@ -11,6 +11,15 @@ "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## WARNING!\n", + "\n", + "**This tutorial does not work with TensorFlow v. 1.9 due to the PrettyTensor builder API apparently no longer being updated and supported by the Google Developers. It is recommended that you use the _Keras API_ instead, which also makes it much easier to train or load multiple models to create an ensemble, see e.g. Tutorial #10 for inspiration on how to load and use pre-trained models using Keras.**" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -44,7 +53,6 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -105,7 +113,6 @@ "cell_type": "code", "execution_count": 3, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -134,9 +141,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -170,9 +175,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -200,9 +203,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -234,9 +235,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "data.test.cls = np.argmax(data.test.labels, axis=1)\n", @@ -274,9 +273,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -303,7 +300,6 @@ "cell_type": "code", "execution_count": 10, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -333,9 +329,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -363,9 +357,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -534,9 +526,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -740,9 +730,7 @@ { "cell_type": "code", "execution_count": 23, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(loss)" @@ -1008,9 +996,7 @@ { "cell_type": "code", "execution_count": 35, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "def optimize(num_iterations, x_train, y_train):\n", @@ -1111,9 +1097,7 @@ { "cell_type": "code", "execution_count": 38, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1928,7 +1912,6 @@ "cell_type": "code", "execution_count": 47, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1958,9 +1941,7 @@ { "cell_type": "code", "execution_count": 48, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1989,7 +1970,6 @@ "cell_type": "code", "execution_count": 49, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -2027,9 +2007,7 @@ { "cell_type": "code", "execution_count": 50, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -2057,9 +2035,7 @@ { "cell_type": "code", "execution_count": 51, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -2105,9 +2081,7 @@ { "cell_type": "code", "execution_count": 53, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "ensemble_incorrect = np.logical_not(ensemble_correct)" @@ -2127,9 +2101,7 @@ { "cell_type": "code", "execution_count": 54, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -2156,9 +2128,7 @@ { "cell_type": "code", "execution_count": 55, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -2186,9 +2156,7 @@ { "cell_type": "code", "execution_count": 56, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -2215,9 +2183,7 @@ { "cell_type": "code", "execution_count": 57, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "best_net_pred_labels = pred_labels[best_net, :, :]" @@ -2294,9 +2260,7 @@ { "cell_type": "code", "execution_count": 61, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -2323,9 +2287,7 @@ { "cell_type": "code", "execution_count": 62, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -2352,9 +2314,7 @@ { "cell_type": "code", "execution_count": 63, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "ensemble_better = np.logical_and(best_net_incorrect,\n", @@ -2372,7 +2332,6 @@ "cell_type": "code", "execution_count": 64, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -2421,7 +2380,6 @@ "cell_type": "code", "execution_count": 66, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -2457,9 +2415,7 @@ { "cell_type": "code", "execution_count": 67, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "def plot_images_comparison(idx):\n", @@ -2569,7 +2525,6 @@ "cell_type": "code", "execution_count": 72, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -2598,9 +2553,7 @@ { "cell_type": "code", "execution_count": 73, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -2624,9 +2577,7 @@ { "cell_type": "code", "execution_count": 74, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -2651,7 +2602,6 @@ "cell_type": "code", "execution_count": 75, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -2684,7 +2634,6 @@ "cell_type": "code", "execution_count": 76, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -2714,7 +2663,6 @@ "cell_type": "code", "execution_count": 77, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -2741,7 +2689,6 @@ "cell_type": "code", "execution_count": 78, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -2767,9 +2714,7 @@ { "cell_type": "code", "execution_count": 79, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -2804,9 +2749,7 @@ { "cell_type": "code", "execution_count": 80, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# This has been commented out in case you want to modify and experiment\n", @@ -2867,9 +2810,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [conda env:tf-gpu]", + "display_name": "Python 3", "language": "python", - "name": "conda-env-tf-gpu-py" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -2881,9 +2824,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.1" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/06_CIFAR-10.ipynb b/06_CIFAR-10.ipynb index 8a145e4..9e45d12 100644 --- a/06_CIFAR-10.ipynb +++ b/06_CIFAR-10.ipynb @@ -11,6 +11,15 @@ "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## WARNING!\n", + "\n", + "**This tutorial does not work with TensorFlow v. 1.9 due to the PrettyTensor builder API apparently no longer being updated and supported by the Google Developers. It would take too much effort to update this tutorial to use e.g. the Keras API, especially because there is already a short [Keras tutorial on CIFAR-10](https://github.com/keras-team/keras/blob/master/examples/cifar10_cnn.py) which does the same.**" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -42,7 +51,6 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -103,7 +111,6 @@ "cell_type": "code", "execution_count": 3, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -132,9 +139,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -198,7 +203,6 @@ "cell_type": "code", "execution_count": 7, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -224,9 +228,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -270,9 +272,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -300,9 +300,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -327,7 +325,6 @@ "cell_type": "code", "execution_count": 11, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -472,7 +469,6 @@ "cell_type": "code", "execution_count": 15, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -508,9 +504,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -712,9 +706,7 @@ { "cell_type": "code", "execution_count": 22, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "distorted_images = pre_process(images=x, training=True)" @@ -1021,9 +1013,7 @@ { "cell_type": "code", "execution_count": 34, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "weights_conv1 = get_weights_variable(layer_name='layer_conv1')\n", @@ -1049,9 +1039,7 @@ { "cell_type": "code", "execution_count": 35, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "def get_layer_output(layer_name):\n", @@ -1075,9 +1063,7 @@ { "cell_type": "code", "execution_count": 36, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "output_conv1 = get_layer_output(layer_name='layer_conv1')\n", @@ -1182,9 +1168,7 @@ { "cell_type": "code", "execution_count": 41, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1559,9 +1543,7 @@ { "cell_type": "code", "execution_count": 50, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "def print_test_accuracy(show_example_errors=False,\n", @@ -1808,7 +1790,6 @@ "cell_type": "code", "execution_count": 56, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1847,7 +1828,6 @@ "cell_type": "code", "execution_count": 57, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [], @@ -1869,7 +1849,6 @@ "cell_type": "code", "execution_count": 58, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1930,7 +1909,6 @@ "cell_type": "code", "execution_count": 59, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -1968,7 +1946,6 @@ "cell_type": "code", "execution_count": 60, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -2048,9 +2025,7 @@ { "cell_type": "code", "execution_count": 62, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -2079,7 +2054,6 @@ "cell_type": "code", "execution_count": 63, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -2109,7 +2083,6 @@ "cell_type": "code", "execution_count": 64, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -2145,9 +2118,7 @@ { "cell_type": "code", "execution_count": 65, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "label_pred, cls_pred = session.run([y_pred, y_pred_cls],\n", @@ -2165,7 +2136,6 @@ "cell_type": "code", "execution_count": 66, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -2197,9 +2167,7 @@ { "cell_type": "code", "execution_count": 67, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -2219,9 +2187,7 @@ { "cell_type": "code", "execution_count": 68, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -2255,9 +2221,7 @@ { "cell_type": "code", "execution_count": 69, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# This has been commented out in case you want to modify and experiment\n", @@ -2315,9 +2279,9 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [conda env:tf-gpu]", + "display_name": "Python 3", "language": "python", - "name": "conda-env-tf-gpu-py" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -2329,9 +2293,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.1" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/07_Inception_Model.ipynb b/07_Inception_Model.ipynb index 1ae5c56..da11720 100644 --- a/07_Inception_Model.ipynb +++ b/07_Inception_Model.ipynb @@ -2,10 +2,7 @@ "cells": [ { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "# TensorFlow Tutorial #07\n", "# Inception Model\n", @@ -16,10 +13,16 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, + "source": [ + "## WARNING!\n", + "\n", + "**This tutorial does not work with TensorFlow v.2. It would take too much effort to update this tutorial to use e.g. the Keras API, especially because Tutorial #10 is somewhat similar.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, "source": [ "## Introduction\n", "\n", @@ -34,20 +37,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Flowchart" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The following chart shows how the data flows in the Inception v3 model, which is a Convolutional Neural Network with many layers and a complicated structure. The [research paper](http://arxiv.org/pdf/1512.00567v3.pdf) gives more details on how the Inception model is constructed and why it is designed that way. But the authors admit that they don't fully understand why it works.\n", "\n", @@ -60,9 +57,6 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -85,10 +79,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Imports" ] @@ -97,9 +88,7 @@ "cell_type": "code", "execution_count": 2, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -115,10 +104,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This was developed using Python 3.5.2 (Anaconda) and TensorFlow version:" ] @@ -126,11 +112,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -149,20 +131,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Download the Inception Model" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The Inception model is downloaded from the internet. This is the default directory where you want to save the data-files. The directory will be created if it does not exist." ] @@ -171,9 +147,7 @@ "cell_type": "code", "execution_count": 4, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -182,10 +156,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Download the data for the Inception model if it doesn't already exist in the directory. It is 85 MB." ] @@ -193,11 +164,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -214,20 +181,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Load the Inception Model" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Load the Inception model so it is ready for classifying images." ] @@ -235,11 +196,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "model = inception.Inception()" @@ -247,20 +204,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Helper-function for classifying and plotting images" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This is a simple wrapper-function for displaying the image, then classifying it using the Inception model and finally printing the classification scores." ] @@ -269,9 +220,7 @@ "cell_type": "code", "execution_count": 7, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -288,20 +237,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Panda" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This image of a panda is included in the Inception data-file. The Inception model is quite confident that this image shows a panda, with a classification score of about 89% and the next highest score being only about 0.8% for an indri, which is another exotic animal." ] @@ -310,9 +253,6 @@ "cell_type": "code", "execution_count": 8, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -350,10 +290,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Interpretation of Classification Scores\n", "\n", @@ -370,20 +307,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Parrot (Original Image)" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The Inception model is very confident (score about 97%) that this image shows a kind of parrot called a macaw." ] @@ -392,9 +323,6 @@ "cell_type": "code", "execution_count": 9, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -431,20 +359,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Parrot (Resized Image)" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The Inception model works on input images that are 299 x 299 pixels in size. The above image of a parrot is actually 320 pixels wide and 785 pixels high, so it is resized automatically by the Inception model.\n", "\n", @@ -457,9 +379,7 @@ "cell_type": "code", "execution_count": 10, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -476,10 +396,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Now plot the resized image of the parrot. This is the image that is actually input to the neural network of the Inception model. We can see that it has been squeezed so it is square, and the resolution has been reduced so the image has become more pixelated and grainy.\n", "\n", @@ -490,9 +407,6 @@ "cell_type": "code", "execution_count": 11, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -513,20 +427,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Parrot (Cropped Image, Top)" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This image of the parrot has been cropped manually to 299 x 299 pixels and then input to the Inception model, which is still very confident (score about 97%) that it shows a parrot (macaw)." ] @@ -535,9 +443,6 @@ "cell_type": "code", "execution_count": 12, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -574,20 +479,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Parrot (Cropped Image, Middle)" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This is another crop of the parrot image, this time showing its body without the head or tail. The Inception model is still very confident (score about 94%) that it shows a macaw parrot." ] @@ -596,9 +495,6 @@ "cell_type": "code", "execution_count": 13, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -635,20 +531,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Parrot (Cropped Image, Bottom)" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This image has been cropped so it only shows the tail of the parrot. Now the Inception model is quite confused and thinks the image might show a jacamar (score about 26%) which is another exotic bird, or perhaps the image shows a grass-hopper (score about 10%).\n", "\n", @@ -659,9 +549,6 @@ "cell_type": "code", "execution_count": 14, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -698,20 +585,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Parrot (Padded Image)" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The best way to input images to this Inception model, is to pad the image so it is square and then resize the image to 299 x 299 pixels, like this example of the parrot which is classified correctly with a score of about 97%." ] @@ -720,9 +601,6 @@ "cell_type": "code", "execution_count": 15, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -759,20 +637,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Elon Musk (299 x 299 pixels)" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This image shows the living legend and super-nerd-hero Elon Musk. But the Inception model is very confused about what the image shows, predicting that it maybe shows a sweatshirt (score about 17%) or an abaya (score about 16%). It also thinks the image might show a ping-pong ball (score about 3%) or a baseball (score about 2%). So the Inception model is confused and the classification scores are unreliable." ] @@ -781,9 +653,6 @@ "cell_type": "code", "execution_count": 16, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": false }, "outputs": [ @@ -820,20 +689,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Elon Musk (100 x 100 pixels)" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "If we instead use a 100 x 100 pixels image of Elon Musk, then the Inception model thinks it might show a sweatshirt (score about 22%) or a cowboy boot (score about 14%). So now the Inception model has somewhat different predictions but it is still very confused." ] @@ -842,9 +705,6 @@ "cell_type": "code", "execution_count": 17, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -881,10 +741,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The Inception model automatically upscales this image from 100 x 100 to 299 x 299 pixels, which is shown here. Note how pixelated and grainy it really is, although a human can easily see that this is a picture of a man with crossed arms." ] @@ -893,9 +750,6 @@ "cell_type": "code", "execution_count": 18, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -916,20 +770,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Willy Wonka (Gene Wilder)" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This image shows the actor Gene Wilder portraying Willy Wonka in the 1971 version of the movie. The Inception model is very confident that the image shows a bow tie (score about 98%), which is true but a human would probably say this image shows a person.\n", "\n", @@ -940,9 +788,6 @@ "cell_type": "code", "execution_count": 19, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -979,20 +824,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Willy Wonka (Johnny Depp)" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This image shows the actor Johnny Depp portraying Willy Wonka in the 2005 version of the movie. The Inception model thinks that this image shows \"sunglasses\" (score about 34%) or \"sunglass\" (score about 18%). Actually, the full name of the first class is \"sunglasses, dark glasses, shades\". For some reason the Inception model has been trained to recognize two very similar classes for sunglasses. Once again, it is correct that the image shows sunglasses, but a human would probably have said that this image shows a person." ] @@ -1001,9 +840,6 @@ "cell_type": "code", "execution_count": 20, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -1040,20 +876,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Close TensorFlow Session" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We are now done using TensorFlow, so we close the session to release its resources. Note that the TensorFlow-session is inside the model-object, so we close the session through that object." ] @@ -1062,9 +892,7 @@ "cell_type": "code", "execution_count": 21, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -1075,10 +903,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Conclusion\n", "\n", @@ -1091,10 +916,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Exercises\n", "\n", @@ -1111,10 +933,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## License (MIT)\n", "\n", @@ -1145,9 +964,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.1" + "version": "3.6.10" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/08_Transfer_Learning.ipynb b/08_Transfer_Learning.ipynb index e90922e..af5aede 100644 --- a/08_Transfer_Learning.ipynb +++ b/08_Transfer_Learning.ipynb @@ -11,6 +11,15 @@ "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## WARNING!\n", + "\n", + "**This tutorial does not work with TensorFlow v. 1.9 due to the PrettyTensor builder API apparently no longer being updated and supported by the Google Developers. It would take too much effort to update this tutorial to use e.g. the Keras API, especially because Tutorial #10 is a similar but more advanced version of Transfer Learning using the Keras builder API. However, you may still want to watch the video for this Tutorial #08 as it explains more details about Transfer Learning than Tutorial #10 does.**" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -52,7 +61,6 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -114,7 +122,6 @@ "cell_type": "code", "execution_count": 3, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -143,9 +150,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -226,9 +231,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -252,9 +255,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -298,9 +299,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -328,9 +327,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -354,9 +351,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -457,9 +452,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -521,7 +514,6 @@ "cell_type": "code", "execution_count": 16, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -558,7 +550,6 @@ "cell_type": "code", "execution_count": 17, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [], @@ -585,9 +576,7 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "from inception import transfer_values_cache" @@ -603,9 +592,7 @@ { "cell_type": "code", "execution_count": 19, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "file_path_cache_train = os.path.join(cifar10.data_path, 'inception_cifar10_train.pkl')\n", @@ -615,9 +602,7 @@ { "cell_type": "code", "execution_count": 20, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -645,9 +630,7 @@ { "cell_type": "code", "execution_count": 21, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -682,9 +665,7 @@ { "cell_type": "code", "execution_count": 22, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -711,9 +692,7 @@ { "cell_type": "code", "execution_count": 23, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -740,9 +719,7 @@ { "cell_type": "code", "execution_count": 24, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "def plot_transfer_values(i):\n", @@ -767,7 +744,6 @@ "cell_type": "code", "execution_count": 25, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -814,7 +790,6 @@ "cell_type": "code", "execution_count": 26, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -892,9 +867,7 @@ { "cell_type": "code", "execution_count": 28, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "pca = PCA(n_components=2)" @@ -910,9 +883,7 @@ { "cell_type": "code", "execution_count": 29, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "transfer_values = transfer_values_train[0:3000]" @@ -947,7 +918,6 @@ "cell_type": "code", "execution_count": 31, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -995,7 +965,6 @@ "cell_type": "code", "execution_count": 33, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1024,9 +993,7 @@ { "cell_type": "code", "execution_count": 34, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "def plot_scatter(values, cls):\n", @@ -1057,7 +1024,6 @@ "cell_type": "code", "execution_count": 35, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -1141,9 +1107,7 @@ { "cell_type": "code", "execution_count": 39, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "transfer_values_reduced = tsne.fit_transform(transfer_values_50d) " @@ -1160,7 +1124,6 @@ "cell_type": "code", "execution_count": 40, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1191,9 +1154,7 @@ { "cell_type": "code", "execution_count": 41, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1324,9 +1285,7 @@ { "cell_type": "code", "execution_count": 46, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Wrap the transfer-values as a Pretty Tensor object.\n", @@ -1417,9 +1376,7 @@ { "cell_type": "code", "execution_count": 50, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "correct_prediction = tf.equal(y_pred_cls, y_true_cls)" @@ -1492,9 +1449,7 @@ { "cell_type": "code", "execution_count": 53, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "session.run(tf.global_variables_initializer())" @@ -1905,7 +1860,6 @@ "cell_type": "code", "execution_count": 63, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1939,9 +1893,7 @@ { "cell_type": "code", "execution_count": 64, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -2058,9 +2010,7 @@ { "cell_type": "code", "execution_count": 65, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -2185,9 +2135,9 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [conda env:tf-gpu]", + "display_name": "Python 3", "language": "python", - "name": "conda-env-tf-gpu-py" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -2199,9 +2149,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.10" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/09_Video_Data.ipynb b/09_Video_Data.ipynb index f0e39d7..44117d1 100644 --- a/09_Video_Data.ipynb +++ b/09_Video_Data.ipynb @@ -11,6 +11,15 @@ "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## WARNING!\n", + "\n", + "**This tutorial does not work with TensorFlow v. 1.9 due to the PrettyTensor builder API apparently no longer being updated and supported by the Google Developers. It would take too much effort to update this tutorial to use e.g. the Keras API, especially because Tutorial #10 is a similar but more advanced version of Transfer Learning using the Keras builder API. However, you may still want to watch the video for Tutorials #08 and #09 as they explain more details about Transfer Learning than Tutorial #10 does.**" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -50,7 +59,6 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -112,7 +120,6 @@ "cell_type": "code", "execution_count": 3, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -141,9 +148,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -243,7 +248,6 @@ "cell_type": "code", "execution_count": 9, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -272,7 +276,6 @@ "cell_type": "code", "execution_count": 10, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -303,9 +306,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# This is the code you would run to load your own image-files.\n", @@ -333,9 +334,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -363,9 +362,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "image_paths_train, cls_train, labels_train = dataset.get_training_set()" @@ -381,9 +378,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -410,9 +405,7 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "image_paths_test, cls_test, labels_test = dataset.get_test_set()" @@ -428,9 +421,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -458,7 +449,6 @@ "cell_type": "code", "execution_count": 17, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -593,9 +583,7 @@ { "cell_type": "code", "execution_count": 20, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -657,7 +645,6 @@ "cell_type": "code", "execution_count": 22, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -694,7 +681,6 @@ "cell_type": "code", "execution_count": 23, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [], @@ -721,9 +707,7 @@ { "cell_type": "code", "execution_count": 24, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "from inception import transfer_values_cache" @@ -739,9 +723,7 @@ { "cell_type": "code", "execution_count": 25, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "file_path_cache_train = os.path.join(data_dir, 'inception-knifey-train.pkl')\n", @@ -751,9 +733,7 @@ { "cell_type": "code", "execution_count": 26, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -777,9 +757,7 @@ { "cell_type": "code", "execution_count": 27, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -810,9 +788,7 @@ { "cell_type": "code", "execution_count": 28, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -839,9 +815,7 @@ { "cell_type": "code", "execution_count": 29, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -868,9 +842,7 @@ { "cell_type": "code", "execution_count": 30, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "def plot_transfer_values(i):\n", @@ -896,7 +868,6 @@ "cell_type": "code", "execution_count": 31, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -943,7 +914,6 @@ "cell_type": "code", "execution_count": 32, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -1021,9 +991,7 @@ { "cell_type": "code", "execution_count": 34, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "pca = PCA(n_components=2)" @@ -1039,9 +1007,7 @@ { "cell_type": "code", "execution_count": 35, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# transfer_values = transfer_values_train[0:3000]\n", @@ -1078,7 +1044,6 @@ "cell_type": "code", "execution_count": 37, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1126,7 +1091,6 @@ "cell_type": "code", "execution_count": 39, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1155,9 +1119,7 @@ { "cell_type": "code", "execution_count": 40, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "def plot_scatter(values, cls):\n", @@ -1191,7 +1153,6 @@ "cell_type": "code", "execution_count": 41, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1275,9 +1236,7 @@ { "cell_type": "code", "execution_count": 45, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "transfer_values_reduced = tsne.fit_transform(transfer_values_50d) " @@ -1294,7 +1253,6 @@ "cell_type": "code", "execution_count": 46, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1326,7 +1284,6 @@ "cell_type": "code", "execution_count": 47, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1459,9 +1416,7 @@ { "cell_type": "code", "execution_count": 52, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Wrap the transfer-values as a Pretty Tensor object.\n", @@ -1552,9 +1507,7 @@ { "cell_type": "code", "execution_count": 56, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "correct_prediction = tf.equal(y_pred_cls, y_true_cls)" @@ -1627,9 +1580,7 @@ { "cell_type": "code", "execution_count": 59, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "session.run(tf.global_variables_initializer())" @@ -1782,9 +1733,7 @@ { "cell_type": "code", "execution_count": 63, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "def plot_example_errors(cls_pred, correct):\n", @@ -2050,7 +1999,6 @@ "cell_type": "code", "execution_count": 69, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -2090,7 +2038,6 @@ "cell_type": "code", "execution_count": 70, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -2120,7 +2067,6 @@ "cell_type": "code", "execution_count": 71, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -2239,9 +2185,9 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [conda env:tf-gpu]", + "display_name": "Python 3", "language": "python", - "name": "conda-env-tf-gpu-py" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -2253,9 +2199,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.1" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/10_Fine-Tuning.ipynb b/10_Fine-Tuning.ipynb index 924ca7c..01bc5c9 100644 --- a/10_Fine-Tuning.ipynb +++ b/10_Fine-Tuning.ipynb @@ -55,16 +55,7 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/importlib/_bootstrap.py:205: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n", - " return f(*args, **kwds)\n" - ] - } - ], + "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", @@ -78,7 +69,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "These are the imports from the Keras API. Note the long format which can hopefully be shortened in the future to e.g. `from tf.keras.models import Model`." + "These are the imports from the Keras API." ] }, { @@ -87,12 +78,41 @@ "metadata": {}, "outputs": [], "source": [ - "from tensorflow.python.keras.models import Model, Sequential\n", - "from tensorflow.python.keras.layers import Dense, Flatten, Dropout\n", - "from tensorflow.python.keras.applications import VGG16\n", - "from tensorflow.python.keras.applications.vgg16 import preprocess_input, decode_predictions\n", - "from tensorflow.python.keras.preprocessing.image import ImageDataGenerator\n", - "from tensorflow.python.keras.optimizers import Adam, RMSprop" + "from tensorflow.keras.models import Model, Sequential\n", + "from tensorflow.keras.layers import Dense, Flatten, Dropout\n", + "from tensorflow.keras.applications import VGG16\n", + "from tensorflow.keras.applications.vgg16 import preprocess_input, decode_predictions\n", + "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", + "from tensorflow.keras.optimizers import Adam, RMSprop" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This was developed using Python 3.6 and TensorFlow version:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'2.1.0'" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tf.__version__" ] }, { @@ -111,7 +131,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -135,7 +155,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -199,7 +219,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -235,7 +255,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -274,7 +294,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -290,8 +310,7 @@ " generator_test.reset()\n", " \n", " # Predict the classes for all images in the test-set.\n", - " y_pred = new_model.predict_generator(generator_test,\n", - " steps=steps_test)\n", + " y_pred = new_model.predict(generator_test, steps=steps_test)\n", "\n", " # Convert the predicted classes from arrays to integers.\n", " cls_pred = np.argmax(y_pred,axis=1)\n", @@ -314,7 +333,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -337,7 +356,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -378,7 +397,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -394,7 +413,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": { "scrolled": true }, @@ -420,7 +439,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": { "scrolled": true }, @@ -449,7 +468,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -472,7 +491,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": { "scrolled": true }, @@ -494,7 +513,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -503,13 +522,13 @@ "(224, 224)" ] }, - "execution_count": 15, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "input_shape = model.layers[0].output_shape[1:3]\n", + "input_shape = model.layers[0].output_shape[0][1:3]\n", "input_shape" ] }, @@ -524,7 +543,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -549,7 +568,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -565,7 +584,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -581,7 +600,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -602,7 +621,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": { "scrolled": true }, @@ -632,7 +651,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -661,7 +680,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -670,7 +689,7 @@ "26.5" ] }, - "execution_count": 22, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -689,7 +708,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -706,7 +725,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -723,7 +742,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -732,7 +751,7 @@ "['forky', 'knifey', 'spoony']" ] }, - "execution_count": 25, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -751,7 +770,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -760,13 +779,13 @@ "3" ] }, - "execution_count": 26, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "num_classes = generator_train.num_class\n", + "num_classes = generator_train.num_classes\n", "num_classes" ] }, @@ -779,16 +798,16 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 28, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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9TojWc7Q4AkW20rtfPT4YDB8WQwB8ZonTG0GYJF9bzByApdfrEScJFt9MCpjP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\njx495smzE0bDEUmS0u32SVttBgPH8fEzwDeT398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p4UZU3bGCNAM6qtHHoLmfDj0bfAvD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lDXQimfs7A9HmlKRr2c/aMHpMMxx6cnnB4fk3h459EjZodHZIMJpfT47Pic849/m3f6S4ajEZ5N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\nGudAm4TpbE6SpOR5wXq9wRhDY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h0gk8e4YjmpFYGwMOEclvMCJd1hsT/F798v9DLLublr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\nkhDypiTjk+zRNjZ0MFTBYdhKpA8RW/oitYwHTFLSLCFJC+rJHQeHB1xcX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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -819,7 +838,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -828,7 +847,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -846,16 +865,16 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([ 1.39839034, 1.14876033, 0.70701933])" + "array([1.39839034, 1.14876033, 0.70701933])" ] }, - "execution_count": 30, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -866,7 +885,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -875,7 +894,7 @@ "['forky', 'knifey', 'spoony']" ] }, - "execution_count": 31, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -897,7 +916,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -935,19 +954,21 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 34, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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IRzi+V6ajk6dAzZno0IkyJGc7GP1YiUNAkkCqjEMl9lAIPBwKXTV6E3bJebaF6xGGXpAk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GrYGpOUkpp0AguvBt77b5tQTXiYh3Y+4jLb5RUCdWEWxZ7Pi6PMjy81vuyRiyKyklPnudagMt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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -975,28 +996,30 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 35, "metadata": {}, "outputs": [ { "data": { - "image/png": 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9/iEAP8XM3wXgp9Lvm6kMhBc5QvkCaQd5EDzsngYpS2Hu9SlByhCqwrd7/sv6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zAAAg\nAElEQVTEpL4vQBPeHaJwVf/fKaLRUfBLQrDVM6OT0hvdv69IV2Q5BEDo+RVaH0wtMGDTG4csT3s3\nbmIuYGJ6j55rXFM8RyXbIjApyPaU2qM5+MLE5/jYSONGvc+OggcJaNNHE3RaP4bvpBnWLo8Hzi87\nG7XNh/Q4i7iBoioR/RU60TGU3bo+znuYXFEvT41V2QdVO3Cq5kO/V4y50ByVCdMh4/Wr1zgej3j1\n6qUemybM0wRhNU8SAedlAYs6QnnpO68zoefouJSqaoMDkUxeDzOhrAXrNGPKGdOcQUmQJyUu52XF\nsq6oFWDO7T3Y3BhVgkbDqLKpq8hpu4Xe2N4dogCgybLxCJGl9HNO1R09Ru5MF9eNNv6EWPrNmwSO\nIeE+g42bAKrUrA6ex1GNP6ovXJrSosNUByMvwb9usdi+g24S5yjuu+DPqeHzpbOV+3Q4AejOVaGP\nJgmIbbRxU49OY1vdfvvsa+bIeL/tuEe34WEuxDMoK+YQC+e4bKVgWlQ/wv0l/rlVJYOKMxD4cQ05\nIJluCGl6D/M0I6WEly9fNhfpOU84HA6Y5lnXiZwBvEKtFbe3t0NR5GnKWNdiFcn1OU4YBIatsJYn\nWLN6SM5zVs02MZazqiFS3SLhUk4y70p2FtBeziEXuqSgb2zvHlEIL+UEoU+ecRLs+87rZfsht9t6\nehHEarooYM5DZmGAB0BBwTfbwL3yUyQAMmAGPU9i98e/ton1nGxStXNIhnDp9w7v65V/VPqJmzWA\naO0Z8Znjsz3gJ2ZPir9j803sHPBtRCGO/TgPvHvNRdIZbLNIda4eehU+q1oxqgKXzlU9otXNqX2z\nSAOEMKwbEsLhcIP333/fci68wvl8xsPDA47HI+7u7vD06VNQUsBvWTXs+unTpyZteFVuW8wGDjbi\nZ5gPmx8HDHNYFrF0eBWlCuoKs7yQv67OY0rd/yOR5tFkV9Xi+DyuvVNEoS0UQNFlUj2rZyk2h5lw\nDcFxg9GEuE2s4l5m+hx1LtmG5wJ9MWj2naIeYcyoRd1M3aVVwSQzPxXNNNxEdXJVvKd573Uwtdce\nh9GJmJibqpqapBSA3adeANLjZV1wAaxdcAHFO/R7tUjEYK1+lm023vy9Qxz8WbVa2DnxcDyK+V1N\noAbGbv0EfIzjpvfrtol7O8FQHd+lxc4/opqo78NVNGMR9lUZvXccqz50RGRl6owpJU1++vTpU9zd\n3aGUD7AsC15+rolaXr16hWVZ8Oy994CUUUvBw8MDXr58idvb2yY1pESY8oSUpmatur29BdcVy3nF\nshSsa9X0cmXViE/WrFK6EDOEM0oh1EqAJBBmxVrmXksVRM2qBmQFV3+TMi99xS2i3Qif1Y+8/b1l\nDnaWWtccf7gstTambe8eYJEpurQwXIu+MB2sGsTZRvF7MU+3Z+811yudODS9PqxKYatfseXYgwgf\nRmyXMLRLTFx1P4sdjCCqPNgnNO28ILbrcUL303eiGwgQdzHXN3NUGyKR2GZyjs/dU0HiOZcOYQDY\nQ46toK509nrtXu4El/MEiKDwagyqe6h6gtfJvCU958Lr+9cQ0aI4Nzc3ENGKVSKCm5sbTFNGnuYm\nsWksxgHLWVDWaqoAG5brhWEr1qVARHMs1JVQSzJAMYNoBSEjyWzbQwCug+Vq1/PzDe2dIAq+8YCu\nQBBR8yhzs85gtzYbe7J5Zksq2J0/TKSimMBlywk7RuAFTVyk80VW11UxBGEDeLoHnuu7Uiog0lQP\nf0YitYF7/kaQovKRU3aXW9j9GBxK1rkIWGuFpKx+FCY9CMswJs2pCozJSrUxuzmuv7PnlxhBPNj4\nBxNjI1BmhUlkyVxingDf3O6TAXhUX3uHpJJOXJxRAtiqDcAYGLVVP7YEoo/B6PMgEKQEI+amCgzE\nbkfdEZcWVXpwi8SIN+i5T58+xTRNuLu7w4sXLxVvWNQ9OueMm5sb1FpbdqcpZ8w3N5jnm6HfKblF\nyzY7VrAoQShrMctHAmFCKYSyrChFUKvjVQlynDBNui6IgZvDjDzdqMgq1GpwPKa9E0QBGCe/Lx5f\nLLWZ5fqmByDqByhmh2kYQFs0rttvuYMM92r6NggSEPCccw9jNVHTF0irJxE4LaFzO39+eJzlfowm\ntujb0PM9iInescX4eiObcB145KZe/s02qTl4+TP6O+Oy/21sTUcTdO+9pHUpZVMAp++VUeIYRDqm\nLhTR2F+PZNxiEnG+olT2ptbfMUgL4uvDwNpWs3F83+0zWt7J7Nah+D03on9zc2OSYQYlAn/+Esui\nBWuixLAsCxYRHJhR62bMxSVXJ6xkWZ3W1hddOxW1AqUwVsvl4IytQKWbnAjugHpzmCFZzdbp8drD\nl6778J8D+LvslA8AfC4iP0lEvw3A3wTwf9p3vygiP/vWZ6BT/pSzZUxA4GI+0ToIOU+AofGd6/ag\nmou7u1gWRPXxvEBo4KaobESBDS9QTpNSrC/hvduoNeibfywP138gYzBQ3xTkRgFEi0B8QNw7kXN6\nyjgYgWmvhm33/F2vWwwaIZNOpIW3J+3jMVsdvun/aTzu/d9iI29KHXbNyuSYQ3uePUojCI0oCUE9\nXWlDFFIjJN3ykoy0OUa0o74YCD3PM54/f45pPiBPMz7//HMsy4JXr14hZ3VlTiljXRc83D/gfNZy\nd80Xo6qE6NW1FcuwMOnKhke5ROsSa7Wx036f1qLBXaSZn3Im1HIA51lren7FmMJ/jE3dBxH558Ok\n/EkAL8L5vyIiP/noHmyau+rWHdG2LzbVESszPKnElhaMkzjiFfvEY7w2udUAAMAqQRAg0iUBT/2F\nAOLt5xZ0VaXXE9DnCNjdtBvH7/30NN5cuUsZDDBJ0xn9GY0omFSQmzQgkKzYzCUC3YnDBQrffvdn\n5Bxdka+dj+GaOBZEWl0qXhd/tgTybfO03/qG97mIKdG0H72OQ2zNDJlSlxRIgCRNrYjvHvtLRHj2\n9BmSmSlfvHiB+/t7vHjxAk+fPlVAkTOOxxOWtWCe5xaWncwkWUsPcPL+eP8rVGJTILqvIxaGsIZ5\ncy0Q0dTuh1l9KHK6jBh6W/uB6j6QjtA/B+Af/YLP3bubglEOFopu+JZfEUHF0NO9h2EzXfQdvuCV\n2hbvd/sdiYdiFf1anSjT85lbeXmg4xC1Vo1fD/1zyUP7z/bZYiAC2q4LgFtIdCsE4v8Hk1rb4OKk\n4ErmIomDIzaO0qSOLcdjVglsm3w2irfbzR1Vo32xH8P32lSsFbounVyVWnBJyPc4t3/vUmOMwNRp\nIUA8K7KXfXP1oUsO8doWfU/xGSOhakQsJdzd3WGalDAcDodWq9KlxmmamvPSuq6KK1mody3VKlOp\nBKBzA3i1arWqmHkdhqtJReWiTIAFXKqWp2O2FLAw57rHtx8UU/iHAHwqIn8rHPvtRPTXALwE8EdE\n5C8/5kaeg0BdM10M7tGGb1sw2zbq7mTc+vrzXT935FdEQEwoNnnMFSlT2NCjDZ3ifdpG9ghPPz7q\n8KoOpRgKDw/uslXaXK5bP+FmRl+cHdnvf/um6bH6TaLeEAb/Lg7h3kbfs0Zsx3lLOC7mpRG0y/tc\ne8bbJDr/vadW+G8nYkpo0dSBAHS099/FL3ak0Pg+zqj88zzPeO+993BzcwMiwqtXr/DixQvknPHk\niVbILqW0H7W2uxVHWoIXVScMczCTvONAgFb9VumggDDZ9wWaBr4ChqeJ0CDNvq39oEThXwTw58Lf\n3wHwW0Xk+0T0UwD+AhH9hIi83F5IsRiMOYVEnbKXTCOIjGXbWQRUt4VmLzsXN6063ShIZM+/EF9d\nUIjIdy1aSpzBiMDcqAaMPhJE3VkqLnqR3qdY8+ECUIPVFQTgnpi68AOI2bhcQjT3xf70547fXb53\nW9I7qsB1otvmI4xDvOaCUJjqFyW/7XP2rtv7PLzrBVHwdTMSwOHaRghHgtDwkyschBzsQVc3munP\nvBWJqGEGn3zyCaZpwqeffmqp3FgjMW9vsSwLXt+/Rj35yEC9GqumdHermaqpWkRWx6w2r9rKRfM0\ncHdES6SRfW1+mSE7++Na+9JEgTSdyz8D4Kf8mKif59k+/xIR/QqAH4dWkRqabIrBxGw4/sI5Z1tI\ndZhY5mrAVRpExJEL723K/aQgcZNrpekuBYi5j+p5aP1rrs6kYcqRmziBGReW1Vbg0VfgkiN7hql4\nLDeAk6Aij5e9G58pzQKjB03SeAP0rLdRiaUzwB1CRRS+H8c6Zq0axzoF4m6vlEaC1e99DSj+Yq1f\n7x6N+6rH22pB7kkK/Z2lHadkgWt6AkCj+nF7e4uvfe1rYBZ873vfbRmYD4eDWl5AKHVBrX3dKHZQ\nUblqGrZioOKON2j1tVpL66PBbGqFEGnWiMe2H0RS+McA/B8i8qtt7Ig+BvCZiFQi+h3Qug9/+zE3\n61yCh3x6ahsfxVP9is1j7tIrMbZLfdcXROeMzjn8/D0O5qTWfRP8JyXPBAUr3tMJiN+vcyAZju19\ndk5qR1o/iXrOBl+AYuJrkn5fgWwWTj83ir3tp6kddLVfEaxrAxXGN0oLsen5wReB0IukDuegSU9b\nrOCxRGLbb/UB8WItPGzU4bq33hhQKezyq2VZepKVWrFWzY8xT5rLQkQDoW5vb/GjP/oxiBjf+c6n\nOB4fMM8T5sMBh8OM4+uzEQHNxThKc2ymx2CyJ4/jKXDPUvezYanINA1ENl1592vtS9V9EJGfg1aX\n/nOb038XgD9KRCtUTv9ZEfns7d3Q3AbMjLUUjV03oC6xT3AHrGB2dHIp2p1sLPefL1LFu8icj7qz\nzJwnM2fqDdZSsVZuAD1XQ/BzApImWGHSDedOUESEnDXxi7uUqsRAqMJg9GSdUtnSmhOmw0GzB5mU\nQSpq6KuJBmOXWpGJMOUn9oKClG9QU4ZbQghixUWAU6lAtRJhlkdhrQA4gSE4iyCbBFJYIFy0VkTV\nJC05T7Dcn1q4diNtpZSG93apx0FIjU/pKet6HIcTXsVVPI252HsnKwTcskUZAUtWpzGK8nqpGG3u\nYN+WKWw/pzTB3dqdMOj6kKaOddmaLqmEeWqOh3WDTXkCBCirAJLUItRUR7Y8FzoeN4cnuLt7jrsn\nR7x48RKvXjxgnoua1+G+ELr5Pay6FiUUmkY+WBzaT4LIDOEKxopkoKR61lZYBgeszF+t85Ls132A\niPzMzrGfB/Dzj366t74nmoOOcmc4eqOnmfNJFEnFOJ462AQ1IvYLfYErx9J7s3h0YPAKZPSKU1kX\niXQMz5K7bAApIkhyDq4v1M1aGsYLOy/ljERazMOpfiN3pHRJzUyk/uotg5EmPhFTHTwVjffJAUl3\n53X1hSFAZUiCpTJzdFsR7om0YIzqxaT3kkv32Dh+fcO3F25cSb1Qczs+XOefbc67dBiBO2kSkV/v\n/SBQGKuukuypHS4laV/35AEajpPPc3ifeO72WsBMxhIsAdCIU7ZqTX4uV48Zybg53OIwa1WnWs7I\nuaAUbtKqE+D+I01SUGnCM3oZ4Mz9/UQBDs3q7CAj9X312PZueDSKg1D74lxM89UWm3+S/vc1Eakt\nMPu/64W+APQ4m/7lHL6ZEZ14XEn6wszItqHhxWzTxt5u/RBmsKlJYOoVyL1HrgKwYJXV8goqJ4Yn\nCmHlAWhELaoH+l5i8R/NIzP1CtttQxvD9feOBu0hHpHUzKdBmD5DffN0ySkP7s9R4tB3d2yoc2Qa\nCIAXWB1NxT6WGmQ45tfYYhGjuqjxKjEWI4LPMRDKOWyTQAaiMKqE/jcTdcsWpIn/usFV2vGNvCwr\nyrogpYx5PmBdC5Z1bVYGoLu9T9PUXei36myb7yAR2/s5Q01E6ucjmveT8MNIFID2sgDceKSHTSRN\nwY6ukIJtalvUCIMGXAG/2HBiO91pjbuR9s8G+JCHO/vvtFl0vaknmiVw0261tFtxwe7b/MNnf64Q\nkrhzli4+lvi+7uJqCwFbjimNs1apALRGYVzqCejhtrYZE2XEGAIATX1zAGs7b23Okm+SUQJoRAHO\nvYMjewAG1fGmC+utaCssjgQhFN1L8V3BHPpYR1+PS6/J+Pe1ebnaSIxRqInYXcBFpK1b9UcoOJ8W\nHB+OGvGIhGk6oFbBUlZ4Eh3HXZyokiQ1O5p0MKoO3TwZ+6quL4zKFVWUNXS57XHtnSAKYtxYf0zn\ntAHSrR8jJUf354Qg4uudjMpqzoOcc6OSJkzbb9/sxqGcw0YugtRDTpuE24f3YhG1RKhJReEAnjXQ\n5zGAD8f7uyOLhk6n5ATBnzvWTehidcjolBQbUdBJ++gyRUpJcwv686wuYUJXtVyT05NspMTlNo35\n91Rj5HiAbVgP2iIiICUbIp2wBpr24e1wwa7EHw7u04ImYXhwmRbFBeK22G6kx7Y90LOtJ3MQi88H\nGKfTGcuy4nQ6q4vzsihWljIO8y3EAuZ8owMd1KQU42i6FNmfHMBdl6xY0SyXUKqIMZTfPD+Fr6yp\nuMNo+WNcV28KaBCtuyKg54Es27Ku1MorqhTjJj0Hg7Stz0EPtL/JxLCgK1MnFV1ycWlmR491NchB\nURcHo9nUi4d41Gdsih57sI2nMx/FxHEtd5WrEwX0zSaCnBVsI7gFQdr53kcAza2bE6MF6JCJ624R\nCoRRAB371OepDh6Zdm4XBOyZWp9gq/451xe7xt/B39BpCcL99thfFLeZK2pNhnOMmyue72P/RQmF\nox1ay2G8p3u8nk5nnM9nnE5nnJYFZdUCsY51zNNBvWahZkfNAyE2cG/rD2FnCUJEpdS1FHCtkEB8\nH9PeGaIgLrbzHucbHX32JjeK6Cq+xXP3J/zaItAFYuvVxbl0ySneZC6LJqS95w1c3QGroT9iG9hd\npI0LJyDxDJBHP2YkkmZ2arZ5vbECm/PUoLrQAUuh71KOvWtbjMkOdceoYdyHW9nYsyCmSIuZklw+\nobB9tuMv4tJh57pxLNtj436Ry03eiYLW/oyP2a6d7TP25irO0RZrGKNXVXJb1xWn06J5Fc9nnI4O\nLNb2HmVR3CfnqSVgWVdVFRDeIZppu7TZQ+2JrNSALdZk5QpqZSzLAuYKj9d5bHsniELnhHHTALH8\n2h5R6OdegkNbjr5HAIbrhkUyRi8S0Kr5EGmEmuu2bo/2Fj0V44T6OZ6rIXpw7hGbJroDyJkwz1r7\nImWCJCtLRrrgE6kFwQlovB9ljyfR1lQvEUsxhyaZbAkrwS4NNRXa2AoBOUoo4xjr56Aq+dwF/X67\nMRVQCarj5n6uBsiGEGCz6TtRYHhk6z4Qeb1dWy+RMBQuhiO4KKqORufzGcfjCefzamXvVYUACIlm\nk5Rq2NgaFel5eVbP4eFBWUnxGUrqeh+zkmlfHbtSwusZwNlUCGG+ir3stXeCKADjZGpLbeP59/G3\n/mH6O7qOeq1d24CXLbguE6n60f4Gtp5wPaimPydGtwHd4Wmr9+/2K3IEUv8NTeI5Ny84TgUsBU24\nJoJWYmA0otDUD8EaAMbobzAQ0c3GbkQB0IAaE/mHAhahxflTaWXETsh0OPf+08dczpmCkR6nsGUA\noX8mTdgfQz9if4CddfOIdikVRInFfldTIUhFl8pKFJazEobltGhl6FKUCFeBUEFKE6ZptnXR50Kj\nUIF1LUYwfH5N7SN3p4d53gZHLZKmfhHQwEauFTJtBukt7Z0hCrUW9TXIkxXD8PJbuklzzs2DMFFu\nIlJz/yVCqUVTZK9ruybmM/DBj1aByNVFWCe6LSwg5YSUZgAE5l4b0O+t55lwTJHy903hiUQi0h2x\nhrjYGIoWu2Si48CaGdicrgB1rVb8oZo0TbbRQiRoSlptSzomEhf6fj6HUbVh8VThmnsQkIHYOddW\nrqUcrVliqD9PAIjNsUtA3dEmuEKTRWUajqSicTXnr4Rs3NEVEVcV47v5+/h6MRrZxnwrlW6v3Tu+\nZVrOMMQyYHGtWBYtUPvw6oTT6UGzI5UCYSAhW75P6DFTT51Aeli6M4B1Xdo60pRuuCis7OtK16St\njFqtJJ2Wt1vPZ9T5gHl6vArxzhAFd7iJnHZPV/Qir2ozDym7oAMeA5W8vV06aGc2IqIbw5B1q11I\n5FWEY9/IxL4El9KuqThbLrZ9x6g2ZEpWjEQ3WtuIwbmoc03VZbE5BvPqHISb7bWP4KBi140yUR+z\nbuPv+AOZ8t8/JyNcXkBnRAy3qdlYuBEATYRN3fPzov/777AlCm9SIbbzsf3c3nazljy9XikF51PB\ncl6xLmd1SKqi0I+9ZxagijsW6TuRvZo4ZkSEnD2n5GL9jgFyhs1QX0MpJcwTkCqhiubbIBB4tShM\nccPk49o7QxSAZKWyepXntnD8bwQgLRGShMhKCLJ0s9/ehPrf1zeCcWHn4hSiIpvephvV07y7j70S\ntHGxRskgPr89bdPHAUvI6h1or299UFdsVV/djBqBsnBfEUhKjROFh6qY6c/WB4+WAO9rGBXhSBLs\nt6t3DaaocPWlKR8S1dlL9UmaOhI4cSSsfQko5NDMdsNLXYx53ORxTlpY/CMYxfacrTm5ckGtjHVZ\nsJwXnE8L1qWgsrjxRvGooOkY6gM1nvux1D8lgkiGe+8yK96gOBbZeDim4EQ3KxMhRk1JzfGVwVLA\nXHYyZr25vTNEISeNIRhE8mHyNENNc3ndLC6ge211lPaaT8BmcYcmm2fCDJieQUlVhE6ldeH1AjEa\n0WhmvkHM1r55Ci5XYbbv0sRBSpv+SyMMzGJSgEky8FgRFdu9gnZzvdro5U1u9Q3nRMvexcrV6kIm\n0oArNnk3dY9CTVZCqOZy1Aq5wgiF6bmKl7FJKZsxF7PLU+hXIwCMHLJmsTCSBCe2RjjiXERswyNe\nqUW/+nxs18YeAbgmLfizmYG1MM6nVTMqLQvW84paWOdHbCyCJEOipu7OQKxup6kGKZsyGGqT9tif\nDECdkoQTUvIxAFKagGy1MnlCrQUpJ1svPcfpY9o7QxQoZ8tSrIvDvRS7OtHDk7cCI5HmpROUYRJT\nos2ZeqXPL1G8my5YJ0zU8DSr8ZguPePGbMxKOOZ5avjHAEoFIuIFVUZMYmdMnINDFwcbeCRVXZzF\n+0yAn8metg1oQVIyjEHY7HaVE2A4J2Ktft0ebJtVVRGBmwvFsAZBbSMYwUByAgR0zhnnUNwrJU4T\nt++TKYh8TXV5u+Zzte0RhsgQ9iwq8VoRQakVy1pwOi84nxesywq24DTA1LbmqNG9XRwDylaoVn3d\nUlvHSAiYWm3YAddOKPoAqCqRiQCacEharb1qLDYSJZMYfujUB+PqNiF1I+IJ0GozNk4QVwQlUM7g\n9QwRR/n9SjulUebtJAeiQN2Jh5IvQ2omLQ+camAY0bDR+3M22ZICQej92XF+Cuc3kdcCYwCygtGa\nwr3L0zp+ki5FZDRpU52ifDQlKZETsTXrq0zE/BRGYqrOeu583lUDaT4ISsRrkwRs8QsNRCDvxI5w\niDdQyhNUou2/jerV18G+7k+dsn/htiXq8f4+xst5xXI+Y11WlHUFr0WTm4R3D974RnepLTl10HOp\nQDc3iECSDcgmlMItI3RNtamM6uzkI6v3SYkwHyZMhxmoAuHaMLAfOklBGbEusG6fdRGum7e2Ovqw\nCOBivTG8HXHv+vM7V2gcpOmhl5vc7xdxBb+PutbqRPdELW9atJd9bO8ZQr5FClJWb0h3qW2bd4Nl\nRHOccuEYOVpBkpEsdkDsJfuCdz62wT5Eruau0K9p4IpoT++EXbWE7mfQLQdd45Yg3cV3aT/kG+yS\nKDwWK/AxfpMFYgsW+3c15NM4nxcsR5MQamdTJB51EskdGRlV5UwJB2yxBqJgV+U0q0qAdQC/4ZcA\nWFd1pW4jTirpztOMfEjdWpejy9jb2ztBFByA2uqDETwDxkmLv7VdTqJz9O2xqwRCpJVXaykOAud1\nVWFbWGZ07HGHkzwsrEgE9p4/vJMvQCMKOrkCrlZktnFkM6eG+zZiwAIhRSW9hgQjhDDbOyYTNrYb\n6pouvek13AR5bUPGV20cX/q4beenp4bbSExBmhFcJwqXbfwurrEvknjEr6m1YlkWc2E+YVnObZ5A\nbVZ0PIL64F74Tf401ZSA5qqv4ycAlDHlNLVx2c4NMzAZEKlSIUNQwaIZnSlNmD3u5gu8J7D1MNlp\nRPRjRPTfE9HfIKK/TkT/ph3/GhH9AhH9Lfv9oR0nIvoPieiXieh/JaK/9629CJLCyOm6jrpdPFs7\nf+Q+P2hzdWCachDt+jM1VVwy99RxCLc+DPG6LQaxx406hkLqfBKi4lpK78078mbzeLbfmCdSRDDk\n7Yt5E9p+e/PY9cXpeS2ucPM2H1FNGFWHKEVduy6e77/jT+UYNbhNirvfr/gub1ovXvcjzhkzo1gt\n0dP5hMUiIPeyTtlq6O8AmAJmc7wZ162W4+uoq73j8di3ZEl+YH1kqRBoomHPFblXku9ae4ykUAD8\nARH5q0T0HoBfIqJfAPAzAP47EfkTRPQHAfxBAP8OgH8CmobtdwL4+wH8aft9vYmAy0kzzlTlgqnZ\nZt0xBmAumr0oO0WtoFRVPNaSvGbyATJyF+BY9fEpT823PFt0INeKlbUgB0HvTSJqY04JGQkiWTeo\nSS4TTQgMysKW9VOy3yVIF6D+43n7U8qAJCR3FTZOwiRWlamgrOdGHCkBqhaaYJoAogxYSjqFFtiC\nlCZILRbkpdl+dZwJSTQJi3MqICPBagmIjmkkajo9W2lJrSy+GT1TkG+yvpg1TRgADQRDUq+/qolI\nABOrSXNGMMTwEzHuZiHJpJlmLJ+xVVZWqanWChLRLEgWnAXS6MzKFaiENCXMKTfVVASYD8G6oMxZ\nVfPJCIDvUvK8mLpO6wKsx4rTccF6r4WGMyVkYVDl/gyLidCQ70nXoAgEGvwkqND8VwKSGdmYT+UE\nShmHmxs8eVJxPC7IdKtzbFIg5YokFVlWUD2bs1cCZVVRyroigZFvD0i575/HtkgT9TcAABzmSURB\nVLdKCiLyHRH5q/b5FbQC1LcA/DSAP2On/RkA/5R9/mkA/4lo+0UAHxDRN97ylIAldFCuwU1NlN2K\nsxEU2/oCSNu0IDRswtPIt3Ty0gEgrwpkjx1yRba/7RhXS6jBAq/O5n11DqzXkT3P1Bl7ke63zu1N\nHBtQz8rIAfvPCKDpItN6j0As9tp/rPPDmG1H39f8pdg+nCdeeWofC2lSgW28DsT2J1VW7IdNeuhq\nADd1wMfIVak45yyuhEC9Cd+oOoyid1RJm0OUdO49SA8bSaWWgvN5xfmsrsvrsuhmNAekpK6jIBak\nquuEDPtJMHqVSP1rrD/qzmXRuTSCo0RJy8DlST0UU9Z0fykDaULKkxHvntgmkUkXgFY2q2yewhV7\nuNi19oUwBSL6bQD+HgB/BcAnIvId++rvAPjEPn8LwP8bLvtVO/YdXGsG5rVcfilE/FF3/3RzWrJN\nzxFsc52MAEIyMMcn1zMVm95rrN1dbB0HoARI3eigiPqr9uBCtCVXc5RSx3iIuOB04sYwVpU8bbOb\nFBDB1stNHeIbdBTAYsbF1i8XO8T6tDPg8JgRbht0OCMQ4r5HfONQI9CuajnOon0XHA5K9FRsdYxI\n3bL3iA6zi9cKRiYjnETjHBCkO1J5kZtAjH3MARO1U+7VvqSbkkXQYgd87MnmhlkL9BARxCSbclpw\nPJ5x/3CP5bxgXWsHCz27VZgqAlrmIwDdeuZ5J8z7MwEo9ryUkgapGSFtqoFd74l0kFSiLJIBmpBK\nAteqEbQG1FIeCxd/kfZookBEz6D5F/8tEXm5AT6E3pYz+/J+re7D+++9Z1yxSwJOFJQD+ku55bo9\nOBAUG2xNJ9QWStucW7DGrw+3ah9sMUjYEdQe5y6mDSYyeWUkAs7/KY1b0hdaf3DAFwBNwsncqLu/\ng+vbg94CWBKTfs6V0YZvaOfcYiIxIxKE0bow3m90pLqUOEacoNYaiu32cF+9z84ilS4V6IbISBau\nHpOPCFjLQaKPsY9Hi6Gwrqp/SQdu22g0iYAB2hetVeKpkCpYzwWn8xnH4xHL0XIiMOB+7UmoZ9EX\n00QGiSp4LJIm01WfM0KBcnUdUmNMJuTmnDFPs6pMsAhcImTJICHIJMgEVCr6t1QjDjreecqWv+PS\n9f9N7VFEgYhmKEH4syLyX9jhT4noGyLyHVMPft2OfxvAj4XLf4sdG5qEug9f//onw/q72LztePzO\nKa8KnPrOCQ3CYffFdymhJ1Ntui9FLmoprtCpvBMEsYc7jYAZlvRRrsf2vguSiY1mTk2daDBzS5kV\nAywVSKzqlmqLYgyAuRhBvViMI3U3jwYGNolBsNnHfSP1rwgxxNqP+rx0ZNy+lTE9WCdaTgD698qd\nXeXpfYtehr0RWuSrnyuAulD3UoJwxYG0702SoHDPPmGtz/67j5EFjNmxlvVY0IoLezn45byAC1uA\nGbXxh01/A2w9C5MwKAuSVf0iUyEgCWBdQ8Ff0+Q3d023MnPz1AKoYH1WYifIwp7bF5iszmTtsS2q\nTswgql8oR+NjrA8E4OcA/E0R+VPhq78I4Pfa598L4L8Mx3+PWSH+AQAvgprxtmfBc/xpE4C6y+le\nUxdk5xLubuuGKrnAIS50RqLGFWotmteONY2V57hT/dVAL0BVE5MGLIWquRRTO58sDTORb7gu3sak\nnL6g/Vg/vtciQi9NqvHjPob9JxDJ7VgjSAlG6PowUdxLG1wAAzHwAjcdvOwtZiWOKfT0nh3juZA6\nGiFLgKibtOI2/q48zmGY14hrNIK1sXS4M5jK4Y5xdOLmBJCrYF0KzsvJzJCaEg9MBhxSS0sPUpaU\n+50BYVBbHYFoB+KaFEjS5EJijulOZJIlcZXu1enZrihlUNaflCbkaULKMyhNEAHWylhKAUvFdDjg\n7unT3XWw1x4jKfyDAP4lAP8bEf0vduwPA/gTAP48Ef0+AP8PtNAsAPwlAP8kgF8G8ADgX35MR4gS\ncurUu6Hu6OLe3uZW0E9NdZMHENm/CPABGLIxuQelT093OhJsaaq7PjdPwaaWjAu9X+C+7aGvcI4G\ndVMmaFp4W1B9Ifd0brF1YgDAE3v4s0mgpfWcAAYw0hKARLCtj28nMi6q96bc3KUsokBQd+agzRft\nHfdjzv3Hee9zMoJ93avQQ7R9vjYEELh4luICMUFP7zPgeILHJ+h6SehSRGU2CUETpiznReuRGBHw\nvLGJlOwm+xukhAFigKkax6A6j7vK67MzaYlHgkoqXAuQDSBms3R5SL/l/SQnOaT5NikBBYKpFYAx\nMLdWnE9nJAIONzPm6eZiTV1rj6n78D9czGRvv3vnfAHw+x/dA2uKrHYRk21Q8rQJo5aElmNfYMh7\n14tFXBDr/gG+SFzvboTCN2OtUADMQhxCi74Dfv/YF3IkzPEDcv0wbjIXgb2vpoA0Mdexg6AbQ1pJ\nsjC6fRGLivstb6MARLmlQ3NpQtpCGlUvGXCK/v0w1uhSSFS7RsKCcP6bI0L3sg+Pf3dhum1sUwca\nUQyYigO3ThS2rflvhLnoxLAT1m7NUQmgihhBOGmZt4cz6lqNzdj4tczT5gRGQAY1l3A/V2NFKkC5\nMQaCYhAsQBZ0wld1UxPQ3JJz1mApIQZSNmzB9ghyCzFPjm0RQFVUqqkF5zNwPh++cj+F34Rm+j7E\nODZhaj7bYyakQdSv6r2VUsaU5rZRctjIIqLgzDRhXVfM84xs9ymlYFkWLGVt0sOovvTFtE3Xvv3s\nnDDR1ETirWPT6lWGRYnYzUHNSY5fRAJQTWYm0jDtlJVrKABJbSHV6sVzfUMmgHqKr8orhHVRIlks\nP7uqNjW9vnlppjGuQ0S5Dnjc+JGjewGTuIndxdbPd4IY8z70MdfziiVxIQqgYXimni/KKJrHn5pv\nt2vE/xbREu+U+7PIRHAR0rXiCL+oBLGsK16+fIXj8YzzWXMrJmgmamFBEvVVkVqQEjClDJLafGCm\nhh0QCq/gQqBpNkkCIC8YbER3kqq+GFyAIkjTDCSgrAVSF9T1hCndYs4HCPR9QCqlTNMNIIzEZyju\nxWC5wbqesK5nsDBevXiJ1y9fPXo3viNEQZtzE7pAhAlEafeadgZRE+kgDMqTio+2ELuJT4nEIN4a\niaU8EgQ//7KPly69ri74hu3XdnXkgpu1PljEo/fL9GDniFtx3KWA4VZgENS1miQB2TaXCKqwBp1z\nAqAb3Dd8CkljPK5g+66eVDcSgi1BdB8T79s1aWL7LlE1dILvJmmf1/iubt3JXppug1P6uTG1/kUz\nKCabNJ+atCdakWmtKIVRiih+IAGwJp83aEi5mJm8zZYDkUCyIDR1fmLAgvVc9UxwK5biDhptTiBh\nTCIQaA5Iomqh2AzLIKzrFSpZgAhkUZaaJ6siJ4LMGWV128Xj2ztBFMJYDyKt243jmVoZiVxRgy/j\nvtDY7Nv2HfWqPY1zsMW7u5MNDLx5C0EAjGPpl/AUZB1TiJum28IFaH833QJWl1I/AU6cxGMR0M67\nIAr2D4OrsZrXGpeMY0TU/OtRAS1KqoCUJmtRn46hUK2MRMBrWjrGcNkux2oAjNHntA+zhPM6IOfH\nGjDX8JAESibFeNGZ3M3P7gLsRKoRhrTNTYEmrRE0fgCswOL/1975hFqXHAX8V93nfbPQgMZIGGLQ\niWSTlQ4hZBGyjCab0V1WZiG4UdCFi5FsslXQhSCCYiCKmE0SzEbwD4Iro1Emk4lhkqgBHcaMIvgn\nM99793SXi6rq7nPuvd97XyYz9zw89Xi8++4993Sd7ur6X9XXDw9cX8+UQ/WDjY0+kpFSf0qNqkN3\nyjpTHVQOwBjHSJvmG2hUawyuRmamfz9FGbWXRKOgVoEpWcnWUsjNJ/vSlDtTVa2WISkTabJneGQg\nawWbYAqx+lo9vKZwLjCiza5uX6YvVfgLjlN0wVJtwTd2tU63Ueif/BQqCU9+LDTHTixV9YM6BhNF\nuz8ALM5ePedAsfLrvtHDmyydOL2JyUKDoWs5i9Cfv48uaz+ayh0bV5ucarao1kg8GnL6g82spPr4\nOqKvnbH5avjYYw7EalkNB4n7LRk/9GcVYaElhHQ24VA742zMRRYDjdpJrHv8Fnq4MpzGtbpZINki\nWIfKq68dvINSsX6dkVSl0fK+BhW2aVB6FmZoC62vknfLsgdRtNRIG3Ot1kRdNuKnilq40s+qyMnO\n+7TuzLO36Ztcq/M0aqV1c7LvZNOaRSFN1NY3826wDaYAgwJFE0Trx1B1MUo4E9snREPQsJMVFmGm\nxVhRXNRGcRdQSEbashOOPHP+BVOSlqnWhKZz+djk4Ujs4+dGHy1ENeJkg3jLNT/JqlpEpD07UexU\nvQ+ftruYCdKPi2+PvGZYGgVeDPiLaxYrpKDfZ/Gguno9/grrJKh2h2FD4y3OV1c0M8zAQoeR8w/a\nzMGuydTWc2LdmFVEmq9jXqFTmX1Nc6tDKTdqNQ3XB+YSSPTEI0ZtwWlRpaIZ64btlxUNA6E2Pqbu\ny4F+rQJZIKuFRM3Ms+5SWixjQZ0x1LlS5xsrp860vAob0qIXFrybgOIHK4FqIqnAiYjWOdgGU3Cp\njKtqkoYwE8cSQN2hYxI9+LRncXlYsrRoQ20e2lEdtoVdHRRCMIPxxxcXbSqYSGoWTCNX6YwEBi/8\nKPmDuEU6l3e1Xmv1/gn1aEN1CQ60Jo1KD3OtpjOkcNy7fd+7JLen9WvakWfJKX6lfWFRmZEVNC1E\nYl36OQUxvmqUj5vJEunFXZU2og2I0LLNMa30e6m5WCZIa6DrbcxDAxjNhNAMarXw3wKSz7VmcrpC\nSN59uTYNDroTcp2F2YUGqB8U3GgizDmN+auIpt7eXkCT1U0gaiFMqSSKmTJltrVIJnxyglkLlAOq\nM1ozRAFUpNa7qRFmcyV74ZULy6MJOA+bYAqhugqYbe9c2aTf2megi8YcnWCWKm/VgpQU67IgLFPb\nutraNJO17b4aT92sGc3TBcEOzU/imezf0CrUkl7AEwNSaJVNw4CulZybrSay2v/ruehJQZIni4gA\n7dCSYSJEjJGOZz5qUcc5ugJZPV/XpIwZ6TCvcSJRtMq3fhKjgzciEb1S0aT5OJdg6WIDs15pL/N8\nAzCUrZuUjO+P8xAmxChcQoMwGqu2od0kC6YmkpvRlQcmGp2om6/QxzTNMMwcv5ZoU+80EOno4SMY\nEmIjE94S54zBhptFcuZqytzcFEotpDKjaaKxpenKnkeNJCQ5U3UBGzbKuSS2U7AJpgDulxGT9ikS\nWqqZCeE8Gn97dz+zk5XKJIO93gjDWPhi8/oGt7JcbI3UdIJ1yGy07wHkxG4NkwL1833DBFnpx30j\nWIh0tKmD9qPTkkXNYmMGFfazFIKwexJThzEfIYnYEfT+oGFbhjQrRZofJe7b8ydsnkqZ0Tq3z5oz\nU6SpxNfX1xwOvUtQSmWhysf7vXy5j+kU4Gds5DYnp56r1UE07tbnKMLQY3r1ek5i3qzAKDsd1OF4\nAM+g9JJ0u36pUYJnIhLMzcdwbao6DaDazTKUKhVNwWhcY1RrUxebWLS0FH1rgqNMkry+Lapye7Qq\nuGZK2TXHNR10oXpX2AZTaGq5dM9yc8UsNYBjD3xstC5lq3t5o1++qpyowfEU6LYx+6Zdj7XGYx0P\njwy0xeOsNZPhGUDIyePeWrsU82FC5c7TGHMPSSdHBLqETvxtzIERlFKd+eCJNXPrbRDfWTAFYD5c\nU8sNi/b7gbOqH3BSmw8n7jEmjq1V++PVOGbA43zH3+jVMLVQqqnpMW4pPQ17ZBC1+TJsHrKAqDRh\nUAteO4A7Yz0xSrKnQh+8KiGQWpsTSxIzzdc1RtcYus4x5pMkkIzUmdYxQitCMS4j1ZKVxHCstSLV\n/Edj5msakIj9kJKtzUjXd4FtMAURqgplLkx5Il1lkEQZkn2myRqkzPNsW1QS1Q9pISXrBA1+vZcg\nM7s9LF5Ge8X1w1fttWTmcm3OvdlOCL66ykS78GYuFG3ag9mYUXblkrr2VGwYCRLfBOJ2ZjUiSDqo\n/j03P3mhTCRJ9TRsLzCq2AEjLoXqwZqXxJHj1Q+MmSbLXrs+4OcPzFAPdj7B6JDLkxX/DAxN1Iiw\nRQPUVNu53lBqQbBuUGN2Z3stUdXpj6ZQBy2m1EKp9jw9QpOIRrtWKm3pvwGqSsZ6CTSGUc1cUJ2b\nve/brqnQySVnss4tUCvJNbPGtG7cVNArSs3MM8xVzXyRYn65arUOQiLrEwgKWkj1gOjkzXCVzERm\nQqiQMyVPzPU1qDfkCbKYh1kqmKM3+yndhSLFWIEImq8sCqFK8oxeLTMihTxV8nyDXs+gD5jSxFXN\nPFBru3alFheRJGgWkEQlc6Ay17H25HbYBlNYQdiWa1UeQv3D89pp9mTKy9BgnOo06vshtWLTR+Zd\n9FQoRZiuulRKAsXdzQJN5C8TY0aNoUu6fg5gDzsutY9+vTXrWEYrircPV7X4dc6ZaKAjCge7ebu2\nqPkGSlFytuPiTCtQslQOh5lD6b0MrHTa6v3XIdzlWuhCcJ+OKshqbs/Aoz5bweNItkfdI6IZS1OF\nhU/AtJ04QuDuXY+7IHC/UxIg0w5G9lBqq6lMkVfiKdZVkBRu8hP394lPIahiX8zFMuezUqSScuZK\nJn+eTpM6LNwj12UFm2EKzSbMy9OYQ21ehvcwgrYPu6TWTqDt+9CcfaOjyTyZLE7kVdRCPjihiOUv\njD4aERbhr1E160zG1MAmRVk5EUU8gmKaz1xuoNSWvt3Cbdi9TjEF04BSS5MezZ+2n3xHjPH6pVlR\nqZK6Wqw6bBZlwVN9HdZ7ZhkZePRGlibT+/Xt9hIWl7Y5C0fL0X1FThL5qfe6OTKMC+54k6N5s1tk\nR+bYlDkxABbhKnZiGYMZ7M0Rjmgunt2sCuy0UNv8VdTP3XHh4fOeHNGCZTxKwX0WZt5M03A6teMR\nFvg6K/Q22ARTUNXWyy8msTLmFyybkMZ1ffJPOFOih0G7vn80+gRq8yhLs+fjXtXtVfVGqMacCofD\n3A7oCKICmnlj3neaBNYmrQZbtxS0Fg6HA3O5QVo/hdLMpUfVsDT79MRiNydkUivDc0KstS4Ouz31\n3ZPE4/b3en8cSd8VDqurT17X59sGipZzC+a0QGX5mZxhEOM3TtvUXpYdpdmqJrklc6dz1oZmGKOW\nFLRkNSVmHi246+CeWmhhLe8kbt2jPMt8hGJFW2HKYMLFqsqTZXhWWmVfnD+xzg1+FGyCKQA2by3V\nuDOBNYgMG53l5pjnuTOIYfaDmdzciIXNEEhYMdTNDaCtIKhGk1OwrrhFvKe/LphAZwruyMQ0iCjq\nefDgCkvQ0Z7PEKnVQx+95ohzIR0e9JxT82906dOfOfINmvYkefGszVWbon5uNYdux98uCO9ui962\nObllvHEsWRdiQFOzkxwzpIUGtPhscHIsbujNWc1N4f4pGa45h2h/PyXc+We+kFg7yx3IpFTMr7Og\n47S4h+JJezXGdu3ECiEwSg6GYfdSz8SNnh4ZpcxiVZTuWGn5CUmW6dl3gE0wBRG8E23y9GEW5kKE\noiJ0p167MB5OorUyXx9a1584YGOUJofDgYcPHxJTZH37HzaToKJMZeWDKL12HkJlz9QqA179+lM+\nAx1ej34AxjZZLt2Tq50issrafNT8yZG93oKcJzaOqsf21xrWOfNBx3sew2iuncTtMa5f4L/exwN+\nZ793widypG0Ipr7V5IlbCXUfgKws/GCuhor2RijQ7bjBBOnCzM0HiUiPPZUxtTiWL/o+jKO5Rgl2\nYLIRjzs5LXSqEnUS5oyuFYoekElhzn6tQs79+29U49Y3ClSVw/yQxGSZczN2Nt98aNw3JrpqxGlZ\nHkhbK/P1TSe08PwPDr885UHFVw7zYXAyWm+7OhBqK/VVqGpc+Fh76TZr4Brvj8whcIj/Z1XSkIb9\nOJy8j7vM/1+o0oJHR6Lwp0P19GlNnejHtTgNA3dYXX9nE+Ix7Nq4tw7zyJmx1mbk2pxchzpt43l+\niwqWy+JdqtRL04HoG3mCO63G7iaQG77Ob3v0KdZAxOpmLDeEptUZ+NlRbj74CF1LrPEda7tG8lC8\nWolUEXH/hHobOMNftDaH/F1gE0yhlMq3//dVpilTvEfhaw9vmOfD6iALMx3McWdMoR3E6TZ6SFmt\n3nIrEpcE89qGz6KWpW2HuhnWF9+0jihfSS7FQ43XVkOh2puz9NyIgShPbKbRVNDq51xgocXoYt0E\n0kDsXYLagkcT2B7ntwScpiEMdms4bM05aXUQ4h4pDbtopSnUcHal21T/u0Uf1o7JI8/DYzKOfutz\nGsmgHQ2XNIXcW+Hb93t/ROAIt6Mx3b+xYEIm0GHQ+qpGJC258zo0Av9OVETbgwBhZJhxkaT1I7P1\nVTGtYRa0JsgzNT2wXpBJ0Wqaj50MNnYyuxtsgimADoeGhNJcjKtKz7KLfdI57BIWEsOJfVza8OQb\nsVteuCxvcFryuROoMRxV1CVKaBOq0ZY+NkivszgyA0S8JLbj7dNgREVFaxqed6lxtGvPSO52nag3\nM/YuVNp9J4oX0CBHBHOagI61n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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ - "31.03% : mosquito_net\n", - " 8.75% : shower_curtain\n", - " 4.29% : ladle\n", - " 2.84% : lab_coat\n", - " 2.69% : window_shade\n" + "50.31% : shower_curtain\n", + "17.08% : handkerchief\n", + "12.75% : mosquito_net\n", + " 2.87% : window_shade\n", + " 1.32% : toilet_tissue\n" ] } ], @@ -1013,28 +1036,30 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 36, "metadata": {}, "outputs": [ { "data": { - "image/png": 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WZVspGeDq6jX+r7/7Q7x4eQPfd1HX+3fNe4qiCKenpxgOh+Ilm6ZBv99Hv9+H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hhKKYRBAF3XSBrTnu+bk3axWMB3XXS7PTjKQ3raISeeU1ZQcFDXQHrKUJU1sixHlubwfB8XF5dC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7rgkI4sKFzMVcgShrpuj8YP/YhC4BNssrgFpAyRiKSynLLFC2vx94y7X0tEDAlTd9HKh3oF3+prgF8zuxUQ5yYobdruFbOuzWNNEvTiKxM14cU3VMgdRKm0AlFjoK2XWap5k7/R1dF2CeSXhrdTZfez9t63YVLJMS7/nTe+9hnSRUCMMzvXdcLhe8ev0Sz58/R60n3cIt8p9jODNLOEo2lJsbwmcvcgb4DP46upJfNwVf4wWc2ncqzKAL8TlCDG8vs2+8PI+IvkJE/zMR/W0i+ltE9O/q9f+IiL5ORH9df/7gLfm53zgEjUbB8sBW0vnRflqHbiP2jvT+wkQV81tu0YkpMDX/eEZT+c1FMP/eirAxwJYX+Pr7hkLNTOi43H7PQN1pxxIah364Tbbp+kObSUcPBhyj3rYac1ipqesR8rqQK7lFGyugZkvuNXggH97kETI8j4Jsa2cAnwCdO+YNWIIV8KZsNNz55ultmMAK4E8w818joo8B/FUi+gX97s8y85++NSOpWx8EcBj+mtIWmPP8gmkutzIAxoje+fu44x2lMcq1TS5wKmhFfTn72sPFhHkGkDMDF6DhxdPfRwKSwAVbYY9+kHxmgT5OGcBTGd5yRpunHkHVztt2qbXixfICIBpckzlF0zHGZtyyl2tAwMy+THx2NTczL681gfiLwlxKR5HdYkFMuo9DB5PFbzgUgBJwv0UTvzEIMPM3AHxD//4WEf0dyFbjb5yu+nCawv8yFCjODpJtsbsffCfpixnHQvPY5BFlZCoZ7xP9mCzvUHqGrd23K6m0O3/bbaZ083VjWvJjZWIdeXCfM+e+oafHUjaDRr5fCMVtm8Tkcf6BuiseGuZb8K7a6BGlVagldnzykYbkChzL17W+Z+yt92fsUHvLbdD/kfXI78hDurubvyN/27LhzPjyjMFU6hyTeJP0TnbrIKLfA+CfBfC/66WfIKK/QUQ/TUSfvyWPjZtE5g4MV6+VYhPE2YQK/F2cOiaNHMwU+w2S0bQKQgWhsPIMUwYQmGRpNE0/qYTa0R1g25rcSpvqekOgLetl9kndNWHb035rWcXSPQyoM1UfqX5ql6m8e27UHh238fDYjEWGAvMSZ7s3L0V+WDEMkoNJDm7QpoxjGw6Yt2m/GSCOCyNtlsrdZcSmq8voEkXbDXauAdGt6a1BgIg+AvDfAfj3mPmbAP4cgN8L4AcgTOHPHDz3VSL6K0T0Vz755BNky3Ek22ZVgvnnzhvuHJ5jvXRI7Whz90ESAbFddbY/9nz3Hx/zBXRYX+eB6/bd7vpQDC953MC3J49yUSk+5h8BxW0Zt60w1//IJ58UcwDMK82CIz8/K/n+QSn5mW159AUM2PwLGtpK7qu1YlkWHdo9Lm/226Uvi/edMYxxgo+VO+ehfWvP4DpwHFRXbw5QtqZkhgOeQEAZYjZE+oytpHxLEHir0QEiOkEA4L9k5v8eAJj519L3fx7AX957lpl/CsBPAcCXv/xPMJHqNMTfW0oZkN5oH5G4hkw8CMTBO5DpsdFC8aGCEpMzgP3esi3BTHCKjdHzeHAGpffIWncAKKL8do9ZsFLUVe5qVcI5JZAvoc5CW0pFLSG0xixkzFqF0S2Hsg6tjgTM5IfBMt5eCKDqKDlaQfLy9N5BhVX4ol1akyHMQtXbVjZaCQXpzjRszYOxBXi9ZgXqvadNOswwFJTKIO7SNsDAOlprSV4CHGeGEbJigBSGJJR+VGQDMkuksgcHcLHe4/BruF0iM3qfMhqXM5KesiFhQheWo/ErZtL7u848gAdCQYTLuqIuC+qVg3dy/ffS24wOEID/DMDfYeb/JF3/UrrtDwP4m7fkl5Ew5TX4iBm9t3RhxxGWuzGKdqLfWxcLcL+xD+8crUamkXuuiwqO/s0a9c1Cy8zoNAppV8tLO5R4rJUJqW1tphabY9x4vDYpQFKIaA97H4bP87tmQfffk7IdydzIbB5gA/5u60sM/b4VbN7LMqXclrpa9fD7o3KODCVPctoCjd5F4dJYW27doAbmlgKNnqnWa2RZQ/9O7z12M/fT2zCBfxHAHwXwfxDRX9drfxLAjxLRD0jJ8SsA/vhDGUlFGnzBBB9XIFOsve8dfF313ZQ8snoRWCqloExrxEcLEQGn/J1TRT+UY6scMt2zb04Jy0GsrPjmYgw+6qCAdjcZg45Q3YZucyJKV9rH3oOg4Ft/d7+vIih59I5xj4QIBk7gdwPlfeiOrQhslXVgQkO5+/R5L/9rVniOmbgQb9rHYDtcMihN1qnxyvw08+EdQ0B1eP9xepvRgf8V+63x6LMGND9AM8zjrFEBCuswPEku6eNQSSixR74fiQPBPIQFyP6C2Zpvyx8vctUdrOvEfuOCOYNDOSWfnFtcTwBnboDXP6u11p7Z6ehGSAgPjuIJLd8J5CE2KB3bIQUL2ZbaAub+HAXNIoq/xwIfFuiUE+ZWu55GVjC7PtdeOI8A7d1kgD0aCbVP6OC0ke7Q/f5Lhwe7MZi+YUoAvO0ek57MjEFHx2S453FWJ7aTALkgasqPxfUbG4ZC6MahF7NQBbZjzoDo2cfTxTey4EcqU0tFIUgsgKqWqw9+ZfZP2AFDKGdj2yyUPDCYN9WISqviOHYKSHD6/82S+KdDQ+VPs9uSrJ/95DMA90BgCNBhBgF95w2bKExGclPOPSARd+7N4+S5rEBmPwBAIF0Al8EsAsNQa58DlCTlsU12snzr/+8iKAg8IRBwQSja4SUawwN8kIho0rd4OiFgDtQFQNyqAOwBmdkV5K7mQRnaIQggFAAdoFpBpWo25MpAuuOw67+P8/ek8AAK2TyZNLSYaL66UbJISPefCzPz5nqf28RIx5Q8COmKHbswj/Ps5XMOCu6ngb8k5bIDX95sPkd2OUSmCPn0J0zve1C59gzV5n3peyooLLtUM1ug1u7NZw2G+ZfdmTUIjeR2EjnIvwsQeCKHjyjVAdxyzZNXjtND990wxLXzzEBnu/nzjNhskzACgDGZ6V2mh6Sdnfbpzzdly+fCkIUilWymn9FaailtSfPgirw1Ekwp0y35u7UVrdnR7Xase4xmjKA555XjQCMQ5HseVcIpOEabImcAGIOh1xMjbcp49f3+FkpnS5b87lSE/DGrg71yajYPPb/lXotPgglY7INV8GdhiPv2hCdYgAVRnJAxYFt4O+Y/Qo66KrVE9sfJKt4BmZUkGup6rNbYZufJpI/R+gjrGHvZ6mCfxuiyknvHHQUfa0inKgRUmVhz6xZh+ykrSMQ25JwDqWPrHefzxZd/W2CzUAGXAlsH0pk1UCrP2XCrF0XXyNtoC+dYxtA218v7uHhAfu6WlHzwZGQ2QGt9A9KR2OxixmhQr6wWP0reocHkziiDzw/YHBOwHg+HAIs3AfsnAQIAQOgg7rEJRDrv7/iZSO5j+Tem/MJjxzjBsWsZBJHcsMt4ew5oJbq/ec5ckEQtVbA9BlBSrCHN2CNmFxD5SsfL9ZUODD4yEOvmbeTAnu0qGdWs3F51aeZbhip77WJ1UtCCHpxJaZSCVCnYKLsJqFFfmZMB6sp8yg4gqrtlIACGxGE0L+HJ8QghNAe6vPZBipy/3wPH+fk99iKpUkWhgrazHfvAQCfDkeUoz1NhbmC0xCKigqz1S4ufsb9nwuOA4EmAAHFHOd8DANZ2QaE7YJFKlkIaDO3yQ80tfHf/WmkWxwQjRghmTDHtOqmjA8Sgpk3v67IJvTUsy+KTa1pXECg6jGnt25vnAxvb5ei0ntiAchyd5NKA1RgCgbuc09cNCCx7MPiygmtFqYuMapeK0gH0FWBGYY0n8AUdK3o/I95WAQI624YTVp7YscesWIzMqFClalYNQDYWIKt0AsDgvmhgqkmdADx//gxmx4QAUUyy6hXgVeE5xM76KITBPnaJlpCUSVc56CYzkHdW0h9GoTYtGkp19t8MUFMctWO7Qnm7T9213zx8P4APgIKKZ/WEvjBetdeyw5JOCqIq8R4/MzLhgbWNHpWE0k8ALeggdG4An0EVUteyoLcm/VgLwCsYF4A7SoFsptLUJfAjGBKwcW6F/fQkQKD3jpevXmFpDaXeoZSOZemo7eJWRHZTabDAEDHpFNoqu8xQARceZhm6daolzg1kVVq1qGQHV9LeOC5ciZXbSx7EuqJtpu+JkWiEO47YSn/DgpjjoaJ5Yo//UgW1gKDMWdDjtlYG8+pugLtQpCMQpQ6zFdWMi12xOhqtN6UhtbRDMQjVYjWlAKyWHLFZqrsklApvw14saryxuZnHWjumiOcYP1FosP7SLdN6XyVo2Jof2VYmVzKCbrED0DXrbt9LYBmpbXOLjLfPgUCiYGOWydHbuvYJlYKlVKCeQEVAqjcdWfH6x8EjVo7cZm+SngQIMMuZbl19Jz1SDk1ppZ+j1yUw53STVeBLQS1Vfmod8kWJ8/Eknha0mdioGDvKeyAL5B3pJwGznjDcBYlNX3wDqBSE6rpfInpSEov0cmwbbj7iEKgyaaHIcwhyEcnq40KCi2NrIo9OlCJrFXIkGhZDSYowByoHBbECGsb4HwaqSdY3qQO6G69yu/Q+mTXp1ebMSoBxZWfTMkNBQKbZ9i50nKycXvJpFMHbVPqaD2PiGRwM6FKHgNwmiGzaEC+FQiLaaR4KjTkm5gbaGxpI2dtYmgTWbKVLgLsTV3lsehIgIJWxwznJkc2slcydbgAnmmaHNOphoLwyGporscimUKTivmdeMadMoCsrUAvWe8eyLDgtJxAt6ExYW5N58qbYaAA3DXzRAALFgUiVv0hHDxNsmMFs589beYJ2ks6xRx/HyuV3EF2pvxzg2noaYSBbJ1CTg3E9XZ1kosAjvyKol+coFLKZ7Zkd2SlK5q7xJt9rp85nXLR32ZqKnqZ1m+UfRwMIdopTJnUWj9mzy1K8eXp00Tqn/kntxIlVuguqzZAnTo3vcRSBKbjIewc3WTnqw7zaTo9P0zuuyMHTAAHOVkBX3u004jDRQ3oUVFXYO7uCD891yAHA0GWmDJBtMskSZ2B1BwBGb7oDrY4jtw6suoONzX4DN3BfYaBl+RgqC/Ow4aDY3DMfjmF1CAbQ/W9Gw1IrTvVOqrnY/cEkCEBvK7ifQXwBqIGwimtUFn2vMZxQXG/y5IvPAJBZiUxKQiwKgmxKGlNag0VRPJzkPiCLJ0gadi2m2WrCn0vIJxSbJYZSGKDOQGnTs9OQKKW8zKpqGeOph9yDmSmZCzQymF0QUCQa1dDpnzMccQdXBf+CQhWxJoXC8FlJTeQ35U5KTztfT+lpgABUyFoD1S5KS2PpC8VMPbvO3MGrs02YshnVdSgwikl6HPjUKE4OCKrEFmAs8Ek7KoiFq672GnegNYWQFXA8gcCI/uxujolFxBeYOzo6KhXcLbpHXr2o0hZcLg3n80VAYL2A+wrCGbJTbdOyLyh1ARX54VJdAEX4dfWj+r2xQlN/1MVgIg8Mspe3aTs3j9ijM6gSqCafgKBAaqwE4G5AkDZO5VEZ9M1D+8itGQjMlRsZlqV5zX1kku/BoCAzSFp5Qnb2phWH9c8gMD4XAJAPLp3fY8zF188QuevmEOtlzqNdss1a4Y5bRtT20pMBAbFWMcFEd1WGWckRsRFoTtHQDyEekNBbs5oX/9nW2dIBezmIAo9jwzQIsj+PrWDJtTIJPGAzyYyhdAbauopAmDuBjlJWUVruaG3VkYKL+pTN8wctAnr1pO6BNZubDxHYApQa69UBOAg4KNQKlIpCEdy8P99jvVzEVeOOsoybfNjbJN+qLgOH4pkbNdD9QBBTMlMoMuFnOW+Awci7lGe3SZ7d9pycVyijRL3fICyPTOPEpJnZRHRhr3xUzKUqAFUZCVCSGSKvwFAKeitgNN18ZD0o0YDIh+V+MiCQWX4gHuCFTz5ZmDXWyLYo0dwJfrteb60BKdJqVLMQqxISAvHJESJ8PVFEgrGNVL6hcBPx49wZ8sz+7Dnbr75t8jHLYCyFQE7FuVe1Ri3l28Xm6tCRWTqJNMf7ucVuPN5ma9BYC+k1/f50OoFZtva+XC4wH5rve2KhZsmLxgVEoOu0G1DCzqg/YegDA1m7r6CAlHGtreiGo5FnKQW1LrtswN0tb6sRkOcZhkcpygraxawAACAASURBVCRCWgr5CEuAluWp9WDHPq/4cNaCGQBCTJ13+2LvKwDLgTTUCxir1t/2pNgt7fR7m54MCACZVpm1ioILHth3ckUYeA8rkdGXBwa5fY+MyuoVaXSbT7AZZkyWsxQCdI97H2UAkHc5AmbLlK1CBoD9RDqr8ChY59TT/0uY766LbiAyWFpMXpYA3caClyFD9zuJCvo8RTW1TR/ctSIuSlr8smKeMhtMYAuKAci5DSoVPdKtodYFl8sJyzK2+7KcEDPird4T2LpyhbuS2y8CzJnx2T0S+C26n6GNHIV/Tmkq72iFffFZartoCxnG5t78We6M3hoYK2oRmbWDRoqCqzp2A8XIw6rmQhylpwECpENzuuJu2/CkfvioOMU2FzEEx6Qgmgf3Lvv6lQJuElTy04BA4oaBhne6pWQZnbDx6RBfHapkA6WwWLM1ycNCJixlZzOKPf8zl6eUAJQAJ54+G21Mz6a8chvFmPsUGORoD2sHG2EZQUDa3na5qTYhy0Y+3N+2XYemdvE22bIiuU1P4ulhoXOUvvcV67plgeuamVQW/nhP6MueuybXDBy3w7Q1vmfgvF7w+nyP1hoWZSGmc7VWZ19AAgELbBKhFJYt52RDSnTIxCxmgDujrWf0fo9aVyy1Y1GxL1QSmxh1wxjVXsxkTk8DBHbSGKiZKxgIZ+PmWxJO/r3fQ8E/iWAeKywAKO9lVdIgI2SMwl/Aw2tiFDzKJjPl4HlG3vb3DW2AUbntvTYicTz9ZGqHnSuM3L4PPUPeDkj1FEUSNmHMLMqpY/GpvtvAWGZHfVA0z4lz3qSwncunwz/pUljBrVTk+lncYS8xc1oMNT3tMmQgsOLV+R69NdRaZbs1ZxbpjcpALX8bMqWygEkmvYEWMIpsJqZrSlq7AHxBLSt67WjVthcBqCtref0KdVkx9s94nNpRemsQIKJfAfAtSNh4ZeYfJKLvAvDfAPg9kN2FfoSZ/9FhJlZQp/k7Kk0l0XdAwkOJgpMpdOpUPc4ZWbjsN8vwi7RNzG3XzFzoCIq6mg+D9eCQUMgMQS6ArrvGTHhQ/L2OGT4f6Ldb+KKjJQ8tICOL0Bt45LbaH/s2gNzm4x+8kHbv4Mr5PWMl5iodi2WUyQK54sZwZGvXhtyDOY5AML37BgTeunH26DThx+puQ83M6GjbVzBgm8/ad3JCkoIo2fmSDUwLOnSuiTMxMTStM7iv4K5zV5rEuM4dgxtkIGCL3rZB20jvign8K8z8D9PnnwTwi8z8p4joJ/Xzv389C+1EDoc0gll2TyB40G81URwR+4gL0CAoe/6d5Wf+oTWavHdiHGqxwuWQ6bM+40XnF4wplMEtJ8adc6IcCfyUAWT24kFBvY82DtI2SbU2Kj1Y9VvSSMezJZV8tno1KqVcYSMV8NBOyj//jjx0pmFa/+BqzbfVwPlGBjr/vQWrOahsuTDP7IGsVhOLMVkxOYy6DIyJQy68UZKs2B+kBo90eJvBaHoCE2sQt15WWKATMLm3fR2ug8CntZ/ADwP4Gf37ZwD8oQef4NQESZG2wpWUE+ZXRqM7Gk+/9xB99kO3IjXmKXdQMIEsyBz03ZiMUeatT0lehihXCV8aMgEq1uLPPzdOEyWDirFOubZXWOLYKk64jvpmbsu9XGj340z/c3CYUhwm8HBS2oMy5E8jNgdLYJUn9lOdMfS50ekhEDyUIzHCg34e3UgDncTC5pbS/GU00OaZmMxm2SIdDanpc5aRMHDX3IF3AQIM4H8ior9KRF/Va19kOaEIAP4BgC/OD1E6d+Dlq3spSgLYWUdHnzJb+/g1KKwH4UYlnv3y0Y9NldrQOUPxSdgHScvfG1iF1YYHMjEJmb0vI6EE2mQ4UDqy+ByG3SI/nHYA9c1Stmr7ox2hAMEayuRmEPaB0fNn1t3ZZAntteLukP7sOWzunN21zD6tHNIvIUdDFvojntms/IlpULiUuUmsT11J/TPB1jdsK5neYfcPHjFt3nPEOnN6F+7Av8TMXyei3wXgF4jo/8xfMjPT9ighcDp34Ht/1xdSD/O+cJu1N+T1nVfjsZ2Hhk8+9dfGxSEZyJDhRK55nOTKgC79HxemuKAxozf2hh/fbe7DXt0M2U1BktLoZvJtw4xYy8fZ23ggKW0dBH8/beMBCCPm9Ul1mtjULOw+fJXrB1aqq5WyfrDZlP5iaw8ReJslGAWb65PZxpbsW5/DJgvZxhKU78jAkOpqDehIUZEbJyubndwUDDL63h4PoChAobElLf5hYOtvSmwAOhvEZk5qruFC32Yl3hoEmPnr+vvXiehnAfw+AL9GRF9i5m+QnEPw6zfk411uldFvcstsn9P/Qmh4+p1yUpPrQ2Qg39BBhq9ne6KWrgf4zG4E5/tY5tXL8F/eJTgLUpTP/PJA95HGbYmuWV9s96dH+Kxj46Qm5AxEJNY1bUXmSQFJ+cz1Xf04PQQe368tO1yj6BVXSIM/grQ1IYFyAoHDAoRi+L1kT08wQOS3a/Un+QoQthb37ymBHqtip7Mnrscbk2RTGgbf1EplScFmYAoY+yEPDWdZGJjlDemt3AEi+pDkRGIQ0YcA/lXIYSM/B+DH9LYfA/CXHs6NlS0r3nETqzBQeqWQNiqQA2muQHZAqfSKr/ADvLHMl6ICXY2si2SsizkdmMUS+UW3+epaWo4AT/bhZJlq8x8ZNJE9CDxe0WOG4l4quvHk2tfRP/XyNLh74kdzJ/dCWlP+7iyTLbvEHLhXMBbIIBON+du9HALWmNFt2rY2twx0dTTuug2Wfpln+kGFixvAK2RDkS4bobAMjxFkZaH2mswGBDkbdnhi9mPIbdWelwcEGSJcQLQgi7QU15RWZInUxZCBI92pp4icSD9Olp8BQBaFlVJRaQFxlXUQVHxDXHMJjG0aXc/BXFncRSErBeCCoQ0LkR+L4hGiUmQdCFWAdHEaSf9ZQJx7A7qstCU/Bi9061p6WybwRQA/qxRoAfBfMfP/QES/BOAvEtEfA/CrAH7kWibazt5AcsX2kiG4ZhIEANzPtMUtmSAlJgWDRNt/oOtqOPO99B4FHplPLqIY1jMhsuXNEQ03w74NFrFfHEc47J5M2cI6Aexy3HoH1aKgFGyj+4pJwJdH2w42EKZbtM0YYU0GZkEdsbUZFDBT6bRd7b3gjoKiE60YrXUfRx8jzxkAGHGgKoHYhnXtztxeqQ85lzcxt25KSt5knSVuXnUWX/Y8bUYf9XABSxVF65SnT2eGZM0RH8zKZ3ct2CTCHiHAc2YuA+MgY4n6bplrhWKibXkByFOoDS6hIFCoohF0Wzrpz+LHyqmBS7pylN4KBJj57wH4Z3au/waAH3qbvCPR9PtxaaRJQclzMCtfn1g4TEiZoyE5S8sjktHATaCGI8/NdY63+d+c6jUE1rLi2A5MuMr3TAVuaV07A6+lpdXXhOuW/DII3J6O7p9p8di/9ve1YPFRfeb78zZ28/v35hdEOXh4jhWsiTQGxcKzFKLcaBnt975igVqZmg1nHh6PGTjw9T56QjMGc2EDyhw8D33Ct0huffOlLEgEdvagVnDaVgyIDrouyom9zMrjwmWFgt8btxjPN4aB0J+U1RxLyO9+bCIKq0xpym+OhOdNXA6Vc6ed44ut0s6sZX4mlGxsn7m9NuscNPU0sSe/dy+fcdQivhemFN9FUNO6aLur0AxSzvzMyJjLB5XD9AyzLlwLcxHxAlX+NzNNTwQEBMVs+e0VgaXxb6Prjo8Hj+YOB5A6MVNl2yBkgwo5ox2LYbGK0Mvt+/eHaAZm4aYhyhwfJgtGcXPvWTBy/dKBFodKGPWyIszljI8BADY27ScBPwiAUgYbEow2dPzbbRcD2LlMPLTRvsWPe3nz2cqwd/9h8ae+N/mx8zKif1QheepQ8LauaXKZqTxZvf2ZXDe9x7rVA8pmH3S7dpOXJJDX6vokQGCbTOCU1uxUIBtCSakJd+iYUbg9lLccc7Ter0YIwq937rEAyGn5WLqRnqdvZldgqHIeZei+e67EJTUAyDrnPN1nLEFcxXG0YW6bTUr4Y4ZpWOLq97E3RGYBlrso1ybr6b2h/A6eB9R5tPZRfnlmZyTk8Pl9IHgIAB7j5uyP0tAEBDMoTPnrPAimtK9EsmsOfPa3uwfm/kVw1+7P5OwfAxCgacJFiQAgp+AG4FZvYoNv+FbCPCljmLiCLh2xL93RsNraPoLhlPzhArpVSbv99LTaUNA9jyx0nazMkzCw1micrPJw2heOTP0jIInBR52VbP+dqsQE5Hnze+86FtTtZiVW7usAMua/991RWY6uj2XdZx6Rxaz4QNRF+StJn2vPpYhB5B+ulgr9HqPwGxPIZBS4kp4GCJC5A/MMspE+qt+wfZ4ZW1Kcvx6DQS7EiKEcQAQ8b1fNZJOLpHEt2mwWikinEMOwJFutoLRDVSnWoI91TXsYqC9bq+4JwByApHkUEDoFSHHPimEuRtct2W/uiZ2krpIUzP1s+z2AQartYBv1Yu8MWTMfbfGQxR188EfQ9737bwWDPVDKfeWADPhW9anE+mz8vc17ZDbeajS09ibP2eE0IwHQwADh6kKqL5/+jMF3kuSk3tigc6tIipZMMrwFeFua2/CQoOxZLp6/s+3D0r2G+r3rfap0pOu/kTtyavA9izQq/1g+ZhuhIF/DTxQ7JvseiACGacl7HsaBSxI3GIDkDTD22ozyI854cp3SE9I+HKwtOByA5OMfWet87VbF5ynf+R2PBRF7ZrPr0pX3j8HDa3UYgcABwU+s2e5cFcqdYwuZaUR9ZZgwMZIHaOGTAAGCTpIo5GcEeNDOVutTke29We63cwJJnykk4/s2jDXkr8ozB3ZsPnq+bnEDs9SNY+6ar3nXvyWJ88Wps4udQHzF9xzcDqP5+k7QNBJiIGcxD0yAgVwvyPM+EwrOlJLBcfYl5Wup7NkdyrRXri1LTS7KGLgrbuUmmm7nAjyQ9oBxb/XbkYWfo/z2O7OuLAd7eeTn5zzs2agzD+5q/u7QZdrVzDFoOE6GEiPnniczYiGTuQHya11XlFpQy2KetMt33oxlTk8CBCSZ8BPAZcRJk1q1LS6YFM96IMysG48WQfIZLbHrBR3f69Q6XTekJ7N16d3z85JHthD77sF47xRhZhMMl4S4rxaZDehzHE0RbU5BRyyJtqg2YuajBbLS+/fKGddSu80pUSv1eGEDadcs6exvz9fz+7NlfhPLfo19zP2f3cTjTIOF7tVnNgQZkERG5ww5/Rwl+07mCWACGn/36JQd5vZkQEAim4m6MiUD5CKqVkg7hXRqJZml5k2jzyl3bg4MZgGZYwf7nULw03SKbYl+7IM+5rqNPHgQblY5ZQpyKnAF0MC9IPvbto5ciwprTFZmI5pvdY9bH1Ysk9wolZwi7IW7anWO6vxppltiD0fuyNF3gEllGJ5IQdMNOOd2NQAQOcs+f97g8bC0/vY8pE1KGXhz7+jSzekJgYD9Vl9mUH647+qKDkbhOOkHbEASUfoH3+kCvaVr22AiNt/L5+xeUFImhOuAoKQ5j/yeuB6gEwHRkcFQ0R13m03JxXEfu4Dm/ewBlCK71RDHZhvJzyciXcsvSl3A2EYYt4I9f8vI+z9lME2C/GDK946AfM3d2rp/0Xe3APR4ZkSqV+qXYIbiavTeVCZMBqAMJpQx4l588PsxyfqOB1bH+bsH0pMBAbhyJ8N1cJ/5YUbp9YuN4ma/abb09owNW83BJBsRsLF48/Nzp8vfRcujXaw0z8EFxwuFRgDYp25mSahMi1H0+95lUQx3WaQU9QUUAbYgYqy+6A7+XMGtaRwmzzNQtmT1mdyWUEcFXYpBrlx+Sn00frfbLKnegO2TF37xGATN5b0GCHn34FtiAnv0HsDY93otDqwhB/8xjf3pfQpATqS+nTm9SXqIBT0dEMD1wu5R/awMpnjXOngbKTZFD+odwZ+mz8Rmk3OQavDlDX/Tib5eMKPg03MZrKRc+3mLzNiZe1PWPVZakqKn++6I6bxjG4iVD6NMyXrIPT6xqsswpHgPO6b+SvJ3Skk218Myjv2TcjjIcwT3Mc+tb2y/ryn6tWtH31u/BBDFoTNSDnj/miEYAfEhNvQmzOCw5IffPB0QMMszdMIYnDHrY/db4nCwJiv/QNJ+CPchaLhtJ+4K64p6YNV9zwEjwIh8UrJdiMnfK2wk/MuDzsrYwjov3kYUOG2SYkm12sogrClcErDNLmOvncVbarURmaTEpkwT0FrbiW4ak8uQAoAot8pQrdmq5wrMLM7ZzfTdeM++Yr9JTOBBz1w70tiOUX9zAZ0nDRkx4OJ6y47Rzo2v35W9rEempwMCGH04sXz6d6bNcoM0tqxXBcMOGr2e5mFCeXoUWEduvWoxBtvlZ5NcOeNoq1JmSz9bAKvv3Hk2/LhXljgavXeJj/Rhl6NYCk3Qw1GRj2nvkKOvE3hCp1jpuQq9y1zE3mVZq12Dbb5aaJboWd2HGg6fyLYfz2W+RWJn6z2yB2A8H2A3B71eSvHlzzazcnzTjmEBxzyK6Z3TCQzqjlkGGaCQXMJwBezGd2Pv3zyXJwEC4uoVoC4AVbCGkmzs3RSpC//Vp8yxFZ9VlDUOsgiDN9Pq9N7ekj9miKvqb0KgNHxzojBCyYmBrUQl2mgCt5FRG9YbT6+JIKJF85F8cwMEWSzStbX0Lfpb2y+gTBgP2Y4hFELKY/uBNN8O+GANkefh/ZXydhbDuQ0tWViQvF+jXVK6JsN7FMcem6j/0QjN7ErmME24TLk4470ScFO3zuIBuT3syDVrE5I5JkW3e/fdzOx/cxtZXhC7YkQJRl4k/5FPEJtvV/6hE9piVsmmyTbpjUGAiP5pyNkClv4pAP8BgM8B+LcA/H96/U8y889fz62A6Rlweo5eFogPLQLTuMuCGW34pkqxQIbHyISYL7qPe6aoIQCye0ykrsG0OOk4orbrGgc81lpR9DcA2eY5C1TyPpwOFt35RY/zziBiE3MiFmBHAFtg1BhKAdHi98kZfBRLHYgAkq2nWbepWbQODhiAH7lue9uLRaoAVzBXgAnMTeMhAGgFSBYqgRYZ+iyAAVYfyt+j4jwTV6XKNh2aAUY+NddGePzLDUbyVi+UBeZ7dNhYJ/NI02xdg2ESGQEt+1fmZk6AUsyBITK+KUAwsEhAt6cSxesEJt25yMGf5bxLRPCbWe4DAUuv3n7mVJY5VpRcoQBVSyX6pzF6ZZFbqyIjHW22TW8MAsz8dwH8gDZYBfB1AD8L4McB/Flm/tM3Z6YG3ewEQxcU2ZfWeAoAhYojrFigvlH6VE79Y66A5T2KXsQA4ILRAaB3perhTrgiM4FJZjWCjOb3Ia9uZyYihCGKwl7ToYAOCjFRiNmG9HRYikQAzC8d4iaWa5k+76QwFknQnJ2YeFr7aqkZmE8VUjhNb7LzCI/emur7UCJsAMB+77GAve/9ns1o59gfBPIl2/k5v8s9IzI8li3qKDk7Bo5BvtKrtE0ZwOSmbeQAQD6+Pto6Pu8STeh0tk3AdUzvyh34IQD/DzP/6kMBmL0kjFN6uBTyOfqO7EgnCk/5M8s+LEeUR5SCUTw6O5fPuwzMQGu6I08CeTsUcg5Cid8IwGmizmrz+IBZjChL7GSon4ehQRUo6BHVYc5V6KLz7e9Si5ch6qx3MYtJYjOzt/UN+/0qqjvKFT/wNrEHbKgxlG/rLQ0NQ9F5NJQzGOD2+dSGqUy3yN9enTBd69yHvGbwlzZKIwGAgt2qrprMr7DRDOS+TkAtRE1lZTy6GLbvWI4Rmbvhc1xUrm1i9t4o2kPpXR0+8kcA/IX0+SeI6G8Q0U8T0edvySCP5RqtluuGqOMYNqBt4Mo4CoUlU1RD7f22iSEeEVjZv61QLGgag3xJ0HxXTBVA0oLtIJKVN76KvwXJA89NLQzE+KB+BqC0p+BkMylTW+1V30s7MqgYdSFhO4xJwKb7sV9vwJZGk/4EgxgVknfe0dPzUY/YWHUU+scI/6auB9f34kAGgMyIsmjcox+WI4xImeTcQNtFZ6QNAxBE3tcBT8DouFcsvTUIENEdgH8dwH+rl/4cgN8LcRW+AeDPHDwXh4+8fOVKuHeu/FHKG2psl19OHcHAEAOMcgxKPYIRgagOwDSMO0+HTmzrqDvFzqfB7G3aAaN3hLfpFqK0N0Ou/EQlAQMQ+8O+nkAUbnfG63tCzll2J6V0NIJbL07XQqlGhR4ZhwHCPgBsinMIDryp57VnD79X+u8Doy4HCgRplqbsIky2n7LKAuAH4Sa3y3+G1++ocvrYKf+w7wRtPziQOeDduAP/GoC/xsy/BgD2GwCI6M8D+Mt7D3E6fORL3/tFlgBabONjVsuLPtF95uFbGJLGd3vUHzD/PT0mqYwAIPMSrB56+Ofw7jRhBep9OV33+rsyzjJo32fV1JDBIFTYAZ8hUTxsaj+CX444mK+LCCs504o1CtmqZ8iwVz887j73jVaZyd0m8zg4lX2PYQAG8PFduFq82zZ77GD4Tt2Mx6ZN3EGzyf1tqkoaTPR9MilqQtHRkJ5g7w/25ktAkPpoCNUoQx7aSq2/tI2/+Gp6FyDwo0iuAOmhI/rxD0POIbg5WftsNWdMgyLqvXvIPVhu73jaBdYZBDo3nSWXTaUJVfivAxQli7YRZlf+qO0gWPoq88kDYqa6wMAhvh8oa35ftvCDkmya6mra15m9xS5bABi+9dtN+BFA5f/ZcGXca89a2wYg5RGH/J5tBQfrnj26nYDyoxKZGSiebxZhysI2GTPj/w4kk1O115I0fL+zmc71LtiktwIBkgNHfj+AP54u/8dE9ANalF+ZvjtODnqmEOTuqKCnRMHjfMGCUiRwIlZkFJQHDdXEIoilE3OAq7fu9I13WjbiFSknZiAdWT1+HdTPAkaz3A0MZ9fND+CQNmF4BD6dI2AC33XM5Q0M31DmrWTN0nz0LHmdXFhV2cl25bF9+H24NqmBWTzLkYHtWoy9vhnjP7n+Wxb5+CRBujH/XqDBwZgBapxKyGfaVUmBnieVjvocvytxPhmV4twbj6/X25478AmAL0zX/uib5EVl0bnx4g/LVl88CYAhuVmCKh6PU2i/E06nLf+E9jOVtVhEtspDBF/uwqy0bJZrAgFzZeTvhjw+NDIEvZdsbUFmHCPNzSva5inUWUgYSDsFRXv46gWl3vt03ixyDvIp+IKxx6qOrSjDTouOS1FKBo+H5DwIKKHYFj8yJihxm2iLKKMBS36HrVKVtpapuzn/h9kks4zDM8WZgyDyqeMyV0OudwVm71nrK20aguZB4ib5FsEmJ3ot9otIoOzUCHIsIkzmOoA6GMNrLOdJzBgECHWpSu0L7CgoqQtpfcPPCyoOGb4DpyOsQwjmacJ7Ow7FvbobUF8TvZP8nK0PdDUJJXOAkSknhcKSsQkDqyGfDCLkeWbjkJUuZhOKgGSKK9aUAxQsHuHtIp+L5xfvIr+RE6iatZJ2rtVEWdjHXlsOaYOYo1vlQj3HfzalDqW272XPhaL9Rr7/Yy5Ldhs2cSLz3yfdyEObGRQyCNt3zfTf6pf7MgGBuz07lt7LZYwnu2+BEvreDvisT41ZjVMRATB6LyjltuFS4MmAALSigAhhzNWeEcwsqVwuAI1iNUfrh3Hdw0Tpt467D5RzFFzrL1O2IAS2kCehVHpuzklS2WEnpEIqbkoeItwrubMOYxZQNkDkZ1s95FteT2Z5pxjGw49pUlXm6au5AK5Dmd9GYJMNXTdgEpnlITcgA7feZcHYByp/NOrj8tTFBbNNbeydQ0AyGSR7KdHMTKL4NDxlVVTw3sjT7b33UHo6IACoK51MIOBWwvbhF0so99TaAOiehL6cdkvdBmvpB5wcKXkqj5hzt5IOQJ3HUbw9k7KTqxRNaCxPArO9D6E7qQ67gGG32z2J8Qi5GtvEhbvINNeHksREtvdldnWr1fGK+ZLroLesBmCw2o4Wmblh+OylHACKMM6ye1zqvaPWilqry10pBafTCQBwvr+fynA8bCmjXiY/MULiE38SwwVImeWEjkNN9T79cGggdtzhvfR0QGAS9lxxP3W3d/khs8Q7BndKY55Bg/caZVYyXU8zlGVkIqqnaf3BkJ9fSd+ZYO/uW7e1cHmYawCCgs29fsWUpZSYk7DbTm+mII9T+CjY5sTfqVyz8kSfje+eD5Gx69lF26Xcbx4d3boaACjvTK2GwA+M1WdK0dODfcOXNKlagaDQapXYdgkn1ql+MGFU/BiaTIugCKNyXKn60wABAoDqQZZBEPQE3nwsdSa3EXPeX1gy5ne85ny+noeSslCpiwZDcHlinGoc+Zmxm6iqAcgE9llIWG8Q12Pr0sg9YTEY7JuO2HtKrX5qc4Yjt715H8K9NDXTUbtd+37KIHzdmVscsCkzdxEMzO7AZCEHq5wzHl9EVIZ22EsGNLZgzD6v6zocP57LzBoXmGWPidy99ICmF5pdiYP02IhU1NLjQJu66nuTaHFuonjgsK5PAgTyJJfBGjN07r4wANuQI4SCNoJq+WwVZ5/O5lK4dWc+eMJkNdE4yIovU3ibjCP52MnAcZqSTQKZ2Q5nVVW0t7j14O8PikKDKnASLneHEIoBpDIkwbmanEgEwGzB9cZMENZQUk9NcKTQMXIj26g7od4I9qgo034N9g4CQB0eTr+S9plJALxZ5KzQuSx77cRkA7pqzNgWgHHIyJTy/BaXrxSPIkqm0AOoxngfrObTAAFA6957agMDhh4baKjghl+bfK20rZdE5RlyVma4Accyu29Z4jvLmAYAEJSyYTWTSXmfjwKoApM8Lkufb+iYh9WLIOPsWj5jJx7HIG8vNuVXX3wEoNsUmQ/udwt2Q6K9DxN1HwTe6C/Z+Epuu6ifPIck/GOeTuoc0Dav3SRb6yAjTMkw6H6Cvdvk4JgKVLX8xY4ZRwxl5qoaEFS9EOW2rjUvAQAAIABJREFU+5KV32m7kTGYsck3bGXsGhg8GRCI+WOzsLFu6hB0Os4tNHYZKBioO4vnkZLDFVbOiI8GM9s8L8fkzNPSq0ImFQj0+675FxPi3WJc6aU9TUufTUiNLRDSMWG2LyJhk4mL8L4BSi/Km1TY52SRj57m9M7Qbeu08Taz0mxAYEoUYJDI8VQ+MxgadN3ElThuTX8EXOw3cBgbawH4lmzO2FJ/FqoygU2rmA+kmTxB/zvmh9jQ7E4jumBtgTj4Y4ILq7oaiREct+nJgACoq5U0C9q9IkSMUmMVW2egkhAqp+QAfIhOW9wCNN7pAxVFCiR1t9g65uPtyOiweV+9276DkKi6lddAxF5lO2nJS+EzES2oxJBn07TbUCe3eWo5pCPtGS+9UCcwdNtxsk0v4uvOLButlIJqC7MSI7CPccSDrpFggH0TFilN0e9Jd8FpWhY5Dk3nQnihk8YPO+EwYJNzeBZ6tfrFhvAiiGtHQ1sZKdUyXElonzcwr8HYfD6DHcJCsI1crL9K4jlj6XnavzExPIivb4fFEgAuHbJpjL2zpC3nitZNrEVX42YjLzaD0MBFNgSx4fIAgXnSUExDYpVhQm+6rwHJRCoDmKP0NECAGEBzIXfapJVlPQpct7vTzpTddBgN5kGLUsy0PzEGYOdvbUx1IKmaMurqKxVacUvYID7rUXqT/R/uB4GEQg57zYcNyCZipn4EC0KG3R2Vjd2qWlt5ns4OAA9cDiY/rJ8po+XfWRqaikul/Ouk+wwWrwZZ2+UKsCpwjlDpDkZ2QIqLsblpztVTrEfzCDzN+QXkCXWXQ1h6b77DlHSQrUFI1pMBmXgTcBJrRDT7hFBErEHUWF3adV6L7dcTjEiBwN6f/XxOxkUvFwK6thOxuLASY8p9mzlLHvZMQd/kFsoetIzSoaNDmSls09MAAdZdeAch1a9SoJAQs938B7HCz8IGnDtUX2CUaDs6EI0NQBDY6KQpPbLCR4O6e+96nJQmBWiChSYEApJViaK463EQ9JyfzTrNM+IrXlGKZdiP1WJoiaFs0/s8CGrvGcf02eQ/Pe3cxkcoeKdvDit4+AUhZB5gVXxbW2JwZvGS/XbcBjXHvtl8l/p1LIshRUJFZ3Ij0GdXVUpWhOnayEF0WKq/gmQXq2PtmMgAtk2a2NcV5bf0NEAAwG5hyYhxIDQhDnsg/x6wBUc7Wdhf3oHzrLLhfjH7QOvg1uCLQcyqk831Hh/Pm8mGdQy959xjOeo8FYXB8VCmvUrvsmVhBGBZTMCFJvyR3AiD4IyX96YD2b0WaLVNQbajFPNkLWvMMVhqxZo5j366JZStj8meyroXYgOIVhjIzIG27bt2UWH3+jhdeS+FYsMpeAhDSfM5Ytq6ca6i+q6shmOhGhurUCAbSjevybha34dB90mAgAsHse7mIxWKgI2tARAhk5OLbcqwNJK0b1ZypVhJ8Y9AYJhg0jUW0XnYansQoTz8g9TEhvi5bmxR/NwtI/hsxv95/G4rhOzAwswTyOSCmril97BYlKSbw3MjkIaAMzcNvI2zMC3LPGqyYVoqoAZUY/uP1vUWILD5IjkA2FpsRzemxC4zbXtEyuxyBs6w/nYx1cVZkPW/jSSRcgil8UTDLsIMiUWw36fzI5g8v1h5CQ1oZ+svucx1OEpPAgRE92yBRPEDOb2ZXdA6bKefsGRBgffnV9MuAPjMr2yRGRr9lZcm476fTKiMjNgblcpb4HFjje1lBzQ1uy5DI6FDADHuzb1L+h5K75tX3bobYYQjt0FW5OxaTOxlf+7FntCZhVMqS9vnbzX+ke+8u5BV1YKs2gvOmGYWZKzyOk3Oc02ijMa0kjuU4i28mYKt7IAhK0ULxQQttoAgECMf8Zz2hAAvuWRhqBEZR4C3BbDVg6Nj8Cw9DRAAu4LbZ2MCo9XhsfM4BNoZugtYcZDYV2MTmJzdluZmANmfHWdsIj1F84UAh83MRLOMBzI5lCfLs8VBVChjAG8sc7c2sLiAj2jsvGvv7xwn2Fj5qO9m2TDCPWPWkZ9i7M6eexwDsLyEleRDXkndpetRcFOkHOGPjOfyj20ff47tsVktnXCZwlRLLMWwSd9tcxEkGYg1B2Mpr+6kbczBMuZYexB1szLERKkYIjxON21mR7Jh6K8T0d9M176LiH6BiP5v/f15vU5E9J8S0S+TbDb6z93yDgzCobPE0mwxqVz+YT0wQ561BUYZESPPLZj4egT/W4Z2uh0NpR02jFMTBiGOMqV30vY75DLr+3K9OSKah22y114RweZ4NcXBpaVU0A4AWps91grvpfC/09nDXtfYBKbbZjBX6/RwYu5obdUfGwmwmMmtezOa5u7Cnr5nNgjjdWdFvo/LqHjeP0rohb53+IALN0DbZ0PXBscxWFhsqJN/Ihgaz+afh9OtrfafA/gD07WfBPCLzPz9AH5RPwOy5+D3689XIRuPPpgI1a1YpjU+/XWvQ5JPbHoqnZEpm1zL1nzbmB2sIMCFdLARsB5rBg5EyJuP+KhFtmiw8fkEIJuOCd8PkMlPcl5JPrdg6y5kMPQ/smvgIyYBXIxQyrn1rLy7/WF1st8bV8DYDZCHzuwnB2/3XrIfo8FOu24j8vZDFK7TXl7X06Qk03OjvBxkwbncIht2OnG4OgYAVu4uB+EoKNi+AG78ewYTuZ/Tz9DnCPmVVDE39iyfe+kmEGDm/wXAb06XfxjAz+jfPwPgD6Xr/wVL+t8AfI6IvnT1BWQ75Ba3lOGnRkcUKhEUnHwkopq+AyyiTSSjCXaCUM5/D1hkcQnHbi+F0GwpKenmJ7X4JJFaC5ZlgU068s4EVBmWQSHiCOt4fykFpRZledGpRAQKA4taY9m0TULpUz0yUHRbdclTfUliMJmRZF0NQUYCE2NMfWIymW6GItgyXMuL6ghQGSzy71yH+e+uw2RUgFoJRX+kvjZTbwtWkY8YCLjRoKs/e6sVLcXoVJUfKihlQa0nrbdcG1ZxysFU/hMbhFjQdPZSePo5cmuTvNDon1wbCbP0NjGBL3JsKPoPAHxR//4+AH8/3fc1vfYNXElhMYeriAraZ/P/5DPrYL3MMZD7ze/KlmQM7sCtzdzARq1y9B9kLooO1ZBOANIyZiXQK2qVVYgSKABby5J9+nTVy25R4ZFZyHOFyOMCdo0LBXjutfXONQsy5ug9eXtktyrdv5uTlM+PBbO+AW/E2OkyjaA4lyvqlhkfos1Umfae91JR7t+ObWH2nxmDoHN90zC1fiXrCmx3LNt41FhXyJQMBjAYHZ0WyduEDRW+qEivsLkVQxnIv5XyAlk2omL9atu8k8AgMzPRA+HWKRHRVyHuAj7zmY/tWr7DfcgYnknWP7kNUgb7z/MflD/75PGu7ErI52zlelcaRqMQ2cERAxVHApZkZXZqvqG3xvBMEV1cUlmzdZIyGnOymXM29oydJcIUspLANMpjjEJYQylF1zlQlOkBhdkDhAwUVrq9R/cDrtYeGEHAnKjhdVvX6TgdcvtNHUbFyWDgpU2ANNhfWJunAVr/m21mKxkIAjaNnFT5B6OYmcGkZmJjcpliLoL0Z8GuGKb0NiDwa6Tbiyvd/3W9/nUAX0n3fVmvDYnzuQNf+t7g/oOS8NgAjnKh0GbtbKbdMCHDKeAYzNH3IzoxxvKH1HWUthBqKaBSwE0oNtRFsZ2Oem8yvGgK61QyWROGd2KAW7IOnAHPAIocpMYYSS4oOfjsJXmXzahI92aGMjAWpANSHsB25a9HtNMtIrJCHKctcPLm0836fiXxIFvA3ujAg3noJqCiwyN4GW7augBhC9rfDAyHuZrisuUjz0jIocHWXDiAk/ck5IpNXt5uqupG5goTeJsTiH4OwI/p3z8G4C+l6/+mjhL8CwB+O7kNh8kNDhXIUGGJoAzb+CpgtM8qJn4vu48bipTo8QHVHBtKA1JdftA6bLECdWnyomyj9w5ixFKWbh1rZkGstANAAisrE9LfVg8BsDQ3PHXeqBvjCMeohHHjfEKxXAt3KoAoP5os0K627bXj7K5sQevo55Y0+ui61yEXt56yYiuDkG0Jpe1JPf0dsvFwvR5ICoAxn99Dyvq9ltPLWMA2K1DdAUCCzn6UmcQKJ5llYG9vQm/DrRofBYL30k1MgIj+AoB/GcB3E9HXAPyHAP4UgL9IRH8MwK8C+BG9/ecB/EEAvwzgJeSU4kek8HNmCp+vm+C5X4nZko4jC9eQUHSdATQUFLHorft8bZk9KKMDelqqW1Tz//Ox2AYOEkOwYR5722TZDHwGRVV7p7+ZbYit6+IQC/bFMWykgZDgFUnJtI2Mbdg+uJJvmv+O0HuJU1gdTLj3/OJc7shhVvAoAabvbwcCgDCsSjSAGwDY/pxchKjY/isTEzoowaaseU1Y9oaNuRWQaLQPn/bAWbPwPfpt5FK0ucLosJOvMw+wnZLmlIPMbx0TYOYfPfjqh3buZQD/zi35Whr6KlmI2Vccg4dBk5ON3eZNaRValDGXV+WogVhCMquxgMagqgCzNqH4tqjIncEpOl889OtFGuMVWxYAjLJJNq2Xxvta6yglMx4oO4wW7NrpRdczh8sU8Qqoe2DlYgSY2CgAqINKh7TIA4nNnTEFSF9pIC7BUOqNODtgN9sh6IPAxXeQNu9kzHRrfgK76EE9DLGJhbaB2A8DRSDcoib9gREY7T2kD8RKAkptlyMA5AbDPc6BOdoz14H2icwYBIxOFRIdstWgVjsZdiroXcCgVtKgB4FcmIrfa8NUhWLNN6u1jKieWccm1o4J7fWK8/ker1+/QmegLAIitRSUWlEKYamLMcFYXqzCTB7lU4XbE7bNRx6+C3YTFru1Bq4dHXZEWtc9BckfFeov77cNQ+wg9M6MooiUiyDnNnR3aeR9MiuPKgG16nSJvaBe+NHc5QDMWrLQhzvUGWjDUJZaT3Mf9CFTDFZ67O6yCnvR0Z/HJ04/wAbcdpnAaImt7BHiSQvMvK9spKIDWAANzmV2Evc3GGKQ7UOJIu0tfiYcArgPPb3V63whFN/E/Rp2PgkQEEK6ACiyWUUXBGU70kl73WJ+RCQy26G7t3QQa4fIsTAiwFTQjIkX2eChkBzXxa0JjQehdwIagVvH+vIl+HIBLmeAV/DZeALQiEBLAZ49l3Hw0wkoFStBDqIgRq3AUgGQ+vidUz07RB4I3CBuRZs2rSgkdVLKFxF2QuOO0nUWpY4K1EIgHfKSOIFumKGbo4jlLAIYJBtigcfh0QVFxXmco2D0m3WjDtvefS91C+oUUZLWlAG4ZSIU6mi9yfFupaLURbfXVkVQ1hZNJhF028vB1sZzl3hRIUYtFntRYe89raFH6IuzithcZfCWEglHcjPtmtwSh9QaYAEUntLEFjo3NJ0ZSJYfdekvRL9WimFdAoN9S7JqRZVnCEIvqAG2mMvzabmkm9gY+rrbb8ATAQEAYJwAFKwWBEUBiuyKAj0huPfmpwq1JtawaYCstBWFu9IvjeQTY9UgS6m2moulQ/JijzOAVa3o5R6VGc8hncjcQDolufWGToTz+SVOz56B6EMs9QVKrWjc0QjohQDqKCzCTh0+lt91155Cge7oLHMlS5G9OqiASwWKspgmda6lqutLMvFpJRB1nIrl23VSUwNqQa0s8t4IFTIZaVxpppaHheVwKegkAgs7Ir5UiZGwLfDS5cQuaM3Zl9QorKQELFcApOyOAFxQdMi1UEF1ysy6cEtAoCXgAMmMTTZmRAtIx9E7ZESmOuuAMJ3GcL/Z4zLyzphbwD6UOrAxi/NwotZFhu6aWiFjYIQq7FF9OXNP7dnWGzp3kIKAK7MmnSMLWqRt5W09bWNhcygWbRkDEN1NaujP1YGIWVzB3jpQdWehpw4CRAWnuzs/pCHUs4JRQD7PX+5dlkWsGheUrorHAPdVx/ALSGerLQBa77otFvkw3ul0Ql871vMFvKrP2tk3FZFU3D83d5q4o58b7vsZrRfcNUJ5/gynWlBZg3bU0UrxekjZpfOrxYmUoXSdmlx8pRij6opKtllp6lcWKg4gRQWOLf8ua+udCJrhSR82lF7vsZmFw5ce9ARk5WbMfBSqHkJlcxfmiLaM9Gg5O6N1gErFcjqh+JZrBUVBnVUWwGnWaGp8bkA9Fbz44APUIgeAnC+v9Tm9NR09N/vb4Veb27FN2bfO8z+0UXKjCkMBOUXlUvw5L7+5Q5q5QZ37+Qoodqtk1B0EmIyZsXP7eajVYj6DS5A9n4O6WnoSIFBrwUcffRQRfQAAy9HguAATCFQDAbV8nVfw5Qy0JgJbCsrphFIqegdW8yOaRNSJZIec8/oqlg53oK/aOV0EpqilimQOCtAuK1p/hXPrWC4r6ukO9bRI3KLohhfyiO9wVUn2+iMwLp19sdKqQlEgCkEA0Bhnnapsym1Tn7sffClC2RqDe4vDMb24Oorhy49FaNUTUisqFDXx3GFkxeYQxOauuhuODk3lIcGZ/hsIaLaqqCxtjS75UviteUqyxxRITv0hPSvyxfNnePHiGQoBy1LxrJ1AveN8fo22rnj27KQjNTH2PgYRTOlCS/YnBcHrZuzTNgiJcwnh5xE6CnE8l8EjB/msGA5eNrLE6TlmB4YovrGMuax5ZSRS3TSW8EAQ5UmAAEA+Lz6Ky+b068QbwFYXlroAJAdDEEH3cL9DXXROd13iHgbuWKifxwR6x/2rl7is3d2C3ju4rSHUZkn1dwUJJdceWHvHujb0fo/LuYFP97h79gyn58+A58/QqKL1hrUzqBQsdcGpVgG0dUUhAYVGDBSZhVgg21Cf1FbMBK7ohCWxPEUVaIJ8wGkwCOH2DHLAYlYZOuK2HeMHAcWCXJDYgO37D8CtXRYwYQTN3xVz70UYS6kKJom9YBVLyR1NXYK6nFDqCbUuWE53uHv2ArWcRGkKowpdQ61yknW73GNdV6yXC5aloNY0hKZl6UMDWJvZMt25BadPXueObaJQ0h6KNz+bc6b0t7unXoq9ctJ0PZctvYCMUWhY2IHk+hD5EwEBDEIIQGfrqfngcdkrISomy3+VxgO60i+NCKCgN40LoKIwcDk33L+6oJ27+IUro62yQ20tFa10NG46Jd32dgkKya1jAWFBAdukpvMFrXeU3lAJuPvgA9S75+ClAjrJpVABWkfvK7g3rNwkYk4dVAoqEagBJ2hsgIUlEctIx4sXL7AsC7qeilNJ/PKlEmSEQ1JZFtSlQoZZBEzFyNh4sio11Ib0C8RH0eOsSw3/VhuWcNIRF3LlN8EqlXTCVkPTwGReGCSAIZFv8fe7jqEzzuu9xBYK4XR3h+fPXuD5i49w9/wFal1AtIB1stilAcAFzB2X+zNevX6Fy+Ue6A3LUvDBBy+EibU+OT5SU59LSoRFrboNqZpMmU8/yKbVU++JCLVt+CGgLIuJuradynTRESNlPvYuKYcBQZZ99udlGFfjYkNdgAAIBTM3mhXjXAoZDr6WngwIDInNe7OJOZJsaAmw5bo9ji6DrRCTlu1se75o6LYTmIDzueHVJ6/x6tW9WMle0NoFYMJS78DUwLWAeyz3qIA5nDJPAIzSjfaKInUAWBu43eP+fAG9vseLjz7C3YcfSHDG5g8UQmUC90WCh7yqsGheVePXVPBBWcRJKBIHef5cRiWYO3pbUYhBOKHf1dH/1RV8pCDAvclIvfrgkoq6KAChobcLWr+AuQEQtiWr4yrWteNyuWBZTqhV2ksWCFWpUhFWVmv1Y7tIA5iFpGy9SdS+94a+NjQdj6i14O7uDi8+fIGPPv4YH7x4gVpP6FyxNsZl7WiX7kuz71+9wuvXL9F6w/lyj7Y23NWC0+kZTksRgA2oS/LEsM3FmRmdfAB2sJLz3BS96swHsCCpjeCI7AlSNgnc55EFMhcgby+P4R3NV7t6KTYqMc6RycxBpd9nqG4eHb2hnfQkQEAY6d40Uq34MNwRUX7vGOpiRYtEtKmUGK+nIhHuwugNuJxX3N9f0BujpM1JSa2cvE+UVfaDLmCN2BaWOEFZKtp5ResriAl1WVJDdgk4fvJtvLyccbm/x91HH+H0wQfAIqMIbnEAFK66ClztclHwKhWVFjAzlmXB6XSKJckoMt2krxHjSOfrZV+ejO6LOXdfG2A9TUftF0n9WKe5lipDtoQqw57LnS+bbu0sLGW5d2sfihJKVUpxUBbG1sC9oZ0qOq84nQo++ugDfPzxR3j2/BmqunCXdRWXqVScThW9rXj16jVevXyNTz75Jlo74+7ZHe5OJ9w9fy7Kw4zL5QzmjufPnmFZluF0ZkBdGB2zryWGVffpcijZGPdIv8ECqgyx+BrUtECpLccuLH5XntSm3QJb2sxqsYnMTek2OTViMWEOEUCAYU8S71+vzgMIgCcCArekYU66BloySwDpvAJFaD8UwtyDTuit43xuaA0AF1HI1mTkgAitrdqngtxyeo/SWTs0AzI2Lw650RKN5Kuinar46/18waV9C+3+gvPLl6Bnz1CfP0e9Oxkj9+hQ2KXi5FLKncbtFdhscU9vHcAqpbVgVYrSE21nDKqHDgAeh2itobem1Fipvo/LS2lifwAZsvL19BQzN6W82Q8mV7RCFdQZ9+tr9L7i448/xHd/zxfw2c9+DCLgfL6XIbXelaFIcPV8f8E3v/VtfOtb38bLl/eoRWj/3bM7LDp0d77c4/XLT1BKwWc/+zE+/vhj1ELIKyw9gq7bq9n6qGMQCN9aXJ8uIxgZwOsCJjmSzOJWvTGWy4K16xRve2aTvwahGcouZWMZRkwJJ4qAuI0W2LMGFDLUGcO6NhNURpHCie0TIOb0JEDAAiQAIBFjRTgOIQqmEBt+UK3Aaj5odZ9TlEMnrawruCzoHXj96rUGE4Wac7uAm82tBpgYl77KcJa2fCX4yi5uEuYvRH54hIAPgq2wsP9TqQImnbG+eo3z/Rl8eg16/hqnD1/g+Ucfir9dxf9vbrHFuTAL21rDuq5YlgXldAJVWztgwEDuWwLQ+RFmNQpKZcje9lZeTuPcWvHErOS9utMR6Ri+EV7ti9jaq/uIBbQPrWwACTtRFtBax7q+wrre48UHz/Hlr3wffvfv/grWdcVv//ZvgegMImApCxoRLuczvvnNb+I3fuO38O1PXqF3Rq0nnO4UjGpF547zy1c4n19jWSo+85mP8fnPfRaLukx5y7OtxMWEnQj8zYpaVSRp871cWzQOpa6GUv+lnvDy/Frk9wQ/zKUY4Gv7WxxrcQrHoBK+vjGrOEkrAq3y/SostXeZnEVVR7ZkpKlSwd3pDmUhZbn76UmAADA2MJt1SeGdGLOVAAuVmBsPqICXiAfYdOLOjFX9yvvLOQQdAEBolPLU2WkSeGseSrIApR3z3Zl1Yo8EFomqTM7RcjIKyI5V68CJCQsDK19wf7lgvX8N4i7Bw2cnVU49poplcs9Sl63w6efWGrqe5kN2XYFRypuTUEk5q0nH4qHAwU3skVly/btWDehBJ954CxtF3d+kIi7pmgMjSq3jfD5jXV/j2bOKL3zhu/A93/Pd+OCDF/jmN7+JZ8/u8Pz5M5zPF3z7k5f4R7/1m/jN3/hNfOvbL/Hy1T1aEwC4e7ZgqQVrW3H/rXu09YICxocffojPfuYjvHjxHLXIpLLsz+/JmmxWmlrpiuOchz3z7w7bSYogw6zqgmksL/ZmEHdUiaLEa1IvcY/VFRIb4NT31q425GrDrjJpyBaWXVYxbtxje/JChOfPn+N0t4D5xWH9nggIhDK7S2MK7oy7o5QelLZ3tzpFp6p2tY5C3409FFwuF7z+/6l711Dbti096Gu9jzHmXGvtvc/Z91Hlrapb3hSkBMsfpQUqiFHUHwpKqUjUHyalIhYoKgRMKuaHJAQimggi5IeUJIEYI5TEIIKWIiQ/LMVKRE09TKq0vPfWed17ztnrOecYvbfmj9Zaf4w55lrrnHOD+/Z791lzjjkevffRe2tfez/M5SWUCsftMx0im+y29pT3M4UZyf32Q4SkBA4ZkQbdWBbaDBbktKjHXogYAiFkJTKHZcZdyghfEQzxJTCoeVSNEYJhVPOYmIdkGZPBPoV3CTDnKO1aTwj6zht0BODZbCAeZtxwN8AIq0sexpUMDTAnuIdgjOQq08Lp0d1JTMxgzPOCJc3Y7Ua8eHmFq6sXSCnju9/VjHWXly9wPB5xff0xPvzwO/jOd76DT968QWZgmva4vNjrnAwDhIGcMtIyIy9L0Sns93tAgJSzekBuaciatw3Q5lR9ttbY/sW/mzkURWdf9DNBQRlMm1A3ekElYkirEgInADrPglr4zEcRzBtWRQGUbFZG1AOptyifDwR7S4jA083NgYDLpdXZRnO5AYCU7K8ErReXlgXzw4z5OCPa5KjjvgYNUb0Azeqvm4NQJtRFADWRRZC5jAoLRk/tzAJZFjAHfQzqNcIM4oywJKSHIw4QDESYXr4ERyCZy26gCZkIQTRHoduzufjFm5tt2fNFGEBRoDKZSC6+jy0Jhp1PQDEZ2rjdMarOteo53Iymj5ByvROnVqauHIyxLAnzsqjlIATs9ztcXOwxDEGDtI4KXK+vb/H+++/ht997Dw93DxABxnHCPg64uLzC5dUVQAHH44yH4wNyYozThJdXV9jtVHGY0oKdOYht+U7UzS84Sx8+a/P5Qp/bp86hzqkXr/GyY72yT60C2fw2nDi1zlYVBfuyrOKxEpjWg1PK+heBotpHCADwlhMB6hmVcX2gMDCDPJ7MkjMXezQALMcjHu4PSMcMZX7maMO5/CNWU1WtAadKHA9wcy8ukBe+AjILAnHRPvsGDRQBAZbjor7x4wiEgCQCSAYxA5kxZgZyQn5zgwNppd/hxSUoqkIzZ0YixsDVlbbVLGt/bA4IJ2KvoN3Uxp0AlIrAUNu1SO4y4tR5diJgqMA8AdX3vj6vXZzttQAwzzOOx2PRZ+x2I4YhGNxVU+FEYOypAAAgAElEQVT1m2t89NF38NFHH+H65gbjOGIaJwzjhMurF3jx4iXiOOJ4nHF9e4Pb21uAI3bTiN1uwjSM1kf1HF0WFTt2uwnTNBYl4+kW/d603lnXn3FqkVARNNQoVpsjz8OoTC3UsucEeH7KDgWihpAbuCi6I9czFJ+D4n349DjeGiLgAwK1q3oLlFdpigJZTXj30zcnEQZSYhzujzjcHS1IJqpizxx1JCeAa4CQysO2WQtsQ+F4EECoOoQsmUGsWm9A4TCJaofzzGASIAwgqkpGYkEwIjSIACkjX9/iaDqA/YtLLKQcm0RlfxQTXDNXsFyDmx5sJzPbT58tXDaHHRYLiCGfW+0rm1eim66qnbsuyl7j7GJKxrIsOB6PWJYFIQSM44BpGszEGDGOI9Ky4Nvf/hbe/+33EELA5X6Pyxcv8Pr1a1xdvcRuv8OyJHz86af45NNrHB4OAAHDGBTuZ8YiC8YxImfBmzefYjkcwCL4gR/8KnbT2IhHspqLz0cWaPW3v29LkdtV21oeuICwzmoTlShXt2/zeUEr3fl9W0JQ3baDW7Jcd4AAzrVm8mM6jyeJABH9pwD+cQAfisjfYcf+fQD/BDT+7jcA/Isi8ikRfQPArwL4dbv8l0TkZ596BtDAHJSlhrYceKVoroWXggKIYKY+vTKljMPDguNhBqds4cI2oZzVvs5Zw4k5o4ZmwqBzCyep/CECKARwCEhLUtNOCIjmm58z2z+j5ol9JABggTJqK49ECMzgZQHf32O5vlZf+KsLDVnmbFJJ4d11npwrxwiYn0BXyLZ0269qA2ak/uyfRBpXFqhIE/zc+q/1zVgTATejpZRwPB5xOBwQgmCaLkwxle18wbIc8XB3h5xmvHh5idfvvsaXv/JVvPv6NcZpwuEw4+NPP8FHH30Xn765RuaMOETEMCCaXd6Vk4fDgof7G8zzEftpwDvvvKNelUbY61i3QPsXawFU79qaaFAVrT7HveIbMDcFFRHNJOtBYK4LiCFotAdBGVSDO+pzdGzkilgiFVX9WUVMOz+O5yCBPwXgPwbwZ5pjvwjg50QkEdG/B+DnAPx+++03ROQnn3Hfk+aEwFa6LrRslVpMWVcXocmlwZe3QaycVRF1XJQwmNKGU0KIBI29z+pZJgmQVDjkFrXstgzBOGaAmdTLxPsm4cxIzOp6ywLKBvFM/cM5gfOiYdKkobqYZxzeXINDwI4CRgslTo085D735I91hMJA0fZbh/sF2I+k/SwbZ5WfReAa7yrn55LLUSs2oYxbjMAtyxHzfISINH4FGZnVBXZZFizzEfv9Hj/6o1/H5dUlfuhrX8PV5RXuHh7w4UffwXvv/zbe/+B93N0fIIZ6QKoA09gP3XzH4xH39/fIacbLl1f46le+jBdXF6r7AfWp91xOLtTwe6MY0KzMtj2d8DYEoff0qzoTuP5IAARFAWwu8CRGXpqioxU5cHlO+ecLAy4eUFMZafXcjfYkERCRv2Qcvj323zVffwnAP/PUfZ5uzm3sm0XZaWIOLhPthUuZBaEU4zB6LKY5nhfkpJs7kCbF4JzNfp7NoUZFAYgTF91eUuTgpmeF05K5/3LtsagcH02Bk5mRWJ1RIhOiIxrTACfrCwDEwcJmU0K+f0CmgMzACyLsXlyp+dI98KjqBk7cYu2bC0/sKNgXkvgi9cnVi51D6v/dmaaFpEaALMQ7JS6EoBASI8rZiO88H8GcMQwqpmVOwKJej8yElFVEePdd5dgXF3u8fPkS19fX+LVf/7/wzW9/C3e39zguaomI44QhqmVgGAYQqxg0zzMOh3tAgNevX+NLX36Ny/0OUpTFPff1BVTnbg3bn2pUhFGph+pmPz29ipQt0SEPbCKXCyofad6Nn6tzzDXlm2U4rhCfDMWKgToTK4yQO9Hertis7XuhE/iXAPz55vvvIKK/CuAawB8Skb+8dRE1dQdev34Nj5lTxK5KtwBBkFCSToQg0ACNiAjXfhJAA4QDOCUsCyy0liF5Aeekjhok4KTWAI2dTzDXQQ37zRkZAoRsFLi63mpqLltUvsEsBjZLxpIW0+QTUvZqRVkRR4YloxANGkpJvfMYGGjAOEEhPTPi/T0CM+R4BL76JYxfelc3UWBABjA0yKmEpQoQEcsLL3CfGEIJIE1UgsBq72fY9TrPwUKINV7AkAzYdCyqBiUAS87gTMW33cOGvSagKr6AcSSEMGJZVOm1LAkpEcZREMMIXoD0kCEvCNOwB0jwwXsf4ld/5dfw/gcf4JNPP7E8igP20x673QWmaYcQxoL+Doc7LMsRQ4x4ebXHNE24vLzAbtB5EGawB+2UObF5aQWCzg33uY26S9g2XpO2BGTwPURCnvVdVFpBig7d38C8UwPUeoJASJ51WEjjVyzXmMeXKDpz9GdmQdG7CFsxGnPbDF5GLzHGYTw7qi9EBIjo34Gmj/mzdug9AD8qIt8lop8C8BeI6CdE5Hp9rTR1B77+9a+LgBv/Lq0LqO4+lTiai7gesYXqQUPKZVRz79r6nBM4LYiGDDkt6MyCjX228EIXMxxySelvmXx4SK+Iih+Leh6OcUDOGTklVewFzfaiZkSGpIyUVR+BACyUlEJbyrGQM3A4YD4ckXICILjY7xDiBOI2LVV5Azpf0hRjDVCYaVxGh+P2aykRcOxIUgTZYhBC0Oil7LbNYHqOBHCupclEFIqLCF69eonLywsMw4iUEm5urnF7e4fD4YCUknHkoaI0Ae7vHvDd73yM27sbfPOb/y8+/vRjLEvCxcUe77x6B7vdHrvdHnGYAATM84L7+wUPDwdwmjEEYJpqQBUBBX1Q68F4smLXMvV5IrA2edbjG8e6z+oZGCqONx8iX8n1bBXbRJkAeXZiu4uYQpmN+Boa8xG4tkOtYo4O6qjY0COAouQ91z43ESCin4EqDP9hsRkTkSOAo33+ZSL6DQA/DuB/fep+yoUqSJNyzDSoNftiFcWDht5kVoVUTtlCWo0AZC7EA9AXROLaWgtPFodn+i9zIyn7PK4mn0hlU0sbgpQz8qJx8TklpCVbli6lxGzycl4ScrLovwjkmLGkVAJzhEh9zkFYbm6wCIO+8iVchpcYxgmBzaRXJok1qUVJQS7KPch1HwTk6sDigqLzR82ozhaRqUlcNNBFG2cLsWZVXM2zilhxiHj58iVevXqFH/uxH8OrV69wOBzwwQcf4Hg8gugegFo33HzF5lCUSXCcj7i5vcGnbz7B7f0d9vs9fvAHX+Hq6grTtMMQlWsd5gW3t/e4ub3DfNTowP1+wjjEkohDfTYUMgOtXkdWf0/X23Paqavw6tpzdKQqCVb3M/+LVTelmG9NNJUMsICN2xv40kxDJpoWlZHXtmRBDULa6tB2+1xEgIj+UQD/NoB/QETum+NfBfCxiGQi+jFoZeLffNZNnZiJwJOsOX/2LSgCSzzZwHXRBZoWRlpUM5+TJvxwkUI3scvlXKiv35fqm+jmqsrQpxNIVF2IMyuHJ9JYhZSSEpJkQSXMWJYZy6zKyhgCwqTwLZv1AhFVUwxBnjOW64QbzkBO2EUC4h4UA8I4lKSgZeG0WX1A5rLsgkOrJbJZNY7jehc2AibQfAQkpKJLk9VpGDRw6OLiAt/4xjfwwz/8I/jaD/0tSEvCb/3Wb+HTN5/i+voa19c3OB5VQRhDhLBgmQeMEcjTgHE34erFJd555x3EIWK/3+HVq5eIMSCljONhxvXNDd68ucbd3QNSZozDhHGcMI2xZPhxvQdb4FEn+7pOpBmzz+0ZdejTS3QDHVSCQGXNSP3RaAGtru2tXZ4X1cOyi+XXlYHi7sQeQQvAnMncg9ZHeU7/9xjRe46JcKvwyM8B2AH4RZsQNwX+LgB/mIgWKMP+WRFZVzM+08kAtwoU6NqU7CKTT0VcNtKZU9OcIKWsmX6yJ9wUEyksDBjZ7uFqfd8YrvDRFgArG1VRgK2nvr+k8nM212UBkDhjySrzB8qWDJSQWT3nlvmo3oUxIsWAxDr9KvYEpJAQoufxy6CUsfAtbjkhEuMlXmO8vDD/CLK6FTZPIp0ZiUpiiYp8pP3cHEvm3utu2LJkE8R0QbIwxmnAO+++wqtXyrF/9G/9Oi72F3j//ffw0Ucf4Td/8zfxzW9+EzfXNzgcDmBDZyKCw/EBgQjTGHCxm5CZsdvv8c47L/Gl3a4499zf3eHjTz7B3d0tbm9ucTjMIATsxqmEUhflevdSKidkyqrIdBh3dvE/jgS2Ns0aCYh5/K9UBZv3cvGyNdmV4/YvkFZZdvQLUsbgik7A40tMeR2cGboz2YYAdEasadtzrANbhUd+/sy5vwDgF5665+a1RRRwRwnXVqNSPzCIBngUIDOQlmwKKEZOufiruy8BoHK7ICNYWvKiCygbAajZV7jufpipzbgmWqBAToByocwpqYYcWRAoFzEt56R9ZM06q/qLjJSzeeGazoAEwUQDNhQzDAHL3R3efKR9ffWVLykqCD5b2jexe+ji999drNGxqgmq6g9U/GEsKcG5U86C+XgEhLDfq2Juv9vjYj/i4mKPcRxwODzg/fffw93dHb75zW/izZs3uLm5wd3dHUTESoZHgAQpJczLESKMeSY8PBxw/3BEyoyHh9eYphHDEJE54fbmBre3d1XZGALGOGIcx1X6uRrtCHtVupns7TjzkCJYwjegI7i1G9rWijw5stpQnrbGRahSOapcXxFXWUuFeVULRqkCRbXmQBXZpJyrJmEqtSIIARJqdO1nHY+3t8NjsHik5boZe/tKpflEqsCigLRkHOcZ83ExAqDw2nUBYrIicwZJAuDpn9sAIvcUbDvUUM0VR5D2pQQLXYYq4I7HBYd5RkT16Mu+6cws6BYHjW1gxSc5Y5ZFuUEgxCFiHAZMU7S4d0I+HnH98ccQAt4NQNhNyCSaTss2uJibsYe0gkLnhJJZFa650Zpn4ZINKC1q/gshYgyqcBNmgAOOxyMeHg5F+TdNk4YBX1+DLavQOIw2Pr0uhqiiDwV9tmUcvns4IH34HVxf32AcNTLQdRpeNCbGqNfb35JFOggQKgctaM0SN/UbQlaf6zt9ynHoOUhA72qx/OUZDdNoCZArcgozq/1saz2Q04nSbc9hEIp44bSNqUcYbb9aR67WrLzV3g4iYE1gFoBVhWCXq1hccNJQzJwZx+OC+TgbcxczL5p5BAQktgw8yu1IsibQLATANkyzKFyRuF4m7XLyxepa3JQzHo5HzMcZuzggUQLFoFCbGZGAGCzNA7mHYQaxyuIsgmEYMY5qX592E3ZTBJosv2mecf3xx2BivPzSa4RpwLLoxnc4zxC9xsaflqyE0bXNzr9Iz9GxRAzjgN1kSjzO4CVhXmYc5yNubxOW5WCLjAzm1xLmasMfTYxhQ0mMGAKGSCr+5AytHlU3/PG4ICXGOASMY1SloEVURqu10AbKqCWFu11SQJpB5D5F11Pc8bM1J/5tU6nE+rJiXFXfVBlOm75apJHhjQDAzJs+TJF6fycwLn7q/tcN3mZ0quKef94O/fb2dhCBBn4X6C/NSzb5SSiCguatY9Y8Aa4LCEwW+FPlefXSM604e8EGi9YiMfrsCh2D/wUh2DP1xx7kiSXmILUVMzPmtOAwHzDPMyRqws0QY6XIISDQoIo9k1mXlJBEnXmGYcDFfofdhdq+d7tR5UPR7L1xGEFBlIt+/AnCboed7HHMCSBDFcwW414lxLTo/HgSQ0cwcRgQSZVyL168UILGGYeHB9zd3uL+7hacE2KIABJEMmIcsdtp8M7xeAARYRxGdZaK0crEcV36wmAeizORsHpJugdoCIQYA2IABsujSOYVGAtXdALQvwN/Py131sX+HA5Pzcb6/K1mJ2qYCEtDDNrYisZMJ9IFbgWqHp4UNNeMim71dw8Zd+9QFjU9UyG6nnzEPAqpH+Nbn1kIcA1qhdu1mRKEgTCEkkCTc5X/dZ+re3HQmuGQ7K6t1TSm8r7LdU7F1+IHCmfxtGFtH9uJdb1DzmoRAAHBub8F//hCzUOEunxOCIP2P0BjAPbThMvLS1xdXmLc7zQjTwCYCCFOiIG0SAk0G/IwTlhSgswLMCiXTZyLcixbYtEQAhJrUs1hHDRFd4gIIWKaJkzTDtM0YRhGHB7ucXd3g7u7Wxzvb5HmozpHDgOmacQ47UAUMY62ZGhniMs3rMatx8FyDLBAUM14zAISzVtYvPYszZfGgJhrdKBaZMUXc3kBKOhFVR+VgIu/32cVUH0eATinTKuuv6vfXb9THuMbzy0WNZ6hAynuCWrIoFo91MwbQuzGGskVoazFxwoy4uLYFkKv9cglmOi0vTVEoLYaRFT9pQVM0fLjagw8F+jPxvXVC1AkA5ZwQ8OFXVnWwHuywB7R6wSWUw/unoSyUM5KmOLKqVhs1ZdXVyp/HxN4WbDkrHkLyV1u1QU654jAgv1uh8urS1xeXlhyzAkI6hotrK62wzQhhIA5LcicMU4T3nn9GuFyhwUAxUEJAAUMY0Q0PUCGbqrEOr5pHBHigEDRsgZrWbP7u3vd+McDluUI4QQSxjhGg+ijcmiCZT2OtgADmDy9thEBEzEggAS3WQOabl0aLt1yQc0nqOEBhBLNGVwxqz4iFNT6QQT1IkWVo721pdL+ZrRTotAr5PRba1lS0cqzR1cMDwC+3lDENCKoVUdqzKZQr0dSXZeKlRKkoEDf8i1iatvbjwRsd7Wu7QAsfkCVbmIulizK5ZekjjeSXcmXNWegZAgv4DwDtqC7F+W6mfJfpbbCdSHXkwveK3oJAgpSiVE5qnLYjGEc9TXFBUiaSmzJg91CwFkrHgsPeLG/VO7/4krrCYQBXuY85wVAxBgHS2JJmoNfBJmCZtt55x3cLjMWYVzsBsSg5wcQiIElJ6ScMREgVvPAdQshBKRlwe3dLW5urrHMM1RpqlaNIZLZ5AcMA2nVJNJgoBiiCbOsabuJ1M2aYNWL/R3qF09tJtHrRDZmMtSkzlS03JUTthumbji3jZ/b6M5RHyEEG3qg71VzphzIvFQF6IN+ShfKBc7ozCgLF+bI4j7M86yIrACM0HlNB0PA1Ix7hVjfeiJQHFoA14SgmrXMj9osCMyCZV7w8PCAeZ7BrJ5VmidgtmChRQtqsMmyRWvbyPstWDJCY4mzCyQThL6f3FfcISKM04jdboeUMmLQijgYbAETYZSxeHSlZUbOCftxwqt3X1nW3J0q49wPAoKMjCCKeJacwJBSZv1hXrCkhBcvX2I/BDwssyVZhaU0y0DKdcEYx3dHGhHB/f0t7m7vcDg+YJmP4KSWk2kI2E0jpmnAEBV2xkimoBssAWkw8535ItiGav9X7dqOEgCRWpuxRvIZ/O0IQMsuTd6FEWusRLOTdt41tl1t9f49J1+3J8WBcvXaAck2qusrgLKu2xwA3X5tLQ+m29Lv5j7vooI/1+bdMxOv57z2YkvE7ttbQQQAW6Su9rS/xSlCAHdeSYlxPB5xPMyqXYeez3mBZCUC6uyu4kBbi9fhvNv+e1OPe2LpGXXh2dXFPKOTXaoAxYjd/lJLmpk8ppta4TMJIwz64ifT/F9MO1xcXliNPVP+ARpWHAgUIyQAC2d4PdoyT0mDdl6+eIXp1RU+vrk2ORAAC5bjjGWeIYEwEJDKfAoOD/e4v7/Dw/095mVWoiaMaVRnpWEImKaAcQxWKRlVVif/jKrVLjUJq6DkU1pt23WGfVk6sQOqKOHcf60PavRtyu2k1QFgRWzMjOhI4rFG6oD2eGvuQe023753qzJ6FIk0/dX9H0FrywJRqWQsrhQzc6QHDQmZWxE5GlClr6yeA+D7gAjYhmSLpoObO0QpICMgxBEiavKaZ/XPp2xoIWdwmiH5qNda9p61Q0gJB265Qf/Hu9NcWYWHFsYCOsFDGHGxv0BOSZ1sLOzZS0irw5NW4xmGQTX/04RoQUgqw1owEtwHIQAxmqMzYQBAIhgRERmQOeNyt8O7r7+MLMBxUdPkkmeDfboYQARZEuZZS3YdHu5wuL/H8XgPgmCaRuX8u8Hs+eanYP4HHg7sytPisIJegXX2tTZWFqByRneucRGrvgFCr1EnrWRsz2JmDBshsS0hqUTpHJevAssTZGK1kev53XFZP6efo7AaUb2X9dFFtVLjQcvZI3p+Qc9oXPVjrkAH6urUOJuKVteI9TGa9HYQAYhVDE7QwAkLBhEAIATSKLSUE47HhLSkmm04ZXBSGZx4gW9hVbKcZp5vDU0UPNimA6BKPDoo14lYjVJQ6wRO04iLy0uQAGmZwTlZPjloVGJU/cEwDhhHrRNICCpLm0MMOzHjBAqEgSJCGJDTAjG/CM1GJJgPB+Q5aZVjEZBo+Ogya0JVBE2yepwX3L65xvHhgJQXHB7ucDw+gCjjYj/h6mKH3W5AiCoIhaCVhsh81DUHg4oDEQoF1py3bWuus4anwWaaLVTMcUF/rya3nrlB6zvwhWzRo+VdntvojxGC57U193wOV3U00G/C9ruUvhePRtv/nhbARwCfB7GowEIRrb4DoF6mFobsuSdOifXjqOQtIQKA2wE9FhqAKY4ITDrA4yHhYGKAyrmClBbkZQblhAFepZjqJG5o+U2DglqJxl4uoQRvUHNRMV3aHdxEA7hmlnA17TFCPeswRPAS8XA84jgfdRwBGGLdRBrxasEvUTkB+UuEmkMxEPJigknOhnAYy+GI+9tbHG9uMT8ckS1ngjtQHQ5HHA4zbm7vkOcF+TjjcHyAcMZ+mjBNAfvdgP1uxDhQLU9IlmMgREskEkHDgFBi1rfgd130HryzVkKVJUhVb+C/FM132WC20BvrgF0KQB2bNDciN7/XQJvqO7BCfCti8JwowvXmeY4f/uk9UGD/VhMRpMzmBa5ZrwIsGY6ImbjJJNIq0GrkZwb5miItl1eqLhEUXbKAKGuJtzPtrSACAtGEGebYUzLkUIAggoWwJMGyZC0FlRMkzcByQEhHkMwImDWLTxOBSNAs7WXxBmoIjEPT5qUK1K2Y3BhpxCLCgnX6Be0KGSEgDIQxjghTRJAdSBgX84KHwwEpLWoLHwbQMGjM+6iFOrMRGGVywZKEqPkzDgGR1CswRE1vvohgEMHD4YhPbt7g4+tPsCSNTTg8PODm+g0Ox4OFV2fwckCejwjCGMYB+/2kij+rXBxik8iUKnQM5pilc4dC9FoOVRysqJ7T/gOpAdbn3KF8JchSEri4OBAjVRGtIdCOzoRFE200hCRAzW8lG/LJBv/8aKBra3riAzFrSa0grNaAUqDV+q/WP6rXWIuBwaq8QTAFsK4/hhBr5WokSz9PliOiGS9nDIbekpnNmb2bBK0veZ5wvRVEADAOIIrfbTogCMii2uiUvAKxqLiQF4APCDggYFGlkaUkcyeNggFabemJPAf7zZaiSQIEKvXcQNXTvBgvYBvDLsoG62KIIKjPf9ztMOx3OB5nK7CpHnI0REgMdr9aPVmDR9RzjxMjjErU5iUh7BRGJwhoHHHMCR98/F28/9EHOM6zKvse7pGXVEqDHecjJD1gCMA4jhjHoCa/gTAMQcUAy9/XbW5f5YpD7bgrp0IhqpXjUr/5USYR7rfupq/qaIOGUbfc3smznO5dUbOkP6/oAaA6ldD2vWmny999+c+3bW6/Mkw20F2XghT5X5WoK58TqfqB0kuCFoJlzUERrXK1Lid9Glsy3EiwADSBe8eARaXnUQmoCMytG8WEq9v8+4AImMsOAICskjALWeYbqNbfFH5SyoI7GKTu+rOyYOH4q99cR9NcTXbfVuPbKWSaisb9o0xeI/eEC9hNE8Tr45mZrTUfKR0JBrldBhakeUFaZgwIGqYsGXNagBhwe3+LT757i/c//BBCWgtosoCdnBLmtGBJCy6mEbtJPQWHIWKIA2LQEmfOccqmPd11qBscBXoX8WkDUhfuXSA97O0056sk1k14C+UfQ+q1am+1TLSiFLC2Svgj6oGK6J6A9TYlWz4JrT5C9T8KyWsygOZ3H9fWM1r0BFh1LO+Zpxe3rNsY1OdCXLNQvQzZSt4BphQ2vZqH1D/W3goioJw+A2LJJUvKKEbKjLRIiRBEVo9AcmLg6/Jk5Wy/4HJUtg6edOwMkHxs8TTymxGBaGnKmdXPP0TXWpjfW7W5lReWmZHnBZJULMjuOEXAMS348KOP8J3bT3FYZlxdXuDq4hKDAHc3N7i/vwEC4fLiAhe7EUMMlQgM6trrZc6f35Sfq4yJGo7soy4M3jZgCAVoFfQkAiZuxI42I86pnXuzF+KOL66TkTKXmsnIcyGu7yHNPZ4hIEi9phUX63i9r/qs6rZcN/16js42QkdAfA5d1+LzRBZqHhBUjDRRq4iTMAcjM0k4cfHisefa56078O8C+FcAfGSn/UER+W/st58D8C9DxfF/Q0T+26dnQSGPwkWNkwYYmQXLkpAXyxSUvVbAov8kl2pBvsieNek+Nv/bAogzyqB634oTTu7lWnB7oS0HUEZffeR14TT8RKDRk/46WT0MJWcsIgAHMGVcXF5i2GnV2/1+h6/8wJcRKWB+eMCbTz7B8fCAcRywv7ywTa9hyb7pNWnJWiO/3To/CnHOU8WY07m2qERXEvjtG811O5+tvf/5763vU7DkLm1KrafuVTnpM5+4OrWiFZ/Dlpu3iKNFBW3/t5AFCiNwVEbkUaeW7FasBibFQgDqXZMRDyqEQLIHVQlEvljswJ/Cad0BAPgPReQ/6AZC9LcD+OcA/ASAHwLw3xPRj8tjPdAR2HgCwNWFI2ctycUpgedZzYicgbRUl2ARy7HnUYLnGxWOW5+rP7TItH2J23DXKXQz7v4cB3OWv6+URmigNQjFR1C7IjYPXM/LuZgYU0o4pgO++uV38fr1l5BHApNg5oRP33yCh+sbTUO2nzQoKJq3n2Uqcu29x+WHxty3HmerBV9v0hPlX7kGRUGoC3Y11828PLXxH45STGIAACAASURBVDfBtcS4FdF62ewxOuDQ+7H2uCXAUYA5+rZzQbVPVTyp19Q+1u/rFO7O9Ysy1hSipeBLyJaIpCp1CZ5ZSlEfcW5W17paVN8+V92BR9pPA/jPRROO/t9E9DcA/N0A/qcnnlJCJ9UFOoMFSEtCmhPScQGnBBKrHWgegQT9ruHCqPL0Bqxs5f12gVD3wYXVrYVI6F8kynmbBEFssxmBCZYKiqFVjSuhkBq+3G4uUcknLxq7TyFgHEZ86fWXcXVxiU8ervHhx9/Bp9fXGAZgIMJuN2E3juqHQDUmvyyUxqy3PcbHx4Yyd09v3swML7hZNpQFCDlqa+fsOTb4dSNS4qmJULy/G85EJ9edJuH4vK1bS9Yf+1BQwrb4WI97MhTO2tuirzCxyR3NGJqlSiNFPXxbYzpIQrP2oFYfIcAU618UCZxr/zoR/R5oJuHfJyKfAPhhaDESb9+yYyeNmroDr169styiOjjJbNVsFvC8gBcVA9QxRgOFwKn4+pvm0AJZqNvwpUnzcoCTdVy3+CnUX7cua0sjJnR83R/nGwCABCqZe9lSnPli1NyGKjKIJU+NRBh2E4ZRIxVnXvDdDz7A+x9+gA8++QgLJVxcXSAOI6Zx1Cw9FsqriUk0WtCrCLk2f2sjnxMNPstGqVwMwFo5t4Lg52zwTz9v/XsvYjy3v0+du0WUTo859y09KeopdwaqlzdIwT+giTlwKaCout2lWE2NFDKCiFV0ApLUCl1K8GNdb8QlQxWQS1/Otc9LBP4kgD9iPf4jAP44tAjJs5s0dQd+6GtfM2cpMkJgSSisUIekRU2CBEAYwXIHABpYnNvF9/STy6cistphDx7q+6lnnh5vYKfjeyMyCu3N8GccvnJh8mTKNt7yEJVsrI9ek26IA5AFD7e3OBwfcPPmDWYShIsRr778DvZXF4hTNPgfMEyj2tqbUTr0p0ZOfxzunrbWStLPwZpoShmTh8G6XnxrQ62Pr9HH6eI9FV3InqmKuOeN5zko6FxriUEnighMGSoQDkUsIGIUXcpqBEJQJgZdMgSCBEWMLBmaicmZha4ZX4wlkcwiGHZDUQg6I6R66qPtcxEBEfmgmZD/BMB/bV+/DeDrzak/YscebZqXP6FkHGaGJNZcgZyhztRsZiDLE4jqDxA8aMIrB7VCPlCVcwazgV72r5pg956jZoH7mMuJ9Z6ib72aa3qYUXIUNJr/ktEXqC9W1A9CLG7CiRJYkJYDlsMRDzc3EEkY9hNeXl1gurzExX6P/f4CrMEFCKHa/0XE5qnPvlPG/4hOo209dNYN75mCuPFTd3TkwUaVAaqMR0S1zPvGM0r/No61XDtGd3zxuVPFV7QsTszhWQu/QLVHTzntT/v3BFFRPVfIUoQLF4uK58JQvUHd+Cw9EdBxAiCxZCAq2mVWUyGZmVmLv2ratyFOmMJYCQ80nR2b/Ps9L0NGRF8Tkffs6z8F4P+0z38RwH9GRH8Cqhj8nQD+lydvKGLhrLFo+kkYlK1WoFNbtsrDFlHVwn6vVbQJ5k2JsoXyT6U2u095yeju2CMAvac7+zjWI4vtZmHz6qpKrOyigP2ec7YsSJYghWu6KZ4TDocH5Psjggh2w4hp3GE37VQplAQDgtYsdI1wRZdetOYZ09/7m5e56ThynQuRNj69EjIpi82Mdg1sLn+lEpbex53Kfdd9qagFq2NudVBOW2sPfG/QwLlrqsKU4GnDNAN2SxDq+BylnmCaRlwQMf0QAGYqpeBAbMrdCCS/SZ1P9oxS1h8iaGAaHNnqIvhCRIC26w78g0T0k9ab/wfAv2qD+mtE9F8A+BVoebJ/7UnLgA+HBVosVBpTIKtFwD/DIwPVKtCC0dYi1esC6geq6uuiAyi527o3RM01+r1dMFobsbm3uLQrzaV6hF1BRt571wloctK0pFJ5mSAAK6xHFhwfHnB4eEBkxsW4w+V+pwt8zmAShIsdgjF3popiyqyIp6k4Twy2lH/nWivStJYDoCocfRM7FhVU5SdJJQJbi3JLLGjPPVVo9gljzrX1+J9jHWjHXK5pCEBVsNp6dB1AuaaKW9USoz1x4tgTIZW32JXk7ETc4ktEc14otVBXZE1Pv+sJgGUlzoZSNQr08TF+T+sO2Pl/FMAffeq+J9dlK+SQGbws4MUKhqZZCYBVEBYxf4Jmv/lG3VrM69etshKVv5/FXabelKwvp7NbqXtTEcj3pL3gzKr3UDFIC3cyZ6uZSMgSTQS4RQyE3RBU8We5/AGtEYicLaMeWbhk0NJsgSyljRGAbnHWfrZceC0etFPZKhVbIqCtZrxxeOxwtBIjyxdh54oozF0jgbb5d09i4seYN7He/y+tVRBq2PjaK7BBjVu6DOoJgteEIFMLBlFC4ChB085p6T0QlYpQen1CzhlD446u6TX1Jn8zFIPf80YAOCkByMvR7GOuABSdCS+7ZYu6kQ5B5dvqvg3HIoPZjhrqQl1Bzcc6agTgscZiemLjityeL65B1o2QOCNxsnwRKvctD0cc7++BlHDx4hKX+wuMYYRXa9RiqKoz4cwgGi0sWropaNFRO7jOh39jvtoZeNp053zWvdxW1/sbIiO6jQlvzV0BdP4L/vz2u/rHNyhk49ytsdHZL+fb5n02Fko9pquyjGezJmCvVyDj8N2jxEUuRQIxBo0CJIGQWpi4QUVeBZszgSnCAUNhgPK46PPWEAEIIMuCPB/By6y+ANBYeaIMT6PUXqAwuHmnZ3BvmXSpG6OYEqU/r1T324BscvLSNybXJjyL5ewDSuYfyw2jikBWE6hyxAAJUhQ9Dw8PwMK4GkaMk1bgIQRw5pKFVwilEOuAUWMtoIpFTVDhWXdXfW3ma0sUWFtZ6jz0dv/qEls9Ix0RACaDehoiaKSf1imo2Ya3RILHdBPeNyqczcKTTBfxbCKA1aY70x4nlKhh5y4eARD06MZRVb2Vfml9N9iQEiQiw8WHAKKq+A4xgoKYiTkgWQSr8poMsnAB9vDBFWB6+4mAAOm4IM1H8KxJOchSg+mK9/JhtvWp0QWgp8TtRj37Eu3fmuWXBX+OEFjQ0HpCC9JodAlorxUPDhFNE50z5uNs5ce1rzkl3N8/4Hh4QM4ZU9Bsv/DrAoAYjJBoDEGGulOHDIRBvCQhivWZUJDTc+X+dh5PCYT/1mrsV+KWz7tfa5+pEMdTMeAp60RrofAcfY5YuEFczyUCxXr0xJQ8NWcUqb7zdk0akVJX7dU9yMfUO/cAhBCrDiEGT4xXqxUpDSdQDIhulRJ1DPIcFyklzXUZY7U6PCE+vRVEgJkxz0fNHpySuTyawkUEAs+oovMlBERpNpx/8kDrDiICNnUdjFe5lVaLAyXFl5/jj2insTVZdZ9XyiC4bCwG40RKmfIlaRYkEWBZMg6HAx7uH4DMmmo8DgCp6S1xtgIUShTJxsisZdFHs48XBOrhv4/M+RqGtyKTOO3t9Aam9T/ZaL2S8FTvYAkyCBpN0vy2bqcKx6faZyFs/WW0KSt9xuaQm6DZnUww7WhHUyS2UyCXFVUDjYhIi9SU4C6BFPEC6GMR+kxCAJBTVivRqIgLqOvTU7RvtbeCCIiIpr3OS/EB8AgCn9oqf8E2F9UMQnC9gF6x+W5bAgDUzdlqBuyH6mHVvLiGELhMX1FBK1PonxB0kwq70xAXJ6gspitgweFwxN3dPQ7HIwIIu2HE4BmCrbCq1xFkQin9JeRihfupKbfwSHlPzdYgdKOjdt7JZm25s59fuXUhKhTMcy10nLn3Ta/ekyaBWB69ZoKa55w0u7gySoP+jaL185j32vacq5+iER0CbGTSkhewOxdVNg/cIS33HWmn0JmXj1t9M7y4josQ9q7NsUQrZDM4MCjW563vvW5vBREgyRiWe3szGZ7Aom4v9w60DVpy1IXqWEMCLVQZygTqohcHjWjz04nda70aikNLgzTEFFtANQMxgCy5WZgVNRDUcYchWvxDdEOnrOXJckpIKeNweMD19Rvc3d8jEGF/cQkaAnKwkVHEkoAIBg0BEYAEthER4k5hn9jkBEviL5YExfP5t/UMtzbemru77N8RBaq4SROkaliyznMwhR1394wWBEMGY0Ose2ULonpxkZ6Mi0+qweXWTKfOQ2z5uMmSpHjKrV53WEU0NL+db89BCrwSR4tHCIDGUzPoL5x0LXkNiKJbMYREvuZFIDDNv2jtRjZHIQYjSDLHMn8XAYkB1sRkSLo44ShNif75UbwVRACiMQH2BZUNOPk0bmK/1ylvQZVY7AADFMvLqO96PQs1WnEtEnTK/O6JfhIpvEeDKspQuARDsbg5zFyhmUsasIeHexwPmvc/GoHJKSEPuSrVLG48sbpKUwggAXJSnUAAEIdQCJQHooRAJdV6CAEU48lG1742s7P6rftOlTD4HOlPrXweunnT11dwnJ3XoKb1uc0znYifcvtKlGL0/ogVQdWxeglzbVyQipqW/S4uduALNmcpKJu6HVOdM0eV9k2kn4tyjo1HpKxcve967TYETQRgQ7RO5AuRpFI097H2dhCBhj+Ur9JsLrLKQNJuu17G98tFDB5189YKDqvnUiUUq3KEJ7zqZEmawquNPFTopYSAKNo78phuwfFwxN3tHY6HBzAnDGHAftxXP/CUQRQByWBzD1VEkZUuEFnUJIFiRBgHhOjc3jXJVB1zmuQhVamG0t/2bzuu02aEWZrvHbTX751uoHmP1fT4+ILcfvbpMUc1a0LmYko/pvX49Nhj3PE5BELrypro488sSKbe3NPicVFuY6OP7bNF5X3PtrxBDEtMgY+O4D5LXQsUCg4+194SIuBafakAAEDL64uIVC9Z38HWpS/EZjH2t2pecL1J3RBVlgWqVCD24wn9pu7WEJhrcM5GjOqiTElRwOFwAAEYwoAhBgwxlgrGOasLNQcBdgMQCLnknvd5IE0YshsxjFrpGCS24aMSC4/LXk3a1rprTXtrfUCd+234Xu9P3fcT/ULhmec32GOmwXqvlmH03Pd0Y53GgLT3e6o9aVEptya05b9P0JYI1ubtRxfzqo+V/0mZ0B41Vb0CQMVrU2NqzhMbb28JEQDQaC/Lsjp56adQsrRmN8rJSS7fSdnVWxPj89xuaGk2jpxcp0iikuLaR60S3W5EwXw84nA4QJgRY8QYNZ9/JMI0TIgxYlkWHI+zFRVlBIrIwlaIBHr+MCDuJ4z7CXGMVg+gigJ10+pz+wVI3d/1Zq2/NZsLzT2lX+DUPKec3YgSJWFGOcWS6/vmdFGmcM/eZiP16V2/z5kofQOcvt+nN0M3A08RgO5k2JCq+3Av4qAwqHYMxfS3QsAtQuvM3ysW1BJdKY50pqQlAVCVMG+/n8CZ5i/Zp6olAjaN5dzi/NNcVxYVtYum2qz7ZovWX0BDe06WU0ul9eGluhEZVAshKiKwRZlzxuEwI6WEQFbkE4QhRgxRqwmPYcAwqg34mBZkZESKmvIcAgQGxQHDFDFcTBj2IzBoJaPQyfTNourSbvlvOqOd9n8ln/bTo0pYnYvKgXs43m+yuoClbI5+rqncuWwQK1jaUiRFEO3Cr+NsOe+WrqPvw2cjAs9qq/Hap+5Yj7Lc0as3sToDXI+hJwIoc75+Xx0xtOlk1hocVf/xlpsIgTowe712tOL3bIMC6sDaa4sGGOWS8pfIuYOe47JU5eBATfJY/9svmQotPXILLic314mQFesMkJzAVjVmnmfM8wwRaM5/yww8jiOmOIIABAFCHBAnlevTKOqQElUbH4eIOA7YXV5gutojTBGIVSFoZMxyGDRlv9BvmPXierKJlYgrXKedYylz2sFQqcfUq/ARR54zfXnepl0jmudlDdpCEX0LT/yOLut12xcx+O/2f9cVUUPgqlm273vft/pPukk/DWzbmtvWF+T7AgmcSEjNpiyVV5uF7RypHbcqS+zXMmYN9QSaRStoft+ULTbgm0JXtduXm1Wll+V3oyAIHLRYCcy7jxmH+YglLSUWHKRKG/3noaN63yFG0LTHYUgICFqExMqXDdOI3Ys9xt1kWv+mm/pA1NiABiqWNdQTgJYonF0oBHWGMXGgcqnyhjoiUO/lNnPpFvvmIzYW8Nb3tdxdCc020djSCdgvZ/tSf3+cUDph3BpX3fw6DyoZqrWifXJrDWnnrTK1jTHBCMpKBGsJTP98rPw4+vbWEIE1HPVGzX/I5e/VnFeq2nIqqXcQlEw99eKtRWDlrXwxEWkcN4Uic7E0Jq8OWXh4shsyDX1AMC8LDvOsMn5UO7aPQ0yMCDFqWLCw5gUcI3hUE08cB4QxYhhGTPsJ48Wo9+h63kr7VcI+Byu9rWXozY0qdTN5Fp96n55TtZuyKLQ23tW6H+tztja7SE2OsRZDRKRLdLJGPus+PgcIPdd60v1uIpMi0HWKsfpbe69t02w/bl+vVSzu+6BrTa1DPQJpEpSeaW8REQB8oL7x60JGJ99076CRHOreXcmffnwVWy0Gm6U5td5SJ7qdes+oI4rtyiJ3DWygXoalGMGL1gGcl1mJSowK723zw5x5nKUHkJaXJkYYB+z3ewzTCMSAYYyYdjvEMRQC46GncPOfDYRs4sQQjc9hnZ8e2ld42X73STG5XlT/4DIss593Dm7WBX1u4/Ra8LXnYT2+hrTr61zvorUH+kjFXnfh/d3o7kb/1t8fI5gtb+65e12/axSg50RU60JPTFvTqj+OWVOtr+ekFSta4qnZic63z1t34M8D+NvslHcBfCoiP0lE3wDwqwB+3X77JRH52aee0bcGZneI4Iw7ME5Bm84LnZ5hFY24oaf9E11u03MByxfUUNO6eFHq7ClRMMgHlZ8RNGyWRTCnBSkndfaJQWvpCQGI4BjAgaAJ07WGHQUBBwCRME4Txv0OFAlh0GiyEEzv0FYSXTGIoosri6iXW/u/LUGgFQHwe3A5R06urwvPOWAv5/r3U3davefzdRSbxKPhmu3fHhli9dtznoVybi8++lrxI43sXSiBfnCLjfIN6pZlNfMJxIIrcrasWdTGEOg5ep4+kSUX0aybO+r3yXP0Kp+r7oCI/LPNQP44gDfN+b8hIj/5jPt2rc8n37JnhzprXNms8fN3LX/U76K6/BptbU+pN5V6Y2F18VgTAYi+FBcJ2n6J6B63jM/qXWgbV5NIAkWBFgIkBnBDRAgCCWoKHHYTdhc77U7JXw5EswjYsCrMtDVKztWpbpJzrShKC6+qhMFhJqQRP7pXIVDPwYrYGgxX30GHFgxlNRD/8cXa/7ZGCwIpSrrnXP9Z27mutebLui5gQ27RhptYcfI+XFdCpLEBrHnHEcIa6bT9MYsZ137UJ+Hk21OE4AvVHSAlQb8bwD/01H2e36QRB1YTXE/Z4P6yetcbC1/E6sYBZffUW+pVAVAX2EbWtCxAvll0H7ZCQ/MIo0xiWnoKmgF4RC4P0ezImgo6U0CmaitWj2EpBCBOg/oCFMZblWxuXhOqbszeKXebaGmrb1LV1Nv3k4ntuUr9xRe8Lm79p6NvOeVa7m6Rk9/Oudn6mh6FNE9e+Sac/I5ez7D1+fTa5yCP080j3RQ9fY+ttdtu2m1FbYsSThFtpTcK99ZKQj93Syez1b6oTuDvB/CBiPz15tjvIKK/CuAawB8Skb/82W974q2/Od20+tYrXE7PtLygWNPL+tj+jiw1nXnHfTYUkCV+iCokFJPjhmnALu4homnBctYKtJmgVYatJj3BLANDRNxFjLsRNPhudm4SjQA1KMYRwbk1KVVXsJ45ac/puDg157Uwu0+BveZQ3WNlDalP0YG+t1ZkaOfVj7f3f5wIbPWjUv3+t6dEkHPEpHz+YgCj9s4IZqv0PPUZaB/oMRkNfmtgoIsYVSTbmpPavigR+OcB/Lnm+3sAflREvktEPwXgLxDRT4jI9fpCaoqPvLi4qLKavbAecW4PoFtO7YakVlYqO+XRgYjUBejZf0TEar1zo/HW87JI6WupvefQS8RLi1idPMK0mxBiRMoZy1FLRwfj2NlhIkWEaUDcjYj7HWgcQTGqc4mNQUuJSWVJLvNTtFLU3RzXFGwFBbR/T0ltR0jh26fOoyOAtUa8zLN/FsK2o9IaEdTj/VN7DuiooyUqz2nbisGn2zkC0P++Rp+nLYSgsSMMNAqDrn+tBWdLgdrOd61jWR9/oqC0ZeG6CP97rn1uIkBEA4B/GsBPNR0+Ajja518mot8A8OPQKkXrwZXiIz/w+h3RQZ4xYzzrBZ6aXoAi2TYegP2L6JNDqp8+UHmHWwT8no4KRKxqkCX6CBQglmorccYCzaLDYpVkx4A4jogyIY7Zco2pTmGwDDQxEKZpwLQbMexGhHFAHAf1B5Ba6jwYwjCVswUaUUcUTnQoT7S1wwqwAZ87rm8LlFF1FQ0hFSMGpz7z7T0q0QbQ+Rl8kbZlXux/c6XqGUSIyhTONyoEv7uu3LInYmV6Gox+TlxZj9+ZUBURFI15GXPA2GYjRrRVpx8zDwJfDAn8IwB+TUS+VYZK9FUAH4tIJqIfg9Yd+M3n3e7Mot1CgM9tTRJ+fTc1nl46IXW9wE+dLSo87hWECrvUpOf5vTIzlryAJUOgpcXDEIBAiKT2fhfSgxCGEDV9dNBEmtMYgUE3NMWAYRi0KAmR1SSQTllKbhIjH4f39XFOteY+5xbkCbjuDrjiEB1aUgJRLScnpt1ntqc5eG83ak1453UCz3Ehdo3/o6dsHyN/hq8lDwCjIpJ639ZrrZ9D7pCX0viw+b5OZuXMe91qn6vugIj8PLT68J9bnf67APxhIlqgvO5nReTjp54BIahNzIMe6wLW/p9W0K0Oxmt5F05yy4aQ5veWk6vjj1FX21KznlStAbCuSPXiUmai+f6E2OR6T/ogmBGwDDvdsEHdejUhhmr0EVA2dCArHBojBiJ1JrLUUhGE4EnFCQAJJPhLJXjRC61roOnMdNNpqW6y+clljLZu3YXXj4f6GzUzVqYTUIVq+ax5DgDNcEOsugzEAGE2i0hDv60wq3PIraxHAtKkss41feJJrPhM+07tvQW9l5X/QBZgydz4CUh5FtCuBRSl6mlrcWHfaHVWobnWWGh1NRmaJCuaqoS9Zp8OpYQOBXUMI+aSMcjXQrB05opeQskqpLfQe0bYxi/EAnDzQQyPo6vPW3cAIvIzG8d+AcAvPHXP7QcB7q9tgK35sbdBo3mV+kdf6JYprOrz+0fVshUquftdM1ZeVqzlz2x85V8YQtkQ7izEwkrPhhEUJwvt1aUQrEyYLhwxK4ARgeiuxJ5MRLtcX1wzZtsLuihiIQBKDFsC0BLLuoGkzLOvlMZ8BfRT28jx0lyvloh+TrNf0ophrYjgegJ/X4VE+fd6LQpJ1geWHjSEqNyB9L6CWuNxa9N3a+BRzmgD3zplfd2GTqVt2pUITWhC0LoQBLKUcyWDk7uSl3sY9DdIb2AKgWIh5msHKvdHqG9mFVfwyIjfGo/BztMOQLOU6hlUZbpy9KzSRtuKXDSLqn6HiC1sT/dkegARq3hcF54TCGJbgGWC9XMgwjjugHFvh/RpAQwEQQAph7dac1HXBir5RrMpfNyWurtofOvslO+h9Uez/AJwpLTyJGwmxNEAmrl3buskov91pSdYzavKyQSJLnI1Zwcq+4tKFt5ei13GtgHriwxusT3VY9F7aojxiUXfzvFps/fVUUa/ZPXdxl2e76hGBCiiJ9nroVrtqp0T0ElG4oLO7LgWEQEkbNUUFLRbglZ1G9yn4LH21hABnSt/mecVAU8NqCcAKk/o4ixHmtZ4DrIgQ7CkpOmaOGthD1/H4kTA4FpWhx4Yhdf7RiAOiOMetLtUqlxwsNZOCPA0WGbGEQYFg3D+wuo+r9uX6uc62FCUcgqGAshdBW0DlmtcRnQca4sToV3AFTfVeTwnFDedxAolhQDpCkLY48qz67tq5dbHzHy9NcJfRr1P97fr/2dtz2cwKMiUSrfac1wzr/78uhYpkK4dqqS5JXYedu12HWGcWKcqCvAEpYIhBhM3K5J8rkXk7SEC6Ka/+dYPpM11p79uD1RWHxT02wFR6snG4dlwZGYpCECyFLkroFY7cilbC2BCtcjB+GYcMI470DhB4mBQP1pVsKy58ERAERiGCHUD1SpLjhSKJO2QvkyK83X97PUNxfQaJQ89qCSs7OfUyGuj6Asni9w3bhU9BLTecxt3rkTAP5/bg2vXWaDfWC0C8Hv1Tez/24FI6/Xx+Ro1wOwRQtCh0hbL9udqn3J7sHqakoNRKzMmgujIgHFimu7nQ9FGqxfGdpcfJQhvDRGonK5ObOXgDRxtoF7L4UEafHOqONT/CqBKK9TFLKBaZls0JZjmCrAUzVYGiqnnMopFk/J3CkYINEcAjRMQJzBpvoAQR1AEgokZArY4gAiiZBVlsq0HlembddUnoCCvPEgoPos+PyIW6mu1hyzst/U8axOlPMYpTwgI1T/P4S2b660hxuvnd3D/uU00lLet5OO9fA4K+KJmyOZO7U0NLbZEyQlKY2mgxnRqc0GFGhgDbOiL6pJi3QvSIx+dAvdMkYIGtgnHaXt7iACcknkoLrrV1Fn0Vte2hKKz+9tC8e9sChnzzrcij4yFM1JOJdlnqXVnFwZAo7YgRcYXqQ5BriwLYQDFHSSOACKYIjIiIoJtbq2qFIoCUDEykeIUxypeWLqD/wb9XeYFnNA1Z5lKvpPfLc+Bo6u1eQ/NGCrp3JrlloNvc9vqpOXvhVAr6Dwfnvq5zzm/h9L+77nk6rF2DomuiFW7Rpuze9HATHy+Vpq+AtA6g+gJRisetcpORad1TquyVH02olef2+rrmfZWEIF24FtYZr0g2kXV/sbkm0Bfh4h7/+nLySXfnRKBUgwkq/yfOWucANvictRBWmcdpPyX4bn/BYJgEDtCwgCJIyhq7UAhKuYrgSUSCYJQ3pQKGkIqXjg1Z4hxc+MAFIoxyQA/eRz0kQAAIABJREFUSgZl0qxDukf1aXqOLhYW17SslnW3ONqtL2i3EIlXf6IWQm28Q8/E07ofS/M+7JGy9u1vicfTxMLNp20/Tm3t646ebuR+3NvPq91oCVuzDrFarSsUWvumnJ8BEEe4U1mx/5cIzS3fARSmtLUHACUMJX/FZ0FT1t4KItD2m0wO3caUfn79jY2zU3OjQhT8BaB3BUZwZaF+zyxI2XQCnK1mgE50UdyDLFuQANL4q1uNAA7K+REiQhwAE00EsOKkPsBoRSkyIEGJCPt4pawq/dbExcMVSbrxxSaLLEuRe8BVmwCgkX+ncr+ASpVgH2NFHhr8VJRWiNoZ0+yjEKt6P/dX8DcCnxtB6U+FuvX9ELWft3UBnSbd7lkLkuo7XxOBvp2uI9fed7NCp+f0Isvp/QpRLty4RWF9H0QqTnXtQSVauVzDzJ0FQAnaqQJ1nTNhPXcnSOH7QScAoEJdqQoWH4RH8alc0C9ucqQqYtpULdbpihbDBfbZ4aJa/1JiC+oxDSwRILmUbcpkxT9ILD24OmY4smDJwEAYLy6wu7oCLD4gxOFEgeSOPYE0J4DwYtzcODoiNB+i7rlx1JTjYh6N9eU7AnDuTIYaPKQ3ar+gNe4Lzwr+fIeoOhDXeRDcMhMaHtkGTgFk9ZV90wZLc165uXLqIucSAWQiGbfvjMp5bTtdrKcQuSbbsN9XIkHX4V6D1D6p+c3Pa7Zx1y9aXed9LcvVSGFQNNq0kuUHAlgtCiIUtOlOa+1lrUPbFjLemid9Bz1R9XdSEuGcaW8VEQBQSe66052oUDdXnSaCFeizvH42kS6HQSwuwLXqQOKMxCYOgI1RGcz3DQKAOIPJqbjG/+vmIUiIoBARhwEhjMo5LSEE2QrRnpNu7EDqQGQbPwTnDcHEj1y9vvw4bGG0SiMAxWMQ9q+koK6lu1kEQczTUGBuxz6HNn+aqbQhVjoPEK798vkn9YBzJxat+ON1CVebqYgFEeQbAXWDnSME9ZW3gkltay4YrL5ZESnJRaaTOz7xuRn72bZCB2UBNutztRk1f2SvsAykCuf2UU8pK9e6gvZ41etU4vJc5edbRwR8w7aypENWRjiB/EB1GwUApm3un4VMJ8DwgiCqD0jInqgS0KhBRwwGY9mQQDYlHhOMww2gOCIMIwQaOEQUEIp3R305vp5zKY0lgGSApaSLKitKtM5cYkZkLrUFBaycnXSiQlR/A6d/ivHdzZjBYh6Y1t+yMcRFCL2m5ZXBoCoEmjHZuZH4Gf27UnfY58Hwel2VfdfrdC0WrJVl52/qKKVO4+rOJ8859YVw7rkdcEPrzd4q6EzEKnSgQUprHVZ7Py1vHss1W8io6jjOj99Nr1u+F9831oGuNQTAG1EsSaB9k9taBeCpvjxhB+BOPSKCDLUSMDu81TJhKWekrHUD9SFruGjpu02RkxU3I5JYqm+yjL8RiQVpnkFREGNGzKkgAQAuiSNTQIwBREYoOAHIIKi7b/WVZ8QI5CVqSnERczxxNBAQhoQ4jPqyQ1D7sjBEMpgTRBIcLWiUoiOUULhX4S4qbGs/i8JK51Eliaq8yzljnheIoBbdPFnAa/ht26c5x+Gr33ctx9b7bG+icobrgJjBWbqsPJ+l1de11nv4c9o+oav1UPwfHqNTIuorEmo8SyGG3f37a2q/nAH6PDXMkFbYpkUdJ16GfXsriEAVnbdfnpjQtf5dCYItEw8BtgAMc/s3JZ/rAsSIATfZgpr7mfWgW5AGa/0dsHEcogEIAxAimIJx9KQpv0Ugwpr+q6RO183FIYCZAGJzHlICEDzSriFrzAkkk3HCrOnJi4tpALGAkqEPq0FYiEBK2h+gbPwQNVpxvWEb3zWANLJR+20KULgtmkyeZRyPC9gqKdVnrHdAv5HCyUbvkcA2p6/OMutMw+vzU9I6D0MeThDGY+30uY7THw/B1QIy2gKFklJufe9lWZBSUvGVWVPMNeG9xf3ZU7g1EpV/r/Pm81xFGABgWQpi8sKs7Xx9X+gEnGv62FvK12tpi5BQNjX8VHHZ2byvWDcjOwGAmwUTcqnkCkPg9V6umGNxJaB52pH5fYWAIY6gECGkZcCEIsqLKQpMJ3D+XQra0BTmVSPvcnfrVKIVlglkMoC6IPtC0Y0dNTyxVuMNAZLE9BYBMEQUQu1WM+sq7qAhVLAqTJZ6XU/PQBFjlAgsy1JEqm0ODqw30clWs7l5XHbt3WXXULc1qQ3DAGbGkIYVqjtt50zOrYhQz9m+VyxKVtu3RCdEoJaWO5oIFzXIzHNShIC180/tJJq1sK0P8AUsyPCwdicEwzCUcX1fEIHaBGuRTOlBHYRI9Y/S76bxF9X4F6WgbTRHAswqAuTMyLZJgxUsybZDAknDGU2hTa3Mp9wwDBGII6z8jyn4zJcfrtmtMKzquBR2e6y9IwZNPuqcRd1MiyyMugwdGjoCEhACDUV2FlfIsTMUQuUepTfQTLVt5/R4NWVVE2d9C0oI9JKnZM3PB8m32lP6AO9HMROy2NQ/rpfwe2+Z19bQf6sVaxLOE4GcM5ZlKRp6snXH2ZgAqZOZ92U9nucRgqp0BVBQQFuU5fuHCNhqd7lfjxn38xcM48xr0UBUHuQmKWg2AuDcLaWkpsBGPBABhAScDUqQFv+offKgnuqcE8KgVYStbDiJO/cYsRC21NGteAGTtQMENVOR15QPJSwYUGWdfvdYf8LgQrVNlRMHH4ctDtvcPldeF9HNk1bZoJk/atZ5e0xWh6ps3hO3c5vTIevjkLq74pHNvkaDjz9buqGc/Lra9Ofu/xQh6OV4J9V0Irr6uELQ/AAVcdLmc7s+OFF/RNkg0v+89pl4igg8GW1BRF8nov+RiH6FiP4aEf2bdvxLRPSLRPTX7e9rO05E9B8R0d8gov+diP6up57hA5GiXm3jCPtFzaAK74XrcePmXvZL5W29NxsxSFk9AnMzScwZklU2F2FIliLHax9C4fYKzSNoGEFhREBAKcvFDGQG5wTOucBpFM7qcFv75TI4FULhXN+pvZmVGm1T+7lOHJqV6L8HuEET1ke0V0r9RgUptP9cPAldaGoriz4KAh5pnvaq/ov9c9H+w+pzyxXNOaogotMpKevmiU2w3drztzdgfXtQdGdI4NyTTiUx6fp47gJfX9z+k/qvR8Sn/zbkwK49BwkkAL9PRP4KEb0E8MtE9IsAfgbA/yAif4yI/gCAPwDg9wP4x6BpxX4ngL8HwJ+0v+ebmPMEnMvr5qvrW5q0TFwGpufrQBcCUgiAbeKiVQUwp4xjXpAEquGyUGFYaK9q8zIyZ0iKGC/2wBAhxJAgEJO7JQTEYYc47cCiOoAYAgYKxuU1qIgCaQ1BsWIiqEoxgqgJEUZXQAAxAjICaRaYzAySjHEYNNuOLXjNRKCksGABZggFgLVkmibXSCBo9KJIgqY3Vy9GEiqohgxZVKIn9XleC6AwN0YIQ5FxlVBVPUiMa+9EX9hVxj5l3P0BkTV3r9fXPhKITB8DgUiG5YmHbPG0FZfUeX/C3Fj6gqIHWaMDWm141oPd0Fx0CwPpmhP1StV7V+eeVhdRCaHU+W+eWaB/dwzgrPcNgczyzGXobWqVrfYkEhCR90Tkr9jnG2iFoR8G8NMA/rSd9qcB/JP2+acB/BnR9ksA3iWirz36jPow29yFtpX/al7OhoNjA/agudbkuizqJMRcA4ZK5KBHDbJq1H2hLSkhJ88GMwAIkBAxTDsM01QWHeB8t+XROobCx8gddCpfL+jAfqsBQxa6CJzZMI8vXOfwdYFTPbx5/XrD1UPntNDafS/8Suur4cNb/77Z3QqU6g0+J8JYdfH0p89iLti88RPzv3n/p5/Zyvmu/3EMtyUurKfnFPqj7p7OW/GLIYG2w98A8HcC+J8B/KCIvGc/vQ/gB+3zDwP4ZnPZt+zYe3ikOdEr343aCdQBiDM3v9UxlTXEolA+W9Y20k3vzkBu6ir4wSCWUsuaS5CBQrWFGSFExHHEsNuZAscoefd04wxwyN5yEn/BMPqgP5RX27zk6hQCKIznbh3VMlR1Y7ZKP6kdg9utu3cv5Clz6j3LsqsEd0PvBILXxgunC6pMw+eF3LbBNrj2aWuF39Ut4ILX9087EQMMvp7ds5VjlhWt9wHcIkJoc0AIil7rTHs2ESCiF9D8gf+WiFyvKJQQPWGTOb1fX3egHUzZqgZ13I96tTpFqkyoud0ZwrmsJc36my08WMCWlZIgiNSGFPsNyRyLFCLFMIKGEeP+Arv9HikJlrxYenHUN0VVXCnw2QhAKOG/9Xxqfm9eo+koTP/gK4GaAKl2/iq5qfNRZswIQOP3UE+iM1xr8y3BKqeaqIDyuT7T/ppHdnucyps4XRp17/r4CvVqutcSnNBd1/WxPbphQjs58oVQwfe+FSuApa+nJtBq2zkKRvTa39ZEtBqgwV3Yxkl7FhEgohFKAP6siPyXdvgDIvqaiLxncP9DO/5tAF9vLv8RO7YaSFN34N13CxvSDV1lH+X6UjZdD3U9JBPmFVSztzA0+2zitopQVp5j1XYoBHUZFg0QRtCwXUZAjCOmi0sM0w5xnACKAGV47IF1x8KJq6+/dm31cmxs/vbcg0/RtsrhjgLEE2VUfV6dM7udCIAgkKDafpB5TDoRCICntzb1wTOan9QvugCNnvSoSr9lCWMQn++NxzTvynUZDSPTPyYqCVBz6rXsvatRedI9bBGZJwHFc9mVbHwuUKkZvCufum5RfWFozqdm8VS7MVwf47ouPeWRjm4sMX+Gcn/q8d0jt3qOdYAA/DyAXxWRP9H89BcB/F77/HsB/FfN8d9jVoK/F8CbRmx4ZFC2TMT3i8v+vjn81ao93veUb24ukVIEUCjWgiwo7sMQFAciJzLMgiQCFt38NEyYLi6wu3xhRGAPCgOy9a91JnHnH6Eq3zs8d25fhieVWHjeOXeQas5SpaZx8IYMNoh5dWO3XKC91Urx1eyjfi34ImytEv39dR0ThGLdxiqwlrku2k/vpH+vkKckZS19LZKv36Z9druFCe0+2m6rH1Zytl/cy9j0+D957LcyMc2kbpzTnrv+Xj5vPcOH0c/Z6Zi3COPq3T2D4D0HCfx9AP4FAP8HEf1vduwPAvhjAP6/9q4m1JbsKn9fVZ1z7n2vGzRGQohBO5JJRtqEkEHIRFDTk9ZZRgYJOImgAwctmWQaQQeCCIqBKGIQVMxAwR8EcWA0SqfTMXTSakSbmFYEIwqx79nLwfrZq3bV+Xk//epcb63Hu/fcOlW7VtVe69trrb322r9N8qMA/gm6MSkA/AGA5wC8CuB/APz4GfdIACDpvanieinpivA61+8pwKXsow6Alu7WDKo8fQKx7cLEcuMBqx9QUEiLfA/YXN/D1dU1ht21ZXfp9JW6DbRyX2nUHY1aVZnCDUAFtKoX+n0p7uOPy2tp1Hs8cNgjRIR4oigBnkDNN3AuOWKz+TBuJz7aSGICOzWq9T4Eoo7+GCDHwlzDogaGo3badGLnJbsTUnXzJNUxMLd22kR48uTvZp6y1z89Zzz74iCXrNIUIzpG5+w78BezHCj9wMz5AuBjp2+drkEyMVGrsJS0Cq8A0KWtBgMe5S9eFUizt6RTSSmWyE2gLojxYKAJbRGoSd0PGDZX2O2usX36PoZhYxtsWF5AN6CUm0iurT57SU9gHpiNalMQ8HMZloBkkEJWJBmN6nOvfxR0bBU9rjvahL5rAKBMMeIYCRDlvVFTVXO7YRjEMyVFRDX5faSulYlaLrKgV1PXLOd0WgIdbbg5IVDxAqlmajaHx0aWALl0WpUvJOOlQkr+dCxp62IyBktCNRHG4h6x72KNAOxYTPNZBqC9AF8Y5MHAzhBG3QE1td3a6PoB/bDFsLvCdneNYbMFtxuNFewBQYeBuiHo3qYl+64HO3VIhL5NlKYo0xL0lZW66i7kkfW47kxctzTzhUGei993tW59HilzWqtbCgKYC2GbVvTuZ+t5xU8WAUuJTSwiWORTlkCUSC++dqAgljKDdWrQi5uIeDUcd4WStULUNrziULYukvvkSFUB02vs17oQfVeDg571CXvXc2EYx8XwjtM5p2KDI8vsQICu9d8zEObrPVvQ97T08/27MdPpPo3lIvln0heSoeIiNA/NswM8ae7wA18MCNQpPwm0K76EV3yPNs9nNzN+X2u1wR667PeakGHKRVGF1dV/3i4VAHZX2Fzfw7C7Bm1/wFKgSz3JZP67INC2ATOTu/QAfIGOmFCYW5ByCequsNYtRZNcarsFIl5xpqRrayeTnspchSRAQJLL0XttBQUCL5Ai4nURNOkp1mVDqjDbSsGaaVZdsc63KguFqECUlaTGRKQGdGHgy7qmoloAbeKOukk+2aQFS/ahxDlT0F+Ryjjhq/CqG8V6Trw2fR+dm8xH6NAswqHjGQhaUDgOAq3lM23Xv5oWUWUMMH6Sl48DAJ/6OjYjcjkgYD+95q4nBhURW+EHFVZRcFAAKNG5GghEWphjizXshN5Grz1sD8DdDtt79zBc3wf6TUwNqlCZhqT+8XdIAxOvG+JfqPL6Sru0+wyqMuhsW61mPO6X1JPhFo1HrLYfQ4Eas3gcTKz+8bn94CM1iAQW4/vMDYwAYvFOFbpS2xv9P8xB5FXAFXpusVKjDHQ7I3+P6TmSfh95Lw86jTh3fgsEczQGp7Argl9IBoH8zHlgIcZi4FaV57/M5lIGXQ4ISM3xFylWv883DnULwbL+0kjloZO9TQWOR7diWfRukvWW391h2GzRb3fgMGixUttEJJRX8iu1YB91lO1IreFHXfijd1S+dQXXjKAHazX02W5/PSc0rdk4BoX2PiYYxQqWhZmYzG0gbcKchlM0Aoh8vzxmWlt5g5PER5qdNn6yP3pAIfKGCKm9CoKWuju5fgwEkmIb4/d2Pgg+FJ1Q9JN4Igc4bNyB8YH0XNGp41Ym6wcO0EWAgIhW5M1K7qsBi0hMSzlI+K6tuhOvAYhF+klbPhMPbhOKhC7yIcBhQLfdottsUEC8YbUFIsgmriihLeiouxCjE80cLDcQdugtcUignT0GAP/GiONu9GBQ60uqwCtIqG/HaN9/w9yOfAdvxndX8I1DORGeTGMFPCgqFnB1lycgQNRVIXT9hxexqXFt579M2macV8nPZTxfivNPtGlOu8zKyi8E1aWrQcqTzkBwNH+cYSydJq9UVN/JuD8O3cvOcBcjeE7ZqbETcgUFd9EyHVvLeREgANS5fF0ZpSDgqwIBqEC4mQ+gpjgY2pXi5QPrO7XSQv7KFEw6oO/RDRuwHwB2VlewoJMuBK8zZdRr9yB0uTCh+QCFHbqCMMcli2v4s4FD8E1GsuhlhM4jfZjCKRMxujRJXd1nAXVHX3qHz6mdN8DpIbuJzAq2g5DOaOQ5jeqjm3DT/VETSAMscTclNZ7zBPR9KCOSnsvkHBCOBHlsmcjom3YAbd0q5fs0DMwPnq3ynrIy2Pxv22nPPeaiuE3m0S0Dh9mg5Qm2El0ECLipvxfP999PFSswsKKiJvv4NmKErt3LCzx9UY51QdeD/QD2A6TrLePOpus8oQimkASIzur9CSBuZaiAqouQ6xz49QVtpZj8DNk89fvlPQEjuk5TsNbkd7OPCNdC/MKqTRV8MC9uo/cfIwdjOhXItemIWDTl9xWfEs3+qEBTHWFWz1g5j1lFiOv1ywAjRwIz9eefZdx24O5E6bIszYDhhA7d8ZBVla5swP0Qr7XP/LP174G7so4QqOBslxuIxuzAmUBwESAAqesD6lJhAwHvqyJ1ib4H40RXCCpo2KYesOk7/ydaSLSjLj1lb/GAYVCIsMq/7Gydv9gW0hZ0E4sAEqI5CFQR6tlFzMJ6IIlVFeb8p3eipOdlOm1UT4+0LLo8itlIaZYFQmEZ4OFmr681PzwkHDBBZSz6NcDZqGvcV2I2I8/UMJ1XR6qkAPMmh52VFcWTs04p7LgFgdjGrDOJSAID0EejQ+P20XTfWepsNsUrFZkyz+EHXIaqFVBnBzg6V9+j7wNx+O6XAQKoMQE3kXVRrUskU3wAcO/Ip71uSkGHzoJ2TcPWH3sU3NwUWw68Qb/donSDLjGGAgw9OAhVcqEHG23eOhSNth0WbJOSPXwu2N0WoCpAWAJdVdKzLMmgmR4UxOxInZbzRnk4GHREGFyBfZQGTOBmT2YjlOksAXIk+4F14jGQ2mSH8+If1+zA/MzF+eS6O3JZkkK7vIxmDtwFDCDArNHiA854IJnSRYBAAfCtMgaAWFgipqDmq3vnFCm6cQgEHFQJCnXXHe73KDd7lDf2tYQme8hmi+H6aeD6KdwMO92MpAOGrkdhQbl5Q6+36r1Ke9zYgoa+1+OajMiocoRCq8BjIyKJKPNlxUt6EiweULNeR2e7//Toe6srQJj1QRBDMmk9MSln4+moUROQbhBpo+zN2tDqQBIujqqH7+Izp8AekNRZE+izNO5SP2ghU5J2H3NfkJKIDD/1sr25Q9632V1iem+VF6/Hp5+VN/ZVosezKRqg1Mv71Lb1P5J7QIGuFz2svJNvOPedr3+spnk91WSh2JQnBLqzlU4jd+wAah/sk8tqQ5AZl8XcUZ8yjV0LAZeVrregbYm+dz+KKOhtI926VHZKFwECAHQEjSII1a8bCWjqfE8bho+EKbsMUSgEpicdpBsw7K6x2V2jG7aQrresQOtI87PDJ4czoJEGT1xSPpCHylkfTtJPvya3HTMP8UyEVvxxceVMe6kxb2WUsjuuUFNN+XofQDAduWSiUKrUo6Ep3X3eNXBAcmDR7Ey/vU2zQld4jjIF4500jlSw6S5BehfNM+QU2kkCEpt3MGc+n6TT51aQQfDi77Iqsn+uVle9JCON9YcYvByyyID6HqP1BKRnWJwXAwIhogECY86z4AoQMwiATt91JFBEVxM66vWMclpdv8HV9T1sd1cYNoMmDRXG5iLAHmAeVUsInscaIuuuS4pmQpwTXEa6pyeGCRc/CB3tyUZo2ww6vyi/j3r/Lvk/mQf/W61yCR84u+LnmLFVbjkLgjFjkwSxTbPNz+j5Cz5C1+CnP2ejDvYyPU6QBwaitQYuk8aAbJ/9GGs+li9s82uSwLypdDEg4K5AgEATXGp/T+ZBrdCnRA4BITblx36DYbvD7voam6srsN/o+v2Ujgq04leyrprpu58IXI3kpxGIObdfLRxlXK2G8SwEY3bAc2aY2gWSasy5+CN+pkrqH8PnzDGKZFnNk5udjfkegFb7g8QIkII/wComt+8tfTZkkhgtxSw8+2xugr+TzoKj7Yg/TUF+NHrUtuauj3Tpkc1TZTbLvIPmxKo8ftcaJDiT/YsAgTDvRef7CwFKHSHc7/QqwjGiudmJorG7UiJ4J3tLm+w6bLdbXN3TAiHsbNtwv3lxUbdRtS2RE6Nwm24h8Ko77S7JaaxNf9v5gfDp7GPC1jYV1xj7RabK1xoOj4kimCbjY3OgnDgNc9bLtpUAPwBErOuoY329dnQ/V/yu17gJzgEBNr+btg/SE7QukjsEJOtGxkvMz2jIfuu+FefSRYAAIDbXPxWBbIbmLZY9Oh9mU7EgSjLn9yIY+h676/u4vv+ULhKygiMSMw0qpF1HBYSoTlSDfDqSSz1mI5/uEajXTVN4vW/ZjMxuDdg1+TnaZz/4trLAFN3WzL4x/YLnLTjAzbbTCFcbFzgUW68j02lFyS5SWLfJwlHEH/Mb16a7kazBQanZlC34asS8m7Qy4fVkYYJzQOD80fbJ04HRY4YuAgREUh1BIBTDl8CqUu/r3DpVccMSkLo+ACIoN7r5B7sOm901ru7dx+bqHm6o+8fHWgGpM6ldZ9kFew0oxugaitKFQGdTDQTQI0p1RzKTAUA15gSRdhuBIo7t4hQvaEe1NmnqQaSvvc18H+RRZ5yfUEf6bALojzYWoPdj+i2m5LD46ZzFpJ+1F9O2cuETSO0H8WnZ1A/ZTYn+mrvPISvhEJ36/mHMrdPg+ajTjtNnP97WRYAA0AqQD2d1FuDm5mZ25BqnEtcMQoFgu7vC9b37GHY7lM6m9ESn5QQ+naIC31EgPpUGq0foYBP7+Hkyj+0DkMA2BNEDYEmhPZglie++T5t82KlOHmyMz0flpjWHm2+bwGOOA/jxvDmmnhNvONqfzFU3965B06n1IBaUIDrznEzhvehpbG0GjeN438w9ZTugJ7l5NMVp7ndWPOC4go2C2SYPFUwTqFa0fUxenPVbmF7H6WJAAGg6WiRlEY6DaZ7AMhJMAr4VcbFS4dvtFpvdFhw6TfrpVOB09FeFl64qLOmjoS5oJoih68GYIpya0GNvrh6Nwhgc57+1p4aq2zMfJXHwcuVsZiVGI77PdKS7PEQQTQObzXPb7fOzj2MD9d7eTwxXJW2LPmcNxT3yIiC1ojR6zqQzY0vgSQcGH0xlZdQX06/nWpuxwA6SzzE8OIzwcaLnwxLJfwPw3wD+fWleHoHeitvNP3D7n+G28w+8uc/w3SLyne3BiwABACD5eRF579J8PCzddv6B2/8Mt51/YJlnOFlyfKWVVvr/TSsIrLTSHadLAoFfWZqBR6Tbzj9w+5/htvMPLPAMFxMTWGmllZahS7IEVlpppQVocRAg+cMkXyH5KskXlubnXCL5NZJfJPkiyc/bsbeQ/GOSX7Xf3740n5lIfork6yRfTsdmeabSL1q/vETy2eU4D17n+P8EydesH14k+Vz67meN/1dI/tAyXFci+U6Sf0by70h+ieRP2fFl+yAn4zzp/wB6AH8P4F0AtgC+AOA9S/L0ALx/DcBbm2M/B+AF+/wCgE8uzWfD3wcBPAvg5VM8Q/eT/ENodsv7AXzuQvn/BICfmTn3PSZPOwDPmJz1C/P/dgDP2uenAXzF+Fy0D5a2BN4H4FUR+QcR+V8AnwHw/MI8PQo9D+DT9vnTAH5kQV4mJCKNGtzgAAACD0lEQVR/DuA/msOHeH4ewK+L0l8C+DbqFvSL0QH+D9HzAD4jIt8SkX+EbpD7vjeNuTNIRL4uIn9rn/8LwJcBvAML98HSIPAOAP+c/v4XO3YbSAD8Ecm/IfkTduxtUrdh/1cAb1uGtQeiQzzfpr75STOXP5VcsIvmn+T3APh+AJ/Dwn2wNAjcZvqAiDwL4EMAPkbyg/lLUXvuVk293EaeAfwygO8F8H0Avg7g55dl5zSRfArA7wD4aRH5Zv5uiT5YGgReA/DO9Pd32bGLJxF5zX6/DuD3oKbmN9xcs9+vL8fh2XSI51vRNyLyDRHZi67Y+lVUk/8i+Se5gQLAb4rI79rhRftgaRD4awDvJvkMyS2ADwP47MI8nSSS90k+7Z8B/CCAl6G8f8RO+wiA31+GwweiQzx/FsCPWYT6/QD+M5msF0ONj/yj0H4AlP8Pk9yRfAbAuwH81ZPmLxN1WeKvAfiyiPxC+mrZPlgyWpoioF+BRm8/vjQ/Z/L8Lmjk+QsAvuR8A/gOAH8K4KsA/gTAW5bmteH7t6Am8xtQ//Kjh3iGRqR/yfrliwDee6H8/4bx95IpzdvT+R83/l8B8KEL4P8DUFP/JQAv2v/nlu6DNWNwpZXuOC3tDqy00koL0woCK610x2kFgZVWuuO0gsBKK91xWkFgpZXuOK0gsNJKd5xWEFhppTtOKwistNIdp/8DUZ+hRMoIF1YAAAAASUVORK5CYII=\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ - " 9.71% : quill\n", - " 7.01% : ladle\n", - " 6.18% : screwdriver\n", - " 4.81% : broom\n", - " 4.26% : nail\n" + "45.08% : shower_curtain\n", + "21.84% : mosquito_net\n", + "11.55% : handkerchief\n", + " 2.02% : window_shade\n", + " 0.91% : Windsor_tie\n" ] } ], @@ -1051,28 +1076,30 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 37, "metadata": {}, "outputs": [ { "data": { - "image/png": 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0hz1sz2aeZViNRlXtNWyiDVW119roFpm2ZlOKqqC7vsZOt8NwuENZLHh5fs7tW7cYDock\nSUIYhiwWC/b391FKbcG8Dd6xwWWUUpyfv2R8ekrQuUWW51RV1aYJpkY6LkEUooymEYbhzs4WLL26\nnpAWOUkSE0Uxlu0iZMVqmaK12UZvbRSm8f2QbldQFIowdAmCkDBsX8YYiqLgfHyOmTvshW16gWnT\nvfl8Tp7n2+fc6XSQUpIkyTai832fMIgo8wJhBGEU4nnuGgCW+JVPWfl4nk/gBzTNOjozYNsOQdAe\nU2tJntWEUUjSJHTDAUnSod/fp98dEUc7SBNgKoG2BNQWgQ2DxOVg55gsThgMO+z0BkS+RVaUNEYT\n+S6W1Dwdj/nN7/4f/OZ3f4Pnp0/oDWOCIMDxLGqtaMqGoi7QaITdVs4aNIoaz7LRUqAxGMsQdEKG\neyM8H3q7ATv7u9iBS6UNi8UKbWm0hLwumKUzGtmghcGLPHo7PdI6o1ItxlBTs0yXn1sfvyBGoc2Z\nq6raGoJNDi3XJa8kSbbKuvm5WTQGgzACz/W2EcI2lF8bgY1n931/q5xFUVCWJfBZKrIoliyXS1ar\nFVEU4XkeQRAwnU55+PAhlmXxS7/0S7iuSxiGKKW4urri6qr1ypPJFbbvMto54PL05TZ/dxwXf+3B\nF2tF6HV77Ozs4EYBnudR12350fN9dnaGXJ5nCMDzPOAz7KUsS2azGYPBgNFoxIsXL1ocYTAgDEO0\n1iyXCx589ID5syvuvrdPlmVkWcZ8PierK6RjE1clduBjey3QZ9s2WZbx8OEjirrky++/x+3bt9Dk\nXM1OtxFcGIbb52BZFnEc4/s+dV0RBCH9fp9ut4vneSyXSy6vxjz66CVDd4d3b/c5Pj5GG83Dhw+3\nRvbdd99FSkm32yUMQ/I859GjRyil8H2fwWDAxcsz0qohDIIWw3FdPNfeYjsbEHS1WlEWNatVhusG\n21KeYwcYLalUhb2Iid0BSdSnl+zT64wIvC51JamVAdPQDEEKSELJ3k6fNA/pdD0i30JiUCbD9RyE\nhE/On/C73/0uv/07v8XJ+VNwNMKxaCzFqlySX+dorciLjKIpEY7AsmwMhko02I5GOgLVNNRSY4c2\nYT/Gdhr8IGLv1iFhP6ECHjz4lHmxxF1OsIRLWmd4kUtpCvIqQ3qSTj9md2+XhoYiL5jOp59bH78g\nRoFtVaANsdXWIADb0uQGK7iJCyilUI1CGEHf7yG13Nbsb6YZm1Rks3gAsizbpg9xHGO5Fk3WbKsa\nwNagFEXBeDzeGp2NUajrmuFwyHK5JMtaUK6tmds4tg2adv9RSLBezBsAs9/vc+/ePaTvksQxes2F\naA2XTVmWSCmRUrJYLLi6uqLT6XB5eUld19y5c4cwbOvWu7u79Hq9lhdRVSwWS87Pz5m/vGRxa0FR\n5CilSLOCvDZ4QZtKDDoJ/cEQW0pWqxWz1ZJnz58hHRsB7O/tUdQLZssLXNejrj8DfDepyuaeblKC\nNu2AoihYLldMZxOW54aDWxEHh3u8cf8+TaO4uLhgPB5zeHjI7du3WSwWdLtdjo+P+fTTT/nwww8p\ny5Lj42O6vS4vTp4hrLWCr5+r53v4lU9euFsHMJvNWS5ajMOynM+eVxChKiiqHHRMLAd0wz6x3yV0\nEhwrRGvRRnYCLG0wCmwJSWRhy4AoaEEygSAOPEAzWZ3x5z/4I/6vP/w9nr14iBMIfC9C65raKMqy\nNaZVXaHXgKiQ68gXgxINtTAIy1CbGm172KGH3/WxbIPl1CTDBDtyuZxd8+LiguvJgkWREoYSGbgE\nsaZUJXmdoXRNFEfsHuyijGJyNflb6eMXxCiYLfC3UV7btonjmL29trR3cnKyBQ03PzcRw4bEtMEK\nLi4uyLJsqyhBEGyjj8vLS5KkLc8ZY+j3+4xGI3zP5/L6kuNbx9y9c5f79+/T6/VYLBZcXl4ipeRL\nX/oSWms+/vhj4jhuQU5gb2+Pt99+mzzP+cmPf8L46hITOMRJzMtnL7h3eMydO3cJHJfx+ILnz58D\n4Pk+3/72t/GTCLcT4XcTfuVXfoXTiwvOzs4QQvDmm2/yla98haIo+PVf/3Wm0ynf/OY3qeuaX/u1\nX+MHP/iEw8MO3/nOd+j1evz0pz/F931sKTk8POT0g6f8xj/9Dd54+z79fo8iFziB4e7dYxAWe8eH\nvPXuOzRNw3g8Rnou3/jGNxjt7hLFEQ8fPqQhJwgC3nn7baq64vr6mvl8ThAEvPHGGxhjtqDiyckJ\nk8mEH/7wh9s0TVWG0X2488YOg96IsipQtaLb7XLr1i2EENvv3b59G39N7vrOd77DRx99xCeffMK3\nvvUtfuWXf4VpqRjP5uTZiiAIGO706fYDotilrkuKsuDk5DlpmnPruDWatuO0aVbRoGrwfZ/9W2+y\nG97GizwwNlUBStckyYjAgdGuIAgE8wkICdKGJATf2yxa0KLkfH7Gg48/4tnpQ9xQkPQDyqpkb2/I\nrVu3OT+/YLVcoZoWsC6KgqIoqGsDWiCMIYh9wJDXOa4r2Nnf4e79O9x78w2k3fDk2UdcL6ZYQnJ8\n9xb/lm1z8fKKqjJ0ki5hFPPB6V8wSoYMdvusiiWe7+JHHifPLnn69Cl5nn9uffxCGAU+Kyhslf7m\n+40B2Pz+pliWhTYaS1h0u12AbTkxyzJmsxmLxQKlFJ7nbcuSG15E0zTbOrnRba18A0wqpbZEqrIs\nKctyzUMItnjF5vyALYnJdh3cToRrWk9qS0kF1Komy/ItdsI6mrEsa30tEs+R+J7PnJZRuCEjXVxc\ntJ59MODWrVssFguqqiIIPjOOnudty5Lz+ZyiaFOjosiZz+cshcBxNd1ewnA4xPV8ev0+vu+3kZEl\n0GsMYLEuXQZBiMCmaQqk8+py2URNWreMviRJyLIWjNykZUopbGmzX+9S1TWPHj7C8SV5XjCfz+l0\nOgghtkSwTfS34YIcHh6Spil5nnN2doY/GHF0dERd5ijT8kMsWbOzM0QIiOOAyfU1V+MJruPjOAGu\nHWEaG8+NQdvUTYVwLCQSWzjY0kcoTWMsHMsiiCD02tShNm1kEHiwDoDaZyXgfHHKeHGKtgrivsvO\nQYdV0WM2n9GIink6wYsc4t4+nuNSlCVXV1dcXl5SlhVhGNFJEqq6s3aMKUZU9Pb6eN0Q4ds0KPxO\nhOvZGC3whCbOO2Sq5WtEYbdNHdWAKA4IuwGWJ1CmoaxL0mLFMl1sn8vnkS+GUVjLTczgL6cJN8uM\nNyMEWJczsVCNeoX1uAESgS2guOEs+L4PsAU0G9XQGI207LXyZluQbLNQ2/Sg5RR0Op1t7rth78Vx\nzNHRId1hj9TUWHWbf9u2TWUMjdaodRViU+LbXJ9oPAwaY1ocxHEc7DBkd3cXz/NYrVrP+M477/D+\n++/z6aef0u/32d9fUdet8fI8j36/j9aa8eSaxWLRLjalmE6nFHlBtxtyeHjAYDDEDwI6gx6+57W0\nZgxpWXB9fc0yXaEOFG/2BzQYFukSWzr/UqD3ZkqVJAnAlv9RliVKNSSdDsU05/GTxxjRkGWt59rf\n39/e540BPD8/x3EcZrMZaZoShiHT6RRLwOFgxM5wiGNbTKZjXp4/x/UNe/t7BIFDrar2uFXZGvM1\nbrRJB123BaMjJyR0I0I/wvcSKrclFTkCuhEELmgN2oBnQ+iCZWgxgMqwmC15Mv6IlZ7hOB7DUcIq\nG1DUKdKFvCi5nl5x+/btNuqKEqqixnFtqrqiqmt2d3c52D9AW30MgrJakZdLev0ALwkxjqFSiniY\n4Do2Va2phSHII7rarKOemMAP2LV3cX2bsBMhXUlZVmRlRl6267NcO4jPI18Yo7BR8JvRQdM0VFXL\nwNvgBPBZeW1DSHJsh0Y1nJ+doxq19cQbavAGQMvzfFvq3FQkNmCk0QajdXsz0zbC2HguYNvbYFlt\nRLIB9TYAY5Zl6+N1cHyPYjFBmpYoZTsujusg9WeA5garaIlJCqoKVSksv1U8z3WJu+HWkzqOw8HB\nAW+tCU1FUXDv3j2apuHy8nJ7XXEcb/kDq+US1nhNWZbkec7x8TF7BwfbsqLneXQ6Haq6RumGVVmw\nmM9btp9l8aX7b+HYTtujcaO3ZGN4N+XfDUi6icQ2FZCiKCjXhrWpLagrLPszkHKD92zARKUUl5eX\nBEHA2dkZURRxfHzM85MTHLvt2aiUwnFcHMclCANcj+1+qrriYH+fXqfP4eExu7uH7O3uMejvEIU9\nXDukNhV3O3fYcXfBhjiQqFoykRKtIPTBc6Cq2ojAkSAl2z6C+eyKk+cnnC2eYcWKXjxE+gY3soh7\nHooOzjJjsVjS3UlI+gmWkJRNjfRswk6I12iiJMaLfMLuCOm4VNWKNJviBSADiZbterddF2FZaF2g\nbXACl7CJUAocx0PaklCGuI7dgpUoal2hjEJI8HyPMAmA4nPp4hfCKIi/9HkDON5kKG6oyTfJS8AW\niNONZjqbvkKZDcPwldw/z/NXPN3GKDiOgyXan6t8znxubxuNNlTrOI4pimJbW+90OttUYxPReJ6H\nY7eeIE2X+JbTkqFku+hd0aLri/mi9XzrY9R5Tik0lu8gG480bZVL2h7T6XQbNe3s7BAEAVVV0e12\neeutt7ZKqLXehohKKWaz2fazZbXsRQzcu3cPJ/CZTCes0pT940P6gwHX19eYpjViVV0zvZ4S+AGr\n1YowsdFr8PXmc9gYBNu2qdbNQsaYrRHfRGpl0WIIbQjv0ht2aVSz7R8JgoAgCEjTlKurK2zb5tat\nW1uuwv7+PnmeczUes5wvKPUptm0IQ5fhYIhl16xWS5qmQqmKd955p+3ZCGN6vR26nVHLTvRipPCo\nTc3eTsLAcSjr1gBYcUvPHl+0zVtCtIbAsdfNXICwBFmaMr58wcuXjymcOZHjY2RNVi2YLyfUpqDT\nj4i7EdEipDtIqHXF5XjCdDpntcyxHIkb+FRNxfl4zK47ILI7YIGwLYxoqHVFpVXLXdA1ZaVYZRmr\nLKdqKoylwbZoqGnqhsYoykZDBVJYKNWyJZNewn61T6/b5RmfrwLxhTAKILbKfrPCsClT3owUNgvx\n5qst+Tl0Oh0cx9l6rptU5TzP8X1/W1m42SNh2zZBENLJOsznyy2LcZOjb8hAm5B4gzVsmJIbL11W\nFbPZlLQsWCyWaNdvS6Z63cfhtOXNcN1bIWVbKSnKGlXn1EJjeS5X0wlFlrJaCNI02x5PSsn5+TnR\nulPu8PBwe3xjDBcXF9trq+uKqq4QUhDHMf1BnzzL2RkOmWcp4/EleZHjOA7dTofJZNLyNdaVhLqu\nmc6njMdjhiRoY9quR32jg3SNhWyiHinltqktW3eS5nmO47lEMiJbqTbS6vVAG8qy5MWLF0TrasLV\n1RUXFxfs7+9jWRYHBwccHBxsqdXT2ZRCujTTOVWZcnz7gDfu38KShsn1grrOSToRd+7cpT8YIIzE\n92NcN8aRzmdcFy1wPY1lYD7N8F2H/sghjgxTKdAKtGqjBNdtnVaZKYpqxfnFCRcvn7OcX8Fug+NL\njFAsVlPGk5c0jeFg/xZJ0iHPFUmnw/jiipPT51ycXyKlS68/wHFt8qJgubxC+z16jcJx2gpCTc0q\nX5FVOUEgKFRJnmfMF0vSNEcpENiATaUaVK3JTQbSUFUutmVjYWG7Nv1Bn9hPMMrwB/zoc2njF8Qo\n8EojlG5ebXyqymqbE94MWzckGikkwhEMBgPsdRi56UdoufcZRVEwHA63+ECaplRV1ZKTbEknSVDN\nDpXXGh9bSgRi3cL7GRhY5Dmnp6eUZYnnuixXq20ZznFsVsuUebps8QPRoGpFVdeYqsKm7XnQWm97\nKeq6ZpWtyLRiWeYIW3I1nzO7vkSaimDNhVitVuuoyKbT6bC3t7ft8Nx46WfPniGEIIqiNb+jJXTt\nDGOGwyETM0EpxXw+Yz6f0x8MGO3ubY1AozXeuhvUOT9ntVpx8vwEIfdw/NYAbsV8FmlJ28aSFsJq\niUUbJmiWZetypYclBcZTeLKNplStgE369Fm/SpIkpGnK97///W0PhWVJriYTdGMoipzCQL5a4HgW\nfiAJI4kx7Xf39/fodnsEXkDTtKVDozXKNAgahGUwGJra4vR0yg9+/AH97oCvfe0t4q7EdaFRoCpw\n/DZSqFXlnFJKAAAgAElEQVTD5dUlZy8fcXHxnMVyQl1V1LpCo9BCUdY5q3yJ47hE3ZDRaEhRKKT0\nsKc2xoJaK5CypTQ7Els72I5kVWS4VUZot8CiUiWrPCMtMoTroIyhVIq0zEjLAks4uK6DMBZaN9RG\n0dCgG4VuDLVocKTElS5hHOEkDo5xPrcufjGMgmCbQ2itUXoNMK77+zc99RvFRLT53WbGgSXbCsSG\nc7DBC5KkbUDaNCt1koTlcrkNtYs8JwhDLGERBgHIIe5uuG3OEQgsu61KF1XJfLng6vKS4vkzRqNd\nuuvIpNPtsLOzg7Qly9mCsiyJhwmmrKlV3YJey5RKZjiWRbpKiaMIx7GpdAvGLYoVosxQFkxmc16e\nnRC7gp//5jdRjWI8HlNVJcfHRwwGAwaDPr1en+FgwGq15MWLU05OThBC8NZbbxEEAVo36EYThgG+\n56F1w8X4gsurMat0xbd+4Vt85SvvoWh7+0tV0Yn69Po9oiTi8uqSF2cv6O9HHAwGGABj2tBVGqTd\nNko50iZdpaQ6JXRDbGHT1A15njObz5gtZmTViv3OLY56h+2CBuI4ZG9vF9d1GI/HDAZ99vb2ePny\nnCdPnlDXFVdXl23fw/Ex/WGXlbKQxsKWDlfjKxbLCXt7Pd56+y5vvX2fw6N9wNCoGkvaCGmwpMBd\nV3UCL0a6kuW84vt/+iN+/w9+l/29uwz6Pd77+UMcG5SCvATbM0hP0GjDfHbFkyefcjE+wXENw36f\nSZ2jlIcThFhSI2h7GXrdiE4nRjcLXMdlMOhxdHiAaQxlpXBsB9dtm+dGgx2uKht7XUKkUpRVQdUo\nyrrCqQ1e4CBribEFSIG0HbwgwLJchFQ0gGM5aA1GGXRTo5TGtiWWIwgcD9fyP7c6fjGMQgt9g1zP\nMLAkWrbU5S06T0tdNtqsh5FY7QCUjRc3MJvNtqFtGIZ0u116/T51VZGm6Tb92FQXirLE8dZATRji\nJyFdTzGdzljM5+2AENWGwMUqoylrsmXK1WRCXVasOh063S5h0IbMjt2elwGEBt/x8GwXVSmKsqIR\nguvpjLIo2dnZwWBR1xVpmrFYrrB8m0YIVvMF2Sqluzfg6OgYVSseP3qCMTAYjDg4OGQwGODIFgsZ\nDkecnr5kPl9gWRZ5XiCEhed6dAchRzu38MOAsnrMfL5kuUyJo4Tjo9v4bsDldAIahBb4jk/tNVhG\noArFbDJjen5N1/eQ0sVxbWRtYymbpmq9rkxsSt0aTa/jUZiCLG0Hi+SrnCatqVcVyRtd9vYPWaxm\nrMqMIIh577331yBwyWg04r333mN3d58wjEmSmMnkmmfPnhKFMUulQTbsjHo0KuTRk0958eQMxDE/\n94332d8/ZjAYsFwuaVSJhYsUHq4dEvld4rhH6CX4ts/pgxf8xY++zwcffMh8lnJx+S3erQ+QDjSG\n7RAfYxnyKmUyueT07AXjyxcEoYsFXFczrL5N6PVwHI+o0yEMkranRIh2oI1r0e11ODw+xJI20+s5\nda0QUhPFAZ2kSzUpCfyWbaspUU3bH1KWBU4BQehjCYlFu9YdW7bRl7CpqwaBwbN9GmOjjFo7UlCm\n5UKYDUjyOeULYRSMMdR5jSMcIi+iE3S2DTfLyZLpeEqv1+N6fE1RFuzt7XF4eIjneZRliYtLEAVc\nWRNKVW/pv2EYUlUV1/MWdLMsCy8MaLKMvCrxfA8v8JktFtiXFww6HZK54vzpOdcvzzi6dcz1fM5P\nfvpjBjtDvnx8h6AW7Hgd5umS6csrltcLilXB7HqG5/sYASqtuTo95f2vfAV27pFlGYdHLZFqAwK+\nnE6ZPHzO+fk5s9kcVdf4gU+/1+d4b4+92zvUIZS5gxAOR4dvo3VDJzmgyCVZKkjTOdPplLOzM/7w\nX/yQ87MlnW6Hv/jBJ9i2zXtf/gX+4bf+Ht+4+1U++vQjfvLRc3ZGuyxXhr/3b3+LfKH5b//L/4Fu\nt8PXv/51bh/d5e3bb/P0yVP+9KyGc8W4OuNPvn/Bc2dIZ2fA3t4BxrV5dn3GaXpG8kaXX/wPbtO7\nLbl4Oeepc4pe1di5ILA83k3ewmt8BnbC9x894Z+//CEBNg8+/oi7d+/yD/7BG3zve39OkhwhRMAP\nf/iYsiypKo8XLxYoBf3+XYrSwjIDfvEf3uYX/92vU6wavvvd3+GDH3/A/XtfIugecXauyFOF7wyh\ntmiMQxQO2IsPGCY7gGR6OmWcZvyf/+wPOJ+f8s3v/Dz337hHsuswy1YkewE9y2KxmvFicsGLnzzn\nydOHPH7yiIeffsLjpw+5uryiSAu6v7TH8bu3eePWkuHOkNHReyyWC3766RnDYdkCpAiMbzO8NUIm\nDvJccnl5SVpNUWVGJqd0gz10uWSxmtCYBlu005Mu0glz3+fEFGR5hmpqPNciHgqCwCBETaMLlMnJ\nywiMRiiFWE/usiyXRnpU0sY4/z8zCoLP+hU2oNkG0LKkhS3slg+wrj3Xdf0Z2CUtpCNx7DZF2ACN\nG3Auz/NtyXCTa29q6RvizeXlJRfjMbHv897hEat0xSJLiZZLaq0wQlBUFXlR0GiDEOCHAbbv4Xku\nYRRigFWWskhXiFqz0xnw6NEjloslrufS7/fXw0laj5CmKbPZjMlkgm3b+IG/pcIeHBxQWZqfPP+I\nqi7Xo9dgOp2vw+ljoiiiaVr+wY9//GMePPiQ1WpFp9t2WMZxzPHxMb1ej3oNisZxvGVzNk3DarXa\nVnaurq624Ox4PKYoC8IwIOomdJVLX4dUNJxfjcmqkrPFmKWV0jF9ulGXhoombwALXdfoWmCVIApQ\npUZmgIBOJ2EYdlG6YTQabYeiDAYDzs7OOD09JY5jbNtumaZ+2/LteS66gjffP2Y4HPEyvWA0HPHW\nl95h2N+BpqFWFXEYUhWg64Z+t8fOsE9/MMBzfNJlzux6xvj6isVizs7OiG/8/Dd488377O3t4vsB\ntmWjjeb5yXM++ODHPHv2lMViSpotWa2WXF1NWK1WdOMuumlYrZYs5nOk3ZbH8zxHCMFqtWJ6fU1/\nMHgFiN1wUJRSa7zDkFcFugbV1ChVY2hwHItKtZyZyfVLqqokCDxkP0FaLc/GWw+T6XQ6PL28oqlb\n/ovQr4L2m9fnlS+GURBtaXFTddgo681qw4ZReLNHAtiGVJsbvsEdNgDWhliUZdm2sWizz3aWXzvY\nIysKVo5DTwgWyyXzxYIwibBdF0taFFVJVpbrk7UIvADLcfACD8u2KeuKNMu4up5gC5vEjvjgww8o\nq4rd0YjhzoBBPcD1HPzAo9NNGAz7qKZG2jb2uhIRRhHdXod50U5yyvOcKIoQwmK1WnF9fX2DKtti\nL6enp0wmE4IgoN9vG44O9g/42te/xuHOIfVVhTYNcRIRJRHD0RBLCtJshREag+H84iUvTk9YLOdt\nE9Pkkk6S0B/2GBAQ5g7LIqesG5Ru8Z3AD0nChE7cY5kv0UojFWiloTJUNVALqAyLVUl/v0f/3bsM\noi57B7vtrIXlvFUEo3Ecm24n4fDokNFol52dYdulaVk0WlEsK3o9j+lkysmzF1RFSTfuII1kejXF\nVBb1qEFoiZQOURCThAmu41FkBednL3n8+DHPn5+glebe/fu8/5Wvcuf2AY5rbdfd+PKcx48e8dOf\n/pQnTx5jSQjDtnJV5BnaaAaDPrm35tKsq2TtWhbbnp2yqrZl7ZtTxBynnePJeg0WeUFTGeqmolYV\nxjRtObSUlJ7LeDzGsgS208eyJLZjY9sS13EQa96LM523DsuAsXQ743QN3DdNg/X/RfoghLgF/K/A\nHi0q8KvGmP9ZCPHfA/85cLne9L9Zz1b4m/a1VeZNyW+jtJsbuikHbqzedtqOblo2otUi4+bGjdhE\nDDfLm3+ZkbepZvi+jwW8ODsDS7TRQpoy9DwczyOvSmpVYzkWQko838cLA6z1oNa8LFmlKassJXB9\nlFFcTScIIeg1PfKqYJUtsd224oAFd+/fZbjbNlNVVY3j2Ph+wHQx5XI+xfO8VwhXG2OwMZKr1Yo0\nTVksFluqcRRF7O3u8dZbX+LerXv0ZMTV5CVFleN4Nq7vMNodtvfVlZRVzunLlF6vC0KwW48wQqON\nYpUtyc4KmqDPob9DqUos6RLHIcqFwi5bkHGRUTcKR7hYRiOwMKZBa0XdGEQDuqkxxqfRNcYoPL+t\nmjx99oTr62vyIuXunTu8++5bW+OWJB0c12lJZXVFHTRUasGzx8959PAxVVERBx1U1VAVBZ2gjzAW\nruMh8XAdD9MIFtM5k/E1jz55xKefPuTpo6e896X7vPPuu+zuDgnDtltRYBhfTfjRj37E85MTxuvZ\nl51OTBT563FxAWEUsb9/wKyfI0L7lTmgwJYx63neXxkt2EY9HlXZDqzVejPAtVmXkUu0VggBTi2o\n65ZiHwTtnNAwDHFcF61bhyYEaG1wLEljSZSl0Vg0pp3bYZoG0zTov8IG+uvlXydSUMB/ZYz5gRAi\nAb4vhPjd9d/+J2PM//h5d7QxChtF11pvvf/NuQWb2QXAlhiz4QHc5A5soolN2/WmdLkZ67VJPTZT\nhDzPI4wiVFly8ehTer0+eVVSFAXSc/CjkKKuEJbA9T2sPEe6DrbntoCQqkjzrN1GWni+RxAF7B20\nzVz7+/uEcdgu7KYmzVMms2uOjo7ww4BK1ZR1hRf42K7D42dPWGQpw+M9ijWNGti2N296Fc7Pz7fl\n0c04t6IosKTFYDBo+RpNjWULVllKYxQIw3A0pGkUqyym2++S5xm2a68HrAosu53KlJcZVaYw8wzl\nrcjLhiTuE8UdPMel0jXL2ZJHHz3C2oFO2KEiA9nQ2ArLUmBpGqGQts18cc30Uc2qN8Rx3e0zT5II\nIQS379zm7t07XF5eslwtuJ6uuRO+TxAGRF5MZQSrZU6Zl0ReRLfTo1yVBHbEaLhLJ+pgCw9pXGwk\n6TJlMc04O3nJi+dnrGYr6kpx/437vHn/TXzPAUw7TVkIHnz4gD//3p9T1vl2bW3WjO/77IxGuI7T\n9loMMkwstryTDaV9kyJsIgbglbXmeR65k2/LsOamo6sb6qZGCI02oHWzJYRFYdSmoGvC3M2OVceS\naEsiRIOGtmSsNagGrIZ/CTv9r5X/10bBGPMSeLl+vxRCPKAd7f63l3XIdTM12FjXjXHYsN6gxQI2\nZKaiKCirEkc62xRhMw0JPnsYtm1vpyNtpjPfbKPeEJ6m8zlRp9P2tTcKx/OI4phllmI5Dr60EPYS\nYcu2eagqWWYpWZGj0YRxTNLp4PoO777/NqPRLnEct2O+V3O0aFB1TVHnZGXaEoJQGEujdIVpNHmV\n4vg2Ozs7FHm+7XDb9BZ0Oh1WqxVnZ2e8fPmSOI65ffs2wHZ4bBTHpGVKrQTSsVikc/K6oGxKDgeH\n7WCYPOXr3/w6URRxcXHB48dPmK1mbUlWV4RhyGjQoxzPOD0/ActtQ1RpYyyLutE084yXz88ZBTv0\nen2WhUE6UDslxhUIB4xtEK6hQZEt5ngI+oNBO0170Md13LapzTJcXl5wfn6+NgzLbaPb3v4uTacm\nSDx6cY9Rb0Sv2yMMYhZmie90ONw/xnc9PBlhCxddGxbzGednV4xfXqIqzf7ogNHwkLfffofhsAei\nHe/eaMPV1ZQ/+ZM/4eNPPiZOQrRqth2beZ6DEAyHgzV25RL4Gu1/NuFrM3h2k8be7OS92cZ/kwm7\npYyvPbkRN/t/PqP6G2OwHXuLm21K8JtI15E2RmpoFLVpy6itJRDoWiE+P6Twd4MpCCHuAl8H/hT4\nNvBPhBD/KfDntNHE38iv3ACNwJZluPHyN3OxzYzFm6nGxvvbsgUjN95n0y+gtd7eyPF4TK/XI45j\nRqPRdhz5fDZrQb/LcYsb2BJjCYy0COOYuCqxZhOk6+D6PubaAiFQuiGrSmarJWm6wvE8+v0eXhgw\nW02598YbbX+Cbnjw4AFPT5/SqIb9/X0Obx8wn83bMWO+pOd32yRMwBtvv0Gvv4OWNi+Wy+01Oo5D\nr9djOBxuW7rruubu3btEUcR4PN4OcN0f7jGZT6maml4UklUZWZWxzJeEndajrfIVe3u73L5zhw8/\n/JBCFa2CzjXSk9ieDTZYvoUTu6iKtmkLgyUkrvSwpYMnfEIZYLsWWtfIRmC5htrVGEeDY2ikwgsc\nSluTZguCyCdqAiwLbKed2vSjH/+APC9apVoPlg2CAM+3yfIVs+sp7x18mcPdA4p5Sa/bR5WKaTXH\n9mw86XF5PuFoNyRKIuZZSr4qSGcpVV4RhzF3bt3j3u1j7tw+wHbMmtLcquTHH3/EH/3xH1FVBY3u\nYdvt/0uoqpzptECuz2lT9TLiMwe1AcA306g3mNZmMM4mWt0A4U2jt2xUW4Z4no0Ret3QpdBawTqa\nnc8XBIG/jhTbdBr96v8byfJFGx1g4bseUljUql4T8Cz0mvz3eeRf2ygIIWL4f6h7k1jJsvPO73fO\nuXPMw5tfzpXFYqmK4iCK3e5e2EAvtGt4Y8ALb7zx1js3vO2NN/a2YRheeNGLNmDDNuR2Q4Ja3aAo\nUiZFFYulLOY8vTnmuBF3vud4cSKisiTSXbDURukCWVUvM16+V/Hu+e453/f///78L8B/aYxZCiH+\nGfBPsbf4PwX+W+A//zWft8t9aLajL5lsNn++6w2860F4d7qwNdwIIdCO3smZm83mro8gpcW3x3G8\nk+F2u136/f7OfVjVVoe/Wq2o0GRlTmlsAykrc4wSKNdFY9ASlOOgBZS1FSZVVQVK4ocBnV4Pz3FY\nXp0TRAGO79iR5WLOeDK231+7yYPeA+aLOUVVWH+G47CMY4SUnN4+pdMbcH452kFT6rrebVG3743n\neRun5CHdbpcgCBiNRvQHfSLZIFMFGTGL5Zw4icmLDOVKpCOQjqDZaRA0ApCGVrvBw/ffoz/os4pX\nSEeQrBOKqiToNjBKMZ+mVg1oNI6URCqkEbU46B3Q9JqUVYqLS4FCSGmJza5BhBJHS1IDRZGRF5Xt\nkguNUmIH1f3880es1wn379/j1q1bNAe9DUBXoHVFViTkSYEMHALXR2hBkRSUaY2IDNSGMq2QODgq\noMqWLCYLpqMZdWU4Pdzjg4fv8eEHByAsjt0Y61xdLGNevXrF9fU13W57M0Wwi9DzfFbrBUpJev2O\nVZHmOUVuwJMgNf4OtPDFg01rvTNqbXcM2+Nfs9lAKQvkyeeQFTllUSCFNbUZU1PrYuc0zbKMsii/\nwOFJG6qzPar4rgsa6qJgvlySbxS9SilMZUN9vur1NyoKQggXWxD+uTHmfwUwxly/8+f/A/D7v+5z\nzV/JfdgmE1klnv7S9GC7DduCV7bn6e2b3mg08FyrYry4uNjtLCaTCScnJ3Q6Hc7Pz/n444+toGlD\nKNqOBNfr9YaFcEiZrXnx6hUA1+Mb/uxnP6XX7xM2GyzWK+p4SavXJk4SVus1WVnS7HY4bLboDXoM\n9w8QuiRySqQvePbqKTc31yySOf2DHmF4jBGav3zyGSfHJxSjnFpUzGZTXr9+Ta/XRzia/OULfN+G\n1VhA6oq9vT2UUjx5YnUIP/jBDxiNRrtFtSU5tZotnlw/4f7xA7LrmH/1r/8Vby/eoJXmwQcPiNOY\noigsUr5KefLiicW7HfS5f/8+nU6H7/zud1gulxyfnPAXP/5T/rd//i+QgWA+mlPmWINRs0ngBgxa\nQzzPZV7mmBqqoqYuKgwaN3RxhUtv0GL88jVFnnAwHFJrTdTwODre49GjRxbeMrOsgU63gZCa84s3\n1HVNp9Phgw++we/+7re5nE+5ej5CaMHkeo4uNJ1Wl0F3j16rx3c+vI/RDrObBZdvL/jJj3/K5dtL\nPnj/Ix7cfY8P39/f3oEYI5hOM16+fMbnv3rEs2dPN87TitVqzvnFGd/85jf4+OOP+NXjz5nPJxwd\nHQEwH88YxWNW65RW0NwdhxzHYbWRvjebltH47nE3DEPu3LnDnTt3dmvhD/73H/P82XOyPOX+/Xt8\n97vfIQw9nr94ytOnT3ZM0NF4xN5+n16/TbBpQltresWw08MYw5ss48/+5EdcXV/z/e9/n3arxTKO\n+fa3v/2V1/XfZPoggP8R+NwY89+98/tHm34DwH8MfPb/9Wts/j6MsRkD2+3Su41H3/d3DZ4tg3E7\nF66qagdr3Qa2bDvA73aKt68HgxsG4Np+gRv4tiHmOghHIY3GSEFtvzFwFUIrhFIIVyKURGPlv1Ez\npNIl0pE0Wg0qXe0KnHVASpAG6dgzvxe4+KGHFzj2SV4JlHK+tEXcfu5Ow7HBoW0bWltgbVmWZEnK\nLJmT1RlRK6TZaWKkRrkS5VmpMMqOwdzA4dbdU8IwpDIlaZEQtUK80KWmQvqS/Vt7CDnHFBJfB3iu\ng68CIjfCxeN0/4Tb3WMu43PG80um8oq4WlKmKXVZMs9K1plF8Duug7ejbBuiKKTTafPBB9/g5ORo\ndxza/syUspOXyXSKrisc7PhWGkUQNul3+nSbXaRRzKcxUru8ePKSn/z4J1y9ueTk4BYff/NjDgYH\nZInAleBEgqrWLBcLrq+vuTg/5/z8jIuLC+q6JIz8Xd/JYI8Z7ubBsyONv9Mz+GskMPEFgOZdaNCX\n7237j8FwwMHBAYvlfDex2CLwu90uhoKiyJhOpsxmU47zA1zXsRJzzzYgV5MV69Uaas3h/oGF3GqD\nqxw85TCfTL/ymvub7BT+AfCfAb8UQnyy+b3/GvhPhRDfxh4fXgH/xd/ga+yKQpIkKKV2C3+7ld6O\nfgCCINyNfIAdQ2AbRbdcLneNxi3gdLudE0KgHEmz3aIoCyptt4ReI8QNfKRSSGOoqK0HwFFIx8Hd\nnCsd3xaPCoPvuUT9LpWuEErQ6XVotBqs1yvi2AadKEfZeF8Jju8QiZBGq0HYDHF8h6IyuJtCt52c\nAF9yZW7NT3Y09cWRygJaXYS4RNcZrW6L/rCHH/k4voPyFAqFFJJ1tsZzPfaP9lFSsVguiJMVB/v7\neK7LZDZFS83pvWOKzFCvBTL3ULgIJEo7OMYlcht4kWJZzIldD8fxAE2tK2pdUhU5QeTR7/Q5PTkG\nBL7vsVzOqcqCKArZ3x/QakX0e10rZZeCqio32+ySeLnACRo40iWvClyh6LU7HO0d0+8MEUaxmMy5\nOLvm5z/5lKePntEM2/zWhx/x4Tc/pBk0yWIQPsR5yvV0xPOnz3n06BFPnz7jbDPJkZuAnDDcFAW9\nheZ+cXSVSmLMF8Kgd52j72aW1L/hLG/vaxsvd3x8TJnXjEY3RI1wV/ijhoXsSFUx2Rw9b25G7N8M\nUM4hStpYQYHg+vKK68sr9vb2uHPrNlmSohC0Gg10Wf3/UxSMMX/CX0chwFfNevjqX2fXoNl+vH1C\nbp/4WmuqsrLqwijaMRW2wqUtKTmO4x0bYYtcE0LguA6ucnE9l2anRZYXVFW52ykoxwFHoXUJNRgp\nEErieC7KOAhX4fk+ynMxQqBcRafb4WY8xnVcoqb1RqhYkZuCdbnGOIZa1tSqRgUKFYQ0e03CZggu\nGGnwPX8He93qNN6lXm/x8++KvbYZFFaxmBCEAj9y6A47hHmAGyhczwFlX58u18zjKa2eVXvOFwtr\nHS9sqO1sNqfSGa1hG69pSdWmUpjCYApDnpZUac30aka+SLlJb5jEUxaLOWmWUlHanIO65uT2Mfce\nfIO9vYH1eyyXnJ+fMx6PkUqTZitW6xXzpYvnupsg4ArH9XFcCbJCCYEjPKS2TdFeu8fh/iGtsMt8\numC1WvPjP/kJjz97yrB7wMcffZtv/da32OvvYzTUucFvwq+eveazx5/z8sULHj16xOXlGXmRsb+/\nTxQFrNZLtC7xPd9qYeoax7H3VZqmSGMfVlVd/TVm6LYoOI6zmwa9u3vYvkZKuYPjKlyCwN/oFAy1\nttRq13Go6oQg8ElSq0l5+vQp0pGcnpxQVxVn19c8+fxXnJ+d8zvf+x5H+wfMJ1OUlPTaXYQ2jG5G\nv255/drra6Fo/HddQgg6nc6XkFrbHYLWmuVyaZV/YWPXT9iOct4VLG3BmdvG3faSUlrrL4JGs4lw\ns83Cc5DKAWnn9qW2YilHeUgcPCkslXfzRHd8b/OxImy5yLlAOFictyMRjqAylWUEOoK8zil1iZGG\nIPCJ2rYAVKakNhWe79FoRNS13gmVrC+g2HW0t/SprQ1722zN8wxjJI2WQ6MtidoNgtpHugo3cJGu\nPecKR5IsE84urbxYSklWZoxejqjriiCIiAJBkq+p65JaVAjhoI2hyEqSZcpiskA1IQtT5uWMVbYi\nXdtUIqlABQqEZrjf4/btWzSbIWBYLhckyZqyyHeNM60riiLHGI2UAs93CMMA33dREpRy8KTCd3wC\n3yoqu60OgdsgNjG6NJy/vUDXhu9993f47rd/l9OTExqBxBGGIoP5VHB+fsPbt6958/oN5+fnxKsl\nYehv8jOarFaL3QNje88EgU9RFMRxTOSH1G5Nrb8oCu/eT1vR3btRBe9qb965uTdRgi7a1MzmU+q6\npCoFQejuema3bp1S64pPP/05v/rVY8Io4OjgkPV6xaeffspf/vKXzKYzPvzGB9w6vUW3bWXYzSii\nKkrmzvwrr7evfVHY7hTa7fZOrPSuCGSbPbhYLAgOwy8tlG1zZ3tE2JKX/moala41NVa44wW+DUjR\n2hYDYazzrNQUeUFpagIipKPwlItSEuk6uJ6P47o2aljYbaG7EcakeUK9rpnPZyyWM9bpGiMMaZaQ\nZtbo4rgRnu8hHUGxSinKajOSsxkL8/ncBs5sILLbKPp3SUhf4N1KbNiqJisNhXAJ/QjPD0EaXN/B\nlx5plhE1I5IsISszmqpJp9uhNhVvzt6wTlbcv/+QShdcjS5ZFyuEhCDwcURInUrKtGRyPaF3q8fB\n8AC9SsHN0RQoU6NFgVNKDJqytlmdeZFsPBxL+v0OjWbI7Tu3KIqC2WyG79kIdWMMUsiNzt+lNhUS\nSejMOqYAACAASURBVBC4GF0R+U1CL0QaiakNQjgo4XDn9A7e7YCPP/yIo/0jPMejGRmkL1i/1vzs\n//6chbbE47IqbZS7sag1G65jG7uuZ4vfemXBsZ7v7GDAsiOovAqNRbbvZPfyC9n91uOQ5/luEgBf\noAcRG2rAhsfR7/fJi4z5fEpRaLxAbbbiguPjY6Io5OXLZzx+/MhK26cTFosFz5494/LsBiUNSggU\ngmDDojRVTZFmeOqrL/WvbVF4B7GwYydoY2fkQopdg7HWdly5ZTMCCCG/BGPZJgTFcWwt1/KLjMld\ndoSoUJ5CuHachhBooymrClPYuXKSpjYdWEqUEkjXQbkujrf55TgYJGYjXfUDu+2fz2fM53Nmsxmz\n2cw+oSUoR5Kka4rSft/KsTdBmqXkpd71T6QUu4bXNg0avlB1/lXDi96wJutKs85XJLXm+PAEz3fR\n6J0IRkjB3JE4riIMA6IoQEh2ctutJ2G1XjGZjShrQRS1GLSHuGWLbFkjlKJIS5RwaLc6LHWTQiYU\ndYKoC2okSoFwPYoy4+rqHITh6vIarTX37z9AKcXdu3fJ82wzjtM709tW9BMEgd11SImvIjzlEHoN\nlHRJk5xSbAtIyD/4+/+Qht/k6PCU0A2RGopckC9Lnjx+ww9/+MccfdBEuWpjTLKfC1i25TqmKjP2\nD4ZUZUm8ijcA2XCHkPMdjzqqqNFWdai3StltT0Fu7st6Nxq39+Y74OGNPmedrPGdkDAKd7vfWtfk\nudUXlKXd7fqBz3A4pNlqsVwuefHixY4gJhEcHxwx7A/QVYXQBiEl45sx52dv7QPrK15fi6JgC4Dt\n/htAb8jHestkNLBerknWa+qqtvQe6dJqtFDCYdqYMZvMWa9X+L6HEAata4oiBwzNRoNWq8VkMsFV\nLko6KOlQ6xqMoCxrqjLDKV38rkdV11S6sl+/VpgSsqJglayRrgJlteVq02B0PNeGpwhBrSGvS9bJ\nhppUl6zWMecXZ4zHE9I0wXU9O9dew2q9sjHtwmxozpAVGVmqUcpi4qxazkcJha6/cJKWRWk1BVLa\n90vrnVhlWxiTOGWVrdgfHGE0lHmJFJu/T1lhzNXVNR9//DGeHzIaTzi7uCAvSqRymc4WxKtLkmKF\nq9oMBn3utO/g5y3mXkKcZBgF8WTJm+c5N9UNsZmSlglg8IKAMPLwlYPII3SpmC8nXF9doxzJd7/3\nLQCCUDGfrxiNrhBCUtUa3/Not9ubWPkmnq+gUkgNYdTAdwM0NfFqgSM8XALarTa3Du8SuVbVGIUu\nLnB1Dj//2S/5+Z//ORdvb6CTkJg5FxdXTOdzHKVoSIc8i5lP55RlSrvdpCqrnYal3WlT6YLxZEy7\n1aYwFaWoLfVI6I2wq0ZIgeNIPM+l3vSAvigKEsex97kBkJL5ZEGnpfADD0e46EpT1xWVZ7GA+TaI\nSMLBwT7feP99rm8uefL0Me1Om9PTE9bnE27fu8Xe4T5JmlBjtSRvzt/w+eefc3Ly1cXGX4uiYIDK\naIQxSGFHcUiLbC+risrUrPOE3BQ4voNsKlRLoSKJJxWtXkB71aBMrYccFJ4f0u0NaDRaaNjtEKQj\nQBoqbQUh8TqmLEtcRyEdRW0MynFxHJe0LNBVicNmnGSgKmtErWm2mtaxJhSudBBG2m6w46CcCt9R\nSK1IVzmz0YLx9YzFIrbHFR+KtCRwI6Sxr6nyGke45EVBnlQ40sf1FWWZoxyFoaasC2pToU2N4ypq\naYNKpBIIIzEINDVFlZMXKWhBkq9I1hPiZEHY9Gg32ri+g+tKfN9BCE2yeTIuZjmvXzzn5cuXVizl\n+6TxkrJOkTTQRlErCb7lUrSVC7ELoeRmesP05obCTyidGNyKMHBwwxDfd2iGAcJXeMLDyIDXr2sc\nPGaTEgwEbsarlze8fHlG1AjwPIdutwvYUaDrBLiOQtQtlGjhOy6e9BGVoNbCHtc8ReD4+J5rF3ko\nCDuG6cWSn/78F/zwh/+Wm5sJnuNzczlhEl+xnN5Q5WucKECIGsdXBI0AT0v8KKA2mmIjUNNlhac8\nQsenWKeoDHzHwSkUTuXgGA9X+NQmp64MSVqAVCjHtcg6ae9BIYQdPG1SqEqRUogU1xGIUCM8Q5Xl\nJEVFkicIR7GMl0glAUOz2eHs7ILR9Q2mltw6ucVB75jfevgRtw7v8PzZU8rE7n6n1xPi2ZJysPeV\n1+PXoihUGJZ1TlALWp5Pv9EkciTrYs3FbMIsn1K0IBi2GZwe0j/oYxqSmZ5QyYTgqOBeZ0hydUSa\nJiSZZDgccufeB2hdMJ7PSKsMrWqMU1CKmHU85vrmnMVySrPR5OHDe3R6EW9vStqDAX7RIr24ZDFb\nEEV2p9Fr2ih4s6747nc+4uL8nOVySVtGG+5iRLfTxXElw/02n3zyC1788pyXzy+JlwVKRCigSioK\nKTg8uk1Ydxm9GNMk4WD/gKv5JTJucP/hPfy24NXlU1zXJc4WjObnIAQnd/fxWxIlHfxmD6UUeVEw\nHo+ZT0aMRiOSdI3vBKR6yVJPeDV6gmiWPPzoNkFgqOsVUkge3NvHFHOu3j7h+vKC6zcXjG5uyLIU\nz/VwXTg9/TYHve8xmd4wSVIS85y9/TW9O3v4KsB1Xd48esnLs+c0Gh5SlviBxrQUplNTkZPLBkVx\nwXv3ThkeH/H2LKLhPuBP/yhmODhice0znbdQ+phW5DHc9+n1W4RhE4zDclaRFjH3wt9mL3iPKk5w\nHRfHsUVRohFlja5rzmZvkdJw+84xcSb4gz/6l/zLP/g/CHyPu9854O3FS7LrEY2y5u5QkXYbJHVJ\nqcfIVsR+t8fprVs0G03W8yUIOOjt0fMa7LX7DN+LePzoEf3wABG5eFlId9GlX+0R0WJUTBnFU95e\nzen0u/S7CozGlRtcvDG4wuBojaMNnfcExpmzrmcosaZpID5fMp4t0JXhdHgbIRU3NxMmNxNev3zL\nzeUCU0nGRUY5u+a3Gg/5R9/8Rxy1Tnh184rRZyOyMkFqwTcO36ftdb7yevxaFAWBxVIjrGhD15p6\nc+4HcKRCBR6eF1i+nXJxpIsyGiMUhWYDEoVlPN8kHE1Yxn1arSZeYMGs8yjE94PNGLPePIHsNrqu\nNbrWm6DUkuViwXw+I8/zTXDMF+RmbezYb7Fcsl6vabZa9vwfBijHoa5L1snabuN0jZRi9/nbc7Ln\nexupL1R1xXw+w/NclCM5PjlkOBzuMi2UtA2qqrITlfl8zs31DcqxWoOttXwynjAejYnjmDAMODo6\noWeaXC4qfD8kTTNurm+IgoAqLyiyguloZJuY8cpmMK5XGw6ABs8mgEfNkA9+6yFSPMRRIfFyzWg0\n4+LsAqMN3W6HIisQyA0wxqPRkkRNhzASKNceeRphj+l4xXz6gihsUOcaKWuaLZd1MsFxNcN2hyAs\nsZmcBl1LPN/HcRv4UUTo+ChhDUz1ZkSsJNaD4Qd4viKIPDxXkucFb9++whjD93/n+5uezghhFGHQ\nQIsMVVU4GDxlpcNOECKVTbBKVmvKrKDX7zG8e49QOqymC2bLOdJRdremBEg7Qt7ehI6SuK6DcQME\nbI51BrP1KP2Vy1RghEX4oYV1bBqBFA4SuDi7wBEOUkiyNMcREt/zKKk2/NIKx3cZT8dcT274s5/+\nhKfPn3J4tE+732a+WnBxcfGV1+PXoygIgZACU9ZoseUjAAik3DQLWy28VnMnWPJcF0VNvenk2lj3\nmDRNWCcrytIBaooyJYo8DDWddofGRv1Y1zYA1ttYXKvSTiuCsM0qjncSaLDx6MBOJ1BvvBJb4Mlg\nMLDJyxuxS5oWLJZLptPpLphGSrlrCm41F9roHb58Pp/vDE+Hh0cMBn3ruTdfRMtthTOr1YrRaLTz\ncWwzMpZLmydRFAWDQZ/3Hz4kY0nydIoSinViu+i+46K1ZQDWtcb3g122o9LQbXfAGFzXoyxTBv0+\nvW6PwHfxvIjAXxDHKTfXY5aLmNlsymoVo6TtuLdaId1BSKPpIFUF5ORlRtBuMb6+5s3LBYfDD5ms\nrBPT8w3zxYzBXsDeYYtax7ieAKM2Z/CQZjjEDQKaVRunEFQVVGVlY92VxHECokZAsxmxXkuqOufN\n69f87M9/iuNI7t69R1kUfPbZZyBh6LpIIairgrq2/AdXSZxmA+U4eL7PfDYjSxL2uj0ODg7RecH0\nxtq59/YPEFHIWlvT09b+zLboex5KuQi56x78xquua4wSOEZt8iVtpJ3Eglovrs5RxqEdtTBaIJWF\n0FIX1KVmHaeM6jGvXr+iqEqev3zB8xfXCFfQHnZQjiJZ/B3LktyKOmyOQk1RFXibiDIh7BvcaXdw\nWrbhZF2Pjk3w1Xa8mGUZ42kBqqbRsufPWlfMlzNG4wzHU7z34C5+4CCloSisAElsQKtpluNlOX5L\n7XIo1+v1biFuR5lbR9x6vWaxWOyIUO/yGdJMsFgsGI/HO4z8lgWxpUvFcUyRF7sUq9VqRZIkdLtd\nawISgmW8RNd6ZxtvNps71ydYc0273aYsLYh2q1EwxuD7AUeHh6zrEPelR1VossxG4TVbbXzHxQyG\nvOE188mMwA9QvQHDTh8lJKauyfKC5WKK1nBxcU5ZZii1gaFuMgXW62QT/lvR7bVwXY9ms0W30yZq\nSgw5eSFI8wRHRugypMxjHCcky2YcndzDiATXL9g72Gf/oMk6AWMqpHJQIsRzOjTDIVG7S5Q1ASsG\nNRvat4NvlaGuQxAppoucN69f8MvPfsHzly85OBgSzX2uRiNuRjegFIOjQ/wwoCwEppZIaTCOwm81\nEZsRttY11NpmQE7GUNr7rTfo0293uCwWLNfZOxGH9U6o5DgOrq8oqtz+v8jfDDnRtYZK2ja7AWls\ntIAUCikVYiOUEsLCgUVDQAmmikmTlNU655cvr9kfDjg8OabRbmGkZjKbc7zBEJamBs6+0nr8ehQF\nLMG52ijEqrKiUgKjDEpJHOnSbDZRDTuycT07YdgKlPLMKhezUhIEntWFC0NZ55R5QZLERI2Adq8N\naIoyozJ2umCEpKxqlqsYIz0GwcGXns7ADoDx7qLeFocsy3a4t1beotW0T/RVHDOdTncz6C27b6s+\nnE6nxHFMp9NhMBjsnHV1XRMvY5bxknkyszP2ZpNGo7HLeej1enQ6nc2N+4U7dPvLGMM6WTOZTsEv\nCfyQOFuRpTnT6Zxuu4vb6dHvD3j14hVv3rylGTVwpaS1ibavy5r1OmMynpIXAZom8XKB6wS02wOa\njS6NRkQQBrY4G41S2/l8gO95+J5DbQxV7dojWunR8I/YG/iUhSbPM+4+OOb586f0hyFHRx3CpoNQ\nEVVZIfDw3Da+28Zzu4TuEKdyqFRhG6wOGK0xoqTWBXmZkeWS5WrBk2dPeP32zOo5lODRr57w+s0b\nPN8jLUsLD3cdVF3hKDAKcB3CIEQFPmmWEUYRjSAkX6ecX1/hImj7AUHTs43w1OZ6CGlZoEmS4hcN\nalPv8jDKOv+NgJOtBsFUZoPFk1CDMAKJgytchFAc7R9hDPgqwDGKwqkQpaRMStb1mmSVMr5Y8OLV\na5q9Lie3TnkYzxBK0my1aHYaGE/++m/i11xfj6IgxEZLbr7YorvS/uClwpV2Ti18f2dequuKYuNt\nWC6XrJOUTv/AMhbyzXm+LPF8l0Y7YtDvEoQ+eZlRZiVZkVFUm+lGXZJkmrTQOI1DhIBut7tLO95u\nz7ff63bu7Pu+nWDE8caQ5dHt9nAcy3ZYLBYbhZwVXm0R9FsOxHK53Fmit4Kroii4vLxkna7AgSiK\nGAwGu2SpbZFpNBpkWcbbt28pNk3GxWKxo/mMx2N+/vM/Z3DcpdXqkaxKynLJp58+Ynw14fjoiFsn\npySrHNcNKIqaUlc4wsN3Q9AKowVZVtNoabr9JoNehyhqAYr1uiBNY6SA01unlvdoNELYoleUGmdr\nkUbheyFl4dAIBwz7Dc7ObjDGMBhGvD2v6A+aNNoKIWvC0Kd2A4QJ8d0urmojdERdRJRliiZFOgYH\niTIKMCT5mmKWEq8dG3azWuJ6LrfvnJAXKX/5+eekecbx6S3mcYyQgkprtACz0bQIRyE9S9p6+fYN\nzSjivTv3SJcrRueXmNqgPJcsz7m+uCQLrM6l2BzpVqsYbx1QypraA/kVl5euLCpfS2OjDoywCU/S\nRSI5OT6lzmuKtKAsKhwBnvLwnQDHOKAN7W7E+eVbvEbA/uE+v/2d7xBEPsO9PlmVsSr/jh0f2JiK\nYMNoFCV17aD0BsXmWo2/kPYGqKuKqixI04z1ylKRy7Ti5KjNarUmKWOSfEVdV3jNDp1hh+HBEFxJ\nXdZkZUGSZyR5zjpPKYqcoshx1hluNGJ/b4+DgwOUsjjurchpK4ayklebpLRVVNpEKpfTk1u712SZ\nBYa0Wi3yPN/FrW+PGdvCALBcLr9gL65XTKYTmt3GroBsST3bz5FSslgsmEwmu5g7IQR7e3b05CjF\nq1evSPUexw8OUcpF1/DLv3zE2+ZbHt6/T74uOD485Ac/+A949eIlo8trVss1rvRoRg263QF3bt/n\n4OiUb3zjPVqNNmHYYjZd8uL5WzCGZrPByekpFxfnXF1dAII0SVkuBForXN8mbHl+gMl8jG5Slmtm\ns5ggCCiqBcP9Bo5fkWaLjc7EIQiauLKNK/s4ok1ZuugYVJWgSFCOj+cptBEUec4yWVrJdFmQ5QnS\nU+yf7NPstlldrTFK0Gi3aPW6GNfBZAlFVVBSU2mDlhIlwThW8p5n1rQmXIdGu0m2apMtV8ziBfF0\nzuXZGcFBm5yaQtcsl0sWiwVuK8B4AlCo6qsJhnStAWkz77XdKQhj9SaOcYi8iCRPyZOYdZyic01V\nlggknvIJvICj2z1evbwkzh7x9zsNPvjmN2m2I7zIY5WuWBd/x4qCEPYNsE/RigpJXWukkRvdgtq8\nRoDZuAEze5bPi5z1OiFf5zh3JV2/hRtIlGcszssT1kuAJmgELNYzlknMMlmT1yW10aRFwWK+wJiY\nVnvOrdNTTk5OdrkRs5mdQrxLgW42m/T7fRaLBcvlktVqRRAEZFlGs92wN3xR7OSr22h7IQTD4RDX\ndZnNZozHY7TWXFxc0G63OT09JV2njEYjpGcR4EmS4LruTk0nhNjFtG+PM0ophsMhg8GAIAhYLhe8\nePaS6WxGtAioa+sInY4npPGaW0cndLo9vvn+NzFaMxmNefqrR4wux7SbLe7fu8vhwRHD4ZB7D+7x\n4MHdTWjtkqLIaLdbOI5HnlW4TsAqXjGdjiiLiiQxIGqKQhBEklYzwmt4SNUgW0nO3o6YTRY8eG/A\n6zdPef+Du7x6+ZyLizUnJ7eRUtHwIwJ/gCP66DIiz6HMcnwnoRHmOI6H40mEUOCUrPKaWTxlNL4h\njmP6/R6NRsSLV6+4uDij1emgqVmsVqR5jikLKlNTSoEGSm3DWb2qoqhKDk6OqYrSCpWiBl4UcHNx\nyeWbM0xZ0ep1KdHEyyWFrlmt1puE7RytQUgHp/QwAkDsWo1bytNWcSsQ6FIjtQQhkcb6OiKvQZ1p\nRK2QWpKsE66vxizHCxzhEAYBjtxMUUqN6ylms4o4yfCjgHavy3h6Q72suXX3Fid3b/HP+J+/0nr8\nWhQFYOMXsLQe22l/15du/11WFbKUeBshSFXZ+PM0TW3yLjmtVouo7RE0HBbzmc1xFBWL1Zzr0RVI\naHdbxKsl8U3MYrmmqgrLXKwF4/FkZ9Pe5iRsF9+WEbnlIux+sJsJSFVVm5GkhcDYSYTNndjmMIzH\nY6rKdvq3vYD53GY71nXN5eWlVW16Hlma0Wnb181mM6bTKdfX1ywWi52fY1u4toCV27dv74JoQbBI\nFiyXa6ihLDVKefQ6fQ6PTjk5ucWgd8BiMWE6WfLpJ6+4fjOnN4R0nbKKU4YDm3Rd1Rmr1ZI0KTHG\noTdosbe/T57WOCrg/OKCsqzodNoURYKhJGr0doGznjLklWQ6XTGbxgghCSMfQ06320A5UNU1QiiU\nCFCqgauaOCKi0iFVDnUtyUyKUAucwMMLLXtgPJ/z+uwFZ2dnFEXBaHSDF7h0em3iZMloOqHVaqHr\nmtl8QaUNzchBaEGe2WBfxw2IohA/CKgw9A/2oapI4zUXV1dki5jryYir0Q2OsV6cWlu5uef5lt60\nmON1Ihq9CE/5vKNbtD6EDb1ZG0MtBEIYjNH4yn5NUQsoBav5iulohkDSDEJevnjJbLIgni5JVikS\nacVuysJpm0ELo3LanRAtC5K84GY64Wp0g+tJbqs79PeHX3kpfvXuw7/Ha9vYs2dlS6tRytk15pSU\nKEdZuenaWnpb7TZ5UXJ9fUOapDSaEcYpSasVJRmNbsDxnSOObx/R2+9RkvPjn/4pldHcf/iQvaMj\nxvMpnz8953o8Z3Cwx+nt27x8+ZLPPvuMx48fk2UZw+GQk5MT9vb2OD09ZX9/n9lsxmKx2DERO50O\nd+/epdez+LCrq0t+//d/n729PZ49e8Ynn3zCwcEBv/d7v8fp6SnPnj3j7du3DIdDtNbMZjObRakU\nP//5z9HGcPeeTZa6e/cuWmuePn3Kzc0N19fXfPLJJ7x+/Zo8z5lMJozHY4wxnJyccHx8vLNVf/93\nvs/f+8Hfo9cZ0mx2KAvNweEJ3/nO93n/4TdJk4KLm2sWyxWvXl3w9JM58RXUpeTp01f8yQ9/TLPR\nwXUdnj79S6LI5+69U+7cuU2n1dqMLV3u3r9DWRTMpgsODvapas1qlXF6eodvffzb9PpDXK/BeLzg\n7ZtzGg1bIP1A8Z3vfURRZNy7d48HDx4SeB2icICjWggTgQnttMNv0Gl3KHTKPJlQywK8itl6zM9+\n8TP+rz/8P/nJz37MfD1jmSy5vLnEj0K+97vf5+57D7i4uebt5QVeFBK0GuztH9Ad9KilldQ3O23u\nPniP/dNjHN/HAPcfPuT07h0eP33CD3/0I7LCTkjWecovPv2U+XzBwf4et2/dQgjJ8+fPefP6DVIq\nut3ubkpkjLGg1U3PKMsy1ptjZxwv6XS6uMKFEtIk5Ze/+Iwf/fBHTK7HBCrgX//hv+HzX/4KZRSD\n7gBXekxGMy7fXhDP7Si42+vzze/d5eDWkM/+8hF/8clf0Ov3ePD+Q6aLOX/64x9/5fX4tdkpbEU4\nqBpTWy6A3JyrpJIbsIhAbNBWVWXj1recOkcpNAVGWLioxtKTakqKqsT1PN7/4H3enL3m+ctnCKG4\nc/8eRVkzmcxYrTM6rXCXnDSfz+l0OlZ/EAQslzY4drlc7nYM26bharXaWbHfvn3LaDym0WhSFAXd\nbpf9/X22qUGe5zEcDsnznCdPnpBlNgbv/fffp9frMZlMWMxtHFyj2dj93VVV4fv+jgy98/VvZuLb\nIwjY/kyj2QBj8IOA9957iK417XaHs/5rbt++S6vVIgwibq5vePb4KfPZhMMHAk8KQj9guUzY3+uy\nv3/Ayekx06VivbbaC0GAJCRZF5y9veaP//jf8ub1GQcHh8znMcJIpHC4uR7TajVR0sPUJUW+oq5q\nvFDhBQKlLG05CELAAe1jCKGOoG6ADAEXowVG1xhdIJWm1W3S6oZc3Zzx6NEj5osxDx7epShK5vMl\naZ6iPAXS4Dgu3V6Xw6MjkiS1o2wEWV3iSsHx8QnCc/BaDWph0FWF47ncfe8eg26f0c0NtYBGt83B\n0SGR4+NIxXKxwHEVRVFSy4R1nVN7gixNybLc3q8bOpIxZiNeMpvdwzuXgDIp0EWNMIK60Cjh4Ds+\nVVYxvply6/A20gioBUVeUuc1jlFoBGVWsarWuGFNWdX4UUi33+P0zm36e0O8KKTKbEP1q15/G+DW\nV0AM1EBljPkdIUQf+BfAXSx96T/5dxKdpcB1HHANihobJiKQyt1ZUKUjkRuPerpRE4LFVrm+R0WB\nMDatVuJQm5rS1JQ6x3U9ens9Lq6vOD+/oBHZROp77z2g2RkjlSTdWFy3QqAsy+j3+zQajV1S9bZP\nsB0hCiF21T8MQ66vrxlPbnbe++FwyPHx8SYNKSaKIvr9Po8fP6YoCo6Pj/noo4/48MMPOTg4YLVa\n8YtPfkH8q5hG1CCO452GIQxDOp3ObsqwWCwIgoD9/f1dFP1qteL09JTh3hCJQEloDvqbZO02Lh77\ngwGeE1FVhulkydnbC4qi5uOPPqbX6vDk8a948yrh5ChAScst6A86xG5OllYYrShzQZYnTGdTHj9+\nTBS1GA6GLBYLewSQhuurEZ1Oh6OjPbTJSbMVlbajvmbbxXUVy+UKgUOz2UWYJlK3UXSR2ImDEc4G\nYFJTljG9foOwYxjNrnjx8jnnV2fgwKA3YBWvGE1GpIWNcZ/HC5rNFm7g0+y0UK5nxWquS1CsCZTB\nazVRoU8lDAU1OJLIb/Lg4UMcpSg/sQKpsBHSGfTZ6/TwXZfpeMJ4em2nVwXE65jC0bQXG0ZEWeL5\nCr2hMyEMwmgEGqGtz0dsThfJKqWsKoQWpHGGzjVKK9ZxymV+yd5gSJ1rVos1WZKhK43v+GgBeVaw\nStf4mdV2uJ5Pu9Om2+/T6XXxQo+kSMnr8iuv6b+tncJ/ZIwZv/PxPwH+yBjz3wgh/snm4//qN33y\nF9JfH5DoOkNXJUILXKWQm5gt6Xk4G0nvbD5jFa8AQRhGhGFASYYUVlIqdYUGamoqU2JqQZqndIdd\nkiJjNl0iHcXp3du89/43uLi64uz1GacnJ7TarV0602Qy2cmIAXzf/xKLPwiCXYGIogiwxCdvw9ob\nDAbs71tY6BYH1263bdCMUty/f59vfetbtDex9oeHh8zvzLkZ37DOVyyXS0smkpJer7fD328FVFLK\nHRzl/Pyc2WxGp9Ph9PQUjCHOLKsPYahrQ1nU5FlF7pVUZWmfpJ0BYdDg9skteq02jz9/TLoSFIVh\nMV/w6PNHFEzYG5zQbncpClgt55sRa8XR0SF7+8fUZc1oPKLVChHCYT5fMJ3OODo+wBhr7pKqHVOE\ntgAAIABJREFUIGq0abVDXLciy1KioIunOkjTodYtWxRMC0yIMaDrCq1zsjzlaK/NupzzF5/+ObPZ\nlO6wS7PRYhmvma+m1EKjPMUqXXF5c0kzSVklCUYKwmZEt9un2x/gJXN0uaYUgrwuqeoao6DRsu9l\na5OtkZQFynXB2ySXKUlvf2h9EVlMXtmsjizLSExBvFrZCViSUKsNu4PaWv6NvSPZFAW7i7D3pdaS\nuqpYzObkSY4wkmydsZ6sORoeUoqaLM0p85LQD2mETdCC9XLNahVbLoQukXXNOk1J8oysLMGTVkLt\nqN+w+v769e/r+PCPgf9w89//E/Bv+H8pCmC3vH7gApI8s4xEoa1j0vV8VkVB0LZQ1sVqxWQ8saM5\n2EmfM5MiN2+2FIraQKnrTTy3Jk5iWp0Ox8oBcQlC0e5YRHrQbOAolzCPCBvhbsfw7NkzsizD930O\nDg4YDAas1+tdkWi32zttxdHREf1ej+vRNY6yKsZut0ur1WI6nbJYLOzWvtHg5OQErTV7e3s0m80d\nLk4qietZXUZSWPzWfD6n3W7jed6OM/AuQ6HRaCCl3MzKV8RxjDYGNsKmap1TlyWjmzE3NyMcoWgE\nEVIIGo02vcEQ34uoa8NqtaYsNc2Wod0KyDPNj/7kh7y8+Au+9fEPOD2+g+u0iBc5q1VKEPjcvnOb\nw/0TxuOJ9aZ4IZ4nSbM1eV5QZCV1rXAcQdiAqOHSaDgb74dCygBHtZC6D7QQpo/QTZAuUFKblMok\n1GZFWmhuZpdc3pwhpWKw36fT6bJ+mZLkKXGypNNv2+SrIsesYypT4W34nSent7j74AFiccPl25e8\nujjnajwCV9HdH9Jqt9EYpos5N1fXLFcxbuBTZyWXo2ukgUGvT29vQPgmRKcaXMeOLyt26tLFYoEq\nEqJmgFR2YmU1ixtsm9l6IQx5VuMIn2SVMLoesYrXCCPRhSZZJay8hLrQmMoQuD69dp9ed4AEll6M\n67ikYk5S5GTZBgTUivBbPoO9PrUwmwfuV7v+NoqCAf5ACGGA/36Dbj94h+h8hc2b/NL1bu5D1IlQ\n0kJZMYJKJRSbouAoB8/zKfIVkbANxzRNd4rAthdsZMABa7Oi0uVOEVZrqHRtiT3CQTiC5WpBVcHx\nrWN8r4FyXZarFcP9PQ73T1idLfEDG/bx+vVrXr9+vWsEHh0d0Wq1vgRS3SLn67pmOBzSanc2Rxi7\nWLd03iSxTMKqquh2u3z88cdcXl6yWq12ScvNZpPlYsnFxQWL5QJtNMk6IU1T2u32lwROQogdhKXZ\nbO7yC7fTjlW8QklLHnZRVHVBlhUkaUpZ2nToRqNJukoYjyYYg8XmFxVCKB6+f8Tdu3dAOIwnY168\nvCLP/oyXg9ccH93jYHib45NDHNmgLAStZoeyrOl2exYt1/ARUuO6HsvlGqGg1W7gOYIwkvi+QLke\nvtOhEfZRsoXQbRzRwRFtpPBBCxt5Rkldr1FuzpPnj1lkF+wf72G0JslWSFcRNQI6vTafP37Mw2+8\nz/3777OKU1w3AARFXqOUQ6fXZf/giFW1Ii9yRqMRZ5fnhJ0Wnf0Bru+RlyUXV5c8f/qM0XiM4yik\nIxlNp0gNjTCi02oThAFIkL5PqgtEYf00WZYxmUwoTMWdB7esGKuuMfz1nQKAqTTChfVqxeXZFfPJ\ngmZodyxKKOazBY5xaEVNup0e+4N92q0OdVnjGg+J5E0xI8tzlsuYvMxo9FoMD/qEzRAvssyPr3r9\nbRSFf2iMORdC7AN/KIT41bt/aIwxm4LBX/n9Xe5D/7hn5OYmxwgSKah3OwXrOKvWVh0nhSLPC+aL\nBev1mrYX2tSeIKDOK7Sj0NpyrrQGbSxHD2FTra+ur9C15M6dOxvtfspiuaTb73O8d8JNLNDYRZxl\n2cY0VAG2gyyEPUKUVblDvdnjQw0YkmTNbD5DgOX1C8F68/Te+iXquubW6Sntdpv1asX52Rl37t7F\n8zzb+Hv2zBqkQscWACyXEsOmkZURbXIFt41QP/AZDAY7P8ZkOiHwXHADAsfFc2vCMCTwQwSCJElR\nUvL08ROePn5CFIWMrmJmN2OiMOLO7Tv0+n3GoxHNZouPP75PEutd1kAYhRweHOCoJqu4AC1pRCGH\nhweUZUG702A47JCm9v0IQ5/+oINu+XiBQioIA59B74jA3UfRABMhZRspPOQGu1PVmrouKOs1SlW8\nevkc4yS8/96HpGnC5cU109mcRtSk1WrsxFR37txmdDMjCCKU4zKf2hj5t2dnoDz+H+reJMa2bL3z\n+q21+/Z00d4+b+br/Vz2a0xjMTIqVSGEYIJgAgLEjBkjGDCgJMQAxJABYkgjD3DZQgUjCkpWYRcl\nePme38uX7c17b8SNiBMRp939XnstBuuckzefn+2ssmW92tLJExE3Ykfk3mt9+2v+zfrlh7x48YLF\ncrXTuLRS/U3TUnfW0fvi4oKrN28IhCVI9XWDMTBozWa73YnlhPhxhAgdvGqLF0eovufu9o5lueb0\n/IQg8FGqx5XC9hQGW+IKY3EoAovHqbYVV1fXbFZrsjAjcANEItncbgiSEQ8fPOLRg8dMRmPQsFlu\nKb0Kz/doq96uSQFyL+rruVYUSPxyduafdfylg4Ix5nL3PhdC/B7wW8DN3v9BCHEOzP/ccwDS9xHC\nYXeJMINmUJbk4gc+Xd+BI3ECD2UU23JDVRbo8Ygw8ImjENO0wE55RAy7ufCAMRZnf3l5SV03qF7w\n6tUr6lpxfHTK2ckRRktubm4QQrBZrbmd31JVFUmSkaU5k+kU1Q+URU2SpBhtqcxGQ101O3Weivvb\nJZ9+8hl4A2Ec0ypFt91SNc2OWHPP9XzOtix49/m7lgsBdErx+uKC93/8Pov7BXEW26e6GVAY5M6V\nalMWFKViNA4ZT0ZEUUwQBEzHU5rzZicEo1gtFiRhjJMoZBLgSYcw8nFdh6ZtuLm54eba8Ed//EfU\nRcm/8rf/Fi+CkOvLS4LQI05CVuslH//8Bc++mfHdH36T26stbWs5JZ+8+Jjb2w3j/IwoGpNnU0bT\nI46anpuba6JkxLNnj3hz+YoPPvwpQno8ejjCDFDWBQMVTiA5Os0QKqbeSszg44nUqkbtLPSkTPC8\nBGUC1NDiuZJOKPquIgg8wjjg1ecXfLb9jDDI+O53v8vZ2TnDoPGDkMl0huv43N9u+PTTz7m8fINw\n/m/M5g1yqPHTlNnJEaPjGdoRzFf3dKrn6PiYsm948eY1p+MZD49OCD2PWTYijEPuVvdIRzAaZfhZ\ngokkKhCY0EExsFqveXn1mu/9C98nSEKU6XbEOvuAYbBygJ50CIMY3w2pqpq7q3uaqkE8EGRJhogl\n9xcLglHIs8fP+cZ7XycKIha3C7b3JapVDK2mKlr6HXZhNJry4PQBJyfnZNmItmto2+or7+m/rENU\nAsidwWwC/E3gPwf+APh3gf9y9/77f955JKDrknx6BJ7D6mYAV+BGPm3X4nc9gfTwlICyY9h2iFbg\ny5DAS1DGoWwUg3LpO7njooMx9mPPdUDbGxVHCY4UbDYrjBHMpmOyPKSqa9argpEc07QNTddYO7WR\nLQHSJMGIgaYrMWJgMB3C0XSqoulLqnbDze3ljiOhSP2Q49GYNLRov64o6cqK0HVZFyUvP/mU54+e\nMD0+tmOpsuL26orF9dzKsDkem+0tsuk5SjJSz4e2wzMGTzh0dYfQhjxNQUCnOoQnSUcZQgrKtiFI\nQlzHpysdZOjjy5TAS6iKhrvtnNVyyf3dHaezGa7jEng+42zMdDKFweHudsH9fMvjp2MSOUXkE4bB\nsFpuWc5X3F9uGOdLppNjmskR49GENNCYSUgeCVxTMbQrhmaD7iS0E8RwhKNifN8lksekzrsMOmUQ\noR1Bms7ap3sghKZpV6y2L2j6K8JIE4cebeFyf7e2Uv3CQ3WKxe2C2ZHHr33ru3iupNiuMUbS1lsI\nQrSuubu94Gc/ex+kJOlb8jDg1ItI/YRxmBPKCNUY5CAoL+/p7gqCTuB3EIuAKMvIooShGdgutiS+\ni+eEpEGOH8RIN6Dsa7q6R9SaqHXp7gpW2npEjPbjWdXheg4ugrZpqIoK5VlkauCEGGnoupaiKQi9\nkMnJmOnphMlpTjKLMIOhFhVbtWTTLyn6LY0p8DJBno85f3TM5MGIdBoRxA49BtP+9XlJngK/t0P0\nucD/aIz534UQ/w/wu0KI/wB4Cfybf95JfCFory959OvfxvV9Pvn0J+hEEh3l3Cxv0b7kLJ4Rl4L6\nbol403Aqj4nPEk5n52wrwZv5iiHOaXuBkg5yx9hzXQfPdwh8kNJjGAaCQDAaTQnDgG1xzcuLLXme\nMp7miAK8xCHOA1od0LYN0tFotwO3Q2nFqrwnzWKiOKJUd2inxo06Lm8+JBml/OYPvo5cKE7ChEB6\nbDcFdy9eUtzMefrkMf7pA168eMH956/IjGQ8GdNtlzhVwzuzY4qqYlisScsO2Wp+/VvfxI8jetXz\n7vEZ5cmSi5t7tosF7z1/TlFueTO/oupbpkczHM+jMIpx7pDnD1GLY8zQEpick9GWm/IF1ark7s01\nTx8+4MHJKX/8D/6Q7ark2cN3efroXdb3W26rhl//+m/z7uwBwysXZwBfGhL/hIdnA8PQ03Q1olzw\ns5//Y/q+5Ye/9T2++fwpfbfm8x+/z/X1JSM0TnHL8sUTYu9fJM8cpsk5mZkyzM8QEnKh0X5H0y7o\nzQrhdCi94OrmJ3z86ftoSp4/f4jucnSdUg2KcmlLpUCmPD57QpqmNMWW15s1SRLjOi6L+efkoxFR\nMJClLWlc8ez5M7ylR7voEIWhv6kJAsPDZERIyMDAH/zd36e7uuKH469Z0521JEkDTN1zt93Sblvi\nSYKufdw4IE8mJGHKXXXLcntPqHMej6fc/sMXfLC44/zxA37wz38PN3RZbgtmR3bUfTt/watXl0RR\njIfLs689ZLFcUjYFd8tbkjTht37rh4wnE/qs4qL5nKoquSqvuNSXLIIlVVoQJYbRdMTJ8QmjUUwb\nrLjafkaiEzvlcNRX3tR/qaBgjPkM+Bu/5Ov3wO981fP4gY8fBPz0Zx9YgEiccLtY0F31nJ6f8fz5\nc9bFlrbv2JQFfhjw7teeMz06Ik4S7m7vKcoNWZ4cVHXtS9P3GugPmgtfuPk01LVH23aofqCua8aZ\n5un0jKubS66u31BVJZPJhOPjY4QQLBb3bLcb4tjW5VZk2gUcjJG4jovqIUhinn79lP/j//z7LJcr\nFotbrq9LhgGKquL8/JSz83PCKKKoSral7Tms1+tDA9P3XDQgfBc3ChlNJ2hjcKuSMA7JsoC26/nk\n00958PQxGguQGXqF6no2RQl01LGP3qyIUocochmNUnz3MaM85NHjM/qmRrUdeZ7R1eqtexKSZpmF\nY3cuLz9rrPdF39J1DU1T07T2JR3N69ev6LuWtp4wv5JMZ2O0PuF4MiUMYzxf8cMf/Ku89873WS4U\nTaOpyo7t5p5BGYSEtisYjUMenT3jfvmaDz6+YrUqefz4PYTTcnHxGeePzwmTjA8/+BPaHbx7Npta\nq7ym4fb21sqz7+wFi6LAGEPTNLx5c02a5IzHU1zj0pgdyElAWVcs1kvEZsetQWMcQd3Z0XFgQpQe\nDgCkXvUc76DDf/LTP6FuaqI4JBtn+FFAWVfcLe+4vr/i7v6Oj15+zPs//xFaWFHgswdWXauua8aj\nI8bjKQCz2dFB1n9PfPNcH3Zl6n6tamUYZWPy1PpMLttTXl++5vOfvD6Mwe8/X6EGxaOHD3n2zjtf\neV//SiAa97Dc2/mcOLXpuh40nWoO4iK9Hlher7m+vmaxWZNPRgfOgeM6thnZW9deBAczjrftu/ZI\nwLfdlOwh8HoHaVw27hqtB8Iw2ElxG+q6BGAY1E4MBjBy92LXRRYYrIryoA2bqqTuWlzfZTSdobRg\nWxTUnWVnniYxOJKm72ibhsVqeZhO+J6H0oq6LOiHgbKuCOrIglW6lq7v6XaW5RiDBBzXw0iI/ACl\nB1zpWJlvo3FcB9eTeL5L4PowRAxJgtY9cRgg1EDfDGi9pKpq6roF45AlY84fPCD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HAAAg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7lru7Ozbbf4kGs3+L78N/AfwHwH3/Zf+5MeZ/6//vPwP+faAD/hNjzP/+dz6KRzfo47ehhw1/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\nY5mfn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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ - "26.50% : orangutan\n", - " 9.93% : spider_monkey\n", - " 4.35% : siamang\n", - " 3.27% : howler_monkey\n", - " 2.88% : capuchin\n" + "26.75% : spoonbill\n", + " 7.06% : black_stork\n", + " 7.04% : wooden_spoon\n", + " 4.21% : limpkin\n", + " 3.72% : paddle\n" ] } ], @@ -1095,7 +1122,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 38, "metadata": { "scrolled": true }, @@ -1104,10 +1131,11 @@ "name": "stdout", "output_type": "stream", "text": [ + "Model: \"vgg16\"\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", - "input_1 (InputLayer) (None, 224, 224, 3) 0 \n", + "input_1 (InputLayer) [(None, 224, 224, 3)] 0 \n", "_________________________________________________________________\n", "block1_conv1 (Conv2D) (None, 224, 224, 64) 1792 \n", "_________________________________________________________________\n", @@ -1173,7 +1201,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -1191,16 +1219,16 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 39, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -1218,7 +1246,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -1235,7 +1263,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 42, "metadata": { "scrolled": true }, @@ -1273,7 +1301,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -1289,7 +1317,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -1305,7 +1333,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -1321,7 +1349,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -1339,7 +1367,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 47, "metadata": { "scrolled": true }, @@ -1383,7 +1411,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -1392,7 +1420,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 49, "metadata": {}, "outputs": [], "source": [ @@ -1402,7 +1430,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 50, "metadata": {}, "outputs": [ { @@ -1444,7 +1472,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -1462,7 +1490,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ @@ -1479,7 +1507,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 53, "metadata": { "scrolled": true }, @@ -1488,56 +1516,65 @@ "name": "stdout", "output_type": "stream", "text": [ + "WARNING:tensorflow:sample_weight modes were coerced from\n", + " ...\n", + " to \n", + " ['...']\n", + "WARNING:tensorflow:sample_weight modes were coerced from\n", + " ...\n", + " to \n", + " ['...']\n", + "Train for 100 steps, validate for 26.5 steps\n", "Epoch 1/20\n", - "100/100 [==============================] - 20s - loss: 1.0910 - categorical_accuracy: 0.4575 - val_loss: 0.8024 - val_categorical_accuracy: 0.7472\n", + "100/100 [==============================] - 32s 322ms/step - loss: 1.1149 - categorical_accuracy: 0.4585 - val_loss: 0.8945 - val_categorical_accuracy: 0.5604\n", "Epoch 2/20\n", - "100/100 [==============================] - 22s - loss: 0.9378 - categorical_accuracy: 0.5600 - val_loss: 0.7077 - val_categorical_accuracy: 0.7566\n", + "100/100 [==============================] - 31s 309ms/step - loss: 0.9385 - categorical_accuracy: 0.5432 - val_loss: 0.7380 - val_categorical_accuracy: 0.7717\n", "Epoch 3/20\n", - "100/100 [==============================] - 19s - loss: 0.8551 - categorical_accuracy: 0.6130 - val_loss: 0.6477 - val_categorical_accuracy: 0.7717\n", + "100/100 [==============================] - 29s 285ms/step - loss: 0.8408 - categorical_accuracy: 0.6186 - val_loss: 0.6924 - val_categorical_accuracy: 0.7321\n", "Epoch 4/20\n", - "100/100 [==============================] - 19s - loss: 0.7747 - categorical_accuracy: 0.6410 - val_loss: 0.7183 - val_categorical_accuracy: 0.6547\n", + "100/100 [==============================] - 30s 299ms/step - loss: 0.7855 - categorical_accuracy: 0.6432 - val_loss: 0.6994 - val_categorical_accuracy: 0.7057\n", "Epoch 5/20\n", - "100/100 [==============================] - 19s - loss: 0.7438 - categorical_accuracy: 0.6645 - val_loss: 0.5706 - val_categorical_accuracy: 0.8113\n", + "100/100 [==============================] - 29s 294ms/step - loss: 0.7128 - categorical_accuracy: 0.6879 - val_loss: 0.7109 - val_categorical_accuracy: 0.6698\n", "Epoch 6/20\n", - "100/100 [==============================] - 19s - loss: 0.6836 - categorical_accuracy: 0.7040 - val_loss: 0.5912 - val_categorical_accuracy: 0.7962\n", + "100/100 [==============================] - 29s 286ms/step - loss: 0.6945 - categorical_accuracy: 0.7015 - val_loss: 0.6150 - val_categorical_accuracy: 0.7604\n", "Epoch 7/20\n", - "100/100 [==============================] - 19s - loss: 0.6527 - categorical_accuracy: 0.7130 - val_loss: 0.5509 - val_categorical_accuracy: 0.8094\n", + "100/100 [==============================] - 28s 282ms/step - loss: 0.6910 - categorical_accuracy: 0.7060 - val_loss: 0.6316 - val_categorical_accuracy: 0.7321\n", "Epoch 8/20\n", - "100/100 [==============================] - 19s - loss: 0.6310 - categorical_accuracy: 0.7275 - val_loss: 0.6414 - val_categorical_accuracy: 0.7038\n", + "100/100 [==============================] - 28s 284ms/step - loss: 0.6269 - categorical_accuracy: 0.7382 - val_loss: 0.5828 - val_categorical_accuracy: 0.7868\n", "Epoch 9/20\n", - "100/100 [==============================] - 19s - loss: 0.6072 - categorical_accuracy: 0.7455 - val_loss: 0.6630 - val_categorical_accuracy: 0.6887\n", + "100/100 [==============================] - 30s 300ms/step - loss: 0.6180 - categorical_accuracy: 0.7362 - val_loss: 0.6337 - val_categorical_accuracy: 0.7377\n", "Epoch 10/20\n", - "100/100 [==============================] - 19s - loss: 0.5986 - categorical_accuracy: 0.7525 - val_loss: 0.6142 - val_categorical_accuracy: 0.7340\n", + "100/100 [==============================] - 30s 297ms/step - loss: 0.5823 - categorical_accuracy: 0.7568 - val_loss: 0.5569 - val_categorical_accuracy: 0.7868\n", "Epoch 11/20\n", - "100/100 [==============================] - 19s - loss: 0.5831 - categorical_accuracy: 0.7525 - val_loss: 0.5202 - val_categorical_accuracy: 0.8057\n", + "100/100 [==============================] - 30s 295ms/step - loss: 0.5969 - categorical_accuracy: 0.7455 - val_loss: 0.6298 - val_categorical_accuracy: 0.7340\n", "Epoch 12/20\n", - "100/100 [==============================] - 19s - loss: 0.5747 - categorical_accuracy: 0.7480 - val_loss: 0.5289 - val_categorical_accuracy: 0.7943\n", + "100/100 [==============================] - 29s 289ms/step - loss: 0.5516 - categorical_accuracy: 0.7704 - val_loss: 0.5804 - val_categorical_accuracy: 0.7566\n", "Epoch 13/20\n", - "100/100 [==============================] - 19s - loss: 0.5735 - categorical_accuracy: 0.7570 - val_loss: 0.6357 - val_categorical_accuracy: 0.6981\n", + "100/100 [==============================] - 30s 297ms/step - loss: 0.5514 - categorical_accuracy: 0.7770 - val_loss: 0.5879 - val_categorical_accuracy: 0.7453\n", "Epoch 14/20\n", - "100/100 [==============================] - 19s - loss: 0.5377 - categorical_accuracy: 0.7760 - val_loss: 0.5130 - val_categorical_accuracy: 0.8113\n", + "100/100 [==============================] - 33s 326ms/step - loss: 0.5239 - categorical_accuracy: 0.7830 - val_loss: 0.5448 - val_categorical_accuracy: 0.7849\n", "Epoch 15/20\n", - "100/100 [==============================] - 19s - loss: 0.5507 - categorical_accuracy: 0.7740 - val_loss: 0.6038 - val_categorical_accuracy: 0.7340\n", + "100/100 [==============================] - 32s 325ms/step - loss: 0.5367 - categorical_accuracy: 0.7760 - val_loss: 0.6596 - val_categorical_accuracy: 0.7226\n", "Epoch 16/20\n", - "100/100 [==============================] - 19s - loss: 0.5228 - categorical_accuracy: 0.7865 - val_loss: 0.5141 - val_categorical_accuracy: 0.7943\n", + "100/100 [==============================] - 28s 282ms/step - loss: 0.5155 - categorical_accuracy: 0.7860 - val_loss: 0.5385 - val_categorical_accuracy: 0.7755\n", "Epoch 17/20\n", - "100/100 [==============================] - 19s - loss: 0.5058 - categorical_accuracy: 0.7855 - val_loss: 0.5561 - val_categorical_accuracy: 0.7698\n", + "100/100 [==============================] - 29s 289ms/step - loss: 0.5058 - categorical_accuracy: 0.7889 - val_loss: 0.6200 - val_categorical_accuracy: 0.7340\n", "Epoch 18/20\n", - "100/100 [==============================] - 19s - loss: 0.4775 - categorical_accuracy: 0.8080 - val_loss: 0.4904 - val_categorical_accuracy: 0.8057\n", + "100/100 [==============================] - 28s 283ms/step - loss: 0.4925 - categorical_accuracy: 0.8030 - val_loss: 0.6469 - val_categorical_accuracy: 0.7151\n", "Epoch 19/20\n", - "100/100 [==============================] - 19s - loss: 0.5360 - categorical_accuracy: 0.7755 - val_loss: 0.6344 - val_categorical_accuracy: 0.7189\n", + "100/100 [==============================] - 31s 312ms/step - loss: 0.4681 - categorical_accuracy: 0.8145 - val_loss: 0.7350 - val_categorical_accuracy: 0.6906\n", "Epoch 20/20\n", - "100/100 [==============================] - 19s - loss: 0.4882 - categorical_accuracy: 0.8100 - val_loss: 0.7323 - val_categorical_accuracy: 0.6660\n" + "100/100 [==============================] - 31s 307ms/step - loss: 0.4743 - categorical_accuracy: 0.8045 - val_loss: 0.5995 - val_categorical_accuracy: 0.7377\n" ] } ], "source": [ - "history = new_model.fit_generator(generator=generator_train,\n", - " epochs=epochs,\n", - " steps_per_epoch=steps_per_epoch,\n", - " class_weight=class_weight,\n", - " validation_data=generator_test,\n", - " validation_steps=steps_test)" + "history = new_model.fit(x=generator_train,\n", + " epochs=epochs,\n", + " steps_per_epoch=steps_per_epoch,\n", + " class_weight=class_weight,\n", + " validation_data=generator_test,\n", + " validation_steps=steps_test)" ] }, { @@ -1549,19 +1586,21 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 54, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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NgwZUWxQq8wlwi//9acBPgBRXb5mUe3nm7rvdBeGVovGCWbP0sIHFVCQnx1nd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\n67o2a+aSaAa2005z4haZmbA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lqgeAUcBVkQoYDcxqN4xiiPeAboRPPnHPSplkhKPcmwDr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\n", 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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1578,18 +1617,30 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 55, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:sample_weight modes were coerced from\n", + " ...\n", + " to \n", + " ['...']\n", + "27/26 [==============================] - 4s 156ms/step - loss: 0.5888 - categorical_accuracy: 0.7377\n" + ] + } + ], "source": [ - "result = new_model.evaluate_generator(generator_test, steps=steps_test)" + "result = new_model.evaluate(generator_test, steps=steps_test)" ] }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 56, "metadata": { "scrolled": true }, @@ -1598,7 +1649,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Test-set classification accuracy: 66.60%\n" + "Test-set classification accuracy: 73.77%\n" ] } ], @@ -1617,14 +1668,14 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 57, "metadata": {}, "outputs": [ { "data": { - "image/png": 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jI4oyp7vWJ0gT8rwgSSOyWuZnv/BFPN1iRc7VecrFYoGiSDimSVmajfBCVXF5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IJNvF7x8vSMd955h9FgwCoKkf++DjtREvE8rwnJTVNEqQntyLLGYDMvcnq9PoIA682a5yfPifOk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\n", 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" ] }, "metadata": {}, @@ -1635,9 +1686,9 @@ "output_type": "stream", "text": [ "Confusion matrix:\n", - "[[151 0 0]\n", - " [102 32 3]\n", - " [ 71 1 170]]\n", + "[[137 9 5]\n", + " [ 56 81 0]\n", + " [ 56 13 173]]\n", "(0) forky\n", "(1) knifey\n", "(2) spoony\n" @@ -1663,7 +1714,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 58, "metadata": {}, "outputs": [], "source": [ @@ -1679,7 +1730,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 59, "metadata": {}, "outputs": [], "source": [ @@ -1700,7 +1751,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 60, "metadata": {}, "outputs": [ { @@ -1742,7 +1793,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 61, "metadata": {}, "outputs": [], "source": [ @@ -1758,7 +1809,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 62, "metadata": {}, "outputs": [], "source": [ @@ -1774,7 +1825,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 63, "metadata": { "scrolled": true }, @@ -1783,56 +1834,65 @@ "name": "stdout", "output_type": "stream", "text": [ + "WARNING:tensorflow:sample_weight modes were coerced from\n", + " ...\n", + " to \n", + " ['...']\n", + "WARNING:tensorflow:sample_weight modes were coerced from\n", + " ...\n", + " to \n", + " ['...']\n", + "Train for 100 steps, validate for 26.5 steps\n", "Epoch 1/20\n", - "100/100 [==============================] - 28s - loss: 0.4756 - categorical_accuracy: 0.8065 - val_loss: 0.5877 - val_categorical_accuracy: 0.7340\n", + "100/100 [==============================] - 34s 339ms/step - loss: 0.4605 - categorical_accuracy: 0.8211 - val_loss: 0.5776 - val_categorical_accuracy: 0.7566\n", "Epoch 2/20\n", - "100/100 [==============================] - 27s - loss: 0.4781 - categorical_accuracy: 0.8035 - val_loss: 0.5577 - val_categorical_accuracy: 0.7717\n", + "100/100 [==============================] - 38s 384ms/step - loss: 0.4683 - categorical_accuracy: 0.8175 - val_loss: 0.5600 - val_categorical_accuracy: 0.7604\n", "Epoch 3/20\n", - "100/100 [==============================] - 27s - loss: 0.4530 - categorical_accuracy: 0.8150 - val_loss: 0.5464 - val_categorical_accuracy: 0.7774\n", + "100/100 [==============================] - 36s 357ms/step - loss: 0.4643 - categorical_accuracy: 0.8095 - val_loss: 0.5748 - val_categorical_accuracy: 0.7528\n", "Epoch 4/20\n", - "100/100 [==============================] - 27s - loss: 0.4440 - categorical_accuracy: 0.8275 - val_loss: 0.5442 - val_categorical_accuracy: 0.7811\n", + "100/100 [==============================] - 37s 371ms/step - loss: 0.4368 - categorical_accuracy: 0.8236 - val_loss: 0.5613 - val_categorical_accuracy: 0.7604\n", "Epoch 5/20\n", - "100/100 [==============================] - 27s - loss: 0.4463 - categorical_accuracy: 0.8345 - val_loss: 0.5536 - val_categorical_accuracy: 0.7811\n", + "100/100 [==============================] - 37s 367ms/step - loss: 0.4140 - categorical_accuracy: 0.8317 - val_loss: 0.5490 - val_categorical_accuracy: 0.7642\n", "Epoch 6/20\n", - "100/100 [==============================] - 27s - loss: 0.4446 - categorical_accuracy: 0.8290 - val_loss: 0.5497 - val_categorical_accuracy: 0.7849\n", + "100/100 [==============================] - 44s 439ms/step - loss: 0.4456 - categorical_accuracy: 0.8155 - val_loss: 0.5488 - val_categorical_accuracy: 0.7660\n", "Epoch 7/20\n", - "100/100 [==============================] - 26s - loss: 0.4474 - categorical_accuracy: 0.8150 - val_loss: 0.5345 - val_categorical_accuracy: 0.7868\n", + "100/100 [==============================] - 45s 454ms/step - loss: 0.4318 - categorical_accuracy: 0.8352 - val_loss: 0.5505 - val_categorical_accuracy: 0.7660\n", "Epoch 8/20\n", - "100/100 [==============================] - 27s - loss: 0.4330 - categorical_accuracy: 0.8305 - val_loss: 0.5437 - val_categorical_accuracy: 0.7811\n", + "100/100 [==============================] - 41s 409ms/step - loss: 0.4283 - categorical_accuracy: 0.8265 - val_loss: 0.5580 - val_categorical_accuracy: 0.7604\n", "Epoch 9/20\n", - "100/100 [==============================] - 27s - loss: 0.4136 - categorical_accuracy: 0.8345 - val_loss: 0.5489 - val_categorical_accuracy: 0.7792\n", + "100/100 [==============================] - 43s 427ms/step - loss: 0.4197 - categorical_accuracy: 0.8392 - val_loss: 0.5496 - val_categorical_accuracy: 0.7679\n", "Epoch 10/20\n", - "100/100 [==============================] - 27s - loss: 0.4262 - categorical_accuracy: 0.8330 - val_loss: 0.5403 - val_categorical_accuracy: 0.7849\n", + "100/100 [==============================] - 43s 431ms/step - loss: 0.4138 - categorical_accuracy: 0.8312 - val_loss: 0.5535 - val_categorical_accuracy: 0.7679\n", "Epoch 11/20\n", - "100/100 [==============================] - 27s - loss: 0.4228 - categorical_accuracy: 0.8320 - val_loss: 0.5425 - val_categorical_accuracy: 0.7811\n", + "100/100 [==============================] - 38s 378ms/step - loss: 0.4373 - categorical_accuracy: 0.8332 - val_loss: 0.5449 - val_categorical_accuracy: 0.7679\n", "Epoch 12/20\n", - "100/100 [==============================] - 26s - loss: 0.4026 - categorical_accuracy: 0.8365 - val_loss: 0.5432 - val_categorical_accuracy: 0.7792\n", + "100/100 [==============================] - 25s 252ms/step - loss: 0.4046 - categorical_accuracy: 0.8470 - val_loss: 0.5396 - val_categorical_accuracy: 0.7698\n", "Epoch 13/20\n", - "100/100 [==============================] - 27s - loss: 0.4248 - categorical_accuracy: 0.8280 - val_loss: 0.5269 - val_categorical_accuracy: 0.7943\n", + "100/100 [==============================] - 23s 234ms/step - loss: 0.4014 - categorical_accuracy: 0.8442 - val_loss: 0.5400 - val_categorical_accuracy: 0.7717\n", "Epoch 14/20\n", - "100/100 [==============================] - 26s - loss: 0.4297 - categorical_accuracy: 0.8305 - val_loss: 0.5288 - val_categorical_accuracy: 0.7925\n", + "100/100 [==============================] - 24s 237ms/step - loss: 0.4141 - categorical_accuracy: 0.8365 - val_loss: 0.5473 - val_categorical_accuracy: 0.7679\n", "Epoch 15/20\n", - "100/100 [==============================] - 26s - loss: 0.3989 - categorical_accuracy: 0.8415 - val_loss: 0.5270 - val_categorical_accuracy: 0.7925\n", + "100/100 [==============================] - 23s 231ms/step - loss: 0.4117 - categorical_accuracy: 0.8307 - val_loss: 0.5436 - val_categorical_accuracy: 0.7698\n", "Epoch 16/20\n", - "100/100 [==============================] - 26s - loss: 0.3801 - categorical_accuracy: 0.8430 - val_loss: 0.5251 - val_categorical_accuracy: 0.7925\n", + "100/100 [==============================] - 23s 229ms/step - loss: 0.3826 - categorical_accuracy: 0.8472 - val_loss: 0.5549 - val_categorical_accuracy: 0.7623\n", "Epoch 17/20\n", - "100/100 [==============================] - 27s - loss: 0.4224 - categorical_accuracy: 0.8315 - val_loss: 0.5336 - val_categorical_accuracy: 0.7830\n", + "100/100 [==============================] - 23s 228ms/step - loss: 0.3979 - categorical_accuracy: 0.8442 - val_loss: 0.5402 - val_categorical_accuracy: 0.7698\n", "Epoch 18/20\n", - "100/100 [==============================] - 26s - loss: 0.4073 - categorical_accuracy: 0.8340 - val_loss: 0.5246 - val_categorical_accuracy: 0.7906\n", + "100/100 [==============================] - 24s 239ms/step - loss: 0.3941 - categorical_accuracy: 0.8447 - val_loss: 0.5313 - val_categorical_accuracy: 0.7774\n", "Epoch 19/20\n", - "100/100 [==============================] - 27s - loss: 0.3952 - categorical_accuracy: 0.8480 - val_loss: 0.5292 - val_categorical_accuracy: 0.7830\n", + "100/100 [==============================] - 24s 244ms/step - loss: 0.3956 - categorical_accuracy: 0.8385 - val_loss: 0.5407 - val_categorical_accuracy: 0.7698\n", "Epoch 20/20\n", - "100/100 [==============================] - 26s - loss: 0.3984 - categorical_accuracy: 0.8425 - val_loss: 0.5220 - val_categorical_accuracy: 0.7925\n" + "100/100 [==============================] - 24s 240ms/step - loss: 0.4037 - categorical_accuracy: 0.8281 - val_loss: 0.5352 - val_categorical_accuracy: 0.7755\n" ] } ], "source": [ - "history = new_model.fit_generator(generator=generator_train,\n", - " epochs=epochs,\n", - " steps_per_epoch=steps_per_epoch,\n", - " class_weight=class_weight,\n", - " validation_data=generator_test,\n", - " validation_steps=steps_test)" + "history = new_model.fit(x=generator_train,\n", + " epochs=epochs,\n", + " steps_per_epoch=steps_per_epoch,\n", + " class_weight=class_weight,\n", + " validation_data=generator_test,\n", + " validation_steps=steps_test)" ] }, { @@ -1844,17 +1904,19 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 64, "metadata": {}, "outputs": [ { "data": { - "image/png": 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gao9jx9r8eEv2IX8r2717N2PGjKFfv37k5eXx9ttvAzaMwYknnkhubi79+/dn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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1864,23 +1926,35 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 65, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:sample_weight modes were coerced from\n", + " ...\n", + " to \n", + " ['...']\n", + "27/26 [==============================] - 3s 118ms/step - loss: 0.5256 - categorical_accuracy: 0.7755\n" + ] + } + ], "source": [ - "result = new_model.evaluate_generator(generator_test, steps=steps_test)" + "result = new_model.evaluate(generator_test, steps=steps_test)" ] }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 66, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Test-set classification accuracy: 79.25%\n" + "Test-set classification accuracy: 77.55%\n" ] } ], @@ -1899,16 +1973,16 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 67, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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ijI3SHaN6JntbCW/90Q8QlBG9Yo6iS2zcf4x74OPkBQgFZE1GcHKO+vuEcYDt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\n", 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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -1919,9 +1993,9 @@ "output_type": "stream", "text": [ "Confusion matrix:\n", - "[[141 3 7]\n", - " [ 65 70 2]\n", - " [ 32 1 209]]\n", + "[[133 7 11]\n", + " [ 48 88 1]\n", + " [ 40 12 190]]\n", "(0) forky\n", "(1) knifey\n", "(2) spoony\n" @@ -1999,7 +2073,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.1" + "version": "3.6.10" } }, "nbformat": 4, diff --git a/11_Adversarial_Examples.ipynb b/11_Adversarial_Examples.ipynb index 8dc1bbb..2c63ec2 100644 --- a/11_Adversarial_Examples.ipynb +++ b/11_Adversarial_Examples.ipynb @@ -11,6 +11,15 @@ "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## WARNING!\n", + "\n", + "**This tutorial does not work with TensorFlow v.2 and it would take too much effort to update this tutorial to the new API.**" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -50,7 +59,6 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -107,7 +115,6 @@ "cell_type": "code", "execution_count": 3, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -168,9 +175,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -204,9 +209,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "model = inception.Inception()" @@ -689,7 +692,6 @@ "cell_type": "code", "execution_count": 16, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -885,7 +887,6 @@ "cell_type": "code", "execution_count": 17, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -975,7 +976,6 @@ "cell_type": "code", "execution_count": 18, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -1152,7 +1152,6 @@ "cell_type": "code", "execution_count": 19, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -1360,7 +1359,7 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -1374,9 +1373,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.10" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/12_Adversarial_Noise_MNIST.ipynb b/12_Adversarial_Noise_MNIST.ipynb index 6a11e12..0688cfd 100644 --- a/12_Adversarial_Noise_MNIST.ipynb +++ b/12_Adversarial_Noise_MNIST.ipynb @@ -11,6 +11,15 @@ "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## WARNING!\n", + "\n", + "**This tutorial does not work with TensorFlow v.2 and it would take too much effort to update this tutorial to the new API.**" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -43,6 +52,8 @@ "source": [ "The following chart shows roughly how the data flows in the Convolutional Neural Network that is implemented below.\n", "\n", + "![Flowchart](images/12_adversarial_noise_flowchart.png)\n", + "\n", "This example shows an input image with a hand-written 7-digit. The adversarial noise is then added to the image. Red noise-pixels are positive and make the input image darker in those pixels, while blue noise-pixels are negative and make the input lighter in those pixels.\n", "\n", "The noisy image is then fed to the neural network which results in a predicted class-number. In this case the adversarial noise fools the network into believing that the 7-digit shows a 3-digit. The noise is clearly visible to humans, but the 7-digit is still easily identified by a human.\n", @@ -54,31 +65,6 @@ "The two optimization procedures are completely separate. The first procedure only modifies the variables of the neural network, while the second procedure only modifies the adversarial noise." ] }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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OrWrYsh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\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pOIiIjEDfvn3xwQcfAAA0Gg2GDh2KrKwswamIiIiIDEtCQgLy8vJExyAiolJIrVZj4sSJ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\ndOv5sFgsHj9+SZCZmen2F+dBQUHO9yA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ARc2ox02S9u/f\nr1GjRhU4u3TlOzUAAAAAnjF48GBt2rRJklSqVCn997//j707D4uqbP8A/p0BWWQRQUVwSUXcQNwy\nNfHNBRdc2zQXXAiXUlPTTF/RQqEyyyRTcssyxaUoBUHUVMglRVERFJe03EuQRUGRZWZ+f/iDV2TO\nmQPMCt/Pdc1VnOc+z3PPmeXI4dzPsxt169Y1cFbVj9EWiRMREQlZsmQJdu/ejYsXL+Lhw4d45513\nEBMTozZWoVAgMTGx1LaioiLExsZi9OjR+kiXiIiIiMjkPXz4UGOMridhyszMxFdffSUa06FDBxw6\ndAgODg4l21xdXdGuXTv4+fnhlVdewZUrVwT3DwkJQUBAAKytrSucZ9++fbFt2zY4OTmVbJs7dy6+\n/vprwZulgKe/u5w5cwY9evQo09awYUMMHDgQu3fvVrvvtm3bsHz5csEbfDZt2iQ4rqWlJSZMmFBm\nu6kc74rQRYGat7c3vL29S34OCgoSLU7z8/NTe9x1wVReS118doiqAp6DpeM5uHqegwGehyv7evIc\nTEREREREpFtVdZGRgIAABAUFITs7W217WFiYaJH4gQMHkJaWJtguNLmeNnGCRumMYYI5XeTw9ddf\nIyUlRXTML774AmPGjIGlpWXJ9uzsbAQHBwte91EoFJg1axaSk5PVfgeEhYWJXr8q1qpVKwQEBKB7\n9+5wcXGBubk5MjIykJycjLi4OPz8888lq/U9S9eTKhrquAFPr4Wpe87PqlOnDt577z306NEDtWrV\nwu3bt7Fr1y5s2rQJSqVSdF8iIiIRVwSoAAAgAElEQVQiIpJu48aN2LhxY8nPK1asQLt27QyYUfVV\nNa8+UZUjl8slPczMzGBhYQFLS0vRh4WFBczMzCCTySQ9quqFWiJTZWlpie+++67ks7lnzx5s3bpV\nbeylS5eQk5NTZntkZKROcyQiIiIiqkrEZnQvpusVBKOjo9X+276YhYUFduzYUaq45ln169fH5s2b\nRVfsuHfvHuLj4yuco7u7OyIjI0sV2BSbOXMm2rdvL7r/5cuXBdumTJki2JaRkSE4cVZeXh4iIiIE\n933zzTfV5msKx7uiqtt1HlN4LXX52SEydTwHS8Nz8P/wHGxcjP315DmYiIiIiIhI96rq78O2trai\nhdwxMTG4efOmYHt4eLhgm4uLC4YMGVKp/KQwpQkaz58/j5kzZ6Jt27Ylk8t98MEHSE5ORosWLUT3\nDwkJQV5eXoVz7Nu3L65cuYK1a9di5syZmDt3Lg4dOoTQ0FDR/YonmNMGXeTw4MEDLFu2THDfmjVr\nIj4+Hm+//XapQmcAcHBwwPLly/Hf//5XcP8LFy5g+/btZbbn5OQgODhYNG+5XI5PPvkEFy5cwAcf\nfIBu3bqhSZMmaNiwIdq1a4exY8di48aN+Oeff7B06VLUqlWr1P7e3t5YtWpVyWPgwIGi4/n5+ZWK\nf/7Rv3//klhDHTfg6fVYoftEi3l4eODChQv46KOP0KtXL3Ts2BFDhw7Fxo0bsX//flhZWYnuT0RE\nRERE0pw9e7bU5FQTJkwQvb+CdIsriZPRq1mzJpydnUVvgCnWpk0bTJw4Eebm4m9tuVwOT09PODo6\nSs6DFwaIjEu3bt3wzjvvICwsDAAwY8YM9OnTB87OzqXinl9FvNj+/ftRWFiIGjVq6DxXIiIiIiJT\nJ7Q65rMePXoEW1tbneWwf/9+0fZhw4bB3d1dNOall16Ct7c3jhw5IjqOr69vhXIMCQkRXcGxR48e\nSEpKEmzPysoSbPP19UXjxo0Fb6j68ccf8dprr5XZvnPnTtHCJKELs6ZwvCuqqt6QJ8QUXktdfnaI\nTB3PwdLwHFwaz8HGw9hfT56DiYiIiIiIdK8q/z48Y8YMrFixAoWFhWXalEol1qxZg08//bRMW15e\nHnbt2iXY79tvvy14D+jZs2crNGmZj48P6tSpU2pbVZqgsWvXrlCpVGpjiieYq8i1g+IJ5tRdP5g5\ncyZ++OEH0WsHly9fRo8ePco9rj5yiImJEb22MWfOHLRt21Y0t0WLFmHlypV49OiR2vYdO3Zg9OjR\npbbFxsYiIyNDtN/g4GAsWLBANAZ4OlnDvHnzoFAoNMZqi6GOG/D08/LkyRPBfs3NzbF9+3bUq1dP\nbXufPn3w0UcfSTq2REREREQkLDMzEyNHjiz593mrVq2wcuVKA2dVvbFInIyeubk57OzsJF0sdXFx\nQZcuXTTeOCeTyWBra8viUCITt3TpUkRHR+PmzZvIyMjArFmzsG3btlIxJ06cULtvdnY24uLi0K9f\nP32kSkRERERk0p6ffV6drKwsnRaoJSQkiLYPGDBAUj8DBgwQLbDRNI4Qa2trDBs2TDTG1dVVtF3s\nJiC5XI6JEyfio48+UtseExODjIyMMqtAbtq0SbDPNm3aCN6YY+zHuzKkTERYlRj7a6nrzw6RqeM5\nWDOeg4XjeA42PGN+PXkOJiIiIiIi0o+qXCTeoEEDvPXWW9iyZYva9u+++w5BQUFl7ueMiopCbm6u\n2n2Kr8UI2bRpE77++uty53rkyBF4e3uX2sYJGjUzhgnmdJXDvn37RMcdOXKkxtysra3Rtm1bwXsU\n4+LiyixkExsbK9qnu7s75s+fr3HsZ5mZmZUrvjIMddwA4NixY6L99u/fH56enqIx06ZNw5IlS0SL\nzYmIiIiISFhhYSGGDx+OK1euAHg6uVpERATs7OwMnFn1VnWvPhERUZVnZ2eHNWvWlPy8fft27Ny5\ns1TMyZMnBfePjIzUWW5ERERERFVJo0aNNMZcvHhRpzmkpaWJtnt4eEjqp02bNpUaR4iXlxcsLS1F\nYzTdRKRUKkXbAwICBFfOKCwsLDNp1j///IODBw8K9ie0gilg/MebpDP211Ifnx0iU8ZzsGY8B6vH\nc7BxMObXk+dgIiIiIiIi/ajqk6bNmTNHsC0tLQ0RERFltm/dulVwn759+6JJkybaSE0jqRM06pI2\nJ5irzDjqGMMEc7rM4fjx46L7eXh4QCaTaXwIFToXj3316tVyjTthwgSjnlzCUMcNAE6fPi06tpTP\ni729PV5++WWNcUREREREpN7EiRNx6NAhAE8nrIqIiJD8d2/SHeP9LZKIiEgCX19fjB07tuTnadOm\nlVycz8vLQ0pKiuC+kZGRUKlUOs+RiIiIiMjUtWzZUmPMH3/8obPxCwoK8PDhQ9GY2rVrS+pLU1x6\nerrkvJ7l4uKiMeb52e7Ly9XVFYMHDxZsf37F0i1btkChUKiNtba2xrhx49S2mcLxJmlM4bXUx2eH\nyJTxHKwZz8EVi+M5WPeM/fXkOZiIiIiIiKozHx8fbNu2rdwPJycnQ6dudNq3b4/evXsLtoeFhZX6\nOSsrC3v37hWMF5tcT9s4QaM4Y5hgTpc56GsSxeev2/z777+i8cZewGyo4ya07VmtW7eW1LfUOCIi\nIiIiKm3p0qX48ccfS37+/PPPJU9uRrqlftkDIiIiPbt69SqaNGkiuCKPmNDQUOzfvx/37t3DP//8\ngw8//BDr16/HuXPnUFhYKLjfnTt3cO7cObRv374yqRPR/yue6VVKnKmRy+WwsrKSHC91AoryHovy\nTGwhNValUkmKNTMzg1KpFP1eLVZUVMRJOCpApVJBqVQKFhI8S6FQlMRrIvWzCTz942h5Xju5XG7w\nzzTfa0T60bVrV40xv/76K4KCgnSfjJGysbHRGGNmZlbpcaZMmYJdu3apbUtMTMTFixdLbmx49oLs\n80aMGAEHB4dK52OKpJw/c3Nz9ZAJAfr77BCZKp6DNeM52HTwHGxceA4mIiIiIqLqzM3NDSNHjjR0\nGlXGnDlzSlYRe96xY8eQnJwMLy8vAEBERAQKCgrUxrq4uGDIkCE6y/N5Uido7Nevn07G5wRzhsuh\noKAADx48qEhK5Xb//v1yjevs7KzrlCrMUMetWPHiQUK09XkhIiIiIqKyduzYgQULFpT8PGXKFMyZ\nM8eAGdGzWCRORERGo23btpDL5ejUqRM6deoEb29vdOjQAXK5XHQ/R0dHfPPNNxgxYgQA4LvvvsOI\nESNw/vx5jWPu3LmTReJEWiCTyWBvby/4h7xnyeVyWFhY6CEr7Wnfvr3oKmDPUigUePLkiaR+a9So\noXFG42KFhYWSb5R+/Pgx/vnnH0nFszk5Obh+/brGWJVKhTNnzuDw4cMa+1SpVLh9+7akXOl/srOz\ncfz4cUkTpjRu3Bj16tWTdLN2zZo14eLiorGYW6lU4vz588jMzJSUr1wuh6enJxwdHSXF64JKpUJa\nWhry8vIEY1hgQKQdXl5eqF+/vujM9ikpKYiLi0OvXr20Pr6FhQXs7e1Fb5TRdFNAsezsbNH2unXr\nlis3fevXrx+aNGmC69evq23ftGkTli5dijNnzoj+TvTOO+8ItlX1452fn68xRtMqDqaiqr+WRNUB\nz8HGg+fgyqtO52Cg6r+eREREREREJE11mDTN19cXrVu3Flx1OywsDGvWrAEAhIeHC/bj7+9foQVG\nKooTNIozhgnmjCGHypJyLxWVxeNGRERERGQ8kpOTMWnSpJJ6g27duiE0NNTAWdGzWCRORERGoXnz\n5vjtt98wePBgbN68GZs3bwbw9Oa2l156qdRDXTHa8OHD8frrr+PXX3+FSqXC5MmT0aVLF43jRkdH\nY/HixVp/PkTVUY0aNSStti2XyzVO/mBs6tSpgz59+kiKLSwsxOPHjyUVaFtaWsLBwUHSSsz5+fnI\nzs6W1O/Dhw/x119/Sfpjc2ZmJuRyucbYoqIinDlzBn///bfGPqli8vPzce/ePUl/wLS2tsaDBw8k\n/4FcpVJpfJ+pVCpkZmbin3/+kdSnmZkZ3N3dJcXqUl5enuhNE4WFhXrMhqjqksvlGDFiBFauXCka\nN2vWLJw8eRKWlpZaz6FevXqiBTapqamSfgdITU3VOI4xk8vlmDRpEgIDA9W2b9myBZ9++qnoCqZe\nXl4ab3wy5eOt6d+amlblUCgUSEpK0mZKBmXKryUR8RxsTHgO1ozn4LJM+fUkIiIiIiIi7agOk6bJ\nZDLMnj0bkyZNUtseHh6OZcuWIScnB0eOHBHsQ2h/XeEEjdWXhYUFatWqJbgqtkwmg5eXl1bGenbV\nak3jAsC9e/fQunVrrYytbYY6bs9uE7s/ROrnRWocEREREREBly5dQp8+fZCTkwPgae1XdHS0pLoR\n0h/Tqs4hIqIqrWHDhjh8+DD69+9fsi09PR0xMTH4+OOP4evrCycnJ7Ro0QJ+fn5YuXIlTpw4UfLH\nlNWrV5dcHLx+/Tr27dunccyzZ8/i1q1bunlCRERERERVyPTp0zVOOJGcnIzp06dLmtREyO7du3Hv\n3r0y2zUVz+zdu1dS/5ripBTpGNrbb78tOFHInTt3sHfvXmzdulVw/ylTpmgcw5SPt62trWj7jRs3\nRNv37NkjenOOFFImAdIXU34tiegpnoONB8/B4ozhHAzwPExERERERET6xUnTnvLz8xOcxCw3Nxc/\n/vgjtm3bJjiBfb9+/dCkSRON44SGhkKlUpX74e3tXaav4gkaNZk1a5akYv+K0DTxm6aJ46TGcYK5\nssSOiUqlwsGDB5GUlFTpx7P3QgKAs7OzaF7Hjx/XyvPTFUMdN0DzZAcXL16U9BykxhERERERVXf3\n7t3DkCFDcP/+fQCAvb09IiIi1C78SYbFInEiIjIq9vb2iI6ORkBAgGDMn3/+ifDwcMycORPdunWD\nnZ0dXnrpJYSEhOD1118vidM0Qyrw9MJkVFSUVnInIiIiIqrK3N3dMW7cOI1xGzZswJgxY/D48eNy\n9X/hwgUMHjwYQ4cOLZl18ln9+vUT3T8yMhJXr14VjUlMTBRcoULqOMagfv36GDZsmGD71KlTkZ6e\nrrbNxsYGfn5+Gscw5ePt4OAg2n748GHBtoKCAsEVYsvDxsZGtD0jI6PSY0hlyq8lET3Fc7Dx4DlY\nnDGcgwGeh4mIiIiIiEi/jGXSNEOzsrLCtGnTBNu//fZb0cn1Jk+erIu0NOIEjdWXpmMSHx+vk3G7\ndesm2v7DDz8ITqZQUdqcVNFQxw0AOnbsKNouZVGhnJwcoy/EJyIiIiIyBpmZmejTp0/J37NtbW2x\nf/9+tGvXzsCZkTosEiciIqNjbm6ODRs2ICQkRNIFysLCQpw6dQqrV6/Gd999V+7xWCRORERERCTN\n0qVLUadOHY1x27ZtQ4sWLbB27VpkZmYKxqWlpWHbtm3o3bs3PD09ERMTIxg7ePBg2NnZCbbn5+dj\n1KhRgjdSpaWlYezYsaI38Dg7O6Nnz56C7cZEbCVSsRvORo0aBXt7e439m/LxbtmypWj7hQsX8Pnn\nn5fZnpWVhTfeeAMpKSmVzkFTkdyOHTt0turI80z5tSSi/+E52HjwHCzMGM7BAM/DREREREREpF/G\nMmmaMZg6dSqsra3VtqWmpuLs2bNq2+rXr4+hQ4fqMjVBnKCx+howYIBoe/Gq9RVRvHDN7du3y7T5\n+vqK7nvlyhV88cUX5RqvqKhItF2bkyoa6rgBgLe3t+j+e/fuRWpqqmjMt99+i7y8vArlR0RERERU\nXTx69AgDBw7EhQsXAACWlpbYuXMnJyAzYuaGToCqDmtrazRt2hQKhUJjrKWlJfr37y8ptmbNmnB2\ndpZUKNqyZUtYWlrC3FzzW1ubM+MRkW4EBgaiSZMmCAgI0OlNe/Hx8Xj48KGkmzSJiIiIiKqz+vXr\n48cff8TgwYM1zmB/584dvPPOO5g6dSo6duyIxo0bw8nJCXl5ecjIyMC1a9dw5coVyWM7Ojpi9uzZ\nWLx4sWBMYmIiPD098eGHH6JXr15wcnLC/fv3sX//fixbtgxpaWmiYyxcuFDw5iVj4+PjAzc3N1y7\ndq1c+4kVtj3LlI93+/btYWFhgYKCAsGY+fPnIzo6GgMHDoSVlRUuXbqEX375RWsri7Zq1Uq0/dSp\nU2jWrBm8vb3h5OQEufx/c3m+8MILmDt3rlbyAEz7tSSi/+E52HjwHCzMGM7BAM/DREREREREpF9S\nJ02bN29eqe1ZWVkYN26c1iZNMwZ16tTBuHHjsHbt2nLt5+/vL+meT11ZunQpYmJicP/+fdG4bdu2\n4fDhw1i0aBGGDx8OR0dHtXFpaWk4ePAg1q9fj7i4ONE+iyeYU1dADvxvgrkDBw6gVq1aasfiBHMV\nM2jQINSqVUtw8r6jR4/i448/xpIlSyT3+fjxY+zcuROff/45UlJScPbsWTRs2LBUjK+vLxwdHUUn\n+VywYAFkMhnmzp0req9zfn4+1q9fj6SkJGzYsEEwTsqkitOnT4elpaVoHGC441Y8tpWVFZ48eaK2\nn6KiIowcORKHDh1SO+lqfHw8goKCJOdFRERExuXEiRPo1q2bYPuYMWOwZcsWPWZEVDUpFAqMHTsW\nCQkJAJ7WX65ZswY+Pj4GzozEsEictKZ+/fp49dVXJcWqVCpMnTpVct9SC7rlcrnki4UsEicyDWPG\njEHz5s0xdOhQjTfDVVRBQQFiYmIwatQonfRPRERERFSV+Pr6Yt26dZg0aZKkWeCVSiUSExORmJhY\n6bHnzJmD8PBw0RUTbt++jRkzZpS77/bt22Py5MmVSU+vZDIZJk2ahPnz50vep1OnTnjxxRclx5vq\n8ba0tMTQoUMREREhGnf06FEcPXpUJzl07NhR9CYVALh79y5++umnMts7deqk1eI0wHRfSyIqjedg\n48BzsDBjOAcDPA8TERERERGRfhnLpGnG4v3338e6deskryRcfK3FkDhBY/Xk4OCADz/8EIGBgYIx\nwcHBOHv2LAIDA9G1a1e1MdevX8fJkycRFRWFyMhI5Obmio5rb2+PwMBAzJkzRzBGqVRi3rx52LRp\nEwICAtC9e3e4uLjAzMwMmZmZuHDhAo4cOYIdO3YgIyMDb7zxhuiY2pxU0VDHDXg6EcWoUaPw/fff\nC8akpKTAw8MDs2bNQo8ePWBvb487d+5g586d2Lhxo6TFzYhIv5o0aYIbN24Itk+ZMgVr1qzRY0ak\nT5pe/4qysbGRdG4hIqLS8vPz8cYbbyAmJqZk2+rVqzFhwgTDJUWSsEictEYmk5VrNscaNWroMBsi\nqkq6dOmC+Ph4DBo0CH///bdOxoiOjmaROBERERGRRAEBAbCxscGECROQn5+vt3Ht7OywZ88edOvW\nTas3TjVo0ADR0dGwsLDQWp/64O/vj0WLFqGwsFBSvNQVTIuZ8vGeOXOmxgI1Iebm5nB3d8fFixcr\nPH7NmjUxcuRI/PDDDxXuQ5tM+bUkotJ4DjYOPAcLM/Q5GOB5mIiIiIiIiPTLWCZNMxYtW7bE4MGD\nsXv3bknxffv2RdOmTXWclWacoLF6mjVrFsLDw5GamioYEx0djejoaDg6OqJNmzaoVasW8vLykJmZ\nidu3b2tcgV6d6dOnIzw8HGfOnBGNS01NFS0ml0rbkyoa6rgBQGBgIHbs2IHHjx8LxqSlpWHBggUV\n6p+IiIi0Jz4+HvHx8YLtr776Ktq3b6+/hIhIVFFREcaPH1+qQPy///0v3n33XQNmRVLJNYcQEREZ\nXuvWrZGYmIj//Oc/Ouk/OjpadEZfIiIiIiIqbeTIkTh58iTatWun13Hd3d1x8OBBuLu7a6W/tm3b\n4tChQ2jQoIFW+tOnevXq4bXXXpMUa2dnV6GJsUz1eHt7e1foArW5uTk2b96Ml19+udI5BAcHo06d\nOpXuR1tM9bUkorJ4DjY8noOFGcM5GOB5mIiIiIiIiPRr5syZFd7X3NwcrVu31mI2hleeolZjKmAO\nCAjA1q1bYWlpqddxiyeYc3Jy0mq/nGBOs5o1a2Lv3r2SrrFkZmbi6NGjiImJwaFDh5CUlFThQmcL\nCwvs2bMHbm5uFdq/vIonVdRmf4Y4bgDg5uaGL774osL7A5pXViciIiLtiI+Px+LFiwUfSUlJhk6R\niP5ffn4+hg0bhh07dpRs++ijj/Dpp58aMCsqDxaJExGRyXB0dMT+/fsxevRorff98OFD/P7771rv\nl4iIiIioKvPy8sLp06exdu1aNGnSpNL9derUCZs3b9a4YkS7du1w5swZTJo0CTVq1KjQWJaWlnjv\nvfeQkJCAFi1aVKgPYyB1ZVI/Pz/Y2tpWaAxTPd6hoaHlKspzdnbG3r17tXaTTMOGDXHw4EGjurnP\nVF9LIiqL52DD4zlYmKHPwQDPw0RERERERKRfxjJpmrF45ZVX0KlTJ41x9evXx9ChQ/WQkXScoLH6\nadSoEeLi4tChQwe9juvs7IxDhw7B29tbL+Npe1JFQx03AJg6dWqFJ+f44IMPMGbMGC1nRERERERk\nup48eYLXXnsNe/bsKdm2dOlSLF682IBZUXmZVJG4SqVCVlYW8vPzDZ2K1uXn5yMrKwsqlcrQqWhd\nVX1ufD+apqr83KoLS0tLbNmyBR9//LHW+46MjNR6n0RVgUqlglKp1PhQqVSSH0qlEgqFQuuP8n6/\nS81DqVRK7lMmk5X8V9OjvKT0WfyQy+WSH1L7NDMzk/wwNzdHjRo1JD3Mzc3L1behH7p8brp6P0gl\nl8vLlW9F3sfapovPGhGVj5mZGSZPnoxr164hNjYWEydORKNGjSTtK5fL4eXlhcDAQJw6dQqJiYnw\n8/OT9J1oa2uLdevW4e+//8b8+fPRpk0bjZ95mUwGT09PLFq0CDdu3MDKlSthbW0tKVdj1atXL0k3\nDEktZBNiisfbwsIC4eHh2LJlC1q2bCkY5+rqivnz5+PixYvo06ePVnPw8vJCSkoKIiMjERAQgA4d\nOqBOnToGXbXDFF9LIlKP52DD4jlYmDGcgwGeh4mIKuvEiROi19z8/Px0sm91lJ6ejvj4eGzevBkr\nVqzAp59+iqVLlyIsLAxbt25FYmIi8vLyDJ0mEYkICQkR/d4LDQ01dIpEpAfGMGmaMZGymri/v3+F\nJ1LTJU7QWP24u7sjISEBgYGBFZ7w8VmOjo7w9/dHw4YNReMaN26M33//HUuXLkXt2rUrPa4YXUyq\naKjjBjz9zg0KCoK5ubmkvs3MzPDZZ59VehVyIiIiIqKqpLCwEGPGjEFsbGzJtv/+97+YN2+eAbOi\nipD2m5GRUCgUOHbsGNq2bYsXXnjB0Olo1b///ouUlBQMGDBA8i+spqKqPje+H01TVX5u1YlMJkNQ\nUBAaNWqEd955B0VFRVrpNyoqCt988w2LyYiec+fOHaSmpsLGxkYwRqVS4d69e3j8+LGkPvPy8rT+\nhz65XA5PT084OjpKilcqlTh//jwyMzM1xtauXRseHh6QyzXPsVRcQCxFcYG2FDVq1ECtWrUkxdra\n2qJ27dqSiubv3r2LtLQ0KBQK0TilUokuXbqgefPmGvuUyWRwdXVFzZo1NcaqVCrcuXMHjx490hir\nS3K5XPLrZmVlBUdHR0mvXX5+PjIyMiT1W55CcUtLSzg7O0v694yVlZWkXIs/Q1JnJpfJZDr/I6WU\nHOrVqwcnJyfBGLHvLiLSLrlcjgEDBmDAgAEAgHv37iE1NRU3b95ERkYG8vLyIJfLYW9vj9q1a8Pd\n3R0eHh6SzhdiGjRogM8++wyfffYZMjMzkZiYiHv37iEzMxO5ubmws7ND7dq1Ub9+fXTu3BkODg4V\nGqdr166VnnBs4sSJmDhxYqX6eJ5MJsOVK1e02qcYUzrewNPjM2bMGIwZMwZXrlzByZMnkZaWhsLC\nQri6usLNzQ1du3Yt8++8DRs2YMOGDZUeH3h6jh86dGilV0PR1jEpZkqvpS4+O0RVCc/BmvEcLF1V\nOgcD2jkPa/scDOjn9TTWzw6RKWvSpAlu3Lih9X5tbGyQm5ur9X7JeKlUKvz222/45ZdfsG/fPknv\nq+Lrt0OGDMGwYcPQuXNnPWRKRERE5VE8adqgQYMQHByMy5cvq41zdXXFuHHj8OGHHxr87626NHz4\ncMybNw+3bt1S2y6TyTBp0iQ9ZyVd8QSNEydOxP79+0v+7Sb0fJ717L/dXn31Vbz44ouSxy2eYO7j\njz/GqlWrEBUVhYsXL4r+ji+TyeDh4YHXXnsN06ZNg7Ozs+Tx6H9q1KiBkJAQfPjhh/j+++/x008/\n4fTp05IWkrKyskLnzp3Ro0cP9OrVCz179pR8f6pcLse8efMwY8YMhIeHIzw8HKdOndJ4H42ZmRna\ntm2LAQMGYPTo0ZLGKp5UMSYmBlFRUThz5gxu3bqFhw8foqCgQFIfzzPUcQOAjz/+GK+//joCAwOx\nb98+tc/BysoKgwcPxoIFCwyy6jkRERFpT/PmzbF582bB9mbNmukxGyLTl5GRgaFDh+KPP/4o2bZs\n2TLMnTvXgFlRRZlUhWRxIU+DBg2qXFFuZmYmzp8/j379+hk6Fa2rqs+N70fTVJWfW3UUEBCARo0a\nYfjw4Xj48GGl+7t16xaSkpJ4MZDoOY8ePUJmZqboxXuVSoVHjx5JusAPAE+ePNFWeiXMzMwkF7cC\nT3POzMzEP//8Iym+eHVuTeRyuU4mIpHL5bC0tJQUa2lpKbkwNi8vD9bW1pKKxJ2dnSWtVCWXy+Hm\n5iapqF2pVKJGjRp48OCBpHx1xczMTPJKYjY2NnB1dZVUeP348WPcvXtX0k3ZhYWFyMnJkRQrl8th\nbW0tqbBdavG7TCaTPMmCMdH0njTGmeeJqgtnZ2e934Ti6OjI3/f0yNSOd4sWLbhqhgBTey2JSBzP\nwVWfqR1vnoPFmdrrSUREFadQKLBp0yZ88skn+Ouvv8q1r1KpRHJyMpKTk/HJJ5+gY8eOeP/99zFq\n1CjJk48SaVN8fDzi4+MF2++QrFkAACAASURBVF999VW0b99efwkRET3n+vXrBhnXUJOmVWaiMF1M\njgY8nWB/6NChWL16tdp2Hx8fjStrG4OqPEGjMUwwZww5PM/e3h4zZ87EzJkzUVBQgLNnz+L69evI\nzs5GVlYWlEolbG1tYWdnh4YNG8Ld3R2NGzeWdE+RGGtr65LnUlRUhOTkZFy7dg1ZWVnIzs5GYWEh\natasiXr16qFZs2bw9PSEnZ1ducfR1uTGzzPUcWvbti2ioqKQnZ2Nw4cP486dO8jOzkbdunXRsGFD\ndO/evcxxWrhwIRYuXFipcYmISD98fHwQEBBQ7v14z2DVVKdOHfj5+Rk6DaIq4ebNm/D19UVqamrJ\nti+//BJz5swxYFZUGSZVJA48/cOXLi5IGZpKpYJSqTR0GjpRlZ8b34+mpyo/t+qqX79+OHLkCAYN\nGoTbt29Xur/IyEgWiRMRERERERERERERERGR1pw/fx7jx4/HmTNntNLfmTNnMHbsWHTt2hXNmzfX\nSp9E5REfH4/FixcLtjdp0oRF4kRU7VX3SdOKioqwe/duwfYpU6boMRvt4QSN1YuFhQW6dOmCLl26\n6HVcc3NzdOzYER07dtTruNpiiOPm4OCg9cJ3IiIyPDc3N4wcOdLQaRARVSkXL16Er68vbty4AeDp\nhHdffvklZs+ebeDMqDJMrkiciIjoeV5eXjhx4gQGDx6MpKSkSvUVGRmJoKAg7SRGRERERERERERE\nRERERNVaREQExo0bh7y8PEOnQkRERKQ3q1atws2bN9W2NW7cGMOGDdNzRkRERETisrKycOrUKaSl\npSEzMxM5OTmwt7dH7dq14ezsjM6dO8PBwUFr4z18+BCXL1/Gn3/+iaysLOTm5qKwsBDW1tawtbWF\ni4sLGjRoAHd3d9jb25vMWKRdCoUCJ0+eREpKCu7fvw8rKyvUrVsXL774Ilq3bq31sVJSUnDt2jVk\nZWUhKysLhYWFsLGxQb169eDm5gYPDw/Y2NhodVxjoe/vAE30+dpT9bF3714MHz4cubm5AABra2ts\n27aNv6NXAUZbJK5UKpGXl4eHDx+WbCsoKEB+fj5yc3NLba8KcnNzkZ+fj4cPH8LCwsLQ6WhVVX1u\nfD+aJn09t4cPHyIvL69KrjRvrBo0aIDDhw9jxIgR2Lt3b4X7SUpKwvXr19GkSRPtJUdERERERERE\nRERERERE1c6mTZvg7+/PvxsTERFRlZWdnY3s7GwAQGFhIe7cuYPIyEisXLlScJ+ZM2fC3Nxob18m\nIiKiauTu3btYtWoVoqKikJqaKnoNRy6Xw8PDA6+++iqmTZsGZ2fnco+Xl5eH9evX46effsLx48eh\nVCol7deoUSN4eXmha9eu6Nq1K3x8fIxqLFMREhKCRYsWCbZ/8803mD59umgfEydOxHfffSfYHhcX\nh549e6ptO3HiBLp16ya475gxY7BlyxYAT+tevvzyS4SFhSE9PV1tfLNmzbBo0SKMGzcOcrlcNG8h\nT548wbZt27BlyxYkJCTg0aNHovFmZmbw8vKCr68vRo0aBU9Pz5K2NWvW4N1335U8tr+/P/z9/QXb\ng4ODsXDhwpKfy3P8pNLXd4AxvvZU/fz0008YP348njx5AgCws7PDL7/8gr59+xo4M9IGo73KcufO\nHZw8eRLA01kJAKCoqAhJSUnIz8/H+fPnDZme1t28eRMXL17E1q1bq9zFr6r63Ph+NE36em55eXk4\nefKk5F+mSDvs7Oywe/duTJs2DevWratwP9HR0Rp/wSMiIiIiIiIiIiIiIiIiErJ//34EBARILhCX\ny+Xo0KEDGjVqhLp16+Lx48fIyMjAX3/9hStXrug4WyIiIqKKCQ0NxeLFiyXHN2nSBFOnTtVhRkRE\nRESa5ebmYs6cOfj+++9RWFgoaR+lUomUlBSkpKRg2bJlmDJlCj7//HNYWVlJ2v/QoUMYO3Ys7t69\nW+58b926hVu3biEmJgYANF5v0udYpH3Hjx/HyJEjcfPmTdG4v/76C/7+/oiOjkZ4eDgsLS0lj6FU\nKrFixQp89tlnyMjIkLyfQqHA2bNncfbsWVy+fBkRERGS9zUmhvgOkEIfrz1VP4WFhZg+fXqpGqtG\njRohNjYWHh4eBsyMtMloqz8fPXqEX3/9Fbt27YJMJgPw9B8XCoUCsbGxVW6mC6VSCaVSib1795Y8\n36qiqj43vh9Nk76em0qlglKpRFFRkc7GIPXMzc2xZs0auLi4YMmSJRX6xTQyMpJF4kTPKP4+e/Y7\nTSaTQS6XV7nzBBGZPqVSCYVCUfJvAE7aQ0RERERERERE+uDj44OAgIBy71ejRg0dZEOGlpaWBj8/\nPygUCo2xrq6uWLRoEYYPHw4nJye1Mffu3cPBgwexfv16xMfHazlbIiIiIv2QyWQICwvTahEFERER\nUXmdO3cOw4cPx59//lnhPvLz87Fy5UrExcUhIiICLVq0EI0/dOgQfH19UVBQUOExpdLnWFWNMdwT\nHRUVhbfeeqtkpV8pfvnlF9jb22Pjxo2S4u/cuQM/P79qe53REN8BUujjtafqJz09HcOHD8fvv/9e\nsq1Vq1bYu3cvXnjhBQNmRtpmtEXiAASLK6X8Ec1U8bmZnqr6vAA+N20yhl8YqhOZTIagoCA0bdoU\nkydPLvcvub///juys7Ph4OCgowyJTMuZM2fw4MEDWFhYAHj6GWvatCk8PT1hZ2dn4OyIiP5HqVTi\n/PnzOHbsGLKysgAAycnJBs6KiIiIiIiIiIiqAzc3N4wcOdLQaZCRmD9/PtLT0zXGjRo1Chs2bEDN\nmjVF45ydnTF69GiMHj0aFy5cwIcffog9e/ZoK10iIiIivQgODoavr6+h0yAiIqJq7OrVq/Dx8cH9\n+/e10l9KSgr69OmDhIQEuLq6qo0pKCjA22+/rZeibX2OVRUZuubj9OnTiIiIQH5+frn3/f777+Hn\n54fevXuLxqWnp6NXr16VKpA2ZYb4DpBCH689VT9XrlzBsGHDcOnSpZJtPXv2xE8//YS6desaMDPS\nBaMqEp81axZGjBgBlUpVoZVfiYiMUfFqu6R/48ePR6NGjfDGG28gOztb8n6FhYXYu3cvb+Qh+n8v\nvPACOnXqVDKTs0wmg52dHWd2JiKjI5PJ0KBBA/znP/8puViWmJiIxMREA2dGRERERERERERE1cXV\nq1exadMmjXETJ07EunXryn3zqYeHB2JiYhAVFVXhyXyzsrJw6tQppKWlITMzEzk5ObC3t0ft2rXh\n7OyMzp0763VCbYVCgZMnTyIlJQX379+HlZUV6tatixdffBGtW7euUuPn5OTg1KlT+Pfff5GZmYmH\nDx/Czs4OTk5OaNy4MTp37gxLS0utjllMoVAgJSUF165dQ1ZWFrKyslBYWAgbGxvUq1cPbm5u8PDw\ngI2NjU7Gryx9H7sHDx7gyJEjuH37NjIyMmBra4vmzZvD29sbtWrV0to4RETVgZ2dHb744gtMmTLF\n0KkQERFRNZaTk4OBAwdqrTi02O3btzFo0CAkJCSULMb0rP379+PGjRuifTRu3Bhubm6wtbXF48eP\n8eDBA1y/fr3cuepzLH1Yu3Yt1q5dW+79bt26hYYNG5Z7P0MXiT9byFkRoaGhooXChYWFGDRoULUt\nEDfUd4AUun7tqfr55ZdfMGHCBOTm5gJ4+v320Ucf4eOPPzb4dx3phlEViTdt2hRNmzY1dBpERFSF\n9O7dG0ePHsWgQYM0/tL7rMjISBaJE/2/unXrws3NzWhvCCEiKiaTyeDk5AQnJ6eSbc/+PxERERER\nERERkakICQnBokWLBNu/+eYbTJ8+XbSPiRMn4rvvvhNsj4uLQ8+ePSuaolYdO3YM3t7egu3jx4/H\nDz/8ILm/wMBAfPrpp4LtO3fuxKuvvlqeFCVbtWoVlEqlaEzbtm2xatWqSt2MNXTo0HLF3717F6tW\nrUJUVBRSU1NFF2+Qy+Xw8PDAq6++imnTpsHZ2VnyOCdOnEC3bt0E28eMGYMtW7YAAHJzc/Hll18i\nLCxMcOX1Zs2aYdGiRRg3bpykydkNPb469+/fx7fffoudO3ciOTkZCoVCMNbKygrdu3fHO++8g9df\nf73SE9I/efIE27Ztw5YtW5CQkIBHjx6JxpuZmcHLywu+vr4YNWoUPD09AQBr1qzBu+++K3lcf39/\n+Pv7C7YHBwdj4cKFGvsxxLG7cOECAgMDERsbq3blNXNzc7z22mtYsmQJWrVqVaExiIiqOgsLCzg6\nOsLDwwP9+/fH+PHjUa9ePUOnRURERNXc8uXLNRbHNmjQAHPnzkXv3r3h5OSE9PR0/Pbbb1i2bJng\ntQMASEpKwrp169Rerzt8+LDgfi1btsTWrVvRsWNHte1paWk4ffo0Dh48iN9++w3Jycmi+etzrKrI\nmBYGdHR0xJtvvok2bdqgoKAAsbGxiIuLE91nz549ePTokeD93mFhYTh16pTGsVu1aoWAgAB0794d\nLi4uMDc3R0ZGBpKTkxEXF4eff/65pPD0WZ6enpg2bVrJzydPnhQdr0+fPqLXVjp37qwx1/Iw1HdA\neenitafqQ6FQIDg4GMHBwSV/p7CyssKaNWswfvx4A2dHumRUReJERES64OHhgePHj2PIkCE4ffq0\npH2K/+Bb0dmciKoSuVxe8hAidiMRGTeZTAZzc82/Fsjlcpibm6NGjRqSY83MzCTF1qhRQ1K/FaFU\nKiW9P4tzlnJDnpmZGZRKpaRYhUIBhUIhKQexG5ueJ5PJSh5EREREREREREREpq579+7o0qULEhIS\n1Lbv2LEDX331FRwdHSX1t23bNsE2V1dXDB48uEJ5aqJSqfDzzz9rjAsNDdXZatHPy83NxZw5c/D9\n99+jsLBQ0j5KpRIpKSlISUnBsmXLMGXKFHz++eewsrLSWl7Hjx/HyJEjcfPmTdG4v/76C/7+/oiO\njkZ4eLjWjps+xs/Pz0dgYCDCwsKQl5cnaZ8nT57g4MGDOHjwIFq1aoX169eLTqAgRKlUYsWKFfjs\ns8+QkZEheT+FQoGzZ8/i7NmzuHz5MiIiIso9tjYY6tiFhIRgyZIlop+VoqIi/Pzzz4iMjMRXX31V\n6uZnIqLqKCgoCEFBQYZOg4iIiEhUZmYmvvrqK9GYDh064NChQ3BwcCjZ5urqinbt2sHPzw+vvPIK\nrly5Irh/SEgIAgICYG1tXWr7v//+K7jPkiVLBIu2AaBevXrw9fWFr68vgKerDYeHhwvG63OsqshY\nisT79u2Lbdu2lVogZ+7cufj6668xa9Yswf0UCgXOnDmDHj16lGnLyclBcHCw6LhyuRzBwcGYP39+\nmWPRsGFDtGvXDmPHjsXKlSuxevXqMp8Hb2/vUtdigoKCRIvE/fz8MGHCBNGctMWQ3wHloYvXnqqP\nq1evYtSoUUhMTCzZ1rRpU+zatQteXl4GzIz0gUXiRERULbi4uOD333/HyJEjER0drTH+wYMHOHz4\nMHx8fPSQHZFxc3V1hYeHB+zs7ARjFAoFkpKSkJ+fr8fMSBvq1q2Lfv36SSpifvz4MYqKijTGyWQy\n1KxZU1LxuUqlQo8ePST1W15KpRLnz59HZmamxlgnJye0bdtW0kW+69evIyoqSlJR9/3793Hu3DlJ\nsU5OTvD09JSUg6WlJWrWrCm5aJ/F5ERERERERERERFVLVbzmN2fOHIwYMUJt25MnT7Bx40Z88MEH\nGvv5448/8Pfffwu2+/v7S7p+XRHJycm4e/euaEzbtm3Ru3dvnYz/vHPnzmH48OEaV8gRk5+fj5Ur\nVyIuLg4RERFo0aJFpfOKiorCW2+9hSdPnkje55dffoG9vT02btxoEuNfuXIFI0aMwLlz5yqaJi5d\nuoRevXph2bJleP/99yXvd+fOHfj5+SE+Pr7CYxuSoY7dBx98gOXLl0seo6CgANOnT0dGRobR3ERO\nRERERERE6kVHRyMnJ0ew3cLCAjt27ChVHPqs+vXrY/PmzejatavgvZb37t1DfHx8SZF1MbHfGW/c\nuCEh+/9p1aqVaKGvPseqiozh93t3d3dERkaqLTSeOXMmfvjhByQlJQnuf/nyZbWFwrGxsRonEgwO\nDsaCBQs05mhra4t58+aVa2EkQzPkd4BUunrtqXrYuXMnJk6cWOqe+f/85z/YsWMH6tevb8DMSF9Y\nJE5ERNWGjY0Ndu3ahZkzZ2L16tUa4yMjI1kkToSnn53atWvD3t5eMEahUOhsJWjSLSsrKzRq1MjQ\naeiEQqFAZmampJslXVxc0Lp1a0mrn+fn5+PBgwcoKCjQGJueno6bN29KuhimUCggk8kkXWg0MzPT\n6QrsREREREREREREZNyqYpH466+/jiZNmuD69etq29esWYM5c+ZofO5bt24VbJPL5Zg0aVJl0hR1\n4sQJjTGvvfaazsZ/1tWrV+Hj44P79+9rpb+UlBT06dMHCQkJcHV1rXA/p0+fRkRERIUmHv7+++/h\n5+dXqSJ7fYx/+/Zt9OrVS+OEAVIUFRVh9uzZMDc3x3vvvacxPj09Hb169arUxACGZKhjt3bt2nIV\niD/r448/Rvv27Su0LxEREREREenH/v37RduHDRsGd3d30ZiXXnoJ3t7eOHLkiOg4zxeINmjQQDA+\nMDAQ9+7dw5AhQ9C2bVs4OjqK5qCJPseqioyhSDwkJER0JeoePXqIFgpnZWWp3R4bGys6rru7O+bP\nny8tyf8n5X5bY2HI7wCpdPXaU9WWm5uLqVOnYvPmzSXbLCwssGzZMsyYMaNK/i2J1DP8GYyIiEiP\nzMzMsGrVKoSGhmr8RS4qKkrSyrpERERERERERERERERUva1duxYymazcj9u3b1dovKp4Y4+ZmRlm\nzZol2H7t2jXs27dPtI+ioiL8/PPPgu39+vXDCy+8UOEcNbl06ZLGmJdfflln4xfLycnBwIEDtVYg\nXuz27dsYNGiQpElUhVy6dKlCBdrFQkNDK7yvPsbPy8vDwIEDNRY529nZoXv37hg0aBC6du0KS0tL\n0fj3338fBw4cEI0pLCzEoEGDTLZA3FDH7u+//8YHH3xQoZyLid2gS0RERERERIaXkJAg2j5gwABJ\n/WiKUzdOr169BOMLCwuxfPly9OzZE05OTnBwcMCLL76IMWPGYMmSJYiMjNS4+rOhxqqKDH3N1dra\nGsOGDRON0TR5o9Bq2cePHxfdb8KECUZRJK8rhvwOkEKXrz1VXefOncPLL79cqkC8cePGOHjwIGbO\nnGnw7zTSL64kTkRE1dLMmTPRsGFDjB07Fnl5eWpjbt68iXPnznHWbyIiIiIiIiIiIiIiIjIqVfWG\nvYCAAAQFBSE7O1tte1hYmOiNeAcOHEBaWppg++TJkyudo5hbt25pjGnTpo1OcwCA5cuXaywUbtCg\nAebOnYvevXvDyckJ6enp+O2337Bs2TKkp6cL7peUlIR169Zh+vTplc7T0dERb775Jtq0aYOCggLE\nxsYiLi5OdJ89e/bg0aNHsLGxMcrxv/76a6SkpIiO+cUXX2DMmDGlipuzs7MRHByMr776Su1+CoUC\ns2bNQnJysuDnPywsDKdOnRLNHwBatWqFgIAAdO/eHS4uLjA3N0dGRgaSk5MRFxeHn3/+Gbm5uaX2\n8fT0xLRp00p+PnnypOhYffr0QatWrQTbO3fuXGaboY5dSEhImef7vDp16uC9995Djx49UKtWLdy+\nfRu7du3Cpk2boFQqRfclIiIiIiIiwxO7XgQAHh4ekvrRdF1H3Ti9e/dG27ZtRX/nLfbgwQOcPn0a\np0+fLtkmk8ng5eUFPz8/TJgwAXXq1BHcX59j6YOPjw8CAgLKvZ+Tk5MOstE9Ly8vjZPh2drairYL\nXaf4999/RffTx8SWhmTI7wApdPnaU9Xz6NEjzJ49Gxs2bCj1uo8dOxarV6+GnZ2dAbMjQ2GROBER\nVVtvvPEGHBwc8OabbwreaBIdHc0icSIiIiIiIiIiIiIiIjIqVbVI3NbWFpMnT8ayZcvUtsfExODm\nzZto3Lix2vbw8HDBvl1cXDBkyBCt5Cnk4cOHGmNq166t0xwyMzMFi2WLdejQAYcOHYKDg0PJNldX\nV7Rr1w5+fn545ZVXcOXKFcH9Q0JCEBAQAGtr6wrn2bdvX2zbtq3UTbtz587F119/LbqivEKhwJkz\nZ9CjR48Kj62r8R88eCD43gWAmjVrIj4+Hm3bti3T5uDggOXLl8PS0hKfffaZ2v0vXLiA7du3Y/To\n0WXacnJyEBwcLDg28PR7Izg4GPPnzy/zHdKwYUO0a9cOY8eOxcqVK7F69epS7wFvb294e3uX/BwU\nFCRaJF58M7lUhjp2GRkZ2Lp1q2huHh4eOHToEOrVq1eyrWPHjhg6dCjGjBmDwYMH48mTJ6J9EBER\nERERkeEUFBRovGYj9XqNpjh1E+/J5XL8+OOP6NWrl+D96mJUKhXOnTuHc+fO4ZNPPkFoaCjGjx+v\nNlafY+mDm5sbRo4cabDx9c3FxUVjTI0aNcrdb0FBAR48eCAa4+zsXO5+TYWhvwOk0NVrT1VPQkIC\nAgICcOHChZJtNWvWRGhoKCZNmmTAzMjQquZfDYmIiCTq06cPEhMT0aJFC7Xtv/76q54zIiIiIiIi\nIiIiIiIiIhJXVYvEAWDGjBmCN7wplUqsWbNGbVteXh527dol2O/bb78Nc/OyaymcPXsW27dvL/fj\n/v37ZfrKz8/X+Py0sQK2mOjoaOTk5Ai2W1hYYMeOHaUKxJ9Vv359bN68GTKZTLCPe/fuIT4+vsI5\nuru7IzIyUu2qTjNnztQ4iffly5crPLYux4+JiUFWVpbgfnPmzFFb5PysRYsWib5HduzYoXZ7bGws\nMjIyRPsODg7GggULNH5/2NraYt68eVi3bp1onDYZ6thFR0eLFnibm5tj+/btpQrEn9WnTx989NFH\nonkRERERERERtW/fHsePH8crr7xSqX6ys7Ph7+8vOlGiPseqaqSsxJybm6uz8aVcNzQzM9PZ+GQ4\nfO1Jk5ycHEyZMgXdunUrVSA+bNgw/PnnnywQJxaJExERubm54Y8//ig183mxpKQk3Lp1ywBZERER\nEREREREREREREalXlYvEGzRogLfeekuw/bvvvkNBQUGZ7VFRUYI3acrlckycOFFt26ZNmzBq1Khy\nPy5dulSmLwsLC43P79GjRxpjKmP//v2i7cOGDYO7u7tozEsvvaT2b6flGUdMSEiI6CrkmlYJFysm\nNuT4+/btE91PyspX1tbWosXQcXFxKCwsLLM9NjZWtF93d3fMnz9f4/jP0ueNp4Y6dseOHRPts3//\n/vD09BSNmTZtGqysrDTmR0RERERERIZhYWEBe3t70Rip1xo0rc5dt25dwbZWrVohPj4ep06dwowZ\nM9CmTRtJYz5PpVLh/fffFy1W1udYVYmUCSD//fdfPWSiXRYWFqhVq5ZozL179/SUjf4Zy3cAUUX9\n9ttv6NSpE9atWweVSgUAsLe3x7fffoudO3fC1dXVwBmSMSg7RTIREVE15OTkhNjYWIwcORIxMTEl\n21UqFTZs2IDFixdL7uvRo0e4cuUKrl+/jtzcXOTl5QEA7OzsYGNjg5YtW6JZs2aCKyAQERERERER\nERERERERiRFb5bkqmDNnDrZs2aK2LS0tDRERERg9enSp7Vu3bhXsr2/fvmjSpIk2U1RL082WwNMb\nDm1tbXWWQ0JCgmj7gAEDJPUzYMAAHDlypMLjCLG2tsawYcNEYzTd1Ca2Urohxz9+/Ljofh4eHuLJ\nSZCTk4OrV6+idevW5Rp7woQJRj25hKGO3enTp0X3kfJ5sbe3x8svv4xDhw5VOkciIiIiIiLSjXr1\n6uHhw4eC7ampqejSpYvGflJTUzWOo8mLL76IF198EcDT60Spqam4dOkSrl69iuvXr+PSpUs4f/48\nioqKBPtIT0/H/v378frrrxvNWKZA07URsfcIACgUCiQlJWkzJb1xdnbGgwcPBNuPHz+Onj176i8h\nPTOm7wAiqZKTk/H++++Xue44fPhwfPPNN3B2djZQZmSMWCRORET0/2xtbbF792589NFHCAkJKdke\nGxsrWiT+4MEDxMbG4uDBg4iLi8Nff/1VMkOPEHNzc3h4eMDHxwd9+/ZF7969WTRORERERERERERE\nRERkonx8fBAQEFDu/ZycnHSQjelr3749evfuLVh0GRYWVqpIPCsrC3v37hXsb8qUKVrPUZ1GjRpp\njLl48aKkuIpKS0sTbZdabKtphSlN4wjx8vKCpaWlaIymInqlUlmhsXU9fkWPSXmlp6eXKRLXtIrV\nyy+/rMuUKs1Qxy49PV00/vnjLBbHInEiIiIiIiLj1aVLF1y9elWwfe/evfD399fYj9j1p+JxyqN2\n7dro3r07unfvXmp7dnY2lixZghUrVgjum5CQUK7CbX2OZaw0XfO5ceOGaPuePXtEC62NWbdu3XDl\nyhXB9h9++AHz5s3T6iSDxjTRqbF+BxCpk5aWhg8++ADh4eGlrkU3aNAA3377LYYMGWLA7MhYsUic\niIjoGTKZDMHBwXjw4AG++eYbAE9n4MnJyYGdnV1JnEqlwr59+/D9998jKioKT548Kdc4RUVFOHfu\nHM6dO4fly5ejTp06GDFiBCZNmoT27dtr9TkRERERERERERERERGRbrm5uWHkyJGGTqNKmTNnjmDR\n5bFjx5CcnAwvLy8AQEREBAoKCtTGuri46O2mqZYtW2qM+eOPP9CvXz+djF9QUKBxxaPatWtL6ktT\nnKbiWiEuLi4aY3Q5ubauxi8oKNDbTcL3798v99jGvKqMIY9dVlaWaLy2Pi9ERERERERkWP369UN4\neLhge2RkJK5evYrmzZsLxiQmJuLIkSMax9EGBwcHfPXVV9i2bZvgxHDamnBNn2MZmoODg2j74cOH\nBdsKCgoQGBio7ZT0xtfXF5s2bRJsv3LlCr744gvMmzdPcp9FRUUwNxcuS7SxsRHdPyMjQ/JYlWVq\n3wFUPSkUCmzcuBELumP3WgAAIABJREFUFy4s9b0rl8sxceJEhISEoG7dugbMkIwZi8SJiIjUWLly\nJQYNGoThw4cjJycHe/bswVtvvQWVSoUdO3bgs88+Q3Jystp9LSws0LRpU7i7u8Pe3r5k1rHs7Gxk\nZmbizz//xK1bt0rN6nP//n2EhYXh22+/Rf/+/bFw4cIyM7URGYpKpSp5iMXoilwulzSbnJmZWbln\nnZPL5TAzM5MUR8ZDqVRKes8pFArJ702VSiU5XqFQlDyk5CqVXC5HjRo1JL0npcQQERERERERERFR\n1SXl2mNubq4eMtEdX19ftG7dGhcvXlTbHhYWhjVr1gCA6A1+/v7+ojcralPXrl01xvz6668ICgrS\nfTJGStPNoYBur4EbenxtEJoQgTTjsSMiIiIiIjIt165dw/bt2yu0b8eOHdGiRQsAwODBg2FnZ4ec\nnBy1sfn5+Rg1ahQOHDiAWrVqlWlPS0vD2LFjRe8vdHZ2Rs+ePctsP3r0KHbv3o3JkyfDzc1Ncv5F\nRUUoKioSbLe0tDToWKZI0wSPFy5cwOeff16mUDorKwvjxo1DSkqKLtPTKV9fXzg6OiIzM1MwZsGC\nBZDJZJg7d67o/dj5+flYv349kpKSsGHDBsE4TUX5O3bswPTp0/Xy/jLkdwCRJiqVClFRUVi0aFGZ\n7xlvb2+sXLkSHTp0MFB2ZCpYJE5ERCSgf//+OHjwIIYMGYLIyEh4enpi6tSpamcJ69y5M4YMGYK+\nffuic+fOGm8cePz4MQ4fPozffvsNO3fuxN9//w3g6T/w9u7di3379mH8+PFYtmwZZ/shg8vKysKd\nO3dEV55QKpV48uSJ1sc2MzND9+7dUb9+fUnx5SnmNjMzg7e3t6SiYJlMxkJxI6FQKHDs2DHBGSuf\nJ7VI+9atW1i/fr2k2IsXLyIqKkr0omgxlUolOYfWrVtjxYoVsLKy0hgrk8n0dlMjERERERERERER\nGZ/8/HyNMVKvoxormUyG2bNnY9KkSWrbw8PDsWzZMuTk5Aiu4CKTyQT31wUvLy/Ur19f9NinpKQg\nLi4OvXr10vr4FhYWsLe3F/2bjqaVk4tlZ2eLtvNvmKVZWFigVq1agitiy2QyeHl5aWWs51et1jQ2\nANy7dw+tW7fWyvjaZshjV7t2bdEJNaR+XqTGERERERERUfkcOHAABw4cqNC+K1asKCkSd3R0xOzZ\ns7F48WLB+MTERHh6euLDDz9Er1694OTkhPv372P//v1YtmyZxtW0Fy5cCGtr6zLbs7OzsWzZMnzx\nxRd46aWXMGDAAPj4+KBt27Zqi1EB4O7du5g9ezbu378vOF7jxo0NOpYpat++PSwsLEQnkZs/fz6i\no6MxcOBAWFlZ4dKlS/jll1/0uuq1Ltjb2yMwMBBz5swRjFEqlZg3bx42bdqEgIAAdO/eHS4uLjAz\nM0NmZiYuXLiAI0eOYMeOHcjIyMAbb7whOmarVq1E20+dOoVmzZrB29sbTk5Ope7TfuGFFzB37tzy\nPUkRhvwOIBKiUqkQERGBoKAgpKamlmpr1qwZQkNDMWTIEANlR6aGVQVEREQiOnfujBMnTqBXr17o\n2LFjqV8K7ezs8O6772LSpElo3rx5ufqtWbMmBgwYgAEDBuDLL7/EyZMn8c0332D79u0lK9n+8MMP\n2LNnD8LDw+Hj46Ptp0ZULoZeSVxXKzaw8Ns0KZVKSat4l0d5VhIvKirSSQ7FK4nXqFFDq/0SEREZ\nUmpqKjZs2IAjR47g+vXryMrKKnUOHTRoEKKjo0t+dnBwKHVD8N9//40mTZoI9u/t7Y1jx46V/Bwb\nG4sBAwZo90kQERGZIJ6DiYhMn6br12JFwMDTCTeTkpK0mZJB+Pn5ITAwUO0NeLm5ufjxxx/x5MkT\nwck6+/XrJ3pOA4DQ0FCEhoZqI13I5XKMGDECK1euFI2bNWsWTp48qZNVcurVqyf6/khNTUWXLl00\n9vP8TWnqxqHS6tWrJ1jorFKpcPDgQTg5OelkbGdnZ9Ei8ePHjxv1SkaGOnZ169bFrVu3BNsvXryI\nPn36aOzn4sWL2kyLiIiIiIiIdGDOnDkIDw/H1atXBWNu376NGTNmlLvv9u3bY/LkyaIxKpUKCQkJ\nSEhIKClUdXFxQYMGDWBvbw8bGxvk5+fj5s2buHz5ssZ7GQcOHGgUY5kSS0tLDB06FBEREaJxR48e\nxdGjR/WUlf5Mnz4d4eHhOHPmjGhcamqqaDG5VB07doSVlZXoImR3797FTz/9VGZ7p06dtFokDhj+\nO4DoWQcOHMBHH32E48ePl9pubW2N2bNn47///S9sbGwMlB2ZIlbFEBH9H3t3H1fz/f8P/HHqdKlr\nXamIEiYXSeaiQjqlqMYssrYw4YOGYTJtw8wUNhdzsRLCspHL5LqQMmOlTQgLLdKVLqRLXZzfH769\nf5061Tmnc1U977eb2633+7zfr9frvDver877/X4+n4S0oLy8HCtWrEBGRgYTIK6kpITly5fj2bNn\nCAkJETpAvDEWi4Xhw4fj119/xYMHDzB58mTmtby8PLi5uWH9+vVt6oMQQgghhJCOLD09HSwWq83/\nLly4IOu3wld7fH+1tbVYunQpBgwYgC1btiApKQmvXr0Se5IVQgghstUe5yhhtMf3R3MwIYR0HBoa\nGi2+/t9//7X4+rlz51oMGG0vVFVVsXDhwmZf3717Nw4fPtzs67J4MC8gIAAsFqvFbe7evYuAgIA2\nJeE9c+YMcnNzm6xvLQBc0L9NWttOkEDzzqa1Y3Lt2jWJ9T1y5MgWX4+IiGg2mYIoWvuMC0tWx87W\n1rbF1y9evNhqG2/evGnyMCchhBDRpaWl4csvv8SoUaNgZGQEFRUVnussHh4eEu3/zZs3iI+PR0RE\nBLZu3Yrvv/8emzdvRmhoKM6cOYMHDx6gqqpKomOQBFkfV0IIIUQeaGpq4ty5c2JPQmZqaoqYmBgo\nKysLvW92djaSkpJw5coVnDlzBpcuXcLDhw9bvWbE4XBgY2Mjt33Js8WLF4u8L5vNxnvvvSfG0UiX\nsrIyzp07B0tLS6n0p66uDh8fH6n0JQh5PAeQzicuLg5jxoyBi4sLzzVFFRUVBAQEID09Hd9//z0F\niBOhUZA4IYQQ0oyCggI4OzsjKiqKWTd8+HD8888/2LRpk0Qylffp0wcnTpzAxYsXYWpqCuDdg51f\nf/01/P39UVNTI/Y+CSGEEEIIIUTcvvzyS2zZsqVND7tLwq5du7BmzRrmX2ZmpqyHRAghhIgVzcGE\nENJx6OjotPj69evXm33t7du3CAoKEveQZGbBggVQU1Pj+9qDBw+QkpLC9zVjY2N4eXlJcmh8WVlZ\nwc/Pr9XtwsPD4evri/LycqHav3//Pjw8PODl5YU3b940ed3V1bXF/U+fPt1itRwASEpKQkJCQovb\ntNZPZ+Tm5tbi61u3bhX57zQul4vo6Gi8ePGC7+vu7u4t7v/48WNs2rRJqD5bujfd2kOKBQUFQvUl\nq2Pn4ODQ4r4XLlzAgwcPWtxm9+7dqKioEGlshHRm9UlVWvunqKgIXV1d9OrVC87Ozvjqq69w5coV\nufveS9quuroaixcvhrW1NTZv3oybN28iLy+PKeohSWVlZQgLC8P7778PHR0djB07FrNmzcIXX3yB\nb775Bl9++SX+97//wcvLC9bW1tDU1ISdnR0CAgJw5swZof+ekyZZHteW/p/funWrTW1HREQ02/bW\nrVvF9A46Fg6HwxyjLl26NPn7pfHvq62FcwghRF5ZWVkhLi4OVlZWYmlv4MCBuHLlCvPMtzR0794d\n+/fv73B9SYuDgwPmz58v9H5sNhuHDh3CqFGjJDAq6TEyMsKVK1davSYiLuvWrYO+vr5U+hJERzgH\nkPanoqIC27Ztg5WVFTgcDs/9HTU1NQQGBuL58+f4+eefYWJiIsORkvaMLesBEEIIIfKooKAAo0eP\n5rnhO3/+fGzdulUqWZ5cXV2RnJwMHx8fJiv63r17UVxcjCNHjkBRUVHiYyCEEEIIIYQQUaSmpjZ5\nAMfOzg7e3t7o3r07lJSUmPXdunWT6th27dqF+/fvM8scDgc9evSQ6hgIIYQQSaE5mBBCOpa+ffu2\n+Pr9+/cREhKCwMBAnvVFRUXw8/NDamqqJIcnVfr6+vDz80NoaKhQ+82aNQtstmweiwkODsbZs2fx\n6tWrFrf77bffcP36dXzzzTfw9vaGnp4e3+3y8vIQFxeHPXv24OrVqy226eHhAU1NTb4B5ABQVVWF\n6dOnIzY2Ftra2nz7+vTTT1sMvjMyMsLYsWNbHEdnNHHiRGhra+P169d8X09MTMTq1avx3XffCdxm\neXk5Tp48iZCQEKSmpiIlJQVmZmZNtnN3d4eenh4KCwubbWvVqlVgsVj48ssvW6wEXlVVhT179uDv\nv/9GeHg4321aS2Rx5MgRBAQEQEVFpcXt6snq2E2cOBGqqqqorKzk20ZNTQ18fHxw5coVvg80X7t2\nDWvWrBF4TIQQ4dXV1aG4uBjFxcXIyMjAlStXEBwcjL59+2Lt2rWYNm2arIcodrt27UJeXh6z/Nln\nn3WK79BLlizBrl27pN7vuXPnMG/evGYTsfBTXV2N5ORkJCcnY+fOnVBVVcXLly+hq6srwZGKRpTj\nKo3P4IEDBzB8+HCR94+IiBDfYDqBoqIixMfHM8uurq7NJuIihJDOYPDgwbhz5w6WLl2KiIgIVFdX\nC92GiooK5s6di5CQkFbPqS19BxeWi4sLDh48CGNjY5n31Z5t3boVxcXF+O233wTa3sjICJGRkXB2\ndkZsbKyERyd5PXr0QHx8PDZt2oSQkBAUFRVJrC8zMzPExcXBx8cHaWlpEutHGNI+B5DO69WrV9ix\nYwd2797N8x0LAFRVVbF48WIsXboUhoaGMhoh6UgoSJwQQghppLi4GOPGjWMCxNlsNsLCwjBr1iyp\njsPIyAixsbH4/PPPsXv3bgDA8ePHMWPGDBw6dEisX+QJIYQQQgjpSJSUlDBu3Dih9zMwMJDAaMRP\n3t9fWFgYz4PkkyZNwvHjx6GgoCCV/gkh8u3t27eoqamBurq6rIdCJEDe56i2kvf3R3MwIaQ5tbW1\nlHy2HbKxsYGysnKLFf5WrlyJmJgYTJgwAaqqqnj48CGOHz8udAXf9uCLL75oMte1hMViYc6cORIe\nVfOMjY1x8OBBeHh4oK6ursVts7Ky8L///Q8LFiyAra0tevToga5du6KiogIFBQV48uQJHj9+LHDf\nenp6WLp0KdauXdvsNklJSRgwYABWrFgBJycndO3aFa9evcKlS5ewcePGJg+sNfb111/Tw4986Ojo\nYMWKFQgKCmp2m3Xr1iElJQVBQUEYMWIE320yMjJw+/ZtREdH4/Tp0ygtLW21by0tLQQFBWHZsmXN\nblNXV4fAwEAcOHAAs2fPhr29Pbp16wZFRUUUFhbi/v37SEhIwJEjR1BQUIApU6Y021a/fv1aHM9f\nf/0FCwsLODg4oGvXrjx/k5qbm+PLL7/k2V5Wx05fXx/Tp09vsSpaamoqrK2tsWTJEjg6OkJLSwtZ\nWVk4efIk9u3bh9ra2hb7IIRIxqNHj+Dj44Pz589j7969Herv3c6YaC0lJaVJILM0Et9t2bIFS5cu\nbXM7lZWVcjkfiHpcpfEZ/P3337FlyxaBE8o09OzZM57Ke6R1MTExqKmpYZYnTZokw9EQQoh80NDQ\nQFhYGFavXo0dO3YgOjoaaWlpLV57YrFYsLa2xuTJk7Fw4UIYGRkJ1NeECROQmpqKa9euITExESkp\nKUhPT2/1mlE9Q0NDuLu747PPPsPo0aPlpq/2TFlZGZGRkZg4cSLWrVuHR48e8d3OxMQEfn5+WLFi\nhVwmBGoLBQUFBAYGYtGiRYiMjERkZCT++usvlJWVtbifoqIiBg4cCDc3N3z88ccC9TVo0CCkpqbi\n7NmziI6Oxp07d/D8+XOUlJS0eP1bkqR5DiCdz7Nnz7Bz507s2bMHJSUlPK+pqanBz88Py5YtE1tF\ne0IAChInhBBCmpg7dy7u3r0L4N0XoAMHDgj8JUbcFBUVsXPnTigrK2Pbtm0AgMjISNjZ2WHJkiUy\nGRMhhBBCCCHyTktLCxcuXJD1MCRG3t9fw0oEAPDll18KHJz24MEDnpuT0q5ySgiRPGVlZQQHByM6\nOhoeHh7w9PSEra0tJcPrIOR9jmoreX9/NAcTQppTW1uLBQsWoKKiAp6ennB1deVbPZjIFxUVFXh5\neeHYsWMtbpeYmIjExEQpjUp2+vbtCw8PD5w5c0ag7V1cXNCrVy8Jj6pl7u7uCAsLw5w5cwQKbq+r\nq0NSUhKSkpLa3PeyZcsQGRmJ9PT0Zrd58eIFFi1aJHTbNjY2mDt3bluG16EtWbIEkZGRTEJyfmJi\nYhATEwM9PT30798f2traqKioQGFhIV68eNFqBfrmBAQEIDIyEnfu3GlxuwcPHrQYTC4IW1vbFitw\nA8DLly9x9OjRJuuHDh3aJEgckN2xCwoKwpEjR1BeXt7sNnl5eVi1apXQbRNCBKepqYkPP/ywyfra\n2loUFRUhNTUVmZmZTV4/cOAA1NTUmOILpH0KCwvjWZZG4rvDhw/zDRC3tLTE9OnTMXLkSFhZWUFb\nWxs1NTUoKirCf//9h6SkJNy4cQNXr14VqeKgNMniuAqqqKgI0dHR8Pb2FnrfAwcOCJw8irxz6tQp\n5mdFRUV4enrKcDSEEMIrIyNDpv2bmppiw4YN2LBhAwoLC5GUlITc3FwUFhaitLQUmpqa0NXVhbGx\nMYYNGwYdHR2h+2CxWBgwYAAGDBiAgIAAAEBFRQWePHmC58+f4+XLlygpKWG+l3bp0gUaGhro3r07\n+vbtC3Nzc4HvpUqzL3GQ5e+fxWLB19cXvr6+ePz4MW7fvo28vDxUV1fDxMQElpaWGDFiRJO/ncLD\nwxEeHi5SnyNGjGjz3zH+/v7w9/dvUxsNqampMW3W1NTg7t27ePLkCYqKilBcXIzq6mqoq6vD0NAQ\nFhYWGDBgADQ1NYXuR1FREV5eXvDy8hJ5rOI4fo1J4xwgrrGL+3dPxKu0tBSHDx9GWFgYkpOTm7xu\nbm6OL774Ap999plI/4cIaQ0FiRNCCCEN/Pjjj4iKimKWf/nlF5kFiNdjsVjYsmULSktLsXfvXgDv\nHvAcOnQoHB0dZTo2wquoqKjDZYojhBBCOhoul4vXr1+LfMGWENIyLpeLtLQ0nnVDhgwReH8TExNx\nD4kQIoe+/fZblJWVYe3atVi7di169eoFNzc3uLu7Y9y4cejSpYush0hIu0NzMCGkJfWJaCdNmoSp\nU6dCXV0dY8eOxYQJE+Dm5gZLS0tZD5E0Y/Hixa0GiTeHzWbDysqqyfzQni1btkzgIHF5CWKePXs2\nunTpgpkzZ6Kqqkpq/WpqauLcuXMYOXKkWCvLm5qaIiYmBsrKymJrs6NRV1fHhQsXMHLkSGRlZbW4\nbWFhoViTPCgrK+PcuXOwt7fHkydPxNYuP+rq6vDx8UFERIRY25TFsbO0tMSmTZuwcOFCkdvo168f\nHj58KJbxENJZGRoatnpOSU5ORmBgIOLi4njW//LLL5g8eTJcXV0lOEIiSQkJCTzLwiS+E0VxcXGT\n4hxsNhs//vgjFi5cyLcyvYmJCaytrTFhwgSmjdOnT2PXrl24ffu2xMbaFtI+rq2xtrZGWloak6gw\nIiJC6CBxLpeLgwcPMsv11Rtzc3PFN9AOprKyEhcvXmSWHR0doaenJ8MREdLx/fHHH8jOzoaLiwu0\ntLRkPRwiBD09Pan9TammpsYEc3ekvtqrPn36oE+fPrIehsyx2WzY2trC1tZW1kORCWmeA0jHERsb\ni4MHD+LUqVN48+ZNk9ft7e0RGBiIiRMnykXCLtJx0aeLEEII+T///vsvgoKCmOV58+Zhzpw5MhzR\n/8disbBz504MHToUAFBTU4PPPvusxczsRPoyMzMxbNgwzJs3D1FRUSgpKZH1kAghhBDSCIvFwqlT\npzBo0CCsXLkSsbGxcp/ln5D2pLS0FDU1NcyykpIS1NTUZDgiQoi8CgkJwYoVKwAAz549w+7du+Hl\n5YWuXbvC1dUVP/30U4cKaCJE0mgOJoS0RlVVFadOncL48eNRXl6Oc+fOISAgAL1790bfvn2xZMkS\nXLx4ke47yBkHBwfMnz9f6P3YbDYOHTqEUaNGSWBUsjNmzBjmXllLjI2N21SRRtx8fHxw+/ZtDB48\nWKr9WllZIS4uDlZWVmJpb+DAgbhy5QpMTU3F0l5H1r17d1y9elWopD3iYmRkhCtXrsDBwUHifa1b\ntw76+vpibVNWx27BggVYvHixSPsuX74cvr6+Yh4RIYSfoUOH4tKlS5gxY0aT11avXi2DERFx4HK5\nTRJtSHoeOHjwIPLz83nWhYeHY9GiRXwDxPnR0dHBjBkzcOvWLdy8eVPuEl/K4ri2xszMDM7Ozszy\nxYsXkZOTI1Qb8fHxePbsGbPs6+sLNpvqpbXk8uXLKCsrY5YnTZokw9EQ0jmMHDkS8fHx0NXVhZ2d\nHfNsSsNr+IQQQghpv3Jzc7Fz5068//77cHFxwaFDh3gCxJWVlfHRRx8hLi4OiYmJ8PT0pABxInH0\nCSOEEEL+z+LFi5kM/gMHDsSWLVva1N7YsWPBZrN5/m3cuFHk9lRUVHDkyBEms2B6ejo2b97cpjES\n8Ro8eDB27NiBI0eOYOrUqTA1NcWkSZMQGhqKzMxMWQ+PEEIIIf9n5syZ+N///oeNGzfCxcUF5ubm\nmD17NqKiolBcXCzr4RHSrpWXl/Ms0wVuQkhLQkJCmjzAW1VVhcuXL2PZsmXo378/DA0NMXXqVBw8\neBCFhYUyGikh8o/mYEKIIFRVVREdHd3kgfDHjx9j27ZtcHNzg6amJhwcHBASEoLk5GRwuVwZjZbU\n27p1K6ZPny7w9kZGRrhw4QJ8fHwkOCrZWbZsWavbzJo1C0pKSlIYjeAGDRqE5ORkhIaGomfPnm1u\nb+jQoTh06BB69erV4naDBw/GnTt3MGfOHJGPiYqKCj7//HPcunWLKioJwcrKCrdu3UJQUBA0NDTa\n3J6enh5mzZoFMzOzVrft0aMH4uPjERwcDF1d3Tb33RwzMzPExcXhvffeE2u7sjp2W7duxZo1awQO\nNFNUVMSGDRuwadOmNo+RyD8ul4v4+Hi6hyAHFBQUEBoaCnNzc571f/75J7Kzs2U0KtIWpaWlqK2t\nZZalkfju9OnTPMtDhgzhm3xAUCNGjJC7ZH2yOK6CmDlzJvNzbW0tfv31V6H2P3DgQLPtEf5OnTrF\ns0xB4oRIHovFwtatWzFz5kwkJycjJCQELi4u6NWrF+bOnYsTJ07wrTRKCCGEEPmVlZWFkJAQWFtb\nw9jYGAEBAfjrr794trG1tUVoaCjy8vIQFRWFcePGyWi0pDOi9GmEEEIIgPPnz+P8+fMA3t3M3bdv\nX5svjNfU1PBcbAeAurq6NrVpaWmJH374AQEBAQCADRs2wN/fH8bGxm1ql4jP8OHDcfHiRYwfPx6v\nX7/G6dOnmZtL1tbWcHd3h7u7OxwcHKCsrCzj0QpGSUkJqqqqUFVVbXYbLpcLfX39FrdpuG1RURGT\nlEFchG2XxWJBV1cXKioqYh1HRyWp35uw6urqJDKGiooKZGZmCvTQa35+vsAPx6qqqsLAwAAsFqvV\nbQXdjhAiHgsWLAAABAQEIDs7G/v27cO+ffvAZrMxatQouLu7w83NDYMHD6b/m4QIgQJIgPv37yMt\nLQ35+fkoKiqCtrY2DAwMYGdnBwsLC4n1+/DhQ/z999/IyspCRUUFtLW14ezsjP79+ze7z3///Yd/\n/vkHL168QElJCWpra6Gurg5tbW2Ym5vDysoKPXr0kNiYCQGANWvWoLKyEiEhIXxfz8/PR1RUFKKi\noqCiogIHBwfmu3VLn29COhuag2kOJkRQysrK+O233zB58mRcuHChyes1NTW4ceMGbty4AQDo27cv\nJkyYAHd3d4wePZqup8qAsrIyIiMjMXHiRKxbtw6PHj3iu52JiQn8/PywYsUKiQaFypq3tzcCAwPx\n/Plzvq+zWCzMmTNHyqMSjKKiIubOnQt/f39cunQJx48fx8WLF5t9Lw0pKChgwIAB8PT0xKRJk2Bn\nZydwvxoaGggLC8Pq1auxY8cOREdHIy0trcW/H1gsFqytrTF58mQsXLgQRkZGAvdH/j8lJSV8//33\nWLFiBfbv34+jR48iOTlZoPscqqqqGDZsGBwdHeHk5MQkKBeUgoICAgMDsWjRIkRGRiIyMhJ//fUX\nTxVJfhQVFTFw4EC4ubnh448/brWfQYMGITU1FWfPnkV0dDTu3LmD58+fo6SkBG/fvhV4vI3J6tit\nXr0aH374IYKCgnDx4kW+70FVVRUeHh5YtWqVzCuyEulhsVgwNjaGtbU1dHR04OnpCQ6HgzFjxshd\nYpLOQEVFBfPnz8fKlSt51l+9elWgcxeRL7JIfHf//n2eZTc3N4n3KW3ymlBw8uTJ0NLSQklJCYB3\nQd/Lly8XaN+ysjIcO3aMWR4yZAgGDhwokXF2FHV1dThz5gyzbGNj0yTJBiFEMhQUFLBnzx4AwL59\n+wAAL168wJ49e7Bnzx4oKSnx3PMaMGCALIdLCCGEED6ys7Nx9OhRREVF4ebNm3xjgbp27Qp/f398\n+umnsLa2lsEoCXmHgsQJIYQQAOvWrWN+XrBggVAPVkjb/PnzceDAAfz1118oLy/H5s2bqaK4nBk+\nfDiuXbsGDoeDgoICZv39+/dx//59bN68GaqqqnBwcACHwwGHw8HQoUNlOOKWaWhoQF9fn6li3xwD\nAwOBHkiura3FjRs3kJOTI64hAnh3Y+PevXsCt6uoqAh7e3tKsiAgYY+vpMcibvn5+bh48SKqq6tb\n3basrKxJEpDmGBgYwMXFRaCHj6ytreXmxiwhncWCBQugpqYGf39/5txSU1OD69ev4/r16/jqq6/Q\ntWtXjBs3DhxBxYpaAAAgAElEQVQOBx4eHjAxMZHxqImsffvttzzfHxQUFHDp0iU4OzsL3MbGjRsR\nGBjIs+748eP48MMPxTZOaVJVVW32Ad2qqqo2JVp49uxZmyus2dnZITk5me9rjo6OLe67ePFibN26\ntdU+srKyEBwcjJMnTyIrK6vZ7Xr37o358+dj4cKFQgXXGBsbIzc3l1lOS0tDv379UFtbi9DQUGzd\nuhX//vtvk/3WrVvXJECtvLwc27ZtQ0REBB4/ftxq30ZGRnBycoKPjw8++OADgcdMiDCCg4MBoNlA\n8XpVVVWIi4tDXFwcli9fDnNzc7i5ucHd3R3Ozs5iqTBH5BfNwU3RHExzMCGiUlVVxcmTJ5sNFG/o\n0aNHePToEbZs2YIuXbpg3LhxTGK11ioYdzQZGRky65vFYsHX1xe+vr54/Pgxbt++jby8PFRXV8PE\nxASWlpYYMWJEk+uL4eHhCA8PF6nPESNGiJyEpC37tobNZsPLyws7d+7k+zqHw5H7z6aCggLc3NyY\nYKTc3Fw8ePAAmZmZKCgoQEVFBRQUFKClpQVdXV1YWVnB2toa6urqberX1NQUGzZswIYNG1BYWIik\npCTk5uaisLAQpaWl0NTUhK6uLoyNjTFs2DDo6OiI1I84fv/+/v7w9/dvl/3zo6WlhcWLF2Px4sV4\n+/YtUlJSkJGRgeLiYhQVFaGurg4aGhrQ1NSEmZkZkzBGHPcM6q9/+vv7o6amBnfv3sWTJ09QVFSE\n4uJiVFdXQ11dHYaGhrCwsMCAAQOgqakpVB+Kiorw8vKCl5dXm8fbmCyO3cCBAxEdHY3i4mJcv34d\nWVlZKC4uhoGBAczMzGBvb9/kGH399df4+uuv2/p2iZzr27cvLly4AGdnZ4SEhCAkJATdunVj/jZy\ncXER+dxJhDdmzJgm61r7e62qqor5+zYnJwdv3ryBsrIydHV1YWJighEjRkg82Y4oicbkRX5+Pv78\n80/k5ubi1atXTMJ0S0tLDBs2DIqKiiK1K+3Ed3V1dXj16hXPOgMDA6mOoTFJHFt5TSiopqaGqVOn\nMt9T7t27h+TkZIGemzp27BhKS0uZZXFWEZeH84Mk3LhxA/n5+cyyPFYRl9S5pTFJJm2khJCkOfwC\nxetVV1fj6tWruHr1KlasWIHu3bszAePOzs5Cfy8jhBBCSNtxuVz8888/uHDhAi5cuIA//viD73Pl\nioqKzH1cb2/vVmMsCJEGChInhBDS6V29ehU3b94E8O7BqFWrVsl4RC1TUFDAmjVrMHHiRADAL7/8\nglWrVkFPT0/GIyMN2djYICYmBuPHj2ey3zZUWVmJ2NhYxMbGAgAGDBjAU2VcnrKNs1gs5l9r2wnT\npiTU1dUJHLwLyO9NMXkl7PFtT7hcLmpqagR6f8IEqbNYLLDZbIFuWlGAOCGyMWvWLJSXl+Pzzz/n\nOy8UFBQw1UvZbDbs7e15qoyTzmfNmjX4448/EBcXB+DdvPDxxx8jJSVFoCQCCQkJCAoK4ln3xRdf\ntNvgtM6urq4Oa9aswaZNm1BZWdnq9unp6Vi2bBm2bduGEydOtClZVF5eHiZNmsR8n+Wn8XktOTkZ\nkydPFqhKXb3c3Fz8/vvvuHz5MgWoEYkKDg6Grq5ukwpQLfnvv/8QGhqK0NBQKCoqwsbGhknsMmrU\nKPobu4OhOZg0RHMwIW2nqqqK6OhoTJ06FadOnRJon7KyMpw5c4apQtazZ0+4urqCw+Fg/Pjx9BCM\nlPTp0wd9+vSR9TBkpqamhqcSXmPz5s2T4mjEw8jISOqVuvX09ODq6irVPsk7ysrKGD58OIYPHy71\nvtlsNmxtbWFrayv1vsVB2sdOR0dHIoHvpH0bOHAg4uLi4OzsjPz8fGRnZ2Pfvn3Yt28f2Gw2Ro4c\nySS0s7Gxkdh9cQK+1wIaB/4CwJMnT3DkyBFcunQJf/75Z7PJ1oB393ZtbGywaNEi+Pr6Cv3cRlsS\njfn5+Uk80Zoo6urqcOjQIezYsQPJycnNPmOhq6sLT09PfP3117Cysmq1XVET382YMQMRERECj58f\nBQWFJu03DKKVFkkc27Yc1/pgbX7E/RmcOXMmTzKrAwcOCHStpuHvXklJCR9//LHAffIj6fODPCS9\nbPx9f/LkyQL3LUmSOrc0JsmkjZQQkghKQUEB4eHhUFdXx44dO5rd7vnz5wgLC0NYWBgUFBQwZMgQ\nuudFCCGESMGLFy9w7tw5xMbGIi4uDoWFhXy3U1JSwvjx4+Ht7Q0vLy9KFEjkDgWJE0II6fQaXkCe\nMWNGu6gq7O7ujkGDBuHu3bsoKytDVFRUu3zopaMbMWIELl682GygeEP37t3DvXv3sGnTJmhqaoLD\n4TBB42ZmZlIaMSGEENI5LVy4EFwuF4sWLWoxgUhNTQ3i4+MRHx+PlStXwtTUlJmvORwOPQwvR7hc\nLlJSUhAfH4/Hjx8jLy8PysrK0NPTg6mpKezt7TF8+HChqkjWU1BQwOHDhzFkyBC8fPkSwLtAoWnT\npuHq1atgs5u/3JaXlwcfHx/U1NQw60aNGtVq5Vxpvj8iuLKyMvj6+uL06dN8X2ez2dDS0sKbN2+a\nZJXNzMzEmDFjcOLECZEeyn/z5g2mTp2K1NTUFrdreE57/Pgxxo0bx/e7iaKiIgwMDKCqqoqysjK8\nfv0ab9++FXpchLRV/cNuwgSK16utrUVycjKSk5MREhICfX19ODk5gcPhwMvLq11c7+kIaA6mOVga\naA4mRHyUlJRw9OhRoQLFG8rIyGAeXmWz2Rg+fDg8PT3B4XDalIyBkJbs2LEDmZmZfF/r0aMHPWxP\nCCFE4hoHiterqalBQkICk6TM2NiYp8p4e6xA295t2bIFS5cuFXj7+u/9s2bNwk8//YRTp07BwsKi\nTWMQJdGYvHj48CG8vb1x7969VrctKirCwYMHcfjwYSxfvhzr16+X62A2IyMjZGVlMcsxMTFYv369\n1BI7dORjKwh7e3tYWVkxSRN+++03/Pjjjy0GXmdkZCA+Pp5Z9vDwgL6+vshjkMb5QR6SXjb8rm9h\nYYFBgwYJvK+kSOvzL8mkjZQQkgiLxWJh27ZtKCsrw/79+1vdvq6ujueeV48ePXiqjGtoaEhh1IQQ\nQkjHlJeXh5s3byIxMRGJiYn466+/mi0wpqCggOHDh2PatGnw9vYW6O94QmSFgsQJIYR0aqWlpTh2\n7BizPH/+fBmORnAsFgvz5s3DwoULAQAHDx6kIHE5NWLECMTHx4PD4aCgoECgfd68eYOTJ0/i5MmT\nAN5dpOdwOEzgOF3kI4QQQsQvICAA6urqmDNnDurq6gTaJysrC+Hh4QgPD+fJ5MzhcDB27NgWA5WI\nZBUWFrZaEUlNTQ0zZ87EsmXLYGlpKVT7hoaGOHr0KMaOHcsEmyUmJmLlypXYvHkz333qH/yoD2oD\nAH19fRw5ckToaiSSfn/CevLkCfMQW35+Ps/YVFRUkJ6eLnBb1tbWrSZYEta5c+eYYCsOh4NHjx4x\nr504cQLDhg1rdl9NTc1mX/Pz82sSnGZtbY3PP/8cHA6HOe5cLhdpaWn4/fffsXXrVrx58wbAuwA3\nHx8fpKSkwNzcXKj3tHz5ciY4TVtbG3PmzMH48eNhbm4ONTU1vHz5EgkJCTA0NGT2CQgI4Dm2qqqq\nWLRoEXx8fDBw4ECecxaXy8WzZ8+QkpKC8+fPIzo6WuBzIyFt1ZZA8YZevXqFqKgoREVFYf78+VRx\nQUpoDqY5uCGag9+hOZjIu/pA8WnTpjHXpEVRU1ODGzdu4MaNGwCAXr16wcXFhaqME5EVFxejuLgY\nAFBdXY2srCycPn0a27dvb3afxYsX0/UYQgghUjFw4EAkJibCycmJ5/tmQzk5Odi/fz8TCNS/f38m\noc6YMWOE/k5KePE77o0DRl+/ft3s/mpqalBXV0dpaSnf6sGpqakYNmwYkpKS0KtXL5HGKEqiMXlx\n8+ZNeHh4NFtBTVtbGxUVFU0SndXU1CA4OBj//vsvDh8+DGVlZWkMV2ijRo1CVFQUs5yamoo1a9Zg\n7dq1Eu+7ox9bQfn5+eGbb74B8O5abkxMTItVrg8cOMDzf2XGjBlt6l8a5wdZJ728e/cunj59yizL\nQ2CytD7/kkzaSAkhiagUFBSwd+9eqKurY+fOnULtm5mZidDQUISGhkJRURE2NjZ0z4sQQggRwNu3\nb3H79m3cuHEDiYmJSE5ORnZ2dov79OzZE15eXvD09MTIkSPRpUsXKY2WkLahu2OEEEI6tatXr6K8\nvBwAMGDAAAwePFjGIxKct7c3lixZgurqavz555949epVmzKkEsmxsbHB5cuX4eLiInCgeENPnz5l\nqrGoqanB3t4eHA4Hnp6e6N+/vwRGTAghhHROn332GQAIFSher3Em54bVSz09PdGtWzdJDJm0QUVF\nBXbv3o3w8HBs3LgRS5YsEWp/e3t7BAcHY/ny5cy6H3/8EQ4ODpg0aVKT7desWcNUCgDe3QSOjIyE\nmZmZ6G+iBW19f8IwNTVlfub3QIsw71ESVToaBmk1Hp+BgYFIv4OtW7fixIkTPOtWr16Nb775BoqK\nijzrWSwW+vfvj++++w4zZszAhAkT8PjxYwDvKiD4+/vj8uXLQvV//fp1AO8C7n777bcm3wXNzMzw\n/vvvM8tZWVmIjY1llpWUlHDlyhWMHDmSb/ssFgsWFhawsLDAlClTUFVVhbNnzwo1RkLaIjAwECwW\niwkYbyuap+ULzcHiQ3PwOzQHE9J2SkpKOHLkSJsDxRt69uwZVRknbbJ161ahgnR69uyJBQsWSHBE\nhBBCCK8+ffrg4sWLcHZ2Rl5eXqvbP3jwAA8ePEBISAiMjIzg5uYGd3d3uLi4QE9PTwoj7ljqv581\n1FwiMB0dHaaq++DBg9GvXz+oqKgwr+fk5ODGjRsIDw/HhQsXmPWFhYXw9vbGrVu3mnznFIQwicYk\nlWhNFDk5Ofjggw+aBHGOHTsWX3zxBTgcDtTV1cHlcvH06VP8/vvvCAkJYZKzAcDx48cRGBiILVu2\n8O1D1MR34no438fHhydIHAC+++47XL9+HcuXL4erq6tEEjlI+ti25bhWV1dL9TPo5+eH1atXM/dk\nIyIimg0S53K5OHjwILNsYGCACRMmCN0nP5I+P8gy6WXDKuIA+F47lSZpnFvqSTJpIyWEJG3BYrHw\n888/A4DQgeL1amtree55GRgYYOzYsfDw8ICnpyd0dXXFOWRCCCGk3aiqqsL9+/dx9+5d5l9KSkqz\nCYrqqaiowN7eHuPHj4erqysGDx4skXvnhEgaBYkTQgjp1Bo+BCjrC6HCMjAwgL29Pa5du4a6ujrE\nxsbCx8dH1sMizRgyZAguX74MDofT6peNllRUVCA2NhaxsbFYuXIlVRknhBBCxKwtgeINUfXS9qO6\nuhpffPEFUlJSEBERIdRF3mXLluHGjRs8QRQzZ87EnTt3YGFhway7ePEi1q9fz7Pv119/DVdX17a/\ngVa05f0R/l6/fo3Vq1fzrPvuu++YihctsbS0xNmzZzF06FDmAZLY2FgkJSXBzs5OqHEMGzYMZ8+e\nFahaSEpKCk+Fjfpst4JSUVHBhx9+KNT4CGmrFStWAIDYAsUbam6e5nA4GDt2LFWflBKag4mwaA4m\nRLIkESher6Uq425ubmIPaiGdD4vFwq5du6CqqirroRBCCOlkBgwYgISEhBYrivOTm5uLAwcO4MCB\nAwCoyriw3r59i927dzdZ7+TkxLPcu3dvhIeH45NPPuEJ+mzM2NgYU6ZMwZQpUxAVFYVPP/2UqR6c\nnJyMY8eOYdq0aUKPU9hEY/XElWhNVLNmzUJ+fj7Puh9++AFfffUVzzoWiwVLS0sEBQXBz88PLi4u\nPIHF27Ztw8SJE8HhcJr0Ic7Ed6KYPHkyRowYgT///JNn/bVr13Dt2jVoa2tj9OjRGDFiBIYNGwY7\nOzuxBN1J+tiK67hK4zPYo0cPODk5Mckdz58/j/z8fBgYGDTZNiEhgacitq+vb5vPk9I8P8gq6WXD\nIHF9fX3Y29sLtb+4SePcAkg2aSMlhCTiUB8ozuVysWvXrja3l5+fz9zzalxl3N7enu5NEEII6XAq\nKyvx7NkzpKenIy0tDf/88w/u3r2Lhw8fMomZWqKhoYHhw4fDwcEBDg4OVC2cdBj0RDIhhJBOLSEh\ngfl5zJgxMhyJaBre4OKXJZnIlyFDhiA2NlasWcDrq4xPnToVhoaGcHFxQUhICNLS0sTWByGEENLZ\nfPbZZwgLCxNbIHd99dKQkBA4OjrCyMgIU6dORVhYGHJycsTSB3lHU1MTkydPxq5du5CYmIicnBxU\nVFSgoqICL168wLlz57B06VK+D/IcPHgQX3/9tdB97t+/H5aWlszy69ev8dFHH6GyshIA8Pz5c3zy\nySc8SQc4HE6TACd5fX+kqV27dvFUCLCxsUFQUJDA+/fu3RtLly7lWcfvgcbW7NmzR6DgNABNElU1\nV1GHEHmzYsUKhISESLSPhvO0i4sLjI2NmXk6Oztbon13JDQH0xwsDTQHEyJ59YHizVVvE5f6KuON\nr2snJydLtF/Sca1btw7u7u6yHgYhhJBOqk+fPrh69SpPcKaw6iuM11cV9/T0RFhYGF68eCHGkXYM\ndXV1mD9/PjIyMnjWv//++zAxMeFZ98knn2D27NktBoA25u3tje3bt/Osq6/4KYr6RGONA8Tl1e3b\nt3mqJQPAkiVLmgRxNta9e3fExsZCR0eHWcflcrF27VqJjLOtWCwWjh8/3uz39NevX+PMmTMICgqC\nq6sr9PT0YGVlhdmzZ+PQoUN4/fq10H12lmMrjJkzZzI/V1dXIzIyku92ERERze4nKmmfH5YtW9bk\nu/bMmTN5gt8B8SW9zMzMREpKCrPs6enZYsVzSZPm51+SSRspISQRFxaLhR07dmDBggVibbe+ynjj\nZ1MOHjyIoqIisfZFCCGESEpdXR1ycnKQnJyMo0ePYsOGDfD394eTkxN69OgBdXV19O/fH15eXggM\nDMThw4dx7969ZgPEjYyM8OGHH2LLli24ffs2ioqKEBsbizVr1oDD4VCAOOkwKEicEEJIp1VbW4uH\nDx8yy/yy88q74cOHMz8/ePBAhiMhgpJEoHi9+irjK1euRP/+/WFpaYl58+bhzJkzzAPShBBCCBHM\n7NmzxRoo3lB99dJ58+bB1NQUdnZ2WLlyJRITE9tUvbwz09bWRkREBHJycnDixAnMnz8f9vb2MDIy\ngqqqKlRVVWFqagp3d3f8+OOPyMzMhL+/f5N2NmzYIHRQgra2No4dO8ZTrSwlJQWff/45qqurMW3a\nNLx69Yp5zdTUFIcPHxbqsyXL90eaavyQ0pIlS4Q+V8yaNYtnOT4+Xqj9HR0dMXjwYIG3b/jwDIAm\n1VEIkWfSCBRvqKCggJmnzczMmHk6NjZWoKzTnQ3NwTQHSxPNwYRIh7QCxetVVlYy17Xt7OyY69pR\nUVF48+aNVMZA2i9NTU388ssvQiUNIYQQQiRBHIHi9UpLSxETE4N58+ahe/fusLa2Zq5NvH37Vgyj\nbb/+/vtvuLu7Y9++fU1eW7Nmjdj6mTNnDk/F3lu3bqG8vFyktoRJNCYPtm3bxrNsZmbWJGC1Ofy2\nTUxMlNtrIiYmJkhOToanp6dA26enp2Pfvn3w8/ODsbExfH198fjxY4H760zHVlAffvghNDU1meUD\nBw402aa8vBzHjh1jlgcPHizUtRlxa8v5QZpJLxtWEQfAt2K5NEnz8y/JpI2UEJKIk6QCxRuqrzI+\nY8YMGBgYwM7ODmvWrEFycjJPwgNCCCFEkiorK5GXl4f09HQkJycjLi4OJ06cwM8//4xvvvkGn332\nGTw8PGBrawsTExMoKyujW7dusLOzw7Rp07Bq1Srs3bsX165dw/Pnz5udw9hsNt577z1MmzYN69ev\nx5kzZ5CRkYGcnBwcP34cS5YswbBhw8Bms6V8BAiRDvpkE0II6bQyMjKYi6wmJibQ0tKS8YiE169f\nP+ZnqhzdftQHinM4nCYXj8Wpvsp4WFgY1NTUYG9vDw6Hgw8++IDns0MIIYQQ/mbPng0AmDt3rsSC\nt+url9Znc9bX14eTkxM8PDzg4eEhkcQyHZGBgQFmzJgh8PYaGhrYs2cPTExM8N133zHruVwuVq1a\nhYsXLwrVv42NDX7++WfMmTOHWRceHo4HDx7g5s2bzDo2m40jR47AwMBAqPZl/f7I/5efn98kQZeg\nD5A11KNHD5iZmTGVgJ48eYL8/HyBPxvjx48Xqr9hw4bxLN+8eROLFi3CDz/8AA0NDaHa6qxOnz4t\n1AN/RPwsLCyaVFWRtMbztLq6OqZPnw53d3dwOBypjkVeyXqOojm486A5uPP6559/sHjxYlkPo1My\nMTFBz549m1RolLSG17WVlZXRp08fLFq0CO7u7jwP4pPOSVlZGXp6erC2tsb48eMxY8YMGBoaynpY\nhBBCCADAysoKV69ehZOTE7KyssTW7oMHD5hK4126dIGTkxM8PT3h7u6O7t27i60fWcvLy+NbHbiu\nrg5FRUW4d+9es3+b+vv7w93dXWxjYbFYGD16NA4fPgwAqKmpQVJSEkaPHi1UO8ImGpM1LpeL8+fP\n86ybM2cO1NXVBW5j1qxZ+Oqrr1BSUsKsO3fuHIYOHSq2cYpT165dER0djevXr2Pz5s24cOECqqur\nW92vsrIShw8fxtGjRxEYGIjvvvuuxUR2nfHYCkJdXR3e3t5M4oe///4bd+/exaBBg5htjh07xpNA\nTBxVxNuiLeeH+qSXI0eOZJ5ZrE96uWvXLrEkvazXMEhcXV0dLi4uQrchLtL+/EsyaSMlhCTiVh8o\nDgC7du2SaF/1VcaTk5Oxdu1aGBoaYsyYMfDw8ICnpyd0dXUl2j8hhJCOISsrC/PmzUN5eTmqqqoA\nAG/fvkVZWRmAd9/hX79+jeLiYpSUlKCkpITZTlyUlZXRq1cvWFlZoU+fPhgwYAAGDRoEa2trngTz\nhHQ2FCROCCGk02p4U87CwqLV7SsrK5mHBwXBr3JzQUEB0tPTBdpfUVERvXr1anGb7t27g81mo6am\nBnl5eaiuroaSkpLAYySyM2TIEFy+fBkuLi4SDRSvV19lvL4ii4WFBTgcDjw8PODq6goVFRWJj4EQ\nQghpj2bPng0ul4t58+ZJpcp3fZXxqKgoKCoqwsbGhpmzR40aJZHK5p3Z2rVrce3aNVy/fp1Zd/ny\nZeTm5sLIyEiotvz9/ZGYmMhT5eCPP/7g2SY4OBj29vZtG7QQxPn+yDu3bt3iyUhraGiI8vJykarI\ndO3alec7ZnZ2tsABakOGDBGqr27dusHLywvR0dHMup9//hkHDhzAlClTMGHCBDg6OtLnogWZmZnI\nzMyU9TCIjJWXl2Pv3r3Yu3cv2Gw21NTUZD2kdovmYCIsmoM7r/T0dGzfvl3WwyAy8vbtW9y7dw9z\n584FAAwaNAhubm48D82TjmvNmjVirQhKCCGESIOkAsXrlZWVISYmBjExMQDePetSH9gzevTodlWx\nurE3b97wrSLcGl9fX+zevVvo/d6+fYs3b97gzZs3qKmpafJ642MpyrVBYRONyVpaWhqKiop41k2Z\nMkWoNtTU1ODh4cEE0ALAjRs3xDI+SRo9ejRGjx6NV69e4dy5c4iPj0diYiL+/fffFiut1tTUYP36\n9bh//z6OHTsGRUVFvtt15mPbmpkzZzJB4gAQERGBn376iWe5npKSEnx9fSU+JkmeHySd9BJ4V+06\nISGBWR4/frxMr2VL+/MvyaSNHS0hZHV1NZ48eSKRtt+8ecMUbaqqqpJYPx3F4sWL8fLlS54ED5KW\nl5fHPJuioqKC999/H2PGjMGUKVNgY2MjtXEQQghpXwoLCxEWFibxfrS1tWFiYoJevXqhT58+6N27\nN6ysrNC7d2+Ym5s3+92LkM6MgsQJIYR0Wg2zjGpra7e6fVJSEhwdHdvU5+bNm7F582aBttXW1kZx\ncXGL2ygoKEBLS4sJMp40aVK7vunXGVlbW+PmzZt8bypIUsNqLEpKSnBycsL06dPh5uYm1XEQQog0\nJSQkwNfXF2w2fRUmwrO2tkZqaqpU+2yYybm+eumnn36KCRMmwNnZGV26dJHqeDqq1atXw9nZmVnm\ncrm4dOkSPv30U6Hb2r17N+7cucP3szJp0iQsW7asTWMVhTjfHwFycnJ4lvPy8sRWrUeY5FGiPBS0\na9cupKSk4Pnz58y6kpIS7N+/H/v37wcAWFpaYuTIkRgzZgw4HA569uwpdD+EdAYWFhZwd3fH5cuX\nea4vEeHQHEyEQXMwIURJSQm6urrQ09OjZLmEEEIIkWuSDhRv6OnTp9i+fTu2b9/eoauM89O7d2+s\nXbsWH3/8sUDbp6en4+jRo7h+/Tru3bsn9O+mcYCjIIRNNCZrja+rdOnSBe+9957Q7djZ2fEEct69\ne7fNY5MWfX19+Pn5wc/PD8C77+8pKSm4desWrl27hri4OLx9+7bJfqdOnUJQUBCCg4P5tkvHtnmO\njo6wtLRkAkgjIyOxceNGsNls/Pfff7h27Rqz7YQJE0S6NtMaaZ8fJJ30MiYmhuc5tMmTJ4vUjrhI\n+/MvyaSNHS0h5NOnT9G7d2+J97N7926REroQ6amqqkJCQgISEhLw/fffw9LSEm5ublIpfkQIIaTj\nU1JSgpaWFrS1taGtrQ0tLS1oaWnB2NgY3bp1g5GREUxMTGBoaAgTExMYGRlRwn5CREBPxhNCCOm0\nSktLmZ/bYybHepqamszFmHPnzsl4NKQ9qq6uxqVLl3Dp0iWwWCz069dP6Da4XC6KiopQVVXV6rZ1\ndXUCbVffrqAXG7lcLrp06YJu3boJtL2CggLevn0r0M0aNpsNDQ0NsFgsgdom8oHL5aKsrAzV1dWt\nbltSUoLy8nKBEjYI+vkF3s0v7733nkBByebm5lQhWEhcLhclJSV8b8TXq6ysZH5+8eIFT7U4Qtqb\n8vJyhCpuGcgAACAASURBVIaGIjQ0FCoqKhg9ejTc3NykUuG8Ixs9ejS0tLRQUlLCrBM1IYCamhq+\n+eYbTJ06lWe9lpYWEwAkbeJ8fwQoKCiQWNtlZWUCbyvKd1hTU1Pcvn0b8+bN43l4paEnT57gyZMn\n+PXXXwEA77//PhYuXAhfX99On4XXy8sL8+fPl/UwOq38/HwEBgYiOztbJv0rKytjwIABGDVqFD7/\n/HP06dMHwLv/I0R0NAcTYdAc3HkNHjwYs2bNkvUwOqXy8nLs3r2bJ8GBtOno6GDIkCEICAgAh8Nh\nKlA1rK5GCCGEECKPpBkoXq+jVhlXUFCApqYmdHR0YGFhgWHDhsHFxQXOzs4C3b/PyMjA8uXLcfz4\n8TaNQ5REgZIIZpWkxt+9Rb1/bWFhwbPcngPMtLS0MGbMGIwZMwYrVqxAcXExdu/ejeDgYJ5rPgDw\n008/Yd68eejVq1eTdujYtszPzw+rV68G8C4x4Pnz5+Hp6YmDBw/yVHKfMWOGWPuV5flBkkkvT548\nyfzMZrPh4eEhclviIIvPvySTNlJCSNIZPHnyBDt37qTn2AghhDRhamqKb7/9FmpqalBVVQXwLgi8\n4T1UXV1dJhBcS0uLAr4JkRIKEieEENJpNaw0IUgAobxqz2Mn8kVJSQmjRo3CkCFDkJaWJtS+dXV1\nuHfvXpOqTi1tL0y7gtzcVVRUxMiRIwXOxlpXV4dHjx4J9JCjrq4u3nvvPQoSb2e4XC6eP3+O4uLi\nVrfNzMxEdna2QEHi9W0Lonv37pg9ezZzMaQlCgoKnf7Bb2FxuVw8fvwY+fn5zW7T0muEtFdaWlrg\ncDhwc3ODu7s7fvrpJ1kPqV1js9no2bMnT7b5vLw8kdoqLCzEl19+2WR9SUkJfv31VwQEBIg8TlGJ\n8/0RtJiYpK0E/fsCgMh/lxobG+P06dO4c+cOIiIicObMGWRkZDS7/e3bt3H79m389NNP+P3330VK\nKNVRmJubw83NTdbD6JSePn2KuXPnSjVAnMViwdbWFhwOBx4eHhgxYoRAiZ+IcGgOJsKgObjzzsG9\ne/fG4sWLZT2MTicnJwfjxo2TeoC4pqYm3NzcwOFwwOFwmjwATgghhBDhFBQU0P3NTqxhlfGuXbsy\n9xQaFlSQJ5aWlkhPTxdrm3/++ScmTJggUhXwxkRJ2NveClY0Pk71SZqEpa2tzbNcVVWFsrIydOnS\nReSxyQsdHR189dVXmDp1KsaNG4fMzEzmterqavzyyy8ICQlpsh8d25bNmDEDa9asYa7RREREwNPT\nk6fStr6+vliDnWV9fpBU0suKigpcunSJWR49ejR0dXVFbk8cZPH5l2TSxo6UEFJJSQndu3eX9TDI\n/6mtrUV2drZEr4ULwsjICJMnT4a7uzs2btyIGzduyHQ8hBBC5Iuenh7mzp0r62EQQvigp5oIIYR0\nWpqamszPomT0lBcNM9MePnxYoEBEIj9+/fVXnDhxQmb9d+3aFUOGDMH06dPx0UcfQUtLC6mpqdi6\ndavQbdXV1aG2tlbsYxTmZgqLxRL4YjqLxQKXyxWofarQ2n4J8zuuq6sT6sFwQbBYLCgpKfEkJiHi\nVf+7a07D36mjoyPmzZsHdXV1aQyNdCBVVVX44YcfZFr50dzcHLa2tvj888/h4OBA5xUxa/xQQePq\nD4Lgcrnw8/PDf//9x/f1ZcuWYfjw4Rg2bJhIY2wLcbw/8k7Xrl15lkeNGtUub4zb2trC1tYW27dv\nx/Pnz3Hjxg388ccfSExMxN9//93kb6J//vkHTk5OuH37Nj0sQqTq6dOncHJykkqAmpaWFsaPHw8P\nDw+4urrC2NhY4n0SmoOJ4GgOpjmYSE99gLiwiURFNXToUCYo3N7enipKEEIIIYRIQEFBASIjIxEZ\nGdlpKkLm5eU1CQBVUFDA+PHj4erqiiFDhsDMzAwGBgZQUVGBiooKz/7Lly/Hjz/+2KYxUJKGjsvS\n0hKRkZFwdHTkWR8bGyujEbVv5ubmGDt2LK5evQoAiImJwenTp/HkyRNmm48//lhs90fl4fwgqaSX\nly5dQnl5ObM8adIkkcfY3kkyaWNHSQhpYWGBhw8fynoYBEBWVhacnJxkEiBef3+Mw+HA1dUVPXv2\nZF5r67mOEEIIIYRIDwWJE0II6bQaPqTZXquMlpWVoaysDMC7zI4+Pj50k6md4HK5WLp0qdQDxKkS\nCyGkMzMzM4Onp6fImalJ51RSUgJ3d3epB4ibmJjAw8MDHA4H48aNaxIUQ8SroKCAZ1lPT0/oNoKD\ng3H27FlmmcVioWfPnnj27BmAd9Uvp06dijt37kg9Y7843h95x8DAgGe54QNK7VX37t3h4+MDHx8f\nAO8ejjp58iS2b9+OBw8eMNvl5OTgq6++YqoeECJp9QHiDavxiFt9UBpVC5cdmoOJoGgOpjmYSEdO\nTg6cnJwk+oAwXaMmhBBCCJE+PT09jB8/HhMmTMCzZ8/w7bffynpIEvftt9/yBICampri9OnTGDp0\nqED7y2vFdUlqfN1E1GR3r1+/5llWUVFp95Wu+XFwcMCgQYNw9+5dZl1z36Xo2LZu5syZTJD427dv\nMXv27Cavi4uszw+STHp56tQpnmV5CBKX9edfkkkbKSEkEYf6APF///1XKv2xWCzY2trS/TFCCCGE\nkA6mc6SFJIQQQvjo06cP8/OjR49arTTr4OAALpcr8D97e/smbWzYsEHg/YuLi1t9D48fP2YuJvbu\n3ZsCxNsJLpeLL774QqRq3aIYOnQoAgMDcfnyZeTm5uLo0aOYO3cuPXxHCCGEtKI+QPyPP/6QeF9s\nNhscDgfBwcFISkrC8+fPERoaCm9vbwoQl7CysrImQUaNg5BaEx8fj2+++YZnXWBgIOLi4qCjo8Os\ny8jIwIwZM5o8ECBJ4nh/5P8bMmQIz3Jubm6Hy7BvaGiIefPm4e7du0zQWr3jx4+joqJCRiMjnYmk\nAsS1tLTg7e2N0NBQPHv2DElJSQgODoaDgwM9ACMDNAcTYdAcTHMwkTxJBog3vEadl5dH16gJIYQQ\nKenatSvq6uron4z+/f333zL7HqisrAwOh4MtW7YgPT0dBQUFOHz4MD755BNoaGjIZEzSVFNTg6io\nKJ51+/fvFzgAFGi/xSbaovH9qMzMzFafpeKnPnFfvY6cNG/w4ME8y+Xl5aisrGyyHR3b1k2ZMoXn\n/NQw+eLAgQObXBsSlTycH/glvezVqxezXJ/0smEguyBqa2sRExPDLNva2spFILI8ff7rkzZu374d\nd+7cQU5ODn755Rf079+fZ7v6pI3y0jbpuF68eCGVAHFtbW14e3vjwIEDePnyJd0fI4QQQgjpgChI\nnBBCSKelo6PD3JCrqKiQaGUqSUlLS2N+bhj0TuRXfYD4tm3bJNaHpqYm89D7kydPmIt6HA4Hampq\nEuuXEEII6UikESBuYmKCuXPn4ujRo8jJycHly5cRGBiIoUOHQkGBLtlIy9mzZ1FVVcWzzs7OTuD9\nc3Nz4ePjg9raWmbd6NGj8f3336NXr17Yv38/z/ZnzpzBpk2b2jZoIbT1/XU0ysrKPMs1NTVC7d+7\nd2/07NmTZ92RI0faOiy5pKioiG3btvEkI6usrER6eroMR0U6A3EHiNcHpSUkJKCgoIAJSmv8f5lI\nH83BnQvNwYKjOZjIgrgDxBsmZnn69CnPNWpVVVWx9EEIIYQQwbBYLPong3/37t2Di4uLVAONzczM\nMHfuXERHR6OwsBCXL1/GkiVLYGlpKbUxyIvHjx+jsLCQWTYxMYGLi4tQbSQlJYl7WHJv0KBBPMul\npaV49OiR0O00PnaN2+1IlJSUeJYVFRWbXAMB6NgKokuXLvjoo4/4vibOKuKyPj9IMullYmIiXr16\nxSzLQxVxQL4//5JM2kgJIUlrJBkgzmKxeO6PvXr1CkePHoWfnx+MjY3F3h8hhBBCCJE9euKYEEJI\np9Ywy+j169dlOBLRxMfHMz+LK2MqkRwul4v58+dLJECcKrEQQggh4iOpAHElJSWqFi5nqqqq8O23\n3/Ksq6/qLoja2lpMnz4dOTk5zDojIyP8/vvvUFRUBPDuAYxly5bx7BcUFITExMQ2jr51bX1/HZGm\npibP8uvXr4VuY+rUqTzLW7Zs4alo0ZEYGhpCW1ubZ11ZWZmMRkM6g2fPnrU5QLy5oDSqhiBfaA7u\nfGgOFg7NwUSasrOzxRIg3vAadW5uLnONumE1NEIIIYSQziA1NRXOzs4SDxBveM/h3r17zD0HT09P\ndOnSRaJ9y7vc3FyeZXNzc6H2v3v3rkwLTbQ10Zqo+vXr16Qy74kTJ4Rqo7KykqdCMgDY29u3eWzy\nqnFlY319fb6JoNvbsZXVZ5BfMDibzcYnn3witj5keX6QdNLLU6dO8SzLS5B4e/j8SzJpIyWEJPxk\nZGTA0dFRrJ8DqhYumOzsbFy+fBnh4eHYvHkzvv/+e2zbtg2HDh3C5cuXUVRUJOshEkIIIYSIhP7i\nI4QQ0qk5OTnh0qVLAIDY2Fj4+fnJeETCiY2NZX52cnKS4UhIa+oDxENDQ8XSnqamJtzc3MDhcODi\n4kIP2hFCCCFiUlJSAjc3N9y8eVMs7ZmammLixIngcDgYN24cBYNLQFZWFkxMTHhurAuipqYGn376\naZNM9R999FGTgJzmrF69GlevXmWWFRQUcPjwYXTr1o1nu+DgYNy8eZNJPFBTUwMfHx+kpKTAwMCg\nxT5k+f46IhMTE57lBw8e4IMPPhCqjeXLl2Pnzp1MoNbr168xbdo0nD9/vknVEEFxuVyhf8fSaD8/\nP79JEF/jzzch4vLy5UuMHz9epAfsevToAXd3d+Z7soaGhgRGSBqjOZg/moP5ozlYODQHE2kpLCzE\nxIkTRQoQV1FRgaOjI9zc3ODu7o7+/ftLYISEEEIIIe2LpAPEzczMMGHCBHh4eMDJyYmugTSj8few\nkpISofbfuHGjOIcjNHEkWhMFi8WCu7s7IiMjmXXh4eFYtmwZVFVVBWrj4MGDKC4u5lk3ceJEsY5T\nHGpqatocMJeTk4OEhASedTY2Nny3bW/HVlafwdGjR2P27NkoLS1l1r333nswNDQUWx+yOj8Ik/Ty\nxx9/ZLYJCgrCqFGj4ODg0Gofp0+fZn62tLTEwIEDRRqruLWXz3990saG/YgraaMk2ybtT0ZGBpyc\nnJCRkdGmdlgsFmxtbcHhcODh4YERI0ZQMHgz/vnnH0REROD06dNNErw0xmKx0KdPH7i7u2PWrFkY\nNGiQlEZJpCEgIAA7d+4Ua5tpaWno16+fWNskhBBCREGVxAkhhHRq48aNY36Ojo5GRUWFDEcjnL/+\n+gtPnz4FAGhoaGDEiBEyHhFpjrgCxJurFk4B4oQQQoh4iCNAvHHljhcvXlC1cAn7+eefYW1tjT17\n9ghcSTI1NRWOjo6IioriWa+kpIT169cL1MaFCxfwww8/8Kxbu3Ytz3eMemw2G0eOHIG+vj6zLisr\nC76+vqirq2uxH1m9v47K1taWZ/ngwYMoLy8Xqg0DA4Mm1WHj4uLg6uqKrKwsgdvhcrm4evUqPvjg\nAxw7dkyoMQhr1apVmDNnDu7duyfwPnV1dVi6dCm4XC6zrnfv3kJX1SBEEM+ePcPIkSPx77//CrS9\npqYmT7Xw//77D7/88gsmTZpED0dLEc3BTdEc3Dyag2kOJvInOzsb9vb2SElJEXif+mvUCQkJKC0t\nxf9j777jmrr6P4B/AjIVAQUFFa2IdYG4cONEWYKopVr3rNY6qKKtdmjVYu1jW0fFUepA63icuIp1\n/FzUBSqKIrhARRBQQBkikPz+8DH1QiA3QBLEz/v18tWekzO+SS65cHO/5xw9ehQzZ85kgjgRERER\n1JMgXtJu4bwGUjxFC5XFx8eL6rtv3z5BIqM2KIpfU6ZNmyYox8XFYcGCBaL6JiYmYu7cuYI6Z2fn\nItcEKoKOHTvit99+Q25ubqn6FxQUYNKkSUV22Pb29i62z7v02mrrGJRIJAgKCsL27dvl/+bNm1eu\nc2jr80GVRS87d+4sL79Z9FLZuSUyMlKQ+FhRdhF/Q5PH/9vX1FQhZtFGdY5N74eyJoi/vVt4YmIi\ndwtXIjo6Gu7u7mjVqhWWLVumNEEceP1zHhMTg2XLlsHR0RHt27cXfH4TVXSBgYGYP3++/F9pFmgn\nIqJ3E38bJCKi95qTkxMaNWqEu3fvIiMjAyEhIRgyZIi2wxJl8+bN8v8fMGAA9PX1tRgNleTLL78s\nVYK4gYEBunbtCnd3d+7EQkREpGZZWVnw9vYuVYK4ubk5+vTpI989zcrKSg0RUkmio6Px6aefYvLk\nyejSpQvatm0LBwcH1K5dG9WrVwfweoe8a9eu4dixYzh58qTCcVavXg1bW1ul8z18+BDDhw8X3Ajg\n5uaGr7/+utg+9erVw5YtW+Du7i7vd/ToUSxcuFDpDS6afn6VmY+PD7788kv5e3Dr1i20aNECH330\nEezs7FC1alVB+2bNmqFt27ZFxpk9ezauXr2Kbdu2yetOnjyJDz/8ECNHjsTAgQPRsWNHwW4b+fn5\nuHPnDq5evYpTp05h//79ePz4MQDgk08+UcfTlcvJyUFQUBCCgoJgb2+PgQMHwtnZGa1atRIkTgKv\ndwQ5fvw4li5dWuQz0c/PT61x0vvp8ePHcHNzU/oFtY2NjWC38MK72ZB28BzMc7BYPAfzHEwVy9On\nT+Hh4aF0B3EDAwN069ZN/vdus2bNNBQhERER0bulPBPE69atC09PT+4WXgaNGzeGtbU1EhMTAbxO\n9pk4cSIOHDgAPT29YvuFhIRg6NChmgqzWG3atMH27dvl5eDgYEyfPh3GxsZqn7t9+/Zwc3NDaGio\nvG7x4sWwtrbG1KlTi+2XmJiIPn36CBbak0gkRRZ7qygePXqEqVOnIiAgAGPGjMHIkSPRpEkT0X0/\n++wzHDx4UFBvZWWF4cOHF9vvXXpttXkMqps2Ph9Ks+hl69atkZqaCuDfRS9DQ0Oho6N4b7i9e/cK\nyhUtSVyTx//cuXORmpqK6dOnw97eXlR8YhdtVOfYVPklJSXB3d1d5QRxOzs7uLm5wcPDA927d68U\nn8WasGzZMsyePRt5eXllGufSpUvo1asXBg4ciN27d5dTdETqExgYiBs3bsjLLi4uqF+/vhYjIiIi\nTWGSOBERvdckEgnGjBmDb775BgCwZMkSDB48GBKJRMuRlSw1NRXr16+Xl8eMGaPFaKg4MpkMkyZN\nwrp160T3adu2LVxcXNCvXz907NiRKzwSERFpgKo7iOvp6aF79+7yc3aLFi3UHCGJlZ+fj1OnTuHU\nqVMq9dPR0cGSJUswbtw4pW3z8vLw8ccfC25GsLGxwZYtW5T+HeHq6opvvvkGCxculNctWLAAXbt2\nRe/evZXOrYnnV9k1btwYQ4cOFez0EBcXh6VLlypsP336dIUJagCwfv166OrqYsuWLfK67OxsrFmz\nBmvWrAEAVK1aFSYmJsjMzERmZmY5PpPSi4qKEuxmamJiAjMzMxgYGCAjI6PYG1l9fHwwefJkTYVJ\n74nHjx+jV69eiI2NLfKYnp4eunbtKk9Kc3Bw0EKEJBbPwYrxHPwvnoN5DqaK4+nTp3BxccHVq1cV\nPt6wYUP5+bdXr15FFnEgIiIiIqGyJojzO4fyJ5FIMGHCBMEutUeOHEHnzp2xcOFC9OrVS74RQ35+\nPsLCwrBq1Srs3LkTwOu/59u1a4eLFy9qJf7yWmittDZs2ICWLVsKjulp06bh8OHD8PPzQ8+ePeWv\n34MHD7Bjxw4EBAQgPT1dMI6fnx9cXFzKLS51SExMREBAAAICAuDo6IiuXbuic+fOsLOzg4WFBUxN\nTZGdnY2UlBRcv34df/31F/bv34+cnJwiYy1btky+qGBx3pXXVtvHoDpp+vNBU4te7tu3T/7/tWrV\nEuxGXlqJiYno169fqfs3aNAAq1atkpc1dfyrc9FGLghJpZWYmIhevXopXbARAIyMjNC9e3f5xkKN\nGzfWQISVh0wmw+TJk+XfFbxNR0cHbdu2haurK9q3bw9LS0tYWlpCKpXi2bNniI2NxT///IODBw/i\n0aNHgr4hISGaegqkQcbGxnB2di7TGFzUi4iIKgpmHRER0XtvzJgxWLRoEV6+fImrV6/i5MmT6Nmz\np7bDKtGaNWuQlZUFAGjevDm6d++u5YhIkdmzZytNEDcwMICzs7P8ot77sBOLRCKBubk5DAwMyn3c\nrKws+Wq/yshkMujr68Pc3FxpWxMTkwq/eIS6SSQS1KhRQ9thQCaTIS0tDbm5uaLapqeni7ohIyMj\nozzC05jc3FykpaUJvkgsjoGBAczNzSvtMWxmZlbi40ZGRhqKhN5VYncQr1Gjhny3cDc3N+4WXonY\n2tpi06ZN6Nq1q6j2s2bNwvnz5+VlPT097NixAzVr1hTVf/78+QgLC8OJEycAvF49fujQobhy5Qrq\n1Kmj+hNQQtXn9z5Ys2YNcnJysGfPnjKNY2hoiM2bN6Nr166YO3cunj17VqRNVlaW/G+34lhaWqJe\nvXplikWZkn4PePHiBV68eFHs47q6upg6dSqWLl1aaX+fIO14kyAeExMjr6tXr548Kc3FxUXpTZX0\nbuM5+P3Dc7AQz8GkDYoSxA0NDQW7hTdt2lSLERIVJZPJEBcXh2vXruHx48dIS0uTX/OsX78+nJyc\nYGpqqpa5MzMzER4ejrt37yI9PR0vX76EqakpatasiZYtW6JZs2bF7uSnKdHR0Vi/fj3CwsLkcb56\n9Ur+uKenZ5FdLomIqPzExsbCzc1N5QRxMzMz9OnTB+7u7nBzc4O1tbWaInx/+fv747///a8gGSs8\nPBzu7u4wMDCAlZUVpFIpnjx5Ijh3AkBAQABSUlK0liRengutlYaVlRX27dsHLy8vwd/coaGhCA0N\nhUQiQc2aNZGdnY3s7GyFYwwaNAg//vhjucWkCZGRkYiMjBQktYohkUiwcuVKDB48WGnbd+W11fYx\nqG6a+nzQ1KKXcXFxiIyMlJe9vLzK5e+U7OxsHDp0qNT9Cy96oo3jX52LNnJBSBIrLi4OPXr0QHx8\nvMLHJRIJ2rRpg379+sHLywutWrWCrq6uhqOsPGbOnKkwQdzT0xM//vgj7O3ti+3boUMHjBgxAoGB\ngThy5Ah++OEHnD17Vp3hkpZZW1sjNDRU22EQERGVCyaJExHRe69OnTr49NNPsWLFCgCvL1xfvny5\nwu7gnJCQgJ9++kle/u6777R+AwoVNWvWrGK/HHnfd2LR0dGBvb19uSfXFRQU4Ny5c3jy5Imo9rq6\nuujUqZOoOCQSyXt/M+6b901MUrI6FRQUICwsDElJSUrbymQy3LlzR+GuhIXl5ORAKpWWR4gakZaW\nhrCwMBQUFChta2VlhS5dulTKLxB0dHSUrphbeLVmordlZWXBw8MDp0+fLvLYmy/i3pyzO3bsWCl/\njioDPz8/NGrUCMeOHcP58+fx4MEDpX2MjIzQtWtXTJw4Ef379xf9u//u3buxfPlyQd2SJUvQqVMn\n0fHq6Ohg69ataN26tXxxm+TkZAwZMgQnTpwoEosmn9/7olq1ati9ezfOnz+PHTt2IDw8HHfu3MHz\n58+Rk5Oj8u87EydOxLBhw7B27Vr8+eefiIyMVPp7RcOGDdG7d294e3vDzc0Nenp6ZXlKSgUEBMDF\nxQWhoaE4c+YMoqKilP4eYW5ujgEDBsDPz487OFO5e3NDzNOnT+Hr6wsXFxe4uLjA1tZW26GRCngO\nLorn4JLxHMxzMGlXYmIievbsidjYWLRt21a+U2XHjh35eUUquX//PsLDw+X/Ll++XGRntzNnzpRp\noZTU1FSEhITgyJEjOHbsGNLS0opt+2YXqEmTJmHo0KEwNDQs9bzA6+vKu3btwpo1a3Dy5MkSzy2m\npqYYOnQopk6dqvFFgPPy8uDv74+VK1dq/bo9EdH7KioqCr1790ZycrLSttwtXPNMTEzw119/wcPD\nA9HR0YLHcnNzFSZqValSBT/99BO++OIL+Pv7aypUhcprobXS6ty5M8LCwuDr6ytIggRe/76Umpqq\nsF+VKlUwc+ZMBAQEVOh7mcaMGYMtW7YU2SVUVU2bNsVvv/1WJGm3JO/Ka6vtY1CdNPX5oKlFL9/e\nRRx4nYRcUWni+Ffnoo1cEJJUlZiYCHd39yKfK8bGxujevTs8PDzg5uYGOzs7LUVYuWzduhW//vqr\noK5KlSr4/fffMXr0aNHjSCQS+QYS27Ztw+eff47nz5+Xc7RERERE5Yvf9hIREeH1CqFBQUHIzs7G\n9evXERQUhEmTJmk7LIXmzJkjv6DYokULfPTRR1qOiAornCBuaGgo2C2cO7G8vmlLHYl2MplMVNLs\nGxKJpEJ/MVnRVJTXSpUvS6RSqajk73cpQRz491gXc7y/a89NVWX5ApDeb5mZmfD09BQkiNeoUQN9\n+/aVf9lTu3ZtLUZIYllZWWHChAmYMGECgNc3ksfExCA+Ph4pKSnIysqCRCKBqakpzM3N8eGHH8LR\n0bFUSRCDBg0qlxuva9eujcePH4tqq8nnVx6srKzK9BoVTixQpiwrd3fs2BEdO3Ysdf+3VatWDTNn\nzsTMmTORnp6OCxcuICkpCU+fPkV2djaqVasGMzMz2NraomnTpqhVq5ZK44tZIKckRkZG8PT0hKen\nJ4DXuz9ER0fj3r17SEpKkv+NaWJiAktLSzg4OKBJkyZMFiK1yM3Nxa5du/Dzzz/DxcVFbbs+kvrx\nHMxzcGnxHMxzMGleVlYWli1bhkmTJsHd3R1NmjTRdkj0Dnn16hW+//57hIeHIyIiQrALXnm7dOkS\nFi5ciNDQUOTl5YnqI5VKcenSJVy6dAkBAQHYtGkTunTpUqr57927h9GjR+PMmTOi2mdkZGD16tUI\nCgrCnDlz8N1332lskUE/Pz8EBgZqZK53RWBgoCBRc+zYsahfv74WIyKiyuz27dtwdXUtMUHc1NQU\n5pbPoAAAIABJREFUffr0kS9G+3ZCH2nGBx98gEuXLmHx4sVYvXq1YOfat+np6cHHxwfz5s2rMAn8\n5b3QWmk0bdoUkZGRCA4OxqpVqxAREVHsvGZmZvDy8sK3336rdJHvimDx4sUICAjAxYsX8ffff+PU\nqVM4f/48srKylPatWrUq+vbti6FDh6J///6lWoDuXXhtK8IxqE7q/nzQ5KKXbyeJV6tWDS4uLqLn\n0AZ1H//qXLSRC0KSKt4s2BgTEwMA+PDDD+X3j3br1g1GRkZajrBySU5OxpQpUwR1Ojo62L17N7y9\nvUs97ieffIKuXbti0KBBZQ2RiIiISK14hwEREREAGxsbzJ8/H7NnzwYAfPHFF2jfvj3atGmj5ciE\n1q9fj82bNwN4nfS2bt067mhZgchkMkycOBFBQUHciYWIiKgCS0tLQ58+fXD9+nX5zqXcuaPysLCw\ngIWFRalvCK/oKvvzqwzMzMzg6uqq7TBKZGxsjLZt26Jt27baDoXeQwYGBlrfiYnUo7Kfoyr786sM\neA4mKl7VqlWxZMkSbYdB76js7GwEBARoZK4jR47gwIEDpe5/9+5ddOvWDVu2bMEnn3yiUt/bt2+j\ne/fu8uQLVeTl5WHBggW4e/cugoOD1b7g6pUrV4okiLdr1w6+vr6wsbERJCpZW1urNZaKJDAwEDdu\n3JCXXVxcmCRORGpx+/Zt9OzZU+EiZI6OjnB3d4ebmxu6dOnC+wRK8Ntvv+G3335T+zxVq1bFokWL\nMG/ePISHh+P69et49uwZpFKpfNG3Dh06oFq1aoJ+S5cuFWwOIEZZFxpTpDwXWisNHR0djB49GqNH\nj0ZKSgrOnTuHJ0+eIDU1FYaGhrC0tISdnR2cnJxKff9SWRe+Ky2JRIIOHTqgQ4cO+PbbbyGVShEX\nF4eYmBgkJCQgIyMD2dnZMDY2RvXq1WFhYQF7e3s0atSoXH7fU/drW16va1mOQU39nJd2R3h1fj5o\natHLp0+fChZzdHV1haGhYanm0tT7Baj3+Ffnoo1cEJLEun//PgYMGID27dtj4cKF6N69u8oLmJJq\nAgICkJaWJqibMWNGmRLE37CxscHJkyfLPA4RERGROvGvDiIiov+ZNm0aNm3ahBs3buDly5cYNmwY\nzp07BzMzs1KNV5bddBS5du0apk2bJi+PGDECnTt3Ltc5qGz+/vtvNG/eHNHR0dyJhYiIqIKSyWTY\nvn07pk+fDldXV34RR0RERERERERUgdWoUQPOzs5wdnbGBx98gNq1ayM/Px8PHjzAiRMnsGPHDrx8\n+VLeXiqVYuTIkahduzZ69eolao68vDz079+/SIJ4nTp1MH36dLi7u6NRo0YwNDTEs2fPcPnyZWze\nvBlbt26FVCqVt//zzz/RsmVL+aLU6rJu3TpB2cfHB7t371Z7cjoREf2bIJ6QkAAAqF69umC38Lp1\n62o5QiqOnp4eOnXqpNIuviRkaWlZLolWFZWOjg5sbW1ha2ur8bkr+2tb0b3Lnw8HDhwQ7GTt4+Oj\nxWhKR93HvzoXbeSCkFQcmUyG8+fPl3rRBlJNRkZGkWslDRs2xKJFi8ptDmNj4zKPcevWLVy9ehUJ\nCQnIycmBqakpevfujebNm5fYLyUlBefPny+ymEajRo3KtFBPYfHx8YiMjMSjR4/w/PlzFBQUwNjY\nGKampmjQoAEaN25c6sX41Dl2ZRAdHY3w8HD5wjAWFhZo1qwZOnToUKE3kqvox3RhBQUFuHTpEqKi\nopCamgo9PT3UrVsXjo6OaNasmVrmJCLSJCaJExER/Y+BgQH27duHtm3b4vnz57h16xZcXV1x4sQJ\nVK1aVauxxcbGwsXFBVlZWQBerz69Zs0arcZERbm6ulb43YqIiIjedxKJBJ999pm2wyAiIiIiIiIi\neidVr14drVu3Rtu2bdGuXTtUrVoV/fv3L9c5dHV14eXlhTFjxsDDw6PYXddGjhyJgIAAjBgxAidO\nnJDX5+fnY/Lkybh+/bpgV+3irF27FtHR0YK6bt26Yd++fTA3NxfUW1hYoG/fvujbty9GjRoFb29v\n5OTkyB9fuHAhxo0bh5o1a6rylFVy5swZQXnWrFlMECci0oAbN26gb9++aN68OaZOnQoXFxe0bt2a\nn8FERKQ1+/btk/9/lSpV0K9fPy1GQ0RvaGPBkffZ9u3bBddmAGDSpEkwMDDQWAxWVlZ48uSJvBwd\nHY2mTZuioKAAa9euxbJly3D79u0i/RYuXKgwoVYqlWLz5s347bffEBERAZlMpnBec3NzeHl54Ztv\nvkHjxo1Vjjs7OxvLly/Hxo0bERsbq7R97dq10bNnTwwZMkTp9UB1jv0uKe7YAIBt27bhhx9+wI0b\nNxT2NTMzg5+fH/z9/UvMY2jXrh0iIiIUPubs7FxifNOnT8eyZctEx12Rj+niYs7OzsZPP/2EVatW\nITU1VWHfFi1aYPbs2Rg5cqTSeX755RfMnDlTXra3t8f169dVivWNS5cuoX379vJylSpVEBcXxwXo\niKhUmCRORET0Fjs7O6xevRrDhw+HTCbDxYsXMXjwYOzcuRNGRkZaienhw4fw9vZGSkoKgNc332ze\nvFlr8dD75+HDh7h69SqqVasG4HVyXY0aNWBlZaXRC2lERMrIZDI8e/YMSUlJyM3NBQA8ffpUy1ER\nERERERERERG9u/T19eHn5ydPCm/SpAkkEon88aioqHKbS1dXF0OHDsW8efPw4YcfiupTp04d/PXX\nX+jTpw9Onz4tr4+JicG+ffvg6+urdIw///xTUK5Rowb27t1bJEG8MBcXF6xYsQITJkyQ12VmZiIk\nJARjx44VFb+qZDIZbt26Jahr3bq1WuYiIqJ/yWQyPHjwABcuXEC9evW0HQ4REREAoHPnzmjVqhWA\n14lRZmZmWo6IiEjz9u/fLyjr6elhzJgxWormX8nJyfDx8cG5c+eKbaMoUfbWrVvw9fUVdc0tLS0N\nwcHB2Lp1K/z9/fHDDz+IXsQqIiICAwYMwMOHD0W1B4AnT55g+/btOHr0aImJ3OocuzLIysrCiBEj\nsHfv3hLbpaenY/78+dizZw+OHDkCKysrDUWoWEU/phW5f/8+PDw8ilxPLezGjRsYNWoUtmzZgp07\nd8LU1LTYtmPHjsV3330n3/gvKioKZ86cUZqQr8jq1asFZW9vbyaIE1GpMUmciIiokKFDhyIhIQGz\nZ88GABw6dAhubm4ICQnR+IXUmzdvws3NTf6HsoGBAfbs2QMHBweNxkHvt5s3b0JfX1+eEK6jowN7\ne3tUr16dSeJEVKHIZDIkJCQgLCwMaWlpAKDSxWYiIiIiIiIiIiISMjY2xq+//qqRuWbNmlXsruEl\n0dfXxx9//IFmzZohPz9fXh8SEqI0STw3NxcXLlwQ1I0ZMwY1atQQNfeYMWMwd+5c+WLPAHD69Gm1\nJYlnZmaioKBAXtbT0+PC0kREGiCRSODu7q7tMIiIiATe3N9IRPS+kslkOHPmjKDO0dERlpaWWoro\ntRcvXuDjjz9Wurtw4YTac+fOoV+/fnj27JnC9qampsjJycGrV68E9fn5+fjxxx9x+/ZtbN26Ffr6\n+iXOGxsbi169euH58+dFHtPV1YWlpSUMDQ2RlZWFjIyMIvNpa+zKIDc3F15eXvi///s/0X2uXbuG\nfv364fz586W6dloeKvoxrUhqaipGjRqFe/fuyeskEgksLCygo6ODlJQUSKVSQZ+jR4/C1dUVR44c\nKTZR3MzMDMOGDcO6devkdYGBgSoniaelpWH79u2CusmTJ6s0BhHR20q/pAYREVElNmvWLHz77bfy\n8unTp9GmTRtcvHhRYzFs2bIFHTp0kCe3GRoaYvv27ejdu7fGYiACgN69e2PatGnw9/eHv78/ZsyY\nATc3N64+S0QVzptFLMaPHy//zGrZsqW2wyIiIiIiIiIiIiIRynKTo52dHbp06SKou3btmtJ+T548\nKXLzYufOnUXPq6uri44dOwrqEhMTRfdXVXZ2tqBclp10iIiIiIiIiIjeZbdv38aLFy8Ede3bt9dS\nNP/y9/eXJ9OamprC398fR48eRWxsLB4+fIgLFy5g6dKlaNiwobxPUlIS+vfvXySZtkePHggJCUFW\nVhbS09Px8uVL3LlzB4sWLYKJiYmg7e7du/Hll18qjW/KlCmCJG5DQ0PMnj0bly9fxsuXL5GYmIj7\n9+8jOTkZL1++xN27d7Fr1y6MGzdOaQK+OseuDGbNmiVPEK9fvz5++eUXREVFITMzE/n5+YiPj8ea\nNWtgY2Mj6BcREYHly5crHPPw4cN4+PAhHj58iCZNmgge27Nnj/wxRf++//57UXFX9GNakWnTpskT\nxBs1aoTNmzcjIyMDycnJSEpKwosXL7Bjxw40a9ZM0O/ChQuYOHFiiWNPmTJFUN6zZw+Sk5NVim/j\nxo3IycmRl5s0acIcESIqE+4kTkREVIwFCxagdu3amDZtGqRSKe7fv4+uXbviiy++wLfffotq1aqp\nZd74+HjMnDkTu3fvlteZmZkhJCQE3bp1U8ucRCWpUqUK9PT0SrUSGxGRpuno6AhujORNkkRERERE\nRERERO8He3t7nDp1Sl5+8uSJ0j6KdisyNzdXad7C7dW5A1LhhPbydOPGDURHRyMlJQVpaWkwNTWF\npaUl2rVrB1tb23KZIzc3FzExMYiJiZHfjKmvrw9zc3PUqVMHHTt2VPn1fx/cunULV69eRUJCAnJy\ncmBqaorevXujefPmovpr4r2Nj49HZGQkHj16hOfPn6OgoADGxsYwNTVFgwYN0LhxY9SvX79c5iIi\nIiIiIiICgLt37xapa9WqlRYiETp9+jQAwMXFBdu2bYOFhYXg8Xr16hVJZh8zZgxSUlIEdQEBAZgz\nZ46gTiKRoFGjRvj6668xcuRI9OnTBzExMfLHly9fDk9PT7i4uCiMLSEhAceOHZOX9fT0cOLECXTq\n1Elhe4lEAltbW9ja2mLQoEHIzc3FoUOHND52ZXH06FEAwOjRo7FmzRoYGBgIHq9fvz4mTpyIjz76\nCD169EBUVJT8sVWrVmHGjBmQSCSCPrVq1ZL/f+FFOC0tLVGvXr0yx12Rj+niXLlyBQDg7u6O3bt3\nw8jISPC4sbExPv74Y/Tv3x/Dhg0T5G3s2LEDgwcPxoABAxSO7eDggO7du8uvRb969QpBQUGYO3eu\nqNhkMhnWrFkjqPvss89EPzciIkWYJE5ERFSCzz//HHZ2dhgxYgRSUlKQl5eHn376CVu2bMGcOXMw\nfvx4GBoalstcycnJ+Pnnn/Hbb78JdgFo2bIldu7ciQ8//LBc5iEiIiIiIiIiIiIiIiKqbArfBKmn\np6e0T+3atSGRSATJ12lpaSrNW3g3HGtra5X6K2NoaIjc3FyFj+Xm5ha5MfSNUaNGYePGjSWOnZCQ\ngB9//BF79+5FQkJCse3s7Ozw2Wef4fPPPy9y86oyd+/exY4dO/D333/j/PnzxT4X4PVNoa1atcK0\nadMwbNgwpe9hu3btEBERofAxZ2fnEvtOnz4dy5YtE9RFRUXBwcFBXm7UqBHu3LlT4jiFjR8/Hn/8\n8Ye8/Ouvv8LPz6/Y9lZWVoIFDaKjo9G0aVMUFBRg7dq1WLZsGW7fvl2k38KFC0tMEtfEe5udnY3l\ny5dj48aNiI2NVdq+du3a6NmzJ4YMGYL+/furNBcRERERERFRYY8fPy5SV7NmTS1EUpSTkxMOHTok\nanOmixcvIjQ0VFDn5+dXJJm2MBsbGxw7dgwODg5IT08H8Dr59Pvvvy82ofbKlSuC62BeXl7FJnEr\nYmBggIEDB2p8bHW4e/dusdfVxFi4cCG++eYblfsNHDgQGzZsKLFNzZo1sWHDBjg5Ocnr7t+/j0uX\nLhVJxtaUinpMl6RZs2YKE8TfZmBggK1bt6JTp064fPmyvH7BggXFJokDwNSpUwULlq5btw5fffWV\nqE2Njh8/LriWZmxsjNGjRyvtR0RUEiaJExERKeHq6opTp07B3d0d8fHxAF5fWJg6dSp++OEHjBs3\nDqNGjULjxo1LNX5YWBg2bdqErVu3IisrS14vkUgwceJE/PLLLyX+cUJUEUgkEtSoUUN026ysLCQm\nJopqa25urvJNKVR5qXKs5efnw8bGBi9fvlTaNi0tDSkpKaJ2YTEyMoKFhYWoC4S1atUq04VEKplM\nJkNaWlqJNxXm5ORoMCIiIiIiIiIiIiLSlnv37gnKYpK1TUxM4ODggGvXrsnr/vnnH9E3pBYUFODc\nuXOCus6dO4vqq01SqRTz58/Hf/7zH1HX0O/cuYOZM2di+fLl2LNnD9q2bStqnl9//RUzZswQHZdM\nJsOVK1cwZswY/PLLL9i3b1+57XT9LklOToaPj0+RY+ttxX2foan3NiIiAgMGDMDDhw9FtQeAJ0+e\nYPv27Th69CiTxImIiIiIiKjMMjMzi9SZmppqIZKifv/9d1HJtMDrnZLfVq9ePfzwww+i+r5p+/nn\nn8vrzp49i4iICIV/4xde7LBBgwai5hFDnWNXFkZGRkV2kC5Ou3bt4OTkhEuXLsnrtJkkXlGP6ZIs\nW7ZMVA6Gvr4+fvvtN8F13atXr+LcuXPFLnTg4+ODevXq4dGjRwCA+Ph4HDp0CF5eXkrnCwwMFJSH\nDh1aYT67iOjdxSRxIiIiJeLj4+Hr64uxY8fC1tYW/v7+8tXck5KS8MMPPyAgIABNmzaFs7MzOnTo\nAAcHBzRv3hxVq1YVjPX06VNcv34d169fxz///IPTp08rXMmuZcuWWL169TtxEwkRAOjo6MDe3l5U\ngu2bG6be3hWhOLq6uujSpQusrKzKI0yqBFQ51vLz8/H06VNRi3jExMQgOjoaUqlUaVsLCwu4ubkV\n2ZVGkebNm4taGZBKRyqV4saNG0hKSiq2TUmPERERERERERERUeWQmZmJEydOCOrEfs82btw4TJ8+\nXV5ev3495syZI2rnqT/++ANPnz6Vl42MjPDJJ5+IjFo7srKyMGzYMISEhCh8vEqVKqhevTpevHiB\nvLw8wWMPHjxA9+7dsWfPHvTt21fpXBkZGcU+ZmRkBGNjY2RmZipcCPT69etwcnJCeHg4GjZsqHSu\nyuLFixf4+OOPcf369RLbKfqeRFPvbWxsLHr16oXnz58XeUxXVxeWlpYwNDREVlYWMjIy8OrVqxLH\nIyIiIiIiIioNRdcTqlWrpoVIhJydneHo6CiqrUwmw19//SWomzBhAoyNjUXPN2bMGMyZM0fwd/rh\nw4cVJtSamZkJyufPnxc9jzLqHLuyGDx4MCwtLUW3d3Z2FiSJ37p1Sx1hiYqjoh7TxbGzsxN1/fKN\nTp06oVWrVrh69aq8bv/+/cUmievq6uKzzz7D119/La9bvXq10iTxhIQEHDhwQFA3efJk0XESERWH\nSeJEREQliIiIgJeXFxITE9G/f384Ojpi4MCBWL9+PX7++WfExcUBeP0HTXR0NKKjo7Fu3bpSz9ei\nRQt88803+Pjjj5lUSO8cVY5ZmUyGgoIC0W2J3ib2WJPJZKhSpQp0dXWVthXT5g2JRCJ6XH6Wq59U\nKi3x84SfIURERERERERERJVfcHAwsrKyBHU+Pj6i+k6aNAm///47oqKiAABpaWnw8fFBSEgIatSo\nUWy/I0eOwM/PT1C3YMECUcnlqrh79678OmdKSgratGkjf8zAwAB37txR2K/wYtZvjBw5skgScYsW\nLTB16lS4uLigUaNGAP79/nP79u1YtmwZXrx4AeB1IvKQIUNw5coV0TtCmZmZwd3dHW5ubnB0dETT\npk1hYGAgfzwpKQlhYWEICgpCaGiovP7Zs2fw9fXFhQsXFF6TP3z4sDwB2cXFBTExMfLH9uzZAycn\np2JjMjExERW7pvn7+8sTxE1NTTFhwgS4urqiQYMGMDIywuPHj3HmzBnUqlWrSF9NvbdTpkwR3KRr\naGiIadOmYciQIXBwcBAssiuTyXD//n1cuXIFf/31F/bv3y9qwV4iIiIiIiIiZd6+tvBG4etD2uDq\n6iq6bXR0NNLS0gR1gwYNUmk+IyMj9OvXD1u3bpXXhYWFKWxb+FrJuXPnMG3aNAQEBJQ5wV6dY6uD\nsbExevbsWer+YjYvKqxXr14qtbezsxOU09PTVZ6zPFTkY7o43t7eKrUHXl9PfjtJ/Ny5cyW2nzBh\nAhYsWCBfsOLIkSO4d+8ebG1ti+2zbt065Ofny8sdO3ZE69atVY6ViKgwJokTEREVY8eOHZg4cSIy\nMjJQv359tGzZEsDrPwqnTJmCzz77DEeOHMH69etx6NAhvHz5slTzmJubY+DAgRg+fDi6devGhEIi\nIiIiIiIiIiIiIiIikVJTU/H9998L6lq2bIkePXqI6q+vr4+DBw+iR48e8gWiz549ixYtWmDatGlw\nd3dHo0aNYGRkhGfPniEiIgKbN2/G9u3bBYtUfvrpp5g5c2Z5PS25unXryv//7eTbN+rVqyd6rGXL\nlmHPnj2Cunnz5uHbb78tkoQtkUjQvHlzLFiwAKNGjYKHhwdiY2MBvE6kHz9+PI4ePVrifHZ2dggK\nCsLw4cMV3rj9hpWVFQYNGoRBgwZh586dGDFihPzmyoiICOzatQuDBw8u0u/tROnCr42lpaVKr01F\ncfr0aQCvk963bdsGCwsLweP16tVD+/bti/TT1HubkJCAY8eOyct6eno4ceJEsbsqSSQS2NrawtbW\nFoMGDUJubi4OHTqk5FUgIiIiIiIiUk5R4rG2kmjfpkrC55uF4t6oWrUqmjVrpvKc7dq1EyTUXrt2\nTWE7a2treHt7Y//+/fK6lStXYtOmTRg0aBA8PDzg7OyM2rVrqxyDOsdWB2traxw8eFCjc75ZwE+s\nwoscvr1onyZV5GO6OG8vtFnaPpGRkSW2t7S0xODBgxEcHAzg9YZHa9euxZIlSxS2z8/PR1BQkKCO\nu4gTUXlhFhoREZECGzZswCeffIKMjAwAgJeXFyQSiaCNrq4uPDw8sGvXLqSnp+PMmTNYvHgxhg8f\nDicnJ1haWsLIyEje3sTEBFZWVujWrRsmTJiAlStX4urVq0hNTUVQUBB69OjBBHEiIiIiIiIiIiIi\nIiIiFUyYMAHJycmCul9//bXId3sladCgASIiIjBixAj593VJSUmYO3cuWrdujerVq0NPTw+1a9eG\nh4cHtm3bJk8Qr1WrFtatW4e1a9eqNKemZWRkYN68eYK6BQsWYP78+Qp36X5bo0aNcOjQIVSvXl1e\nd+zYMYSHh5fYb/jw4Rg3blyJCeKF+fr6YsWKFYK6lStXiu5fGTg5OeHQoUNFEsSLo8n39sqVK4LF\nEby8vIpNEFfEwMAAAwcOFN2eiIiIiIiIqDjW1tZF6p4+faqFSIQsLS1Fty0cb4MGDUp1L3nhnYuf\nPXtWbNvAwEDY2NgI6p4/f44NGzbA19cXVlZWsLOzw4gRIxAUFCRfVFEMdY5dGZiZmanUvvCiiAUF\nBeUZjmgV/ZhWpH79+irP2aBBA0E5IyND6Ws+depUQXn9+vXyxS8L27dvHx4/fiwvW1hY4OOPP1Y5\nTiIiRZiJRkRE9BaZTIbvv/8e48aNE3y57ezsXGI/AwMDdO3aFV999RU2b96MixcvIjk5GdnZ2ZDJ\nZJDJZHj+/DkSExNx6tQprFu3DlOmTIGjoyMTw4mIiIiIiIiIiIiIiIhKYfHixdi3b5+gbuLEiejV\nq5fKY9WoUQPBwcGIjIxEly5dlLavUqUKZs+ejXv37mHChAkqz6dpgYGBgt2GWrVqha+//lp0fzs7\nO8yYMUNQt3r16nKL720TJkwQ7AJ+4cIFZGdnq2Wuiuj333+Hvr6+6PaafG8L35Bb+OZZIiIiIiIi\nIk1RtCvz1atXtRCJkKIdzouTlpYmKL+9iJsqTE1NBeXc3FxkZWUpbFu3bl1cvHgR3t7exY539+5d\nbNmyBRMmTEDDhg3RoUMHBAcHK02YVefYlcG7mjNQ0Y9pRUozb+E5ZTIZ0tPTS+zTrl07dOjQQV5O\nTU3Fzp07FbYtfL1t7NixKi2uSURUknfzDENERKQGOTk58PX1xfz58wUJ4kZGRlzNnIiIiIiIiIiI\niIiIiKgC2bVrV5FE2JYtW+LXX38t1XjPnj2Dv78/OnXqhLCwMKXt8/Pz8dNPP6F9+/bYunVrqebU\npD///FNQ9vPzU/nG1DFjxgjKp06dKnNcikgkEnTr1k1ezs/PV7preWXh7OwMR0dHlfpo8r0tvOPV\n+fPnVZqHiIiIiIiIqLw0bty4SPLqpUuXtBTNvyQSibZDUMrKygohISGIiIjA1KlT8cEHH5TY/uLF\nixg1ahTatm2LW7duaW1s0o534ZjWpsK7iQcGBhZpExMTgxMnTsjLOjo6mDRpktpjI6L3B5PEiYiI\nAKSkpKB3797YvXt3kcd8fHygp6enhaiIiIiIiIiIiIiIiIiIqLDjx49j+PDhgoWf69Spg5CQEBgZ\nGak83vnz5+Hg4ICff/4ZmZmZgjE/+eQTfPXVV1iwYAG++OIL9OrVC4aGhvI2N2/exLBhwzBw4EDk\n5OSU7YmpSUpKCm7evCmo8/LyUnmc+vXrC3b4vnv3LlJSUkoV06tXr/D06VPExcXhzp07Rf4V3kn7\nwYMHpZrnXePq6qpSe02/t05OToLyuXPnMG3aNMHPDREREREREZEm6OjooGvXroK6q1evIjU1VUsR\nqc7c3FxQfv78eanGycjIEJQNDAxQtWpVpf3atGmDFStW4P79+3jw4AG2bduGqVOnonXr1goTgyMj\nI9GzZ088fPhQq2NTxaXtY7os8xaeUyKRFFkwURFfX1/Url1bXj537hwiIyMFbQrvIu7m5oaGDRuq\nHCMRUXGqaDsAIiIibXvw4AE8PT0RFRWl8PHSfIlOREREREREREREREREROXvn3/+Qf/+/ZGbmyuv\nq1mzJv7++2+lOxMpEhkZib59++LFixfyulq1amHFihXw9fVVuCNzSkoKFi1ahJUrV8oT1fe1NsBe\nAAAgAElEQVTu3YuBAwfi8OHDFW53nQsXLggS6mvVqoXs7GxkZ2erPFbNmjXx6NEjeTkxMRGWlpZK\n+925cwf//e9/cfr0aURFRSEhIUGledPS0lSO9V3UunVrldpr+r21traGt7c39u/fL69buXIlNm3a\nhEGDBsHDwwPOzs6CG2OJiIiIiIiI1MXb2xuhoaHycl5eHjZs2IBZs2ZpMSrxatasKSg/ePAAUqlU\n4fWokty/f19QrlGjhsqx2NjYYMiQIRgyZAgAIDk5GXv37sWKFSsEC9QlJSVhzpw52LJlS4UYmyqW\ninJMl2bByfj4eEHZ1NQUurq6Svvp6+vj008/xcKFC+V1gYGBWLt2LQAgOzsbmzZtEvSZPHmyyvER\nEZWEO4kTEdF77ezZs2jTpk2xCeL6+vrw9PTUcFREREREREREREREREREVFh4eDg8PDyQlZUlrzM1\nNcXff/+NFi1aqDxeQUEBhg0bJkgQt7a2xsWLFzF48OBib160tLTE8uXLsWbNGkF9aGgoVq5cqXIc\n6paUlCQoJycnw8bGplT/Cu+C8+zZsxLnjouLw0cffYTGjRvj66+/xpEjR1ROEAcgeI8qMzEJ92/T\nxnsbGBgIGxsbQd3z58+xYcMG+Pr6wsrKCnZ2dhgxYgSCgoIQFxen0nMiIiIiIiIiEmvIkCEwNDQU\n1K1ZswavXr3SUkSqadmypaCcmZmJmJgYlccJDw8vcdzSqFWrFiZOnIhr167Jk7vf2L17N3Jycirk\n2KRdFeWYvnz5sspzFu7j6Ogouu+kSZNQpcq/+/j++eef8t3Mt23bhvT0dPljDRs2hLu7u8rxERGV\nhEniRET03tqxYwdcXFzw9OnTYtv06NED1atX12BURERERERERERERERERFRYZGQkXF1dkZGRIa+r\nVq0a/vrrL7Rp06ZUY+7Zswc3btwQ1K1duxYNGjQQ1f/TTz/FoEGDBHU//vhjhbsRuaTvQ8vq7YT9\nws6fP482bdpg9+7dZZ5HKpWWeYx3QbVq1VRqr433tm7durh48SK8vb2L7Xv37l1s2bIFEyZMQMOG\nDdGhQwcEBwejoKBAXeESERERERHRe8jc3Bzjx48X1N27dw/fffdduc2RnZ1dbmMV1rRp0yI7JO/Z\ns0elMV6+fIlDhw4J6rp06VLm2N7Q1dXF8uXLIZFIBHPeuXOnQo/9PtLX1xeU8/PzNR5DRTmmDxw4\noFJ7AAgJCRGUO3bsKLpvnTp1MHDgQHk5KysLwcHBAIDVq1cL2k6cOFHlndWJiJSporwJERFR5TN/\n/nwsWLAAMpmsxHYlfbFNRP+SyWRIS0tDbm6u0rZSqVRUO1VJJJIiFxZKoqOjAwMDg3KPIzc3F2lp\naUo/XwDAwMAA5ubmggtc7xtVjp039PX1RV0gycvLw/3790WtQvjo0SNR7xkA6OnpwdTUVLDqX3Gq\nVaumlvfXwMAAVlZWom6Kq1Gjxnt9jBEREREREREREdG7LyoqCi4uLoKdjY2MjHDgwAF06tSp1OMW\nTl62tbWFl5eXSmNMnz5dME5iYiLOnTuH7t27lzqu8qbOpPXirq0nJyfDw8MDaWlp8jodHR24urqi\nb9++aN26NerVqwdLS0sYGBgU+c7G398fP//8s9rirqhUvZ6vjfcWAKysrBASEoLLly9j48aNOHDg\nQIk7hl+8eBEXL17EL7/8gu3bt6Np06ZqiJiIiIiIiIjeR9988w22bNki2Kn3P//5D7p16wYPD48y\njf3w4UMMGjQIFy9eLGuYCkkkEri7u+PPP/+U1wUFBWHmzJlFdkgvTnBwsOC5A4Cnp2e5xlmrVi2Y\nmpoK5ilp4cCKMvb7xsTERFB+e7FNTakox/Tt27dx7NgxuLi4iGp//vx5XLlyRVCnah7J1KlT8d//\n/ldeXrNmDdq3b4+IiAh5nYGBAcaNG6fSuEREYlSoJPHjx48jPDxc22EQEZW7L7/8Utsh0P8UFBRg\nxowZWLFihdK2EokEPj4+GoiK6N0nlUoRFRWFpKQk0e3Lm46ODuzt7UUn+r7pU97S0tIQFhYmaicE\nKysrdOnSBbq6uuUex7tC1WPnzWIAYi4WvXr1Cnv37hV1gVYmk4nevaJq1apo0qQJ9PT0lLatU6eO\nWhK0zc3NRa+MKJFIKvWqgzKZrMSfe1U+E4iIiIiIiIiIiKjiiY6ORu/evZGamiqvMzAwwN69e9Gj\nR48yjf32DXoA0LVrV5XH6NSpE3R1dQXXmCMiIipUknjNmjUF5c6dOyMsLEytc3733XeCBPG6desi\nJCQEbdu2FdU/MzNTXaGplaZ3PNfGe/u2Nm3aoE2bNlixYgUePnyIsLAw/PPPPzh79iyuXr1a5Bp9\nZGQkevbsiYsXL8LGxkZjcRIREREREVHlVbt2bSxfvhyjRo2S10mlUvj4+OCPP/7AiBEjSjXutm3b\nMGXKFLUn2k6bNk2QUBsXF4cFCxYgICBAad/ExETMnTtXUOfs7Iw2bdoobC+TyUp1P2NKSkqR18Ha\n2lpjY5M4derUEZRv3ryJ/v37azwOTR7TJZk+fToiIiKU3m+cl5eHKVOmCOocHR3RuXNnlebr2rUr\nWrVqhatXrwIAbty4gfHjxwva+Pr6wsLCQqVxiYjEqFBJ4vv37xeVtEdE9K6ZPXs2d/CsALKzszF0\n6FCEhISIat+6dWvUrVtXzVERVR5SqVR0kq26VIRE2DfJxmJeC03fKFRRqXLsSCQSSKVSUa/dm3Hz\n8/PLGmKRGHR0dEQdb+o6/0skkvd6cQEioorg/v37iIyMRHx8PDIzM6Gvrw9zc3M0btwYLVu2hLm5\nubZDJCIiKndZWVmIiYlBfHw8Hj9+jKysLOTl5aF69eowNzdH8+bNYW9vD319fbXFwHMwERHR+yU2\nNha9evVCcnKyvE5PTw87d+6Eq6trmcd/+vSpoFy7dm2Vx6hSpQpq1KiBlJSUYsfVNktLS0H57t27\nap0vPz8fO3fuFNRt2LBBdII4AMHrqSmFr7uX5nuvtxPjNUHT721JbGxsMGTIEAwZMgTA693k9+7d\nixUrVuDmzZvydklJSZgzZw62bNmirVCJiIiIiIiokhk5ciTCw8OxcuVKeV1eXh5GjhyJXbt2YfHi\nxWjevLnScWQyGf7++28sWrQIZ8+eBVD0ekF5a9++Pdzc3BAaGiqvW7x4MaytrTF16tRi+yUmJqJP\nnz6C61ASiQTfffddsX3mzp2L1NRUTJ8+Hfb29qLik0qlmDFjhmAhODs7OzRo0EBjY5M4bdq0wfbt\n2+Xl4OBgTJ8+HcbGxhqNQ5PHdElu3rwJX19f7Ny5s9hE8by8PAwfPrzIYqLffvttqeacMmWKIDH8\n+vXrgscnT55cqnGJiJSpUEniRERE6vLkyRN4eXnh0qVLovtoY+UsIiIiIiJS7v79+wgPD5f/u3z5\nMtLT0wVtzpw5U6rdv0qSkZGBwMBAbNy4EbGxscW2k0gkaN68Odzc3DB06NBSrWZLRERUETx+/BhH\njhzByZMncfHiRcTGxipdtEtfXx+enp4YP348PDw8yiUOnoOJiIjeT3fu3EHPnj2RlJQkr9PV1cXW\nrVvh5eVVLnMYGRkJknpzcnJKNU5WVpagrOkbL5Vp3bq1oPzkyRPcunULTZs2Vct8sbGxePbsmbxc\np04d9OnTR6UxwsPDyzsspUxMTATlFy9eqDzGvXv3yiscUTT93qqiVq1amDhxIsaPH4/hw4cLblLe\nvXs3fv/9dxgZGWkxQiIiqoiio6Nx5coVJCQk4NWrV6hevTrs7OzQqVMnmJmZaTs8REdHY/369QgL\nC8Pdu3eRnp6OV69eyR/39PTEwYMHtRghERHR+2vZsmXIzs7GH3/8Iajfv38/Dh48CCcnJ7i6usLJ\nyQm1atWChYUFZDIZnj59itjYWPzzzz84ePAgHj58qPHYN2zYgJYtWwoWzZs2bRoOHz4MPz8/9OzZ\nU75I84MHD7Bjxw4EBAQUuVfGz88PLi4uxc6Tk5ODoKAgBAUFwd7eHgMHDoSzszNatWpVZHfjjIwM\nHD9+HEuXLsW5c+eKzKPJsdUhMTER/fr1K9MYAwcOxNixY8sporLz8fHBl19+KU+6v3XrFlq0aIGP\nPvoIdnZ2qFq1qqB9s2bNVFrUURWaOqaL4+joiMjISBw8eBCOjo5YsGABvLy85NdtX758icOHD2Pe\nvHmIiooS9P3oo48waNAglecEgKFDh2L27NmCa6NvtGrVCp06dSrVuEREylTYJHEzAwM0MjaGka4u\n3uy9l1lQgPS8PNQ1NESp9uMzNARq1ADE7OZnZARUEfny5OQAqqwebGQEvLWakEwmQ8LjxzAzM0O1\nQiddWemeabmSQKa80f8oijcrKxPp6emoW7ee6mPKZEBCApCZKToGTSnz8QgA1apBVreeuGNSQ2Qy\nGR4/ToCZmRmqVq329iOQZGUBInfilFWtClTRE9VWlWMML18CKSmvjw1ljIwACwvB65uZlfX6eKxT\np/Q7exYUvI6jmBhkAHJycxEbH48X2dmlm4PKVXR0NDw9PXH//n2V+nl7e6spIiIiIiIiUsWrV6/w\n/fffIzw8HBEREVrZjSs4OBgzZswQNbdMJsONGzdw48YNxMXFYdeuXRqIsHIKDAwU7BY3duxY1K9f\nX4sRVVx8rYhIHQICArBq1SqV+rx69Qp79+7F3r174eLignXr1qFhw4aljoHnYO3geUU8vlZEROpx\n//599OrVC48fP5bX6ejoIDg4GB999FG5zWNpaSmY49atWyqP8fDhQ2QX+l648O7O2mZnZ4cPPvgA\ncXFx8rodO3Zg3rx5apnvyZMngrKquz9du3YNDx48UKnPm5tK38gXeW/D2wonnj19+hTp6emiE9JS\nUlKK7A6kbpp+b0tDV1cXy5cvx44dO+Q3Kb98+RJ37tyBg4ODlqMjIiJFNL1Yb35+PtauXYvly5fj\n9u3bCttUqVIFHh4e+Oabb+Dk5FQu86oiLy8P/v7+WLlypWCnSyIiIqo4dHR0EBQUhCZNmmDu3LmC\nawNSqRQXLlzAhQsXVB538ODB5RmmQlZWVti3bx+8vLwEyaWhoaEIDQ2FRCJBzZo1kZ2dXeQ61BuD\nBg3Cjz/+KHrOqKgoQXKsiYkJzMzMYGBggIyMDEFy79t8fHyU7oaszrHLS3Z2Ng4dOlSmMSrCIn1v\na9y4MYYOHYo///xTXhcXF4elS5cqbD99+nS1JYlr45h+28qVKzFy5EjExcUhNjYWQ4YMga6uLmrX\nrg0dHR0kJSUpvH7Yrl07/P7776WaE3i9KOm4cePwn//8p8hjn332WanHJSJSpsImiTcyNsZ3dnaw\nNTaGzv8SOW++eIGzz55hjI0N9HR0VB+0bl2gd29BgrZCEglgawuYmiofUyoF4uIAsasHSyTABx8A\n1avLq/Ly8rBh40Z07dIFzZs3l9fL8CbpWpsJxDJIRKaqFxfvzZs3ERZ2FhMmfKrymMjLA37/HYiO\nVilqTSjz8QhA1qw5ZBM+BfTEJVNrQl5eHjZu3IAuXboKjkdIpZDERENS6GKvIjIdHciaNAXMzEXM\nqMLxAADx8cDBg+IWZmjQAPD0FCz4cPPmTZwNC8OY0aOhV9rX/cUL4MGDYpPEpTIZ7iUkYN7vv+Nq\nCbvakGacPHkSAwcOFOwAIEbDhg3RqlUrNUVFRERERESqyM7ORkBAgFbmLigowJQpU7BmzRqtzP++\nCwwMxI0bN+RlFxcXJl0Vg68VEVVEx44dQ+fOnXH06FHY29ur1JfnYO3ieUU8vlZEROXv4cOH6NWr\nl2DXJolEgj/++ANDhw4t17ne7CjzxpkzZ5CcnIxatWqJHkPRwjSOjo7lEl95+vjjj/HTTz/Jy7/+\n+iumTJmCmjVrlvtchRdsf/78uUr9345TrMK7gGdkZKg8RrVq1VC3bl0kJCTI606fPi16cfHAwECt\nJI1p8r0trVq1asHU1FSQYJiVlaXFiIiI6G3aXKw3Li4OgwYNwuXLl0tsl5+fL98F9Msvv8SiRYug\nU8r7NkvDz88PgYGBGpvvXcCF84iIqKKaNWsW3N3dMWPGDBw9erTU4zg7O2PJkiUa2/m3c+fOCAsL\ng6+vb5GdjWUyGVJTUxX2q1KlCmbOnImAgAClvx+VtMneixcv8KKEvChdXV1MnToVS5cuVTiOOscm\n8dasWYOcnBzs2bNH26Fo5JgujqWlJY4fPw4PDw/ExMQAeP3989sLhhbWu3dv7Nq1S/SCkcWZPHky\nfv75Z0ilUnmdqakphg0bVqZxiYhKUmGTxI2rVIGtsTGamZjIk0ezCwoQk5WFpiYm0CvNib9GDaB+\nfeU7hOvoAE2aAGI+2GWy1+3FfrmkowN8+KEgAT0vLw+Wlpb4oGHDIivJyKCjyh7L5e51yrdUabs3\nraUKUn1zcrIRGxsjeG46/0spVyovD7C0BN768reiKPPxCACWFpA2bVrhksQtLS3RsOEHwuNRKoXO\nq5eAmAvAOjqQNf4QMpFfOIo+HoDXizxYWIjb0dzaGmjaVPAzn52Tg5jYWDRt2rT0SeLPn7+Oo7id\nxP/3y5yRgUHpxqdys2XLFowbNw6vXr1SuW+/fv3UEBEREREREb1rPv30U6xfv75IvYODA3x8fGBv\nbw8rKysAwLNnz3D9+nWcP38ex48fR25urqbDJSIiUhtzc3N06tQJjo6OaNy4MerUqQMTExMUFBQg\nLS0NN2/exOHDh3H27FlBYkxSUhL69euHGzduoGrVqqLn4zmYiIjo/fT48WP06tVLsCuyRCLB2rVr\nMXr06HKfz93dHcHBwfJybm4uZs+ejY0bN4rq//jxYyxevFhQV7NmTa3sbqmMv78/Vq1aJU/MzcjI\nwODBg/HXX3+V+rtzmUym8MbdOnXqCMo3b95EfHy8qB3F9+3bJ9jtSCxFc/bv31/lcdq3b4+9e/fK\ny6tXrxaVJB4VFYUlS5aoPF950OR7W1y9MikpKUUS962trUsVGxERlT9tLdZ7//59ODs7CxZoUUYq\nlWLx4sVISkpSeO1IHa5cuVIkQbxdu3bw9fWFjY2N4Hz7Pp3fuHAeERFVZPb29vj7779x9epVbNiw\nASEhIYiPjy+xj0QiQZMmTdCvXz+MHTsWzZo101C0/2ratCkiIyMRHByMVatWISIiotgF6czMzODl\n5YVvv/0WjRs3FjV+QEAAXFxcEBoaijNnziAqKgoFSjbvMzc3x4ABA+Dn5wcHBwetjE3iVatWDbt3\n78b58+exY8cOhIeH486dO3j+/DlycnI0vsChuo/pktja2uLy5ctYsmQJAgMDi01Kb968OWbNmlVu\n158/+OADODo64sqVK/K6kSNHqvRdORGRqipskrgEgI5EIv8v3iq/XafaoJJ//4lpJ2bFEan0dTux\n8SgYWyKRQCKRQEciKbLKiYp7LJe717OLW3nldUtFKwLpvH5+bz/n/z0z5QGIfM+0oMzHIwDZ/953\nUceahrw5HiUSnSLH45v3UsQgkOlIIBP5vEQfD8C/P28i44COjuD11ZE/v6I/b6K9GVeqeAEFqY6O\n/Pgg7Zk/fz4WLFhQ6j9kxK4GT0REREREmle9enW0bt0abdu2Rbt27VC1atVS3fSrzOrVq4vcYNSg\nQQOsXLkSXl5eCvv4+PgAADIzM7F9+3Y8evSo3OMiIiLSlGbNmmHBggXw8vKCo6NjideHvb298dVX\nX+HChQsYNmwY7t69K38sPj4eixcvxqJFi0TNy3MwERHR+yk5ORm9e/fGnTt3BPUrV67EhAkT1DLn\ngAEDUL9+fTx48EBet2nTJlhYWODHH39ElRI2Ibh37x58fHyQkpIiqJ8yZYpGd7UUy9LSEt999x2+\n/PJLed3x48fRt29fbNmyBXXr1hU1jkwmw8mTJ7Fs2TIMHz4cvr6+Rdo0btwY1tbWSExMlPeZOHEi\nDhw4UGLSckhISKl3i2/Tpg22b98uLwcHB2P69OkwNjZWaRxfX19BknhoaChWrVqFzz//vNg+4eHh\n8Pb2Rk5OjuqBlwNNvrdz585Famoqpk+fDnt7e1HjSqVSzJgxQ/DdvZ2dnahFA4iIqPJ6+fIlfHx8\niiSId+/eHf7+/nBycoK5uTkePHiAPXv24JdffsGTJ0/k7TZs2AAHBwd88cUXao913bp1grKPjw92\n795dIX/nIyIiIqFWrVph+fLlWL58ORISEhAVFYX4+Hikp6fj1atXMDExgbm5OerUqYN27dqVagfh\npKSkco1ZR0cHo0ePxujRo5GSkoJz587hyZMnSE1NhaGhISwtLWFnZwcnJyfo6uqqNLaRkRE8PT3h\n6ekJ4PViQdHR0bh37x6SkpLku32bmJjA0tISDg4OaNKkSYnXyP6fvTuPi6r6/wf+ujPAsG+Koagk\n4ob6ETdEzfpUlvueaWlWH/Nji2ua/rKP2mrZx8otc8EsM9NSU/uYWFqaueOW5AYo4gahCLIJzMz9\n/UHM1wuznMEZZgZez8djHg/unXPOfc/cOxzmct7nVEXbtrB48WIsXry4So5V5l6vjeHDh2P48OGV\nqhsbG4vY2NhK1XWla9oSb29vvPXWW5g1axYOHz6MU6dO4caNG9BoNKhbty6io6MRFRVl02NeuHAB\nJ06cUOx7+eWXbXoMIqLynDZJnIiIqDJKSkowduxYrFq1qtJtBAUF4aGHHrJhVEREREREdC88PDww\nadIkQ1J4s2bNFElqiYmJNj/m5cuXFQNrgdLZbffs2YM6depYrO/r64sXXnjB5nERERFVJXOJMKZ0\n6tQJe/bsQevWrXHr1i3D/jVr1gglibMPJiIicj7btm3DZ599ZvS5ssGdd5s+fTqCgoKMln/mmWcw\nbNgwo8+NHz8eZ8+eVewLDAzE9u3bsX37diujLvX555+b/RtCo9Hgww8/rDDY8qOPPsKPP/6IV155\nBY8++ijCw8Ph6emJ7OxsnDx5Et9//z1WrlxpWLm5THh4OKZOnVqpWKvCtGnTcOLECXzzzTeGfbt3\n70bTpk0xatQoDB48GLGxsfDz8zM8r9VqkZycjBMnTmDPnj3YunUrrl27BgB46qmnjB5HkiSMGTMG\nb7/9tmHfjh070KVLF7zzzjt45JFH4OHhYWh/3759+PTTT/Hdd98BKB042qFDBxw+fFj4tQ0cOBDT\np083JCKfPXsWLVu2xBNPPIHIyMgKK/W0aNEC7du3r9DO4MGDERYWpkhYGzduHPbv348XX3wRbdu2\nhbe3N7KyspCQkIB169ZhzZo10Ol08Pb2Rtu2bbFv3z7huG2lqs5tYWEh4uLiEBcXh1atWmHw4MHo\n1q0boqOjUbt2bUXZnJwc7Nq1C/PmzcOBAwcUz02aNMlWL52IiOygKibrXbhwIf744w/Fvtdeew1z\n585V/A8oMjIS06ZNw4gRI9CzZ0/F/4T+85//4KmnnkJoaKhNYytv7969FeJkgjgREZHrCQsLE55I\nzVmEhITYdeEzb29vtG/f3ug9Emdum1yXva9pU9RqNTp37ozOnTvb/VjLli1TTJb48MMPo3nz5nY/\nLhHVbE6bJJ6n0+F0bi4KdDrDKs0XCgrgVg1X55UkCUFBQdBoNI4OxS40Go3JfwC7Mo1KhSB3d7GV\ntV1ITbkebXneioqLkX7zJrJu34Ysy9DLMlKvXUOeg2YJr8lycnLwxBNPYOfOnffUTs+ePc3OYE9E\nRERERFXL29sbn3zySZUec8KECYqB7n5+fvjpp5+EktOIiIhqurCwMEycOBFvvvmmYd+lS5eQlpaG\nhg0bmq3LPpiIiMj5XLx4Edu2bRMuv3//fpPPmVu9pnzCNQBkZ2dbdezyCgoKLJYZNmwYzp8/j1mz\nZin2nzlzBuPGjRM+Vq1atRAfHw9fX1+r46xKn3/+OdRqNdasWWPYV1BQgKVLl2Lp0qUAAB8fH/j5\n+SEvLw95eXmVOs7UqVPx7bffKhL/ExIS0KtXL2g0GoSGhkKv1yMjIwPFxcWKunPmzEFmZqZVSeJN\nmjTB008/ja+//tqwLzU1FfPmzTNafuLEiUYHKWs0Gixfvtyw6lWZtWvXYu3atSaPr1Kp8OWXXyI+\nPt4hSeJA1Z3bMomJiYpEPT8/PwQGBkKj0SAnJweZmZlG6w0cOJCrJxEROZmqnqw3JycHc+fOVezr\n06cPPvzwQ5N1wsLCsHnzZvzjH/8w/I1XUFCAd999164rQsqyXGEio7Zt29rteERERERE5LoKCgqw\ncuVKxb7x48c7KBoiqkmcdiq77JIS/J6VhR2ZmYj/6y/E//UXLuTno4WvryFpvLpQq9Xo2qWL3Wcz\ndJTQ0FB06dLV0WHYXKhGg67BwVA7OhAbU6vV6NKla7W+Hrt26QK12nZnruDOHZxKScGOQ4cQf/Ag\ndhw8iN9PnkT2Pf5DlayTlpaGBx544J4TxAE4ZHYqoupApVJBrVYLPVQqlfBDr9dDp9MJPe6eec1R\nJEmy6n0g668drVaL4uJioYderxeKwZrzZs2D59j+LP0OqW6TOhFR1UhJScGWLVsU+9588000aNDA\nQRERERG5nocffrjCvrtXYzSGfTARERE5ysyZM/HVV18hMDCwUvUffPBBHDlyxCVWhPH09MRXX32F\npUuXIjg42GiZ/Px8pKenm00iDgkJQf369U0+7+fnh+3bt6NFixYVnisqKsKlS5dw+fJlRYK4m5sb\nPv74Y0yfPt2KV/R/li5disGDB1eq7t169+6N5cuXC48r8PHxwXfffYcnnnjino99L6ri3Jq7556b\nm4vLly8jOTnZaIK4Wq3GpEmTsGHDBt67JyJyMmWT9Y4cORLNmze3++/pFStWICsry7Dt5uaGJUuW\nWKzXuHHjCn8nLF++HDdv3rR5jGXy8vKg0+kM2+7u7vDy8rLb8YiIiIiIyHUtWrRI8f2kUaNGGDBg\ngAMjIqKawmlXEg/z9MTzDRqguZ+fYeVwCYBKkqCuZv8oUKlUaNWqVbX9B0hwcHC1XIlxqDUAACAA\nSURBVEk82MMDQe7u1W7SgppyPdoyYSvQzw89O3fG47GxgCyXzh6amorDf/6JtPR0mx2HTDt+/Dj6\n9u2La9eu3XNbHh4e6NWrlw2iIqpZyvqPJk2aWCwryzLy8/NRUlIi1PalS5eQlJQkHIOpQS9VJSgo\nCF27dhVKWNdoNDU+idiaawcAiouL8cEHH+D48eMWy+r1epw/f16o3ZCQEMTExAidj5YtW6Jbt25w\nd3e3WJbn2L5UKhUiIyNRr149k2Vq165dhRERUXWxYsUKRV8eGBjocqsbZWZm4uDBg8jIyMCNGzfg\n6emJkJAQNG7cGB07drTp5GnVgU6nw5EjR5CYmIgbN27A3d0dYWFhaNOmjdHB7DXJpUuXcPLkSVy5\ncgW3b9+GTqeDt7c3AgICEB4ejiZNmlhcFZiIaiZjCVaW7juzD6552Aebxj6YiKjqjRw5Ej179sTK\nlSvx+eefW7y/7OHhge7du+PFF19Enz59XO5e8NixYzFixAgsW7YMX3/9NU6ePGlx4tVGjRrh0Ucf\nRf/+/dGzZ0+L98nvv/9+HDlyBO+//z4+++wzRTLY3dzd3TFw4EDMnj0bLVu2rPRr8vX1xcaNG3Hw\n4EGsX78eCQkJSE5Oxu3bt1FYWGjVZMNjxoxBdHQ0ZsyYgV27dhmt6+7ujieffBLvvfcewsPDKx23\nrdnz3M6ZMwfdu3dHfHw89u7di8TEREXinDFBQUEYNGgQJk2ahNatW1f6dRERUfWxceNGxXafPn2E\nv+P++9//xjvvvAOtVgsAKCkpwQ8//IDnnnvO1mECgGHV8jKu9jcfERERERFVjePHj+Odd95R7Pt/\n/+//8TsEEVUJp00SlwC4q1Rwl6Rql4RrTHX+pS9JUrVMOJZgeUCbq+L1aH2bbncN7NPLMtzd3Krt\n9eFsfvzxRwwbNszsTOfWeOihhxAQEGCTtohqEkmShJOzZVlGdna2YmUIU/R6Pa5cuSI067NarRZO\nNLYnjUaD0NBQR4fhMqy5dgDgzp07SE1NxaFDh2wah5eXFxo2bCg0WL9BgwaoW7euUJI42ZckSQgI\nCICPj4/JMp6enlUYERFVF19++aVie9iwYS7x+0Sv1+Orr77C4sWLcfToUZMDn4OCgtCvXz/85z//\nservp8TERMVA3saNGyM5OdmqGF944QWsXLnSsP3JJ59g0qRJijIdOnTA0aNHjdbv1q2b2fYnTpyI\n+fPnV9gfGhqKjIwMw/aZM2fQvHlzFBQU4MMPP8Snn36KGzduGG2zZcuWmDZtGkaNGmX22Hdz5fcK\nKB30tmDBAnzxxRdCk+7cd999ePjhhzF8+HDOgExEBpcvX66wr1GjRmbrsA82zpX7FfbBFbEPJiJX\nNG7cOIwbN87ux/nf//5n92NYUrt2bUyfPh3Tp09HZmYmEhIScPXqVWRnZ6OoqAh+fn4ICgpCs2bN\nEB0dDQ8PjyqNLzQ01KpEZ0t8fX0xZcoUTJkyBdnZ2Th06BDS09Nx8+ZNFBQUwNfXF4GBgYiIiEDz\n5s1Rp04dq4/h4+ODd999F7Nnz0ZCQgJOnTqFrKws6PV6BAUFoWnTpujUqRN8fX0V9ebNm4d58+ZV\n6nXFxsYiNja2UnXv1rFjR/z888/IzMzEb7/9hmvXriEnJwe+vr5o0qQJHnjggQr/W46Li0NcXJzw\nMdLtNPG8vc6tl5cX+vTpgz59+gAo/fvlzJkzuHDhAtLT05GbmwugdCX5kJAQtG7dGs2aNYObm9MO\nUSMioiqWnp5e4f/9Tz/9tHD90NBQPPLII/jpp58M+zZv3my3JHFb/u1V3p9//okzZ84gMzMTt27d\nQkBAAEJCQtChQwdERETY5BhFRUU4d+4czp07Z+irPTw8EBQUhHr16iE2NrZaLgJlC2fPnsWJEydw\n9epVFBYWIiAgAI8++iiioqIs1q2Kc8sJBomIiIgc5+zZs0hISAAAZGVl4dixY1i7dq1iAbXmzZvj\nX//6l6NCJKIaxvXvwGs0QHAwIJKMed99kIOCATcLiR+SBEmrBUQSDmUZ8PSEbGRFDONtqwB3dwBi\nyaMlJRJ05ifzBQCoypq1tdu3gQspgIUZhQFAKk2bFmpWggxA4OaZXg85pA7ktu0EyspQXUwpjdmB\nZD9/6Bs1FrompfvqQkpOAlSWk5FkHx/o69UXu9YhXEzk1P5dEFD7+YuVVUmAmxskvfnZog0kCbLo\n6/L2ASIjAQszUQMAwsLE34jiYiA7u/QzbYlWC/j5mX5erwd8fQH+k9Puli5divHjxxtmhrWF/v37\n26wtIiIiIiJyPUlJSRUG57rC94SzZ89i6NChSExMtFj21q1bWL16NdauXYupU6fivffeq9YTxply\n8eJF9O7dG2fPnjVb7s8//8Szzz6LNWvW4Lvvvqv2E4sdPXoUgwYNMprcaUpGRgbWrVuHn3/+mQlq\nRGSwYcMGxXabNm1w3333mSzPPrjmYB9sHPtgIiLnExISgl69ejk6jCoTGBiIHj162K19d3d3dO7c\nGZ07d7bbMewlJCQEQ4YMcXQYlWbPc+vt7Y327dujffv2dmmfiIiqn19//bVC4rWlCdfK69atmyJJ\n/JdffoEsyzZb1MXT0xNFRUVGnysqKjJ5nGeffRZffPGF2bavXr2KDz74AN9//z2uXr1qslxkZCRe\neuklvPLKK9BoNMKxA0BKSgrWr1+Pn376CQcPHjT5WoDSidmjo6MxYcIEjBgxwuJk+baeOK+qJvwr\nz9TEhjqdDsuWLcP8+fORlJRUod4777xjMkm8Ks4tJxgkIiIicg7x8fGYPHmyyefd3NywatUqTpxI\nRFXG9X/bBAcD3boBAiv+yaF1IUe1tFxWloEraZD++svy8VUqyPUbQPbxtVy2jCSWTC3LQG5eae6q\nJR4eQGCgeD6ssAsXgP/+F7hrNhNzbL5usrs7dBNehb51G8tlS0rg9vGHUJ08busorKJv1BjFE6dB\n9rCcta9OSYJm7VpAZznBVR/ZFCXDnoUs8EeCJAl9JACUnlqRfGtJUsEzPEK4XUlbDKnY9M3FuxqG\n7O4BWRIcCFevHqThwwWDsOKNyM4GjhwRy5oPCQE6dCidncEYWS59Y11ghRtXpdfrMXnyZCxcuNCm\n7UqS5BIDT4mIiIiIyH6OHDlSYV+nTp0U22lpaTh48CCuXLkCvV6PkJAQhIWFoXPnzvDx8amqUA0O\nHDiAvn37Iisry+jzAQEBKCwsRHG5m0xarRYffPABkpKSsHbt2ipf/cyRbty4gWeffRYXLlww7JMk\nCbVr14ZKpUJmZib05e4R/Pzzz+jRowd27NhRbZPUzp8/j0ceeQS3jUzCqFarERISAk9PT+Tn5yMn\nJ6fCNUVEVOaHH37A6tWrFfumT59utg774JqBfbBx7IOJiIiIiIiopjh9+rRiu2HDhqhbt65VbZSf\ndCY3NxdXrlxBgwYN7jk+e9Hr9XjzzTfx3//+F3fu3LFYPjk5GVOmTMGCBQuwadMm4QlZPvnkE7z6\n6qvCccmyjOPHj+P555/Hxx9/jM2bN9tspWtX89dff2HgwIE4cOCAyTLGVpavqnPLCQaJiIiIXIOn\npydWrVqF2NhYR4dCRDWI6yeJlyWBiiSCqlWlSZ2WVmWQ5dJFrkVWFNbrS2Ow40oPImHYjf7vZFdH\nDjZRqUuz4IXK2jxN3XqSVJog7i4Qs1pduiq1ViAJX6uDLLL6upVEry9Zxt+fH9GWBc+FtRe4JNln\nhW5ZLv08i2TMy7L5z71eb9ffCTXdnTt3MGrUKHz33Xc2bzs6OhoNGza0ebtEREREROQ6yieoNWrU\nCLVq1QJQurrFzJkzsW/fPqN1PTw88OCDD2LGjBl4+OGH7R4rAKSnp2PAgAEVktP++c9/YvLkyeje\nvTu8vb0hyzIuXLiAdevWYe7cucjNzTWU3bhxI6ZPn45PPvmkSmK25McffzQkPnXv3h3nzp0zPLdp\n0yZ07NjRZF0/Pz+hY0yYMMGQnNa4cWO8+eabGDBggKF+QUEB/ve//+HNN9/EmTNnDPUOHTqEsWPH\nYt26dVa/Lnuw9Xs1btw4RXKap6cnJkyYgOHDh6N169aKGY5lWcbFixdx/PhxbN++HVu3bq2Q1EdE\nNU9qaioWLVqEhQsXKn4nPPPMM3jqqafM1mUf7Hjsg8WxDyYiIiIiIiKqnLu/7wOl9wesZazOmTNn\nnDZJPD8/HyNGjMCWLVuMPu/m5gZ/f3/k5uaipNyCTmlpaXjooYewadMmPP744xaPlZOTY/I5Ly8v\neHt7Iy8vz+jq4qdOnULHjh2RkJCARo0aWTxWdZKbm4snn3wSp06dMluufJJ4VZ1bTjBIRERE5Ny8\nvb1x//33o3v37hg/fjwiIyMdHRIR1TCunyROREQ1xu3bt/Hkk09ix44ddmm/b9++dmmXiIiIiIhc\nR/nZ9xs2bAitVovXXnsNCxYsMLpCQJni4mLs3LkTO3fuxKBBg/Dll18KJ0xV1vPPP4/MzEzFvjlz\n5uD1119X7JMkCY0bN8Ybb7yBUaNG4bHHHlMkMy1YsAB9+vRB9+7d7RqviDp16hh+dis3UVxISAjq\n169/z8c4fvw4AKBXr17YuHEjvLy8FM97e3vjySefxIABAzBixAhs3LjR8Nz69esxbNgwDBo06J7j\nuFe2fK+uXr2KnTt3Grbd3d3xyy+/VFiRpYwkSYiIiEBERASGDBmCoqIibNu2zcpXQESuaOLEiYrB\nplqtFtnZ2Th37hySk5MVZSVJwuTJk/Hhhx9abJd9MPtggH0wwD6YiIiIiIiIqre774sAqNSCHvXr\n14dKpVJMmnbu3DmhJGoRKSkphntRmZmZaNeuneE5jUZT4R5YGR8fH6P7R40aVSGJuGXLlhg/fjy6\nd+9uSHqXZRlnzpzBunXrMH/+fMNkg/n5+Rg+fDiOHz+O8PBwodcQGBiIXr16oWfPnmjTpg2aN28O\njUZjeD49PR379u1DXFwc4uPjDfuzsrIwdOhQHDp0CGoji3dVxSSDjjB16lRDgnhAQADGjBmDHj16\nIDw8HF5eXrh27Rr27t2ruCcEVN255QSDREREROalp6dX6fEmTZqESZMmVekxiYjMYZI4ERG5hKSk\nJPTu3dvkTXZbcIbBhURERERE5FjZ2dmK7bp16+Lf//43Vq1aZVU733//PZKSkrBnzx4EBwfbMkSD\nw4cPKwbuAKX/hCifnFZegwYNsHPnTrRu3drwemVZxltvveUUCWpVpUWLFkaT0+6m0Wiwdu1adO7c\nGceOHTPsf/vtt6vdd8jjx48rEjD79etnMjnNGI1Gg8GDB9sjNCJyMuvXr0dGRobZMoGBgejfvz9e\nffVVtGnTRqhd9sHsg+/GPph9MBEREREREVVPWVlZiu169epZ3YabmxtCQkIU96jKt3svwsLCFMcq\nz5rJ4ebPn49NmzYp9s2ePRszZ86skIQtSRKioqLw9ttv49lnn0Xv3r1x/vx5AMCtW7fwwgsv4Oef\nfzZ7vMjISMTFxWHkyJGKpPDyQkNDMWTIEAwZMgTfffcdnnnmGcPq4kePHsWGDRswbNiwCvWqYpJB\nR/jtt98AlCa+f/PNN6hdu7bi+fr16yMmJkaxr6rOLScYJCIiIiIiIktUjg6AiIjIkpMnT+KRRx6x\na4J4/fr1ER0dbbf2iYiIiIjINZRPUPvpp58UyWmNGzfGkiVLkJSUhMLCQmRnZ+PIkSN4/fXXK6wQ\nkZiYiBEjRthtdv4FCxYotuvXr4/33ntPqK6xsr///juOHj1qs/ic3fz5880mp5Xx8PDA4sWLFftO\nnDiBAwcO2Cs0hyg/gE50NRIiImO8vLyg0WjMrv5dHvtg9sHlsQ8mIiIiIiIiqn7y8vIU297e3pVq\np3y98u06g5ycHMyePVux7+2338abb75pdJXuuzVu3Bjbtm2Dv7+/Yd/OnTuRkJBgtt7IkSMxevRo\nswni5Q0dOhQLFy5U7Fu0aJFw/eqiY8eO2LZtW4UEcWOq8txygkEiIiIiIiKyhEniRETk1Hbs2IEH\nH3wQV65csetx+vXrB0mS7HoMIiIiIiJyfuUHEd2dtPP000/j9OnTeOmllxAZGQlPT08EBASgQ4cO\nmDNnDk6dOoUmTZoo6sfHx2P16tU2j1OWZWzfvl2xb8yYMVYNpnr++ecVA1AA4Mcff7RJfM4uMjIS\njz/+uHD5zp07V5hYbOvWrbYOy6ECAwMV2wcPHnRQJERUHVy/fh0rVqxA27Zt8dxzzyE3N9diHfbB\n7IONYR9MREREREREVL3k5+crtj09PSvVTvkJ6JwxSXzJkiW4ffu2YTs6OhpvvPGGcP3IyEi8+uqr\nin2fffaZzeK725gxYxSrgB86dAgFBQV2OZazWrFiBTw8PITKVuW55QSDREREREREZAmTxImIyGmt\nXLkSffv2VdxQtcTT0xORkZFWH2vAgAFW1yEiIiIiourH1GCkBx54AF999ZXZwSGNGjVCfHw8fH19\nFfs/+OADm69keubMGdy6dUuxb8iQIVa14eXlhb59+yr27du3755jcwX9+/e3us7AgQMV29VtFdOO\nHTsqtg8cOIAJEyY45cA6InKs9PR0yLJseOTn5+PKlSv48ccfMXnyZNSqVUtR/ssvv0T37t0t3uNj\nH8w+2BT2wURERERERETVQ0lJCXQ6nWKfaFJueeVXyi4sLKx0XPby9ddfK7YnTZoElcq6YdvPP/+8\nYnvPnj33HJcxkiThwQcfNGxrtVqLq5ZXJ926dUObNm2Ey1flueUEg0RERERERGQJk8SJiMjp6PV6\nTJw4ES+88AK0Wq3JchqNBl27dsWECRPw5ZdfIiUlBampqRVmz7QkMDAQjzzyyL2GTVRt6fV66HQ6\niw9ZloXblCRJ+GGPWF3xYetB7c5Ep9NBq9UKP6y51qwhSRJUKpXFhyRJwtdadT5vzqAsKUWv15t8\n2Ot6IaLqq3xyWZnFixcLDe6IiIjAtGnTFPvOnTtn84E0p06dUmz7+PigRYsWVrfToUMHxfYff/xx\nT3G5inbt2t1znZMnT9oqHKdQt27dCol7ixYtQlhYGP71r39hw4YNyMjIcFB0ROTMvL29ERYWhl69\neuHjjz9GSkoKRo4cqShz+PBhvPjii2bbYR/MPli0DvtgIiIiIiIiItfk7u4OtVqt2FdcXFyptoqK\nihTblV2R3F4yMzNx+vRpxb5+/fpZ3U7Dhg0VK3ynpKQgMzOzUjEVFxfj5s2bSE1NRXJycoVH+YT9\ntLS0Sh3HFfXo0UO4bFWfW04wSERERERERJa4OToAIiKiuxUVFWH06NEVZtsESgfLxcTEICYmBp06\ndUKHDh0QEBCgKDN06FBDknizZs2QkpJiNtEcAB577DG4u7vb7kUQVTPJycnYv38/fHx8TJZRqVRo\n1aoVgoODLbYnSRJ8fHzg7e1tsaxOp4Obm9ifrHq9HomJiUhKShIq72qCg4PRqlUrq2cednY6nQ6/\n/vorrl69KlS+pKQE165ds3kcPj4+aNq0qVB/4OPjg/379wtNYlBdz5uzkGUZSUlJuHnzpsky5p4j\nIjLGWIJa+/btrVo9YPTo0Zg1a5Zi3+7duxETE3PP8ZUp//stPDy8Uv1NRESEYtvaSbdcVcOGDa2u\nEx4ertjOycmBTqerMKDNlS1ZsgTHjx/H5cuXDftu376NVatWYdWqVQCAxo0bo3PnznjooYfQvXt3\n3H///Q6KloicVUBAAFavXg2VSoXVq1cb9n/zzTeYMGECYmNjjdZjH8w+2BT2weyDiYiIiIiIqPrw\n8fHB7du3Ddt37typVDvlVw43NQGhoxw6dEgxoXmdOnVQUFCAgoICq9uqVasWrly5Yti+fv06QkJC\nLNZLTk7Gt99+i99++w2JiYnC4zLK3Lp1y+pYXVXbtm2Fy1b1uS2bYHDr1q2GfYsWLcKXX36JIUOG\noHfv3ujWrRvuu+8+q49PRERERERE1YPzJ4lbSryQJEClKn3ca1sGcmlZkfJVkOwhFLYsA6KrFJa9\nZyLNArBu/U4bkwFotYDIbJElJYDe8asEyjJQIji5pU4rQZLdSl+nxXbVpeUEVkIsfdvEzpxOJ37p\n6HTiHyOVDKHXBUkSeUkGdrsmrfhciPx+cPyV6Jpu3bqFQYMGYc+ePfD19UX79u0NCeExMTFo0KCB\n2fqbNm3Chg0bAJQmrK5atQovv/wyTpw4YbbegAEDbPYaiKqj27dvIyMjA15eXibLqNVqNGnSRLhN\n0YkZdDqd8EBrWZar/YDq6rgisizLuHr1qnByv1arrdQ/1ixxd3dHYGCg8LWZnp4u3HZ1PG/OQpZl\n5OTkmJ0pvfzgACIiS4xNetOtWzer2qhXrx4iIiJw4cIFwz5br3hZfmCOv79/pdopP/FWUVER8vPz\nzU4QVB1U5v0q/17Jsozs7GzUqlXLVmE5XFhYGA4fPoyxY8cqBhvdLSUlBSkpKVizZg0AICYmBq+8\n8gpGjBhRrZL1iOjeSJKEBQsWYOvWrcjOzjbsX7FihckkcfbB7INNYR9cin0wERERERERVQe+vr6K\nJPHK/v+//P+B/fz87ikuWys/puCvv/6yOPZNlKWxMampqZg6dSo2btx4T8fJzc29p/quRCTpvowj\nzi0nGCQiIiIiIiJznDdJXKUCNBrA09N8MmZgIBARAQisMCn5+f+dXWo5SUMKDgL8BGcW1GjEyllJ\nkkpfvsjimW552VAdOQvIlrN95YAg6Jo0F0qIVQkmD9uNTgv1hnVQ/fSj5bJ6GaqLKfaPyYKLF4Fv\nFgAlApnMUkF9qK6NEkr8jgx0x9Dbt+AmkLeUnqXBgT/9odNbDsLdXXyOhTp1ADP5gYqygf7u0HiI\n/Yrx9VcJvS4AUKkkuLnZIVE8MAhSx45C5wIaDWTJ9IArGSrIkCA7dooFl1NcXIwvvvgCzzzzDBYt\nWoSoqCirBrZlZWXhlVdeMWy/+OKL6Ny5M2JiYswmibu5uaF37973FDsREREREVUfzZs3r7Cv/OqV\nIu6//35Fglr5VUeJnFVoaCi2bNmCY8eO4YsvvsAPP/yA1NRUk+UPHz6Mw4cP4+OPP8a6deuMfoaI\nqGYKDAxEv3798NVXXxn2/fbbbybLsw+mmo59MBEREREREdUEQUFBuHbtmmH7+vXrVreh1WorTCQe\nFBR0z7HZkj3vSeXn55t87uDBg+jdu7dNVgHXi64+VA1YsxK9I84tJxgkIiIiIiIic5w3SVySALW6\n9GEuSdzDAwgIEMuk9vL6uykLSaASShO/BVcRhB2/PFt6+YZyumJIf/0F6HUWy8r6v1czFFq9WiBI\ne9LrIaUku1SqbW4u8McfgNhi4j4Amok1XHgHUnEm1AInpSgXuHQJ0Fq+HODlJfbxKVtoW2QRE0kC\n3NxU0AlcP5IEaLSAyqqPkWTzlbolD4/SLHgBlhLAZfutd16teXh4YPLkyZWuP3XqVMMsnQ0bNsQH\nH3wAAOjUqROWL19ust4///lPp/snAREREREROU5UVFSFfZVZfaJ8nZycnErHZEz57zF3r7phjfJx\naTQau65g6iwDiirzfpV/ryRJQmBgoK1CqsDR71W7du3Qrl07LFy4EJcvX8a+ffuwf/9+/P777zhx\n4kTp/cW7nDx5Eg8//DAOHz5ssxUziMj1tWrVSrGdlpZmsiz7YPbBprAPZh9MRERERERE1UezZs3w\n559/GrbN3S8y5erVq9DplIMjmzUTHIdZRYqLxUaQVkb5ewNl/vrrrwoJ4iqVCj169MDjjz+Otm3b\non79+ggJCYFGo4Gm3AJZU6dOxUcffWS3uJ2ZJDJQ+2+OOLcAJxgkIiIiIiIi05w3SZyIiEjQjh07\nsGrVKsP20qVLDYNBY2JizNbt37+/XWMjIiIiIiLXUj6ZDQDy8vKsbic3N1exbetEplq1aim209LS\noNfroVKprGrn4sWLiu3g4GCTZcuvMlB+AJYIW6xcYQuVGXR26dIlxXZAQIDJlReq03sFAA0aNMDw\n4cMxfPhwAKUDzb7//nssXLgQp0+fNpRLT0/H66+/blilgoiofNKzVqs12V+xD2YfbAr7YPbBRERE\nREREVH20aNFCsZ2cnGx1GykpKRbbdbTy95C6dOmCffv22fWYs2bNUtzXCAsLw5YtW9C+fXuh+pW5\nF+cMqnrCP0ec27txgkEiIiIiIiIqz7oRK0RERE4mNzcXY8eONWw/88wz6NWrl2E7KioK/v7+RutK\nkoQBAwbYPUYiIiIiInIdzZo1Q/369RX7yicmiShfp3bt2vcUV3n/+Mc/FNt5eXk4d+6c1e0kJCSY\nbfdu5VdmLZ+EJ+LChQtW17GHY8eO3XOdNm3amCxbnd4rY+rUqYOxY8fijz/+MCStldm4cSMKCwsd\nFBkROZuMjAzFdq1atUwmU7MPZh8sWod9MPtgIiIiIiIicl1RUVGK7bS0NFy/ft2qNvbv36/Y9vX1\ndbrk15CQEMW2scR2W9Jqtfjuu+8U+1atWiWcIA4AmZmZtg7LIlec8K+qz605ZRMMLly4EMeOHUN6\nejqWLl1a4XNWNsEgERERERERVU9MEiciIpf2xhtvGAZ+3nfffZg/f77ieZVKZfJmd5s2bdCwYUO7\nx0hERERERK5DkiQMGjRIsW/v3r1WtXH9+vUKA0Latm17z7HdrXnz5hVWHN20aZNVbdy5cwfbtm1T\n7OvatavJ8uVXYr158yays7OFj5eZmYlTp05ZFaOHh4diW6vVWlXflB9++MHqOlu2bFFsx8bGmixb\nnd4rc9RqNRYsWABJkgz77ty5U6mVX4ioetq9e7di29xgXfbB7INNYR9cEftgIiIiIiIiclUPP/yw\n4vssYP09oPLljbXpaOXvSWVkZODs2bN2O9758+eRlZVl2K5Xrx4ee+wxq9ooP6lhVXDFCf+q+txa\ngxMMEhERERER1UxMEiciIpf1+++/49NPPzVsL1q0qMIATQDo0KGD0fp9+/a1zsGrAwAAIABJREFU\nW2xEREREROS6hg4dqthOSEiwKllo5cqVFfZ17979nuO6myRJ6NWrl2JfXFwc7ty5I9zG6tWrKyRN\n9enTx2R5X19fhIWFKfb99ttvwsdbsmQJZFkWLg9UHByUk5NjVX1TkpKSsHPnTuHyBw8exPHjxxX7\n+vfvb7J8dXqvLKlTpw4CAgIU+/Lz86vk2ETk3BISErBv3z7Fvscff9xsHfbBxlWnfoV9sO2wDyYi\nIiIiIiJXVLduXcTExCj2ffPNN8L109PT8csvvyj2lZ940BlERkbi/vvvV+xbv3693Y6XkZGh2A4P\nD7eq/h9//IG0tDSr6thi4jxHTPh3r6r63FYGJxgkIiIiIiKqWZgkTkRELunOnTt44YUXoNfrAQCD\nBw+uMIi0TKdOnYzud8Z/EBARERERkeN169YN3bp1U+wbP3684fuHOampqfjwww8V+zp06IB//OMf\nNo0RACZMmFDh2G+//bZQ3evXr2PGjBmKfd26dUO7du3M1is/cOuzzz4TOl5iYiLmzp0rVPZu9erV\nU2yfPn3a6jZMmThxolBCX0lJCcaNG6fY16ZNG3Tp0sVsPVd7r6xNiCuTmZlZIRmubt26lWqLiJzL\n+fPnodPpKlX3+vXrGDlyZIW+84knnjBbj32waa7Wr5jDPliJfTARERERERHVNOXHbP3vf//D5cuX\nheouX75ckYzs7u6Ofv362TQ+W3nyyScV25988glu3rxpl2OVX0n99u3bVtUvf19NhC0mznPEhH+2\nUJXntrI4wSAREREREVHNwSRxIiJySe+88w7OnTsHAAgKClKsKF5e+UGBANCgQQO0bdvWbvERERER\nEZFrKz8YZs+ePXj++edRUlJisk5aWhp69eqF3Nxcxf4333zTHiEiJiYGPXv2VOx7//33sWjRIrP1\nrl+/jscee0wxWEWSJMyaNcviMctPzhUfH2/2+xhQugrs448/jsLCQovtl1c+YW716tUoKCiwuh1j\nTp8+jaFDh5pNUispKcHIkSNx9OhRxf6ZM2dabN/V3qsZM2ZgzJgxSExMFK6j1+vx6quvKgZgRUZG\nWr1CCRE5p4ULF6JFixZYtmwZbty4IVRHlmVs2rQJnTp1Mty7KzN8+HC0b9/eYhvsg41ztX7FHPbB\nSuyDiYiIiIiIqKYZO3asYgVprVaLl156yWK9lJSUChO8jR49GrVr17Z5jLYwdepU+Pj4GLZzcnIw\nbNgws/e5LDGVEG1sErtLly4Jtbl582Z8/fXXVsdiq0kGq3rCP1uoynPLCQaJiIiIiIjIEiaJExGR\nyzl69KhisOi8efMQGhpqsnyDBg0q3JTu379/hRlUicg4SZKgUqmgVqvNPqz5TOn1euh0OqGHJEkW\nj13Zh6N/D1jz2iRJEn7PRFZYszedTgetViv00Ov1kGXZqocotVoNNzc3iw+1Wi3cpjXnTaWq3l+5\n9Hq98MNes3db+v1U3c8BUU2ybds29O3b1+jjlVdeqVB++vTpJsuvX7/e4vFiY2MrDEhavXo1Wrdu\njbi4OFy6dAklJSUoKCjAyZMnMWvWLLRq1Qpnz55V1BkzZgz69Olzby/ejFWrViEkJESxb8KECejV\nqxd27NiB4uJiw/60tDT897//RVRUFP78809FnUmTJqF79+4Wjzd48OAKKzqMGzcOI0aMwN69e5GX\nlwe9Xo8bN24gPj4ezz33HGJjY3H9+nV4e3uja9euVr2+gQMHKv5mO3v2LFq2bInXXnsNy5Ytw5o1\naxSP8olkprRp0wZA6eokbdq0wfr16xXJXHfu3MGmTZvQrl07fPvtt4q6TzzxBIYMGWLxGK72XhUW\nFiIuLg6tW7dG69atMXv2bOzcudNoYmhOTg42bdqEBx54AGvWrFE8N2nSJKviJiLnlpSUhBdffBGh\noaF48MEHMXnyZHz++ef48ccfsW/fPhw4cAA//fQT4uLi8MorryA8PBxDhgypsPJTeHg4Pv74Y6Fj\nsg82ztX6FVPYB7MPJiIiIiIiIgoMDMS0adMU+7Zt24Zp06aZ/L/y1atXMXDgQMV9BC8vL6EJ5Rwl\nJCSkwuSAu3btwuOPP46rV68KtyPLMn799VcMGDAAGzZsMFqmSZMmigRgWZYxduxYi0nLW7ZswdNP\nPy0cy91sNclgVU/4ZwtVeW45wSARERERERFZ4uboAIiIiKxRUlKC0aNHQ6vVAgB69OiBf/3rXxbr\nxcTEYPPmzYbt/v372y1GouomMjISXbp0gZ+fn8kykiQhKChIqD29Xo/ExERkZWVZLCtJEsLCwtC0\naVPheEVZE4e9BAUFoVWrVkJJrPn5+Thw4IBQom1wcLBwu/ag0+lw8OBBZGRkWCyr1+tx/vx5ZGZm\nCrWt1WoVA+3NUavVePjhhytMFGJM7dq1hRPFrTlvGo2m2iYp6/V6pKamVlipzxhJktCoUSOzv0cq\nQ6VSoVWrVmjSpInJMlu2bLHpMYnIcS5evIht27YJl9+/f7/J52JjY4XamD9/PlJTU7F9+3bDvnPn\nzmHMmDFC9Xv06IHFixcLla2s0NBQbN68Gf369VP8XRMfH4/4+HhIkoRatWqhoKDA5MCcIUOG4IMP\nPhA6nkajwfLlyysk3a1duxZr1641WU+lUuHLL79EfHw89u3bJ3QsoHRQ09NPP61YwSI1NRXz5s0z\nWn7ixIlCq9QuWrQIo0aNQmpqKs6fP4/hw4dDrVbjvvvug0qlQnp6uuF75906dOiAFStWCMXuyu9V\nYmKiYrCRn58fAgMDodFokJOTY/Jvt4EDB+Lll18WjpmIXIdOp8PevXuxd+9eq+vef//9+OWXX6xa\npYZ9cEWu3K/cjX0w+2AiIiIiIiJyTtu2bTO5grOx/wlPnz7d5DiRZ555BsOGDTN7vEmTJmHt2rWK\n78H//e9/ceTIEUydOhUdO3ZEYGAg0tLS8P333+Ojjz6qMAbh7bffFhoP4EjTpk3DiRMn8M033xj2\n7d69G02bNsWoUaMwePBgxMbGKv6XrtVqkZycjBMnTmDPnj3YunUrrl27BgB46qmnjB5HkiSMGTMG\nb7/9tmHfjh070KVLF7zzzjt45JFH4OHhYWh/3759+PTTT/Hdd98BKL0v0qFDBxw+fFj4tQ0cOBDT\np083jGMpmzjviSeeQGRkpGKlbQBo0aKF0XsiZRP+3Z1cPW7cOOzfvx8vvvgi2rZtC29vb2RlZSEh\nIQHr1q3DmjVroNPp4O3tjbZt21p1L8dWqurclk0wGBcXh1atWmHw4MHo1q0boqOjUbt2bUXZnJwc\n7Nq1C/PmzcOBAwcUz3GCQSIiIiIiourLeZPEJQlwcwP+vilhkloN6PWlD0usWrlOcFVJZ1mFVpYB\nnQ7Q6yyX1WmB4hJAJVBWqwUE3zZZUkGvErukhBe3lIUP7zS0dvpYyXqgRCt2vWm1ktDHQpLEPz6S\n9PclJlhWlsU/ctaULSsvwpqPpywDkix4YUoSIFlKInO1K9d1zJs3DydPngQA+Pr6YtmyZUL1OnTo\nYEgS9/Pzw0MPPWS3GImqm4CAAISGhsLf398m7cmyjKysLFy/ft1iWbVajaZNm1o1kFyUTqdDUlKS\nzdu1hkajQWhoqFBy8vXr15GRkQGdTuBvOMBuqzaLHjsjIwOpqakWy+r1emRnZwvPJl22WroISZJQ\nr149swnEZby9vYVXlrfmvFV3ubm5uHnzpsVyKpWqwiputiBJEoKDg82W8fb2tvlxiajm8PDwwObN\nmzF58mQsWbJEuJ5KpcLkyZMxd+7cKukvunTpgn379mHo0KEVVhGQZdnoKpQA4ObmhilTpmDOnDlW\nTWrSu3dvLF++HC+99JJQv+zj44PVq1dj8ODBiI+PFz5OmaVLl6KwsBCbNm2yuq4pISEh2LVrF3r3\n7o1z584BKP07o2wgjjGPPvooNmzYgMDAQOHjuNJ7Ze5vodzcXLMTw6jVaowfPx7z5s0T/puKiJyf\nu7v7PdVXqVR48cUX8f7771t9T4F9sHGu1K+Ywj64IvbBRERERERE5AyqerJeLy8vbN68Gd26dVOM\nH9m9ezd2795tsf6oUaMwZcoUoVgd7fPPP4darcaaNWsM+woKCrB06VIsXboUQOm9CT8/P+Tl5SEv\nL69Sx5k6dSq+/fZbnD171rAvISEBvXr1Moxz0Ov1yMjIqDA5/5w5c5CZmWlVkritJs6r6gn/bKmq\nzm0ZTjBIRI5y/vx5+Pr6OjoMIiJygOTkZEeHQEQCnDdJvF49YNQooFEjwNw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qKiqyKLNq1SoNGjRIP/30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- "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import Image\n", - "Image('images/12_adversarial_noise_flowchart.png')" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -88,11 +74,18 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", + " from ._conv import register_converters as _register_converters\n" + ] + } + ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", @@ -101,68 +94,34 @@ "from sklearn.metrics import confusion_matrix\n", "import time\n", "from datetime import timedelta\n", - "import math\n", - "\n", - "# We also need PrettyTensor.\n", - "import prettytensor as pt" + "import math" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "This was developed using Python 3.5.2 (Anaconda) and TensorFlow version:" + "This was developed using Python 3.6 (Anaconda) and TensorFlow version:" ] }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'0.12.0-rc0'" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tf.__version__" - ] - }, - { - "cell_type": "markdown", + "execution_count": 2, "metadata": {}, - "source": [ - "PrettyTensor version:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, "outputs": [ { "data": { "text/plain": [ - "'0.7.1'" + "'1.9.0'" ] }, - "execution_count": 4, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "pt.__version__" + "tf.__version__" ] }, { @@ -181,40 +140,25 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Extracting data/MNIST/train-images-idx3-ubyte.gz\n", - "Extracting data/MNIST/train-labels-idx1-ubyte.gz\n", - "Extracting data/MNIST/t10k-images-idx3-ubyte.gz\n", - "Extracting data/MNIST/t10k-labels-idx1-ubyte.gz\n" - ] - } - ], + "execution_count": 3, + "metadata": {}, + "outputs": [], "source": [ - "from tensorflow.examples.tutorials.mnist import input_data\n", - "data = input_data.read_data_sets('data/MNIST/', one_hot=True)" + "from mnist import MNIST\n", + "data = MNIST(data_dir=\"data/MNIST/\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The MNIST data-set has now been loaded and consists of 70,000 images and associated labels (i.e. classifications of the images). The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial." + "The MNIST data-set has now been loaded and consists of 70.000 images and class-numbers for the images. The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial." ] }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, + "execution_count": 4, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -222,72 +166,45 @@ "text": [ "Size of:\n", "- Training-set:\t\t55000\n", - "- Test-set:\t\t10000\n", - "- Validation-set:\t5000\n" + "- Validation-set:\t5000\n", + "- Test-set:\t\t10000\n" ] } ], "source": [ "print(\"Size of:\")\n", - "print(\"- Training-set:\\t\\t{}\".format(len(data.train.labels)))\n", - "print(\"- Test-set:\\t\\t{}\".format(len(data.test.labels)))\n", - "print(\"- Validation-set:\\t{}\".format(len(data.validation.labels)))" + "print(\"- Training-set:\\t\\t{}\".format(data.num_train))\n", + "print(\"- Validation-set:\\t{}\".format(data.num_val))\n", + "print(\"- Test-set:\\t\\t{}\".format(data.num_test))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The class-labels are One-Hot encoded, which means that each label is a vector with 10 elements, all of which are zero except for one element. The index of this one element is the class-number, that is, the digit shown in the associated image. We also need the class-numbers as integers for the test-set, so we calculate it now." + "Copy some of the data-dimensions for convenience." ] }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "data.test.cls = np.argmax(data.test.labels, axis=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Data Dimensions" - ] - }, - { - "cell_type": "markdown", + "execution_count": 5, "metadata": {}, - "source": [ - "The data dimensions are used in several places in the source-code below. They are defined once so we can use these variables instead of numbers throughout the source-code below." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": true - }, "outputs": [], "source": [ - "# We know that MNIST images are 28 pixels in each dimension.\n", - "img_size = 28\n", + "# The number of pixels in each dimension of an image.\n", + "img_size = data.img_size\n", "\n", - "# Images are stored in one-dimensional arrays of this length.\n", - "img_size_flat = img_size * img_size\n", + "# The images are stored in one-dimensional arrays of this length.\n", + "img_size_flat = data.img_size_flat\n", "\n", "# Tuple with height and width of images used to reshape arrays.\n", - "img_shape = (img_size, img_size)\n", - "\n", - "# Number of colour channels for the images: 1 channel for gray-scale.\n", - "num_channels = 1\n", + "img_shape = data.img_shape\n", "\n", "# Number of classes, one class for each of 10 digits.\n", - "num_classes = 10" + "num_classes = data.num_classes\n", + "\n", + "# Number of colour channels for the images: 1 channel for gray-scale.\n", + "num_channels = data.num_channels" ] }, { @@ -306,10 +223,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": true - }, + "execution_count": 6, + "metadata": {}, "outputs": [], "source": [ "def plot_images(images, cls_true, cls_pred=None, noise=0.0):\n", @@ -360,16 +275,14 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, + "execution_count": 7, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -378,10 +291,10 @@ ], "source": [ "# Get the first images from the test-set.\n", - "images = data.test.images[0:9]\n", + "images = data.x_test[0:9]\n", "\n", "# Get the true classes for those images.\n", - "cls_true = data.test.cls[0:9]\n", + "cls_true = data.y_test_cls[0:9]\n", "\n", "# Plot the images and labels using our helper-function above.\n", "plot_images(images=images, cls_true=cls_true)" @@ -421,10 +334,8 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": true - }, + "execution_count": 8, + "metadata": {}, "outputs": [], "source": [ "x = tf.placeholder(tf.float32, shape=[None, img_size_flat], name='x')" @@ -439,10 +350,8 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": true - }, + "execution_count": 9, + "metadata": {}, "outputs": [], "source": [ "x_image = tf.reshape(x, [-1, img_size, img_size, num_channels])" @@ -457,10 +366,8 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": true - }, + "execution_count": 10, + "metadata": {}, "outputs": [], "source": [ "y_true = tf.placeholder(tf.float32, shape=[None, num_classes], name='y_true')" @@ -475,13 +382,11 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": true - }, + "execution_count": 11, + "metadata": {}, "outputs": [], "source": [ - "y_true_cls = tf.argmax(y_true, dimension=1)" + "y_true_cls = tf.argmax(y_true, axis=1)" ] }, { @@ -502,10 +407,8 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": true - }, + "execution_count": 12, + "metadata": {}, "outputs": [], "source": [ "noise_limit = 0.35" @@ -522,10 +425,8 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": true - }, + "execution_count": 13, + "metadata": {}, "outputs": [], "source": [ "noise_l2_weight = 0.02" @@ -542,10 +443,8 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": true - }, + "execution_count": 14, + "metadata": {}, "outputs": [], "source": [ "ADVERSARY_VARIABLES = 'adversary_variables'" @@ -560,13 +459,11 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": true - }, + "execution_count": 15, + "metadata": {}, "outputs": [], "source": [ - "collections = [tf.GraphKeys.VARIABLES, ADVERSARY_VARIABLES]" + "collections = [tf.GraphKeys.GLOBAL_VARIABLES, ADVERSARY_VARIABLES]" ] }, { @@ -578,10 +475,8 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, + "execution_count": 16, + "metadata": {}, "outputs": [], "source": [ "x_noise = tf.Variable(tf.zeros([img_size, img_size, num_channels]),\n", @@ -599,10 +494,8 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": true - }, + "execution_count": 17, + "metadata": {}, "outputs": [], "source": [ "x_noise_clip = tf.assign(x_noise, tf.clip_by_value(x_noise,\n", @@ -619,10 +512,8 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": true - }, + "execution_count": 18, + "metadata": {}, "outputs": [], "source": [ "x_noisy_image = x_image + x_noise" @@ -637,10 +528,8 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false - }, + "execution_count": 19, + "metadata": {}, "outputs": [], "source": [ "x_noisy_image = tf.clip_by_value(x_noisy_image, 0.0, 1.0)" @@ -657,51 +546,50 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We will use PrettyTensor to construct the convolutional neural network. First we need to wrap the tensor for the noisy image in a PrettyTensor-object, which provides functions that construct the neural network." + "We will use the Layers API to construct the convolutional neural network, see Tutorial #03-B." ] }, { "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "x_pretty = pt.wrap(x_noisy_image)" - ] - }, - { - "cell_type": "markdown", + "execution_count": 20, "metadata": {}, - "source": [ - "Now that we have wrapped the input image in a PrettyTensor object, we can add the convolutional and fully-connected layers in just a few lines of source-code." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": true - }, "outputs": [], "source": [ - "with pt.defaults_scope(activation_fn=tf.nn.relu):\n", - " y_pred, loss = x_pretty.\\\n", - " conv2d(kernel=5, depth=16, name='layer_conv1').\\\n", - " max_pool(kernel=2, stride=2).\\\n", - " conv2d(kernel=5, depth=36, name='layer_conv2').\\\n", - " max_pool(kernel=2, stride=2).\\\n", - " flatten().\\\n", - " fully_connected(size=128, name='layer_fc1').\\\n", - " softmax_classifier(num_classes=num_classes, labels=y_true)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that `pt.defaults_scope(activation_fn=tf.nn.relu)` makes `activation_fn=tf.nn.relu` an argument for each of the layers constructed inside the `with`-block, so that Rectified Linear Units (ReLU) are used for each of these layers. The `defaults_scope` makes it easy to change arguments for all of the layers." + "# Start the network with the noisy input image.\n", + "net = x_noisy_image\n", + "\n", + "# 1st convolutional layer.\n", + "net = tf.layers.conv2d(inputs=net, name='layer_conv1', padding='same',\n", + " filters=16, kernel_size=5, activation=tf.nn.relu)\n", + "net = tf.layers.max_pooling2d(inputs=net, pool_size=2, strides=2)\n", + "\n", + "# 2nd convolutional layer.\n", + "net = tf.layers.conv2d(inputs=net, name='layer_conv2', padding='same',\n", + " filters=36, kernel_size=5, activation=tf.nn.relu)\n", + "net = tf.layers.max_pooling2d(inputs=net, pool_size=2, strides=2)\n", + "\n", + "# Flatten layer.This should eventually be replaced by:\n", + "# net = tf.layers.flatten(net)\n", + "net = tf.contrib.layers.flatten(net)\n", + "\n", + "# 1st fully-connected / dense layer.\n", + "net = tf.layers.dense(inputs=net, name='layer_fc1',\n", + " units=128, activation=tf.nn.relu)\n", + "\n", + "# 2nd fully-connected / dense layer.\n", + "net = tf.layers.dense(inputs=net, name='layer_fc_out',\n", + " units=num_classes, activation=None)\n", + "\n", + "# Unscaled output of the network.\n", + "logits = net\n", + "\n", + "# Softmax output of the network.\n", + "y_pred = tf.nn.softmax(logits=logits)\n", + "\n", + "# Loss measure to be optimized.\n", + "cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(labels=y_true,\n", + " logits=logits)\n", + "loss = tf.reduce_mean(cross_entropy)" ] }, { @@ -720,26 +608,25 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 21, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ - "['layer_conv1/weights:0',\n", + "['layer_conv1/kernel:0',\n", " 'layer_conv1/bias:0',\n", - " 'layer_conv2/weights:0',\n", + " 'layer_conv2/kernel:0',\n", " 'layer_conv2/bias:0',\n", - " 'layer_fc1/weights:0',\n", + " 'layer_fc1/kernel:0',\n", " 'layer_fc1/bias:0',\n", - " 'fully_connected/weights:0',\n", - " 'fully_connected/bias:0']" + " 'layer_fc_out/kernel:0',\n", + " 'layer_fc_out/bias:0']" ] }, - "execution_count": 25, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -759,10 +646,8 @@ }, { "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": false - }, + "execution_count": 22, + "metadata": {}, "outputs": [], "source": [ "optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(loss)" @@ -784,10 +669,8 @@ }, { "cell_type": "code", - "execution_count": 27, - "metadata": { - "collapsed": false - }, + "execution_count": 23, + "metadata": {}, "outputs": [], "source": [ "adversary_variables = tf.get_collection(ADVERSARY_VARIABLES)" @@ -802,9 +685,8 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 24, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -814,7 +696,7 @@ "['x_noise:0']" ] }, - "execution_count": 28, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -834,10 +716,8 @@ }, { "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": true - }, + "execution_count": 25, + "metadata": {}, "outputs": [], "source": [ "l2_loss_noise = noise_l2_weight * tf.nn.l2_loss(x_noise)" @@ -852,10 +732,8 @@ }, { "cell_type": "code", - "execution_count": 30, - "metadata": { - "collapsed": true - }, + "execution_count": 26, + "metadata": {}, "outputs": [], "source": [ "loss_adversary = loss + l2_loss_noise" @@ -870,10 +748,8 @@ }, { "cell_type": "code", - "execution_count": 31, - "metadata": { - "collapsed": false - }, + "execution_count": 27, + "metadata": {}, "outputs": [], "source": [ "optimizer_adversary = tf.train.AdamOptimizer(learning_rate=1e-2).minimize(loss_adversary, var_list=adversary_variables)" @@ -899,13 +775,11 @@ }, { "cell_type": "code", - "execution_count": 32, - "metadata": { - "collapsed": true - }, + "execution_count": 28, + "metadata": {}, "outputs": [], "source": [ - "y_pred_cls = tf.argmax(y_pred, dimension=1)" + "y_pred_cls = tf.argmax(y_pred, axis=1)" ] }, { @@ -917,10 +791,8 @@ }, { "cell_type": "code", - "execution_count": 33, - "metadata": { - "collapsed": true - }, + "execution_count": 29, + "metadata": {}, "outputs": [], "source": [ "correct_prediction = tf.equal(y_pred_cls, y_true_cls)" @@ -935,10 +807,8 @@ }, { "cell_type": "code", - "execution_count": 34, - "metadata": { - "collapsed": true - }, + "execution_count": 30, + "metadata": {}, "outputs": [], "source": [ "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))" @@ -962,10 +832,8 @@ }, { "cell_type": "code", - "execution_count": 35, - "metadata": { - "collapsed": true - }, + "execution_count": 31, + "metadata": {}, "outputs": [], "source": [ "session = tf.Session()" @@ -982,10 +850,8 @@ }, { "cell_type": "code", - "execution_count": 36, - "metadata": { - "collapsed": false - }, + "execution_count": 32, + "metadata": {}, "outputs": [], "source": [ "session.run(tf.global_variables_initializer())" @@ -1000,10 +866,8 @@ }, { "cell_type": "code", - "execution_count": 37, - "metadata": { - "collapsed": true - }, + "execution_count": 33, + "metadata": {}, "outputs": [], "source": [ "def init_noise():\n", @@ -1019,10 +883,8 @@ }, { "cell_type": "code", - "execution_count": 38, - "metadata": { - "collapsed": false - }, + "execution_count": 34, + "metadata": {}, "outputs": [], "source": [ "init_noise()" @@ -1046,10 +908,8 @@ }, { "cell_type": "code", - "execution_count": 39, - "metadata": { - "collapsed": true - }, + "execution_count": 35, + "metadata": {}, "outputs": [], "source": [ "train_batch_size = 64" @@ -1066,10 +926,8 @@ }, { "cell_type": "code", - "execution_count": 40, - "metadata": { - "collapsed": false - }, + "execution_count": 36, + "metadata": {}, "outputs": [], "source": [ "def optimize(num_iterations, adversary_target_cls=None):\n", @@ -1081,7 +939,7 @@ " # Get a batch of training examples.\n", " # x_batch now holds a batch of images and\n", " # y_true_batch are the true labels for those images.\n", - " x_batch, y_true_batch = data.train.next_batch(train_batch_size)\n", + " x_batch, y_true_batch, _ = data.random_batch(batch_size=train_batch_size)\n", "\n", " # If we are searching for the adversarial noise, then\n", " # use the adversarial target-class instead.\n", @@ -1155,10 +1013,8 @@ }, { "cell_type": "code", - "execution_count": 41, - "metadata": { - "collapsed": true - }, + "execution_count": 37, + "metadata": {}, "outputs": [], "source": [ "def get_noise():\n", @@ -1178,10 +1034,8 @@ }, { "cell_type": "code", - "execution_count": 42, - "metadata": { - "collapsed": false - }, + "execution_count": 38, + "metadata": {}, "outputs": [], "source": [ "def plot_noise():\n", @@ -1215,10 +1069,8 @@ }, { "cell_type": "code", - "execution_count": 43, - "metadata": { - "collapsed": true - }, + "execution_count": 39, + "metadata": {}, "outputs": [], "source": [ "def plot_example_errors(cls_pred, correct):\n", @@ -1235,13 +1087,13 @@ " \n", " # Get the images from the test-set that have been\n", " # incorrectly classified.\n", - " images = data.test.images[incorrect]\n", + " images = data.x_test[incorrect]\n", " \n", " # Get the predicted classes for those images.\n", " cls_pred = cls_pred[incorrect]\n", "\n", " # Get the true classes for those images.\n", - " cls_true = data.test.cls[incorrect]\n", + " cls_true = data.y_test_cls[incorrect]\n", "\n", " # Get the adversarial noise from inside the TensorFlow graph.\n", " noise = get_noise()\n", @@ -1262,10 +1114,8 @@ }, { "cell_type": "code", - "execution_count": 44, - "metadata": { - "collapsed": true - }, + "execution_count": 40, + "metadata": {}, "outputs": [], "source": [ "def plot_confusion_matrix(cls_pred):\n", @@ -1275,7 +1125,7 @@ " # all images in the test-set.\n", "\n", " # Get the true classifications for the test-set.\n", - " cls_true = data.test.cls\n", + " cls_true = data.y_test_cls\n", " \n", " # Get the confusion matrix using sklearn.\n", " cm = confusion_matrix(y_true=cls_true,\n", @@ -1305,10 +1155,8 @@ }, { "cell_type": "code", - "execution_count": 45, - "metadata": { - "collapsed": false - }, + "execution_count": 41, + "metadata": {}, "outputs": [], "source": [ "# Split the test-set into smaller batches of this size.\n", @@ -1318,7 +1166,7 @@ " show_confusion_matrix=False):\n", "\n", " # Number of images in the test-set.\n", - " num_test = len(data.test.images)\n", + " num_test = data.num_test\n", "\n", " # Allocate an array for the predicted classes which\n", " # will be calculated in batches and filled into this array.\n", @@ -1336,10 +1184,10 @@ " j = min(i + test_batch_size, num_test)\n", "\n", " # Get the images from the test-set between index i and j.\n", - " images = data.test.images[i:j, :]\n", + " images = data.x_test[i:j, :]\n", "\n", " # Get the associated labels.\n", - " labels = data.test.labels[i:j, :]\n", + " labels = data.y_test[i:j, :]\n", "\n", " # Create a feed-dict with these images and labels.\n", " feed_dict = {x: images,\n", @@ -1353,7 +1201,7 @@ " i = j\n", "\n", " # Convenience variable for the true class-numbers of the test-set.\n", - " cls_true = data.test.cls\n", + " cls_true = data.y_test_cls\n", "\n", " # Create a boolean array whether each image is correctly classified.\n", " correct = (cls_true == cls_pred)\n", @@ -1394,9 +1242,8 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 42, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -1404,17 +1251,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "Optimization Iteration: 0, Training Accuracy: 12.5%\n", - "Optimization Iteration: 100, Training Accuracy: 90.6%\n", - "Optimization Iteration: 200, Training Accuracy: 84.4%\n", + "Optimization Iteration: 0, Training Accuracy: 20.3%\n", + "Optimization Iteration: 100, Training Accuracy: 71.9%\n", + "Optimization Iteration: 200, Training Accuracy: 90.6%\n", "Optimization Iteration: 300, Training Accuracy: 84.4%\n", - "Optimization Iteration: 400, Training Accuracy: 89.1%\n", - "Optimization Iteration: 500, Training Accuracy: 87.5%\n", - "Optimization Iteration: 600, Training Accuracy: 93.8%\n", + "Optimization Iteration: 400, Training Accuracy: 87.5%\n", + "Optimization Iteration: 500, Training Accuracy: 93.8%\n", + "Optimization Iteration: 600, Training Accuracy: 89.1%\n", "Optimization Iteration: 700, Training Accuracy: 93.8%\n", - "Optimization Iteration: 800, Training Accuracy: 93.8%\n", - "Optimization Iteration: 900, Training Accuracy: 96.9%\n", - "Optimization Iteration: 999, Training Accuracy: 92.2%\n", + "Optimization Iteration: 800, Training Accuracy: 92.2%\n", + "Optimization Iteration: 900, Training Accuracy: 90.6%\n", + "Optimization Iteration: 999, Training Accuracy: 93.8%\n", "Time usage: 0:00:03\n" ] } @@ -1432,9 +1279,8 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 43, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -1442,15 +1288,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "Accuracy on Test-Set: 96.3% (9633 / 10000)\n", + "Accuracy on Test-Set: 94.2% (9417 / 10000)\n", "Example errors:\n" ] }, { "data": { - "image/png": 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JkyZNiqaIRABCx/m0r1qdNm2ayQ8++KDJ9mkIu1vMfkyTPfBGtOzH5NmPhmrX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\nadYEcnxEigbhF5F73gfcG/ok/f+gK30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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1477,10 +1323,8 @@ }, { "cell_type": "code", - "execution_count": 48, - "metadata": { - "collapsed": false - }, + "execution_count": 44, + "metadata": {}, "outputs": [], "source": [ "init_noise()" @@ -1495,9 +1339,8 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 45, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -1505,18 +1348,18 @@ "name": "stdout", "output_type": "stream", "text": [ - "Optimization Iteration: 0, Training Accuracy: 6.2%\n", - "Optimization Iteration: 100, Training Accuracy: 93.8%\n", - "Optimization Iteration: 200, Training Accuracy: 96.9%\n", + "Optimization Iteration: 0, Training Accuracy: 7.8%\n", + "Optimization Iteration: 100, Training Accuracy: 92.2%\n", + "Optimization Iteration: 200, Training Accuracy: 93.8%\n", "Optimization Iteration: 300, Training Accuracy: 98.4%\n", - "Optimization Iteration: 400, Training Accuracy: 95.3%\n", - "Optimization Iteration: 500, Training Accuracy: 96.9%\n", - "Optimization Iteration: 600, Training Accuracy: 100.0%\n", - "Optimization Iteration: 700, Training Accuracy: 98.4%\n", + "Optimization Iteration: 400, Training Accuracy: 92.2%\n", + "Optimization Iteration: 500, Training Accuracy: 95.3%\n", + "Optimization Iteration: 600, Training Accuracy: 96.9%\n", + "Optimization Iteration: 700, Training Accuracy: 96.9%\n", "Optimization Iteration: 800, Training Accuracy: 95.3%\n", - "Optimization Iteration: 900, Training Accuracy: 93.8%\n", - "Optimization Iteration: 999, Training Accuracy: 100.0%\n", - "Time usage: 0:00:03\n" + "Optimization Iteration: 900, Training Accuracy: 96.9%\n", + "Optimization Iteration: 999, Training Accuracy: 92.2%\n", + "Time usage: 0:00:02\n" ] } ], @@ -1533,9 +1376,8 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 46, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1546,14 +1388,14 @@ "Noise:\n", "- Min: -0.35\n", "- Max: 0.35\n", - "- Std: 0.195455\n" + "- Std: 0.19540551\n" ] }, { "data": { - "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1577,9 +1419,8 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 47, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1587,15 +1428,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "Accuracy on Test-Set: 13.2% (1323 / 10000)\n", + "Accuracy on Test-Set: 13.8% (1378 / 10000)\n", "Example errors:\n" ] }, { "data": { - "image/png": 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GOwF448aNofPZicL2OuyE5ebNm4fKkeJTam8CYX9++umnqpcsWaLaTla3oyHn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\nTkSuQmCC\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1606,16 +1447,16 @@ "output_type": "stream", "text": [ "Confusion Matrix:\n", - "[[ 85 0 0 895 0 0 0 0 0 0]\n", + "[[ 115 0 0 863 0 0 0 2 0 0]\n", " [ 0 0 0 1135 0 0 0 0 0 0]\n", - " [ 0 0 46 986 0 0 0 0 0 0]\n", + " [ 0 0 146 886 0 0 0 0 0 0]\n", " [ 0 0 0 1010 0 0 0 0 0 0]\n", - " [ 0 0 0 959 20 0 0 0 3 0]\n", - " [ 0 0 0 847 0 45 0 0 0 0]\n", - " [ 0 0 0 914 0 1 42 0 1 0]\n", - " [ 0 0 0 977 0 0 0 51 0 0]\n", - " [ 0 0 0 952 0 0 0 0 22 0]\n", - " [ 0 0 1 1006 0 0 0 0 0 2]]\n" + " [ 0 0 0 966 16 0 0 0 0 0]\n", + " [ 0 0 0 865 0 27 0 0 0 0]\n", + " [ 0 0 0 946 0 1 11 0 0 0]\n", + " [ 0 0 0 981 0 0 0 47 0 0]\n", + " [ 0 0 0 968 0 0 0 0 6 0]\n", + " [ 0 0 1 1008 0 0 0 0 0 0]]\n" ] } ], @@ -1640,10 +1481,8 @@ }, { "cell_type": "code", - "execution_count": 52, - "metadata": { - "collapsed": false - }, + "execution_count": 48, + "metadata": {}, "outputs": [], "source": [ "def find_all_noise(num_iterations=1000):\n", @@ -1675,83 +1514,81 @@ }, { "cell_type": "code", - "execution_count": 53, - "metadata": { - "collapsed": false - }, + "execution_count": 49, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Finding adversarial noise for target-class: 0\n", - "Optimization Iteration: 0, Training Accuracy: 9.4%\n", - "Optimization Iteration: 100, Training Accuracy: 90.6%\n", - "Optimization Iteration: 200, Training Accuracy: 92.2%\n", - "Optimization Iteration: 299, Training Accuracy: 93.8%\n", + "Optimization Iteration: 0, Training Accuracy: 4.7%\n", + "Optimization Iteration: 100, Training Accuracy: 93.8%\n", + "Optimization Iteration: 200, Training Accuracy: 93.8%\n", + "Optimization Iteration: 299, Training Accuracy: 89.1%\n", "Time usage: 0:00:01\n", "\n", "Finding adversarial noise for target-class: 1\n", - "Optimization Iteration: 0, Training Accuracy: 7.8%\n", - "Optimization Iteration: 100, Training Accuracy: 62.5%\n", - "Optimization Iteration: 200, Training Accuracy: 62.5%\n", - "Optimization Iteration: 299, Training Accuracy: 75.0%\n", + "Optimization Iteration: 0, Training Accuracy: 14.1%\n", + "Optimization Iteration: 100, Training Accuracy: 68.8%\n", + "Optimization Iteration: 200, Training Accuracy: 67.2%\n", + "Optimization Iteration: 299, Training Accuracy: 60.9%\n", "Time usage: 0:00:01\n", "\n", "Finding adversarial noise for target-class: 2\n", "Optimization Iteration: 0, Training Accuracy: 7.8%\n", - "Optimization Iteration: 100, Training Accuracy: 93.8%\n", - "Optimization Iteration: 200, Training Accuracy: 95.3%\n", - "Optimization Iteration: 299, Training Accuracy: 96.9%\n", + "Optimization Iteration: 100, Training Accuracy: 96.9%\n", + "Optimization Iteration: 200, Training Accuracy: 93.8%\n", + "Optimization Iteration: 299, Training Accuracy: 98.4%\n", "Time usage: 0:00:01\n", "\n", "Finding adversarial noise for target-class: 3\n", - "Optimization Iteration: 0, Training Accuracy: 6.2%\n", - "Optimization Iteration: 100, Training Accuracy: 98.4%\n", - "Optimization Iteration: 200, Training Accuracy: 96.9%\n", - "Optimization Iteration: 299, Training Accuracy: 98.4%\n", + "Optimization Iteration: 0, Training Accuracy: 12.5%\n", + "Optimization Iteration: 100, Training Accuracy: 93.8%\n", + "Optimization Iteration: 200, Training Accuracy: 100.0%\n", + "Optimization Iteration: 299, Training Accuracy: 100.0%\n", "Time usage: 0:00:01\n", "\n", "Finding adversarial noise for target-class: 4\n", "Optimization Iteration: 0, Training Accuracy: 12.5%\n", - "Optimization Iteration: 100, Training Accuracy: 81.2%\n", - "Optimization Iteration: 200, Training Accuracy: 82.8%\n", - "Optimization Iteration: 299, Training Accuracy: 82.8%\n", + "Optimization Iteration: 100, Training Accuracy: 87.5%\n", + "Optimization Iteration: 200, Training Accuracy: 81.2%\n", + "Optimization Iteration: 299, Training Accuracy: 92.2%\n", "Time usage: 0:00:01\n", "\n", "Finding adversarial noise for target-class: 5\n", - "Optimization Iteration: 0, Training Accuracy: 7.8%\n", - "Optimization Iteration: 100, Training Accuracy: 96.9%\n", - "Optimization Iteration: 200, Training Accuracy: 96.9%\n", - "Optimization Iteration: 299, Training Accuracy: 98.4%\n", + "Optimization Iteration: 0, Training Accuracy: 15.6%\n", + "Optimization Iteration: 100, Training Accuracy: 92.2%\n", + "Optimization Iteration: 200, Training Accuracy: 92.2%\n", + "Optimization Iteration: 299, Training Accuracy: 93.8%\n", "Time usage: 0:00:01\n", "\n", "Finding adversarial noise for target-class: 6\n", - "Optimization Iteration: 0, Training Accuracy: 6.2%\n", - "Optimization Iteration: 100, Training Accuracy: 93.8%\n", - "Optimization Iteration: 200, Training Accuracy: 92.2%\n", - "Optimization Iteration: 299, Training Accuracy: 96.9%\n", + "Optimization Iteration: 0, Training Accuracy: 9.4%\n", + "Optimization Iteration: 100, Training Accuracy: 98.4%\n", + "Optimization Iteration: 200, Training Accuracy: 95.3%\n", + "Optimization Iteration: 299, Training Accuracy: 98.4%\n", "Time usage: 0:00:01\n", "\n", "Finding adversarial noise for target-class: 7\n", - "Optimization Iteration: 0, Training Accuracy: 12.5%\n", - "Optimization Iteration: 100, Training Accuracy: 98.4%\n", - "Optimization Iteration: 200, Training Accuracy: 93.8%\n", - "Optimization Iteration: 299, Training Accuracy: 92.2%\n", + "Optimization Iteration: 0, Training Accuracy: 14.1%\n", + "Optimization Iteration: 100, Training Accuracy: 85.9%\n", + "Optimization Iteration: 200, Training Accuracy: 85.9%\n", + "Optimization Iteration: 299, Training Accuracy: 87.5%\n", "Time usage: 0:00:01\n", "\n", "Finding adversarial noise for target-class: 8\n", - "Optimization Iteration: 0, Training Accuracy: 4.7%\n", - "Optimization Iteration: 100, Training Accuracy: 96.9%\n", - "Optimization Iteration: 200, Training Accuracy: 93.8%\n", - "Optimization Iteration: 299, Training Accuracy: 96.9%\n", + "Optimization Iteration: 0, Training Accuracy: 15.6%\n", + "Optimization Iteration: 100, Training Accuracy: 95.3%\n", + "Optimization Iteration: 200, Training Accuracy: 92.2%\n", + "Optimization Iteration: 299, Training Accuracy: 93.8%\n", "Time usage: 0:00:01\n", "\n", "Finding adversarial noise for target-class: 9\n", - "Optimization Iteration: 0, Training Accuracy: 7.8%\n", - "Optimization Iteration: 100, Training Accuracy: 84.4%\n", - "Optimization Iteration: 200, Training Accuracy: 87.5%\n", - "Optimization Iteration: 299, Training Accuracy: 90.6%\n", + "Optimization Iteration: 0, Training Accuracy: 9.4%\n", + "Optimization Iteration: 100, Training Accuracy: 87.5%\n", + "Optimization Iteration: 200, Training Accuracy: 81.2%\n", + "Optimization Iteration: 299, Training Accuracy: 85.9%\n", "Time usage: 0:00:01\n", "\n" ] @@ -1777,10 +1614,8 @@ }, { "cell_type": "code", - "execution_count": 54, - "metadata": { - "collapsed": false - }, + "execution_count": 50, + "metadata": {}, "outputs": [], "source": [ "def plot_all_noise(all_noise): \n", @@ -1812,17 +1647,16 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 51, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ { "data": { - "image/png": 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7FMWpBPqFLIQQQmQATchCCCFEBmi3ZM3BLqrIDXHLgEudekM5IgHncysX8jxk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\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1867,10 +1701,8 @@ }, { "cell_type": "code", - "execution_count": 56, - "metadata": { - "collapsed": true - }, + "execution_count": 52, + "metadata": {}, "outputs": [], "source": [ "def make_immune(target_cls, num_iterations_adversary=500,\n", @@ -1922,10 +1754,8 @@ }, { "cell_type": "code", - "execution_count": 57, - "metadata": { - "collapsed": false - }, + "execution_count": 53, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1933,23 +1763,23 @@ "text": [ "Target-class: 3\n", "Finding adversarial noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 3.1%\n", - "Optimization Iteration: 100, Training Accuracy: 93.8%\n", - "Optimization Iteration: 200, Training Accuracy: 93.8%\n", - "Optimization Iteration: 300, Training Accuracy: 96.9%\n", - "Optimization Iteration: 400, Training Accuracy: 96.9%\n", + "Optimization Iteration: 0, Training Accuracy: 1.6%\n", + "Optimization Iteration: 100, Training Accuracy: 95.3%\n", + "Optimization Iteration: 200, Training Accuracy: 96.9%\n", + "Optimization Iteration: 300, Training Accuracy: 92.2%\n", + "Optimization Iteration: 400, Training Accuracy: 93.8%\n", "Optimization Iteration: 499, Training Accuracy: 96.9%\n", - "Time usage: 0:00:02\n", + "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 14.4% (1443 / 10000)\n", + "Accuracy on Test-Set: 13.3% (1326 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 42.2%\n", - "Optimization Iteration: 100, Training Accuracy: 90.6%\n", - "Optimization Iteration: 199, Training Accuracy: 89.1%\n", + "Optimization Iteration: 0, Training Accuracy: 12.5%\n", + "Optimization Iteration: 100, Training Accuracy: 89.1%\n", + "Optimization Iteration: 199, Training Accuracy: 95.3%\n", "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 95.3% (9529 / 10000)\n" + "Accuracy on Test-Set: 93.3% (9327 / 10000)\n" ] } ], @@ -1966,10 +1796,8 @@ }, { "cell_type": "code", - "execution_count": 58, - "metadata": { - "collapsed": false - }, + "execution_count": 54, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1977,23 +1805,23 @@ "text": [ "Target-class: 3\n", "Finding adversarial noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 7.8%\n", - "Optimization Iteration: 100, Training Accuracy: 32.8%\n", + "Optimization Iteration: 0, Training Accuracy: 9.4%\n", + "Optimization Iteration: 100, Training Accuracy: 17.2%\n", "Optimization Iteration: 200, Training Accuracy: 32.8%\n", - "Optimization Iteration: 300, Training Accuracy: 29.7%\n", - "Optimization Iteration: 400, Training Accuracy: 34.4%\n", - "Optimization Iteration: 499, Training Accuracy: 26.6%\n", - "Time usage: 0:00:02\n", + "Optimization Iteration: 300, Training Accuracy: 28.1%\n", + "Optimization Iteration: 400, Training Accuracy: 21.9%\n", + "Optimization Iteration: 499, Training Accuracy: 18.8%\n", + "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 72.1% (7207 / 10000)\n", + "Accuracy on Test-Set: 80.0% (8002 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 75.0%\n", - "Optimization Iteration: 100, Training Accuracy: 93.8%\n", + "Optimization Iteration: 0, Training Accuracy: 78.1%\n", + "Optimization Iteration: 100, Training Accuracy: 90.6%\n", "Optimization Iteration: 199, Training Accuracy: 92.2%\n", "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 95.2% (9519 / 10000)\n" + "Accuracy on Test-Set: 92.3% (9235 / 10000)\n" ] } ], @@ -2012,10 +1840,8 @@ }, { "cell_type": "code", - "execution_count": 59, - "metadata": { - "collapsed": false - }, + "execution_count": 55, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -2023,203 +1849,203 @@ "text": [ "Target-class: 0\n", "Finding adversarial noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 4.7%\n", - "Optimization Iteration: 100, Training Accuracy: 73.4%\n", - "Optimization Iteration: 200, Training Accuracy: 75.0%\n", - "Optimization Iteration: 300, Training Accuracy: 85.9%\n", - "Optimization Iteration: 400, Training Accuracy: 81.2%\n", - "Optimization Iteration: 499, Training Accuracy: 90.6%\n", - "Time usage: 0:00:02\n", + "Optimization Iteration: 0, Training Accuracy: 9.4%\n", + "Optimization Iteration: 100, Training Accuracy: 75.0%\n", + "Optimization Iteration: 200, Training Accuracy: 76.6%\n", + "Optimization Iteration: 300, Training Accuracy: 82.8%\n", + "Optimization Iteration: 400, Training Accuracy: 85.9%\n", + "Optimization Iteration: 499, Training Accuracy: 85.9%\n", + "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 23.3% (2326 / 10000)\n", + "Accuracy on Test-Set: 24.6% (2464 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 34.4%\n", - "Optimization Iteration: 100, Training Accuracy: 95.3%\n", - "Optimization Iteration: 199, Training Accuracy: 95.3%\n", + "Optimization Iteration: 0, Training Accuracy: 37.5%\n", + "Optimization Iteration: 100, Training Accuracy: 93.8%\n", + "Optimization Iteration: 199, Training Accuracy: 98.4%\n", "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 95.6% (9559 / 10000)\n", + "Accuracy on Test-Set: 93.9% (9387 / 10000)\n", "\n", "Target-class: 1\n", "Finding adversarial noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 12.5%\n", - "Optimization Iteration: 100, Training Accuracy: 57.8%\n", - "Optimization Iteration: 200, Training Accuracy: 62.5%\n", - "Optimization Iteration: 300, Training Accuracy: 62.5%\n", - "Optimization Iteration: 400, Training Accuracy: 67.2%\n", - "Optimization Iteration: 499, Training Accuracy: 67.2%\n", - "Time usage: 0:00:02\n", + "Optimization Iteration: 0, Training Accuracy: 7.8%\n", + "Optimization Iteration: 100, Training Accuracy: 62.5%\n", + "Optimization Iteration: 200, Training Accuracy: 78.1%\n", + "Optimization Iteration: 300, Training Accuracy: 65.6%\n", + "Optimization Iteration: 400, Training Accuracy: 78.1%\n", + "Optimization Iteration: 499, Training Accuracy: 71.9%\n", + "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 42.2% (4218 / 10000)\n", + "Accuracy on Test-Set: 32.6% (3260 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 59.4%\n", + "Optimization Iteration: 0, Training Accuracy: 45.3%\n", "Optimization Iteration: 100, Training Accuracy: 93.8%\n", - "Optimization Iteration: 199, Training Accuracy: 95.3%\n", + "Optimization Iteration: 199, Training Accuracy: 96.9%\n", "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 95.5% (9555 / 10000)\n", + "Accuracy on Test-Set: 94.0% (9401 / 10000)\n", "\n", "Target-class: 2\n", "Finding adversarial noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 6.2%\n", - "Optimization Iteration: 100, Training Accuracy: 43.8%\n", - "Optimization Iteration: 200, Training Accuracy: 57.8%\n", - "Optimization Iteration: 300, Training Accuracy: 70.3%\n", - "Optimization Iteration: 400, Training Accuracy: 68.8%\n", - "Optimization Iteration: 499, Training Accuracy: 71.9%\n", - "Time usage: 0:00:02\n", + "Optimization Iteration: 0, Training Accuracy: 9.4%\n", + "Optimization Iteration: 100, Training Accuracy: 57.8%\n", + "Optimization Iteration: 200, Training Accuracy: 81.2%\n", + "Optimization Iteration: 300, Training Accuracy: 73.4%\n", + "Optimization Iteration: 400, Training Accuracy: 87.5%\n", + "Optimization Iteration: 499, Training Accuracy: 79.7%\n", + "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 46.4% (4639 / 10000)\n", + "Accuracy on Test-Set: 26.2% (2620 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 59.4%\n", - "Optimization Iteration: 100, Training Accuracy: 96.9%\n", - "Optimization Iteration: 199, Training Accuracy: 92.2%\n", + "Optimization Iteration: 0, Training Accuracy: 37.5%\n", + "Optimization Iteration: 100, Training Accuracy: 95.3%\n", + "Optimization Iteration: 199, Training Accuracy: 95.3%\n", "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 95.5% (9545 / 10000)\n", + "Accuracy on Test-Set: 93.8% (9380 / 10000)\n", "\n", "Target-class: 3\n", "Finding adversarial noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 6.2%\n", - "Optimization Iteration: 100, Training Accuracy: 48.4%\n", - "Optimization Iteration: 200, Training Accuracy: 46.9%\n", - "Optimization Iteration: 300, Training Accuracy: 53.1%\n", - "Optimization Iteration: 400, Training Accuracy: 50.0%\n", - "Optimization Iteration: 499, Training Accuracy: 48.4%\n", - "Time usage: 0:00:02\n", + "Optimization Iteration: 0, Training Accuracy: 14.1%\n", + "Optimization Iteration: 100, Training Accuracy: 50.0%\n", + "Optimization Iteration: 200, Training Accuracy: 57.8%\n", + "Optimization Iteration: 300, Training Accuracy: 59.4%\n", + "Optimization Iteration: 400, Training Accuracy: 64.1%\n", + "Optimization Iteration: 499, Training Accuracy: 59.4%\n", + "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 56.5% (5648 / 10000)\n", + "Accuracy on Test-Set: 46.3% (4631 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 54.7%\n", - "Optimization Iteration: 100, Training Accuracy: 93.8%\n", - "Optimization Iteration: 199, Training Accuracy: 96.9%\n", + "Optimization Iteration: 0, Training Accuracy: 46.9%\n", + "Optimization Iteration: 100, Training Accuracy: 95.3%\n", + "Optimization Iteration: 199, Training Accuracy: 90.6%\n", "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 95.8% (9581 / 10000)\n", + "Accuracy on Test-Set: 93.6% (9358 / 10000)\n", "\n", "Target-class: 4\n", "Finding adversarial noise ...\n", "Optimization Iteration: 0, Training Accuracy: 9.4%\n", - "Optimization Iteration: 100, Training Accuracy: 85.9%\n", - "Optimization Iteration: 200, Training Accuracy: 85.9%\n", - "Optimization Iteration: 300, Training Accuracy: 87.5%\n", - "Optimization Iteration: 400, Training Accuracy: 95.3%\n", - "Optimization Iteration: 499, Training Accuracy: 92.2%\n", - "Time usage: 0:00:02\n", + "Optimization Iteration: 100, Training Accuracy: 82.8%\n", + "Optimization Iteration: 200, Training Accuracy: 92.2%\n", + "Optimization Iteration: 300, Training Accuracy: 90.6%\n", + "Optimization Iteration: 400, Training Accuracy: 93.8%\n", + "Optimization Iteration: 499, Training Accuracy: 95.3%\n", + "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 15.6% (1557 / 10000)\n", + "Accuracy on Test-Set: 16.9% (1689 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", "Optimization Iteration: 0, Training Accuracy: 18.8%\n", "Optimization Iteration: 100, Training Accuracy: 95.3%\n", - "Optimization Iteration: 199, Training Accuracy: 96.9%\n", - "Time usage: 0:00:01\n", + "Optimization Iteration: 199, Training Accuracy: 92.2%\n", + "Time usage: 0:00:00\n", "\n", - "Accuracy on Test-Set: 95.6% (9557 / 10000)\n", + "Accuracy on Test-Set: 93.3% (9332 / 10000)\n", "\n", "Target-class: 5\n", "Finding adversarial noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 18.8%\n", - "Optimization Iteration: 100, Training Accuracy: 71.9%\n", - "Optimization Iteration: 200, Training Accuracy: 90.6%\n", - "Optimization Iteration: 300, Training Accuracy: 95.3%\n", - "Optimization Iteration: 400, Training Accuracy: 89.1%\n", - "Optimization Iteration: 499, Training Accuracy: 92.2%\n", - "Time usage: 0:00:02\n", + "Optimization Iteration: 0, Training Accuracy: 10.9%\n", + "Optimization Iteration: 100, Training Accuracy: 65.6%\n", + "Optimization Iteration: 200, Training Accuracy: 71.9%\n", + "Optimization Iteration: 300, Training Accuracy: 78.1%\n", + "Optimization Iteration: 400, Training Accuracy: 75.0%\n", + "Optimization Iteration: 499, Training Accuracy: 76.6%\n", + "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 17.4% (1745 / 10000)\n", + "Accuracy on Test-Set: 26.4% (2638 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 15.6%\n", - "Optimization Iteration: 100, Training Accuracy: 96.9%\n", - "Optimization Iteration: 199, Training Accuracy: 95.3%\n", - "Time usage: 0:00:01\n", + "Optimization Iteration: 0, Training Accuracy: 29.7%\n", + "Optimization Iteration: 100, Training Accuracy: 92.2%\n", + "Optimization Iteration: 199, Training Accuracy: 93.8%\n", + "Time usage: 0:00:00\n", "\n", - "Accuracy on Test-Set: 96.0% (9601 / 10000)\n", + "Accuracy on Test-Set: 94.1% (9407 / 10000)\n", "\n", "Target-class: 6\n", "Finding adversarial noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 10.9%\n", - "Optimization Iteration: 100, Training Accuracy: 81.2%\n", - "Optimization Iteration: 200, Training Accuracy: 93.8%\n", - "Optimization Iteration: 300, Training Accuracy: 92.2%\n", + "Optimization Iteration: 0, Training Accuracy: 6.2%\n", + "Optimization Iteration: 100, Training Accuracy: 85.9%\n", + "Optimization Iteration: 200, Training Accuracy: 90.6%\n", + "Optimization Iteration: 300, Training Accuracy: 93.8%\n", "Optimization Iteration: 400, Training Accuracy: 89.1%\n", - "Optimization Iteration: 499, Training Accuracy: 92.2%\n", - "Time usage: 0:00:02\n", + "Optimization Iteration: 499, Training Accuracy: 89.1%\n", + "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 17.6% (1762 / 10000)\n", + "Accuracy on Test-Set: 15.8% (1584 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 20.3%\n", - "Optimization Iteration: 100, Training Accuracy: 93.8%\n", - "Optimization Iteration: 199, Training Accuracy: 95.3%\n", + "Optimization Iteration: 0, Training Accuracy: 17.2%\n", + "Optimization Iteration: 100, Training Accuracy: 90.6%\n", + "Optimization Iteration: 199, Training Accuracy: 93.8%\n", "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 95.7% (9570 / 10000)\n", + "Accuracy on Test-Set: 93.8% (9385 / 10000)\n", "\n", "Target-class: 7\n", "Finding adversarial noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 14.1%\n", - "Optimization Iteration: 100, Training Accuracy: 93.8%\n", + "Optimization Iteration: 0, Training Accuracy: 10.9%\n", + "Optimization Iteration: 100, Training Accuracy: 96.9%\n", "Optimization Iteration: 200, Training Accuracy: 98.4%\n", - "Optimization Iteration: 300, Training Accuracy: 100.0%\n", + "Optimization Iteration: 300, Training Accuracy: 96.9%\n", "Optimization Iteration: 400, Training Accuracy: 96.9%\n", - "Optimization Iteration: 499, Training Accuracy: 100.0%\n", - "Time usage: 0:00:02\n", + "Optimization Iteration: 499, Training Accuracy: 98.4%\n", + "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 12.8% (1281 / 10000)\n", + "Accuracy on Test-Set: 13.2% (1319 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 12.5%\n", - "Optimization Iteration: 100, Training Accuracy: 98.4%\n", - "Optimization Iteration: 199, Training Accuracy: 98.4%\n", - "Time usage: 0:00:01\n", + "Optimization Iteration: 0, Training Accuracy: 23.4%\n", + "Optimization Iteration: 100, Training Accuracy: 93.8%\n", + "Optimization Iteration: 199, Training Accuracy: 90.6%\n", + "Time usage: 0:00:00\n", "\n", - "Accuracy on Test-Set: 95.9% (9587 / 10000)\n", + "Accuracy on Test-Set: 93.6% (9357 / 10000)\n", "\n", "Target-class: 8\n", "Finding adversarial noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 4.7%\n", - "Optimization Iteration: 100, Training Accuracy: 64.1%\n", - "Optimization Iteration: 200, Training Accuracy: 81.2%\n", - "Optimization Iteration: 300, Training Accuracy: 71.9%\n", - "Optimization Iteration: 400, Training Accuracy: 78.1%\n", - "Optimization Iteration: 499, Training Accuracy: 84.4%\n", - "Time usage: 0:00:02\n", + "Optimization Iteration: 0, Training Accuracy: 9.4%\n", + "Optimization Iteration: 100, Training Accuracy: 68.8%\n", + "Optimization Iteration: 200, Training Accuracy: 73.4%\n", + "Optimization Iteration: 300, Training Accuracy: 89.1%\n", + "Optimization Iteration: 400, Training Accuracy: 89.1%\n", + "Optimization Iteration: 499, Training Accuracy: 76.6%\n", + "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 24.9% (2493 / 10000)\n", + "Accuracy on Test-Set: 26.9% (2694 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 25.0%\n", + "Optimization Iteration: 0, Training Accuracy: 23.4%\n", "Optimization Iteration: 100, Training Accuracy: 95.3%\n", - "Optimization Iteration: 199, Training Accuracy: 96.9%\n", + "Optimization Iteration: 199, Training Accuracy: 95.3%\n", "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 96.0% (9601 / 10000)\n", + "Accuracy on Test-Set: 94.5% (9452 / 10000)\n", "\n", "Target-class: 9\n", "Finding adversarial noise ...\n", "Optimization Iteration: 0, Training Accuracy: 9.4%\n", "Optimization Iteration: 100, Training Accuracy: 48.4%\n", - "Optimization Iteration: 200, Training Accuracy: 50.0%\n", - "Optimization Iteration: 300, Training Accuracy: 53.1%\n", - "Optimization Iteration: 400, Training Accuracy: 64.1%\n", - "Optimization Iteration: 499, Training Accuracy: 65.6%\n", - "Time usage: 0:00:02\n", + "Optimization Iteration: 200, Training Accuracy: 51.6%\n", + "Optimization Iteration: 300, Training Accuracy: 51.6%\n", + "Optimization Iteration: 400, Training Accuracy: 53.1%\n", + "Optimization Iteration: 499, Training Accuracy: 50.0%\n", + "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 45.5% (4546 / 10000)\n", + "Accuracy on Test-Set: 46.6% (4657 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 51.6%\n", - "Optimization Iteration: 100, Training Accuracy: 95.3%\n", - "Optimization Iteration: 199, Training Accuracy: 95.3%\n", - "Time usage: 0:00:01\n", + "Optimization Iteration: 0, Training Accuracy: 50.0%\n", + "Optimization Iteration: 100, Training Accuracy: 89.1%\n", + "Optimization Iteration: 199, Training Accuracy: 93.8%\n", + "Time usage: 0:00:00\n", "\n", - "Accuracy on Test-Set: 96.2% (9615 / 10000)\n", + "Accuracy on Test-Set: 94.6% (9462 / 10000)\n", "\n" ] } @@ -2245,10 +2071,8 @@ }, { "cell_type": "code", - "execution_count": 60, - "metadata": { - "collapsed": false - }, + "execution_count": 56, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -2256,355 +2080,361 @@ "text": [ "Target-class: 0\n", "Finding adversarial noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 7.8%\n", - "Optimization Iteration: 100, Training Accuracy: 53.1%\n", - "Optimization Iteration: 200, Training Accuracy: 73.4%\n", - "Optimization Iteration: 300, Training Accuracy: 79.7%\n", - "Optimization Iteration: 400, Training Accuracy: 84.4%\n", - "Optimization Iteration: 499, Training Accuracy: 95.3%\n", - "Time usage: 0:00:02\n", + "Optimization Iteration: 0, Training Accuracy: 10.9%\n", + "Optimization Iteration: 100, Training Accuracy: 60.9%\n", + "Optimization Iteration: 200, Training Accuracy: 76.6%\n", + "Optimization Iteration: 300, Training Accuracy: 73.4%\n", + "Optimization Iteration: 400, Training Accuracy: 57.8%\n", + "Optimization Iteration: 499, Training Accuracy: 71.9%\n", + "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 29.2% (2921 / 10000)\n", + "Accuracy on Test-Set: 36.0% (3601 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 29.7%\n", - "Optimization Iteration: 100, Training Accuracy: 96.9%\n", - "Optimization Iteration: 199, Training Accuracy: 95.3%\n", - "Time usage: 0:00:01\n", + "Optimization Iteration: 0, Training Accuracy: 45.3%\n", + "Optimization Iteration: 100, Training Accuracy: 93.8%\n", + "Optimization Iteration: 199, Training Accuracy: 93.8%\n", + "Time usage: 0:00:00\n", "\n", - "Accuracy on Test-Set: 96.2% (9619 / 10000)\n", + "Accuracy on Test-Set: 94.7% (9474 / 10000)\n", "\n", "Target-class: 0\n", "Finding adversarial noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 1.6%\n", - "Optimization Iteration: 100, Training Accuracy: 12.5%\n", - "Optimization Iteration: 200, Training Accuracy: 7.8%\n", - "Optimization Iteration: 300, Training Accuracy: 18.8%\n", - "Optimization Iteration: 400, Training Accuracy: 9.4%\n", - "Optimization Iteration: 499, Training Accuracy: 9.4%\n", - "Time usage: 0:00:02\n", + "Optimization Iteration: 0, Training Accuracy: 14.1%\n", + "Optimization Iteration: 100, Training Accuracy: 9.4%\n", + "Optimization Iteration: 200, Training Accuracy: 12.5%\n", + "Optimization Iteration: 300, Training Accuracy: 9.4%\n", + "Optimization Iteration: 400, Training Accuracy: 6.2%\n", + "Optimization Iteration: 499, Training Accuracy: 6.2%\n", + "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 94.4% (9437 / 10000)\n", + "Accuracy on Test-Set: 93.3% (9334 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 89.1%\n", - "Optimization Iteration: 100, Training Accuracy: 98.4%\n", - "Optimization Iteration: 199, Training Accuracy: 93.8%\n", - "Time usage: 0:00:01\n", + "Optimization Iteration: 0, Training Accuracy: 87.5%\n", + "Optimization Iteration: 100, Training Accuracy: 96.9%\n", + "Optimization Iteration: 199, Training Accuracy: 98.4%\n", + "Time usage: 0:00:00\n", "\n", - "Accuracy on Test-Set: 96.4% (9635 / 10000)\n", + "Accuracy on Test-Set: 95.2% (9524 / 10000)\n", "\n", "Target-class: 1\n", "Finding adversarial noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 7.8%\n", - "Optimization Iteration: 100, Training Accuracy: 42.2%\n", - "Optimization Iteration: 200, Training Accuracy: 60.9%\n", - "Optimization Iteration: 300, Training Accuracy: 75.0%\n", - "Optimization Iteration: 400, Training Accuracy: 70.3%\n", - "Optimization Iteration: 499, Training Accuracy: 85.9%\n", - "Time usage: 0:00:02\n", + "Optimization Iteration: 0, Training Accuracy: 6.2%\n", + "Optimization Iteration: 100, Training Accuracy: 53.1%\n", + "Optimization Iteration: 200, Training Accuracy: 73.4%\n", + "Optimization Iteration: 300, Training Accuracy: 73.4%\n", + "Optimization Iteration: 400, Training Accuracy: 82.8%\n", + "Optimization Iteration: 499, Training Accuracy: 81.2%\n", + "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 28.7% (2875 / 10000)\n", + "Accuracy on Test-Set: 25.4% (2543 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 39.1%\n", - "Optimization Iteration: 100, Training Accuracy: 98.4%\n", - "Optimization Iteration: 199, Training Accuracy: 95.3%\n", - "Time usage: 0:00:01\n", + "Optimization Iteration: 0, Training Accuracy: 21.9%\n", + "Optimization Iteration: 100, Training Accuracy: 93.8%\n", + "Optimization Iteration: 199, Training Accuracy: 93.8%\n", + "Time usage: 0:00:00\n", "\n", - "Accuracy on Test-Set: 96.4% (9643 / 10000)\n", + "Accuracy on Test-Set: 94.9% (9492 / 10000)\n", "\n", "Target-class: 1\n", "Finding adversarial noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 7.8%\n", - "Optimization Iteration: 100, Training Accuracy: 15.6%\n", - "Optimization Iteration: 200, Training Accuracy: 18.8%\n", - "Optimization Iteration: 300, Training Accuracy: 12.5%\n", - "Optimization Iteration: 400, Training Accuracy: 9.4%\n", - "Optimization Iteration: 499, Training Accuracy: 12.5%\n", - "Time usage: 0:00:02\n", + "Optimization Iteration: 0, Training Accuracy: 12.5%\n", + "Optimization Iteration: 100, Training Accuracy: 23.4%\n", + "Optimization Iteration: 200, Training Accuracy: 10.9%\n", + "Optimization Iteration: 300, Training Accuracy: 10.9%\n", + "Optimization Iteration: 400, Training Accuracy: 10.9%\n", + "Optimization Iteration: 499, Training Accuracy: 9.4%\n", + "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 94.3% (9428 / 10000)\n", + "Accuracy on Test-Set: 91.9% (9188 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", "Optimization Iteration: 0, Training Accuracy: 95.3%\n", "Optimization Iteration: 100, Training Accuracy: 95.3%\n", - "Optimization Iteration: 199, Training Accuracy: 92.2%\n", + "Optimization Iteration: 199, Training Accuracy: 89.1%\n", "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 96.9% (9685 / 10000)\n", + "Accuracy on Test-Set: 95.5% (9545 / 10000)\n", "\n", "Target-class: 2\n", "Finding adversarial noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 6.2%\n", - "Optimization Iteration: 100, Training Accuracy: 60.9%\n", - "Optimization Iteration: 200, Training Accuracy: 64.1%\n", - "Optimization Iteration: 300, Training Accuracy: 71.9%\n", - "Optimization Iteration: 400, Training Accuracy: 75.0%\n", - "Optimization Iteration: 499, Training Accuracy: 82.8%\n", + "Optimization Iteration: 0, Training Accuracy: 7.8%\n", + "Optimization Iteration: 100, Training Accuracy: 62.5%\n", + "Optimization Iteration: 200, Training Accuracy: 70.3%\n", + "Optimization Iteration: 300, Training Accuracy: 78.1%\n", + "Optimization Iteration: 400, Training Accuracy: 73.4%\n", + "Optimization Iteration: 499, Training Accuracy: 71.9%\n", "Time usage: 0:00:02\n", "\n", - "Accuracy on Test-Set: 34.3% (3427 / 10000)\n", + "Accuracy on Test-Set: 34.7% (3474 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 31.2%\n", - "Optimization Iteration: 100, Training Accuracy: 100.0%\n", - "Optimization Iteration: 199, Training Accuracy: 98.4%\n", + "Optimization Iteration: 0, Training Accuracy: 51.6%\n", + "Optimization Iteration: 100, Training Accuracy: 87.5%\n", + "Optimization Iteration: 199, Training Accuracy: 95.3%\n", "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 96.6% (9657 / 10000)\n", + "Accuracy on Test-Set: 95.2% (9520 / 10000)\n", "\n", "Target-class: 2\n", "Finding adversarial noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 6.2%\n", - "Optimization Iteration: 100, Training Accuracy: 9.4%\n", - "Optimization Iteration: 200, Training Accuracy: 14.1%\n", - "Optimization Iteration: 300, Training Accuracy: 10.9%\n", - "Optimization Iteration: 400, Training Accuracy: 7.8%\n", - "Optimization Iteration: 499, Training Accuracy: 17.2%\n", - "Time usage: 0:00:02\n", + "Optimization Iteration: 0, Training Accuracy: 12.5%\n", + "Optimization Iteration: 100, Training Accuracy: 15.6%\n", + "Optimization Iteration: 200, Training Accuracy: 12.5%\n", + "Optimization Iteration: 300, Training Accuracy: 18.8%\n", + "Optimization Iteration: 400, Training Accuracy: 10.9%\n", + "Optimization Iteration: 499, Training Accuracy: 14.1%\n", + "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 94.3% (9435 / 10000)\n", + "Accuracy on Test-Set: 90.5% (9051 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 96.9%\n", - "Optimization Iteration: 100, Training Accuracy: 98.4%\n", - "Optimization Iteration: 199, Training Accuracy: 96.9%\n", + "Optimization Iteration: 0, Training Accuracy: 87.5%\n", + "Optimization Iteration: 100, Training Accuracy: 93.8%\n", + "Optimization Iteration: 199, Training Accuracy: 93.8%\n", "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 96.6% (9664 / 10000)\n", + "Accuracy on Test-Set: 95.3% (9535 / 10000)\n", "\n", "Target-class: 3\n", "Finding adversarial noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 14.1%\n", - "Optimization Iteration: 100, Training Accuracy: 20.3%\n", - "Optimization Iteration: 200, Training Accuracy: 40.6%\n", - "Optimization Iteration: 300, Training Accuracy: 57.8%\n", - "Optimization Iteration: 400, Training Accuracy: 54.7%\n", - "Optimization Iteration: 499, Training Accuracy: 64.1%\n", - "Time 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(9650 / 10000)\n", + "Accuracy on Test-Set: 95.4% (9537 / 10000)\n", "\n", "Target-class: 3\n", "Finding adversarial noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 4.7%\n", + "Optimization Iteration: 0, Training Accuracy: 12.5%\n", "Optimization Iteration: 100, Training Accuracy: 10.9%\n", - "Optimization Iteration: 200, Training Accuracy: 17.2%\n", - "Optimization Iteration: 300, Training Accuracy: 15.6%\n", - "Optimization Iteration: 400, Training Accuracy: 1.6%\n", + "Optimization Iteration: 200, Training Accuracy: 18.8%\n", + "Optimization Iteration: 300, Training Accuracy: 10.9%\n", + "Optimization Iteration: 400, Training Accuracy: 9.4%\n", "Optimization Iteration: 499, Training Accuracy: 9.4%\n", - "Time usage: 0:00:02\n", + "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 95.7% (9570 / 10000)\n", + "Accuracy on Test-Set: 94.6% (9464 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 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"Accuracy on Test-Set: 97.0% (9699 / 10000)\n", + "Accuracy on Test-Set: 95.5% (9550 / 10000)\n", "\n", "Target-class: 8\n", "Finding adversarial noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 7.8%\n", - "Optimization Iteration: 100, Training Accuracy: 48.4%\n", - "Optimization Iteration: 200, Training Accuracy: 46.9%\n", - "Optimization Iteration: 300, Training Accuracy: 71.9%\n", - "Optimization Iteration: 400, Training Accuracy: 70.3%\n", - "Optimization Iteration: 499, Training Accuracy: 75.0%\n", - "Time usage: 0:00:02\n", + "Optimization Iteration: 0, Training Accuracy: 12.5%\n", + "Optimization Iteration: 100, Training Accuracy: 26.6%\n", + "Optimization Iteration: 200, Training Accuracy: 43.8%\n", + "Optimization Iteration: 300, Training Accuracy: 60.9%\n", + "Optimization Iteration: 400, Training Accuracy: 62.5%\n", + "Optimization Iteration: 499, Training Accuracy: 64.1%\n", + "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 36.8% (3678 / 10000)\n", + 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"Optimization Iteration: 100, Training Accuracy: 12.5%\n", + "Optimization Iteration: 200, Training Accuracy: 15.6%\n", + "Optimization Iteration: 300, Training Accuracy: 15.6%\n", + "Optimization Iteration: 400, Training Accuracy: 7.8%\n", "Optimization Iteration: 499, Training Accuracy: 9.4%\n", - "Time usage: 0:00:02\n", + "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 96.2% (9625 / 10000)\n", + "Accuracy on Test-Set: 95.5% (9555 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", "Optimization Iteration: 0, Training Accuracy: 96.9%\n", @@ -2612,47 +2442,47 @@ "Optimization Iteration: 199, Training Accuracy: 95.3%\n", "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 97.2% (9720 / 10000)\n", + "Accuracy on Test-Set: 96.5% (9650 / 10000)\n", "\n", "Target-class: 9\n", "Finding adversarial noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 9.4%\n", - "Optimization Iteration: 100, Training Accuracy: 23.4%\n", - "Optimization Iteration: 200, Training Accuracy: 43.8%\n", - "Optimization Iteration: 300, Training Accuracy: 37.5%\n", - "Optimization Iteration: 400, Training Accuracy: 45.3%\n", - "Optimization Iteration: 499, Training Accuracy: 39.1%\n", - "Time usage: 0:00:02\n", + "Optimization Iteration: 0, Training Accuracy: 7.8%\n", + "Optimization Iteration: 100, Training Accuracy: 28.1%\n", + "Optimization Iteration: 200, Training Accuracy: 39.1%\n", + "Optimization Iteration: 300, Training Accuracy: 42.2%\n", + "Optimization Iteration: 400, Training Accuracy: 46.9%\n", + "Optimization Iteration: 499, Training Accuracy: 48.4%\n", + "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 64.9% (6494 / 10000)\n", + "Accuracy on Test-Set: 64.1% (6415 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 67.2%\n", - "Optimization Iteration: 100, Training Accuracy: 95.3%\n", - "Optimization Iteration: 199, Training Accuracy: 98.4%\n", - "Time usage: 0:00:01\n", + "Optimization Iteration: 0, Training Accuracy: 68.8%\n", + "Optimization Iteration: 100, Training Accuracy: 98.4%\n", + "Optimization Iteration: 199, Training Accuracy: 96.9%\n", + "Time usage: 0:00:00\n", "\n", - "Accuracy on Test-Set: 97.5% (9746 / 10000)\n", + "Accuracy on Test-Set: 96.3% (9629 / 10000)\n", "\n", "Target-class: 9\n", "Finding adversarial noise ...\n", "Optimization Iteration: 0, Training Accuracy: 9.4%\n", - "Optimization Iteration: 100, Training Accuracy: 7.8%\n", - "Optimization Iteration: 200, Training Accuracy: 10.9%\n", - "Optimization Iteration: 300, Training Accuracy: 15.6%\n", - "Optimization Iteration: 400, Training Accuracy: 12.5%\n", + "Optimization Iteration: 100, Training Accuracy: 3.1%\n", + "Optimization Iteration: 200, Training Accuracy: 3.1%\n", + "Optimization Iteration: 300, Training Accuracy: 10.9%\n", + "Optimization Iteration: 400, Training Accuracy: 9.4%\n", "Optimization Iteration: 499, Training Accuracy: 4.7%\n", - "Time usage: 0:00:02\n", + "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 97.1% (9709 / 10000)\n", + "Accuracy on Test-Set: 96.1% (9614 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", - "Optimization Iteration: 0, Training Accuracy: 98.4%\n", + "Optimization Iteration: 0, Training Accuracy: 96.9%\n", "Optimization Iteration: 100, Training Accuracy: 100.0%\n", "Optimization Iteration: 199, Training Accuracy: 95.3%\n", "Time usage: 0:00:01\n", "\n", - "Accuracy on Test-Set: 97.7% (9768 / 10000)\n", + "Accuracy on Test-Set: 96.7% (9666 / 10000)\n", "\n" ] } @@ -2686,9 +2516,8 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 57, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -2699,14 +2528,14 @@ "Noise:\n", "- Min: -0.35\n", "- Max: 0.35\n", - "- Std: 0.270488\n" + "- Std: 0.27831247\n" ] }, { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2726,24 +2555,22 @@ }, { "cell_type": "code", - "execution_count": 62, - "metadata": { - "collapsed": false - }, + "execution_count": 58, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Accuracy on Test-Set: 97.7% (9768 / 10000)\n", + "Accuracy on Test-Set: 96.7% (9666 / 10000)\n", "Example errors:\n" ] }, { "data": { - "image/png": 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3bq1js70333yzjs1S7y233KLj8847L1yDQ/jwww913KdPH1Srxs856ZTJ/Awj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\nd+ZJRESUqqp45klERJQSdp5ERESW2HkSERFZYud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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2754,16 +2581,16 @@ "output_type": "stream", "text": [ "Confusion Matrix:\n", - "[[ 972 0 1 0 0 0 2 1 3 1]\n", - " [ 0 1119 4 0 0 2 2 0 8 0]\n", - " [ 3 0 1006 9 1 1 1 5 4 2]\n", - " [ 1 0 1 997 0 5 0 4 2 0]\n", - " [ 0 1 3 0 955 0 3 1 2 17]\n", - " [ 1 0 0 9 0 876 3 0 2 1]\n", - " [ 6 4 0 0 3 6 934 0 5 0]\n", - " [ 2 4 18 3 1 0 0 985 2 13]\n", - " [ 4 0 4 3 4 1 1 3 950 4]\n", - " [ 6 6 0 7 4 5 0 4 3 974]]\n" + "[[ 966 0 0 0 0 1 5 1 5 2]\n", + " [ 0 1113 4 2 0 0 3 1 11 1]\n", + " [ 2 1 991 8 6 0 0 7 16 1]\n", + " [ 0 0 4 978 0 11 0 7 9 1]\n", + " [ 0 0 4 0 946 0 6 0 3 23]\n", + " [ 2 1 1 6 0 870 3 2 2 5]\n", + " [ 3 3 2 0 6 11 926 1 6 0]\n", + " [ 0 3 24 6 1 0 0 972 3 19]\n", + " [ 4 0 3 8 5 4 3 4 939 4]\n", + " [ 3 6 1 11 8 3 0 5 7 965]]\n" ] } ], @@ -2783,10 +2610,8 @@ }, { "cell_type": "code", - "execution_count": 63, - "metadata": { - "collapsed": false - }, + "execution_count": 59, + "metadata": {}, "outputs": [], "source": [ "init_noise()" @@ -2801,24 +2626,22 @@ }, { "cell_type": "code", - "execution_count": 64, - "metadata": { - "collapsed": false - }, + "execution_count": 60, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Accuracy on Test-Set: 92.2% (9222 / 10000)\n", + "Accuracy on Test-Set: 87.6% (8764 / 10000)\n", "Example errors:\n" ] }, { "data": { - "image/png": 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RFrkziMgGEbmtgGWUx4HDWu0EcCjst4ui2AH7zQTI68Zyt3OFiJwL4AkA9wfT+sMeHQwO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\nAAAASUVORK5CYII=\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2829,16 +2652,16 @@ "output_type": "stream", "text": [ "Confusion Matrix:\n", - "[[ 970 0 1 0 0 1 8 0 0 0]\n", - " [ 0 1121 5 0 0 0 9 0 0 0]\n", - " [ 2 1 1028 0 0 0 1 0 0 0]\n", - " [ 1 0 27 964 0 13 2 2 1 0]\n", - " [ 0 2 3 0 957 0 20 0 0 0]\n", - " [ 3 0 2 2 0 875 10 0 0 0]\n", - " [ 4 1 0 0 1 1 951 0 0 0]\n", - " [ 10 21 61 3 14 3 0 913 3 0]\n", - " [ 29 2 91 7 7 26 70 1 741 0]\n", - " [ 20 18 10 12 150 65 11 12 9 702]]\n" + "[[ 948 0 3 0 0 1 27 1 0 0]\n", + " [ 0 1110 5 1 0 0 19 0 0 0]\n", + " [ 5 1 1022 0 0 0 0 0 4 0]\n", + " [ 0 1 22 955 1 23 1 5 2 0]\n", + " [ 0 3 2 0 941 0 36 0 0 0]\n", + " [ 1 0 1 1 0 869 20 0 0 0]\n", + " [ 1 1 1 0 1 1 953 0 0 0]\n", + " [ 7 32 77 2 43 4 1 855 7 0]\n", + " [ 13 7 45 8 7 39 107 1 747 0]\n", + " [ 11 18 6 10 465 79 32 8 16 364]]\n" ] } ], @@ -2863,10 +2686,8 @@ }, { "cell_type": "code", - "execution_count": 65, - "metadata": { - "collapsed": false - }, + "execution_count": 61, + "metadata": {}, "outputs": [], "source": [ "# This has been commented out in case you want to modify and experiment\n", @@ -2948,9 +2769,9 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [conda env:tf-gpu]", + "display_name": "Python 3", "language": "python", - "name": "conda-env-tf-gpu-py" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -2962,9 +2783,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.10" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/13B_Visual_Analysis_MNIST.ipynb b/13B_Visual_Analysis_MNIST.ipynb index b5098dd..6441ae1 100644 --- a/13B_Visual_Analysis_MNIST.ipynb +++ b/13B_Visual_Analysis_MNIST.ipynb @@ -2,10 +2,7 @@ "cells": [ { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "# TensorFlow Tutorial #13-B\n", "# Visual Analysis (MNIST)\n", @@ -16,10 +13,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Introduction\n", "\n", @@ -30,20 +24,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Flowchart" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The following chart shows roughly how the data flows in the Convolutional Neural Network that is implemented below. Note that there are two separate optimization loops here:\n", "\n", @@ -54,20 +42,23 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "![Flowchart](images/13b_visual_analysis_flowchart.png)" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, + "source": [ + "## TensorFlow 2\n", + "\n", + "This tutorial was developed using TensorFlow v.1 back in the year 2016. There have been significant API changes in TensorFlow v.2. This tutorial uses TF2 in \"v.1 compatibility mode\", which is still useful for learning how TensorFlow works, but you would have to implement it slightly differently in TF2 (see Tutorial 03C on the Keras API). It would be too big a job for me to keep updating these tutorials every time Google's engineers update the TensorFlow API, so this tutorial may eventually stop working." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, "source": [ "## Imports" ] @@ -75,47 +66,57 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", - "import tensorflow as tf\n", "import numpy as np\n", "from sklearn.metrics import confusion_matrix\n", "import math" ] }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /home/magnus/anaconda3/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/compat/v2_compat.py:88: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "non-resource variables are not supported in the long term\n" + ] + } + ], + "source": [ + "# Use TensorFlow v.2 with this old v.1 code.\n", + "# E.g. placeholder variables and sessions have changed in TF2.\n", + "import tensorflow.compat.v1 as tf\n", + "tf.disable_v2_behavior()" + ] + }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This was developed using Python 3.6 (Anaconda) and TensorFlow version:" ] }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 3, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'1.3.0'" + "'2.1.0'" ] }, - "execution_count": 2, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -126,67 +127,39 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Load Data" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The MNIST data-set is about 12 MB and will be downloaded automatically if it is not located in the given path." ] }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Extracting data/MNIST/train-images-idx3-ubyte.gz\n", - "Extracting data/MNIST/train-labels-idx1-ubyte.gz\n", - "Extracting data/MNIST/t10k-images-idx3-ubyte.gz\n", - "Extracting data/MNIST/t10k-labels-idx1-ubyte.gz\n" - ] - } - ], + "execution_count": 4, + "metadata": {}, + "outputs": [], "source": [ - "from tensorflow.examples.tutorials.mnist import input_data\n", - "data = input_data.read_data_sets('data/MNIST/', one_hot=True)" + "from mnist import MNIST\n", + "data = MNIST(data_dir=\"data/MNIST/\")" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ - "The MNIST data-set has now been loaded and consists of 70,000 images and associated labels (i.e. classifications of the images). The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial." + "The MNIST data-set has now been loaded and consists of 70.000 images and class-numbers for the images. The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial." ] }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 5, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -194,103 +167,57 @@ "text": [ "Size of:\n", "- Training-set:\t\t55000\n", - "- Test-set:\t\t10000\n", - "- Validation-set:\t5000\n" + "- Validation-set:\t5000\n", + "- Test-set:\t\t10000\n" ] } ], "source": [ "print(\"Size of:\")\n", - "print(\"- Training-set:\\t\\t{}\".format(len(data.train.labels)))\n", - "print(\"- Test-set:\\t\\t{}\".format(len(data.test.labels)))\n", - "print(\"- Validation-set:\\t{}\".format(len(data.validation.labels)))" + "print(\"- Training-set:\\t\\t{}\".format(data.num_train))\n", + "print(\"- Validation-set:\\t{}\".format(data.num_val))\n", + "print(\"- Test-set:\\t\\t{}\".format(data.num_test))" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ - "The class-labels are One-Hot encoded, which means that each label is a vector with 10 elements, all of which are zero except for one element. The index of this one element is the class-number, that is, the digit shown in the associated image. We also need the class-numbers as integers for the test-set, so we calculate it now." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, - "outputs": [], - "source": [ - "data.test.cls = np.argmax(data.test.labels, axis=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, - "source": [ - "## Data Dimensions" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, - "source": [ - "The data dimensions are used in several places in the source-code below. They are defined once so we can use these variables instead of numbers throughout the source-code below." + "Copy some of the data-dimensions for convenience." ] }, { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ - "# We know that MNIST images are 28 pixels in each dimension.\n", - "img_size = 28\n", + "# The number of pixels in each dimension of an image.\n", + "img_size = data.img_size\n", "\n", - "# Images are stored in one-dimensional arrays of this length.\n", - "img_size_flat = img_size * img_size\n", + "# The images are stored in one-dimensional arrays of this length.\n", + "img_size_flat = data.img_size_flat\n", "\n", "# Tuple with height and width of images used to reshape arrays.\n", - "img_shape = (img_size, img_size)\n", - "\n", - "# Number of colour channels for the images: 1 channel for gray-scale.\n", - "num_channels = 1\n", + "img_shape = data.img_shape\n", "\n", "# Number of classes, one class for each of 10 digits.\n", - "num_classes = 10" + "num_classes = data.num_classes\n", + "\n", + "# Number of colour channels for the images: 1 channel for gray-scale.\n", + "num_channels = data.num_channels" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Helper-functions for plotting images" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Function used to plot 9 images in a 3x3 grid, and writing the true and predicted classes below each image." ] @@ -298,11 +225,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "def plot_images(images, cls_true, cls_pred=None):\n", @@ -336,10 +259,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Function used to plot 10 images in a 2x5 grid." ] @@ -347,11 +267,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "def plot_images10(images, smooth=True):\n", @@ -386,10 +302,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Function used to plot a single image." ] @@ -397,11 +310,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "def plot_image(image):\n", @@ -412,10 +321,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Plot a few images to see if data is correct" ] @@ -423,17 +329,13 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" ] }, "metadata": {}, @@ -442,10 +344,10 @@ ], "source": [ "# Get the first images from the test-set.\n", - "images = data.test.images[0:9]\n", + "images = data.x_test[0:9]\n", "\n", "# Get the true classes for those images.\n", - "cls_true = data.test.cls[0:9]\n", + "cls_true = data.y_test_cls[0:9]\n", "\n", "# Plot the images and labels using our helper-function above.\n", "plot_images(images=images, cls_true=cls_true)" @@ -453,10 +355,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## TensorFlow Graph\n", "\n", @@ -465,20 +364,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Placeholder variables" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Placeholder variables serve as the input to the TensorFlow computational graph that we may change each time we execute the graph.\n", "\n", @@ -488,11 +381,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "x = tf.placeholder(tf.float32, shape=[None, img_size_flat], name='x')" @@ -500,10 +389,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The convolutional layers expect `x` to be encoded as a 4-rank tensor so we have to reshape it so its shape is instead `[num_images, img_height, img_width, num_channels]`. Note that `img_height == img_width == img_size` and `num_images` can be inferred automatically by using -1 for the size of the first dimension. So the reshape operation is:" ] @@ -511,11 +397,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "x_image = tf.reshape(x, [-1, img_size, img_size, num_channels])" @@ -523,10 +405,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Next we have the placeholder variable for the true labels associated with the images that were input in the placeholder variable `x`. The shape of this placeholder variable is `[None, num_classes]` which means it may hold an arbitrary number of labels and each label is a vector of length `num_classes` which is 10 in this case." ] @@ -534,11 +413,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "y_true = tf.placeholder(tf.float32, shape=[None, num_classes], name='y_true')" @@ -546,10 +421,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We could also have a placeholder variable for the class-number, but we will instead calculate it using argmax. Note that this is a TensorFlow operator so nothing is calculated at this point." ] @@ -557,11 +429,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "y_true_cls = tf.argmax(y_true, axis=1)" @@ -569,10 +437,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Neural Network\n", "\n", @@ -582,11 +447,7 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "net = x_image" @@ -594,10 +455,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The input image is then input to the first convolutional layer, which has 16 filters each of size 5x5 pixels. The activation-function is the Rectified Linear Unit (ReLU) described in more detail in Tutorial #02." ] @@ -605,12 +463,21 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From :2: conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use `tf.keras.layers.Conv2D` instead.\n", + "WARNING:tensorflow:From /home/magnus/anaconda3/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/layers/convolutional.py:424: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Please use `layer.__call__` method instead.\n" + ] + } + ], "source": [ "net = tf.layers.conv2d(inputs=net, name='layer_conv1', padding='same',\n", " filters=16, kernel_size=5, activation=tf.nn.relu)" @@ -618,10 +485,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "After the convolution we do a max-pooling which is also described in Tutorial #02." ] @@ -629,22 +493,25 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From :1: max_pooling2d (from tensorflow.python.layers.pooling) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use keras.layers.MaxPooling2D instead.\n" + ] + } + ], "source": [ "net = tf.layers.max_pooling2d(inputs=net, pool_size=2, strides=2)" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Then we make a second convolutional layer, also with max-pooling." ] @@ -652,11 +519,7 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "net = tf.layers.conv2d(inputs=net, name='layer_conv2', padding='same',\n", @@ -666,11 +529,7 @@ { "cell_type": "code", "execution_count": 19, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "net = tf.layers.max_pooling2d(inputs=net, pool_size=2, strides=2)" @@ -678,10 +537,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The output then needs to be flattened so it can be used in fully-connected (aka. dense) layers." ] @@ -689,14 +545,20 @@ { "cell_type": "code", "execution_count": 20, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From :1: flatten (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use keras.layers.Flatten instead.\n" + ] + } + ], "source": [ - "net = tf.contrib.layers.flatten(net)\n", + "net = tf.layers.flatten(net)\n", "\n", "# This should eventually be replaced by:\n", "# net = tf.layers.flatten(net)" @@ -704,10 +566,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We can now add fully-connected (or dense) layers to the neural network." ] @@ -715,12 +574,18 @@ { "cell_type": "code", "execution_count": 21, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From :2: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use keras.layers.Dense instead.\n" + ] + } + ], "source": [ "net = tf.layers.dense(inputs=net, name='layer_fc1',\n", " units=128, activation=tf.nn.relu)" @@ -728,10 +593,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We need the neural network to classify the input images into 10 different classes. So the final fully-connected layer has `num_classes=10` output neurons." ] @@ -739,11 +601,7 @@ { "cell_type": "code", "execution_count": 22, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "net = tf.layers.dense(inputs=net, name='layer_fc_out',\n", @@ -752,10 +610,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The outputs of the final fully-connected layer are sometimes called logits, so we have a convenience variable with that name which we will also use further below." ] @@ -763,11 +618,7 @@ { "cell_type": "code", "execution_count": 23, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "logits = net" @@ -775,10 +626,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We use the softmax function to 'squash' the outputs so they are between zero and one, and so they sum to one." ] @@ -786,11 +634,7 @@ { "cell_type": "code", "execution_count": 24, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "y_pred = tf.nn.softmax(logits=logits)" @@ -798,10 +642,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This tells us how likely the neural network thinks the input image is of each possible class. The one that has the highest value is considered the most likely so its index is taken to be the class-number." ] @@ -809,11 +650,7 @@ { "cell_type": "code", "execution_count": 25, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "y_pred_cls = tf.argmax(y_pred, axis=1)" @@ -821,20 +658,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Loss-Function to be Optimized" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "To make the model better at classifying the input images, we must somehow change the variables of the neural network.\n", "\n", @@ -846,22 +677,15 @@ { "cell_type": "code", "execution_count": 26, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ - "cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_true, logits=logits)" + "cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(labels=y_true, logits=logits)" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We have now calculated the cross-entropy for each of the image classifications so we have a measure of how well the model performs on each image individually. But in order to use the cross-entropy to guide the optimization of the model's variables we need a single scalar value, so we simply take the average of the cross-entropy for all the image classifications." ] @@ -869,11 +693,7 @@ { "cell_type": "code", "execution_count": 27, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "loss = tf.reduce_mean(cross_entropy)" @@ -881,10 +701,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Optimization Method\n", "\n", @@ -896,11 +713,7 @@ { "cell_type": "code", "execution_count": 28, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(loss)" @@ -908,10 +721,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Classification Accuracy\n", "\n", @@ -923,11 +733,7 @@ { "cell_type": "code", "execution_count": 29, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "correct_prediction = tf.equal(y_pred_cls, y_true_cls)" @@ -935,10 +741,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The classification accuracy is calculated by first type-casting the vector of booleans to floats, so that False becomes 0 and True becomes 1, and then taking the average of these numbers." ] @@ -946,11 +749,7 @@ { "cell_type": "code", "execution_count": 30, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))" @@ -958,20 +757,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Optimize the Neural Network" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Create TensorFlow session\n", "\n", @@ -981,11 +774,7 @@ { "cell_type": "code", "execution_count": 31, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "session = tf.Session()" @@ -993,10 +782,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Initialize variables\n", "\n", @@ -1006,11 +792,7 @@ { "cell_type": "code", "execution_count": 32, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "session.run(tf.global_variables_initializer())" @@ -1018,20 +800,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Helper-function to perform optimization iterations" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "There are 55,000 images in the training-set. It takes a long time to calculate the gradient of the model using all these images. We therefore only use a small batch of images in each iteration of the optimizer.\n", "\n", @@ -1041,11 +817,7 @@ { "cell_type": "code", "execution_count": 33, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "train_batch_size = 64" @@ -1053,10 +825,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This function performs a number of optimization iterations so as to gradually improve the variables of the neural network layers. In each iteration, a new batch of data is selected from the training-set and then TensorFlow executes the optimizer using those training samples. The progress is printed every 100 iterations." ] @@ -1064,11 +833,7 @@ { "cell_type": "code", "execution_count": 34, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# Counter for total number of iterations performed so far.\n", @@ -1084,7 +849,7 @@ " # Get a batch of training examples.\n", " # x_batch now holds a batch of images and\n", " # y_true_batch are the true labels for those images.\n", - " x_batch, y_true_batch = data.train.next_batch(train_batch_size)\n", + " x_batch, y_true_batch, _ = data.random_batch(batch_size=train_batch_size)\n", "\n", " # Put the batch into a dict with the proper names\n", " # for placeholder variables in the TensorFlow graph.\n", @@ -1113,20 +878,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Helper-function to plot example errors" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Function for plotting examples of images from the test-set that have been mis-classified." ] @@ -1134,11 +893,7 @@ { "cell_type": "code", "execution_count": 35, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "def plot_example_errors(cls_pred, correct):\n", @@ -1155,13 +910,13 @@ " \n", " # Get the images from the test-set that have been\n", " # incorrectly classified.\n", - " images = data.test.images[incorrect]\n", + " images = data.x_test[incorrect]\n", " \n", " # Get the predicted classes for those images.\n", " cls_pred = cls_pred[incorrect]\n", "\n", " # Get the true classes for those images.\n", - " cls_true = data.test.cls[incorrect]\n", + " cls_true = data.y_test_cls[incorrect]\n", " \n", " # Plot the first 9 images.\n", " plot_images(images=images[0:9],\n", @@ -1171,10 +926,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Helper-function to plot confusion matrix" ] @@ -1182,11 +934,7 @@ { "cell_type": "code", "execution_count": 36, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "def plot_confusion_matrix(cls_pred):\n", @@ -1196,7 +944,7 @@ " # all images in the test-set.\n", "\n", " # Get the true classifications for the test-set.\n", - " cls_true = data.test.cls\n", + " cls_true = data.y_test_cls\n", " \n", " # Get the confusion matrix using sklearn.\n", " cm = confusion_matrix(y_true=cls_true,\n", @@ -1223,20 +971,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Helper-function for showing the performance" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Below is a function for printing the classification accuracy on the test-set.\n", "\n", @@ -1248,11 +990,7 @@ { "cell_type": "code", "execution_count": 37, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# Split the test-set into smaller batches of this size.\n", @@ -1262,7 +1000,7 @@ " show_confusion_matrix=False):\n", "\n", " # Number of images in the test-set.\n", - " num_test = len(data.test.images)\n", + " num_test = data.num_test\n", "\n", " # Allocate an array for the predicted classes which\n", " # will be calculated in batches and filled into this array.\n", @@ -1280,10 +1018,10 @@ " j = min(i + test_batch_size, num_test)\n", "\n", " # Get the images from the test-set between index i and j.\n", - " images = data.test.images[i:j, :]\n", + " images = data.x_test[i:j, :]\n", "\n", " # Get the associated labels.\n", - " labels = data.test.labels[i:j, :]\n", + " labels = data.y_test[i:j, :]\n", "\n", " # Create a feed-dict with these images and labels.\n", " feed_dict = {x: images,\n", @@ -1297,7 +1035,7 @@ " i = j\n", "\n", " # Convenience variable for the true class-numbers of the test-set.\n", - " cls_true = data.test.cls\n", + " cls_true = data.y_test_cls\n", "\n", " # Create a boolean array whether each image is correctly classified.\n", " correct = (cls_true == cls_pred)\n", @@ -1327,10 +1065,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Performance before any optimization\n", "\n", @@ -1340,17 +1075,13 @@ { "cell_type": "code", "execution_count": 38, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Accuracy on Test-Set: 9.3% (933 / 10000)\n" + "Accuracy on Test-Set: 8.7% (871 / 10000)\n" ] } ], @@ -1360,10 +1091,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Performance after 10,000 optimization iterations\n", "\n", @@ -1374,9 +1102,6 @@ "cell_type": "code", "execution_count": 39, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -1384,108 +1109,108 @@ "name": "stdout", "output_type": "stream", "text": [ - "Optimization Iteration: 1, Training Accuracy: 14.1%\n", - "Optimization Iteration: 101, Training Accuracy: 73.4%\n", + "Optimization Iteration: 1, Training Accuracy: 10.9%\n", + "Optimization Iteration: 101, Training Accuracy: 82.8%\n", "Optimization Iteration: 201, Training Accuracy: 89.1%\n", - "Optimization Iteration: 301, Training Accuracy: 92.2%\n", - "Optimization Iteration: 401, Training Accuracy: 87.5%\n", + "Optimization Iteration: 301, Training Accuracy: 90.6%\n", + "Optimization Iteration: 401, Training Accuracy: 89.1%\n", "Optimization Iteration: 501, Training Accuracy: 93.8%\n", - "Optimization Iteration: 601, Training Accuracy: 95.3%\n", - "Optimization Iteration: 701, Training Accuracy: 95.3%\n", - "Optimization Iteration: 801, Training Accuracy: 92.2%\n", - "Optimization Iteration: 901, Training Accuracy: 96.9%\n", + "Optimization Iteration: 601, Training Accuracy: 87.5%\n", + "Optimization Iteration: 701, Training Accuracy: 92.2%\n", + "Optimization Iteration: 801, Training Accuracy: 95.3%\n", + "Optimization Iteration: 901, Training Accuracy: 95.3%\n", "Optimization Iteration: 1001, Training Accuracy: 95.3%\n", - "Optimization Iteration: 1101, Training Accuracy: 96.9%\n", - "Optimization Iteration: 1201, Training Accuracy: 95.3%\n", + "Optimization Iteration: 1101, Training Accuracy: 98.4%\n", + "Optimization Iteration: 1201, Training Accuracy: 96.9%\n", "Optimization Iteration: 1301, Training Accuracy: 93.8%\n", "Optimization Iteration: 1401, Training Accuracy: 95.3%\n", - "Optimization Iteration: 1501, Training Accuracy: 98.4%\n", - "Optimization Iteration: 1601, Training Accuracy: 95.3%\n", + "Optimization Iteration: 1501, Training Accuracy: 100.0%\n", + "Optimization Iteration: 1601, Training Accuracy: 96.9%\n", "Optimization Iteration: 1701, Training Accuracy: 98.4%\n", - "Optimization Iteration: 1801, Training Accuracy: 98.4%\n", - "Optimization Iteration: 1901, Training Accuracy: 98.4%\n", - "Optimization Iteration: 2001, Training Accuracy: 98.4%\n", - "Optimization Iteration: 2101, Training Accuracy: 98.4%\n", - "Optimization Iteration: 2201, Training Accuracy: 98.4%\n", + "Optimization Iteration: 1801, Training Accuracy: 100.0%\n", + "Optimization Iteration: 1901, Training Accuracy: 100.0%\n", + "Optimization Iteration: 2001, Training Accuracy: 95.3%\n", + "Optimization Iteration: 2101, Training Accuracy: 95.3%\n", + "Optimization Iteration: 2201, Training Accuracy: 96.9%\n", "Optimization Iteration: 2301, Training Accuracy: 96.9%\n", - "Optimization Iteration: 2401, Training Accuracy: 98.4%\n", - "Optimization Iteration: 2501, Training Accuracy: 98.4%\n", - "Optimization Iteration: 2601, Training Accuracy: 98.4%\n", - "Optimization Iteration: 2701, Training Accuracy: 98.4%\n", - "Optimization Iteration: 2801, Training Accuracy: 96.9%\n", + "Optimization Iteration: 2401, Training Accuracy: 95.3%\n", + "Optimization Iteration: 2501, Training Accuracy: 95.3%\n", + "Optimization Iteration: 2601, Training Accuracy: 95.3%\n", + "Optimization Iteration: 2701, Training Accuracy: 92.2%\n", + "Optimization Iteration: 2801, Training Accuracy: 98.4%\n", "Optimization Iteration: 2901, Training Accuracy: 98.4%\n", "Optimization Iteration: 3001, Training Accuracy: 100.0%\n", - "Optimization Iteration: 3101, Training Accuracy: 96.9%\n", - "Optimization Iteration: 3201, Training Accuracy: 98.4%\n", - "Optimization Iteration: 3301, Training Accuracy: 96.9%\n", - "Optimization Iteration: 3401, Training Accuracy: 96.9%\n", + "Optimization Iteration: 3101, Training Accuracy: 100.0%\n", + "Optimization Iteration: 3201, Training Accuracy: 96.9%\n", + "Optimization Iteration: 3301, Training Accuracy: 98.4%\n", + "Optimization Iteration: 3401, Training Accuracy: 100.0%\n", "Optimization Iteration: 3501, Training Accuracy: 96.9%\n", - "Optimization Iteration: 3601, Training Accuracy: 100.0%\n", + "Optimization Iteration: 3601, Training Accuracy: 95.3%\n", "Optimization Iteration: 3701, Training Accuracy: 98.4%\n", - "Optimization Iteration: 3801, Training Accuracy: 95.3%\n", + "Optimization Iteration: 3801, Training Accuracy: 98.4%\n", "Optimization Iteration: 3901, Training Accuracy: 98.4%\n", - "Optimization Iteration: 4001, Training Accuracy: 98.4%\n", - "Optimization Iteration: 4101, Training Accuracy: 96.9%\n", - "Optimization Iteration: 4201, Training Accuracy: 98.4%\n", - "Optimization Iteration: 4301, Training Accuracy: 98.4%\n", - "Optimization Iteration: 4401, Training Accuracy: 100.0%\n", + "Optimization Iteration: 4001, Training Accuracy: 100.0%\n", + "Optimization Iteration: 4101, Training Accuracy: 98.4%\n", + "Optimization Iteration: 4201, Training Accuracy: 100.0%\n", + "Optimization Iteration: 4301, Training Accuracy: 95.3%\n", + "Optimization Iteration: 4401, Training Accuracy: 98.4%\n", "Optimization Iteration: 4501, Training Accuracy: 100.0%\n", - "Optimization Iteration: 4601, Training Accuracy: 96.9%\n", - "Optimization Iteration: 4701, Training Accuracy: 100.0%\n", - "Optimization Iteration: 4801, Training Accuracy: 98.4%\n", + "Optimization Iteration: 4601, Training Accuracy: 100.0%\n", + "Optimization Iteration: 4701, Training Accuracy: 96.9%\n", + "Optimization Iteration: 4801, Training Accuracy: 100.0%\n", "Optimization Iteration: 4901, Training Accuracy: 98.4%\n", - "Optimization Iteration: 5001, Training Accuracy: 98.4%\n", - "Optimization Iteration: 5101, Training Accuracy: 98.4%\n", + "Optimization Iteration: 5001, Training Accuracy: 96.9%\n", + "Optimization Iteration: 5101, Training Accuracy: 100.0%\n", "Optimization Iteration: 5201, Training Accuracy: 100.0%\n", - "Optimization Iteration: 5301, Training Accuracy: 100.0%\n", - "Optimization Iteration: 5401, Training Accuracy: 98.4%\n", - "Optimization Iteration: 5501, Training Accuracy: 95.3%\n", - "Optimization Iteration: 5601, Training Accuracy: 98.4%\n", + "Optimization Iteration: 5301, Training Accuracy: 98.4%\n", + "Optimization Iteration: 5401, Training Accuracy: 100.0%\n", + "Optimization Iteration: 5501, Training Accuracy: 100.0%\n", + "Optimization Iteration: 5601, Training Accuracy: 96.9%\n", "Optimization Iteration: 5701, Training Accuracy: 98.4%\n", - "Optimization Iteration: 5801, Training Accuracy: 98.4%\n", - "Optimization Iteration: 5901, Training Accuracy: 100.0%\n", + "Optimization Iteration: 5801, Training Accuracy: 100.0%\n", + "Optimization Iteration: 5901, Training Accuracy: 96.9%\n", "Optimization Iteration: 6001, Training Accuracy: 98.4%\n", "Optimization Iteration: 6101, Training Accuracy: 100.0%\n", - "Optimization Iteration: 6201, Training Accuracy: 98.4%\n", - "Optimization Iteration: 6301, Training Accuracy: 100.0%\n", - "Optimization Iteration: 6401, Training Accuracy: 98.4%\n", - "Optimization Iteration: 6501, Training Accuracy: 100.0%\n", + "Optimization Iteration: 6201, Training Accuracy: 96.9%\n", + "Optimization Iteration: 6301, Training Accuracy: 98.4%\n", + "Optimization Iteration: 6401, Training Accuracy: 100.0%\n", + "Optimization Iteration: 6501, Training Accuracy: 98.4%\n", "Optimization Iteration: 6601, Training Accuracy: 98.4%\n", "Optimization Iteration: 6701, Training Accuracy: 98.4%\n", "Optimization Iteration: 6801, Training Accuracy: 100.0%\n", - "Optimization Iteration: 6901, Training Accuracy: 100.0%\n", - "Optimization Iteration: 7001, Training Accuracy: 100.0%\n", - "Optimization Iteration: 7101, Training Accuracy: 100.0%\n", + "Optimization Iteration: 6901, Training Accuracy: 98.4%\n", + "Optimization Iteration: 7001, Training Accuracy: 96.9%\n", + "Optimization Iteration: 7101, Training Accuracy: 96.9%\n", "Optimization Iteration: 7201, Training Accuracy: 100.0%\n", - "Optimization Iteration: 7301, Training Accuracy: 100.0%\n", - "Optimization Iteration: 7401, Training Accuracy: 98.4%\n", + "Optimization Iteration: 7301, Training Accuracy: 98.4%\n", + "Optimization Iteration: 7401, Training Accuracy: 96.9%\n", "Optimization Iteration: 7501, Training Accuracy: 100.0%\n", - "Optimization Iteration: 7601, Training Accuracy: 96.9%\n", + "Optimization Iteration: 7601, Training Accuracy: 100.0%\n", "Optimization Iteration: 7701, Training Accuracy: 100.0%\n", "Optimization Iteration: 7801, Training Accuracy: 100.0%\n", - "Optimization Iteration: 7901, Training Accuracy: 100.0%\n", - "Optimization Iteration: 8001, Training Accuracy: 100.0%\n", - "Optimization Iteration: 8101, Training Accuracy: 96.9%\n", + "Optimization Iteration: 7901, Training Accuracy: 96.9%\n", + "Optimization Iteration: 8001, Training Accuracy: 98.4%\n", + "Optimization Iteration: 8101, Training Accuracy: 100.0%\n", "Optimization Iteration: 8201, Training Accuracy: 100.0%\n", - "Optimization Iteration: 8301, Training Accuracy: 98.4%\n", - "Optimization Iteration: 8401, Training Accuracy: 98.4%\n", - "Optimization Iteration: 8501, Training Accuracy: 100.0%\n", + "Optimization Iteration: 8301, Training Accuracy: 100.0%\n", + "Optimization Iteration: 8401, Training Accuracy: 100.0%\n", + "Optimization Iteration: 8501, Training Accuracy: 98.4%\n", "Optimization Iteration: 8601, Training Accuracy: 100.0%\n", "Optimization Iteration: 8701, Training Accuracy: 100.0%\n", "Optimization Iteration: 8801, Training Accuracy: 100.0%\n", - "Optimization Iteration: 8901, Training Accuracy: 98.4%\n", - "Optimization Iteration: 9001, Training Accuracy: 96.9%\n", + "Optimization Iteration: 8901, Training Accuracy: 100.0%\n", + "Optimization Iteration: 9001, Training Accuracy: 100.0%\n", "Optimization Iteration: 9101, Training Accuracy: 100.0%\n", - "Optimization Iteration: 9201, Training Accuracy: 98.4%\n", - "Optimization Iteration: 9301, Training Accuracy: 98.4%\n", - "Optimization Iteration: 9401, Training Accuracy: 100.0%\n", - "Optimization Iteration: 9501, Training Accuracy: 100.0%\n", + "Optimization Iteration: 9201, Training Accuracy: 100.0%\n", + "Optimization Iteration: 9301, Training Accuracy: 100.0%\n", + "Optimization Iteration: 9401, Training Accuracy: 98.4%\n", + "Optimization Iteration: 9501, Training Accuracy: 98.4%\n", "Optimization Iteration: 9601, Training Accuracy: 98.4%\n", "Optimization Iteration: 9701, Training Accuracy: 100.0%\n", - "Optimization Iteration: 9801, Training Accuracy: 100.0%\n", + "Optimization Iteration: 9801, Training Accuracy: 96.9%\n", "Optimization Iteration: 9901, Training Accuracy: 98.4%\n", - "CPU times: user 38.6 s, sys: 4.3 s, total: 42.9 s\n", - "Wall time: 31 s\n" + "CPU times: user 25.6 s, sys: 2.81 s, total: 28.4 s\n", + "Wall time: 24.7 s\n" ] } ], @@ -1498,9 +1223,6 @@ "cell_type": "code", "execution_count": 40, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -1508,15 +1230,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "Accuracy on Test-Set: 98.9% (9888 / 10000)\n", + "Accuracy on Test-Set: 98.8% (9881 / 10000)\n", "Example errors:\n" ] }, { "data": { - "image/png": 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\n", 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" ] }, "metadata": {}, @@ -1527,26 +1249,28 @@ "output_type": "stream", "text": [ "Confusion Matrix:\n", - "[[ 977 0 0 0 0 0 1 0 1 1]\n", - " [ 0 1134 0 0 0 0 0 1 0 0]\n", - " [ 2 3 1021 0 1 0 0 4 1 0]\n", - " [ 1 0 1 999 0 3 0 3 1 2]\n", - " [ 0 0 0 0 981 0 0 0 0 1]\n", - " [ 2 0 0 3 0 883 1 1 0 2]\n", - " [ 3 3 0 0 4 2 946 0 0 0]\n", - " [ 0 2 5 0 1 0 0 1019 1 0]\n", - " [ 7 2 4 2 3 1 4 4 941 6]\n", - " [ 1 5 0 0 10 3 0 2 1 987]]\n" + "[[ 977 0 1 0 0 0 0 1 1 0]\n", + " [ 0 1125 4 0 0 1 1 3 1 0]\n", + " [ 1 0 1029 0 0 0 0 2 0 0]\n", + " [ 0 0 3 1000 0 4 0 0 2 1]\n", + " [ 0 0 3 0 973 0 1 1 0 4]\n", + " [ 2 1 0 3 0 883 3 0 0 0]\n", + " [ 6 2 0 1 1 4 943 0 1 0]\n", + " [ 1 0 8 1 0 0 0 1016 1 1]\n", + " [ 5 0 11 2 1 4 1 2 945 3]\n", + " [ 3 3 1 0 4 4 0 3 1 990]]\n" ] }, { "data": { - "image/png": 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MeF+ZvVkMPDGuCzQlW22RSPoSEzSFbG/erIqIHkxhRqaODwIXSdoOuBf4ACVnsqSTgPuA\nd5dzvwccBawCni7n9qRO1+YH4/bnAO/khQM0EdFEi4HE9gpgou7PkgnONXBKG/VuNZDY/sb415L+\nBfhJG5XH1LSRlOiVN7ZzV8TK125opZwYDb38VO3H89NHEdFQy12bvqgzRrKe5xtf2wCPsdmUUkQ0\nMOoZ0iSJapnt2JTQptKviog2mJG4E23S6d8SNL5XHga+MUEkon0t32vTF3VWtq6QlCfmRXRlBNII\nbLFrI2m27Q1Uj928XtI9VEtuRdVY2eotx+WGvSep7s3ZsJVVeREz04AHiTomGyP5N6p1+8c0rOPN\nWQkbMbFh6LbUMVkgEYDte6bpWiJmphGftXmxpI9s6U3bn6lRvoErJRn4Z9vnTPUCI0beiLdIZgE7\n0+yhG39qe42klwBXSbrT9rXjTyi3Qp8MMIcdG1QVMZw0AtO/kwWStbY/0aRw22vKn+skXQYsAq7d\n7JxzgHMAXqTdRyA2R0zBiIyRTDb926jjJmknSbuM7QNHALc1KTNiJI3y9C8T3C04RXsCl1WLY5kN\nfM325Q3LjBg9Ax4k6thiILH9WJOCbd9Ltbw+IiYx6l2biIha8hDxiH4bgRZJAklEP3n0p39jBLWV\n2ezo29e3Us53/mS3VsoZammRREQTYjQGWxNIIvotgSQiGhmRla0JJBH9lkASEU2NwqxNpwvSJM2V\ntFTSnZJWSnp9l/VFDKURv9emDZ8HLrf9rvIIweQJiBhvCIJEHZ0FEkm7AocB7wew/SzwbFf1RQyr\nURhs7bJrsx/wMPBlSTdLOrekE4iI8Uaga9NlIJlNlTz6bNuHUGWg/4Mn9Ek6WdINkm54jmc6vJyI\nwTRTnmvTq9XAatvLy+ulVIHlBWyfY3uh7YXbsn2HlxMxoNIi2TLbDwIPSDqgHFoC3NFVfRHDqG5r\nZCotEkmzynDCd8rr/SQtl7RK0jfKxAeSti+vV5X39+31e3Sdj+SDwEWSbgEOBv53x/VFDJ/2WySn\nAivHvf4U8FnbLwfWAyeV4ycB68vxz5bzetJpILG9onRbXmX7ONvt3DIaMULabJFIWgC8Azi3vBZw\nONXQAsAFwHFl/9jymvL+knL+lCVDWkS/tdsi+RzwMWBsvewewOPl8btQjV3OL/vzgQcAyvtPlPOn\nLIEkot/qB5J5YzOcZTt5fDGSjgbW2b5xGq8eyL02Ef01tYHUR2wvnOT9Q4FjJB0FzAFeRLW6fK6k\n2aXVsQBYU85fA+wDrJY0G9gVeHTqXyKBZHK9dRf/kAd87q4HbWU2O+OeW1op55Mve1Ur5fRFSz8e\nts8AzgCQ9Cbgr23/J0nfAt4FfB04Efh2+ciy8vqn5f0f2r39sKZrE9Fn2lRva+DjwEckraIaAzmv\nHD8P2KMc/wgTLBitKy2SiD7rYtWq7WuAa8r+vVSPy938nN8Bf95GfQkkEf00BKtW60ggiei3BJKI\naGJUssh3Ntgq6QBJK8Ztv5Z0Wlf1RQytEbhpr7MWie27qO6vQdIsqjnry7qqL2JYaQSWB0xX12YJ\ncI/t+6apvojhkEd2TsnxwMXTVFfEcBn+Bkn3C9JK7oNjgG9t4f1kSIsZLRnS6nk7cJPthyZ6MxnS\nYsbLYGstJ5BuTcTEhqC1UUfXD8jaCXgrcGmX9UQMtbRIJmf7KXpMlBIxE4zKgrSsbI3oM20a/kiS\nQBLRT0PQbakjgSSiz7IgrQttZCVra8nxCCxdHnRtZTZ7++2Pt1LO9w9qIfPbVH9sRuDHbPACScQM\nk8HWiGjGjETLN4Ekos8yRhIRjWQdSUQ0Z49E16brJfIflnS7pNskXSxpTpf1RQyj3P07CUnzgQ8B\nC20fBMyiyksSEePlXpta5e8g6TlgR+BXHdcXMXQGvbVRR2ctEttrgE8D9wNrgSdsX9lVfRFDycAm\n19sGWJddm92AY4H9gL2BnSS9Z4LzkiEtZrRpeGRn57ocbH0L8AvbD9t+jionyRs2PykZ0mLGG5u5\n2do2wLocI7kfWCxpR+C3VJnkb+iwvoihlDGSSdheDiwFbgJuLXWd01V9EUOp7ozNgAebrjOknQmc\n2WUdEcOsWtk64FGihqxsjei3AR9IrWM6HkcREZOQXWvbajnSPpJ+JOmOsqL81HJ8d0lXSfp5+XO3\nclySviBplaRbJL2m1++QQBLRT665hqTeOpINwEdtHwgsBk6RdCBwOnC17f2Bq8trqJ45tX/ZTgbO\n7vVrDF7Xpo3+YhtZ1mDwpty2mdW8jE0bm5cxgL7/J3NbKeeku+9tXMY975zaeqi2Zm1sr6Va/Int\nJyWtBOZTred6UzntAuAa4OPl+IW2DVwnaa6kvUo5UzJ4gSRipqn/H9Y8SeOXUJxje8KZUEn7AocA\ny4E9xwWHB4E9y/584IFxH1tdjiWQRAwVT2nV6iO2F27tJEk7A5cAp9n+tca10G1ban/lSsZIIvqt\nxZWtkralCiIX2R57wuVDkvYq7+8FrCvH1wD7jPv4gnJsyhJIIvqtpQVpqpoe5wErbX9m3FvLgBPL\n/onAt8cdf1+ZvVlMdWPtlLs1kK5NRN+1uCDtUOC9wK2SVpRjfwOcBXxT0knAfcC7y3vfA44CVgFP\nAx/oteJOA0mZx/4LqgV8X7L9uS7rixg6Bja2E0hs/4Tqd20iSyY438ApbdTdZRqBg6iCyCLg1cDR\nkl7eVX0Rw0jUW4w26MvouxwjeSWw3PbTtjcAPwb+rMP6IobTCKQR6DKQ3Aa8UdIeJZXAUbxwhDgi\nYCQCSWdjJLZXSvoUcCXwFLAC+INllZJOplqeyxx27OpyIgaTyU17W2P7PNuvtX0YsB64e4JzkiEt\nZrRRGCPpetbmJbbXSXop1fjI4i7rixhKAx4k6uh6HcklkvYAngNOsf14x/VFDBcbNg1/36brDGlv\n7LL8iJEw/HEkK1sj+m3Qxz/qSCCJ6LcEkohoZOxJe0NuoALJk6x/5Adeet9WTpsHPDLpGVv/d9l6\nGfVMbzlbT242nN9rgK7lB/u3Us4f1bskgMFfbFbHQAUS2y/e2jmSbqiT3KXrMlLO9JQzSNfSZjkv\nkEASEY0Y2Dj80zYJJBF9ZXACST+08djPth4dmnK6L2eQrqXNcp43Al0beQS+xKiRtJHqecmzgZXA\nibaf7rGsNwF/bftoSccAB9o+awvnzgX+o+3/O8U6/ifwG9uf7uUaZ7Jdt9vTb/h3J9Q69/IHPn9j\n6+MzLUnO1sH0W9sH2z4IeBb4r+PfLDk2p/xvZ3vZloJIMRf4b1MtNxoagTQCCSSD71+Bl0vaV9Jd\nki6kyvWyj6QjJP1U0k2SvlUeQ4CkIyXdKekmxiWTkvR+Sf9U9veUdJmkn5XtDVS5PV8maYWkfyzn\n/XdJ15dHOv7duLL+h6S7Jf0EOGDa/jZG0QgEkmEcI5kxJM2meqzi5eXQ/lTdnOskzQP+FniL7ack\nfRz4iKR/AL4EHE6V1PcbWyj+C8CPbb9T0ixgZ6pHOR5k++BS/xGlzkVUuUCXSTqMKr/M8cDBVD9D\nNwE3tvvtZwgbNg7/0w8TSAbTDuOygP8r1SMG9gbus31dOb4YOBD4f+UBSNsBPwVeAfzC9s8BJH2V\nkjhqM4cD7wOwvRF4Yuzh0uMcUbaby+udqQLLLsBlY+M2kpY1+rYz3YC3NupIIBlMvx1rFYwpweKp\n8YeAq2yfsNl5L/hcQwI+afufN6vjtBbriBEIJBkjGV7XAYeOZeaXtJOkPwbuBPaV9LJy3pamBK4G\n/rJ8dpakXYEnqVobY64A/vO4sZf5kl4CXAscJ2kHSbsA/6Hl7zaDuLrXps42wBJIhpTth4H3AxdL\nuoXSrbH9O6quzHfLYOu6LRRxKvBmSbdSjW8caPtRqq7SbZL+0faVwNeAn5bzlgK72L6JauzlZ8D3\nges7+6KjzmBvqrUNsqwjieijXWe/2K9/0XG1zr1i/bkDu44kYyQR/TYC/5knkET0U6Z/I6INTvLn\niGhm8Fet1pFAEtFPI5JqMdO/Ef3mTfW2Gsp9VndJWiXp9I6v/PfSIonoIwNuqUVS7pn6IvBWYDVw\nvaRltu9opYJJpEUS0U92my2SRcAq2/fafhb4OnBsp9dfpEUS0Wdub/p3PvDAuNergde1VfhkEkgi\n+uhJ1l/xAy+dV/P0OZJuGPf6HNvtp37sQQJJRB/ZPrLF4tYA+4x7vaAc61zGSCJGx/XA/pL2k7Qd\nVfKpackVkxZJxIiwvUHSX1Glf5gFnG/79umoO3f/RkRj6dpERGMJJBHRWAJJRDSWQBIRjSWQRERj\nCSQR0VgCSUQ0lkASEY39f2GvAFCULtk9AAAAAElFTkSuQmCC\n", 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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1557,10 +1281,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Optimizing the Input Images\n", "\n", @@ -1571,20 +1292,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Helper-function for getting the names of convolutional layers" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Function for getting the names of all the convolutional layers in the neural network. We could have made this list manually, but for larger neural networks it is easier to do this with a function." ] @@ -1592,11 +1307,7 @@ { "cell_type": "code", "execution_count": 41, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "def get_conv_layer_names():\n", @@ -1612,11 +1323,7 @@ { "cell_type": "code", "execution_count": 42, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "conv_names = get_conv_layer_names()" @@ -1625,16 +1332,12 @@ { "cell_type": "code", "execution_count": 43, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['layer_conv1/convolution', 'layer_conv2/convolution']" + "['layer_conv1/Conv2D', 'layer_conv2/Conv2D']" ] }, "execution_count": 43, @@ -1649,11 +1352,7 @@ { "cell_type": "code", "execution_count": 44, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -1672,20 +1371,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Helper-function for finding the input image" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This function finds the input image that maximizes a given feature in the network. It essentially just performs optimization with gradient ascent. The image is initialized with small random values and is then iteratively updated using the gradient for the given feature with regard to the image." ] @@ -1693,11 +1386,7 @@ { "cell_type": "code", "execution_count": 45, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "def optimize_image(conv_id=None, feature=0,\n", @@ -1818,10 +1507,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This next function finds the images that maximize the first 10 features of a layer, by calling the above function 10 times." ] @@ -1829,11 +1515,7 @@ { "cell_type": "code", "execution_count": 46, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "def optimize_images(conv_id=None, num_iterations=30):\n", @@ -1882,10 +1564,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### First Convolutional Layer\n", "\n", @@ -1895,17 +1574,13 @@ { "cell_type": "code", "execution_count": 47, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Layer: layer_conv1/convolution\n", + "Layer: layer_conv1/Conv2D\n", "Optimizing image for feature no. 0\n", "Optimizing image for feature no. 1\n", "Optimizing image for feature no. 2\n", @@ -1920,9 +1595,9 @@ }, { "data": { - "image/png": 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"text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -1935,20 +1610,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Note how these are very simple shapes such as lines and angles. Some of these images may be completely white, which suggests that those features of the neural network are perhaps unused, so the number of features could be reduced in this layer." ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Second Convolutional Layer\n", "\n", @@ -1958,17 +1627,13 @@ { "cell_type": "code", "execution_count": 48, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Layer: layer_conv2/convolution\n", + "Layer: layer_conv2/Conv2D\n", "Optimizing image for feature no. 0\n", "Optimizing image for feature no. 1\n", "Optimizing image for feature no. 2\n", @@ -1983,9 +1648,9 @@ }, { "data": { - "image/png": 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nIzQ0dNnzL3n58fHxbN++XRjm2traNUVEUVFRZGdns2XLFmw225oFl9ZkkO12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\nBgbocZTcP0+a3/T0NAYHByGTyShVbTdNBqFQiKNHj2JychJKpRKNjY30M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\n", 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" ] }, "metadata": {}, @@ -1998,10 +1663,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Final output layer\n", "\n", @@ -2012,9 +1674,6 @@ "cell_type": "code", "execution_count": 49, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -2023,54 +1682,54 @@ "output_type": "stream", "text": [ "Iteration: 0\n", - "Predicted class: 1, score: 79.35%\n", - "Gradient min: -0.564165, max: 0.727934, stepsize: 5.89\n", - "Loss: 0.974301\n", + "Predicted class: 8, score: 89.26%\n", + "Gradient min: -0.846559, max: 0.558144, stepsize: 5.45\n", + "Loss: 0.3713841\n", "\n", "Iteration: 1\n", "Predicted class: 2, score: 100.00%\n", - "Gradient min: -0.570292, max: 0.710277, stepsize: 5.12\n", - "Loss: 26.5427\n", + "Gradient min: -0.535254, max: 0.621156, stepsize: 5.34\n", + "Loss: 34.539898\n", "\n", "Iteration: 2\n", "Predicted class: 2, score: 100.00%\n", - "Gradient min: -0.409457, max: 0.470393, stepsize: 6.22\n", - "Loss: 37.2067\n", + "Gradient min: -0.664117, max: 0.673569, stepsize: 5.53\n", + "Loss: 45.18827\n", "\n", "Iteration: 3\n", "Predicted class: 2, score: 100.00%\n", - "Gradient min: -0.496705, max: 0.523132, stepsize: 5.94\n", - "Loss: 38.8357\n", + "Gradient min: -0.549961, max: 0.498956, stepsize: 5.67\n", + "Loss: 48.934826\n", "\n", "Iteration: 4\n", "Predicted class: 2, score: 100.00%\n", - "Gradient min: -0.408904, max: 0.465926, stepsize: 5.98\n", - "Loss: 41.3122\n", + "Gradient min: -0.540532, max: 0.564252, stepsize: 5.45\n", + "Loss: 50.952587\n", "\n", "Iteration: 5\n", "Predicted class: 2, score: 100.00%\n", - "Gradient min: -0.476362, max: 0.522812, stepsize: 5.89\n", - "Loss: 42.0313\n", + "Gradient min: -0.581356, max: 0.486933, stepsize: 5.69\n", + "Loss: 51.000446\n", "\n", "Iteration: 6\n", "Predicted class: 2, score: 100.00%\n", - "Gradient min: -0.398669, max: 0.488273, stepsize: 6.10\n", - "Loss: 42.4584\n", + "Gradient min: -0.578246, max: 0.520858, stepsize: 5.55\n", + "Loss: 51.367252\n", "\n", "Iteration: 7\n", "Predicted class: 2, score: 100.00%\n", - "Gradient min: -0.470078, max: 0.545729, stepsize: 5.88\n", - "Loss: 42.6654\n", + "Gradient min: -0.592440, max: 0.511202, stepsize: 5.60\n", + "Loss: 51.47485\n", "\n", "Iteration: 8\n", "Predicted class: 2, score: 100.00%\n", - "Gradient min: -0.470376, max: 0.456324, stepsize: 6.17\n", - "Loss: 43.9691\n", + "Gradient min: -0.589151, max: 0.507705, stepsize: 5.53\n", + "Loss: 51.796883\n", "\n", "Iteration: 9\n", "Predicted class: 2, score: 100.00%\n", - "Gradient min: -0.471091, max: 0.535464, stepsize: 5.92\n", - "Loss: 43.4301\n", + "Gradient min: -0.614109, max: 0.527479, stepsize: 5.59\n", + "Loss: 51.947083\n", "\n" ] } @@ -2082,10 +1741,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Note how the predicted class indeed becomes 2 already within the first few iterations so the optimization is working as intended. Also note how the loss-measure is increasing rapidly until it apparently converges. This is because the loss-measure is actually just the value of the feature or neuron that we are trying to maximize. Because this is the logits-layer prior to the softmax, these values can potentially be infinitely high, but they are limited because we limit the image-values between 0 and 1.\n", "\n", @@ -2096,17 +1752,14 @@ "cell_type": "code", "execution_count": 50, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": false }, "outputs": [ { "data": { - "image/png": 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" ] }, "metadata": {}, @@ -2119,10 +1772,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Although some of the curves do hint somewhat at the digit 2, it is hard for a human to see why the neural network believes this is the *optimal* image for the digit 2. This can only be understood when the optimal images for the remaining digits are also shown." ] @@ -2131,9 +1781,6 @@ "cell_type": "code", "execution_count": 51, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": false }, "outputs": [ @@ -2156,9 +1803,9 @@ }, { "data": { - "image/png": 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XsLUnz2q32+l2mnxPkvwiCbVInHGDwYBvvvkGWq0WDAYDVqsVCwsLyMjIeOBE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KxIT0/HD3/4Qzz33HNUCYxgdnYWPT09mJqaglwux8rKCsbHxwO0kO/evYtf/OIXYLPZsFqtAQHh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\n+H+odrS0zIdNqQAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": {}, @@ -2171,10 +1818,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "These images may vary each time you run the optimization. Some of the images can be seen to somewhat resemble the hand-written digits. But the other images are often impossible to recognize and it is hard to understand why the neural network thinks these are the *optimal* input images for those digits.\n", "\n", @@ -2187,20 +1831,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Close TensorFlow Session" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We are now done using TensorFlow, so we close the session to release its resources." ] @@ -2208,11 +1846,7 @@ { "cell_type": "code", "execution_count": 52, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# This has been commented out in case you want to modify and experiment\n", @@ -2222,10 +1856,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Conclusion\n", "\n", @@ -2236,10 +1867,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Exercises\n", "\n", @@ -2260,10 +1888,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## License (MIT)\n", "\n", @@ -2294,9 +1919,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.1" + "version": "3.6.10" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/13_Visual_Analysis.ipynb b/13_Visual_Analysis.ipynb index 49286db..bcefc5a 100644 --- a/13_Visual_Analysis.ipynb +++ b/13_Visual_Analysis.ipynb @@ -11,6 +11,15 @@ "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## WARNING!\n", + "\n", + "**This tutorial does not work with TensorFlow v.2 and it would take too much effort to update this tutorial to the new API.**" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -46,7 +55,6 @@ "cell_type": "code", "execution_count": 44, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -102,7 +110,6 @@ "cell_type": "code", "execution_count": 3, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -163,9 +170,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -219,9 +224,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "conv_names = get_conv_layer_names()" @@ -237,9 +240,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -266,9 +267,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -299,9 +298,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -339,9 +336,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "def optimize_image(conv_id=None, feature=0,\n", @@ -691,9 +686,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -860,9 +853,7 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -897,7 +888,6 @@ "cell_type": "code", "execution_count": 18, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -932,9 +922,7 @@ { "cell_type": "code", "execution_count": 19, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -967,9 +955,7 @@ { "cell_type": "code", "execution_count": 20, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1002,9 +988,7 @@ { "cell_type": "code", "execution_count": 21, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1037,9 +1021,7 @@ { "cell_type": "code", "execution_count": 22, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1072,9 +1054,7 @@ { "cell_type": "code", "execution_count": 23, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1107,9 +1087,7 @@ { "cell_type": "code", "execution_count": 24, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1142,9 +1120,7 @@ { "cell_type": "code", "execution_count": 25, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1177,9 +1153,7 @@ { "cell_type": "code", "execution_count": 26, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1212,9 +1186,7 @@ { "cell_type": "code", "execution_count": 27, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1247,9 +1219,7 @@ { "cell_type": "code", "execution_count": 28, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1282,9 +1252,7 @@ { "cell_type": "code", "execution_count": 29, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1317,9 +1285,7 @@ { "cell_type": "code", "execution_count": 30, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1352,9 +1318,7 @@ { "cell_type": "code", "execution_count": 31, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1387,9 +1351,7 @@ { "cell_type": "code", "execution_count": 32, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1422,9 +1384,7 @@ { "cell_type": "code", "execution_count": 33, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1457,9 +1417,7 @@ { "cell_type": "code", "execution_count": 34, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1493,7 +1451,6 @@ "cell_type": "code", "execution_count": 35, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1545,7 +1502,6 @@ "cell_type": "code", "execution_count": 36, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1591,9 +1547,7 @@ { "cell_type": "code", "execution_count": 37, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -2110,9 +2064,7 @@ { "cell_type": "code", "execution_count": 38, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -2209,7 +2161,7 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -2223,9 +2175,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.10" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/14_DeepDream.ipynb b/14_DeepDream.ipynb index 3f8f0fb..6f031d4 100644 --- a/14_DeepDream.ipynb +++ b/14_DeepDream.ipynb @@ -41,7 +41,6 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -80,9 +79,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -140,9 +137,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -181,9 +176,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import inception5h" @@ -217,9 +210,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -263,7 +254,6 @@ "cell_type": "code", "execution_count": 9, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -671,7 +661,8 @@ " # Calculate the value of the gradient.\n", " # This tells us how to change the image so as to\n", " # maximize the mean of the given layer-tensor.\n", - " grad = tiled_gradient(gradient=gradient, image=img)\n", + " grad = tiled_gradient(gradient=gradient, image=img,\n", + " tile_size=tile_size)\n", " \n", " # Blur the gradient with different amounts and add\n", " # them together. The blur amount is also increased\n", @@ -839,9 +830,7 @@ { "cell_type": "code", "execution_count": 21, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -870,7 +859,6 @@ "cell_type": "code", "execution_count": 22, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -900,9 +888,7 @@ { "cell_type": "code", "execution_count": 23, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1147,7 +1133,6 @@ "cell_type": "code", "execution_count": 25, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -1350,9 +1335,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "layer_tensor = model.layer_tensors[6]\n", @@ -1371,9 +1354,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "layer_tensor = model.layer_tensors[7][:,:,:,0:3]\n", @@ -1392,9 +1373,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "layer_tensor = model.layer_tensors[11][:,:,:,0]\n", @@ -1413,9 +1392,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "image = load_image(filename='images/giger.jpg')\n", @@ -1425,9 +1402,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "layer_tensor = model.layer_tensors[3]\n", @@ -1439,9 +1414,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "layer_tensor = model.layer_tensors[5]\n", @@ -1461,7 +1434,6 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [], @@ -1473,9 +1445,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "layer_tensor = model.layer_tensors[6]\n", @@ -1561,7 +1531,7 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -1575,9 +1545,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.6" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/16_Reinforcement_Learning.ipynb b/16_Reinforcement_Learning.ipynb index 505f087..fcffd32 100644 --- a/16_Reinforcement_Learning.ipynb +++ b/16_Reinforcement_Learning.ipynb @@ -2,10 +2,7 @@ "cells": [ { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "# TensorFlow Tutorial #16\n", "# Reinforcement Learning (Q-Learning)\n", @@ -16,10 +13,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Introduction\n", "\n", @@ -36,10 +30,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## The Problem\n", "\n", @@ -52,40 +43,28 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "![Illustration of the problem](images/16_problem.png)" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The problem is that there are 10 states between the ball going downwards and the paddle hitting the ball, and there are an additional 18 states before the reward is obtained when the ball hits the wall and smashes some bricks. How can we teach an agent to connect these three situations and generalize to similar situations? The answer is to use so-called Reinforcement Learning with a Neural Network, as shown in this tutorial." ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Q-Learning" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "One of the simplest ways of doing Reinforcement Learning is called Q-learning. Here we want to estimate so-called Q-values which are also called action-values, because they map a state of the game-environment to a numerical value for each possible action that the agent may take. The Q-values indicate which action is expected to result in the highest future reward, thus telling the agent which action to take.\n", "\n", @@ -108,10 +87,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Simple Example\n", "\n", @@ -122,20 +98,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "![Q-values Simple Example](images/16_q-values-simple.png)" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Detailed Example\n", "\n", @@ -144,26 +114,20 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "![Q-values Detailed Example](images/16_q-values-details.png)" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ - "The Q-values for the possible actions have been estimated by a Neural Network. For the action NOOP in state *t* the Q-value is estimated to be 2.900, which is the highest Q-value for that state so the agent takes that action, i.e. the agent does not do anything between state *t* and *t+1* because NOOP means \"No Operation\".\n", + "The Q-values for the possible actions have been estimated by a Neural Network. For the action NOOP in state $t$ the Q-value is estimated to be 2.900, which is the highest Q-value for that state so the agent takes that action, i.e. the agent does not do anything between state $t$ and $t+1$ because NOOP means \"No Operation\".\n", "\n", - "In state *t+1* the agent scores 4 points, but this is limited to 1 point in this implementation so as to stabilize the training. The maximum Q-value for state *t+1* is 1.830 for the action RIGHTFIRE. So if we select that action and continue to select the actions proposed by the Q-values estimated by the Neural Network, then the discounted sum of all the future rewards is expected to be 1.830.\n", + "In state $t+1$ the agent scores 4 points, but this is limited to 1 point in this implementation so as to stabilize the training. The maximum Q-value for state $t+1$ is 1.830 for the action RIGHTFIRE. So if we select that action and continue to select the actions proposed by the Q-values estimated by the Neural Network, then the discounted sum of all the future rewards is expected to be 1.830.\n", "\n", - "Now that we know the reward of taking the NOOP action from state *t* to *t+1*, we can update the Q-value to incorporate this new information. This uses the formula above:\n", + "Now that we know the reward of taking the NOOP action from state $t$ to $t+1$, we can update the Q-value to incorporate this new information. This uses the formula above:\n", "\n", "$$\n", " Q(state_{t},NOOP) \\leftarrow \\underbrace{r_{t}}_{\\rm reward} + \\underbrace{\\gamma}_{\\rm discount} \\cdot \\underbrace{\\max_{a}Q(state_{t+1}, a)}_{\\rm estimate~of~future~rewards} = 1.0 + 0.97 \\cdot 1.830 \\simeq 2.775\n", @@ -176,10 +140,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Motion Trace\n", "\n", @@ -192,20 +153,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "![Motion Trace](images/16_motion-trace.png)" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Training Stability\n", "\n", @@ -216,20 +171,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "![Training Stability](images/16_training_stability.png)" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "If we were to train a Neural Network to estimate the Q-values for the two states $t$ and $t+1$ with Q-values 0.97 and 1.0, respectively, then the Neural Network will most likely be unable to distinguish properly between the images of these two states. As a result the Neural Network will also estimate a Q-value near 1.0 for state $t+2$ because the images are so similar. But this is clearly wrong because the Q-values for state $t+2$ should be zero as we do not know anything about future rewards at this point, and that is what the Q-values are supposed to estimate.\n", "\n", @@ -238,10 +187,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Flowchart\n", "\n", @@ -256,20 +202,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "![Flowchart](images/16_flowchart.png)" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Neural Network Architecture\n", "\n", @@ -286,30 +226,21 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Installation" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The [documentation](https://github.com/openai/gym) for OpenAI Gym currently suggests that you need to build it in order to install it. But if you just want to install the Atari games, then you only need to install a single pip-package by typing the following commands in a terminal." ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "- conda create --name tf-gym --clone tf\n", "- source activate tf-gym\n", @@ -318,30 +249,31 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This assumes you already have an Anaconda environment named `tf` which has TensorFlow installed, it will then be cloned to another environment named `tf-gym` where OpenAI Gym is also installed. This allows you to easily switch between your normal TensorFlow environment and another one which also contains OpenAI Gym." ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "You can also have two environments named `tf-gpu` and `tf-gpu-gym` for the GPU versions of TensorFlow." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## TensorFlow 2\n", + "\n", + "This tutorial was developed using TensorFlow v.1 back in the year 2016-2017. There have been significant API changes in TensorFlow v.2. This tutorial uses TF2 in \"v.1 compatibility mode\". It would be too big a job for me to keep updating these tutorials every time Google's engineers update the TensorFlow API, so this tutorial may eventually stop working." + ] + }, { "cell_type": "markdown", "metadata": { "colab_type": "text", - "deletable": true, - "editable": true, "id": "xu2SVpFJjmJr" }, "source": [ @@ -351,39 +283,49 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", - "import tensorflow as tf\n", "import gym\n", "import numpy as np\n", "import math" ] }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /home/magnus/anaconda3/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/compat/v2_compat.py:88: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "non-resource variables are not supported in the long term\n" + ] + } + ], + "source": [ + "# Use TensorFlow v.2 with this old v.1 code.\n", + "# E.g. placeholder variables and sessions have changed in TF2.\n", + "import tensorflow.compat.v1 as tf\n", + "tf.disable_v2_behavior()" + ] + }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The main source-code for Reinforcement Learning is located in the following module:" ] }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 3, + "metadata": {}, "outputs": [], "source": [ "import reinforcement_learning as rl" @@ -391,30 +333,23 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This was developed using Python 3.6.0 (Anaconda) with package versions:" ] }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 4, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'1.1.0'" + "'2.1.0'" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -426,21 +361,18 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ - "'0.8.1'" + "'0.17.1'" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -452,10 +384,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Game Environment\n", "\n", @@ -464,12 +393,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 6, + "metadata": {}, "outputs": [], "source": [ "env_name = 'Breakout-v0'\n", @@ -478,22 +403,15 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This is the base-directory for the TensorFlow checkpoints as well as various log-files." ] }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 7, + "metadata": {}, "outputs": [], "source": [ "rl.checkpoint_base_dir = 'checkpoints_tutorial16/'" @@ -501,22 +419,15 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Once the base-dir has been set, you need to call this function to set all the paths that will be used. This will also create the checkpoint-dir if it does not already exist." ] }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 8, + "metadata": {}, "outputs": [], "source": [ "rl.update_paths(env_name=env_name)" @@ -524,62 +435,16 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Download Pre-Trained Model\n", "\n", - "You can download a TensorFlow checkpoint which holds all the pre-trained variables for the Neural Network. Two checkpoints are provided, one for Breakout and one for Space Invaders. They were both trained for about 150 hours on a laptop with 2.6 GHz CPU and a GTX 1070 GPU.\n", - "\n", - "#### COMPATIBILITY ISSUES\n", - "\n", - "These TensorFlow checkpoints were developed with OpenAI gym v. 0.8.1 and atari-py v. 0.0.19 which had unused / redundant actions as noted above. There appears to have been a change in the gym API since then, as the unused actions are no longer present. This means the vectors with actions and Q-values now only contain 4 elements instead of the 6 shown here. This also means that the TensorFlow checkpoints cannot be used with newer versions of gym and atari-py, so in order to use these pre-trained checkpoints you need to install the older versions of gym and atari-py - or you can just train a new model yourself so you get a new TensorFlow checkpoint.\n", - "\n", - "#### WARNING!\n", - "\n", - "These checkpoints are 280-360 MB each. They are currently hosted on the webserver I use for [www.hvass-labs.org](www.hvass-labs.org) because it is awkward to automatically download large files on Google Drive. To lower the traffic on my webserver, this line has been commented out, so you have to activate it manually. You are welcome to download it, I just don't want it to download automatically for everyone who only wants to run this Notebook briefly." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, - "scrolled": false - }, - "outputs": [], - "source": [ - "# rl.maybe_download_checkpoint(env_name=env_name)" + "The original version of this tutorial provided some TensorFlow checkpoints with pre-trained models for download. But due to changes in both TensorFlow and OpenAI Gym, these pre-trained models cannot be loaded anymore so they have been deleted from the web-server. You will therefore have to train your own model further below." ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, - "source": [ - "I believe the webserver is located in Denmark. If you are having problems downloading the files using the automatic function above, then you can try and download the files manually in a webbrowser or using `wget` or `curl`. Or you can download from Google Drive, where you will get an anti-virus warning that is awkward to bypass automatically:\n", - "\n", - "* [Download Breakout Checkpoint from Google Drive](https://drive.google.com/uc?export=download&id=0B2aDiIly76ZvUjZTcXRuRFY0RjQ)\n", - "\n", - "* [Download Space Invaders Checkpoint from Google Drive](https://drive.google.com/uc?export=download&id=0B2aDiIly76ZvWDR4TExwdmw1RVE)\n", - "\n", - "You can use the checksum to ensure the downloaded files are complete:\n", - "\n", - "* [SHA256 Checksum](http://www.hvass-labs.org/projects/tensorflow/tutorial16/sha256sum.txt)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Create Agent\n", "\n", @@ -590,39 +455,31 @@ "cell_type": "code", "execution_count": 9, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[2017-05-15 15:48:47,348] Making new env: Breakout-v0\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ + "WARNING:tensorflow:From /home/magnus/development/TensorFlow-Tutorials/reinforcement_learning.py:1189: conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use `tf.keras.layers.Conv2D` instead.\n", + "WARNING:tensorflow:From /home/magnus/anaconda3/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/layers/convolutional.py:424: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Please use `layer.__call__` method instead.\n", + "WARNING:tensorflow:From /home/magnus/development/TensorFlow-Tutorials/reinforcement_learning.py:1205: flatten (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use keras.layers.Flatten instead.\n", + "WARNING:tensorflow:From /home/magnus/development/TensorFlow-Tutorials/reinforcement_learning.py:1209: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use keras.layers.Dense instead.\n", + "WARNING:tensorflow:From /home/magnus/anaconda3/envs/tf2/lib/python3.6/site-packages/tensorflow_core/python/training/rmsprop.py:119: calling Ones.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Call initializer instance with the dtype argument instead of passing it to the constructor\n", "Trying to restore last checkpoint ...\n", - "INFO:tensorflow:Restoring parameters from checkpoints_tutorial16/Breakout-v0/checkpoint-127639066\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[2017-05-15 15:48:47,868] Restoring parameters from checkpoints_tutorial16/Breakout-v0/checkpoint-127639066\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Restored checkpoint from: checkpoints_tutorial16/Breakout-v0/checkpoint-127639066\n" + "INFO:tensorflow:Restoring parameters from checkpoints_tutorial16/Breakout-v0/checkpoint-1175644\n", + "Restored checkpoint from: checkpoints_tutorial16/Breakout-v0/checkpoint-1175644\n" ] } ], @@ -635,10 +492,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The Neural Network is automatically instantiated by the Agent-class. We will create a direct reference for convenience." ] @@ -646,11 +500,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "model = agent.model" @@ -658,10 +508,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Similarly, the Agent-class also allocates the replay-memory when `training==True`. The replay-memory will require more than 3 GB of RAM, so it should only be allocated when needed. We will need the replay-memory in this Notebook to record the states and Q-values we observe, so they can be plotted further below." ] @@ -669,11 +516,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "replay_memory = agent.replay_memory" @@ -681,10 +524,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Training\n", "\n", @@ -695,9 +535,6 @@ "cell_type": "code", "execution_count": 12, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -705,7 +542,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "87584:127639721\t Epsilon: 0.10\t Reward: 12.0\t Episode Mean: 12.0\n" + "2388:1176704\t Epsilon: 0.10\t Reward: 26.0\t Episode Mean: 26.0\n" ] } ], @@ -715,10 +552,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "In training-mode, this function will output a line for each episode. The first counter is for the number of episodes that have been processed. The second counter is for the number of states that have been processed. These two counters are stored in the TensorFlow checkpoint along with the weights of the Neural Network, so you can restart the training e.g. if you only have one computer and need to train during the night.\n", "\n", @@ -726,26 +560,20 @@ "\n", "```\n", "source activate tf-gpu-gym # Activate your Python environment with TF and Gym.\n", - "python reinforcement-learning.py --env Breakout-v0 --training\n", + "python reinforcement_learning.py --env Breakout-v0 --training\n", "```" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Training Progress" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Data is being logged during training so we can plot the progress afterwards. The reward for each episode and a running mean of the last 30 episodes are logged to file. Basic statistics for the Q-values in the replay-memory are also logged to file before each optimization run.\n", "\n", @@ -757,11 +585,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "log_q_values = rl.LogQValues()\n", @@ -770,10 +594,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We can now read the logs from file:" ] @@ -782,9 +603,6 @@ "cell_type": "code", "execution_count": 14, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [], @@ -795,10 +613,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Training Progress: Reward\n", "\n", @@ -808,20 +623,18 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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A5L8TESYl7VxAhySuOZDGpDAkUwqnXR7+QEX0cPkFXszNRq4Po2kOu5iPUdp4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\n3SjZ08d/0AAAAABJRU5ErkJggg==\n", 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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -835,10 +648,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Training Progress: Q-Values\n", "\n", @@ -853,20 +663,19 @@ "cell_type": "code", "execution_count": 16, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ { "data": { - "image/png": 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eDkUppRqVXyf3owWlAMS2DPVyJEop1bj8Orln5pUAmtyVUs2PXyf3rMrkHqnJ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\nF5F+xpjjXo3Kc1OB2caYZ0X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UsN/SIKCUqlAfrErm4U82MqRjE94cF0WdWlrN+DPdOkqpCjP9+x08/nkc53Vtxqs3DSAkOMjXSVInoEFAKVUhXl6ayH8XxHNxr5a8OKaf9gNURWgQUEqdsue+iWfKkkRGRbbm2ev6UjNIA0BVoUFAKXVKXl6ayJQliVwf1Zb/XN2HoBraEVxVokFAKfWbzfh+B/9dEM9V/drw5NV9qKEBoMop1zGbiIwUkXgRSRSRB8r4/RwRWSciRSJy7XG/FYtIjPvMr6iEK6V8a9bqZB77PI4RPVvw32s1AFRVJzwSEJEg4GXgQiAVWCMi840xcV6jJQPjgfvLmEWuMSayAtKqlPITn8bs4sF5Gzk3ohlTxvbTawBVWHlOBw0CEo0x2wFEZBYwCjgaBIwxSe63ktOQRqWUH1mweS/3ztnAoPDGvHbTAGrX1NtAq7LyhO82QIrX91Q3rLxCRCRaRFaKyJVljSAik9w40RkZGScxa6VUZfp2WwZ/+mA9vds0YPr4gYTW0gBQ1VXGMdwZxpgo4AbgBRHpdPwIxphpxpgoY0xUs2b6kgml/NGq7Zn88b1oOjWvxzsTBlGvtt5XUh2UJwjsArzf/9bWDSsXY8wu93c7sAzodxLpU0r5gZiUg9z8ju0O+r2bB9GgjvYFVF2UJwisAbqISAcRqQWMAcp1l4+INBKR2u7/psBZeF1LUEr5vy17shg3YzWN6gYz85YztTfQauaEQcAYUwTcBSwAtgBzjDGbReQxEbkCQEQGikgqcB3wuohsdpN3B6JFZAOwFHjyuLuKlFJ+7KeMw/x++ipCg4P44JYzadlAXwhT3YgxxtdpOEZUVJSJjo72dTKUCngp+3O4/vUfKSwuYfYfh9CpWT1fJ0n9ChFZ666/nhS9sqOU+pm9h/K48c1V5BQUM2vSmRoAqjF9wkMpdYx9h/O58c2V7D9SwLsTB9G9VZivk6ROIw0CSqmjDuUU8vvpq9l1MJfp46Lo266hr5OkTjMNAkopAA7nFzHurdX8lH6Y138fxeCOTXydJFUJ9JqAUorcgmJufnsNG3cd4pUb+3NuhD60GSj0SECpAJdfVMxt769lddJ+nru+LyN6tvR1klQl0iCgVAArKi7h7g9j+HZbBv+5qjejIk+mWzBVHWgQUCpAlZQY/jI3lq837+Wfl/VgzKD2vk6S8gENAkoFIGMMf/90E/PW7+IvI7oycVgHXydJ+YheGFYqwJSUGJ74cgsfrErmjuGduPO8zr5OkvIhDQJKBQhjDIu2pPPMgnji07IZPzScv4zo6utkKR/TIKBUAFiRuI+nF8QTk3KQDk3rMnVsPy7r0woRfS9woNMgoFQ1tj75AM98E88PiZm0bhDCU9f05pr+bfWdwOooDQJKVUPxe7N59pt4volLo0ndWvzzsh7cMLg9IcH6Okh1LA0CSlUjOzOP8MKiBD6J2UW9WjW578IIJgzroK+CVL9IS4ZS1UBRcQkvLU3kpSWJ1AwSJp3TkdvP7UTDOrV8nTTl5zQIKFXF7dh3hHtmx7Ah5SCjIlvz0CXdaRGmbwBT5aNBQKkqyhjDh6tTePzzOIKDhKlj+3F539a+TpaqYjQIKFUFZWTn88DHsSzems5ZnZvwzHV9adUg1NfJUlWQBgGlqpiFcWk88HEs2flF/POyHowfGk6NGnq/v/ptNAgoVUUcyS/i8c/jmLUmhR6twvhwTCQRLer7OlmqitMgoFQVsHbnAe6dE0Py/hxuO7cT914YQa2a+sCXOnUaBJTyY0fyi5iyOIE3lm+nVYNQZt16pr72UVUoDQJK+SFjDAs2p/HYZ5vZfSiP0VHtePiy7oSFBPs6aaqa0SCglJ9Jzszh0c82s2RrOt1a1mfK2H5EhTf2dbJUNaVBQCk/kV9UzBvfbWfqkkRq1hD+fml3xg0NJ1g7e1OnkQYBpfzAisR9/P3TTWzPOMIlvVvyj8t66H3/qlJoEFDKh9Kz83jiiy18GrOb9o3r8NaEgZzXtbmvk6UCiAYBpXwgr7CY937cyZTFCeQXlfDn87twx/BO2tWzqnQaBJSqRIXFJcxdm8qLixLYm5XHORHNePTyHnRsVs/XSVMBSoOAUpWgpMTwWexunl+4jaTMHPq1b8hzo/sytFNTXydNBTgNAkqdRsYYFm9J55lv4tm6N5tuLeszfVwUv+vWXN/vq/yCBgGlTpMff8rkvwu2si75IOFN6vDimEgu79NaO3tTfkWDgFIVbNOuQzz19VaWJ+yjZVgI/7m6N9cOaKv3+yu/pEFAqQpyKLeQZxbE8/6qnTSqU4u/X9qdm848Q+/4UX5Ng4BSp8gYwycxu3jiiy3sP1LAuCHh3HtRhPbzo6qEch2fishIEYkXkUQReaCM388RkXUiUiQi1x732zgRSXCfcRWVcKX8QWJ6Nje8sYrJszfQplEd5t81jEev6KkBQFUZJzwSEJEg4GXgQiAVWCMi840xcV6jJQPjgfuPm7Yx8AgQBRhgrZv2QMUkXynfyC0oZuoS28VzaHAQT1zVizED2xOkF31VFVOe00GDgERjzHYAEZkFjAKOBgFjTJL7reS4aUcAC40x+93vC4GRwIennHKlfGTxljQemb+Z1AO5XNO/LQ9e0o2m9Wr7OllK/SblCQJtgBSv76nA4HLOv6xp2xw/kohMAiYBtG/fvpyzVqpy7TqYy6PzN7MwLo0uzesxe5K+4EVVfX5xYdgYMw2YBhAVFWV8nByljrH/SAGvLkvknR93EiTCAxd34+ZhHfSWT1UtlCcI7ALaeX1v64aVxy5g+HHTLivntEr5VHZeIW8u38Gby7eTW1jM1f3bMvnCCNo01C6eVfVRniCwBugiIh2wlfoY4IZyzn8B8G8RaeS+XwQ8eNKpVKoS5RUW8+6PSby67CcO5BRySe+W3HthBJ2b1/d10pSqcCcMAsaYIhG5C1uhBwEzjDGbReQxINoYM19EBgLzgEbA5SLyL2NMT2PMfhF5HBtIAB7zXCRWyt8UFpcwe00KU5ckkJaVzzkRzfjLRV3p3baBr5Om1GkjxvjXKfioqCgTHR3t62SoAFJcYvhsw26eW7iN5P05DDijEX8Z0ZUz9aKvqkJEZK0xJupkp/OLC8NK+UrK/hz++N5a4vZk0b1VGDPGR3FeV+3hUwUODQIqYG1IOcjN76yhsNhoD58qYGkQUAFpYVwaf/5wPU3q1WLWpEF0bq5v9lKBSYOACjjv/pjEo/M306tNA6aPG0iz+vq0rwpcGgRUwCgpMTz59VamfbedC7o3Z8rYftSppbuACmy6B6iAkFdYzH1zNvDFxj38/swzePSKntrZm1JoEFAB4MCRAm59N5ronQd46JJu3Hp2R737RylHg4Cq1nZmHmHCW2tIPZjLSzf047I+rX2dJKX8igYBVW2tTz7ALe9EU2wMH9wymKjwxr5OklJ+R4OAqlYKikr4dlsG8zfsZsHmvbQMC+HtCQPp2ExvAVWqLBoEVJVXXGJYtSOT+TG7+WrTXg7lFtKoTjDXR7Xlngsi9IUvSv0KDQKqSjLGEJt6iPkbdvPZht2kZ+dTp1YQI3q25Iq+rRnWpan2969UOWgQUFVKdl4hbyzfwfyYXSRl5lArqAbDuzbjisjWnN+tBaG1gnydRKWqFA0CqsrYfTCXiW+vYVtaNkM6NeGO4Z0Z0aslDUKDfZ00paosDQKqSti8+xAT315DTn4x7908mLM6N/V1kpSqFjQIKL/37bYM7nh/LWGhwXx0+xC6tQzzdZKUqjY0CCi/NntNMg/N20REi/q8NX4gLRuE+DpJSlUrGgSUXzLG8NzCbUxdksg5Ec145cb+1KutxVWpiqZ7lfI7BUUlPPBxLP9bv4sxA9vx+JW99HZPpU4TDQLKrxzKLeS299by4/ZM7r8ogjvP66ydvSl1GmkQUH5j18FcJry1mh37jvD86L5c1a+tr5OkVLWnQUD5hfXJB/jje2vJLSzmnYmDGNpJbwFVqjJoEFA+VVhcwktLEnlpaSItw0J4/5bBRLSo7+tkKRUwNAgon9mx7wj3zI5hQ8pBrurXhn+N6klYiD79q1Rl0iCgKp0xhg9WJ/N/n2+hVs0a+rIXpXxIg4CqVBnZ+TzwcSyLt6YzrHNTnrmurz4AppQPaRBQlWZhXBoPfBxLdn4Rj1zeg3FDwqmhL3tXyqc0CKjT7kh+EY99Fsfs6BR6tApj1phIuujFX6X8ggYBdVrkFhSTkJ7Nlj1ZvLLsJ5L353D78E5MviCCWjX16V+l/IUGAXVKiopLSMrMIX5vNvFp2cTvzSJ+bzY79+dgjB2nXeNQZk8awqAO+qJ3pfyNBgF10vYfKeD1737i+4R9JKQfpqCoBIAaAuFN6tK9VRhX9mtDt5b16doyjPaN6xCk5/6V8ksaBFS5ZecV8ubyHby5fDu5hcUM7dSUcUPOoGvLMLq1rE/n5vUICdbXOypVlWgQUCeUV1jMuz8m8cqynziYU8jFvVpy30URdG6uF3eVquo0CKhfVFhcwuw1KUxdkkBaVj7nRDTj/osi6NO2oa+TppSqIBoE1M8Ulxg+27Cb5xZuI3l/DgPOaMSLY/pxZscmvk6aUqqClSsIiMhI4EUgCHjTGPPkcb/XBt4FBgCZwGhjTJKIhANbgHg36kpjzG0Vk3R1OiyLT+c/X24lPi2b7q3CeGv8QIZ3baZ9+itVTZ0wCIhIEPAycCGQCqwRkfnGmDiv0W4GDhhjOovIGOApYLT77SdjTGQFp1udBh+sSubhTzYS3qQuU8f249LerfSJXqWqufIcCQwCEo0x2wFEZBYwCvAOAqOAR93/c4GXRJuOVcr073fw+Odx/K5bc165sb/e5aNUgCjPo5ttgBSv76luWJnjGGOKgEOA5wRyBxFZLyLfisjZZS1ARCaJSLSIRGdkZJzUCqhT9/LSRB7/PI6Le7XktZsGaABQKoCc7uf39wDtjTH9gHuBD0Qk7PiRjDHTjDFRxpioZs2aneYkKQ9jDM8siOe/C+K5MrI1U8f20y4dlAow5dnjdwHtvL63dcPKHEdEagINgExjTL4xJhPAGLMW+AmIONVEq1NnjOGJL7bw0tJExgxsx7PXR1IzSAOAUoGmPHv9GqCLiHQQkVrAGGD+cePMB8a5/68FlhhjjIg0cxeWEZGOQBdge8UkXf1WJSWGv3+yiTe/38H4oeH8+6re2q2DUgHqhBeGjTFFInIXsAB7i+gMY8xmEXkMiDbGzAemA++JSCKwHxsoAM4BHhORQqAEuM0Ys/90rIgqn+ISw1/nxvLxulRuO7cTfxvZVW//VCqAifF09egnoqKiTHR0tK+TUS0VFpcweXYMn8fuYfIFEfz5/M4aAJSqJkRkrTEm6mSn0yeGA0R+UTF3zlzPoi1pPHhxN/54bidfJ0kp5Qc0CFRjGdn5xO/NZuveLBZs3suapAM8NqonfxgS7uukKaX8hAaBauBIfhHb0rJdhW//bkvLJvNIwdFxmtarxX+v7cN1Ue1+ZU5KqUCjQaCKMsbwacxupixOYPu+I0eHhwYHEdGyPud3b360n/+IFvVpVr+2D1OrlPJXGgSqoMT0bP7xyWZ+3J5J7zYNuO/CCLq2rE/XlvVp16iO9vejlCo3DQJVSG5BMVOXJPDG8u2EBgfxxFW9GDOwvd7jr5T6zTQIVBGLt6TxyPzNpB7I5Zr+bXnwkm40raeneJRSp0aDgJ/bdTCXR+dvZmFcGl2a12P2pDMZrC93UUpVEA0CfqqgqITp3+9gyuIEAB64uBs3D+tAsPbvo5SqQBoE/ExRcQlfb97Li4sSSEg/zEU9WvDPy3vQtlEdXydNKVUNaRDwEwdzCpi1JoV3VySx+1Ae4U3qMH1cFOd3b+HrpCmlqjENAj6WmH6Yt1fs4OO1u8gtLGZIxyb8a1Qvftetud71o5Q67TQI+IAxhu8S9jHj+x18uy2DWkE1GBXZmglndaBH65+9c0cppU4bDQKVKK+wmI/XpfLWD0kkph+mab3a3HthBDcMbq+3eyqlfEKDQCUoLC5hTnQKUxYnkJaVT8/WYTx3fV8u7dOK2jX1fb5KKd/RIHAaFZcYPtuwm+cWbiN5fw4DzmjE86MjGdKxifbjr5TyCxoETgNjDAvj0nj2m2syM+kAABywSURBVG3Ep2XTvVUYM8ZHcV7X5lr5K6X8igaBCrYicR9PL4gnJuUgHZrWZerYflzau5V26qaU8ksaBCrI+uQDPPNNPD8kZtKqQQhPXdOba/q3paY+4auU8mMaBE7BoZxCvtq0h09jdvPj9kya1K3FPy7rwY2D2xMSrBd8lVL+T4PAScotKGbRljQ+jdnNt9vSKSw2dGhal7+M6Mq4oeHUq61ZqpSqOrTGKofC4hKWJ2QwP2Y338SlkVNQTIuw2owbEs6oyDb0ahOmF3yVUlWSBoFfYIxhXfJBPl6Xylcb93Agp5AGocGMimzDFX1bM6hDY+3WQSlV5WkQOE5BUQlfbtzDWz/sYEPqIUKDg7ioZwuu6Nuas7s0o1ZNvdCrlKo+NAg4mYfz+XB1Mu/+uJP07Hw6NqvL41f24up+bair5/mVUtVUwNduW/dm8db3ScyL2UVBUQnnRDTj6WvDOadLM723XylV7QVkECgpMSyNT2fGDzv4ITGTkOAaXDugLROGhtOlRX1fJ08ppSpNwAWBTbsOce+cGLalHaZVgxD+NrIbYwe1o2GdWr5OmlJKVbqACQLFJYbXvv2J5xduo2m92rw4JpJLerfSd/YqpQJaQASB5MwcJs+JYe3OA1zWpxX/d2UvbfkrpRTVPAgYY/hobSr/mr+ZGjWEF0ZHMiqytT7YpZRSTrUNAvuPFPDg/2JZsDmNMzs25tnrI2nTMNTXyVJKKb9SLYPA0q3p/GVuLFm5hTx0STduGdZRb/dUSqkyVKsgkFtQzBNfxvH+ymS6tqjPezcPonsrfXG7Ukr9kmoTBFL25zBuxmq27zvCLcM6cP+Irtqds1JKnUC57o8UkZEiEi8iiSLyQBm/1xaR2e73VSIS7vXbg254vIiMqLikH6t5WG06NK3LB7cM5u+X9dAAoJRS5XDCIwERCQJeBi4EUoE1IjLfGBPnNdrNwAFjTGcRGQM8BYwWkR7AGKAn0BpYJCIRxpjiil6R2jWDmD5+YEXPVimlqrXyHAkMAhKNMduNMQXALGDUceOMAt5x/88Fzhd7H+YoYJYxJt8YswNIdPNTSinlB8oTBNoAKV7fU92wMscxxhQBh4Am5ZwWEZkkItEiEp2RkVH+1CullDolftFngjFmmjEmyhgT1axZM18nRymlAkZ5gsAuoJ3X97ZuWJnjiEhNoAGQWc5plVJK+Uh5gsAaoIuIdBCRWtgLvfOPG2c+MM79fy2wxBhj3PAx7u6hDkAXYHXFJF0ppdSpOuHdQcaYIhG5C1gABAEzjDGbReQxINoYMx+YDrwnIonAfmygwI03B4gDioA7T8edQUoppX4bsQ12/xEVFWWio6N9nQyllKpSRGStMSbqZKfziwvDSimlfMPvjgREJAPYeQqzaArsC6DxfLlsfx/Pl8v29/F8uWx/H8+Xyz6ZNB7vDGPMyd9eaYypVh/sdYqAGa8qpFHzxv/Gqwpp1LypnI+eDlJKqQCmQUAppQJYdQwC0wJsPF8u29/H8+Wy/X08Xy7b38fz5bJPJo0Vwu8uDCullKo81fFIQCmlVDlpEFBKqQDml6+XFJF2wLtAC8AA04wxL4pIY2A2EA4kAdcDWUAyUA/Y4X5rAGwFfgT+CJS431oBxe57CpDrFtkRqI9930GYG6cFcATbVUawG68GIG54CpAD9AJCgANAI2ATtrvsRm6afdjnHr4CrgK6unnmA7WBQmC3m1e4S1uWW1ZD9399INTNLwnbVXewS3eRG34Ie49xLZdnRUCGy4uaLo27XXpCgMZuvp7xawDb3LCaQDOXxiJKy8lhN21t99tOt8wGLj3iPmnAXmAzcD7QHFjp8raTW6a4ee/FdiyY75aR4vKnrZuncXlS7D413Lh1gDw3fX23vYqBg+6vZz2+d8MGAy3d9MYr/ze6NNUDNrhlFgB93Xg52G3bzA0PwnaC6FkmXuvyk5tPXez234YtY53cPNq58Ypc2msC27HbPcvNp5lXfidjt2snt9xo7Ds6ZgIXuWFg+/cKcfkUCkS4tNTCduke6vKgEbYMNPTaBsnYMtja5am49V6FLat1sds91K1DDZe/bd2yC10aG7h1DcFu/xZuGUVu+lxK94kabrrt2PLTyg3PB9Ziu5650Gtb/eTS18BrmcFuvjXd903ub1ev5dQC1rvlXErpfrzNpaulW76njBlsHRDstklN7Dav4T4Zbr3y3bhBbj4lXuPUcNMJtszXcd/3AO2x/ad58mubm39nN68sl9873Pa63a3Po5SWqXTgTWPMkyIShu2S5xNjzF14EZH5QEdjTC9OwF+PBIqA+4wxPYAzgTvdW8oeABYbY7oAi933u4FvgeXGmEjgBWAp0ANbcG9x49QE4o0xIcBoINuNfzl2ox/BVtI5wF3YSrsImGKMqQU8B3zi0ncvtpIoBK5wy1+NfflOb2zfSfuwFdRsYx/l/q/bIE9gO9Z7B0gA7sfuBDHu/z9gd8x8Y0wocA12xz3ohpdgC3YEcLWbXy6219bbgJeAJ7EFtgZ2R4l0aRxjjOkLXODS9in2JUBHgBRjTFeXjixsRVeC3XlaYgtfI7e8Nm6912Mr0lddWna5/Nvp0tIcW9BzgN5unb/CBueRlFaaa9y6/g5b4b0PnOPy+s/AOuBh4H/YnWoVsMVtpxxspTYfu0NkY3eYm40xtbGVVQ7QDXgQ+BgbGLJdem7BBp5CVx7GubR+BixzedME6I+tbD9z6f8Eu6O/h624s4B4bOW8GlvJ3+jmuR9bVjLdp7/bZvEuTVe6da/j5rMYW2FswVZG/wYuc3k1w63LIeA+bKW1wBjTB1uWPZXZaJdP2cCbbrkLseXiWmCJW/9k4HHsvvI0MMzle2dsI8MTBJ4BvnTTZ7u/B7GV2nq3zn9ywzKx5f95t34LsY2yw8aYULddpgGfY/exbLedU1x+jwT6u/3uH9gylOE+r7jtXAj0duXmNWxliVvuB9h3l+QBK7Bl9WKXx89jg3FNt9xMbJ1RDExx2/cHYI77vR2w3Cu/YoERbrkfu/QeAv4GLMLWC+e4bbDS7W+93DgF2PL/FDYwDcHu+weBEdjg0t3Ve4+75T0InO3y6XxsvTbW1YePA99xHBG52m2zcvHLIGCM2WOMWef+z8buDG049g1m72AL86XYQu4RAXxo7BXv1UB3bCEQbCsabCXgaX28hm0xZLrvnpaFYFt7S93wl4Gz3PA5xr485yugn/u9M7YCxRjzDaWtBc86ZXmlsRa2VVwH2yLIwm60MGAsthDvduOeja3Af8IWykRsYQbbmn/HzWOPMeZtbCX6Nbaw1nfLOOjG9xz5ZLr1bITdsYuB713L4gJsICoCSowx3wI3YHcKAXYaY9JdvlyFDQKvGGM+xe4sniMnAc5109bAVgIXYHfWj7DbLhdbgce6dF3o8i0H25oscfMKxm6zUS7dJW49P6H0aOhjbCXRBFtxzRWRBi4PmrvljMQGrCA3fDbwX5d/hS4NN2G32/sufWku/Y3cd+O2Tbpbx49dGmKxFehWbGeL3kfZjdx2ywaSjTFbsBVUJnCZMWYpcKfbDruxAXkXcAa2rMS4/O6CrSxqYVuW61x6DrnlPI89sinBBp4o4EW3HTZSWqZaYN/w94Mbdja2gorFBhrc9xA3XrxLbzG2ot7vxkkzxux0+RPptkcNN88Sl6c1sGUw3w3DvXUw1KW9nsuvIy7Perrp6rtlBLnpjmCDmueotQQwbl5nuOF5bpyR2AoyD1tGawJbjDHfYctoXew+dxC7z81wy3kHG5CnYytbsEc/R9y4GV7fcesb7JaTDSS5betpQHmOXGpiy/0qbLkZiH1D4wjsfl3ipv8cGxjAHkV0wtY7EdgG5hpT+nbH27Hb8Ru8iEg9bCP1/yivyn467WQ/2JZQsttYB72GC7agDgCGuwysgy2gjd3GWYdtzY3FBpIUbKtlFfA2tlJZ4aY5jN0JfsTu7CXYymUttmDeiy3IxdiKpo4bdyo2Gue5dMZiC1WqS186MMml+QlKWwRp2B3zfWxlsMItt8j93Yit/Oe4v//EHvJOp7SFk+zWyWArnxg37nRsYY1xvyVgC/BlbthhN21b7NFHCfZ0yfNAgktrErblBralFO3GS8UGiavdvDPdOEFuGcat23bP9nLL24OtTGZ5refXbvw47I5lsDtQHWzFatwnGXskYLA744/AXDfvg9ijmvOwp588p41iXd5ucHlxALtjeU5/7APucdsv2i0/1q3jZ26dj7i0J7nhOS6tYW67GEqPwg5gj4BaYBsHnm2y1o2z061zFjZY5LlPFra8LHfr4Sl3X2PLtMEeDV7ttbwYt7wEbHl6iNKWa7JL83BsI2iVy49VLj/udunNcXl9BqWnzJ53afGcMo1z2y0HW/Z6Y/ejeS6NmW5dn3PjpLjhm7z+z3NpzHXz2u/+X+62s+eUWR52vyjGBmfPtInYYFaArdj2UFrGst2n0OXjMjfeXuwRcR6ljbEUl1ZPpfu+yyuDLZcGGzgOYgOn56i7CBvUXsHuK8uwgTHG/bbIre94bONmM6Wnv27BBriDLr01sfXOXuyRkGf4D9j9fQ92u4tbVhL26Gwrth4Z6cr8H9y2a+uW+5JXvfg8NvCFA5uq/BPDLqp9DNxzXEsa7BFAiTFmrdewy4EfjDH7sRstBVuQE7DRdCe2Mu+CLTwPYVsVntb4VdiMfQMbSJa5v2uxLRPBbrhvsBsrhtKgsB7ogG0R7XHf07EVyp0ico4x5mGXjkfdcldjK99Q7OmM/7j5LcNWqn/FHRpjW+xQej43AZhsjGmH3QFTsS06gw16dbGFaC1wHXbne8SdnvgXdmdp5fLjCLYQTgAeK2NTdHRp+Jtb52nYAJqPPX+JsV2ET3d5nojdQeq6owtcXrfDHjGlumWeid2BZxpj6mJbPY2wp9N2YSvoOJe/D2F3xq4unzeLyO9dPu7EHtF5lrUT29pqiD0UzwM+xFaEy9x88rFHI8HuN+OWEY9tBX6LbX2vcXl5LrYS+R5bUQ5y6x6BvX5VE3va7HM3j/3A7yltqUdid/hQN588bBlagi0vXbAB5CVsi/RCbOPDYI9WrnHTFLl12g/ciq2oLsJW5m2xlWswtvz0d2nfit3evbGtz50ubZOxp7MisY2MxtgKRrBl9wZsZZmFvQ4xC1tB73Pp+KvbRn8CXnZl8U73exI2SLzl8vFVbAXZFFtRNcUGpHBs+boTWy6K3fqcRelR5D9cei5zebnTreMm7DZf5PJ1s5tHqFu/YErPx6e69Wzu8uwDt54l2P1wB7Z818PWDUdcvtXDNmhuolQJtsL/2uVvbTc8zRjTE7t9g4A4Y8wI7Om9OGxDpSn2VO8kbFDbAKxyp5HfxJbtfwM3Y8tRF+ARbDl5Q0QaYk8LJRljUr3ShIhEAp2MMfM4Gb5u6f/KEUAwNore6zUsHmjl/p+CrUCSsJE1Bxu9b3CZ9gm2Uk3FVkzF2Oicja0kF2NbrsWUXqzLwJ6//MIr4r7ilnGRW95mr/T8G7jDTffwcUcv8W7ZL2Er/fu9fvec/wt306ZjC3A7N82j2ILX3KXXc/FwK7Zw7qX0QmIrt6wsl2cJbj0jKD0FFoat/Dwt+5nYYFboPnnYnb0Eu4MlUXrB6wJsBbLcK/3TsefOS4BlXsOnYVuKnorDc+rEcwSxwI03CdvC2evyv50bPtat3zfu++tu2D9dHqRhdyLPee692IomCVvxlGBbyPuwO//Vbt22u+Xvcd9L3Ced0lMcnlMoh9y2OYAtUxnYxgbY8jDH/X4WtnwOcdsm3aVd3PolYwPHPrfM3ZSeNngBeyT3iMuHcDfPXK+8jMYGnFzsOeZ7XFoL3bYpwAbkFLfN010+pLt895R7T8s7y2ubZmFP9XhugvjG5fMo93++S2+YW79C7DWKApcvh9280ikNdG28jtBzsGVkGbYR5NmPPBeCw93/ycBWN9147L53wCu/xaU1E3jIDZuHDdBbgUNu2G2UHhmlu22WRekNBZ51+Q+l295TZxzC7kNJ2CPFJOy+/jmuJe3Sn48th8uw++eP2COZ57Dlajx2X/fUPQeA37vpd3ilqcDl35Uuz2d6Led1bPk6hN1/X8M2zIZgy9pibOMmBrv/JmHLVxb2OuDtbl2TKD0Tscy7Xq0yRwLuPN907Hm857x+8n6D2W7geWNMOLbl+B02ajfEVrJjjTEPGmPauhbKNmxhHYFtTa7DtjDex27QvcCN2As7qS4Nl2ILSg1sC3mD+x8RaY+tZD53y9zklc7R2MNOsJXRRbjzqCLSCnuo9xP2yGMTtjClYC9eiUtfiJtvTfdpgt15sl2a87CVzARKj3ZmY1uELxtjtmEvHH7njqJaUNo76y3YQv0ltkDOwVY47xljznB5moqtPP6HbWkViEgLEamLPXoZ7dLcXkQGiEgzbMDIcGmY7NL4D5eHi4DFrrVyh1teU2zFPtalawe2FbdTRPphj4K+w1ZOLbEt5OuxlcLZ2ILfHVtJ5mN3iMewLc9r3fAibEt9OHYnW4zdOfZgd+7vsTtZjjGmsVumYFu5q7FlplhEumN33AuwZWcPdscc5/IiFHvU1RDbMt6LDQrnum0/EFuxJ7t1Dnbb/01s8MwCskSkrytbXdywfcBQ7MXnZdhW9Drsjn4P7hSoMaa5225PuDRf4bZ3OvbIZSWu4sQGoBJKj4peBCZiy/8qt913YMtMHWxFOgFboSe7cVa67eg5WuniysalbrlDsOV4L7blaty8Mt16F7g8qy0ivd32b+vWy4hIhMu7TDfdQjfsPDdeC5cWz3Q1sQ2QXthA9qxL93Ig1e0D32Arz93YfeAH7FH/S9hGWHfgC+wR4XI4+rrc31N6OrAZtsK/wq3TMLcuTV1ejMCWHc91ENy2XII9pVmIvUnkE2z5GQmkiEgj7P5/CfC6238/wZbbNS4vu2P3OQHOctv7fuBdY8wDxphXjTGt3fBhwDZjzHBOxNct/l84ChjmMjgWG/ViXOY0we7ECdhKpbEbf7gbZxalt+p5pvunG2e525C52B1hPXYnGIk9TMx3BcFz/i3PbfRcbGE7gt2ZPRe8UrAtg/0ce5QRiy34xuuTgy2Am3AtHZfGVGyF4DkayceeBtmP3RliXFo3UXrXR5GbLseN7zl09VwbKPH65LvhBZSe3/QcWXhuvcvHVtyxlJ5zfNqN60l/odc8Cl3+bMPenXMJpefMvdOYgN3RVrvhyW65BZS2Zve6vM9y+ZdL6bUbzziedB/CnrI6gq1sC13+pLrfi73yI9flTza2vKzFVtpZlLaMh2HPz96GPfVSjA3ysdjTWYVumOfceZ5bdjE26B6ktKXpqSASXNqzvfIqG1uhxnltk8OU3tq8hdLrNp7y5Tn3vxlboXparIuxp2wuccv1LmOec/Gfue2ZiK3sPHlT6NKf5Ja10y0rntJrDMal7ztsBXXIa/0910YedfM54L5nYY/K17p1OYItV97pK8YGM882zsKeMtmNPTXpGdeTPwle+b0Vu68ud3lZ7OYf79Lkua6yhtJrS9mU3t78tUuX53uOy4sD2LKYT2lZy3PLjuXY8l9C6RGk9zp5jsjMcb95j5PttstUbDDMwAbQTW45sS4N+S7teZTWXdHYI404l9d73Lp7n3UYj9c1gePORpTrmoB2G6GUUgHML08HKaWUqhwaBJRSKoBpEFBKqQCmQUAppQKYBgGllApgGgSqIRF5WEQ2i0isiMSIyGA3/B4RqVOO6cs1XhnTRYjIlyKSICLrRGSOiLQ48ZQntYwrXedZZf3WTERWich6ETn7FJZxr4hsFZGNIrJBRJ4TkeATT1kxRCTJLTvGfab8xvlcISIPVHT6KpKIhIvIDb5ORyDTIFDNiMgQ7OP1/Y3tWfIC7L3rUPpw0YmUdzzv5YZgH7R51RjTxRjTH/uUaLOTmU85XElp517HOx/YaIzpZ4xZXp6ZiUjQcd9vwz7cd6axj/IPxD4XEVrG5KfTecaYSPf582+ZgTFmvjHmyeOHuweg/EU49il/5Su+fjBMPxX+oN3VwGdlDP8z9sGWjcBSN+xV7AMpm4F//cp4F2Gfql6H7TOlXhnzn4h9crGsNIVg+5DZiH3w5zw3fDzHdn71OTDc/X8Y+/TrBuzDNS2wT87uxz58FYPtJ8UzbST24asM91so9knkjdgHc57yGvcw9onSDcCw49KaAnT4lfz9WZ654UnYbgk8D/n0xz5w9RNwm9d4f8E+2BTrPf1xy0gCmpYxfBm2G+LV2If1znbDVwI9jxsvyjt/sQ/GvYZ92vc5l18rXTrmAY1OsIzx2CdYF7r03YXtd2q9m4/nwU1PL7ZrsQ94dfNa/hTs08rbgWu90n7I5dtkX+8/gfjxeQL0U8Eb1HadEeN24FeAc71+O6Zy8dpxg9zO3+f48bCPw38H1HXf/4Z7Cvu45T4H3P0LaboPmOH+74atrEP49SBggMvd/08Df3f/v+2pQMpYjnel19otx/OSliXAlV7zvr6M6cOAAyfI31/Ls9vd/89T2pV3M2zHYmCD6TTsY/813PqeU8YykrDBy/Pk6GQ3fBnwrPv/EmCR+38ypUG8Ffa9Gcfnx9tueUHue6ynbGC72njhBMsYj30K2bNOh3DBza3vPe7/xUAX9/9gYInX8j9y690D2zUyuB6Afb3fBPJHTwdVM8aYw9jutSdhW8WzRWT8L4x+vYisw7bmelL2aZYz3fAfRCQG21fOGSeZrGHY/o4wxmzFdlkQcYJpCrCVFthWZfhJLnMgtvOsDGPf/TCT0hfVFGN7p/1VIjLCnZNPEpGhbvCv5dl893cjtmfIbGNMBpDven+8yH3WY4+qumH7lSmL9+mg572G/8/99c6TOdi+ksD2rTT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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -879,10 +688,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Testing\n", "\n", @@ -894,11 +700,7 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -917,10 +719,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We will now instruct the agent that it should no longer perform training by setting this boolean:" ] @@ -928,11 +727,7 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "agent.training = False" @@ -940,10 +735,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We also reset the previous episode rewards." ] @@ -951,11 +743,7 @@ { "cell_type": "code", "execution_count": 19, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "agent.reset_episode_rewards()" @@ -963,10 +751,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We can render the game-environment to screen so we can see the agent playing the game, by setting this boolean:" ] @@ -974,11 +759,7 @@ { "cell_type": "code", "execution_count": 20, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "agent.render = True" @@ -986,10 +767,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We can now run a single episode by calling the `run()` function again. This should open a new window that shows the game being played by the agent. At the time of this writing, it was not possible to resize this tiny window, and the developers at OpenAI did not seem to care about this feature which should obviously be there." ] @@ -997,75 +775,36 @@ { "cell_type": "code", "execution_count": 21, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "87586:127639767\tQ-min: 1.765\tQ-max: 1.783\tLives: 5\tReward: 1.0\tEpisode Mean: 0.0\n", - "87586:127639820\tQ-min: 1.608\tQ-max: 1.619\tLives: 5\tReward: 2.0\tEpisode Mean: 0.0\n", - "87586:127639882\tQ-min: 1.712\tQ-max: 1.734\tLives: 5\tReward: 3.0\tEpisode Mean: 0.0\n", - "87586:127639931\tQ-min: 1.968\tQ-max: 1.998\tLives: 5\tReward: 4.0\tEpisode Mean: 0.0\n", - "87586:127639963\tQ-min: 1.953\tQ-max: 1.988\tLives: 5\tReward: 5.0\tEpisode Mean: 0.0\n", - "87586:127639985\tQ-min: 0.013\tQ-max: 0.184\tLives: 4\tReward: 5.0\tEpisode Mean: 0.0\n", - "87586:127640039\tQ-min: 1.651\tQ-max: 1.664\tLives: 4\tReward: 6.0\tEpisode Mean: 0.0\n", - 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"metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Mean Reward" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The game-play is slightly random, both with regard to selecting actions using the epsilon-greedy policy, but also because the OpenAI Gym environment will repeat any action between 2-4 times, with the number chosen at random. So the reward of one episode is not an accurate estimate of the reward that can be expected in general from this agent.\n", "\n", @@ -1100,11 +833,7 @@ { "cell_type": "code", "execution_count": 22, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "agent.reset_episode_rewards()" @@ -1112,10 +841,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We disable the screen-rendering so the game-environment runs much faster." ] @@ -1123,11 +849,7 @@ { "cell_type": "code", "execution_count": 23, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "agent.render = False" @@ -1135,10 +857,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We can now run 30 episodes. This records the rewards for each episode. It might have been a good idea to disable the output so it does not print all these lines - you can do this as an exercise." ] @@ -1146,1734 +865,710 @@ { "cell_type": "code", "execution_count": 24, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "87588:127641897\tQ-min: 1.755\tQ-max: 1.774\tLives: 5\tReward: 1.0\tEpisode Mean: 0.0\n", - "87588:127641950\tQ-min: 1.634\tQ-max: 1.650\tLives: 5\tReward: 2.0\tEpisode Mean: 0.0\n", - "87588:127642002\tQ-min: 1.849\tQ-max: 1.872\tLives: 5\tReward: 3.0\tEpisode Mean: 0.0\n", - "87588:127642037\tQ-min: 1.930\tQ-max: 1.966\tLives: 5\tReward: 4.0\tEpisode Mean: 0.0\n", - "87588:127642067\tQ-min: 1.936\tQ-max: 1.970\tLives: 5\tReward: 5.0\tEpisode Mean: 0.0\n", - "87588:127642101\tQ-min: 1.950\tQ-max: 1.963\tLives: 5\tReward: 9.0\tEpisode Mean: 0.0\n", - "87588:127642136\tQ-min: 2.189\tQ-max: 2.341\tLives: 5\tReward: 13.0\tEpisode Mean: 0.0\n", - "87588:127642159\tQ-min: 1.926\tQ-max: 2.292\tLives: 5\tReward: 14.0\tEpisode Mean: 0.0\n", - "87588:127642178\tQ-min: 1.976\tQ-max: 2.286\tLives: 5\tReward: 15.0\tEpisode Mean: 0.0\n", - "87588:127642199\tQ-min: 2.169\tQ-max: 2.290\tLives: 5\tReward: 16.0\tEpisode Mean: 0.0\n", - "87588:127642218\tQ-min: 2.243\tQ-max: 2.338\tLives: 5\tReward: 17.0\tEpisode Mean: 0.0\n", - "87588:127642240\tQ-min: 2.127\tQ-max: 2.307\tLives: 5\tReward: 24.0\tEpisode Mean: 0.0\n", - "87588:127642261\tQ-min: 2.328\tQ-max: 2.408\tLives: 5\tReward: 25.0\tEpisode Mean: 0.0\n", - "87588:127642280\tQ-min: 2.272\tQ-max: 2.454\tLives: 5\tReward: 26.0\tEpisode Mean: 0.0\n", - "87588:127642302\tQ-min: 2.251\tQ-max: 2.401\tLives: 5\tReward: 27.0\tEpisode Mean: 0.0\n", - "87588:127642323\tQ-min: 2.339\tQ-max: 2.423\tLives: 5\tReward: 31.0\tEpisode Mean: 0.0\n", - "87588:127642343\tQ-min: 2.365\tQ-max: 2.458\tLives: 5\tReward: 32.0\tEpisode Mean: 0.0\n", - "87588:127642364\tQ-min: 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0.0\n", - "87588:127642703\tQ-min: 2.254\tQ-max: 2.433\tLives: 4\tReward: 49.0\tEpisode Mean: 0.0\n", - "87588:127642716\tQ-min: -0.187\tQ-max: 0.041\tLives: 3\tReward: 49.0\tEpisode Mean: 0.0\n", - "87588:127642759\tQ-min: 2.005\tQ-max: 2.038\tLives: 3\tReward: 50.0\tEpisode Mean: 0.0\n", - "87588:127642805\tQ-min: 2.048\tQ-max: 2.137\tLives: 3\tReward: 54.0\tEpisode Mean: 0.0\n", - "87588:127642854\tQ-min: 2.376\tQ-max: 2.597\tLives: 3\tReward: 58.0\tEpisode Mean: 0.0\n", - "87588:127642875\tQ-min: 2.335\tQ-max: 2.511\tLives: 3\tReward: 59.0\tEpisode Mean: 0.0\n", - "87588:127642897\tQ-min: 2.480\tQ-max: 2.536\tLives: 3\tReward: 63.0\tEpisode Mean: 0.0\n", - "87588:127642910\tQ-min: 0.048\tQ-max: 0.153\tLives: 2\tReward: 63.0\tEpisode Mean: 0.0\n", - "87588:127642960\tQ-min: 2.096\tQ-max: 2.239\tLives: 2\tReward: 64.0\tEpisode Mean: 0.0\n", - "87588:127643019\tQ-min: 1.741\tQ-max: 1.920\tLives: 2\tReward: 68.0\tEpisode Mean: 0.0\n", - "87588:127643087\tQ-min: 1.986\tQ-max: 2.044\tLives: 2\tReward: 69.0\tEpisode Mean: 0.0\n", - "87588:127643135\tQ-min: 2.272\tQ-max: 2.352\tLives: 2\tReward: 70.0\tEpisode Mean: 0.0\n", - "87588:127643168\tQ-min: 2.441\tQ-max: 2.554\tLives: 2\tReward: 74.0\tEpisode Mean: 0.0\n", - "87588:127643202\tQ-min: 2.076\tQ-max: 2.292\tLives: 2\tReward: 78.0\tEpisode Mean: 0.0\n", - "87588:127643225\tQ-min: -0.176\tQ-max: 0.268\tLives: 1\tReward: 78.0\tEpisode Mean: 0.0\n", - "87588:127643281\tQ-min: 1.966\tQ-max: 2.221\tLives: 1\tReward: 82.0\tEpisode Mean: 0.0\n", - "87588:127643349\tQ-min: 1.627\tQ-max: 2.724\tLives: 1\tReward: 86.0\tEpisode Mean: 0.0\n", - "87588:127643370\tQ-min: 2.374\tQ-max: 2.479\tLives: 1\tReward: 93.0\tEpisode Mean: 0.0\n", - "87588:127643390\tQ-min: 2.446\tQ-max: 2.602\tLives: 1\tReward: 94.0\tEpisode Mean: 0.0\n", - "87588:127643412\tQ-min: 1.203\tQ-max: 1.788\tLives: 1\tReward: 98.0\tEpisode Mean: 0.0\n", - "87588:127643435\tQ-min: 2.395\tQ-max: 2.539\tLives: 1\tReward: 102.0\tEpisode Mean: 0.0\n", - "87588:127643448\tQ-min: -0.182\tQ-max: 0.072\tLives: 0\tReward: 102.0\tEpisode Mean: 102.0\n", - "87589:127643490\tQ-min: 1.753\tQ-max: 1.763\tLives: 5\tReward: 1.0\tEpisode Mean: 102.0\n", - "87589:127643540\tQ-min: 1.656\tQ-max: 1.658\tLives: 5\tReward: 2.0\tEpisode Mean: 102.0\n", - "87589:127643604\tQ-min: 1.705\tQ-max: 1.724\tLives: 5\tReward: 3.0\tEpisode Mean: 102.0\n", - "87589:127643649\tQ-min: 1.970\tQ-max: 1.978\tLives: 5\tReward: 4.0\tEpisode Mean: 102.0\n", - "87589:127643683\tQ-min: 1.972\tQ-max: 2.004\tLives: 5\tReward: 5.0\tEpisode Mean: 102.0\n", - "87589:127643716\tQ-min: 1.970\tQ-max: 1.998\tLives: 5\tReward: 6.0\tEpisode Mean: 102.0\n", - "87589:127643751\tQ-min: 1.816\tQ-max: 1.837\tLives: 5\tReward: 7.0\tEpisode Mean: 102.0\n", - "87589:127643802\tQ-min: 1.631\tQ-max: 1.676\tLives: 5\tReward: 8.0\tEpisode Mean: 102.0\n", - "87589:127643865\tQ-min: 1.726\tQ-max: 1.738\tLives: 5\tReward: 9.0\tEpisode Mean: 102.0\n", - "87589:127643930\tQ-min: 1.688\tQ-max: 1.705\tLives: 5\tReward: 10.0\tEpisode Mean: 102.0\n", - "87589:127643992\tQ-min: 1.655\tQ-max: 1.675\tLives: 5\tReward: 11.0\tEpisode Mean: 102.0\n", - "87589:127644039\tQ-min: 1.976\tQ-max: 1.991\tLives: 5\tReward: 12.0\tEpisode Mean: 102.0\n", - "87589:127644071\tQ-min: 1.870\tQ-max: 1.899\tLives: 5\tReward: 13.0\tEpisode Mean: 102.0\n", - "87589:127644104\tQ-min: 1.978\tQ-max: 2.014\tLives: 5\tReward: 14.0\tEpisode Mean: 102.0\n", - "87589:127644140\tQ-min: 2.065\tQ-max: 2.085\tLives: 5\tReward: 18.0\tEpisode Mean: 102.0\n", - "87589:127644176\tQ-min: 2.025\tQ-max: 2.114\tLives: 5\tReward: 19.0\tEpisode Mean: 102.0\n", - "87589:127644209\tQ-min: 2.068\tQ-max: 2.149\tLives: 5\tReward: 23.0\tEpisode Mean: 102.0\n", - "87589:127644246\tQ-min: 2.119\tQ-max: 2.161\tLives: 5\tReward: 24.0\tEpisode Mean: 102.0\n", - "87589:127644276\tQ-min: 2.175\tQ-max: 2.211\tLives: 5\tReward: 25.0\tEpisode Mean: 102.0\n", - "87589:127644310\tQ-min: 2.073\tQ-max: 2.097\tLives: 5\tReward: 26.0\tEpisode Mean: 102.0\n", - "87589:127644341\tQ-min: 2.106\tQ-max: 2.172\tLives: 5\tReward: 30.0\tEpisode Mean: 102.0\n", - "87589:127644377\tQ-min: 2.312\tQ-max: 2.571\tLives: 5\tReward: 34.0\tEpisode Mean: 102.0\n", - "87589:127644392\tQ-min: 0.043\tQ-max: 0.267\tLives: 4\tReward: 34.0\tEpisode Mean: 102.0\n", - "87589:127644446\tQ-min: 1.782\tQ-max: 1.803\tLives: 4\tReward: 35.0\tEpisode Mean: 102.0\n", - "87589:127644499\tQ-min: 1.961\tQ-max: 2.130\tLives: 4\tReward: 36.0\tEpisode Mean: 102.0\n", - "87589:127644540\tQ-min: 2.092\tQ-max: 2.265\tLives: 4\tReward: 37.0\tEpisode Mean: 102.0\n", - "87589:127644581\tQ-min: 2.203\tQ-max: 2.238\tLives: 4\tReward: 38.0\tEpisode Mean: 102.0\n", - "87589:127644611\tQ-min: 2.169\tQ-max: 2.270\tLives: 4\tReward: 39.0\tEpisode Mean: 102.0\n", - "87589:127644644\tQ-min: 2.176\tQ-max: 2.307\tLives: 4\tReward: 43.0\tEpisode Mean: 102.0\n", - "87589:127644677\tQ-min: 2.138\tQ-max: 2.240\tLives: 4\tReward: 44.0\tEpisode Mean: 102.0\n", - "87589:127644725\tQ-min: 1.803\tQ-max: 1.827\tLives: 4\tReward: 45.0\tEpisode Mean: 102.0\n", - "87589:127644792\tQ-min: 1.820\tQ-max: 1.887\tLives: 4\tReward: 46.0\tEpisode Mean: 102.0\n", - "87589:127644855\tQ-min: 1.821\tQ-max: 1.853\tLives: 4\tReward: 47.0\tEpisode Mean: 102.0\n", - "87589:127644924\tQ-min: 1.803\tQ-max: 1.938\tLives: 4\tReward: 48.0\tEpisode Mean: 102.0\n", - "87589:127644973\tQ-min: 2.182\tQ-max: 2.255\tLives: 4\tReward: 52.0\tEpisode Mean: 102.0\n", - "87589:127645008\tQ-min: 2.057\tQ-max: 2.107\tLives: 4\tReward: 53.0\tEpisode Mean: 102.0\n", - "87589:127645029\tQ-min: -0.272\tQ-max: 0.309\tLives: 3\tReward: 53.0\tEpisode Mean: 102.0\n", - "87589:127645074\tQ-min: 1.963\tQ-max: 2.158\tLives: 3\tReward: 57.0\tEpisode Mean: 102.0\n", - "87589:127645121\tQ-min: 2.300\tQ-max: 2.361\tLives: 3\tReward: 58.0\tEpisode Mean: 102.0\n", - "87589:127645164\tQ-min: 2.119\tQ-max: 2.211\tLives: 3\tReward: 59.0\tEpisode Mean: 102.0\n", - "87589:127645204\tQ-min: 2.328\tQ-max: 2.377\tLives: 3\tReward: 63.0\tEpisode Mean: 102.0\n", - "87589:127645242\tQ-min: 1.591\tQ-max: 2.503\tLives: 3\tReward: 70.0\tEpisode Mean: 102.0\n", - "87589:127645265\tQ-min: 1.942\tQ-max: 2.711\tLives: 3\tReward: 74.0\tEpisode Mean: 102.0\n", - "87589:127645289\tQ-min: 1.739\tQ-max: 3.524\tLives: 3\tReward: 81.0\tEpisode Mean: 102.0\n", - "87589:127645319\tQ-min: 1.548\tQ-max: 5.599\tLives: 3\tReward: 88.0\tEpisode Mean: 102.0\n", - "87589:127645326\tQ-min: 3.214\tQ-max: 6.187\tLives: 3\tReward: 95.0\tEpisode Mean: 102.0\n", - "87589:127645332\tQ-min: 4.149\tQ-max: 7.073\tLives: 3\tReward: 102.0\tEpisode Mean: 102.0\n", - "87589:127645338\tQ-min: 2.279\tQ-max: 6.700\tLives: 3\tReward: 109.0\tEpisode Mean: 102.0\n", - "87589:127645344\tQ-min: 3.218\tQ-max: 6.832\tLives: 3\tReward: 116.0\tEpisode Mean: 102.0\n", - "87589:127645348\tQ-min: 3.802\tQ-max: 5.502\tLives: 3\tReward: 123.0\tEpisode Mean: 102.0\n", - "87589:127645354\tQ-min: 1.270\tQ-max: 6.387\tLives: 3\tReward: 130.0\tEpisode Mean: 102.0\n", - "87589:127645360\tQ-min: 2.805\tQ-max: 6.095\tLives: 3\tReward: 137.0\tEpisode Mean: 102.0\n", - "87589:127645397\tQ-min: 2.879\tQ-max: 6.591\tLives: 3\tReward: 144.0\tEpisode Mean: 102.0\n", - "87589:127645404\tQ-min: 3.505\tQ-max: 6.818\tLives: 3\tReward: 151.0\tEpisode Mean: 102.0\n", - "87589:127645410\tQ-min: 3.764\tQ-max: 6.270\tLives: 3\tReward: 158.0\tEpisode Mean: 102.0\n", - "87589:127645415\tQ-min: 3.677\tQ-max: 5.796\tLives: 3\tReward: 165.0\tEpisode Mean: 102.0\n", - "87589:127645421\tQ-min: 2.668\tQ-max: 5.257\tLives: 3\tReward: 172.0\tEpisode Mean: 102.0\n", - "87589:127645427\tQ-min: 3.923\tQ-max: 5.098\tLives: 3\tReward: 179.0\tEpisode Mean: 102.0\n", - "87589:127645432\tQ-min: 1.970\tQ-max: 5.844\tLives: 3\tReward: 186.0\tEpisode Mean: 102.0\n", - "87589:127645437\tQ-min: 2.983\tQ-max: 5.170\tLives: 3\tReward: 193.0\tEpisode Mean: 102.0\n", - "87589:127645442\tQ-min: 0.927\tQ-max: 5.070\tLives: 3\tReward: 200.0\tEpisode Mean: 102.0\n", - "87589:127645449\tQ-min: 2.823\tQ-max: 4.550\tLives: 3\tReward: 207.0\tEpisode Mean: 102.0\n", - "87589:127645457\tQ-min: 2.697\tQ-max: 4.629\tLives: 3\tReward: 214.0\tEpisode Mean: 102.0\n", - "87589:127645463\tQ-min: 2.279\tQ-max: 3.921\tLives: 3\tReward: 221.0\tEpisode Mean: 102.0\n", - "87589:127645469\tQ-min: 1.649\tQ-max: 4.535\tLives: 3\tReward: 228.0\tEpisode Mean: 102.0\n", - "87589:127645477\tQ-min: 1.761\tQ-max: 4.724\tLives: 3\tReward: 235.0\tEpisode Mean: 102.0\n", - "87589:127645485\tQ-min: 2.276\tQ-max: 4.794\tLives: 3\tReward: 242.0\tEpisode Mean: 102.0\n", - "87589:127645493\tQ-min: 1.740\tQ-max: 4.088\tLives: 3\tReward: 246.0\tEpisode Mean: 102.0\n", - "87589:127645500\tQ-min: 2.749\tQ-max: 4.242\tLives: 3\tReward: 253.0\tEpisode Mean: 102.0\n", - "87589:127645507\tQ-min: 1.777\tQ-max: 4.064\tLives: 3\tReward: 260.0\tEpisode Mean: 102.0\n", - "87589:127645516\tQ-min: 1.485\tQ-max: 3.484\tLives: 3\tReward: 267.0\tEpisode Mean: 102.0\n", - "87589:127645523\tQ-min: 2.242\tQ-max: 4.177\tLives: 3\tReward: 274.0\tEpisode Mean: 102.0\n", - "87589:127645531\tQ-min: 2.252\tQ-max: 3.996\tLives: 3\tReward: 281.0\tEpisode Mean: 102.0\n", - "87589:127645566\tQ-min: 1.331\tQ-max: 4.973\tLives: 3\tReward: 285.0\tEpisode Mean: 102.0\n", - "87589:127645575\tQ-min: 1.970\tQ-max: 3.440\tLives: 3\tReward: 289.0\tEpisode Mean: 102.0\n", - "87589:127645584\tQ-min: 1.505\tQ-max: 3.210\tLives: 3\tReward: 293.0\tEpisode Mean: 102.0\n", - "87589:127645592\tQ-min: 1.477\tQ-max: 3.720\tLives: 3\tReward: 300.0\tEpisode Mean: 102.0\n", - "87589:127645600\tQ-min: 2.563\tQ-max: 3.410\tLives: 3\tReward: 304.0\tEpisode Mean: 102.0\n", - "87589:127645608\tQ-min: 1.711\tQ-max: 3.448\tLives: 3\tReward: 311.0\tEpisode Mean: 102.0\n", - "87589:127645615\tQ-min: 2.012\tQ-max: 3.991\tLives: 3\tReward: 318.0\tEpisode Mean: 102.0\n", - "87589:127645624\tQ-min: 1.686\tQ-max: 3.728\tLives: 3\tReward: 325.0\tEpisode Mean: 102.0\n", - "87589:127645632\tQ-min: 1.994\tQ-max: 3.683\tLives: 3\tReward: 329.0\tEpisode Mean: 102.0\n", - "87589:127645638\tQ-min: 2.120\tQ-max: 4.264\tLives: 3\tReward: 336.0\tEpisode Mean: 102.0\n", - "87589:127645646\tQ-min: 2.023\tQ-max: 4.184\tLives: 3\tReward: 340.0\tEpisode Mean: 102.0\n", - "87589:127645655\tQ-min: 2.003\tQ-max: 2.833\tLives: 3\tReward: 344.0\tEpisode Mean: 102.0\n", - "87589:127645665\tQ-min: -0.107\tQ-max: 1.473\tLives: 3\tReward: 345.0\tEpisode Mean: 102.0\n", - "87589:127645674\tQ-min: 2.355\tQ-max: 4.078\tLives: 3\tReward: 349.0\tEpisode Mean: 102.0\n", - "87589:127645713\tQ-min: 1.669\tQ-max: 2.720\tLives: 3\tReward: 353.0\tEpisode Mean: 102.0\n", - "87589:127645721\tQ-min: 2.809\tQ-max: 4.243\tLives: 3\tReward: 357.0\tEpisode Mean: 102.0\n", - "87589:127645753\tQ-min: 1.922\tQ-max: 2.913\tLives: 3\tReward: 361.0\tEpisode Mean: 102.0\n", - "87589:127645772\tQ-min: 2.140\tQ-max: 2.795\tLives: 3\tReward: 362.0\tEpisode Mean: 102.0\n", - "87589:127645783\tQ-min: 0.064\tQ-max: 0.209\tLives: 2\tReward: 362.0\tEpisode Mean: 102.0\n", - "87589:127645841\tQ-min: 1.845\tQ-max: 2.442\tLives: 2\tReward: 366.0\tEpisode Mean: 102.0\n", - "87589:127645919\tQ-min: 0.936\tQ-max: 2.705\tLives: 2\tReward: 370.0\tEpisode Mean: 102.0\n", - "87589:127645949\tQ-min: 1.205\tQ-max: 3.975\tLives: 2\tReward: 374.0\tEpisode Mean: 102.0\n", - "87589:127645956\tQ-min: 3.382\tQ-max: 4.514\tLives: 2\tReward: 381.0\tEpisode Mean: 102.0\n", - "87589:127645979\tQ-min: 0.130\tQ-max: 0.357\tLives: 1\tReward: 381.0\tEpisode Mean: 102.0\n", - "87589:127646023\tQ-min: 2.012\tQ-max: 2.867\tLives: 1\tReward: 385.0\tEpisode Mean: 102.0\n", - "87589:127646070\tQ-min: 2.544\tQ-max: 2.705\tLives: 1\tReward: 386.0\tEpisode Mean: 102.0\n", - "87589:127646095\tQ-min: 0.001\tQ-max: 0.201\tLives: 0\tReward: 386.0\tEpisode Mean: 244.0\n", - "87590:127646139\tQ-min: 1.787\tQ-max: 1.799\tLives: 5\tReward: 1.0\tEpisode Mean: 244.0\n", - "87590:127646182\tQ-min: 1.817\tQ-max: 1.831\tLives: 5\tReward: 2.0\tEpisode Mean: 244.0\n", - "87590:127646227\tQ-min: 1.924\tQ-max: 1.953\tLives: 5\tReward: 3.0\tEpisode Mean: 244.0\n", - "87590:127646262\tQ-min: 2.057\tQ-max: 2.093\tLives: 5\tReward: 4.0\tEpisode Mean: 244.0\n", - "87590:127646293\tQ-min: 1.956\tQ-max: 1.980\tLives: 5\tReward: 5.0\tEpisode Mean: 244.0\n", - "87590:127646324\tQ-min: 1.892\tQ-max: 1.911\tLives: 5\tReward: 6.0\tEpisode Mean: 244.0\n", - "87590:127646358\tQ-min: 1.726\tQ-max: 1.840\tLives: 5\tReward: 7.0\tEpisode Mean: 244.0\n", - "87590:127646406\tQ-min: 1.681\tQ-max: 1.705\tLives: 5\tReward: 8.0\tEpisode Mean: 244.0\n", - "87590:127646469\tQ-min: 1.489\tQ-max: 1.679\tLives: 5\tReward: 9.0\tEpisode Mean: 244.0\n", - "87590:127646534\tQ-min: 1.689\tQ-max: 1.710\tLives: 5\tReward: 10.0\tEpisode Mean: 244.0\n", - "87590:127646601\tQ-min: 1.625\tQ-max: 1.658\tLives: 5\tReward: 11.0\tEpisode Mean: 244.0\n", - "87590:127646650\tQ-min: 1.949\tQ-max: 1.968\tLives: 5\tReward: 12.0\tEpisode Mean: 244.0\n", - "87590:127646683\tQ-min: 1.941\tQ-max: 1.961\tLives: 5\tReward: 13.0\tEpisode Mean: 244.0\n", - "87590:127646715\tQ-min: 1.990\tQ-max: 2.067\tLives: 5\tReward: 14.0\tEpisode Mean: 244.0\n", - "87590:127646738\tQ-min: -0.265\tQ-max: 0.133\tLives: 4\tReward: 14.0\tEpisode Mean: 244.0\n", - "87590:127646782\tQ-min: 1.799\tQ-max: 1.840\tLives: 4\tReward: 15.0\tEpisode Mean: 244.0\n", - "87590:127646825\tQ-min: 1.948\tQ-max: 1.967\tLives: 4\tReward: 16.0\tEpisode Mean: 244.0\n", - "87590:127646871\tQ-min: 1.927\tQ-max: 1.959\tLives: 4\tReward: 17.0\tEpisode Mean: 244.0\n", - "87590:127646904\tQ-min: 2.043\tQ-max: 2.058\tLives: 4\tReward: 18.0\tEpisode Mean: 244.0\n", - "87590:127646935\tQ-min: 1.972\tQ-max: 2.004\tLives: 4\tReward: 19.0\tEpisode Mean: 244.0\n", - "87590:127646966\tQ-min: 2.014\tQ-max: 2.062\tLives: 4\tReward: 20.0\tEpisode Mean: 244.0\n", - "87590:127646985\tQ-min: -0.062\tQ-max: 0.107\tLives: 3\tReward: 20.0\tEpisode Mean: 244.0\n", - "87590:127647040\tQ-min: 1.724\tQ-max: 1.740\tLives: 3\tReward: 21.0\tEpisode Mean: 244.0\n", - "87590:127647095\tQ-min: 1.943\tQ-max: 1.955\tLives: 3\tReward: 22.0\tEpisode Mean: 244.0\n", - "87590:127647142\tQ-min: 2.024\tQ-max: 2.103\tLives: 3\tReward: 26.0\tEpisode Mean: 244.0\n", - "87590:127647182\tQ-min: 1.959\tQ-max: 2.022\tLives: 3\tReward: 27.0\tEpisode Mean: 244.0\n", - "87590:127647218\tQ-min: 2.041\tQ-max: 2.136\tLives: 3\tReward: 31.0\tEpisode Mean: 244.0\n", - "87590:127647255\tQ-min: 2.036\tQ-max: 2.070\tLives: 3\tReward: 32.0\tEpisode Mean: 244.0\n", - "87590:127647290\tQ-min: 1.980\tQ-max: 2.191\tLives: 3\tReward: 36.0\tEpisode Mean: 244.0\n", - "87590:127647342\tQ-min: 1.706\tQ-max: 1.736\tLives: 3\tReward: 37.0\tEpisode Mean: 244.0\n", - "87590:127647409\tQ-min: 1.795\tQ-max: 1.842\tLives: 3\tReward: 38.0\tEpisode Mean: 244.0\n", - "87590:127647473\tQ-min: 1.780\tQ-max: 1.805\tLives: 3\tReward: 39.0\tEpisode Mean: 244.0\n", - "87590:127647542\tQ-min: 1.660\tQ-max: 1.909\tLives: 3\tReward: 40.0\tEpisode Mean: 244.0\n", - "87590:127647597\tQ-min: 2.079\tQ-max: 2.107\tLives: 3\tReward: 41.0\tEpisode Mean: 244.0\n", - "87590:127647629\tQ-min: 1.994\tQ-max: 2.072\tLives: 3\tReward: 42.0\tEpisode Mean: 244.0\n", - "87590:127647665\tQ-min: 2.155\tQ-max: 2.173\tLives: 3\tReward: 43.0\tEpisode Mean: 244.0\n", - "87590:127647699\tQ-min: 2.047\tQ-max: 2.189\tLives: 3\tReward: 47.0\tEpisode Mean: 244.0\n", - "87590:127647736\tQ-min: 2.219\tQ-max: 2.495\tLives: 3\tReward: 51.0\tEpisode Mean: 244.0\n", - "87590:127647756\tQ-min: 2.399\tQ-max: 2.565\tLives: 3\tReward: 55.0\tEpisode Mean: 244.0\n", - "87590:127647778\tQ-min: 2.284\tQ-max: 2.583\tLives: 3\tReward: 59.0\tEpisode Mean: 244.0\n", - "87590:127647800\tQ-min: 2.406\tQ-max: 2.620\tLives: 3\tReward: 63.0\tEpisode Mean: 244.0\n", - "87590:127647822\tQ-min: 2.359\tQ-max: 2.564\tLives: 3\tReward: 67.0\tEpisode Mean: 244.0\n", - "87590:127647833\tQ-min: -0.036\tQ-max: 0.223\tLives: 2\tReward: 67.0\tEpisode Mean: 244.0\n", - "87590:127647884\tQ-min: 1.975\tQ-max: 2.198\tLives: 2\tReward: 71.0\tEpisode Mean: 244.0\n", - "87590:127647929\tQ-min: 2.128\tQ-max: 2.484\tLives: 2\tReward: 75.0\tEpisode Mean: 244.0\n", - "87590:127647972\tQ-min: 2.320\tQ-max: 2.380\tLives: 2\tReward: 76.0\tEpisode Mean: 244.0\n", - "87590:127648014\tQ-min: 2.009\tQ-max: 2.463\tLives: 2\tReward: 80.0\tEpisode Mean: 244.0\n", - "87590:127648049\tQ-min: 2.390\tQ-max: 2.437\tLives: 2\tReward: 81.0\tEpisode Mean: 244.0\n", - "87590:127648081\tQ-min: 2.299\tQ-max: 2.460\tLives: 2\tReward: 82.0\tEpisode Mean: 244.0\n", - "87590:127648112\tQ-min: 2.319\tQ-max: 2.551\tLives: 2\tReward: 86.0\tEpisode Mean: 244.0\n", - "87590:127648165\tQ-min: 1.847\tQ-max: 2.040\tLives: 2\tReward: 87.0\tEpisode Mean: 244.0\n", - "87590:127648241\tQ-min: 1.213\tQ-max: 2.371\tLives: 2\tReward: 91.0\tEpisode Mean: 244.0\n", - "87590:127648256\tQ-min: 0.018\tQ-max: 0.151\tLives: 1\tReward: 91.0\tEpisode Mean: 244.0\n", - "87590:127648314\tQ-min: 2.263\tQ-max: 2.612\tLives: 1\tReward: 95.0\tEpisode Mean: 244.0\n", - "87590:127648335\tQ-min: 2.463\tQ-max: 2.630\tLives: 1\tReward: 96.0\tEpisode Mean: 244.0\n", - "87590:127648357\tQ-min: 2.585\tQ-max: 2.698\tLives: 1\tReward: 100.0\tEpisode Mean: 244.0\n", - "87590:127648378\tQ-min: 2.491\tQ-max: 2.898\tLives: 1\tReward: 104.0\tEpisode Mean: 244.0\n", - "87590:127648400\tQ-min: 2.554\tQ-max: 2.852\tLives: 1\tReward: 108.0\tEpisode Mean: 244.0\n", - "87590:127648421\tQ-min: 2.414\tQ-max: 2.958\tLives: 1\tReward: 115.0\tEpisode Mean: 244.0\n", - "87590:127648445\tQ-min: 2.390\tQ-max: 3.135\tLives: 1\tReward: 119.0\tEpisode Mean: 244.0\n", - "87590:127648468\tQ-min: 2.370\tQ-max: 3.299\tLives: 1\tReward: 126.0\tEpisode Mean: 244.0\n", - "87590:127648494\tQ-min: 2.755\tQ-max: 3.314\tLives: 1\tReward: 133.0\tEpisode Mean: 244.0\n", - "87590:127648509\tQ-min: -0.140\tQ-max: 0.334\tLives: 0\tReward: 133.0\tEpisode Mean: 207.0\n", - "87591:127648551\tQ-min: 1.768\tQ-max: 1.783\tLives: 5\tReward: 1.0\tEpisode Mean: 207.0\n", - "87591:127648601\tQ-min: 1.657\tQ-max: 1.664\tLives: 5\tReward: 2.0\tEpisode Mean: 207.0\n", - "87591:127648652\tQ-min: 1.851\tQ-max: 1.862\tLives: 5\tReward: 3.0\tEpisode Mean: 207.0\n", - "87591:127648690\tQ-min: 2.005\tQ-max: 2.023\tLives: 5\tReward: 4.0\tEpisode Mean: 207.0\n", - "87591:127648723\tQ-min: 1.894\tQ-max: 1.913\tLives: 5\tReward: 5.0\tEpisode Mean: 207.0\n", - "87591:127648758\tQ-min: 1.980\tQ-max: 2.014\tLives: 5\tReward: 6.0\tEpisode Mean: 207.0\n", - "87591:127648791\tQ-min: 1.818\tQ-max: 1.831\tLives: 5\tReward: 7.0\tEpisode Mean: 207.0\n", - "87591:127648813\tQ-min: -0.174\tQ-max: 0.101\tLives: 4\tReward: 7.0\tEpisode Mean: 207.0\n", - "87591:127648866\tQ-min: 1.657\tQ-max: 1.681\tLives: 4\tReward: 8.0\tEpisode Mean: 207.0\n", - "87591:127648929\tQ-min: 1.683\tQ-max: 1.697\tLives: 4\tReward: 9.0\tEpisode Mean: 207.0\n", - "87591:127648981\tQ-min: 1.868\tQ-max: 1.886\tLives: 4\tReward: 10.0\tEpisode Mean: 207.0\n", - "87591:127649018\tQ-min: 1.920\tQ-max: 2.034\tLives: 4\tReward: 11.0\tEpisode Mean: 207.0\n", - "87591:127649052\tQ-min: 1.979\tQ-max: 2.015\tLives: 4\tReward: 15.0\tEpisode Mean: 207.0\n", - "87591:127649089\tQ-min: 1.940\tQ-max: 2.027\tLives: 4\tReward: 19.0\tEpisode Mean: 207.0\n", - "87591:127649121\tQ-min: 1.964\tQ-max: 2.039\tLives: 4\tReward: 20.0\tEpisode Mean: 207.0\n", - "87591:127649168\tQ-min: 1.749\tQ-max: 1.771\tLives: 4\tReward: 21.0\tEpisode Mean: 207.0\n", - "87591:127649232\tQ-min: 1.642\tQ-max: 1.716\tLives: 4\tReward: 22.0\tEpisode Mean: 207.0\n", - "87591:127649294\tQ-min: 1.752\tQ-max: 1.774\tLives: 4\tReward: 23.0\tEpisode Mean: 207.0\n", - "87591:127649359\tQ-min: 1.725\tQ-max: 1.774\tLives: 4\tReward: 24.0\tEpisode Mean: 207.0\n", - "87591:127649412\tQ-min: 1.957\tQ-max: 1.990\tLives: 4\tReward: 25.0\tEpisode Mean: 207.0\n", - "87591:127649447\tQ-min: 2.097\tQ-max: 2.117\tLives: 4\tReward: 26.0\tEpisode Mean: 207.0\n", - "87591:127649478\tQ-min: 2.056\tQ-max: 2.085\tLives: 4\tReward: 27.0\tEpisode Mean: 207.0\n", - "87591:127649509\tQ-min: 2.032\tQ-max: 2.131\tLives: 4\tReward: 31.0\tEpisode Mean: 207.0\n", - "87591:127649544\tQ-min: 2.193\tQ-max: 2.302\tLives: 4\tReward: 35.0\tEpisode Mean: 207.0\n", - "87591:127649565\tQ-min: 2.264\tQ-max: 2.424\tLives: 4\tReward: 36.0\tEpisode Mean: 207.0\n", - "87591:127649584\tQ-min: 2.031\tQ-max: 2.287\tLives: 4\tReward: 37.0\tEpisode Mean: 207.0\n", - "87591:127649596\tQ-min: -0.144\tQ-max: 0.076\tLives: 3\tReward: 37.0\tEpisode Mean: 207.0\n", - "87591:127649649\tQ-min: 1.760\tQ-max: 1.798\tLives: 3\tReward: 38.0\tEpisode Mean: 207.0\n", - "87591:127649719\tQ-min: 1.564\tQ-max: 1.715\tLives: 3\tReward: 42.0\tEpisode Mean: 207.0\n", - "87591:127649777\tQ-min: 2.073\tQ-max: 2.168\tLives: 3\tReward: 46.0\tEpisode Mean: 207.0\n", - "87591:127649821\tQ-min: 2.199\tQ-max: 2.354\tLives: 3\tReward: 50.0\tEpisode Mean: 207.0\n", - "87591:127649856\tQ-min: 2.322\tQ-max: 2.456\tLives: 3\tReward: 54.0\tEpisode Mean: 207.0\n", - "87591:127649869\tQ-min: 0.025\tQ-max: 0.418\tLives: 2\tReward: 54.0\tEpisode Mean: 207.0\n", - "87591:127649930\tQ-min: 1.381\tQ-max: 1.778\tLives: 2\tReward: 58.0\tEpisode Mean: 207.0\n", - "87591:127650007\tQ-min: 1.768\tQ-max: 2.462\tLives: 2\tReward: 62.0\tEpisode Mean: 207.0\n", - "87591:127650027\tQ-min: 2.428\tQ-max: 2.487\tLives: 2\tReward: 63.0\tEpisode Mean: 207.0\n", - "87591:127650047\tQ-min: 2.191\tQ-max: 2.541\tLives: 2\tReward: 64.0\tEpisode Mean: 207.0\n", - "87591:127650066\tQ-min: 2.439\tQ-max: 2.499\tLives: 2\tReward: 65.0\tEpisode Mean: 207.0\n", - "87591:127650086\tQ-min: 2.355\tQ-max: 2.534\tLives: 2\tReward: 66.0\tEpisode Mean: 207.0\n", - "87591:127650107\tQ-min: 2.535\tQ-max: 2.644\tLives: 2\tReward: 67.0\tEpisode Mean: 207.0\n", - "87591:127650128\tQ-min: 2.424\tQ-max: 2.611\tLives: 2\tReward: 71.0\tEpisode Mean: 207.0\n", - "87591:127650149\tQ-min: 2.291\tQ-max: 2.567\tLives: 2\tReward: 72.0\tEpisode Mean: 207.0\n", - "87591:127650173\tQ-min: 2.357\tQ-max: 2.671\tLives: 2\tReward: 76.0\tEpisode Mean: 207.0\n", - "87591:127650198\tQ-min: 1.918\tQ-max: 3.150\tLives: 2\tReward: 83.0\tEpisode Mean: 207.0\n", - "87591:127650223\tQ-min: 1.593\tQ-max: 3.739\tLives: 2\tReward: 90.0\tEpisode Mean: 207.0\n", - "87591:127650253\tQ-min: 2.184\tQ-max: 6.902\tLives: 2\tReward: 97.0\tEpisode Mean: 207.0\n", - "87591:127650258\tQ-min: 2.303\tQ-max: 6.611\tLives: 2\tReward: 104.0\tEpisode Mean: 207.0\n", - "87591:127650263\tQ-min: 3.708\tQ-max: 6.076\tLives: 2\tReward: 111.0\tEpisode Mean: 207.0\n", - "87591:127650267\tQ-min: 2.672\tQ-max: 6.862\tLives: 2\tReward: 118.0\tEpisode Mean: 207.0\n", - "87591:127650272\tQ-min: 1.783\tQ-max: 6.216\tLives: 2\tReward: 125.0\tEpisode Mean: 207.0\n", - "87591:127650277\tQ-min: 2.125\tQ-max: 5.576\tLives: 2\tReward: 132.0\tEpisode Mean: 207.0\n", - "87591:127650283\tQ-min: 3.175\tQ-max: 5.794\tLives: 2\tReward: 139.0\tEpisode Mean: 207.0\n", - "87591:127650288\tQ-min: 4.208\tQ-max: 6.206\tLives: 2\tReward: 146.0\tEpisode Mean: 207.0\n", - "87591:127650293\tQ-min: 3.793\tQ-max: 5.610\tLives: 2\tReward: 153.0\tEpisode Mean: 207.0\n", - "87591:127650298\tQ-min: 3.057\tQ-max: 5.366\tLives: 2\tReward: 160.0\tEpisode Mean: 207.0\n", - "87591:127650304\tQ-min: 3.419\tQ-max: 5.766\tLives: 2\tReward: 167.0\tEpisode Mean: 207.0\n", - "87591:127650308\tQ-min: 2.737\tQ-max: 6.274\tLives: 2\tReward: 174.0\tEpisode Mean: 207.0\n", - "87591:127650313\tQ-min: 4.419\tQ-max: 6.346\tLives: 2\tReward: 181.0\tEpisode Mean: 207.0\n", - "87591:127650319\tQ-min: 2.684\tQ-max: 5.516\tLives: 2\tReward: 188.0\tEpisode Mean: 207.0\n", - "87591:127650322\tQ-min: 3.003\tQ-max: 4.892\tLives: 2\tReward: 195.0\tEpisode Mean: 207.0\n", - "87591:127650328\tQ-min: 3.717\tQ-max: 5.497\tLives: 2\tReward: 202.0\tEpisode Mean: 207.0\n", - "87591:127650334\tQ-min: 3.530\tQ-max: 5.096\tLives: 2\tReward: 209.0\tEpisode Mean: 207.0\n", - "87591:127650341\tQ-min: 3.669\tQ-max: 5.190\tLives: 2\tReward: 216.0\tEpisode Mean: 207.0\n", - "87591:127650346\tQ-min: 2.570\tQ-max: 3.876\tLives: 2\tReward: 223.0\tEpisode Mean: 207.0\n", - "87591:127650353\tQ-min: 3.737\tQ-max: 5.336\tLives: 2\tReward: 230.0\tEpisode Mean: 207.0\n", - "87591:127650360\tQ-min: 2.802\tQ-max: 5.215\tLives: 2\tReward: 237.0\tEpisode Mean: 207.0\n", - "87591:127650366\tQ-min: 1.970\tQ-max: 5.470\tLives: 2\tReward: 244.0\tEpisode Mean: 207.0\n", - "87591:127650373\tQ-min: 2.147\tQ-max: 5.573\tLives: 2\tReward: 251.0\tEpisode Mean: 207.0\n", - "87591:127650379\tQ-min: 2.106\tQ-max: 4.824\tLives: 2\tReward: 258.0\tEpisode Mean: 207.0\n", - "87591:127650386\tQ-min: 2.746\tQ-max: 4.343\tLives: 2\tReward: 265.0\tEpisode Mean: 207.0\n", - "87591:127650425\tQ-min: 1.870\tQ-max: 5.265\tLives: 2\tReward: 272.0\tEpisode Mean: 207.0\n", - "87591:127650432\tQ-min: 3.059\tQ-max: 4.681\tLives: 2\tReward: 279.0\tEpisode Mean: 207.0\n", - "87591:127650438\tQ-min: 2.537\tQ-max: 4.428\tLives: 2\tReward: 286.0\tEpisode Mean: 207.0\n", - "87591:127650445\tQ-min: 1.591\tQ-max: 4.291\tLives: 2\tReward: 290.0\tEpisode Mean: 207.0\n", - "87591:127650454\tQ-min: 2.578\tQ-max: 3.873\tLives: 2\tReward: 294.0\tEpisode Mean: 207.0\n", - "87591:127650462\tQ-min: 3.137\tQ-max: 4.334\tLives: 2\tReward: 301.0\tEpisode Mean: 207.0\n", - "87591:127650469\tQ-min: 2.537\tQ-max: 3.330\tLives: 2\tReward: 305.0\tEpisode Mean: 207.0\n", - "87591:127650491\tQ-min: -0.535\tQ-max: 0.537\tLives: 1\tReward: 305.0\tEpisode Mean: 207.0\n", - "87591:127650552\tQ-min: 1.948\tQ-max: 2.805\tLives: 1\tReward: 309.0\tEpisode Mean: 207.0\n", - "87591:127650573\tQ-min: 2.012\tQ-max: 3.112\tLives: 1\tReward: 313.0\tEpisode Mean: 207.0\n", - "87591:127650586\tQ-min: -0.178\tQ-max: 0.125\tLives: 0\tReward: 313.0\tEpisode Mean: 233.5\n", - "87592:127650631\tQ-min: 1.738\tQ-max: 1.756\tLives: 5\tReward: 1.0\tEpisode Mean: 233.5\n", - "87592:127650681\tQ-min: 1.608\tQ-max: 1.681\tLives: 5\tReward: 2.0\tEpisode Mean: 233.5\n", - "87592:127650745\tQ-min: 1.693\tQ-max: 1.710\tLives: 5\tReward: 3.0\tEpisode Mean: 233.5\n", - "87592:127650791\tQ-min: 2.007\tQ-max: 2.043\tLives: 5\tReward: 4.0\tEpisode Mean: 233.5\n", - "87592:127650823\tQ-min: 1.982\tQ-max: 1.999\tLives: 5\tReward: 5.0\tEpisode Mean: 233.5\n", - "87592:127650855\tQ-min: 1.947\tQ-max: 1.978\tLives: 5\tReward: 6.0\tEpisode Mean: 233.5\n", - "87592:127650891\tQ-min: 1.860\tQ-max: 1.888\tLives: 5\tReward: 7.0\tEpisode Mean: 233.5\n", - "87592:127650936\tQ-min: 1.647\tQ-max: 1.686\tLives: 5\tReward: 8.0\tEpisode Mean: 233.5\n", - "87592:127650996\tQ-min: 1.627\tQ-max: 1.706\tLives: 5\tReward: 9.0\tEpisode Mean: 233.5\n", - "87592:127651058\tQ-min: 1.615\tQ-max: 1.658\tLives: 5\tReward: 10.0\tEpisode Mean: 233.5\n", - "87592:127651120\tQ-min: 1.606\tQ-max: 1.666\tLives: 5\tReward: 11.0\tEpisode Mean: 233.5\n", - "87592:127651171\tQ-min: 2.078\tQ-max: 2.122\tLives: 5\tReward: 15.0\tEpisode Mean: 233.5\n", - "87592:127651194\tQ-min: -0.342\tQ-max: 0.180\tLives: 4\tReward: 15.0\tEpisode Mean: 233.5\n", - "87592:127651249\tQ-min: 1.698\tQ-max: 1.723\tLives: 4\tReward: 16.0\tEpisode Mean: 233.5\n", - "87592:127651314\tQ-min: 1.764\tQ-max: 1.785\tLives: 4\tReward: 17.0\tEpisode Mean: 233.5\n", - "87592:127651374\tQ-min: 1.671\tQ-max: 1.684\tLives: 4\tReward: 18.0\tEpisode Mean: 233.5\n", - "87592:127651421\tQ-min: 1.929\tQ-max: 1.945\tLives: 4\tReward: 19.0\tEpisode Mean: 233.5\n", - "87592:127651453\tQ-min: 1.996\tQ-max: 2.010\tLives: 4\tReward: 20.0\tEpisode Mean: 233.5\n", - "87592:127651485\tQ-min: 1.951\tQ-max: 2.018\tLives: 4\tReward: 21.0\tEpisode Mean: 233.5\n", - "87592:127651521\tQ-min: 2.054\tQ-max: 2.101\tLives: 4\tReward: 25.0\tEpisode Mean: 233.5\n", - "87592:127651571\tQ-min: 1.697\tQ-max: 1.773\tLives: 4\tReward: 26.0\tEpisode Mean: 233.5\n", - "87592:127651639\tQ-min: 1.759\tQ-max: 1.799\tLives: 4\tReward: 30.0\tEpisode Mean: 233.5\n", - "87592:127651707\tQ-min: 1.742\tQ-max: 1.765\tLives: 4\tReward: 31.0\tEpisode Mean: 233.5\n", - "87592:127651752\tQ-min: 0.088\tQ-max: 0.286\tLives: 3\tReward: 31.0\tEpisode Mean: 233.5\n", - "87592:127651796\tQ-min: 1.841\tQ-max: 2.045\tLives: 3\tReward: 35.0\tEpisode Mean: 233.5\n", - "87592:127651844\tQ-min: 2.039\tQ-max: 2.095\tLives: 3\tReward: 39.0\tEpisode Mean: 233.5\n", - "87592:127651899\tQ-min: 1.763\tQ-max: 1.800\tLives: 3\tReward: 40.0\tEpisode Mean: 233.5\n", - "87592:127651950\tQ-min: 2.205\tQ-max: 2.345\tLives: 3\tReward: 44.0\tEpisode Mean: 233.5\n", - "87592:127651973\tQ-min: 2.190\tQ-max: 2.513\tLives: 3\tReward: 48.0\tEpisode Mean: 233.5\n", - "87592:127651997\tQ-min: 2.491\tQ-max: 2.628\tLives: 3\tReward: 52.0\tEpisode Mean: 233.5\n", - "87592:127652020\tQ-min: 2.352\tQ-max: 2.592\tLives: 3\tReward: 53.0\tEpisode Mean: 233.5\n", - "87592:127652042\tQ-min: 2.402\tQ-max: 2.633\tLives: 3\tReward: 57.0\tEpisode Mean: 233.5\n", - "87592:127652066\tQ-min: 2.368\tQ-max: 2.562\tLives: 3\tReward: 61.0\tEpisode Mean: 233.5\n", - "87592:127652088\tQ-min: 2.472\tQ-max: 2.575\tLives: 3\tReward: 62.0\tEpisode Mean: 233.5\n", - "87592:127652110\tQ-min: 2.395\tQ-max: 2.561\tLives: 3\tReward: 63.0\tEpisode Mean: 233.5\n", - "87592:127652132\tQ-min: 2.375\tQ-max: 2.585\tLives: 3\tReward: 67.0\tEpisode Mean: 233.5\n", - "87592:127652157\tQ-min: 2.473\tQ-max: 2.893\tLives: 3\tReward: 71.0\tEpisode Mean: 233.5\n", - "87592:127652176\tQ-min: 2.306\tQ-max: 2.499\tLives: 3\tReward: 72.0\tEpisode Mean: 233.5\n", - "87592:127652197\tQ-min: 2.349\tQ-max: 2.556\tLives: 3\tReward: 76.0\tEpisode Mean: 233.5\n", - "87592:127652210\tQ-min: -0.203\tQ-max: 0.078\tLives: 2\tReward: 76.0\tEpisode Mean: 233.5\n", - "87592:127652270\tQ-min: 2.092\tQ-max: 2.818\tLives: 2\tReward: 80.0\tEpisode Mean: 233.5\n", - "87592:127652291\tQ-min: 2.315\tQ-max: 2.478\tLives: 2\tReward: 81.0\tEpisode Mean: 233.5\n", - "87592:127652312\tQ-min: 2.388\tQ-max: 2.683\tLives: 2\tReward: 85.0\tEpisode Mean: 233.5\n", - "87592:127652333\tQ-min: 2.325\tQ-max: 2.544\tLives: 2\tReward: 86.0\tEpisode Mean: 233.5\n", - "87592:127652356\tQ-min: 2.417\tQ-max: 2.561\tLives: 2\tReward: 93.0\tEpisode Mean: 233.5\n", - "87592:127652380\tQ-min: 2.176\tQ-max: 2.591\tLives: 2\tReward: 97.0\tEpisode Mean: 233.5\n", - "87592:127652400\tQ-min: 2.240\tQ-max: 2.582\tLives: 2\tReward: 104.0\tEpisode Mean: 233.5\n", - "87592:127652423\tQ-min: 2.176\tQ-max: 2.865\tLives: 2\tReward: 111.0\tEpisode Mean: 233.5\n", - "87592:127652447\tQ-min: 2.034\tQ-max: 3.135\tLives: 2\tReward: 118.0\tEpisode Mean: 233.5\n", - "87592:127652472\tQ-min: 2.173\tQ-max: 3.220\tLives: 2\tReward: 125.0\tEpisode Mean: 233.5\n", - "87592:127652494\tQ-min: 2.264\tQ-max: 3.115\tLives: 2\tReward: 126.0\tEpisode Mean: 233.5\n", - "87592:127652514\tQ-min: 2.601\tQ-max: 3.100\tLives: 2\tReward: 127.0\tEpisode Mean: 233.5\n", - "87592:127652534\tQ-min: 2.156\tQ-max: 2.808\tLives: 2\tReward: 131.0\tEpisode Mean: 233.5\n", - "87592:127652555\tQ-min: 2.541\tQ-max: 2.808\tLives: 2\tReward: 132.0\tEpisode Mean: 233.5\n", - "87592:127652577\tQ-min: 2.678\tQ-max: 3.090\tLives: 2\tReward: 136.0\tEpisode Mean: 233.5\n", - "87592:127652590\tQ-min: 0.011\tQ-max: 0.967\tLives: 1\tReward: 136.0\tEpisode Mean: 233.5\n", - "87592:127652640\tQ-min: 2.543\tQ-max: 3.653\tLives: 1\tReward: 143.0\tEpisode Mean: 233.5\n", - "87592:127652665\tQ-min: 2.447\tQ-max: 4.720\tLives: 1\tReward: 150.0\tEpisode Mean: 233.5\n", - "87592:127652689\tQ-min: 2.704\tQ-max: 4.175\tLives: 1\tReward: 157.0\tEpisode Mean: 233.5\n", - "87592:127652711\tQ-min: 3.109\tQ-max: 4.645\tLives: 1\tReward: 161.0\tEpisode Mean: 233.5\n", - "87592:127652725\tQ-min: -0.014\tQ-max: 0.422\tLives: 0\tReward: 161.0\tEpisode Mean: 219.0\n", - "87593:127652768\tQ-min: 1.803\tQ-max: 1.816\tLives: 5\tReward: 1.0\tEpisode Mean: 219.0\n", - "87593:127652826\tQ-min: 1.633\tQ-max: 1.654\tLives: 5\tReward: 2.0\tEpisode Mean: 219.0\n", - "87593:127652889\tQ-min: 1.706\tQ-max: 1.731\tLives: 5\tReward: 3.0\tEpisode Mean: 219.0\n", - "87593:127652934\tQ-min: 1.998\tQ-max: 2.011\tLives: 5\tReward: 4.0\tEpisode Mean: 219.0\n", - "87593:127652964\tQ-min: 2.007\tQ-max: 2.038\tLives: 5\tReward: 5.0\tEpisode Mean: 219.0\n", - "87593:127652996\tQ-min: 1.879\tQ-max: 1.935\tLives: 5\tReward: 6.0\tEpisode Mean: 219.0\n", - "87593:127653026\tQ-min: 1.763\tQ-max: 1.822\tLives: 5\tReward: 7.0\tEpisode Mean: 219.0\n", - "87593:127653049\tQ-min: -0.249\tQ-max: 0.235\tLives: 4\tReward: 7.0\tEpisode Mean: 219.0\n", - "87593:127653091\tQ-min: 1.865\tQ-max: 1.886\tLives: 4\tReward: 8.0\tEpisode Mean: 219.0\n", - "87593:127653140\tQ-min: 1.693\tQ-max: 1.709\tLives: 4\tReward: 9.0\tEpisode Mean: 219.0\n", - "87593:127653201\tQ-min: 1.706\tQ-max: 1.724\tLives: 4\tReward: 10.0\tEpisode Mean: 219.0\n", - "87593:127653248\tQ-min: 1.998\tQ-max: 2.038\tLives: 4\tReward: 11.0\tEpisode Mean: 219.0\n", - "87593:127653281\tQ-min: 1.933\tQ-max: 2.007\tLives: 4\tReward: 12.0\tEpisode Mean: 219.0\n", - "87593:127653315\tQ-min: 1.936\tQ-max: 1.983\tLives: 4\tReward: 16.0\tEpisode Mean: 219.0\n", - "87593:127653350\tQ-min: 2.174\tQ-max: 2.322\tLives: 4\tReward: 20.0\tEpisode Mean: 219.0\n", - "87593:127653365\tQ-min: 0.036\tQ-max: 0.426\tLives: 3\tReward: 20.0\tEpisode Mean: 219.0\n", - "87593:127653408\tQ-min: 1.847\tQ-max: 1.862\tLives: 3\tReward: 21.0\tEpisode Mean: 219.0\n", - "87593:127653462\tQ-min: 1.701\tQ-max: 1.795\tLives: 3\tReward: 22.0\tEpisode Mean: 219.0\n", - "87593:127653529\tQ-min: 1.778\tQ-max: 1.801\tLives: 3\tReward: 23.0\tEpisode Mean: 219.0\n", - "87593:127653578\tQ-min: 2.010\tQ-max: 2.020\tLives: 3\tReward: 24.0\tEpisode Mean: 219.0\n", - "87593:127653611\tQ-min: 2.053\tQ-max: 2.078\tLives: 3\tReward: 25.0\tEpisode Mean: 219.0\n", - "87593:127653647\tQ-min: 1.991\tQ-max: 2.196\tLives: 3\tReward: 29.0\tEpisode Mean: 219.0\n", - "87593:127653682\tQ-min: 2.111\tQ-max: 2.268\tLives: 3\tReward: 30.0\tEpisode Mean: 219.0\n", - "87593:127653729\tQ-min: 1.767\tQ-max: 1.797\tLives: 3\tReward: 31.0\tEpisode Mean: 219.0\n", - "87593:127653799\tQ-min: 1.709\tQ-max: 1.831\tLives: 3\tReward: 32.0\tEpisode Mean: 219.0\n", - "87593:127653866\tQ-min: 1.755\tQ-max: 1.803\tLives: 3\tReward: 33.0\tEpisode Mean: 219.0\n", - "87593:127653933\tQ-min: 1.789\tQ-max: 1.811\tLives: 3\tReward: 34.0\tEpisode Mean: 219.0\n", - "87593:127653987\tQ-min: 2.132\tQ-max: 2.257\tLives: 3\tReward: 38.0\tEpisode Mean: 219.0\n", - "87593:127654008\tQ-min: 2.290\tQ-max: 2.414\tLives: 3\tReward: 39.0\tEpisode Mean: 219.0\n", - "87593:127654029\tQ-min: 2.187\tQ-max: 2.466\tLives: 3\tReward: 43.0\tEpisode Mean: 219.0\n", - "87593:127654049\tQ-min: 2.475\tQ-max: 2.549\tLives: 3\tReward: 47.0\tEpisode Mean: 219.0\n", - "87593:127654072\tQ-min: 2.380\tQ-max: 2.476\tLives: 3\tReward: 48.0\tEpisode Mean: 219.0\n", - "87593:127654092\tQ-min: 2.384\tQ-max: 2.493\tLives: 3\tReward: 52.0\tEpisode Mean: 219.0\n", - "87593:127654105\tQ-min: 0.072\tQ-max: 0.224\tLives: 2\tReward: 52.0\tEpisode Mean: 219.0\n", - "87593:127654162\tQ-min: 1.802\tQ-max: 1.834\tLives: 2\tReward: 53.0\tEpisode Mean: 219.0\n", - "87593:127654229\tQ-min: 1.875\tQ-max: 1.913\tLives: 2\tReward: 54.0\tEpisode Mean: 219.0\n", - "87593:127654301\tQ-min: 1.697\tQ-max: 1.889\tLives: 2\tReward: 58.0\tEpisode Mean: 219.0\n", - "87593:127654351\tQ-min: 2.277\tQ-max: 2.401\tLives: 2\tReward: 59.0\tEpisode Mean: 219.0\n", - "87593:127654375\tQ-min: 0.016\tQ-max: 0.213\tLives: 1\tReward: 59.0\tEpisode Mean: 219.0\n", - "87593:127654435\tQ-min: 1.763\tQ-max: 1.906\tLives: 1\tReward: 60.0\tEpisode Mean: 219.0\n", - "87593:127654499\tQ-min: 1.852\tQ-max: 1.911\tLives: 1\tReward: 61.0\tEpisode Mean: 219.0\n", - "87593:127654566\tQ-min: 1.833\tQ-max: 1.903\tLives: 1\tReward: 62.0\tEpisode Mean: 219.0\n", - "87593:127654618\tQ-min: 1.870\tQ-max: 2.419\tLives: 1\tReward: 66.0\tEpisode Mean: 219.0\n", - "87593:127654631\tQ-min: -0.017\tQ-max: 0.137\tLives: 0\tReward: 66.0\tEpisode Mean: 193.5\n", - "87594:127654685\tQ-min: 1.651\tQ-max: 1.682\tLives: 5\tReward: 1.0\tEpisode Mean: 193.5\n", - "87594:127654736\tQ-min: 1.850\tQ-max: 1.864\tLives: 5\tReward: 2.0\tEpisode Mean: 193.5\n", - "87594:127654780\tQ-min: 1.891\tQ-max: 1.934\tLives: 5\tReward: 3.0\tEpisode Mean: 193.5\n", - "87594:127654817\tQ-min: 1.999\tQ-max: 2.044\tLives: 5\tReward: 4.0\tEpisode Mean: 193.5\n", - "87594:127654850\tQ-min: 1.971\tQ-max: 1.982\tLives: 5\tReward: 5.0\tEpisode Mean: 193.5\n", - "87594:127654882\tQ-min: 1.880\tQ-max: 1.913\tLives: 5\tReward: 6.0\tEpisode Mean: 193.5\n", - "87594:127654916\tQ-min: 1.829\tQ-max: 1.878\tLives: 5\tReward: 10.0\tEpisode Mean: 193.5\n", - "87594:127654937\tQ-min: -0.237\tQ-max: 0.162\tLives: 4\tReward: 10.0\tEpisode Mean: 193.5\n", - "87594:127654990\tQ-min: 1.683\tQ-max: 1.710\tLives: 4\tReward: 11.0\tEpisode Mean: 193.5\n", - "87594:127655040\tQ-min: 1.909\tQ-max: 2.004\tLives: 4\tReward: 12.0\tEpisode Mean: 193.5\n", - "87594:127655083\tQ-min: 2.002\tQ-max: 2.042\tLives: 4\tReward: 16.0\tEpisode Mean: 193.5\n", - "87594:127655127\tQ-min: 1.953\tQ-max: 1.996\tLives: 4\tReward: 20.0\tEpisode Mean: 193.5\n", - "87594:127655164\tQ-min: 2.028\tQ-max: 2.074\tLives: 4\tReward: 21.0\tEpisode Mean: 193.5\n", - "87594:127655196\tQ-min: 2.035\tQ-max: 2.054\tLives: 4\tReward: 22.0\tEpisode Mean: 193.5\n", - "87594:127655229\tQ-min: 2.120\tQ-max: 2.405\tLives: 4\tReward: 26.0\tEpisode Mean: 193.5\n", - "87594:127655251\tQ-min: 2.246\tQ-max: 2.396\tLives: 4\tReward: 27.0\tEpisode Mean: 193.5\n", - "87594:127655268\tQ-min: 2.243\tQ-max: 2.392\tLives: 4\tReward: 28.0\tEpisode Mean: 193.5\n", - "87594:127655286\tQ-min: 2.245\tQ-max: 2.390\tLives: 4\tReward: 29.0\tEpisode Mean: 193.5\n", - "87594:127655308\tQ-min: 2.304\tQ-max: 2.416\tLives: 4\tReward: 30.0\tEpisode Mean: 193.5\n", - "87594:127655330\tQ-min: 2.293\tQ-max: 2.345\tLives: 4\tReward: 34.0\tEpisode Mean: 193.5\n", - "87594:127655343\tQ-min: -0.039\tQ-max: 0.290\tLives: 3\tReward: 34.0\tEpisode Mean: 193.5\n", - "87594:127655383\tQ-min: 2.074\tQ-max: 2.111\tLives: 3\tReward: 35.0\tEpisode Mean: 193.5\n", - "87594:127655434\tQ-min: 1.858\tQ-max: 1.963\tLives: 3\tReward: 36.0\tEpisode Mean: 193.5\n", - "87594:127655487\tQ-min: 2.120\tQ-max: 2.189\tLives: 3\tReward: 37.0\tEpisode Mean: 193.5\n", - "87594:127655527\tQ-min: 2.145\tQ-max: 2.264\tLives: 3\tReward: 41.0\tEpisode Mean: 193.5\n", - "87594:127655559\tQ-min: 2.238\tQ-max: 2.304\tLives: 3\tReward: 42.0\tEpisode Mean: 193.5\n", - "87594:127655580\tQ-min: -0.035\tQ-max: 0.260\tLives: 2\tReward: 42.0\tEpisode Mean: 193.5\n", - "87594:127655636\tQ-min: 1.821\tQ-max: 2.036\tLives: 2\tReward: 46.0\tEpisode Mean: 193.5\n", - "87594:127655705\tQ-min: 1.870\tQ-max: 1.949\tLives: 2\tReward: 47.0\tEpisode Mean: 193.5\n", - "87594:127655772\tQ-min: 1.753\tQ-max: 1.917\tLives: 2\tReward: 48.0\tEpisode Mean: 193.5\n", - "87594:127655821\tQ-min: 2.071\tQ-max: 2.226\tLives: 2\tReward: 49.0\tEpisode Mean: 193.5\n", - "87594:127655852\tQ-min: 2.208\tQ-max: 2.255\tLives: 2\tReward: 50.0\tEpisode Mean: 193.5\n", - "87594:127655885\tQ-min: 2.251\tQ-max: 2.297\tLives: 2\tReward: 51.0\tEpisode Mean: 193.5\n", - "87594:127655906\tQ-min: -0.061\tQ-max: 0.177\tLives: 1\tReward: 51.0\tEpisode Mean: 193.5\n", - "87594:127655952\tQ-min: 2.156\tQ-max: 2.214\tLives: 1\tReward: 52.0\tEpisode Mean: 193.5\n", - "87594:127655991\tQ-min: 2.186\tQ-max: 2.215\tLives: 1\tReward: 53.0\tEpisode Mean: 193.5\n", - "87594:127656034\tQ-min: 2.292\tQ-max: 2.545\tLives: 1\tReward: 57.0\tEpisode Mean: 193.5\n", - "87594:127656055\tQ-min: 2.133\tQ-max: 2.445\tLives: 1\tReward: 64.0\tEpisode Mean: 193.5\n", - "87594:127656076\tQ-min: 2.376\tQ-max: 2.508\tLives: 1\tReward: 65.0\tEpisode Mean: 193.5\n", - "87594:127656099\tQ-min: 2.311\tQ-max: 2.453\tLives: 1\tReward: 69.0\tEpisode Mean: 193.5\n", - "87594:127656122\tQ-min: 2.194\tQ-max: 2.632\tLives: 1\tReward: 73.0\tEpisode Mean: 193.5\n", - "87594:127656135\tQ-min: 0.041\tQ-max: 0.202\tLives: 0\tReward: 73.0\tEpisode Mean: 176.3\n", - "87595:127656179\tQ-min: 1.787\tQ-max: 1.804\tLives: 5\tReward: 1.0\tEpisode Mean: 176.3\n", - "87595:127656232\tQ-min: 1.614\tQ-max: 1.645\tLives: 5\tReward: 2.0\tEpisode Mean: 176.3\n", - "87595:127656293\tQ-min: 1.658\tQ-max: 1.683\tLives: 5\tReward: 3.0\tEpisode Mean: 176.3\n", - "87595:127656342\tQ-min: 2.007\tQ-max: 2.039\tLives: 5\tReward: 4.0\tEpisode Mean: 176.3\n", - "87595:127656371\tQ-min: 1.982\tQ-max: 2.006\tLives: 5\tReward: 5.0\tEpisode Mean: 176.3\n", - "87595:127656402\tQ-min: 1.909\tQ-max: 1.933\tLives: 5\tReward: 6.0\tEpisode Mean: 176.3\n", - "87595:127656436\tQ-min: 1.743\tQ-max: 1.803\tLives: 5\tReward: 7.0\tEpisode Mean: 176.3\n", - "87595:127656456\tQ-min: -0.313\tQ-max: 0.038\tLives: 4\tReward: 7.0\tEpisode Mean: 176.3\n", - "87595:127656499\tQ-min: 1.838\tQ-max: 1.848\tLives: 4\tReward: 8.0\tEpisode Mean: 176.3\n", - "87595:127656537\tQ-min: 1.963\tQ-max: 1.978\tLives: 4\tReward: 9.0\tEpisode Mean: 176.3\n", - "87595:127656590\tQ-min: 1.693\tQ-max: 1.720\tLives: 4\tReward: 10.0\tEpisode Mean: 176.3\n", - "87595:127656638\tQ-min: 1.960\tQ-max: 1.993\tLives: 4\tReward: 11.0\tEpisode Mean: 176.3\n", - "87595:127656657\tQ-min: -0.459\tQ-max: 0.010\tLives: 3\tReward: 11.0\tEpisode Mean: 176.3\n", - "87595:127656706\tQ-min: 1.820\tQ-max: 1.841\tLives: 3\tReward: 12.0\tEpisode Mean: 176.3\n", - "87595:127656748\tQ-min: 1.909\tQ-max: 1.929\tLives: 3\tReward: 13.0\tEpisode Mean: 176.3\n", - "87595:127656793\tQ-min: 1.927\tQ-max: 1.964\tLives: 3\tReward: 14.0\tEpisode Mean: 176.3\n", - "87595:127656833\tQ-min: 2.082\tQ-max: 2.122\tLives: 3\tReward: 18.0\tEpisode Mean: 176.3\n", - "87595:127656868\tQ-min: 1.989\tQ-max: 2.011\tLives: 3\tReward: 19.0\tEpisode Mean: 176.3\n", - "87595:127656900\tQ-min: 2.109\tQ-max: 2.127\tLives: 3\tReward: 23.0\tEpisode Mean: 176.3\n", - "87595:127656934\tQ-min: 2.041\tQ-max: 2.196\tLives: 3\tReward: 24.0\tEpisode Mean: 176.3\n", - "87595:127656983\tQ-min: 1.759\tQ-max: 1.890\tLives: 3\tReward: 25.0\tEpisode Mean: 176.3\n", - "87595:127657045\tQ-min: 1.616\tQ-max: 1.682\tLives: 3\tReward: 26.0\tEpisode Mean: 176.3\n", - "87595:127657107\tQ-min: 1.712\tQ-max: 1.735\tLives: 3\tReward: 27.0\tEpisode Mean: 176.3\n", - "87595:127657169\tQ-min: 1.645\tQ-max: 1.729\tLives: 3\tReward: 28.0\tEpisode Mean: 176.3\n", - "87595:127657218\tQ-min: 2.122\tQ-max: 2.151\tLives: 3\tReward: 29.0\tEpisode Mean: 176.3\n", - "87595:127657251\tQ-min: 2.031\tQ-max: 2.106\tLives: 3\tReward: 30.0\tEpisode Mean: 176.3\n", - "87595:127657285\tQ-min: 2.027\tQ-max: 2.106\tLives: 3\tReward: 34.0\tEpisode Mean: 176.3\n", - "87595:127657318\tQ-min: 2.200\tQ-max: 2.330\tLives: 3\tReward: 38.0\tEpisode Mean: 176.3\n", - "87595:127657356\tQ-min: 2.014\tQ-max: 2.550\tLives: 3\tReward: 42.0\tEpisode Mean: 176.3\n", - "87595:127657379\tQ-min: 2.378\tQ-max: 2.420\tLives: 3\tReward: 46.0\tEpisode Mean: 176.3\n", - "87595:127657402\tQ-min: 2.039\tQ-max: 2.519\tLives: 3\tReward: 50.0\tEpisode Mean: 176.3\n", - "87595:127657423\tQ-min: 2.143\tQ-max: 2.505\tLives: 3\tReward: 54.0\tEpisode Mean: 176.3\n", - "87595:127657436\tQ-min: 0.032\tQ-max: 0.222\tLives: 2\tReward: 54.0\tEpisode Mean: 176.3\n", - "87595:127657488\tQ-min: 1.849\tQ-max: 1.884\tLives: 2\tReward: 55.0\tEpisode Mean: 176.3\n", - "87595:127657542\tQ-min: 2.099\tQ-max: 2.417\tLives: 2\tReward: 62.0\tEpisode Mean: 176.3\n", - "87595:127657558\tQ-min: 0.040\tQ-max: 0.255\tLives: 1\tReward: 62.0\tEpisode Mean: 176.3\n", - "87595:127657615\tQ-min: 1.877\tQ-max: 1.897\tLives: 1\tReward: 63.0\tEpisode Mean: 176.3\n", - "87595:127657683\tQ-min: 1.949\tQ-max: 2.054\tLives: 1\tReward: 64.0\tEpisode Mean: 176.3\n", - "87595:127657735\tQ-min: 2.190\tQ-max: 2.218\tLives: 1\tReward: 65.0\tEpisode Mean: 176.3\n", - "87595:127657773\tQ-min: 2.213\tQ-max: 2.361\tLives: 1\tReward: 69.0\tEpisode Mean: 176.3\n", - "87595:127657812\tQ-min: 2.257\tQ-max: 2.287\tLives: 1\tReward: 70.0\tEpisode Mean: 176.3\n", - "87595:127657846\tQ-min: 2.189\tQ-max: 2.253\tLives: 1\tReward: 71.0\tEpisode Mean: 176.3\n", - "87595:127657881\tQ-min: 2.073\tQ-max: 2.765\tLives: 1\tReward: 78.0\tEpisode Mean: 176.3\n", - "87595:127657904\tQ-min: 2.204\tQ-max: 2.673\tLives: 1\tReward: 85.0\tEpisode Mean: 176.3\n", - "87595:127657925\tQ-min: 2.340\tQ-max: 2.609\tLives: 1\tReward: 89.0\tEpisode Mean: 176.3\n", - "87595:127657946\tQ-min: 2.491\tQ-max: 2.714\tLives: 1\tReward: 96.0\tEpisode Mean: 176.3\n", - "87595:127657963\tQ-min: -0.053\tQ-max: 0.182\tLives: 0\tReward: 96.0\tEpisode Mean: 166.2\n", - "87596:127657995\tQ-min: 0.108\tQ-max: 0.173\tLives: 4\tReward: 0.0\tEpisode Mean: 166.2\n", - "87596:127658040\tQ-min: 1.857\tQ-max: 1.868\tLives: 4\tReward: 1.0\tEpisode Mean: 166.2\n", - "87596:127658091\tQ-min: 1.656\tQ-max: 1.679\tLives: 4\tReward: 2.0\tEpisode Mean: 166.2\n", - "87596:127658146\tQ-min: 1.914\tQ-max: 1.934\tLives: 4\tReward: 3.0\tEpisode Mean: 166.2\n", - "87596:127658180\tQ-min: 1.994\tQ-max: 2.027\tLives: 4\tReward: 4.0\tEpisode Mean: 166.2\n", - "87596:127658215\tQ-min: 1.923\tQ-max: 1.957\tLives: 4\tReward: 5.0\tEpisode Mean: 166.2\n", - "87596:127658246\tQ-min: 1.904\tQ-max: 1.943\tLives: 4\tReward: 6.0\tEpisode Mean: 166.2\n", - "87596:127658277\tQ-min: 1.882\tQ-max: 1.923\tLives: 4\tReward: 7.0\tEpisode Mean: 166.2\n", - "87596:127658297\tQ-min: -0.045\tQ-max: 0.174\tLives: 3\tReward: 7.0\tEpisode Mean: 166.2\n", - "87596:127658341\tQ-min: 1.873\tQ-max: 1.892\tLives: 3\tReward: 8.0\tEpisode Mean: 166.2\n", - "87596:127658396\tQ-min: 1.697\tQ-max: 1.740\tLives: 3\tReward: 9.0\tEpisode Mean: 166.2\n", - "87596:127658450\tQ-min: 1.920\tQ-max: 1.935\tLives: 3\tReward: 10.0\tEpisode Mean: 166.2\n", - "87596:127658492\tQ-min: 1.899\tQ-max: 1.933\tLives: 3\tReward: 11.0\tEpisode Mean: 166.2\n", - "87596:127658523\tQ-min: 1.970\tQ-max: 2.013\tLives: 3\tReward: 12.0\tEpisode Mean: 166.2\n", - "87596:127658557\tQ-min: 2.045\tQ-max: 2.082\tLives: 3\tReward: 16.0\tEpisode Mean: 166.2\n", - "87596:127658589\tQ-min: 2.011\tQ-max: 2.075\tLives: 3\tReward: 17.0\tEpisode Mean: 166.2\n", - "87596:127658635\tQ-min: 1.676\tQ-max: 1.695\tLives: 3\tReward: 18.0\tEpisode Mean: 166.2\n", - "87596:127658696\tQ-min: 1.684\tQ-max: 1.778\tLives: 3\tReward: 19.0\tEpisode Mean: 166.2\n", - "87596:127658764\tQ-min: 1.651\tQ-max: 1.702\tLives: 3\tReward: 20.0\tEpisode Mean: 166.2\n", - "87596:127658827\tQ-min: 1.536\tQ-max: 1.713\tLives: 3\tReward: 21.0\tEpisode Mean: 166.2\n", - "87596:127658880\tQ-min: 1.987\tQ-max: 2.016\tLives: 3\tReward: 22.0\tEpisode Mean: 166.2\n", - "87596:127658911\tQ-min: 2.022\tQ-max: 2.084\tLives: 3\tReward: 23.0\tEpisode Mean: 166.2\n", - "87596:127658941\tQ-min: 2.085\tQ-max: 2.137\tLives: 3\tReward: 24.0\tEpisode Mean: 166.2\n", - "87596:127658975\tQ-min: 2.006\tQ-max: 2.028\tLives: 3\tReward: 25.0\tEpisode Mean: 166.2\n", - "87596:127659009\tQ-min: 1.986\tQ-max: 2.021\tLives: 3\tReward: 26.0\tEpisode Mean: 166.2\n", - "87596:127659040\tQ-min: 1.968\tQ-max: 2.005\tLives: 3\tReward: 27.0\tEpisode Mean: 166.2\n", - "87596:127659073\tQ-min: 1.963\tQ-max: 2.024\tLives: 3\tReward: 31.0\tEpisode Mean: 166.2\n", - "87596:127659106\tQ-min: 2.057\tQ-max: 2.103\tLives: 3\tReward: 32.0\tEpisode Mean: 166.2\n", - "87596:127659126\tQ-min: -0.127\tQ-max: 0.197\tLives: 2\tReward: 32.0\tEpisode Mean: 166.2\n", - "87596:127659171\tQ-min: 1.811\tQ-max: 1.927\tLives: 2\tReward: 36.0\tEpisode Mean: 166.2\n", - "87596:127659220\tQ-min: 2.315\tQ-max: 2.631\tLives: 2\tReward: 40.0\tEpisode Mean: 166.2\n", - "87596:127659241\tQ-min: 2.398\tQ-max: 2.459\tLives: 2\tReward: 44.0\tEpisode Mean: 166.2\n", - "87596:127659263\tQ-min: 2.251\tQ-max: 2.506\tLives: 2\tReward: 48.0\tEpisode Mean: 166.2\n", - "87596:127659284\tQ-min: 2.396\tQ-max: 2.576\tLives: 2\tReward: 55.0\tEpisode Mean: 166.2\n", - "87596:127659310\tQ-min: 2.400\tQ-max: 2.546\tLives: 2\tReward: 59.0\tEpisode Mean: 166.2\n", - "87596:127659333\tQ-min: 2.413\tQ-max: 2.656\tLives: 2\tReward: 63.0\tEpisode Mean: 166.2\n", - "87596:127659353\tQ-min: 2.393\tQ-max: 2.510\tLives: 2\tReward: 64.0\tEpisode Mean: 166.2\n", - "87596:127659367\tQ-min: 0.114\tQ-max: 0.326\tLives: 1\tReward: 64.0\tEpisode Mean: 166.2\n", - "87596:127659412\tQ-min: 2.210\tQ-max: 2.251\tLives: 1\tReward: 65.0\tEpisode Mean: 166.2\n", - "87596:127659466\tQ-min: 1.759\tQ-max: 1.894\tLives: 1\tReward: 66.0\tEpisode Mean: 166.2\n", - "87596:127659525\tQ-min: 2.140\tQ-max: 2.177\tLives: 1\tReward: 67.0\tEpisode Mean: 166.2\n", - "87596:127659555\tQ-min: -0.112\tQ-max: 0.265\tLives: 0\tReward: 67.0\tEpisode Mean: 155.2\n", - "87597:127659610\tQ-min: 1.648\tQ-max: 1.659\tLives: 5\tReward: 1.0\tEpisode Mean: 155.2\n", - "87597:127659675\tQ-min: 1.680\tQ-max: 1.706\tLives: 5\tReward: 2.0\tEpisode Mean: 155.2\n", - "87597:127659734\tQ-min: 1.666\tQ-max: 1.694\tLives: 5\tReward: 3.0\tEpisode Mean: 155.2\n", - "87597:127659781\tQ-min: 1.980\tQ-max: 2.012\tLives: 5\tReward: 4.0\tEpisode Mean: 155.2\n", - "87597:127659813\tQ-min: 1.925\tQ-max: 1.954\tLives: 5\tReward: 5.0\tEpisode Mean: 155.2\n", - "87597:127659847\tQ-min: 1.937\tQ-max: 1.965\tLives: 5\tReward: 6.0\tEpisode Mean: 155.2\n", - "87597:127659880\tQ-min: 1.783\tQ-max: 1.804\tLives: 5\tReward: 7.0\tEpisode Mean: 155.2\n", - "87597:127659930\tQ-min: 1.663\tQ-max: 1.684\tLives: 5\tReward: 8.0\tEpisode Mean: 155.2\n", - "87597:127659994\tQ-min: 1.693\tQ-max: 1.731\tLives: 5\tReward: 9.0\tEpisode Mean: 155.2\n", - "87597:127660061\tQ-min: 1.694\tQ-max: 1.711\tLives: 5\tReward: 10.0\tEpisode Mean: 155.2\n", - "87597:127660122\tQ-min: 1.646\tQ-max: 1.711\tLives: 5\tReward: 11.0\tEpisode Mean: 155.2\n", - "87597:127660171\tQ-min: 1.918\tQ-max: 1.954\tLives: 5\tReward: 12.0\tEpisode Mean: 155.2\n", - "87597:127660203\tQ-min: 1.916\tQ-max: 1.961\tLives: 5\tReward: 13.0\tEpisode Mean: 155.2\n", - "87597:127660239\tQ-min: 1.961\tQ-max: 2.006\tLives: 5\tReward: 17.0\tEpisode Mean: 155.2\n", - "87597:127660273\tQ-min: 1.975\tQ-max: 2.003\tLives: 5\tReward: 18.0\tEpisode Mean: 155.2\n", - "87597:127660309\tQ-min: 1.822\tQ-max: 1.941\tLives: 5\tReward: 19.0\tEpisode Mean: 155.2\n", - "87597:127660343\tQ-min: 2.056\tQ-max: 2.079\tLives: 5\tReward: 20.0\tEpisode Mean: 155.2\n", - "87597:127660382\tQ-min: 1.792\tQ-max: 2.034\tLives: 5\tReward: 24.0\tEpisode Mean: 155.2\n", - "87597:127660416\tQ-min: 2.032\tQ-max: 2.174\tLives: 5\tReward: 25.0\tEpisode Mean: 155.2\n", - "87597:127660446\tQ-min: 2.066\tQ-max: 2.134\tLives: 5\tReward: 26.0\tEpisode Mean: 155.2\n", - "87597:127660470\tQ-min: -0.343\tQ-max: -0.004\tLives: 4\tReward: 26.0\tEpisode Mean: 155.2\n", - "87597:127660516\tQ-min: 1.972\tQ-max: 2.023\tLives: 4\tReward: 27.0\tEpisode Mean: 155.2\n", - "87597:127660567\tQ-min: 1.783\tQ-max: 1.794\tLives: 4\tReward: 28.0\tEpisode Mean: 155.2\n", - "87597:127660620\tQ-min: 2.041\tQ-max: 2.067\tLives: 4\tReward: 29.0\tEpisode Mean: 155.2\n", - "87597:127660663\tQ-min: 1.952\tQ-max: 2.087\tLives: 4\tReward: 30.0\tEpisode Mean: 155.2\n", - "87597:127660699\tQ-min: 2.036\tQ-max: 2.089\tLives: 4\tReward: 31.0\tEpisode Mean: 155.2\n", - "87597:127660735\tQ-min: 2.154\tQ-max: 2.557\tLives: 4\tReward: 35.0\tEpisode Mean: 155.2\n", - "87597:127660758\tQ-min: 2.147\tQ-max: 2.391\tLives: 4\tReward: 36.0\tEpisode Mean: 155.2\n", - "87597:127660778\tQ-min: 2.132\tQ-max: 2.248\tLives: 4\tReward: 37.0\tEpisode Mean: 155.2\n", - "87597:127660790\tQ-min: -0.046\tQ-max: 0.138\tLives: 3\tReward: 37.0\tEpisode Mean: 155.2\n", - "87597:127660845\tQ-min: 1.827\tQ-max: 1.883\tLives: 3\tReward: 38.0\tEpisode Mean: 155.2\n", - "87597:127660908\tQ-min: 1.814\tQ-max: 1.831\tLives: 3\tReward: 39.0\tEpisode Mean: 155.2\n", - "87597:127660964\tQ-min: 2.022\tQ-max: 2.082\tLives: 3\tReward: 40.0\tEpisode Mean: 155.2\n", - "87597:127661005\tQ-min: 2.147\tQ-max: 2.237\tLives: 3\tReward: 44.0\tEpisode Mean: 155.2\n", - "87597:127661038\tQ-min: 2.118\tQ-max: 2.151\tLives: 3\tReward: 45.0\tEpisode Mean: 155.2\n", - "87597:127661061\tQ-min: -1.010\tQ-max: 0.276\tLives: 2\tReward: 45.0\tEpisode Mean: 155.2\n", - "87597:127661107\tQ-min: 2.039\tQ-max: 2.158\tLives: 2\tReward: 49.0\tEpisode Mean: 155.2\n", - "87597:127661158\tQ-min: 1.998\tQ-max: 2.660\tLives: 2\tReward: 53.0\tEpisode Mean: 155.2\n", - "87597:127661181\tQ-min: 2.162\tQ-max: 2.353\tLives: 2\tReward: 57.0\tEpisode Mean: 155.2\n", - "87597:127661194\tQ-min: 0.028\tQ-max: 0.147\tLives: 1\tReward: 57.0\tEpisode Mean: 155.2\n", - "87597:127661243\tQ-min: 1.971\tQ-max: 2.025\tLives: 1\tReward: 61.0\tEpisode Mean: 155.2\n", - "87597:127661293\tQ-min: 2.387\tQ-max: 2.612\tLives: 1\tReward: 65.0\tEpisode Mean: 155.2\n", - "87597:127661307\tQ-min: -0.086\tQ-max: 0.145\tLives: 0\tReward: 65.0\tEpisode Mean: 146.2\n", - "87598:127661349\tQ-min: 1.720\tQ-max: 1.728\tLives: 5\tReward: 1.0\tEpisode Mean: 146.2\n", - "87598:127661392\tQ-min: 1.806\tQ-max: 1.829\tLives: 5\tReward: 2.0\tEpisode Mean: 146.2\n", - "87598:127661434\tQ-min: 1.861\tQ-max: 1.879\tLives: 5\tReward: 3.0\tEpisode Mean: 146.2\n", - "87598:127661471\tQ-min: 1.963\tQ-max: 1.981\tLives: 5\tReward: 4.0\tEpisode Mean: 146.2\n", - "87598:127661506\tQ-min: 1.988\tQ-max: 2.017\tLives: 5\tReward: 5.0\tEpisode Mean: 146.2\n", - "87598:127661527\tQ-min: -0.344\tQ-max: 0.013\tLives: 4\tReward: 5.0\tEpisode Mean: 146.2\n", - "87598:127661571\tQ-min: 1.917\tQ-max: 1.947\tLives: 4\tReward: 6.0\tEpisode Mean: 146.2\n", - "87598:127661611\tQ-min: 1.861\tQ-max: 1.881\tLives: 4\tReward: 7.0\tEpisode Mean: 146.2\n", - "87598:127661665\tQ-min: 1.710\tQ-max: 1.722\tLives: 4\tReward: 8.0\tEpisode Mean: 146.2\n", - "87598:127661713\tQ-min: 1.970\tQ-max: 2.007\tLives: 4\tReward: 9.0\tEpisode Mean: 146.2\n", - "87598:127661746\tQ-min: 1.936\tQ-max: 1.950\tLives: 4\tReward: 10.0\tEpisode Mean: 146.2\n", - "87598:127661767\tQ-min: 0.092\tQ-max: 0.242\tLives: 3\tReward: 10.0\tEpisode Mean: 146.2\n", - "87598:127661820\tQ-min: 1.649\tQ-max: 1.680\tLives: 3\tReward: 11.0\tEpisode Mean: 146.2\n", - "87598:127661876\tQ-min: 1.952\tQ-max: 2.013\tLives: 3\tReward: 12.0\tEpisode Mean: 146.2\n", - "87598:127661899\tQ-min: -0.122\tQ-max: 0.316\tLives: 2\tReward: 12.0\tEpisode Mean: 146.2\n", - "87598:127661943\tQ-min: 1.914\tQ-max: 1.941\tLives: 2\tReward: 13.0\tEpisode Mean: 146.2\n", - "87598:127661993\tQ-min: 1.659\tQ-max: 1.671\tLives: 2\tReward: 14.0\tEpisode Mean: 146.2\n", - "87598:127662061\tQ-min: 1.709\tQ-max: 1.782\tLives: 2\tReward: 15.0\tEpisode Mean: 146.2\n", - "87598:127662109\tQ-min: 1.955\tQ-max: 1.971\tLives: 2\tReward: 16.0\tEpisode Mean: 146.2\n", - "87598:127662142\tQ-min: 1.949\tQ-max: 1.971\tLives: 2\tReward: 17.0\tEpisode Mean: 146.2\n", - "87598:127662173\tQ-min: 1.925\tQ-max: 1.955\tLives: 2\tReward: 18.0\tEpisode Mean: 146.2\n", - "87598:127662208\tQ-min: 2.023\tQ-max: 2.068\tLives: 2\tReward: 22.0\tEpisode Mean: 146.2\n", - "87598:127662256\tQ-min: 1.701\tQ-max: 1.749\tLives: 2\tReward: 23.0\tEpisode Mean: 146.2\n", - "87598:127662319\tQ-min: 1.701\tQ-max: 1.724\tLives: 2\tReward: 24.0\tEpisode Mean: 146.2\n", - "87598:127662392\tQ-min: 1.767\tQ-max: 1.814\tLives: 2\tReward: 28.0\tEpisode Mean: 146.2\n", - "87598:127662461\tQ-min: 1.711\tQ-max: 1.729\tLives: 2\tReward: 29.0\tEpisode Mean: 146.2\n", - "87598:127662513\tQ-min: 2.016\tQ-max: 2.035\tLives: 2\tReward: 30.0\tEpisode Mean: 146.2\n", - "87598:127662549\tQ-min: 2.117\tQ-max: 2.186\tLives: 2\tReward: 31.0\tEpisode Mean: 146.2\n", - "87598:127662581\tQ-min: 2.076\tQ-max: 2.144\tLives: 2\tReward: 35.0\tEpisode Mean: 146.2\n", - "87598:127662616\tQ-min: 2.132\tQ-max: 2.203\tLives: 2\tReward: 36.0\tEpisode Mean: 146.2\n", - "87598:127662651\tQ-min: 1.996\tQ-max: 2.062\tLives: 2\tReward: 37.0\tEpisode Mean: 146.2\n", - "87598:127662685\tQ-min: 2.059\tQ-max: 2.100\tLives: 2\tReward: 38.0\tEpisode Mean: 146.2\n", - "87598:127662718\tQ-min: 2.218\tQ-max: 2.223\tLives: 2\tReward: 42.0\tEpisode Mean: 146.2\n", - "87598:127662753\tQ-min: 2.221\tQ-max: 2.278\tLives: 2\tReward: 43.0\tEpisode Mean: 146.2\n", - "87598:127662787\tQ-min: 2.014\tQ-max: 2.063\tLives: 2\tReward: 47.0\tEpisode Mean: 146.2\n", - "87598:127662822\tQ-min: 2.088\tQ-max: 2.438\tLives: 2\tReward: 51.0\tEpisode Mean: 146.2\n", - "87598:127662844\tQ-min: 2.259\tQ-max: 2.415\tLives: 2\tReward: 52.0\tEpisode Mean: 146.2\n", - "87598:127662864\tQ-min: 2.365\tQ-max: 2.445\tLives: 2\tReward: 56.0\tEpisode Mean: 146.2\n", - "87598:127662884\tQ-min: 2.356\tQ-max: 2.439\tLives: 2\tReward: 60.0\tEpisode Mean: 146.2\n", - "87598:127662905\tQ-min: 2.422\tQ-max: 2.500\tLives: 2\tReward: 64.0\tEpisode Mean: 146.2\n", - "87598:127662919\tQ-min: 0.224\tQ-max: 0.494\tLives: 1\tReward: 64.0\tEpisode Mean: 146.2\n", - "87598:127662966\tQ-min: 2.476\tQ-max: 2.743\tLives: 1\tReward: 68.0\tEpisode Mean: 146.2\n", - "87598:127662989\tQ-min: 2.496\tQ-max: 2.813\tLives: 1\tReward: 69.0\tEpisode Mean: 146.2\n", - "87598:127663010\tQ-min: 2.405\tQ-max: 2.518\tLives: 1\tReward: 73.0\tEpisode Mean: 146.2\n", - "87598:127663024\tQ-min: -0.284\tQ-max: 0.232\tLives: 0\tReward: 73.0\tEpisode Mean: 139.5\n", - "87599:127663067\tQ-min: 1.753\tQ-max: 1.762\tLives: 5\tReward: 1.0\tEpisode Mean: 139.5\n", - "87599:127663109\tQ-min: 1.800\tQ-max: 1.813\tLives: 5\tReward: 2.0\tEpisode Mean: 139.5\n", - "87599:127663160\tQ-min: 1.662\tQ-max: 1.687\tLives: 5\tReward: 3.0\tEpisode Mean: 139.5\n", - "87599:127663207\tQ-min: 1.974\tQ-max: 1.987\tLives: 5\tReward: 4.0\tEpisode Mean: 139.5\n", - "87599:127663242\tQ-min: 1.966\tQ-max: 1.994\tLives: 5\tReward: 5.0\tEpisode Mean: 139.5\n", - "87599:127663274\tQ-min: 1.901\tQ-max: 1.921\tLives: 5\tReward: 6.0\tEpisode Mean: 139.5\n", - "87599:127663308\tQ-min: 1.814\tQ-max: 1.833\tLives: 5\tReward: 7.0\tEpisode Mean: 139.5\n", - "87599:127663357\tQ-min: 1.644\tQ-max: 1.661\tLives: 5\tReward: 8.0\tEpisode Mean: 139.5\n", - "87599:127663424\tQ-min: 1.685\tQ-max: 1.703\tLives: 5\tReward: 9.0\tEpisode Mean: 139.5\n", - "87599:127663486\tQ-min: 1.663\tQ-max: 1.695\tLives: 5\tReward: 10.0\tEpisode Mean: 139.5\n", - "87599:127663556\tQ-min: 1.684\tQ-max: 1.699\tLives: 5\tReward: 11.0\tEpisode Mean: 139.5\n", - "87599:127663606\tQ-min: 2.030\tQ-max: 2.084\tLives: 5\tReward: 15.0\tEpisode Mean: 139.5\n", - "87599:127663628\tQ-min: -0.448\tQ-max: 0.082\tLives: 4\tReward: 15.0\tEpisode Mean: 139.5\n", - "87599:127663684\tQ-min: 1.722\tQ-max: 1.736\tLives: 4\tReward: 16.0\tEpisode Mean: 139.5\n", - "87599:127663748\tQ-min: 1.720\tQ-max: 1.772\tLives: 4\tReward: 17.0\tEpisode Mean: 139.5\n", - "87599:127663813\tQ-min: 1.686\tQ-max: 1.717\tLives: 4\tReward: 18.0\tEpisode Mean: 139.5\n", - "87599:127663861\tQ-min: 2.008\tQ-max: 2.113\tLives: 4\tReward: 22.0\tEpisode Mean: 139.5\n", - "87599:127663896\tQ-min: 2.009\tQ-max: 2.052\tLives: 4\tReward: 23.0\tEpisode Mean: 139.5\n", - "87599:127663929\tQ-min: 2.039\tQ-max: 2.057\tLives: 4\tReward: 24.0\tEpisode Mean: 139.5\n", - "87599:127663961\tQ-min: 2.076\tQ-max: 2.184\tLives: 4\tReward: 25.0\tEpisode Mean: 139.5\n", - "87599:127664008\tQ-min: 1.692\tQ-max: 1.707\tLives: 4\tReward: 26.0\tEpisode Mean: 139.5\n", - "87599:127664077\tQ-min: 1.739\tQ-max: 1.818\tLives: 4\tReward: 27.0\tEpisode Mean: 139.5\n", - "87599:127664144\tQ-min: 1.713\tQ-max: 1.725\tLives: 4\tReward: 28.0\tEpisode Mean: 139.5\n", - "87599:127664208\tQ-min: 1.743\tQ-max: 1.789\tLives: 4\tReward: 29.0\tEpisode Mean: 139.5\n", - "87599:127664255\tQ-min: 2.060\tQ-max: 2.139\tLives: 4\tReward: 30.0\tEpisode Mean: 139.5\n", - "87599:127664291\tQ-min: 2.060\tQ-max: 2.154\tLives: 4\tReward: 34.0\tEpisode Mean: 139.5\n", - "87599:127664325\tQ-min: 2.065\tQ-max: 2.095\tLives: 4\tReward: 35.0\tEpisode Mean: 139.5\n", - "87599:127664358\tQ-min: 2.066\tQ-max: 2.166\tLives: 4\tReward: 36.0\tEpisode Mean: 139.5\n", - "87599:127664391\tQ-min: 1.992\tQ-max: 2.050\tLives: 4\tReward: 37.0\tEpisode Mean: 139.5\n", - "87599:127664420\tQ-min: 2.124\tQ-max: 2.158\tLives: 4\tReward: 38.0\tEpisode Mean: 139.5\n", - "87599:127664455\tQ-min: 2.036\tQ-max: 2.213\tLives: 4\tReward: 42.0\tEpisode Mean: 139.5\n", - "87599:127664490\tQ-min: 2.089\tQ-max: 2.205\tLives: 4\tReward: 43.0\tEpisode Mean: 139.5\n", - "87599:127664513\tQ-min: -0.521\tQ-max: -0.111\tLives: 3\tReward: 43.0\tEpisode Mean: 139.5\n", - "87599:127664559\tQ-min: 1.911\tQ-max: 1.972\tLives: 3\tReward: 44.0\tEpisode Mean: 139.5\n", - "87599:127664607\tQ-min: 2.068\tQ-max: 2.141\tLives: 3\tReward: 45.0\tEpisode Mean: 139.5\n", - "87599:127664656\tQ-min: 2.022\tQ-max: 2.095\tLives: 3\tReward: 49.0\tEpisode Mean: 139.5\n", - "87599:127664699\tQ-min: 2.059\tQ-max: 2.756\tLives: 3\tReward: 53.0\tEpisode Mean: 139.5\n", - "87599:127664719\tQ-min: 2.275\tQ-max: 2.449\tLives: 3\tReward: 54.0\tEpisode Mean: 139.5\n", - "87599:127664732\tQ-min: 0.060\tQ-max: 0.301\tLives: 2\tReward: 54.0\tEpisode Mean: 139.5\n", - "87599:127664778\tQ-min: 2.109\tQ-max: 2.157\tLives: 2\tReward: 58.0\tEpisode Mean: 139.5\n", - "87599:127664820\tQ-min: 2.384\tQ-max: 2.539\tLives: 2\tReward: 62.0\tEpisode Mean: 139.5\n", - "87599:127664842\tQ-min: 2.198\tQ-max: 2.455\tLives: 2\tReward: 66.0\tEpisode Mean: 139.5\n", - "87599:127664864\tQ-min: 2.476\tQ-max: 2.600\tLives: 2\tReward: 70.0\tEpisode Mean: 139.5\n", - "87599:127664886\tQ-min: 2.233\tQ-max: 2.521\tLives: 2\tReward: 74.0\tEpisode Mean: 139.5\n", - "87599:127664907\tQ-min: 2.210\tQ-max: 2.531\tLives: 2\tReward: 78.0\tEpisode Mean: 139.5\n", - "87599:127664929\tQ-min: 2.236\tQ-max: 2.470\tLives: 2\tReward: 82.0\tEpisode Mean: 139.5\n", - "87599:127664951\tQ-min: 2.313\tQ-max: 2.487\tLives: 2\tReward: 83.0\tEpisode Mean: 139.5\n", - "87599:127664973\tQ-min: 2.177\tQ-max: 2.553\tLives: 2\tReward: 87.0\tEpisode Mean: 139.5\n", - "87599:127664987\tQ-min: 0.015\tQ-max: 0.203\tLives: 1\tReward: 87.0\tEpisode Mean: 139.5\n", - "87599:127665035\tQ-min: 2.091\tQ-max: 2.143\tLives: 1\tReward: 91.0\tEpisode Mean: 139.5\n", - "87599:127665089\tQ-min: 1.995\tQ-max: 2.141\tLives: 1\tReward: 92.0\tEpisode Mean: 139.5\n", - "87599:127665161\tQ-min: 1.824\tQ-max: 2.351\tLives: 1\tReward: 96.0\tEpisode Mean: 139.5\n", - "87599:127665203\tQ-min: -0.230\tQ-max: -0.095\tLives: 0\tReward: 96.0\tEpisode Mean: 135.9\n", - "87600:127665258\tQ-min: 1.623\tQ-max: 1.681\tLives: 5\tReward: 1.0\tEpisode Mean: 135.9\n", - "87600:127665323\tQ-min: 1.673\tQ-max: 1.694\tLives: 5\tReward: 2.0\tEpisode Mean: 135.9\n", - "87600:127665382\tQ-min: 1.696\tQ-max: 1.725\tLives: 5\tReward: 3.0\tEpisode Mean: 135.9\n", - "87600:127665429\tQ-min: 1.982\tQ-max: 2.019\tLives: 5\tReward: 4.0\tEpisode Mean: 135.9\n", - "87600:127665458\tQ-min: 1.942\tQ-max: 1.968\tLives: 5\tReward: 5.0\tEpisode Mean: 135.9\n", - "87600:127665490\tQ-min: 2.015\tQ-max: 2.054\tLives: 5\tReward: 6.0\tEpisode Mean: 135.9\n", - "87600:127665526\tQ-min: 1.801\tQ-max: 1.817\tLives: 5\tReward: 7.0\tEpisode Mean: 135.9\n", - "87600:127665574\tQ-min: 1.669\tQ-max: 1.709\tLives: 5\tReward: 8.0\tEpisode Mean: 135.9\n", - "87600:127665615\tQ-min: -0.153\tQ-max: 0.143\tLives: 4\tReward: 8.0\tEpisode Mean: 135.9\n", - "87600:127665660\tQ-min: 1.875\tQ-max: 1.899\tLives: 4\tReward: 9.0\tEpisode Mean: 135.9\n", - "87600:127665705\tQ-min: 1.866\tQ-max: 1.891\tLives: 4\tReward: 10.0\tEpisode Mean: 135.9\n", - "87600:127665747\tQ-min: 1.927\tQ-max: 1.947\tLives: 4\tReward: 11.0\tEpisode Mean: 135.9\n", - "87600:127665784\tQ-min: 1.979\tQ-max: 2.002\tLives: 4\tReward: 12.0\tEpisode Mean: 135.9\n", - "87600:127665815\tQ-min: 1.992\tQ-max: 2.034\tLives: 4\tReward: 13.0\tEpisode Mean: 135.9\n", - "87600:127665849\tQ-min: 1.990\tQ-max: 2.037\tLives: 4\tReward: 17.0\tEpisode Mean: 135.9\n", - "87600:127665882\tQ-min: 1.974\tQ-max: 2.028\tLives: 4\tReward: 18.0\tEpisode Mean: 135.9\n", - "87600:127665933\tQ-min: 1.660\tQ-max: 1.804\tLives: 4\tReward: 19.0\tEpisode Mean: 135.9\n", - "87600:127665995\tQ-min: 1.712\tQ-max: 1.726\tLives: 4\tReward: 20.0\tEpisode Mean: 135.9\n", - "87600:127666054\tQ-min: 1.695\tQ-max: 1.713\tLives: 4\tReward: 21.0\tEpisode Mean: 135.9\n", - "87600:127666119\tQ-min: 1.669\tQ-max: 1.699\tLives: 4\tReward: 22.0\tEpisode Mean: 135.9\n", - "87600:127666168\tQ-min: 1.967\tQ-max: 2.047\tLives: 4\tReward: 23.0\tEpisode Mean: 135.9\n", - "87600:127666189\tQ-min: 0.049\tQ-max: 0.201\tLives: 3\tReward: 23.0\tEpisode Mean: 135.9\n", - "87600:127666233\tQ-min: 1.899\tQ-max: 1.930\tLives: 3\tReward: 24.0\tEpisode Mean: 135.9\n", - "87600:127666287\tQ-min: 1.692\tQ-max: 1.740\tLives: 3\tReward: 25.0\tEpisode Mean: 135.9\n", - "87600:127666345\tQ-min: 1.973\tQ-max: 2.027\tLives: 3\tReward: 29.0\tEpisode Mean: 135.9\n", - "87600:127666390\tQ-min: 2.341\tQ-max: 2.606\tLives: 3\tReward: 33.0\tEpisode Mean: 135.9\n", - "87600:127666409\tQ-min: 2.472\tQ-max: 2.503\tLives: 3\tReward: 34.0\tEpisode Mean: 135.9\n", - "87600:127666429\tQ-min: 2.264\tQ-max: 2.515\tLives: 3\tReward: 38.0\tEpisode Mean: 135.9\n", - "87600:127666447\tQ-min: 2.428\tQ-max: 2.572\tLives: 3\tReward: 39.0\tEpisode Mean: 135.9\n", - "87600:127666466\tQ-min: 2.377\tQ-max: 2.562\tLives: 3\tReward: 43.0\tEpisode Mean: 135.9\n", - "87600:127666488\tQ-min: 2.328\tQ-max: 2.461\tLives: 3\tReward: 47.0\tEpisode Mean: 135.9\n", - "87600:127666510\tQ-min: 2.380\tQ-max: 2.607\tLives: 3\tReward: 51.0\tEpisode Mean: 135.9\n", - "87600:127666533\tQ-min: 2.363\tQ-max: 2.568\tLives: 3\tReward: 55.0\tEpisode Mean: 135.9\n", - "87600:127666553\tQ-min: 2.401\tQ-max: 2.500\tLives: 3\tReward: 56.0\tEpisode Mean: 135.9\n", - "87600:127666575\tQ-min: 2.114\tQ-max: 2.592\tLives: 3\tReward: 60.0\tEpisode Mean: 135.9\n", - "87600:127666598\tQ-min: 2.280\tQ-max: 2.538\tLives: 3\tReward: 64.0\tEpisode Mean: 135.9\n", - "87600:127666620\tQ-min: 2.412\tQ-max: 2.506\tLives: 3\tReward: 65.0\tEpisode Mean: 135.9\n", - "87600:127666632\tQ-min: 0.158\tQ-max: 0.395\tLives: 2\tReward: 65.0\tEpisode Mean: 135.9\n", - "87600:127666681\tQ-min: 2.151\tQ-max: 2.262\tLives: 2\tReward: 69.0\tEpisode Mean: 135.9\n", - "87600:127666728\tQ-min: 2.302\tQ-max: 2.573\tLives: 2\tReward: 73.0\tEpisode Mean: 135.9\n", - "87600:127666748\tQ-min: 2.387\tQ-max: 2.597\tLives: 2\tReward: 77.0\tEpisode Mean: 135.9\n", - "87600:127666769\tQ-min: 2.162\tQ-max: 2.485\tLives: 2\tReward: 81.0\tEpisode Mean: 135.9\n", - "87600:127666791\tQ-min: 2.353\tQ-max: 2.633\tLives: 2\tReward: 85.0\tEpisode Mean: 135.9\n", - "87600:127666811\tQ-min: 2.475\tQ-max: 2.603\tLives: 2\tReward: 86.0\tEpisode Mean: 135.9\n", - "87600:127666832\tQ-min: 1.714\tQ-max: 2.607\tLives: 2\tReward: 93.0\tEpisode Mean: 135.9\n", - "87600:127666847\tQ-min: 0.025\tQ-max: 0.334\tLives: 1\tReward: 93.0\tEpisode Mean: 135.9\n", - "87600:127666884\tQ-min: -0.352\tQ-max: 0.459\tLives: 0\tReward: 93.0\tEpisode Mean: 132.6\n", - "87601:127666925\tQ-min: 1.749\tQ-max: 1.771\tLives: 5\tReward: 1.0\tEpisode Mean: 132.6\n", - "87601:127666967\tQ-min: 1.770\tQ-max: 1.802\tLives: 5\tReward: 2.0\tEpisode Mean: 132.6\n", - "87601:127667011\tQ-min: 1.867\tQ-max: 1.932\tLives: 5\tReward: 3.0\tEpisode Mean: 132.6\n", - "87601:127667047\tQ-min: 1.946\tQ-max: 1.969\tLives: 5\tReward: 4.0\tEpisode Mean: 132.6\n", - "87601:127667077\tQ-min: 1.974\tQ-max: 2.042\tLives: 5\tReward: 5.0\tEpisode Mean: 132.6\n", - "87601:127667111\tQ-min: 1.965\tQ-max: 1.987\tLives: 5\tReward: 6.0\tEpisode Mean: 132.6\n", - "87601:127667142\tQ-min: 1.820\tQ-max: 1.833\tLives: 5\tReward: 7.0\tEpisode Mean: 132.6\n", - "87601:127667162\tQ-min: -0.263\tQ-max: 0.170\tLives: 4\tReward: 7.0\tEpisode Mean: 132.6\n", - "87601:127667206\tQ-min: 1.866\tQ-max: 1.882\tLives: 4\tReward: 8.0\tEpisode Mean: 132.6\n", - "87601:127667250\tQ-min: 1.988\tQ-max: 2.027\tLives: 4\tReward: 9.0\tEpisode Mean: 132.6\n", - "87601:127667294\tQ-min: 1.814\tQ-max: 1.851\tLives: 4\tReward: 10.0\tEpisode Mean: 132.6\n", - "87601:127667331\tQ-min: 1.933\tQ-max: 1.976\tLives: 4\tReward: 11.0\tEpisode Mean: 132.6\n", - "87601:127667363\tQ-min: 1.970\tQ-max: 2.004\tLives: 4\tReward: 12.0\tEpisode Mean: 132.6\n", - "87601:127667397\tQ-min: 1.976\tQ-max: 2.032\tLives: 4\tReward: 16.0\tEpisode Mean: 132.6\n", - "87601:127667432\tQ-min: 1.974\tQ-max: 2.020\tLives: 4\tReward: 17.0\tEpisode Mean: 132.6\n", - "87601:127667478\tQ-min: 1.677\tQ-max: 1.694\tLives: 4\tReward: 18.0\tEpisode Mean: 132.6\n", - "87601:127667544\tQ-min: 1.690\tQ-max: 1.733\tLives: 4\tReward: 19.0\tEpisode Mean: 132.6\n", - "87601:127667606\tQ-min: 1.794\tQ-max: 1.832\tLives: 4\tReward: 20.0\tEpisode Mean: 132.6\n", - "87601:127667671\tQ-min: 1.756\tQ-max: 1.784\tLives: 4\tReward: 21.0\tEpisode Mean: 132.6\n", - "87601:127667718\tQ-min: 2.080\tQ-max: 2.130\tLives: 4\tReward: 22.0\tEpisode Mean: 132.6\n", - "87601:127667751\tQ-min: 2.029\tQ-max: 2.068\tLives: 4\tReward: 23.0\tEpisode Mean: 132.6\n", - "87601:127667781\tQ-min: 2.010\tQ-max: 2.046\tLives: 4\tReward: 24.0\tEpisode Mean: 132.6\n", - "87601:127667812\tQ-min: 2.073\tQ-max: 2.228\tLives: 4\tReward: 28.0\tEpisode Mean: 132.6\n", - "87601:127667835\tQ-min: -0.028\tQ-max: 0.177\tLives: 3\tReward: 28.0\tEpisode Mean: 132.6\n", - "87601:127667891\tQ-min: 1.767\tQ-max: 1.842\tLives: 3\tReward: 29.0\tEpisode Mean: 132.6\n", - "87601:127667946\tQ-min: 1.970\tQ-max: 2.021\tLives: 3\tReward: 30.0\tEpisode Mean: 132.6\n", - "87601:127667991\tQ-min: 2.062\tQ-max: 2.180\tLives: 3\tReward: 31.0\tEpisode Mean: 132.6\n", - "87601:127668030\tQ-min: 2.031\tQ-max: 2.068\tLives: 3\tReward: 35.0\tEpisode Mean: 132.6\n", - "87601:127668063\tQ-min: 2.128\tQ-max: 2.416\tLives: 3\tReward: 39.0\tEpisode Mean: 132.6\n", - "87601:127668085\tQ-min: 2.294\tQ-max: 2.450\tLives: 3\tReward: 43.0\tEpisode Mean: 132.6\n", - "87601:127668108\tQ-min: 2.309\tQ-max: 2.417\tLives: 3\tReward: 47.0\tEpisode Mean: 132.6\n", - "87601:127668130\tQ-min: 2.342\tQ-max: 2.450\tLives: 3\tReward: 54.0\tEpisode Mean: 132.6\n", - "87601:127668154\tQ-min: 2.212\tQ-max: 2.443\tLives: 3\tReward: 58.0\tEpisode Mean: 132.6\n", - "87601:127668176\tQ-min: 2.301\tQ-max: 2.701\tLives: 3\tReward: 65.0\tEpisode Mean: 132.6\n", - "87601:127668199\tQ-min: 2.417\tQ-max: 2.585\tLives: 3\tReward: 69.0\tEpisode Mean: 132.6\n", - "87601:127668226\tQ-min: 2.306\tQ-max: 2.719\tLives: 3\tReward: 76.0\tEpisode Mean: 132.6\n", - "87601:127668247\tQ-min: 2.650\tQ-max: 3.426\tLives: 3\tReward: 80.0\tEpisode Mean: 132.6\n", - "87601:127668270\tQ-min: 1.950\tQ-max: 3.292\tLives: 3\tReward: 84.0\tEpisode Mean: 132.6\n", - "87601:127668284\tQ-min: 0.406\tQ-max: 0.723\tLives: 2\tReward: 84.0\tEpisode Mean: 132.6\n", - "87601:127668333\tQ-min: 2.601\tQ-max: 3.435\tLives: 2\tReward: 91.0\tEpisode Mean: 132.6\n", - "87601:127668364\tQ-min: 2.556\tQ-max: 5.411\tLives: 2\tReward: 98.0\tEpisode Mean: 132.6\n", - "87601:127668369\tQ-min: 2.444\tQ-max: 5.953\tLives: 2\tReward: 105.0\tEpisode Mean: 132.6\n", - "87601:127668374\tQ-min: 3.765\tQ-max: 5.552\tLives: 2\tReward: 112.0\tEpisode Mean: 132.6\n", - "87601:127668378\tQ-min: 3.498\tQ-max: 6.066\tLives: 2\tReward: 119.0\tEpisode Mean: 132.6\n", - "87601:127668384\tQ-min: 3.315\tQ-max: 6.838\tLives: 2\tReward: 126.0\tEpisode Mean: 132.6\n", - "87601:127668388\tQ-min: 3.629\tQ-max: 6.227\tLives: 2\tReward: 133.0\tEpisode Mean: 132.6\n", - "87601:127668394\tQ-min: 3.125\tQ-max: 5.766\tLives: 2\tReward: 140.0\tEpisode Mean: 132.6\n", - "87601:127668399\tQ-min: 1.872\tQ-max: 5.354\tLives: 2\tReward: 147.0\tEpisode Mean: 132.6\n", - "87601:127668405\tQ-min: 1.891\tQ-max: 5.260\tLives: 2\tReward: 154.0\tEpisode Mean: 132.6\n", - "87601:127668444\tQ-min: 3.421\tQ-max: 5.474\tLives: 2\tReward: 161.0\tEpisode Mean: 132.6\n", - "87601:127668450\tQ-min: 3.133\tQ-max: 5.921\tLives: 2\tReward: 168.0\tEpisode Mean: 132.6\n", - "87601:127668454\tQ-min: 1.863\tQ-max: 5.537\tLives: 2\tReward: 175.0\tEpisode Mean: 132.6\n", - "87601:127668459\tQ-min: 1.319\tQ-max: 5.649\tLives: 2\tReward: 182.0\tEpisode Mean: 132.6\n", - "87601:127668463\tQ-min: 3.745\tQ-max: 5.352\tLives: 2\tReward: 189.0\tEpisode Mean: 132.6\n", - "87601:127668468\tQ-min: 3.931\tQ-max: 5.100\tLives: 2\tReward: 196.0\tEpisode Mean: 132.6\n", - "87601:127668473\tQ-min: 3.457\tQ-max: 5.721\tLives: 2\tReward: 203.0\tEpisode Mean: 132.6\n", - "87601:127668479\tQ-min: 2.423\tQ-max: 3.470\tLives: 2\tReward: 210.0\tEpisode Mean: 132.6\n", - "87601:127668488\tQ-min: 1.242\tQ-max: 2.573\tLives: 2\tReward: 211.0\tEpisode Mean: 132.6\n", - "87601:127668496\tQ-min: 1.938\tQ-max: 3.770\tLives: 2\tReward: 218.0\tEpisode Mean: 132.6\n", - "87601:127668503\tQ-min: 2.297\tQ-max: 3.498\tLives: 2\tReward: 222.0\tEpisode Mean: 132.6\n", - "87601:127668511\tQ-min: 1.545\tQ-max: 3.395\tLives: 2\tReward: 226.0\tEpisode Mean: 132.6\n", - "87601:127668520\tQ-min: 1.693\tQ-max: 3.364\tLives: 2\tReward: 233.0\tEpisode Mean: 132.6\n", - "87601:127668527\tQ-min: 1.683\tQ-max: 3.130\tLives: 2\tReward: 240.0\tEpisode Mean: 132.6\n", - "87601:127668565\tQ-min: 1.745\tQ-max: 5.647\tLives: 2\tReward: 247.0\tEpisode Mean: 132.6\n", - "87601:127668571\tQ-min: 1.912\tQ-max: 3.448\tLives: 2\tReward: 251.0\tEpisode Mean: 132.6\n", - "87601:127668611\tQ-min: 1.719\tQ-max: 3.410\tLives: 2\tReward: 255.0\tEpisode Mean: 132.6\n", - "87601:127668619\tQ-min: 2.959\tQ-max: 4.002\tLives: 2\tReward: 259.0\tEpisode Mean: 132.6\n", - "87601:127668655\tQ-min: 0.753\tQ-max: 4.262\tLives: 2\tReward: 263.0\tEpisode Mean: 132.6\n", - "87601:127668663\tQ-min: 2.337\tQ-max: 4.141\tLives: 2\tReward: 267.0\tEpisode Mean: 132.6\n", - "87601:127668715\tQ-min: 0.070\tQ-max: 0.779\tLives: 1\tReward: 267.0\tEpisode Mean: 132.6\n", - "87601:127668780\tQ-min: 0.650\tQ-max: 2.531\tLives: 1\tReward: 274.0\tEpisode Mean: 132.6\n", - "87601:127668794\tQ-min: 0.176\tQ-max: 0.366\tLives: 0\tReward: 274.0\tEpisode Mean: 142.7\n", - "87602:127668849\tQ-min: 1.660\tQ-max: 1.689\tLives: 5\tReward: 1.0\tEpisode Mean: 142.7\n", - "87602:127668901\tQ-min: 1.762\tQ-max: 1.772\tLives: 5\tReward: 2.0\tEpisode Mean: 142.7\n", - "87602:127668955\tQ-min: 1.668\tQ-max: 1.699\tLives: 5\tReward: 3.0\tEpisode Mean: 142.7\n", - "87602:127669000\tQ-min: 1.975\tQ-max: 2.019\tLives: 5\tReward: 4.0\tEpisode Mean: 142.7\n", - "87602:127669020\tQ-min: -0.215\tQ-max: 0.113\tLives: 4\tReward: 4.0\tEpisode Mean: 142.7\n", - "87602:127669074\tQ-min: 1.651\tQ-max: 1.672\tLives: 4\tReward: 5.0\tEpisode Mean: 142.7\n", - "87602:127669129\tQ-min: 1.880\tQ-max: 1.922\tLives: 4\tReward: 6.0\tEpisode Mean: 142.7\n", - "87602:127669171\tQ-min: 1.946\tQ-max: 1.986\tLives: 4\tReward: 10.0\tEpisode Mean: 142.7\n", - "87602:127669212\tQ-min: 1.944\tQ-max: 2.002\tLives: 4\tReward: 11.0\tEpisode Mean: 142.7\n", - "87602:127669242\tQ-min: 1.973\tQ-max: 2.019\tLives: 4\tReward: 12.0\tEpisode Mean: 142.7\n", - "87602:127669274\tQ-min: 1.926\tQ-max: 1.975\tLives: 4\tReward: 13.0\tEpisode Mean: 142.7\n", - "87602:127669307\tQ-min: 2.105\tQ-max: 2.278\tLives: 4\tReward: 17.0\tEpisode Mean: 142.7\n", - "87602:127669357\tQ-min: 1.747\tQ-max: 1.796\tLives: 4\tReward: 18.0\tEpisode Mean: 142.7\n", - "87602:127669417\tQ-min: 1.715\tQ-max: 1.727\tLives: 4\tReward: 19.0\tEpisode Mean: 142.7\n", - "87602:127669482\tQ-min: 1.681\tQ-max: 1.718\tLives: 4\tReward: 20.0\tEpisode Mean: 142.7\n", - "87602:127669553\tQ-min: 1.743\tQ-max: 1.765\tLives: 4\tReward: 21.0\tEpisode Mean: 142.7\n", - "87602:127669603\tQ-min: 2.072\tQ-max: 2.117\tLives: 4\tReward: 22.0\tEpisode Mean: 142.7\n", - "87602:127669636\tQ-min: 2.084\tQ-max: 2.119\tLives: 4\tReward: 23.0\tEpisode Mean: 142.7\n", - "87602:127669670\tQ-min: 2.010\tQ-max: 2.060\tLives: 4\tReward: 24.0\tEpisode Mean: 142.7\n", - "87602:127669704\tQ-min: 1.898\tQ-max: 1.979\tLives: 4\tReward: 28.0\tEpisode Mean: 142.7\n", - "87602:127669738\tQ-min: 2.096\tQ-max: 2.180\tLives: 4\tReward: 29.0\tEpisode Mean: 142.7\n", - "87602:127669772\tQ-min: 2.142\tQ-max: 2.170\tLives: 4\tReward: 33.0\tEpisode Mean: 142.7\n", - "87602:127669807\tQ-min: 2.072\tQ-max: 2.183\tLives: 4\tReward: 34.0\tEpisode Mean: 142.7\n", - "87602:127669841\tQ-min: 2.174\tQ-max: 2.224\tLives: 4\tReward: 38.0\tEpisode Mean: 142.7\n", - "87602:127669873\tQ-min: 2.093\tQ-max: 2.191\tLives: 4\tReward: 39.0\tEpisode Mean: 142.7\n", - "87602:127669910\tQ-min: 2.197\tQ-max: 2.437\tLives: 4\tReward: 43.0\tEpisode Mean: 142.7\n", - "87602:127669931\tQ-min: 2.435\tQ-max: 2.479\tLives: 4\tReward: 44.0\tEpisode Mean: 142.7\n", - "87602:127669952\tQ-min: 2.251\tQ-max: 2.526\tLives: 4\tReward: 45.0\tEpisode Mean: 142.7\n", - "87602:127669974\tQ-min: 2.238\tQ-max: 2.452\tLives: 4\tReward: 46.0\tEpisode Mean: 142.7\n", - "87602:127669993\tQ-min: 2.353\tQ-max: 2.468\tLives: 4\tReward: 47.0\tEpisode Mean: 142.7\n", - "87602:127670013\tQ-min: 2.226\tQ-max: 2.469\tLives: 4\tReward: 51.0\tEpisode Mean: 142.7\n", - "87602:127670035\tQ-min: 2.220\tQ-max: 2.600\tLives: 4\tReward: 55.0\tEpisode Mean: 142.7\n", - "87602:127670058\tQ-min: 2.358\tQ-max: 2.584\tLives: 4\tReward: 59.0\tEpisode Mean: 142.7\n", - "87602:127670082\tQ-min: 2.341\tQ-max: 2.521\tLives: 4\tReward: 63.0\tEpisode Mean: 142.7\n", - "87602:127670103\tQ-min: 2.369\tQ-max: 2.522\tLives: 4\tReward: 67.0\tEpisode Mean: 142.7\n", - "87602:127670126\tQ-min: 1.917\tQ-max: 2.413\tLives: 4\tReward: 68.0\tEpisode Mean: 142.7\n", - "87602:127670145\tQ-min: 2.342\tQ-max: 2.609\tLives: 4\tReward: 69.0\tEpisode Mean: 142.7\n", - "87602:127670158\tQ-min: -0.303\tQ-max: 0.424\tLives: 3\tReward: 69.0\tEpisode Mean: 142.7\n", - "87602:127670202\tQ-min: 2.325\tQ-max: 2.465\tLives: 3\tReward: 73.0\tEpisode Mean: 142.7\n", - "87602:127670224\tQ-min: 2.308\tQ-max: 2.635\tLives: 3\tReward: 74.0\tEpisode Mean: 142.7\n", - "87602:127670245\tQ-min: 2.460\tQ-max: 2.646\tLives: 3\tReward: 78.0\tEpisode Mean: 142.7\n", - "87602:127670269\tQ-min: 2.455\tQ-max: 2.712\tLives: 3\tReward: 85.0\tEpisode Mean: 142.7\n", - "87602:127670292\tQ-min: 2.240\tQ-max: 2.845\tLives: 3\tReward: 92.0\tEpisode Mean: 142.7\n", - "87602:127670315\tQ-min: 2.779\tQ-max: 3.246\tLives: 3\tReward: 96.0\tEpisode Mean: 142.7\n", - "87602:127670328\tQ-min: 0.010\tQ-max: 0.268\tLives: 2\tReward: 96.0\tEpisode Mean: 142.7\n", - "87602:127670382\tQ-min: 0.438\tQ-max: 2.764\tLives: 2\tReward: 103.0\tEpisode Mean: 142.7\n", - "87602:127670406\tQ-min: 2.238\tQ-max: 4.566\tLives: 2\tReward: 110.0\tEpisode Mean: 142.7\n", - "87602:127670429\tQ-min: 2.586\tQ-max: 3.575\tLives: 2\tReward: 114.0\tEpisode Mean: 142.7\n", - "87602:127670457\tQ-min: 2.830\tQ-max: 6.067\tLives: 2\tReward: 121.0\tEpisode Mean: 142.7\n", - "87602:127670462\tQ-min: 3.539\tQ-max: 6.227\tLives: 2\tReward: 128.0\tEpisode Mean: 142.7\n", - "87602:127670467\tQ-min: 2.655\tQ-max: 5.876\tLives: 2\tReward: 135.0\tEpisode Mean: 142.7\n", - "87602:127670472\tQ-min: 2.309\tQ-max: 6.446\tLives: 2\tReward: 142.0\tEpisode Mean: 142.7\n", - "87602:127670478\tQ-min: 2.977\tQ-max: 6.508\tLives: 2\tReward: 149.0\tEpisode Mean: 142.7\n", - "87602:127670483\tQ-min: 2.145\tQ-max: 5.609\tLives: 2\tReward: 156.0\tEpisode Mean: 142.7\n", - "87602:127670487\tQ-min: 1.552\tQ-max: 5.483\tLives: 2\tReward: 163.0\tEpisode Mean: 142.7\n", - "87602:127670493\tQ-min: 0.675\tQ-max: 6.091\tLives: 2\tReward: 170.0\tEpisode Mean: 142.7\n", - "87602:127670499\tQ-min: 3.525\tQ-max: 5.526\tLives: 2\tReward: 177.0\tEpisode Mean: 142.7\n", - "87602:127670506\tQ-min: 3.911\tQ-max: 5.610\tLives: 2\tReward: 184.0\tEpisode Mean: 142.7\n", - "87602:127670513\tQ-min: 3.107\tQ-max: 4.286\tLives: 2\tReward: 191.0\tEpisode Mean: 142.7\n", - "87602:127670519\tQ-min: 3.875\tQ-max: 5.261\tLives: 2\tReward: 198.0\tEpisode Mean: 142.7\n", - "87602:127670524\tQ-min: 3.681\tQ-max: 4.988\tLives: 2\tReward: 205.0\tEpisode Mean: 142.7\n", - "87602:127670530\tQ-min: 3.829\tQ-max: 5.252\tLives: 2\tReward: 212.0\tEpisode Mean: 142.7\n", - "87602:127670537\tQ-min: 3.661\tQ-max: 5.947\tLives: 2\tReward: 219.0\tEpisode Mean: 142.7\n", - "87602:127670543\tQ-min: 0.818\tQ-max: 6.100\tLives: 2\tReward: 226.0\tEpisode Mean: 142.7\n", - "87602:127670550\tQ-min: 1.349\tQ-max: 4.426\tLives: 2\tReward: 233.0\tEpisode Mean: 142.7\n", - "87602:127670556\tQ-min: 3.577\tQ-max: 4.942\tLives: 2\tReward: 240.0\tEpisode Mean: 142.7\n", - "87602:127670563\tQ-min: 1.653\tQ-max: 3.518\tLives: 2\tReward: 244.0\tEpisode Mean: 142.7\n", - "87602:127670569\tQ-min: 1.497\tQ-max: 4.850\tLives: 2\tReward: 251.0\tEpisode Mean: 142.7\n", - "87602:127670606\tQ-min: 3.148\tQ-max: 6.722\tLives: 2\tReward: 258.0\tEpisode Mean: 142.7\n", - "87602:127670611\tQ-min: 3.101\tQ-max: 4.130\tLives: 2\tReward: 265.0\tEpisode Mean: 142.7\n", - "87602:127670616\tQ-min: 4.503\tQ-max: 5.602\tLives: 2\tReward: 272.0\tEpisode Mean: 142.7\n", - "87602:127670623\tQ-min: 2.842\tQ-max: 5.075\tLives: 2\tReward: 279.0\tEpisode Mean: 142.7\n", - "87602:127670629\tQ-min: 2.633\tQ-max: 5.970\tLives: 2\tReward: 286.0\tEpisode Mean: 142.7\n", - "87602:127670635\tQ-min: 2.501\tQ-max: 5.630\tLives: 2\tReward: 293.0\tEpisode Mean: 142.7\n", - "87602:127670641\tQ-min: 2.565\tQ-max: 5.003\tLives: 2\tReward: 300.0\tEpisode Mean: 142.7\n", - "87602:127670648\tQ-min: 1.812\tQ-max: 2.868\tLives: 2\tReward: 304.0\tEpisode Mean: 142.7\n", - "87602:127670655\tQ-min: 2.751\tQ-max: 3.801\tLives: 2\tReward: 311.0\tEpisode Mean: 142.7\n", - "87602:127670661\tQ-min: 2.372\tQ-max: 4.040\tLives: 2\tReward: 318.0\tEpisode Mean: 142.7\n", - "87602:127670682\tQ-min: -0.290\tQ-max: 0.641\tLives: 1\tReward: 318.0\tEpisode Mean: 142.7\n", - "87602:127670749\tQ-min: 1.195\tQ-max: 2.524\tLives: 1\tReward: 322.0\tEpisode Mean: 142.7\n", - "87602:127670757\tQ-min: 1.663\tQ-max: 3.343\tLives: 1\tReward: 326.0\tEpisode Mean: 142.7\n", - "87602:127670781\tQ-min: -0.047\tQ-max: 0.144\tLives: 0\tReward: 326.0\tEpisode Mean: 154.9\n", - "87603:127670833\tQ-min: 1.652\tQ-max: 1.663\tLives: 5\tReward: 1.0\tEpisode Mean: 154.9\n", - "87603:127670884\tQ-min: 1.845\tQ-max: 1.871\tLives: 5\tReward: 2.0\tEpisode Mean: 154.9\n", - "87603:127670925\tQ-min: 1.900\tQ-max: 1.927\tLives: 5\tReward: 3.0\tEpisode Mean: 154.9\n", - "87603:127670963\tQ-min: 2.001\tQ-max: 2.066\tLives: 5\tReward: 4.0\tEpisode Mean: 154.9\n", - "87603:127670996\tQ-min: 1.983\tQ-max: 2.002\tLives: 5\tReward: 5.0\tEpisode Mean: 154.9\n", - "87603:127671027\tQ-min: 1.927\tQ-max: 1.968\tLives: 5\tReward: 6.0\tEpisode Mean: 154.9\n", - "87603:127671060\tQ-min: 1.748\tQ-max: 1.767\tLives: 5\tReward: 7.0\tEpisode Mean: 154.9\n", - "87603:127671081\tQ-min: -0.046\tQ-max: 0.168\tLives: 4\tReward: 7.0\tEpisode Mean: 154.9\n", - "87603:127671130\tQ-min: 1.708\tQ-max: 1.726\tLives: 4\tReward: 8.0\tEpisode Mean: 154.9\n", - "87603:127671184\tQ-min: 1.876\tQ-max: 1.902\tLives: 4\tReward: 9.0\tEpisode Mean: 154.9\n", - "87603:127671225\tQ-min: 1.927\tQ-max: 1.958\tLives: 4\tReward: 10.0\tEpisode Mean: 154.9\n", - "87603:127671266\tQ-min: 2.034\tQ-max: 2.070\tLives: 4\tReward: 11.0\tEpisode Mean: 154.9\n", - "87603:127671303\tQ-min: 1.926\tQ-max: 1.949\tLives: 4\tReward: 12.0\tEpisode Mean: 154.9\n", - "87603:127671336\tQ-min: 2.010\tQ-max: 2.036\tLives: 4\tReward: 16.0\tEpisode Mean: 154.9\n", - "87603:127671366\tQ-min: 1.950\tQ-max: 1.971\tLives: 4\tReward: 17.0\tEpisode Mean: 154.9\n", - 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0.204\tLives: 2\tReward: 32.0\tEpisode Mean: 154.9\n", - "87603:127671947\tQ-min: 2.019\tQ-max: 2.045\tLives: 2\tReward: 33.0\tEpisode Mean: 154.9\n", - "87603:127672004\tQ-min: 1.658\tQ-max: 1.772\tLives: 2\tReward: 34.0\tEpisode Mean: 154.9\n", - "87603:127672069\tQ-min: 1.687\tQ-max: 1.790\tLives: 2\tReward: 35.0\tEpisode Mean: 154.9\n", - "87603:127672121\tQ-min: 2.131\tQ-max: 2.251\tLives: 2\tReward: 36.0\tEpisode Mean: 154.9\n", - "87603:127672157\tQ-min: 2.166\tQ-max: 2.329\tLives: 2\tReward: 37.0\tEpisode Mean: 154.9\n", - "87603:127672190\tQ-min: 2.115\tQ-max: 2.157\tLives: 2\tReward: 41.0\tEpisode Mean: 154.9\n", - "87603:127672224\tQ-min: 2.188\tQ-max: 2.245\tLives: 2\tReward: 42.0\tEpisode Mean: 154.9\n", - "87603:127672270\tQ-min: 1.796\tQ-max: 1.822\tLives: 2\tReward: 43.0\tEpisode Mean: 154.9\n", - "87603:127672330\tQ-min: 1.802\tQ-max: 1.857\tLives: 2\tReward: 44.0\tEpisode Mean: 154.9\n", - "87603:127672396\tQ-min: 1.711\tQ-max: 1.784\tLives: 2\tReward: 45.0\tEpisode 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"87603:127672827\tQ-min: 0.138\tQ-max: 0.258\tLives: 0\tReward: 75.0\tEpisode Mean: 149.9\n", - "87604:127672885\tQ-min: 1.693\tQ-max: 1.714\tLives: 5\tReward: 1.0\tEpisode Mean: 149.9\n", - "87604:127672948\tQ-min: 1.663\tQ-max: 1.679\tLives: 5\tReward: 2.0\tEpisode Mean: 149.9\n", - "87604:127673000\tQ-min: 1.873\tQ-max: 1.875\tLives: 5\tReward: 3.0\tEpisode Mean: 149.9\n", - "87604:127673035\tQ-min: 2.045\tQ-max: 2.064\tLives: 5\tReward: 4.0\tEpisode Mean: 149.9\n", - "87604:127673066\tQ-min: 1.967\tQ-max: 1.997\tLives: 5\tReward: 5.0\tEpisode Mean: 149.9\n", - "87604:127673101\tQ-min: 1.931\tQ-max: 1.941\tLives: 5\tReward: 6.0\tEpisode Mean: 149.9\n", - "87604:127673134\tQ-min: 1.836\tQ-max: 1.872\tLives: 5\tReward: 7.0\tEpisode Mean: 149.9\n", - "87604:127673182\tQ-min: 1.651\tQ-max: 1.662\tLives: 5\tReward: 8.0\tEpisode Mean: 149.9\n", - "87604:127673246\tQ-min: 1.671\tQ-max: 1.690\tLives: 5\tReward: 9.0\tEpisode Mean: 149.9\n", - "87604:127673310\tQ-min: 1.638\tQ-max: 1.679\tLives: 5\tReward: 10.0\tEpisode Mean: 149.9\n", - "87604:127673378\tQ-min: 1.585\tQ-max: 1.661\tLives: 5\tReward: 11.0\tEpisode Mean: 149.9\n", - "87604:127673428\tQ-min: 1.931\tQ-max: 1.982\tLives: 5\tReward: 12.0\tEpisode Mean: 149.9\n", - "87604:127673461\tQ-min: 1.894\tQ-max: 1.993\tLives: 5\tReward: 16.0\tEpisode Mean: 149.9\n", - "87604:127673496\tQ-min: 1.928\tQ-max: 1.944\tLives: 5\tReward: 17.0\tEpisode Mean: 149.9\n", - "87604:127673529\tQ-min: 2.050\tQ-max: 2.108\tLives: 5\tReward: 18.0\tEpisode Mean: 149.9\n", - "87604:127673550\tQ-min: -0.158\tQ-max: 0.070\tLives: 4\tReward: 18.0\tEpisode Mean: 149.9\n", - "87604:127673605\tQ-min: 1.720\tQ-max: 1.787\tLives: 4\tReward: 19.0\tEpisode Mean: 149.9\n", - "87604:127673657\tQ-min: 1.970\tQ-max: 2.001\tLives: 4\tReward: 20.0\tEpisode Mean: 149.9\n", - "87604:127673701\tQ-min: 1.980\tQ-max: 2.030\tLives: 4\tReward: 21.0\tEpisode Mean: 149.9\n", - "87604:127673738\tQ-min: 2.030\tQ-max: 2.057\tLives: 4\tReward: 22.0\tEpisode Mean: 149.9\n", - "87604:127673774\tQ-min: 2.097\tQ-max: 2.124\tLives: 4\tReward: 23.0\tEpisode Mean: 149.9\n", - "87604:127673807\tQ-min: 2.042\tQ-max: 2.060\tLives: 4\tReward: 27.0\tEpisode Mean: 149.9\n", - "87604:127673841\tQ-min: 2.120\tQ-max: 2.154\tLives: 4\tReward: 31.0\tEpisode Mean: 149.9\n", - "87604:127673889\tQ-min: 1.773\tQ-max: 1.815\tLives: 4\tReward: 32.0\tEpisode Mean: 149.9\n", - "87604:127673952\tQ-min: 1.786\tQ-max: 1.805\tLives: 4\tReward: 33.0\tEpisode Mean: 149.9\n", - "87604:127674015\tQ-min: 1.828\tQ-max: 1.850\tLives: 4\tReward: 34.0\tEpisode Mean: 149.9\n", - "87604:127674080\tQ-min: 1.758\tQ-max: 1.826\tLives: 4\tReward: 35.0\tEpisode Mean: 149.9\n", - "87604:127674132\tQ-min: 2.034\tQ-max: 2.092\tLives: 4\tReward: 36.0\tEpisode Mean: 149.9\n", - "87604:127674166\tQ-min: 2.050\tQ-max: 2.077\tLives: 4\tReward: 37.0\tEpisode Mean: 149.9\n", - "87604:127674199\tQ-min: 2.194\tQ-max: 2.223\tLives: 4\tReward: 38.0\tEpisode Mean: 149.9\n", - "87604:127674237\tQ-min: 2.105\tQ-max: 2.220\tLives: 4\tReward: 42.0\tEpisode Mean: 149.9\n", - "87604:127674268\tQ-min: 2.101\tQ-max: 2.207\tLives: 4\tReward: 43.0\tEpisode Mean: 149.9\n", - "87604:127674302\tQ-min: 2.200\tQ-max: 2.251\tLives: 4\tReward: 44.0\tEpisode Mean: 149.9\n", - "87604:127674332\tQ-min: 2.115\tQ-max: 2.179\tLives: 4\tReward: 45.0\tEpisode Mean: 149.9\n", - "87604:127674364\tQ-min: 2.114\tQ-max: 2.247\tLives: 4\tReward: 49.0\tEpisode Mean: 149.9\n", - "87604:127674400\tQ-min: 2.137\tQ-max: 2.320\tLives: 4\tReward: 50.0\tEpisode Mean: 149.9\n", - "87604:127674435\tQ-min: 2.079\tQ-max: 2.216\tLives: 4\tReward: 54.0\tEpisode Mean: 149.9\n", - "87604:127674475\tQ-min: 2.200\tQ-max: 2.377\tLives: 4\tReward: 58.0\tEpisode Mean: 149.9\n", - "87604:127674499\tQ-min: 2.350\tQ-max: 2.537\tLives: 4\tReward: 62.0\tEpisode Mean: 149.9\n", - "87604:127674520\tQ-min: 2.408\tQ-max: 2.597\tLives: 4\tReward: 66.0\tEpisode Mean: 149.9\n", - "87604:127674543\tQ-min: 2.412\tQ-max: 2.525\tLives: 4\tReward: 67.0\tEpisode Mean: 149.9\n", - "87604:127674565\tQ-min: 2.482\tQ-max: 2.556\tLives: 4\tReward: 68.0\tEpisode Mean: 149.9\n", - "87604:127674585\tQ-min: 2.394\tQ-max: 2.539\tLives: 4\tReward: 72.0\tEpisode Mean: 149.9\n", - "87604:127674600\tQ-min: 0.053\tQ-max: 0.197\tLives: 3\tReward: 72.0\tEpisode Mean: 149.9\n", - "87604:127674656\tQ-min: 1.584\tQ-max: 1.884\tLives: 3\tReward: 76.0\tEpisode Mean: 149.9\n", - "87604:127674714\tQ-min: 2.218\tQ-max: 2.353\tLives: 3\tReward: 80.0\tEpisode Mean: 149.9\n", - "87604:127674759\tQ-min: 2.189\tQ-max: 2.243\tLives: 3\tReward: 81.0\tEpisode Mean: 149.9\n", - "87604:127674802\tQ-min: 2.331\tQ-max: 2.365\tLives: 3\tReward: 85.0\tEpisode Mean: 149.9\n", - "87604:127674843\tQ-min: 2.299\tQ-max: 2.737\tLives: 3\tReward: 92.0\tEpisode Mean: 149.9\n", - "87604:127674865\tQ-min: 2.367\tQ-max: 2.514\tLives: 3\tReward: 96.0\tEpisode Mean: 149.9\n", - "87604:127674889\tQ-min: 2.248\tQ-max: 2.649\tLives: 3\tReward: 103.0\tEpisode Mean: 149.9\n", - "87604:127674912\tQ-min: 2.439\tQ-max: 3.002\tLives: 3\tReward: 107.0\tEpisode Mean: 149.9\n", - "87604:127674938\tQ-min: 2.280\tQ-max: 2.987\tLives: 3\tReward: 114.0\tEpisode Mean: 149.9\n", - "87604:127674963\tQ-min: 2.794\tQ-max: 3.503\tLives: 3\tReward: 121.0\tEpisode Mean: 149.9\n", - "87604:127674991\tQ-min: 2.500\tQ-max: 7.708\tLives: 3\tReward: 128.0\tEpisode Mean: 149.9\n", - "87604:127674997\tQ-min: 2.156\tQ-max: 7.000\tLives: 3\tReward: 135.0\tEpisode Mean: 149.9\n", - "87604:127675002\tQ-min: 2.477\tQ-max: 6.028\tLives: 3\tReward: 142.0\tEpisode Mean: 149.9\n", - "87604:127675007\tQ-min: 4.239\tQ-max: 6.303\tLives: 3\tReward: 149.0\tEpisode Mean: 149.9\n", - "87604:127675011\tQ-min: 3.918\tQ-max: 7.023\tLives: 3\tReward: 156.0\tEpisode Mean: 149.9\n", - "87604:127675017\tQ-min: 3.540\tQ-max: 6.474\tLives: 3\tReward: 163.0\tEpisode Mean: 149.9\n", - "87604:127675023\tQ-min: 1.202\tQ-max: 6.183\tLives: 3\tReward: 170.0\tEpisode Mean: 149.9\n", - "87604:127675028\tQ-min: 0.965\tQ-max: 5.638\tLives: 3\tReward: 177.0\tEpisode Mean: 149.9\n", - "87604:127675035\tQ-min: 2.349\tQ-max: 4.229\tLives: 3\tReward: 181.0\tEpisode Mean: 149.9\n", - "87604:127675042\tQ-min: 2.396\tQ-max: 5.087\tLives: 3\tReward: 188.0\tEpisode Mean: 149.9\n", - "87604:127675048\tQ-min: 3.254\tQ-max: 4.975\tLives: 3\tReward: 195.0\tEpisode Mean: 149.9\n", - "87604:127675054\tQ-min: 3.008\tQ-max: 5.374\tLives: 3\tReward: 202.0\tEpisode Mean: 149.9\n", - "87604:127675060\tQ-min: 3.376\tQ-max: 5.494\tLives: 3\tReward: 209.0\tEpisode Mean: 149.9\n", - "87604:127675065\tQ-min: 3.540\tQ-max: 5.774\tLives: 3\tReward: 216.0\tEpisode Mean: 149.9\n", - "87604:127675070\tQ-min: 3.060\tQ-max: 4.781\tLives: 3\tReward: 223.0\tEpisode Mean: 149.9\n", - "87604:127675077\tQ-min: 1.581\tQ-max: 5.604\tLives: 3\tReward: 230.0\tEpisode Mean: 149.9\n", - "87604:127675081\tQ-min: 1.432\tQ-max: 4.240\tLives: 3\tReward: 237.0\tEpisode Mean: 149.9\n", - "87604:127675086\tQ-min: 3.265\tQ-max: 4.325\tLives: 3\tReward: 244.0\tEpisode Mean: 149.9\n", - "87604:127675092\tQ-min: 2.793\tQ-max: 4.364\tLives: 3\tReward: 251.0\tEpisode Mean: 149.9\n", - "87604:127675098\tQ-min: 1.659\tQ-max: 4.788\tLives: 3\tReward: 258.0\tEpisode Mean: 149.9\n", - "87604:127675104\tQ-min: 2.185\tQ-max: 3.692\tLives: 3\tReward: 265.0\tEpisode Mean: 149.9\n", - "87604:127675110\tQ-min: 3.059\tQ-max: 5.168\tLives: 3\tReward: 272.0\tEpisode Mean: 149.9\n", - "87604:127675116\tQ-min: 2.764\tQ-max: 3.735\tLives: 3\tReward: 279.0\tEpisode Mean: 149.9\n", - "87604:127675154\tQ-min: 2.274\tQ-max: 4.377\tLives: 3\tReward: 283.0\tEpisode Mean: 149.9\n", - "87604:127675162\tQ-min: 2.393\tQ-max: 5.473\tLives: 3\tReward: 287.0\tEpisode Mean: 149.9\n", - "87604:127675199\tQ-min: 2.164\tQ-max: 3.879\tLives: 3\tReward: 294.0\tEpisode Mean: 149.9\n", - "87604:127675206\tQ-min: 1.621\tQ-max: 4.900\tLives: 3\tReward: 301.0\tEpisode Mean: 149.9\n", - "87604:127675213\tQ-min: 2.385\tQ-max: 4.757\tLives: 3\tReward: 308.0\tEpisode Mean: 149.9\n", - "87604:127675220\tQ-min: 3.074\tQ-max: 4.138\tLives: 3\tReward: 315.0\tEpisode Mean: 149.9\n", - "87604:127675226\tQ-min: 1.986\tQ-max: 2.857\tLives: 3\tReward: 319.0\tEpisode Mean: 149.9\n", - "87604:127675250\tQ-min: -0.037\tQ-max: 0.340\tLives: 2\tReward: 319.0\tEpisode Mean: 149.9\n", - "87604:127675303\tQ-min: 1.394\tQ-max: 2.884\tLives: 2\tReward: 326.0\tEpisode Mean: 149.9\n", - "87604:127675327\tQ-min: 1.153\tQ-max: 2.299\tLives: 2\tReward: 330.0\tEpisode Mean: 149.9\n", - "87604:127675348\tQ-min: 1.783\tQ-max: 2.660\tLives: 2\tReward: 334.0\tEpisode Mean: 149.9\n", - "87604:127675371\tQ-min: 2.439\tQ-max: 3.126\tLives: 2\tReward: 338.0\tEpisode Mean: 149.9\n", - "87604:127675391\tQ-min: 2.384\tQ-max: 2.837\tLives: 2\tReward: 339.0\tEpisode Mean: 149.9\n", - "87604:127675412\tQ-min: 1.705\tQ-max: 4.095\tLives: 2\tReward: 343.0\tEpisode Mean: 149.9\n", - "87604:127675431\tQ-min: 1.738\tQ-max: 3.458\tLives: 2\tReward: 347.0\tEpisode Mean: 149.9\n", - "87604:127675462\tQ-min: 2.482\tQ-max: 4.338\tLives: 2\tReward: 354.0\tEpisode Mean: 149.9\n", - "87604:127675485\tQ-min: 0.097\tQ-max: 0.350\tLives: 1\tReward: 354.0\tEpisode Mean: 149.9\n", - "87604:127675583\tQ-min: -0.169\tQ-max: 0.298\tLives: 0\tReward: 354.0\tEpisode Mean: 161.9\n", - "87605:127675625\tQ-min: 1.748\tQ-max: 1.756\tLives: 5\tReward: 1.0\tEpisode Mean: 161.9\n", - "87605:127675666\tQ-min: 1.814\tQ-max: 1.842\tLives: 5\tReward: 2.0\tEpisode Mean: 161.9\n", - "87605:127675705\tQ-min: 1.902\tQ-max: 1.919\tLives: 5\tReward: 3.0\tEpisode Mean: 161.9\n", - "87605:127675743\tQ-min: 1.980\tQ-max: 2.007\tLives: 5\tReward: 4.0\tEpisode Mean: 161.9\n", - "87605:127675778\tQ-min: 1.964\tQ-max: 1.994\tLives: 5\tReward: 5.0\tEpisode Mean: 161.9\n", - "87605:127675810\tQ-min: 1.965\tQ-max: 2.003\tLives: 5\tReward: 6.0\tEpisode Mean: 161.9\n", - "87605:127675843\tQ-min: 1.766\tQ-max: 1.801\tLives: 5\tReward: 7.0\tEpisode Mean: 161.9\n", - "87605:127675891\tQ-min: 1.605\tQ-max: 1.707\tLives: 5\tReward: 8.0\tEpisode Mean: 161.9\n", - "87605:127675954\tQ-min: 1.706\tQ-max: 1.718\tLives: 5\tReward: 9.0\tEpisode Mean: 161.9\n", - "87605:127676023\tQ-min: 1.680\tQ-max: 1.760\tLives: 5\tReward: 10.0\tEpisode Mean: 161.9\n", - "87605:127676089\tQ-min: 1.653\tQ-max: 1.678\tLives: 5\tReward: 11.0\tEpisode Mean: 161.9\n", - "87605:127676138\tQ-min: 1.868\tQ-max: 2.061\tLives: 5\tReward: 12.0\tEpisode Mean: 161.9\n", - "87605:127676172\tQ-min: 1.976\tQ-max: 1.995\tLives: 5\tReward: 13.0\tEpisode Mean: 161.9\n", - "87605:127676193\tQ-min: -0.059\tQ-max: 0.295\tLives: 4\tReward: 13.0\tEpisode Mean: 161.9\n", - "87605:127676235\tQ-min: 1.906\tQ-max: 1.942\tLives: 4\tReward: 14.0\tEpisode Mean: 161.9\n", - "87605:127676288\tQ-min: 1.763\tQ-max: 1.829\tLives: 4\tReward: 15.0\tEpisode Mean: 161.9\n", - "87605:127676346\tQ-min: 2.014\tQ-max: 2.055\tLives: 4\tReward: 19.0\tEpisode Mean: 161.9\n", - "87605:127676385\tQ-min: 2.041\tQ-max: 2.080\tLives: 4\tReward: 20.0\tEpisode Mean: 161.9\n", - "87605:127676418\tQ-min: 2.044\tQ-max: 2.170\tLives: 4\tReward: 21.0\tEpisode Mean: 161.9\n", - "87605:127676451\tQ-min: 2.075\tQ-max: 2.162\tLives: 4\tReward: 25.0\tEpisode Mean: 161.9\n", - "87605:127676475\tQ-min: -0.099\tQ-max: 0.215\tLives: 3\tReward: 25.0\tEpisode Mean: 161.9\n", - "87605:127676530\tQ-min: 1.829\tQ-max: 1.890\tLives: 3\tReward: 29.0\tEpisode Mean: 161.9\n", - "87605:127676595\tQ-min: 1.862\tQ-max: 1.896\tLives: 3\tReward: 30.0\tEpisode Mean: 161.9\n", - "87605:127676649\tQ-min: 1.995\tQ-max: 2.130\tLives: 3\tReward: 31.0\tEpisode Mean: 161.9\n", - "87605:127676685\tQ-min: 2.184\tQ-max: 2.242\tLives: 3\tReward: 32.0\tEpisode Mean: 161.9\n", - "87605:127676718\tQ-min: 2.100\tQ-max: 2.182\tLives: 3\tReward: 33.0\tEpisode Mean: 161.9\n", - "87605:127676752\tQ-min: 2.150\tQ-max: 2.231\tLives: 3\tReward: 34.0\tEpisode Mean: 161.9\n", - "87605:127676785\tQ-min: 2.192\tQ-max: 2.229\tLives: 3\tReward: 35.0\tEpisode Mean: 161.9\n", - "87605:127676836\tQ-min: 1.745\tQ-max: 1.770\tLives: 3\tReward: 36.0\tEpisode Mean: 161.9\n", - "87605:127676902\tQ-min: 1.591\tQ-max: 1.756\tLives: 3\tReward: 37.0\tEpisode Mean: 161.9\n", - "87605:127676973\tQ-min: 1.822\tQ-max: 1.921\tLives: 3\tReward: 41.0\tEpisode Mean: 161.9\n", - "87605:127677046\tQ-min: 1.600\tQ-max: 2.286\tLives: 3\tReward: 45.0\tEpisode Mean: 161.9\n", - "87605:127677099\tQ-min: 2.053\tQ-max: 2.102\tLives: 3\tReward: 46.0\tEpisode Mean: 161.9\n", - "87605:127677134\tQ-min: 2.109\tQ-max: 2.140\tLives: 3\tReward: 47.0\tEpisode Mean: 161.9\n", - "87605:127677167\tQ-min: 2.327\tQ-max: 2.429\tLives: 3\tReward: 51.0\tEpisode Mean: 161.9\n", - "87605:127677189\tQ-min: -0.038\tQ-max: 0.213\tLives: 2\tReward: 51.0\tEpisode Mean: 161.9\n", - "87605:127677235\tQ-min: 2.000\tQ-max: 2.037\tLives: 2\tReward: 52.0\tEpisode Mean: 161.9\n", - "87605:127677280\tQ-min: 2.027\tQ-max: 2.063\tLives: 2\tReward: 53.0\tEpisode Mean: 161.9\n", - "87605:127677324\tQ-min: 2.117\tQ-max: 2.134\tLives: 2\tReward: 54.0\tEpisode Mean: 161.9\n", - "87605:127677362\tQ-min: 2.116\tQ-max: 2.305\tLives: 2\tReward: 58.0\tEpisode Mean: 161.9\n", - "87605:127677399\tQ-min: 2.268\tQ-max: 2.319\tLives: 2\tReward: 62.0\tEpisode Mean: 161.9\n", - "87605:127677433\tQ-min: 2.207\tQ-max: 2.433\tLives: 2\tReward: 66.0\tEpisode Mean: 161.9\n", - "87605:127677453\tQ-min: 2.363\tQ-max: 2.507\tLives: 2\tReward: 67.0\tEpisode Mean: 161.9\n", - "87605:127677474\tQ-min: 2.441\tQ-max: 2.623\tLives: 2\tReward: 71.0\tEpisode Mean: 161.9\n", - "87605:127677487\tQ-min: 0.035\tQ-max: 0.164\tLives: 1\tReward: 71.0\tEpisode Mean: 161.9\n", - "87605:127677533\tQ-min: 2.462\tQ-max: 2.580\tLives: 1\tReward: 75.0\tEpisode Mean: 161.9\n", - "87605:127677558\tQ-min: 2.145\tQ-max: 2.673\tLives: 1\tReward: 82.0\tEpisode Mean: 161.9\n", - "87605:127677581\tQ-min: 2.687\tQ-max: 2.993\tLives: 1\tReward: 86.0\tEpisode Mean: 161.9\n", - "87605:127677602\tQ-min: 2.374\tQ-max: 2.936\tLives: 1\tReward: 90.0\tEpisode Mean: 161.9\n", - "87605:127677625\tQ-min: 2.129\tQ-max: 2.879\tLives: 1\tReward: 97.0\tEpisode Mean: 161.9\n", - "87605:127677647\tQ-min: 2.395\tQ-max: 2.794\tLives: 1\tReward: 101.0\tEpisode Mean: 161.9\n", - "87605:127677663\tQ-min: 0.015\tQ-max: 0.272\tLives: 0\tReward: 101.0\tEpisode Mean: 158.6\n", - "87606:127677716\tQ-min: 1.678\tQ-max: 1.684\tLives: 5\tReward: 1.0\tEpisode Mean: 158.6\n", - "87606:127677767\tQ-min: 1.834\tQ-max: 1.844\tLives: 5\tReward: 2.0\tEpisode Mean: 158.6\n", - "87606:127677810\tQ-min: 1.907\tQ-max: 1.941\tLives: 5\tReward: 3.0\tEpisode Mean: 158.6\n", - "87606:127677848\tQ-min: 1.949\tQ-max: 2.005\tLives: 5\tReward: 4.0\tEpisode Mean: 158.6\n", - "87606:127677881\tQ-min: 1.976\tQ-max: 2.021\tLives: 5\tReward: 5.0\tEpisode Mean: 158.6\n", - "87606:127677913\tQ-min: 1.949\tQ-max: 1.972\tLives: 5\tReward: 6.0\tEpisode Mean: 158.6\n", - "87606:127677945\tQ-min: 1.789\tQ-max: 1.822\tLives: 5\tReward: 7.0\tEpisode Mean: 158.6\n", - "87606:127677991\tQ-min: 1.646\tQ-max: 1.659\tLives: 5\tReward: 8.0\tEpisode Mean: 158.6\n", - "87606:127678056\tQ-min: 1.690\tQ-max: 1.730\tLives: 5\tReward: 9.0\tEpisode Mean: 158.6\n", - "87606:127678100\tQ-min: -0.085\tQ-max: 0.158\tLives: 4\tReward: 9.0\tEpisode Mean: 158.6\n", - "87606:127678143\tQ-min: 1.937\tQ-max: 1.976\tLives: 4\tReward: 10.0\tEpisode Mean: 158.6\n", - "87606:127678186\tQ-min: 1.940\tQ-max: 1.950\tLives: 4\tReward: 11.0\tEpisode Mean: 158.6\n", - "87606:127678243\tQ-min: 1.645\tQ-max: 1.722\tLives: 4\tReward: 12.0\tEpisode Mean: 158.6\n", - "87606:127678289\tQ-min: 2.028\tQ-max: 2.049\tLives: 4\tReward: 13.0\tEpisode Mean: 158.6\n", - "87606:127678321\tQ-min: 1.960\tQ-max: 1.993\tLives: 4\tReward: 14.0\tEpisode Mean: 158.6\n", - "87606:127678357\tQ-min: 2.025\tQ-max: 2.069\tLives: 4\tReward: 18.0\tEpisode Mean: 158.6\n", - "87606:127678393\tQ-min: 1.958\tQ-max: 2.482\tLives: 4\tReward: 22.0\tEpisode Mean: 158.6\n", - "87606:127678413\tQ-min: 2.310\tQ-max: 2.430\tLives: 4\tReward: 23.0\tEpisode Mean: 158.6\n", - "87606:127678436\tQ-min: 2.148\tQ-max: 2.430\tLives: 4\tReward: 27.0\tEpisode Mean: 158.6\n", - "87606:127678449\tQ-min: -0.273\tQ-max: 0.286\tLives: 3\tReward: 27.0\tEpisode Mean: 158.6\n", - "87606:127678495\tQ-min: 1.984\tQ-max: 2.000\tLives: 3\tReward: 28.0\tEpisode Mean: 158.6\n", - "87606:127678538\tQ-min: 2.054\tQ-max: 2.119\tLives: 3\tReward: 29.0\tEpisode Mean: 158.6\n", - "87606:127678592\tQ-min: 1.758\tQ-max: 1.815\tLives: 3\tReward: 30.0\tEpisode Mean: 158.6\n", - "87606:127678635\tQ-min: -0.036\tQ-max: 0.414\tLives: 2\tReward: 30.0\tEpisode Mean: 158.6\n", - "87606:127678679\tQ-min: 1.944\tQ-max: 1.981\tLives: 2\tReward: 31.0\tEpisode Mean: 158.6\n", - "87606:127678735\tQ-min: 1.654\tQ-max: 1.956\tLives: 2\tReward: 32.0\tEpisode Mean: 158.6\n", - "87606:127678793\tQ-min: 1.934\tQ-max: 1.967\tLives: 2\tReward: 33.0\tEpisode Mean: 158.6\n", - "87606:127678831\tQ-min: 2.178\tQ-max: 2.211\tLives: 2\tReward: 34.0\tEpisode Mean: 158.6\n", - "87606:127678865\tQ-min: 2.051\tQ-max: 2.127\tLives: 2\tReward: 35.0\tEpisode Mean: 158.6\n", - "87606:127678900\tQ-min: 2.133\tQ-max: 2.280\tLives: 2\tReward: 36.0\tEpisode Mean: 158.6\n", - "87606:127678933\tQ-min: 2.284\tQ-max: 2.330\tLives: 2\tReward: 37.0\tEpisode Mean: 158.6\n", - "87606:127678983\tQ-min: 1.702\tQ-max: 1.807\tLives: 2\tReward: 38.0\tEpisode Mean: 158.6\n", - "87606:127679049\tQ-min: 1.585\tQ-max: 1.716\tLives: 2\tReward: 42.0\tEpisode Mean: 158.6\n", - "87606:127679097\tQ-min: -0.064\tQ-max: 0.265\tLives: 1\tReward: 42.0\tEpisode Mean: 158.6\n", - "87606:127679155\tQ-min: 1.777\tQ-max: 1.864\tLives: 1\tReward: 46.0\tEpisode Mean: 158.6\n", - "87606:127679215\tQ-min: 2.128\tQ-max: 2.154\tLives: 1\tReward: 47.0\tEpisode Mean: 158.6\n", - "87606:127679261\tQ-min: 2.095\tQ-max: 2.386\tLives: 1\tReward: 51.0\tEpisode Mean: 158.6\n", - "87606:127679301\tQ-min: 2.240\tQ-max: 2.281\tLives: 1\tReward: 52.0\tEpisode Mean: 158.6\n", - "87606:127679336\tQ-min: 2.182\tQ-max: 2.221\tLives: 1\tReward: 53.0\tEpisode Mean: 158.6\n", - "87606:127679371\tQ-min: 2.146\tQ-max: 2.200\tLives: 1\tReward: 57.0\tEpisode Mean: 158.6\n", - "87606:127679403\tQ-min: 2.166\tQ-max: 2.235\tLives: 1\tReward: 58.0\tEpisode Mean: 158.6\n", - "87606:127679452\tQ-min: 1.818\tQ-max: 1.949\tLives: 1\tReward: 59.0\tEpisode Mean: 158.6\n", - "87606:127679521\tQ-min: 1.925\tQ-max: 2.035\tLives: 1\tReward: 60.0\tEpisode Mean: 158.6\n", - "87606:127679585\tQ-min: 1.918\tQ-max: 2.098\tLives: 1\tReward: 64.0\tEpisode Mean: 158.6\n", - "87606:127679662\tQ-min: 1.814\tQ-max: 2.746\tLives: 1\tReward: 68.0\tEpisode Mean: 158.6\n", - "87606:127679687\tQ-min: 2.350\tQ-max: 2.740\tLives: 1\tReward: 72.0\tEpisode Mean: 158.6\n", - "87606:127679702\tQ-min: -0.177\tQ-max: 0.151\tLives: 0\tReward: 72.0\tEpisode Mean: 154.0\n", - "87607:127679743\tQ-min: 1.756\tQ-max: 1.772\tLives: 5\tReward: 1.0\tEpisode Mean: 154.0\n", - "87607:127679792\tQ-min: 1.627\tQ-max: 1.654\tLives: 5\tReward: 2.0\tEpisode Mean: 154.0\n", - "87607:127679845\tQ-min: 1.796\tQ-max: 1.842\tLives: 5\tReward: 3.0\tEpisode Mean: 154.0\n", - "87607:127679882\tQ-min: 1.859\tQ-max: 1.878\tLives: 5\tReward: 4.0\tEpisode Mean: 154.0\n", - "87607:127679915\tQ-min: 1.970\tQ-max: 2.004\tLives: 5\tReward: 8.0\tEpisode Mean: 154.0\n", - "87607:127679949\tQ-min: 1.986\tQ-max: 2.019\tLives: 5\tReward: 9.0\tEpisode Mean: 154.0\n", - "87607:127679978\tQ-min: 1.781\tQ-max: 1.889\tLives: 5\tReward: 10.0\tEpisode Mean: 154.0\n", - "87607:127680025\tQ-min: 1.672\tQ-max: 1.699\tLives: 5\tReward: 11.0\tEpisode Mean: 154.0\n", - "87607:127680088\tQ-min: 1.748\tQ-max: 1.833\tLives: 5\tReward: 12.0\tEpisode Mean: 154.0\n", - "87607:127680154\tQ-min: 1.716\tQ-max: 1.752\tLives: 5\tReward: 13.0\tEpisode Mean: 154.0\n", - "87607:127680222\tQ-min: 1.707\tQ-max: 1.769\tLives: 5\tReward: 14.0\tEpisode Mean: 154.0\n", - "87607:127680274\tQ-min: 1.999\tQ-max: 2.047\tLives: 5\tReward: 15.0\tEpisode Mean: 154.0\n", - "87607:127680306\tQ-min: 2.011\tQ-max: 2.029\tLives: 5\tReward: 16.0\tEpisode Mean: 154.0\n", - "87607:127680328\tQ-min: -0.113\tQ-max: 0.253\tLives: 4\tReward: 16.0\tEpisode Mean: 154.0\n", - "87607:127680375\tQ-min: 1.915\tQ-max: 1.935\tLives: 4\tReward: 17.0\tEpisode Mean: 154.0\n", - "87607:127680421\tQ-min: 1.981\tQ-max: 2.003\tLives: 4\tReward: 18.0\tEpisode Mean: 154.0\n", - "87607:127680463\tQ-min: 1.955\tQ-max: 1.961\tLives: 4\tReward: 19.0\tEpisode Mean: 154.0\n", - "87607:127680500\tQ-min: 2.005\tQ-max: 2.065\tLives: 4\tReward: 20.0\tEpisode Mean: 154.0\n", - "87607:127680533\tQ-min: 1.985\tQ-max: 1.999\tLives: 4\tReward: 21.0\tEpisode Mean: 154.0\n", - "87607:127680565\tQ-min: 2.096\tQ-max: 2.145\tLives: 4\tReward: 22.0\tEpisode Mean: 154.0\n", - "87607:127680599\tQ-min: 2.082\tQ-max: 2.113\tLives: 4\tReward: 23.0\tEpisode Mean: 154.0\n", - "87607:127680649\tQ-min: 1.753\tQ-max: 1.813\tLives: 4\tReward: 24.0\tEpisode Mean: 154.0\n", - "87607:127680714\tQ-min: 1.711\tQ-max: 1.752\tLives: 4\tReward: 25.0\tEpisode Mean: 154.0\n", - "87607:127680781\tQ-min: 1.694\tQ-max: 1.736\tLives: 4\tReward: 26.0\tEpisode Mean: 154.0\n", - "87607:127680848\tQ-min: 1.669\tQ-max: 1.821\tLives: 4\tReward: 30.0\tEpisode Mean: 154.0\n", - "87607:127680899\tQ-min: 2.096\tQ-max: 2.222\tLives: 4\tReward: 34.0\tEpisode Mean: 154.0\n", - "87607:127680931\tQ-min: 2.128\tQ-max: 2.170\tLives: 4\tReward: 35.0\tEpisode Mean: 154.0\n", - "87607:127680961\tQ-min: 2.139\tQ-max: 2.193\tLives: 4\tReward: 36.0\tEpisode Mean: 154.0\n", - "87607:127680996\tQ-min: 2.079\tQ-max: 2.133\tLives: 4\tReward: 37.0\tEpisode Mean: 154.0\n", - "87607:127681031\tQ-min: 2.045\tQ-max: 2.180\tLives: 4\tReward: 38.0\tEpisode Mean: 154.0\n", - "87607:127681064\tQ-min: 2.042\tQ-max: 2.128\tLives: 4\tReward: 42.0\tEpisode Mean: 154.0\n", - "87607:127681098\tQ-min: 2.222\tQ-max: 2.232\tLives: 4\tReward: 43.0\tEpisode Mean: 154.0\n", - "87607:127681135\tQ-min: 2.349\tQ-max: 2.475\tLives: 4\tReward: 47.0\tEpisode Mean: 154.0\n", - "87607:127681156\tQ-min: 2.286\tQ-max: 2.440\tLives: 4\tReward: 48.0\tEpisode Mean: 154.0\n", - "87607:127681171\tQ-min: 0.047\tQ-max: 0.248\tLives: 3\tReward: 48.0\tEpisode Mean: 154.0\n", - "87607:127681217\tQ-min: 2.070\tQ-max: 2.095\tLives: 3\tReward: 49.0\tEpisode Mean: 154.0\n", - "87607:127681276\tQ-min: 1.874\tQ-max: 2.028\tLives: 3\tReward: 53.0\tEpisode Mean: 154.0\n", - "87607:127681334\tQ-min: 2.101\tQ-max: 2.147\tLives: 3\tReward: 54.0\tEpisode Mean: 154.0\n", - "87607:127681371\tQ-min: 2.116\tQ-max: 2.239\tLives: 3\tReward: 55.0\tEpisode Mean: 154.0\n", - "87607:127681408\tQ-min: 2.468\tQ-max: 2.547\tLives: 3\tReward: 59.0\tEpisode Mean: 154.0\n", - "87607:127681431\tQ-min: 2.280\tQ-max: 2.690\tLives: 3\tReward: 63.0\tEpisode Mean: 154.0\n", - "87607:127681453\tQ-min: 2.054\tQ-max: 2.563\tLives: 3\tReward: 67.0\tEpisode Mean: 154.0\n", - "87607:127681466\tQ-min: -0.662\tQ-max: 0.099\tLives: 2\tReward: 67.0\tEpisode Mean: 154.0\n", - "87607:127681513\tQ-min: 2.119\tQ-max: 2.174\tLives: 2\tReward: 71.0\tEpisode Mean: 154.0\n", - "87607:127681557\tQ-min: 2.152\tQ-max: 2.228\tLives: 2\tReward: 72.0\tEpisode Mean: 154.0\n", - "87607:127681615\tQ-min: 2.401\tQ-max: 2.595\tLives: 2\tReward: 76.0\tEpisode Mean: 154.0\n", - "87607:127681628\tQ-min: 0.216\tQ-max: 0.360\tLives: 1\tReward: 76.0\tEpisode Mean: 154.0\n", - "87607:127681673\tQ-min: 2.201\tQ-max: 2.312\tLives: 1\tReward: 80.0\tEpisode Mean: 154.0\n", - "87607:127681716\tQ-min: 2.240\tQ-max: 2.314\tLives: 1\tReward: 81.0\tEpisode Mean: 154.0\n", - "87607:127681758\tQ-min: 2.284\tQ-max: 2.382\tLives: 1\tReward: 82.0\tEpisode Mean: 154.0\n", - "87607:127681799\tQ-min: 2.332\tQ-max: 2.451\tLives: 1\tReward: 86.0\tEpisode Mean: 154.0\n", - "87607:127681835\tQ-min: 2.389\tQ-max: 2.679\tLives: 1\tReward: 90.0\tEpisode Mean: 154.0\n", - "87607:127681848\tQ-min: -0.115\tQ-max: 0.157\tLives: 0\tReward: 90.0\tEpisode Mean: 150.8\n", - "87608:127681894\tQ-min: 1.754\tQ-max: 1.766\tLives: 5\tReward: 1.0\tEpisode 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1.993\tQ-max: 2.024\tLives: 5\tReward: 12.0\tEpisode Mean: 150.8\n", - "87608:127682452\tQ-min: 1.965\tQ-max: 2.005\tLives: 5\tReward: 13.0\tEpisode Mean: 150.8\n", - "87608:127682486\tQ-min: 1.916\tQ-max: 2.050\tLives: 5\tReward: 14.0\tEpisode Mean: 150.8\n", - "87608:127682517\tQ-min: 1.985\tQ-max: 2.014\tLives: 5\tReward: 15.0\tEpisode Mean: 150.8\n", - "87608:127682549\tQ-min: 1.885\tQ-max: 1.936\tLives: 5\tReward: 19.0\tEpisode Mean: 150.8\n", - "87608:127682585\tQ-min: 1.965\tQ-max: 1.990\tLives: 5\tReward: 23.0\tEpisode Mean: 150.8\n", - "87608:127682620\tQ-min: 2.079\tQ-max: 2.127\tLives: 5\tReward: 24.0\tEpisode Mean: 150.8\n", - "87608:127682653\tQ-min: 2.049\tQ-max: 2.104\tLives: 5\tReward: 25.0\tEpisode Mean: 150.8\n", - "87608:127682685\tQ-min: 2.052\tQ-max: 2.088\tLives: 5\tReward: 26.0\tEpisode Mean: 150.8\n", - "87608:127682717\tQ-min: 2.025\tQ-max: 2.052\tLives: 5\tReward: 27.0\tEpisode Mean: 150.8\n", - "87608:127682748\tQ-min: 2.003\tQ-max: 2.024\tLives: 5\tReward: 28.0\tEpisode Mean: 150.8\n", - "87608:127682782\tQ-min: 2.108\tQ-max: 2.209\tLives: 5\tReward: 32.0\tEpisode Mean: 150.8\n", - "87608:127682818\tQ-min: 2.094\tQ-max: 2.108\tLives: 5\tReward: 33.0\tEpisode Mean: 150.8\n", - "87608:127682849\tQ-min: 2.079\tQ-max: 2.142\tLives: 5\tReward: 34.0\tEpisode Mean: 150.8\n", - "87608:127682881\tQ-min: 2.000\tQ-max: 2.105\tLives: 5\tReward: 35.0\tEpisode Mean: 150.8\n", - "87608:127682916\tQ-min: 2.170\tQ-max: 2.207\tLives: 5\tReward: 36.0\tEpisode Mean: 150.8\n", - "87608:127682950\tQ-min: 2.288\tQ-max: 2.493\tLives: 5\tReward: 40.0\tEpisode Mean: 150.8\n", - "87608:127682972\tQ-min: 2.439\tQ-max: 2.481\tLives: 5\tReward: 41.0\tEpisode Mean: 150.8\n", - "87608:127682990\tQ-min: 2.269\tQ-max: 2.471\tLives: 5\tReward: 42.0\tEpisode Mean: 150.8\n", - "87608:127683012\tQ-min: 2.143\tQ-max: 2.405\tLives: 5\tReward: 46.0\tEpisode Mean: 150.8\n", - "87608:127683033\tQ-min: 2.332\tQ-max: 2.442\tLives: 5\tReward: 50.0\tEpisode Mean: 150.8\n", - "87608:127683056\tQ-min: 2.458\tQ-max: 2.539\tLives: 5\tReward: 54.0\tEpisode Mean: 150.8\n", - "87608:127683078\tQ-min: 1.800\tQ-max: 2.672\tLives: 5\tReward: 58.0\tEpisode Mean: 150.8\n", - "87608:127683092\tQ-min: -0.059\tQ-max: 0.069\tLives: 4\tReward: 58.0\tEpisode Mean: 150.8\n", - "87608:127683140\tQ-min: 2.305\tQ-max: 2.477\tLives: 4\tReward: 62.0\tEpisode Mean: 150.8\n", - "87608:127683162\tQ-min: 2.416\tQ-max: 2.487\tLives: 4\tReward: 63.0\tEpisode Mean: 150.8\n", - "87608:127683176\tQ-min: -0.108\tQ-max: 0.026\tLives: 3\tReward: 63.0\tEpisode Mean: 150.8\n", - "87608:127683224\tQ-min: 2.500\tQ-max: 2.744\tLives: 3\tReward: 70.0\tEpisode Mean: 150.8\n", - "87608:127683244\tQ-min: 2.376\tQ-max: 2.586\tLives: 3\tReward: 71.0\tEpisode Mean: 150.8\n", - "87608:127683258\tQ-min: -0.027\tQ-max: 0.220\tLives: 2\tReward: 71.0\tEpisode Mean: 150.8\n", - "87608:127683306\tQ-min: 2.301\tQ-max: 2.759\tLives: 2\tReward: 78.0\tEpisode Mean: 150.8\n", - "87608:127683330\tQ-min: 2.334\tQ-max: 2.649\tLives: 2\tReward: 82.0\tEpisode Mean: 150.8\n", - "87608:127683350\tQ-min: 2.408\tQ-max: 2.508\tLives: 2\tReward: 83.0\tEpisode Mean: 150.8\n", - "87608:127683362\tQ-min: 0.001\tQ-max: 0.376\tLives: 1\tReward: 83.0\tEpisode Mean: 150.8\n", - "87608:127683416\tQ-min: 2.042\tQ-max: 2.116\tLives: 1\tReward: 84.0\tEpisode Mean: 150.8\n", - "87608:127683469\tQ-min: 2.351\tQ-max: 2.478\tLives: 1\tReward: 85.0\tEpisode Mean: 150.8\n", - "87608:127683513\tQ-min: 2.162\tQ-max: 2.434\tLives: 1\tReward: 89.0\tEpisode Mean: 150.8\n", - "87608:127683553\tQ-min: 2.296\tQ-max: 2.536\tLives: 1\tReward: 93.0\tEpisode Mean: 150.8\n", - "87608:127683588\tQ-min: 2.540\tQ-max: 2.689\tLives: 1\tReward: 97.0\tEpisode Mean: 150.8\n", - "87608:127683622\tQ-min: 2.363\tQ-max: 2.576\tLives: 1\tReward: 98.0\tEpisode Mean: 150.8\n", - "87608:127683651\tQ-min: 2.445\tQ-max: 2.640\tLives: 1\tReward: 99.0\tEpisode Mean: 150.8\n", - "87608:127683703\tQ-min: 2.028\tQ-max: 2.240\tLives: 1\tReward: 103.0\tEpisode Mean: 150.8\n", - "87608:127683779\tQ-min: 2.230\tQ-max: 2.499\tLives: 1\tReward: 107.0\tEpisode Mean: 150.8\n", - "87608:127683858\tQ-min: 2.058\tQ-max: 3.091\tLives: 1\tReward: 111.0\tEpisode Mean: 150.8\n", - "87608:127683881\tQ-min: 2.198\tQ-max: 3.193\tLives: 1\tReward: 115.0\tEpisode Mean: 150.8\n", - "87608:127683903\tQ-min: 2.595\tQ-max: 3.421\tLives: 1\tReward: 122.0\tEpisode Mean: 150.8\n", - "87608:127683925\tQ-min: 2.155\tQ-max: 3.768\tLives: 1\tReward: 129.0\tEpisode Mean: 150.8\n", - "87608:127683954\tQ-min: 2.611\tQ-max: 5.506\tLives: 1\tReward: 136.0\tEpisode Mean: 150.8\n", - "87608:127683959\tQ-min: 2.963\tQ-max: 5.873\tLives: 1\tReward: 143.0\tEpisode Mean: 150.8\n", - "87608:127683965\tQ-min: 2.843\tQ-max: 5.788\tLives: 1\tReward: 150.0\tEpisode Mean: 150.8\n", - "87608:127683994\tQ-min: 2.415\tQ-max: 4.049\tLives: 1\tReward: 154.0\tEpisode Mean: 150.8\n", - "87608:127684008\tQ-min: -0.035\tQ-max: 0.309\tLives: 0\tReward: 154.0\tEpisode Mean: 151.0\n", - "87609:127684063\tQ-min: 1.627\tQ-max: 1.654\tLives: 5\tReward: 1.0\tEpisode Mean: 151.0\n", - "87609:127684114\tQ-min: 1.841\tQ-max: 1.865\tLives: 5\tReward: 2.0\tEpisode Mean: 151.0\n", - "87609:127684168\tQ-min: 1.662\tQ-max: 1.703\tLives: 5\tReward: 3.0\tEpisode Mean: 151.0\n", - "87609:127684216\tQ-min: 2.002\tQ-max: 2.020\tLives: 5\tReward: 4.0\tEpisode Mean: 151.0\n", - "87609:127684247\tQ-min: 1.967\tQ-max: 1.995\tLives: 5\tReward: 5.0\tEpisode Mean: 151.0\n", - "87609:127684279\tQ-min: 1.965\tQ-max: 1.986\tLives: 5\tReward: 6.0\tEpisode Mean: 151.0\n", - "87609:127684314\tQ-min: 1.795\tQ-max: 1.854\tLives: 5\tReward: 10.0\tEpisode Mean: 151.0\n", - "87609:127684360\tQ-min: 1.695\tQ-max: 1.724\tLives: 5\tReward: 11.0\tEpisode Mean: 151.0\n", - "87609:127684423\tQ-min: 1.746\tQ-max: 1.772\tLives: 5\tReward: 12.0\tEpisode Mean: 151.0\n", - "87609:127684484\tQ-min: 1.606\tQ-max: 1.772\tLives: 5\tReward: 13.0\tEpisode Mean: 151.0\n", - "87609:127684549\tQ-min: 1.759\tQ-max: 1.908\tLives: 5\tReward: 14.0\tEpisode Mean: 151.0\n", - "87609:127684595\tQ-min: 2.024\tQ-max: 2.086\tLives: 5\tReward: 15.0\tEpisode Mean: 151.0\n", - "87609:127684628\tQ-min: 1.998\tQ-max: 2.026\tLives: 5\tReward: 16.0\tEpisode Mean: 151.0\n", - "87609:127684661\tQ-min: 1.952\tQ-max: 2.014\tLives: 5\tReward: 17.0\tEpisode Mean: 151.0\n", - "87609:127684694\tQ-min: 2.034\tQ-max: 2.061\tLives: 5\tReward: 18.0\tEpisode Mean: 151.0\n", - "87609:127684726\tQ-min: 2.015\tQ-max: 2.051\tLives: 5\tReward: 19.0\tEpisode Mean: 151.0\n", - "87609:127684754\tQ-min: 2.143\tQ-max: 2.173\tLives: 5\tReward: 20.0\tEpisode Mean: 151.0\n", - "87609:127684788\tQ-min: 1.963\tQ-max: 2.037\tLives: 5\tReward: 21.0\tEpisode Mean: 151.0\n", - "87609:127684823\tQ-min: 2.093\tQ-max: 2.138\tLives: 5\tReward: 22.0\tEpisode Mean: 151.0\n", - "87609:127684846\tQ-min: -0.186\tQ-max: 0.232\tLives: 4\tReward: 22.0\tEpisode Mean: 151.0\n", - "87609:127684889\tQ-min: 1.932\tQ-max: 1.958\tLives: 4\tReward: 23.0\tEpisode Mean: 151.0\n", - "87609:127684930\tQ-min: 2.099\tQ-max: 2.142\tLives: 4\tReward: 24.0\tEpisode Mean: 151.0\n", - "87609:127684972\tQ-min: 1.992\tQ-max: 2.033\tLives: 4\tReward: 25.0\tEpisode Mean: 151.0\n", - "87609:127685009\tQ-min: 2.075\tQ-max: 2.135\tLives: 4\tReward: 29.0\tEpisode Mean: 151.0\n", - "87609:127685033\tQ-min: 0.000\tQ-max: 0.368\tLives: 3\tReward: 29.0\tEpisode Mean: 151.0\n", - "87609:127685077\tQ-min: 2.295\tQ-max: 2.514\tLives: 3\tReward: 33.0\tEpisode Mean: 151.0\n", - "87609:127685100\tQ-min: 2.149\tQ-max: 2.658\tLives: 3\tReward: 40.0\tEpisode Mean: 151.0\n", - "87609:127685123\tQ-min: 1.812\tQ-max: 3.201\tLives: 3\tReward: 47.0\tEpisode Mean: 151.0\n", - "87609:127685140\tQ-min: -0.555\tQ-max: 0.613\tLives: 2\tReward: 47.0\tEpisode Mean: 151.0\n", - "87609:127685185\tQ-min: 2.128\tQ-max: 2.161\tLives: 2\tReward: 48.0\tEpisode Mean: 151.0\n", - "87609:127685233\tQ-min: 1.913\tQ-max: 2.194\tLives: 2\tReward: 52.0\tEpisode Mean: 151.0\n", - "87609:127685280\tQ-min: 2.126\tQ-max: 2.274\tLives: 2\tReward: 53.0\tEpisode Mean: 151.0\n", - "87609:127685319\tQ-min: 2.312\tQ-max: 2.400\tLives: 2\tReward: 57.0\tEpisode Mean: 151.0\n", - "87609:127685357\tQ-min: 2.606\tQ-max: 2.748\tLives: 2\tReward: 61.0\tEpisode Mean: 151.0\n", - "87609:127685377\tQ-min: 2.519\tQ-max: 3.619\tLives: 2\tReward: 62.0\tEpisode Mean: 151.0\n", - "87609:127685389\tQ-min: 0.083\tQ-max: 0.184\tLives: 1\tReward: 62.0\tEpisode Mean: 151.0\n", - "87609:127685436\tQ-min: 2.103\tQ-max: 2.290\tLives: 1\tReward: 63.0\tEpisode Mean: 151.0\n", - "87609:127685494\tQ-min: 1.586\tQ-max: 3.198\tLives: 1\tReward: 70.0\tEpisode Mean: 151.0\n", - "87609:127685515\tQ-min: 2.646\tQ-max: 2.753\tLives: 1\tReward: 71.0\tEpisode Mean: 151.0\n", - "87609:127685529\tQ-min: -0.298\tQ-max: 0.219\tLives: 0\tReward: 71.0\tEpisode Mean: 147.3\n", - "87610:127685572\tQ-min: 1.771\tQ-max: 1.787\tLives: 5\tReward: 1.0\tEpisode Mean: 147.3\n", - "87610:127685612\tQ-min: 1.795\tQ-max: 1.833\tLives: 5\tReward: 2.0\tEpisode Mean: 147.3\n", - "87610:127685653\tQ-min: 1.886\tQ-max: 1.906\tLives: 5\tReward: 3.0\tEpisode Mean: 147.3\n", - "87610:127685684\tQ-min: -0.097\tQ-max: 0.119\tLives: 4\tReward: 3.0\tEpisode Mean: 147.3\n", - "87610:127685727\tQ-min: 1.869\tQ-max: 1.902\tLives: 4\tReward: 4.0\tEpisode Mean: 147.3\n", - "87610:127685773\tQ-min: 1.935\tQ-max: 1.952\tLives: 4\tReward: 8.0\tEpisode Mean: 147.3\n", - "87610:127685826\tQ-min: 1.682\tQ-max: 1.699\tLives: 4\tReward: 9.0\tEpisode Mean: 147.3\n", - "87610:127685876\tQ-min: 1.935\tQ-max: 1.961\tLives: 4\tReward: 10.0\tEpisode Mean: 147.3\n", - "87610:127685910\tQ-min: 1.936\tQ-max: 1.984\tLives: 4\tReward: 11.0\tEpisode Mean: 147.3\n", - "87610:127685941\tQ-min: 1.960\tQ-max: 1.993\tLives: 4\tReward: 12.0\tEpisode Mean: 147.3\n", - "87610:127685973\tQ-min: 1.965\tQ-max: 2.007\tLives: 4\tReward: 13.0\tEpisode Mean: 147.3\n", - "87610:127685996\tQ-min: -0.274\tQ-max: 0.102\tLives: 3\tReward: 13.0\tEpisode Mean: 147.3\n", - "87610:127686040\tQ-min: 1.890\tQ-max: 1.914\tLives: 3\tReward: 14.0\tEpisode Mean: 147.3\n", - "87610:127686082\tQ-min: 1.975\tQ-max: 2.052\tLives: 3\tReward: 15.0\tEpisode Mean: 147.3\n", - "87610:127686126\tQ-min: 1.971\tQ-max: 2.005\tLives: 3\tReward: 16.0\tEpisode Mean: 147.3\n", - "87610:127686165\tQ-min: 2.055\tQ-max: 2.075\tLives: 3\tReward: 17.0\tEpisode Mean: 147.3\n", - "87610:127686201\tQ-min: 1.981\tQ-max: 2.010\tLives: 3\tReward: 21.0\tEpisode Mean: 147.3\n", - "87610:127686236\tQ-min: 1.987\tQ-max: 2.014\tLives: 3\tReward: 22.0\tEpisode Mean: 147.3\n", - "87610:127686270\tQ-min: 2.048\tQ-max: 2.107\tLives: 3\tReward: 26.0\tEpisode Mean: 147.3\n", - "87610:127686319\tQ-min: 1.779\tQ-max: 1.806\tLives: 3\tReward: 27.0\tEpisode Mean: 147.3\n", - "87610:127686378\tQ-min: 1.763\tQ-max: 1.814\tLives: 3\tReward: 28.0\tEpisode Mean: 147.3\n", - "87610:127686441\tQ-min: 1.784\tQ-max: 1.854\tLives: 3\tReward: 29.0\tEpisode Mean: 147.3\n", - "87610:127686505\tQ-min: 1.767\tQ-max: 1.807\tLives: 3\tReward: 30.0\tEpisode Mean: 147.3\n", - "87610:127686552\tQ-min: 2.130\tQ-max: 2.151\tLives: 3\tReward: 31.0\tEpisode Mean: 147.3\n", - "87610:127686586\tQ-min: 2.020\tQ-max: 2.132\tLives: 3\tReward: 35.0\tEpisode Mean: 147.3\n", - "87610:127686619\tQ-min: 2.229\tQ-max: 2.302\tLives: 3\tReward: 39.0\tEpisode Mean: 147.3\n", - "87610:127686653\tQ-min: 2.146\tQ-max: 2.168\tLives: 3\tReward: 40.0\tEpisode Mean: 147.3\n", - "87610:127686688\tQ-min: 2.089\tQ-max: 2.103\tLives: 3\tReward: 41.0\tEpisode Mean: 147.3\n", - "87610:127686722\tQ-min: 2.044\tQ-max: 2.106\tLives: 3\tReward: 42.0\tEpisode Mean: 147.3\n", - "87610:127686758\tQ-min: 2.272\tQ-max: 2.601\tLives: 3\tReward: 46.0\tEpisode Mean: 147.3\n", - "87610:127686778\tQ-min: 2.339\tQ-max: 2.568\tLives: 3\tReward: 47.0\tEpisode Mean: 147.3\n", - "87610:127686793\tQ-min: -0.078\tQ-max: 0.390\tLives: 2\tReward: 47.0\tEpisode Mean: 147.3\n", - "87610:127686843\tQ-min: 2.456\tQ-max: 2.751\tLives: 2\tReward: 54.0\tEpisode Mean: 147.3\n", - "87610:127686864\tQ-min: 2.580\tQ-max: 2.685\tLives: 2\tReward: 55.0\tEpisode Mean: 147.3\n", - "87610:127686883\tQ-min: 2.495\tQ-max: 2.593\tLives: 2\tReward: 56.0\tEpisode Mean: 147.3\n", - "87610:127686902\tQ-min: 2.405\tQ-max: 2.642\tLives: 2\tReward: 57.0\tEpisode Mean: 147.3\n", - "87610:127686922\tQ-min: 2.280\tQ-max: 2.503\tLives: 2\tReward: 58.0\tEpisode Mean: 147.3\n", - "87610:127686941\tQ-min: 2.468\tQ-max: 2.574\tLives: 2\tReward: 62.0\tEpisode Mean: 147.3\n", - "87610:127686962\tQ-min: 2.408\tQ-max: 2.591\tLives: 2\tReward: 66.0\tEpisode Mean: 147.3\n", - "87610:127686985\tQ-min: 2.310\tQ-max: 2.628\tLives: 2\tReward: 70.0\tEpisode Mean: 147.3\n", - "87610:127687008\tQ-min: 2.512\tQ-max: 2.766\tLives: 2\tReward: 74.0\tEpisode Mean: 147.3\n", - "87610:127687030\tQ-min: 2.183\tQ-max: 2.847\tLives: 2\tReward: 78.0\tEpisode Mean: 147.3\n", - "87610:127687043\tQ-min: 0.031\tQ-max: 0.250\tLives: 1\tReward: 78.0\tEpisode Mean: 147.3\n", - "87610:127687082\tQ-min: 2.263\tQ-max: 2.332\tLives: 1\tReward: 79.0\tEpisode Mean: 147.3\n", - "87610:127687134\tQ-min: 2.101\tQ-max: 2.186\tLives: 1\tReward: 80.0\tEpisode Mean: 147.3\n", - "87610:127687203\tQ-min: 2.399\tQ-max: 2.755\tLives: 1\tReward: 84.0\tEpisode Mean: 147.3\n", - "87610:127687217\tQ-min: 0.028\tQ-max: 0.155\tLives: 0\tReward: 84.0\tEpisode Mean: 144.6\n", - "87611:127687262\tQ-min: 1.731\tQ-max: 1.745\tLives: 5\tReward: 1.0\tEpisode Mean: 144.6\n", - "87611:127687313\tQ-min: 1.651\tQ-max: 1.690\tLives: 5\tReward: 2.0\tEpisode Mean: 144.6\n", - "87611:127687367\tQ-min: 1.864\tQ-max: 1.887\tLives: 5\tReward: 3.0\tEpisode Mean: 144.6\n", - "87611:127687403\tQ-min: 1.930\tQ-max: 1.957\tLives: 5\tReward: 4.0\tEpisode Mean: 144.6\n", - "87611:127687433\tQ-min: 1.943\tQ-max: 1.969\tLives: 5\tReward: 5.0\tEpisode Mean: 144.6\n", - "87611:127687467\tQ-min: 1.962\tQ-max: 1.975\tLives: 5\tReward: 6.0\tEpisode Mean: 144.6\n", - "87611:127687500\tQ-min: 1.687\tQ-max: 1.752\tLives: 5\tReward: 7.0\tEpisode Mean: 144.6\n", - "87611:127687549\tQ-min: 1.655\tQ-max: 1.674\tLives: 5\tReward: 8.0\tEpisode Mean: 144.6\n", - "87611:127687613\tQ-min: 1.662\tQ-max: 1.678\tLives: 5\tReward: 9.0\tEpisode Mean: 144.6\n", - "87611:127687651\tQ-min: -0.105\tQ-max: 0.128\tLives: 4\tReward: 9.0\tEpisode Mean: 144.6\n", - "87611:127687705\tQ-min: 1.583\tQ-max: 1.631\tLives: 4\tReward: 10.0\tEpisode Mean: 144.6\n", - "87611:127687773\tQ-min: 1.727\tQ-max: 1.743\tLives: 4\tReward: 11.0\tEpisode Mean: 144.6\n", - "87611:127687828\tQ-min: 1.937\tQ-max: 1.957\tLives: 4\tReward: 12.0\tEpisode Mean: 144.6\n", - "87611:127687864\tQ-min: 1.992\tQ-max: 2.010\tLives: 4\tReward: 13.0\tEpisode Mean: 144.6\n", - "87611:127687897\tQ-min: 1.985\tQ-max: 2.009\tLives: 4\tReward: 14.0\tEpisode Mean: 144.6\n", - "87611:127687937\tQ-min: 2.044\tQ-max: 2.064\tLives: 4\tReward: 18.0\tEpisode Mean: 144.6\n", - "87611:127687970\tQ-min: 2.033\tQ-max: 2.052\tLives: 4\tReward: 19.0\tEpisode Mean: 144.6\n", - "87611:127688019\tQ-min: 1.712\tQ-max: 1.740\tLives: 4\tReward: 20.0\tEpisode Mean: 144.6\n", - "87611:127688085\tQ-min: 1.721\tQ-max: 1.762\tLives: 4\tReward: 21.0\tEpisode Mean: 144.6\n", - "87611:127688149\tQ-min: 1.650\tQ-max: 1.725\tLives: 4\tReward: 22.0\tEpisode Mean: 144.6\n", - "87611:127688214\tQ-min: 1.663\tQ-max: 1.756\tLives: 4\tReward: 23.0\tEpisode Mean: 144.6\n", - "87611:127688263\tQ-min: 2.057\tQ-max: 2.084\tLives: 4\tReward: 24.0\tEpisode Mean: 144.6\n", - "87611:127688299\tQ-min: 1.769\tQ-max: 1.903\tLives: 4\tReward: 28.0\tEpisode Mean: 144.6\n", - "87611:127688335\tQ-min: 2.005\tQ-max: 2.049\tLives: 4\tReward: 29.0\tEpisode Mean: 144.6\n", - "87611:127688367\tQ-min: 2.091\tQ-max: 2.151\tLives: 4\tReward: 30.0\tEpisode Mean: 144.6\n", - "87611:127688399\tQ-min: 2.170\tQ-max: 2.212\tLives: 4\tReward: 31.0\tEpisode Mean: 144.6\n", - "87611:127688431\tQ-min: 2.025\tQ-max: 2.069\tLives: 4\tReward: 32.0\tEpisode Mean: 144.6\n", - "87611:127688465\tQ-min: 1.990\tQ-max: 2.026\tLives: 4\tReward: 33.0\tEpisode Mean: 144.6\n", - "87611:127688500\tQ-min: 2.126\tQ-max: 2.194\tLives: 4\tReward: 37.0\tEpisode Mean: 144.6\n", - "87611:127688519\tQ-min: 0.121\tQ-max: 0.461\tLives: 3\tReward: 37.0\tEpisode Mean: 144.6\n", - "87611:127688552\tQ-min: 0.028\tQ-max: 0.190\tLives: 2\tReward: 37.0\tEpisode Mean: 144.6\n", - "87611:127688599\tQ-min: 1.996\tQ-max: 2.115\tLives: 2\tReward: 41.0\tEpisode Mean: 144.6\n", - "87611:127688647\tQ-min: 2.269\tQ-max: 2.482\tLives: 2\tReward: 45.0\tEpisode Mean: 144.6\n", - "87611:127688661\tQ-min: -0.118\tQ-max: 0.097\tLives: 1\tReward: 45.0\tEpisode Mean: 144.6\n", - "87611:127688708\tQ-min: 2.254\tQ-max: 2.884\tLives: 1\tReward: 52.0\tEpisode Mean: 144.6\n", - "87611:127688732\tQ-min: 2.380\tQ-max: 2.510\tLives: 1\tReward: 53.0\tEpisode Mean: 144.6\n", - "87611:127688751\tQ-min: 2.509\tQ-max: 2.535\tLives: 1\tReward: 54.0\tEpisode Mean: 144.6\n", - "87611:127688773\tQ-min: 2.214\tQ-max: 2.313\tLives: 1\tReward: 55.0\tEpisode Mean: 144.6\n", - "87611:127688784\tQ-min: -0.137\tQ-max: -0.064\tLives: 0\tReward: 55.0\tEpisode Mean: 140.8\n", - "87612:127688825\tQ-min: 1.743\tQ-max: 1.756\tLives: 5\tReward: 1.0\tEpisode Mean: 140.8\n", - "87612:127688868\tQ-min: 1.769\tQ-max: 1.795\tLives: 5\tReward: 2.0\tEpisode Mean: 140.8\n", - "87612:127688922\tQ-min: 1.657\tQ-max: 1.705\tLives: 5\tReward: 3.0\tEpisode Mean: 140.8\n", - "87612:127688973\tQ-min: 1.955\tQ-max: 1.978\tLives: 5\tReward: 4.0\tEpisode Mean: 140.8\n", - "87612:127689006\tQ-min: 1.986\tQ-max: 2.008\tLives: 5\tReward: 5.0\tEpisode Mean: 140.8\n", - "87612:127689035\tQ-min: 1.966\tQ-max: 1.974\tLives: 5\tReward: 6.0\tEpisode Mean: 140.8\n", - "87612:127689066\tQ-min: 1.749\tQ-max: 1.813\tLives: 5\tReward: 7.0\tEpisode Mean: 140.8\n", - "87612:127689111\tQ-min: 1.692\tQ-max: 1.708\tLives: 5\tReward: 8.0\tEpisode Mean: 140.8\n", - "87612:127689176\tQ-min: 1.702\tQ-max: 1.741\tLives: 5\tReward: 9.0\tEpisode Mean: 140.8\n", - "87612:127689241\tQ-min: 1.659\tQ-max: 1.676\tLives: 5\tReward: 10.0\tEpisode Mean: 140.8\n", - "87612:127689302\tQ-min: 1.630\tQ-max: 1.654\tLives: 5\tReward: 11.0\tEpisode Mean: 140.8\n", - "87612:127689355\tQ-min: 1.973\tQ-max: 2.007\tLives: 5\tReward: 12.0\tEpisode Mean: 140.8\n", - "87612:127689378\tQ-min: -0.112\tQ-max: 0.204\tLives: 4\tReward: 12.0\tEpisode Mean: 140.8\n", - "87612:127689419\tQ-min: 1.939\tQ-max: 1.980\tLives: 4\tReward: 13.0\tEpisode Mean: 140.8\n", - "87612:127689465\tQ-min: 1.912\tQ-max: 1.950\tLives: 4\tReward: 14.0\tEpisode Mean: 140.8\n", - "87612:127689522\tQ-min: 1.673\tQ-max: 1.718\tLives: 4\tReward: 15.0\tEpisode Mean: 140.8\n", - "87612:127689570\tQ-min: 1.962\tQ-max: 1.988\tLives: 4\tReward: 16.0\tEpisode Mean: 140.8\n", - "87612:127689603\tQ-min: 2.014\tQ-max: 2.075\tLives: 4\tReward: 20.0\tEpisode Mean: 140.8\n", - "87612:127689627\tQ-min: -0.091\tQ-max: 0.199\tLives: 3\tReward: 20.0\tEpisode Mean: 140.8\n", - "87612:127689682\tQ-min: 1.711\tQ-max: 1.726\tLives: 3\tReward: 21.0\tEpisode Mean: 140.8\n", - "87612:127689724\tQ-min: -0.040\tQ-max: 0.209\tLives: 2\tReward: 21.0\tEpisode Mean: 140.8\n", - "87612:127689767\tQ-min: 1.848\tQ-max: 1.880\tLives: 2\tReward: 22.0\tEpisode Mean: 140.8\n", - "87612:127689811\tQ-min: 1.961\tQ-max: 1.978\tLives: 2\tReward: 23.0\tEpisode Mean: 140.8\n", - "87612:127689853\tQ-min: 1.906\tQ-max: 1.925\tLives: 2\tReward: 24.0\tEpisode Mean: 140.8\n", - "87612:127689894\tQ-min: 2.037\tQ-max: 2.090\tLives: 2\tReward: 28.0\tEpisode Mean: 140.8\n", - "87612:127689933\tQ-min: 2.121\tQ-max: 2.210\tLives: 2\tReward: 32.0\tEpisode Mean: 140.8\n", - "87612:127689973\tQ-min: 2.198\tQ-max: 2.401\tLives: 2\tReward: 36.0\tEpisode Mean: 140.8\n", - "87612:127689995\tQ-min: 2.288\tQ-max: 2.436\tLives: 2\tReward: 37.0\tEpisode Mean: 140.8\n", - "87612:127690009\tQ-min: -0.031\tQ-max: 0.069\tLives: 1\tReward: 37.0\tEpisode Mean: 140.8\n", - "87612:127690057\tQ-min: 2.013\tQ-max: 2.063\tLives: 1\tReward: 38.0\tEpisode Mean: 140.8\n", - "87612:127690116\tQ-min: 1.884\tQ-max: 1.909\tLives: 1\tReward: 39.0\tEpisode Mean: 140.8\n", - "87612:127690174\tQ-min: 2.133\tQ-max: 2.171\tLives: 1\tReward: 40.0\tEpisode Mean: 140.8\n", - "87612:127690200\tQ-min: -0.037\tQ-max: 0.172\tLives: 0\tReward: 40.0\tEpisode Mean: 136.8\n", - "87613:127690252\tQ-min: 1.621\tQ-max: 1.693\tLives: 5\tReward: 1.0\tEpisode Mean: 136.8\n", - "87613:127690315\tQ-min: 1.643\tQ-max: 1.659\tLives: 5\tReward: 2.0\tEpisode Mean: 136.8\n", - "87613:127690366\tQ-min: 1.864\tQ-max: 1.898\tLives: 5\tReward: 3.0\tEpisode Mean: 136.8\n", - "87613:127690401\tQ-min: 1.925\tQ-max: 1.938\tLives: 5\tReward: 4.0\tEpisode Mean: 136.8\n", - "87613:127690435\tQ-min: 1.933\tQ-max: 1.949\tLives: 5\tReward: 5.0\tEpisode Mean: 136.8\n", - "87613:127690469\tQ-min: 1.935\tQ-max: 1.977\tLives: 5\tReward: 9.0\tEpisode Mean: 136.8\n", - "87613:127690504\tQ-min: 2.179\tQ-max: 2.305\tLives: 5\tReward: 13.0\tEpisode Mean: 136.8\n", - "87613:127690525\tQ-min: 2.128\tQ-max: 2.241\tLives: 5\tReward: 14.0\tEpisode Mean: 136.8\n", - "87613:127690545\tQ-min: 1.717\tQ-max: 2.213\tLives: 5\tReward: 15.0\tEpisode Mean: 136.8\n", - "87613:127690565\tQ-min: 2.135\tQ-max: 2.274\tLives: 5\tReward: 16.0\tEpisode Mean: 136.8\n", - "87613:127690585\tQ-min: 2.200\tQ-max: 2.348\tLives: 5\tReward: 17.0\tEpisode Mean: 136.8\n", - "87613:127690605\tQ-min: 2.118\tQ-max: 2.305\tLives: 5\tReward: 18.0\tEpisode Mean: 136.8\n", - "87613:127690627\tQ-min: 2.129\tQ-max: 2.312\tLives: 5\tReward: 19.0\tEpisode Mean: 136.8\n", - "87613:127690641\tQ-min: 0.014\tQ-max: 0.224\tLives: 4\tReward: 19.0\tEpisode Mean: 136.8\n", - "87613:127690695\tQ-min: 1.714\tQ-max: 1.727\tLives: 4\tReward: 20.0\tEpisode Mean: 136.8\n", - "87613:127690750\tQ-min: 2.040\tQ-max: 2.111\tLives: 4\tReward: 21.0\tEpisode Mean: 136.8\n", - "87613:127690792\tQ-min: 2.043\tQ-max: 2.105\tLives: 4\tReward: 22.0\tEpisode Mean: 136.8\n", - "87613:127690828\tQ-min: 2.116\tQ-max: 2.167\tLives: 4\tReward: 23.0\tEpisode Mean: 136.8\n", - "87613:127690860\tQ-min: 2.157\tQ-max: 2.172\tLives: 4\tReward: 24.0\tEpisode Mean: 136.8\n", - "87613:127690893\tQ-min: 2.138\tQ-max: 2.167\tLives: 4\tReward: 28.0\tEpisode Mean: 136.8\n", - "87613:127690915\tQ-min: -0.427\tQ-max: 0.563\tLives: 3\tReward: 28.0\tEpisode Mean: 136.8\n", - "87613:127690960\tQ-min: 2.085\tQ-max: 2.117\tLives: 3\tReward: 29.0\tEpisode Mean: 136.8\n", - "87613:127691006\tQ-min: 2.026\tQ-max: 2.062\tLives: 3\tReward: 30.0\tEpisode Mean: 136.8\n", - "87613:127691053\tQ-min: 1.836\tQ-max: 2.660\tLives: 3\tReward: 34.0\tEpisode Mean: 136.8\n", - "87613:127691075\tQ-min: 2.278\tQ-max: 2.366\tLives: 3\tReward: 38.0\tEpisode Mean: 136.8\n", - "87613:127691089\tQ-min: -0.006\tQ-max: 0.138\tLives: 2\tReward: 38.0\tEpisode Mean: 136.8\n", - "87613:127691129\tQ-min: 1.988\tQ-max: 2.011\tLives: 2\tReward: 39.0\tEpisode Mean: 136.8\n", - "87613:127691170\tQ-min: 2.044\tQ-max: 2.122\tLives: 2\tReward: 40.0\tEpisode Mean: 136.8\n", - "87613:127691224\tQ-min: 1.812\tQ-max: 1.864\tLives: 2\tReward: 41.0\tEpisode Mean: 136.8\n", - "87613:127691272\tQ-min: 2.171\tQ-max: 2.215\tLives: 2\tReward: 42.0\tEpisode Mean: 136.8\n", - "87613:127691301\tQ-min: 2.344\tQ-max: 2.418\tLives: 2\tReward: 43.0\tEpisode Mean: 136.8\n", - "87613:127691334\tQ-min: 2.161\tQ-max: 2.215\tLives: 2\tReward: 44.0\tEpisode Mean: 136.8\n", - "87613:127691372\tQ-min: 2.097\tQ-max: 2.232\tLives: 2\tReward: 48.0\tEpisode Mean: 136.8\n", - "87613:127691422\tQ-min: 1.736\tQ-max: 1.760\tLives: 2\tReward: 49.0\tEpisode Mean: 136.8\n", - "87613:127691494\tQ-min: 1.720\tQ-max: 1.880\tLives: 2\tReward: 53.0\tEpisode Mean: 136.8\n", - "87613:127691560\tQ-min: 1.820\tQ-max: 1.934\tLives: 2\tReward: 54.0\tEpisode Mean: 136.8\n", - "87613:127691626\tQ-min: 1.752\tQ-max: 2.086\tLives: 2\tReward: 58.0\tEpisode Mean: 136.8\n", - "87613:127691676\tQ-min: 2.129\tQ-max: 2.214\tLives: 2\tReward: 59.0\tEpisode Mean: 136.8\n", - "87613:127691710\tQ-min: 2.299\tQ-max: 2.406\tLives: 2\tReward: 63.0\tEpisode Mean: 136.8\n", - "87613:127691745\tQ-min: 2.035\tQ-max: 2.164\tLives: 2\tReward: 64.0\tEpisode Mean: 136.8\n", - "87613:127691785\tQ-min: 2.293\tQ-max: 2.472\tLives: 2\tReward: 71.0\tEpisode Mean: 136.8\n", - "87613:127691808\tQ-min: 2.143\tQ-max: 2.440\tLives: 2\tReward: 75.0\tEpisode Mean: 136.8\n", - "87613:127691829\tQ-min: 1.921\tQ-max: 2.489\tLives: 2\tReward: 79.0\tEpisode Mean: 136.8\n", - "87613:127691841\tQ-min: 0.072\tQ-max: 0.313\tLives: 1\tReward: 79.0\tEpisode Mean: 136.8\n", - "87613:127691888\tQ-min: 2.163\tQ-max: 2.211\tLives: 1\tReward: 83.0\tEpisode Mean: 136.8\n", - "87613:127691932\tQ-min: 2.358\tQ-max: 2.504\tLives: 1\tReward: 84.0\tEpisode Mean: 136.8\n", - "87613:127691978\tQ-min: 2.047\tQ-max: 2.285\tLives: 1\tReward: 85.0\tEpisode Mean: 136.8\n", - "87613:127692017\tQ-min: 2.251\tQ-max: 2.327\tLives: 1\tReward: 86.0\tEpisode Mean: 136.8\n", - "87613:127692055\tQ-min: 2.361\tQ-max: 2.563\tLives: 1\tReward: 93.0\tEpisode Mean: 136.8\n", - "87613:127692078\tQ-min: 2.379\tQ-max: 2.775\tLives: 1\tReward: 97.0\tEpisode Mean: 136.8\n", - "87613:127692101\tQ-min: 2.345\tQ-max: 3.082\tLives: 1\tReward: 104.0\tEpisode Mean: 136.8\n", - "87613:127692125\tQ-min: 2.469\tQ-max: 3.301\tLives: 1\tReward: 111.0\tEpisode Mean: 136.8\n", - "87613:127692151\tQ-min: 2.195\tQ-max: 3.053\tLives: 1\tReward: 115.0\tEpisode Mean: 136.8\n", - "87613:127692174\tQ-min: 2.053\tQ-max: 5.052\tLives: 1\tReward: 122.0\tEpisode Mean: 136.8\n", - "87613:127692196\tQ-min: 3.218\tQ-max: 3.777\tLives: 1\tReward: 129.0\tEpisode Mean: 136.8\n", - "87613:127692225\tQ-min: 2.640\tQ-max: 7.819\tLives: 1\tReward: 136.0\tEpisode Mean: 136.8\n", - "87613:127692231\tQ-min: 3.148\tQ-max: 6.144\tLives: 1\tReward: 143.0\tEpisode Mean: 136.8\n", - "87613:127692236\tQ-min: 3.346\tQ-max: 6.952\tLives: 1\tReward: 150.0\tEpisode Mean: 136.8\n", - "87613:127692241\tQ-min: 3.525\tQ-max: 6.480\tLives: 1\tReward: 157.0\tEpisode Mean: 136.8\n", - "87613:127692245\tQ-min: 2.161\tQ-max: 6.309\tLives: 1\tReward: 164.0\tEpisode Mean: 136.8\n", - "87613:127692249\tQ-min: 3.869\tQ-max: 6.309\tLives: 1\tReward: 171.0\tEpisode Mean: 136.8\n", - "87613:127692253\tQ-min: 3.571\tQ-max: 5.777\tLives: 1\tReward: 178.0\tEpisode Mean: 136.8\n", - "87613:127692258\tQ-min: 3.166\tQ-max: 5.918\tLives: 1\tReward: 185.0\tEpisode Mean: 136.8\n", - "87613:127692262\tQ-min: 2.885\tQ-max: 5.234\tLives: 1\tReward: 192.0\tEpisode Mean: 136.8\n", - "87613:127692270\tQ-min: 2.900\tQ-max: 3.831\tLives: 1\tReward: 193.0\tEpisode Mean: 136.8\n", - "87613:127692278\tQ-min: 2.557\tQ-max: 5.713\tLives: 1\tReward: 200.0\tEpisode Mean: 136.8\n", - "87613:127692284\tQ-min: 4.380\tQ-max: 4.935\tLives: 1\tReward: 207.0\tEpisode Mean: 136.8\n", - "87613:127692320\tQ-min: 2.739\tQ-max: 5.609\tLives: 1\tReward: 214.0\tEpisode Mean: 136.8\n", - "87613:127692327\tQ-min: 3.526\tQ-max: 5.042\tLives: 1\tReward: 218.0\tEpisode Mean: 136.8\n", - "87613:127692334\tQ-min: 4.065\tQ-max: 5.043\tLives: 1\tReward: 225.0\tEpisode Mean: 136.8\n", - "87613:127692339\tQ-min: 1.078\tQ-max: 4.988\tLives: 1\tReward: 232.0\tEpisode Mean: 136.8\n", - "87613:127692343\tQ-min: 4.129\tQ-max: 5.668\tLives: 1\tReward: 239.0\tEpisode Mean: 136.8\n", - "87613:127692348\tQ-min: 3.897\tQ-max: 6.170\tLives: 1\tReward: 246.0\tEpisode Mean: 136.8\n", - "87613:127692355\tQ-min: 3.166\tQ-max: 5.217\tLives: 1\tReward: 253.0\tEpisode Mean: 136.8\n", - "87613:127692395\tQ-min: 2.545\tQ-max: 3.698\tLives: 1\tReward: 260.0\tEpisode Mean: 136.8\n", - "87613:127692402\tQ-min: 3.140\tQ-max: 4.613\tLives: 1\tReward: 267.0\tEpisode Mean: 136.8\n", - "87613:127692408\tQ-min: 2.794\tQ-max: 4.494\tLives: 1\tReward: 274.0\tEpisode Mean: 136.8\n", - "87613:127692414\tQ-min: 2.961\tQ-max: 4.157\tLives: 1\tReward: 278.0\tEpisode Mean: 136.8\n", - "87613:127692420\tQ-min: 2.604\tQ-max: 4.554\tLives: 1\tReward: 285.0\tEpisode Mean: 136.8\n", - "87613:127692427\tQ-min: 2.394\tQ-max: 4.361\tLives: 1\tReward: 292.0\tEpisode Mean: 136.8\n", - "87613:127692465\tQ-min: 2.877\tQ-max: 4.571\tLives: 1\tReward: 299.0\tEpisode Mean: 136.8\n", - "87613:127692485\tQ-min: -1.033\tQ-max: 0.680\tLives: 0\tReward: 299.0\tEpisode Mean: 143.0\n", - "87614:127692527\tQ-min: 1.766\tQ-max: 1.789\tLives: 5\tReward: 1.0\tEpisode Mean: 143.0\n", - "87614:127692565\tQ-min: 1.803\tQ-max: 1.843\tLives: 5\tReward: 2.0\tEpisode Mean: 143.0\n", - "87614:127692591\tQ-min: -0.225\tQ-max: 0.154\tLives: 4\tReward: 2.0\tEpisode Mean: 143.0\n", - "87614:127692638\tQ-min: 1.861\tQ-max: 1.883\tLives: 4\tReward: 3.0\tEpisode Mean: 143.0\n", - "87614:127692677\tQ-min: 1.971\tQ-max: 1.990\tLives: 4\tReward: 4.0\tEpisode Mean: 143.0\n", - "87614:127692719\tQ-min: 1.868\tQ-max: 1.902\tLives: 4\tReward: 5.0\tEpisode Mean: 143.0\n", - "87614:127692757\tQ-min: 1.970\tQ-max: 2.008\tLives: 4\tReward: 6.0\tEpisode Mean: 143.0\n", - "87614:127692789\tQ-min: 1.890\tQ-max: 1.911\tLives: 4\tReward: 7.0\tEpisode Mean: 143.0\n", - "87614:127692823\tQ-min: 1.920\tQ-max: 1.938\tLives: 4\tReward: 8.0\tEpisode Mean: 143.0\n", - "87614:127692854\tQ-min: 1.956\tQ-max: 2.014\tLives: 4\tReward: 9.0\tEpisode Mean: 143.0\n", - "87614:127692900\tQ-min: 1.622\tQ-max: 1.698\tLives: 4\tReward: 10.0\tEpisode Mean: 143.0\n", - "87614:127692963\tQ-min: 1.506\tQ-max: 1.674\tLives: 4\tReward: 14.0\tEpisode Mean: 143.0\n", - "87614:127693028\tQ-min: 1.711\tQ-max: 1.739\tLives: 4\tReward: 15.0\tEpisode Mean: 143.0\n", - "87614:127693095\tQ-min: 1.765\tQ-max: 1.803\tLives: 4\tReward: 16.0\tEpisode Mean: 143.0\n", - "87614:127693141\tQ-min: -0.065\tQ-max: 0.344\tLives: 3\tReward: 16.0\tEpisode Mean: 143.0\n", - "87614:127693194\tQ-min: 1.681\tQ-max: 1.725\tLives: 3\tReward: 17.0\tEpisode Mean: 143.0\n", - "87614:127693248\tQ-min: 1.985\tQ-max: 1.995\tLives: 3\tReward: 18.0\tEpisode Mean: 143.0\n", - "87614:127693305\tQ-min: 1.598\tQ-max: 1.688\tLives: 3\tReward: 19.0\tEpisode Mean: 143.0\n", - "87614:127693358\tQ-min: 1.962\tQ-max: 1.997\tLives: 3\tReward: 20.0\tEpisode Mean: 143.0\n", - "87614:127693390\tQ-min: 2.017\tQ-max: 2.048\tLives: 3\tReward: 21.0\tEpisode Mean: 143.0\n", - "87614:127693423\tQ-min: 2.144\tQ-max: 2.350\tLives: 3\tReward: 25.0\tEpisode Mean: 143.0\n", - "87614:127693444\tQ-min: 2.376\tQ-max: 2.448\tLives: 3\tReward: 26.0\tEpisode Mean: 143.0\n", - "87614:127693462\tQ-min: 2.332\tQ-max: 2.458\tLives: 3\tReward: 27.0\tEpisode Mean: 143.0\n", - "87614:127693483\tQ-min: 2.290\tQ-max: 2.491\tLives: 3\tReward: 28.0\tEpisode Mean: 143.0\n", - "87614:127693504\tQ-min: 2.354\tQ-max: 2.425\tLives: 3\tReward: 29.0\tEpisode Mean: 143.0\n", - "87614:127693522\tQ-min: 2.388\tQ-max: 2.483\tLives: 3\tReward: 33.0\tEpisode Mean: 143.0\n", - "87614:127693541\tQ-min: 2.270\tQ-max: 2.347\tLives: 3\tReward: 34.0\tEpisode Mean: 143.0\n", - "87614:127693562\tQ-min: 2.352\tQ-max: 2.491\tLives: 3\tReward: 38.0\tEpisode Mean: 143.0\n", - "87614:127693585\tQ-min: 2.124\tQ-max: 2.533\tLives: 3\tReward: 42.0\tEpisode Mean: 143.0\n", - "87614:127693605\tQ-min: 2.291\tQ-max: 2.583\tLives: 3\tReward: 43.0\tEpisode Mean: 143.0\n", - "87614:127693625\tQ-min: 2.335\tQ-max: 2.473\tLives: 3\tReward: 44.0\tEpisode Mean: 143.0\n", - "87614:127693644\tQ-min: 2.395\tQ-max: 2.536\tLives: 3\tReward: 45.0\tEpisode Mean: 143.0\n", - "87614:127693657\tQ-min: 0.026\tQ-max: 0.412\tLives: 2\tReward: 45.0\tEpisode Mean: 143.0\n", - "87614:127693699\tQ-min: 2.104\tQ-max: 2.139\tLives: 2\tReward: 46.0\tEpisode Mean: 143.0\n", - "87614:127693754\tQ-min: 1.830\tQ-max: 1.937\tLives: 2\tReward: 47.0\tEpisode Mean: 143.0\n", - "87614:127693819\tQ-min: 1.974\tQ-max: 2.017\tLives: 2\tReward: 48.0\tEpisode Mean: 143.0\n", - "87614:127693870\tQ-min: 2.307\tQ-max: 2.335\tLives: 2\tReward: 49.0\tEpisode Mean: 143.0\n", - "87614:127693905\tQ-min: 2.022\tQ-max: 2.130\tLives: 2\tReward: 53.0\tEpisode Mean: 143.0\n", - "87614:127693942\tQ-min: 2.223\tQ-max: 2.292\tLives: 2\tReward: 54.0\tEpisode Mean: 143.0\n", - "87614:127693964\tQ-min: -0.345\tQ-max: 0.457\tLives: 1\tReward: 54.0\tEpisode Mean: 143.0\n", - "87614:127694008\tQ-min: 2.152\tQ-max: 2.202\tLives: 1\tReward: 58.0\tEpisode Mean: 143.0\n", - "87614:127694057\tQ-min: 2.324\tQ-max: 2.604\tLives: 1\tReward: 62.0\tEpisode Mean: 143.0\n", - "87614:127694079\tQ-min: 2.336\tQ-max: 2.557\tLives: 1\tReward: 66.0\tEpisode Mean: 143.0\n", - "87614:127694099\tQ-min: 2.449\tQ-max: 2.512\tLives: 1\tReward: 67.0\tEpisode Mean: 143.0\n", - "87614:127694120\tQ-min: 2.384\tQ-max: 2.646\tLives: 1\tReward: 71.0\tEpisode Mean: 143.0\n", - "87614:127694134\tQ-min: 0.130\tQ-max: 0.253\tLives: 0\tReward: 71.0\tEpisode Mean: 140.4\n", - "87615:127694189\tQ-min: 1.683\tQ-max: 1.693\tLives: 5\tReward: 1.0\tEpisode Mean: 140.4\n", - "87615:127694238\tQ-min: 1.845\tQ-max: 1.865\tLives: 5\tReward: 2.0\tEpisode Mean: 140.4\n", - "87615:127694287\tQ-min: 1.685\tQ-max: 1.715\tLives: 5\tReward: 3.0\tEpisode Mean: 140.4\n", - "87615:127694335\tQ-min: 1.930\tQ-max: 1.947\tLives: 5\tReward: 4.0\tEpisode Mean: 140.4\n", - "87615:127694365\tQ-min: 1.954\tQ-max: 1.982\tLives: 5\tReward: 5.0\tEpisode Mean: 140.4\n", - "87615:127694400\tQ-min: 1.906\tQ-max: 1.926\tLives: 5\tReward: 6.0\tEpisode Mean: 140.4\n", - "87615:127694420\tQ-min: -0.096\tQ-max: 0.063\tLives: 4\tReward: 6.0\tEpisode Mean: 140.4\n", - "87615:127694462\tQ-min: 1.833\tQ-max: 1.868\tLives: 4\tReward: 7.0\tEpisode Mean: 140.4\n", - "87615:127694502\tQ-min: 1.910\tQ-max: 1.920\tLives: 4\tReward: 8.0\tEpisode Mean: 140.4\n", - "87615:127694531\tQ-min: -0.029\tQ-max: 0.218\tLives: 3\tReward: 8.0\tEpisode Mean: 140.4\n", - "87615:127694582\tQ-min: 1.631\tQ-max: 1.664\tLives: 3\tReward: 9.0\tEpisode Mean: 140.4\n", - "87615:127694635\tQ-min: 1.867\tQ-max: 1.879\tLives: 3\tReward: 10.0\tEpisode Mean: 140.4\n", - "87615:127694683\tQ-min: 1.914\tQ-max: 1.928\tLives: 3\tReward: 11.0\tEpisode Mean: 140.4\n", - "87615:127694720\tQ-min: 1.920\tQ-max: 1.953\tLives: 3\tReward: 12.0\tEpisode Mean: 140.4\n", - "87615:127694753\tQ-min: 1.966\tQ-max: 2.038\tLives: 3\tReward: 16.0\tEpisode Mean: 140.4\n", - "87615:127694786\tQ-min: 1.915\tQ-max: 1.953\tLives: 3\tReward: 17.0\tEpisode Mean: 140.4\n", - "87615:127694818\tQ-min: 2.045\tQ-max: 2.117\tLives: 3\tReward: 21.0\tEpisode Mean: 140.4\n", - "87615:127694863\tQ-min: 1.640\tQ-max: 1.660\tLives: 3\tReward: 22.0\tEpisode Mean: 140.4\n", - "87615:127694936\tQ-min: 1.704\tQ-max: 1.790\tLives: 3\tReward: 23.0\tEpisode Mean: 140.4\n", - "87615:127695003\tQ-min: 1.607\tQ-max: 1.693\tLives: 3\tReward: 24.0\tEpisode Mean: 140.4\n", - "87615:127695066\tQ-min: 1.545\tQ-max: 1.706\tLives: 3\tReward: 25.0\tEpisode Mean: 140.4\n", - "87615:127695117\tQ-min: 1.944\tQ-max: 1.970\tLives: 3\tReward: 26.0\tEpisode Mean: 140.4\n", - "87615:127695148\tQ-min: 2.001\tQ-max: 2.020\tLives: 3\tReward: 30.0\tEpisode Mean: 140.4\n", - "87615:127695185\tQ-min: 1.505\tQ-max: 2.459\tLives: 3\tReward: 34.0\tEpisode Mean: 140.4\n", - "87615:127695206\tQ-min: 2.292\tQ-max: 2.447\tLives: 3\tReward: 35.0\tEpisode Mean: 140.4\n", - "87615:127695226\tQ-min: 2.223\tQ-max: 2.418\tLives: 3\tReward: 36.0\tEpisode Mean: 140.4\n", - "87615:127695245\tQ-min: 2.313\tQ-max: 2.373\tLives: 3\tReward: 37.0\tEpisode Mean: 140.4\n", - "87615:127695267\tQ-min: 2.310\tQ-max: 2.449\tLives: 3\tReward: 41.0\tEpisode Mean: 140.4\n", - "87615:127695283\tQ-min: 0.034\tQ-max: 0.248\tLives: 2\tReward: 41.0\tEpisode Mean: 140.4\n", - "87615:127695327\tQ-min: 1.973\tQ-max: 2.000\tLives: 2\tReward: 42.0\tEpisode Mean: 140.4\n", - "87615:127695368\tQ-min: 2.111\tQ-max: 2.145\tLives: 2\tReward: 43.0\tEpisode Mean: 140.4\n", - "87615:127695414\tQ-min: 2.005\tQ-max: 2.129\tLives: 2\tReward: 47.0\tEpisode Mean: 140.4\n", - "87615:127695454\tQ-min: 2.254\tQ-max: 2.286\tLives: 2\tReward: 48.0\tEpisode Mean: 140.4\n", - "87615:127695489\tQ-min: 2.176\tQ-max: 2.210\tLives: 2\tReward: 49.0\tEpisode Mean: 140.4\n", - "87615:127695521\tQ-min: 2.159\tQ-max: 2.215\tLives: 2\tReward: 50.0\tEpisode Mean: 140.4\n", - "87615:127695555\tQ-min: 2.226\tQ-max: 2.292\tLives: 2\tReward: 54.0\tEpisode Mean: 140.4\n", - "87615:127695612\tQ-min: 1.677\tQ-max: 1.804\tLives: 2\tReward: 55.0\tEpisode Mean: 140.4\n", - "87615:127695679\tQ-min: 1.717\tQ-max: 1.984\tLives: 2\tReward: 59.0\tEpisode Mean: 140.4\n", - "87615:127695750\tQ-min: 1.788\tQ-max: 1.857\tLives: 2\tReward: 60.0\tEpisode Mean: 140.4\n", - "87615:127695817\tQ-min: 1.672\tQ-max: 1.933\tLives: 2\tReward: 64.0\tEpisode Mean: 140.4\n", - "87615:127695868\tQ-min: 2.129\tQ-max: 2.172\tLives: 2\tReward: 65.0\tEpisode Mean: 140.4\n", - "87615:127695900\tQ-min: 2.162\tQ-max: 2.207\tLives: 2\tReward: 69.0\tEpisode Mean: 140.4\n", - "87615:127695934\tQ-min: 2.146\tQ-max: 2.353\tLives: 2\tReward: 70.0\tEpisode Mean: 140.4\n", - "87615:127695970\tQ-min: 2.184\tQ-max: 2.295\tLives: 2\tReward: 74.0\tEpisode Mean: 140.4\n", - "87615:127696006\tQ-min: 2.072\tQ-max: 2.672\tLives: 2\tReward: 78.0\tEpisode Mean: 140.4\n", - "87615:127696031\tQ-min: 2.447\tQ-max: 2.703\tLives: 2\tReward: 85.0\tEpisode Mean: 140.4\n", - "87615:127696059\tQ-min: 2.400\tQ-max: 2.714\tLives: 2\tReward: 92.0\tEpisode Mean: 140.4\n", - "87615:127696080\tQ-min: 2.617\tQ-max: 2.961\tLives: 2\tReward: 93.0\tEpisode Mean: 140.4\n", - "87615:127696101\tQ-min: 2.716\tQ-max: 3.000\tLives: 2\tReward: 97.0\tEpisode Mean: 140.4\n", - "87615:127696124\tQ-min: 2.552\tQ-max: 3.052\tLives: 2\tReward: 101.0\tEpisode Mean: 140.4\n", - "87615:127696147\tQ-min: 2.177\tQ-max: 3.415\tLives: 2\tReward: 108.0\tEpisode Mean: 140.4\n", - "87615:127696169\tQ-min: 2.833\tQ-max: 3.527\tLives: 2\tReward: 112.0\tEpisode Mean: 140.4\n", - "87615:127696199\tQ-min: 1.329\tQ-max: 7.426\tLives: 2\tReward: 119.0\tEpisode Mean: 140.4\n", - "87615:127696204\tQ-min: 2.727\tQ-max: 7.415\tLives: 2\tReward: 126.0\tEpisode Mean: 140.4\n", - "87615:127696208\tQ-min: 4.507\tQ-max: 7.164\tLives: 2\tReward: 133.0\tEpisode Mean: 140.4\n", - "87615:127696213\tQ-min: 4.021\tQ-max: 7.264\tLives: 2\tReward: 140.0\tEpisode Mean: 140.4\n", - "87615:127696218\tQ-min: 4.524\tQ-max: 6.419\tLives: 2\tReward: 147.0\tEpisode Mean: 140.4\n", - "87615:127696224\tQ-min: 4.086\tQ-max: 6.433\tLives: 2\tReward: 154.0\tEpisode Mean: 140.4\n", - "87615:127696229\tQ-min: 3.476\tQ-max: 6.955\tLives: 2\tReward: 161.0\tEpisode Mean: 140.4\n", - "87615:127696233\tQ-min: 4.404\tQ-max: 6.600\tLives: 2\tReward: 168.0\tEpisode Mean: 140.4\n", - "87615:127696237\tQ-min: 4.124\tQ-max: 6.776\tLives: 2\tReward: 175.0\tEpisode Mean: 140.4\n", - "87615:127696242\tQ-min: 3.961\tQ-max: 5.944\tLives: 2\tReward: 182.0\tEpisode Mean: 140.4\n", - "87615:127696247\tQ-min: 4.544\tQ-max: 5.938\tLives: 2\tReward: 189.0\tEpisode Mean: 140.4\n", - "87615:127696254\tQ-min: 3.912\tQ-max: 6.480\tLives: 2\tReward: 196.0\tEpisode Mean: 140.4\n", - "87615:127696262\tQ-min: 4.059\tQ-max: 5.634\tLives: 2\tReward: 203.0\tEpisode Mean: 140.4\n", - "87615:127696268\tQ-min: 3.037\tQ-max: 4.725\tLives: 2\tReward: 210.0\tEpisode Mean: 140.4\n", - "87615:127696274\tQ-min: 2.643\tQ-max: 5.178\tLives: 2\tReward: 217.0\tEpisode Mean: 140.4\n", - "87615:127696280\tQ-min: 3.061\tQ-max: 4.737\tLives: 2\tReward: 224.0\tEpisode Mean: 140.4\n", - "87615:127696286\tQ-min: 3.398\tQ-max: 4.795\tLives: 2\tReward: 231.0\tEpisode Mean: 140.4\n", - "87615:127696292\tQ-min: 3.533\tQ-max: 4.552\tLives: 2\tReward: 238.0\tEpisode Mean: 140.4\n", - "87615:127696298\tQ-min: 3.064\tQ-max: 5.131\tLives: 2\tReward: 245.0\tEpisode Mean: 140.4\n", - "87615:127696302\tQ-min: 3.054\tQ-max: 4.238\tLives: 2\tReward: 252.0\tEpisode Mean: 140.4\n", - "87615:127696309\tQ-min: 3.495\tQ-max: 4.537\tLives: 2\tReward: 259.0\tEpisode Mean: 140.4\n", - "87615:127696318\tQ-min: 2.442\tQ-max: 3.859\tLives: 2\tReward: 263.0\tEpisode Mean: 140.4\n", - "87615:127696325\tQ-min: 2.675\tQ-max: 4.446\tLives: 2\tReward: 267.0\tEpisode Mean: 140.4\n", - "87615:127696332\tQ-min: 2.903\tQ-max: 4.970\tLives: 2\tReward: 271.0\tEpisode Mean: 140.4\n", - "87615:127696339\tQ-min: 3.041\tQ-max: 5.599\tLives: 2\tReward: 278.0\tEpisode Mean: 140.4\n", - "87615:127696347\tQ-min: 3.840\tQ-max: 5.684\tLives: 2\tReward: 285.0\tEpisode Mean: 140.4\n", - "87615:127696354\tQ-min: 3.338\tQ-max: 5.494\tLives: 2\tReward: 292.0\tEpisode Mean: 140.4\n", - "87615:127696359\tQ-min: 3.294\tQ-max: 5.389\tLives: 2\tReward: 299.0\tEpisode Mean: 140.4\n", - "87615:127696366\tQ-min: 2.294\tQ-max: 3.083\tLives: 2\tReward: 300.0\tEpisode Mean: 140.4\n", - "87615:127696373\tQ-min: 2.104\tQ-max: 3.364\tLives: 2\tReward: 307.0\tEpisode Mean: 140.4\n", - "87615:127696379\tQ-min: 1.393\tQ-max: 4.202\tLives: 2\tReward: 314.0\tEpisode Mean: 140.4\n", - "87615:127696402\tQ-min: 0.235\tQ-max: 0.449\tLives: 1\tReward: 314.0\tEpisode Mean: 140.4\n", - "87615:127696467\tQ-min: 1.152\tQ-max: 2.538\tLives: 1\tReward: 318.0\tEpisode Mean: 140.4\n", - "87615:127696475\tQ-min: 2.140\tQ-max: 4.579\tLives: 1\tReward: 325.0\tEpisode Mean: 140.4\n", - "87615:127696481\tQ-min: 1.566\tQ-max: 2.914\tLives: 1\tReward: 332.0\tEpisode Mean: 140.4\n", - "87615:127696491\tQ-min: 1.893\tQ-max: 2.961\tLives: 1\tReward: 336.0\tEpisode Mean: 140.4\n", - "87615:127696499\tQ-min: 2.189\tQ-max: 3.113\tLives: 1\tReward: 340.0\tEpisode Mean: 140.4\n", - "87615:127696523\tQ-min: 0.266\tQ-max: 0.417\tLives: 0\tReward: 340.0\tEpisode Mean: 147.5\n", - "87616:127696566\tQ-min: 1.755\tQ-max: 1.768\tLives: 5\tReward: 1.0\tEpisode Mean: 147.5\n", - "87616:127696607\tQ-min: 1.822\tQ-max: 1.860\tLives: 5\tReward: 2.0\tEpisode Mean: 147.5\n", - "87616:127696650\tQ-min: 1.905\tQ-max: 1.942\tLives: 5\tReward: 3.0\tEpisode Mean: 147.5\n", - "87616:127696686\tQ-min: 1.920\tQ-max: 1.984\tLives: 5\tReward: 4.0\tEpisode Mean: 147.5\n", - "87616:127696717\tQ-min: 1.984\tQ-max: 2.012\tLives: 5\tReward: 5.0\tEpisode Mean: 147.5\n", - "87616:127696748\tQ-min: 1.915\tQ-max: 1.942\tLives: 5\tReward: 6.0\tEpisode Mean: 147.5\n", - "87616:127696778\tQ-min: 1.698\tQ-max: 1.758\tLives: 5\tReward: 7.0\tEpisode Mean: 147.5\n", - "87616:127696825\tQ-min: 1.582\tQ-max: 1.633\tLives: 5\tReward: 8.0\tEpisode Mean: 147.5\n", - "87616:127696887\tQ-min: 1.685\tQ-max: 1.710\tLives: 5\tReward: 9.0\tEpisode Mean: 147.5\n", - "87616:127696929\tQ-min: -0.020\tQ-max: 0.235\tLives: 4\tReward: 9.0\tEpisode Mean: 147.5\n", - "87616:127696986\tQ-min: 1.568\tQ-max: 1.667\tLives: 4\tReward: 13.0\tEpisode Mean: 147.5\n", - "87616:127697052\tQ-min: 1.726\tQ-max: 1.766\tLives: 4\tReward: 14.0\tEpisode Mean: 147.5\n", - "87616:127697114\tQ-min: 1.681\tQ-max: 1.714\tLives: 4\tReward: 15.0\tEpisode Mean: 147.5\n", - "87616:127697161\tQ-min: 2.009\tQ-max: 2.051\tLives: 4\tReward: 16.0\tEpisode Mean: 147.5\n", - "87616:127697180\tQ-min: -0.006\tQ-max: 0.308\tLives: 3\tReward: 16.0\tEpisode Mean: 147.5\n", - "87616:127697223\tQ-min: 1.883\tQ-max: 1.921\tLives: 3\tReward: 17.0\tEpisode Mean: 147.5\n", - "87616:127697264\tQ-min: 1.967\tQ-max: 1.993\tLives: 3\tReward: 18.0\tEpisode Mean: 147.5\n", - "87616:127697320\tQ-min: 1.846\tQ-max: 1.892\tLives: 3\tReward: 19.0\tEpisode Mean: 147.5\n", - "87616:127697370\tQ-min: 2.026\tQ-max: 2.101\tLives: 3\tReward: 20.0\tEpisode Mean: 147.5\n", - "87616:127697406\tQ-min: 2.037\tQ-max: 2.075\tLives: 3\tReward: 21.0\tEpisode Mean: 147.5\n", - "87616:127697436\tQ-min: 2.030\tQ-max: 2.055\tLives: 3\tReward: 22.0\tEpisode Mean: 147.5\n", - "87616:127697469\tQ-min: 2.059\tQ-max: 2.080\tLives: 3\tReward: 23.0\tEpisode Mean: 147.5\n", - "87616:127697521\tQ-min: 1.619\tQ-max: 1.701\tLives: 3\tReward: 24.0\tEpisode Mean: 147.5\n", - "87616:127697583\tQ-min: 1.657\tQ-max: 1.745\tLives: 3\tReward: 25.0\tEpisode Mean: 147.5\n", - "87616:127697651\tQ-min: 1.690\tQ-max: 1.752\tLives: 3\tReward: 26.0\tEpisode Mean: 147.5\n", - "87616:127697718\tQ-min: 1.667\tQ-max: 1.827\tLives: 3\tReward: 27.0\tEpisode Mean: 147.5\n", - "87616:127697766\tQ-min: 2.042\tQ-max: 2.062\tLives: 3\tReward: 31.0\tEpisode Mean: 147.5\n", - "87616:127697800\tQ-min: 2.033\tQ-max: 2.101\tLives: 3\tReward: 32.0\tEpisode Mean: 147.5\n", - "87616:127697830\tQ-min: 2.066\tQ-max: 2.106\tLives: 3\tReward: 33.0\tEpisode Mean: 147.5\n", - "87616:127697863\tQ-min: 1.952\tQ-max: 2.108\tLives: 3\tReward: 34.0\tEpisode Mean: 147.5\n", - "87616:127697898\tQ-min: 2.262\tQ-max: 2.323\tLives: 3\tReward: 38.0\tEpisode Mean: 147.5\n", - "87616:127697912\tQ-min: 0.078\tQ-max: 0.422\tLives: 2\tReward: 38.0\tEpisode Mean: 147.5\n", - "87616:127697968\tQ-min: 1.802\tQ-max: 1.856\tLives: 2\tReward: 39.0\tEpisode Mean: 147.5\n", - "87616:127698031\tQ-min: 1.838\tQ-max: 1.876\tLives: 2\tReward: 40.0\tEpisode Mean: 147.5\n", - "87616:127698099\tQ-min: 1.856\tQ-max: 1.898\tLives: 2\tReward: 41.0\tEpisode Mean: 147.5\n", - "87616:127698150\tQ-min: 2.508\tQ-max: 2.578\tLives: 2\tReward: 45.0\tEpisode Mean: 147.5\n", - "87616:127698172\tQ-min: 2.409\tQ-max: 2.576\tLives: 2\tReward: 49.0\tEpisode Mean: 147.5\n", - "87616:127698193\tQ-min: 2.089\tQ-max: 2.658\tLives: 2\tReward: 56.0\tEpisode Mean: 147.5\n", - "87616:127698216\tQ-min: 2.436\tQ-max: 2.506\tLives: 2\tReward: 57.0\tEpisode Mean: 147.5\n", - "87616:127698236\tQ-min: 2.420\tQ-max: 2.482\tLives: 2\tReward: 58.0\tEpisode Mean: 147.5\n", - "87616:127698257\tQ-min: 2.457\tQ-max: 2.508\tLives: 2\tReward: 62.0\tEpisode Mean: 147.5\n", - "87616:127698278\tQ-min: 2.433\tQ-max: 2.551\tLives: 2\tReward: 66.0\tEpisode Mean: 147.5\n", - "87616:127698291\tQ-min: -0.079\tQ-max: 0.126\tLives: 1\tReward: 66.0\tEpisode Mean: 147.5\n", - "87616:127698343\tQ-min: 1.843\tQ-max: 1.872\tLives: 1\tReward: 67.0\tEpisode Mean: 147.5\n", - "87616:127698399\tQ-min: 2.090\tQ-max: 2.351\tLives: 1\tReward: 71.0\tEpisode Mean: 147.5\n", - "87616:127698456\tQ-min: 1.968\tQ-max: 2.183\tLives: 1\tReward: 75.0\tEpisode Mean: 147.5\n", - "87616:127698512\tQ-min: 2.150\tQ-max: 2.390\tLives: 1\tReward: 79.0\tEpisode Mean: 147.5\n", - "87616:127698549\tQ-min: 2.398\tQ-max: 2.454\tLives: 1\tReward: 80.0\tEpisode Mean: 147.5\n", - "87616:127698584\tQ-min: 2.203\tQ-max: 2.419\tLives: 1\tReward: 84.0\tEpisode Mean: 147.5\n", - "87616:127698620\tQ-min: 2.034\tQ-max: 2.903\tLives: 1\tReward: 88.0\tEpisode Mean: 147.5\n", - "87616:127698641\tQ-min: 2.441\tQ-max: 2.569\tLives: 1\tReward: 89.0\tEpisode Mean: 147.5\n", - "87616:127698661\tQ-min: 2.446\tQ-max: 2.627\tLives: 1\tReward: 93.0\tEpisode Mean: 147.5\n", - "87616:127698684\tQ-min: 2.499\tQ-max: 2.649\tLives: 1\tReward: 94.0\tEpisode Mean: 147.5\n", - "87616:127698707\tQ-min: 2.364\tQ-max: 2.641\tLives: 1\tReward: 98.0\tEpisode Mean: 147.5\n", - "87616:127698721\tQ-min: -0.372\tQ-max: 0.212\tLives: 0\tReward: 98.0\tEpisode Mean: 145.8\n", - "87617:127698766\tQ-min: 1.752\tQ-max: 1.767\tLives: 5\tReward: 1.0\tEpisode Mean: 145.8\n", - "87617:127698808\tQ-min: 1.814\tQ-max: 1.833\tLives: 5\tReward: 2.0\tEpisode Mean: 145.8\n", - "87617:127698846\tQ-min: 1.934\tQ-max: 2.003\tLives: 5\tReward: 3.0\tEpisode Mean: 145.8\n", - "87617:127698884\tQ-min: 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16.0\tEpisode Mean: 145.8\n", - "87617:127699403\tQ-min: 2.019\tQ-max: 2.053\tLives: 4\tReward: 17.0\tEpisode Mean: 145.8\n", - "87617:127699434\tQ-min: 2.157\tQ-max: 2.220\tLives: 4\tReward: 18.0\tEpisode Mean: 145.8\n", - "87617:127699468\tQ-min: 1.990\tQ-max: 2.004\tLives: 4\tReward: 19.0\tEpisode Mean: 145.8\n", - "87617:127699501\tQ-min: 1.990\tQ-max: 2.028\tLives: 4\tReward: 20.0\tEpisode Mean: 145.8\n", - "87617:127699549\tQ-min: 1.681\tQ-max: 1.707\tLives: 4\tReward: 21.0\tEpisode Mean: 145.8\n", - "87617:127699614\tQ-min: 1.751\tQ-max: 1.785\tLives: 4\tReward: 22.0\tEpisode Mean: 145.8\n", - "87617:127699674\tQ-min: 1.735\tQ-max: 1.746\tLives: 4\tReward: 23.0\tEpisode Mean: 145.8\n", - "87617:127699736\tQ-min: 1.770\tQ-max: 1.788\tLives: 4\tReward: 24.0\tEpisode Mean: 145.8\n", - "87617:127699784\tQ-min: 1.994\tQ-max: 2.046\tLives: 4\tReward: 25.0\tEpisode Mean: 145.8\n", - "87617:127699816\tQ-min: 2.013\tQ-max: 2.036\tLives: 4\tReward: 26.0\tEpisode Mean: 145.8\n", - 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2.474\tLives: 3\tReward: 51.0\tEpisode Mean: 145.8\n", - "87617:127700200\tQ-min: 2.233\tQ-max: 2.483\tLives: 3\tReward: 52.0\tEpisode Mean: 145.8\n", - "87617:127700221\tQ-min: 2.048\tQ-max: 2.191\tLives: 3\tReward: 56.0\tEpisode Mean: 145.8\n", - "87617:127700245\tQ-min: 2.330\tQ-max: 2.450\tLives: 3\tReward: 60.0\tEpisode Mean: 145.8\n", - "87617:127700271\tQ-min: 2.389\tQ-max: 2.500\tLives: 3\tReward: 61.0\tEpisode Mean: 145.8\n", - "87617:127700293\tQ-min: 2.307\tQ-max: 2.508\tLives: 3\tReward: 65.0\tEpisode Mean: 145.8\n", - "87617:127700306\tQ-min: -0.006\tQ-max: 0.113\tLives: 2\tReward: 65.0\tEpisode Mean: 145.8\n", - "87617:127700351\tQ-min: 1.970\tQ-max: 2.111\tLives: 2\tReward: 69.0\tEpisode Mean: 145.8\n", - "87617:127700399\tQ-min: 2.366\tQ-max: 2.450\tLives: 2\tReward: 73.0\tEpisode Mean: 145.8\n", - "87617:127700422\tQ-min: 2.140\tQ-max: 2.488\tLives: 2\tReward: 74.0\tEpisode Mean: 145.8\n", - "87617:127700442\tQ-min: 2.122\tQ-max: 2.493\tLives: 2\tReward: 78.0\tEpisode Mean: 145.8\n", - "87617:127700464\tQ-min: 2.243\tQ-max: 2.550\tLives: 2\tReward: 82.0\tEpisode Mean: 145.8\n", - "87617:127700488\tQ-min: 2.414\tQ-max: 2.670\tLives: 2\tReward: 86.0\tEpisode Mean: 145.8\n", - "87617:127700510\tQ-min: 2.252\tQ-max: 2.626\tLives: 2\tReward: 93.0\tEpisode Mean: 145.8\n", - "87617:127700533\tQ-min: 2.150\tQ-max: 2.691\tLives: 2\tReward: 100.0\tEpisode Mean: 145.8\n", - "87617:127700556\tQ-min: 1.972\tQ-max: 2.779\tLives: 2\tReward: 101.0\tEpisode Mean: 145.8\n", - "87617:127700567\tQ-min: -0.020\tQ-max: 0.190\tLives: 1\tReward: 101.0\tEpisode Mean: 145.8\n", - "87617:127700613\tQ-min: 2.301\tQ-max: 2.671\tLives: 1\tReward: 105.0\tEpisode Mean: 145.8\n", - "87617:127700659\tQ-min: 2.437\tQ-max: 2.946\tLives: 1\tReward: 106.0\tEpisode Mean: 145.8\n", - "87617:127700709\tQ-min: 0.855\tQ-max: 2.276\tLives: 1\tReward: 113.0\tEpisode Mean: 145.8\n", - "87617:127700735\tQ-min: 1.749\tQ-max: 3.022\tLives: 1\tReward: 120.0\tEpisode Mean: 145.8\n", - 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0.0\n", + "2392:1178269\tQ-min: 1.254\tQ-max: 1.491\tLives: 3\tReward: 8.0\tEpisode Mean: 0.0\n", + "2392:1178308\tQ-min: 0.082\tQ-max: 0.123\tLives: 2\tReward: 8.0\tEpisode Mean: 0.0\n", + "2392:1178355\tQ-min: 1.247\tQ-max: 1.398\tLives: 2\tReward: 9.0\tEpisode Mean: 0.0\n", + "2392:1178382\tQ-min: 0.131\tQ-max: 0.157\tLives: 1\tReward: 9.0\tEpisode Mean: 0.0\n", + "2392:1178441\tQ-min: 1.198\tQ-max: 1.503\tLives: 1\tReward: 10.0\tEpisode Mean: 0.0\n", + "2392:1178506\tQ-min: 1.218\tQ-max: 1.342\tLives: 1\tReward: 11.0\tEpisode Mean: 0.0\n", + "2392:1178573\tQ-min: 1.211\tQ-max: 1.554\tLives: 1\tReward: 12.0\tEpisode Mean: 0.0\n", + "2392:1178628\tQ-min: 1.272\tQ-max: 1.546\tLives: 1\tReward: 13.0\tEpisode Mean: 0.0\n", + "2392:1178650\tQ-min: 0.079\tQ-max: 0.122\tLives: 0\tReward: 13.0\tEpisode Mean: 13.0\n", + "2393:1178697\tQ-min: 1.203\tQ-max: 1.427\tLives: 5\tReward: 1.0\tEpisode Mean: 13.0\n", + "2393:1178739\tQ-min: 1.233\tQ-max: 1.548\tLives: 5\tReward: 2.0\tEpisode Mean: 13.0\n", + "2393:1178793\tQ-min: 1.309\tQ-max: 1.414\tLives: 5\tReward: 3.0\tEpisode Mean: 13.0\n", + "2393:1178835\tQ-min: 0.102\tQ-max: 0.131\tLives: 4\tReward: 3.0\tEpisode Mean: 13.0\n", + "2393:1178878\tQ-min: 1.257\tQ-max: 1.521\tLives: 4\tReward: 4.0\tEpisode Mean: 13.0\n", + "2393:1178921\tQ-min: 1.275\tQ-max: 1.446\tLives: 4\tReward: 5.0\tEpisode Mean: 13.0\n", + "2393:1178966\tQ-min: 1.297\tQ-max: 1.528\tLives: 4\tReward: 6.0\tEpisode Mean: 13.0\n", + "2393:1178997\tQ-min: 0.083\tQ-max: 0.126\tLives: 3\tReward: 6.0\tEpisode Mean: 13.0\n", + "2393:1179043\tQ-min: 1.246\tQ-max: 1.419\tLives: 3\tReward: 7.0\tEpisode Mean: 13.0\n", + "2393:1179098\tQ-min: 1.231\tQ-max: 1.501\tLives: 3\tReward: 8.0\tEpisode Mean: 13.0\n", + "2393:1179151\tQ-min: 1.264\tQ-max: 1.522\tLives: 3\tReward: 9.0\tEpisode Mean: 13.0\n", + "2393:1179183\tQ-min: 0.069\tQ-max: 0.107\tLives: 2\tReward: 9.0\tEpisode Mean: 13.0\n", + "2393:1179239\tQ-min: 1.253\tQ-max: 1.325\tLives: 2\tReward: 10.0\tEpisode 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20.5\n", + "2420:1205985\tQ-min: 0.097\tQ-max: 0.133\tLives: 1\tReward: 11.0\tEpisode Mean: 20.5\n", + "2420:1206029\tQ-min: 1.203\tQ-max: 1.463\tLives: 1\tReward: 15.0\tEpisode Mean: 20.5\n", + "2420:1206076\tQ-min: 0.830\tQ-max: 1.096\tLives: 1\tReward: 19.0\tEpisode Mean: 20.5\n", + "2420:1206098\tQ-min: 1.233\tQ-max: 1.522\tLives: 1\tReward: 20.0\tEpisode Mean: 20.5\n", + "2420:1206112\tQ-min: 0.140\tQ-max: 0.153\tLives: 0\tReward: 20.0\tEpisode Mean: 20.5\n", + "2421:1206156\tQ-min: 1.244\tQ-max: 1.475\tLives: 5\tReward: 1.0\tEpisode Mean: 20.5\n", + "2421:1206183\tQ-min: 0.072\tQ-max: 0.114\tLives: 4\tReward: 1.0\tEpisode Mean: 20.5\n", + "2421:1206238\tQ-min: 1.167\tQ-max: 1.451\tLives: 4\tReward: 2.0\tEpisode Mean: 20.5\n", + "2421:1206303\tQ-min: 1.246\tQ-max: 1.382\tLives: 4\tReward: 3.0\tEpisode Mean: 20.5\n", + "2421:1206357\tQ-min: 1.233\tQ-max: 1.420\tLives: 4\tReward: 4.0\tEpisode Mean: 20.5\n", + "2421:1206395\tQ-min: 1.247\tQ-max: 1.588\tLives: 4\tReward: 5.0\tEpisode 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24.0\tEpisode Mean: 20.5\n", + "2421:1206875\tQ-min: 0.094\tQ-max: 0.119\tLives: 2\tReward: 24.0\tEpisode Mean: 20.5\n", + "2421:1206925\tQ-min: 0.921\tQ-max: 1.427\tLives: 2\tReward: 28.0\tEpisode Mean: 20.5\n", + "2421:1206940\tQ-min: 0.208\tQ-max: 0.244\tLives: 1\tReward: 28.0\tEpisode Mean: 20.5\n", + "2421:1206992\tQ-min: 0.711\tQ-max: 1.110\tLives: 1\tReward: 32.0\tEpisode Mean: 20.5\n", + "2421:1207005\tQ-min: 0.121\tQ-max: 0.141\tLives: 0\tReward: 32.0\tEpisode Mean: 20.9\n" ] } ], @@ -2883,10 +1578,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We can now print some statistics for the episode rewards, which vary greatly from one episode to the next." ] @@ -2894,21 +1586,17 @@ { "cell_type": "code", "execution_count": 25, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Rewards for 30 episodes:\n", - "- Min: 40.0\n", - "- Mean: 145.166666667\n", - "- Max: 386.0\n", - "- Stdev: 105.131372842\n" + "- Min: 8.0\n", + "- Mean: 20.866666666666667\n", + "- Max: 40.0\n", + "- Stdev: 8.155706931686273\n" ] } ], @@ -2923,10 +1611,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We can also plot a histogram with the episode rewards." ] @@ -2934,20 +1619,18 @@ { "cell_type": "code", "execution_count": 26, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -2957,10 +1640,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Example States\n", "\n", @@ -2972,11 +1652,7 @@ { "cell_type": "code", "execution_count": 27, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "def print_q_values(idx):\n", @@ -3009,10 +1685,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This helper-function plots a state from the replay-memory and optionally prints the Q-values." ] @@ -3020,11 +1693,7 @@ { "cell_type": "code", "execution_count": 28, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "def plot_state(idx, print_q=True):\n", @@ -3056,10 +1725,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The replay-memory has room for 200k states but it is only partially full from the above call to `agent.run(num_episodes=1)`. This is how many states are actually used." ] @@ -3067,16 +1733,12 @@ { "cell_type": "code", "execution_count": 29, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "656" + "1061" ] }, "execution_count": 29, @@ -3091,10 +1753,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Get the Q-values from the replay-memory that are actually used." ] @@ -3102,11 +1761,7 @@ { "cell_type": "code", "execution_count": 30, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "q_values = replay_memory.q_values[0:num_used, :]" @@ -3114,10 +1769,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "For each state, calculate the min / max Q-values and their difference. This will be used to lookup interesting states in the following sections." ] @@ -3125,11 +1777,7 @@ { "cell_type": "code", "execution_count": 31, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "q_values_min = q_values.min(axis=1)\n", @@ -3139,10 +1787,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Example States: Highest Reward\n", "\n", @@ -3155,16 +1800,13 @@ "cell_type": "code", "execution_count": 32, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ - "41" + "42" ] }, "execution_count": 32, @@ -3179,10 +1821,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This state is where the ball hits the wall so the agent scores a point. \n", "\n", @@ -3194,20 +1833,18 @@ { "cell_type": "code", "execution_count": 33, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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ylKAgdBFhsVZKUS6XWVxcZHFxkWw2W+ejhyvhlCLy3Y0IvSB0EeVyOYiy8X0f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\ngiAIXY4IvSAIQpcjQi8IgtDl/H+HLMff60gFRgAAAABJRU5ErkJggg==\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -3216,23 +1853,23 @@ "text": [ "Action: Q-Value:\n", "====================\n", - "NOOP 1.576 (Action Taken)\n", - "FIRE 1.573 \n", - "RIGHT 1.564 \n", - "LEFT 1.574 \n", - "RIGHTFIRE 1.571 \n", - "LEFTFIRE 1.571 \n", + "NOOP 1.188 \n", + "FIRE 1.169 \n", + "RIGHT 1.148 \n", + "LEFT 1.278 (Action Taken)\n", "\n" ] }, { "data": { - "image/png": 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -3241,23 +1878,23 @@ "text": [ "Action: Q-Value:\n", "====================\n", - "NOOP 1.630 (Action Taken)\n", - "FIRE 1.626 \n", - "RIGHT 1.610 \n", - "LEFT 1.617 \n", - "RIGHTFIRE 1.606 \n", - "LEFTFIRE 1.625 \n", + "NOOP 1.220 \n", + "FIRE 1.206 \n", + "RIGHT 1.163 \n", + "LEFT 1.310 (Action Taken)\n", "\n" ] }, { "data": { - "image/png": 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l2dv3fwS+rZRKAB8A/57KzeNvlFKfBz4EfrvJYwhCO+gK296yZQvJZJJisUhf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\n2La8uvXViB3GYsCUxDGFjUb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\n", 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vDZoR+glgQmv9evX9d6lcHFeUUrcCVP9OrbC+IMSVrrFtI+pQybIxAm86ZbPZ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\nuhwRekEQhC5HhF4QBKHL+f8NrAmvL0LNtwAAAABJRU5ErkJggg==\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -3291,23 +1928,23 @@ "text": [ "Action: Q-Value:\n", "====================\n", - "NOOP 1.710 (Action Taken)\n", - "FIRE 1.703 \n", - "RIGHT 1.694 \n", - "LEFT 1.703 \n", - "RIGHTFIRE 1.693 \n", - "LEFTFIRE 1.705 \n", + "NOOP 1.301 \n", + "FIRE 1.335 (Action Taken)\n", + "RIGHT 1.243 \n", + "LEFT 1.305 \n", "\n" ] }, { "data": { - "image/png": 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Ly1y4cIELFy6Ez6P96mUO2P7RTh39fuAK8FdKqcPA68AXgB1a60u1Zd4HdjRb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\nhBCiz0mgF0KIPhenQP9UtxO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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -3316,23 +1953,23 @@ "text": [ "Action: Q-Value:\n", "====================\n", - "NOOP 1.743 \n", - "FIRE 1.736 \n", - "RIGHT 1.741 \n", - "LEFT 1.739 \n", - "RIGHTFIRE 1.725 \n", - "LEFTFIRE 1.747 (Action Taken)\n", + "NOOP 1.307 \n", + "FIRE 1.337 \n", + "RIGHT 1.255 \n", + "LEFT 1.435 (Action Taken)\n", "\n" ] }, { "data": { - "image/png": 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pLMuqy0QpMfPdQ7Mdpr4K/IvW+iZgP/A68AXgGa31PuCZ2rwgdBpdaduxWCzo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\nVUN9y21bENpN01E3Wmu9Fj+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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -3341,23 +1978,23 @@ "text": [ "Action: Q-Value:\n", "====================\n", - "NOOP 1.768 (Action Taken)\n", - "FIRE 1.749 \n", - "RIGHT 1.753 \n", - "LEFT 1.757 \n", - "RIGHTFIRE 1.747 \n", - "LEFTFIRE 1.764 \n", + "NOOP 1.362 \n", + "FIRE 1.359 \n", + "RIGHT 1.260 \n", + "LEFT 1.496 (Action Taken)\n", "\n" ] }, { "data": { - "image/png": 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TbTzOZDLJwMAAiUSCmZkZpqenwx6xZh/hypihJqJ2YIavFtaGZDKJ4zjk83kA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\nTbHn3JAAAAAASUVORK5CYII=\n", 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\n", 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KYQ/YbGu8/1YRM9klRnzNcTpp/OZY1Wo1eNporV1j6pbcqt2daEu9Xm9K1TTHMRk27fCX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\nAAAAAElFTkSuQmCC\n", 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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -3391,12 +2028,10 @@ "text": [ "Action: Q-Value:\n", "====================\n", - "NOOP 0.796 \n", - "FIRE 0.806 (Action Taken)\n", - "RIGHT 0.794 \n", - "LEFT 0.790 \n", - "RIGHTFIRE 0.797 \n", - "LEFTFIRE 0.791 \n", + "NOOP 0.394 \n", + "FIRE 0.386 \n", + "RIGHT 0.369 \n", + "LEFT 0.436 (Action Taken)\n", "\n" ] } @@ -3408,10 +2043,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Example: Highest Q-Value\n", "\n", @@ -3421,16 +2053,12 @@ { "cell_type": "code", "execution_count": 34, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "161" + "517" ] }, "execution_count": 34, @@ -3447,20 +2075,19 @@ "cell_type": "code", "execution_count": 35, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ { "data": { - "image/png": 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UHNbVaMeo0U5sez3CI5bDI2NbHWYJlyYGIjkDmdAbNIZuFhcXuXDhAsePH+fS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\nC4Ig9Dgi9IIgCD2OCL0gCEKP8/8B8s+udmS2ndkAAAAASUVORK5CYII=\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -3469,23 +2096,23 @@ "text": [ "Action: Q-Value:\n", "====================\n", - "NOOP 2.008 \n", - "FIRE 2.006 (Action Taken)\n", - "RIGHT 1.995 \n", - "LEFT 2.014 \n", - "RIGHTFIRE 1.996 \n", - "LEFTFIRE 2.006 \n", + "NOOP 1.289 \n", + "FIRE 1.206 \n", + "RIGHT 1.333 \n", + "LEFT 1.653 (Action Taken)\n", "\n" ] }, { "data": { - "image/png": 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MJkM2mwUIBHg5wuszmQypVIp4PB4IH7Bs6Cac0RKPx0mlUsRiscCbjcViQS2n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\n6Befi7weAAAAAElFTkSuQmCC\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -3494,23 +2121,23 @@ "text": [ "Action: Q-Value:\n", "====================\n", - "NOOP 0.982 (Action Taken)\n", - "FIRE 0.977 \n", - "RIGHT 0.975 \n", - "LEFT 0.977 \n", - "RIGHTFIRE 0.968 \n", - "LEFTFIRE 0.980 \n", + "NOOP 1.073 \n", + "FIRE 1.088 \n", + "RIGHT 1.106 \n", + "LEFT 1.239 (Action Taken)\n", "\n" ] }, { "data": { - "image/png": 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nMkqg3qM3opZIJEin02QyGYBAgJcj/H06nSaZTBKLxQLhA5YN3YQzWmKxGMlk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\neDx4iAh3D7Zt1024vWPHDo4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\n", 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SKWKxWODNxmKxoJbT29sbHNuEe9Y6drPXxIit4zhBuXp7e5mbmwMIYvEAPT09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\nOnSIoaGhunZArXUQMjbORG9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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -3544,23 +2171,23 @@ "text": [ "Action: Q-Value:\n", "====================\n", - "NOOP 1.053 (Action Taken)\n", - "FIRE 1.058 \n", - "RIGHT 1.056 \n", - "LEFT 1.051 \n", - "RIGHTFIRE 1.058 \n", - "LEFTFIRE 1.055 \n", + "NOOP 0.506 \n", + "FIRE 0.514 \n", + "RIGHT 0.564 (Action Taken)\n", + "LEFT 0.548 \n", "\n" ] }, { "data": { - "image/png": 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PhR4gmUzau7g5vvGYATtA2fx9TLuNR5rP55mamiIWi9mewWpCbwwrmUwyNzdH\nuVy2PYFmkXMch/n5eaampnAch3K5vOq+23FOwjH6hYUFex7M72dCbSaMND09TT6fp1wubyhGH7aP\nIAhs9pM5h+HnGsLLgrAemrUlk8kwPj7O8PAwlUqFZDLJ6OgoIyMjANZZW237XhMpoQ9nqRjBuHnz\nJmfPnuXKlStks1mbzmTW7xYmoyYej6O1thk35rNarXZX7zH81Oji4iITExM2k+he3yc8oFsqlZia\nmrKhIJO9Ysjn81y8eJFisYjjOB09V+amZjz3K1euUCgU7GdmzAKWwlWXL1+2cU3T7V2vEJvvano0\nhUKB/fv3c+DAAYCGmaailuImbB6a7aZcLpPL5WyP3jg2juPYdOcoEymhhztP8I0bN3jzzTf58MMP\nGR4eJplMUi6Xu+rRhzGx+mKxyOLion3/XiGC8OeFQoEPP/yQGzdu2IyetR7bjAeYkEjzE3uFQoEP\nPviA6enpde27Fcw5WVxctDF6wN5kYCnGefnyZebm5lp6FsL87sYO5ubmmJmZ4dlnn71jPUHYKM3C\n/cEHH1AsFjl8+DD79u0jl8tx+vRplFIkEglu375t1+1gNdUNE2mh11rbEEexWKRYLNoLfDNTr9fJ\n5XI2vt9OgiBgbm6Oubm5tu+7VRYWFu45aL1WwnYwNTV1x35F6IV24HlLEml6rLt372b//v0Ui0Xe\nfvttu154ciTT+48SmyKAGT5pYS9R2LqEezFRu6iEzY9JgjAZbsPDw3zqU5/iqaeeYnBw0KYKG4wu\nRXVMKHIefTNmENIQzjARti7hLKZkMmk9L0PUBsOEzYWplGsE/IEHHuCFF17gwIEDNkzjOA5nzpzh\n/fff72VT10TkhL75Ag0PzJpMDjPI1su7Z6vds+Yn79p57Fb23QrdaJcZAA7bgbkoBaFTmDkPBgcH\nqdfrlMtl8vk8AwMDNjEgykRO6JuFol6vW8/NpCduhvrP96KTcbwoxgihfe0KlyYGIjkDmdBfVKtV\ncrkcU1NTXL9+ncnJSSYnJ5meno68yEMEhV4QBKHXmGc1DCMjI4yPj3Pr1i1eeeUVfvGLX9jUyjBR\ndLBgEwzG9ioMIWwexD6EdhN+8hqWpjs9cuQIsViMkydPks/nyeVyDAwMNExhKUIvCIKwSWh2MF3X\nZXBwsCEx5MCBAzz22GMcPHiQgYGBVbeNAhK6EQRBaKJ53OfWrVv8zd/8De+++y6ZTIYgCDh06BDj\n4+M4jsPntzNUAAATN0lEQVTc3Jx9Uh76dOIRQRCEfiL8nIbnebz11ltcunTJToK0c+dOWx47SoK+\nGiL0giAIK2C8cvNUbJhCocDk5CTbtm27o0QxRC9WL0IvCILQRPg5jZXI5XKUy2Wq1SrpdDpyMflm\nROgFQRCWCc/3qrVmbGyMhx9+mHK5zPvvv98Qhy+Xy5RKJdLp9IrlxaPk1UvWjSAIwjLNJVc++clP\n8vWvf53f//3fJ51O2/fDqZebARF6oSVMvrGUIRD6ATPvg+GZZ57h2LFjvPjiiw0VKgcGBuy8xWaK\nTDObm5QpFvqOcFmDKHVVBWGjhO3YFDCbmppqeN+UYymXy9RqNTKZDFprZmdn7WxnUUKEXmgJEXeh\nnwiCoKF2zXe/+12q1ap9EtZgZlKDpXh8KpVCa83CwoIV+ig9LStCL7REOGRjvBxB2KzUarWGHPrr\n16/zh3/4h3fMimZKpZtYfa1Wo1qtRraEugi9sC7C2QTpdJrdu3ezfft2fN/n+vXrXL9+3Rp7K1MG\nCkI3MWNNtVoNrTW7d+/m+PHj7Nu3j2vXrnH69GkmJibsTcBcB2Y6S9d1qVarDb2BKHjyBhF6Yc04\njmMHoAAymQyPPvoojzzyCKVSidOnT3Pr1i0RemHT4bou8XjcCvV9993Hl7/8ZY4dOwbAn/zJn/DV\nr37Vhm8ymQyLi4uUy+VNMbWpCL2wLsKZNbFYjJGREXbv3k2hUGBgYKDhc8nCETYLzWWJBwYGOHLk\niF0+fPgw8XjcLjfPaBZ1JL1SWDPNE4fUajVmZmb46KOPuHr1KvPz83dM7i4ImwGtdUN8PZ/Pc+LE\nCbv8xhtvNJQ62AyTjYRp6baklBoG/hR4HNDA/wRcAL4HHAAmgd/RWs+11EohEjRfDIuLi7z33ntM\nT0/j+z43b95sGMjazGEbse2thXkS1nDp0iW+9rWv8fDDD1MoFHjzzTcbJhmpVCr2f6UUiUTC1qWv\n1WqUy+VIDcy22v/4JvDftNa/rZSKA2ng3wA/1lr/O6XUV4CvAH/Q4nGEiBC+GEqlEpcuXWJyctJ+\n1pxrvIkR295ChDPGlFLcvHmTV1999Y71woXODFprYrEYAwMDeJ5HqVTC930r9FEoh7Dh0I1Sagg4\nDnwLQGtd1VrngBeAby+v9m3gxVYbKUQX8xRgeA7XzY7Y9tblXpOGrPaZeaLW87xIPiXeSoz+IHAL\n+I9KqTNKqT9VSmWAca319PI614HxlTZWSr2klDqllDplnj4TNh9mEMsUg+oT2mbbXWqv0CaM46KU\nIhaLkUwmSSQSdvA17PWH8X2fUqlEsVikUqk09Gaj4AC1IvQe8BTwx1rrI0CBpa6sRS99wxW/pdb6\nZa31Ma31sdHR0RaaIfSSKE6b1gbaZtsdb6nQEYwDs1odp2bxLpfL5HI5ZmdnWVhYiFR8HloT+qvA\nVa21GZr+c5YujhtKqV0Ay39vttZEIcrU63WCIOir0A1i21ueer1OpVKhUChQLBbvmWUTBAGVSsXW\nqO8boddaXweuKKUOLb/1OeA94FXgi8vvfRF4paUWCkKXEdsW+o1Ws27+F+A7y1kJHwL/I0s3j/+k\nlPoS8BHwOy0eQxB6gdi2sC5MuCeKtZ9aEnqt9VlgpTjk51rZryD0GrFtYSPE43GbT1+tViOTT7+5\nnuMVBEGIEOEcea01ruuSTCbtAG5U4vVSAkEQBKENhOvPNz882GvEoxcEQdggzbWdqtWqjdXXarX+\niNELgiAIH2Nq0puQTlS8ehF6QRCENhIlgTdIjF4QBKHPEY9eEAShjSilbOkEWHpqtrkMcrcRoRcE\nQWgznufdkU8vQi8IgtBHmLLF5v9eF/4ToRcEQWgz9Xod3/dRSkWi4J8IvSAIQhsx+fRaazzPQ2st\nHr0gCEK/Ua/X7cNTm3oqQUEQBGF1ei3uYUToBUEQOoDjfCyvvQ7diNALgiB0ADP/rKlqGRb+biNC\nLwiC0AFMKQSlFJ7n4Xlezzx7EXpBEIQOYgZkexm+kawbQRCEDqG1thOP9LIMggi9IAhChzB1bqC3\nWTgi9IIgCB0iKimWIvSCIAgdxlSy7FU5BBF6QRCEDuI4ToPQ96QNPTmqIAjCFiEK4Rvx6AVBEDpI\nOPNGsm4EQRD6FCP0vUJCN4IgCH2OCL0gCEKfI0IvCILQJXpVCkGEXhAEoQsopXAcpydVLFs6olLq\n95VS55RS7yqlvquUSiqlDiqlTiilJpRS31NKxdvVWEHoFmLbQifYdNUrlVJ7gP8VOKa1fhxwgd8F\nvgZ8XWv9IDAHfKkdDRWEbiG2LXSKXqVXttqH8ICUUsoD0sA08Bzw58uffxt4scVjCEIvENsW2oqp\nTw90PXyz4aNpraeA/we4zNJFMA+cBnJaa395tavAnpW2V0q9pJQ6pZQ6dfv27Y02QxDaTjttuxvt\nFTYfJl7fLVoJ3YwALwAHgd1ABvi1tW6vtX5Za31Ma31sdHR0o80QhLbTTtvuUBOFTUwvMm9auaX8\nI+CS1vqW1roG/AXwKWB4ubsLsBeYarGNgtBtxLaFjhGeS7ZbtCL0l4FnlVJptXR7+hzwHvBT4LeX\n1/ki8EprTRSEriO2LXSEer1uX5tC6LXWJ1gamHoTeGd5Xy8DfwD8K6XUBLAd+FYb2ikIXUNsW+gk\nm64evdb63wL/tuntD4FnWtmvIPQasW2h05g4fTeEX6pXCoIgdJnmjJtOi72UQBAEQehzxKMXBEHo\nMmEPvhuhG/HoBUEQukz4KdluIEIvCILQI7ol9iL0giAIPaJbT8iK0AuCIPQI8egFQRCEtiBCLwiC\n0OeI0AuCIPSQblSzFKEXBEHoETIYKwiC0Od0azBWnowVBEHoIfJkrCAIgtAykRL6XkyxJWxOwl5Q\nvV5fcR2xJUFYIlKhm5XqP/SiSL8QDcJC3WwH4TKvruuilLLrGDsyU7YJgsHYSfgvfGxfKzkH93IY\n1utQhG3ZbHs3W2/HbFSREfp6vY7rug3vichvTVbq2YUvSKUUjuPgOA71eh3HcRqE3qwn9iPAx/a0\nFkE26xnbMXbWvK2xQ2OL6yHsgJj9N39u2lKv16lUKvi+v65jNBMZoTcnM3xCJZSzNbmXSGut8X3f\nXizVatUKPizZjeu61tMXtjb9ctNvdmbWQyRi9OE7bvgOKUIvrEY4JFMulwmCAM9b8ls8z8NxHBF7\noW9oVQsj4dFrrQmCAPh4lvTm/4WtgxHolUIyxnNPpVKUSiXK5TKZTAbXdW331vd9giCgVqv1jTcn\nbBzP80gkEneEhuHOeVuNg2Dei8fjJJNJPM+7IzSolCIWi5FIJO7pbWutrQNbq9WoVCo4jkMymcRx\nnIZtgyDAdV1isRjlcplr164xOzu74pjCms/ButbuEFprarUavu9TrVYJgoB0Ot2W2JSwufA8j+Hh\nYcbGxhgeHsbzvIYwjSGRSFCpVFhcXGTfvn0MDQ2Rz+fRWrOwsEC9XrcXp3EihK1Bs+iOjIywe/du\nBgYGcF33jsFYEws3upPNZoElp2Lnzp0cOnSIkZERKpUK9XodpRS1Wg3P89izZw+7du0iFovZEGL4\nRmH2Xa/XrahPT09z+fJlkskk999/P0NDQ1QqFYIgwHEcSqUSqVSKnTt38uGHH/KNb3yDv/qrvwI+\n7q1WKpV1nZNICH0QBBQKBRzHoVqt2jtwsVi0XpnQnzR7KPF4nL1793L06FEOHTpEKpWiXC5Tq9Ua\nsmvMQGwQBGQyGbZv3861a9eIxWLWZoynVKvVevkVhS7TLPSJRIJt27axffv2VYU+CAJ832dwcJBt\n27YBSz3D+++/n2PHjrFz506KxaIV40qlQjwe58EHH7Q3hrXy+OOPc/78eTKZDAcPHrzruvv27eMv\n//Iv7fJKg7drIRJCby5GpZS9K1arVevld3t+RaG7hC/MWCzG2NgYTz75JM8++yzDw8MsLCxQKpWI\nx+O4rmvTzcxNwmQmzM/Pr9i9lV7h1sboS61Wa+jdhTO4giAgCAKq1SrlchlYckCLxSKFQoF8Pk+x\nWLShQyP0uVxu3UKfy+VYWFiwx2wOKVUqFRKJhP1/vd77SkRG6MvlshV6z/MoFouUSiXx6Lcg4Quu\nVCpRKpVs19YIfTNKKeLx+Ir724gHJPQXZrwnnJIbTs0NJ4KEQy9mvMjzPGKxGEEQoJTC8zz73nqJ\nxWJ2vyuNG4T3GYvF2pJMEAmhNyfOxLPMCTTxKKG/Cd/Iq9Uq09PTnDp1itnZWTvo6vt+Q+jGdV07\n4Do0NMShQ4c4ePAg8XicarVq1wkPgglbk7CAh0Mf5v1wD9CIuhnE9zyPeDxOPB63HrhZPx6PW897\nPSQSCftaibC9Oo5js8laIRJC77ouw8PDDTH64eFhtNak0+mGLy6pcv1Fc2+tWq0yNTVFsVjk3Llz\nVtDNIJjBxOILhQLj4+MsLi7ieR7pdJpisWgvUq011Wq1219L6CHNNlUoFLh+/TqLi4t3rGu0JTxg\nms1m7UNNMzMzzMzMMDg4aMPKSil838fzPMbGxhgbG8PzPOtgNOuV2XcikcBxHG7evMn09DSJRIJ9\n+/aRzWYb9l0ul0kmk+zYsYMrV65w/vx5u78gCDYU4YiE0AdBQC6Xs6PZxhPL5XKUSiWJ0W8hgiAg\nn8+zsLBwx1OK0JjWZmKuN27cIJFI2GwFYzPGjhYWFnr4jYRu06wR8/PzLC4uruvJWIPx8JtTII0d\nrpYGvNq+gYaegYlkrLZv3/cbblAbHW+KhNDPzMzwne98B8COaqdSKYrFIqdOnaJYLNp1JVWu/1lL\n7nvYDkqlEu+99x63b99uSMc0Qp/P5zvdZCHC9MPzOOFB4w1tHwUPORaL6e3btwONNSS01nbUe7P/\nUEJnuduTg8tZOj2J+Smlen+BCX3NWmz7nkKvlPoPwG8AN7XWjy+/tw34HnAAmAR+R2s9p5autG8C\nzwNF4H/QWr95z0bIxSCEWKkIVThtMrxsHIJ7eTorXQxi21uDjRQ1g4+fZu10UbO77XstRc3W5MSE\nS7qu9AKOA08B74be+7+Bryz//xXga8v/Pw/8f4ACngVO3Gv/y9tpecmrky+xbXn162tNdrhGYz1A\n48VwAdi1/P8u4MLy//8v8IWV1rvbSyml4/F4wyuRSOh4PK5d1+35iZRX9F9KKe267oovWP1ioMO2\n3evzIq/+f61Fwzc6GDuutZ5e/v86ML78/x7gSmi9q8vvTdOEUuol4CWzLClwQiusJXyzRtpu24LQ\na1rOutFa643EIbXWLwMvg8QxhWgiti30Cxt9ZPCGUmoXwPLfm8vvTwH7QuvtXX5PEDYLYttC37FR\noX8V+OLy/18EXgm9/8/VEs8C86FusCBsBsS2hf5jDYNJ32UpDlljKS75JWA78GPgIvAjYNvyugr4\nQ+AD4B3gmGQmyCsKL7FtefXray12GIkHpiSOKXQaLQ9MCX3KWmxbyvoJgiD0OSL0giAIfY4IvSAI\nQp8TieqVwG2gsPw3aowi7VoPUWzX/h4eW2x7/Ui71s6abDsSg7EASqlTWutjvW5HM9Ku9RHVdvWS\nqJ4Tadf6iGq71oKEbgRBEPocEXpBEIQ+J0pC/3KvG7AK0q71EdV29ZKonhNp1/qIarvuSWRi9IIg\nCEJniJJHLwiCIHSASAi9UurXlFIXlFITSqmv9LAd+5RSP1VKvaeUOqeU+r3l97cppX6olLq4/Hek\nB21zlVJnlFLfX14+qJQ6sXzOvqeUine7TcvtGFZK/blS6hdKqfNKqU9G4XxFAbHrNbcvcrbdb3bd\nc6FXSrksFYv6deBR4AtKqUd71Bwf+LLW+lGWpov7F8tt+QrwY631QywVvOrFRft7wPnQ8teAr2ut\nHwTmWCrI1Qu+Cfw3rfUjwGGW2hiF89VTxK7XRRRtu7/sei2Vzzr5Aj4J/CC0/FXgq71u13JbXgE+\nzyrTy3WxHXtZMqzngO+zVEnxNuCtdA672K4h4BLLYz2h93t6vqLwErtec1siZ9v9aNc99+hZfYq2\nnqKUOgAcAU6w+vRy3eIbwL8G6svL24Gc1tpMDd+rc3YQuAX8x+Wu958qpTL0/nxFAbHrtRFF2+47\nu46C0EcOpVQW+C/Av9Ra58Of6aXbeddSlZRSvwHc1Fqf7tYx14EHPAX8sdb6CEuP+jd0Z7t9voTV\niZJdL7cnqrbdd3YdBaGP1BRtSqkYSxfDd7TWf7H89mrTy3WDTwH/WCk1CfwZS13cbwLDSilTq6hX\n5+wqcFVrfWJ5+c9ZukB6eb6igtj1vYmqbfedXUdB6N8AHloeaY8Dv8vStG1dRymlgG8B57XW/z70\n0WrTy3UcrfVXtdZ7tdYHWDo3P9Fa/1Pgp8Bv96JNobZdB64opQ4tv/U54D16eL4ihNj1PYiqbfel\nXfd6kGB5YON54H2Wpmn733vYjk+z1B17Gzi7/HqeVaaX60H7Pgt8f/n/+4GTwATwn4FEj9r0S8Cp\n5XP2V8BIVM5Xr19i1+tqY6Rsu9/sWp6MFQRB6HOiELoRBEEQOogIvSAIQp8jQi8IgtDniNALgiD0\nOSL0giAIfY4IvSAIQp8jQi8IgtDniNALgiD0Of8/W5dRFxjoYUAAAAAASUVORK5CYII=\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -3569,12 +2196,10 @@ "text": [ "Action: Q-Value:\n", "====================\n", - "NOOP 1.050 (Action Taken)\n", - "FIRE 1.028 \n", - "RIGHT 1.035 \n", - "LEFT 1.041 \n", - "RIGHTFIRE 1.022 \n", - "LEFTFIRE 1.043 \n", + "NOOP 0.503 \n", + "FIRE 0.513 \n", + "RIGHT 0.559 (Action Taken)\n", + "LEFT 0.520 \n", "\n" ] } @@ -3586,10 +2211,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Example: Loss of Life\n", "\n", @@ -3599,16 +2221,12 @@ { "cell_type": "code", "execution_count": 36, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "217" + "115" ] }, "execution_count": 36, @@ -3625,20 +2243,19 @@ "cell_type": "code", "execution_count": 37, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": false }, "outputs": [ { "data": { - "image/png": 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D3HXXXfzKr/wKrutSLpfRWuM4Tsvy+L6Pbdvkcjny+TwvvPACV65c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\nAElFTkSuQmCC\n", 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\n", 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KUYVrOI1Gg6tXrwZevRm/aLNCbM7V1GhKpRJ33HEHhw8fBmiaaarXfS6E/sM4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\nOSL0giAIfc7/D4oTSAUqWcXyAAAAAElFTkSuQmCC\n", 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\n", 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Dhw8DNM001e8+F8LgYYQeVjsVUSZSQg+rb9rMzAyvv/4658+fZ3R0lGQySaVS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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -3697,23 +2314,23 @@ "text": [ "Action: Q-Value:\n", "====================\n", - "NOOP 0.258 \n", - "FIRE 0.317 \n", - "RIGHT 0.035 \n", - "LEFT 0.463 (Action Taken)\n", - "RIGHTFIRE 0.183 \n", - "LEFTFIRE 0.227 \n", + "NOOP 0.378 \n", + "FIRE 0.380 (Action Taken)\n", + "RIGHT 0.375 \n", + "LEFT 0.360 \n", "\n" ] }, { "data": { - "image/png": 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SQg83nrRr165x6tQpzp8/z+joKMlkknK53FOPPoyJ1ReLRVZXV4PlN3tsC39f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\n", 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\n", 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kZIREIkGlUumrRx/GVNVKpRKFQiFYvl6IIPx7sVjko48+YmZmJsjo2eixTXtA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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -3747,23 +2364,23 @@ "text": [ "Action: Q-Value:\n", "====================\n", - "NOOP 0.193 \n", - "FIRE 0.247 (Action Taken)\n", - "RIGHT -0.042 \n", - "LEFT 0.119 \n", - "RIGHTFIRE -0.032 \n", - "LEFTFIRE 0.120 \n", + "NOOP 0.197 \n", + "FIRE 0.203 \n", + "RIGHT 0.217 (Action Taken)\n", + "LEFT 0.208 \n", "\n" ] }, { "data": { - "image/png": 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K5XIYt40KvTGiRqNBJpMhm83y9ttvc+bMmZZ9m/WEwWOyMoyYVyoV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\nAAAASUVORK5CYII=\n", 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\n", 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stvbdCuaaFIvFBqGPevee53HlyhVyuVxLfSHMfTd2sLS0xMLCAo899thN6wlC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\nEaEXBEHoc0ToBUEQ+hwRekEQhD7n/weEbO2POhAScgAAAABJRU5ErkJggg==\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -3797,23 +2414,23 @@ "text": [ "Action: Q-Value:\n", "====================\n", - "NOOP -0.076 \n", - "FIRE 0.045 \n", - "RIGHT -0.298 \n", - "LEFT 0.085 \n", - "RIGHTFIRE 0.018 \n", - "LEFTFIRE 0.106 (Action Taken)\n", + "NOOP 0.187 \n", + "FIRE 0.177 \n", + "RIGHT 0.194 (Action Taken)\n", + "LEFT 0.191 \n", "\n" ] }, { "data": { - "image/png": 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6sHfvui7Xrl0jl8t11BfC/O/GDhYWFpibm+OjH/3oTesJQi/ZcXn03SRcYaZb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\nYII=\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -3822,23 +2439,23 @@ "text": [ "Action: Q-Value:\n", "====================\n", - "NOOP -0.075 \n", - "FIRE 0.067 \n", - "RIGHT -0.214 \n", - "LEFT 0.122 \n", - "RIGHTFIRE 0.073 \n", - "LEFTFIRE 0.148 (Action Taken)\n", + "NOOP 0.149 (Action Taken)\n", + "FIRE 0.113 \n", + "RIGHT 0.141 \n", + "LEFT 0.126 \n", "\n" ] }, { "data": { - "image/png": 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -3847,23 +2464,23 @@ "text": [ "Action: Q-Value:\n", "====================\n", - "NOOP -0.428 \n", - "FIRE -0.168 \n", - "RIGHT -0.416 \n", - "LEFT -0.043 (Action Taken)\n", - "RIGHTFIRE -0.103 \n", - "LEFTFIRE -0.119 \n", + "NOOP 0.140 (Action Taken)\n", + "FIRE 0.108 \n", + "RIGHT 0.132 \n", + "LEFT 0.111 \n", "\n" ] }, { "data": { - "image/png": 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N9vzY22W90I15C+qWsB3Mz8/f1Bge9QdP2P3sJo9+VwQwwzfShCKEvU34LWY3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\nc6tneKfP+FaP3y9MZWvCar0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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -3872,12 +2489,10 @@ "text": [ "Action: Q-Value:\n", "====================\n", - "NOOP -0.179 \n", - "FIRE -0.293 \n", - "RIGHT -0.518 \n", - "LEFT -0.155 \n", - "RIGHTFIRE 0.095 (Action Taken)\n", - "LEFTFIRE -0.152 \n", + "NOOP 0.137 (Action Taken)\n", + "FIRE 0.109 \n", + "RIGHT 0.130 \n", + "LEFT 0.105 \n", "\n" ] } @@ -3889,10 +2504,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Example: Greatest Difference in Q-Values\n", "\n", @@ -3903,16 +2515,13 @@ "cell_type": "code", "execution_count": 38, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ - "503" + "699" ] }, "execution_count": 38, @@ -3928,20 +2537,18 @@ { "cell_type": "code", "execution_count": 39, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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PNE07YNYwFYTVaDQadv76jcxwKQg7mUgJffix2/yfy+WaJhoTdjftPHnjEJiZRsOevXj1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\nF7/4BbDk4MRisaZy2tE6ZUL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uPiE86CRK3O0b04j7/Pw8Dz30EK1WS25YYQ9aa3K5HHNzc6TT6bhmL7nvwoNOosQd2mtcSilyuRzz8/P4vi/iLuxBa43ruuTz+T2+IwgPMokT915ILUzohqmhi38IQjuJF3eT2x5FkdTGhD3I2AdB6E7ixT2VSuE4TtyJajrShAcb2w8cx5FUSEHoILHibmpijuOQzWZxnB1TTWeZ8GBj+0E6ncZxHPENQbBIrLjD/XllzI0rYRmhE5NVJTV3QWgn0eIO9wXe5LgLgo0MZhOE7iRe3G2kyS0IgtAfx0Lc7aldB6HbfvKgGJxe/4dJlKmkQQpCd46FuJvQzCib39KUHz2TKlP5XwrCXhIv7vZCHYe9iQ8a4DKOh8Y0k+TylP+hILSTeHG3GaT53Y/YSLO+f6Q8BeF4MNXinkql4iwbOwfavDfzgYsY9UcSy1Nq7ILQnWMl7oOEZfpZ2EMEoj+kPAXh+JB4cTeDmPqtDdpTvjabTSqVCs1mMz6WqVk6jkOhUGB2dpZMJjNwRs44M0eSkOVjl0mr1aJcLtNsNomiKB44ZN6b8nRdd8++40T6TQRhL4kXd3vwkn0D28LROYe3WYJtc3OTGzducPfuXYB4jpowDMnn8ywtLVEoFHBdN558qt+Rjv2K7CCiM85j93t+uzxNmZTLZZaXl1ldXSUMw3hKiCAIcF2XixcvcuXKFbLZbDyh16iFt/OBIYOYBKE7iRZ3e2TqQTewWaHJ1Mp936der3Pr1i3eeOMNoiiKRdzzPBYWFsjn81y6dCn+PgzDvpdqOyi2PIyojfPYg9hhFsFotVrcuXOH1157Dd/3YxFvtVrMzMzgOA5LS0sDleco7JS+E0G4z9DirpRKA98ElrXW71ZKXQE+C5wEvgX8itbaG+L4bXOH2OEAg/nO1DZNDT0IAkqlEhsbG0D7TIKe51GtVoGdGn0QBPG5HnRMedoPyzAMCcOQUqnE+vo60F6etVqNSqXSNteLPXXEuGw0701n7igZt28LwjgZxV33G8Ar1uePAh/XWj8KbAEfGObgnXnu6XS6LZ+6U/w7c63tG77beyM+thj18zJTEbuui+u6ZDIZMplM/N7Y2WnTpI/dz8sc/yCBtqdetsvW3q/b/2uUNnYL2Y2Qsfq2IIyToWruSqmHgH8J/Bfgt9TOHfZTwC/tbvJp4D8BfzjoOUxzu58sDXtbIzx2TTyTycQ1UDMHuKn1mb/9YhYQ6VZbtAVxENE56Nj9hqqGxcTNbdLpdNynYTqigyCI4+92+R+mPIe1cwy19rH7tiCMk2HDMn8A/A4wt/v5JFDSWge7n28DF4c5gRHjg7Bj7iYkEwRB3KnX7eaPogjf92m1WrEg9QrLdOZ012o1SqUSjUYj/t5s5zgOc3NzFIvFvjJxOo9drVbZ3t6Os3zsEEkmk2F+fp75+XkymUwsvqMWemOLXaa+7xOG4b615SAI8DwvFnmz/bjj4WN40I3dtwVhnAws7kqpdwPrWutvKaV+coD9nwWeBThx4kTXbUyt0Ij0QdhpjkEQ0Gw2CYIgFhb7QRGGIZ7n0Ww24+26xfM7j29qrevr61y7do3Nzc1YdMMwJAgCCoUCS0tLAMzOzsY18X6Pvba2xvXr19nc3ARo60MoFApcunSJhx9+mJmZmTjWPI5avBF188Az5WSXoV22nudRr9ep1+tH2qFqwlhm3v9hGaVvC8KkGKbm/g7gZ5VSTwM5YB74BFBUSjm7NZyHgOVuO2utnwOeA1haWuparTO1P8/z2mqM9u/mr1IqFh0j7raww/1wSacgNZvNtnBNN+wl3TzP4969e1y/fp3l5Z3Ls8V9fn4ex3FYXFyMv+/VKjAtDvvYGxsbXLt2jTt37uw5drFYxHVdTp06FT8MBknh3G9+GDsVMoqieJyB7/v71sDtlpAR926hqUEEuNNu+/9o4u92X8SQjMy3lVKSwiNMhIHFXWv9EeAjALu1m3+vtf5lpdSfAr/ATlbBM8DzwxhoBMYIRSedHZEmDNNvXNqEHcyrWwvBFmCzfbPZpFQqUalU9mzv+z61Wi0W3c7QSadQ2ceOoohms8n29naczdNZHvV6PX5wDRLX3q9cuoVcOr/rFmbp1sFrf7avedAQTa8O1FG3Wo7KtwVhnIwjz/1DwGeVUv8Z+Efgk8MesFfnZLcsilQqFedl91Ob7ZYl0+v89qCcgwTSZOAYe8y5oF3gOkXK3te20V4gvNcDrR8Ost3ezn5QmvLcbz/7muF+52vnNQ9CL7v7sWuEjNy3BWFcjETctdZfA762+/4a8LZRHBfu5zCbPPRuv5naa+cI1VqtRhAE3Q4bb2+ObUInvTJU7N+CIIiH2+fzebTWuK4bH2tubg6lFNVqta0z1E7XNNk69u/m2OYaDCbMZEIkQHwuO/R0kHjanc2dnbzd3neGZWq1Ws/QjBnMVK1WKZVKcdl2pkUOEjqxO2dtu40dmUyG2dnZniG1YRinbwsCtFdeO8PIdlLDYUnsCFVzYUEQUK/X8X1/TxPf8zzK5TL1er1N4I1grq2txdks5jf7vRnFmsvlesbFbSExomseNMViMX54mNaCEUPf97l9+zau6+75h2WzWebm5uJRnXZfQavVirN3utlt4tr1ej2OxXdrpXQKoOmTqNfrXefb6VX+9rHv3r1LrVZrO4cdWiqXy9y6dYtKpRI7pBHyTCbD3NwchUIhziCyz9PtwWLbXa1WKZfL+L4PtGcQzc7OcuHCBXK5XPwgGlcnsyCMmnQ6TTabjVvIdos5iiJarda+ldReJE7cO290UxtsNpt7btZarcbKygrr6+txbdoQRRGNRqOnGJljVyqVuBD3y2jpFKMwDFlYWIgnybIJw5BGo8H3v//92C67w3dhYYHz589z8uTJPeJu4vVGxMzxTFgmCAIajQblcjm2Y78QlLE7lUoRBAEbGxusrKxQLpeB9oeSTWcoCGg7b2d5RlFEqVTi+9//Ptlstm2UK0ChUODcuXOcPn06/t2U50F2e57H3bt3WV1d3TOqWGvNqVOnyOVynD59uq2VZW4WQUgyJtsrnU7HOmSLu60FhyFx4m5jau7NZpNGo9FWyzNhj/X1dW7fvo3neXGt2xSOKSj7eN2ObeZCOShd0SaVSjE/P8/c3Fx8PGNXs9nk5s2brK2tUavV4ri7efrWajXy+Tz5fB7XdeOQi+u6eJ6H7/ttdncKYBAEtFqt+MHQr7j7vk+pVOLOnTtsbW3F3/crgJ0Pgc7yrFar1Ov1PR2vURQxNzdHJpOhUCj09VAy/8N0Ok2r1WJra4vl5WW2t7fjm8GE03zf59KlS10zqgQh6di1dTsEM0xIBhIu7vthCsL3/TjE0E8uvGHYGl06nW6rtRtxNw8YpVScEmji3AaTL244jN2HpbPj1jwY7Fz1UZ6r1/E6r/mwmAdxr/I8qtGwgjBq7Ey9znDmMDqVeHE/KCvFHg5/lHQKmT0S1o7DG+wQhelMhfudKfZ2h6FXzNz81i3DyLZrv/2HxT62ueZu2UHd0iq72W13mNr/c3tuIUE4bnSmDdvvh2mJJlLc7U41Exev1Wp7hMFkw6RSqbbMDFtoewmXfWxgzxOzHxs7wxKwIzSNRiNuTcD9FEGzj9Y7efKmL8E8DEyoodFo7Nuhauy2M3wOil2n0+m2Vo4pQ9uuzs7Ng665G/a+dgcR3O8AN4PSOrfvZbfneXje/ckXzQPKXPsIBy8JwpHTmf5sh3inpubemSURRRG1Wo319XW2trb21M6MQBqh6BUP7vZdFEVxzL5UKsW/DyoStt2+71Mul2PRtgdKwc6KRpubm/GDqfOajQB2szsMQyqVCqurq2QymQPt7jz29vY2rVYr/q0zxtd5vkHLwb5uII73A20ra/VjdxAEbG9vx2Vix+zNeQThuGJX+jorUFNVc7efWraQra+vx81v03EWhmEchz0sURRRqVTwPG/PE/OwdHsodaYv2f8wI+6VSmXPvlrvpGjuJ+5mqbt+7O48tud5sbh3HnvU2Mc24l6v1weyu9Vq9SyTfloUgpBUwjCk1Wq1tXRNn94wA/QSJ+6dmHU7TfjEjl0PgxEMW+iOiiiKqNfrA+1rQjp2eOU4ME67h41NCsIkiaKoreICxON6hpkML/Hi3q3jUhAEYdoxWW2DJoskPsWgM7tDlsETBOFBwNToBxX3xNfcD0oPGjbWOq7mfD92HRRvHmTfg5h0bPq42i0IR0U2m42zxIYZG5J4ce+cMmCYEVu9jj8pRpWVcpw4rnYLwlGRyWTI5/OkUqmu0373S+LDMoIgCA8SZroS13XjdOdBSHzNXRAE4UHCniTRLLE5SNxdxF0QBCFBmFh7NpuNR6+KuAuCIBxTOhfsMAvMP3CzQgqCIEwTmUyGmZmZeFI83/f3TP99GETcBUEQJkDnxGCu6zI7O0s2m8XzPKrVas9lLftBsmUEQRAmwH4z1o5iviSpuQuCICQAM99UJpOJR6cOM92KiLsgCEICMLPgdq4FPSgi7oIgCBPEDFgy6yy3Wq2RjOQWcRcEQThCOjtSs9ksi4uL5HI5PM9ja2srnuK82/b9Ih2qgiAIR4w9gZ7jOMzMzLCwsMDc3By5XG4ks9+KuAuCIEwQ03nabDbjkIwdd5dBTIIgCMeATrE2awTX6/V4ic6pWyBbEAThQcMsP5lKpeJl9cz6wiLugiAIxxwTnrEXkB+GoWLuSqmiUuoLSqlXlVKvKKV+VCm1qJT6a6XU67t/TwxloSBMAPFt4SjotrpcFEUjWZRo2A7VTwB/pbV+C/BDwCvAh4GvaK0fA76y+1kQjhvi28JYcV2XkydPsrS0xIULF5ibmxvp8QcWd6XUAvDjwCcBtNae1roEvAf49O5mnwZ+blgjBeEoEd8WxkFnDT2Xy3HmzBmWlpY4d+4chUKh7Xc7Y2YQhtn7CnAX+B9KqX9USv2RUqoAnNVar+xuswqcHcpCQTh6xLeFsWCLt1KKTCYTL6eXyWSGFnSbYY7kAD8M/KHW+kmgRkczVe8EjboGjpRSzyqlvqmU+matVhvCDEEYOSPz7bFbKhwr7Dh6GIZUq1U2NzcplUq0Wq2R5LcbhhH328BtrfWLu5+/wM4NsaaUOg+w+3e9285a6+e01k9prZ8qFApDmCEII2dkvn0k1grHAnsaX6UUnuexvr7OzZs3WV1dxVRyTe1+YuKutV4Fbiml3rz71TuBl4EvAc/sfvcM8PxQFgrCESO+LYwbrTWe51GpVNja2qJUKtFoNIaaBbKTYfPcfx34E6WUC1wDfpWdB8bnlVIfAG4A7xvyHIIwCcS3hSNllMIOQ4q71vrbQLem5zuHOa4gTBrxbWFcpFIpstksjrMjv77vD70wRzdkhKogCMIY6Yyh5/N5Ll68yIkTJwiCgLW1NdbW1mJxH3baAYPMCikIgjBm7CwY13U5ffo0Dz/8MBcvXmR2dnZPiuRIzjmSowiCIAg9sWviURQRhiG+7+P7/tjOKWEZQRCEMdIZYomiiLt379JsNvE8j83NTcIw7Ln9oIi4C4IgjBk7v71er3Pr1q04th6GoYi7IAjCcaabmI8LibkLgiBMIVJzFwRBGBN2WqPruiwuLjI7OwsQj071PG/PtqNAxF0QBGEMKKVIpVJxCGZmZoZHH32UK1eu4Hke3/ve99je3o63t7cdBSLugiAIY8LOWU+n0xSLRc6fP0+r1WJ5eXks+e0GEXdBEIQx0TnFb7lcZm1tDc/zqFarbb+PMiQDIu6CIAhjwWTGGIIg4MaNG2xsbOB5HltbW22DmGRuGUEQhGNItVqlWq32/H3UNXdJhRQEQRgTo46jHwapuQuCIIwJrTVKKVzXjaf4DYKAIAjGPpBJxF0QBGHEpNPpWLwLhQJPPPEEly5dIggCrl+/ztWrV+Nl9VKp1Mjj7SDiLgiCMFI689td12VpaYm3ve1t8e/Xr1+Pt0+n0yLugiAIxw2tNVEUxeGZXC7XNr/7uOLyIu6CIAhjxIj33bt3KZVKvP7667Rarfj3ccXeRdwFQRBGjF0zz2aznDt3jjAM+cY3vsG3v/1tABzHGWvHqoi7IAjCCDExd8PJkyd561vfSqPRYGNjI/4+l8tRr9fHEm8HyXMXBEEYKyBavwEAAA/dSURBVOl0GqUUjuPgum78/agHLXUiNXdBEIQRYjpQDa+++irPP/88uVyOZrMZf99qtcZWawcRd0EQhJGitW6bM6bVavHlL3+ZbDa7Z66ZcSLiLgiCMCLM4KUoisjlcjzyyCOcOXOGer3O8vIya2tr8bajXpyjExF3QRCEEaCUIpPJxLXzubk5nn76ad773veilOKFF17gM5/5DMvLy8DO4Cbf98cWmhFxFwRBGAFKKdLpdPzZcRze8pa38GM/9mPATp77F7/4xbbfxxmaEXEXBEEYAZ0dqVEUsb6+ztraGq7rcuPGjbbBS1EUSVhGEAQh6Wit48WuYacm/3d/93eUSiU8z+Oll17i3r178e++7ydX3JVS/w74IKCB7wK/CpwHPgucBL4F/IrW2ut5EEFIIOLbwiDY2TCrq6u88MILvPDCC8DevPZxZ8sMPIhJKXUR+LfAU1rrtwJp4P3AR4GPa60fBbaAD4zCUEE4KsS3hUGxR6bCjqCb15HbMuT+DpBXSjnADLAC/BTwhd3fPw383JDnEIRJIL4tHBoTc3cch3w+z8zMDLlcDsdxjnxVpoHDMlrrZaXUx4CbQAP43+w0VUtaa9PeuA1cHNpKYSoxzj6JWs1+iG8LhyWbzcadpbOzs7z3ve/lXe96F67r8vWvf50XXniBq1evAjvCH0XRWEenwhDirpQ6AbwHuAKUgD8FfuYQ+z8LPAtw4sSJQc0QjjFJE3XDKH1beDBwXTcW95mZGZ5++mne//73A/DmN7+Zr3/96/G2uVxu7FMPwHBhmX8BXNda39Va+8AXgXcAxd2mLMBDwHK3nbXWz2mtn9JaP1UoFIYwQxBGzsh8+2jMFSZJt3CLPUHYI488Qj6fjz93xuXHxTBnuQm8XSk1o3au7p3Ay8DfAL+wu80zwPPDmShMI47jMDc3R7FYJJvNTtqcTsS3hb7p7DDVWlMqleLPX/rSl9qmHRh3CqRhYHHXWr/ITufSP7CTKpYCngM+BPyWUuoqOyljnxyBncIUYNdwZmZmWFpa4vLly21huaPudOqG+LZwWGy/dV2XxcVFNjY2+PjHP86HPvQhXn311bjG3mg0xp4GCUPmuWutfxf43Y6vrwFvG+a4wnRiT5SUy+U4f/48MzMz1Ot11tbW0FonppNVfFs4DI5zX0pzuRw/+IM/yOLiIl/72te4efMmAMVikXq93jbt7ziRxTqEiWBWqzELGQjCcUUp1ebDSilmZ2dJpVJtsfbO7caNTD8gHBl2bbzVanH37l3K5TLVajX+bVIDPgRhUDqnHajVavzlX/4ljzzySNtcMvV6vW2e93Ej4i4cGbZo12o1bt68STqdplardd1GEI4LjUYjfn/v3j0++tGPMjMzE0/vC9BsNo/Uv0XchYng+z6bm5uTNkMQRoKZUyaTyeB5Hq+++mrb7+NemKMbEnMXBEEYEfbEYYZJCDtIzV2YIEnJjBGEURFFEUopcrkcqVSKVqt1JGmP3RBxFyaGiLowjWitabVaKKXGPsXAfoi4C4IgjJhJirpBYu6CIAhTiIi7IAjCFCLiLgiCMIWIuAuCIEwhIu6CIAhTiIi7IAjCFCLiLgiCMIWIuAuCIEwhMohJmAqO62hXM8d3N/tHMfd35xTKqVSq7+MOY5PWOhEDeR5kRNxHwDA34XEVpSRjBC3pZZtKpXAcp6e4H0aIDeY4Zr8gCOI5xJVSZDIZMplM29D4XucwAm2OmUql9l3c2aykpZQiDENarVbXibSEoyGx4n6cVudJuog8CBh/sVe7mdRsfP0SRVHbIg/jxJRFq9VqW0DiqM4rHD2Jibn3WoLqOIm8MFlMrdL4kv1KEpOwyV7j8yhJYvk/KCSm5m6myjRPedMcTPpT3zRVB3FgrTVhGCb+Go8DURQRhmH8SvKyfcaeQqHA/Pw8juPENpt7IJVKkclkYlHu9xrMMdLpNFEUUSqV2NjYwPd90uk0Fy9e5NSpUyil8H2fKIpwHKft+HZIp9lsEgQBqVQK13XJZrOxjXYYxviy4zg4jkO1WuX27dtUq1Vg5z5J4v9imkmEuGutCYIgjtXZzpV08ZuZmWFhYYFcLgfsjXma7+wHl/mt0WhQKpWo1+tHbPV0EUURQRDQarXIZDIEQYDWOha4pHTsGRE1ceg3velN/MRP/AQnTpygUqkQBAGO4xAEAa7rcubMGU6cOAEQx817xbyNT7VaLRzHoVgsUq1W+epXv8rnP/95tNbMzc3xwQ9+kJ//+Z/HdV1WV1cByOfzbRUN13UB2Nzc5NatW9y7d49cLsdDDz3E+fPnyWazeJ5HFEWk02nS6TSe59FqtSgWiywuLvLiiy/ysY99jBdffBGAXC5HEAR4nrdv3N5w0D2fZE1ICokRdxN7NDX4KIrwfT9x4m4vMJFKpVhYWODKlSvxTWjs77afqbmYWszGxgZRFNFoNPY8AIT+iaKIZrNJpVKJa/BG3JOUtdHpF4uLizz++OOcO3eOzc1NfN+Pl2nL5/MsLS1x9uxZ4L64m2vqdmylFLVaLX4wbG1tcf369Xgb13V58skneeKJJwB49NFHD7T5tddeY2VlhUKhwA/8wA8wPz/f17XOzMzwqU99Kv6cyWS6tg4Gwb5X9jvOfvfSJENFR3WPJ0Lc4f78x3YPvt1Tn0SUUhQKBc6dO8e5c+diIelVMzHXZm5Qx3FYX1/fc8wkX3MSMS2/VqtFOp2Oxd38H5Ii7tB+YwdBQL1ep1arUa/XY3H3fR+tNdVqlZmZGZRSceXHXFOnj9itQdd1yeVyVCoVms1m27lNmASIy6lXPL5cLlMul+OHZqlU2lfcm81m3ILd3t6OH0jm3If1626t4G7bdK7odRjhHmSfw3IU5+hGIsTd3JzQLu7HISxjaoomFLCfuNspeqYZnCThmSb2yx9PEiasYeLk5q95mVRJ4yem1deJ8TkTWzex705BSafTXd93w8T8HcchnU6TyWQO3N7gOE5f4Zf9GOT/d1gBPQrBfeDXULXT18zfJPa02zWFKIqoVqvcunWLarUaC3avm8bOF9Zas7W1Ra1Wa/vHJ12MkorxFbvjLon+Y9tjRDiTyeC6Lp7nxX8dx8F1XVzXbdunH3E3+7mu2ya4Sqk4nt4P2Wy27VgH7Wv7vel47XXt/XLQPvbvSftf20zCtkSIu1IqrmWYDlVTYxg0E2Wc2JkY29vbhGHIyspK/F2/zchWq9XWTBZh7x+7rMIwpF6vx6GAzrBMUgbSdIYZV1dXefHFF1lYWKBWq8UVA5N1cvLkSebn5+NWLPQWCfO953mk02nm5+ep1Wp897vfjbdpNBp89atfjWvk9+7dQ2tNLpdrG7BkHgjb29usrKxQKpXIZrOcOXOGs2fPxp3WppWaTqfxfR/P85ifn2dhYYGXXnqJ5eXl+Ny+78fX0M3PO++bfjtUB71nHoR7LRHiHoYh1Wp1j7jXajVarVaiQxeNRmPgQSGSGjYafN/n3r17OI5DNpuNxdx+iCaBzofMtWvXuH379p7Whvl70IjQbnSmJ7ZardjHqtUqn/rUp/jc5z4HtIdAu3V2mpCj3VdkQked29vJAqlUCs/zqFQq8TbNZvPA9FS5F0ZLIsS90Wjw0ksvxbFF49jNZpM7d+603ZxJdIAkP3ymFdsPPM/j7t27VKvVrtkkSRF3gxFP3/fbOh3HiemoNZ2kR4VJR03ifTvtqIMKXSn1x8C7gXWt9Vt3v1sEPgdcBt4A3qe13lI7XvsJ4GmgDvxrrfU/HGSE4zi6WCx2njeen6LZbIqACgey3xwpWus9Px6FbyulRNWEsdLNt6E/cf9xoAp8xroB/iuwqbX+PaXUh4ETWusPKaWeBn6dnRvgR4BPaK1/5CDjjvsNMGzOrjB+eoj7RH17v4nDBk0o6AzL2BOHwf1OUmgPy/Q6lkwclnx6ifue9LxuL3ZqMf/P+vw94Pzu+/PA93bf/3fgF7ttd8DxtbzkNc6X+La8pvXVy/cGTUQ9q7Ve2X2/CpzdfX8RuGVtd3v3uwMxtYLOV9IyZYRkYtd0O1+HZOS+LQiTYOgOVa21HiSsopR6FnjWfJaYujAM4whvjcq3BWESDFpzX1NKnQfY/WvG0C8DS9Z2D+1+twet9XNa66e01k8NaIMgjAPxbWEqGFTcvwQ8s/v+GeB56/t/pXZ4O7BtNXEF4Tggvi1MB310CP1PYAXw2YkzfgA4CXwFeB34P8Di7rYK+G/A94HvAk/12WE78U4JeU33S3xbXtP66uV7B6ZCHgXHPRVSSD4908XGjPi2MG56+XZiltkTBEEQRoeIuyAIwhQi4i4IgjCFiLgLgiBMIYmYFRLYAGq7f5PGKcSuw5BEux6e4LnFtw+P2NU/PX07EdkyAEqpbyZx0IfYdTiSatckSWqZiF2HI6l29ULCMoIgCFOIiLsgCMIUkiRxf27SBvRA7DocSbVrkiS1TMSuw5FUu7qSmJi7IAiCMDqSVHMXBEEQRkQixF0p9TNKqe8ppa7uLm02KTuWlFJ/o5R6WSn1T0qp39j9flEp9ddKqdd3/56YgG1ppdQ/KqX+fPfzFaXUi7tl9jmllHvUNu3aUVRKfUEp9apS6hWl1I8mobySgPh13/Ylzrenwa8nLu5KqTQ7s+29C3gC+EWl1BMTMicAfltr/QTwduDXdm35MPAVrfVj7MwYOIkb9TeAV6zPHwU+rrV+FNhiZ0bDSfAJ4K+01m8BfogdG5NQXhNF/PpQJNG3j79f9zNt6ThfwI8CX7Y+fwT4yKTt2rXleeCn6bGu5hHa8RA7zvRTwJ+zM/3sBuB0K8MjtGsBuM5u3431/UTLKwkv8eu+bUmcb0+LX0+85k5C16ZUSl0GngRepPe6mkfFHwC/A5i1CE8CJa11sPt5UmV2BbgL/I/dZvUfKaUKTL68koD4dX8k0benwq+TIO6JQyk1C/wZ8Jta67L9m955bB9ZipFS6t3Autb6W0d1zkPgAD8M/KHW+kl2htm3NVWPuryE3iTJr3ftSapvT4VfJ0Hc+16b8ihQSmXYuQH+RGv9xd2ve62reRS8A/hZpdQbwGfZab5+AigqpczcQJMqs9vAba31i7ufv8DOTTHJ8koK4tcHk1Tfngq/ToK4/z3w2G4PuQu8n531Ko8cpZQCPgm8orX+feunXutqjh2t9Ue01g9prS+zUzZf1Vr/MvA3wC9MwibLtlXgllLqzbtfvRN4mQmWV4IQvz6ApPr21Pj1pIP+u50TTwOvsbM+5X+coB3/nJ2m1neAb+++nqbHupoTsO8ngT/fff8I8A3gKvCnQHZCNv0z4Ju7Zfa/gBNJKa9Jv8SvD2Vjonx7GvxaRqgKgiBMIUkIywiCIAgjRsRdEARhChFxFwRBmEJE3AVBEKYQEXdBEIQpRMRdEARhChFxFwRBmEJE3AVBEKaQ/w/neC08BYCX9wAAAABJRU5ErkJggg==\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -3950,23 +2557,23 @@ "text": [ "Action: Q-Value:\n", "====================\n", - "NOOP 0.666 \n", - "FIRE 0.905 \n", - "RIGHT 0.768 \n", - "LEFT 0.408 \n", - "RIGHTFIRE 1.149 (Action Taken)\n", - "LEFTFIRE 0.213 \n", + "NOOP 0.428 \n", + "FIRE 0.428 \n", + "RIGHT 0.906 (Action Taken)\n", + "LEFT 0.358 \n", "\n" ] }, { "data": { - "image/png": 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t7VgOX8NKpcL8/Dye5zWlV25E6MPz5pj+BKWUbUhb+xUEQYig0LfeqK0dn2bw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\nE088QbFY5Nq1a1t2TCG6RMK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7nVSJu31hGnFfXFzk7NmztFotuWCFfWityefzLCws4DhO0rKX3HfhXidV4g7tLS6lFPl8nsXFRYIgEHEX9qG1xvM8CoXCPt8RhHuZ1Il7L6QVJnTDtNDFPwShndSLu8ltj+NYWmPCPmTsgyB0J/XinslkyGazSSeq6UgT7m1sP8hms5IKKQgdpFbcTUssm82Sy+XIZndNNZ1lwr2N7QeO45DNZsU3BMEiteIOd+aVMReuhGWETkxWlbTcBaGdVIs73BF4k+MuCDYymE0QupN6cbeRR25BEITDMRXibk/t2g/dvic3iv7p9X+YRJ1KGqQgdGcqxN2EZob5+C2P8sNnUnUq/0tB2E/qxd1eqOOoF/FBA1xGcdOYZdJcn/I/FIR2Ui/uNv08fh9GbOSx/vBIfQrCdDDT4p7JZJIsGzsH2rw384GLGB2ONNantNgFoTtTJe79hGUOs7CHCMThkPoUhOkh9eJuBjEdtjVoT/nabDapVCo0m82kLNOyzGazFItF5ufncV2374ycUWaOpCHLx66TVqtFuVym2WwSx3EycMi8N/Xped6+744S6TcRhP2kXtztwUv2BWwLR+cc3mYJttu3b3P58mVu3LgBkMxRE0URhUKBtbU1isUinuclk08ddqTjYUW2H9EZZdmHPb9dn6ZOyuUy165dY3NzkyiKkikhwjDE8zzuv/9+Lly4QC6XSyb0Grbwdt4wZBCTIHQn1eJuj0w96AI2KzSZVnkQBNTrda5cucLrr79OHMeJiPu+z9LSEoVCgXPnziXboyg69FJtB8WWBxG1UZbdjx1mEYxWq8X6+jqvvPIKQRAkIt5qtZibmyObzbK2ttZXfQ7DTuk7EYQ7DCzuSikHeB64prV+v1LqAvA0sAJ8E/hFrbU/QPltc4fY4QCD2WZam6aFHoYhpVKJmzdvAu0zCfq+T7VaBXZb9GEYJue61zH1ad8soygiiiJKpRLXr18H2uuzVqtRqVTa5nqxp44YlY3mvenMHSaj9m1BGCXDuOp+FXjJ+vx7wB9orb8P2AY+NEjhnXnujuO05VN3in9nrrV9wXd7b8THFqPDvMxUxJ7n4Xkeruvium7y3tjZadOkyz7My5R/kEDbUy/bdWt/r9v/a5g2dgvZDZGR+rYgjJKBWu5KqbPA+4D/Avya2r3Cfgz4+b1DPgv8J+CP+z2Hedw+TJaGfawRHrsl7rpu0gI1c4CbVp/5e1jMAiLdWou2IPYjOgeVfdhQ1aCYuLmN4zhJn4bpiA7DMIm/2/V/lPoc1M4RtNpH7tuCMEoGDct8EvgNYGHv8wpQ0lqHe5+vAvcPcgIjxgdhx9xNSCYMw6RTr9vFH8cxQRDQarUSQeoVlunM6a7VapRKJRqNRrLdHJfNZllYWGB5eflQmTidZVerVXZ2dpIsHztE4roui4uLLC4u4rpuIr7DFnpji12nQRAQRdFdW8thGOL7fiLy5vhRx8NHcKMbuW8LwijpW9yVUu8Hrmutv6mU+tE+vv8k8CTAsWPHuh5jWoVGpA/CTnMMw5Bms0kYhomw2DeKKIrwfZ9ms5kc1y2e31m+abVev36dS5cucfv27UR0oygiDEOKxSJra2sAzM/PJy3xw5a9tbXFa6+9xu3btwHa+hCKxSLnzp3jgQceYG5uLok1j6IVb0Td3PBMPdl1aNet7/vU63Xq9fpYO1RNGMvM+z8ow/RtQZgUg7Tc3wn8pFLqvUAeWAQ+BSwrpbJ7LZyzwLVuX9ZaPwU8BbC2tta1WWdaf77vt7UY7f3mr1IqER0j7raww51wSacgNZvNtnBNN+wl3Xzf59atW7z22mtcu7b782xxX1xcJJvNcvz48WR7r6cC88Rhl33z5k0uXbrE+vr6vrKXl5fxPI8TJ04kN4N+UjjvNj+MnQoZx3EyziAIgru2wO0nISPu3UJT/Qhwp932/9HE3+2+iAEZmm8rpSSFR5gIfYu71voTwCcA9lo3/15r/QtKqb8CfprdrIIngC8OYqARGCMUnXR2RJowzGHj0ibsYF7dnhBsATbHN5tNSqUSlUpl3/FBEFCr1RLR7QyddAqVXXYcxzSbTXZ2dpJsns76qNfryY2rn7j23eqlW8ilc1u3MEu3Dl77s/2b+w3R9OpAHfZTy7h8WxBGySjy3H8TeFop9Z+BfwQ+M2iBvTonu2VRZDKZJC/7MK3Zblkyvc5vD8o5SCBNBo6xx5wL2gWuU6Ts79o22guE97qhHYaDbLePs2+Upj7v9j37N8OdztfO39wPvew+jF1DZOi+LQijYijirrX+e+Dv995fAt4+jHLhTg6zyUPvts+0XjtHqNZqNcIw7FZscrwp24ROemWo2PvCMEyG2xcKBbTWeJ6XlLWwsIBSimq12tYZaqdrmmwde78p2/wGgwkzmRAJkJzLDj0dJJ52Z3NnJ2+3951hmVqt1jM0YwYzVatVSqVSUredaZH9hE7szlnbbmOH67rMz8/3DKkNwih9WxCgveHSGUa2owpHJbUjVM0PC8OQer1OEAT7KsD3fcrlMvV6vU3gjWBubW0l2Sxmn/3ejGLN5/M94+K2kBjRNTea5eXl5OZhnhaMGAZBwNWrV/E8b98/LJfLsbCwkIzqtPsKWq1Wkr3TzW4T167X60ksvttTSqcAmj6Jer3edb6dXvVvl33jxg1qtVrbOezQUrlc5sqVK1QqleSGZYTcdV0WFhYoFotJBpF9nm43FtvuarVKuVwmCAKgPYNofn6eM2fOkM/nkxvRqDqZBWHYZDIZXNdtu+ZsrTNZakcldeLeeaGb1mCz2dx3sdZqNTY2Nrh+/XrSmjbEcUyj0egpRqbsSqWSCMXdMlo6xSiKIpaWlpJJsmyiKKLRaPC9730vscvu8F1aWmJ1dZWVlZV94m7i9UbETHkmLBOGIY1Gg3K5nNhxtxCUsTuTyRCGITdv3mRjY4NyuQy035RsOkNBQNt5O+szjmNKpRLf+973yOVybaNcAYrFIvfddx8nT55M9pv6PMhu3/e5ceMGm5ub+0YVa605ceIE+XyekydPtj1lmXCSIKQZOxTbKe5HCbl2kjpxtzEt92azSaPRaGvlmbDH9evXuXr1Kr7vJ61uUyFGsO3yupVt5kI5KF3RJpPJsLi4yMLCQlKesavZbPLGG2+wtbVFrVZL4u6mlV+r1SgUChQKBTzPS0Iunufh+z5BELTZ3fnPDcOQVquV3BgOK+5BEFAqlVhfX2d7ezvZflgB7LwJdNZntVqlXq/v63iN45iFhQVc16VYLB7qpmT+h47j0Gq12N7e5tq1a+zs7CSpjyacFgQB586d65pRJQjTgB1+6byu+iXV4n43jHgHQZCEGA6TC28YtEXnOE5bq92Iu7nBKKWSlEAT5zaYfHHDUew+Kp0dt+bGYOeqD/Ncvcrr/M1HxdyIe9XnuEbDCsKw6RT2Tm3qV6tSL+4HZaXYw+HHSaeQ2SNh7Ti8wQ5RmM5UuPNIZh93FHrFzM2+bhlGtl13+/6g2GWb39wtO6jz/L3stjtM7f+5PbeQIEwjd+tQnamwjN2pZuLitVptnzCYbJhMJtOWmWELbS/hssuGO/nrh63IXo9PjuPQaDSSpwlgXzxN6908edOXYG4GJtTQaDTu2qFq7LYzfA6KXTuO0/aUY+qwM853UFkH3Qjs75qy7Zk4y+VyMiit8/hedvu+j+/fmXzR3KDMbx/i4CVBGDudqc2d1+BMtNw7syTiOKZWq3H9+nW2t7f3tc6MQBqh6BUP7rYtjuMkZl8qlZL9/YqEbXcQBJTL5US07YFSsLui0e3bt5MbU+dvNgLYze4oiqhUKmxubuK67oF2d5a9s7NDq9VK9tlpmJ1/B6kH+3cDSbwfaFtZ6zB2h2HIzs5OUid2zN6cRxCmlV5hmUEbLKkSd2jvmLSF7Pr168njt+k4i6IoicMelTiOqVQq+L7fNrionwrtdlNqtVptcWFb9Iy4VyqVfd81qU93E3ez1N1h7O4s2/f9RNw7yx42dtlG3Ov1el92t1qtnnVymCcKQUgrpu+w8ynapEfOVFjGxqzbacIndux6EIxg2EI3LuI4pl6v9/VdE9KxwyvTwCjtHuQCEIRJY/fT2Zjw48yKe7eOS0EQhFnHpET3q3mpTzHozO6QZfAEQbgXMC36fsU99S13+5Hb7lU2DBprHdXj/GHsOije3M93D2LSselptVsQxoXrusmI8kFSvFMv7nZnWWd2x7DKnxTDykqZJqbVbkEYF2b9ZN/32+bGOiqpD8sIgiDcS5hQtFldrF9S33IXBEG4l/B9Pwk/myU2+4m7i7gLgiCkCBNr9zwvGbXaj7hLWEYQBCEFGCE3SQedI9uPirTcBUEQUkA2myWfzyej8M1qazObCikIgnAv4LouhUIB13UJgoBGozHQNNkSlhEEQUgRw0oXlpa7IAhCCjDzLpkMGROSkZi7IAjCFGMmFLQX7rgnl9kTBEGYBTKZTDK9bxzH+L4/lNCMiLsgCMIEcV2XhYUFcrkcQRBQqVQGmnbAIB2qgiAIY6Rz8jzHccjn8xQKBebm5vA8r23VuZmdz10QBGGW6Ay5mKl9gyAgCILZXENVEAThXiOKImq1WrJkaBAEbZ2q/SLiLgiCMEF8308mCzPL6kmHqiAIwoxgwjMmJDOowA/UoaqUWlZKfV4p9bJS6iWl1KNKqeNKqS8rpV7d+3tsIAsFYQKIbwvjotvqcsNouQ+aLfMp4G+11m8Bfgh4Cfg48BWt9YPAV/Y+C8K0Ib4tjBTXdVlaWuLkyZOsrKwwNzc31PL7Fnel1BLwOPAZAK21r7UuAR8APrt32GeBnxrUSEEYJ+LbwijobKG7rsuxY8c4deoUx48fJ5/P7zt+EAZpuV8AbgB/ppT6R6XUp5VSReC01npj75hN4PRAFgrC+BHfFkaOWWnJ8zxc1yWbzbbltw/KICVlgR8G/lhr/TagRsdjqt4NHHUNHimlnlRKPa+Uer5Wqw1ghiAMnaH59sgtFaaGzji6mUumXC5TrVaTFMhexx+VQcT9KnBVa/31vc+fZ/eC2FJKrQLs/b3e7cta66e01g9rrR8uFosDmCEIQ2dovj0Wa4WpwQi2UoowDCmVSmxtbXHr1q2hTDlg07e4a603gStKqTfvbXo38CLwJeCJvW1PAF8cyEJBGDPi28Ko0VoTBAH1ep1KpUK1WqXVau0bnToIg+a5/wrwF0opD7gE/BK7N4zPKaU+BFwGPjjgOQRhEohvC2NlWCmQhoHEXWv9AtDt0fPdg5QrCJNGfFsYFUqppAMVSOaVGaawg4xQFQRBGDn2lAK5XI4TJ04wPz9PHMdsb29z+/Ztoijad+wgiLgLgiCMkM5JwFzXZXl5mZMnT9JqtWi1Wmxvb7cdn4YRqoIgCMIRiOM4mf1xkDVSD0Ja7oIgCCOks6M0jmNKpRK+7xMEAeVymTiO244fBiLugiAIY6TVanHjxg1gV8hNS94g4i4IgjCFdIr5qJCYuyAIwgwiLXdBEIQRYWe+ZLNZFhcXk6l9a7UalUqFMAxHcm4Rd0EQhBHQmQKZz+c5c+YMq6urhGHIlStXqFaryfGZTGao4RoRd0EQhBFhzxOTyWSYn59nZWWFIAi4detW2/5hzSljEHEXBEEYEZ0pkPV6ne3tbXzfp16vt+2X6QcEQRCmgM789iiK2NraYmdnhzAMqVQqyZQD5vhhIuIuCIIwBhqNxl3nbB+2uEsqpCAIwgwiLXdBEIQRYtZKdRwH2A3PRFE08oFMIu6CIAhDxk5rzOfzPPDAA5w+fZooitjY2ODatWs0m01geLNAdiLiLgiCMESUUm3ins1mOXXqFG95y1uSYzY3N5P3juOMZCCTxNwFQRCGTGeKoxF613VxXXfoOe3dkJa7IAjCkOk2OGlnZ4dqtcr6+jpBECT7RxV7F3EXBEEYMra4e57H8ePHieOYl19+mYsXLwK74ZhRdqyKuAuCIAyZTOZOxHtpaYkLFy7g+z47OzvJds/zaLVaIxN3ibkLgiCMEDOBWCaTIZu9057uHME6bKTlLgiCMGTs1vgbb7zBs88+Sy6Xw/f9ZHsQBCLugiAI04LWum3OmCAIeP7553Fdt0307WNGgYi7IAjCkDD57XEc43keq077+L8AABBiSURBVKurHDt2jGazya1bt9je3k6OHdXgJYOIuyAIwhAw0wyY0EuhUOAd73gH73rXuwB47rnn+PKXv8zNmzeB3cFNYRiOTOBF3AVBEIaEnSWTzWZZW1vj0UcfJZfLUSqVePbZZ5P9JhVSxF0QBCHldC7OUSqVeOONN8hms1y/fr1t8JJkywiCIEwBWus28VZK8eKLL1KtVgnDkIsXL1Iul5P9owzJwIDirpT6GPBhQAP/BPwSsAo8DawA3wR+UWvt9yxEEFKI+LbQD3Y2zO3bt3nuued47rnnuh476myZvgcxKaXuBz4CPKy1/gHAAX4W+D3gD7TW3wdsAx8ahqGCMC7Et4V+sWPuk2ZQS7JAQSmVBeaADeDHgM/v7f8s8FMDnkMQJoH4tnBkTMvdcRw8zyOXy+G67kREv++wjNb6mlLqvwFvAA3gf7P7qFrSWpvJia8C9w9spSCMEfFt4ai4rpvE2+fm5nj88cd55JFHcByHb3/72zz33HOsr68Du8Ifx/FI4+0wWFjmGPAB4AJwBigC7znC959USj2vlHq+Vqv1a4YgDJ1h+vaITBRShj1njOd5PPzww3z4wx/mYx/7GO973/tYXFxs2z+OlvwgZ/hXwGta6xta6wD4AvBOYHnvURbgLHCt25e11k9prR/WWj9cLBYHMEMQhs7QfHs85gppQ2tNsVhkfn6e1dVVcrlcsi+TyYxlsY5BxP0N4B1KqTm1a+m7gReBrwI/vXfME8AXBzNREMaO+LYwEHEcc/XqVV544QWeffbZtmkHwjAc+eLYMIC4a62/zm7n0rfYTRXLAE8Bvwn8mlLqIrspY58Zgp2CMDbEt4WjYodlXNfloYcewvM8Pve5z/FHf/RHyUAmpdRI53Bvs2mQL2utfxv47Y7Nl4C3D1KuIEwa8W3hKDiOk7xfWVnhR37kRzh37hyvvvoqpVIJgMXFRer1Os1mcyw2pScpU7hnMIsXCMIsks/nkw5UO9Y+bmT6AWHsjDoFTBDGjR1meeWVV3j66ad56KGHqFQqyfZms0kYht2+PhJE3IWxY1ru44g7CsI4aLVayft6vc7v/M7vcOrUKa5du5NQ1Ww2x9qwEXEXxorruiwtLeG6LtVqta1lIwjThpm2t9Vq4bou3//934/neXzjG9/gypUrwG5na+fqTONAYu7CyLHj64VCgbNnz/KmN72JlZWVZDCHxOGFaaRQKCTv5+fn+chHPsIf/uEf8hM/8RPJ9lwu15ZNMy5E3IWRY4u253msrKxw8uRJ5ufnk30i7MI04nle8j6Xy/Ge97yHt7/97Tz22GPJdsdxJuLfIu7CWDFzXgdB0Na5JJ2swjTSuTjHrVu3gN3pfieNxNyFkWNfAM1mk/X1dUqlEqVSqa1TVQRemDbsnPV6vc6nP/1pzp8/z9e+9rVku+/7Y4+3g4i7MAZs0W40Gqyvr+M4Dq1WK9knwi5MI41GI3lfq9X4kz/5ExzHaRP9cQ1a6kTEXRgrcRxTr9cnbYYgDJVsNksYhm2+bUatTqLVDiLugiAIA9NtcNKkRN0g4i6MHTtzQMIxwixRKBTIZDL4vt+2WPYkEHEXxo4IujCr2DH4SSOpkIIgCDOIiLsgCMIMIuIuCIIwg0jMXZgJpjWOP45h6XbdHHUOn37t01onA9QGWTM0juOp/d9OGhH3lDDIRS7O347WOnmlmUwm0yZ8WmuUUsnfQXzClBNFUVtKXjabTSaxMvXTeR6z3dh2WDuM3ZlMhiAIksE7nufheV6b4Pcq0/7tcRzTarXGOgf6LJFacb/XJpJKuxClHXsCMvt9mus1juOxzWlv6sLM6zNOms3mxEZp3sukJubeq4Vwr4m80D+d0wfbrzQxCXvsNT6nDfOEIxyN1LTc4zhua2mZWFuaW17DQimVTAt6lNameXQdZwswrcRxnIQgoihqm7MmbT5k7Mnn8xSLxWTBBxulFNlsNhHlw/4GE9YwZdZqNXZ2dgjDkEwmw4kTJ1hcXCSTySThjkwm0xaiMb7kui65XC7Zb8ruxA4BZbNZcrkc29vbXL58mSiKOHfuHCdPniQIAnzfT8S6sy/AlOE4Dq7rJpPMmRkW7fCVcDCpEHetNWEYopQiiqLknxiGYduFOqvk83mWlpaYm5trE3c7/mpjb2u1WpTLZSqVyszXUy/iOCYMw2Q1nDAM0VrjOE6qbnyO47TFne+//35+8Ad/kIWFBer1eiJsURThui7Ly8ssLCwAd4ayH9TqD4IAx3GYn5+nXq/zwgsv8NWvfhXYHT35vve9j8cff5xsNsv29jawOw+5aVwppfB9H601J0+e5OzZsywsLCQzG5o4vO1rjuMQBAH1ep1Tp06xurrKM888w2/91m9Rq9X46Ec/ys/8zM+wvb3NtWvXKBQKeJ7X1llq/lf1ep1iscjp06e5ePEin/zkJ/nSl74E7N5slFJtS9oJvUmNuPu+D9xpwcdxTBAEMyvu9gVSLBY5e/Ysp0+fJpPJtF1o3X673XG1s7PD66+/Tr1eT1piaY81D5s4jmk2m1QqlaQFb8TdFtNJ0ynMCwsLPPDAAxw/fpxyuZyIexiG5HI5Tp06xbFjx1BKJXFyE57o/P+a7c1mk2w2y7Fjx6hUKmxubibHuK7Lgw8+yGOPPYbneWxubqK1plAoEMdxWxlaa9bW1lhdXe3rtz722GPMzc0RRRGPPPIIp06d4tSpU7z5zW8+dBlra2t84QtfSD53Lnpx0I3ubtfAoKGxabi+UiHucGf1cLs3/V5JgyoUCpw+fZpz5861PaL3ijOaCzGTyXDjxg1u3brVFm++1zBPfq1WK6k/rXVSJ2kR984QkbG50WgkWSFmdkGtNfV6Hc/zkuwTOLy4e55HvV5v6zzVWtNoNCiXy7iumzztmSdk02Aw4r6zs5M0OA5DtVplfn4egHK5nNxY7XVy7WO60Wq1yOVywO786HYr/SAt6JX9M4zv9ApJpZlUiLtxMGgX93slLGNijeaiNhdaL1Ey4m7CWPfKTfAoHLX/Yhx02mNu0I7jJO/tl+M4ZLPZNl+wn+xsjD84jtP26nacSYc0TzbmHKYMs6BzNps9Ukem67rJe1OmKc9gL0vXDftY13X3nf9uAtuP+E6bYB+FVIg7tKeymb9pzHQYBfV6nY2NDYIgSC5e6O145vFdKUWlUmFnZyf5zjS2MIaB8ZXOzr+01UVnWMEIuBFdW3yz2WwimAf9HrPddd3ke67rtmXJGKE1eecm99zzvLZ4umksmBb0YbGPt0W81/tu2PZ2ivsk/5dp86PDkApxN05nd6ga5x5kdFuasVtwtVqNq1evcvPmzWTf3X6zvT8IAqrV6j23XJ39G6Mool6vs7Oz09ZPY4Rh0vNqGzqfsG7fvs1LL71EsVik0WgkT2RxHJPNZllcXGRubi75LhwsMiYrplgs0mw2ee2115J9vu/zrW99K7m2dnZ2gF0RtX3KPEEeP36c++67j7m5ueQpultL3mTeNBqNZPHzr33ta+zs7NBoNHjmmWcA2NnZYWtri3w+j+u6bT5rfnez2aRQKHDixAkuX77Myy+/3PbbhjVd9L1wjag0/Mj77rtPP/HEE/vEvVqt8p3vfIcXXngh6dm3W7azxCCtzDSm+40aO8RRKBQ4c+YMp0+fJpfL7css+eY3v0mlUplIC0Ep1fMfY1L+Op827A7zo/qE3cI3g5ZM3FopRaFQSFrYvW4YdgaLeUI8qHPS2G/CSc1mM4m7LywsMDc3l3R2dwuZ2WWYJ5owDKlWqzIA6gC01l2dJBXivrS0pB999NHkkdDu2FlfX+fKlSvJ8lVpi6MKk8dxHIrFIoVCIYkj29y8eRPf91Mj7pPwYdNRO42krWM8bfQt7kqpPwXeD1zXWv/A3rbjwF8C54HXgQ9qrbfVbhPgU8B7gTrwb7XW3zrIuGw2q5eXlzvPSxRFtFotms2m/GOFA+nVyt1rEe7bOQ7fvlvLXRCGwSDi/jhQBf7cugD+K3Bba/27SqmPA8e01r+plHov8CvsXgCPAJ/SWj9ykHFyAcjEYaOmh7hP1Lc7Jw7r8t2+R2Wa75ksLIPpcLUzcHqFZYY1cVgul2ubOOww/UnGPpk47GB6iXsS57rbi91WzHesz98FVvferwLf3Xv/P4Cf63bcAeVreclrlC/xbXnN6quX7/U7G89prfXG3vtN4PTe+/uBK9ZxV/e2HUi3PN9ZzZQRho+dOtv5OiJD921BmAQDp0JqrXU/YRWl1JPAk+azxNSFQRhFaGpYvi0Ik6DflvuWUmoVYO/v9b3t14A167ize9v2obV+Smv9sNb64T5tEIRRIL4tzAT9ivuXgCf23j8BfNHa/m/ULu8AdqxHXEGYBsS3hdngEB1C/xPYAAJ244wfAlaArwCvAv8HOL53rAL+O/A94J+Ahw/ZYTvxTgl5zfZLfFtes/rq5XupGMQkqZDCqOmZLjZixLeFUdPLt2XtKkEQhBlExF0QBGEGEXEXBEGYQUTcBUEQZpBUzOcO3ARqe3/TxgnErqOQRrsemOC5xbePjth1eHr6diqyZQCUUs+ncdCH2HU00mrXJElrnYhdRyOtdvVCwjKCIAgziIi7IAjCDJImcX9q0gb0QOw6Gmm1a5KktU7ErqORVru6kpqYuyAIgjA80tRyFwRBEIZEKsRdKfUepdR3lVIX95Y2m5Qda0qpryqlXlRK/bNS6lf3th9XSn1ZKfXq3t9jE7DNUUr9o1Lqb/Y+X1BKfX2vzv5SKeWN26Y9O5aVUp9XSr2slHpJKfVoGuorDYhfH9q+1Pn2LPj1xMVdKeWwO9vevwbeCvycUuqtEzInBH5da/1W4B3AL+/Z8nHgK1rrB9mdMXASF+qvAi9Zn38P+AOt9fcB2+zOaDgJPgX8rdb6LcAPsWtjGuproohfH4k0+vb0+/Vhpi0d5Qt4FPg76/MngE9M2q49W74I/Dg91tUcox1n2XWmHwP+ht3pZ28C2W51OEa7loDX2Ou7sbZPtL7S8BK/PrQtqfPtWfHribfcSenalEqp88DbgK/Te13NcfFJ4DcAsxbhClDSWptl4SdVZxeAG8Cf7T1Wf1opVWTy9ZUGxK8PRxp9eyb8Og3injqUUvPAXwMf1VqX7X1697Y9thQjpdT7geta62+O65xHIAv8MPDHWuu3sTvMvu1Rddz1JfQmTX69Z09afXsm/DoN4n7otSnHgVLKZfcC+Aut9Rf2NvdaV3McvBP4SaXU68DT7D6+fgpYVkqZuYEmVWdXgata66/vff48uxfFJOsrLYhfH0xafXsm/DoN4v4N4MG9HnIP+Fl216scO0opBXwGeElr/fvWrl7rao4crfUntNZntdbn2a2b/6u1/gXgq8BPT8Imy7ZN4IpS6s17m94NvMgE6ytFiF8fQFp9e2b8etJB/73OifcCr7C7PuV/nKAd72L3UevbwAt7r/fSY13NCdj3o8Df7L3/F8D/Ay4CfwXkJmTTvwSe36uz/wUcS0t9Tfolfn0kG1Pl27Pg1zJCVRAEYQZJQ1hGEARBGDIi7oIgCDOIiLsgCMIMIuIuCIIwg4i4C4IgzCAi7oIgCDOIiLsgCMIMIuIuCIIwg/x/ymk2SXhrij8AAAAASUVORK5CYII=\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -3975,23 +2582,23 @@ "text": [ "Action: Q-Value:\n", "====================\n", - "NOOP 0.784 \n", - "FIRE 0.383 \n", - "RIGHT 0.674 \n", - "LEFT 0.731 \n", - "RIGHTFIRE 0.611 \n", - "LEFTFIRE 1.086 (Action Taken)\n", + "NOOP 0.391 \n", + "FIRE 0.426 \n", + "RIGHT 0.849 (Action Taken)\n", + "LEFT 0.311 \n", "\n" ] }, { "data": { - "image/png": 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3N3nyRng7O5aj17BarbKwsEAQBG3plVsR+ui8OaY/QSllG9LOfgVBEGIo9J03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\nJ4iKeK1W47XXXuPKlStMT0/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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -4000,23 +2607,23 @@ "text": [ "Action: Q-Value:\n", "====================\n", - "NOOP 1.094 \n", - "FIRE 1.070 \n", - "RIGHT 0.808 \n", - "LEFT 1.409 (Action Taken)\n", - "RIGHTFIRE 1.315 \n", - "LEFTFIRE 0.993 \n", + "NOOP 0.413 \n", + "FIRE 0.486 \n", + "RIGHT 0.852 (Action Taken)\n", + "LEFT 0.306 \n", "\n" ] }, { "data": { - "image/png": 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jvK0dy+FrWC6XmZubw/f9pvTKjQh9eN4c05+glLINaWu/giAIERT61hu1tePT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\niNALgiD0OCL0giAIPY4IvSAIQo/z/wMphXH5TzEoOQAAAABJRU5ErkJggg==\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -4025,23 +2632,23 @@ "text": [ "Action: Q-Value:\n", "====================\n", - "NOOP 1.382 \n", - "FIRE 1.363 \n", - "RIGHT 1.342 \n", - "LEFT 1.431 (Action Taken)\n", - "RIGHTFIRE 1.374 \n", - "LEFTFIRE 1.368 \n", + "NOOP 0.409 \n", + "FIRE 0.434 \n", + "RIGHT 0.737 (Action Taken)\n", + "LEFT 0.332 \n", "\n" ] }, { "data": { - "image/png": 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NnrwR3uaO5fA1LJfLLCws4HleQ3rlZoQ+PG+O6U9QStmGtLlfQRCECAp9843a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\nEAShyxGhFwRB6HJE6AVBELqc/x+kQhBdGpYT4gAAAABJRU5ErkJggg==\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -4050,12 +2657,10 @@ "text": [ "Action: Q-Value:\n", "====================\n", - "NOOP 1.345 (Action Taken)\n", - "FIRE 1.331 \n", - "RIGHT 1.317 \n", - "LEFT 1.345 \n", - "RIGHTFIRE 1.284 \n", - "LEFTFIRE 1.295 \n", + "NOOP 0.443 \n", + "FIRE 0.692 (Action Taken)\n", + "RIGHT 0.585 \n", + "LEFT 0.308 \n", "\n" ] } @@ -4067,10 +2672,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Example: Smallest Difference in Q-Values\n", "\n", @@ -4082,16 +2684,12 @@ { "cell_type": "code", "execution_count": 40, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "630" + "134" ] }, "execution_count": 40, @@ -4107,20 +2705,18 @@ { "cell_type": "code", "execution_count": 41, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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yfd+37e/7PgsLC1y5coWlpSVKpVJLttB27MW01fDwMIODg4yPj69rR4Kwn4mN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\nBHieRyqVolwuW69M2F84jtP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\n", 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TJwFsrNiEFKL53qbDsl6vUyqVbJz9vvvuw/M8SqWSzZxpxaQSaq3JZrPU63Wu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\narVaj89G2AybDXmsJaibZS0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QBJw+fZpcLifP1xUiJnHB3QRsrTW2bTM3N8fFixdZXFzEsiyUUntOwSXIj7/uvHr8ID8zM8Pc3FxHZ7vUCTHpEhXc4zumCe6zs7OcP3+eRqMhO6zYQ2tNJpNhZmYG27ajlr2MfReTLlHBHTpbXEopMpkMs7OzeJ4nwV3sobUmlUqRzWb31B0hJlnigvtBpBUm9mNa6FI/hOiU+OBuxraHYSitMbGHXPsgxP4SH9wty8JxnKgT1XSkickWrweO48hQSCG6JDa4m5aY4zik02kcp1VU01kmJlu8Hti2jeM4UjeEiElscIc795UxO66kZUQ3M6pKWu5CdEp0cIc7Ad6McRciTi5mE2J/iQ/ucXLKLYQQhzMS57Iy1E0cROqGEPsbiZa7Sc3I6bfYj9QLIfZKfHCPP6hDdmJxEKkbQnRKfHCPk9NvIYQ4HAnuYqRJi12I/Y1UcJcdWQghDifxwd1cxCStdnEQ6Y8RYq/EB/f4xUvxHVguXplM3f93qQdC7C/RwT1+ZarswOIgcttfIfbqObgrpWzgJeCm1vpJpdQl4IvAIvBN4Oe11s0evr/j3iFhGMp9RERHPTDPUu13cB903RZikPoRJX8ReCP2+28Cv621/gCQBz7dy5d3j3O3bbvjoiZ5TeYrXg/i9aTPBlq3hRiknlruSqnzwL8C/hvwy6q1h/0o8Kn2JF8A/gvwu8ddhjndDoKgl6KKMTaIlMxJ1G0hBqnXtMzvAL8KzLR/XwQKWmu//fsN4FwvCwiCQAK7OJQ+t94HXreFGKRjB3el1JPAltb6m0qpHznG/M8AzwDMz8/vO43WGt/38X1fnr4kDmRZFq7rRqmaXvWzbgsxLL203D8M/IRS6mNABpgFPgfMKaWcdgvnPHBzv5m11s8BzwGsra3te05t0jHNZpMgCAaVV+27eIpgv3TBgPPEQ2PW9W7r3P2+H8s0Qb2P9/zvW91WSskQHjEUxw7uWuvPAp8FaLdu/qPW+ueUUl8BforWqIKngRd6KaB5AHIQBCM1SuZeAXxch+7FOzn30+/1Ng9O7+d3nlTdFmKQBjHO/deALyql/ivwMvD5Xr+wz62yExEf2dFtXMdlH2ad++2Er4Poe90WYlD6Ety11n8H/F37/RXgQ/34Xrgzhtn3/ZEJ7iadFARB1LI0n5tA5DjOyB2w7sacYfm+HwXy7lSMbdt9y4vHlwsMrNN9kHVbiEFK7BWq5lTb932q1Sqe50WBMSktXlOWeJmUUnieR7lcplwu43lex7QAmUyGmZkZpqamsG27Y97u70uag9Y5CAIqlQqlUolGo9ExLYDruszMzJDL5XBdt+d1NtNrrXFdF9d19yxTiEmWuOAeb/FprWk0GpTLZWq1WtTSTeLOa8pkWRb1ep2trS02Njao1+tYloVlWfh+axTdqVOnWFlZYXFxEcdxopFAo9aKj69zs9lke3ub9fV1yuUyQLRuYRiSy+U4c+YMy8vLpNPpntc5HtwzmQyZTCY6WJqyjdr2FKKfEhfc40zLvV6vJz64m/SLbdtUq1W2t7e5efMm5XI5SkeY4F6pVMhkMmSzWVzXHfngbts2zWaTnZ0dNjY2yOfzUerJpKZmZmai1rtJW2mtj91JHg/uSqkoHSSEaEl0cI8bpcCntabZbFKv1zvG6hv1ej3KEcfTEqOmO5iaA7E5WMXz4PV6vWMbCCEGayTGFo5a4DMteMe5c+yMt1BNZ2p8+vjPUbDfyBhzhmLst87dnayjtM5CjJJEttzjHWzNZpNSqUS5XE50WsaU2bIsarUajUYjClwm5x4fAlmv16OUzTikZTzPizpS4c7wVSBav2azSbFYjC5KM9MdRzwtEwQB8/Pzd72ISohJk6jg3j0CIwxDSqUSW1tb5PP5KEiGYZi4VEa83L7vUywWo5Eyprxmmkajwc7ODr7vR0HfzDtKuv9XhUKBZrMZ/S1+G17P8ygUCgAdT9Y66jrHD6Kms3Z+fp6FhYV9h19KoBeTKlHBHTrHgpvhdZubm2xtbUX3de+11TcI8cAShiGNRqMjxxwPMia4l0qlPQe0UdI9nLHZbEbBPf53uBPcq9Vq1KI/zgE6frZgroFoNpucP39+zzUFQkyyxAX3bo1Gg2KxSKlUAohabKMsDEOq1eqwi3GiTCqqXq/35fvi9SCbzdJoNEa+XgjRT4nvUO2+l7vswAI664FJewkh7kh8cDcjT4z4ezG5ukfljNJN5YQ4CYlPy3Q/Si1+C4Ik56gP05JMcvmP4yTWOd4nMwr1QIhhSXxwj48yMTenGpchb6Ne/uPoxzrvVw8mcVsKcTdyLiuEEGNIgrsYC5KaEaKTBHchhBhDEtyFEGIMSXAXQogxJMFdCCHGkAR3IYQYQxLchRBiDElwF0KIMSTBXQghxpAEdyGEGEMS3IUQYgxJcBdCiDEkwV0IIcaQBHchhBhDEtyFEGIM9RTclVJzSqnnlVJvKqXeUEr9sFJqQSn1N0qpt9o/5/tVWCFOitRtMep6bbl/DvgrrfUDwPcDbwDPAl/XWt8HfL39uxCjRuq2GGnHDu5KqVPAE8DnAbTWTa11AXgK+EJ7si8An+i1kEKcJKnbYhz00nK/BNwG/kAp9bJS6veUUjlgWWu90Z5mE1jutZBCnDCp22Lk9RLcHeAHgN/VWj8KVOg6TdWtpxbv++RipdQzSqmXlFIvVSqVHoohRN/1rW4PvKRCHKCX4H4DuKG1frH9+/O0dohbSqkVgPbPrf1m1lo/p7V+TGv9WC6X66EYQvRd3+r2iZRWiH0cO7hrrTeB60qp+9sffQR4Hfgz4On2Z08DL/RUQiFOWJLqtlJKHv4tjsXpcf7PAH+klEoBV4B/S+uA8WWl1KeBq8Ane1yGEMMgdVuMtJ6Cu9b6FWC/U8+P9PK9Qgyb1G0x6uQKVSFGgKRmxFH1mpYRQgxQa1BOiwnw8c+EOIi03IUYERLUxVFIcBdihEiAF4clwV2IESXDJMXdSM5diBEVD+zSohfdpOUuxIgyAd2yLGnBiz0kuAsxouKtdRPg4y8x2SQtI8QIC8MwCuzxFnwYhpKqmXDSchdixMUDuYyFF4YEdyHGgAnm3UFeTC4J7kKMGa01Sils2x52UcQQSXAXYoxorTuCuwT4ySUdqkKMAa31nty7UgrHcVBK4fv+kEsoTpq03IUYE/FO1DAMCcMwCvCOc6cdJ/n4ySAtdyHGkNYa3/ej9IzjOFjWnbac7/uEYTjEEopBk+AuxJgyAR7oCPBBEBAEwZBLJwZN0jJCjLEwDPF9f08wl3Hw40+CuxBjzuTf4c5oGrkfzfiT4C7EBDDB3AyTTKVSZDKZjqGSEuzHiwR3ISaAyb97nkcYhjiOQyqV6uhklVTNeJEOVSEmQBAE+L4fpWRc18W2bWmtjzEJ7kJMgPiwxzAM8TwP3/c7RtMopQiCQFrwY0KCuxATyKRnoBXYp6amUEpRqVSikTUmRy9Gk+TchZhA8Za8ZVmk02lSqVRHB6vpfJWHf4wmabkLMYHirXJzXxrzPk5a7qNLgrsQEy4MQ2q1WsctC9LpNGEY0mw2h108cUySlhFiAnXfZKxer1Ov19Fak06nmZ2dZXp6umOopBgt0nIXQnQEe3ORUzqdBlqdr5Zl4fs+tVpNbjg2Ino6LCul/oNS6p+UUt9RSv2xUiqjlLqklHpRKXVZKfUlpVSqX4UV4qRMct0OgoBGowHA7Owsy8vLLC0tkclk9hwERHIdO7grpc4B/x54TGv9MGADPwP8JvDbWusPAHng0/0oqBAnZdLrtud5FItFKpUKAJlMBtd1o/vSGBLck63XhJoDZJVSDjAFbAA/Cjzf/vsXgE/0uAwhhmEi67YZRdNoNKjVatGVreZiJ7ldweg4dnDXWt8E/gdwjVbF3wW+CRS01uaZXjeAc70WUoiTNKl1u7slbh70obXG8zyy2Sxnzpwhl8sBreAutzBIrl7SMvPAU8AlYBXIAT9+hPmfUUq9pJR6yZz+CZEE/azbAyriQHS3xC3LIgxDKpUKtVqNbDbL2bNnWVhYiKYJgkCCe0L1MlrmXwDvaK1vAyilvgp8GJhTSjntFs554OZ+M2utnwOeA1hbW5PzO5EkfavbSqmRqtvxAO95Hvl8nnq9DsDU1BTpdDoaRROfxzyIOwxDecpTQvSSc78GPK6UmlKtQ/dHgNeBvwV+qj3N08ALvRVRiBMndRtoNBqUSiU8z8PzPMrlMvl8nnK5HE1jbhtsbicsgT05esm5v0irc+lbwGvt73oO+DXgl5VSl4FF4PN9KKcQJ0bq9v5KpRLr6+vcunULgLNnz3Lx4kWmp6eHXDKxn54uYtJa/zrw610fXwE+1Mv3CjFsUrfvMCNoms1mNP4dYHFxkdXVVVzXpdFoEIYhmUyGRqNBo9GIbjgmFz0Nh1yhKoS4q4OGPAZBgOM4nDt3jvn5eRzHoVar8c4777C1tRXl4gEJ8EMgN44QQhyKUqpjnPvt27dZX19Ha83a2hr33Xcfp0+f3tMpK4F9OKTlLoQ4FBOkzRDJ7e3tKP2ysLAQXfwkd5JMBgnuQogjMcEdoFwu02w2sSwrStOsrKwwNTUVTRe/lYE4ORLchRA9McMhwzBkamqK7/u+72N2dhbXdbl+/Trf+ta3JLgPgQR3IcSRxHPorutSLpd5++23ow7UCxcucOnSJaampqhUKlSr1Wj6U6dO4Xle9HAQMTgS3IUQRxIP7r7vs76+zrVr12g2m8zPz5PL5aJcfLVa7QjiQRAQBIEE9hMgo2WEEMemtaZarUadqMViEdd1Adjd3SWVSvHII49w/vx5oJWjj4+VF4MjwV0I0RMzlh1a95/JZrNUq1Vu3LhBJpPhox/9KB//+MeZn58fYiknj6RlhBA9MePfTbrm2rVr3Lhxg83NTe6//36eeOIJPvCBD6C15urVq0xPT/Pee+/x7W9/m3w+TzabRWtNrVYb8pqMFwnuQoieeJ4Xva9UKrz66qvRnSR93+c73/kOjz/+OB//+MdZWlpCKcULL7zAK6+8EqV1stnssIo/tiS4CyH6JgzDKLAD3Lhxgz//8z+nXC7z5JNP8sEPfpBqtYrv++Tz+Wg6abX3nwR3IUTfOY6D4zjU63W+973vYVkWP/iDP0i9XqdYLBKGIXNzcxQKBaCVq48PmRS9k+A+gsxd+oRIqlQqheu6HemZ2dlZstkstVqNxx57jHPnzpHNZmk2m/zDP/wDf/mXf0mpVGJ6ehqttVz41CMJ7iNIArtIunq93vHgjunpaarVKm+++SbFYpHl5WV+8id/Mho2WavV+PKXvwy0hktOTU0NpdzjRIK7EKLvwjDsuIHYzs4Ozz//PF/5ylfwfZ8nnniCD33oQ1FwLxaLHfNLiqZ3EtxHTPxhxNKCv0O2RfLE/yfmKlbDdd1oGCTAzMwMKysrbG5usry8TBiGbG1tAWDb9r7fG/9+s18c5mHdWuuB1hfz3d1l6f580HVWgvsIcV2XTCaDbdsEQUC9Xu8YhhbXyxPpRz1Qmp131NdjnGWzWc6cORP9/mM/9mNcunSJbDZLuVzmT//0T/nqV7+K7/tMT09jWVZ0SwNo3cbAjKs3T3yyLCt6H6e17gio8Yd4xz83r173naPUu8NMe9z74Sc2uPeygcdFd8dpJpNhcXExGlmwvb3dEdzj009aYIu33OLvJ207JJVt26TT6Y5O0tdee42HHnqIra0t5ubm+Omf/unob9/4xjfwfR9o3cZAHF1igvt+R1zz+aTqPn3LZDIsLCwwMzNDqVSiXC5TKpWiaSc9mJmnBHWfok9yHUqKZrPZ0QJ99dVXefbZZ6nX67iuy1NPPcVnPvOZ6O87OzvDKOZYSUxwD8OwIziFYSin1vuwLCt63S2n1z3Nvbaj2fbmtHXUHo1mTrW77zoodWhwDmqQ7TddGIYdZ5nvvPMOb7/9dvT7ysoK9XqdTCZDsVhkdXWVBx54gGq1yunTp7Ftm2q1iuM4eJ7XcQMy13WxbZtMJhPdWz7+PzepFtu28X0/ug2x1hrbtlFKEQQBvu93pHrMvIfdFlrrPemi/b7DfG72s4NSQeb7fN/vGHl0WIkI7lprfN+PNrJZUbNSk7pzdgemZrNJoVCg2WxSrTUmsn0AAAoDSURBVFY77q4XD+yZTIbp6WmmpqZwXbfjew46IJh7gzQaDcrlMtVq9cB8ftKEYYjv+zQaDVzXxff9aMcdxQPVqHAch1QqFQVvOPgsKZ7PDsOQVCrVMUJmaWmJTCYDtM5QP/WpT/GJT3yiYx7XdUmn02xsbPDSSy/x7rvvYts2CwsLLC4usra2xurqKtlsNgrUJqY4jkM2m6VUKvHyyy/z+uuv4/s+MzMz2LZNuVymUChEo3TMzdDuFXu6DxzlcjlKPZkDR3eq1Jxhmv3Y9/0900KrX6FcLrO9vR2doZttfJiYmJjgboZNmX+IOdJPenCPq9VqvPfeeziOg+/7HZd5G5ZlMTMzw+rqKmfOnCGbzUYBLp66ibfUTeUMgoBCocDGxgabm5sH5vOTxlzyXiqVoha8Ce7mTET0n23bOI5zqOBuhGGIZVnkcjlyuRwbGxvMzMyQTqfZ3t5mcXERy7J4+OGHD/yOhx9+GK01qVSKVCrF2bNnWVlZ4YEHHmBpaeme5c5kMlF8WVhYwHVddnZ2uH37NsViEaXUnkbRQcz+4zgOjUaDQqFAuVyOHlxi1jm+/5gz6mq1SrlcxvO8jhFB5juDIMCyrI4+h6OkGBMR3OFOj3C8kphTFtHieV5U+eJBK15xlFJks1mWlpY4d+4cs7OzUaoCiAKeYXY2c8AwoxW2t7dPfgWPyZz5NRqN6CCltY5aSBLcB6M7+B1mXzXTxFMgvu9z8+ZNXn/9dR555BFc140aHPH5TGDb3d2lXC5Tq9WiNEulUmF3d/dQwb1UKlGr1fA8L0r11Go16vV6dDZs9hcTmA9i9h/f9/E8D8/zoobqfqkU0+iAVsvd9/1oW8TvrGm+u5fGbSKCu9k5oTO4T3paptth88cmVxcEAc1mM/o93lo34pXKpDfGIU8tHcwn46jb2dQ1M5QXWmekZpjjzMzMgcsxXNfFcRxs2+54xe8rf68ymEZAfP54f5ZpGHQv+6Dviw/FNDl/E8RNCzx+9mwOXt3DOM105jsO26+xn0QEd9h/hEMvKzapzD05tra2CIKATCazp5XfPX28UhUKBQqFwp58e9KDZHwniaebpP6cnLtt7+4LjszZItxJ76TT6UMtJ51ORwHecRxc1yWVSh1pfsdxCMMwmj9+sDBlu9uY93gQBqI0jpnfTGPW1/weD/rxcfnmfbze9nrGmYjgbnJW8Q5V84/bb1TIpOtuKXW3xMvlMuvr62xvb0eV9G498vHWV7PZjFpSSdbd8VStVtnd3e3opzE713FGGoh7M2eGR2m5xzsXzf9Fa80777zD1772Nd58882OtIwJckEQREH99u3bvPbaa9y8eRPbtpmbm2Nubo5XXnmFM2fOkMlkojNQE1PMaJpKpcIbb7zB5cuXOy6QqlarFItF6vV6FGzj5e3WnQo1y6lWq9EZyUFpwXiHar1e7zhImGWa9Y4/wvBu5dlPIoK76RXuDu6VSoVGoyE50y53+wdrrWk0GtFOd9zvT3pLPc7zvOhAlk6n91x9mPQD1ag67hA96PyfhGHIyy+/HAX2g8SHEDabzWjZJo1iWt4HHWxMa9vkxuPfac5uDxpVdliH7SeMp0jv1vDqZUBAIoJ7rVbj29/+dnS0MkfOer3O+vr6vkP+xN2NWoA+qu4hordv36ZcLu/pMAYJ7oPUrzrWaDTk/9Rn6hAXt/w+8CSwpbV+uP3ZAvAl4CLwLvBJrXVetQ4/nwM+BlSBf6O1/ta9CuE4jp6bm+teLkEQ0Gg0olMXIe7mHmOs9/zxJOq2Ump8j7AiEfar23C44P4EUAb+MLYD/HdgR2v9G0qpZ4F5rfWvKaU+BnyG1g7wQ8DntNY/dK/CyQ7Qf730U4xji/+A4C51OyFMx+Zh+thMvn6/G4fFc9fdTCokPjQ4npa5W4rksI56xnyYae/VsD0ouHes1EEvWq2Y78R+/y6w0n6/Any3/f5/AT+733T3+H4tL3kN8iV1W17j+jqo7h18mLu7Za31Rvv9JrDcfn8OuB6b7kb7s3vqHmN60P1ThNhPfOhs9+uI+l63hRiGnjtUtdb6OKeeSqlngGfM75JTF70YRCqpX3VbiGE4bsv9llJqBaD9c6v9+U1gLTbd+fZne2itn9NaP6a1fuyYZRBiEKRui7Fw3OD+Z8DT7fdPAy/EPv/XquVxYDd2iivEKJC6LcbDITqE/hjYADxaecZPA4vA14G3gP8DLLSnVcD/BN4GXgMeO2SH7dA7JeQ13i+p2/Ia19dBde+eQyFPggwXE4N24HCxAZO6LQbtoLp93LSMEEKIBJPgLoQQY0iCuxBCjCEJ7kIIMYYScVdI4D2g0v6ZNEtIuY4iieV63xCXLXX76KRch3dg3U7EaBkApdRLSbzoQ8p1NEkt1zAldZtIuY4mqeU6iKRlhBBiDElwF0KIMZSk4P7csAtwACnX0SS1XMOU1G0i5TqapJZrX4nJuQshhOifJLXchRBC9EkigrtS6seVUt9VSl1uP9psWOVYU0r9rVLqdaXUPymlfrH9+YJS6m+UUm+1f84PoWy2UuplpdTX2r9fUkq92N5mX1JKpU66TO1yzCmlnldKvamUekMp9cNJ2F5JIPX60OVLXN0eh3o99OCulLJp3W3vo8BDwM8qpR4aUnF84Fe01g8BjwO/0C7Ls8DXtdb30bpj4DB21F8E3oj9/pvAb2utPwDkad3RcBg+B/yV1voB4PtplTEJ22uopF4fSRLr9ujX68PctnSQL+CHgb+O/f5Z4LPDLle7LC8A/5IDnqt5guU4T6sy/SjwNVq3n30PcPbbhidYrlPAO7T7bmKfD3V7JeEl9frQZUlc3R6Xej30ljsJfTalUuoi8CjwIgc/V/Ok/A7wq4B5FuEiUNBa++3fh7XNLgG3gT9on1b/nlIqx/C3VxJIvT6cJNbtsajXSQjuiaOUmgb+BPglrXUx/jfdOmyf2BAjpdSTwJbW+psntcwjcIAfAH5Xa/0orcvsO05VT3p7iYMlqV63y5PUuj0W9ToJwf3Qz6Y8CUopl9YO8Eda66+2Pz7ouZon4cPATyil3gW+SOv09XPAnFLK3BtoWNvsBnBDa/1i+/fnae0Uw9xeSSH1+t6SWrfHol4nIbj/I3Bfu4c8BfwMredVnjillAI+D7yhtf6t2J8Oeq7mwGmtP6u1Pq+1vkhr2/xfrfXPAX8L/NQwyhQr2yZwXSl1f/ujjwCvM8TtlSBSr+8hqXV7bOr1sJP+7c6JjwHfo/V8yv88xHL8c1qnWq8Cr7RfH+OA52oOoXw/Anyt/f6fAd8ALgNfAdJDKtMjwEvtbfa/gfmkbK9hv6ReH6mMiarb41Cv5QpVIYQYQ0lIywghhOgzCe5CCDGGJLgLIcQYkuAuhBBjSIK7EEKMIQnuQggxhiS4CyHEGJLgLoQQY+j/A81RVNH9sppSAAAAAElFTkSuQmCC\n", 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bzVKv17l8+TKnTp1ienoa13Wt1x31jltfkhGlVCqRz+dJJBJWeJeXl20sfGJi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\nHBF6QRCEPkeEXhAEoc/5/+ZhsAJWyUBuAAAAAElFTkSuQmCC\n", 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\n", 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0ZGPhBw8etPU3rZW+vj4rlidOnKBWq5HNZm3mTr1eZ2ZmhnvuuYeJiQmSySSL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\nUqlkvTJhd+E4TpM3fvDgQZ5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\n", 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Zs4exsTGSySQLCws2/dP3/SaRX+o6fd+3+fvFYtHG+13XJZ/P8/rrr/OLX/wC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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -4229,12 +2825,10 @@ "text": [ "Action: Q-Value:\n", "====================\n", - "NOOP 0.861 (Action Taken)\n", - "FIRE 0.854 \n", - "RIGHT 0.851 \n", - "LEFT 0.846 \n", - "RIGHTFIRE 0.853 \n", - "LEFTFIRE 0.845 \n", + "NOOP 0.686 (Action Taken)\n", + "FIRE 0.660 \n", + "RIGHT 0.675 \n", + "LEFT 0.675 \n", "\n" ] } @@ -4246,10 +2840,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Output of Convolutional Layers\n", "\n", @@ -4261,11 +2852,7 @@ { "cell_type": "code", "execution_count": 42, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "def plot_layer_output(model, layer_name, state_index, inverse_cmap=False):\n", @@ -4328,10 +2915,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Game State\n", "\n", @@ -4342,20 +2926,19 @@ "cell_type": "code", "execution_count": 43, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ { "data": { - "image/png": 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UHNbVaMeo0U5sez3CI5bDI2NbHWYJlyYGIjkDmdAbNIZuFhcXuXDhAsePH+fS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\nC4Ig9Dgi9IIgCD2OCL0gCEKP8/8B8s+udmS2ndkAAAAASUVORK5CYII=\n", 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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -4366,10 +2949,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Output of Convolutional Layer 1\n", "\n", @@ -4382,9 +2962,6 @@ "cell_type": "code", "execution_count": 44, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": false }, "outputs": [ @@ -4397,9 +2974,9 @@ }, { "data": { - "image/png": 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QumuvvTZJcuuttxauhOVqJ93oViKyatUzeUQP0pCWSUYAgKI6SkZWr17drTqG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\nytC74IILkhw/t+mhhx5KMtq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+ "image/png": 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\n", 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" ] }, "metadata": {}, @@ -4412,10 +2989,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Output of Convolutional Layer 2\n", "\n", @@ -4426,9 +3000,6 @@ "cell_type": "code", "execution_count": 45, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -4441,9 +3012,9 @@ }, { "data": { - "image/png": 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sv0Xt3btXRMamvypQ1+0f1le+8hWN3/3ud0/6fElNUcVdh2oZN6rsxLX/hje8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\n", 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\n", 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" ] }, "metadata": {}, @@ -4456,10 +3027,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Output of Convolutional Layer 3\n", "\n", @@ -4476,9 +3044,6 @@ "cell_type": "code", "execution_count": 46, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -4491,9 +3056,9 @@ }, { "data": { - "image/png": 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B4pQPpYqw+SJV6jMf+MAHAM+P8ZFHHhm0bUlTTokxqimpEqTE+FTfyNdLC9bX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heN7Av+PTTwVhqXXHKJ+5qtRqIoS/2BfJFPV1xxRRLNESnBUtIfffTRAHz84x/vcUxfiramHcsZ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\n", 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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -4508,8 +3073,6 @@ "cell_type": "markdown", "metadata": { "colab_type": "text", - "deletable": true, - "editable": true, "id": "Nv2JqNLBhy1j" }, "source": [ @@ -4524,11 +3087,7 @@ { "cell_type": "code", "execution_count": 47, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "def plot_conv_weights(model, layer_name, input_channel=0):\n", @@ -4599,10 +3158,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Weights for Convolutional Layer 1\n", "\n", @@ -4615,9 +3171,6 @@ "cell_type": "code", "execution_count": 48, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -4625,15 +3178,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "Min: -0.68262, Max: 0.14787\n", - "Mean: -0.05167, Stdev: 0.11923\n" + "Min: -0.24494, Max: 0.13658\n", + "Mean: -0.01729, Stdev: 0.06023\n" ] }, { "data": { - "image/png": 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"text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -4646,10 +3199,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We can also plot the convolutional weights for the second input channel, that is, the motion-trace of the game-environment. Once again we see that the negative weights (blue) have a much greater magnitude than the positive weights (red)." ] @@ -4658,9 +3208,6 @@ "cell_type": "code", "execution_count": 49, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -4668,15 +3215,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "Min: -0.95588, Max: 0.09746\n", - "Mean: -0.03578, Stdev: 0.15025\n" + "Min: -0.56904, Max: 0.06957\n", + "Mean: -0.05132, Stdev: 0.12694\n" ] }, { "data": { - "image/png": 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"text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -4689,10 +3236,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Weights for Convolutional Layer 2\n", "\n", @@ -4704,25 +3248,21 @@ { "cell_type": "code", "execution_count": 50, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Min: -0.30984, Max: 0.24492\n", - "Mean: -0.02332, Stdev: 0.09427\n" + "Min: -0.24590, Max: 0.14826\n", + "Mean: -0.00605, Stdev: 0.06365\n" ] }, { "data": { - "image/png": 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" ] }, "metadata": {}, @@ -4735,10 +3275,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Weights for Convolutional Layer 3\n", "\n", @@ -4751,9 +3288,6 @@ "cell_type": "code", "execution_count": 51, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -4761,15 +3295,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "Min: -0.33228, Max: 0.24060\n", - "Mean: -0.02068, Stdev: 0.09566\n" + "Min: -0.25325, Max: 0.17733\n", + "Mean: -0.03257, Stdev: 0.07194\n" ] }, { "data": { - "image/png": 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\n", 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" ] }, "metadata": {}, @@ -4782,10 +3316,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Discussion\n", "\n", @@ -4804,10 +3335,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Exercises & Research Ideas\n", "\n", @@ -4824,8 +3352,7 @@ "You may find it helpful to add more command-line parameters to `reinforcement_learning.py` so you don't have to edit the source-code for testing other parameters.\n", "\n", "* Change the epsilon-probability during testing to e.g. 0.001 or 0.05. Which gives the best results? Could you use this value during training? Why/not?\n", - "* Continue training the agent for the Breakout game using the downloaded checkpoint. Does the agent get better or worse the more you train it? Why? (You should run it in a terminal window as described above.)\n", - "* Try and change the game-environment to Space Invaders and re-run this Notebook. The checkpoint can be downloaded automatically. It was trained for about 150 hours, which is roughly the same as for Breakout, but note that it has processed far fewer states. The reason is that the hyper-parameters such as the learning-rate were tuned for Breakout. Can you make some kind of adaptive learning-rate that would work better for both Breakout and Space Invaders? What about the other hyper-parameters? What about other games?\n", + "* Try and change the game-environment to Space Invaders and re-run this Notebook. The hyper-parameters such as the learning-rate were tuned for Breakout. Can you make some kind of adaptive learning-rate that would work better for both Breakout and Space Invaders? What about the other hyper-parameters? What about other games?\n", "* Try different architectures for the Neural Network. You will need to restart the training because the checkpoints cannot be reused for other architectures. You will need to train the agent for several days with each new architecture so as to properly assess its performance.\n", "* The replay-memory throws away all data after optimization of the Neural Network. Can you make it reuse the data somehow? The ReplayMemory-class has the function `estimate_all_q_values()` which may be helpful.\n", "* The reward is limited to -1 and 1 in the function `ReplayMemory.add()` so as to stabilize the training. This means the agent cannot distinguish between small and large rewards. Can you use batch normalization to fix this problem, so you can use the actual reward values?\n", @@ -4843,10 +3370,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## License (MIT)\n", "\n", @@ -4878,9 +3402,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.0" + "version": "3.6.10" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/17_Estimator_API.ipynb b/17_Estimator_API.ipynb index e26fcbf..4d90650 100644 --- a/17_Estimator_API.ipynb +++ b/17_Estimator_API.ipynb @@ -2,10 +2,7 @@ "cells": [ { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "# TensorFlow Tutorial #17\n", "# Estimator API\n", @@ -16,10 +13,16 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, + "source": [ + "## WARNING!\n", + "\n", + "**This tutorial does not work with TensorFlow v.2 and it would take too much effort to update this tutorial to the new API.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, "source": [ "## Introduction\n", "\n", @@ -38,10 +41,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Imports" ] @@ -49,18 +49,14 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/importlib/_bootstrap.py:205: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n", - " return f(*args, **kwds)\n" + "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", + " from ._conv import register_converters as _register_converters\n" ] } ], @@ -73,10 +69,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This was developed using Python 3.6 (Anaconda) and TensorFlow version:" ] @@ -84,16 +77,12 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'1.4.0'" + "'1.9.0'" ] }, "execution_count": 2, @@ -107,67 +96,39 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Load Data" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ - "The MNIST data-set is about 12 MB and will be downloaded automatically if it is not located in the given path." + "The MNIST data-set is about 12 MB and will be downloaded automatically if it is not located in the given dir." ] }, { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Extracting data/MNIST/train-images-idx3-ubyte.gz\n", - "Extracting data/MNIST/train-labels-idx1-ubyte.gz\n", - "Extracting data/MNIST/t10k-images-idx3-ubyte.gz\n", - "Extracting data/MNIST/t10k-labels-idx1-ubyte.gz\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ - "from tensorflow.examples.tutorials.mnist import input_data\n", - "data = input_data.read_data_sets('data/MNIST/', one_hot=True)" + "from mnist import MNIST\n", + "data = MNIST(data_dir=\"data/MNIST/\")" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ - "The MNIST data-set has now been loaded and consists of 70,000 images and associated labels (i.e. classifications of the images). The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial." + "The MNIST data-set has now been loaded and consists of 70.000 images and class-numbers for the images. The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial." ] }, { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -175,205 +136,65 @@ "text": [ "Size of:\n", "- Training-set:\t\t55000\n", - "- Test-set:\t\t10000\n", - "- Validation-set:\t5000\n" + "- Validation-set:\t5000\n", + "- Test-set:\t\t10000\n" ] } ], "source": [ "print(\"Size of:\")\n", - "print(\"- Training-set:\\t\\t{}\".format(len(data.train.labels)))\n", - "print(\"- Test-set:\\t\\t{}\".format(len(data.test.labels)))\n", - "print(\"- Validation-set:\\t{}\".format(len(data.validation.labels)))" + "print(\"- Training-set:\\t\\t{}\".format(data.num_train))\n", + "print(\"- Validation-set:\\t{}\".format(data.num_val))\n", + "print(\"- Test-set:\\t\\t{}\".format(data.num_test))" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ - "The class-labels are One-Hot encoded, which means that each label is a vector with 10 elements, all of which are zero except for one element. The index of this one element is the class-number, that is, the digit shown in the associated image. We also need the class-numbers as integers so we calculate that now." + "Copy some of the data-dimensions for convenience." ] }, { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ - "data.train.cls = np.argmax(data.train.labels, axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, - "outputs": [], - "source": [ - "data.test.cls = np.argmax(data.test.labels, axis=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, - "source": [ - "This is an example of one-hot encoded labels:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.],\n", - " [ 0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],\n", - " [ 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n", - " [ 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.],\n", - " [ 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n", - " [ 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.],\n", - " [ 0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n", - " [ 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", - " [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n", - " [ 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.]])" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data.train.labels[0:10]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, - "source": [ - "These are the corresponding class-numbers:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([7, 3, 4, 6, 1, 8, 1, 0, 9, 8])" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data.train.cls[0:10]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, - "source": [ - "## Data Dimensions" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, - "source": [ - "The data dimensions are used in several places in the source-code below. They are defined once so we can use these variables instead of numbers throughout the source-code below." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, - "outputs": [], - "source": [ - "# We know that MNIST images are 28 pixels in each dimension.\n", - "img_size = 28\n", + "# The number of pixels in each dimension of an image.\n", + "img_size = data.img_size\n", "\n", - "# Images are stored in one-dimensional arrays of this length.\n", - "img_size_flat = img_size * img_size\n", + "# The images are stored in one-dimensional arrays of this length.\n", + "img_size_flat = data.img_size_flat\n", "\n", "# Tuple with height and width of images used to reshape arrays.\n", - "img_shape = (img_size, img_size)\n", - "\n", - "# Number of colour channels for the images: 1 channel for gray-scale.\n", - "num_channels = 1\n", + "img_shape = data.img_shape\n", "\n", "# Number of classes, one class for each of 10 digits.\n", - "num_classes = 10" + "num_classes = data.num_classes\n", + "\n", + "# Number of colour channels for the images: 1 channel for gray-scale.\n", + "num_channels = data.num_channels" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Helper-function for plotting images" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Function used to plot 9 images in a 3x3 grid, and writing the true and predicted classes below each image." ] }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 6, + "metadata": {}, "outputs": [], "source": [ "def plot_images(images, cls_true, cls_pred=None):\n", @@ -407,28 +228,21 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Plot a few images to see if data is correct" ] }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 7, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -437,10 +251,10 @@ ], "source": [ "# Get the first images from the test-set.\n", - "images = data.test.images[0:9]\n", + "images = data.x_test[0:9]\n", "\n", "# Get the true classes for those images.\n", - "cls_true = data.test.cls[0:9]\n", + "cls_true = data.y_test_cls[0:9]\n", "\n", "# Plot the images and labels using our helper-function above.\n", "plot_images(images=images, cls_true=cls_true)" @@ -448,62 +262,46 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Input Functions for the Estimator" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Rather than providing raw data directly to the Estimator, we must provide functions that return the data. This allows for more flexibility in data-sources and how the data is randomly shuffled and iterated.\n", "\n", - "Note that we will create an Estimator using the `DNNClassifier` which assumes the class-numbers are integers so we use `data.train.cls` instead of `data.train.labels` which are one-hot encoded arrays.\n", + "Note that we will create an Estimator using the `DNNClassifier` which assumes the class-numbers are integers so we use `data.y_train_cls` instead of `data.y_train` which are one-hot encoded arrays.\n", "\n", "The function also has parameters for `batch_size`, `queue_capacity` and `num_threads` for finer control of the data reading. In our case we take the data directly from a numpy array in memory, so it is not needed." ] }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 8, + "metadata": {}, "outputs": [], "source": [ "train_input_fn = tf.estimator.inputs.numpy_input_fn(\n", - " x={\"x\": np.array(data.train.images)},\n", - " y=np.array(data.train.cls),\n", + " x={\"x\": np.array(data.x_train)},\n", + " y=np.array(data.y_train_cls),\n", " num_epochs=None,\n", " shuffle=True)" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This actually returns a function:" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 9, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -513,7 +311,7 @@ ".input_fn>" ] }, - "execution_count": 13, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -524,31 +322,24 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Calling this function returns a tuple with TensorFlow ops for returning the input and output data:" ] }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 10, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "({'x': },\n", + "({'x': },\n", " )" ] }, - "execution_count": 14, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -559,62 +350,44 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Similarly we need to create a function for reading the data for the test-set. Note that we only want to process these images once so `num_epochs=1` and we do not want the images shuffled so `shuffle=False`." ] }, { "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 11, + "metadata": {}, "outputs": [], "source": [ "test_input_fn = tf.estimator.inputs.numpy_input_fn(\n", - " x={\"x\": np.array(data.test.images)},\n", - " y=np.array(data.test.cls),\n", + " x={\"x\": np.array(data.x_test)},\n", + " y=np.array(data.y_test_cls),\n", " num_epochs=1,\n", " shuffle=False)" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "An input-function is also needed for predicting the class of new data. As an example we just use a few images from the test-set." ] }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 12, + "metadata": {}, "outputs": [], "source": [ - "some_images = data.test.images[0:9]" + "some_images = data.x_test[0:9]" ] }, { "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 13, + "metadata": {}, "outputs": [], "source": [ "predict_input_fn = tf.estimator.inputs.numpy_input_fn(\n", @@ -625,33 +398,23 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The class-numbers are actually not used in the input-function as it is not needed for prediction. However, the true class-number is needed when we plot the images further below." ] }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 14, + "metadata": {}, "outputs": [], "source": [ - "some_images_cls = data.test.cls[0:9]" + "some_images_cls = data.y_test_cls[0:9]" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Pre-Made / Canned Estimator\n", "\n", @@ -660,12 +423,8 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 15, + "metadata": {}, "outputs": [], "source": [ "feature_x = tf.feature_column.numeric_column(\"x\", shape=img_shape)" @@ -673,22 +432,15 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "You can have several input features which would then be combined in a list:" ] }, { "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 16, + "metadata": {}, "outputs": [], "source": [ "feature_columns = [feature_x]" @@ -696,22 +448,15 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "In this example we want to use a 3-layer DNN with 512, 256 and 128 units respectively." ] }, { "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, + "execution_count": 17, + "metadata": {}, "outputs": [], "source": [ "num_hidden_units = [512, 256, 128]" @@ -719,29 +464,22 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The `DNNClassifier` then constructs the neural network for us. We can also specify the activation function and various other parameters (see the docs). Here we just specify the number of classes and the directory where the checkpoints will be saved." ] }, { "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 18, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Using default config.\n", - "INFO:tensorflow:Using config: {'_model_dir': './checkpoints_tutorial17-1/', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_service': None, '_cluster_spec': , '_task_type': 'worker', '_task_id': 0, '_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n" + "INFO:tensorflow:Using config: {'_model_dir': './checkpoints_tutorial17-1/', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_service': None, '_cluster_spec': , '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n" ] } ], @@ -755,10 +493,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Training\n", "\n", @@ -769,69 +504,70 @@ }, { "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 19, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "INFO:tensorflow:Calling model_fn.\n", + "INFO:tensorflow:Done calling model_fn.\n", "INFO:tensorflow:Create CheckpointSaverHook.\n", - "INFO:tensorflow:Saving checkpoints for 1 into ./checkpoints_tutorial17-1/model.ckpt.\n", - "INFO:tensorflow:loss = 300.688, step = 1\n", - "INFO:tensorflow:global_step/sec: 370.039\n", - "INFO:tensorflow:loss = 26.462, step = 101 (0.271 sec)\n", - "INFO:tensorflow:global_step/sec: 521.366\n", - "INFO:tensorflow:loss = 22.0528, step = 201 (0.191 sec)\n", - "INFO:tensorflow:global_step/sec: 549.886\n", - "INFO:tensorflow:loss = 32.07, step = 301 (0.182 sec)\n", - "INFO:tensorflow:global_step/sec: 548.856\n", - "INFO:tensorflow:loss = 13.8037, step = 401 (0.182 sec)\n", - "INFO:tensorflow:global_step/sec: 516.064\n", - "INFO:tensorflow:loss = 23.2653, step = 501 (0.194 sec)\n", - "INFO:tensorflow:global_step/sec: 552.268\n", - "INFO:tensorflow:loss = 17.7141, step = 601 (0.180 sec)\n", - "INFO:tensorflow:global_step/sec: 529.426\n", - "INFO:tensorflow:loss = 25.7157, step = 701 (0.189 sec)\n", - "INFO:tensorflow:global_step/sec: 513.375\n", - "INFO:tensorflow:loss = 5.08285, step = 801 (0.195 sec)\n", - "INFO:tensorflow:global_step/sec: 536.319\n", - "INFO:tensorflow:loss = 10.3937, step = 901 (0.187 sec)\n", - "INFO:tensorflow:global_step/sec: 534.847\n", - "INFO:tensorflow:loss = 3.12976, step = 1001 (0.187 sec)\n", - "INFO:tensorflow:global_step/sec: 540.827\n", - "INFO:tensorflow:loss = 5.54126, step = 1101 (0.185 sec)\n", - "INFO:tensorflow:global_step/sec: 483.467\n", - "INFO:tensorflow:loss = 10.2708, step = 1201 (0.209 sec)\n", - "INFO:tensorflow:global_step/sec: 527.042\n", - "INFO:tensorflow:loss = 7.62363, step = 1301 (0.187 sec)\n", - "INFO:tensorflow:global_step/sec: 557.67\n", - "INFO:tensorflow:loss = 2.30585, step = 1401 (0.180 sec)\n", - "INFO:tensorflow:global_step/sec: 547.406\n", - "INFO:tensorflow:loss = 7.69151, step = 1501 (0.182 sec)\n", - "INFO:tensorflow:global_step/sec: 557.682\n", - "INFO:tensorflow:loss = 10.7881, step = 1601 (0.179 sec)\n", - "INFO:tensorflow:global_step/sec: 547.859\n", - "INFO:tensorflow:loss = 7.09411, step = 1701 (0.184 sec)\n", - "INFO:tensorflow:global_step/sec: 544.495\n", - "INFO:tensorflow:loss = 2.6387, step = 1801 (0.182 sec)\n", - "INFO:tensorflow:global_step/sec: 549.648\n", - "INFO:tensorflow:loss = 0.772691, step = 1901 (0.182 sec)\n", + "INFO:tensorflow:Graph was finalized.\n", + "INFO:tensorflow:Running local_init_op.\n", + "INFO:tensorflow:Done running local_init_op.\n", + "INFO:tensorflow:Saving checkpoints for 0 into ./checkpoints_tutorial17-1/model.ckpt.\n", + "INFO:tensorflow:loss = 300.61185, step = 0\n", + "INFO:tensorflow:global_step/sec: 453.729\n", + "INFO:tensorflow:loss = 33.910957, step = 100 (0.221 sec)\n", + "INFO:tensorflow:global_step/sec: 545.745\n", + "INFO:tensorflow:loss = 38.821697, step = 200 (0.183 sec)\n", + "INFO:tensorflow:global_step/sec: 510.96\n", + "INFO:tensorflow:loss = 36.428062, step = 300 (0.196 sec)\n", + "INFO:tensorflow:global_step/sec: 509.188\n", + "INFO:tensorflow:loss = 10.77646, step = 400 (0.196 sec)\n", + "INFO:tensorflow:global_step/sec: 525.229\n", + "INFO:tensorflow:loss = 20.211845, step = 500 (0.190 sec)\n", + "INFO:tensorflow:global_step/sec: 529.656\n", + "INFO:tensorflow:loss = 16.973766, step = 600 (0.189 sec)\n", + "INFO:tensorflow:global_step/sec: 518.829\n", + "INFO:tensorflow:loss = 9.104766, step = 700 (0.193 sec)\n", + "INFO:tensorflow:global_step/sec: 517.877\n", + "INFO:tensorflow:loss = 11.87432, step = 800 (0.194 sec)\n", + "INFO:tensorflow:global_step/sec: 513.369\n", + "INFO:tensorflow:loss = 7.3187075, step = 900 (0.194 sec)\n", + "INFO:tensorflow:global_step/sec: 531.02\n", + "INFO:tensorflow:loss = 5.238852, step = 1000 (0.188 sec)\n", + "INFO:tensorflow:global_step/sec: 493.925\n", + "INFO:tensorflow:loss = 6.4892335, step = 1100 (0.203 sec)\n", + "INFO:tensorflow:global_step/sec: 513.837\n", + "INFO:tensorflow:loss = 10.295633, step = 1200 (0.194 sec)\n", + "INFO:tensorflow:global_step/sec: 516.007\n", + "INFO:tensorflow:loss = 4.5178833, step = 1300 (0.194 sec)\n", + "INFO:tensorflow:global_step/sec: 501.485\n", + "INFO:tensorflow:loss = 2.4612594, step = 1400 (0.200 sec)\n", + "INFO:tensorflow:global_step/sec: 508.118\n", + "INFO:tensorflow:loss = 10.878417, step = 1500 (0.197 sec)\n", + "INFO:tensorflow:global_step/sec: 505.549\n", + "INFO:tensorflow:loss = 22.480297, step = 1600 (0.198 sec)\n", + "INFO:tensorflow:global_step/sec: 512.93\n", + "INFO:tensorflow:loss = 6.8385906, step = 1700 (0.195 sec)\n", + "INFO:tensorflow:global_step/sec: 520.968\n", + "INFO:tensorflow:loss = 1.8562572, step = 1800 (0.192 sec)\n", + "INFO:tensorflow:global_step/sec: 547.812\n", + "INFO:tensorflow:loss = 4.875979, step = 1900 (0.183 sec)\n", "INFO:tensorflow:Saving checkpoints for 2000 into ./checkpoints_tutorial17-1/model.ckpt.\n", - "INFO:tensorflow:Loss for final step: 7.35222.\n" + "INFO:tensorflow:Loss for final step: 2.701511.\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 23, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -842,10 +578,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Evaluation\n", "\n", @@ -854,21 +587,23 @@ }, { "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 20, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "INFO:tensorflow:Starting evaluation at 2017-11-17-12:07:56\n", + "INFO:tensorflow:Calling model_fn.\n", + "INFO:tensorflow:Done calling model_fn.\n", + "INFO:tensorflow:Starting evaluation at 2018-07-16-11:23:09\n", + "INFO:tensorflow:Graph was finalized.\n", "INFO:tensorflow:Restoring parameters from ./checkpoints_tutorial17-1/model.ckpt-2000\n", - "INFO:tensorflow:Finished evaluation at 2017-11-17-12:07:56\n", - "INFO:tensorflow:Saving dict for global step 2000: accuracy = 0.9727, average_loss = 0.0934177, global_step = 2000, loss = 11.825\n" + "INFO:tensorflow:Running local_init_op.\n", + "INFO:tensorflow:Done running local_init_op.\n", + "INFO:tensorflow:Finished evaluation at 2018-07-16-11:23:09\n", + "INFO:tensorflow:Saving dict for global step 2000: accuracy = 0.972, average_loss = 0.09360652, global_step = 2000, loss = 11.848927\n", + "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 2000: ./checkpoints_tutorial17-1/model.ckpt-2000\n" ] } ], @@ -878,23 +613,19 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 21, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'accuracy': 0.9727,\n", - " 'average_loss': 0.093417682,\n", + "{'accuracy': 0.972,\n", + " 'average_loss': 0.09360652,\n", " 'global_step': 2000,\n", - " 'loss': 11.825023}" + " 'loss': 11.848927}" ] }, - "execution_count": 25, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -905,18 +636,14 @@ }, { "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 22, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Classification accuracy: 97.27%\n" + "Classification accuracy: 97.20%\n" ] } ], @@ -926,10 +653,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Predictions\n", "\n", @@ -942,12 +666,8 @@ }, { "cell_type": "code", - "execution_count": 27, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 23, + "metadata": {}, "outputs": [], "source": [ "predictions = model.predict(input_fn=predict_input_fn)" @@ -955,18 +675,19 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 24, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "INFO:tensorflow:Restoring parameters from ./checkpoints_tutorial17-1/model.ckpt-2000\n" + "INFO:tensorflow:Calling model_fn.\n", + "INFO:tensorflow:Done calling model_fn.\n", + "INFO:tensorflow:Graph was finalized.\n", + "INFO:tensorflow:Restoring parameters from ./checkpoints_tutorial17-1/model.ckpt-2000\n", + "INFO:tensorflow:Running local_init_op.\n", + "INFO:tensorflow:Done running local_init_op.\n" ] } ], @@ -976,21 +697,18 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 25, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": false }, "outputs": [ { "data": { "text/plain": [ - "array([7, 2, 1, 0, 4, 1, 4, 9, 5])" + "array([7, 2, 1, 0, 4, 1, 4, 9, 6])" ] }, - "execution_count": 29, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -1002,18 +720,14 @@ }, { "cell_type": "code", - "execution_count": 30, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 26, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1028,20 +742,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "# New Estimator" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "If you cannot use one of the built-in Estimators, then you can create an arbitrary TensorFlow model yourself. To do this, you first need to create a function which defines the following:\n", "\n", @@ -1058,12 +766,8 @@ }, { "cell_type": "code", - "execution_count": 31, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 27, + "metadata": {}, "outputs": [], "source": [ "def model_fn(features, labels, mode, params):\n", @@ -1166,10 +870,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Create an Instance of the Estimator\n", "\n", @@ -1178,12 +879,8 @@ }, { "cell_type": "code", - "execution_count": 32, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 28, + "metadata": {}, "outputs": [], "source": [ "params = {\"learning_rate\": 1e-4}" @@ -1191,10 +888,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We can then create an instance of the new Estimator.\n", "\n", @@ -1205,11 +899,8 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 29, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -1218,7 +909,7 @@ "output_type": "stream", "text": [ "INFO:tensorflow:Using default config.\n", - "INFO:tensorflow:Using config: {'_model_dir': './checkpoints_tutorial17-2/', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_service': None, '_cluster_spec': , '_task_type': 'worker', '_task_id': 0, '_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n" + "INFO:tensorflow:Using config: {'_model_dir': './checkpoints_tutorial17-2/', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_service': None, '_cluster_spec': , '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n" ] } ], @@ -1230,10 +921,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Training\n", "\n", @@ -1242,11 +930,8 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 30, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -1254,58 +939,63 @@ "name": "stdout", "output_type": "stream", "text": [ + "INFO:tensorflow:Calling model_fn.\n", + "INFO:tensorflow:Done calling model_fn.\n", "INFO:tensorflow:Create CheckpointSaverHook.\n", - "INFO:tensorflow:Saving checkpoints for 1 into ./checkpoints_tutorial17-2/model.ckpt.\n", - "INFO:tensorflow:loss = 2.33444, step = 1\n", - "INFO:tensorflow:global_step/sec: 190.454\n", - "INFO:tensorflow:loss = 0.810317, step = 101 (0.527 sec)\n", - "INFO:tensorflow:global_step/sec: 198.129\n", - "INFO:tensorflow:loss = 0.349305, step = 201 (0.504 sec)\n", - "INFO:tensorflow:global_step/sec: 184.116\n", - "INFO:tensorflow:loss = 0.288062, step = 301 (0.543 sec)\n", - "INFO:tensorflow:global_step/sec: 195.138\n", - "INFO:tensorflow:loss = 0.0948148, step = 401 (0.512 sec)\n", - "INFO:tensorflow:global_step/sec: 199.116\n", - "INFO:tensorflow:loss = 0.203272, step = 501 (0.502 sec)\n", - "INFO:tensorflow:global_step/sec: 190.777\n", - "INFO:tensorflow:loss = 0.22347, step = 601 (0.524 sec)\n", - "INFO:tensorflow:global_step/sec: 198.669\n", - "INFO:tensorflow:loss = 0.161297, step = 701 (0.505 sec)\n", - "INFO:tensorflow:global_step/sec: 192.277\n", - "INFO:tensorflow:loss = 0.154663, step = 801 (0.518 sec)\n", - "INFO:tensorflow:global_step/sec: 158.865\n", - "INFO:tensorflow:loss = 0.136487, step = 901 (0.634 sec)\n", - "INFO:tensorflow:global_step/sec: 121.05\n", - "INFO:tensorflow:loss = 0.144933, step = 1001 (0.826 sec)\n", - "INFO:tensorflow:global_step/sec: 118.257\n", - "INFO:tensorflow:loss = 0.103951, step = 1101 (0.848 sec)\n", - "INFO:tensorflow:global_step/sec: 118.136\n", - "INFO:tensorflow:loss = 0.133236, step = 1201 (0.845 sec)\n", - "INFO:tensorflow:global_step/sec: 112.046\n", - "INFO:tensorflow:loss = 0.060983, step = 1301 (0.896 sec)\n", - "INFO:tensorflow:global_step/sec: 99.9212\n", - "INFO:tensorflow:loss = 0.0838628, step = 1401 (0.997 sec)\n", - "INFO:tensorflow:global_step/sec: 115.121\n", - "INFO:tensorflow:loss = 0.118691, step = 1501 (0.868 sec)\n", - "INFO:tensorflow:global_step/sec: 96.8269\n", - "INFO:tensorflow:loss = 0.179758, step = 1601 (1.038 sec)\n", - "INFO:tensorflow:global_step/sec: 99.8103\n", - "INFO:tensorflow:loss = 0.0996531, step = 1701 (0.998 sec)\n", - "INFO:tensorflow:global_step/sec: 128.677\n", - "INFO:tensorflow:loss = 0.097964, step = 1801 (0.775 sec)\n", - "INFO:tensorflow:global_step/sec: 124.224\n", - "INFO:tensorflow:loss = 0.086759, step = 1901 (0.806 sec)\n", + "INFO:tensorflow:Graph was finalized.\n", + "INFO:tensorflow:Running local_init_op.\n", + "INFO:tensorflow:Done running local_init_op.\n", + "INFO:tensorflow:Saving checkpoints for 0 into ./checkpoints_tutorial17-2/model.ckpt.\n", + "INFO:tensorflow:loss = 2.328683303358867, step = 0\n", + "INFO:tensorflow:global_step/sec: 30.0746\n", + "INFO:tensorflow:loss = 1.0425889833487076, step = 100 (3.326 sec)\n", + "INFO:tensorflow:global_step/sec: 30.7697\n", + "INFO:tensorflow:loss = 0.4519329631053862, step = 200 (3.250 sec)\n", + "INFO:tensorflow:global_step/sec: 30.5945\n", + "INFO:tensorflow:loss = 0.28173916577119856, step = 300 (3.269 sec)\n", + "INFO:tensorflow:global_step/sec: 30.3772\n", + "INFO:tensorflow:loss = 0.41579200542133726, step = 400 (3.292 sec)\n", + "INFO:tensorflow:global_step/sec: 31.44\n", + "INFO:tensorflow:loss = 0.2537537261934676, step = 500 (3.181 sec)\n", + "INFO:tensorflow:global_step/sec: 32.2734\n", + "INFO:tensorflow:loss = 0.2306796091927107, step = 600 (3.103 sec)\n", + "INFO:tensorflow:global_step/sec: 32.4727\n", + "INFO:tensorflow:loss = 0.16169791614095563, step = 700 (3.075 sec)\n", + "INFO:tensorflow:global_step/sec: 32.9575\n", + "INFO:tensorflow:loss = 0.24491770370504626, step = 800 (3.034 sec)\n", + "INFO:tensorflow:global_step/sec: 31.4056\n", + "INFO:tensorflow:loss = 0.1723769961825516, step = 900 (3.185 sec)\n", + "INFO:tensorflow:global_step/sec: 31.8268\n", + "INFO:tensorflow:loss = 0.0865023047044578, step = 1000 (3.142 sec)\n", + "INFO:tensorflow:global_step/sec: 33.1043\n", + "INFO:tensorflow:loss = 0.08865380930537742, step = 1100 (3.021 sec)\n", + "INFO:tensorflow:global_step/sec: 33.0132\n", + "INFO:tensorflow:loss = 0.09500106271291871, step = 1200 (3.029 sec)\n", + "INFO:tensorflow:global_step/sec: 32.2879\n", + "INFO:tensorflow:loss = 0.048251991971276796, step = 1300 (3.097 sec)\n", + "INFO:tensorflow:global_step/sec: 32.4468\n", + "INFO:tensorflow:loss = 0.0965478484811222, step = 1400 (3.082 sec)\n", + "INFO:tensorflow:global_step/sec: 31.0871\n", + "INFO:tensorflow:loss = 0.06810141978839185, step = 1500 (3.217 sec)\n", + "INFO:tensorflow:global_step/sec: 31.6667\n", + "INFO:tensorflow:loss = 0.13537004696386645, step = 1600 (3.158 sec)\n", + "INFO:tensorflow:global_step/sec: 31.98\n", + "INFO:tensorflow:loss = 0.08716099232839157, step = 1700 (3.127 sec)\n", + "INFO:tensorflow:global_step/sec: 32.1884\n", + "INFO:tensorflow:loss = 0.06138957874514458, step = 1800 (3.107 sec)\n", + "INFO:tensorflow:global_step/sec: 32.1328\n", + "INFO:tensorflow:loss = 0.11381113679326431, step = 1900 (3.113 sec)\n", "INFO:tensorflow:Saving checkpoints for 2000 into ./checkpoints_tutorial17-2/model.ckpt.\n", - "INFO:tensorflow:Loss for final step: 0.0712585.\n" + "INFO:tensorflow:Loss for final step: 0.09910375161965862.\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 34, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -1316,10 +1006,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Evaluation\n", "\n", @@ -1328,11 +1015,8 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 31, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -1340,10 +1024,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "INFO:tensorflow:Starting evaluation at 2017-11-17-12:08:18\n", + "INFO:tensorflow:Calling model_fn.\n", + "INFO:tensorflow:Done calling model_fn.\n", + "INFO:tensorflow:Starting evaluation at 2018-07-16-11:24:18\n", + "INFO:tensorflow:Graph was finalized.\n", "INFO:tensorflow:Restoring parameters from ./checkpoints_tutorial17-2/model.ckpt-2000\n", - "INFO:tensorflow:Finished evaluation at 2017-11-17-12:08:18\n", - "INFO:tensorflow:Saving dict for global step 2000: accuracy = 0.9761, global_step = 2000, loss = 0.0760049\n" + "INFO:tensorflow:Running local_init_op.\n", + "INFO:tensorflow:Done running local_init_op.\n", + "INFO:tensorflow:Finished evaluation at 2018-07-16-11:24:20\n", + "INFO:tensorflow:Saving dict for global step 2000: accuracy = 0.9769, global_step = 2000, loss = 0.0701695\n", + "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 2000: ./checkpoints_tutorial17-2/model.ckpt-2000\n" ] } ], @@ -1353,20 +1043,16 @@ }, { "cell_type": "code", - "execution_count": 36, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 32, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'accuracy': 0.97610003, 'global_step': 2000, 'loss': 0.076004863}" + "{'accuracy': 0.9769, 'global_step': 2000, 'loss': 0.0701695}" ] }, - "execution_count": 36, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -1377,18 +1063,14 @@ }, { "cell_type": "code", - "execution_count": 37, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 33, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Classification accuracy: 97.61%\n" + "Classification accuracy: 97.69%\n" ] } ], @@ -1398,10 +1080,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Predictions\n", "\n", @@ -1410,11 +1089,8 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 34, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [], @@ -1424,18 +1100,19 @@ }, { "cell_type": "code", - "execution_count": 39, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 35, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "INFO:tensorflow:Restoring parameters from ./checkpoints_tutorial17-2/model.ckpt-2000\n" + "INFO:tensorflow:Calling model_fn.\n", + "INFO:tensorflow:Done calling model_fn.\n", + "INFO:tensorflow:Graph was finalized.\n", + "INFO:tensorflow:Restoring parameters from ./checkpoints_tutorial17-2/model.ckpt-2000\n", + "INFO:tensorflow:Running local_init_op.\n", + "INFO:tensorflow:Done running local_init_op.\n" ] }, { @@ -1444,7 +1121,7 @@ "array([7, 2, 1, 0, 4, 1, 4, 9, 5])" ] }, - "execution_count": 39, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -1456,18 +1133,14 @@ }, { "cell_type": "code", - "execution_count": 40, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "execution_count": 36, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1482,10 +1155,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Conclusion\n", "\n", @@ -1501,10 +1171,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Exercises\n", "\n", @@ -1526,10 +1193,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## License (MIT)\n", "\n", @@ -1560,9 +1224,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.1" + "version": "3.6.10" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/18_TFRecords_Dataset_API.ipynb b/18_TFRecords_Dataset_API.ipynb index c3c6b91..99a8d54 100644 --- a/18_TFRecords_Dataset_API.ipynb +++ b/18_TFRecords_Dataset_API.ipynb @@ -2,10 +2,7 @@ "cells": [ { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "# TensorFlow Tutorial #18\n", "# TFRecords & Dataset API\n", @@ -16,10 +13,16 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, + "source": [ + "## WARNING!\n", + "\n", + "**This tutorial does not work with TensorFlow v.2 and it would take too much effort to update this tutorial to the new API.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, "source": [ "## Introduction\n", "\n", @@ -32,10 +35,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Imports" ] @@ -43,11 +43,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stderr", @@ -70,10 +66,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This was developed using Python 3.6 (Anaconda) and TensorFlow version:" ] @@ -81,11 +74,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -104,10 +93,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Load Data" ] @@ -116,9 +102,7 @@ "cell_type": "code", "execution_count": 3, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -127,10 +111,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The data dimensions have already been defined in the `knifey` module, so we just need to import the ones we need." ] @@ -139,9 +120,7 @@ "cell_type": "code", "execution_count": 4, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -150,10 +129,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Set the directory for storing the data-set on your computer." ] @@ -162,9 +138,7 @@ "cell_type": "code", "execution_count": 5, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -173,10 +147,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The Knifey-Spoony data-set is about 22 MB and will be downloaded automatically if it is not located in the given path." ] @@ -184,11 +155,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -204,10 +171,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Now load the data-set. This scans the sub-directories for all `*.jpg` images and puts the filenames into two lists for the training-set and test-set. This does not actually load the images." ] @@ -215,11 +179,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -236,10 +196,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Get the class-names." ] @@ -247,11 +204,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -271,20 +224,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Training and Test-Sets" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This function returns the file-paths for the images, the class-numbers as integers, and the class-numbers as One-Hot encoded arrays called labels.\n", "\n", @@ -295,9 +242,7 @@ "cell_type": "code", "execution_count": 9, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -306,10 +251,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Print the first image-path to see if it looks OK." ] @@ -317,11 +259,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -340,10 +278,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Get the test-set." ] @@ -352,9 +287,7 @@ "cell_type": "code", "execution_count": 11, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -363,10 +296,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Print the first image-path to see if it looks OK." ] @@ -374,11 +304,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -397,10 +323,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The Knifey-Spoony data-set has now been loaded and consists of 4700 images and associated labels (i.e. classifications of the images). The data-set is split into 2 mutually exclusive sub-sets, the training-set and the test-set." ] @@ -408,11 +331,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -432,20 +351,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Helper-function for plotting images" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Function used to plot 9 images in a 3x3 grid, and writing the true and predicted classes below each image." ] @@ -454,9 +367,7 @@ "cell_type": "code", "execution_count": 14, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -514,20 +425,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Helper-function for loading images" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This dataset does not load the actual images, instead it has a list of the images in the training-set and another list for the images in the test-set. This helper-function loads some image-files." ] @@ -536,9 +441,7 @@ "cell_type": "code", "execution_count": 15, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -552,10 +455,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Plot a few images to see if data is correct" ] @@ -564,9 +464,6 @@ "cell_type": "code", "execution_count": 16, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -594,20 +491,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Create TFRecords" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "TFRecords is the binary file-format used internally in TensorFlow which allows for high-performance reading and processing of datasets.\n", "\n", @@ -616,10 +507,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "File-path for the TFRecords file holding the training-set." ] @@ -628,9 +516,6 @@ "cell_type": "code", "execution_count": 17, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -652,10 +537,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "File-path for the TFRecords file holding the test-set." ] @@ -663,11 +545,7 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -687,10 +565,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Helper-function for printing the conversion progress." ] @@ -699,9 +574,7 @@ "cell_type": "code", "execution_count": 19, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -720,10 +593,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Helper-function for wrapping an integer so it can be saved to the TFRecords file." ] @@ -732,9 +602,7 @@ "cell_type": "code", "execution_count": 20, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -744,10 +612,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Helper-function for wrapping raw bytes so they can be saved to the TFRecords file." ] @@ -756,9 +621,7 @@ "cell_type": "code", "execution_count": 21, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -768,10 +631,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This is the function for reading images from disk and writing them along with the class-labels to a TFRecords file. This loads and decodes the images to numpy-arrays and then stores the raw bytes in the TFRecords file. If the original image-files are compressed e.g. as jpeg-files, then the TFRecords file may be many times larger than the original image-files.\n", "\n", @@ -782,9 +642,7 @@ "cell_type": "code", "execution_count": 22, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -836,10 +694,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Note the 4 function calls required to write the data-dict to the TFRecords file. In the original code-example from the Google Developers, these 4 function calls were actually nested. The design-philosophy for TensorFlow generally seems to be: If one function call is good, then 4 function calls are 4 times as good, and if they are nested then it is exponential goodness!\n", "\n", @@ -851,11 +706,7 @@ { "cell_type": "code", "execution_count": 23, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -874,10 +725,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Convert the test-set to a TFRecords-file:" ] @@ -886,9 +734,6 @@ "cell_type": "code", "execution_count": 24, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -909,20 +754,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Input Functions for the Estimator" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The TFRecords files contain the data in a serialized binary format which needs to be converted back to images and labels of the correct data-type. We use a helper-function for this parsing:" ] @@ -931,9 +770,7 @@ "cell_type": "code", "execution_count": 25, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -971,10 +808,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "Helper-function for creating an input-function that reads from TFRecords files for use with the Estimator API." ] @@ -983,9 +817,7 @@ "cell_type": "code", "execution_count": 26, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -1039,10 +871,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This is the input-function for the training-set for use with the Estimator API:" ] @@ -1051,9 +880,7 @@ "cell_type": "code", "execution_count": 27, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -1063,10 +890,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "This is the input-function for the test-set for use with the Estimator API:" ] @@ -1075,9 +899,7 @@ "cell_type": "code", "execution_count": 28, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -1087,20 +909,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Input Function for Predicting on New Images" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "An input-function is also needed for predicting the class of new data. As an example we just use a few images from the test-set.\n", "\n", @@ -1111,9 +927,7 @@ "cell_type": "code", "execution_count": 29, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -1131,9 +945,7 @@ "cell_type": "code", "execution_count": 30, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -1145,10 +957,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The class-numbers are actually not used in the input-function as it is not needed for prediction. However, the true class-number is needed when we plot the images further below." ] @@ -1157,9 +966,7 @@ "cell_type": "code", "execution_count": 31, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -1168,10 +975,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Pre-Made / Canned Estimator\n", "\n", @@ -1181,11 +985,7 @@ { "cell_type": "code", "execution_count": 32, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "feature_image = tf.feature_column.numeric_column(\"image\",\n", @@ -1194,10 +994,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "You can have several input features which would then be combined in a list:" ] @@ -1206,9 +1003,7 @@ "cell_type": "code", "execution_count": 33, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -1217,10 +1012,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "In this example we want to use a 3-layer DNN with 512, 256 and 128 units respectively." ] @@ -1229,9 +1021,7 @@ "cell_type": "code", "execution_count": 34, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -1240,10 +1030,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The `DNNClassifier` then constructs the neural network for us. We can also specify the activation function and various other parameters (see the docs). Here we just specify the number of classes and the directory where the checkpoints will be saved." ] @@ -1251,11 +1038,7 @@ { "cell_type": "code", "execution_count": 35, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1276,10 +1059,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Training\n", "\n", @@ -1290,9 +1070,6 @@ "cell_type": "code", "execution_count": 36, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -1326,10 +1103,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Evaluation\n", "\n", @@ -1340,9 +1114,6 @@ "cell_type": "code", "execution_count": 37, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -1364,11 +1135,7 @@ { "cell_type": "code", "execution_count": 38, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -1391,11 +1158,7 @@ { "cell_type": "code", "execution_count": 39, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1411,10 +1174,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Predictions\n", "\n", @@ -1428,11 +1188,7 @@ { "cell_type": "code", "execution_count": 40, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "predictions = model.predict(input_fn=predict_input_fn)" @@ -1441,11 +1197,7 @@ { "cell_type": "code", "execution_count": 41, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1463,9 +1215,6 @@ "cell_type": "code", "execution_count": 42, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": false }, "outputs": [ @@ -1488,11 +1237,7 @@ { "cell_type": "code", "execution_count": 43, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -1513,20 +1258,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Predictions for the Entire Test-Set" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "It appears that the model maybe classifies all images as 'spoony'. So let us see the predictions for the entire test-set. We can do this simply by using its input-function:" ] @@ -1535,9 +1274,7 @@ "cell_type": "code", "execution_count": 44, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -1548,9 +1285,6 @@ "cell_type": "code", "execution_count": 45, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -1570,11 +1304,7 @@ { "cell_type": "code", "execution_count": 46, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "cls_pred = np.array(cls, dtype='int').squeeze()" @@ -1582,10 +1312,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The test-set contains 530 images in total and they have all been predicted as class 2 (spoony). So this model does not work at all for classifying the Knifey-Spoony dataset." ] @@ -1593,11 +1320,7 @@ { "cell_type": "code", "execution_count": 47, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -1616,20 +1339,14 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "# New Estimator" ] }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "If you cannot use one of the built-in Estimators, then you can create an arbitrary TensorFlow model yourself. To do this, you first need to create a function which defines the following:\n", "\n", @@ -1647,11 +1364,7 @@ { "cell_type": "code", "execution_count": 48, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "def model_fn(features, labels, mode, params):\n", @@ -1753,10 +1466,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Create an Instance of the Estimator\n", "\n", @@ -1766,11 +1476,7 @@ { "cell_type": "code", "execution_count": 49, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "params = {\"learning_rate\": 1e-4}" @@ -1778,10 +1484,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "We can then create an instance of the new Estimator.\n", "\n", @@ -1794,9 +1497,6 @@ "cell_type": "code", "execution_count": 50, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -1817,10 +1517,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Training\n", "\n", @@ -1831,9 +1528,6 @@ "cell_type": "code", "execution_count": 51, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": false }, "outputs": [ @@ -1867,10 +1561,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Evaluation\n", "\n", @@ -1881,9 +1572,6 @@ "cell_type": "code", "execution_count": 52, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -1905,11 +1593,7 @@ { "cell_type": "code", "execution_count": 53, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -1929,11 +1613,7 @@ { "cell_type": "code", "execution_count": 54, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1949,10 +1629,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Predictions\n", "\n", @@ -1963,9 +1640,6 @@ "cell_type": "code", "execution_count": 55, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [], @@ -1976,11 +1650,7 @@ { "cell_type": "code", "execution_count": 56, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -2008,11 +1678,7 @@ { "cell_type": "code", "execution_count": 57, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -2033,10 +1699,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "### Predictions for the Entire Test-Set\n", "\n", @@ -2047,9 +1710,7 @@ "cell_type": "code", "execution_count": 58, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -2060,9 +1721,6 @@ "cell_type": "code", "execution_count": 59, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "scrolled": true }, "outputs": [ @@ -2115,10 +1773,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "The Convolutional Neural Network predicts different classes for the images, although most have just been classified as 0 (forky), so the accuracy is horrible." ] @@ -2126,11 +1781,7 @@ { "cell_type": "code", "execution_count": 60, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -2150,11 +1801,7 @@ { "cell_type": "code", "execution_count": 61, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -2174,11 +1821,7 @@ { "cell_type": "code", "execution_count": 62, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { @@ -2197,10 +1840,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Conclusion\n", "\n", @@ -2209,10 +1849,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## Exercises\n", "\n", @@ -2232,10 +1869,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "## License (MIT)\n", "\n", @@ -2266,9 +1900,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.1" + "version": "3.6.10" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/19_Hyper-Parameters.ipynb b/19_Hyper-Parameters.ipynb index ca10b25..d20ff3a 100644 --- a/19_Hyper-Parameters.ipynb +++ b/19_Hyper-Parameters.ipynb @@ -8,7 +8,7 @@ "# Hyper-Parameter Optimization\n", "\n", "by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n", - "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)" + "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsl1877BS8m3yt8t_wq2IWji)" ] }, { @@ -54,16 +54,7 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/magnus/anaconda3/envs/tf-test/lib/python3.6/importlib/_bootstrap.py:205: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n", - " return f(*args, **kwds)\n" - ] - } - ], + "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", @@ -85,15 +76,14 @@ "metadata": {}, "outputs": [], "source": [ - "# from tf.keras.models import Sequential # This does not work!\n", - "from tensorflow.python.keras import backend as K\n", - "from tensorflow.python.keras.models import Sequential\n", - "from tensorflow.python.keras.layers import InputLayer, Input\n", - "from tensorflow.python.keras.layers import Reshape, MaxPooling2D\n", - "from tensorflow.python.keras.layers import Conv2D, Dense, Flatten\n", - "from tensorflow.python.keras.callbacks import TensorBoard\n", - "from tensorflow.python.keras.optimizers import Adam\n", - "from tensorflow.python.keras.models import load_model" + "from tensorflow.keras import backend as K\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import InputLayer, Input\n", + "from tensorflow.keras.layers import Reshape, MaxPooling2D\n", + "from tensorflow.keras.layers import Conv2D, Dense, Flatten\n", + "from tensorflow.keras.callbacks import TensorBoard\n", + "from tensorflow.keras.optimizers import Adam\n", + "from tensorflow.keras.models import load_model" ] }, { @@ -111,16 +101,27 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "**NOTE:** This Notebook requires features in `scikit-optimize` that have not been merged into the official release at the time of this writing. If this Notebook cannot run with the version of `scikit-optimize` installed by the command above, you may have to install `scikit-optimize` from a development branch by running the following command instead:\n", + "**NOTE:** This Notebook requires plotting functions in `scikit-optimize` that have not been merged into the official release at the time of this writing. If this Notebook cannot run with the version of `scikit-optimize` installed by the command above, you may have to install `scikit-optimize` from a development branch by running the following command instead:\n", "\n", - "`pip install git+git://github.com/Hvass-Labs/scikit-optimize.git@610ce8d3e3e82d76f798ad90984c5888a204884e`" + "`pip install git+git://github.com/Hvass-Labs/scikit-optimize.git@dd7433da068b5a2509ef4ea4e5195458393e6555`" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/magnus/anaconda3/envs/tf2/lib/python3.6/site-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n", + " warnings.warn(msg, category=FutureWarning)\n", + "/home/magnus/anaconda3/envs/tf2/lib/python3.6/site-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.metrics.scorer module is deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.metrics. Anything that cannot be imported from sklearn.metrics is now part of the private API.\n", + " warnings.warn(message, FutureWarning)\n" + ] + } + ], "source": [ "import skopt\n", "from skopt import gp_minimize, forest_minimize\n", @@ -148,7 +149,7 @@ { "data": { "text/plain": [ - "'1.4.0'" + "'2.1.0'" ] }, "execution_count": 4, @@ -168,7 +169,7 @@ { "data": { "text/plain": [ - "'2.0.8-tf'" + "'2.2.4-tf'" ] }, "execution_count": 5, @@ -359,35 +360,24 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The MNIST data-set is about 12 MB and will be downloaded automatically if it is not located in the given path." + "The MNIST data-set is about 12 MB and will be downloaded automatically if it is not located in the given dir." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Extracting data/MNIST/train-images-idx3-ubyte.gz\n", - "Extracting data/MNIST/train-labels-idx1-ubyte.gz\n", - "Extracting data/MNIST/t10k-images-idx3-ubyte.gz\n", - "Extracting data/MNIST/t10k-labels-idx1-ubyte.gz\n" - ] - } - ], + "outputs": [], "source": [ - "from tensorflow.examples.tutorials.mnist import input_data\n", - "data = input_data.read_data_sets('data/MNIST/', one_hot=True)" + "from mnist import MNIST\n", + "data = MNIST(data_dir=\"data/MNIST/\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The MNIST data-set has now been loaded and consists of 70,000 images and associated labels (i.e. classifications of the images). The data-set is split into 3 mutually exclusive sub-sets." + "The MNIST data-set has now been loaded and consists of 70.000 images and class-numbers for the images. The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial." ] }, { @@ -401,23 +391,23 @@ "text": [ "Size of:\n", "- Training-set:\t\t55000\n", - "- Test-set:\t\t10000\n", - "- Validation-set:\t5000\n" + "- Validation-set:\t5000\n", + "- Test-set:\t\t10000\n" ] } ], "source": [ "print(\"Size of:\")\n", - "print(\"- Training-set:\\t\\t{}\".format(len(data.train.labels)))\n", - "print(\"- Test-set:\\t\\t{}\".format(len(data.test.labels)))\n", - "print(\"- Validation-set:\\t{}\".format(len(data.validation.labels)))" + "print(\"- Training-set:\\t\\t{}\".format(data.num_train))\n", + "print(\"- Validation-set:\\t{}\".format(data.num_val))\n", + "print(\"- Test-set:\\t\\t{}\".format(data.num_test))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The class-labels are One-Hot encoded, which means that each label is a vector with 10 elements, all of which are zero except for one element. The index of this one element is the class-number, that is, the digit shown in the associated image. We also need the class-numbers as integers for the test-set, so we calculate it now." + "Copy some of the data-dimensions for convenience." ] }, { @@ -426,7 +416,24 @@ "metadata": {}, "outputs": [], "source": [ - "data.test.cls = np.argmax(data.test.labels, axis=1)" + "# The number of pixels in each dimension of an image.\n", + "img_size = data.img_size\n", + "\n", + "# The images are stored in one-dimensional arrays of this length.\n", + "img_size_flat = data.img_size_flat\n", + "\n", + "# Tuple with height and width of images used to reshape arrays.\n", + "img_shape = data.img_shape\n", + "\n", + "# Tuple with height, width and depth used to reshape arrays.\n", + "# This is used for reshaping in Keras.\n", + "img_shape_full = data.img_shape_full\n", + "\n", + "# Number of classes, one class for each of 10 digits.\n", + "num_classes = data.num_classes\n", + "\n", + "# Number of colour channels for the images: 1 channel for gray-scale.\n", + "num_channels = data.num_channels" ] }, { @@ -442,48 +449,7 @@ "metadata": {}, "outputs": [], "source": [ - "validation_data = (data.validation.images, data.validation.labels)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Dimensions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The data dimensions are used in several places in the source-code below. They are defined once so we can use these variables instead of numbers throughout the source-code below." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# We know that MNIST images are 28 pixels in each dimension.\n", - "img_size = 28\n", - "\n", - "# Images are stored in one-dimensional arrays of this length.\n", - "img_size_flat = img_size * img_size\n", - "\n", - "# Tuple with height and width of images used to reshape arrays.\n", - "# This is used for plotting the images.\n", - "img_shape = (img_size, img_size)\n", - "\n", - "# Tuple with height, width and depth used to reshape arrays.\n", - "# This is used for reshaping in Keras.\n", - "img_shape_full = (img_size, img_size, 1)\n", - "\n", - "# Number of colour channels for the images: 1 channel for gray-scale.\n", - "num_channels = 1\n", - "\n", - "# Number of classes, one class for each of 10 digits.\n", - "num_classes = 10" + "validation_data = (data.x_val, data.y_val)" ] }, { @@ -502,7 +468,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -544,14 +510,14 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -560,10 +526,10 @@ ], "source": [ "# Get the first images from the test-set.\n", - "images = data.test.images[0:9]\n", + "images = data.x_test[0:9]\n", "\n", "# Get the true classes for those images.\n", - "cls_true = data.test.cls[0:9]\n", + "cls_true = data.y_test_cls[0:9]\n", "\n", "# Plot the images and labels using our helper-function above.\n", "plot_images(images=images, cls_true=cls_true)" @@ -580,7 +546,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -589,17 +555,17 @@ " # all images in the test-set.\n", "\n", " # Boolean array whether the predicted class is incorrect.\n", - " incorrect = (cls_pred != data.test.cls)\n", + " incorrect = (cls_pred != data.y_test_cls)\n", "\n", " # Get the images from the test-set that have been\n", " # incorrectly classified.\n", - " images = data.test.images[incorrect]\n", + " images = data.x_test[incorrect]\n", " \n", " # Get the predicted classes for those images.\n", " cls_pred = cls_pred[incorrect]\n", "\n", " # Get the true classes for those images.\n", - " cls_true = data.test.cls[incorrect]\n", + " cls_true = data.y_test_cls[incorrect]\n", " \n", " # Plot the first 9 images.\n", " plot_images(images=images[0:9],\n", @@ -622,7 +588,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": { "scrolled": true }, @@ -707,11 +673,11 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ - "path_best_model = '19_best_model.keras'" + "path_best_model = '19_best_model.h5'" ] }, { @@ -723,7 +689,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -741,7 +707,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -782,14 +748,13 @@ " callback_log = TensorBoard(\n", " log_dir=log_dir,\n", " histogram_freq=0,\n", - " batch_size=32,\n", " write_graph=True,\n", " write_grads=False,\n", " write_images=False)\n", " \n", " # Use Keras to train the model.\n", - " history = model.fit(x=data.train.images,\n", - " y=data.train.labels,\n", + " history = model.fit(x=data.x_train,\n", + " y=data.y_train,\n", " epochs=3,\n", " batch_size=128,\n", " validation_data=validation_data,\n", @@ -797,7 +762,7 @@ "\n", " # Get the classification accuracy on the validation-set\n", " # after the last training-epoch.\n", - " accuracy = history.history['val_acc'][-1]\n", + " accuracy = history.history['val_accuracy'][-1]\n", "\n", " # Print the classification accuracy.\n", " print()\n", @@ -843,7 +808,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 25, "metadata": { "scrolled": false }, @@ -859,23 +824,23 @@ "\n", "Train on 55000 samples, validate on 5000 samples\n", "Epoch 1/3\n", - "55000/55000 [==============================] - 3s - loss: 2.2525 - acc: 0.1995 - val_loss: 2.1754 - val_acc: 0.3578\n", + "55000/55000 [==============================] - 4s 64us/sample - loss: 2.2207 - accuracy: 0.2039 - val_loss: 2.0769 - val_accuracy: 0.3326\n", "Epoch 2/3\n", - "55000/55000 [==============================] - 2s - loss: 2.0279 - acc: 0.4612 - val_loss: 1.8432 - val_acc: 0.5558\n", + "55000/55000 [==============================] - 2s 39us/sample - loss: 1.8787 - accuracy: 0.4489 - val_loss: 1.5766 - val_accuracy: 0.6934\n", "Epoch 3/3\n", - "55000/55000 [==============================] - 4s - loss: 1.6227 - acc: 0.5998 - val_loss: 1.3877 - val_acc: 0.6654\n", + "55000/55000 [==============================] - 2s 39us/sample - loss: 1.3661 - accuracy: 0.7220 - val_loss: 1.0646 - val_accuracy: 0.8080\n", "\n", - "Accuracy: 66.54%\n", + "Accuracy: 80.80%\n", "\n" ] }, { "data": { "text/plain": [ - "-0.66539999999999999" + "-0.808" ] }, - "execution_count": 26, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -899,7 +864,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -913,615 +878,618 @@ "\n", "Train on 55000 samples, validate on 5000 samples\n", "Epoch 1/3\n", - "55000/55000 [==============================] - 2s - loss: 2.2287 - acc: 0.1868 - val_loss: 2.1264 - val_acc: 0.3182\n", + "55000/55000 [==============================] - 3s 47us/sample - loss: 2.2126 - accuracy: 0.3096 - val_loss: 2.0888 - val_accuracy: 0.4494\n", "Epoch 2/3\n", - "55000/55000 [==============================] - 2s - loss: 1.9607 - acc: 0.4438 - val_loss: 1.7713 - val_acc: 0.5082\n", + "55000/55000 [==============================] - 2s 38us/sample - loss: 1.9393 - accuracy: 0.4852 - val_loss: 1.7300 - val_accuracy: 0.5342\n", "Epoch 3/3\n", - "55000/55000 [==============================] - 5s - loss: 1.5763 - acc: 0.5579 - val_loss: 1.3832 - val_acc: 0.6166\n", + "55000/55000 [==============================] - 2s 38us/sample - loss: 1.5558 - accuracy: 0.5634 - val_loss: 1.2548 - val_accuracy: 0.7250\n", "\n", - "Accuracy: 61.66%\n", + "Accuracy: 72.50%\n", "\n", - "learning rate: 6.1e-04\n", - "num_dense_layers: 2\n", - "num_dense_nodes: 474\n", - "activation: sigmoid\n", + "learning rate: 7.0e-04\n", + "num_dense_layers: 1\n", + "num_dense_nodes: 365\n", + "activation: relu\n", "\n", "Train on 55000 samples, validate on 5000 samples\n", "Epoch 1/3\n", - "55000/55000 [==============================] - 3s - loss: 1.3354 - acc: 0.5258 - val_loss: 0.3002 - val_acc: 0.9112\n", + "55000/55000 [==============================] - 3s 46us/sample - loss: 0.2217 - accuracy: 0.9350 - val_loss: 0.0633 - val_accuracy: 0.9828\n", "Epoch 2/3\n", - "55000/55000 [==============================] - 4s - loss: 0.2336 - acc: 0.9269 - val_loss: 0.1626 - val_acc: 0.9538\n", + "55000/55000 [==============================] - 2s 39us/sample - loss: 0.0576 - accuracy: 0.9822 - val_loss: 0.0507 - val_accuracy: 0.9870\n", "Epoch 3/3\n", - "55000/55000 [==============================] - 7s - loss: 0.1403 - acc: 0.9563 - val_loss: 0.1113 - val_acc: 0.9692\n", + "55000/55000 [==============================] - 2s 39us/sample - loss: 0.0397 - accuracy: 0.9878 - val_loss: 0.0396 - val_accuracy: 0.9892\n", "\n", - "Accuracy: 96.92%\n", + "Accuracy: 98.92%\n", "\n", - "learning rate: 6.1e-06\n", - "num_dense_layers: 2\n", - "num_dense_nodes: 333\n", + "learning rate: 6.8e-03\n", + "num_dense_layers: 4\n", + "num_dense_nodes: 466\n", "activation: relu\n", "\n", "Train on 55000 samples, validate on 5000 samples\n", "Epoch 1/3\n", - "55000/55000 [==============================] - 3s - loss: 2.1702 - acc: 0.5067 - val_loss: 1.9186 - val_acc: 0.6892\n", + "55000/55000 [==============================] - 3s 53us/sample - loss: 2.3098 - accuracy: 0.1117 - val_loss: 2.3022 - val_accuracy: 0.1060\n", "Epoch 2/3\n", - "55000/55000 [==============================] - 3s - loss: 1.4878 - acc: 0.7480 - val_loss: 1.0546 - val_acc: 0.7940\n", + "55000/55000 [==============================] - 2s 44us/sample - loss: 2.3015 - accuracy: 0.1123 - val_loss: 2.3016 - val_accuracy: 0.1060\n", "Epoch 3/3\n", - "55000/55000 [==============================] - 7s - loss: 0.8226 - acc: 0.8264 - val_loss: 0.6324 - val_acc: 0.8514\n", + "55000/55000 [==============================] - 3s 47us/sample - loss: 2.3017 - accuracy: 0.1123 - val_loss: 2.3019 - val_accuracy: 0.1060\n", "\n", - "Accuracy: 85.14%\n", + "Accuracy: 10.60%\n", "\n", - "learning rate: 1.7e-04\n", - "num_dense_layers: 4\n", - "num_dense_nodes: 252\n", + "learning rate: 9.8e-04\n", + "num_dense_layers: 5\n", + "num_dense_nodes: 122\n", "activation: sigmoid\n", "\n", "Train on 55000 samples, validate on 5000 samples\n", "Epoch 1/3\n", - "55000/55000 [==============================] - 3s - loss: 2.3075 - acc: 0.1058 - val_loss: 2.2968 - val_acc: 0.1126\n", + "55000/55000 [==============================] - 3s 54us/sample - loss: 2.3093 - accuracy: 0.1038 - val_loss: 2.3061 - val_accuracy: 0.1060\n", "Epoch 2/3\n", - "55000/55000 [==============================] - 4s - loss: 1.5272 - acc: 0.4944 - val_loss: 0.8210 - val_acc: 0.7386\n", + "55000/55000 [==============================] - 2s 42us/sample - loss: 2.3057 - accuracy: 0.1078 - val_loss: 2.3039 - val_accuracy: 0.1060\n", "Epoch 3/3\n", - "55000/55000 [==============================] - 7s - loss: 0.6595 - acc: 0.7967 - val_loss: 0.4940 - val_acc: 0.8544\n", + "55000/55000 [==============================] - 2s 41us/sample - loss: 2.3053 - accuracy: 0.1073 - val_loss: 2.3054 - val_accuracy: 0.0978\n", "\n", - "Accuracy: 85.44%\n", + "Accuracy: 9.78%\n", "\n", - "learning rate: 7.3e-03\n", + "learning rate: 3.7e-04\n", "num_dense_layers: 3\n", - "num_dense_nodes: 166\n", - "activation: relu\n", + "num_dense_nodes: 72\n", + "activation: sigmoid\n", "\n", "Train on 55000 samples, validate on 5000 samples\n", "Epoch 1/3\n", - "55000/55000 [==============================] - 3s - loss: 0.1821 - acc: 0.9431 - val_loss: 0.0705 - val_acc: 0.9808\n", + "55000/55000 [==============================] - 3s 48us/sample - loss: 2.3049 - accuracy: 0.1066 - val_loss: 2.3042 - val_accuracy: 0.1060\n", "Epoch 2/3\n", - "55000/55000 [==============================] - 3s - loss: 0.0605 - acc: 0.9829 - val_loss: 0.0678 - val_acc: 0.9848\n", + "55000/55000 [==============================] - 2s 40us/sample - loss: 2.3008 - accuracy: 0.1220 - val_loss: 2.2349 - val_accuracy: 0.2808\n", "Epoch 3/3\n", - "55000/55000 [==============================] - 7s - loss: 0.0549 - acc: 0.9855 - val_loss: 0.0736 - val_acc: 0.9846\n", + "55000/55000 [==============================] - 3s 55us/sample - loss: 1.1898 - accuracy: 0.7546 - val_loss: 0.5112 - val_accuracy: 0.9176\n", "\n", - "Accuracy: 98.46%\n", + "Accuracy: 91.76%\n", "\n", - "learning rate: 6.1e-05\n", - "num_dense_layers: 2\n", - "num_dense_nodes: 209\n", + "learning rate: 6.7e-04\n", + "num_dense_layers: 3\n", + "num_dense_nodes: 230\n", "activation: sigmoid\n", "\n", "Train on 55000 samples, validate on 5000 samples\n", "Epoch 1/3\n", - "55000/55000 [==============================] - 2s - loss: 2.3187 - acc: 0.1073 - val_loss: 2.3030 - val_acc: 0.0924\n", + "55000/55000 [==============================] - 3s 57us/sample - loss: 1.9215 - accuracy: 0.2895 - val_loss: 0.5522 - val_accuracy: 0.8572\n", "Epoch 2/3\n", - "55000/55000 [==============================] - 2s - loss: 2.3016 - acc: 0.1121 - val_loss: 2.2993 - val_acc: 0.1126\n", + "55000/55000 [==============================] - 5s 90us/sample - loss: 0.3142 - accuracy: 0.9098 - val_loss: 0.1402 - val_accuracy: 0.9612\n", "Epoch 3/3\n", - "55000/55000 [==============================] - 5s - loss: 2.2858 - acc: 0.1573 - val_loss: 2.2243 - val_acc: 0.2898\n", + "55000/55000 [==============================] - 3s 54us/sample - loss: 0.1451 - accuracy: 0.9567 - val_loss: 0.0997 - val_accuracy: 0.9708\n", "\n", - "Accuracy: 28.98%\n", + "Accuracy: 97.08%\n", "\n", - "learning rate: 1.8e-04\n", - "num_dense_layers: 4\n", - "num_dense_nodes: 453\n", + "learning rate: 9.7e-03\n", + "num_dense_layers: 3\n", + "num_dense_nodes: 132\n", "activation: relu\n", "\n", "Train on 55000 samples, validate on 5000 samples\n", "Epoch 1/3\n", - "55000/55000 [==============================] - 3s - loss: 0.3601 - acc: 0.8920 - val_loss: 0.1234 - val_acc: 0.9640\n", + "55000/55000 [==============================] - 3s 53us/sample - loss: 0.2108 - accuracy: 0.9304 - val_loss: 0.0595 - val_accuracy: 0.9840\n", "Epoch 2/3\n", - "55000/55000 [==============================] - 3s - loss: 0.0850 - acc: 0.9741 - val_loss: 0.0576 - val_acc: 0.9830\n", + "55000/55000 [==============================] - 2s 45us/sample - loss: 0.0719 - accuracy: 0.9801 - val_loss: 0.0487 - val_accuracy: 0.9874\n", "Epoch 3/3\n", - "55000/55000 [==============================] - 7s - loss: 0.0566 - acc: 0.9824 - val_loss: 0.0535 - val_acc: 0.9856\n", + "55000/55000 [==============================] - 3s 50us/sample - loss: 0.0673 - accuracy: 0.9824 - val_loss: 0.0474 - val_accuracy: 0.9870\n", "\n", - "Accuracy: 98.56%\n", + "Accuracy: 98.70%\n", "\n", - "learning rate: 5.5e-06\n", + "learning rate: 1.6e-04\n", "num_dense_layers: 4\n", - "num_dense_nodes: 186\n", - "activation: sigmoid\n", - "\n", - "Train on 55000 samples, validate on 5000 samples\n", - "Epoch 1/3\n", - "55000/55000 [==============================] - 3s - loss: 2.3129 - acc: 0.1039 - val_loss: 2.3025 - val_acc: 0.1100\n", - "Epoch 2/3\n", - "55000/55000 [==============================] - 4s - loss: 2.3016 - acc: 0.1106 - val_loss: 2.3010 - val_acc: 0.1126\n", - "Epoch 3/3\n", - "55000/55000 [==============================] - 7s - loss: 2.3013 - acc: 0.1123 - val_loss: 2.3011 - val_acc: 0.1126\n", - "\n", - "Accuracy: 11.26%\n", - "\n", - "learning rate: 3.1e-05\n", - "num_dense_layers: 3\n", - "num_dense_nodes: 427\n", + "num_dense_nodes: 112\n", "activation: sigmoid\n", "\n", "Train on 55000 samples, validate on 5000 samples\n", "Epoch 1/3\n", - "55000/55000 [==============================] - 3s - loss: 2.3132 - acc: 0.1070 - val_loss: 2.3007 - val_acc: 0.1126\n", + "55000/55000 [==============================] - 8s 149us/sample - loss: 2.3076 - accuracy: 0.1080 - val_loss: 2.3030 - val_accuracy: 0.1060\n", "Epoch 2/3\n", - "55000/55000 [==============================] - 4s - loss: 2.3029 - acc: 0.1080 - val_loss: 2.3020 - val_acc: 0.1126\n", + "55000/55000 [==============================] - 6s 118us/sample - loss: 2.2995 - accuracy: 0.1135 - val_loss: 2.2653 - val_accuracy: 0.1128\n", "Epoch 3/3\n", - "55000/55000 [==============================] - 7s - loss: 2.3021 - acc: 0.1093 - val_loss: 2.3016 - val_acc: 0.1126\n", + "55000/55000 [==============================] - 6s 115us/sample - loss: 1.7192 - accuracy: 0.4761 - val_loss: 1.1804 - val_accuracy: 0.6904\n", "\n", - "Accuracy: 11.26%\n", + "Accuracy: 69.04%\n", "\n", - "learning rate: 1.4e-04\n", - "num_dense_layers: 2\n", - "num_dense_nodes: 29\n", + "learning rate: 7.7e-05\n", + "num_dense_layers: 5\n", + "num_dense_nodes: 154\n", "activation: relu\n", "\n", "Train on 55000 samples, validate on 5000 samples\n", "Epoch 1/3\n", - "55000/55000 [==============================] - 2s - loss: 0.8474 - acc: 0.7524 - val_loss: 0.2954 - val_acc: 0.9190\n", + "55000/55000 [==============================] - 3s 56us/sample - loss: 0.8023 - accuracy: 0.7727 - val_loss: 0.2123 - val_accuracy: 0.9404\n", "Epoch 2/3\n", - "55000/55000 [==============================] - 2s - loss: 0.2392 - acc: 0.9315 - val_loss: 0.1741 - val_acc: 0.9512\n", + "55000/55000 [==============================] - 3s 49us/sample - loss: 0.2085 - accuracy: 0.9384 - val_loss: 0.1236 - val_accuracy: 0.9644\n", "Epoch 3/3\n", - "55000/55000 [==============================] - 5s - loss: 0.1643 - acc: 0.9517 - val_loss: 0.1346 - val_acc: 0.9612\n", + "55000/55000 [==============================] - 7s 125us/sample - loss: 0.1413 - accuracy: 0.9564 - val_loss: 0.0950 - val_accuracy: 0.9746\n", "\n", - "Accuracy: 96.12%\n", + "Accuracy: 97.46%\n", "\n", - "learning rate: 3.7e-04\n", + "learning rate: 2.9e-03\n", "num_dense_layers: 4\n", - "num_dense_nodes: 338\n", + "num_dense_nodes: 156\n", "activation: sigmoid\n", "\n", "Train on 55000 samples, validate on 5000 samples\n", "Epoch 1/3\n", - "55000/55000 [==============================] - 3s - loss: 2.1610 - acc: 0.1844 - val_loss: 1.0813 - val_acc: 0.6678\n", + "55000/55000 [==============================] - 4s 75us/sample - loss: 2.3168 - accuracy: 0.1028 - val_loss: 2.3084 - val_accuracy: 0.1060\n", "Epoch 2/3\n", - "55000/55000 [==============================] - 4s - loss: 0.5982 - acc: 0.8131 - val_loss: 0.3252 - val_acc: 0.9100\n", + "55000/55000 [==============================] - 3s 51us/sample - loss: 2.3055 - accuracy: 0.1059 - val_loss: 2.3023 - val_accuracy: 0.1060\n", "Epoch 3/3\n", - "55000/55000 [==============================] - 7s - loss: 0.2712 - acc: 0.9201 - val_loss: 0.1858 - val_acc: 0.9468\n", + "55000/55000 [==============================] - 6s 101us/sample - loss: 2.3016 - accuracy: 0.1119 - val_loss: 2.3027 - val_accuracy: 0.1060\n", "\n", - "Accuracy: 94.68%\n", + "Accuracy: 10.60%\n", "\n", - "learning rate: 1.7e-06\n", - "num_dense_layers: 4\n", - "num_dense_nodes: 512\n", + "learning rate: 1.1e-05\n", + "num_dense_layers: 1\n", + "num_dense_nodes: 496\n", "activation: relu\n", "\n", "Train on 55000 samples, validate on 5000 samples\n", "Epoch 1/3\n", - "55000/55000 [==============================] - 3s - loss: 2.2568 - acc: 0.3895 - val_loss: 2.1984 - val_acc: 0.6048\n", + "55000/55000 [==============================] - 10s 185us/sample - loss: 1.8064 - accuracy: 0.6548 - val_loss: 1.0878 - val_accuracy: 0.8440\n", "Epoch 2/3\n", - "55000/55000 [==============================] - 4s - loss: 2.0854 - acc: 0.6719 - val_loss: 1.9276 - val_acc: 0.7052\n", + "55000/55000 [==============================] - 7s 124us/sample - loss: 0.7844 - accuracy: 0.8393 - val_loss: 0.4786 - val_accuracy: 0.9046\n", "Epoch 3/3\n", - "55000/55000 [==============================] - 7s - loss: 1.7106 - acc: 0.7158 - val_loss: 1.4589 - val_acc: 0.7290\n", + "55000/55000 [==============================] - 7s 128us/sample - loss: 0.4776 - accuracy: 0.8791 - val_loss: 0.3288 - val_accuracy: 0.9226\n", "\n", - "Accuracy: 72.90%\n", + "Accuracy: 92.26%\n", "\n", - "learning rate: 1.4e-03\n", + "learning rate: 3.4e-03\n", "num_dense_layers: 2\n", - "num_dense_nodes: 62\n", + "num_dense_nodes: 451\n", "activation: relu\n", "\n", "Train on 55000 samples, validate on 5000 samples\n", "Epoch 1/3\n", - "55000/55000 [==============================] - 2s - loss: 0.2396 - acc: 0.9249 - val_loss: 0.0643 - val_acc: 0.9822\n", + "55000/55000 [==============================] - 3s 62us/sample - loss: 0.1423 - accuracy: 0.9556 - val_loss: 0.0378 - val_accuracy: 0.9904\n", "Epoch 2/3\n", - "55000/55000 [==============================] - 2s - loss: 0.0587 - acc: 0.9819 - val_loss: 0.0536 - val_acc: 0.9838\n", + "55000/55000 [==============================] - 3s 52us/sample - loss: 0.0438 - accuracy: 0.9869 - val_loss: 0.0379 - val_accuracy: 0.9896\n", "Epoch 3/3\n", - "55000/55000 [==============================] - 6s - loss: 0.0427 - acc: 0.9867 - val_loss: 0.0480 - val_acc: 0.9842\n", + "55000/55000 [==============================] - 6s 106us/sample - loss: 0.0342 - accuracy: 0.9892 - val_loss: 0.0370 - val_accuracy: 0.9890\n", "\n", - "Accuracy: 98.42%\n", + "Accuracy: 98.90%\n", "\n", - "learning rate: 2.7e-03\n", - "num_dense_layers: 2\n", - "num_dense_nodes: 364\n", - "activation: sigmoid\n", - "\n", - "Train on 55000 samples, validate on 5000 samples\n", - "Epoch 1/3\n", - "55000/55000 [==============================] - 3s - loss: 1.3014 - acc: 0.5223 - val_loss: 0.2531 - val_acc: 0.9232\n", - "Epoch 2/3\n", - "55000/55000 [==============================] - 3s - loss: 0.1956 - acc: 0.9386 - val_loss: 0.1221 - val_acc: 0.9650\n", - "Epoch 3/3\n", - "55000/55000 [==============================] - 5s - loss: 0.1138 - acc: 0.9646 - val_loss: 0.0846 - val_acc: 0.9758\n", - "\n", - "Accuracy: 97.58%\n", - "\n", - "learning rate: 5.6e-04\n", + "learning rate: 1.2e-05\n", "num_dense_layers: 5\n", - "num_dense_nodes: 13\n", + "num_dense_nodes: 182\n", "activation: relu\n", "\n", "Train on 55000 samples, validate on 5000 samples\n", "Epoch 1/3\n", - "55000/55000 [==============================] - 3s - loss: 0.9357 - acc: 0.6775 - val_loss: 0.3024 - val_acc: 0.9184\n", + "55000/55000 [==============================] - 3s 59us/sample - loss: 1.9464 - accuracy: 0.4581 - val_loss: 1.0907 - val_accuracy: 0.8004\n", "Epoch 2/3\n", - "55000/55000 [==============================] - 3s - loss: 0.2416 - acc: 0.9338 - val_loss: 0.1749 - val_acc: 0.9520\n", + "55000/55000 [==============================] - 3s 48us/sample - loss: 0.6953 - accuracy: 0.8265 - val_loss: 0.3989 - val_accuracy: 0.8992\n", "Epoch 3/3\n", - "55000/55000 [==============================] - 7s - loss: 0.1685 - acc: 0.9525 - val_loss: 0.1541 - val_acc: 0.9570\n", + "55000/55000 [==============================] - 4s 78us/sample - loss: 0.4034 - accuracy: 0.8862 - val_loss: 0.2750 - val_accuracy: 0.9256\n", "\n", - "Accuracy: 95.70%\n", + "Accuracy: 92.56%\n", "\n", - "learning rate: 1.0e-02\n", - "num_dense_layers: 5\n", - "num_dense_nodes: 352\n", + "learning rate: 2.8e-04\n", + "num_dense_layers: 2\n", + "num_dense_nodes: 512\n", "activation: sigmoid\n", "\n", "Train on 55000 samples, validate on 5000 samples\n", - "Epoch 1/3\n" + "Epoch 1/3\n", + "55000/55000 [==============================] - 3s 56us/sample - loss: 1.8869 - accuracy: 0.3200 - val_loss: 0.5330 - val_accuracy: 0.8646\n", + "Epoch 2/3\n", + "55000/55000 [==============================] - 3s 52us/sample - loss: 0.3947 - accuracy: 0.8847 - val_loss: 0.2235 - val_accuracy: 0.9346\n", + "Epoch 3/3\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "55000/55000 [==============================] - 3s - loss: 2.3316 - acc: 0.1049 - val_loss: 2.3019 - val_acc: 0.1070\n", - "Epoch 2/3\n", - "55000/55000 [==============================] - 3s - loss: 2.3024 - acc: 0.1090 - val_loss: 2.3017 - val_acc: 0.1126\n", - "Epoch 3/3\n", - "55000/55000 [==============================] - 8s - loss: 2.3020 - acc: 0.1104 - val_loss: 2.3014 - val_acc: 0.1126\n", + "55000/55000 [==============================] - 4s 78us/sample - loss: 0.2316 - accuracy: 0.9292 - val_loss: 0.1444 - val_accuracy: 0.9592\n", "\n", - "Accuracy: 11.26%\n", + "Accuracy: 95.92%\n", "\n", - "learning rate: 1.5e-03\n", - "num_dense_layers: 1\n", + "learning rate: 3.4e-03\n", + "num_dense_layers: 3\n", "num_dense_nodes: 5\n", "activation: sigmoid\n", "\n", "Train on 55000 samples, validate on 5000 samples\n", "Epoch 1/3\n", - "55000/55000 [==============================] - 2s - loss: 1.7072 - acc: 0.4784 - val_loss: 1.2153 - val_acc: 0.6980\n", + "55000/55000 [==============================] - 4s 64us/sample - loss: 2.3031 - accuracy: 0.1100 - val_loss: 2.3020 - val_accuracy: 0.1060\n", "Epoch 2/3\n", - "55000/55000 [==============================] - 2s - loss: 0.9949 - acc: 0.7914 - val_loss: 0.7749 - val_acc: 0.8564\n", + "55000/55000 [==============================] - 3s 46us/sample - loss: 2.3020 - accuracy: 0.1118 - val_loss: 2.3021 - val_accuracy: 0.1060\n", "Epoch 3/3\n", - "55000/55000 [==============================] - 6s - loss: 0.6663 - acc: 0.8663 - val_loss: 0.5469 - val_acc: 0.9014\n", + "55000/55000 [==============================] - 4s 70us/sample - loss: 2.3019 - accuracy: 0.1128 - val_loss: 2.3018 - val_accuracy: 0.1060\n", "\n", - "Accuracy: 90.14%\n", + "Accuracy: 10.60%\n", "\n", - "learning rate: 1.0e-03\n", - "num_dense_layers: 3\n", - "num_dense_nodes: 496\n", - "activation: relu\n", - "\n", - "Train on 55000 samples, validate on 5000 samples\n", - "Epoch 1/3\n", - "55000/55000 [==============================] - 3s - loss: 0.1843 - acc: 0.9426 - val_loss: 0.0483 - val_acc: 0.9852\n", - "Epoch 2/3\n", - "55000/55000 [==============================] - 5s - loss: 0.0506 - acc: 0.9840 - val_loss: 0.0471 - val_acc: 0.9856\n", - "Epoch 3/3\n", - "55000/55000 [==============================] - 7s - loss: 0.0347 - acc: 0.9889 - val_loss: 0.0451 - val_acc: 0.9856\n", - "\n", - "Accuracy: 98.56%\n", - "\n", - "learning rate: 3.7e-03\n", - "num_dense_layers: 5\n", - "num_dense_nodes: 512\n", + "learning rate: 1.0e-02\n", + "num_dense_layers: 2\n", + "num_dense_nodes: 104\n", "activation: relu\n", "\n", "Train on 55000 samples, validate on 5000 samples\n", "Epoch 1/3\n", - "55000/55000 [==============================] - 4s - loss: 0.2060 - acc: 0.9377 - val_loss: 0.0739 - val_acc: 0.9832\n", + "55000/55000 [==============================] - 3s 52us/sample - loss: 0.1708 - accuracy: 0.9451 - val_loss: 0.0552 - val_accuracy: 0.9836\n", "Epoch 2/3\n", - "55000/55000 [==============================] - 5s - loss: 0.0781 - acc: 0.9814 - val_loss: 0.0765 - val_acc: 0.9842\n", + "55000/55000 [==============================] - 3s 46us/sample - loss: 0.0630 - accuracy: 0.9813 - val_loss: 0.0551 - val_accuracy: 0.9836\n", "Epoch 3/3\n", - "55000/55000 [==============================] - 8s - loss: 0.0908 - acc: 0.9818 - val_loss: 0.1368 - val_acc: 0.9766\n", + "55000/55000 [==============================] - 3s 46us/sample - loss: 0.0514 - accuracy: 0.9851 - val_loss: 0.0415 - val_accuracy: 0.9904\n", "\n", - "Accuracy: 97.66%\n", + "Accuracy: 99.04%\n", "\n", - "learning rate: 1.0e-02\n", - "num_dense_layers: 5\n", - "num_dense_nodes: 512\n", - "activation: relu\n", + "learning rate: 5.5e-04\n", + "num_dense_layers: 3\n", + "num_dense_nodes: 277\n", + "activation: sigmoid\n", "\n", "Train on 55000 samples, validate on 5000 samples\n", "Epoch 1/3\n", - "55000/55000 [==============================] - 3s - loss: 2.3199 - acc: 0.1105 - val_loss: 2.3015 - val_acc: 0.1126\n", + "55000/55000 [==============================] - 4s 78us/sample - loss: 2.0127 - accuracy: 0.2534 - val_loss: 0.6090 - val_accuracy: 0.8268\n", "Epoch 2/3\n", - "55000/55000 [==============================] - 4s - loss: 2.3020 - acc: 0.1104 - val_loss: 2.3011 - val_acc: 0.1126\n", + "55000/55000 [==============================] - 3s 50us/sample - loss: 0.3406 - accuracy: 0.9004 - val_loss: 0.1451 - val_accuracy: 0.9582\n", "Epoch 3/3\n", - "55000/55000 [==============================] - 8s - loss: 2.3018 - acc: 0.1110 - val_loss: 2.3013 - val_acc: 0.1126\n", + "55000/55000 [==============================] - 4s 80us/sample - loss: 0.1563 - accuracy: 0.9533 - val_loss: 0.0969 - val_accuracy: 0.9700\n", "\n", - "Accuracy: 11.26%\n", + "Accuracy: 97.00%\n", "\n", - "learning rate: 1.9e-04\n", - "num_dense_layers: 4\n", - "num_dense_nodes: 418\n", + "learning rate: 2.8e-03\n", + "num_dense_layers: 2\n", + "num_dense_nodes: 441\n", "activation: relu\n", "\n", "Train on 55000 samples, validate on 5000 samples\n", "Epoch 1/3\n", - "55000/55000 [==============================] - 3s - loss: 0.3595 - acc: 0.8999 - val_loss: 0.0888 - val_acc: 0.9732\n", + "55000/55000 [==============================] - 3s 58us/sample - loss: 0.1397 - accuracy: 0.9561 - val_loss: 0.0428 - val_accuracy: 0.9864\n", "Epoch 2/3\n", - "55000/55000 [==============================] - 3s - loss: 0.0868 - acc: 0.9738 - val_loss: 0.0686 - val_acc: 0.9782\n", + "55000/55000 [==============================] - 3s 49us/sample - loss: 0.0471 - accuracy: 0.9853 - val_loss: 0.0401 - val_accuracy: 0.9892\n", "Epoch 3/3\n", - "55000/55000 [==============================] - 8s - loss: 0.0584 - acc: 0.9821 - val_loss: 0.0478 - val_acc: 0.9850\n", + "55000/55000 [==============================] - 7s 120us/sample - loss: 0.0316 - accuracy: 0.9905 - val_loss: 0.0482 - val_accuracy: 0.9882\n", "\n", - "Accuracy: 98.50%\n", + "Accuracy: 98.82%\n", "\n", - "learning rate: 2.4e-03\n", - "num_dense_layers: 4\n", - "num_dense_nodes: 144\n", + "learning rate: 5.0e-04\n", + "num_dense_layers: 2\n", + "num_dense_nodes: 309\n", "activation: relu\n", "\n", "Train on 55000 samples, validate on 5000 samples\n", "Epoch 1/3\n", - "55000/55000 [==============================] - 3s - loss: 0.1906 - acc: 0.9390 - val_loss: 0.0576 - val_acc: 0.9834\n", + "55000/55000 [==============================] - 3s 56us/sample - loss: 0.2504 - accuracy: 0.9302 - val_loss: 0.0794 - val_accuracy: 0.9784\n", "Epoch 2/3\n", - "55000/55000 [==============================] - 3s - loss: 0.0550 - acc: 0.9840 - val_loss: 0.0402 - val_acc: 0.9890\n", + "55000/55000 [==============================] - 3s 49us/sample - loss: 0.0642 - accuracy: 0.9801 - val_loss: 0.0631 - val_accuracy: 0.9814\n", "Epoch 3/3\n", - "55000/55000 [==============================] - 7s - loss: 0.0380 - acc: 0.9885 - val_loss: 0.0459 - val_acc: 0.9880\n", + "55000/55000 [==============================] - 5s 83us/sample - loss: 0.0439 - accuracy: 0.9861 - val_loss: 0.0465 - 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- val_loss: 0.0496 - val_acc: 0.9852\n", + "55000/55000 [==============================] - 4s 81us/sample - loss: 0.0942 - accuracy: 0.9724 - val_loss: 0.0687 - val_accuracy: 0.9806\n", "\n", - "Accuracy: 98.52%\n", + "Accuracy: 98.06%\n", "\n", - "learning rate: 2.5e-04\n", + "learning rate: 5.5e-05\n", "num_dense_layers: 2\n", - "num_dense_nodes: 435\n", + "num_dense_nodes: 512\n", "activation: relu\n", "\n", "Train on 55000 samples, validate on 5000 samples\n", "Epoch 1/3\n", - "55000/55000 [==============================] - 3s - loss: 0.3258 - acc: 0.9131 - val_loss: 0.1024 - val_acc: 0.9676\n", + "55000/55000 [==============================] - 3s 58us/sample - loss: 0.7263 - accuracy: 0.8272 - val_loss: 0.1854 - val_accuracy: 0.9470\n", "Epoch 2/3\n", - "55000/55000 [==============================] - 4s - loss: 0.0856 - acc: 0.9742 - val_loss: 0.0603 - val_acc: 0.9812\n", + "55000/55000 [==============================] - 3s 51us/sample - loss: 0.1967 - accuracy: 0.9415 - val_loss: 0.1209 - val_accuracy: 0.9666\n", "Epoch 3/3\n", - "55000/55000 [==============================] - 7s - loss: 0.0601 - acc: 0.9819 - val_loss: 0.0477 - val_acc: 0.9868\n", + "55000/55000 [==============================] - 6s 115us/sample - loss: 0.1333 - accuracy: 0.9604 - val_loss: 0.0909 - val_accuracy: 0.9748\n", "\n", - "Accuracy: 98.68%\n", + "Accuracy: 97.48%\n", "\n", - "learning rate: 2.5e-06\n", - "num_dense_layers: 1\n", - "num_dense_nodes: 409\n", + "learning rate: 3.5e-05\n", + "num_dense_layers: 4\n", + "num_dense_nodes: 512\n", "activation: relu\n", "\n", "Train on 55000 samples, validate on 5000 samples\n", "Epoch 1/3\n", - "55000/55000 [==============================] - 3s - loss: 2.2504 - acc: 0.3689 - val_loss: 2.1796 - val_acc: 0.5498\n", + "55000/55000 [==============================] - 4s 70us/sample - loss: 0.7778 - accuracy: 0.8057 - val_loss: 0.1853 - val_accuracy: 0.9496\n", "Epoch 2/3\n", - "55000/55000 [==============================] - 3s - loss: 2.0835 - acc: 0.6384 - val_loss: 1.9688 - val_acc: 0.6812\n", + "55000/55000 [==============================] - 3s 54us/sample - loss: 0.1914 - accuracy: 0.9435 - val_loss: 0.1170 - val_accuracy: 0.9678\n", "Epoch 3/3\n", - "55000/55000 [==============================] - 6s - loss: 1.8409 - acc: 0.7098 - val_loss: 1.6977 - val_acc: 0.7404\n", + "55000/55000 [==============================] - 6s 107us/sample - loss: 0.1281 - accuracy: 0.9622 - val_loss: 0.0848 - val_accuracy: 0.9778\n", "\n", - "Accuracy: 74.04%\n", + "Accuracy: 97.78%\n", "\n", - "learning rate: 4.4e-03\n", - "num_dense_layers: 3\n", - "num_dense_nodes: 311\n", + "learning rate: 3.6e-05\n", + "num_dense_layers: 5\n", + "num_dense_nodes: 446\n", "activation: relu\n", "\n", "Train on 55000 samples, validate on 5000 samples\n", "Epoch 1/3\n", - "55000/55000 [==============================] - 3s - loss: 0.1504 - acc: 0.9523 - val_loss: 0.0746 - val_acc: 0.9800\n", + "55000/55000 [==============================] - 3s 63us/sample - loss: 0.7959 - accuracy: 0.8041 - val_loss: 0.1977 - val_accuracy: 0.9454\n", "Epoch 2/3\n", - "55000/55000 [==============================] - 4s - loss: 0.0559 - acc: 0.9842 - val_loss: 0.0751 - val_acc: 0.9812\n", + "55000/55000 [==============================] - 3s 53us/sample - loss: 0.1957 - accuracy: 0.9419 - val_loss: 0.1231 - val_accuracy: 0.9658\n", "Epoch 3/3\n", - "55000/55000 [==============================] - 7s - loss: 0.0431 - acc: 0.9884 - val_loss: 0.0500 - val_acc: 0.9870\n", + "55000/55000 [==============================] - 6s 110us/sample - loss: 0.1334 - accuracy: 0.9598 - val_loss: 0.0973 - val_accuracy: 0.9734\n", "\n", - "Accuracy: 98.70%\n", + "Accuracy: 97.34%\n", "\n", - "learning rate: 2.1e-03\n", - "num_dense_layers: 5\n", - "num_dense_nodes: 436\n", + "learning rate: 6.6e-05\n", + "num_dense_layers: 4\n", + "num_dense_nodes: 143\n", "activation: relu\n", "\n", "Train on 55000 samples, validate on 5000 samples\n", "Epoch 1/3\n", - "55000/55000 [==============================] - 3s - loss: 0.1884 - acc: 0.9418 - val_loss: 0.0664 - val_acc: 0.9840\n", + "55000/55000 [==============================] - 4s 67us/sample - loss: 0.8583 - accuracy: 0.7593 - val_loss: 0.2149 - val_accuracy: 0.9410\n", "Epoch 2/3\n", - "55000/55000 [==============================] - 4s - loss: 0.0598 - acc: 0.9837 - val_loss: 0.0454 - val_acc: 0.9880\n", + "55000/55000 [==============================] - 3s 50us/sample - loss: 0.2304 - accuracy: 0.9302 - val_loss: 0.1499 - val_accuracy: 0.9538\n", "Epoch 3/3\n", - "55000/55000 [==============================] - 8s - loss: 0.0435 - acc: 0.9887 - val_loss: 0.0553 - val_acc: 0.9864\n", + "55000/55000 [==============================] - 5s 86us/sample - loss: 0.1615 - accuracy: 0.9511 - val_loss: 0.1022 - val_accuracy: 0.9700\n", "\n", - "Accuracy: 98.64%\n", + "Accuracy: 97.00%\n", "\n", - "learning rate: 1.9e-04\n", - "num_dense_layers: 3\n", - "num_dense_nodes: 441\n", - "activation: relu\n", + "learning rate: 2.2e-04\n", + "num_dense_layers: 2\n", + "num_dense_nodes: 195\n", + "activation: sigmoid\n", "\n", "Train on 55000 samples, validate on 5000 samples\n", "Epoch 1/3\n", - "55000/55000 [==============================] - 3s - loss: 0.3664 - acc: 0.8989 - val_loss: 0.1076 - val_acc: 0.9698\n", + "55000/55000 [==============================] - 3s 53us/sample - loss: 2.2477 - accuracy: 0.1821 - val_loss: 1.6547 - val_accuracy: 0.6054\n", "Epoch 2/3\n", - "55000/55000 [==============================] - 4s - loss: 0.0872 - acc: 0.9736 - val_loss: 0.0626 - val_acc: 0.9816\n", + "55000/55000 [==============================] - 3s 49us/sample - loss: 0.8448 - accuracy: 0.7859 - val_loss: 0.4386 - val_accuracy: 0.8970\n", "Epoch 3/3\n", - "55000/55000 [==============================] - 7s - loss: 0.0583 - acc: 0.9824 - val_loss: 0.0504 - val_acc: 0.9856\n", + "55000/55000 [==============================] - 3s 61us/sample - loss: 0.4028 - accuracy: 0.8893 - val_loss: 0.2623 - val_accuracy: 0.9310\n", "\n", - "Accuracy: 98.56%\n", + "Accuracy: 93.10%\n", "\n", - "learning rate: 1.7e-03\n", + "learning rate: 4.2e-05\n", "num_dense_layers: 1\n", "num_dense_nodes: 512\n", "activation: sigmoid\n", "\n", "Train on 55000 samples, validate on 5000 samples\n", "Epoch 1/3\n", - "55000/55000 [==============================] - 3s - loss: 1.2528 - acc: 0.5598 - val_loss: 0.2764 - val_acc: 0.9186\n", + "55000/55000 [==============================] - 3s 61us/sample - loss: 2.3073 - accuracy: 0.1076 - val_loss: 2.2982 - val_accuracy: 0.1126\n", "Epoch 2/3\n", - "55000/55000 [==============================] - 3s - loss: 0.2010 - acc: 0.9369 - val_loss: 0.1251 - val_acc: 0.9592\n", + "55000/55000 [==============================] - 3s 49us/sample - loss: 2.2914 - accuracy: 0.1344 - val_loss: 2.2685 - val_accuracy: 0.1802\n", "Epoch 3/3\n", - "55000/55000 [==============================] - 6s - loss: 0.1203 - acc: 0.9629 - val_loss: 0.0916 - val_acc: 0.9734\n", + "55000/55000 [==============================] - 4s 79us/sample - loss: 2.1696 - accuracy: 0.3317 - val_loss: 1.9508 - val_accuracy: 0.5936\n", "\n", - "Accuracy: 97.34%\n", + "Accuracy: 59.36%\n", "\n", - "learning rate: 1.5e-03\n", - "num_dense_layers: 5\n", - "num_dense_nodes: 285\n", - "activation: sigmoid\n", + "learning rate: 5.1e-06\n", + "num_dense_layers: 3\n", + "num_dense_nodes: 512\n", + "activation: relu\n", "\n", "Train on 55000 samples, validate on 5000 samples\n", "Epoch 1/3\n", - "55000/55000 [==============================] - 3s - loss: 2.1378 - acc: 0.1588 - val_loss: 1.2723 - val_acc: 0.4116\n", + "55000/55000 [==============================] - 3s 59us/sample - loss: 2.0897 - accuracy: 0.5289 - val_loss: 1.6486 - val_accuracy: 0.7448\n", "Epoch 2/3\n", - "55000/55000 [==============================] - 4s - loss: 0.5670 - acc: 0.7991 - val_loss: 0.2616 - val_acc: 0.9266\n", + "55000/55000 [==============================] - 3s 57us/sample - loss: 1.1361 - accuracy: 0.7831 - val_loss: 0.6805 - val_accuracy: 0.8716\n", "Epoch 3/3\n", - "55000/55000 [==============================] - 8s - loss: 0.1877 - acc: 0.9460 - val_loss: 0.1365 - val_acc: 0.9618\n", + "55000/55000 [==============================] - 6s 118us/sample - loss: 0.5963 - accuracy: 0.8549 - val_loss: 0.4089 - val_accuracy: 0.9120\n", "\n", - "Accuracy: 96.18%\n", + "Accuracy: 91.20%\n", "\n", - "learning rate: 3.3e-04\n", - "num_dense_layers: 5\n", - "num_dense_nodes: 5\n", + "learning rate: 1.4e-06\n", + "num_dense_layers: 4\n", + "num_dense_nodes: 512\n", "activation: sigmoid\n", "\n", "Train on 55000 samples, validate on 5000 samples\n", "Epoch 1/3\n", - "55000/55000 [==============================] - 3s - loss: 2.3570 - acc: 0.0907 - val_loss: 2.3175 - val_acc: 0.0868\n", - "Epoch 2/3\n", - "55000/55000 [==============================] - 3s - loss: 2.3074 - acc: 0.0952 - val_loss: 2.3029 - val_acc: 0.1126\n", - "Epoch 3/3\n" + "55000/55000 [==============================] - 4s 70us/sample - loss: 2.3907 - accuracy: 0.0994 - val_loss: 2.3302 - val_accuracy: 0.0986\n", + "Epoch 2/3\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "55000/55000 [==============================] - 8s - loss: 2.3019 - acc: 0.1123 - val_loss: 2.3013 - val_acc: 0.1126\n", + "55000/55000 [==============================] - 3s 52us/sample - loss: 2.3122 - accuracy: 0.0994 - val_loss: 2.3043 - val_accuracy: 0.0986\n", + "Epoch 3/3\n", + "55000/55000 [==============================] - 7s 121us/sample - loss: 2.3020 - accuracy: 0.1115 - val_loss: 2.3020 - val_accuracy: 0.1060\n", "\n", - "Accuracy: 11.26%\n", + "Accuracy: 10.60%\n", "\n", - "learning rate: 2.3e-04\n", - "num_dense_layers: 4\n", - "num_dense_nodes: 512\n", + "learning rate: 4.8e-04\n", + "num_dense_layers: 2\n", + "num_dense_nodes: 225\n", "activation: sigmoid\n", "\n", "Train on 55000 samples, validate on 5000 samples\n", "Epoch 1/3\n", - "55000/55000 [==============================] - 3s - loss: 2.1591 - acc: 0.1861 - val_loss: 1.0381 - val_acc: 0.6422\n", + "55000/55000 [==============================] - 3s 59us/sample - loss: 1.5780 - accuracy: 0.4531 - val_loss: 0.3782 - val_accuracy: 0.9030\n", "Epoch 2/3\n", - "55000/55000 [==============================] - 5s - loss: 0.6686 - acc: 0.7868 - val_loss: 0.4403 - val_acc: 0.8662\n", + "55000/55000 [==============================] - 3s 52us/sample - loss: 0.2985 - accuracy: 0.9146 - val_loss: 0.1567 - val_accuracy: 0.9564\n", "Epoch 3/3\n", - "55000/55000 [==============================] - 8s - loss: 0.3814 - acc: 0.8831 - val_loss: 0.2920 - val_acc: 0.9090\n", + "55000/55000 [==============================] - 7s 118us/sample - loss: 0.1712 - accuracy: 0.9491 - val_loss: 0.1077 - val_accuracy: 0.9704\n", "\n", - "Accuracy: 90.90%\n", + "Accuracy: 97.04%\n", "\n", - "learning rate: 2.6e-03\n", + "learning rate: 5.5e-03\n", "num_dense_layers: 1\n", - "num_dense_nodes: 126\n", - "activation: sigmoid\n", + "num_dense_nodes: 512\n", + "activation: relu\n", "\n", "Train on 55000 samples, validate on 5000 samples\n", "Epoch 1/3\n", - "55000/55000 [==============================] - 2s - loss: 1.1633 - acc: 0.5922 - val_loss: 0.1928 - val_acc: 0.9422\n", + "55000/55000 [==============================] - 4s 69us/sample - loss: 0.1434 - accuracy: 0.9557 - val_loss: 0.0516 - val_accuracy: 0.9834\n", "Epoch 2/3\n", - "55000/55000 [==============================] - 2s - loss: 0.1490 - acc: 0.9550 - val_loss: 0.0859 - val_acc: 0.9778\n", + "55000/55000 [==============================] - 3s 49us/sample - loss: 0.0457 - accuracy: 0.9858 - val_loss: 0.0441 - val_accuracy: 0.9874\n", "Epoch 3/3\n", - "55000/55000 [==============================] - 5s - loss: 0.0885 - acc: 0.9729 - val_loss: 0.0735 - val_acc: 0.9786\n", + "55000/55000 [==============================] - 6s 110us/sample - loss: 0.0330 - accuracy: 0.9894 - val_loss: 0.0582 - val_accuracy: 0.9854\n", "\n", - "Accuracy: 97.86%\n", + "Accuracy: 98.54%\n", "\n", - "learning rate: 5.7e-04\n", - "num_dense_layers: 1\n", - "num_dense_nodes: 246\n", + "learning rate: 2.4e-03\n", + "num_dense_layers: 3\n", + "num_dense_nodes: 5\n", "activation: relu\n", "\n", "Train on 55000 samples, validate on 5000 samples\n", "Epoch 1/3\n", - "55000/55000 [==============================] - 6s - loss: 0.2579 - acc: 0.9261 - val_loss: 0.0748 - val_acc: 0.9782\n", + "55000/55000 [==============================] - 3s 63us/sample - loss: 1.1980 - accuracy: 0.5365 - val_loss: 0.3991 - val_accuracy: 0.8454\n", "Epoch 2/3\n", - "55000/55000 [==============================] - 6s - loss: 0.0691 - acc: 0.9787 - val_loss: 0.0502 - val_acc: 0.9858\n", + "55000/55000 [==============================] - 3s 50us/sample - loss: 0.3059 - accuracy: 0.9074 - val_loss: 0.1823 - val_accuracy: 0.9488\n", "Epoch 3/3\n", - "55000/55000 [==============================] - 6s - loss: 0.0465 - acc: 0.9854 - val_loss: 0.0423 - val_acc: 0.9880\n", + "55000/55000 [==============================] - 4s 77us/sample - loss: 0.1815 - accuracy: 0.9497 - val_loss: 0.1492 - val_accuracy: 0.9606\n", "\n", - "Accuracy: 98.80%\n", + "Accuracy: 96.06%\n", "\n", - "learning rate: 2.4e-04\n", + "learning rate: 6.6e-04\n", "num_dense_layers: 1\n", - "num_dense_nodes: 164\n", + "num_dense_nodes: 512\n", "activation: relu\n", "\n", "Train on 55000 samples, validate on 5000 samples\n", "Epoch 1/3\n", - "55000/55000 [==============================] - 2s - loss: 0.4321 - acc: 0.8849 - val_loss: 0.1429 - val_acc: 0.9608\n", + "55000/55000 [==============================] - 3s 61us/sample - loss: 0.2145 - accuracy: 0.9392 - val_loss: 0.0638 - val_accuracy: 0.9830\n", "Epoch 2/3\n", - "55000/55000 [==============================] - 2s - loss: 0.1163 - acc: 0.9654 - val_loss: 0.0821 - val_acc: 0.9766\n", + "55000/55000 [==============================] - 3s 51us/sample - loss: 0.0591 - accuracy: 0.9817 - val_loss: 0.0539 - val_accuracy: 0.9856\n", "Epoch 3/3\n", - "55000/55000 [==============================] - 4s - loss: 0.0794 - acc: 0.9762 - val_loss: 0.0679 - val_acc: 0.9796\n", + "55000/55000 [==============================] - 3s 62us/sample - loss: 0.0395 - accuracy: 0.9879 - val_loss: 0.0454 - val_accuracy: 0.9890\n", "\n", - "Accuracy: 97.96%\n", + "Accuracy: 98.90%\n", "\n", "learning rate: 1.0e-06\n", + "num_dense_layers: 5\n", + "num_dense_nodes: 512\n", + "activation: relu\n", + "\n", + "Train on 55000 samples, validate on 5000 samples\n", + "Epoch 1/3\n", + "55000/55000 [==============================] - 3s 64us/sample - loss: 2.2933 - accuracy: 0.2440 - val_loss: 2.2784 - val_accuracy: 0.4338\n", + "Epoch 2/3\n", + "55000/55000 [==============================] - 4s 68us/sample - loss: 2.2577 - accuracy: 0.5072 - val_loss: 2.2230 - val_accuracy: 0.5994\n", + "Epoch 3/3\n", + "55000/55000 [==============================] - 8s 154us/sample - loss: 2.1768 - accuracy: 0.6255 - val_loss: 2.0998 - val_accuracy: 0.7048\n", + "\n", + "Accuracy: 70.48%\n", + "\n", + "learning rate: 2.9e-03\n", "num_dense_layers: 2\n", - "num_dense_nodes: 5\n", + "num_dense_nodes: 299\n", "activation: relu\n", "\n", "Train on 55000 samples, validate on 5000 samples\n", "Epoch 1/3\n", - "55000/55000 [==============================] - 2s - loss: 2.3000 - acc: 0.1046 - val_loss: 2.2987 - val_acc: 0.1122\n", + "55000/55000 [==============================] - 4s 69us/sample - loss: 0.1429 - accuracy: 0.9546 - val_loss: 0.0407 - val_accuracy: 0.9882\n", "Epoch 2/3\n", - "55000/55000 [==============================] - 2s - loss: 2.2981 - acc: 0.1124 - val_loss: 2.2965 - val_acc: 0.1224\n", + "55000/55000 [==============================] - 3s 59us/sample - loss: 0.0452 - accuracy: 0.9855 - val_loss: 0.0391 - val_accuracy: 0.9882\n", "Epoch 3/3\n", - "55000/55000 [==============================] - 4s - loss: 2.2959 - acc: 0.1221 - val_loss: 2.2941 - val_acc: 0.1290\n", + "55000/55000 [==============================] - 7s 132us/sample - loss: 0.0310 - accuracy: 0.9903 - val_loss: 0.0370 - val_accuracy: 0.9894\n", "\n", - "Accuracy: 12.90%\n", + "Accuracy: 98.94%\n", + "\n", + "learning rate: 1.8e-04\n", + "num_dense_layers: 3\n", + "num_dense_nodes: 512\n", + "activation: relu\n", "\n", - "learning rate: 1.3e-05\n", + "Train on 55000 samples, validate on 5000 samples\n", + "Epoch 1/3\n", + "55000/55000 [==============================] - 3s 62us/sample - loss: 0.3695 - accuracy: 0.8938 - val_loss: 0.0923 - val_accuracy: 0.9740\n", + "Epoch 2/3\n", + "55000/55000 [==============================] - 3s 54us/sample - loss: 0.0903 - accuracy: 0.9729 - val_loss: 0.0664 - val_accuracy: 0.9808\n", + "Epoch 3/3\n", + "55000/55000 [==============================] - 7s 126us/sample - loss: 0.0613 - accuracy: 0.9809 - val_loss: 0.0611 - val_accuracy: 0.9830\n", + "\n", + "Accuracy: 98.30%\n", + "\n", + "learning rate: 1.0e-06\n", "num_dense_layers: 2\n", "num_dense_nodes: 512\n", "activation: relu\n", "\n", "Train on 55000 samples, validate on 5000 samples\n", "Epoch 1/3\n", - "55000/55000 [==============================] - 3s - loss: 1.6243 - acc: 0.6472 - val_loss: 0.7587 - val_acc: 0.8260\n", + "55000/55000 [==============================] - 9s 168us/sample - loss: 2.2758 - accuracy: 0.2655 - val_loss: 2.2455 - val_accuracy: 0.4236\n", "Epoch 2/3\n", - "55000/55000 [==============================] - 4s - loss: 0.5184 - acc: 0.8724 - val_loss: 0.3656 - val_acc: 0.9038\n", + "55000/55000 [==============================] - 8s 138us/sample - loss: 2.2145 - accuracy: 0.5053 - val_loss: 2.1717 - val_accuracy: 0.6046\n", "Epoch 3/3\n", - "55000/55000 [==============================] - 7s - loss: 0.3292 - acc: 0.9091 - val_loss: 0.2724 - val_acc: 0.9268\n", + "55000/55000 [==============================] - 7s 128us/sample - loss: 2.1320 - accuracy: 0.6202 - val_loss: 2.0713 - val_accuracy: 0.6850\n", "\n", - "Accuracy: 92.68%\n", + "Accuracy: 68.50%\n", "\n", - "learning rate: 7.6e-05\n", - "num_dense_layers: 1\n", - "num_dense_nodes: 241\n", + "learning rate: 1.0e-02\n", + "num_dense_layers: 3\n", + "num_dense_nodes: 5\n", "activation: relu\n", "\n", "Train on 55000 samples, validate on 5000 samples\n", "Epoch 1/3\n", - "55000/55000 [==============================] - 2s - loss: 0.7636 - acc: 0.8233 - val_loss: 0.2393 - val_acc: 0.9368\n", + "55000/55000 [==============================] - 3s 60us/sample - loss: 1.5650 - accuracy: 0.3523 - val_loss: 1.2933 - val_accuracy: 0.4494\n", "Epoch 2/3\n", - "55000/55000 [==============================] - 2s - loss: 0.1961 - acc: 0.9448 - val_loss: 0.1449 - val_acc: 0.9612\n", + "55000/55000 [==============================] - 3s 48us/sample - loss: 0.9217 - accuracy: 0.6472 - val_loss: 0.6613 - val_accuracy: 0.7856\n", "Epoch 3/3\n", - "55000/55000 [==============================] - 6s - loss: 0.1309 - acc: 0.9617 - val_loss: 0.1068 - val_acc: 0.9688\n", + "55000/55000 [==============================] - 5s 85us/sample - loss: 0.6513 - accuracy: 0.7897 - val_loss: 0.4982 - val_accuracy: 0.8736\n", "\n", - "Accuracy: 96.88%\n", + "Accuracy: 87.36%\n", "\n", - "learning rate: 2.0e-03\n", - "num_dense_layers: 4\n", + "learning rate: 1.4e-03\n", + "num_dense_layers: 1\n", "num_dense_nodes: 512\n", + "activation: sigmoid\n", + "\n", + "Train on 55000 samples, validate on 5000 samples\n", + "Epoch 1/3\n", + "55000/55000 [==============================] - 3s 62us/sample - loss: 1.3383 - accuracy: 0.5226 - val_loss: 0.2304 - val_accuracy: 0.9322\n", + "Epoch 2/3\n", + "55000/55000 [==============================] - 3s 53us/sample - loss: 0.2098 - accuracy: 0.9346 - val_loss: 0.1193 - val_accuracy: 0.9642\n", + "Epoch 3/3\n", + "55000/55000 [==============================] - 7s 123us/sample - loss: 0.1266 - accuracy: 0.9606 - val_loss: 0.0846 - val_accuracy: 0.9750\n", + "\n", + "Accuracy: 97.50%\n", + "\n", + "learning rate: 4.6e-04\n", + "num_dense_layers: 3\n", + "num_dense_nodes: 105\n", "activation: relu\n", "\n", "Train on 55000 samples, validate on 5000 samples\n", "Epoch 1/3\n", - "55000/55000 [==============================] - 3s - loss: 0.1668 - acc: 0.9474 - val_loss: 0.0605 - val_acc: 0.9832\n", + "55000/55000 [==============================] - 9s 168us/sample - loss: 0.3599 - accuracy: 0.8924 - val_loss: 0.0980 - val_accuracy: 0.9706\n", "Epoch 2/3\n", - "55000/55000 [==============================] - 4s - loss: 0.0548 - acc: 0.9845 - val_loss: 0.0419 - val_acc: 0.9902\n", + "55000/55000 [==============================] - 7s 124us/sample - loss: 0.0884 - accuracy: 0.9728 - val_loss: 0.0749 - val_accuracy: 0.9788\n", "Epoch 3/3\n", - "55000/55000 [==============================] - 8s - loss: 0.0408 - acc: 0.9890 - val_loss: 0.0596 - val_acc: 0.9844\n", + "55000/55000 [==============================] - 7s 124us/sample - loss: 0.0643 - accuracy: 0.9799 - val_loss: 0.0548 - val_accuracy: 0.9860\n", "\n", - "Accuracy: 98.44%\n", + "Accuracy: 98.60%\n", "\n", - "learning rate: 2.2e-03\n", + "learning rate: 1.0e-03\n", "num_dense_layers: 2\n", - "num_dense_nodes: 326\n", + "num_dense_nodes: 512\n", "activation: sigmoid\n", "\n", "Train on 55000 samples, validate on 5000 samples\n", "Epoch 1/3\n", - "55000/55000 [==============================] - 3s - loss: 1.1358 - acc: 0.5865 - val_loss: 0.2104 - val_acc: 0.9356\n", + "55000/55000 [==============================] - 3s 62us/sample - loss: 1.1924 - accuracy: 0.5680 - val_loss: 0.1600 - val_accuracy: 0.9518\n", "Epoch 2/3\n", - "55000/55000 [==============================] - 3s - loss: 0.1576 - acc: 0.9505 - val_loss: 0.0999 - val_acc: 0.9712\n", + "55000/55000 [==============================] - 3s 54us/sample - loss: 0.1456 - accuracy: 0.9542 - val_loss: 0.0808 - val_accuracy: 0.9764\n", "Epoch 3/3\n", - "55000/55000 [==============================] - 6s - loss: 0.1011 - acc: 0.9679 - val_loss: 0.0856 - val_acc: 0.9726\n", + "55000/55000 [==============================] - 6s 117us/sample - loss: 0.0892 - accuracy: 0.9727 - val_loss: 0.0612 - val_accuracy: 0.9806\n", "\n", - "Accuracy: 97.26%\n", - "\n" + "Accuracy: 98.06%\n", + "\n", + "CPU times: user 12min 22s, sys: 2min 47s, total: 15min 9s\n", + "Wall time: 9min 35s\n" ] } ], "source": [ + "%%time\n", "search_result = gp_minimize(func=fitness,\n", " dimensions=dimensions,\n", " acq_func='EI', # Expected Improvement.\n", @@ -1542,7 +1510,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 27, "metadata": { "scrolled": true }, @@ -1550,21 +1518,23 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 28, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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2zZs3r2BMnZ2dtLS0lPV73PyfT/LQk2s4a+aBHDZlfFnXlqOS2AaLY6uMY6uM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\nAElFTkSuQmCC\n", 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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1583,7 +1553,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 28, "metadata": { "scrolled": true }, @@ -1591,10 +1561,10 @@ { "data": { "text/plain": [ - "[0.0023584457378584664, 4, 144, 'relu']" + "[0.01, 2, 104, 'relu']" ] }, - "execution_count": 29, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -1614,7 +1584,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -1630,19 +1600,19 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'activation': 'relu',\n", - " 'learning_rate': 0.0023584457378584664,\n", - " 'num_dense_layers': 4,\n", - " 'num_dense_nodes': 144}" + "{'learning_rate': 0.01,\n", + " 'num_dense_layers': 2,\n", + " 'num_dense_nodes': 104,\n", + " 'activation': 'relu'}" ] }, - "execution_count": 31, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -1660,7 +1630,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 31, "metadata": { "scrolled": true }, @@ -1668,10 +1638,10 @@ { "data": { "text/plain": [ - "-0.98799999999999999" + "-0.9904" ] }, - "execution_count": 32, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -1691,7 +1661,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 32, "metadata": { "scrolled": true }, @@ -1699,49 +1669,49 @@ { "data": { "text/plain": [ - "[(-0.98799999999999999, [0.00057102338020535671, 1, 246, 'relu']),\n", - " (-0.98799999999999999, [0.0023584457378584664, 4, 144, 'relu']),\n", - " (-0.98699999999999999, [0.0043924439217142824, 3, 311, 'relu']),\n", - " (-0.98680000000000001, [0.00025070302453255417, 2, 435, 'relu']),\n", - " (-0.98640000000000005, [0.0020904801989242469, 5, 436, 'relu']),\n", - " (-0.98560000000000003, [0.00017567744133971055, 4, 453, 'relu']),\n", - " (-0.98560000000000003, [0.00018871091218374878, 3, 441, 'relu']),\n", - " (-0.98560000000000003, [0.0010013922052631494, 3, 496, 'relu']),\n", - " (-0.98519999999999996, [0.006752254693985822, 2, 105, 'relu']),\n", - " (-0.98499999999999999, [0.0001905308801138268, 4, 418, 'relu']),\n", - " (-0.98460000000000003, [0.0073224617473678331, 3, 166, 'relu']),\n", - " (-0.98440000000000005, [0.0020143982003767271, 4, 512, 'relu']),\n", - " (-0.98419999999999996, [0.0014193250864683331, 2, 62, 'relu']),\n", - " (-0.97960000000000003, [0.00023735076383216567, 1, 164, 'relu']),\n", - " (-0.97860000000000003, [0.0026064900033469073, 1, 126, 'sigmoid']),\n", - " (-0.97660000000000002, [0.0037123587226393501, 5, 512, 'relu']),\n", - " (-0.9758, [0.0027230837381696737, 2, 364, 'sigmoid']),\n", - " (-0.97340000000000004, [0.0016597651372777609, 1, 512, 'sigmoid']),\n", - " (-0.97260000000000002, 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-1761,19 +1731,21 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 33, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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JiMbc65uBSkmT+6l7dUTMjYi5lVSPaJxmZvuToiYGSYdIUu71ybl4dhYzJjOz\n/V2qp5Ik/QJYCEyW9BzwT0AlQERcBbwb+LikTqAFOD8iIs2YzMxsYKkmhoh47z7mX0H2clYzMysR\npXBVkpmZlRAnBjMzy+PEYGZmeZwYzMwsjxODmZnlcWIwM7M8TgxmZpbHicHMzPI4MZiZWR4nBjMz\ny+PEYGZmeZwYzMwsjxODmZnlcWIwM7M8TgxmZpbHicHMzPKkmhgk/VhSg6T1/cyXpMslbZL0sKQT\n04zHzMz2Le0WwzXAOQPMfxswK/e4EPh+yvGYmdk+pD205ypJMweoch6wLDfO832SJkqaGhHb04xr\nOBoyW9kZzzNJhzClbHqxw0msoXMLOzq3MbliGlMqZhQ7nJKx8y8beGnHRiZMPZpJU48pdjh5dt67\niWce2syhp0xj9pmHjOi2N965nc2rGzhi3hRmv2XqiG67p9/f2spdK1tZuKAG4JXX5yyuSbSeFbc0\ncfuqFhbNr2XJ2ePSCHVMSDUxDMJ04Nke08/lykoyMTRktvJIZjUZutgWT3Es80ZVcmjo3MK6lpVk\n6GJrx5McX7vAyYFsUnhs7c/JZDp4fms9R8+9oGSSw677nmTzN1eQaetk680bqKmYz4z5I/M327xy\nK7dcWk9HaxfrbtrCO785lzedNWFEtt3THbe28pmLX6KlBX7682YA2tvhF9c384MrDxp0clhxSxPv\n/8QLNLcEy65rZNmVDCk5TCxrZlemLvFyo0pEpPoAZgLr+5m3Ajijx/QdwNx+6l4I1AP1M2bMiGK4\n6KKLAnjlcdFFFxUljqEa7fGnpZT3SzFjK5X90juOocZUKu+nmID6GMTndrGvStoKHNZj+tBc2V4i\n4uqImBsRcw8++OARCa63xYsXU1eX/aZQV1fH4sWLixLHUI32+NNSyvulmLGVyn7pGUdVVRXV1dVD\niqlU3s9ooGwSSXED2T6GFRExp4955wIXA28HTgEuj4iT97XOuXPnRn19fYEjHZzly5dz6623snjx\nYpYuXVqUGIZjtMefllLeL8WMrVT2S884gCHHVCrvp1gkrYmIufusl2ZikPQLYCEwGfgL8E9AJUBE\nXCVJwBVkr1xqBj4UEfv8xC9mYjAzG60GmxjSvirpvfuYH8BFacZgZmbJFLuPwczMSowTg5mZ5XFi\nMDOzPE4MZmaWx4nBzMzyODGYmVme1H/glgZJLwNPpLiJCcDuFJfbV73+5vdVPpiy3tOTgR2DiHOo\nirn/ks4tEcpZAAAGNklEQVRLuv/G8r4baP5gy/fnY2+g+aXyv/vaiNj3rSMGc9+MUnswyPt9DGP9\nV6e53L7q9Te/r/LBlPUxPWb3X9J5SfffWN53A80fbPn+fOwl3X+l+L/b/fCppL79NuXl9lWvv/l9\nlQ+mbKjvZ6iKuf+Sziu1/Teajr2+yvfnY2+g+aPh2HvFaD2VVB+D+Fm39c37b+i874bH+294Rmr/\njdYWw9XFDmCU8/4bOu+74fH+G54R2X+jssVgZmbpGa0tBjMzS4kTg5mZ5XFiMDOzPGMuMUgqk/R1\nSd+T9IFixzPaSFoo6W5JV0laWOx4RhtJ4yTVS1pS7FhGG0mvzx13N0j6eLHjGW0kvUPSDyVdL2lY\n45aWVGKQ9GNJDZLW9yo/R9ITkjZJ+uI+VnMe2bGjO4Dn0oq1FBVo/wXQCNSwH+2/Au07gC8Av0wn\nytJViP0XEY9FxMeA/wmcnma8paZA+++miPgo8DHgb4YVTyldlSRpPtkPpWWRGyNaUjmwETiL7AfV\nA8B7gXLgG71W8eHc46WI+IGkGyLi3SMVf7EVaP/tiIiMpNcA/x4RF4xU/MVUoH13PDCJbFLdEREr\nRib64ivE/ouIBklLgY8D10bEz0cq/mIr1P7LLfcd4GcR8eBQ40l1aM+kImKVpJm9ik8GNkXEZgBJ\n1wHnRcQ3gL2a65KeA9pzk13pRVt6CrH/engJqE4jzlJUoGNvITAOeAPQIunmiMikGXepKNSxFxHL\ngeWS/h+w3ySGAh1/Av4V+N1wkgKUWGLox3Tg2R7TzwGnDFD/18D3JL0ZWJVmYKNEov0n6a+Bs4GJ\nwBXphlbyEu27iLgMQNIHybW8Uo2u9CU99hYCf032C8nNqUY2OiT97PsksAiYIOmoiLhqqBseDYkh\nkYhoBj5S7DhGq4j4NdnkakMUEdcUO4bRKCLuAu4qchijVkRcDlxeiHWVVOdzP7YCh/WYPjRXZoPj\n/Td03nfD4/03PEXbf6MhMTwAzJJ0uKQq4HxgeZFjGk28/4bO+254vP+Gp2j7r6QSg6RfAKuBoyU9\nJ+kjEdEJXAzcAjwG/DIiHi1mnKXK+2/ovO+Gx/tveEpt/5XU5apmZlZ8JdViMDOz4nNiMDOzPE4M\nZmaWx4nBzMzyODGYmVkeJwYzM8vjxGBmZnmcGGxMkdQ4AttYOsixGdLY9jskvaEY27b9h3/gZmOK\npMaIGF+A9ZRHRFFu2z7QtiVdA6yIiBtGNirbn7jFYGOWpM9LekDSw5K+0qP8JklrJD0q6cIe5Y2S\nviNpHTBP0tOSviLpQUmPSHpdrt4HJV2Re32NpMsl3Stps6R358rLJF0p6XFJt0m6uXteP7E+Lemb\nkh4E3iPpo7nY10m6UVKdpNOApcC3Ja2VdGTu8fvc+7m7O0az4XBisDFJ2TFvZ5Ed7OQE4KTcKFmQ\nHe3qJGAu8ClJk3Ll44D7I+L4iLgnV7YjIk4Evg98rp/NTQXOIDt4yr/myv4amEl20J6/BeYNIuyd\nEXFiRFwH/Doi3hQRx5O9T85HIuJesjdR+3xEnBARfwauBj6Zez+fA64cxHbMBjTmxmMwy1mcezyU\nmx5PNlGsIpsM3pkrPyxXvpPsiH839lpP99gUa8h+2PflptygPBtyQ6JCNlH8Klf+vKQ7BxHz9T1e\nz5H0NbIDJo0neyO1PJLGA6cBv8oO3gXsR6PuWXqcGGysEvCNiPhBXmF2lLBFwLyIaJZ0F9kxmgFa\n+zi335Z77qL//5e2Hq/VT53BaOrx+hrgHRGxLjci3MI+6pcBuyLihGFs02wvPpVkY9UtwIdz36qR\nNF3SFGAC8FIuKbwOODWl7f8ReFeur+E19P3BPpADgO2SKoELepS/nJtHROwBnpL0HsiO+Svp+GFH\nbvs9JwYbkyLiVrKDya+W9AhwA9kP1N8DFZIeI9sfcF9KIdxIdozeDcBPgQeB3QmW/z/A/WQTzOM9\nyq8DPi/pIUlHkk0aH8l1mD8KnFeA2G0/58tVzVIiaXxENOY6t/8EnB4Rzxc7LrN9cR+DWXpWSJoI\nVAH/4qRgo4VbDGYjSNJvgMN7FX8hIva66sisWJwYzMwsjzufzcwsjxODmZnlcWIwM7M8TgxmZpbH\nicHMzPL8fy6cRQja3MY6AAAAAElFTkSuQmCC\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1829,7 +1803,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -1851,19 +1825,21 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 36, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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UN95Jqlof4n33uHYYsacB1ao6vv0TqnqRiHwLOBaYJyITVHVrGPcwKcjaNEyq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\n+VF3Tz0MvEDg5TarH56Z/Y3c2wLHcRynE0RtNMrN7BsRX9N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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1880,19 +1856,21 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 37, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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OGsyND5/PxH99zK5te+jWtxPX/O0cOnaLvt5iAVKSE7jv1nHcfvVYyspctG+b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\nbTAYDPUxaxLeLATusO+2EZE+ItIdaxdLvq0ghgFnhaj9FcC19tpED/z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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1911,7 +1889,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -1927,20 +1905,20 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " 8960/10000 [=========================>....] - ETA: 0s" + "10000/10000 [==============================] - 1s 64us/sample - loss: 0.0490 - accuracy: 0.9882\n" ] } ], "source": [ - "result = model.evaluate(x=data.test.images,\n", - " y=data.test.labels)" + "result = model.evaluate(x=data.x_test,\n", + " y=data.y_test)" ] }, { @@ -1952,15 +1930,15 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "loss 0.0363312054525\n", - "acc 0.9888\n" + "loss 0.048988553384863916\n", + "accuracy 0.9882\n" ] } ], @@ -1978,14 +1956,14 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "acc: 98.88%\n" + "accuracy: 98.82%\n" ] } ], @@ -2004,11 +1982,11 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ - "images = data.test.images[0:9]" + "images = data.x_test[0:9]" ] }, { @@ -2020,11 +1998,11 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ - "cls_true = data.test.cls[0:9]" + "cls_true = data.y_test_cls[0:9]" ] }, { @@ -2036,7 +2014,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -2052,7 +2030,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -2061,14 +2039,14 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 46, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" ] }, "metadata": {}, @@ -2094,11 +2072,11 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ - "y_pred = model.predict(x=data.test.images)" + "y_pred = model.predict(x=data.x_test)" ] }, { @@ -2110,11 +2088,11 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ - "cls_pred = np.argmax(y_pred,axis=1)" + "cls_pred = np.argmax(y_pred, axis=1)" ] }, { @@ -2126,14 +2104,14 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 49, "metadata": {}, "outputs": [ { "data": { - "image/png": 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BOP300wEoO/2kX2co8f169eoBcMUVVwDQsWNHAPbdd18AtthiiyxGHI0yTRGRCJRpSkEY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\niETw/wHzcqwbcSXHTgAAAABJRU5ErkJggg==\n", 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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -2207,7 +2185,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.1" + "version": "3.6.10" } }, "nbformat": 4, diff --git a/20_Natural_Language_Processing.ipynb b/20_Natural_Language_Processing.ipynb index 076fb73..a24a12b 100644 --- a/20_Natural_Language_Processing.ipynb +++ b/20_Natural_Language_Processing.ipynb @@ -124,16 +124,7 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", - " from ._conv import register_converters as _register_converters\n" - ] - } - ], + "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", @@ -155,12 +146,11 @@ "metadata": {}, "outputs": [], "source": [ - "# from tf.keras.models import Sequential # This does not work!\n", - "from tensorflow.python.keras.models import Sequential\n", - "from tensorflow.python.keras.layers import Dense, GRU, Embedding\n", - "from tensorflow.python.keras.optimizers import Adam\n", - "from tensorflow.python.keras.preprocessing.text import Tokenizer\n", - "from tensorflow.python.keras.preprocessing.sequence import pad_sequences" + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense, GRU, Embedding\n", + "from tensorflow.keras.optimizers import Adam\n", + "from tensorflow.keras.preprocessing.text import Tokenizer\n", + "from tensorflow.keras.preprocessing.sequence import pad_sequences" ] }, { @@ -180,7 +170,7 @@ { "data": { "text/plain": [ - "'1.5.0'" + "'2.1.0'" ] }, "execution_count": 3, @@ -202,7 +192,7 @@ { "data": { "text/plain": [ - "'2.1.2-tf'" + "'2.2.4-tf'" ] }, "execution_count": 4, @@ -288,7 +278,11 @@ "outputs": [], "source": [ "x_train_text, y_train = imdb.load_data(train=True)\n", - "x_test_text, y_test = imdb.load_data(train=False)" + "x_test_text, y_test = imdb.load_data(train=False)\n", + "\n", + "# Convert to numpy arrays.\n", + "y_train = np.array(y_train)\n", + "y_test = np.array(y_test)" ] }, { @@ -341,7 +335,7 @@ { "data": { "text/plain": [ - "'A simple comment...

What can I say... this is a wonderful film that I can watch over and over. It is definitely one of the top ten comedies made. With a great cast, Jack Lemmon and Walter Matthau wording a perfect script by Neil Simon, based on his play.

It is real to life situation done perfectly. If you have digital cable, one gets the menu on bottom of screen to give what is on. It usually gives this film ***% stars but in reality it deserves **** stars. If you really watch this film, one can tell that it will be as funny and fresh a hundred years from now.'" + "'Return To the Lost World was filmed back-to-back with the 1992 version of The Lost World.

In this sequel, the same five people, lead by Challenger return to the plateau where a group has started drilling for oil which is threatening to destroy the land. Gomez has something to do with this. They manage to defeat the drillers and the plateau is saved, much to the delight of the natives.

Like in The Lost World, what few dinosaurs we see are made of rubber and these include a T-Rex and Ankylosaurus.

John Ryhs-Davies and David Warner reprise their roles as Challenger and Summerlee and three of the other actors are also back.

Despite reading several bad reviews of this and those cheap looking rubber dinosaurs, I enjoyed Return to the Lost World.

Rating: 3 stars out of 5.'" ] }, "execution_count": 11, @@ -429,8 +423,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 10.6 s, sys: 16 ms, total: 10.6 s\n", - "Wall time: 10.6 s\n" + "CPU times: user 8.21 s, sys: 33.2 ms, total: 8.25 s\n", + "Wall time: 8.25 s\n" ] } ], @@ -604,8 +598,8 @@ " 'say': 131,\n", " 'these': 132,\n", " 'here': 133,\n", - " 'why': 134,\n", - " 'scenes': 135,\n", + " 'scenes': 134,\n", + " 'why': 135,\n", " 'while': 136,\n", " 'something': 137,\n", " 'such': 138,\n", @@ -720,8 +714,8 @@ " 'sure': 247,\n", " 'rather': 248,\n", " 'hard': 249,\n", - " 'girl': 250,\n", - " 'anyone': 251,\n", + " 'anyone': 250,\n", + " 'girl': 251,\n", " 'each': 252,\n", " 'played': 253,\n", " 'day': 254,\n", @@ -771,8 +765,8 @@ " 'instead': 298,\n", " 'high': 299,\n", " 'during': 300,\n", - " 'year': 301,\n", - " 'said': 302,\n", + " 'said': 301,\n", + " 'year': 302,\n", " 'half': 303,\n", " 'everyone': 304,\n", " 'later': 305,\n", @@ -804,8 +798,8 @@ " 'nice': 331,\n", " 'budget': 332,\n", " 'poor': 333,\n", - " 'completely': 334,\n", - " 'short': 335,\n", + " 'short': 334,\n", + " 'completely': 335,\n", " 'second': 336,\n", " \"you're\": 337,\n", " '3': 338,\n", @@ -855,8 +849,8 @@ " 'remember': 382,\n", " 'mean': 383,\n", " 'came': 384,\n", - " 'understand': 385,\n", - " 'getting': 386,\n", + " 'getting': 385,\n", + " 'understand': 386,\n", " 'perhaps': 387,\n", " 'moments': 388,\n", " 'name': 389,\n", @@ -887,11 +881,11 @@ " 'went': 414,\n", " 'finally': 415,\n", " 'mother': 416,\n", - " 'case': 417,\n", - " 'title': 418,\n", + " 'title': 417,\n", + " 'case': 418,\n", " 'absolutely': 419,\n", - " 'live': 420,\n", - " 'boy': 421,\n", + " 'boy': 420,\n", + " 'live': 421,\n", " 'yes': 422,\n", " 'laugh': 423,\n", " 'certainly': 424,\n", @@ -974,15 +968,15 @@ " \"they're\": 501,\n", " 'act': 502,\n", " 'art': 503,\n", - " 'matter': 504,\n", - " 'kill': 505,\n", + " 'kill': 504,\n", + " 'matter': 505,\n", " 'etc': 506,\n", " 'tries': 507,\n", " \"won't\": 508,\n", " 'past': 509,\n", " 'town': 510,\n", - " 'turns': 511,\n", - " 'enjoyed': 512,\n", + " 'enjoyed': 511,\n", + " 'turns': 512,\n", " 'brilliant': 513,\n", " 'gave': 514,\n", " 'behind': 515,\n", @@ -1041,11 +1035,11 @@ " 'extremely': 568,\n", " 'score': 569,\n", " 'violence': 570,\n", - " 'involved': 571,\n", - " 'police': 572,\n", + " 'police': 571,\n", + " 'involved': 572,\n", " 'strong': 573,\n", - " 'chance': 574,\n", - " 'lack': 575,\n", + " 'lack': 574,\n", + " 'chance': 575,\n", " 'cannot': 576,\n", " 'hit': 577,\n", " 'roles': 578,\n", @@ -1061,16 +1055,16 @@ " 'looked': 588,\n", " \"wouldn't\": 589,\n", " 'crap': 590,\n", - " 'simple': 591,\n", - " 'please': 592,\n", - " 'murder': 593,\n", - " 'cool': 594,\n", + " 'please': 591,\n", + " 'simple': 592,\n", + " 'cool': 593,\n", + " 'murder': 594,\n", " 'obvious': 595,\n", " 'happened': 596,\n", " 'complete': 597,\n", " 'cut': 598,\n", - " 'serious': 599,\n", - " 'age': 600,\n", + " 'age': 599,\n", + " 'serious': 600,\n", " 'gore': 601,\n", " 'attempt': 602,\n", " 'hell': 603,\n", @@ -1091,18 +1085,18 @@ " 'across': 618,\n", " 'none': 619,\n", " 'hero': 620,\n", - " 'possible': 621,\n", + " 'exactly': 621,\n", " 'today': 622,\n", - " 'exactly': 623,\n", + " 'possible': 623,\n", " 'alone': 624,\n", " 'sad': 625,\n", " 'brother': 626,\n", " 'number': 627,\n", - " 'saying': 628,\n", - " 'career': 629,\n", + " 'career': 628,\n", + " 'saying': 629,\n", " \"film's\": 630,\n", - " 'usually': 631,\n", - " 'hours': 632,\n", + " 'hours': 631,\n", + " 'usually': 632,\n", " 'cinematography': 633,\n", " 'talent': 634,\n", " 'view': 635,\n", @@ -1125,8 +1119,8 @@ " 'level': 652,\n", " 'ends': 653,\n", " 'started': 654,\n", - " 'call': 655,\n", - " 'female': 656,\n", + " 'female': 655,\n", + " 'call': 656,\n", " \"i'll\": 657,\n", " 'husband': 658,\n", " 'four': 659,\n", @@ -1138,8 +1132,8 @@ " 'change': 665,\n", " 'mostly': 666,\n", " 'usual': 667,\n", - " 'silly': 668,\n", - " 'scary': 669,\n", + " 'scary': 668,\n", + " 'silly': 669,\n", " 'rating': 670,\n", " 'beyond': 671,\n", " 'somewhat': 672,\n", @@ -1156,11 +1150,11 @@ " 'apparently': 683,\n", " 'non': 684,\n", " 'strange': 685,\n", - " 'upon': 686,\n", - " 'attention': 687,\n", + " 'attention': 686,\n", + " 'upon': 687,\n", " 'finds': 688,\n", - " 'basically': 689,\n", - " 'single': 690,\n", + " 'single': 689,\n", + " 'basically': 690,\n", " 'cheap': 691,\n", " 'modern': 692,\n", " 'due': 693,\n", @@ -1189,12 +1183,12 @@ " 'earth': 716,\n", " 'tells': 717,\n", " 'predictable': 718,\n", - " 'songs': 719,\n", - " 'team': 720,\n", + " 'team': 719,\n", + " 'songs': 720,\n", " 'comic': 721,\n", " 'straight': 722,\n", - " 'whether': 723,\n", - " '8': 724,\n", + " '8': 723,\n", + " 'whether': 724,\n", " 'die': 725,\n", " 'add': 726,\n", " 'dialog': 727,\n", @@ -1224,12 +1218,12 @@ " 'sequel': 751,\n", " 'clear': 752,\n", " 'falls': 753,\n", - " 'needs': 754,\n", - " \"haven't\": 755,\n", + " \"haven't\": 754,\n", + " 'needs': 755,\n", " 'dull': 756,\n", " 'suspense': 757,\n", - " 'eye': 758,\n", - " 'bunch': 759,\n", + " 'bunch': 758,\n", + " 'eye': 759,\n", " 'surprised': 760,\n", " 'showing': 761,\n", " 'sorry': 762,\n", @@ -1240,8 +1234,8 @@ " 'ways': 767,\n", " 'theme': 768,\n", " 'theater': 769,\n", - " 'named': 770,\n", - " 'among': 771,\n", + " 'among': 770,\n", + " 'named': 771,\n", " \"what's\": 772,\n", " 'storyline': 773,\n", " 'monster': 774,\n", @@ -1256,31 +1250,31 @@ " 'using': 783,\n", " '9': 784,\n", " 'feature': 785,\n", - " 'comments': 786,\n", - " 'buy': 787,\n", + " 'buy': 786,\n", + " 'comments': 787,\n", " \"'\": 788,\n", - " 'typical': 789,\n", - " 't': 790,\n", + " 't': 789,\n", + " 'typical': 790,\n", " 'sister': 791,\n", " 'editing': 792,\n", " 'tale': 793,\n", " 'avoid': 794,\n", - " 'deal': 795,\n", - " 'mystery': 796,\n", - " 'dr': 797,\n", + " 'dr': 795,\n", + " 'deal': 796,\n", + " 'mystery': 797,\n", " 'doubt': 798,\n", " 'fantastic': 799,\n", - " 'kept': 800,\n", - " 'nearly': 801,\n", + " 'nearly': 800,\n", + " 'kept': 801,\n", " 'subject': 802,\n", - " 'okay': 803,\n", - " 'feels': 804,\n", + " 'feels': 803,\n", + " 'okay': 804,\n", " 'viewing': 805,\n", " 'elements': 806,\n", - " 'oscar': 807,\n", - " 'check': 808,\n", - " 'points': 809,\n", - " 'realistic': 810,\n", + " 'check': 807,\n", + " 'oscar': 808,\n", + " 'realistic': 809,\n", + " 'points': 810,\n", " 'greatest': 811,\n", " 'means': 812,\n", " 'herself': 813,\n", @@ -1289,8 +1283,8 @@ " 'imagine': 816,\n", " 'rent': 817,\n", " 'viewers': 818,\n", - " 'crime': 819,\n", - " 'richard': 820,\n", + " 'richard': 819,\n", + " 'crime': 820,\n", " 'form': 821,\n", " 'peter': 822,\n", " 'actual': 823,\n", @@ -1301,8 +1295,8 @@ " 'believable': 828,\n", " 'period': 829,\n", " 'red': 830,\n", - " 'brought': 831,\n", - " 'move': 832,\n", + " 'move': 831,\n", + " 'brought': 832,\n", " 'material': 833,\n", " 'forget': 834,\n", " 'somehow': 835,\n", @@ -1321,11 +1315,11 @@ " 'average': 848,\n", " 'open': 849,\n", " 'sequences': 850,\n", - " 'killing': 851,\n", - " 'atmosphere': 852,\n", + " 'atmosphere': 851,\n", + " 'killing': 852,\n", " 'eventually': 853,\n", - " 'tom': 854,\n", - " 'learn': 855,\n", + " 'learn': 854,\n", + " 'tom': 855,\n", " 'premise': 856,\n", " '20': 857,\n", " 'wait': 858,\n", @@ -1335,17 +1329,17 @@ " 'expected': 862,\n", " 'whatever': 863,\n", " 'indeed': 864,\n", - " 'particular': 865,\n", - " 'note': 866,\n", - " 'poorly': 867,\n", + " 'note': 865,\n", + " 'poorly': 866,\n", + " 'particular': 867,\n", " 'lame': 868,\n", " 'dance': 869,\n", " 'imdb': 870,\n", " 'situation': 871,\n", " 'shame': 872,\n", " 'third': 873,\n", - " 'york': 874,\n", - " 'box': 875,\n", + " 'box': 874,\n", + " 'york': 875,\n", " 'truth': 876,\n", " 'decided': 877,\n", " 'free': 878,\n", @@ -1357,9 +1351,9 @@ " 'acted': 884,\n", " 'leaves': 885,\n", " 'unless': 886,\n", - " 'emotional': 887,\n", + " 'romance': 887,\n", " 'possibly': 888,\n", - " 'romance': 889,\n", + " 'emotional': 889,\n", " 'sexual': 890,\n", " 'gay': 891,\n", " 'boys': 892,\n", @@ -1369,14 +1363,14 @@ " 'forced': 896,\n", " 'credits': 897,\n", " 'memorable': 898,\n", - " 'doctor': 899,\n", + " 'reading': 899,\n", " 'became': 900,\n", - " 'reading': 901,\n", + " 'doctor': 901,\n", " 'otherwise': 902,\n", - " 'begin': 903,\n", + " 'de': 903,\n", " 'air': 904,\n", - " 'crew': 905,\n", - " 'de': 906,\n", + " 'begin': 905,\n", + " 'crew': 906,\n", " 'question': 907,\n", " 'meet': 908,\n", " 'society': 909,\n", @@ -1388,8 +1382,8 @@ " 'hands': 915,\n", " 'superb': 916,\n", " 'screenplay': 917,\n", - " 'beauty': 918,\n", - " 'interested': 919,\n", + " 'interested': 918,\n", + " 'beauty': 919,\n", " 'street': 920,\n", " 'features': 921,\n", " 'perfectly': 922,\n", @@ -1399,24 +1393,24 @@ " 'stage': 926,\n", " 'nature': 927,\n", " 'effect': 928,\n", - " 'comment': 929,\n", - " 'forward': 930,\n", + " 'forward': 929,\n", + " 'comment': 930,\n", " 'nor': 931,\n", - " 'badly': 932,\n", - " 'sounds': 933,\n", + " 'e': 932,\n", + " 'badly': 933,\n", " 'previous': 934,\n", - " 'e': 935,\n", + " 'sounds': 935,\n", " 'japanese': 936,\n", " 'weird': 937,\n", " 'island': 938,\n", - " 'inside': 939,\n", - " 'personal': 940,\n", + " 'personal': 939,\n", + " 'inside': 940,\n", " 'quickly': 941,\n", " 'total': 942,\n", " 'keeps': 943,\n", " 'towards': 944,\n", - " 'result': 945,\n", - " 'america': 946,\n", + " 'america': 945,\n", + " 'result': 946,\n", " 'battle': 947,\n", " 'crazy': 948,\n", " 'worked': 949,\n", @@ -1429,16 +1423,16 @@ " 'writers': 956,\n", " 'fire': 957,\n", " 'copy': 958,\n", - " 'unique': 959,\n", - " 'dumb': 960,\n", + " 'dumb': 959,\n", + " 'unique': 960,\n", " 'realize': 961,\n", " 'powerful': 962,\n", - " 'mark': 963,\n", - " 'lee': 964,\n", + " 'lee': 963,\n", + " 'mark': 964,\n", " 'business': 965,\n", " 'rate': 966,\n", - " 'dramatic': 967,\n", - " 'older': 968,\n", + " 'older': 967,\n", + " 'dramatic': 968,\n", " 'pay': 969,\n", " 'following': 970,\n", " 'directors': 971,\n", @@ -1453,24 +1447,24 @@ " 'appear': 980,\n", " 'brings': 981,\n", " 'front': 982,\n", - " 'ask': 983,\n", - " 'dream': 984,\n", + " 'dream': 983,\n", + " 'ask': 984,\n", " 'water': 985,\n", - " 'admit': 986,\n", - " 'bill': 987,\n", + " 'bill': 986,\n", + " 'admit': 987,\n", " 'rich': 988,\n", " 'apart': 989,\n", " 'joe': 990,\n", " 'political': 991,\n", " 'fairly': 992,\n", - " 'reasons': 993,\n", - " 'leading': 994,\n", - " 'portrayed': 995,\n", - " 'spent': 996,\n", + " 'leading': 993,\n", + " 'reasons': 994,\n", + " 'spent': 995,\n", + " 'portrayed': 996,\n", " 'telling': 997,\n", " 'cover': 998,\n", " 'outside': 999,\n", - " 'wasted': 1000,\n", + " 'present': 1000,\n", " ...}" ] }, @@ -1514,7 +1508,7 @@ { "data": { "text/plain": [ - "'A simple comment...

What can I say... this is a wonderful film that I can watch over and over. It is definitely one of the top ten comedies made. With a great cast, Jack Lemmon and Walter Matthau wording a perfect script by Neil Simon, based on his play.

It is real to life situation done perfectly. If you have digital cable, one gets the menu on bottom of screen to give what is on. It usually gives this film ***% stars but in reality it deserves **** stars. If you really watch this film, one can tell that it will be as funny and fresh a hundred years from now.'" + "'Return To the Lost World was filmed back-to-back with the 1992 version of The Lost World.

In this sequel, the same five people, lead by Challenger return to the plateau where a group has started drilling for oil which is threatening to destroy the land. Gomez has something to do with this. They manage to defeat the drillers and the plateau is saved, much to the delight of the natives.

Like in The Lost World, what few dinosaurs we see are made of rubber and these include a T-Rex and Ankylosaurus.

John Ryhs-Davies and David Warner reprise their roles as Challenger and Summerlee and three of the other actors are also back.

Despite reading several bad reviews of this and those cheap looking rubber dinosaurs, I enjoyed Return to the Lost World.

Rating: 3 stars out of 5.'" ] }, "execution_count": 19, @@ -1541,17 +1535,19 @@ { "data": { "text/plain": [ - "array([ 3, 591, 929, 7, 7, 48, 67, 10, 131, 11, 6,\n", - " 3, 393, 19, 12, 10, 67, 103, 121, 2, 121, 9,\n", - " 6, 406, 27, 4, 1, 342, 713, 1317, 90, 16, 3,\n", - " 78, 174, 694, 4910, 2, 2556, 3599, 3, 399, 227, 31,\n", - " 4033, 2628, 441, 20, 24, 288, 7, 7, 9, 6, 144,\n", - " 5, 114, 871, 221, 922, 43, 22, 25, 3639, 1897, 27,\n", - " 217, 1, 9206, 20, 1306, 4, 258, 5, 197, 48, 6,\n", - " 20, 9, 631, 411, 11, 19, 405, 18, 8, 614, 9,\n", - " 1003, 405, 43, 22, 62, 103, 11, 19, 27, 67, 380,\n", - " 12, 9, 80, 26, 14, 152, 2, 1451, 3, 2997, 153,\n", - " 36, 146])" + "array([1037, 5, 1, 432, 181, 13, 748, 141, 5, 141, 16,\n", + " 1, 7418, 318, 4, 1, 432, 181, 7, 7, 8, 11,\n", + " 751, 1, 167, 707, 83, 469, 31, 1037, 5, 1, 117,\n", + " 3, 547, 45, 654, 15, 3336, 60, 6, 3746, 5, 2222,\n", + " 1, 1379, 45, 137, 5, 77, 16, 11, 33, 1894, 5,\n", + " 4399, 1, 2, 1, 6, 2011, 72, 5, 1, 3029, 4,\n", + " 1, 5836, 7, 7, 37, 8, 1, 432, 181, 48, 171,\n", + " 3847, 73, 63, 23, 90, 4, 4118, 2, 132, 1469, 3,\n", + " 789, 4019, 2, 7, 7, 315, 4180, 2, 611, 2991, 65,\n", + " 578, 14, 2, 2, 277, 4, 1, 79, 150, 23, 81,\n", + " 141, 7, 7, 467, 899, 439, 74, 838, 4, 11, 2,\n", + " 143, 691, 256, 4118, 3847, 10, 511, 1037, 5, 1, 432,\n", + " 181, 7, 7, 670, 338, 405, 41, 4, 447])" ] }, "execution_count": 20, @@ -1702,7 +1698,7 @@ { "data": { "text/plain": [ - "0.94534" + "0.94528" ] }, "execution_count": 26, @@ -1823,17 +1819,19 @@ { "data": { "text/plain": [ - "array([ 3, 591, 929, 7, 7, 48, 67, 10, 131, 11, 6,\n", - " 3, 393, 19, 12, 10, 67, 103, 121, 2, 121, 9,\n", - " 6, 406, 27, 4, 1, 342, 713, 1317, 90, 16, 3,\n", - " 78, 174, 694, 4910, 2, 2556, 3599, 3, 399, 227, 31,\n", - " 4033, 2628, 441, 20, 24, 288, 7, 7, 9, 6, 144,\n", - " 5, 114, 871, 221, 922, 43, 22, 25, 3639, 1897, 27,\n", - " 217, 1, 9206, 20, 1306, 4, 258, 5, 197, 48, 6,\n", - " 20, 9, 631, 411, 11, 19, 405, 18, 8, 614, 9,\n", - " 1003, 405, 43, 22, 62, 103, 11, 19, 27, 67, 380,\n", - " 12, 9, 80, 26, 14, 152, 2, 1451, 3, 2997, 153,\n", - " 36, 146])" + "array([1037, 5, 1, 432, 181, 13, 748, 141, 5, 141, 16,\n", + " 1, 7418, 318, 4, 1, 432, 181, 7, 7, 8, 11,\n", + " 751, 1, 167, 707, 83, 469, 31, 1037, 5, 1, 117,\n", + " 3, 547, 45, 654, 15, 3336, 60, 6, 3746, 5, 2222,\n", + " 1, 1379, 45, 137, 5, 77, 16, 11, 33, 1894, 5,\n", + " 4399, 1, 2, 1, 6, 2011, 72, 5, 1, 3029, 4,\n", + " 1, 5836, 7, 7, 37, 8, 1, 432, 181, 48, 171,\n", + " 3847, 73, 63, 23, 90, 4, 4118, 2, 132, 1469, 3,\n", + " 789, 4019, 2, 7, 7, 315, 4180, 2, 611, 2991, 65,\n", + " 578, 14, 2, 2, 277, 4, 1, 79, 150, 23, 81,\n", + " 141, 7, 7, 467, 899, 439, 74, 838, 4, 11, 2,\n", + " 143, 691, 256, 4118, 3847, 10, 511, 1037, 5, 1, 432,\n", + " 181, 7, 7, 670, 338, 405, 41, 4, 447])" ] }, "execution_count": 32, @@ -1896,20 +1894,20 @@ " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", - " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", - " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", - " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", - " 0, 0, 0, 3, 591, 929, 7, 7, 48, 67, 10,\n", - " 131, 11, 6, 3, 393, 19, 12, 10, 67, 103, 121,\n", - " 2, 121, 9, 6, 406, 27, 4, 1, 342, 713, 1317,\n", - " 90, 16, 3, 78, 174, 694, 4910, 2, 2556, 3599, 3,\n", - " 399, 227, 31, 4033, 2628, 441, 20, 24, 288, 7, 7,\n", - " 9, 6, 144, 5, 114, 871, 221, 922, 43, 22, 25,\n", - " 3639, 1897, 27, 217, 1, 9206, 20, 1306, 4, 258, 5,\n", - " 197, 48, 6, 20, 9, 631, 411, 11, 19, 405, 18,\n", - " 8, 614, 9, 1003, 405, 43, 22, 62, 103, 11, 19,\n", - " 27, 67, 380, 12, 9, 80, 26, 14, 152, 2, 1451,\n", - " 3, 2997, 153, 36, 146], dtype=int32)" + " 0, 0, 0, 0, 0, 0, 0, 1037, 5, 1, 432,\n", + " 181, 13, 748, 141, 5, 141, 16, 1, 7418, 318, 4,\n", + " 1, 432, 181, 7, 7, 8, 11, 751, 1, 167, 707,\n", + " 83, 469, 31, 1037, 5, 1, 117, 3, 547, 45, 654,\n", + " 15, 3336, 60, 6, 3746, 5, 2222, 1, 1379, 45, 137,\n", + " 5, 77, 16, 11, 33, 1894, 5, 4399, 1, 2, 1,\n", + " 6, 2011, 72, 5, 1, 3029, 4, 1, 5836, 7, 7,\n", + " 37, 8, 1, 432, 181, 48, 171, 3847, 73, 63, 23,\n", + " 90, 4, 4118, 2, 132, 1469, 3, 789, 4019, 2, 7,\n", + " 7, 315, 4180, 2, 611, 2991, 65, 578, 14, 2, 2,\n", + " 277, 4, 1, 79, 150, 23, 81, 141, 7, 7, 467,\n", + " 899, 439, 74, 838, 4, 11, 2, 143, 691, 256, 4118,\n", + " 3847, 10, 511, 1037, 5, 1, 432, 181, 7, 7, 670,\n", + " 338, 405, 41, 4, 447], dtype=int32)" ] }, "execution_count": 33, @@ -1980,7 +1978,7 @@ { "data": { "text/plain": [ - "'A simple comment...

What can I say... this is a wonderful film that I can watch over and over. It is definitely one of the top ten comedies made. With a great cast, Jack Lemmon and Walter Matthau wording a perfect script by Neil Simon, based on his play.

It is real to life situation done perfectly. If you have digital cable, one gets the menu on bottom of screen to give what is on. It usually gives this film ***% stars but in reality it deserves **** stars. If you really watch this film, one can tell that it will be as funny and fresh a hundred years from now.'" + "'Return To the Lost World was filmed back-to-back with the 1992 version of The Lost World.

In this sequel, the same five people, lead by Challenger return to the plateau where a group has started drilling for oil which is threatening to destroy the land. Gomez has something to do with this. They manage to defeat the drillers and the plateau is saved, much to the delight of the natives.

Like in The Lost World, what few dinosaurs we see are made of rubber and these include a T-Rex and Ankylosaurus.

John Ryhs-Davies and David Warner reprise their roles as Challenger and Summerlee and three of the other actors are also back.

Despite reading several bad reviews of this and those cheap looking rubber dinosaurs, I enjoyed Return to the Lost World.

Rating: 3 stars out of 5.'" ] }, "execution_count": 36, @@ -2007,7 +2005,7 @@ { "data": { "text/plain": [ - "'a simple comment br br what can i say this is a wonderful film that i can watch over and over it is definitely one of the top ten comedies made with a great cast jack lemmon and walter matthau a perfect script by neil simon based on his play br br it is real to life situation done perfectly if you have digital cable one gets the menu on bottom of screen to give what is on it usually gives this film stars but in reality it deserves stars if you really watch this film one can tell that it will be as funny and fresh a hundred years from now'" + "'return to the lost world was filmed back to back with the 1992 version of the lost world br br in this sequel the same five people lead by return to the where a group has started for oil which is threatening to destroy the land has something to do with this they manage to defeat the and the is saved much to the delight of the natives br br like in the lost world what few dinosaurs we see are made of rubber and these include a t rex and br br john davies and david warner their roles as and and three of the other actors are also back br br despite reading several bad reviews of this and those cheap looking rubber dinosaurs i enjoyed return to the lost world br br rating 3 stars out of 5'" ] }, "execution_count": 37, @@ -2087,17 +2085,7 @@ "cell_type": "code", "execution_count": 41, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:From /home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/tensorflow/python/keras/_impl/keras/backend.py:1456: calling reduce_sum (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "keep_dims is deprecated, use keepdims instead\n" - ] - } - ], + "outputs": [], "source": [ "model.add(GRU(units=16, return_sequences=True))" ] @@ -2177,17 +2165,7 @@ "cell_type": "code", "execution_count": 46, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:From /home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/site-packages/tensorflow/python/keras/_impl/keras/backend.py:1557: calling reduce_mean (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "keep_dims is deprecated, use keepdims instead\n" - ] - } - ], + "outputs": [], "source": [ "model.compile(loss='binary_crossentropy',\n", " optimizer=optimizer,\n", @@ -2203,21 +2181,22 @@ "name": "stdout", "output_type": "stream", "text": [ + "Model: \"sequential\"\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "layer_embedding (Embedding) (None, 544, 8) 80000 \n", "_________________________________________________________________\n", - "gru_1 (GRU) (None, None, 16) 1200 \n", + "gru (GRU) (None, 544, 16) 1248 \n", "_________________________________________________________________\n", - "gru_2 (GRU) (None, None, 8) 600 \n", + "gru_1 (GRU) (None, 544, 8) 624 \n", "_________________________________________________________________\n", - "gru_3 (GRU) (None, 4) 156 \n", + "gru_2 (GRU) (None, 4) 168 \n", "_________________________________________________________________\n", - "dense_1 (Dense) (None, 1) 5 \n", + "dense (Dense) (None, 1) 5 \n", "=================================================================\n", - "Total params: 81,961\n", - "Trainable params: 81,961\n", + "Total params: 82,045\n", + "Trainable params: 82,045\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] @@ -2249,22 +2228,19 @@ "text": [ "Train on 23750 samples, validate on 1250 samples\n", "Epoch 1/3\n", - "23750/23750 [==============================]23750/23750 [==============================] - 464s 20ms/step - loss: 0.6517 - acc: 0.6002 - val_loss: 0.6218 - val_acc: 0.6752\n", - "\n", + "23750/23750 [==============================] - 16s 690us/sample - loss: 0.4935 - accuracy: 0.7452 - val_loss: 0.3798 - val_accuracy: 0.8328\n", "Epoch 2/3\n", - "23750/23750 [==============================]23750/23750 [==============================] - 447s 19ms/step - loss: 0.4292 - acc: 0.8102 - val_loss: 0.6701 - val_acc: 0.6512\n", - "\n", + "23750/23750 [==============================] - 12s 510us/sample - loss: 0.2919 - accuracy: 0.8887 - val_loss: 0.3111 - val_accuracy: 0.8736\n", "Epoch 3/3\n", - "23750/23750 [==============================]23750/23750 [==============================] - 445s 19ms/step - loss: 0.3092 - acc: 0.8765 - val_loss: 0.3182 - val_acc: 0.8752\n", - "\n", - "CPU times: user 35min 19s, sys: 2min 41s, total: 38min\n", - "Wall time: 22min 37s\n" + "23750/23750 [==============================] - 12s 511us/sample - loss: 0.2210 - accuracy: 0.9211 - val_loss: 0.3090 - val_accuracy: 0.8760\n", + "CPU times: user 48.9 s, sys: 1.58 s, total: 50.5 s\n", + "Wall time: 40.7 s\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 48, @@ -2296,10 +2272,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "25000/25000 [==============================]25000/25000 [==============================] - 175s 7ms/step\n", - "\n", - "CPU times: user 2min 59s, sys: 340 ms, total: 2min 59s\n", - "Wall time: 2min 55s\n" + "25000/25000 [==============================] - 12s 493us/sample - loss: 0.3331 - accuracy: 0.8674\n", + "CPU times: user 14 s, sys: 404 ms, total: 14.4 s\n", + "Wall time: 12.4 s\n" ] } ], @@ -2311,13 +2286,15 @@ { "cell_type": "code", "execution_count": 50, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Accuracy: 86.71%\n" + "Accuracy: 86.74%\n" ] } ], @@ -2343,8 +2320,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 7.01 s, sys: 0 ns, total: 7.01 s\n", - "Wall time: 6.88 s\n" + "CPU times: user 1.08 s, sys: 23.5 ms, total: 1.1 s\n", + "Wall time: 1.03 s\n" ] } ], @@ -2418,7 +2395,7 @@ { "data": { "text/plain": [ - "121" + "132" ] }, "execution_count": 55, @@ -2445,7 +2422,7 @@ { "data": { "text/plain": [ - "13" + "18" ] }, "execution_count": 56, @@ -2473,7 +2450,7 @@ { "data": { "text/plain": [ - "'I would like to start by saying I can only hope that the makers of this movie and it\\'s sister film The Intruder (directed by the great unheralded stylist auteur that is Jopi Burnama) know in their hearts just how much pleasure they have brought to me and my friends in the sleepy north eastern town of Jarrow.

From the opening pre credit sequence which manages to drag ever so slightly despite containing a man crashing through a window on a motorbike, the pitiless destruction of a silence lab, the introduction of one of the most simultaneously annoying and anaemic bad guys in movie history and costume design that Jean Paul Gautier would find ott and garish. Make no mistake; this is a truly unique experience. Early highlight - an explosion (get used to it, plenty more where that came from!) followed by a close up of our chubby heroine and the most hilarious line reading of the word \"dad\" in living memory. And then... the theme song...

Yeah, this deserves its own paragraph. Sung by AJ, written by people who really should wish to remain anonymous, it makes the songs written for the Rocky films sound like Schubert. This is crap 80\\'s hero motivation narcissism at an all time high, with choice lyrics such as \"its only me and you, its come down to the wire\" and much talk of having to \"cross the line\" (it\\'ll make sense in time - our hero cares little for the boundaries of bona fida police work) abounding. Not to mention the Indonesian Supremes cooing the film\\'s title seductively. At this point anyone wishing to switch off officially has no pulse.

Our hero is Semitic cop Peter Goldson (essayed brilliantly by Intruder star Peter O\\'Brien), the \"stabilizer\" of the title. The man\\'s bull in a china shop approach to crime fighting and particularly his less than inconspicuous undercover work truly leaves much to be desired, but he is without question an entertaining guide through the mean streets of downtown Jakarta, with local sleaze ball connection Captain Johnny in tow, as well as Peter\\'s own waste of space partner in fashion crime Sylvia Nash, who does little. So many highlights, so little time - the \"slide please\" arrogance of Peter\\'s not all too convincingly argued case against chief baddie Greg Rainmaker (Intruder fans will know hirsute slimy bastard Craig Gavin as the monstrous John White - helluva name eh? No! Oh well...), the x marks the spot location map stupidity, our hero taking horrible advantage of heroine Tina Probost during a moment of weakness on her behalf, the latter turning up at a sting operation dressed like a member of a particularly flamboyant dancing troop. And believe me that barely covers it.

There wasn\\'t even time to go into the plot revolving around the hunt for a drug detection system and a kidnapped professor with an alarming but commendable amount of national pride. Or our hero turning up at a funeral dressed as if an extra on Boogie Nights. Or the absolutely hysterical craic between Captain Johnny and Goldson - two guys have never made more heavy weather of buddy buddy shtick than these clowns. The trowel was possibly too subtle me thinks.

Ah it tails off people, and you never thought scenes of wanton destruction and general mayhem could be so unbelievably boring, but the character interaction is stupendous, the dialogue truly priceless and the incompetence on show somehow endearing. Oh and the shoes people - watch out for the shoes!'" + "\"This HAS to be my guilty pleasure. I am a HUGE fan of 80's movies that were designed to entertain and they didn't care if they offended anyone. This move has no meat, not substance, no deep thought provoking scenes. Just plain old college kids having fun and if a few breasts have to be shown, then so be it! This movie is for when you just want to relax and NOT think. Viva la nudity!\"" ] }, "execution_count": 57, @@ -2501,7 +2478,7 @@ { "data": { "text/plain": [ - "0.08332923" + "0.27373913" ] }, "execution_count": 58, @@ -2619,14 +2596,14 @@ { "data": { "text/plain": [ - "array([[0.868934 ],\n", - " [0.72526425],\n", - " [0.33099633],\n", - " [0.49190348],\n", - " [0.3054021 ],\n", - " [0.14959489],\n", - " [0.5235635 ],\n", - " [0.21565402]], dtype=float32)" + "array([[0.95301837],\n", + " [0.92733926],\n", + " [0.79257476],\n", + " [0.9019553 ],\n", + " [0.5875022 ],\n", + " [0.55110747],\n", + " [0.89896274],\n", + " [0.33616564]], dtype=float32)" ] }, "execution_count": 63, @@ -2785,8 +2762,8 @@ { "data": { "text/plain": [ - "array([0.86528164, 0.6867993 , 0.4362397 , 0.66128314, 0.11546915,\n", - " 0.94507647, 0.32628497, 0.535881 ], dtype=float32)" + "array([ 0.01839033, 0.05229224, 0.0848575 , 0.03222338, -0.03947427,\n", + " -0.03776564, -0.01149088, -0.07443853], dtype=float32)" ] }, "execution_count": 69, @@ -2806,8 +2783,8 @@ { "data": { "text/plain": [ - "array([ 1.0691622 , 1.124244 , -0.04477464, -0.05861434, 0.16965319,\n", - " 1.2626944 , 0.76136374, -0.00998422], dtype=float32)" + "array([-0.14307617, 0.08333486, 0.15650608, 0.08930028, -0.08659173,\n", + " -0.12289459, -0.14367667, -0.10402057], dtype=float32)" ] }, "execution_count": 70, @@ -2844,8 +2821,8 @@ { "data": { "text/plain": [ - "array([ 0.31903917, 0.53934103, 1.3727672 , 1.4083829 , 0.8475107 ,\n", - " -0.22946651, 0.0251075 , 0.77032244], dtype=float32)" + "array([ 0.05553182, -0.09014519, -0.06248455, -0.11525143, 0.14601274,\n", + " 0.07451952, 0.10784499, 0.10799433], dtype=float32)" ] }, "execution_count": 72, @@ -2865,8 +2842,8 @@ { "data": { "text/plain": [ - "array([ 0.47915924, 0.12226178, 0.90192014, 0.742338 , 0.58730644,\n", - " 0.32736972, -0.17633988, 1.3744307 ], dtype=float32)" + "array([ 0.15100664, -0.13359004, -0.15154287, -0.12776676, 0.10830297,\n", + " 0.15224072, 0.13508266, 0.14284784], dtype=float32)" ] }, "execution_count": 73, @@ -2969,26 +2946,26 @@ "text": [ "Distance from 'great':\n", "0.000 - great\n", - "0.016 - touching\n", - "0.017 - arguments\n", - "0.025 - nevertheless\n", - "0.031 - elmer\n", - "0.032 - 8\n", - "0.036 - ritter\n", - "0.037 - juliet\n", - "0.041 - randy\n", - "0.045 - afterward\n", + "0.012 - spring\n", + "0.013 - 1980\n", + "0.013 - permanent\n", + "0.013 - robinson\n", + "0.015 - anime\n", + "0.015 - pleasantly\n", + "0.016 - inter\n", + "0.016 - profit\n", + "0.017 - ramones\n", "...\n", - "1.057 - rubbish\n", - "1.060 - dull\n", - "1.064 - disappointing\n", - "1.069 - unlikeable\n", - "1.078 - uninspired\n", - "1.083 - lacks\n", - "1.188 - worst\n", - "1.225 - waste\n", - "1.247 - awful\n", - "1.282 - terrible\n" + "1.988 - mst3k\n", + "1.988 - consist\n", + "1.988 - save\n", + "1.989 - unless\n", + "1.990 - ripoff\n", + "1.991 - insipid\n", + "1.994 - avoid\n", + "1.995 - drivel\n", + "1.995 - expand\n", + "1.995 - profile\n" ] } ], @@ -3016,26 +2993,26 @@ "text": [ "Distance from 'worst':\n", "0.000 - worst\n", - "0.047 - embarrassingly\n", - "0.053 - terrible\n", - "0.094 - retarded\n", - "0.095 - poor\n", - "0.095 - stereotyping\n", - "0.096 - uninspired\n", - "0.099 - awful\n", - "0.100 - severed\n", - "0.108 - lacks\n", + "0.004 - horrible\n", + "0.004 - dull\n", + "0.005 - below\n", + "0.005 - boredom\n", + "0.006 - conceived\n", + "0.008 - salvage\n", + "0.009 - slapped\n", + "0.009 - fails\n", + "0.010 - virus\n", "...\n", - "1.167 - restraint\n", - "1.168 - available\n", - "1.176 - foremost\n", - "1.188 - great\n", - "1.193 - mesmerizing\n", - "1.222 - highly\n", - "1.229 - exploration\n", - "1.239 - delightful\n", - "1.268 - wonderfully\n", - "1.323 - 7\n" + "1.989 - cried\n", + "1.989 - compelling\n", + "1.990 - carell\n", + "1.990 - stadium\n", + "1.991 - deanna\n", + "1.992 - eddie\n", + "1.992 - resolved\n", + "1.992 - sirk\n", + "1.994 - sidney\n", + "1.997 - concentrates\n" ] } ], @@ -3107,7 +3084,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.1" + "version": "3.6.10" } }, "nbformat": 4, diff --git a/21_Machine_Translation.ipynb b/21_Machine_Translation.ipynb new file mode 100644 index 0000000..94146fe --- /dev/null +++ b/21_Machine_Translation.ipynb @@ -0,0 +1,2040 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# TensorFlow Tutorial #21\n", + "# Machine Translation\n", + "\n", + "by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n", + "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "Tutorial #20 showed how to use a Recurrent Neural Network (RNN) to do so-called sentiment analysis on texts of movie reviews. This tutorial will extend that idea to do Machine Translation of human languages by combining two RNN's.\n", + "\n", + "You should be familiar with TensorFlow, Keras and the basics of Natural Language Processing, see Tutorials #01, #03-C and #20." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Flowchart\n", + "\n", + "The following flowchart shows roughly how the neural network is constructed. It is split into two parts: An encoder which maps the source-text to a \"thought vector\" that summarizes the text's contents, which is then input to the second part of the neural network that decodes the \"thought vector\" to the destination-text.\n", + "\n", + "The neural network cannot work directly on text so first we need to convert each word to an integer-token using a tokenizer. But the neural network cannot work on integers either, so we use a so-called Embedding Layer to convert each integer-token to a vector of floating-point values. The embedding is trained alongside the rest of the neural network to map words with similar semantic meaning to similar vectors of floating-point values.\n", + "\n", + "For example, consider the Danish text \"der var engang\" which is the beginning of any fairytale and literally means \"there was once\" but is commonly translated into English as \"once upon a time\". We first convert the entire data-set to integer-tokens so the text \"der var engang\" becomes [12, 54, 1097]. Each of these integer-tokens is then mapped to an embedding-vector with e.g. 128 elements, so the integer-token 12 could for example become [0.12, -0.56, ..., 1.19] and the integer-token 54 could for example become [0.39, 0.09, ..., -0.12]. These embedding-vectors can then be input to the Recurrent Neural Network, which has 3 GRU-layers. See Tutorial #20 for a more detailed explanation.\n", + "\n", + "The last GRU-layer outputs a single vector - the \"thought vector\" that summarizes the contents of the source-text - which is then used as the initial state of the GRU-units in the decoder-part.\n", + "\n", + "The destination-text \"once upon a time\" is padded with special markers \"ssss\" and \"eeee\" to indicate its beginning and end, so the sequence of integer-tokens becomes [2, 337, 640, 9, 79, 3]. During training, the decoder will be given this entire sequence as input and the desired output sequence is [337, 640, 9, 79, 3] which is the same sequence but time-shifted one step. We are trying to teach the decoder to map the \"thought vector\" and the start-token \"ssss\" (integer 2) to the next word \"once\" (integer 337), and then map the word \"once\" to the word \"upon\" (integer 640), and so forth.\n", + "\n", + "This flow-chart depicts the main idea but does not show all the necessary details e.g. regarding the loss function which is also somewhat complicated." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Flowchart](images/21_machine_translation_flowchart.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import tensorflow as tf\n", + "import numpy as np\n", + "import math\n", + "import os" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We need to import several things from Keras." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# from tf.keras.models import Model # This does not work!\n", + "from tensorflow.keras.models import Model\n", + "from tensorflow.keras.layers import Input, Dense, GRU, Embedding\n", + "from tensorflow.keras.optimizers import RMSprop\n", + "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard\n", + "from tensorflow.keras.preprocessing.text import Tokenizer\n", + "from tensorflow.keras.preprocessing.sequence import pad_sequences" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This was developed using Python 3.6 (Anaconda) and package versions:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'2.1.0'" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tf.__version__" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'2.2.4-tf'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tf.keras.__version__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Data\n", + "\n", + "We will use the Europarl data-set which has sentence-pairs in most European languages. The data was created by the European Union which translates a lot of their communications to the languages of the member-countries of the European Union.\n", + "\n", + "http://www.statmt.org/europarl/" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import europarl" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this tutorial I have used the English-Danish data-set which contains about 2 million sentence-pairs. You can use another language by changing this language-code, see `europarl.py` for a list of available language-codes." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "language_code='da'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order for the decoder to know when to begin and end a sentence, we need to mark the start and end of each sentence with words that most likely don't occur in the data-set." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "mark_start = 'ssss '\n", + "mark_end = ' eeee'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can change the directory for the data-files if you like." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# data_dir = \"data/europarl/\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This will automatically download and extract the data-files if you don't have them already.\n", + "\n", + "**WARNING: The file for the English-Danish data-set is about 587 MB!**" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data has apparently already been downloaded and unpacked.\n" + ] + } + ], + "source": [ + "europarl.maybe_download_and_extract(language_code=language_code)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load the texts for the source-language, here we use Danish." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "data_src = europarl.load_data(english=False,\n", + " language_code=language_code)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load the texts for the destination-language, here we use English." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "data_dest = europarl.load_data(english=True,\n", + " language_code=language_code,\n", + " start=mark_start,\n", + " end=mark_end)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will build a model to translate from the source language (Danish) to the destination language (English). If you want to make the inverse translation you can merely exchange the source and destination data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example Data\n", + "\n", + "The data is just a list of texts that is ordered so the source and destination texts match. I can confirm that this example is an accurate translation." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "idx = 2" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Som De kan se, indfandt det store \"år 2000-problem\" sig ikke. Til gengæld har borgerne i en del af medlemslandene været ramt af meget forfærdelige naturkatastrofer.'" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_src[idx]" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"ssss Although, as you will have seen, the dreaded 'millennium bug' failed to materialise, still the people in a number of countries suffered a series of natural disasters that truly were dreadful. eeee\"" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_dest[idx]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Error in Data\n", + "\n", + "The data-set contains about 2 million sentence-pairs. Some of the data is incorrect. This example appears to be French (or some other weird language I don't understand), although the Danish text is also included." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "idx = 8002" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\"Car il savait ce que cette foule en joie ignorait, et qu\\'on peut lire dans les livres, que le bacille de la peste ne meurt ni ne disparaît jamais, qu\\'il peut rester pendant des dizaines d\\'années endormi dans les meubles et le linge, qu\\'il attend patiemment dans les chambres, les caves, les malles, les mouchoirs et les paperasses, et que, peut-être, le jour viendrait où, pour le malheur et l\\'enseignement des hommes, la peste réveillerait ses rats et les enverrait mourir dans une cité heureuse.\" (Thi han vidste det, som denne glade forsamling ikke vidste, og som man kan læse i bøger, at pestens bacille aldrig dør og aldrig forsvinder, at den kan sove i mange år i møbler og linned, at den venter tålmodigt i kamre, kældre, kufferter, lommetørklæder og papirer, og at den dag måske kommer, hvor pesten til menneskenes skade og oplysning vågner sine rotter og sender dem ud for at dø i en lykkelig by.)'" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_src[idx]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'ssss \"He knew what those jubilant crowds did not know but could have learned from books: that the plague bacillus never dies or disappears for good; that it can lie dormant for years and years in furniture and linen-chests; that it bides its time in bedrooms, cellars, trunks, and bookshelves; and that perhaps the day would come when, for the bane and the enlightening of men, it would rouse up its rats again and send them forth to die in a happy city.\" eeee'" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_dest[idx]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tokenizer\n", + "\n", + "Neural Networks cannot work directly on text-data. We use a two-step process to convert text into numbers that can be used in a neural network. The first step is to convert text-words into so-called integer-tokens. The second step is to convert integer-tokens into vectors of floating-point numbers using a so-called embedding-layer. See Tutorial #20 for a more detailed explanation.\n", + "\n", + "Set the maximum number of words in our vocabulary. This means that we will only use e.g. the 10000 most frequent words in the data-set. We use the same number for both the source and destination languages, but these could be different." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "num_words = 10000" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We need a few more functions than provided by Keras' Tokenizer-class so we wrap it." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "class TokenizerWrap(Tokenizer):\n", + " \"\"\"Wrap the Tokenizer-class from Keras with more functionality.\"\"\"\n", + " \n", + " def __init__(self, texts, padding,\n", + " reverse=False, num_words=None):\n", + " \"\"\"\n", + " :param texts: List of strings. This is the data-set.\n", + " :param padding: Either 'post' or 'pre' padding.\n", + " :param reverse: Boolean whether to reverse token-lists.\n", + " :param num_words: Max number of words to use.\n", + " \"\"\"\n", + "\n", + " Tokenizer.__init__(self, num_words=num_words)\n", + "\n", + " # Create the vocabulary from the texts.\n", + " self.fit_on_texts(texts)\n", + "\n", + " # Create inverse lookup from integer-tokens to words.\n", + " self.index_to_word = dict(zip(self.word_index.values(),\n", + " self.word_index.keys()))\n", + "\n", + " # Convert all texts to lists of integer-tokens.\n", + " # Note that the sequences may have different lengths.\n", + " self.tokens = self.texts_to_sequences(texts)\n", + "\n", + " if reverse:\n", + " # Reverse the token-sequences.\n", + " self.tokens = [list(reversed(x)) for x in self.tokens]\n", + " \n", + " # Sequences that are too long should now be truncated\n", + " # at the beginning, which corresponds to the end of\n", + " # the original sequences.\n", + " truncating = 'pre'\n", + " else:\n", + " # Sequences that are too long should be truncated\n", + " # at the end.\n", + " truncating = 'post'\n", + "\n", + " # The number of integer-tokens in each sequence.\n", + " self.num_tokens = [len(x) for x in self.tokens]\n", + "\n", + " # Max number of tokens to use in all sequences.\n", + " # We will pad / truncate all sequences to this length.\n", + " # This is a compromise so we save a lot of memory and\n", + " # only have to truncate maybe 5% of all the sequences.\n", + " self.max_tokens = np.mean(self.num_tokens) \\\n", + " + 2 * np.std(self.num_tokens)\n", + " self.max_tokens = int(self.max_tokens)\n", + "\n", + " # Pad / truncate all token-sequences to the given length.\n", + " # This creates a 2-dim numpy matrix that is easier to use.\n", + " self.tokens_padded = pad_sequences(self.tokens,\n", + " maxlen=self.max_tokens,\n", + " padding=padding,\n", + " truncating=truncating)\n", + "\n", + " def token_to_word(self, token):\n", + " \"\"\"Lookup a single word from an integer-token.\"\"\"\n", + "\n", + " word = \" \" if token == 0 else self.index_to_word[token]\n", + " return word \n", + "\n", + " def tokens_to_string(self, tokens):\n", + " \"\"\"Convert a list of integer-tokens to a string.\"\"\"\n", + "\n", + " # Create a list of the individual words.\n", + " words = [self.index_to_word[token]\n", + " for token in tokens\n", + " if token != 0]\n", + " \n", + " # Concatenate the words to a single string\n", + " # with space between all the words.\n", + " text = \" \".join(words)\n", + "\n", + " return text\n", + " \n", + " def text_to_tokens(self, text, reverse=False, padding=False):\n", + " \"\"\"\n", + " Convert a single text-string to tokens with optional\n", + " reversal and padding.\n", + " \"\"\"\n", + "\n", + " # Convert to tokens. Note that we assume there is only\n", + " # a single text-string so we wrap it in a list.\n", + " tokens = self.texts_to_sequences([text])\n", + " tokens = np.array(tokens)\n", + "\n", + " if reverse:\n", + " # Reverse the tokens.\n", + " tokens = np.flip(tokens, axis=1)\n", + "\n", + " # Sequences that are too long should now be truncated\n", + " # at the beginning, which corresponds to the end of\n", + " # the original sequences.\n", + " truncating = 'pre'\n", + " else:\n", + " # Sequences that are too long should be truncated\n", + " # at the end.\n", + " truncating = 'post'\n", + "\n", + " if padding:\n", + " # Pad and truncate sequences to the given length.\n", + " tokens = pad_sequences(tokens,\n", + " maxlen=self.max_tokens,\n", + " padding='pre',\n", + " truncating=truncating)\n", + "\n", + " return tokens" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now create a tokenizer for the source-language. Note that we pad zeros at the beginning ('pre') of the sequences. We also reverse the sequences of tokens because the research literature suggests that this might improve performance, because the last words seen by the encoder match the first words produced by the decoder, so short-term dependencies are supposedly modelled more accurately." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 2min 18s, sys: 940 ms, total: 2min 19s\n", + "Wall time: 2min 19s\n" + ] + } + ], + "source": [ + "%%time\n", + "tokenizer_src = TokenizerWrap(texts=data_src,\n", + " padding='pre',\n", + " reverse=True,\n", + " num_words=num_words)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now create the tokenizer for the destination language. We need a tokenizer for both the source- and destination-languages because their vocabularies are different. Note that this tokenizer does not reverse the sequences and it pads zeros at the end ('post') of the arrays." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 1min 49s, sys: 752 ms, total: 1min 50s\n", + "Wall time: 1min 50s\n" + ] + } + ], + "source": [ + "%%time\n", + "tokenizer_dest = TokenizerWrap(texts=data_dest,\n", + " padding='post',\n", + " reverse=False,\n", + " num_words=num_words)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Define convenience variables for the padded token sequences. These are just 2-dimensional numpy arrays of integer-tokens.\n", + "\n", + "Note that the sequence-lengths are different for the source and destination languages. This is because texts with the same meaning may have different numbers of words in the two languages. \n", + "\n", + "Furthermore, we have made a compromise when tokenizing the original texts in order to save a lot of memory. This means we only truncate about 5% of the texts." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1968800, 47)\n", + "(1968800, 55)\n" + ] + } + ], + "source": [ + "tokens_src = tokenizer_src.tokens_padded\n", + "tokens_dest = tokenizer_dest.tokens_padded\n", + "print(tokens_src.shape)\n", + "print(tokens_dest.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the integer-token used to mark the beginning of a text in the destination-language." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "token_start = tokenizer_dest.word_index[mark_start.strip()]\n", + "token_start" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the integer-token used to mark the end of a text in the destination-language." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "token_end = tokenizer_dest.word_index[mark_end.strip()]\n", + "token_end" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example of Token Sequences" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the output of the tokenizer. Note how it is padded with zeros at the beginning (pre-padding)." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "idx = 2" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3069,\n", + " 3374, 43, 7, 1386, 108, 1995, 7, 178, 9, 3, 302,\n", + " 19, 2076, 8, 20, 39, 285, 499, 69, 136, 5, 166,\n", + " 24, 10, 13], dtype=int32)" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tokens_src[idx]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can reconstruct the original text by converting each integer-token back to its corresponding word:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'naturkatastrofer forfærdelige meget af ramt været medlemslandene af del en i borgerne har gengæld til ikke sig problem 2000 år store det se kan de som'" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tokenizer_src.tokens_to_string(tokens_src[idx])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This text is actually reversed, as can be seen when compared to the original text from the data-set:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Som De kan se, indfandt det store \"år 2000-problem\" sig ikke. Til gengæld har borgerne i en del af medlemslandene været ramt af meget forfærdelige naturkatastrofer.'" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_src[idx]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the sequence of integer-tokens for the corresponding text in the destination-language. Note how it is padded with zeros at the end (post-padding)." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 2, 404, 19, 43, 26, 20, 618, 1, 1451, 5, 9785,\n", + " 174, 1, 81, 7, 9, 214, 4, 67, 2200, 9, 1596,\n", + " 4, 892, 1762, 8, 1480, 107, 5494, 3, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", + " dtype=int32)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tokens_dest[idx]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can reconstruct the original text by converting each integer-token back to its corresponding word:" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'ssss although as you will have seen the failed to materialise still the people in a number of countries suffered a series of natural disasters that truly were dreadful eeee'" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tokenizer_dest.tokens_to_string(tokens_dest[idx])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compare this to the original text from the data-set, which is almost identical except for punctuation marks and a few words such as \"dreaded millennium bug\". This is because we only use a vocabulary of the 10000 most frequent words in the data-set and those 3 words were apparently not used frequently enough to be included in the vocabulary, so they are merely skipped." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"ssss Although, as you will have seen, the dreaded 'millennium bug' failed to materialise, still the people in a number of countries suffered a series of natural disasters that truly were dreadful. eeee\"" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_dest[idx]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Training Data\n", + "\n", + "Now that the data-set has been converted to sequences of integer-tokens that are padded and truncated and saved in numpy arrays, we can easily prepare the data for use in training the neural network.\n", + "\n", + "The input to the encoder is merely the numpy array for the padded and truncated sequences of integer-tokens produced by the tokenizer:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "encoder_input_data = tokens_src" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The input and output data for the decoder is identical, except shifted one time-step. We can use the same numpy array to save memory by slicing it, which merely creates different 'views' of the same data in memory." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1968800, 54)" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "decoder_input_data = tokens_dest[:, :-1]\n", + "decoder_input_data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1968800, 54)" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "decoder_output_data = tokens_dest[:, 1:]\n", + "decoder_output_data.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For example, these token-sequences are identical except they are shifted one time-step." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "idx = 2" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 2, 404, 19, 43, 26, 20, 618, 1, 1451, 5, 9785,\n", + " 174, 1, 81, 7, 9, 214, 4, 67, 2200, 9, 1596,\n", + " 4, 892, 1762, 8, 1480, 107, 5494, 3, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", + " dtype=int32)" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "decoder_input_data[idx]" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 404, 19, 43, 26, 20, 618, 1, 1451, 5, 9785, 174,\n", + " 1, 81, 7, 9, 214, 4, 67, 2200, 9, 1596, 4,\n", + " 892, 1762, 8, 1480, 107, 5494, 3, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", + " dtype=int32)" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "decoder_output_data[idx]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we use the tokenizer to convert these sequences back into text, we see that they are identical except for the first word which is 'ssss' that marks the beginning of a text." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'ssss although as you will have seen the failed to materialise still the people in a number of countries suffered a series of natural disasters that truly were dreadful eeee'" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tokenizer_dest.tokens_to_string(decoder_input_data[idx])" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'although as you will have seen the failed to materialise still the people in a number of countries suffered a series of natural disasters that truly were dreadful eeee'" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tokenizer_dest.tokens_to_string(decoder_output_data[idx])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create the Neural Network\n", + "\n", + "### Create the Encoder\n", + "\n", + "First we create the encoder-part of the neural network which maps a sequence of integer-tokens to a \"thought vector\". We will use the so-called functional API of Keras for this, where we first create the objects for all the layers of the neural network and then we connect them later, this allows for more flexibility than the so-called sequential API in Keras, which is useful when experimenting with more complicated architectures and ways of connecting the encoder and decoder." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the input for the encoder which takes batches of integer-token sequences. The `None` indicates that the sequences can have arbitrary length." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "encoder_input = Input(shape=(None, ), name='encoder_input')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the length of the vectors output by the embedding-layer, which maps integer-tokens to vectors of values roughly between -1 and 1, so that words that have similar semantic meanings are mapped to vectors that are similar. See Tutorial #20 for a more detailed explanation of this." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "embedding_size = 128" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the embedding-layer." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "encoder_embedding = Embedding(input_dim=num_words,\n", + " output_dim=embedding_size,\n", + " name='encoder_embedding')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the size of the internal states of the Gated Recurrent Units (GRU). The same size is used in both the encoder and decoder." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "state_size = 512" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This creates the 3 GRU layers that will map from a sequence of embedding-vectors to a single \"thought vector\" which summarizes the contents of the input-text. Note that the last GRU-layer does not return a sequence." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "encoder_gru1 = GRU(state_size, name='encoder_gru1',\n", + " return_sequences=True)\n", + "encoder_gru2 = GRU(state_size, name='encoder_gru2',\n", + " return_sequences=True)\n", + "encoder_gru3 = GRU(state_size, name='encoder_gru3',\n", + " return_sequences=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This helper-function connects all the layers of the encoder." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "def connect_encoder():\n", + " # Start the neural network with its input-layer.\n", + " net = encoder_input\n", + " \n", + " # Connect the embedding-layer.\n", + " net = encoder_embedding(net)\n", + "\n", + " # Connect all the GRU-layers.\n", + " net = encoder_gru1(net)\n", + " net = encoder_gru2(net)\n", + " net = encoder_gru3(net)\n", + "\n", + " # This is the output of the encoder.\n", + " encoder_output = net\n", + " \n", + " return encoder_output" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note how the encoder uses the normal output from its last GRU-layer as the \"thought vector\". Research papers often use the internal state of the encoder's last recurrent layer as the \"thought vector\". But this makes the implementation more complicated and is not necessary when using the GRU. But if you were using the LSTM instead then it is necessary to use the LSTM's internal states as the \"thought vector\" because it actually has two internal vectors, which we would need to initialize the two internal states of the decoder's LSTM units.\n", + "\n", + "We can now use this function to connect all the layers in the encoder so it can be connected to the decoder further below." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "encoder_output = connect_encoder()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create the Decoder\n", + "\n", + "Create the decoder-part which maps the \"thought vector\" to a sequence of integer-tokens.\n", + "\n", + "The decoder takes two inputs. First it needs the \"thought vector\" produced by the encoder which summarizes the contents of the input-text." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "decoder_initial_state = Input(shape=(state_size,),\n", + " name='decoder_initial_state')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The decoder also needs a sequence of integer-tokens as inputs. During training we will supply this with a full sequence of integer-tokens e.g. corresponding to the text \"ssss once upon a time eeee\". \n", + "\n", + "During inference when we are translating new input-texts, we will start by feeding a sequence with just one integer-token for \"ssss\" which marks the beginning of a text, and combined with the \"thought vector\" from the encoder, the decoder will hopefully be able to produce the correct next word e.g. \"once\"." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "decoder_input = Input(shape=(None, ), name='decoder_input')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the embedding-layer which converts integer-tokens to vectors of real-valued numbers roughly between -1 and 1. Note that we have different embedding-layers for the encoder and decoder because we have two different vocabularies and two different tokenizers for the source and destination languages." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "decoder_embedding = Embedding(input_dim=num_words,\n", + " output_dim=embedding_size,\n", + " name='decoder_embedding')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This creates the 3 GRU layers of the decoder. Note that they all return sequences because we ultimately want to output a sequence of integer-tokens that can be converted into a text-sequence." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "decoder_gru1 = GRU(state_size, name='decoder_gru1',\n", + " return_sequences=True)\n", + "decoder_gru2 = GRU(state_size, name='decoder_gru2',\n", + " return_sequences=True)\n", + "decoder_gru3 = GRU(state_size, name='decoder_gru3',\n", + " return_sequences=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The GRU layers output a tensor with shape `[batch_size, sequence_length, state_size]`, where each \"word\" is encoded as a vector of length `state_size`. We need to convert this into sequences of integer-tokens that can be interpreted as words from our vocabulary.\n", + "\n", + "One way of doing this is to convert the GRU output to a one-hot encoded array. It works but it is extremely wasteful, because for a vocabulary of e.g. 10000 words we need a vector with 10000 elements, so we can select the index of the highest element to be the integer-token." + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "decoder_dense = Dense(num_words,\n", + " activation='softmax',\n", + " name='decoder_output')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The decoder is built using the functional API of Keras, which allows more flexibility in connecting the layers e.g. to route different inputs to the decoder. This is useful because we have to connect the decoder directly to the encoder, but we will also connect the decoder to another input so we can run it separately.\n", + "\n", + "This function connects all the layers of the decoder to some input of the initial-state values for the GRU layers." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "def connect_decoder(initial_state):\n", + " # Start the decoder-network with its input-layer.\n", + " net = decoder_input\n", + "\n", + " # Connect the embedding-layer.\n", + " net = decoder_embedding(net)\n", + " \n", + " # Connect all the GRU-layers.\n", + " net = decoder_gru1(net, initial_state=initial_state)\n", + " net = decoder_gru2(net, initial_state=initial_state)\n", + " net = decoder_gru3(net, initial_state=initial_state)\n", + "\n", + " # Connect the final dense layer that converts to\n", + " # one-hot encoded arrays.\n", + " decoder_output = decoder_dense(net)\n", + " \n", + " return decoder_output" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Connect and Create the Models\n", + "\n", + "We can now connect the encoder and decoder in different ways.\n", + "\n", + "First we connect the encoder directly to the decoder so it is one whole model that can be trained end-to-end. This means the initial-state of the decoder's GRU units are set to the output of the encoder." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "decoder_output = connect_decoder(initial_state=encoder_output)\n", + "\n", + "model_train = Model(inputs=[encoder_input, decoder_input],\n", + " outputs=[decoder_output])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we create a model for just the encoder alone. This is useful for mapping a sequence of integer-tokens to a \"thought-vector\" summarizing its contents." + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "model_encoder = Model(inputs=[encoder_input],\n", + " outputs=[encoder_output])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we create a model for just the decoder alone. This allows us to directly input the initial state for the decoder's GRU units." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "decoder_output = connect_decoder(initial_state=decoder_initial_state)\n", + "\n", + "model_decoder = Model(inputs=[decoder_input, decoder_initial_state],\n", + " outputs=[decoder_output])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that all these models use the same weights and variables of the encoder and decoder. We are merely changing how they are connected. So once the entire model has been trained, we can run the encoder and decoder models separately with the trained weights." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compile the Model\n", + "\n", + "The output of the decoder is a sequence of one-hot encoded arrays. In order to train the decoder we need to supply the one-hot encoded arrays that we desire to see on the decoder's output, and then use a loss-function like cross-entropy to train the decoder to produce this desired output.\n", + "\n", + "However, our data-set contains integer-tokens instead of one-hot encoded arrays. Each one-hot encoded array has 10000 elements so it would be extremely wasteful to convert the entire data-set to one-hot encoded arrays.\n", + "\n", + "A better way is to use a so-called sparse cross-entropy loss-function, which does the conversion internally from integers to one-hot encoded arrays.\n", + "\n", + "We have used the Adam optimizer in many of the previous tutorials, but it seems to diverge in some of these experiments with Recurrent Neural Networks. RMSprop seems to work much better for these." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "model_train.compile(optimizer=RMSprop(lr=1e-3),\n", + " loss='sparse_categorical_crossentropy')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Callback Functions\n", + "\n", + "During training we want to save checkpoints and log the progress to TensorBoard so we create the appropriate callbacks for Keras.\n", + "\n", + "This is the callback for writing checkpoints during training." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "path_checkpoint = '21_checkpoint.keras'\n", + "callback_checkpoint = ModelCheckpoint(filepath=path_checkpoint,\n", + " monitor='val_loss',\n", + " verbose=1,\n", + " save_weights_only=True,\n", + " save_best_only=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the callback for stopping the optimization when performance worsens on the validation-set." + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "callback_early_stopping = EarlyStopping(monitor='val_loss',\n", + " patience=3, verbose=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the callback for writing the TensorBoard log during training." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "callback_tensorboard = TensorBoard(log_dir='./21_logs/',\n", + " histogram_freq=0,\n", + " write_graph=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "callbacks = [callback_early_stopping,\n", + " callback_checkpoint,\n", + " callback_tensorboard]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load Checkpoint\n", + "\n", + "You can reload the last saved checkpoint so you don't have to train the model every time you want to use it." + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " model_train.load_weights(path_checkpoint)\n", + "except Exception as error:\n", + " print(\"Error trying to load checkpoint.\")\n", + " print(error)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train the Model\n", + "\n", + "We wrap the data in named dicts so we are sure the data is assigned correctly to the inputs and outputs of the model." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "x_data = \\\n", + "{\n", + " 'encoder_input': encoder_input_data,\n", + " 'decoder_input': decoder_input_data\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [], + "source": [ + "y_data = \\\n", + "{\n", + " 'decoder_output': decoder_output_data\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We want a validation-set of 10000 sequences but Keras needs this number as a fraction." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0050792360828931325" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "validation_split = 10000 / len(encoder_input_data)\n", + "validation_split" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can train the model. One epoch of training took about 1 hour on a GTX 1070 GPU. You probably need to run 10 epochs or more during training. After 10 epochs the loss was about 1.10 on the training-set and about 1.15 on the validation-set.\n", + "\n", + "The batch-size was chosen to keep the GPU running at nearly 100% while being within the memory limits of 8GB for this GPU." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "model_train.fit(x=x_data,\n", + " y=y_data,\n", + " batch_size=384,\n", + " epochs=10,\n", + " validation_split=validation_split,\n", + " callbacks=callbacks)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Translate Texts\n", + "\n", + "This function translates a text from the source-language to the destination-language and optionally prints a true translation." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "def translate(input_text, true_output_text=None):\n", + " \"\"\"Translate a single text-string.\"\"\"\n", + "\n", + " # Convert the input-text to integer-tokens.\n", + " # Note the sequence of tokens has to be reversed.\n", + " # Padding is probably not necessary.\n", + " input_tokens = tokenizer_src.text_to_tokens(text=input_text,\n", + " reverse=True,\n", + " padding=True)\n", + " \n", + " # Get the output of the encoder's GRU which will be\n", + " # used as the initial state in the decoder's GRU.\n", + " # This could also have been the encoder's final state\n", + " # but that is really only necessary if the encoder\n", + " # and decoder use the LSTM instead of GRU because\n", + " # the LSTM has two internal states.\n", + " initial_state = model_encoder.predict(input_tokens)\n", + "\n", + " # Max number of tokens / words in the output sequence.\n", + " max_tokens = tokenizer_dest.max_tokens\n", + "\n", + " # Pre-allocate the 2-dim array used as input to the decoder.\n", + " # This holds just a single sequence of integer-tokens,\n", + " # but the decoder-model expects a batch of sequences.\n", + " shape = (1, max_tokens)\n", + " decoder_input_data = np.zeros(shape=shape, dtype=np.int)\n", + "\n", + " # The first input-token is the special start-token for 'ssss '.\n", + " token_int = token_start\n", + "\n", + " # Initialize an empty output-text.\n", + " output_text = ''\n", + "\n", + " # Initialize the number of tokens we have processed.\n", + " count_tokens = 0\n", + "\n", + " # While we haven't sampled the special end-token for ' eeee'\n", + " # and we haven't processed the max number of tokens.\n", + " while token_int != token_end and count_tokens < max_tokens:\n", + " # Update the input-sequence to the decoder\n", + " # with the last token that was sampled.\n", + " # In the first iteration this will set the\n", + " # first element to the start-token.\n", + " decoder_input_data[0, count_tokens] = token_int\n", + "\n", + " # Wrap the input-data in a dict for clarity and safety,\n", + " # so we are sure we input the data in the right order.\n", + " x_data = \\\n", + " {\n", + " 'decoder_initial_state': initial_state,\n", + " 'decoder_input': decoder_input_data\n", + " }\n", + "\n", + " # Note that we input the entire sequence of tokens\n", + " # to the decoder. This wastes a lot of computation\n", + " # because we are only interested in the last input\n", + " # and output. We could modify the code to return\n", + " # the GRU-states when calling predict() and then\n", + " # feeding these GRU-states as well the next time\n", + " # we call predict(), but it would make the code\n", + " # much more complicated.\n", + "\n", + " # Input this data to the decoder and get the predicted output.\n", + " decoder_output = model_decoder.predict(x_data)\n", + "\n", + " # Get the last predicted token as a one-hot encoded array.\n", + " token_onehot = decoder_output[0, count_tokens, :]\n", + " \n", + " # Convert to an integer-token.\n", + " token_int = np.argmax(token_onehot)\n", + "\n", + " # Lookup the word corresponding to this integer-token.\n", + " sampled_word = tokenizer_dest.token_to_word(token_int)\n", + "\n", + " # Append the word to the output-text.\n", + " output_text += \" \" + sampled_word\n", + "\n", + " # Increment the token-counter.\n", + " count_tokens += 1\n", + "\n", + " # Sequence of tokens output by the decoder.\n", + " output_tokens = decoder_input_data[0]\n", + " \n", + " # Print the input-text.\n", + " print(\"Input text:\")\n", + " print(input_text)\n", + " print()\n", + "\n", + " # Print the translated output-text.\n", + " print(\"Translated text:\")\n", + " print(output_text)\n", + " print()\n", + "\n", + " # Optionally print the true translated text.\n", + " if true_output_text is not None:\n", + " print(\"True output text:\")\n", + " print(true_output_text)\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Examples\n", + "\n", + "Translate a text from the training-data. This translation is quite good. Note how it is not identical to the translation from the training-data, but the actual meaning is similar." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input text:\n", + "De har udtrykt ønske om en debat om dette emne i løbet af mødeperioden.\n", + "\n", + "Translated text:\n", + " you have expressed a desire to speak on this subject during the debate eeee\n", + "\n", + "True output text:\n", + "ssss You have requested a debate on this subject in the course of the next few days, during this part-session. eeee\n", + "\n" + ] + } + ], + "source": [ + "idx = 3\n", + "translate(input_text=data_src[idx],\n", + " true_output_text=data_dest[idx])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is another example which is also a reasonable translation, although it has incorrectly translated the natural disasters. Note \"countries of the European Union\" has instead been translated as \"member states\" which are synonyms in this context." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input text:\n", + "I mellemtiden ønsker jeg - som også en del kolleger har anmodet om - at vi iagttager et minuts stilhed til minde om ofrene for bl.a. stormene i de medlemslande, der blev ramt.\n", + "\n", + "Translated text:\n", + " in the meantime i have a part of the house who have also asked for a member of the victims of the terrible victims of the tragedy of which we were affected eeee\n", + "\n", + "True output text:\n", + "ssss In the meantime, I should like to observe a minute' s silence, as a number of Members have requested, on behalf of all the victims concerned, particularly those of the terrible storms, in the various countries of the European Union. eeee\n", + "\n" + ] + } + ], + "source": [ + "idx = 4\n", + "translate(input_text=data_src[idx],\n", + " true_output_text=data_dest[idx])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example we join two texts from the training-set. The model first sends this combined text through the encoder, which produces a \"thought-vector\" that seems to summarize both texts reasonably well so the decoder can produce a reasonable translation." + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input text:\n", + "De har udtrykt ønske om en debat om dette emne i løbet af mødeperioden.I mellemtiden ønsker jeg - som også en del kolleger har anmodet om - at vi iagttager et minuts stilhed til minde om ofrene for bl.a. stormene i de medlemslande, der blev ramt.\n", + "\n", + "Translated text:\n", + " you have a part of this debate in which i have had a request to speak during the debate in the portuguese presidency as a member of the victims of the tragedy of which we have been victims of this morning eeee\n", + "\n", + "True output text:\n", + "ssss You have requested a debate on this subject in the course of the next few days, during this part-session. eeeessss In the meantime, I should like to observe a minute' s silence, as a number of Members have requested, on behalf of all the victims concerned, particularly those of the terrible storms, in the various countries of the European Union. eeee\n", + "\n" + ] + } + ], + "source": [ + "idx = 3\n", + "translate(input_text=data_src[idx] + data_src[idx+1],\n", + " true_output_text=data_dest[idx] + data_dest[idx+1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we reverse the order of these two texts then the meaning is not quite so clear for the latter text." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input text:\n", + "I mellemtiden ønsker jeg - som også en del kolleger har anmodet om - at vi iagttager et minuts stilhed til minde om ofrene for bl.a. stormene i de medlemslande, der blev ramt.De har udtrykt ønske om en debat om dette emne i løbet af mødeperioden.\n", + "\n", + "Translated text:\n", + " in the meantime i have also asked a member of the members who have asked for a debate on the victims of this type of attack and that you have expressed a part of this debate as part of the spanish presidency eeee\n", + "\n", + "True output text:\n", + "ssss In the meantime, I should like to observe a minute' s silence, as a number of Members have requested, on behalf of all the victims concerned, particularly those of the terrible storms, in the various countries of the European Union. eeeessss You have requested a debate on this subject in the course of the next few days, during this part-session. eeee\n", + "\n" + ] + } + ], + "source": [ + "idx = 3\n", + "translate(input_text=data_src[idx+1] + data_src[idx],\n", + " true_output_text=data_dest[idx+1] + data_dest[idx])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is an example I made up. It is a quite broken translation." + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input text:\n", + "der var engang et land der hed Danmark\n", + "\n", + "Translated text:\n", + " there were a member of the european commission eeee\n", + "\n", + "True output text:\n", + "Once there was a country named Denmark\n", + "\n" + ] + } + ], + "source": [ + "translate(input_text=\"der var engang et land der hed Danmark\",\n", + " true_output_text='Once there was a country named Denmark')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is another example I made up. This is a better translation even though it is perhaps a more complicated text." + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input text:\n", + "Idag kan man læse i avisen at Danmark er blevet fornuftigt\n", + "\n", + "Translated text:\n", + " read you read that the netherlands has been sensible eeee\n", + "\n", + "True output text:\n", + "Today you can read in the newspaper that Denmark has become sensible.\n", + "\n" + ] + } + ], + "source": [ + "translate(input_text=\"Idag kan man læse i avisen at Danmark er blevet fornuftigt\",\n", + " true_output_text=\"Today you can read in the newspaper that Denmark has become sensible.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a text from a Danish song. It doesn't even make much sense in Danish. However the translation is probably so broken because several of the words are not in the vocabulary." + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input text:\n", + "Hvem spæner ud af en butik og tygger de stærkeste bolcher?\n", + "\n", + "Translated text:\n", + " who is a of the and the eeee\n", + "\n", + "True output text:\n", + "Who runs out of a shop and chews the strongest bon-bons?\n", + "\n" + ] + } + ], + "source": [ + "translate(input_text=\"Hvem spæner ud af en butik og tygger de stærkeste bolcher?\",\n", + " true_output_text=\"Who runs out of a shop and chews the strongest bon-bons?\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "This tutorial showed the basic idea of using two Recurrent Neural Networks in a so-called encoder/decoder model to do Machine Translation of human languages. It was demonstrated on the very large Europarl data-set from the European Union.\n", + "\n", + "The model could produce reasonable translations for some texts but not for others. It is possible that a better architecture for the neural network and more training epochs could improve performance. There are also more advanced models that are known to improve quality of the translations.\n", + "\n", + "However, it is important to note that these models do not really understand human language. The models have no knowledge of the actual meaning of the words. The models are merely very advanced function approximators that can map between sequences of integer-tokens." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercises\n", + "\n", + "These are a few suggestions for exercises that may help improve your skills with TensorFlow. It is important to get hands-on experience with TensorFlow in order to learn how to use it properly.\n", + "\n", + "You may want to backup this Notebook before making any changes.\n", + "\n", + "* Train for more than 10 epochs. Does it improve the translations?\n", + "* Increase the size of the vocabulary. Does it improve the translations? Would it make sense to have different sizes for the vocabularies of the source and destination languages?\n", + "* Find another data-set and use it together with Europarl.\n", + "* Change the architectures of the neural network, for example change the state-size for the GRU layers, the number of GRU layers, the embedding-size, etc. Does it improve the translations?\n", + "* Use hyper-parameter optimization from Tutorial #19 to automatically find the best hyper-parameters.\n", + "* When translating texts, instead of using `np.argmax()` to sample the next integer-token, could you sample the decoder's output as if it was a probability distribution instead? Note that the decoder's output is not softmax-limited so you have to do that first to turn it into a probability-distribution.\n", + "* Can you generate multiple sequences by doing this sampling? Can you find a way to select the best of these different sequences?\n", + "* Disable the reversal of words for the source-language. Does it improve the translations?\n", + "* What is a Bi-Directional GRU and can you use it here?\n", + "* We use the **output** of the encoder's GRU as the initial state of the decoder's GRU. The research literature often uses an LSTM instead of the GRU, so they used the encoder's **state** instead of its output as the initial state of the decoder. Can you rewrite this code to use the encoder's state as the decoder's initial state? Is there a reason to do this, or is the encoder's output sufficient to use as the decoder's initial state?\n", + "* Is it possible to connect multiple encoders and decoders in a single neural network, so that you can train it on different languages and allow for direct translation e.g. from Danish to Polish, German and French?\n", + "* Explain to a friend how the program works." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## License (MIT)\n", + "\n", + "Copyright (c) 2018 by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n", + "\n", + "Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n", + "\n", + "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n", + "\n", + "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE." + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.10" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/22_Image_Captioning.ipynb b/22_Image_Captioning.ipynb new file mode 100644 index 0000000..d5782f5 --- /dev/null +++ b/22_Image_Captioning.ipynb @@ -0,0 +1,2353 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# TensorFlow Tutorial #22\n", + "# Image Captioning\n", + "\n", + "by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n", + "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "Tutorial #21 on Machine Translation showed how to translate text from one human language to another. It worked by having two Recurrent Neural Networks (RNN), the first called an encoder and the second called a decoder. The first RNN encodes the source-text as a single vector of numbers and the second RNN decodes this vector into the destination-text. The intermediate vector between the encoder and decoder is a kind of summary of the source-text, which is sometimes called a \"thought-vector\". The reason for using this intermediate summary-vector is to understand the whole source-text before it is being translated. This also allows for the source- and destination-texts to have different lengths.\n", + "\n", + "In this tutorial we will replace the encoder with an image-recognition model similar to Transfer Learning and Fine-Tuning in Tutorials #08 and #10. The image-model recognizes what the image contains and outputs that as a vector of numbers - the \"thought-vector\" or summary-vector, which is then input to a Recurrent Neural Network that decodes this vector into text.\n", + "\n", + "This is a somewhat advanced tutorial and you should be familiar with TensorFlow, Keras, Transfer Learning and Natural Language Processing, see Tutorials #01, #03-C, #08, #10, #20, and #21." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Flowchart\n", + "\n", + "We will use the VGG16 model that has been pre-trained for classifying images. But instead of using the last classification layer, we will redirect the output of the previous layer. This gives us a vector with 4096 elements that summarizes the image-contents - similar to how a \"thought-vector\" summarized the contents of an input-text in Tutorial #21 on language translation. We will use this vector as the initial state of the Gated Recurrent Units (GRU). However, the internal state-size of the GRU is only 512, so we need an intermediate fully-connected (dense) layer to map the vector with 4096 elements down to a vector with only 512 elements.\n", + "\n", + "The decoder then uses this initial-state together with a start-marker \"ssss\" to begin producing output words. In the first iteration it will hopefully output the word \"big\". Then we input this word into the decoder and hopefully we get the word \"brown\" out, and so on. Finally we have generated the text \"big brown bear sitting eeee\" where \"eeee\" marks the end of the text.\n", + "\n", + "The flowchart of the algorithm is roughly:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Flowchart](images/22_image_captioning_flowchart.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import tensorflow as tf\n", + "import numpy as np\n", + "import sys\n", + "import os\n", + "from PIL import Image\n", + "from cache import cache" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We need to import several things from Keras." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras import backend as K\n", + "from tensorflow.keras.models import Model\n", + "from tensorflow.keras.layers import Input, Dense, GRU, Embedding\n", + "from tensorflow.keras.applications import VGG16\n", + "from tensorflow.keras.optimizers import RMSprop\n", + "from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard\n", + "from tensorflow.keras.preprocessing.text import Tokenizer\n", + "from tensorflow.keras.preprocessing.sequence import pad_sequences" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This was developed using Python 3.6 (Anaconda) and package versions:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'2.1.0'" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tf.__version__" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'2.2.4-tf'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tf.keras.__version__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Data\n", + "\n", + "We will use the COCO data-set which contains many images with text-captions.\n", + "\n", + "http://cocodataset.org/" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import coco" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can change the data-directory if you want to save the data-files somewhere else." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# coco.set_data_dir(\"data/coco/\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Automatically download and extract the data-files if you don't have them already.\n", + "\n", + "**WARNING! These data-files are VERY large! The file for the training-data is 19 GB and the file for the validation-data is 816 MB! **" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading http://images.cocodataset.org/zips/train2017.zip\n", + "Data has apparently already been downloaded and unpacked.\n", + "Downloading http://images.cocodataset.org/zips/val2017.zip\n", + "Data has apparently already been downloaded and unpacked.\n", + "Downloading http://images.cocodataset.org/annotations/annotations_trainval2017.zip\n", + "Data has apparently already been downloaded and unpacked.\n" + ] + } + ], + "source": [ + "coco.maybe_download_and_extract()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the filenames and captions for the images in the training-set." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "- Data loaded from cache-file: data/coco/records_train.pkl\n" + ] + } + ], + "source": [ + "_, filenames_train, captions_train = coco.load_records(train=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Number of images in the training-set." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "118287" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "num_images_train = len(filenames_train)\n", + "num_images_train" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the filenames and captions for the images in the validation-set." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "- Data loaded from cache-file: data/coco/records_val.pkl\n" + ] + } + ], + "source": [ + "_, filenames_val, captions_val = coco.load_records(train=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Helper-Functions for Loading and Showing Images\n", + "\n", + "This is a helper-function for loading and resizing an image." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def load_image(path, size=None):\n", + " \"\"\"\n", + " Load the image from the given file-path and resize it\n", + " to the given size if not None.\n", + " \"\"\"\n", + "\n", + " # Load the image using PIL.\n", + " img = Image.open(path)\n", + "\n", + " # Resize image if desired.\n", + " if not size is None:\n", + " img = img.resize(size=size, resample=Image.LANCZOS)\n", + "\n", + " # Convert image to numpy array.\n", + " img = np.array(img)\n", + "\n", + " # Scale image-pixels so they fall between 0.0 and 1.0\n", + " img = img / 255.0\n", + "\n", + " # Convert 2-dim gray-scale array to 3-dim RGB array.\n", + " if (len(img.shape) == 2):\n", + " img = np.repeat(img[:, :, np.newaxis], 3, axis=2)\n", + "\n", + " return img" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a helper-function for showing an image from the data-set along with its captions." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def show_image(idx, train):\n", + " \"\"\"\n", + " Load and plot an image from the training- or validation-set\n", + " with the given index.\n", + " \"\"\"\n", + "\n", + " if train:\n", + " # Use an image from the training-set.\n", + " dir = coco.train_dir\n", + " filename = filenames_train[idx]\n", + " captions = captions_train[idx]\n", + " else:\n", + " # Use an image from the validation-set.\n", + " dir = coco.val_dir\n", + " filename = filenames_val[idx]\n", + " captions = captions_val[idx]\n", + "\n", + " # Path for the image-file.\n", + " path = os.path.join(dir, filename)\n", + "\n", + " # Print the captions for this image.\n", + " for caption in captions:\n", + " print(caption)\n", + " \n", + " # Load the image and plot it.\n", + " img = load_image(path)\n", + " plt.imshow(img)\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example Image\n", + "\n", + "Show an example image and captions from the training-set." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "A giraffe eating food from the top of the tree.\n", + "A giraffe standing up nearby a tree \n", + "A giraffe mother with its baby in the forest.\n", + "Two giraffes standing in a tree filled area.\n", + "A giraffe standing next to a forest filled with trees.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "show_image(idx=1, train=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pre-Trained Image Model (VGG16)\n", + "\n", + "The following creates an instance of the VGG16 model using the Keras API. This automatically downloads the required files if you don't have them already.\n", + "\n", + "The VGG16 model was pre-trained on the ImageNet data-set for classifying images. The VGG16 model contains a convolutional part and a fully-connected (or dense) part which is used for the image classification.\n", + "\n", + "If `include_top=True` then the whole VGG16 model is downloaded which is about 528 MB. If `include_top=False` then only the convolutional part of the VGG16 model is downloaded which is just 57 MB.\n", + "\n", + "We will use some of the fully-connected layers in this pre-trained model, so we have to download the full model, but if you have a slow internet connection, then you can try and modify the code below to use the smaller pre-trained model without the classification layers.\n", + "\n", + "Tutorials #08 and #10 explain more details about Transfer Learning." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "image_model = VGG16(include_top=True, weights='imagenet')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Print a list of all the layers in the VGG16 model." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"vgg16\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "input_1 (InputLayer) [(None, 224, 224, 3)] 0 \n", + "_________________________________________________________________\n", + "block1_conv1 (Conv2D) (None, 224, 224, 64) 1792 \n", + "_________________________________________________________________\n", + "block1_conv2 (Conv2D) (None, 224, 224, 64) 36928 \n", + "_________________________________________________________________\n", + "block1_pool (MaxPooling2D) (None, 112, 112, 64) 0 \n", + "_________________________________________________________________\n", + "block2_conv1 (Conv2D) (None, 112, 112, 128) 73856 \n", + "_________________________________________________________________\n", + "block2_conv2 (Conv2D) (None, 112, 112, 128) 147584 \n", + "_________________________________________________________________\n", + "block2_pool (MaxPooling2D) (None, 56, 56, 128) 0 \n", + "_________________________________________________________________\n", + "block3_conv1 (Conv2D) (None, 56, 56, 256) 295168 \n", + "_________________________________________________________________\n", + "block3_conv2 (Conv2D) (None, 56, 56, 256) 590080 \n", + "_________________________________________________________________\n", + "block3_conv3 (Conv2D) (None, 56, 56, 256) 590080 \n", + "_________________________________________________________________\n", + "block3_pool (MaxPooling2D) (None, 28, 28, 256) 0 \n", + "_________________________________________________________________\n", + "block4_conv1 (Conv2D) (None, 28, 28, 512) 1180160 \n", + "_________________________________________________________________\n", + "block4_conv2 (Conv2D) (None, 28, 28, 512) 2359808 \n", + "_________________________________________________________________\n", + "block4_conv3 (Conv2D) (None, 28, 28, 512) 2359808 \n", + "_________________________________________________________________\n", + "block4_pool (MaxPooling2D) (None, 14, 14, 512) 0 \n", + "_________________________________________________________________\n", + "block5_conv1 (Conv2D) (None, 14, 14, 512) 2359808 \n", + "_________________________________________________________________\n", + "block5_conv2 (Conv2D) (None, 14, 14, 512) 2359808 \n", + "_________________________________________________________________\n", + "block5_conv3 (Conv2D) (None, 14, 14, 512) 2359808 \n", + "_________________________________________________________________\n", + "block5_pool (MaxPooling2D) (None, 7, 7, 512) 0 \n", + "_________________________________________________________________\n", + "flatten (Flatten) (None, 25088) 0 \n", + "_________________________________________________________________\n", + "fc1 (Dense) (None, 4096) 102764544 \n", + "_________________________________________________________________\n", + "fc2 (Dense) (None, 4096) 16781312 \n", + "_________________________________________________________________\n", + "predictions (Dense) (None, 1000) 4097000 \n", + "=================================================================\n", + "Total params: 138,357,544\n", + "Trainable params: 138,357,544\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "image_model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will use the output of the layer prior to the final classification-layer which is named `fc2`. This is a fully-connected (or dense) layer." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "transfer_layer = image_model.get_layer('fc2')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We call it the \"transfer-layer\" because we will transfer its output to another model that creates the image captions.\n", + "\n", + "To do this, first we need to create a new model which has the same input as the original VGG16 model but outputs the transfer-values from the `fc2` layer." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "image_model_transfer = Model(inputs=image_model.input,\n", + " outputs=transfer_layer.output)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model expects input images to be of this size:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(224, 224)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "img_size = K.int_shape(image_model.input)[1:3]\n", + "img_size" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For each input image, the new model will output a vector of transfer-values with this length:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4096" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "transfer_values_size = K.int_shape(transfer_layer.output)[1]\n", + "transfer_values_size" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Process All Images\n", + "\n", + "We now make functions for processing all images in the data-set using the pre-trained image-model and saving the transfer-values in a cache-file so they can be reloaded quickly.\n", + "\n", + "We effectively create a new data-set of the transfer-values. This is because it takes a long time to process an image in the VGG16 model. We will not be changing all the parameters of the VGG16 model, so every time it processes an image, it gives the exact same result. We need the transfer-values to train the image-captioning model for many epochs, so we save a lot of time by calculating the transfer-values once and saving them in a cache-file.\n", + "\n", + "This is a helper-function for printing the progress." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "def print_progress(count, max_count):\n", + " # Percentage completion.\n", + " pct_complete = count / max_count\n", + "\n", + " # Status-message. Note the \\r which means the line should\n", + " # overwrite itself.\n", + " msg = \"\\r- Progress: {0:.1%}\".format(pct_complete)\n", + "\n", + " # Print it.\n", + " sys.stdout.write(msg)\n", + " sys.stdout.flush()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the function for processing the given files using the VGG16-model and returning their transfer-values." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "def process_images(data_dir, filenames, batch_size=32):\n", + " \"\"\"\n", + " Process all the given files in the given data_dir using the\n", + " pre-trained image-model and return their transfer-values.\n", + " \n", + " Note that we process the images in batches to save\n", + " memory and improve efficiency on the GPU.\n", + " \"\"\"\n", + " \n", + " # Number of images to process.\n", + " num_images = len(filenames)\n", + "\n", + " # Pre-allocate input-batch-array for images.\n", + " shape = (batch_size,) + img_size + (3,)\n", + " image_batch = np.zeros(shape=shape, dtype=np.float16)\n", + "\n", + " # Pre-allocate output-array for transfer-values.\n", + " # Note that we use 16-bit floating-points to save memory.\n", + " shape = (num_images, transfer_values_size)\n", + " transfer_values = np.zeros(shape=shape, dtype=np.float16)\n", + "\n", + " # Initialize index into the filenames.\n", + " start_index = 0\n", + "\n", + " # Process batches of image-files.\n", + " while start_index < num_images:\n", + " # Print the percentage-progress.\n", + " print_progress(count=start_index, max_count=num_images)\n", + "\n", + " # End-index for this batch.\n", + " end_index = start_index + batch_size\n", + "\n", + " # Ensure end-index is within bounds.\n", + " if end_index > num_images:\n", + " end_index = num_images\n", + "\n", + " # The last batch may have a different batch-size.\n", + " current_batch_size = end_index - start_index\n", + "\n", + " # Load all the images in the batch.\n", + " for i, filename in enumerate(filenames[start_index:end_index]):\n", + " # Path for the image-file.\n", + " path = os.path.join(data_dir, filename)\n", + "\n", + " # Load and resize the image.\n", + " # This returns the image as a numpy-array.\n", + " img = load_image(path, size=img_size)\n", + "\n", + " # Save the image for later use.\n", + " image_batch[i] = img\n", + "\n", + " # Use the pre-trained image-model to process the image.\n", + " # Note that the last batch may have a different size,\n", + " # so we only use the relevant images.\n", + " transfer_values_batch = \\\n", + " image_model_transfer.predict(image_batch[0:current_batch_size])\n", + "\n", + " # Save the transfer-values in the pre-allocated array.\n", + " transfer_values[start_index:end_index] = \\\n", + " transfer_values_batch[0:current_batch_size]\n", + "\n", + " # Increase the index for the next loop-iteration.\n", + " start_index = end_index\n", + "\n", + " # Print newline.\n", + " print()\n", + "\n", + " return transfer_values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Helper-function for processing all images in the training-set. This saves the transfer-values in a cache-file for fast reloading." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "def process_images_train():\n", + " print(\"Processing {0} images in training-set ...\".format(len(filenames_train)))\n", + "\n", + " # Path for the cache-file.\n", + " cache_path = os.path.join(coco.data_dir,\n", + " \"transfer_values_train.pkl\")\n", + "\n", + " # If the cache-file already exists then reload it,\n", + " # otherwise process all images and save their transfer-values\n", + " # to the cache-file so it can be reloaded quickly.\n", + " transfer_values = cache(cache_path=cache_path,\n", + " fn=process_images,\n", + " data_dir=coco.train_dir,\n", + " filenames=filenames_train)\n", + "\n", + " return transfer_values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Helper-function for processing all images in the validation-set." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "def process_images_val():\n", + " print(\"Processing {0} images in validation-set ...\".format(len(filenames_val)))\n", + "\n", + " # Path for the cache-file.\n", + " cache_path = os.path.join(coco.data_dir, \"transfer_values_val.pkl\")\n", + "\n", + " # If the cache-file already exists then reload it,\n", + " # otherwise process all images and save their transfer-values\n", + " # to the cache-file so it can be reloaded quickly.\n", + " transfer_values = cache(cache_path=cache_path,\n", + " fn=process_images,\n", + " data_dir=coco.val_dir,\n", + " filenames=filenames_val)\n", + "\n", + " return transfer_values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Process all images in the training-set and save the transfer-values to a cache-file. This took about 30 minutes to process on a GTX 1070 GPU." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing 118287 images in training-set ...\n", + "- Data loaded from cache-file: data/coco/transfer_values_train.pkl\n", + "dtype: float16\n", + "shape: (118287, 4096)\n", + "CPU times: user 187 ms, sys: 621 ms, total: 807 ms\n", + "Wall time: 806 ms\n" + ] + } + ], + "source": [ + "%%time\n", + "transfer_values_train = process_images_train()\n", + "print(\"dtype:\", transfer_values_train.dtype)\n", + "print(\"shape:\", transfer_values_train.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Process all images in the validation-set and save the transfer-values to a cache-file. This took about 90 seconds to process on a GTX 1070 GPU." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processing 5000 images in validation-set ...\n", + "- Data loaded from cache-file: data/coco/transfer_values_val.pkl\n", + "dtype: float16\n", + "shape: (5000, 4096)\n", + "CPU times: user 7.16 ms, sys: 32.7 ms, total: 39.8 ms\n", + "Wall time: 37.8 ms\n" + ] + } + ], + "source": [ + "%%time\n", + "transfer_values_val = process_images_val()\n", + "print(\"dtype:\", transfer_values_val.dtype)\n", + "print(\"shape:\", transfer_values_val.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tokenizer\n", + "\n", + "Neural Networks cannot work directly on text-data. We use a two-step process to convert text into numbers that can be used in a neural network. The first step is to convert text-words into so-called integer-tokens. The second step is to convert integer-tokens into vectors of floating-point numbers using a so-called embedding-layer. See Tutorial #20 for a more detailed explanation.\n", + "\n", + "Before we can start processing the text, we first need to mark the beginning and end of each text-sequence with unique words that most likely aren't present in the data." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "mark_start = 'ssss '\n", + "mark_end = ' eeee'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This helper-function wraps all text-strings in the above markers. Note that the captions are a list of list, so we need a nested for-loop to process it. This can be done using so-called list-comprehension in Python." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "def mark_captions(captions_listlist):\n", + " captions_marked = [[mark_start + caption + mark_end\n", + " for caption in captions_list]\n", + " for captions_list in captions_listlist]\n", + " \n", + " return captions_marked" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now process all the captions in the training-set and show an example." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['ssss Closeup of bins of food that include broccoli and bread. eeee',\n", + " 'ssss A meal is presented in brightly colored plastic trays. eeee',\n", + " 'ssss there are containers filled with different kinds of foods eeee',\n", + " 'ssss Colorful dishes holding meat, vegetables, fruit, and bread. eeee',\n", + " 'ssss A bunch of trays that have different food. eeee']" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "captions_train_marked = mark_captions(captions_train)\n", + "captions_train_marked[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is how the captions look without the start- and end-markers." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Closeup of bins of food that include broccoli and bread.',\n", + " 'A meal is presented in brightly colored plastic trays.',\n", + " 'there are containers filled with different kinds of foods',\n", + " 'Colorful dishes holding meat, vegetables, fruit, and bread.',\n", + " 'A bunch of trays that have different food.']" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "captions_train[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This helper-function converts a list-of-list to a flattened list of captions." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "def flatten(captions_listlist):\n", + " captions_list = [caption\n", + " for captions_list in captions_listlist\n", + " for caption in captions_list]\n", + " \n", + " return captions_list" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now use the function to convert all the marked captions from the training set." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "captions_train_flat = flatten(captions_train_marked)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Set the maximum number of words in our vocabulary. This means that we will only use e.g. the 10000 most frequent words in the captions from the training-data." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "num_words = 10000" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We need a few more functions than provided by Keras' Tokenizer-class so we wrap it." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "class TokenizerWrap(Tokenizer):\n", + " \"\"\"Wrap the Tokenizer-class from Keras with more functionality.\"\"\"\n", + " \n", + " def __init__(self, texts, num_words=None):\n", + " \"\"\"\n", + " :param texts: List of strings with the data-set.\n", + " :param num_words: Max number of words to use.\n", + " \"\"\"\n", + "\n", + " Tokenizer.__init__(self, num_words=num_words)\n", + "\n", + " # Create the vocabulary from the texts.\n", + " self.fit_on_texts(texts)\n", + "\n", + " # Create inverse lookup from integer-tokens to words.\n", + " self.index_to_word = dict(zip(self.word_index.values(),\n", + " self.word_index.keys()))\n", + "\n", + " def token_to_word(self, token):\n", + " \"\"\"Lookup a single word from an integer-token.\"\"\"\n", + "\n", + " word = \" \" if token == 0 else self.index_to_word[token]\n", + " return word \n", + "\n", + " def tokens_to_string(self, tokens):\n", + " \"\"\"Convert a list of integer-tokens to a string.\"\"\"\n", + "\n", + " # Create a list of the individual words.\n", + " words = [self.index_to_word[token]\n", + " for token in tokens\n", + " if token != 0]\n", + " \n", + " # Concatenate the words to a single string\n", + " # with space between all the words.\n", + " text = \" \".join(words)\n", + "\n", + " return text\n", + " \n", + " def captions_to_tokens(self, captions_listlist):\n", + " \"\"\"\n", + " Convert a list-of-list with text-captions to\n", + " a list-of-list of integer-tokens.\n", + " \"\"\"\n", + " \n", + " # Note that text_to_sequences() takes a list of texts.\n", + " tokens = [self.texts_to_sequences(captions_list)\n", + " for captions_list in captions_listlist]\n", + " \n", + " return tokens" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now create a tokenizer using all the captions in the training-data. Note that we use the flattened list of captions to create the tokenizer because it cannot take a list-of-lists." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 8.43 s, sys: 20.6 ms, total: 8.45 s\n", + "Wall time: 8.45 s\n" + ] + } + ], + "source": [ + "%%time\n", + "tokenizer = TokenizerWrap(texts=captions_train_flat,\n", + " num_words=num_words)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the integer-token for the start-marker (the word \"ssss\"). We will need this further below." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "token_start = tokenizer.word_index[mark_start.strip()]\n", + "token_start" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the integer-token for the end-marker (the word \"eeee\")." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "token_end = tokenizer.word_index[mark_end.strip()]\n", + "token_end" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Convert all the captions from the training-set to sequences of integer-tokens. We get a list-of-list as a result." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 7.8 s, sys: 27 ms, total: 7.83 s\n", + "Wall time: 7.83 s\n" + ] + } + ], + "source": [ + "%%time\n", + "tokens_train = tokenizer.captions_to_tokens(captions_train_marked)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Example of the integer-tokens for the captions of the first image in the training-set:" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[[2, 841, 5, 2864, 5, 61, 26, 1984, 238, 9, 433, 3],\n", + " [2, 1, 429, 10, 3310, 7, 1025, 390, 501, 1110, 3],\n", + " [2, 63, 19, 993, 143, 8, 190, 958, 5, 743, 3],\n", + " [2, 299, 725, 25, 343, 208, 264, 9, 433, 3],\n", + " [2, 1, 170, 5, 1110, 26, 446, 190, 61, 3]]" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tokens_train[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These are the corresponding text-captions:" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['ssss Closeup of bins of food that include broccoli and bread. eeee',\n", + " 'ssss A meal is presented in brightly colored plastic trays. eeee',\n", + " 'ssss there are containers filled with different kinds of foods eeee',\n", + " 'ssss Colorful dishes holding meat, vegetables, fruit, and bread. eeee',\n", + " 'ssss A bunch of trays that have different food. eeee']" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "captions_train_marked[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Generator\n", + "\n", + "Each image in the training-set has at least 5 captions describing the contents of the image. The neural network will be trained with batches of transfer-values for the images and sequences of integer-tokens for the captions. If we were to have matching numpy arrays for the training-set, we would either have to only use a single caption for each image and ignore the rest of this valuable data, or we would have to repeat the image transfer-values for each of the captions, which would waste a lot of memory.\n", + "\n", + "A better solution is to create a custom data-generator for Keras that will create a batch of data with randomly selected transfer-values and token-sequences.\n", + "\n", + "This helper-function returns a list of random token-sequences for the images with the given indices in the training-set." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "def get_random_caption_tokens(idx):\n", + " \"\"\"\n", + " Given a list of indices for images in the training-set,\n", + " select a token-sequence for a random caption,\n", + " and return a list of all these token-sequences.\n", + " \"\"\"\n", + " \n", + " # Initialize an empty list for the results.\n", + " result = []\n", + "\n", + " # For each of the indices.\n", + " for i in idx:\n", + " # The index i points to an image in the training-set.\n", + " # Each image in the training-set has at least 5 captions\n", + " # which have been converted to tokens in tokens_train.\n", + " # We want to select one of these token-sequences at random.\n", + "\n", + " # Get a random index for a token-sequence.\n", + " j = np.random.choice(len(tokens_train[i]))\n", + "\n", + " # Get the j'th token-sequence for image i.\n", + " tokens = tokens_train[i][j]\n", + "\n", + " # Add this token-sequence to the list of results.\n", + " result.append(tokens)\n", + "\n", + " return result" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This generator function creates random batches of training-data for use in training the neural network." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "def batch_generator(batch_size):\n", + " \"\"\"\n", + " Generator function for creating random batches of training-data.\n", + " \n", + " Note that it selects the data completely randomly for each\n", + " batch, corresponding to sampling of the training-set with\n", + " replacement. This means it is possible to sample the same\n", + " data multiple times within a single epoch - and it is also\n", + " possible that some data is not sampled at all within an epoch.\n", + " However, all the data should be unique within a single batch.\n", + " \"\"\"\n", + "\n", + " # Infinite loop.\n", + " while True:\n", + " # Get a list of random indices for images in the training-set.\n", + " idx = np.random.randint(num_images_train,\n", + " size=batch_size)\n", + " \n", + " # Get the pre-computed transfer-values for those images.\n", + " # These are the outputs of the pre-trained image-model.\n", + " transfer_values = transfer_values_train[idx]\n", + "\n", + " # For each of the randomly chosen images there are\n", + " # at least 5 captions describing the contents of the image.\n", + " # Select one of those captions at random and get the\n", + " # associated sequence of integer-tokens.\n", + " tokens = get_random_caption_tokens(idx)\n", + "\n", + " # Count the number of tokens in all these token-sequences.\n", + " num_tokens = [len(t) for t in tokens]\n", + " \n", + " # Max number of tokens.\n", + " max_tokens = np.max(num_tokens)\n", + " \n", + " # Pad all the other token-sequences with zeros\n", + " # so they all have the same length and can be\n", + " # input to the neural network as a numpy array.\n", + " tokens_padded = pad_sequences(tokens,\n", + " maxlen=max_tokens,\n", + " padding='post',\n", + " truncating='post')\n", + " \n", + " # Further prepare the token-sequences.\n", + " # The decoder-part of the neural network\n", + " # will try to map the token-sequences to\n", + " # themselves shifted one time-step.\n", + " decoder_input_data = tokens_padded[:, 0:-1]\n", + " decoder_output_data = tokens_padded[:, 1:]\n", + "\n", + " # Dict for the input-data. Because we have\n", + " # several inputs, we use a named dict to\n", + " # ensure that the data is assigned correctly.\n", + " x_data = \\\n", + " {\n", + " 'decoder_input': decoder_input_data,\n", + " 'transfer_values_input': transfer_values\n", + " }\n", + "\n", + " # Dict for the output-data.\n", + " y_data = \\\n", + " {\n", + " 'decoder_output': decoder_output_data\n", + " }\n", + " \n", + " yield (x_data, y_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Set the batch-size used during training. This is set very high so the GPU can be used maximally - but this also requires a lot of RAM on the GPU. You may have to lower this number if the training runs out of memory." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "batch_size = 384" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create an instance of the data-generator." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "generator = batch_generator(batch_size=batch_size)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Test the data-generator by creating a batch of data." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "batch = next(generator)\n", + "batch_x = batch[0]\n", + "batch_y = batch[1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Example of the transfer-values for the first image in the batch." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0. , 0. , 1.483, ..., 0. , 0. , 0.813], dtype=float16)" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "batch_x['transfer_values_input'][0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Example of the token-sequence for the first image in the batch. This is the input to the decoder-part of the neural network." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 2, 1, 21, 80, 13, 34, 315, 1, 69, 20, 12,\n", + " 1, 1083, 3, 0, 0, 0, 0, 0, 0, 0],\n", + " dtype=int32)" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "batch_x['decoder_input'][0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the token-sequence for the output of the decoder. Note how it is the same as the sequence above, except it is shifted one time-step." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1, 21, 80, 13, 34, 315, 1, 69, 20, 12, 1,\n", + " 1083, 3, 0, 0, 0, 0, 0, 0, 0, 0],\n", + " dtype=int32)" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "batch_y['decoder_output'][0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Steps Per Epoch\n", + "\n", + "One epoch is a complete processing of the training-set. We would like to process each image and caption pair only once per epoch. However, because each batch is chosen completely at random in the above batch-generator, it is possible that an image occurs in multiple batches within a single epoch, and it is possible that some images may not occur in any batch at all within a single epoch.\n", + "\n", + "Nevertheless, we still use the concept of an 'epoch' to measure approximately how many iterations of the training-data we have processed. But the data-generator will generate batches for eternity, so we need to manually calculate the approximate number of batches required per epoch.\n", + "\n", + "This is the number of captions for each image in the training-set." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "num_captions_train = [len(captions) for captions in captions_train]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the total number of captions in the training-set." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "total_num_captions_train = np.sum(num_captions_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the approximate number of batches required per epoch, if we want to process each caption and image pair once per epoch." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1541" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "steps_per_epoch = int(total_num_captions_train / batch_size)\n", + "steps_per_epoch" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create the Recurrent Neural Network\n", + "\n", + "We will now create the Recurrent Neural Network (RNN) that will be trained to map the vectors with transfer-values from the image-recognition model into sequences of integer-tokens that can be converted into text. We call this neural network for the 'decoder' as it is almost identical to the decoder when doing Machine Translation in Tutorial #21.\n", + "\n", + "Note that we are using the functional model from Keras to build this neural network, because it allows more flexibility in how the neural network can be connected, in case you want to experiment and connect the image-model directly to the decoder (see the exercises). This means we have split the network construction into two parts: (1) Creation of all the layers that are not yet connected, and (2) a function that connects all these layers.\n", + "\n", + "The decoder consists of 3 GRU layers whose internal state-sizes are:" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "state_size = 512" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The embedding-layer converts integer-tokens into vectors of this length:" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "embedding_size = 128" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This inputs transfer-values to the decoder:" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "transfer_values_input = Input(shape=(transfer_values_size,),\n", + " name='transfer_values_input')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We want to use the transfer-values to initialize the internal states of the GRU units. This informs the GRU units of the contents of the images. The transfer-values are vectors of length 4096 but the size of the internal states of the GRU units are only 512, so we use a fully-connected layer to map the vectors from 4096 to 512 elements.\n", + "\n", + "Note that we use a `tanh` activation function to limit the output of the mapping between -1 and 1, otherwise this does not seem to work." + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "decoder_transfer_map = Dense(state_size,\n", + " activation='tanh',\n", + " name='decoder_transfer_map')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the input for token-sequences to the decoder. Using `None` in the shape means that the token-sequences can have arbitrary lengths." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "decoder_input = Input(shape=(None, ), name='decoder_input')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the embedding-layer which converts sequences of integer-tokens to sequences of vectors." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "decoder_embedding = Embedding(input_dim=num_words,\n", + " output_dim=embedding_size,\n", + " name='decoder_embedding')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This creates the 3 GRU layers of the decoder. Note that they all return sequences because we ultimately want to output a sequence of integer-tokens that can be converted into a text-sequence." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "decoder_gru1 = GRU(state_size, name='decoder_gru1',\n", + " return_sequences=True)\n", + "decoder_gru2 = GRU(state_size, name='decoder_gru2',\n", + " return_sequences=True)\n", + "decoder_gru3 = GRU(state_size, name='decoder_gru3',\n", + " return_sequences=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The GRU layers output a tensor with shape `[batch_size, sequence_length, state_size]`, where each \"word\" is encoded as a vector of length `state_size`. We need to convert this into sequences of integer-tokens that can be interpreted as words from our vocabulary.\n", + "\n", + "One way of doing this is to convert the GRU output to a one-hot encoded array. It works but it is extremely wasteful, because for a vocabulary of e.g. 10000 words we need a vector with 10000 elements, so we can select the index of the highest element to be the integer-token." + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "decoder_dense = Dense(num_words,\n", + " activation='softmax',\n", + " name='decoder_output')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Connect and Create the Training Model\n", + "\n", + "The decoder is built using the functional API of Keras, which allows more flexibility in connecting the layers e.g. to have multiple inputs. This is useful e.g. if you want to connect the image-model directly with the decoder instead of using pre-calculated transfer-values.\n", + "\n", + "This function connects all the layers of the decoder to some input of transfer-values." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "def connect_decoder(transfer_values):\n", + " # Map the transfer-values so the dimensionality matches\n", + " # the internal state of the GRU layers. This means\n", + " # we can use the mapped transfer-values as the initial state\n", + " # of the GRU layers.\n", + " initial_state = decoder_transfer_map(transfer_values)\n", + "\n", + " # Start the decoder-network with its input-layer.\n", + " net = decoder_input\n", + " \n", + " # Connect the embedding-layer.\n", + " net = decoder_embedding(net)\n", + " \n", + " # Connect all the GRU layers.\n", + " net = decoder_gru1(net, initial_state=initial_state)\n", + " net = decoder_gru2(net, initial_state=initial_state)\n", + " net = decoder_gru3(net, initial_state=initial_state)\n", + "\n", + " # Connect the final dense layer that converts to\n", + " # one-hot encoded arrays.\n", + " decoder_output = decoder_dense(net)\n", + " \n", + " return decoder_output" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Connect and create the model used for training. This takes as input transfer-values and sequences of integer-tokens and outputs sequences of one-hot encoded arrays that can be converted into integer-tokens." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "decoder_output = connect_decoder(transfer_values=transfer_values_input)\n", + "\n", + "decoder_model = Model(inputs=[transfer_values_input, decoder_input],\n", + " outputs=[decoder_output])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compile the Model\n", + "\n", + "The output of the decoder is a sequence of one-hot encoded arrays. In order to train the decoder we need to supply the one-hot encoded arrays that we desire to see on the decoder's output, and then use a loss-function like cross-entropy to train the decoder to produce this desired output.\n", + "\n", + "However, our data-set contains integer-tokens instead of one-hot encoded arrays. Each one-hot encoded array has 10000 elements so it would be extremely wasteful to convert the entire data-set to one-hot encoded arrays. We could do this conversion from integers to one-hot arrays in the `batch_generator()` above.\n", + "\n", + "A better way is to use a so-called sparse cross-entropy loss-function, which does the conversion internally from integers to one-hot encoded arrays.\n", + "\n", + "We have used the Adam optimizer in many of the previous tutorials, but it seems to diverge in some of these experiments with Recurrent Neural Networks. RMSprop seems to work much better for these." + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "decoder_model.compile(optimizer=RMSprop(lr=1e-3),\n", + " loss='sparse_categorical_crossentropy')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Callback Functions\n", + "\n", + "During training we want to save checkpoints and log the progress to TensorBoard so we create the appropriate callbacks for Keras.\n", + "\n", + "This is the callback for writing checkpoints during training." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "path_checkpoint = '22_checkpoint.keras'\n", + "callback_checkpoint = ModelCheckpoint(filepath=path_checkpoint,\n", + " verbose=1,\n", + " save_weights_only=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the callback for writing the TensorBoard log during training." + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [], + "source": [ + "callback_tensorboard = TensorBoard(log_dir='./22_logs/',\n", + " histogram_freq=0,\n", + " write_graph=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "callbacks = [callback_checkpoint, callback_tensorboard]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load Checkpoint\n", + "\n", + "You can reload the last saved checkpoint so you don't have to train the model every time you want to use it." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " decoder_model.load_weights(path_checkpoint)\n", + "except Exception as error:\n", + " print(\"Error trying to load checkpoint.\")\n", + " print(error)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train the Model\n", + "\n", + "Now we will train the decoder so it can map transfer-values from the image-model to sequences of integer-tokens for the captions of the images.\n", + "\n", + "One epoch of training took about 7 minutes on a GTX 1070 GPU. You probably need to run 20 epochs or more during training.\n", + "\n", + "Note that if we didn't use pre-computed transfer-values then each epoch would take maybe 40 minutes to run, because all the images would have to be processed by the VGG16 model as well." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "decoder_model.fit(x=generator,\n", + " steps_per_epoch=steps_per_epoch,\n", + " epochs=20,\n", + " callbacks=callbacks)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate Captions\n", + "\n", + "This function loads an image and generates a caption using the model we have trained." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "def generate_caption(image_path, max_tokens=30):\n", + " \"\"\"\n", + " Generate a caption for the image in the given path.\n", + " The caption is limited to the given number of tokens (words).\n", + " \"\"\"\n", + "\n", + " # Load and resize the image.\n", + " image = load_image(image_path, size=img_size)\n", + " \n", + " # Expand the 3-dim numpy array to 4-dim\n", + " # because the image-model expects a whole batch as input,\n", + " # so we give it a batch with just one image.\n", + " image_batch = np.expand_dims(image, axis=0)\n", + "\n", + " # Process the image with the pre-trained image-model\n", + " # to get the transfer-values.\n", + " transfer_values = image_model_transfer.predict(image_batch)\n", + "\n", + " # Pre-allocate the 2-dim array used as input to the decoder.\n", + " # This holds just a single sequence of integer-tokens,\n", + " # but the decoder-model expects a batch of sequences.\n", + " shape = (1, max_tokens)\n", + " decoder_input_data = np.zeros(shape=shape, dtype=np.int)\n", + "\n", + " # The first input-token is the special start-token for 'ssss '.\n", + " token_int = token_start\n", + "\n", + " # Initialize an empty output-text.\n", + " output_text = ''\n", + "\n", + " # Initialize the number of tokens we have processed.\n", + " count_tokens = 0\n", + "\n", + " # While we haven't sampled the special end-token for ' eeee'\n", + " # and we haven't processed the max number of tokens.\n", + " while token_int != token_end and count_tokens < max_tokens:\n", + " # Update the input-sequence to the decoder\n", + " # with the last token that was sampled.\n", + " # In the first iteration this will set the\n", + " # first element to the start-token.\n", + " decoder_input_data[0, count_tokens] = token_int\n", + "\n", + " # Wrap the input-data in a dict for clarity and safety,\n", + " # so we are sure we input the data in the right order.\n", + " x_data = \\\n", + " {\n", + " 'transfer_values_input': transfer_values,\n", + " 'decoder_input': decoder_input_data\n", + " }\n", + "\n", + " # Note that we input the entire sequence of tokens\n", + " # to the decoder. This wastes a lot of computation\n", + " # because we are only interested in the last input\n", + " # and output. We could modify the code to return\n", + " # the GRU-states when calling predict() and then\n", + " # feeding these GRU-states as well the next time\n", + " # we call predict(), but it would make the code\n", + " # much more complicated.\n", + " \n", + " # Input this data to the decoder and get the predicted output.\n", + " decoder_output = decoder_model.predict(x_data)\n", + "\n", + " # Get the last predicted token as a one-hot encoded array.\n", + " # Note that this is not limited by softmax, but we just\n", + " # need the index of the largest element so it doesn't matter.\n", + " token_onehot = decoder_output[0, count_tokens, :]\n", + "\n", + " # Convert to an integer-token.\n", + " token_int = np.argmax(token_onehot)\n", + "\n", + " # Lookup the word corresponding to this integer-token.\n", + " sampled_word = tokenizer.token_to_word(token_int)\n", + "\n", + " # Append the word to the output-text.\n", + " output_text += \" \" + sampled_word\n", + "\n", + " # Increment the token-counter.\n", + " count_tokens += 1\n", + "\n", + " # This is the sequence of tokens output by the decoder.\n", + " output_tokens = decoder_input_data[0]\n", + "\n", + " # Plot the image.\n", + " plt.imshow(image)\n", + " plt.show()\n", + " \n", + " # Print the predicted caption.\n", + " print(\"Predicted caption:\")\n", + " print(output_text)\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Examples\n", + "\n", + "Try this with a picture of a parrot." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicted caption:\n", + " a bird is sitting on a tree branch eeee\n", + "\n" + ] + } + ], + "source": [ + "generate_caption(\"images/parrot_cropped1.jpg\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Try it with a picture of a person (Elon Musk). In Tutorial #07 the Inception model mis-classified this picture as being either a sweatshirt or a cowboy boot." + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicted caption:\n", + " a man in a suit and tie is standing outside eeee\n", + "\n" + ] + } + ], + "source": [ + "generate_caption(\"images/elon_musk.jpg\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Helper-function for loading an image from the COCO data-set and printing the true captions as well as the predicted caption." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [], + "source": [ + "def generate_caption_coco(idx, train=False):\n", + " \"\"\"\n", + " Generate a caption for an image in the COCO data-set.\n", + " Use the image with the given index in either the\n", + " training-set (train=True) or validation-set (train=False).\n", + " \"\"\"\n", + " \n", + " if train:\n", + " # Use image and captions from the training-set.\n", + " data_dir = coco.train_dir\n", + " filename = filenames_train[idx]\n", + " captions = captions_train[idx]\n", + " else:\n", + " # Use image and captions from the validation-set.\n", + " data_dir = coco.val_dir\n", + " filename = filenames_val[idx]\n", + " captions = captions_val[idx]\n", + "\n", + " # Path for the image-file.\n", + " path = os.path.join(data_dir, filename)\n", + "\n", + " # Use the model to generate a caption of the image.\n", + " generate_caption(image_path=path)\n", + "\n", + " # Print the true captions from the data-set.\n", + " print(\"True captions:\")\n", + " for caption in captions:\n", + " print(caption)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Try this on a picture from the training-set that the model has been trained on. In some cases the generated caption is actually better than the human-generated captions." + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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ypJpFzl+8ZNhSjBI2rxdC4Igc3QnQ2gZ3LYyt1ghbBZKAloIsr+hbxFzwhGKBdCUs7ABxXvkRUOTUP1F+ZpyAuJBXF2UVow3GFBE+95wWD1mjShNLmBQM/afKKUWJ0fMlZd9hOtdE8XmFwQBKZVRKiiQVnPYF9abLEzMn0+C42joCc+5dhbDXhTaUfUHgOvT6MZGEeaLzKoTKGXh7N4uCnjBM5pof3u7SrHqkxoUoIwBU4BBnBt/xmM0SkghqlYwHpwP+/P0J73w8wvMEOjM0agphJI/PEnrzhAeP9sm0xvcEcWrvO80Eo3HMZqtKPQwwjNFCMBpFVNwK16/ssFpNuP844/STY9AwHM5587UXiEZDbuy6nJ6N+LWX13n3x31+5atf5fr2Gt94scul7QaT+D6Xt13OemdUautMZ5ogdNhp7VBpPyCsuDx8UEaYKZMxrNY7DKMxWvp0+3vMoyH1Zg1vuY1fb3D/ww/QqX1at/eOWFpdoey1iNITTnsnPPzoT8iiE9avf4PG+nMYkREGJbau7JBlmmajaSs7RlhUJh1K5Rpf+NrXKVeWaa00cYMqOnV4/Stf48UvfoHWyjL1RoVyqYSUDlmWEJVKuL5Dq72M63hIIwjCEjduvMjVF67xvX/1HW5/cp9apUqnWaFc8nGVQmhBZmnWnLHXaC2tIQuDuMjgFwllTtDK3BnYoJQrWa6jlkMQKHEehBCCtCAfEQU+QAr73u5XcBEGKXOq7TPkZ8gJ5NBHChyl0DpblEkMgLb5EUbmjLxNG4o8KtOahcEtuACJ6yk8z6Fed3FUzFma5tva/etNxQu7IcePU7oDSb+fMRyB5wjL+oPNyYoHauzTz4xkNMkIag6NSsjxcGY9uTFokyKFREmZfwfkbLyQKfNE0h3FZCKlVZM0y4rj45gk1TlL7TCYxmRKMp1qbt0aEacSJTWOMly9FDLoJzw+gzQxPHo8odoMKfuKKDl3qM1GmXKlzjC6D9JAZmhUKyjtcNIdsFxvcHO3zbsPugxTcJ05Z8MeKx2H/hBeurpFQ7b4l9/5Pf6X7/4er964xGzSYu90wo31Zd68+hzvfHCP4+MBjXLIeruG4wquV7ZR9SqtsMGTboYTnaKMZHRyQm8S0T07w/NLTNI50zQlTSZM5xFRbFCOYafeZNQb8d0PPsbzy3giYdj9AamAndAHDFoIlBdQrTs40kE5ntWj/J+SLqVySGdpmdfebOL7Cql83JJi6dIGRqeUSiG+HyKUk/dlODhSkRlNu7VEuVKm25XU6sts71zm2osv0Wg2aLbexeg61WqZMAhwhECo3LSNQQjrFBaBQAhsdmDOOz2KgJ2jTJvm2P0NT4UODHZ/VeT9CoQ+J8zThRsAJQRGWIL50+nCZ8nPhBMoIrbWJmdVi/zFFrLKZZdm3efkZEaamQUxZ4xt+hHigrM4Pypg04PpKMZUFY2KS2+Q5Oc0SCHJMkUQOKyvgeilnI0sx+AoacmjzCxYWoFhqeFRDhUPnsT0ZynTOEViU5LimosaUdHAIqRAIEnz/ofxNEZJhes6NCseyUzSnxcMsEZKQ5pCaqaMJ3OSWNjBNYqVpuHVa1U+/GRE4KRoBJ1mgOsImlWH00mMRCIFNOs1tBSMohSdgSMVg3HELEmQxsMRiue2mqzWyvSmfRItmUymNJ67jKumvHnpMv/jb/8JMXM2N7b5ytuX+fDDDzn65Af88MOQZmOD3rDPTnOJm1fXUBWHRlnh6RFPnkzo6zpVz6daWsYArXaVmJRKVGIaa8bZiNrqMp31lzi4P2Ge9lApxFqTcMhLV1ymXKJSyQiClObGv4PnNiGdk0QZrgqRSiIdtciv88EFBI7j0qjVKQUax1M4jkQKF9etIgU5Aa2wNJ41UKlcXNens7RMvdGEh/tU6qtc2r7MzvYum+ubLK9sc/vjA/zAxfVcNAZlBEpZjkpi+SGjz41TYhl7bViQgAWMV5bfs8Thonvpgg6bi4SidSMyJx81FmVYPsKgLnSj2bcSbTRp9jOeDhhjS3Qi79LIsuw8msMiNwpDj8k0eappCFj0FRTIQcjzh5BlFj4dHM1p1nykIyBPCYSEyTRj/2jOtctl/FpG/0Nbv5XqnJQpCEthwHEEmxuK055gMDPERj8F94SQi7RG5iy2bU684KlyhKFJWKpXefP6Cv/iu/dJtMZRAmUcpAOO8lDaJUORoikpw+ayT6ddo1KSROkQKV2C0GN9o0xvCncPT5ESJjE8Po5Z8zNcpYAUBERpSn+acjSaUAva7Gw+x7WtA+6dDElTTe90ymQasNHuYKYOg/kJN18IeG73Ei5toqTOOD7m9ZseZ5zh1BTj3gytJX7YoL1+mcnwmFJ6xO29A1aWLrHUruB5DkG1xtS4jBPJ2cExThDy3M4mlbLH+z4oJdCpvfarKyG1uWSYLVGvL1HyPHqH7yDmp5SrS5wc36W1dAWkR33zOVRQytHfOUAGF+UISsqAI5HCQQjLN50XjETOwNuPGolSDu1Om7W1DQ72uqytbbO0us7y0gpCSkrlOuVym2qtbIlobXGeLCJ2HsdRBa9k0EYvzimkXJDdTxe4rYIUqLMw5yKtlHn4Kzix86LUeS6cmULn7GeNbW7L9P//fQJ/DSkMx97dxXLgfK45jWNQtvEDXfhXK3ma9Jfad60BW+cQ64yTXpQ3AOV0S95HcHiUkWQRZdcwmlqUYIxZdI5JIezDlJLBNOPJkcRIgREpygiM1rZaI/LBNQbpCHzPOpJppBeE5fklGjZWAl66UeXlF1r88XuPuX8aY4TE8wxhyUE4M066AyazDN+RXN3wuLwR0BsOORmOQEs0MI4SGm3NxmaNjb0RB2cRjmOYRwOOT6VtSsqTUImgPxqyfzLjyvI69XqVl6+t8MP7h0RzODh9yAd34PJrf5+PHzxguSO4euM5HKm5ffcWP/jxI+plxe6168SHx4yHcDw745OHh2xf3iVs36R+9Ytkf/Fn3Pmj7zKOFDe2buK4AdPYMBynTKYRj49O2RvOCZtdnrvUwzGnuNIQC0GSJPRHDhmCQO0zGQUY7TMf/wAnWefsyRQdz5lNXsDxtmhsXuOi6S+MyYARCiXIuR0W3XwyH1wjrDu2OmH1QkpFrdnk8nNXeXj3lLXVNZY6S5QrFRylcJRDKaxaV6KkJZGxXJEoFC+Hjyrv7eMCSSdy6GpTTdA5dF8Et/w/cwHmF7qTGluZKnL8xR3nwcoYG3QyWDCT9trOS+Oflp+Z9QSEsDeyKGWY8wifZpo4se3DBRkiLsApCjJECDxH4ub52TkxZ5BaAHrRN1AgBiEEiTEMhxlPTgyzeYbKvbvROu/myp2uEJDBTkNxtRMijcq5W3Hu00UB/wS+K6iWJKFrI43ISU5LLgo8IdhcDim1q1QqjuUDtGZ33WNz2WFzpYPj+ZQUfOXFJt9+e42S4xMlMUbECCHRWcZwpGlXQ5otzdqajxKCkmeoVRJG0wSMQMq8HGUgjTWrLZ+lpRKNdpOrly/x8gsN1huKzVaNL21fZv/BE570D4niiAf7GaP5BOVkrK2EfPMrr9DeuILrNDHJEzJvwgcP7zEZT/CUg6ZO6DXZqIRsVz38sEpmJI+fHHPWH3DS6zKXhvraDrFqM5gnGJESOApHKhqVCq3aMqXQg+yM2fQJRkKSZIzGdxiN7tLrjzk5O+akP0YbZcd0Md7FiNkx0xTROv/VnKM8Ka0dK2nbfO18DZdqpcz1my9y/cYNtjaW6Swt4fklXC+gXK7QbNRptpp5CnCuU4i8B82cG2VBXEthUMKghIXzhdux1SNbotU5AuYiUV70ugh7/Vm+bXH8gmgsMonMnJ8/M3k68XehOoCwNdTFHIHCphZQTaNTbApHniIUDUEYa2DClg2L9ssLbVpPeVNMwStIpDJ4CtJUM5vb80ojbFuxsCfL8r0ltpTphz7fvNpiFB1x92iMVkXGV3AB1oHoTNJoOJSU5FGSEKcaKSRCQmoE43GMxue0P2dtBfyPJXFs2GoHXFmrU6HDWvuYX/t6wLd/6XkCx+NPz35Mue7hCoUUCQLFLDIEQY3d7RofftJjsyOphh7TWcIsnlOvCroDLKtsBP1JQvcsZjJJ8Nd8mrUmLzxXxblS4fnlCn5W4zsf/Jj9/hkqFDw82Ceazmh8cYX5dEa3Lzg9/ZjbH9/n5Rd3aQyPee/7t/no40c0WhUO9w6JJ5rZOKF/0uP4sIvwHQajIZmOMNJh+8ouYfsacWLwvClh6FFyRd6RpxiPFXOdcdJzaS77zMePEEyZjcYo5TJOElpVhyiLSeIEt+x+yvzztHDRVScWximx+bQd5wJVWxZfIhDKoRwEXNm9QvT1jFKpQqVaxZF2fovnChxVZPoXdTh/NVhyTljHUzD7Rbg4nwom0HlFYBHx82B2UaS4GNTIHU1eOctT5qLxyBp9jqYxaCNRIjsvHf4E+dlxAp8S8Wkjz0st522/F5BAnjwZY4hTG8mffo5P04bFO6MzalWXZsfn6CRhlqS2rIIhRSIyg+uIvBKR5T0Jgt44pVw3/PJbHf7geyn3j2KSbKF6CAyZgXmSkqS2G6+4HiFFEYhwXIeP7h8xiMdcvVKn8e6Eg5OUVjNkubnMcJCyug67u2vEjAlNi1qrxGQ+JzEKKQ1aaGZRRm84p1ILePG5Jtsth7BUIo01XppydJxiMjDKIKXtlYiUZpqOyJIhjXKFzU6D9eqctXKHP/rwPQaccjqZMDvyePl6g7t7Q3TS4bXn1/no9gOSSR+RgTY+l9eXeNfd50/e/wHXXr5KxU95dDLjzkmfWqXKZDjAlHw8z6FWrdCbzRDBkHRyl1r9OtVSHQdIUsM8Mdw/POTE9KlXIoRsM5mOUdkpSsxp1ctU/IAwVUhxQLtSZ7z/HjK7gXZ8wko5b6MtuvGKwc6BsxCLzj1rdDlnLwwq/90IiesGtJodXnjZRwpBqVzKjTHnfZzzaEtO+Nk+gdzYc5Jh0T7MeaQWwtiqEdKmmYvcPucBRPHRLLSXpz7b3FfnASfTxnbXIhbko8mdhMkdUpb+HUgHgEUjjrnAcNrvi1e9IAy1zvIJRMWPdjutjfWSnDuSiw6gSDPA4DuSasnhynaZdsV2zgkgQ9Iqe9xYq1EJDZ5zDs0yY5gngkE648q1Ot/+6haNkpOXK83FYEDouHhC0Qg9Kr4tQxW4TilBb2x494M+73444t5RzErbYWsloLPmkeiUWTJkc7OOMgH37j9mOh3RalU4OZkxmCVIxzrDOIs5Pu2RxTE7a8u8eGODlfUS1aZHRsBgliGVvSjPgUZdceXKGpVaBYGhXCpzZXmdVrjKrUGC19C8/solvvX2VTaaHstVl1ZF8ORJhO82OTo65c7BkEEsuL+3RxTFBKWA/dEeH955SKuzBHLO5kadJE2ZzWeMxxOUVKRaMJmPqFZTluopTtyF2Zh6ycUImCeG0JM0y9AOQ1SWEqohjmOoVap4KmA8jwiVQIop8fhHPP7kn7P3F7/Fkw//b0wS2/w7H4dCB851odAVywelef6sTZEu2FmVRij8sERnqc3S8hK+7y9mrgopLaIrUg7M+WQ2IWwVu+j8oyC3C0eQR2mRV6hyTV/0DsAiJVgEwKd+ZUEcZlqTGEFiJLGWpJltWsqMbYnP8nuy7z8bCfzMOAHbYWc7AhfXW3Tn5Xj+Am2ycBYX703k5EuW2o2LISrq5hfJQykkG0sONU9wdDylVCr6tiUKw+WVgC+/tEytZOv9Sp0TSeO5Jp4bZlGfS1s+V3Y8ArcYThaoREnBSr3Ci7tt1tqlRfOGwOai80RTqfv0BnN+8P4ZQmm+dLOGUREHvQOqnRLSKTEaT2jWKqRpwJNBn/WlGoErbE+4ErQaDoaMk96Yk96U7jAmSTTCsfP719ou9bJlxJs1h9XlEpVSC5Rn52o4CkGb43nAaTpne2ONyZlCmiobay3evzvk5esd9g9P+ejDYxqNNo96KakSOCrm8VGfRs1DuFM+vPVjpqOM1dUOz790nbPJkMlsSpwmSCU56w0ZzyfM5po0UhCdMh0d4rqKTsVju+6wVPd57eoK13baBGFKLfCI4ghH1IinhoeHY0bTiPl8SpL0GE/vcHrw25w8+AEnm78AACAASURBVF2SKEaoTzn+C1IQd0LIXK3MAhWc880GjUQ4Ln4Y4AV+XuY9J/aUEEiRO4Vcv2wQOu9eLZITKYUtRRU5uxbnTXA51JdCLBCsgHxKsFikvYv7EZY/SLUh0RCnhiQztl0Ziwq0OadIixqD+LvQNqyUWMzyE+RooKi7GYEpZl/lXy1KghdYUrvfOWxCnJOIBXQT2MaiRtlhue3R8Ks87vXxlYLMkopLDYkg4/BsSLUcMpxEzKPzgZ1GCWQpGMNSp8W3vrRKv3/AJ4/mCJVvJwSzuUY4HpWGRzmQOFKS6JwAFSDQ1KuKSkly65FGOZJaSRJ4hiSNGI0NrnA4GZ3QbK4xmA2oNwPE3CGUAk8JSmWHmzshrYbPSW/CfD5lPo9oVstMJob5LGFzrcxoKhlOJjQaPsudgNlkyshPGAcJrpsxiTS3nnQ56h9zdNjl8GTMLPFZa4X0xoqlzhJzk9BI69w9VQROhhsajISz0YCSL2k2XPrTE3pnQzobbdzmEn1cZklKWVpidjibME9HPD44Y9gdI0XI5uaY4WCGJyT1msKVKVJpjB+yuu4wGvSZxRpRhUcnE/ZPx9SqZXw3JdYZwlNgZsySiNl4Rliv5WMlFg75IgqwLzkvZIydentBlxTSRlthCd5zJFEoGCBsF5/dSOb7Wy+ycBfinPhbsBULkHsezFiku5Y81gt7L9hGFk1nBusAMg3a5FPYzYUU4sI9XkyDpVSfaXs/M0ggSayRS2Wd5tMpwYXoX3xTMLILlHRufE8vLnKOAiyUs4OlHMNwqqmVQy63Kmw2FbubAZWa4PpzFbZWy2AyXrvRxPdcksz2cxd1aCkzykGLWnWDjdUOl9ZDHHE+CAJblrx12GO/OyTWMXYexDl95UiJowxJltFpSDbqAeNpzFKrwtpyi9FkSJxOCEshjvDwXJ92awk/bILIaDck1y+VqFc9QuPSLJdo1sr4gUuUZUgJcZYSlF2uXWnQaflowHNc0njKsHfKLIqRApJkxkf3H3DwuM9f/PgJmIx+/5QsjfnWm5vM0yk3n7vB9euXGM0183FC93hGOveQMuCwGyOEwvNiznonJLOMVrnNpd1NZskcjGE6mTCb96mEGaErmYzGPD46pdudMh0ltiIDhCUwxCRpTK3s4gcGoTWecEjSgLPejGlkOOlrnnRTSl6IAcolRffgHdLJGKkcFi20TxFEuaEWSPGCb9Cm6M6zBu4IeSESX8jJhcjJRYHKo7jMWf9z5t82DRVNPQvGSICQGiE0iy5SIfOyn20sk1LYBXAsubBIc1MNaWbItEDroteAp2bTF0ilsI3CIQn1d8AJGJ2bhuAp4z7nA+yrhV8XB1WglMyXx7LzwcUFWLVICoztLrToQRKlhsk44WQwxCu5nEQxr3yhQbtjWFkJeOPVJb74hWVCf4Y0T1FMVEqCerNOpbbCXIfIwKVe83HcAnqJBQGlnALSmWKErMJpu5RXuy5pllO2GprtdYcgyBiP55RLy2ihqLc7bGw+T61aQinBbBLR7Y8QrqJWlQQlwTDKODqbMp2MCZyMTruBH0pcX7PSccmyhGbTY3szJJrNuHXnjLt3T8hGZ7ikCJMxiSZM5hGZI5hGcGNzm2tbDT66dYrvRvRGI2ZDlzuPBzihbceOpinVcoXA93BdQMPyWhXhZggMjaDMjZ0tgsBBKZdYa1ZWa1zd2SJLPKRUtBtlpCgxmph85qghDH18zwEzRzBiZalKOYC9ox5JrFHCIUsTeoMZo2nEbJaACXCyIePue8STUU7PXaDTn1K2C2/ydLOI2XbVn3PU91QnIovN8xhrMELbSoMQC6eg8khfEJC6SF0xF44sc2dyfvxFAfyC09E6/5cfJ8tzfiMKh5Q3BF20CHHOYRXO6u8EErCrr5BPGjr/3hrPOSv6lBMwBQA6dwBSKQSg8llYYOdYKyFoVBycxUwOTeAqTmcRj7pjHh6m7B2MqQUeIs7I0Lz+/FXu7s9I0xhHSbQWtEsO9ZrE8wJmUczR2QghQjZWW3QaPsUgFinIaj2gWStRLXlUQ4XnSDaaHoFra9uhI9jdLLPUceksK7Y32iB8Do9HZDplPIJSuYN0XBw3xkxnHB4eIlyX0AWjYzKtGcQxJ4MR3WEfT0lCt4wUcOO5LV64to5QCZW6YXurDNowGU5peAppDHEy56w/4fgoZj43TKYJDw+nXNna5NJmiTv3ewxHmseHp9zfv0eplLJzqUy7GVBuurgq4etfvszupTZRBqVSiWmc8vDBQxwt6bTblCshszQjUxKtfEaTCIwkDHx0KjgdxJzNIkZJyjTJUNKhP5gRxxmh0lTLJU4GI2ZZj40Vj07NRRqHcsVjNNMcnjqcdvuk8wHj/iFa55MoyPvoC+u98GK/ypFBka8XpBqWbX+aT7P6pBcR+NyozeKfzA3VlmMTI0iNzF+L41uckGmJMbYsnuVEn9bk047JI78m0TbnNyIvbuZqv8j3i7vJUxhDjjFyVKKEQInPgRMQQmwB/zOwkj/P3zTG/DMhxH8B/MfASb7pf56vLfDTDoZS0rb4XoD8+Y/n5TWehmRQTEHOcB3bG260jcKuUqRak2S2bbJcllzZDdjbN0xmsFpTREZj4gSReOgMjg4ilJGMS/DwYMzqWsyLN9rM4lN678/wXJebuz6NTkYUZxx1x9zd38f3qtg1Gwrt0ovRUCpByohySRD4glIouX4p4NFhhOcKVtsVWs067dqMmh9wYIaM4hJ7R0esL5X46N4HjCYD4pmhVTI4RnM6nTAZp7QqHrVQcTaaUs5KuJlLqRMgVYxGMp4kKBFwdWud+VTR649ZqVeY9SVH/R69WcQsjvFVwmQa4RqDj8NgrLn9+JBf/eVrBGGFJ4+P2T/pYkrH7Oys8sH9+1zdrTKZxWTpGM9RLLW2mEUPefywT7Pd5Lg34l//4Idc2VrhxStrZI5HfzTi48e3WV0OqDc8xmPFSW+Ip2aM5gnDuabaFDhhxmw+5mwwZhJBMo0YTyM2VmoImTE3A7xQ4LsuaeRw+yjibHLGzd06orvP0rSLIx07z0Ra2E2RXgppx6foE+ZiQLF5vAGyTJAJbSH+wnmIhTPRFypBdlqvjfxC5At75HAhB3+IhePIt9XFWpWWrLJViYLOk4vIbz5tD4ZPIeFzN1B4LEtVWFQiCqbAfD7EYAr8Z8aYd4UQVeAHQojfz3/7r40x/+Vf+UhFlxQglFzwJov5VCZnc6X5CU5Ak6UGz1EEDsQa4lQgMktaKUC6UCpDf5BipF2e6fJOheW6w4/uTen2EtbrAuM7DM8E1UqIrxT/+x++w99/u0Gjusqjxw9plSq8/cYuZ/N7OB4cnPTo9QZ0z05sI4lKUdJgjGSl6bFcdWhVHRwR4yr7fbkkWVsvMY8zKp5Dtd5hOJywuuYSiDpJ3EVPI4Sx6xy6qsS9h7epei3MLGBpuYVMHvLClToi9pjqhCRJSOYpoxksaYdQOggS6nWfJJlzdjrEVx6toEorqDEuxQxmHkmmwGREaUal7PIP/8GXee/H+9zzJ5QrHj/8pMuj/Sd88UqHiRkSlAKMEYRixOvPbXDntMfBo0d0llf54ONDvHLMzmaFICgzj3sc9EY06mUELoNBxOHJMUcnJzTra1SCEjqLSdOYdDYni21ve63hUq0HjOaGSqlKoCQihUpYZzrTGJ0RBAGzPngCjoczjroDhKuYzSMq/hnj4ZBxb8ho1EUoj0ZnGS/wMSZC65xgzknBQpeKPn4jinJa3qQmrTJemG3AOdQXORuvKVb+EwUZZ0xeURB2TcS8G1CLfC2AwlFcSHftSlmFoygu78J5jfnL6UnhFPJrXqSenC+lYxBo89npwF/bCRhjDoHD/P1ICPERdqnxv5YseuuFwXU8WwbJMrs2mjFok9muwLwkUDiBYgCN1iiTITSUfYEnYTwXVKqSRtm60/Eooj8RpKmh7EvefnWLveN99p8csbXWoHOlzlFvzBdfvkp7rcL4z76DJ0JWOjG/9OYajXKLpaWAyZHPk5MJrkq4shbyxaurOL7io/s9vvv9U/pjQz2U3Lxcpd5UdIdTTvoZ2lhHtrIcoKXDv/7BMe1bR1zd1SwvbTA8henM5uTrSw1qjRK6N6c3mDCcpSxfCjDGQWqIElirVugfnwEeg1FKpOHBkzFbiUO7XmNro8nx8ZgPbh9wMpiRJSlXlqpkYYQXjMhclzSNQRk6jRp+GDKK7rG7UeJsMOb/+MN3eOlyjaW1bRJnlZPuGaNI02orNls7PO4ZjB7TPemTeAM6aYuNzU3mo5jDvSMczyGO5pyNJ5z0h0xmfer1gCzJODwbEM09fOkwiTK74o8n2dpaBm0YTTJWV1aoBiHDUQ+dCZaWFbfvPCLqe1zqhMROwl6qaNRKJGmG8Vbxl1YYnzyi//F3GaoQVW6RJlOkG7Cyvp4bSB4hLxLIC1B9kYwumHqbkhbTyG0kt0GqaMu1i7faBp5FcUrnnYkF35BPNCq2WUx7BzBi0duw2H+BPOy1GFnsx1PXmG9MwTpYxuGcJzCmWJ34J8u/lRKhEGIHeA34c+At4B8LIf4D4B0sWuj91Q5UODSDxOBI+/iuXq4yHEY8eDxFqvPlmYpnYIykUXF5cTdgNEkZzOHSisvDgxn4iksrkpoviCPJu5/EPOnPGfUUSebhOg7jWUajXefO7TkrO5JZOmEWe9y8fpl2eZNudEi51mN7q0q56jJ5IDl4PKdU0Xzt9U0uL++A0tRqHuOx4NbtLi9cLfP89RpGJDw+GnD/YE7gwJdfbRCbKUmqubHrsNrss7vxHBWvxpnJmOiMSdKjkQiiWHDc6zKaZRDFVPwyDT9ke22dRwdnyGXBNM6IkjlSBbiOSyOs48ga9x51aTYbuBjieIpizI3dDRr1kHsnjxmMe5ychcw3BYEA13G5u3eLTkdQCwzroY9G4AiXJydT5hG0Wg6dTojjvMjv/+gh0XhOtV6l2vDxyx0+/nif1ZKLuBTR7XYJPYeSD5mOmUUzplFEo1HGExkHozndiWap5gEaV1kFSDMP1w/wjeH4ZMC0ZhiNptTLFYQoE2cOodFMJ+AYB52MCMIqK0sZ29tvUFl7AXH8IdODdzGbX6Wz1iaZRPiBJEszlOtgjF06brFIxwVjepoD1rYFt1gNOJ/vazv8tJ2TgO3Ms2vWmotqaaM/+eQzc6Fhx+TrCIpiJaALJcTi/yJ/eNo8zsuAP8V+hLnQhbj46TP3+ps7ASFEBfgXwH9qjBkKIf5b4DfyS/gN4L8C/qOfsN/i7w5YNJMvnSBsH78sZvA50GrAF5/v8EffO+Pe/hiZz9UmP4nnSNaWXZ6/oiBx+f5HM7TM2N3yeXSY4LuwtQ61SoVyY8rt2xlhkPL+o8eMRkPeeLlGEqf0hgnr45Cj7gH1Tsr+0YjQPUXjMZ8PGQ0e4jgrZCJGuoJHx3OOuxFXN1ymiaZeL/GLb+6wUYXOpseV3Q5Jojk4nbDyOGa547O7VkEol7gy5PpmhVdubtEdutzbHzGdGpKZ5vKlTW7fPyQcp5DFtMqCYSp5cNjlshdSrpaoeANKvuLNV1c56XYZD2Pmmcd0GDEdntId9qjeq3JpqUmrGeD7mpV2m6VmE/OxYDoGY1IcB0q+i/QDpHJYqSpUs832zi73Tk95/4d3eKyG+LUml5cvMRrFNDstPr7zDsvNCr2Z5iRK6VTrJKmiXe1QWV8hkS5KSuqVMklqOwG7gxluUKER+oSOIPQMjZokHUkmKDKpGfR6mNk6lZZHFE3Q4xnHB8eINYeDbo/+OCJ0G8TpBOXbFYn9IKThRvSe/JjJWcK1a1fo9w/QZ0Ou19YZiSdU622EcLB0HxfSzZ+m3A5CgSEDk9ll6IWd9m4Xrbdr3du4dfEPmdjOPS01wthJxhdXoH6Kh1hUAvJTUtjup6wYQ1ELvEAFfvqC7d8cKDKSC9sK8Tk5ASGEi3UA/9wY878BGGOOLvz+3wG//ZP2vfh3BzxXLp6QIS+raOu9jBZM+hmN65r1JYcHe9ZhSGk7/NJMo5Rg+9IyW5dCzo5OcKTmybHmtRcCJmNIEpcknYEH127U2Vhy2d3eZv9gQqwf8+2XdqgT0JsYblxe5k73HpNejSuNEhETKl6Nr3zxJfR0RiwTrl9Zo1lNefCwy8ZKm1QIxgmsrNykFPZJ4j6VMKEeSMYINpYbfP11n2kyw6DxXc3mep04nrJ3FvH4wTE+hnKjSX8cM/64z0E/wRE93nrrEs1GyK0PHoAb89HefQ66ESZLuNRsUG4ExPMeWxtN7j1MeH7rKtL1+IM/+xOkmLHU2cIrCfTZCeVWlVKjTKNdwj+UKBSu4xN4Zc66dxmmc3ZWdvG05ocf3maS+AReQBz38ZKM4WyDB/uHbKVTXnxxk48/OqJ3GnNzdwmvNQblsbmxzXwy4vj0jCwz6MyQpJppNEc4hlkyYjCdElQ81sI6S6GgJwxJlhB4LpvtGh3HJZsYlkt1zgZjtND0xmOiRBCEPse9ES8sLRHrjKX2KqfDGTXpUcpO8MQE19nEyAbx5JTDvT3WdzbsXBPOuz6LJbhyteOiSRUl6ng2IpllhNUS+B5IidF2WfzUaIRJIBXnnJs5N2BHOaQmyVf/vegAnrKgpz9dQPdPb3xu9AWdsbjcBR+wsKwL91CsKyA+daan5W9SHRDAfw98ZIz5pxe+X8v5AoB/ALz/Vzuig8grtMbkK69IAVIyN5LhUPD4MAbH3o7jSMqhYh6lmFQzn0/Ar9Jeb7B+oDm9PSVODVsbAY8eRzhU0Kmh2V6l6g1wfEFYdbmxWyMUKZ5veOlFj+dfr+M9WKakJlxZ3+V3v3ePWrPH9qUdmp0ttJG8d+sTTgcRN6+s8fzudR4+fsL+0R4602QqRfgJWpV5/1EfR2UgodGKqUtIZhGz6Zh6LcQLBHHU58q2j+eVmGSa4+GUUKZc26kRBhJPlBn0QpabAcsra3zv/Y/QBoKGy8koIijXWA0b+LUSb3+hysH+lCfHT9i5FHB9d4tarY3wBOWqR7VU4vhkznzmstFp4bs+cWIrKGGgqGX/L3Nv9iNpeqX3/b59jS/2iIzcs/au6rWa3SSbPSSH45E0lkayMIZgQTagG3t0b8AW/Bf4wje6MyBABgzB1liyxRnPSnGGy5BNNptd3V37lvsa+/Ltuy+yqruGYNMGNGP0e5MRX0ZEZkbGOe95n/M8z2nwcP8Mfz6mYslIasqlKz2ura5wNAh4urcHSUQYaty+PycvJTbWmqx2W4yLEfVak3Z7iSxPCIIYQQAv8Oh2uohihCLHdJoGjl0yGGZoeYBcynhh8sznQWQeiUz9FFM3MS2HvFxQMwzCNMVzQTdAsyRGQY4qRlhai7GfE0QJFxoqmXOJ+SLk5OQRzeVXODm8j+VUabSquLMRmm4ha8qnrbS/ure++AEXgYzR2UNqyQbV9jKBP+fcwUFGNnUkJNzFDN2uIkjnXamSc9LQbHSKYlTRTI1z9daLqP55l6IsXzipC+Wnm+DzVuR5PJ1/+znd/LPHc25790zx8PyvOMcxi2dPF194+Oengf+YSuBrwH8F3BEE4eNn1/4H4B8LgvD6s99sD/jd/7cXEsVnvmicl1GGWqBqCl5UIAolYRDz6GnOPIjO30zxmeuwKGJYMqFfMBx7HPX7XNnqsLbR4uHuMVECth6hUVCEBoYOChM6dQVRStl6Y4MyC9HFEsMyWa+VTKcxly9ukgYeR7OEihIxHYZExQ6XewX+VMHzJqwt1bAUkdPpnMlsgKEljOdnCJJIo945B2dyicOTIVVTZqO3REFKpWhwcDxhlk651uuRhDaCuMCpL9HQulxeXVDKPq9d22I8m7J3coeLvRscL1LCZIokG6z3UnSlxt5wyHAS8M6ldebRENHU0CyXSl6C4DAbT3CsBkV6biK6/fghP/7okLZtc32jQZEJ6HoFQUzp9hpkQ4+6VWVHHOP7MRdWK9QcE0le4ge3v0NDh9+4sspx7rJwZwiiTHOritW02dk+od3QadSrnIz6lEVBFPt0mxvU6w51T2e5VaEmWCzXGjStDDmCvb05cZJTpAV2TUetyAwjj2R2jOE6VEuDNbXCWZEzxePkbMb6+isYpozouWTFCF0UWGldIs5Sslxn3t/BcQxSLFZ6m5imQp6LxFlIPI9pLfdI0wxRkD4l3fxibV2UJapZod5aRVU13NkpcRyhayIHD+9Tay9RxgIL94z1K1/B6DikXogiO2h2Qf9oj+7aNXRbJU/KZ8zi83aDQEmwmKGbFc7V6uWniN8vK/Sfs2efA4Uv6iPPK4NfEuDPS+pPS4fPX/8x3YEffc6r/3+eNfDi0hTI0vNpK69erZFlOR/cd9EMlbLUmXkFUfL8h3+mmTYUkVgo8Lyco/2QwfCAIBLx4pQgtej0BMyFz/ZZgNo2adoGRVZSqag4ukG13uHk4JCL3ZRslPHx9n3saoOVjolQyqyvdpAGU7bPXKLpY1RdQFcsKFRW1teZjDzCaI5QJHjRPrV6HT+vMJxNcL2Sk5FLo6FhGEuQKiiWyOWrK+wdxUSuDqVE5MXERczR9AmmEZMLFR48PcW2XNasOpkUIykWeXZGiU4WqqR5zBuXejzcOWbntE+Rx4jhAZW8oC7IDNKSVJ4iCC5ZUnB4OGY4CRGFnE6rQZ6rpOnsPECSBESRV165yvbuARviOqfjA+4+3kXTHV66INA0XJxKFTGRWFEavPtmyMGpz41LVykzDV00UHILuSzxJjNEVaUiCTgVhzQTQJDorfaQgpizE59mq4qIyHByeu7pKIusLFVoG2BbKieJRJaB58WURgGaSq1ik5QJCz+l3nbIhAamsKB3qU4Wxkjtq4xn+2hpili9ybC/w+rSBsfjIbniEJY+W1uvQKEgCPlfLaN/SYkdhQHD4ZhGR8WwTZxml+lom3gxIJQl5sN7DM+OEJUur66/w9nDn/P4wS4v3XwH3xuiqq8hCSqillNk+TOgUCRLPUZH+6xdufHLTv7PjgV/9Xjyy0SAz6uYZ02LT7UFzyuJ8gV+wK9CP74QAqJzjrREKeQoQsk331pj4cfcfhxSIJy7xxSQZCKicD5o5LmV01JTQixzojRH1iAvckQho1aXGAw9blzr8OWbXR7snPB0f8iSbiDIBmd+zGo+5eh4H6tVsH3UJ/NAllVqZgl5QRLnZG0D00756lKdYJrgCymjUcBqu4nrF5zNply4sokcJzy5P2B7b0zF8YmSnOkgYD7LmUc5sjLmQkvDU3zmEwk/lNGMhK21Brf3crbvfsTKRotWt4KXRlgVk8A3mPZd6oqJpCyQpZJVS2bqgiYXKOYWphZytvCZRRlyVNBUI/JcYejNaNZkZpMxQl4nCkWuX+zRbjQQSx1JDNE0GQrQxQxdhMBP2Ds94sGTHbbWl3jrZpvpOEcKIn77ay+xPQ65sztBi2OWX2+gx9Afj5ENB6ft8MrmJpkXcLp3SIlMVS2YTmbkkkWRphwej1hvOjRkiZpQZZSmRHlGFBd0aga6LbC+UUfMwXtm3x7GCaNUxZQFTBO2Kj3i2GXoSRyOAnpNk9EkQVdisukudauFX87IJBExPsa9+28x9Spqt0uYOuxkIhekl3EaNbIi+QyP/wy4p0RAkSUGpzvE/hyxWIFMIUekvXyB4dJd7v7sz1DNHnbrAvWWTZp4HB/coywSPvjL36eztIlhmkyPdxjsHeN0l1m+uIkX+sz7u0Suh6opxGlyzmwVhM9K/ueZQfgsAbyokfn0pvDZl+d4o1Ceewo8P5qcXxb4FbjgFyMJ5HmJ+Iz0c33TZK2VcyyL6LpCTsqVLZMgyHm8G5+/Wc8sw/MclpdNHFvlp7dcPrlf8PJ1nQvdBqk3opBjpKg4V4XFCRVD5+H2KRkGrW7OZDpAk3V+/Y2vUpDwB3/0IXvbE65d6FAWEodnMypVlUajQq2+ydN4SOjt020vIaUFUhxjySJlICAKNXJtQq6bxJicDQdEbk4UFGhqia4KiJpCOA+pmgqemIGQkwYzvNxHrCo4ZpOyiFFTkdX2ZW6PbxHLJccLj62qyqIQOeqPqTpt7u3NuPXw97F1lVpVRHNkbF0mSs79BV9/aZM8L8nSBWkWYldSmo6AiEiaN7i+1kRkhlKmyLJGGM7onxwQzF021g2Q51jGGpIVMOmPSYoe00nAk9GUrqLxVv0CZWkhyvDk8V3q9WWazjK+P8F1F58KaSxTxzYkZDFBKGKGowXt9jolKtPpiIkbIlCwuloBvSSVLGq2SLI/Ybl5gd35HmeLGUuSiVLKfO2rv0V/uMOH9z9Elgsc3WIRSUS6jF7skKUNbrzzuxw//CHu/AmqvUaQFhRRj7TwaVTAtNVzk5hfLGRfkPMupmNKStIk4O4Hf45hN2n21tE0h/bym7j+HxEfB7z9n/wjrO4W3nzMupZw7do17j3+iHHo8uDDv2R08gnR6ICl6F28+RSn6jA6PcSsriFIIiQ5CDLwfIzeMxDxhd39fD1vH3y2179QuvAclSzKFzLD8wzyK7wE4AuSBCjPFVtRlrN5SaOzvMaxO0RTZeI8Z31FoQhFdnZ90lJEFOVntEoRWbKpVzMQ5uydhDSbMlsdm1ojYLzweLo9YUdY8PIbVVRFYH3tEn/6Zx+hmiab60vkZcZ0MeTBA5/BqYsklfy7/+MJl1+tEQUpZz8Z8dJmG4o64+kJVcWkJgpIusD2cI9F6CKbJUI5xS8SPD9k/2CCpYsIukAwT4jmGWVRp1KvcrTwScKQMw+GwxOOtgWcdYVI01DSBaIukEsSD57cwfVcKprEk8Mh3qhFZ1VmudvCTyLWV3QOjgviOCFNTARJRrVNdCVja2WVdq3FyeCYaqXBIkwxCh+1ktE2Whye6Yi6Q9tUEAQBL86YzV1028Y0Taxqk7WNOj+984h6ZiKpAmejPmUikUQZh/OQqgMhegAAIABJREFU45Mp1YbKeB7RrS9zdjbjuLHHpW9+C0k3COIxqCVeXNLVVTRFYsNxMCULRXVwo4TBZMx8FlF1NFY32wSknI0WLF/fotecIBc2VadGWHiURcmSs8TP37tDpwfthk2R5fS9BKVyBbm6ha2kLFyPRx/8OadnP0BWbfbHQ+qrl5hODqksv8zWlbeQdZW8OPdeFJ431IWCLM0IvZiigGAx4WwwYefxz0gCn1fe/juYus+H3/lXzP2crBAoDZFbH/yAfHCXjXaLr19eYa+/R7D9iEfHP6Jz+R22j3axjB756R7+ne9SMR10s8PG3/oySZggCdI53fgF0g/lZxLk80vPzwPlp8cFQSgpnwN/5YsDbl6I+V887nzO+kIIiAQBFn7G5TUduyLwcDfkbDSm180oEpHZImJry+TG5da5YQggiAVRmPLgyYjxIsTUFYqiZD6LmfZHZEFGHsh4fkJUlvi+ih8JVFor/O4//U2iSCRFoCYrPPxwwv/5Fz9HcSTqVRtNlXn0eEJvuYZdszk+c3mwfZcLHYU0nVPkKaPFlM5ynYbdpggTpnOX7cMBQRIhygVukICi0OrVsZsG01mAjMFL114iL1Ky+Ix3N9Z49VILu3qFarVKvaGjGTWiMmNtpcHLr67S3WhzZX2FYRwxmktUrCZffX2LbmOJr315lRsvXyAVZPqncyCjs9QkjBJms5BZGDJ2MygM8sgky1SKLGIyGlEKKgUySamQSzJhlKOKNpfWtzAklWogs8IajmmiVx3645Tbj4/oD0PyXOGDhwdUKk2W2wWi4JEmCaaiI6kGCRJhmkFeEqUZuqGjKCZlLKGKNnGeEYUhc9dFFjMsUyWXUlRV4eDkkB/89KcUbkF/+wGmUFDKMs1ajc1GBd/b42A4pucYlLmHUCYImcu4/5h0EVKEx4xP/pCqVqFZ2aDaWadMpkSLOXHi4M7mTPsTkiDlOcW3KAsQZSzLYDp8xHvf+V85fPIx8WyfLBhjiAbRxMULEjIpREweUfoew+kp71xQecmZceuH3+V/+p//b/7Fv/zX3Hq6D7UO20/eY3H2mP3tn5FO9hBKl2hxC0Mc8vjuY+IoOa8G+Mw56EUO4y8GSfnCbYAyz8nTjM8K/+dj+wooC567Gp2fNj6/GviCJAGBb1zR+NvftOi1VB7t32YaeKys2AgimKaDoho0rBJDF8nIERFBEFm4KYNR8Knx4vZZzMHCxamL1GoaK5sN4rTkz3+8z8IDb3LCaNbn9GyC02gRF0s8ftxn5BZcuNDAizKmacbmSoPD/hStEMjEAkl0ORwOEI2CmTAjFTLalomYhwzmYzI55vqNJjdfXmW1a6NqEKcFfpRCUZIGAQ8/ucdo2KdWMbmyvIpt1zGrXU7O9skDiclU5uRkzN7RKbOpROZaPL67oJQNVjsWiRgwdPt4gcpgdkbd7NCumjRtma0NgyQOkUqHLJeI8hSBgumsT5zP6NShSOaE+YSJe8jB0YCsMAm9hCJM0VSRRJ4TI7OzH/C4P2Ps7zDyEqJQYf9kytpKFbMtsLqqcWGtyvb+PsORz6X1y+iVKYvEJ08zjgcDktAnLQriNCeJI7JEJM1lpos5glASxCnjiYeja6hKhSQ2UdSC5SWdtiORpx4rNRlBlM8rP1Gga1b4219/Gz8aEcc5smjgLTLyaIfVqo5pqTQ7GzSqV0lzjTiLketbFLFHUXgsRrc52rvD2fEjhLJAFhUQBHTNokgzdp/sgGrza7/121y7cYmKZdNcuoppQz69y87tD7Fr13nllW/Rrov8zteu0JVy/tW//zN+uPcJD0cfojd9PDknwMZa3uCVK1UuNWFJD1gjoNN4F3Pt1zCrMpIukj/TKxYvIv4vaOmfdwSK8lwwXArP2ICyzGxwTOKHlLLyzJ/gl7UCn7cc/gYZg38dy7El/sHfXWX/xH0m4DDY2R3hejKSJnF4PGV4tIC0wLFEgimUokAhnb8pUViQpjmSINDUVC53WtimxONdn+AkoMwzBguBwey8x11GAm++fJXvfP+Ypl3lNBxyYV3h7uMhaaHgpRmSWpLGBXZDpW46nB66nPqnvPt6i7Vel8ns/APfW2nhhjrTechoEDIY+/SqKm+sN/lob4ZQCnQbKq/fWCPzYk4HE7rrS6w2uhztn/HkeIxYlak0QxqtGpURuGfLTPpzEiFAFETSImJ1tUGaZBh2g+Es5+bNmwx2hzi6Sbsj0mhcRJEkAt9F0xU0S6BZSASKyGwxRhMrpIVIkftYSkj/ZIdks0qauGiKRX/ocrRzTFLG7G9PUIRV3n79GrNZwv/140/oddfZWusRRQ/IJIVW1+Dp7l1evfomP/74Pldf/gZ1o0kcLEhTAUPNGY3HyLnAmy+XQMo8C2nUq0iyymzWJ45Tlro1Ki2LOC6oyw6WICDHJXbFoqHILFsCoyDiRusymiTypz+5zXJV5mTQxy1EcvF8PqNQ9BGtN5HEmLD8gOFgn87F32bmp6hpjDs5ple/iO8GaJHAdDQmSWIMw+bJw0+YT0+IwgVxrDJ2GvSUMYY7xD35hNnkhIVu4KYlmeDg1+r85+9+ndJz+e/+xb/la7/zT/napRof/cm/RtBF1KyKZlX5ym/8Fu79P0C3XOLpU6RhjGZmBJrK5eVNDM0gz0AQMvJf8Kz4K60/4dwOTxTE8/F8CJSpz/h4m0plGVEoUY0KqIBQfmptf86B+qxb8HnrC5EELMPgzlmIP/HR0RAkhVZTRJYzJvOUEoN55KNLGpok0XJEuu2cOIVCElCUHFEAWYfuksjNaxeoWRZ3dyZkxQJV1ChGMQ+ezNHEx9iWzHgk8OjoiK+uq1i6zd6hx6405T97u0u33uDR2Rl/51vXkNIqp32XI3/Ml1bWsYoqsmCiGiaTSKXpVGnU1gln9xGymDAoCGuw2WtzsSw5GLhUHJ1qtYFWF0FK0a0GBibW+hpHfsp0PqTeW+f4sEArdCSnIBUjhERBoGQygdlscm52KZUUakr/eMjyRocPP7pF6ImYYkCo5Fy+sokoa9z+6AmpH7O8bFOpgmkIDE98FosYQRPwkjHTwSEKApgFYdxHkxLMREbfcLjQ22D3MGd16wa12h7bu8eUpcTfevsbvH/3A3R1hdHoI+7lffp+TLmbsXRDp4xTihIc06YsI0RZIE+hVm0iIFA1TNI0YzgZnRuuKApTN0O3Uw72PS6tNZDlgkq1xuVOl+bNN7Af3uLihSuc3LvLa1t17s0SXEkmlQTywmcRq2juguPRt3EUCbs4xRQriLFPRxYobBmt+iZhkbH/4LsIikZefoPrr36F0fCYB3f+lOHJKZZpI2RzplnCqVCiajnLzYgkcyhij2Q65KVLTf7uV64xGo34vT/6Y6xmRrgY8bO/uIWi3WRlQ+PkyV3evPlNnOYy46xk5k7Q+wEVWaf18m/SffUqoZcS+S5ZmKIoKpKpnbf2n0uUX9i4SwQoSqLQR5EVJF3iZOc+88kxmqrw0Q9vs3T5TVYvrVHkBYqmfooh8Awt+BthDP51rrSIqdsq9+976IuIVh2++fYl6rVlbt0e8GB7h81rbYKFgHgQUEHkN95ZZjwOkZWMmiPwx98fczZKyLKUf/Pn77GxVKPVjnj9ah1VbdFpnDAcRJzsntGt1tmdhWhyxj/4+3+fP/zB+3z7e2N+5x82We3BwtNwkwpHTxeMpy5zf87FS1WuX7lIEOU8PhyiyCUIPqq0xensAC8K8XOBJM44Op6jGwK1psZwGrKYJoRRhlUDBJk4kCiY07B00kxh2pf4zuEuIT5V0+DKloqXKtw/m9HWLeJY4PJam/FszJ2PH/PKzYu0uzoHu4ekgcGH944pRJlLFzVu3z88/98XMYKqIWMTzX0sDKQ0wk8SVjYqpPMSSS2wdIsoLjA0lbPBhOWuzY2VS6Tzgoe797i994Qbl9Y4PbuHmAr8+M7PEJKIg71PqFZNwnLISttivtjFC9aZjyqQZSRIiCJc2qjTcGTSwqBRrUOhYukaZAkVVUVWdSJguAh4eb2C6wWgVpBjkbKxgSBWeO21bxAXAqtfanHc/30mUUJdEVhkGtgagR8yHI+pWAo1QWCRK2RElOkRav4aewuRCIEifEQoyCyt3KRIJX76o+8ShgsMs0p7uSD1TxDjEzQxJ08KJCqYravY4kPGgcd6b4WX11b4oz/7AQ92HzDNx/Q2LpKMfkbpz3DlPie7Orq5zHg842T/26izKXYRMXBzysuvkwyPcE5XaV9cZXj6mFt/8T0uX73J6quvPxMTnQN+L6L/giBQ5DGT00OsWouKYpCnGVsvfYXB2VMyYjrLDean++hOG1XXP8UGnvse/hUPsl9YX4gkIIsi9U6TrQ0PQZzztbdWuFB7iZ8/PmY8n2NKICFRcyKsKwo7Jxn3nkzp9316NQG/JqOKCsttgVarADHDy13MsuDkLODiJZE3X6mihBKH0zqaatEtQ7738yn/2598wJWlGv/sv7yEJEd4qUSlXedVzUIcFaTyAmoyb6xsMl0kzKOA+czjresXWQQ5T4+3UWSdXCmYzueMhiFXLtlUzJLB2YIkhmhR8pfvbfPVdy5w4/I603nAzhOP3XzEztN9bCPl0isyZ0OJJBXJQoEwyKibEk8Ox7R0ndsPQ0y7pLcucXL2CEu/wmw+ZxHNqegZZZkh5l3Gk0fUzSYNRwRVY5KkHJ/MmZwusCyLqqbTqTWRHY208AgzFQkNSouJt+DaehNvBG4eYrerLC05iIVGmibEyT6mU6PeaqDGKTe21hlNPNJ5ge4oPHi6T50ahiEzO5xjyhI1w8YLZmTBnEudVayKxWg0Jc5yslKgaunUTYVFEJAkAoWSIyQlpmPizsfY8wp+WJBEAUvrHc78IXESo4glRSKS5CUqKZol0dJttpZKvnc/wFSaqGnM3uFPSakTBB+QZGMM8wKLo/c52f7x+QATZ53KhbeotS9SyBlHhx+DImBWHHx3gHJsYlaqRNGMjc4yf/gnP+DEH/LSjTpbcgMvtViyJI4yiaScoGUKebTg9GFErRbjTkdkvkTRWmGoOQQffBslTvDiL1PtaNhVmengFGd6icZSkyxJKcr8fD7i806BBGmwIPenyI023njC4GiMaoUEswnX3/wNqlWVg3u7bF5tn88ihGeCvGeW5b+iTfiFSAJemPLxx0946+UGqlhlcpJTjh4RBRNgSsPROT12MVWJd7+8zNffsvj+zwaMBgtqVZvTvkdepHzp1RZffnWZEui06/gzj7uPThm4E756vYs7FRHSiLpZZTQMuLTeYvfoIcu1Nb7+a0uc9E/QFIf5bMYbl1/ik3TEvH9Mb2OT/3Cnj6GmbHaqXLq8ycCX2T0YUcouo+mEbs3hylaFiiLh+ymnZzLzCWRpjudG7B1OqbUMFFFHlnQ+enKAGw1xtAqrqw5bm6vs7NzFDQNGk4zcK7GrFmECmVTS932qgUB3ZY1XrmwyPzsljUNqjQqmbbOm24wGQ2zFgiRHk03isiDOcmqdKlmagmVg5CHD0Ygss2irFnYHZEGmqut84+YFrq1d4y/e+4jtucvG8jLeGCpmxEZHZnWpSYpI3ZYwigU/eT/k/mkfRzHprilUDZmxl7O5toHn+SzVLdI4ZP8oQlNyVleW6HaX+Mn4ffwgJJc1kFWUBHRBoFQKqrLGtbUN6vUummYx2N/hyc8+5tVf/yaD0ZzN7iU+OnrKgWtipDPKIqPXW4IooNdSeTqOUNISq2GiWS0m3iFZ8ARDqWLpayz8KXnpAzKSIJKGJYNTA1G5TunF5EFJpiZMU5+0yCjLXcqFSq+5xKPHe/TdIZ0LDsfDkuWaiCzLxFGArpbUDQ0ZiaudOseez3SmUBQaXnaK3LxM4h9R39jgdH4X7+cB1177MrZYcHb0Pnb7AqIgIqsyiqkiAFL5bMBoqSIKKp4/xxifMDj5S9yTfQJXQNCX6C6NOQz6jI4OqNWvoNXrPBcdnzsqF78CFvyCJAFNFfjW1y7gewtsp8FSO6ZXt/jN1juczjO+/95tbt3fRtF13r/l8ZUvdbl5o0qaiuSERCls9GxWGhr+LODipZco1BputkcqiVSlFEu3GGsxr1y7yHwy5+3eZfAPUM06p5OYneNTrl5cpaqt89Enj3i6HZEEIT3bpD86xfMEYk3kZx+dcD1ImfkhF7caXFz6En/8o4848Sa8dmWThlhyOhgxzxOWuiKL8bmjy8tvNWhURe493Mb3YmJvQaMi44cZ2/2McXKKbdjE8ZCDfo5SaOxMfRwBgiAlF0syv2Rv+xStskYaBJwtAro9B1vKmaciumlg5BYZE6IkIkwjupUK9nKHICsQSoUoTIncQ4pkyjyVWc4zkjyiWW1R2g6LSOPqxU1a0TFJKbBzckYQity43mX70GcwSelUBbxcYWd0hFO1MSgxUKkbOSNvRLNqg6zS7S2RLUaUgCQZ9FbXMA0dTZVp1yq4aUmURURySVKk2GqTmmOhIeDORxidJtXGVbaEBLlis//oIbO9AW9sXeX240+48dav8fLb3+K9P/hfiFyJk3HI0PfQbZ3TxQAnzzDVCoIRUUQpfr4gLiIMQWHVlFlQEgsJYrRHcBwhZjJeWhCmEbIaogoCpVzhlYuXGIx9fnLrDusXO+RJhB+n7A18ep2EKMqQVYEoLAjjiFa1RhrHzEYBK3UJq2nzePAJq50mVb1BIQWUwS3u/sXPUESPxQIe3/p9hocrrFy6ycYrN8nj8LwdLgkkyYjDe+/R37lDFkwIwpxShP7wIbVawMPbAWXhM9o/wDJrNNaWUG312ZHgRcbBL19fiCQgCxr7R336o4S1Nrx8eYWDScwoPGE286nUNGqWwWgUkJQx//Lf/ZxvvLHKu9dq7I0gjSMsRWQ0DilFAfFogJ/2cd0JogTLvRoTb04c63juEE1OmI0S2k2TUhYQhYTZomThquTBGcudNg+2J1x6ycLqX+fej79Prd6g27LILIN7jxY0OirBrOQnp8dcf6lHkMyQypTVJQvT7BCWU1Qt5UQNqCYyRSTx+HiCbpXIUsGrL3dY6fZ4un/M7smC0dRlrWty49o6lWrC48cjDBUUCTShIE9yZFNHk+Hp7l3qlSbLay2kQmE+82iv2DjaCsl4SEOtMZsXzHOZummwmPvsHE2YLAI6nQavbfZ4snPIeDbDb3dQFIXUD0liF0E+JYmgYjVYRAlieS7X9QON0eghmgG2vcbE30M1CyQxotFtY5YSk4lPzUmZzgNKYs4GQ9abNjMvoFt1aDRr+NM5aVYiqSo8s2HP0gxBLpjHOZtWhc3NCzzen3KyF/FacMiVr/8X7Hz3D+hduUiz16Q/OuXlb/0TXv3y3+PJj3+PbDKlZ1c4zJNzj0lFQJJzslImz0ImswUFIqZRosomrYpFxxQQQp8Tb0JeRoSpT5lESLKPLIg0DYU4jpHlKk/3ZvzpD39Kq2ay1jRxy4zcklFFGVnImcYRRaSyrInMk5xHx0NsscAgZjRUWAQJc1lEH0MSv48bxJi6ia1rFIhUnBqGXqApObIqM9q9y+Bkm8XEo7d5k0ZP52znfYZHexRlyfKVm+iagiRXsRyFxw8ekrkjmmtfotKqURYFoiiQpwJZkiPrIhLK58ff/4+x/rkryVPihcj9R3OCsKBe1ZjMSoJgj42VKkfHHrmQE+QJeSmQZgV/8N4uD3o2zYpC09bZXNXY2ujgRhof3nvC5noXRI00CCiLBl6a4UZz+uM5lliglCWJYGPYEtW2RU+1EYWM+48X1CoNuktbTMNTrl4T+dEth1JIqFYVrE6Dh3tPCc58LFWkPz5DkKpIUkGllSDUdNbqDlGiEyY+2rqKpKicTmJKNKbzBFUtSTODBwczFvOY5eU2d++fkIsxWVKlomtcvqIxmSiMRguMSoX0YMIhyTnn3Q/pL47oLleZTI/ZWGqx3mlz6/YdrNLkyJtTXa0RpDIfH46JgpjES8jlAjuOOTpJaDQbtNp1ZNWi26gymyv0/TFL9TqJpROWE4TI4+pGm9ORz//+7fdZbjt85dUlHj8dUJNV2u0aJ/2SlqMilQGFZzLzU7z+gHHk0rSr1Cs2oiizcvUCmmayc/yQ6TzAqjgE4oK4TEmzDF1SGM+mOLUvoUglnzz4hD/9/r/hn/03/zW//bqCaWgU04jG5uusvv0PUatNvNM7fPTT7zJIzz3+l+sSolIQTEKaisEsi8mknHa7ThFmRHnBxXaV8Sjg0INMAEe1EESRlJhEzDAdAzMrmLoJSArz0xF3791CVAR6a1WCIiJMc0SgroCWC3iqwczzGXgFOCWzMObyssNpAE/3JiQZ5IYBMswXCYahAhJlKZKkCYWcU0Yu4tynCFy8cMZsuEOZZvgDmWCckRYgqTpl6BNMJ4yGAxTRYTqe0a3pTIsKneYSwWTEbDjCqGwQzXe58/4jvvqbfw93tve58feFSAKiKKAZNvFowr6coKZzRm5Gs57Sa2a8/eYWsxl8770neF5CGMRIqcCdJy6GVvDm1Qpvvr7EbBZzPBhz40qbvWHE/Ydj2kbB/fSAXM25/FKPKzWT0ZELhUir1kZUZjimw1qzjet7fBwdImkCycQlSRMud02uXKwz8SNktcf7nzwhTxIMR2ARRliOgaxKmFqBIKjs9ye0rBJNbYGg4IUB2ztzZM3AsgwGo4hhXLBwj7BtDT+L6fddTFNlMZdI6glR4uP5Iu7CBR8GqU8plRiFRO6V7M1DNlcqxGHMWruKILr0Rwd0W00ooD+dEJ4F1Ns241GALIOsKtR0GdfzOMozfvvNd1HEGtOTAAkBVbVQ0iaHwzM0zWJtuYWISCk22O9/jGlKVG0Zj5y2KTFZzNF1CSH0qVCgtRrIko6SpshySddS6dgWF1bW2Z+71C5/icw9Iy8TTNtENzXETERQchRZRtV0yjjirL/g+pWX6S11satPkAqP5OM/wt66TDreYfrRd6lu/rdMH77HyZN7jEdniIKFLFmkiY83D0ASMbQYMYhIYwM5E5BFiaokEeUxohIRxjl153wTGbgebpAhFiICKl7qMw0COtU6/ckUpIIvv9kDEaqKjm6ALJWczYPzgShlganqOGLGPJHRTIH9gUdMSh2NI1lAiQvSoqBh22i6QlRUaa6vcLL9MWESUkgTgkXB2tbrpKpKnOjYloOgwPxklzTIycsAf76LsLOgPz7FdDapV00kvU738hKH+z+m8ESmgUxrtU0UzBmePOTOhw6KFH5u/H0hkkCSlKz1LuFmB6TjjMo1C7VVYaWmsrmsoogORsvmxms1njx6QhAeEUUluiZSFCKiouE4Oo5TodpaomLVKbJDnhRn7B5niOICxcp5cC9FwMKxJMo45snjQ65cW8W2LQ5HUyy7ysWLaxwfeWSBi6JkPBmkeIVK4E65e+s2b7+5ymhksz+Ys5hEZERc3nQwFZkoyohygbN0Tln6FJLJYFRwNvaxzIS2ZZLE4NgaXVtnaaNGJmS8994+w9MA35LRVI3MzRlPXSJFRVAzDAm2qjqaaCGJBZogIksJN69tkeU5B8fH1PSCilywP0+QdAHXD4mLFNvSIM8RhAxRUBBzCVWRmc8m2EaGKKqUyjOSzlxGkk2OJhOOxwtkFLaPnvC1r1wleSdltdel3arw7T++R60mUq1U+MZrTT7e2cceGLz7xrc42p9jGga1Sp1MULBWlulYHioSqeujajWq1Qh9NEEOpyjyOfiVZBFNy8Qdu8xO+pRIrK1tsNxrY6oJ2C3cKKG24TB8+h6PP3nK1N1munDRNJtp7DM8nlJkoLYqJGGBUipYqoRjiFTsOuPJEFsUcHSHXI7xhQw3lJiFOXkq0DAMJEFCFgQMy6HTsNnfO6PXrGBrEmd+BJlPRTUoihJTO3cVWmt1GA58uh0VK0jxy5j+NIJCwjF1or0RzV4XBIk8i9k9mlOv2njzKUFeEKYqapihqzH7T35MkCbMp0ek1WXmusDg+D6SXEevXUEpAvz5EaQpQtLn8CjGtK/xytoVRv0+QhDQcUwO7v2Ik5N9Gr0Gmhii65XPjb8vRBIIo4zdwxA3BMtRKOUme/sx4zMfXQhodQskIeboBHSrycb6jLSIGSx8VFlivMjY3nO5fNFkPJV5sHNIxYRXrtexKmBrNfaOppweTjgdzHjpss36WhdNjhjPRsRZikBGVlTIItBlkfqKwVKjxscHLj27Ql2TODzs485dzIpKM9YIVQtBjDg4HrPaqyMLCuP5iJWVKmKRIQoLDDWmYiksd6o45KgXKmi6gS2JyLpBlpSMp5DGJZmW8+jRCVvtDnmmoespc7HA9TLWHJOzhYeoiTRr0G05SKnJ8ewpS0tdgoWHaUM9U3HUBruDBQs/wamoJHGGJIoEcYQoFWi6zNnkCFOt0TUvQlEQJAmKLLMYeszDBYNRQuaDoCTMJglL7R6f3HnKS5cu0HZ0rAZc6bVxrCa7XgBpyWg0Q1RDgrmMSEFc2hTVLixS8nhGUZbMvIDdwyPm8wWWek6eEgQFRdbo1arEWciT7W2GR2dsLK3Qu3ABof8RIgLz/g6aILM/HHP78S1UY4FsFqSpT5IpNOsOsRshJimlKJCXBQIxjlOlLHxKcmaTFBIBTZE4ywLcZIZQitR0G1s10YwcfyFR0x1koaCkoFFVyXKJtCiRtQIvjrAEGTk/H64hDEMWYYCYJmRSRiIo6HqF+XzBXM0RlILF3EU1Siy7Qt1REIoh/nCEWhQkxSmRD1kos5hvY5o2elYQjn0KMUYVQRBSJv0ziiIgi+eoskGeThkNZgjjBTUzhqKO3rI4fPodHP8lrn/5He598AGhl4A4/9z4++swGt0DXM5djbOyLL8kCEID+D1gk3N3oX/0Kx2HS/jpnadoUo5i1fjejw9QtJzm9Zv8++/+hEaz4MrmMl3T5PD4gOX1CrJiMPczhrOcnRMP647E4VnCLHfEAAAgAElEQVTGxC3YORyxviLxG++2sRWF/Z0ZSRCgihorTYul2io/ufWUm680SMOCyWLESqfCSX+IYSi0202mgyMyJaDr6GRFBEabIEoZT+YE2QgKE1VxGI0LVlo6p4cBFV1nqeEQuxlpWiDJJUkqYBk2omySSgVpFnJ9dYMyLHhwuodcVWl0NEZnGZoikWUygRggmxpKIeIYGfvzgFmZMU5z9DRFkxRu3R0QhwWKVpCnPpoFuZgiCgJW1eKqrrEIE1o1g6XWEh/ePWDveIhqCIiznEpNJREy1GiBoyuIJCComLZNQw7xkxw3yegs1xl6x3TqFVY3VG4/2ebly6uoRolpCzzZ/4SXL25wepIyd4dYpoTrg2mYeNMz4uEYZ22F0p0yn7kc90/pjwaEaU6nZaGKMZqs41RqdNpdFouATx48ZO6lrK81cZoNytZXiDEw26v4ew8YHe1hSkccjyOKUmIRh4TRlJVqHVESaVc1wrwky2U0pUTIcipawl7iESQgCyVSIpMIYBgaaRQjyAWFDDM/Jcoseg2Lx7tPSZKCekcnEmKysCAsBLJEoNPREAsYLiCLIc9zPBXms4RChjyJqIg2tpHT6AlopUImng/YaTcrjL2EMC6IkgSzUkPTCtKkQCgEFLlEVkqyPMYNfYIoJc1coixCLDNkHKQyBVni4lqHmRviuYf4XoyxdI3B2SHXmleZHk8o0wmDh0+59u4//ptLAs/Wr5dlOXrh/j8H/rwsy/9REIR//uz+f/95T7YtmRtrGdOhiLtIkXORxlqVjctf4off+4Tdk1OmScirFy38rGA4nNNtV3lpS6d4FOJGBaNRwHAY0J+kiAKYssLPPppxcaVGmRWkkUQQFqRliaQJqGbJo6dTzIqCbmis9io8fbrP0lIHOU3orXTpeyM2rDqTWYqlS2So+KmIqStYSglJziQHWzexHQMtEzkbT+i7AZpW0qhriJLItfUuimawu39M/3TCodwnKRRm4ZymbRDFBctbJrGXMxyXzMOM5aUG2SIkXhRoskGCiGnLXGhZfPWNHneenqA5Ct12hdloQsusIUgCquBTZAqba2uMp5Nzu2tRxnEMKlMdCpidRewkY7qrNobTJM1KZEEgiUJU1SCaKJydzmjUKgyOF1y90aAsIpY7PSpGQBKAt5jTqbVQVJkiialaDTS5YDQbgaJiaAqiraMZMoKi4A6HnB7tE3hTOjWbpiiy0lawDZXV9VVyCcbDCZoqUak6uGnIdB7Qf/8Dav/pP2Fy9/uwcFFrVbg3oUgyEi8mkgBJRFcUxmGEKcmohsRiDnmhMJgOKYOSU0XAD0UUWSRNMjLl/2HuzXqtWdI7r19EZOS45rX2/M5nPnVqHky33bbbbrWvLCRAoEZCXHDD5+CKr8AlQuICgVpgIVluhNtuu9zlKp9Tc533fc877nntveacIzKCi12NjOQySGBUeZOpXFLerHz+GfE8/0Ew7EfYTqCCHlaGrK2mLA2jrM+2KDi9yenP+tzmLU44OiGpO41XiqUNiHTCILJoDVoK8sbT4cm6juvKoLKK0AfszQYELqSyDh0EuM7Qmpq6tVSmI0ZjbcE6L0ii9I5dGkd03lI3LdZ0gCGLAgZpBh42ZUsaZmjZMIzAWs9wcg/V73Owt4fdPOP5937BeK9PrFZc/PiPf2Xx/kNtB/594Hd/ef3fAv+avwcEkijgg3dmfHGes10VbApDU2S8evaCfk9RFQE359c8N0smYxj3Mk4mKRdnJcNBA9ozHkuiQLHcdORlx3LZIbod26LlYBrTSoGhZTRJmB13fGdwxMXZDUrF5NZzdnFLvi15ay54fBgg/JSj8T0ury9xnaXbCJp6gRQwHvR5b3/KeiW5Wr6lqhqSniLuD1mcVpxd7UgHISeHMx4e9HFIQhWwP0qoFjFPv3hLJz1xLBg8SPjSBwGb0lAGFrVs6VrB7eKafpiy3XUYY1EkCNcx2ptx8uQdTh7t4eWEfgLn52+g3VF5iVSSxeqGy+WW2ThFCcmZcQzHCe+IITc3G8qdoNw0NOOIIqixTuGdZ9DrcXW15OXlBlMqogPF5XVOXcdkgeHl6ZJYQtVZpn3B9dUrkmSf7WaDbTTTkz3WRchorJjMBiyXDQ6PjkJ8pDF1zqgXE4YpbedRnSHVPbIooT+IGQUB54uC+XVHURaE2nJ1+ZbJ3/wJb37xI0a9EdP37jGOY374psANQkZKkDcNxgt8pKlLy/bWYMU+O7tGa42QDbtNh86GjAYZtzcXGAy2VWRRn+lgwGp3w64qUToiCVsuTm+IdUiYhZjOMcg0rTQEWEpruck1h5MEoRYYK1EiIBQtu9YxDB17/RgrWvpJH+cM83VNEGis7lBKMUpCRKLIq5LV9ookFGzyDbazZJOMoilojCMIFFmcEseaXVGzLTv6iSaJoDaGsrMM+gOu5wt6FgYyZDo7gPwaPZqRTGfc1PDBR9/4lcX6/wUIeOBPxB1J+b/5pZX4wd9yHL7iLq/w/3L87dyBNFYUOP6z/+gDPv3RFX/5acHbF6dcXVyShAl7Ryl1WSOdJYpCdKppXEMcOZyx+A6KylEicd1dyINXAuc05xeGMHIMM8l770V8/ME9bBPz4KjH3kTz2adz5itDUQi6VnD/3pAoMixXOWGgWRc1VWmIo4ZvfHjMZz+cgxPsD47Zbud31tMKQmWp7IonxxnFpiIvYdAfcXSyz3az5erylMkwpjqe0J6tuL0pkDbAFgqhtqxWOZ1M6A0z0kBhqg2tu4vmEs4QBD0yFZOXNX/211/w4eN9Qr2h3EnSZMgyX5ONBrQ2IGok662hKBzFZkPllzx4MGU4TmhchxMlCRrlQ+ra0OFxzpHEPdqq48XpnP1Bn7q2EHjaxuJSQZoIjg8mtEri6x1Xb+bs37/HZLLP9SVYs+Ngbw9hO6LEIwOJaQ3aWlQYMZ1OcdZytSi5mF+Q51tCFfPs5ZwP3jni0aMxm+WG65s1N5cXRN2MLu3z53/2v/OLH77g4y99xG9/8BEP3nvC/ttTVjpBdtAPFW1sya2jai1XxhIcTkiHM5rVOYGqeDdVzIXmdrWj6Syuc6xsiaRDqwJbO/pJxnQwpt5umC9zsiREC0WtHZnyRCiUkrRVR2sqwqDPVx4OeXWWM68FaahYecOmaFBBhwxDTNEwnSR0/ZKqLuklfYTvAHlnThOCUHdR4704IlAC03YYBx6NFJ6u69jmhuvVFmMgi/ZI4h7r1lA0mscffYOzzadsdjua5jnGHtOXAbO9Q6rtEmFnZHvf/AcFgd/y3p8LIfaBfyWE+Pxv/+i99+LvcDT427kDWkv/3U/f8F/+i9/kK18acHb9jE1RoANHbbaYXUCgoOjAdpL1psHahkh7+pnmanvnqydxeOnIYoEXjigTzLKQUdIxiCT3j1I647CuRscTrpaG29UN3noCYqo2B9eBn/Ls5RlZWjDbHyFdxcEk4PFsDz5IeDV/wdV2ztHxhHu7OSf7Iw4HGVfLBQ7HqJeQOoFtDNeLDa2xeJ2h44xPPhpyNFnzp3/1nOW6oDaWNI3pug24jqpoaCT0VIjSAbVtSHRAXlT0ehFlXhCHAWc3C+r8hizRnMwGGCNoSlhuDfuHJ9QsmF9viERAWRcUO0MapAyiADeMmaVTbpYF+W5L+8gROEfhKtI4xVWCSrWsnGeSpShVcHzyCYG0nF9fkckel+uC9VYzbltcMMbJBb1UMcoUYRAT6+hu3FaV6PUtoq5J4wwlJE3bcnm1xpgGIStW2yWjnufwQPPFyyt2q4JeDK665fnnz3n+8g03F2tmT95lvqo5ev9b7P/13+BbwWmzZhDFWAVd0yC9wAeOul1TNO5ulo6FNKEuS7p2Q0966gCqTlM0EAd3vJFJb8Agyvj87C2Vh0g5YiEYxClNa9CRRypBrByttdgmZ7NSpGi0qNlVljj0dA6M8HSmZl06hv0h91PBLq7QgcQbR2U8MgRFyDDQ7IwhjiLq2tFFHi0ldVtjvCLSmm1ekReWvUGGNTU6ecDJyYB8sUA0DmMUvXiKklvK1RsGkzFvX3wPGWlcnXF1evoPBwLe+/NfnudCiH8JfAe4/nf5A0KII2D+9z8DLm8sTdkxHQyxTYh17m455j2dt+AkeeVYrjqm44hOOXQU4L0jkLBY32XZCemRWkAgqIxhEmjKUpKFAZ1NiZOYRFh+/vQti/UO6QxVZRmmd56FV+s1lU95M2+4f1gxqHscHewxHSR4GbF/4Cj8CBF2rKtrfuPLh7zz8H3KXLCqW+YXBVtjaaqWL16cUTYpSguu5oY3ZznvPAo5Pd2yyhu88OSmvBPoWMEwVUgceS7QqaC2grI19LTCI6lbQb7ZYG1MJx3TQUwvCfFKEWYJN7e3vHhzg0eRRTG7cMNBf0B+WbNcFkyyPkfjIfszRSQGzG8KXHcXoBVpj21ASMV4OMT5lji6A7KLRU7w7IaP3p/wixen3Bu8Q6/fJ3rPsb+Xsp23jDLFvcNjMnEHxoFwNH1B25b4WtEVOd471nnO6c2S0hq6tkRrSagUcRRifEBrPDqKme4n7A8Czq/nWN8hMHz5259w/bMfwPmIg8NDLn74OV3mWTcl1kmcAYsmSSV19Yq6hWma0ImAU+Mpqi3aaxDqTnRVeboOykKSJRJrG55/8Ybz+ZzpYZ8gCfCBJgwTutDQtDX6lynHsZZsy4p2KdkbD0iDGtMKOiTJKEFFmtVmTafgbLGgJyxSh+ikZV8ratNxayvqBnrJXVJyXjaUpaOXxJjW0XlQoSaJQ6yRtL0YHQZUHaj6Bl+0hGzJb3/EMFghiFC2IG+hanOqtsOZAZEr2F386viP/7cJRBkgfxlImgH/HPivgP8F+M+B//qX5//5738OFFv43o9uiMMrHt7v8eMXHVmqWNV34xxjOnQoAUfQxQTWEUQwHAWMm475wmKMR6q7fPlIOVLdsTdLSXopw7TjZlMj4oooqGjbO3bi1brChwE32wpvGtJ4ghAepQPwLWkKy90tmimX9YZE7u584XzMuliRRD1WK09Vwc+ebvn81YZeDK0x3CwLvHakScxm27C4WfHi9RrbtUTe00o4Pd+QhZosUsSxJFAO1wnyGvy2Jks0rXGkv/Sstz7gzeWGg5NHTEYjtosLNAGj2QgnSqqy4dXbC+Ig4eFBxmF/gBABxrdI1dF2DWnapypqTo4yxqMeeEugWrQKmYx6/MHvfsxf//AXXC03eONInGS3fQHsyOI+2/qGk8f3IJiRxiN8b0cQxFQ7h7UlYSgYDlL2ZgnjvkS27V0jz1hq0xGFAQ8O+qzmORaH7xzr5ZYvns8pGsf17ZKuE3g3INQh06MjDvaPSLM+u3LJ7c0lxx+dkOhn9LVEp/5OaRgEhP2AKPDEoWLlHcrVGBOwLCu8q5GhpvUCbRTaO1pvKVD0kwHeKt5eLkj6AfvTCCEUedOyyh2R9qShpzYddSfZy2KqytLQ4YOSWErCKOTspkYEkhBPqCVOSazwXBQVBzZAxxGl9WyKAgKHtQKZOFZFw660JDqiaQx00AkHqsKaDiEUvV5GWRW0XYnvOorihqb1HOynjHuCXVmSFyVBtIdpDVnkuF1t6I+H7Ob/5h8GBLjb6//LXyarBMB/773/YyHE94H/QQjxXwBvgP/4/w4E2rbhe5++oD8U7I977PXDO5ILAa7zWOvI0pDZLCWQmmmS0B+njIYzji9X/OTpLVEY4pzn1UXO3jjgS08CDg4jHtw/xNYNP39xw3I3Z5yk3BsccHs55/37IxAR11VLsW0JhOarH7zDbDim3l3i7BonEl5cvqYoDff6mjjWNG1NUxmevrpkvvasVwW7csmkp5BW0QnJrjAUpwVQ0bSOMneMRjlZKqkrj04Fo8GAUCl01JBEmq98vMcPfrhilzvGE8kgTbi8rbFNhxM1WjuUlFxelWzWS44iS9kqKiPZbBoEUFYGF8JkdsS0n6KUxwhNFCUUTY0Qio4aqT1ZKkC1CBESBgG9OEX3GvAdm63F2o7DLGQw1NT1hiAI2R/us9+fsci35OsNh6OMw9mI1y8XHE4yyiJHSn+XQ1jdspy3YDTGg5Ca4/0B00HC6WvDT57fUFQtX5xe4S9zdm2HbXOKXcVmdcXhwSGZHROrlNfPvqBaLRBOo3TK/myMrXOufMVmazg6GFA1MUksME2HpqVo78ZtrjOEWjEdaExh8GgQBi8dSSiYpj3ml2uapuH4YZ8sUwSdQirPujCITjPtZWzrmrK9e09cXWLigJVx9LKEXVPhVcAgkZjWIX1I0xm80CTZkIge1kTIvkCEW7pO0XYBhRWsi4pIh0Shw3lHFGi8d0jpaE0LaKSUJKFEtRIlHKuyxjtNXrWkqSBLBFUjsMbQeoG3jiyWNG3Obrf4hwEB7/1L4Kt/x/0F8Pv/T58jhWT/5D0u3r7iSAbU7RYvBduiQ0tF1XRIJRiNB4ynfbZly3B4wIdP7rPJC0aDjEB5oiijbSWbzUu01Dx89ABHhXSatlP0M02WxgziAWEr0QKO94esdjW1jrHO0/oCKeDL7z1ktUxYbd9wcO8Tfv7iJ2SUSBRKOVTQcjzt0bqAF6e3nF2ume0F3NvL2KzBbiqKtcGWd38kgLEWZEzZOla55d1JjyiKWKxqTo4H+NZwsJeyXpd8+uOc9cbzzQ8PWK1qqsqTxHdN1DiIePnFGbNDhckEfQ+91GDqCqXuQjT7vYRt0RL5Da3NcVrSi2IyrYgCTTxOmV/P0cKTZBpT3SXj6CDgYln9ckwF0/GQ73zrMa3J6aWCNxcbNqsdex+PmPR6XJ6dsbd/QM9ZujYnSQaEWZ+26bi6uuZWb6i3lkCOCMIMiefxoykPj2OaqsG/WGFomG8rwlChdEC/FyJ9y3pruLq8Qi62ZMbyB7/3NWb9e+RnC4JOcfTgHjef/YhF01DUsG0dptmxyD0aT6gjpL677ikFyuF9QKYV3jmckogo5cPDe5iy49X5nDRWpHGAtRDKkH4aUZuctrYsVi1eQyAd5/kO5QQykLy9bbkvByRRzDCqGGpLrSzr3N4ZhQQw7Q+IgoByk+NdxNEkYZ531E5T1BaEZjockESe1nps21E1HR2KIADhDbYzhAEQ6LuEJSFpSoEzHU0D/Tikl4QYY5BdgtIhg17H26slWo1+Zf39WjAGPZBkU1p7zcVlQW+osMZhO49UHnXnsYy3gtO3OZ0tqB5PsY0hCj1JJBikMbu8ZjpO+MPfOeZyXbEtApRTbIKc28rRdSH5rUOPOuIE7k8HNJsK0auJCkkSQiTBOsvO5qgkxtcHbMuOQTLCSEUU9LldX5L2LMNhyrg3ZVV4nr66Rm0cpeswrcOJjihTuFqipCCKBGkUsakahHPEUjG/bri+uUb4jrY2bHc1e5clSSZ49CCgM4K6rklCTxRrhBJoHRBFjl4q0SqiaDxPpor9/QRsx652xL2Qzabh7HKFPh4RqIDdbocTkjiKKBYL4iAji3rEwZjdriGVCcp5hNTQOJ48jngoY77zyZc5PBrxF//2L+mlBzy+L7m9WHO+u+T9+yfowz22ZYsr12RpjEwzNB3r3ZbaS/xuwf7kmLbxVFVOHIeMBz0EDi8EcarZlIq6dhjbEChLoDxSeE6OR+wfj3j6asW4CTk5OiDdWtRiTdwbMJ08Jvr+ZxxEfZY9y3pr0KqhaAxHoz7CmztVIZBEKUXXUpYtURDjpMN6zyQdUxWC7//kOVYYJpOUbeuJnIPYoT3QGVrr2DhBU1q8BK0C2qAlcYJAOYrWkfWHVOYW4wY8nvWoqzlblzFMQ7R0VKZABZYmhy4wCBXSSx153hLKkG3REqiAQCrKrqVsDFVriaIA70ArhVYSHd4ZpFrjKGpLmioiH1A1HbudRQeaIIQwHtI0l4S6z4PjR8Cf/53192sBAq5zfPGznzI9GLO4MbhtQxYK6DwdHVoJaiNZLTaUGu4dxkSpYL48ZzIbEmhN01hG2vDoMCYdH9C/WLGYL7hdV6xyh/MhoQxIwpDr5S1rHfLgcB+iGz4Xlj3twShEI9ltrnn+xjGdjvj0x2853M+5tzdlnm9YU2LrgE5ZGlETxDDMAkY9wWKdM4kS+nFEURvq2pLnjlAJRkqRpTHrwtA2Dm86KhyTmURKxcXFjnVuWd827E9DHj5KODwe8vzZkmlfk057nF3mlJXDC0XSC9mb9KCTKOnZFobh7IB1YdAB3NqGvIrYVoJRJmgbw2pVEISeqraM4pqPn3xEWRuubxc82r9/50TjAwbDHr9x/30EgixLMKYiTcdc3S45mAw4PBixLLekvQ8RlWV1tUBOBhxM9mhbxeLmlvW2wkc9TqZ9Br0+8Tjl9emCBkHdOKwtCFVHPw1YhQFV23G52BF5Ry+T4Do+eDLkG//oQ4h+zvyHK7zrqIoSHWrCw2Nsfo4OQgYo4hjWTc2F8RyNYu6NQtqmIW888+LOzVepkLizmKamDizj0YSjrM9Pfv4SIwsePxhStlA1HcZC63ZME00soQ0DjNd44zCd4+FoSpfs2JWeuC8pTclq44n0HcX49LrE+IDDUZ9ZFvDmasWubtChJpKOVWkZzHqEsiVEscphkRdIHdNPY6CjcxbTScIwIE0jQnX3LoWhxjioG4u1LZHq048iFuWO0hiECZnt9WmsozUx94/20Vr9yvr7tQCBIBBEsgSjaV3HvX5EEii+uCroZZIglNRVhy87fKRoKnh7UaPue1LbUBeeZp3TPxDoMGF53WB3DcMsIMkGbKsaDIz6IVkUs809r+Y559c1sex4u8v52scTkghao5if7nhxvcI5weX5GuUddJ6Xp7eMs4ReGGI0KGFZzJf00oAnD4ZEtwm+9WS9kNC5O123AGMc28LiRYP0HQ5PYwUnY810EnI2LxgNI9ra8817Q3ay4PGTJzx+fMzt/LuUNTSbLaFSVIVjXuZM+lAWBbsKhFakqeXBvX2s0LTVjgeHQy6uS66vlxy8P2E83me+rBCdJQ4lMlTkXYOQYH1N2ZRMsyHGePrxhKTXIwkMb25eoMIR+6MxT28umTx5wO08597sCFpLWRv2hyHj2QEXN9fky4KL683dFyxtaXojPn/xmsO9ewhncZ3BdRYVwmjQw1mJ7Tx7kx55CavlBuMF476mcYbFZkUy8PTGPQb7BxSL50RRig9i2pslMo3wpoauYygkV8GdcW3btPhGEHhFqCxa1thWYJB01qDDmIPBjMurW5ZFwfuPZ4z7cFPeLc1ba8FYSiALAyKgbVsSFfBoElO2Jdo6xqmkizRB4zC2xHrD7a4mwOOUYqw9ibLsZ4LcOsrOEMcamUWkUmCNZ1M3lI1lr58QKOisYZBpiqokzxtcFBKPFFkSIBwIKdluG7yH6TAikBKPpJ9kNMZxNa+4WtSkOiaJh5RVwW5X/Or6+/+t0v+eI4kU47HG2Jxx6JgmEdvaIiVU7d3ITyiwnWCYRuRFx8s3Cyb7Q7JKcH5WMBtGeKn50fMNJ8MQiWBVwd7BECtTPv/pFe2sYzowVK2iKgzntzkKwW1ZYfwNAx8TOsX8UlF5g/IFGMG2yDHW4eoGG0qqrsNrBVKxWi/wKmO1MGx2hrJssa1FCvCiuwu+5I5vXuZ3xJwoloTK8Fu//QlppDi8uuZ6tSZ9lPD+oz1WRCTplPmi5MG9Hm3P05URl5uGnxZrBoEl6AJyawhCWK4Mh/sjvnh1hm0amrKl6xKqqgNX0Noxaa9PM8+xbc5wb0xeN/zgx18ghWI8llRdiwPiCMJuwO3tc9o0YpvXRFlJqD1PHj9ktRZ88P6XefjogJunz5gvC8aJRy9u+OLpW2TUY1vC1dWcvcMe89CxWLZczRuOJxOsa9C6xrcNRWlYbwrqwjDqC2YDhbcBvnNEkUZmIet2DXHA/fcfsji9JtIJMg1AQLPckPYyuK6xgcMphTCCynnWZUtsoHWKgySjdQVtHOF1R10okiijLBt+9uqC/b0ZwySmqTdID3EiSe2dbBsC8qqjEZ4oANE5AhGS6ZrGeuIwIu88h2NFvm1oTEvpPUkYIIzGFy21cBzpiGLQcLV1OC8QvmNXNASdBddxOEg5zjJ2puAyLylFzHQ4IAsNSZwQSkVR1CRJgLOGUEsmw5CqsVwuCmZeMOgpyspgjeHq+orZcMAuVwhR0Yt+zVWEtnPEsaafgIott7uWyt65o5QNNMYQKEEYCrQKcDji2DPqpQTBkPc/3iOOFD96es3nXzxj9o0TskGfz396yXJrWG5azq5y4jRk0Dek0ZTDXo+LVzVPHod86WTK67MN1xc5B8Me6Ugjd32ub2tc51E4mtLy8CCjURYVgHUti01DFEo+/2JBub1j91nnKcsSHUuyGKrC05g7/oLwgsf3emyrBiUkH793n0DBu48PKK3nZnWOMpbEOM4vforpYkZxx8HJfVzVMRtG/OKsYNBXfPTohLe7NaKtubltaMuGy4tbnPOESlDebCl3Dt1XlLuCXhoxzkBkdy7Ao7DP/OqW15eX/OZ33kcEGusUsXTEoSTMM16fv2E0GpKXNYWSCBKyNENHAUFjqcuWMAwYpTFv3i55++aGyaHjdlmjI4USkkBGHExCdjtH1TTcrtfs7UkWqyXPnl9jmpJIeiSOjruuuA47kp5mWTQ01xadSGJf8fTf/Gs++cdfZfj7/xRbrWmLNb3RlPB8QXYw42c3c+a3Dcp5ZlkPu9uy8C2ui5mvNkz2PdIpojAkDRN2mzWtzbF6wEXuER7SfsY01ZSbHQs8kU4wdkNhLDoIkNZzvizR2lHVYMqKNI1JhhIpNNvOUUuHEh5pPauyhK2j8Zpt1KFkyDCLEEYx6wlO5yX3kj5ploEx9FXA57uClpZHR2PUqGO+NWzzBuc7wvBuSjDoRfQSxbM3lxgXscoLrm4sV6uavWlMpGKy1HF6sSbtRYRB/Svr79cCBFrraH1LlQv2U421NQtbYA8AACAASURBVKF0iNaTpIK2gqLwhFpwsS1IlSCvAt6etvT6O+6dpAgfMjsYMr7s8+zNhs7ueHG2YTCMmIwTnHOEIuHeyR7Pvlhys/WM9mIa3/HNjx8huw0XZz+mDjxfeTDh+rTm+eUt/YFkMMw4P12yTXv0ZzFf++gdqnLJtjBoGZBle/zgszM22wIcd197Ar7yeMZ6r+XHTxesdhZbez5+75DDkz5vX59RrN+i0oTaFBwdv8d4dMLVm5+zbTue3Buw2lU4k5FvdlxdrogCgQpbHJqz9YZIByRhxtpXvL1YoCNBVYFxdwSq2jR0pEiVoJM+B/uapt6yWNbMDgd87aMZcdKRJRFbY0hVS6RACUEcDVBKcLMwLLYLGqVZbtb87jdnqEhyc3rFel2iI80Gy5vLC9LUs9vkHOyl7M96lDvDB+8/5Oz8nKbxNLahNS2dc5xd5/zs8zmbTY4OI3qhRktB3RYcPxkzPejx8mZL6UMePDjAi5qb+YKq/ibJ7Ji6SkmHPVxRobXmZp1zs87pvMIS0DQd0DGKQ1a7Ld4rWtdiGomOIlIteJ2vmR4O2JsIvG1ogNZb5qua1boiDlKsM1QI4jCgbhocIaEOaBpHbu6W5DtX0gURSS1oG4eRlizrk3cdhZAI67DaMu2HJEnKKu+Yppr9sKNJY0I1Ilc5y+stUaOQKuLB8RC8YbkpuVmVTEcjemmGsR4pJXXXkCSCx0dj3lxVgEIHnlBJRBeQpSEHgxiF4eK2JZsNfmX9/VqAAF4wv/bsjQWkGp+3d9rtwBMlIcO9DtDsth2taVHKs15WfPdvzogCzycfDhlMMzYlFHnFfJFTG8d4kFI1HW1R8vBQc3KQIO2EzXrF+e2Gb344pS08v/jxOZ/+bEMYKNbbmh/+fM4/+uQQu97j9Zsr1iuDNx7fOULRcXmxopdppDNYY0miMSoIqZoNB9OUewcZWRwx6WseP5owHGV8/7MLJuMM4xSbneXde/vUTnDx+orJoMfrt2+wpiWtQwLXcrvI6WUxDx8c8Bd/84aj4yPObl7xW9865mcvF1ycV4iu4eT+lCTNuLxdkw40Uayo845IaHTgcLFi3RqC+RZMiQxqhnHEbNzDWsnXP3iCSASvzq8ISsHscEZtGnpJim57lH7Hw8fv8eztDf/4q1/i1avnPDwe8tMv3hBYw148xDgDOFQQcHOz46N3H3HvMOL6ekm5bTAuJs9v2ZZbwjBgW3eUraNDsqo901QTpQGPTlLuH6eMDvuUnSHZaYajEXGgWFU5/SgkzDSnf/Tfke09Yfrt30b81R+jQgVFiYwUQefuxElpRZT2aeuIDx/0QEiWxYKia+lnfQLhudls0dM+kZNI59k0Lct1if93vBTVkooQGyTE0jLNUtZ1w6ZuEEpDKGkQVNZQbi1hrQklWBxL41jUDbEOORr2UVXLXk+TxhnVpuP+WNLtDJOoT5cYytIgtOf0tkINEqxpuag9/TTj+CDDdx1l1YJXWCROCuIoIpAerVtCFRDEsNlJik3Lo+MxdeMYZQlxEtI0v+bOQs572q7FdgNevikQ3hJGkMWKpjHogaSnYdrTyCBicV1SlC1FZ6iNYJG3HB6sSZRmvqxpO5AIqqJDKYEViv39ISoe8vnrU7IoIWTJbJggBfzxT854fVXyzkmPTdlQXGzIhoLluuX4IOXl84qq7KhnkrZ1vHxzxmw25sHJkPPTDbvqhkEv5tRJDg/6fOfrT8jilHy35vLymtkw4/d/5zGvzhZ8+tPXHOz3cPcHOBWglEaFHtyC9aIDkZEmGYVquV4X9HuO5dayzVtuF4p79zZ8dJLwaVVhckm+WWNFQL8fUZYtW2cYpZK6qaitYNjLEKahyg3T0RilI6IoYLdrOb9eMJ6kDJIBWbRhnc9Z5DHDNEGFAYN+n+3mlv2DfTbFlry45mA2xHcN66bm9qbgaG/M1bplYyDREaO4pag2dP4Yg+SHT18y2xsRBFCZLXmneBSOiYbw3icx99+dEuuEk5MhcXLMYlHzxdUluamxXUcaxxzs7/H21Zx/9jv/AbOHD3j+r/5X9l6/5eQP/wWjr36b/dfXbDc5SQSbShArwayn+MnbNYNwyH/6z/+Qz09fcfbjDcNJTBoJ8m1BVVuUcaw2FWkQEoqAvKwJgog41sTKEilP8MvRqbOKTEvatmVT5YRhiAOK2jAaZgSRRgeKRAds8ppEKcZZj0DeSZ1Prw1abxAmYrMZ0tY1+a5EdYYARRIH6JlHaJjfbgmCCC8k1niySFK3BhUE1M2OxvaI0wGRNIwyfdezEtDLFOPjISqwrDY5s/GINJaYtvmV9fdrEUgKEMaKxbrA1oa69ahQ8fi4x+PjAYHSfPzhhHffTSFwOK8xrcS0HhEIpvsxYSjZ7DoiHSOlxnaCxapBeEHbgSXi9GaOShuG45Q4DAhFyg/OrghaQxIJNuaOvz/ZD5hfNdzsKprOcu9RhvXw7LTgp5+XnF9brlcFh/sHdN7zs6fnVGXJg8MeH9w/4Gh0xCgdkg4GXC5Lzq9XlMYTCM3Xv3LM4/sjoihivlqzXG9J05jZZMbhcYyVHW8W15StAXp8+ou3VHXHpz97zabYsFp7vvLJx/zWtx7cueRWnvW6RjvYH4XYBqIuohfETDPF8VRzMO1xsD/h/sMD7j04QCcZN8szOnZY5dk1aw4Pj/ChYl17QuFQGMbZlCZXvH05R7uY/+2vPiOOh3z60yX7e3tEgaULBH/z8zecX68R3tNaQbGr+OsfveK7P3rFrqgptiW+M2gtQbT4ruHB8TGffPJlPvjwfaLekKKLqFGs6yV5syNJIg5GfSZxwPp2hZQBtUi4ePqauoObasfmB39Bdvgljt65R9I1xLVgby8ljiKuNgGHkxn/4e/9U8J0RF5tyasKYQQhkqotiRLNII4JAklFx840eGlomprVcktZB9hO42zHVZ5zts1ZVoa6s3RSILjrc1TOE4QxMlV0oWKgUmbJkJPJAO8svg1IsoTcKuquI44LSllRyJBl3XKzdGgXMur1GE8nTEcpvTggEp62Ljm/2bLeSsb9HnHQMe5LFBtOz86pG8NkkBHIDmNDJuOMVHuqqiFOMt5cbZivGur217wngOfOlwhB5wWhlOzWHdue5cHBlJvNkvFowIOTPpNhzmfmnJsrj3VACIFWNE6yqEqUa+k6gXWghOR2VVLvNHXRMJt57k+PKDpBp+Dz02s6BekwZmgqmqZCRwFxEKO85yDSvD4tCFSLihTLpaOqDIns6PUjnr1a0MoxZXGNa3O+/sEBv/nld/HJmMU25/XbmrdvLUlYc2I0rmq5OKuRoePoYIwXIVq27PUSpJJ0mcRPb3kw6XF6WVJvO1yoefnqBl+F/N4fPObtiw0/+MklJ4cZ7z7c4xc/XHLbtthewL1xDxUYbhYlg55mOJJEsWI4HSCc4Nmbc8ajfRKdItWSfFnx9PUXDEZ9hLzm/HTOt95RfDi7B9ISxj3uje/zP/3Zn5GmPUTV4+WrC0ZhQC4iPvn4XWoruZgXaK15em5YbjxXRcEn785IsphAS/Jtyd6whxWKRjbYztAZmK9WbDZb6hoK1zFfG+6dZAwn+1xc1QyiPqF3nN5uSftDPvvun/GlyYBgEGHzms3NOcFPf8rxt3+L+M//kq6VqCAkAH7nva8xTAWpr7g6+5yrxQIVWrrGkI4TrtqO6bBHIDxt69iVBVGQcJxOCGJNNh7z7M2ap28r+mMQWqOlojUWZx1KRsQ6omtKRCAxxhIikYlCdiE69Fyu1wQyZBJZgp6EuiIWU2aJ5bzc0JaSMI0AixI168qyaySRAicdjfFEnULjWK2WpFGf2TgjwLPqFkglUUHAZleyqzuqOqf2MD0aUViFsbBZ1xwejBnFv+aW4x7wBhCetoMs9BxNMlaLikCtsNbx7OkFV/OMr37yLvpbQ/7HP/oeuXF3oo+zgl4siaylMdy5/6aaOAmZr0pq4+j7mEE4ZXEW8ov5Jd5p3pzuePioT4tkuc159519DtIJP3p+zW99/RFvry6o3IooDKhLj/MGISVRHBFFmuv5mut5w7/39Qn3Hw746MEjvAr4oz/9S07PK5Y3OdfXBb1U0zYlv/HJlE44VsWWi7MVi8bgKss3Pux4cJywuL1FtRnbxhMLRzLoiJKYN/2YL339kAe9D3lefZ9Al3zvsxs2a9gfpkRdwE3V8PL1hiS+k8dubYe2iqdfrCiKjkf39kmDmKc/f4GpLWEokIni6GjKj39yStMZjkYpXhhqBAPhqDDIcMhvfvRt/vR7f8X9e49phaesb8n6E6QM+fSzl9RlzpulYmPm1I3hOAt5dJwyylLevl7x0bt77I1izm7X6FFIbrZczW+pqoZQ3rHblIL5tiLZZNyfDFiFLU3nublZsKktkaqx6yWbEPYmx1jZ0SUxu5c/Ze/JHxIfHDM4u+TFdkevN2QyiJiGG/I25/Zmzcvzc+q6RkVDLi6XfP5szT/5ynu0cY0TNe/dG9NuWzI1ZrC/x+dvFxTrBhF11M5gShCRIpQhSU8zv1oijMeXBlqH8SXJ0QMC6RmmmrPtKctFThT1+No39ijrNUpI4qDj9a5gYxoSpUH02esntEHD+bLAK02/1yNUkhUW5xOO9wY8OJgQa8nbyzVNawiCjLZrWSw3BFow7mWkYY33CbtO4n1HIA0HBynCakaDX3PasAC89hRlwPsPxP8pL/0nj474xfma+bYgERlPX9/y42cbtNaoJCIRhrLqKPKOMFBEQD/VGCfYrC2CjlEUgRY4wZ30s1mz2eZsdh3tzvH+O/cRfkMoBf005Haz5ltfOmHST/iTf3tLW3c8eTLB+w1h6sEZsl4A0nF9teYr70x57/1DVDxmMhmx2LWM+wlvqivmqy27zlPtWoqqZnTl+f3vvMc39CM+ff2GtChJoowfff6ag+OvcbFesz6v2Fl450GCVpJlYZiOeqig4tnbz/j4qwe8PVvy9OmKZdkyG6T0lKeoDaFUxKGic4am5W6f2EFVVbw5u+B474B3Th6y3S5Z7hbcLnPatiINNfmyZno/Y7de8fnLS969d0wcZ7yzF2C7Eb/7rW8zHWg6YykDz/3ZAWma0NUvcSJgsif4T776PlYGfPf7z3j54oreIOHituTd91OWleF6uyIRCV2g6aQiHaRU24aqgrb2DCcT8tLwsi5ABuyqgqpzxFFAEimWy5rtruNJlmGTBLW7wcmMeDhh/8kjumevGWdT/tlHn9C1JYWIKE3BZaUIY0WSZPQzxcvnFwwnMV/+ypiu23J6IynbFB1lvF1tuDo9Y7mrmPaHlLWFMGCbbyiKgr1hn9kgJu5rrv8P5t4j1rMtO+/7nRz/OdycKle9nDqH126RapEEbVGBsgAnwkEDe6SRbcATTQzDhgHDgAHLcABsSTBtEwSbtEk12c1Or7tfqqpX9Srdqpvv/edwct4e3KZBEGzBAppAr8nB2Qdnz75vr733Wt+39JENE2QJQ9MIgillKmiu9OgLmYUCuRwxPw+wFIFZyFAticqMjmqQlhKqpqOrEqUoycuSPNY5TTLitMQ2dVxD45Ur13n3C58nTqb80z/4Q8Z+gG3JICvIqkGjaRH4IZ12nSxJOB4vqFkWpmKjaylFlXI6/tk7/18IEkCC2BP0mgI/k8m8jJVaSWtljc0yx27qfPo8oChLtLDCcWJsR8cSOmGYkeQVQawSq+BPUihleo5G3VJAgZ4qM15WfPJ0wa/9yiY715qMBjHepOTHH+3zxg2XzdUuT58d8eU3brPb2+D+wQviXNCo1xiPp6RRQdvUsSyLIAoQIwlFzomos761Q5jJBHGAY2hkBYyXFWUlIatQCplC1Xj/2RJJPee1zT6aKnj9Vpsolqkqm7OxTyYM3npzm0U8o1ErKTMNv1yys7ugbe/xykvv8CfvfcLJcMhv/sbrHN6f8H9//IJBrmBqMopbMfdjXEPGcSW6bZNOy+XK1RaGkpOFIfM4wW3baEqd2EtI44z1voNbq3CaCt2OThwGBFmOF85ZbVm4Sk5z26WmlkRhSaG06RkSmcj40tvX8dOMnJx+xyLJVZJUIsoqxMKDVEE1HI4nFyRhSXtFRUbQr/fIq4o8W0Ih8+LpjP5aRr/vkmUVNcMAKUaIEiFdHr4pmgJSiWO6rH7pq+jrDbJnz6ncDh3HYFVz+cZn38DUQvyljyhdIqEyDUaomuBax6GpGPx4EaBoCn9691PSPMIPFOa+wDQ0un2bRs9AMiW0SqKKC5ZhQllJmKqOpBpUgK7bqHqO7tq4moYlKeRFhFBhHBZc31jj+SzG6beIRc4yFMSiZM2s8BSVZQ5mWWI5KaWUc+qV5EIlznLC2YzpomCtv82v/qtf4dW9DaQ4JQxC4iKlWW/QqNs8eXHGNI8oypSylGhbEtNlgaV3aLomXhhTN5sUVcJ0+bN1fn8hSMA2FX7tr61ya7vN//K7z5EqFVEInj694MF5xOqKzWs3bExD58VphJAKwrBAETKWISiETBhmOIZM0xRYusZq0yVMA65ca6AA5+dzyirndH/K22/dJo9O8eY5nZpGLBJ0u2LvapeHj0aUks7TswF6CZXILquzuhaVX9GzbI4TH0SJLiRGo5D3P3yB65p8+uwCTTERlcTuSgdVFpzOPJZ+eVmnoAqiICXWZQbnEUpTQRgF3tTDLbbpOjI7e7cZ3v8JXqyztbqJXlN5/ORjDi+WXL8m2Nlo0qtfp5wJ3nt4zvVdlYt5Rk82sNsaX/raLQI/5exsTN3QkBWJsjLY3esTeWMOLmKeHowIowzPT7m22+erb93iBx88YGOjR7tpc3IyJs41dLlk/zxArnJ6rYCmVRFVFistg0FWkkcBhWJRioRb6xvoeoafCUpZYjQLaDkau3sb9Ho1lMpDkhUsyWG1piKbOlNvhiQLHFumWYMwiMgNhYZmgZxSU0AydaZRhiYk6g44horjGKj1HvVr7yDe+NvETz/AyQosW6EIZ4RqyTKKMcqESV5DkxVe3mzx1vY6/+M3P+V4FnHj5iaxcDAsB0VKaRiChu3SbVmEizF5FBOmMbZxKeopSvCLHDGP6BgqaZrQrjsEaUrNcknDJaGAlmswTxfcOxPkuoAk4iJNSXOdriGRDUuWFlzES97cWsd1MwZzn+m8YjTJWAwEd+7s8Le/8RIv712l7Th4ixkvzk95dHjMaBxhGgphOEdXJGRJp0gFKx2bOC0YzyM0KcTU+6z264RBRhilWLb9M/H3C0ECDdfkl9+9TbgM+Y/+3TscHhb8/h9/QngUkFJxdDynvWJxZatGli+wXRVVNmjWZCRFZzzO0GRB24JFlDJZ5oxnE5odhdUo59reCjU7JF0KXhykNNsz7j9bcHrg8+tf7OGsuYRRTq1usZzPeHh0wGKek6EST2NcwyCSSjYaJkm0YKPZJBUJ3baOo8LdB5/y7DAkywVSpeHFBS1Xo2Gb2KrGtZd0HF1hEZTU6gqD8QxJN8njkp3OKn3TpUgDbm71iaWUk8GUdtvCcZfsH0+YeU2eHz1mNP4m//7f+1tUis/BMw+7eZkJmYqMkGVkFYpcYekrZIkCREh6xXgMT6U5Kz2DqkwZDoa8dGWNr3/uOmmasJzN0JQMEhlXccnKM54cveDO9haKAWfDlLIKsFc1pkuFJJ3ixwaGKTGez7m51cDVm9TsjCIJCcOKIo+RKofF4zMMx0IhR9ddkkJmtIhodW263S1G8xfEccrLV/ss44I8KUgyBcewUNWCIhU4uoY3zWl2NNA0attbPPvx96m/93023/0NSjlBKUo6q00mx+dI6+sgLpWYwiRETlReeXmb+4dLPnp6wtaVFt0NnUXgES0FjqWjahKOqqEWBmmmU9NLQiEo9QJXspFllTKLabsWpmVhZAmGoZFqgtLUWa3XKaZzFEq2SsH+KKK93gI5oUhdEr3EqNUYipJUFVx1G+hIrHUdTCQCT+ZL37jDdneLKxubGGpJvDhhMnnOe49OeO/eMXEiqLVULMdE0VSKecD5IOHK+jpRmPHgyQW91f5lxWBNI/F9JFnFMTXS9K8gE5Ak6SaX3gJ/FleA/wxoAv8el74MAP+JEOIP/kVzaaqKIQwGScA/+91P0UyB27BZBClFWfHazSaiNPjk0wFxUhGHFXeu1slLwckgwgtybK1ke6ON6rh0HZmyzNi9bmNbGmEQcvVqg/lHQ46DnPGP95EruHnDJZV1iHSSvOLi3gRJszg4m6OhkZYxb72+Sse1OF3OadU1jMwhiZdEqUTTVeiuaVxv9rlzTeX9Dwc8PpwTphmOY7LRa6MpEm694ua1HrZsMBjOOVosubZzhYODQ0xLp93uk2QTFL3O8ekFGys1HEUjDWSODkaUiczLO23cTp0nzx5y4+U3sVoSm6bC+y8KjJZBZ9UkS3O+870H5JVKzdDZ6jbwwhx/FrGxolOUOptbLeZhyftPxgi9TseVOZ5MiLMMaf4c3ZRp1G0Op8eM/CaapOD7PnVD5WhsM556jPMhF7Mcye7hBxWrrYLGjiDKXWBOkuVYqkmW5Wxv1LFrKvfuP6XTanA8mCCZMVuSQg+HKi8J4wLPC1FlMHSFRFQky4pShskywbRs4irG8zTkDQet2SVMPLKFj/GjP6bzlS+TCUGv2+XRk+dobkpeJIyWGZ1uh9de2qEoNb79g3t0Om1eurnJMgpwbQctT9EqCdSKVEpRU5VcklENFZGlZJlC4kckWUi/oVMUEc/OfdZ7LSI/Q9VUAi+gu16jZhkU84o0LXHKklmSs264nIUL6r0Gg7lHGJfU2i5Oy8KcxiwXMpqzw699/VVubW+Q+WPS6QnDMOR8cszDw2OOpimVVFJSISqHhmOTZBFlWRGXKc/OzjFUkGsuaelxdBaw0m9xdb2J58+Jo8tGtp87CQghngCv/5QQFOAM+B3g3wH+ayHEf/n/ey4qVnZMvn/P5HScUm+oiCJHNQRVUSKJCseRCOYlqiaoNzR2OjXmScXj4xn1ukKeSvzk3pROx+T2lsUrWx0UU6W0JJqNJra9S6N5lwd3RzTNNrGW4jYLfMlncDhno9PhdBLi50vCSkYuSna6DlubBltr61zPuxycTzFcCRHo9AyLlV4NRRO0Wx2ur2wwHOo8PJpCVaHJJWudGlpd8PDgkKTK+eLt60iKxXQ2xlu+oKaofPpgxmL5lC998Tr74yEnL44w1BqGeVm5OBj7/IPf/BLNnszSV7j30YRv/+NvkmQFRZFw81aTaVARBhmuAYpQ0GQFLw745CTHUBTaLY262WIyCECTubXWQ0kkHh+c0HQqLEtmo7eBrpq4NZvjp09o9Bp89OlTXr++QyWNUMzbSJZNmE/48Scz8gKuXmkjZJPvfnhAmJp8+aVd0lJBl2XKqiSpZLw04PGTfZZBiWsLRFHghxGD8Qs2Vxp4Ycw8Lln6CR1ZcGOzTVBm5KVFXlZYpo6mlnQaNaRKZ/32K3ijC/xlippWOJHHmutQFBWmYRHGGep8ySicoXc6BFFMla7yYvyCKM2ouzaTizGm42AYNXZ21jibnuLlGSKJsCwHkBh5EXEKoqowigIpEeS2Ck2bmiYTiYpcVKiFjmPkDAZTAgF1p8m0WjLTZGpVRbCI0V2NNA4JqxxVl8lin8EwJvFl7tz6LH/j3S9QLkb4iwecjqZ8uO9xNJ7iJUu8MEGUKisdh3otI0oSdMWhVAS2afLynkFSqYwuFpAVoOrUHAMKuBiFmDokErx24wZw9+dLAn8hvg48F0Ic/VRq7F8qwjDmO3865A/+9AMaDYWyEBRxiqppuLrO4WGMqqf0VzU2OhrhTFCWAs0sePVGmzyGpV8wL1J0WWZ1u4nVgcEg4mB/Qb1WsbG+oCpLrlwxKOY5SWbym5+/TqPt8uLZgMf7EVbfQ4tKspGHrhhIWkW/36PVWWH/6WPeXG/x/f0Bfrhkq6FTSDLjcUqajHgym/Cjj46ZLHNWGy5v7PaoqoAw8NhYc7nWbWFjoDpLbt+oEfoZV9e7jGcFuiogSHh8PEfSbBK5Yme7zfTeglZnhQdPp2yHNvtnE6ZTjffunrLasfjylzosvILpcomsCgbjFNWooakVWVZQs3QWoY9rN5lOMwbDBVM/5pVX1/jMFza4tsy598kRH96f8LWv3aHhqPhBzusvv31pEBs+5E9+8ICirDgaP8ZSXUaDAceDkral0W5VzM/nFIuAka/w29/+mF5DRzc10iylSCvkCkxNYbb0cGsOVtNg4QuWi5i5l1JzFUQFTUfH1RRM18HRJeZLgZ9o5EmIqxt4EWBkXARLXpI3KIqCeRTgTsekEw8hqWS+R9MyGM09lKaLH2c0ajLNWsndpwuMpoWmgKbpyFJJVUZ4ESyzGFFIqJLAK5eEeUxUSCBD05WIA4Uyr8gpCJMIVa2ThDEt06AMQmwLapbBKK94OjlnxarTNdskWUJpqnR0QRT5+IUgTXMajstec4vPfeMzXFlZIRyeUkkhdw8u+NG9J4RlhKwpuJZFmuTM44RCGPT7NaKsRCgaaZlSlTJlmjIPQ1b7DhvdOgs/YrIIsXUYz3LaNeh3ajw6OP6Z+Pt5kcDfA/7pn3v/DyVJ+jeBD4B/+C+0IAM0TeajH91n6Ze4LohcwjI1lgtB2xYUqoQ/ge1thVbbwtAyXkxGLOOCmztdtLrOzJvSaKqsuTaFJzNVbHJRcGNvnSgJeHF+gRco1A0Doy2zms1ZxClO2eP/fO8pLw7nOI5Gq2Wz1m0yOF1yeqHxrW+94OaNAKtuM5R73N4QYNTIPZ92zWEyvEAqLYSm02zbbFQl19eaJHnJdJnx2s4688wnL2U+Prjg5Tt1VvsKnhey2rQZRidYVoOwEpiOgc+C2ztdPnm25CtfvMPv/u6HXLgyn/nsTc6GPt5oxq/+0lXefG2Lb377IdNhSBRWvPRGi3a/xQcfHmMZMY7FwwAAIABJREFUBrXmOq/d3kRXYk5Oxtx/PMKtSzgNg7QqGC9iFlMP16mxswMfvn+ftbUGV3ZXePnVdxgcf8Bu36T+13b5vd/9Mc8+mlI3NHoNlRsdmM7npMsNGh2Zd97d4NotD9+3ePDAA3SoBKt1iVdvrgAKTw+nUAnURMFOVUpJoW1prK4YOK7BxaQkEzIJJX3dpLJlslLF83KeHEZs9mxIBMHFCPOdt9A7NZLhiNjzSeOYytQwNQPHscm9JZJ8CeJWu0MUxvh+QL/bAFUh5VJqTFJKlnFEUWoQR8zzGNutYxka0yDBUBV0RWLkZSSmjlQU9OQ6iiqwTAezKrEtha1mA0OpcOKKU2lClvjIlYsrG4zTBUYuoWoKrqnz5c98jjdffZWtbhtbKUi8Cx6Pz/je3Uc8Gw3prdg09QZDL2ExS1FUg35bQ1FV4gSWM5+LsyWGYSJJMmFSMZ0XyFJJtBzTbFpQKUiywLVUVtdNxrMF+8f5z8Tfz8OLUAd+HfiPfzr03wH/iMsaoH8E/FfAb/0l//1/5iOtusrOqs23HwZ0ujYoOUUqY2mCeVrQNVQScs7HMPEmNB2DpVewSCCKFkhlRZwUdFsGRZHzrT8+5Mp1h7/zG2/x0b7HvWdDmqbCclEwqiLafZebq7v87//Pj9m9ecJ46mEZCmVZsvAiNE1i/ZZLtCiYTOc8+uaIlV6NRu0Jn31rlTVrjVJVmfkT+j2XImvT6Vl89XMp4XzM1uoqf/yjU4IsotSa2HqNyWzBk+MJp8MJN2/YvH5nBxXYXK3x5OkAXwhuXl1nw75CFEwZzgPW11usrpr4Xsk/+b8+Ig8yFFfHj6b8r789ZZrO6Ta6JKMFgS9IipgsKXjj9gq6pfHi8AUv3dri9tWrfPNbHzOKBG/e3EIvLFJfpt/bIYsGtJyC48GCG3aXpu3w3R99m9WWTKuQcdZc/q3f+gJ/9K3HeJOEJMup123uXG2xea2JpIXIlsnu3lWODg/57Gcb/N7hEUKX6HSa7G7t8MmTY0xNJU4zXMei5dSYpzGxYqCZbQwTDG1JOs+oXIekykDIaGpFLvnYmoYtKQhd4+DgKY8+WMeuNZAMhVLKIVogKTKqItja3uDk4hxhuNRUndWGTZoHmI6Bpth4FXheRK9ZIwp8kijHSwtUSUM3NVTbpmHV0DCYRCFhLljtNMldmTDL0LAQaQGSwvnYp20phGcX1FKZWrfHV9/8HK/t3MSUdHRJw8uWnM9n6IrFjSvXWKtpRHFE5p2zf3HC+58+52QywhQJbZHjRQHLUCZKBRUV7YaNqirEiSCNKzqtJi1yBqOA44uYKq+4eqXLSsdFFCWp0kZLx7RqHbJySRSqlJVBkcz+6kgA+BvAR0KIIcCfPX8K9H8MfPMv++nPm4+sr+jipVdMftXr8+B0xj/4W1/kv//tTyjKGS9turzzyg6PnxY8PnpBGKrM8pI33txi7pc8+vQEx1Foti2u7LYpUoE7y0BV+O1vfszJWYyrGSy0iiAuKdF4tvQ4O3rEak/nwcMLorigZhg0XZlGV+diHBNGOc2mQpaZuK0aeVrxd9/9MsvC5+MHz7i12WKyGNJuNgnCCXmu02+6vPH6HUZzn89+tosXpwzDMSYm6aJitW4SRAGOW+fkPEarEqyWweuvbXDyKMYqC0Rl86OPTthbdTk5nXPj9k2+/Z2PMAybfqtOHEWUOty50mHn1hXiROV3xnd5cTxHZUrDrXM0jEiSmMlwTugXfP72bdpOn/uHz2nbE/S8yagQtHoKeanT7Bj83Tu3MeQcqczxgyOSqc5afxMlSfD9kqs3+6RXY1zdoFer4dRb3N5u8t33jjkfnFAzd5gvKtpmB10/JA4rBAq6oiOLEtWAMM6IkhKZirphkSwj1MSllCQMTaJWU7jwAuobfXp1ixu2zq3NPp8+nzAeLdHdDNKKwdkJq3vrOLqEVAm0lS7GmUqVCayGi1RpzIcxnbZJzbRZ6ipCdXh+PmWZcVluXhasNU0adZti7pMmFbKqkFchaSmjOxZkPkEUI9UtCpFSVjmH3jmmreHECraqYUkqK70dbl+9zvb2Go16jciPOR2eULMN1KLgSrdHr+HitgXLxRl3H53z9HzI2cWQ02iAUzdoxBJOrlJaGrIMllpRWTKFBJZuM/MCNKFQKpCXEEQFiyjF1k1KoZAmGcejBTP/nPVGAy+acjr10YuKl+7UePcdgz/4wdFfGQn86/y5rcCfmY789PVvAj/b9eCnoWsytX6br76c0Gzm/MknL5jFEe9+vs8//Ptv8E/+jyH7s5NLR1lVgAJn5zPCVKDpQFUxnaT8cHJGr6fz139lFz2TGZwntDQ4GPg0HIl+R2Hkj3l9o4FutkD32C6a/PPvDvCTiq21Bl6QUOUK3ixFciTirGS1r/H1L23zydE9bm2ssbVSx9AF2xu7PH08YrbwKDWZ3uoOszJktPQI4gBZhhXTZa/mcqIkdHdruNYGB4Mxy+GI670eHzw8xstL3v3iFb765Xf5b/7n3+bh/gjX2CHOFvhzBbexQpmEqGodya5Y65d89eUrPD/2ePT0FCFKgrjgSttCEinzacZGp85f/9UtwlCQVwK7qdDtW1wsEzQRECUx0eEFSZxhOyp3XoJ0saTXE+Q5+EHBcbDP5tY24WhIko9RdYtmvSCYp1i1ijBqs7vb4oZtMzo5ZWvtUmW4EAJJljFtiQqBpRnUDIvFIoCyoNc1udHRKUSBLalUEty6dZUsE/gnczY217h+/QZ1TeXe3bvU6oLJUkapZF660sHzJ9TzPldvvcre9T1ar7+K2bbxPvohWRrR7bucnvrU+l22OibVTCPJKhyrIC0EfqrhxRmqWuJqGoouU9cy9LJCEw6GKqhbcHv7JVRVZ+4HGIqErhuYuossQdNx2F5dR9d0kCUqTUGSSk5OX5CmCUlZcHp8ganqRGGBXuuweCy4+5OfkBJj6ypbrRaa1WFBQVEqLIWElMrUGjKKCoOTGcMsQVcDiqKkblq0LYvROGQ8K+i4NYIs4/h8zMTQWW3aNG2NVHZxHWhmCrP5GD+2cNXiZ+Lv52E+8kvAf/Dnhv8LSZJe53I7cPgXvv3l88gG01nE3lWTTn+H3//eU166KfjcGzr7w4CTYIhmJly/YpNXgqOziONjj0KqsHSZOBckaUHdUbmy3URVZG5da/CFt27xv/3Rc4ZPzrm512Sl5bKXNvDFjH/t7VcZBxf83ofHbPU1zgc5Hz0YIyklV27UkLlcnUYT2OhI7GytsLdzg3jh4co5y2rMds1lbavNvDAI5wH7hwk7vYCea1FvCjS1Ig1hrmZce2OTTEgMpx5f6LicNsb85O4F01mBWdfQdJs//sGPuLG3haM63H8wI5VzIm/ELCyJUnhy/oQbOx16K9s8G5b85N4hF/MFa12HeVDglSUNraCmSFy/2sBwNB4dHTOYnRLHBdd32ty8sspOb53v/nCfDx7v021p7K2o5P4xRWmimy3Sec7Qn7PW6fLs2THXNrvUqph6o8tmo0fm+tS7m6BW1FwJRW6yvqHxyaNPUfQW4qfCJO1Wg/FyyfFoRJZftnXXbIP1bp9GN+N0MkFyHbZ3N3Etk6YukW2uk4iS5wf7GJrDaHlpzCm0jK7bpl2rM5sskfyca1/5It31DpPvfA/Z7rP2d/5TismntH/ygOp4Qdc20KoziiKmUCQ2dreJXlwwC2IkXyFAgmpB1zW4uXWLK+ubXGnrtNQKoRp4mcrEExT1mLrrUHMuV91MqCBrzBdjjgYetqUwGB0zmYccng9ZeBmqBUkRU5aC+SJFQ6EoMt585SrbGx30HFI/Ickk8lJBFFCoBYYuoUsahirRb9rc3/dwdEG3qWJYGSfziOOzBC9KWG1bbHQsJFlGlUsqWcNw64yGU9JGn5f31jmuzfEXKat7jb8aEhBChEDnL4z9G/+y84RRjKLraKtXuLMLfdPiUTzi5tUdDo9Krq00qVkqwhS4NlSSwouzEF1WoJTQkSiVCllWeH64ZDqMaLzr8uT4nPl8zNc+20Epdfwk4a0bt5kvBLN8RFuv87mXdtj7NYePPin5z//bb/Fv/82X+PRszHAR8M61Hkno8+v/yqu06m0S4fAn3/uQt99ZZzGo85O7I9bX1hgNRlwMQ56/mLLXa7C97qK6OetrJt1mg6q6vBabzH1Gs4i0YWI5Cs2+RpymJF7CvQ+es77Vw6o1MPUGWTwgrEo2dhwwoJjmpGnMbB5z/IO7dBsOa60Ohiz99CRZYrHIUGSFdsPg2bMpnzwMkI0SXddpNmvc2Ovy2s0tWm6Hs+GCZxfnrHcsFosCcsHedZOGrePs7TAeZFwMxmzsuQznC77w9ldoEXHy+CkDpUQfVPzq599gJOacno94cTZA1WX88xl5IpHlBYOLhPF8n/F0Ss22kCWJPLu0KDNtFbchU2Qx8+k5Vr+PYa+hWjJSIbiYjtnff0YaXzrwXO+0WXNXGM8ikiwnLxKC5ZKzhx9x+uA+bUXHbLcJju5j2BYNIaHlFe89F6hSSiYEo1lM7OXUbJuXejU+d6vHVldje2UTq32FKPLIF8/JWDJdSlzMM+a+RL3WYB5JRGFCXBpYTo2jkxGuVfLscMhg7pFlCZZtUAhBalSYLZtVrY5aFqz1BUWeEGoakSIzmfkgcpJMRtI1tLggDEp66w3apsDzIuJUp2GrfOGWy8wvL68KhUwpyWSlYHerzfXtOoPRkvG0YKWloeoyQraRpQmHF3MatToPnl6gVTaa+ld4MPjzCFNTWe/oFNGS5+ESudmjrxo8PvCI4znnXszTo5C//xufodNpYeqfUKuNODrz2Wg12V1tMfFnLIIEUzO4tdvFMmSajsmJqTOZRFy7VrK2Xqe11qK343D04oJQj9nZ6TIaTbmxt8XNrRUOhnPmi5Q0lbi3P6Nj6/z4/hl+LnE0fsosCZgsYibLjO+9f8H2ZkwlcuaLGN1UeHbhI1SJG40+VAZCMVnM5wyGB6yv7bG12uDoeJ+t1QZr3Rpv3Ozzve+f8PwwYji9IJdOqcqcdsNEjWROzpfcuN4lDksGYxBSwrWdLmoa8eL0hLqlkfs+c6/Aci0EcDZJMcgwzYK1molmSGxvNXnj5VeIfZ9/fv8BVr3B+laf6WxM3RGYlkZelaS5yv7ZBVZDZzCq8MKKft1lPplzpe+w6LlUmcJ4OSaqBOQ217aaBIlHUlqo3RaK9ADTVEjKmKYm021ql30CVGi6gqEbTLwpkiphZgnLWUaj2ebJ6YAgXOClHuPznFtr68SaQtNt0pBL/DBA7bjMFyGObTI9eczFg/tIAlJFJfMmeBcn2KpJb72PVltnc6PPw+cPyYs5UpVzrdfllz//Dm/fXsWfDxhMQs4WBVp0wNI7ZrZcsvBCJouUoqhICpmcEYtlQhBmeElFIRV4QcmdGyuMgjmdVotZoLJxY5XByQRdk3BsmSzPWSQSmiQoUaBSCbOEtPqpkIdU0tB1Wm2XTJkhVxkiV5kkBRQqd+o6lVGxLCoaZg21KsnTnL0NBUmRWMxzLkYJ86Si1dYJpzFO0yEvdDoNk6enC8qyot5QmM6Cn4m/XwgSSIuSw+cpd+7UiZcSg8WQxSSmsiK+8Mot5HjM0j+lLH2ySKemw1c+t0uYuXxy9xhVVrh5rc/qio1ILKaLBY7Z4eXrV+i31/j2j37I+qrN+toqkpwTJgmVmvP0ZIQxLjg5n3AxfEyK4P27A1650yaNTOZBQhCk7A/nHJ/POV+EKFLF7SslZ8cpk1mKkAJWWha9nkmQ5diGyfZ6l5d2euSSYOFFyLJKr22R5BE5cG23x05nk9WGR7fj8MOfTEgqD/yCSaDRX9FwHZOFF1EYFn6p4DR01lKbIi1plRn9XouDWYKjKUhyRbOjUwCWrbOcpRimxI2bPTb6JnJZ0KgZDIcXPHg65ONHz+l0WrSaNt1rTbotmTzJmY9Tnj8/I84Fv/KNz/Pps8fcfXDO6cEFN3cK7mx9iSu33yachkzCgAcnn1DXHNSojqrXiCdzzkdzXFtj6afkeYznVeimTllcquoGcUJegiIZnE0iDMWk2aoTpxnD+RC5Mrh99Q2++toasqrw7P6H6IqJ4tSp/I/J44CWDZ21PsvlgBKVIi8wKJDiEtl2UCWBZJsUaLSbnUvtx7Lgtd1dfv1rX0fLPI4GPuPRKVk24ngksX+6JM0jikohiAQICRQJoShk5WX2EWeCXq9Fv63g+iFDf06j06bpNCnlOR/cO0akCjeu9UijBEvXkfQYVTU5vBiiqpe3I6KQ0TXpUkOiUpArna3eOuFkyYm3ZBFX1HXwfI0oiQmSAkWWaJs67bUaR37Goxch3VrO5rrLF/pdjqdTiqIiCo8RhUKeyfilhK44xFFKlFU/E3+/ECQgo/CTj845Wyx46+pN1EpQq1nU2w0+eTjDjwq2t1axnR5CBKytOsSihj/NKSuVrJRYq3WwFIEnRewfn/HwxRn7hyds76zw5tu3uffoIUE85DMvbXJ2PGDmp5yOE0L/Iapc49Fzn6gQtByblVqDKo2Q0gyvktAMhf3DJaUKHUtndJBSxhV13SDxS079mJotIVcqSVbwYjCis1LhtDUs08RSXMIiYbY4x3F04kJhmF+QKgkHny4ZTgrqrknLMvCKnCzOsdZsnLAk9AteHHrUbBlVN+i1JKyazqIsadcsDENQKpfSWGmQ40gGq21Bt6WhWQa61ebquoGh6QzmKRezIZp62axTZRFmTyYQKpZi0nRdzi8CvvDZV/nwg4fM/Ahd1chMgaVUfPfu+7y2s8d0dkyUTmjXN5mNl1zML2i1+1zf7TD1HqLoErIkoyKhKQqWoZPnFUkh8OKUSRhyve+yVAskq4NlmaTpDG/ksdneRY809g8+YjKdkhQJlWRRty5bxaMsYt3t093Y5PToKXF5uaymZcb8+ARztUO9ZmDPDR5/9ClemvLho3PeeX2Pb7y2S744YhrMmC+nPDo+5/B8wCTQkVSHZq2Hayi0+iqaVpElKWkhiKoKlJJFmCKJkiRQqTkNbEtgSAaD8RQ/FpSxRrsBJycTJn5Mt9Hl9vUmReTTsGxKTSGNUmTFoKJAKxRSWeI0SUiDEluWCUsQXJ4NrPQalAUkFylJkWPoOnGeIaQS26xIU4WdjRqpKFiMCzRNZW2rxeQiJIpj5mGIUuhoqkA1fjbUfyFIQJIKfvPXX2H/JOJidEqn32UxFPjPLhhMK37//QmyJvPex0d8/StrvHprm8VZzv7jZxiVRd3U8NMJfpXQbOu89Xab/Wcxx2cjuitNnFaLer3J1bVVTMngfLzA90PazRr+PGS0DNBVmWZNoaCkzAtub7U5vJij2TpFXtJt2HhRTqdhIQuJo/MFKy0LzTB49HxMlcgIS8eUJWxLAUmnymS6/TZlnhAFHq1WnZpWhyhlFi7RbP0yw5gH9NoWEjKiihktCtxhiGMr9CoYBRneJEJ3TWq1Orlh8/jZgH7LxKprHJzMUKoSS4Gzic/qusnuVpu8gPtPDuh3b+LUmhwcPiNbxOystknRSL2AXr2Bods8OhgwSwaYpc184TNfBJwcTbEbJq++fJ3Nts6Dx0/53r1P+ZUv32Jjs0lWVAgy1lb7aFLOMgi4eX0b/+KcMpZxDA3NELRqLlGckZUwWsQsvQQ/NCil4tLBOcpwNY13dq4RxoIHT39Mq+Yg6QKBRpVXxEmCn+RcLBNqKxqGIoi9gBKJMitJVZnF6XNufu23qPe3uXjvT/jeH/1P/OHHn/D5L90m8kv+h9/5Ie++uU2SD/jO3SF3X6T0mzWu7nbQLZOyLEninKoSTJYhZAU1SyOMUtK0xG2ZxFHCbJHS1V0UVE5Gc1xHufxuFSiaja2VeKcVihKwWAiUn/pQSKWEW6tRGRqpH6IpCo5hEIuCXM2JhYTrWDiWTq/eQFbAS1IcW6NVQZFleIVEIQwcXeP4fEqWh6iawmKZ8+Yr6yRFju6YhMsFtmUznYeYsow//QXPBBRV4f7BmKpY4moNPH9Gx9X49JlPmMkkaUa0EFDpjC5CjvUXNO0mv/zlW4zPQ7pNA7NZXNobyyW9jk2wgOdxwL1HT/h68ypq2uDgbM5okhH6HsNpRLsDiyCnACQKblxt0lvr8cPvP+FwtGRzz2G963B8FiJlYGQSmiLz7GzEKMzQLZl+TWBbJqZWItsCUQiyJGM2XbJidEgKCz+MyYVJUdXwFiPMCsbziFTELLwYxyoJ04IsKQhjiSxTeXE840rfpShlDE1CKhRUVUXONYLQQ5MqvDRmrWnT8XSWy4x5JohyQSOROD/z6DRt1hoa6cLn2TznfDjCVlUaDZ1KVfn0LGHmr/P6rS3CTLAqd7h/75D7+48oAwlJSHQaLWQhY9Z6zBZP0WsZB5MBb13ZZbqImPsRwluwt7HCIkxYX+mz2Q/Zny7RTZ1KZCAgywqKEpZRyuPjKVlZ4rgKbVdmpeaQRrBIcipVx3FdskwQxSUqOqoiuBgHnE0iDoYL3r71FTRHvzQVLSvkqkJTII08skWE9/gRW06DN15eR3oyJBifcH+UESQyR8MJqAK/VFnpuRiyxNgLqOclhqZQiYqKS2UezdWxHRsjTlkkCdVSJkkLdEnC1E2kKqfd1i6rE70c5JJlHFJzNdotndW+QZJmVEXOdJnQrNdRVQVJlAhLoWE56IoJUkpu5IRRhaaYqJpGJSSiOGbV1LHkiuk0JI1LSs3gaOiRZyWikrkYp1imwu5mnQgZR23grteQdI08mLHSVLDdkk/3s5+Jv18IEqhExV7f5HRSMvF9tCCnubvBRIFKLXjzZo3BqOA3fmmT9c0+4/khqlHQ6zVo1128YEGWJ1RJju/HKHbOxvYKhdrn4uSM2TQhDDQ+fPKcfk/jjfUN4vQMR8loNQzyqmQyFdiGQeYn+HGJqcmsOyrbGw5CEjw7iEhLQZzEeFGFLiuUVUGaJMg6mDWdjTWdpV+w1rG5vtUiklQevzimKHLCcIntzpFLifraBjV5Rr4IiLIChIo3T2l0LXQrI4pLXEelWzc4mWckeUHPlek2LM6GC+QyQ9Uk0kQQeAW9Xh1NDclSGYGErQkuZiFCqOytmSz9iFzJaXRMasJFziuajsrbb60zWyx5eJSx3nOZzny8uMTJFeZRgY5GLREYZcHj/WccDyZ8eWOPkxOP5XwfDR1hWGhlha7UcFSBWpSYpqASBZWoQFZQNJ0oyqjykqISHAxmjL2EZs1gtsy5suGiSgWmbmAYFWUWowiNmm0RRjnHZxM+PhhjGQqqrmJsbGNcv45VtwhmC2xLwjZUert7JOMnnL33h8jtNfZuXuf04AKtLhC6xLMzOFsECN3m9rUWtsKlA1Je4loqpuFSSjFBHlO3dIoSgjBD1RQsQ0Xklx6Skm4TB4L1lTrrPYnT4QK/yNioGdiSwM8zWm0Z2ylI/BRd1lFQSdOCUhfYqiAuU1BqBPGCJM3JRYWcC5RSuZQJ12XyrKQsFBAyqSoRqDAOYoKoombreH7EMizZXGuxutGmyoEipCbDi8URoadxY6+Fa8KNrYrv/OjwL8XfLwQJiKICAb4fIRsGkS8TFyXrqzKrLQNb6hP7Ou2ezsVkgZdKlIRI6imKpFEzdRYLGZGAKUzOBgviUtBotOm8vMWzoynRcoKhShRZitY2eb1xg8EkwLFOeHYSklQSzy9m5H6FrGmsdnS6jopu2vS6MvOp4GCcgKahajKrLZ2oSHn2IkGWJQpHAknmym6DtbrOdBEyXI6ZhQlpDAs/4/qNHvWawmI5Z6Np0DY1okxlNEuRyxJ/maEpAksX2LLKk1MfzVXpr1iopUSChCIEi6RE5CAkmeeHS5pNA0M12Nu1sHSF0SQiC0okWWA1msz8kLpjsrPpcnExRMoFa1adlbZJozFjeDbhyfMR83mOpsqsuXWC5ZLX91Y5Gy/Yf3KCYpRopklUJSiSQku1mU3HrO1sgdCQNR1Vt1kkBbJpUAnQdJ1MJIRJgKpJWDpElUReCoI0JS9L5l5MGDb4zO1VOvUWQbxk6YXYeoNlmHPv8SFeENNp/L/MvUmsbdl53/dba+1+79Pfvnl9V6+qyGJRJEsUpdBWrNC24NiC08EBZMRAJkkGziSZZepx5gnieJDEgwSSYsWdqIZmI4tNkfWKVa9ef++7zemb3TdrrwxuCWCUUBEiK+AHHOx9vrOwcSb/b639Nf9/hBtCPEmI1xuqvGXn+iH5+ZjQtjh+9ysc/+p/QLZ4gex0yZOMXifC7US8eS2ie1Dx0XlMt9sFSyEExHnFJqvxXEWDIclyjHWlSh0oSV1VrJKUwHPwPZdeENLokotxTloW5E2AyFviVUkUWCihUEKTKYUuGopSEdougRDEQpAXBZmv0KUBzdU4s3/VSbpISupC4kmF7Ri0jq/0GUyDJWscR5JtDMtNg2dZuEoSeJL5piYtWmw0rZAUVYptG472I86oeTVe8OatXbaiv6BmoX9Tpg3Ykcc7D+7x9OI1rtGEXdhvt2llhmu5+K7he88veH56wdG1LrbVZTpN2d3us9zktE1FN+pSmJq9UDLb5CCX3Lq+x4sXDZqYrd2QjmtIpku8aMgsyej2Nd2FxZNXDS9OC6Ro6YYWF3HO/bcHWK3Do/efoy2B4zokaUOWNQS+TWVatAJpDMZoVuuUUSQYdDpUBiZJiSUUxtIMuxZpsmYw7LPcxCSLK4myl+Mcx1booCJQLaHt0XM8JssM21HovOZ4LyTNWxbLjACojKDnepR1Td0YXl9mdCKPG7eGHO73eH76hLaoqauKyXxDzw8IpE9ZJPQGAVWmWaYxGTm2tNjZ3sXLS7QVs7Nn41k+R3s1g90Rv/voJdMiZ9R1cRxBmmp2t3yMqhkNRqzH5wz3Qp68PGW6jIkiH9/xcDwbIQRGC3xbMey7lI2hnDY0raLWgGWom5ahDemiAAAgAElEQVTHp2sGnYj+YJ9SJ0yWJav1BSeXGbrV3D3eo9P1mecJw62K5fgVl7/ze+zduc/qww8ZHdzi8Je/xvrlE6yujzPsU5/P2MQtW1sDdFYzXklUELC31WOR5JxfpLg22I6FwZBVBZbr0dYtQkhcx0UYi/kip6gK/H7A3nALT1RYekNuQVUVVIUm6oaUaUwSV9zu+WRNDoGDrg1eFLFlG17NN4RhiJY1LQ6R51CUBa5yCX3FZuPQCVzCjqJpM0JLgyypDCSVRhfQFCA1mFYQdXwMist5Q1M3eLZFZsCJemRFgmu79LsNlgzIypjl7GdcfMSyFI9PTwjdA3rdiHCk6PW66Fax2byiaQyVXuEEC6Rj47o+rmcjmpaet8/pyWvqcsPWtQ5lA5bbEgaCxTrl7HJGXmW8OF3y8+/dY9Sx6FQpo57kZLFhZxTQ8waML1/T6XrMVwVt0SIjh/3eLo0I0FXJraMBrtE8f7XiYOShPEXTCKRsKbMaN7TZ3orY7g3QraJMC462R+zvGqTUOJ5P0ZQk+YbLScbpSUrdQEuL40gQNi0G4SoiVzGODcJu6UQOHc/h8CDggz+6RLQNlt0iRcu13SGT9epKlEIoJicbghriTUFVNrQkrPOCWwcjbKkpuRpJbRqbstmgMkPH9zHKwYsivKJht9vn9vXrrNdjfvxsTtJWWFIgtcRWsF7mNEWDczsgsFyens4ZZRWjrQ6eLbCUwQt8jJiSJiWdrseo2yHPU9iyWCUx60yAMRR1DQbSouFyXnE2mfJ6csmL04T5usS2JLcPIro9jzgvyCrN9shjvHjF2Y8t7h/9Cnff+xL+7c8xffyIk//1H/Hw7/x9hrfvkZ9fIhrF9WvHvDp9Slo4XD+MqOsr2vG2EpTa4AYujnOl9ltVDVVTU7QV0kCobFzfAceiF3mYpkJbFo7tYnkGz1Y0RpAWBUmiUULj2wpHtyjbIkkryqom04Z+5IEFNaAag+8rTO2QZS0dq+VG1EdZLvN2DbpGCMnAdsjykqQCq20JpWB/K6JpBWVdsc5ztDEMwxBLCCy7QimXLFFIZdjqRTRac7ks2SQ/PTH4MyE+YluSbrfHxTwmzWo2uWQx3TDsuex29llsNIluuH+nz/62w/bQZ39ryO5WjySbEfolngO5TtEG5qsaRI3v+pyeX9CKKY6vWMUbEq15FedMlxt6PcnWoMfOTsjf/dUD/su/8y7vvrVNnld87XP3uLtzwHff/wSv65DXLS0FrmMhhGAQSh5eC7m2E9IJbGxlrtpbXZscjVCCLG8pGs1w2MP3I6rCQpcOaW5IU0OSa1qtMRjCwMbzPGqj8foWo4ELShH6knc/c5Pt7hbpOmNnK2DU91gnFVIJ0IJeqBh2FYvXS5589BqMwfVtPNfCVgojrgZiamM4OZ+yXqVIPGrtkBSSwPXY7fXwlKBB0umEV000l1OijoXvW1iOQdctVaVJs4qLiw2bIqexAl5NMtrG8Pk37xFZFtPJGmELyrJmMBhyvH9A17fohRaOK0irmqZtaTRXCr1AUlS8PFtyMYtJy5r9vYDPPOjjdRTTdI2QFpgWU0NeZyzSmNmjR+x+6WvYoc3Fv/xN6qaluJgQXb93BTajOTg+pLOzSz2r2LVdOt2A67tdbh71sGzYZCXagJSCqm5ptcBqJbURVBZ0+wG7wx79IKJsKjZNQSFqQtemF/qUbUWWZoS+ouPZnFcVWW6okoqOtKGueL3JcB0P11HQSpRrs0xzilSDsLBan8DYWL4DSESrSOMGHbdUuUApxcC12Ot5uA7YrqTRmlHHI3Qkyrma0DStoK1TQj8gCh3yuubZecp8VbE7+hnnGGyNYdAL6XdHLF6/YpZMaHJJmuUcbx8S9VtWdQwy5OaNgKqpqOqcXjdkuloS9itcz+UymXN4dI+3Du7y4cePEBhsZbgcJyRFyfn4kmFXojyX0/kap9ujSRT9rs3tBzfRssO9WyPstuXdBzf53e+94MXrFXv7LuPLDG3gcD8gXlUsZzVISa9vY1veFROyFKySFbNVg64E44spxm5pKk2ZwicvZyAMCIWyJaYwhIGDkC1KNLiWjRES37s6yom0RCiHT15NOTvbEHQc7ryxzS1l8Tvfeclkvbx6pw0skrxGyytK9E7Po98PyIuSxkgsT5BWBYtNQq0zpGPje31qUeGokK7bJU0XjPo9lIHHL57w6mJKlhTYbYPf8+h0FEVWYBpDUzWs4wzbEQx7DgoPW/ro1sF3uuzuSKbjkvE6J8srBC69TodWFESeC9QIAyAQAnRTczabUZNwvOuwu92jFhobQ1MaoGWT5Yy8EGW15GVO0miS89eUWc3m+WOqokRFPdYvP2LwhXfwOhFdd8jo7XdZm5KTJ6fU85hwOyQ6HrLJMxaVS7mqSYsSbTS+bWM7Fq60aRUUzVUloK4q0IaqKbGVhevZdIII0RQ0dU3Xs3AdSZ4YhNJYQNUYbOlgW4pFkeLLKx1FW7RkLZi64Ua/y9aOi2wc0kxitQLTWpSNQuiGJq1Y6RrXAse10baiKCEuakByfS+iqRrs0GVTt3RVy3RVsjtsaExLnFRMZznbox5B1/+p+PuZCAICWMwSKq2pK4MtFLZtMVtmWOqCqNOjKUPOxgW+7WGJltfjCZ+8tMjKkutHEcpykEELzlXS6XxasFzOCCMXxygeHO6yymNevJhx69Yu/nbE+LxgPU14+NkjXkwrVstnDNwee59VrLKEP3z0lJ4XUOUNeQUIg+sa6lajkDw/jenEDqHroJRHUSpenaUkSUHXVziuRVwaLsYr8pXkcpKRlDXbgxAwCNHgeB6Rp1huMnRtaITN2fmKumhJW8P20GW6WLFKEkY7XS4nKfcejLh1IyLOalqjMUIwX1e4tsB1LGhh1O+wzh3iLMFyW2brFWlZ0e3YWEIz6A5pdMl0NuUky+ht24wGHYwxTJYLeh2Jc2Ob6fgS4di4jsFzAlQLxijSsqLKBW+8cUzo2QRWQNsarh1cI21THj8eYymJkobJbIk2Do6l2e55BHZK1giMEQSOdUWeGVkMt1wGoUfHsZjFyVVFQUhCz2VR1xjhIIzCdiQ1oAVks1Oyl09pLRcpHTaTE+rJgr0vfoWmMKxnM9jUvPXmfV69PKFa5pTRlRqP6zhIVVPVNcoCyxJIYWHZNkbXtAZqKWlpca0K6QikaHGFQ1NV1Js1vmWTm5amaSiblkDZ2EKglUE6FrYjCFxBSYM0ilY06LIlclx6vkWrMyptaBSsV2u0rul7NnmtmeiCViqGtoVoDMJqcaOIIFIoUWM5Nr2eT9UUCG2T5gVV0/Ls9YzAd+mENhaaqmp5PdU/FX8/E0HAsiSRE/HND55SlaCUx94IDqKAFycL3rjvEVqK3IApK4YDl6WE56dTjG5IhwGWczWJt1znvD55zGKzJPAlrgfdyOLu7i6fvFQskxmvXk852N9jvkwRTczOZMxsCQc7EYf7Qz4+O+Fi8hHvvDHg42c5l5McAVgSQg9sv0MSx7i+xWpVkVkNjtJIBY0oqNuW0AvZ23IpZ5rxJEY1NkKClBZppVFS4Sqb+aqiDB0mS40vbExbs2wa1rnAdcA9VuzvdBC2ZHGx4SjYZh5XjMcxnic52u+QVS1Vk2Eag5IWWVwzGccQ2HSjgPEkJ7QtDgfbFPWa9Srmw09esDsM8TxYpxucagdslyxL8Dyb7VGAH/TpdQ2rRUXgNAy3D3F9h9dnF3hJgiU9LicZbz24xkj5WEqijUHnOVVR4bsejpS0WhP6HYzRHO50ub4seXqRILAYRgHXrrkMB1dgs7RFpCzo2Jw3DUJc7c79rmGVJYjUYnffR2uN4weUqxmriwkIi7zK0Rgmf/gNDn/1b/DyG/+M1dMfU+Zw4859ultdPvzBRxQnS6KtDj3bZq3ASIFtSarKoAQo24CBTuiRZAWilez0u8SVRjclZWuIixQlGhogrTS0mlJrsqTERhI6CsdtCGyJ7Adc5hrfXPEEZEWLdB3WSUZb1TRUGJGxKSt2wh5H/ZBE1mQahHLwlCAvC2xliERNGEU4lkutK3xfUS9zknWNdAWDjs+jp2OGtaLXUYSBoMgzTOv8dPz9/4b0P8VMK3j/oznn5xvq1qJtS5LUwjuCeaoxQOgGRFsdnl1MmMdrOp0+xwd9luuStBQMQsF8kbFarljOSxxPYPsueV6zteMSdSz2tjz6ww5JIUhXMb7vMZstMDLCcVOODvaIhkc4i4zpZsqW69BUGbUQuJWhaFuG1yK2BwEfbNZcPxywWdes4wzhQJ5WuKEEDUnaYFC4UrEpKoQwSFvgSciqlp3Q5WDgcZGVnE8qkkJRqJodR5GW0BqgqJFAlrX4rkOr4Pada8jWcOeg5Wwxxgs96rbGNKBagRHQSMnJ5YYSQyd0sduGu7suh9civN5tNnnD+fmCloaH996iqGo+en7C2bjGdz1M21KUFY7KGG0NSLKYwEsRskLaHe7euEVVTnn88Rl5LFjPUqKe5mw+ZZ2XaG0omhIjHeKypFf7dD0PJS2iwOW9t7vc2F9xPolxPI9eFOG6hjhdQ1NRGUEpBJZrkRYtddaQ5iVCSRpj2MQNjWpRYZckTRiPV9hhRJFnWJ5DPXlF25QUyym+HeJ1fQbHRzRVyvXDA3RTktblVbfhTodG16wTjTFcybMpQ1aUyMygG4OyFXXj4sgaLWGjc2Rbk9U1WdPQGE1baRxLIpSF1LDVDWhkjdu0DLYDZrM1Rhtu7XV4cZ4Q+gYbQ6JbpATHqohQeFjUlcWgO2C/SRknKdO6YifyGPgwXyU0lYPRirJtGPQ8qCR5VZCn0AsctgcO+8MOYaDZP+jQVgr1pyD9ZyII1JVhNk/wPZ9sWbGzZZNkDY3lstV38ByXrIDVIiOuc6pCczZZMOx5jIZ9St0Sp5qT05jTkw26abl+fYtk0zCeJPTdDk/aKd2OoisiMr3h7DRFCZeqEXz8ck2nazHLFImZ4Dst04uCtK3QlYa2xZGSohHMlgVbQcsocpEI7tyIePZK4DhXlC912WILi45n4YeSQycisgyboqHCYZ3URB7sdXyqsmZ/p0dWlSyTJbaUrEtBLQWRLaCyeHUeoyYxx9civMDlk9NTdrsRN65HWL2Gk0lCUQqaxlDWhgPfwwvg5PUaIQ1JlXMwdPB8SSMEqAjHrel2c5arhEdPXhN4Ia/PUj5+OuGdN2+h25L5PGHnjW0cT3GqCsLAx5Lw/OUZkRfy8M6Aw72YVgXM1zXb/YhHz56zzmt8t0fY7RBvoCwr4nxN1HVxPI90k7K/c8hWv8di9RhHKnqOjW5TaBuEgERctcvWBoZdj/Ekp60tfKWoTI2lBJ4r6O3tkds159MlfQSO0bjC0H/4Ds7WNkGoqGJNb+8GLoKzi3PcVnD89s9DaHjxvW/iNZqFadB1QeWAcgWNaYjzmnRT0emEuJFNVbeUTU1eFQglCDxFpcE2gjjTCCRKKjxlYymNoyTKVbSzmtDAKHShuhokm8UF0sD+dsDZGgLLR5uGsjV4nsOqqFiUBfM8vjp9aE2pDUUtMEBaZijhkJQ1w06P/kCiV5qszjmdxvSiLr3IJxx6GKVQjUJI9VPx92eqDggh/nshxEQI8egnfEMhxL8QQjz59Dr41C+EEP+tEOKpEOJHQoh3/9+erzE8uNNnZ9AlDCUHux6eJ6kbzXie8+3vnnJ2GvPjV6ccXwvpdgY8fbri2aslaZny9PmS3/3Gaz56toIKrvsh44uU56cbpvOSH3005uOTKRfzDKTLetVwPovJ6xTPcri8XLLeSL7+nY+Yzl9xsD8kUhaXsw11LSizhqw2GCMYX8acz2KiKIAWHt4f0Q9dFouStpU0GVhCMugJhkGLrQyjvs2XP3fMG3eHbA89vvrubW4eRliBZBAFvHV7h62ujW0J0lYjHYESV8q05+OU8Szl5YsVCMPZ+ZjvffiSpk3pdFuqrGIxi9GfUmCXdUOrwRiBDdRNQ1xrRNgjbQN+9OiU549OsKuWbhjwgw+f8s+//l0ef3JJXRlevDjh/HzOfFnzybOYPNP80mffxDYOQRBx78Ytnp2cM45h/2iPRrSMdvc4nV7VyQMv5Pa1I3ZGu5RVgy0FtBm1rlGOj8IwX655fjohS3OOt0O+/NljfuW9N/jqZx+yPehjOS67nT73d0d88c41vvKZ6/z63/x5dvsRpnE4P89xR8fs/uIv4YUBZZ6SpVcCJAef+wq9L/4S6x9/xP67f/kqa390C8qYMFL4rqB/fIuB73Hn1l0eHh5x5HvsDlyEMGR5RZIUlFlF0xhqbbCVg23DOk+IyxqpNdUfT/YFLoF1tZdWeUVbaDzPxlItgaUodcNqY9jyuzhSsNqUWFLiWh7TzZXQiu06FI2mbqCUkOqUeRJTorFdgTSSdF2z2dQ0tSarik9ViFukAhSs04pNXFGULVHg0doGZUl0fZU38r2fvt//WUuE/wPwtT/h+6+B3zHG3AV+59PvcMU5ePfTz3/KFfHon2qWpVjHMa3R7IxCwtDBtIbx2Zr9SLDruvSclq986QbHw10cY1OW8OxVxstXCfGm4uw8ZbGqSXODqQRpXHO5KFDKQ1mKXhCgU8PlpODFqwTftzjcCzjY9ekGHfa3B3R8wXS2ZL2YcHx4m54PXR+O+wECw85WyM9/9phNWtColsP9HueXC8JQ4jgtTiixQ8GNaw73bobsRgG2kPS2+mzvj4h8G9M0dPyQ0cDjwT2XvNowWSw4Hni40uDYGomh0YYWELJFCMVsnJPnhr2dEX6gEEoxnlQ4VsPBjk/Xt4gCxWyaMr7cIIQg9D12Bj1uHO9j2R5nkxln5xdMLxdkVUu3N2BvZ0Cn4+AHEDqSusxZLWKSZcb7Pzzn6ZM1y1lFWrcYItJ4hefAd/7oOfMYVsuKRx9/9CnphYdn9zk63GPQ9TC0BIGPbRnqfE2VZkgktmWzP+pw52jA9eMtpPKoyxDR9LDNELcdstc55Kh/TJVa3Nw7Yifssu1vs1gVPPpkSuZsYYchXn/EcNunLXJ23niH8N5dPvrNf8TsD/45luiw++/8x4R33qD37l/h1t/6T3j4t/8e/fv3MMkcL8u5feMmt4728JRBCkOZFrRVTSe06fU8tG4pas16XuBzRYoSaoknJE3Z4NqK3YFNLzBEkaKsC9a1oKLFaMngaJ/asomXCbqBuhb4BkxVcDJPyDOLrLLJ0hrPDciKlDhdoesSW4orFmOvYZOlnMxrWhFiS4uqrAhdF99WiLYBA1LY3DoasT0w5OWay4tzFos1bhBg2X9OQVJjzB8IIW78Cfe/C3z10/t/CPwe8F996v8fjTEG+I4Qov8neAf/b6ZbTalrvI6DSGyKvCGwFHePQ7q24uaNHqPtPXAFVqOolitsZXMyLVitY0LbRmrJOm9xvJZiRyNNw8hWfPHhIa6qeT5Z4htJnF+Rj/SUQ9R1eeP2DZ6/OGP/MGR7y+L0fM5mUuPLhp2eyzoDR7qcr3OKpmW9TvFtC1vUBG7D2fkG27HZ3VacnGd0Oy5v3h8yGrrQSEYjgeVEnF8mnL26Yj163jtjOAqIs4aoI1ilNclSc3wUkiUNi7imag0GiSNaOgE4YZfZpODGNZ+/9N7P8ejpR3zw8ZjQs9na7rLV1rS1ZrHWWNKwG3i0CIRqsV2brGxZLnLWmSY3LavHY950Ivb2eti2obvOCG1FmjcMO13G5YphKNgN+3z/Ry+RoaY7rDk5fYaS8PLljDLLaBt4PYl5cPuYdW6h1zlPn73gcP8ao60hTSuRSmGrlrTIScqSyGk43B7QNprVqmC+OkMqSZ6XpIVGt1DXCRfzBtEKeoM95sucvMjJsxyt4fHLc57803/C1t17DPf3SMYFt37hPX70z3+L/OQV+we7bD74DoO/8mvE3/oN8tIQryZURcvOm19g60u/QpNU+Ac3cF69z2XyW5zPn1JaEuEohNLUVYNuDLPplMtcsjt0UUJQNi1hx2FoNZQ0WDh0rIbCkhhskioFYzEMXJS64g2oauh0PBZpCmXNMjNYQlIYQ5Kn2Jahba4k5SIvoEaTNA0DX9A6FlnaMlnXHIg+Hd9QNwVSQhZrPN9hf2fArVsWg7ClyAocIUnygiQrGU/XBG745wsCP8V2fwLYl8Dup/eHwOlPrHv9qe+nBoG2ba+OMk2BTiWztubNt2/w7mdu8fiTxzxbzZlXOXXWMIh2cbuCRghW6wohJZmqOOo5PDiIOFsWrJoGJ1B84c6Qr7xzyP/xe0+ZTlKGXZujax7ORmB0y3ic8cZNm45do82c/e0DHlx7yOx8SZKf86MfWyzSEkFBWhrkOuejuGA4cBBGM92saIS6+h9GEvout6532d3q4TmC9XrJZlUwX6/x3ZaH17s8uHnI2XjGMtnQD/soueIX/upb/OAHMWfTU/z9Dp+8WFM1hsUqR2hBnjVEUY0WBa506Hoh6aYmSTSTRcsinvL2vSFVVhGEBf3QYyc4YHdvxMfnF3xyMsOWLR0Pru2OaEzLycWC77//lKZtODyIGPUMHc8gkoBR1+b+zTuYVjNbLJHC5+XLM8JwjpIOSmmkMJDCxXxNbSw2q5hNknOwHbGaxCTpBa2EqmypPUmlK9JKYQApJHHWMl/HV68vwsJSDgiBbq8621oNtS5x7YA0TRBSM13NKHWDsgzf+P73eddb8Xk/4v6XfommFlw+e8z5xx8TdXtoozB1TTV+zvzDP2KdN2wWU1rdYquQwd1/j2b1nGKyYHD4Np95Z8zLi0vyRYq0QSmIbMWkLDCtQlgNq9rg+xbjPGcgXXYjyTQtSZcNbVGxsSS+GxDaCieUVORMl4Zu6DEYuSRlg66vYKdsiWgNeAGWWqPQxOWGmZaM3AjLcVBthsSwSEqMhOF2QEtLWUmkPWQ8W+DtOeiiRSJQ1IynBWXjcfvaIefLR+S1xXS6YGfrpwP530hi0BhjhLhq//iz2k/qDnQii00smC1KwtAm8m0+fHKG45V84eE1TsbnhF6AHfTZlAn40Ov4XNvrMV9X6KpC0fLgZo8bd7d5vVzQ6Rre+dxNeoM9bh1lWE3B6GjIpqlYvb5ENYZks2E0eMwvvHODcZqiqLlxfR9w+PaHj+l0fDa1Jq01e32XsmjxpKCuC7JCMZ60YEk810YYiWOHPLizz3G3T2BJztGMJw1bnYB3v/gApRpW8YLdvW3qsqQuA3YPrmOUIWtek9eadp0RRDY6ztgZOdi2RZ4rpNXw13/hLZTVYKwp24HL4cjlZFZT5ZrLi4x+36EbdAk9GyM1D27v4fqG+XpCUrbUWuH4DoEj6ecJF2cJQc+lynK8gYMXNbiV5sGNB+z0BoyzlIvkOa9+/JJFUfPBo1Nct2WVt2ySmipJcaWNUS37XQ/TNvieTRWvePL0OV01wPIHGAyurVDSUFWapqrITYpoNXVZkRUpVdXSCgvHsXAdi/bTLrhJHiPJqaqWR89mnyZpFZ+cXvLRWcTeD7/Pe7/+d5mcfMw3/rv/DYSFbgW6lVhhn2q94HIyIa4NpijxXB8v6jD53r9g/J2vs3P75+i99RkGreDte29z/r3vUZkK11aEysXZUqyzlp4TkLY5jiU4GnZI0opFUmKlLXGc0iKRykJKjeXbbA89yjRh2TRMNnMOnBDHcxFlhEbT2C1KSkYjyZY3RCcF9XyFURaVrtgUFauy4jzWRK0ksASlZ6jqkrwKcDxFVpQ0rU9dQasN4w2UhcX+0ObVLCcMuoRORZxWKPsvZnZg/MfHfCHEPjD51H8GHP/EuqNPff8X+0ndgZ1t3zi+RVloGuHQ6UoOdnrs7Qxwg20Wq5d0DwRxnFCIjPl6ze6upGoC0kozTVtKzyNWECcp01nK/sEO603F+x98F4nA7wdM5gkvpwtEXTMa2URRROA5JDqnG3b5+PWMxea7+O6AxXJJMPTIpyUGg+MIykaiRU1k2ShtYUqN513N+Tu2oiprXjyd4C43YLdc5i1aCz7/1g08d4dHzz7k7PyEUeTw9p1dDkZ3qLRNYQreON5nukrYZBkCH90I+l2HNK7pRorQt5gva/rbNb1hxI27R5xMl1c7ZVmx3w/oDj0W64TdwZDj3REfv3zObJ4wCj26kU2pW9KsoNlUHOxEtFULKPa6IVbeEPQCbu61KM8i1jWNNry+jImrGi0l83mKsvRVSbbrczrJeLgbktWa60dDNnlCkRYEkY9Msqu23DRnZ2RRNS2R3wNTUFUlvhfSjXrE2YzLZcYqKTAYfN/FUYq6bmnrhqOdPicXC378csbFosa2Wm72Ya1cFmXF+PUFyeScj779LebTBWGnQ09YqLbF3t8hySfEy5jScpCyodfdpn90zPLJ7yMtFxm6lNma+PkzHuwd8/rNhA8++SGR6zEKbKRymWxKkrbBbiU0IMqGKqvRssUTNV7ksEk0oyjA7TmkpsBuJbWU+K5AuC6JAbdtwRHEWYlpGlrPoS5tVrWiTitsy0LTfio8KvFsRb7WeI5N1RjSXCOERGARuIb717bouB61yghDl3qWs9PvEDoFm+qEyO9gRQUfPV/x0Yu/GFKR3wR+HfgHn15/4yf8/7kQ4n8GvgSs/7R8wKchgf4gIIwWaKVYLAu2+pInT+Hpsxl1U5JuXjIYuhgp6ESS1UqwXGR0bIW3E+CFEi0lRWUQBtLM8O3vn3BysmC33+EL7xxgxmu+eH+H46MBrWqxpSRyutR1iWV8xq81F/yQyDuiWNcc3A6xLUmyqvBCRVHXeF2fVVHgeYaDToAuId80lM2VOMWMnM3Cw9g16wxu7m6RzlNOn36DP3xyRlFJzK0ByC6Wt8XFfMrxzoij7YCm+oivfuUhk8uaf/avPsT3Gw4OQ85e5azinI+ervlbX7vLv/7eOeNFxcWs4s3bXa4dX6NNWj548Zr+KOJg22d7O2QmG5LzkoNBl7iq+bymCMcAACAASURBVOQ842Ar4Atvv0UYujwePeHFqwnH1/YYeh2kVeGNXM4XU9aLGl3WrBYZ00TTGyksfNZFQVa3OE3JG3seRkry1RUj8PWDESeLDevYJnQCbNdivahIUkknkEhh040cxvMVzXKFLSzKquH1PGYW1xghEG2OKyVaN9w6HOF3Is4v5kSuxVff9Ng0DdcPh3h9C6tyWK4z1qcnrE9f0bYKrSWWK3GjEO/4DuXZCi1LZAOeEmzf+yzKM4jNmmgwwAl8dJmSTM7ZckLe3r7Gi9NTknTFfi/E82wcS1FT8ex8jTA2lmmxmoZSGoxno6uKXhTRH0bMyiWRcpitEnAsAunRD1o2raRMDEHQshUqdKZJTcVkURIIge1bVFWDoyEzGj/w2O/bREh01qBsARUUpU2SLZiNJW/e28FyKrSQZLnmaNembTOQDtdH21S1xLEE1w7XPHn+52QbFkL8T1wlAbeEEK+B/+ZT8P9jIcTfA14B//6ny38b+GvAUyDjSqX4T7VOGOLYAVK4NFXJnc/ss5ysWW6mqMbh7u2I42tdZuc5Bwc9vCDCkQ1ogW1HrLMVSbJgPJ5RlgbXhkEgObq1j2ps4nSO2zrYlsvNgz7d/tUgx2/900d89t0bKOHz7OVHxNOYz34poqkz3v3FPb77rUsGXZc3bh9xcjKmY7V0uhaNkuhaEK8rggB2BwH9YY/ZfMNqonHckGmSMVkmjDo1jvb44q0dfvRkwjK7qoWfX4zZrCyGWx3ef/QDPK35m1/+LCKy2e16/JNvvM90qen3Qq7fDvn9b664fatP01j8L7/xQzq+RZZLltcqtvKCyHf43MMR4HNrZxc3EEh8ltsdhsrmTtdlujlByIBFkpKWCaN+yKB/g7pxwDXMVzEf/PBj/uov/1skm5esNxOub9s0q5yu47Czs8W3Hj2jqg3Hhwfcjlx+9GqB1Qkp65a0LPCQVEISdgLaoiIMfUptoQTE2ZoGhWX76MaQ5DFg2O93KMs1Ycfj7n6IFDVREBD6LrN4ymCouHtrj9E2WNIiqyvSwsMPArJ4RVNrpISyFXSVhVfD4At/ickPvk5btdz86l/m2W//Br3du/Ru3aG4eELbcgVwI0nKnLou2ExO2R8NeWd7wB9dxJwvU3zLoTU1fc/QD1zK2lAYQ6MEykiyTFCVhtKu2VEFQ8fGk5KXy5TQdlGh4CzRSCOYL0qO2g6jLcn7sw0Km47vEbiCNC9Z5hXCOIx2BriqZV2VlKrC8yVeBbqUoCSRKyjzmiTOOLw55GKWUZgG11hUCKaLDYuVRSdwuXtrwDu+w5u3BH/wh+f/34OAMeY/+ik//fL/w1oD/Gd/luf+sW3ilDSdk64rnFByOd1w47DD+KJilVWcjDfsXOth211OXi7wu2uWGXSGLpfjDYOhzc7OLlneUJU2os15426X+zevcfvaLvPxOVXWcvPY5g8/OOHe/ZD3Hjzk4rIm/d4LRp2IHz+b89XP3sbRNapT8W9/+dfwxTf5xnd/xPX9EdPzGGNyNnHBvZvbhE6HdbYgHBgCT3D3eki51+E75SvWzRxjtxxseRwPexRNTV4GvHf/EMdtEb7AI8DkMxw3QAUtm7nm9777mIcPPaRx2dt2WG5axpMVb7y1x9v3Dzk5X/Gjx3NuH+1SFJrx/JKzS8md4wFatCjXxXE0k82cQTNgOo9Jy4aL5ZLdLGS773N2ueD3X55weOhx48YB3dAnWY4Z7Fwny3qsVi85Pb/g+sEx6+WK4yOXNx/e4vuPznBdgcdV30TU6dIGLq/nL7n78AaTeYwWGttxcYRGKMn2zoipSEFDoxuqBopGUxYFCEXbtvi+Q6/rU5xv2AkcdnZcJFeZ9ZezGCu0iBwLQ05R2vRCC9/SXFQZw96QeVzQNBIpFZYlsJUg7A3p7G/z5Df/d9Yrw/2v/TVWu9+hu38bGbgk56+wbBdjDK1UmFagq4wEg2cpfu7G21wYxcnpCzKrwnEdQgxlk6Fsnyy7IpJphWC+SrHckK0tF9tRFGXDosiIq5pRx6MUNefLklHgEfQCdkaKOKvYDiLGaUUv8DmObD4+m9E0IKRhsUjwbUlx1Y1MaVpCZeMEFm0j2drqczlbo43i9XTDfL2ixSXPBMOBz05XEoUutu2y3Kz4+Mmau0eDn4q/n4mOwTQrefJ8w9aejyU0lq548cmUG8chdZJiK5uvf/05d49HGGA21qRljOUHjPZ66Dwn6jhorfjkxYyDkUsUBqRljRE5vW5AGfpkZcVsc878ewvuHd3i3oNtZmlCUjbcutej3w/5zL0DXi5f8+1//UfEWczf/tov8ff/wW9jGUFkCbaHPicvx3R3Yn7xM3c4uXjNclnzWqa4tuHmzS6H/V26XoARNlGvoazmnFY59996g6ZRlKLGdWJezad88/3v8rk7nyfLVvzc5wMspenbmv/i177GP/76t7lcF3z0aIwlXZbrmjg759b1EQcDm8/dvU9v5NLzK3yrz7d+fIKS4HkpwpzzwcdjlJRsRSEvX63wlKS/HfKl995mf2+bl68umEzXfOWLP89mFfOd9/8Vs2XO81djlHTZGR2QrEu+8t7ncaMeqrV4NYmJ4zHnl69xbtwEKXn97Iym36FoGg6vdYmcltPZOUmaMgoH1GtDcUU1iG0p+jsD1pucJE4wpkHRYiQs04IsDej4sNjkrApN27Zc7wX0O1DkFnlToIzAKWvG6ZgkzUjS9RWtmmtjCTi4/5ByMyGZjlmOY5YvZlz78i9jZz71aoZIUnQQYUuFkhJLCZzAhUIje4ccfPnX+PzWIeOLf0he1+RlhchhXbS4fkOZl4w8l1lS40chw45PEHg0taZqBYU2dLshJQ5lldJxHYra0AkdlnlLswFl2wR2g1KayaIE52rwKIs1aVoROxY24Ps2KQbfsdnuOMw2Ge8/XmEZj6Ntm8mmIAxs5qscN/QpygLjdDBZwSAwvJouiesG/eftE/gLNyNI8pYyy7EQ7O5abCpJXOa885kuz55fyW9PZgXdjuL67T1ePA749reecXgt4f79PXb2bsAs4WCrxpaaT55MeHBXUmQag2KRZDz68QXrWULdtvzww1MuzitEIOj1A6bzBR/qS17PJwSqJnDOsTpdzl/XvLG3x/7IxZEWJ6sLdOuTriouVlOu7+/x/PUlB1GH3d1d2lbSdVsWVcbZcskasJ2aRbZi/TLmaHDIaG8bz+9yYNt4KmKnu8s3V6fcGHW4nC84TxR/4/MWHaX4cFoQOBa9geI//Os3GW67vP/+c77yhTu0RvPe2yM2m5x/+Z1zihrSTcpys+FgP+D20YjNRrDe5FzMYqLQxuq6TOcbNumSZy/mhF6Hk/GcqqpZZYb1RvP+B2cs5gtcZ0gvCjk9O2G52GC5HfphQGDAMjaXlwv8ViK0IS81QtpcvJjw8N4utlA4rs3edo9JBVmV4zoSqXyKLKepKzzv012/0+X5NPs/mXvTX8uu88zvt9ae95nvOXe+t25NrCoW58myKKklWZZnC0i6gza64QDJpwBG/oCGE0AddAIEAZIvRiNoIEHbSdxttN0e5NZASaQ1UZxEFotkkTXf+d5zzzzsea+18uEwiNEQ4UwIuD+dvYB9vpz9POddw/v8SJUmiXIKbUgw5GWJp2wGUU6uBX5FsVqrUrNgfzCgKAoKv4obtrDCAEdatHyP6uY54oPbxJMZmYoZvfc6F//z30c6mvF3/xDjuSgEjgGKnFqriVjdQFfWcLevEb/177hSqXPw6DP85J03SbIcVzhYjsVgOqFUkrzMsVE0WqvUGxYnvRFGWCRK06zWCMwiM0JaFlXXZprlCKF5OEpwUoNVhY7n08RwMkkZa4iNwPMctLGIk5SNlocRCjdfZEZ4oYdn+1R8j+WqQ9Pz2DRVCpUxmybYjiQq5iRJwmrDoz8X9KdzJmPFKz++94ny+1SYgONKmqGme6a4enmFXi/lC19osLbcYZIM0b6iUhXYvmJltUGnGeJec7HsdeI0I51rRv2MeJbgBza9kyndbp+zwYQkKVhb2yaaG+7fPsUPfJCKh90zxnHG5e0q8WzCrOth51NuHgz5e888QmfN4fyFbe7c6vI7v34FlRX0RgU3HpyQ5DPWmy4Nq2Q+GdMbz9Fuypk9IUokySBic7WC5dl49WVcy6OuxyAi2qs+/dGI8W6fumcDAYPxgFbD5uWf3uDxa+fZ7c757//sFY6OxnjSI5rP+Q9/4xEubK/QXPJ4/Oomf/Rv3mXznEe1mpIlNrmU2JZiqeWTZjEONrWKy3TWwwodNraXGIzmDMYJo3f3uXB+hSgWfHjrAMtyadYXeKxazSNJc4bTnMPjXQLLsLTk8PIP7hCGNvunKR0bTg8jmqs2zXqAsgKkTDAqx3Mc+sMJFAJZSE76fWaxTd1zEYuldbQWYBZZAo5joawEz9f0ejGDxNCpVOk0HObOjOlMMUhztBuw1XTYH6U0bdhYrjCjhrcZYTc6VMIKUuVU/QDhOySHD4hyRakFZXxA+tEHuBs11OgEy24ijcGTCruzTuVzX6Py/K+CgOE73+P09pu0Vna4VAn4cakJHYHtCopS40hBlpco1wLbIxIxaqbpVAMkJWURMI3n5AYcx8GyXIQyNIIQ17aYCgt/uclqJWU0SZiOFbntUChFS3o0fejLiNCxqNmCKC/BkhTaYJKS0PLYXt9inoy42x8tosyUxrYlrlfB1iGVisFgKHLYWGkSejHjyaccQ+bYLrZsMY5PWNlYQ8kxw0nMjXcPcfyCZt2n14s5d2GVPIfRcMIwyti8sIQuDdNxSpFMqVUa3Lo9JksMK80Wo2lBe71JnBse3usiSsFokoAPtx9q1lZtRrMxjbDCL31lmZbfZPKd97l9d07YWuG9777L539hh3Z1g2+/9hrPnV/n89e3ee3+EV/43AoX2nUSu+R4lnLYG9JZDtmqNyjCGpNZzMZKkyzJOZuOUXG+iDQ7PWA6jclSzUwEnPaPkKLPfD6H0uVbL7/Pcj2gP454/sVtbr83ZvvaJq2lGfvdhMNBlY21LTw74PbtHrZRbNZaPLpzjv7kDg3X44VfeYw3PnrIfn9Is+kSGofxWNOqB1giZzYpeeXlXRCGvDS8/KO7/PavPsbm5hLHZyMMAltqLMfh6sYGb755zDQpGEYJSmnOhEQaAfOU9XbAE9sb3N3bJ4lS5lmJmAuW1mv4ts9Su86gf4wqLOwwJKyvMp9NmEcTPNfDFuBKw1YzYB6VeKGPFprH1wJafsCdo4iJsIgKxfFRwXFmiD2JF/igUuZqxJ/+u+/wD7/8OHd+9haWt0Rja4t7f/6QNM5ouoomAqfWoIj62MagVImxLYLtp3B2rjD8wb+kPNjDa6wh7QLb8xn3Tjm33ORR3+Zn/QjLlmhtCOwKqyuKKC9o1UAkDusdiSM9cqE43B0ghQXG0EAyzAqqYYCFJE9jpNRseJKaLznuTrm9n5MaBztwuHyuwlpoaFRCDscFZSpZC3xEDXJj4fkCzYxhami6dXZaisnUJROauc6J4oQst2g1JFlucB3odjOMK5Ge+ET9fSpMIEoydk9HKG3zk1fv0uxIJsOS4bCg3qniOnMee3yDcTcn86ecnoBIEt5JC5LEsNyoIMyUYdaj5msevdIiKlL8HDpuyI2PhpzNZlQaHkwVEnBsm5XlKnv7I8ooZ7U251xnibWVCjc+6PLYYAnPcpAGXn/7dbJJybVLTYSdMQ/rfO7zTzJ9OMAXEZcvtMgeaHpnijgco5KS8Syj1IpRlCC1okhgHvu445hqaHHp4g6VRoOtec63v/sWD+7N8MKA9eUml853uGrlXLvaxhElO2ttnnvmIi//6IS//OYbPPvUmFk+IU8ld25POQoLzl+I2F6tE2UJo/kInZc0a1VUkZEkc6T2OLftsbnZ5GA/JU6HxElBxRc4xrC7PyaKBLORZnnZJcpsGh68cGGTb9y4S1YqltpVppOYNNNgNEpBEitU3INCIY2NZcN0FlNrK5bbdc5vblMmMDxN8PwKQVhlNBzh2A4CiRKawgiW2zV6iUK4Er9qGKcFj6yuMJhIakR8MC45TR08FzIN9dgQyZRCWdx6/03Gn3mB6vIyTr2Ks/koG1/6DSbf/2vWGxfY+uo/wjl/Ee4dk+Q5xlN4lTX8K4+Rv/ctuHWDUkgct4q7vIodBlhGUX/2a/zWM19l8Ed/QHc8QGjDdsfGFRWWLJtmA8oK3D8ZY9sWAYaK7ZHoktQIlOMyT+e4qWClUcHveORnBeVkzCxRSNdjWpYMhorHrtexQyg8mzsPpuydlZzrVGlULcJSUKYFZ7Mcz9WEzhTZEDRDSZQoHOkyHkJioDSSXDeotx3GvV2Oeimfe3yNaTL+RP19KkxAKyhjyc6SR72lKfOSK1c6FLngwcGUwQBUMcJxbI7OZlQ9hyVfUvE8atWAh7tz0mTGcKLY2nR57plNLq0+xfdeuk2STnFDA8ImyktKITCZoRFITvY1pDaJ1ozmGbt7+/zWV65QrQZUfcELj+6wceF5ZoPXefFRn/nA4r2HfXwU//rP3yDpx3zxq1dZ3a5RX61jG02WWnx0+wEV3yWNMyaDjM8+vs7t3Qlv3+rSWfYoFZxNBZd2lllprLPSaPOBTlBpCSgsKdjaWcMSFk8+tUoZWfRPqrz3YZ/9I0Wt2WU2MoRViyeuX6Q3jTB2xpc//zgv//RdjrsDlgKBZUssq0IUOeRacuniBlurPk9da/Hk9VPG4xn1ygoqzfj+a7eIdcLjj7bZXF3joD/gH375Rb75zbd4uH+EXw2J5jG2Y2OlOe2lGpZZZCSOI0XNd8izAmlKsCXzqYINQ3spZL5co3scMRyPOTrt4TsVQi9AGU2pFs0vVd+jXXeITUyc29w/i6npU3qpYKwVdVvi1lxcCUu5wbc8douSZkWzdd7ib37wA66sb+Ft79D9zv/E7DThl/7LP0QbwfTVbzG59RrtX/9H6NEeycNDgp3Hmd1/k/yj9ygrLYo8xzeCPJngGBf3+V9kNHjAUin47V/9Hf7wr/4VVccQOjmzRFOvCHxcbgxPKDKHhvEIbIhMjqVKjDE8HMxxrUXLb9sqGM0t5knGMLeQGk7mGUudGtWGxPMtDuY5r92ZcNIrcaSzOC+DJg0Mk7gg9V22w5DQcziOh5STkuE8w7J9MlVw58EUadfYejygZVmcZoKlqsXpOOfgJPpE/Vlf//rX/38T+ydd//Sffv3rzz7doOMqhnPF+qZFtxtTrQiwNKFvsblR5WyYkGYKPwhora3SH6TkCoaTktE4BSRKGzZWatTdgON+j4OzCUlakM4XFUCeG7QylFGOV5dYtkOUarY7C+T06XDOlct1kmhKK2zz3R/cxLZjPvvMU3zw3i5HvRl+aLO3G3MyTtl+ZIvZLKcS5Ky0qoyGisNuytZWlUceWaJmK65sLZNi+ODhhKWlFrNI8NG9HrZcvCy7R2dMZylxUmBLl1IXeHbGxlodjUU0qXC6f8QompGkCa1qQN0WOFbB+fM1NlcrBF5Amioe3Jlzd++MsOrhuzaWFFTDCnmucCzwHJu15iqPX7zAWmuZqiexXY0U8JUvfwbLlPzo1T2+8JlHmQ3P+Msf38X3XWxKVldaDEYxeaqxHYEQKZaRHHYjLm4tkasMVxoqlRq+5eBYMI/HiypBheRFvqjCLA9LSmAR5yUlKKPwHMF6xyfKSozQaCxmqmBtuQWeJpqVXG3WCUpBMop5oCJsR9J24JWf3ubZ609z8Yltjt96hdde+RHlRz9j+s53iQ7usXTtOaz1LbwLz2EFFdJ0yvTmT7D8KkanSA3B9nX8J76A/+gzZA/fJ3rnDZTnsUSKlZxxPDwj1y6OpRmkUwaTOXkhsAKfuU5wsIm6M/xWk0gVeLaHZ3mUKEajiLkCy5JEmYVTcQh8l4YvaNfg5GzCaF6glUUjtNAGXCPwypyBKohdSegLHEdSoolLSErwSoEdVpCWZjou6PYNh90+4yRi1I/peE12JxWi3oiD3vzk61//+r/49/X3qTCB/+a//mdft7QFUpBZgqVln7PTHGPg3v2Ees1mueozHmVIqXAsG0c4jPolRWIzGidIIWk1HVZXXNY6AY9falLx6xweThgNMnBASygKjbRBKcEoKjBGc2m5wbNP7tAdGu4c3qVddXhsZ5tpNOAzT1/jndsjomhEaQQbqzUeeeIyloiIooL7t4/pn40xwuJsHHE2HjPqp4SuQ6Me8vjVNS6cX2MQ5+ydRRydDCjyhDQDxw04PJtz5+6Q0LUI/YCsLEAKlpdbXFgPSSNDoUJ2j7tMBhGzScr1yy2q1QZPXt7isD9gNJ1jjOGj+0PeeHeXs2lJlhXYtoXrONS9OltrHcKKjx94DPt9Hu4dYDsB7doqeyd9GkHBufUmWAtDOLfSotGpkiVwb++UjbUmlqWxXXfR5lxo4mwR3VavVgmqDpal0I7NUrvC6oqL7zpcuXyFdrPJ8WEXz6tTqhKJwLZtDItKQBmNMRrLkoSBoF0POL8SokyGkhZa55xfshlNIyazAl9rJq7LxIEiThFpycPjkmeuXeaxnTbvvX0ToyTrbRtElUZrneDqs+R/87/AvMR9+otMXv8LimyBPfOdgPq1z5CplPmbL+N6ATjgn7+Kt75BPhlw6bEXKKIJ3dMj2tUKmXIodQW74TO4f4DtWgxUhiwtlMixKzYaQCpWW22kLimURWgpxuMZwpJsNzwurAZkaYFlWQgNgSvwQ4l0NHFakmChvICVtSVaoYMlXNxAMp3FoG0qTkiuSgIXmsshnVbIlfMVKmHO/cOIu8cJSk3ZuVDj1p3Bp9gE/tl/9fVaxaG0NWsNi+7xHGzBLFM42MR5wWQUIxyBNA5ZVnB0OkaWNheWW1xdq7DZtjh3vs6Xv/A0SaI56fY5OBsymS/O/s9izXyuwRhs26YSCFQuMJHkN59fRTRq3PvgjEol4IWnz9HqdLh0fpP4/jHaznn79l3eutVlba3CubUWXrWCzkLuPTgiLwq6gxlRHtGoSrKsJM1KlCpJC8EsMZwNUqbzlDguuXx5G8c2DHoTuoMYlECVFukkpTCaSt2l3bI4Phjz6tsHtMMKvXHEjTsnbC1X6fWnfOa5i/zic1c5OI2593DMtZUatmVY3mxx7/6ELNWYHIzKsEWKdF200bhugB2ETKcpeRbTGydMkhnVhs/x8UOSWcZmJ+SNd/dYaixhOx5bnYCskByNpmh30RDl2CX9acHORpNffnaVWw9i2o0GD3sxj11eQfolZ4MISUSUD8hSB13YuLZhudWmEviUWiOEoSwVAtBCkakMR2qWmyFZCqHvstx0FjsJFY/jaU6t4TAyBZbymcUptg3V0Gc2zGl4itP9Q1qtBtWwjmsLli4+je9MmO/dZTaP8MIKanaCiTJc3yc4d5ksnjL44Mckw1MQHs7GReZH91FRSf3ZLzK9+zZrQRWkT2/QJ3EyplJiF4AryKVAaOj4AX7Nx7N9sjhhZ3ONy2sVDvsRlrTJS8VsBJ+72qY0KaaweXqnTiJKdKmJipws17jSZZIUnE5LQsdlo12hXbHQH9OdLcBWmnmW4diCuoE4N1y8sE4QCgbjDCWWELbi+etVBrM5d+/Pfq4JfCrWBAqtmaQZbiGpeRb1JY/Dbkk6F7zwdIXh0DAZJeisZL39cXeXdjk+iXhfx9QrHhjFileQJSdE0Zz3PjxF2g5paYjSAs+ROEIuSs2ywLMFtiuRUvO9V/dYuyh47e4JVU+yvhbguUNWlyUf3phy/6zPL764weFZj298/wH3Twc0nZAfvHVCq24zjjPSxCAd2Ohonn6ySlJIbn004oP7PTbaa5z1ZwzjjChKGI4mpElOWWhmY4FfWZTDjbpHFCcImfDk1eu8dfOAg90+m6urnHZjVtsNNrfrnFvfodpu841XbpFmMWiLhwcZX/x7l9kflfysfUqeZCRZyijx2ayGzHKNW4Aq5qQqJ/AVrXad3mDIcJhz896Ipx7b5N6DfTZWMpyq4huvvM7Gcgtt2dw9OCCseHS8EIqY3HK5dsljOBqTpHW6Z302N+uc36myvNXgzv0+w9mU8a2Eixc3aNZszmYxlrSARSqyFUu8oEqzJuiPR0gMfuiji5T+2ZxS2LRbVcimzMaSXGvWmlX8RoNg2COJY9babUK/YCWccnJgkLakGXhsLNXRGsLAJ1jb4Oz975EWAs/T6PkYz/EXHInOFvH4mMmDO8gCaiuXcJZXOfnpN4mO9nGx0bOE6lPPIcc9vrR5kdqtCq8/uMW6cPmwf4bneSR5Qt1xWcPleDxjXHcQvoejFWrexzMa7UosI6lUBWepIc4NZTJnNE9IS8VyxaUwNoWGJM2xXcFSs8LyUoVOw2OlZrOb9snTHI2kqi3CqoeWJd1JSlD3Oew94J07MxwqCBEyixPeux0zGn5yvNinwgQApGOTx4p6JeBzz23xx3/2DkJ63NktEbrAlgaFw+5pRqft4xmF40CnEzCfZ8wSwWalwVy5yNDGrQakqc1wFIM21KuSRjtgEqWossCXElxJbgTDqeHBO/ephyGlLnn/nQmRmfDE1Sa9XsYkEuzdzZgmCYUlefO9IduNBGUUpxNNIB12NhzWt32SSBA6DS6eX2Y6f8D9e0Pu3OsyTwRaaurVGpNxxnRaYIwAqRFGIKXkwtU2ay2HoshoNGpcv7zF4f0ujge//svXuXnzFjtb6wzGBT/8izcYDVKeee4KYd3mtQ932Tq/hrYUZVyQpxolJXGesLSUsLUWkOSKojTcP+rx9PUG8yTnbDgn05pud054vYHUhvc/GHH58hJ35nPiusBvVGkuNUimM568cJlY9fjwwRlPXDtHGsc8OInYuFjFNiBUyQ9f+4B26LPWXufg+BghNNLxKMoMJQ0rmxvUAp/Ds5v4UpDFBapUCAHd4ZwwtAk8n8BJJw/A/QAAIABJREFUebjfJS0lS0AYBsS5Zj8+wjUukyRlqxbiWBZV2+ZYGoJKi3qjRVqU+LahsnmdWXzGrHuGdkLssqTQNt7qY7S2nmQ+3GN84w20VaF25WkaT3x2MV2Mj1GTPnqeU+RTkttv03//TYbjnJotuSYtXhsfIgtrwYzQmlqscVzNhZVlBsWEJNeMkwKykK2a5lhrdlaa1L2Y6awgjRVtVzKLFUGrghaCUiV4to/tWMg6pIWhIMF3OowmBfN0UdG26hITLohISWEoXI+NTptmkSIu23z31VN64xEXVps89WiNstR8+OD052rvU2ECUoDQGizBJEn58duHzJSL5wp6Z3MCz9Bq2YSuQ5xJslFKIAztZY9paSi0oR667O8lvH+rT+BpAidkNkspyfEs0GXOqJ8TOALbdnCl4PQsJnPcxUp6qvnqE1vs9WYUUjEaW5x2A+72+xTK5ebuAMe3UbnBswS7vYjQk6y3oQk8/8IGqury6k8P+c6rx/jeCa4QrLYb9MYD4llB6ECZLbaGjCVxHYe2U1JmhqoH9YpkuVXDnQv6g0Oef/QStz9sUaQFhoLzFy/yJ998i1rVw2gbr+Lx5o09BoM5f/9z2yTDIctNh6evrPCz22fUKz6j8ZzuyZxGNcR3IE1jlCq596BPFhseHkY8PBmhM4tiGhPGVU4eDknVgMuXaux3Z3hpzlqrzllU8v23f8b1S6t4hab3YMRzn3uEQmhuv3+MZUFbhohMUbXAC3yqWxdpuIKgKlhqVyE3VF3B3sEu0zjC9V3iLEcrjWVLcEJmeUSqZmxUq9R8F8sopokiTSPCaoDJbCZxRuD5nM1SmrHFZnuZsphy2B0gooTlVp1mtYV3/gq9N/+ctLSwpF6wI6TAvnSdbG8PK1xh6YVfJzh3FWEyhj/5E0xp0fn816hd+UWS3gn+8jr55ATnZJNOQ+KunePaxatU997h3/7pn3LJqTDyAnShcXLoThNErUWjEtPXklSGbK91eHDvFsdKkpYFWgKeIqzXoIwZzRIEFmEYcmEloOa4nI4ld84iHNun208wSYyxBStLIapIKJVLw5ZkmcHYNqfDHoNuzs5Gk1/6jOIvv9ej3fZZCmt0OhXg1s/V36fCBACkMJRScHSScnwyx7IdfFtjKhZFYRiMBTYpvido1Ryur9TJ/QDjacbjhIPTmOFZj/FcEwQWq0s2RVHg2gJhJEIK8qwkzQ2evWhgyZDM54qmZxMrzUs/u4cTQFIoKlWL3mCEsm3yIgPl4muL/jhluW6zXndYrQbMZEYqSronEff7E3YfzhDWgmxbqzo0GwFGS3QJSljYUqCLgorrEAQCz3I4OZ5y6WITN3RIVMH1SxtMnJxapcmLn3mcf/PNW+we36BZqZPMNOudkCJXnA6mdAcFX/3CFr/8G+ucdTOK7oglL6fTdOkNUxzHQpqS/YMegScJAgffF2AJRuMp59c3eOHJx9B5zO7BiMPxhKQsuLZ2nhdfWEG9/oCHRxHT0xGWJViq1piOYny/jijhowcnfP75J3hz9BBRsXnmQhslNH7FwTIljz/+CFEx4+2bd7CLDhdWVyhVyVFvhJAWjuviFQuopgGEUWhhsG1DUSoyDDEFwnGphj7nmhZv702ZRoJa1WYe5wS2ZHc0pVJzgEW4qeM4OOsXyeIzpr0xyvKoBx6VegfqLWY//AbO5nWstW3cJMLKU5LjD4lP9tG4RH/9hwSrl2g9+RyDB3fxNJz7tf8YVY7ovf5Ddl97hUfWtvna577ESy99D7XiM8sKzsYFVruKyGMuBFW8msXx0W08+xKX1la4s39KJXCI8xQpbWwXKD1ajkLg4oQhRZkxzDIsaXOuUyHNYnrzOZ4MCG0b17Gx/AYVpSkLRRg6FAgqrs9eNuP9BwN+7UuPkGUOeycFCklvMvxE7X0qTMCwiHPyLUPgSVaXm5z0EpACrRVGSIS9wHwVuUIZDxwHFaWcnCYM05yzSU5eCFzXQRnDwdkU25K4jkCimStIU0EUK+o1+XEai0ShGc0KpCPIMo10beIC7EIgiwKNTaRKdFrg+h66tMgLQRhKskLRmy065ibTHhsbVZ58rMZpP6U/FswiyGYxwhbkWkMJtut8nAZcMBtpZCXk8sUmV3c2eHCYcjjf58LGJt39IUvhPo89coHJV3z+hz/4DlImVOshrY7Hvds9PBRXtgM6oc2d3TnnNzaoLS9hrUxYujTm9Z916R6nRFFBFpekDsShw/bWMmtrVfLU8P69Q550NDW7xps3dzmbJjQcw+lpj/2DOi8+9QhXVo748Y0jjkYZUQaBEzJMYyLboXI0Yn4up1rx8KsBx7OUZjug0WogSgjqVW7deIiZGbBKbMfjwcN9zs5GrKwuEUUR0TxGiEXCslSCQoLt2EQ6ZZIuIseqoWY0niFinyKzcTwXaTlUHMH+WZ9xarPT2aFmSjKjCVyX6tZ5oruvkGmLpYrDUjXE3bhEPulCpYFSfUZ//kd4TgvrqS8iPAekTRa0qW9doX75Kt17rzO8eRMnLbCSgtpnv4QThLjjLoO3vs21tQucPvECb+2+R8NxSZcCgnpAWqQfw0wNa0ttToZdKn5l0d9Q9XCikl4iGMwTWrbHTCkqbso4ijgYChqugy2hWa2wGlYYV2J6s5RBlJMqj0urTcJSsT8uyF1Jux1gdMnKUh3HEdzfnfHElW2eedxweHTKmzcHn6i/T4UJAFgCkDalKqnVbA6PDIFnYbOIaLaFwJIaheDuUU6aDmhVfe515+SmBCTGgLFKmnUPx/IXdJ9MU6sZDIoSixKb8bxAmwVlJvAFSgtsCX4o+MrVHWam4ObhkCxJiaIUzxXowjCf5dRcG10W9DOXrFik7xSpxmo5tDouq5sV1J0xvfGUWaLwhYWrF7sSRi+2fVzXJrANSaGZRhHCqjCMCp485+PzNNnxnHJeMO2lvDnfZ2NlGUsKJvOcZqfO7bsROi8JhOAXHt/iwuVlbu8esHfY5/NPP8J0rpgMU37nV57mp2+e8N2f3qbuCxxpkUSK/f0hO+dWuPrIGrVA0O/tMXOrXL9SZ2UY8NH+hMG44Fvf/5Avf/48m50VKpUpm37AaJowixKa9RozFbG8tMT79+8wmitm+gyh67xw6QL11WVuvH2Tjx5IJqMpW25I7DhEac5gGuH5DnEUk6YZWoHvudiWjWvZ1IIKlAW+7zHJFtudnsxQlk1XaZS0yIocz2ge3VzHFgWH/Rnr7WWqQmN0wtLGBShHJKMByg7QwsWuruFeewadTwCb3st/jLbaOJeewT13DVdepCgU4bhPfHafCIdG5xxp8wFMM5x6G9Hfo3PhSazP/CZq1uPknZf4D579MvmPHI77xzy13ubW0R5LzQZoGz+JiYucphZ053OqXoDOFR1/idJSbFR8GoHkg5OEWbqI2TeFIM0LnMCjP8tQvk2j4mIamt50caCs3x+TpIp+apCFwfIFRbn4M5tNYz66N2E0cVnuNOn255z180/U3qeCSiwArRcldJ7B/t4YIQSjcY5lOdQ8SWDbGG1TaIGxFJPYMM1KwsCiYts4UlLzLRq+Rb3m0ukEeA4IS5MWAq0sHGvRugk2lr3YGZCWxAltsKAEbvbOeO7qRX7vN79AO2ihtMQThrS0mM6hNCW6dOgNUwazAiEEjz3W4NpjDe7cHfLaq31mQ81SxaMeOhRSo4zAFRJHCLQyJHGKIxUYwzxT3H3QZzRVyLZL55Et7JWA9a0d/uSl+6Bs/vrl98m1wfMsBoMZh/tD6o7N1Qsttncq1DybRzt1fFdy48N9JsOIuhUQTxRaldTrDo1WiG0LVLHAYA3HCUlS4lUb1JfXMCKnUoOtNZd2a0EyOp3N+dNvv8/t/SGVuoMrNFJIlFFUPItW1cXyJVcuPUKhS1TpYFUkRaE4OunTO5tx9+59LNdjYjSZEou5sKXRWlEUCm0E0nbQxlAqjYVAak2poVCaeRoxLxKmeYp0XEKhqLiGVrtCnseMZ308p4BSs95YRgaKtcYSS5/9NZz1VWzLxsGA5aK3rmP6uzDokY1PKCYjKltXcOoVxj/4Xyn2dmldfxHKjKT7kPjBe1TDFZpLy4TVVeT6GoMbL3H6/X9J/y/+OfHbP+Hci79L7Qu/wa9caVNGKXGqqbse8/kMHWfgCQajCKUUnusgA5/prOTkdMQsSZknOfN5jDGSSa4JggqNQGKMIi8VxhTMo4woEmQJlBqMkVhAVhZEpcIYi+Ew46QfM4ty+tOI4UxTCx2OT4dkMex0ap+ov7+zEhBC/M/AbwFnxpjHPx7774DfBnLgPvCfGGPGH8eSfwjc/vjx14wx/9nfbQMLeo5lCbSGycwQBoKw4tKu2bi25KibMZmXaMB1IVYl5UxhS7CQeDaEvsQWEI1T0qTElqAsyHMFto1tW/i+ROUaYwTCs8m1Ic01ndAiTQw3H054/YPv8nt//yl+7z/6Ar//P36LRCUYZfAqGt93SKaKQhs828ZzfKaznK2NJtWm4t6DMb5ts9xyadcN5URTlgbfEvi2zTwu0QiUWdBktIaG7zEejgmCVWrNGsejU55/9Cn++b9+nW//6DZ7uxHDeUG76mGVJbaw6U8UYT2heH+fX/7sEwxmPtMootlwmCWaeJwwnZ/SnU544olVAtfn7t1TsihBSs39vS5KGA4OTgirATub27z11h2SJMEAVx9dJTjwONqb8O0ffEij6uPKklw61FzJZDiiudHhaDwhv3eXRy9vUGQz6rUGJ6Mh/WmCtH08U5AXJSdxjCthdlziWwaBRmuDEOLjN8AghYUUhuFojuMu8nOrng9YrIYh9apkMjBEicZUEjzfQxhNu7HEeCZo1xpsr69hX2uhJnv49VWcS9fhrXex/CrCtxjfeAUn7JA7AuG3cZZXSU4+IhoPSe+/zUrgs/rsL9NsrVAOxjiBT6Xaot5eR0wPSIYDClxMluAJG50NUffmdNYvcc1/lVv9ER4l0nZRRi3Op0gLPHdBnSLDqQSMmFOolN5UU1oS6bmEaKqeTb0SIJZcoqjkcDgnwGelUSfSJWWUMpzEFK0qk0wz7Bnc1YC0iBjNU/Ji0U8wjUreuGnYWa/x9KPLDCYl/HD3/5kJsACP/AHwR39r7LvAPzHGlEKI/xb4JyyYAwD3jTFP/1/43v/TAoz4Pz7g2BIhwHYNl3c8OrWA7lnBbD7DcSShb1MJbKSj2T1ICD3JUt2hVXfptF2KzDCeZfSmiwYexxUIKSjKBRswyTVNy8JIQyYFoQOBdAnrEkdkzAuLPLT5F9+4wf4zE373S9f5aDjiJ+/cB1tQFga/ZtOwPFRpyHKFLSXtZsB8qmk25jTqdZJ5ynScYQHSAssSWNJgWQahbcpEkmYloQtaCR7sDbCtBm/97Bb/9lvv8mfV94lTyZ3bXaJMY9kuxhiEbRCFIckM44linsa8WTnib17d4/knN/nVL15nPkv45ks3+GhvRKXeoLPcYDCcMc8MmTLIVHFyOKM/SihLqLckly5k7Ow0uHVXo7OcbD7kyQst1mou909jkqwADUqDdjymsxlykpCXhqMkIo4UT19YozudY8mShh+S5hGNSo2DyZQChUXGZAqi4mFZFmWhsFn89kJKNGaxcIpFlCpWqgHCAdfAhaU6qV0wdEJ8Iylkhio1+8MZURlxvtmkYXLan/ldpg9fZ/zqX+C0L+FeuIqR7+Gtb6PzEVF/QOXyDjqdEK6co0yHjI8PydwWTl4Q7T+g87nfxr/wNIy6UKkjHAddJvTe/g55qhDGEFx4Av+FzzF+66+QR8cEL/46Tz33DDd+8BarOxsQDZmmOXpcEGUlO6HLumUxtQRpZmi0G2STCWmWM5QeGEOnGmJMTq5gKXBYDkPAMIwU4yzC8yQ122ZaZBwcJ/gVyZWLTVY7IUpbfHSvZDwtEFi06z6dhsf57Q5al5z2e5+ov7/TBH4eeMQY89Lfun0N+Af/d0T/cy8JymhcaxEWWfckz16sMc0D9o8GtDsOnuMQ+rDcCTk8SVDCItMfr+ZrmCTQbrhUaz5ROmY0y/AcG21KtLYoSoM0FpYlqdYNbsVi0tN4XkmuHOq1Gnu9EZ4QZMLjr9/YZXx9zj/+7Rc5PhzwMJqys+1xbiMkmkvevTUkSkpCb5n+qGA0jTBIJmmGUBqhDRYCpMSVAmuBiaFUmlmqGeeGJQFKSaJI8vqNh7z6wwcMpwUH3ZjQdnnyco2fvNej4tuEoSHLwUgoMMSpQec5L/9ol0QJJlHJ4KSHMZDmmr3unFpWgpcynuScDlJ0aWjWbc7tLHFwMmEyKajWNf3+jEcfaXLSTTjcK9BjhdeI+Qdfe4ajoxn/6ts3SKc59bqHdAS2CUkzxZUra5wczHjywnnGsz69yYC1eoXlToXMhtD1GCUxdtOl29M4QmJjMFKgAAQIAcZAqQ2h5eEKlyhL0DiUKqfdXERspwU0HI/90Riv6tKuBZxNI0pVsF1zaHU2iPdvEL3xErZXZ352QCpcWleepXX9eYbvvsQsEYT1Ns2rV1BZyemb32eSgtuooJIxZakpTm4h7xxiJXOktvGe+DWSpI8qigUaPFyj8vgv0L/xXdL9XQJbIg97XPnKP+b63hHDKMV3co7nJSQpmQ5QZclpP2JuQW7ZaCHIlUA4FhklltYIHPJcE4QeeaGgKDjXCbB9RZ4qLONSC6q4pJSWTafh4ziLSPF5WjKalBQKLOnx7JVNHrlk8dObXcgNQmSfKL3/LxYG/1PgT/7W/QUhxDvAFPgvjDE/+nkP/W3ugG2BY0OpBUWh8aSFJyRNx2Y4U0hp8DxJsyJoNX1ajZBeP6dTl0gBRanYP0tIDxK2Vn2qjs1sbohT0LrAsiTGGFxrcUw1M4qq9Gk3Pfr9OeCwFNoIJbCEQH4sVDvw+cndMWd//DIVz0UVhmhcsKdnVAIbIcEIm+Ewx7YSLKmxhObgcIIlBU3bxhhDzqL0zxXoQoAwRKXCkzaOxQJnFVjcePsucV6ytuGhEqi4PqNxBlpgSYFGgjAYYVBCME8LHBeiVCGwePO9U1arLqnKeOfOGWkmSPs5SarxPAfPc4hVAZbAsjT1qkWelPjGYTBIkcLGUgJjNI12g0mq+bPvvMdXf/Ey640aVtMnqFooSyOUT1E6NCo+W1caPHppnRu7M9KeYpaXlAaU0EyHEzqeZLne4XtHXYQvsb2AIotYRF+AxCCMwGgQSHzpIPIZSRoReBbLgWCWjMntkJYjuWsizk76LFW3Ob+1RHE44FJzFXd9m/6bf0VZCHxHkwtD3jvFvXSV+ckhhXGJpYVV9cgLDUIRj0c4tWVcz2LWndNa9ynmY4q9u9jCYGuNt3sT9/KTrP3C18gGQ4Kdx5ievMX07k20W8HOU+ywii40z1x9nL954yekpcB1DWelZH2lxuF0TF0LHNdjLkDFGVI6+J6N0CnzTNGd5dQDB8so8gJKISmVYJYrhLFwS4kyGsu3CLHxbQ/Pg+Foxu7RnHmiGUwKHFtiu4oPbnc5Oc1Y67gI88lYkP9XJiCE+H0W62n/28dDJ8A5Y8xACPEc8BdCiMeMMdN//9m/zR2oBJZ5dL3O0Tyl2815dMOj5S54hNLKWWl6TGYxVd9iY7XJPClZWfLRWhMlJcOJJs4MupScdXO6JqXQIC1JWhhsA1oLpBCLeacUiMIwGeZkpSIpbVbaIfvHCUtVj0mW46Ip0VRDi4enMYE/J/Ad5vFivrWzBRsbLh/eTRlFKUstiVdThKGkEbpM5iWxgFJBQUGhQBdQFgLXkZRG0gos6q5GaYlwNUWq8XyLwBd0llzSWPDwOAbLIokNeVHSChwyUVJoQWkMlhIEPmSZBgSvvXeM1gXCgOssYK9FbghdQbPmotSiQaha8XFsw1a9Qc0POJj2eevGMTJdmOSN+2d4jkUa59RCmycfWSesF7x1a59zyy3GcU6gA5qVKvk05pW33uTipSuMZw8QMmcUJWw1anRHp5z2YtZaVZ5fX+awFNTqDWbTkjgrEcZgLLCkXEBYlca1bSzXptCGdc9bBHKgF9RnnWNbi/78UINja85XG6y2t1Aipt8bY9wqTSsl1QZtSd774Svs1FeoXb5AISWTe+8zPx6w8sxTCD/ACUL+d+beLEa2PL/z+vyXs8eJJSPXm3eve2u5VV1Vru62u721ZzxeMIxmHuYBNAJGQkLmgQd4APyGBvECQiOhkXhAgxiEEAiM0CAY9Rg83ba7vVW7l6rqqrp193tzz9gjzvpfeDjXYIFrPGJAqiOllIoMRSgz8v87//93rRdTFtMZO7albQsa7wmUQqc5DBKaB9/FrTaYsqIKEpLRDlGWsyotpmqRvRGLh3/McHpKL85olg1OQd7XNJsZJ5uKcNyjosUZQHUBp03ryMIASU1jHKGS1I1hXpX0wohl6zmfl4yTBN/WKB0SxynWdRhTYxzzlWVTOmZrQ13B3ddCwrBkU0iu7CSEseDWbg78M6QNf84A+Ft0gOEvvkwYxntfA/XL778nhHgIvAq8/096La0Ed/ZTJk8tsW4oW0tsPMIp7uyEHAjPthbcuT1kamBZVIx6AbO55umqwTqB8A4vLP04pJ+GHC1aGmOQUpFIQeW6Bh7vJaOe5+39Hi0QEHAxr3n8oqTaNGSxZllVDFNBYTxeSoSUaCUJpGBjutrpTekYjDRaeaarCnXS4l60HOxkHOwn2OcltrEI6dHCgxc40U1MjMN5WDeGnoK0r/HC4r0mzTSLqcHWjvPphrp1naTSQWAEwUv8RHmBcx2gGoUS5z3ew2JdE2Wafq5Yb7p8RaEEUniE8KSpJo4kBJJrW7vc6W/z8NkZ5aKByuEBLRV1bfDOs5VmUBeEccTBziH94TlBGFOeV7x+Zwe85Gh+wWA4ZDXfsFp2W9iTiynb0RVK6ykqx3LT8Nr+gMw5zowFD3kvxNhOnyEQSATGOLSUSOVxUrJoDSena3aHMQPtWZgaUGRJSGMaQuW4NxyxfesNFidP2Mxn6EGA0QFV3RJu5+jhmNn5c6yocDphOZszX9fsx1sMbr/B+vSMy7MjVrWhWK7o5wqlNJFwxDu3aMKI4skHFBdnlE1LdvSU8b/wrxDkGdHiFJ1tIwbbhG3FplhwoxdyclEjY8UgERwtCmKhCHREUVeoQLKuSsJGE2YBpYNAB0gpkRK8625eyta0MiTQmlRA1joWzpBGCVGouJyveHJRYBqLlCG9FN64lvK1t0dcLBtGwwznNiyWJX47/vz19/9yAPwq8O8A3/DeF3/m8R1g6r23QojbdM3Ej/6i1wuUQMWCTAsOBgGF87Sris+O13zj7R1EGnM46tEEMK3XDGJF04IXCqXBIzBVFzd9YycmixXzTUssIAw7ai5QnsJ4+qHk7Zs9furWDkIKHp0XXA5bHp6viSPFat0ivCaPFGXbdGczYdnOUwSC2WwDkaC2gtOzTm+QhBLvBVWtWBeGXk8z7EfU6wanWla1wHlBEgik72zMzjkkUDaOuOdZF4KqhmGkuJy3XE7XgCBSAmM90EVcLxpLYyBUAie6yCvRus7XjiAZKF7/0i7zy4rpaokIFJESZJFm7yBnXTs2yw2PH59y+6d+AhdoTucFpvXoAOpSkPdjyrrCWwE64M1re0zbFaiMvdEWP/rsDFuGtC08PzplkA+QBPzww0fEkSTAcXw0ZysZUntNkAWEOwE2MGTGUC4qsJ5+L2JTWerK4vEI0fHkURKSpVHXd6gkjdRsygarHJOiRusYI+BoseT17R3u/cRPk7/5Dsvv/0OElJi2xpGgRY3SAb1+zvr+FKklbvcuy82ExlugIdm7yvH9j7g8n5AkQ1LlCZXCSIlXIf7gLn75DNMYiPuoqMWWa9zZKf1X3iFU++Rf+llcqvGLjPju21x5+ClhFpJrRe0aSufYHvVIIsGm1BgcVVUTWAUOjJNkcURZFngaCmPRQtEag/GOXtZDKYdTjlXRYKxgU1pOJzXDXkoaa8aDkE25Ypz3CGOFWrdczivKytNLI8r2n6GB6HOKR34DiIDfeknx/CkV+PPA3xZCtIADft17//l6xZeXB3wIX7/X4+Q0YLJs2emlzNYN02lL3ipKpfjWJ8eEkef6lYTHU8O6NNzYj5ktDItlwbgXoqXgfNoQa8XuIOB4YbgsG0b9gDxTvHcw4Bu39tCBxPiKrUSRjwIqZ3h4XtJaRWVblrWktR0vm+mAnSziYtHQC2KWheH8wnSSZCew1tMYhxSC5dxRFg29sIvaKhuJMQ4vunN9ILrnCiHIQ0/VSDaThqpRtBY21QZQCKFQCrxzGCuwrjvTdRsDjXGWbgPWhXZ6r2i9RwYBB7sDQh1wPimxLeSR5Oo4YmsQIiYlOlG0bcTx2QWPm5pH55d478iiiDs7fTbNhheVo/GW1pTEsca3IX/84SfcvrJNsX5BWQl+8MFDEmnZD/tkImC+rBFecnneIqzvbMFpRKxbrKx4cl5gdUigE8I8ZLlaMVtXKKE7dEB2/wzWOrz1eAOubLDOsfaCaVkzry0Kg8CgbMqXv/yrXPnalzn6o39EurtPPB6wvJjR9PtIKfCtQdAy2VTIgSXyjrqsiNOEo+99m2z7kPVyShTG7I775LFBNgu8aXHpEBeFsJog8SShJqwtIssJ4pTwtX+O7NYZcjVh+cHvsLpsSb/yUxyMb/Bm/9t8509+SGsk/SgjCjTWWoxvaCpLszGspKGqBYFVrIsK7aGIBZvKsR1GSC94Pq/wQpENQpa0LJuG69GAdBxweNh1SwoJytU0bYDzIKTA+IbJfEMUpAyGEfwTqkL/adiBP6945O99znN/E/jNv+g1/++XsQ7ZtGzvhuxnI/7kkylONIz7msXEEAjH6XyNdBWrheB+7fnsWU1lDHeu9rANKCEoas/HJwWhlhxsBWxliqcLQ9F67MZyqxdzeytiK1WcrlomTYFXIR89nXNxtvHSAAAgAElEQVS0aDmZVAip8HhmhcVbCCNBLgJOpyXr0hDFAUp5Aim5sRczXzScrSzGeKTrEO+2sJjG4axnWXicAy893gk0nkAJAi1AwAYIrew+SEHHYKjOTNUZq0CYLslHCuhFkqb1VKb7nb0HYzwOEBKK0vDJ/Qt6WcLeOGG9KHDesqla5g8uqZqa69dGXN/eZ1KVXJYFeSLxRrE/HvHOKwdMZ3PkZkKUK169s4sDzmcNp5MF3/jKl7h17Tnv/2jChIBxL6RsW7ybstWL2BSOTes43BuRRCGt6HZq87risihpVcDB7ohERcxWa6zzSC3+r4GGx1pHKBT4pjtCVQ0LL1CuA3il8oi25Nbhl3j3L32Dz377v+foH3+bL/1L/wZRf0B49ATbjFhbj10sCbcizkvBOMiQrqXBMp9PmZ3VHOqESCp2X3uT/TfuEWrwy3MC59DpCLc4pZmeIqUk8J5wfBV5+x28ruHFfYJxzuYH/xuuMYimpnr0mK0vv8vN+XV+p3qfIBpwaz9istywKFsq1+0uq9aycgWJjvCNRXnJ3m7GZG0QTiK1h9ZjhKdta9a1oBcF7PQVu0YxGuRMo4qL5RqHQwtJnsZIWXI+XXN82bA9CDmflkRhRhL9/wQM/n93CfaSDLGC268MWNWOo+UaIxwnmyVWCYxqePNazqOHNU+PGk5mJXd3Qw51xEx60iBgUxlaK3j1ekiWSCaVozAOISTr2qA9xJGgxGFrQ4PnYl3w0cM1x4XFGY8ShiDq7t6hAGctrdRUxhMkirI2RFpwZzfhvdtDLhaWHz5ZMN1U6MCTas2yMKwKj/Wyuzt7ifZwaztl3RjqyhAKy7ICQ1cy6QU450EoBA4pPFtJzLSoiQKB0h1HH+KRSmK8oHUQyg5sVAFgoW4sZ+drqp7lYBCzFWgeXSyZlS2B1OggolhVTOMVResZDWKywHJ9d58sGZGO+hxvCkwv4PZhwC99/ZDZ2nFyWnJeL3gxXfPa9SvUl5aPz1asK0Ox3tDrB9y8OmY6FcxrQ5SGSKWJMET9kOfzFZWCPNUI6agqi3OA6KLHtVIdXQhY64l0SNILcLZECIlrW/pZ1gWwtI5MpFy9us+T7/4jPvq972CNZXn0gt2f+mXy3jarpz+gsYLFbMGd936BKnyf/v4tdq4O+Pj7R3z66Dn7412uioitwzv0trexrUXt3CIIImQ0Rr36dRxrRBCQeEcwvEbw9i9g6zMWH/weggGjn/55XBgibESYh/iipDo9wb845WBrhzZU1E1D2ovYzDuZebFxqEBTrw11FbA13IVmjvGeurTECspNgzeONO+hwhAFeCXIQsHUVjgTYuqKYrHh0+MNBzs5eVyjtWEyr6hKi28V01nDoJegg+pzV98XYwg4+PjpitduJcwWK1Qm2B/mTJ/VtL6h6lnqyjD0EVd7KedLQxjD3Z0eWyoCVxIGntZCIAWJhCwIWawrvHdY78mjAFM5NnhWlIzznIuZJcsbeqnGrRq0kDgsWmpUINDSE6JIAtge56zWJVZ5qtZwdy+kHwWQROwOKpZlSRR1gRledLSnsx4VgJaCYaS5uZXwfFVyXnfvta4dTgpq0d3pERIlPc4Lboz7pEJxMq8IpISXGAIoTNMJrKzrQMNAd9oBITpBEq0jjgRVacBAWxsa5UmGIcM8hqbms+NjqgZ+5Wt3uHvjBnm2hfWKHz98waOTE87Lhn6RMTUT4uGAvf0tBlmMn835+Xffxiw8D09/xE7eudwaEyDVmq1hRM8kGGtp25LhIKE2MN0siMMALRTTyzmSHkootFIIQMrO+4F3eOcRKNargsbXOBcwSiOGYYhuJKeLgrlShFrw4+98m4tli3eWcrVBWMngja+T373H5A//IWIds/3K6wz3tmmaEnyPorLMy5axdQRxRn4w4OLRJ9jJnPBrCdmb7yJsg3AFOsrgS38Fzo9Q+S6mOKX+5PfZTJcoJRl6sFLS1AXR8JDg1j3Cg13GmxnjiyNelGsmy4IkD9mUDeXavqxlcwQqAJ9w/epNqvln1LZBKMd6XVHUnjgOQECWSwaDjKatudwUpFmfREuGtSfDsV5bZuGSZy9KilLRyyUKTyNgNAqYzAqU+IIPAakF7z+do3uWNE4p8aw2JWkWwkLiGxj0Q6I45Wk1Y9JW3NkPGYw0Pzpb8fh8g3sZVdU6y3xp8G1NURpwntYYDnZj0lgxsxW3VUYkJOumpWpbnHEIZzvHYaBpak8USzyOr1zbYuTBRhoiSWksj6Yl1gmiMCBIHTvjhE3d4LxksqhQEkQoySKFdTBbtYhQARJXv5QcJ4pAOGojSLMAryxt2211vZcs1g2T1uEtVNYTBJJEe6yHZWURSqFVxxBIuvBUKSRaekxr2ZQthakp5iVOeqrCYdsVoc7ZGYR4LWFZkYaOQRYyL+cEUnPtYET+cMInD59x38OPnjqeH9/n5KThvTsD3r77OnVZsTveZTtLGA4jpEpwsqs9n7QVKgrIdATWkCpJXSnK0hFqiW1bMJowUWgjEYBAYFqD0hotBFJJWuPYlDU2gvWmJiKktRW7IqT0AQtRd1iBaTierYmUwkjFs+/+L2Q+4pVf+xfJ/uq/ydZkyuT7v011fkF58xWO73+KeFmBHocRXrRcPvmI04cPGcQjhPfY9SX+5FOCeoEPImRvD3HzXczFfZoPv4uta2SgoZohapBxDNWE9NZtjl/cZ6u15PtX6WvJs8sVO3HO0WTKallRrCz9XsDF0pL2FHW94MXjR1zdjSnqik3ZMFtUNFZxNY8JrWU5W6ElOAGtD/BWUpY1UVPz+PmaPIZQaba3e7w4rrBWsLcTo6RgXVZ8+Mkp1672Pnf9fSGGQBopfvqtEctlw58czbl3NyfXfdamwluJqD1JL2bdGo4XBTs9yXuv7GKAta0IQ0jTkNrCbNlyvGq5WBvujFLiXLPfazkcxiSx49WdAfEmoWhr+lLwzQ8umS8dd3dSZhvHtHIESrCTJGSxIootb+3sMLWOyWSKloJxrOkFKaMgo5QlY6dZbRKeXxTU1iCVwJpuMSrRMQKzVcsH7RzvOqmxjSS7meRobdDC46RAa0VlDB7PZFl1DIJQWDzeO9JIURlogQSP83RlmoAX3fFAOIExnrPLgkHcUW1WAN6z2VTMZpo0UFSVY7Vs+d4nL5iXG6TU7Pa3ma0sZxcrnID5quZbf/CCF8czTNPST0p0b8DZccl60XLv+iEqb/AC0ijicgk+cGActTes5y3CS3bHWxwMM+ZliVFRJ2ZRiiQKWW6KDsDzDtFNBLztKNVIBSzqljhReGtJWk2eSa4MMnZ34cXpKVvBkLJ9jiVH5xkX909YFZ6r3/8tRve+hrj5Go++9V8xXRZc1Jb0bErei3FeoZOMJFK8ePCU9XKJ0Dt4rdk8/4jqxQPyJARnCc9OCfsHyGJGaw1GKkLZEgQSihXZG79IunfJpp2y+fD3iVaW4Zs3yZuWat3ieobVssL5gDhT2NoyHOTsbKfUmwZBhRUx0gvaBqQM0ErSOoi1QirPfLmmMI5IBeQ6YBhL9NqxWcH11/qM+4qz+Yb+IOTutT6L1YbTi4KLRYttHdXj/4dU5/+8vhAuQu8cP/vWNu++NubodIGUhhu7GaM0YWsrJdnWFDjWWKJBxPXdHnmk0I1kuxfSSzRV47gSS965knClF5EIxaKoWbR1t7XXgjwJSJ3nZDEn7QVkIsJawTtXB/zcO2OcbdlUhv1hzNVRhPWeEvidZ5c8na54UTnmxrHXj+iHgp0042CrTyw7Wmy4FRAGGucFQnUsgHGWUHucFZysDLOipW5hVnego1BQtI6yMSA84yzG4XECnJMoHFJ0aLl1grJ1hIFAi45rhw7nULID1zydZbmuHJuypfGesobWC4QWzNad26wqm047oEK2RkP297ZZVI73PzxCaY/yXd3Ww8eX1HWD8ZYX5y2ffPyYH378kIfPjjherrl7sM8reR9pG3zQLeB+qLkxDhDCMlsUVEXFjfGAcRJjWkNRN5iX+IeSXd6DfKkd9t4hsOAdeA1WYpqGqmzJdEKhHVHaEqmAx08eEfX7HPQ1aRKQ9RO8BRvFNJspxaffhcqzc++rBGrD73/r21xcLhDGopOERghm64Ig6VNsCgorsK6iXc8w7qXjs2qomxakR6Q5WnVUbKQientX4eotdNIneu1tgv6IsJfgl+dEW1e4fniVa5mmVi1pEpAmgl4aoVVAP4+5dbjP7f0+o4Gkqjc0xtK0DUI6vHVoHZBkKUGgcF4QhRHeedq2Ynq+ZjKruHV7h2t7Q3q9jH4Wc3pR8IcfnPPJ0xVnk5rp1FDUioPtrc9df1+IISAQzE7X3NrTfPXWHtXEUroK2VpM1aBziTBdLPXuXkSWKRbSsqChdYbKWmZrg5KCQaRIhEAJybrtBETDfsDxbM33n654NqlIc8lQxYzyPv/amzf56vaIBydrXr+RM+hJFpuG3jjjyWzJJ0drPjoq+c5nlxyvG+ZNy8a1pIOus6+YL7FVyzCNGCUxPaWRVhJrTb/XiYnA460hlJY09GglWFaOSWkRiJfyWYHWcOdgRCY7N2QSSVItiYQiUCHrGpx0DAZJ1+XnPVIJtO4wg1AJQi1I4oBAKLyTNEbQGqhNZ9LBwnJtaJ0kDCOEi7k4r/n4wRm/8yef8vhsRlE3RErhLCgVkES6q7S6rJBhTBp5rg4STLPg4YtTRG24sb3Nlf4YtWxQmSYIHDr0iMAyLzcUBnpasZ31qNqWZV0wXS1prEHqTsUpBWgdksQhAtBKY43n+LihkgETbzkvap7Ml3z4yZqj2Yw2yblz44BBGpJFIYH3CBVQWHBGEEQhB+/9Mu/+7E9RLi44mqyZrCoGwz7T1YyPP32KyncJ05zDwwPyWOCXCxIpUYM+QRgQ9kf40Q4MxqRZRD+R9EJFcOU6jAbwwf9I+b//151x572v09/eI7j6Klv9nGGuWa4tURyQJ5JiVlL4mtPLOccnMy42DkOfQOe0rUQISRgHRKEC11A0XVFrGIdd2lJrQWrOC0GxPWb7esz7n55wdOnZHg55+9U9aus4OStROuT29Yx3X9vmZ3/yyueuvy/EccAYz2TpkI8rtnq6U8vNSowRmNozrD3jOOesXBL1FXKj8VLThgaZCAaRZKkdl7VhaT3PFzXrqtPYxwGkRYnxkrI0nMwL3tvaI4xjro4EOwz4/vmCp0cVvUSQB4pV3fLpiylfvjnkxazESst6baixbCKNGyZclIaoXtK4lkGecVpVHE8XiJdqQmpHHoGkO6c3CnohxKGjnyhOF55Z3bnmpPYIKaid5bPjC7aSAKVbjHS4wmONQIYWH3g0itWy7Ka3AOctjVFI3QFrQoIOFaascS1EgScIXLeLqCTeesrWEISaUMEffXzMg7MMj6XY1CgpWcwdzlq06ppxhklAYyTOCqbLkiROmSwbXrnRBxzToiIJWnIZ8NXXrnBclTx4NqNWGpUGTOuSoYoprSOyLVJJtPJIFWCkQdB5QJTsNA94UMLjvMc5i5aCysJKOoyC0xND6zxBAI9fnPDzb73HJ0++jcODgLpuWBU1W4MQbxa07/8D9GpB7QTHizX5bElv1OfZsyNOSbmyv8P2/k16o12y228Qb28h2wJ7/pQo7xFcv4mIQ1z/FdzP/g3Uco6cnWNvvouY3MeuJlSTkvNnz9h58xcY/dovsZkdE412EbrP5ckTkjRkOWmI45B10SIJWSzmRMk+ab6LdZdEkWNdSvppxJKGs8uCra2E/iAmSQLKpiFNPatyg5aeyle4paMsLZKaR6clKM2vfO0af/TD53z2vOUrbx1w71bOZLb43PX3hRgCDo/OJB8/mBHvR2wnKWfTmkEUsjtO2FJ9rPdUlUHFgq1Bj2nbUHlHKyS1FVzfTdFa8+C4YNN4nPRo5dnKQ7a3407QU7co6Sirmotmw4PLBd/8w3MeLVcYD8erbuu+M4g4vlxzc3vMvPRcLkp6UUiqoT8KcAl8+4cvuJ7n9PoJztV8dF6wWNb0VScSqo2nsYIQiJXAa0msLFp0bjljO6ff1f0R0tVs6goHrF/KYmNgWnscEi8dAkUUOtra03qH1mCtohd2lJkXnqYBKSGOLCBxvusMDBRYA5XpDFLeCqrGIgKH0jCblZStQQpLPwppGkldv6QuW8dgv0ftAmzh2CxbEi2pfUOQJIyFJEFxNFuSZAEizhjnQ4qJ5uFsglctrhGMe4ooilhPFwgnyfMQGyjgT3sN3Ut2wBIEmn4vZrYqSJKUsZEYYymsxTvLZt0QDyPSKOTJ88944/oeLu7x/PnzjpIVLauioqgrwvWczeyIxfSU02nBXj9FWottWxarivl8yf7HT/j6V95h+43XQQtUWyBWl4jNkvVqhfrsI9KqRXz5n8dHQ/y1K5jbX4V4hPjkH+BLSyUTvJvx5Okz+lcClj/6jOFP3KNXbBgMU7SQTI1BxI40iHHW460nCR1RWDG/nCNRpElAWTbMFl2yUpKmJLFGiq5rIIoCik0XulKXJXUjeevuFkkkOHpSsa4ct67c5O27V7hzI+TujYgHL8750Udf8IxBlGS+qbGRZGYsLnCIlSfShq08I6lCzpZrZBAQOolxlsWy5PnTFQ9PKjYvM9uLuWNeWKyzoCX9QHJ7J+BwK+HscoO0sJdvsYkkk/mC33tywpNVQes6RR9S0ljL0bRCyoAfPVvT1J5xP6YXOvpJwlYcMMgDPpo1PJ6WbG0KRABRKLl9rc84jJAvppyuLFIJvJS0jUPhSUKFUoLnc09hPUpKQt8yHiQcLWrqBhBdVsJOP6c5KTABbKRAWIdrOvzkpQWfUHm87ahIBGgrkQaKtUVaR5AIhAyRzuAkOKHQqjuaVKWnbRyNAYGnaSU67DIdhfQ03qEVrOuGp2c1o8wSGtg0Bh8JnFRcFCv284TWRSzPC5ajkBfrmiYJuZKOeLN/jQezM9Zty2S25sp+yvSsxVRgqpZemlFXFVVVdj6IuiEKA7RU3fAKND0ZM21XOODF2aI79vRCWm8paweu4cMPvkfpQxbnJYPhiPnpKZulZ1EckGNfOgY7o9KmNhRGMR4MEfIZs7Li/uMj7t17gzyRtGfPaB98iGoa4t0xoTMsT08JRrcIJk8RH/5jXH8bFYX43ddxV94ifv0B2fMT7j9/xoP7f8CAI8zJOTv3vsY3fvUv862//98xbwIOr2e0tmb1vEIGGpVGVO6CxfGMLJLUdUtTWOaFIY4SBrmmLAxbox3SxBDoLtqtbR2mdazrFoTmbNLSWoNG8xOvbtPYNb0RjJXihx8852xWcTY3n7v8vhBDwHlYto42ijh/tiC2igxPfzSinCuELHARVBtLLwiYLgoCJ7mWpVzGlsBqhJJMy4rGe7IwIlCOOJHcGCbsqIgodehUMF0tUIljSMhmAVuB4CIE13hC4REILBLhHWUluXuYc7gHk4uGxzNDYiVhq1gWDd+4t0MaCWoX8/h8xjgPuBKFVIMek3KOsRapHIeHObL1SN1pDKQ2DGKL8R6UJe2ltJMFoejUcmvv6bUtw17EsiwRgaf2f+pAFFjvURIUXS69ll2ctgmgtI5Adnboxii88/QjjcRjbY0IFEnYtSTZSmJd131gjMAhaBTgLWGgQDqklCzXFeM4pGkM603dnTm8xTQpRaA4X2049Y6w6LTi7bRgHVS8c+8m4UIxny7QTcT+OGdTGOaNYVg5tvopYbihLEukUp16UHRuTy0USRijvSSLQlZNS9N6tJd4/RJnUTDoD3h+tiDXCcO9A7YObjM4OMIsplwuVmzPXgCefi8ljzV5ntPfHVPVFUkSorWkbhua6RHV0WOy/R2cDvCFxW/fJh6NCWrQV2/j11P8/AIlFaLWcPZd5F/+m4g7b7L5wQesjODMzXiwOmQ/TKkefcr1977OiP+WAkdraqJQkw8SvPLM1xuQAf08o2hrFkVJ1TjmdUvsA3SlqYsNUTAlPkywtsLYBh8KysZzdFwym9fIMALreOuVEWXV8OLxAuHgdPmcUTTgS/d2OT1//rnr7wsxBHAOn4RMpmuqhSc/EOQuYTUriYI+M1/yoiyYVhUykx3ibyU2Fty6kbI1UGRO8dHHgqfTGrxDZoo3DxNub/W4uz1kNWg4Wq6ZWcFkUXF7b8i7Nwd8777lYlmicGjhiZSisY7Cwit78N5bit045zwtOF0uuL4bcmMrZ7MoSRpL5QRlULHVi9jWAYujknnRod9aSqJAoVtPP4q4aA2PLyqcbcmVQMeSwSDi7GzCei24uhujJDw+K9DKEquWJFBsBQKz1efZ2YwwfKkN0ALTOoSWhKEj0o6iFjQ19AK6Xba3nUag7WK78qQ7qhhjaVyHCjvAGonw4EznZcgCiHQ3eKBTN2qp2QhNi8AIi5UhD47mRFLSVi1hGBMCy8bQWsM4jvnjjx8Rac3VnTFFWSFQxGEfYdcY0eCxJGHEQgic91hnieOcPE0xtkYHnXy2sQ0PTy8RYZe753RM3INYSOqmZl3XSDWgrC55/P0paZYxHF1n90qGMi3T1YpepNkbRLgg4OJyyno2I8liRr2Aoqq4OD1mdXrE1s4IL3XHkKgQf/AqKtvCX72J/+RbVGWJXq6ItreRzRq3miAGh6gg5u2f+QVOfvd/5X/+1p/wb/9b/x6DN16nHSS8cu9nOP3gA65d2WEzW7BkznJtcE6QDbrPb7O0BHGEdTVJoMFWHJ1sqGpHYyxaZ2Sppm48m8K9/Ht5dKBpm5pRGtK6mt/94ZTzY8etKxlrI5mvSszHLcXna4W+GEMgCLoijnXrSYYRa+EhLeklIbKyDAYDLo2mXpaIkSaoc9bNAqcUV7f7bGcwajSvvz3k2XzNZ5MlTSA4SBWZlkRJwsWipV7V7PYirtzcpfAFoTIMc89uI4nymNf3c+x8w/vnFa10/PV3DhimEaFWbF/JuFh57u32GA1D/E/ucfF4g9SO8ZZGypD6oqF8WdYZy4AoEjSN4bPpmn7eIrTGWY9QAqMVq7VFRI7bh0MuFpeEgSKOFcMtQy4CjHOsbEkqAmxVEacRbVvh6e7y/chRW0BYpJCkoUD5zm9gvCcLRffcxuC8RIYK38FnBEp0CLx0XdaB93gJm8aRJgEBYFvoaYGLFFEWY03L9u6ATTlHCkmvl1IBtq3ZjhXTQvPixZLRSDNZFcR5QlE6ernt2IWLGb2+xCYpdVuhtaTXy7FnZ+A9AlgVJdJa4kTTiyNKaxn1BgTBHLxCRo4o0mxWBY12iFITRRlPJhO+/70/YFe3TFcbhIhI0xF/6Vf+CoWIMc2c/a2Yp9OKB49nSBHwymjA3jCmXDXMioZisaBdL7pg2GwI2zfh6ltwXeKFhOwH6EizmZ5iq5p0OERsjnG7X2L76z9H8eyMn//aL/Gb3/zP+Y//9t/hP/wv/1NGTz7kr71+jR/c/yFPT07xbZeA1RrBMEtQynM5XaAaj4o1VWsIlCJMdKdD8BLrJU9PKoxtqWrD/jChbQyhVuxsBxzu77C9F+KtJzwV5JHm3t2I3/2jIx6fFBz0tunFCTD7c9ffF2IICCnQZUtiFcZUXKwcr17fY9CkJI3m7GwGfRBW4zeKMBJIEbGVBoRhB4AlYYQNDHtbCfvjhKP1hrNJwST31Gcznp1fsL+VwjjAC0fsU94+zNGpZvSoQG0HfPXOmGYxJDtcQGJ47dqI2VGDyhTT2Zx3747om5jpWcOkKjhrC26HY2KjebaumZwUtFVDDXjnqVrLaBiTRJ7LVUu9KRlkil4Sstg0OK84OS042ErZHgZgHIlK2CwX1HgCbaid4sJ4tpKGduWphUI500VuEyHDbmtcVl1Ia9OC1l1kV227IFP3py5E0e1ORAeZoEOBFlBUFqUVjbNkaRd02ZYbvAowoSQLPOu1pSwb4iBnOVOkuSRNBaUxtDrm/v0VTWUIvCPbDwnjENN6Hr+YsT2KSIWCesMwj5AG6rbibDZhJx+hhaI2FWEYUZuWQgukhcNhTt80fHq0pBfF+FjS9lp6ImQ9bxAqRIqG+aoE5zg5uqCMFdPVBkNBmjbsfvaAqq4xXqEDgWlbhHx5pFIRt2/fpJwtGeQJrbXUZUMWROjxDXw2QHz6HdxoD5GEcO9niOen6NMj2hqaxYLw8UPEnXeRb3yV09/6T7j5V/9VXr/9P/H77z/m7/1H/xm//rd+keu65df/xr/Mb/zdv0vc28EGkkzXbA9D5mWX46CdxFpQOqQoW3zdBdR4PItlzWppqRvPlXGfcR5x/8WCu4dj7t0a0t/SzBczrNDEQcDOlZA80Lx2ewfvCoJQcuNawPt/fgHRF2MIrDeGF0cFq6qgP87YGsVgG1oT0qYR3/rjCUW/ZisKaYcztrf3iJttWlvgWkNjHI/8gp7QXMlTVCmZthWTwpBPClZhQSUd2/0EhcUHAf6ypm4Fb13pszoVXK4K2kbQzwO+Mtzj8eKSF+2cSdiSFSHjYc4qMRzP1lxOFngr2M9yFIa2UMwvN5zNFxStxUuJV5rGelZli6Tri6say82DjH5fU3rDaJjS1rCalygVMCsMxxdnxDKibg3beY6SLcu2pawd26ngonJsrMcpzfmmYUsqQiUoKrCtQ2qNcI5+LDuA1HuQnjCQCOk7Xb7r7vwoCyisUEilGOYp0lcI5xgmAUXVUjnV5Qooi4w15+cXYCMC6zl7VHPnjQG2WeMsFLVACEEloF1uaCuFaDvji5GebBgSCI+VnnlpOJ+tGIQDtA6p2ppAKZR1SC9QMqCoag7HQ64UFaUrOVttqJeKRbNmsmkYWEUgHb0koddXBBiOLxY4HbKuLJUp+PiTHyCASQWBSpCiRkqDcY7ZfMnh/lUO9w/YHedEosUJjXzjK4jBLv7Jd+HZQ9QbPwfzF/grb2BufhmV7yPdCvfJJ4j+ENYnCNcQHNwkSwK+cjvh+XHG85c0pdcAACAASURBVPWMD59fcu9rf42fufke//qPP+SbDy+4nJwRI7iYl8g4I8415XKJ8pKm6cLoJApXddjLKJXsDhTnS8u0WTP7bMPWMKF2FR88OWX544qD8RZRtmY2XxOPQ86nMWmm+Mn3RoRSM8gT4Omfu/6+EEPAeU8purvRZtMyVZZ2dwehArZ3Mw5vpXxwXEOoiGQCTU25UczWFeNewI5OWZQlK+doU8FWP0dNag72BuAarm0PSDINKdSFZ1UW2NgTKInup6zcGRdHU6pXRxymMT01ZErJqzLnj5IJJ+sFB2FOM62oTUG4HXFy3HD0tOD6lZg0cty6NSYKJU+OF2SJIowUCIGxcLGqCQNLpDv6p6hrWucARxJJ5usWW1vWjaO0ggzL1e0IFwsmc4s3gr2dPlG1QemAh9MKLbtcAWccrYfWS6RUOGtxSmK8eilEcoQhZFqyaaBsXWdpDhWxUvjGoqVHCg0ipPQO5bquBCVglMJ8I/DeUlQG4zVFXdJPcmZ1w/NnG9CWLILzecvVa2PyccLsbEow1NzKBV4IenmOziTCSXouZF4uKZsCqSFJI9bVujNdCUnRNOjS8+PJhEePJb1+j2rTUJYNDlhMGlosjTEUpuL6To9GCrLxmNVmg7WeOJT0+iGX64qDRDFIB1ys1uRZgDOOpnakacS6KDC1ZdTvk127zejOG8j9Q/zsOe2jHyNbjc5z5NrD0X3Ul/863PpFPCXqzgtcXLL55v+AHl+ld3CIsCveeefL/GDhKOYr0r3rPHrwffYLxd/8d/8D+t/8bf6bv/93sHVDYEMCDy+mFUJG7KYKY7tchhpP1gvZyVPixFGYio2tqGeWUV+TRo6Hx2vm8wYlY/Z3JaM4593XhjSuZblqyCJPXZcMsj6N2Xzu+vsLFYNCiP9CCHEuhPjwzzz27wshjoQQP3j59Wt/5me/IYR4IIT4VAjxK/80QyBJArI8YVV7ZvOWq1d6jAYBwyimLVp2RzGph+W6Yjbf8KOPL7mYLlgsGzaNwdAwiCIcjpNyyY+PnlMXBcGioikasp4kbiUvzpasN4ZkoTidb6jDlh98+Jyj9bJLGn40xeiIp+tTbg5SvI9RpWY87nHpa+aTmtBIhBNshZo88ARByHtv3iKQMYVpuXGtz93DMVdHQ16/vc/Na53RZjsL6CeSNO7Tz0e8d+eQm4dXqOuOGqtag5LgpSMfBozGGReXU5RsSbRHJgGbBvxLMBBAeUtlQCLQ0hAHnnEaECrJojCs6y5noG7gYmmZlR5HgHMebxzhy+alLNKEQFWsEBYWlWBtY0wQUwqBjyTrVUNTKWQrSLOMyarB2pb7z5YsTksupzWxFvRTweXlkv7OiHwQvZQgQ1VsXipACzCOURagQosIQArVCYaUQgUaLzqMKAljwiTl4fERQmi2ehFhojF1i6olezsDxnmG9Q6pLE/O1sgkwrYbelkISiEauLZ9wC9/42to1+KdZ7y7xXAU0ctSkIJNWWKtJLh2i2o9ofjDbyFWJa3z/wdzbxor65afd/3W8M411x7PPN177tzDbU9tOm0TO3GTeIpj4oghQv4QCfhAMFI+ICGhKEh8AISQGBJhRY5ixUFYGIxFYmN1m063e77dfYdz77ln3ufsqeaqd14DH+oYWeC2g+1YvaQt7XpVtXepVOv/rrX+z/P8qJsaOzvj5M5bLD68gy/P8eV9ZDXBjq8hsz0Wz55w9v4Tst0h+fmCj/7r/yGvfuxjnNy9y1u/+VVuXj3kV//H/4az9+/wlz/1Uf7qX/85VvNzso4kCwXz6ZxnJ2tmS4NTmkCnXN0b8vFXLnB4IQDlkLpLr5+CtxxPa+49LVnOLNOlo98J2c0CNo2jNhWFachSzV4W0xERk3WOqL9znsC/iGz4HwA/9gdc/6+89x99/vMbzwvAK8DPAa8+f81/K4T4zmD056OuDUenBShJ5QQfPllw72TOYBgQBV0enNW0OBItSQYRvhdxcrqmaVrKoqUVgkeTBWeTBU/PV3zr4ZxV2yIjz81bY5TuclIW1FZQh9ue6r7ucSgvUDSStJcQD1LyleXRnRlPj0ruPJjztWdPsbWha2PWtuZ+tcTZgMQHhDspWReatuTr7z7k5HjCsJNw5UKX3igkGycouUWWRaEm1JoojJDeEgtLpiVVtWDTNAgnGWSSXiboBiGRAhFo+mkICAoDb98746xt0RIuJZoolshQ0DaSaenRPmAnTkFanDTEwdbObCxYt41j00CoPUkgwBrmRc2igbwBayyaLfEmChS9yKMxrHNQUcYg7ZCImssHHUzZorUj6ySEOuDa1RGjUUSrJNK1VEXDfLJhs65wctuG7HR7COeIY0nT1IyCDrFQtJUhCkP880IQSkU3STkc73IwzpDCcnFvF60EgRWslwUffeU2xsGyrFFRyOPjU/Z6Y37q1Wv85I//FDdfeh3ygpevXOUn/srP8pd/7mdIY00nVpSbDYtZSb83IEwEg9GY/rC31W00BSdvf43Z02P8zhWy194kUi2sN3ivmTx5RHv0Ic3v/EPqX/nP0V/6JVyTs/vpv8T4xgGP7j3meNIgx9f55A98L3HouPP1b5C04Nspv/jf/9fcf/CMv/LXfoFf+Nv/GXEn5bye8anvv8knXt4HYTENJFlAlBqOjh9zOlkhQ0WStpR5SdEIjucFeW6JIsVOJwBp+fzbj/na2+esC4P2La6qmJ1stolIsSRc/wl0An8Qd+APGT8J/OPngaMPhBAfAt8LfPEPf5ngfNHSiwUaeHrmOOhbPuge09sdcL7egIBuGjM98RzspFS6JUlSytxse+lOUbsA7QS7lxN63QwTON57Mucb58/IDgKyLCI1Ca3QNHnLu6sjGmm4cWOHBx9M2fiGR4GlqSrEOmexUYz2E4KR4NbOiCeTigfzkhf2Uqyp6XRSitrz4NmGa1c7JGHGOrd0OyHYluN5jsCyO0opKsF0WrLWjriRDIcZr13fpyks9x6u/p/IsCSUxCqhXBeMkpByUVFWFoVgXXlUaBhJTdDarRdAO3oSDoYxtWs5zyHQW26jcZLWbz9TJbdQlwiJ8wYjJQ5J3XikMehIIK3Hy61xp24FQgagNGWx4cbugHMsJ7OKyjguXO4z7Di+98aA3Rd2qL/+hAvjHYYHIYvFgvm6YFFqJJAq2KwXqFBDBJPS4P22j5k3FTudLuq5VyGJNXW7jfK6df0mHzw45nw2Z20M68Lx9P6KVy/A979+lXvPTuikPYbDjNaG/MgnbyHjjO95469ydrKkP+xydnxMOLhAEJ2SjXYITyumiw3DUQfrKmaTCaPBkMY023OSIKJta7AV8tYPEsZjfDZkR5YMojFysEP13ozm7BkYTXz1E6S3X2HtoHryIRffvEjr1ty+sssPvn7AZO8mRaSotKB38SW++t5d7t55mx//+b/FJ77vJf7h//CPOH78DpdvdkHvk9uaxbJkpytYrRzvP8zpt5L93YTDvYijZxtuXuqzPw54eJIzHMVUZcu4k9LpCGLfELmYYGm4EXY4FXBSLtkc/8vhDvz7Qoh/m22S8C947+fARbYwkt8bR8+v/X+n/e/jDoShRFnD4X4Hs3CI1nLpWpe6Ubz73hldJdkZdhmMM55Nlxx0Y155bch0XnH0tOaLi4K9TNNJFAe9AWGoKEpDZBX51LGpJdMzy6Vdz3K5pmTOyzs71L5kIAP2uzHsddECjlXNS6MuJF3q0zmbacuyY3m8XnJ2lrM3zMjQVLnjwdmaOIrZGQ9Yr1tymaMDhVkZnDWEGhbLhl4/odcNOD3P2ckyXrjaZ3cYE2D4gY/fpNs9ZbLKWTwtuHi4w3xe8PKlfeaLOXFucbHDese89By3Fp1J9oKIvKnY7WukCJk0LStvuH2xx3JZM88rAqVxssVbR6jAIjDCoZVCGocXEKntlkEpjVbb+DPTNDjZxSkIohBbthydP8WScr4w7B0kXN7TdExLL5K895VHHFwYsjk+4ekk48IgJk1CzmcNcaC37xHD0fFmqwzUnk5kuTgeEEYerSAJwu3hZK9D03p2Rn1CFWw7GUohpALpMSrhg7sP2RsPSBQs1gUvX9vh2XTGWx88oity9ga73P7hH+fOF3+bR+8dcXDzNQZ7u2zyYrsSqQVVWRIHmrbOt3FvQrOcz4mjBOopvPsF/Evfh73x/chwiNpsCImwOzfovvljVOX/QqP7RJli9cXPMd1Ihhcvk+10YfGQoH/AD/+1n+af/OYdjt++x8deGvHZ+2cEF27z1S/8bzy8e4cf+sxP8Lf+4/+UB/ce8iu/9Pf55rffZd3M6A8yummMDjQHewmrdYspU165cciV/R1ircmrJUXVMDmvsRZ6iSbpOkIXU52X1LXlfNayCBrKRNIR33mq/3GLwH8H/B22Zta/A/wXbCEk/8Lj93MHokj5q5c73LgxZPpBjm4t9ycVu3GKkZ4k1uR5TV+G7A8ztJf0lcL1A9o6YddLPnZlzM5+yuPTOafnJbNFhQ+AnqDfz3DSMOwn+NazrGt80xAOOsxmG5onU147uMAwVXz54TGrdc1yWZOnW8lvv4CsJxhejvn45UucHeesfIFWmtW6ZpQFpHFC0xrWK481BYeHfdJA07ae0/Mc6UL2Bl16/QgRaO6fzVFCIZly8dKYZBVw7/GGh09mbMqCol4hUcTSUdSWTsrWh49kXTdkqeLqMOW0dpyWJUEkGGQBR5MFdekwThPi6SYBZetQHkIc8XaLiXWeVgrCUCC9pDIWJyANNVEbUBUWkTlsU5KIlDgOOV/X7O8HvPbGJZrNhOOTlqmeEiQB955O6cmUYmaoNXjhSJSgG3l6SUDoFSdhTYtiLwvRgca0LZ1I4oXermqQ9Dv9bTBrGDJbb0jCgCyJcIutStIIw6oWPLl/ws2LI2bzmpP5hsmq5nRyjb39IcS71ITUk2PWTpBv1qwXD8nXS5qqYJBFiLakzmO63QzbFljnKSqLyWfY0yPOq4pRskvw6FuI4SHu1g/S3noTIVvMC58m2b1KtDzHF0uO3voyZ3qXSy9/L+H4gKo4Q2SH3PqRn6D7u9/mm19+h73bkm9+8x2ezlqQkq9//vP8xhfe4af//Ou89vF/lf/gb/8n5Ms1n/2t3+Ebb32O88l7xGlAlkYo7/DOcn66JEwkT1YVq2XF9YOIS+OAsBOzN4gAS9laLIpkGPPgaMkwTYmV47z9UzYQee9Pf+93IcTfB379+cOnwOXf99RLz6/9oUPiKWrHbFEwuhjQ8QonIiLhKX1A2nOgNO3KoHsBztVsakvTwP5OzBsvXmbPBzhtOfYL6mW71cpHnt1+QJM3uDjkLK/IPOgg4MOTDVFZcX5ecPtSj5XdkLiAT+5e4/+694QP5yvCfpdeFtM2NcpmzJ45PnQLVospOopwgeP0SbFN0jmMCYWksNuT+/VqzaO8xVgBrWS6qllWFVFH0LYxxoTMljnDfkjbBDx5MiUIAlZlg2ktizwkVhUvXd0lz88glSRWoGxLFkXIforxLYu8ZZBKolAgnUAjaYUkUB4hDbb0CCfodKKtdNgZAq2IBLRGYr1ASItQmjgB5yVWWnZEzMxrtJc8W6zpBRFr5+hpwRe+9D7GKvqB5IVrO0wXOda32Eyxs5vy4bNT0AHDTKGEZ7GxGAM6DKiLGqUzdJLwZLLg0eMNL95Q9DoJq1XOdLkgCgK8s4RJRtYxlKbEe09rHJe6A27sh7z9aMbZIscKT2MiPIb75wt+4ud/Dn3z44iqYnj9Fdbf/j947+F9djLJ7s6Y6aKgbFr6+yPapiAvK/Y7YywGE2rGwxdo+2OqekkpJLapcF/6LZLsAHXzB+H9XwXdob32aRhex7z3z1iWFU+bihe7XdpIoRljyzVZN0EVK57OW/xOQiJi3nzjRd6+f4fJWc7FV4Z8cPwOX/nFL2H+XpcXbr7Epz/9ab7vb/67vP/gfb7wz3+Ns7Mj+v2UyXLNopTs9BSpdPRHCf1AUzeWINbMiwbbQJKGVHFNKEDuZORlC2XDYJQC0z+9IiCEOPTeHz9/+NPA73UO/lfgl4UQ/yVwgS134Mt/1N9zCPras95YDq6OsK0jEiWtsXT6MWbjMLKmijw3ehkXkg7zYsPUbJDeMJ+eI2xKLGJi2SEetTTeIAM4GCWMhgnPZhveL5fkQUAceY5LqJcFL39kxF4/4ezBmnacsR/nfM8L+wQHhq++MyWMupQnFtoNs6VB3VAM1Zhy1hI5hZQNCMflcUpjNV7Oma8c07VjtWiJI03Wy3B5jvfb9KDRqMf5vRNWeUscKorScuvGLt4+Y7aGp60A33Lt4gBEw7gH3WFG0wFRObRTGOd5tCjJwoQkgL29HmdPp0i1VQJJK+jobQtRKTjox2RJ+Fyf3rApBU54QqGIAnBKIhRUK0uWRZyUG8ayS+sUpoK1sQwyxWS+wSUJYSSoW8Odu1NeuL0L5QprDXlTo9OUUCr6/YhItswXNTaJkLSY1nA+ySk3La7etk1bW3Nh0CfPa/LaMhwOsE1NoGOSNEaXW1NRGse88eKQa4cZ58s1o0s9nhyfIVRMN4t5+/H7PHjnPpdNyfx3P8elH/kprn5wxG/+s1/nz33y+zEE1O22M3Iy2bA7SomjACU91qz54J2vE778BuOLV9l88C2SxYp0lNB2Rjgd4qsH+M0Uf/cLKFcgogFx1kGN9wlPY3o70dZifPQY0g70XuLla5d4pFp0aHjtI1cwUvPk8X1uX9un3jT0owsEl0N+87MPeP/BMXfvf5OPvfEqf/7Hfpa/8e/8R3ztn/4qjx7dI4gjnjw959lJy41BRiA8qhNzdT9hNl8SOkVjW9ZPV8h+yGS1ZlFvo+r0pManwXecf39c7sAPCSE+ynY78BD4mwDe+3eEEP8EeJctbOff897bP+p/4OHxxHBVCiariki3XOnF7I76rNc1D90SKype2RswHIawbomUYNxNEd6xCWG9KXh6dM5ZUdLpKmgVTWNRImWv06GZtDwOIua5RRQtwjuME5ytS24kmsuXO9RdhzgtebDOCXohN27u8fhoQSYDHp8tGXQET1dzTG0pK8dkCjpQFE3LyXRGIwNyKzmbGebLDdcudsjiiA+f5eR5g2+3wMhn53NGowwhNA9PFmyaU25d6/HCjSFfemtCHDk+eqWzzf3H8unXr7NoNwTdPpOTmrqsMcbzpCnYG3nGg5QXX9wnywRvv3e6XQ04g2klCEGWBnR7MVf6A/YiwZ2zUzZHG9JkG0bS6YQ4JZhMCg7HY0IVcpQ3LHxDGgR0ogDrLVkYcRpuAz9SobBC0FBTuYp+oOj3OiyLlksHXeqypPGeybKmqTyBaLe6g36XunRUZUsSRwTx1iodRRpjG5o6xDaO4bVDskRw8uwZx6eTbcCIL0g7EhVoKmMxtaOf9BBKc+naDm5aMDlbEeQfcnz/Id2zZ7z0A5/gNz73Wb7wpS+xbCLSTpe2LDidLlmuKl576RI62nZv8vkZH37r69j6FUKV0iZd3MFl4uQCaAtf/iziysucf+uLFL/8i3SvvcHwR/8CVnTIxjGp0DTrNbq1cPV1HCF/7t/4eX7tl/4xm+Ujnpw84u3PP+BHf+QvkppvsFjlBMow+XDK99y8wid/4CqPl6c8mj7hf/6VX+Qv/Gs/xV/6G/8W733+S/zvv/1ZzhYThkIQO0MYWspyxclpRWmhdg4TNMzXNYEA4TxZEoBtMc8BPH/sIvD/hzvw/Pl/F/i7f+TE/38Na+D+05ZKTHnzpT6xSHi6ypmvGjSWP3f7gL2dHo9mE1YNJDqiC6xdQ1FqelHM1dcT1Nma+dOc5STn8GYXLKzrnM5BRvVgzmJTQmtARGgNcSlYLT26U0MsmIYNs7plcWRpAo2pPdnQEzYh4KlXLYMopIoctlnhrOToxFObFYe7Cd5qGiu4sDdmPOiwaSxS1YwHMatcslyWnE033L55mTiC6aKirjznk2O+7xPXiTtdVJFzYafL8nhJrhXrZs1hHHP3fMK7dxeEkaYqLRLJIOuxu9Pjc59/j2XhAE+qBVaFCLGVx2ZJwPULCYeppbfUZFXIfhoTxyEGz96ow4Nnp1wd9HEYZusVXknyjSWXHic8zjgmbU0Yapq2pWoaTONIRz2qVcOF3Zg2aBgdxmAMJZrVZsOi8gx68XbLV1oCael2Y1rvqUvP2Swn0hv2uz2wBqW2SDmdJYwHAbRDtA6QoqXTSdjUBXcfLSgcLMoNOwcD8qKlrNf0u/sMrl1nc7KmNCHL2YLVg3f51Ce/l//zd7/CcrFmNOjiA8jSEOvg6fEZy/WKwbBPN02Im4bF+Smj/QOUAqE04rU3IT+hefo2wbWP0fvoZzCr36bz0hvU03OOF0sG4z70EhhcwF5+kVDEmHpNNtilO+jy8NGaUec2P/zRFlZvI6KKIFQ4H/GpN2+zbkueTs+ZLyuMqSlkwFtf+RqTR49ZLSaUa8lLly7gijmz2YZ+LJCdkKKwjPe7+LrCt5rTzDLqJnR6EbatmC8Fz1SLKP8EBKI/i+E9hAouXeuzMwqReKbrnPuTKdqHXNlJGMYa3QiqXLJoCobGk3iBkArbOmrVbEMa0oiJLUEHFAYeHW84rjcUWnB6WjDUis4wYb02NMLxwsGAyFhyazk6bhg0imXhmeYl2gTE2hFGATvdHvPZlEsHI56cr6B1XL3e54O7K/b7MZ/+nqtgWt55+4RQKA4Ph8yXFW+//4wLOzG9XofFumG1rFjXlqOz99jb6dKJAoR0oBTnZxuWyyVVK/nynVOuxiGlkeSFp9dLGNmaQT9DCkUvFrS0KFVSVB3qWm2RZD4kSCRSClrbUteONJZc63cJMZy2G/bGKS6RfPPJhKjbJRARg86A1aIkikMGWcJsXdIYi3ce4zyBFLS1wJgGayXGR8jQslpVvH5tjM4kjdfM1xWh1qyLCttKbOso1jVlZQgCRTeWZF1BVTsW85y68bTWEYYhVw53ma4KiCNmZ2dc6l1gbzxEK0EWJtjGsjGOtjXkZY2fRtT5kqJtuXJ9jBo0fPj273Jjr8PtH/2LyKjl/N4d3njzU/RHI375f/p1nNsWz0CwTZfWknVlmRzNWVbH3L58kSiOyfMFq9lTLrz1OS794GdIujBfLOl/+G2SH/hxLt9+Ex9HuOk98tUZvfQQ8/AOujNBXHydpuO2h4+//Q9QJ49JoiHaTMi6GUjLuhIUq5KgGxN3M0wjkU1LUdaUIkJIwwcPP+Db79xDYmlai44ls9pQeUtZaS5nEu8dzaZCBYJawv7FAZeTGFMa6iCi0DX9LEF0NXD6B86/74oiIASkHcmgF9CammYTk8uWIAjoWUkkPOd1iWocVlS0Eh48WZIiEAcx46FmqBTaB8zLDau8ZrOpKM9bWgTLeYtooFGOPFF86nrGCwddztwG1dfY8xZZwMMna5qlJ4gE0ajD8njGRz+yw/DigPOnaz7y4j5eaR5PVwx7MSqOGAwM168kmLbk0WnOwktUAMV6wTAOePlSnySTLDYNnSigkpK6KsBrVrOKOFYoAS+/cIWy8Dw7L1BasmgDglgz6IS0NDw4n9HKLdFnOS053A8YhAFhN6VqN1y+GnP8rGSWt/hA88rFhGczx/lMEmjB+8drrvY7lEmMU7DfhuzECQ9mOcU659qlEROpSYH52lA0gNJI3xKqAO8NdW2pWofUkjQFjGPQTWic5eioZHBhwMPHK9LnLb1l0bDZtORSkYSa1cawO0gJAoFrPVEiQW6DTJZ5SaYcoXCknZgyL3l6dIozNc4rsiigMDkXDveoFwtkdMo8b9jrdUg7kvN5zksXxsxOHrK8c8Ynf+bfJMm63Pihn2Q83GVWfJswCtiU9famUZstpyGQRFmAaw0iVEzrNWfrnFEnYXU2Z1lMIPsGF2+OadcbMCva2beofISZP6OZPuLR/TOuXEuwB4eIr3wZeleR2T6kEerjP8PLi1/j3je+wTgVjK7s8uB0wmq14dalA7qJY5lvaEVIr9ehtg2PT5ZsVhukDclzRdu2oKBZtJyucnIs0U7IqmhQg4TcW2IniQVYb3B5xbIypECER8eWIPgTnAn8mQwBmxKm04rxMGS+qQj7mm5HMw5DDoa9bYiG8QSVIpPQDmMm04IdHGkTEAhJG1mCGC5eiYiChFkJD56soIH+KGKQaFywdR1GMuKVnT65qPH9lGnRknZjPIZxEtDUZnt3kQFDHTPRJccmpx9mXD7so4SgtYbRICaIA5yEIIjoJp5IepIQgthzcDmjLA0da7l2sMP7DxeczHI2xhKH2yX7bFnxha/cxXmJQGGNZZlblHKsqpJVYCmihgsHmlEa8+y4pKw0kbDks4KFMVzfj7n2+ogvfHON1jGPZzm2FnTSgOFOl1XdcDQrWAPnkzXXw4idXsTMb7MEz883zGaWeDein8ZctY5nZUMUxjR1Q2UAPEorWtfStBphBH0gVBIfWY6P5zSVJ5GO1tdUjcRYCQiqumWde6Ybw2jkGHcClLHM8LRNw6qoUElIJ01xeUWnmxIkCU8entK0hjSMEVrzzfffpyka6raltoLKWrqh3u5/ncJGY0Y7mtM7H4C2TI+OuPnxH6asLGVjWeZb3YT3EAVbL4OrW7Tw9NOALA04Wy0oq2b73Rzt4bIuKuuRDQfo3UsIKZj8+t9jdlTChR1OpktOT+7z1c8GfORj30dn9wBTPcaePUMffoLDzwR03n+Xqiw5Xp9Tq5bdTp9XRxmn6zNqEWGdY74u2JQtpnHsjQY4A6vpmqKwpB2NEIIkDBGuJekoiigk1BZReVQoaLxls25RTUN3t0+qLE3RkoUC+4dog787igCglGNdVGht2N8LSaIQ5RviTDA1OUw9l5IBIQneboi6klG3S2Q9x8dz1kFAOk4IQrh+dcgw1tydLCiriKiXstMPaEpH6VqCKEYos727NRV565nWhnhumr2VjQAAIABJREFUuNKPkIcp+0HKyjkePDzmMI7pxz1OVlMmyyV73Q5KekbDAZ6G5abidLrdjnQzaGuPR1MUWy13N1N09xMCGZG3LdZbupGikwXkZUNtFOvSEqiWKN7mz/2e2ShOBIQhT/Oa9vGGQkE3USA0vb0Rk/mC13Z2WZQbaunwgeHp6Rxax8W9hMODmOUqR3nBvM6ZrAx7YQopdOKYW8OMTVnz/t0Jy9pw2WUYDydrizWS3YshJ7MG5SXeS0xrt7DU1pP0I+rQUtQNw3FGtWgJhCBsaiojSa0iTjxeC1ZrT6AhDRX9MEI2jlG/g1cFGIEUlk1VoZxjUNckSUBRV1t/vVYUdYlQgtmyIBIZF3cHlJuaJFJ471AuodProFXGjdsfY2fQJy/X1NMlZ48/JBr2iUKNcw3OC5Ta0k+rqkaakLSfYoSn3NTMI4lSEaEUHC/WfHD/GePDIVqkiKSDCkLipuba7dvclw3St/iqYP7ueyQ/9hlsdY5763OIVUVTrZjfvUPYSVGxZVKsyVSH6wchVWSY5RHLeYUONGkakYYRN650CbRjMV9hfUXVejIREicho1HCcOwRwVYSPjmb4pbQGcYobQiWLVqGiLakENttlxIgm385isE/taGk4CO3ezR2a3W9ftihnHpiqZAeROgRNuDBYgG1QAhH2NcEOqDcVOgsQsYRWkT0Q8k4DPBFyzhNyK7EqArAswgakkIgDLhBSF1WVMZTFw2RCCiEII4Dns02JGNNZSW28RQGdi4IkjzhzoM1TV4y7mniRBOoCiktzinSNCYOFeVyjQeMCjGmQQtFKCXnq5JNbhmnMbeuDjmfb1jlHiUtSgvCMKSXaFb5NnxTCEknilAhKDQii7CFYZApssyTdTUNHc4mU5YGqoUhjRVXDhWmsZRVS15tqb6isoRekSJ5cbfD1VHIw8WKXHryqiEJA/oWatNQWU9eG9IsRAqPsQphHaG0WLllBaaJYjAIeXJaMOoKBoMI05aEkURZmBeeEM84VbQKlIROlPCJy5e5mCYUmw2PNzOQgjQQrFY5oVJ0laJarZi17dZY5MHjaDyEUjOOUq7u7HFhP+Xo0ZwPJznz1RrdXRCFLzI5esTnj5/y8TdeYTWdcPMT/wrryYSCFqm29GMlJWGg8G1L5T3OtBDUEAkaUzOZrVktS/YHA9Kg5Om9+6xfe5HLN19CDTLM6RP0zgtE114kfPIuQgR0ezu8fvsi3peYd76NmOSEP/TXcR9+jt1Ll0mO7vH4aEoxK9FZwCIT1KVisamRSuK8pzWWbidDCMFmvcEUFu231mprBUptKdlVawkQKK0IggCTWMrG4Oc1rVWUu4L5PCcNI2rnCAdd4vV3dhF+VxSBIJDsDCOEtLhWEiM52ItxsqUxLbn3bEzJujQspoYoUexHKTr2OCGorWGdF6RCEocx+aaicYZRmJJFmk4/4HGxhkQgRjFloTk6WXKtHyGRpDJEZZLmUpe7syVxFvNstmE1WeNrzfmmoLNqubG7Q21C7t5f0jYV6BhjDImWNFSs1gbdSenFESfzHJTn5pU9RNDSNBVx4TjoxUilkcISx4ooVFinsM7SyRI+9bGb/NaXP2C1LklCyaWLV6jrBa9eGTEaJrz1/im612FdzFms55ArHk1LKqlwccBov0/rKhZrw2qWw6Th+sUxrTZMpzkXRwmRcpzlBWYQ0uaGalUxSAKwjlVRbk9pBcTCItVzWzRbJFpjPMJL8I7z4y1/sTsIMDQkcURlGgIVMOwKpIEw8Bjj6Hc1vUgh64bKbmPBAysQGjbrNcJDkGYgNHVRUecNqJA0Dbh96TKPzk4oassPvnCdUX+IdBu+cbbgwfGGwSgh9BZhNGXZcvLgPfazjL2dXaw1XHnlFt/82hfQWqOE2LIdPUipEc6ilaBpLVJv/RV52VJUC9Io4dblF3jt1hWUb/B713FhgLr8KuMX3kQrRf3oXbx2lK2jEgHHX/3nDC+/SufHfgpDhSJA3n6d+rd/HYqauiyZRJq4zbZpUHGElwJbGXBiiy83DowijALCWGC9o2lblA6QXiAJSBXb7agOOJIVrQgw2jKvDaMSolKSCEmbSs6nSzL3nfcD3xVFQEoBMuRwJ6YTyeeZ+mYbzpkL0H67BOtrVF0hwpCm3ibj0EjMxkE/pI0k801D13m6/YguAZu6ZdIsCfS2jMZRTEVDzwkaKXCFIeqmdLUk1zW+1mwwJN4RpjHLpmFZbKiKEatFjaXi8LBDpDzn6xVl0TAeJARaMpms2KxzxnHI6XlOGEkO9mOsdTirkEAYWlQQsCxaTmY1i8ISyC27vpsEnM2XtE2NDsMtROJswq0LHQg8UTzglRtbOW0zqeh0Msq6JOnFFIVlrQIOO10+fLbeYtCswFrJzqCPEpKnJ2tmm4b7rUH3NDoJqJoavMBgtiBXFTBvDVI7ejqiKgxYR2kdjfVIqVDSUDTbcJKLVyPs87gz4802uVgr4kDjCoPFIcMIZw0KD9ZhlUcoGKuYjSlZWo8QFu9aAhmhEUilKPOCV1+8xuUr13l9ecIX3/qAO+8+phv3aUzL/dMFVeuoS81oGHH08IhAhuhsACgObt9C2AXnj08pJidkUUAUBYQoirrdshtVsE1slVt6UtMYrBN4Kai9Z7S3z62XrpM/vsPsbsC+ybE+QNz+HgJarBQYW/PgyRP2RyG9omB86w1MFOOf3kXuXMQGXV7d71OfPuSRk/SdJK8LhBdoJXHO0jYNSktoLcJto+GVkqhQIRSEYYiUIKRGO4FvS9q6wZstl8IlCmJNpwHVetbzknEcUklLs6iRWfYd5993RREIAkUah3gnMIUnFC0+haKA6tjgDj0EWzT4aDclL7atonJTUjcWAsnaOKp5hbOWl3sRQ0KMEJRBw9F8SRaE2CCgg+OgH1L3QiprOOimkEUEecuJ1Fgh0NKx3jh6o4j+bsDFwwFNY3j0bM0sbOgPQmgEj57lNHXLsJ/S72csS898ucGqkMN+ByMsTeupjaUsa6RO6Xa7nJ4vUbHg6kGHLGs5XxiqumZTFJS55ubFHovCEeiE5ariNMlJ4wSkw/oGFdW8/OohbV1xMOgy2Nnw7HRD0yoeHc+YLWoiJUkzTVXXTM9nvHhhTC9RTDcFKkzpRxAZyyY3FLVBGEtXBpTS4axgLwio8NiyQQqo8Rg8w0zRHyWcTmquXo6RyjObbtjdGeCt2yoAtUQ6aJwlDjTaWAwOHWrCOCKKElzlqEyOiBX7aUpVh2inibVEK5BKcePmFdJQcjw9o2oKfuetB3zlKw/52c8MCKlJogDd1FRVy2TachKu2O1nDC/d4sK166xWU+zZY6QQ9PoDss4EHShCr/FOYgCUx1qD99uiJtmeMeA9pq44Pz2nWF0kaBsC0+BDRfPBu4R7l3H1FJk3NCEIZ4lQXHv1IyS7Q0x7jIhDxOAVlHnMajPnw+mGnd0+abDtRmgZ4bxFOksSBti6xWBRUYTzMJ83qCBG6WJrpdaeuiowUUbtLXm+jWr3rcUHNVJrVOjoD3vY1rL0BuFAdWKq+jtr9r4rigB4rDdUbYv0ktmipLejyFee82mBDzzxKAQHwmzFJJWTNOuGOAnwwOl5Tts6bhx2EVIz25QM4w6xDDjIBqjK86Ss6MSeS7LDeV4zETkO8OsaJxwiVJwuCj7y0phNYEkDzfigT6QFQRgSRJ5ls6SpGzYLi2m2nL+q9SRAaTydMCNfGZJQMNjpUDUNZW2wRhBpS68reXjcEhnNqzcyrlvJV95dEKttZHS30+fu42OuHWakccyX3yk4PSsZZprgdIanZdgPCbVAqpBAaoxJGXZajk7XLNYOU3mEdux0AtrCMFsUzLOULFQsNo75qsaFnrRRlFVLkGoSKxFGkFcWiUdryaS1eKCyjn4Sslk39Echg1HC0WlO1g8IFTSVYNRLsUpwtmmYTQu6WUirBJF0IAydTG+FQEqgI41vBcu6xQaefi/EbkA3EolDCEFrSsq25mRlufvht/jGB6f80y894Yc+fgVhKlBQG4sTgkgJ0jAiTEPKesMmr+kf7GJMzeErn0BgWa2WqG/fIw4EIYI4yWiFoChyTO3xzwnPxjgE0Ik0ZbnhZDqjaiTXbr5G59pN5KUD3GSKO75H+/g9hFJUUrHcLCk2jviFl/Gdi8jyCaKd4/UrsJpz78kzOv0BShlUpBkHGUoIaivIa4cWknLZ0NQOX9bUjdt2TAJNoMB7RxBsI+TzuiH2Eqc0la3YrBtUG2ADQ1VtVxUiE9i8YZwkJGnLovkTKAb/LIZ34JwnFgHdfsDJpGV1nKOdRGjF2SQn8J7dboduEtLSstyUOAvWSUwLVaUQjWdoAszKYULHMqwoGkPgFblrWS1bVsUc0RWczDecq4pR13OYhazrlqgT0SJYb0peuHlAEilqC+ezDWkc0Ek0o6BLYwxZFvPGrTFv33/EJjeEYUVqAwIpuHu+Ihtuwx7b2hCGkqgXIIVmtihRWrApDatNw7jb58p+hCCk34/wTlE3Fq23bML9seRwNML6gKZpSWPBYtbQ72qctdRNyaZwVJVDK0knBvvcfqulIOqFBEpTtZ5BlnK6LliXhnbiGXSeC4yUIEw166UhCAMCPOfrBmMFUUehK0GoFDvjiCQVONOy0w+oCkM6iLi00yH14JIIv2yoN5ZAWTr9hE4EiQThHQpQfkvScW1DoCReQFFYmlbg2warAkKt8c7x2a+8zfXrl8lLSxwH9AYdglgxGPQ5n6/Y1IZQCZx1xFqyLKfQtFTThm9/5Xe5dusF3GiEMhuQwRZJHgeEXqK1xIVbaKozjspbhBCEgWYQegItMDgmxZxp3vDiJ15HXL6CEw41vIBTCnnpZfaamv1+wKPTOTLqoDoJ3lpYniE2Lf5QYmUf60J0aJhM1mQdyXhsKZsGJxSrvKaTZBinaBqPjD1Ixc6oQ2u2IS9aKbpJQo1k+8WXZGlE1TZYC5nWNKGneR4c6wroy4iylCjpSVT4Heffd0URkHIbfLEqWjZNS+UcR/dL0qEm8JBbQVzCggbvty041xgG4y6RDnn8ZMVkUXMYB5jccrws2dlNEbqgKVuK1mNC6GUR7z9dcTY7oZMGzGcNUite2+szIuWb+YxeN6CRgnVe4LzGGot1mtmqxHdT0jhGARkRnf4OZ6sZUQS9KGZ8qLhzf0brHWEUUtUtYRASKEtVtxjrMUawO8qQ0tE0hlZodvZHlOsJWoYcny8Z9QRRGCKk5GMvDoijhEcnLbPFknCnx2azJfVIETCZL1E6QOsQUy1pC4drPY1zTK1nbxSSdgJEKAmiCBlIAifIOiFZP6RY1aw3La4WWAtRvM0pNGK7ArjYSZi7inigiAOBt5YwlLz5ykVqVxNrEHVLW7asaocOPBf2UxqgrVtqITkYpgwCjfeOUEmk3HIOpN8CUo2xCCtQKLyURFlCGqWc3XkXpSN2hwEH44h+Ao+Ol7x64YAPn5wihEA+pzAvVgUyS/CN3WobNjluk1PPJrh8gesMkEAQalIVoFAQabzKtgeQVGghcC6gNhWLTYmPBdP1nMfPnvGR1U2CdhfiPvLqi7jFGeLCiwzmR2jvKc2G0+Uc1gtoFoigA4eXkR58dpFwtEv56DFZOCL2mrNZgXUtm9LgvSZMf+8AdpuN6GxDICXztUcpSRwljIc95m6FrRp06AkTT1FE9AeCzkixMQ3DNEElntJavNJYq+gmXWrRfsf5911RBGKlGMgQug5XKs5ETaWCbYiiaXFhgKscpq2w1tEaRSQUh50Y5yXnSiK8J28N92Y5XhhOMQTnEAchU1Mx7gdcHPZRp5ppWZJFwfY02EkWC0OiJMYYDnYiokAzmxueHOf0O4p1KRgMAjZ5hY4DlhuDbyt6yRBNzHy1JosrumlAg98qIANFU8Jq2ZAkniAMMA5aY7DWstPvsJ5vePxsTr8f4WyAQPJ/M/dmMZZteXrXbw173mc+MWZk5HDnW/fW3NUD1WWVuyRb0MgYqW1GC0sg/IaQDG+8wCtIIBkwSGBAgmaQ3YDBpm3TuHqw29U1153zZmZkRmRMJ06ceY9r4OHclsstX7pRCVTrKWLHiX0ezln/vdZ/fd/3y1LHbmdEpEI8bjtZlGC5XiOFY1U1WNdQtwpnW+rGkYYBOvRUjcOgsN5SV1uM2HxjSbuKnf0MvOXJTYQVLb1+ghRQ1Z7WKQILaay3xiPrGKQa6y1tbahbyOQW7+6tZjTQHB1mLBu4nSypCsHEVGgZYpUiGca084JyaRinMbkW9JSgcoJAa6IwQAjFuqpYiZZeHm+FU6VASE2Sp4Sx5GCU453FtIraOR4cpFwvHN/94ClPrxYoKTDe47zDS40OFW1R44OM47e+zKAjifM+nf0DLm+ucNajdUAapygnUXGIrWpErkk6Md632FpyNSkwzhL4AOUcl5dnXD89JUsCgt07yJ17NB9+i/ryBn20w/W0YtCXvDg/ZX2zID/s4kav4VSCaCfIzRVtU7CeFbz90kOkWXM2KzHa43HEWlCUFSAJY08YasIgwdYlbePIsoQsjel2E6q6oVUCL1o2G0MUSPJcsGksIgrQBHS0I8wDvDTEQoGQ9MJPzxj8qSgCARA3CoxhkKfctiGdXkV17fFi6zWvK0PW0cg4xpTbM+/lbYXzHlMbWrt92l9UFpUIwtpQzVu6uQXhEIWjKZdsqpZOHrDX6bB36BjGEafXCxpjEX3Jg90ORaU4uymZzizzjUcaTxqDUpLVsmC+sNR1y92dCkXExdU5koZ7R0NGg4yrqyW2tpTOcHqzIokCssTh/Dbosio9s6BltWnodGLG/YDlwjOdrdGBJ0u6LBaLrR9dxrz//JYX13NePRqghMcIxWplKcqGMIzo90IaKjo7MSwtOlXMFw3ewGLRkOYVzjckUhGEAaJ1lEWDt46ycTTWkcWaNNWsNxbnBEmwXaFdL2uMFXgnGOQxUijujDMCZcC1rBuHkRLSGGsF2nmE8JRlQ/vJysLalkXVYmTAuBttJ7zfBps4rViXLQSCxAuUlARaEyhBEmkWdYtUCVXp2R9nnN5e897zCqUCrN9+sRWQxxqPpW6hdkvmmzWvvvomwjqMq8Ab0m7KoqnIki7dJMW4llZoQiQ6rlgXNUp7ghiSRmEdeGd49uKc61nF3fkMP70gdKDTnPLZFd03HtIb3OH88jmn4oqb2yWdt19F4LdqRDfDVRMCZ1DacHpzQTeR3NQlm8rTUYK2qRGF42A0YtkseHFd8eqDlzjY28VygwpqtIpIwgAlocKjLCRaE6SStqkxLiINNSoMkE1FGmoSFYGEsoE8yz91/v1UFAFjHUEO05UnkA3dNCA3lo1U3BYtu5nm5QcpnoAWiVAV64VlerbkThLhC0vVtnQyhRGO1jh6SYTRnsq27MQBPZlwcVNwsaj4TD/j4U6HOvFIZ2h6Eee3LYmVDDspN7MNt/MKaxS1a3iwm287ucKzWjY8OV1hnODu3oZBN/+HBF88cRYRRCGLVYkKDOv1NtMuCEK8cySBZneUcDVdMVvU/LH7CaaxNKYkTgKuJyXPLk4w1qG0RIuAx2e3RJ/InevK0hqJkAFRHDIYS3oDyfVty/FexiRpuZiUaOkxUhJFIUoLFrOSMErwjcc0jlVjcHha4xF+CyWNpadl25kv2R4LdvOATqIY92O6eUAUg9bbJKBIhUS6pP2kB4FwNI1F1NCUBqkVpbFcLz31pmVvd0AYR0gpyeOMz/X7ZLsDRBBSlwXz61tCFYDYTu4kDFmWDUkQsVIaR0MaeqbGkSmxpRYJCJQkEIpUxKT9iE215qPvfZ+vfPkzRN0uy49+iKjXhHGIc579nV2+8sXPcjm/5lvff5fGQd5NsM2GomqxYouKd9ZRGM/z2YyPz8747JdeQVcGP7kk6gwQOwa9uuHt4yGnZ8+Zmw0Xl1MeTie4rkB2DhDk+PSQ23XJ+e0KO91wd2eAFpL5dAV5QhQoOhEgtj2ORgmqZsO6cERRSr+jaWuL3dSYumC5rskDQRR46nWFthqVKrppgBOOjYdcQx6ErNqaJEiIdfSp8++nowgID6FkmGasTMNy3bKut5BSA3hb88q9ITcLz3K6YacTs04Szu2a2kEpBXEcEseaKAsItCSot3n6ZWGxgUdlisREBJEjygLmZk1uE3oyhEywdjWtq1msV3TzgCSSVFj2d1L2xyGtiXl+OUV6wWrTUFvF8+sln7s/4N7hmNEoAARFU6MiyaCXsDMa0OstuZgsOZ+VDFLNIAsYDvR2j5fHZKknUIpB7wEnZxcM+ppq2vL0xZL9UUYWB7y8N2BjWloviZKQ+eWGoB+xN4CudpSLNQOj6YQBK1GzXjQYD71uSBprnLFcTCryUYDyW85D6zzOeELvaRE0BsragPAEyfbcX0nBoB+z2xUMdzOCEISwtEKyKTfgFJ1OSJwErJYW60t0qFgtWpyBfleDCriar/DGcZAopFZ4IM0TDncGHNwZ0dsdMFuu+G5Z4qxmK+dtCXUAvsYYSyglZSsY9xNurlvwHuc9Ukp8oFhsCh4c7ZAPBI9OK3bvvEochHhTsPv5X+D2w39AYxzeSnZGQ+7d22f46jFLGfDoBz9EhyAUlMZSlC229XgnEFJQm5IPnj5lNvs5evfuglMoFLrbofzBd/hsx/CdccLaNjw7O+ef8D+/7Qn4Bms3iPqK+WLN7bqhE2e0jUdJhxaCvUEHrQWtrzhfTrl/sIdbz6nKNbPWUBQKHYRbEG1ZIW1DVTVY43HNBl0bgiQlih1OWJpqa/GOvcTUawoTcTga8uq486nz748SKvJfAr8MXHvv3/rk2v8AvPbJS/rA3Hv/+U9Sid8HPvzkb7/rvf8Lf2gVkALbOqqiYd1YLqcFk9KQ5oqdTkYaWC5PVzQWjkdd+nmHedvQywLObwuaosLVUBtH0Fi8EFxXNWVrUMazMpYssvhaksaSPIlY1ZaAisT3eOFX5E6ShDnrumI/kqz2+yyqgjyAm7mlrtfMbyukVoRaU7eGye2a4GXP4XjExe05+8MOm3VJFkv29jLiBDpOcz1VJGHEW6+N6UQCLwz39nOE96yLivuHxxR1jPXQ6WSEkaFsDC/t9TnYG5AEkm9/eEVZ1QihuX+0y+V0zrjXpSMMT+awm6Tk0nGnl3A2KNnUnmE/QAnH5LbFeM9hnqCFxDQWITzS+m3kmBfUraKoLGkWYqQDYxn0YtrGM1sY4m7DIMuoVi1pKCjKBmMlykOeBSBhtYqZz1cs5y1KSqSSFGVFXbWfcAa3CDAtJU+vZrw7nTC62YaI2NYia8WdvQMCvc2UTDsd+sazWJXEeY6r1ng0Sims3RKkkRJjLFGqSBJBEDQI2dA56tPd2+H2g3eI3vw58jxBfes76DhhdPc+ejyi2zS8+sabnJ88Zl5M8cRo6dGiwLuGqvFkOiKWgsvJhGdPTznaH6CVYXN+i8pi5qua+698nvTjKVfnVzw+e0ajc3Syg7cVQkfI9CHD3QccXi3I85x7h4e4tiYMK/b6PWbrBaaRlMZxvSjYLBuicMtibGtL6A3OQQzYxoDzeBTWRPTCiE6eYU1JtaoYphFW11AbTBMggi4v7T7glaOfTCz0XwF/Cfhvfv+C9/7P/liR+A+AH08xfOy9//wf4b4/NhyL24YX1QpTtczWFmNahv2ULAFaz9V1xb2DmDu7OdOyYVNv6MUaP4opGse6uKX2Ft1KTN2wXFjWteFwFLG7mxJrzeOrOdEg2BpZnOJm2YAuqZUkrSy3tJ+Eg5YonWz3cUIzXTe0RcPewZD3Ht9Q1pa6tUznBSfXt8RRn+cvVuwNOwx7OZ28wivD6YslzgVESURQWKazNeG4Q6g0y2WLoaEbJaS6z0enT9kdJTjhMabkjQf77A4y2rZhuio4OuhyM61oGkG/b7izG5DFEetFQSugjjxZqtnPunw21Dx6eotgK8RSoqFtW2ovQEgiCeNUgxdUVmDKhkA5QID1uFZA6wn1dtWw2ThupgX9fkxlG1brAoHGeke9LqlqT3804MX5lKurmghJIDym8Tjf0u2n1K3BuK2jEOFZrlectxsmVcDF+YI4DHnr+AFIjZAKhyfq9ugrTzEvGB/cYVHN2axmtLYBpxEAHpQHqQLKut3GqYWai8c/YvWjDt2je6AluloQCYFRHpkGBMMhXDxHTU62wMZWkIUxpIJVtKZtLGXdUmxqIi1YsuDp81Neu7dLYApuL27Ye+kVdGeXwf0D3nhxxvnkmpPZORcfn3Bv/5hGBrjlnCDo8vLdMSePYx4c7KOkZLPxjJMOyoe0VtNJYpLYEwURKtUUpaGWgiRQeGuoC0ebeCoHqAARCka6x700o5+GLFY3nLQNqnAUqxJkSpRm3Nu9j8r6LOyn4z9+Iu6AEEIAfwb44//vJv0/OqQTSC+YVwZvHW3ruLubs64aDvZT+kFKoiR5J+PFYsWqahCtwHtJWLV0NaRaMJlWFJEnjSTKOzTghGBIxGpSsC4bstgzXxnK1pDHghftGnFTo4Y9LsqSfmIYxCHzqxsCoagKx6AjKaTg/Gq1NbNYj7GeZdHwYrLgZ97YJQ4SitrQ1iVJTyB0Ql1LFsuC2nqEhKYBEcagHeXckmUp/e6Ad5+cooMIxZpRNyAJcsoq4Px6ymrlCGI4u5zz4GjIq3f7zK4v6Q9inJPMNprSWuqqZVrWyCxC6a3sVFiJ9ordUcJmZcG7bay4FGRS4jQ0tUUpSRhrvLdY6zGtR3qJby2FsURSUTZwNV3T78ewacgCSas0z28bOolgOJZksSJWkp1+RL1sqFct+ThBxYIkCZCfFBXvIY0UojRMb7aqNm8NRVFS19vmqbEWodT2+DDYWpGb1jDsdgjCIY+enCKlQHj/ibHGMi0K8lzhgoAov0v8yucI9/doP/4BSkh2OjnnyQ1+OYHnJ4jhLi59Aa0lUppfXUaaAAAgAElEQVRVs2ZRbgjSkBTHdNVQ1dsThaoxPH32nBcPD9jvpqggYVNWVMWafNPypcMh305TIi04e/SY+z//BQhG+PkU5CUP779MN/8eYSyxpQUpqJ3DrkvKjWXvzogkUQRBTBaFzGYllRH0OiGT61tM6wnTkDiPsasS5QRKa4xXKOM5ynpcrNZczCvMMmLV6/PKwUN64z5l03C7+fSewB+FQPT/NH4RuPLeP/qxaw+EEN8TQnxTCPGLf5SbOMD2LK0QWA/jTockDul1NGVlIFHEnZjrZcX7H00wjaGTxahQEmUR1nqKytFUgs3KUBnD7nFOt7ONqi6WNU3tMEbiK8/z50uenC4RSYgwgsm64KPJlNjniDDhYtlS3VZMJhWnpytOni5QSjKMBbvdnG6uCAOoK8f1bYGQDmMEZ1cbwjAkVDHOQhpL6rbl8mZDHAoG3ZAnT16gXcLnX36Ve+Mxz64mfPujZ0xnV9SN4/yyQMqGPLeY1jCbrTi/3LAzGrPTFSgt6Y16hCpjXm4gCMjClEB3uJwV21DPVgMhdeM+6XALkiQkDUM0mmXpeTxvOF20rEpP0XhWG4OWmqI2NK1BasiigDQIEMpTLVecT0riFhIPLw0HLGZrio3HtIKzq3O6XfjM60P6w5DxfgclLda0oBSbym8FQWyfSEkYUKxrrq5WaKmI1VYgZFqLx9NULVE6INQJp1dzFpuauq05Phhxp98B02zJykoilUDhMU7go5BAa7LdLum9V2iTHlxewngHk0kab6mKAqIQ3xvT9vZBSHQUMSsbCmfw2uOUoHEOJwTWeqyxzOZLrhYbNipGj3epWsPk8pSr02vu7e6yl0tk2PLxixPMfIakBe1oZcbum2/z+v0eMiywQUWnF3N3PyPQhnG/y939ffYHI7IoYnfUZW+3SxorBNsg3lRDphSBVoQafGFxraUVHpFoCtNQtp6VTIkPD/n8Z97gwcNjwtAjmxLK9afOv5+0MfjPA7/6Y79fAMfe+6kQ4kvA/yyE+Iz3fvkH//HH4SPdXDNdGlLl6GcDumGPR5NTGu/xC0OvawhaR98F9IYH1LqhLFpmkxqTCD46WXExachCTSdTZKlCtUANi7LlnfWGPAgIIqhqi9EgjebxdYGvDKtaMJuVmPNnvPSgQ9U0tIVl7QzSBywLh2DF3jjHRAq/dDjvGXc1zhhabzjcGzJfThn2E6IkpKoKdscJTStZbq4Z9RVKW16/f5/D0R1+60cfMq/mZFHCdGZIE8ugl/Di4gWtibl3HPHKy/eJ4jOePSuRoiTNR1xNb1msKh7ePUQay95el6J21MWC8XiAtWDQdJOc6/WMooLWB1RVyzixBIGk9gbnJapl+yVTsGkti1YROUsiJT4S+NYy7keEmaQtIO3ECNOi45imNszXhn4g0Rik6NBWjiwRnJ7M6XUSDg8zLAHrdcVkZoldgxQCAcSRot9LmRaW2aJkHWqGuQW2SUPOCoZ3jqjaJYl6zmq5pmksWSpoNxsiqXEIhIKmcezvD3BGEDvBRgqa84/Z/K3/juCtX8D/zDdw1YzNfENtcnTYww+P8FLA7SVRGGOaiiRMkZGiqSuMdOx0Em7XNbUxaCe4up0x31Q0tsatapAS4Q3VqqT79kPeOB7yN354wsnFc1689xF3v3YPN3oNe/ID8gcPGO6PmT9+Rhr36YQJO+MB3V7EshD0OwlRqKmtJQ4DrCmoy4rV0hPoAIXjanJD0FoO45iJs/TQxBZaC5PWMsrH7OQDXrp/j+M7O9RVi5GSQDWkyf8HyUJCCA38s8CXfv/aJ/ix+pOfvyOEeAy8ypZS9I+MH4ePjAehD8KYJHHcG+9yeVJRbkrGd3p4Y3l2sqGXS37m1SOSOOZsPuX6rOS9JwsKKbhdtFjviTPFYBQjtGVZVqzahlpCszBMlEErgWwskdFIHE8vNuSh4nA/x6xKulHCZLJm/05C95Uuk5sVj57NcUHIyW3Nxgk6PQM+YNAL+Orn7iB8QCQdX3jtgKeXDQpPAKxLw7ptaL0jjgOm85p7e4ccDsf8r7/5XR6fXeO8I4kD6qql3wtRomB3p8f0ZkG3L7G2IE8G9AaQ6JZF0VAWFdoL2nZDVdUEcUwgA1wgKEqHlBE0Hl8Z6spinKNxLboR7ISKWwTDPCWSFi0UrYVGQhBuE3byPCKSgmXVkCU5vVFEHjrmpibJJLOyRpcVRqTU1vGlwz7TqqKNA7p5yuMPTynXkp1uhMPTCM/5dUHjAuIgwHkHXuKsBRxRqCgLT1k7mtZuhTJaE4qYLN4ji58inOHsxQVHeznT24rIJ8QRlG5r9pFS0u1A0MlZbgpkq3G91wn3X6I1JUxP8Eqhm4I8jcj7O9DdwV09wl48oWpLtFIMspxnkxWNteRxyDosCbUCK0A6Gl9xObnkcBBgZysefOaLHB3f5ebZJXS/xtt/7E/yzff+Y86vrzk/OWX02Qlx7xDiDB102du/x3vvnJCgCMOIKOmzOxpzM11jbctyU2Oso2kURVHirKOq6q20mxaRRmStwJQVodrKhxebAqkkOu3RiSWjO/vcO95jNOhRrpeoZcX+KGW5nn/qXP5JtgPfAD7w3p/9WGHY+X0AqRDiIVvuwJM/7EY6UDTKsNML2QkGLJ0nSUIe7nU46uX0s5zWKT66nPD46gqrJE2gKPC0jWCYh3Tj7dZBKksnkYjWIaQgihVxK9BOUlWe2VoyWyjyfszRXhcfSubrDUq33Dvu8ktfOeDVozF7u32iboSII1rjuDMakceK+3eGvHo84GivA95wvJ8x6nfpp5DEYBC0tkJoy7o0tE3N3kDxM6/cY68z5Ne++X3eP73cqgGd4HZVk6aC1+526Xb3UEGMIedbPzql3+kznRc8PrtEKsXkco41kt6ow3y5RgrPcramLlZICXmWgNZczedIbdjb7WNRjGLNbq6Z1w1B6MgiS9VayrbGtCXFZoOUjr29lEhptPP005SDwz4SgVsY+r0erfPgPL1el7kTjDtdJhbCfsL+sEMee3Y6CXt3uuwcpIx3+0gpsFJjncd6cNYjhYBWsJnXOCxRtg378M4jpAfvicKQbO+QNE0Z9kf0sows05y+uKGfxqTR9unonaOTRMg04GJyw2zVsJytcC4m+Nk/hZQR7gffQsQpdnCXTpbTPbwDSQeRdXH9EWW5pnYttakJVECxabi5XTNbWcrGY9wW3eac4fT0GZdXU8K8Bx527hyQJpLixSkPP/N19o8ecHb+nJNnz7CTC5So8b0d0Pvc++yfpZPuolC0BJiyZFPWpHlGlIZsioq6Niw3Fauipq237tKiblAyYKg1sZI4IdhJc0b9AVGnj9ExmoBuJyFUgul8yd/9rR9RrBt2RhmDPOZO7yc4HfjHcQe89/8FW/rwr/6Bl38N+HeFEC3brf5f8N7f/mHv0TSWVdHS1oJqtyBUG+7udwh8zUE/Js+hrCW5TliW2w/IWIvQmmLZMshDxr2Au/dT9oYR54+XtDNL6AU+EMSpJAwcqhdwNXdEgedmUbG72yGLI56e3/Lzb98lUSnPLl5wZ9yjlo7jewOc1Tx5OuXN13YIgobFwuHrNfuHfYaZoKwWOAXDvI9wgvXSYbWil/cZHEjyJOHe/h5Pz+b8H7/7Q1ZFRT8O2dQ1nVwzDBPu3e1TmIrHz065vNywrDxhljBfzZmtrxl3IiKV4lNPWVueXdww7MbEBQSBYOFAG8iilvWyQmLoj2IUIa5uUaKm0w14cNjjZlnTtgJhBAhBJw8pVy3lytAf58S9ALzFiQAVtrhZy3UjiKKKcrOhO+jTALezilzELDYb0qxDVTc8+vgaXX0iid5UBLEhbAV3s4ynmzXrjSNQAUoJlBJ0ewlF07ApLF5sFYpCghQSrxxgsGXBcNil0JLV8gnGWfI0pp/F3G7WWCCMA/JuznJdUzYN69ITd0YIWWDzIfrhF2nalqaqKYwm3jmEegFRDzm8z7r6JqtiiYwCWhrWRcWyaBBaIlpP2wCmRQjFdLZGBilBt8Pk5oL7x28yvLvP1be/w0vDPq+/+bP86Lsf8eTygtnJCd2DEcZtm3ed8Su8/tkv8+EPv8NqsybraNblBikDkiyj281ZbzZMLuesZxuWlUUpCNXWs+GMJ5QRWQxaaXbvDHjQH6CDiDiQtK6hKC2rzYbrdcWjkwn7m4R5pOnonyBU5FO4A3jv/5V/zLW/CvzVP+yef3BIIVBOkEU5J+cTRmNN526Onc6QjSeXMTJq0M7RlDWzZUE9c8S1ZdQV9HuSMIh4ME4wM0u9tJQ4dCTQkcRJwfGow1uv7/HNdy948uECrz2rxS1HOxlff/uItJds94btDh+fXbK7MyIODEkiefut++TZ1rWnE8HJI4/KGu70O5zfFtwNMw77u6hXNEJJmrLC+4YsS1msW377nY/4nR88Z1m07O30WSwLilrReMPxTpdX9iNkkJIFmuncsp6uie2a7723Jg8E3/jKq+zcfZnfffcHvHd6xte/+rMsFjMmk3O+8NI9niwK5rcrrq2h2+uQZhmiqLk7zogPcz44b3n/YsOg36ClBqlRgae1hk3Z4iwUzrHwhvt7XUTZsmgFSjjSvQ5iWbGoGvZ2D7hcL4g7GXEUMJ9u+x62qnl2PqVYwDgLaasGawTGtcyXBctFi7Fuu+x0FmMFYRyilGCzapgvDWkSEqchcRSig4C62fDrf+2v8OZOxOnpKVelp59lBEGN9Wqbr6A2WAOrukI4QyeLKBY1Lx8lpF2Hdxo9vIPYu48olgRuRUBGsnmCXx3gZQ/Z1vT6O+h2iU4USrXEi4jrtUfjkQKssHR64VZzMVsyW21IV3P2Ox02ixUqTjh4/RVEEvOFvQ6/nmUs7ZrJrOVYW+qrJ0TxANKU43t3OP3gBwinCcMEKT3T2YLGeyRiWyh1SpDCQFuUqVluKlyoWStFR8cMBzlhJ2bn7g79JEbplE1Zspo3eBESB4b9ccKXv/CQk6ennE0KOnH8qfPvp0IxGMeKl+4OkesanXbYJDU3kxt6KuRsVbJaVWSHMbdXlmlZ0zaQFIJBFFIkcHeckHVSjIXrcsm0sgSh4uG9HlVrWF5XhG1DFDbs7CQ8PikY9iWvH3X4yp273JQVH1+X3JoJo1RxeLDD/LblxWbG0fEhvTwE27DZhPRST7+jiSOP0iGv3Osw6nTwFibzG67WK07OJtQGhBRcXS+5nJbgBVkgefZsgpOSQZ4yGqe01hFlQ4JAY2mJk4LCLPCFISCg00vYTTSLzZxRP+Tzn73Hh4/eRaqAz7z8OrerJaeXU5SPWK83dDt9dsZdpGmxEbRVjdCQJAFWasqNxVlPbRy1hUZAWVt0HOBqwdXFLeNRn9QYpqsSGyoQLbY1hDHUVxtcX3O3J3lmEq5WBX1aAhUiRUuYbDP9fQvWObCC802LQRCHEqm3fEJvLdWyxteeThYQhoJAaZQUtHVNUVasblaEB68w7kW88/gFycMU6T1KwN64z7KueHR2SxR1GO8PeHLylFE3pZSSTtpDRAf4zUdQz1C25ujOQ/qjAcmdt7B+hYz6VNWK9fIan4Z45+lkHXb3LDfLhvWiwlmB89tlrXcei+OD9z5mkKdMGsuo30NVa5SIiI+P6aYpgU548vyWoi6R0bZnFKgZXrbkgSXtZEyvblkvQ/I8xJYlNzdLpgvD0TDjuCeYFCX9Qcyov8PJVUHpFHk3Q8hPPi+dYDaeedFQViVeeVSoP4lOazGV4ONn5+z0OxzsbeXSnzZ+KoqANY7JixtC4O2DI56IBR3fY3pRMT9bc/d+xovHG04vazYoEi3JE0gHMefnBavVjJcfOpTW3C5KskzzuTdGHHRTzq5L5qFk2cI7H85oNg4VtCzWimApcDsNhdmKZXwTc3E5R4clNgx58/U7KBkwXxZsVgXOKpSyvPJghMByO59TlIKdbMjVfM6jy0uc02xWhslsw2JjMF7StAopIMs1v/L1z/POyYzb8pqX9wNOXlT87b//DjjBerXBi4hiXdIbx8zWLZvNmrP5Lray3M4WrJcLukmIlDEZhm+++4JgENCTLd1+DxEnLKqS9WZFqjX9POHwsE+0qsnj7X5yb5xgbMJ0vmFTttTW8PpLO5Rlw+RKIFky7GfbEMvGEcqQfkdzfnPD7qBPeb2kFIooifngckPbH9KJ2i1SDI/95CgL66idQEcBgfcoPN46ZKBQWrJxnoV1HOxn9POQKArxHrzb+j+SPObg+IjFckKWXRBqjRQB1rYMugmDRYQWkqK0PH8xwQvP+KiPsIr+/g7ICtndRZgN5upHlEYT93bQ/X3MSqCyfbLdl0FLiqagqRzWeKxx5KlkvZR00oC390M+PF2zNg5jDY2Q5EnAar3kt7/1I7765c/QjT1uegVOkuo+g70hqprw7q//BsXtnPzZKYO9IYubFUkg2e2GzF48Z1q3NA0oFC8PB+ztplSbkk4Q4Z1k4xTJaEyMIvykX3Jn3EGHIVmWbjmevR5Jrvng0VNuL1e0zrNcVcjCUEznBAoQP+XcAec8xgeoQHIyveFFu2DHxWR1jw/rJXoh+J0fLZFJSJpYtKpJDwaEw4SkMkzOGz662BAEmiwJOT4K6UaKxcUKHbQc7WVIEfPu+YQXC8PGCVRluXEtN3KNMBITeOLGMa8lu90BqTKcPb9kUbY8eOklIi/YbFqmtyuGoxjTthTWcX1T8vaDEC1SHtwdU1c1y2lIuBvx1s6As9Mp755MUEqjAs233/+QX/mln+PkZp9f/51v8+f+5Nf5n37j+7zz4ow4CBHCkqUp+4cHnDw+43MP7jKdexq5JkhzoqgmHe7SlTHfe/eC33rngof7Pb76pTucT5ZMbq9I8wRXQ0d7qqZkbQymaWjrllRvcxyt94Qi4cpYZmgmi4aBFviqpNl0aPtbitHN+Zx80EFlAmNChCkpCkM8yJk8m/HSYMS/9i//Of7W3/ibPFk8JgkzPJ62rQl1RNkYmsbQCSWK7bm+ktvwzMoLlq1HzUriQBMMA6QUKKVQWtOWNYKW1XKDtwplJHXZgLMI7yCARgrK9Yp33mmIRczZ0xn9w5R3fuvv8sVf+AzCtvjkHji39QIIg9g8AdVByoK0f8BwdES9eEFZ1njhSOOYTMdsyg2jVHO8E3N204BXrJYLnGuJ4pD3P74kiYf89nfe5f7xHqOV4HJ6zet373L3YMzxbszJow+RSrO4uub7v/mb9OwCmfZI6gZfeRrVpzfu0+11CeKApXEUIoa4RaqYwmj6WUQWS6pPkquFDti0lrZssMZwW3h2+inSCLJI0LYt+V4HHQR4PFVR0Jif8pWAR3C+LNiNQtJAIE2Eax1JqPnK8QNOL6boQDDqag76MVFnm9DTLGpEECGjkNW8pZN62ihkMq+4OV/SrAzj/Yg3Ozlx5VgOEpZVyelU0u86jh9mjOI+3//wFJNGXFzVXM8qDg/GiKDEVRlaF8wnZ+yNh2BDLsqIi8sprzzYo58rpNpjf3yHJ8+veOfdE7q9gM9/YZ+2Fnz/vUvqas2DcUjW6bI76vDByRV/6df+Dv/mn/kTTF5+g28//og3jwdc3t5S1hWdbsp8XXGzKFDWEsYpr965x/nymh+cnDIapzx9OuOV3V2+fXJDPwvR0rNYFuRph/nlDeu04f5RTpCEnF6vmFytCIQiCkOiUKKko60btPMMUo0UkkAZAr312NdYVkWDMJai8mwmK447XaqbDYODhEJoNsuWmam5M+zztbfuo/Qv8x/+R/8JeEcYaIRVKC+2wiMlCYTcKkMB/Lar3zQtWnnuDDPGSUwk/mEBkFJQrRu6eUyWh2gUR7tDpjczBAJTW2K1xa1LoZndVtzf65DmIZnoc/zwS7hyDd0HW7dh7w5pN6L2Dp/tYasNfvUC1V4RRhF3+/foHeU8ujhhuZ4zzjscdR2RF3z7cUvVbGEzd3bGaF/xvXffY77coALNx88q3n98zt3DEWGoMaZlMb/k7313gg5CAhXQSkXQ6XO91pjbmm63R3pwxH5/RDbo44VjOp/iqRn1Ypo2oKxauknIoBNwfTvjerYhCELSJKJuDZcXN3z44Qnf+/iaP/HVz/HlV/eZLZcEQhKGAU4KsjSj3+ni5E+5izDUCiklVVtxeHdEu16RbFaMsox5WfKkXdLfj+lEIa00DLoR3Tjjdu04X2xIupKDYYdxHrBaWC4uKqwSZPsJu3mKxNNIw+TGcHY6Y5wF/PxXdjBC8d7TS7QKmawXHI5HfPG1PX7v/SvaquH4KCcUjjDucXaz4mivx5c/f5eLyZz5ckGxgsO9HZIgZGPW9AdbqKZvGjZLi7Yhcdyh2xV0Ek2/n7E7POSbv3fGv/+rf5u/+Od/me+8e0s5P2HUVZzdSharCoDb5YxuP+FsecM/OXyN779zxQcfntO5jHn54BDbOm5WFeM04s4gYdzvolVAyis8OX/ErZyxChWtVWi2UdtlXZPEAV5sRU5RJBkdDPjCqEuQSh4/v6a9bRGtQYcRVbGkN8j48HyOOFsihERYzXtPlyT9mJ2DDueX1/ynf/kv8yv/0j/H137xF/ned/+vbXY+mrb2VKXFVhafbSlN3m9VjFoqxt2EYBhz/7CDXVuatv2kQBgmy4K1aWk2C5z3vPHGMd1cIdVWeiwVKKUQXtOsFbpTczmfI0gZxbt89ms/h3a3VPaaoCqR1Q1kGbePL5AiJKqfgQoImxljLci7B3z2rVf4SvWQH73/iKv5itePHVQFznrma4sUgtEg5na5pFiXjPo5q9UK51okgk0ZUJVb5Nd8FeCcI9AROoiRUpMlMf3uLsluQBAK4qRPlOcoLSmXK5rlmtYYZJZQm5qmqZm5llVd0rQ11m3dlAhB21ZEieDtz9/hg8kN7z+94itv7JCGAXVjqJuWKImp64a5cdw9/AlchP9/DGctb92JqEVOs6wp3YYX6xt0onk2LVhv5rzxxpjjgyFVVVO1FpmDrysCJSmqlsVGkacJjfbcmJoAGOc93pms0U9usTsRjbVEccRbn8u4N8wojOCiKhhry4EJ+ehszrpuiAPN/m6GExKlA9rW45zk8dNbdLBivWkZD/uksaDT6zOtNrzz8Q+3TS9peTGZo2WXh4dDGm8Ju5IwMMxma5omJogCnlw2/Hv/2f/Cv/UvfIPo4Zd59/lvY2yFDAXegmw0X/rSHR49OqNgzu7effTH51QlvH484v3TKzo5HOymvHw8pj/I0Rb+/K/8U/yP/9tv8t47v0d/rNkULVW7dQvGQUSWJuSdhDBVpGFEpDXr2RK/9vTThO5wF9vWTKc3eGMIEolOA84n0E0sHwcb1kYyEBKzFDTtmv/8b/4uB6OUf+br/zTLmxl5DB89eR8rPF4E6Fhh/La75rfbWpwWjPYyOoGlGwuKWhFojfdgPzE1TecbilZwerXgYlKz0xltScHSoaVHiJjj3df4t//in+a//u//W77/w4/QJmQY3LBZ3bB3/wHp9e9Rt5LJb/0d1PMF/VLz9Dd+jWI22U6+1ZpNUSHDNdfzG5qyBhcy7o4w5ZoWh1QBO7sxSIWWBh1pVusly01BWdQUxZpuFrOaX5OnCc4FYEN2Rjt0uh1c06JUQN7tEQWS2kHdemgbXFnRyWOyLEAe9lkuZjRmg/MG57fhL7LyaGnBQlF4qlahlSdOFfWmIlSG8+ktV1crekmIqQxRFJHo7RGywjG/PvvU+fdTUQSMh0fPNvzMK0fELmRcZbhen0W1Zm8n5+fGR4jQspjdorVmoCUmkOyNO4Qiomhbun3N/bsZi6uax2JObT0nlzPmU0eMpHxWcHSY8+pnRtxMbrkJlhyMc1463uVm4fn+8oKrTcPR/SNuZqeIpaDFQNUiw5isk/H8xS1Hh33efuOAWKe8uLwkTfrcrBpKo+jqjPWmwjlJLxW88dlXeXF7w9Or54RCcX254r1nZywLQxYIpoXk3/kr/zv/xp/+Kl95dY8nF9dYI3EWXn4tZ2+gyN4ccz1f88Uvvsz/+f3tE0VYx7OLOeN+ThJ4bFOxujXs9QacfvhDfumrX6IsCzbugoOe5uZ6hfEeKwyphP1uhhcpRdVSVRWR1jTWEIaeKGlwoSCcb336rdXY2vDgaEAsYt5/fsWoF7BaGx6dLdnrpXzji5/jd37wEV/+hRn/6r/4pyiLOX/trxv+wTvvISNBgkQ7j/LbIzfwCG/ZGWRUwlKvLOXaItMtPrsxnsEgQ17PuV4UrOoaqTw6CsEpvNU4m5AGGV//hXv88a/d5fDgX+fX//rfo2rWHB8OuHjn71PePObsh7+HaitWtaKxEIXwfzP3ZjG2Zfd93reGPZ655lu37tzdt5vdZF822WySTapJStTkQaCiyBFsxzLsN+chQB5i+MHIU+K8BFACxECMBImSOIlsCLKSyJZpiiJFiqS62QO7yZ77TlW35qoz7XFNedhXAQOQchK+cL0cYFedc1CFvf57Db/1fa9+63WsgLKpmc5mFEVNflxycrB4yPqPGI97ZMM+vt9p2YKA2lpCkKyMepyfHxILyzjxDKKU0hhCUHjriLIBWT6gDSkNCVkSgQws2iVFC1VlUcmIEDlib7t1C2eo64bWGJq2pqpamsbhEdimxpsFjkBvssnWxhZVeY61ll6SMezHHJ5VVHXNoxdX2NwaonXC2XTGbLnEO8fqcPRj+99PRRGAQKEk946XfHhzA7UMFGbJZHXMqlBcGa7zwdEpWT9m3I+w08BR3SBix8ZORK+Xo2pBdVSx/2DJcm6pAKUVvnE43ZIFhfKO5bLk3dsFV9dzqpMWRgKfCPrjAfmp4hsvvs3lnXUeHJaMe4Kt9RFf+dYuThyyvZ4wGUu0NCzKU6oaEqE4P5sz6icMsxUeHJzw+JULRBb+7FuvMVqfsJVOWNZTdi5fYFp6wsmSsvbk2hPCkP/kt7/Ob37xCf7Bb/wl/vs//LP8IxUAACAASURBVDp7sxl5GtFaicpS3j+ueOqa4FNP32T/8Ji9w5bj85JJBL1kwng4Io8FdVPzpy9/j7/0S+v88s99jt/9l7+HVkXHsHNAHFFiSWPBvf1zShdYGfXo5Qm+WDI7L7AVmLZAa01VG/oqQFCUGO4+OGdlmFCWjg9dXWP9aMrzzzxKGwre+H7L1778dT7/cy/w9g/e5YXPfJxX3rqLkuckMhAlEVneEYmEDAQpWNQFJlG4ECgbg3MSKTvZhgXqouLLX30JFUuGvZS2slzfuUSWpqSDy/zi8x9j6/oqh3dP6dsFv/gLT9EUDU1dsPve67z4b/YpjOTJx3awvqGfZ1jvMTJgrcHLgMWzMhowHo4Yj3M21sb4ILDeIiOFB7x1nB7vUx09ACTWe5QQaAVBeU6qqgshpT2SfMx4kDItCs72TxkOBgyzmDjWSCVIowghNU6VuKVFeUsmLFGsOraDb6nbOY1piaIUqYBMkegxi+WCs7NdJn1JXdecLiqaakpRFVSN48F8zug0op+mWOs4PD1FycBo0GM6b39s7/upKALOBm7fPaMYLbmysYUNmtVBjkTjjOTwzjG9xGO85/SoojIRp7RgFbqIUGUgzSXT2nB01tLaQO0CobL0lMLUslNoGcvhnZLVccbGVs6a1xwZyYmruLt/xP5eg45iJJobV9ZZHWekvYTV1SUPDg/ZubhNEuXMFwkPTs+YjPtMBkOms1MiW9GczSlbw+Fyzo3tK1wd9VlbGTGMAw9Ob/Py/UPWJn1OpnBmzslThcIyHPT4vW+/x/XNHr/xc5/kP//nf8j7H8xYzBY889QmM7fg26/e5cb2JpcurPODD3YZpNAWjtY8RHkLQdLPObWel156iZ95/nN85mPP853Xv8H2esxsVpN7cLVnb7akn8aYRcvB0ZQjB01Vk40yYm0JQlIsDc3CswxLZoVDzGrSfszto4qe9Lz03gOMEhx87TWKBq6sreKjCR+8d4eibEkWR/z8zz3PP/6n/yfbWymIjhcpkSjhMCawf2hoIs12PuDSZs7m+gXiKMHYmmE84hNPPY2xDZP1Pk3jGW1ucOl6jyzSZPkKSRpQRcWjN4YsTjyz4xP25ic0bYUKDpXmrPRkt8/vHefnZ6BiqqolH/RQXjJIcybjAeuTAbZt2dvfJ85TculZHuwznS/JVyYI4RgqAb0RjXMEHWFMS60iFucL6lpweWeLLPHsHd7jZLpEhhjvSuZzQRonZEmK1JIoTtHxAiEi5MqETHsSoHJTajelbGZ4H9O2JUWxwFcFo35KiCKapmT/8JAkTjFlSV2V5EpiQs3t/T2UOWM0nBBkdxZjPl2ytjpAef9j+99PRREwIXBtMGFjM6INJdv9Ld4+POE0mlEPMubFnCvDTaa+RljPwbLidFESZ4r1cco4GtMECSOF6NfUu57RSPP4zpCzqaU2nqcv5Gxs9XjtzilXb/TIg2Zx4tg/OmM3VNw78cxqyfWLI6Ztzb33ZoBl3O/x9Icv8PSTayRakEQR/UHE3b2M+3cWrP78KqfHZ1y0KYUMXJwk2KqhLM957PoV1lZGHD84ZLr02FYhYsm1iz2UbjiZtwQcw1gBA/6z/+VF/uHf+Axf+NBj3G8O6Q8GlCbw1u2aRz4S8TO3PsSsN+BPXvqviOKEug0E19GLpYyIpKQ/GHA0n/Pya3/Gs5/6Agezp3j37W+Q5ZJIeWKpyWNFpBouJhH7pwZhQdmcel6TBo9zEc9+5BaHB8f84K03GUaBZmEJqca0nrkSBOcRaNJMcPFyzmqk+fRnb/HKK6/y2NWrvPbq29z69NP8lRc+yx+9+mdc2RyBA680OsnZ2XqU/vgqOukiv+sbK0ilqKsCUyY8+fELXLh2le9+9/vs704ZjRO0DijliNMUqSzD1RELZ7j33dfo9WOUgqPDU8rW4YVkfXPMYDTowBzBYawjThKapmY6m2JEIE5AiwolU9QgJiEljxIwJW7QY9G0lEXNcHWVaQPTkynB1NR1ibGO2WyBFBErkxV0rGhMwXTWsJgZlvMl6HOGgz7jcU5ja4KHOFVoKTFO4F2DcyPS3HNyssfp8TllYVE6YzQcUxaB2dJy5+yc82lNEIEL6w07GytoGZOlA7KkwNkZ0nrK0vHt117HIxn3U9ZXYl5/6z6zwv7Y/vdTUQSqyrJsKz422GISrbBcFpBJ+vmYsrDoNOGgXtI0DRtpn1iloB29QdLpqk2DryUiUlwYJkSPjOn1YSXTzM9rfv1TV7g87nN2tmB+MWUYDzj54BRnFad1txKeSEHU6yKc59MaoTStsZS9litrKTuXVjC2IksF3pdsbgiESfHFkpXFlAtP3OT/eOUBB4fHbG2uorG89c67nJYGRSChJUkEd/bPePzRy8Q6ZZIfkySSveOWed3io4RvvX2PX33+ef7JV8+YDCP+9df2mNcN5Y2S9SsXqXcPqJeCyjiSOKKumm6hjUAcJ2BbjFQUxvLe26/y1KNPcOf2PlIfMOrnKKmxwVHZQK4EW6s9pk2FHiQ0B46qinnisUe5srnKztYaH9zfpV96Fq3k+LjgkbUeZRXYqyvawvLklQ221ifcGPbx8/tc2hxxtLfPk889w9e++qf81c99kl4caJo5G+NN4nwV288ZjQJpNQdn0VJQz86orEUITV01mGrKYlrTLg2DfkzbNjRl50s8L5dc2NnCs2Dz8Ruc33kHUwhUFjFeXYGyAqVp2wJsSVAZQYKOFdYHeLgNmUgQzrFYLCkbg9YRaQQLDdZarA/Ifh9v4d7hHsvZnMVpwWA0pt8bsqyXiFTTFDW7t+/y/ffv8diVVdrWUpUVtXGYxlG3BicMaa0RIZDWEcZahMwZTzYZDlNsuyRROcJMOTmcQWQo28D5fM7BWcHJ6ZJFZcgjxXLW4IxAKijKmtlijpDw3oMZ89NpR2H2gkQpmsYCku31BO7+aKbAT0URGKQp33u34urKMRcnOxxV50RaYk1LbTyxgrJ1BKd4+/YRpRe0CESr6Y+HtHiMMcyOlqz0Mz57fQthPOW0ZvOJAWljOTuasjLqcXXQ56z2uCTl9TuHLBvHIOvy9HlPYouAcRIdPJiIlY0eLQl3Dk65sjOhKB2NM/SSHqM84/i9e1y+foVrn3wBu/I9/ujF10iyQCI9Qkas5CvUjUGzYJIJPrAL/vhb77A2zlkZ9MhyxTt7B3g8g0jx4vtH/I1fUFyfXCXIY1Z6OUOteOf2B/x3/9M/5S9/8edR+QR5PqdpW07mgbNZRZLAuYf5okHoTmLx2htvc+vD8Muf/zhf/eY3OD2vWF9N0MQsm5bG12TZgBqJp8GowJWtTZ6+uY1cPuDi5Ue4ur3OK7snnBSezZU+8xoMhs8+e516Kvn4tSu88PHHuHF5jfnshEFvyfxszhNP3eT2UcHrb7zG3/7S59k9KqidJktzUuUp5sccW09jAvkgIeDRISIShmHfM8yGSB3TtBXTZUvTthwczbj11FUurOeEMOXFb9zmQ8Wck8WSnBn1meNoZpBxRF1V2LohqAo1EBgFIQSiKCeIgNQp3rToWLAsHWZZIF0DoUFGGiUlwVToKOV04ZieTXFKkowmROkYFStSqWinJ5R1yXxRMm0hFpZerokiUDZgG7pDUntnjAcp/V6E9Z6Apt/vk2nN7PSA06NDykWDVB0W/sF0zg/uHZEpGI9S8o2Mw3NJ1TqOzkuOF3so5WjqQC/y5Cpm77xgrgX9KBAULPYblIK6MUTFT3licJDFrPWG7M88RT1lJR+xFDFlmLGdZpwtliwXBlM11EoTYoVYGOqlw41bZBAkSnJ5fYzDMW8KhJOMR3C5P2T/pKSaLZFxRBz3kLZkUQQO557xMCfLNMVJSV17HBIRCbwLRLHn3n6FMQ/o6xh84MrFDfb2ZkSy4cMfuUQeYPuxK7jmnGw1pZ96yrLl/skZUaT4a3/9Syg55hvf/AZS17zbL3nz9imzRcNeqojjGIGkrwWFk8zmLf/8j1/iVz/9Sf7L37/LebvAG8GdMqZ65X0+/dF9PvuJT/L+77xP1s+oWs98WXZHm4nIssBwZZWqnHFwvOArf/Rt/uoXE5798Md47+4dirLkfF7Qix21E0zbipWR4vzYc2Glj7ZT5vffZ2c1JdeBL/3C5zlYwOu397m40udDN24w6XWhnNFkxMZQMkzh8OABpYx4+fVddFtw9MHb/PzPfJTf+R/f5Q//+I944fkXGF68TG91jerkmJO7NUkvYbpYULcGQcDbmnndcGGsuz1uL2iC7Dpp4/Ci5c6DO8xmjsO9XXzIGQ8HnC8KSuM7n2Sw+AbqZYEzhv6kh8ViDEgJpp1j24bWamwwVG2BsSVJJEh6AWMempMaSzAlZVFw78ESlQ5I4pQge/RGK/R7AkEP65ZMT2YY16Uwl3WLDw4ddW7DxkJpO+lM6yuKskVGFRa4mY+5en2DL3/lTzCLKRdWB5RVxxEsTEdTjuOYxbKlaR1t62jbFoSgrj1SwCDtRjh95Rj3NItS8qBuGWpBWRtaJxFSUi3KH9v/fiqKgPOGLzx1kd9//fs8cb7DI8McWxu28pzae0rr6GUR50tHUzc02pBFmigGLyJsMKTWQdB4KRAG4gy0Tjk6XnI4XTAvDYYlE53Ry3Ly4OknmrWrPSaZYnfpkMay9BYtBEEoCNBWLReGI65eXSFKEnScIV3CnfcPqDemDK+uc+e736V/9TFsIemnMY9e2WI8vsX79w44Ot7n2iXFZBiRIZHWd1FOEyhbC64lzQSRihBCMswTvvnKB/zaZ27xa5/7OP/sGy+ye2fOdDHluU98hK9/8zs8/4Uv8u1v3eKDwzdY66XkWY/+uLMCnz044fbuHld2thkOxrx3sMeLb/2AT996jp1PPM27tz/o4sXTimyQMsk185MFG71tNkMgDpZeJEmjiOX0hGgw4W/+0jMcnUzJsh6TtU0W1ZKy6oSgy6KgWFhmJXxwd58kSXn6wx/C1RXVg7d49tZFvvK1l/nDL3+ZZz99iytP3aIul0znU7yAuiqYLysiJUi1IKQRS+OpzuegE6q6xJiCLPHM6oJvfvtlprOGpvE8+8QOd3YPSXs9kggWZUUsoLEtmQ7ISBNLj28LIqXAC1prWC4WzBYlXjnaeo7EUwZI0xTrPWXV4nwgjSVCCi5fXMWKiMo4RqMBq6sj0sRSLKao0N1nPhgS4WmcIDSWUIFHsXAWYwPGQ116Gu1xwWODwLbvM5ufcf/uEamKmTWCylgOzytOpiX9VFHWDdYEekLgQyeG8aFjcgYlSCPNrDTYVvHYds7VjYg/fWtBv+8ZDhTzBlZ6mkmc83uvnPzI/vdTUQRCEGxuOf7uzscZxWssilNc5Oh7jXIx68mEk+aUqBexOC1YNpYsCeSp5nyxYHvcJyPlrd05Asv1jS4yW5kWLSSzs5qpcUgnSQYeQv3weKbkg9vnXJxkaOFZOgNBIYIlS7p/dOM0w7U+i6rBL5YUdY2MI57eXuHxJ3boxyucH+ySjNcY+QUrkxEP9u/SFseIRvPiN+9yZ/NdVla2ibOcW49d5/L2ZRKVEtqSKNKoWPPNN97h3b0HKC1IejFfe+NdfuNnf4Y/+NbrzM0porZMEsFLb51y7fpb/Oavf5b/9Lfus73eZ7S2SvARBIP0KcFYzk5LVsYDNi+sc2//nPr8q3z+s8/zzGM3uDrKsW1FiaREcHy85MrODttJytnhHjGnjBOJCIb93V1mRoFXRP0J++dL2sbhXUN/mHF8WvLWBwe0ZcP2+gZPPn6ZlXFGJAR37z3AOMGVK1f49osvoWRFhMOHhGVZcXI+pW0tSkksEhfHxLGkrloWs5qibRBNSyzBuZrlYolvI84LTw2U85ajwyNWNjboZTGNMQQ0tWnxrmPzGWdxvqMYOREwDopihnUG07RY24ALRCLpDj/5hsY2BCEwdcf8F8JinOF8XtHrreDMlAfHJ5xPTzg7OaO1gUhCIiWnlUXKgEDicBgX8A6EDCgPWaqQUlDVoHVHTIoiyXldMrvfYq2nbANKK0ToBK5d2Fri6QJVsZKYAGkac32nR9saTKu5tjJh+wKczxzvnS5QsiWgERLi/CcQkgohLtHhxjeBAPw3IYTfEkKsAP8bcBW4A/x6COH8IYH4t4BfBkrgN0MIL/+F3xFJpo1hK5N4e0oexaRxhHCCgQ8opWl7PXxkGfRifBOIgqCtPamRHB+VjOKYQS8m0hHOOaplicwVeZ7itWb3oCCtIobDnOl5hegpmhPwXlFXgUFfczBvkY1HCsGor3n68piX3jvn5bcOubm9RT9RfO/VXSYh8MLPPE3e6zG6dpN0+3HK5S4ewTu7R9zfvc3jlze5ceVR1ta2+f4Hd8j6hsp5Pvnpp3Gm4e79e0ivuftgyrKdIpVB4DEGlk7z+994k+efuM6F3ibB30MqhcJTCcF3vveAX/nZTf7OX/sURkp6Wcr6qIfWku3JKtOl6ea/sediWKVcFhwc7XPvcJdr1y6SbQ44PjNkUU5aGNpMY82MzScvcfXDlzl88yXqekldO0gGSA1S5kyLEq0skW6ZL5cI0fLSG7ucz2puXR2zvTkmTSTBtCxMy/niGBWlZLmAWPD67fuMN9fY3rhEmkckhaKsSuIkJU01rbV442iamtPzGfNFiZaBOIambYl0TJIkBFnTSzRV0zBbzBmN+5y7kuVDTHjtatqqRWkFOBwe4wzWWhTdUduyamlMS9l0+PLL60O0iFg0BadFxaKwCKeo2o5JgfScnlW8894hT93YxvmGvcMzRBtogkRp8RDsGujg7b4TuQSB15096s9Tf6NeigydW0CIiLOjBhErdAxKSCICSoIPgO9yMNe3xvRSQT+NGPU0s7YlShSb62PaumI596xM+sjYcfVGzt58yWkZIFhq07A/+8mmAxb4j0IILwshBsB3hRBfBn4T+EoI4R8JIf4+8PeB/xj4JTqs2KPAc8A/fvj6Y1uwHtoYFQlO5zMm0Sq2qcj7I2bNjGBLXPAsq4phpli9PCSKu2FR2wgOioam77i4krIz7JH1IpAev2yw3kAfGqVoJSxrQ7s0VKqzG1ss21fHBKO4v1vSzxSzICiXFY9cvMTb92acloZBT+J9xDgIPn/zGleuXeTozn2S0RBIONzb571Fw3u7D1gb9yi9Y1qe88iNdW6qHULjuLd3wPl8yp3bR/yrl96k9hWmUVgC2UNKbiQ9Wa6YFp5/9rXv8ve+9CX+6NW3Oa/POFsEzqeO42HNG69/n49+9CnmyxpnGrQMJFqwMnSMhjneBcq2wemYpVliI1jaOXvnD8jjAQuRYa3jtJwyq5bcufc+VFNuXN1hf3+Pedki4wnj9RGxgOJsQV3X5IMMGSlGw5wo6zNIcp54apOVvsBLQZoMICw4PTvEOIOQmuWyYbooaFvL/d0TelmXXquqBcaU1G2DlCl1Y5nN5h1mDMl40KeoFpxNT2htQEWSynehl14aUwB39o/xWHq9jPN5RdUEgnTEOsY1DWVrMLLzIyZRTFu2tKbBeEWWys5ZsSjJdMZKf4PF0rN3Zmm8Jw2eorCISjAYKGrrmM0t1Ru3SbKEsmzBSLzyaAHLpsOfxzKQ5RoVSdrCgRIsqodU5KhT1G9v9egNMrTSHPcLqtqR9wVl2VDWBh1rpBdsDHIevbjKlZ11LJZFVRBJgTHgJVQYVB4xSD37izm7ewWTYcxzz2zwvXdOaS1EGrQUvH2v+v9XBEII+3QUYUIICyHEm8BF4FfosGMA/wPwxw+LwK8Avx1CCMC3hRBjIcSFh5/zI5v1nigyeNcy6PVRXnJSt3jRIuKUD05q3n1wik1hJY0RSpD1UpSJULWnxmCdQ7eQtaB7gWJpmM8ayuBoFFy82qcou736eBCzd9oZiqyFoZaMBznvjxKmZxVCSYyUnC6bbijnPAfHZzRnFZ+6POSLX/olZu8fgaxZ3N3DxxH3D07YnR5x9VLCrGh5/e4x01lFuSx57MYNnvjUZxEvvsNb3/s2H39km1lZcud0xsl8iQseKXn4JBGMhznBt/zBd+7yK5+7x6+98FF+6/f/DUEpApJYeW48chmhYkxTgVDUrcW1XQxYSoeKYqZ1wbsnB8yrORurfZK+4HB+xDBzNL5hWi04LU5494N99nZPyaIFQ3WOiFJGKxtE+SpxKmmbmjSTqDzG+RLpOyagcwWffWaHQarYPTjh4voGl69cJ4Q5p4tDTGVp64L3do+pTaCXpyzqBYcnD9BC01rTPaGXgbpaUNUN82WFImM8GNO2JcEWNFWNE5L5omE+b8kigbIVtpdSloaX39pjMExRQdDULY0JqAyCMZzPPU51VKhEa2rjkbEkEpqtSUxjHcYH6iDxUmFCwATPYC3CLw09ociymOE4Qacli7Lh3DgS7xjkOYVr8Q91eUJCpCXBOpwTnRJPalKtiLRn2E+YjDtt28W1MZOVHnEao4NkWVuMbHG15eS8QCcSLWAy6DHIUxZ1SVCOQtfooNA9ybI1NM2MndUVpI7oKejVNcN+TpLD0/GEWGvKpaUpPH/Cj4aN/n9aE3goIfko8B1g84c69gHddIGHBeL+D71t9+G1H1sEtBL0hwl5lOFjzXTakPcTghe0zvLuScFRaRnpmCTLyPqKQR7TLjxGCirjWRY1PSkRGcRTQbuw+EhSaRj3+tzMexzOG8ppC07QOEPkA5cuDBhmGb0guHKhx/GsZhgrCu/5/r0zjLFMtCabNnx4Y5UPXRggw4xLn/4UZvqAxdERSwK3D3+ATyw+GHb351R1Qu/aJT44mVKJd3nkk5/k8qUJt9/wrPZbvnBrhz99X/DSDzrx5GiUMrUNW6s5T1yesNiQ7B/O+K9/96v8w9/4Vf7Fd1axwfHcE1f52IdX2NnZwppOqVa5Ep1rmsrQEAjBcn625M39E6xq2NpKWRtGlLbqDrmUBbYqmTUzGmeYhwY9ypjheWNvxng9Y2slQUqLNQ3IgKVh3pwjvCP2miAVwi1JRcbCQi+LuXJ5na3HrtKEJdl7L3M2P2FZGo7nDTKKEEpQmJLbu/fppz3SNMZ7R+tahOhYfvOiIE2h5zLquqAsS6qmxSNpG9eBWmRASo2vatI84/C4pfCBYa6xxtF4zeysRXkwLcS5IAjJvKpxUjHKNMOBQsaBgYpIrWey1qdycLSs0BlkmWbZdvzLcV8xryoiFXN1O4LGM8oiauURmwmHuzVNU5P29UOlniTKIkSkGI40sQ4s24ZhP2U4jEmGkqjv0NoRIoNFkieKBokcpuhhoLUlkU5AeApZIgaCOFKksSYWMUpLYteipERFAikl2gQuXspItGdeWAZSsZ70GV3qYZaW3/7X936yIiCE6NPxA//DEMK8m/p3LYQQhBA/nlrwoz/v//YOTPoRw3QMThMpTUgaIi9IvMK4mp3NESKHaxsZG+sDhFAIAW3U4GcVW1HMfOq5f1xSSMvGWg9vWmKh6Y1y1uOM7XyAx1KgWT5wuFnD5x67zCefvMEJ58wP5mgvWV3p85GVHgdlRV17NrOUlVjy7FNbPHPrBXZf+TrTg0OkfwOtoXftEV791rc5mC+JMjC149aNR0hVj6P5Od95Y49UQR7/71xdGbI16nFwUjAtaw6OznCNpbWe4/OaUT/lxqUR1y4PkTLn7bfv8t23TngwO+c3vnCLsjrjYx99gjwVnE/n1L4CZRE6MKtnVEWFNY7Swf5phU4iNlY0UdTQ4Khsi9SBJGgWtuK8LtEKJlsp/cpSCAfKE6klhU0RymGDxVjH8XzOYfmAWEuGqo8VktYqchlYG/TY3JiAXHJ2+ibl9IwmtBTGslha1jZGyAhi2cll7p2d009qhnmC1B3rMI5T1iZrTKuWZVvD7IRgDfOqpmos82VN3XYi1cp5auMIM8vqpqJyglTAtWvrvH/7gMFQ0pxqoiCoCjDekqaCfJiC6LwUw3HE5mrGZJRStYbQGKbVAiM9w3HGai9FYehlIEQgjxWpFlzZ6qOEQUuIk5iicSSJopxFpLlCJAKlBf1+Stm2BGGJZYz3Ap0K0kHEeBDo9R1JYmgwqLSH1rAsKtrWIFS3gCiCJYokQiukVKRRQn+QIEO3/eiFBglN3eCFQWY1mVDIEEitojWOo9mUpVsSBfFj++L/qyIghIgeFoD/OYTwuw8vH/75MF8IcQE4enh9D7j0Q2/feXjt/9F+2Dtwab0fWtNgaggU6EhgWocVNW1oWVlRrF5YJZMCnCci4HRg0Jf00h6LxjFfeE7OWnQCUWaJpKYpLI2qyVLF3eUpx+2SRKQsg+Dzz36CX/vEY2zdvMmdD86xgzd5qmm5/eCIm+s93j845Wg25fLFi2Qu5spTV7j+zCeY379HnA7Yf+c9Bhsr7N895eXvv4FKUvIIRv2UW49c4mxZ0d6e8cvPPYJSCm0ts+kZN66tULeON7/9No9ONhiEjKOi5Whe8PHHr7I6UpyfFZwsC6JEA5Kvvvp9/t1ffI4HBxFrkxiZ5kSy5J379xCuJU1SCtcQnKSsLEeLCqFSLl8c4NUC7zRJlBCERMsYSXdoJ4sygvPEiaHfS7ANmNhQu4rCLaiqktq2VGVFYRwL14DzhDRikO6wvXGVXmqJREs6gDfff5XmTcf0fM694zOWpmVjY8x4bYWVZUqqEoqqohIekLT6z2O7MXkvZbAWcyXb4vBoSjNtiAR4HdC9iFC2FM5RB4iFQiWqW+TzMJqkaB24uDVhXlXMasvNxwZoJAf7FTLyGGHI8oi2sfTzmMkoYTBM6A8zdKMohEFGjus3V4ilZCVVrPUDKooQkaTb8HNIGSOUY1mUpGkMleXS5ZRikWJai8wgTTUrwwGNbTqJrI9JpKRqPSEY6lbiK0/pPFGSMNKOOFPkXhGpP1+/0GilkErRhIAPkiA6hbzxDus8QoIIDhsM3nsq2+KNItNdmnDYF5QS6lDRup+ABU636gAAIABJREFUNvxwtf+/Bd4MIfwXP/Sj3wf+FvCPHr7+ix+6/h8IIf5XugXB2V+0HgAdJKJsAsX0nFY3rKRjEhVTec/cVcRRQNCZWILxREFQFh0gI5cxZ7bFtpZMSZYLR5xbVnsx9cKyCJ5cRNAEWi04Pp4hRtv8+3/332M9S4kvXuDRyQmv7L/GrWef4KnZ42QrQwbfv8vtu2/y0U99lP7KVaanRxzf+y47zz2DWy5w9hCxdolvfe3LvPzuPlevbPLo9iWi4Ll7uE+eJ2yOI+bLBcvWci40Vas5fGtOP424uD1kkmieYY1l3VAFxfbWkCDgbFpy7/4Bl7bWWZ/0iRNFSFqyFYVLSmrZMndLZqHAtJahDIg4RkhNWbRkw5SNjQlxKlhWEi0ESsiu01tojCFKY/I8o64NtipJUsWwr4mjjEx05uXKdl6ANMlwsSP2DY5AZQM3V67wqY9/lO+9+U3Ol0fIEHNcL1nOKk7nC9o00B8OWFkfkuWCuJ+TRim2SciGqtvnDgYnLUJIcLB/fMgwnzDsJzQhYmWQkdcxVgQGkx79RcOocEQhIJUgTiGOY3oDaE1AxYJbT19m72TBIO9W10eTjDQNzKqKgOzukySmn8UM+gl5kpDGMYMsEBAdM0AIMiVJdITzgSAgVhECgVMWqbrTfq2BYlERlh6nDKJSyBh6ecZw2GO6sLS24zQiIDhYFgZHROok6JbhUGJagw8WY7viKAUY2/kVVQArJEpIghcY73G+y5rIIGmMZ1lZ6sYghSA8jEVrqbu4epxQeYPkJ8OLPQ/8TeB1IcSrD6/9g4ed/3eEEH8HuEsnJgX4A7rtwffotgj/9r/tC0QQpCKh0i0ehdQJTbNAZT2yRDKIIm6flJC25M5jlCaKFK7wzGXLfG7JFJwEx/m5I+kp4tBydFxjl4p67lkdRUReY6aBX/3CI/TLU1hfw9ew/OBlDs5a5Hsv8vQX/xZHe7tcffZJ+htj0qEm3xpz+O5b7L/5Ta5/9nnWHn2O4CRHs3MOizlKKXbWNrFO8Z0fvM2gF5NEMbfvHTEvKoo24FqLEmC9ZJBKokSzOsq5MsoISvKhJ3fQsSFXKZs3H+eRy1dp5RxjDE61zE2BkYaD6phF3SBwxFlOEikiBXGa4RxM1hW9PO7os0oz0as442jbBi0EOpKkiUKlHc+vaRuGTYYUAaklic4YhYRECVQaU9UlmR4TuZY0TwhCUcxrDvfv8dYPau4evEeSK5qgSfOcJI9Ro25bTaoYjUDgSaXA2gKVSiaRQgiJq8F6RZ4PcY0hi2OyOKOJJKEvGPUHxJWHGHYurXE2nyOMwEmLC51ANU0i0p7FOk2qUoZJwng9Z1lUxJGmNCWRVlwQY4zz2NYgXSBLUgSBVCREeUwQDg9IqZBOIF1ACYkXYIND6YgsiQjSYr2hdSCVwtpA5jzGVARvQTkQMC+XFG2NkBGRhAqwztF6B1oSqxgtJUVhUaomSiSttTgfEEKiVAenRTl0pMliiQ2GprUE75ACEhXjrcP5QCQEeR6DFHinIXiMa0mSiJGOifRPUARCCN+gU9b9qPazP+L3A/D3/m2f+8NNCIil7mzDqiZLNXOjGGUZVeNYTzKmMdxbzoi1RGaCcZywKAz3iwX3jwz7D0qM96yNY3COuwc184VFFJ4z3zIZK/pC85ef+zQfWd/g7L3b5HuvsvmZX+N874ityxfYf+2b3Hj2kHe++lU2rw3Z+chnePdbL1Gc7pIO1rCtpDg65+LNliKL+ZN/+QesbeVcv3oBqRRff+V15lXN1eur4AWr7YCL2QjvPUdHJY0JSAlZDL1BThwrahGwTnHqKqaVZdCbcGkUyFY03noiJ3A+MHcVg0kP7yFKU5xv8T4QywjvG7IkwVsI9GltS+NbEh2RRulD0WeMNfbhQlJEFGm87yzFeRxhXItU3fDaegtSEYRCRRlWdKO1OIo7HkAINOaM124fMpj0SNIUpEAFRdlW6EQjBZ0xWAqM853QJBhcSDAB2taQq4ReHJHFGS2SfpKQq5x5pmm9wfkK3VMEAb0sJUgLTaBqG3QvJ9YC4SDKBAZFpAXUDhkp4l5nKdK203XlvRjnfAe1rQ2RSsF5fNshxQGUlCRRjA0WrKc1LTYSeOUxWFonkS48tCV52rahDS1BiE4pLqKuELiADYZICZSXeAUyFuhMYq2jbmsipYhFF5QTS4NsJD44ggyARGtPJBVSSVywKKWIw0OcO92agJCB9CGwJJae1nbgGB1FaCHwKFSQaC1Auh/b/34qEoMCgfOC3fMCzYwrk2362ZByWVF6w9wrkkww8DGJgso4ZKg5qVqO5i2N85xXHqkCS+so5pZ56eiAtIKq9pzuWX7lU0/zwqefYOOjL3D/T7/J6ZuvMth5kWi0TTh4nUc/8UWqpaWcLnj/xfcZXbwCJmJ28IDLt55gsnOd/qXHeOsbX+HlvSlzD49trTGIe/zZm+8w6CluPrbD2nqKs47BZIW0p/HOc/myIUhJcJZ+L2I8zJHBY1QgUhFaO9AJwXtOF+8ySHssbQ3eIISjdA5Rd2w+hKSuG2IVUbsGRItvPZKUWIPFgwKHoXHh4ZOje9IjBBYLPuBdlz9HR1S2RnlBlkQsyhLRCiKdIIUiSgSyhdZ7nDNEqUZtJDS1YDgaEAtFTycoZ2mcQHqJkhLrLMZ5TACHJ1YRuUqJRIwOjiyKkUoQREDHMW3d4EM35RBSErRD2s5WZOqaONGgBcZ68iwmSWOq8wKHJtYpIZSIRCGCQmuHdYYkilFC0jYteZwRlKfxkjRKCDZgpe1sVgGE1ATXDZwbYfERWGG7eXfbEocUKQXGtJ0LUIILTbf/rx1RpNBEONP9vcE5tAJLIPG6C7JZz6ysqWyDbQEpsc6jI0kcdch2IcE7i1IRaZqRqJgMTaYUSgectSxagxcBh8Ajui1buvBREnc2aeMFrrEIAf4vMA7+VBSBbn7jGSc5UjuC9fTSmKqtEbHiyNaU3pFmkvmyRYnAg1lD0ziyNKKoDMOepKkdJ2cWmXTzr+Cgri2jvMfnnrzGX/+FZ9i6fAPVzxhtbWI+2ODwe68yfPwF6tcLLj066Ei3aUQ1j5gd77N29RrHd04YrI248cLzFE3DWw/uorNVbt7cYHtjyOlywc2PrJCnF0hjaJ2hbQORSsizFCcCtW9AOPCBQZoTnATfsDQlhBZhY6TWmGApyoJISZq2Za2fczo/I9YpWYg4mE1xFhIhEEpTmBqlBUhLFNdoZxFIhIDGVLRBIFFIuvMQ3nmsNQQJUquuOCCxviWgqI2nKmukUMhehPfdHFwJTxwJjHBoFRFkRJpE6KijChMC3hl0pHDWE4SAwMNhraZ2LSF0ijGpJAGBkx4nOrGHVIpKGJxryKOUPETUCFSkWE5rTg5L1i5lZGlCNslBgZSS1ZUxc1uTRgprBVoqIqlxTU0cxeAtaZqjA8RB4QI4JCp4jHc436CkJFIJiVZAtwMRBAjv0UIgxZ+7EkHKGILFGEukNf0kBR2oVcC0hiSOaWpHqhUYibMNUaSZxBHBGKwTeBx1Y7CmG3U1ypCnMbGMcc52gFIlCKkkAVZ0yjBNMBgsFhFLYjqZrDUOicT77nh0HMekEnKtmLYeFQms7bycP679VBSBxlqWpmI1T0h6gWwyQrvAZJhhipiZLxilOdN5ReICkVaYRAKWOBfE8+6GMx7SSCJCd3xTy5jnnnyMTz+2zcdubHLzQ9fpXbrB8u23yLZW2P7Cv8O7f/BP2Hga1jYexZoWd3aGTGKMULRnc7ZuDrHqCnfuv0KtHGXrUTsjNrOc/6u9N4mxbUvzu36r293pI+LGjdu85r4u82XZzoaiXAhXyTUBVwlUMPMAUQgkJrZkDzwoyxNPQcIDJIQEwpJBCE8A4QFINLJEJ8qVBVnZ583X3Xf76E6329Uy2FGup3Q+VRWlIu7Ti38oFCf2OYP/1jrr22t96//9P4TAskeVLXfKEgLYwRE6TxpgNsvRXrAfAs6PuvRMSaKLRBeRWjMVFb6PGDXHO4HJE3lKWBcYugFvDMJDaTQ6BAjjl1EYg1SG5Pw4eXuL85ZMeqZ5Sec7WtdgpKYspoDEeYd3EeEDUXqQiRA8SiqSGJ9Au+ioN45FOSfLclLwxDSAEjRdgzSASUx1NTYrTRGXBD4OuBCwLiA0eOchQS4VXRiPMaML9G1HUoIoQcixSCtFwSRGZtJSe4/ycHtyzPN+O9qfry0IgxIZyQmc96gox45HlaGMEq0DuSxp+xaUI5MKEwVBBga3Rys5it5jwGhIWIQCGSE32ZXJqYMYEcKTqYRIYnyCJonOxw5FRiimkzkpWYJ3aJPjBwcIsixnsAO61FTaMyiFChm9DZRKk6LHD5ZMBpyIDH3EhzEYN84R+8Ss0OMRoIsIFdFAISWF1PS+x4lRDXmrmtN0lt53lHlGf1WolCfNXGekGCBGgnc4l9Bafe78eyWCgA2Rfd+zOpgQTKBzLUor5pOSh89b2ugoy2J8gilogscoQSkk9TqyvfDUfaCYGQ6mmnYTOJ7M+PVf+xW+9eYdDhc5t19/bdQWvHiEHTxht2H5/jc4+t7Xmd59m9eKN6jPvsf88B72BwpzZ0p5fMSH5w+Jpuf08iVaSqIw3L99RBYUdR3Z1jVaJuq6Q1OAmKK6gYlWaGvY71qULpiJGc71KOFRQaMxWAtH8xkhl6Az6t4hpedguuC0fU7hBbYPZFFS13sufKRxgVxl9NFRFUuSs/hmnGTJi6tMu2DMURUkqbEJrOtw/YASmhQdeSmQPtG3A70Hkym0MQSXyKWGKGnrBi0jzieSVlystygpqCaBfKkQUZGCILo0GnYET0qBaCPROaTSBO8gOFIU6KQYbEQiSTLSx44kxuScipHoW/CBPrY8vbSIgwJkYnU41pIsJyW9c5DG04TcezQDIkZia6nUjMxkOBxWSETy9IOlHgbKUjPRUChD7x2d7Sm0ocgMpMi+2+NTpDIZMo59AdEKASTv0UKSl+XYKg0JXmFTHI/qjGLYtFTTgukkY7CJKDyl0diYENHjBgvDuEUIPiARSC8JrcfJgBKSLAOBxgNI0EFSiJwMwzAEQhoVicaLUbtgNHmYIL0m9h0aT1Uq5qZicI7MKYTy4zbu842FXo0goK+ysK2tWZQT+ralkwGfJH0PKk553DcMcmDfQ9t5qkwwzTVPn/d8+LRluMqknvWWr9y/y7/1r/4V/uI33qU/2xDDZjQHfXqOVIrp6+8S1i2bf/KPOfm1X0NVRxT9J/zgxx9w9/2O7KRiWmm6ueLFxadAhwkV/daynEwQPfQ+EmpwO4s3EiUnZMUCpQuCPccPPcwnGKlHKWmSKKVxPjBYECIjCYG1Cm1GBR4EVCwIQ2R3vsY3PflrGcOFI+QaLQ2x8UQNi/mU3GccMqFPHYNzHC7nDDUUYkGZr0gIktDUQ0O9OUP4iFKSQTjq6FA99A1IDSZkGD8lup6gHVF4go3EHOjGis0in7C52KPiwN70ZC7DEcdGFzER24HetZhMYxL0biApQSUVy3xOM1gudi3CKxARKwIhOebTfNzadCBdJFqHkw2lMYhCUpaQ6UgMA0O0lJlBRfD9gJoYMgnKJjLhCYMjCItAkaTD9paUJNGNe2eNoj1rSMWoTwh9z/muJmrNwWJBKQqk0ez7mtrWGK3Jpaarm3ElpQ0iRSql0Llk03W03mGSoWRcAbSbnsuhZzovKU3B+rxl2PVoxHhUZ8eA6JoO240FThMpUUHSxpYoJFWV0+0iZ7EGD3mVYSpDLhNt19GFlqpQFKrCKEMZBrZdT/SBdidxUWCiQRIxwtP3r7i9WCQhteJss2GhNfu9YzCemfS8Pl/x6GXH7358CToShKCtPUeHhttHgieXPds2UJaG5ARf/+o7/Na/8iv8c+/dp3/yfSZHb3D2+89Ju5cs3vlFhtrRvfguR3/x32b77f8d8sDgYfOj38EXhp8+/ykBwZuLW7w433C+6VmKEmEl3uYEcuokmS/nNNueJCfk2RRjcgQSqRST5RJNJCSBNOPyVRDxUWCDQGeG6WxKvanZtj3GGLTytHXLZDae5e+e9RQlnO8vOZC3uDU9xGjBLBeUuWJ9+hFxECzMgtYOlMWSRX6ILnP2g0dLRUpjNnyeZehZhpGCFBynm3PWuy2lNOTkJO+QrqIsD9D0nNWnmLxntVzQ2wFXR7K0wETNSbmg228R0zlFLIlhh8kSDI7UDezWDXlesZhmJCIuRHRZMSkm7LqeZjcwNQdMJzOCCAxDQ33e0RuBzityGYgykpUFyWUIDPVmR5sn8spQ1w5FYDEpKLIpjz+u0cZQZTCoLU1v6cPAcnpAvfe0UVJUhkIXdPUox5V+tDMOrQSboUOJTR7XBGJKIAJ+CPjg0TLhfSQETX05sLY7RAoczEtSnuNbha1ryqVmokus91ycX9L6gTRocgM6ROreI7OcMESadWBwnq6ODA6UVNgU2HU95aTk+PYhWkvW6y37zUCG4iCNR40pSprWcVF3HJQZPg8QByamYEiOdm1RvgYlyMsK4wSZ0iT7+VP9lQgCgwtsOsuzFxuO5xnz5RGZKdjWO56fb/k/Hp7y8UWHEqMSKlyZQ5ye97S94L23FkyqxFsHFf/Gb3ydd+8ccPbwR+xffMSbB8cc/vl/AXf2EcpMiOffBq0grJm9c4w5uk/7+CHPL57hDiS6TayKA54/veTDRy+YHt1FkiOlQJpEHw1SSi43O4aoSPkCLzRKZYjk2e0aYgwUmSKvClxn6VuLUpKEBKEIHnbbHX0/kBfFqADznhQlRYjkJuPte+/w4cUjiqD4yoN3ubisEUmwKEtkUqwfO7gN+TJx7/h1Zqs5wY9+dl1nkWlMnCFGXfkknyNItL5jopfk8xkpQZlXCBJd5/A+YVTFIj/mcv0J0UTyNMH1nny14q65jZKBZ52mTLc4ni/YrDvsZpQrx5QIdjReGTo35m72ifPLlkuT8EIwL29xOL8FSeG9RUtY1y2pyDhcHDLJNOvLHZtNR1kJShT9VoBSzNSEpciJSiG8uJK/GA6mS4KreXz2CIQiyYpeVjz9cEd5nFO6KQyG3jr6wVJpyX67RguPDBmmgHwqGdqBTy87eutJIjFdGoL1aJVQRrBcZMTLfpRLm5xgNLdvHVHLjOe7S4Z0igsGomRhFBdPak5eW6Jdgi4ipIYgcV3P5qJBZQYRJGVZMKvGbHaWZTgUfXPlBYDn8qLGdYHZwQydCy7agdNNIhVQlB1VKbFprLTd1omUBNNSIXwiBEXtBK59xY8IBx852+6ZT4/4+PmaBw9mJGc4b2s+uNzz4WWN0UCMKBRlEcFJhDJ86xszMglnL2u+eu8Ory8PMFWFtZp8covpvQf4bcP05F10ecLux4nqL/x5YnvJy+/9b4SjA843Lzg1jhePLvnqnbf44AePOdtccvzGA7SvwEQ6Fxh8wsee3GiSh85rQooYkei6jugcMUScCzS1pexzQhzPhjOVo6/O5oONCOK4DUjgbCCl0dxyt/WUt3NkbtCx5M3ZPZqhu9IHTLCdJ8sl7339W9jQs1hMx+RaCEQSzeDoO89iXhJ8YDyAFWg9Jsa0hKPlFBuhd3HUDWiJNjm73R6nQEnDPD9kVSzResrto2KUcocxMXawOqTQJSHU9HVLlmfsNx6MQDiBiYn6oqUwBUaWVPMFspxjbUQaTVEUtG1HSpFcG1577R6mqFBCIY1kvsqI7El4SInppCTTJUeTQ2wMOCQiWUhw+zCjKqDrJszKWxhAZFPqfeCb3/jnefLylEU1uvtOJlPmqznt/pLvP/w9gghUBWMjWB9wLiKToUiGTAWyNK46z9f7cYujK2RSVMlC68gjBLaUE0lWC0QTsbs1B9OSrmsIfuR/9ryj33u0HnNH3nm00qQoWS1nTBYVQkq0FCymo/dh23XE5JhOC5Qy7DtLf7FnMp9Q78GIGaaYoUSk0ArvWpqNpasNy9UBw3aDz3pEynh61lDpV9xj0AjJ6eWGu+WKZ+s9778tCX483yx04pv3F7zYWTZDT5lJykwhhGB6oDlaSB49apiUc77+za9zfOcBfXvB/a+9gZTvsX3+kLpew8vI3ffBPDjm8bPvop/8Ps/rNe32Ieu6RjvDLJ6wPg/YmLh1/x2UntD2A1lUDA6MybHDgDGGfJrRbNuxZZgQOOdx1iHk2K1GyIwYNCkyCmZcYLtpWK4mmGzsMhTimOkftViR3CjyMiOGwA9/+IS3HxyxyEoGJ9h2DpP2FHnGbmNHZVhS7FuPEgGEp+4tXe9I0dN1HUopytKglBg9/EJgMZuQG83lvsMkj1QK6wMxwq1bd0F6drsN08MjDlcHKJ1T9x0RQ6YjpclxQeG942zzgrYZXYbuHN/icn9BlRsuThu2lw1COu6/dkK/3qI7we27twhSMAQI6SpPYnJMnqGUIvmxj8J+X5OERyuFUgrDuJ2q+x4vFdY5qgKkKiAlHn/6Em0yhr3lvNmR4p4333qXw+MlR7eWNM2aR4+e8NbJPcqJYXuZWJVHhGiZ5OM2cr8eRg7a4K2jNAPt3tK3Be1eQXJEsWda5cwzyYtHF3RJoXLN4bwgpsR63VGKhJOR9VYh8oxNI9nvYFauGHpPvd3RNYmQNHlWMp1NMVct2XNj0NoQQ2Ba5linwBvQo526Eob9ZUPbdLz17usUZYG3gWqac7l2DAMUeYUMjsms4MnpGqPAD5HV4Svei7DKNGJQvP72lH0dWG96pAiEoefPvX6b9TLy8e/9lKLUTAqJlmPp5aISoAXTRckvvHaHdx6cMOiBZ5ePUaVmE2HTPCPEDhNLHv+wZhES69jhJQil0B70FrJsyXQ1p3OO6eERg5M0dcPtWwvO1g0xJpztiCHR9wM+gXeRRGSz3RFDIIYAJIzWVJMCKSXRjcdmy2nF8apk11yZWKZIUZSEBF3bjyYSBObzjO1Fy/17tzg8WtA6x35wzJdL1ptLtHRsNw6hNFKPe84AOGdpO4fzAqMU3o3NJry3WCfwLpBCIDOKUFVIbYjWk3xAK4UNnvv3j9hst/RNw6zK8cGTFyUpQpKeIUg8DjfUPPv0EbcWE/rOUxULfJR0fQHCMJ1HnM84uv8a00rx8U8+ZHkYSeIqNRbHpp1IsN6CC6zKHIdn3w6oLCMNibbuGLTEmIwsK3EuonOD0YLgPEE5YgwcHh4SQuLjjz5gsZyTZ5oQPIvFjLbecnp2gTYZbd/w7e/8BNd1LCvFfl/j2wLrIkaDkIHWD+QTgQ2wvgwkD9NyTj/sUUIjEzS9Q2jD0EaCFRzOV/ihpyoVJvacbxPkd7h3e0aZTVl96z5Du+ajDz6m6zRlNWF+uCDPDSiJNAYtR63FervDtg1D05HIODpcMV1M6e2ADXBwdIu82FBv1rR1znyxwkcBAXQQDPWavTFsiWyebZguIgfTCb7efu78eyWCwBACB1rz5HzN+++e0PYtqhJIaQg20Mmek7tT4hD5hXeW5Pl45ppJxe17S6ZvZhwflbzcfcqnW4dNGw7MIcMwIDKN8BVSw/n+nFoEvFLEZDjJKtp1yyS/TeMzdt1ACJHNfrSIms9zut4SIwQXiBJgFOmAxDk3npM7j9KS4BNaCKy1SCWoJhVCjI4yQo1Cp84FpJBX8l3FMPSje+zQM1tOIEaM1Nx56w51XfPi5SXldEHbNwy2RwkNImCycuTlA9YHvI9YO2bqIY4iHq3pBs9gA0qO7aptSNQXO6SUKCmIIpFJzWIx5fTslLYZgMR+t6WXmq6PZFVJpgVD11FOlnz43U8JXYOcz1kuRgPWbLLiqDhCG1i/XHN60bE4WGJ05ODkBCkVzkXyQpMX0Awea69cdIcGIwNSj54DafBkeUZZFvQ2IqViiAGlEsk7BjuKycLQY4xEK/jJjz8iyzOKQlPvLb17zMOfBIbe0bQdWkuePnmCUQqXItv9QPCSlBfkmSCEYQwGmSLiefbCUag5RwdTlE7krYeQk5cVTTuw3jdMpyVlnlHmUw6OT3j5+Kc8etJw+/57zA9XrJZTUkoo6dlvGnwwlLMJ00XOfF7ivUNkGS6MR4QxBOpmwLuId4qQBNl0yuzwELHbMM8kt+8sefjdF3ifWKxyYrD0XnD24iW7F1vs4JlUGU3v0ZkmuA7pHP5V70AUExwcT/joacPdu55JVmBThygkrlDcmc54470VmUy8dXdJkpHBW0yWY40nBM/Wbjh/cUFlI9oIRAbBS6zrQEBtHVJIdj4Rt4GD8gDtSvZ9ZEgKJeBytycJQ15WrDc7Siu53NfEBJkZ1XbWeoKArEijbPXKmbar2zETX5VkecbluqbrPdVkgnOBdhhQUiEl5Jnh8GDGbt8iwvgUWr/YUMnED7/7hK+8/yY+TghRUpgSlTS7yzVRBGworqSmAS0TMCrlBhvHPb9SdP2A8w5jsrEYRRqkhBAi3nqkEBS5IclRgosYW8PHkFAqAgnEjDIvyaqM3vV4G1nmYyDwMWd1NKWYHbKqSmQuEVqSvGboBo7uHTAIhdYGa2sefvAJJish16yYMp3k2K6jawaEEAitqFuLUBHi2No9ecOkypmvZlfdnC2lKrH9gAuJkAIqBRAaHyUuKtwAjx/tWa4MKTh+/KMPCFd1C/PFClOUGGm4dXhM07W4zmLyCQerFYHAYC1d19BZT5Hl3D6a0g9rnj5dMy1KVvOS2fIAXTpWt++SlOby5UtkniO0xkyOUaWmWhxxcvuIjz58yOxwga89T5/uWBwc8d6tFZu2I6aA0vlVdaGnaVqM0pRlSX+lpqxyAwTa3iJkhu0CLz5dQxpVi0JLnj95TLOvibYl2kA1LamdxczmDE3LfKIgCUSxAj7+ufPvlQgCkNB64Fd/5S4x87x5b0EubrMfWooZzKYFfdeijSIvFU1n2dY7CDmxV+grsUXEo2c59+dLbLCsu5pAN1JnAAAP00lEQVTLrkNmFpEKZukWpoGpWpKFCR+/WNMGDQwoAoNLWD+gZE8MgZQSRVXQdjV5ntMNDqXNuB0Y5fl0bYcQiYPFAus8MQ6YTLFYVPSDp27Hp5UUChktJ7cOuNx2XJyfo9W4ll8sMjJu8eTRR7z31bc5unPIxdmGPDcUs/nIy0Wkclyua8pyQrQ9J8crYgqsNxuUzphOKpBgTIELHus8RZZRZApEIsRIFIIiH9Vx1sG0KLi1nOKdpcYRFbgAOtPk0wIfHUM/kGnBy0bQ246333+TssgheZKI9MGRrKLzA0oIrNfcvX/Mya0lm43mwdvvjKb/GPohsttdsL/cYJRhOi9JyZOERqZIs9+PNQ2lht4ijb4S2ET2u4YqL8iMwBQF9XaHEInzi0veff+rnD57SVWV7Lbn1HXD7dtvst2eE/qeZm85WR7x/PkzZtWEPJ+B75gvV0SZECKyWi04WB3SOUvfNrT1lk8+WbPbgzzWFOXAk4cfs5gdcnQyIQrF4e0TRIq0TY+eL7j3IGd5POVrf+EBMjmenTWsd5YHr7/DfJVTdz2ZkWOCWIzOw3mW/dPcjEYieosygnKicXagHzwHRyuafU2KHlVNqUzGfF7R7raocgKTit3lKUUOBMlioTmLij5l1I2C4RVPDM6rnD/3y/fI54LKTCklTDJDhRkrtYJlEB3bXSDsBbUdUEly//gORI/wezb9wODBGEk/OPZDT58SMSb6HUzUlOOjNzCZpNnu2NYtjdNEIkY4EgofDTGM/eIOD2d4F7CDGzPCVcnFZYPODGVREGNiX3ckItF5Lv0GLccS56F3SAnL5ZS68wSXGHqJ7T39sCaRMJnm4KCibjdkRcbLl5fcuvuAe/fucnp6xnQ6oe06ButoO0cSBlIi1xolxWgg0lrqrmc2m5NSIhLxLmG9I6SESDCbVxweLTg/39JuarSSJCLew9A7CiXoekkIiRQCzjq8DegioquM3XmNcx7vEkobhDQ0rQcBmQKkQARJjNC0liI3eN+jB8tTO8qNl6uKrgvMpjkkRz4tkHGBHyzWjiUwKSaCNiQ1qgQPD1d07cB2211JeUEgGdyAQWN3LYhEriX3bx8ghaOY51TTGV4Ezs53XK53HN+5y+nj56xWU4pqwr3XXsP1nr4fmC4X6FKRGcX2ouPy4jmLw0OM0WRFQTc4jk7e4M13psymOf3Qk7uG1x/cZbma8smnZ7SDw0SLnk7oG09RFKwvtvzut3/Mfr9Bqhl/+S9/g3tHOT/86Uf8n7/7U6QUTKcLehfY7ffjKiwlBu9QSLRWWK2xHooE1gfapufwcElV5jRNj/cerSOzxYJtaDh5/T4qL+k3L4jJ0u06Uip5vpO8/fY9jpdL+O9/5+fOv1ciCFQzRTnxnG4bZlJSLI+5jDXeD3jvaUWHjT1KKzI14bXlCb3tOJws+ODZR7g4oDLNtJjgQ+SH60syrREyIZTkq3e+wsydUG8957tz8kwzOEWWa4ahpWkG3nznAe7Zdtw2qERmJBKBsx6T51xu9iglKIwixICUinj1I5ShtQO5Hh1xfRoTNcN+QCtJnsNsBvpgTl9bnA244Hj68hJdKB5+8IJJmfPV9+9zeblDCIUPY2OPg4NjrN1gYySlDIEiy6AsS6JPY0Y7JUgJITRKSVzfE2Ok0Jp6V9M29Vi74BOZUWPtNiCNpu49dbtFa4USCak0Sina1vLk8ROUGD3vR538KGzxMSJCRIgxQad1NhYkIbB2FNcMuPG0orV455lMMra7PcknilwSwli9lxuFEIq+9wwxkJclInrWmw1VWaGSxPaOlEZjjpDGMQnBM50UnO9bEgJVS5TQDEPAB8V8ecTd+7cpqwnHhwt2zZ666wCBKgpyrckKyb7e0Gx2ZLpCqAyQKCGIEaQsODqekBUS7wWoCbOV4cXLC87OzlF5gQsJjML1nuAi2SSnbzoef/KcybSkrCwnJ1P2u1MePT3l4HDJ0DcURU7X7ykLQ0qSYCGGRFEoinyGzjKUkmTFqK7UCgY7sK07iImi1PTe8vEnz2j3gtU9xckbD4i3D3n8+AkvnzcUs4r3v3KfkzsrQnzFW5MrIWiS4/BkhrGSWBXsu4YYPVJLEg6RxkldtzuaYUDpyI9e7NCmgFjQuQYVE9IYMqMpTEbXD9ye3OFWfkLfwm67xfrAtu7orUZrObZ00IZnz16wqyNVqZkUOdvLnvmioswLkvB0TUBpxXQ6Yd90eB8gjbXo0TuqzHB8uEQIcD5wud5Rty1HqwW5Kbi86PAucuv2lCFFJllBWzteXF4yKycUecHTpy/QOgcZ6YcBgWK3rel7h1SKyMB0YsbJECJDZ5GZIaQ4Wnd4h9QKrTRSQ5VnxBTHSdmPTT5C8HgHCEEmBUWe45wjyzXejwUtSilCH4lRkhTkRiLFmHF3TkCKZFk1VhiaDBc8Xe9QWpIIeOswIuGdGwVeuaTrBtwQWS0qQhjzKFmRj0U8AkyeYztHJgVlVdJ3A/WuxxiB0QbrwhhykwQEQijOLxqCh8Vqyna/J9MZWVmiMs1b79xnvd9j9wFXGja7gW5wCKFIsccYTUqa/dax3wyc3DuinE4YhoGh7zH6yr/PjS7QzlmKUqOlJ2lDSgEfNHbw1M2AMZHVfIKzAyYT+KAIV+mVDz/6mPPzM2xIDNYilebJ81Ps4DCZRGcF3dAjhAAxrgp8CsSYmOqClCLBWayAXWPHYGEFQ7vB9S2z5SF5LjG5pPVTnpw5jJrw7i+8TTEx1G1N1/7pnIX+7CGhLCY0uzUqK3h5+pyUAiEGhIgcLkqIGkGgmuUEKdDe0DuP0I7QWXzvENmoly51gSexKOYcmzv4JuPl9pwoFSaT9N6QpB33yH5AyES39yByiklGJgWvf+0+u33NbtOBEEwmBdZ6LtcbrB1XGCGO1XLz2YxpVdC0Lc455tOK1WLCdFICAhsiZZVfOep6bGdp64bVas6SGf/iL73DTz48x7YDUo17+d2mRQhFVUXeeusNTs92bDbPcdrR7RLVbIJShn4YQArKvADGqkCjFYJEbweAsROvAG3GUtmUEnmmyXOJtY7BeULy47Ed4xdVa4334+lD0pAXhhAi9X5gNi2w7urJksD6iE+Q5QVt15CZgmAtUiuSd9jeY4xmujJkmSB4Td+NDVLwYx2+MgV5kdF2LSFlZHpUYPZ9Q2Y02mi6bqCaTAgx0HYtEkEg0HcNVTXFe8++7pnNJgQh6DpLVUl2e08Eskzj3LjaSWksw51M56wODkehVdOSYqKoSpRRqGAJPiIVLOcTEoJ6N/pCCK3ZbDpef/2Yuq5Zbxu8deyHFq0zMJoQHYTEJ09qXr7cMV/MyTJBNwzoYkJkQCrJrm0IcTzBifEPt3MpJKyNJGk5HyxJaAKC+XzKpNLsT2vCYFncLdis95RlznRW8vZ7bzIvJ4Qk2OwcmVEk+s+dfq9EEBi85+X5OaSEk2um2QJhNJnUkBxN37GcL2n7QL3dcPfghHU/IFWkazsUhuV8jvSJVTZlVZS0wiLTimYX6fot6UqzX7cDzkUKoxHR09nAbFpgUkmRjVlygMdPTpF6lCc3fc/h0ZwiV1xcCjwWLRJKj6KXmAJNa4khYHTCuY6qqphMCoKPCBmZTnNSGP0De2uJSfDsdM+D+4ecX44qsn1n0b2nqjK0kRitKIuMX/7mA77zo8e07RqTj0/5fnCQAjIJjNZoxegsExN5BilEQkjkxagH0Ho0rQghkgR0vR0FNi4SoyDFcdkvpUcEgQ3j1iETEoIlRo3rA7PJmJsIMZFfmWCQAjGMfntCSuq6xWQK4SLWOaQ2VFXO3TsLzi927HctWfYHuY2E99B0PVqPQjCtBFWlCC5ByJCMJdoxSi7XLbN5hjEZ9b4Bo7EWUnRolVFVE1YHM548O8PF0YuyKEucc3RtT55naDOOsxCaBPQu0PcWUkJKifGRPoxZeaUUWmt6G8ajO2GIPkDwiGi5d+cQG2bs/p+HCC0oZEVKAmkUJMXlrsO5HpNlPH38iMxUVPMDVGoxWtIOHdv1jrzISNHRJkeWj9WLIUY668Bo+mFcQSgp2O8DeM36bIeUJTorOH36nFt3TogiMp9PyLKctu1xzhO8GLeonwMxuoFdL4QQZ0AD/PyOiV8MHPHF5g9f/Hv4ovOHP9t7eCOldOtnL74SQQBACPHtlNIvXjeP/6/4ovOHL/49fNH5w/Xcw+cbj93gBjf4UuAmCNzgBl9yvEpB4D+5bgJ/SnzR+cMX/x6+6PzhGu7hlckJ3OAGN7gevEorgRvc4AbXgGsPAkKIvyKE+IkQ4gMhxG9fN58/LoQQnwghvieE+I4Q4ttX1w6EEP+TEOKnV39X183zsxBC/H0hxKkQ4vufufZzOYsR/+HVuHxXCPGt62P+T7n+PP5/Vwjx9GocviOE+I3PvPe3r/j/RAjxL18P6z+EEOI1IcQ/FkL8UAjxAyHE37i6fr1jkFK6tl/GQrwPgbeADPh94GvXyelPwP0T4Ohnrv37wG9fvf5t4N+7bp4/w+9XgW8B3/+jODP2k/wfGG2Pfhn4nVeU/98F/tbP+ezXrr5POfDg6numrpn/HeBbV69nwMMrntc6Bte9Evgl4IOU0kcpJQv8Q+A3r5nTnwa/CfyDq9f/APjXrpHLP4OU0v8KXP7M5c/j/JvAf55G/F/A8qoF/bXhc/h/Hn4T+IcppSGl9DFjg9xf+jMj98dASul5Sun/vnq9B34E3OOax+C6g8A94PFn/n9yde2LgAT8j0KI3xNC/LtX126nP2zD/gK4fT3U/kT4PM5fpLH561fL5b//mS3YK81fCPEm8E3gd7jmMbjuIPBFxl9KKX0L+HXgrwkhfvWzb6ZxPfeFOnr5InIG/mPgbeAbwHPgP7heOn80hBBT4L8G/mZKaffZ965jDK47CDwFXvvM//evrr3ySCk9vfp7Cvy3jEvNl3+wXLv6e3p9DP/Y+DzOX4ixSSm9TCmFlFIE/lP+cMn/SvIXQhjGAPBfppT+m6vL1zoG1x0Efhd4VwjxQAiRAX8V+EfXzOmPhBBiIoSY/cFr4F8Cvs/I/beuPvZbwH93PQz/RPg8zv8I+DevMtS/DGw/s2R9ZfAze+R/nXEcYOT/V4UQuRDiAfAu8E/+/+b3WQghBPCfAT9KKf29z7x1vWNwndnSz2RAHzJmb//OdfP5Y3J+izHz/PvAD/6AN3AI/C/AT4H/GTi4bq4/w/u/YlwyO8b95b/zeZwZM9L/0dW4fA/4xVeU/39xxe+7V5Pmzmc+/3eu+P8E+PVXgP9fYlzqfxf4ztXvb1z3GNwoBm9wgy85rns7cIMb3OCacRMEbnCDLzlugsANbvAlx00QuMENvuS4CQI3uMGXHDdB4AY3+JLjJgjc4AZfctwEgRvc4EuO/xeBT1kMPeUaJAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicted caption:\n", + " a giraffe standing in a field next to a tree eeee\n", + "\n", + "True captions:\n", + "A giraffe eating food from the top of the tree.\n", + "A giraffe standing up nearby a tree \n", + "A giraffe mother with its baby in the forest.\n", + "Two giraffes standing in a tree filled area.\n", + "A giraffe standing next to a forest filled with trees.\n" + ] + } + ], + "source": [ + "generate_caption_coco(idx=1, train=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is another picture of giraffes from the training-set, so this image was also used during training of the model. But the model can't produce an accurate caption. Perhaps it needs more training, or perhaps another architecture for the Recurrent Neural Network?" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicted caption:\n", + " a giraffe standing next to a tree in a forest eeee\n", + "\n", + "True captions:\n", + "A couple of giraffe snuggling each other in a forest.\n", + "A couple of giraffe standing next to some trees.\n", + "Two Zebras seem to be embracing in the wild. \n", + "Two giraffes hang out near trees and nuzzle up to each other.\n", + "The two giraffes appear to be hugging each other.\n" + ] + } + ], + "source": [ + "generate_caption_coco(idx=10, train=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is a picture from the validation-set which was not used during training of the model. Sometimes the model can produce good captions for images it hasn't seen during training and sometimes it can't. Can you make a better model?" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicted caption:\n", + " a dog is standing on a grassy field eeee\n", + "\n", + "True captions:\n", + "A big burly grizzly bear is show with grass in the background.\n", + "The large brown bear has a black nose.\n", + "Closeup of a brown bear sitting in a grassy area.\n", + "A large bear that is sitting on grass. \n", + "A close up picture of a brown bear's face.\n" + ] + } + ], + "source": [ + "generate_caption_coco(idx=1, train=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "This tutorial showed how to generate captions for images. We used a pre-trained image-model (VGG16) to generate a \"thought-vector\" of what the image contains, and then we trained a Recurrent Neural Network to map this \"thought-vector\" to a sequence of words.\n", + "\n", + "This works reasonably well, although it is easy to find examples both in the training- and validation-sets where the captions are incorrect.\n", + "\n", + "It is also important to understand that this model doesn't have a human-like understanding of what the images contain. If it sees an image of a giraffe and correctly produces a caption stating that, it doesn't mean that the model has a deep understanding of what a giraffe is; the model doesn't know that it's a tall animal that lives in Africa and Zoos.\n", + "\n", + "The model is merely a clever way of mapping pixels in an image to a vector of floating-point numbers that summarize the contents of the image, and then map these numbers to a sequence of integers-tokens representing words. So the model is basically just a very advanced function approximator rather than human-like intelligence." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercises\n", + "\n", + "These are a few suggestions for exercises that may help improve your skills with TensorFlow. It is important to get hands-on experience with TensorFlow in order to learn how to use it properly.\n", + "\n", + "You may want to backup this Notebook before making any changes.\n", + "\n", + "* Train the model for more epochs. Does it improve the quality of the generated captions?\n", + "* Try another architecture for the Recurrent Neural Network, e.g. change the number of GRU layers, their internal state-size, the embedding-size, etc. Can you improve the quality of the generated captions?\n", + "* Use another transfer-layer from the VGG16-model, for example the flattened output of the last convolutional layer.\n", + "* Try adding more dense-layers to the mapping between the transfer-values and the initial-state in the decoder.\n", + "* When generating captions, instead of using `np.argmax()` to sample the next integer-token, could you sample the decoder's output as if it was a probability distribution instead? Note that the decoder's output is not softmax-limited so you have to do that first to turn it into a probability-distribution.\n", + "* Can you generate multiple sequences by doing this sampling? Can you find a way to select the best of these different sequences?\n", + "* Connect the image-model directly to the decoder so you can fine-tune the weights of the image-model. See Tutorial #10 on Fine-Tuning.\n", + "* Can you train a Machine Translation model from Tutorial #21 and then connect its decoder to a pre-trained image-model to make an image captioning model? Perhaps you need an intermediate fully-connected layer that you will train.\n", + "* Can you measure the quality of the generated captions using some mathematical formula?\n", + "* Modify the decoder so it also returns the states of the GRU-units. Then change `generate_caption()` so it only inputs and outputs one integer-token in each iteration. You need to get the GRU-states out of `decoder_model.predict()` and feed them back in next time you call it. Now you compute less in each iteration, but there is still a lot of overhead, so it may not be much faster when using a GPU?\n", + "* Explain to a friend how the program works." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## License (MIT)\n", + "\n", + "Copyright (c) 2018 by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n", + "\n", + "Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n", + "\n", + "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n", + "\n", + "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE." + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.10" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/23_Time-Series-Prediction.ipynb b/23_Time-Series-Prediction.ipynb new file mode 100644 index 0000000..f8c5759 --- /dev/null +++ b/23_Time-Series-Prediction.ipynb @@ -0,0 +1,3067 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# TensorFlow Tutorial #23\n", + "# Time-Series Prediction\n", + "\n", + "by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n", + "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "This tutorial tries to predict the future weather of a city using weather-data from several other cities.\n", + "\n", + "Because we will be working with sequences of arbitrary length, we will use a Recurrent Neural Network (RNN).\n", + "\n", + "You should be familiar with TensorFlow and Keras in general, see Tutorials #01 and #03-C, and the basics of Recurrent Neural Networks as explained in Tutorial #20." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Location\n", + "\n", + "We will use weather-data from the period 1980-2018 for five cities in [Denmark](https://en.wikipedia.org/wiki/Denmark):\n", + "\n", + "* **[Aalborg](https://en.wikipedia.org/wiki/Aalborg)** The weather-data is actually from an airforce base which is also home to [The Hunter Corps (Jægerkorps)](https://en.wikipedia.org/wiki/Jaeger_Corps_(Denmark)).\n", + "* **[Aarhus](https://en.wikipedia.org/wiki/Aarhus)** is the city where [the inventor of C++](https://en.wikipedia.org/wiki/Bjarne_Stroustrup) studied and the [Google V8 JavaScript Engine](https://en.wikipedia.org/wiki/Chrome_V8) was developed.\n", + "* **[Esbjerg](https://en.wikipedia.org/wiki/Esbjerg)** has a large fishing-port.\n", + "* **[Odense](https://en.wikipedia.org/wiki/Odense)** is the birth-city of the fairytale author [H. C. Andersen](https://en.wikipedia.org/wiki/Hans_Christian_Andersen).\n", + "* **[Roskilde](https://en.wikipedia.org/wiki/Roskilde)** has an old cathedral housing the tombs of the Danish royal family." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following map shows the location of the cities in Denmark:\n", + "\n", + "![Map of Denmark](images/Denmark.jpg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following map shows the location of Denmark within Europe:\n", + "\n", + "![Map of Europe](images/Europe.jpg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Flowchart\n", + "\n", + "In this tutorial, we are trying to predict the weather for the Danish city \"Odense\" 24 hours into the future, given the current and past weather-data from 5 cities (although the flowchart below only shows 2 cities).\n", + "\n", + "We use a Recurrent Neural Network (RNN) because it can work on sequences of arbitrary length. During training we will use sub-sequences of 1344 data-points (8 weeks) from the training-set, with each data-point or observation having 20 input-signals for the temperature, pressure, etc. for each of the 5 cities. We then want to train the neural network so it outputs the 3 signals for tomorrow's temperature, pressure and wind-speed." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Flowchart](images/23_time_series_flowchart.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import tensorflow as tf\n", + "import numpy as np\n", + "import pandas as pd\n", + "import os\n", + "from sklearn.preprocessing import MinMaxScaler" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We need to import several things from Keras." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Input, Dense, GRU, Embedding\n", + "from tensorflow.keras.optimizers import RMSprop\n", + "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard, ReduceLROnPlateau\n", + "from tensorflow.keras.backend import square, mean" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This was developed using Python 3.6 (Anaconda) and package versions:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'2.1.0'" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tf.__version__" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'2.2.4-tf'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tf.keras.__version__" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'1.0.3'" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.__version__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Data\n", + "\n", + "Weather-data for 5 cities in Denmark will be downloaded automatically below.\n", + "\n", + "The raw weather-data was originally obtained from the [National Climatic Data Center (NCDC), USA](https://www7.ncdc.noaa.gov/CDO/cdoselect.cmd). Their web-site and database-access is very confusing and may change soon. Furthermore, the raw data-file had to be manually edited before it could be read. So you should expect some challenges if you want to download weather-data for another region. The following Python-module provides some functionality that may be helpful if you want to use new weather-data, but you will have to modify the source-code to fit your data-format." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import weather" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Download the data-set if you don't have it already. It is about 35 MB." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data has apparently already been downloaded and unpacked.\n" + ] + } + ], + "source": [ + "weather.maybe_download_and_extract()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "List of the cities used in the data-set." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Aalborg', 'Aarhus', 'Esbjerg', 'Odense', 'Roskilde']" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cities = weather.cities\n", + "cities" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load and resample the data so it has observations at regular time-intervals for every 60 minutes. Missing data-points are linearly interpolated. This takes about 30 seconds to run the first time but uses a cache-file so it loads very quickly the next time." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 13.9 ms, sys: 55.4 ms, total: 69.3 ms\n", + "Wall time: 157 ms\n" + ] + } + ], + "source": [ + "%%time\n", + "df = weather.load_resampled_data()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These are the top rows of the data-set." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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AalborgAarhusEsbjergOdenseRoskilde
TempPressureWindSpeedWindDirTempPressureWindSpeedWindDirTempPressureWindSpeedWindDirTempPressureWindSpeedWindDirTempPressureWindSpeedWindDir
DateTime
1980-03-01 11:00:005.0000001007.76666710.2280.0000005.01008.30000015.4290.06.083333NaN12.383333310.0000006.1428571011.06666712.585714290.05.000000NaN11.466667280.000000
1980-03-01 12:00:005.0000001008.00000010.3290.0000005.01008.60000013.4280.06.583333NaN12.883333310.0000007.0000001011.20000011.300000290.05.000000NaN12.466667280.000000
1980-03-01 13:00:005.0000001008.0666679.7290.0000005.01008.43333315.4280.06.888889NaN13.244444309.4444447.0000001011.30000012.118182290.05.166667NaN13.133333278.333333
1980-03-01 14:00:004.3333331008.13333311.1283.3333335.01008.26666714.9300.06.222222NaN12.911111306.1111116.8571431011.40000012.742857290.05.833333NaN12.300000270.000000
1980-03-01 15:00:004.0000001008.20000011.3280.0000005.01008.10000017.0290.05.555556NaN12.577778302.7777786.0000001011.50000012.400000290.04.833333NaN12.300000270.000000
\n", + "
" + ], + "text/plain": [ + " Aalborg Aarhus \\\n", + " Temp Pressure WindSpeed WindDir Temp \n", + "DateTime \n", + "1980-03-01 11:00:00 5.000000 1007.766667 10.2 280.000000 5.0 \n", + "1980-03-01 12:00:00 5.000000 1008.000000 10.3 290.000000 5.0 \n", + "1980-03-01 13:00:00 5.000000 1008.066667 9.7 290.000000 5.0 \n", + "1980-03-01 14:00:00 4.333333 1008.133333 11.1 283.333333 5.0 \n", + "1980-03-01 15:00:00 4.000000 1008.200000 11.3 280.000000 5.0 \n", + "\n", + " Esbjerg \\\n", + " Pressure WindSpeed WindDir Temp Pressure \n", + "DateTime \n", + "1980-03-01 11:00:00 1008.300000 15.4 290.0 6.083333 NaN \n", + "1980-03-01 12:00:00 1008.600000 13.4 280.0 6.583333 NaN \n", + "1980-03-01 13:00:00 1008.433333 15.4 280.0 6.888889 NaN \n", + "1980-03-01 14:00:00 1008.266667 14.9 300.0 6.222222 NaN \n", + "1980-03-01 15:00:00 1008.100000 17.0 290.0 5.555556 NaN \n", + "\n", + " Odense \\\n", + " WindSpeed WindDir Temp Pressure WindSpeed \n", + "DateTime \n", + "1980-03-01 11:00:00 12.383333 310.000000 6.142857 1011.066667 12.585714 \n", + "1980-03-01 12:00:00 12.883333 310.000000 7.000000 1011.200000 11.300000 \n", + "1980-03-01 13:00:00 13.244444 309.444444 7.000000 1011.300000 12.118182 \n", + "1980-03-01 14:00:00 12.911111 306.111111 6.857143 1011.400000 12.742857 \n", + "1980-03-01 15:00:00 12.577778 302.777778 6.000000 1011.500000 12.400000 \n", + "\n", + " Roskilde \n", + " WindDir Temp Pressure WindSpeed WindDir \n", + "DateTime \n", + "1980-03-01 11:00:00 290.0 5.000000 NaN 11.466667 280.000000 \n", + "1980-03-01 12:00:00 290.0 5.000000 NaN 12.466667 280.000000 \n", + "1980-03-01 13:00:00 290.0 5.166667 NaN 13.133333 278.333333 \n", + "1980-03-01 14:00:00 290.0 5.833333 NaN 12.300000 270.000000 \n", + "1980-03-01 15:00:00 290.0 4.833333 NaN 12.300000 270.000000 " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Missing Data\n", + "\n", + "The two cities Esbjerg and Roskilde have missing data for the atmospheric pressure, as can be seen in the following two plots. \n", + "\n", + "Because we are using resampled data, we have filled in the missing values with new values that are linearly interpolated from the neighbouring values, which appears as long straight lines in these plots.\n", + "\n", + "This may confuse the neural network. For simplicity, we will simply remove these two signals from the data.\n", + "\n", + "But it is only short periods of data that are missing, so you could actually generate this data by creating a predictive model that generates the missing data from all the other input signals. Then you could add these generated values back into the data-set to fill the gaps." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df['Roskilde']['Pressure'].plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before removing these two signals, there are 20 input-signals in the data-set." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(333109, 20)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.values.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we remove the two signals that have missing data." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "df.drop(('Esbjerg', 'Pressure'), axis=1, inplace=True)\n", + "df.drop(('Roskilde', 'Pressure'), axis=1, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now there are only 18 input-signals in the data." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(333109, 18)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.values.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can verify that these two data-columns have indeed been removed." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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AalborgAarhusEsbjergOdenseRoskilde
TempPressureWindSpeedWindDirTempPressureWindSpeedWindDirTempWindSpeedWindDirTempPressureWindSpeedWindDirTempWindSpeedWindDir
DateTime
1980-03-01 11:00:005.01007.76666710.2280.05.01008.315.4290.06.08333312.383333310.06.1428571011.06666712.585714290.05.011.466667280.0
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" + ], + "text/plain": [ + " Aalborg Aarhus \\\n", + " Temp Pressure WindSpeed WindDir Temp Pressure \n", + "DateTime \n", + "1980-03-01 11:00:00 5.0 1007.766667 10.2 280.0 5.0 1008.3 \n", + "\n", + " Esbjerg Odense \\\n", + " WindSpeed WindDir Temp WindSpeed WindDir Temp \n", + "DateTime \n", + "1980-03-01 11:00:00 15.4 290.0 6.083333 12.383333 310.0 6.142857 \n", + "\n", + " Roskilde \\\n", + " Pressure WindSpeed WindDir Temp WindSpeed \n", + "DateTime \n", + "1980-03-01 11:00:00 1011.066667 12.585714 290.0 5.0 11.466667 \n", + "\n", + " \n", + " WindDir \n", + "DateTime \n", + "1980-03-01 11:00:00 280.0 " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data Errors\n", + "\n", + "There are some errors in this data. As shown in the plot below, the temperature in the city of Odense suddenly jumped to almost 50 degrees C. But the highest temperature ever measured in Denmark was only 36.4 degrees Celcius and the lowest was -31.2 C. So this is clearly a data error. However, we will not correct any data-errors in this tutorial." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df['Odense']['Temp']['2006-05':'2006-07'].plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This can also be confirmed to be an error by considering the temperatures in some of the other cities in Denmark for that period, which was only around 10 degrees. Because the country is so small, it is not possible for one city in Denmark to have 50 degrees while another city only has 10 degrees." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df['Roskilde']['Temp']['2006-05':'2006-07'].plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Add Data\n", + "\n", + "We can add some input-signals to the data that may help our model in making predictions.\n", + "\n", + "For example, given just a temperature of 10 degrees Celcius the model wouldn't know whether that temperature was measured during the day or the night, or during summer or winter. The model would have to infer this from the surrounding data-points which might not be very accurate for determining whether it's an abnormally warm winter, or an abnormally cold summer, or whether it's day or night. So having this information could make a big difference in how accurately the model can predict the next output.\n", + "\n", + "Although the data-set does contain the date and time information for each observation, it is only used in the index so as to order the data. We will therefore add separate input-signals to the data-set for the day-of-year (between 1 and 366) and the hour-of-day (between 0 and 23)." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "df['Various', 'Day'] = df.index.dayofyear\n", + "df['Various', 'Hour'] = df.index.hour" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Target Data for Prediction\n", + "\n", + "We will try and predict the future weather-data for this city." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "target_city = 'Odense'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will try and predict these signals." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "target_names = ['Temp', 'WindSpeed', 'Pressure']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following is the number of time-steps that we will shift the target-data. Our data-set is resampled to have an observation for each hour, so there are 24 observations for 24 hours.\n", + "\n", + "If we want to predict the weather 24 hours into the future, we shift the data 24 time-steps. If we want to predict the weather 7 days into the future, we shift the data 7 * 24 time-steps." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "shift_days = 1\n", + "shift_steps = shift_days * 24 # Number of hours." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a new data-frame with the time-shifted data.\n", + "\n", + "**Note the negative time-shift!**" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "df_targets = df[target_city][target_names].shift(-shift_steps)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**WARNING!** You should double-check that you have shifted the data in the right direction! We want to predict the future, not the past!\n", + "\n", + "The shifted data-frame is confusing because Pandas keeps the original time-stamps even though we have shifted the data. You can check the time-shift is correct by comparing the original and time-shifted data-frames.\n", + "\n", + "This is the first `shift_steps + 5` rows of the original data-frame:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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TempWindSpeedPressure
DateTime
1980-03-01 11:00:006.14285712.5857141011.066667
1980-03-01 12:00:007.00000011.3000001011.200000
1980-03-01 13:00:007.00000012.1181821011.300000
1980-03-01 14:00:006.85714312.7428571011.400000
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" + ], + "text/plain": [ + " Temp WindSpeed Pressure\n", + "DateTime \n", + "2018-03-01 19:00:00 NaN NaN NaN\n", + "2018-03-01 20:00:00 NaN NaN NaN\n", + "2018-03-01 21:00:00 NaN NaN NaN\n", + "2018-03-01 22:00:00 NaN NaN NaN\n", + "2018-03-01 23:00:00 NaN NaN NaN" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_targets.tail()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### NumPy Arrays\n", + "\n", + "We now convert the Pandas data-frames to NumPy arrays that can be input to the neural network. We also remove the last part of the numpy arrays, because the target-data has `NaN` for the shifted period, and we only want to have valid data and we need the same array-shapes for the input- and output-data.\n", + "\n", + "These are the input-signals:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "x_data = df.values[0:-shift_steps]" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Shape: (333085, 20)\n" + ] + } + ], + "source": [ + "print(type(x_data))\n", + "print(\"Shape:\", x_data.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These are the output-signals (or target-signals):" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "y_data = df_targets.values[:-shift_steps]" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Shape: (333085, 3)\n" + ] + } + ], + "source": [ + "print(type(y_data))\n", + "print(\"Shape:\", y_data.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the number of observations (aka. data-points or samples) in the data-set:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "333085" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "num_data = len(x_data)\n", + "num_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the fraction of the data-set that will be used for the training-set:" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "train_split = 0.9" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the number of observations in the training-set:" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "299776" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "num_train = int(train_split * num_data)\n", + "num_train" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the number of observations in the test-set:" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "33309" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "num_test = num_data - num_train\n", + "num_test" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These are the input-signals for the training- and test-sets:" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "333085" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_train = x_data[0:num_train]\n", + "x_test = x_data[num_train:]\n", + "len(x_train) + len(x_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These are the output-signals for the training- and test-sets:" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "333085" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_train = y_data[0:num_train]\n", + "y_test = y_data[num_train:]\n", + "len(y_train) + len(y_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the number of input-signals:" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "20" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "num_x_signals = x_data.shape[1]\n", + "num_x_signals" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the number of output-signals:" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "num_y_signals = y_data.shape[1]\n", + "num_y_signals" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Scaled Data\n", + "\n", + "The data-set contains a wide range of values:" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Min: -27.0\n", + "Max: 1050.8\n" + ] + } + ], + "source": [ + "print(\"Min:\", np.min(x_train))\n", + "print(\"Max:\", np.max(x_train))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The neural network works best on values roughly between -1 and 1, so we need to scale the data before it is being input to the neural network. We can use `scikit-learn` for this.\n", + "\n", + "We first create a scaler-object for the input-signals." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "x_scaler = MinMaxScaler()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We then detect the range of values from the training-data and scale the training-data." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "x_train_scaled = x_scaler.fit_transform(x_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Apart from a small rounding-error, the data has been scaled to be between 0 and 1." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Min: 0.0\n", + "Max: 1.0000000000000002\n" + ] + } + ], + "source": [ + "print(\"Min:\", np.min(x_train_scaled))\n", + "print(\"Max:\", np.max(x_train_scaled))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We use the same scaler-object for the input-signals in the test-set." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "x_test_scaled = x_scaler.transform(x_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The target-data comes from the same data-set as the input-signals, because it is the weather-data for one of the cities that is merely time-shifted. But the target-data could be from a different source with different value-ranges, so we create a separate scaler-object for the target-data." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "y_scaler = MinMaxScaler()\n", + "y_train_scaled = y_scaler.fit_transform(y_train)\n", + "y_test_scaled = y_scaler.transform(y_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Generator\n", + "\n", + "The data-set has now been prepared as 2-dimensional numpy arrays. The training-data has almost 300k observations, consisting of 20 input-signals and 3 output-signals.\n", + "\n", + "These are the array-shapes of the input and output data:" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(299776, 20)\n", + "(299776, 3)\n" + ] + } + ], + "source": [ + "print(x_train_scaled.shape)\n", + "print(y_train_scaled.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instead of training the Recurrent Neural Network on the complete sequences of almost 300k observations, we will use the following function to create a batch of shorter sub-sequences picked at random from the training-data." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "def batch_generator(batch_size, sequence_length):\n", + " \"\"\"\n", + " Generator function for creating random batches of training-data.\n", + " \"\"\"\n", + "\n", + " # Infinite loop.\n", + " while True:\n", + " # Allocate a new array for the batch of input-signals.\n", + " x_shape = (batch_size, sequence_length, num_x_signals)\n", + " x_batch = np.zeros(shape=x_shape, dtype=np.float16)\n", + "\n", + " # Allocate a new array for the batch of output-signals.\n", + " y_shape = (batch_size, sequence_length, num_y_signals)\n", + " y_batch = np.zeros(shape=y_shape, dtype=np.float16)\n", + "\n", + " # Fill the batch with random sequences of data.\n", + " for i in range(batch_size):\n", + " # Get a random start-index.\n", + " # This points somewhere into the training-data.\n", + " idx = np.random.randint(num_train - sequence_length)\n", + " \n", + " # Copy the sequences of data starting at this index.\n", + " x_batch[i] = x_train_scaled[idx:idx+sequence_length]\n", + " y_batch[i] = y_train_scaled[idx:idx+sequence_length]\n", + " \n", + " yield (x_batch, y_batch)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will use a large batch-size so as to keep the GPU near 100% work-load. You may have to adjust this number depending on your GPU, its RAM and your choice of `sequence_length` below." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "batch_size = 256" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will use a sequence-length of 1344, which means that each random sequence contains observations for 8 weeks. One time-step corresponds to one hour, so 24 x 7 time-steps corresponds to a week, and 24 x 7 x 8 corresponds to 8 weeks." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1344" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sequence_length = 24 * 7 * 8\n", + "sequence_length" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We then create the batch-generator." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "generator = batch_generator(batch_size=batch_size,\n", + " sequence_length=sequence_length)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then test the batch-generator to see if it works." + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "x_batch, y_batch = next(generator)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This gives us a random batch of 256 sequences, each sequence having 1344 observations, and each observation having 20 input-signals and 3 output-signals." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(256, 1344, 20)\n", + "(256, 1344, 3)\n" + ] + } + ], + "source": [ + "print(x_batch.shape)\n", + "print(y_batch.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can plot one of the 20 input-signals as an example." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "batch = 0 # First sequence in the batch.\n", + "signal = 0 # First signal from the 20 input-signals.\n", + "seq = x_batch[batch, :, signal]\n", + "plt.plot(seq)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also plot one of the output-signals that we want the model to learn how to predict given all those 20 input signals." + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "seq = y_batch[batch, :, signal]\n", + "plt.plot(seq)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Validation Set\n", + "\n", + "The neural network trains quickly so we can easily run many training epochs. But then there is a risk of overfitting the model to the training-set so it does not generalize well to unseen data. We will therefore monitor the model's performance on the test-set after each epoch and only save the model's weights if the performance is improved on the test-set.\n", + "\n", + "The batch-generator randomly selects a batch of short sequences from the training-data and uses that during training. But for the validation-data we will instead run through the entire sequence from the test-set and measure the prediction accuracy on that entire sequence." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "validation_data = (np.expand_dims(x_test_scaled, axis=0),\n", + " np.expand_dims(y_test_scaled, axis=0))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create the Recurrent Neural Network\n", + "\n", + "We are now ready to create the Recurrent Neural Network (RNN). We will use the Keras API for this because of its simplicity. See Tutorial #03-C for a tutorial on Keras and Tutorial #20 for more information on Recurrent Neural Networks." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now add a Gated Recurrent Unit (GRU) to the network. This will have 512 outputs for each time-step in the sequence.\n", + "\n", + "Note that because this is the first layer in the model, Keras needs to know the shape of its input, which is a batch of sequences of arbitrary length (indicated by `None`), where each observation has a number of input-signals (`num_x_signals`)." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "model.add(GRU(units=512,\n", + " return_sequences=True,\n", + " input_shape=(None, num_x_signals,)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The GRU outputs a batch of sequences of 512 values. We want to predict 3 output-signals, so we add a fully-connected (or dense) layer which maps 512 values down to only 3 values.\n", + "\n", + "The output-signals in the data-set have been limited to be between 0 and 1 using a scaler-object. So we also limit the output of the neural network using the Sigmoid activation function, which squashes the output to be between 0 and 1." + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "model.add(Dense(num_y_signals, activation='sigmoid'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A problem with using the Sigmoid activation function, is that we can now only output values in the same range as the training-data.\n", + "\n", + "For example, if the training-data only has temperatures between -20 and +30 degrees, then the scaler-object will map -20 to 0 and +30 to 1. So if we limit the output of the neural network to be between 0 and 1 using the Sigmoid function, this can only be mapped back to temperature values between -20 and +30.\n", + "\n", + "We can use a linear activation function on the output instead. This allows for the output to take on arbitrary values. It might work with the standard initialization for a simple network architecture, but for more complicated network architectures e.g. with more layers, it might be necessary to initialize the weights with smaller values to avoid `NaN` values during training. You may need to experiment with this to get it working." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "if False:\n", + " from tensorflow.python.keras.initializers import RandomUniform\n", + "\n", + " # Maybe use lower init-ranges.\n", + " init = RandomUniform(minval=-0.05, maxval=0.05)\n", + "\n", + " model.add(Dense(num_y_signals,\n", + " activation='linear',\n", + " kernel_initializer=init))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loss Function\n", + "\n", + "We will use Mean Squared Error (MSE) as the loss-function that will be minimized. This measures how closely the model's output matches the true output signals.\n", + "\n", + "However, at the beginning of a sequence, the model has only seen input-signals for a few time-steps, so its generated output may be very inaccurate. Using the loss-value for the early time-steps may cause the model to distort its later output. We therefore give the model a \"warmup-period\" of 50 time-steps where we don't use its accuracy in the loss-function, in hope of improving the accuracy for later time-steps." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "warmup_steps = 50" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "def loss_mse_warmup(y_true, y_pred):\n", + " \"\"\"\n", + " Calculate the Mean Squared Error between y_true and y_pred,\n", + " but ignore the beginning \"warmup\" part of the sequences.\n", + " \n", + " y_true is the desired output.\n", + " y_pred is the model's output.\n", + " \"\"\"\n", + "\n", + " # The shape of both input tensors are:\n", + " # [batch_size, sequence_length, num_y_signals].\n", + "\n", + " # Ignore the \"warmup\" parts of the sequences\n", + " # by taking slices of the tensors.\n", + " y_true_slice = y_true[:, warmup_steps:, :]\n", + " y_pred_slice = y_pred[:, warmup_steps:, :]\n", + "\n", + " # These sliced tensors both have this shape:\n", + " # [batch_size, sequence_length - warmup_steps, num_y_signals]\n", + "\n", + " # Calculat the Mean Squared Error and use it as loss.\n", + " mse = mean(square(y_true_slice - y_pred_slice))\n", + " \n", + " return mse" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compile Model\n", + "\n", + "This is the optimizer and the beginning learning-rate that we will use." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "optimizer = RMSprop(lr=1e-3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We then compile the Keras model so it is ready for training." + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [], + "source": [ + "model.compile(loss=loss_mse_warmup, optimizer=optimizer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a very small model with only two layers. The output shape of `(None, None, 3)` means that the model will output a batch with an arbitrary number of sequences, each of which has an arbitrary number of observations, and each observation has 3 signals. This corresponds to the 3 target signals we want to predict." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "gru (GRU) (None, None, 512) 820224 \n", + "_________________________________________________________________\n", + "dense (Dense) (None, None, 3) 1539 \n", + "=================================================================\n", + "Total params: 821,763\n", + "Trainable params: 821,763\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Callback Functions\n", + "\n", + "During training we want to save checkpoints and log the progress to TensorBoard so we create the appropriate callbacks for Keras.\n", + "\n", + "This is the callback for writing checkpoints during training." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "path_checkpoint = '23_checkpoint.keras'\n", + "callback_checkpoint = ModelCheckpoint(filepath=path_checkpoint,\n", + " monitor='val_loss',\n", + " verbose=1,\n", + " save_weights_only=True,\n", + " save_best_only=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the callback for stopping the optimization when performance worsens on the validation-set." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "callback_early_stopping = EarlyStopping(monitor='val_loss',\n", + " patience=5, verbose=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the callback for writing the TensorBoard log during training." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "callback_tensorboard = TensorBoard(log_dir='./23_logs/',\n", + " histogram_freq=0,\n", + " write_graph=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This callback reduces the learning-rate for the optimizer if the validation-loss has not improved since the last epoch (as indicated by `patience=0`). The learning-rate will be reduced by multiplying it with the given factor. We set a start learning-rate of 1e-3 above, so multiplying it by 0.1 gives a learning-rate of 1e-4. We don't want the learning-rate to go any lower than this." + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [], + "source": [ + "callback_reduce_lr = ReduceLROnPlateau(monitor='val_loss',\n", + " factor=0.1,\n", + " min_lr=1e-4,\n", + " patience=0,\n", + " verbose=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [], + "source": [ + "callbacks = [callback_early_stopping,\n", + " callback_checkpoint,\n", + " callback_tensorboard,\n", + " callback_reduce_lr]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Train the Recurrent Neural Network\n", + "\n", + "We can now train the neural network.\n", + "\n", + "Note that a single \"epoch\" does not correspond to a single processing of the training-set, because of how the batch-generator randomly selects sub-sequences from the training-set. Instead we have selected `steps_per_epoch` so that one \"epoch\" is processed in a few minutes.\n", + "\n", + "With these settings, each \"epoch\" took about 2.5 minutes to process on a GTX 1070. After 14 \"epochs\" the optimization was stopped because the validation-loss had not decreased for 5 \"epochs\". This optimization took about 35 minutes to finish.\n", + "\n", + "Also note that the loss sometimes becomes `NaN` (not-a-number). This is often resolved by restarting and running the Notebook again. But it may also be caused by your neural network architecture, learning-rate, batch-size, sequence-length, etc. in which case you may have to modify those settings." + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:sample_weight modes were coerced from\n", + " ...\n", + " to \n", + " ['...']\n", + "Train for 100 steps, validate on 1 samples\n", + "Epoch 1/20\n", + " 99/100 [============================>.] - ETA: 0s - loss: 0.0086\n", + "Epoch 00001: val_loss improved from inf to 0.00398, saving model to 23_checkpoint.keras\n", + "100/100 [==============================] - 68s 684ms/step - loss: 0.0085 - val_loss: 0.0040\n", + "Epoch 2/20\n", + " 99/100 [============================>.] - ETA: 0s - loss: 0.0048\n", + "Epoch 00002: val_loss did not improve from 0.00398\n", + "\n", + "Epoch 00002: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n", + "100/100 [==============================] - 71s 713ms/step - loss: 0.0048 - val_loss: 0.0043\n", + "Epoch 3/20\n", + " 99/100 [============================>.] - ETA: 0s - loss: 0.0031\n", + "Epoch 00003: val_loss improved from 0.00398 to 0.00258, saving model to 23_checkpoint.keras\n", + "100/100 [==============================] - 71s 712ms/step - loss: 0.0031 - val_loss: 0.0026\n", + "Epoch 4/20\n", + " 99/100 [============================>.] - ETA: 0s - loss: 0.0029\n", + "Epoch 00004: val_loss improved from 0.00258 to 0.00250, saving model to 23_checkpoint.keras\n", + "\n", + "Epoch 00004: ReduceLROnPlateau reducing learning rate to 0.0001.\n", + "100/100 [==============================] - 67s 670ms/step - loss: 0.0029 - val_loss: 0.0025\n", + "Epoch 5/20\n", + " 99/100 [============================>.] - ETA: 0s - loss: 0.0028\n", + "Epoch 00005: val_loss improved from 0.00250 to 0.00248, saving model to 23_checkpoint.keras\n", + "100/100 [==============================] - 71s 713ms/step - loss: 0.0028 - val_loss: 0.0025\n", + "Epoch 6/20\n", + " 99/100 [============================>.] - ETA: 0s - loss: 0.0028\n", + "Epoch 00006: val_loss improved from 0.00248 to 0.00243, saving model to 23_checkpoint.keras\n", + "100/100 [==============================] - 68s 678ms/step - loss: 0.0028 - val_loss: 0.0024\n", + "Epoch 7/20\n", + " 99/100 [============================>.] - ETA: 0s - loss: 0.0027\n", + "Epoch 00007: val_loss did not improve from 0.00243\n", + "100/100 [==============================] - 65s 651ms/step - loss: 0.0027 - val_loss: 0.0024\n", + "Epoch 8/20\n", + " 99/100 [============================>.] - ETA: 0s - loss: 0.0027\n", + "Epoch 00008: val_loss did not improve from 0.00243\n", + "100/100 [==============================] - 65s 650ms/step - loss: 0.0027 - val_loss: 0.0024\n", + "Epoch 9/20\n", + " 99/100 [============================>.] - ETA: 0s - loss: 0.0027\n", + "Epoch 00009: val_loss improved from 0.00243 to 0.00239, saving model to 23_checkpoint.keras\n", + "100/100 [==============================] - 65s 652ms/step - loss: 0.0027 - val_loss: 0.0024\n", + "Epoch 10/20\n", + " 99/100 [============================>.] - ETA: 0s - loss: 0.0026\n", + "Epoch 00010: val_loss improved from 0.00239 to 0.00239, saving model to 23_checkpoint.keras\n", + "100/100 [==============================] - 65s 650ms/step - loss: 0.0026 - val_loss: 0.0024\n", + "Epoch 11/20\n", + " 99/100 [============================>.] - ETA: 0s - loss: 0.0026\n", + "Epoch 00011: val_loss improved from 0.00239 to 0.00231, saving model to 23_checkpoint.keras\n", + "100/100 [==============================] - 65s 652ms/step - loss: 0.0026 - val_loss: 0.0023\n", + "Epoch 12/20\n", + " 99/100 [============================>.] - ETA: 0s - loss: 0.0026\n", + "Epoch 00012: val_loss improved from 0.00231 to 0.00229, saving model to 23_checkpoint.keras\n", + "100/100 [==============================] - 65s 651ms/step - loss: 0.0026 - val_loss: 0.0023\n", + "Epoch 13/20\n", + " 99/100 [============================>.] - ETA: 0s - loss: 0.0026\n", + "Epoch 00013: val_loss improved from 0.00229 to 0.00228, saving model to 23_checkpoint.keras\n", + "100/100 [==============================] - 65s 652ms/step - loss: 0.0026 - val_loss: 0.0023\n", + "Epoch 14/20\n", + " 99/100 [============================>.] - ETA: 0s - loss: 0.0026\n", + "Epoch 00014: val_loss did not improve from 0.00228\n", + "100/100 [==============================] - 65s 653ms/step - loss: 0.0026 - val_loss: 0.0023\n", + "Epoch 15/20\n", + " 99/100 [============================>.] - ETA: 0s - loss: 0.0026\n", + "Epoch 00015: val_loss did not improve from 0.00228\n", + "100/100 [==============================] - 65s 653ms/step - loss: 0.0026 - val_loss: 0.0023\n", + "Epoch 16/20\n", + " 99/100 [============================>.] - ETA: 0s - loss: 0.0025\n", + "Epoch 00016: val_loss did not improve from 0.00228\n", + "100/100 [==============================] - 66s 657ms/step - loss: 0.0025 - val_loss: 0.0023\n", + "Epoch 17/20\n", + " 99/100 [============================>.] - ETA: 0s - loss: 0.0025\n", + "Epoch 00017: val_loss did not improve from 0.00228\n", + "100/100 [==============================] - 67s 665ms/step - loss: 0.0025 - val_loss: 0.0023\n", + "Epoch 18/20\n", + " 99/100 [============================>.] - ETA: 0s - loss: 0.0025\n", + "Epoch 00018: val_loss did not improve from 0.00228\n", + "100/100 [==============================] - 69s 685ms/step - loss: 0.0025 - val_loss: 0.0023\n", + "Epoch 00018: early stopping\n", + "CPU times: user 15min 17s, sys: 4min 8s, total: 19min 26s\n", + "Wall time: 20min 4s\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%time\n", + "model.fit(x=generator,\n", + " epochs=20,\n", + " steps_per_epoch=100,\n", + " validation_data=validation_data,\n", + " callbacks=callbacks)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load Checkpoint\n", + "\n", + "Because we use early-stopping when training the model, it is possible that the model's performance has worsened on the test-set for several epochs before training was stopped. We therefore reload the last saved checkpoint, which should have the best performance on the test-set." + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " model.load_weights(path_checkpoint)\n", + "except Exception as error:\n", + " print(\"Error trying to load checkpoint.\")\n", + " print(error)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Performance on Test-Set\n", + "\n", + "We can now evaluate the model's performance on the test-set. This function expects a batch of data, but we will just use one long time-series for the test-set, so we just expand the array-dimensionality to create a batch with that one sequence." + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "1/1 [==============================] - 1s 729ms/sample - loss: 0.0023\n" + ] + } + ], + "source": [ + "result = model.evaluate(x=np.expand_dims(x_test_scaled, axis=0),\n", + " y=np.expand_dims(y_test_scaled, axis=0))" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss (test-set): 0.002279780339449644\n" + ] + } + ], + "source": [ + "print(\"loss (test-set):\", result)" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [], + "source": [ + "# If you have several metrics you can use this instead.\n", + "if False:\n", + " for res, metric in zip(result, model.metrics_names):\n", + " print(\"{0}: {1:.3e}\".format(metric, res))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate Predictions\n", + "\n", + "This helper-function plots the predicted and true output-signals." + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_comparison(start_idx, length=100, train=True):\n", + " \"\"\"\n", + " Plot the predicted and true output-signals.\n", + " \n", + " :param start_idx: Start-index for the time-series.\n", + " :param length: Sequence-length to process and plot.\n", + " :param train: Boolean whether to use training- or test-set.\n", + " \"\"\"\n", + " \n", + " if train:\n", + " # Use training-data.\n", + " x = x_train_scaled\n", + " y_true = y_train\n", + " else:\n", + " # Use test-data.\n", + " x = x_test_scaled\n", + " y_true = y_test\n", + " \n", + " # End-index for the sequences.\n", + " end_idx = start_idx + length\n", + " \n", + " # Select the sequences from the given start-index and\n", + " # of the given length.\n", + " x = x[start_idx:end_idx]\n", + " y_true = y_true[start_idx:end_idx]\n", + " \n", + " # Input-signals for the model.\n", + " x = np.expand_dims(x, axis=0)\n", + "\n", + " # Use the model to predict the output-signals.\n", + " y_pred = model.predict(x)\n", + " \n", + " # The output of the model is between 0 and 1.\n", + " # Do an inverse map to get it back to the scale\n", + " # of the original data-set.\n", + " y_pred_rescaled = y_scaler.inverse_transform(y_pred[0])\n", + " \n", + " # For each output-signal.\n", + " for signal in range(len(target_names)):\n", + " # Get the output-signal predicted by the model.\n", + " signal_pred = y_pred_rescaled[:, signal]\n", + " \n", + " # Get the true output-signal from the data-set.\n", + " signal_true = y_true[:, signal]\n", + "\n", + " # Make the plotting-canvas bigger.\n", + " plt.figure(figsize=(15,5))\n", + " \n", + " # Plot and compare the two signals.\n", + " plt.plot(signal_true, label='true')\n", + " plt.plot(signal_pred, label='pred')\n", + " \n", + " # Plot grey box for warmup-period.\n", + " p = plt.axvspan(0, warmup_steps, facecolor='black', alpha=0.15)\n", + " \n", + " # Plot labels etc.\n", + " plt.ylabel(target_names[signal])\n", + " plt.legend()\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now plot an example of predicted output-signals. It is important to understand what these plots show, as they are actually a bit more complicated than you might think.\n", + "\n", + "These plots only show the output-signals and not the 20 input-signals used to predict the output-signals. The time-shift between the input-signals and the output-signals is held fixed in these plots. The model **always** predicts the output-signals e.g. 24 hours into the future (as defined in the `shift_steps` variable above). So the plot's x-axis merely shows how many time-steps of the input-signals have been seen by the predictive model so far.\n", + "\n", + "The prediction is not very accurate for the first 30-50 time-steps because the model has seen very little input-data at this point.\n", + "The model generates a single time-step of output data for each time-step of the input-data, so when the model has only run for a few time-steps, it knows very little of the history of the input-signals and cannot make an accurate prediction. The model needs to \"warm up\" by processing perhaps 30-50 time-steps before its predicted output-signals can be used.\n", + "\n", + "That is why we ignore this \"warmup-period\" of 50 time-steps when calculating the mean-squared-error in the loss-function. The \"warmup-period\" is shown as a grey box in these plots.\n", + "\n", + "Let us start with an example from the training-data. This is data that the model has seen during training so it should perform reasonably well on this data." + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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oUrStpBFMkaJyUMkECiGEEEI0Clprpq6M452lB3B1srB8bxLtm3sTl5QJwPK9pxjWIYgQX/d6HqloqiQIdBStK10iQhc3hpEgUAghhBCioSq02vjTV9tIOJ1NfqGNQylZTOgVyqsTuvG/rQn8sO04j4xqz5iuIUz8ZD1vLd7Puzf2rPqCSftMkiCki+M+hGgy6qwcVCk1QymVpJSKKbUtQCm1TCkVa39sZt8+Xim1UykVrZTaopQaVuqcKfbjY5VSU+pqvHVOa/NQVXdQm9XRIxJCCCGEELVk7cFUlu89hb+nC+2be/P8VZ15/6Ze+Li7cNfQCBb8eRhPXNGJHq39uXtYBN9vSyD62JnKL5ZzBqaPgE8Gw7b/OvaDiCahLucEfgmMLbftGWCF1roDsML+GvvznlrrXsDdwOdggkbgRWAgMAB4sShwbHC0PcirqjuoliBQCCGEEKKh+nFbAn4eLsy8ewDT7+jHvcMjUeV6QRR55NL2BPu48erCPZVfbP8vUJhrni9+Fs7KHEJRu+osCNRarwHSym0eD8y0P58JTLAfm6m1PVUGXkDR8yuAZVrrNK31aWAZFQPLhsGe6auQCbTIYvFCCCGEEA1ZVl4hS3af4qoeLXFzdjrv8d5uzjw8sh2xRxLYe+RExQNifgC/NvDodpMoWPAXs5zY8a2QXf72Woiac3R30BCtdaL9+UkgpGiHUuo6pdQ+4GdMNhCgFXCs1PkJ9m0NT1GQV64xTMmcQMkECiGEEEI0RItjTpJTYOW63tW/TZ3QLYCf3Z4lYmY/SNhasiM7DQ6thK4TICASLnsR4pbBK83gs0vh435w5ljVFxaiGuptiQh75k+Xev2j1joKkx18tabXU0rdb59PuCU5ObkWR1pListBy5UFSDmoEEIIIUSD9uP244QFeNCvbfVnLfnv+g9hKhl3WxaF274q2bH/F7AVQtfrzOsB90OX8eZ5+9FQkANr3qrF0YumyNFB4CmlVEsA+2NS+QPsZaSRSqkg4DgQVmp3a/u2CrTW07XW/bTW/YKDg2t/5H9UcTloVZlAKQcVQgghhGhoEs/msPZgCtf1alXlHMBK7V1AemAvVll7kh27umR7/O/gFQyhvc1riwUmzYRHo+HW/0Hr/nBqd+1+CNHkODoI/Ako6vA5BZgPoJRqr+z/apRSfQA3IBVYAoxRSjWzN4QZY9/W8BSXg0p3UCGEEEKIxqDAauOJ73bgYrFwQ9/W1T8x9yyc2IZPl9Hsc+uOb8ZByDILynN8G7TqW7Z6TCkIiDCPwVGQvL+487wQF6LO1glUSs0GRgJBSqkETJfPN4G5Sql7gCPAjfbDbwDuUEoVADnATfZy0TSl1KvAZvtxr2itG+ZsWHsQWL4xjHQHFUIIIYS4uK2NS+Ef82LwdnemY4gPgyIDGdM1hHeW7GdtXCrvTOpJ20Cv6l8wfi1oGypyJAFnT8HOr0iIXkHrfuMg5QBHQq/kaGwywztUUt0W3BHyMyH9OPjVIPAUopQ6CwK11pOr2HVZJcf+E/hnFdeZAcyoxaHVD1vlS0RId1AhhBBCiNqXW2Bl9qaj7Eo4y6sTuuHldmG3vXM3H+PZH3fRJtATX3cXVu1P4n9bE3D5QVFg1dx/SSQTa5IFBDi8Gpw9IGwAlwXkk7vTlc2rF9A8KABXNK9sc2P1ls18de9ABkUGlj03OMo8Ju+XIFBcsDoLAkU5Va4TKN1BhRBCCNEAnDkGi54CV28Y+wZ4BdX3iKq0YMcJXv9lL4lnzVp7uYVWpt7Sp0Zz9mw2zbvL9jN15UGGdwhi6q198HV3QWvN9mNnWLDjBFrD02Ojaj7AQ6uh7WBwdiPQz43TIX2JStxB9MJPicKL4359aWtx48GvtjL/4WG0CfQsOTewg3lMjYP2FXIrQlSLBIGOUlwOWm7tGCkHFUIIIURD8Ns7pnOlskBeOtzybX2PqAKtNe8vj+WjFbH0bO3Hu5N6EnPiLK//so9pqw7y8Kj2lZ6XlpXPophEVuxNom2gJ1d1b8nM9UdYsOMEkweE8cr4brg4mXs2pRR92jSjT5vqdwIlOw1+fACad4F2oyB5L/S8uXh3s55X0ezU85BxlDmMYdqdQ7Eoxfipa7n3v5tZ+OfhuDrb7xm9m4OLJ5w+csE/JyEkCHSU4sYvZb+BKpoTqGRyrxBCCCEcLPFsDh//GseimJO8eE0XxveqYp27ghyzgHmPm8G/Dax5G9IOm2Yl9WzW+njeXXaA5j5ueLk5s/3oGSb1bc3/XdcdV2cLg9sFsut4Ou8s3U+XUF9GdWpe5vyV+5J4YNZW8q02wgI8+D0uhf+sjQfgudFtuDfpNdS7W2DwwyZw8w2t+SA3fgqxS81/az8wP8Pet5fs73sXLH0egE4TXyYy2BuAdyb15L7/bmHe9uPc2N/eMF8paBYOp+NrPg4h7OptncAmR1e+RETx/wQyJ1AIIYQQDvTNxqOMeHsVc7cco5mnC4/NieZfK2LRlX0xfXSDyf51uwH63glo2P2jo4dchtaa13/Zyz/m76ZTiA9tAjzJyC3k6bFRvDWxR3HmTCnFP2/oTqcQHx6bvZ1jadnF1ziVnstf50bTrrk3ix7sw5rm77JjQhrfjExn4bUW7kt8ARW7FDz8YcXL8F4XOLoRjqwDa0H1BpqXCRv/DVFXw5QFcNkLcNci8Co118/NGx7aCI9up3e3LsWbR3duTvdWfkxbFUehtdS9ogSB4g+STKCj2KpaIqLotWQChRBCCOEYqw8k8/y8XQxtH8Tr13UnxNedZ77fydYVczm16TsCb/wXLu2Gl5xwdANaWZib1IqP5+1ntlNr3PeuIXDY4zVbG6+WaK156afdzFx/hDsGt+XFa7riZKl6HJ6uzky/vR9XffQbj83ZztwHBqOU4i9zosktsDH1+kgiNz4H8b/jEf87Q0qffO2/oOdk2DYTljwPM8aY7SOfhZFPn3+w22ZC7hkY+hcI6w8Rl1R+XPOKcwuVUjw8qj1/+morP+9KLMnUNgs38wq1LruUhBDVJEGgo1SxTqC2/8OVxeKFEEIIUds2HErlhfkxFNo0TkoxKDKQQZGBPPPDTjqG+PDpbX2Lu2a+2+MYtn3v4pRn5ezXt8FfduHn6wtAVuxvHFfhPL0wnl5h/kTndWLo8Y0Mf3MFrq4lt5MK6BnmzzU9QxnWPqh4Hl1NWW2aDYdSWbDjBLFJmYzqFMzVPUIJD/JCa80L83cza8MR7hsewbPjOlcrEG0T6Mlr13XjsTnRjHh7FUpBwukc3rq+G5HL74cjv0PHK83SC/3vNSc17wxhA8zz/vdC22Gw/mPYPgs2fw7DHgdn13O/8e55ENrHBIAXYEyXEDqGeDN1ZRyjoppjUQrvZuFQkGXWFvQONsGgrRCcXC7oPUTTI0GgoxSXg1aRCZQgUAghhBC17N+rD5KUkcew9kFk51v5busxZm04QqCXK59P6Vdm2QS18nWcgqPY0OYeBm35K8989C96jrmdIG83BhyPJsZpGP+5sz8jOwVj3XYI5wUrGNsinZNu4cXXyC+0sWzPKX7YdpyBEQHMvm8QlsoydKfj4WwCtOgB7r5ldlltmikzNvF7XAperk5EBnvzztIDvLP0AIFergT7uLHvZAYPXBLJM1dG1SgTOb5XK9Ky8tl65DQAtw9qyySv7SYAvOo96H/PuS/QPArGfwydxsGcyRC/BtqPrvr4wnxI3AED7qv2GMuzWEw28LE50fR4aSkAL3a0cReYn6N3MKz+J/z+ATwZZ0pLhTgPCQIdpXidwPJzAot+cUk5qBBCCCFqT+LZHFYfSOahke154opOAGTlFbJqfzIdQrxp3azUsgP52ZC8D4Y/waARUyiIeZ2r9Xpu+6EXPmSzyz2Ly4cOxCfKNFVxbjsYgOd7pEPfPmXeN6/Qypdr43lj0T5+3H6cG0qvoac1rH4LVr0BaPBvC49sBme34kOmrYzj97gUnh0XxR2Dw3F3ceLEmRyW7j7J3sQM4pIzefKKTjw0st0FlaLeNTSCu4aWamgz+x/g09I+17Ga2o0y6/wdWHLuIPBUDFjzoHW/Go+ztGt6hJJbYCUjt5C9iRl8vT2Bu9wwQWBYf/vPE9OA5pIn/tB7iaZBgkBHKV4iovJMoKwTKIQQQoja9L8tCdg03NgvrHibl5szV/VoWfHgpD3mXqVlT3ByxqX9CIYmbOaLCf04sncL7ASfkFKBU2A78AwyTVLKBU9uzk7cNzySX2JO8t9Fa7iiyw14e9iDvC1fwKrXofuNENQBVv4f7F0A3Sea3fFpfLAilmt7hnLf8MjiIC/U34M7h9agE2luOqDByRXSDkFI18qPy8uEuGXQ/z6wlP+i/hxcPCByJBxYDFe+VfW8vCPrzGOrPxYEWiyKm/q3AUym9M60NDgJacf3ExCeWHJgUYlqTT6LaJKkO6ijFC8WX/kSEcgSEUIIIUSjl19o4/7/bmHy9A18vfEIp7Pyqz747HGzLEPS3hq/j82mmbv1GIMjA8suNF6VxGjz2LKneQxsjzqbwGUd/Lm7mz1n4FcSTKIUtBkER9dXejmLRfHGMBfmFz5oOmqunwq56dg2f8Eht84M2DuJAb/1ItESwuFf/8OGQ6m8vWQfD8zaSqi/O69d1+3CG85oDW+Gwb8vgQ3T4JMhcGiV/Qdjhf2LIHm/eX1iO1jzTWavptpfBmeOwunDZbdbC2DrTFj3Max608wH9A+r/BoXwMmieGvyIJJoxvYd0XDwV7NjxNOQkQjR39Tae4nGSzKBjlLcHbSqxeJlTqAQQgjRmGmteX7eLpbuOUWbAE+e+zGGfy7ax6e39WVI+6CyB+dnw7+HQ3aquVe4e0lJg5Jq2HAolWNpOfzt8k7VGRhs/8p0nPSzl24GRJp7k9NH4Owxs618INO6H+xbCDmnwaPiwuldOASAU346LHkWve2/WJL38XXBrQzr0Rw3Fyf2xvakV9o6Rk1fj5PFwpB2gTw7rjO+7n+gwcnh1ebxdLxpnAKw/GW4fyQseQ42fgKu3jDyGROoAbTqW/P3iRhhHg+tNj8vgLwMmHU9JGwqOW7wwxfyKc6ppZ8Hp/zb4pWWQPruxfh6h8Dwv8G+X+CnRyCkS8XPlHEKfEJqfSyiYZJMoKNUUQ6qkcXihRBCiKZg1oYjzN2SwJ8vbc/qJ0ey4JFhhPi6c8eMTczdfKzMOnDZuxZAdippQ57H6hkMi58xO/YuJG/BE3y7PpZbPtvA2A/W8MXvh0nPLVmzLiuvkA9XxOLj7szYbi3OPahTe+Dnv5qM2PAnSiqWAtqZx7SDcOaYKav0KrvIenHWcPvX8FFvyDhZdn/yfrTFmbtb/MAbBZNRyfsACBs0kfdu6sUb13fn0tFXEaAy+e+EIDY+exmz7hlI55ZlG8XUWFEmzM0XCvPM8xPbzOeI/gbCBpo5iEufh0z7mD0Dav4+QR3AJ7Qk6ATYMccEgNdNhz9vg/tWmrUV64BfaEei1FHcDq+AdpeazzTlJ7Pz4MqSA202+OF+eLejmcMoBJIJdJzictDycwKLSh0kEyiEEEI0VjsTzvDqwj2M6hTM46M7opSie2s//vfgEB76eitPfb+T5+fHEBnkxZnsAv6R8zl9LAEM+TWKO53H8GLWLA7s2kybeQ/gbs0kwLqVU77/wMfTjVcX7uG9pfuZ2Lc1V/cM5aWfdrM3MZ03b+iBu0sVc8OsBaaJyIpXTeOS/vdBr1tK9gfag8DUg5ByAHxbgaXcPUwLexC49DnzeGgV9Ly5ZH/KAVRAJF/eN4S/z3Xlgz156PBL+MvVpUov7XPlLvE4At6DLvwHXPpzHVhsnudnmfX5iiz5O+SdNWWTrl7ms5/cBb1vvbD3UgoiR5jAymYzP5+YHyC4M/S86Y9/lvNw7zUJ9z1zwQq6y3jTatAzAJp3KZmLCKZz6M5vzfPYZdDxijofm7j4SRDoKDZZIkIIIYRois5mF/DQ19sI9nbjvRt7lVkywc/DhS/vGsAvuxLZcyKd2KRMurT0ZcCxdKyeXXl/cB+OHGmGdftXpH/3EO6WTI64deLyvG2MviwB1ecOdiac4ct18czedIyZ64/g6erE51P6cWlUqdK//GwTFHkHm9e/v2+askRcAlxGqfoAACAASURBVBM+Bb9WZQftGQDNIkoCvCF/rvjBvALNPMGictGcM2X3pxyAoI64OTvx7uQBbDjUnj5t/cvO9WveGbxDYM/8ygOn9BOmXLX8+KpybCPknoWwQXBsg1mGomVPSE80DWi8gk0Zp5Mz3PXzH19sPWIE7JhtuoB6Bpo5kqOevfDr1USHyzkVPIQVie50ch9IcfFn2yEmI2kthMOrYPWb0Os2M18w/nfHjE1c9KQc1FF0FUtEFP3ikXJQIYQQotGx2TR/+y6ak2dz+fjWPjTzqriwuIuThfG9WvH3cZ2ZcWd/3rupF8E6jRatI5jQuxWPTRhGQYcr6Wc5gM3JjbZ/WwmBHVA75wLQI+N33st7hegBK3h+XBTfPzikbACYmQz/6gPvtIfFz5qAaO1HEHU1TFlQdYA16UtTAuriBQMfrPyYMa+BxT5/78zRUh/cCmmHTckkoJRicLtA3JzL3QdZnKDHTRC7BDKTyu47vs00lXm/K2yZUdWPuKyjG8xj56vNY9ph8Ago6WAacYkJAIv8kQAQTCYQIG457JkHaOh6/R+7ZnUphdc9P/Ey9zM/+kTJ9jaDIT8TTu6A5S+Z0t6r3oXwoZC8t2SepGjSJAh0FHd/sloOxurmV2Zz8ZxAKQcVom7t/A6+mihfuAghHOrDFbEs35vE81d1pk+bis1TKmUtgKxkM9/Mzv0yMyfQMvolU8rY9To4stY0dPn2Vji4As/tn3Fv21MV59Qtfd4EWJGjYMNUeC8KbAVw+SvnHkdoL/jbfnjiQNWBYtcJ8PwpCI6CM0dKtp9NMO/RrBrLOvSZYoLGjf8uu33TdEBDi+6w5HmTyTyfY5sgqGPJ+2YlgYe/ac7SZQKMeu7816gJ31DTgGX3j2ZuZIseENS+dt/jHLzdXRjdJYSfdyaWzCltO8Q8LnvRlLte8iS4uEP4cLP9yFqHjU9cvCQIdJSWPTg+6iPy/cv9YpAlIoRwjIV/MWtByR8/IYSDLNtzig9XxHJDn9ZMGRJe/RMzTgIafEut59eyJzwRC4PsGbnet5mpJPMfhqBO8NRhcPe3B06lrvN2B9g5BwY+ALf/CN0nmeOufr9k3t+5WCzg5n2eY5zAv03ZIPB0vHlsFn7+9whqD12uhQ2fwG/vwWeXwpp3zPy6/vfC2DehIAv2/Vz2vPwsk9lc8Qrs+QkKciFhs+miWrpbqbu/CQRvnFm9z1xTXa+DkzshaTcMfaz2r38e43uGkpqVz9qDqWaDb6gJguN/Mz//7pPM9tDe4OLZdEtC5V67DJkTWN+Ky0ElEyhEnWrRA46uM3M3wofV92iEEI1cXFImj38bTY/WfvxfTde8y7Av/l0qEwiAd6nunM3amgzPjm9h8mwzh6/3babZS3qiCSBjfjCZMDBBoFJw/WfmZrh8k5c/yr+tKcUsmmNXFAQGVHOB97FvwsmrYcXL5vXxreax/70myPVrY+a59bjRbE8/AbOuA3vH0TI6X1s2CPTwv6CPVG197zINdGwFjisFLWVEp2B83Z2ZH32cER3tcz5HvwRbvjBlvEXlr04uJkAu3TSmqYhbAbMnw92LLmw5kEZIMoH1rGSJCAkChahT+Rnm8fSRcx8nhBB/UIHVxp9nb8fN2cKnt/WtukNnVdLt87tKZwIrc+nz8NiOkuxW/3tNWeVPj0BWqmmE0rwLPB1fkpFTqvYDQDA31nnpkLjDvD4dDxZn01W0OnxD4YE1MH4qPBoNna6CLuNN4xiLxTSNObSyZBmKVW+Y+X63/wg3fGE+u5svtL8cOoypmAmsS27ecM0HZux18bM939s7OzGue0uWxJwkK6/QbOw6wcz3jBpX9uBWfSFpr8maNgRxK2Dth38si2ezwZxbTRfcw2tqb2wNnASB9e0Cu4M6ZyXikbS9DgYkRCOVmWwe89LrdxxCiEZv+ppD7E1M5/XruxPq71HzC1SVCaxM6aAjIAKufs8s1fBRL1P90OPGShdyr3UdLgdUyTp0p+NNiailBgGwm7fJZgZEwORvYNLMkn09bjb3Spu/sK/3Nxv63GHWx+s+0TQ+eToebplrAt3S2b+6zgReBCb2bU1WvpWfdyWe+8AWPUyzwqQ9jhnYH3FiO3x1PSx7AQ6uuPDrnNwJhTnmeUpc7YytEZAgsL4VLxlR/W84XNLjiZx/LWHL78cpRzo8CXFeNptpsgCmdbgQQtSRg8mZfLgilnHdW3BF1/Ms1F6VtMPg6n1hC5j3uxse+M3MIWx/OQx+5MLGUFNeQabUcM88k7VJ3g+Bf7BBSukS2qD20Pka2DAN5j0I6Irz7yxOJUGxS6ngu64zgReBvm2bERnsxdzNx859YIvu5vHkrpJtWpvgyL6c2UUjbjkA2tndzBe9UIdXm8fgqIYR/DqIBIH1zf4LribloD5Hfy1+7p66u9aHJESjk3O6ZJmWXMkECiHqhtaav/+wCw8XJ166tuuFXyjtIAREXvjyBSFd4M6FcNv/zDwwR+lxk7nJPrYJUvZDSLfavf5lL5mltuJ/M/Pw/MPOffwjW2DIoyZb2MgppbipXxhbjpwmLimz6gObRZiy2RPbSrZt/Q983Bc+GQp5GXU/2GrKP/gbx1wimGcdiu1E9IVf6Mh6COwAkSPNHFKbTMECCQIvClpZalQO6nViLXl+kWjlhHuqfKNRXcV18tU+IaWkBj3tkGnZLRqmzFPmsVm4KQeVDmFCiDqwM+Esmw6n8fjoDjT3cb/wC6UerJsulnWt+0QzD/D398FWCCF/IBCuTFB7uHcZTJ4DV75VjeM7wJhXwd33/Mc2Atf3aY2rk4WXftpNgbWK+0qLxayVeGCpCYYO/wa/vmb2Je+DFa86bsDnsGrfSQqPbGBNXgd257fAkp0C2WkXdrGME6bEOLgTFGRD+vHaHWwDJUHgRUFR7XJQmxX31N1khQ4jz7897qkxdTqyhq7AamPhzhNM/GQd3V5awue/HareiQeWwNvtYPbN8P198FFvU5feUCZSi7KKuuMFtjc3JgU59TseIUSj9O2WY7i7WLi+b+sLv4i1wCy6HtAAg0B3P5PBPLDIvK7tTCCYG/lOV9ZLA5aLXbCPG69d143f41L4x7wYdFVfeHa+xgRGBxaZ+xw3X3hoA3S7HmK+vygyZe/PX48neYwZcQn+YebLhKwTey/sYlkp4NXc/H8T4PThWhplwyb/gi4CWlmqXQ7qlHcapa0UeLUgN7CbyQRKZ1GjMA+W/gPOHCXxbA5TV8ZxyVsreeGb1WSmn2ZwZCCv/byX13/Zi812jqA7PxvmPWSeH1hsfiG6eJqOUtFfO+aziNqVZZ87W3RTJc1hhBC1LCffyoLoE4zr1hJf9xqWYGanwZLnzN+f00dM+XpDzASCWagdzN/NhvoZGrAb+4XxyKj2zNl8jH+vqeKL745jTbOgObeYL0Vv/c50Ye14JWSnlC0VrQfxKVkUnjHZuuDQcMaMMMs6bdy0oXoXKP2FvdamJ4BXkCmFBVPdJaofBCqlPJRSnepyME2WslS7PM051ywEavUIJDewC04FmbhkHK3L0TUYeudcWPcRh6Zez5A3f+XtJfvpHqjZ6PMki5z+yqxbo5gyuC3T1xzir3OjyS+sInje9l/zS/Cmr+DSf8BTh+DZE6at8oZpjv1QTUX0N7D2o7q7flEJSdG3gNIcRghRy37ZlUhGXiE39j/PPLVKT34S1n8MsUtNR08oaeDR0AR1MI+t+jp2PqIo9tfLO3JNz1DeXLSPn3acqHiAhz9c+y/wC4PxH5f8b9b+MjPncs98xw64nN9ik2mp7H+3fULp2KkbBbhwInYb2fnnmdoTvxb+LwRil5svVXLPgjUfvILBrzVYXEzjJVG9IFApdQ0QDSy2v+6llPqpLgfWtCigmplAezfQQvdAcgNNetw9RZrD5BUUcnzJhwBEFsTywmBXVj0xkultVuBSkIHKPInTug946dquPDW2E/OiT3DPzM0V5wlmp8HqNyF8uCmXuOQJ88tSKbMAbGpcSVZJ1I6MU6bT27J/mPWA1rwDO+fW7ntkpwLKLK4M0hxGCFHrvt1yjPBATwZG1LCjp9aw314+mXsG9vxkFl6vi1JKR/AMNI/+bep3HE2YxaJ4e2IPBkQE8Pi30SzdfbLiQZ2vgcdjoNctJds8A0yWcOe3YK1hH4VatPpACp297M1tfEPB4kRecHeirAdYUFlQW1rROpVf3wBfjiu5Z/MKNt1jm7WVclC76mYCXwIGAGcAtNbRQEQdjanpqUFjGOcckwks9Agi3zccm7MH7mkXWCPdUKUehJ//VpzdOZtdwGuffknrvFiiQ28G4K6wU4T7WUz5ZrcbIOpq2DYLlXuWh0a25+2JPVgbl8IdMzZxNqdUw5etX5pOkpVNOG8eZR6T99XxB2xidswuef7NjfDrq/DDfZBUiz/n7FQzV6Voraw8yQQKIWpPXFIGmw6ncWP/MFRNO3qmHYKCLPM8Jdas8df5mgvvDFrfOl5psi0D/1TfI2nS3F2c+GJKP7q38uPhb7axcn9S9U7sdYtpphZfP4uq5xfaWH8whT7NckxW0rs5AF7th9DdcphFO85T/Vb6382J7SXLQ3kHm8dmEVIOalfdILBAa13+rkna69USrVS15wQWl4O6B4LFiQLv1rhknedbkcakIBemDYLNn8PuHygotDLjs/cZnPwdBc4+9JryjrnZP/grbP/KlAH0uQMGPWgCgekjwFrIpH5hTLu1DzsTzjD53+tJzcght8BKbswCdGgf0167vODO5jGpiQXdde1UjClJ6XWradoS2MFMUv/t3dp7j+xU8+20m71DnGQChRC1RGvN8/Ni8HF35sZ+F1AKerjUzfbueWArgIgRtTdARwtqDy+kQMse9T2SJs/H3YWZdw+gY4gPf5q1lXVx1ahkancpOLtD7LIqD/l64xHmRx/HWq6/gtaaY2nZFbaz4VPTs6Eath89TVa+lQ4emeAdYrJ3gAobgBsFZB7eQkpmXtUXyE4tee7fpqQxnJc9CPQPg7PSHRSqHwTuVkrdAjgppToopf4FrKvDcTUtyqnacwKdclKxunibhTOBAq+WuGQl1uXoLi77FprabkDHr+Wrr2fw+On/Y5xlAy7dJ4CbD7QeYJq5/PIEhA0yf0zDh8GEaXA6Ho6ZicVjm59l9rVeRKUtI/DdFhx6tTfup7YxN7MHx89U0j3SNxRcfcwCuKL2JO01C7h2utK87n0bdJ1gyqMKz/GLviZy0kwQ6O5nXsucQCFELZmz+RgbDqXx3LjOBHm71ezk1IOw4hVzsxs2ENITTHVQm4F1M1jR5Ph5uDDrnoGEB3pxz8wtxJ46zzqArp7mnunAkkrvTWdtOMJzP8bw2Jxoxn6whs9/O8Ss9fG8t+wAY95fw/C3VvKnr7aWXaJi8dOw7iNzb3Yea2KTcbIoWqhU8G1ZsiN8ODaLK1epdSyKqaS8tUh2KngGQd87TdObokxgURDo28rcE+Rnn3csjV11g8A/A12BPOAb4Czwl3OdoJSaoZRKUkrFlNoWoJRappSKtT82s2+/VSm1Uym1Sym1TinVs9Q5Y5VS+5VScUqpZ2r6ARuEGmYCrR6Bxa8LvFrinJnYNNY90xrrps/J927F0VbjyNy/irzYVSX7O19jHi99Di55ygQT13xYUhpQtP/Lq+CriTBtIP0WXctz7UxtuFdAC7aH3c6HZ4Yx9v01/PXbaP42dwdTV8ZRaLWZ6zSPglMyB/Nc8gqt7DuZzpb4NLbEp5GUfo5lNayFkHLAdCXrcIVpxNN3CnQZD/kZ5O1fQeq5vvGrrqJMYNFaUdIdVIiG5+zxel/IeveJs8zedJSXF+zmjUV7+XbzUV7/eS+DIwO5qbKGMHt+KvvF4dEN8PEASNwJx7fByv8zN6S9bim5SW3Ro+QLKyFqQYCXK/+9ZwAFVhs/bK9GFqzTOEg7CCd3ldm8Li6Fl37azaVRzfn4lt7YtOa1n/fyj/m7+WhFLM08XbltUBuW7TnF47O3UlhoLVs9Ne+hitVU5ZajWHMghd5h/jhnnjRfvhfxDEB1uZY7nJeRsmFO1WPPTiup/MlNh8wkQJnAEEwQCJDRhBIoVXA+3wFKKSfgZ631KOC5Glz7S+Bj4L+ltj0DrNBav2kP6J4BngYOAyO01qeVUlcC04GB9veeClwOJACblVI/aa0b1QrpGgvVbgyTe5pCt5JJ5wXeoTgVZmHJT8fm1vj+aBxLy+a7LcfYfyqDwIQVvJ63npcK7iQ1zZdprr/wJ+eFaIsLqtOVEDnSnBTa2/xXnpsP9LzFZALjSsocAo8th67X03bSf2gLzEnN5oWfYth4OA2tNd9vS2BzfBof39IH71Z9YetME7w4nfefT5NyNDWbR2ZvY/eJ9DKlIJ6uTsy6ZwB921bSLCE1zmR2m3cBZ1fTiAdMYx5nd7atnsffszxY9eSoPza47DRo0dO0LAf5BlCIhiZxJ/znSnMDd8+Skvm9DpKRW8ArC/bw3dYEADxcnOhkO8RymzMFzmG8cX33inMBkw/A3NvN86fjzZg3fwEp++Hfw0uO6zIeRr8E8x8xr0c9W9cfRzRBIb7uDIgIYPmeUzw9NurcB3e9DhY9DTvmFJf1pmbm8eDX24gM8uLDm3vh4+7CuG4tScs21VmuzpbipVHaBHjSetmfcH5tU8k1J86AHx80vReu/CecOWbWKLQ4wwOrAUjKyCXmxFkeH90RNiWW3NfZqVHPkhX7O/edfo9TqfcTEljJ74HsVNPgxt0XrHlm/p9Pi5J7Nj97EHg2ockvYXLeTKDW2grYlFI1ijC01muAtHKbxwMz7c9nAhPsx67TWp+2b98AFK2yOgCI01of0lrnA3Ps12hclKp2Js9SmI2t6EYWKPQyqfLGWBKampnH5M828PHKOOKSMpns/Cvpbi0YeuMTPPGXJ9H2QE+Nnwo3zQLnapThXPcJPLod+t9bsq0wB9oMLn7ZJtCTL+8awNpnLmXd3y/jjeu781tsChM/WcdWW3tzfJJkA0vLyC3gnpmbOZKazYMj2vHhzb2Ydc8A/nNXf0J83blzxmZ2JpypeOLuHwAFbYeU3e7sBq37E5K2hfjUbNJzCyqeW11al/xRsDiZuQ5FTRiEEA3D7++ZqRMp+818bwfafeIsYz/4je+3JfDwqHb89tQodj/chh9dnmWF25OsGx5DuKqkPG3D1JLn/wyHT4aaErvQ3jDk0ZJ9RX9/LnsRbv8ROl5Rp59HNF2jO4cQm5RJfMp5/gZ6BpjlIvb/XLzpl5iTnM0p4AN7AAimC2mQtxtB3m5l1sa8v48P45w2lb1mhzHQaawpCbUWwOJnTE+AxGhTFg38sjMRrWFcJx9TsVO6HBQgsB0ZY97HW+Xy/dyZzN18jCOp5T5Lzml75Y+/eZ20tyT7ByXP05tQP40qVLccNBPYpZT6Qin1UdF/F/B+IVrromjlJBBSyTH3APZeybQCjpXal2Df1rjUoDuoKsxBO3sUvy7wMqlyl8zGNck1t8DKg19tIzkjjx8fGsqKR/rSPXcbvr1vYGzPMCKb+6Junwd/OwA9b6r5G1z5NjxeKqHc5doqD508oA0z7uzPmewCHvvd/JLbu2l5zd+zkbLaNI/NieZQShaf3NqHJ67oxPherRjeIZhRnZrz9b0D8fN04fYvNpGUYS8N3fQZvBVpmr+0H12ydEMpuu1QwgsP4Ucm8ceOw+q3iv9Q1EhBNhTmlrQtd/E08wSEEA1HSiy0GWQ6+x2t5oLRF2BvYjqPTfueD37eWlzaftvnG9Fa892fhvDkFVGEBXhi2fxvFBqadyFg3Wvwrz6wvlTQl58Fu/5nGl4VTUWw5psb20uegjGvwrh3zPa2Q82jd7BpyiFEHbm8i7ntXr731PkPbneZ6aNgX1NvcUwikcFedGnpe/5z9y0E4B23hxnn8V9yHtlpqrF63Wbm6H06zBzT6zZz/MFfAVi4M5GoFj60d7eXffuEVrh0q15jyHLyo2XiCp76fidj3l/DL7tKJUKyU03WvagRXNKesmWlRc/TE87/ORq56taz/WD/r9ZorbVSqkz6Syk1ChMEDqvp9ZRS9wP3A7Rp07DWptFYzB+TarAUZmNzLskE5vu2QaNwPXsIuKyORuhYhVYbj87ezqb4ND6a3JueYf6w4RPzB7RLqUSwh/+Fv4nFYkoC7l5iFg/1aXHOw0d0DGbtM5ey+XAqJ756g6yYn9GDr0LZCqFFA13LqZa8tXgfv+5L4tUJ3RjSPqjC/lB/D2bc2Z8x769h/pbD3Hf0KdMNr81gaNkT+kyp9Lpnwy7Dnze52mkDenss7Hkb1v3LlFXZu4VVS6b9j13RfBtXLykHFaIhsdlMSVfEJeAVBAcWmwx/LS6hYLNppv92iOPLp/Ku5T9knPLksX1vsCWnJa7OFmbfP4i2gV7m4JQ42P61aTwx7h1I2GLm9v32LvS9yzTWiP4G8jOh9+0Q3Ak6XQU9bgJtLVlAvf+9Zk02/wvoKCrEBQgL8CSqhQ/L957i3uGR5z44cqR5nD4Sq0cA16SEk9/z9uotgXJyF3g0Y/jEv/LxZxt5YeUZxnX3QNGDoX6RuCTvMyWn13xg7geOrOVEx9vYcuQ0T4zpCOn2xIZvxSAQJ2e82g9jQtIeuk8ewdPf7+Shr7fx4eAcxhcsMnP9PJqV9ADQNnOfV8TFw3wpLB1CqxcEaq1nnv+oajmllGqptU5USrUEihctUUr1AD4HrtRaF/V3PQ6U/u3Y2r6tsjFOx8wlpF+/fg2rS4pS1c4EWgpysJXKBGpnDwq8Q3E72zjWPNFa8/cfdrF0zylevKYL1/YMNTfsq98yv5DCBtTuG7YZVO1DnSyKQe2CiGk7lp6HZ6Gm2bu3vXDaBJVN0PdbE/j3mkPcPqgttw+qmM0r0jHEh15h/qRt/g6y15iboaveAzfvKs85YGmHjy2Mm5xW4nvcHsDlpZvSjpoE3mft3/YV/RFw8ZBy0OpIPWgytpe/XL1SayHqSkaiyegHtgMnV7P+a8oBE1zVkneW7ufUmv/wrusXFDbvhk/aIW7P/IItTs/z9b2lAkCAte+bfxMjnzUBXdvBMOo5+M9YU6raZpBphx8+3DxXCnpNtp9c6m+FUhIACocb3TmET1Yf5Ex2Pv6erlUfGNTBZAMPrsAp9ww3Ox0i71g8FF5t5vCfy5mj4N+Wge2CuH1QW2ZtOFI8n7adepBI5xROJg6n4/d7eMo9nJDUOH7eabJ5V/cINV+sQOVBIECbgaj9P9PeM5uv7x3I377bQYutD4LFvr6wf5uSTCCULQctuq6Ug1avHFQpdVgpdaj8fxfwfj8BRV/7TwHm26/fBpNpvF1rfaDU8ZuBDkqpCKWUK3Cz/RoNTsLpbP768zF+2nuG0zmFZORZiTmZw6nMguqXg2pdYU4gQL5fJK5nD+KSkUDYkjvxOzC3jj5F3Vh3MIU/z97OPxfv48n/7eS7rQk8dlkH7hoaYQ44sNh0Txv214ti8dzwUXfioqwlG47aV0uJXXZh5YoN1NYjp/n7D7sY0i6QF66pZF3Fcq7v04oRmb+Q7xsOEz49ZwAIcDAli6+to+lhOUz42U0lJVXHNtZsoEXf9hUHgZ5NNhOYX2hjw6HUc3dsLbLgMdj4CeyYXfcDE7UvM6nxdI1Os/9eDWgHbezzh4+ur7XLz9t+nGmrDvKY/2/o5l1wfmA1ziOeZKiOZuWtAbQPLhUAag2xy838Jp9SM1raDjbVDYufgc9HmzlVN3xxUfzNEqK00V1CsNo0q/Ynn/tApeCWb+HW//Fo2Pf8n/OfTcLh35dATiVz/Es7c7T4C45Xxndl4Z+HMe/hoXz3p8E8cP2VtBk4AX8vV1YfSObn457kJ8XxU/RxurfyIzzIC06bElR8WlZ+/TD7F/jHNuLu4sQHYwMZaNnH5y63kH/3CpOBdy8VBPqVDwJbl2Qbm7DqloP2K/XcHZgEVNLqr4RSajYwEghSSiUALwJvAnOVUvcAR4Ab7Ye/AAQC0+xp5kKtdT+tdaFS6hFgCeAEzNBaN8iOHKfS88jIs/Hx+mSmrk8uLv50d1Zs9aZaS0QoWwFKW8tkAgHy/Nrhffw3wpbdi3NuKh6pu8kJ7k1+sw518Elq19zNx3j2x114uTmTlVdIoU1z55Bw/jK61NhjvgfvFmbdmouAd9veLGt+N71O/UCwOgM750JIV5g92Yzxjnn1PUSHeHnBbkL83Jh2ax9cnM7/fdI17V3xVftY5303w6uROT2YlMkCNZKXmIUzhXD5K3Bsk/mv/z3VH2hRJrDoG0VXryY3JzAtK5+3Fu/jl12JpOcWYlEwKDKQ7q39UCgycgtISDzFoXT4YHIf+gZjfs4Aaz+C3nc02Wx3g3RsE3xxObQdBnfMb/idjNPs3zkHtgO/MFPafWS9KcesgeSMPBbuPMGAiAC6hvqhtWbl/iSe+n4n49oU0iZpNwx+wfy8+t4JK17GZ+al0O9uU7mglFkiKPOkaZpR3pjX4PPLwGo1N88+lbU9EKJ+9WjlRwtfd+ZsPsr4XqHnLu90ciEjbCSLDy3n9sG3QEQ/+N9dsOJluPr9ys/R2nT+7DAGAKUU3VqV9JbsH14SPlhtmqVfrsP16GJOnTjCPVcONvN/135o5sq6ela4PAChvcDJzcwP7nwNLvFrAJid2ZuCQ814sI172WVWmpf7otqvVa1+kdRQVbccNLXcpg+UUlsxwVtV50yuYleF35xa63uBeys5Fq31L8Av1Rnnxaxv22Z8dn1bDp/OY218Jq5OilBfV77YkkJqjg2n3MLzXkMVmuyFdi77jyKr1TD8Di/E6urDqQF/p9WaJ/BKXHfRB4FTV8bx9pL9DO8QxNRb++Du7ERaVj4t/NxLDrLZ4PBv0HV8zeaB1bHm17zIgKmXsjxyDu32zDMtlG0FcHi1KTGoqoShkUg8m8POVMTd3wAAIABJREFUhLM8PTbq3OUkpTRLWAlK81lSFENsGifLub8hP5icSWhwIDPC5/LFxlP87heOS4vukLz3nOdVkJ5gbhpd7F+euHhCdkrNrnGRyi2w8tWGI2w4lEpEkBdRLXwZ2SmYwFILVttsmsfmbGfjoTSu7tGSy7uEsO9kBgt3nmDLWtOU+Trn9UznE1ap/jz53ZMsGbQLF2seDHscfn8fDq2s/KZXXJzW/cs8HvnddN5r3e/cx9eDuKRMPFydaOXvcf6Dzxw1nUF9Qk0g1mYQHFlb7XmB+YU2Pll1kE9XHySnwFRxjOvegtTMfDYeTiMy2Iu3+p+Bnym+ccUzwDRwWfMWbJlhOg2Oeg4O2PvWtavk30PrfvCntYCGFt2r94MQwsEsFsUDIyJ5ecEefo9LYXiH4HMevyjmJPlWG1d2awHhXSBhM6z/GCJHVd5ULyvFdFH3O3+ps5NFceWIYTDrXYYHnGV8r1aw5W3TPXTijKpPdHaDVn1KKoOS94GzO5HhPfn411iu692KFqXLQcuXjvuGQu4Z08DJ1YumqlpBoFKqT6mXFkxmsIF/tVg/Ipq5EdGs5AatY5Ab+icLOxKzCMwppJlH1T9WS6HJXpTPBOYG9+TQdYuKX+f5d8DrxHpOd6m84cbFYH70cd5esp/rerfirYk9ijNJZQJAMPM+8s5C2MB6GGXVerT2o0uoPzPO9uf/chfCz38zgUZWMv/P3lmHR3F2ffie3Y0rcYgSQiC4u0tpkbaUAoVCDUrdXb+38tb7VmihXtpSQQptKRR3DxYSNEISEogRd9n5/ji7xJOFYoG5ryvXZmfHNtmZfc5zzvn9+GIg+PWEyfOb/gx8Paw7Iu28I9t5Wb7R8ZUU2XqzObsFm46nMaxtw7PksekFdPJzwcO/Jak7ikk4U0iIWytI3HVuohA5SdX7Aazsropy0LWHU3nlzyhO5xQT6G7P5ugMSsuN6HUK/Vq5M61PINe18+bbrSfYEp3Bf8d34Pbe0rd5Q8fmPDEytHJnc96AtFJGqduIyPKjcMtmXPx7w5AXxBfz4AItCGwqZCeK6l7nqRDxiwRLlzMIzE+TAKpKD9GR07lMmLsdvU7hi2nd6V+HoFQ1sk/KNWy+n7a+Do4sg1P7ZSDYAPsTs3ju94McT81ndEcf7h/cijWHU/lu6wnsbQy8dmN7buvlj83GN8WvzKPKYHHYSzDkefj7CbGoSNgGWQlyf68pXW/mGhcK02gaTO0dwDdbTvD+qmMMCPGoNxsYnZrHG8sO076FM90CTJ58w1+FhO3w50NSBVXTay87UR5dLRRpdJPtPxzuAM42cOQvCOrfqGAf/r1FkbesSIJAj9a8NLYDoz7ezE2fb+X9WzowCCoVSKvibGoPyT0lvY/XKJbW93xY5edtoDuVpZwa/wIvRys8HKwxGo38ElHTVrE6ujIZuNbsCaxJoXdPbDMiwdh4dvFyEHEym2cXH6RXkBvvTujUcClhkqkk7QoLAhVFYWrvAH47E4yK6ebZ/W65mRVmyGzxmlehvLT6hgVnYP4E2PiOZDmbKGuPpBLkbk8rz4b7+s5SUQaxG7BqOwpfV3se+WU/G46l1bt6cVkFJ7MKCfFyPHuM2PR8+bIpzZNg2xKMRplIqKoMZu0gIhNNlIz8Eh7+ZR8zf9yDs60Vv97bh03PDOXwa6NY/ugA7h8czImMAu77aS9jZ2/lvVVHub69D1MD8+Cn8bDyBSgvqdxhWbF8gQ54EgIH8KzVAuyK04nv8rTMtgb2Q00KZ1vM1ZE9verZ/TWgSADjHiJlk5eLrAT4tBv8fs/ZRRn5Jcz8QT67zV1sufO73Sze24hUe87J6gIqbcdKwBb1e61Vi8sqWBmVwvurjnLPvHBumbudvOJyvrurB3Nu704nP1eeuq4Ne18ZybbnhnFnvyBsDHqRkXdvXVvwQqeHcZ/AzXMl65CfUl2lWkOjCWJj0PP4iNYcTMphZVQdHpdAVkEpM3/cg42Vnq/u6IHOXL1jsIFJP4iexaI7a49zzP18dVg/1YmLv1TopB2VyZ2M49Dh1sa38+0uFVhph2VbzzCCPBz4/YF+ONtacce8vUxyX8yE5Nt45Nf95BRV8RuuahhfE1WFlCgZr9VHcQ7smAN5FlhtXMFYFASqqjq0ys9IVVXvVVX12MU+uWsFg0FHc0c9y4/mkJJXvyl2fZnAmhS7h6EzlmKdc+KCnueFYOOxNGb8EI6How1zp3XD2tDIR/DUfqnrdg+5NCd4DtzUxRdba2sOOg+RBR1vhb4Pyu++3cUoeMN/q2/018MQsxY2vi3ZlSZIQUk522POMCLM2zKpaJC6/dI8DG2v5/cH+hHo7sDMH/awaM/JOlc/kVGAqkIrT0eCTaIMsen54GaStLZUgOf4PzIrGValZMXKvkkGgYWl5czdGMvwDzex6lAKT4wIZdkjA+jbSvwPDfmnaf/PRJ7x2MnGp4fw/q2dyCkqw9vZlncmdETZ9K54Me2cU1kuCFJeq1aIXcctX1I08AXu1b/OG5GmWV/f7iiZcTz0zVqOnM69DO9c45w4tkK85lz8RKgkccflmXAyGuV+V5onA7tk8d17YP5ezhSU8PUdPVj8QD96B7vx7OIIopJz6t9XTlL10jJ7NxGK2vO9CD+VFpKZX8KTCw/Q/Y013D9/L19siuNkZiH39G/J6icG1ao8sDXosNZXuX+lHgbvegSuFAW6TIV7VsP4r8TaQUOjiXNLNz9CvBx5f9Uxissqqr0Wk5bP9O92cTq7mC+nd69dtu0aID2BKZEypqmK+fvZrRELCjM6HXi2lWBu07vg1V78NRvDu708Ju6Stg9TyWcHXxeWPTKAh4a2wsbeGTsba1ZGnebu73eTX1Jeef5QGbBW5fhK+KI//C8M0kWr0mhU+WF7PN3fWMMNH67l1P8GwaoX4McbZSK1iWKpOuhjiqI4K8I3iqLsUxTluot9ctcOOgJdrVAUhZ/21z/zUNkT2EgQ2KwtALZZRy/cKf4LzuSXsD02gxeWRHLX9+G4OVjzwz29qvUt1UtGtNwcrkCFNUcbAzd28eWuzDvJn7xEbkA9ZsAzcXDveinH2jmnUtTAlA2j133gGSZlDE1QvW9LdAalFUaGh52D6EH0atBZQcvB+LjYsuj+vvQJduOlP6IkuKuBeVkrT0ecbK3wcbYlNq2gShAY0/gx47fBortkmw63VC63bjrqoGm5xXy6LpqHftnHoPc28O7Ko3QNcGX5owN5bETr6pMoO+dIpmLZYxgK05jYw5+NTw9h7ZODpW8zea/MrgYNFA8z82fv1AF59OkILn7YDX+ejr1HsP5YGiczC6lo0R2ALrpYIpMaGKhrXH7y0+TaMItoBfaXvpdz7aO9EOz7Qfy/rvsv6KxQD/3Jy0ujCI/P4oOJneno54KzrRVzpnbHzcGGF5dGUmGs435YUS4lWzWtFIa+LNUuH7WDt5qT8vEQ1hxMYFznFvwyszeHXx/FmicH88rYdjjZWlXfNvc0fDUY/npEnhdmQk5i5aCyPgJ6Q+fJlf3FGhpNGL1O4ZWx7YjLKODzDZXfqUv3JzHm0y0kZRUxd1o3ugc2q3sHYePEby+yhiL9mRhTdu8crhOvdjJhlRol39eWtNI0CwKDXeWkZkDfsy/ZWul5ZlRbfprRm/kzezN7SlciknKYMS9cAl4Xf0kwnD5YbZfZhaUkbJf3Y9TbUPznE6w/msod3+3m//46RKi3E/cZ/qZFaTy/VwyA9KOkR64hYvsqTn88lJKUK2PcbSmWloPeo6pqLnAdouI5HVH61LgQKDps9XBTmAvrYvPYGJdX55dhZSaw4XLQMucAjAZ7bDIv74dRVVVeW3aI7m+uZerXu/gtPJFZg4L56+EBhHiZyghPHWg45Z5xXEp0rlBu7x1AVrk1v2eZauIVBRwkM8OI/xNPq9WvyPPTB6VZOrAv9H0IUiNlkNTE2HQ8HUcbAz2C6vliqIvoNVLjb7KFcLAx8NHkLtgadLzweyTGGp/32LQCFAVaekgWsJWXgwSGroFgsBWvwMbY/L58Qd2zutKcGSQTWF7UJMpxH19wgP+tOU5kUg49g9xYfH9f5t3di1Bvp+orVpTB/p/A3tRbZfr7GPQ6bK30MojOTZbesE6TRXL/1D7ppdj+qQTKzVqe3d2U3gEowK+7E9mWL8F+K+UUh07lyIC5Cfztrnb+PJDM2yuO8PveJKKSc2RgY1a7C+xX/TFhu0X7VFWVvQlZvLvy6LkH/HGbZNa8ohzyUmDd66JO2vch8O9N5sF/WLQ3iUeHtxYfMBMu9lb837h2HEzK4Yft8bX3m3dKMtVVS7oBPELgvs0YBz7LDocRtCs/zNJOu3lnQif6hXhIiWd9rH4ZTkfINRO9FuK3yvLAK0OBWkPjUjE41JPxXX2ZuzGWQ6dyeHvFEZ5YEEHXAFdWPz6o4clevZWURh9fVT0bdiamdp9gY3iFQYWprLRKMNcgOr1MvuedEk2GBnyfr+/QnP9N6syuE5l8vTlOxmo+neQ+gNi4zfwhnJ7/XYNd/Hr+rujD7MIRWJ/cxnPz1rIvMYu3xnfklxHF3Jw1j4p244nr8xYFqg0rl8xj84pf8Mw6QFxRw+PzKw1LVSvMaZjRwI+qqh5SLK4D02gM1WQWf1snN3YmFvDWxhS8HQ2MC3PlhlBnnGzky+xsT2AjmUAUHaXOgVjnJl7sU2+Qn3Ym8P22eCb18OPGzr60be6ER9XsX0mezMZ6d4QHttbeQVE25Kde0U27HXxdaN/CmQXhJ7mjb2D18kgnH+jzoKjL5SSJUh+Iv41dM5FY3vEZBA++PCd/HqiqyqZjafQPcbfIFgKQMqv0I9DtjmqLvZxseXlMO579/SC/7E5kWhWz+dj0fHxd7bCzls9+K09Hlu5PRtXpUXw6iuJhQ5w+KIqWw14BxxrKZ+ae2rLCRr0KLycJZwrYHnuGp68L5eFhjVwDSXukR2H0B7DiaUg/Bq2GVn8dRNDCLRiWPSpf3OnHJFN9++/VLCB8Xe0YEebNgvCTRPg40gtrOjkXsCVhH3w4VL5sb/sFbGoEoxr/nr0/QGm+BE/18OeBZB777QCKUpnQ1SnwpvMypigGlOZdZKFrAEZnP+J3LeOJ8HYEuNnT1seJwaGetG/hjKIoqKrK4dO5LIs4zbKIUyRny2Tj3vgsFt5v4WAs97SURYFM0uisxPt29PugKCS69SEg4QNuDbPj8eFVPssmgaexnZqzeG8SH64+xvUdfGhRtfQs39Q77FRbcVn1bMOL2eP47UwXtvkVEZL0Bxhfb1hJuqxYyr263C5WGsufFHEZK4dGRWY0NK5GXhnbjk3H07llznZKyo1M7xPIq+PaWfYdH3q9qOcmbpcydFWVScaOE8/tJEKGw+qX5PdzuQ79e8t4oOe9jSrI39TFl78PnuarzXFM6xNIs+adYffX5BUUcs+8cE5nF/NEdxu8DmbTfsA4jLowdNuW8PvgVJoNnSQVBd/fDy5+6G+ewzPW9mSnD2Vi8i5UW2dUp+6EtbRQDOcKwdJM4F5FUVYjQeAqRVGcAG0q+IIhZvHOtnq+viWQV4c1x8fRim/CM5i64ARf7U7HqKoWZwIByuy9MRRevobVrdEZvLbsMCPCvHjnlk4MaO1RPQAEiFknj6mRdddUm0v+PEJrv3YFMb1PIIdP5/L2P0dRa5Z3djE5pRxcAHvnQYtuoipnZSs3rejVMhBvIsSk5XMqp5ghbc5BFXTbJzLA6nxbrZcm9vCjf4g7b684cjbzUF5h5PDp3MpsMRIE5hWXk5JbDC26yuydsaLW/gBRElzxjJR61NW7Y5aDvsK9AhftSUKnwITufo2vHLdBmvQ73ioTDDXL/5LCJSvt01H6qfx6ykA4cpFkV+tQ/pzeN5AzBaVsi82kyNaHEJts7jrzkczWntgMS2bBoaVQlFW5UUUZJO0VL7X6/j/FuU2yDPqSkJ0on93Vr5ztq8kvKee5xQfp8vpqXl92mA1H084Kax1+7XrWPjmYObd345FhrfEqP81J1Z1DaUUUl1XwzdYTLMzrSPOMHVirxexPzOLHVTv4bc7/MeaDVUz8YjudX1vNmE+3YLPtA+6138iHt3bkseGt2R2fydEUC3tAo1fLY9dp0O1OCB0F05eCdztUVeW7GJkseHOATaW4ROoh+LgjHFqKoii8eXMHKlSV//urhhWwOQh0qK0g+sm6aH4LP8nDQ0PwHTJD+oKSwhs+17iNEmR3mAA3fipZ8kNL5RrQWzW8rYbGVYibgzVv3twBg07hv+M78MbNHSyf5A0aKH59x1fJ89xTMiF5ruM2rzB4YDvcvvjcykhH/ReeiYUhz1m0+tPXtSG/tJwvNsVKsFlRwifzFxObXsDcad15MEzGoi3b9+XGkcOhRTcCIj/DqSRV2igStkHv+876F7pe9wK2ZVnY5SVgFTri3N7zFYClmcAZQBcgTlXVQkVR3IC7L95pXWMoCpjs4/U6hQFBjgwIciT2TAmLo7JYHJWNjUHH43amnkALLpBye28cUnafm5T+BeJERgEP/ryXEE9HPr6ta+WXfkm+ZMU8QmWwcHxl5UbxW6D1yOo7SjssjzX9Xa4wJvf05/DpXL7aHIetQceT11U5X7dgKW1Y97o8n/Rj5Ws9Z8CWD6WXa9wnl/akz5ONx0SVc3Bow75CZzm6HA7+Bv0fl+CjBoqi8MHEzkz8YgfTv9vFt3f24LP1McSk5XN3/6Cz6/UJdkevU3h7xVE+adsFZfdXMklQ87OREgXzxkiAN+ZDsHOtfU5nM4EFgIXv4xJTYVRZvDeJQaGeNHex4AsxerWIEdk1k37TmuWyyXslADSYJmLCbqycdR30TJ33iP6tPGjp4UBiZiH2nv60P7kBFEgd/gneuhxRvz22ArpOh5s+kzLABdMrfdTajhWblKr7LsqG90NERn/GGm3QXZP1b8qj3gp2fcHBTi/x4M/7OJVdRP8QD+ZtP8F3207g18yOudO6YWetJ8TLkRAvR0Z3bE5pTD4H0ny475tdWBt0pOaWcL//YOzS/2FR2HZwbo5x7WvoSnLJLvqDBfpphHaezFi20DdiEWQCutZk9ZvM3E2xzN+ZwJs3W+B3d3yV9Njc+Fmtz9I/USlszHAEG7DNjQf6y/fS7zNF9XPju9B+PP5u9jw2PJR3Vx5lw7E0hponmsxKwA7Vr9VfdiXy8dpoJnTz46nrQiHHdJ2kHWmwLIzkPeI5GNhPBpsz10hGsMvUxt+nhsZVyuiOzbm+vU/leM1SrO2h7RjYPx8GP1c5CXM+tjTe7Rvvy62J3qrOCaL6aOPjxPguvszbHk+ocwjjUXBI3Mj/jXuVAa09YOMhQAEv0dag38Ow+B74yHReLQdXn1xu0QWm/CatTU1QMMrSTGBf4JiqqtmKokwDXgY0hYALhKroUNTas+at3G14dpA314c68/OBTBLS5U9u1FsQBDp4oysvRFdWW3TjYpJTVMaMH8Ix6HV8c2cPHG2qzDPsnCNZoY2mdtK0wzJwhbqzYacPgrVjtV6lKxFFUfjPuPZM7uHPp+tjqjVYAzDoaXl0C4a24yqXO3hIdiziNzFXbQJsOp5OqLdj9XKt+shJgj8egOZdYOiL9a7W3MWOX+/tg61Bz4S5O9hcw9MO5Mb9xIjW/BVxijXZprKwU/tr72zXXMlAPbgDuk2v+4DmSZQrTRymtFD8JnOS2Xw8nZTcYib3aNxsl6x4+VuEmT5bAb2l/LPQZDlTUS6v+/Ws3KZqaW6fB+vcrU6n8N+bO/DmzR2waSbnEW/0ZqfjcOj/GMxcL/26kYtFJvvXyRIA9pghAjRH/4Y/HxYBJHMPYdoRkfQ+tV8yMhryt4n4DT7pLBUDfR6AVsNQj63g8d/2YzSqLLyvLz/N6M3aJwczc0BL5t3ds05hLevcRMLadcLe2kCgmwMLZvXh+QdmQchI2PIBLH8KnXd7uOVrXN28uK/gK/7rs5m+ka/IderVDrbPppmdgXGdWrB0XzJ5xZWK1VHJOby27BDbq9qFlBVLJjp0VK0AsLzCyAerj2Hr0RJV0VeKZJ2OkPu/R6hkrU2TFjMGtKSlhwNv/H2Y0nLTZ+ZsECgDPVVV+XRdNC8ujWRIG09RvlUU8f0y2DUuGpUSKcc13wdadJWZfa20WeMa55wDQDODn5Xsevi3pqoTG2nzuUJ5YmQoRlXlqb+TOKKEMMX9ONPN7SipkTJWM1cMtb9FspOeYXJ/vO3n2pnKNjfA0BfOKRi9UrA0EzgX6KwoSmfgKeAb4Eeg6TQzXckounrLoxRF4ZG+XiTllLI/IYOuVjaN1j2DlIMCGApTKbW+NF9uFUaVR37dz8nMQubP6I2/W42yVXOgZx4oZMVDx0lS9pQZK3+D2PWiaGdlCykHJXuhs3Su4vKh0ym8dUtHSsoreH/VMWwMOmYONClZthoOt34vin0130ufB0VFb893ciO9giksLWf3iUzu7Geh90/4t5L9nfh9ZQaqHvzd7Pnl3t68tuwwd/cPqrPc9IEhIWyNyeCpDZlEWNuhO7W/eolpRRkc+Rvajm64Kf1sOWiVILC0QIJHW2fL3tsFRFVVYtMLyN63lB7h31C8bwHznWfj7uDYuAKr0QjrTTYkZhuM9uNh60diuNv9Lsl8lBVW99q0dYY7/hKvtToytGb6hXjQDyBbsjDb6Uj86Xxu6gr4dZdM31eD4UNT6c/IN6D/oxJ4ph2BA/Nl+an9IieeXkWsKmFb7ez/tYCqyr3Qs43cC3d/BSufE4uOYS/LPSFyMcqxFViXHOXpqTfTI0j+R8Gejrw8tg4bA1WFtf8HRZk4tWjN1luHVu9Pvu1nCbrtPaQESlHk8/BZD1j1okiyT18qwdzieyDqd+7oO5Lf9yXxxaZYWnk6suZQCmcOb+Sk6sX329y5q18Qz9/QFtv4rfL5Cr2+1mkt2Z9MXHoBX0zrjrIuoDIIPPyH9A3e9gvM6SNZhFH/xdqg45WxYdwzbw/3zAuXcvG0ZFwMDszZlIRRVTmWksfKQync0tWXd6r6zOp0ct1nRDf890+JlO8YDQ2NC4NXGLQcBPt/BBtnyY7V9Nu8gvB3s2f+jN4oikK76DEoOz8X71wrW0k+tOhSubKiyPdU8FARqGpkLNPUsHR0Xa5Ks9NNwGeqqn4OaNNmFwpFegLrw0qv8NLQ5thRTJFia9Euy01BoFXBpesL/GF7PJuPp/PajR3oHexee4Vsk1BNwRnpIyrOEYlf91YyOIhdB/NvkT4lY4V8WTfvfMnO/9+i10lp4+iOPry5/AgLzR54iiKSx4519NF5tZVZ+l1fQPK+S3vC58iO2DOUVhgZHGpBP6CqipFzq6EWewUFezrywz296u031OsUPprcBSsrayIrAilO3Ft9hbhNIofffnzDBzIHgaVVsuTfjoJPOl3yXrWi0goe/HkfI/63iZ1b1wBgW5HHO5mPMbOXZ+M+mrHrRZ574NPgZsqY+3SSGcttn4qJ79HlMuCu2fcXPFgUWy2hRP5WWU5tRSHUjFdbCR7M9L5PHvUGKX3u9wj0eUiu6XcC4dg/JgGOHmLhcS0SuRjm9IZlj8nzpHAppbx3o5TmWjugmsSiRrkkcn17n8b3mRknVRYArjUEqkAGLqGjJHA3v9YsEHrNkt/7PCCTAe3Gy8Tbhjfp3NyeTn4ufL4hlvcWrmdWzIMstHmDTcE/cle/IOZtj+e5BeFS4m/tVGlLYUJVVb7ZEkcHX2dGtfeW+0CmyT8seZ+UBHu0luAx4rezfeFD23jxzKg2xKTl89hvB9gWcZTkUkc+WnucT9ZFs+5oKo+PaM2HkzrXvj7cQ+BMA0GgWSXXp0Pjf1MNDQ3L6TFDxnipUZZ5/F1mege706ulG4pfN6lOST0E+emQnSDfTzXRG666ABAsDwLzFEV5AbGGWK4oig7QmjkuECo6FBoefLrbGwh0KCe7wobyuryUalB+NhOYckHOsTFOZhby/qpjDG3jyZRe9ZSwmYPAsgJIM2UEmgWZBgdVBjFpRyQ7WFYoA9omhEGv45PbutI32J03/j7MmfySxjca8X9SPvHrFMlmXaFsOp6OnZWeni0tsIZI2iM30w63XtBzaO5ix88ze3OIVqinDxKXml354qGlMgvZaljDOzGXfZkCG7JPSglIUZb47F0icgrLmPL1TlaajN/vCjyD0aczTPoJTyWHB+zWNr6Tk7tkEmngk5XLFEUycpmxsPEtyQgGDRChnPNlwBPQaTKpLW8kKjm3ugBSYD+YtQnu3VD9S9IjBK57Uxr3x30is6jRq8Az1KToFnFuQXdJnpQYN3UOmYLmfT+Ib2jWCbkHVqkSWHfKllzVjht9Mi0r0YrfIo8hIyB4iOXnMuR5uP5dsQ0BOYfh/5EqjT8f5LOJbZl7ezdW9jxAF8MJCLsR61Ph/KdbMfcNCibs6GfyGbxpdq0SqQMnszmems/tvU1BqUdryIiR7HXa4cren173QmGG9A4j1S8PDQ1h+/PDWDCrD/19VLyb+xHz3xuIf2cM0f8dzeMjQmsHuiDHyEqQyY+62PWlXC9tx1r+N9LQ0GicdjdJJUPYuCYRBJ6lhUmJ9NQ+qZqB8+tnbKJYGgROBkoQv8AUwA94/6Kd1bWGySKiMQIcKsgz2rAzsaDRdcvtPDHqrLHKrxw0KWUF2KYfbGCr80NVVV5cGikS5eM71v3lXFYM+SnStwEiVAGVQWB2YqVnXsYxKQWFJpUJNGOl1/HGzR0oKq3gg9UWKH/6dISx/5O/z+E/JXNTknfxT/QcUFWVjcfS6dfKvWH/LTORi0Qqvu2YC34uYc2dGTr0Ouwo4cWvlxCTlieDy8N/yPEam62zNqmOmv/GR5ZVvnbojwt+vnWRXVjK7d/u5PCpXObe3p3HhgXjmHEQnV93aHejBElH/258R0m7ZTBtzm6aaT0UUBeRAAAgAElEQVRCxJe2fiQD+u53/rsTbhYIt3xFqJ8POUVlZ20EztKiS/2y3ooiZakdJsjztmPAyRsqSqpnYxvj1ynSnF9yafucLyhF2VJy2XU6uAaIX11mXGUWFymr/3RDDCf0QQRXxFu23xObwdFHelfqEkOqDxsn6HN/9dKtkOHQ92GIXERA/BJuaOeBa+wfKG1Gw81zZKJl5+fc0zKLe/V/E+l1Y53Z94V7TmJnpWdsp+aywCtMJgCT90qfn7cpG9dysNznt8+u5j+p0yn0DnbHQ8nFztUHgyVqhe4hMtmQdaL2a2XFsOd7KZs+Vw8zDQ2NhlEUqWSYPN8yo/crBRc/EZ1K3GGaVNVLf/Q1gkVBoCnw+x0wj64ygKX1b6FxTtTRE+i5533cIr+ptszdUEqpzo5V0RZo8uj0lDn5Y52bcHaR9+63CVgzA9ejv/6r052/M4FvtsSx+Xg6v+9N4o7vdrMlOoPnbmiLb32CIeYZfHOttVlBqllg9V6S1teJQfzpA5Idu8KVQesjxMuRO/sF8Vv4SaKSLfh/hYwEp+aw9H74bSr81EhJ4yXmREYBiZmFDGljgZpmUZaUKIaOumg9ds3DxAS7rTGGaXM2UPjTFLFAGPJC4xubM4HmACQzFmxd5cafcfHtOrIKSpn69S6Op+Tz5fTuXN/BR7JiJbmVvUqthovaWMGZyg1VtXqGo6Jcyur8etV9oNEfyrXVvHNlv+C/pH0L+X9GJVtoHVCVcZ/Aw3tloGA2tbdUEKkoqzLbdeCXcz/2lcLKF6T3pNe9MOI1KZ0qyqomfjV3YwwHk3JwCuiCknbYsmzpqf3g3+vCKEErimRvPULh2HKIXgOFZ6DzFLl2ut0BUb/jvXAMOQZ3Hj5za6WIi4nC0nKWRZxmTKfm4q0FUqIMMkEElZlARRGhoTMxcGhJ7fMpSLNccMHd5EFYV19g9CooyanlV6qhoXENoyhSGRD1u1SjBQ8+a/9wLWBREKgoyr3AYuBL0yJf4NJMmV8DqCgoNWwXmx1fiEfkl9WW6SsKsbN3IDypkIyC8kb3W+ocgHWelGDqi7NwSpTyMo+Iz9EXNTL4Or4a5k8Qz68q7E3I4uU/onhz+RHu+G43Ty2KIC69gMdHtGZaFTXHWmTHy6M5WxC3QfzJbJxk2UPhUk7m20PK8xJ3gXe7Ji0h/+jw1rjZW/P634cbX1lvkBIKo6kcNClceiavED5aG421XsewxoRKQIRKinOkT+1i4R4C1o481aGQ/1jNwzY7mvxxX8qkQmPUzARmJ4Krf2W52kVkZ9wZbvtqJzHp+Xx1R3eGtjX1P5qz4C0HyWPIcECFo6YsZWkB/HwrvN8KfrwZEndKUFSSW90UvipWtjB1gfSaWSAmZQlhzZ2xt9az4WjauW9s4yQlogD2pp7hwjP1r1+V6DWVv0cuPPdjX0JScop5cuEB/ok8Xf2FnCQpeezzgATm7cdXBvCmvtm9CVl8tDaaGzu3oGWHPvL/NfkF1ktpIWSeOHdp9cZoOwbit8rAyMGzsqe036PyqBpJ6v8WCQUGVh2q3naw/OBp8kvKmdyzSmuAp0ly/cDP8uhTRT0w7Cb5m/z1qPTmmCnKlqyhhX3FZz9fdfUFHv4THLwk86ihoaFhxjwxpBqh132X91wuMZaWgz4E9AdyAVRVjQbOwS1ao0EaEoap0iOmKy+imbMTRhXmHzhT25i8BqVOgVjlJYGxnGZH5oNqJHnw/1CM5bgd/rH+DY0VsGQmxKyFrf+r9tLnG2JoZm/FlmeH8su9vVn6YD+2PjeUx0eENty7csakCmce9BTnVC/19AyVYNArDFClzK2J9QPWxMXOikeGhbD7RCZ7E7Ia36DbHZIh6fuwPD8dcXFP0ELWHUllWcQpHh4WUn+m10z8Vgj/Wm6kzS/i/0+nA78eOB1ZyPVl65hdcTOriupQTawLKzu55kqr9AS6BEgWIefkRTGRP5VdxLRvdnHbVzvJLirl2zt7VBfAiV0nEtRm8SDf7iJdv+wx+PMhUVCMWSvXZtwGWHof7JwrAW1IIwa1F1Bd19ZU3vf3wVMUlDQ+EVUv5syOpUFgRrT8z/o/JuWERdmNb3MZ+CviFKM+3sySfck8uTCCmLR8OLpCroudcyWrZxZjURQY/Z5kyPx6kFdcxmO/7aeFqy1vju+AEijZbhIaEdDJOAaopnvnBaTrdDCWw8md4qFnnpBz8oZZG+Gmz+kwZBL+bnb8uCO+2vfRgvCTBHs60COwSv+wrbOUwJbmS0BoV+U1vQGmLpSy5sUzKq9Bs6K0OYBsDFsXCfRqTuaoqggRtRzUtErVNDQ0Lj6+3WDqIpjwrVQwXUNYOjooUVX1bB2SoigGaETJRMNyFKXekh+bnLjK1coKsbF1ZEJ7V1Ycy+W7vQ0HgmXOgShqBbaZx3A9voC8oOsp8B1IbsvRuET/jr4wve4NT+2XIM3ZVwzdTSVbUck5rD+axowBLfF3s6dfKw+6BjSruwewJmdiREHOq8pAva4goerscBPsB6zJpJ7+uNhZ8e3WuMZXbhYIz8aKCAdIOeBlJruwlBeXRtLG24n7B1vQR7PpPXBqIWI3F5vh/wflxahBA1loP5V/oiwUQVIU+SyW5Mt1l3PSlAkMAdTGMy9mSvItEvJJzS1m6tc7iTiZzctjwtj0zFAGtq5SVpuXAie2SC9g1XMc9bZcL8dXSbZywBPw0im4a7n0+UWvgm531vYsushM7ulPQWkFyw+ebnzl+jBnAi0tB82MEwXN0OtlwuzEpvM/9kViyb4kHv11Py09HPhtVh9srXQsm/cu/DYF5o2BHZ9Jtr9qtrpFV/G0dG7BV5vjSMoq4uPJXXG2tZLMtL0HJGxv+MBmkS0vCydBLMW9lSi7thxUu8y6RVfoOg2dTuGe/i0Jj886e/39E3maPQlZTO9Th0qpWSjKuUXt4zn5wPgvxDdw3euyzGwp4mVhEAhSxppxvPK5qko2Mz8FAvtavh8NDY1rh9DroOOtF6akvglhaRC4SVGUFwE7RVFGAouAZY1so2ExOpSqmUBj5Qy7TVZlj5KuvAijlR2zenkwpo0zCw5m8eXuDArL6s4iFnlJ/51X+FvoKkrIbi3CDGc6zEBRK/A4OBfb9AgMBTUGz7HrAQXGfCjnkrgTgDkbY3CyMXBnWxW+uwF2fWW5ul9mLLgHm2Z/TRdZVfNqM82CwMakYngVBIH21gam9g5gZVQKJzMtNCd38JDMlFmp6jJhFvzJLCitW469JifDZXDe+75LE5j4doMHd6BMXcDIDr5sjk63PDtl4yQZiaIseXTxr+wnakhi3sy+n+BtX+nfbICM/BKmfr2T9LwSfpjRi5kDg7G1qlGaGbUEUGsrqQb2lQDh6Wh4ZB8Me1WWBw2Ametg9AeivnmJ6RbQjGBPBxaYLVDOh7OZwHMIAt2C5Z5h71HZV3aFcCKjgFf+iKJXSzcW39+XPsHuvD+hE+PzF5Bi11o8Evs+DKPr1lNLzyvh260nGNOpOd3N2TNFEQuPE5savs+mRkn/dJW+wgvG9W/BncsavJ6n9wmkg68zr/55iMQzhbz61yE6+DpXmi9XxSxQ1HV63TsLGS4CQgcXiEhM+jExgHcJsPycvdpK8Gj+mx1dLh6KoPkDamhoaFTB0iDwOSAdiATuA1YAL1+sk7rWUGuUg+rKK8vR9MVnqiwvxGiwEwP5fl6MbevCkkPZ3LHwBIsisyiu0Zxf5uhHkXtHbLOOU27rTrGHZNnKHX3JaXUjLnHLCFgzk6Blt0gmwkziDukvCR4iZtLJe4lOzeOfqBTu7BeE07rnIXE7/PMMrHnFskDwTIz0cel08FgE3Lu+7t4MRZEMoaK/8D0ul4k7+wahUxS+21aHYl19BPaT8qVL7FtXlT/CT7AiMoUnR7ahg68F9gKrXxKFwp4zL/7JmfFsA9YO3NDBh9JyIxuOWdirZuNoshwwBTKu/pWKgZb0BcaY7BuiV0smrw6yCkqZ9s0ukrOL+O6unnQLqMdaI3KRTHh4htb9uqLIuVUt6/TrIeIiF7DU01IURWFyD3/2JmSJMuv5YO0ogcu5ZALdgqUksfNt4jcYd2VkA0vLjTz6634Meh0fT+5yVsVyhGsyQbpU/pc7lEMtJojYSj2CA59viKGk3MhTI2t8BkKvh7zTUp1RH6f2SwXFZSpzNOh1vDuhE1mFpYydvYXMglLendCpbjXPZkHwyhnxTa2PVsOlTDj9iPRGe7c7t8+5V5j0UuYmy/NdX8jjxHkXvmRWQ0NDownT6J1VURQ9cERV1a9VVZ2oquqtpt+1ctALhaKDKsIwurJKCwi9+XdjBbqKEowGGUToFIVH+3kxe5w/oR62fB2ewV2L4vnrcDYVVXwEMzvcQ6FXNzI6P2A6jpDe7UlSe73I6b6vUW7nCatekJlXo1F6bvx6yOyvd3tI3sOcjbHYGvTc2ypL+pdGviGD/e2zpdfFTKlJAvxMrPwO0nOVnShBIEg5lG/3+tPuXabKjPElLnO7WPi42HJj5xYsDD9JTpGFPoBBAyRLkn6eapX7fhJxnfNBVclf/BA3Lu/GEz4RzBpURZTBWCEB0JlYWP2KqB2CDNJP7hJzcBvH8zvuv6BHkBsejtastLQk1NoUBJoDOGdf6Udy9rMsE5h6qDJzWIdaZU5RGXd8t5u4jAK+uaMnvYPd697PmVjxJ+o40bLzvkK4pZsfBp3Cwj3n6dunKFISWpjZ+LqFmVCcXSkO0ucBEZX6ZZJkci8zczfGEpmcw7sTOtHC1U7UP3+8Cb4ehmrtyE7rvry27HC9pftJWYX8siuRid39CPasce20HiX37WMr6j64sULKxuuz57hEtG/hwqxBweQWlzNrUDDtWzQwadRYsNpyoDxG/Cq94aE3nNvJmMti045ASpQIKI18vU4bCw0NDY1rmUanDlVVrVAU5ZiiKAGqqiZeipO65lB0KFUGCLryyrJBXZmIV+gqJDuoGqoHRm08bXlrlC8HU4qYtzeDz3amk15YzoweUm5V4DuAAt8BtQ6pGmzJCZEvRcVYhs+uNyElAqwcpB/Q12SW6dcL4/6f2FsYwe39euJy8BPpp+p+lwyks+Jh07viSZawHf64v3JgZucmsuVph8HKXmbwLaHLVPm5irirfxBL9iez/OBppva2oLSp5SAZ/M3tBz4dTAqPFs6Gn9gCfz0sAgnPWBDQ1KBw25c4Rs0HBR4wLENffL+U8SoKLLpLzMer0vk2Eb6Ai+ILaAl6ncLIdj78dSCZ4rKK2iWXNbFxlDLQ/FR5bhZk8QipW16+KqWFUt486BkZgG96VzJ5JvVEVVW576c9HE3J5avpPRjQugF5+8hFgFLpoddE8HSyoX+IB+uPpvHi6PPMrji4W1YOmm2yuTH30rn4wcTv4ctBELlYMqKXiTP5JXy1OZbRHX3E6gNgx+cQtxEA5ZavmZXdnpf/iGJFZApjzJ55VXh7xVFQRE24Fg7uYmZsvr5qkrhTvPdaXN4gEOCJEaF09nOpVLw9X1wD5Ptn+2x5HnaOxu5mEZnUQ6IIamWv2UJoaGho1IGlNRbNgEOKoqxTFOUv88/FPLFrCbWGWbyuijqhOSuomJaZM4E16eRjx4ej/biutROLIrOIPVNS7fWknFJmb08jvaB2JqqghSlIjF0vM68gmUCAfo9QatTzhuE77u3rI8baHSeI0ptOB0Nfkln6f56DBdPkC/zmL2Dw8+J7lxUvJYK3/WK5zPdVSEdfF0K8HFm638LMSbNAMX529BaV0HMRwtjxmTyW5J5zOWl8RgHH1//EYTWIqF7vYZ1xGN5rKb1vu7+WALDzFAlanH2lf3P7bCnP82pXzfT6UnNDBx8KSitYd8SCklBrRxF2MQeBDqaBq3uIZOca+rulH5Xr1bs93PSZ9GL9ehvki9DSzrhMdsZl8vKYdg0PiFVVgsCgAXULZVzh9Al2JyYtn4z8ksZXrgu7ZpbZoGSbSnZdqtgNNO8sJZD755/fsS8Qn2+IpaisgidHmvxMVRX2zoPgofCfHGg7mim9Aghr7sybyw+zNTqjWkZw1aEUlkee5rHhrSWLWBcBfaS6oqy4+vKiLPhlsky2BQ+5GG/vnLA26Li+Q3NsDBfAjuSGd2WycdCz517Cae8mZafHV8r11fm26kqkGhoaGhqA5UHgK8BY4HXgwyo/GheE6uWgSnllOajOJGNvzg4aDfWXSCqKwn29PHGx1fPh1tSzZaGRKUU89vdJlh3N4bl/ksksrC6eUWHnLgOqmHXSZ+PgSYFzK1JzizlS3IzPysYyWHcA7/2zZda5fZV+Dt9uYrId8YsEfdP/gC5TYOgL8OB2eGgXzFwjBpzXMIqiML6rL+HxWZYLxIQMh0f3i5G52VurMSrKTWqCCpQXQ5blfYgnMwuZMGcrrYyxeLQdQIfR98Edf0Gb0VKOtsLk+zfwabj1O3g8CjpPloFW/BZoc45lWxeYfq3cCXK3Z+6mmEbtU84Kw+Sniay8la0sd28thtIF9SjngpS+gqgQOnrBrd9CRSkcFuvUb7fG4e5gXd0jrS6yTkiv7AUycr/U9GrpBsCeeAtKOuvCxtmyIPBs32aNDHrX6XD6AKREnt/x/yXJ2UXM35nArd39CPEylXGmH5XMZbubzq6n1ym8Nb4DpeVGpn27i+s+2szPuxJIzS3mlT+iCGvuXL3kuiaB/eTzVbMvMHotlObBlF/BuXaGsUnj1wOeT4RhL53f9kEDpLe9vPia8/3S0NDQsJQGg0BFUWwVRXkcmAi0BbapqrrJ/HNJzvBaoIZFhK5MgoQKK8ezmUCzWIzRqu5MoBknGz0P9fEk5kwJTy5P4qXVyTy/MhkXWz0vDPYho7Cc51Ymk1VUPRA0thkrflSRC1lT3Jb2/1lD77fWccMnW/i5YiRGGxfxDGwWVFthrfd9klmZOE9mYTXq5KYuku35Y3+y5RtZ2cqA8tg/lvnXpURIBrD/Y/I8eZ9FhzErgXqWn8aJIrxCTX6OwYNh8nwY/qr0+w3/v0pDZp2uughMm8tTCmrGoNfx4JAQopJz2XS8gSAOJAgsyZMg0NG7crn5vTVUEpptqoo3Z6a824vH374fiEvNYe2RNKb1CWy8JDXe5P9mNohvYnT0dcHWSseuE+cZBNq6Wp4JtHKonc3pOBF0VhDx2/kd/1/y8RqxIXhsRBUxlyMm0ezQ66ut2zWgGdueH8YHE0Vl96WlUfR9ex0Z+SW8O6EjVnWJqJjx7yOPiTuqLz/+j5i4m71Xrzb+jeiR+TvKt8e52UtoaGhoXEM0dpf9AeiBqILegJb9uyioSnWLCHPWr9zes7In0IJMoJmBQY5M7tSMClUlp7iC/oEOfDzWn6GtnHhjZAtO55XxwB+J7D5ZQEFpBUsPZTMhvM3Z7ZO8hvL0daG8Nb4jb43vyOczhqKb8qv0nUz8oXZjf5ep8Gwc+HX/t3+Kqxq/Zvb0bunG0v3JjWeqqtL+Zsla/f2k9KM1xIFfRFm11yww2FocBC7Zl8yW6Aye72KyA63q4ajTw8CnxI5g4JPVN/RsA3etkLKtFl0tf08XiZu7+uLrasfs9Y1kA81BYF5K9SDQEpuInJMSkFQVwBn0NKREEvHXp1gbdEyrSx6/JgnbxO7As03j616BWBt0dAtoRvj5ZgJtXSzPBLr61xaSsneD1iPh0FIRtLqE7Iw7w6K9SdzVPwhfcxmnqoqYSdDAOjNztlZ6bu3ux9+PDGDR/X0Z17kFL44Oo5Ofa8MHc3AHjzZnrXoA8aeMXivGxpdBIfaKp+0Y8WSc9MPlPhMNDQ2NK5bGhGHaqaraEUBRlG+B3Rf/lK5FamQCzUGgnTfWeZJ1UMrNwjANZwJBSg/NwjA16dzcnk/G+vPu5hReXnMKG4NCSblKtwAvtnf9ll6Brtzdengdyp0eMGtD/Qc12DR6Xhowvqsvzy+JJCIphy7+jQz+zAQNkmxgxK/SGzTmg7qzR9mJsOd76HE3uPhK31Ty3kZ3n5JTzBvLD9M9sBmDnfaILci5GE8H9ZefKwBrg477Bgfz6p+H2BmXSd9W9ahyOjUHtUI81kJHVS538ZfgucFM4Mnq/WkAHSZQvuEdmp1cx6QeN+PpZMH1kLxPhJOasDltzyA3Zq+PJre4TAzOzwVbF5ncqChvWDEyO7H239tMhwlSqhy50HLhqX9JcVkFLyyJxN/NjsdHVBFzCf9GSoUHPt3g9oqi0DPIjZ5B51A1EdBHyo2NRgn6ErZL2XKb0ef5Lq5ybF3g5jmX+yw0NDQ0rmgam0I8qyKiqqqFLswa50wti4i6MoFmYZh/b5vQyt2Gz8b5M62LG8OCnZg9zp8lD/an38hbMYSOaNKD0iudGzo2x95az4tLIskprBTpyS8p57P10Qx8bz3rj6ZW30hvgEk/wu2LpCT0jwcr7TeqErlYApt+j8hz3+4iKlNR96WbW1zGh6uPMfzDjRSVVvDOLR1RUiJEiKEJB/WTevjj4WjNTzvj61/JHFSU5lfPBOp04NZKevXqI+dk7f40RSFKH0YX5TizBlggjlNeIgqjTdy3rHdLN4wq7E04D6sGW2d5LMlteD1zJrAu2t0EAf3gr0fl838J+GRdNCcyCnh7fCfsrQ3Sq/frFOmZDb3+4th9tBwkWdPYdfL82D8yWRE85MIfS0NDQ0PjmqCxILCzoii5pp88oJP5d0VRGvnm1rCUWuWgZQWoKJTbeUhPoKqeDQwb6wm0FGuDjju6ufPEAG/aeNpekH1qNI6LnRVzp3UnJi2fO77bxd6ETN755yiD39vAB6uPk1tUzpMLI0jJKa69ceuRcN3rMih+qwUk1cjyRS0B/97StwkSBJYXiUVHDYrLKpj85U5mr49hSBsvlj86gNZejhI0+nS+8G/8EmJrpee69j5sOpZOSXlF3Su5+FX5vUaA4RUmnod1lSqqap2ZwJzCMhaltsBVKSDAaIEC7JkYMJafW8b1CqRrQDMMOoXd59MXaGvykmuoJLQkX1Qw68sE6q3gtp9F2GrFM7UVNC8w22Mz+GpzHBO7+zGAA7D2P/DVEMlGdpwkkzUG6wt/4LAbwSUANr4tn8FjKyQAtHa48MfS0NDQ0LgmaDAIVFVVr6qqs+nHSVVVQ5XfnS/VSV711LSIKC/CaLDHaOWIolagVBRf0EygxuVlcKgnn9/ejUOncpkwdwffbImji78rSx/sx9IH+1FabuTxBfvPqrtWo82Yyt67Q0sql+eegtTI6j59ZpuPpPBau3l7xRGOnM7l6zt68Pnt3QjxcpJ9FGZIGWkTZ2SYNwWlFeyMqyc4qRoEVu1/BOj/KBRly+D+TGz114qyRCG3Rmbqp53x7CxrJU8sKMEl7Yg8ejZt0Qo7az2d/FzOOQiMTs3jvY0pAIQfi6+/f7M+ZdCq2LvB8FegKFP6Ay8S8RkFPDB/H8EeDrw6rh0sfwK2fiQvTvgWJnx98TLoBmvpO03eC6+5igJpDfEZDQ0NDQ2Nc0HrKL8SUPTVegKVimJUgw1GKxGe0JXmo5j6BC3pCdS48hnZzpsf7unFO7d0ZPdLI/j2rp50DWhGsKcj/7mxPTvjMvlqc1ztDQ3WMGsjhIyEo8srPzdxJrHe4KGV67oGiv9djSBw7eFUftiRwIwBLRnZrkoppNmLMKD3BXufl4u+rdyxs9Kz9nBq3SvYVenH9OlY/bXmneHmuZB5Aja+U/21msqgQGm5kR92JOAf0hEMdmJS3Riph+S696jDILyJ0TvYnYNJ2WQXlja6rtGo8s2WOMbM3srxXPn6+d+ycO78PrzuSY+6PALrouVgUWjd9vGFE4kpr/Q/zM3NYvE3bxHGCb69sydOar58Foa8APdukN7Ei02XqWJLYu0oIl1h4y7+MTU0NDQ0rlq0IPAKQEVBqeoTaCxH1Rkqg8CyAnTlRaiKHlV3juILGlcs/UM8uK1XAG4O1cvHJnb347p23ny6LprTOfXYQoSOEp85s2dd7DqwdwfvDpXrKAr49awWBO5NyOSpRRG0b+HMs9fXUKWMXiP9cT41MmNNEFsrPYNCPVh7JLVxJVZzWWJVukyB3vdLZimzitfi2cxUZVDyT9Rp0vNKuHtgiJSSplrgW3dis5TrNuHeSzPjOrWgrELlzwOnGlwvKauQqd/s5M3lRxjU2oP3bh8AwNROzmw+ns6Go2m1N8oxBd319QSaURTJlKUfhbj15/M2qpMRDe8GySRARTknv7ubp4s/42fdqwRY58Hpg7KeX0/xSr0UfdR6K7hvs/jnzdoADnWLf2loaGhoaFjCRQsCFUX5TlGUNEVRoqosc1MUZY2iKNGmx2am5W0VRdmhKEqJoihP19jP9YqiHFMUJUZRlOcv1vleVhRdtXJQxViOqhgwWkm/h74sH11ZIUaDvSbacg2gKAqvjG1Hharyzj9H617JnPE7sQmKcyUr2HZsbbl4/55wJoai7HSWRZxiyte7aGZvxdzbu2NjqOJjl5UA0aslw3iVfMZGhHlzOqeYQ6fqaV8e94n81Ef/R0V8Y0WVW9LZzFRleeL32+IJ9nBgYIgH+HSAlKhqmf1aFGXBqX3Qamj96zQh2rVwpoOvM7+Fn6w34D6YlM0NH28hMimH9yZ04us7euDm5gnADa0daO5iy7zt8bU3zD4pXoCOPo2fSNsxoLeB2AZUjC3l8B9QVggb36b8w3a0z95ApOtw9MZS2Pw+pJiCwEtdOm1lJ5YtGhoaGhoa/5KLmQmcB9RsWngeWKeqamtgnek5QCbwKPBB1ZUVRdEDnyMehe2AKYqiNG0lhbqoYRaPsRx0BiqszZnAfOkTtNL6Aa8V/N3suW9QMH8eOMWeunzY3FuBsy/Eroeo32XA2u2OaqsUlJTzwSFp3X3gvS955Nf9dPJ1YcmD/Qlwr1JWXFEOv8+UyYjBz1zMt3VJGdbWC0WBNYdTSc0tJvA6z6cAAB7ZSURBVCo5p3rJYfe75Kc+nFvAoKcgZq0EdiCZQIOd9KEB+xOzOHAymzv7BaHTKeDdUXrT8k7Xv9/jq2TSp9Wwf/0erxQm9/DnyOlcopJrB9yqqvLm30ewsdKz8vFBTOrpj6IoZzOwhtJcpvUJZGtMBtGpeZUbVpRJhtutpWVeeFZ2Usoct/Hfv6FjK6F5F9Rud5FQ5sITPI3fzF+h570Q/jXs+hLcQ7RsnIaGhoZGk+WiBYGqqm5Ggruq3IQY0GN6vNm0bpqqquFUsaQw0QuIUVU1TlXVUuA30z6uMnTVy0FVczmoZAKlHNSUCdS4ZnhgSCt8nG15/e/DtTMsiiKZj+OrRJzCs62UF5pIzS1m0pc7+D7eFSN6HmmdxZs3d2D+zN61yk85MB+SdsPYjyqVRa8C3B1t6B7QjE/WRdP7rXWMnb2Vfu+s4+0VRziWktf4DgC63SnZwHWvS7CcnVjNuPyH7fE42hiY0N0kNONjKsdtqC9w91diSu/f9HsvzdzYxRcbg44FexJrvbbxeDq74zN5bHgI/m5V7mE2zoACxTlM6RWAtUFXmQ2M3wYL74CUSBj2suUn0nKQeD8WnYdlhZnSQsnUhozg78DnGJ73H7pcN51mjjYw8jW51nJOymdDQ0NDQ0OjiXKpewK9VVU1T5GnAN4NrQz4AierPE8yLbuqUBVddWGYWj2B+SjlRaiaMug1hb21gSevC+VgUg4bj6XXXqHnTKgoFaXArtPPBiZpucVMmLud+IwCPrtrIDqf9nTXxzCtTyC2VnWUkp3YIlnFjrde5Hd06Xl6VBum9wnk9Zva879Jneno68q3W08w6uPN3PbVDtYeTsVYlyCJGXs3GPk6RK+CnXPkb21SqkzLLWZ55Gkm9vDD0cZkdm62fEippy8waa8oPPaaddWU3YJYn9zQwYc/D5yiqLTSlsNoVHl/5TH83eyY3LOGwqdOJ4FgcQ5uDtbc3KUFS/YlU7jrR5g3WmwQBjwpXoCW0tyknGuJOE99pB0B1ciSVE8e+20/7Vs4c3tv07lb2cHEedDldug2/fyPoaGhoaGhcZkxXK4Dq6qqKorSiGKD5SiKMguYBRAQ0ICc+JVIDYsIxVgOiqGaOqhkArUg8FpjfFdfPlkbzafroxnSxlPK6Mx4toGb5kB+KvS4G4C84jLu+j6czIJSfr23D539XSGmF0QsAGNF3f1ESeGVdhJXGX2C3ekT7H72+S3d/DiTX8Lv+5KYty2emT/uoaWHA/cMaMmEbr5i/l2T3vfB0b9FebLwDPSXMs75uxIpN6rc2Teocl07V+kXTI2qvR+QUkJrR+h82wV8l1cGU3sH8seBU4z6eDOPj2hNoLs964+mcfh0Lh9N7oy1oY45R1vnsz6Bd/YLYuGeJIq2zMbeuyPcNv/cM9PmTGxKFAQNOK/3UZCwDwfgf5E23Nrdj1fGtsOgr3LuXmFw85zz2reGhoaGhsaVwqXOBKYqitIcwPRYhxxcNZKBqrJwfqZltVBV9StVVXuoqtrD09PzgpzspUNXqydQMoFSOqUvK0BXVqSVg16DWOl13D+kFfsTs9kRe6b2Cl1vh4FPgrUDpeVG7p+/l+Opecy5vZsEgCAKhqV5kH6s9vb5aZLd8ut5cd/IFYS7ow2zBrVi87NDmT2lK862Bl75I4qB727gmy1xlFXUYTHQ5yEJAAGad+FUdhHfboljRJg3QR41DLubd5Jsn6rKz6K7RWly1UsQuRg6T5Hg5yqjV0s3vr+7J062Bp5cGMGEuTv4fEMsvYLcuLFzPQUcti4ibAS0b+HC4EAbXPNjMLYZfX6lyY7eYO9hmUJrHRxLyWPNhjXkqA48PmEE793aGSdbTZFZQ0NDQ+Pq41JnAv8C7gTeMT3+2cj64UBrRVFaIsHfbcDUi3qGlwFVUVDUyhIqc08gio4Kg4NJGEbLBF6rTOzux+x10Xy2IYZ+IXULURiNKs8ujmBbzBk+mNiZIW28Kl80B3iJO8C7hq5SwnZ5vIr60yzFoNcxrnMLxnZqzp6ELD5ee5w3lx9hT3wWs6d2xapq9idk+Nlf1RZdefmPKIwqvDq2Dp2qVsMkc5h+FBJ3wqElYt+x4zN5vde9F/mdXT6GtvFicGtPtsRkUGE0EurthK+rXfUMdlVsXc5mAgEeCc1Gn2pke3lr+p3PCSiK+D6eijjnTaNT87hlzjYW6WPQtejErT0asaXQ0NDQ0NBowlxMi4hfgR1AG0VRkhRFmYEEfyMVRYkGRpieoyiKj6IoScCTwMum9Z1VVS0HHgZWAUeAhaqq/otmjysURQ/U6AlUpGzPaO1wVh1UtdIygdcitlZ6Zg0KZnvsGfYm1KEUCry78ih/HDjFM6PacKtZpMSMWzA0awlHltXeMG6D9GW16HYRzrxpoCgKPYPc+HlmH14Z246Vh1J4+Jd9lJZXyQjqraDNaAD+SrBi/dE0nh7VprrQiZm2YwAF5vSFvx+XAPup43DL13DD+1LGexWj0ykMDvVkWFtv/JrZ1x8AQq0gsJuVCMu8F+XYcK9mQwQNkExgfmOFJpWoqspryw7jqCshjHicQvqf37E1NDQ0NDSaCBdTHXSKqqrNVVW1UlXVT1XVb1VVPaOq6nBVVVurqjpCVdVM07oppnWcVVV1Nf2ea3pthaqqoaqqtlJV9b8X63wvK7V6AitAJ0lao5UjurICFC0TeE0ztbeYyn+2PqbWa7/vTeLLzXFM7xPIg0Na1d5YUaDDLWJQXpBRuVxVxVMtaCDoL1t78BXFjAEt+c+4dqw6lMpDNQPBST+ScO8RXvv7CF38XbmrX1DdO3HyEeNyB08Y+DTc+bf8fTtNgt6zLsn7aDLYOFcLAnW5JymxduVAmpHVh1PPb58hI+Qx1nLT+DWHU9kak8H/dS+Vqgz/Xud3bA0NDQ0NjSbCpe4J1KgDVdGhVAkCMZeDAkarykyg1hN47WJvbWDGgJZsOJZOVHLloDm3uIy3Vhyhe2Az/nNj+/qzLm1Gg1oB8Vsrl52OkH7A1iMv8tk3Le7q35LXb2rPmsOp3PndbnIKxbnmZE4Zt/14BAX4cFJn9LoGMlzDXoZnomH4K2Cwrn+9a50amUBykrFu5o+3sw3LIk6d3z59OklvYF2Z7zooKa/gzeVHaO3lyChXU8v5NdQjq6GhoaFxbaIFgVcECrXKQatkAg2FaSiq8axaqMa1yfS+gTjZGvh8Q2U2cPa6aDILS/nPuPYNByU+nUBvI0qgFWWSBdz3I+iszk2C/xrhjr5BfDy5C3sTshjw3nomfbmDyV/uoLC0gp9m9KaVp3YtXhBsXaAkF4ymSbCcJBRXf3q3dGdPQmZtf0xL0Omgw63ioVlYd/l0Vb7deoLEzEJeHdcOfWaMZHDt3c79uBoaGhoaGk0ILQi8EqiRCZSeQAkCK6wcscmNB6Dc3quurTWuEZxtrbi7XxD/RKXw98FT7D6Ryffb4pnU3Z+Ofi4Nb2ywhv9v796D7KzrPI+/v91JJ+RCbh2C5AKBNHJxDYQIDAQGdAe8sIO6KrhMmUJcZ1ccnS3dLd2yitrZZUe3rHW1dJhiFIcZF1jH0R1mdtdZBl0uMoBxGBHkkosQgiTppHOTmEv3+e4fz9PdJzcgTZ4+z+l+v6pS5zm/8zsnv05+9XR/+nc76Rx48nvwxdPh8yfDqm/Am6/xB94jePe587nrdy/kqjefxEAjOf64ifz5Dedz1kljb2fPlpk8A8hi91qAHRtgxgKWnzKLTTv3smHbr0f2uUuvgcb+YlOeV7Bp5x6++oM1/NZZ87ikZy70rYPZh5lSLUnSGONCoBrIKLN4ZrF+q9HftCZwePt5Q6Cuv3gxdzy6no/f8RgA0ydN4NNXvsaNRk69HO77fHF9xlWw+FJYfkNFLR0bli2axbJFs1rdjLFr8KiMPTuAgL074Pj5nHdy8W++6vm+w2++82pOfDPMPbM4H/MtHzlitS98/2n6B5LPvevMomDr2gN2gpUkaawyBNZCOY0vGxCdw0dEAAOTZg7V2j9lXisapxqZNbWLH376Mn7+y508u2kXPfOmM3f6pNf25ks+BdPnwYyFrgNUPUwuR7D37IB9LxfXMxZwxonHM23SBFY9t433nLvgyO8/kgh48/vh3j+AX/XCtEPPjn1s/Ta++w8v8q8vO42T50yFvb+CX20sdtOVJGmMMwTWweBIYLkusHlN4N5Zpw9VcyRQANMnT+SCU+dwwalzju6NE7pg+YeraZQ0EseVo6y7tw6HwFmn0NkRnLtoJque2zbyz37D0uJx6+pDQuCGbbv51F/8lBOmT+LGy5cUhX3risc5TgeVJI19rgmsgSx3dBxcFxiNASjXBO6Z03QYdYeZXdIYMnNR8bjtedjybHHd3QPAW06ZzbObd7Hj1/tH9tmDa/u2rj2g+PEN23nPHz1E7669fOWD5zJtUnlf7Vt74PskSRrDDIF1MLQmsNwcptFPdhSHxfdPPalFjZKkih2/oPjl1rbnYMtqmHbi0BTR5SfPKjaxXT/C0cAZC4vP7hsOgRt37OGDtz7MpAkdfO9jF3Fh82j6YFh0OqgkaRxwaKkODgqBzWsCiWDL0o/RmDD1CG+WpDbVOaEIa9t+UewMWo4CApyzaCadHcGq5/q4/I0jmArfOaE4L/DBLxXTTi/+JLf8vzXs7W9wx0cuZNGcgzac6VtXhNBJHv8hSRr7HAmsgSz/G4LB6aDDR0QA9J19Pdvf+IGWtE2SKjV7cRHAep89IARO6ZrA2Scdz49fz7rAwV+mPXwLG3fs4c4fv8D7zltQBMA9O2H/nuG6W9e6HlCSNG4YAusgBncHTcgkcsD1f5LGh1mL4aWfFsdDnLTsgJfOP2U2/7h+O7/eNzCyz37/N4vHrqn88X1raTRyeCOYO66Bm+cNTwPtW+tUUEnSuGEIrIPm6aBZ/LCThkBJ48GCtwxfn/qbB7y0oqebfQMNHn2ub2SfPf88uOgT5Pb13Pnoc7x32fzi3MF9L8P6h4o6f3cTbF8PL/fCvDeN8IuQJKm9GAJrYfiw+Gj0F5eGQEnjwdnvGb4e3C20dMHiOXR1dvDg6t6Rf/6sk4mBfXQ3+vj45eV0018+VjzOexM89Tfwk9uL54svHfnfI0lSGzEE1sDQERE0hkNgGAIljQMTJ8PKv4YP3X3IS8d1dXLeybN4YPWWEX/89knzAbju9BzeDOaFR4vH995aPD7wRZg6F044c8R/jyRJ7cQQWAfN00HLEOiaQEnjxuJLD5kKOmhFTzdPb9xF7669I/robz1T3F8/cFp53uC25+CndxaHyc87G869rii/4F8Nr8+WJGmMMwTWQdNh8ZGD00E7W9kiSaqFS3q6AfjRmqMfDdy8aw9f+2k/+2IS3S+vKQLgl5cWB9MvW1lUesd/gd+9Hy799DFstSRJ9WYIrIGhqZ/pdFBJanb2STOYOWXiiKaE3nrfOvYOQOOEs2Djz+DxvyheOPOfwdJri+uuqcWooCRJ44hJow7K6aCDo4CA00ElCejsCC4+rZsH1/SSmcRrnLLZu2sv33rked597nwmH7e0CICbnoBFF8E136q41ZIk1ZsjgTWQg2sCG8NrAt0dVJIKK3q62bRzL2s2/+o1v+fW+9eyr7/B7721pxjp2/8yzFgAv/2VClsqSVJ7MGnUQZTr/w5YE+h/jSQBrFhSrAt8YPUWeuZNf9X6vbv28q2H13P1OfNZ3D0VZl4H0+ZBzxXQObHq5kqSVHuOBNZADk0HHSAa5WHxrgmUJAAWzp7CKXOm8OBr3BzmS3/3LPsHGvzeW5cUBRMmwRnvMgBKklQyBNZBR9NIoEdESNIhVvR08/C6rezrb7xivdWbdnHXo+v5nQtP5tS500apdZIktRdDYA1kOR00cgCcDipJh1ixZC679w3w2Pptr1jvD//P00ydNIFPvK1nlFomSVL7MQTWwdBh8QPDR0QYAiVpyG+cNoeO4BWnhP5ozRZ+8PRmPn75EmZP7RrF1kmS1F4MgTUwvCawaTpoeFi8JA2acdxEli6cecTzAgcayX/6X0+xYNZxrLzolNFtnCRJbcYQWAdNu4M6HVSSDu+SJd08vmE7O3bvP+S1h9Zu4amXdvKpK05n8kR/iSZJ0isxBNZA85pAp4NK0uFdcvpcGgl/v+7Q0cD7numlq7ODK88+sQUtkySpvRgC62DosHiPiJCkIzln4UymdnUedl3gA6u38JbFs5jS5b1TkqRXYwisgeGRQI+IkKQjmdjZwQWnzuGhNVsPKN+4Yw/PbNrFpT1zW9QySZLaiyGwDpp2B3VNoCQd2UWnzWHdlpd5cfuvh8oeWN0LwKWnGwIlSXotKguBEXFbRGyOiCeaymZHxD0Rsbp8nFWWR0R8JSLWRMTjEbGs6T0ry/qrI2JlVe1tqcOMBBoCJelQK3q6geI4iEH3r97C3OmTOOPE6a1qliRJbaXKkcA/Bd5+UNlngHszswe4t3wO8A6gp/zzUeAWKEIjcBNwAXA+cNNgcBxLsmNwd9CmjWFcEyhJh3jjvOl0T+vioTIEDjSSB1f3cklPNxHR4tZJktQeKguBmXk/0HdQ8dXA7eX17cC7m8r/LAsPAzMj4g3AlcA9mdmXmduAezg0WLa94XMCB1wTKEmvICK46LRufrR2K5nJk7/cwbbd+/lNp4JKkvSajfaawHmZ+VJ5vRGYV17PB15oqrehLDtS+dgytCaw+ZxAz7mSpMO5eMkcenft5YkXd/LVH6whAlYs6W51syRJahstG27KzIyIPFafFxEfpZhKyqJFi47Vx46OwTWBjYZHREjSq7i4DHzXff1hdu7p53PvOpM50ya1uFWSJLWP0R4J3FRO86R83FyWvwgsbKq3oCw7UvkhMvPWzFyemcvnzm2vaUHZtDtopNNBJemVLJg1hcXdU9nT3+Br/2IZH7nk1FY3SZKktjLaSeNuYCXw+fLxr5rKPx4Rd1FsArMjM1+KiL8F/nPTZjBXAJ8d5TZXb2hjmAa4O6gkvao/+dB5ACw5wR1BJUk6WpUljYi4E7gM6I6IDRS7fH4e+HZE3AA8D3ygrP6/gXcCa4DdwPUAmdkXEf8R+HFZ7w8y8+DNZtre8GHxxcYwGR3D6wQlSYcw/EmSNHKVhcDM/OARXnrbYeomcOMRPuc24LZj2LT6OWh3UNcDSpIkSaqKw001MDgSyOBh8U4FlSRJklQRQ2AdNG0MQ/a7HlCSJElSZQyBNTC8JrAYCTQESpIkSaqKIbAOBqeDNoo1gUPPJUmSJOkYMwTWQA5tDNOAHHAkUJIkSVJlDIF10HxYvLuDSpIkSaqQIbAGXBMoSZIkabQYAuugY/CIiAEiPSJCkiRJUnUMgXXQdFg8jgRKkiRJqpAhsCYyOocOi3dNoCRJkqSqGAJrIqODGNwYxpFASZIkSRUxBNZFdBQjgdk/vEZQkiRJko4xQ2BNZEwgGuWaQKeDSpIkSaqIIbAuBkcCGx4WL0mSJKk6hsC66CjXBHpEhCRJkqQKGQJrYnB3UI+IkCRJklQlQ2BdNO8OGm4MI0mSJKkahsCaKEYCPSJCkiRJUrUMgXURnUSj4ZpASZIkSZUyBNZERgfkgGsCJUmSJFXKEFgXQ0dEeE6gJEmSpOoYAmsio5MYDIEdbgwjSZIkqRqGwLro6CRyoJgS6nRQSZIkSRUxBNbE4JpAp4NKkiRJqpIhsC6ik2j0E6Qbw0iSJEmqjCGwJjI6iIF9xbUhUJIkSVJFDIF1ER3EwN7i2hAoSZIkqSKGwJrImDAUAl0TKEmSJKkqhsC6iA46Gk4HlSRJklQtQ2BNNK8JdDqoJEmSpKoYAusiOon+PQBkx8QWN0aSJEnSWNWSEBgRn4yIJyLiyYj4/bJsaUT8fUT8LCL+OiKOb6r/2YhYExHPRMSVrWhz5aKDjv7dADQmTG5xYyRJkiSNVaMeAiPiTcC/BM4HlgJXRcQS4OvAZzLznwDfA/5tWf8s4FrgbODtwB9FROdot7tq2dFJx+DGMJ2GQEmSJEnVaMVI4JnAI5m5OzP7gfuA9wKnA/eXde4B/nl5fTVwV2buzcxfAGsoAuTY0pRrG52TWtgQSZIkSWNZK0LgE8AlETEnIqYA7wQWAk9SBD6A95dlAPOBF5rev6EsO0BEfDQiVkXEqt7e3soaX5WM4f+KdDqoJEmSpIqMegjMzKeALwD/F/g+8I/AAPBh4GMR8RNgOrDvKD/31sxcnpnL586de4xbPQqaQmDD6aCSJEmSKtKSjWEy8xuZeV5mXgpsA57NzKcz84rMPA+4E1hbVn+R4VFBgAVl2ZiSTdNB0+mgkiRJkirSqt1BTygfF1GsB7yjqawD+Bzwx2X1u4FrI2JSRCwGeoBHR7/V1WoOfk4HlSRJklSVVp1K/pcRMQfYD9yYmdvLYyNuLF//LvBNgMx8MiK+Dfwc6C/rD7Sk1RVqTJw2fO1IoCRJkqSKtCQEZuYlhyn7MvDlI9S/Gbi56na1UmPi1KFrRwIlSZIkVaUl00F1qIGu4ZHA7OhqYUskSZIkjWWGwJpoHgkkonUNkSRJkjSmGQJronlNoCRJkiRVxRBYEweMBEqSJElSRQyBNeFIoCRJkqTRYAisCUcCJUmSJI0GQ2BNDDgSKEmSJGkUGAJrwpFASZIkSaPBEFgTOeG4VjdBkiRJ0jhgCKwLzwaUJEmSNAoMgTWzZ/aZrW6CJEmSpDFsQqsboGFr3v9DsqOr1c2QJEmSNIYZAmvEswIlSZIkVc3poJIkSZI0jhgCJUmSJGkcMQRKkiRJ0jhiCJQkSZKkccQQKEmSJEnjiCFQkiRJksYRQ6AkSZIkjSOGQEmSJEkaRwyBkiRJkjSOGAIlSZIkaRyJzGx1G465iOgFnm91Ow6jG9jS6kZoTLOPqUr2L1XJ/qWq2cdUpTr2r5Mzc+7hXhiTIbCuImJVZi5vdTs0dtnHVCX7l6pk/1LV7GOqUrv1L6eDSpIkSdI4YgiUJEmSpHHEEDi6bm11AzTm2cdUJfuXqmT/UtXsY6pSW/Uv1wRKkiRJ0jjiSKAkSZIkjSOGwFESEW+PiGciYk1EfKbV7VH7iYiFEfHDiPh5RDwZEZ8sy2dHxD0Rsbp8nFWWR0R8pexzj0fEstZ+BWoHEdEZEY9FxN+UzxdHxCNlP/ofEdFVlk8qn68pXz+lle1We4iImRHxnYh4OiKeiojf8B6mYyUi/k35/fGJiLgzIiZ7D9PrERG3RcTmiHiiqeyo71kRsbKsvzoiVrbiazmYIXAUREQn8DXgHcBZwAcj4qzWtkptqB/4VGaeBVwI3Fj2o88A92ZmD3Bv+RyK/tZT/vkocMvoN1lt6JPAU03PvwB8KTOXANuAG8ryG4BtZfmXynrSq/ky8P3MPANYStHXvIfpdYuI+cAngOWZ+SagE7gW72F6ff4UePtBZUd1z4qI2cBNwAXA+cBNg8GxlQyBo+N8YE1mrsvMfcBdwNUtbpPaTGa+lJn/UF7vovjhaT5FX7q9rHY78O7y+mrgz7LwMDAzIt4wys1WG4mIBcC7gK+XzwN4K/CdssrB/Wuw330HeFtZXzqsiJgBXAp8AyAz92XmdryH6diZABwXEROAKcBLeA/T65CZ9wN9BxUf7T3rSuCezOzLzG3APRwaLEedIXB0zAdeaHq+oSyTRqSctnIu8AgwLzNfKl/aCMwrr+13Olr/Dfh3QKN8PgfYnpn95fPmPjTUv8rXd5T1pSNZDPQC3yynHH89IqbiPUzHQGa+CHwRWE8R/nYAP8F7mI69o71n1fJeZgiU2kxETAP+Evj9zNzZ/FoW2/265a+OWkRcBWzOzJ+0ui0asyYAy4BbMvNc4GWGp1EB3sM0cuX0uqspftlwEjCVGoy2aGxr53uWIXB0vAgsbHq+oCyTjkpETKQIgP89M79bFm8anCJVPm4uy+13OhoXA78dEc9RTFl/K8X6rZnl1Co4sA8N9a/y9RnA1tFssNrOBmBDZj5SPv8ORSj0HqZj4Z8Cv8jM3szcD3yX4r7mPUzH2tHes2p5LzMEjo4fAz3lDlVdFAuV725xm9RmyrUK3wCeysz/2vTS3cDgTlMrgb9qKv9QuVvVhcCOpukL0gEy87OZuSAzT6G4R/0gM68Dfgi8r6x2cP8a7HfvK+u35W9DNToycyPwQkS8sSx6G/BzvIfp2FgPXBgRU8rvl4P9y3uYjrWjvWf9LXBFRMwqR6yvKMtaysPiR0lEvJNivU0ncFtm3tziJqnNRMQK4AHgZwyv2fr3FOsCvw0sAp4HPpCZfeU3wa9STIfZDVyfmatGveFqOxFxGfDpzLwqIk6lGBmcDTwG/E5m7o2IycCfU6xN7QOuzcx1rWqz2kNEnEOx8VAXsA64nuIX0t7D9LpFxH8ArqHYTfsx4CMUa6+8h2lEIuJO4DKgG9hEscvn/+Qo71kR8WGKn9kAbs7Mb47m13E4hkBJkiRJGkecDipJkiRJ44ghUJIkSZLGEUOgJEmSJI0jhkBJkiRJGkcMgZIkSZI0jhgCJUmSJGkcMQRKkiRJ0jhiCJQkSZKkceT/Aw8EM9jL026ZAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_comparison(start_idx=100000, length=1000, train=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model was able to predict the overall oscillations of the temperature quite well but the peaks were sometimes inaccurate. For the wind-speed, the overall oscillations are predicted reasonably well but the peaks are quite inaccurate. For the atmospheric pressure, the overall curve-shape has been predicted although there seems to be a slight lag and the predicted curve has a lot of noise compared to the smoothness of the original signal." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Strange Example\n", + "\n", + "The following is another example from the training-set.\n", + "\n", + "Note how the temperature does not oscillate very much within each day (this plot shows almost 42 days). The temperature normally oscillates within each day, see e.g. the plot above where the daily temperature-oscillation is very clear. It is unclear whether this period had unusually stable temperature, or if perhaps there's a data-error." + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_comparison(start_idx=200000, length=1000, train=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As a check, we can plot this signal directly from the resampled data-set, which looks similar." + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df['Odense']['Temp'][200000:200000+1000].plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can plot the same period from the original data that has not been resampled. It also looks similar.\n", + "\n", + "So either the temperature was unusually stable for a part of this period, or there is a data-error in the raw data that was obtained from the internet weather-database." + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df_org = weather.load_original_data()\n", + "df_org.xs('Odense')['Temp']['2002-12-23':'2003-02-04'].plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example from Test-Set\n", + "\n", + "Now consider an example from the test-set. The model has not seen this data during training.\n", + "\n", + "The temperature is predicted reasonably well, although the peaks are sometimes inaccurate.\n", + "\n", + "The wind-speed has not been predicted so well. The daily oscillation-frequency seems to match, but the center-level and the peaks are quite inaccurate. A guess would be that the wind-speed is difficult to predict from the given input data, so the model has merely learnt to output sinusoidal oscillations in the daily frequency and approximately at the right center-level.\n", + "\n", + "The atmospheric pressure is predicted reasonably well, except for a lag and a more noisy signal than the true time-series." + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_comparison(start_idx=200, length=1000, train=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "This tutorial showed how to use a Recurrent Neural Network to predict several time-series from a number of input-signals. We used weather-data for 5 cities to predict tomorrow's weather for one of the cities.\n", + "\n", + "It worked reasonably well for predicting the temperature where the daily oscillations were predicted well, but the peaks were sometimes not predicted so accurately. The atmospheric pressure was also predicted reasonably well, although the predicted signal was more noisy and had a short lag. The wind-speed could not be predicted very well.\n", + "\n", + "You can use this method with different time-series but you should be careful to distinguish between *causation and correlation* in the data. The neural network may easily discover patterns in the data that are only temporary correlations which do not generalize well to unseen data.\n", + "\n", + "You should select input- and output-data where a *causal* relationship probably exists. You should have a lot of data available for training, and you should try and reduce the risk of over-fitting the model to the training-data, e.g. using early-stopping as we did in this tutorial." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercises\n", + "\n", + "These are a few suggestions for exercises that may help improve your skills with TensorFlow. It is important to get hands-on experience with TensorFlow in order to learn how to use it properly.\n", + "\n", + "You may want to backup this Notebook before making any changes.\n", + "\n", + "* Remove the wind-speed from the target-data. Does it improve prediction for the temperature and pressure?\n", + "* Train for more epochs, possibly with a lower learning-rate. Does it improve the performance on the test-set?\n", + "* Try a different architecture for the neural network, e.g. higher or lower state-size for the GRU layer, more GRU layers, dense layers before and after the GRU layers, etc.\n", + "* Use hyper-parameter optimization from Tutorial #19.\n", + "* Try using longer and shorter sequences for the batch-generator.\n", + "* Try and remove the city \"Odense\" from the input-signals.\n", + "* Try and add last year's weather-data to the input-signals.\n", + "* How good is the model at predicting the weather 3 or 7 days into the future?\n", + "* Can you train a single model with the output-signals for multiple time-shifts, so that a single model predicts the weather in e.g. 1, 3 and 7 days.\n", + "* Explain to a friend how the program works." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## License (MIT)\n", + "\n", + "Copyright (c) 2018 by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n", + "\n", + "Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n", + "\n", + "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n", + "\n", + "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE." + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.10" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/README.md b/README.md index 277cf5a..d09884e 100644 --- a/README.md +++ b/README.md @@ -11,53 +11,120 @@ Original author is [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org) * The source-code is well-documented. * There is a [YouTube video](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ) for each tutorial. -## Tutorials +## Tutorials for TensorFlow 2 -1. Simple Linear Model ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/01_Simple_Linear_Model.ipynb)) +The following tutorials have been updated and work with **TensorFlow 2** +(some of them run in "v.1 compatibility mode"). -2. Convolutional Neural Network ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/02_Convolutional_Neural_Network.ipynb)) +1. Simple Linear Model +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/01_Simple_Linear_Model.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/01_Simple_Linear_Model.ipynb)) -3. Pretty Tensor ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/03_PrettyTensor.ipynb)) +2. Convolutional Neural Network +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/02_Convolutional_Neural_Network.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/02_Convolutional_Neural_Network.ipynb)) -3-B. Layers API ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/03B_Layers_API.ipynb)) +3-C. Keras API +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/03C_Keras_API.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/03C_Keras_API.ipynb)) -3-C. Keras API ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/03C_Keras_API.ipynb)) +10. Fine-Tuning +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/10_Fine-Tuning.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/10_Fine-Tuning.ipynb)) -4. Save & Restore ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/04_Save_Restore.ipynb)) +13-B. Visual Analysis for MNIST +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/13B_Visual_Analysis_MNIST.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/13B_Visual_Analysis_MNIST.ipynb)) -5. Ensemble Learning ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/05_Ensemble_Learning.ipynb)) +16. Reinforcement Learning +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/16_Reinforcement_Learning.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/16_Reinforcement_Learning.ipynb)) -6. CIFAR-10 ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/06_CIFAR-10.ipynb)) +19. Hyper-Parameter Optimization +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/19_Hyper-Parameters.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/19_Hyper-Parameters.ipynb)) -7. Inception Model ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/07_Inception_Model.ipynb)) +20. Natural Language Processing +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/20_Natural_Language_Processing.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/20_Natural_Language_Processing.ipynb)) -8. Transfer Learning ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/08_Transfer_Learning.ipynb)) +21. Machine Translation +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/21_Machine_Translation.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/21_Machine_Translation.ipynb)) -9. Video Data ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/09_Video_Data.ipynb)) +22. Image Captioning +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/22_Image_Captioning.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/22_Image_Captioning.ipynb)) -10. Fine-Tuning ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/10_Fine-Tuning.ipynb)) +23. Time-Series Prediction +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/23_Time-Series-Prediction.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/23_Time-Series-Prediction.ipynb)) -11. Adversarial Examples ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/11_Adversarial_Examples.ipynb)) +## Tutorials for TensorFlow 1 -12. Adversarial Noise for MNIST ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/12_Adversarial_Noise_MNIST.ipynb)) +The following tutorials only work with the older **TensorFlow 1** API, so you +would need to install an older version of TensorFlow to run these. It would take +too much time and effort to convert these tutorials to TensorFlow 2. -13. Visual Analysis ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/13_Visual_Analysis.ipynb)) +3. Pretty Tensor +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/03_PrettyTensor.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/03_PrettyTensor.ipynb)) -13-B. Visual Analysis for MNIST ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/13B_Visual_Analysis_MNIST.ipynb)) +3-B. Layers API +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/03B_Layers_API.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/03B_Layers_API.ipynb)) -14. DeepDream ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/14_DeepDream.ipynb)) +4. Save & Restore +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/04_Save_Restore.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/04_Save_Restore.ipynb)) -15. Style Transfer ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/15_Style_Transfer.ipynb)) +5. Ensemble Learning +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/05_Ensemble_Learning.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/05_Ensemble_Learning.ipynb)) -16. Reinforcement Learning ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/16_Reinforcement_Learning.ipynb)) +6. CIFAR-10 +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/06_CIFAR-10.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/06_CIFAR-10.ipynb)) -17. Estimator API ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/17_Estimator_API.ipynb)) +7. Inception Model +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/07_Inception_Model.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/07_Inception_Model.ipynb)) -18. TFRecords & Dataset API ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/18_TFRecords_Dataset_API.ipynb)) +8. Transfer Learning +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/08_Transfer_Learning.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/08_Transfer_Learning.ipynb)) -19. Hyper-Parameter Optimization ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/19_Hyper-Parameters.ipynb)) +9. Video Data +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/09_Video_Data.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/09_Video_Data.ipynb)) -20. Natural Language Processing ([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/20_Natural_Language_Processing.ipynb)) +11. Adversarial Examples +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/11_Adversarial_Examples.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/11_Adversarial_Examples.ipynb)) + +12. Adversarial Noise for MNIST +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/12_Adversarial_Noise_MNIST.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/12_Adversarial_Noise_MNIST.ipynb)) + +13. Visual Analysis +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/13_Visual_Analysis.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/13_Visual_Analysis.ipynb)) + +14. DeepDream +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/14_DeepDream.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/14_DeepDream.ipynb)) + +15. Style Transfer +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/15_Style_Transfer.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/15_Style_Transfer.ipynb)) + +17. Estimator API +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/17_Estimator_API.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/17_Estimator_API.ipynb)) + +18. TFRecords & Dataset API +([Notebook](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/18_TFRecords_Dataset_API.ipynb)) +([Google Colab](https://colab.research.google.com/github/Hvass-Labs/TensorFlow-Tutorials/blob/master/18_TFRecords_Dataset_API.ipynb)) ## Videos @@ -67,15 +134,31 @@ These tutorials are also available as [YouTube videos](https://www.youtube.com/p These tutorials have been translated to the following languages: -* [Chinese](https://github.com/thrillerist/TensorFlow-Tutorials) +* [Chinese](https://github.com/Hvass-Labs/TensorFlow-Tutorials-Chinese) + +### New Translations You can help by translating the remaining tutorials or reviewing the ones that have already been translated. You can also help by translating to other languages. +It is a very big job to translate all the tutorials, so you should just start with Tutorials #01, #02 and #03-C which are the most important for beginners. + +### New Videos + +You are also very welcome to record your own YouTube videos in other languages. It is strongly recommended that you get a decent microphone because good sound quality is very important. I used `vokoscreen` for recording the videos and the free [DaVinci Resolve](https://www.blackmagicdesign.com/products/davinciresolve/) for editing the videos. + ## Forks See the [selected list of forks](forks.md) for community modifications to these tutorials. -## Downloading +## Installation + +There are different ways of installing and running TensorFlow. This section describes how I did it +for these tutorials. You may want to do it differently and you can search the internet for instructions. + +If you are new to using Python and Linux then this may be challenging +to get working and you may need to do internet searches for error-messages, etc. +It will get easier with practice. You can also run the tutorials without installing +anything by using Google Colab, see further below. Some of the Python Notebooks use source-code located in different files to allow for easy re-use across multiple tutorials. It is therefore recommended that you download the whole repository @@ -93,33 +176,26 @@ This also makes it easy to update the tutorials, simply by executing this comman git pull -### Zip-File +### Download Zip-File You can also [download](https://github.com/Hvass-Labs/TensorFlow-Tutorials/archive/master.zip) the contents of the GitHub repository as a Zip-file and extract it manually. -## Installation - -There are different ways of installing and running TensorFlow. This section describes how I did it -for these tutorials. You may want to do it differently and you can search the internet for instructions. - -If you are new to using Python and Linux, etc. then this may be challenging -to get working and you may need to do internet searches for error-messages, etc. -It will get easier with practice. - -### Python Version 3.5 or Later - -These tutorials were developed on Linux using **Python 3.5 / 3.6** (the [Anaconda](https://www.continuum.io/downloads) distribution) and [PyCharm](https://www.jetbrains.com/pycharm/). - -There are reports that Python 2.7 gives error messages with these tutorials. Please make sure you are using **Python 3.5** or later! - ### Environment -After installing [Anaconda](https://www.continuum.io/downloads), you should create a [conda environment](http://conda.pydata.org/docs/using/envs.html) +I use [Anaconda](https://www.continuum.io/downloads) because it comes with many Python +packages already installed and it is easy to work with. After installing Anaconda, +you should create a [conda environment](http://conda.pydata.org/docs/using/envs.html) so you do not destroy your main installation in case you make a mistake somewhere: conda create --name tf python=3 +When Python gets updated to a new version, it takes a while before TensorFlow also +uses the new Python version. So if the TensorFlow installation fails, then you may +have to specify an older Python version for your new environment, such as: + + conda create --name tf python=3.6 + Now you can switch to the new environment by running the following (on Linux): source activate tf @@ -128,8 +204,6 @@ Now you can switch to the new environment by running the following (on Linux): The tutorials require several Python packages to be installed. The packages are listed in [requirements.txt](https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/requirements.txt) -First you need to edit this file and select whether you want to install the CPU or GPU -version of TensorFlow. To install the required Python packages and dependencies you first have to activate the conda-environment as described above, and then you run the following command @@ -137,18 +211,50 @@ in a terminal: pip install -r requirements.txt -Note that the GPU-version of TensorFlow also requires the installation of various -NVIDIA drivers, which is not described here. +Starting with TensorFlow 2.1 it includes both the CPU and GPU versions and will +automatically switch if you have a GPU. But this requires the installation of various +NVIDIA drivers, which is a bit complicated and is not described here. -### Testing +### Python Version 3.5 or Later -You should now be able to run the tutorials in the Python Notebooks: +These tutorials were developed on Linux using **Python 3.5 / 3.6** (the [Anaconda](https://www.continuum.io/downloads) distribution) and [PyCharm](https://www.jetbrains.com/pycharm/). + +There are reports that Python 2.7 gives error messages with these tutorials. Please make sure you are using **Python 3.5** or later! + +## How To Run + +If you have followed the above installation instructions, you should +now be able to run the tutorials in the Python Notebooks: cd ~/development/TensorFlow-Tutorials/ # Your installation directory. jupyter notebook This should start a web-browser that shows the list of tutorials. Click on a tutorial to load it. +### Run in Google Colab + +If you do not want to install anything on your own computer, then the Notebooks +can be viewed, edited and run entirely on the internet by using +[Google Colab](https://colab.research.google.com). There is a +[YouTube video](https://www.youtube.com/watch?v=Hs6HI2YWchM) explaining how to do this. +You click the "Google Colab"-link next to each tutorial listed above. +You can view the Notebook on Colab but in order to run it you need to login using +your Google account. +Then you need to execute the following commands at the top of the Notebook, +which clones the contents of this repository to your work-directory on Colab. + + # Clone the repository from GitHub to Google Colab's temporary drive. + import os + work_dir = "/content/TensorFlow-Tutorials/" + if not os.path.exists(work_dir): + !git clone https://github.com/Hvass-Labs/TensorFlow-Tutorials.git + os.chdir(work_dir) + +All required packages should already be installed on Colab, otherwise you +can run the following command: + + !pip install -r requirements.txt + ## Older Versions Sometimes the source-code has changed from that shown in the YouTube videos. This may be due to diff --git a/coco.py b/coco.py new file mode 100644 index 0000000..42cecf9 --- /dev/null +++ b/coco.py @@ -0,0 +1,205 @@ +######################################################################## +# +# Functions for downloading the COCO data-set from the internet +# and loading it into memory. This data-set contains images and +# various associated data such as text-captions describing the images. +# +# http://cocodataset.org +# +# Implemented in Python 3.6 +# +# Usage: +# 1) Call set_data_dir() to set the desired storage directory. +# 2) Call maybe_download_and_extract() to download the data-set +# if it is not already located in the given data_dir. +# 3) Call load_records(train=True) and load_records(train=False) +# to load the data-records for the training- and validation sets. +# 5) Use the returned data in your own program. +# +# Format: +# The COCO data-set contains a large number of images and various +# data for each image stored in a JSON-file. +# Functionality is provided for getting a list of image-filenames +# (but not actually loading the images) along with their associated +# data such as text-captions describing the contents of the images. +# +######################################################################## +# +# This file is part of the TensorFlow Tutorials available at: +# +# https://github.com/Hvass-Labs/TensorFlow-Tutorials +# +# Published under the MIT License. See the file LICENSE for details. +# +# Copyright 2018 by Magnus Erik Hvass Pedersen +# +######################################################################## + +import json +import os +import download +from cache import cache + +######################################################################## + +# Directory where you want to download and save the data-set. +# Set this before you start calling any of the functions below. +# Use the function set_data_dir() to also update train_dir and val_dir. +data_dir = "data/coco/" + +# Sub-directories for the training- and validation-sets. +train_dir = "data/coco/train2017" +val_dir = "data/coco/val2017" + +# Base-URL for the data-sets on the internet. +data_url = "http://images.cocodataset.org/" + + +######################################################################## +# Private helper-functions. + +def _load_records(train=True): + """ + Load the image-filenames and captions + for either the training-set or the validation-set. + """ + + if train: + # Training-set. + filename = "captions_train2017.json" + else: + # Validation-set. + filename = "captions_val2017.json" + + # Full path for the data-file. + path = os.path.join(data_dir, "annotations", filename) + + # Load the file. + with open(path, "r", encoding="utf-8") as file: + data_raw = json.load(file) + + # Convenience variables. + images = data_raw['images'] + annotations = data_raw['annotations'] + + # Initialize the dict for holding our data. + # The lookup-key is the image-id. + records = dict() + + # Collect all the filenames for the images. + for image in images: + # Get the id and filename for this image. + image_id = image['id'] + filename = image['file_name'] + + # Initialize a new data-record. + record = dict() + + # Set the image-filename in the data-record. + record['filename'] = filename + + # Initialize an empty list of image-captions + # which will be filled further below. + record['captions'] = list() + + # Save the record using the the image-id as the lookup-key. + records[image_id] = record + + # Collect all the captions for the images. + for ann in annotations: + # Get the id and caption for an image. + image_id = ann['image_id'] + caption = ann['caption'] + + # Lookup the data-record for this image-id. + # This data-record should already exist from the loop above. + record = records[image_id] + + # Append the current caption to the list of captions in the + # data-record that was initialized in the loop above. + record['captions'].append(caption) + + # Convert the records-dict to a list of tuples. + records_list = [(key, record['filename'], record['captions']) + for key, record in sorted(records.items())] + + # Convert the list of tuples to separate tuples with the data. + ids, filenames, captions = zip(*records_list) + + return ids, filenames, captions + + +######################################################################## +# Public functions that you may call to download the data-set from +# the internet and load the data into memory. + + +def set_data_dir(new_data_dir): + """ + Set the base-directory for data-files and then + set the sub-dirs for training and validation data. + """ + + # Ensure we update the global variables. + global data_dir, train_dir, val_dir + + data_dir = new_data_dir + train_dir = os.path.join(new_data_dir, "train2017") + val_dir = os.path.join(new_data_dir, "val2017") + + +def maybe_download_and_extract(): + """ + Download and extract the COCO data-set if the data-files don't + already exist in data_dir. + """ + + # Filenames to download from the internet. + filenames = ["zips/train2017.zip", "zips/val2017.zip", + "annotations/annotations_trainval2017.zip"] + + # Download these files. + for filename in filenames: + # Create the full URL for the given file. + url = data_url + filename + + print("Downloading " + url) + + download.maybe_download_and_extract(url=url, download_dir=data_dir) + + +def load_records(train=True): + """ + Load the data-records for the data-set. This returns the image ids, + filenames and text-captions for either the training-set or validation-set. + + This wraps _load_records() above with a cache, so if the cache-file already + exists then it is loaded instead of processing the original data-file. + + :param train: + Bool whether to load the training-set (True) or validation-set (False). + + :return: + ids, filenames, captions for the images in the data-set. + """ + + if train: + # Cache-file for the training-set data. + cache_filename = "records_train.pkl" + else: + # Cache-file for the validation-set data. + cache_filename = "records_val.pkl" + + # Path for the cache-file. + cache_path = os.path.join(data_dir, cache_filename) + + # If the data-records already exist in a cache-file then load it, + # otherwise call the _load_records() function and save its + # return-values to the cache-file so it can be loaded the next time. + records = cache(cache_path=cache_path, + fn=_load_records, + train=train) + + return records + +######################################################################## diff --git a/download.py b/download.py index 676abc5..57bec13 100644 --- a/download.py +++ b/download.py @@ -34,6 +34,9 @@ def _print_download_progress(count, block_size, total_size): # Percentage completion. pct_complete = float(count * block_size) / total_size + # Limit it because rounding errors may cause it to exceed 100%. + pct_complete = min(1.0, pct_complete) + # Status-message. Note the \r which means the line should overwrite itself. msg = "\r- Download progress: {0:.1%}".format(pct_complete) @@ -44,6 +47,35 @@ def _print_download_progress(count, block_size, total_size): ######################################################################## +def download(base_url, filename, download_dir): + """ + Download the given file if it does not already exist in the download_dir. + + :param base_url: The internet URL without the filename. + :param filename: The filename that will be added to the base_url. + :param download_dir: Local directory for storing the file. + :return: Nothing. + """ + + # Path for local file. + save_path = os.path.join(download_dir, filename) + + # Check if the file already exists, otherwise we need to download it now. + if not os.path.exists(save_path): + # Check if the download directory exists, otherwise create it. + if not os.path.exists(download_dir): + os.makedirs(download_dir) + + print("Downloading", filename, "...") + + # Download the file from the internet. + url = base_url + filename + file_path, _ = urllib.request.urlretrieve(url=url, + filename=save_path, + reporthook=_print_download_progress) + + print(" Done!") + def maybe_download_and_extract(url, download_dir): """ diff --git a/europarl.py b/europarl.py new file mode 100644 index 0000000..de0852b --- /dev/null +++ b/europarl.py @@ -0,0 +1,136 @@ +######################################################################## +# +# Functions for downloading the Europarl data-set from the internet +# and loading it into memory. This data-set is used for translation +# between English and most European languages. +# +# http://www.statmt.org/europarl/ +# +# Implemented in Python 3.6 +# +# Usage: +# 1) Set the variable data_dir with the desired storage directory. +# 2) Determine the language-code to use e.g. "da" for Danish. +# 3) Call maybe_download_and_extract() to download the data-set +# if it is not already located in the given data_dir. +# 4) Call load_data(english=True) and load_data(english=False) +# to load the two data-files. +# 5) Use the returned data in your own program. +# +# Format: +# The Europarl data-set contains millions of text-pairs between English +# and most European languages. The data is stored in two text-files. +# The data is returned as lists of strings by the load_data() function. +# +# The list of currently supported languages and their codes are as follows: +# +# bg - Bulgarian +# cs - Czech +# da - Danish +# de - German +# el - Greek +# es - Spanish +# et - Estonian +# fi - Finnish +# fr - French +# hu - Hungarian +# it - Italian +# lt - Lithuanian +# lv - Latvian +# nl - Dutch +# pl - Polish +# pt - Portuguese +# ro - Romanian +# sk - Slovak +# sl - Slovene +# sv - Swedish +# +######################################################################## +# +# This file is part of the TensorFlow Tutorials available at: +# +# https://github.com/Hvass-Labs/TensorFlow-Tutorials +# +# Published under the MIT License. See the file LICENSE for details. +# +# Copyright 2018 by Magnus Erik Hvass Pedersen +# +######################################################################## + +import os +import download + +######################################################################## + +# Directory where you want to download and save the data-set. +# Set this before you start calling any of the functions below. +data_dir = "data/europarl/" + +# Base-URL for the data-sets on the internet. +data_url = "http://www.statmt.org/europarl/v7/" + + +######################################################################## +# Public functions that you may call to download the data-set from +# the internet and load the data into memory. + + +def maybe_download_and_extract(language_code="da"): + """ + Download and extract the Europarl data-set if the data-file doesn't + already exist in data_dir. The data-set is for translating between + English and the given language-code (e.g. 'da' for Danish, see the + list of available language-codes above). + """ + + # Create the full URL for the file with this data-set. + url = data_url + language_code + "-en.tgz" + + download.maybe_download_and_extract(url=url, download_dir=data_dir) + + +def load_data(english=True, language_code="da", start="", end=""): + """ + Load the data-file for either the English-language texts or + for the other language (e.g. "da" for Danish). + + All lines of the data-file are returned as a list of strings. + + :param english: + Boolean whether to load the data-file for + English (True) or the other language (False). + + :param language_code: + Two-char code for the other language e.g. "da" for Danish. + See list of available codes above. + + :param start: + Prepend each line with this text e.g. "ssss " to indicate start of line. + + :param end: + Append each line with this text e.g. " eeee" to indicate end of line. + + :return: + List of strings with all the lines of the data-file. + """ + + if english: + # Load the English data. + filename = "europarl-v7.{0}-en.en".format(language_code) + else: + # Load the other language. + filename = "europarl-v7.{0}-en.{0}".format(language_code) + + # Full path for the data-file. + path = os.path.join(data_dir, filename) + + # Open and read all the contents of the data-file. + with open(path, encoding="utf-8") as file: + # Read the line from file, strip leading and trailing whitespace, + # prepend the start-text and append the end-text. + texts = [start + line.strip() + end for line in file] + + return texts + + +######################################################################## diff --git a/images/21_machine_translation_flowchart.png b/images/21_machine_translation_flowchart.png new file mode 100644 index 0000000..6437f12 Binary files /dev/null and b/images/21_machine_translation_flowchart.png differ diff --git a/images/21_machine_translation_flowchart.svg b/images/21_machine_translation_flowchart.svg new file mode 100644 index 0000000..b8f8a61 --- /dev/null +++ b/images/21_machine_translation_flowchart.svg @@ -0,0 +1,4475 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + image/svg+xml + + + + + + + + + Decoder + + "once" + + + + "upon" + + + + "a" + + + + "time" + + + Layer 4 + Layer 5 + Layer 6 + [vector] + [vector] + + StartMarker"ssss" + + + + EndMarker"eeee" + + + + Tokenizer (Destination) + + + + Embedding (Decoder) + + + 2 + 337 + 640 + 9 + 79 + 3 + + + + + + + + + + + + + + + + + + + [vector] + Destination (English) + + + + + + GRU4 + + + + + + + GRU5 + + + + + + + GRU6 + + + + + Dense + + + + + + + + GRU4 + + + + + + + GRU5 + + + + + + + + GRU6 + + + + + Dense + + + + + + + + GRU4 + + + + + + + GRU5 + + + + + + + + GRU6 + + + + + Dense + + + + + + + + GRU4 + + + + + + + GRU5 + + + + + + + + GRU6 + + + + + Dense + + + + + + + + GRU4 + + + + + + + GRU5 + + + + + + + + GRU6 + + + + + Dense + + + + + + + + GRU4 + + + + + + + GRU5 + + + + + + + + GRU6 + + + + + Dense + + + + 337 + 640 + 9 + 79 + 3 + + "once" + + + + "upon" + + + + "a" + + + + "time" + + + + "eeee" + + + + "" + + + 0 + + Initial State + + + + + + + + FirstStateZero + + + + + + + + + + FirstStateZero + + + + + + + FirstStateZero + + + + + + Layer 1 + Layer 2 + Layer 3 + Encoder + + "der" + + + + "var" + + + + "engang" + + + + Tokenizer (Source) + + + + Embedding (Encoder) + + + + + + + + + 12 + 54 + 1097 + [vector] + [vector] + + + GRU1 + + + + GRU1 + + + + GRU1 + + + + + + + + GRU2 + + + + + + GRU2 + + + + + + GRU2 + + + + + + + + GRU3 + + + + GRU3 + + + + GRU3 + + + ThoughtVector + + + Source (Danish) + + + + + diff --git a/images/22_image_captioning_flowchart.png b/images/22_image_captioning_flowchart.png new file mode 100644 index 0000000..af3446f Binary files /dev/null and b/images/22_image_captioning_flowchart.png differ diff --git a/images/22_image_captioning_flowchart.svg b/images/22_image_captioning_flowchart.svg new file mode 100644 index 0000000..2df11f7 --- /dev/null +++ b/images/22_image_captioning_flowchart.svg @@ -0,0 +1,4209 @@ + + + + + + + + + + + + + + + + + + + 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+ + Convolution Block 2 + + + + Convolution Block 3 + + + + Convolution Block 4 + + + + Convolution Block 5 + + + + + + + + + + + + + + + Input Image + VGG16 Image-Model + + + Dense Map + + + + [4096] + Decoder + + "big" + + + + "brown" + + + + "bear" + + + + "sitting" + + + Layer 1 + Layer 2 + Layer 3 + [vector] + [vector] + + StartMarker"ssss" + + + + EndMarker"eeee" + + + Tokenizer + + Embedding + + 2 + 165 + 110 + 102 + 13 + 3 + + + + + + + + + + + + + + + + + + + [vector] + + + + + + GRU1 + + + + + + + GRU2 + + + + + + + GRU3 + + + + + Dense + + + + + + + + GRU1 + + + + + + + GRU2 + + + + + + + + GRU3 + + + + + Dense + + + + + + + + GRU1 + + + + + + + GRU2 + + + + + + + + GRU3 + + + + + Dense + + + + + + + + GRU1 + + + + + + + GRU2 + + + + + + + + GRU3 + + + + + Dense + + + + + + + + GRU1 + + + + + + + GRU2 + + + + + + + + GRU3 + + + + + Dense + + + + + + + + GRU1 + + + + + + + GRU2 + + + + + + + + GRU3 + + + + + Dense + + + + 165 + 110 + 102 + 13 + 3 + + "eeee" + + + + "" + + + 0 + + Initial State + + + + + + + + [512] + + "big" + + + + "brown" + + + + "bear" + + + + "sitting" + + + + + + + + + + diff --git a/images/23_time_series_flowchart.png b/images/23_time_series_flowchart.png new file mode 100644 index 0000000..552148d Binary files /dev/null and b/images/23_time_series_flowchart.png differ diff --git a/images/23_time_series_flowchart.svg b/images/23_time_series_flowchart.svg new file mode 100644 index 0000000..079319f --- /dev/null +++ b/images/23_time_series_flowchart.svg @@ -0,0 +1,769 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + image/svg+xml + + + + + + + + + Temp + Pressure + WindSpeed + WindDir + + + + + + + + + + + + + + + + + + Aalborg + + + + + + + + + + + + + + + + + + + + + Temp + Pressure + WindSpeed + WindDir + + Roskilde + + + + + + + + + + + + + + + + + Temp + Pressure + WindSpeed + + Odense + + + + + + Gated Recurrent Unit + + + + Dense + + + + + + + + + + + + + + + + + + + + + + + + Input Signals + Output Signals + 512 + + NeuralNetwork + + diff --git a/images/Denmark.jpg b/images/Denmark.jpg new file mode 100644 index 0000000..239d139 Binary files /dev/null and b/images/Denmark.jpg differ diff --git a/images/Europe.jpg b/images/Europe.jpg new file mode 100644 index 0000000..b5a6219 Binary files /dev/null and b/images/Europe.jpg differ diff --git a/imdb.py b/imdb.py index 3121899..db3094f 100644 --- a/imdb.py +++ b/imdb.py @@ -54,7 +54,7 @@ def _read_text_file(path): It is returned as a single string where all lines are concatenated. """ - with open(path, 'rt') as file: + with open(path, 'rt', encoding='utf-8') as file: # Read a list of strings. lines = file.readlines() diff --git a/mnist.py b/mnist.py new file mode 100644 index 0000000..6b7bbb4 --- /dev/null +++ b/mnist.py @@ -0,0 +1,186 @@ +######################################################################## +# +# Downloads the MNIST data-set for recognizing hand-written digits. +# +# Implemented in Python 3.6 +# +# Usage: +# 1) Create a new object instance: data = MNIST(data_dir="data/MNIST/") +# This automatically downloads the files to the given dir. +# 2) Use the training-set as data.x_train, data.y_train and data.y_train_cls +# 3) Get random batches of training data using data.random_batch() +# 4) Use the test-set as data.x_test, data.y_test and data.y_test_cls +# +######################################################################## +# +# This file is part of the TensorFlow Tutorials available at: +# +# https://github.com/Hvass-Labs/TensorFlow-Tutorials +# +# Published under the MIT License. See the file LICENSE for details. +# +# Copyright 2016-18 by Magnus Erik Hvass Pedersen +# +######################################################################## + +import numpy as np +import gzip +import os +from dataset import one_hot_encoded +from download import download + +######################################################################## + +# Base URL for downloading the data-files from the internet. +base_url = "https://storage.googleapis.com/cvdf-datasets/mnist/" + +# Filenames for the data-set. +filename_x_train = "train-images-idx3-ubyte.gz" +filename_y_train = "train-labels-idx1-ubyte.gz" +filename_x_test = "t10k-images-idx3-ubyte.gz" +filename_y_test = "t10k-labels-idx1-ubyte.gz" + +######################################################################## + + +class MNIST: + """ + The MNIST data-set for recognizing hand-written digits. + This automatically downloads the data-files if they do + not already exist in the local data_dir. + + Note: Pixel-values are floats between 0.0 and 1.0. + """ + + # The images are 28 pixels in each dimension. + img_size = 28 + + # The images are stored in one-dimensional arrays of this length. + img_size_flat = img_size * img_size + + # Tuple with height and width of images used to reshape arrays. + img_shape = (img_size, img_size) + + # Number of colour channels for the images: 1 channel for gray-scale. + num_channels = 1 + + # Tuple with height, width and depth used to reshape arrays. + # This is used for reshaping in Keras. + img_shape_full = (img_size, img_size, num_channels) + + # Number of classes, one class for each of 10 digits. + num_classes = 10 + + def __init__(self, data_dir="data/MNIST/"): + """ + Load the MNIST data-set. Automatically downloads the files + if they do not already exist locally. + + :param data_dir: Base-directory for downloading files. + """ + + # Copy args to self. + self.data_dir = data_dir + + # Number of images in each sub-set. + self.num_train = 55000 + self.num_val = 5000 + self.num_test = 10000 + + # Download / load the training-set. + x_train = self._load_images(filename=filename_x_train) + y_train_cls = self._load_cls(filename=filename_y_train) + + # Split the training-set into train / validation. + # Pixel-values are converted from ints between 0 and 255 + # to floats between 0.0 and 1.0. + self.x_train = x_train[0:self.num_train] / 255.0 + self.x_val = x_train[self.num_train:] / 255.0 + self.y_train_cls = y_train_cls[0:self.num_train] + self.y_val_cls = y_train_cls[self.num_train:] + + # Download / load the test-set. + self.x_test = self._load_images(filename=filename_x_test) / 255.0 + self.y_test_cls = self._load_cls(filename=filename_y_test) + + # Convert the class-numbers from bytes to ints as that is needed + # some places in TensorFlow. + self.y_train_cls = self.y_train_cls.astype(np.int) + self.y_val_cls = self.y_val_cls.astype(np.int) + self.y_test_cls = self.y_test_cls.astype(np.int) + + # Convert the integer class-numbers into one-hot encoded arrays. + self.y_train = one_hot_encoded(class_numbers=self.y_train_cls, + num_classes=self.num_classes) + self.y_val = one_hot_encoded(class_numbers=self.y_val_cls, + num_classes=self.num_classes) + self.y_test = one_hot_encoded(class_numbers=self.y_test_cls, + num_classes=self.num_classes) + + def _load_data(self, filename, offset): + """ + Load the data in the given file. Automatically downloads the file + if it does not already exist in the data_dir. + + :param filename: Name of the data-file. + :param offset: Start offset in bytes when reading the data-file. + :return: The data as a numpy array. + """ + + # Download the file from the internet if it does not exist locally. + download(base_url=base_url, filename=filename, download_dir=self.data_dir) + + # Read the data-file. + path = os.path.join(self.data_dir, filename) + with gzip.open(path, 'rb') as f: + data = np.frombuffer(f.read(), np.uint8, offset=offset) + + return data + + def _load_images(self, filename): + """ + Load image-data from the given file. + Automatically downloads the file if it does not exist locally. + + :param filename: Name of the data-file. + :return: Numpy array. + """ + + # Read the data as one long array of bytes. + data = self._load_data(filename=filename, offset=16) + + # Reshape to 2-dim array with shape (num_images, img_size_flat). + images_flat = data.reshape(-1, self.img_size_flat) + + return images_flat + + def _load_cls(self, filename): + """ + Load class-numbers from the given file. + Automatically downloads the file if it does not exist locally. + + :param filename: Name of the data-file. + :return: Numpy array. + """ + return self._load_data(filename=filename, offset=8) + + def random_batch(self, batch_size=32): + """ + Create a random batch of training-data. + + :param batch_size: Number of images in the batch. + :return: 3 numpy arrays (x, y, y_cls) + """ + + # Create a random index into the training-set. + idx = np.random.randint(low=0, high=self.num_train, size=batch_size) + + # Use the index to lookup random training-data. + x_batch = self.x_train[idx] + y_batch = self.y_train[idx] + y_batch_cls = self.y_train_cls[idx] + + return x_batch, y_batch, y_batch_cls + + +######################################################################## diff --git a/reinforcement_learning.py b/reinforcement_learning.py index 3805ab2..d5f0e09 100644 --- a/reinforcement_learning.py +++ b/reinforcement_learning.py @@ -7,7 +7,7 @@ # To train a Neural Network for playing the Atari game Breakout, # run the following command in a terminal window. # -# python reinforcement-learning.py --env 'Breakout-v0' --training +# python reinforcement_learning.py --env 'Breakout-v0' --training # # The agent should start to improve after a few hours, but a full # training run required 150 hours on a 2.6 GHz CPU and GTX 1070 GPU. @@ -18,14 +18,13 @@ # Once the Neural Network has been trained, you can test it and # watch it play the game by running this command in the terminal: # -# python reinforcement-learning.py --env 'Breakout-v0' --render --episodes 2 +# python reinforcement_learning.py --env 'Breakout-v0' --render --episodes 2 # # Requirements: # # - Python 3.6 (Python 2.7 may not work) # - TensorFlow 1.1.0 # - OpenAI Gym 0.8.1 -# - PrettyTensor 0.7.4 (not required if you use tf.layers instead) # # Summary: # @@ -156,16 +155,19 @@ # ######################################################################## +# Use TensorFlow v.2 with this old v.1 code. +# E.g. placeholder variables and sessions have changed in TF2. +import tensorflow.compat.v1 as tf +tf.disable_v2_behavior() + import numpy as np -import tensorflow as tf import gym -import scipy.ndimage +import PIL.Image import sys import os import time import csv import argparse -import download ######################################################################## # File-paths are global variables for convenience so they don't @@ -216,29 +218,6 @@ def update_paths(env_name): # File-path for the log-file for Q-values. log_q_values_path = os.path.join(checkpoint_dir, "log_q_values.txt") -######################################################################## -# Download TensorFlow checkpoints. - -# URL's for the checkpoint-files. -_checkpoint_url = { - "Breakout-v0": "http://hvass-labs.org/projects/tensorflow/tutorial16/Breakout-v0.tar.gz", - "SpaceInvaders-v0": "http://hvass-labs.org/projects/tensorflow/tutorial16/SpaceInvaders-v0.tar.gz" -} - - -def maybe_download_checkpoint(env_name): - """ - Download and extract the TensorFlow checkpoint for the given - environment-name, if it does not already exist in checkpoint_base_dir. - You should first set this dir and call update_paths(). - """ - - # Get the url for the game-environment. - url = _checkpoint_url[env_name] - - # Download and extract the file if it does not already exist. - download.maybe_download_and_extract(url=url, - download_dir=checkpoint_base_dir) ######################################################################## # Classes used for logging data during training. @@ -430,6 +409,9 @@ def print_progress(msg): # Size of each image in the state. state_img_size = np.array([state_height, state_width]) +# Size of each image in the state. Reversed order used by PIL.Image. +state_img_size_reverse = tuple(reversed(state_img_size)) + # Number of images in the state. state_channels = 2 @@ -460,12 +442,19 @@ def _pre_process_image(image): """Pre-process a raw image from the game-environment.""" # Convert image to gray-scale. - img = _rgb_to_grayscale(image) + img_gray = _rgb_to_grayscale(image=image) + + # Create PIL-object from numpy array. + img = PIL.Image.fromarray(img_gray) + + # Resize the image. + img_resized = img.resize(size=state_img_size_reverse, + resample=PIL.Image.LINEAR) - # Resize to the desired size using SciPy for convenience. - img = scipy.misc.imresize(img, size=state_img_size, interp='bicubic') + # Convert 8-bit pixel values back to floating-point. + img_resized = np.float32(img_resized) - return img + return img_resized class MotionTracer: @@ -1100,23 +1089,15 @@ class NeuralNetwork: better at estimating the Q-values. """ - def __init__(self, num_actions, replay_memory, use_pretty_tensor=True): + def __init__(self, num_actions, replay_memory): """ :param num_actions: Number of discrete actions for the game-environment. :param replay_memory: Object-instance of the ReplayMemory-class. - - :param use_pretty_tensor: - Boolean whether to use PrettyTensor (True) which must then be - installed, or use the tf.layers API (False) which is already - built into TensorFlow. """ - # Whether to use the PrettyTensor API (True) or tf.layers (False). - self.use_pretty_tensor = use_pretty_tensor - # Replay-memory used for sampling random batches. self.replay_memory = replay_memory @@ -1187,115 +1168,78 @@ def __init__(self, num_actions, replay_memory, use_pretty_tensor=True): # You can experiment with values between 1e-2 and 1e-3. init = tf.truncated_normal_initializer(mean=0.0, stddev=2e-2) - if self.use_pretty_tensor: - # This builds the Neural Network using the PrettyTensor API, - # which is a very elegant builder API, but some people are - # having problems installing and using it. - - import prettytensor as pt - - # Wrap the input to the Neural Network in a PrettyTensor object. - x_pretty = pt.wrap(self.x) - - # Create the convolutional Neural Network using Pretty Tensor. - with pt.defaults_scope(activation_fn=tf.nn.relu): - self.q_values = x_pretty. \ - conv2d(kernel=3, depth=16, stride=2, name='layer_conv1', weights=init). \ - conv2d(kernel=3, depth=32, stride=2, name='layer_conv2', weights=init). \ - conv2d(kernel=3, depth=64, stride=1, name='layer_conv3', weights=init). \ - flatten(). \ - fully_connected(size=1024, name='layer_fc1', weights=init). \ - fully_connected(size=1024, name='layer_fc2', weights=init). \ - fully_connected(size=1024, name='layer_fc3', weights=init). \ - fully_connected(size=1024, name='layer_fc4', weights=init). \ - fully_connected(size=num_actions, name='layer_fc_out', weights=init, - activation_fn=None) - - # Loss-function which must be optimized. This is the mean-squared - # error between the Q-values that are output by the Neural Network - # and the target Q-values. - self.loss = self.q_values.l2_regression(target=self.q_values_new) - else: - # This builds the Neural Network using the tf.layers API, - # which is very verbose and inelegant, but should work for everyone. - - # Note that the checkpoints for Tutorial #16 which can be - # downloaded from the internet only support PrettyTensor. - # Although the Neural Networks appear to be identical when - # built using the PrettyTensor and tf.layers APIs, - # they actually create somewhat different TensorFlow graphs - # where the variables have different names, which means the - # checkpoints are incompatible for the two builder APIs. - - # Padding used for the convolutional layers. - padding = 'SAME' - - # Activation function for all convolutional and fully-connected - # layers, except the last. - activation = tf.nn.relu - - # Reference to the lastly added layer of the Neural Network. - # This makes it easy to add or remove layers. - net = self.x - - # First convolutional layer. - net = tf.layers.conv2d(inputs=net, name='layer_conv1', - filters=16, kernel_size=3, strides=2, - padding=padding, - kernel_initializer=init, activation=activation) - - # Second convolutional layer. - net = tf.layers.conv2d(inputs=net, name='layer_conv2', - filters=32, kernel_size=3, strides=2, - padding=padding, - kernel_initializer=init, activation=activation) - - # Third convolutional layer. - net = tf.layers.conv2d(inputs=net, name='layer_conv3', - filters=64, kernel_size=3, strides=1, - padding=padding, - kernel_initializer=init, activation=activation) - - # Flatten output of the last convolutional layer so it can - # be input to a fully-connected (aka. dense) layer. - # TODO: For some bizarre reason, this function is not yet in tf.layers - # TODO: net = tf.layers.flatten(net) - net = tf.contrib.layers.flatten(net) - - # First fully-connected (aka. dense) layer. - net = tf.layers.dense(inputs=net, name='layer_fc1', units=1024, - kernel_initializer=init, activation=activation) - - # Second fully-connected layer. - net = tf.layers.dense(inputs=net, name='layer_fc2', units=1024, - kernel_initializer=init, activation=activation) - - # Third fully-connected layer. - net = tf.layers.dense(inputs=net, name='layer_fc3', units=1024, - kernel_initializer=init, activation=activation) - - # Fourth fully-connected layer. - net = tf.layers.dense(inputs=net, name='layer_fc4', units=1024, - kernel_initializer=init, activation=activation) - - # Final fully-connected layer. - net = tf.layers.dense(inputs=net, name='layer_fc_out', units=num_actions, - kernel_initializer=init, activation=None) - - # The output of the Neural Network is the estimated Q-values - # for each possible action in the game-environment. - self.q_values = net - - # TensorFlow has a built-in loss-function for doing regression: - # self.loss = tf.nn.l2_loss(self.q_values - self.q_values_new) - # But it uses tf.reduce_sum() rather than tf.reduce_mean() - # which is used by PrettyTensor. This means the scale of the - # gradient is different and hence the hyper-parameters - # would have to be re-tuned. So instead we calculate the - # L2-loss similarly to how it is done in PrettyTensor. - squared_error = tf.square(self.q_values - self.q_values_new) - sum_squared_error = tf.reduce_sum(squared_error, axis=1) - self.loss = tf.reduce_mean(sum_squared_error) + # This builds the Neural Network using the tf.layers API, + # which is very verbose and inelegant, but should work for everyone. + + # Padding used for the convolutional layers. + padding = 'SAME' + + # Activation function for all convolutional and fully-connected + # layers, except the last. + activation = tf.nn.relu + + # Reference to the lastly added layer of the Neural Network. + # This makes it easy to add or remove layers. + net = self.x + + # First convolutional layer. + net = tf.layers.conv2d(inputs=net, name='layer_conv1', + filters=16, kernel_size=3, strides=2, + padding=padding, + kernel_initializer=init, activation=activation) + + # Second convolutional layer. + net = tf.layers.conv2d(inputs=net, name='layer_conv2', + filters=32, kernel_size=3, strides=2, + padding=padding, + kernel_initializer=init, activation=activation) + + # Third convolutional layer. + net = tf.layers.conv2d(inputs=net, name='layer_conv3', + filters=64, kernel_size=3, strides=1, + padding=padding, + kernel_initializer=init, activation=activation) + + # Flatten output of the last convolutional layer so it can + # be input to a fully-connected (aka. dense) layer. + net = tf.layers.flatten(net) + + # First fully-connected (aka. dense) layer. + net = tf.layers.dense(inputs=net, name='layer_fc1', units=1024, + kernel_initializer=init, activation=activation) + + # Second fully-connected layer. + net = tf.layers.dense(inputs=net, name='layer_fc2', units=1024, + kernel_initializer=init, activation=activation) + + # Third fully-connected layer. + net = tf.layers.dense(inputs=net, name='layer_fc3', units=1024, + kernel_initializer=init, activation=activation) + + # Fourth fully-connected layer. + net = tf.layers.dense(inputs=net, name='layer_fc4', units=1024, + kernel_initializer=init, activation=activation) + + # Final fully-connected layer. + net = tf.layers.dense(inputs=net, name='layer_fc_out', units=num_actions, + kernel_initializer=init, activation=None) + + # The output of the Neural Network is the estimated Q-values + # for each possible action in the game-environment. + self.q_values = net + + # TensorFlow has a built-in loss-function for doing regression: + # self.loss = tf.nn.l2_loss(self.q_values - self.q_values_new) + # But it uses tf.reduce_sum() rather than tf.reduce_mean() + # which is used by PrettyTensor. This means the scale of the + # gradient is different and hence the hyper-parameters + # would have to be re-tuned, because they were tuned for + # the original version of this tutorial using PrettyTensor. + # So instead we calculate the L2-loss similarly to how it is + # done in PrettyTensor. + squared_error = tf.square(self.q_values - self.q_values_new) + sum_squared_error = tf.reduce_sum(squared_error, axis=1) + self.loss = tf.reduce_mean(sum_squared_error) # Optimizer used for minimizing the loss-function. # Note the learning-rate is a placeholder variable so we can @@ -1480,12 +1424,8 @@ def get_weights_variable(self, layer_name): you must use the function get_variable_value() for that. """ - if self.use_pretty_tensor: - # PrettyTensor uses this name for the weights in a conv-layer. - variable_name = 'weights' - else: - # The tf.layers API uses this name for the weights in a conv-layer. - variable_name = 'kernel' + # The tf.layers API uses this name for the weights in a conv-layer. + variable_name = 'kernel' with tf.variable_scope(layer_name, reuse=True): variable = tf.get_variable(variable_name) diff --git a/requirements.txt b/requirements.txt index 43c0e28..d303a8b 100644 --- a/requirements.txt +++ b/requirements.txt @@ -7,6 +7,7 @@ # Python packages by running the following commands in a shell: # # conda create --name tf python=3 +# source activate tf # pip install -r requirements.txt # # Note that you have to edit this file to select whether you @@ -23,20 +24,22 @@ Pillow scikit-learn ################################################################ -# TensorFlow can be installed either as CPU or GPU versions. -# You select which one to install by (un)commenting these lines. +# TensorFlow v.2.1 and above include both CPU and GPU versions. -tensorflow # CPU Version of TensorFlow. -# tensorflow-gpu # GPU version of TensorFlow. - -# Builder API for TensorFlow used in many of the tutorials. -prettytensor +tensorflow ################################################################ -# The tutorial on Reinforcement Learning uses OpenAI Gym. -# Uncomment this line if you want to run that tutorial. +# Some tutorials use other individual Python packages. +# Uncomment the relevant lines for the tutorials you want to run. -# gym[atari] +# gym[atari] # Tutorial #16 on Reinforcement Learning. +# pandas # Tutorial #23 on Time-Series Prediction. ################################################################ +# PrettyTensor was used as the builder API for several of the +# earlier tutorials. PrettyTensor is apparently no longer being +# maintained and may not work with newer versions of TensorFlow. + +# prettytensor +################################################################ diff --git a/weather.py b/weather.py new file mode 100644 index 0000000..e706034 --- /dev/null +++ b/weather.py @@ -0,0 +1,255 @@ +######################################################################## +# +# Functions for downloading and re-sampling weather-data +# for 5 cities in Denmark between 1980-2018. +# +# The raw data was obtained from: +# +# National Climatic Data Center (NCDC) in USA +# https://www7.ncdc.noaa.gov/CDO/cdoselect.cmd +# +# Note that the NCDC's database functionality may change soon, and +# that the CSV-file needed some manual editing before it could be read. +# See the function _convert_raw_data() below for inspiration if you +# want to convert a new data-file from NCDC's database. +# +# Implemented in Python 3.6 +# +# Usage: +# 1) Set the desired storage directory in the data_dir variable. +# 2) Call maybe_download_and_extract() to download the data-set +# if it is not already located in the given data_dir. +# 3) Either call load_original_data() or load_resampled_data() +# to load the original or resampled data for use in your program. +# +# Format: +# The raw data-file from NCDC is not included in the downloaded archive, +# which instead contains a cleaned-up version of the raw data-file +# referred to as the "original data". This data has not yet been resampled. +# The original data-file is available as a pickled file for fast reloading +# with Pandas, and as a CSV-file for broad compatibility. +# +######################################################################## +# +# This file is part of the TensorFlow Tutorials available at: +# +# https://github.com/Hvass-Labs/TensorFlow-Tutorials +# +# Published under the MIT License. See the file LICENSE for details. +# +# Copyright 2018 by Magnus Erik Hvass Pedersen +# +######################################################################## + +import pandas as pd +import os +import download + +######################################################################## + +# Directory where you want to download and save the data-set. +# Set this before you start calling any of the functions below. +data_dir = "data/weather-denmark/" + + +# Full path for the pickled data-file. (Original data). +def path_original_data_pickle(): + return os.path.join(data_dir, "weather-denmark.pkl") + + +# Full path for the comma-separated text-file. (Original data). +def path_original_data_csv(): + return os.path.join(data_dir, "weather-denmark.csv") + + +# Full path for the resampled data as a pickled file. +def path_resampled_data_pickle(): + return os.path.join(data_dir, "weather-denmark-resampled.pkl") + + +# URL for the data-set on the internet. +data_url = "https://github.com/Hvass-Labs/weather-denmark/raw/master/weather-denmark.tar.gz" + + +# List of the cities in this data-set. These are cities in Denmark. +cities = ['Aalborg', 'Aarhus', 'Esbjerg', 'Odense', 'Roskilde'] + + +######################################################################## +# Private helper-functions. + + +def _date_string(x): + """Convert two integers to a string for the date and time.""" + + date = x[0] # Date. Example: 19801231 + time = x[1] # Time. Example: 1230 + + return "{0}{1:04d}".format(date, time) + + +def _usaf_to_city(usaf): + """ + The raw data-file uses USAF-codes to identify weather-stations. + If you download another data-set from NCDC then you will have to + change this function to use the USAF-codes in your new data-file. + """ + + table = \ + { + 60300: 'Aalborg', + 60700: 'Aarhus', + 60800: 'Esbjerg', + 61200: 'Odense', + 61700: 'Roskilde' + } + + return table[usaf] + + +def _convert_raw_data(path): + """ + This converts a raw data-file obtained from the NCDC database. + This function may be useful as an inspiration if you want to + download another raw data-file from NCDC, but you will have + to modify this function to match the data you have downloaded. + + Note that you may also have to manually edit the raw data-file, + e.g. because the header is not in a proper comma-separated format. + """ + + # The raw CSV-file uses various markers for "not-available" (NA). + # (This is one of several oddities with NCDC's file-format.) + na_values = ['999', '999.0', '999.9', '9999.9'] + + # Use Pandas to load the comma-separated file. + # Note that you may have to manually edit the file's header + # to get this to load correctly. + df_raw = pd.read_csv(path, sep=',', header=1, + index_col=False, na_values=na_values) + + # Create a new data-frame containing only the data + # we are interested in. + df = pd.DataFrame() + + # Get the city-name / weather-station name from the USAF code. + df['City'] = df_raw['USAF '].apply(_usaf_to_city) + + # Convert the integer date-time to a proper date-time object. + datestr = df_raw[['Date ', 'HrMn']].apply(_date_string, axis=1) + df['DateTime'] = pd.to_datetime(datestr, format='%Y%m%d%H%M') + + # Get the data we are interested in. + df['Temp'] = df_raw['Temp '] + df['Pressure'] = df_raw['Slp '] + df['WindSpeed'] = df_raw['Spd '] + df['WindDir'] = df_raw['Dir'] + + # Set the city-name and date-time as the index. + df.set_index(['City', 'DateTime'], inplace=True) + + # Save the new data-frame as a pickle for fast reloading. + df.to_pickle(path_original_data_pickle()) + + # Save the new data-frame as a CSV-file for general readability. + df.to_csv(path_original_data_csv()) + + return df + + +def _resample(df): + """ + Resample the contents of a Pandas data-frame by first + removing empty rows and columns, then up-sampling and + interpolating the data for 1-minute intervals, and + finally down-sampling to 60-minute intervals. + """ + + # Remove all empty rows. + df_res = df.dropna(how='all') + + # Upsample so the time-series has data for every minute. + df_res = df_res.resample('1T') + + # Fill in missing values. + df_res = df_res.interpolate(method='time') + + # Downsample so the time-series has data for every hour. + df_res = df_res.resample('60T') + + # Finalize the resampling. (Is this really necessary?) + df_res = df_res.interpolate() + + # Remove all empty rows. + df_res = df_res.dropna(how='all') + + return df_res + + +######################################################################## +# Public functions that you may call to download the data-set from +# the internet and load the data into memory. + + +def maybe_download_and_extract(): + """ + Download and extract the weather-data if the data-files don't + already exist in the data_dir. + """ + + download.maybe_download_and_extract(url=data_url, download_dir=data_dir) + + +def load_original_data(): + """ + Load and return the original data that has not been resampled. + + Note that this is not the raw data obtained from NCDC. + It is a cleaned-up version of that data, as written by the + function _convert_raw_data() above. + """ + + return pd.read_pickle(path_original_data_pickle()) + + +def load_resampled_data(): + """ + Load and return the resampled weather-data. + + This has data-points at regular 60-minute intervals where + missing data has been linearly interpolated. + + This uses a cache-file for saving and quickly reloading the data, + so the original data is only resampled once. + """ + + # Path for the cache-file with the resampled data. + path = path_resampled_data_pickle() + + # If the cache-file exists ... + if os.path.exists(path): + # Reload the cache-file. + df = pd.read_pickle(path) + else: + # Otherwise resample the original data and save it in a cache-file. + + # Load the original data. + df_org = load_original_data() + + # Split the original data into separate data-frames for each city. + df_cities = [df_org.xs(city) for city in cities] + + # Resample the data for each city. + df_resampled = [_resample(df_city) for df_city in df_cities] + + # Join the resampled data into a single data-frame. + df = pd.concat(df_resampled, keys=cities, + axis=1, join='inner') + + # Save the resampled data in a cache-file for quick reloading. + df.to_pickle(path) + + return df + + +########################################################################