From 7aeb6cbf142fe5a69fd32dca2d03315846abd396 Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Wed, 6 Nov 2019 22:18:35 -0800 Subject: [PATCH 01/24] Update tensorflow v2 installation --- tensorflow_v2/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow_v2/README.md b/tensorflow_v2/README.md index d5151917..ed23a174 100644 --- a/tensorflow_v2/README.md +++ b/tensorflow_v2/README.md @@ -43,10 +43,10 @@ To install TensorFlow 2.0, simply run: ``` -pip install tensorflow==2.0.0-beta1 +pip install tensorflow==2.0.0 ``` or (if you want GPU support): ``` -pip install tensorflow_gpu==2.0.0-beta1 +pip install tensorflow_gpu==2.0.0 ``` From c5772812faea39e0d0b1cc1dbd937b8ecb20091b Mon Sep 17 00:00:00 2001 From: Nikhil Kilari <36819773+kilarinikhil@users.noreply.github.com> Date: Sun, 12 Apr 2020 07:51:02 +0530 Subject: [PATCH 02/24] A minor mistake in cross entropy loss (#357) tf.reduce_mean(-tf.reduce_sum(y_true * tf.math.log(y_pred),1)) or else it simply finds the sum and the reduced mean remains the sum itself. --- tensorflow_v2/notebooks/2_BasicModels/logistic_regression.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow_v2/notebooks/2_BasicModels/logistic_regression.ipynb b/tensorflow_v2/notebooks/2_BasicModels/logistic_regression.ipynb index c29c42b9..b9b1ccc4 100644 --- a/tensorflow_v2/notebooks/2_BasicModels/logistic_regression.ipynb +++ b/tensorflow_v2/notebooks/2_BasicModels/logistic_regression.ipynb @@ -109,7 +109,7 @@ " # Clip prediction values to avoid log(0) error.\n", " y_pred = tf.clip_by_value(y_pred, 1e-9, 1.)\n", " # Compute cross-entropy.\n", - " return tf.reduce_mean(-tf.reduce_sum(y_true * tf.math.log(y_pred)))\n", + " return tf.reduce_mean(-tf.reduce_sum(y_true * tf.math.log(y_pred),1))\n", "\n", "# Accuracy metric.\n", "def accuracy(y_pred, y_true):\n", From 39d9d0efa11fafc7eb1e6305a62910d72e04e0df Mon Sep 17 00:00:00 2001 From: lkdmttg7 Date: Sun, 17 May 2020 00:16:40 +0530 Subject: [PATCH 03/24] Update convolutional_network_raw.ipynb (#366) --- .../notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb b/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb index 80adb3f0..c0ffbc42 100644 --- a/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb +++ b/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb @@ -217,7 +217,7 @@ " loss = cross_entropy(pred, y)\n", " \n", " # Variables to update, i.e. trainable variables.\n", - " trainable_variables = weights.values() + biases.values()\n", + " trainable_variables = list(weights.values()) + list(biases.values())\n", "\n", " # Compute gradients.\n", " gradients = g.gradient(loss, trainable_variables)\n", From 2cf9bfd3609fdb03c5f8499b18a4b6bd7c43b374 Mon Sep 17 00:00:00 2001 From: Dragon-Yu <769888056@qq.com> Date: Sun, 17 May 2020 02:47:36 +0800 Subject: [PATCH 04/24] Update neural_network.ipynb (#361) Add the missing fully connected layer 2 in the RNN example --- tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb b/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb index 2bcd1860..857610ca 100644 --- a/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb +++ b/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb @@ -116,6 +116,7 @@ " # Set forward pass.\n", " def call(self, x, is_training=False):\n", " x = self.fc1(x)\n", + " x = self.fc2(x)\n", " x = self.out(x)\n", " if not is_training:\n", " # tf cross entropy expect logits without softmax, so only\n", From e7353b776165d124f0f560af87992311bc1984c8 Mon Sep 17 00:00:00 2001 From: Nikhil Kilari <36819773+kilarinikhil@users.noreply.github.com> Date: Sun, 17 May 2020 00:19:17 +0530 Subject: [PATCH 05/24] output layer aactivation, add fc2 in call (#358) softmax applied during training phase to output layer and fc2 layer is unused Co-authored-by: Aymeric Damien --- tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb b/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb index 857610ca..77926535 100644 --- a/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb +++ b/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb @@ -111,12 +111,12 @@ " # First fully-connected hidden layer.\n", " self.fc2 = layers.Dense(n_hidden_2, activation=tf.nn.relu)\n", " # Second fully-connecter hidden layer.\n", - " self.out = layers.Dense(num_classes, activation=tf.nn.softmax)\n", + " self.out = layers.Dense(num_classes)\n", "\n", " # Set forward pass.\n", " def call(self, x, is_training=False):\n", " x = self.fc1(x)\n", - " x = self.fc2(x)\n", + " x = self.fc2(x)\n" " x = self.out(x)\n", " if not is_training:\n", " # tf cross entropy expect logits without softmax, so only\n", From 922833aff43d616bd7d0cddb43366ac93215f6d7 Mon Sep 17 00:00:00 2001 From: Sebastian Stein Date: Sat, 16 May 2020 20:49:42 +0200 Subject: [PATCH 06/24] make TensorFlow 2 examples compatible with Python 3 (#339) --- tensorflow_v2/notebooks/1_Introduction/helloworld.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow_v2/notebooks/1_Introduction/helloworld.ipynb b/tensorflow_v2/notebooks/1_Introduction/helloworld.ipynb index 8a9479c0..a1fdad54 100644 --- a/tensorflow_v2/notebooks/1_Introduction/helloworld.ipynb +++ b/tensorflow_v2/notebooks/1_Introduction/helloworld.ipynb @@ -37,7 +37,7 @@ "source": [ "# Create a Tensor.\n", "hello = tf.constant(\"hello world\")\n", - "print hello" + "print(hello)" ] }, { @@ -55,7 +55,7 @@ ], "source": [ "# To access a Tensor value, call numpy().\n", - "print hello.numpy()" + "print(hello.numpy())" ] } ], From 3a767b1e712c386dfbb525589685cce569619953 Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Sat, 16 May 2020 13:14:52 -0700 Subject: [PATCH 07/24] update docs (#367) --- README.md | 129 ++-- examples/README.md | 5 + notebooks/README.md | 5 + tensorflow_v1/README.md | 93 +++ .../1_Introduction/basic_eager_api.py | 68 ++ .../1_Introduction/basic_operations.py | 75 ++ .../examples/1_Introduction/helloworld.py | 25 + .../gradient_boosted_decision_tree.py | 85 ++ .../examples/2_BasicModels/kmeans.py | 93 +++ .../2_BasicModels/linear_regression.py | 89 +++ .../linear_regression_eager_api.py | 69 ++ .../2_BasicModels/logistic_regression.py | 71 ++ .../logistic_regression_eager_api.py | 105 +++ .../2_BasicModels/nearest_neighbor.py | 55 ++ .../examples/2_BasicModels/random_forest.py | 77 ++ .../examples/2_BasicModels/word2vec.py | 195 +++++ .../examples/3_NeuralNetworks/autoencoder.py | 142 ++++ .../3_NeuralNetworks/bidirectional_rnn.py | 126 +++ .../3_NeuralNetworks/convolutional_network.py | 125 +++ .../convolutional_network_raw.py | 141 ++++ .../examples/3_NeuralNetworks/dcgan.py | 167 ++++ .../examples/3_NeuralNetworks/dynamic_rnn.py | 193 +++++ .../examples/3_NeuralNetworks/gan.py | 157 ++++ .../3_NeuralNetworks/multilayer_perceptron.py | 104 +++ .../3_NeuralNetworks/neural_network.py | 103 +++ .../neural_network_eager_api.py | 133 ++++ .../3_NeuralNetworks/neural_network_raw.py | 101 +++ .../3_NeuralNetworks/recurrent_network.py | 115 +++ .../variational_autoencoder.py | 143 ++++ .../examples/4_Utils/save_restore_model.py | 140 ++++ .../examples/4_Utils/tensorboard_advanced.py | 143 ++++ .../examples/4_Utils/tensorboard_basic.py | 97 +++ .../build_an_image_dataset.py | 212 +++++ .../tensorflow_dataset_api.py | 130 ++++ .../examples/6_MultiGPU/multigpu_basics.py | 94 +++ .../examples/6_MultiGPU/multigpu_cnn.py | 198 +++++ .../0_Prerequisite/ml_introduction.ipynb | 48 ++ .../0_Prerequisite/mnist_dataset_intro.ipynb | 94 +++ .../1_Introduction/basic_eager_api.ipynb | 238 ++++++ .../1_Introduction/basic_operations.ipynb | 220 ++++++ .../notebooks/1_Introduction/helloworld.ipynb | 87 +++ .../gradient_boosted_decision_tree.ipynb | 266 +++++++ .../notebooks/2_BasicModels/kmeans.ipynb | 226 ++++++ .../2_BasicModels/linear_regression.ipynb | 236 ++++++ .../linear_regression_eager_api.ipynb | 181 +++++ .../2_BasicModels/logistic_regression.ipynb | 174 +++++ .../logistic_regression_eager_api.ipynb | 258 +++++++ .../2_BasicModels/nearest_neighbor.ipynb | 332 ++++++++ .../2_BasicModels/random_forest.ipynb | 229 ++++++ .../notebooks/2_BasicModels/word2vec.ipynb | 724 ++++++++++++++++++ .../3_NeuralNetworks/autoencoder.ipynb | 310 ++++++++ .../3_NeuralNetworks/bidirectional_rnn.ipynb | 301 ++++++++ .../convolutional_network.ipynb | 423 ++++++++++ .../convolutional_network_raw.ipynb | 303 ++++++++ .../notebooks/3_NeuralNetworks/dcgan.ipynb | 333 ++++++++ .../3_NeuralNetworks/dynamic_rnn.ipynb | 352 +++++++++ .../notebooks/3_NeuralNetworks/gan.ipynb | 323 ++++++++ .../3_NeuralNetworks/neural_network.ipynb | 390 ++++++++++ .../neural_network_eager_api.ipynb | 287 +++++++ .../3_NeuralNetworks/neural_network_raw.ipynb | 224 ++++++ .../3_NeuralNetworks/recurrent_network.ipynb | 292 +++++++ .../variational_autoencoder.ipynb | 316 ++++++++ .../4_Utils/save_restore_model.ipynb | 252 ++++++ .../4_Utils/tensorboard_advanced.ipynb | 307 ++++++++ .../notebooks/4_Utils/tensorboard_basic.ipynb | 217 ++++++ .../build_an_image_dataset.ipynb | 291 +++++++ .../image_transformation.ipynb | 418 ++++++++++ .../5_DataManagement/load_data.ipynb | 577 ++++++++++++++ .../tensorflow_dataset_api.ipynb | 222 ++++++ .../5_DataManagement/tfrecords.ipynb | 261 +++++++ .../6_MultiGPU/multigpu_basics.ipynb | 179 +++++ .../notebooks/6_MultiGPU/multigpu_cnn.ipynb | 328 ++++++++ 72 files changed, 14174 insertions(+), 48 deletions(-) create mode 100644 examples/README.md create mode 100644 notebooks/README.md create mode 100644 tensorflow_v1/README.md create mode 100644 tensorflow_v1/examples/1_Introduction/basic_eager_api.py create mode 100644 tensorflow_v1/examples/1_Introduction/basic_operations.py create mode 100644 tensorflow_v1/examples/1_Introduction/helloworld.py create mode 100644 tensorflow_v1/examples/2_BasicModels/gradient_boosted_decision_tree.py create mode 100644 tensorflow_v1/examples/2_BasicModels/kmeans.py create mode 100644 tensorflow_v1/examples/2_BasicModels/linear_regression.py create mode 100644 tensorflow_v1/examples/2_BasicModels/linear_regression_eager_api.py create mode 100644 tensorflow_v1/examples/2_BasicModels/logistic_regression.py create mode 100644 tensorflow_v1/examples/2_BasicModels/logistic_regression_eager_api.py create mode 100644 tensorflow_v1/examples/2_BasicModels/nearest_neighbor.py create mode 100644 tensorflow_v1/examples/2_BasicModels/random_forest.py create mode 100644 tensorflow_v1/examples/2_BasicModels/word2vec.py create mode 100644 tensorflow_v1/examples/3_NeuralNetworks/autoencoder.py create mode 100644 tensorflow_v1/examples/3_NeuralNetworks/bidirectional_rnn.py create mode 100644 tensorflow_v1/examples/3_NeuralNetworks/convolutional_network.py create mode 100644 tensorflow_v1/examples/3_NeuralNetworks/convolutional_network_raw.py create mode 100644 tensorflow_v1/examples/3_NeuralNetworks/dcgan.py create mode 100644 tensorflow_v1/examples/3_NeuralNetworks/dynamic_rnn.py create mode 100644 tensorflow_v1/examples/3_NeuralNetworks/gan.py create mode 100644 tensorflow_v1/examples/3_NeuralNetworks/multilayer_perceptron.py create mode 100644 tensorflow_v1/examples/3_NeuralNetworks/neural_network.py create mode 100644 tensorflow_v1/examples/3_NeuralNetworks/neural_network_eager_api.py create mode 100644 tensorflow_v1/examples/3_NeuralNetworks/neural_network_raw.py create mode 100644 tensorflow_v1/examples/3_NeuralNetworks/recurrent_network.py create mode 100644 tensorflow_v1/examples/3_NeuralNetworks/variational_autoencoder.py create mode 100644 tensorflow_v1/examples/4_Utils/save_restore_model.py create mode 100644 tensorflow_v1/examples/4_Utils/tensorboard_advanced.py create mode 100644 tensorflow_v1/examples/4_Utils/tensorboard_basic.py create mode 100644 tensorflow_v1/examples/5_DataManagement/build_an_image_dataset.py create mode 100644 tensorflow_v1/examples/5_DataManagement/tensorflow_dataset_api.py create mode 100644 tensorflow_v1/examples/6_MultiGPU/multigpu_basics.py create mode 100644 tensorflow_v1/examples/6_MultiGPU/multigpu_cnn.py create mode 100644 tensorflow_v1/notebooks/0_Prerequisite/ml_introduction.ipynb create mode 100644 tensorflow_v1/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb create mode 100644 tensorflow_v1/notebooks/1_Introduction/basic_eager_api.ipynb create mode 100644 tensorflow_v1/notebooks/1_Introduction/basic_operations.ipynb create mode 100644 tensorflow_v1/notebooks/1_Introduction/helloworld.ipynb create mode 100644 tensorflow_v1/notebooks/2_BasicModels/gradient_boosted_decision_tree.ipynb create mode 100644 tensorflow_v1/notebooks/2_BasicModels/kmeans.ipynb create mode 100644 tensorflow_v1/notebooks/2_BasicModels/linear_regression.ipynb create mode 100644 tensorflow_v1/notebooks/2_BasicModels/linear_regression_eager_api.ipynb create mode 100644 tensorflow_v1/notebooks/2_BasicModels/logistic_regression.ipynb create mode 100644 tensorflow_v1/notebooks/2_BasicModels/logistic_regression_eager_api.ipynb create mode 100644 tensorflow_v1/notebooks/2_BasicModels/nearest_neighbor.ipynb create mode 100644 tensorflow_v1/notebooks/2_BasicModels/random_forest.ipynb create mode 100644 tensorflow_v1/notebooks/2_BasicModels/word2vec.ipynb create mode 100644 tensorflow_v1/notebooks/3_NeuralNetworks/autoencoder.ipynb create mode 100644 tensorflow_v1/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb create mode 100644 tensorflow_v1/notebooks/3_NeuralNetworks/convolutional_network.ipynb create mode 100644 tensorflow_v1/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb create mode 100644 tensorflow_v1/notebooks/3_NeuralNetworks/dcgan.ipynb create mode 100644 tensorflow_v1/notebooks/3_NeuralNetworks/dynamic_rnn.ipynb create mode 100644 tensorflow_v1/notebooks/3_NeuralNetworks/gan.ipynb create mode 100644 tensorflow_v1/notebooks/3_NeuralNetworks/neural_network.ipynb create mode 100644 tensorflow_v1/notebooks/3_NeuralNetworks/neural_network_eager_api.ipynb create mode 100644 tensorflow_v1/notebooks/3_NeuralNetworks/neural_network_raw.ipynb create mode 100644 tensorflow_v1/notebooks/3_NeuralNetworks/recurrent_network.ipynb create mode 100644 tensorflow_v1/notebooks/3_NeuralNetworks/variational_autoencoder.ipynb create mode 100644 tensorflow_v1/notebooks/4_Utils/save_restore_model.ipynb create mode 100644 tensorflow_v1/notebooks/4_Utils/tensorboard_advanced.ipynb create mode 100644 tensorflow_v1/notebooks/4_Utils/tensorboard_basic.ipynb create mode 100644 tensorflow_v1/notebooks/5_DataManagement/build_an_image_dataset.ipynb create mode 100644 tensorflow_v1/notebooks/5_DataManagement/image_transformation.ipynb create mode 100644 tensorflow_v1/notebooks/5_DataManagement/load_data.ipynb create mode 100644 tensorflow_v1/notebooks/5_DataManagement/tensorflow_dataset_api.ipynb create mode 100644 tensorflow_v1/notebooks/5_DataManagement/tfrecords.ipynb create mode 100644 tensorflow_v1/notebooks/6_MultiGPU/multigpu_basics.ipynb create mode 100644 tensorflow_v1/notebooks/6_MultiGPU/multigpu_cnn.ipynb diff --git a/README.md b/README.md index 50cc4fa4..00610dcf 100644 --- a/README.md +++ b/README.md @@ -4,69 +4,51 @@ This tutorial was designed for easily diving into TensorFlow, through examples. It is suitable for beginners who want to find clear and concise examples about TensorFlow. Besides the traditional 'raw' TensorFlow implementations, you can also find the latest TensorFlow API practices (such as `layers`, `estimator`, `dataset`, ...). -**Update (08/17/2019):** Added new [TensorFlow 2.0 examples](tensorflow_v2)! (more coming soon). - -*If you are using older TensorFlow version (0.11 and under), please take a [look here](https://github.com/aymericdamien/TensorFlow-Examples/tree/0.11).* +**Update (05/16/2020):** Moving all default examples to TF2. For TF v1 examples: [check here](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1). ## Tutorial index #### 0 - Prerequisite -- [Introduction to Machine Learning](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/0_Prerequisite/ml_introduction.ipynb). -- [Introduction to MNIST Dataset](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb). +- [Introduction to Machine Learning](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/0_Prerequisite/ml_introduction.ipynb). +- [Introduction to MNIST Dataset](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb). #### 1 - Introduction -- **Hello World** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1_Introduction/helloworld.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1_Introduction/helloworld.py)). Very simple example to learn how to print "hello world" using TensorFlow. -- **Basic Operations** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1_Introduction/basic_operations.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1_Introduction/basic_operations.py)). A simple example that cover TensorFlow basic operations. -- **TensorFlow Eager API basics** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1_Introduction/basic_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1_Introduction/basic_eager_api.py)). Get started with TensorFlow's Eager API. +- **Hello World** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/1_Introduction/helloworld.ipynb)). Very simple example to learn how to print "hello world" using TensorFlow 2.0. +- **Basic Operations** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/1_Introduction/basic_operations.ipynb)). A simple example that cover TensorFlow 2.0 basic operations. #### 2 - Basic Models -- **Linear Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/linear_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/linear_regression.py)). Implement a Linear Regression with TensorFlow. -- **Linear Regression (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/linear_regression_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/linear_regression_eager_api.py)). Implement a Linear Regression using TensorFlow's Eager API. -- **Logistic Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/logistic_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/logistic_regression.py)). Implement a Logistic Regression with TensorFlow. -- **Logistic Regression (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/logistic_regression_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/logistic_regression_eager_api.py)). Implement a Logistic Regression using TensorFlow's Eager API. -- **Nearest Neighbor** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/nearest_neighbor.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/nearest_neighbor.py)). Implement Nearest Neighbor algorithm with TensorFlow. -- **K-Means** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/kmeans.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/kmeans.py)). Build a K-Means classifier with TensorFlow. -- **Random Forest** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/random_forest.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/random_forest.py)). Build a Random Forest classifier with TensorFlow. -- **Gradient Boosted Decision Tree (GBDT)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/gradient_boosted_decision_tree.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/gradient_boosted_decision_tree.py)). Build a Gradient Boosted Decision Tree (GBDT) with TensorFlow. -- **Word2Vec (Word Embedding)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/word2vec.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/word2vec.py)). Build a Word Embedding Model (Word2Vec) from Wikipedia data, with TensorFlow. +- **Linear Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/linear_regression.ipynb)). Implement a Linear Regression with TensorFlow 2.0. +- **Logistic Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/logistic_regression.ipynb)). Implement a Logistic Regression with TensorFlow 2.0. +- **Word2Vec (Word Embedding)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/word2vec.ipynb)). Build a Word Embedding Model (Word2Vec) from Wikipedia data, with TensorFlow 2.0. #### 3 - Neural Networks ##### Supervised -- **Simple Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/neural_network_raw.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/neural_network_raw.py)). Build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. Raw TensorFlow implementation. -- **Simple Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/neural_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/neural_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. -- **Simple Neural Network (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/neural_network_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/neural_network_eager_api.py)). Use TensorFlow Eager API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. -- **Convolutional Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/convolutional_network_raw.py)). Build a convolutional neural network to classify MNIST digits dataset. Raw TensorFlow implementation. -- **Convolutional Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/convolutional_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/convolutional_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a convolutional neural network to classify MNIST digits dataset. -- **Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/recurrent_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/recurrent_network.py)). Build a recurrent neural network (LSTM) to classify MNIST digits dataset. -- **Bi-directional Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/bidirectional_rnn.py)). Build a bi-directional recurrent neural network (LSTM) to classify MNIST digits dataset. -- **Dynamic Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/dynamic_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/dynamic_rnn.py)). Build a recurrent neural network (LSTM) that performs dynamic calculation to classify sequences of different length. +- **Simple Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb)). Use TensorFlow 2.0 'layers' and 'model' API to build a simple neural network to classify MNIST digits dataset. +- **Simple Neural Network (low-level)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network_raw.ipynb)). Raw implementation of a simple neural network to classify MNIST digits dataset. +- **Convolutional Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network.ipynb)). Use TensorFlow 2.0 'layers' and 'model' API to build a convolutional neural network to classify MNIST digits dataset. +- **Convolutional Neural Network (low-level)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb)). Raw implementation of a convolutional neural network to classify MNIST digits dataset. +- **Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/recurrent_network.ipynb)). Build a recurrent neural network (LSTM) to classify MNIST digits dataset, using TensorFlow 2.0 'layers' and 'model' API. +- **Bi-directional Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb)). Build a bi-directional recurrent neural network (LSTM) to classify MNIST digits dataset, using TensorFlow 2.0 'layers' and 'model' API. +- **Dynamic Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/dynamic_rnn.ipynb)). Build a recurrent neural network (LSTM) that performs dynamic calculation to classify sequences of variable length, using TensorFlow 2.0 'layers' and 'model' API. ##### Unsupervised -- **Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/autoencoder.py)). Build an auto-encoder to encode an image to a lower dimension and re-construct it. -- **Variational Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/variational_autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/variational_autoencoder.py)). Build a variational auto-encoder (VAE), to encode and generate images from noise. -- **GAN (Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/gan.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/gan.py)). Build a Generative Adversarial Network (GAN) to generate images from noise. -- **DCGAN (Deep Convolutional Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/dcgan.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/dcgan.py)). Build a Deep Convolutional Generative Adversarial Network (DCGAN) to generate images from noise. +- **Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/autoencoder.ipynb)). Build an auto-encoder to encode an image to a lower dimension and re-construct it. +- **DCGAN (Deep Convolutional Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/dcgan.ipynb)). Build a Deep Convolutional Generative Adversarial Network (DCGAN) to generate images from noise. #### 4 - Utilities -- **Save and Restore a model** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/4_Utils/save_restore_model.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/save_restore_model.py)). Save and Restore a model with TensorFlow. -- **Tensorboard - Graph and loss visualization** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/4_Utils/tensorboard_basic.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/tensorboard_basic.py)). Use Tensorboard to visualize the computation Graph and plot the loss. -- **Tensorboard - Advanced visualization** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/4_Utils/tensorboard_advanced.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/tensorboard_advanced.py)). Going deeper into Tensorboard; visualize the variables, gradients, and more... +- **Save and Restore a model** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/save_restore_model.ipynb)). Save and Restore a model with TensorFlow 2.0. +- **Build Custom Layers & Modules** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/build_custom_layers.ipynb)). Learn how to build your own layers / modules and integrate them into TensorFlow 2.0 Models. #### 5 - Data Management -- **Build an image dataset** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/5_DataManagement/build_an_image_dataset.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/5_DataManagement/build_an_image_dataset.py)). Build your own images dataset with TensorFlow data queues, from image folders or a dataset file. -- **TensorFlow Dataset API** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/5_DataManagement/tensorflow_dataset_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/5_DataManagement/tensorflow_dataset_api.py)). Introducing TensorFlow Dataset API for optimizing the input data pipeline. -- **Load and Parse data** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/5_DataManagement/load_data.ipynb)). Build efficient data pipeline (Numpy arrays, Images, CSV files, custom data, ...). -- **Build and Load TFRecords** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/5_DataManagement/tfrecords.ipynb)). Convert data into TFRecords format, and load them. -- **Image Transformation (i.e. Image Augmentation)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/5_DataManagement/image_transformation.ipynb)). Apply various image augmentation techniques, to generate distorted images for training. +- **Load and Parse data** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/5_DataManagement/load_data.ipynb)). Build efficient data pipeline with TensorFlow 2.0 (Numpy arrays, Images, CSV files, custom data, ...). +- **Build and Load TFRecords** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/5_DataManagement/tfrecords.ipynb)). Convert data into TFRecords format, and load them with TensorFlow 2.0. +- **Image Transformation (i.e. Image Augmentation)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/5_DataManagement/image_transformation.ipynb)). Apply various image augmentation techniques with TensorFlow 2.0, to generate distorted images for training. -#### 6 - Multi GPU -- **Basic Operations on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/6_MultiGPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/6_MultiGPU/multigpu_basics.py)). A simple example to introduce multi-GPU in TensorFlow. -- **Train a Neural Network on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/6_MultiGPU/multigpu_cnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/6_MultiGPU/multigpu_cnn.py)). A clear and simple TensorFlow implementation to train a convolutional neural network on multiple GPUs. -## TensorFlow 2.0 +## TensorFlow v1 -The tutorial index for TF v2 is available here: [TensorFlow 2.0 Examples](tensorflow_v2). +The tutorial index for TF v1 is available here: [TensorFlow v1.15 Examples](tensorflow_v1). Or see below for a list of the examples. ## Dataset Some examples require MNIST dataset for training and testing. Don't worry, this dataset will automatically be downloaded when running examples. @@ -93,11 +75,62 @@ pip install tensorflow_gpu For more details about TensorFlow installation, you can check [TensorFlow Installation Guide](https://www.tensorflow.org/install/) -## More Examples -The following examples are coming from [TFLearn](https://github.com/tflearn/tflearn), a library that provides a simplified interface for TensorFlow. You can have a look, there are many [examples](https://github.com/tflearn/tflearn/tree/master/examples) and [pre-built operations and layers](http://tflearn.org/doc_index/#api). -### Tutorials -- [TFLearn Quickstart](https://github.com/tflearn/tflearn/blob/master/tutorials/intro/quickstart.md). Learn the basics of TFLearn through a concrete machine learning task. Build and train a deep neural network classifier. +## TensorFlow v1 Examples - Index + +The tutorial index for TF v1 is available here: [TensorFlow v1.15 Examples](tensorflow_v1). + +#### 0 - Prerequisite +- [Introduction to Machine Learning](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/0_Prerequisite/ml_introduction.ipynb). +- [Introduction to MNIST Dataset](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/0_Prerequisite/mnist_dataset_intro.ipynb). + +#### 1 - Introduction +- **Hello World** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/1_Introduction/helloworld.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/1_Introduction/helloworld.py)). Very simple example to learn how to print "hello world" using TensorFlow. +- **Basic Operations** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/1_Introduction/basic_operations.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/1_Introduction/basic_operations.py)). A simple example that cover TensorFlow basic operations. +- **TensorFlow Eager API basics** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/1_Introduction/basic_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/1_Introduction/basic_eager_api.py)). Get started with TensorFlow's Eager API. + +#### 2 - Basic Models +- **Linear Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/linear_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/linear_regression.py)). Implement a Linear Regression with TensorFlow. +- **Linear Regression (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/linear_regression_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/linear_regression_eager_api.py)). Implement a Linear Regression using TensorFlow's Eager API. +- **Logistic Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/logistic_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/logistic_regression.py)). Implement a Logistic Regression with TensorFlow. +- **Logistic Regression (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/logistic_regression_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/logistic_regression_eager_api.py)). Implement a Logistic Regression using TensorFlow's Eager API. +- **Nearest Neighbor** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/nearest_neighbor.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/nearest_neighbor.py)). Implement Nearest Neighbor algorithm with TensorFlow. +- **K-Means** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/kmeans.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/kmeans.py)). Build a K-Means classifier with TensorFlow. +- **Random Forest** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/random_forest.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/random_forest.py)). Build a Random Forest classifier with TensorFlow. +- **Gradient Boosted Decision Tree (GBDT)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/gradient_boosted_decision_tree.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/gradient_boosted_decision_tree.py)). Build a Gradient Boosted Decision Tree (GBDT) with TensorFlow. +- **Word2Vec (Word Embedding)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/word2vec.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/word2vec.py)). Build a Word Embedding Model (Word2Vec) from Wikipedia data, with TensorFlow. + +#### 3 - Neural Networks +##### Supervised + +- **Simple Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/neural_network_raw.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/neural_network_raw.py)). Build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. Raw TensorFlow implementation. +- **Simple Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/neural_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/neural_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. +- **Simple Neural Network (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/neural_network_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/neural_network_eager_api.py)). Use TensorFlow Eager API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. +- **Convolutional Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/convolutional_network_raw.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/convolutional_network_raw.py)). Build a convolutional neural network to classify MNIST digits dataset. Raw TensorFlow implementation. +- **Convolutional Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/convolutional_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/convolutional_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a convolutional neural network to classify MNIST digits dataset. +- **Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/recurrent_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/recurrent_network.py)). Build a recurrent neural network (LSTM) to classify MNIST digits dataset. +- **Bi-directional Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/bidirectional_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/bidirectional_rnn.py)). Build a bi-directional recurrent neural network (LSTM) to classify MNIST digits dataset. +- **Dynamic Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/dynamic_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/dynamic_rnn.py)). Build a recurrent neural network (LSTM) that performs dynamic calculation to classify sequences of different length. + +##### Unsupervised +- **Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/autoencoder.py)). Build an auto-encoder to encode an image to a lower dimension and re-construct it. +- **Variational Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/variational_autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/variational_autoencoder.py)). Build a variational auto-encoder (VAE), to encode and generate images from noise. +- **GAN (Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/gan.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/gan.py)). Build a Generative Adversarial Network (GAN) to generate images from noise. +- **DCGAN (Deep Convolutional Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/dcgan.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/dcgan.py)). Build a Deep Convolutional Generative Adversarial Network (DCGAN) to generate images from noise. + +#### 4 - Utilities +- **Save and Restore a model** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/4_Utils/save_restore_model.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/4_Utils/save_restore_model.py)). Save and Restore a model with TensorFlow. +- **Tensorboard - Graph and loss visualization** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/4_Utils/tensorboard_basic.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/4_Utils/tensorboard_basic.py)). Use Tensorboard to visualize the computation Graph and plot the loss. +- **Tensorboard - Advanced visualization** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/4_Utils/tensorboard_advanced.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/4_Utils/tensorboard_advanced.py)). Going deeper into Tensorboard; visualize the variables, gradients, and more... + +#### 5 - Data Management +- **Build an image dataset** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/build_an_image_dataset.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/5_DataManagement/build_an_image_dataset.py)). Build your own images dataset with TensorFlow data queues, from image folders or a dataset file. +- **TensorFlow Dataset API** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/tensorflow_dataset_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/5_DataManagement/tensorflow_dataset_api.py)). Introducing TensorFlow Dataset API for optimizing the input data pipeline. +- **Load and Parse data** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/load_data.ipynb)). Build efficient data pipeline (Numpy arrays, Images, CSV files, custom data, ...). +- **Build and Load TFRecords** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/tfrecords.ipynb)). Convert data into TFRecords format, and load them. +- **Image Transformation (i.e. Image Augmentation)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/image_transformation.ipynb)). Apply various image augmentation techniques, to generate distorted images for training. + +#### 6 - Multi GPU +- **Basic Operations on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/6_MultiGPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/6_MultiGPU/multigpu_basics.py)). A simple example to introduce multi-GPU in TensorFlow. +- **Train a Neural Network on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/6_MultiGPU/multigpu_cnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/6_MultiGPU/multigpu_cnn.py)). A clear and simple TensorFlow implementation to train a convolutional neural network on multiple GPUs. -### Examples -- [TFLearn Examples](https://github.com/tflearn/tflearn/blob/master/examples). A large collection of examples using TFLearn. diff --git a/examples/README.md b/examples/README.md new file mode 100644 index 00000000..196ed016 --- /dev/null +++ b/examples/README.md @@ -0,0 +1,5 @@ +## Deprecated - Please Read + +Due to TensorFlow radically changing their API in v2, the examples index have been split between [v1](../tensorflow_v1) and [v2](../tensorflow_v2). + +The following examples are the original TF v1 examples, and will be deprecated entirely in favor of [tensorflow_v1](../tensorflow_v1) directory in a future release. diff --git a/notebooks/README.md b/notebooks/README.md new file mode 100644 index 00000000..196ed016 --- /dev/null +++ b/notebooks/README.md @@ -0,0 +1,5 @@ +## Deprecated - Please Read + +Due to TensorFlow radically changing their API in v2, the examples index have been split between [v1](../tensorflow_v1) and [v2](../tensorflow_v2). + +The following examples are the original TF v1 examples, and will be deprecated entirely in favor of [tensorflow_v1](../tensorflow_v1) directory in a future release. diff --git a/tensorflow_v1/README.md b/tensorflow_v1/README.md new file mode 100644 index 00000000..93a8c3a9 --- /dev/null +++ b/tensorflow_v1/README.md @@ -0,0 +1,93 @@ +# TensorFlow v1 Examples + +All the following examples are the original TF v1 examples. + +*If you are using older TensorFlow version (0.11 and under), please take a [look here](https://github.com/aymericdamien/TensorFlow-Examples/tree/0.11).* + +#### 0 - Prerequisite +- [Introduction to Machine Learning](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/0_Prerequisite/ml_introduction.ipynb). +- [Introduction to MNIST Dataset](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/0_Prerequisite/mnist_dataset_intro.ipynb). + +#### 1 - Introduction +- **Hello World** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/1_Introduction/helloworld.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/1_Introduction/helloworld.py)). Very simple example to learn how to print "hello world" using TensorFlow. +- **Basic Operations** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/1_Introduction/basic_operations.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/1_Introduction/basic_operations.py)). A simple example that cover TensorFlow basic operations. +- **TensorFlow Eager API basics** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/1_Introduction/basic_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/1_Introduction/basic_eager_api.py)). Get started with TensorFlow's Eager API. + +#### 2 - Basic Models +- **Linear Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/linear_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/linear_regression.py)). Implement a Linear Regression with TensorFlow. +- **Linear Regression (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/linear_regression_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/linear_regression_eager_api.py)). Implement a Linear Regression using TensorFlow's Eager API. +- **Logistic Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/logistic_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/logistic_regression.py)). Implement a Logistic Regression with TensorFlow. +- **Logistic Regression (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/logistic_regression_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/logistic_regression_eager_api.py)). Implement a Logistic Regression using TensorFlow's Eager API. +- **Nearest Neighbor** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/nearest_neighbor.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/nearest_neighbor.py)). Implement Nearest Neighbor algorithm with TensorFlow. +- **K-Means** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/kmeans.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/kmeans.py)). Build a K-Means classifier with TensorFlow. +- **Random Forest** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/random_forest.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/random_forest.py)). Build a Random Forest classifier with TensorFlow. +- **Gradient Boosted Decision Tree (GBDT)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/gradient_boosted_decision_tree.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/gradient_boosted_decision_tree.py)). Build a Gradient Boosted Decision Tree (GBDT) with TensorFlow. +- **Word2Vec (Word Embedding)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/word2vec.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/word2vec.py)). Build a Word Embedding Model (Word2Vec) from Wikipedia data, with TensorFlow. + +#### 3 - Neural Networks +##### Supervised + +- **Simple Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/neural_network_raw.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/neural_network_raw.py)). Build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. Raw TensorFlow implementation. +- **Simple Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/neural_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/neural_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. +- **Simple Neural Network (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/neural_network_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/neural_network_eager_api.py)). Use TensorFlow Eager API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. +- **Convolutional Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/convolutional_network_raw.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/convolutional_network_raw.py)). Build a convolutional neural network to classify MNIST digits dataset. Raw TensorFlow implementation. +- **Convolutional Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/convolutional_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/convolutional_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a convolutional neural network to classify MNIST digits dataset. +- **Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/recurrent_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/recurrent_network.py)). Build a recurrent neural network (LSTM) to classify MNIST digits dataset. +- **Bi-directional Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/bidirectional_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/bidirectional_rnn.py)). Build a bi-directional recurrent neural network (LSTM) to classify MNIST digits dataset. +- **Dynamic Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/dynamic_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/dynamic_rnn.py)). Build a recurrent neural network (LSTM) that performs dynamic calculation to classify sequences of different length. + +##### Unsupervised +- **Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/autoencoder.py)). Build an auto-encoder to encode an image to a lower dimension and re-construct it. +- **Variational Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/variational_autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/variational_autoencoder.py)). Build a variational auto-encoder (VAE), to encode and generate images from noise. +- **GAN (Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/gan.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/gan.py)). Build a Generative Adversarial Network (GAN) to generate images from noise. +- **DCGAN (Deep Convolutional Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/dcgan.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/dcgan.py)). Build a Deep Convolutional Generative Adversarial Network (DCGAN) to generate images from noise. + +#### 4 - Utilities +- **Save and Restore a model** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/4_Utils/save_restore_model.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/4_Utils/save_restore_model.py)). Save and Restore a model with TensorFlow. +- **Tensorboard - Graph and loss visualization** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/4_Utils/tensorboard_basic.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/4_Utils/tensorboard_basic.py)). Use Tensorboard to visualize the computation Graph and plot the loss. +- **Tensorboard - Advanced visualization** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/4_Utils/tensorboard_advanced.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/4_Utils/tensorboard_advanced.py)). Going deeper into Tensorboard; visualize the variables, gradients, and more... + +#### 5 - Data Management +- **Build an image dataset** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/build_an_image_dataset.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/5_DataManagement/build_an_image_dataset.py)). Build your own images dataset with TensorFlow data queues, from image folders or a dataset file. +- **TensorFlow Dataset API** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/tensorflow_dataset_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/5_DataManagement/tensorflow_dataset_api.py)). Introducing TensorFlow Dataset API for optimizing the input data pipeline. +- **Load and Parse data** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/load_data.ipynb)). Build efficient data pipeline (Numpy arrays, Images, CSV files, custom data, ...). +- **Build and Load TFRecords** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/tfrecords.ipynb)). Convert data into TFRecords format, and load them. +- **Image Transformation (i.e. Image Augmentation)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/image_transformation.ipynb)). Apply various image augmentation techniques, to generate distorted images for training. + +#### 6 - Multi GPU +- **Basic Operations on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/6_MultiGPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/6_MultiGPU/multigpu_basics.py)). A simple example to introduce multi-GPU in TensorFlow. +- **Train a Neural Network on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/6_MultiGPU/multigpu_cnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/6_MultiGPU/multigpu_cnn.py)). A clear and simple TensorFlow implementation to train a convolutional neural network on multiple GPUs. + +## Installation + +To download all the examples, simply clone this repository: +``` +git clone https://github.com/aymericdamien/TensorFlow-Examples +``` + +To run them, you also need the latest version of TensorFlow. To install it: +``` +pip install tensorflow==1.15.0 +``` + +or (with GPU support): +``` +pip install tensorflow_gpu==1.15.0 +``` + +For more details about TensorFlow installation, you can check [TensorFlow Installation Guide](https://www.tensorflow.org/install/) + +## Dataset +Some examples require MNIST dataset for training and testing. Don't worry, this dataset will automatically be downloaded when running examples. +MNIST is a database of handwritten digits, for a quick description of that dataset, you can check [this notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb). + +Official Website: [http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/). + +## More Examples +The following examples are coming from [TFLearn](https://github.com/tflearn/tflearn), a library that provides a simplified interface for TensorFlow. You can have a look, there are many [examples](https://github.com/tflearn/tflearn/tree/master/examples) and [pre-built operations and layers](http://tflearn.org/doc_index/#api). + +### Tutorials +- [TFLearn Quickstart](https://github.com/tflearn/tflearn/blob/master/tutorials/intro/quickstart.md). Learn the basics of TFLearn through a concrete machine learning task. Build and train a deep neural network classifier. + +### Examples +- [TFLearn Examples](https://github.com/tflearn/tflearn/blob/master/examples). A large collection of examples using TFLearn. diff --git a/tensorflow_v1/examples/1_Introduction/basic_eager_api.py b/tensorflow_v1/examples/1_Introduction/basic_eager_api.py new file mode 100644 index 00000000..e00719d3 --- /dev/null +++ b/tensorflow_v1/examples/1_Introduction/basic_eager_api.py @@ -0,0 +1,68 @@ +''' +Basic introduction to TensorFlow's Eager API. + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ + +What is Eager API? +" Eager execution is an imperative, define-by-run interface where operations are +executed immediately as they are called from Python. This makes it easier to +get started with TensorFlow, and can make research and development more +intuitive. A vast majority of the TensorFlow API remains the same whether eager +execution is enabled or not. As a result, the exact same code that constructs +TensorFlow graphs (e.g. using the layers API) can be executed imperatively +by using eager execution. Conversely, most models written with Eager enabled +can be converted to a graph that can be further optimized and/or extracted +for deployment in production without changing code. " - Rajat Monga + +''' +from __future__ import absolute_import, division, print_function + +import numpy as np +import tensorflow as tf +import tensorflow.contrib.eager as tfe + +# Set Eager API +print("Setting Eager mode...") +tfe.enable_eager_execution() + +# Define constant tensors +print("Define constant tensors") +a = tf.constant(2) +print("a = %i" % a) +b = tf.constant(3) +print("b = %i" % b) + +# Run the operation without the need for tf.Session +print("Running operations, without tf.Session") +c = a + b +print("a + b = %i" % c) +d = a * b +print("a * b = %i" % d) + + +# Full compatibility with Numpy +print("Mixing operations with Tensors and Numpy Arrays") + +# Define constant tensors +a = tf.constant([[2., 1.], + [1., 0.]], dtype=tf.float32) +print("Tensor:\n a = %s" % a) +b = np.array([[3., 0.], + [5., 1.]], dtype=np.float32) +print("NumpyArray:\n b = %s" % b) + +# Run the operation without the need for tf.Session +print("Running operations, without tf.Session") + +c = a + b +print("a + b = %s" % c) + +d = tf.matmul(a, b) +print("a * b = %s" % d) + +print("Iterate through Tensor 'a':") +for i in range(a.shape[0]): + for j in range(a.shape[1]): + print(a[i][j]) + diff --git a/tensorflow_v1/examples/1_Introduction/basic_operations.py b/tensorflow_v1/examples/1_Introduction/basic_operations.py new file mode 100644 index 00000000..e1775069 --- /dev/null +++ b/tensorflow_v1/examples/1_Introduction/basic_operations.py @@ -0,0 +1,75 @@ +''' +Basic Operations example using TensorFlow library. + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +''' + +from __future__ import print_function + +import tensorflow as tf + +# Basic constant operations +# The value returned by the constructor represents the output +# of the Constant op. +a = tf.constant(2) +b = tf.constant(3) + +# Launch the default graph. +with tf.Session() as sess: + print("a=2, b=3") + print("Addition with constants: %i" % sess.run(a+b)) + print("Multiplication with constants: %i" % sess.run(a*b)) + +# Basic Operations with variable as graph input +# The value returned by the constructor represents the output +# of the Variable op. (define as input when running session) +# tf Graph input +a = tf.placeholder(tf.int16) +b = tf.placeholder(tf.int16) + +# Define some operations +add = tf.add(a, b) +mul = tf.multiply(a, b) + +# Launch the default graph. +with tf.Session() as sess: + # Run every operation with variable input + print("Addition with variables: %i" % sess.run(add, feed_dict={a: 2, b: 3})) + print("Multiplication with variables: %i" % sess.run(mul, feed_dict={a: 2, b: 3})) + + +# ---------------- +# More in details: +# Matrix Multiplication from TensorFlow official tutorial + +# Create a Constant op that produces a 1x2 matrix. The op is +# added as a node to the default graph. +# +# The value returned by the constructor represents the output +# of the Constant op. +matrix1 = tf.constant([[3., 3.]]) + +# Create another Constant that produces a 2x1 matrix. +matrix2 = tf.constant([[2.],[2.]]) + +# Create a Matmul op that takes 'matrix1' and 'matrix2' as inputs. +# The returned value, 'product', represents the result of the matrix +# multiplication. +product = tf.matmul(matrix1, matrix2) + +# To run the matmul op we call the session 'run()' method, passing 'product' +# which represents the output of the matmul op. This indicates to the call +# that we want to get the output of the matmul op back. +# +# All inputs needed by the op are run automatically by the session. They +# typically are run in parallel. +# +# The call 'run(product)' thus causes the execution of threes ops in the +# graph: the two constants and matmul. +# +# The output of the op is returned in 'result' as a numpy `ndarray` object. +with tf.Session() as sess: + result = sess.run(product) + print(result) + # ==> [[ 12.]] diff --git a/tensorflow_v1/examples/1_Introduction/helloworld.py b/tensorflow_v1/examples/1_Introduction/helloworld.py new file mode 100644 index 00000000..1c40f315 --- /dev/null +++ b/tensorflow_v1/examples/1_Introduction/helloworld.py @@ -0,0 +1,25 @@ +''' +HelloWorld example using TensorFlow library. + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +''' + +from __future__ import print_function + +import tensorflow as tf + +# Simple hello world using TensorFlow + +# Create a Constant op +# The op is added as a node to the default graph. +# +# The value returned by the constructor represents the output +# of the Constant op. +hello = tf.constant('Hello, TensorFlow!') + +# Start tf session +sess = tf.Session() + +# Run the op +print(sess.run(hello)) diff --git a/tensorflow_v1/examples/2_BasicModels/gradient_boosted_decision_tree.py b/tensorflow_v1/examples/2_BasicModels/gradient_boosted_decision_tree.py new file mode 100644 index 00000000..00501a2b --- /dev/null +++ b/tensorflow_v1/examples/2_BasicModels/gradient_boosted_decision_tree.py @@ -0,0 +1,85 @@ +""" Gradient Boosted Decision Tree (GBDT). + +Implement a Gradient Boosted Decision tree with TensorFlow to classify +handwritten digit images. This example is using the MNIST database of +handwritten digits as training samples (http://yann.lecun.com/exdb/mnist/). + +Links: + [MNIST Dataset](http://yann.lecun.com/exdb/mnist/). + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" + +from __future__ import print_function + +import tensorflow as tf +from tensorflow.contrib.boosted_trees.estimator_batch.estimator import GradientBoostedDecisionTreeClassifier +from tensorflow.contrib.boosted_trees.proto import learner_pb2 as gbdt_learner + +# Ignore all GPUs (current TF GBDT does not support GPU). +import os +os.environ["CUDA_VISIBLE_DEVICES"] = "" + +# Import MNIST data +# Set verbosity to display errors only (Remove this line for showing warnings) +tf.logging.set_verbosity(tf.logging.ERROR) +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=False, + source_url='http://yann.lecun.com/exdb/mnist/') + +# Parameters +batch_size = 4096 # The number of samples per batch +num_classes = 10 # The 10 digits +num_features = 784 # Each image is 28x28 pixels +max_steps = 10000 + +# GBDT Parameters +learning_rate = 0.1 +l1_regul = 0. +l2_regul = 1. +examples_per_layer = 1000 +num_trees = 10 +max_depth = 16 + +# Fill GBDT parameters into the config proto +learner_config = gbdt_learner.LearnerConfig() +learner_config.learning_rate_tuner.fixed.learning_rate = learning_rate +learner_config.regularization.l1 = l1_regul +learner_config.regularization.l2 = l2_regul / examples_per_layer +learner_config.constraints.max_tree_depth = max_depth +growing_mode = gbdt_learner.LearnerConfig.LAYER_BY_LAYER +learner_config.growing_mode = growing_mode +run_config = tf.contrib.learn.RunConfig(save_checkpoints_secs=300) +learner_config.multi_class_strategy = ( + gbdt_learner.LearnerConfig.DIAGONAL_HESSIAN)\ + +# Create a TensorFlor GBDT Estimator +gbdt_model = GradientBoostedDecisionTreeClassifier( + model_dir=None, # No save directory specified + learner_config=learner_config, + n_classes=num_classes, + examples_per_layer=examples_per_layer, + num_trees=num_trees, + center_bias=False, + config=run_config) + +# Display TF info logs +tf.logging.set_verbosity(tf.logging.INFO) + +# Define the input function for training +input_fn = tf.estimator.inputs.numpy_input_fn( + x={'images': mnist.train.images}, y=mnist.train.labels, + batch_size=batch_size, num_epochs=None, shuffle=True) +# Train the Model +gbdt_model.fit(input_fn=input_fn, max_steps=max_steps) + +# Evaluate the Model +# Define the input function for evaluating +input_fn = tf.estimator.inputs.numpy_input_fn( + x={'images': mnist.test.images}, y=mnist.test.labels, + batch_size=batch_size, shuffle=False) +# Use the Estimator 'evaluate' method +e = gbdt_model.evaluate(input_fn=input_fn) + +print("Testing Accuracy:", e['accuracy']) diff --git a/tensorflow_v1/examples/2_BasicModels/kmeans.py b/tensorflow_v1/examples/2_BasicModels/kmeans.py new file mode 100644 index 00000000..ed4bf91b --- /dev/null +++ b/tensorflow_v1/examples/2_BasicModels/kmeans.py @@ -0,0 +1,93 @@ +""" K-Means. + +Implement K-Means algorithm with TensorFlow, and apply it to classify +handwritten digit images. This example is using the MNIST database of +handwritten digits as training samples (http://yann.lecun.com/exdb/mnist/). + +Note: This example requires TensorFlow v1.1.0 or over. + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" + +from __future__ import print_function + +import numpy as np +import tensorflow as tf +from tensorflow.contrib.factorization import KMeans + +# Ignore all GPUs, tf k-means does not benefit from it. +import os +os.environ["CUDA_VISIBLE_DEVICES"] = "" + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) +full_data_x = mnist.train.images + +# Parameters +num_steps = 50 # Total steps to train +batch_size = 1024 # The number of samples per batch +k = 25 # The number of clusters +num_classes = 10 # The 10 digits +num_features = 784 # Each image is 28x28 pixels + +# Input images +X = tf.placeholder(tf.float32, shape=[None, num_features]) +# Labels (for assigning a label to a centroid and testing) +Y = tf.placeholder(tf.float32, shape=[None, num_classes]) + +# K-Means Parameters +kmeans = KMeans(inputs=X, num_clusters=k, distance_metric='cosine', + use_mini_batch=True) + +# Build KMeans graph +training_graph = kmeans.training_graph() + +if len(training_graph) > 6: # Tensorflow 1.4+ + (all_scores, cluster_idx, scores, cluster_centers_initialized, + cluster_centers_var, init_op, train_op) = training_graph +else: + (all_scores, cluster_idx, scores, cluster_centers_initialized, + init_op, train_op) = training_graph + +cluster_idx = cluster_idx[0] # fix for cluster_idx being a tuple +avg_distance = tf.reduce_mean(scores) + +# Initialize the variables (i.e. assign their default value) +init_vars = tf.global_variables_initializer() + +# Start TensorFlow session +sess = tf.Session() + +# Run the initializer +sess.run(init_vars, feed_dict={X: full_data_x}) +sess.run(init_op, feed_dict={X: full_data_x}) + +# Training +for i in range(1, num_steps + 1): + _, d, idx = sess.run([train_op, avg_distance, cluster_idx], + feed_dict={X: full_data_x}) + if i % 10 == 0 or i == 1: + print("Step %i, Avg Distance: %f" % (i, d)) + +# Assign a label to each centroid +# Count total number of labels per centroid, using the label of each training +# sample to their closest centroid (given by 'idx') +counts = np.zeros(shape=(k, num_classes)) +for i in range(len(idx)): + counts[idx[i]] += mnist.train.labels[i] +# Assign the most frequent label to the centroid +labels_map = [np.argmax(c) for c in counts] +labels_map = tf.convert_to_tensor(labels_map) + +# Evaluation ops +# Lookup: centroid_id -> label +cluster_label = tf.nn.embedding_lookup(labels_map, cluster_idx) +# Compute accuracy +correct_prediction = tf.equal(cluster_label, tf.cast(tf.argmax(Y, 1), tf.int32)) +accuracy_op = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + +# Test Model +test_x, test_y = mnist.test.images, mnist.test.labels +print("Test Accuracy:", sess.run(accuracy_op, feed_dict={X: test_x, Y: test_y})) diff --git a/tensorflow_v1/examples/2_BasicModels/linear_regression.py b/tensorflow_v1/examples/2_BasicModels/linear_regression.py new file mode 100644 index 00000000..cfb1c2fa --- /dev/null +++ b/tensorflow_v1/examples/2_BasicModels/linear_regression.py @@ -0,0 +1,89 @@ +''' +A linear regression learning algorithm example using TensorFlow library. + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +''' + +from __future__ import print_function + +import tensorflow as tf +import numpy +import matplotlib.pyplot as plt +rng = numpy.random + +# Parameters +learning_rate = 0.01 +training_epochs = 1000 +display_step = 50 + +# Training Data +train_X = numpy.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167, + 7.042,10.791,5.313,7.997,5.654,9.27,3.1]) +train_Y = numpy.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221, + 2.827,3.465,1.65,2.904,2.42,2.94,1.3]) +n_samples = train_X.shape[0] + +# tf Graph Input +X = tf.placeholder("float") +Y = tf.placeholder("float") + +# Set model weights +W = tf.Variable(rng.randn(), name="weight") +b = tf.Variable(rng.randn(), name="bias") + +# Construct a linear model +pred = tf.add(tf.multiply(X, W), b) + +# Mean squared error +cost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples) +# Gradient descent +# Note, minimize() knows to modify W and b because Variable objects are trainable=True by default +optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) + +# Initialize the variables (i.e. assign their default value) +init = tf.global_variables_initializer() + +# Start training +with tf.Session() as sess: + + # Run the initializer + sess.run(init) + + # Fit all training data + for epoch in range(training_epochs): + for (x, y) in zip(train_X, train_Y): + sess.run(optimizer, feed_dict={X: x, Y: y}) + + # Display logs per epoch step + if (epoch+1) % display_step == 0: + c = sess.run(cost, feed_dict={X: train_X, Y:train_Y}) + print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c), \ + "W=", sess.run(W), "b=", sess.run(b)) + + print("Optimization Finished!") + training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y}) + print("Training cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n') + + # Graphic display + plt.plot(train_X, train_Y, 'ro', label='Original data') + plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line') + plt.legend() + plt.show() + + # Testing example, as requested (Issue #2) + test_X = numpy.asarray([6.83, 4.668, 8.9, 7.91, 5.7, 8.7, 3.1, 2.1]) + test_Y = numpy.asarray([1.84, 2.273, 3.2, 2.831, 2.92, 3.24, 1.35, 1.03]) + + print("Testing... (Mean square loss Comparison)") + testing_cost = sess.run( + tf.reduce_sum(tf.pow(pred - Y, 2)) / (2 * test_X.shape[0]), + feed_dict={X: test_X, Y: test_Y}) # same function as cost above + print("Testing cost=", testing_cost) + print("Absolute mean square loss difference:", abs( + training_cost - testing_cost)) + + plt.plot(test_X, test_Y, 'bo', label='Testing data') + plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line') + plt.legend() + plt.show() diff --git a/tensorflow_v1/examples/2_BasicModels/linear_regression_eager_api.py b/tensorflow_v1/examples/2_BasicModels/linear_regression_eager_api.py new file mode 100644 index 00000000..a9b2b2f7 --- /dev/null +++ b/tensorflow_v1/examples/2_BasicModels/linear_regression_eager_api.py @@ -0,0 +1,69 @@ +''' Linear Regression with Eager API. + +A linear regression learning algorithm example using TensorFlow's Eager API. + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +''' +from __future__ import absolute_import, division, print_function + +import matplotlib.pyplot as plt +import numpy as np +import tensorflow as tf + +# Set Eager API +tf.enable_eager_execution() +tfe = tf.contrib.eager + +# Training Data +train_X = [3.3, 4.4, 5.5, 6.71, 6.93, 4.168, 9.779, 6.182, 7.59, 2.167, + 7.042, 10.791, 5.313, 7.997, 5.654, 9.27, 3.1] +train_Y = [1.7, 2.76, 2.09, 3.19, 1.694, 1.573, 3.366, 2.596, 2.53, 1.221, + 2.827, 3.465, 1.65, 2.904, 2.42, 2.94, 1.3] +n_samples = len(train_X) + +# Parameters +learning_rate = 0.01 +display_step = 100 +num_steps = 1000 + +# Weight and Bias +W = tfe.Variable(np.random.randn()) +b = tfe.Variable(np.random.randn()) + + +# Linear regression (Wx + b) +def linear_regression(inputs): + return inputs * W + b + + +# Mean square error +def mean_square_fn(model_fn, inputs, labels): + return tf.reduce_sum(tf.pow(model_fn(inputs) - labels, 2)) / (2 * n_samples) + + +# SGD Optimizer +optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate) +# Compute gradients +grad = tfe.implicit_gradients(mean_square_fn) + +# Initial cost, before optimizing +print("Initial cost= {:.9f}".format( + mean_square_fn(linear_regression, train_X, train_Y)), + "W=", W.numpy(), "b=", b.numpy()) + +# Training +for step in range(num_steps): + + optimizer.apply_gradients(grad(linear_regression, train_X, train_Y)) + + if (step + 1) % display_step == 0 or step == 0: + print("Epoch:", '%04d' % (step + 1), "cost=", + "{:.9f}".format(mean_square_fn(linear_regression, train_X, train_Y)), + "W=", W.numpy(), "b=", b.numpy()) + +# Graphic display +plt.plot(train_X, train_Y, 'ro', label='Original data') +plt.plot(train_X, np.array(W * train_X + b), label='Fitted line') +plt.legend() +plt.show() diff --git a/tensorflow_v1/examples/2_BasicModels/logistic_regression.py b/tensorflow_v1/examples/2_BasicModels/logistic_regression.py new file mode 100644 index 00000000..f38ea81c --- /dev/null +++ b/tensorflow_v1/examples/2_BasicModels/logistic_regression.py @@ -0,0 +1,71 @@ +''' +A logistic regression learning algorithm example using TensorFlow library. +This example is using the MNIST database of handwritten digits +(http://yann.lecun.com/exdb/mnist/) + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +''' + +from __future__ import print_function + +import tensorflow as tf + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) + +# Parameters +learning_rate = 0.01 +training_epochs = 25 +batch_size = 100 +display_step = 1 + +# tf Graph Input +x = tf.placeholder(tf.float32, [None, 784]) # mnist data image of shape 28*28=784 +y = tf.placeholder(tf.float32, [None, 10]) # 0-9 digits recognition => 10 classes + +# Set model weights +W = tf.Variable(tf.zeros([784, 10])) +b = tf.Variable(tf.zeros([10])) + +# Construct model +pred = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax + +# Minimize error using cross entropy +cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1)) +# Gradient Descent +optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) + +# Initialize the variables (i.e. assign their default value) +init = tf.global_variables_initializer() + +# Start training +with tf.Session() as sess: + + # Run the initializer + sess.run(init) + + # Training cycle + for epoch in range(training_epochs): + avg_cost = 0. + total_batch = int(mnist.train.num_examples/batch_size) + # Loop over all batches + for i in range(total_batch): + batch_xs, batch_ys = mnist.train.next_batch(batch_size) + # Run optimization op (backprop) and cost op (to get loss value) + _, c = sess.run([optimizer, cost], feed_dict={x: batch_xs, + y: batch_ys}) + # Compute average loss + avg_cost += c / total_batch + # Display logs per epoch step + if (epoch+1) % display_step == 0: + print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost)) + + print("Optimization Finished!") + + # Test model + correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) + # Calculate accuracy + accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})) diff --git a/tensorflow_v1/examples/2_BasicModels/logistic_regression_eager_api.py b/tensorflow_v1/examples/2_BasicModels/logistic_regression_eager_api.py new file mode 100644 index 00000000..c65205e7 --- /dev/null +++ b/tensorflow_v1/examples/2_BasicModels/logistic_regression_eager_api.py @@ -0,0 +1,105 @@ +''' Logistic Regression with Eager API. + +A logistic regression learning algorithm example using TensorFlow's Eager API. +This example is using the MNIST database of handwritten digits +(http://yann.lecun.com/exdb/mnist/) + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +''' +from __future__ import absolute_import, division, print_function + +import tensorflow as tf + +# Set Eager API +tf.enable_eager_execution() +tfe = tf.contrib.eager + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=False) + +# Parameters +learning_rate = 0.1 +batch_size = 128 +num_steps = 1000 +display_step = 100 + +dataset = tf.data.Dataset.from_tensor_slices( + (mnist.train.images, mnist.train.labels)) +dataset = dataset.repeat().batch(batch_size).prefetch(batch_size) +dataset_iter = tfe.Iterator(dataset) + +# Variables +W = tfe.Variable(tf.zeros([784, 10]), name='weights') +b = tfe.Variable(tf.zeros([10]), name='bias') + + +# Logistic regression (Wx + b) +def logistic_regression(inputs): + return tf.matmul(inputs, W) + b + + +# Cross-Entropy loss function +def loss_fn(inference_fn, inputs, labels): + # Using sparse_softmax cross entropy + return tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits( + logits=inference_fn(inputs), labels=labels)) + + +# Calculate accuracy +def accuracy_fn(inference_fn, inputs, labels): + prediction = tf.nn.softmax(inference_fn(inputs)) + correct_pred = tf.equal(tf.argmax(prediction, 1), labels) + return tf.reduce_mean(tf.cast(correct_pred, tf.float32)) + + +# SGD Optimizer +optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate) +# Compute gradients +grad = tfe.implicit_gradients(loss_fn) + +# Training +average_loss = 0. +average_acc = 0. +for step in range(num_steps): + + # Iterate through the dataset + d = dataset_iter.next() + + # Images + x_batch = d[0] + # Labels + y_batch = tf.cast(d[1], dtype=tf.int64) + + # Compute the batch loss + batch_loss = loss_fn(logistic_regression, x_batch, y_batch) + average_loss += batch_loss + # Compute the batch accuracy + batch_accuracy = accuracy_fn(logistic_regression, x_batch, y_batch) + average_acc += batch_accuracy + + if step == 0: + # Display the initial cost, before optimizing + print("Initial loss= {:.9f}".format(average_loss)) + + # Update the variables following gradients info + optimizer.apply_gradients(grad(logistic_regression, x_batch, y_batch)) + + # Display info + if (step + 1) % display_step == 0 or step == 0: + if step > 0: + average_loss /= display_step + average_acc /= display_step + print("Step:", '%04d' % (step + 1), " loss=", + "{:.9f}".format(average_loss), " accuracy=", + "{:.4f}".format(average_acc)) + average_loss = 0. + average_acc = 0. + +# Evaluate model on the test image set +testX = mnist.test.images +testY = mnist.test.labels + +test_acc = accuracy_fn(logistic_regression, testX, testY) +print("Testset Accuracy: {:.4f}".format(test_acc)) diff --git a/tensorflow_v1/examples/2_BasicModels/nearest_neighbor.py b/tensorflow_v1/examples/2_BasicModels/nearest_neighbor.py new file mode 100644 index 00000000..ea40d68e --- /dev/null +++ b/tensorflow_v1/examples/2_BasicModels/nearest_neighbor.py @@ -0,0 +1,55 @@ +''' +A nearest neighbor learning algorithm example using TensorFlow library. +This example is using the MNIST database of handwritten digits +(http://yann.lecun.com/exdb/mnist/) + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +''' + +from __future__ import print_function + +import numpy as np +import tensorflow as tf + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) + +# In this example, we limit mnist data +Xtr, Ytr = mnist.train.next_batch(5000) #5000 for training (nn candidates) +Xte, Yte = mnist.test.next_batch(200) #200 for testing + +# tf Graph Input +xtr = tf.placeholder("float", [None, 784]) +xte = tf.placeholder("float", [784]) + +# Nearest Neighbor calculation using L1 Distance +# Calculate L1 Distance +distance = tf.reduce_sum(tf.abs(tf.add(xtr, tf.negative(xte))), reduction_indices=1) +# Prediction: Get min distance index (Nearest neighbor) +pred = tf.arg_min(distance, 0) + +accuracy = 0. + +# Initialize the variables (i.e. assign their default value) +init = tf.global_variables_initializer() + +# Start training +with tf.Session() as sess: + + # Run the initializer + sess.run(init) + + # loop over test data + for i in range(len(Xte)): + # Get nearest neighbor + nn_index = sess.run(pred, feed_dict={xtr: Xtr, xte: Xte[i, :]}) + # Get nearest neighbor class label and compare it to its true label + print("Test", i, "Prediction:", np.argmax(Ytr[nn_index]), \ + "True Class:", np.argmax(Yte[i])) + # Calculate accuracy + if np.argmax(Ytr[nn_index]) == np.argmax(Yte[i]): + accuracy += 1./len(Xte) + print("Done!") + print("Accuracy:", accuracy) diff --git a/tensorflow_v1/examples/2_BasicModels/random_forest.py b/tensorflow_v1/examples/2_BasicModels/random_forest.py new file mode 100644 index 00000000..daff4721 --- /dev/null +++ b/tensorflow_v1/examples/2_BasicModels/random_forest.py @@ -0,0 +1,77 @@ +""" Random Forest. + +Implement Random Forest algorithm with TensorFlow, and apply it to classify +handwritten digit images. This example is using the MNIST database of +handwritten digits as training samples (http://yann.lecun.com/exdb/mnist/). + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" + +from __future__ import print_function + +import tensorflow as tf +from tensorflow.contrib.tensor_forest.python import tensor_forest +from tensorflow.python.ops import resources + +# Ignore all GPUs, tf random forest does not benefit from it. +import os +os.environ["CUDA_VISIBLE_DEVICES"] = "" + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=False) + +# Parameters +num_steps = 500 # Total steps to train +batch_size = 1024 # The number of samples per batch +num_classes = 10 # The 10 digits +num_features = 784 # Each image is 28x28 pixels +num_trees = 10 +max_nodes = 1000 + +# Input and Target data +X = tf.placeholder(tf.float32, shape=[None, num_features]) +# For random forest, labels must be integers (the class id) +Y = tf.placeholder(tf.int32, shape=[None]) + +# Random Forest Parameters +hparams = tensor_forest.ForestHParams(num_classes=num_classes, + num_features=num_features, + num_trees=num_trees, + max_nodes=max_nodes).fill() + +# Build the Random Forest +forest_graph = tensor_forest.RandomForestGraphs(hparams) +# Get training graph and loss +train_op = forest_graph.training_graph(X, Y) +loss_op = forest_graph.training_loss(X, Y) + +# Measure the accuracy +infer_op, _, _ = forest_graph.inference_graph(X) +correct_prediction = tf.equal(tf.argmax(infer_op, 1), tf.cast(Y, tf.int64)) +accuracy_op = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + +# Initialize the variables (i.e. assign their default value) and forest resources +init_vars = tf.group(tf.global_variables_initializer(), + resources.initialize_resources(resources.shared_resources())) + +# Start TensorFlow session +sess = tf.Session() + +# Run the initializer +sess.run(init_vars) + +# Training +for i in range(1, num_steps + 1): + # Prepare Data + # Get the next batch of MNIST data (only images are needed, not labels) + batch_x, batch_y = mnist.train.next_batch(batch_size) + _, l = sess.run([train_op, loss_op], feed_dict={X: batch_x, Y: batch_y}) + if i % 50 == 0 or i == 1: + acc = sess.run(accuracy_op, feed_dict={X: batch_x, Y: batch_y}) + print('Step %i, Loss: %f, Acc: %f' % (i, l, acc)) + +# Test Model +test_x, test_y = mnist.test.images, mnist.test.labels +print("Test Accuracy:", sess.run(accuracy_op, feed_dict={X: test_x, Y: test_y})) diff --git a/tensorflow_v1/examples/2_BasicModels/word2vec.py b/tensorflow_v1/examples/2_BasicModels/word2vec.py new file mode 100644 index 00000000..094fca8c --- /dev/null +++ b/tensorflow_v1/examples/2_BasicModels/word2vec.py @@ -0,0 +1,195 @@ +""" Word2Vec. + +Implement Word2Vec algorithm to compute vector representations of words. +This example is using a small chunk of Wikipedia articles to train from. + +References: + - Mikolov, Tomas et al. "Efficient Estimation of Word Representations + in Vector Space.", 2013. + +Links: + - [Word2Vec] https://arxiv.org/pdf/1301.3781.pdf + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" +from __future__ import division, print_function, absolute_import + +import collections +import os +import random +import urllib +import zipfile + +import numpy as np +import tensorflow as tf + +# Training Parameters +learning_rate = 0.1 +batch_size = 128 +num_steps = 3000000 +display_step = 10000 +eval_step = 200000 + +# Evaluation Parameters +eval_words = ['five', 'of', 'going', 'hardware', 'american', 'britain'] + +# Word2Vec Parameters +embedding_size = 200 # Dimension of the embedding vector +max_vocabulary_size = 50000 # Total number of different words in the vocabulary +min_occurrence = 10 # Remove all words that does not appears at least n times +skip_window = 3 # How many words to consider left and right +num_skips = 2 # How many times to reuse an input to generate a label +num_sampled = 64 # Number of negative examples to sample + + +# Download a small chunk of Wikipedia articles collection +url = 'http://mattmahoney.net/dc/text8.zip' +data_path = 'text8.zip' +if not os.path.exists(data_path): + print("Downloading the dataset... (It may take some time)") + filename, _ = urllib.urlretrieve(url, data_path) + print("Done!") +# Unzip the dataset file. Text has already been processed +with zipfile.ZipFile(data_path) as f: + text_words = f.read(f.namelist()[0]).lower().split() + +# Build the dictionary and replace rare words with UNK token +count = [('UNK', -1)] +# Retrieve the most common words +count.extend(collections.Counter(text_words).most_common(max_vocabulary_size - 1)) +# Remove samples with less than 'min_occurrence' occurrences +for i in range(len(count) - 1, -1, -1): + if count[i][1] < min_occurrence: + count.pop(i) + else: + # The collection is ordered, so stop when 'min_occurrence' is reached + break +# Compute the vocabulary size +vocabulary_size = len(count) +# Assign an id to each word +word2id = dict() +for i, (word, _)in enumerate(count): + word2id[word] = i + +data = list() +unk_count = 0 +for word in text_words: + # Retrieve a word id, or assign it index 0 ('UNK') if not in dictionary + index = word2id.get(word, 0) + if index == 0: + unk_count += 1 + data.append(index) +count[0] = ('UNK', unk_count) +id2word = dict(zip(word2id.values(), word2id.keys())) + +print("Words count:", len(text_words)) +print("Unique words:", len(set(text_words))) +print("Vocabulary size:", vocabulary_size) +print("Most common words:", count[:10]) + +data_index = 0 +# Generate training batch for the skip-gram model +def next_batch(batch_size, num_skips, skip_window): + global data_index + assert batch_size % num_skips == 0 + assert num_skips <= 2 * skip_window + batch = np.ndarray(shape=(batch_size), dtype=np.int32) + labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32) + # get window size (words left and right + current one) + span = 2 * skip_window + 1 + buffer = collections.deque(maxlen=span) + if data_index + span > len(data): + data_index = 0 + buffer.extend(data[data_index:data_index + span]) + data_index += span + for i in range(batch_size // num_skips): + context_words = [w for w in range(span) if w != skip_window] + words_to_use = random.sample(context_words, num_skips) + for j, context_word in enumerate(words_to_use): + batch[i * num_skips + j] = buffer[skip_window] + labels[i * num_skips + j, 0] = buffer[context_word] + if data_index == len(data): + buffer.extend(data[0:span]) + data_index = span + else: + buffer.append(data[data_index]) + data_index += 1 + # Backtrack a little bit to avoid skipping words in the end of a batch + data_index = (data_index + len(data) - span) % len(data) + return batch, labels + + +# Input data +X = tf.placeholder(tf.int32, shape=[None]) +# Input label +Y = tf.placeholder(tf.int32, shape=[None, 1]) + +# Ensure the following ops & var are assigned on CPU +# (some ops are not compatible on GPU) +with tf.device('/cpu:0'): + # Create the embedding variable (each row represent a word embedding vector) + embedding = tf.Variable(tf.random_normal([vocabulary_size, embedding_size])) + # Lookup the corresponding embedding vectors for each sample in X + X_embed = tf.nn.embedding_lookup(embedding, X) + + # Construct the variables for the NCE loss + nce_weights = tf.Variable(tf.random_normal([vocabulary_size, embedding_size])) + nce_biases = tf.Variable(tf.zeros([vocabulary_size])) + +# Compute the average NCE loss for the batch +loss_op = tf.reduce_mean( + tf.nn.nce_loss(weights=nce_weights, + biases=nce_biases, + labels=Y, + inputs=X_embed, + num_sampled=num_sampled, + num_classes=vocabulary_size)) + +# Define the optimizer +optimizer = tf.train.GradientDescentOptimizer(learning_rate) +train_op = optimizer.minimize(loss_op) + +# Evaluation +# Compute the cosine similarity between input data embedding and every embedding vectors +X_embed_norm = X_embed / tf.sqrt(tf.reduce_sum(tf.square(X_embed))) +embedding_norm = embedding / tf.sqrt(tf.reduce_sum(tf.square(embedding), 1, keepdims=True)) +cosine_sim_op = tf.matmul(X_embed_norm, embedding_norm, transpose_b=True) + +# Initialize the variables (i.e. assign their default value) +init = tf.global_variables_initializer() + +with tf.Session() as sess: + + # Run the initializer + sess.run(init) + + # Testing data + x_test = np.array([word2id[w] for w in eval_words]) + + average_loss = 0 + for step in xrange(1, num_steps + 1): + # Get a new batch of data + batch_x, batch_y = next_batch(batch_size, num_skips, skip_window) + # Run training op + _, loss = sess.run([train_op, loss_op], feed_dict={X: batch_x, Y: batch_y}) + average_loss += loss + + if step % display_step == 0 or step == 1: + if step > 1: + average_loss /= display_step + print("Step " + str(step) + ", Average Loss= " + \ + "{:.4f}".format(average_loss)) + average_loss = 0 + + # Evaluation + if step % eval_step == 0 or step == 1: + print("Evaluation...") + sim = sess.run(cosine_sim_op, feed_dict={X: x_test}) + for i in xrange(len(eval_words)): + top_k = 8 # number of nearest neighbors + nearest = (-sim[i, :]).argsort()[1:top_k + 1] + log_str = '"%s" nearest neighbors:' % eval_words[i] + for k in xrange(top_k): + log_str = '%s %s,' % (log_str, id2word[nearest[k]]) + print(log_str) diff --git a/tensorflow_v1/examples/3_NeuralNetworks/autoencoder.py b/tensorflow_v1/examples/3_NeuralNetworks/autoencoder.py new file mode 100644 index 00000000..9d3ba60e --- /dev/null +++ b/tensorflow_v1/examples/3_NeuralNetworks/autoencoder.py @@ -0,0 +1,142 @@ +""" Auto Encoder Example. + +Build a 2 layers auto-encoder with TensorFlow to compress images to a +lower latent space and then reconstruct them. + +References: + Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. "Gradient-based + learning applied to document recognition." Proceedings of the IEEE, + 86(11):2278-2324, November 1998. + +Links: + [MNIST Dataset] http://yann.lecun.com/exdb/mnist/ + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" +from __future__ import division, print_function, absolute_import + +import tensorflow as tf +import numpy as np +import matplotlib.pyplot as plt + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) + +# Training Parameters +learning_rate = 0.01 +num_steps = 30000 +batch_size = 256 + +display_step = 1000 +examples_to_show = 10 + +# Network Parameters +num_hidden_1 = 256 # 1st layer num features +num_hidden_2 = 128 # 2nd layer num features (the latent dim) +num_input = 784 # MNIST data input (img shape: 28*28) + +# tf Graph input (only pictures) +X = tf.placeholder("float", [None, num_input]) + +weights = { + 'encoder_h1': tf.Variable(tf.random_normal([num_input, num_hidden_1])), + 'encoder_h2': tf.Variable(tf.random_normal([num_hidden_1, num_hidden_2])), + 'decoder_h1': tf.Variable(tf.random_normal([num_hidden_2, num_hidden_1])), + 'decoder_h2': tf.Variable(tf.random_normal([num_hidden_1, num_input])), +} +biases = { + 'encoder_b1': tf.Variable(tf.random_normal([num_hidden_1])), + 'encoder_b2': tf.Variable(tf.random_normal([num_hidden_2])), + 'decoder_b1': tf.Variable(tf.random_normal([num_hidden_1])), + 'decoder_b2': tf.Variable(tf.random_normal([num_input])), +} + +# Building the encoder +def encoder(x): + # Encoder Hidden layer with sigmoid activation #1 + layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['encoder_h1']), + biases['encoder_b1'])) + # Encoder Hidden layer with sigmoid activation #2 + layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['encoder_h2']), + biases['encoder_b2'])) + return layer_2 + + +# Building the decoder +def decoder(x): + # Decoder Hidden layer with sigmoid activation #1 + layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['decoder_h1']), + biases['decoder_b1'])) + # Decoder Hidden layer with sigmoid activation #2 + layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['decoder_h2']), + biases['decoder_b2'])) + return layer_2 + +# Construct model +encoder_op = encoder(X) +decoder_op = decoder(encoder_op) + +# Prediction +y_pred = decoder_op +# Targets (Labels) are the input data. +y_true = X + +# Define loss and optimizer, minimize the squared error +loss = tf.reduce_mean(tf.pow(y_true - y_pred, 2)) +optimizer = tf.train.RMSPropOptimizer(learning_rate).minimize(loss) + +# Initialize the variables (i.e. assign their default value) +init = tf.global_variables_initializer() + +# Start Training +# Start a new TF session +with tf.Session() as sess: + + # Run the initializer + sess.run(init) + + # Training + for i in range(1, num_steps+1): + # Prepare Data + # Get the next batch of MNIST data (only images are needed, not labels) + batch_x, _ = mnist.train.next_batch(batch_size) + + # Run optimization op (backprop) and cost op (to get loss value) + _, l = sess.run([optimizer, loss], feed_dict={X: batch_x}) + # Display logs per step + if i % display_step == 0 or i == 1: + print('Step %i: Minibatch Loss: %f' % (i, l)) + + # Testing + # Encode and decode images from test set and visualize their reconstruction. + n = 4 + canvas_orig = np.empty((28 * n, 28 * n)) + canvas_recon = np.empty((28 * n, 28 * n)) + for i in range(n): + # MNIST test set + batch_x, _ = mnist.test.next_batch(n) + # Encode and decode the digit image + g = sess.run(decoder_op, feed_dict={X: batch_x}) + + # Display original images + for j in range(n): + # Draw the original digits + canvas_orig[i * 28:(i + 1) * 28, j * 28:(j + 1) * 28] = \ + batch_x[j].reshape([28, 28]) + # Display reconstructed images + for j in range(n): + # Draw the reconstructed digits + canvas_recon[i * 28:(i + 1) * 28, j * 28:(j + 1) * 28] = \ + g[j].reshape([28, 28]) + + print("Original Images") + plt.figure(figsize=(n, n)) + plt.imshow(canvas_orig, origin="upper", cmap="gray") + plt.show() + + print("Reconstructed Images") + plt.figure(figsize=(n, n)) + plt.imshow(canvas_recon, origin="upper", cmap="gray") + plt.show() diff --git a/tensorflow_v1/examples/3_NeuralNetworks/bidirectional_rnn.py b/tensorflow_v1/examples/3_NeuralNetworks/bidirectional_rnn.py new file mode 100644 index 00000000..2ff862ae --- /dev/null +++ b/tensorflow_v1/examples/3_NeuralNetworks/bidirectional_rnn.py @@ -0,0 +1,126 @@ +""" Bi-directional Recurrent Neural Network. + +A Bi-directional Recurrent Neural Network (LSTM) implementation example using +TensorFlow library. This example is using the MNIST database of handwritten +digits (http://yann.lecun.com/exdb/mnist/) + +Links: + [Long Short Term Memory](http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf) + [MNIST Dataset](http://yann.lecun.com/exdb/mnist/). + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" + +from __future__ import print_function + +import tensorflow as tf +from tensorflow.contrib import rnn +import numpy as np + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) + +''' +To classify images using a bidirectional recurrent neural network, we consider +every image row as a sequence of pixels. Because MNIST image shape is 28*28px, +we will then handle 28 sequences of 28 steps for every sample. +''' + +# Training Parameters +learning_rate = 0.001 +training_steps = 10000 +batch_size = 128 +display_step = 200 + +# Network Parameters +num_input = 28 # MNIST data input (img shape: 28*28) +timesteps = 28 # timesteps +num_hidden = 128 # hidden layer num of features +num_classes = 10 # MNIST total classes (0-9 digits) + +# tf Graph input +X = tf.placeholder("float", [None, timesteps, num_input]) +Y = tf.placeholder("float", [None, num_classes]) + +# Define weights +weights = { + # Hidden layer weights => 2*n_hidden because of forward + backward cells + 'out': tf.Variable(tf.random_normal([2*num_hidden, num_classes])) +} +biases = { + 'out': tf.Variable(tf.random_normal([num_classes])) +} + + +def BiRNN(x, weights, biases): + + # Prepare data shape to match `rnn` function requirements + # Current data input shape: (batch_size, timesteps, n_input) + # Required shape: 'timesteps' tensors list of shape (batch_size, num_input) + + # Unstack to get a list of 'timesteps' tensors of shape (batch_size, num_input) + x = tf.unstack(x, timesteps, 1) + + # Define lstm cells with tensorflow + # Forward direction cell + lstm_fw_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0) + # Backward direction cell + lstm_bw_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0) + + # Get lstm cell output + try: + outputs, _, _ = rnn.static_bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x, + dtype=tf.float32) + except Exception: # Old TensorFlow version only returns outputs not states + outputs = rnn.static_bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x, + dtype=tf.float32) + + # Linear activation, using rnn inner loop last output + return tf.matmul(outputs[-1], weights['out']) + biases['out'] + +logits = BiRNN(X, weights, biases) +prediction = tf.nn.softmax(logits) + +# Define loss and optimizer +loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits( + logits=logits, labels=Y)) +optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate) +train_op = optimizer.minimize(loss_op) + +# Evaluate model (with test logits, for dropout to be disabled) +correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1)) +accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) + +# Initialize the variables (i.e. assign their default value) +init = tf.global_variables_initializer() + +# Start training +with tf.Session() as sess: + + # Run the initializer + sess.run(init) + + for step in range(1, training_steps+1): + batch_x, batch_y = mnist.train.next_batch(batch_size) + # Reshape data to get 28 seq of 28 elements + batch_x = batch_x.reshape((batch_size, timesteps, num_input)) + # Run optimization op (backprop) + sess.run(train_op, feed_dict={X: batch_x, Y: batch_y}) + if step % display_step == 0 or step == 1: + # Calculate batch loss and accuracy + loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x, + Y: batch_y}) + print("Step " + str(step) + ", Minibatch Loss= " + \ + "{:.4f}".format(loss) + ", Training Accuracy= " + \ + "{:.3f}".format(acc)) + + print("Optimization Finished!") + + # Calculate accuracy for 128 mnist test images + test_len = 128 + test_data = mnist.test.images[:test_len].reshape((-1, timesteps, num_input)) + test_label = mnist.test.labels[:test_len] + print("Testing Accuracy:", \ + sess.run(accuracy, feed_dict={X: test_data, Y: test_label})) diff --git a/tensorflow_v1/examples/3_NeuralNetworks/convolutional_network.py b/tensorflow_v1/examples/3_NeuralNetworks/convolutional_network.py new file mode 100644 index 00000000..e7088f1f --- /dev/null +++ b/tensorflow_v1/examples/3_NeuralNetworks/convolutional_network.py @@ -0,0 +1,125 @@ +""" Convolutional Neural Network. + +Build and train a convolutional neural network with TensorFlow. +This example is using the MNIST database of handwritten digits +(http://yann.lecun.com/exdb/mnist/) + +This example is using TensorFlow layers API, see 'convolutional_network_raw' +example for a raw implementation with variables. + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" +from __future__ import division, print_function, absolute_import + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=False) + +import tensorflow as tf + +# Training Parameters +learning_rate = 0.001 +num_steps = 2000 +batch_size = 128 + +# Network Parameters +num_input = 784 # MNIST data input (img shape: 28*28) +num_classes = 10 # MNIST total classes (0-9 digits) +dropout = 0.25 # Dropout, probability to drop a unit + + +# Create the neural network +def conv_net(x_dict, n_classes, dropout, reuse, is_training): + # Define a scope for reusing the variables + with tf.variable_scope('ConvNet', reuse=reuse): + # TF Estimator input is a dict, in case of multiple inputs + x = x_dict['images'] + + # MNIST data input is a 1-D vector of 784 features (28*28 pixels) + # Reshape to match picture format [Height x Width x Channel] + # Tensor input become 4-D: [Batch Size, Height, Width, Channel] + x = tf.reshape(x, shape=[-1, 28, 28, 1]) + + # Convolution Layer with 32 filters and a kernel size of 5 + conv1 = tf.layers.conv2d(x, 32, 5, activation=tf.nn.relu) + # Max Pooling (down-sampling) with strides of 2 and kernel size of 2 + conv1 = tf.layers.max_pooling2d(conv1, 2, 2) + + # Convolution Layer with 64 filters and a kernel size of 3 + conv2 = tf.layers.conv2d(conv1, 64, 3, activation=tf.nn.relu) + # Max Pooling (down-sampling) with strides of 2 and kernel size of 2 + conv2 = tf.layers.max_pooling2d(conv2, 2, 2) + + # Flatten the data to a 1-D vector for the fully connected layer + fc1 = tf.contrib.layers.flatten(conv2) + + # Fully connected layer (in tf contrib folder for now) + fc1 = tf.layers.dense(fc1, 1024) + # Apply Dropout (if is_training is False, dropout is not applied) + fc1 = tf.layers.dropout(fc1, rate=dropout, training=is_training) + + # Output layer, class prediction + out = tf.layers.dense(fc1, n_classes) + + return out + + +# Define the model function (following TF Estimator Template) +def model_fn(features, labels, mode): + # Build the neural network + # Because Dropout have different behavior at training and prediction time, we + # need to create 2 distinct computation graphs that still share the same weights. + logits_train = conv_net(features, num_classes, dropout, reuse=False, + is_training=True) + logits_test = conv_net(features, num_classes, dropout, reuse=True, + is_training=False) + + # Predictions + pred_classes = tf.argmax(logits_test, axis=1) + pred_probas = tf.nn.softmax(logits_test) + + # If prediction mode, early return + if mode == tf.estimator.ModeKeys.PREDICT: + return tf.estimator.EstimatorSpec(mode, predictions=pred_classes) + + # Define loss and optimizer + loss_op = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits( + logits=logits_train, labels=tf.cast(labels, dtype=tf.int32))) + optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) + train_op = optimizer.minimize(loss_op, + global_step=tf.train.get_global_step()) + + # Evaluate the accuracy of the model + acc_op = tf.metrics.accuracy(labels=labels, predictions=pred_classes) + + # TF Estimators requires to return a EstimatorSpec, that specify + # the different ops for training, evaluating, ... + estim_specs = tf.estimator.EstimatorSpec( + mode=mode, + predictions=pred_classes, + loss=loss_op, + train_op=train_op, + eval_metric_ops={'accuracy': acc_op}) + + return estim_specs + +# Build the Estimator +model = tf.estimator.Estimator(model_fn) + +# Define the input function for training +input_fn = tf.estimator.inputs.numpy_input_fn( + x={'images': mnist.train.images}, y=mnist.train.labels, + batch_size=batch_size, num_epochs=None, shuffle=True) +# Train the Model +model.train(input_fn, steps=num_steps) + +# Evaluate the Model +# Define the input function for evaluating +input_fn = tf.estimator.inputs.numpy_input_fn( + x={'images': mnist.test.images}, y=mnist.test.labels, + batch_size=batch_size, shuffle=False) +# Use the Estimator 'evaluate' method +e = model.evaluate(input_fn) + +print("Testing Accuracy:", e['accuracy']) diff --git a/tensorflow_v1/examples/3_NeuralNetworks/convolutional_network_raw.py b/tensorflow_v1/examples/3_NeuralNetworks/convolutional_network_raw.py new file mode 100644 index 00000000..d063f21f --- /dev/null +++ b/tensorflow_v1/examples/3_NeuralNetworks/convolutional_network_raw.py @@ -0,0 +1,141 @@ +""" Convolutional Neural Network. + +Build and train a convolutional neural network with TensorFlow. +This example is using the MNIST database of handwritten digits +(http://yann.lecun.com/exdb/mnist/) + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" + +from __future__ import division, print_function, absolute_import + +import tensorflow as tf + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) + +# Training Parameters +learning_rate = 0.001 +num_steps = 200 +batch_size = 128 +display_step = 10 + +# Network Parameters +num_input = 784 # MNIST data input (img shape: 28*28) +num_classes = 10 # MNIST total classes (0-9 digits) +dropout = 0.75 # Dropout, probability to keep units + +# tf Graph input +X = tf.placeholder(tf.float32, [None, num_input]) +Y = tf.placeholder(tf.float32, [None, num_classes]) +keep_prob = tf.placeholder(tf.float32) # dropout (keep probability) + + +# Create some wrappers for simplicity +def conv2d(x, W, b, strides=1): + # Conv2D wrapper, with bias and relu activation + x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME') + x = tf.nn.bias_add(x, b) + return tf.nn.relu(x) + + +def maxpool2d(x, k=2): + # MaxPool2D wrapper + return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1], + padding='SAME') + + +# Create model +def conv_net(x, weights, biases, dropout): + # MNIST data input is a 1-D vector of 784 features (28*28 pixels) + # Reshape to match picture format [Height x Width x Channel] + # Tensor input become 4-D: [Batch Size, Height, Width, Channel] + x = tf.reshape(x, shape=[-1, 28, 28, 1]) + + # Convolution Layer + conv1 = conv2d(x, weights['wc1'], biases['bc1']) + # Max Pooling (down-sampling) + conv1 = maxpool2d(conv1, k=2) + + # Convolution Layer + conv2 = conv2d(conv1, weights['wc2'], biases['bc2']) + # Max Pooling (down-sampling) + conv2 = maxpool2d(conv2, k=2) + + # Fully connected layer + # Reshape conv2 output to fit fully connected layer input + fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]]) + fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1']) + fc1 = tf.nn.relu(fc1) + # Apply Dropout + fc1 = tf.nn.dropout(fc1, dropout) + + # Output, class prediction + out = tf.add(tf.matmul(fc1, weights['out']), biases['out']) + return out + +# Store layers weight & bias +weights = { + # 5x5 conv, 1 input, 32 outputs + 'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])), + # 5x5 conv, 32 inputs, 64 outputs + 'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])), + # fully connected, 7*7*64 inputs, 1024 outputs + 'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])), + # 1024 inputs, 10 outputs (class prediction) + 'out': tf.Variable(tf.random_normal([1024, num_classes])) +} + +biases = { + 'bc1': tf.Variable(tf.random_normal([32])), + 'bc2': tf.Variable(tf.random_normal([64])), + 'bd1': tf.Variable(tf.random_normal([1024])), + 'out': tf.Variable(tf.random_normal([num_classes])) +} + +# Construct model +logits = conv_net(X, weights, biases, keep_prob) +prediction = tf.nn.softmax(logits) + +# Define loss and optimizer +loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits( + logits=logits, labels=Y)) +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) +train_op = optimizer.minimize(loss_op) + + +# Evaluate model +correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1)) +accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) + +# Initialize the variables (i.e. assign their default value) +init = tf.global_variables_initializer() + +# Start training +with tf.Session() as sess: + + # Run the initializer + sess.run(init) + + for step in range(1, num_steps+1): + batch_x, batch_y = mnist.train.next_batch(batch_size) + # Run optimization op (backprop) + sess.run(train_op, feed_dict={X: batch_x, Y: batch_y, keep_prob: 0.8}) + if step % display_step == 0 or step == 1: + # Calculate batch loss and accuracy + loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x, + Y: batch_y, + keep_prob: 1.0}) + print("Step " + str(step) + ", Minibatch Loss= " + \ + "{:.4f}".format(loss) + ", Training Accuracy= " + \ + "{:.3f}".format(acc)) + + print("Optimization Finished!") + + # Calculate accuracy for 256 MNIST test images + print("Testing Accuracy:", \ + sess.run(accuracy, feed_dict={X: mnist.test.images[:256], + Y: mnist.test.labels[:256], + keep_prob: 1.0})) diff --git a/tensorflow_v1/examples/3_NeuralNetworks/dcgan.py b/tensorflow_v1/examples/3_NeuralNetworks/dcgan.py new file mode 100644 index 00000000..2de85441 --- /dev/null +++ b/tensorflow_v1/examples/3_NeuralNetworks/dcgan.py @@ -0,0 +1,167 @@ +""" Deep Convolutional Generative Adversarial Network (DCGAN). + +Using deep convolutional generative adversarial networks (DCGAN) to generate +digit images from a noise distribution. + +References: + - Unsupervised representation learning with deep convolutional generative + adversarial networks. A Radford, L Metz, S Chintala. arXiv:1511.06434. + +Links: + - [DCGAN Paper](https://arxiv.org/abs/1511.06434). + - [MNIST Dataset](http://yann.lecun.com/exdb/mnist/). + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" + +from __future__ import division, print_function, absolute_import + +import matplotlib.pyplot as plt +import numpy as np +import tensorflow as tf + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) + +# Training Params +num_steps = 20000 +batch_size = 32 + +# Network Params +image_dim = 784 # 28*28 pixels * 1 channel +gen_hidden_dim = 256 +disc_hidden_dim = 256 +noise_dim = 200 # Noise data points + + +# Generator Network +# Input: Noise, Output: Image +def generator(x, reuse=False): + with tf.variable_scope('Generator', reuse=reuse): + # TensorFlow Layers automatically create variables and calculate their + # shape, based on the input. + x = tf.layers.dense(x, units=6 * 6 * 128) + x = tf.nn.tanh(x) + # Reshape to a 4-D array of images: (batch, height, width, channels) + # New shape: (batch, 6, 6, 128) + x = tf.reshape(x, shape=[-1, 6, 6, 128]) + # Deconvolution, image shape: (batch, 14, 14, 64) + x = tf.layers.conv2d_transpose(x, 64, 4, strides=2) + # Deconvolution, image shape: (batch, 28, 28, 1) + x = tf.layers.conv2d_transpose(x, 1, 2, strides=2) + # Apply sigmoid to clip values between 0 and 1 + x = tf.nn.sigmoid(x) + return x + + +# Discriminator Network +# Input: Image, Output: Prediction Real/Fake Image +def discriminator(x, reuse=False): + with tf.variable_scope('Discriminator', reuse=reuse): + # Typical convolutional neural network to classify images. + x = tf.layers.conv2d(x, 64, 5) + x = tf.nn.tanh(x) + x = tf.layers.average_pooling2d(x, 2, 2) + x = tf.layers.conv2d(x, 128, 5) + x = tf.nn.tanh(x) + x = tf.layers.average_pooling2d(x, 2, 2) + x = tf.contrib.layers.flatten(x) + x = tf.layers.dense(x, 1024) + x = tf.nn.tanh(x) + # Output 2 classes: Real and Fake images + x = tf.layers.dense(x, 2) + return x + +# Build Networks +# Network Inputs +noise_input = tf.placeholder(tf.float32, shape=[None, noise_dim]) +real_image_input = tf.placeholder(tf.float32, shape=[None, 28, 28, 1]) + +# Build Generator Network +gen_sample = generator(noise_input) + +# Build 2 Discriminator Networks (one from real image input, one from generated samples) +disc_real = discriminator(real_image_input) +disc_fake = discriminator(gen_sample, reuse=True) +disc_concat = tf.concat([disc_real, disc_fake], axis=0) + +# Build the stacked generator/discriminator +stacked_gan = discriminator(gen_sample, reuse=True) + +# Build Targets (real or fake images) +disc_target = tf.placeholder(tf.int32, shape=[None]) +gen_target = tf.placeholder(tf.int32, shape=[None]) + +# Build Loss +disc_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits( + logits=disc_concat, labels=disc_target)) +gen_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits( + logits=stacked_gan, labels=gen_target)) + +# Build Optimizers +optimizer_gen = tf.train.AdamOptimizer(learning_rate=0.001) +optimizer_disc = tf.train.AdamOptimizer(learning_rate=0.001) + +# Training Variables for each optimizer +# By default in TensorFlow, all variables are updated by each optimizer, so we +# need to precise for each one of them the specific variables to update. +# Generator Network Variables +gen_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='Generator') +# Discriminator Network Variables +disc_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='Discriminator') + +# Create training operations +train_gen = optimizer_gen.minimize(gen_loss, var_list=gen_vars) +train_disc = optimizer_disc.minimize(disc_loss, var_list=disc_vars) + +# Initialize the variables (i.e. assign their default value) +init = tf.global_variables_initializer() + +# Start training +with tf.Session() as sess: + + # Run the initializer + sess.run(init) + + for i in range(1, num_steps+1): + + # Prepare Input Data + # Get the next batch of MNIST data (only images are needed, not labels) + batch_x, _ = mnist.train.next_batch(batch_size) + batch_x = np.reshape(batch_x, newshape=[-1, 28, 28, 1]) + # Generate noise to feed to the generator + z = np.random.uniform(-1., 1., size=[batch_size, noise_dim]) + + # Prepare Targets (Real image: 1, Fake image: 0) + # The first half of data fed to the discriminator are real images, + # the other half are fake images (coming from the generator). + batch_disc_y = np.concatenate( + [np.ones([batch_size]), np.zeros([batch_size])], axis=0) + # Generator tries to fool the discriminator, thus targets are 1. + batch_gen_y = np.ones([batch_size]) + + # Training + feed_dict = {real_image_input: batch_x, noise_input: z, + disc_target: batch_disc_y, gen_target: batch_gen_y} + _, _, gl, dl = sess.run([train_gen, train_disc, gen_loss, disc_loss], + feed_dict=feed_dict) + if i % 100 == 0 or i == 1: + print('Step %i: Generator Loss: %f, Discriminator Loss: %f' % (i, gl, dl)) + + # Generate images from noise, using the generator network. + f, a = plt.subplots(4, 10, figsize=(10, 4)) + for i in range(10): + # Noise input. + z = np.random.uniform(-1., 1., size=[4, noise_dim]) + g = sess.run(gen_sample, feed_dict={noise_input: z}) + for j in range(4): + # Generate image from noise. Extend to 3 channels for matplot figure. + img = np.reshape(np.repeat(g[j][:, :, np.newaxis], 3, axis=2), + newshape=(28, 28, 3)) + a[j][i].imshow(img) + + f.show() + plt.draw() + plt.waitforbuttonpress() diff --git a/tensorflow_v1/examples/3_NeuralNetworks/dynamic_rnn.py b/tensorflow_v1/examples/3_NeuralNetworks/dynamic_rnn.py new file mode 100644 index 00000000..faad368e --- /dev/null +++ b/tensorflow_v1/examples/3_NeuralNetworks/dynamic_rnn.py @@ -0,0 +1,193 @@ +""" Dynamic Recurrent Neural Network. + +TensorFlow implementation of a Recurrent Neural Network (LSTM) that performs +dynamic computation over sequences with variable length. This example is using +a toy dataset to classify linear sequences. The generated sequences have +variable length. + +Links: + [Long Short Term Memory](http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf) + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" + +from __future__ import print_function + +import tensorflow as tf +import random + + +# ==================== +# TOY DATA GENERATOR +# ==================== +class ToySequenceData(object): + """ Generate sequence of data with dynamic length. + This class generate samples for training: + - Class 0: linear sequences (i.e. [0, 1, 2, 3,...]) + - Class 1: random sequences (i.e. [1, 3, 10, 7,...]) + + NOTICE: + We have to pad each sequence to reach 'max_seq_len' for TensorFlow + consistency (we cannot feed a numpy array with inconsistent + dimensions). The dynamic calculation will then be perform thanks to + 'seqlen' attribute that records every actual sequence length. + """ + def __init__(self, n_samples=1000, max_seq_len=20, min_seq_len=3, + max_value=1000): + self.data = [] + self.labels = [] + self.seqlen = [] + for i in range(n_samples): + # Random sequence length + len = random.randint(min_seq_len, max_seq_len) + # Monitor sequence length for TensorFlow dynamic calculation + self.seqlen.append(len) + # Add a random or linear int sequence (50% prob) + if random.random() < .5: + # Generate a linear sequence + rand_start = random.randint(0, max_value - len) + s = [[float(i)/max_value] for i in + range(rand_start, rand_start + len)] + # Pad sequence for dimension consistency + s += [[0.] for i in range(max_seq_len - len)] + self.data.append(s) + self.labels.append([1., 0.]) + else: + # Generate a random sequence + s = [[float(random.randint(0, max_value))/max_value] + for i in range(len)] + # Pad sequence for dimension consistency + s += [[0.] for i in range(max_seq_len - len)] + self.data.append(s) + self.labels.append([0., 1.]) + self.batch_id = 0 + + def next(self, batch_size): + """ Return a batch of data. When dataset end is reached, start over. + """ + if self.batch_id == len(self.data): + self.batch_id = 0 + batch_data = (self.data[self.batch_id:min(self.batch_id + + batch_size, len(self.data))]) + batch_labels = (self.labels[self.batch_id:min(self.batch_id + + batch_size, len(self.data))]) + batch_seqlen = (self.seqlen[self.batch_id:min(self.batch_id + + batch_size, len(self.data))]) + self.batch_id = min(self.batch_id + batch_size, len(self.data)) + return batch_data, batch_labels, batch_seqlen + + +# ========== +# MODEL +# ========== + +# Parameters +learning_rate = 0.01 +training_steps = 10000 +batch_size = 128 +display_step = 200 + +# Network Parameters +seq_max_len = 20 # Sequence max length +n_hidden = 64 # hidden layer num of features +n_classes = 2 # linear sequence or not + +trainset = ToySequenceData(n_samples=1000, max_seq_len=seq_max_len) +testset = ToySequenceData(n_samples=500, max_seq_len=seq_max_len) + +# tf Graph input +x = tf.placeholder("float", [None, seq_max_len, 1]) +y = tf.placeholder("float", [None, n_classes]) +# A placeholder for indicating each sequence length +seqlen = tf.placeholder(tf.int32, [None]) + +# Define weights +weights = { + 'out': tf.Variable(tf.random_normal([n_hidden, n_classes])) +} +biases = { + 'out': tf.Variable(tf.random_normal([n_classes])) +} + + +def dynamicRNN(x, seqlen, weights, biases): + + # Prepare data shape to match `rnn` function requirements + # Current data input shape: (batch_size, n_steps, n_input) + # Required shape: 'n_steps' tensors list of shape (batch_size, n_input) + + # Unstack to get a list of 'n_steps' tensors of shape (batch_size, n_input) + x = tf.unstack(x, seq_max_len, 1) + + # Define a lstm cell with tensorflow + lstm_cell = tf.contrib.rnn.BasicLSTMCell(n_hidden) + + # Get lstm cell output, providing 'sequence_length' will perform dynamic + # calculation. + outputs, states = tf.contrib.rnn.static_rnn(lstm_cell, x, dtype=tf.float32, + sequence_length=seqlen) + + # When performing dynamic calculation, we must retrieve the last + # dynamically computed output, i.e., if a sequence length is 10, we need + # to retrieve the 10th output. + # However TensorFlow doesn't support advanced indexing yet, so we build + # a custom op that for each sample in batch size, get its length and + # get the corresponding relevant output. + + # 'outputs' is a list of output at every timestep, we pack them in a Tensor + # and change back dimension to [batch_size, n_step, n_input] + outputs = tf.stack(outputs) + outputs = tf.transpose(outputs, [1, 0, 2]) + + # Hack to build the indexing and retrieve the right output. + batch_size = tf.shape(outputs)[0] + # Start indices for each sample + index = tf.range(0, batch_size) * seq_max_len + (seqlen - 1) + # Indexing + outputs = tf.gather(tf.reshape(outputs, [-1, n_hidden]), index) + + # Linear activation, using outputs computed above + return tf.matmul(outputs, weights['out']) + biases['out'] + +pred = dynamicRNN(x, seqlen, weights, biases) + +# Define loss and optimizer +cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y)) +optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cost) + +# Evaluate model +correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1)) +accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) + +# Initialize the variables (i.e. assign their default value) +init = tf.global_variables_initializer() + +# Start training +with tf.Session() as sess: + + # Run the initializer + sess.run(init) + + for step in range(1, training_steps + 1): + batch_x, batch_y, batch_seqlen = trainset.next(batch_size) + # Run optimization op (backprop) + sess.run(optimizer, feed_dict={x: batch_x, y: batch_y, + seqlen: batch_seqlen}) + if step % display_step == 0 or step == 1: + # Calculate batch accuracy & loss + acc, loss = sess.run([accuracy, cost], feed_dict={x: batch_x, y: batch_y, + seqlen: batch_seqlen}) + print("Step " + str(step*batch_size) + ", Minibatch Loss= " + \ + "{:.6f}".format(loss) + ", Training Accuracy= " + \ + "{:.5f}".format(acc)) + + print("Optimization Finished!") + + # Calculate accuracy + test_data = testset.data + test_label = testset.labels + test_seqlen = testset.seqlen + print("Testing Accuracy:", \ + sess.run(accuracy, feed_dict={x: test_data, y: test_label, + seqlen: test_seqlen})) diff --git a/tensorflow_v1/examples/3_NeuralNetworks/gan.py b/tensorflow_v1/examples/3_NeuralNetworks/gan.py new file mode 100644 index 00000000..dd5977ad --- /dev/null +++ b/tensorflow_v1/examples/3_NeuralNetworks/gan.py @@ -0,0 +1,157 @@ +""" Generative Adversarial Networks (GAN). + +Using generative adversarial networks (GAN) to generate digit images from a +noise distribution. + +References: + - Generative adversarial nets. I Goodfellow, J Pouget-Abadie, M Mirza, + B Xu, D Warde-Farley, S Ozair, Y. Bengio. Advances in neural information + processing systems, 2672-2680. + - Understanding the difficulty of training deep feedforward neural networks. + X Glorot, Y Bengio. Aistats 9, 249-256 + +Links: + - [GAN Paper](https://arxiv.org/pdf/1406.2661.pdf). + - [MNIST Dataset](http://yann.lecun.com/exdb/mnist/). + - [Xavier Glorot Init](www.cs.cmu.edu/~bhiksha/courses/deeplearning/Fall.../AISTATS2010_Glorot.pdf). + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" + +from __future__ import division, print_function, absolute_import + +import matplotlib.pyplot as plt +import numpy as np +import tensorflow as tf + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) + +# Training Params +num_steps = 100000 +batch_size = 128 +learning_rate = 0.0002 + +# Network Params +image_dim = 784 # 28*28 pixels +gen_hidden_dim = 256 +disc_hidden_dim = 256 +noise_dim = 100 # Noise data points + +# A custom initialization (see Xavier Glorot init) +def glorot_init(shape): + return tf.random_normal(shape=shape, stddev=1. / tf.sqrt(shape[0] / 2.)) + +# Store layers weight & bias +weights = { + 'gen_hidden1': tf.Variable(glorot_init([noise_dim, gen_hidden_dim])), + 'gen_out': tf.Variable(glorot_init([gen_hidden_dim, image_dim])), + 'disc_hidden1': tf.Variable(glorot_init([image_dim, disc_hidden_dim])), + 'disc_out': tf.Variable(glorot_init([disc_hidden_dim, 1])), +} +biases = { + 'gen_hidden1': tf.Variable(tf.zeros([gen_hidden_dim])), + 'gen_out': tf.Variable(tf.zeros([image_dim])), + 'disc_hidden1': tf.Variable(tf.zeros([disc_hidden_dim])), + 'disc_out': tf.Variable(tf.zeros([1])), +} + + +# Generator +def generator(x): + hidden_layer = tf.matmul(x, weights['gen_hidden1']) + hidden_layer = tf.add(hidden_layer, biases['gen_hidden1']) + hidden_layer = tf.nn.relu(hidden_layer) + out_layer = tf.matmul(hidden_layer, weights['gen_out']) + out_layer = tf.add(out_layer, biases['gen_out']) + out_layer = tf.nn.sigmoid(out_layer) + return out_layer + + +# Discriminator +def discriminator(x): + hidden_layer = tf.matmul(x, weights['disc_hidden1']) + hidden_layer = tf.add(hidden_layer, biases['disc_hidden1']) + hidden_layer = tf.nn.relu(hidden_layer) + out_layer = tf.matmul(hidden_layer, weights['disc_out']) + out_layer = tf.add(out_layer, biases['disc_out']) + out_layer = tf.nn.sigmoid(out_layer) + return out_layer + +# Build Networks +# Network Inputs +gen_input = tf.placeholder(tf.float32, shape=[None, noise_dim], name='input_noise') +disc_input = tf.placeholder(tf.float32, shape=[None, image_dim], name='disc_input') + +# Build Generator Network +gen_sample = generator(gen_input) + +# Build 2 Discriminator Networks (one from noise input, one from generated samples) +disc_real = discriminator(disc_input) +disc_fake = discriminator(gen_sample) + +# Build Loss +gen_loss = -tf.reduce_mean(tf.log(disc_fake)) +disc_loss = -tf.reduce_mean(tf.log(disc_real) + tf.log(1. - disc_fake)) + +# Build Optimizers +optimizer_gen = tf.train.AdamOptimizer(learning_rate=learning_rate) +optimizer_disc = tf.train.AdamOptimizer(learning_rate=learning_rate) + +# Training Variables for each optimizer +# By default in TensorFlow, all variables are updated by each optimizer, so we +# need to precise for each one of them the specific variables to update. +# Generator Network Variables +gen_vars = [weights['gen_hidden1'], weights['gen_out'], + biases['gen_hidden1'], biases['gen_out']] +# Discriminator Network Variables +disc_vars = [weights['disc_hidden1'], weights['disc_out'], + biases['disc_hidden1'], biases['disc_out']] + +# Create training operations +train_gen = optimizer_gen.minimize(gen_loss, var_list=gen_vars) +train_disc = optimizer_disc.minimize(disc_loss, var_list=disc_vars) + +# Initialize the variables (i.e. assign their default value) +init = tf.global_variables_initializer() + +# Start training +with tf.Session() as sess: + + # Run the initializer + sess.run(init) + + for i in range(1, num_steps+1): + # Prepare Data + # Get the next batch of MNIST data (only images are needed, not labels) + batch_x, _ = mnist.train.next_batch(batch_size) + # Generate noise to feed to the generator + z = np.random.uniform(-1., 1., size=[batch_size, noise_dim]) + + # Train + feed_dict = {disc_input: batch_x, gen_input: z} + _, _, gl, dl = sess.run([train_gen, train_disc, gen_loss, disc_loss], + feed_dict=feed_dict) + if i % 1000 == 0 or i == 1: + print('Step %i: Generator Loss: %f, Discriminator Loss: %f' % (i, gl, dl)) + + # Generate images from noise, using the generator network. + f, a = plt.subplots(4, 10, figsize=(10, 4)) + for i in range(10): + # Noise input. + z = np.random.uniform(-1., 1., size=[4, noise_dim]) + g = sess.run([gen_sample], feed_dict={gen_input: z}) + g = np.reshape(g, newshape=(4, 28, 28, 1)) + # Reverse colours for better display + g = -1 * (g - 1) + for j in range(4): + # Generate image from noise. Extend to 3 channels for matplot figure. + img = np.reshape(np.repeat(g[j][:, :, np.newaxis], 3, axis=2), + newshape=(28, 28, 3)) + a[j][i].imshow(img) + + f.show() + plt.draw() + plt.waitforbuttonpress() diff --git a/tensorflow_v1/examples/3_NeuralNetworks/multilayer_perceptron.py b/tensorflow_v1/examples/3_NeuralNetworks/multilayer_perceptron.py new file mode 100644 index 00000000..cf04b015 --- /dev/null +++ b/tensorflow_v1/examples/3_NeuralNetworks/multilayer_perceptron.py @@ -0,0 +1,104 @@ +""" Multilayer Perceptron. + +A Multilayer Perceptron (Neural Network) implementation example using +TensorFlow library. This example is using the MNIST database of handwritten +digits (http://yann.lecun.com/exdb/mnist/). + +Links: + [MNIST Dataset](http://yann.lecun.com/exdb/mnist/). + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" + +# ------------------------------------------------------------------ +# +# THIS EXAMPLE HAS BEEN RENAMED 'neural_network.py', FOR SIMPLICITY. +# +# ------------------------------------------------------------------ + + +from __future__ import print_function + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) + +import tensorflow as tf + +# Parameters +learning_rate = 0.001 +training_epochs = 15 +batch_size = 100 +display_step = 1 + +# Network Parameters +n_hidden_1 = 256 # 1st layer number of neurons +n_hidden_2 = 256 # 2nd layer number of neurons +n_input = 784 # MNIST data input (img shape: 28*28) +n_classes = 10 # MNIST total classes (0-9 digits) + +# tf Graph input +X = tf.placeholder("float", [None, n_input]) +Y = tf.placeholder("float", [None, n_classes]) + +# Store layers weight & bias +weights = { + 'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])), + 'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])), + 'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes])) +} +biases = { + 'b1': tf.Variable(tf.random_normal([n_hidden_1])), + 'b2': tf.Variable(tf.random_normal([n_hidden_2])), + 'out': tf.Variable(tf.random_normal([n_classes])) +} + + +# Create model +def multilayer_perceptron(x): + # Hidden fully connected layer with 256 neurons + layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1']) + # Hidden fully connected layer with 256 neurons + layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2']) + # Output fully connected layer with a neuron for each class + out_layer = tf.matmul(layer_2, weights['out']) + biases['out'] + return out_layer + +# Construct model +logits = multilayer_perceptron(X) + +# Define loss and optimizer +loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits( + logits=logits, labels=Y)) +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) +train_op = optimizer.minimize(loss_op) +# Initializing the variables +init = tf.global_variables_initializer() + +with tf.Session() as sess: + sess.run(init) + + # Training cycle + for epoch in range(training_epochs): + avg_cost = 0. + total_batch = int(mnist.train.num_examples/batch_size) + # Loop over all batches + for i in range(total_batch): + batch_x, batch_y = mnist.train.next_batch(batch_size) + # Run optimization op (backprop) and cost op (to get loss value) + _, c = sess.run([train_op, loss_op], feed_dict={X: batch_x, + Y: batch_y}) + # Compute average loss + avg_cost += c / total_batch + # Display logs per epoch step + if epoch % display_step == 0: + print("Epoch:", '%04d' % (epoch+1), "cost={:.9f}".format(avg_cost)) + print("Optimization Finished!") + + # Test model + pred = tf.nn.softmax(logits) # Apply softmax to logits + correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(Y, 1)) + # Calculate accuracy + accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) + print("Accuracy:", accuracy.eval({X: mnist.test.images, Y: mnist.test.labels})) diff --git a/tensorflow_v1/examples/3_NeuralNetworks/neural_network.py b/tensorflow_v1/examples/3_NeuralNetworks/neural_network.py new file mode 100644 index 00000000..1fff2d54 --- /dev/null +++ b/tensorflow_v1/examples/3_NeuralNetworks/neural_network.py @@ -0,0 +1,103 @@ +""" Neural Network. + +A 2-Hidden Layers Fully Connected Neural Network (a.k.a Multilayer Perceptron) +implementation with TensorFlow. This example is using the MNIST database +of handwritten digits (http://yann.lecun.com/exdb/mnist/). + +This example is using TensorFlow layers, see 'neural_network_raw' example for +a raw implementation with variables. + +Links: + [MNIST Dataset](http://yann.lecun.com/exdb/mnist/). + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" + +from __future__ import print_function + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=False) + +import tensorflow as tf + +# Parameters +learning_rate = 0.1 +num_steps = 1000 +batch_size = 128 +display_step = 100 + +# Network Parameters +n_hidden_1 = 256 # 1st layer number of neurons +n_hidden_2 = 256 # 2nd layer number of neurons +num_input = 784 # MNIST data input (img shape: 28*28) +num_classes = 10 # MNIST total classes (0-9 digits) + + +# Define the neural network +def neural_net(x_dict): + # TF Estimator input is a dict, in case of multiple inputs + x = x_dict['images'] + # Hidden fully connected layer with 256 neurons + layer_1 = tf.layers.dense(x, n_hidden_1) + # Hidden fully connected layer with 256 neurons + layer_2 = tf.layers.dense(layer_1, n_hidden_2) + # Output fully connected layer with a neuron for each class + out_layer = tf.layers.dense(layer_2, num_classes) + return out_layer + + +# Define the model function (following TF Estimator Template) +def model_fn(features, labels, mode): + # Build the neural network + logits = neural_net(features) + + # Predictions + pred_classes = tf.argmax(logits, axis=1) + pred_probas = tf.nn.softmax(logits) + + # If prediction mode, early return + if mode == tf.estimator.ModeKeys.PREDICT: + return tf.estimator.EstimatorSpec(mode, predictions=pred_classes) + + # Define loss and optimizer + loss_op = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits( + logits=logits, labels=tf.cast(labels, dtype=tf.int32))) + optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate) + train_op = optimizer.minimize(loss_op, + global_step=tf.train.get_global_step()) + + # Evaluate the accuracy of the model + acc_op = tf.metrics.accuracy(labels=labels, predictions=pred_classes) + + # TF Estimators requires to return a EstimatorSpec, that specify + # the different ops for training, evaluating, ... + estim_specs = tf.estimator.EstimatorSpec( + mode=mode, + predictions=pred_classes, + loss=loss_op, + train_op=train_op, + eval_metric_ops={'accuracy': acc_op}) + + return estim_specs + +# Build the Estimator +model = tf.estimator.Estimator(model_fn) + +# Define the input function for training +input_fn = tf.estimator.inputs.numpy_input_fn( + x={'images': mnist.train.images}, y=mnist.train.labels, + batch_size=batch_size, num_epochs=None, shuffle=True) +# Train the Model +model.train(input_fn, steps=num_steps) + +# Evaluate the Model +# Define the input function for evaluating +input_fn = tf.estimator.inputs.numpy_input_fn( + x={'images': mnist.test.images}, y=mnist.test.labels, + batch_size=batch_size, shuffle=False) +# Use the Estimator 'evaluate' method +e = model.evaluate(input_fn) + +print("Testing Accuracy:", e['accuracy']) diff --git a/tensorflow_v1/examples/3_NeuralNetworks/neural_network_eager_api.py b/tensorflow_v1/examples/3_NeuralNetworks/neural_network_eager_api.py new file mode 100644 index 00000000..2151bba9 --- /dev/null +++ b/tensorflow_v1/examples/3_NeuralNetworks/neural_network_eager_api.py @@ -0,0 +1,133 @@ +""" Neural Network with Eager API. + +A 2-Hidden Layers Fully Connected Neural Network (a.k.a Multilayer Perceptron) +implementation with TensorFlow's Eager API. This example is using the MNIST database +of handwritten digits (http://yann.lecun.com/exdb/mnist/). + +This example is using TensorFlow layers, see 'neural_network_raw' example for +a raw implementation with variables. + +Links: + [MNIST Dataset](http://yann.lecun.com/exdb/mnist/). + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" +from __future__ import print_function + +import tensorflow as tf + +# Set Eager API +tf.enable_eager_execution() +tfe = tf.contrib.eager + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=False) + +# Parameters +learning_rate = 0.001 +num_steps = 1000 +batch_size = 128 +display_step = 100 + +# Network Parameters +n_hidden_1 = 256 # 1st layer number of neurons +n_hidden_2 = 256 # 2nd layer number of neurons +num_input = 784 # MNIST data input (img shape: 28*28) +num_classes = 10 # MNIST total classes (0-9 digits) + +# Using TF Dataset to split data into batches +dataset = tf.data.Dataset.from_tensor_slices( + (mnist.train.images, mnist.train.labels)) +dataset = dataset.repeat().batch(batch_size).prefetch(batch_size) +dataset_iter = tfe.Iterator(dataset) + + +# Define the neural network. To use eager API and tf.layers API together, +# we must instantiate a tfe.Network class as follow: +class NeuralNet(tfe.Network): + def __init__(self): + # Define each layer + super(NeuralNet, self).__init__() + # Hidden fully connected layer with 256 neurons + self.layer1 = self.track_layer( + tf.layers.Dense(n_hidden_1, activation=tf.nn.relu)) + # Hidden fully connected layer with 256 neurons + self.layer2 = self.track_layer( + tf.layers.Dense(n_hidden_2, activation=tf.nn.relu)) + # Output fully connected layer with a neuron for each class + self.out_layer = self.track_layer(tf.layers.Dense(num_classes)) + + def call(self, x): + x = self.layer1(x) + x = self.layer2(x) + return self.out_layer(x) + + +neural_net = NeuralNet() + + +# Cross-Entropy loss function +def loss_fn(inference_fn, inputs, labels): + # Using sparse_softmax cross entropy + return tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits( + logits=inference_fn(inputs), labels=labels)) + + +# Calculate accuracy +def accuracy_fn(inference_fn, inputs, labels): + prediction = tf.nn.softmax(inference_fn(inputs)) + correct_pred = tf.equal(tf.argmax(prediction, 1), labels) + return tf.reduce_mean(tf.cast(correct_pred, tf.float32)) + + +# SGD Optimizer +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) +# Compute gradients +grad = tfe.implicit_gradients(loss_fn) + +# Training +average_loss = 0. +average_acc = 0. +for step in range(num_steps): + + # Iterate through the dataset + d = dataset_iter.next() + + # Images + x_batch = d[0] + # Labels + y_batch = tf.cast(d[1], dtype=tf.int64) + + # Compute the batch loss + batch_loss = loss_fn(neural_net, x_batch, y_batch) + average_loss += batch_loss + # Compute the batch accuracy + batch_accuracy = accuracy_fn(neural_net, x_batch, y_batch) + average_acc += batch_accuracy + + if step == 0: + # Display the initial cost, before optimizing + print("Initial loss= {:.9f}".format(average_loss)) + + # Update the variables following gradients info + optimizer.apply_gradients(grad(neural_net, x_batch, y_batch)) + + # Display info + if (step + 1) % display_step == 0 or step == 0: + if step > 0: + average_loss /= display_step + average_acc /= display_step + print("Step:", '%04d' % (step + 1), " loss=", + "{:.9f}".format(average_loss), " accuracy=", + "{:.4f}".format(average_acc)) + average_loss = 0. + average_acc = 0. + +# Evaluate model on the test image set +testX = mnist.test.images +testY = mnist.test.labels + +test_acc = accuracy_fn(neural_net, testX, testY) +print("Testset Accuracy: {:.4f}".format(test_acc)) diff --git a/tensorflow_v1/examples/3_NeuralNetworks/neural_network_raw.py b/tensorflow_v1/examples/3_NeuralNetworks/neural_network_raw.py new file mode 100644 index 00000000..9c9962ba --- /dev/null +++ b/tensorflow_v1/examples/3_NeuralNetworks/neural_network_raw.py @@ -0,0 +1,101 @@ +""" Neural Network. + +A 2-Hidden Layers Fully Connected Neural Network (a.k.a Multilayer Perceptron) +implementation with TensorFlow. This example is using the MNIST database +of handwritten digits (http://yann.lecun.com/exdb/mnist/). + +Links: + [MNIST Dataset](http://yann.lecun.com/exdb/mnist/). + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" + +from __future__ import print_function + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) + +import tensorflow as tf + +# Parameters +learning_rate = 0.1 +num_steps = 500 +batch_size = 128 +display_step = 100 + +# Network Parameters +n_hidden_1 = 256 # 1st layer number of neurons +n_hidden_2 = 256 # 2nd layer number of neurons +num_input = 784 # MNIST data input (img shape: 28*28) +num_classes = 10 # MNIST total classes (0-9 digits) + +# tf Graph input +X = tf.placeholder("float", [None, num_input]) +Y = tf.placeholder("float", [None, num_classes]) + +# Store layers weight & bias +weights = { + 'h1': tf.Variable(tf.random_normal([num_input, n_hidden_1])), + 'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])), + 'out': tf.Variable(tf.random_normal([n_hidden_2, num_classes])) +} +biases = { + 'b1': tf.Variable(tf.random_normal([n_hidden_1])), + 'b2': tf.Variable(tf.random_normal([n_hidden_2])), + 'out': tf.Variable(tf.random_normal([num_classes])) +} + + +# Create model +def neural_net(x): + # Hidden fully connected layer with 256 neurons + layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1']) + # Hidden fully connected layer with 256 neurons + layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2']) + # Output fully connected layer with a neuron for each class + out_layer = tf.matmul(layer_2, weights['out']) + biases['out'] + return out_layer + +# Construct model +logits = neural_net(X) +prediction = tf.nn.softmax(logits) + +# Define loss and optimizer +loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits( + logits=logits, labels=Y)) +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) +train_op = optimizer.minimize(loss_op) + +# Evaluate model +correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1)) +accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) + +# Initialize the variables (i.e. assign their default value) +init = tf.global_variables_initializer() + +# Start training +with tf.Session() as sess: + + # Run the initializer + sess.run(init) + + for step in range(1, num_steps+1): + batch_x, batch_y = mnist.train.next_batch(batch_size) + # Run optimization op (backprop) + sess.run(train_op, feed_dict={X: batch_x, Y: batch_y}) + if step % display_step == 0 or step == 1: + # Calculate batch loss and accuracy + loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x, + Y: batch_y}) + print("Step " + str(step) + ", Minibatch Loss= " + \ + "{:.4f}".format(loss) + ", Training Accuracy= " + \ + "{:.3f}".format(acc)) + + print("Optimization Finished!") + + # Calculate accuracy for MNIST test images + print("Testing Accuracy:", \ + sess.run(accuracy, feed_dict={X: mnist.test.images, + Y: mnist.test.labels})) diff --git a/tensorflow_v1/examples/3_NeuralNetworks/recurrent_network.py b/tensorflow_v1/examples/3_NeuralNetworks/recurrent_network.py new file mode 100644 index 00000000..fbc3d271 --- /dev/null +++ b/tensorflow_v1/examples/3_NeuralNetworks/recurrent_network.py @@ -0,0 +1,115 @@ +""" Recurrent Neural Network. + +A Recurrent Neural Network (LSTM) implementation example using TensorFlow library. +This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/) + +Links: + [Long Short Term Memory](http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf) + [MNIST Dataset](http://yann.lecun.com/exdb/mnist/). + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" + +from __future__ import print_function + +import tensorflow as tf +from tensorflow.contrib import rnn + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) + +''' +To classify images using a recurrent neural network, we consider every image +row as a sequence of pixels. Because MNIST image shape is 28*28px, we will then +handle 28 sequences of 28 steps for every sample. +''' + +# Training Parameters +learning_rate = 0.001 +training_steps = 10000 +batch_size = 128 +display_step = 200 + +# Network Parameters +num_input = 28 # MNIST data input (img shape: 28*28) +timesteps = 28 # timesteps +num_hidden = 128 # hidden layer num of features +num_classes = 10 # MNIST total classes (0-9 digits) + +# tf Graph input +X = tf.placeholder("float", [None, timesteps, num_input]) +Y = tf.placeholder("float", [None, num_classes]) + +# Define weights +weights = { + 'out': tf.Variable(tf.random_normal([num_hidden, num_classes])) +} +biases = { + 'out': tf.Variable(tf.random_normal([num_classes])) +} + + +def RNN(x, weights, biases): + + # Prepare data shape to match `rnn` function requirements + # Current data input shape: (batch_size, timesteps, n_input) + # Required shape: 'timesteps' tensors list of shape (batch_size, n_input) + + # Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input) + x = tf.unstack(x, timesteps, 1) + + # Define a lstm cell with tensorflow + lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0) + + # Get lstm cell output + outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32) + + # Linear activation, using rnn inner loop last output + return tf.matmul(outputs[-1], weights['out']) + biases['out'] + +logits = RNN(X, weights, biases) +prediction = tf.nn.softmax(logits) + +# Define loss and optimizer +loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits( + logits=logits, labels=Y)) +optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate) +train_op = optimizer.minimize(loss_op) + +# Evaluate model (with test logits, for dropout to be disabled) +correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1)) +accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) + +# Initialize the variables (i.e. assign their default value) +init = tf.global_variables_initializer() + +# Start training +with tf.Session() as sess: + + # Run the initializer + sess.run(init) + + for step in range(1, training_steps+1): + batch_x, batch_y = mnist.train.next_batch(batch_size) + # Reshape data to get 28 seq of 28 elements + batch_x = batch_x.reshape((batch_size, timesteps, num_input)) + # Run optimization op (backprop) + sess.run(train_op, feed_dict={X: batch_x, Y: batch_y}) + if step % display_step == 0 or step == 1: + # Calculate batch loss and accuracy + loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x, + Y: batch_y}) + print("Step " + str(step) + ", Minibatch Loss= " + \ + "{:.4f}".format(loss) + ", Training Accuracy= " + \ + "{:.3f}".format(acc)) + + print("Optimization Finished!") + + # Calculate accuracy for 128 mnist test images + test_len = 128 + test_data = mnist.test.images[:test_len].reshape((-1, timesteps, num_input)) + test_label = mnist.test.labels[:test_len] + print("Testing Accuracy:", \ + sess.run(accuracy, feed_dict={X: test_data, Y: test_label})) diff --git a/tensorflow_v1/examples/3_NeuralNetworks/variational_autoencoder.py b/tensorflow_v1/examples/3_NeuralNetworks/variational_autoencoder.py new file mode 100644 index 00000000..8a8fd378 --- /dev/null +++ b/tensorflow_v1/examples/3_NeuralNetworks/variational_autoencoder.py @@ -0,0 +1,143 @@ +""" Variational Auto-Encoder Example. + +Using a variational auto-encoder to generate digits images from noise. +MNIST handwritten digits are used as training examples. + +References: + - Auto-Encoding Variational Bayes The International Conference on Learning + Representations (ICLR), Banff, 2014. D.P. Kingma, M. Welling + - Understanding the difficulty of training deep feedforward neural networks. + X Glorot, Y Bengio. Aistats 9, 249-256 + - Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. "Gradient-based + learning applied to document recognition." Proceedings of the IEEE, + 86(11):2278-2324, November 1998. + +Links: + - [VAE Paper] https://arxiv.org/abs/1312.6114 + - [Xavier Glorot Init](www.cs.cmu.edu/~bhiksha/courses/deeplearning/Fall.../AISTATS2010_Glorot.pdf). + - [MNIST Dataset] http://yann.lecun.com/exdb/mnist/ + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" +from __future__ import division, print_function, absolute_import + +import numpy as np +import matplotlib.pyplot as plt +from scipy.stats import norm +import tensorflow as tf + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) + +# Parameters +learning_rate = 0.001 +num_steps = 30000 +batch_size = 64 + +# Network Parameters +image_dim = 784 # MNIST images are 28x28 pixels +hidden_dim = 512 +latent_dim = 2 + +# A custom initialization (see Xavier Glorot init) +def glorot_init(shape): + return tf.random_normal(shape=shape, stddev=1. / tf.sqrt(shape[0] / 2.)) + +# Variables +weights = { + 'encoder_h1': tf.Variable(glorot_init([image_dim, hidden_dim])), + 'z_mean': tf.Variable(glorot_init([hidden_dim, latent_dim])), + 'z_std': tf.Variable(glorot_init([hidden_dim, latent_dim])), + 'decoder_h1': tf.Variable(glorot_init([latent_dim, hidden_dim])), + 'decoder_out': tf.Variable(glorot_init([hidden_dim, image_dim])) +} +biases = { + 'encoder_b1': tf.Variable(glorot_init([hidden_dim])), + 'z_mean': tf.Variable(glorot_init([latent_dim])), + 'z_std': tf.Variable(glorot_init([latent_dim])), + 'decoder_b1': tf.Variable(glorot_init([hidden_dim])), + 'decoder_out': tf.Variable(glorot_init([image_dim])) +} + +# Building the encoder +input_image = tf.placeholder(tf.float32, shape=[None, image_dim]) +encoder = tf.matmul(input_image, weights['encoder_h1']) + biases['encoder_b1'] +encoder = tf.nn.tanh(encoder) +z_mean = tf.matmul(encoder, weights['z_mean']) + biases['z_mean'] +z_std = tf.matmul(encoder, weights['z_std']) + biases['z_std'] + +# Sampler: Normal (gaussian) random distribution +eps = tf.random_normal(tf.shape(z_std), dtype=tf.float32, mean=0., stddev=1.0, + name='epsilon') +z = z_mean + tf.exp(z_std / 2) * eps + +# Building the decoder (with scope to re-use these layers later) +decoder = tf.matmul(z, weights['decoder_h1']) + biases['decoder_b1'] +decoder = tf.nn.tanh(decoder) +decoder = tf.matmul(decoder, weights['decoder_out']) + biases['decoder_out'] +decoder = tf.nn.sigmoid(decoder) + + +# Define VAE Loss +def vae_loss(x_reconstructed, x_true): + # Reconstruction loss + encode_decode_loss = x_true * tf.log(1e-10 + x_reconstructed) \ + + (1 - x_true) * tf.log(1e-10 + 1 - x_reconstructed) + encode_decode_loss = -tf.reduce_sum(encode_decode_loss, 1) + # KL Divergence loss + kl_div_loss = 1 + z_std - tf.square(z_mean) - tf.exp(z_std) + kl_div_loss = -0.5 * tf.reduce_sum(kl_div_loss, 1) + return tf.reduce_mean(encode_decode_loss + kl_div_loss) + +loss_op = vae_loss(decoder, input_image) +optimizer = tf.train.RMSPropOptimizer(learning_rate=learning_rate) +train_op = optimizer.minimize(loss_op) + +# Initialize the variables (i.e. assign their default value) +init = tf.global_variables_initializer() + +# Start training +with tf.Session() as sess: + + # Run the initializer + sess.run(init) + + for i in range(1, num_steps+1): + # Prepare Data + # Get the next batch of MNIST data (only images are needed, not labels) + batch_x, _ = mnist.train.next_batch(batch_size) + + # Train + feed_dict = {input_image: batch_x} + _, l = sess.run([train_op, loss_op], feed_dict=feed_dict) + if i % 1000 == 0 or i == 1: + print('Step %i, Loss: %f' % (i, l)) + + # Testing + # Generator takes noise as input + noise_input = tf.placeholder(tf.float32, shape=[None, latent_dim]) + # Rebuild the decoder to create image from noise + decoder = tf.matmul(noise_input, weights['decoder_h1']) + biases['decoder_b1'] + decoder = tf.nn.tanh(decoder) + decoder = tf.matmul(decoder, weights['decoder_out']) + biases['decoder_out'] + decoder = tf.nn.sigmoid(decoder) + + # Building a manifold of generated digits + n = 20 + x_axis = np.linspace(-3, 3, n) + y_axis = np.linspace(-3, 3, n) + + canvas = np.empty((28 * n, 28 * n)) + for i, yi in enumerate(x_axis): + for j, xi in enumerate(y_axis): + z_mu = np.array([[xi, yi]] * batch_size) + x_mean = sess.run(decoder, feed_dict={noise_input: z_mu}) + canvas[(n - i - 1) * 28:(n - i) * 28, j * 28:(j + 1) * 28] = \ + x_mean[0].reshape(28, 28) + + plt.figure(figsize=(8, 10)) + Xi, Yi = np.meshgrid(x_axis, y_axis) + plt.imshow(canvas, origin="upper", cmap="gray") + plt.show() diff --git a/tensorflow_v1/examples/4_Utils/save_restore_model.py b/tensorflow_v1/examples/4_Utils/save_restore_model.py new file mode 100644 index 00000000..56af08b1 --- /dev/null +++ b/tensorflow_v1/examples/4_Utils/save_restore_model.py @@ -0,0 +1,140 @@ +''' +Save and Restore a model using TensorFlow. +This example is using the MNIST database of handwritten digits +(http://yann.lecun.com/exdb/mnist/) + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +''' + +from __future__ import print_function + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) + +import tensorflow as tf + +# Parameters +learning_rate = 0.001 +batch_size = 100 +display_step = 1 +model_path = "/tmp/model.ckpt" + +# Network Parameters +n_hidden_1 = 256 # 1st layer number of features +n_hidden_2 = 256 # 2nd layer number of features +n_input = 784 # MNIST data input (img shape: 28*28) +n_classes = 10 # MNIST total classes (0-9 digits) + +# tf Graph input +x = tf.placeholder("float", [None, n_input]) +y = tf.placeholder("float", [None, n_classes]) + + +# Create model +def multilayer_perceptron(x, weights, biases): + # Hidden layer with RELU activation + layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1']) + layer_1 = tf.nn.relu(layer_1) + # Hidden layer with RELU activation + layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2']) + layer_2 = tf.nn.relu(layer_2) + # Output layer with linear activation + out_layer = tf.matmul(layer_2, weights['out']) + biases['out'] + return out_layer + +# Store layers weight & bias +weights = { + 'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])), + 'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])), + 'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes])) +} +biases = { + 'b1': tf.Variable(tf.random_normal([n_hidden_1])), + 'b2': tf.Variable(tf.random_normal([n_hidden_2])), + 'out': tf.Variable(tf.random_normal([n_classes])) +} + +# Construct model +pred = multilayer_perceptron(x, weights, biases) + +# Define loss and optimizer +cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y)) +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) + +# Initialize the variables (i.e. assign their default value) +init = tf.global_variables_initializer() + +# 'Saver' op to save and restore all the variables +saver = tf.train.Saver() + +# Running first session +print("Starting 1st session...") +with tf.Session() as sess: + + # Run the initializer + sess.run(init) + + # Training cycle + for epoch in range(3): + avg_cost = 0. + total_batch = int(mnist.train.num_examples/batch_size) + # Loop over all batches + for i in range(total_batch): + batch_x, batch_y = mnist.train.next_batch(batch_size) + # Run optimization op (backprop) and cost op (to get loss value) + _, c = sess.run([optimizer, cost], feed_dict={x: batch_x, + y: batch_y}) + # Compute average loss + avg_cost += c / total_batch + # Display logs per epoch step + if epoch % display_step == 0: + print("Epoch:", '%04d' % (epoch+1), "cost=", \ + "{:.9f}".format(avg_cost)) + print("First Optimization Finished!") + + # Test model + correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) + # Calculate accuracy + accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) + print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})) + + # Save model weights to disk + save_path = saver.save(sess, model_path) + print("Model saved in file: %s" % save_path) + +# Running a new session +print("Starting 2nd session...") +with tf.Session() as sess: + # Initialize variables + sess.run(init) + + # Restore model weights from previously saved model + saver.restore(sess, model_path) + print("Model restored from file: %s" % save_path) + + # Resume training + for epoch in range(7): + avg_cost = 0. + total_batch = int(mnist.train.num_examples / batch_size) + # Loop over all batches + for i in range(total_batch): + batch_x, batch_y = mnist.train.next_batch(batch_size) + # Run optimization op (backprop) and cost op (to get loss value) + _, c = sess.run([optimizer, cost], feed_dict={x: batch_x, + y: batch_y}) + # Compute average loss + avg_cost += c / total_batch + # Display logs per epoch step + if epoch % display_step == 0: + print("Epoch:", '%04d' % (epoch + 1), "cost=", \ + "{:.9f}".format(avg_cost)) + print("Second Optimization Finished!") + + # Test model + correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) + # Calculate accuracy + accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) + print("Accuracy:", accuracy.eval( + {x: mnist.test.images, y: mnist.test.labels})) diff --git a/tensorflow_v1/examples/4_Utils/tensorboard_advanced.py b/tensorflow_v1/examples/4_Utils/tensorboard_advanced.py new file mode 100644 index 00000000..45a7f962 --- /dev/null +++ b/tensorflow_v1/examples/4_Utils/tensorboard_advanced.py @@ -0,0 +1,143 @@ +''' +Graph and Loss visualization using Tensorboard. +This example is using the MNIST database of handwritten digits +(http://yann.lecun.com/exdb/mnist/) + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +''' + +from __future__ import print_function + +import tensorflow as tf + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) + +# Parameters +learning_rate = 0.01 +training_epochs = 25 +batch_size = 100 +display_step = 1 +logs_path = '/tmp/tensorflow_logs/example/' + +# Network Parameters +n_hidden_1 = 256 # 1st layer number of features +n_hidden_2 = 256 # 2nd layer number of features +n_input = 784 # MNIST data input (img shape: 28*28) +n_classes = 10 # MNIST total classes (0-9 digits) + +# tf Graph Input +# mnist data image of shape 28*28=784 +x = tf.placeholder(tf.float32, [None, 784], name='InputData') +# 0-9 digits recognition => 10 classes +y = tf.placeholder(tf.float32, [None, 10], name='LabelData') + + +# Create model +def multilayer_perceptron(x, weights, biases): + # Hidden layer with RELU activation + layer_1 = tf.add(tf.matmul(x, weights['w1']), biases['b1']) + layer_1 = tf.nn.relu(layer_1) + # Create a summary to visualize the first layer ReLU activation + tf.summary.histogram("relu1", layer_1) + # Hidden layer with RELU activation + layer_2 = tf.add(tf.matmul(layer_1, weights['w2']), biases['b2']) + layer_2 = tf.nn.relu(layer_2) + # Create another summary to visualize the second layer ReLU activation + tf.summary.histogram("relu2", layer_2) + # Output layer + out_layer = tf.add(tf.matmul(layer_2, weights['w3']), biases['b3']) + return out_layer + +# Store layers weight & bias +weights = { + 'w1': tf.Variable(tf.random_normal([n_input, n_hidden_1]), name='W1'), + 'w2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2]), name='W2'), + 'w3': tf.Variable(tf.random_normal([n_hidden_2, n_classes]), name='W3') +} +biases = { + 'b1': tf.Variable(tf.random_normal([n_hidden_1]), name='b1'), + 'b2': tf.Variable(tf.random_normal([n_hidden_2]), name='b2'), + 'b3': tf.Variable(tf.random_normal([n_classes]), name='b3') +} + +# Encapsulating all ops into scopes, making Tensorboard's Graph +# Visualization more convenient +with tf.name_scope('Model'): + # Build model + pred = multilayer_perceptron(x, weights, biases) + +with tf.name_scope('Loss'): + # Softmax Cross entropy (cost function) + loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y)) + +with tf.name_scope('SGD'): + # Gradient Descent + optimizer = tf.train.GradientDescentOptimizer(learning_rate) + # Op to calculate every variable gradient + grads = tf.gradients(loss, tf.trainable_variables()) + grads = list(zip(grads, tf.trainable_variables())) + # Op to update all variables according to their gradient + apply_grads = optimizer.apply_gradients(grads_and_vars=grads) + +with tf.name_scope('Accuracy'): + # Accuracy + acc = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) + acc = tf.reduce_mean(tf.cast(acc, tf.float32)) + +# Initialize the variables (i.e. assign their default value) +init = tf.global_variables_initializer() + +# Create a summary to monitor cost tensor +tf.summary.scalar("loss", loss) +# Create a summary to monitor accuracy tensor +tf.summary.scalar("accuracy", acc) +# Create summaries to visualize weights +for var in tf.trainable_variables(): + tf.summary.histogram(var.name, var) +# Summarize all gradients +for grad, var in grads: + tf.summary.histogram(var.name + '/gradient', grad) +# Merge all summaries into a single op +merged_summary_op = tf.summary.merge_all() + +# Start training +with tf.Session() as sess: + + # Run the initializer + sess.run(init) + + # op to write logs to Tensorboard + summary_writer = tf.summary.FileWriter(logs_path, + graph=tf.get_default_graph()) + + # Training cycle + for epoch in range(training_epochs): + avg_cost = 0. + total_batch = int(mnist.train.num_examples/batch_size) + # Loop over all batches + for i in range(total_batch): + batch_xs, batch_ys = mnist.train.next_batch(batch_size) + # Run optimization op (backprop), cost op (to get loss value) + # and summary nodes + _, c, summary = sess.run([apply_grads, loss, merged_summary_op], + feed_dict={x: batch_xs, y: batch_ys}) + # Write logs at every iteration + summary_writer.add_summary(summary, epoch * total_batch + i) + # Compute average loss + avg_cost += c / total_batch + # Display logs per epoch step + if (epoch+1) % display_step == 0: + print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost)) + + print("Optimization Finished!") + + # Test model + # Calculate accuracy + print("Accuracy:", acc.eval({x: mnist.test.images, y: mnist.test.labels})) + + print("Run the command line:\n" \ + "--> tensorboard --logdir=/tmp/tensorflow_logs " \ + "\nThen open http://0.0.0.0:6006/ into your web browser") diff --git a/tensorflow_v1/examples/4_Utils/tensorboard_basic.py b/tensorflow_v1/examples/4_Utils/tensorboard_basic.py new file mode 100644 index 00000000..81216c0b --- /dev/null +++ b/tensorflow_v1/examples/4_Utils/tensorboard_basic.py @@ -0,0 +1,97 @@ +''' +Graph and Loss visualization using Tensorboard. +This example is using the MNIST database of handwritten digits +(http://yann.lecun.com/exdb/mnist/) + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +''' + +from __future__ import print_function + +import tensorflow as tf + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) + +# Parameters +learning_rate = 0.01 +training_epochs = 25 +batch_size = 100 +display_epoch = 1 +logs_path = '/tmp/tensorflow_logs/example/' + +# tf Graph Input +# mnist data image of shape 28*28=784 +x = tf.placeholder(tf.float32, [None, 784], name='InputData') +# 0-9 digits recognition => 10 classes +y = tf.placeholder(tf.float32, [None, 10], name='LabelData') + +# Set model weights +W = tf.Variable(tf.zeros([784, 10]), name='Weights') +b = tf.Variable(tf.zeros([10]), name='Bias') + +# Construct model and encapsulating all ops into scopes, making +# Tensorboard's Graph visualization more convenient +with tf.name_scope('Model'): + # Model + pred = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax +with tf.name_scope('Loss'): + # Minimize error using cross entropy + cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1)) +with tf.name_scope('SGD'): + # Gradient Descent + optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) +with tf.name_scope('Accuracy'): + # Accuracy + acc = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) + acc = tf.reduce_mean(tf.cast(acc, tf.float32)) + +# Initialize the variables (i.e. assign their default value) +init = tf.global_variables_initializer() + +# Create a summary to monitor cost tensor +tf.summary.scalar("loss", cost) +# Create a summary to monitor accuracy tensor +tf.summary.scalar("accuracy", acc) +# Merge all summaries into a single op +merged_summary_op = tf.summary.merge_all() + +# Start training +with tf.Session() as sess: + + # Run the initializer + sess.run(init) + + # op to write logs to Tensorboard + summary_writer = tf.summary.FileWriter(logs_path, graph=tf.get_default_graph()) + + # Training cycle + for epoch in range(training_epochs): + avg_cost = 0. + total_batch = int(mnist.train.num_examples/batch_size) + # Loop over all batches + for i in range(total_batch): + batch_xs, batch_ys = mnist.train.next_batch(batch_size) + # Run optimization op (backprop), cost op (to get loss value) + # and summary nodes + _, c, summary = sess.run([optimizer, cost, merged_summary_op], + feed_dict={x: batch_xs, y: batch_ys}) + # Write logs at every iteration + summary_writer.add_summary(summary, epoch * total_batch + i) + # Compute average loss + avg_cost += c / total_batch + # Display logs per epoch step + if (epoch+1) % display_epoch == 0: + print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost)) + + print("Optimization Finished!") + + # Test model + # Calculate accuracy + print("Accuracy:", acc.eval({x: mnist.test.images, y: mnist.test.labels})) + + print("Run the command line:\n" \ + "--> tensorboard --logdir=/tmp/tensorflow_logs " \ + "\nThen open http://0.0.0.0:6006/ into your web browser") diff --git a/tensorflow_v1/examples/5_DataManagement/build_an_image_dataset.py b/tensorflow_v1/examples/5_DataManagement/build_an_image_dataset.py new file mode 100644 index 00000000..8993665b --- /dev/null +++ b/tensorflow_v1/examples/5_DataManagement/build_an_image_dataset.py @@ -0,0 +1,212 @@ +""" Build an Image Dataset in TensorFlow. + +For this example, you need to make your own set of images (JPEG). +We will show 2 different ways to build that dataset: + +- From a root folder, that will have a sub-folder containing images for each class + ``` + ROOT_FOLDER + |-------- SUBFOLDER (CLASS 0) + | | + | | ----- image1.jpg + | | ----- image2.jpg + | | ----- etc... + | + |-------- SUBFOLDER (CLASS 1) + | | + | | ----- image1.jpg + | | ----- image2.jpg + | | ----- etc... + ``` + +- From a plain text file, that will list all images with their class ID: + ``` + /path/to/image/1.jpg CLASS_ID + /path/to/image/2.jpg CLASS_ID + /path/to/image/3.jpg CLASS_ID + /path/to/image/4.jpg CLASS_ID + etc... + ``` + +Below, there are some parameters that you need to change (Marked 'CHANGE HERE'), +such as the dataset path. + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" +from __future__ import print_function + +import tensorflow as tf +import os + +# Dataset Parameters - CHANGE HERE +MODE = 'folder' # or 'file', if you choose a plain text file (see above). +DATASET_PATH = '/path/to/dataset/' # the dataset file or root folder path. + +# Image Parameters +N_CLASSES = 2 # CHANGE HERE, total number of classes +IMG_HEIGHT = 64 # CHANGE HERE, the image height to be resized to +IMG_WIDTH = 64 # CHANGE HERE, the image width to be resized to +CHANNELS = 3 # The 3 color channels, change to 1 if grayscale + + +# Reading the dataset +# 2 modes: 'file' or 'folder' +def read_images(dataset_path, mode, batch_size): + imagepaths, labels = list(), list() + if mode == 'file': + # Read dataset file + with open(dataset_path) as f: + data = f.read().splitlines() + for d in data: + imagepaths.append(d.split(' ')[0]) + labels.append(int(d.split(' ')[1])) + elif mode == 'folder': + # An ID will be affected to each sub-folders by alphabetical order + label = 0 + # List the directory + try: # Python 2 + classes = sorted(os.walk(dataset_path).next()[1]) + except Exception: # Python 3 + classes = sorted(os.walk(dataset_path).__next__()[1]) + # List each sub-directory (the classes) + for c in classes: + c_dir = os.path.join(dataset_path, c) + try: # Python 2 + walk = os.walk(c_dir).next() + except Exception: # Python 3 + walk = os.walk(c_dir).__next__() + # Add each image to the training set + for sample in walk[2]: + # Only keeps jpeg images + if sample.endswith('.jpg') or sample.endswith('.jpeg'): + imagepaths.append(os.path.join(c_dir, sample)) + labels.append(label) + label += 1 + else: + raise Exception("Unknown mode.") + + # Convert to Tensor + imagepaths = tf.convert_to_tensor(imagepaths, dtype=tf.string) + labels = tf.convert_to_tensor(labels, dtype=tf.int32) + # Build a TF Queue, shuffle data + image, label = tf.train.slice_input_producer([imagepaths, labels], + shuffle=True) + + # Read images from disk + image = tf.read_file(image) + image = tf.image.decode_jpeg(image, channels=CHANNELS) + + # Resize images to a common size + image = tf.image.resize_images(image, [IMG_HEIGHT, IMG_WIDTH]) + + # Normalize + image = image * 1.0/127.5 - 1.0 + + # Create batches + X, Y = tf.train.batch([image, label], batch_size=batch_size, + capacity=batch_size * 8, + num_threads=4) + + return X, Y + +# ----------------------------------------------- +# THIS IS A CLASSIC CNN (see examples, section 3) +# ----------------------------------------------- +# Note that a few elements have changed (usage of queues). + +# Parameters +learning_rate = 0.001 +num_steps = 10000 +batch_size = 128 +display_step = 100 + +# Network Parameters +dropout = 0.75 # Dropout, probability to keep units + +# Build the data input +X, Y = read_images(DATASET_PATH, MODE, batch_size) + + +# Create model +def conv_net(x, n_classes, dropout, reuse, is_training): + # Define a scope for reusing the variables + with tf.variable_scope('ConvNet', reuse=reuse): + + # Convolution Layer with 32 filters and a kernel size of 5 + conv1 = tf.layers.conv2d(x, 32, 5, activation=tf.nn.relu) + # Max Pooling (down-sampling) with strides of 2 and kernel size of 2 + conv1 = tf.layers.max_pooling2d(conv1, 2, 2) + + # Convolution Layer with 32 filters and a kernel size of 5 + conv2 = tf.layers.conv2d(conv1, 64, 3, activation=tf.nn.relu) + # Max Pooling (down-sampling) with strides of 2 and kernel size of 2 + conv2 = tf.layers.max_pooling2d(conv2, 2, 2) + + # Flatten the data to a 1-D vector for the fully connected layer + fc1 = tf.contrib.layers.flatten(conv2) + + # Fully connected layer (in contrib folder for now) + fc1 = tf.layers.dense(fc1, 1024) + # Apply Dropout (if is_training is False, dropout is not applied) + fc1 = tf.layers.dropout(fc1, rate=dropout, training=is_training) + + # Output layer, class prediction + out = tf.layers.dense(fc1, n_classes) + # Because 'softmax_cross_entropy_with_logits' already apply softmax, + # we only apply softmax to testing network + out = tf.nn.softmax(out) if not is_training else out + + return out + + +# Because Dropout have different behavior at training and prediction time, we +# need to create 2 distinct computation graphs that share the same weights. + +# Create a graph for training +logits_train = conv_net(X, N_CLASSES, dropout, reuse=False, is_training=True) +# Create another graph for testing that reuse the same weights +logits_test = conv_net(X, N_CLASSES, dropout, reuse=True, is_training=False) + +# Define loss and optimizer (with train logits, for dropout to take effect) +loss_op = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits( + logits=logits_train, labels=Y)) +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) +train_op = optimizer.minimize(loss_op) + +# Evaluate model (with test logits, for dropout to be disabled) +correct_pred = tf.equal(tf.argmax(logits_test, 1), tf.cast(Y, tf.int64)) +accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) + +# Initialize the variables (i.e. assign their default value) +init = tf.global_variables_initializer() + +# Saver object +saver = tf.train.Saver() + +# Start training +with tf.Session() as sess: + + # Run the initializer + sess.run(init) + + # Start the data queue + tf.train.start_queue_runners() + + # Training cycle + for step in range(1, num_steps+1): + + if step % display_step == 0: + # Run optimization and calculate batch loss and accuracy + _, loss, acc = sess.run([train_op, loss_op, accuracy]) + print("Step " + str(step) + ", Minibatch Loss= " + \ + "{:.4f}".format(loss) + ", Training Accuracy= " + \ + "{:.3f}".format(acc)) + else: + # Only run the optimization op (backprop) + sess.run(train_op) + + print("Optimization Finished!") + + # Save your model + saver.save(sess, 'my_tf_model') diff --git a/tensorflow_v1/examples/5_DataManagement/tensorflow_dataset_api.py b/tensorflow_v1/examples/5_DataManagement/tensorflow_dataset_api.py new file mode 100644 index 00000000..dad0132a --- /dev/null +++ b/tensorflow_v1/examples/5_DataManagement/tensorflow_dataset_api.py @@ -0,0 +1,130 @@ +""" TensorFlow Dataset API. + +In this example, we will show how to load numpy array data into the new +TensorFlow 'Dataset' API. The Dataset API implements an optimized data pipeline +with queues, that make data processing and training faster (especially on GPU). + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" +from __future__ import print_function + +import tensorflow as tf + +# Import MNIST data (Numpy format) +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) + +# Parameters +learning_rate = 0.001 +num_steps = 2000 +batch_size = 128 +display_step = 100 + +# Network Parameters +n_input = 784 # MNIST data input (img shape: 28*28) +n_classes = 10 # MNIST total classes (0-9 digits) +dropout = 0.75 # Dropout, probability to keep units + +sess = tf.Session() + +# Create a dataset tensor from the images and the labels +dataset = tf.data.Dataset.from_tensor_slices( + (mnist.train.images, mnist.train.labels)) +# Automatically refill the data queue when empty +dataset = dataset.repeat() +# Create batches of data +dataset = dataset.batch(batch_size) +# Prefetch data for faster consumption +dataset = dataset.prefetch(batch_size) + +# Create an iterator over the dataset +iterator = dataset.make_initializable_iterator() +# Initialize the iterator +sess.run(iterator.initializer) + +# Neural Net Input (images, labels) +X, Y = iterator.get_next() + + +# ----------------------------------------------- +# THIS IS A CLASSIC CNN (see examples, section 3) +# ----------------------------------------------- +# Note that a few elements have changed (usage of sess run). + +# Create model +def conv_net(x, n_classes, dropout, reuse, is_training): + # Define a scope for reusing the variables + with tf.variable_scope('ConvNet', reuse=reuse): + # MNIST data input is a 1-D vector of 784 features (28*28 pixels) + # Reshape to match picture format [Height x Width x Channel] + # Tensor input become 4-D: [Batch Size, Height, Width, Channel] + x = tf.reshape(x, shape=[-1, 28, 28, 1]) + + # Convolution Layer with 32 filters and a kernel size of 5 + conv1 = tf.layers.conv2d(x, 32, 5, activation=tf.nn.relu) + # Max Pooling (down-sampling) with strides of 2 and kernel size of 2 + conv1 = tf.layers.max_pooling2d(conv1, 2, 2) + + # Convolution Layer with 32 filters and a kernel size of 5 + conv2 = tf.layers.conv2d(conv1, 64, 3, activation=tf.nn.relu) + # Max Pooling (down-sampling) with strides of 2 and kernel size of 2 + conv2 = tf.layers.max_pooling2d(conv2, 2, 2) + + # Flatten the data to a 1-D vector for the fully connected layer + fc1 = tf.contrib.layers.flatten(conv2) + + # Fully connected layer (in contrib folder for now) + fc1 = tf.layers.dense(fc1, 1024) + # Apply Dropout (if is_training is False, dropout is not applied) + fc1 = tf.layers.dropout(fc1, rate=dropout, training=is_training) + + # Output layer, class prediction + out = tf.layers.dense(fc1, n_classes) + # Because 'softmax_cross_entropy_with_logits' already apply softmax, + # we only apply softmax to testing network + out = tf.nn.softmax(out) if not is_training else out + + return out + + +# Because Dropout have different behavior at training and prediction time, we +# need to create 2 distinct computation graphs that share the same weights. + +# Create a graph for training +logits_train = conv_net(X, n_classes, dropout, reuse=False, is_training=True) +# Create another graph for testing that reuse the same weights, but has +# different behavior for 'dropout' (not applied). +logits_test = conv_net(X, n_classes, dropout, reuse=True, is_training=False) + +# Define loss and optimizer (with train logits, for dropout to take effect) +loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits( + logits=logits_train, labels=Y)) +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) +train_op = optimizer.minimize(loss_op) + +# Evaluate model (with test logits, for dropout to be disabled) +correct_pred = tf.equal(tf.argmax(logits_test, 1), tf.argmax(Y, 1)) +accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) + +# Initialize the variables (i.e. assign their default value) +init = tf.global_variables_initializer() + +# Run the initializer +sess.run(init) + +# Training cycle +for step in range(1, num_steps + 1): + + # Run optimization + sess.run(train_op) + + if step % display_step == 0 or step == 1: + # Calculate batch loss and accuracy + # (note that this consume a new batch of data) + loss, acc = sess.run([loss_op, accuracy]) + print("Step " + str(step) + ", Minibatch Loss= " + \ + "{:.4f}".format(loss) + ", Training Accuracy= " + \ + "{:.3f}".format(acc)) + +print("Optimization Finished!") diff --git a/tensorflow_v1/examples/6_MultiGPU/multigpu_basics.py b/tensorflow_v1/examples/6_MultiGPU/multigpu_basics.py new file mode 100644 index 00000000..b31120fa --- /dev/null +++ b/tensorflow_v1/examples/6_MultiGPU/multigpu_basics.py @@ -0,0 +1,94 @@ +from __future__ import print_function +''' +Basic Multi GPU computation example using TensorFlow library. + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +''' + +''' +This tutorial requires your machine to have 2 GPUs +"/cpu:0": The CPU of your machine. +"/gpu:0": The first GPU of your machine +"/gpu:1": The second GPU of your machine +''' + + + +import numpy as np +import tensorflow as tf +import datetime + +# Processing Units logs +log_device_placement = True + +# Num of multiplications to perform +n = 10 + +''' +Example: compute A^n + B^n on 2 GPUs +Results on 8 cores with 2 GTX-980: + * Single GPU computation time: 0:00:11.277449 + * Multi GPU computation time: 0:00:07.131701 +''' +# Create random large matrix +A = np.random.rand(10000, 10000).astype('float32') +B = np.random.rand(10000, 10000).astype('float32') + +# Create a graph to store results +c1 = [] +c2 = [] + +def matpow(M, n): + if n < 1: #Abstract cases where n < 1 + return M + else: + return tf.matmul(M, matpow(M, n-1)) + +''' +Single GPU computing +''' +with tf.device('/gpu:0'): + a = tf.placeholder(tf.float32, [10000, 10000]) + b = tf.placeholder(tf.float32, [10000, 10000]) + # Compute A^n and B^n and store results in c1 + c1.append(matpow(a, n)) + c1.append(matpow(b, n)) + +with tf.device('/cpu:0'): + sum = tf.add_n(c1) #Addition of all elements in c1, i.e. A^n + B^n + +t1_1 = datetime.datetime.now() +with tf.Session(config=tf.ConfigProto(log_device_placement=log_device_placement)) as sess: + # Run the op. + sess.run(sum, {a:A, b:B}) +t2_1 = datetime.datetime.now() + + +''' +Multi GPU computing +''' +# GPU:0 computes A^n +with tf.device('/gpu:0'): + # Compute A^n and store result in c2 + a = tf.placeholder(tf.float32, [10000, 10000]) + c2.append(matpow(a, n)) + +# GPU:1 computes B^n +with tf.device('/gpu:1'): + # Compute B^n and store result in c2 + b = tf.placeholder(tf.float32, [10000, 10000]) + c2.append(matpow(b, n)) + +with tf.device('/cpu:0'): + sum = tf.add_n(c2) #Addition of all elements in c2, i.e. A^n + B^n + +t1_2 = datetime.datetime.now() +with tf.Session(config=tf.ConfigProto(log_device_placement=log_device_placement)) as sess: + # Run the op. + sess.run(sum, {a:A, b:B}) +t2_2 = datetime.datetime.now() + + +print("Single GPU computation time: " + str(t2_1-t1_1)) +print("Multi GPU computation time: " + str(t2_2-t1_2)) diff --git a/tensorflow_v1/examples/6_MultiGPU/multigpu_cnn.py b/tensorflow_v1/examples/6_MultiGPU/multigpu_cnn.py new file mode 100644 index 00000000..be003ebd --- /dev/null +++ b/tensorflow_v1/examples/6_MultiGPU/multigpu_cnn.py @@ -0,0 +1,198 @@ +''' Multi-GPU Training Example. + +Train a convolutional neural network on multiple GPU with TensorFlow. + +This example is using TensorFlow layers, see 'convolutional_network_raw' example +for a raw TensorFlow implementation with variables. + +This example is using the MNIST database of handwritten digits +(http://yann.lecun.com/exdb/mnist/) + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +''' + +from __future__ import division, print_function, absolute_import + +import numpy as np +import tensorflow as tf +import time + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) + +# Training Parameters +num_gpus = 2 +num_steps = 200 +learning_rate = 0.001 +batch_size = 1024 +display_step = 10 + +# Network Parameters +num_input = 784 # MNIST data input (img shape: 28*28) +num_classes = 10 # MNIST total classes (0-9 digits) +dropout = 0.75 # Dropout, probability to keep units + + +# Build a convolutional neural network +def conv_net(x, n_classes, dropout, reuse, is_training): + # Define a scope for reusing the variables + with tf.variable_scope('ConvNet', reuse=reuse): + # MNIST data input is a 1-D vector of 784 features (28*28 pixels) + # Reshape to match picture format [Height x Width x Channel] + # Tensor input become 4-D: [Batch Size, Height, Width, Channel] + x = tf.reshape(x, shape=[-1, 28, 28, 1]) + + # Convolution Layer with 64 filters and a kernel size of 5 + x = tf.layers.conv2d(x, 64, 5, activation=tf.nn.relu) + # Max Pooling (down-sampling) with strides of 2 and kernel size of 2 + x = tf.layers.max_pooling2d(x, 2, 2) + + # Convolution Layer with 256 filters and a kernel size of 5 + x = tf.layers.conv2d(x, 256, 3, activation=tf.nn.relu) + # Convolution Layer with 512 filters and a kernel size of 5 + x = tf.layers.conv2d(x, 512, 3, activation=tf.nn.relu) + # Max Pooling (down-sampling) with strides of 2 and kernel size of 2 + x = tf.layers.max_pooling2d(x, 2, 2) + + # Flatten the data to a 1-D vector for the fully connected layer + x = tf.contrib.layers.flatten(x) + + # Fully connected layer (in contrib folder for now) + x = tf.layers.dense(x, 2048) + # Apply Dropout (if is_training is False, dropout is not applied) + x = tf.layers.dropout(x, rate=dropout, training=is_training) + + # Fully connected layer (in contrib folder for now) + x = tf.layers.dense(x, 1024) + # Apply Dropout (if is_training is False, dropout is not applied) + x = tf.layers.dropout(x, rate=dropout, training=is_training) + + # Output layer, class prediction + out = tf.layers.dense(x, n_classes) + # Because 'softmax_cross_entropy_with_logits' loss already apply + # softmax, we only apply softmax to testing network + out = tf.nn.softmax(out) if not is_training else out + + return out + + +def average_gradients(tower_grads): + average_grads = [] + for grad_and_vars in zip(*tower_grads): + # Note that each grad_and_vars looks like the following: + # ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN)) + grads = [] + for g, _ in grad_and_vars: + # Add 0 dimension to the gradients to represent the tower. + expanded_g = tf.expand_dims(g, 0) + + # Append on a 'tower' dimension which we will average over below. + grads.append(expanded_g) + + # Average over the 'tower' dimension. + grad = tf.concat(grads, 0) + grad = tf.reduce_mean(grad, 0) + + # Keep in mind that the Variables are redundant because they are shared + # across towers. So .. we will just return the first tower's pointer to + # the Variable. + v = grad_and_vars[0][1] + grad_and_var = (grad, v) + average_grads.append(grad_and_var) + return average_grads + + +# By default, all variables will be placed on '/gpu:0' +# So we need a custom device function, to assign all variables to '/cpu:0' +# Note: If GPUs are peered, '/gpu:0' can be a faster option +PS_OPS = ['Variable', 'VariableV2', 'AutoReloadVariable'] + +def assign_to_device(device, ps_device='/cpu:0'): + def _assign(op): + node_def = op if isinstance(op, tf.NodeDef) else op.node_def + if node_def.op in PS_OPS: + return "/" + ps_device + else: + return device + + return _assign + + +# Place all ops on CPU by default +with tf.device('/cpu:0'): + tower_grads = [] + reuse_vars = False + + # tf Graph input + X = tf.placeholder(tf.float32, [None, num_input]) + Y = tf.placeholder(tf.float32, [None, num_classes]) + + # Loop over all GPUs and construct their own computation graph + for i in range(num_gpus): + with tf.device(assign_to_device('/gpu:{}'.format(i), ps_device='/cpu:0')): + + # Split data between GPUs + _x = X[i * batch_size: (i+1) * batch_size] + _y = Y[i * batch_size: (i+1) * batch_size] + + # Because Dropout have different behavior at training and prediction time, we + # need to create 2 distinct computation graphs that share the same weights. + + # Create a graph for training + logits_train = conv_net(_x, num_classes, dropout, + reuse=reuse_vars, is_training=True) + # Create another graph for testing that reuse the same weights + logits_test = conv_net(_x, num_classes, dropout, + reuse=True, is_training=False) + + # Define loss and optimizer (with train logits, for dropout to take effect) + loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits( + logits=logits_train, labels=_y)) + optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) + grads = optimizer.compute_gradients(loss_op) + + # Only first GPU compute accuracy + if i == 0: + # Evaluate model (with test logits, for dropout to be disabled) + correct_pred = tf.equal(tf.argmax(logits_test, 1), tf.argmax(_y, 1)) + accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) + + reuse_vars = True + tower_grads.append(grads) + + tower_grads = average_gradients(tower_grads) + train_op = optimizer.apply_gradients(tower_grads) + + # Initialize the variables (i.e. assign their default value) + init = tf.global_variables_initializer() + + # Start Training + with tf.Session() as sess: + + # Run the initializer + sess.run(init) + + # Keep training until reach max iterations + for step in range(1, num_steps + 1): + # Get a batch for each GPU + batch_x, batch_y = mnist.train.next_batch(batch_size * num_gpus) + # Run optimization op (backprop) + ts = time.time() + sess.run(train_op, feed_dict={X: batch_x, Y: batch_y}) + te = time.time() - ts + if step % display_step == 0 or step == 1: + # Calculate batch loss and accuracy + loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x, + Y: batch_y}) + print("Step " + str(step) + ": Minibatch Loss= " + \ + "{:.4f}".format(loss) + ", Training Accuracy= " + \ + "{:.3f}".format(acc) + ", %i Examples/sec" % int(len(batch_x)/te)) + step += 1 + print("Optimization Finished!") + + # Calculate accuracy for MNIST test images + print("Testing Accuracy:", \ + np.mean([sess.run(accuracy, feed_dict={X: mnist.test.images[i:i+batch_size], + Y: mnist.test.labels[i:i+batch_size]}) for i in range(0, len(mnist.test.images), batch_size)])) diff --git a/tensorflow_v1/notebooks/0_Prerequisite/ml_introduction.ipynb b/tensorflow_v1/notebooks/0_Prerequisite/ml_introduction.ipynb new file mode 100644 index 00000000..fe84ef52 --- /dev/null +++ b/tensorflow_v1/notebooks/0_Prerequisite/ml_introduction.ipynb @@ -0,0 +1,48 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Machine Learning\n", + "\n", + "Prior to start browsing the examples, it may be useful that you get familiar with machine learning, as TensorFlow is mostly used for machine learning tasks (especially Neural Networks). You can find below a list of useful links, that can give you the basic knowledge required for this TensorFlow Tutorial.\n", + "\n", + "## Machine Learning\n", + "\n", + "- [An Introduction to Machine Learning Theory and Its Applications: A Visual Tutorial with Examples](https://www.toptal.com/machine-learning/machine-learning-theory-an-introductory-primer)\n", + "- [A Gentle Guide to Machine Learning](https://blog.monkeylearn.com/a-gentle-guide-to-machine-learning/)\n", + "- [A Visual Introduction to Machine Learning](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/)\n", + "- [Introduction to Machine Learning](http://alex.smola.org/drafts/thebook.pdf)\n", + "\n", + "## Deep Learning & Neural Networks\n", + "\n", + "- [An Introduction to Neural Networks](http://www.cs.stir.ac.uk/~lss/NNIntro/InvSlides.html)\n", + "- [An Introduction to Image Recognition with Deep Learning](https://medium.com/@ageitgey/machine-learning-is-fun-part-3-deep-learning-and-convolutional-neural-networks-f40359318721)\n", + "- [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/index.html)\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "IPython (Python 2.7)", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/tensorflow_v1/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb b/tensorflow_v1/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb new file mode 100644 index 00000000..6b96dc0f --- /dev/null +++ b/tensorflow_v1/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb @@ -0,0 +1,94 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# MNIST Dataset Introduction\n", + "\n", + "Most examples are using MNIST dataset of handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flatten and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "## Overview\n", + "\n", + "![MNIST Digits](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "## Usage\n", + "In our examples, we are using TensorFlow [input_data.py](https://github.com/tensorflow/tensorflow/blob/r0.7/tensorflow/examples/tutorials/mnist/input_data.py) script to load that dataset.\n", + "It is quite useful for managing our data, and handle:\n", + "\n", + "- Dataset downloading\n", + "\n", + "- Loading the entire dataset into numpy array: \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Import MNIST\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)\n", + "\n", + "# Load data\n", + "X_train = mnist.train.images\n", + "Y_train = mnist.train.labels\n", + "X_test = mnist.test.images\n", + "Y_test = mnist.test.labels" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- A `next_batch` function that can iterate over the whole dataset and return only the desired fraction of the dataset samples (in order to save memory and avoid to load the entire dataset)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Get the next 64 images array and labels\n", + "batch_X, batch_Y = mnist.train.next_batch(64)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Link: http://yann.lecun.com/exdb/mnist/" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/tensorflow_v1/notebooks/1_Introduction/basic_eager_api.ipynb b/tensorflow_v1/notebooks/1_Introduction/basic_eager_api.ipynb new file mode 100644 index 00000000..6780a3ff --- /dev/null +++ b/tensorflow_v1/notebooks/1_Introduction/basic_eager_api.ipynb @@ -0,0 +1,238 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Basic introduction to TensorFlow's Eager API\n", + "\n", + "A simple introduction to get started with TensorFlow's Eager API.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### What is TensorFlow's Eager API ?\n", + "\n", + "*Eager execution is an imperative, define-by-run interface where operations are\n", + "executed immediately as they are called from Python. This makes it easier to\n", + "get started with TensorFlow, and can make research and development more\n", + "intuitive. A vast majority of the TensorFlow API remains the same whether eager\n", + "execution is enabled or not. As a result, the exact same code that constructs\n", + "TensorFlow graphs (e.g. using the layers API) can be executed imperatively\n", + "by using eager execution. Conversely, most models written with Eager enabled\n", + "can be converted to a graph that can be further optimized and/or extracted\n", + "for deployment in production without changing code. - Rajat Monga*\n", + "\n", + "More info: https://research.googleblog.com/2017/10/eager-execution-imperative-define-by.html" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", + "import numpy as np\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Setting Eager mode...\n" + ] + } + ], + "source": [ + "# Set Eager API\n", + "print(\"Setting Eager mode...\")\n", + "tf.enable_eager_execution()\n", + "tfe = tf.contrib.eager" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Define constant tensors\n", + "a = 2\n", + "b = 3\n" + ] + } + ], + "source": [ + "# Define constant tensors\n", + "print(\"Define constant tensors\")\n", + "a = tf.constant(2)\n", + "print(\"a = %i\" % a)\n", + "b = tf.constant(3)\n", + "print(\"b = %i\" % b)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running operations, without tf.Session\n", + "a + b = 5\n", + "a * b = 6\n" + ] + } + ], + "source": [ + "# Run the operation without the need for tf.Session\n", + "print(\"Running operations, without tf.Session\")\n", + "c = a + b\n", + "print(\"a + b = %i\" % c)\n", + "d = a * b\n", + "print(\"a * b = %i\" % d)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mixing operations with Tensors and Numpy Arrays\n", + "Tensor:\n", + " a = tf.Tensor(\n", + "[[2. 1.]\n", + " [1. 0.]], shape=(2, 2), dtype=float32)\n", + "NumpyArray:\n", + " b = [[3. 0.]\n", + " [5. 1.]]\n" + ] + } + ], + "source": [ + "# Full compatibility with Numpy\n", + "print(\"Mixing operations with Tensors and Numpy Arrays\")\n", + "\n", + "# Define constant tensors\n", + "a = tf.constant([[2., 1.],\n", + " [1., 0.]], dtype=tf.float32)\n", + "print(\"Tensor:\\n a = %s\" % a)\n", + "b = np.array([[3., 0.],\n", + " [5., 1.]], dtype=np.float32)\n", + "print(\"NumpyArray:\\n b = %s\" % b)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running operations, without tf.Session\n", + "a + b = tf.Tensor(\n", + "[[5. 1.]\n", + " [6. 1.]], shape=(2, 2), dtype=float32)\n", + "a * b = tf.Tensor(\n", + "[[11. 1.]\n", + " [ 3. 0.]], shape=(2, 2), dtype=float32)\n" + ] + } + ], + "source": [ + "# Run the operation without the need for tf.Session\n", + "print(\"Running operations, without tf.Session\")\n", + "\n", + "c = a + b\n", + "print(\"a + b = %s\" % c)\n", + "\n", + "d = tf.matmul(a, b)\n", + "print(\"a * b = %s\" % d)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iterate through Tensor 'a':\n", + "tf.Tensor(2.0, shape=(), dtype=float32)\n", + "tf.Tensor(1.0, shape=(), dtype=float32)\n", + "tf.Tensor(1.0, shape=(), dtype=float32)\n", + "tf.Tensor(0.0, shape=(), dtype=float32)\n" + ] + } + ], + "source": [ + "print(\"Iterate through Tensor 'a':\")\n", + "for i in range(a.shape[0]):\n", + " for j in range(a.shape[1]):\n", + " print(a[i][j])" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/tensorflow_v1/notebooks/1_Introduction/basic_operations.ipynb b/tensorflow_v1/notebooks/1_Introduction/basic_operations.ipynb new file mode 100644 index 00000000..9d60c1aa --- /dev/null +++ b/tensorflow_v1/notebooks/1_Introduction/basic_operations.ipynb @@ -0,0 +1,220 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Basic Operations example using TensorFlow library.\n", + "# Author: Aymeric Damien\n", + "# Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Basic constant operations\n", + "# The value returned by the constructor represents the output\n", + "# of the Constant op.\n", + "a = tf.constant(2)\n", + "b = tf.constant(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a=2, b=3\n", + "Addition with constants: 5\n", + "Multiplication with constants: 6\n" + ] + } + ], + "source": [ + "# Launch the default graph.\n", + "with tf.Session() as sess:\n", + " print \"a: %i\" % sess.run(a), \"b: %i\" % sess.run(b)\n", + " print \"Addition with constants: %i\" % sess.run(a+b)\n", + " print \"Multiplication with constants: %i\" % sess.run(a*b)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Basic Operations with variable as graph input\n", + "# The value returned by the constructor represents the output\n", + "# of the Variable op. (define as input when running session)\n", + "# tf Graph input\n", + "a = tf.placeholder(tf.int16)\n", + "b = tf.placeholder(tf.int16)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Define some operations\n", + "add = tf.add(a, b)\n", + "mul = tf.multiply(a, b)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Addition with variables: 5\n", + "Multiplication with variables: 6\n" + ] + } + ], + "source": [ + "# Launch the default graph.\n", + "with tf.Session() as sess:\n", + " # Run every operation with variable input\n", + " print \"Addition with variables: %i\" % sess.run(add, feed_dict={a: 2, b: 3})\n", + " print \"Multiplication with variables: %i\" % sess.run(mul, feed_dict={a: 2, b: 3})" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ----------------\n", + "# More in details:\n", + "# Matrix Multiplication from TensorFlow official tutorial\n", + "\n", + "# Create a Constant op that produces a 1x2 matrix. The op is\n", + "# added as a node to the default graph.\n", + "#\n", + "# The value returned by the constructor represents the output\n", + "# of the Constant op.\n", + "matrix1 = tf.constant([[3., 3.]])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Create another Constant that produces a 2x1 matrix.\n", + "matrix2 = tf.constant([[2.],[2.]])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Create a Matmul op that takes 'matrix1' and 'matrix2' as inputs.\n", + "# The returned value, 'product', represents the result of the matrix\n", + "# multiplication.\n", + "product = tf.matmul(matrix1, matrix2)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 12.]]\n" + ] + } + ], + "source": [ + "# To run the matmul op we call the session 'run()' method, passing 'product'\n", + "# which represents the output of the matmul op. This indicates to the call\n", + "# that we want to get the output of the matmul op back.\n", + "#\n", + "# All inputs needed by the op are run automatically by the session. They\n", + "# typically are run in parallel.\n", + "#\n", + "# The call 'run(product)' thus causes the execution of threes ops in the\n", + "# graph: the two constants and matmul.\n", + "#\n", + "# The output of the op is returned in 'result' as a numpy `ndarray` object.\n", + "with tf.Session() as sess:\n", + " result = sess.run(product)\n", + " print result" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "IPython (Python 2.7)", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2.0 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.8" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/tensorflow_v1/notebooks/1_Introduction/helloworld.ipynb b/tensorflow_v1/notebooks/1_Introduction/helloworld.ipynb new file mode 100644 index 00000000..9d7f0ace --- /dev/null +++ b/tensorflow_v1/notebooks/1_Introduction/helloworld.ipynb @@ -0,0 +1,87 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Simple hello world using TensorFlow\n", + "\n", + "# Create a Constant op\n", + "# The op is added as a node to the default graph.\n", + "#\n", + "# The value returned by the constructor represents the output\n", + "# of the Constant op.\n", + "\n", + "hello = tf.constant('Hello, TensorFlow!')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Start tf session\n", + "sess = tf.Session()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello, TensorFlow!\n" + ] + } + ], + "source": [ + "# Run graph\n", + "print(sess.run(hello))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "IPython (Python 2.7)", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2.0 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.8" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/tensorflow_v1/notebooks/2_BasicModels/gradient_boosted_decision_tree.ipynb b/tensorflow_v1/notebooks/2_BasicModels/gradient_boosted_decision_tree.ipynb new file mode 100644 index 00000000..09e4b270 --- /dev/null +++ b/tensorflow_v1/notebooks/2_BasicModels/gradient_boosted_decision_tree.ipynb @@ -0,0 +1,266 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Gradient Boosted Decision Tree\n", + "\n", + "Implement a Gradient Boosted Decision tree (GBDT) with TensorFlow to classify\n", + "handwritten digit images. This example is using the MNIST database of\n", + "handwritten digits as training samples (http://yann.lecun.com/exdb/mnist/).\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import print_function\n", + "\n", + "import tensorflow as tf\n", + "from tensorflow.contrib.boosted_trees.estimator_batch.estimator import GradientBoostedDecisionTreeClassifier\n", + "from tensorflow.contrib.boosted_trees.proto import learner_pb2 as gbdt_learner\n", + "\n", + "# Ignore all GPUs (current TF GBDT does not support GPU).\n", + "import os\n", + "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "# Import MNIST data\n", + "# Set verbosity to display errors only (Remove this line for showing warnings)\n", + "tf.logging.set_verbosity(tf.logging.ERROR)\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=False,\n", + " source_url='http://yann.lecun.com/exdb/mnist/')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Parameters\n", + "batch_size = 4096 # The number of samples per batch\n", + "num_classes = 10 # The 10 digits\n", + "num_features = 784 # Each image is 28x28 pixels\n", + "max_steps = 10000\n", + "\n", + "# GBDT Parameters\n", + "learning_rate = 0.1\n", + "l1_regul = 0.\n", + "l2_regul = 1.\n", + "examples_per_layer = 1000\n", + "num_trees = 10\n", + "max_depth = 16" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Fill GBDT parameters into the config proto\n", + "learner_config = gbdt_learner.LearnerConfig()\n", + "learner_config.learning_rate_tuner.fixed.learning_rate = learning_rate\n", + "learner_config.regularization.l1 = l1_regul\n", + "learner_config.regularization.l2 = l2_regul / examples_per_layer\n", + "learner_config.constraints.max_tree_depth = max_depth\n", + "growing_mode = gbdt_learner.LearnerConfig.LAYER_BY_LAYER\n", + "learner_config.growing_mode = growing_mode\n", + "run_config = tf.contrib.learn.RunConfig(save_checkpoints_secs=300)\n", + "learner_config.multi_class_strategy = (\n", + " gbdt_learner.LearnerConfig.DIAGONAL_HESSIAN)\\\n", + "\n", + "# Create a TensorFlor GBDT Estimator\n", + "gbdt_model = GradientBoostedDecisionTreeClassifier(\n", + " model_dir=None, # No save directory specified\n", + " learner_config=learner_config,\n", + " n_classes=num_classes,\n", + " examples_per_layer=examples_per_layer,\n", + " num_trees=num_trees,\n", + " center_bias=False,\n", + " config=run_config)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Active Feature Columns: ['images_0', 'images_1', 'images_2', 'images_3', 'images_4', 'images_5', 'images_6', 'images_7', 'images_8', 'images_9', 'images_10', 'images_11', 'images_12', 'images_13', 'images_14', 'images_15', 'images_16', 'images_17', 'images_18', 'images_19', 'images_20', 'images_21', 'images_22', 'images_23', 'images_24', 'images_25', 'images_26', 'images_27', 'images_28', 'images_29', 'images_30', 'images_31', 'images_32', 'images_33', 'images_34', 'images_35', 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'images_680', 'images_681', 'images_682', 'images_683', 'images_684', 'images_685', 'images_686', 'images_687', 'images_688', 'images_689', 'images_690', 'images_691', 'images_692', 'images_693', 'images_694', 'images_695', 'images_696', 'images_697', 'images_698', 'images_699', 'images_700', 'images_701', 'images_702', 'images_703', 'images_704', 'images_705', 'images_706', 'images_707', 'images_708', 'images_709', 'images_710', 'images_711', 'images_712', 'images_713', 'images_714', 'images_715', 'images_716', 'images_717', 'images_718', 'images_719', 'images_720', 'images_721', 'images_722', 'images_723', 'images_724', 'images_725', 'images_726', 'images_727', 'images_728', 'images_729', 'images_730', 'images_731', 'images_732', 'images_733', 'images_734', 'images_735', 'images_736', 'images_737', 'images_738', 'images_739', 'images_740', 'images_741', 'images_742', 'images_743', 'images_744', 'images_745', 'images_746', 'images_747', 'images_748', 'images_749', 'images_750', 'images_751', 'images_752', 'images_753', 'images_754', 'images_755', 'images_756', 'images_757', 'images_758', 'images_759', 'images_760', 'images_761', 'images_762', 'images_763', 'images_764', 'images_765', 'images_766', 'images_767', 'images_768', 'images_769', 'images_770', 'images_771', 'images_772', 'images_773', 'images_774', 'images_775', 'images_776', 'images_777', 'images_778', 'images_779', 'images_780', 'images_781', 'images_782', 'images_783']\n", + "WARNING:tensorflow:From /Users/aymeric.damien/anaconda2/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/estimators/head.py:678: __new__ (from tensorflow.contrib.learn.python.learn.estimators.model_fn) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "When switching to tf.estimator.Estimator, use tf.estimator.EstimatorSpec. You can use the `estimator_spec` method to create an equivalent one.\n", + "INFO:tensorflow:Create CheckpointSaverHook.\n", + "WARNING:tensorflow:Issue encountered when serializing resources.\n", + "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", + "'_Resource' object has no attribute 'name'\n", + "INFO:tensorflow:Graph was finalized.\n", + "INFO:tensorflow:Running local_init_op.\n", + "INFO:tensorflow:Done running local_init_op.\n", + "WARNING:tensorflow:Issue encountered when serializing resources.\n", + "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", + "'_Resource' object has no attribute 'name'\n", + "INFO:tensorflow:Saving checkpoints for 0 into /var/folders/p7/9nxn5g_s5kv636j86zdg2hrr0000gr/T/tmpE_8oiQ/model.ckpt.\n", + "WARNING:tensorflow:Issue encountered when serializing resources.\n", + "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", + "'_Resource' object has no attribute 'name'\n", + "INFO:tensorflow:loss = 2.3025992, step = 1\n", + "INFO:tensorflow:Saving checkpoints for 2 into /var/folders/p7/9nxn5g_s5kv636j86zdg2hrr0000gr/T/tmpE_8oiQ/model.ckpt.\n", + "WARNING:tensorflow:Issue encountered when serializing resources.\n", + "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", + "'_Resource' object has no attribute 'name'\n", + "INFO:tensorflow:Saving checkpoints for 94 into /var/folders/p7/9nxn5g_s5kv636j86zdg2hrr0000gr/T/tmpE_8oiQ/model.ckpt.\n", + "WARNING:tensorflow:Issue encountered when serializing resources.\n", + "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", + "'_Resource' object has no attribute 'name'\n", + "INFO:tensorflow:global_step/sec: 0.199624\n", + "INFO:tensorflow:loss = 0.32783023, step = 101 (500.943 sec)\n", + "INFO:tensorflow:Requesting stop since we have reached 10 trees.\n", + "INFO:tensorflow:Saving checkpoints for 161 into /var/folders/p7/9nxn5g_s5kv636j86zdg2hrr0000gr/T/tmpE_8oiQ/model.ckpt.\n", + "WARNING:tensorflow:Issue encountered when serializing resources.\n", + "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", + "'_Resource' object has no attribute 'name'\n", + "INFO:tensorflow:Loss for final step: 0.21336032.\n" + ] + }, + { + "data": { + "text/plain": [ + "GradientBoostedDecisionTreeClassifier(params={'head': , 'weight_column_name': None, 'feature_columns': None, 'center_bias': False, 'num_trees': 10, 'logits_modifier_function': None, 'use_core_libs': False, 'learner_config': num_classes: 10\n", + "regularization {\n", + " l2: 0.0010000000475\n", + "}\n", + "constraints {\n", + " max_tree_depth: 16\n", + "}\n", + "learning_rate_tuner {\n", + " fixed {\n", + " learning_rate: 0.10000000149\n", + " }\n", + "}\n", + "pruning_mode: POST_PRUNE\n", + "growing_mode: LAYER_BY_LAYER\n", + "multi_class_strategy: DIAGONAL_HESSIAN\n", + ", 'examples_per_layer': 1000})" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display TF info logs\n", + "tf.logging.set_verbosity(tf.logging.INFO)\n", + "\n", + "# Define the input function for training\n", + "input_fn = tf.estimator.inputs.numpy_input_fn(\n", + " x={'images': mnist.train.images}, y=mnist.train.labels,\n", + " batch_size=batch_size, num_epochs=None, shuffle=True)\n", + "\n", + "# Train the Model\n", + "gbdt_model.fit(input_fn=input_fn, max_steps=max_steps)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Active Feature Columns: ['images_0', 'images_1', 'images_2', 'images_3', 'images_4', 'images_5', 'images_6', 'images_7', 'images_8', 'images_9', 'images_10', 'images_11', 'images_12', 'images_13', 'images_14', 'images_15', 'images_16', 'images_17', 'images_18', 'images_19', 'images_20', 'images_21', 'images_22', 'images_23', 'images_24', 'images_25', 'images_26', 'images_27', 'images_28', 'images_29', 'images_30', 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'images_675', 'images_676', 'images_677', 'images_678', 'images_679', 'images_680', 'images_681', 'images_682', 'images_683', 'images_684', 'images_685', 'images_686', 'images_687', 'images_688', 'images_689', 'images_690', 'images_691', 'images_692', 'images_693', 'images_694', 'images_695', 'images_696', 'images_697', 'images_698', 'images_699', 'images_700', 'images_701', 'images_702', 'images_703', 'images_704', 'images_705', 'images_706', 'images_707', 'images_708', 'images_709', 'images_710', 'images_711', 'images_712', 'images_713', 'images_714', 'images_715', 'images_716', 'images_717', 'images_718', 'images_719', 'images_720', 'images_721', 'images_722', 'images_723', 'images_724', 'images_725', 'images_726', 'images_727', 'images_728', 'images_729', 'images_730', 'images_731', 'images_732', 'images_733', 'images_734', 'images_735', 'images_736', 'images_737', 'images_738', 'images_739', 'images_740', 'images_741', 'images_742', 'images_743', 'images_744', 'images_745', 'images_746', 'images_747', 'images_748', 'images_749', 'images_750', 'images_751', 'images_752', 'images_753', 'images_754', 'images_755', 'images_756', 'images_757', 'images_758', 'images_759', 'images_760', 'images_761', 'images_762', 'images_763', 'images_764', 'images_765', 'images_766', 'images_767', 'images_768', 'images_769', 'images_770', 'images_771', 'images_772', 'images_773', 'images_774', 'images_775', 'images_776', 'images_777', 'images_778', 'images_779', 'images_780', 'images_781', 'images_782', 'images_783']\n", + "INFO:tensorflow:Starting evaluation at 2018-07-26-01:00:06\n", + "INFO:tensorflow:Graph was finalized.\n", + "INFO:tensorflow:Restoring parameters from /var/folders/p7/9nxn5g_s5kv636j86zdg2hrr0000gr/T/tmpE_8oiQ/model.ckpt-161\n", + "INFO:tensorflow:Running local_init_op.\n", + "INFO:tensorflow:Done running local_init_op.\n", + "INFO:tensorflow:Finished evaluation at 2018-07-26-01:00:07\n", + "INFO:tensorflow:Saving dict for global step 161: accuracy = 0.9273, global_step = 161, loss = 0.23841818\n", + "WARNING:tensorflow:Issue encountered when serializing resources.\n", + "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", + "'_Resource' object has no attribute 'name'\n", + "Testing Accuracy: 0.9273\n" + ] + } + ], + "source": [ + "# Evaluate the Model\n", + "# Define the input function for evaluating\n", + "input_fn = tf.estimator.inputs.numpy_input_fn(\n", + " x={'images': mnist.test.images}, y=mnist.test.labels,\n", + " batch_size=batch_size, shuffle=False)\n", + "\n", + "# Use the Estimator 'evaluate' method\n", + "e = gbdt_model.evaluate(input_fn=input_fn)\n", + "print(\"Testing Accuracy:\", e['accuracy'])" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/tensorflow_v1/notebooks/2_BasicModels/kmeans.ipynb b/tensorflow_v1/notebooks/2_BasicModels/kmeans.ipynb new file mode 100644 index 00000000..1a64ba2f --- /dev/null +++ b/tensorflow_v1/notebooks/2_BasicModels/kmeans.ipynb @@ -0,0 +1,226 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# K-Means Example\n", + "\n", + "Implement K-Means algorithm with TensorFlow, and apply it to classify\n", + "handwritten digit images. This example is using the MNIST database of\n", + "handwritten digits as training samples (http://yann.lecun.com/exdb/mnist/).\n", + "\n", + "Note: This example requires TensorFlow v1.1.0 or over.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import print_function\n", + "\n", + "import numpy as np\n", + "import tensorflow as tf\n", + "from tensorflow.contrib.factorization import KMeans\n", + "\n", + "# Ignore all GPUs, tf random forest does not benefit from it.\n", + "import os\n", + "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "# Import MNIST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)\n", + "full_data_x = mnist.train.images" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Parameters\n", + "num_steps = 50 # Total steps to train\n", + "batch_size = 1024 # The number of samples per batch\n", + "k = 25 # The number of clusters\n", + "num_classes = 10 # The 10 digits\n", + "num_features = 784 # Each image is 28x28 pixels\n", + "\n", + "# Input images\n", + "X = tf.placeholder(tf.float32, shape=[None, num_features])\n", + "# Labels (for assigning a label to a centroid and testing)\n", + "Y = tf.placeholder(tf.float32, shape=[None, num_classes])\n", + "\n", + "# K-Means Parameters\n", + "kmeans = KMeans(inputs=X, num_clusters=k, distance_metric='cosine',\n", + " use_mini_batch=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Build KMeans graph\n", + "(all_scores, cluster_idx, scores, cluster_centers_initialized, \n", + " cluster_centers_vars,init_op,train_op) = kmeans.training_graph()\n", + "cluster_idx = cluster_idx[0] # fix for cluster_idx being a tuple\n", + "avg_distance = tf.reduce_mean(scores)\n", + "\n", + "# Initialize the variables (i.e. assign their default value)\n", + "init_vars = tf.global_variables_initializer()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 1, Avg Distance: 0.341471\n", + "Step 10, Avg Distance: 0.221609\n", + "Step 20, Avg Distance: 0.220328\n", + "Step 30, Avg Distance: 0.219776\n", + "Step 40, Avg Distance: 0.219419\n", + "Step 50, Avg Distance: 0.219154\n" + ] + } + ], + "source": [ + "# Start TensorFlow session\n", + "sess = tf.Session()\n", + "\n", + "# Run the initializer\n", + "sess.run(init_vars, feed_dict={X: full_data_x})\n", + "sess.run(init_op, feed_dict={X: full_data_x})\n", + "\n", + "# Training\n", + "for i in range(1, num_steps + 1):\n", + " _, d, idx = sess.run([train_op, avg_distance, cluster_idx],\n", + " feed_dict={X: full_data_x})\n", + " if i % 10 == 0 or i == 1:\n", + " print(\"Step %i, Avg Distance: %f\" % (i, d))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test Accuracy: 0.7127\n" + ] + } + ], + "source": [ + "# Assign a label to each centroid\n", + "# Count total number of labels per centroid, using the label of each training\n", + "# sample to their closest centroid (given by 'idx')\n", + "counts = np.zeros(shape=(k, num_classes))\n", + "for i in range(len(idx)):\n", + " counts[idx[i]] += mnist.train.labels[i]\n", + "# Assign the most frequent label to the centroid\n", + "labels_map = [np.argmax(c) for c in counts]\n", + "labels_map = tf.convert_to_tensor(labels_map)\n", + "\n", + "# Evaluation ops\n", + "# Lookup: centroid_id -> label\n", + "cluster_label = tf.nn.embedding_lookup(labels_map, cluster_idx)\n", + "# Compute accuracy\n", + "correct_prediction = tf.equal(cluster_label, tf.cast(tf.argmax(Y, 1), tf.int32))\n", + "accuracy_op = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n", + "\n", + "# Test Model\n", + "test_x, test_y = mnist.test.images, mnist.test.labels\n", + "print(\"Test Accuracy:\", sess.run(accuracy_op, feed_dict={X: test_x, Y: test_y}))" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/tensorflow_v1/notebooks/2_BasicModels/linear_regression.ipynb b/tensorflow_v1/notebooks/2_BasicModels/linear_regression.ipynb new file mode 100644 index 00000000..2c6692db --- /dev/null +++ b/tensorflow_v1/notebooks/2_BasicModels/linear_regression.ipynb @@ -0,0 +1,236 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "# Linear Regression Example\n", + "\n", + "A linear regression learning algorithm example using TensorFlow library.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "import numpy\n", + "import matplotlib.pyplot as plt\n", + "rng = numpy.random" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Parameters\n", + "learning_rate = 0.01\n", + "training_epochs = 1000\n", + "display_step = 50" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Training Data\n", + "train_X = numpy.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,\n", + " 7.042,10.791,5.313,7.997,5.654,9.27,3.1])\n", + "train_Y = numpy.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,\n", + " 2.827,3.465,1.65,2.904,2.42,2.94,1.3])\n", + "n_samples = train_X.shape[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# tf Graph Input\n", + "X = tf.placeholder(\"float\")\n", + "Y = tf.placeholder(\"float\")\n", + "\n", + "# Set model weights\n", + "W = tf.Variable(rng.randn(), name=\"weight\")\n", + "b = tf.Variable(rng.randn(), name=\"bias\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Construct a linear model\n", + "pred = tf.add(tf.multiply(X, W), b)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Mean squared error\n", + "cost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples)\n", + "# Gradient descent\n", + "optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Initialize the variables (i.e. assign their default value)\n", + "init = tf.global_variables_initializer()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0050 cost= 0.195095107 W= 0.441748 b= -0.580876\n", + "Epoch: 0100 cost= 0.181448311 W= 0.430319 b= -0.498661\n", + "Epoch: 0150 cost= 0.169377610 W= 0.419571 b= -0.421336\n", + "Epoch: 0200 cost= 0.158700854 W= 0.409461 b= -0.348611\n", + "Epoch: 0250 cost= 0.149257123 W= 0.399953 b= -0.28021\n", + "Epoch: 0300 cost= 0.140904188 W= 0.391011 b= -0.215878\n", + "Epoch: 0350 cost= 0.133515999 W= 0.3826 b= -0.155372\n", + "Epoch: 0400 cost= 0.126981199 W= 0.374689 b= -0.0984639\n", + "Epoch: 0450 cost= 0.121201262 W= 0.367249 b= -0.0449408\n", + "Epoch: 0500 cost= 0.116088994 W= 0.360252 b= 0.00539905\n", + "Epoch: 0550 cost= 0.111567356 W= 0.35367 b= 0.052745\n", + "Epoch: 0600 cost= 0.107568085 W= 0.34748 b= 0.0972751\n", + "Epoch: 0650 cost= 0.104030922 W= 0.341659 b= 0.139157\n", + "Epoch: 0700 cost= 0.100902475 W= 0.336183 b= 0.178547\n", + "Epoch: 0750 cost= 0.098135538 W= 0.331033 b= 0.215595\n", + "Epoch: 0800 cost= 0.095688373 W= 0.32619 b= 0.25044\n", + "Epoch: 0850 cost= 0.093524046 W= 0.321634 b= 0.283212\n", + "Epoch: 0900 cost= 0.091609895 W= 0.317349 b= 0.314035\n", + "Epoch: 0950 cost= 0.089917004 W= 0.31332 b= 0.343025\n", + "Epoch: 1000 cost= 0.088419855 W= 0.30953 b= 0.370291\n", + "Optimization Finished!\n", + "Training cost= 0.0884199 W= 0.30953 b= 0.370291 \n", + "\n" + ] + }, + { + "data": { + "image/png": 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5eOSRR3a731//+ld8Ph8rVqxoaJs4cSIHHXRQokPMeEoSREQ6kAULFuDz+SK+Gq+I6PP5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\n4IFd2wUFsFqprIh0YKrdIB3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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Start training\n", + "with tf.Session() as sess:\n", + " sess.run(init)\n", + "\n", + " # Fit all training data\n", + " for epoch in range(training_epochs):\n", + " for (x, y) in zip(train_X, train_Y):\n", + " sess.run(optimizer, feed_dict={X: x, Y: y})\n", + "\n", + " #Display logs per epoch step\n", + " if (epoch+1) % display_step == 0:\n", + " c = sess.run(cost, feed_dict={X: train_X, Y:train_Y})\n", + " print \"Epoch:\", '%04d' % (epoch+1), \"cost=\", \"{:.9f}\".format(c), \\\n", + " \"W=\", sess.run(W), \"b=\", sess.run(b)\n", + "\n", + " print \"Optimization Finished!\"\n", + " training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y})\n", + " print \"Training cost=\", training_cost, \"W=\", sess.run(W), \"b=\", sess.run(b), '\\n'\n", + "\n", + " #Graphic display\n", + " plt.plot(train_X, train_Y, 'ro', label='Original data')\n", + " plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')\n", + " plt.legend()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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OVIcOHbR3714tXLhQmzdv1jPPPKOoqChJ0pQpU5Senq7t27dX3Nqwf//++uUv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\nWrlyZbVfV1RUpCuuuEKtWrXStGnTdOjQIU2fPl1ffvmlPB6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+ "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Regression result" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/tensorflow_v1/notebooks/2_BasicModels/linear_regression_eager_api.ipynb b/tensorflow_v1/notebooks/2_BasicModels/linear_regression_eager_api.ipynb new file mode 100644 index 00000000..f517dc15 --- /dev/null +++ b/tensorflow_v1/notebooks/2_BasicModels/linear_regression_eager_api.ipynb @@ -0,0 +1,181 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Linear Regression with Eager API\n", + "\n", + "A linear regression implemented using TensorFlow's Eager API.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Set Eager API\n", + "tf.enable_eager_execution()\n", + "tfe = tf.contrib.eager" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Training Data\n", + "train_X = [3.3, 4.4, 5.5, 6.71, 6.93, 4.168, 9.779, 6.182, 7.59, 2.167,\n", + " 7.042, 10.791, 5.313, 7.997, 5.654, 9.27, 3.1]\n", + "train_Y = [1.7, 2.76, 2.09, 3.19, 1.694, 1.573, 3.366, 2.596, 2.53, 1.221,\n", + " 2.827, 3.465, 1.65, 2.904, 2.42, 2.94, 1.3]\n", + "n_samples = len(train_X)\n", + "\n", + "# Parameters\n", + "learning_rate = 0.01\n", + "display_step = 100\n", + "num_steps = 1000" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Weight and Bias\n", + "W = tfe.Variable(np.random.randn())\n", + "b = tfe.Variable(np.random.randn())\n", + "\n", + "# Linear regression (Wx + b)\n", + "def linear_regression(inputs):\n", + " return inputs * W + b\n", + "\n", + "# Mean square error\n", + "def mean_square_fn(model_fn, inputs, labels):\n", + " return tf.reduce_sum(tf.pow(model_fn(inputs) - labels, 2)) / (2 * n_samples)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# SGD Optimizer\n", + "optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + "\n", + "# Compute gradients\n", + "grad = tfe.implicit_gradients(mean_square_fn)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial cost= 31.307329178 W= -0.7870768 b= -0.2507985\n", + "Epoch: 0001 cost= 9.502781868 W= -0.26173288 b= -0.17560114\n", + "Epoch: 0100 cost= 0.114994615 W= 0.36224815 b= 0.014603348\n", + "Epoch: 0200 cost= 0.106785327 W= 0.34959725 b= 0.104292504\n", + "Epoch: 0300 cost= 0.100346453 W= 0.33839324 b= 0.1837239\n", + "Epoch: 0400 cost= 0.095296182 W= 0.32847065 b= 0.25407064\n", + "Epoch: 0500 cost= 0.091335081 W= 0.3196829 b= 0.3163719\n", + "Epoch: 0600 cost= 0.088228233 W= 0.31190023 b= 0.37154746\n", + "Epoch: 0700 cost= 0.085791394 W= 0.30500764 b= 0.42041263\n", + "Epoch: 0800 cost= 0.083880097 W= 0.2989034 b= 0.46368918\n", + "Epoch: 0900 cost= 0.082380980 W= 0.2934973 b= 0.50201607\n", + "Epoch: 1000 cost= 0.081205189 W= 0.28870946 b= 0.5359594\n" + ] + }, + { + "data": { + "image/png": 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U83hXoHvtyc651Wb2fWAmMBn4BLjAOdf4jgcREWmhhQthRKNl7Hbvhv3396ee\nvamqqmJs375cUVnJjGAQAxywcNYsxi5axLzycgWFFBbpOgkT9/H4hDBtr+AtvCQiIjHYtQsOOCC0\n7Z//hCFDfCmnRe6cNo0rKisZEQzWtRkwIhjEVVZy1/TpzCgr869A2Svt3SAikiSxzAEzCw0Iw4d7\nQwupHBAAFi9YwGkNAkJDI4JBFs+fn+SKJBIKCSIiCVRVVcUNkyczrKCAMd27M6yggBsmT6aqqqpF\nz7/ggvBLKS9cmIBi48w5R251Nc1NUzQgp7o6pvAkiaWtokVEEiSW8fg334Tjjgtt+/JLaHRXeEoz\nM7ZnZeEgbFBwwPasLN3tkMLUkyAikiANx+NrPwZrx+On1IzHNxYMej0HDQPCY495vQfpFBBq9R81\nioWB8B81zwUCDBg9OskVSSQUEkREEiTS8XgzaNOm/vjQQ71wcM45iawysaaWlnJ3YSHPBgLUDio4\n4NlAgJmFhVxZUuJnebIPCgkiIgkQyXj8LbeEn3fw2WeJrjLx8vLymFdeztJJkxien88Z3boxPD+f\npZMm6fbHNKA5CSIiCdCS8fiN1oNAIPTRNWugR49kVJg8eXl53m2OZWVacTHNqCdBRCRB9jYeH8Cx\n+JMldcelpV7vQaYFhMYUENKLehJEJOWl67fPqaWljF20CNdg8mI3PuEzuoWcpzsAJVWpJ0FEUlKs\n6wukgobj8cd0nozhQgLCnj0KCJLa1JMgIiknk9b73707jxt/E7rs8L//3XQNBJFUpJ4EEUk50awv\nkIrMoEuX+uNzz/V6DhQQJF0oJIhIykn39f5zcsLf0jhnjj/1iERLIUFEUko6r/f/+ONeONi5s75t\n2zbNO5D0pZAgIiml4foC4aTiev+7d3vh4Kyz6ttmz/bCQW6ub2WJxEwhQVJCKn4rFP+k03r/ZpCd\nHdrmHJx3nj/1iMSTQoL4JhNucZPESIf1/gcPDj/vQHlXMolCgvii9ha3vrNm8fzq1Tz56ac8v3o1\nfWfNYmzfvgoKrVwqr/e/dKkXDl55pb7ts88UDiQzaZ0E8UXDW9xq1d7i5mpucZtRVtb8C0jGS7X1\n/p2DxiMg06ZBCnRqiCSMehLEF+l+i5skl98BwaxpQHBOAUEyn0KCJF063+ImrcukSU3nHQSDGlqQ\n1kPDDZJ0LdlCN9VucZPWZfVqKCgIbXvzTTjmGF/KEfGNehLEF+l0i5u0LmahAeGMM7yeAwUEaY3U\nkyC+CLfMkhLmAAAe20lEQVSFrsMLCDMLC5mnwV5JsnAdVxpWkNZOPQnii1S+xU1al1/+smlA+Oor\nBQQRUE+C+CjVbnGT1mXjRujcObTtscfgnHP8qUckFSkkSEpQQJBk0tCCSMsoJIhIq6FwIBIZzUkQ\nkYz3xz82DQgbNyogiOyLQoKIZKyvvvLCQcMdGadN88JBp07+1SWSLjTcICIZSUMLIrFTT4KIZJT8\nfG3hLBIvCgkirVAm7ouxeLEXDtasqW977z2FA5FYKCSItBJVVVXcMHkywwoKGNO9O8MKCrhh8mSq\nqqr8Li0mznnhYMCA+rbTT/fae/f2ry6RTKA5CSKtQFVVFWP79uWKykpmNFgGe+GsWYxdtChtV7nU\nvAORxFJPgkgrcOe0aVzRYJ8M8HbgHBEMMqWykrumT/ezvIj9+MfawlkkGRQSRFqBxQsWcFowGPax\nEcEgi+fPT3JF0Vm1ygsHf/tbfduLL9YPOYhIfGm4QSTDOefIra6muc9QA3Kqq1N+/4zGpXXsCJs3\n+1OLSGuhkCCS4cyM7VlZOAgbFBywPSsrZQOCH/MOUj0wiSSLhhtEWoH+o0axMBD+n/tzgQADRo9O\nckX7dsstTQPCzp2JCwiZeveHSCzUkyDSCkwtLWXsokW4BpMXHV5AmFlYyLySEr9LrLN1K3ToENr2\nu9/BRRcl7pqZeveHSKwi6kkws+vM7HUz22pm68zsf83sqH08Z7CZBRv97DGzg2MrXURaKi8vj3nl\n5SydNInh+fmc0a0bw/PzWTppUkp9AJo1DQjOJTYgQObd/SESLxbJymtm9gwwF1iG1wtxC3A0UOic\n29nMcwYDi4CjgLp+O+fc+r1cpwioqKiooKioqMX1iUjLpNqYu9/rHQwrKOD51aubnbMxPD+f51et\nSl5BIjFYvnw5xcXFAMXOueWxvFZEPQnOudOdc4865yqdc28D5wM9gOIWPP0L59z62p8oahWROEmV\ngPDww00DwuefJzcgRHL3h0hrE+vExY54QXvTPs4z4A0z+8zM/s/M+sV4XRFJY19/7YWDiRPr28aM\n8cJB167JraXh3R/hpPrdHyKJFHVIMO9fzD3Aa865FXs59XPgImAs8APgY+AlMzsu2muLSPoyg6ys\n0Dbn4H//1596ID3v/hBJhojmJIQ80ex+4DSgv3Pu8wif+xKwxjl3XjOPFwEVgwYNokOjWUzjxo1j\n3LhxUdUsIv7Jzobdu0PbUqUHv/buhinN3f2RQpM7RRqaO3cuc+fODWnbsmULr7zyCsRhTkJUIcHM\n7gVGAQOdc2ujeP7teOGifzOPa+KiSIZ49VUYNCi07V//gpNO8qee5lRVVXHX9Oksnj+fnOpqdmRl\n0X/0aK4sKVFAkLQSz4mLEa+TUBMQzgAGRxMQahyHNwwhIhms8TB+p06wcaM/texLXl4eM8rKoKws\n5e7+EPFLRCHBzO4DxgGjge1mdkjNQ1ucc7tqzrkZ6FY7lGBmlwOrgHeBbOBC4GTg1Li8AxFJOX7f\n0hgrBQQRT6QTFy8G2gMvAZ81+PlRg3MOBbo3ON4fuAt4q+Z53wFOcc69FE3BIpkoU26vGzGiaUD4\n+uv0CggiUi+ingTn3D5DhXNuQqPjO4A7IqxLJONVVVVx57RpLF6wgNzqarZnZdF/1Cimlpam3Rj4\n6tVQUBDa9uijMH68L+WISJxo7wYRH2TSXgHpPrQgIs3TLpAiPsiEvQLMmgYE5xQQRDKJQoKIDxYv\nWMBpwWDYx0YEgyyePz/JFbXc9dc3DQdffqlwIJKJNNwgkmSR7BWQSrPst22DxiMgV10Ft9/uTz0i\nkngKCSJJ1nCvgOZ2HUy1vQI070CkddJwg4gP0mWvAM07EGndFBJEfDC1tJS7Cwt5NhCo233QAc/W\n7BVwZUmJn+Xxt781DQcffqhwINLaaLhBxAd5eXnMKy/nrunTubvRXgHzfNwrIBiENm1C2wYOBG+v\nGBFpbRQSRHySansFaN6BiDSm4QaRFOBnQOjRo2lACAYVEEREIUGk1aqo8MLBxx/Xt734ohcOUujG\nChHxkYYbRFohDS2ISEsoJIi0IgoHIhIJDTeItAJXXdU0IOzerYAgInunngSRDLZhA3TpEto2dy78\n5Cf+1CMi6UUhQSRDaWhBRGKlkCCSYRQORCReNCdBJEPMnt00IGzapIAgItFTSBBJc7t3e+FgwoT6\nthtu8MLBgQf6V5eIpD8NN4ikMQ0tiEgiqSdBJA1166YtnEUk8RQSRNLIK6944eCzz+rbtIWziCSK\nhhtE0oBzEGgU6UePhief9KceEWkdFBJEUpzmHYiIXzTcIJKixo7VFs4i4i+FBJEU89FHXjh44on6\ntn/+U1s4i0jyabhBJIU0DgFdusD69f7UIiKikCCSAjTvQERSkYYbRHz00ENNA8LOnQoIIpIaFBJE\nfLBtmxcOLrywvu33v/fCQXa2f3WJiDSk4QaRJGvcc9CxI2ze7E8tIiJ7o54EkSTp3Tv8UsoKCCKS\nqhQSRBLshRe8cPDBB/VtGzdq3oGIpD4NN4gkyJ49sF+jf2G9Dizl7PHryMoqBfJ8qUtEpKUUEkQS\nIOwtjRhuMyycFWDsokXMKy8nL09BQURSl4YbROLoxz8OM+8Aw+E1GjAiGGRKZSV3TZ+e/AJFRCKg\nkCASBytWeOHgb3+rb+vbbQhBwq+jPCIYZPH8+UmqTkQkOgoJIjEyg29/u/544kQIBh1d+E8zEcHr\nUciprsZp9qKIpDDNSRCJ0kEHwaZNoW31n/nG9qwsHIQNCg7YnpWFaccmEUlh6kkQidCcOV7vQcOA\nsGdP01sa+48axcJA+H9izwUCDBg9OoFViojETj0JIi20eTN06hTa9tZb8J3vhD9/amkpYxctwlVW\nMiLozU5weAFhZmEh80pKEl2yiEhMIupJMLPrzOx1M9tqZuvM7H/N7KgWPG+ImVWY2S4z+8DMzou+\nZJHkMwsNCBdd5PUcNBcQAPLy8phXXs7SSZMYnp/PGd26MTw/n6WTJun2RxFJC5H2JAwEfgssq3nu\nLcD/mVmhc25nuCeYWT7wFHAfcDYwDHjIzD5zzj0fZd0iSTFkCLz8cmhbJHMN8/LymFFWBmVlOOc0\nB0FE0kpEIcE5d3rDYzM7H1gPFAOvNfO0S4CVzrmra47fN7MBwBRAIUFS0osvwrBhoW27dkHbttG/\npgKCiKSbWCcudsQbZt20l3P6AC80alsI9I3x2iJxt3u3N7TQMCC88ILXexBLQBARSUdRT1w072vR\nPcBrzrkVezm1K7CuUds6oL2ZtXXO7Y62BpF4avxFf/BgeOklX0oREUkJsdzdcB/wLaB/nGppYsqU\nKXTo0CGkbdy4cYwbNy5Rl5RW6OKL4YEHQtu0xpGIpIO5c+cyd+7ckLYtW7bE7fUtmhXfzOxeYBQw\n0Dm3dh/nvgxUOOeuaNB2PjDTOXdgM88pAioqKiooKiqKuD6Rlnj7bTjmmNC2TZvgwLB/K0VE0sPy\n5cspLi4GKHbOLY/ltSKek1ATEM4ATt5XQKhRDpzSqG14TbtI0jnnDS00DAizZ3vtCggiIvUiGm4w\ns/uAccBoYLuZHVLz0Bbn3K6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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Initial cost, before optimizing\n", + "print(\"Initial cost= {:.9f}\".format(\n", + " mean_square_fn(linear_regression, train_X, train_Y)),\n", + " \"W=\", W.numpy(), \"b=\", b.numpy())\n", + "\n", + "# Training\n", + "for step in range(num_steps):\n", + "\n", + " optimizer.apply_gradients(grad(linear_regression, train_X, train_Y))\n", + "\n", + " if (step + 1) % display_step == 0 or step == 0:\n", + " print(\"Epoch:\", '%04d' % (step + 1), \"cost=\",\n", + " \"{:.9f}\".format(mean_square_fn(linear_regression, train_X, train_Y)),\n", + " \"W=\", W.numpy(), \"b=\", b.numpy())\n", + "\n", + "# Graphic display\n", + "plt.plot(train_X, train_Y, 'ro', label='Original data')\n", + "plt.plot(train_X, np.array(W * train_X + b), label='Fitted line')\n", + "plt.legend()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/tensorflow_v1/notebooks/2_BasicModels/logistic_regression.ipynb b/tensorflow_v1/notebooks/2_BasicModels/logistic_regression.ipynb new file mode 100644 index 00000000..39465835 --- /dev/null +++ b/tensorflow_v1/notebooks/2_BasicModels/logistic_regression.ipynb @@ -0,0 +1,174 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Logistic Regression Example\n", + "\n", + "A logistic regression learning algorithm example using TensorFlow library.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting MNIST_data/train-images-idx3-ubyte.gz\n", + "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n", + "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n", + "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "\n", + "# Import MINST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Parameters\n", + "learning_rate = 0.01\n", + "training_epochs = 25\n", + "batch_size = 100\n", + "display_step = 1\n", + "\n", + "# tf Graph Input\n", + "x = tf.placeholder(tf.float32, [None, 784]) # mnist data image of shape 28*28=784\n", + "y = tf.placeholder(tf.float32, [None, 10]) # 0-9 digits recognition => 10 classes\n", + "\n", + "# Set model weights\n", + "W = tf.Variable(tf.zeros([784, 10]))\n", + "b = tf.Variable(tf.zeros([10]))\n", + "\n", + "# Construct model\n", + "pred = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax\n", + "\n", + "# Minimize error using cross entropy\n", + "cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1))\n", + "# Gradient Descent\n", + "optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)\n", + "\n", + "# Initialize the variables (i.e. assign their default value)\n", + "init = tf.global_variables_initializer()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0001 cost= 1.182138959\n", + "Epoch: 0002 cost= 0.664778162\n", + "Epoch: 0003 cost= 0.552686284\n", + "Epoch: 0004 cost= 0.498628905\n", + "Epoch: 0005 cost= 0.465469866\n", + "Epoch: 0006 cost= 0.442537872\n", + "Epoch: 0007 cost= 0.425462044\n", + "Epoch: 0008 cost= 0.412185303\n", + "Epoch: 0009 cost= 0.401311587\n", + "Epoch: 0010 cost= 0.392326203\n", + "Epoch: 0011 cost= 0.384736038\n", + "Epoch: 0012 cost= 0.378137191\n", + "Epoch: 0013 cost= 0.372363752\n", + "Epoch: 0014 cost= 0.367308579\n", + "Epoch: 0015 cost= 0.362704660\n", + "Epoch: 0016 cost= 0.358588599\n", + "Epoch: 0017 cost= 0.354823110\n" + ] + } + ], + "source": [ + "# Start training\n", + "with tf.Session() as sess:\n", + " sess.run(init)\n", + "\n", + " # Training cycle\n", + " for epoch in range(training_epochs):\n", + " avg_cost = 0.\n", + " total_batch = int(mnist.train.num_examples/batch_size)\n", + " # Loop over all batches\n", + " for i in range(total_batch):\n", + " batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n", + " # Fit training using batch data\n", + " _, c = sess.run([optimizer, cost], feed_dict={x: batch_xs,\n", + " y: batch_ys})\n", + " # Compute average loss\n", + " avg_cost += c / total_batch\n", + " # Display logs per epoch step\n", + " if (epoch+1) % display_step == 0:\n", + " print \"Epoch:\", '%04d' % (epoch+1), \"cost=\", \"{:.9f}\".format(avg_cost)\n", + "\n", + " print \"Optimization Finished!\"\n", + "\n", + " # Test model\n", + " correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\n", + " # Calculate accuracy for 3000 examples\n", + " accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n", + " print \"Accuracy:\", accuracy.eval({x: mnist.test.images[:3000], y: mnist.test.labels[:3000]})" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/tensorflow_v1/notebooks/2_BasicModels/logistic_regression_eager_api.ipynb b/tensorflow_v1/notebooks/2_BasicModels/logistic_regression_eager_api.ipynb new file mode 100644 index 00000000..06aa5bca --- /dev/null +++ b/tensorflow_v1/notebooks/2_BasicModels/logistic_regression_eager_api.ipynb @@ -0,0 +1,258 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Logistic Regression with Eager API\n", + "\n", + "A logistic regression implemented using TensorFlow's Eager API.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Set Eager API\n", + "tf.enable_eager_execution()\n", + "tfe = tf.contrib.eager" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "# Import MNIST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Parameters\n", + "learning_rate = 0.1\n", + "batch_size = 128\n", + "num_steps = 1000\n", + "display_step = 100" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Iterator for the dataset\n", + "dataset = tf.data.Dataset.from_tensor_slices(\n", + " (mnist.train.images, mnist.train.labels))\n", + "dataset = dataset.repeat().batch(batch_size).prefetch(batch_size)\n", + "dataset_iter = tfe.Iterator(dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Variables\n", + "W = tfe.Variable(tf.zeros([784, 10]), name='weights')\n", + "b = tfe.Variable(tf.zeros([10]), name='bias')\n", + "\n", + "# Logistic regression (Wx + b)\n", + "def logistic_regression(inputs):\n", + " return tf.matmul(inputs, W) + b\n", + "\n", + "# Cross-Entropy loss function\n", + "def loss_fn(inference_fn, inputs, labels):\n", + " # Using sparse_softmax cross entropy\n", + " return tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(\n", + " logits=inference_fn(inputs), labels=labels))\n", + "\n", + "# Calculate accuracy\n", + "def accuracy_fn(inference_fn, inputs, labels):\n", + " prediction = tf.nn.softmax(inference_fn(inputs))\n", + " correct_pred = tf.equal(tf.argmax(prediction, 1), labels)\n", + " return tf.reduce_mean(tf.cast(correct_pred, tf.float32))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# SGD Optimizer\n", + "optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + "\n", + "# Compute gradients\n", + "grad = tfe.implicit_gradients(loss_fn)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial loss= 2.302584887\n", + "Step: 0001 loss= 2.302584887 accuracy= 0.1172\n", + "Step: 0100 loss= 0.952338457 accuracy= 0.7955\n", + "Step: 0200 loss= 0.535867393 accuracy= 0.8712\n", + "Step: 0300 loss= 0.485415280 accuracy= 0.8757\n", + "Step: 0400 loss= 0.433947206 accuracy= 0.8843\n", + "Step: 0500 loss= 0.381990731 accuracy= 0.8971\n", + "Step: 0600 loss= 0.394154936 accuracy= 0.8947\n", + "Step: 0700 loss= 0.391497582 accuracy= 0.8905\n", + "Step: 0800 loss= 0.386373103 accuracy= 0.8945\n", + "Step: 0900 loss= 0.332039326 accuracy= 0.9096\n", + "Step: 1000 loss= 0.358993769 accuracy= 0.9002\n" + ] + } + ], + "source": [ + "# Training\n", + "average_loss = 0.\n", + "average_acc = 0.\n", + "for step in range(num_steps):\n", + "\n", + " # Iterate through the dataset\n", + " d = dataset_iter.next()\n", + "\n", + " # Images\n", + " x_batch = d[0]\n", + " # Labels\n", + " y_batch = tf.cast(d[1], dtype=tf.int64)\n", + "\n", + " # Compute the batch loss\n", + " batch_loss = loss_fn(logistic_regression, x_batch, y_batch)\n", + " average_loss += batch_loss\n", + " # Compute the batch accuracy\n", + " batch_accuracy = accuracy_fn(logistic_regression, x_batch, y_batch)\n", + " average_acc += batch_accuracy\n", + "\n", + " if step == 0:\n", + " # Display the initial cost, before optimizing\n", + " print(\"Initial loss= {:.9f}\".format(average_loss))\n", + "\n", + " # Update the variables following gradients info\n", + " optimizer.apply_gradients(grad(logistic_regression, x_batch, y_batch))\n", + "\n", + " # Display info\n", + " if (step + 1) % display_step == 0 or step == 0:\n", + " if step > 0:\n", + " average_loss /= display_step\n", + " average_acc /= display_step\n", + " print(\"Step:\", '%04d' % (step + 1), \" loss=\",\n", + " \"{:.9f}\".format(average_loss), \" accuracy=\",\n", + " \"{:.4f}\".format(average_acc))\n", + " average_loss = 0.\n", + " average_acc = 0." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testset Accuracy: 0.9083\n" + ] + } + ], + "source": [ + "# Evaluate model on the test image set\n", + "testX = mnist.test.images\n", + "testY = mnist.test.labels\n", + "\n", + "test_acc = accuracy_fn(logistic_regression, testX, testY)\n", + "print(\"Testset Accuracy: {:.4f}\".format(test_acc))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.14" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/tensorflow_v1/notebooks/2_BasicModels/nearest_neighbor.ipynb b/tensorflow_v1/notebooks/2_BasicModels/nearest_neighbor.ipynb new file mode 100644 index 00000000..c8fba06f --- /dev/null +++ b/tensorflow_v1/notebooks/2_BasicModels/nearest_neighbor.ipynb @@ -0,0 +1,332 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Nearest Neighbor Example\n", + "\n", + "A nearest neighbor learning algorithm example using TensorFlow library.\n", + "This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/)\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting MNIST_data/train-images-idx3-ubyte.gz\n", + "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n", + "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n", + "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import tensorflow as tf\n", + "\n", + "# Import MINST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# In this example, we limit mnist data\n", + "Xtr, Ytr = mnist.train.next_batch(5000) #5000 for training (nn candidates)\n", + "Xte, Yte = mnist.test.next_batch(200) #200 for testing\n", + "\n", + "# tf Graph Input\n", + "xtr = tf.placeholder(\"float\", [None, 784])\n", + "xte = tf.placeholder(\"float\", [784])\n", + "\n", + "# Nearest Neighbor calculation using L1 Distance\n", + "# Calculate L1 Distance\n", + "distance = tf.reduce_sum(tf.abs(tf.add(xtr, tf.negative(xte))), reduction_indices=1)\n", + "# Prediction: Get min distance index (Nearest neighbor)\n", + "pred = tf.argmin(distance, 0)\n", + "\n", + "accuracy = 0.\n", + "\n", + "# Initialize the variables (i.e. assign their default value)\n", + "init = tf.global_variables_initializer()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test 0 Prediction: 7 True Class: 7\n", + "Test 1 Prediction: 2 True Class: 2\n", + "Test 2 Prediction: 1 True Class: 1\n", + "Test 3 Prediction: 0 True Class: 0\n", + "Test 4 Prediction: 4 True Class: 4\n", + "Test 5 Prediction: 1 True Class: 1\n", + "Test 6 Prediction: 4 True Class: 4\n", + "Test 7 Prediction: 9 True Class: 9\n", + "Test 8 Prediction: 8 True Class: 5\n", + "Test 9 Prediction: 9 True Class: 9\n", + "Test 10 Prediction: 0 True Class: 0\n", + "Test 11 Prediction: 0 True Class: 6\n", + "Test 12 Prediction: 9 True Class: 9\n", + "Test 13 Prediction: 0 True Class: 0\n", + "Test 14 Prediction: 1 True Class: 1\n", + "Test 15 Prediction: 5 True Class: 5\n", + "Test 16 Prediction: 4 True Class: 9\n", + "Test 17 Prediction: 7 True Class: 7\n", + "Test 18 Prediction: 3 True Class: 3\n", + "Test 19 Prediction: 4 True Class: 4\n", + "Test 20 Prediction: 9 True Class: 9\n", + "Test 21 Prediction: 6 True Class: 6\n", + "Test 22 Prediction: 6 True Class: 6\n", + "Test 23 Prediction: 5 True Class: 5\n", + "Test 24 Prediction: 4 True Class: 4\n", + "Test 25 Prediction: 0 True Class: 0\n", + "Test 26 Prediction: 7 True Class: 7\n", + "Test 27 Prediction: 4 True Class: 4\n", + "Test 28 Prediction: 0 True Class: 0\n", + "Test 29 Prediction: 1 True Class: 1\n", + "Test 30 Prediction: 3 True Class: 3\n", + "Test 31 Prediction: 1 True Class: 1\n", + "Test 32 Prediction: 3 True Class: 3\n", + "Test 33 Prediction: 4 True Class: 4\n", + "Test 34 Prediction: 7 True Class: 7\n", + "Test 35 Prediction: 2 True Class: 2\n", + "Test 36 Prediction: 7 True Class: 7\n", + "Test 37 Prediction: 1 True Class: 1\n", + "Test 38 Prediction: 2 True Class: 2\n", + "Test 39 Prediction: 1 True Class: 1\n", + "Test 40 Prediction: 1 True Class: 1\n", + "Test 41 Prediction: 7 True Class: 7\n", + "Test 42 Prediction: 4 True Class: 4\n", + "Test 43 Prediction: 1 True Class: 2\n", + "Test 44 Prediction: 3 True Class: 3\n", + "Test 45 Prediction: 5 True Class: 5\n", + "Test 46 Prediction: 1 True Class: 1\n", + "Test 47 Prediction: 2 True Class: 2\n", + "Test 48 Prediction: 4 True Class: 4\n", + "Test 49 Prediction: 4 True Class: 4\n", + "Test 50 Prediction: 6 True Class: 6\n", + "Test 51 Prediction: 3 True Class: 3\n", + "Test 52 Prediction: 5 True Class: 5\n", + "Test 53 Prediction: 5 True Class: 5\n", + "Test 54 Prediction: 6 True Class: 6\n", + "Test 55 Prediction: 0 True Class: 0\n", + "Test 56 Prediction: 4 True Class: 4\n", + "Test 57 Prediction: 1 True Class: 1\n", + "Test 58 Prediction: 9 True Class: 9\n", + "Test 59 Prediction: 5 True Class: 5\n", + "Test 60 Prediction: 7 True Class: 7\n", + "Test 61 Prediction: 8 True Class: 8\n", + "Test 62 Prediction: 9 True Class: 9\n", + "Test 63 Prediction: 3 True Class: 3\n", + "Test 64 Prediction: 7 True Class: 7\n", + "Test 65 Prediction: 4 True Class: 4\n", + "Test 66 Prediction: 6 True Class: 6\n", + "Test 67 Prediction: 4 True Class: 4\n", + "Test 68 Prediction: 3 True Class: 3\n", + "Test 69 Prediction: 0 True Class: 0\n", + "Test 70 Prediction: 7 True Class: 7\n", + "Test 71 Prediction: 0 True Class: 0\n", + "Test 72 Prediction: 2 True Class: 2\n", + "Test 73 Prediction: 7 True Class: 9\n", + "Test 74 Prediction: 1 True Class: 1\n", + "Test 75 Prediction: 7 True Class: 7\n", + "Test 76 Prediction: 3 True Class: 3\n", + "Test 77 Prediction: 7 True Class: 2\n", + "Test 78 Prediction: 9 True Class: 9\n", + "Test 79 Prediction: 7 True Class: 7\n", + "Test 80 Prediction: 7 True Class: 7\n", + "Test 81 Prediction: 6 True Class: 6\n", + "Test 82 Prediction: 2 True Class: 2\n", + "Test 83 Prediction: 7 True Class: 7\n", + "Test 84 Prediction: 8 True Class: 8\n", + "Test 85 Prediction: 4 True Class: 4\n", + "Test 86 Prediction: 7 True Class: 7\n", + "Test 87 Prediction: 3 True Class: 3\n", + "Test 88 Prediction: 6 True Class: 6\n", + "Test 89 Prediction: 1 True Class: 1\n", + "Test 90 Prediction: 3 True Class: 3\n", + "Test 91 Prediction: 6 True Class: 6\n", + "Test 92 Prediction: 9 True Class: 9\n", + "Test 93 Prediction: 3 True Class: 3\n", + "Test 94 Prediction: 1 True Class: 1\n", + "Test 95 Prediction: 4 True Class: 4\n", + "Test 96 Prediction: 1 True Class: 1\n", + "Test 97 Prediction: 7 True Class: 7\n", + "Test 98 Prediction: 6 True Class: 6\n", + "Test 99 Prediction: 9 True Class: 9\n", + "Test 100 Prediction: 6 True Class: 6\n", + "Test 101 Prediction: 0 True Class: 0\n", + "Test 102 Prediction: 5 True Class: 5\n", + "Test 103 Prediction: 4 True Class: 4\n", + "Test 104 Prediction: 9 True Class: 9\n", + "Test 105 Prediction: 9 True Class: 9\n", + "Test 106 Prediction: 2 True Class: 2\n", + "Test 107 Prediction: 1 True Class: 1\n", + "Test 108 Prediction: 9 True Class: 9\n", + "Test 109 Prediction: 4 True Class: 4\n", + "Test 110 Prediction: 8 True Class: 8\n", + "Test 111 Prediction: 7 True Class: 7\n", + "Test 112 Prediction: 3 True Class: 3\n", + "Test 113 Prediction: 9 True Class: 9\n", + "Test 114 Prediction: 7 True Class: 7\n", + "Test 115 Prediction: 9 True Class: 4\n", + "Test 116 Prediction: 9 True Class: 4\n", + "Test 117 Prediction: 4 True Class: 4\n", + "Test 118 Prediction: 9 True Class: 9\n", + "Test 119 Prediction: 7 True Class: 2\n", + "Test 120 Prediction: 5 True Class: 5\n", + "Test 121 Prediction: 4 True Class: 4\n", + "Test 122 Prediction: 7 True Class: 7\n", + "Test 123 Prediction: 6 True Class: 6\n", + "Test 124 Prediction: 7 True Class: 7\n", + "Test 125 Prediction: 9 True Class: 9\n", + "Test 126 Prediction: 0 True Class: 0\n", + "Test 127 Prediction: 5 True Class: 5\n", + "Test 128 Prediction: 8 True Class: 8\n", + "Test 129 Prediction: 5 True Class: 5\n", + "Test 130 Prediction: 6 True Class: 6\n", + "Test 131 Prediction: 6 True Class: 6\n", + "Test 132 Prediction: 5 True Class: 5\n", + "Test 133 Prediction: 7 True Class: 7\n", + "Test 134 Prediction: 8 True Class: 8\n", + "Test 135 Prediction: 1 True Class: 1\n", + "Test 136 Prediction: 0 True Class: 0\n", + "Test 137 Prediction: 1 True Class: 1\n", + "Test 138 Prediction: 6 True Class: 6\n", + "Test 139 Prediction: 4 True Class: 4\n", + "Test 140 Prediction: 6 True Class: 6\n", + "Test 141 Prediction: 7 True Class: 7\n", + "Test 142 Prediction: 2 True Class: 3\n", + "Test 143 Prediction: 1 True Class: 1\n", + "Test 144 Prediction: 7 True Class: 7\n", + "Test 145 Prediction: 1 True Class: 1\n", + "Test 146 Prediction: 8 True Class: 8\n", + "Test 147 Prediction: 2 True Class: 2\n", + "Test 148 Prediction: 0 True Class: 0\n", + "Test 149 Prediction: 1 True Class: 2\n", + "Test 150 Prediction: 9 True Class: 9\n", + "Test 151 Prediction: 9 True Class: 9\n", + "Test 152 Prediction: 5 True Class: 5\n", + "Test 153 Prediction: 5 True Class: 5\n", + "Test 154 Prediction: 1 True Class: 1\n", + "Test 155 Prediction: 5 True Class: 5\n", + "Test 156 Prediction: 6 True Class: 6\n", + "Test 157 Prediction: 0 True Class: 0\n", + "Test 158 Prediction: 3 True Class: 3\n", + "Test 159 Prediction: 4 True Class: 4\n", + "Test 160 Prediction: 4 True Class: 4\n", + "Test 161 Prediction: 6 True Class: 6\n", + "Test 162 Prediction: 5 True Class: 5\n", + "Test 163 Prediction: 4 True Class: 4\n", + "Test 164 Prediction: 6 True Class: 6\n", + "Test 165 Prediction: 5 True Class: 5\n", + "Test 166 Prediction: 4 True Class: 4\n", + "Test 167 Prediction: 5 True Class: 5\n", + "Test 168 Prediction: 1 True Class: 1\n", + "Test 169 Prediction: 4 True Class: 4\n", + "Test 170 Prediction: 9 True Class: 4\n", + "Test 171 Prediction: 7 True Class: 7\n", + "Test 172 Prediction: 2 True Class: 2\n", + "Test 173 Prediction: 3 True Class: 3\n", + "Test 174 Prediction: 2 True Class: 2\n", + "Test 175 Prediction: 1 True Class: 7\n", + "Test 176 Prediction: 1 True Class: 1\n", + "Test 177 Prediction: 8 True Class: 8\n", + "Test 178 Prediction: 1 True Class: 1\n", + "Test 179 Prediction: 8 True Class: 8\n", + "Test 180 Prediction: 1 True Class: 1\n", + "Test 181 Prediction: 8 True Class: 8\n", + "Test 182 Prediction: 5 True Class: 5\n", + "Test 183 Prediction: 0 True Class: 0\n", + "Test 184 Prediction: 2 True Class: 8\n", + "Test 185 Prediction: 9 True Class: 9\n", + "Test 186 Prediction: 2 True Class: 2\n", + "Test 187 Prediction: 5 True Class: 5\n", + "Test 188 Prediction: 0 True Class: 0\n", + "Test 189 Prediction: 1 True Class: 1\n", + "Test 190 Prediction: 1 True Class: 1\n", + "Test 191 Prediction: 1 True Class: 1\n", + "Test 192 Prediction: 0 True Class: 0\n", + "Test 193 Prediction: 4 True Class: 9\n", + "Test 194 Prediction: 0 True Class: 0\n", + "Test 195 Prediction: 1 True Class: 3\n", + "Test 196 Prediction: 1 True Class: 1\n", + "Test 197 Prediction: 6 True Class: 6\n", + "Test 198 Prediction: 4 True Class: 4\n", + "Test 199 Prediction: 2 True Class: 2\n", + "Done!\n", + "Accuracy: 0.92\n" + ] + } + ], + "source": [ + "# Start training\n", + "with tf.Session() as sess:\n", + " sess.run(init)\n", + "\n", + " # loop over test data\n", + " for i in range(len(Xte)):\n", + " # Get nearest neighbor\n", + " nn_index = sess.run(pred, feed_dict={xtr: Xtr, xte: Xte[i, :]})\n", + " # Get nearest neighbor class label and compare it to its true label\n", + " print \"Test\", i, \"Prediction:\", np.argmax(Ytr[nn_index]), \\\n", + " \"True Class:\", np.argmax(Yte[i])\n", + " # Calculate accuracy\n", + " if np.argmax(Ytr[nn_index]) == np.argmax(Yte[i]):\n", + " accuracy += 1./len(Xte)\n", + " print \"Done!\"\n", + " print \"Accuracy:\", accuracy" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/tensorflow_v1/notebooks/2_BasicModels/random_forest.ipynb b/tensorflow_v1/notebooks/2_BasicModels/random_forest.ipynb new file mode 100644 index 00000000..4b212efc --- /dev/null +++ b/tensorflow_v1/notebooks/2_BasicModels/random_forest.ipynb @@ -0,0 +1,229 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Random Forest Example\n", + "\n", + "Implement Random Forest algorithm with TensorFlow, and apply it to classify \n", + "handwritten digit images. This example is using the MNIST database of \n", + "handwritten digits as training samples (http://yann.lecun.com/exdb/mnist/).\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import print_function\n", + "\n", + "import tensorflow as tf\n", + "from tensorflow.python.ops import resources\n", + "from tensorflow.contrib.tensor_forest.python import tensor_forest\n", + "\n", + "# Ignore all GPUs, tf random forest does not benefit from it.\n", + "import os\n", + "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n", + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "# Import MNIST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Parameters\n", + "num_steps = 500 # Total steps to train\n", + "batch_size = 1024 # The number of samples per batch\n", + "num_classes = 10 # The 10 digits\n", + "num_features = 784 # Each image is 28x28 pixels\n", + "num_trees = 10\n", + "max_nodes = 1000\n", + "\n", + "# Input and Target data\n", + "X = tf.placeholder(tf.float32, shape=[None, num_features])\n", + "# For random forest, labels must be integers (the class id)\n", + "Y = tf.placeholder(tf.int32, shape=[None])\n", + "\n", + "# Random Forest Parameters\n", + "hparams = tensor_forest.ForestHParams(num_classes=num_classes,\n", + " num_features=num_features,\n", + " num_trees=num_trees,\n", + " max_nodes=max_nodes).fill()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Constructing forest with params = \n", + "INFO:tensorflow:{'valid_leaf_threshold': 1, 'split_after_samples': 250, 'num_output_columns': 11, 'feature_bagging_fraction': 1.0, 'split_initializations_per_input': 3, 'bagged_features': None, 'min_split_samples': 5, 'max_nodes': 1000, 'num_features': 784, 'num_trees': 10, 'num_splits_to_consider': 784, 'base_random_seed': 0, 'num_outputs': 1, 'dominate_fraction': 0.99, 'max_fertile_nodes': 500, 'bagged_num_features': 784, 'dominate_method': 'bootstrap', 'bagging_fraction': 1.0, 'regression': False, 'num_classes': 10}\n", + "INFO:tensorflow:training graph for tree: 0\n", + "INFO:tensorflow:training graph for tree: 1\n", + "INFO:tensorflow:training graph for tree: 2\n", + "INFO:tensorflow:training graph for tree: 3\n", + "INFO:tensorflow:training graph for tree: 4\n", + "INFO:tensorflow:training graph for tree: 5\n", + "INFO:tensorflow:training graph for tree: 6\n", + "INFO:tensorflow:training graph for tree: 7\n", + "INFO:tensorflow:training graph for tree: 8\n", + "INFO:tensorflow:training graph for tree: 9\n" + ] + } + ], + "source": [ + "# Build the Random Forest\n", + "forest_graph = tensor_forest.RandomForestGraphs(hparams)\n", + "# Get training graph and loss\n", + "train_op = forest_graph.training_graph(X, Y)\n", + "loss_op = forest_graph.training_loss(X, Y)\n", + "\n", + "# Measure the accuracy\n", + "infer_op, _, _ = forest_graph.inference_graph(X)\n", + "correct_prediction = tf.equal(tf.argmax(infer_op, 1), tf.cast(Y, tf.int64))\n", + "accuracy_op = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n", + "\n", + "# Initialize the variables (i.e. assign their default value) and forest resources\n", + "init_vars = tf.group(tf.global_variables_initializer(),\n", + " resources.initialize_resources(resources.shared_resources()))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 1, Loss: -0.000000, Acc: 0.112305\n", + "Step 50, Loss: -123.800003, Acc: 0.863281\n", + "Step 100, Loss: -274.200012, Acc: 0.863281\n", + "Step 150, Loss: -425.399994, Acc: 0.872070\n", + "Step 200, Loss: -582.799988, Acc: 0.917969\n", + "Step 250, Loss: -740.200012, Acc: 0.912109\n", + "Step 300, Loss: -895.799988, Acc: 0.939453\n", + "Step 350, Loss: -998.000000, Acc: 0.924805\n", + "Step 400, Loss: -998.000000, Acc: 0.940430\n", + "Step 450, Loss: -998.000000, Acc: 0.914062\n", + "Step 500, Loss: -998.000000, Acc: 0.927734\n", + "Test Accuracy: 0.9204\n" + ] + } + ], + "source": [ + "# Start TensorFlow session\n", + "sess = tf.train.MonitoredSession()\n", + "\n", + "# Run the initializer\n", + "sess.run(init_vars)\n", + "\n", + "# Training\n", + "for i in range(1, num_steps + 1):\n", + " # Prepare Data\n", + " # Get the next batch of MNIST data (only images are needed, not labels)\n", + " batch_x, batch_y = mnist.train.next_batch(batch_size)\n", + " _, l = sess.run([train_op, loss_op], feed_dict={X: batch_x, Y: batch_y})\n", + " if i % 50 == 0 or i == 1:\n", + " acc = sess.run(accuracy_op, feed_dict={X: batch_x, Y: batch_y})\n", + " print('Step %i, Loss: %f, Acc: %f' % (i, l, acc))\n", + "\n", + "# Test Model\n", + "test_x, test_y = mnist.test.images, mnist.test.labels\n", + "print(\"Test Accuracy:\", sess.run(accuracy_op, feed_dict={X: test_x, Y: test_y}))" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + }, + "varInspector": { + "cols": { + "lenName": 16.0, + "lenType": 16.0, + "lenVar": 40.0 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/tensorflow_v1/notebooks/2_BasicModels/word2vec.ipynb b/tensorflow_v1/notebooks/2_BasicModels/word2vec.ipynb new file mode 100644 index 00000000..5d9d83d4 --- /dev/null +++ b/tensorflow_v1/notebooks/2_BasicModels/word2vec.ipynb @@ -0,0 +1,724 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Word2Vec (Word Embedding)\n", + "\n", + "Implement Word2Vec algorithm to compute vector representations of words.\n", + "This example is using a small chunk of Wikipedia articles to train from.\n", + "\n", + "More info: [Mikolov, Tomas et al. \"Efficient Estimation of Word Representations in Vector Space.\", 2013](https://arxiv.org/pdf/1301.3781.pdf)\n", + "\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import division, print_function, absolute_import\n", + "\n", + "import collections\n", + "import os\n", + "import random\n", + "import urllib\n", + "import zipfile\n", + "\n", + "import numpy as np\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Training Parameters\n", + "learning_rate = 0.1\n", + "batch_size = 128\n", + "num_steps = 3000000\n", + "display_step = 10000\n", + "eval_step = 200000\n", + "\n", + "# Evaluation Parameters\n", + "eval_words = ['five', 'of', 'going', 'hardware', 'american', 'britain']\n", + "\n", + "# Word2Vec Parameters\n", + "embedding_size = 200 # Dimension of the embedding vector\n", + "max_vocabulary_size = 50000 # Total number of different words in the vocabulary\n", + "min_occurrence = 10 # Remove all words that does not appears at least n times\n", + "skip_window = 3 # How many words to consider left and right\n", + "num_skips = 2 # How many times to reuse an input to generate a label\n", + "num_sampled = 64 # Number of negative examples to sample" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading the dataset... (It may take some time)\n", + "Done!\n" + ] + } + ], + "source": [ + "# Download a small chunk of Wikipedia articles collection\n", + "url = 'http://mattmahoney.net/dc/text8.zip'\n", + "data_path = 'text8.zip'\n", + "if not os.path.exists(data_path):\n", + " print(\"Downloading the dataset... (It may take some time)\")\n", + " filename, _ = urllib.urlretrieve(url, data_path)\n", + " print(\"Done!\")\n", + "# Unzip the dataset file. Text has already been processed\n", + "with zipfile.ZipFile(data_path) as f:\n", + " text_words = f.read(f.namelist()[0]).lower().split()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Words count: 17005207\n", + "Unique words: 253854\n", + "Vocabulary size: 50000\n", + "Most common words: [('UNK', 418391), ('the', 1061396), ('of', 593677), ('and', 416629), ('one', 411764), ('in', 372201), ('a', 325873), ('to', 316376), ('zero', 264975), ('nine', 250430)]\n" + ] + } + ], + "source": [ + "# Build the dictionary and replace rare words with UNK token\n", + "count = [('UNK', -1)]\n", + "# Retrieve the most common words\n", + "count.extend(collections.Counter(text_words).most_common(max_vocabulary_size - 1))\n", + "# Remove samples with less than 'min_occurrence' occurrences\n", + "for i in range(len(count) - 1, -1, -1):\n", + " if count[i][1] < min_occurrence:\n", + " count.pop(i)\n", + " else:\n", + " # The collection is ordered, so stop when 'min_occurrence' is reached\n", + " break\n", + "# Compute the vocabulary size\n", + "vocabulary_size = len(count)\n", + "# Assign an id to each word\n", + "word2id = dict()\n", + "for i, (word, _)in enumerate(count):\n", + " word2id[word] = i\n", + "\n", + "data = list()\n", + "unk_count = 0\n", + "for word in text_words:\n", + " # Retrieve a word id, or assign it index 0 ('UNK') if not in dictionary\n", + " index = word2id.get(word, 0)\n", + " if index == 0:\n", + " unk_count += 1\n", + " data.append(index)\n", + "count[0] = ('UNK', unk_count)\n", + "id2word = dict(zip(word2id.values(), word2id.keys()))\n", + "\n", + "print(\"Words count:\", len(text_words))\n", + "print(\"Unique words:\", len(set(text_words)))\n", + "print(\"Vocabulary size:\", vocabulary_size)\n", + "print(\"Most common words:\", count[:10])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "data_index = 0\n", + "# Generate training batch for the skip-gram model\n", + "def next_batch(batch_size, num_skips, skip_window):\n", + " global data_index\n", + " assert batch_size % num_skips == 0\n", + " assert num_skips <= 2 * skip_window\n", + " batch = np.ndarray(shape=(batch_size), dtype=np.int32)\n", + " labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)\n", + " # get window size (words left and right + current one)\n", + " span = 2 * skip_window + 1\n", + " buffer = collections.deque(maxlen=span)\n", + " if data_index + span > len(data):\n", + " data_index = 0\n", + " buffer.extend(data[data_index:data_index + span])\n", + " data_index += span\n", + " for i in range(batch_size // num_skips):\n", + " context_words = [w for w in range(span) if w != skip_window]\n", + " words_to_use = random.sample(context_words, num_skips)\n", + " for j, context_word in enumerate(words_to_use):\n", + " batch[i * num_skips + j] = buffer[skip_window]\n", + " labels[i * num_skips + j, 0] = buffer[context_word]\n", + " if data_index == len(data):\n", + " buffer.extend(data[0:span])\n", + " data_index = span\n", + " else:\n", + " buffer.append(data[data_index])\n", + " data_index += 1\n", + " # Backtrack a little bit to avoid skipping words in the end of a batch\n", + " data_index = (data_index + len(data) - span) % len(data)\n", + " return batch, labels" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Input data\n", + "X = tf.placeholder(tf.int32, shape=[None])\n", + "# Input label\n", + "Y = tf.placeholder(tf.int32, shape=[None, 1])\n", + "\n", + "# Ensure the following ops & var are assigned on CPU\n", + "# (some ops are not compatible on GPU)\n", + "with tf.device('/cpu:0'):\n", + " # Create the embedding variable (each row represent a word embedding vector)\n", + " embedding = tf.Variable(tf.random_normal([vocabulary_size, embedding_size]))\n", + " # Lookup the corresponding embedding vectors for each sample in X\n", + " X_embed = tf.nn.embedding_lookup(embedding, X)\n", + "\n", + " # Construct the variables for the NCE loss\n", + " nce_weights = tf.Variable(tf.random_normal([vocabulary_size, embedding_size]))\n", + " nce_biases = tf.Variable(tf.zeros([vocabulary_size]))\n", + "\n", + "# Compute the average NCE loss for the batch\n", + "loss_op = tf.reduce_mean(\n", + " tf.nn.nce_loss(weights=nce_weights,\n", + " biases=nce_biases,\n", + " labels=Y,\n", + " inputs=X_embed,\n", + " num_sampled=num_sampled,\n", + " num_classes=vocabulary_size))\n", + "\n", + "# Define the optimizer\n", + "optimizer = tf.train.GradientDescentOptimizer(learning_rate)\n", + "train_op = optimizer.minimize(loss_op)\n", + "\n", + "# Evaluation\n", + "# Compute the cosine similarity between input data embedding and every embedding vectors\n", + "X_embed_norm = X_embed / tf.sqrt(tf.reduce_sum(tf.square(X_embed)))\n", + "embedding_norm = embedding / tf.sqrt(tf.reduce_sum(tf.square(embedding), 1, keepdims=True))\n", + "cosine_sim_op = tf.matmul(X_embed_norm, embedding_norm, transpose_b=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 1, Average Loss= 520.3188\n", + "Evaluation...\n", + "\"five\" nearest neighbors: brothers, swinging, dissemination, fruitful, trichloride, dll, timur, torre,\n", + "\"of\" nearest neighbors: malting, vaginal, cecil, xiaoping, arrangers, hydras, exhibits, splits,\n", + "\"going\" nearest neighbors: besht, xps, sdtv, mississippi, frequencies, tora, reciprocating, tursiops,\n", + "\"hardware\" nearest neighbors: burgh, residences, mares, attested, whirlwind, isomerism, admiration, ties,\n", + "\"american\" nearest neighbors: tensile, months, baffling, cricket, kodak, risky, nicomedia, jura,\n", + "\"britain\" nearest neighbors: superstring, interpretations, genealogical, munition, boer, occasional, psychologists, turbofan,\n", + "Step 10000, Average Loss= 202.2640\n", + "Step 20000, Average Loss= 96.5149\n", + "Step 30000, Average Loss= 67.2858\n", + "Step 40000, Average Loss= 52.5055\n", + "Step 50000, Average Loss= 42.6301\n", + "Step 60000, Average Loss= 37.3644\n", + "Step 70000, Average Loss= 33.1220\n", + "Step 80000, Average Loss= 30.5835\n", + "Step 90000, Average Loss= 28.2243\n", + "Step 100000, Average Loss= 25.5532\n", + "Step 110000, Average Loss= 24.0891\n", + "Step 120000, Average Loss= 21.8576\n", + "Step 130000, Average Loss= 21.2192\n", + "Step 140000, Average Loss= 19.8834\n", + "Step 150000, Average Loss= 19.3362\n", + "Step 160000, Average Loss= 18.3129\n", + "Step 170000, Average Loss= 17.4952\n", + "Step 180000, Average Loss= 16.8531\n", + "Step 190000, Average Loss= 15.9615\n", + "Step 200000, Average Loss= 15.0718\n", + "Evaluation...\n", + "\"five\" nearest neighbors: three, four, eight, six, seven, two, nine, one,\n", + "\"of\" nearest neighbors: the, is, a, was, with, in, and, on,\n", + "\"going\" nearest neighbors: time, military, called, with, used, state, most, new,\n", + "\"hardware\" nearest neighbors: deaths, system, three, at, zero, two, s, UNK,\n", + "\"american\" nearest neighbors: UNK, and, s, about, in, when, from, after,\n", + "\"britain\" nearest neighbors: years, were, from, both, of, these, is, many,\n", + "Step 210000, Average Loss= 14.9267\n", + "Step 220000, Average Loss= 15.4700\n", + "Step 230000, Average Loss= 14.0867\n", + "Step 240000, Average Loss= 14.5337\n", + "Step 250000, Average Loss= 13.2458\n", + "Step 260000, Average Loss= 13.2944\n", + "Step 270000, Average Loss= 13.0396\n", + "Step 280000, Average Loss= 12.1902\n", + "Step 290000, Average Loss= 11.7444\n", + "Step 300000, Average Loss= 11.8473\n", + "Step 310000, Average Loss= 11.1306\n", + "Step 320000, Average Loss= 11.1699\n", + "Step 330000, Average Loss= 10.8638\n", + "Step 340000, Average Loss= 10.7910\n", + "Step 350000, Average Loss= 11.0721\n", + "Step 360000, Average Loss= 10.6309\n", + "Step 370000, Average Loss= 10.4836\n", + "Step 380000, Average Loss= 10.3482\n", + "Step 390000, Average Loss= 10.0679\n", + "Step 400000, Average Loss= 10.0070\n", + "Evaluation...\n", + "\"five\" nearest neighbors: four, three, six, seven, eight, two, one, zero,\n", + "\"of\" nearest neighbors: and, in, the, a, for, by, is, while,\n", + "\"going\" nearest neighbors: name, called, made, military, music, people, city, was,\n", + "\"hardware\" nearest neighbors: power, a, john, the, has, see, and, system,\n", + "\"american\" nearest neighbors: s, british, UNK, john, in, during, and, from,\n", + "\"britain\" nearest neighbors: from, general, are, before, first, after, history, was,\n", + "Step 410000, Average Loss= 10.1151\n", + "Step 420000, Average Loss= 9.5719\n", + "Step 430000, Average Loss= 9.8267\n", + "Step 440000, Average Loss= 9.4704\n", + "Step 450000, Average Loss= 9.5561\n", + "Step 460000, Average Loss= 9.1479\n", + "Step 470000, Average Loss= 8.8914\n", + "Step 480000, Average Loss= 9.0281\n", + "Step 490000, Average Loss= 9.3139\n", + "Step 500000, Average Loss= 9.1559\n", + "Step 510000, Average Loss= 8.8257\n", + "Step 520000, Average Loss= 8.9081\n", + "Step 530000, Average Loss= 8.8572\n", + "Step 540000, Average Loss= 8.5835\n", + "Step 550000, Average Loss= 8.4495\n", + "Step 560000, Average Loss= 8.4193\n", + "Step 570000, Average Loss= 8.3399\n", + "Step 580000, Average Loss= 8.1633\n", + "Step 590000, Average Loss= 8.2914\n", + "Step 600000, Average Loss= 8.0268\n", + "Evaluation...\n", + "\"five\" nearest neighbors: three, four, six, two, seven, eight, one, zero,\n", + "\"of\" nearest neighbors: and, the, in, including, with, for, on, or,\n", + "\"going\" nearest neighbors: popular, king, his, music, and, time, name, being,\n", + "\"hardware\" nearest neighbors: power, over, then, than, became, at, less, for,\n", + "\"american\" nearest neighbors: english, s, german, in, french, since, john, between,\n", + "\"britain\" nearest neighbors: however, were, state, first, group, general, from, second,\n", + "Step 610000, Average Loss= 8.1733\n", + "Step 620000, Average Loss= 8.2522\n", + "Step 630000, Average Loss= 8.0434\n", + "Step 640000, Average Loss= 8.0930\n", + "Step 650000, Average Loss= 7.8770\n", + "Step 660000, Average Loss= 7.9221\n", + "Step 670000, Average Loss= 7.7645\n", + "Step 680000, Average Loss= 7.9534\n", + "Step 690000, Average Loss= 7.7507\n", + "Step 700000, Average Loss= 7.7499\n", + "Step 710000, Average Loss= 7.6629\n", + "Step 720000, Average Loss= 7.6055\n", + "Step 730000, Average Loss= 7.4779\n", + "Step 740000, Average Loss= 7.3182\n", + "Step 750000, Average Loss= 7.6399\n", + "Step 760000, Average Loss= 7.4364\n", + "Step 770000, Average Loss= 7.6509\n", + "Step 780000, Average Loss= 7.3204\n", + "Step 790000, Average Loss= 7.4101\n", + "Step 800000, Average Loss= 7.4354\n", + "Evaluation...\n", + "\"five\" nearest neighbors: three, four, six, seven, eight, two, one, nine,\n", + "\"of\" nearest neighbors: and, the, its, a, with, at, in, for,\n", + "\"going\" nearest neighbors: were, man, music, now, great, support, popular, her,\n", + "\"hardware\" nearest neighbors: power, system, then, military, high, against, since, international,\n", + "\"american\" nearest neighbors: english, british, born, b, john, french, d, german,\n", + "\"britain\" nearest neighbors: government, second, before, from, state, several, the, at,\n", + "Step 810000, Average Loss= 7.2603\n", + "Step 820000, Average Loss= 7.1646\n", + "Step 830000, Average Loss= 7.3155\n", + "Step 840000, Average Loss= 7.1274\n", + "Step 850000, Average Loss= 7.1237\n", + "Step 860000, Average Loss= 7.1528\n", + "Step 870000, Average Loss= 7.0673\n", + "Step 880000, Average Loss= 7.2167\n", + "Step 890000, Average Loss= 7.1359\n", + "Step 900000, Average Loss= 7.0940\n", + "Step 910000, Average Loss= 7.1114\n", + "Step 920000, Average Loss= 6.9328\n", + "Step 930000, Average Loss= 7.0108\n", + "Step 940000, Average Loss= 7.0630\n", + "Step 950000, Average Loss= 6.8371\n", + "Step 960000, Average Loss= 7.0466\n", + "Step 970000, Average Loss= 6.8331\n", + "Step 980000, Average Loss= 6.9670\n", + "Step 990000, Average Loss= 6.7357\n", + "Step 1000000, Average Loss= 6.6453\n", + "Evaluation...\n", + "\"five\" nearest neighbors: four, three, six, eight, seven, two, nine, zero,\n", + "\"of\" nearest neighbors: the, became, including, first, second, from, following, and,\n", + "\"going\" nearest neighbors: near, music, popular, made, while, his, works, most,\n", + "\"hardware\" nearest neighbors: power, system, before, its, using, for, thus, an,\n", + "\"american\" nearest neighbors: b, born, d, UNK, nine, john, english, seven,\n", + "\"britain\" nearest neighbors: of, following, government, home, from, state, end, several,\n", + "Step 1010000, Average Loss= 6.7193\n", + "Step 1020000, Average Loss= 6.9297\n", + "Step 1030000, Average Loss= 6.7905\n", + "Step 1040000, Average Loss= 6.7709\n", + "Step 1050000, Average Loss= 6.7337\n", + "Step 1060000, Average Loss= 6.7617\n", + "Step 1070000, Average Loss= 6.7489\n", + "Step 1080000, Average Loss= 6.6259\n", + "Step 1090000, Average Loss= 6.6415\n", + "Step 1100000, Average Loss= 6.7209\n", + "Step 1110000, Average Loss= 6.5471\n", + "Step 1120000, Average Loss= 6.6508\n", + "Step 1130000, Average Loss= 6.5184\n", + "Step 1140000, Average Loss= 6.6202\n", + "Step 1150000, Average Loss= 6.7205\n", + "Step 1160000, Average Loss= 6.5821\n", + "Step 1170000, Average Loss= 6.6200\n", + "Step 1180000, Average Loss= 6.5089\n", + "Step 1190000, Average Loss= 6.5587\n", + "Step 1200000, Average Loss= 6.4930\n", + "Evaluation...\n", + "\"five\" nearest neighbors: three, four, six, seven, eight, two, nine, zero,\n", + "\"of\" nearest neighbors: the, and, including, in, first, with, following, from,\n", + "\"going\" nearest neighbors: near, popular, works, today, large, now, when, both,\n", + "\"hardware\" nearest neighbors: power, system, computer, its, both, for, using, which,\n", + "\"american\" nearest neighbors: born, d, john, german, b, UNK, english, s,\n", + "\"britain\" nearest neighbors: state, following, government, home, became, people, were, the,\n", + "Step 1210000, Average Loss= 6.5985\n", + "Step 1220000, Average Loss= 6.4534\n", + "Step 1230000, Average Loss= 6.5083\n", + "Step 1240000, Average Loss= 6.4913\n", + "Step 1250000, Average Loss= 6.4326\n", + "Step 1260000, Average Loss= 6.3891\n", + "Step 1270000, Average Loss= 6.1601\n", + "Step 1280000, Average Loss= 6.4479\n", + "Step 1290000, Average Loss= 6.3813\n", + "Step 1300000, Average Loss= 6.5335\n", + "Step 1310000, Average Loss= 6.2971\n", + "Step 1320000, Average Loss= 6.3723\n", + "Step 1330000, Average Loss= 6.4234\n", + "Step 1340000, Average Loss= 6.3130\n", + "Step 1350000, Average Loss= 6.2867\n", + "Step 1360000, Average Loss= 6.3505\n", + "Step 1370000, Average Loss= 6.2990\n", + "Step 1380000, Average Loss= 6.3012\n", + "Step 1390000, Average Loss= 6.3112\n", + "Step 1400000, Average Loss= 6.2680\n", + "Evaluation...\n", + "\"five\" nearest neighbors: four, three, six, two, seven, eight, one, zero,\n", + "\"of\" nearest neighbors: the, its, and, including, in, with, see, for,\n", + "\"going\" nearest neighbors: near, great, like, today, began, called, an, another,\n", + "\"hardware\" nearest neighbors: power, computer, system, for, program, high, control, small,\n", + "\"american\" nearest neighbors: english, german, french, born, john, british, s, references,\n", + "\"britain\" nearest neighbors: state, great, government, people, following, became, along, home,\n", + "Step 1410000, Average Loss= 6.3157\n", + "Step 1420000, Average Loss= 6.3466\n", + "Step 1430000, Average Loss= 6.3090\n", + "Step 1440000, Average Loss= 6.3330\n", + "Step 1450000, Average Loss= 6.2072\n", + "Step 1460000, Average Loss= 6.2363\n", + "Step 1470000, Average Loss= 6.2736\n", + "Step 1480000, Average Loss= 6.1793\n", + "Step 1490000, Average Loss= 6.2977\n", + "Step 1500000, Average Loss= 6.1899\n", + "Step 1510000, Average Loss= 6.2381\n", + "Step 1520000, Average Loss= 6.1027\n", + "Step 1530000, Average Loss= 6.0046\n", + "Step 1540000, Average Loss= 6.0747\n", + "Step 1550000, Average Loss= 6.2524\n", + "Step 1560000, Average Loss= 6.1247\n", + "Step 1570000, Average Loss= 6.1937\n", + "Step 1580000, Average Loss= 6.0450\n", + "Step 1590000, Average Loss= 6.1556\n", + "Step 1600000, Average Loss= 6.1765\n", + "Evaluation...\n", + "\"five\" nearest neighbors: three, four, six, two, seven, eight, one, zero,\n", + "\"of\" nearest neighbors: the, and, its, for, from, modern, in, part,\n", + "\"going\" nearest neighbors: great, today, once, now, while, her, like, by,\n", + "\"hardware\" nearest neighbors: power, system, high, program, control, computer, typically, making,\n", + "\"american\" nearest neighbors: born, english, british, german, john, french, b, d,\n", + "\"britain\" nearest neighbors: country, state, home, government, first, following, during, from,\n", + "Step 1610000, Average Loss= 6.1029\n", + "Step 1620000, Average Loss= 6.0501\n", + "Step 1630000, Average Loss= 6.1536\n", + "Step 1640000, Average Loss= 6.0483\n", + "Step 1650000, Average Loss= 6.1197\n", + "Step 1660000, Average Loss= 6.0261\n", + "Step 1670000, Average Loss= 6.1012\n", + "Step 1680000, Average Loss= 6.1795\n", + "Step 1690000, Average Loss= 6.1224\n", + "Step 1700000, Average Loss= 6.0896\n", + "Step 1710000, Average Loss= 6.0418\n", + "Step 1720000, Average Loss= 6.0626\n", + "Step 1730000, Average Loss= 6.0214\n", + "Step 1740000, Average Loss= 6.1206\n", + "Step 1750000, Average Loss= 5.9721\n", + "Step 1760000, Average Loss= 6.0782\n", + "Step 1770000, Average Loss= 6.0291\n", + "Step 1780000, Average Loss= 6.0187\n", + "Step 1790000, Average Loss= 5.9761\n", + "Step 1800000, Average Loss= 5.7518\n", + "Evaluation...\n", + "\"five\" nearest neighbors: four, three, six, seven, eight, nine, two, zero,\n", + "\"of\" nearest neighbors: the, from, in, became, and, second, first, including,\n", + "\"going\" nearest neighbors: today, which, once, little, made, before, now, etc,\n", + "\"hardware\" nearest neighbors: computer, power, program, system, high, typically, current, eventually,\n", + "\"american\" nearest neighbors: b, d, born, actor, UNK, robert, william, english,\n", + "\"britain\" nearest neighbors: government, state, country, from, world, great, of, in,\n", + "Step 1810000, Average Loss= 5.9839\n", + "Step 1820000, Average Loss= 5.9931\n", + "Step 1830000, Average Loss= 6.0794\n", + "Step 1840000, Average Loss= 5.9072\n", + "Step 1850000, Average Loss= 5.9831\n", + "Step 1860000, Average Loss= 6.0023\n", + "Step 1870000, Average Loss= 5.9375\n", + "Step 1880000, Average Loss= 5.9250\n", + "Step 1890000, Average Loss= 5.9422\n", + "Step 1900000, Average Loss= 5.9339\n", + "Step 1910000, Average Loss= 5.9235\n", + "Step 1920000, Average Loss= 5.9692\n", + "Step 1930000, Average Loss= 5.9022\n", + "Step 1940000, Average Loss= 5.9599\n", + "Step 1950000, Average Loss= 6.0174\n", + "Step 1960000, Average Loss= 5.9530\n", + "Step 1970000, Average Loss= 5.9479\n", + "Step 1980000, Average Loss= 5.8870\n", + "Step 1990000, Average Loss= 5.9271\n", + "Step 2000000, Average Loss= 5.8774\n", + "Evaluation...\n", + "\"five\" nearest neighbors: four, three, six, seven, eight, two, nine, zero,\n", + "\"of\" nearest neighbors: and, the, from, in, within, first, including, with,\n", + "\"going\" nearest neighbors: today, before, another, little, work, etc, now, him,\n", + "\"hardware\" nearest neighbors: computer, program, system, both, making, designed, power, simple,\n", + "\"american\" nearest neighbors: actor, born, d, robert, john, b, german, writer,\n", + "\"britain\" nearest neighbors: government, state, following, great, england, became, country, from,\n", + "Step 2010000, Average Loss= 5.9373\n", + "Step 2020000, Average Loss= 5.9113\n", + "Step 2030000, Average Loss= 5.9158\n", + "Step 2040000, Average Loss= 5.9020\n", + "Step 2050000, Average Loss= 5.8608\n", + "Step 2060000, Average Loss= 5.7379\n", + "Step 2070000, Average Loss= 5.7143\n", + "Step 2080000, Average Loss= 5.9379\n", + "Step 2090000, Average Loss= 5.8201\n", + "Step 2100000, Average Loss= 5.9390\n", + "Step 2110000, Average Loss= 5.7295\n", + "Step 2120000, Average Loss= 5.8290\n", + "Step 2130000, Average Loss= 5.9042\n", + "Step 2140000, Average Loss= 5.8367\n", + "Step 2150000, Average Loss= 5.7760\n", + "Step 2160000, Average Loss= 5.8664\n", + "Step 2170000, Average Loss= 5.7974\n", + "Step 2180000, Average Loss= 5.8523\n", + "Step 2190000, Average Loss= 5.8047\n", + "Step 2200000, Average Loss= 5.8172\n", + "Evaluation...\n", + "\"five\" nearest neighbors: three, four, six, eight, two, seven, one, zero,\n", + "\"of\" nearest neighbors: the, with, group, in, its, and, from, including,\n", + "\"going\" nearest neighbors: produced, when, today, while, little, before, had, like,\n", + "\"hardware\" nearest neighbors: computer, system, power, technology, program, simple, for, designed,\n", + "\"american\" nearest neighbors: english, canadian, german, french, author, british, film, born,\n", + "\"britain\" nearest neighbors: government, great, state, established, british, england, country, army,\n", + "Step 2210000, Average Loss= 5.8847\n", + "Step 2220000, Average Loss= 5.8622\n", + "Step 2230000, Average Loss= 5.8295\n", + "Step 2240000, Average Loss= 5.8484\n", + "Step 2250000, Average Loss= 5.7917\n", + "Step 2260000, Average Loss= 5.7846\n", + "Step 2270000, Average Loss= 5.8307\n", + "Step 2280000, Average Loss= 5.7341\n", + "Step 2290000, Average Loss= 5.8519\n", + "Step 2300000, Average Loss= 5.7792\n", + "Step 2310000, Average Loss= 5.8277\n", + "Step 2320000, Average Loss= 5.7196\n", + "Step 2330000, Average Loss= 5.5469\n", + "Step 2340000, Average Loss= 5.7177\n", + "Step 2350000, Average Loss= 5.8139\n", + "Step 2360000, Average Loss= 5.7849\n", + "Step 2370000, Average Loss= 5.7022\n", + "Step 2380000, Average Loss= 5.7447\n", + "Step 2390000, Average Loss= 5.7667\n", + "Step 2400000, Average Loss= 5.7625\n", + "Evaluation...\n", + "\"five\" nearest neighbors: three, four, six, seven, two, eight, zero, nine,\n", + "\"of\" nearest neighbors: the, and, from, part, in, following, within, including,\n", + "\"going\" nearest neighbors: where, once, little, now, again, while, off, produced,\n", + "\"hardware\" nearest neighbors: system, computer, high, power, using, designed, systems, simple,\n", + "\"american\" nearest neighbors: author, actor, english, born, writer, british, b, d,\n", + "\"britain\" nearest neighbors: great, established, government, england, country, state, army, former,\n", + "Step 2410000, Average Loss= 5.6953\n", + "Step 2420000, Average Loss= 5.7413\n", + "Step 2430000, Average Loss= 5.7242\n", + "Step 2440000, Average Loss= 5.7397\n", + "Step 2450000, Average Loss= 5.7755\n", + "Step 2460000, Average Loss= 5.6881\n", + "Step 2470000, Average Loss= 5.7471\n", + "Step 2480000, Average Loss= 5.8159\n", + "Step 2490000, Average Loss= 5.7452\n", + "Step 2500000, Average Loss= 5.7547\n", + "Step 2510000, Average Loss= 5.6945\n", + "Step 2520000, Average Loss= 5.7318\n", + "Step 2530000, Average Loss= 5.6682\n", + "Step 2540000, Average Loss= 5.7660\n", + "Step 2550000, Average Loss= 5.6956\n", + "Step 2560000, Average Loss= 5.7307\n", + "Step 2570000, Average Loss= 5.7015\n", + "Step 2580000, Average Loss= 5.6932\n", + "Step 2590000, Average Loss= 5.6386\n", + "Step 2600000, Average Loss= 5.4734\n", + "Evaluation...\n", + "\"five\" nearest neighbors: four, three, six, seven, eight, nine, two, zero,\n", + "\"of\" nearest neighbors: the, and, in, from, became, including, for, with,\n", + "\"going\" nearest neighbors: little, again, just, a, now, where, to, for,\n", + "\"hardware\" nearest neighbors: computer, program, system, software, designed, systems, technology, current,\n", + "\"american\" nearest neighbors: actor, d, writer, b, born, singer, author, robert,\n", + "\"britain\" nearest neighbors: great, established, government, england, country, in, from, state,\n", + "Step 2610000, Average Loss= 5.7291\n", + "Step 2620000, Average Loss= 5.6412\n", + "Step 2630000, Average Loss= 5.7485\n", + "Step 2640000, Average Loss= 5.5833\n", + "Step 2650000, Average Loss= 5.6548\n", + "Step 2660000, Average Loss= 5.7159\n", + "Step 2670000, Average Loss= 5.6569\n", + "Step 2680000, Average Loss= 5.6080\n", + "Step 2690000, Average Loss= 5.7037\n", + "Step 2700000, Average Loss= 5.6360\n", + "Step 2710000, Average Loss= 5.6707\n", + "Step 2720000, Average Loss= 5.6811\n", + "Step 2730000, Average Loss= 5.6237\n", + "Step 2740000, Average Loss= 5.7050\n", + "Step 2750000, Average Loss= 5.6991\n", + "Step 2760000, Average Loss= 5.6691\n", + "Step 2770000, Average Loss= 5.7057\n", + "Step 2780000, Average Loss= 5.6162\n", + "Step 2790000, Average Loss= 5.6484\n", + "Step 2800000, Average Loss= 5.6627\n", + "Evaluation...\n", + "\"five\" nearest neighbors: four, six, three, seven, eight, nine, two, one,\n", + "\"of\" nearest neighbors: the, in, following, including, part, and, from, under,\n", + "\"going\" nearest neighbors: again, before, little, away, once, when, eventually, then,\n", + "\"hardware\" nearest neighbors: computer, system, software, program, systems, designed, for, design,\n", + "\"american\" nearest neighbors: actor, writer, singer, author, born, robert, d, john,\n", + "\"britain\" nearest neighbors: established, england, great, government, france, army, the, throughout,\n", + "Step 2810000, Average Loss= 5.5900\n", + "Step 2820000, Average Loss= 5.7053\n", + "Step 2830000, Average Loss= 5.6064\n", + "Step 2840000, Average Loss= 5.6891\n", + "Step 2850000, Average Loss= 5.5571\n", + "Step 2860000, Average Loss= 5.4490\n", + "Step 2870000, Average Loss= 5.5428\n", + "Step 2880000, Average Loss= 5.6832\n", + "Step 2890000, Average Loss= 5.5973\n", + "Step 2900000, Average Loss= 5.5816\n", + "Step 2910000, Average Loss= 5.5647\n", + "Step 2920000, Average Loss= 5.6001\n", + "Step 2930000, Average Loss= 5.6459\n", + "Step 2940000, Average Loss= 5.5622\n", + "Step 2950000, Average Loss= 5.5707\n", + "Step 2960000, Average Loss= 5.6492\n", + "Step 2970000, Average Loss= 5.5633\n", + "Step 2980000, Average Loss= 5.6323\n", + "Step 2990000, Average Loss= 5.5440\n", + "Step 3000000, Average Loss= 5.6209\n", + "Evaluation...\n", + "\"five\" nearest neighbors: four, three, six, eight, seven, two, zero, one,\n", + "\"of\" nearest neighbors: the, in, and, including, group, includes, part, from,\n", + "\"going\" nearest neighbors: once, again, when, quickly, before, eventually, little, had,\n", + "\"hardware\" nearest neighbors: computer, system, software, designed, program, simple, systems, sound,\n", + "\"american\" nearest neighbors: canadian, english, author, german, french, british, irish, australian,\n", + "\"britain\" nearest neighbors: established, england, great, government, throughout, france, british, northern,\n" + ] + } + ], + "source": [ + "# Initialize the variables (i.e. assign their default value)\n", + "init = tf.global_variables_initializer()\n", + "\n", + "with tf.Session() as sess:\n", + "\n", + " # Run the initializer\n", + " sess.run(init)\n", + "\n", + " # Testing data\n", + " x_test = np.array([word2id[w] for w in eval_words])\n", + "\n", + " average_loss = 0\n", + " for step in xrange(1, num_steps + 1):\n", + " # Get a new batch of data\n", + " batch_x, batch_y = next_batch(batch_size, num_skips, skip_window)\n", + " # Run training op\n", + " _, loss = sess.run([train_op, loss_op], feed_dict={X: batch_x, Y: batch_y})\n", + " average_loss += loss\n", + "\n", + " if step % display_step == 0 or step == 1:\n", + " if step > 1:\n", + " average_loss /= display_step\n", + " print(\"Step \" + str(step) + \", Average Loss= \" + \\\n", + " \"{:.4f}\".format(average_loss))\n", + " average_loss = 0\n", + "\n", + " # Evaluation\n", + " if step % eval_step == 0 or step == 1:\n", + " print(\"Evaluation...\")\n", + " sim = sess.run(cosine_sim_op, feed_dict={X: x_test})\n", + " for i in xrange(len(eval_words)):\n", + " top_k = 8 # number of nearest neighbors\n", + " nearest = (-sim[i, :]).argsort()[1:top_k + 1]\n", + " log_str = '\"%s\" nearest neighbors:' % eval_words[i]\n", + " for k in xrange(top_k):\n", + " log_str = '%s %s,' % (log_str, id2word[nearest[k]])\n", + " print(log_str)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/tensorflow_v1/notebooks/3_NeuralNetworks/autoencoder.ipynb b/tensorflow_v1/notebooks/3_NeuralNetworks/autoencoder.ipynb new file mode 100644 index 00000000..68318441 --- /dev/null +++ b/tensorflow_v1/notebooks/3_NeuralNetworks/autoencoder.ipynb @@ -0,0 +1,310 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Auto-Encoder Example\n", + "\n", + "Build a 2 layers auto-encoder with TensorFlow to compress images to a lower latent space and then reconstruct them.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Auto-Encoder Overview\n", + "\n", + "\"ae\"\n", + "\n", + "References:\n", + "- [Gradient-based learning applied to document recognition](http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf). Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Proceedings of the IEEE, 86(11):2278-2324, November 1998.\n", + "\n", + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import division, print_function, absolute_import\n", + "\n", + "import tensorflow as tf\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "# Import MNIST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Training Parameters\n", + "learning_rate = 0.01\n", + "num_steps = 30000\n", + "batch_size = 256\n", + "\n", + "display_step = 1000\n", + "examples_to_show = 10\n", + "\n", + "# Network Parameters\n", + "num_hidden_1 = 256 # 1st layer num features\n", + "num_hidden_2 = 128 # 2nd layer num features (the latent dim)\n", + "num_input = 784 # MNIST data input (img shape: 28*28)\n", + "\n", + "# tf Graph input (only pictures)\n", + "X = tf.placeholder(\"float\", [None, num_input])\n", + "\n", + "weights = {\n", + " 'encoder_h1': tf.Variable(tf.random_normal([num_input, num_hidden_1])),\n", + " 'encoder_h2': tf.Variable(tf.random_normal([num_hidden_1, num_hidden_2])),\n", + " 'decoder_h1': tf.Variable(tf.random_normal([num_hidden_2, num_hidden_1])),\n", + " 'decoder_h2': tf.Variable(tf.random_normal([num_hidden_1, num_input])),\n", + "}\n", + "biases = {\n", + " 'encoder_b1': tf.Variable(tf.random_normal([num_hidden_1])),\n", + " 'encoder_b2': tf.Variable(tf.random_normal([num_hidden_2])),\n", + " 'decoder_b1': tf.Variable(tf.random_normal([num_hidden_1])),\n", + " 'decoder_b2': tf.Variable(tf.random_normal([num_input])),\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Building the encoder\n", + "def encoder(x):\n", + " # Encoder Hidden layer with sigmoid activation #1\n", + " layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['encoder_h1']),\n", + " biases['encoder_b1']))\n", + " # Encoder Hidden layer with sigmoid activation #2\n", + " layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['encoder_h2']),\n", + " biases['encoder_b2']))\n", + " return layer_2\n", + "\n", + "\n", + "# Building the decoder\n", + "def decoder(x):\n", + " # Decoder Hidden layer with sigmoid activation #1\n", + " layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['decoder_h1']),\n", + " biases['decoder_b1']))\n", + " # Decoder Hidden layer with sigmoid activation #2\n", + " layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['decoder_h2']),\n", + " biases['decoder_b2']))\n", + " return layer_2\n", + "\n", + "# Construct model\n", + "encoder_op = encoder(X)\n", + "decoder_op = decoder(encoder_op)\n", + "\n", + "# Prediction\n", + "y_pred = decoder_op\n", + "# Targets (Labels) are the input data.\n", + "y_true = X\n", + "\n", + "# Define loss and optimizer, minimize the squared error\n", + "loss = tf.reduce_mean(tf.pow(y_true - y_pred, 2))\n", + "optimizer = tf.train.RMSPropOptimizer(learning_rate).minimize(loss)\n", + "\n", + "# Initialize the variables (i.e. assign their default value)\n", + "init = tf.global_variables_initializer()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 1: Minibatch Loss: 0.438300\n", + "Step 1000: Minibatch Loss: 0.146586\n", + "Step 2000: Minibatch Loss: 0.130722\n", + "Step 3000: Minibatch Loss: 0.117178\n", + "Step 4000: Minibatch Loss: 0.109027\n", + "Step 5000: Minibatch Loss: 0.102582\n", + "Step 6000: Minibatch Loss: 0.099183\n", + "Step 7000: Minibatch Loss: 0.095619\n", + "Step 8000: Minibatch Loss: 0.089006\n", + "Step 9000: Minibatch Loss: 0.087125\n", + "Step 10000: Minibatch Loss: 0.083930\n", + "Step 11000: Minibatch Loss: 0.077512\n", + "Step 12000: Minibatch Loss: 0.077137\n", + "Step 13000: Minibatch Loss: 0.073983\n", + "Step 14000: Minibatch Loss: 0.074218\n", + "Step 15000: Minibatch Loss: 0.074492\n", + "Step 16000: Minibatch Loss: 0.074374\n", + "Step 17000: Minibatch Loss: 0.070909\n", + "Step 18000: Minibatch Loss: 0.069438\n", + "Step 19000: Minibatch Loss: 0.068245\n", + "Step 20000: Minibatch Loss: 0.068402\n", + "Step 21000: Minibatch Loss: 0.067113\n", + "Step 22000: Minibatch Loss: 0.068241\n", + "Step 23000: Minibatch Loss: 0.062454\n", + "Step 24000: Minibatch Loss: 0.059754\n", + "Step 25000: Minibatch Loss: 0.058687\n", + "Step 26000: Minibatch Loss: 0.059107\n", + "Step 27000: Minibatch Loss: 0.055788\n", + "Step 28000: Minibatch Loss: 0.057263\n", + "Step 29000: Minibatch Loss: 0.056391\n", + "Step 30000: Minibatch Loss: 0.057672\n" + ] + } + ], + "source": [ + "# Start Training\n", + "# Start a new TF session\n", + "sess = tf.Session()\n", + "\n", + "# Run the initializer\n", + "sess.run(init)\n", + "\n", + "# Training\n", + "for i in range(1, num_steps+1):\n", + " # Prepare Data\n", + " # Get the next batch of MNIST data (only images are needed, not labels)\n", + " batch_x, _ = mnist.train.next_batch(batch_size)\n", + "\n", + " # Run optimization op (backprop) and cost op (to get loss value)\n", + " _, l = sess.run([optimizer, loss], feed_dict={X: batch_x})\n", + " # Display logs per step\n", + " if i % display_step == 0 or i == 1:\n", + " print('Step %i: Minibatch Loss: %f' % (i, l))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original Images\n" + ] + }, + { + "data": { + "image/png": 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ITCEgICCCpohTGDBggJ02bVrVO5uackqiDho0CCjEGEi/fv755wEX3adXFdiQ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\nIJ1b7EhzUSOSLCRkrfMSi1N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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Testing\n", + "# Encode and decode images from test set and visualize their reconstruction.\n", + "n = 4\n", + "canvas_orig = np.empty((28 * n, 28 * n))\n", + "canvas_recon = np.empty((28 * n, 28 * n))\n", + "for i in range(n):\n", + " # MNIST test set\n", + " batch_x, _ = mnist.test.next_batch(n)\n", + " # Encode and decode the digit image\n", + " g = sess.run(decoder_op, feed_dict={X: batch_x})\n", + " \n", + " # Display original images\n", + " for j in range(n):\n", + " # Draw the generated digits\n", + " canvas_orig[i * 28:(i + 1) * 28, j * 28:(j + 1) * 28] = batch_x[j].reshape([28, 28])\n", + " # Display reconstructed images\n", + " for j in range(n):\n", + " # Draw the generated digits\n", + " canvas_recon[i * 28:(i + 1) * 28, j * 28:(j + 1) * 28] = g[j].reshape([28, 28])\n", + "\n", + "print(\"Original Images\") \n", + "plt.figure(figsize=(n, n))\n", + "plt.imshow(canvas_orig, origin=\"upper\", cmap=\"gray\")\n", + "plt.show()\n", + "\n", + "print(\"Reconstructed Images\")\n", + "plt.figure(figsize=(n, n))\n", + "plt.imshow(canvas_recon, origin=\"upper\", cmap=\"gray\")\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2.0 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/tensorflow_v1/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb b/tensorflow_v1/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb new file mode 100644 index 00000000..2435b229 --- /dev/null +++ b/tensorflow_v1/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb @@ -0,0 +1,301 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Bi-directional Recurrent Neural Network Example\n", + "\n", + "Build a bi-directional recurrent neural network (LSTM) with TensorFlow.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## BiRNN Overview\n", + "\n", + "\"nn\"\n", + "\n", + "References:\n", + "- [Long Short Term Memory](http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf), Sepp Hochreiter & Jurgen Schmidhuber, Neural Computation 9(8): 1735-1780, 1997.\n", + "\n", + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "To classify images using a recurrent neural network, we consider every image row as a sequence of pixels. Because MNIST image shape is 28*28px, we will then handle 28 sequences of 28 timesteps for every sample.\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "from __future__ import print_function\n", + "\n", + "import tensorflow as tf\n", + "from tensorflow.contrib import rnn\n", + "import numpy as np\n", + "\n", + "# Import MNIST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Training Parameters\n", + "learning_rate = 0.001\n", + "training_steps = 10000\n", + "batch_size = 128\n", + "display_step = 200\n", + "\n", + "# Network Parameters\n", + "num_input = 28 # MNIST data input (img shape: 28*28)\n", + "timesteps = 28 # timesteps\n", + "num_hidden = 128 # hidden layer num of features\n", + "num_classes = 10 # MNIST total classes (0-9 digits)\n", + "\n", + "# tf Graph input\n", + "X = tf.placeholder(\"float\", [None, timesteps, num_input])\n", + "Y = tf.placeholder(\"float\", [None, num_classes])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Define weights\n", + "weights = {\n", + " # Hidden layer weights => 2*n_hidden because of forward + backward cells\n", + " 'out': tf.Variable(tf.random_normal([2*num_hidden, num_classes]))\n", + "}\n", + "biases = {\n", + " 'out': tf.Variable(tf.random_normal([num_classes]))\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def BiRNN(x, weights, biases):\n", + "\n", + " # Prepare data shape to match `rnn` function requirements\n", + " # Current data input shape: (batch_size, timesteps, n_input)\n", + " # Required shape: 'timesteps' tensors list of shape (batch_size, num_input)\n", + "\n", + " # Unstack to get a list of 'timesteps' tensors of shape (batch_size, num_input)\n", + " x = tf.unstack(x, timesteps, 1)\n", + "\n", + " # Define lstm cells with tensorflow\n", + " # Forward direction cell\n", + " lstm_fw_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0)\n", + " # Backward direction cell\n", + " lstm_bw_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0)\n", + "\n", + " # Get lstm cell output\n", + " try:\n", + " outputs, _, _ = rnn.static_bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x,\n", + " dtype=tf.float32)\n", + " except Exception: # Old TensorFlow version only returns outputs not states\n", + " outputs = rnn.static_bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x,\n", + " dtype=tf.float32)\n", + "\n", + " # Linear activation, using rnn inner loop last output\n", + " return tf.matmul(outputs[-1], weights['out']) + biases['out']" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "logits = BiRNN(X, weights, biases)\n", + "prediction = tf.nn.softmax(logits)\n", + "\n", + "# Define loss and optimizer\n", + "loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(\n", + " logits=logits, labels=Y))\n", + "optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + "train_op = optimizer.minimize(loss_op)\n", + "\n", + "# Evaluate model (with test logits, for dropout to be disabled)\n", + "correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1))\n", + "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n", + "\n", + "# Initialize the variables (i.e. assign their default value)\n", + "init = tf.global_variables_initializer()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 1, Minibatch Loss= 2.6218, Training Accuracy= 0.086\n", + "Step 200, Minibatch Loss= 2.1900, Training Accuracy= 0.211\n", + "Step 400, Minibatch Loss= 2.0144, Training Accuracy= 0.375\n", + "Step 600, Minibatch Loss= 1.8729, Training Accuracy= 0.445\n", + "Step 800, Minibatch Loss= 1.8000, Training Accuracy= 0.469\n", + "Step 1000, Minibatch Loss= 1.7244, Training Accuracy= 0.453\n", + "Step 1200, Minibatch Loss= 1.5657, Training Accuracy= 0.523\n", + "Step 1400, Minibatch Loss= 1.5473, Training Accuracy= 0.547\n", + "Step 1600, Minibatch Loss= 1.5288, Training Accuracy= 0.500\n", + "Step 1800, Minibatch Loss= 1.4203, Training Accuracy= 0.555\n", + "Step 2000, Minibatch Loss= 1.2525, Training Accuracy= 0.641\n", + "Step 2200, Minibatch Loss= 1.2696, Training Accuracy= 0.594\n", + "Step 2400, Minibatch Loss= 1.2000, Training Accuracy= 0.664\n", + "Step 2600, Minibatch Loss= 1.1017, Training Accuracy= 0.625\n", + "Step 2800, Minibatch Loss= 1.2656, Training Accuracy= 0.578\n", + "Step 3000, Minibatch Loss= 1.0830, Training Accuracy= 0.656\n", + "Step 3200, Minibatch Loss= 1.1522, Training Accuracy= 0.633\n", + "Step 3400, Minibatch Loss= 0.9484, Training Accuracy= 0.680\n", + "Step 3600, Minibatch Loss= 1.0470, Training Accuracy= 0.641\n", + "Step 3800, Minibatch Loss= 1.0609, Training Accuracy= 0.586\n", + "Step 4000, Minibatch Loss= 1.1853, Training Accuracy= 0.648\n", + "Step 4200, Minibatch Loss= 0.9438, Training Accuracy= 0.750\n", + "Step 4400, Minibatch Loss= 0.7986, Training Accuracy= 0.766\n", + "Step 4600, Minibatch Loss= 0.8070, Training Accuracy= 0.750\n", + "Step 4800, Minibatch Loss= 0.8382, Training Accuracy= 0.734\n", + "Step 5000, Minibatch Loss= 0.7397, Training Accuracy= 0.766\n", + "Step 5200, Minibatch Loss= 0.7870, Training Accuracy= 0.727\n", + "Step 5400, Minibatch Loss= 0.6380, Training Accuracy= 0.828\n", + "Step 5600, Minibatch Loss= 0.7975, Training Accuracy= 0.719\n", + "Step 5800, Minibatch Loss= 0.7934, Training Accuracy= 0.766\n", + "Step 6000, Minibatch Loss= 0.6628, Training Accuracy= 0.805\n", + "Step 6200, Minibatch Loss= 0.7958, Training Accuracy= 0.672\n", + "Step 6400, Minibatch Loss= 0.6582, Training Accuracy= 0.773\n", + "Step 6600, Minibatch Loss= 0.5908, Training Accuracy= 0.812\n", + "Step 6800, Minibatch Loss= 0.6182, Training Accuracy= 0.820\n", + "Step 7000, Minibatch Loss= 0.5513, Training Accuracy= 0.812\n", + "Step 7200, Minibatch Loss= 0.6683, Training Accuracy= 0.789\n", + "Step 7400, Minibatch Loss= 0.5337, Training Accuracy= 0.828\n", + "Step 7600, Minibatch Loss= 0.6428, Training Accuracy= 0.805\n", + "Step 7800, Minibatch Loss= 0.6708, Training Accuracy= 0.797\n", + "Step 8000, Minibatch Loss= 0.4664, Training Accuracy= 0.852\n", + "Step 8200, Minibatch Loss= 0.4249, Training Accuracy= 0.859\n", + "Step 8400, Minibatch Loss= 0.7723, Training Accuracy= 0.773\n", + "Step 8600, Minibatch Loss= 0.4706, Training Accuracy= 0.859\n", + "Step 8800, Minibatch Loss= 0.4800, Training Accuracy= 0.867\n", + "Step 9000, Minibatch Loss= 0.4636, Training Accuracy= 0.891\n", + "Step 9200, Minibatch Loss= 0.5734, Training Accuracy= 0.828\n", + "Step 9400, Minibatch Loss= 0.5548, Training Accuracy= 0.875\n", + "Step 9600, Minibatch Loss= 0.3575, Training Accuracy= 0.922\n", + "Step 9800, Minibatch Loss= 0.4566, Training Accuracy= 0.844\n", + "Step 10000, Minibatch Loss= 0.5125, Training Accuracy= 0.844\n", + "Optimization Finished!\n", + "Testing Accuracy: 0.890625\n" + ] + } + ], + "source": [ + "# Start training\n", + "with tf.Session() as sess:\n", + "\n", + " # Run the initializer\n", + " sess.run(init)\n", + "\n", + " for step in range(1, training_steps+1):\n", + " batch_x, batch_y = mnist.train.next_batch(batch_size)\n", + " # Reshape data to get 28 seq of 28 elements\n", + " batch_x = batch_x.reshape((batch_size, timesteps, num_input))\n", + " # Run optimization op (backprop)\n", + " sess.run(train_op, feed_dict={X: batch_x, Y: batch_y})\n", + " if step % display_step == 0 or step == 1:\n", + " # Calculate batch loss and accuracy\n", + " loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x,\n", + " Y: batch_y})\n", + " print(\"Step \" + str(step) + \", Minibatch Loss= \" + \\\n", + " \"{:.4f}\".format(loss) + \", Training Accuracy= \" + \\\n", + " \"{:.3f}\".format(acc))\n", + "\n", + " print(\"Optimization Finished!\")\n", + "\n", + " # Calculate accuracy for 128 mnist test images\n", + " test_len = 128\n", + " test_data = mnist.test.images[:test_len].reshape((-1, timesteps, num_input))\n", + " test_label = mnist.test.labels[:test_len]\n", + " print(\"Testing Accuracy:\", \\\n", + " sess.run(accuracy, feed_dict={X: test_data, Y: test_label}))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/tensorflow_v1/notebooks/3_NeuralNetworks/convolutional_network.ipynb b/tensorflow_v1/notebooks/3_NeuralNetworks/convolutional_network.ipynb new file mode 100644 index 00000000..19590f46 --- /dev/null +++ b/tensorflow_v1/notebooks/3_NeuralNetworks/convolutional_network.ipynb @@ -0,0 +1,423 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Convolutional Neural Network Example\n", + "\n", + "Build a convolutional neural network with TensorFlow.\n", + "\n", + "This example is using TensorFlow layers API, see 'convolutional_network_raw' example\n", + "for a raw TensorFlow implementation with variables.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## CNN Overview\n", + "\n", + "![CNN](http://personal.ie.cuhk.edu.hk/~ccloy/project_target_code/images/fig3.png)\n", + "\n", + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "from __future__ import division, print_function, absolute_import\n", + "\n", + "# Import MNIST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=False)\n", + "\n", + "import tensorflow as tf\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Training Parameters\n", + "learning_rate = 0.001\n", + "num_steps = 2000\n", + "batch_size = 128\n", + "\n", + "# Network Parameters\n", + "num_input = 784 # MNIST data input (img shape: 28*28)\n", + "num_classes = 10 # MNIST total classes (0-9 digits)\n", + "dropout = 0.25 # Dropout, probability to drop a unit" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Create the neural network\n", + "def conv_net(x_dict, n_classes, dropout, reuse, is_training):\n", + " \n", + " # Define a scope for reusing the variables\n", + " with tf.variable_scope('ConvNet', reuse=reuse):\n", + " # TF Estimator input is a dict, in case of multiple inputs\n", + " x = x_dict['images']\n", + "\n", + " # MNIST data input is a 1-D vector of 784 features (28*28 pixels)\n", + " # Reshape to match picture format [Height x Width x Channel]\n", + " # Tensor input become 4-D: [Batch Size, Height, Width, Channel]\n", + " x = tf.reshape(x, shape=[-1, 28, 28, 1])\n", + "\n", + " # Convolution Layer with 32 filters and a kernel size of 5\n", + " conv1 = tf.layers.conv2d(x, 32, 5, activation=tf.nn.relu)\n", + " # Max Pooling (down-sampling) with strides of 2 and kernel size of 2\n", + " conv1 = tf.layers.max_pooling2d(conv1, 2, 2)\n", + "\n", + " # Convolution Layer with 64 filters and a kernel size of 3\n", + " conv2 = tf.layers.conv2d(conv1, 64, 3, activation=tf.nn.relu)\n", + " # Max Pooling (down-sampling) with strides of 2 and kernel size of 2\n", + " conv2 = tf.layers.max_pooling2d(conv2, 2, 2)\n", + "\n", + " # Flatten the data to a 1-D vector for the fully connected layer\n", + " fc1 = tf.contrib.layers.flatten(conv2)\n", + "\n", + " # Fully connected layer (in tf contrib folder for now)\n", + " fc1 = tf.layers.dense(fc1, 1024)\n", + " # Apply Dropout (if is_training is False, dropout is not applied)\n", + " fc1 = tf.layers.dropout(fc1, rate=dropout, training=is_training)\n", + "\n", + " # Output layer, class prediction\n", + " out = tf.layers.dense(fc1, n_classes)\n", + "\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Define the model function (following TF Estimator Template)\n", + "def model_fn(features, labels, mode):\n", + " \n", + " # Build the neural network\n", + " # Because Dropout have different behavior at training and prediction time, we\n", + " # need to create 2 distinct computation graphs that still share the same weights.\n", + " logits_train = conv_net(features, num_classes, dropout, reuse=False, is_training=True)\n", + " logits_test = conv_net(features, num_classes, dropout, reuse=True, is_training=False)\n", + " \n", + " # Predictions\n", + " pred_classes = tf.argmax(logits_test, axis=1)\n", + " pred_probas = tf.nn.softmax(logits_test)\n", + " \n", + " # If prediction mode, early return\n", + " if mode == tf.estimator.ModeKeys.PREDICT:\n", + " return tf.estimator.EstimatorSpec(mode, predictions=pred_classes) \n", + " \n", + " # Define loss and optimizer\n", + " loss_op = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(\n", + " logits=logits_train, labels=tf.cast(labels, dtype=tf.int32)))\n", + " optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n", + " train_op = optimizer.minimize(loss_op, global_step=tf.train.get_global_step())\n", + " \n", + " # Evaluate the accuracy of the model\n", + " acc_op = tf.metrics.accuracy(labels=labels, predictions=pred_classes)\n", + " \n", + " # TF Estimators requires to return a EstimatorSpec, that specify\n", + " # the different ops for training, evaluating, ...\n", + " estim_specs = tf.estimator.EstimatorSpec(\n", + " mode=mode,\n", + " predictions=pred_classes,\n", + " loss=loss_op,\n", + " train_op=train_op,\n", + " eval_metric_ops={'accuracy': acc_op})\n", + "\n", + " return estim_specs" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Using default config.\n", + "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpdhd6F4\n", + "INFO:tensorflow:Using config: {'_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_tf_random_seed': 1, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_save_checkpoints_steps': None, '_model_dir': '/tmp/tmpdhd6F4', '_save_summary_steps': 100}\n" + ] + } + ], + "source": [ + "# Build the Estimator\n", + "model = tf.estimator.Estimator(model_fn)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Create CheckpointSaverHook.\n", + "INFO:tensorflow:Saving checkpoints for 1 into /tmp/tmpdhd6F4/model.ckpt.\n", + "INFO:tensorflow:loss = 2.39026, step = 1\n", + "INFO:tensorflow:global_step/sec: 238.314\n", + "INFO:tensorflow:loss = 0.237997, step = 101 (0.421 sec)\n", + "INFO:tensorflow:global_step/sec: 255.312\n", + "INFO:tensorflow:loss = 0.0954537, step = 201 (0.392 sec)\n", + "INFO:tensorflow:global_step/sec: 257.194\n", + "INFO:tensorflow:loss = 0.121477, step = 301 (0.389 sec)\n", + "INFO:tensorflow:global_step/sec: 255.018\n", + "INFO:tensorflow:loss = 0.0539927, step = 401 (0.392 sec)\n", + "INFO:tensorflow:global_step/sec: 254.293\n", + "INFO:tensorflow:loss = 0.0440369, step = 501 (0.393 sec)\n", + "INFO:tensorflow:global_step/sec: 256.501\n", + "INFO:tensorflow:loss = 0.0247431, step = 601 (0.390 sec)\n", + "INFO:tensorflow:global_step/sec: 252.956\n", + "INFO:tensorflow:loss = 0.0738082, step = 701 (0.395 sec)\n", + "INFO:tensorflow:global_step/sec: 253.222\n", + "INFO:tensorflow:loss = 0.134998, step = 801 (0.395 sec)\n", + "INFO:tensorflow:global_step/sec: 255.606\n", + "INFO:tensorflow:loss = 0.00438448, step = 901 (0.391 sec)\n", + "INFO:tensorflow:global_step/sec: 256.306\n", + "INFO:tensorflow:loss = 0.0471991, step = 1001 (0.390 sec)\n", + "INFO:tensorflow:global_step/sec: 255.352\n", + "INFO:tensorflow:loss = 0.0371172, step = 1101 (0.392 sec)\n", + "INFO:tensorflow:global_step/sec: 253.277\n", + "INFO:tensorflow:loss = 0.0129522, step = 1201 (0.395 sec)\n", + "INFO:tensorflow:global_step/sec: 252.49\n", + "INFO:tensorflow:loss = 0.039862, step = 1301 (0.396 sec)\n", + "INFO:tensorflow:global_step/sec: 253.902\n", + "INFO:tensorflow:loss = 0.0520571, step = 1401 (0.394 sec)\n", + "INFO:tensorflow:global_step/sec: 255.572\n", + "INFO:tensorflow:loss = 0.0307549, step = 1501 (0.392 sec)\n", + "INFO:tensorflow:global_step/sec: 254.32\n", + "INFO:tensorflow:loss = 0.0108862, step = 1601 (0.393 sec)\n", + "INFO:tensorflow:global_step/sec: 255.62\n", + "INFO:tensorflow:loss = 0.0294434, step = 1701 (0.391 sec)\n", + "INFO:tensorflow:global_step/sec: 254.349\n", + "INFO:tensorflow:loss = 0.0179781, step = 1801 (0.393 sec)\n", + "INFO:tensorflow:global_step/sec: 255.508\n", + "INFO:tensorflow:loss = 0.0375271, step = 1901 (0.391 sec)\n", + "INFO:tensorflow:Saving checkpoints for 2000 into /tmp/tmpdhd6F4/model.ckpt.\n", + "INFO:tensorflow:Loss for final step: 0.00440777.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Define the input function for training\n", + "input_fn = tf.estimator.inputs.numpy_input_fn(\n", + " x={'images': mnist.train.images}, y=mnist.train.labels,\n", + " batch_size=batch_size, num_epochs=None, shuffle=True)\n", + "# Train the Model\n", + "model.train(input_fn, steps=num_steps)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Starting evaluation at 2017-08-21-14:25:29\n", + "INFO:tensorflow:Restoring parameters from /tmp/tmpdhd6F4/model.ckpt-2000\n", + "INFO:tensorflow:Finished evaluation at 2017-08-21-14:25:29\n", + "INFO:tensorflow:Saving dict for global step 2000: accuracy = 0.9908, global_step = 2000, loss = 0.0382241\n" + ] + }, + { + "data": { + "text/plain": [ + "{'accuracy': 0.99080002, 'global_step': 2000, 'loss': 0.038224086}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Evaluate the Model\n", + "# Define the input function for evaluating\n", + "input_fn = tf.estimator.inputs.numpy_input_fn(\n", + " x={'images': mnist.test.images}, y=mnist.test.labels,\n", + " batch_size=batch_size, shuffle=False)\n", + "# Use the Estimator 'evaluate' method\n", + "model.evaluate(input_fn)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Restoring parameters from /tmp/tmpdhd6F4/model.ckpt-2000\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 7\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 0\n" + ] + } + ], + "source": [ + "# Predict single images\n", + "n_images = 4\n", + "# Get images from test set\n", + "test_images = mnist.test.images[:n_images]\n", + "# Prepare the input data\n", + "input_fn = tf.estimator.inputs.numpy_input_fn(\n", + " x={'images': test_images}, shuffle=False)\n", + "# Use the model to predict the images class\n", + "preds = list(model.predict(input_fn))\n", + "\n", + "# Display\n", + "for i in range(n_images):\n", + " plt.imshow(np.reshape(test_images[i], [28, 28]), cmap='gray')\n", + " plt.show()\n", + " print(\"Model prediction:\", preds[i])" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tensorflow_v1/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb b/tensorflow_v1/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb new file mode 100644 index 00000000..d7f2c15d --- /dev/null +++ b/tensorflow_v1/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb @@ -0,0 +1,303 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Convolutional Neural Network Example\n", + "\n", + "Build a convolutional neural network with TensorFlow.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## CNN Overview\n", + "\n", + "![CNN](http://personal.ie.cuhk.edu.hk/~ccloy/project_target_code/images/fig3.png)\n", + "\n", + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "from __future__ import division, print_function, absolute_import\n", + "\n", + "import tensorflow as tf\n", + "\n", + "# Import MNIST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Training Parameters\n", + "learning_rate = 0.001\n", + "num_steps = 500\n", + "batch_size = 128\n", + "display_step = 10\n", + "\n", + "# Network Parameters\n", + "num_input = 784 # MNIST data input (img shape: 28*28)\n", + "num_classes = 10 # MNIST total classes (0-9 digits)\n", + "dropout = 0.75 # Dropout, probability to keep units\n", + "\n", + "# tf Graph input\n", + "X = tf.placeholder(tf.float32, [None, num_input])\n", + "Y = tf.placeholder(tf.float32, [None, num_classes])\n", + "keep_prob = tf.placeholder(tf.float32) # dropout (keep probability)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Create some wrappers for simplicity\n", + "def conv2d(x, W, b, strides=1):\n", + " # Conv2D wrapper, with bias and relu activation\n", + " x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')\n", + " x = tf.nn.bias_add(x, b)\n", + " return tf.nn.relu(x)\n", + "\n", + "\n", + "def maxpool2d(x, k=2):\n", + " # MaxPool2D wrapper\n", + " return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],\n", + " padding='SAME')\n", + "\n", + "\n", + "# Create model\n", + "def conv_net(x, weights, biases, dropout):\n", + " # MNIST data input is a 1-D vector of 784 features (28*28 pixels)\n", + " # Reshape to match picture format [Height x Width x Channel]\n", + " # Tensor input become 4-D: [Batch Size, Height, Width, Channel]\n", + " x = tf.reshape(x, shape=[-1, 28, 28, 1])\n", + "\n", + " # Convolution Layer\n", + " conv1 = conv2d(x, weights['wc1'], biases['bc1'])\n", + " # Max Pooling (down-sampling)\n", + " conv1 = maxpool2d(conv1, k=2)\n", + "\n", + " # Convolution Layer\n", + " conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])\n", + " # Max Pooling (down-sampling)\n", + " conv2 = maxpool2d(conv2, k=2)\n", + "\n", + " # Fully connected layer\n", + " # Reshape conv2 output to fit fully connected layer input\n", + " fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]])\n", + " fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])\n", + " fc1 = tf.nn.relu(fc1)\n", + " # Apply Dropout\n", + " fc1 = tf.nn.dropout(fc1, dropout)\n", + "\n", + " # Output, class prediction\n", + " out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Store layers weight & bias\n", + "weights = {\n", + " # 5x5 conv, 1 input, 32 outputs\n", + " 'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])),\n", + " # 5x5 conv, 32 inputs, 64 outputs\n", + " 'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])),\n", + " # fully connected, 7*7*64 inputs, 1024 outputs\n", + " 'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])),\n", + " # 1024 inputs, 10 outputs (class prediction)\n", + " 'out': tf.Variable(tf.random_normal([1024, num_classes]))\n", + "}\n", + "\n", + "biases = {\n", + " 'bc1': tf.Variable(tf.random_normal([32])),\n", + " 'bc2': tf.Variable(tf.random_normal([64])),\n", + " 'bd1': tf.Variable(tf.random_normal([1024])),\n", + " 'out': tf.Variable(tf.random_normal([num_classes]))\n", + "}\n", + "\n", + "# Construct model\n", + "logits = conv_net(X, weights, biases, keep_prob)\n", + "prediction = tf.nn.softmax(logits)\n", + "\n", + "# Define loss and optimizer\n", + "loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(\n", + " logits=logits, labels=Y))\n", + "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n", + "train_op = optimizer.minimize(loss_op)\n", + "\n", + "\n", + "# Evaluate model\n", + "correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1))\n", + "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n", + "\n", + "# Initialize the variables (i.e. assign their default value)\n", + "init = tf.global_variables_initializer()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 1, Minibatch Loss= 63763.3047, Training Accuracy= 0.141\n", + "Step 10, Minibatch Loss= 26429.6680, Training Accuracy= 0.242\n", + "Step 20, Minibatch Loss= 12171.8584, Training Accuracy= 0.586\n", + "Step 30, Minibatch Loss= 6306.6318, Training Accuracy= 0.734\n", + "Step 40, Minibatch Loss= 5113.7583, Training Accuracy= 0.711\n", + "Step 50, Minibatch Loss= 4022.2131, Training Accuracy= 0.805\n", + "Step 60, Minibatch Loss= 3125.4949, Training Accuracy= 0.867\n", + "Step 70, Minibatch Loss= 2225.4875, Training Accuracy= 0.875\n", + "Step 80, Minibatch Loss= 1843.3540, Training Accuracy= 0.867\n", + "Step 90, Minibatch Loss= 1715.7744, Training Accuracy= 0.875\n", + "Step 100, Minibatch Loss= 2611.2708, Training Accuracy= 0.906\n", + "Step 110, Minibatch Loss= 4804.0913, Training Accuracy= 0.875\n", + "Step 120, Minibatch Loss= 1067.5258, Training Accuracy= 0.938\n", + "Step 130, Minibatch Loss= 2519.1514, Training Accuracy= 0.898\n", + "Step 140, Minibatch Loss= 2687.9292, Training Accuracy= 0.906\n", + "Step 150, Minibatch Loss= 1983.4077, Training Accuracy= 0.938\n", + "Step 160, Minibatch Loss= 2844.6553, Training Accuracy= 0.930\n", + "Step 170, Minibatch Loss= 3602.2524, Training Accuracy= 0.914\n", + "Step 180, Minibatch Loss= 175.3922, Training Accuracy= 0.961\n", + "Step 190, Minibatch Loss= 645.1918, Training Accuracy= 0.945\n", + "Step 200, Minibatch Loss= 1147.6567, Training Accuracy= 0.938\n", + "Step 210, Minibatch Loss= 1140.4148, Training Accuracy= 0.914\n", + "Step 220, Minibatch Loss= 1572.8756, Training Accuracy= 0.906\n", + "Step 230, Minibatch Loss= 1292.9274, Training Accuracy= 0.898\n", + "Step 240, Minibatch Loss= 1501.4623, Training Accuracy= 0.953\n", + "Step 250, Minibatch Loss= 1908.2997, Training Accuracy= 0.898\n", + "Step 260, Minibatch Loss= 2182.2380, Training Accuracy= 0.898\n", + "Step 270, Minibatch Loss= 487.5807, Training Accuracy= 0.961\n", + "Step 280, Minibatch Loss= 1284.1130, Training Accuracy= 0.945\n", + "Step 290, Minibatch Loss= 1232.4919, Training Accuracy= 0.891\n", + "Step 300, Minibatch Loss= 1198.8336, Training Accuracy= 0.945\n", + "Step 310, Minibatch Loss= 2010.5345, Training Accuracy= 0.906\n", + "Step 320, Minibatch Loss= 786.3917, Training Accuracy= 0.945\n", + "Step 330, Minibatch Loss= 1408.3556, Training Accuracy= 0.898\n", + "Step 340, Minibatch Loss= 1453.7538, Training Accuracy= 0.953\n", + "Step 350, Minibatch Loss= 999.8901, Training Accuracy= 0.906\n", + "Step 360, Minibatch Loss= 914.3958, Training Accuracy= 0.961\n", + "Step 370, Minibatch Loss= 488.0052, Training Accuracy= 0.938\n", + "Step 380, Minibatch Loss= 1070.8710, Training Accuracy= 0.922\n", + "Step 390, Minibatch Loss= 151.4658, Training Accuracy= 0.961\n", + "Step 400, Minibatch Loss= 555.3539, Training Accuracy= 0.953\n", + "Step 410, Minibatch Loss= 765.5746, Training Accuracy= 0.945\n", + "Step 420, Minibatch Loss= 326.9393, Training Accuracy= 0.969\n", + "Step 430, Minibatch Loss= 530.8968, Training Accuracy= 0.977\n", + "Step 440, Minibatch Loss= 463.3909, Training Accuracy= 0.977\n", + "Step 450, Minibatch Loss= 362.2226, Training Accuracy= 0.977\n", + "Step 460, Minibatch Loss= 414.0034, Training Accuracy= 0.953\n", + "Step 470, Minibatch Loss= 583.4587, Training Accuracy= 0.945\n", + "Step 480, Minibatch Loss= 566.1262, Training Accuracy= 0.969\n", + "Step 490, Minibatch Loss= 691.1143, Training Accuracy= 0.961\n", + "Step 500, Minibatch Loss= 282.8893, Training Accuracy= 0.984\n", + "Optimization Finished!\n", + "Testing Accuracy: 0.976562\n" + ] + } + ], + "source": [ + "# Start training\n", + "with tf.Session() as sess:\n", + "\n", + " # Run the initializer\n", + " sess.run(init)\n", + "\n", + " for step in range(1, num_steps+1):\n", + " batch_x, batch_y = mnist.train.next_batch(batch_size)\n", + " # Run optimization op (backprop)\n", + " sess.run(train_op, feed_dict={X: batch_x, Y: batch_y, keep_prob: dropout})\n", + " if step % display_step == 0 or step == 1:\n", + " # Calculate batch loss and accuracy\n", + " loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x,\n", + " Y: batch_y,\n", + " keep_prob: 1.0})\n", + " print(\"Step \" + str(step) + \", Minibatch Loss= \" + \\\n", + " \"{:.4f}\".format(loss) + \", Training Accuracy= \" + \\\n", + " \"{:.3f}\".format(acc))\n", + "\n", + " print(\"Optimization Finished!\")\n", + "\n", + " # Calculate accuracy for 256 MNIST test images\n", + " print(\"Testing Accuracy:\", \\\n", + " sess.run(accuracy, feed_dict={X: mnist.test.images[:256],\n", + " Y: mnist.test.labels[:256],\n", + " keep_prob: 1.0}))\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/tensorflow_v1/notebooks/3_NeuralNetworks/dcgan.ipynb b/tensorflow_v1/notebooks/3_NeuralNetworks/dcgan.ipynb new file mode 100644 index 00000000..661cc74a --- /dev/null +++ b/tensorflow_v1/notebooks/3_NeuralNetworks/dcgan.ipynb @@ -0,0 +1,333 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Deep Convolutional Generative Adversarial Network Example\n", + "\n", + "Build a deep convolutional generative adversarial network (DCGAN) to generate digit images from a noise distribution with TensorFlow.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## DCGAN Overview\n", + "\n", + "\"dcgan\"\n", + "\n", + "References:\n", + "- [Unsupervised representation learning with deep convolutional generative adversarial networks](https://arxiv.org/pdf/1511.06434). A Radford, L Metz, S Chintala, 2016.\n", + "- [Understanding the difficulty of training deep feedforward neural networks](http://proceedings.mlr.press/v9/glorot10a.html). X Glorot, Y Bengio. Aistats 9, 249-256\n", + "- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://arxiv.org/abs/1502.03167). Sergey Ioffe, Christian Szegedy. 2015.\n", + "\n", + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import division, print_function, absolute_import\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "# Import MNIST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Training Params\n", + "num_steps = 10000\n", + "batch_size = 128\n", + "lr_generator = 0.002\n", + "lr_discriminator = 0.002\n", + "\n", + "# Network Params\n", + "image_dim = 784 # 28*28 pixels * 1 channel\n", + "noise_dim = 100 # Noise data points" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Build Networks\n", + "# Network Inputs\n", + "noise_input = tf.placeholder(tf.float32, shape=[None, noise_dim])\n", + "real_image_input = tf.placeholder(tf.float32, shape=[None, 28, 28, 1])\n", + "# A boolean to indicate batch normalization if it is training or inference time\n", + "is_training = tf.placeholder(tf.bool)\n", + "\n", + "#LeakyReLU activation\n", + "def leakyrelu(x, alpha=0.2):\n", + " return 0.5 * (1 + alpha) * x + 0.5 * (1 - alpha) * abs(x)\n", + "\n", + "# Generator Network\n", + "# Input: Noise, Output: Image\n", + "# Note that batch normalization has different behavior at training and inference time,\n", + "# we then use a placeholder to indicates the layer if we are training or not.\n", + "def generator(x, reuse=False):\n", + " with tf.variable_scope('Generator', reuse=reuse):\n", + " # TensorFlow Layers automatically create variables and calculate their\n", + " # shape, based on the input.\n", + " x = tf.layers.dense(x, units=7 * 7 * 128)\n", + " x = tf.layers.batch_normalization(x, training=is_training)\n", + " x = tf.nn.relu(x)\n", + " # Reshape to a 4-D array of images: (batch, height, width, channels)\n", + " # New shape: (batch, 7, 7, 128)\n", + " x = tf.reshape(x, shape=[-1, 7, 7, 128])\n", + " # Deconvolution, image shape: (batch, 14, 14, 64)\n", + " x = tf.layers.conv2d_transpose(x, 64, 5, strides=2, padding='same')\n", + " x = tf.layers.batch_normalization(x, training=is_training)\n", + " x = tf.nn.relu(x)\n", + " # Deconvolution, image shape: (batch, 28, 28, 1)\n", + " x = tf.layers.conv2d_transpose(x, 1, 5, strides=2, padding='same')\n", + " # Apply tanh for better stability - clip values to [-1, 1].\n", + " x = tf.nn.tanh(x)\n", + " return x\n", + "\n", + "\n", + "# Discriminator Network\n", + "# Input: Image, Output: Prediction Real/Fake Image\n", + "def discriminator(x, reuse=False):\n", + " with tf.variable_scope('Discriminator', reuse=reuse):\n", + " # Typical convolutional neural network to classify images.\n", + " x = tf.layers.conv2d(x, 64, 5, strides=2, padding='same')\n", + " x = tf.layers.batch_normalization(x, training=is_training)\n", + " x = leakyrelu(x)\n", + " x = tf.layers.conv2d(x, 128, 5, strides=2, padding='same')\n", + " x = tf.layers.batch_normalization(x, training=is_training)\n", + " x = leakyrelu(x)\n", + " # Flatten\n", + " x = tf.reshape(x, shape=[-1, 7*7*128])\n", + " x = tf.layers.dense(x, 1024)\n", + " x = tf.layers.batch_normalization(x, training=is_training)\n", + " x = leakyrelu(x)\n", + " # Output 2 classes: Real and Fake images\n", + " x = tf.layers.dense(x, 2)\n", + " return x\n", + "\n", + "# Build Generator Network\n", + "gen_sample = generator(noise_input)\n", + "\n", + "# Build 2 Discriminator Networks (one from noise input, one from generated samples)\n", + "disc_real = discriminator(real_image_input)\n", + "disc_fake = discriminator(gen_sample, reuse=True)\n", + "\n", + "# Build the stacked generator/discriminator\n", + "stacked_gan = discriminator(gen_sample, reuse=True)\n", + "\n", + "# Build Loss (Labels for real images: 1, for fake images: 0)\n", + "# Discriminator Loss for real and fake samples\n", + "disc_loss_real = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(\n", + " logits=disc_real, labels=tf.ones([batch_size], dtype=tf.int32)))\n", + "disc_loss_fake = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(\n", + " logits=disc_fake, labels=tf.zeros([batch_size], dtype=tf.int32)))\n", + "# Sum both loss\n", + "disc_loss = disc_loss_real + disc_loss_fake\n", + "# Generator Loss (The generator tries to fool the discriminator, thus labels are 1)\n", + "gen_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(\n", + " logits=stacked_gan, labels=tf.ones([batch_size], dtype=tf.int32)))\n", + "\n", + "# Build Optimizers\n", + "optimizer_gen = tf.train.AdamOptimizer(learning_rate=lr_generator, beta1=0.5, beta2=0.999)\n", + "optimizer_disc = tf.train.AdamOptimizer(learning_rate=lr_discriminator, beta1=0.5, beta2=0.999)\n", + "\n", + "# Training Variables for each optimizer\n", + "# By default in TensorFlow, all variables are updated by each optimizer, so we\n", + "# need to precise for each one of them the specific variables to update.\n", + "# Generator Network Variables\n", + "gen_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='Generator')\n", + "# Discriminator Network Variables\n", + "disc_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='Discriminator')\n", + "\n", + "# Create training operations\n", + "# TensorFlow UPDATE_OPS collection holds all batch norm operation to update the moving mean/stddev\n", + "gen_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, scope='Generator')\n", + "# `control_dependencies` ensure that the `gen_update_ops` will be run before the `minimize` op (backprop)\n", + "with tf.control_dependencies(gen_update_ops):\n", + " train_gen = optimizer_gen.minimize(gen_loss, var_list=gen_vars)\n", + "disc_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, scope='Discriminator')\n", + "with tf.control_dependencies(disc_update_ops):\n", + " train_disc = optimizer_disc.minimize(disc_loss, var_list=disc_vars)\n", + " \n", + "# Initialize the variables (i.e. assign their default value)\n", + "init = tf.global_variables_initializer()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 1: Generator Loss: 3.590350, Discriminator Loss: 1.907586\n", + "Step 500: Generator Loss: 1.254698, Discriminator Loss: 1.005236\n", + "Step 1000: Generator Loss: 1.730409, Discriminator Loss: 0.837684\n", + "Step 1500: Generator Loss: 1.962198, Discriminator Loss: 0.618827\n", + "Step 2000: Generator Loss: 2.767945, Discriminator Loss: 0.378071\n", + "Step 2500: Generator Loss: 2.370605, Discriminator Loss: 0.561247\n", + "Step 3000: Generator Loss: 3.427798, Discriminator Loss: 0.402951\n", + "Step 3500: Generator Loss: 4.904454, Discriminator Loss: 0.554856\n", + "Step 4000: Generator Loss: 4.045284, Discriminator Loss: 0.454970\n", + "Step 4500: Generator Loss: 4.577699, Discriminator Loss: 0.687195\n", + "Step 5000: Generator Loss: 3.476081, Discriminator Loss: 0.210492\n", + "Step 5500: Generator Loss: 3.898139, Discriminator Loss: 0.143352\n", + "Step 6000: Generator Loss: 4.089877, Discriminator Loss: 1.082561\n", + "Step 6500: Generator Loss: 5.911457, Discriminator Loss: 0.154059\n", + "Step 7000: Generator Loss: 3.594872, Discriminator Loss: 0.152970\n", + "Step 7500: Generator Loss: 6.067883, Discriminator Loss: 0.084864\n", + "Step 8000: Generator Loss: 6.737456, Discriminator Loss: 0.402566\n", + "Step 8500: Generator Loss: 6.630128, Discriminator Loss: 0.034838\n", + "Step 9000: Generator Loss: 6.480587, Discriminator Loss: 0.427419\n", + "Step 9500: Generator Loss: 7.200409, Discriminator Loss: 0.124268\n", + "Step 10000: Generator Loss: 5.479313, Discriminator Loss: 0.191389\n" + ] + } + ], + "source": [ + "# Start Training\n", + "# Start a new TF session\n", + "sess = tf.Session()\n", + "\n", + "# Run the initializer\n", + "sess.run(init)\n", + " \n", + "# Training\n", + "for i in range(1, num_steps+1):\n", + "\n", + " # Prepare Input Data\n", + " # Get the next batch of MNIST data (only images are needed, not labels)\n", + " batch_x, _ = mnist.train.next_batch(batch_size)\n", + " batch_x = np.reshape(batch_x, newshape=[-1, 28, 28, 1])\n", + " # Rescale to [-1, 1], the input range of the discriminator\n", + " batch_x = batch_x * 2. - 1.\n", + "\n", + " # Discriminator Training\n", + " # Generate noise to feed to the generator\n", + " z = np.random.uniform(-1., 1., size=[batch_size, noise_dim])\n", + " _, dl = sess.run([train_disc, disc_loss], feed_dict={real_image_input: batch_x, noise_input: z, is_training:True})\n", + " \n", + " # Generator Training\n", + " # Generate noise to feed to the generator\n", + " z = np.random.uniform(-1., 1., size=[batch_size, noise_dim])\n", + " _, gl = sess.run([train_gen, gen_loss], feed_dict={noise_input: z, is_training:True})\n", + " \n", + " if i % 500 == 0 or i == 1:\n", + " print('Step %i: Generator Loss: %f, Discriminator Loss: %f' % (i, gl, dl))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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6VWlTPKyiPmRNdO/ePcySVIRH1A+fDUjxSHkrG1cRH8pSVY2WTKL8BD2WhmoF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\nNLSZp2OoG2VZU9CVwr755psDPsnpvffeS+o4K8I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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Testing\n", + "# Generate images from noise, using the generator network.\n", + "n = 6\n", + "canvas = np.empty((28 * n, 28 * n))\n", + "for i in range(n):\n", + " # Noise input.\n", + " z = np.random.uniform(-1., 1., size=[n, noise_dim])\n", + " # Generate image from noise.\n", + " g = sess.run(gen_sample, feed_dict={noise_input: z, is_training:False})\n", + " # Rescale values to the original [0, 1] (from tanh -> [-1, 1])\n", + " g = (g + 1.) / 2.\n", + " # Reverse colours for better display\n", + " g = -1 * (g - 1)\n", + " for j in range(n):\n", + " # Draw the generated digits\n", + " canvas[i * 28:(i + 1) * 28, j * 28:(j + 1) * 28] = g[j].reshape([28, 28])\n", + "\n", + "plt.figure(figsize=(n, n))\n", + "plt.imshow(canvas, origin=\"upper\", cmap=\"gray\")\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tensorflow_v1/notebooks/3_NeuralNetworks/dynamic_rnn.ipynb b/tensorflow_v1/notebooks/3_NeuralNetworks/dynamic_rnn.ipynb new file mode 100644 index 00000000..31aa32ee --- /dev/null +++ b/tensorflow_v1/notebooks/3_NeuralNetworks/dynamic_rnn.ipynb @@ -0,0 +1,352 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Dynamic Recurrent Neural Network.\n", + "\n", + "TensorFlow implementation of a Recurrent Neural Network (LSTM) that performs dynamic computation over sequences with variable length. This example is using a toy dataset to classify linear sequences. The generated sequences have variable length.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## RNN Overview\n", + "\n", + "\"nn\"\n", + "\n", + "References:\n", + "- [Long Short Term Memory](http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf), Sepp Hochreiter & Jurgen Schmidhuber, Neural Computation 9(8): 1735-1780, 1997." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import print_function\n", + "\n", + "import tensorflow as tf\n", + "import random" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ====================\n", + "# TOY DATA GENERATOR\n", + "# ====================\n", + "\n", + "class ToySequenceData(object):\n", + " \"\"\" Generate sequence of data with dynamic length.\n", + " This class generate samples for training:\n", + " - Class 0: linear sequences (i.e. [0, 1, 2, 3,...])\n", + " - Class 1: random sequences (i.e. [1, 3, 10, 7,...])\n", + "\n", + " NOTICE:\n", + " We have to pad each sequence to reach 'max_seq_len' for TensorFlow\n", + " consistency (we cannot feed a numpy array with inconsistent\n", + " dimensions). The dynamic calculation will then be perform thanks to\n", + " 'seqlen' attribute that records every actual sequence length.\n", + " \"\"\"\n", + " def __init__(self, n_samples=1000, max_seq_len=20, min_seq_len=3,\n", + " max_value=1000):\n", + " self.data = []\n", + " self.labels = []\n", + " self.seqlen = []\n", + " for i in range(n_samples):\n", + " # Random sequence length\n", + " len = random.randint(min_seq_len, max_seq_len)\n", + " # Monitor sequence length for TensorFlow dynamic calculation\n", + " self.seqlen.append(len)\n", + " # Add a random or linear int sequence (50% prob)\n", + " if random.random() < .5:\n", + " # Generate a linear sequence\n", + " rand_start = random.randint(0, max_value - len)\n", + " s = [[float(i)/max_value] for i in\n", + " range(rand_start, rand_start + len)]\n", + " # Pad sequence for dimension consistency\n", + " s += [[0.] for i in range(max_seq_len - len)]\n", + " self.data.append(s)\n", + " self.labels.append([1., 0.])\n", + " else:\n", + " # Generate a random sequence\n", + " s = [[float(random.randint(0, max_value))/max_value]\n", + " for i in range(len)]\n", + " # Pad sequence for dimension consistency\n", + " s += [[0.] for i in range(max_seq_len - len)]\n", + " self.data.append(s)\n", + " self.labels.append([0., 1.])\n", + " self.batch_id = 0\n", + "\n", + " def next(self, batch_size):\n", + " \"\"\" Return a batch of data. When dataset end is reached, start over.\n", + " \"\"\"\n", + " if self.batch_id == len(self.data):\n", + " self.batch_id = 0\n", + " batch_data = (self.data[self.batch_id:min(self.batch_id +\n", + " batch_size, len(self.data))])\n", + " batch_labels = (self.labels[self.batch_id:min(self.batch_id +\n", + " batch_size, len(self.data))])\n", + " batch_seqlen = (self.seqlen[self.batch_id:min(self.batch_id +\n", + " batch_size, len(self.data))])\n", + " self.batch_id = min(self.batch_id + batch_size, len(self.data))\n", + " return batch_data, batch_labels, batch_seqlen" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ==========\n", + "# MODEL\n", + "# ==========\n", + "\n", + "# Parameters\n", + "learning_rate = 0.01\n", + "training_steps = 10000\n", + "batch_size = 128\n", + "display_step = 200\n", + "\n", + "# Network Parameters\n", + "seq_max_len = 20 # Sequence max length\n", + "n_hidden = 64 # hidden layer num of features\n", + "n_classes = 2 # linear sequence or not\n", + "\n", + "trainset = ToySequenceData(n_samples=1000, max_seq_len=seq_max_len)\n", + "testset = ToySequenceData(n_samples=500, max_seq_len=seq_max_len)\n", + "\n", + "# tf Graph input\n", + "x = tf.placeholder(\"float\", [None, seq_max_len, 1])\n", + "y = tf.placeholder(\"float\", [None, n_classes])\n", + "# A placeholder for indicating each sequence length\n", + "seqlen = tf.placeholder(tf.int32, [None])\n", + "\n", + "# Define weights\n", + "weights = {\n", + " 'out': tf.Variable(tf.random_normal([n_hidden, n_classes]))\n", + "}\n", + "biases = {\n", + " 'out': tf.Variable(tf.random_normal([n_classes]))\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def dynamicRNN(x, seqlen, weights, biases):\n", + "\n", + " # Prepare data shape to match `rnn` function requirements\n", + " # Current data input shape: (batch_size, n_steps, n_input)\n", + " # Required shape: 'n_steps' tensors list of shape (batch_size, n_input)\n", + " \n", + " # Unstack to get a list of 'n_steps' tensors of shape (batch_size, n_input)\n", + " x = tf.unstack(x, seq_max_len, 1)\n", + "\n", + " # Define a lstm cell with tensorflow\n", + " lstm_cell = tf.contrib.rnn.BasicLSTMCell(n_hidden)\n", + "\n", + " # Get lstm cell output, providing 'sequence_length' will perform dynamic\n", + " # calculation.\n", + " outputs, states = tf.contrib.rnn.static_rnn(lstm_cell, x, dtype=tf.float32,\n", + " sequence_length=seqlen)\n", + "\n", + " # When performing dynamic calculation, we must retrieve the last\n", + " # dynamically computed output, i.e., if a sequence length is 10, we need\n", + " # to retrieve the 10th output.\n", + " # However TensorFlow doesn't support advanced indexing yet, so we build\n", + " # a custom op that for each sample in batch size, get its length and\n", + " # get the corresponding relevant output.\n", + "\n", + " # 'outputs' is a list of output at every timestep, we pack them in a Tensor\n", + " # and change back dimension to [batch_size, n_step, n_input]\n", + " outputs = tf.stack(outputs)\n", + " outputs = tf.transpose(outputs, [1, 0, 2])\n", + "\n", + " # Hack to build the indexing and retrieve the right output.\n", + " batch_size = tf.shape(outputs)[0]\n", + " # Start indices for each sample\n", + " index = tf.range(0, batch_size) * seq_max_len + (seqlen - 1)\n", + " # Indexing\n", + " outputs = tf.gather(tf.reshape(outputs, [-1, n_hidden]), index)\n", + "\n", + " # Linear activation, using outputs computed above\n", + " return tf.matmul(outputs, weights['out']) + biases['out']" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/aymeric.damien/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/gradients_impl.py:93: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n", + " \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n" + ] + } + ], + "source": [ + "pred = dynamicRNN(x, seqlen, weights, biases)\n", + "\n", + "# Define loss and optimizer\n", + "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))\n", + "optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cost)\n", + "\n", + "# Evaluate model\n", + "correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))\n", + "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n", + "\n", + "# Initialize the variables (i.e. assign their default value)\n", + "init = tf.global_variables_initializer()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 1, Minibatch Loss= 0.864517, Training Accuracy= 0.42188\n", + "Step 200, Minibatch Loss= 0.686012, Training Accuracy= 0.43269\n", + "Step 400, Minibatch Loss= 0.682970, Training Accuracy= 0.48077\n", + "Step 600, Minibatch Loss= 0.679640, Training Accuracy= 0.50962\n", + "Step 800, Minibatch Loss= 0.675208, Training Accuracy= 0.53846\n", + "Step 1000, Minibatch Loss= 0.668636, Training Accuracy= 0.56731\n", + "Step 1200, Minibatch Loss= 0.657525, Training Accuracy= 0.62500\n", + "Step 1400, Minibatch Loss= 0.635423, Training Accuracy= 0.67308\n", + "Step 1600, Minibatch Loss= 0.580433, Training Accuracy= 0.75962\n", + "Step 1800, Minibatch Loss= 0.475599, Training Accuracy= 0.81731\n", + "Step 2000, Minibatch Loss= 0.434865, Training Accuracy= 0.83654\n", + "Step 2200, Minibatch Loss= 0.423690, Training Accuracy= 0.85577\n", + "Step 2400, Minibatch Loss= 0.417472, Training Accuracy= 0.85577\n", + "Step 2600, Minibatch Loss= 0.412906, Training Accuracy= 0.85577\n", + "Step 2800, Minibatch Loss= 0.409193, Training Accuracy= 0.85577\n", + "Step 3000, Minibatch Loss= 0.406035, Training Accuracy= 0.86538\n", + "Step 3200, Minibatch Loss= 0.403287, Training Accuracy= 0.87500\n", + "Step 3400, Minibatch Loss= 0.400862, Training Accuracy= 0.87500\n", + "Step 3600, Minibatch Loss= 0.398704, Training Accuracy= 0.86538\n", + "Step 3800, Minibatch Loss= 0.396768, Training Accuracy= 0.86538\n", + "Step 4000, Minibatch Loss= 0.395017, Training Accuracy= 0.86538\n", + "Step 4200, Minibatch Loss= 0.393422, Training Accuracy= 0.86538\n", + "Step 4400, Minibatch Loss= 0.391957, Training Accuracy= 0.85577\n", + "Step 4600, Minibatch Loss= 0.390600, Training Accuracy= 0.85577\n", + "Step 4800, Minibatch Loss= 0.389334, Training Accuracy= 0.86538\n", + "Step 5000, Minibatch Loss= 0.388143, Training Accuracy= 0.86538\n", + "Step 5200, Minibatch Loss= 0.387015, Training Accuracy= 0.86538\n", + "Step 5400, Minibatch Loss= 0.385940, Training Accuracy= 0.86538\n", + "Step 5600, Minibatch Loss= 0.384907, Training Accuracy= 0.86538\n", + "Step 5800, Minibatch Loss= 0.383904, Training Accuracy= 0.85577\n", + "Step 6000, Minibatch Loss= 0.382921, Training Accuracy= 0.86538\n", + "Step 6200, Minibatch Loss= 0.381941, Training Accuracy= 0.86538\n", + "Step 6400, Minibatch Loss= 0.380947, Training Accuracy= 0.86538\n", + "Step 6600, Minibatch Loss= 0.379912, Training Accuracy= 0.86538\n", + "Step 6800, Minibatch Loss= 0.378796, Training Accuracy= 0.86538\n", + "Step 7000, Minibatch Loss= 0.377540, Training Accuracy= 0.86538\n", + "Step 7200, Minibatch Loss= 0.376041, Training Accuracy= 0.86538\n", + "Step 7400, Minibatch Loss= 0.374130, Training Accuracy= 0.85577\n", + "Step 7600, Minibatch Loss= 0.371514, Training Accuracy= 0.85577\n", + "Step 7800, Minibatch Loss= 0.367723, Training Accuracy= 0.85577\n", + "Step 8000, Minibatch Loss= 0.362049, Training Accuracy= 0.85577\n", + "Step 8200, Minibatch Loss= 0.353558, Training Accuracy= 0.85577\n", + "Step 8400, Minibatch Loss= 0.341072, Training Accuracy= 0.86538\n", + "Step 8600, Minibatch Loss= 0.323062, Training Accuracy= 0.87500\n", + "Step 8800, Minibatch Loss= 0.299278, Training Accuracy= 0.89423\n", + "Step 9000, Minibatch Loss= 0.273857, Training Accuracy= 0.90385\n", + "Step 9200, Minibatch Loss= 0.248392, Training Accuracy= 0.91346\n", + "Step 9400, Minibatch Loss= 0.221348, Training Accuracy= 0.92308\n", + "Step 9600, Minibatch Loss= 0.191947, Training Accuracy= 0.92308\n", + "Step 9800, Minibatch Loss= 0.159308, Training Accuracy= 0.93269\n", + "Step 10000, Minibatch Loss= 0.136938, Training Accuracy= 0.96154\n", + "Optimization Finished!\n", + "Testing Accuracy: 0.952\n" + ] + } + ], + "source": [ + "# Start training\n", + "with tf.Session() as sess:\n", + "\n", + " # Run the initializer\n", + " sess.run(init)\n", + "\n", + " for step in range(1, training_steps+1):\n", + " batch_x, batch_y, batch_seqlen = trainset.next(batch_size)\n", + " # Run optimization op (backprop)\n", + " sess.run(optimizer, feed_dict={x: batch_x, y: batch_y,\n", + " seqlen: batch_seqlen})\n", + " if step % display_step == 0 or step == 1:\n", + " # Calculate batch accuracy & loss\n", + " acc, loss = sess.run([accuracy, cost], feed_dict={x: batch_x, y: batch_y,\n", + " seqlen: batch_seqlen})\n", + " print(\"Step \" + str(step) + \", Minibatch Loss= \" + \\\n", + " \"{:.6f}\".format(loss) + \", Training Accuracy= \" + \\\n", + " \"{:.5f}\".format(acc))\n", + "\n", + " print(\"Optimization Finished!\")\n", + "\n", + " # Calculate accuracy\n", + " test_data = testset.data\n", + " test_label = testset.labels\n", + " test_seqlen = testset.seqlen\n", + " print(\"Testing Accuracy:\", \\\n", + " sess.run(accuracy, feed_dict={x: test_data, y: test_label,\n", + " seqlen: test_seqlen}))" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/tensorflow_v1/notebooks/3_NeuralNetworks/gan.ipynb b/tensorflow_v1/notebooks/3_NeuralNetworks/gan.ipynb new file mode 100644 index 00000000..1bfb0bd5 --- /dev/null +++ b/tensorflow_v1/notebooks/3_NeuralNetworks/gan.ipynb @@ -0,0 +1,323 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Generative Adversarial Network Example\n", + "\n", + "Build a generative adversarial network (GAN) to generate digit images from a noise distribution with TensorFlow.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## GAN Overview\n", + "\n", + "\"nn\"\n", + "\n", + "References:\n", + "- [Generative adversarial nets](https://arxiv.org/pdf/1406.2661.pdf). I Goodfellow, J Pouget-Abadie, M Mirza, B Xu, D Warde-Farley, S Ozair, Y. Bengio. Advances in neural information processing systems, 2672-2680.\n", + "- [Understanding the difficulty of training deep feedforward neural networks](http://proceedings.mlr.press/v9/glorot10a.html). X Glorot, Y Bengio. Aistats 9, 249-256\n", + "\n", + "Other tutorials:\n", + "- [Generative Adversarial Networks Explained](http://kvfrans.com/generative-adversial-networks-explained/). Kevin Frans.\n", + "\n", + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import division, print_function, absolute_import\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "# Import MNIST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Training Params\n", + "num_steps = 70000\n", + "batch_size = 128\n", + "learning_rate = 0.0002\n", + "\n", + "# Network Params\n", + "image_dim = 784 # 28*28 pixels\n", + "gen_hidden_dim = 256\n", + "disc_hidden_dim = 256\n", + "noise_dim = 100 # Noise data points\n", + "\n", + "# A custom initialization (see Xavier Glorot init)\n", + "def glorot_init(shape):\n", + " return tf.random_normal(shape=shape, stddev=1. / tf.sqrt(shape[0] / 2.))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Store layers weight & bias\n", + "weights = {\n", + " 'gen_hidden1': tf.Variable(glorot_init([noise_dim, gen_hidden_dim])),\n", + " 'gen_out': tf.Variable(glorot_init([gen_hidden_dim, image_dim])),\n", + " 'disc_hidden1': tf.Variable(glorot_init([image_dim, disc_hidden_dim])),\n", + " 'disc_out': tf.Variable(glorot_init([disc_hidden_dim, 1])),\n", + "}\n", + "biases = {\n", + " 'gen_hidden1': tf.Variable(tf.zeros([gen_hidden_dim])),\n", + " 'gen_out': tf.Variable(tf.zeros([image_dim])),\n", + " 'disc_hidden1': tf.Variable(tf.zeros([disc_hidden_dim])),\n", + " 'disc_out': tf.Variable(tf.zeros([1])),\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Generator\n", + "def generator(x):\n", + " hidden_layer = tf.matmul(x, weights['gen_hidden1'])\n", + " hidden_layer = tf.add(hidden_layer, biases['gen_hidden1'])\n", + " hidden_layer = tf.nn.relu(hidden_layer)\n", + " out_layer = tf.matmul(hidden_layer, weights['gen_out'])\n", + " out_layer = tf.add(out_layer, biases['gen_out'])\n", + " out_layer = tf.nn.sigmoid(out_layer)\n", + " return out_layer\n", + "\n", + "\n", + "# Discriminator\n", + "def discriminator(x):\n", + " hidden_layer = tf.matmul(x, weights['disc_hidden1'])\n", + " hidden_layer = tf.add(hidden_layer, biases['disc_hidden1'])\n", + " hidden_layer = tf.nn.relu(hidden_layer)\n", + " out_layer = tf.matmul(hidden_layer, weights['disc_out'])\n", + " out_layer = tf.add(out_layer, biases['disc_out'])\n", + " out_layer = tf.nn.sigmoid(out_layer)\n", + " return out_layer\n", + "\n", + "# Build Networks\n", + "# Network Inputs\n", + "gen_input = tf.placeholder(tf.float32, shape=[None, noise_dim], name='input_noise')\n", + "disc_input = tf.placeholder(tf.float32, shape=[None, image_dim], name='disc_input')\n", + "\n", + "# Build Generator Network\n", + "gen_sample = generator(gen_input)\n", + "\n", + "# Build 2 Discriminator Networks (one from noise input, one from generated samples)\n", + "disc_real = discriminator(disc_input)\n", + "disc_fake = discriminator(gen_sample)\n", + "\n", + "# Build Loss\n", + "gen_loss = -tf.reduce_mean(tf.log(disc_fake))\n", + "disc_loss = -tf.reduce_mean(tf.log(disc_real) + tf.log(1. - disc_fake))\n", + "\n", + "# Build Optimizers\n", + "optimizer_gen = tf.train.AdamOptimizer(learning_rate=learning_rate)\n", + "optimizer_disc = tf.train.AdamOptimizer(learning_rate=learning_rate)\n", + "\n", + "# Training Variables for each optimizer\n", + "# By default in TensorFlow, all variables are updated by each optimizer, so we\n", + "# need to precise for each one of them the specific variables to update.\n", + "# Generator Network Variables\n", + "gen_vars = [weights['gen_hidden1'], weights['gen_out'],\n", + " biases['gen_hidden1'], biases['gen_out']]\n", + "# Discriminator Network Variables\n", + "disc_vars = [weights['disc_hidden1'], weights['disc_out'],\n", + " biases['disc_hidden1'], biases['disc_out']]\n", + "\n", + "# Create training operations\n", + "train_gen = optimizer_gen.minimize(gen_loss, var_list=gen_vars)\n", + "train_disc = optimizer_disc.minimize(disc_loss, var_list=disc_vars)\n", + "\n", + "# Initialize the variables (i.e. assign their default value)\n", + "init = tf.global_variables_initializer()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 1: Generator Loss: 0.774581, Discriminator Loss: 1.300602\n", + "Step 2000: Generator Loss: 4.521158, Discriminator Loss: 0.030166\n", + "Step 4000: Generator Loss: 3.685439, Discriminator Loss: 0.125958\n", + "Step 6000: Generator Loss: 4.412449, Discriminator Loss: 0.097088\n", + "Step 8000: Generator Loss: 3.996747, Discriminator Loss: 0.150800\n", + "Step 10000: Generator Loss: 3.850827, Discriminator Loss: 0.225699\n", + "Step 12000: Generator Loss: 2.950704, Discriminator Loss: 0.279967\n", + "Step 14000: Generator Loss: 3.741951, Discriminator Loss: 0.241062\n", + "Step 16000: Generator Loss: 3.117743, Discriminator Loss: 0.432293\n", + "Step 18000: Generator Loss: 3.647199, Discriminator Loss: 0.278121\n", + "Step 20000: Generator Loss: 3.186711, Discriminator Loss: 0.313830\n", + "Step 22000: Generator Loss: 3.737114, Discriminator Loss: 0.201730\n", + "Step 24000: Generator Loss: 3.042442, Discriminator Loss: 0.454414\n", + "Step 26000: Generator Loss: 3.340376, Discriminator Loss: 0.249428\n", + "Step 28000: Generator Loss: 3.423218, Discriminator Loss: 0.369653\n", + "Step 30000: Generator Loss: 3.219242, Discriminator Loss: 0.463535\n", + "Step 32000: Generator Loss: 3.313017, Discriminator Loss: 0.276070\n", + "Step 34000: Generator Loss: 3.413397, Discriminator Loss: 0.367721\n", + "Step 36000: Generator Loss: 3.240625, Discriminator Loss: 0.446160\n", + "Step 38000: Generator Loss: 3.175355, Discriminator Loss: 0.377628\n", + "Step 40000: Generator Loss: 3.154558, Discriminator Loss: 0.478812\n", + "Step 42000: Generator Loss: 3.210753, Discriminator Loss: 0.497502\n", + "Step 44000: Generator Loss: 2.883431, Discriminator Loss: 0.395812\n", + "Step 46000: Generator Loss: 2.584176, Discriminator Loss: 0.420783\n", + "Step 48000: Generator Loss: 2.581381, Discriminator Loss: 0.469289\n", + "Step 50000: Generator Loss: 2.752729, Discriminator Loss: 0.373544\n", + "Step 52000: Generator Loss: 2.649749, Discriminator Loss: 0.463755\n", + "Step 54000: Generator Loss: 2.468188, Discriminator Loss: 0.556129\n", + "Step 56000: Generator Loss: 2.653330, Discriminator Loss: 0.377572\n", + "Step 58000: Generator Loss: 2.697943, Discriminator Loss: 0.424133\n", + "Step 60000: Generator Loss: 2.835973, Discriminator Loss: 0.413252\n", + "Step 62000: Generator Loss: 2.751346, Discriminator Loss: 0.403332\n", + "Step 64000: Generator Loss: 3.212001, Discriminator Loss: 0.534427\n", + "Step 66000: Generator Loss: 2.878227, Discriminator Loss: 0.431244\n", + "Step 68000: Generator Loss: 3.104266, Discriminator Loss: 0.426825\n", + "Step 70000: Generator Loss: 2.871485, Discriminator Loss: 0.348638\n" + ] + } + ], + "source": [ + "# Start Training\n", + "# Start a new TF session\n", + "sess = tf.Session()\n", + "\n", + "# Run the initializer\n", + "sess.run(init)\n", + "\n", + "# Training\n", + "for i in range(1, num_steps+1):\n", + " # Prepare Data\n", + " # Get the next batch of MNIST data (only images are needed, not labels)\n", + " batch_x, _ = mnist.train.next_batch(batch_size)\n", + " # Generate noise to feed to the generator\n", + " z = np.random.uniform(-1., 1., size=[batch_size, noise_dim])\n", + "\n", + " # Train\n", + " feed_dict = {disc_input: batch_x, gen_input: z}\n", + " _, _, gl, dl = sess.run([train_gen, train_disc, gen_loss, disc_loss],\n", + " feed_dict=feed_dict)\n", + " if i % 2000 == 0 or i == 1:\n", + " print('Step %i: Generator Loss: %f, Discriminator Loss: %f' % (i, gl, dl))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Ew8UXX5y1MaULpXdL/UgVSSnELTa5OKZMmVLk802aNMnwSMqPkpekYNWuMJtF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\nl19+OVDu3Z50rrfeeisQswF1bZX/oFlV1dF9pgxXzWAnTZo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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Testing\n", + "# Generate images from noise, using the generator network.\n", + "n = 6\n", + "canvas = np.empty((28 * n, 28 * n))\n", + "for i in range(n):\n", + " # Noise input.\n", + " z = np.random.uniform(-1., 1., size=[n, noise_dim])\n", + " # Generate image from noise.\n", + " g = sess.run(gen_sample, feed_dict={gen_input: z})\n", + " # Reverse colours for better display\n", + " g = -1 * (g - 1)\n", + " for j in range(n):\n", + " # Draw the generated digits\n", + " canvas[i * 28:(i + 1) * 28, j * 28:(j + 1) * 28] = g[j].reshape([28, 28])\n", + "\n", + "plt.figure(figsize=(n, n))\n", + "plt.imshow(canvas, origin=\"upper\", cmap=\"gray\")\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tensorflow_v1/notebooks/3_NeuralNetworks/neural_network.ipynb b/tensorflow_v1/notebooks/3_NeuralNetworks/neural_network.ipynb new file mode 100644 index 00000000..62e70727 --- /dev/null +++ b/tensorflow_v1/notebooks/3_NeuralNetworks/neural_network.ipynb @@ -0,0 +1,390 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Neural Network Example\n", + "\n", + "Build a 2-hidden layers fully connected neural network (a.k.a multilayer perceptron) with TensorFlow.\n", + "\n", + "This example is using some of TensorFlow higher-level wrappers (tf.estimators, tf.layers, tf.metrics, ...), you can check 'neural_network_raw' example for a raw, and more detailed TensorFlow implementation.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Neural Network Overview\n", + "\n", + "\"nn\"\n", + "\n", + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "from __future__ import print_function\n", + "\n", + "# Import MNIST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=False)\n", + "\n", + "import tensorflow as tf\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Parameters\n", + "learning_rate = 0.1\n", + "num_steps = 1000\n", + "batch_size = 128\n", + "display_step = 100\n", + "\n", + "# Network Parameters\n", + "n_hidden_1 = 256 # 1st layer number of neurons\n", + "n_hidden_2 = 256 # 2nd layer number of neurons\n", + "num_input = 784 # MNIST data input (img shape: 28*28)\n", + "num_classes = 10 # MNIST total classes (0-9 digits)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Define the input function for training\n", + "input_fn = tf.estimator.inputs.numpy_input_fn(\n", + " x={'images': mnist.train.images}, y=mnist.train.labels,\n", + " batch_size=batch_size, num_epochs=None, shuffle=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Define the neural network\n", + "def neural_net(x_dict):\n", + " # TF Estimator input is a dict, in case of multiple inputs\n", + " x = x_dict['images']\n", + " # Hidden fully connected layer with 256 neurons\n", + " layer_1 = tf.layers.dense(x, n_hidden_1)\n", + " # Hidden fully connected layer with 256 neurons\n", + " layer_2 = tf.layers.dense(layer_1, n_hidden_2)\n", + " # Output fully connected layer with a neuron for each class\n", + " out_layer = tf.layers.dense(layer_2, num_classes)\n", + " return out_layer" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Define the model function (following TF Estimator Template)\n", + "def model_fn(features, labels, mode):\n", + " \n", + " # Build the neural network\n", + " logits = neural_net(features)\n", + " \n", + " # Predictions\n", + " pred_classes = tf.argmax(logits, axis=1)\n", + " pred_probas = tf.nn.softmax(logits)\n", + " \n", + " # If prediction mode, early return\n", + " if mode == tf.estimator.ModeKeys.PREDICT:\n", + " return tf.estimator.EstimatorSpec(mode, predictions=pred_classes) \n", + " \n", + " # Define loss and optimizer\n", + " loss_op = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(\n", + " logits=logits, labels=tf.cast(labels, dtype=tf.int32)))\n", + " optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " train_op = optimizer.minimize(loss_op, global_step=tf.train.get_global_step())\n", + " \n", + " # Evaluate the accuracy of the model\n", + " acc_op = tf.metrics.accuracy(labels=labels, predictions=pred_classes)\n", + " \n", + " # TF Estimators requires to return a EstimatorSpec, that specify\n", + " # the different ops for training, evaluating, ...\n", + " estim_specs = tf.estimator.EstimatorSpec(\n", + " mode=mode,\n", + " predictions=pred_classes,\n", + " loss=loss_op,\n", + " train_op=train_op,\n", + " eval_metric_ops={'accuracy': acc_op})\n", + "\n", + " return estim_specs" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Using default config.\n", + "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpu7vjLA\n", + "INFO:tensorflow:Using config: {'_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_tf_random_seed': 1, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_save_checkpoints_steps': None, '_model_dir': '/tmp/tmpu7vjLA', '_save_summary_steps': 100}\n" + ] + } + ], + "source": [ + "# Build the Estimator\n", + "model = tf.estimator.Estimator(model_fn)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Create CheckpointSaverHook.\n", + "INFO:tensorflow:Saving checkpoints for 1 into /tmp/tmpu7vjLA/model.ckpt.\n", + "INFO:tensorflow:loss = 2.44919, step = 1\n", + "INFO:tensorflow:global_step/sec: 602.544\n", + "INFO:tensorflow:loss = 0.344767, step = 101 (0.167 sec)\n", + "INFO:tensorflow:global_step/sec: 618.839\n", + "INFO:tensorflow:loss = 0.277633, step = 201 (0.162 sec)\n", + "INFO:tensorflow:global_step/sec: 626.418\n", + "INFO:tensorflow:loss = 0.407796, step = 301 (0.160 sec)\n", + "INFO:tensorflow:global_step/sec: 624.765\n", + "INFO:tensorflow:loss = 0.376889, step = 401 (0.160 sec)\n", + "INFO:tensorflow:global_step/sec: 624.091\n", + "INFO:tensorflow:loss = 0.319697, step = 501 (0.160 sec)\n", + "INFO:tensorflow:global_step/sec: 616.907\n", + "INFO:tensorflow:loss = 0.39049, step = 601 (0.162 sec)\n", + "INFO:tensorflow:global_step/sec: 623.371\n", + "INFO:tensorflow:loss = 0.336831, step = 701 (0.161 sec)\n", + "INFO:tensorflow:global_step/sec: 617.429\n", + "INFO:tensorflow:loss = 0.312776, step = 801 (0.162 sec)\n", + "INFO:tensorflow:global_step/sec: 620.825\n", + "INFO:tensorflow:loss = 0.312817, step = 901 (0.161 sec)\n", + "INFO:tensorflow:Saving checkpoints for 1000 into /tmp/tmpu7vjLA/model.ckpt.\n", + "INFO:tensorflow:Loss for final step: 0.24931.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Train the Model\n", + "model.train(input_fn, steps=num_steps)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Starting evaluation at 2017-08-21-13:57:02\n", + "INFO:tensorflow:Restoring parameters from /tmp/tmpu7vjLA/model.ckpt-1000\n", + "INFO:tensorflow:Finished evaluation at 2017-08-21-13:57:02\n", + "INFO:tensorflow:Saving dict for global step 1000: accuracy = 0.9189, global_step = 1000, loss = 0.286567\n" + ] + }, + { + "data": { + "text/plain": [ + "{'accuracy': 0.91890001, 'global_step': 1000, 'loss': 0.28656715}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Evaluate the Model\n", + "# Define the input function for evaluating\n", + "input_fn = tf.estimator.inputs.numpy_input_fn(\n", + " x={'images': mnist.test.images}, y=mnist.test.labels,\n", + " batch_size=batch_size, shuffle=False)\n", + "# Use the Estimator 'evaluate' method\n", + "model.evaluate(input_fn)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Restoring parameters from /tmp/tmpu7vjLA/model.ckpt-1000\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 7\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 0\n" + ] + } + ], + "source": [ + "# Predict single images\n", + "n_images = 4\n", + "# Get images from test set\n", + "test_images = mnist.test.images[:n_images]\n", + "# Prepare the input data\n", + "input_fn = tf.estimator.inputs.numpy_input_fn(\n", + " x={'images': test_images}, shuffle=False)\n", + "# Use the model to predict the images class\n", + "preds = list(model.predict(input_fn))\n", + "\n", + "# Display\n", + "for i in range(n_images):\n", + " plt.imshow(np.reshape(test_images[i], [28, 28]), cmap='gray')\n", + " plt.show()\n", + " print(\"Model prediction:\", preds[i])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/tensorflow_v1/notebooks/3_NeuralNetworks/neural_network_eager_api.ipynb b/tensorflow_v1/notebooks/3_NeuralNetworks/neural_network_eager_api.ipynb new file mode 100644 index 00000000..346f2e5d --- /dev/null +++ b/tensorflow_v1/notebooks/3_NeuralNetworks/neural_network_eager_api.ipynb @@ -0,0 +1,287 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Neural Network with Eager API\n", + "\n", + "Build a 2-hidden layers fully connected neural network (a.k.a multilayer perceptron) with TensorFlow's Eager API.\n", + "\n", + "This example is using some of TensorFlow higher-level wrappers (tf.estimators, tf.layers, tf.metrics, ...), you can check 'neural_network_raw' example for a raw, and more detailed TensorFlow implementation.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Neural Network Overview\n", + "\n", + "\"nn\"\n", + "\n", + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import print_function\n", + "\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Set Eager API\n", + "tf.enable_eager_execution()\n", + "tfe = tf.contrib.eager" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "# Import MNIST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Parameters\n", + "learning_rate = 0.001\n", + "num_steps = 1000\n", + "batch_size = 128\n", + "display_step = 100\n", + "\n", + "# Network Parameters\n", + "n_hidden_1 = 256 # 1st layer number of neurons\n", + "n_hidden_2 = 256 # 2nd layer number of neurons\n", + "num_input = 784 # MNIST data input (img shape: 28*28)\n", + "num_classes = 10 # MNIST total classes (0-9 digits)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Using TF Dataset to split data into batches\n", + "dataset = tf.data.Dataset.from_tensor_slices(\n", + " (mnist.train.images, mnist.train.labels))\n", + "dataset = dataset.repeat().batch(batch_size).prefetch(batch_size)\n", + "dataset_iter = tfe.Iterator(dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Define the neural network. To use eager API and tf.layers API together,\n", + "# we must instantiate a tfe.Network class as follow:\n", + "class NeuralNet(tfe.Network):\n", + " def __init__(self):\n", + " # Define each layer\n", + " super(NeuralNet, self).__init__()\n", + " # Hidden fully connected layer with 256 neurons\n", + " self.layer1 = self.track_layer(\n", + " tf.layers.Dense(n_hidden_1, activation=tf.nn.relu))\n", + " # Hidden fully connected layer with 256 neurons\n", + " self.layer2 = self.track_layer(\n", + " tf.layers.Dense(n_hidden_2, activation=tf.nn.relu))\n", + " # Output fully connected layer with a neuron for each class\n", + " self.out_layer = self.track_layer(tf.layers.Dense(num_classes))\n", + "\n", + " def call(self, x):\n", + " x = self.layer1(x)\n", + " x = self.layer2(x)\n", + " return self.out_layer(x)\n", + "\n", + "\n", + "neural_net = NeuralNet()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Cross-Entropy loss function\n", + "def loss_fn(inference_fn, inputs, labels):\n", + " # Using sparse_softmax cross entropy\n", + " return tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(\n", + " logits=inference_fn(inputs), labels=labels))\n", + "\n", + "\n", + "# Calculate accuracy\n", + "def accuracy_fn(inference_fn, inputs, labels):\n", + " prediction = tf.nn.softmax(inference_fn(inputs))\n", + " correct_pred = tf.equal(tf.argmax(prediction, 1), labels)\n", + " return tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n", + "\n", + "\n", + "# SGD Optimizer\n", + "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n", + "\n", + "# Compute gradients\n", + "grad = tfe.implicit_gradients(loss_fn)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial loss= 2.340397596\n", + "Step: 0001 loss= 2.340397596 accuracy= 0.0703\n", + "Step: 0100 loss= 0.586046159 accuracy= 0.8305\n", + "Step: 0200 loss= 0.253318846 accuracy= 0.9282\n", + "Step: 0300 loss= 0.214748293 accuracy= 0.9377\n", + "Step: 0400 loss= 0.180644721 accuracy= 0.9466\n", + "Step: 0500 loss= 0.137285724 accuracy= 0.9591\n", + "Step: 0600 loss= 0.119845696 accuracy= 0.9636\n", + "Step: 0700 loss= 0.113618039 accuracy= 0.9665\n", + "Step: 0800 loss= 0.109642141 accuracy= 0.9676\n", + "Step: 0900 loss= 0.085067607 accuracy= 0.9746\n", + "Step: 1000 loss= 0.079819344 accuracy= 0.9754\n" + ] + } + ], + "source": [ + "# Training\n", + "average_loss = 0.\n", + "average_acc = 0.\n", + "for step in range(num_steps):\n", + "\n", + " # Iterate through the dataset\n", + " d = dataset_iter.next()\n", + " \n", + " # Images\n", + " x_batch = d[0]\n", + " # Labels\n", + " y_batch = tf.cast(d[1], dtype=tf.int64)\n", + "\n", + " # Compute the batch loss\n", + " batch_loss = loss_fn(neural_net, x_batch, y_batch)\n", + " average_loss += batch_loss\n", + " # Compute the batch accuracy\n", + " batch_accuracy = accuracy_fn(neural_net, x_batch, y_batch)\n", + " average_acc += batch_accuracy\n", + "\n", + " if step == 0:\n", + " # Display the initial cost, before optimizing\n", + " print(\"Initial loss= {:.9f}\".format(average_loss))\n", + "\n", + " # Update the variables following gradients info\n", + " optimizer.apply_gradients(grad(neural_net, x_batch, y_batch))\n", + "\n", + " # Display info\n", + " if (step + 1) % display_step == 0 or step == 0:\n", + " if step > 0:\n", + " average_loss /= display_step\n", + " average_acc /= display_step\n", + " print(\"Step:\", '%04d' % (step + 1), \" loss=\",\n", + " \"{:.9f}\".format(average_loss), \" accuracy=\",\n", + " \"{:.4f}\".format(average_acc))\n", + " average_loss = 0.\n", + " average_acc = 0." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testset Accuracy: 0.9719\n" + ] + } + ], + "source": [ + "# Evaluate model on the test image set\n", + "testX = mnist.test.images\n", + "testY = mnist.test.labels\n", + "\n", + "test_acc = accuracy_fn(neural_net, testX, testY)\n", + "print(\"Testset Accuracy: {:.4f}\".format(test_acc))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.14" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/tensorflow_v1/notebooks/3_NeuralNetworks/neural_network_raw.ipynb b/tensorflow_v1/notebooks/3_NeuralNetworks/neural_network_raw.ipynb new file mode 100644 index 00000000..6d9dbd24 --- /dev/null +++ b/tensorflow_v1/notebooks/3_NeuralNetworks/neural_network_raw.ipynb @@ -0,0 +1,224 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Neural Network Example\n", + "\n", + "Build a 2-hidden layers fully connected neural network (a.k.a multilayer perceptron) with TensorFlow.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Neural Network Overview\n", + "\n", + "\"nn\"\n", + "\n", + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "from __future__ import print_function\n", + "\n", + "# Import MNIST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)\n", + "\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Parameters\n", + "learning_rate = 0.1\n", + "num_steps = 500\n", + "batch_size = 128\n", + "display_step = 100\n", + "\n", + "# Network Parameters\n", + "n_hidden_1 = 256 # 1st layer number of neurons\n", + "n_hidden_2 = 256 # 2nd layer number of neurons\n", + "num_input = 784 # MNIST data input (img shape: 28*28)\n", + "num_classes = 10 # MNIST total classes (0-9 digits)\n", + "\n", + "# tf Graph input\n", + "X = tf.placeholder(\"float\", [None, num_input])\n", + "Y = tf.placeholder(\"float\", [None, num_classes])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Store layers weight & bias\n", + "weights = {\n", + " 'h1': tf.Variable(tf.random_normal([num_input, n_hidden_1])),\n", + " 'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),\n", + " 'out': tf.Variable(tf.random_normal([n_hidden_2, num_classes]))\n", + "}\n", + "biases = {\n", + " 'b1': tf.Variable(tf.random_normal([n_hidden_1])),\n", + " 'b2': tf.Variable(tf.random_normal([n_hidden_2])),\n", + " 'out': tf.Variable(tf.random_normal([num_classes]))\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Create model\n", + "def neural_net(x):\n", + " # Hidden fully connected layer with 256 neurons\n", + " layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])\n", + " # Hidden fully connected layer with 256 neurons\n", + " layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])\n", + " # Output fully connected layer with a neuron for each class\n", + " out_layer = tf.matmul(layer_2, weights['out']) + biases['out']\n", + " return out_layer" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Construct model\n", + "logits = neural_net(X)\n", + "\n", + "# Define loss and optimizer\n", + "loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(\n", + " logits=logits, labels=Y))\n", + "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n", + "train_op = optimizer.minimize(loss_op)\n", + "\n", + "# Evaluate model (with test logits, for dropout to be disabled)\n", + "correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(Y, 1))\n", + "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n", + "\n", + "# Initialize the variables (i.e. assign their default value)\n", + "init = tf.global_variables_initializer()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 1, Minibatch Loss= 13208.1406, Training Accuracy= 0.266\n", + "Step 100, Minibatch Loss= 462.8610, Training Accuracy= 0.867\n", + "Step 200, Minibatch Loss= 232.8298, Training Accuracy= 0.844\n", + "Step 300, Minibatch Loss= 85.2141, Training Accuracy= 0.891\n", + "Step 400, Minibatch Loss= 38.0552, Training Accuracy= 0.883\n", + "Step 500, Minibatch Loss= 55.3689, Training Accuracy= 0.867\n", + "Optimization Finished!\n", + "Testing Accuracy: 0.8729\n" + ] + } + ], + "source": [ + "# Start training\n", + "with tf.Session() as sess:\n", + "\n", + " # Run the initializer\n", + " sess.run(init)\n", + "\n", + " for step in range(1, num_steps+1):\n", + " batch_x, batch_y = mnist.train.next_batch(batch_size)\n", + " # Run optimization op (backprop)\n", + " sess.run(train_op, feed_dict={X: batch_x, Y: batch_y})\n", + " if step % display_step == 0 or step == 1:\n", + " # Calculate batch loss and accuracy\n", + " loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x,\n", + " Y: batch_y})\n", + " print(\"Step \" + str(step) + \", Minibatch Loss= \" + \\\n", + " \"{:.4f}\".format(loss) + \", Training Accuracy= \" + \\\n", + " \"{:.3f}\".format(acc))\n", + "\n", + " print(\"Optimization Finished!\")\n", + "\n", + " # Calculate accuracy for MNIST test images\n", + " print(\"Testing Accuracy:\", \\\n", + " sess.run(accuracy, feed_dict={X: mnist.test.images,\n", + " Y: mnist.test.labels}))" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/tensorflow_v1/notebooks/3_NeuralNetworks/recurrent_network.ipynb b/tensorflow_v1/notebooks/3_NeuralNetworks/recurrent_network.ipynb new file mode 100644 index 00000000..48fe57a8 --- /dev/null +++ b/tensorflow_v1/notebooks/3_NeuralNetworks/recurrent_network.ipynb @@ -0,0 +1,292 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Recurrent Neural Network Example\n", + "\n", + "Build a recurrent neural network (LSTM) with TensorFlow.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## RNN Overview\n", + "\n", + "\"nn\"\n", + "\n", + "References:\n", + "- [Long Short Term Memory](http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf), Sepp Hochreiter & Jurgen Schmidhuber, Neural Computation 9(8): 1735-1780, 1997.\n", + "\n", + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "To classify images using a recurrent neural network, we consider every image row as a sequence of pixels. Because MNIST image shape is 28*28px, we will then handle 28 sequences of 28 timesteps for every sample.\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "from __future__ import print_function\n", + "\n", + "import tensorflow as tf\n", + "from tensorflow.contrib import rnn\n", + "\n", + "# Import MNIST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Training Parameters\n", + "learning_rate = 0.001\n", + "training_steps = 10000\n", + "batch_size = 128\n", + "display_step = 200\n", + "\n", + "# Network Parameters\n", + "num_input = 28 # MNIST data input (img shape: 28*28)\n", + "timesteps = 28 # timesteps\n", + "num_hidden = 128 # hidden layer num of features\n", + "num_classes = 10 # MNIST total classes (0-9 digits)\n", + "\n", + "# tf Graph input\n", + "X = tf.placeholder(\"float\", [None, timesteps, num_input])\n", + "Y = tf.placeholder(\"float\", [None, num_classes])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Define weights\n", + "weights = {\n", + " 'out': tf.Variable(tf.random_normal([num_hidden, num_classes]))\n", + "}\n", + "biases = {\n", + " 'out': tf.Variable(tf.random_normal([num_classes]))\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def RNN(x, weights, biases):\n", + "\n", + " # Prepare data shape to match `rnn` function requirements\n", + " # Current data input shape: (batch_size, timesteps, n_input)\n", + " # Required shape: 'timesteps' tensors list of shape (batch_size, n_input)\n", + "\n", + " # Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input)\n", + " x = tf.unstack(x, timesteps, 1)\n", + "\n", + " # Define a lstm cell with tensorflow\n", + " lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0)\n", + "\n", + " # Get lstm cell output\n", + " outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32)\n", + "\n", + " # Linear activation, using rnn inner loop last output\n", + " return tf.matmul(outputs[-1], weights['out']) + biases['out']" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "logits = RNN(X, weights, biases)\n", + "prediction = tf.nn.softmax(logits)\n", + "\n", + "# Define loss and optimizer\n", + "loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(\n", + " logits=logits, labels=Y))\n", + "optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + "train_op = optimizer.minimize(loss_op)\n", + "\n", + "# Evaluate model (with test logits, for dropout to be disabled)\n", + "correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1))\n", + "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n", + "\n", + "# Initialize the variables (i.e. assign their default value)\n", + "init = tf.global_variables_initializer()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 1, Minibatch Loss= 2.6268, Training Accuracy= 0.102\n", + "Step 200, Minibatch Loss= 2.0722, Training Accuracy= 0.328\n", + "Step 400, Minibatch Loss= 1.9181, Training Accuracy= 0.336\n", + "Step 600, Minibatch Loss= 1.8858, Training Accuracy= 0.336\n", + "Step 800, Minibatch Loss= 1.7022, Training Accuracy= 0.422\n", + "Step 1000, Minibatch Loss= 1.6365, Training Accuracy= 0.477\n", + "Step 1200, Minibatch Loss= 1.6691, Training Accuracy= 0.516\n", + "Step 1400, Minibatch Loss= 1.4626, Training Accuracy= 0.547\n", + "Step 1600, Minibatch Loss= 1.4707, Training Accuracy= 0.539\n", + "Step 1800, Minibatch Loss= 1.4087, Training Accuracy= 0.570\n", + "Step 2000, Minibatch Loss= 1.3033, Training Accuracy= 0.570\n", + "Step 2200, Minibatch Loss= 1.3773, Training Accuracy= 0.508\n", + "Step 2400, Minibatch Loss= 1.3092, Training Accuracy= 0.570\n", + "Step 2600, Minibatch Loss= 1.2272, Training Accuracy= 0.609\n", + "Step 2800, Minibatch Loss= 1.1827, Training Accuracy= 0.633\n", + "Step 3000, Minibatch Loss= 1.0453, Training Accuracy= 0.641\n", + "Step 3200, Minibatch Loss= 1.0400, Training Accuracy= 0.648\n", + "Step 3400, Minibatch Loss= 1.1145, Training Accuracy= 0.656\n", + "Step 3600, Minibatch Loss= 0.9884, Training Accuracy= 0.688\n", + "Step 3800, Minibatch Loss= 1.0395, Training Accuracy= 0.703\n", + "Step 4000, Minibatch Loss= 1.0096, Training Accuracy= 0.664\n", + "Step 4200, Minibatch Loss= 0.8806, Training Accuracy= 0.758\n", + "Step 4400, Minibatch Loss= 0.9090, Training Accuracy= 0.766\n", + "Step 4600, Minibatch Loss= 1.0060, Training Accuracy= 0.703\n", + "Step 4800, Minibatch Loss= 0.8954, Training Accuracy= 0.703\n", + "Step 5000, Minibatch Loss= 0.8163, Training Accuracy= 0.750\n", + "Step 5200, Minibatch Loss= 0.7620, Training Accuracy= 0.773\n", + "Step 5400, Minibatch Loss= 0.7388, Training Accuracy= 0.758\n", + "Step 5600, Minibatch Loss= 0.7604, Training Accuracy= 0.695\n", + "Step 5800, Minibatch Loss= 0.7459, Training Accuracy= 0.734\n", + "Step 6000, Minibatch Loss= 0.7448, Training Accuracy= 0.734\n", + "Step 6200, Minibatch Loss= 0.7208, Training Accuracy= 0.773\n", + "Step 6400, Minibatch Loss= 0.6557, Training Accuracy= 0.773\n", + "Step 6600, Minibatch Loss= 0.8616, Training Accuracy= 0.758\n", + "Step 6800, Minibatch Loss= 0.6089, Training Accuracy= 0.773\n", + "Step 7000, Minibatch Loss= 0.5020, Training Accuracy= 0.844\n", + "Step 7200, Minibatch Loss= 0.5980, Training Accuracy= 0.812\n", + "Step 7400, Minibatch Loss= 0.6786, Training Accuracy= 0.766\n", + "Step 7600, Minibatch Loss= 0.4891, Training Accuracy= 0.859\n", + "Step 7800, Minibatch Loss= 0.7042, Training Accuracy= 0.797\n", + "Step 8000, Minibatch Loss= 0.4200, Training Accuracy= 0.859\n", + "Step 8200, Minibatch Loss= 0.6442, Training Accuracy= 0.742\n", + "Step 8400, Minibatch Loss= 0.5569, Training Accuracy= 0.828\n", + "Step 8600, Minibatch Loss= 0.5838, Training Accuracy= 0.836\n", + "Step 8800, Minibatch Loss= 0.5579, Training Accuracy= 0.812\n", + "Step 9000, Minibatch Loss= 0.4337, Training Accuracy= 0.867\n", + "Step 9200, Minibatch Loss= 0.4366, Training Accuracy= 0.844\n", + "Step 9400, Minibatch Loss= 0.5051, Training Accuracy= 0.844\n", + "Step 9600, Minibatch Loss= 0.5244, Training Accuracy= 0.805\n", + "Step 9800, Minibatch Loss= 0.4932, Training Accuracy= 0.805\n", + "Step 10000, Minibatch Loss= 0.4833, Training Accuracy= 0.852\n", + "Optimization Finished!\n", + "Testing Accuracy: 0.882812\n" + ] + } + ], + "source": [ + "# Start training\n", + "with tf.Session() as sess:\n", + "\n", + " # Run the initializer\n", + " sess.run(init)\n", + "\n", + " for step in range(1, training_steps+1):\n", + " batch_x, batch_y = mnist.train.next_batch(batch_size)\n", + " # Reshape data to get 28 seq of 28 elements\n", + " batch_x = batch_x.reshape((batch_size, timesteps, num_input))\n", + " # Run optimization op (backprop)\n", + " sess.run(train_op, feed_dict={X: batch_x, Y: batch_y})\n", + " if step % display_step == 0 or step == 1:\n", + " # Calculate batch loss and accuracy\n", + " loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x,\n", + " Y: batch_y})\n", + " print(\"Step \" + str(step) + \", Minibatch Loss= \" + \\\n", + " \"{:.4f}\".format(loss) + \", Training Accuracy= \" + \\\n", + " \"{:.3f}\".format(acc))\n", + "\n", + " print(\"Optimization Finished!\")\n", + "\n", + " # Calculate accuracy for 128 mnist test images\n", + " test_len = 128\n", + " test_data = mnist.test.images[:test_len].reshape((-1, timesteps, num_input))\n", + " test_label = mnist.test.labels[:test_len]\n", + " print(\"Testing Accuracy:\", \\\n", + " sess.run(accuracy, feed_dict={X: test_data, Y: test_label}))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/tensorflow_v1/notebooks/3_NeuralNetworks/variational_autoencoder.ipynb b/tensorflow_v1/notebooks/3_NeuralNetworks/variational_autoencoder.ipynb new file mode 100644 index 00000000..76ae0a91 --- /dev/null +++ b/tensorflow_v1/notebooks/3_NeuralNetworks/variational_autoencoder.ipynb @@ -0,0 +1,316 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Variational Auto-Encoder Example\n", + "\n", + "Build a variational auto-encoder (VAE) to generate digit images from a noise distribution with TensorFlow.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## VAE Overview\n", + "\n", + "\"vae\"\n", + "\n", + "References:\n", + "- [Auto-Encoding Variational Bayes The International Conference on Learning Representations](https://arxiv.org/abs/1312.6114) (ICLR), Banff, 2014. D.P. Kingma, M. Welling\n", + "- [Understanding the difficulty of training deep feedforward neural networks](www.cs.cmu.edu/~bhiksha/courses/deeplearning/Fall.../AISTATS2010_Glorot.pdf). X Glorot, Y Bengio. Aistats 9, 249-256\n", + "\n", + "Other tutorials:\n", + "- [Variational Auto Encoder Explained](http://kvfrans.com/variational-autoencoders-explained/). Kevin Frans.\n", + "\n", + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import division, print_function, absolute_import\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from scipy.stats import norm\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n", + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "# Import MNIST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Parameters\n", + "learning_rate = 0.001\n", + "num_steps = 30000\n", + "batch_size = 64\n", + "\n", + "# Network Parameters\n", + "image_dim = 784 # MNIST images are 28x28 pixels\n", + "hidden_dim = 512\n", + "latent_dim = 2\n", + "\n", + "# A custom initialization (see Xavier Glorot init)\n", + "def glorot_init(shape):\n", + " return tf.random_normal(shape=shape, stddev=1. / tf.sqrt(shape[0] / 2.))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Variables\n", + "weights = {\n", + " 'encoder_h1': tf.Variable(glorot_init([image_dim, hidden_dim])),\n", + " 'z_mean': tf.Variable(glorot_init([hidden_dim, latent_dim])),\n", + " 'z_std': tf.Variable(glorot_init([hidden_dim, latent_dim])),\n", + " 'decoder_h1': tf.Variable(glorot_init([latent_dim, hidden_dim])),\n", + " 'decoder_out': tf.Variable(glorot_init([hidden_dim, image_dim]))\n", + "}\n", + "biases = {\n", + " 'encoder_b1': tf.Variable(glorot_init([hidden_dim])),\n", + " 'z_mean': tf.Variable(glorot_init([latent_dim])),\n", + " 'z_std': tf.Variable(glorot_init([latent_dim])),\n", + " 'decoder_b1': tf.Variable(glorot_init([hidden_dim])),\n", + " 'decoder_out': tf.Variable(glorot_init([image_dim]))\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Building the encoder\n", + "input_image = tf.placeholder(tf.float32, shape=[None, image_dim])\n", + "encoder = tf.matmul(input_image, weights['encoder_h1']) + biases['encoder_b1']\n", + "encoder = tf.nn.tanh(encoder)\n", + "z_mean = tf.matmul(encoder, weights['z_mean']) + biases['z_mean']\n", + "z_std = tf.matmul(encoder, weights['z_std']) + biases['z_std']\n", + "\n", + "# Sampler: Normal (gaussian) random distribution\n", + "eps = tf.random_normal(tf.shape(z_std), dtype=tf.float32, mean=0., stddev=1.0,\n", + " name='epsilon')\n", + "z = z_mean + tf.exp(z_std / 2) * eps\n", + "\n", + "# Building the decoder (with scope to re-use these layers later)\n", + "decoder = tf.matmul(z, weights['decoder_h1']) + biases['decoder_b1']\n", + "decoder = tf.nn.tanh(decoder)\n", + "decoder = tf.matmul(decoder, weights['decoder_out']) + biases['decoder_out']\n", + "decoder = tf.nn.sigmoid(decoder)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Define VAE Loss\n", + "def vae_loss(x_reconstructed, x_true):\n", + " # Reconstruction loss\n", + " encode_decode_loss = x_true * tf.log(1e-10 + x_reconstructed) \\\n", + " + (1 - x_true) * tf.log(1e-10 + 1 - x_reconstructed)\n", + " encode_decode_loss = -tf.reduce_sum(encode_decode_loss, 1)\n", + " # KL Divergence loss\n", + " kl_div_loss = 1 + z_std - tf.square(z_mean) - tf.exp(z_std)\n", + " kl_div_loss = -0.5 * tf.reduce_sum(kl_div_loss, 1)\n", + " return tf.reduce_mean(encode_decode_loss + kl_div_loss)\n", + "\n", + "loss_op = vae_loss(decoder, input_image)\n", + "optimizer = tf.train.RMSPropOptimizer(learning_rate=learning_rate)\n", + "train_op = optimizer.minimize(loss_op)\n", + "\n", + "# Initialize the variables (i.e. assign their default value)\n", + "init = tf.global_variables_initializer()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 1, Loss: 645.076538\n", + "Step 1000, Loss: 173.018188\n", + "Step 2000, Loss: 165.299225\n", + "Step 3000, Loss: 172.933685\n", + "Step 4000, Loss: 161.475052\n", + "Step 5000, Loss: 179.529831\n", + "Step 6000, Loss: 166.430023\n", + "Step 7000, Loss: 167.152176\n", + "Step 8000, Loss: 159.920242\n", + "Step 9000, Loss: 160.172363\n", + "Step 10000, Loss: 150.077652\n", + "Step 11000, Loss: 162.774567\n", + "Step 12000, Loss: 156.187820\n", + "Step 13000, Loss: 148.331573\n", + "Step 14000, Loss: 153.757202\n", + "Step 15000, Loss: 158.050598\n", + "Step 16000, Loss: 163.068939\n", + "Step 17000, Loss: 152.765152\n", + "Step 18000, Loss: 151.136353\n", + "Step 19000, Loss: 157.889664\n", + "Step 20000, Loss: 149.112473\n", + "Step 21000, Loss: 151.694885\n", + "Step 22000, Loss: 153.153229\n", + "Step 23000, Loss: 152.662323\n", + "Step 24000, Loss: 150.556198\n", + "Step 25000, Loss: 142.779984\n", + "Step 26000, Loss: 148.985382\n", + "Step 27000, Loss: 150.923401\n", + "Step 28000, Loss: 161.761551\n", + "Step 29000, Loss: 144.045578\n", + "Step 30000, Loss: 151.272964\n" + ] + } + ], + "source": [ + "# Start Training\n", + "# Start a new TF session\n", + "sess = tf.Session()\n", + "\n", + "# Run the initializer\n", + "sess.run(init)\n", + "\n", + "# Training\n", + "for i in range(1, num_steps+1):\n", + " # Prepare Data\n", + " # Get the next batch of MNIST data (only images are needed, not labels)\n", + " batch_x, _ = mnist.train.next_batch(batch_size)\n", + "\n", + " # Train\n", + " feed_dict = {input_image: batch_x}\n", + " _, l = sess.run([train_op, loss_op], feed_dict=feed_dict)\n", + " if i % 1000 == 0 or i == 1:\n", + " print('Step %i, Loss: %f' % (i, l))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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R6/XU19eza9cuqqqquHv37oae72pDTavV8vTTT/P4449z+PBhCgsLSSaTTE5Oyjv5pZde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WBggFWIqHKSyWSYYrXYjSwWi/Hcc8/h8ccfh9FoxKVLl3D+/Pn7CICIa5h6h8UGFyqV\nCt///vfR3t6OUCiEc+fOsdoVPQ9aez3UpNQ3fuKJJ3Do0CHMz8/j1VdfxYcffgi/3w+pVIqKigp4\nvd68SkZrrS2Xy1FWVoa//du/xcMPPwyPx4OPPvoIV69exeLiItra2lhvPZsbvpi1RSIRjh49ihde\neAE7duyAWCzGK6+8glu3bkEmk8HpdMJsNvP9KRQgktMjh/fSSy/hT//0T1FWVoZUKoXXX38dsViM\n+fjPnz/PwXIxQS2xDrrdbuzYsQPf+973mFd/cHCQ17Xb7SxJmS9YzG5LikQibN++HU1NTTh06BBX\nP4mvnoIO4sAmVrR8a8tkMohEIiiVSuzcuRPPPvssLBYLU/Z+8skn7BTb29vR1dXFmgS5jAisyIHu\n2LEDDocDzc3NCIVCWFhYwOzsLG7fvo2Kigrs2LEDzz33HH7605/mfO/o89SPJklOp9OJtrY2RKNR\nvPnmm9zqU6vVeOWVV3Ds2DF0dnbmfW657EvlqEkwgEonwWAQV69ehcfjQTQahcvlgkgkQjwex/j4\nOJaWlphHm5xprheYMmKSBwwGg7h+/TozC9ntdlZwWlhYQDQahVgsRiAQyAtqSaVSWFhYQCKRQCgU\nwtzcHK5evYq+vj4kEgmW3KOeN4lzeDyevD3JlZUVLC4uYnZ2FoFAAFNTU7h16xbLZlL5jdjaSPUr\nHA4X1Wuam5vD9PQ0wuEwQqEQrl+/jnA4jKWlJRZ1sFqt3COXyWRFMSatrKwgFothdHSUNV4DgQA8\nHg+USiVKS0uRSqXQ0tICjUYDi8VSNK0jSWWOjIxgeHiYpfxisRjMZjMfTMQfHA6H1xQ7yWVUhvd6\nvZiensbU1BQ7OrrHJNxB/d9ijIIXir7v3LnDfOYUYNH69H4X6/SkUimqq6thNpsxNTWFjz76CKFQ\niN9XOkiEQiGXTYt11EqlEo2NjUilUujo6MCbb77JwghisZjpH9cD+qJsyWQy4ejRo5iZmcH58+fx\n+uuvY3JykltZarUaFouloHTm6vshFouxe/dubNu2DQKBACdPnsQHH3yAYDAIsViM0tJSVFZWMj98\nseuSo3744YdRU1MDgUCAgYEB/PKXv0QsFoPVaoVGo4HNZisqM129rtlsxrFjx1k4/xEAACAASURB\nVFBWVobl5WV0d3fj7bffhlarRUtLCzuaYjnKyaHa7XZs3boVJ06cgFqtxtTUFDo6OnDz5k0YjUYo\nlUoIBAI4nc6isnV6l2QyGVpbW7F//35s3boVmUwG77//PoaHh7G4uAiRSISHHnoItbW1uHDhArMy\n5jPCiZSWlqK6uhqlpaVcdbtz5w56enqwsrICl8vF1dZses5c94I0HOhckEql6O/vR19fH27dusWt\nloWFBTz//POwWq1FBVq0n4F7CRsleT6fDzdu3OCgh+RXjUYj8/z/UWfUkUgEvb29rOpEXLJ+vx9m\nsxl6vR7APSBBOp3mUm12DzmXxeNx3Lx5kwXvSXZRp9Oxw1heXmYZM7VajWg0ys4119qpVArT09O4\nevUqampqWJM2k8lAp9NxuToYDLIToXXzHZqZzD0JuJs3b2J5eRlGoxEjIyOsCUzyclqtlstEfr8f\nc3NzBfuymUwGg4ODiMfjsFgsSKVSGB8fZzWr5eVldhoKhQIWi4VVogq9YKTTfP36dUilUqZ3pe/N\nRoQaDAYurxcyokEkKUwivo/FYlz2Bu5RGhoMBmg0GhYUKWbtVCqFSCTCamM+nw9LS0ssBEKgKpPJ\nhJ6enqLL9bS+SqWCUCjE9PQ05ubm+B5LJBIOtsLhMPx+Pyv6FOtQXS4Xkskkent70d3dzRztFGBQ\nK4RKkMVkegC4MrKwsICzZ8/izp07SKfTfN1Ef5nJZPJKcq42mUyGXbt2obq6GteuXcObb76JiYkJ\nFkwgFLhOp8PQ0FDRQUAmk4HD4cCxY8dgsVjg9Xrx/vvvw+v1IpVKQaFQMHBybGyMMS3FGJ0J+/fv\nh0wmQ39/P959912Mj49zMJtKpeB2uxnMWIxRX3rnzp2oqqpiYNcbb7yBoaEhmEwmOBwO2O126HQ6\nxl8UY3q9Hrt378bXv/51uN1uTExM4OzZszh9+jQ8Hg+amprQ2toKg8GApaWlgo6aAL1yuRw2mw2P\nPPIIKisrEYvFMDExgY8//pg11I1GI44dO8Y0qYUcNZWmHQ4HWlpaUFtbi+HhYYyOjmJwcBDj4+OY\nmpqCXC6Hy+XiRKVQm4+Cb9K4VqlUkMlkOH/+PNNP07lENNIE2s33bggE9wR7JBIJdDodn2Hnzp3j\n6iAh0wnELBKJUFpaColEklPPIJd9qRw1ZcaEhlapVGhoaIDD4UB1dTXEYjEfzFQuikajnDnli1Ro\nvGZhYYGVonbv3o3y8nIuE4VCIRa/EIlEGBkZAZC/V5bJZBAOhzE2NoaysjLEYjE4HA7WBKYsLBwO\ns6b1xMQEX2uua6YyKI3xVH6mxKXT6VggwWazsaazSqVCLBZjIvpCFovFMD4+zoFIKpXiF5WEPzQa\nDYB7G560cws5DyLADwQC941n0DMD7jnzaDTKcm/FOFQKapLJJGeMVJGgnma2oEUmk7kv4i1kNOZB\n5X8KBqm/rNFoWHCFAgw63ApdO0mVEsCGAj/qW9fW1sJsNrPYA6kQFcNdT5lnNBplLW86PAwGAywW\nCwdaY2NjGB0dLdjXo2vesWMHt216e3uxtLTE7xrJR+p0OvT29mJoaIgPvHwmENzj7D927BhMJhOu\nX7/OOvOEE7BaraitrYVUKsUnn3xS9DiLQCDAgw8+iC1btiCTybCgTnbG5XQ6YbFY4PF40N/fX/SB\nKZFI0NDQwHK7b731Fj744APec1TONxqN/K4XU72QSCSora3F1772NQDA7du38frrr+PTTz9FOBzm\nqQy9Xs8/p9CoHWXq27Ztw1e/+lXU19cjnU7jt7/9Ld9vuqdU5fJ6vQXvAZ2TGo0GLS0t3GLq6enB\npUuX0N/fj1gsxqNlJpMJ6XQaNpst7z6kqoJarWZQpM1mw29/+1uMj4/zNAuJA1Fypdfr+UzJty4F\nlbW1tRAKhbh58ybGx8chlUp5bEqpVDJQjXrr+YyCVcLBUJBNOAhSSqTpAKVSeZ+i1nqtoKMWCAS/\nAHAcgD+TyTR/9jkjgN8AqAAwBuDZTCaz8Nn/fRfA/wCQBvBXmUzmw2IvhuTzxsfHceTIEZSUlKCi\nogJKpRIikQhTU1OYmZmB3W6HzWbDiy++CKFQCI/Hg46ODvziF7/IiTAk/We9Xs8RVktLC0vWESCJ\n9I61Wi38fj+uXbuGU6dOYWZmZs0yanYfMBgMQq/Xw2Qyoba2lg/+5eVl6PV6BkWk02loNBpcunQp\nZ2k9O8uj0QSRSITa2lpUVlZypKpSqXjkq7GxETKZDCsrK+jq6sqbWROIaWFhgctC5eXlMBgM0Gq1\ncDqdnNV5PB6srKxArVazpGauA5kOa8qOMpkMFAoFGhoaoNfrUVZWBrvdjtnZWYyPj2NychKBQOA+\nUv5c10y/J12XQqGARqOBy+VitD0FRKOjo5iZmeHvKVS9oMCPesZU7lepVHA6nSz2QK2C1Qpd+aym\npgYulwsLCwuIRCJwuVzskCoqKrBr1y4sLy9jdnaWkaM0xpHPkSgUCjzwwANIp9M8VaDVarF9+3bU\n1NTAbDYz1oCy3nfffRcffPBB3nXFYjG2bNmCZ555Bj6fD++//z78fj8MBgN27NjB4CFaf3JyEv/y\nL/+Cnp6egmhZs9mMl156CTt37kQymcSHH37I+0Ov16Ourg4tLS2oqamByWTCW2+9xc62UNCiVCrx\n7W9/GwaDAWfPnsUPf/hDzqRJbpUApFNTU1CpVAWnDei9eOCBB/B3f/d3GBgYwHe/+110dXVxIKdU\nKhncODg4WFSAKBDck3h85JFH8J3vfAfV1dX45je/iU8++YQPdKVSyYGFyWRCMpnMG9Rmj62Wlpbi\n5Zdfht1ux+3bt/HjH/8YFy5cQCZzT1CHRj4pw8slh5t9HxwOBxoaGnD48GG0tLTgwoULeO211zjQ\nolaOwWBASUkJ9Ho9+vr6GDSZa129Xg+Hw4Gvfe1rsFqt0Gq1LEtK2KBkMgm1Wo2qqiqWBh0cHMy5\nLrUT6Lxpbm6GxWLBnTt3OBCORCJIJBIQCoXcmxYIBOjt7c15L+ge0ySBxWKBRCLhdqpWq0UqlcLM\nzAzkcjlXYUKhEDo7OzE8PJzvtchpxWTU/wrg/wD4Zdbn/heAjzOZzP8WCAT/67N//0+BQNAI4P8B\n0ATAAeD3AoGgNpPJFIXYoPLm3bt30dvbi8XFRT48Q6EQRkZGGDwkFothtVpRWVmJtrY2lJWV4dat\nWzh79mzOksX09DS6u7u5LEE9XVLCod6Ky+VCWVkZtmzZwlnD2bNn8fvf/z7ndY+Pj3PEq9frEQwG\nuXyiVCq5zK7ValFSUoInn3wSarUa169fR3d395ovHL2g4XAYgUCAJeumpqbgdru5tBwIBBAKhQDc\nI2CgURG/358z+s7OUlKpFBOIlJSUwGKxQKfTwev18jiVSqWC2WzmbLjQAUel4WzCCYvFArfbDbFY\njKGhIS6LkyQmAZ7yZZKU4dLMs9FoxJYtW3gulHpNyWQScrkccrmc2wzZimWrjbIWylxWVlZgs9mg\nUqlQX1/PJbGFhQWWPqTsOx6Pc4lttQkEAi7RUQ+1vLycnVJZWRkHeX6/H3q9Hi0tLRgZGcHU1BTP\n8K+1rk6ng06nQzAYxNTUFObn52GxWHiygX6m1+tFfX09g/YuXryYV/aSMn29Xo/h4WEMDg6y5GxL\nSwuMRiMUCgXi8ThKS0thNBqxe/duhEKhgo6a5siFQiHGxsawsrIClUoFq9XKI5MajQZarRZutxvl\n5eWYn58vmEUSoYxOp4PP58M777yDhYUF6HQ6fg+qqqpgMpl4MkOn0zHqN5cR2csTTzwBh8OBH/3o\nRxgcHOQKFIEYq6ur4XA4MDw8zKCoQqVTl8uFp556CkajEVNTU4yZIeS42+1GfX096urquLVDla1c\nplAo4HK5sHPnTiQSCXR2duLkyZPo7OxknggKPltaWlBZWYnZ2dmCpXqxWIwHH3wQdXV12LJlC7xe\nL9577z1MTk5yYEEkQUajEU1NTQDuYWHy3WO1Wo1du3Zhz549aGtrw8DAAMbGxu6b2kgmkwxOa2tr\nQ11dHZaXlxlDspaZTCY8+uij2L59O/exabS3oqICwWAQHo8HKpUKRqMRe/bsgdPpZD+Tz6xWK55/\n/nmkUinY7XYEAgH4fD6UlJSwbzp79ixKS0tRVlaGPXv28KTH7OxswWx9zftf6AsymcwFgUBQserT\nTwI49NnH/wbgHID/+dnnf53JZBIARgUCwRCAdgBXir0gkqa7cuUK96oXFxd5JpQcaywWw+LiInbu\n3ImjR4/Cbrfj+PHjuHXrVk7puUQiga6uLu4hEGKaQGlLS0tYXFzksl59fT2sViuOHz8OjUaDjz/+\nOOeLQWMUMpkMNpuNI0GNRoNoNIrJycn7os0XXngBhw8fhkqlYgBCjvvPGtPkdAgYRICqoaEhHqKn\n+cDa2lpEo1Fm6cp3vymjNhgMUKlUnNH19fVhbm6O0fV0aEUiES6Z51qbyv4WiwUOh4O1uSUSCZaW\nlu7T+JbL5VAqlfcxlq3Vd6JM12QyoaysDEajEXa7Hdu3b2dpUp/Px6UnuvfZRDTEwJRtQqEQBoMB\ndrudZ+EBcClOp9NhaWmJSVRo9tJkMiGVSiEejyMQCKzpTIRCIbRaLZejqWxfV1cHs9kMjUaD4eFh\neL1ehEIhaLValJWVQS6XM2J1ZmZmzXVJHpICEK1WC+CeM8xkMhgbG4PP54NYLEZNTQ1WVlZgsVig\n1WqRSCTW7FdnE2MsLy9zAEjYjlQqhdHRUUSjUUilUjz++OMQiURMQrNa13e1UbsGACOCCWQjFArh\n9/u5vE6EFzKZrCBCWywWo6ysDDKZDAMDA1yKJ8317BE5GhkqZm5dKpWiqqoK7e3tkMvl6Ojo4PIu\n9aaJ38DpdGJ8fByJRCLvnDZlZVu3bkVTUxNWVlbQ0dHB95qut7a2lmf6iRgo3z0gMqfW1lbs3bsX\ngUAAZ86cQWdnJ7dEqLLQ3NyMAwcOQCgUFpztJZ6IQ4cOcatwZGQEIyMjWFpaYoBgZWUlGhsb0djY\niAMHDqCnpwd37tzB7OzsmhUGQo4/9thj2L59OxKJBCYmJjA5OYm5uTlmZVOpVKipqcGJEyewa9cu\nCIVC3L59G7Ozszl7308++SSeeuoplJaWQqlU4rXXXmNgosFgwPz8PPR6PVpbW1FbW4vHHnsM0WiU\nWwNUWVv9/JRKJb7xjW/gscceg1KpxOLiIgYHB5HJZFBWVobBwUHMzMzg2WefRVNTE7RaLex2O371\nq18hEAjwc1ovucxGe9TWTCZDTY0ZANbPPnYCuJr1dVOffa5oI8f06aefwmAwcMmS0NV0GK6srMDn\n8+HOnTvQ6/U4ePAgZxK5UJfpdBpjY2N8GNDPi8fj9zFDeb1e9PX1Yffu3Xjsscdgs9nQ2tqa97oX\nFhYQi8WgVCoxNzcHjUbD/dlkMslkIel0Gnq9HkePHkVNTQ0WFhZgtVrXdNS02ena5ufn+cDp7e1l\nR0xIdaIGrKioQHl5OYaHh3OOPVEJLZ1Oc4bqcDig0WgQDAbZeZCjpn51SUkJgsEg5ubm1nzZqC9E\nFQu1Ws2MZxKJhLNx6gNTr0en02F5eZlHraifn71u9riJyWRiMhVCqS8vLzPAiUbrKGNPJpPQ6XRr\nRss0ZaDValFbWwuNRgOlUsmAQJVKxaxytNHj8TgMBgMikQhXZNa6F0TGQlm6Wq3mMi3hCmhEhJ6D\nxWJhpGh2kLH6+SWTSczMzCAYDEIkEsHhcDCRCiFQJycnUV1dzXO+NOqSz4lQ346mKAijIBAIuMcd\njUZ5fxJYsBj0vkwmg1qtZnAb7cN0Os10pNTnJcBgMYQt5Kjj8Tg8Hg+PQYrFYsTjcQ58xGIxotEo\nO9RC6yqVSuzatQtWqxWxWAx+v/++d5wCb0Igj4yMFASgAvec1IEDB2AwGODxeNDZ2ckBJgUA9fX1\nqK2thVwuR1dXFzPY5VqXQKXt7e1wu93o7e1FX18fAoEA0uk0lEolzGYztm3bhn379sFgMKC7uxvX\nr1/HyMhI3oCeKF6TySQmJycxOjqKYDDIkzQajQaHDx9Gc3MzqqurodPpcPLkSdy5cwfz8/NrVhfE\nYjHa2trYWZ46dQqTk5P8TieTSRiNRjQ3N+ORRx7B7t27IRaL0dvbi97eXszPz+cEoj7yyCNobGyE\n2Wzm0Vi/34/5+Xk+m/bs2YPjx4/zmUGgtb6+vpwAtfLychw+fBiVlZWYn5/H0tISQqEQAoEAtFot\nxsfHYTAY8PzzzwO4F+wPDw9jbm4OExMTSKVS6waSAV8AmCyTyWQEG9CTFggEfw7gz7M/R6VS6hvT\nzaKxhGg0yiNCAPjQ/9nPfoZ9+/bBbrfD5XKt+SJT74aa+0S4QPyrkUjkPscjFArx29/+FgcPHoRa\nreYZ7rXWFYvFvAmol6lQKDA7O4uJiQkWhCeqOXKqBPQxmUxrrkuoa5VKhaamJu6rLy8v47333sPE\nxAQikQgfHPQ9W7ZsQSAQ4JGdNe49NBoN93ddLhdeeukliEQi+P1+3L17F319fQycopKW2+2GXC7H\nrVu3mBpv9boEJNm9ezfcbjdaWlqwbds2xGIxeDweTE1NIRaLoaamBqWlpYx+DofD6Ovrw+zs7JrO\nVCQSwW63o66uDocPH0ZpaSlKS0uZDIJmkq1WK9rb2zlzTiQSuHLlCubn59HT07MmfV9paSmam5ux\ndetWuFwuVFRUIBQKYXl5mQ9Oo9HIz5lY6/x+PwYGBnDmzJk1WwHUfyNini1btqC9vf2+DU7jJg0N\nDTAYDEin0/jwww8ZCEVZ1ur7TK2CoaEhAMCxY8ewvLyM6elpdHR0IJlMQqvVorm5GSdOnIBYLIbH\n48HHH3/Mo4xrGWXEw8PD6O7uxpEjR1BVVYUbN25gZmYGk5OT0Gq1qK6uxt69e3km96OPPmKgXC4T\nCoUc5JWWlqKtrQ01NTX8/GQyGbZu3YqjR4/C6XRiaGiISS0KratQKLjyQQxz6XQa0WiUiTMOHToE\noVCI9957D9euXSuKp9zlcqG+vh7xeByjo6OQSqVQqVSM5H3qqaewb98+2Gw2DA8Po7+/n2mD8znU\n8vJyVFdXY3FxER0dHeju7obFYoFGo4HdbsfDDz+MP/mTP8HKygpOnz6Nf//3f2eHl2tdhUKBP//z\nP8fu3buxsrKCnp4ehEIhVFVVQS6X4/HHH0d7ezuPuf7617/G7373O/T09BSsWNCM+I0bN3Dz5k3M\nzs7ioYceYnIqp9OJxsZGBINBZmg7efIklpaW8vJmNzY2Ys+ePQiHw/jNb36DxcVFWCwWbN++HceO\nHYPdbodGo4FIJMK1a9cQCARw7tw5XL9+ndH8axmxIgaDQfzTP/0Trly5wnPSDocDJ06cYJKhzs5O\nBINBdHR04OLFi1wdW+t8I7KUaDSKv/qrv0JnZycsFgueffZZ2O12vPzyy1wCv3DhAieWo6OjmJub\nQzAY/C911D6BQGDPZDJegUBgB+D/7PPTAMqyvq70s8/9B8tkMj8F8FMAIEdPN4bAPUKhEHa7nQFE\nVAIkp0QEBwaDATKZDCMjIzh//vyaF0yZOUXZQqEQZWVliEajGBwc5HEWmuVLpVKoqqqCWq1GKpXC\nnTt3cq5LmROVexsaGpDJZLCwsMA/j8g+KEq0Wq1Ip9O4efMm+vr6cq5L85BisRjNzc2w2WxMqECA\nEOp963Q6ZvJ59dVX4fV68wJPskcRtFotO71YLIaWlhbugQL3ImqlUomVlRVcuXJlzYMzG8Uuk8k4\nK5DL5ZiammIwHgFNqN9GTq+rq4tHk9aybEYph8MB4F47Y3Z2lrMm6mcRO1AqlUIwGMTMzEzOKDkW\niyESiTBQjEBc1G6g343Y1ebn5xEMBhGNRjE0NMSlztX3mrJNCj6ampp4aoHKumKxGCUlJZDL5Ugk\nEhgaGkJfXx/jMXIdcMlkEpFIhHkBqJpgNpvhdrvv418Wi8UYGBjA5cuX0dHRkfdAXllZQTKZRCAQ\nQFdXF44fPw673c6Hvdlshk6nQ1lZGVwuF27evIlTp04x4jefZTIZzM7OYnp6GjU1NRwoZrPCHTx4\nEBaLBVNTU3jrrbewuLhY1AgVYRNoHSr1p9NpVFVV4cCBA9i2bRs++eQTvPfeewWJMoA/jPYQFa1M\nJoPVasXS0hIUCgWqq6tx6NAhGAwGjI+P4/XXX+fydCHgG51fQqGQsRtyuRzl5eU4cOAAWlpaEI1G\ncf78ebz99tvo6ekpal0AfI7t2bOHmRdtNhsOHToEtVrNOKA333wTg4ODRRG/xONxnkOXSqWcrZrN\nZqhUKs4cr1+/jtu3bzMGhTAnaxmJ9FAL4qtf/SoHoTRHLRKJMDc3h5s3b+LDDz/EzMwMvF4vjwPm\nuh8E9FxZWcH27dvR3NwMjUaDuro6bkVNT0/j7NmzGBgYQCAQwOzsbF7QImGRJBIJ4vE49/hramqw\nfft2aDQaPuNPnjzJTJHEuUCV4Y3YRh31uwC+AeB/f/b3O1mfPykQCP4/3AOT1QC4vp6F6aEJhULY\nbDa43W7YbDYuhxBTkUqlYr7qr3/96xCLxfjlL3+J2dnZnGtTximVSmGxWLBr1y74/X6mXZybm0NJ\nSQkMBgOWl5fx9a9/HTKZDBMTE3jllVfWXJNKheT0bDYbnE4nlzGrqqoYzEOlQpvNxr3J06dPr5mN\n0cFPiHI6IIlL9/DhwwwwczqdPDJjNpsBACMjIywGslZkSONB1EemEjvNzqZSKS5dZzIZKJVKLC0t\nYWxsDOFweE2wDB0U9AyNRiO/nMlkkkcUBAIBo9XpOubn5xkpvrrvlN0CoK+Px+OQSqXM/kNjfYTw\npfGqubk5dti5QByxWAxLS0uYmZnhcvb09DSjWIk+FQAWFxcxPj7OgiIUfOTa3BSESCQS3L59m1sf\nRBZC92p4eJhLet3d3QV5AejZLi0t4fe//z2kUinsdjsj7Ak4BAC/+93v0NnZiY6Ojrz7g55dKpVC\nKBTC1atXMTs7y5UKq9WKaDTK42perxcnT57ErVu3WDUqn2UyGfj9fpw5c4YdNfXqKVh0Op3o7+/H\nm2++iUuXLhWVfRAIlcbTrFYrWlpauB1QW1uL+vp6CAQC/OQnP8HIyAgLthSyeDx+X8907969WFxc\nRFlZGdra2qDX6zE5OYl//ud/xtmzZ4uaf6fDm9jjXC4Xjh8/Dp/Px+OoEokEb7/9Nl599VUOgoop\n/1+/fh0tLS2wWq1obW1FQ0MDn6dSqRRTU1O4e/cuLly4wCDWQmV6OuMikQgH7DSNQy2n8fFx/Ou/\n/iuDwYgrotB9mJ6extDQEOx2O3bs2MHPRKPRYH5+HuPj47h06RI6OjowOjp6X5syOzFYbcPDw3wf\nH3vsMYyPjyMcDkMsFmN+fh6dnZ24ePEirl27xtTUmUwmb6JA59vU1BQaGhrwjW98g2fxI5EIJicn\noVarMT4+jt/85jc86ru8vIz5+Xkm9NqIFTOe9SruAcfMAoFgCsD3cc9BvyYQCP4HgHEAzwJAJpPp\nFggErwHoAZAC8P8Wi/j+7Pv5oEgkEhxh1tfXY//+/WhtbUV/fz8DTUiYobKykueN870cy8vLSCQS\nCIfDKCsrQ1NTE9rb25HJZJg2VKlUwmKxMMpwbm4OFy5cwNjYWM6siWZ6qS+mUqnQ3t6OBx54APPz\n8wiFQpydUqY5PT2NixcvYm5ubs1rJiedTqfZQZEjMpvNsNvt7PQoc6JKA5GZEIJ0rdGvdDrN10x/\npFIpswGFQiF2kATU8nq9CAQC7OBzIdUJhLSwsMCIT4lEwu2FaDTKIBS6ZiKEIWe91rpEHTo5Ocml\nzmQyicHBQSYKoWyernF+fp5LoLk2dSKRgMfjgUwm4wONxtDu3r2L0dFRPhgoOyBRlXyOhIIUuoaL\nFy/C4/HA7XZzby2ZTGJ6ehper5fxBsSTnOsApc/Rpj979iz8fj/q6+t5LIuEXzweDz744ANWKyvk\nSLLfjcnJSbzxxhs8PywUCrmXPDMzg66uLty8eZOvtxhbWlrCxYsXodfrsX//fg5aCdB47tw5nD9/\nHl1dXTwWWIzRSExvby9cLhcaGxs5S9Xr9QgEAvjwww8xNja2LupQ0hZYWFiAyWTC008/zRmrXC5H\nZ2cn/u3f/g03btxAMBgs6EzJiMmQ6GktFgs/c+KZ//GPf1yQwTDblpeXMTIygqtXr+LAgQMoLS2F\nQHBPdjIUCuHjjz/mc6enp6eoXjrZ1NQU3nvvPbS1tfHvT33f/v5+3L59G52dnVhcXGQwL93jXOuv\nrKxwcLNt2zYoFAoEAgEkEgnMzc2hv7+fxzdJKIl+H6qW5LJf/epXmJ2dRW1tLbRaLbPqBYNBvh8E\naKVxU9obVCFbbeTIf/zjH+Mv//IvIZVK8emnn+Ly5cs8CUBMb3Nzc1ydUiqVXKGjGe1i32syQbEv\n1n+mUembMh6KAPV6PXQ6HcrLy9HY2Mh9ADpwqUl/5coVXLp0CXfu3Ml5A2hN6jmq1Wq0tLSgvr4e\nW7duRW1tLaxWKx+sHo8Hv/71r9HZ2Qmfz5eXX5eQsuSkiRGIQBJESDE7O4uxsTF4PB688cYbTD1a\naGaPwFZqtRoGgwFmsxmPPvooj0tRmTIUCmF6epo3UL5DmSoLhPA1mUxMZ2kwGCCVSrG4uMgvWDgc\nZjnRcDicl0KTRkBo5ItmxwnNSpEljcnRC00YhFwAEXp+xHREjiOb9YzaGxKJhB0lPbtc0SwFJNRu\noflv+kPPnf6fWIno/4pl+sr+WfQxtXnoc+vh417rdyDQXfbPIDT9RvY6VTgIUUyEGzQrvxE1p+zn\nuGXLFp7P9/v9nI1lk9cUY4SPIOGdbdu2QSaTsQPxer1rIv4LGYHfysvL0draiqNHjyIYDGJkZARX\nrlzB5cuXi5Z/zTaJRIKamhrU1dWhrq4ODocDAwMD6O7uRl9fHwOl1nNvsRH9GwAAIABJREFUaRKA\nZCFra2tZa3psbAxDQ0P/4Uwodn0aOyUnTRUuCvJXPy/Kwou5D3K5nPcC7eFswh/6Q3uU9ke+a6f2\nEu3PtbJkWoNwRvQcC107EdrQddD5vdq5032iUjkFGauu+0Ymk9lR6D59qRz1Zx+zw6aHSBlSa2sr\nrFYrszgNDQ1hcHAQc3NzBXsWwP2BADkTAgiZTCaUlJQwoGxubg6jo6NF8VvTmvSHDjfqI0skEi69\nEip3PbzL2etn36Ps8nj2xxs56Ff/rLX+vfrnfF5b7cC+qHVX/4wvwzuez77oa1xd4l+vE1lt2e81\nHVD0Z6PXTUEPOX8ADP7b6PUSZoUAX5lMhkcJ6WzYSABENLLEcEUAVCrDbtRkMhmkUik/Lwrasx3U\nRowcz+ozYaP629nrriYPWv032Xrf6bUyWFp39f993rONvj/7XMsmkvki1qf1soOLHO/1H6ejXvX5\n//Bv+pMdyWT//3oc31ovF/D5HtSmbdqmbdqm/fHYf0Ywn51YFQg8i3LUXyqu79W2Vj94PV+/3q/d\ndNCbtmmbtmn/vew/49z/oquD6+cy27RN27RN27RN27T/Mtt01Ju2af8XLLs09kWvS2CXL3r9bGzE\npm3apv3X2Ze69L1pm1bI/jP6S9lAnGyE9+c1IvZQqVRQKBSIxWIIhUIsGrJRo6kDnU4Hi8WCLVu2\noKenB16vl+laPw+gTCgUMieAxWLhMblQKFRwLrsYI2pYjUaDkpISJBIJTE9PF0VJWsgIBCeTyVgL\nvhD//XqMpjKMRiOSySSLknxRYEsSJaJ3pBBJy3qMZB4puFsv/3Quyw5CJRLJutHr+dYF7ge1bnSd\nfOC3ja69lkrfF4V72nTU/80tH8KbbKNjPcAf1H5kMhmPS2XPQW4EhSuRSJjVSyaTwWAwIBwOM4nF\nwsLChhDJhNavqKhg2c9UKoWuri4mAaG5y/VeM2mJW61WnDhxAgAwNDSEqakpjI+PM4/9RhwIoacN\nBgMOHToEo9HINJojIyOYmJj4XEhqkUgEo9GI8vJyPPTQQ4jH47hy5Qpu3br1uQMlmls3m814+OGH\n4XA4cOHCBQQCAR7T+jxG1LOHDh2C3W7H22+/zajtLwINX1lZCZfLBZ1Oh4mJCeYm+LzXLRaLYbPZ\noFarmWKX+N+zRwbXe7005UIUqJlMhmlxSZtgvWsCYH59YpEkDXqabsk3HpnLaIxPpVJBq9UyiQqN\nYJL283ruNQURdG6QkMzU1BQHLbFY7D6q6vWsS2O6bW1tkEgkWFxcxNzcHJLJ5H1c7evdM19KR03z\nyHa7nWeeicifsgO1Wg2//x5z6fLyMqLRKDNQ5TqkKevQ6XQs6mA2m2E0GpmZTCwWM8k6cYkTUUeu\nyFAgEKCsrIwlIi0WC3bu3AmLxQKfzwe/389SaETrOTk5yevmmhnN1pbdu3cvnE4nKisrUVpaikwm\ng5mZGWbmunjxIkf0CwsLzBSV64VQq9VwuVyoq6tDVVUVHnzwQTidTpb+vHPnDvx+P3p6etDf34/h\n4WHezPkcLGVfBw8eRFNTE9xuN6qqqtDS0oJ4PI5QKAS/348PPvgAN27cgN/v59+h0MtL78KRI0dY\n/q+2thYqlYozpWAwiB/96EfweDws31doI5Mz0uv1qKiowLPPPou2tjYeAyRSms7OTpw/fx537txZ\n10YmEYDS0lJs3boVR44cwezsLCwWC2ZmZmA0GtHd3c3sbOs50AQCAZPzPPnkkzh48CAuXboEr9eL\n2dlZnnffCMkCAKaB/drXvoa2tjbmLLh9+zaT4nyeMaJt27bhySefxKFDh6BUKvGP//iPLCbzeYx0\nov/hH/4BDz/8MEwmE4aHh/Huu+9CKpVueASK3hWtVouWlhb87Gc/QyqVwtjYGBMj5eNGKHTNcrkc\nRqMRjY2N+Ou//mtkMvdoK2/fvo3f/OY3ANY/bkftClIjJDIYoue8c+cOU7AWa9QGIcdvNBpRXV0N\no9GIlZUVaLVa9PT0YGpqCn6/v6gxtuzRXKFQCKfTibq6uvvkXK1WKyuknTt3DktLS0Vdr0QiAfCH\nM+Sb3/wm9Ho9TCYTa3ErlUoMDAzg6tWruHbtWsFrBcC8HMSf0d7ejieffBJutxtzc3Pwer0oKSnB\nxMQEPvjgA1y7do391nrsS+eoqYzU2NiIbdu2wWq1oqysjKUjifFqamqKf+lYLMZau8QHvVZpw2q1\noqmpCRUVFXA6ndixYwer3iSTSYyPj2NiYgIejwejo6MYGhqCXq+HXC5nvt/VRtHZzp07UVtbi/Ly\nctjtdrjdbgBAXV0dwuEwbt68yfJ+ExMT0Gq1/HPX2tS0Ll3n448/zhKBpLtsNBqxsLCAiYkJDAwM\nsCi5SqVilq61TCAQwGQyoampCXv27IHb7WZ9axKnJ0H5ZDKJUCiEsbExprzMdwgJBPekKLdv3476\n+nom8ydWrHQ6DY1Gg+rqambhop5qoXWFQiFUKhXcbjfKyspgNpsRi8Xg9XqZWEWpVKK+vv4+nvVi\nDk062DUaDdRqNdMXEiOVQqGA2+2G1+tFd3f3mmWuXEYHmlarhcViQX9/PwYHB+Hz+ZBKpaDRaGAw\nGNjxrddUKhVqa2uxdetWyOVyjI6OwufzFUXrmc/ocD98+DAeeOABKJVK9Pb2YmRkhKsKn8dJS6VS\nPP300zh+/DgMBgOmpqYwNTVVNHdBvrWVSiWqqqrw1FNPQaPRYHFxEdPT0ywSsdG1s8mS6LAPBAKY\nm5vj+5I9QlqsUZnbZDJh9+7deOaZZ+B2uzExMYH5+XmMjo6uGx+QPf8ul8tRU1ODnTt3Yv/+/TAY\nDOjo6MDY2BjkcjnUanXR61K1jObKiVv94Ycf5syXxCdItrgQD3z2ukRY5HQ60dDQgJqaGqTTaT7f\n4/E4du/ejY6OjqIcdTZxFPkXu92OmpoazM3NYXFxkVXt9u7di2QyWdBRU/mduAWIB99qvSckOTc3\nh08//ZSDwpaWFgQCAQwPDxclBrPavlSOWqVSoby8HDU1Ndi2bRv27t3L/Ssq2xgMBmg0GigUCty6\ndQsKhYJp2YA/qP+sNqVSyYxF1dXVsNlsLIRAWsgqlQpmsxmBQACZTIYdSL6XjGhOW1tbUVNTA5vN\nBplMhpmZGZbUoxeFtJbJCVNPaK3NJ5FIWAyB2M0kEgl8Ph/TcFJJLBKJsNKTXC7nDZ3rsBAIBJyd\nG41GyOVyDA0NcWkmHo9DoVDwgUkHH2kv5zuEiLOZrmNpaQl+vx/T09OIRCIsQEEsaBqNBpFIBIFA\nIO/hTM5OoVCw2ll/fz9HrQ6Hg5XIqKpBTGqF2KhWPx+fz8esd4FAAA0NDXC5XCyColKp1s0gRprX\n5Ozu3r3Loh9WqxUKhQJKpZLLj+vhA6BgjioAfX19zHqXyWSYaQ1YXzYmEomwdetWFsrwer2sZkVZ\nfzZDU7FGB5zBYMCRI0dgMBiwuLiIs2fPsp440dZuJHskDYCvfOUr7KTPnDmDjo4OPqyLYZRb67rV\najXq6urw9NNP48CBA5ibm8Pdu3dx48YNJBKJnPrI+dak+2G329HS0oLnnnsOW7Zs4YQEAIur0PlW\nDAFTtmSmw+HAsWPH0N7eDpvNhnQ6DYvFgoqKCubtHxwcLIplj85ik8nEFM7Nzc1oaGiAXC7nwFkm\nk0EkEjGNcqG16dlRWXr37t1oaGjg/jlpBuj1elbHovelkBFxltFoRH19PSwWC6anpzEwMACRSIS7\nd+9i+/btqKiowP79+/GTn/wk77rZJX+FQgGLxcLnaSKRwGuvvYZEIoGRkRHYbDY8+OCD2Lp1Kz76\n6KMNBYlfKkftdDqxdetWNDc3w+12c42fXiBSeAqHwxgeHobH40EymYRarUY4HM7bY6Es3el0QqvV\nIpPJ8OaitSORCMbGxjgjIe7pfFG4UqnkTJe0b0kJKh6PQ6PRQCgUYnJyksuxxGdNjmStdSnDU6vV\nkEqlWFpawvT0NMvoLS8vM2NbNoctEeXn2xTkmDKZDGsZ37hxg0vHMpkM5eXlzDscj8e59ZDvkMgO\nOHw+HwtZkM4rOeeKigoWAlEqlazQVYwRpzMBmih6J6J8EusgkZJiIm7qoVGQ0tPTw2pt9AxlMhkL\nlhA71XpYqSQSCTQaDRKJBEvehUIhaLVaaLVapnIlSsZiTSAQoKqqCm63G2q1GgMDA5ienkYsFuN3\ngGha1wPCoYN+3759qKurQzqdxvT0NDweDweYhQLCXOvSgVxZWQmr1Yrl5WVcvnwZ77//Pst6SqVS\nxjOs52CjoPLRRx/FkSNHEAqFcP78ebz77rsYHx+/79lt5LrLy8tx4sQJHDt2DBqNBqdPn8bHH3+M\nSCTCuuXF3me6d5Ttbd++HS+88AK2bNkCkUiEU6dOwefzMRaDqhjFrk3fV1lZicbGRjz22GOs+z42\nNsbVPAqKimmPZKv5tbS0oK6uDm1tbSgpKYFMJoPX68Xk5CRSqRQ7ML1eX9TaFEQplUq0tLSgoaGB\n21qRSAR3796FRCKB2WxGTU0N9Hp9UfciW4zJ5XKhpKSE6ZYHBwdZNYsqoy6XK+96ADgApj1AGXks\nFsPw8DBu3ryJhYUFxGIxbp05HI6cwkCF7EvlqH0+H4aGhpikfmlpCVevXsXU1BQkEglKSkoglUox\nPz/PfY9UKoWFhQW+2blehkAggFu3bgEAC0ScOnWKH2BJSQmSySRmZmbg8/nu087NByBaWlrC3bt3\nYbPZ0NbWBpFIhL6+PkxOTnJWrNPpWIOYaFFHRkbySuIlk0mMjo4iEAjA4/Fgfn4eExMTmJmZwfLy\nMhwOB6xWK6OSdTodhEIhZ5iFpAxv374Nj8eDyspKaLVaVqYBAJPJBLvdDqlUCrVaDZvNhtHRUQY8\n5doYmUyGBS5OnTrFjomcvVarRVVVFcLhMMrLy+FwOBAMBuH3+wseyBQ0eDwenDlzhjNgkres+ExA\nxWg0wmw2w2azYXp6umjgF3GYT05OYmFhgdWNpFIpysrKEAqFYLFYYLPZoNVq1412FolEWFhYQDqd\nxuXLl1mZS6FQQKvVchAAIG/bYq11d+3aBYvFgitXruAXv/gFFhYW+L1WqVQAwFztxZZ9hUIhtFot\nnnjiCSQSCfz617/GK6+8glAoxDgEqVQKiUTCMpvFZqgikQgNDQ34wQ9+gN7eXvz85z/HmTNneH9o\nNBq+N/mkWlebQCCAVqvFt7/9bbzwwgtIp9N46aWX0NXVhWg0ColEgrq6OqjVat5bxVwzOVS5XI4f\n/ehHqK6uRjwexxtvvIG///u/h0BwTymutrYWVVVV6O7uLhpBTYHF9u3b8cMf/hBSqRSTk5N46623\n8Prrr3MG73A4oNfr83Lsr75eykqfeeYZtLS0YGFhAadOncInn3yC2dlZOBwOVFVVwel0suRuoXUp\nyLJYLNyLTSaTGBsbwxtvvIHFxUWEQiGYTCb8xV/8BcxmM1cy8mEvCCRqMplQXV2NiooKdHd3Y2Zm\nBhMTE/D5fJifn4dGo0F7ezsMBgOsViukUmleISa6F2q1GlqtFsvLyxgdHcXly5exsLCAcDjM6odW\nq5VppYnTPt+6ALjiODU1hYmJCZw9exaJRIKfE51bMpmMsSq5qr757EvlqClLSqVSiMViMJlMKCsr\nYx1YkUjEYBvSh43H4wiHwwXnUombNxwOM79ua2srSkpK2Il4PB4uL0kkEoyOjnK2lStyS6fTXLql\nw6umpgZNTU1Ip9Nc5qWDRyAQwOfzYWRkJO8hQYpc8XgcPp8PAFBaWgqj0ch9ar1ez1+n0WgwOjrK\nIJx8kWYmk2HNYaPRyJuaxC7UajW/tIlEgpGn9L35jH4nAmgIBPeI6AkQotPp+D4olUrmxS1k9HNJ\nUi5bFYyQltn851TuLuaQp68hpTJCBQN/yExITIR0x9fbSyZE6NLSEve2aWSIJEwBIBKJYHR0tOhs\nTywWw+VyYXl5GR6Ph99vg8HAAi5SqRQ+n481wYsRphCJRFzu93q9uH37NhYWFqBWq6FSqaDRaGAy\nmbjSUyiIy74PKpUKR48eRUVFBd566y10dHQgFosxMp6C23A4zOprxTrrxsZGPP7441CpVCwdmq0y\nV1lZCeCe+tl6wFNUmm5sbEQymcSlS5fw6quvIpFIcEDkdDohFArR19dXVEuAqhYulwvPPvssFAoF\nhoeH8eabb+LChQuIxWKMmaCWEYlHFDKBQICamhocP34cbW1tEAgEeOedd3Dnzh0MDg4CuHeeaDQa\nDnKLWZP2g8PhQGlpKXw+HwYGBtDV1YXJyUkkEgnOYEtKSjA/P8/YnHzr0t5VKpUcOFy/fp2TmUgk\nwvgXh8MBjUbDZ1KhdckxyuVyzM7OcrtQIBAwGp367AqFoqj3gtal8zebp53+nwIAk8kEsVjMFcCN\n4FC+VI6aHHQ0GoVKpeJ+Qk1NDex2O9LpNPx+P2w2G2ZnZxGLxSCRSDA8PIz5+Xku/65lpLJEfeh0\nOs2jLJSVTUxMcAa9srICqVTKoIhcs6gEriL5SYVCAafTCavVikQiwaUbvV6PyspKBleQxmwuMBkF\nCFRiMRqNrEiVnX0B9zRzLRYLSkpKOBAIBoN57zUdfjTqYLPZUFpaikQiAZfLhYqKCggE9/SOo9Eo\nZzmFQFT0f6TYRDOsFHSVlpairKyMy0RUdivWaByD7gvdd4PBwIGL3+9HIpFgub31WiaTYZUgAhMC\n4MrAetel6xQKhUgkEowStVqtKCkpgV6vZxDO9PT0mtKkaxk5PY1Gw9Khy8vLqKioQFlZGSwWCwdc\nY2NjXEkqlFVT9tjU1AQA8Hq9mJ6ehlgs5pIjVV2CwSB6enpw+/btokZwRCIRysrKsHv3biiVSly8\neBHhcBhyuRxKpRKlpaWoqamBxWLB4uIibt68WXT5WyAQ4PDhw6iqqkI6ncbp06f5fpMUbEVFBVZW\nVjgYKsYoIzt48CCkUimuXbuGV155Bf39/dyLpf1HQKFi1qYA+dChQzh8+DD6+/vxy1/+EpcvX0Yg\nEIBIJIJGo4FWq2XMSKGgNhuNvXfvXrS3t0MsFuPGjRv48MMPWZ6VyuIkFxuPxwuuTVUaCliCwSCu\nXLmCjo4OBvkCYAwRoagpsM53zdTzttlsEIvFmJycxOTkJF9TJBLh5ISCLdJKz5VRZ5fpqXVH5xmd\nU4SDMJvNaGpqQiwWQ29vL+tI5zJq61G5nlqo5JzpTKPppWAwiI6ODt7f6x1V+9I5auo/P/DAAxCL\nxSgpKeEeHmWWFKVUVFTwRiTkJfW1VtvKygo8Hg8UCgXm5+cZdERgCxqNUavVqKyshEKhgEgkQiQS\nQUdHBwCsOTJCTkmj0SAYDEKj0XBPUyKR8AtMsnZSqRSRSARtbW24fPkylpaWchJeZDIZBnZRuZE0\nrUmyj2Z/aZaT7qHP58ub4ZDsJEWC5OiUSiUA8Muv0WiQyWRgs9ng8/kQiUQKjp9QkEGVCCrfyWQy\nLsWurKwgFotxtlMMipqyb/oaUj2zWCzQ6XRchg2FQizUXmwPNTuqJ0ISEn6nGXCRSIRQKMT9zWLX\nJizB8vIyOyOxWAyNRgOdToeSkhKOxilSp42cb23KQKnUlkwmYbFYGChIABeqbiQSCUxNTXGWku9e\n6HQ61NTUIBQKYXR0FABgsVjQ0tLCe48OP5lMBo/Hw/iRQveiqakJTqcTqVSKgUcajQYOhwNbt26F\ny+WCVqtFIpHAW2+9xXPx+YwO+3379kEqlWJ4eBjXrl3j+5ytbR+Px9Hf38/TE4XWlUgkjCCfm5vD\nO++8g/7+fiQSif+fvTeNjfO8zoYvzr7v+3DnkMNNpEhRErVYUbQ4jpPalpM4AZKgKBoUbdEi7d8W\nCFAgRYGmSNAlSPImSNPGWZzF8aLYsR1Lsi3KtmSJkihxEbfhcDicjbOTs5Ez7w/1nAwZznCopO+n\n9uMBBMtabj1zz/3cZ7vOdTEYsL29naUqCduyk6lUKgwMDOCJJ56AWq3Gj370I7z55ptcIdTpdFxF\nczgcUCgUm4CB2z0rlabtdjvvxeTkJH72s58hEAjwmaWRqoaGBhSLxR2dh0Ag4BHDw4cPo6enBx6P\nh8c48/k8O3KtVgun0wngfguP7oxK6yoUCuj1euzbtw9msxkGg4EDFbpfKdDq7OxEW1sbtzwrrUsj\ndCQPbDKZoNFoEAqF4Pf7OVGgROLw4cNoa2tDKpXC7OxsVSctFArZd8hkMpbrXVlZYXDzysoKI8GP\nHTuGQCDArYUHGd17qBw1AGQyGdy4cQMKhQLNzc2bxrJSqRTi8Tijc48cOYL29nY8/fTTOH36NP7u\n7/4O7777bsWMZ2lpCS+++CIj/QhtSoLkiUQChUIBTqcTLpcLn/3sZ3HmzBmMj4/jxz/+Mc6fP7/t\nusViESMjIwgGgzyf7Pf7kUwmGbGYyWRgtVrR0dGB9vZ2fOlLX8LExAR++MMf4q233qoYBOTzeXi9\nXrz88stoaGhg5y2RSBjVG4/H0dPTA7vdjuPHj2P//v1IJBK4c+cOMplMxWfOZrNYWVnh9Xw+H5RK\nJcxmM8bHx+H3+wHcjwr7+vpgsVhw9+5dTExMwO/37+hESqUSdDod9Ho9NBoNGhsbIRQKkUqlMDY2\nxqNfDQ0NCIVC7ATLS0jlRmh5YrCy2+1wOp04cOAA98D8fj+PJVmt1k0oaoqgt1NdI61vnU6HwcFB\nLvFarVaoVCoOqDKZDMuhEj6AvqftLrq6ujo0NTWhtbUVHR0dyOfzcDgcXL7T6/WIRqM892yz2eB0\nOpHJZJBOp7nUv9Xo4qTWDU0qdHV14fjx49DpdAiHw5idnUUmk0F7ezvjGH7xi19weX+7MUaFQgGL\nxQKDwYDZ2Vl4PB60traiubkZHR0dXO4GgKeeeoqRrpcvX8b4+HhVp1pfX4/HHnsMGo0GU1NTkEgk\nOH78OMxm86aqTWtrK0wmEzo6OjAxMVG1WgbcD94bGxvR1dWF6elp/Nu//Rs8Hg8GBga43dLW1ob+\n/n7k83ncvHkTOp1uRzyAXC5He3s7vvKVr6CzsxN//ud/zgEAjWH29/eju7sb7e3teP3117ndVU3/\nXCgU4qMf/Sj++q//GhqNBhcvXsQLL7yAUqkEs9kMuVyOc+fOwe12o7Ozkz9/tUydtKhPnjyJw4cP\nI5fL4Xvf+x4uXrwIn88HnU4HlUqF9vZ2dHZ24qmnnkI+n8cbb7wBj8dTcV2BQICGhgb81V/9FZqa\nmiAWi3H9+nX8+te/Rjweh1wuh1qtxv79+9HR0YHm5mb09PTgzTffxMjICJaXl7fN1sViMY4dO4ZT\np06ht7cXAoEAV65cYRwOVSCB+y2N06dPo7W1FdFoFM8//zzC4XDFKsBnPvMZnDlzBo2NjVCpVHjn\nnXf4HRaLxfB4PIhEIti/fz+6urrQ0tKChYUFvPzyy/D5fNxi2Ho2FAoFPve5z+Hzn/88VCoVNjY2\nuOQvkUjg8XgQi8Wg0WjQ09PDScuXvvQlTsikUun/7Iwa+I1zGh0dhd/v5zKOUCjE8vIyb3SxWITP\n58PAwABfKv39/bhz505FIMfGxgbm5+cRDocZNFAqlZBOp5HL5XjzPB4Prl+/juPHj6O+vh69vb0Y\nHBzE+fPnKz73ysoK95QmJydRKpX4MqR1vV4vbt26ha6uLgwODqKzsxO9vb24fv161Uwkk8kwcYBY\nLOZMn6oHGxsb8Hq9aGlpgVarhcPhQGNjI6anpysiiKk/Q2NegUCAh/8DgQDi8TgDLQgVX1d3n9jF\n7/cjEAhUvDjpMqHLinRsCf2YSqXg9/uxurrKvWWNRsPOjvraW40idoPBwH10l8vFTjqbzTL5DdEj\nUjabz+chFou33edywgKa0TabzUysQz1pakUoFAoeGaSIfrtWA2VjMpkMNpuNcQpUriZWpPX1dQY7\niUQinjunfl8lRw2AiV7IURHKPRgMIhgMYmlpCSaTif+sTCbjva3WviBwmEAgYNCiRCJh/fdYLAa9\nXo+6ujpuC9VChUqjN4Swp/FAQiLT2aFSaa3IbyLIWF9fZ0S9SqWCVCrlPmkkEuFxy0gkUlN7QSaT\n4dChQ2hubsb6+joWFxe5J03fJWWFdXV1CAQCNTF8kZMym82IRqMYHx/neVyaWHA4HGhqaoJUKoXH\n4+E2XCUTCoWwWq04ePAgXC4XFhcX2SGVSiWYTCY0Nzdj37596O7uhkajwc2bN3H37l34fL6qo5Fm\nsxlDQ0MoFAoIh8PcXtvY2IBQKITT6cRjjz2GxsZGmM1m7tUvLCxUJB2SyWQYGhrCoUOH0NnZiZGR\nEaRSKaTTaSSTSeRyObhcLrS3t+ORRx5BfX09UqkUpqamsLCwULWyd/r0aQwPD8NqtTLwM5/P8z3Q\n0tKCI0eO4MSJExAIBPB6vbhx4wai0Sii0WjFdRsbG/HYY49hcHAQiUQCy8vLPGlDpCqtra348Ic/\nzO/F22+/zfcT8L8koyajMSfgN+wvNJZEh4Mo5I4cOYKTJ0+iu7sbWq0WgUBg2zXJAVCfmH6NLngq\nla6urkIgEODOnTtoamriub1qB5mes/yyImF5Ykujsua9e/c422xsbOQe6HbrEgIR+M0XXCgU+CDT\nhUAlWq/XC7vdznPXlYwoBDUaDZekKQuOxWJYWlra1COnS5XGiCpF9lR6MxqNkMlkXH41mUyIRCLw\n+XzcZiiVStybLM9atzvIdInpdDo4nU44HA64XC44HA5sbGwgGo1idXUVEomEWw/UU6bLrVLvl5x+\nV1cXrFYrUzcCvwk6NBoNBAIB1tbWEIlE+DLJZDI8x77VKDutr6/ny51AMMVikf8OVQjorBCamioA\n261L54IcPU0BpFIpRCIRrK6uYnV1lRHxGo2Gpxgq4SJobQpK/H4/BgcH0dbWBrVajXQ6jYWFBV63\ntbWVgxWv18tZfSWj3je9F1KpFHa7HaVSCT6fD5lMBl1dXejs7ITuwstrAAAgAElEQVROp0M+n0ck\nEtkRE0CVFofDgVwuh1QqxW0RKvcqFAo4HA4G9ZAz3ykAMBgMGBgY4HFKajfIZDKk02koFAq4XC40\nNTUhnU4zkrjaLDy1V/r6+lAqlRAMBjE1NQWdTscBgNPpRE9PD2w2G3w+H27cuMEVp0rrikQiHDhw\nAG1tbTAajRgZGYHf74dEIuGyMY09uVwu+P1+3LhxgzkDqgVu1GIiIqj5+XkmZKLSf29vL0wmE1ZX\nVzlICIfDjPnZahRcDQ4OAgBef/11TE5OArj/Tn74wx+G2+2G2+3etO7KygozMFYKtghns76+jlu3\nbuGFF15goFqpVMLHPvYx1NfXQ6VSQSAQ4OrVq/zdVAsOe3t74XK5IBQKMTo6ipdeegnBYBBHjx6F\nRCKB2+1GfX09pFIpBAIB5ufnkUqloNfrkclkEAqFHoiv/aFy1FQSIsdHF5FareYLnMp1BFQKBoNY\nXFyETqfbsdxElxsBAZRKJVZXV5nthi4veg6/388ArkoXGzlTYvehDJH4fgmlTiUQ4H6GTC8kse1s\nty45X4VCwdR5CoUCkUiEx9GoHCaVSvmCpcy4Us+3XBzCYrHAaDTiwIEDiMViHAFT758cs16vh8vl\n4rJypWem7JScaVtbGwYGBjA3N4d0Os1z5Hq9HnK5HEajEWq1GgCYwnVrFYC+O+LK7uvrQ3NzM7q7\nu7GxsYF79+5hZWUFAoEAarUaTU1NXNEwGAwIh8PY2NhAPB7fdj8MBgOcTicaGhqYZIZAJ9Q3pl40\nobTFYjHC4TC8Xi8WFxcr7jOdCbFYDK1Wi7a2NqanpV439RRpRlypVGJpaali2Zv2o1gsIpFIQCAQ\noKOjA8D9TDoWi0Eul3Nv3eVycaZCNLvVzjPxpQcCAdTX10Ov17PIh1qt5j3Yv38/YrEY5ubm4PP5\ndhxJqqurYywJXdI2mw2BQIAJh1paWhjLMTExwSNtOzlqCrA3NjZgMBi4JZBIJGCxWJggI5PJ4N13\n38XCwkJN8+pEbkP0t3QX0EjWwYMHUV9fDwCYnZ1FMBjckV62ru4+MyBVtFZWVrC+vs6jhU6nE8PD\nw9BqtYhEInj77bfxwQcfMGCp0l5IJBLs37+fBUKKxSIMBgMT6pw4cQJut5v35tKlSxgZGYHX663Y\nIiOjilo4HOZyNzGcdXd3w2q1QqfT8fuwuLiIubk5Tqa2M8rylUolIpEIvF4vj7S6XC585CMf4dZT\nLpfD9evXsba2htnZWSwsLFS836i/LxKJEIlE8PLLL/NedHV1wW63o7Ozk6stN27c4Pd9ZWWFW4rb\nrZtKpaBSqRCPx/Gtb30Lk5OTzJJIrRCdTodAIID333+fwWqU/e9mdr/cHipHXe7MCOlMoxpEPkHC\nC0TW3t3djeHhYdTV1eG73/0uvF5vxfXJoep0Ouh0OnR1dcHj8WB5eRlyuZyRhZRlfvzjH0epVMLt\n27fxT//0TxXXpflNQn62t7djYWEBi4uLSCQSSKVSsNvtsFqt0Gq16O3txfr6Ol5//XX867/+67YV\nAAKGEGK6q6sLPT09kEqlmJ6ehk6nQzqdhlarRXNzM/r6+tg5plIp/OpXv+KqwXZr0yykxWJBZ2cn\nDh8+jIWFBcjlcrhcLmg0GjQ1NcFisfCIyPz8PD744AMsLCxUXJeqH3a7HQMDAzh48CBUKhXC4TB6\ne3vR0tICvV4Pt9vNY1qpVArf/e53Nx3m7faiWCxCq9Wip6eHX7Tl5WVGZ1OFgHqm8Xgc09PTm9oP\n2z1zOXucXq+HQCDgrJ9aLnK5nIMIKjdnMpmqWWSpVEIikUA0GoVer2eQCWWT5AAEAgECgQDm5uaY\nLYki7+0u/FKpxIQm6+vruHz5MhQKBRoaGqBWq3HixAkGCxYKBTz33HMYHR3F1NTUjsAsAp2trKzg\n9ddfx2c+8xlYrVa43W50dXVheHiYg9G5uTn87d/+LXNz71TSKxaLWF5exg9/+EPGaxw5cmTTbLbT\n6cQHH3yAn/70p7h161ZNHPAUlI2PjyMej8NiseDs2bNYWFiA1WplPv9EIoFnnnmGW2i1ANToDHV3\nd0Ov1+OZZ55BMplEU1MTmpqaUFdXh7GxMTz77LO4efMm97x3wm+QYwCAgYEBNDU1IRKJwOFwQKVS\noVAo4Jvf/CbeeOMN+Hw+fjd2Wvftt99GY2MjWlpa8IUvfAGZTIarhtlsFnNzc3jhhRcwNjaGK1eu\n1FSmJ+DV+vo6Dh8+jAMHDvDeUeXG6/Xiq1/9KoLBIAKBAFZXV7nNVGn9YrHIDlqj0eAf//EfubKp\nUqkQCAQwPj6O27dv4/bt21zFonGtaplpNBpFIpGATCbD3/zN3yAUCjFY2O/34yc/+QlGR0cxPj4O\niUTCjJEUNFUyj8eDVCoFnU6Hv//7v+f2YD6fx7179+DxeBAMBvHCCy8wiyHtv0gk2hVJUrk9VI6a\nbGNjg0uA9fX1cDqd6O3txcTEBEddYrEYg4ODOHDgANxuN+Lx+I6qNTQylMlk0NDQgKGhIRw4cIBL\nerFYjGlMKcNaXl7GxYsXK/aQqTy5vr6OXC7Hfc4jR44gn88zWUljYyMaGhoYWDU5OYnXXnttE4NU\nuVHWSlUEjUYDm80Gh8OBrq4unDhxgrMQjUbDM5yrq6ubIrntIjgKiKgkTz1Ui8WC4eFhrK6uQiwW\nM0hEKBQiHA5jenqaxwu24+amdem5yfnJZDIMDg5yj40AFcD96kIqlUImk4FCoUAqlfotBCpdfNR/\nplaFXC6HwWCA3++HUChEPB5HMpnky5IiZKlUilQqVRF4ksvlmC40FotxOS+dTmN6epoRvvT3xWIx\nVlZWKpb0yq1QKCAUCkGpVGJubg75fB4ikQihUIhJSJaXlxnImE6nkUgkdqTPpNJ5qXSfYS+bzTJD\nGaH/I5EIFhcXcenSJfh8PgZi7oSsp7VjsRi+853v4NChQ2hvb4dSqUQ+n0c4HMbMzAxu3ryJe/fu\n7UotKpfLYWJiAt/+9rfx2GOPwe12QywWc1b8wx/+kMUtdiqllxsFQRcuXMDAwADUajX6+/v5Hbh5\n8ybOnz9fs5MmW11dxezsLKanp9HW1oYDBw4w/qFYLOL8+fN4/vnnMTc3x0lELZbP5zE3Nwej0QiN\nRsNBfCqV4grF888/zxz5tVCe5nI5zM7O4tKlS9jY2IDb7eZKQyAQwGuvvYZkMomJiQn4fL4dmQbL\nbWZmBq+88go6Ozv5/aRkZHFxkYlJkskkByDVBJLo91999VVEo1H09PTwfZlIJBAOh+HxeJDJZJhE\nJZFI8KQK3QfbteBKpRKeffZZLC0tMQ/HL37xC+4/x2IxrK+vM66IyIeolUio++3WDQQC+Pd//3f8\n4R/+IQDg9u3buHnzJsbGxhCNRrnSRZUjqhpEo1GsrKygUCjUxNK21R4qR02bQxdFNpvF4uIiGhoa\n4HA4OAuTy+UM1ycQy/j4OA/yVzJq6NPIVF1dHZr/S9Lw2LFjUKlUTGG3vr6OpaUlXLhwAT/96U93\nVDwhBxMKhTAzM4NDhw4xiQWVsOmQrays4KWXXsLbb7/NBBjbGQHrEokEAoEA7t27B5PJxHtBXOR1\ndXVIp9Mcgf7qV7/il6XS2Fc+n2fhikAggGg0CpPJxEhk6udSOfbdd9/FyMgIxsfHK473UNBSKBSY\nbvL9999HZ2cnlxAFAgGy2Szu3buHcDiMXC7H/NSUPW2NOmndfD6PpaUlzM/PI5FIoKGhgUFI1Lei\n3rdSqYRYLGamMcoUt3vmbDaLhYUFvlgoO6RLc3x8nFGdJMlIjp1AfdsZ7Z/X60WhUMDi4iJsNhsK\nhQLi8Thn5QS4k0qlPGpFF2il74/wFsViEQsLC4hEIrh69Spn/jQZQbzou5F2JGedzWbx2muv4b33\n3mOHQsC3VCqFRCKxa63rjY0NJJNJjIyMYGZmBsePH4dcLkepVGImQuJIqBUZS8+bTqfxwgsvYHl5\nGXa7HXa7HZFIBHfv3sXo6Cg7p92UHtfW1jA1NQW73Y5EIoGuri5oNBokk0ncvXsX3/jGN1hVrlaj\nUujIyAg2NjZgt9u5dz87O4vR0VGMjo4iGAzy3tbyzJlMBn6/HxcvXkQikWACJKrqvPfeewgGg5va\nfOVnrNooZyAQwHPPPYempiYIhUIUCgXMzs4in88zToMCVxqjpJ9XOscEGg6FQrhw4QJKpRI7ZDpX\nRG5FgT/hdShIrXT2XnnlFczOznJQ4fV6N2Xh5UkF8XWXtywrBZ6rq6v4z//8T4yNjTHlMlVN6TNS\nC5IquNTqovtlt04aAOoepF7++7a6urrSf/13E+rTaDSyDm53dzc++clPsjoJkRaEw2HcvXsXly9f\nxuXLl6uCZKiHSrN+R48exb59+9DX14eWlhYYjUYGmgUCATz77LMMyKh2IZVTKur1elgsFjz99NOs\n1EXAGFJPuXfvHr7+9a8jHo9XnXUu56i1Wq0wm8085vPRj34UOp2OL77XXnsN09PTuHnzJpaWlnYk\nwSc0skwm43Ky3W5HU1MTmpubIRKJmOji3r17XIbM5XI7gpFIPEWv10Or1TLJDJGH0GwuiYCUq+1U\n65/SzKPFYmFHXH74CaREFwWVlqnXXK0vS2x0hBKnS6IcF0Ho9bq6Ou4B1uL8ynEXW+dgyenSulT1\n2c3LXP7eUPuBKinlDv1BLgh6Lmpp0AVM+7PbMZPy5yU+BNJFpsue9ns3dxOtqVarYbfb0dDQwMQZ\nNGFQ7WxVsnKRCIfDgcOHD7Oymsfj2QS63M2zEsmQ1WqFw+GAwWDAzMwMFhYW+D0rf9Za1qe7gvAn\n5ah8AhLSs9J5qXWfxWIxT8rQOaKWUvn3VX4OazkbtL/ljpPW37qvxBlB71y156aqK61TaRSxfN8I\nAwBURmbTd0eJDCURW9em+4RwUYQ12eY5rpdKpaGd9umhctRbjS4JMmKLokuY0KDlxBO1fp7yv7NV\nXGDrpbYbAED5C7C1fPIw7PWe7dme7dme/fdbuS+oYjU56oeq9L3VtmYAlVCau42+t/6dnaLs3ay9\nNVvasz3bsz3bs///2e/z/n8wza0927M927M927M9+39ie456z/Zsz/Zsz/bsIbaHuvS9Z3v2/5WV\n4wt+nyUsAmWVs5M9CCBru3VJP7y1tZWlJ2uZv93JiG2utbUVVqsVmUwGy8vLSKVSiEajv/OzE1BJ\np9Ohra0NhUIBk5OTNdN8VjMC/xAHv1AoxN27dx8IsLadCYVCKBQKdHV1QSKR4Nq1azyu+btaubAE\nTSJUIu3ZjdHZJtAkAUtrkbqsZW0CThJgcLdI+0pWLtzzoMQh9FzlCO2t6z3o2ts9Xy1iQ7XYnqPe\ns/8224pGBjbjDh708BKiki5hqVS6ibhhtyNDZITqJoEIkuOcmZnh+Uqih9yN0eVFM+9nz56FUqnE\n4uIilpeX4fV6Wc/5QZ+bONCPHTsGp9OJQCDAa3u93gd2HPTsRHd79uxZ5HI5vP/++xgdHUUsFvu9\nBAEmkwkf+9jHsG/fPrz11lvwer0PNG+61UQiEZxOJ86dO4d9+/bhxRdfhMfj4VGd3+XZ6+rqmK/f\n5XIhHA4jFAptQoI/yPrk4Orr62E2m1lJrHxWe7c0lPQuUjBHbHlErxqNRneUdqy2rkAgYJldGgsk\ngiAKSneDuKdJA2KmJJU4+vwCgYCDlt0i7suJpIxGI4RCIbPjEUlRLZrt5bZ1cmRoaAgikQjZbJZp\nVJeXlx/4zD2Ujro8Kqv0opaPoZTPnFbbBPo7ADZFPvT/5Yduu3V3GgcoX2srKUj589LIBKHLq61L\nXz6NsJC2cfnBL8/+aAZ3J81hmvFTKpWQyWRQq9Uwm80wGo0IhUJMFUqz58SotROJAa3tdDqZ3MVo\nNOLEiROsEJXNZuH3+5kzOxKJVCR+KTciTyF9cuJDNpvNiMfjSKVSCAQCuHTpEiKRCJNF7HRB0HdD\nercf+tCHcPDgQezbtw/ZbBZLS0vwer2YmJjgef2diEPKjcbK9Ho9Wlpa8OlPfxrFYhHJZBLhcBiv\nvPIKpqammKN7tyNElDG2t7fj1KlTkEqlMBgMKBaLvCe/y3iWTCZDZ2cnenp6oNVqMTU1xeejfF72\nQUwul7OQxIEDB+D3+3m+/kF0e8uNRCo+8pGP4MyZM1CpVOysdqLMrGZ0XiQSCR5//HGcOHECSqUS\nN2/exJUrVx7YSVOgSM7pD/7gD+ByuZBMJrGwsMBiPw8SKNJYKjEzmkwmro6srq4iHA7vet3yu0km\nkzGXOpFVJZNJLC4u8l1a67PS/hKjnMVigcPhYJ6FXC6HZDKJ27dv1xxY0N6KRCJoNBocOHAAnZ2d\naGxshEgkwszMDOLxOEKhEG7cuMGSyrU8L1VVSESlvb0d586dAwAkk0msrKzg+vXrePXVV7G0tPQ/\nn+sb+M0HJ3Uh4D5Bu0wmw+rqKg+kK5VKHrAn2rnV1dWqkRtp06pUKhZC0Gq10Gq1zCQjl8t5rpdY\nsEjYoRKpAfH+kqSjUqnEwYMHYTKZEA6HEYvFsLa2hlgshmAwiPX1dYRCIZa/rDTfSZeKxWJBf38/\nLBYL2tra0NTUBOA+NzY953vvvcfKVPF4nPmtK+0FiRi0tbWhubkZp06dYh3gZDLJdJOzs7OYnJyE\n1+tl4YtKwhnAbzjK+/v70dPTg9bWVrhcLrjdbiabiUQieOutt/DBBx9gZWUFAFg2s9LlVu5MH3nk\nEZb/I+3wtbU1JksgFaZCocAsXztZXV0dc02fPHkS/f39MJvNWF9fh9VqRXd3N2w2G/L5PBYXFzmy\nr+Uyrqurg1wuZ0dtMpmYq14ikWBwcBCxWKwmre/tTCwWQ6PRYP/+/Whra8Py8jLi8fimYHM7pqVa\njC7MoaEhHDt2DJOTkxgfH+eL7Xcp69XV1cFkMuH48eN44oknYDKZ8Pzzz2NsbIxZ1B7U6FI+e/Ys\nzp07B5fLBa/Xi3v37jH5y4M+MwUvDocDTz/9NBQKBZaWljijftAqQHnG63A4cPz4cYhEIng8nk1j\no7sdFSUnZTAYmG++s7MTwH2qzbGxMchksl2Ptm7Vn25vb2fxDKlUijt37iCdTtdMBkN7S1UWq9WK\ngYEBZk0MBoMsCDQ1NYXJycmavkcKVKiqpVar8eijj8LpdMJisSAej8NsNgMA7t69i1QqVZOjpj0Q\ni8X8jH19fTh16hSam5sRi8UgkUhYm3tpaYnZyXZrD5WjJkWe1tZWDAwMYP/+/Sx2UV5GpY186aWX\nmIoxl8vB7/czo9nWQyeXy3H69GkMDg6isbERDoeDvxxSWiLlIcqc7t27x5SjJIKx3bpNTU04d+4c\nSySS0pJIJGIe6cXFRczMzODu3bus5ZtKpRAKhXhovtxIWailpQWDg4M4c+YMtFotk3ikUik0NTUx\n1eX8/Dx8Ph+LlROn83b9FpFIxFKb3d3daGlpgUQiwfz8PNbW1vhFI8lFyrL1ej1KpVLFLJUckslk\nwr59+9DR0QGDwYBAIMB6sAaDgak/GxoaWBpUIpFU7aXSy6vX66FWqyEQCDA+Po4LFy7A4/EweYTN\nZsO+ffs4eNvY2NixPLuVxODevXu4fv06YrEYVlZWMDg4CLfbDZvNhsOHD+P27dsoFos1Z2V0dkky\n8//8n/+Dubk5rK+vw2QyYXh4GH19fcjn8ygUCjtWQ7aubTabcfLkSRw7dgypVArf//73sby8zBlv\n+d7uxokIBAJ0dXXhz/7sz3jtL3/5y/B6vb8lYvMg5UeFQoF//ud/Rnd3N9bX1/HLX/4SV65c2cTM\ntluj71Kj0WB4eBhf/vKXkc/n8etf/xqvvPIKkxc96NoKhQJmsxknTpzAX/zFX0Cj0eDatWt48cUX\nMTMzU5G1r9qadFcQ4dBTTz2FkydPolAoYGRkBPfu3cPExARX1GrZG3L6dH82NTXh7Nmz6OvrYxGR\nt956C9FoFE6nk9XSalmX2k1NTU3o6upivXXS0SZ8REtLC6ampvDrX/8ac3NzO65NrJAqlQo2mw1P\nP/00WlpasL6+jmAwiEwmg1wuh5aWFnz+85/H2NgYbt26VdM+a7Vaplru7OyExWJBKpXC1atX+Y44\nefIkPvWpT6G/vx+XL1/ecU3aY5PJBLfbjY6ODhiNRuZTJ2pqh8OBr3zlKzAajRwI7NYeKkdts9ng\ndrvR29uLjo4O1iienp5GLpfjqK9UKrHcGSlRlfdGtjsQZrMZXV1dqK+vh1arhVgsxtjYGGe0crkc\nGxsb8Pl8mJubY3k46k1Wujy1Wi1sNhv0ej3EYjE2NjYQDocxOTnJ/NV1dXXw+/3w+XxYXl5m1jCS\nqqyk1arRaFjvNpVKMaNXLBZjkneJRIJ4PI7l5WWk02l+hmrsPcSORaISCoUCc3NzWFlZQTgchlAo\nhN1uZ/rJlZUVSCQSZLPZqrzOFEgR/apUKmVK1YmJCQBg8QiStSyVSkwvutOLTOxjRIxPgKZQKITF\nxUW0t7czCxmVg2OxWNU1gd/QCWYyGcTjcYyOjqJYLCIcDiOfz0Ov10Mmk6GhoYHFYHYDoipvnYRC\nISSTSaZyJM5hkuesJHlayerq7isFWSwWqFQqzM3NYX5+ngVFqDRZjU2u0roCgQDd3d0YGhqCVCrF\n4uIilpaWOECh7OdBSt8ElOrp6YFMJsO1a9fwq1/9irkSKGjabXZKTq+9vR3PPPMMisUixsbG8NJL\nL2FsbIz7nrvdC1rbYDBgaGgIf/zHf4ympia8+eabeP755zn4BmrHXpRnkCKRCG63G+fOncOxY8eg\nVqvxyiuvwOPxsI4xZWK1BEbkUKlV5HK5MDQ0BJ1Ox/cnnT9iA9uOv3+rUcVMIpGgt7cXra2tm6pa\ni4uL8Pl8EIvFcDgc0Ov1nGjt9Mwk76pQKFi8Z21tDaFQCAsLC5iamoLZbIZCoUB3dzff+zutS/tL\nlSeBQIDFxUXMz8/j7t27iMVizGi3f/9+GI3GquuV7zHJ0RK3fi6XQ6FQwMWLF1lAJBwOY21tDVqt\nlluvu7WHylFns1koFArmyE4kEpienma9VJIqk8lkrD5DQIV0Ol21H7m2tgaBQLCpRP7OO+8gFAoh\nlUpBrVbD4XAgFoshHA6zCAfJA1YqV1D5OhaLcZ96fn4ely9fRiwWg06ng8FgwPr6Oq8rEAiwsrJS\nVbeW+jCRSATLy8tM5zk/P8+gBKLlJJBFOSF8NVGHjY0NFmygPuD4+Dj8fj9CoRBrtMpkMu4dF4tF\npvmstC5l26lUioXd19fXEY1GMTU1BZlMxrJ1xKuuVqv5z1V74ehlIJEEumji8ThL6VEmRhkVOdpa\nLk5SwInH4xAKhfydUpBE0XypdF9QZLfOiaJ2pVK5yZGm02nOTuVyOUuw7sZEIhGMRiP8fj+uXbvG\noiSUUVG1ANg9y97Ro0chl8vh9/vx5ptvcrupnE51N1aOA+nr6+Nq009+8hPcvn2bec+pV7ub8jc9\ni0ajwZEjR9DT04OpqSl8+9vfxvvvv49cLsdUv7ulaSVraWnh3nE+n8e3vvUtLCwscIWAlJh287wC\ngQAajQanTp3CyZMnoVarMTc3h5dffpnvROB+xlkLyImctEgkgtlsRn19PbduFhYWMDExwQIxxWKR\n8Sm1rCuVSjnYMRgMLIGbSqXw8ssvMy2yWq1mhTuRSFTTOaGzIZfLWWSHtLonJiZQKBQQDoeh0+mY\nm7uW80xUw6VSCel0Gnfu3MGdO3fg8/m4RajX6+Hz+ThgrmVd+lyZTAZLS0uYnp5m9DwJcpCCnUgk\nglwuZ2nb3Qa2D5WjTqfTWFxc5Owgk8kwanVtbY0jOXLSKpWKs126rCttwOrqKmZmZlBXV4fm5mas\nra2xHBu9tMlkki9khULBogvVsr1sNguv14vp6Wm+wAOBAKs7aTQa6HQ6FvWgZyYHWOmZKdOi3jj1\nPEhso76+HjabjR0ojXCQili1g1As3peXW19f3wRuI8SzzWZDfX09MpkMA+BIdajaHlMJn763QqHA\nTk+tVsNkMqGtrY2xAeV7vtPBpbVJfIQcWjabhUaj4VaGQqFg4v5ahR3KATrpdBpCoZAdPIm/0FgV\nKUjt5kUjUB5hHyhrlEqlXA6nH5UUvqqZWq1GsVhEKBTC/Pw8XxASiWSTnnK1IGs7EwqFaG1t5XbA\nyMgI/zrR+e4kvLDVqF9O6PdUKoU33ngD165d4+CF1Kl2O4ZDwUl/fz+efPJJaDQafO9738PY2BgH\n6pQBUVC/m6BFLBbjU5/6FA4fPoxisYjJyUkGFsrlcgD39aurYTi2W5fKyKdPn4ZMJsPS0hIuXbqE\n2dlZiMVinkKQSqUVVfy22sbGBlQqFZqbmzEwMICenh54PB5cu3YNN2/eRCQSgcVigd1uh06n4xbd\nTpbP56FQKKBWq6FUKiGVSjl5IP3p9fV11NfXw2Aw8MjXTtl6+ciYUChkHW7SXaBkiUSDqNJYyzOT\nytbq6ir3+0mulp5JKBTC6XRyhbKWc0dtUZlMhsXFRa5ilq9JylzUBqW7abf2UDnqtbU1TE5Owul0\noqmpiUUoCKRF2WJ5CVYsFmNhYYF1iStlOoTepaZ/oVDA448/DqPRiNXVVajVang8Hgb1FAoF3Llz\nZ9NB2+6SI7GNZDIJp9PJQJCnnnoKuVwOarUaqVQKs7OziMfjyGaziEaj8Hq9VS8iutjpgDU2NnKZ\nUKvVQqFQsODEysoK/H4//H4/rl+/vuM8JAHwKNixWq145JFHYDKZkM/nYTab2SkSgCwcDiMQCOx4\nedLlR07aZrOxeH1TUxMDA2OxGOLxOABgeXm5JkdNP0jJSqVSweVyobm5GWazGXa7HWKxGIFAAAKB\nYFfjTlT+puDFaDSiVLovUWcymSAWixGJRLiC8iAlWQrkCGlvNBphsVgAgJ2qRCLZlXMSCoUsPUjS\nmy6Xi6stOp0Oq6urnEVFo9GagheRSASHw4H6+nqsrKzgtX7w1bMAACAASURBVNdeQzgcxqFDh6BU\nKqHVaqHT6XDr1i14PB6EQqGaslTS/P6jP/ojPPnkk3j22Wfx3HPPYX19HU6nEzabDTabjRWPRkdH\na0bY19XV4dFHH8WXv/xlWCwWvPfee3j55ZeRz+dhMpmg0WjQ1dWFUCiEyclJzihrMaVSiaNHj+LT\nn/40FhYW8LWvfQ3vvPMOj661tbXBarXyyFotveS6ujro9XqcOnUKX/ziF7G2toavfe1ruHz5MkKh\nEOtzWywW6HQ6jI+PQyqV1qQfLRQKcfToUTz99NPQarUYGxvD9773Pa7gSCQSdHV1oaWlBU6nEx6P\nZ8cKBgWrarUaNpsNi4uLuHbtGiOZE4kEI+0bGhqg1WoZc1AtAC2vAJRKJcTjcQQCAaytrfE+5vN5\nWCwWDAwM4PDhw3w+qgUAtC5lvVRJpd8r/3OPPPIIjh07hmg0ih/96Ec7jgaSw6WWWfmfpYCwrq4O\nFosFx44dw9zcHL71rW8hHA7X1GL4rX9vV3/6v9mohD09PY3Dhw/zZeFyuSCTyZBIJGA0GnmeVS6X\nc4alVCqRSCQqXnKlUglLS0s4dOgQgsEgjEYj+vr6uDRD2Sr1hWmESK1WI5PJVD1o6+vrXPJQqVTQ\n6/WwWq0sEL+6ugqbzcblMoqYyzPU7Z6ZerJUClKpVFz6p3J3oVCAVquF1WqF1Wpl+cpEIlH1pVtf\nX+eXXiQSwWAwsOJOLpdDLpeDSCRCa2srcrkcuru7eXxop4ySfo/2jC4yGvUSCAQwGo0Qi8VIp9Ow\n2+3cN6+l90Z7o9VqYTabIZPJ2DFTgEAvS60RbDlYUalUchZTPqImlUo5E9tNyZdKuaVSibMRIpog\ntHktY2TbPTNdbhTJ0yyr2WyGwWBAY2MjlpaWEAgEoFAodjwXtK5YLIbFYmGN4GKxCLPZjN7eXuh0\nOtTX13OrhRD3tQQwVI7t7u6GUCiEx+OBWCyG3W5Hc3Mz9u3bx++cwWDA+Ph4zSNJQqEQp0+fhslk\nYslMWluj0aCpqQkulwsejwfhcBjJZLLmvXA6nXjiiSewtraGCxcu4L333kM6nYbJZEJjYyNjamZm\nZn5rfLKSqdVq9PT04LHHHoPT6cTXv/51XLx4EdFolDM8h8MBk8kEm80GpVJZNaOmMyyRSGA2m3H4\n8GGYzWZuxcXjcW5pEa7B6XSy1ONOGS+p4DU2NqK+vp7lWunskhoffZfpdBqxWKxqZYsCWIVCAbvd\nzpWl8s9JqHEq4RuNRsZ4VFM0pFYSKWnReaX7nMZN5XI5+vv7YTAYWGa02jmmNgftd11dHcuUEkYo\nn89DrVaz8mM58O1BgIwPlaMG7n8pCwsLGB8f59EQms0jrV3KKjUaDZeYabC8WlQfi8Vw79496PV6\nZLNZqNVqzlqpzEuHWCAQ4NSpUyiVSrhx4waSyWTFMYNSqcRAMYrEqJeUTCaZfYrKNnK5HEeOHMFb\nb73F/eRKwQX1Nufn55mZCAC/dPl8HjKZDL29vWhqaoLBYGAEdzUwDmWmqVQKi4uL0Gg0zFC0urrK\nJUi73Q6tVovTp09DKBQimUwiFApV7ZXRZ0mlUlCpVAgEAlCpVADA419qtZoDhAMHDuDSpUvMvrRT\nFkI9TiphUf+HSvjAfTS+w+HYpBW707r0QsvlcshkMq7i2Gw2APdL1fQZEokEA7Rq6ZFJpVLWUler\n1VhfX+cAi/rphUIBKpXqt2b9K1l5mTiRSCAWi3FFh6oXcrkcPp+PM+NEIrFjpYHW1ev1DNQjoNvQ\n0BC0Wi0kEglisRgGBweRSqWwsrLClZRqJpFI0NLSAovFwqN+DoeDR+3a2tq4WtbQ0IDz58/zmGG1\nvaALf//+/SgUChgdHcXExAR6e3vR0tKChoYGOJ1OCAQCWK1Wxr2Q/GC1vVCpVPjQhz6EQ4cO4erV\nq3j77beRyWRgsVhw8uRJdHR08Bm8ceMGg2CrjSVRtens2bNoa2tDJBLB9evXkUwmoVAoYDKZcObM\nGZ68IFBWtWoZjR8RetxgMGBxcRE3btxANBplgKrZbEZ7eztPqUxNTe3Y+5bJZBgYGIDFYkFnZye3\nErVaLQeKSqWS0d8NDQ1YWlqCx+NBLperGNgSY1xDQwM6Ojp45j+ZTPKdQfifY8eOwWq1YnV1FVev\nXq161qjaZrVaOZiltge1E5eWliASidDT0wO9Xg+Px4PXXnsNs7OzVQNxhUKB4eFh5PN52O12pNNp\nWK1WJJNJRKNRiEQihMNhHDx4EB0dHQCAl19+GcFgkEGYu63IPXSOmiL0+fl5Lu9Sz1AulzNQJpvN\noqurixGAfr8fN27c4It6O8vn8/D5fAxmojLm6uoqZDIZkzjU19fDbreju7sb8Xgc6XQa4+PjFaNZ\nQvOOjY3B7/ezUHgymYRQKOT+OV1ENJJDUf9OAC0aI1hZWeGSOM14l5fYrFYrGhsbMTQ0hOeee27H\n0h7tB4m/03gQXf75fB42mw09PT04ePAg3G43uru7eRyumtHcuEwmQz6fRzKZhEaj2XTZ1tfXM+GF\nyWRisFY1oBZl6TKZjJH+dXV1iMViXE5XKBScAc7Pz7NTr7ZuuTO12WzQ6XRMHCIQCHjP8/k8BzWU\nrVdbmzIRjUaDxsZGmEwmBg+JxWLo9Xqsrq5yD5VAdnV1dfzdbZeNUGBBQSvNhwL3JxyUSiWTLVAw\nS0EIneNq2Uj5uBo9r9FohEAg4AmDUqmEtrY25iVYXV3lX69kpCdOACSpVIp9+/bB4XAwW5tMJkN9\nfT1XIFKp1I7tAMpy7HY7EokEZmZmkMlkGMVLQKy2tjaEw2EO+ndal8ZvTp48CbPZjJ/97GcIBoOM\nt7Db7VAqlVztKi+tVlqbgHK9vb0YGhqCSqWCx+NBJBKBRqOB3W6H1WpFfX09NBoNLBYLIpFIVYAo\nAJ7VHxwcRFdXF4RCIWZnZxEIBJDL5dDQ0ACr1crtoq6uLkSjUZ6Jr2RU2Tx9+jRn9hMTE5smFYgP\noL29nQG03/nOdxCLxTZVq7buLe1Bd3c3t1IIjU7fez6fx759+zA4OAgAmJiYwMjICM9Gb1cJGB4e\nxqFDh+ByubitWQ6A9Pv9cLlc6O3thdvtRjQaxfvvv4+rV6/y97PdOy2VSnHs2DF87nOf4+rp8vIy\nNBoNNjY2EAgEEIvFYLVa0d7ejmg0ivn5eYyNjfF7TeDZ3dhD56hLpRLy+Tzef/99jI+PcwZNZVJy\nPuRsT58+jTNnzuBDH/oQ3nrrLWSz2YqRVrFYxNzcHMLhMIrFIiMHaeOo90ncw//yL/+C48ePw2Kx\n4Pvf/z4DwrZaXV0d987r6urw9ttvQyQSMVSfkKxisRgqlQpHjx7lGcxIJIJUKlXxMhYIBMhkMrh1\n6xZu377N0Rhln3SRjo2N4ciRI/jc5z6HhoYG1NfXVy1zUpS5traGpaUlBINBds5U+qYy2sWLF/EP\n//APMJvN6O/vZ2aqSpcQAZloNIHmBgkESNURo9GIoaEhdtaUVW5nNA+sVCphNBqhUqmYB3l6ehqR\nSAT5fJ7LfnShmEwmLCwsoFAoVHyp1Wo1jEYjbDYb5HI5XC4XZwrZbBbBYJBHK6gyolarGaRVqWxN\njrerqwt6vZ5BekStSKAvKoVns1mk02moVCrkcrmKoCQCIFEZPZPJcGslFAohFAohGAzyBWk2mxGJ\nRBitu1MgRN9hNptlgJ7FYsHKygoHlgqFAu3t7QwWpHbUTlULQgnncjkolUoMDg4im81icnISiUSC\nx4ho/j6RSOxYUqfSo9vtRrFY5Mxl//798Hq9mJmZgcFg4FbX4uIigsEgksnkjtUQh8OBz3/+8+jv\n78fa2hqCwSDa29shkUiwurqKRCLBI0qRSISJgYgQp9Lz2mw2fOELX4DD4YDH48HIyAiampoYgEqJ\ngkajwcLCAkZGRngssdK6arUazzzzDI4fPw6VSoWLFy9idnYW2WwWLpcLnZ2daGtrY4c9NTWFK1eu\n4Pr165idna0auO3btw9PP/005ubmcPfuXczMzEAgEKClpYWxDENDQ0xHOjU1BY/Hg2AwWHEvdDod\nnnzySXz+859HqVTCV7/6VczNzTEXxOnTp+F0OmG1WiGXyxEMBvHBBx/g7t27fE9VeuY//dM/xdmz\nZ1EoFPDuu+9idHQUWq0WTU1NUKvV+OxnP8vo7o2NDfzHf/wHlpaWoNPpuKKz3dk4efIkvvSlL6Gv\nrw+jo6N46623EAqFcOzYMYhEIhw4cICDcRptnZ6eRmNjI8Lh8AMT+Tx0jpoyJuoDElEE9WRp7piy\n4bGxMQwPD6O7uxsikagqsKA8yqWZumw2y2UqKolQvzOdTmNgYABisbji85YTFlDplDI4Ko0SGIaA\nSj6fDzabjf/Mdl8cRXVELqDVavnfIbYtKvNTsEEctQaDgR15pT2WyWQcgRO4iZzd+vo6BxdUqiHg\ni1wur4jSJmcql8sZAEPlfqp20KWr1Wqh1+vhdDqh0Wg2kXJsXZvWpb/T0dHBLG2xWAyLi4uIRqP8\nuerr69HY2IhMJoNUKoUbN24A2D6DrKu7zypHl43RaERvby8SiQRTm9L3RnO0RqMRdrude1GVgguh\nUAitVss0iDabDc3NzQyeo++dAk+NRsOOmyoWlRw1nTMKelpbW/nXwuEwZ89EX0pgy52wAPT+0Xig\nxWLhc0pcBoTBaG9vx/z8PEKhUM0I+0wmw98VlXSXlpbYcbe0tKC5uRnA/cyplr43ZWvUs9TpdDAa\njezUbDYbGhoa0NDQgGQyiVu3bmFpaalq9Y1MpVJtepeVSiV/NwaDAf39/TCZTCgUCpienq6JUEUg\nEPC+UnBWLBaZe9pkMnE53e/34+rVq7h+/fqO0xFSqRS9vb3QaDSMaCZiIblcjs7OTm6JhMNh3L59\nGzdv3oTP5+NJhEr7SwBTn88Hv98PAOjv74dWq0VLSwsMBgM2NjYwMTGBQCCAaDSKYDDI40iVrL6+\nHmq1Gj6fD7du3YJYLOZqhdvtZg6DcDiMsbExDmBpzLXS8zocDkgkEgQCAfzoRz9CKBTiINHhcPAk\nx/z8PE/sUEWK3pHt1i2VSjCbzVhZWcHXvvY1TE9Po6GhAcPDw2hoaGA+jfHxcczOzjJ1MFXrqA23\nW3voHHU5uCqTyUCn03EppLzvU06nR5kJEYhUWre8BEMODsBv9bXJSZKDEggEO/aGyFGLRCIuVZV/\nFiKfICdWLBaxurqK5eXlir0sWlcul0OhUKC+vp573sBmtKTVakVXVxcHNaFQqGoPh1DThCA3Go3I\nZrPM4kU84BaLhTPCtbU1eL3eisw65axFer0eOp0OAwMDyGQyuHr1KorFIpfLjh8/joGBAbjdbh5z\nqFaqLx+PslgsGB4eBgD+e2q1mpHlJ0+ehEajwfz8PBYXF3fkJy+V7s9GEwcytVoIIVuewavVau7Z\nqlSqilUW2g9ypEqlEo2NjVzmBn4zikIldAKi0OxlNbRzOT2sVCpFPB6Hw+Hg8i9VonQ6HRYWFrgk\nt9OIFoGgCoUCotEos8nRpATNqNM8/A9+8ANMT09XDQzLjWZh6XPZ7XZkMhl0dXVBLpejr68PADA6\nOooXX3yxJoEVwnJQ1YT2OpvNQq/Xw263M9HRD37wA+aCrwVIRq2VYrEImUzGNLvUW21tbUU6ncbb\nb7+NS5cucRC9UwUAuP/9KxQKHhddXV2FRqPh0aaJiQlcvnwZExMTmJqaYnKSWqohZrMZp06dgs/n\ng0Kh4CyVlMnu3LmDixcvwuv1MjHHTu+HUCiEy+VCfX09crkcNBoNpFIpk/S88cYbmJ+fZ1U1yqQr\nneNCoQClUsmto3PnznEC0dDQwOea+uzJZBLJZBKxWGzHCQO5XM6BUEdHBw4fPgybzYbW1lZIJBL4\n/X7cu3cPN27cYJxPsViEVCrdpNuw9XtbWVmBVCpFMpnk4M/lcmH//v0QiUTMNfHSSy9xoE7AzFwu\nh3A4vGvuAeAhdNTFYpHLEdQ3JLRt+Xwl9aSam5thNBoZgLCTlaMM6e9R1EdjDRR5Eer11q1bVRmu\n6CCr1WpGxFJWSjJvlL3W19fj+PHjSCaTuHTpUtV1qfSsVCrR1tbGlw8hF6lUbzAY8Nhjj+HQoUMw\nm808d1htDwhYYjQa0djYCJvNxmuvr69Dp9PB5XKhq6sLbrcbGo0GY2NjuHbtWtXAgoBIlBU0Nzej\nWCyio6ODA5bGxkY8/vjjsFqt3PMkysvtjDJBchI9PT1wOBwQCoVYW1vDsWPHsLGxAbvdjoaGBjQ3\nNzNIiBjEKvUMKVgjop3m5mY4HA6k02n+N4rFIvR6PVO4EmiNMrLt1qWXkc4toeqFQiFjC8hpFgoF\nBINBzM7O8nwnvQeV+pyFQoFR+ysrK8yZToCeUun+THEul8PIyAjm5ua4t1ytL0uXJiF67969i46O\nDm4JOJ1ObgHcunULV65c2TRjX80IyDM9PY0rV65AoVAwpSyh4NfX1/HGG2/g5z//OWZmZmpGwhOh\nkN/vh8VigcFgQHt7OwPfSqUSZmdn8eMf/5gFZmoB9MTjcczOzsLn86G5uRmDg4NIJpMwm83Q6XQ8\nonT+/HlMT0/XpI4kEAg46zQYDJyZR6NRroIsLS3h/PnzuH79OveYd6JsFQgEuHDhAp544gk0Njai\nsbGR9RKi0Sju3bvHrQCfz4fbt29zBY0Sme3WJZzNxsYGVwKpmkSYnOXlZVy4cAG5XA7pdBqJRIJb\nC5WmJMRiMesfKBQKHDhwgJMLgUCAixcvIhAIMBkTTfbQ91KteppMJpFOp2E0GvHoo49uIhi6ffs2\nc9Ynk0kYjUau6NHkUKV15XI5T/F86lOf4veUyvJLS0uIx+OYnJxEU1MT8zLQ1MiDWt2DpOG/b6ur\nqyuV/RwAGO2oVqvR1tbGgBmVSsVlhNbWVh7Yn5+fx1/+5V8yino7IydCiODHH3+cL1AA/HOz2czB\nwnvvvYdnn30Ws7Oz2zo/yrg1Gg3PaTY3N+NjH/sYR4tyuRxarRYqlYrnDb/xjW/gl7/8JYN9trvo\nieBFo9HgIx/5CNxuN4aGhmA0GpkFiTJ5QjGOjo7ixz/+MUZGRqoCnEhExOl0Ynh4GJ/4xCeg0+l4\n1InAWsViEdFoFN/85jfx6quvwuv1VszKqFeo0WgwMDCAEydOoLm5GW63GwaDgQMhAMxPPjY2huvX\nr+OXv/wlX/Zb16aeLAFr/uRP/gROpxONjY0Qi8VcdiUwExE6BAIBTExMwOPxcKtgu2emoKyzsxPH\njh1Db28vI+qXl5dx9epVjuJpNG1ubo5Rw5WqOHTe2tvbMTAwAL1ej4aGBmSzWS4hBoNBxONxiEQi\nKBQKZl6rRgFbjnynaN1ms3HmRFzw1CqibI16b7WWkqnHTmQ1JDVIdLapVIrLy7XeI+WtIrVajf37\n928iv7l9+zZSqVTVz1/peQlJ7XK5uM0QiUQwOTkJj8fDAja1jAHSulRpIYR0X18fdDodVlZWcOfO\nHbz22mubyv61OH+qdgwNDcHlcvGI4draGubm5ph5j8CtALZ9L7YaAQqNRiMLZJR/V/Pz8zySRs6T\nKpPV9looFHLgTtXAXC4Hr9fLrR8KMIHfBOwUXFRamxj1mpqamMBobW0NiUSC36tyilMCrRGpUrVq\nCwnpUBnb5/NtwsiQj6CAloRy0ul01Xeagu6DBw8il8txkEwMZ/Q8VNmh6SRS86O7s2w/rpdKpaGq\nXyweYkdd7vxodu/cuXOsHFUoFLismUqlcP36dfzsZz+rOuNLFwRd+gcPHkRPTw/6+vrQ0tICs9mM\nQqGAZDLJEeI777yD2dlZLg9VeH4u2ZhMJjidTnz6059GZ2cnnE4ntFotZygejweTk5P46le/umNk\nT88rk8l4xKS3txc9PT0MGCEQ0ptvvokbN27gnXfewdzc3I7AHiq7KZVKWK1WnD17Fm63G01NTUwc\nEo1GMTs7iytXruDnP/85BxU7gZEkEgkTV9BM5eDgIOx2O5eNXn31VYyNjSEYDCIcDiORSGxqZWy3\nx1Sup0tIr9dzVgYAoVAIPp8Ps7OzTBVbXlKv9My0xxRQ0SwyOfdIJMLZM2Wb1DvdqQRHFRwKqqRS\nKb/QlJXQ56NSea162uUOm9o0FAyVf176t3bjUMufv9y50nqUKe129pueGwCPQ1LwRkIqD/Kc9NmJ\nK5qAQnTx0ve027EYsVjMz0mjT0QyRAyDdGZ3QpCXP6tIJOK2E7GOhUKh32pPlK+709p0vsqZ7qgl\nUO5Igd9UkmrdE6rslTt3AlGWK7QRk6FAIOB3ptJz0/cllUr5fSs/p+Xr0ntE2elOZ49AwQQgpWSo\n/AedbUr8yImXSpXHDOm7o+pueWm//HNSm5MAy4Sq3+bd/p/pqP/r/ze9dHSYjx8/jp6eHtjtdiST\nSdy4cQNTU1MMXKDxpWpOhNaWSCRc8m1ubkZbWxuGh4cRi8V4bOr8+fM8d1otE6EDSpmoVqtFT08P\n6uvr2cHSnPfly5dx584dLvXu1MuiS5jQoBqNBnq9ngf/JRIJvF4v3nnnHSYBqGW2t/xyJ6AWOROa\nX4xGo/yCUyZSi9Fzl/f56dfKS6vVHPNOz77VaI2H4Tz/b7St5cDfdZ/LA/JyR7RbR7p1TToblElV\nAijuxsrPMDmq8gv/QW2rQMODBCjb2XYtk9/XurWsVeufe5B/u9x+n5+pfL9+n+vSeaxytv/nOmoy\ncib0c61WuymqIoAXvaC7Idrf6qjKS8j0g6I5oLaSFq1b/kz0b1BW8yAZCK233f/vOag927M927OH\nz2oMWP7nO+o927M927M927P/xVaTo969VM+e7dme7dme7dme/T+zPUe9Z3v2v8weZE5zN2v/d6+/\nZ3u2Z5vtoZuj3rM9q8XKHUY5GInwAL/LuoQYJY1hmj0m9OiDrE+jVGKxGG63G1KplCVESWGtVjnH\n7Z5ZKBTyyCIJXpBIQDAYfGBsBD07iRwcPXoUpVIJU1NTmJiYgN/v/52BWkTGceLECZw9exYffPAB\n3njjDczNzVUVtqjFRCIRbDYbnnzySRw/fhw3b97ECy+8wNS2O4mIVDI6fx0dHdi3bx8OHDiAYrGI\nK1euYHR0FKFQiHEpu1mT/kuTE8TwV1dXh/n5efh8Ph4T3I0RXobQ6wT2BMCys7Uoq1VaVyAQwGaz\n8XtCFMSpVIo5/HdzTghMXM7ut7Gxsek7I1KpB3lehUKxSRo3GAzyO7gTWVSldcs14E+ePMkqh8Fg\nEIuLi0xl/CD2UDrq8kt4O+Ri+XgHHYxaDsLWbGC7kYrt0J1bn2M7KweRlYPQyp+5/N/fzTOXI2PL\nka3bfS667He6IOhyL/9BLENbOaZLpRKTcNSC1BYIBJvI+pVKJTo7OxEKhXgEgghDCoUCv8w7oV7J\n2ZEykM1mQ0tLC8RiMRKJBLLZLJLJJKamppgdqRrFZ7kR0Y3BYMAjjzyCvr4+dHR0IJlM8kz23Nwc\n5ubmMDExsePIV7nR2IzBYEBLSwu++MUvAgBfPK+//jomJyfh9/tZNnA3Roxtra2t+MQnPoHGxkYk\nk0lMTk5iYmICq6urTFjxIGNPpHjV39/P/NnlFKUPsi4ZMdW53W4cOnQI6XSaqWy3oqJ3azTiOTAw\ngOHhYdTX12NkZIR//0EDunIGsP7+fnz84x9HY2Mj5ufnWef9Qdan95AcyZEjR9Df388TI3fv3uUR\nvlqtfN6eGA6JNVCv1yOfz2N1dRWxWGzXwRyJwkilUua0J3IqIuKZmZnB+vp6zWe6/HltNhusVita\nW1vR1NSETCaDZDKJeDyOWCyGy5cv17wXFJjIZDJYrVYMDAxgaGiI74/p6WmEQiEsLS3hzTffrMo4\nuN0+6PV69PT0wGazob29HZ/85Cc5UPN6vbh8+TKee+45lozdrT10jrp8xlCj0TAbjFQqRSKR4Png\ncm3gcq7uaiMexGxFMoZGoxFqtRoKhYKH1iUSCatJpdNpPnTV9IKJQKSckKWnpwcGgwHRaBThcJj5\naePxOPL5PI99Ebq8GqezTqdDc3Mz1Go1j3tJJBKEQiEmeR8bG0M2m2VVLWLuqfTMEokERqMRFosF\ndrsdJ06cgMvlYpKEqakpJJNJLC4uYn5+HoFAgAkDSEWq0l6IxWLs27eP57LdbjcGBgaYzGBlZQXv\nvvsu7ty5wwpPPp+PZwwrkalQAHD27Fm0t7ejs7MTzc3NTBZBUo/f/va3mduaxu2qBRcUtBBhzeOP\nP47e3l7o9XqUSiU+d7dv38aFCxewtLTEMpe1OCgiP7Barejs7ERHRwcTqhC/PJ05osvdjeMTi8VQ\nq9UYGBhAX18fE/i0trYiFotVVRnayYjsYmBgAGfOnGGnQYQcv6spFAq43W48/vjjaG5uxnPPPYf3\n33+fJWsf1Og77ezsxJkzZ9DT04N4PM4qdDvxDFRbl864VqvFxz/+8U0qSbsN4srXJQ4CYk189NFH\noVQq4fV6EQgEmO1wN2OS5XO/drsdBoMBzf9FQkSB7Z07d6BUKpndrtZ16VnNZjOPt5IWQC6Xw82b\nN5FMJuH1emty1BQACYVCDu6JFMZutyMUCjHZya1bt/Duu+/WdEZoD6RSKcxmM6xWK55++mk0NzdD\nr9cjHo/DYrGwSuHCwkJNjprOmFgsZhbGw4cP4+DBg7BYLEgmk6irq0NfXx+Wl5dZuW0n5cHt7KFz\n1MSV3dHRAafTCZ1OB4fDAbVajXg8jng8jtXVVZ6b9vv9WFtbY5lK4ubd7jCTYD3R/w0PD7OMYTab\nhdfrhdfrRSQSQTAYZNF6yvoq0SSKRCLs37+fVWmcTieOHj3K4h6pVAq3b9+Gz+fD3NwcSy+ur68z\nwUGlvTCZTOju7sbp06fR2NgIl8vFcnqkWOTz+QAAs7OznBlmMhkWSN/O8en1erjdbuzfvx+tra3M\njw3cJ56w2WwIBoNwOBwsL0ovfKFQqEiVSE7p8OHD+xfFLQAAIABJREFUrDFst9s3CWuQBGOhUMDY\n2BhTupLwQbWzQSIApHtbKpW4/EovuMvlQjweh0QigUwm25EIn15kUsWSSqWIRCJYXFxEOp1mMRK7\n3Y729naMjIww3WUt0TFdPlS1+OCDD5gvnRS+2traMD8/v+sskp7dYDCgra0NAHDnzh3O7ujfpT+7\nW+chlUrR1dWFU6dOobm5GS+88AJu3brFim+/S5tBIBDA5XLh3LlzOHbsGNLpNMbGxliJ7ncZZSQS\nm8985jP48Ic/DACYmprCzMwMUqnUrqsAVLGiMU6VSoWenh4MDw9jbW0Ns7OzrC+/m/ZIeaWNOMRt\nNhuGh4cxMDCA5eVlRCIR3LlzZ0fO+q1GtLsqlQoWiwWHDh1CW1sb+vr6oNVqMTo6ikAgAIVCwW2e\nWozOs9FoRENDAwYGBuByudDf349MJoNYLIZkMomWlhaEw2EsLS3VvG758544cYJ1swEgEAjwPdLV\n1cXSpbUYBVakfa7T6ZgpktTWKGgcGxuraU1y1BKJBA0NDWhvb4fFYkE2m8XKygpmZ2eRSCTgcDjQ\n1tYGm83Gqoq7tYfKUWu1WnR2dsLtdsPtdrOM3sLCAuuxqlQqVj+5efMmDAYDTCYTZ6vA/2XvzYLj\nPM9zwefvRu/73uhudGPfQYCbuSuUh9RiWV5SzlQpVZNETvlUUnMxyUV8ZqZyk4tTOVWpnItUxbZ8\nNE55Gc/YsmXHi6TIEi2KoigSBLEQIHagsTUajUYv6H2fC+p91aCB7h+0fYZJ4a1SgQUBH77+/u9/\n1+d93v3T1GazGc8++yy6u7thtVphsViwtbWF6elp5symsYk0zcZqtSKTyWBnZ+fAiMxisaCtrQ1P\nP/00rFYr1zyuX7/Oc66lUimnbCgClsvlNQcZEL1nc3Mzent70d7eDqVSifHxcSa8J9pJclh2d3d5\nsANFe/spDLVaDZfLBafTyenut956C+FwGKFQCFKpFDabjaPUQCDAgyrIwB7ksFAqLBgMQqfTIZvN\n4t/+7d8wMTHBmRCXy4VKpYJgMIhUKsU0oLUyFpT+z2azzH+8tbWFeDyO1dVVZm+juyCRSGA0GjnL\nUksoo7Gzs4PZ2Vl84xvfYO7ofD6Pnp4e5rmuVCqwWq08CUyM0DjMVCqF8fFx3Lt3j++By+XC888/\nv2foipjxi9Wi0+ng8Xig1WoxMTGBH/7whygWi9BoNDCZTDCbzTxk5DC1QkEQ0NHRga985Svo7u5G\nNBrF66+/voef/nFS3/Q81Wo1/v7v/x5tbW1YWlrCN7/5TSwtLe2ZnHdYISNit9tx9epVfPGLX4Tf\n78crr7yC999/XxQn+X77pT2bTCZ4vV68/PLLeOaZZzAzM4Pvf//7mJqa2kOOJMYpogiSiJ3OnDmD\nU6dO4cyZM7Db7fjoo48wNjaG7e1t1hU0IbCeI0Bpf7vdjuPHj6OpqQm9vb3Mqz4zM4NIJMI0nqlU\nCvPz86LWValU0Ol0+MxnPgO3283879euXUMsFkMymYTT6cTg4CBP6pqfn697Hmq1GkajkUd8GgwG\nBINBfPjhh1hcXEQkEkF7ezvPpnY4HDVnGlTv2WazweVy8SSxt99+G4uLi1hZWUEikUBHRweeeeYZ\nPPXUU3j22Wfx2muviVpXo9HAaDQCAPx+P+bn5xGLxXjamUwmg8fjwX//7/8dFy9exLvvvstzCA4j\nT5ShpkEcBoOB5yXPzs7y+Lh4PA673Q673Q6VSsW1AaJ5rHWBaTgCpRnT6TTu3r2LtbU1hMNhKBQK\nHDt2DBKJhOlCadZuLSNCfLTRaJTp8La3t/GrX/0KoVAIGo0GTqcTJpNpz8zkaDT6G7yzj65LnM2b\nm5sIBoOIxWJYWFjA6uoqUqkUHA4HDw7JZDL8+YjPt9ZZxGIxbGxsMDXf7Ows1tbWsLm5CbVajaGh\nIQiCwNNg6DMS4GI/IcaxVCqFtbU1yGQyrKysIBKJ4P79+1AqlTzzWaFQ8LSaetSRZFxo9ChlPciY\n0qD2YrG4hyGOQFpiFDOl1Eh5EU0krUep43K5DKlUeqioic6F0tBUlqBhEdlsFrlcjiOgwxoS+twb\nGxtYXV3l7AdRKAKHZ4uin6eSSzQaZQeDDEZDQ8Njo7QFQYDFYoHJZOIBNTRh7VE8x2FFoVCgqakJ\nly9fRjQaxY9//GNcv34du7u7bMgfl1XMbDbjwoULDBb69re/jYmJCS7bNDQ0iKblpM9I5YXOzk5c\nunQJdrsdGxsbeOONN3jQEBl0Mc4hpear5yVQ1Ly+vo7Z2VmEQiEuA+p0OthsNlGfXy6X850qlUqI\nRCLQarWIRCL4+c9/jkKhwOnvU6dOwW63i1qX9l1d3x4dHcXa2hrW19exubkJjUYDmUyG3t5eqNVq\n0Q4RzX+PRCIol8uYnp7Gzs4OEokEn6dWq+XMrFqtFrVfYlokjnpyhInREQAHQvT8/kOAyfL5PBKJ\nBKLRKKxWK+LxOLa2tuD3+7lmRbywcrkcdrsdsVgMOzs7dZGFuVwOgUCAp1xRJEZRHV02QRCQz+eZ\neJ888Frrbm9vw+/3M3hqfX2djTINt0gmkygWixxlElf0QWvTmEFCUHZ1dSEQCHDNVavVwmg0Mtes\nRqPhGd31Bg9Q9FgoFNjZSafTKBQK0Ov1XBpIpVIMFNnY2GBA2UHrVioVdhAooqYJTmazGY2NjWhv\nb4dOp+PInIxpvZQeRb1kQGlGNAC0tLSgq6sLTqeTMwrl8sMxomLQsbQ2AWvoc2o0Guj1ejQ3N/PM\nbKVSWdNZqXUu+Xyep/goFApYrVY4nU420Llc7rHSYiSEg6CJc0qlco+xPoxUKg+5kD0eD2dtxsbG\n9tT7qkGTh5WGhgZ0dHRAIpFgdnYWN2/eRCKRYIAo8HhgL6lUCqvViueffx79/f0YGRnBrVu3uNRA\nPOiPQ1/b0NCAc+fO4cUXX4Rer8fW1hbGxsaQyWT2gKCqneR6d5pSuTabDWfPnoXFYmEjtbi4yClx\nlUrFXOv1hO4zUS/TsJaVlRVMTk5iYWEBu7u7PL7WYrEgHA6LepY0EZBGRdLMZ9LDpVIJqVSK678r\nKys8nbDeWVM2gozo1NQUZ6LofaeRvmq1WvQ7SGN0acQqvYsUIFG5kIaD7OzsiNovDfGggK4ayEsO\nWKlUgl6vh1Qq3TPJ77BO4hNlqMvlMkKhEI9Ko9oaIUBp8lQ2m4XVaoXVauV2A+DgUYbAJwjb7e1t\n9PT0cKSk0+lQKpW4RUGtVkMQBDa69VosSqUSotEoNjc3MTAwAKlUCp1OB7fbjXg8DovFApVKxUaR\nPN16dKJ04WUyGba3t9k7LhQKPImL0vQUAVKEU6+2R6M9SXkpFAp4vV40NTWhWCxyLYVSNPQc6qXc\nqlPtlN632WyQSCTo6uqCw+GAxWJBsVjkGmr1nmoJGQX6bKSEKCXd2NgIpVLJz4PSsWLrnPSz5XKZ\nkbEAGHAnl8vZiB+2VkgGgQy0Xq9nLIbJZGLQYvVEn8MIDRUgZU4jVWnCGn2/untAjAiCgObmZsjl\nckQiEfj9fuj1er7HNDbwsEZPEAQYjUacO3cO+XweH374ITY2NtgoSSQS1gGHBdap1WocP34cTz/9\nNCwWC9544w0GFhJVMO35MHdDKpVCq9XiC1/4Atra2rCzs4O3334bmUyGB2EoFAoGBIo5Z7rTVqsV\nHR0d8Hg8CIVCuHPnDm7fvs0YAYPBAACcxRATlVHNlVqEEokEHjx4gLW1Nc7+abVaNtRiQVnkdBaL\nRfj9fh55Sl0cdE7kNJM+F7M2IebJiFbfKUEQYDAYGKNDxlfsnsmZoowNOUnV6H2TyYRUKoWlpSVR\n6wKf6MRHA4LqVq3jx48jGo3yGNTHkSfKUBeLRUQiETx48ACnT58GAAYVVM9EpfSj3W5HMplEIBDg\nA6hlqKenp9HS0oJ0Og2tVosXX3yRDT2NISsUClAqldjd3cXU1FRdwEy5XEY6nYZMJkMqlYLJZILP\n58OFCxd4fmoymcTW1hYEQUAwGIRSqWSg2kF7plRvNBplwFtrayvOnz8PrVYLiUTCI+wymQyCwSBm\nZmYwPDwsyjOmwe9UV75y5QoDLPL5PA/4IEdhbW2NMwy1hIw1DRGhweqdnZ38IpZKJXR0dCAajcLr\n9eLGjRuYn5+vq9yqAWNmsxn9/f1oaWnhzAKBAgcHBzny3dzcFFUTqkZw+nw+tLW18bB3StOTwqyO\n+sQIKQOZTAaz2Yzu7m7eU7lchlwuZ8dmv5bBevumOehUbyyVStBoNFAqlbDZbIyzIMdM7J5VKhXc\nbjcikQiWlpag1Wrx1FNPccdAsVjEz3/+c1HZlup1SXlduXIFt2/fxtTUFNxuN06ePAmv14tSqQS/\n34+pqSnMzc2JqskCD5/L5cuX8bd/+7ewWCy4c+cOlpeXGTxEGbi5uTke+VjPiFD2zu124+rVq+jp\n6cGHH36IH/zgB/D7/ejr64Pb7YZer+f1Hu1COUhMJhPa29tx4cIFvPDCC/jXf/1X3LlzB6FQCBKJ\nBL29vZxxkUqlmJ+fZ2fgoL3Sfs1mM3p7e9Ha2opEIoGbN29ibW2NjX9jYyO6u7thMpkQDAbrAr6o\n+4bGnlJrHtXlAbBjfvHiRQwMDHA/fK17V90vTWvS+0XGj4zq1atXcerUKTQ0NOAXv/hFzfea1qVM\nB5U8GhoaODqn/2e323H69GmUy2V897vfxfe///26Z0FBEekBpVLJOogCP7fbjVOnTuHUqVP42te+\nhl/+8pf/MQw18NBYB4NBLC8vQ6PRMPCI6o4KhYKBFZSupoHx0Wi05gu9u7uLpaUluN1u5HI5tLa2\ncosBtRnkcjkeeTYzM8PtVLUOuFwuY3NzE9vb25BKpWhpaeGUdENDAxMW0BhMlUqFmzdv1u0vpCiP\n6slqtRrRaJR/h5Q6IXNphGImk0Eqlaqp8Kk2mkgksL6+zrVNjUbDqFgiumhubsZTTz3FgJPqIfYH\nrU3EAWTcaBwpjd7z+XxwOBxsHG/evCkK+AV8kuJdXV1FNpuFXq+HWq3ml9zj8aCtrQ35fB7RaBSx\nWExUvzpFnYlEAsFgEJVKBblcjvdpNBphMBjg9Xq53CImKqvuVS+Xy4x7oNGaer2eDTaNZxRzDtUg\nO3re+XyecRFarRZ6vR5KpRJGo5EdUTE99pTGjcfjTDJB7WukqLe2ttDW1ranc6He2uRo0XsdDodh\nsVjgdrvh8/nQ1NSEYDDI6fulpaWaLZck5AydOnWKJ8pNTU3taRPUarUYGRlhspnq4TsHCfWo9/f3\n4/z581haWsL4+DijeT/3uc+hsbGR0dnT09NIp9OM7TjofBsaGtDY2IgzZ86gt7cXmUwG29vbyGQy\n0Ov1cLvdeO655/ZgArRaLdesDzpbimadTid8Ph9kMhmy2Sxn3wisNTg4CLVaDYVCIcoBl8vl6Ozs\nhMlkgsfj4c9HbUiVSoX7kx0OBwRBwNjYWN02JyoBGY1G+Hw+7OzsQKFQAABnVfP5POx2O06dOoVc\nLof79+/jvffeq5lBlcvlnCIHHhpSrVYLlUrFuqhQKMBms+HcuXOIx+OYm5vDtWvXuK3qoHshk8nQ\n1NSEUqnEGKGWlhZud8vlcrBYLPiDP/gDWK1WrK+v46OPPkI6na6551ryxBlqmq+8sbGBpqYm9lQq\nlQo0Gg3y+Ty0Wi3i8Ti0Wi1KpRJ6e3sxNjZWtxZH4KP5+XkYjUZm6CGEcPXMVpVKhRMnTsDv99dE\nZ5PQgybkOdVRdTod12AbGxuh1+vhcrngcDiQTqeRSCTq1n0zmQzu37/Pzgl9P51Os5d4/vx5NDY2\n4tKlS9jY2MDa2lpd5ZbP57G+vo58Pg+ZTMZrymQylMtlZtEiMoqlpSWEQiEG7tUSqvdXKhWsra3h\nzp07aGho4Lp9b28vjh07BqVSif7+fuj1+pro+mqh3ut8Po9AIACdTseZkIaGBly9ehUqlQqdnZ17\nMgy11q2e1JbP5xEOhxGLxaDVarkEkUqlGNk/PT39G3OfDxJKFZNDEY/Huc0tk8lwxEHpfAKpkBy0\nPj17g8EAnU7HJRWqZWYyGY5Uqu+6GGIZMnxKpZKNWWNjI6RSKWKxGLe82e12dnDElBoI7d3W1saO\nRVtbGxwOB/R6PSKRCKRSKXw+H/L5PH+mesqNar1DQ0NcQguHwxgcHITFYoFSqUSxWERbWxtyuRw0\nGs0e9PpBolQqYTabcerUKXR2dmJ0dBQbGxvwer3wer1wuVyMlyGdQpHcQUIlkM7OTgwODsLhcPCZ\nulwuWK1WHsFrtVpRKpWwtrZWN5Oj1+vhdDpx8uRJWK1WuFwu7O7uspEaHByE3W5He3s7mpqaUCgU\nMD8/j5WVlZp4A3IqLl++zPPayUkmbIdSqeSMiCAICIfDmJ2d5ch2vzZRmUyG1tZWtLW1cdbN7/dD\noVBwiYJKlH19fXA4HFhYWOD6PemT/aSjo4PXJVS20WiESqXi91sQBHR3d6OpqQm//vWvcfv2bayv\nr3N5YT/92dDQgO7ubly9ehVGoxFdXV3Y2tpCa2srcrkcQqEQ1/9dLheWl5cxPz+Pra0tjsIfZy76\nE2eogYde+fj4ODQaDaanp2G322G1WrG0tITd3V1uHcrn8wxSMhqNe+bGHoQe3tjYQD6fh8ViQSQS\ngcViYWNJToHL5YJer0dLSwvsdjt7nLWUBaXUtra2MD8/j7a2Nm7ByeVysFqtHEU2NzfD5/MhHA5z\nKriWpNNpzM3NYXV1FUajEUqlkkERVJskkhGKJj/88EOuOx2050KhwJEh1bqpP5YU6vr6Ora2tvD5\nz38ePp8PExMTe6L6/YQiPACsKIgEggxJOBxGIpHAhQsXYDKZoNFoGGxRa13gE2AgITSJJEQulzOx\nQ39/P9RqNbRarSglT0qQ0nmURo9EIhzd0ctLNV+qz9U6YzKm1UjScrnMrXTVSFAaMl89t7vengmw\nQr3UVO/O5/NQqVQolUrcI0uZqXpnTBE19bdrtVpotVrONBCgiNLr9HtiogQillGr1dzhATx0jlZW\nVlAqlfbgT2jftdam/VIJgOqXFosFUqkUW1tbrJibmppE96oLggCNRoO2tjYcP36cuRr0ej23vkUi\nESYlIseNMmW17oRer8fg4CAr+FwuxxGfz+djg0/Ay2QyiWw2WxMcSZiNoaEh2Gw2rhsTYJSyIZ2d\nnVAqldjY2OBsSC0sAJWDnnrqKXZ4JBIJwuEwO0jNzc0YGBiAIAjIZDLMwFVNVfqoqFQq9PT04OLF\nizh58iTS6TTXiclRbGxs5BIMGUFqIa3VdXDy5ElcuXIFx44dQ6lUQjweZ8dVKpXixIkT0Gg0DN4L\nBALc5SGXyw/MMGg0Gpw/fx5/8Rd/wRmZ7u5u7ipqbW2FRCJBY2MjNjY2IJVKsbCwsKej43HS30+c\noSbAUDgcxu3bt6FWq+FwOFAqlRAMBjl1p1Qqcfz4cVy6dAktLS344he/iA8//LButJDL5bg1qlKp\nYGlpiVOpqVSKwTctLS34m7/5G/zpn/4pvvOd7+CDDz44cF1SftV0mLOzsxyh53I5LCwsYHx8HD6f\nD5/5zGfwZ3/2ZwCAN99880AmLlJApIyp9WlnZ4dTd9QSAjysebW1teFLX/oSvvOd7xyo4MjLJWVL\nqSYik6E6m1QqRTgcRiaTwR//8R/jqaeewtjY2IFgC3q5qFZKylgqlbIHXq0Q7HY795AScG+//VJ6\nnBjlHA4Hp41DoRD3OwuCwMxwbrcbiUQCjY2NNRU91aj0ej10Oh00Gg2ampqY9W13dxflchkejwcS\niQQulwuZTAYGgwHb29sA9jfS1bXCpqYm6HQ6mEwmNDc3Y25uDolEgvdsNBr3EL5QFHnQ2oRSJRS6\nzWZDX18fn+Hk5CS0Wi0sFgvMZjOXQsjZqPV+UARDRqf5Y+a3XC6H6elpBINBTlNbrVa8+uqrooF7\ndJ8rlQp0Oh3sdjtaW1uZ0auhoQFPP/00WlpaEAwGMTs7y3emllT3IhOIhygyZ2dnuc/c6/UiFoth\nYmICm5ubovrVvV4vurq6OMIjo0xObWdnJxMF3bt3D3Nzc/xuHrR2Q0MDzpw5g56eHmZ6i0Qi6Ozs\nZMfb7XZzG+fMzAxGRkawublZszxksVjwla98BTqdDqFQCAsLC5DJZOjo6GBmPLvdjmKxiEAggIWF\nBSwuLrIBPMjoCYLAQcBHH32Eubk5FAoFnD59mlPpKpUKgUCA38VoNLrHmdxPlEolnnnmGXzpS1+C\n3+/HP/7jPzKfQG9vL2NNAOD+/fuIxWIQBAEtLS1MJ3rQu/fSSy/h0qVLWFxcxCuvvIKdnR20tLSg\nv78fXq8XjY2NKJfLGB4eRigUgtPpxJkzZzAzM4NwOLxvtkUikaC5uRlf/vKXUalU8A//8A+Ym5uD\ny+XCF7/4RfT09MBms0EqlWJ8fBx+vx8NDQ3o7e3lZ7y1tfUfw1DTC09Qer1ezz2KyWSSyd5lMhk/\nKDI49XpbyRDk83kUi0UGQhB0n7inKf1OaFq1Wl33cOmSP9qCRfVtMmLAQ6+MIrJaipMUG/2s2Wzm\n2nI1wIYUn8ViYTa0egqZ0pqEDNXr9dwvTmA9QoYSMxDhAQ5SnPRciIGMWh7C4TCWl5c5omtoaEBb\nWxsuXboEl8uFXC6HWCx24LMjo0cIWJvNhvb2dkQiEb4DxCzm9Xpx+fJlzoQEg8G6aSaKeC0WCwwG\nA7fm0V3wer3o6+vD0NAQOjo68ODBA1GDBsiBM5lMMJlM6Ozs3NO7abFY4HQ6+eUmQ0rGrNa9oPUJ\ntLK7u8v3g1Km1GpH9LJi0m3Vf1MQBGxvb8Pr9cJsNrND09jYCI/Hg0qlgtXVVa7Vi4moKatCOAa7\n3Y54PI7u7m7odDo0Nzczx8Ho6KiodemsSBeoVCrYbDYkk0l+LywWCwRBwOuvv47x8XHmSqglgiBw\nS6hCoYDRaITX6+Xo2WQywWazYXt7G2NjYxgdHWUcQL1sCBGSWK1WmM1mZsmiGr5KpcLExATm5uaw\ntrbGxEy1HAC5XM6GsbGxEQaDAdFolKmYK5UKVlZWsLGxwf/5/X7OqtV6/wg70dLSAo/Hw8+OEPqx\nWAzvvfce0uk0NBoNUqkU65ODnqEgPKReBh7eiytXrrAu8ng8DICcm5vD4uIiPB7PHuKXWo4FAUwL\nhQLsdjvzcLe1tUEikWBychJTU1NYXl5mfJJOp+POmoPWpmxIJpOBSqXCqVOn4PV6cfz4cUilUvj9\nfhSLRbz77rusV6xWK9rb2zmD8Th0vk+koaaXWCqV8gWiy08gJ+qpJZIRMekscgKq16gGBFEUS0qT\nwFnb29t125KATxQnAbIoLURRL1HjNTY2MrBNDEhGJpNxzy21QhBCklrJuru7mTlrdXW1rgGhfnKz\n2QyHw8G1WCIckEgksFgs6Ovrw6VLl5DNZjE2Noa1tbWaKT2KbEiZO51OWCwWBgUJwkOii5deegkn\nTpyASqVi7uxaqTdKw1osFgwODsLj8TDqHXiYRuvo6MDp06fR3NyMeDzOTEG1hNY2mUxwOp1ob2/n\ntCGBF/v7+7mW1dDQgO3t7bqThkgZSyQSmM1mtLa2wuv1MtkOkb5QbXNzcxOLi4t1aVRJiDCFnCmF\nQoFUKgW73Q6Px8OMSYVCgQFOlOKsJdVGr1AoYHFxkVn7BgcHUalUGGw5MjKCQCDA3RhihEgyRkZG\n0NbWBovFgoGBAZRKJQZ5vfnmm3jzzTexvr4u2rkggND6+jrX+ltbW5mAhPpy33zzTS7diHEsIpEI\nGzeLxYKurq49ZYuRkRGMjY0xwrweOI3uxOzsLPNFUO2UwGSZTAazs7N4//33ub5JQUQtkUgkuHXr\nFs6dO8d0mTR1an19HVNTU1hYWEA4HEY2m8Xy8jITKx1Uoyb9AoD1EOErcrkcZmZmsLq6yr3fVF+O\nRqNMMHLQndPpdACAaDQKo9GIoaEh1s3JZBI//elPkUgksLy8jGKxyHV8mmVQy7EgbA91DVGAsLW1\nhZGREYyPjyMQCDBXQDKZZB6Kg8pDEokEVquVneEvfOELqFQqzJ1x9+5dzM7OIpfLIZVKMbc6ZStU\nKhXkcnnNZ3iQPHGGmoSMKj0MuVzORpTSc80fN9UrFAr2fOv1XdJLXT35pbqHjwgTTp8+DZlMhkgk\nwj9fS8nRfik1ZjabmUkrnU5z/eK5555DU1MT3nvvPQZLiAHgUNuXWq2G0+nE6uoq906bTCZ87nOf\ng81mw+7uLu7cuVMTdFKNFler1WhqasLAwABaWloQj8cRiUS4terkyZPw+Xy4f/8+bt26VbN3kUAS\ntKfW1lZ0dXVBIpHA7XYjm81Cq9XC4XDg4sWLe14capnY79nRPSBnhwhOZDIZ+vr6EI/HodfrOXIq\nFotYWlrC7du365IX0FnQkBaPx4OBgQH09vYyAYpGo4FGo+GIJBAI8DmIaSlTq9WMs6BzITKbSqWC\n6elppkisvr/19l19H0kJEMCyXC5jcXER8/PzPBRGDJsa1cbJiSU0fldXFzQaDYrFIhYWFjAyMoLR\n0dG6LHj77Tkej+NXv/oVisUiXn75ZcaWbG9v49VXX8XIyIgoJHK1lEolJJNJfO9738Nzzz2Hrq4u\nGI1GBINBrK6ucm8yGWmxYB4CiRLm4/jx40yL6ff78frrr2N6eppR0GLAhcTyNzY2hmKxyKWcjY0N\nzM/PcwfG8PAw9+1TFrDW2qFQCB999BED9KhVLxgM4sGDB7h//z7jUSgypV71g9amZzY2NgafzweD\nwcBZyPn5eczOzmJzc5MpaulclUol19UP0puJRALXrl1DIpGAzWbDzs4OQqEQO1y3bt3a06VAiPhI\nJIJ4PH5gur5UKuHtt9/GhQsXYDAYoFQqcePGDeYdn5ubw/b2NhQKBSQSCdLpNJqamhCNRhEMBlln\n7PfcVlZW8Ktf/QovvPACGhsbsbCwgLGxMQymlZM5AAAgAElEQVQPD2NmZoazvlarFdvb2zAajXA4\nHJiamuLOpMeJqIXDwsR/HyIIwm9sgtKdlPJUqVRcX6iG3Le2tkKn02F8fBz/9E//VJPMv7rmq1Qq\n4fF44Ha70dbWhr6+PgZ2EGtWNpvFtWvXcOfOHa69HCQUmVFq9vnnn0dvby86OjqYMzwWi2Frawuh\nUAhf+9rXsLKyUrP/tDq6b2trQ3NzM7q7uzEwMICLFy9Cp9NxKvzmzZu4f/8+f41EIjVfakKC6vV6\n+Hw+fOELX0BnZyc8Hg8sFgtzkQcCAVy/fh1f//rXEQqFOGKoFVWrVCo0NzczorO7uxtnzpyB2Wxm\noMbbb7+NN954g8c71hucQXVZs9mMEydOwOfzobW1FU6nE62trRAEAaFQCOPj4/iXf/kXZnATE6Gq\n1Wqe1uZyuXDy5El+wRsaGjAxMYH5+XmuXe3u7tbkUq9+fuS00MAFi8XCALJUKoVQKMTROZVlxLSp\nVfeGUu2eMiyCIDDYkowuUdWKed9JSVU7xpQtobUoUjoM8cuja1O2iCg9qeR12DVpvepeeHLsyUGp\nzhQcZm1ah2rfKpUKuVyOmfweZSATC6ijti9ieQMeYkSIsfDRsxKDFCbMQnWGkcpglDancyKkvhii\nHXLqqRZPd4nKevS8CIhF94Xe6VqRL2UuZTIZOyN0T6u5MSjlTORO9HkOElqTOlio9EP7oTIc6RWi\nEM1kMqxTD9ozBXl0Z6v3TGdIZCeUNSB9TPq+SkYqlcqpAz8I/d0n2VBXg5OkUilcLhdaWlrQ3NzM\nAJlisQitVov5+Xncu3evbs2XvpICpb5Dr9eLZ599ltMrKysrnNYh8FkthU9KktKEZPjb29vR3d2N\nXC7HALn79+9jbm6uLtqS1qV+Zqpp+nw+XL58GR6PByqVCmtra/j2t7/NgwHE9PfSmRKTWnd3Nxob\nG7n9yGQyYXp6GgsLC5icnMTGxoZoJVfNykP0h9QiI5FIkEqlMDU1xQhlsdENGSeixqQXmIwRoZ0P\ny2b1qPGgf9PnFUMHKfZvVMuT8O4dRh6n/7PWWvut+9ue8aPnfFjD/D9iTRKxbXiHFTHtiL9P+X39\n/f2ew+9iTTLav4s7WL1u9VfgQPbFf9+GGgCnxD7+mT1RTiaT4eEDlPKuF5U9ujYZQQJUqVQqBkFk\nMhmub9UCROy3bnUPKtVsyAusjpbEvvDVCHA6D0rZU2pKLFnIo+tWf63+92H3WGv96vWO5EiO5EiO\nhOXfv6EWI2RsDxtFiZH9mvSP5EiO5EiO5Eh+RyLKUD+xYDKxUm+4xW8jR0b6SI7kSI7kSP7/lrrz\n7wRBaBIE4deCIDwQBGFKEIT/7ePvmwVB+JUgCPMffzVV/c7/IQjCgiAIs4IgPPv7/ABHciRHciRH\nciT/kaVu6lsQhEYAjZVK5Z4gCDoAIwC+AODPAEQqlcp/FQThfwdgqlQq/1kQhF4A/w+ATwFwAXgH\nQGelUjkw7P1tUt9HciRHslfqEaY8rhD4kNCuv6u/QaBAmthFgyRSqdRvvX41ylmj0aCjowMzMzN7\n+Bl+G6EzodZAIlIJBoO/VaavGhVfPY9gc3Pzsdd9FJNSDaQizorfZr+PotRJfluMy6Pr/i7uBH2t\nBo3+tutW918/CiKrsfbvJvVdqVQ2AWx+/O+EIAjTANwAPg/g8sc/9m0A7wH4zx9///+tVCo5AMuC\nICzgodG+Ve9vHcmRPAnyqIL4XSFzSTFQy081T8DjEPU/ujYR2VBLCv0Nosv9bYR62J1OJ5RKJdLp\nNLa3t0VNJqu3b2ITc7vd+MM//EOsrKzg1q1bWFtbq0sgUm9taus5ffo0Ll68yK1w4XCYB+c8biuY\nIDzkDne73eju7mYO/omJCcRiMe5CECsEFqXOBhryIZfLIZFImHeeWq4OI+RQaDQaaLVaBqES9wOx\nNB7mDtJeydA3NzcDeNi+ReBZ6rN/nDMmQh+z2QyPx4Nyucxtl8Rcedg1yWnT6XRoampCZ2cnFAoF\nlpeXsb29jUQigVAoJGo+d7VU0xwbDAa89NJLzA3v9/sxOjqK9fX1Q69LcqgatSAIzQCOA7gNwPGx\nEQeAIADHx/92A/io6tfWP/7eo2v9JwD/ad9NfawUDAbDHuax7e1tnm5Fh0KKiJrwa3lGtK5SqWSC\nC/LgQ6EQotEot1nR4ATqM6xH6mAwGJhuVKVSob29nYkRiAmIemTpAlMvar09E0+vTqeD0+mEy+VC\nQ0MDgsEgj1VbX19nEv7qXtyD9kwj8YidbGhoCG1tbXC5XEilUpicnEQikcDGxgbP6qZ2snpjP6VS\nKSswu92Ozs5OPP3008jn80xq8NFHH2FtbQ3pdBrJZJKHA9TCHFDEdfHiRXi9XvT09KCvrw8mkwmJ\nRAI7OzsIh8P44Q9/iN3dXSZFoFGBtfo5aRxpZ2cnvvSlL+H48eOw2+2oVCrcP03EBlNTU9zzKka5\nEQlOU1MTBgcH8Zd/+Zc8gzqfz2N4eBgffPABFhcXmS/5MJGDSqWC2WzGpUuX8MILL6C1tRXpdBq7\nu7u4f/8+vve972F7e5sZxA6jNIkq89KlS3j22WfhdruxtraG999/H5OTk3zfHjfSIWKd5557DqdO\nnWLKzHw+D7lcfihDVy1kPFpbW3H16lWePf/P//zPPI5QLDtZtZDBI3KZP//zP8e5c+dQqVQwNTWF\nmzdvMv2sWGNKBkStVnPvrdVqxcsvv8xRdCAQwA9+8IPfILqpty7xMGg0GgwNDcHr9aKlpQVmsxnx\neBzJZBIjIyOYnp6G3+8/1H6NRiOMRiPa2towNDTE73g+n0cikcBbb72F0dFRrKysiDqL6v2azWY8\n++yzuHDhAtrb25nhK5fLIRgM4tq1a/jWt74l6izIGaSRvW1tbfirv/orOJ1OyOVypNNpSCQSBINB\n/PznP8fPfvYzzMzMiFqX2Cefe+45ZnHs7u6GRqPhziGJRIKvf/3r+P73v4/FxcXHyoiINtSCIGgB\n/BjAX1Uqld1HWm8qh01fVyqVbwL45sdr8+9SX6/L5YLX64XVaoXFYoFCoUAoFOLpPTRhhSa4ELtO\nrRdbr9fD6/UybePJkyd5bOTu7i5WVlaYND2VSmF6epr7c4lAYj+RSqXo6OiAy+WCxWJBU1MTTp8+\nzTy48Xgco6OjbKSWl5fZSJOy309hUDTQ2tqK8+fPM+EJGRAycAsLC3j//fexsLCARCLBnhxx1u63\ntlarRXt7O3p6etDU1ITPfvazPEe2XC7j5MmT8Pv98Pv9mJycxN27d3lwRDweP9DwUTRw6dIltLe3\no7W1FT09PbDb7Uw8kMlkoNPpcO/ePUxPT3OqligH99szGVMaOdjd3Y3Ozk7YbDYUCgXu1bbb7Th2\n7Bju37/PTlA9Xmfy3ol+k5ysVCrFoxZNJhO6u7uRSCSwsLDAkY0YRUG0qjSGMBAIMPkETQhqbm7G\n5ubmnhY8MUJ7t9lsPBWJjH2lUuH2wMcxokRIQY5Wd3c35ufncf/+fYTDYXZiH3dtQRB4NOunP/1p\naLVa/OhHP4Lf70csFjsUM9mja1Mv/6VLl/D5z38eLpcLS0tLGBkZ2TPc5zBrAp8QaWi1WjQ1NbED\nMDs7i/HxcWxsbDDngth2TjJQLpcLdrsdDocDnZ2dOHv2LE+LWltbOzQZDLWuWq1WtLW14bOf/Sw7\nz1KpFIuLi5ibm2OOA7FC7acul4vndHd0dMDtdiMajWJnZwcmkwktLS1YWlqqO7GNhBwgImF66qmn\n0NLSwjztgUCAh+d0dnaKXpfuMVH5Dg0NcXklnU5jY2ODA62enh5cu3ZN9LoNDQ1M8NTZ2Qm1Ws16\nmZ4ZEWrRdLzfm6EWBEGGh0b6/65UKq9//O0tQRAaK5XK5sd1bJoQvgGgqerXPR9/r64QsYfBYIDV\nasXQ0BAsFgv3MmezWeh0Ojae5HkD2DO7dL+DIM5lUorNzc08QSWXy/FsVIfDgUAggPX1dWg0Gl73\nIC5xqVTK0VJHRwdaWlrgcDh44AZxRnd0dPAgEbVajd3dXV53v7SnVCrl2a/t7e04fvw4XC4XdDod\nzzCuVvTElkOZBVLO+zkXUqkUNpuNyWM6OjqYZ5loTyuVCk/yoZQqeZ4HvSDVhC9EzuJwOFAoFDA7\nO4tYLMZpX0q7EaEN/f5BRorqdTTtq6GhgRXY8vIy810TwxGxEtUi7390fcqcLC8vMxdzKBSCx+OB\nw+Fg54p6+Q9jUIGHBjsWi+H69etYX1/nuiPNUKa05GFSeoLwkP7UZDKxs3Pv3j3s7u4y3zdlkMRS\nXFYLjXulKWQjIyOcbclms0yB+zipTZlMhu7ubpw9exZ2ux3r6+uYnJzkNOzjOAAUSZMxJeO0srKC\nt956i9Pdj7NnYjSkQQsXL16EVCrF2toa3n33XR6cITZ9XF0KMRqNaP6YErm1tRXHjx9HOp2G3+9H\nIBBAMpnk8apiygEUoXs8HrS2tqK3txdOpxMymQzRaBSCICCZTEKtVjOR1OzsrKh1q8sJ/f39aGxs\nhFwux927d1Eul7GzswO9Xo/Ozk5sb29jYWFBVP2bsjcUoSsUCk4dr6+vIxgMorm5GV1dXUwhLCbj\nUqlUYLPZ0NbWhq6uLrhcLkxNTSEcDnNWr729HefPn0d7ezsGBgZw9+7duucAPHSGnE4nTxCjNPet\nW7d4dPCpU6fw/PPPo6enR1Skvp/UNdTCwx39XwCmK5XKf6v6Xz8D8KcA/uvHX/+16vvfFwThv+Eh\nmKwDwB0xm6EXwWAwwGw2M7fs2toaU29SzaJQKECn0/G0Fkox10ohU6qGJgHF43EEAgFOQ7tcLub7\npglQALCzs3Pgy0fREtWTtFotk/XHYjEmPhEEgXl7aUwnRXr7rU2Gl5i96AWdnZ3F1tYWkskke4mR\nSATpdJoVZ0NDQ01lRNEGORPFYhHj4+MIhULY2tqCTCZDY2Mj4vE4QqEQR7rEi3tQupccpUqlgng8\njlgshmAwiOnpaYyNjQF4SK3ncDiwvb3Nwy3K5TIPBNnPkNBLQRH52toaOz2JRALz8/OQyWRoamqC\nQqHAzs4OkskkALDxrSeUpdnY2MDw8DCKxSLW1tZ4nF44HGaaQXLaxNbeyuUyMpkMotEolpaWeAxg\nsVjkwSXAJ2Q5j5NGprNbXV3F/fv3IQgC9Ho98vk8OzYHZVcOEnICiKa3UChgbGwM29vbrLCpVHQY\nqSbwOXv2LPr6+pBOp3Hr1i1+16qBPo+ztlKphNfr5bnwv/71r3H9+vU9JSyxa1fXYrVaLZcZrly5\ngvX1dbzzzju4d+8eP9PqIT+1/gadIbH4uVwuNDU1wev1Qi6X80jZ3d1dZDIZzjCIORcy1CaTCf39\n/bBYLEilUpxNiMfjMJvNnKoWW2Igx0Kj0cDj8XBkSlm9dDoNg8GAwcFBCILA87DFCNWkydFaX19H\nKBTC9PQ0AoEATCYTjEYj61Wx65ITns1mEY/HMT09jfn5eSwvL3NZTKPRYGtrCxqNRrTxp8xCKpXC\n8vIyNjc3mVt+fn4elUoFFosFVquVOebFZgEeFTER9QUA/wuA+4IgjH38vf8TDw30DwVB+HMAKwD+\n548/wJQgCD8E8ABAEcD/WgvxXS1UnyTjptfrMTExgeHhYfj9fiQSCebSdrlcDA558OABVlZWataR\nKS1OQxYEQcAvf/lLLC8vM8PZwMAAp8UHBgZ4oHgtvmhac3V1FQ6HA+l0GrOzs3j99dexu7sLhULB\noAWpVAqfz4dyuYy7d+8yL/l+axPJPY1p7Ovrw+TkJObm5rC0tIRSqYSWlhae6Uw1p3A4vKe2ftCe\ng8EgI2sFQcD8/Dz8fj+PBjxz5gxKpRIymQzUajWy2Syi0WhNkn3i/43FYrh37x6y2SwmJyeRTCYx\nPz8Ps9mMtrY25uCmiDccDvNwgP2eH9Xac7kcNjc3MTo6itXVVUgkEkSjUeh0Ovh8PlgsFuRyOWaU\ni8Vi2N7erhs90do0wpMcnWw2C5vNhmPHjkEQBB5DGI/HDwW+oWdJ06AkEgkMBgOam5vh8Xi4Xp1I\nJJBKpQ4F6Kl8zEtcKBQQCASQzWaZGpcUaiqV4rt2GMNHisZmsyEWi+HBgwc8k1mpVO4Zt/o4BtVk\nMuHEiRNIJpP45S9/iTfffJMjbbpjhzXWZEz7+/vx5S9/GcFgEN/61rdw48YNfn70fA+D9KV9dXR0\n4OrVq3juueeQyWTw13/91zzJqVwuQ6FQcAZQzJoE8KK0d09PDzKZDG7cuIEPPvgAMpkMDoeDnfXD\nMC9ShmlhYYEn9a2trWF3dxcSiQSf//znYbPZoFQqMT09LfocyPF888032dFPpVJIJBJQq9VwOBxo\na2tDa2sryuUyl3nq7Z0wLLlcjst4pMdoFvyFCxfgdDohCIJo56JSqSAYDCIajWJ8fJy5zyuVyh6u\nfBoTHIvFRK1LAUY6nca3v/1t1l/0jqtUKoTDYQ6yaBDP44gY1PcHAA5ymf+nA37nvwD4L4+zIQKE\nVc/njcVizOGcSCQAgOftFgoFJouv8zl4pBsNnSBgGima7e1tjqJpDGH1/NyD1k2lUojFYlCr1ex5\n0yhAmUyGTCaDXC7HBPTVqd9a62YyGcjlcs4UkGGjjIJUKkUymYRer98TiYmZIEYzZKm2T1O0kskk\nrFYrKyaKTKmVpdba1LYDgFOANHhgcHAQjY2NsNls/LcpNV/vjGltAKzAtVotNBoNmpub4fP5uFad\nTCaxuLjIRkRs1Fu9d4vFwqksSmslEgmeakTKQ6xBpb9Po0V7enqg0+nQ2NgIhUKBlZUVrskeFiFb\nqVR47q3b7UahUMDu7i7kcjlKpRIb/scFe1mtVthsNh5daDAY+Fk9OjziMCKTyWC322E2mzE9PY2Z\nmRnk83mo1Wp+Nx4HIUtO68WLF3HixAn8+Mc/xvz8PBtw4PFIkihC7e/vx7lz51AoFNgJJRR1qVRi\nJS/mTCqVCq9Lad9QKIT19XU8ePAASqUSOp0Oer2e0dNihd6pnZ0dDhgIRAeAHTAqvYk1TpVKhctr\nNGOe7hY5hjRAaHd3F5ubm6JBdXT3qexB94vKY11dXejt7YVUKsXW1pbo86hGuNM7QDaGAMY0T3pj\nYwPBYFDUugD4Hj2qv2hdt9uN06dPY3V1FVtbW4/d2fFEMZPRw5FIJPD5fMhms1wvJUg9AJ6epVAo\nYLPZMDo6umem6n4KiS6AwWCA0WhEOp1mxVkqlRgBTX+fjGC9uh5dAkJRk4Fwu92MxqWLR96oQqGo\nO4WK1s1kMpyOkUgkcDgcsNvtUKvV7BWSsaWBF2JqWISqpejF4/Hw+TqdTkY60/lnMhlRUQg9Q5oI\nRGMtOzs74XA4eK7s5uYmp+DrofUfFaqDu1wudHd3o7u7G+VymaNWqiMS8FCsUMrN4/HgxIkTMJvN\ncDqdyOVynG4nJPJh1qX7pNFo4HQ6cfLkSVgsFkilUhSLRayurvIYz8O23VCa12w286xcq9WKbDaL\nSCTCP/e4UW9jYyO0Wi2Xh5xOJ2dE6GcOuybhDTo7O1EsFnk8oNFo3OO4HRZMRmfR39+PT3/605DL\n5RgeHkahUIDZbOaINJvNHhqwR9OjLl68CIVCgfHxcbzzzjtsTAmdXT3URUzqm/AOGo0G6XQaExMT\n3M1ht9vR0dHBoKfqlHq9/VLESaNwM5kMt+nJZDIYDAZoNBrE43GeJy1mv/S1OptULpf53TCbzejv\n74dOp8Pt27exuroqGpldbfRpH1RaUSqVOHnyJHQ6HYNzS6WS6D0/ql/oHtLscoPBgFgshjt37mBl\nZUX0uiSk30loMuHTTz8Nk8mEn/3sZwgEAgeCZevJE2eoi8Uidnd34Xa7IZfLYTKZ4PV6YTab+edU\nKhVcLhe0Wi3i8Tgbx3pED/F4HAaDAalUils3KJqk2aYE6gCwZzRarZeDLur29jbPlj19+jQMBgO3\nNVUqFeh0Oq73PhqRHORc5HI5CIKATCYDhUKBjo4O6PV6diTImaAIW2ykQJc8Go0ik8mgv78fDocD\nNpuN67DUy0n1FrHGlAw7KXOv14tjx45xRqRQKGBwcJABW2JTelQXImVIYLXq7AilJ1OpFAKBgGiF\nXG1AjEYj9wxX77m5uRkKhQLXr18/VK2JUpyUaTEajTw5LJfLwWKxcEZHbN2tWqjOCTwcX7q6usoR\nKX0WAuwdZs+UoqehL/l8HhqNhmtziUQCDQ0NhybKoLQs1Y+DwSA7G3K5nB0Bwl2IVWwSiYRBTna7\nnceykhOqUqnw4MEDLhWIyYrQXo1GI5xOJywWC1ZWVjAyMoJQKMQAVLoP6+vrjAeo12mg1+t5Wl1H\nRwcCgQAikQg759Thsbu7i2w2WxPIWb1fQiJTTVcikXBUTbiWjo4OAJ+UwWrNmaf9UmRLaHLKeJLx\nt9lsaG9vh8vlQj6fx4MHD0QNSiLMB030o5amcrm8B7fk8/m4/FUP7EXrku6uHtdanZo2mUwYGhpC\nLpfDysoK7ty5I4pop9ppobp9tVgsFpw+fRoejweZTAYjIyN1z7iWPFGGGnhoQAhA4PF4cPXqVZw9\ne5aHcZMCyWQyaGlpwerqKvr6+jA2NsbApINe7GQyicnJSS7wX7x4EcBD5W6xWAAAqVQKBoMBUqkU\nAwMDnGKpBok8KpVKBUtLS0za0NPTA7PZDJlMhubmZq7hkNJTqVS4efOmqDR1qVRCOBzGjRs3cOzY\nMUYe22w2yOVyWK1W5HI5tLW1wWq1QqPR4M6dO5ziqpeyn5+fRywWg0qlwurqKgRB4MyCIAhwuVx7\nADkEgKsXVZOSpJnL1N6VTqc5hXjlyhUcO3YMIyMjeOWVV0SlfcvlMhYXF5HP5xEKhXDjxg2OyrVa\nLYxGI1544QX09/ejr68P3/nOd7C2tibqxSMQ3I0bN3D37l1UKhVWSDabDQMDAxgaGsKLL76I1157\njevf9YQUBoFuvvGNb3BLodFoxOXLl9HV1cXtfGJTZKS4SRHfu3eP6+dWq5WNAKUhKQ0u5izoPVOp\nVAgEApicnEQsFoPZbIbNZoPZbEaxWMTCwgIWFxdFR+zkDBESeXh4GGq1GseOHYPZbIZWq0UymYTT\n6cTw8DAmJyfrOojVRuT555/HqVOnsLm5ieHhYTz99NNwOBywWCzsaCgUCszMzCCRSNS8y4IgwGw2\nw2Aw4PTp0zh79iz8fj9mZmYglUpx8eJFfOpTn2KchdFo5FowOdD7rUmz4I8fP86fmzJbLS0t0Gq1\ncLlcOHHiBGKxGJaXlxGNRjmLdpDCp2fe0tLCpRDK3gEPM5E2mw0dHR0wm824c+cOpqensby8XLPj\nQiqVwm63Y2BggNtFo9Eol9woCDl37hy0Wi12dnYwNjaGsbExlEolnkH/6DlLpVI4nU6YTCZYrVac\nO3cOsVgMRqMRKpVqzzvd09ODnZ0dfPjhh7h27RqWl5c5A7Xf83O73dyP7nK54Ha74XA4oNFoGH9i\ntVrR29sLhUKBV199Fe+88w5jgg4ayERZJq/Xi6amJgwNDSGdTqO7uxvFYhHBYBASiQTPPPMMAOCD\nDz7AT37yE8zNzbFD8zgdDU+coQYeKuNbt26hpaWFjahKpcLm5iZSqRTXhGw2G3u89Hv1DAg19be3\nt3O9mkBb1DpEnhfV5ugy1vLsCU0YiUSwsrKCM2fOcBScTCbhcrng8/kAAA6HAwqFgl+4ehFDLpfD\n4uIitra2uFWIWr8sFgu0Wi0GBgbQ1NSEXC6HyclJNtS11i4Wi1xzvXHjBsxmM2MANBoNzGYz+vr6\n0NTUhOPHj+Pu3btMmlFPaED66OgoZ0PIMaHSxdDQENxuN6ft6wl9FqpDb21tQaVSIZlMolQqcanh\n/PnzcLlc6Ozs5HR+vXWBT8Bwfr+fwVKUxrfZbHwm1A4ntu2LJJlMcusVpQ6NRiOOHz/OnQNi+1lJ\nsVIaMp1OQ6fTYWdnh+uNOp2O2wcPai+stS7Vvqm+SaApwkpUYy7od+sZVHpfrVYrzGYz/H4/OzGh\nUAhyuZwNikqlAlAfnV2dxuzo6GCHUC6Xo1Kp8Dx5iiYVCoWo+0Z1bbr/LpcLsVgMSqWSa8qUnatu\nHQRw4DtCaXRy/AYGBgA8xOYYjUZu95HL5SiXy5DL5VyiqpcVMRqNaGlpwdDQEGMsqNMEeKjzFAoF\n3G430uk0P2O9Xo9MJnPgfSY8wZUrV9DY2Mh8CpTt0Ov1sNlscLvdjKfJ5/PQarWMk9jPaaHukr6+\nPpw7dw5tbW2cRSyVSjAYDDAYDNz58v777yMSiXCLJIF89zOoHo8Hp0+fxvnz52G1WmE0GjmDRZlC\no9EImUyGmZkZbGxsoFgscjdMrW4cr9eLr371qzCbzfxuZTIZBvzJ5XJYLBaMjo4imUxy2YyyqI/D\nQvjEGWoCWgUCAVy7dg0mkwl2ux1yuZxRx0qlEk6nE16vlyn2KNVUr+5LLQqJRII9Nmr7oSb+bDYL\nn88Hs9nMirOeV0/15I2NDUQiEVbkVM8k5G1zczN7e/Wi3up9EytPpfKQKYuifOqjbmtrg8/nQ7FY\nhNlsZkCEGM+NEM9Er0jZA5vNBuChAmhtbYXT6UQoFKqz2ifKvlgsQhAErm/SWQiCgAcPHqC9vR2d\nnZ3weDx1FWd1lEdZhWw2i1wuh1QqxU4HpdwprajT6USl3ohhiPZdnZqm3ycEJ/WW11u3Og1JtTYy\nSlRzLBQKrCzFslmRgqU9U+q7WCyiUCgw6Y1SqYTD4cDm5qboeeV0xnQmZJSozikID2kzqeRQHR2I\nMajUVqPX69koE6WnIAjo7++HwWA4FPUknQORyhCwS6lUYn5+HuVyGSaTCe3t7fzsxAAYBUGA1WpF\nR0cHK+RqR4VKFT6fjwmZyIDViprkcjm6urrQ39/PxhR4yG6oUChQLBah0+n4/pDDUCurBzx0rik7\nUy6XsbW1xQ6MXq/ncydHrlwuw2w2I5kcXKcAACAASURBVBaLIRwO1zxjs9mMM2fOYHd3l9siidVQ\nq9VCq9UiEokgkUhwuc7j8fA7tJ8TIJFI4PF48Cd/8ifchkvskEajERaLhct8VBJQqVRoampipPpB\nZ9zb24uXX34ZarUa8Xgcs7OzzDdAGQKiZSV8hNfrRSQSwc7ODgcsj4pCocCFCxdw/Phxzl5tbGzw\nHTCbzVxKJXZAk8kEh8OBeDzOWZzDyhNnqCnqImAQpZDS6TQrHIlEgtbWVpw9exZKpZJ7nusZ02o2\nMABML5nL5ZBMJpHNZhEMBpHP59lbcjgcCAaDooAF1FoGgEFdVGcjggiz2QyLxQKfz8fr1kq90X+k\njCnKSCaTzGqWTCaRSCQ4ldTR0YGpqSlR0Q2tW00kU2381Go1UqkULBYL2tvb8eDBA1H7pUhApVJB\nr9dz6xMB3ahWr1AooNVqa0Zj1SleAu1RSwW1dVHUkMvloNFooFKp9pzXQULGiCJEuktk9MlwVkfs\nfr+/bjRNKV61Ws1RKSnM5eXlPaxeGo2GwSxiAFT0zMiA0j2tpmAl8JpcLmfaWjEAJ3IoyEgYjcbf\niECp3isIAkdmYurIdGZE5kPOCd03iqAo20QdB/XuMTkVAJikh/r4S6USLBYLPB4PTCYTFhYW+J0X\nU5+2Wq1cowfA95fQ2k1NTZBKpUxZSx0qtfYsk8nQ1dXFziW9dzqdjp8r4VvI6IdCoZp9uJSmJ9at\njY0NRKNRBrvR/QPAxojSwKlU6kACH7oLzc3NkMlkCAQCzDbW29vLOkIqlWJ+fh5bW1sMIKXnQa2d\nj0pDQwP6+vrQ3t6OUCiEt99+m7MN5HxRi+js7CwKhQJ8Ph+kUimy2SwTUu235xMnTsDr9SIYDOLt\nt9/G5uYml/F0Oh2/K0tLS5iZmWE2QkpRb21t7buuQqHAyZMnUSwW8aMf/QhTU1Mwm824evUqhoaG\nADzUQRsbG5iZmWFSn2w2y3Xsek7RfvLEGWqKvPL5PCYmJnh6DKFiyVNOJpOYm5tDX18fk4zUe/EI\nSUrGPhqNMqiKXgDyCMngaTSauqATci4oaiZHg15oQq/LZDIMDAyw11WPP7w6UiEngHqfyXkRBAGp\nVApKpRJWq1VUJFKtiKmlQqfTYXt7mw0TMXsZDAYGRPj9fkZoH7QupQEtFgvMZjNcLhcKhQKi0Sgr\nfLlcjvPnz6Ovrw8AGOBzkBCilJC3HR0dcDqd2N3dxfT0NCswAnD09/cjFovhxz/+MUZGRg5cFwAD\nSkwmE/R6PRobG6FUKnH//n1IpVKYzWacPXsWV69excmTJ6FSqfDVr34VgUCgpmdM56rRaNDa2orG\nxka43W6OSNxuN9xuNz71qU/h8uXLCAQCeOeddzAzMyOKO5yQ7xqNBhcuXIBcLkcwGOQzOHbsGDo7\nO1Eul3Hjxg1OGYpxOOnZm81mrK6uwmq1or29HW63G52dnXC73cywRgMMxGIAyIFJp9NYW1vD6dOn\n0dXVBeBh5mZ9fR0/+clPcOPGDa4X1luXnBNqIZTL5QzyIudwZ2cHw8PDePXVV5l8otba5BhGo1Eu\nexC/fDwe53fv5s2bmJubw/z8PFZWVpiw5aAyHGVYZmdnMTg4CK/Xy6W8ra0tbG1tIZ1OY3R0FHNz\nc9jZ2WGednJKDzprp9OJ5eVldHV1wev1orOzE/l8HplMBuvr67h16xYWFhaQyWRQLpcZt0DYnv3W\nFQQBFosFfX19UKvV6OnpwcDAAGfhJiYm8Itf/ALZbBarq6v8jhI1Z7WT86i4XC5mYNPr9fijP/oj\n5jTY3d3F3/3d36FQKGBnZwdyuRw+n49T6ZVK5UCyHYlEgsHBQc6snTt3jm1IOp3G22+/jfHxcaRS\nKeh0OshkMrhcLv6qVCr3ZRCjALG3txd6vR6XL1/GmTNnoNfrUS6X8e6773LZUavVIp/Pw2azwWAw\n4NSpU9je3sadO3ceq5/6iTPU1UIGqhp1Td4ztUQR/SehUeu92NXGj1CFVMsiQ6NQKJhIhOrV9dau\nXk8QBO7DLhaLPECEjD/VV8RGIpVKhV9wUs7EmV0ul7lGTTSfYlstqiNU4tQlLm8C7z311FPo7OzE\nvXv3EAgERClkWtPtdqO9vZ1bIOLxOEciV65cgclkwtbWFoaHh+tiC8ibNZlM6OjoQEdHBxoaGtDd\n3Y1QKMTDHbq7u5HJZDA8PIxbt27VRVpSVE+1546ODvT09OD8+fOsEE6cOMF1w4WFBSbXqScEHmls\nbERXVxeamppgMBhw/vx5VCoV7lEOBoN49913MTs7K7olic5Zr9fD4XAwiFEul8PtdnNL1fDwMDuk\nYjAcdCaC8LCVMJFIwOFwwOv1cnS2sbGB69evc+uT2G4AADxEh0CdL730Eiu6cDiM1157DePj4zzY\nQqxQnf6dd96BVCpFX18f9Ho9R0w0TKWaqKbeWZDxVKvVePDgAaRSKUdTW1tbmJ+fx3vvvYfl5WUk\nk0ku7dQ7j0wmg9XVVSwtLSGXy8FgMECn0zGh0ebmJhKJBJaWlvh8KQg4aN+VSgWBQAD37t1DMpmE\nw+FgnUTR3ezsLO8xm81CoVAgHA5zueigdQuFAqampuDxeKDRaJBIJBCNRjExMYHFxcU9ACyJ5OFw\nC8qi0RSt/dbOZrMYGRlhZ2BnZwebm5sIhULw+/3MEEhdC5TZI5bKg6h2CeNEXTwWiwUfffQRNjc3\n4ff7MT09jWAwyOyUVJIJhUL89/dzAEqlEnZ3d3Hr1i185jOfgc/nw+LiIpaWlnDr1i2Mj4/zrAhi\n2wuHw/B4PEzKFQqFHovv+4k21MAnSro6TUtTqgiBrFAo0NjYWLc9q1qqgS00nYtSb729vRxhU21K\nLHCIHoLFYuHpX0qlEjabDYODg0zFKJFI9iAtD/JmH4X/0+AJk8kEpVKJcrnMHODJZBKbm5t1lT39\nLVrXbDajq6trD9BGrVbDbrfDYrEwuUOtaJrWpedFFIM2mw09PT349Kc/zesSGcD6+jref/99phc9\nSKg2R2dLfOqtra24dOkS5HI5lEolP7Nf/OIX+NGPfsRMYLWEjEGhUIBSqURTUxP6+/uhVqv3AMZ2\nd3cxMTGBn/70p0ilUjXXpPWIIIZqbjRaj+pjhI5/7bXXMDY2Jnq8HhldmmZGWSaLxcLp0+npaXz4\n4YeYmppih06MkHKn/fv9fi5jmEwm3Lx5Ew8ePMDS0hI2NjYO3Z9NhnplZQVvvvkmG39iPrtz5w63\nZ4nZM905SpHeuHEDy8vL8Hq9aG9vx+joKGZmZnjaGRkNMc5xuVxmJycajeL+/fsYHh6GRCLB2toa\ng5Aooqd162FZiKf/vffe4/eYyDYCgQDC4fCe0aSEm6gHQpqfn0cymcTy8jLcbjdKpRISiQRzIhD5\nCBkmQoTXmlpXqTzshHj//ffZUFLZbXJykiN9ej8JK5DJZPi8D8ocbm9v4yc/+Qnm5+dhMBg4SAiF\nQoz1AcDc5E6nE+l0GrFYbA9r2X57/u53v4vp6WmuSZNxDgQC7DxQuVCtVkMQBEQiEa6/H2RINzY2\n8MorryAUCjEAMhAIMHCMArDR0VGoVCp2IDc2Nhhn9DikJ0+0oSYlSRe0Gl1psVgQDod/41DFRJK0\nLqGNGxoaYDAYcOzYMaZejEaj3AYmxkjvJ3q9Hr29vWhvb+coml7CnZ0dUcqChC57Op2GUqnEiy++\nCLvdDuChZ7q4uIhoNIrR0VFRNRA6U3o54vE4fD4fOjs7Of2TSqWwvb2NsbEx3L59mwEWB50xKUxa\nk9DT1AZhMpmYMGR4eBg/+9nPMDIygqWlpbp7pSlkcrkcExMTyOVyPE6TDB/xfn/jG9/AxsYGEomE\nqHLIzs4OO2OlUokHFdBEnQcPHuCDDz7A8PAwZmZmRL1sZDgikQjGxsYQDAY5w9De3o5yucxta9ev\nX+dpZ2J5yUmRr62t4Y033sDo6CgMBgOUSiUikQjm5+c5Wqomq6knFE0XCgVEIhHcuXMHk5OTXOPc\n2tri8tBh5hdXR+ukvDOZDL75zW9CIpFwpEdGo/p36q1LzmGxWGRu+unpabz11lt7WtLEGNJqodpx\nJBJBPB7H+vo67t27xwyHVDYjEZMdI+BquVzG5OQkADDxDaXk6fNQ3VgikYhq6aH6+M7ODubm5ngE\nKXWIVJf5CIVcjcM4SLLZLDY3N3Hnzh2OxglbUH0HiJqVyg/VzI/7CTlslLWgOQLkNNC+KAAikFc6\nneaukoOEShFKpZI/J+lQcqqIe4Ic0mpK5YPOOplMcnRMn42c2mpHqhr8CzzEBdC6h3FsSYTH+aXf\ntQgHjMh81ECSZ0/oSwLiyOVyxGIxRlGL/Jv8H828prYDilIofVitPOoJRcrEIavRaLimTqQRRBJR\nr0ZdLWRIKA1vtVoZdZtKpfZ8drH0ltXlBFqfzphqe9XG4zB3hRRCdetOteN1WN7p6nUPykL8Lu6y\nGGV7JEdyJEfyO5KRSqVyqt4PPdGGup5QNExtUL/Lz0Ie7WEpHcWImFr640itNPqRHMmRHMmRPHEi\nylA/0anvekKp29+HUGrk9yG/DyMNHBnoIzmSIzmS/4jyeMMxj+RIjuRIjuRIjuR/iPy7jqiP5Ej+\nP/beLDbO8zoDfoaz7/vK4XC47xRJrZYsW7Zkp4qT1CkSp21apGkTXxRtgaILUPSuBYpeFChQoJdp\niwIpGiNtErdx7cSOFclUJFmUKJGSuHPIGc6+cFYus/0X6jn+qJCcb+j8P5T+OoBgQZZevvPO971n\ne87zPLP/WybEpfyiW1nUzhIC9j5tdUuIdSFMBrG4HXX/QlwH8MnkCwGffhF7BfZSLn8abIbw89N+\nj6LWdth+qV34i1hXOHFD0xfNysvut1chGQ1R9h6VMvRJe+aon9kz+z9mTyKRf1GtFiE7HK37i2g9\n0cUpFLEhhO9RQYfAJ7PmJF7jcrkAAOFwmMdmhPKRzeyX9myz2aBUKqHVagGAdbszmQyPPok1AooK\n2fcIVVyv13nsksCYYk14DjSCSqNnhEwmVHQzRuvS2seOHUNLSwsKhQKznxHqvlkjxkCVSgWfz4eR\nkRHmzY7H48wq2ex+CTDrcDhw7NgxnDp1CiqVCleuXEEoFEI4HGbSmmb3q1arYTQa4XQ68ad/+qeQ\ny+WIRqO4efMmfvKTn4gi8DnInmpHLYxQaHSGfk8vtjAaahaVLCQcobEUegGJVOUo6wr3Tl+MMIqj\nPTdjwsv3STCa8JyOGnE+GRE/+TM/zbr0QgtHaciOEn0Lvzu63Ghd4QhOI+73g9Ym5LtSqWQiB+FI\nh1CjXOzadL4KhQI+n4+Z5ij7ol+lUqlpYCSdg0ajgcvlgsPh4Mt4a2sLy8vLTJt5FOUeIlYxGo14\n6aWXWGgmkUjg9u3b2NraYnWkZtcmngGiv7xw4QKrL83NzSEUCh04L3uYCQlhPB4PxsbGcOLECaTT\naZZXJW6AZoxGm0iwpKuriwl4UqkUUqkUPvroI6jV6qaDGKVSCZVKBYPBAKvVCr/fD5PJhFgshp2d\nHVajasb50z2pUqnQ2dmJnp4etLa2QiKRsIjQ9vY2VlZW9qXjbLRfg8EAvV6Pjo4OfPWrX4VUKsXm\n5iZmZmZw69YtLC4uNlVdEE7MDA4OYmRkBBcvXoTf78fW1hZmZmYwNTWFhYUF3LhxQ/Re6XvT6/UY\nGhrCl770JTz33HOw2+2o1+sYHBzEzZs3MTk5iZ/97Gc8WibGpFIpWltbcfHiRQwPD2NiYgJjY2N8\nZ5w6dQq5XA4ffPCBKA6G/eypdNRUPqK5UJpBTqfTPLCvUChYnYUUYvaTUttvXaJ3pC+pVquxbjTR\nJ0ql0j0iFY1KI3S5E22kw+Fg+Tt6AciR0JwxcY+L2TOpZNlsNpjNZp7vpNExmm2lyLjRZU/rEuEE\niWM4nU5mRcrn89jc3ESxWOQ5X4rmG0WGer0eJpMJZrMZHo8Hly5d4lnReDyOu3fvMgnF9vY2U6/S\n93GQ0ey0xWLhSLunpweFQgHxeBzRaBQffvghSqUSEy7QRdFIH5hUnU6fPo3jx4+ju7sbCoUCiUQC\niUQCMzMzWFxcRCQSYdIEGuc7zITat93d3XjzzTeZZGF3dxd37txhwhNiZ6LRPTEXnEKhYBWu8+fP\n7+EcDgaD+P73v49gMIhYLNY0SFImk7ETPX/+PM6cOYPt7W0EAgHcuXOHn0WaBW7GJBIJU9SeO3cO\nZ86cgcViwfT0NAs+EBteM6VZuuyVSiV8Ph9OnjyJl19+Ga2trZienkYkEsHq6iqA5svrQm58u92O\nixcvYmRkBEajEfF4HG+99RakUmlTmToFnWq1Gk6nE+3t7fD5fJiYmGDe8kwmw8602XMg53/x4kV0\ndXXB4XCgWq2iu7sbyWQS169fRyaTEe2oab+kmjU6Oorz589jcHCQ6Z/b29sRDocRj8dFj7cKvzen\n04lLly7h3Llz8Hq9TM7y4osvwuPx4Dvf+U5T0zMymQxarRZ9fX3o6+vDhQsXYDQaATweZx0dHYXJ\nZML6+jpmZmZEOWph8H3mzBn09vbi3Llz8Pl8HBQrFAr09PSgra0NWq22KfKhPftv+l/8v2hCxhyt\nVosXX3wRPp+P+0o00E8sOFReIaaaVCp1IF0dsWUR1eTAwABzTdfrdaRSKWZDIjWZR48eoVgs8qzy\nQfqyJE3X2dmJ4eFh9PX1wWg0MqMREZVEIhGsr69jbW2Nh/rJsT75INO6JPn35S9/Gf39/TCbzZDJ\nZEyYUq8/ln185513sLq6ymISRACx3+VJL5nb7cbAwADGx8fx/PPPM186CQUUCgUsLS1henoaU1NT\nTNRA9KX7nTNFw5cuXcL4+Dj6+vrgdrt51h14nJ0PDAxgamoK8/PzyOVyKBQKLASyH5EGZQZ2ux1f\n+cpXMDg4yA9/PB6H1WpFW1sbZ7yLi4vIZDJM2kKZ5H4vCTGm+Xw+HDt2DG+88QbMZjMTaNBzQ1rM\nk5OTyGazzNvdqDyr0+nYYUxMTECv1yMcDjO5gs/nQ61Ww8LCAjMkib3kpVIpOjo68Nxzz+Gll15C\nW1sbZmdnWW5QpVLBaDQyX3IzWR6JMVy+fBmXL1+GyWTCjRs3WMt5c3PzyFUnek4uXryI559/njOQ\nb3/721haWkIikeB3spn1KRA3GAwwmUz45je/ibGxMdRqNQQCAXzve9/D2traniBX7FnIZDL4/X4W\n1hkaGsLnP/95SKVSrKysYHl5GfPz83wujdami54oiy9fvgyPxwO3281CK6Tfvrm5Ca1Wy3z/Ys7Y\nYDCgvb0do6OjGB8fx8jICHMutLS0wOfzwWQysWhQMBgUtS6pvb3xxhsYGRmB2+2GTCbDBx98gGq1\nCrlcjt7eXpw/fx6pVEr08yyXy2E2m9HX14dz585hdHSUqyvBYBAajQanTp2C0+nEiy++iB/84Aei\n+SIcDgcGBwfx4osvwu/3s6b12toaarUavvKVr6C7uxsXLlzA3bt3G6oEUlVQoVDA4/Hg7NmzaG1t\nxczMDD744AO8//77AAC/349XXnkFFy5cwE9/+lMWI2rWnjpHTYpAarUaw8PD6OrqQjabRTAY5CyU\nROstFgvK5TIMBgNisdiB3K+0Nq1rsVjQ09MDs9m8p+dht9tZDYZEx4kR56CLQlh2NBqNaGtrg8fj\nwdzcHOLxOIrFIpdcKIMlB5zL5Q50HsKzIHYvo9GIYrGIjY0NhEIhzgBJ/QYAl2sP6w0Js2lSd0ok\nEohGo4hEIqjX6/B4PNwHojItiSkcRuAvZD4CwFzD9+7dY3Y5m82GjY0NZkWSSCR7mH2eXPvJUjdl\n4WtraygUCpifnwcA2Gw2lMtl5HI5pvIDIKoETj3XXC6H9fV1hEIhLCwsIJPJwGKx8OfZ3Nzkvyss\ngR9mLS0tfBnG43GsrKzw82yz2dDa2oqtrS2+zJopTwuBQeVyGXNzc3jnnXdQr9ehVqthMpn2MGiJ\nvSToe1SpVJyBpVIpfPDBB4jFYvwcE2im2TYAre/z+eD1ejkwnpqa2kO0c5T2AimH2Ww2uN1uJJNJ\nzM3N4datW/yui6mEPLlf4m0nbvy2tjasra0hFApxxWJzc1N0Ni0MWKxWK5xOJ4xGI3Z2dhAOhxEK\nhbgas7m5uUfop9GZSCSPOfXNZjOGh4eh1+sxPz+PpaUl5rD//Oc/j2q1ynsWuy5VFex2Owt+rK6u\nYnJyEhKJBENDQ+jr62M1OLFa6EIdh0qlwoH88vIySqUSxsfHcerUKa7MiQXXUds0l8shFoshmUxi\ncnISkUgEu7u7kMvl+NKXvoRCocBsfo2sXq9zf357exuLi4uYn5/HysoKc3rL5XKUy2U8//zzLNEs\nbPs1Y0+Vo6aHkHoqvb29kEqlCAaDuH//Pubn56HRaOB2u+H3+1kjORaLIRwOH3pp0sHKZDLYbDb4\nfD48fPgQ9+/fx+rqKnZ3d9Hf34/Ozk5WPCHe2cOoEoUXiV6vh8vlQrFYxI0bN7CysoJKpcK9PQow\ndnZ2mLy90br0xRoMBqRSKayurmJ2dhZLS0usakSXh8ViQSaTYerMw84C+IRDWyaTYWZmBnNzc1ha\nWkJLSwvOnj0Li8XCDlqhUDCXrZjLnkrPa2tryGaz+NGPfoSWlhbY7XZ0dnbyQ07tC6I43C87FZ7D\n7u4uEokEZ5/BYBDz8/NwOBzY2tqCwWDgrIr2cNC6QiNVnmQyiYWFBaTTaSwsLGBnZwcjIyOsv6xU\nKlGv17nCIuZCJgWjYDDIUnq5XI55kZVKJVeJCL8gtuxNeyHN3Y2NDcRiMRZ6MBqNyGQyB1YqGq0t\nk8lQKBSwvLyMbDbLcqUU4K2vrzeFmBVeVMRfv7Ozw+2Q3d1dvoAJ19CsKZVKyGQy6PV61Go1PHjw\nAFevXkUikWDqWfp8Yvcs1Ogmh61Wq/eAhCjoFGu0rkwmg9lsRqFQgFKpZEedzWbh8/lgs9n4exMb\nuNA+1Go160RT1p/NZmE2m/HKK68w06NYZ0rrVioVLC0tQSKRsAxlKBSCwWBAa2sr6vU6NBoNl6fF\nBAEEmMtms7h9+zZyuRzW1taYLrejowMA9uBHxO65WCwiGo3i6tWrKJVKjH0AwGV1ompt5h2pVqvI\nZrN47733uCJIFUmFQsFc683Q+O5nT5WjBsDRv06ng9VqRSAQQDAY5AyEJCo1Gg1effVVLq006k/T\nAy6VSrm0FAwGsba2hmQyydzL1Hvq6enBvXv32DEd5vSo16zX66FSqZBKpRAMBvegB1OpFNra2tDZ\n2ckcvIcBkmjd3d1dFAoFRlNGo1GEQiFsbm5CqVQik8mwLm46nUYymRS1552dHe4Rl8tlzqjpZxGw\niTSu6RI9iLxfeMYka0mZRTabZepU4cVgMpk4s27kTKnHnM/nkcvlWEilUCgw97vZbAYAqFQq1umm\ntRs9G3TOpCm8tbUFlUoFk8mEzs5OvmgkEgkymQyvK+alJvwEOVSilnW5XGhvb2ee483NTX5mxL7Q\n5NhJZAAAent7YbfbodFooNVqMT09zapRR7koCEy3vb0Nq9UKAFwyJUGGZvYrrJBYLBa0tLSwdKRG\no0GtVmPMRbP7JUS2SqWC3+9HuVxGLBZDJpOBQqE4MisgVaFaWlqg1+thMBhQqVQQCAR+ztE1cx6E\nt6lWqwgEAsjn89ja2kIikYBKpdrDUS2suog1AnZREErvN1XgKFDPZrOi1qN7aWdnh6UiSdlLIpFA\npVJBoVAwzieXy4l+nqkVGI/Hkclk9rRW5HI5dDoda9cD4qtDdHapVIr51KmNSQJBFouF7y+xYD3h\n1INQnIUqh4Tcd7lcWF5e/r/jqOmgTCYTLl68CI1Gg2QyiaWlJVZS2dnZYdCD1+uF0Wjcg948LHKT\nyWQYGBjA5cuX0dLSwkpTpMBFZRCSDQTAjuywA65UKjCZTDh79ix0Oh1isRhH3mq1miNxynAMBgOv\ne9jDRipb1Wp1D7+53W5nxR0qF9PDm8/nuZd8mAk1skulEtRqNQYGBmCxWGA2myGXyznLpF6sGKdH\nqFQSpHA6nTAYDPiVX/kVLk3X63WsrKwgFouxY2xU5iRHTUA8q9UKq9WKY8eOwePxMKgwHo9jenqa\nX8pG5P20NmVElUoFVqsVly5dYok8Eg+4e/cu9zcpCBGb9VJg4vF48I1vfIOrFdvb23j77bexurrK\nF1SzjsRoNMJut2N8fBwulwsGgwHb29tYXV3FysoKZ2diqyFCIyGR1tZWDmSDwSAD7Jp11MIyssfj\nwfPPP4/FxUVsbGywhGKxWOT2RjNGjsLj8eDLX/4yXnjhBbz11ltIJBKc5REgVWyQRUa4meHhYQwM\nDCCXy2FhYQE2mw0WiwUqlQp37tzhYJb20+hcqHpAbbtIJMIKcydPnkRbWxsCgQBWV1d5XK3RuvT/\n8/k8lpeX+c+pCtLW1obz589Do9FgcnISH330UcOeLBm9h1tbW1hYWOB9EGjqhRdewGuvvQaNRoN3\n3nmHgZFizoIqfJlMhn8WtQY8Hg9+//d/H2azGTdv3sQPfvAD0UELBbNCxDWBR91uN06ePAmn04mb\nN2/i+9///qEt1P3OQiKR7ClrE+5kfHwcv/EbvwGdToe///u/RyKREF1deNKeKkcNfBJxDw8Ps6Mz\nm817Xlqz2cxOmjIJYenwoEOo1Wro6ekB8NhpU+/N7/djd3cXFosFHo+HsxxhCfmwkla9XmctZ1q7\nu7sbg4ODjPJzOBxobW3lF5kuCVr3oD1TEEFlVrvdzvq9FFhQiY/Q02IuIHpgUqkUcrkcg7Gee+45\n5PN5BoAQkEWMhq/wPHK5HLLZLLRaLXw+H06cOIGtrS3utWUyGcRisT2IejGlMQCc7ZtMJni9Xu6n\nS6VSJJNJ7ic360BoDwQc6+zsBAD+zhwOB79szfZOKVgj8Akhvnd2dmCxWGAwGKBQKJp+iSnTo2kF\nk8mEcrnMmRipwdG70Yy1tLTA+IoPIAAAIABJREFUYDBApVLxKKRw4kKpVB7p0qE9O51OBoVWq1Vo\ntVqYzWbUajXm8G+2PE3jbwQUpSydEL4rKyuQy+UMuGz0M6gfq1Kp+Fkul8vc6/Z4PDCZTNzKoL00\nKoHTOapUKs7oqNpEZ+N2u7nSIzbLE5bpqXJDfV+z2QyXy4X+/n60t7cjEokgFAqJBnsJBYdIyIjG\nF3U6HYaHhzE8PAyZTMY4GjGCPnRWwtYC/ZlSqYTVakV/fz+USiVCoRDu37/P6lWNzoKMzlWIdbFa\nrRxcBINB3L17l1s5YkxYGZLL5XvGRAmYJpPJEAgEEAqFmlKFe9KeSkedz+dRKBQgk8nQ39+PfD6P\naDTKIz+kvUvKVHSRUIZ5kFH/g8BAZ86cQTwe5z4h6fnSBULZMPXKDnuYs9kstre3USqV4Ha7cfz4\ncdaj1uv1kEgkPPpEZVty2Id9cYQYj8fjcDgcfJk7nU6e1SRdVAL4CFWrDstQd3d3kclkEAqFYLfb\nuexvt9vR0dGBZDLJxBDCeWUxDrVUKiEWi8FsNiOfzzO4yWg0cjZaLBYRDoe5DCfGCJVeLBYRi8Xg\ncDhgMBigVCq5XdLd3Y10Os0tETFGn4vAMVarlS886sl2dXWhWq1icnKyqTIkIfDp887OzrJz3d3d\n5XEOAkSKJXIQ9u2pt76zs7Pn4iP8AsmWikXJ0oVfKpUYIb2ysgKlUslVl0qlAqVS2dQsstCRGAwG\nBINBhEIhAOBgdn5+HslkEsViUfQ8q7Dk7Xa7Ua/XsbGxAQBoa2tjYpKpqSnWXBfTTyZnajQa94yD\nEqqeQKl37tzhAIOCmoOeD7rYjUYjZ+NGoxFmsxnZbBYqlQoulwterxcPHz7kEScSCTpoXQpUyIka\njUaYTCYAYMnWrq4ujI+Pw+fz4fvf/z7C4XDDtiGtTfeL0Wjk9iT1odVqNZ5//nl0dXUhHA7j9u3b\ne8BvBwVEtGcAjC+h8jlVDXt7e3H27FmEw2FMTk6y9vNhQRYFQvV6HS0tLfz5dTodn+Pw8DDOnTuH\n4eFhfOtb38KdO3dEjb9RoEmAPblczhVYwoK89tprGBsbw8zMDN577z2u7B0lsAWeQkcNgNG39Xod\nra2t3Is1GAz8glHfolQq7YlkgMNLTpTJmc1mZrupVquM8K5UKuygySmKAUSQFnKtVkN3dze6uroY\nPAU8vkx1Ot0eSUnhr8O+PCozGQwGqNVqZloCAK1Wy6hv6olQMCKm1BSNRtHS0oL+/n6USiVEo1G4\nXC5IJI/nOg0GAzo6OriPKtZyuRw2NjZgNpuhUqkwOzsLh8PBACWj0Yi+vj4Eg0Ekk0nkcjnRgJN0\nOs1jU1KpdM/MYr1eR39/PzKZDFKpVFMkDtTXDofDqFQqmJubY8Q3AQV7enpgs9lYzF5s741Q39Fo\nFP/1X/8FrVbLPd+zZ8/C6/Wivb0dwWCQHYxYq1QqyOVyWFlZ4RKpw+FAe3s7WltbYbfbEY1Gm1qT\nAhe1Wo1kMomHDx8imUxCq9XC6/XCZDLB7/czIrkZozZQR0cH7t27h2g0imq1CrVaDYfDwfO9Yvum\n5PgpMGlvb8fm5iaWl5f5QtVqtVzZymazDYNOyrq0Wi2jsr1eL1eKAHAgvr29Db1ezy0j+vcHOSbC\nDhDiX61Ww2azcRZM88kUZNJe6TMehHQ2Go3M46BUKtHW1gaz2cw/0+12cwBA4675fJ4Dh4MCLqqs\nWCwW6PV6dHZ2olar8dgljU2OjIxw0EjPGwXhB0226PV6KBQKqNVqnD17FsDj6Q2NRsNtvra2NvT3\n9+ODDz5gsCT1gQ/6DmnKRqPRwOFwYHR0FC6Xi58DpVKJ7u5uDA0NoVQqIRwOIxqNolKp8H1/0LOh\n0+lgNBrhcrkwMjICqVSK/v5+VKtVJBIJWCwWfO5zn0M+n0csFuPyv5BQ6pe+9A0AxWIR3/rWt7C4\nuAir1QqTyYSWlhYEg0F2SidOnIDf70cmk8GDBw+YeemwQ6hUKvjwww8RCARgMplw7NgxzjRoZGFi\nYoIju0QisaeXfNhLTTOa9AKOjo7yBb29vY3nn38eExMTDM6ifgz1Rw+zQqGAd999F9evX4fP54Pd\nbmdhdY1Gw6xLY2NjePToEQBxTGLCl7VSqcDhcECpVKJUKkGhUGB0dBSnT59GV1cXXC4X4wTE2M7O\nDhKJBK5fv46lpSXMzs6iWCxid3cXKpUKf/iHf4jBwUEYjUYWpheT7dXrdR7JorGbH/zgB9jZ2YFS\nqcTQ0BD+7M/+DD6fD9vb2wiHww1nh4WZ1fb2Nh48eIDl5WXO+iUSCUZGRvDGG29gdHQUQ0NDe3p/\nh61LgR6BmkqlEhYWFpikR6vV4uzZs+ju7kZLSwvu378val166WUyGXK5HOLxONLpNINw7HY7PvvZ\nz+LkyZPo6OjAzMyMqMtByFctk8lQrVaxvr6O+/fvY2NjA16vF263G4ODg0ilUntY8RrtmYJfIvYw\nm824desWl6N3d3dx8eJFWCwWrK6uYmlpqeG6xLtAxD0nTpyA2+1GNpvFxsYGzwyrVCq0t7fDbrcj\nHA43zKZbWlqg1WoxNDSEtrY2uN1uWCwW1pGnoDufzzMtJwH4qGW23xlQWZveVyp3E9aEPovZbEYu\nl4NcLofD4UBLSwvzLxzE5+B2u/HKK6+gu7sbKpUKLS0t2NnZ4fUoe8/n84hEIrDZbBgeHobdbsfq\n6iqjlvf73lwuF/7yL/+SK5jb29s8QaPVannEc2lpCaVSCXq9HiMjI1hfX2fU+ZOVACIv+q3f+i0M\nDg7CbDZjc3OTA8TOzk6o1WoAj8FamUwGarUa/f39SKVSmJ+fP7C91dvbizfffBN9fX2Qy+UcnFEr\n1ev1AnhMJzs9PQ2FQoGuri5kMhmuxu23rlwux4kTJ/B3f/d3fHcnk0no9XpYrVa+Q6PRKKanpxEI\nBJiylLgijmJPnaOmXms0GsXMzAyMRiMcDgdSqRTW19f5waMMmGZ96UE/zCqVCg/2E4EAldey2Sz3\nJ8fHxxlcRl9Go7XL5TI7vJaWFiwsLGB3dxe5XI77kAMDA9Dr9Xv6GWKdU7VaZcYxInmhL10qleLs\n2bMwmUzck2oWhatQKFAulznzo3nh3t5eWCwWGI1GyGQyUYxn9F8qYxG4qVAocKUkm83yaJnNZhNV\nbhL2g6ifCXzSsy4WiwiFQtweoQCv0bpP9t6sViuTulBZkNDahFKmszvIqBRLPTfq6xqNRoTDYZ7B\npt6h1WpFMBgUVaoXzi/LZDKYTCaYTCZGTtNlS6VJAvc1Mjpb4JPerEajYUwB7U2n0wEA98LFrEtn\nS/+lbDQSiXAbgUrGBFykWfhGPWS9Xs8jWWq1GvV6HclkEqlUijNJr9fLhC+NSr30PKhUKnR0dHCr\nTC6XI5VKAQCXlikooIv9sF4yPQ9GoxHHjx/nLFalUu3p9VJGTI5Zp9MhGo1y8HTQGTudTpw/fx46\nnQ6RSIQDKaKpJfBiLBZDIBCAy+VCrVbjufiD1qV5d7/fj+XlZSwsLPCIGpHLlMtlBAIBrK+vQ6lU\norOzE8FgEHq9nu/G/c6ju7sbL730EqRSKf77v/+bqxgej4cnZIrFIlZXV7kVSvSvRqNx3+ePAuvz\n58+jVqvh2rVrWFtbg9lsRmdnJ/+77e1tBINBBAIBJleZn59n7M5+6xLIz2g04oc//CEePHgAhUKB\nl19+GQ6HA8Dj6mk0GsX8/Dz0ej1OnjyJer2OWCwGqVTaVMJD9tQ56nq9zn2Ye/fuseMpl8vsJKxW\nK5dWqEwhpl9IYw7xeBzJZJLRxoRENhgMWFtbw+bmJnQ6XVM9WQoCCLGaTqc5a65UKlheXkYsFmMk\nMf07MedRq9W4JxMKhZj0Y2trC0qlEktLS9wnMRqNorMcIdqZLkehWMH6+joikQj6+/ths9ma7q/U\n64+ZsQglWigUuFVB42WUhTQyYXYqlUphNpuhUCj20HjKZDIO5Gh2UsxzIQSxKJVK7v1TJYUoI202\nG9RqNdbX1xsSLQgBJkqlEhqNBq2trXzewCdO1uFwQC6XI5PJiCpRU7mRfu//XyGL7e1t/vxerxed\nnZ3Q6/WIRCIoFAqigkJq99A4jLBH3dLSgra2NrS2tvKFI5bHmRwGBTtut3tPy8bhcKCrqwtbW1tY\nX1/nalajtcl5Uf+xvb2dZ6gJse71emGxWPgdJOT3YfgNav2YzWYYjUZ0dnbCZDLxHD31wslBbWxs\nIB6Po1Ao8P315PrUL7VYLMw8RiNYwpFDnU6HbDaLpaUlXisQCPCzftC+29raOLunSkO1WoVKpYJS\nqUQymcT6+jpisRg2NjZgMpmQSCR4Tvkgk8vl6OvrYwyLwWDA5uYmTCYTFAoF1tfXsb29jdnZWaTT\naeh0Ouj1ena6FPQ/aQqFgulXy+UyV8EsFgscDgeWlpYYuU4jYIQVIPDdfuN2EokE4+PjMBgMnBhQ\n66KtrQ0tLS0MHKO7zuFw8Huv0WiwtLS0L1ukUqnExMQE6vU6t66IR10ul/Po7Pz8PHZ3d6HX67m6\nYLfbMT09jUwm83+j9A18klkLy5aURVAkT72xg6LBg9alfnMymQTwSaRLPcpoNMoPGyDOoQrXJQAY\n9aKFo0Mej4ejYrGOmjJqeqHohSdSCJoLJCSjGKOfTXumTIP2S+cizM7ElNOFWAFygAQ4EmaYxHVN\nc6Nis3S6yHQ6HcxmM8+bAo/JHdrb27lcu7q62tChEhqUSo5UwdnZ2eEeuNfrxUsvvcSUjisrKw0D\nAEJhEy0iOadKpcLgKYvFgvPnz/PI09tvvy2q30sBBQUVra2tTJWq0+nQ39+Pz3/+8zhx4gTK5TKm\npqZEzSTT90VgHgKiFQoFuN1uSKVSXL58GUNDQ4ySFbsuvQvkqEnk4/z58yiXy3C5XOjt7cXi4iJm\nZmawvLwsurpAs8C079bWVng8HgwODkKtVnMba3Z2FnNzc6JAdUSqQdMkhKh3uVxcdZLJZJiensbs\n7Cwzk9G4J7B/n1qr1UKv1yMUCsHr9XKVxWq1IpfLIZlMMq83cXuTIycQ5n5OTyKRcAJDWBW3283v\nVyQSYU4KCphDoRBXzw5qD0kkn9ANUzJTq9V4DJUUp4jViwieIpEIj0cehKImwZR8Pg+dTgev18uj\nTul0Gu+++y6vQQx5NH5K+93v2ZNIJPw9qVQq9PX18Zlls1ncunULP/nJT7jETu9puVzm3x9kFETo\ndDqcOnWKR0VLpRLu3buHu3fvIhwO87qE3Nfr9RyYigExPmlPraMmI0ciLD9JJBLOPgqFApxOJ5f4\nxBo5POGhqdVqdiZ0OWm12qbnT4mVhvpPGo0G7e3t8Hq9nBFqNBrRo1TCs6jVatwP0Wq10Ol0GB8f\nh06nQz6fZwS8WCPnp1Kp0NraCofDweXEsbExHDt2bA9Pd6Osmr4rId9yR0cHxsfHAYD3R2NgN27c\nYArQg4wCE7qM+/r60N7ejuHhYbjdbqhUKqjVai5HXrt2Df/xH/+B+fn5hudbq9Wg0Wj40hgZGcHl\ny5cZGETAJ+IPf+uttxjxe5jRS6/T6XDs2DGMjIzA4/Ggt7cXTqeTHe3W1hb+4R/+Ae+++y5CoZAo\nLu5arcZ9x46ODpw9exYdHR0wmUx85pFIBB988AHefvttJBIJUZUFQi2TCIzX68WFCxfg8/ngcrmg\n0Whw7949fOc738GVK1f2zTr2M3J2hIgmohqJRII33ngD9Xod6+vr+PDDD/Hd736X0fpi3g1ilKKg\ne2pqCtlsluf3//Vf/5WR09QaaFSBo2wpHo/jypUr8Hg8WF1d5ckImqFeWlpCPB7nig79u4OqcDSx\ncPv2bUQiEUSjUUY2EzYiHA7/nLwnBdKESN5v7Wq1infeeYcZC51OJ1KpFBKJBEKhEDKZDJLJJH8P\nFDSVSiUUCoUDdQGIsOjb3/42otEoLBYLstksUqkUrl+/jkKhwD15rVa7hy45GAxyJXS/7zKfz+Mf\n//Efcfr0aTgcDi4ZExEVVZfozvB4PNx+ELY899vz3/zN3+DChQtwuVxQKBT4+OOPsbq6io2NDZ7Q\nIWCuWq2G1+vldQ8K5Kgf/ed//uf45je/CQAIBoNYXl7GtWvXWNoUeDxCTMGSQqHgTJtosJu1p95R\nkwmzVSqBxuNxjviaGfF5ck16+ClLS6VSjBQ9yroA+KHXaDSw2Wzo6elBqVRiGkOx5en91t3d3UVr\nayvGxsbYuQojWLEmDFKozOtwODA+Po6BgQFIJBJWYIrH46IrAHSmdNns7u5iaGiIS7G1Wg3Ly8u4\ndesWPv74Y75kDzO6XOv1OgullMtlmEwmHD9+nPtwkUgE3/ve9zA3NyeqdEqVG41Gw4jVYrEIt9vN\n5bBoNIo7d+7g3XffxbVr10Q7PZrxT6VSDIyyWCwMZCFylnfeeQcbGxui5S0po1OpVIhEIpibm+O+\nda1Ww61bt/Dhhx/i448/ZgS72O9ue3sbcrmcndHU1BSXM4mcZWFhAclkUtRYD61LpBfkLJPJJPND\nZ7NZZuWioEIsMlbIwheNRvGjH/0IV69eZerTaDT6czgTMRclAQlJ0Yx6kaVSiVnTKAABPunlisGy\nUCBCGSi905lMhj+7kLJUoVCIkhENh8PMiUCjqDQRQeckBAvWajUGeB5mpVIJgUAA7777LlfednZ2\nGHxGe6LgtFQq7RnbO+hMiJo2FApBp9NxaZ8CH7pDqXJIiRq1/Q4760ePHmF5eZlBdaSkJ7xHKpUK\nK53t7Oww8LfRdM/9+/fxF3/xFzxiW6lU9lQ7ATB/OlWcaKqlGdZBof1SOGrhB6PDoLJLOp1GIpE4\nsoA9rU3rBoNB5o2mGcOjGD1kxKS2srIC4HF0GIvFmtbBpcuGHopoNIqNjQ3o9XoGPiwvL2NxcVF0\nZUG4Zj6fRzgc5vKhxWJBNBpFsVjEwsICVlZWRMsjEs6AaDFXV1cxNTXFDpCi/5/97GdYWloSNYZD\ne93Z2UEkEoFEImG0M805R6NRTE5O4tatWz+XlRxmxHa2s7OzZyyQetIff/wxpqamGLUtFgBIqmsP\nHjxAIBDA7Ows3n33Xeh0OpTLZc54iERFrFGLZmdnhyll33rrLW6H5PN5ZllrZl0CQxFVazKZxL/8\ny7/w5Ub99aOMlwixEBLJY4UwysiFoMpm1xXuq6WlZU//7yj7FNru7i5isdgeToIn12s2O6JAkyg9\nD9qfkKpX7B2Uz+cZ0X2Q0ZqEaWhmzwsLC4f+PXKuYu+2er3OTvewlg89H81OnQhpdQ9bl95TsXsm\nyWWhPVnOpoCDKkf0b49qkk/zj39RJpFIRG1CWD4jpiAqUxOX8VGMyt1UuiHGJYpKjyo6QGVEogSk\n6FsqlfLsaDPrCn+ZTCbodDp+cMg5U2TYDCcw9Wlp3pL4dIWaywcBZA7bL5XYCOyj0WgYCEdCEUDz\nyktU2iewHxEmkHNqVheZ1qWs6Mn9fNoLn9antZ7ZM3tmz+x/baper59o9Jd+qRz1M3tmz+yZPbNn\n9n/IRDnqozVKn9kze2bP7Jk9s2f2/4k9c9TP7Jk9s2f2zJ7ZU2y/FGCyZ/bMntkz+/+LPalZ8Itq\nT9IsO9lRsBxPmhDbQRicwzTrmzGavxeu+2k0nYXrCsWLJBJJQxR5IyMsDvAJ9zyhvIXSp0e1p95R\nCx9aoTQkzf8Kx4COsjatLxz7EvIzH/WhoHUIjk8PHP3ZUV8S2rNwxlI4GnJU+L8QqCacXafzOeoZ\n09pP0poKwVVHfUGe3KPwXOiC+zRnQReEcO/0vNHvj7I2cQEIFckIvfxpLjkhmQxRagJgVifih292\n33QJyWQyZuiSyWSoVCoIBAJMlUtMfM2YkFnM4/FgbGwMUqkUsVgMqVQKsViMZ1ubeWdoz0QmYrPZ\nmLSDxvEWFxexvLwsSjGJjM6YxDO8Xi80Gg1UKhXziq+srDATXzPTHQTktNvt8Pl80Gg0KBaLiEQi\nKBaLiEajjMoX+87Q80Z686dPn4bT6US1WsWPf/xjhMNhFItFpFKppidn6AzMZjOGhobwO7/zO5DJ\nZAiHw7h16xZ++MMfIhaLNQXyFYJPT506hbNnz+LFF1+Ew+FAqVTC7du38f7772NmZkYUFzwZjbkZ\nDAYMDg7iD/7gDzA8PAyj0Yh6vY7p6Wm89957+PDDDzE3N9fUWRBn+fnz53H27FmcOnUKXV1dfBcv\nLCzgj//4j1mv/Cj21DpqIs0ghiCC0NOcLjHIUMQi1vnRBUyMUXS50BycUqlkzmGioBTD9U3r0gC9\nVqtFvV5n/m9iSBKOQ4l94YicgGQiaVaRuJCJVWd3d5d1iMW8HOQsSCOZGL+Ec4q0JtGtig0GiERF\no9HAbDZjcHAQAHiuMxQK8UgUzWWKDTJIBMBsNsPn86Gvr4/noZPJJO7evcszkTRvLcZpE4saUU92\nd3ezPnIymUQgEMDq6iqPAxLCXMy+ZTIZFAoFrFYrXn/9dRYzkEgkWFpawtzcHEtcptPpPTzzYs5a\nq9Wio6MDg4ODOHv2LItGFAoFXLlyBevr60z2cBhxxn77NplM6OrqwsTEBF599VWoVCoek5uensb8\n/DxWVlaQSCREz1YDj5+/1tZWtLW1YWBgAKOjoxgYGEAqleJZ6/feew/379/nd1zs+0J84uPj42ht\nbYXf70d7ezvUajVyuRxCoRDq9TpWV1dZylSMyWQyaLVa2O12dHZ2wu/3Mw1svV7Hw4cPUalUsLi4\nKJqBigJDjUYDj8eD8fFxeDwe/u6Hhoawvb2N//mf/0EkEmlKXlUqlUKpVMJgMODSpUvw+Xx8X1y6\ndAmRSAR37txBpVJBIpEQtS4A5iV3uVwYHx/H6dOnefrC4XDg4sWLuHfvHo+Mif3eaLLHYrHg4sWL\nGB0dhVqtRrFYhFwux5kzZ6BUKlkdTeyzRvccPWudnZ0ol8tIJBKQSCQYGhpCS0sLVldXOfgUs18K\nLPr6+tDb24vjx4/DYrEgFosBAMvjdnR0YG5u7sjJ31PpqOnLIn5lnU4HhULBUTuNCpF6DYA9alSN\n1iXu5f7+fr4syRmVy2VIJBJ2JnRBECHIQUYjY0SFNzQ0BKVSyYIIJM1WKBQQj8exvb0NqVTakPWM\nLh2Se+vu7obL5YJer2eRi3Q6jdXVVUQiER7XasS+RA8YaThfvnwZnZ2dsNlsLPwejUYRjUYRCATw\n6NEjdqYU1R9mpCbk9/sxOjqKV199lYVO0uk0rl27hocPH7IsIyn9NCptEaWn3+/H2NgYTp8+jZ6e\nHpZ6JOL7hYUFZiCiYElYlXnSSFnHbrfji1/8Il544QW0t7dDq9Uin8/zGX/88cf4yU9+gmKxyDPy\nh2kEA+DMjqLuN998E0ajkc9yfX0ds7OzmJqa4nn1XC4nSpRCIpHA4XDgxIkT+NVf/VUcP34cTqeT\nz5LELYj9qlKpiJb+bGlpgdvtxssvv4wvfvGLGB4eZlrNra0teDweRCIRrK+v8yUtxuhCViqVuHTp\nEk6dOoWBgQHYbDYOZigYoj2LLXlS2ZHel/Pnz8Pv96O/vx9GoxGpVAqBQGDPWKNYJyKRSFh5qaen\nB/39/ejv72f5xFKphEgkwnrXYhwqnQVJJZ48eRLDw8P88ygwDwQCuHbt2s+Vrw8zEkCxWCwYHx/H\n8PAwFAoFs4mNjo4ywRNxEzRzxg6HA8899xyOHTsGq9WKWCyGTCYDs9kMp9MJt9uN5eXlpgIWmUwG\ng8GA9vZ2dHR0YHd3F6urqyiVStBoNOjo6IDD4WhKe4DuUJvNhq6uLhw/fhyxWIypTzc3N/H6669D\nq9XuEd0Rsy6dcU9PDwYHB7Gzs8O8Eel0GqOjo5iYmOD9it3zk/ZUOWphOVAul8Pn86G9vZ3J8KVS\nKdO7VatVVlQhdplGFyb1JtRqNdra2jA8PAyNRgOj0ciiFJVKhR/cVCrFDDaHRdz04BIV3eDgIPr6\n+pi8fnd3F11dXYhGo1hbW0OxWEQ2m2Vmo4OyGzoPck4jIyPw+Xyw2Wwol8vQ6/XweDw8l7y5uckl\nzkaaqnQWGo2G9V7pwkmn03A6naz0lcvluNRJe2p0zhqNhp0pBUSpVIql/DweDxKJBFKpFGfZQvKL\n/S5Pei46OzsxNjaGwcFBuFwuJj0h5iKXy8UVACLxkEgkB1Yw6DNptVp0dXVhfHwcXq8XcrmcX+ZK\npcJydRaLhfcjzPQOOmuVSoW2tja88MILOHfuHHQ6HVKpFDKZDJelZTIZXC4XisUiwuEwcyQ3ag1I\npVKMjY3h1VdfxcmTJ+FwOBAKhRCJRJDP5/liNxqNXC0SY3S5HTt2DJcuXcLg4CAUCgWWlpawtrbG\nJV1Sn2uGCEUieSxu4HA4MDY2xuXjra0t5uIul8sIh8NIJpNNUfjSRe/3+9Hb24u2tjY4HA6mwkwk\nEsx7TcGz2POQyWTo6OhAX18f7HY7q9VpNBrm4CYSkWaciEwmg9ls5ueONNaNRiPLd5KQB4l4iFmf\nJDJHRkbQ39+PYrGItbU11nIfHR1FuVzmzxIMBkWfhVKpxOjoKPPiBwIB3LlzBwqFAj09PfB4PExz\nTHddIyOeebfbjf7+fgQCAWSzWayuriIWi+HEiROsVU0OVez3R1rnra2tyOVymJ6eZgrYlpYWnD59\nmjXFxWpHEIMc8beHw2HcvXsXkUgEk5OTkEqliEQiTOtL7ZGj2FPpqKmn8rWvfQ2dnZ2IxWKYmprC\nzZs3odfr4XK50N3djf7+foTDYYRCIczMzBzKQiMs8/p8Pvzar/0a7HY7Pv74Y0xOTiKXy6GnpwcD\nAwPo6+tDMpmERCLBwsKCKLUTuVwOp9OJz3zmMxgeHsYHH3yAhw8fYnNzEyqVCpcvX8bo6CisVit0\nOh3S6TRyudyhF7ywtHL27FmMjo6iVCphenoaH330EQccQ0ND6OnpYfKPjY0NbhMcdh4kNDA6Ogq5\nXI7r169jZmYGm5ubXMq9+DrDAAAgAElEQVQxGo1wOp1M+N8oA6EMwWQyMb95tVrF3/7t32JzcxNq\ntRo+nw8+nw9qtZqDjnQ63bDNQFmYxWLhtsVPf/pTvP/++1AoFPB6vRy5GgwGpnukvu9BlwW1WUwm\nE8skXr16FTdu3EA0GoXT6YTdbmeVH7PZjHg8zpn6YeUsiUTCcpPUm/6rv/orLC4uYnt7G3q9HqdO\nneLMSavVIpfLcQXnsLOmVotWq2Xlt3//93/H22+/DYnksezh4OAg2tra9lCainGoFMh1dnZCKpXi\n/fffx/3793HlyhW+jLu7u1EsFrG5ucnVqEYm5Gz3er1IJBJIp9MIhUJYXV2FSqVi7n5iHyQZzEZG\nLSKv1wufzweZTIa5uTncuHEDiUSC22gkbZhIJESVIunu0Ol0GBkZgVKpZFGK27dvcwm4VnssqRiN\nRrn612hduug7Ozuh0WiQy+WwuLiIQCAAq9WKiYkJtLa2IhAIIBqNHipG8eTaSqUSZrMZOp0OyWQS\nN2/eZP5tv9+PCxcuoFwuIx6Po1QqiXL+QkxIOBzmdge1stxuNwf5dAZiM0mJRIJKpYJMJoPJyUlc\nuXKFWRxbWlrQ19cHk8mEzc1NpnkWuy6pshHRVDKZ5OqpXq9nPQMqe4tdt/6/9KD//M//zM9SoVBA\ntVqFWq1GR0cHstkst+OOmlU/VY6aQDVKpRJarRb9/f3Y3d3F+vo6AoEAkskktra2oFAokEgk8Npr\nr3F20uiFo/9HAuH9/f2Ynp7mknGpVILVakUymYTP58Pw8DAmJyf3cAkftjaVCbu7uyGTyRAIBPiS\nUavVSCaT8Hg86Ovr4yxbzLr0/6m3RFJ18XicqwHpdBodHR2IRCIIBoOiOKMpUyNnmc1mEQ6HmS+4\ntbUVm5ubcDgc8Hg8TIknpq9er9e5dUFVkFQqhVQqBYPBAJvNhlqtBpvNhtXVVa4CHNa6oMuB+Hmr\n1SqX/kmWlPrpGo2GNcZJurNRW4QcWaVSYeF4kiylHiZl9JStiinVA2D8ANE7Ek82ZX8Gg4EdCEkG\nkqMW83wQJ3UsFsPCwgJf0B6PBz6fD9lsFhsbG6wpLeaSoL9TKpWwsbGB9fV1BINBDpJ6e3vhcDgw\nNzfHMpjN9KbpTFOpFPNyy2Qy2Gw26PV6pNNpRKPRpnp6FHCR7jL10kl7WKPRIJvNciVDLLBH6FBL\npRJrJMvlctYpJ1Un4ngW81zQOZAY0MLCArcm8vk8bDYbisUiMpkMc/mLrS6QQyAnWi6X2dELpVxj\nsRiy2SxT54q1arWKUCjE+6V1CVwGPH52COsi9pmjtg+JhdB7SzgahULBbadm1iXAIwDG3gDgfrjB\nYOCETGyWTv6qWq2yQAjtl7AdZrOZlefo3xzFnipHDTw+RKlUyrrNoVBoj9Yr9aLdbjeMRiMfMtC4\n3ER9yDNnzsDtduPHP/4x0uk004RGIhH09PRApVJxqUKI8j3I6vU6lwm9Xi+2t7eRy+V4v5QVazSa\nPaUyMevS52ptbYVcLt8DvpJKpUilUlxqbgb5TQ+k2WyG3W7nviU5tkwmw4hckrskJ93IedCDrlAo\nGLlKTo0cFoG/iLO6kRADrUvgMGp5kFymRPJYQEStViORSCCTyfBnERNYkHQqlXFJ5jSfz3PlgbjA\n6WISg3KmtSkYKZVKsNlsqFQqLN1HHNXBYLChVvKTRu9LoVBgrWOFQgG73Q6v1wuz2YyZmRlEo1Hm\nABdr1C7I5XLQaDTwer3wer1wu92wWq3Y2tpCMBjkUvVRkMhOp5NVygwGA/R6Peu5x2KxpvjK6/U6\nAzl1Oh08Hg8jsqlUmkgkuP/fzKgPUQtvb29Dp9MxtqWrqwsWi4WBoyTBKJYPnvqhpC1AAalGo0Fv\nby8HyaSdLBb0Ri07Ag8SiFWj0cDhcODYsWOQyWRYWFjAxsZGU2I+dCeSUA+9A06nE8ePH0dfXx8H\nns3QGdPdQvzYdB8olUro9XoMDAxApVLxnsV+d3R3ULuG1iad+bGxMZ4CCIfDosVxhHedMOir1x8L\nPA0PD7OyIVUjxbaHnrSnylHX63VGC4+PjzNYLBwOI5vNMmqa+m6E/CXn1cgInTc6OgqlUolUKsUl\nYuIO12q1sFqtUKvVzHPdyCQSCdxuN8bGxqDVavniEvKHkyYxldCaGTUxGAws40kZEYm22+12BuEQ\nWEvM2vSg0kVZLpehUCjYQZFAPP0/AvWIfchUKhWq1So7aCoft7S0wG63Y3d3l6NQMcpAtGfgcV+0\nUCgwsvf48eOoVCowmUyQy+XY2NjgrIdeOjH7rtVqnDHZ7XYMDQ2hr6+Ps79QKMQlPirHNpMp5PN5\nlEolfraBxw5gdXUVoVAI0WgUqVSqKfUooTNtb2/nfqNcLucMLxAI8IiPWKdHc7G12mNZVafTyQER\nqYxRBabZdQknYjQa0d/fz/3YUqmE5eVl5HI5hMNhZDKZpkb3KHDT6/Xw+Xzo6enBzs4OC9dQ5YEq\nJc3smSRrFQoF9/vNZjO3NiqVCm7dusWa0c2Aski8QafTsYYxjX6ZzWbcvHkTGxsbTWsOUPBJ96XJ\nZILNZsPAwAC6u7uxsrKCubk5luoUa+Sg6J6hMntfXx/6+/vh9/uxsrKCSCQi2unRugB+TuVLo9Gg\nu7sbvb29CAQCuHfvXlOOWrg2OWlqv7S3t2NiYoIBs+vr60ceoaL1W1pa4PP5cOLECZw7dw6rq6tY\nW1v7VOO+T5WjBj4pU7S1tXE5+fjx47Db7ZDL5TAYDNDpdOju7obT6UQ8Hkcmk+EL4LAIvFaroa2t\njctj586dg8fjYaEP6m0NDg5ySUc4HH/QF1iv1+FwOKBSqSCRSODxePDaa69xFG6xWNDZ2Qmfz4fN\nzU0OSAg4d1g5uV6vs5OmeUWLxQKj0cjAB+oHkWIMPSyHZb8UzVPJ2263Y3R0FOfPn2ekaL3+eBRu\nc3OT16WIv1EZOZlMYm5ujjVfv/rVr3IPtlqtsiJXLpdrSsWnVqthaWmJs7GOjg587nOf4zNaWlpi\nsJ7Yy5icYqlUQjgcxszMDCYmJjAyMgK1Wo1qtYrZ2VnOfGlsT6wzpQoFIbq/+c1vQq/XQ6VSoVAo\nYGlpiaVECbcg1mq1GuLxOOtTnz59Gna7nSU/k8kkQqEQV3fElk2pSlQsFiGVSuF0OiGTybC7u4tU\nKsXyn2LL/8K1VSoV/wLAFReZTIb29nY8fPiQM02xwQo5UbrYNRoNB/kajQYmkwlerxcfffQR5HK5\n6CyPJkQsFgvkcjkUCgUkEglnfSMjI9Dr9fjZz37GAUsjlDM5IMIWUKXP6/VCqVTC6XTC7/fD5/Ph\n2rVrrLEtfP8OW5fuQhq3pGejra0Ng4ODGBwchN1ux1//9V9jZWWFk5VGmBaqYqnVakilUsbb0L38\nu7/7u/B4PLhy5QreeecdfpYPq3TSdweAkxphVUShUODkyZN44YUXcP36dXz3u9/F7OzsoWpbZMQj\nQGBKWlMul0Mmk+H06dO4cOEC+vr68Ed/9EeYnZ0V/f6R4BDtmd4P4HEA9id/8icYHh7GzZs38U//\n9E8cgB/VnkpHXavVWGTbarXi7Nmz6OjogEKhYG1Sk8mEer2OSCSyJ7JrtHahUOAZyhMnTsDn82Fr\na4vnZXU63Z4SMvAJ4cVhD9zW1haWlpagVCrh9/tx5swZVCoVmM1mLt8QQQvNK4pFLVLpy263o729\nHWazGQ6HY89npr4W7e9Jko79rFKpYG1tDSaTCT09PVxRcLlcjDylchn9HDGjFhKJBJlMBoFAAH6/\nnwkWSPgdeCysTr2sJ9duVFpPJpOIx+MMylOpVHyBWK1WWK1WrK2tiR4LoXVJPjKfzyMYDPLYhVQq\nhdFoZPKMw5D6+xmNSeVyOeTzeTx48ACdnZ38YguV4JrVKa9WqzwzvrGxgUePHiEajUKpVHL5U8y5\nPmnUZpDL5YjH46jVatwKMZlMAB6P4NEzLdbo/San9+GHH3IQ4fV60dnZiba2NkQiEYTD4YbrUVBK\nv0h/mPStS6USo4gBwGaz8fvXqE1Gmb9SqeTviPq5dFd0dHTwO2IwGPacx37rU8WQ+sRmsxlSqZRb\nFhS8bG9vIxaLYXd3lz9bo++RyvyUXLjdblgsFnbWfr+fq2/xeJyrQjKZjJ//g86YOBFUKhVaW1uh\nUCjQ0dHBAYfNZoPdbsfm5iZCoRCPXFLydNC6NIkgl8t5ZtrlcvEZWa1W9Pf3o6urCzdv3uQ+PX3v\nBzk/WlepVMJoNDIojzgyTCYThoaGMDg4yJKjlIg1upepFG+1WjEyMsLTAPX6Y9lOm82GU6dOcYUo\nmUzy56UR0WYz66fSUVerVUxOTqKtrQ0qlQoGgwEqlQobGxsMd+/u7mboPs0l0wEc5FDr9Tru3bsH\nnU7HqGaFQsFgm62tLXR1dXEWSf1CMUQLa2truHr1KjKZDNbW1tDd3c0XRTwex+joKKRSKYNkxJKH\n0F6uXbuGWCzGyMdgMIhMJgOTyQS3241cLsfZqdh1d3d3Wbu2t7cXXq+XgUE0u22xWPbMkZODaoQH\nIM3sn/70pxgdHYVKpUK5XObKgN/vx8OHDyGTyZpi+iLw1MLCAlcb5ubmUKlUeByExswO0/vdb10C\nmYRCIZjNZi6hU+Wiv79/j3a2WPQmOcxUKgWVSoW1tTUkk0ns7OzA5/PB6/UiGAxibW2NtcWbsc3N\nTZhMJlSrVTx69AiFQgGdnZ3o7u7mNk6zJrwIq9Uq7t27h0QigY6ODpw5cwZ2ux1Wq5WzoUZr0VkR\nAQcFguvr60gmkxxYnDlzBtvb2xwQNTKhlCoh4KvVKhKJBL9nCoUC29vbcDqd3H5qlJmSQ6VMzOVy\nQa1Wo1KpcGuLft/S0sIjVAqFgsennnw+qDdP4zw+nw8Oh4PL6sLKAPD4e1UqlbDb7chkMohGo3w/\nPvncUXZH5Cs2mw39/f2QSqWo1WrQaDRwuVw88kXAQKrwCQFaTxpl/KOjo2hra8PExAQ0Gg0HKNQG\n0Gq1mJ+fx/b2NmfFJL+7H0iLAgCbzYaOjg584xvfgEajQb3+mD3OZDLBZDJx1YGqCkqlEsAnbIn7\n7Zkcfk9PD48v0v1DAT1hm2ZnZxmkqlKpDiXXoXN+6aWXcOLECZw6dYpH5uRyOaRSKd8XDx484GCL\nkkwa4/uld9QA+LL84Q9/CKvVypcQ1flNJhO6u7sBAPF4fA+13mEHUKlUkEwmce/ePSwtLWF9fR31\nen0Pctjv9/NLnMlk9gAADivf5PN5nstrbW3FwsIC9wi3trYwODjILyKBehqtCzx+wHd2djA9PY1E\nIoGNjQ3o9Xom83C73Xj99df5xRFSDDZ6GKrVKiM/b9++jWAwCLVajUKhALPZjM9+9rPQarU8hypc\nU0w/eWtrC8lkEmtrazCbzfxCOBwOfO1rX0NXVxe/mGLXpX0TEC2dTmNqaor7yi+88AJGR0cxMzOD\nQCAgek0Ae5wJEaisr69Do9HghRdegN/vR6VSwb/927+JXpccHgU3NLHw4MEDJsj4+te/ju3tbQSD\nQVGzrMIKBGVPCoUCmUwG2WwWwWAQlUoF3d3d8Hq9fNGJ7ZvSZUiXy9raGpaXlxGJRBgI6XA44HK5\nRJ8D9WOpVEh99UAgwP1Zh8PBlQtyhI2MshuiTgXAz100GoVWq2XEvl6vP9DRPXm+VDnw+/3QaDTM\nkigE5NHzQsBTAp/SmTz5M6jdZTKZcOzYMc50FQoFqtUqZ8NarZYzWIfDwed+WHBBWvInT56E2+3m\nDJ94C8xmM/R6/R60tsFggNVq5TvmoHVp7O3VV1+FzWaDyWTC7u4u5HI5rFYrZ78PHz5EOp2GyWSC\nxWKBxWLhTHW/8yan9/LLL+PEiRNwOp0oFos8Xun1eqFSqbC9vY2HDx/yLLREIkEymTx0BNVkMuEL\nX/gCTp8+DYvFwp+PWpE0FrqwsIBYLAa32w2ZTIZ0Os0A14POw+Fw4M033+TqDfFMEOJdq9XyNAS1\nQontkRKpZu2pc9QUTe3s7CCdTvPsKWWgEokEPp+Pe6yrq6s8w9nowqD+GvW0p6am9vSHOzo68IUv\nfIF7McJeZKM9U1mzpaUFDx8+5D+nf/vrv/7rjIYslUqiyx/klFZWVhCNRjEzM8MZWr1eh/9/SUWI\nKEEspSX9nXw+j2KxiNu3b++hZLXZbGhvb4fT6dxDZyg2g6ToUaVSIR6P4+rVq3xGVqsVv/mbv4me\nnh50dHTg0aNHTWW+lEmrVCoUi0U8evQI6XSaZ0Y/+9nPYmBgAHfv3hXtTOmXXC5HR0cHlEolFhYW\nsLa2xmW+sbExTExMiKayFAJiCAXa1dWF69evI5FI8PPc2toKrVaLjz76SBSQhTIwAjm9+OKLUKlU\nuHfvHhYXF6FUKlGr1fhCIsRrI+dEfUchrWylUsHdu3cRj8cBAHa7HQ6Hg+kXs9lswxIyodsNBgPP\nqxNBRDqd5p91/PhxpFIpzMzMIBgMNuxDKhQKjI2NobW1FXq9HsViERqNhsveGo0Gr7/+OkZGRuBw\nOLC4uIjFxcWGRCeUSR8/fhyXLl3iUaxSqYRHjx5hYGAAra2t6OrqYpKWUCiEtbU1xnLsdybUfz1+\n/DheeeUV6HQ6bvOVy2VYLBZuPyUSCdy4cYMpODc3NyGXyw98PlpaWpidjrAw6+vrPPplsVhQqVR4\nBLBQKDC+JZvNchvpoOfi5MmT+MxnPoNEIoF8Po9CoQCXywW5XI6trS2EQiHcvHmT3/muri6k02lI\npVIkk8l9q1tSqRTnz5/H17/+deh0OkxPTyOXy8Fut8NsNiOZTGJjYwNra2uIRCLQ6/U4ffo0MpkM\nlpaWsLOzg1gs9nPrtrS04HOf+xx+7/d+D7VaDffv30coFOKJBYlEgsnJSWxsbGBjYwM7Ozt45ZVX\nsLm5iZWVFYTDYdy4cWPfdZVKJX77t38bfr8fV65cYTbIS5cuQaVSIZVKYXZ2Fjdv3mQ+is7OTn5G\nFhcXuTLSjD11jpqMLnth9CoscVEZudEFtN+6VM4T9rYp2wHAqNZm90tlGIoKhRd1pVLB9vY20ul0\n018SOWYawRECO2h0Kp/P7wFmNLPner3O41hUKpNKpSiXyyiVSgz0OSjK3M/IsdMlRwhU6olRpYI+\nS7PnQeUpAroB2JPViGVvIhM+Xzqdbs+8MYk6AGBUr9j1yFFRf9tsNjPiVKlUor+/H/V6HeFwGEtL\nS6LGpwj8SCIDbreb6TBlMhl8Ph9Onz4Nv9+PTCaD1dVVUesSfwE5097eXuzs7OxhXzp37hx8Ph+m\np6cxMzMjKrCg2XmtVgun04nW1lZuaQGPx3pOnjyJ8+fPY2pqCg8fPsTi4mLDPddqNUa52+12Fomg\nFoVOp8PExAQsFgs2NjYwNzeHpaWlhuNT1WqVpzR0Oh1sNhtXGEZGRriqoFQq8ejRI8zNzWFtbQ0b\nGxuHsiPW64/HdojK1O/3M5MeTYkQ58DCwgJWV1e52tdohloikTAdKLV+2tvbucdNFb9Hjx4hmUxC\nKpUil8vx+NZB3yOVpx0OB3Z3dxkcSpWK+fl5phuem5uD2WzmmWQSUjnoeyTSG+I+cLvdcLvdAB5X\nP7/3ve8hGo0ikUigUqlgbGyM7/xGbJGEHSBKWpfLxXflrVu38KMf/YjbTH6/HxsbG9jd3eV36yDT\n6/Xo7e2FSqVCR0cHXC4XV3xv3bqFO3fucGDi9/uZzEmtVkOj0RwpmwaeYkf9pAn7osQX7XK5mO3l\n06xJtru7i0QiwbO0v6h1JRIJCw0c1FMRu65wPI0c9ebmJpxO5x5JuGbXrVare4ROVCoVvwjEXdzs\nmgScqNVqMBqNXEKzWq0MTGqEZD3IKLCy2WwoFAowmUzw+XxwuVwM3GrmnMmpSqVS2Gw2Hv2SSB4T\n9tN8/MrKSlPoTbroDAYDXC4X2tracObMGUQiEbS2tuLs2bMIhUK4cuUKotGoaFQ2IW1tNhscDgdk\nMhmee+45JBIJvPrqqzh+/DhqtRquX78uanyKghJCODudTni9XuY/lkql6OzsxHPPPYdwOIyrV6/i\n0aNHotYlXn6NRoOBgQEuJ586dQparZYv92q1iuvXr+PRo0dcdTrMqFJAI2R+v5/FZSjo3NraQjQa\nxeTkJCOzG5H2EKiQ6GMtFgtzIBASnrAoN2/exOLiItLpNM9QH1R5omAqGAxyBQAAv3OLi4sIBoNI\nJpNMXkT8BqQgdlDgWalUmJufFLhI5CUejyMUCmF+fp7vNqq6JJNJJu/Zz+i7I0Id2nc8HsfHH3/M\nLREKCAj3olarecb+oLVlMhlisRjW1tY4wQiHwwgGg1haWsLVq1cZ0Eg9X41Gg7W1NQYb73d3UOC7\nvr6O1tZWuN1uPHz4EIFAAA8ePMDMzAxisRgHuhKJBFarFSsrKygUCsx69qQRoHJ9fR0nTpxAd3c3\nUqkUwuEw/vM//xP37t1DNptFuVyGy+XiNoPRaMTDhw95Quko990vjaMGPpmz1mq1MBqNe7LAozTo\nhetKJI/nIQmJnM/n2ZF82nWVSiX3QIrFIpRK5ZHWFQYA1Ovy+Xzo7e3F9vY2gxmaXVv44FBEOTEx\ngZMnT0Kv1yMajTbtqIVgGspQ/h/23jQ2svM8F3xqYRVr3zdWsVjcdzbZZK/qdkuWLHk37AC2EsMe\nZxIYSBBMnD++dzABBpgfxp0gCAwYkx8GgsiwM3ZubNiWo7Zsq1uS1S313uxu7lstLJK1sfaNVcWq\n+UG9bxcbTdYpSvdC9vAFhBa4fDz1nXO+d3ve5zGZTHA4HBgZGYFWq0U4HObgpRmjDLxSqTAQxWQy\nobu7G/39/bh9+3ZTPM5UpWhpaYFGo4FCoUBvby9EIhELRqhUKgQCAdy8eVPwyBcFUyQyQGpfpNRD\n87j//u//jlu3bjUsI9cbzby73W54PB7Y7XacPn2a+cjT6TSuX7+Oq1evCqqEELiQULzd3d0YHh7G\nwMAAi80oFArMzc3hd7/7He7evctAxEaWyWSwvb0NuVwOs9kMl8vFmTA5aHKmt2/fRiwWEzTyVavV\n4PP5OHAigBYFgZVKBZcvX8bNmzextLTEmZmQdaPRKJaWlnD79m1sb2/DbDYzA1k4HMbCwgK8Xi9T\ncta3nA5av1arIZlMYnZ2lvcaACtXPXz4kBMFaosQApmu+6D1q9UqHj16xMRNDocDfr+fM30Sn6Dn\nnBDR9X3Tpz3X1Iqcnp7Gm2++ySOxoVAI77zzzr6ZdIVCwVgfYC+4OExDulgs4t1330WlUuGZd5/P\nh8XFRYTDYcTjcU5MCEBGEyVE33vQNb/22mvI5/Po6+uDwWDA66+/zs4yk8mwUE08Hsf29jZMJhOS\nySRz1x8UjKfTafzwhz9kMNrs7CxmZ2fx5ptv7lNETKfTjAxfX1/H9vY2O/Gj+JM/OEdNqDmv18uR\nfrOl0yfXBB5HS16vFxaLBZFIpKky8pNWPz+4u7sLn88HpVK5b5ysWaP1qOwjlUrhcrkQDAa5PHZU\n3WgaIarVajAajRgdHUUwGEQ0GmWwVjNWf6jk83kkEgmcOHEC586dg8vlwtWrV/H222/jzp07TWn2\nAo9BcIFAABKJBH/5l38Jt9uNanWPa/l73/sefD6f4FI93StqIVy7dg3ZbBYTExPo6OhAIBDAb37z\nG9y+fRter1dwRk0HVDwex6NHj5BOp+H3+/H8889DoVBgdXUV09PTeOWVVwRlkGRUUiRAHbDnpGhk\nb35+nvmihTJDUatpeXmZM7rbt28zEC2dTnNJloIroXgFqkSsr68jHA7zoUs0r0Qs1EhU52lrLyws\nYGlpCVKpFD/5yU94QoGqT7SnzbbHCoUCVldX4fV6941FHbUaVr+uz+eDz+fDG2+80fS1HWa0LiB8\nIkGI5fN5+Hw+fPe73z3050ql0r6pCCHrrq6uYnV19dCfI8KlZtZeWFjAwsLCU79He0PrJpNJwYIk\nmUwG09PT+MY3vrFvj5/Mkon/Xui6jUz0Yd3MD3QRIpGgi6Bo0Gw2Y2RkBKlUissgzXANH7R2W1sb\nent7YTKZsLq6ivn5+aaYdZ62JvUoibUsEAggHA7zWMRR1gQeZ1TUSwuFQkyb2Wwg8CSYSi6Xo729\nnUuHsViMWcSasfr5VmI8o4oFkXAI4bR+0qhyQJmT3W5nQYN4PC5IROWgPSCGN9oLkgUUyt/8NCOO\n8CdHdqrVqiAq2UbXXU8OItSBHtuxHdtHwu7WarWpRj/0B+WogcfIOzrwm2GgamQymYzn//L5fNN9\nzoOsvtxEB3MznMuN1gYeE5x8GNf7tL/xUXhOju3Yju3Y/sjsj9NRH9uxHduxHdux/ZGYIEfdPET4\n2I7t2I7t2I7t2P6n2R8UmOzYju3Yju2P3eqBSR9mxbNepIJAqR/UCCNBYiCEUP8guAsyahfSdRPo\n8INikQh7QgIrBCI9atuQ9oDWIypSwt/QSOAHsWNHfWzHdmx/9HbY7OpRAYIAWNCCpjvq12wW20Fz\n/AS+FIn2NNYJg/NBHAmN3blcLuj1ehQKBQbLHnVShJyTXC6HVqvFF7/4RUY6k3zmURwUOTuz2Qyb\nzYYXX3wR/f39mJ6exjvvvAO/389A1GaMKFy1Wi3Onz+PL33pS+jo6EA+n8f3vvc9zM3NIRqN7iM7\nEmK1Wo3pVN1uN06dOoW/+Iu/QLlchtfrxU9/+lP853/+56Fz8I3sI92jJsQwKdLU3xjadELONktu\nQQhfIuWg6JLmsgEwSX2z7FY090fkLLVajXl8ATBLV7MvMbGykdxgrVZjcB2RDZButND9oGiQKDml\nUimTvdST3xPNodCXuj7SlsvlsFqtfKCRrjhdbz6f58NIyNqE+JbL5dDr9WhrawOwBwYslUqsDFQo\nFPhvCEVDEzGHyWRCW1sbdDodNBoNCoUCEokEsw6Vy+V9I0WN1q6fAJiYmIDRaIRWq4VKpUIymcTG\nxgbi8TgzRdHzISHzqWQAACAASURBVOTZpiyBBFpOnjwJi8UCjUaDcrmMe/fuYWtri5WfSOdZyCFK\n12w0GtHR0YFPfOITrJ5FY1dE/rGysoJUKiX4cBaJRDAajTAYDEzc0tnZue/vZrNZXL9+HVtbW0gk\nEoLHBOn5IMUkkWhPv97pdMJqtUKpVCKfz+PKlSsIh8PIZDKC1pVIJCysYTAYmEecSDnUajVyuRxT\nbabTacF7QcxqdrsdarWanRE9X9FotGm5Uno2VCoVpqamWEMbAMLhMCKRCF9rM2OSxNNtNpvR09OD\nrq4u5oqQy+XY2trCT37yE8Tj8aayYDrvdTod/vRP/xRut5sZ86xWK5LJJK5cuYK33nqLR9GEGN03\nu92OiYkJfPWrX2W6U+I0ePPNN/HjH/8YXq9XMPcArT0wMICLFy/i05/+NHOhA4DT6USpVMI3vvEN\nzM3NPW06SVCP+iOZUddThSqVSi6BkFOlIfpSqbTP4Qk50MjBUwSkUCi4bFMvalEsFrG9vc1rN3rQ\nyMHT2BTRJJKsGr1gNEJEN0zIC0fOgxR67HY7LBYLH+SFQgGBQAChUGifTnKja653pMS7bTKZoNFo\n2MGR5OPc3BxnCUKcB9FkajQatLW14ZOf/CR0Oh076HA4zHOqEomEkfBCBBMUCgWTW5w6dQqTk5Mc\nyJVKJbz77rtYXV1lgv0nZ2sP22e5XI7h4WE888wzGBoagsFgYKR+MpnEwsICrl27xgcbia4cNrpF\nQaFOp0NXVxf+6q/+ChaLBTKZjJ+NtbU1zM/Pw+/38whfPp+HRCJp6Jy0Wi26u7vx/PPPY3x8HGNj\nYxzklstluN1uzMzMwOv1YmNjg8t8jfZDJBLBarViYmICFy9exJkzZ1ies1wuI5fLYXp6Gqurqyxo\nQFlDo+eDHPHp06cxOTmJ7u5uOBwOWK1WZLNZnjleWFhgti5SwTrM6klmDAYDTp06BaPRCIfDAZvN\nxmxzRD9748YNHvdrFGAQy5xKpUJ7ezvMZjPzl1utVhgMBhQKBUSjUSwvL2NpaUmQo6YgTqvVoq2t\nDWazGRqNhlnh9Ho9stksbt26hVqtxupdzQSHpOwFgLkNxsfHEQqFMDc3x3P5QoNwctRtbW0wGAys\nGUCCLfT1dDotmOSjXqpUr9dDKpXC7/cjnU6jVtvTNejp6UFvby/u3bsnuGJB+6DRaGA2m+HxeDA9\nPY2NjQ1+t19++WU4nU7mDRBq5E9IE4H2c25uDsVikYmjqEJyVPtIOer6eVMS47bZbFCpVPwSE3ds\nsVjEtWvXsL29jUKhsE/l6mlGa1IAoNfrmc6QRCfIOVF2kMvlBMlGUhQok8lgMBjQ1taGc+fOwe12\nc/RbLBbh8/kwPz+PnZ0dZr857GGjB4xeYqfTCafTiYGBATgcDv49YmCil4I0iA97iOurFcRENTo6\nCrfbzS9stVpFMBhEoVCATCYTHFzQYUnSer29vRgdHeXPTJWAbDaLVCqFUqnEDpXWP2xPVCoVnE4n\nBgcHMTIywv0gOkidTicf6JVKhSsD9Q77oGvW6XSYmprCqVOnYLVa2RlTma+trQ1dXV0sgUmHFpGQ\nPM2kUilMJhNOnDiBM2fOYGhoaJ/2dalUgkajQU9PDwwGA2QyGVpaWphS9DDnJJFIMDU1hUuXLuG5\n556D0+lkSU3KyFQqFbq6upDP56FUKuH1ejmwOIzjWS6XY2JiAl/60pdw9uxZGI1Glm0lFSnqH5IA\nCAWLhz0jJJtosVhYQMVmswHY01Xf3d3d93ySOpZCoWjo+Oh57unpwfDwMF588UWm/9RoNACwjzef\ngj76uwcZvYs2mw2dnZ1wu93o7+9n0iES09jY2GB5yo2NjYbaxvTctbS0wOVyoa2tDZ2dnSiVShga\nGoLdbofdbkcsFkOxWMTy8jJWVlb2VQEP22fSYya5zlwuh1KpxEEMVZ92dnaYSUxIAED3hchwRCIR\notEon9UdHR1wOByIxWLY3d0VxHNB6yoUCqjVaiwsLHDCtLOzA6VSidHRUbhcLqbNFco0RxUPmUyG\njY0NZlcrFouQSqX4/Oc/z0xiJDQixMivVKtVrKys4Pbt20ilUlhbW+OkxuVyMW3yYRrah9lHylET\ngQWVNvv7+zE5OQmXywWZTIZkMolSqQSTyYRisch6s3K5nJWZDrL6/o9SqURHRwfLQ1LmqFarUalU\nuGQYiUSYWekgqycLIe3YqakpnDx5kllv8vk8P1jJZBKxWIxvrhATiUTQ6XQYHh7GiRMnYLFYEAgE\nkEqlYLFYuJ8TDAahVCr3iZkI2XOVSoUzZ86gra0N2WwW0WgUAJgLV6fTQaFQcL9M6LoKhQIdHR2Y\nnJxk6sVwOAydTrcPzEHyhI2qF7TPCoUCnZ2dmJychNPpxNzcHDY3N3kdehH0ej3zMtPXDwu2qNd2\n5swZtLe3IxaLIRAIsNgCKRGZTCZ2WNRHPOxekjzf2NgYzp49y6xXJDxBXOgUKNCB0traytd+0DWT\nTvDZs2fh8XhQKpXw2muvIRKJIBaLsXgARf27u7tQKpWHEthQRUupVHImrVarkclk8B//8R/w+/1M\nt0oSgsSs1uigJwesVCoxPDyM4eFhaLVabGxssIwrfS6SN1xdXUWhUGAWtoOMKhcOhwPnz59HX18f\nyxlSJkpKT6FQiNn84vG4oMNTKpVicHAQY2Nj6OnpgVarRa22J3NJWTkJacRiMUHSohQAqFQqDAwM\noL29HQqFgml3HQ4HLBYLK1uRBKaQFgA909TKoX40JUTt7e3cKpPL5dySEpqlUuuxUCggl8shm80y\nx4XH42GRGJlM1lBEo96oorm5uYlcLseBQGtrK9rb27GwsIBMJrOvCtKoCkcVGpIHpmCT9tRqtTLN\nbDP4AkoOo9Eoky2VSiVkMhl+fxUKBQKBADMgHoWX4iPlqCkrouz05Zdfhl6vRzQaxXvvvYcHDx6w\nqs/AwAC+9rWv4a233sJ7773XkF6OonySMfzCF74AqVSKt956CwsLC0ilUpiamsL4+DjOnj3L0mTE\nIHbQxtZnmGazGZ/4xCcwOjqKN998E48ePWKO469//esYGxuDxWIBsMfM1ag8Xf/98+fP49y5c5BK\npXjvvfdw+fJliMViDAwMYGpqCs888wyy2SxmZ2cFM2iRkzx58iRcLhcePXqE69evIxgMoru7GydO\nnEB/fz8kEgneffddftGFZNQU/T777LNwu934wQ9+gJWVFVQqFbjdbrzwwgtwuVwIh8NMI9mo90bP\nxujoKM6dO4fu7m5EIhH88pe/RD6fh8lkgtVqRXd3N0vOJRIJzuQPWpsOLsp6bTYbAoEArl27hsXF\nRRQKBajVarS3t/OhWV+ePixbIP3aiYkJ5qF+5ZVXsLS0xM5yeHgYfX19kEgkSCQSePDgAZftDzqQ\naS+oLy0Wi3Hjxg288cYbePvtt1npaGRkBD09PUin01hfX8fMzAwLSBy218QA2NHRAZ/PhwcPHuDm\nzZuYm5uDTCZDf38/enp6kMvlsLm5iUePHiEYDDbMnESiPZWnwcFBXLhwASsrK1heXsbc3BwLSiiV\nSsjlcpTL5X3XK6Q03dvbi5deegnDw8OQSqW4fPkyB7XkrNPpNCqVClKpFOLxeEMHQsFhW1sbPvax\nj7FKFSlnkXRtqVSC1+tlxyVEWlQqlcJgMKC3txd2ux35fB4rKyuIx+Nwu928/s2bN+H3+xtyUdev\nTbgFqiitra1xZmo2m7G8vIxXX30VXq8X0WhUcDmdSrjk6HZ2dlAsFqFWq2Gz2VjTnp7fRmpl9UZt\nzVAoBJFIxNek1WrhdruZProZXFI9XajX6+WWEAAG2MlkMqaNJu5/+t3D1qVEbnl5mb9GvOc2mw29\nvb0cBJFM6VGy6o+UowbAHNakjVupVFjybWtrC+l0Gjs7O2hra4PH44FarYZarRYc/UgkEvT19aG9\nvR2zs7MIBoNYX1/nl2x0dJRFwEkyUUhpRSwWo7u7G319fdDr9QiHw9jc3EShUIBCoYBcLmewCPWt\nmwGpDQ4Owmq1IhgMMsG7XC5HMpnksha9+EKvGQCDQarVKrLZLEKhEFKpFHK5HAOeqNwoFHlaq9Wg\n0WjgcrngdDohlUo5gxGLxcjn8wy6IdCGUN1vYK/MarFYuE2yvb2NSqXCcpLFYpFL0UIBgfRs0L2R\nSCTMUV6r1dDW1gapVIpiscjqXFRGbGR0MNCoBpWzC4UCpFIplxDpcNve3uY+ZCOj6Hx7exuZTIbv\nfWtrK9xuNzo7O1GtVrkcFwqF+OA8bE/owKJ9JMEBrVYLl8uFgYEB2O12rK2t8btZ//cPW7e1tRUO\nhwNarZYzZSrRk342/c10Os1OWkjfe3BwEO3t7VCpVPs04uVyOZd36bmjcrcQLIdSqURvby8D3Ij7\nvKWlBZVKhZ8VytiE8JaTM9VoNFAqldzGSyQSaGlp4Yyc7hu1tpoBXe7s7EAmk7E8JJ1rRJO8traG\nra0tDtyayfRIe5vul1arhcfjYeGOzc1N3guh5zMAfi/oLCO8QXt7O7LZLAd19XicRkaZP+FyAECt\nVsNkMuHkyZOoVCqYm5tDOBw+VAHtoGsmUCEFMlarFZOTkxgeHoZEIkE4HGb8U7PZNPARc9T1H8Bs\nNkMikTDcn6D+Tx4yKpWqYX+p3nQ6HUZGRqDRaBAOh7n/QaValUoFrVaLXC63b/6tUblCo9FgZGSE\nJRJJKYUAcSaTCXq9njPsZl4KhULBfNZUYqI+TltbG0ficrm8YaZERqV6l8sFm822T+CBesB2ux0a\njYZLUc1EgQaDARqNhnvbEokEarUacrkcdrudgUFSqZTBQ0KsXh2MkN0EUrNYLFCr1RxY0OEkZK/p\nBS4WixwMarVamEwmLsUSoIz6kM2MtmSzWcTjce7hUUmMNLRJOtDr9XJ2JgRYJxKJEIvFGBnb3t6O\ngYEBKJVKtLe3w+FwMG+91+tFMplsWHGhbF0sFiMej8NiscDlcgEAurq64HQ6OTh++PAhVlZWeD8a\n7QW1F0jrWSwWM7o+n8+zE93a2mJwmtAqDnHUW61WyOVyyOVyDA4OIhqNYn19Hel0mpH6FGQJuXck\np0qlbsq2ent74ff7+X4RbkHoQU9BC/A4O9vd3WWefaPRiHQ6zepXQgKs+v2g9ajsbLFY9gHWrl+/\njnA4jEKhcCRKY1qfWjZdXV2cUV++fJnxF80GAPSz9VruHR0dMJlMeP3117GwsMDKWs0kO/VlZ1rX\n6XTC4/HgjTfewPLyMqLRaFModXoe6qmcpVIpHA4Huru7MTg4iF//+tf71v2Dd9QAePONRiM7iGq1\nCrFYDL1ez+hn6uURKAcQxklNETuhnSlSLhaLjKam0la91nWjtQmlSCh1rVaL0dFRFAoFGAwG2O12\nGAwGBkA8+eAc1pdVq9XQarXsnHU6Hfr7+2EwGNDf38+HMaGegcfc34ddM41j6fV6Bn+dOnUKsViM\n1zUYDPvWFbIXtGeU5ZfLZUxOTmJ9fR1yuRxutxt6vZ4P5ic1xQ/bCxpxy+fzAPai776+PpTLZR71\nWV9fRzKZ5CxQSBlLLBZzr3VzcxNyuRwDAwOMhiV9bhrPamZ0j7Ik0rIeGxtDS0sLtra2IJPJWIs4\nGo0iGAwKzpqo7Ob1egEAfX19OH36NNRqNa9bKpXw6NEjeL1eJBIJweI19DPFYhESiQTDw8Po7u5m\nZ0cIeJ/Px05aSIDY0tLCVTAAHAxmMhlks1ksLi6ys6ZRLyHXS4BLjUbDvXLSGiY8RCgUwvr6OgqF\nguBsjEBTVEKuB42SNkAsFmPAJR3cQoJaAjhRaZoqDQaDgR347OwsgzubRTnXjzCqVCp0dnbCZrPB\n4/FAJpNhc3OTA0JA2Dx5/eQMZefUlz579iwcDge2trYwPz/fNKEK4RcouVEqlQyiJd326elpbG9v\nNxVY0DXXTxKp1WrGMvT29uJf//VfudrUjPOnxIF8CbVJTp06hYGBAXi9Xjx8+LCp8v/T7CPnqKmf\nsL6+jt/97ncYGBjA2NgY7HY7gL2Dw2QywW63QyKRIBgM7qv/H/YwV6tVxONxTE9Po1wu4/z58+jq\n6mIgGc37EkiC1gXQMKovFApYW1uD1WrF2bNn8fWvfx3ZbBYajQbFYhFWq5VnnUOhEJdthagn1Wo1\neL1etLe3Y2pqCh0dHezwlUolrFYra60S606jjIxeZhKy/+QnPwmLxYJyucyHEomTkHZufTmuUcm3\nWq1iY2MDJ0+ehNlsxnPPPQez2cwv4vr6OiqVCgup06y8kCwyGAwilUoxwveb3/wmlxGj0Sju3LnD\nLzN9TiF7TGNugUAAJpMJ7e3tePnllwHsyeZdv36dy6nNRPOZTIZnMwuFAoaGhvCpT32KR6/+4R/+\nAWtra/vmZIU46Wq1ikwmg6WlJXZMHR0deOmll3h06urVq5ienuasSajkZaFQwPb2Nra2tmAymRi9\nSv352dlZ3Lt3jwkihB5CJFdIvX+bzQatVotqtQqlUgmHw8G9TUIhH2YU7BKw6M6dO5ifn+fqm0Kh\ngNlsRl9fHzo7OxnxWyqVDnUk9QhkKsGurq6ybCGNaV26dAmdnZ0MSq1H1D/tHtbzIVDSUavtjUst\nLy9DpVJxpkftFr1ej2QyyZWig54NQsbX975tNhs0Gs2+sTW5XL4viG0k51sPLpRKpZwkEX6FEpOu\nri6kUincvHmTwXR0vQddM41xUmVFrVajs7OTeSc0Gg2GhoYwMDCAH/3oR1hbW0Mmk2kIyqJqBSUg\n/f396Orq4hE1hUKBwcFBjI+PI5fLYWZmhoGbjcao6NwhPXiFQoHh4WHs7u4in8/DaDTiq1/9KvL5\nPH74wx9idnaW26P/wzJqkUjUCuD3AOTv//xPa7Xa/ykSiYwA/h2AB4APwJdrtVri/d/53wH8BYBd\nAP9brVb7TTMXVavVEIlEWKDeZDJBrVYjGo0inU6ju7sbExMT8Pl8zFIjJHqr1WqIxWK4efMmfD4f\nzp07B6VSiVAohHK5DIvFgsHBQc5w6kFIjTY3mUzi/v37SCaT8Hq9GBkZwdbWFqPHR0ZGUKlUsLi4\niK2tLcHrAnti5W+++SYSiQR6e3uhVCrh8/kQjUbR29sLq9WK1dVVxGIx7ikKeSDy+TzW19dx+/Zt\n9Pf385gK9bJ0Oh3EYjG2trb2lSCFAE7i8TiWl5dx9epVjI6OQqPRIJ1Oo1AocAmOyvj1s7eN1q5U\nKohEIrh16xbGx8fh8XgY5U5zpzTnS+V6IXtMB3sul0MqlYLH44FCoeDvm81m9Pb2IhwON62bTNcS\ni8XgcDigUCg4G97d3YXT6cTm5qbgTAx4XNWoVqvI5/NQqVTo6OiASCTiNtHu7u6++c1mDggKcAwG\nA0wmE9LpNMLhMCQSCSqVClQq1b65XCHrAY9739TTjUajyOVyfEB7PB5oNBp2Lo2M1pPL5QwIAsBt\nEaVSic7OTnR1dUGn08FisTRclxxTS0sLjEYjt392d3eZW4H2dmpqCnq9Hi6Xi0F9h61LWbRer+c2\nWT3BEgVU1WoVVqsVVqsVLpeLP9dB104ATpK9lclkPKtP/AAE8iqVSkin07BarVy1OAxFTq2fwcFB\nbi/QnDdVNWlfKVnQ6/WcoR7kVAnJbTQa4Xa7cf78ea5UVKtVyGQyxsnQc1fPTXHYeKtCoYDBYEB3\ndzfMZjNeeuklXkMikcBqtUKj0UAqlbKuPQUdjRI+hUKByclJtLW1YWpqCkajESLRHhUpjRVLpVJ4\nvV4G3NG69LmbddZCMuodAB+v1WpZkUjUAuCaSCT6NYAvAbhSq9X+m0gk+q8A/iuA/yISiYYAvAxg\nGEAbgDdEIlFfrVYTfLJR439zcxPb29vY2NhAS0sLstks9zoNBgPC4TCi0SjP4jY66OlwJOBNLpeD\nTCbjUtvU1BTUajWTITSjl0wHcS6Xw8bGBubm5hj5KRaL8Xd/93fY3d1lbWcCWDRyqJS9PnjwAFtb\nW3j48CGkUum+8Y9z584hGo3yKJhQR12pVJBMJjE3N4fLly9DJpNhd3cXuVwOzzzzDIaHh5HL5bjX\nS8GQkGwvl8vB7/ejXC4jk8nwSJ1YLMaJEyfQ3d3NoKx6h9po7Wq1ikQiAa/XC61Wi1KpBJ/PB7FY\njK6uLoyPj0MikfA9bQZwQpkklcBTqRSi0SgfJIVCAXNzc01raFNGTwxIOzs7ePjwIVcv+vr6mLCG\n1hXq/CjjItBeJBJBMBhEW1sbjEYjenp6IJPJkM/nBZeQ6WAm5rRoNIq1tTWsra3h4x//OGw2G1wu\nFwKBAKanpwXtAZWgKfgrlUqIxWJYXFxEpVKB2Wxm1jaLxcIERI2MRiKVSiWPcBLbXbFYhE6n49FI\nIq9pVAEgUJBer0dXVxdzTBNIsVwucymVWnJ0uB8WaFGZlGb1c7kcRCIRisUiA7OIeZCchkaj4WoT\ncPC7R/fs9OnTUKlUvMdEwFR7H5QlEomwsbGBarXKY4AtLS0HMnFR4CGXy/HMM8/wKFomk4HNZuPg\nQ6VS4dGjR5yVEtMh8X4fVF1obW1lcqGOjg6kUinGtqjVaqhUKhSLRdy/f59HZ3U6HZ8bT1uXgpaP\nf/zjOHnyJKxWK4rFIlcEWltbYbfbsbOzg5mZGfj9fm7xESDyoGePWix/9md/Bq1WC6lUytdMgZLB\nYMDc3By2traws7MDo9GIXC4HAIJbT09aQ0dd21uVBhhb3v+vBuALAJ59/+s/APAWgP/y/td/UqvV\ndgB4RSLRCoDTAN4TelEULdEBThlMPYJRrVZjZ2eHwWBCe3qE0CyXyzyHTf9tb2/z/KpQQA8ZkSiU\nSiXs7OxwVlMul6FUKvehaOvJPYTsRaVS4REVYjUrFApMkgFg39iGUIdHD2a5XMb09DR/3p2dHXR3\nd3P5v555S+g1E7qZMgDK/PR6PaxWK4aHh1EqlRqOTj25LpWXcrncPpQsjfSMjY1BJBI1FbAAjzNq\nAhXSLOzW1hZOnDgBj8cDq9UKlUoleI8pk6CxQIfDgb6+PmatqtVqPJpltVobEuvUr0vPk1QqxcTE\nBIaHh5FOp3H//n2kUikoFAr09/dz+VTo4UAHN/XwlEolbt++jVu3bmFnZweXLl3injIFtUKul0rQ\nNAEhkUgQi8UQjUaZEMZisXC5koh7DjOpVMqUkCKRCJubm1yhoeeK+Af6+vq4UtcoaKGesdvtxsjI\nCHw+HzY3NyGVSjmzVqlUcLvd6O3tZcrQcDh8aDBLPWyr1YrR0VGsr68jFApBr9dzcEEMVw6HA8lk\nkoPGRq0mAp8NDw/D4XAwmxvwuNzudDpRq+3N/EYiEQ7KCAN00L0D9sChFy9exPr6OoLBIIC9SpFW\nq4XVaoVIJGLswu7uLgcYFPw/LYCp1Wowm804c+YMxsbGcP36dZRKJSQSCQZ5iUQiRtNXq1U4HA4+\nrw8CEYtEIrS1teH5559Hb28vVlZWsLS0hNbWVnR1dUGtVkOhUCAajSIWi2FjYwOdnZ1MUiORSJ7K\nF0Hvc2dnJyYmJvDo0SPGEJw+fRp6vR4Wi4UDZp/Ph1qtht7eXhQKBW6ZCaEnfdIE9ahFIpEEwF0A\nPQD+n1qtdlMkEtlqtdrW+z8SAmB7//+dAG7U/Xrw/a8JNjoECTBERhnk6Ogo9Ho97t+/3xT3La27\ns7PD4xnvfz6IRCKMjIxAqVTyLGszUHoauaHeGq1LL4BYLGZ6uWZRhYQ2TiaT/OLQiEF/fz92d3ex\nurrK+yDUmZLTy+fzvDa91C6XC8VikftyzfRW6N7RugQiI9INrVaL1dVVPtiamYekvjYxhlEZzGAw\n4Ny5c0in0zwnK3QvyEHSz29tbWFzcxPJZBKhUIizqmb5zqk0TWXSrq4uaDQa/OxnP8P6+jrPz1ar\nVe6xCski6XltaWmBwWDA+fPnUa1WcfnyZczMzKC3t5cPjUAgwEjnRtdMNIsmkwkjIyM4deoUpqen\n8fbbb6NQKMBkMmFiYgIqlQq3b99GIBAQdOgQ8Y3VasXFixchk8kQDodx7949eDweDA0N4Utf+hIm\nJibw85//HG+99Rbu3r3bcO1arYaxsTG0t7fzZ15eXsby8jIqlQocDge+8pWvwGQyYXNzE7/4xS/w\n9ttvN8xqdnd3WWDh5MmTeOGFF7hiRwEylVbv3buHt956C3Nzc/D5fIc+c4QpMZvN2NjYwKc//Wmo\n1WrOmCmJoH741atXGSfQaPSNph42NzfR0dHBZySV1qPRKBYXFzE/P49EIoFEIsEjk1RCPqgkK5VK\n0d7ejt3dXVgsFvT19TGBVCAQwLvvvot4PI6VlRXs7OxwD5z0Dqha8OTaMpkMo6Oj/HOnT5+GQqFg\nR/nKK69ge3sbwWCQKzyFQoEJnhKJxIHPNWFiqPT/+c9/HiKRCOl0GisrK/jnf/5nBINBrrJQib2l\npQWJRGIffXS9UTZNWf+FCxdgs9mQTCaxsrKCH/3oRzwxVCqVYDabmSgnFothYWGBK3LNmCBH/X7Z\nelwkEukB/FwkEo088f2a6ABhjYNMJBJ9E8A3m/kd4LFiC6FxPwypNjKpVMoOXMh8rBCjAzWXy+0r\neR/VarXH4wCtra0MKDrKzT9oXTo4aFb0g+wxResEoHE4HPwyCCVmedq10uGiUqmYxU6n0zEH/FH3\nolqtQq1WM8ClVqvB6XRCIpEgk8nwgdmMUbCm0+mg1WrhcDg4MOzt7eVebbNcy5T5EqOU2+3Gzs4O\nPvvZz6K7uxs7OzsIBAI8oibEFAoF88kbDAYma5HJZDhx4gTPTtcHcI2MAhaNRoPu7m6YTCbE43H0\n9vbyrL3D4cDOzg7u3r2Lhw8fCtqLarWKaDQKu90OsVgMi8WCrq4uvPjii4zIlkql2Nrawu9//3vO\n2BoZYRVWVlaY8Y64CmgmPpPJYHNzE2+99Rbu37+PSCTSkKGOpifIoQcCAW4vVKtVrK6uMhI7GAwy\nzzWh1A97gkBenAAAIABJREFUD3d3dxEOh5l60+FwYHNzk1s4S0tL+54FmlOnsv5Ba9P7u729Db/f\nz0FPPB7H3NwcZmdnEQqFUKvVIJfL+fwolUosyHFQO4ACCHKYEokEa2tr8Pv9WF9fx9WrVxmEpdVq\nGQAXj8eZaOhpAUC1WkUoFMLW1hYUCgW6urqwvLyMhYUFrKys4MGDB0gkEgymBcAc7TTX/7R1a7Ua\nByfE4x0Oh7G1tYUf//jHnDiKRCI4HA6USiWkUim0trYiGAzyDP9RrCnUd61WS4pEojcBfBJAWCQS\nOWq12pZIJHIAiLz/YxsA2ut+zfX+155c6/sAvg8crJ71NCP6SIVCwYjhD2r0MLhcLn7YCFn4QZwq\nGaEkqR/1YVwzMV45HA60trbyQ/tBjfpkHR0dDEQSqlh0mBG45cyZMxgcHMTs7KzgvunTrpEOfwK5\nPPfcczh16hSUSqVgSsgn16Se7+7uLux2O2w2G/R6PT72sY9BLBbj3r17jPptxggVSwCZZ599lsFT\nVqsV//Zv/wa/39+UvB7dJ71eD71eD7VaDb1ej+eeew5jY2Mol8tYWVlhNiohRu8BzdF7PB4MDAyg\nVquxOMTy8jLu37+P2dlZBjg1snK5zFkcAa96e3sZ1QsAoVAId+/exbVr1xCPxwUHACsrKxwI6nQ6\n2O12nnzIZrN44403cO3aNTx48IBLtkIxFpTVhkIh2O12aLVaJBIJVjrb3NzEwsLCvjZLo3VLpRLW\n19eRy+W4zy2VShGNRuH3+5mkhwJvoe2QWq2GYDCIYrGIcDgMh8OBVCqFzc1NHssjZ0zUvbVajVsM\nB2XrlOl7vV68/vrrcDqdiMViSKfTmJ6e3td2pB4w9fjrdRKeZpVKBTMzMyiVSggEAqjVagiHw1he\nXma8DQUKBJgUiUSM/TmssvXOO+8gl8tx++e3v/0t5ufnWUSH5r93dna4507O/zAMQz6fx89+9jOY\nTCY4HA5MT09jeXmZ/x7hFIibgwJ+askcNTkRgvq2ACi/76QVAD4B4P8G8CqA/wXAf3v/31++/yuv\nAvh/RSLRP2EPTNYL4FbTV/YUq9VqkMlkjKqjg/XDyH6pX0KMVFRa/qBrAo/niWndZtRZDlvXbDbD\nbDbz1z+ooybn19LSwgIoVEb+IEYvst1ux9TUFGw2G65evcpEJ80+uPUtjGKxyPSqRDdLJfWjvBC7\nu7uIRqPo7+/H6Ogouru7uVR/7949+P3+I61L1Yl8Po+PfexjXOLL5/O8rlCHWn+Ak2Sqx+NhGtJo\nNIpAIIArV65wqa2Z68xkMky+8uKLLzLHeTKZxK9+9Sv8/ve/5+xMiFHrZm1tDTdu3MDW1hb6+vpg\nNBqRSqUwMzODd999F3fv3sX29nZTQL1EIoHZ2VnkcjkEg0EsLy8ziOnRo0f8nB2W2R10zYVCAbOz\ns/D7/Xwe0P6Qg6IDnQI9IeuWy2Vsb2/j5s2b3L8lsaF6B1HPdCXk2knidXt7G4uLi1xZetKhEY0l\nrduo2lKpVFAoFPDee3swI7rOJ9+xTCbD5xt9/7A9oYxzenoaMzMzvC5dL13X7u7uvj2nPTvsGQmH\nw7hy5QrefvttPivqf6d+HSKoEjIqWygU4Pf78Z3vfIcDnfq9oPUzmQxXUgn/AhxN+xxAYz1qkUg0\nhj2wmASAGMB/r9Vq/5dIJDIB+O8A3AD82BvPir//O/8HgP8VQAXAt2q12q8b/A1BVy+RSOByufCV\nr3wFzz77LL797W9jbW2tqTnOg0wqleJb3/oWzp49i0ePHuEHP/jBkQ9lMgokOjo68O1vfxu3bt3C\nL3/5Sy77fhBrbW1FT08PLly4AKvVin/6p3/iCPSo1wo8nhH8whe+gHK5jNu3bzN6+6hrEpjIYDBg\naGgIKpUKb775JtLp9JGul/pjlJH09PTA4/Fwn2xzc7PpNek6CW1KgiRmsxnhcJhZp44UDb8fWcvl\ncv6XZt1JC/goVi9wUU8d2yy/8tOM8Ao0OlQfHHwYdpSRsWM7to+qfYDqqyA96oaO+n+GCXXUdCgN\nDQ3h4sWLeO211+Dz+T6U0iwJXIyNjaFareLatWtHPvDrjQ7/0dFR5hMnRPQHXZeG+ltbW5FKpY6c\nRT7N6qk6m6UAPLZjO7ZjOzZB9sfnqN//Wf7/j8K1H9uxHduxHduxHdEEOeqPHIVoIzt2zsd2bMd2\nbMf2/yf7g3PUx3Zsx3Zsx9a8PQli/TDwBoSTqCdPEQLKamQikYi5F0hvoVAoNMVB8eR6dL1yuZx5\n0YE9JDeRUB3FCMtBQjMi0Z6QUiaTYQXID4pJOnbUx3Zsx3ZsR7QPGxRHKG8C8wkdzxKyJvGMEz83\niZ4cdW0Cc7a0tECpVGJ8fBzb29tIJBKIRCLM4d+s0bgdjUCdPXsWNpsN169fx8zMDGKxWNN8+7Va\nja/VYDDg4x//OF588UXo9XpsbGzg+9//PtbW1gTrwNdbtVqFSqViZbLz58/jM5/5DBOcvPrqq7h+\n/fqR6UOBP3BH/T8KOVpP/fhhr0n2Ya9N0fKHCfx68sA4yijVQfa0KPzDWvvJEbgPSjJTf9/ocAIe\nK6odNXuoX7d+P4jQ5aj3sv5ZozXp60JGWw4zoi6lfahUKkwC8kH3ggCS9ZSn9axdRxnDfPK9q5+X\nB/b2p1AoNLUf9dMMBLik+0b/0vebyaTqWQzpc9O/9ajiZq+13lHTf/VrAzjSfavfS41Gw8Qkra2t\nzLnebCZZf+ZoNBq4XC6+V+Pj4/D5fEw61Mw111+rVCrF0NAQf3YSayHBnWZBuZSlt7a24sSJEzhx\n4gQTc509exYPHz7EnTt3jkQdSvaRdtRPvhD1N4bmAI/qPOhhqEc216971LXrDxwAfCjWEwHQ/OtR\nXjh6iWndJw83ijSbCQRoDZKxpLVpXSIGaHbt+j2u1wmuPzRIfCGdTu8TKhGyLr0gxDkN7O03MVJR\nEFD/Agq5ZhqnIkpBckT0WSgrIWGJWCwmaE9on202GxQKBZRKJR/s9dSkra2tzL5E852NrH4vOjo6\noNFoWHSB9JKBvTLfxsYGc2ELMdoPtVqNEydOsAABqYIBwNbWFnw+H2KxWFOHM3GLKxQKVpUC9sYP\nNRoNz7Cn02lmjhJi9IzUK3zRBIZKpWJ6zXg8ztrlQtetfw/pvtP7QsRG9fPRR1mX1q5UKuyo6icw\nmg3064PAQqHApDD1CclRHZ9KpUKlUkEwGIRKpUK5XIbBYGBCo2bXpX+NRiNisRhSqRT0ej1sNhs6\nOjpYgrUZx0fnOZHsLC0tYWlpCUajEa2trejt7WWeA9KYaHYfDAYD9Ho9ZmZmsLCwwNShPT09XMY/\nagn8I+Wo6/sHMpkMdrudifydTiecTie/ANlsFr/97W8RiUQEae3SLC+JqRuNRvT19cHtdqOjowPA\nY+GI1dVVzM7O7iMNOMx5kIwcMUWZzWZcuHABnvdJ5ekgXltbw9zcHFZXV1kp6bAXrv7FNRqNMJlM\ncDqdGB4eRmdnJ0fw1WoVV69excLCApecGgUC5HRIZ3ZsbAzj4+Po6upiZywS7clVrq+vs8wmObxG\ne02HllarRXt7O7785S8zxzqRPqRSKWxtbWFjYwNer5d7UIftC91HrVYLl8uFU6dOwWq1soYx7Ue5\nXGaubgAc4R82X0xqPuPj45iammJay42NDc4cac46m82ipaUFgUAAN27cOFTMnjRx29ra0N/fj89+\n9rMQiUTIZrOIRCJIJpOQyWTsANVqNfx+P+7cuYNwOIyFhYVD7+PQ0BDGx8dx4cIFdHV1QaVSIRaL\nIRKJMJ+9RCJhlZ9XXnkFyWSSBSoO2ueWlhYMDQ3hueeew/nz59Hb24uWlhbmnScpyVgsBqlUisXF\nRfzqV7/id/Kwa6Z5/U996lOYmprC6OgoZDIZl2Tp2ZZIJExeEQwGcf369QPXpbVJUcxsNuP555+H\nXq+HVquFwWBgNjuSOrxy5Qrm5+dx48YNflYOW5sCFqPRCJvNBrPZDK1WC41Gw/KONpsNu7u7uHbt\nGn75y1/yPThon+k/pVLJdKUKhQIqlYqfCWBPLCSfz2Nubo5V+BpdL+0h8QLQmSKXy3H27FkUi0Wm\nryV2vEbOhM4OmUzG96xarSIWiyGTyaCjowOTk5N47733EIlEUCwW+Yw+zGgktKWlBXK5nHksKEge\nHR3FmTNn4HA4cOfOHaysrHDPulFwT8Em8Re88cYb7OjFYjH+9m//FqdOnUKlUsHS0pKgKgudn5TQ\nJBIJ/Mu//AvTOotEIoyOjuITn/gE2tvbWQPiKBWtj5SjJuUq2tCTJ0/ixIkTrH9KNHg6nQ65XA6x\nWAy3bt1CNptlTtyDTCwWcxan0+nQ09ODl19+GWazGbXanogEUXG63W7mCE6lUocGAfWRsEKhgM1m\nw9TUFD75yU+iWq0im80im82yVjIxX21vbzfUPa1/ibVaLYaHh3H27Fnmh47H41Aqldjd3YXb7UYw\nGOTMV0ifpd6hPvfccxgZ2aNwj8VizBCl1WphNBphNBqbijTpcGhra8PFixdht9uZOAQAv4zENFdP\nzdnoIW5paUFbWxsuXLiAEydOIBQKwe/3I5/P80EhkUjgdDohEong9/ufyqT05F6QctalS5cwMjKC\nUqmEcDjMKk+tra1wOp0YHBxEPp9HPB7n4O6wgIueuaGhITz77LNwOByIRCIIhUIIhUIIBoOw2+1Q\nKBSw2+2QSqUIh8PQ6/Xw+/0HrkuAG3KmJ0+ehFwux/Xr17GysgKfz4dKpYLu7m50d3fD4/FAKpXC\n4/FgZmam4V7I5XJcvHgRn/vc5+B0OtHS0oKrV69ieXmZaRhJdYgk/qiSdNg103V3dXXhy1/+Mjo6\nOph7OhAIIJvNchapVquRTqf5/W9kEokEer0eFy9e5P2up3KMx+MsuJDNZlmFr1FZna7b4/FgeHgY\nAwMDaG9vZ6pdorkMhUJQqVSQy+WIx+N49dVXG14zOb2+vj5YLBY4nU5otVp0dXXBZDJBJBLB6/Wy\nBKZCocDNmzcFOWr67CaTCTabjfnhKZlYWlrCysoKZ3uhUIhVnhpdM/Wm6QyhatHp06cxPDyM2dlZ\nAEA2m0UikeAA7CCrL/FTVYJ0HSQSCQYHB3HixAnk83kONihjP8z51SdLRBcKPGZoMxqN8Hg8CAQC\nyOfzkMlk3E9utA8UJBArXLFYZO1p4uHX6/XY3t7mSuhRyFE+Uo6aNHvrlaH6+vqgVCqRy+Xw6NEj\nGI1GFi8/d+4cEokEfD7foVEr8Pjwr1b3dFjb29tZJ3R5eRmRSARDQ0Po6emB3W5HLpfD9evXOQo6\nbF26oRKJBBaLBZ2dnchkMpifn8f6+jrEYjFeeukldHZ2IpvNIhAIwOfzNdyP+mvWarXweDxwOp0o\nFAr49a9/jWKxCJvNhuHhYXR3d7Oj3t7eFrDbj18MrVaLzs5OAMDMzAxmZ2dRKpXQ1dWF3t5e1tzd\n2toS/IBRv4ruoc/nw4MHD7CxsQGRSIQzZ85Ap9NBKpVyNtqoL0uHpclkwtDQEPr7+2EwGHDlyhX4\nfD4UCgWIRCIMDQ3BaDRCKpUiFAoxh2+jtUk0pLu7G0qlEqFQCF6vF4FAALlcjg9Ni8WCra0tpitt\nJMagUqngeZ832+l0wufzYXV1lcvFlUoFBoOBKWGJXY1EGQ7bY6o2ORwOFItF+Hw+3o9kMgm1Wo2x\nsTEYjUYug6dSKXa0h60tl8sxMDAApVKJZDIJn8+Hn/3sZ4hGo6wDPTAwALVava9a0ahKJJFIWJPZ\n4XCgVqshFAphfn6e1eVUKhXLESaTSSwtLTV8F2mvqbowMTEBvV7P2Wc6ncbCwgLLP6rVajx69Aib\nm5uCSuoymQzPPfcczp49C7fbDbFYzNliMpnE5uYmlpeXYbVaEY/H+d42MolEAoPBgNOnT2NwcJAp\nW6k1QopwWq0W6+vrSKfTgiiOqfpkNBrhdDoxMDDAfXOn08mOxGKxoKWlBcvLy1CpVA0Jmegdor6s\ny+XiQKetrQ0ulwsmkwkulwupVAqhUIjFjg575ugspV692WxGNpvlikJnZyf0ej2MRiMsFgsHXET3\netjZRJ+HqGSpVw2ABVwsFgu0Wi1r2ZMDPmwv6EwhMQ6tVrtPlbG/vx+12p5gCcmnUiLVjLP+yDlq\nKsVRL4kyl4WFBTx8+JAP/52dHQwMDODevXusRnOY1feFKcoJh8N8OJDes0QiwdDQEHp7ewWzctHh\nRI5aoVDg3r17mJ6extbWFvcqzGYzPB4PtFqtIE1j+r5IJILT6YTNZkO1WsXKygoePXrEJRaDwYDJ\nyUnuFTXTF6OgR6PRIBgMYnZ2FvPz81y2Hh4ehk6n46xDaI+axFOGhobgcrlw8+ZNrK2tIZFIMPrU\n4XCgXC4jGAwKlo8Ui8Xo6+vD0NAQuru7kc1mEQwGEY1GsbOzw1mvWCxGKpVizerD5DQpgzQajRgc\nHORgiPSow+EwarU9Lng6lEKhEDY3N7n/fVgAQHKWJpMJMpkMm5ubWF9fZ51jm80Gp9MJvV6P3d1d\n+Hw+1hM+KACtr+IYjUZUKhXEYjF+nkkPnfaZRCrW19cRDoeRSCQOrRJRZqrRaFAsFhGJRHD9+nXM\nzc1hZ2cHdrsd3d3dUCgUKBaLCAQC8Pv9iEQihz5/hFfo7OzE4OAgyuUylpeXcfPmTczMzHBwoVQq\neT/C4TCCwWDD0rRIJILb7caFCxcwPj4OnU6He/fuYWFhAWtra/taIXT4BwKBhpS+hEuw2Ww4c+YM\nPB4PACASieDGjRtIpVIIBAKIRqMol8uswkdVs8PWpWoLaRyTstjm5iZyuRzC4TBLxUokEq4kNsqm\n6d3W6XQwGAzo7+9n7APRzE5PT+Pu3busCx8MBgUhtamHTsJACoVi3zuUyWSwsbGBaDSKaDTKghSN\njErqhJOh/ny5XIbFYkGtVuOqC+FdhJx39Z+H7g993Ww2o7+/n7EsJDsKQNC61FOndSlbNxqN6Orq\n4sqi2Wxm9a9G1b2n2UfKUddqNc5ODAYDCoUCAoEA7ty5g+XlZYTDYSgUCsRiMbS1tXGDXmhJtlQq\nobW1FX19fdDpdHjvvffw4MEDeL1eiMViGAwGXLhwARqNBltbW7xuow2t1fbEQsbGxvhA/ulPf8q9\nRZVKhfb2dlit1n1RoJAbReCiM2fOYGBgAAsLCyxeQFnHxMQEP9xClXyAPWd6+vRpXLp0CZlMBvfv\n38f9+/dRKBQwOjqKS5cuYWxsDMFgEJlMRvA+i8ViXLx4ERcuXMCZM2ewvb2Nu3fvIh6PQywWw2az\n4fz58yiVSiyZJ2SvKbN/8cUXMTk5iVwuh/n5eQQCARaxn5iYgMfjQSQSwdzcHBYXFxvKPEokEphM\nJjz//PMYGRlBpVLB+vo6FhYWsLS0xOXic+fOYXx8HA8fPsSVK1cQCAQa8pVLpVJMTk7C7XbDZrMh\nn89je3sbXq8XsVgMcrkcn/rUp3D+/HlkMhnMzc3h8uXLCIfDhwK+qF9KQvXhcJhFPkqlEmw2GwYG\nBvDCCy9Ar9fj4cOHePfddxEIBLC4uHiok65HsSaTSUilUi7/t7e3Y2BgACdPnoTdbsdbb73FqmIE\nxjnMKJA6deoUurq6sLa2hnfeeQerq6soFApwu91QKpXIZrPY2NjA/Pw8Yy4aZepKpRJ//ud/jmee\neQYymQxerxc///nPEQ6HmWKXHBEB4RrpBFBWOjAwgM985jPo6enB9vY2rl27hkAggLm5OaRSKRSL\nRb5fFBQ2CmplMhmsVivGxsbwzDPPQKVS4ebNmyysQcpXhA2hgFCIhrtYLIbZbEZHRwe6urpgs9mw\nsLDAPeh4PI7p6el91SYhWV49SK+trQ0mkwmZTAaVSoXL/aQlTS2nZkbMqE+t1Wr571BbqFgs4jvf\n+Q6fGULBp7QfdJZSa9XhcPDZ/I//+I8cxDY7hUIBF0n5ymQydHd3Y2xsDD09Pfj+97+P+/fv73se\n/qBL3/WjAmKxGIlEAnK5nHtTer0ecrmc+6a7u7vI5XKMLmxU+6dsOp1Oc6Qrl8vR29uLQqHAWatK\npUKpVGJJSoqaDlubQArUd9VoNBgdHUUul4NWq4XT6YRKpUKxWOQyiZB1KTI2GAxQKpUMZhkYGIBe\nr8fY2Bg8Hg9WVlZQKBT2ASQOe5lJzpAOTvo7J0+eRCKRwMTEBHp7e2EwGHi+kK6n0TWLxWLodDq4\n3W4oFAqUy2WMjIwgFApxBk+6xoVCAYlEAgD2odmfZi0tLTCbzWhvb4dUKkUqlUIqlYLb7UattifF\nODIywn1jKkvTS3rQunRgUkmN+tIul4uDi6GhIQwMDKBSqcDn8zF2gfbjICOgoVwuRzqdRrFYhNPp\nxPr6OoPi+vr6ONt7+PDhPgR8IyuVStjY2MDOzg4MBgPcbjcmJibQ1taGgYEB2O123LhxAzdu3IDX\n60UqlRLcd6tWq4jH49BoNGhra8PU1BROnjzJz8vKygrefvttbG5uCtZwp8NMo9Egn89Dr9ejv78f\ner0epVIJmUyGM7HNzc19WchhJhKJGORFAatYLEZnZye0Wi3i8Tg7EXpPhGi407odHR0wGAy8Lx0d\nHWhpaUGxWGRApFgs5pKpkGumQMtkMnG52+PxQKPRwOFwsAZ6/bMrxEmT4yAAndPphE6nQ3t7O/e5\nCd9Tv55QR0o9aY/HA4vFwvKNXV1dqFarmJ+f3zetINRJk2NWqVRwuVzc+9fr9ejq6oLP52OApJCK\n5JP7IZfLYbVaYbFYAAAGgwEDAwNoa2vDq6++ytcs9HopaCHEt8ViQaVSgVqtxvj4ONxuNxYXF7G1\ntfWBnDTwEXPUwOMRhFwuh4WFBUZO63Q6AOCymclkQiwWY0UjIdKRVPLJZrPw+XzQ6XRwOBz8vcHB\nQRiNRohEjzWY6ZBvdPPoYI3FYujp6UFXVxevazabGWhDGU/9uvRzh113qVSCQqGAyWTivi85LZPJ\nhAcPHnBgIZVKGbXdyKGSfq9Op8Pg4CBHkyMjIwxsyufz+0bAGqGy6w8cGrE5ffo0SqUSP9AymQzF\nYpEDCwKNHLTP9Ll0Oh10Oh331+x2OyN7jUYjtFotC7ST1iztx0HW0tLCh6NKpUI8HodMJuNyNM1y\nKpVKBvHRM3eYo6a9IIKJTCaDra0tuFwuXLx4kfenXC4jEAjA6/Wy3KWQ9gLdh0AggFgshv7+fnR3\nd+NrX/saFAoF6xH/5je/4RJqI2YnOvyouuX1emG1WuFyuTA5OQm5XI5QKIS1tTX87ne/g9frZTCd\n0JGhWq0Gv9/PB9rw8DB6e3uRy+Vw7do1hEIh7sUKqQ7VB71+vx+7u7t8yBMgi0BeKysrTamL0UFc\nLpcRCoWwuLjIGblcLkd3dzf0ej2DRoUe9BSwiMViZDIZbG5uQiQScX9Yr9djZGQE6+vriEajgqtw\n9I6SrjqwX35SqVTC4XDAaDQKyvqf3It6DW2amU6lUmhtbUVHRweq1Spu3LjRVA+W5rkJwGiz2dDb\n24tYLAadTgeNRoOuri7Mz8+jUCgIVkukM5amLux2O4aHh9Ha2op8Pg+1Wo2enh7GKglN+IDH73ZL\nSwtUKhW3HtfX11lv3WazcdXigzhp4CPoqAHwi0AapfVAo2KxCIVCAblcjlgsxmLwjTIbWnd3dxeB\nQADFYhHnzp1Da2srcrkc5HI5FAoFZ8bUyxKyLgA+MAnNarVakU6n0draCgDsmKiv2SjjfdK8Xi9G\nR0dhMpn2gRx0Oh33PSORCD9AjYyizGg0ilgshoGBAe5jUt9TqVQin88jEonwAy/kxaZeGlUYOjo6\n2NGVSiXu0VMWSaNPjZweZb7AXjuBULJKpRKVSoWRp7du3WIEZrVabbgfMpkMFouFR8cGBweRzWb5\n61KpFMlkEtFolJHltHaj50MsFiMajUKpVMJms8HhcKC9vR3t7e1Qq9XY3d3Fa6+9ho2NDfj9fsRi\nMUEjJ/T9RCKBfD4Pg8HAo2O9vb3I5/Pw+Xy4ceMGNjY2kM1m+fMJMQpolUol96ztdjuSySTC4TDe\neecdRn8346RLpRJyuRwKhQKy2Sy2t7eh0+nQ2tqKWq3Gc800ztMMeJECCyqLEhZFrVajs7OTP/th\nAWG90Tuys7ODeDyOxcVFrK6uMnpYrVZjdHQUu7u7sNls2N7eZmRzowCZHCpNFlDPWC6Xs2MqlUow\nmUyIRqOCqDMpKCRHWqvVeCwR2Msg1Wo14xnUarWg8VMAPH8skUh45j0SiSAej6NUKvF4mt/vh0wm\ng1wu30fCdNg1q1QqrlwQqIsoPavVKjweDwwGA3Z3d7k3Tme5kHWlUim3AQAwiry/vx/t7e3Y2Njg\n8w1o7Ewp4KGRN6VSCZVKBWAv6Ke1g8Eg4x/qiXD+KDJqYO+DUH86GAzuQ+hptVoMDAzA4XDgu9/9\nLjPVCAVQUUYbCoWwurqKlpYWfpCGhoZgMBjg8/nw+uuvCy67AUAul2MQ0LVr12AymfglsNvt+Ou/\n/ms8ePAAr732Gvx+/76IvtGBnEql8KMf/Qi/+MUvYDKZeJbV7Xbjb/7mb6DRaPCrX/2KswUhUX2t\nVkMkEuE+/dWrV5mXtlAo4Dvf+Q40Gg1u3bqFV199lWeJhZSbqtUq3njjDdy+fRsOhwNtbW1cdvR4\nPPiTP/kT+Hw+XL58GQ8fPuS/Sb97kFUqFczOzuL73/8+LBYLXC4Xg8acTifOnj0Lo9GIt99+G3fu\n3EEoFGLh9kbrzs/Po1Kp4M6dO5iamoLBYIBUKkUkEuHJg2QyiZmZGczMzPCzIQS/4Pf7sb6+DrPZ\njMnJSSiVSqRSKZRKJaTTaQYjra+vIxKJcPm0UUuE7jFVgwYHByGTyeD3+xEMBrGwsMBBaalUElw2\npUPObrfj1KlT0Gg0WFxcxL1796DRaLC8vMwAQKHlR+Bx31Sr1aJcLiMSieCdd96BWq3G8PAw7HY7\nKpVGZXKrAAAgAElEQVQKVCrVPnayRmtSyVutVuP69etc+q5Wq5iYmMCZM2fQ0dHB4z5CS5vELJXP\n55FMJhEMBrltJZfL4fF4cOnSJSiVShQKBe7JHgYsBPaAbJcuXeKZ8VQqxWOLCoUCHR0d0Ov1aGtr\ng8PhwMbGBhKJRMO1VSoVPvvZz3JAtbq6yhXHnZ0dWK1W7OzsIBQKoVAoQKVScdXioPOTqhUqlQrf\n+ta3UKvVsLm5iWQyCaPRiHQ6DYfDAZPJhKWlJa7aEGfFYe0Qundf/OIXOTje3t6GRqPhCirNwBPQ\nkHrVhwUY1OP+3Oc+xwQ9Xq+XiW4SiQT6+/uhVCoxMzMDv9/PBD6lUunQ0Vaq7v793/89j/NGIhE4\nnU6k02m89NJLsNvtmJubw9LSEgPUaLT4qKyAH0lHTVbfK6OIslqtcjRDJU6hETKVNagHXiqVuMxI\naFRiyqrXdxYK+qr/eSI0oXGiWq2GXC7HEa7Qw4I+M4HEqL9Wq9VgtVoZGUtMVvWH92FG0WgikUCh\nUMCDBw84C6b+FjFv1SObhVxzuVzmhz8UCvH4W2trK6xWK5RKJSKRCAKBACNjhfRNC4UCwuEwpqen\nYTQasba2hlQqBblcjpdeegnAHrHJ8vIys28J2ed8Po+trS3kcjloNBro9Xo+uLRaLRwOByqVCq8t\ntCQL7AUB4XAYBoOBM5Hl5WUA4HtHSF4C1QkJDOvJcAiYlU6nsbm5CY1Gg93dXX6+hb4fwGNyFrPZ\njMHBQcjlcty9exc3b96ExWLBCy+8wKhVIXiF+utVKpUwmUxQqVSQyWRYXFzE/Pw8PB4PVCoVdDod\nH2bNXK/b7YbH40EsFkMoFGIMikgkQl9fH3p6eqBUKvdVABpda622N1IzPj6OpaUlpFIp/nqtVoPB\nYMDY2BhMJhMePnzI/X8h67a2tqKzsxOFQgGrq6sAwEhxj8eDyclJ9PX14d69ewgGg4xZaLS2QqFA\nW1sbhoeHmc2NKlUSiQTnz5+H6v9j781i20yv8/GH+75TFBdR+y7L8ibb4/HYHs9MJjOTrUmKIGlS\nFEWbIiiQ3hS9zEUvgqJFC7QIWhQJijTpRdqiSzJLMvFMZjLjZWx50b5RokhK3HeRFElJJP8XxjlD\nKbb4UXZQz+/vAxiaxTp8+X7v9579eTQabGxscH9II8NBa9ZoNBgbG8Pa2hry+TxqtRrsdju6u7u5\nB+fmzZvcV0B7fFDGqVarwWAwcHPizZs3GV2P5r5bW1sRiUS4WY9S740Al6xWK86ePYuRkRGsra2h\nVCrxu+10OtHf388d/16vl7N9tK4HOS7ktDidToyPj2NpaQl+vx+pVArDw8NwOBxwuVxoaWnB9evX\n4fV6USqVYDAYuDeivtzZjDyxhrq+FkqbRkP5Q0NDAMBpU6EvNemlP/Wzd1KpFIODg6hUKggGg0gk\nEk1FC/Rw9w+0y+Vy/qyVlRUkk8mmvKr6tVINml52iUQCp9OJXC7H4BvN1AlpvTTjSI4FRSjJZBKT\nk5OCITJJb6VSQTabRS6XQzKZ5LQPzWtqNBrcvXsXkUhEUEMP6SVUNL/fj3g8zqhIvb292Nragkgk\ngtfrxfr6elPOEHnRW1tbSKfTPLpDIDWbm5uIxWKYm5tDMBhsqmOfENhoQiGfz/N4DV2swWCQGXaE\npqbJ6aRZ51qtBp/Ph7m5OW6Mo67m+ui7kcjlcphMJnR3d+PZZ5+Fx+PBe++9h2QyCaVSiUqlwql/\noc8O+HhWeHBwEDabjWuvYrEYDocDnZ2d0Gq1fG6EsiRRQ9bAwADa2tpw/fp17gGhTn6CoqQLW+je\najQadHd3Q6vVwmaz8b1gtVpx7NgxvPDCC0gmk5iYmMDCwgIbsIP00pozmQw3bG5tbaFQKECr1cLp\ndKK3txeVSgV37txBIBDgjGEj3TQnv729jZ6eHvT39wMAoz3q9XrE43FkMhksLi7y8xPiuMjlcm7c\nHBoaQrFYhMFgYHAPGmf0+/3IZDLcvV0/bvUgvRTJptNpPPPMMygWizxnT1Mhs7OzCIfD2Nzc5OZU\nGod90NprtRo3HmcyGeh0Oly4cAEymYzxBObn5zE9PY1AIIBAILCH+YpKrA8SiUSCzs5OBsii5tWO\njg7G+5iamsLq6ipWV1e5r4ZszcOcgEbyxBpq4MGeularRWdnJyqVCnfTHSaVUG+E6UDZ7XaUSiUs\nLS1xw9Cj6hWLxWhvb0ehUMD09HTDl1mIXgAMLK/RaBAOhxuiYzXSS79LNSi5XI7V1VUe1zqM3noo\nUqq9DQwMQCQSYWpq6lDUcnTQCW4UANd/d3d3EQgEuHvzMM6bSHQf2pM6UAnQIh6PY2VlhVOQQoXG\nXqh+KBaLsbGxgVQqBafTCZFIxGNNQhuc6tetVCqhUCiQzWaxtLSEQCAAnU4Hk8nEWSEhKW8S6sge\nHBzkyCCRSHAkQ1kAcm6E6qXO2BMnTkAkEiGdTjP64MWLF9Hf388lqc3NTcFMQ5RV0Gq16OnpgUgk\nQiwWg9FoxIkTJzA8PIx8Po8PPvgAMzMzgvWKRCLs7OwgkUigvb2da8YymQwWi4WnMP73f/8Xs7Oz\nSKVSgpDTSO/i4iJGR0fR1dXFfSzA/fOSzWYZLCmXyzUECiGp1WqYm5vbM/Mtk8kgEomQSCQwNTWF\n2dlZdjgoTX2QE1er1bhpamlpCXK5HGNjY9BqtdjZ2WEEvM3NTSwsLPB8d/3Y4sN0k/O2srLC+2k0\nGlGtVpFMJvH6669zWYjq+vUNqLSfD9Lf1dWFjY0NmM1mLCws4OWXX0a5XEYoFMLi4iImJiYQCAQY\n4IVKoIRd/rB7WqFQ4OjRo9BoNLhz5w7jRCwsLGB1dRVLS0solUqMr28wGFCtVrmeXR9sNSNPtKHe\nLzKZDC+99BK3wR/W6O0XShspFArkcjlMT08/EtMJCXn1X/3qVxEOhzEzM/PITE603r6+Pnz5y19G\nPp/HwsLCoVmL6kUikUCv1+PixYtIJBKYmJiA1+t9JC5VMn7t7e34yle+gnPnzjEi12H3grxeGrf4\n+te/jmPHjjG61WHp5AicwOFwoL29HWazmdGcKFXfCAHvQUKjMV1dXejq6uKmL6fTiUKhwFC1zXDt\nEiDJyZMnWc9zzz2H06dPo7+/n8FHqNQiVK/BYMCxY8fw3HPPoaenBwqFAl/4whfgcDhgMBjwX//1\nX1hYWEAqlRIEYkFC55NQ0i5duoTd3V3Y7XaUy2VMT0/jf/7nf/i8CXUAaJZeJBLhwoUL+MIXvgCt\nVsvkEN///vfx7rvvYmVlpan7gkbTfvCDH8DtdjP6G0FQZjIZeL1enk0WmmGp1Woc1QaDQZ5X39nZ\n4bNFo2P1MJZCynrhcBjJZBIejwcqlQr/8i//wiQT9TpkMhlnp4T0slC25kc/+hF0Oh3+7d/+DZVK\nBZFIZM+9Q4BL5Ew3Os/VahX37t3D+vo6pqamIJVKGeSF5r1rtRo7YpShoqbIh915tVoNv/jFL7Cw\nsICbN2/C6XTiW9/6FqMXkoNPnevU2Ep6H5a9qNXuN//94Ac/QCwWQ0dHB370ox8hGo1iamqKR0Gr\n1SqDEFHmiRyMw97TnyhDTd6cTqfj9FYzdbKDhOqH+Xye06iPKtTU0Nrayg0QFL0/6nptNhs0Gg2q\n1aogaEUha6UUl8vl4s7QZrlZH6bXYDBwBElNG4fdAzL+ALgbWSaToVKpYGNj41BZFtJXrVbZSaPu\n7HA4DK/XK6hW+CCRSqV75ncHBwehUqm4l6GZ2jQJpTqJfcrtdqO1tRXlcpmZgebm5pDJZJrSm8/n\nUSwWkcvlkM1mMTY2xpFzJBLBhx9+uKe+KVSq1So2NjZw9+5ddoRUKhWWl5dx9+5dvP3221haWmoa\nsalSqTAiW6FQQDAYhEqlgkwmg9frxTvvvLOng7yZd69Wq3Hvx/LyMmQyGQDwc2zGodivl6L1+uzb\n/jT0Ye4gMsD1MKD0GfST7k6h5RDSE4vFGBDkQXo3Nze5K1uIE07vaSwW28O6tl8vGVKayBGy7kKh\ngOXl5T2OX31WkhygVCrFjouQe2NnZwfJZBL//u//zu9FvV76Dul0mjMAQrkXDpJPlKGu7/7e3NyE\nXC7n2bdHFUp9pFIprv8+ipCBkslk3AQllUofmwMAANlsFmKxGCsrK4+sk4RGq8LhMGZnZw9dWtgv\nVPunzvhHPbj0om1tbWFpaQmZTAYzMzOYnp5+JAegWq3yTCt181I9q1ljCtx/VuVyGZubm5idncXq\n6ioGBwcB3N+TmZkZwanN/WstFovweDz44IMPmMWnWr0PODE5OckGtRnJ5XK4c+cOxGIxent7eawp\nkUjA4/FgamqqqWkIEkrp/uxnP4PD4YBer8fu7i4zvlFT42FKIUSIsLW1Ba/Xy3C3QqK6RropuiUD\nT8/pUd6J+lLLQbqa/Yxm+0ia1d1IfzM9Ms3+DuF5N6N3e3v7N7rO6TnWO0bNZAzpue3PrNGdTPof\nh3Gul0+UodbpdJDL5ezZCRljESKEpEWGhCApH0XqUzbFYhGLi4uIRqOPxfBR3Sgej2N1dZVxvx9V\n6AVOp9N46623sLCw8NgcoWQyiaWlJZTLZVy7du2RLlB6KSqVCjY3N3H16lXIZDJMTEw0xIM+SOgs\nbW5uIp/PIxqNwmazYW5uThCj0MPWurW1xelOsViMiYkJKBQKrmUdprRAl8Xs7Cwzj1HdmKL3w6x3\nd3cXwWAQkUiE0ZwqlQqP/QlBH3vYeiuVCm7dusW9G5Rdof9/WCFjnUqlHluGjeQwxkeoPK41PpXm\n5XGVTPfr+W2dFdGTcFhEIpGgRVDqqRn4OKFC0cjj3o9G8JWHlceRPn8qT+WpPJWn8n8qd2q12qlG\nf+kTFVE/zlTCfnkczVgPkqfe+FN5Kk/lqTyVR5HGWJNP5ak8lafyVJ7K/6PyOPqGHiSNYJGbkU9U\nRP1UnspTeSpP5dHlcZXO6nH06ycnHscIKoFF1fNDNzsVsH9thNNNGASEc3AQpWwjIYwBg8HATZYq\nlQrFYpF5Av5/1fW9Xx5348h+3b8tvdRx+Dil/oV53DVx8gz3d6s+Dr2EYNQMbKQQvcSaRYxlj6sp\niPCJie2L0L8OI/XeNgE60GTA9vb2gbOijfTSMyP4SPrneoKbwwhdStRoViqVmBDjsOutXzfNsteP\nUlE3/mEb2er116NlkRF4HJgJ9Z9R//OwDYj18jiMHv2kP8DHvT6HvevojNF7TIaPJhIO2zhLI14a\njQZ6vZ6Z8nZ2drC2tsYjWs3uLZ1blUqFZ599FhaLBWKxGJubm3jnnXeQz+cPxD1/mNRqNajVapjN\nZhw/fhwnTpyAXq9HJBKBXq/H9773vUduUP5EGOqHGeRHNab1h7Yem7bemB7We9uvo/6CoO9y2Fnf\n/Q1qIpGIaezoEm72wqx/6eq/d73Rq1QqPKcpZO31F0Q9RSbpJcYywjkm3PZGa6/XW8+mQ3/UajW0\nWi1Doa6traFQKKBQKAhaN3Hu7tdNRsRut8PtdkMkEmFjYwPz8/OC8blp7Iso/eovOjKCVqsV8Xic\n4UaFPEsaBaT1GY1GRlkitipCXQuHw011hZMToVQqMTw8zNjOwP150XK5jGAwyFCozZw9+u40E04U\nqIS4JpFIsLGxwcQYzThF9e8b/TvRS2o0GkgkEhSLRYjF4qZGf/aP4tQLIYHVzywfRi9JPWZA/T0o\n9O54kJGmqZH6+4/0HmatdDeUy2WG9a2H92xWL/19uVyOTCbDyGGEmkjjT80YatJLd+Pc3BwDD+n1\n+j085s04GLSHNBFRq9Xwy1/+ElarFUqlEhaLBQ6H4zc4qZuVJ9JQ08VAlxe9xPXeNUUzzTCS0KGi\nF5gQjOig0QGg+TualROim/TSZWu32/dAAxKiDoHLN6Ob9kMmkzGxgV6v5+9EI0W5XG4PBWMj3bQP\nYrEYer2eSezpJaRnUC6XsbGxgVwuBwCCHAHSSy/X4OAgpFIpG3mr1bpnXwhDu1AoNHTA6j14s9kM\nq9XKoAYqlQqtra1MtUe4vWtra4wVf9B+iEQixvimyJlwffV6PXQ6HVwuFyPZ6fX6PSxFB+mWSqXQ\narXMxkVOT0tLCwPuyOVySKVS+P1+TE9PQ6FQYGtr68C9Jr29vb1oa2uD2+2GQqGAwWCASqViruRI\nJIJ0Oo1IJMLPotHFQcbT7Xajp6cHly5dgt1u52ja5/NhdXUVer0e1WqVnSGhjgtxixMF6LFjx7C7\nuwu5XM6jgjqdDgsLCwdiMO8XOiPkDBEwDDlwWq0WarUaPp+PGbWEQoDWO931xk4kuo+Tr1Qq2Slq\ndk53v776/05TL3TnCXFo6w098PFoZ31Kmf77fvAOIWul9ZAOej7EAV+vU8h9VL+vm5ubEIlEKJVK\nzN1N9zU5hM04AeR4V6tVpNNpZvkCgLa2Nsjlcni9XsEB4P5ntb29jRs3bkAkus85UKlUoNfr0d3d\nvSerdZjg74ky1BRl0YXV2dkJs9nM6F4tLS0A7nutW1tbeOedd+Dz+ZDNZnnO82FSH8mQsRscHGR4\nQHqZy+UyXz7Xrl0T5GFR1KlUKmEwGGC1WvHcc8/B7XazwRKJRIhEIvD7/ZidncX8/HzDdG+9ITUY\nDDCZTEx+3tvby5c/ACwtLWFxcRFra2uIxWKCdJN+pVKJtrY2jI6Ooq+vb8+LTNCG169fh9/v5xe6\nkSNAa1OpVLBYLDh79ix7njs7O3yBEtJPNpvF1tYWw+0dpJuMtNFoRHt7Ozo6OhjcQiwWw2g0wmQy\nQSwWI5VKwWKxIJFIcHr2IN1SqRRmsxltbW1obW0FAE6dk+6uri7s7u5CpVJha2sLKpWKU9UP2wu5\nXA6DwQC73Y4TJ04wtzdhfNPzJTIBmUyGUCjE9bOD9tlsNqOzsxNnzpxBf38/bDYbO4Pb29vQaDRQ\nq9WQSCRwu92Ynp5GLpfjz36YXrFYDJPJhKGhIYyPj2N0dBTd3d1Mz1gqlRiGkRzHUCjEQCEHrZnO\nh8vlQl9fH86ePcuOIrGTbW9vo7OzE52dney8xGKxh+qtX7dCoWAudKPRCJvNBovFwu+/SCRCd3c3\nlpeXsbq6ysxPjXSTI07Bg81m28N9bLfb0dnZCYPBAJ/Ph7fffrvhbD9d9vWZHHIsqBxC0LYajYYd\nrkAg0FDv/si3/j6irEK1WoVSqUQul2PUtUZczw+K1Gl/yIElDIb9rH8H6a3fDzLudG/odDq43W5k\nMhk25ELT4PXlO7qDAHBg0NHRAZ/PB5PJxIx+jZyLWq3G92891kD97+n1elitVphMJoYYPUyd/Ykz\n1DqdDnq9HlKpFH/wB3+Avr4+xu31er3Q6XSwWq1oaWnB+Pg43nzzTdy+fRvLy8vI5XIHbgCRqnd1\ndeG5557Dq6++ilrtPkZuNBqF3W6H0+nESy+9xPRli4uLzGl8kNEjgPlTp07hueeew8jICDY2NrC2\ntoZqtYrR0VGMjY0hkUjAbrcz4H6jw0AvQF9fH55//nmMj49Dr9fjF7/4BTY3N6FQKOBwOHD+/Hlm\nTbp16xbz2zbSSxfAn//5n8PhcGBjYwNTU1Pw+XwwGAzo6+vDwMAAp9XX1tYAHJwJoEuBDPSlS5eg\n1WoxPT0Nn8+HVCqF7u5ujIyMwGKxoFKpYH5+nhGlGukmcodz585hYGAAExMTWF5eRjAYRLFYxKuv\nvoqRkRGYzWYsLy/j6tWrTHB/0DMkwoXf+73fY/5iv9+PK1euIJfLwWw2w+124+zZs4jFYpifn2cM\n6YMuNoVCAavVitHRUZw5cwYulwt+vx8ejwc+nw9qtRoGgwE9PT3o6Ohg6M9isdjQSMtkMnR3d+Ol\nl17CkSNHoFQq8Z//+Z/w+XzY2tpCa2srXn31VbhcLrS3tyMcDsNisTQEsqFneO7cOXz6059GX18f\n8vk8vve97yESiUCr1bJzZ7fbsbOzwxSmjfDQRaL72Po9PT34zne+A5fLxfCft2/fxs7ODlQqFSqV\nChsQwmBuJAqFAi6XC6dPn8bly5cxNja2h4/b6/UiGo1CJLoPRENZLiKTaLQfFy5cwPnz5zE6Ogqr\n1QqtVguJRIJ0Oo14PI5QKASXy4VarQa9Xo+f/vSnDfUSMNJzzz2HEydOoKuri++5YrHIMLPDw8MA\ngLW1NUxPT+Of/umfDtRNTrhWq4XVasWLL74Is9kMpVIJk8nEgDvEEjY/P4+FhQW89dZbB1J2UlRO\nGUmHwwGTyQSbzQaXy8XZxNXVVdhsNmxvb2NxcRE//elPDzwb5AxTBtHhcDCZhUQiwec//3mYzWZm\nsdve3sbGxgZKpRJWV1cfWr6oT3uToaSMGQB0dHTg5ZdfxtTUFKampuByuRAOhzm4OsiZ3d3dZcrk\nnZ0d6PV61Gr3KVLtdjtefPFFmEwmXL9+HU6nE+VymZEKm8m0PFGGmlJn5BFvb2/z4SecYJVKBZfL\nhZGREQwODjLXc6M0EKEX0d9VKBQIBALY2NjA0tISwuEwnE4nxsbGcPLkSVitVk5NNhIytjKZDK2t\nrVAqlZicnMTU1BT8fj+n6S9cuMBpayH8zvUeZV9fH9xuN2MYX79+HcViEUqlEu3t7Thx4gR0Oh03\nNwlJfZNjdPToUbhcLiSTSdy7dw9TU1NIJBIwGo1wu92w2WyYmppCNpvlZqRGotfrcfz4cVy4cAFj\nY2O4cuUKFhcXsb6+jq2tLXR1dcHpdEKhUCAcDgtK2VO0NDY2xgQUIpEIfr8ffr+f6Tjb29uZJo+I\nCg6qaZFeYvc6duwYNBoNlpeXEQqFuL5ks9mYRSkUCmF5eRkbGxsNm0SUSiXsdjtHdYVCAT6fD0tL\nS0gkEhgaGkJXVxdMJhPDam5sbHCm6GFCl7xer2e6wXw+j/n5eSQSCYjFYrjdbjgcDkilUiQSCQQC\nAa77HRSF1DsuwP3Iw+v1YmZmBoVCAQMDAzCZTJzdikQiiEajTOPaSC+xXRHLl8fjwfT0NMLhMNfX\n6YJNpVJYWVkRFEHq9XocPXoUzz//PIaHh5FKpbC+vo7V1VUkk0msrKxwBCmXy7G2tga/398w7U2Z\ngxdffBHj4+MwmUzY3t7G8vIywuEws0gB4JJXNBptSFxCEX5bWxs+85nPYGBgAHK5HIVCASsrK/D7\n/ezUr66uMmb61NRUw71QKpWwWq2w2+0cQNRTiIZCIaRSKajVamxubmJxcRGTk5MHnmdyWKRSKXQ6\nHXp7e5m6lEpohI4nkUgQj8dRqVSQTCYb3keUmqZ6dFtbG0wmE2q1Gmw2G5drgPtcB8lkEgCYd/th\nUt8jBNzndbDb7cwTQE55pVJBV1cXIpEIstks43U/zFDXlw+IIQu4f/c5nU60tbWxg9DT04NwOIxS\nqcSZi0+soaaUGbXOp1Ip7O7uMtH55OQkZDIZpxIGBwcZh5d+/yChl1GtVqNSqcDj8WBhYYFTgfF4\nHF1dXQDAKQ0h9RX6/1arFQaDAUqlEtevX8fNmzcZNvKZZ57htD69KEIbC0QiERu2bDaLubk5rKys\noFarQaVSob+/Hzqdji9IoakV4lY9fvw4KpUKFhcXcfPmTayvr6NarcLtdsPpdMJsNnOtSAgDjEgk\nQk9PD5599lmcOHECCoUCExMTWFxc5Dp3R0cHR1IikUgQe1R9lHfmzBmYzWYsLS1henqa0/0tLS3o\n6emBSqVCKpVCJBLhiKyR3ra2NoyMjMDlciEajbLufD4Pq9WKrq4u9PX1oVKpIBAIcHTWaC8orU3E\nGbdv38bKygoTiAwPD6Ovrw9GoxG5XA5ra2tYW1s7kO2pPn1MJZ1qtYpYLIZ4PM483c8++yysVivS\n6TTW1tawurrKNKAHperra7zkMEejUWSzWbS1teHEiRMYGRlBoVBAIBDA/Pw81tbWGjYDUrTU2tqK\njo4O5PN5ZLNZ3L17l6MZ+n3qLaBn2MghEovF6O7uxvj4OBwOB3Z3d/Huu+9icnKSua8p3U99KdFo\ntCG9KFGednR0YHBwEAqFAqFQCJOTk3j77beRz+fZQaHnIuRdIb0OhwPj4+Po7e3Fzs4OZ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19XXek4PqLvsPWUtLC7q6ujA4OMgXJRnparUKn88Hj8eDfD7P5BiNDjOB6+t0OoyPj0Or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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Testing\n", + "# Generator takes noise as input\n", + "noise_input = tf.placeholder(tf.float32, shape=[None, latent_dim])\n", + "# Rebuild the decoder to create image from noise\n", + "decoder = tf.matmul(noise_input, weights['decoder_h1']) + biases['decoder_b1']\n", + "decoder = tf.nn.tanh(decoder)\n", + "decoder = tf.matmul(decoder, weights['decoder_out']) + biases['decoder_out']\n", + "decoder = tf.nn.sigmoid(decoder)\n", + "\n", + "# Building a manifold of generated digits\n", + "n = 20\n", + "x_axis = np.linspace(-3, 3, n)\n", + "y_axis = np.linspace(-3, 3, n)\n", + "\n", + "canvas = np.empty((28 * n, 28 * n))\n", + "for i, yi in enumerate(x_axis):\n", + " for j, xi in enumerate(y_axis):\n", + " z_mu = np.array([[xi, yi]] * batch_size)\n", + " x_mean = sess.run(decoder, feed_dict={noise_input: z_mu})\n", + " canvas[(n - i - 1) * 28:(n - i) * 28, j * 28:(j + 1) * 28] = \\\n", + " x_mean[0].reshape(28, 28)\n", + "\n", + "plt.figure(figsize=(8, 10))\n", + "Xi, Yi = np.meshgrid(x_axis, y_axis)\n", + "plt.imshow(canvas, origin=\"upper\", cmap=\"gray\")\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tensorflow_v1/notebooks/4_Utils/save_restore_model.ipynb b/tensorflow_v1/notebooks/4_Utils/save_restore_model.ipynb new file mode 100644 index 00000000..f70b2429 --- /dev/null +++ b/tensorflow_v1/notebooks/4_Utils/save_restore_model.ipynb @@ -0,0 +1,252 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Save & Restore a Model\n", + "\n", + "Save and Restore a model using TensorFlow.\n", + "This example is using the MNIST database of handwritten digits\n", + "(http://yann.lecun.com/exdb/mnist/).\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting MNIST_data/train-images-idx3-ubyte.gz\n", + "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n", + "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n", + "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "from __future__ import print_function\n", + "\n", + "# Import MINST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)\n", + "\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Parameters\n", + "learning_rate = 0.001\n", + "batch_size = 100\n", + "display_step = 1\n", + "model_path = \"/tmp/model.ckpt\"\n", + "\n", + "# Network Parameters\n", + "n_hidden_1 = 256 # 1st layer number of features\n", + "n_hidden_2 = 256 # 2nd layer number of features\n", + "n_input = 784 # MNIST data input (img shape: 28*28)\n", + "n_classes = 10 # MNIST total classes (0-9 digits)\n", + "\n", + "# tf Graph input\n", + "x = tf.placeholder(\"float\", [None, n_input])\n", + "y = tf.placeholder(\"float\", [None, n_classes])\n", + "\n", + "\n", + "# Create model\n", + "def multilayer_perceptron(x, weights, biases):\n", + " # Hidden layer with RELU activation\n", + " layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])\n", + " layer_1 = tf.nn.relu(layer_1)\n", + " # Hidden layer with RELU activation\n", + " layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])\n", + " layer_2 = tf.nn.relu(layer_2)\n", + " # Output layer with linear activation\n", + " out_layer = tf.matmul(layer_2, weights['out']) + biases['out']\n", + " return out_layer\n", + "\n", + "# Store layers weight & bias\n", + "weights = {\n", + " 'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),\n", + " 'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),\n", + " 'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]))\n", + "}\n", + "biases = {\n", + " 'b1': tf.Variable(tf.random_normal([n_hidden_1])),\n", + " 'b2': tf.Variable(tf.random_normal([n_hidden_2])),\n", + " 'out': tf.Variable(tf.random_normal([n_classes]))\n", + "}\n", + "\n", + "# Construct model\n", + "pred = multilayer_perceptron(x, weights, biases)\n", + "\n", + "# Define loss and optimizer\n", + "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))\n", + "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n", + "\n", + "# Initializing the variables\n", + "init = tf.global_variables_initializer()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# 'Saver' op to save and restore all the variables\n", + "saver = tf.train.Saver()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting 1st session...\n", + "Epoch: 0001 cost= 187.778896380\n", + "Epoch: 0002 cost= 42.367902536\n", + "Epoch: 0003 cost= 26.488964058\n", + "First Optimization Finished!\n", + "Accuracy: 0.9075\n", + "Model saved in file: /tmp/model.ckpt\n" + ] + } + ], + "source": [ + "# Running first session\n", + "print(\"Starting 1st session...\")\n", + "with tf.Session() as sess:\n", + " # Initialize variables\n", + " sess.run(init)\n", + "\n", + " # Training cycle\n", + " for epoch in range(3):\n", + " avg_cost = 0.\n", + " total_batch = int(mnist.train.num_examples/batch_size)\n", + " # Loop over all batches\n", + " for i in range(total_batch):\n", + " batch_x, batch_y = mnist.train.next_batch(batch_size)\n", + " # Run optimization op (backprop) and cost op (to get loss value)\n", + " _, c = sess.run([optimizer, cost], feed_dict={x: batch_x,\n", + " y: batch_y})\n", + " # Compute average loss\n", + " avg_cost += c / total_batch\n", + " # Display logs per epoch step\n", + " if epoch % display_step == 0:\n", + " print \"Epoch:\", '%04d' % (epoch+1), \"cost=\", \\\n", + " \"{:.9f}\".format(avg_cost)\n", + " print(\"First Optimization Finished!\")\n", + "\n", + " # Test model\n", + " correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\n", + " # Calculate accuracy\n", + " accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n", + " print(\"Accuracy:\", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))\n", + "\n", + " # Save model weights to disk\n", + " save_path = saver.save(sess, model_path)\n", + " print(\"Model saved in file: %s\" % save_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting 2nd session...\n", + "Model restored from file: /tmp/model.ckpt\n", + "Epoch: 0001 cost= 18.292712951\n", + "Epoch: 0002 cost= 13.404136196\n", + "Epoch: 0003 cost= 9.855191723\n", + "Epoch: 0004 cost= 7.276933088\n", + "Epoch: 0005 cost= 5.564581285\n", + "Epoch: 0006 cost= 4.165259939\n", + "Epoch: 0007 cost= 3.139393926\n", + "Second Optimization Finished!\n", + "Accuracy: 0.9385\n" + ] + } + ], + "source": [ + "# Running a new session\n", + "print(\"Starting 2nd session...\")\n", + "with tf.Session() as sess:\n", + " # Initialize variables\n", + " sess.run(init)\n", + "\n", + " # Restore model weights from previously saved model\n", + " load_path = saver.restore(sess, model_path)\n", + " print(\"Model restored from file: %s\" % save_path)\n", + "\n", + " # Resume training\n", + " for epoch in range(7):\n", + " avg_cost = 0.\n", + " total_batch = int(mnist.train.num_examples / batch_size)\n", + " # Loop over all batches\n", + " for i in range(total_batch):\n", + " batch_x, batch_y = mnist.train.next_batch(batch_size)\n", + " # Run optimization op (backprop) and cost op (to get loss value)\n", + " _, c = sess.run([optimizer, cost], feed_dict={x: batch_x,\n", + " y: batch_y})\n", + " # Compute average loss\n", + " avg_cost += c / total_batch\n", + " # Display logs per epoch step\n", + " if epoch % display_step == 0:\n", + " print(\"Epoch:\", '%04d' % (epoch + 1), \"cost=\", \\\n", + " \"{:.9f}\".format(avg_cost))\n", + " print(\"Second Optimization Finished!\")\n", + "\n", + " # Test model\n", + " correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\n", + " # Calculate accuracy\n", + " accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n", + " print(\"Accuracy:\", accuracy.eval(\n", + " {x: mnist.test.images, y: mnist.test.labels}))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [default]", + "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": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/tensorflow_v1/notebooks/4_Utils/tensorboard_advanced.ipynb b/tensorflow_v1/notebooks/4_Utils/tensorboard_advanced.ipynb new file mode 100644 index 00000000..62aa8d76 --- /dev/null +++ b/tensorflow_v1/notebooks/4_Utils/tensorboard_advanced.ipynb @@ -0,0 +1,307 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tensorboard Advanced\n", + "\n", + "Advanced visualization using Tensorboard (weights, gradient, ...). This example is using the MNIST database of handwritten digits\n", + "(http://yann.lecun.com/exdb/mnist/).\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "from __future__ import print_function\n", + "\n", + "import tensorflow as tf\n", + "\n", + "# Import MNIST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Parameters\n", + "learning_rate = 0.01\n", + "training_epochs = 25\n", + "batch_size = 100\n", + "display_step = 1\n", + "logs_path = '/tmp/tensorflow_logs/example/'\n", + "\n", + "# Network Parameters\n", + "n_hidden_1 = 256 # 1st layer number of features\n", + "n_hidden_2 = 256 # 2nd layer number of features\n", + "n_input = 784 # MNIST data input (img shape: 28*28)\n", + "n_classes = 10 # MNIST total classes (0-9 digits)\n", + "\n", + "# tf Graph Input\n", + "# mnist data image of shape 28*28=784\n", + "x = tf.placeholder(tf.float32, [None, 784], name='InputData')\n", + "# 0-9 digits recognition => 10 classes\n", + "y = tf.placeholder(tf.float32, [None, 10], name='LabelData')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Create model\n", + "def multilayer_perceptron(x, weights, biases):\n", + " # Hidden layer with RELU activation\n", + " layer_1 = tf.add(tf.matmul(x, weights['w1']), biases['b1'])\n", + " layer_1 = tf.nn.relu(layer_1)\n", + " # Create a summary to visualize the first layer ReLU activation\n", + " tf.summary.histogram(\"relu1\", layer_1)\n", + " # Hidden layer with RELU activation\n", + " layer_2 = tf.add(tf.matmul(layer_1, weights['w2']), biases['b2'])\n", + " layer_2 = tf.nn.relu(layer_2)\n", + " # Create another summary to visualize the second layer ReLU activation\n", + " tf.summary.histogram(\"relu2\", layer_2)\n", + " # Output layer\n", + " out_layer = tf.add(tf.matmul(layer_2, weights['w3']), biases['b3'])\n", + " return out_layer\n", + "\n", + "# Store layers weight & bias\n", + "weights = {\n", + " 'w1': tf.Variable(tf.random_normal([n_input, n_hidden_1]), name='W1'),\n", + " 'w2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2]), name='W2'),\n", + " 'w3': tf.Variable(tf.random_normal([n_hidden_2, n_classes]), name='W3')\n", + "}\n", + "biases = {\n", + " 'b1': tf.Variable(tf.random_normal([n_hidden_1]), name='b1'),\n", + " 'b2': tf.Variable(tf.random_normal([n_hidden_2]), name='b2'),\n", + " 'b3': tf.Variable(tf.random_normal([n_classes]), name='b3')\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Encapsulating all ops into scopes, making Tensorboard's Graph\n", + "# Visualization more convenient\n", + "with tf.name_scope('Model'):\n", + " # Build model\n", + " pred = multilayer_perceptron(x, weights, biases)\n", + "\n", + "with tf.name_scope('Loss'):\n", + " # Softmax Cross entropy (cost function)\n", + " loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))\n", + "\n", + "with tf.name_scope('SGD'):\n", + " # Gradient Descent\n", + " optimizer = tf.train.GradientDescentOptimizer(learning_rate)\n", + " # Op to calculate every variable gradient\n", + " grads = tf.gradients(loss, tf.trainable_variables())\n", + " grads = list(zip(grads, tf.trainable_variables()))\n", + " # Op to update all variables according to their gradient\n", + " apply_grads = optimizer.apply_gradients(grads_and_vars=grads)\n", + "\n", + "with tf.name_scope('Accuracy'):\n", + " # Accuracy\n", + " acc = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\n", + " acc = tf.reduce_mean(tf.cast(acc, tf.float32))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Initialize the variables (i.e. assign their default value)\n", + "init = tf.global_variables_initializer()\n", + "\n", + "# Create a summary to monitor cost tensor\n", + "tf.summary.scalar(\"loss\", loss)\n", + "# Create a summary to monitor accuracy tensor\n", + "tf.summary.scalar(\"accuracy\", acc)\n", + "# Create summaries to visualize weights\n", + "for var in tf.trainable_variables():\n", + " tf.summary.histogram(var.name, var)\n", + "# Summarize all gradients\n", + "for grad, var in grads:\n", + " tf.summary.histogram(var.name + '/gradient', grad)\n", + "# Merge all summaries into a single op\n", + "merged_summary_op = tf.summary.merge_all()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0001 cost= 59.570364205\n", + "Epoch: 0002 cost= 13.585465186\n", + "Epoch: 0003 cost= 8.379069252\n", + "Epoch: 0004 cost= 6.005265894\n", + "Epoch: 0005 cost= 4.498054792\n", + "Epoch: 0006 cost= 3.503682522\n", + "Epoch: 0007 cost= 2.822272765\n", + "Epoch: 0008 cost= 2.306899852\n", + "Epoch: 0009 cost= 1.912765543\n", + "Epoch: 0010 cost= 1.597006118\n", + "Epoch: 0011 cost= 1.330172869\n", + "Epoch: 0012 cost= 1.142490618\n", + "Epoch: 0013 cost= 0.939443911\n", + "Epoch: 0014 cost= 0.820920588\n", + "Epoch: 0015 cost= 0.702543302\n", + "Epoch: 0016 cost= 0.604815631\n", + "Epoch: 0017 cost= 0.505682561\n", + "Epoch: 0018 cost= 0.439700446\n", + "Epoch: 0019 cost= 0.378268929\n", + "Epoch: 0020 cost= 0.299557848\n", + "Epoch: 0021 cost= 0.269859066\n", + "Epoch: 0022 cost= 0.230899029\n", + "Epoch: 0023 cost= 0.183722090\n", + "Epoch: 0024 cost= 0.164173368\n", + "Epoch: 0025 cost= 0.142141250\n", + "Optimization Finished!\n", + "Accuracy: 0.9336\n", + "Run the command line:\n", + "--> tensorboard --logdir=/tmp/tensorflow_logs \n", + "Then open http://0.0.0.0:6006/ into your web browser\n" + ] + } + ], + "source": [ + "# Start training\n", + "with tf.Session() as sess:\n", + "\n", + " # Run the initializer\n", + " sess.run(init)\n", + "\n", + " # op to write logs to Tensorboard\n", + " summary_writer = tf.summary.FileWriter(logs_path,\n", + " graph=tf.get_default_graph())\n", + "\n", + " # Training cycle\n", + " for epoch in range(training_epochs):\n", + " avg_cost = 0.\n", + " total_batch = int(mnist.train.num_examples/batch_size)\n", + " # Loop over all batches\n", + " for i in range(total_batch):\n", + " batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n", + " # Run optimization op (backprop), cost op (to get loss value)\n", + " # and summary nodes\n", + " _, c, summary = sess.run([apply_grads, loss, merged_summary_op],\n", + " feed_dict={x: batch_xs, y: batch_ys})\n", + " # Write logs at every iteration\n", + " summary_writer.add_summary(summary, epoch * total_batch + i)\n", + " # Compute average loss\n", + " avg_cost += c / total_batch\n", + " # Display logs per epoch step\n", + " if (epoch+1) % display_step == 0:\n", + " print(\"Epoch:\", '%04d' % (epoch+1), \"cost=\", \"{:.9f}\".format(avg_cost))\n", + "\n", + " print(\"Optimization Finished!\")\n", + "\n", + " # Test model\n", + " # Calculate accuracy\n", + " print(\"Accuracy:\", acc.eval({x: mnist.test.images, y: mnist.test.labels}))\n", + "\n", + " print(\"Run the command line:\\n\" \\\n", + " \"--> tensorboard --logdir=/tmp/tensorflow_logs \" \\\n", + " \"\\nThen open http://0.0.0.0:6006/ into your web browser\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loss and Accuracy Visualization\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Computation Graph Visualization\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Weights and Gradients Visualization\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Activations Visualization\n", + "" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/tensorflow_v1/notebooks/4_Utils/tensorboard_basic.ipynb b/tensorflow_v1/notebooks/4_Utils/tensorboard_basic.ipynb new file mode 100644 index 00000000..71a15649 --- /dev/null +++ b/tensorflow_v1/notebooks/4_Utils/tensorboard_basic.ipynb @@ -0,0 +1,217 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Tensorboard Basics\n", + "\n", + "Graph and Loss visualization using Tensorboard. This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/).\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import print_function\n", + "\n", + "import tensorflow as tf\n", + "\n", + "# Import MINST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Parameters\n", + "learning_rate = 0.01\n", + "training_epochs = 25\n", + "batch_size = 100\n", + "display_epoch = 1\n", + "logs_path = '/tmp/tensorflow_logs/example/'\n", + "\n", + "# tf Graph Input\n", + "# mnist data image of shape 28*28=784\n", + "x = tf.placeholder(tf.float32, [None, 784], name='InputData')\n", + "# 0-9 digits recognition => 10 classes\n", + "y = tf.placeholder(tf.float32, [None, 10], name='LabelData')\n", + "\n", + "# Set model weights\n", + "W = tf.Variable(tf.zeros([784, 10]), name='Weights')\n", + "b = tf.Variable(tf.zeros([10]), name='Bias')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Construct model and encapsulating all ops into scopes, making\n", + "# Tensorboard's Graph visualization more convenient\n", + "with tf.name_scope('Model'):\n", + " # Model\n", + " pred = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax\n", + "with tf.name_scope('Loss'):\n", + " # Minimize error using cross entropy\n", + " cost = tf.reduce_mean(-tf.reduce_sum(y * tf.log(pred), reduction_indices=1))\n", + "with tf.name_scope('SGD'):\n", + " # Gradient Descent\n", + " optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)\n", + "with tf.name_scope('Accuracy'):\n", + " # Accuracy\n", + " acc = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\n", + " acc = tf.reduce_mean(tf.cast(acc, tf.float32))\n", + "\n", + "# Initializing the variables\n", + "init = tf.global_variables_initializer()\n", + "\n", + "# Create a summary to monitor cost tensor\n", + "tf.summary.scalar(\"loss\", cost)\n", + "# Create a summary to monitor accuracy tensor\n", + "tf.summary.scalar(\"accuracy\", acc)\n", + "# Merge all summaries into a single op\n", + "merged_summary_op = tf.summary.merge_all()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0001 cost= 1.182138961\n", + "Epoch: 0002 cost= 0.664609327\n", + "Epoch: 0003 cost= 0.552565036\n", + "Epoch: 0004 cost= 0.498541865\n", + "Epoch: 0005 cost= 0.465393374\n", + "Epoch: 0006 cost= 0.442491178\n", + "Epoch: 0007 cost= 0.425474149\n", + "Epoch: 0008 cost= 0.412152022\n", + "Epoch: 0009 cost= 0.401320939\n", + "Epoch: 0010 cost= 0.392305281\n", + "Epoch: 0011 cost= 0.384732356\n", + "Epoch: 0012 cost= 0.378109478\n", + "Epoch: 0013 cost= 0.372409370\n", + "Epoch: 0014 cost= 0.367236996\n", + "Epoch: 0015 cost= 0.362727492\n", + "Epoch: 0016 cost= 0.358627345\n", + "Epoch: 0017 cost= 0.354815522\n", + "Epoch: 0018 cost= 0.351413656\n", + "Epoch: 0019 cost= 0.348314827\n", + "Epoch: 0020 cost= 0.345429416\n", + "Epoch: 0021 cost= 0.342749324\n", + "Epoch: 0022 cost= 0.340224642\n", + "Epoch: 0023 cost= 0.337897302\n", + "Epoch: 0024 cost= 0.335720168\n", + "Epoch: 0025 cost= 0.333691911\n", + "Optimization Finished!\n", + "Accuracy: 0.9143\n", + "Run the command line:\n", + "--> tensorboard --logdir=/tmp/tensorflow_logs \n", + "Then open http://0.0.0.0:6006/ into your web browser\n" + ] + } + ], + "source": [ + "# Start Training\n", + "with tf.Session() as sess:\n", + " sess.run(init)\n", + "\n", + " # op to write logs to Tensorboard\n", + " summary_writer = tf.summary.FileWriter(logs_path, graph=tf.get_default_graph())\n", + "\n", + " # Training cycle\n", + " for epoch in range(training_epochs):\n", + " avg_cost = 0.\n", + " total_batch = int(mnist.train.num_examples / batch_size)\n", + " # Loop over all batches\n", + " for i in range(total_batch):\n", + " batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n", + " # Run optimization op (backprop), cost op (to get loss value)\n", + " # and summary nodes\n", + " _, c, summary = sess.run([optimizer, cost, merged_summary_op],\n", + " feed_dict={x: batch_xs, y: batch_ys})\n", + " # Write logs at every iteration\n", + " summary_writer.add_summary(summary, epoch * total_batch + i)\n", + " # Compute average loss\n", + " avg_cost += c / total_batch\n", + " # Display logs per epoch step\n", + " if (epoch+1) % display_epoch == 0:\n", + " print(\"Epoch:\", '%04d' % (epoch+1), \"cost=\", \"{:.9f}\".format(avg_cost))\n", + "\n", + " print(\"Optimization Finished!\")\n", + "\n", + " # Test model\n", + " # Calculate accuracy\n", + " print(\"Accuracy:\", acc.eval({x: mnist.test.images, y: mnist.test.labels}))\n", + "\n", + " print(\"Run the command line:\\n\" \\\n", + " \"--> tensorboard --logdir=/tmp/tensorflow_logs \" \\\n", + " \"\\nThen open http://0.0.0.0:6006/ into your web browser\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loss and Accuracy Visualization\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Graph Visualization\n", + "\n", + "" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [default]", + "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": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/tensorflow_v1/notebooks/5_DataManagement/build_an_image_dataset.ipynb b/tensorflow_v1/notebooks/5_DataManagement/build_an_image_dataset.ipynb new file mode 100644 index 00000000..9df1396d --- /dev/null +++ b/tensorflow_v1/notebooks/5_DataManagement/build_an_image_dataset.ipynb @@ -0,0 +1,291 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "# Build an Image Dataset in TensorFlow.\n", + "\n", + "For this example, you need to make your own set of images (JPEG).\n", + "We will show 2 different ways to build that dataset:\n", + "\n", + "- From a root folder, that will have a sub-folder containing images for each class\n", + "\n", + "```\n", + " ROOT_FOLDER\n", + " |-------- SUBFOLDER (CLASS 0)\n", + " | |\n", + " | | ----- image1.jpg\n", + " | | ----- image2.jpg\n", + " | | ----- etc...\n", + " | \n", + " |-------- SUBFOLDER (CLASS 1)\n", + " | |\n", + " | | ----- image1.jpg\n", + " | | ----- image2.jpg\n", + " | | ----- etc...\n", + "\n", + "```\n", + "\n", + "- From a plain text file, that will list all images with their class ID:\n", + "\n", + "```\n", + " /path/to/image/1.jpg CLASS_ID\n", + " /path/to/image/2.jpg CLASS_ID\n", + " /path/to/image/3.jpg CLASS_ID\n", + " /path/to/image/4.jpg CLASS_ID\n", + " etc...\n", + "```\n", + "\n", + "Below, there are some parameters that you need to change (Marked 'CHANGE HERE'), \n", + "such as the dataset path.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import print_function\n", + "\n", + "import tensorflow as tf\n", + "import os\n", + "\n", + "# Dataset Parameters - CHANGE HERE\n", + "MODE = 'folder' # or 'file', if you choose a plain text file (see above).\n", + "DATASET_PATH = '/path/to/dataset/' # the dataset file or root folder path.\n", + "\n", + "# Image Parameters\n", + "N_CLASSES = 2 # CHANGE HERE, total number of classes\n", + "IMG_HEIGHT = 64 # CHANGE HERE, the image height to be resized to\n", + "IMG_WIDTH = 64 # CHANGE HERE, the image width to be resized to\n", + "CHANNELS = 3 # The 3 color channels, change to 1 if grayscale" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Reading the dataset\n", + "# 2 modes: 'file' or 'folder'\n", + "def read_images(dataset_path, mode, batch_size):\n", + " imagepaths, labels = list(), list()\n", + " if mode == 'file':\n", + " # Read dataset file\n", + " with open(dataset_path) as f:\n", + " data = f.read().splitlines()\n", + " for d in data:\n", + " imagepaths.append(d.split(' ')[0])\n", + " labels.append(int(d.split(' ')[1]))\n", + " elif mode == 'folder':\n", + " # An ID will be affected to each sub-folders by alphabetical order\n", + " label = 0\n", + " # List the directory\n", + " try: # Python 2\n", + " classes = sorted(os.walk(dataset_path).next()[1])\n", + " except Exception: # Python 3\n", + " classes = sorted(os.walk(dataset_path).__next__()[1])\n", + " # List each sub-directory (the classes)\n", + " for c in classes:\n", + " c_dir = os.path.join(dataset_path, c)\n", + " try: # Python 2\n", + " walk = os.walk(c_dir).next()\n", + " except Exception: # Python 3\n", + " walk = os.walk(c_dir).__next__()\n", + " # Add each image to the training set\n", + " for sample in walk[2]:\n", + " # Only keeps jpeg images\n", + " if sample.endswith('.jpg') or sample.endswith('.jpeg'):\n", + " imagepaths.append(os.path.join(c_dir, sample))\n", + " labels.append(label)\n", + " label += 1\n", + " else:\n", + " raise Exception(\"Unknown mode.\")\n", + "\n", + " # Convert to Tensor\n", + " imagepaths = tf.convert_to_tensor(imagepaths, dtype=tf.string)\n", + " labels = tf.convert_to_tensor(labels, dtype=tf.int32)\n", + " # Build a TF Queue, shuffle data\n", + " image, label = tf.train.slice_input_producer([imagepaths, labels],\n", + " shuffle=True)\n", + "\n", + " # Read images from disk\n", + " image = tf.read_file(image)\n", + " image = tf.image.decode_jpeg(image, channels=CHANNELS)\n", + "\n", + " # Resize images to a common size\n", + " image = tf.image.resize_images(image, [IMG_HEIGHT, IMG_WIDTH])\n", + "\n", + " # Normalize\n", + " image = image * 1.0/127.5 - 1.0\n", + "\n", + " # Create batches\n", + " X, Y = tf.train.batch([image, label], batch_size=batch_size,\n", + " capacity=batch_size * 8,\n", + " num_threads=4)\n", + "\n", + " return X, Y" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# -----------------------------------------------\n", + "# THIS IS A CLASSIC CNN (see examples, section 3)\n", + "# -----------------------------------------------\n", + "# Note that a few elements have changed (usage of queues).\n", + "\n", + "# Parameters\n", + "learning_rate = 0.001\n", + "num_steps = 10000\n", + "batch_size = 128\n", + "display_step = 100\n", + "\n", + "# Network Parameters\n", + "dropout = 0.75 # Dropout, probability to keep units\n", + "\n", + "# Build the data input\n", + "X, Y = read_images(DATASET_PATH, MODE, batch_size)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Create model\n", + "def conv_net(x, n_classes, dropout, reuse, is_training):\n", + " # Define a scope for reusing the variables\n", + " with tf.variable_scope('ConvNet', reuse=reuse):\n", + "\n", + " # Convolution Layer with 32 filters and a kernel size of 5\n", + " conv1 = tf.layers.conv2d(x, 32, 5, activation=tf.nn.relu)\n", + " # Max Pooling (down-sampling) with strides of 2 and kernel size of 2\n", + " conv1 = tf.layers.max_pooling2d(conv1, 2, 2)\n", + "\n", + " # Convolution Layer with 32 filters and a kernel size of 5\n", + " conv2 = tf.layers.conv2d(conv1, 64, 3, activation=tf.nn.relu)\n", + " # Max Pooling (down-sampling) with strides of 2 and kernel size of 2\n", + " conv2 = tf.layers.max_pooling2d(conv2, 2, 2)\n", + "\n", + " # Flatten the data to a 1-D vector for the fully connected layer\n", + " fc1 = tf.contrib.layers.flatten(conv2)\n", + "\n", + " # Fully connected layer (in contrib folder for now)\n", + " fc1 = tf.layers.dense(fc1, 1024)\n", + " # Apply Dropout (if is_training is False, dropout is not applied)\n", + " fc1 = tf.layers.dropout(fc1, rate=dropout, training=is_training)\n", + "\n", + " # Output layer, class prediction\n", + " out = tf.layers.dense(fc1, n_classes)\n", + " # Because 'softmax_cross_entropy_with_logits' already apply softmax,\n", + " # we only apply softmax to testing network\n", + " out = tf.nn.softmax(out) if not is_training else out\n", + "\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Because Dropout have different behavior at training and prediction time, we\n", + "# need to create 2 distinct computation graphs that share the same weights.\n", + "\n", + "# Create a graph for training\n", + "logits_train = conv_net(X, N_CLASSES, dropout, reuse=False, is_training=True)\n", + "# Create another graph for testing that reuse the same weights\n", + "logits_test = conv_net(X, N_CLASSES, dropout, reuse=True, is_training=False)\n", + "\n", + "# Define loss and optimizer (with train logits, for dropout to take effect)\n", + "loss_op = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(\n", + " logits=logits_train, labels=Y))\n", + "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n", + "train_op = optimizer.minimize(loss_op)\n", + "\n", + "# Evaluate model (with test logits, for dropout to be disabled)\n", + "correct_pred = tf.equal(tf.argmax(logits_test, 1), tf.cast(Y, tf.int64))\n", + "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n", + "\n", + "# Initialize the variables (i.e. assign their default value)\n", + "init = tf.global_variables_initializer()\n", + "\n", + "# Saver object\n", + "saver = tf.train.Saver()\n", + "\n", + "# Start training\n", + "with tf.Session() as sess:\n", + "\n", + " # Run the initializer\n", + " sess.run(init)\n", + "\n", + " # Start the data queue\n", + " tf.train.start_queue_runners()\n", + "\n", + " # Training cycle\n", + " for step in range(1, num_steps+1):\n", + "\n", + " if step % display_step == 0:\n", + " # Run optimization and calculate batch loss and accuracy\n", + " _, loss, acc = sess.run([train_op, loss_op, accuracy])\n", + " print(\"Step \" + str(step) + \", Minibatch Loss= \" + \\\n", + " \"{:.4f}\".format(loss) + \", Training Accuracy= \" + \\\n", + " \"{:.3f}\".format(acc))\n", + " else:\n", + " # Only run the optimization op (backprop)\n", + " sess.run(train_op)\n", + "\n", + " print(\"Optimization Finished!\")\n", + "\n", + " # Save your model\n", + " saver.save(sess, 'my_tf_model')" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tensorflow_v1/notebooks/5_DataManagement/image_transformation.ipynb b/tensorflow_v1/notebooks/5_DataManagement/image_transformation.ipynb new file mode 100644 index 00000000..d55f63c2 --- /dev/null +++ b/tensorflow_v1/notebooks/5_DataManagement/image_transformation.ipynb @@ -0,0 +1,418 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Image Transformation (i.e. Image Augmentation)\n", + "\n", + "Learn how to apply various image augmentation techniques with TensorFlow. The transformations are meant to be applied for each image sample when training only, and each transformation will be performed with random parameters.\n", + "\n", + "**Transformations:**\n", + "- Random flip left-right\n", + "- Random contrast, brightness, saturation and hue\n", + "- Random distortion and crop\n", + "\n", + "For more information about loading data, see: [load_data.ipynb](load_data.ipynb)\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", + "from IPython.display import Image as IImage, display\n", + "import numpy as np\n", + "import PIL\n", + "from PIL import Image\n", + "import random\n", + "import requests\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Download an image.\n", + "d = requests.get(\"https://www.paristoolkit.com/Images/xeffel_view.jpg.pagespeed.ic.8XcZNqpzSj.jpg/")\n", + "with open(\"image.jpeg\", \"wb\") as f:\n", + " f.write(d.content)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load image to numpy array.\n", + "img = PIL.Image.open('image.jpeg')\n", + "img.load()\n", + "img_array = np.array(img)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display image.\n", + "PIL.Image.fromarray(img_array)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create TensorFlow session.\n", + "session = tf.Session()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Randomly flip an image.\n", + "def random_flip_left_right(image):\n", + " return tf.image.random_flip_left_right(image)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display randomly flipped image.\n", + "PIL.Image.fromarray(random_flip_left_right(img_array).eval(session=session))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Randomly change an image contrast.\n", + "def random_contrast(image, minval=0.6, maxval=1.4):\n", + " r = tf.random.uniform([], minval=minval, maxval=maxval)\n", + " image = tf.image.adjust_contrast(image, contrast_factor=r)\n", + " return tf.cast(image, tf.uint8)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display image with different contrast.\n", + "PIL.Image.fromarray(random_contrast(img_array).eval(session=session))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Randomly change an image brightness\n", + "def random_brightness(image, minval=0., maxval=.2):\n", + " r = tf.random.uniform([], minval=minval, maxval=maxval)\n", + " image = tf.image.adjust_brightness(image, delta=r)\n", + " return tf.cast(image, tf.uint8)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display image with different brightness.\n", + "PIL.Image.fromarray(random_brightness(img_array).eval(session=session))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Randomly change an image saturation\n", + "def random_saturation(image, minval=0.4, maxval=2.):\n", + " r = tf.random.uniform((), minval=minval, maxval=maxval)\n", + " image = tf.image.adjust_saturation(image, saturation_factor=r)\n", + " return tf.cast(image, tf.uint8)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display image with different staturation.\n", + "PIL.Image.fromarray(random_saturation(img_array).eval(session=session))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Randomly change an image hue.\n", + "def random_hue(image, minval=-0.04, maxval=0.08):\n", + " r = tf.random.uniform((), minval=minval, maxval=maxval)\n", + " image = tf.image.adjust_hue(image, delta=r)\n", + " return tf.cast(image, tf.uint8)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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mV+/cufPeD26sra0xBzEbIYEQIvEAuWaQ1rJhNiIiunDhAuV059E1MzA2du/X068Cp/D+e5uNbbO+UvzCN5aCZu7t+ybFqkgU22EYYwQFBpJtrYSEWCCECKFkBpyQ8BxBPtn6/eSn41+OO10AAELoeIf4+BeCRAghQIADRiknmAiCEEWRBFTfiNYW17fp9ub9nXQ6rS+kh8Nh4FAW2jvebq6YlWQJkXShVKjXq/n5bN/s7h807l57txC9Zo3gpkvVwukcyrZdDrkEoBpS5AWBIkGRCB5NjH8TEn4azjmAf73+nHTACQnPEYRzfvzRHv953Olyzk3TPP6efzoqAQHGGQSAMRZFARYxIUQQiGcFH37w4alTp+Zn523b9n2feRQAIIiEEGIYRsgCSRb7/UGn3a3P1L72O7+6JK9L4f3+PWtxufDqq+c7I+A72ds/7A7bcuDkQqpHjALMAWQSkZIOOCEhjqT3TUh4HondA/Z9HyEkCMInffDx5x2EPqWhKIqQQAA5xghhgDAEkEdRdH/jfqFUAJBHNPBDL51Jh14kqQLAGiFElmRVVVzLH/WMb//hH7/x0jcu137hYZvTYBaCgiyEgQEfXlMtE0Y0ZfmQAV/AAAAG+dMJ6UxIePb5ZACd9MEJCc8X/7kgLIQQIQRjfByKBQCglELIKaAccUkUKYMIQwZZyEJN0xRF6Q/7pmVEPChXyxCBg6P9XLogyRLjzHGcKIokQQYMCliEpn1wpyVUX5NpvddSIIxkEeuEh3bZGnuylosi0wcTSRF5xECMRighIQH8LVFHEoSVkPBcQMDHUtFPbwZDCDU1TSkNw9BzvWMpMCEEYxGASGCAhYBhLkgSxkiW5Wqx1ux2R3YfqiElSBU0HkIkg9nF+sGNZuai9LVfeuODWzfMvo+t1Ep2tahXD1ozrle43xFzeRXKiu8zynDfcDSZSxrgYJRRAABpEAEOwCRiZMpzJ6fW3cLHX0/4yRwqRMB094Hg8VOZaXWWwpS+sNMers1jguOmvQ9jj1/kmDbfpi33aYk74oKgKcs3Zqoal/64+mDbSrYUOoHLcEFNTYYD6PiFKJoI4tMZszLw+HKMS38cT8s39/Omi4177rTfy0mdW/Cs+SWfGDH5/NHa8vE683EU5XGRRFF0LAL+5BfGWBRFn1x5/MtxJ+37vqzKmGAIEOLEtfx+b5Av5v7rf/jbq0srZt/NpHP/9L/9J2987VUj7I5pxxftarVWrc3mMjkIEGfg+EGylHioJSRMged5EENCCKX0eKWKYJTJZJ52uhISEv5uPgrC+kTA8Em0cxAEx1ccd8Cf/C5i4ZNfKKWMffRPqBBxwEQiFXMlEKBGp7F/sHf/Yf7LX/z6t9/5ix+8/cMXwQvnr65X63PjPff3//c/+8WLv57OFl0X2Q6C6Fi8GEEIEyfxhISfHV1XXNu6cWPrsJFzbImQHGMRjjk2KyEh4ZmCgL+pIPzkWMrjdelPFig+iY6m9KNvmzEG4EcTa875xJnQCBAZjI0R9xFEIAj92w9uHsJe122gDq21sxzxzVtt4ldqufX+0MrkYEgRYwBjBCGPoggjBtDTOUowIeF5RFbkoTHsXH8/l08L5EVZJp4d2r4lK9LTTlpCQsLfAflkhfmTDvijXzgAfzOa4/j34wMpAQCMcYg+6rk551iCqaJSq1dZAPa3jkRVPH12uTSXHRnmL7745tLawsL84rUfP9y83ZuRl15a/8bOFnc9RiMSUUgEBADlgPFk+puQMA2+53LOKKWu66p5FHkRhkRVZJZ8SgkJzzzk0xNf8DdCsf560/jTPTRl7OMlaMYZJwQf98f5fL5Qz5w+txJFzA4M07A4YpIiKZAiGZu2sbe7F1j0yy/90nzmF0eHIJW2EVIoAwCgMKKURbKIIeQ8OfAqIeFnRiCARAhGPAhcAChnIUCyKguWGzztpCUkJPwd/HUH/MnaMgAAQkgpQwhh/FH/+sm69LEw6fhkyuNB9vEMOAiCiAbN4YEXOlS2uR/0xh21J3aa3pY/FiVWLlX8iQSHiDiDlLKSSikMCJzz49AtzjlCmJ9QKF1CwucEIhLuRoz6IWDHHymCIIyS3jch4Tngo5hv/imOfzneAz5WHwmCgDE+vuy4A/6kVwYfz4x9L5BUyQ6M7qQppGCxmlUUCWM82qbY0N0hNweeLCiuZz3cuW+HNkRCFFLOuShIH90NcM6SDjghYQp814EQZjOZbDarqwrnnLEIJOtICQnPAySfzg9H/ZD6HABBAXpad103cKkspzjnYRh6nnd86XH3nMvkgiAIwlCSJCxiP3BFVVhdXD4INrcfbS8tLp+dfbHRPXCpI6Zwd9jOpbPeEPIgfbb2m3XxvHMqcGCATAeKKYxhJABKXQoQF4jt+1CKMiD92ITG+drGvtiU+tHY62PkeU/6rIM43eS0v3P4+HyD/OnoDmP9cdHJ5CcCJ6Nki9UTx8gU0Qn54E6rn9aJGmGFiYKmIgLMel42LHVohwJ+fLnH65ineuzU94kr9zif42cNGvO9xL3XtPkZlw9x9/dj5ipSTBDrtOcETAuPqbdx9USIM8h9zplWz02+8s/PTvrW+2/dxJ5q9b3BrrWyfKpttJEAj3vcT9ucAQBGo9GxGQMACCGkKEqpWJyZmTmzvPL9t76/e7DjBDbmxBgERrOjqDIIogiDQAn33Hc96mr5FCTw9PxXP7wZClQEjPAIUEYRx5qgQSCAGCF/QkLC34ZS6jp+ELiESBFhURDZbui6gaAnaoKEhGcdkqkpl69eJFD41h+8hZBQnS+1RwdAApORByDABBNCjgX+x3PQbDrLOWeAEXJsEhwahtFsNmfOXXnp5RcebN892GpFYywwBYjUCK1gMp5ZLc/NZzqD6wdHGwtkOZUpqz09EM4GHha5CAFk0I8gFZEsAMKTDjgh4WcHYc+P/CBK6RIFCEGAEMZESgayCQnPPgRE+Dt/9R0MhfOvLx9tt3VRKKI5GrBe0/A8z/f9KIqOp8KCIIiiyBgLwxAggAnBCEMGx5OJ47o3tq7XT+frq8V8tnB0d9Te79fmi1e+eHp1tZrNFoQM2ri39/af384sgje+fOZS7tK/+R99xw9BFEEEKHFFhXKfuZ4vC8rTzpOEhOcGCiLKGQAQIcQBDVkAENBUHbDJ005aQkLC3wHhOMrX9N2D3cJqnuiV8b6fgplrH9yS04rv+5TS46grxtjxErTjOJRSLGBMAEccIYQBDsNQzahrp1Zf+4WrMCLvgGu2bzLVHoSN87m6G9jcUX7rl3/76uxXG5O9TI50/Qc59QoQwyiKIICAQURFjkhIoym3bhMSPteEzFZkCMVUJqWIIrM80wsUCFHcXnVCQsKzA/n2H/zw8ssXQIDHk9FXf/HrOsm9/8NbubbodWAURSxkx4HQAIAoihhjCCJJkgRJ4CByXVcQsazJGONx1Ldte2/rwDJtl5mnrywKKeBGk0azJ4Tq/Xvv7tzoC2627ewaUXUuc0oXYKR6LvQxk7EnCZFGBBkJ9tPOkISE5wlRCrKCnMrki2URQpFF3HN8Gnqx0YMJCQnPDOSLr335nXffGRtDWZNuyLdfff2VF76xdvEri9//H3YODg4ajUYYhMfnvAuCIEmS7/oAAIQQZYAxhpCgyDKEkAX87p17Gw8fiIIkCpLneUTA1ZmyerpUzFf2j1o3710fHwYkBzIzWrZS2oTXeN7NSXlq1mAng0IBc5ECOxm4JyT87EDsSDiEwKOhL6m2olJdB77NApo4eCYkPOsQMDNaebHywXeH7ohuX2+2d/+iulhoDQ69Xmk8HodBKIiCoijHVki+70uSBP6mgZLreY7jVC6nX736WuTzzY1N27Np4Bldi5vyt4dv1Wo1rYYry6XW0cPhUd//nmM9EFA2J+ZQfvksGquHtkjHmhUwm06yODFySUj4WXHdURT2DOvIcWkmTyAoYJwjmCYdcELCsw/53r+9BSDX5RQVIymDFlbnMELcEQ4mDzBSCSSUUupTSEVFEajiBWHg2q4IRUmSwpCBIMppqVIpG5gHeTFVn52LHNMmfVHLsgA2drrhyBiN3XR5br6+HF2EzcPe0O5///CPLsKVDFdbGwY7esMwKj5xMA1LEvR4jC8vPpnN4ZPS78bp2yCK8XNl0+nkaOwSYowOGE235Bina4zLn2l1jXHE+cXGvW5c/sSB+OM7nrhyifU9ZY+vb9Pmw7Q+uHF4PoCAMBhxHMpYyksaEh2fdAC7T8OxPYFpUi2rBccPsGLbtm26s7oiR1EEOFRkIaRREDqYIOGENoeFmP49TgfJ+OPLPa4enlR9i2NafW0ccfpdyuP07o+v0CTGBzdOF65Mqd/F8PHmHPHtz3TfS5wfNsQx7WFMPZn2e3/S9eRJ+zQTo+mHLFA0Sc1KlmkfHh5ceeniP//GP/0D/p13vn0NQKynFMf2ccSZD4AQuWYkSAIhBCAgqSJlkelOGNJSvPhX3/n+6tklUSMyS0tAqK6UipViYHDMhZnqvCgog96tyWQgIAn6UhEu27brQ0nSgRIIfCJBDrGCgPdE3zch4blEkSGj0A15SKkAKIeRJIVYYghgxGUB6ZRbrjdRlAwDvqpi23MQYQICgEGEIIgiBBCBic4+IeEZgmSkomGNgQ8JkwLPPdw7qtWrr7yiXL7wwoNru74dQTECfiSJqh/6siAQAkRRZJzJijS3OMMhO2odjKyu10iFksVTrFgqZNR8s9HpTvozZwq7DzeRq+5ttDEWBqNOOp/OSrUMLGfYLAihj/NEkgSRF0TZCwSfBkkDkZDwt5FlSgPBjzBnMEIBxFySbU23J5Mx446sUMe2ev3dTAG7vuNHRJAgEVUMJUYFDJhAMQZYwGLA3Kf9KgkJCR9BlByRswVBJHpad1x7Z8f8wX/6YP9up6IvprW0xS0P+YVqFgWyN3B9P9LkNEDApy6AXJCInlUoqZA+DEZE07KhR5rNgbCgMxHsNY86bjuD1GK1eLTVdsZuqVIoFkooFFfna7QrscmqY856jkCCKKMAzknHNlNSokNKSPhpEAohEiWCGeeiGGDiIDRGoBtGIx55xaKiqIrncYhNDqmsqhy0ZUUXhRRgWhQwwEUQKQigxKUhIeHZgYhFGgXMMR2358KQCH5qMjC3e91duaeIuumMSZZSAu2JqyiSD1zOOQRQkqTJZPLBex9mK1p9rjYzM9MAHccIUmqmb7VumTdKC5lcJt896r/w0ounLizPLDUaB63I4oHrBGxoKerMmcvyoxLdnTEdjuTQl3wfoiCMQNIBJyT8LRw/kIGiSQQjionBkBUEXY+28xktCMFMvaZq4mRiWjYDOCCCPBn0JdEjcgQ4d+xIYjr1RUhjt+ATEhJ+/pCF1dmdzd1Wsx8aIJ3S02pOorpnhY43QSxAIpxdrrmeM+mMdFmiLqWYIgBSWkqgZGQMRKTUSjOVahlLdNiwEAfMj7JVdaZWzqqFtfK6Z4WNRosovFwptOyBKknZlBRyI1WVWGcioLEKC5STgWeHEVblkzkZPCHhM4YXQEWAkhAhwWCoTelgYvSCqAuRF/i2rqqnTi2mdOT5Zi5D9LQuBhMkmkBAlEM/CKMQIiqFUSIOTkh4hiBWO4SeLAqYIooIyJb1iWg0e31ZkgkiuVJhbqkuySIJdzu7IxYBSRcopbZtI4J0JR269OHt7b2HR/l5AEUvCCxZ5evzpxQkj1rjakl2mffgdtOxLYEQZ2xXiiXFkyZNIwc6igRSdVEYKoNJ2gw4hkyXCGNx9kM/34xJSHiW4FyFgBBoQTR02aHvDwJ34niTfrvp+SYNorSawRIJg6hYzuuZDOuPfe5DybV9BGDAAI6AGnEKwOOjYRMSEn7+oLf++MPBgZUS8jQCpm0pBbL6wuziizkBC6qqer63t78jqeLC4rwoCcvLixGLGGCe57GIFbJFTUq5pu9ZwbhnaBnhN373G7/xO79s9u33/+rm1v2t3aNHL3/56tnTFwQvNWl4gR04hj06MlhXuPvulhk2lq/6etn3QhaGEkACifHOS0j4vAMVxjHkAaeTKBr44cBxTTfwZ2cW65VFQVAphY7pmRPLtX1rMskpooY5ZAGAHmUO5S6AQcT9p/0aCQkJfw2ZXa7LqnTYOlg4XTvYaz3ae/jVr3/5qytf/v3eX3bsrhRJ7RsjhHcvXD1bD0udRk8tY8Bh2PfSed0L3VRaF3V81Dgq56NvvPrNN196w/HDftt4sLWDXfnoUdB7cOvS0i/3W5yZ98sZ7DdChnIt09XDdGej0LxzK1ffKi+f9e6fB37RSzepW6HIBtgVMZaARoDgU99nroC0x74Aho+fGof08b9D+Hjh2PT64Jjr+eP3sOPsd3GMnhLx6QRudEofWTFG/hqrC5zSjzmOuHyO09sROJ2/aZz/cZxPcMDDx/4eJ9+NW6CJ1y+ejM61kOqKounwgyDYIcSf+FbfHC8vrr34wgpC6M6dO+99+L4gCGEYjg2zWCwywI2x+eDudq/ricJqdeaNUvWUHSBNmG4VelpdphCjR4/1A47ZlI75fGPzLS6dcc8N4vxxY3xq2ZTfV5x+F8KYDz7mfafdso/Ln1h9NozR6cbU27jy4jHlzmLOP4h73zh9+dMCxeizT4qPrAaLhUK5XPZCxw2czc3N8Xisz8Fzp6+GLr19/yZX/UxFOU2WhpO+se+mtHQ2kxWI6DhjblEOmZqWVqsXf/xXd+9e33/jy2+eP3N1b7u7s32gqfKP/3L7TuX3kKKUlDm741LO8qmZ/YPDcDAoVE/1OkZzrzM7s5TWdMqU0MMERxGngEF2bNIOAcZYwEISPJLweYYz2/fGouS5kTkeHdrOBEMa+JON+w/r9XoxX+IUGIbR6/R3tnYRQuXcrCTKMBAW6jPl8iVZWQRICwWRAetpv0pCQsJHkMP9xux8fWlleWl1YX5x7kc/fnsyNlKp1JU3F77x5V8yBw76i/Cotb93uFfJ1hZmlx7s7wROxCgP3DGAnIjYizw1LW/uHIlI7jYHfHy3OlPjvgwJHvq9Ej4TQKuQVbJhTfbKPuCTrq1TNUPqFf1SipS0slmu53c3Bv4wTCtqIISQQ8ohYJBSxmGEMMcw5ryZhOeNJ32yzGeVdrvRHzzCpMnAEQTjbF6XJMk0x82DPdOwlpdXlhZXAAAry9ZgMFBVtXHQSumZYr6aK9Yz2TnbFno9I/AkIif5n5DwrEAkWbAsa293HxF46eoFXdc7vU6tVmv3Wzdv3ajl586fuyBJ0sHO4d32lj3wc2rJtpzAD3zf1zIyhyygvibJIMV1MaW6JSEoGQ2u8tzc0pyQpeZDv6Liq1fP6NH5rXdq7UOZGf1T87Jgz2lCRUnny2t7Sj2w6La1P1PEetNCAsAhFTjFUQgwjBhgBMccZBfPtA193PXTLpkmJDwJRCkrClk36AcRTGf0TLaKkOD7LKulacSiMGICwxgrsqKpWrVaXVivgwDREEIi+5GDkKlECgWi5yf1NiHhWYGUiuXhaNBsGv1xDyBuTEzXiHrdfqpMbty8wddxLlc4dWoVY9xv3OSAt1qtMIhkWYYQiqKIMc5kMrVazYRW+6gpU/Pcgq5pmiYUqZQ6au/2xB1j23751bO1eYhVp5Y9u1R5YRzt+nDeiHRmeebDm8T8cPX8ev2V6tGHm407FQ5UAaVCJkAMKAsFCDFEMTt1Cc8Z0w50Eo4pFldSGd205VbLC6PhcMQQoiktGwSNMAw93/N8jzGWSqUABBGNJsORN/ERE9yIjoxIVk9LUAr9EIDc036VhISEjyDmxGIMVEoVP3In48n66TOj6gAg8PDmVn22oGe0kHkDsyulUWUpCwLSvjOxDFsUBEKIrqYc16IwaBy0zl18YXDwAddtv9TpDcZ2wxcjbePOZvWrQk2dD0HYdba0ubo1OYBIBFQwhLbZ1vIiYkPcaF5/OHl3aXHZ7NtC+C9EDCDUESQAMoARIUAUiDdlNMhJzWiTjiHhWcBy3CCwx+OJadmqxjgHjHPKIYKsUi0tLc8bhjEYDACkYeTt7m2trq9hwdVIeuiMTWyKBGtIEJV0NzmJMiHhmYH4vo8EFAahKEn1ev3SCxev3/jQtIwaKRXk6s69o/2j3c74qD5fXViZzyr58e4HlmNGPNAkTRAE5nBZ0QLHD53QGtozeqmer4wb3cbe/qnlta9+7ZX79juv/uLVbDlrG6NBdDQmW3LwqiqdlbnkIF4oFFO5pcEIbbXujYJOPlyYE0MJAUoxZzCIGEQRAERAGIBnS6GUdNgJP088+6jRuheEW6rklEvi6mpNlTWEhFyhkslkzp8/zzlvt9thGA6Hw8Fg0Go3qAtd6BiBRWTCoRn4RiG30HWTHjgh4VmB+F5QL9TG5tCduP3e4NHG5rvvvnvu/Nnf/to/vf7+rR/80U+scJKdUSIbjjvWg8NtP3SxCGWiCCKxLWfYHy2tLGbk7Ae334EMAEO6+53dh1sHxZq2fLrsI2umU+2b3a3uJjfoqNVT9PIrL339/ndd1c5SUaTICuQWqXlFVa2pp5aM13kkEiZEAfZDEEURgGEIefiEY8ETEp5xAr/tOw0t5acyUBX9jE7rMxnAycQet9qNQjF3+vTpTDZFKWWMUUr3O/soQP3mcL+za/DAMsftw4f67DyIkWMlJCT8/CGSTsajiSBKDnIj3by/c8ttwwDlDuqHV1+86NHRxuYDRZB7O0NXoRmpkK77477hjANrMIlCmlVS7YOji1cunH3tCxxyJIGNjQfYYqffWAkx+eBHj2CV7/35BzpIz6dPX639g3L2NfthrVQiw1EhX+wKZHR030UzteVzAHV4OSxe754GXGAMAmJD1SEAYCBhIKOYY+Rj55v88TPmaYOnptWBndTMeOol9Cl9Z+N0lnE6vHBKHeS0xL0vjfFnjSsXFqNXjvNhFac8GSru/hjH1LcYHSSN8b2GnIqiyDkPggBjLIqi53mTycQe31T1iSgHtfKsDKXDjeBgZ6tndP2hI4jk7vs7FESnTp86e2E9nUnVarXlwlJ1NbO1u/mG9AIC+O7O9r+6/30jvET53GOfG5efcTr1OOLWqViMXh/F6PLj9Ltx+u+4z5THpChOp8vj9OhxvtFT8qSDN+O+6zj9LoExPr4x/tlxO4Fxv0/bfp7UOudJ5fO0t5n2uYREsuv6vu8LGcBEn+qWWAm3rGt3/yjMFTP5cnZtbW1/f384GOOC2G50lCzL5YqDcb9SnSFQ6PeHIhKbR63UMggiT89qL7384vkLrucG3Ub7zMVTuXqtudVEFnaHkZ02SYk3m61RH+cKsDG5qSl+dVYnctbutAugUK8vHDRlGuAggF4IaMgQBlAgAAOQRGElfA745AP+9JcMIYTAK+Q1LBMvdEeTkW26HggGk0E4sfL5vBcG3V7n1u27qb9MSbKQyaavXLiQmVFzufSFFVmRpJn67Je++OX9jUQEnJDwDEEkTeCY+tgur6bSswJhaEaXW3fsAq2Z9qS/0StVC4Ik6Jru+z4DTNLJ7ErVss1W+4hHACEMIbQDML/w8u7ezq1bN4mAc4Wsqqoja1wqlRWekqNMTs9Z3Oaqg7M92EDV0vx2749Jpa/mRGSCwHTUIJ/VS0fto5BmMU9JMC2JUgA1hqKQcjPywBOegSU8m5yUnOw54pNFDggh5xxCiDFO5eR8QTE9t9FuuhOfMQwFURBVJSeUazXKqeq5kcn9gLpB2O2P79x6GGAnn8+cXliolEpLp8/XK28yR7u/8XRfLiEh4a8hBh1UFgsUo3RJGY77HjdX1k8tLEC6WbMd69Hmw157kMmlBSJSSjVdxZDXyrXKV6t/9Z3vcs7PnTt37969iNo3b18nhORyOduxwjA8c3Y9nU4TQv7tv/5jaIuLpSWXmfvbGxvWnXn15VMlNSvvN/EDJwprmfmipjARAkxpepyt97xRQB0AoqwEic8R45EfUYEke1cJnyOORxLHnTHGWBBFN3D6g26n08ZclMS0QIRsOuUMB5l8vtFoAEBkJYUQ4pwHvpHNZyMeBbb1aHOv2+ld+3D7lVfT5eJrT/u1EhIS/hqCdaAX5Mk4CAzAbDSxwGF/TIDc3rpLIyYSWZUZYmSmPut6zqMHW5Dqo64l60K+kjl1ZuWrv/Dl+ofZ4Whw7/YDVdEEQchl86VyMZvKd7rtw6ODhbNlt09b3V2oBZl5ubAYFXMmpbdSGad3//Z4NFHPRqsLZzYO9sfu+PJadWYlNzwUutvAGHh+gEMACSJYIIkb0ueTz9sM+HjWC/5mB0wIabXHXjSaWMMgoIqAoyjyLEPUoKLL9blab9hlQAEu8H0/lUqJRPQCpol5C4iS4uey+e3NcfPQBsAFIPWU3zAhIeFjSKmSlbBKbcu2KSEys6Ltrb4iKhPXFgRBwKLn+vm5YkpNG+ZEVLHAFKNvN9ujhaW581fXQ+y43NhuPKzOliZjY/feGDCgvZFShVTktxwjsIOxIKsumsgiKlYqq6vVak5pbT1yqFmeyZG0bYZNpswtLs26DqQstG3f9whlEEAOIOcMRCzCgMQFayQkfJb4qT1gxtjxErRtCb2xSwFXZF2WFMihIMCMrsgSSqUlxvx0RqbMoxFP6XLbnGh6hnqAhVDK6vX5lXyqUChenfjKU325hISEvwEpFPI0oJIiWGPXm3iZfCpT0V546fJ3/+LHUcAIFJGCRUGyLSelp//Bb/9WOIi4ELUngZ5W/cBzXDw7O7u4uGiMzdFgoooHhzutR/d3UkouiiLgk9buaP10+Qsvr7iBYzSH9ycPR/VhSlc3tna0AqiUZ48eHdwHd+eKp6OJsr/TgpOQeZhEKmYKhAAhHzDKaAiExMf088jn8ECV433fT6bCAACM8ezcJdPzXZ/KhMuCqEoCkYGukuW1uXxBK9cyy/Mr9+89GBM8W6se7u2m0gpCWICoUCjMzZ/W5s+1u3VzwMVkHJuQ8MxA5lZrd+/cM9kEZXB1MX36wsrQbcPZ8e/+7u/++3/3+yk1szi3ZNpGGPpIBK1Wyx95AXDL87nBeGDdHa+fO01D8PpLrz3afzDo31xYWJgtLd384M7Oxr6sShNjrILUoGmUMzMVfa6emlM1pGfhytn5rd3xxvat6mxW09LmxPrJxrV0sHB+7vXJuCaBLAZZn2EMAgYjjrhAUIyqKCHhs8knM+Djv6+tXGr3m2BkYOTJRFBEKQysQbt59ZUz+WLq8uVzL1y6ahgjSSQXL50ZDnt6WpVBef+oNTNbmV9YcnvlXtcNaTrpgBMSnh3IS195aXZ17nvffmtvs7l2araxNzp/8cpMvv4Xf/IWRMwDI1uSXWjIKWF2vn7/wd3FxflzS6vv/vhHEhFOLV7pbLYPdg7ISKAE795qY4IK+WKpnu+1+v1hpMkptUQ8MLi++9bKzPqF5ZfKxaqcxYOxDbrepcI69a1B0FOLhXLlPHt0wTe/5NK8CwAAxidJhABFDMT5IcXpBXGMgJHSx9/nSfs+xhGnK40jboYX53cbN/OL01PG6QhRjN/nkyZWDxrjYxpHnN5x2plxXD2J9XmN/fnvEthyAAHAHz9tODo1W/HHgwnjll5dbDZ2CaYq4ALH5WxR4LjVOFyYrzn2+LCx1Rse/ON/9t8dPWgY5lE5t+BYp2/cYRHL6RKLU7NOu3f+tExK4vTuiD8+P+PS87RiBZ7WcwU0bRDr469HMfrgOJ73fJ7Wl3paiEutbDn9C7/0jT8x/+LmB3etLt+8vV8oFJyhSzSUKWskxecLRSUrRsDKzgvFekrMgExB8Z1QUNCXX36j0+kcHR7+yZ9+L7BZsVAIPWZPXAzEUr5Qq9ZH4DBFFFmTHN/Y6d/3sTHeGzmBdWi4dn+8vDZbL61SK5fxroD0xVEvCXVOSHgMCEJVVTU1RbnDGWUsRITJsvzo0aPTp09HUSSKYiqVmp+f1zRtbm7u/v37QigVS/lSuSiJkiRy30WO7xIp2cpJSHhWIObY0uXM5YtX7n+4PWw4KEupxduTYeCHWlFcPTevKICREEu41W7Y7mi//1DOoIXV2d1HB9/+7nf6xuALb75SX33BYsHmvZ32/sByfBlrkHIQoWq5OujuSVhaXztz1Nnf721w1R12jP2HTRmtdex2rgbq6UW3W/T8cyJZdkxLffxE5cR43qNkEz6fME4VWZfVrOONBQIkCQBOGYOU0mKxOBwOwzAkhJw9e5ZS2mg0IECZTIbVgSiKg/7QdzWMFcyTBeiEhGcI8u/+1R9dPH+x+Ctz1WJ9drZvjA3P9RHEmIqGP6yWa1DzOsN9goDVsd0Bq5VR4/BAAZkzy2cWayEmyDCtVE6/+splAUre+J7P+Vxl0TTN3cOtD29e6/vtfDaffiW/vIwbo70I+rO1+d5D1zx0Za6Pbwf7nUkeraZFjTkgL5Z8YD/tPElIiOVpDeCiKMJE1NR8RNsIAVmCoR+5blStru/s7HznO99ZWlqCEFqWlUql1tfXAUKaoHIGZEVqNV0aKRBBgPjJHdH3bA1kn7X0JCT8LJCwI22Fzf9p5986gcVAZNKJmBIUVT7YbGhpkqmpEaTD3d5wFGIGL6yfjrjT3O+2Hx1kcqmZ2mJ/0tvZPlw/v1YoFfKZYrVS2xs1iIQWqrM2GBrOEFFh1DPf+u47a5fmL5y5VCnUVK98oE5mzs0gNw0mlYy7XKmcpzQ9tqlMZD96sh1w8qEmPI9EUSRBWdPLY3PbtEacWYRwQOFgMFhbW+v1ekEQTCaTVCoVhuFPfvKTSnmWYI4wXsgvE0RUSfNCFDJXQckSdELCswJJp9K2ae9uHtXPps9eOL3f2On3B6OGmVvSAm5f23g7VSQL60UQ0e5h1w8mhVwVFqQe2OrsmKo45oD4Nmjsjb/zxz+UBCVyGeUMKiHQPK56ge9IUEMYbm/viVl+4dIFp0ddc1LIF7uhI5j5krQ+m3kl9NSx6wcI254hncyZ5wkJnykgBBBiWc0BgMxJH0IzpagRhjMzMwghjLEkSQsLC/l8nhBy69YtyEQ/sLAIFFWKIsoZ5JyjKZ0VEhISniiElIIsEsI0yFSUgdHtNgcsArlsmoZ8ZnYml1NzVYHgcNLr6RlBJKx12F6cW/raL1fu3HvY6bRYhL701a9dufqi0wwarUPbsZDCW8b+EGCQ9ubq+d6mky6qmaWsoKPbN+4cbvQycmFuZoH5Ac5YHO3T/IJvpXmINTE7cA0oqI9N6EnpOD8DOtGEp8jTqj+EED9gHEgQiQhSTZFlmXR6hmEYd+7c+d73vrezs3PmzJnf+q3fevPNNyGE+zstDtRsSa/Vavehc+wyAxGPiyJ+0m5dT5pnLT0JCT8LJLtMBEFIhzOyoA6ak8gD3MMIqCGnO8NW65C9+JXlpbXybLHqG+77P7zW2wa7G+3ZtXJpJgclsrfTvnb9gx/84B3JUCmkqWzKJ6YZDvV0ca2+WCoX+gX33s4NSUYRVoYD2zadccfZPzxcSp02lEa3dDtVHxbZJXywnIKZLFbbiV9LQsLfQhAEy7UjxjERVE3LZ0gYhoPBIAiC3d1dSZKuXLmSTqffeusty7J2d3e//5135xcrL79xZXkNIYgJFiCHlEWJp0lCwrMDCScwxEEEgv1O48K5CyziR1s9I+pzFKVJmlAqEJGk5XOnX9Z4moHM+9LtdqvXHA1mUvLLX75UXswcHB7WT+c790G3OwgCIhJJRJI9Apw5pez82kvl9Qvr73zrwxt/+SCj5rmrLCzU5+dnr935YR7lsjZuP/jBvcl/vPzC67MrX7Eb0nDyHwMrI7ZfzaE3A5QeoBYPSR4sW8HTCc560nrHOL1yHNOO6ON0xgQ8Ptw8Th8cp8eNI+4+0xLnYzqt/3Hc9SdVjk+6nlihLUlRluB+CwzHzPZcDKNCsSILqVHfbh2M/u//t//+e9/73nvXfrQwu3b/zuYLF075odzcoQfZERI1g9uMiTVZMYOY8oWPL98nvfI0LXH19lnT+57UuQJx32+8Hj3GV/uEvutp/ZvjOKn2IY6n5bs8LWg0nACKZqvziqQcHTQQwDO1euBE2UxaVIkfeFY/6O5Odh8esBB85ZVfuLD6wmx1Npct2I6xffRQSIGZ5dLY7dv+pDJXlDTB8U2OmO/7hwdHN6/fttrhSv3M19/8pXyu2G0MEccsZDdv3kQ4O7aCjjnxIbN5sNfaO7K25UVv6ZVy8aymLTiuMKEcEKfIXWXodk7mdRMSnk8oZRgRggVOGQ0Z5wBBDAESJOHixQszs/XvvvUdJS2pmhIEvuVYZ8+e/eY3v+n7fq/XzucyfuAJgoDQExb5JSQkTAOZdM2sntPljCTIruVnVE2QRBYBe+JBiQY27e9bvh1NGtfZBL1++Su7W/uWaa+cm4dK1Go1WQBkoo/3AzcIZ/N1LaP0B9x3A+ACwEl7b/Bu59Zkn1XzM/Pzi4HDAAAjYzKaTArl2QAbKI31WtoBXrt/dG/3vW6wSzh3TV0gEdD72KllYdkN/ACNQfi0syoh4ekBGSKQECRgKCBAEBAwoIBCLIJX3njp7t273/rWt9fW1haX5ueXZ6u7pU6re9T8MUAgndJMJyQC9n1flQWQ2Io9VySqjc8GceVIFFlXRDUMQkmQ1bwOQmxPHDUlZzNZMR/1O33qAmaSjtf/XvsHux/2sMYixx2ZvapeycjFSd/3J5I2mA/F3rhnQgREqIwGY1lSqqWFVqszMOy/PPpeuVICEVayimt7ZmCkKirGEEIWQg9AltEzkRPxwDDGtjK+KNlLPJqBKvHg2HdUxAQkiE/6KOikoif8l/Ck64+ARUY5ZEgWFUDSgsRZZLOIqykZAI4IOn/+HGPs7Pmzq2srHEd/8G/+gEHtxRdfrlTz4y2fccIBpHTqz+jzZgeZkPDzhMzO1mVVpowSCQW+TyMe8EjRRUWRstVUAI3I80IuiqLYPGhu32guf6mcr6SscNwahBmlVChpphdBXQpD2j0YWKajpAVAAXSAhwCyVa0il1I5LoaH+4fUxoV0ORi7tuVkypkwoCzgaTGfqRbRaIuYlIbBzMVZ8egMG53qGMD3gsCfqFiTIGbJyD3hcwwhJIrcEIWSJEliBqDQNp0oigzD6Ha7x0dR+r6v63q5XO4NO4qqn7/yKoAc4TCfK7puL5+dsewuwMlpr88TyYDms0HsDFjWRdMbwyD0mN3pjEUZKqruOmZvFORWZ2srxclo3B8cZUItX8rirDwejy6/eH7iDvujHiGjbKncm/QOOx3Fn9WEtA2cyKO6rltjr2G1a6UZP7TWVheY4HeHvfHQz6mVjFzqjdrFbBGNJALVOf3lcnqucZtDy9OhCGZ8mTGjT6SwoFCKtRAjziED9MmGbyYyhoT/Ep50/eGQARAF1A9pgAETBEQEDBAP3OjhxqNep9fr9efm5u7curN+9tTRfrNcm93d28mkUxBSPS2pmhJGUUgZmVIK/Dm0g0xI+LlBrHA8cUaBaHIp1CogpaV4gBRKhECglObLenqR7z50gBul07l0Tt/aMbrNoes6BGs0wrKql/KVrjz0Rn6mpM+maiH3dF0XJWHUtSgOcrmSQGQPBeVijbatkrA0U5418pNURRsI/vBIGG+uh1pmeDS3vDC7VFhQRpPJEE4CE9Ac5jIA1At8kWGIn45+ImlQEp4F/MhTBQQhGBtD6PcLZSbIApFwIVt+eG9zOBh//au/oGna7/3+v5//0QdYwCFlj7a2Lp5fN02DMltVpfHIF3FyDNZzRtL+fDaInQGH3Oc4soJJqZY/f2Wdh2Bv6yidTQ13rc2Hu3kAVt/Ilk5JsJUORt7RsBtEtLHfrpVmFipL7799/f733lNJyjWJlIITc5QqSqomZQt6dbb86N7ufmvrFy/9lqoQWSSp1WJu5Fb11ZnU4ujoTmpuLVueLzNFjApG3zMn9chf5fZi5q5i+23KAiuc0ED2QSAiIYXz1qcMChMSPm+EYSBoCmJwYhqR3cvk06okiJJgGVY2m9O11Gxl/v3331dkVRSlZrPhu/4//sf/uNXYebhxt1yrWBZSlErkRzDZyklIeGYgXHNWVucG44HjOB9++GHkgepsARCaKYiQqO13HNCMZCU1sYbVmXJ7J6CG6nhOe29rbf3cK2988f1v3xv0hxYbIzNVqtYiO+i3e92D8fr51Suvnb1z/xbDfo4t5fLpgbLfL205VtTqLT3cVSC++qlkKOvrv0IBeNgAm4gCUAIAIASQDAQgAwAsMIl7AcgePzMOY8JN4kaOAppuSS2IEYI9eV/Vx/8epxfEeLoGN86nlvOY+8fo//iUJz7E+v5OqReMy7e494orRyEm+XHpiauHsf7NU+pEc3qeUcO2hyldxjivC7Jl9VQV12Zzy8trIQ//7Id/ASFcPDUfMtsLx2kuFev6xFjobzuLS7mF5aB3NMlmiGFN99xnzSf7aaXnWfO1nTY9cfrpuHYyzveX8pj0oMfr9eOYVsc/LU+6npzUygThIa4UZgKHHXWPJFEXCKcWkVT10msro94YXEVZvXCwd0iGOK1l5xaA21Es2+wMeu9d+3Fg8E7fUrCup3THG44DKsqCoosQiQTIwBWoITDKZVHZerR9b+vHzMUKtBQYXLl8xU1OvEpI+JmhlAoC5JwriiIIGmMhY8zzPN/3l5aWfv8P/mh7Z2d2ZoZRr1qtptPa7q329//qncAU1pfOaWk4GRmeLXMG4ozWExISfv4QQcFeaLuB45iBzQIWAEOwBZGMnX2ZaFfOviQAhAhcWz5VLpc9z7s+2qmlUh42GIrElFCqZxWS9iLbDW2EgKAgRKHlWIYzVNJEz8qTodGSjg6a+5HPX7xweVG90N0VBn0fwsef+fykiR9R/pwTkvCZ4knPkKIoEkVIKVVVVZZTjPUppZ7nybI8GRuZdPbM+rqiKPls6rUvvD6e9G/95PcsG5Sz+VxBkHU3JWuRmPOgQZ6Tip5E/yZ8HiDZutIY71IhzM6IZj/gEtBTCo0YBdHcai1XTd+/vnn37v1ittw67Ha7XXMIMpk0hFDSSCFfsAUWGcgzWT1f9yNXTctOaA4bvZa7g7kLyvbR3UN75IuaUMlVOGNE44pOeJs+72fSPmu+qkmD9ZkHYxyG4bHxkeMyzjml1LX9Gx/e9Bxv0BsCAMr5847lcgr1XKaoFPNahkJTy/JcOtPdY5ZgZ5M4rISEJ8bUWwOUUt/3FVnlEfSVUFVSiqQ4tnvm4sLFs5fdcUQprdVqiqB5ttdrjWbmFyOfRREN/DDi/tAZmQPPNB2L9yMa1jM1KUdSkYAlCDGXZZGK4mFzFxKIQbS7vc2v5MvyfDY7Ozaf9NEaj2faGXASbZjws/CkB0AIIQh5EARR5KsqgxBCCAkhjuPKkmYYRhCElUplPB7/5Cc/yeUziqa02m1bMGZEDWMsiCSIAqzAJ733dlIkA8qEzwNk50anNl+ShPRRY8e3uVyA/eHItYP2Pl4o2hLQKoUaiZRBd+hFdnk+M3L73iQKaBj5TJKkfCWTyWQMc4JyvjkOAsECkUyYFA7YwdEwcMOipFHogwhyQAVJmqCO4rfDaA6A53skflKb8InMIOFnAWPMWEgpNU1TUagoCQghWZY5BY82Hh3sH/3CL/0aIeQ73/4zxGkun+21e4btZarLtkF3t1seVVLZrEsk4D/tN0lISPgY4k2ATNTlhRXHdBtHDS2lEhETyd2/OUqxrfXTZ+2J1zhsTswxRODU6bW7DzclRRQi7Ex8w7CyxWy5VBkZ/Ykxpl4vMKlj28AVgYuCoYeA0sctoksL84syQB6d2OHAQx1JhNBLOp6TJJkxPF1OKvr9PwOllHNuGEY6jfWUeNwBz87OtptdTdXy+Xyz2fQ9X9dSgiCMu51zb15ZqJ5/cP3h/vYkBEEE7ch1Baw/6XSeCMnANOF5ZNp6S2bX04VK5vzVdTUnfv8HYyHFUnLKtPnpsy/tbO/enNztdrpz83O//pu/2um3AGQh4XQoDjqTg+5OS2/VarULl8+awegH//6GaIXcDn3Tx0wQuKgIRNdSco7vt3f6/TYKueH1F9Pqck0E46ez/pyQ8JxyvOMLAPB9PwwFQdAghAhh3/dfeOGFXKF6/fp1x3HW1tYQQlEU2cNhqZYTZWHY92vLBawqljfM6UqYzIATEp4ZiOM4lNL2UWfQGUNfVJS87md7W6HzpYkFnIOdbj6f71qDb/3wrzSUffnC6xkt9EHw5lff3Nne/eM//ZPb0WZGL66cXvy13/iV//V/+t/ajV5GLhCgAIo0RcpmsoKQq2XFo+aGoPD60ozuzchuHorDQ65KhIhQAgyzCFEKIgYA5CJ5fHQWitGJxp0RjXGMLyZ/vK1SMOXAGqHp/C/jdI0sTuc65YQW8sfrXON0e3HEZH/8yA48/r2EE5K7TKsnZuzx5RKnJkYoLiYgRh8MH/9e0+qJGZxSNyn4iIYEorXVU1b4CKuIAdhrjJEWvfnlr7/1//h/vffjDxcXFxcW63pFB8C7+rVvfPsPrlWKo0p9Lq/qhmmW8sJgLMvk8c+NYobEUkw9j9NDx90Hx+TntMCY8ooDxXwXAX98PsS1M0/t/JIpnzv1SgycTu8bR1y5x+lx49qZk2La9E8dPBVz+dT3yZLavfe3jzZ7vX7Xt0BQ4oFzGLhR/35GY3lPgKPeJOg7WlrN5xlT3d/8rW8+fPAIUHD29Pl6Yf6tt9759/+fP19bW5ufr05abjUzjyNp2JlUCpV8tjgej2unarokZhRJ0JmqZugkFRp4Ll0aOekwjEDIGOUYAIQRwjRK1pcSEh5HFEUEQSJgUVZdAw8HwyiK9LSuKMrOzk6hUPiVX/mVKIowYadPn15aqv2P/8N/0FM6INzyDSLDnJDm22YQBDKZbuCYkJDw5CB+DwiRJkOdeX1JQBktdzg8BBCwgeq4I9tyRI2IKBO4nu2ZQparor68uPLg/sbmo618pjRTn92+v9/eHvZabWvilrKqJiu5DCKEuL7teFbgea7vBDQAIWeUSgL2o/HEOQLerAAUiBQGGOM+AxEFXBIEGk43M3heiB0ZJUOOhJ8BTjkDLIzCrKrqNGVbLUopwtC2bBp1up2e71LDMGbmKulURtf0aqlcKIkhwwBANQt9FzHqy2Lmab9HQsLnkdizoBFCl69emlutzR7UjvaatuHKslybqbY3GgO7DSWQyxdlqAduGAX+/vYBHt8/bO1LKplfm221Go8at0Ha09S0JmXzpXxjt31wcJjL5lyuUScKabjXuetEEyvsqBE6N3fp3Hp9NVc0e0PjRsb1KA0445gQjjDADIVs6u4oCT5K+DxAkBhRd2wMMzlB13TbksPAsmwrDKL93YeSIG8/eiRJUr0688Pv//DipVOe503s0dh1c4Wc7Q4wyFBOZUlkdLqjPZ93koHvZ5vnvXxJo78/hwpSrvba6ks33r33Z//hO1pWKteK5VI5EMpcDIftydaHezJIQQjf/daN9+nWUevwa7/ypVdffdU27H5vQCDRVF3CpF6ZjRxuDPZNe5LNp0VF3tndZn0kaDRT0HJVJTuj1ZZTesErraclgvb3nf1d17VJLiWoisB96FoMwOf8hI4YnveKkvB0kUXF9SamY3R6UanIBEGURGnMQ0EQBoNBqzV44403RFE8Otrn0Mnllf29XSuMIgAlRThs7C/NLmiq1G07opgcRZmQ8PMmdgbsTEB/1OtPCssrS4VCQZSJqqqjyXB+YVbQtVI530i1t643JwOTAFGcyEwcqorW2R9+/09/vL9zEHQIBsJhcxBBu6UPJUFZXljqdrucgeWlFdu2BzuWJMsz1Zl0WekcDP6y8ZeKqJ0+ta7bLzBUEJRi4CHbG3MKONcwSsXEsiQkfK4RBcnzIQB8NBqqiqTKRBRFPaUBjiZjMwjC2Zm54XB4dNSo1rKypLZbh6cvvFCpL4QRbey1K1lXkWUIo8/bWdDJClnCswyZKVXDMRy2jPZR3x55xWxZzYhOYP3xv/0eFsG582fnFmdOnV41B47ZDoVIc6DJKXh0Z9vsuVEUWZaVSqf0oqTK6YP9Qx6CWr1m+/bQ5Hru0uxKdXLwiGMqy7KM1V5/2OoNRv1Bd6d9sZL1QxFGcxJRCaecWQiJkogc/7N5kEXSECT8l4Ah9gIfEhDRSBTSqoQ9eyjJRJE0VdZOf+Hyo41Hk8nk0sXLW1t3RSIPJ4OUqpUL1eHAdMc9HgGEkCiTmODfhITnkvh29dlq/+MgwQDf3T2AGFxef0mX05qscxCkMynB1822fa2xcbjQyc9ry6cWW2DU3TE6nUGpVKI+6Pf7UKL6DFk8kxdT+OuXf3Xz4ebNm7cppVkulGppkqV2q5+qSkoWI5UqaWl9bV09nd7ZeKTI0kJxodHOGBYSoVTI5hRZdT00MCh43g+JTkh4AnAOPM+DkAuSkMlkVNHrT1gURYqi/MN/9A//8D/82aMH25lMRhzhldWV0WhUq1cghP12LwpALlUoFypHR3ZIQ/I5mwEnJDzLkMChgq88+F5r+OgPzWCkZUUoscEQ5CuSb9jIUuz7UB5IwxbNKpWXvvLm9Tt3tg83LdvJzItShowMc9i0RKC0Vjd+43e/Prda/NP/+BdZXZ8p1OvpmZ803xeBmdazoT96tN0SoZ6Xi/X18pn1NSV6UFrY1617w54XTiLIli20ZpKSTB8vk0AxHXPcTDdWj4in0xnHEffcOF1v3HgM48frFKd9buwTYmY8cSPHk9oCoDEPjkt/XP7zGB1nXD6TGJ3otL7LUUw+8Jg4QR6j643Xl0+XTi8yMUTLs6cN457j9IgYWHaPAwqlMKWlMcAzS1XbtiM/Wl1emz+VxeCX33nrpjlu1RfOLS5lLffezMz5G7dpPjPdWeg+i/E/nvJM9bh6Ne0KVlz+Y/h4v9sg5ruIr28x1z9hf9lp7z+tX/K0Ptlx5yWc1EpeFFOOcemPKxcaU7Gmzc+4+8e1z3GfNY0tl5g9YMBAOpWxA4NznslkQu7YljUzV6/NzEBPslqhKqY7g9bm7mZ1tuAG9oXX18ADAxxOJAnllNykY3SHvctXzz3YuEsgah8OOKOyKLcP+z82r3kDjiAXiLiydmowGv/k3ZsDwbhy6aLjeep4BgGRikGreGOItyW7WrH/T/PoVBfYf2dmJSR83lAUDWPsBT5C0PMDMLGIIIiiLIpio9EAAGQyGUVRCqlCOp2+dOnSo3vNSqV46cKFo7alqPLC4vxffa+RTi8B4D3tV0lISPgI4vs+ARgjQrCQSss+Q87QGvQHfieEUMQCCpkHBVCZzxVLhQ+ufQDro0AaFxYRCDxCuRopms6+9tXXW+NNwENBgrW5sj9hj+7sH2y0NC0rpPnANg8OmxPboQBSxLcO9rb2tt84+6vAnAujxcW5vAP+YGAfFtIb3J4D7umnnSefKZK952Oe93ywLCsIXATDUrkAILAskzJkmmaz2XRHZDwe942+bdtXzl1xXddxHCxCUYbFstboTSzHlFQlk6ERNQl5/Ewxjuc93z6rJOVysjwtW1iCCPJ9X9JJEASto5GalXKZ/MQcdzsjd0gjG2pyyvYtiLSZytwL514+DN9P17ORSqyBN3zUGbdsIoJ3v/uj137pEkKkVIDyOb3TGIcB7jUmNMSKXiwVqpMBt0Px9S/8ckpP72/uhL7vq2lzt6hMXtZ6wizM5TPXMeY0ewsMTqYDTiroZ4PYpc6nZKsXfyTnk70/hAAC37T7igI45EFINDU9mViapt29dr9arQYgUBRlaWnpxo3rhRmh2xsoinDv/ocTA6ULtcOjw3R2NmITAAonlNKnw5P+rpN24+fDtPn8WS0XomVky44QgY7jmLZlWtbMfGWusnSw31UQzFdKlFIIuT2x3nvrg9Wl00PWzK2Twro4v1YZHW5DAGq1cm9v+O73Pszk8oxCVc/uH7QsP8jOle/febCKXzid/SJAYm8wWiCrwAO2n5mrz/UfnbPHBdMW9UgC0roU6og4EXq+PQqfQZ6XKPEnzWcgHyCIHLtvSJCIEiYpSQHIaBFCBoPB0cHDr//K1wkhDx48aHX2trYKDze3zi4vTYYdVZ+VJGlze0dRMild4sGUD33+8+0zyWe1Q3paPLUZMFQoiCKfBnpKq9WrjUajvT/EM4oiaAASRvnewR7HPF1UTMPc3t4GkjdxorEFXvpivVyp8HPm+bWzR4cNo8dQwFvNoev2+m1j/cy5K+evUlN17644+8vFwlI+8Lx9OYh82k+FqBqNXmTc4VnDQn3MoMaWZSpBdmK1Kqmgnw2elxnwSU2B4zs8BkCIuBcEUFJ0jAXGfYyxYRiqqhJCcrmcbduNRoOIqFwuu/bd/Z399fVzlfr56/c2HTtcXZEFHE7Z/z5zPK38T3i6fFbLhXASACEkkBBCdCUlIdUZjRtuL6elbMcQBXlmuTx2+47jEgWqGTGbWTzobh+9y0X/oD5XO3WuRnRgNUftzbBaUJiR83oAGNn06sXxvjY+kuZq5yktRn4eccCYiHEoSUrgayEMMA4xACCSEZUZEI2QMkSntgFKSPgcoGnaZAQxjhjllGNOAQAsn89HUYQQ+sY3vnHv3r3BYHDu3Ll33v0rhJAsqCwIirlcOqVbE0sS0+lUBtDO036PhJPheV+Z+Kx2qFP7AQsaUpGkSel2o3e006Y+qFVqUUjNYKSkNcS5khYlnFdTCuHSzbc3eyNQyJQDHu5fN8wBXbpQJRm4enVBjYSFwhmNLTArHXjSuQuX7u1uE/cwd1pXU6mh5e4fHoXICiIzdMPVhdNpvQoplRnRRZUBceQEth9S4qtQfZL5k5DwXGJZFo28VErN5jU1W+kPx+ORjbRwdXU1K8//6O1rDx49gBDOlGbOnj3bbDY9j75y9ULrqNFoerlsSZFTjuVqqhI871PghITPEIQPsORn/RBrtEClkYs8wzGL6swgHEZoKKhg4cW55bXT59fPMxvglLHx/YAJ0GM2jSJsSPe+vV1ZziyertD06Fan/Q+++n9eqyzcvvHg1uH/HIjjl78Ov/7bHOKO5C/+/r/a+PGPv5Wbwdnsai1/fuPwhzX9rEQKXc8BskdkSUdKFKkcxDiGo8frq+LGUSRuTYpNFwUapyeOe3KcD+W0OuOT0rnGETsCZVPqHWPKJfbyKfMh/j7THSgRp5uMY1p9NgKPD1+I2Mn0eAL2iZ4TpBe7rXunK+2s3uYTvz8cSoJcrxdFVVhYmRmNRvuNvddevbq6Uq3kMrsfRu/e6admxJfPLJYygqYFPoMsRk8fJ5uM1ZtOW474ZOpJ3HeBYvSsT5pnbSY3tY42pr2K01XH6d3DGD1unL/7tJyUDvukVg6mTU/sWdC5WiqywdFeb3BkkjxbXF1ATGjuN1MF7ezVVaA6C+vlN7/yWlrJCoH0L//lv/xX2v8GYXTUCju7Q0809YrKRXjj5n0s68wT3n7vB7uFo4cbmz3jYG6lpEvi6O5OZnH9aNTuhl03ExDBKOjCUff3DhpSYV3N1VOSiyZ26Do8pBgiLDxb9Tkh4ZkAIRTSQCFQkoTBaGKPRxijfD4fhuGo249CSghZW1ujDvTc4MUXX9x8cO/t//SDr3/9SweD0dbmxtXf/AZVRMAHACQrTAkJzwrkoLlTLyzMLszQ6HA8MdtHPU3TJFVk3Hc8W8twQGihmpOBSiegaw1Xr6Y6vV5FUwWNMQcBTurz9QqqXv/hXcbJj37ydkq/7UWumiZ6paaq8Pvv/+Wv1xe1SDxTezEa+l3n3ebgwaPeu3O1r7noB4ejJvcWVbKIhSxlPOCOiBLD8KfJtCP652PH6enNVE7quZZljYb9bD1VqZZsblt2pBJMw3DQHz64dxSG4YMHG7Ztf/HVr+i6HoV0YrtXrr5gghCTcGa2OBl2q2v55QX19t2TSf+0QXDPSz1JSPh5QnyXdfrtXKo6Nz8rdnrdQZ9SWqqVKQu3d7cWlMJh0/vhWz+4tH5ltXb63bd+cuv6NYiEfLqMclI/NMdjq47w0urq6MBmHDXaHcM2OWAIk8lw7Dk4cyaVOaXMkmIlpSl4+8++749Ci+SErvW+p2+fr9BKul6WShNTo5EbegrAjz8CLeHvx9QdQNJSPpMoiu65E8f0ReIbnoOQKoqiFQ4t0+l2epKgKorq+34pX/Jc/+23ftTs9GqpUxsP7ocSCiHa3Nq4/MbLCO0CkH7ar5LwFJg2enxavfuzthQ/LU8r/eT1b1zZ2txuNHZ1RS+vlvPL+tgcjf2myBCWAGdgf/uo2Wz2DobhK9SnTtgXBEHpd539gwZFAAqo1Wr5YShpkFJeq5WC/ZbreNyC7a1+vVZtKO133vve1fNOcb1WM1V1Q+21wNL8gsbttatLVy4sp/q19sbIdfoilCp6zvqcGYY/ayQz4GfzuYIgRKE7Hg3TugiRlM6mJO43x0Y6nbYmluUML5y7OD8/H5r02vvvCtr5R1v7nowlie82doWSkC2nHzx6yGIOdv57pD+ZASck/JdDoMQWz9WVGu51RhMUlMv1lJAbmhz1xUw+Nep3Q8pW507N1xcB4i++dgVO9B+/e23z4XbEwLkXTkOFGc5kbLUlEQx6BvVlTpkm6DJRQcTTSnpldsXYcLbGzjAjuN2rC5mwXLh98XXN6B3OzOWx6JrBw3Eoq7VMhlSABazudEFSCQmfBzzPi6gXRqYoFrCSI1gJjDbnHDDUarUgkWvVOuBwY2Oj0+mo8quSJDWaR5XF6sLCwlzptDFs3b+/ly9cfdrvkfB0mNa276RmzAn/eci1H95dvTBXn6shmXcb477ZmZubO7dwlveg7weHe/sE48hCteK8LMuAsNRiZvSDYSj7a6sLZ15essJBu+NGXijrSoQCZ4iqpOwagW2My5V0ZkbGRGjutrfevVbIawvVL6/k/4HBaio5UitIlBAWWGlNnlvPhKnQHfQefm8H9l5+bEKfF33bs8bUR75Nq8N+Topl6pn9CdW3EzthB0BNkyViIMQDxn0/IgCvra6ahpXLFlZOrSOE7t+/TykrFSsCESvV8oc3P5xfn3nt8qtHj6LN7cHXTl0NIvmk0v9ZrScJn0+e2hJ0gS10N42xaegFtZSVB32zvTUMLThothEjxUw5o6cf3t4evjHOFpSHmw8ajjXBfZTiTWvPvdNS0jCd1kUFHzV3BSmTKaTy4szWw91OM7j0euELX7lkiCo66Nzb/dOy/Yi7/bL4ywK+uHsHmOw7GUPMZCullZTFhtvjW1bASiur+7tPJR8SEp5pJpOJSFA6rRUKhTGlnbYj0bCWz2ua9tJLLyl6+sO712zbXqov7zpWOp1+79aNUqWQzWV2d3fNcRmTVHfg5LLK036PhISEv4YEWevypSsjp1dZyfTco1HU7m3abieElFXmU5KOLMMIfHbtnQff+dbbu81H/9Xv/g6GBInhf/Pf/NPbN26+992bX/rFS6ZhwH3JRW53sL+H9qvFen0m8+D6Hve+O7uin12qffXV/27cZe/+5Y3bd949t/r6mZUrE/u/Ptz71sHCrsIgdGAK68JQFcflii70rF4qXYKRNrAHQsamFsmyqoMCDhgAgAMOAPj474zRGD/LWL3gtDrX6bbN4vw1GYsx5o0hboYR79c73ZJR/AxvunSSKXWHjE93fdzINE4PGssJzcCmTSeI1VnG3ufTj4AQHisO3dCuVV5ttW5YXveFyzPeZKvba2zcl//Fv1islRb/8I++OzaYYYQZzT51Zjmg43wmv37l/P0b20cHo9OnXz61spKScxCPKZMIIRiLAIAwYpRCAACERIjR6cbp4OP8nuNUDAhO68/6+PvTmNNq+RM+QS9O/xpXr05K7z6tbjWu3WD88d+LgB+vp49i9N8s5v5xutg49fe0M864di9OZjz9/WN0ulN2F3E6+7j7kHaze0e89cbXXkUqbXT5Ym11f9wcNSZpmNm6t+e7IFcRa6XZB7ce9SadF79wmcHw/KUzt27deLR/X0pjooHrt64JSPA8dubyWplqvhsu1dYmPWdjY6PVPQionn6puHS2vqs1yNzwpdp8RcNheGR4/Qfb9w8G5uHa6qmV1eVzayQLonw/Q7vWduR77sS2fJ+meTYlqKou2LYHPu59PwFO25smJDyfyLKIsacpku8HzXbX831d1wVBgAC1O+1MJp3x7VKlsFyfaxxuLC68MjNz6vf/9z/FkT4/vyyKoqxLsuynUvJxw8Q5TzbtEhKeOiSdSR3udHdXdyr10kJ5LaWmFZB+f3KdWXBmdr5ULfS6fdsOPC/SlezDe7v327e+8UtfP31ubfvgUTlfOnNlYdib9A7HnIAja1PRxdXzp5Zrsx+8fdPlxtnV9W6r++Gtn8gVKOqCPDvmAZDyWkXnQ9grRBk9pY1G5ns//nDvsLO8fn5lZf3KAhT+AjU2gyELZCWTkqqA+QOvy6Y8+Sgh4bMEDcchivL5nGmOBsOx7XjplMAAazTajYPG3t4ekJDnhu12u1qqlkpVhFA6pVTL6wRkj476UGClQqY2gxBCP9UH/9ScO+Gp8KzZ86FYGVIybjtJSCU1S9mea/qzlYXTK2e2djbrK6WqK4/fjwx7XEYFhCEmwPbNlZUVVVdu7Vx/69s/DpidyqrpuUy+WmiQTv/w5rkv1nOVdG/U6ZqHK0vLv/gbX15cre839l7+6pXxpMexv3x6QVaj9378/oP+/hjN7AkTtcqKWhEEUfOoP9huG7uacQa/+ApfXDmtohTcFXsj3rE6EIVKOuQUfbJ6DP/GX5K241kkfuns55yQE+Zp2ZbRaGz7bqqeoizdH/TCMGQMMx42W4ORYRDEe8PuzvbO5TMX52cvXvvwzvxMfmV5aTTye+Mhx4hxV5FCxAyMU0/idRISngrPewUmZifASHFH4dvfffeDH384dLpLVyrrVxcPxv6je7vv/PCGmgO/+uu/fOc2aI8amp8uylVm03q5Ikho48YuoNsSUTKpLJF4oZgiSrR5f2fj0d2vvlY/dWbpqL0TyZPVuUUQ8nAQykACzLXwuOG3A00NbP5gr8EcHIxQTqjX8vMr1bO2/WEqE6bmTL1fsX0NqmEUEermEQrYR403RABAgJL154TPDykd7+23ND1kPMRIUGQNAMp56HnB1uZ2fW6+Z3b0lHxq/VTjqHnQNttHWc8VRmPUbPnl8ozrR41Go1Q6hTHmnDN2vIkIk+nvM0LcjJPFlM6010/Ls2YD+lmFDPtDl5vleuHB3UdIAVffPMNABAJeXS6raS2t5SRJknTJjmwvCoZHTR2mIYaRNVFSQiqbThcVJSVJEt5p3BxPRqVqvlaZaR+1/+AP/iCbziEB+tyBnG8/3G8+6pVKWVXIMhCEzBEgkEoSoD5XRSwxjw5G4lHHyZVRlC4aWGrWzAinlZ5peqNcmuSGUQQ4AgyBT0XxQACTGfCzyfM+Mo3jpA5zn/Y+ihQE7tgwsCCiTLaAsWyZR77jqnp2d+9gPDbOvrD22hdeCgy2ubk9f7ra6xmDgT2/eLHR3He9SbE4s380Xl7BCCHGGOf8uAP++yU+IeEZ4Xm3ZUT52ZSWUvd3Du0+0PNida5s9l02VN547U3P9o92WwKQPSvMqLl6eV5gynAwHvXHg85IRKom683ukU8mr//qCy+fez0vlbxxqEB1tjyXUlPj0ahxcOj3gtZeh7rAs2hGr7588asV/bTgVSRbBWFoGKbtu1JOUuqIZAH2pHZHB0E9pemFc6PqlUdq6SaW30H6OwAygKJj7x0GGEjGYgmfJ3zfIIRKAomiSJGUVCqFMRYVUVMzgiD4gVefqWayqY2HDyzbTum5hfm1iWnZ9rBYTi0uzeqZ0mAI7z90PtkDPgZC+FkdKiX8lwCn5Gmn93mFDOxWuVrb2TyoraXOXVp6tPVo473W+QtrP3Y/GPbN/YftnYeHRIHm2NNETWCKlqECEgcN4+Gd7dqKLlUiprgbrRunF17sdDqtZkvF6bny3ITbjuXKsrJ5c3duDtTK9aOD9lGjffb8Oa8vNTf9lIj0RSVfp1m6JBBlQkc6yuvebLuZ2ZngQNotnrOXLhci5r/XuLlx+CdL+r8DHALIAGSAfyT1gQDFB7onJHx24MzTdE3R1ElnMhxHRYIkWSgVao1GAxH8C9/4hus5t27fzOfyHCJd10U5ZVtus3n45hd/IZtf+Pa33hsO6KtfWEnayoSEZwconAIoFDJ6Vq+I+TWJaLy9N2QT4R/9s2+++51rN3+04UyCdEldWK1DAg92D4ihACXkUkAwkokkazhVI6maeNDtzc/XT59aVkVJIDKBCqOkVpv90Xfe2rM3vvjLb84W1//wf/6Lnbtbi7U57sH6hTWpIDLF6XX2F5SFWXK+s8epmC7lV7aPro3MD/OZcO1U7dWXv+q72n/64++6R/894IgzDCjhjDCGMSCUAwAfr2971pYgptaPTnmfp0aMf/DTOkmK8sebecT5m06rJz4x3+UYBAyPl4g/UQBTSimlxuSvAsPn3F1azxeK4ubDB77fWz5XfuFCfngEIMl/9+13DcN4/aUXAreDUxMtdf7BzY2XXnrJDIzR0NbJ7KBnf/nrXzb68od32NjKKXIEgB8xKQioLAcRfbZUBtPqaOPKfdr7x9WHactxej/jx1+P4/S1MbpkHHOfuHMR4njSA7Vp28OTSk+sXjnO9zrmegFPl544v2QyNzNn9B3LtB0wUWulnJ5CDI/H1sN7W93mAHKcyabLlUJ1piqKhHPe3GpzRnUhnUmnvMDygFXK5OdX66996Ru7O1uTgWljGwG0vHyqWpvzvCg9K9aMGh/LCKRnU2uP3M1H2w9r5aoE3Uf3bl58/UK1kDUH7Q3fvLvVvXzqFybUPjR+wtye54ft9qh9mJopvgwm5z7aAP50lgEGkjishM8if3tZL62n5HRpa/uO41l8SLudRj6r7m42/tn/5WsboPf2O7ckSfJ9XxKFKMC6rstatLwy3+2OHu1tq3KmvKwKddV0+wDOgI/PsfmEqU+nSUhIOAlI6FJAIYt4YAfeJAo0xjwYONE73/2JMwxFrkGAWu2mw8e6rhqGZQcWYDDDc0EYjZyBXOC52fTiyrxO8hJUc7lKqZxrdRsM0DAMbt2+vb+7Gwb8rTvXwtE923REUZSITA0yGjcKZU0h0usv/fJk0vmrt79FMpEzcZruNQjNXCmLQnHYAf29vGStqrzuHbcYHAGAP26fIAAQPOeb8J9Vnhf3oalPGnryes2/HZkMIWQRByLAAuuPW4LjYoLTqYzjU8aA7/uFYmHsuS+9fGVvf29+NruwMDOztHjrvYP7dzoiKWpy3raD+kwum+dDj3PEAOQc/PWZUp+BdemTOvN82siSuOeimPvE6Wjjrk/4+fDUzoJuHrRVomOAIwo7jcFwOAodjji2Bz6MBEVRAea2Mx72fcexXDtU5ZTv+1Zocs9lEldrhHN+6/rGzsN3ugedM6unvvSV12vlGaKSwXiwu7vb37Qa+70gBOfWrxZqtf6QIlNQvKKk55YvrEWGrFsvFEpWtbSbipRc+5JtdDLaTE7LhobgBZTDc545Z4wUKHgAAMARBOiTSvzpRuSnOKkMfdYO5U/4bPPp3veTgzIAACldY2G4sDS3231vbA7rudnAC0Qit/vD4WRyeLgjyxLgfj6fGpsmkWeXVkvv/eCuIqtz86eMiT8eTgolrOnqCDEOGUSMQw4A/ziGJpkBJ/xc+by1h3HvS3QlLSDBj3yCSOCErgEwhnm9BEBkDl3P9WVdLJaK6ZIKIew2ezLNelHHAZaeB4W6ODs361nR1sbW0sopboFxz966tze7Vg5G/t5BY9Afry+fz6n91tFEx3kFiigUS7nKC5des6Iz7JESDNPvXNfnz7Isvkx9LyMtw90vhwSOJeDYEaSymlrEWEOEHe/h0eN3+eh9OISxI9ZkBvx0idMpTsu0usaTslF7ujPgn/qHEMIwdDBEui6jPo/CUMSQBxEmwDSdvb09APnO3sNer/fK5Zcrlbxte65t5nLpcnbOdiXPDZV8FgKqKiLAACIGEf8kRyBEMOZg6ueI6fdop5uhTr1nOeUMO9F0PF2e2gyYc275pud5ak7UUhnXcyDDjDEEIWMsZCH0OcJUcUUBSygSQ4ciBaVmlGxdIgjavUCRtHp91mfOaDQyDgJ7YuUrXy4tFLf9I+CT+5t3Lpy5Qvwc7ysSyNPJQSj6xTU1vP6V8SELLDmfUm68/b6jg3xmfuAIF5f+iWkPvcDM5qKAMcAky7IYgAQo4FNbvn9n6POzNgNOSJiW49nwcU1+uPNgvlod244I5Xy6GEWskNa9yGi2+w8ePXzt5dePujsABGHg1+dmd/a2ALJbR1Y2re7styyLFlOFnW2jupDhKI8EDgkDkHH+0WeEIJ7WhCMh4b+E512/e1IQQSKiRrDKC6V8sVwIwxAyxDlvNRuiJMhEpTA0jElEw7SSA54QYK+0kMstCp7ttHcNFHnzp2FtPaunMr3doU0C1/Q5hQszS/dv7RxtX08T7fDgqHVkzuuXUSAPjiaOOzSDkR3sacqCNQiiVNoytFFvYeXllycC6E4YozqCkiAggkAY0TACBEIMCAfs48JhAAAAGQAAxLjrPGskFe7Z5EmPfKefMX90SvNPzYMNp98bIUEAgiLnMhmBAtMeB8CKGPUD78Pr1y6cPzs/N7O2eGZre3Ozcb9a+mKnPQBcCdkwiHxJzhOq7+30i7VliPjxf8cGORhiBHjSAf/9eNIz4KR9+PnwpNvn2CXo0XioZ7Qg9LuDtuUaCOKUlhYFMQj9IKCAQYA4hJAgEXMRRL7Bezmp6Ae0fTRyjkAxm2UcGmFPw3q1VlnOrW9uPPzed34QocgxvUKmLIxU6kObjY7sRwyEjLFhe/K//X9/b2ZhlCOvFqpfGruhqueK+a83H3Gfwgg3VSXFfDCe+IqgqUoaA+g4nqAxCBD8WwGcCQmfMR4bhEUk3uq01lcXQo4yqVxOUd9/fztbFlRF4xAOBr0FWvvSV7/03T/74dbBrpACqpyn0a4k4nQW9weDcqWQS9e+/+7bxdrLEP90m/J525BLSHhGIFjkCHKRylKgjDsTTVcjeTwZW5msDoCLRJDL50wLY4yLM1m/6BsPR94BOrzZLc/mKxdSR+1DsV8QhcqN97YUVb794IMvv/51JNHvff9tVdbM0ZhEMBxHeiYNqN0MNhxpFGIHzmlSbliSdGZptgcFHbiQcUgkIoV+QEUGIM7KZRELIXUwpFlJ6rkTSZY5BkEYMMZkIipEhIxbQYyuLka/FUecDgyix0TEAADi7H2Fj5/7STN6fOwfw4BzDjg6/vPYJpZzgPCUPsEnpBs+sRHf32rNj0ExvshxxD03zm84TtdIpnTNivWNjnnutPlGPr4e/s2/+OzxulWEURRRiICqSjT0AWREwM1mu4Cd/NkXxg5zbVsWcCMwoCyL8ghH1Dfcl179gq7r9+7t0JAUlOJ40pZFkNJSW492ilXxv/3d3wRh6ff+8M7AWwQKK2qZaCQ5YSjIFIVuxAXDwxhPV15xIB7jhx2jW42vb9ONs6f2G465f5wia9rvIu59CXi8Hj2uHmL4eF0vjPGvjXtuXP6clG74xNrbuKjyE/oeT0pnHOf7G09M+5BVCqIgTpwJJiiV1mVJwRCLadX0RwIRKaW+72NEPM9tNpuccySAMAxVVXUmPouACrNWLzK7R6VMKp0r5FP+zWu3sU7La9kwCkzLQ/7EiwRBJCktk9OLKSUzdnvWxKysqaLtKXKEWNp0PAAjRUQIh3ImDEMeAeTjKAAS4wRhghQgmJCx8LjQjhsLxhh68sszny74v1lIcVE9/OP//dfB2hACABgEkAP6kYMT5Agg/tybAyX8HXyqof/k4GUA4oPUEIIYIwwBQohCDiDnnGPACqUFw/JkRV2aXxAA7HXDVGZ2ebk2Moarayv1av2gfdTvDaqZai6bGtw9pFFgGJNMtiAqZGe/J3ACEKmWMwhyBhgHDHAEOYYQfKzoS2piQsLPG2L646yU80LHY46iyIKqBXaEuRB4oZJTAAC25QSh77sh57xarerZ1O7unoCFlJ7iHmKM+X4gy/Le1qFjuwISJ64ZRKYeANsz83Ni87aTSperuRnCJe7CTLqQL2c9MNls3pX7WpksFIoXFVkMIhqAIIiMbFBGHGgSDqhn2FbIKEQcEqqrWd+PwpBBjAkigMEwohjyk1JQxI+8/sbvPyUR+Vnuc/xLxCgAHAAIIOMcQoAYYAiiRMf82ean6sNf15+YCSfCECHEARMFyDkCAAgEUkDXFi++fe1BVgD5nDbqtDrtppbFqZz4yoV5FGgS0R3TO9g9KpwtlCqZQi3j+ebMzEx19vTW4fZ772/XS1ImkxFShJAAoQgjACjgEHCOEUL4+beJTHi++LztesTuASuzNJdTnECyxj4RcKagj+A4cBxN1wgWIhqGfhRGEQAAI0IIgQpAGAgSKZaKjmePxkNKIjN05QwYOX1RFIqzpXStlJ4ju3sTUReyFb3b7UJfvLB8RRQkk42ymfT82pmj3hCARWCqNCIE6gCKVuiFJBjRcSDulUpCVkhVVDs/b4kKjIZw+06JcT+KOAQC5JhzyjmnP0f5xCdN58d/eXyGUv43loA+WbjGiAOOADxedoYAcMBh/Er2ifG8VPRnLZ0ndqDHp6oo5x8tinDOccwSFiGQc4QxFETIASEEIsQJgXqqLkl7EbVCvx+FhiITQVAtKzx7cT00dpq7o1KuYlQsGrEg8s+cX5ckUq6mxxOj03UMW9FVXC5m5SwRcCSLEYKMA8gYOp4Cixj54bMVhPW814cnzbOWnmeNk8qfJ53P5B/+X38j6OAb4dbBRpP73Bzbg8EgCtn8zLxhGJwDQRAghFAGjLF2q2MxQ5JFQSAUBhH0hSyvVQvNZnvxTLFUKdojj3BF0IGkw2I1P2m6WKVzK3WnF77zk7dWT62+8fVXpTTc2LyfVivFco4pEPE+IZiFAXVDTQXlWvdB+IMog1PimiLzProlh3g1c7WpMt9HYcAYA4ABRgEDQMAnZsQRdx8W00XGP/ejfd+PL0MQQoQgRujjDWDIOeD8o5aZxURxJzPgzwafriafqjMQxWyfYEwBAALBmGAJYUFAnHNCoGEH1epMGB31e03quZlcWdH0iA8ghJZljUajTD6/trbGA8q5XymXL12++OBW9+0fvwPEzKkz54wus2xTzaqKHAmEY8wBx5wjyiFBHPwc9nISEj7F520AEX8QR1a+d3u/1WwHdhRAD4oMcZKWVcdyfd+XZZlxFgQBxpgQIonyS29cEYl8uH/IGLOCCedRvpbWKqRYltfWlpq73WHLOGwdAdMvFctLK4ssaGelrFDVdDlbKOc3m3e23t10XfvMhVL5dG61mAr5NqRRZLFKhNLVsux0Ht55T1WrpWL9/p2Ntx/+Oabya1UJiucBhKIg0RCHEYAAQUx/DqX4iRDzZwzCOjY8P+b4PH2MMYSQCOj4XzHGOAPHlqyQwye99ZbIn546j60/ccF3EHKEEEAMEwopIARSSikLjcmgWMiPzFG/M+JhIMqZCHBEzMZ+q9vueoHb3X0IOCzoOZmgYW+UTmfnZ+FsLe0ASdMkE3sAsYiakphVZAoR5AAyjgBjGD35Wphw0kx74EzC348nPgPea23t9XdY1lXTRIJ0pp41O+r40DMM4ziOl1EmSwpEADCo67ob2J7rGxPTNlwv9H0e9Y8m1YWiO3B2gsOD7Ubocko40WAURXpWX7u0sFo709zp72zvGqPmyBgM7HZWzy+cyadmbCt7a+z2vP5QFTLV/KJUHI0O9zMqm53NCrmutbsNhND2grvGj6roTRHlMRIZhMd9B4SQQQZjNtNOyrUGfsoZ89hL9RjPDR6foQL6dAeMMcYYI4REETAKGGaMIk45pZQxwPl/5jDNzxfP2kDhxE7IgvTjGgQ+qT8AAIJjYhegDxHEGHAWURpRyqPI833bYyOKURjyiImyIApEwgQoujjojhpHzXS6cLRzMByM1hfWT51atMfmh7fuAActzOePBkbjaEMWFvO5lOc3ZRkQwjEGgCPA2cdBv/RZ8zU5qWj/k+JZq5/PO0+6fJ+X8iImHWTmhZnlsqiRCHhnly+1bzvv/vFdOgwZY57jCxJZXFykLNre3m43O1vtnaWVuqZmnFGQkQuyXhs2O0bf9kxrZrEIuRjZPpN5WtYQQoIocOpMvMG97ZtH+60X3ji3PDvrRQ5z2b1rve0Hd7JzeaQ50WhSoTU5UzD73HAxba9MtjKDzAHn/Kvrv9RvDO7tfSgL/UxKUuUsRhCBj44q4DwmGP/k+KTRRB/zcRsa0wETwjk/DrL/dAeMMUeI0wgCQCnniCPOadxGcsJnj58awxEhNooeIcY5ZIwxFkWUBUHgeZ6GfHPQs70JhCIRBAAwZR6Eoe8EzUarNjOfzuidbsvx7HQ6K0S80+nv3N2sVIqMWa2WUS3khkPaGe1CcEoiH6WBf6SRS2phQsLTgYRBhDjxvKA9aJx7ZbUXHqROFcDsUB8Ve1ETIRSayPO8UJsEUVhTF3y2JWosW0Omy/kAcYYFGZ36Qk1RxUnP2ri+GxhAlHH/0D/3Wq5D+u3xdrmW0et0NVtempuToIqXxbd/+O6k6WK1WehqPrAL2dz6F64Kgrh5eyPT+41qjnJvbHUkODwdCutZUFrP+FxaooxZPuc0wEjWBAFyEXLmwOl8QEVEKAMAAAYgZQB+fAyuIlicc0ohQhggDAFmjAUhF0VLEARZlkURI4QopUEQRFGkyh91sQBABhD6pKMVEQ1YFDHOGEJIFgAhECEQ0QgCQDDACDAMGOOUcsYYQxRwxDgHHDEGGfgobuekdJnT6u1ObE/9hIZGsenkMf6+Mf7QcXpHAT5eNxyng4xLjyaolEWcU4gAwoxgQFlEWRgxiDEmBAuCQAg57oABAJLkAQ4p44DDKOIRZZxDxkEUlIgcMe45PnE9RZYJj7qe1dnrXa9UVjIpuevQ6lz2aHM7JXI5l3N9YWtvnE4drcwU6xVBzc/vjpp3b1z/5V/79VztcsR94oVawCLbZWrq5asv/8n/sbf+sujjSVk5PwqxyxwRqhEdClB77HvFETeTYChOv/v4fBNifJqnhU7pCz5t/T8xXWnMdjuMWQmLOyErTheLY/T3cfU/lilXFk8qP2OTM+X9p23Hpm1vp/UPjt0DPr96WVpOPbq7vbO9O2hNZudmPCtMaekH9oGsSJqmeZ531N9NAaU0kxn1+/lSfn+zLeo4VRW2WvsowNV8CZrKL33zq/vbR+29Nk/B5eXlnYMtAaNiNqcop5mLUlKuMldZO7UKGQKYZR8pZof71M/n58RUcX935ztv/cWVC1fXXlodNAYiTXm9XGCmRTVDSTGwa4pa8MIhggRiAgimEXdCTyJQEFHMRPQ/m3fgo74TIQDgR6f9YwGIgowxppSFQeSHIUY4pZF8ST3e/8YYAsAZA1GEKMUCTgVB4LhBFEQRiyAkooAxwRQyhiLOIw74cVlwzigFUcg+mnZwwDnkDAKO4HEwDkeAc84ghPD/z95/BUmWnWmC2P//51zl2sND64xIWVlVWRIF1QAa6J6e6emR3OHscHe4XGrjG/lO4zNfaJwX2pJrtFmzEdyhrY3YHnb3NNAQ3UAJlFapMyNDSw/X7lec8/98uB4iE+kAAh2JrATiqzAvT/fr5557zr3nO7+Gg9wdZ1LJMwYSBAQBIVaKlCMIygFiNkSoFCjFRH2SEBFrLQJZyyBorVjLImhZrLVJkgAYa8FaMgbEGAAwWsjB/d39uXNXSr7Lo3Dnzk9H5wLHVZ7ndbvdTDAxPTF+Z7X62ef3up3OTv329PDrS9frvuMOT+XJl+euXN1Z3STJsfER2AgjMpFoAuQTrs5nOMMZTgOau2rh/IWL0y9MTk7e27q5dHO1uRM6jjv34uj6nZ1eDK9/8xp45s6NJbEqmHIRsdGF3e293LA38pwXNbi6t4+h96//u3/90rVrf+sPvzM5ORnHce2PN0eGypfmL7773odeOXdh/jKLff+D96yYUrk0fWFs3092qzu1WsMJSSvHzzkdqG317vcWN21nwqPzTjsLnO21sdfuKMx4bi+JCW1GyFGaGUzMhi0C+Ce7YhRHoQAbFiQGFAUAyEGA2awTBEEcx81mhFHkeV4m483Puwf65zR3FTFrEQXKC7vYbkm3w3EkIOhqEZSQhRMhNIioiADZWrHWWsuH9r9+R1JVJGphRBEGSGn3C2ak+BXxpJ0XBrZ+wty8pwVBK8gMFlFAoXLQc1xFhNg70DyDiOUDiGgRFhFhYRbmVO0sSZIQJojGWjCGlBJOEqVUrpDzMhm32Yua3d0wJPbyxbJbUK1WPZ/Pjo6M5fIlttLrRGEnYptUKlyvNus17bjdc8+Vp2fnTVNtL0thqixJVjhmBkGjCQIFDLp7MkXSbx2e+P38lO7bZx0nnZcvWniSznj5O7fvjQ9NXrx4KZQ2uhKPc94t7a7tJz3TrLfzU86FK5c5kfff/FQFQMbxdUbaGjLuwvnpTj28ubmyvbvfw9ZwqXzxyvnRiWJ1f39mbvzyhYuO6CuXrnZ7nVajaSTZ29+Nk1iU9bPu/HMjF3Bhb6N6+9adrO9Rydtc3e512rM8127dSbAxe+Wa6gyxjaiw5Y3ttG7N7GzGtd1eHAFqBYgiQDIwkeNA1Qf0tbyuAwKSOqMiSTaLuRzmcmAMeS4lifZ9J5vTw6MRERGJUqk9mABIRBrtKJPBXM7pdshEEEUMBkWMRCiOEWYR0hoBxFibJMZYOlZ+NWViBAClURCBGQREwNovVjjmGX5JGDbMFsASAJCQcl1fO5oyvgMHdyMzWwvWCrNEkQLBtL4IkZAAMxChiBhjtE7dCIiZjTGe55EzEbaTF56/2DUtEbW+srq4cCXv22azOlQpX75ylaT+3vtvoTf0wgvXHtz6cNQrr1oyKqedbC6XQ5GN7e39XlyCSU4yRGBQCIQUaBJNTjc5WerHwc/XFwu/hkCJZxrPOoF90XDS69J//qffm59eGPvyRGVoOBNk0eNed7/W3SlNF1740iU2AJ7xsurv/IO/rcV/66dvCRvbBWXLMTgr4Y5lQ6RiTorFfGO/uba2NjE1Xi6VFhYWFOilOys//fw913Vn52cvXrowNFK6fuP6xvpmLpcbmisuTF24Aw/ufryiINvZsm1TKzxXKmzPQNgqj5SzFUky9y3EcbFTxd252f8qYqfbzcVNsUYAKeNkM67XCHsnumBBQURFohRoLUqj1oAEuTxkgsRx0Q+gkPcBXc/TGV/ns6AUKkVK0eEyKiLdMNGKHAddRycxcAJJxNZy0gZEICTL/dhfY1hE+MDGg3j4l3rliAAKgHC/QLp84RaxXwXPigR8Wl6R1loRRpQj6z4zAGUCfaj5SDXPBzKwA4JIzFYARMASoeWjIPJUbk7dpDKZDNLCnRs/GanQ+ELp3v2N+8srI8NXXFZTCyO7GxJk8tWdra2N/dGZkXOzc9zaXX9v8/xXf2e3s1IsjDWr7dW7S7nSi/64Se3QSqlUG05Eh4F2Z/g5eFpeu2cS8M/HqUUrnNJ5TwrtSfalq68Wg6HPPrixurQ5dW7MWruxvf71N75RH+2cm1n08jpfyM1NTr/02gtr66vlsULSxK0H1epWPZu4PdOOenZ8dnJxYbqXdB7c21xf/RPf9+/culcuDJ+fv/jtb/1eu9Pa3dt55513Mnm/XClJze7t7yjfWZy+MDo6WiqWHXbKhZKpxyv3N3rddz0uzUk56jZX95eavT2MobXXql75vmpMOfq1fDboxGJNjEDIA2MnBg+QIILW6Lrg+47jiVaoNRSLRmvrunHgO0GgXYe0RqXEdb1jCyjbVJvMrDWxYQEkJa5LhhA1guiMtYqUUiLWYVbWoKswQUxXOjiozfCIHHy6k3qGXz9QEEGJgDCbGBJllFKHlQkOzP+CiNbaA1es/i2Q7sYOg+tEhIisTTUunBJwNhhtjq7uVTeLk/b2vRuF4qSrs/kg+OpXLi/f7W5ubsa9cHxyRpEbd8PxsRFudwsFd3TCbddbu2vt0coL9UY4NqPcJNJe6DhoUDEjAMVGRJ24wthpOdc8aTwr/fxtw9lal0KrXu69H322sfHnW9vrCxfnX7g4oQrue29/2Lna3e1swIYdKU80a+24Z5q9Gkfqq1/9aiFXePenH3z88cfZnE8dm4RtCOLESL3abXXiZquRzWbDNrQ46nSj9k40v3jh4sKVdz74yeb+2kRpZGxuyK/Q0s3lSuHmlekXL79w4eP3PgoSLIxkVteW4kJrYfhldBVBeXZolJOksblXk/bSzffHfF3xmEkjxeSx5Tjhk5XsAAAAIQKl0XWV72OQQddDrTGfJ6XEcSgIIBug54nWDABR7An3BRprU+kXRUiMAgERCyTCDAqRBIR8D1OfrSRBk1AsBGS01lY0iDAzC4sIQF/edYXSxFhp5/or8qnO8Rl+DVCYqpqVkYQE2EASM4GNE3MgywIAHNqArQUQtJbZijFsjGUGy3JgG+4fn4a0ua67urU1MTmeRDsrd9YgzpbLo7v7dfI2Xe+q4+j6TlcpGBmZbHUliU0uyF58fYadpoJer9Gr7slzl1+48/47V0oV8jvKy7qeFxOAUaKxk7Cnv3BxwL9tOKkE/KTr1A6sNXO2oQGA0/P61h/95DoiOq62Yj+qXk9i85XfffWlK6/++K9+4uTw7Q/vNe7J+Suzf/gPf788XIpa5p//v//54pX50kg+P63q1b1zF87Pfmuu1+vd+OzG9tbO6NgI2qDXkkKuYq394INPPnnn5tjU8MtvvFAqltmPmJKN6uZ+o5oJxn74/R/RV/XI1BB8mnShcW56ug3ZkXhktjjuaGO7cUFPGZvs+jv1oftz2bkCTHhJ0GnFvajj5a3SGgyB+VUcOJFEaXQc5bro+0orzGSEFLgO+B5ox5ISrUQr6fUca621YowVkYN4YGRjGJBQCzErBjDWGGZ2nDwiKkSF1GMUZCJCskqplL2PT0YqDB8Q8MOi8G/X/fzMQ6EGFERCQYEEGMEiW4lNckjAKZtyP8IXQTAxlq0kCSdHBOwJswimmpL0HlFKsRtlClnHjD5Yu/v6C1+9e3cvSerDs7i0vJQkST6fZ8OdqJ3LZIvFIvei4ct5T5V++MOPKvmRdZDVzboJKWNdckLtxqS0RhJRIGCYlToj4DOc4SlAS5ygpxPG8tBktbm7fr/+o/q7+Zyf1NEZc2xVLn6p8rd/929dHH3xL959s9luOBW7X1vfeUBj08OL3yldvXZ+ojCHrpTHvH/33/15Yz0cGi0XZryd1nZ1rZXXw9qTrbX9P33wExvHOm8uf3Vq9sWZ0qhfqT0/57V+8B9+pH2qTBVI4/KtzbHCxden/7fxlnWKnXvRh7OVKM8vDHuXuuO2ndzz1Ua89yCXv2LJ77TCLkIxyCIOiEPCx2vVehJ5QBoQlNEu+Z7KZ1TgUSEbpwulIoWi2FDMlCBaCQUFiEn3G0RkRCzlXWutNSJC1irLrtXWGIkg6HFi2DBznNgoNizk+26vnrqZChESKTjYK3Ufdj9VhGmKJAuc6roRMU1myczWWn1Cr+/Tiic+KQbVWz0tDDQwDIg/lgH3Aw/YmToD6goPqrdqqd3vGKFSSjkOaQ2o2ECSSJSYXshRiInRhhEE5aC05iPtxInRjivghHHCYnwvEanWaw8m85Ms6s563GrJ7mcfX7zylVxuXMH925/tNltr1Wr15s2bMzMzIuJnzg8NDYWr2p9pXbhSKGF5ZIia9q3KxISvp7LwzpXhqXCy+c7akuc/l3RqlYJqdj2tHz9ug+JHYx6QiObE99vJdpqDjh4YRzsgLnPQduNJq0ZPapu0MkDJNyjx2oAvGAbUYx7wnD5p56ynVT94EE7LZnzSdnRJjUSSRBC1413we0GlmGCv3uoUhrPV5s7Xv/mlc+fOf/rezR9vfXjn1hK4JhMEQNZgkth4c7VuDbcnzehcyQnU4pXZ7eXm7v72yMXzs0Mzjuw6nOvstXO5kgdeq9sCtwdWafBL2crU6NDrX3k5Wwg+/PBDw8n83IxJbKO6F1zdGptz3r7+vdvXb2EHvjn1NV7OzoWVln7AZpUxsCabd877UOmYXpc7MKDA9SBochAABYCBjRCwAvWwUbZvpk3JD8ECCKEgHbrGIACIZUQQEmABQgBGAVAC4rA13dCGESQxsCVjkfrOLnIoAeNBZuBD2ejRCetHESMCYhoxDL/G2k9nOCHSHOBwaNc9QJKIMWyNsIG+YxajDBY3D2+UVAuS2oybzabr5Ld2mu1WozyUQxW0mjutVn18pNNq0fb29vDw8OXLl2u1GiJmMplKpXJn5bMZ+6JpY11XVzbvV0qzfqb0YP32zHDUbvccJ4tKJVFkbMKMSjtwwgLsZzjDGf760EHBpQQUUa/TdTKqWPRdn8nlC+cWNneCr7z+O2t397737/8qajMjjC8UpqfH46AeFZOp6dE2VLdWqnH9VuSPJeLNXZhqNcP6at2KmZqcING7G404YdchK4isHAoU+kVveGg6u7tenRidKlSyoxMjoK3nefXW1kZ19eP1713JX5irTOVfyH33h3+sxqIXL/3BcP3F1V1uRi3H3wdb91hcyHatSaip7eMJeOCOQzwSRrZiLaXVVxEckn5lXhRJCyUc5JI83HnhwzCSWoNZjvxogFCMVWEEna7thWyNjmIQUAqU1swsACmvAyKkjjgo6rhzFhyQsXDqLkYICIIACAKE6kw1/cUEokY83EgRM1oLIhJGli3EMcSJtQaZmS0x2EHlCInIHtvsE5ExplqtmqRXLI7GgUvYGxkpRybeq+5opYIg6HQ6586dy2azu7u7ly9fnpiY2NnZSXC3VItbtVCXTM80Nx5sexl/q9ks+v76VgukhIJxHGfcXzER1RfNieaL1p9nBc/KuD0r/RyEgXwUDm1FdchwRXq5wHV89jDbDMbh/OV5L+Ntr1R//J9+2t7isfFhk+lOnRt/6ZXna3pld29vbLTs9aRRbVcbe6OJf+3a14KrpUpp/Ac/+OHG+sbY7Ojo+MhPf/JJ0Y60wp7pthKTuLFsLe+VxrLZXEbFwQfvf/TOO++QwrGx0Wp1b31lw/Zgb30Th19sLUf7YWNudkSV6p+3/nwiu7H5iVsoT3o0B35FethJ2tZawOCkA0FAChhBHCLPIa3Rd8lx8Ljm7JALAQDBwlEiXzwgTmQBEUYAEEYEpQgsK6DYQJJwHNk4EmutsRpBAZDSiIwCAJhKwIAkRAhMh2c8zsEKEBAFDkJE0l8i2ifslPGs4yk+qIdzlxr7+1JsV5g5MWIMpkHAIgwAgD+HgB+KRDLGtNvthfnKzNzc2lrYbm2VCrS9t9/Yf6AWZmq12u7u7kcffTQ3N3flypWJiYkf//jH6+vrL74yWfPrn17/bO78eDE/tL6z73H+0vnnO/ubG1udXJmtBSLOZoPYRnEce+6ZDfjXgSet2j3Dr4aTOlWdFvRr37l656OVndu7YVMcN9OoiRewFwW3b9/+9KObjfVwfzkaLo8gKxNz1Itvr1zPzwM4CZPxAweJ67XW7u7u+/vvB1LUlP/yG19576P3mvvd8+cnBKHZbFsDLjh+JiDH1Gvt1Qc72Wy2mCsMVUqzV0ZD20EVxbVYhJXSWc/3M4DDrb2798ZLl2Y2vjpcmd9311vxJapfcDPPgww3QtO1NQDflQJD50QD5xIAgqfR98DzVCajvQAdR5DwuG/U4Ssgp9Sb/h3GjUCSnuLRnyRJYm3qroVEhIwgwMyOJoR+udcjjffPzRmLfZH8iJjTKftV5vkMTxiJYTjaBqX0yQBgGZlRBKxFZgbpZxv9OZ7uh7cEIqbZshzHcRR/9tlnW5tLjrNdGcLV9d1Oe5doWmsdhuHNmzdHRkauXbu2vr7+/vvvLyws9PZVa6Txye0Pw87lKxcuDE1kR8fG5yYv/mC5mm9TpuAhWRFGArG/irX+i0YMA71Pf839eNbwRZvHQXhW+jkIAyXgv/sH//DtwlvvO+/URuvDwz6iSNtp3ICZl7ycV3SHC0Wle3XY3tqJkuTO5w+Mlz//+mtFbxhjRzvWdYLeXmvzzj6OFH/60ccj+dn/1X/5vyfrvfX+W2OliW99+2t/+i9+goJBMSgU80JxjJ3mbvvW9fvjE0Nf/s7LF3LntvbWR4cm5sYWP/vgxo9++OO6bH38+YegrTskXamv3dqaWPhaScZR6ySa6ZpxwxJKW3kWDbiiwxNmjiLFjgbtoOthNktBFt0AHMcCwjEVohxmY1ak8OFSSEdtCQGLMAqTtWytGGvZhCJGOwikhB2IJE4SRFEKiYAIlaJDB1cAYUuPCK99aVv1TYlp+NOhSnyQs8mZBPx0YfomVDw2EQIAzASgDmacAH5BEes0Sjg9XmllbYyIw8PDk9OT+4290tCUTZrVvd1CTlcqcwBQKpUcx3n55ZfPnTu3tLTU7Xa//vWvK6WSTuznfcPJ8v3Vsp+/9o3L8wsLG3d2ak09LnntZLUbmq4kcUjIjuPCACedMzxdPOvE86zjiUvANpSXrj0/N1c2NlSgP3nv9uZqe/XBrksPLHKxVFyt7nYSG2R8h1SrW7u8cPVrL37n07du//TD97EUQqilR7JdqCyMjpR7n7+19Pbse3s7zTvvb5VLD775N37nxvm1Rq0hiam3a+RItuh0u9HGhzsZ37n50b1Prn8U95LJb1298uLr9pxz6+bdsYXCqJn40R+/k5/Ha/9wLmfy3bsbhe7VjIyTcttJhMxIpDkPSKFpn3TgPI8cLZ4Prk9OgK4PyrGoE3zY6/XQG0trOCb+HpE0iQYRYUEWscBGxIJJxJquIpsJCNBLYhIx1hoWBnAQgQiIKE0GfBCQchTxebzbQUanC7G1Nkk4zVCJCFH415/032QMjmt8shuU4451xz9MElIEAoxIiIygFQoPdL4Ga63nqJS8U/2zUmpiYuLevWXBobHxsbXVO+1OZ2SkMjYxXi7n9/b2SqXSiy++2Gq13n777fHx8VdeeeXNN980JnqFvpz3RzIFJzfiZd2iI3q/tZmYwHXLAg6BIU3CCepfxU/+aY3zIDwr8alnhPqr4UnP79OaF720cffla1cvXZxVNtnc3L7xyf1CJSjFwf5mU2cJETd2VwMc0jpo73e9IHvvxvLW3drnP1n6yf/4eeUqjAyPmrrqGBGL//gf/ef/7N5/+90/+34xO6wV1ndaNz699fpXvnTnxu0H9x+0uq3KcPHc4qJQ+PFnH1bXmxA69TsWusGbG7dv/2CPC+3x4elGY/X5q9nh2WKr2fmzf/Oj7/yd7AuvO8mthndjEdwQg54J0eOipqCd9Aw1NOdOdsEOkRbHFddDxyPtiPYMiAVJRUwAOFpEEICO4XCSRARBiRgSsmzFili0xoJFY3uAKuM7SLpHGMaJo8EYi6BTMzIAMIIFSf9Lfa2P9zD9pxdoxDQzsAARJLbfvTMC/oJCiTzkAp1a+kXAMgKQVkiIAowICDho3WBmxH4GytQHkIiGhoY+uPdhaWQ6lysFfl6jt7a21kvCq1e+s9+ovfrqq3t7e3/8x38sIr1eL5fLeZ63unM3q4vj5emROfeF1y4le7DSXWFllB7RbhBFCTN7nqdRGCFJkjMb8BnO8OsH/vf3/tlGdSUxvdnJ+a17u3/5/b+MOZxbnPjggzu7q7XqSifcE2185SA6KMpMTlYw0c1mU7ykbevZEapUKp1GNDZXHpkYunXjTlJXbpwvDGcufG1sj+5D6FZyYz/94aedHfull79mAe/dv9vsNpTw+NiEiXh1dTWReGSiPDxVypezU5XAyy4UgsutB27teqc4GQ/Pz8rWpbX1K4+9AK0QABgAgUTIct81yvNbSQyWtaNdItIk2Qzlc05ldM3znULBy+f8TEb5LpAyBJKKmIKpOzQiAipBRBoQ+9OoO9aySWwSi4nAGmQWEFxZN0r7ruNZI51O1OuGYWQJSDIhM4E4wspYlcRgErQWmBWhpIXhPFdcVzkOaQVT4woAWCiKjUmsNWJYCFWXI5NwHNs4AbYq/UvbOXT/6U9tX4N9Oju709ppnlZ8HvAAuY0GqFIHtHPSqxoYf3zCdgb1x1PCQAlCbBMEm3GhtrM8PzP81ps/Cs361ZdyboY/fHulurrPev0f/G9e+p/+nf/8n/83/+q73/0uuonnONGeHR0Z+5v/4NtvvvPTucuV2mr1latfbSZGyYWRwuvvff757OWRK5XSUK7w06X4xnriyjwkwBT5JxSDB83LwPkdVD/4pO2csD+DMGgeT2pLPi256cTxpidtf8DnJx3nge2fUrzsoP48rfXnSbej/+r7b4JnHA9MF5q7zTiJt2ubDzZvmSRglCDrY09sD5RWTqCU622ubylwKiNDQ1MjWByqTOdfee2VjJe9cePG5u762PTwdlRfe7Cmq9ii7ZlrlV6wa7shZ3v5kUKj3Vi/v7e5sus5QeTVfJ1FIBHJZDLFcoGItre3bWcIwltjFXu1/AcLV567v3N389Z2QfYHXcCh6k9EBCwCIollIcwgMaV3qlhF1nW05+lsLnBd5TpOKl8yC4LEYhUq7CeBlENPq4dDOh8eOCfNvA86jeMgUQwsmM0rpRggIRLPJgIYZFwAdPMEqIS1NRRFEIU2ijhJxFpLyIDgaHEdcl1wXSIi0gYRwYrvYUwQkfhAAuAkiSLyXFdAsVVxxN2OiSRmzsExzfmp3Bxn+PVDgBkAWKOkd6ONkyhJQoe8Ymlka21Decm1Ky90R8NPb3/P5Uw28EdK5csXLulAHOW1y3G71Xn77Q9f+9IrX/7G1c/fvf7Z+5+3TPylly80mzu7e1vncTqWuvjaUeRDToQBRaM+iwM+wxl+/dD/4v/1b779N79x6eq5W5/d+enbb9d3W9e+cunqC2+srtbjhqzf2tsMax0TAokFE8eJ9j1rrcqq3eauSdrOMORHcvlcfhHOd7Ed70VOQZ27OsMhVTe3O92NoResVyhOjk91d2Rnd7vXTQpuCRLtlU2n3UPETJB1M8TMwuB7wfX7S2Yjnsztl5+/dq7yHRKfSBeL+f3HOzunxQWPu54iAGgFxnoAjCgAFjAhbbwAcjmnXM4oIq3B0UDI1nJiDBFrR/cJ+IB3UwIetNdUmkkJajFRP7GlCFoL5eE0EiWJIkMOBzl0tHa1ky2BMFpLSYJhaDsdiUI2CVvrCmBqF9RKlGbfsUistWVARyNbYALtEAMiIiVKhAgVgssMvR64Wro9W6s/1L1De+Rp2TaeFVI/rZ3404IVsCKAgKAQjFhrknarQy9ceS5Otm7dfxA1ms/NDZ0brXTDex4E1a2tvc3tkcKQV/bY0HPz50i5tx7cmp0cGRkeGh+bvu2sjZRy5bK3dHtrfDyPialFK1NFX+tchgo9I9qxCl3zq6RV/w3EicOEntJ5T9z+E239NxdP3Amr4Jff/fFH7/zkHSdD07OjC+dnxmZH9mo7r73+ZQn1ncwqRrc2zE6320UiRDFJkiRJYqLNnV10IZttrt7aqtY/f/eDn770+tV8KdusdjjBndV9SZxatRvW2Ywm+WIp3G9tru4KO/li0URxa7+DBHPzs7PnpnaqWysrS7PnZr78tTcWLm7hTj7c9WpNm4W66wbgD3XAGez0kY6TYBrbQzZlnShmIlLERKyVyWZtsSSlIchlPQAgtIAsIiwMCMyHAUb9W/WQuQaGM3nWWlSKtBKHkQ2kEZ5+VtiytRxF1iSIiK6jHMfJ5owwJgmYhLs9yQYQRyo2gOKmNV+NZYTUDs0CbAGIFBEBooNgWTQqRHS9DDNbIyIJMyqSjKcKOa/eOJJ9f9Yb6K+PL5rzyGnFU570qp78KAgAghAiEyiQpBs2mq1obLZS3U0cV4ehXV1f0pNQHi4yJw/u3neVRs+5v7QUR4iz5ZHRqZHRuVIxx4YVZS5dfMEtCVIIYOfnJzEx4LZyY8ZzxRXdM6HFCNiCPplK7bTuh6fVzhfrbn56eFrP9W9qPPRJ+6lHhyZ3q9uO40yOjSzMT7biqrVWa+dP/uRPJoZma+tht9sVEa31yEglW/Q3NzcZ7cz8jM7olZWVjZuNd52brXZ95uL8xNiscnDrQX1zZ6fX45I3YtraLJuVB82JmWy2OORyr9Fp+joplstBjvaqu72o6zhOLpeLOsnKvfXxyvrXv/XK6MI3lj7VG0tE6A3lM0013un+nHw9SsCmAbWkbCrdEgnbSDtKaVRkPV9KZTU0pIeG0HVFRBBY+lmDMc1hNAiDJCSFpDQwiGa0LOwKW2BhYBYAtuL7ylhA0YpQKat1ZC1oQWZxXVQErkvWOlEE1gopIUPW9pNgWkERscwixAzGsjAKChGSVixiLYuIQkqziui+HvxIBX0mAT/6+aAffMGuS9I4JhFkTSqxNu71Gu1249PbXS2NIKMSRffXrjcbO+TWhrl099b9wCuUS5mbq3ea7bjebE1Medeunn/nJ//fWvj82p1ec7+jux2Xm0rN9aJuZ68+WmiG3Y5SRQCjlPJ8H4yK+Is1Dk8LZxLwGY7jiUvASZTs79RdV41OVhzt9ho92GeNamN9q7kVLX++1dpKHPARsVFvdsNOo9M6f/ncH/6DPyoXy//mX/4Pf/Gffvjh29dnF6ZX7+70Wvaf/tf/xc5i58Enf9ELY+m0hrNjXam66GbcgsteyRsNVMlxtYeZb/7+G3eX7m5ura+sr7iuWymMxaHZedAIOpeaGxe5NeqqEI3fqZrdyOuaTDBgHFiAUCGwUqw0+i46DpHCwO16nqcdJYh+QENDqljCTEY0mdRZ6VBlnaKfSeOgSqCIgMjh548ZONV3irYopMCwIHEaNoSgmJSjka1iRhC2LEmSsIgxaBlZCEABChKGYSQiURpoJMoyAChE7PUiIiJAw5IkqWHQKoLQmrRjjtYKCVP3MTaIDpxJwCc//mktuIPuK0YGUCCEiIQoHMdRo9mKSPcmh7OkfKPdXhgubywFfvv13GS7E7//wcdf+drXh0fH795/t1KcGB0vtDs7t2/fGZo71+pmgmyJ3I3t9b2cO9KLyEEvbKmtpbZSbiQROZ7WnmELzzgBn0nAv9n4oq0/pwW9tbPVaxnISbcVAmDGyyDjznZ1qDictKRVjZM2ZgouEYmBXjsKw3B5dfnt937y0gsvVet7gkCevPD65V4z+fCjD/7jf/+9+/fvF4JKMM7V9WbCcUK9fKZobNLYakRJnClmmZKQ2p/e+NjxHXJxe2dLiU56UgjKY4XprTuzsJuxrJEy7TAXxQwe5QqW2wMWUFHYT1ZFrkOeR66rHI2VgnY9R4iMiOtSoYh+IEw9QCFACwIIIP0kGCykheGAgPuL4/HXn0GgHCsgwEQQW6MEAIDFihAIgSAIKVSpmtsasSQsJEIimkXiBEwicRJ3emAtWhZrMGVfAA2ELiUKFQsYi2w4SqwIIdpGLyEEV2vPI0cTITCzHLg6nzlhPSv4hd62WqFCSMSYpNuF1vzsfD5woqhVKY4Xc6P3o/tx1C54ha4TLW/unNvbL5bKL750dWpkMkxqqxt3xajAq5TK4x6qCLq71Tvb4bJfmXr1lde8GKvLvaASiBcmoUpaUWIT382eSv9Pit/UhfWkeFoS8NPSkD0r837S+OMTq6D3d5peRg0Vh6p7+599/FmX62NjY0nP7LebWVV0fKSMJtaNRs1KzsvqqZmZjb3Vn370zvT89Pnnz1Umy7cefL7VWV39qNGpJ+/++GPDybVr15bvrDEyagx00Ov2onCPDYMre90G5c3i/Pz96/cmJsa0S9PTk1Ojc1HLNra7EOq9xqjfJtdviMube76Js4XcvuNtc3tq0DWwgO5naRZS6Gh0XRofdR3XMaCMRSGbCUCREUkIlAUhEuY02hJY6MCXSwTkKB/WzyVgEVCIVoiQNVIMDCwigqJEQBhFEAWEJU5ErIgmQg2oSWs0bNh0wziJreGAhRODJsHEgjAwMxBqzxHExApbjmOJYgBgFuhGgAKRY6M48VztakRUPxtr9CSE4Gcaz8xQELMFAiQgAGDmxCQsPQ1ubafuKLMwN6VcL26ZZjWOGnZzuzY1vbi0vIqZ6B/9o7+/cm/jX/5//nmmDNgwiL5ShfW1jfyInZ095+sLExdecBzoLdP2dvV8RXl5SyqQCF0d2bP48jOc4dcOrQy88M35zIzdbTcuXJ3Mqec/eOvTkdLE9uatkXNDy8FyYpLp+cyCFO/evC9dt1QoXLr6xsXLFzKevnrt0u5adXVp/foP1y5PPFfRI/vVWtSL9m+1sanHgomoGxlHs4cWbeDnsjpXi3a0DWdGhn/n//Q7cdyeGZ28+956vD48M/3tVavvP9gb9ifaANADDLGQbyA2wRDXJ1zKW7EARimDaLW2WosAG9sTRkR0XMllVbkkQ2UoZMENDGASEAqKo0ERAoIGLzEADxc/ELEA0O1hKkbjw4knlYpS5yxERAIkQURU0kniRzZBSikFqhNZAGAWZLAs1pJCZUhcIBICgcQYAkSNmayWQNcykTBag2LQxJQkLIaNgY51mIVZrAFrKPXwEhGOsyKWYysOExvtk+uC0pLNeMZak3BiLVsAIQFEUYin4906iMAG1ekchNOqAyoDwmZoQJGD04o3HYRB9D5wfAapxE02cB3HcdrtRrnkL681ur3swvzi1HTl1r22kSncGKnvrGpUHY4SbQuVcHnt3bGxsa++/sp+dX1rfaMYjIAI6qUct1BXb+33VlbChXl+/as5HXQ+fdv75Hrp0oXhQmbftlWnl3g+1ZpxyfNOZRwG4gsmMQ+qE4wD6kYPjP+Wx7ej8PGB1RaEEPpuK8CADCKAjKgPGnwovbyXOq0cZIM/NJklZoBkdkpx/wMxaHxOetoBdbufNOiE8e5pFdrHYJDEP2B8FA3IBf38703OXR2ZmB+t12anhmcvXbhyYfFCO24sXFlYun3PLcDCpen5mVnX8UYWCkmYzL947tPPPvnwer1crrRrvV6bSbSJeH1/CREzlez86GRlaHh3e89au7uz5+Sy69vr1trE9tpJ3c/qibmx0siQG+vmXrha39paa9JeYVjn8tnJK5dfjEOAnxHgENGaSCkRNETiOOI4pLQQYZxgasBS9JDDkWUAQABxFBkriKAQDQtgqnZGETj8A0iTBSKyEBGyEEmq2c6oNO8VplWM+kdbNAkgHpXyPXxIEHX6sDBjYoStgLCxgg4gCDMYiyCISNz3uE6F79QqDAdO2H03K7b9lhFIgBEQ2BAyKeu52vd1JqOUBkfhXvXohkDEsyT0zy6YOY5jRExTlRaLxUwmU6vuTU2M1Vq92emJYkCdNhdHL1nBVqNx7tz5r3/tmzqjb954oDnz0osvb+2vVS6PghcQ63Pnp7c3ewQRsQ9JTlM7n3OVQmZynQyGSjjO+mdlLk8ZLI9PCMOghAGQFKWRjkQIgCxwyAyHiXQAAIyRhwm4/3dmxf7NgP7P/qu/6xecVqfhZZz5mblEoq3aelByLlxarNZ2v/zN184tzN+/f3e7tnz5pYtXLl5a39mbSyare/ukRIDv3rsTN8V3gkZnb3R01Ei31trWnjBFxVIhTLxb95anp8cvXrmw+mDtzs17Q5nRqfEF2/G+9+/fBID56Ysj/lV/5LLrTDdrWStFxH5650c4WKkIERyHXRe0g4GP2iHHwXaHrWVrGVGUAqUEAKxYSGu+AVlEQEiMgAJESvhA23zstn745pZUm516Y/mOIKIgg6TF0gGJETGOjzZ9qVdXWgSJWVsRsSQMxhIKMSMIdqMEwAogsLJW2KKwSqzYRAuQsBhLzIQABgRQEpsAk4iwRRFi4ZShPc8iotYqCJTnK9fVjk7V33zMA+vXt5o+cdvVs6I6Pj2k95JSxMxJkgwPD2ezmaU7H37pa2+MjBaTpLW2tuy48chUZXx6bHvz3uLchdmZ524/WF663z43MTI5MecEUI32dNbLOJDpxiOjxaGcL3F+dxcSWy+VR7VjolgcJyAisXHG90z4bIzz07rfTsuvQlgJCjJZFAJUiIKMoI4lsHtIpDbWHkoLkCYpAAQBNUBx88Sfl1Nr/2mFIT3ZtfHENuBP37xTS7ZGzuVGZ4bv7nwGhpjsznatY/Xa+qbjoJEkU/abMbRNbS/cnpufm52e2tjYHi6Ot+php9VbX9qybF957tq1a9eWH6x89tlnGztrUS+utapLd1eyMOaanBP7KlQlvzyWnS7Z6WgVop3pjOtFNNHWI0F+mpMAWfnaC6Vz/EoOr8f12FGiNGgHfB89nxwHtCZASRIyBhFEa0FEAmEWtATAaVoDRaAVRSyawPJDAushARNpOTgvHtQcQgSTAIAAChEZZMA0T5YkkTrsm4hYK9Yis8SJsZIqKDQCIWgRBQARJyJiLbAVtmwNGQMiEicoktZTQpMgWzFGhNMUkgJAQJIWcQAAEcxmEBGVAs9VjkJrbZwYFCviHHuGEQ6yDT/pB/LM4et0gf0Ebex5LpEkSTJcKbguEapuu74wkbt3bzOMejNzs7X29TCKvv27v/v5Bxvb253aPheLs1oXksiOjlQ++OEH33jjZY2m0+1UhifzQWV53exWGaVRKM4Ffs/aSCnlKi3MCqyBX6Ukw7OLwV7xJ25pwOcDJGAmQFaEwMLEYJEIBUCgr7qUR6t6aIBDqfehlk7a0TPAF895UL/35off/MOv9Lr1D9/6dHx0QkRi6M2cm6rf7qBV95aXtre3Fy/MZTPFTjuuVZufv/MXOzu7nXZ47cVXy/mhqy9cvHrt8ieffFLrbqztZGPs6YwJe52gFMxMj3RMNVp2lu7ejzoNUjI2Wm7Umzc+Xinlpl+68L/U6PbaSbeRtMQDTrKZIJ9112sPOfQeXqfvEyKSEs8DpVFr0VqRwnxGRYm1BhPLjkZFwmKZwcRCiAmKowlILIsmNCDSD7Q8roIWAExzSh87Yz+7VhsE+wk6bJoWI+VgEx5VMRIBa4EZmTEtj8iMKCgCzELAViBiYAZrJDHWJmwZrCEREdYiVoSEMUnEWrTWMiMAAQAw9XcCRNBnY5Om6DIsELOIxCYBNmku6GOhv6k/9KncJD8PZxLw6YKIrLVKKa01kY3jWKkAQCZnFuOku719v9Vu2aS7t7u/XduOo+FLl198/837O+tLoaUgCMIw3N4Oy0Cz42NZnSTQ1k4nsuVuXba2TWwKGdUtDCnHFUWRolhRwEBx8guKJH5x8Mz0c4CEqpT0a3EQIzAiCDACqIdyzsvhyyAfhcHjcCYB/3w8LQl4QDjrH/1nfxAm4cqt9sZS93rtg/Jk5tXfv4J+8pWv/u63v/O773/0/p//2Z/cufmg1axrRzmvZv/gO7+/vr797/7t//hn/+570/OTz129cvWlFy5eXnz3g798sHrLc3wnK9X9dnEkePWN5xcuTf+Lf/YXiepanQGXs5ViI+z09vbj1sjUzMVSdjSrOclEjocIkiRhrb6JB84Lj/rxIimFSGxFNIBlQcsKwPUYEIwCZclRqFMFsOWEERG0QmtZKdAKLbIiNAeq2keF4GPJ/Y/7QYRhWsEGiRCIU3pD7G9w5aD+QRo2DKBAKWE0BtiCMWyNFUZjxRBYg9ZKHFuTgDVirQijUpxa+wDIMqc/ZGHQAEwAaQl3RBQQspBGMAMiKqWIUASMIWsPK872B+1MLn1GkRKw4zipJJTmnQs8PTQ22w3v3Ln7WS47QsB3bt3JFnq+cpPEsCQTE0NW6Xq9HWAwNpoX3Xnt5efPzY4Kxasb95ZW7leG5jOZocbWflBwDRutw2IhyARRoxmheFEozs9JdfPbhNNSQQ8iTkWcSsCInHq6EyEAOM6RLez4axjZwz31YU9EBPHxTnNnD/6vB6cmAd9dulPbq3/69l2OoJAp1R/E+3fD+bn5m6ufXnvxpWuvXt3f365X60v3lvY2d+5+tnzt2sXX33i5urf/p//he51a96dvfvDJh59Pzk5dvXyxsREGXiDaDVQPe25ju5fLlKenp6NuO+ur3dpqsVSoDA/ncldVNNuqxz6gxpzWRcZeL24SO3FXvMxDvleHr4kRY4FIHGEAFGQRsYKFjAGglI8UptzD1lpjFZKktf5EIP2RVmAGqKDTBo9/lfYhjp1+lTmCfqEkAkTMOYdlfdPnAREJERm0ZTaGbQK9yIoBY4SZI3bZQpKAsWQTYEZrEQFdF1gAES0zMzJzPx1WDP0iTwDABEJ9D0pESNOPGCRCZrIWrD2y+6b6Z0gv+4snMZx0ITtp/591lXiqgtZax3HPdRQzh2FYLhYTDDpRVGtVC/nC+cXFRXaqjQ+jZv2n778f2V4hY7f21+uNfZ2b7Pa4kzRKI2O5SqZQKPq5DWvrviflTBTWO34w1On2Srn2+HBhd583NnuIGWEPIHral/5L4UmrEAc1f1KJk9QAr3tIEJEUKCKlwHFSepXA18cXpcOyZti3efU5+GCvD4l5PMHbZzyhyiCc3ryfSjOnBn33s5VcIfPa118cLozf/Oje8v2VlY/373/yF72J+kZ19aXnX6pMDlUqFRLVbYTrD7b//Id/IiCjI5WFhYWlO2thL+npuFtbevc/fqS1PnfuXK+TZMzw3q3Ov/noj4MgyHozmESTk5Nikm4thjC+cO78G1f//vv3GIXDMLTKTSDqJVExV3CCALGZ9uwRCTiKmUgUiQAAWTAAiAoEwBICKErdxTUhoYgIi4AFQVAKjAFRqfgLnBY8kuN/AoAJ28dKxmGo+o8ISvrkpF3yCawVazl9VIhIKSKiOLYsYo3EiVgLYiE2IgJhhGyEGY0Ba5CFCAhRMSeWAQAE0DL0NwICzPYY+wqAFUYAdl3HMgujAUCLlsFaMBZcfSD/HuBMDn4WkRIwIsZxzIEnIsYYrfXS6irbenmouLm5nnVzr73yZeE7xWxwf/VBELjdXv3evQ+j2MbtcGV12agkN6K39nPklUqV8sJCQZkk2l+9MDm6bfPNRgwSZrKQywpKLJgFzDwrBPysYCBhUKKISIHroOuQ0sp1FKBkAzykXmZmxgNJtx8YeUjA6YJTb55M1XmGLyZ0q7mvff7b3/kDcnRXte9U763s7ILRrU+T+odv498crm403nrz7eGJElGRQn3/zfj//tN/df7lKc7EwZS4LXXtueeev3r5//nP/sQPvN1Oc3dvp1AoXLh28YP3Puh0G0EwlCnkri+veY5fzpSrdXn3w/vK3cxlX0pYYpcthOR4npNrm+0g0BQX054daoHT/zuEIMiMSYxpkYMUYcdHsAxW6UQ7oj30PFIq8FWS/jAt9Gv6YXPiOoCAhhEErQVjGRgSK2GsDwfluAoojtzHDlwzsMycap7TqglaayLqtPo/PFBNHxB558hjiyh1kbICttP72QcGEdAa/TMfAwBE5jjPWiBQBMpRbPo2X0x3eSp1qhFrTxaHelpxhE/L2eGkKsSTHn9aqshBiEPKFwLGiBGzvlmcaEamVmv0nE7+jVfO3bn++dTMxFrVLu/t393c+N3CxNK7y3k1PD+9mHUm9jYfJHpvcm7sj/7x3/rBn7/9sa/h6tjmfdOqd8sFvVbfUT3diy5l5aZt72y3yjGWvKC8vd2ZGJNmZ9D1nk6c9CA8LQ3NoHhfQX6sBo4GSJZpO+lalNJkenwu30vtVkqjUqAVEBECmNgQkdZEOnI0agdQiULMu/4j+uf+MtIXGMRyWr0UrLUgkCu1rUW2ZGKdJMRGCStrsTcgvvak8fqDcUrzNaDO+uD74en0f1B/BoT19g0Kv/x59be+9ntrG2tv/+j9+cWFmbFzWt41oR0dGSsVclu7m9/73vcw0Ybj3d1da23gZjt7nXMvzI7kR+/eu9mrRUVvqFOX5dt7uWFXhIkoW/QMhbMXx85f+7sPHjzYeqtDoEwT252GN5Kdm3hhtPLKcHG0GOTWtjc9kmKlFMVJlGitJpKWBW9AQfXHob8fTPNHGkACMpAQAovA4xcOg/24W8sCDMagtSyCJunf6HBE+igicTRg4glShgUARCESZZgIzGMSfaTZLo+3fyzW/gkvcGd4tqC1TpJOmHSsBWul1+t1o7Y4vdxQ6fN7t0ZGJ8Zn5taq+2vLnw+Xcp22mRyd+8H/78feVzNXnrscJT2yzj/4O/+Tzz//oNlsDg0NGWNMbDKZPGnDNnEQOp2NsfGC8swHHyyNjc9mfB4aKnRNfBZXmmKwanrAQkwEBwR8XFdsWBSmLs0KBQyAFkASQJ06aCFAWnWS0uYV4IFyLj1h+r/E9NcrBHFcQAOgSBh89NmCSRAERDBhMGyI1Nk8PlvQP/ruO/l8fn3n1sfv3Hr++ReV8TzPCTuhE0g+lzexdV2VL2eTyJQLeRHBBOOa3VmqmTZlnKFOA27tbbXH9PjUaJLYrbVtBAVoG53a5OhoG6rdXiISEerAz7naHxkuBxlne/dBt1S79eC9IJ+8Mn+x1+p1W95U6dVE51u/XOKmQw6zFlikfx/HiAgCYmlgAhOXwTACSGqFZebUg8nG6iFePJKAH99OYkHkSMerFBIBEcAgJy9JnbwOfbD7QU8nVRgNlsBOtnAMbOUJx0Ge4RcitWjk81nEbrvd7kZtL29Y1VfWd7WfH4F8eSi8cf1mznM8DLq1SIlTr9ezw+7C+YUXLrz84bufhBLGcdjtNq2F4ZGxWzeXPbeLBNtbGzOLi9OTE1HLXV7+fGaKCOrCw40kzg5w6vlNxS/0Vn3EBKbUwDCtwyOPE3ASg+3nXWJBUkCpDUuhDyKWARmIwRoEBUiC6oh4j/ctZguAhJJmFFAkac0XII8tGGIQFLapMwqzATjzpvt5OC2N2mkpbvTWeq2dSzrNpNFkldw3bZspZNmadqfrkp8pZeYvzpjY3Pl8KZ8r7Dd3Ay/TqLeUy6V82Rqqt5pOpHFoZDjnT1Sm3978act2fF/fvb5nMSiVZjf03fp+LVcYCYJMNdxzqjfyXWPscNnnmv0gSuIHjY/q+63ufiHr64AuA5R/fo8PhM4+h0WJRQBAUJIG/pCKiRSgfbxkGVNKh8gCqQsxi7BFEyHAoZDap0kASCXjn4U1eEiuxxVQaTjBoSSdfg+Q+kml/T96BQAZqFl5NgjvtG7oJ41npZ8mMVqBUioIgjDcN8aUSqWpqanVteVsdmh5rV0oRsVyPpuLVjfuu8VvdhvRxYXL+Vyu3qx/5zvf/sl33115sBrajjtkjTG7u7thnN3Y2HF0a25uevHiYml4hI1euWE0TLiuygd2r2q8XA67g3a+v6lhZj/v+TrOvn0V9ABTQmppOlyO5CCKly0ggmZkS1qjFiEiQrGYJt/oZw5QLFojIsXGHDvj0bAQkSCApKFLgIJMLIz9jJZA0I+TtAfBxI8n4C+aM+YgPCv9HIST9l//0//1f/Enf/ynhCUbCFrVa/aQeHZ+usWqVzWtZmvu/NTU1NTWg2qz1oo5TmI7NFqcnBhrdmvrW1vF0sxM5eWJwsW3v/f+0O8sDKGvoRU12iuffSJ1KU2N5QPGYjI9NQ/S29i+W6/vWOUEfuvGg9WcZ4h6W0tbzW4yM/GVSjko6vLdnV98bcelMWMFAAiQGQmRWSyhIrTm8cx2EO+rLEM/fldEGOP4qOWHRGH7M7ZYAAA44NP+I4eIbAERDR6p0B+SGh8nXsORZPyYMwz4/GR40jbRM5wuiIiZIxMpisJGw/f9ycmhUqnUrjU8r7S7u+l5xa2dB7nyUKNzH91or1qNmsnodOXL3/ryW2+9ZQxMT09v72+sbd8s5LO1/Vav6zpudm97yUr8T3/nDxiLd5aq29vtixfm9+t1P1NRFmWQvug3F79Q1fyIBDzo+MOwwEdaYFAEmIioNPWzBUUA2Dc4EUFMTATaAcdBIuo4/cQDh/7O6f+USwAgFhnEWDDGptXPTEjWYmJskkBixBgQAH7G2evXgC+cBHzu5ZmvhF9yOfPhmx/Xq/VcKZst6F7SiNl4brbW2gtNe3J2tFAodGt7oth1fLDU3u/U6vVeO37pwpW5kZdz+lJzc7+5PXV5/jXPcSMbzk4tKS+5v3Sr5F4+tzg6NTNz/977cXNNF3s22VcFp6XvLl54bsgv31v63HpmbDFfKnJrawdg9JcZiEM+YEEUYEhVPMBMCsGm+SQfByIhVCxCqKwFy5Lag1MfhePW3/QNDZIADpwdUt+ng4TSj+QYP/ZYyuHu4fiJwA4itkGn/YIR4WkR/CCc1vU+KxsR13UZdcKpR32cz+c9z7t169Zz556r1VeuXp6dmJj6q5+8f/7izMRkLew1lSeVsfJ3fu/bb3/8V3t71awqjo6NdqXhtgWQi6XK8Oil5eW9ZiNDYPdru56T7/Sa2lGun9mur43l5lH8uNd0IXii1zUIX3wJGH6JTh6/Kw58PrQgEoOQMAuRGBRCFEYRUSSCohQ6VhID6mC9OThRqlcDAAgCBAAWtAxsxRhM0mx6kWUhy2AMJCwsyIwDF5NnB8+6BHxS6H/3o3/9pee/OuxMvPOTtxOJxsbK+SGv1t2dn1zYudMUhs3qxo1717udXqVUCbv1gHPhXri8XmfHjIxMT41dCmQ+R+f/5h9ecF3XyRYTNp6vz19YZDH12vBIOVsamtQga6tvbW41Rv2Ook55yM262A7rGT1swPOyudLwMFNondYgAj6O47e7tZgWmmAGTJmVSACSAbciISIKCwmIMDITi4Ao1BYA4LAW4eGbAd53h7fJofYJ+vHEA5yqDmoPP2oGHlQV5LfrPnxm8KQXCBFRWgVB4DhOFEWFQiGbDZbfv3Pp3HOEzamJkXarrnC0kH0x60Y+BNkh/5uvf/Ptt9/ebuwkUdLiZqGUG5st62CBELP5QpAZ6fXM81dfmJoqaI0r92u19u7U1CiiMQQxZ4rZoUh3sHcy783fVPws6fYl2gFexI+9H0QEwQGBflChEUYGZAQQAUI0BgFBKWZBbYEIjNHwOCIPewAAgiIWWChJhBkYBBIUQcPIIkaIGRmARR6vr/vtI7bTwkkl4BOroDf3VzD7BpO59urVqbEZgGRidmh8uuJnR/9v/5f/ZmNvVTu0vr7abDZfungxqbeqH4flQgEYTBSPZieHs9PYGfegMjZd2qvuhCCdsOt5HltQ6MzOvWS5nSmMCvWCcn5srpydjP1hU5mlfO2llc+2XJeCeEarTHPVaXR7TvSLE9L+rAjCAiBEACyokFKltB1AwIzAfBBZwAgAxgKAKOfgATugXoFUV/x4VbADfe/HVO6F/lMHlh8i4KP5GKSCHnCZZ4/LbyeiKPIUC0qv12s2m0MTmenpqXJ5eWt3I+t3jdm/eePezPRrQ6VL9a07lUzlv/xf/ON/9d/+D0rcfCXDVm7duB7azgtfvmC3bBAEvV6vVt8wxg5VRpO4vbu9Kck4kSwuju/XQjcX1PciMUjOoMfltx2PNXsdx3Hb8OExiGiMIgQRQGAAOKzYjaAFrWEhJAAGREICQE4UHIs+OtyjW2sIU3sZAqRx/0pENGjDwgJWxIgIAIsw4CACPsMXE3r3L+HjcFlg+fbKZyPTuX/yT/7+69eurtxd+/STD668Ntvj+vJb1UsXK/Wtzf3xT//ut974t+t7o6XprFu6dfPu0sfqyszc5cU37t6sY7bpOr6JwFN5MEAAIuB5nnYl7Bgw+ZcX/+czlZdurf7LmG+U3Ins4nAerzjtazn/tZ2qqa97w+XhxMSoBzg9HW1AH9FFpxyWblB/iVgxcQD6jJn6IJMGABDzKPf/fApMDk//8C8eeVAf89ziQ63TIArmAY/SCVXWA3dkA9phfvyGAwdJ9gPw88M2HnfeAXU0B3ufnqidQThpP609Hdv8QHgqIKeZhNptXpwTPzf84w+2Li++rP117QQf3NpIcObyhYut9vpu60YwWbGdYq7ka627nValUq6NlBxw2mvm3OyVbHF0r7U2Ouz93nfmuKdu3d0qjThW8PZ1k8+2J0fJs6jHqx/0utV2dmhQfwbWx338vAx8agbF3Q64/3FQeN6AdgbeVxSnrSEolWa1QNRIoY3gYVVzP+rBiCLSCrQjSoEmQbSAbCWfsiMfAwDwAGdPgIgfMjbhQVmFhAUAiQHYKmMhigAA8Cge4qEFQvEj49z/vHek4kPdr2GatvN4Z7rBsvHj8aRNLSeWyOnxz91J4+zVoOVwkKQ7QAAbiBOGsWlF8Ob3f+pmITukd9d33nv/ncCjzZXtzQebbN2hQmZ3q3Xv+rLv5NCW/+zf/1Th1Pr66u9942Vj8hvbHaTM3fsr5fJcPa4/9gSNelsBe47jZ3KzhfPsvrGyW7v3UeulV86N5Sccfy6JMsW8sggiolzn9ALGz3CGZw8sJgxNwjab9yxRL+wqjZVKZbe2FGQwn3fCbhj21jvt3TgOO714fXOtUMr1OuGV5y43qs2vffXrSun6fmN6amJ8rFRv7A0Plx0bb6xWHRdyWb22Xh0bHSKgJGoPVbTjK7kXuVL5Tc2EJaAUIqIiRDxQahlmTo1HB69woIvSSiEJaSAixHR7jwSqT7cPZ9f5dfT/hGGHZ3i2oP/J/+GP4mZy/aMbjWa1MjUSdbrvvvO+Ri+x9bHRcygOs3v9k/tgdLeLmzudst+xMezXkueufqsw0qo3oJx37eBgmrHhcWscY9DYWDuFkaFrOzs7Dz67PV1Ws+MLrjfVinzf9RIxiYl810vCX+fln+FRDN6ZPhvhKE/a1vXEbWksAIACwiaKusq1jtKasFbdZ46SpNpp7yt2wl43jnutVnL77j0GU2vsd2/2djZ2S8WhuelznVYvm/ECz9HK5vNOdy9sd/Zdj1FFjcbazOT5QDvtbmNqtqC80NrYswGq+ETdxEFx5wN/cELv08EqnZO1I1oACTEdVASxLAislAP9TDiHZwQA0BpQHZqWAABEiKWvWflZ9j2tOPtBOLW4/xNuGL5wz9EzYsM+sQ24nVl55eXXM6Vk+Z67sHhOkb57Z622tTsxPTY8cr5Y8GanFXffvHXz9srqquc5yDaKeGuzc25xZmLSq1ZbfpDr9jowQMPR68SWTWzY2ETIKReff/Xq2P7+1t7yBxU3bz3V61lBZKaYjXYIfsvqkj4tnIUbfTGhSPJBwFHY6ew3qruFSiCY2d7e9AI/jutRXM+4gYJN5Mj3kXRmdWN1bHjswsXzH73/qed5jVpz5KXRi4uXdrfvVKvldqce9hrr26uN2l6mmKk1djMZP3Bhf3srlo3pBTMyNBy4lHSe+XRsPyesCEVYJC2pjYiOg4gkRCmTHjfcAoBSFg5WexExBxkoDwn4eMuHUb+PPfGpXNdJn8ffNmer0worOq0N00mh3337k6XPVxcnzl16bh5JZufnn3v+tb/8/rv57stB67nAukr5Rd0cyUKnt8dxL7Rur62Ghy+02y5rf2JqtLZdz3tZOyBuVSkXCUCJsmQNhF3Xd+crxRFIolYjZzKiyElY2Aqhjo0ZZFs6w68HZxLw023f2lhrT7MKe8ledTcSH/yRloSZArbasav1UKEQdTqt1i64YRLL1voWx3Lp4uVer8exDJWHHU95Gffe7c3pc+PGmGazub6+Vm82imOFtbWdwJmqN/a2tvZU0Nnd2S+PxKVyJWzZ5KQ5cp8RCdjB1MxsEUUpcB3SWmtNielXBGc+JGCA1KNSBCC19dJB0KAc5qw7zL0DfQ4+nQX9SYfbPa12Tq39Z2RjcdL51V5n9OaHO90Z8/IrV+pRvWecV19afP7Frzbf/Bu9jQJbJWyw/cqrF1+NTPWHf/ln7NH5869VKucdt9BNuNPpeJ6XOvQ9FrERQEmSRDsOAiWxBVYs3vTEK+24x+i4nicJMRNpYDxTQJ/htxpsbRiGDJwJ3MREzXovO1wOcn51b7Mb1sfHs8Mj46utjUZ9Lz/kWSEHg1a9u7tdnRifXFlZWVic36tt31u6wxJGIbtOphvZMIobvW6r2+0l4ebyvZnp/PjUeK44tLfzIJttuO4EuBGcTAP9zCDj9/0tlRKtUTvguqC0JAmKwGE1MzxAEiub1hkVZJY0hTuwAuovcI+ECPOA4iVPmgiflsT2rOOkTqlPXAL2ZXx0yI1b3ep2WBmdCTeCtUQV+CVSI92e4+oCW5qb+dZQJReb5q1R8grNV176fa3KjvJdCGv16vjIlOkmg04gCjWpKIrAWqUx5tiyFbKkRzQ0QXcNREQZRLQGwDnzov814eQ31pNVqZ2WZHBaD8zTUsVrTSLGGqtc8Rwd5FQmH4C1KF0RUU7B9YcR9x3HKxaLQeC19rva0btO9cLF8xcuXLBsbt++tbWzuTg9kRgMI3FD8YL8bvW+pQe5cgAWh0aGhkdnCTrXP3nQzDFlNXt1iDMn6udAle/gH5ys/RNKwIP6k88AELlagRKlQWkmYiIwTpoH/qD4HyIiElGIrrFoDAmwiIAgMwESQJye5ZCAD+KDT3RZJ8bpEfAXy7R0WhLws77hoDt378Y2jo1t15ISTS34Xx5pvDIZfd0diiDbbZj9Rhx5xZlqO4hk/I2v/ZMvvfGHudxUsTBWr9dZkrHx4Uaj6rqPr9kHAFEck9aOdl1H+55yXKu8WHlRsxUzUMRxu9dMTMRW4ig5M0Ce4bccnuf5vs/MvV6XFI2NjZWLxWazqRwQkV5PavV4r9aNYsPM7WaD2OEYtra29/b2rl174bPrn9y485mg7XTjdjuuNbq9MCLHr9WaS8srOsi89NIr07NzidjafitsQMYZ8v1s21Sf9nU/Kfg+FbKUyznlgpfPqyAgz2OtE98D3wPPFddhz5XDP+2Q62it+iDQhBrp0eD+X4jT6v+Tbv8MTxd6Njf78uJXRguzRjpgws9W3n3w0Z/NlS/lxv9IJJiYvBRajKK655I13bmZod39C5GByCRe4IGFuM2+m+kl3UHxWBkHbNTVGqzYXmQBXE4AwBGvZRmIswCQWAvKekqBHRj3OTAQ/pQGYhAGhUWdNP7spBhUxpf048dBBsYjnhADd5oD4oZPLKEO+maAZPyEZ/jUnDhOPA6Pn+Cs29uouuXycNh+oAMvV5jO5kZ6QxHTC63WR9eee4Pl3MpuLZtLnCB0HafZrXXj7re+8425xfnv/egvfW9oeuwagTLanRzLIfPWaufKxYvbr8GPfvju5z9+b/5/5l9emN5Z0v/2L02NX7riNRYrG3c+Op/ogUqsx18vPl5njegeH5DDYaF+luOH9Lf9JFGPIxWFivuqYDn+EyIF/WqsgsiEaQUCCLsFJFGKSUVKJ0FGcjnXD1Slsg9CkFpzBZEpLeMdJ6Em1C4gquO9UrplLVoLxoBJtEmsscCMcpSfQI7fqyZWIiKMLMKC1qKIgJDWj59fHnifDxjnQRn9TpgmwBlQJMkMWDcGxcsOwqD1UGBQkY9B+QZOxwdlkGJi0HPqDOCd08orMOhzjRnT5J32Vv2D2+9Yvzc8OpTk1I1qbev9rYXFa5WxYjY/pbQLojudaL8an4WfneEMTw6en8nlfFKwt7c3NDTUDePVtZsjo6Wd/U6hUHBd99adB4vn5oJADVf2e52OtfKVr3xjfubi7lZtZ7M5OjI+OZ67cePm9taGVsoYMz4+bU2cy/rGGkJV1OObO3ErYTfoTGcW2/u7ZsxLnAbI6aighR+1laZvrJGf/aFImqFVfpaAhQ0iEh6qovvHWCZAtiwAQIgMgEQIkMuGjkZURjvWcdjzJQis64IiHwCQUESEARAcpUSEqL8XPFb5QBDRgtK6Xx0cBC0DCSpEM2BJdzQBgBVgAyxChGKJAU6av+Fp4Ww5f7rQd+/dXV/a8jBo2dri/MxrX3mpvtX5+M0bBsLd/fbte16p9FylfLWYP9frSJI45HZPdIIT24q+YHha2p7TksxOC4MF1zOrwWmi0WhZViiq02m+8tKl3b3Og5UbiwsT1Trkc7k4idbXNxfOXzbxrqOVZfPcC9fOL1wC9gOSjBOZSA9Pji2ex1v3akOVcqujosjLZHTg05UrF2enjMejy6vIkgTZ9vxcaWdnr9lVTqmZ1E6HgBkOqgPh4WGCiKTUceHjsPiAQJohA0Qe8m9CirXWWisAMMb0iw4RsRWUfjwuIxIRiUKiQqHjOtp1UTvkeai0uC5oDQIOACAhiiAJMKRcrNyjCKJjb8CiyyxaIaIgCDOgALOoQdEZGkUEGUWDYUEGAUAQ86wQ8KCEYydsRwalgjixqfeUnNdO+PnAdp60E9Z4Zaq+1XQ8v6AqG7erP2m8n3S4utwcPUetxu33P9p31YWrz/3Rc5fzjpMHChhORsBn+M3Gk9+gPBsboNNqX8TxXAcxUkoK+dLKWhMAOr0mgInjeHd703EwCnvbm6uI3dnZNy4/ZzY3d22iRobGPMrt7dSy+XqpMvRq4arWOo7Ne++///f+6OuBLy+//GJG71YbvTAutFrVYo7yhWh739Y7zayXbZzwegepHA8LGz50gSKpaelnc0il5UEP9cyHonA+oxxHO44jIlFk49hyP4e728+FgYCigIQQiVBp0FqUVtpBVKQVgkiSsGFCFKXQ1QpJUDOhJUT1kAr3kIzFCrLFCNhhYQvgAAhbK8CDmMoKAghYFARAEQZBoYGpW5+wt+1J21FP+vn6LTNXn3T89eL5hevtW5iQC5nObm+n1swGxeHcpIGt8kjed/Xu+kqrc0vwGjpAoAakCj5xh56VafmiScBPK/zgTAL+9UDrDABEUUsprNaaYQwXL14miPf394vZblXU+MTzO9tb65vrE+O5xYWFTz/d31pfL+SHFYnSODc7nQ28brt16dJUEkU3btz+7Prtv/eHX56cHF5Zt0vLK0GxN1z6Smtve3q62DG7pYnYmP2Ahhon7OdAm5ZzVJoejpncSCVyDEfeROqo/ED6AREhYqnEngeuiyIQxxTHxAyImCQkIsawsZIWx2WxANCLIErEjZk0+S5imkZSiHSoFDoOWY99Dz1XSKFWSD9jgz8Qyg0SOAotsqPBGNYKFYIZ4JSRsE3zWyIAAggwCrIYAP+EI/p0MMgX4aRP9eAN5em0c1I8MxJwM9rvcatj2jldzOfzruMZY3u2067tX7wwVhkqiA1LlW5luNPr7jebNYDhJ9qhMzxbeNYl4JOGRZ20nZOiF8akLEGvmA+azU42KJRKfqP2qYhxXOVqGBkqPVjeEI5dR4dRl8CUisHYWDmJTc7g0HDeWtPsdBztVobLyvEvXLggaC4sTn/6+Qf7tWap4I7plpIQcajZ7gyNdzJIHUsnn8cB40b9zBSPlNZWFB1nXyJKiZYUpxUO0qMPPy8VE9dF1wWllIhi9iAVkYGs5TC2YShxbMNUOLYQxkoMRzEjqE5a85tZBIOs1RoDnxFRKXQcIARAtscSaBzfKyi0FkQAtEa26LsYozDjoERDjkqty5gYFmEFIMTE+KRrdgzCyU1Xp6U6HvCFfVZEracD3dxvl4plF4K4bcJO1EzqsQ2Dgj9cnhPX7UZdy+1G8+ZHn/773W1EzF29/L870Ql+UyXgs4ipMxzHadnskVzPVZ52TehGYaLcyv7+Xn1/e2pylKDGzI1mY3io1G54XuCtLN/fWV9Jkmh374EF2W/XN3fvO06mkC9qxaVSaW52zvc6vqtGR8q+pyvDI4qcbrikc92OHQ/butLdvvxc7uNWF6BwGsPQd8JK/adSKk2v1PftQdqpPvtqrYlIq36JofSBIiKlFCKWSy2txXHYcZy0HQBABE1GBBNDcYJRpLo9TmIbJVxreklsk8hYaxCQABmIAG1CBGgMGJMqwIGQBdhaF46o9/jjbBBJkQCR4xAhKQXGotDjvcQVOcaKMCjNSSLMYi2LYJx8sRI+DAINsuWfkhe0fcYLXT5pXxwd7nIccph02WImk/MwqXZ76IiG7PZ61XObaMDp7X3++V92Ov5L1752otbPcIYznAiEGkASm7Q7rSSh0UK2uruUywTDw7mdjc12u9Xsbk7PLmxueLlMUK/Xd3e2yqOlam2jZ7rV/cbmyvbs5GKlWMjl/GazGRt+8ODBt74+LWJzuVyvE2g3342W/KGsEeBeSUV2ajT7kW4CjJ9K/y0niEhIRIoUEPVpOJ/3Dg3AKcvqfrStOR5ulH5FROWCIkrTV7FSSKnrlDBSD1EDOCJOGFM+wSSGxFjtOr1eHHYxiQWFRDQBWsYwIWZkBgQQsSxsxSqUQ5XyI/UVCJkIQEiIHI0imG4J7IAyf4q0FjEGNINS1hhgFmaBzqkM55PHaUnA9KyIVF8s6BqveoG3sHh5b73e2KsGRc9ziYRtM86oLAkaTLb2l/2sX5zL4dDH1n23lF3stLK92CGXANmRIO7Y4axjIY4kArRMzBaFHbD6tFI7D4wzG7CjPKmEelpxvaeV6WlQvO/gYhWnMw74aP3RXwB1WpmnBnx+auUpB9SXRRp05tOJbx48Oo//JlsQiY3tNbZWPxueLO/UwtGJku06H3z0H4cnR7PjxfNzQ9Jrtar7E+PfMCDb26ug1XDl3E/e/qjXw4w7ND09dnFx9K23/4q1Zdsuus6D23Zl/bpyVgO3lYRra5uVYvGaTXZ8/ZNb11dfeuW52cng3t2Qgdi6IgoAlBLtGIWi1ENEBQd3VBIpRyERkQJXAwOnKzARa42eB67PhZzNBNrxRCnUKjoemHQoGfeOpd9BxJR9icjPK0QkQkRGlIMUzeA5QcrEImFGceD36TOxTiHPxhhr7XGXLkcd2ZsPJHKNjBlNBxeVSsD9awxjL+2MIgBirRnAAIAvyWP7z9xBxETEWokj6HUxicQmlM9zP57YirHETMzIgi4NqKM8oN7toHXD0SQiABaYAfsh0Ugi4j7cvT4cchDR1UQKEAFJnPSBoLT+FjCTZUTBxLKIhHy04egbC4gQMdCRIkQCpUQTCnAac5vx+s5xh1Ur0g5s7HqHnZHj1ZRFP+KRlx42lLVIgARIKMBWYsuJiKApAQCzxBZYxIikEdWO6LTz6cIoQmn2UH3CuPaBJpWTLm+DVAf0+HVMf+sb3+w0Q9MRcjBXzpJC07M61pLr7e7Xfd8PvMxQuWgNt/aa98K7y533X3upMFQuovYSjBmZKTJoOxZYLAMLWhKlwHOcwPHcdtQ84RWc4Qy/vYjj2AFCxCRJOp2Omyu3Wq3O/q7W2ibSkxCElldW0mxZANhqm0yz5zqNXIBJGJ2bn//S61/9/vf+fPFyWaGq1auL5xd39rbKOtjfr+f9fJfRcVQUbXpOkMmWx+aKy8sbfrbiueOJZYsJWyEi7bBWolDUQQKHlNQA+q++B1ohkjiKlJI0GleAfddTChwHPA88DzxftANaCSo5iOWF9C/9p090fBUmAiJAFKUEERCZiI5VBnxolTxOToGPzMisrH3En8s+ljjFPFpesC+CD9rwMR5vBw6WZkXKiihG1GINOo6AJQUIERD2Y4exH3KFKvXTOg0ciexEgKIUKUIkCTw4NsLA3P+LjUUSREKNhEIIjIwAYhWk7CsAAFbAMoCgEnU4yChISASEgJ5nEVErBJTUkU0EFYpSqRcaiBzubPqx2ml3AQABUiZXRKDk+OAf3hi5rEICIEACAbDssCAzs2EEMlbIgGUgi1ZALKbDiSQgnJ6acHBx3C8e9Ltvf6SVwwl42h+dGo3jOEpiIuqFtSBDlXJ+Y31TUWV8dDyXney0u8VMdnI0yGZUrR21TSLIwC4CWSUiIEAijjCJKGbhAbR/hjP8kjg929ggEfUpeY8PgFLKd7xm24RhGCRuRuswDMMwHBkdSRijHiYRV6u7oxMjSqk4imudZOf6jT/6w/nJyZHLlxfn5s7dX15J0OvVkzjiWqP22kuz3//uD/Nji3vbtVhhMF7udtbGRmu/85VXlVJbW/d//Na7f+/vfqdQkCiSKDYirLV2HNJKiIzjHkpsR0G6AOB7QgREQspqRUQCIJrAdx0i0Bpch1FZ17FILChaH4b5Hv4hAOS1Oi4JHfIjHNiPUxwewMCAcCC1ioAAAgLmcsiMIn0CPZTYXH2k+Th+L3V7AAAPi78gInaASpYTOt7IYVMKKWEmASvADrJFYkwQEViQAYEAmUQxMqDgwIxyg+7zn685S4eRFCkFWhGSZDIJER0jYOmrxI1BRIWAJIoAiUWEAKI2Ge7nveKDAGsQUHh0valNQZFCRM9TiEjIaZxVOjMIrFXft64/XNz3rcv6D5VxPJzQBM3DEnC634Jiqe+IJ321B4iQYZMkbK1YC3ECNlEmgdgAEMaJRUDuJ1kBwLTy5KnhxOvPQFXYgKgBG5OX8SzEkY1rzXrSM0o8MORlvAvnz7/+pdd+/OMfr22sNcO9ifmh8fnJ0YCcYK3W2ItsMZ8fjmM/7KgMBb2wpRQhOYoQU10Bh2HS0+4AlcvJLuvEOL2F+8m2/4Xr5xcsbu+3zdmt2+16Ob2/vw8AhULB9/2d/QYzR1GCmMn6pd3tvVqtlssme7W9ILaOUwKhOIq+9KWXHcf5T3/2g63dZjY33Apstxd1e2EYheu7Gxf4HKJ78/rdc8HL5+bHXro6k81GzWYQRkGhMOIoWypRFJleaIW164LriVYgAtp5qPre4ZtsPkqVxVoBkUGSVAWddQQASFlEIWWRmEgEWLv4WAnYc9Lo2cOJljSlg7UPCaypN7VIP/Fkn3cR8MDLOpezD5lyqX8ifZAA5PgrAIBKJW884OA+ow1K8Rj3jqjioXtSwFNohROLrEQ0xRYIgAiARSlgRgKxwMCp/Hf69/OBIl1AhIVAqD82ACz9PxJQKAhAaZpJoTRZF4NVRJYNAAIgAxOhZdEHA5EOIxEQChGCmHRGlAKFIABEIoICCSKlDnYAAMjWWiTJBA/FeadO7kRo6XD385AEHGQZURA51Rv01dqCkbFswSREhJZsjEoRYP/qRAmRgBWL0leoyBdsHRsEjaxAUJGOw6TVa2W8XKFYNAknHN69db9QzIem27H1Xrc2joXpxeemz3dN6073ARTteKXs7W8nKqLAzW71kARJFAoobcE1CCJgOT6r73uGXx2/bRKw53nZbFZEFhcXL1xZiKGU9GK3FG/VNibHp+emr966eU85qtHaDaOe6zpra+uBB1q7cWwePFipVmtjo1PKyeULEkcmTmR5dVVEdcNweGT09nv3tfY9N8pmMfBs5Kn1zb2Fc7NxbEaHodODXg9FwHXBdVFr0IQHGSTlUDZFRADJFSwiKyRSoBWSEgBAEY1huvwLilbCaDWhgKSS9CN6YER0nUedodIF1Jrj7Hv0bWIBfkYMBQDHObKhHqd5fSzO+CFtM9JBiNQhB6e/HTAxB4k4jtgOQESI2YgAgNZgDVllHU3cr2pFaEQE0KCIoJJDPfYvj8GSMRz1hwURLAiKdLvquPP5oQra0wgCrqbUTEoIAqQIEidOE0unVmq0LIxKRGE/cUraDpEQCSIzhyLoagWCqNI8KqgQje0hESl1SMCAzMz5fF8jDf0YM1EKEAW0/RkVNCKidk2qqO7/SlBESCRhBiEidjWFFlzFRsBYVBoVgGUGIWS0IghWDlTrf308cQk4TMKAM772EeMgk3Mdr9Vu93q9Ylm1eq1au0E+gidjC5XRxdL05ZHZF7G+2V7duVdr3lCy17a+VfNB4VLFKwqDtRwbZrZAjK4BBBzgjn/iqMMvmCT081VDZzjDrwZrbavV2trampp0arVaM+wppTzljY2MjlTGirmySXhkdIQFRkaGO82qo/FLX3r9uReuvfXWj3u93rVXXiN0d3dr+YIfJyabLYC4i+cuRSGPjAwXhoqjo6WNlRs7Ozsjz80rHcRRI5sdb7Sj0pD1PAgDEkHHAa3F0aKUROZQAn4ombPvIGnQBKSAiAkRkAmBUgkJJVVcAvbJwPdSjeUR9R6s7IfPUd9p6EALquDYU5bqMxHT1fW4NrvfjlZHtt7j7SuFxzTMcEi6SEck+hDxD3CiSbNDp0cd9FEAAAy7iDELM7oOA6uEwQJ6njIGABhS8gWxloUGpn6Uk8b99Nnx6H2qsbUWmIWoP1+HKuisi0SkCbVS6d6IUAlwNisAVoQOdMZoLYtImnApFaoBoG8DR0SwjiIk0Vq5WpFCpQQRIUEiTO33AEAKmRFAec5DxgVEIWJEJOcwAAwOphIAxFgU4XSLg6nhWAABxChhBO4PO6MFIhDQDgJAGniNxCjI3E8vftLxfCrQheH8UHGIIm+v1RgqjKHCnb3dcqVog/3RkZGL1y76Re1d11dePF8ZL+cqxXurH3mca4a3b91+cMd7M5edmSm9XFM3TOerCC5I1lWZ2Go2FFvFwAPrFD5hPGnV7knPO4iYnzSRP+sq6NPDsyEB+55vE6uUmpiYuLd6u9Zxrly4HDX3PM9T6Ozt7LPhbtydWxgplUrVnfWXXjz/xpdfv31npVa35fJYvRE+f3V2v7oZI7mBPz48MV6ZzDqlRrhbLBcm5ycI2vPz87ns0Mb65t7+8uVLEwsLC/eXbo2OJ1qD6yljgci6jjjaEoHuJ1KQR25Ix0FEdBQBgVJAxACkEFynz7gmFZ4oXXc50HScGg8JMuLkuKRLRAccDHAwOanIiWkfUgI7aOAgQhhQPVTUCA72C+lxh5LcsavAI7X2MW7W6vEM6brp/iAl4CMOJlSIiALGEiKwJRQERY7jKMWIyMJEDBYQEYQH3xADTHUD14ejohdIeGgD9jxzfHgPnY7JUUQCCpSTBnqBUgKAGU2GAYFEyLIAQ2JRRMKIRPrkfXzqg8BxFKEiR6PrKCQgEkR03INMKQ/Pgu9F0Pej7lNs6jWt3Yd0EoduAZ1QiQgbsJYBgK0gIgqK0czCFoVBRASFSQBZsyMIxEgEVgANEIEIPK1EKCeFbnaaI8UxBFKsK8XRMO4kSTw8Vn7x26/+2Z//yXff/O61V6+qjAuO85++//16vfbG1ydePP+in7Hg1iLdcdz4Zme326DxBvhuORdM5/0Zz6lY9tlYa+1phSGd4Qy/DYjjmKyUy+XFxZnrdz9ttULf9zcf7OfLJvB8jtzhysj9T25evHKxWC4MD4/8jd+f/9PvfjdJnHx+ZG1tr9HYHipnclk/tr1CqRB3taMCSXqEOpfPjk+P9br746Nz+UI5E5R2dm6Ojc0ws3YrWhkmRSSOCIDVSpRiRwNQXxUJD0ciEbpaERGQAkehIKU+sb7vsAAiIqeqROQ+lQ7wRj5ceg/IFhFBBI+lfjzOoMcF8ePtHEvW8RDR9iWhQzeuA2LQnLLBMWcuAAHRA+JZHUUAwAjMzIB8oLjWpCyIEkIUEeU4SKIsgKNVYoAIFAERpHI4i9AAEfik2205ls5TKdIaHa2QJJ/t4YEDWjpfaVyWKKY0tYgCpfo+cUTiakQgFhQhEIgN6zQTGKXlINPdBh+Op++7ROQoIoVa99lXE+Cxdf741KDuIiJRn4MPSVc7/as4roIGAM0eGwAQAWRBQAEBQATWYkVYrACgAAmSoCAaEhEmQUEFCIQMB2L1swBdMotLdxsLC+XiUNaliULJL+XWMuXK6k+2eRs6fv3mnU8n5qeW7q/3dnxTrbz9F/elNzw+Punm9tvVyLbt8ARkJpPNnf9HRlUm539nZvxVCScb+/mCGe11UccUjDXc4rbtAcalep1azUzGm/WdNkqQWKeXmFg1QIcKlGLPDFDF2AFlJZ+WpDsIg/pzWv180hIzDIhHfNIYNDoD45IH9FMGHC8DK4SerD8nHZ+TzpcDw4l8oLL1ve6L25ujr1ybun/r7VbYtc3Se59vZjPjhcxc0aufG3v9448+/sf/9e/9n/+P/9dqdePq1atbW0vNZsNx9OfXP3zjjTdsXJudHDPNXTdWth2JW5+antlYornFb2+s39veXnvtS/PhZ36rFc/ONGcqVUrapcqleisfJkAYxabpKm0SN4rNsQvBwxoDKFYsE4DGVAwC0g4iWEyQUAC0ephoCQSO+Fu4/0aRmxbY5WPBIwiojhVpEJEDTTI6D6/XiH2GtQcpDx8ty3PsX8fnop+JEh8yAAMgD6hz7LgPhS0ddAwshw4pAQQhZmusNQkb5oStHAt7PWynWu+bZkXQsjCjMLKARu9o03DMXywWACabqr5tWqEJWcBRrogoEkez1hB4qLUgIaGrFGgNpNlzgRQoBYToexaREZEevoEtGHiEMhERMYnbjx0HEklL+R6fXADwguTheem/GuM9duMVxQmgaEIhUYSIFgBYLIolEhErJKAg5WwAQNcSCwkqi8YAikplXNFoGFAoMWAsC7K1KABmgKqfB9QXH+Qbd9J68DhQ4Hz88doG+8NDQae1k/GG7y5/ZEIczk8tfbhVr9+InfDFr1zJT+X2dncVioeZersb5PIrtzbr263abiPqGhHJjbmVobzO98rTegM/tLo6Or+gdG7zTjQ3+lI5O7JS+6tmZ3esMlzOjY3ni50u7a59tLc5rqWIWBGtEWMT2yQESBIv97SU1mc4w9MHIRsbI8nyg/uTUxNaQ+A71Wa0MDy9vLaZzxVRdT0/2dtdGZrMQJLMzMwsLCwkSaK1npiYiON4fHx8fX39D/7GG6Njozc/W9M5Aoc7vU4mm5menazXa71eL1sO9vZqxhg/yNVqzW69Nn5uMkrCXuQCuoBAqEicxPRtk4go0n+F/vLaX8cPtbjMnBp/IZWAD9SK0K8zKMd55a+Px2mV4fg/D4yFD2lEH+Lgn+nPMTn7USAdhbce1wSk+4Z0dwLHaEZpFjk0SB61nM+nYVeUpsZMs3SJiE2Od+PolY0VZBRgQQK0IkygAAgUISsFREIoLGIZQSQgAQQkdBQhseOAIsXAWsOhxv74uBEcUeYjWoTHgi0fXCYceOQBIqb5RQ6UzIdfCcsxrcbBt/32BS2njuxgBVKXagESQZE01IwFJHXndnRGDIsAcFqOEtOgqcRgahRIrACjZbIsg7LufAGhR4ZLYnB/q1GaKnAubMXtxelXZ3DmxlJtt7ENiSct19adublzQSXffPCOZ/Tug1Y0ZAMv5/i20d2PuAtu5m/8w6/fX7sTSXV4ZizqfO4M5ScveoEOe15pY/ev1pZv5+7lRocmh2fH0c/udrpR9ztarIuudgpaO9pzSDtkvYjDx3b0WZF0nxa+aP05NZzQq3DQ56emIXjCQORupylid3e35ucvdBt3AIyrZWZiZGXljuB+GIVupru0/NnU7MUH9+5HUbS7u1upVBzHyWazSinP8yYmJuYWplzXrdX3s6rWCuth1PN9f2Z+4nt/fr3Xa+SzmZ2dnUwmPzo2aZNdx884HnXjXpiQ1kVhcFABu0lkVbqwpuzyUCiwhSNCOiJge7japlbW9HgRZOirFaTfoKQVBU8+Lz9Looh4WKXtgL36zdojEyMSHem6WcwjjRw29diTKjoqp3j8eBaDSHJwoSmNEQA5RkTwmIq1H3blKxBIrICkebIYBRMLYfehbh+9iTi9CrHI0mdrBlCSirPiagIEQIsALKw1IIHrgOOC0uQ6QkqUQq0BjinqDwdKKzhQ0h+pExDBdR4vyiU2zTB13BUOAcBxjtzlDt6ksuOjZSjTN8Y+5NnH3A9JipnYAjOxAbYkIggkImGsLTNbMAaSRMSiMSwCUcLpASLK2jSU+SFTxV8TT3pd1e0HnajHynF29ja4ENf1/p+/uTZTuZTL+p1WfOuzpUzej01c1uOSdXv7UZDDKDSxzxnP74WtqG1qO030bIFuD1UKnYbyo2xBOZt7W++/++HYyGjlwisLr5yzTvjudz/+uPlg4dLFN974nTcuf3mpe1VxIYlzvRjCuAcqIRJNJ9UUnuEMvxRO+iANPPoJmxIcDb1uS6AX+Plc1t3bbHW6LSLoNnaSqBqH2Oj1clm/09rL5196cGd1ZGSk1+tls9ler7e3t7e7u5vP559//vm92o4Rs7e312l9vr29PTKWC3uh9sgLPLFOrVZrtQVpvLpXs6ZFEKGLJkqiOGaOXQRAcBSCVaB+8QMpIsxCSJKa4w64KPWITWnJPqwZPlyO6Zju75eZo59l3/6bhxMwHa34fJQVC45JqI8Ivr+wD+pYPPFxGiaiA3+uI0IiokOV9fGTIqLTt8yKtWBsWiKCRcRRR2FOD42PwwDA0CfgVO/KIGBQqz6hpsWKOU1EosVRpB3RDvkeKI1aMxI4Sh3XDx/233XhUFo9JtSKHWDKcR5KkXt83PQjQ3pItI8MWv8CWaU/txZFxNi+X1sYi4iwBWYWRuprbqnZSlgkDQU2hoUprVplGTCVswGYSQBASACIBtgsv2DQYMFzXC/r3dm6mQV38fyFpjJJQ2019keGpotDAfnSbDce3HuQ9faKuXxsq0HWd13XhNytxS7m3MTnuhOVkqyDiv0kJD9X0EHcMfrW8k6y8VdXX7jUqsWOW9RF1J5vYru3sePnDYQWRaIE0aIoQERUqj9jP4MvmoT3tPozSGL4oo3PqeG3TAJWZC2HYdSYqczWaruBT51u1O7UNzaXigWnXKR2cx/8ook6WtH6xuadO9szMzOtVqvVavV6vXw+/53vfAcRP7/+2Wh5ttPubm5Vu93u+QuvrS/v6MBsba6KSbrdbrGMY+Mzd++tIbSQa9OXhpkhMdYaI0ocMkYLAB0Sw6PKSSJ82Es5lYCT5Gg1P+ZcJaQefa77TfGRN68cOBb9kmP1EHHKgXh9kFUj/TKRQw9bIZLDBB3Hy/AdP+PPOftx2j7U4qbK50OxO+0+9ontKMbmMK+XhhgAQDA2rBiVSkcPlHrI2e3wAl0fRUAAU2uxZWFBALTxkcxqbeqxDMzse4J0wL6OuK44GlPnpOP8eojjtszj1z4wB/sAG2eSHPb5YXk3dXbrx24d+ryJSRgAmNEypLWqrJXESC8xwsjMYtOUkpaQAKQTWmBiQbFsLVkrIMBCIhZAEBVz6q+lTlf//MQl4FA1lXJURl544cVctjReuaAy5Vsfb25H70+PjXVstdnbzxS92u5+rbpbKYyUS4XiUF5p7LaipMuKdHsv7nXDEpaq68vZiUxjo91ohJ09bsfO9v1WIct/ufSDdrtdzgyVKll/uOlW9muNpj80Iu2hBKaAc5RgnLCJMOLE9Z8Z9f0ZniE8LQn45BsmQ5B0Oo047oWt1vhwpttzzU6n08HhseJQpdBtFzq1jjUsgK2wu7+/v7CwkMvlPvnkE0T8xje+oZR6//33K6MWOlkTSmxC19eeytz69O74fIGtVSTdbjeMt7O5xe3ddilPYnl3s+Znhwhdk4hGQoDYWnQc0r1HDKgHjEoE/eq/h7Y9ETH8eG9nsIe20sOmBABSCfu4YPpLztSjkuKxGUspLWViK3C4GyARTIOJAVzn0Xn5+ed9RLY+/Cf23bjkMJAJ+jbLowQUhyI4IpIkzAJIWoux4lJK3uR66Vn6fTm4LrQsAMgi1gIIcpqrGcD0xB6oZxMLzCgC1irtiNbgOqA0OA5rDaSEjtJD4cFMcUqFiXl86i+TPF4QIv2YjQIAxAe+a+kVAxxsUI5ivfAYAQMIMqNljGJJEogTiGNkhlYokqqj+SD/thAiguo7iwGmmxVEQBayNk1FyUohMx0kSntmoDV6naTb2O9UKlPQLXS2Mteuvj5c6L73XuvKlbnPlt5ar9YnxxeCHC3fXo9Ne2fd9LphlPQkwW4zzOfzJLqYy63c3fnqd95YuDpzf+s+2fDOB5+G+zg9PdVar+cyhdJoaXJyEtyuxdDG3QByu81t1U5ULwc2o5WvtAvsAGtje097TH4pPOmd0bMisZ3hdGE5abeb3Xaz3W444Ie9nueqsdGhTtKgpjgOlfPl6kotk81n8nk/l6vVam+99daXv/zlfD7v+75S6u23397e3p6aWUwi6TaikemRTCbTa8YPlleHp650e53A1VrrByvrleHO5OTM3MyQws6De5+cu7BA4oOg4ziIiSB7nkJ1tOU4NBACwIFKl/sEfHBQGkPSFwSPpZw8oKK0haOnx8KxnJPHvvhlnq/jNID0qDn5Z5XMzEebAFZ8XB97/HXAvBy3eR+RcZpZI/3gIGQWAUAdc9o6vDAEQAJFCIJISmuwjKnEpvVRRrDjb4wFAGERFDI2VTOzAHSM1ZD6Xae7IAIgpchxUCsgJVqLo5GIkWwaGnt47SIpJYuIWKsee8lhPKB6j300tWf6Joz6B+DDcVZpNa2Dgw/YFxBACWCflRmMEWPYWjCRc9AmghCCIiIR8DybWs0FkFONswAwKwdYRANZZkQBIBabaqFPBYPuw9Nan3VjnYdnR2OKl5Y2dc/kM0E3/LGThW47mZtexEI7ur8/PJZvOuBnCA2MD00lEm3vbZuY457VWqucVxoqdOudT9773Kcsdzxf3FFnOHMus7u7Oz19ySvqpeX7GxuNkakCNO3y503YT2YvTUA8qmXCSrGXiKFIsAcACAOSsZ7hDL8FUGhr9f0w6lkb53OlnZ17QTaemZvd21na298VYxemzoeduFAsCqJByeVye3t7m5ubv//7v58kyQ9+8IOdnZ2hoaF8phzv62attfjSfLFY3Ltbb1U7DgX3799fmJtZWFjohlvj4+NzcxfjqL69U93Z3pubB6UcRFEukWjQjC4qOiKt4+zIoo59dLQYGX5Iou1TjwjK0efHVc3HKWqQOvTn4EgnDISYxjL1ldHp749rUo+36bnHfnvklzRwVT2ssgfHaPj4jw6pN/23c5Rr+kgIBgBSigXS2GDL4ChKCZhUeLwPhz1JLQCGxVrWCMJoBUTE1ZLYvn49tUSLIIBoBUCgNaSeYYhMKOmQPzxs/UwiiZHjnx5dr328JpL5SLo9lPsBIIoP75OjycVjUU+PjDaRRkBrBVLX7jQiS4CRAEhAIE1qTSSSllDqCTNR6vzMzH0Z2nMBACz3a3Kk6TsAwT4jvkQaLzWdSnb7/k5O51vJpqbV+2uJn3UZvf/wwxVdsuMzBSrH9XD1wt+uFAoF2hq/e/tuo5uQgkvXLkVJb3h8qBtJLi4sf768vr45tTgdSiSeirWzZdpXc5cg39wor3e327Xb6oWLX75y/m9tqiBpvi4iIbNwjEAeH5STHFAHF9XjH8hBtoqTSqh84g3NCWd4UD3aAf180vWJnxkMdAJ6/LjhL+E09NfDqc3Lcb3rYVH6MFne3tq5sHju0vnzn362tlvNRWu9oVInlrGvf/n1H33/329sdsbnJoxt1mvh5oNihSaWm3e6sDlzOfMX/3/2/jPYkiRLD8TOcffQV4unRWa+fCkqs7J0d3V1d3WPQPcMMMDsAMslCIJrWCy5MFsYjUbjf/6nGX/QjD+4IAkszbjELpcgiAVmBtMzPTPdPS1KdcnU6ml1tQzp7oc/4j6RWRkNPGzWlJj8rOzVfTfv8xvhEeHHzznf+c4f/Gzt/sZUfWb/oN/vdhTD7fHD4mblzW9f/oM777iGNxzUhVd65Y1/9N7bH585a11+Po7VXadw9u5DY2H5b4aJX6s0RFDQMRRzGMogjkXOtQGAkBARkFKJfGRksvhRb3VCr+2NY4I0JYlaIRGlYowWGCeX4KOQLFrR0TwwdlwM4zkaYCJkCZNOA0BEGo3D2QNEQpg0+0vkifpdBMSJD5oXCQCotHsDgNSQ6oSM4gR0OjpHQMYYAgcA0wiVJilBA2nNNCFwhogQI0FajqvSOGfaVgGVd3RBJ5FWBH7C235s72JZRx5kqjdxGLoHle4ZkA5NMjAAMEwhNTENkjCMSUmQChKpWz12YhMAR00OwkQxDgTM0KhJm4KTYhq0YOIxzzX92RuetKbHECnrWyMRAeHhrgZCqeBTuwQiyrk6vVJp7GNSe0aglTienBMvekOTiKRWSlKSUJxoTVqRthkHSLMYk8LldPai4Lg8Ne1ZmSI67DUwGX9ynRVmFOTyrL6/GQIsQBnvZ3TNOm1gVCzOLnebPcGNSIamK2L0z6zME1OVwgIIPUp6e5sNpyfOr1xhHIbd4cHaRhLL6akpmUgZqzCI93cbyGA65yon/sY3XivPVN/94APLch3T1TG+++57K9+Yztc8y4SkG4/kmEWllxe/9eF269AmISISUEr0j+G0jZSf4Rm+fDjpAsIhdRYAENCyrPMr52emZ9fWW6Y5XczPbmx8cu35y4aRMJ5z7PrC4sVcPqhUKpWy96O/uPe117/z7W9845/+V/+yUpy9fOXFfi9wHcsQZhLHtnAWZ5cf3N04v3KhlCsVC6ULKxdzXqGQz8XR7rA3KNbK/mgUjhuVs+cNMxScTItDgoyDAExSlfuJGhEdKjwTaUB2VOA7KU86XHz5I1sjYqnIVKJOhIsPM7IAZPBj9ixjkFoRREzEI5KKx8YGFehDHWcgUpOVM0vDOTWrMu1iCKD0UZmqCak/pxlNcsYaAJRgihCISZ0aYEr7C1tMpBsLAAFMp/smRJQJwLEpwiOX9+hMjyYi/Z84rvfFEz+B8UnjgSNKVwqpSGlKJGiFidRKYpKQ0kSpJ3qC66QJSFOcoNBH38aQQDLgjIUS4DgdS0cOfVp/fDJEnB4AcSCVWl82ObBjR/8JBjiKDXjShgPjCQnr6NvTU06ShDQq0iohpUhrrdOKMUhD95Pkekrj+7I7Eb8CIhqo9kHXNE2NsjJV2T3Yjo3izPzUVC334Qcfbm/vmtwslAv1C4sHrf1xW42GY9Q87xU6rW632VckYUyGza2lwrcuvv7Gm6/fe7g2HA7Ho7iQo6uXrx50QscoT3vnTFfv4IPWePdB72dBqcDFIgCkd79WCggVKQbsWQT6Gf4q4DHre+T5bd6/v7iwCMz66ONPXMd2HDQNWSxWTIMbhsgXFgdDk/H6hYvVufkZQ7y/uLpy9blv3Hy/2dhEY9mYnV0qlrSMNKiQaX7l8rX5maUHD3cXZhZL+dLuzt6FC5dG/Y4l0HLzSIIkjAbtM/OFWj0fxi3BpeAiAWYZSMyQChIlEVGnfJhU/08jIEklHvWAj0KLjBC0TK2yJko5NQSTPnFHfwJaEyJSJI68/5SozDkwhozrT8WlJ1wvIsUQSGHaNSAlIh0pYT2GWBlwGN+Sh9ZXT9b3SbmLkkCatFZAYDBTAyKiBpSKtNZKa621Y6ZpSw2gGTOQA0vrenR8chKOjnNC8f5UHloeRu5PAgBSLtfRfoVNaltRSVSaJYmOEx3HKCVFiSadtjqYjHRkVom0H5DgIAQaEk2DEgGcMcQ0eUwnaFATu6jUsUd7EhM9SI2pV5mGHxCYkk8ulwqV8embAeBofDj8OXk/kZKIaa2VBimPQ9AsgwRATyny9FnjtJFXsbO5S0SSkrmF2ouvXrEf6OZox46o22yEVn9qsTRdmr978+F7b33YajWlSsAUkR/n3aJjeY7lMMHa3aYKaXNze2ZuPhhHrYN27EdBPErixLR4ZbUyt1CreksF0y2V3ffX3+tSD7q3bTnNGGPIARgyAkIGHBEpw7U/7QmfdiI+W0rV6b/htMf/DF9MZKUA8ATfh4jYITrdxsr5Vc7g1q3rL7/0SpxE4/FYaoGobKdYKM4iL4wDa2+ve26lYojwje+8cfujrf314dULb+71dgcjdWn1MoWg9XvFQqlYmjvYbRbcYhIpaau9vb2/+Y1fe+etbdfGc8tnbJu3mv3t9b0rF8/kcyLsxIBKCETJOOcCAYnFUiMiMExNLwIgJyCI5ROsLwAgcNKpepEGYpN0o0al8NAHOupwhwCAkmuN6RIMQJyDEMgYYtozJ23NfuJZ4EKnFl1JhoSpkhQAygwDnEQCIG2hBHLiY4E+pE0pSVoypZRSk2ENxnXq7k8c94lspM/iQ/YZANOcc8aBMTRNdTQPR9YUAA41pY+3WYfO8cQzTguijsqTeOr5aSQNWmsgSLcsQYhS6SjWMoEo0TJBKVEDKX2UUAdIO/9p0lqDT5yDIUgaWhkoBHBOnFEYnWQ149FydLgFfDz7rkinB5D6wTRhXOskOWJ3H1O+4UQo+OQgAJBM2NSPuPsAILjWoEGjIiJiQJyASJ9c/Y8SFulvWQY48/nK+PzTwdMaX7z6xsthNP7kzofaiIw8/Nbv/ub16x/GSRiywdJKfdiIhKHjZBRFo6WlOUC69urX3v7FO5sPt5NASUc5ri1jbZiisTt67+fXtx40FShKcH5q9tyllc3N9W7Y2TeKlBvtj6JQDpeWFufg1Wn2vQf9gKNgjGlNMialYtKIiNw0nsqJPcMzfBnhOsJxHNLAhZUkwXDUVYlqNTurF2qDwaDfD86eexV59Mn1n3z96/MvvnDhJ3/x4du/eOv3/sY/WN8+aDQHkuLVsytI4Bbcs2fOfvjh/Q8/ur+6+jxnhlZyerau1SgKWwW35HleojXjHkGn4HlxEpGWUkpElUiRSK4BlDQ4xgSQNltFBGQEEpBT2qUmPeaTNhhx4vJOApga0zyiUsce2GE9TFplwpUipVjK5eCcKYWcs7SRw5GVOpofIbTWCMSkRCVRSiLNaVJu9AT444mrl6hJNjO1wqlh0ApIodaoFU+dPGMiuKE0KQINOOk3AAzSdr6ElPbgYwwYI62O2yaeDMAq8YjBO7LB6jj3n5rhdDaAJWxyPESkWcqRQmB+CImERFISQywhiUlrJEzTo8eXQOt0L4IJaq0Y6FTLEZUiQ5BE9P1jzgGeUJ0UXMGj5iR9LXWqZJWylPFo55Akj28s0p/+ieKVo30GwKQr0afNlecAAFeagDikvi8x4iCTk4NMXgIAZeViv2A4tQf8W3/717d3Nttyu905eOuDn/6nz//91159dXtn56NP3uNOkCTJ6pWzZxaX+v3hcDAaDAbFct6whNRJtV53LIeIcIBxHFfLs0W7tn5nT0rJTZNCbmmrXpwyjVahwpg76u83MdLLS3NVU+3dv5kkCyTI4AYiQ0aMGDBgjGVlsbM8iac1EZ+XB/wrPKSn8q2nnbdn+MvByeuChzwsIuKcPDe3tdPg3BACZTgKJZxbeW5vby/n6nb7YG5hwFio4mGp6GiotHc3X3p5tTZnN4aQG/BKOW+IqJC3phfr5y+t/P4f/Hkcx+12u1ypDtrdC5fPm7Y8f34KpDn0/eZQFquzfuzeW9+fmStFsbJiHcdJHBgMINYUagTkk76s6UtkyAgS0OzImcMTL8AUmohNanIm5gQBUMrDwpdH2bCkUwUJpImUFSNiWp/wDfGRR4EhSAVKgVaYxEwmKBVojTIjcBaFBABKExEpojQIrTQl8cTFBGKk0vA4AwDLAEWaIREoZGSYAGlclIlJJDvlcxMSodZwGIE+KrGdvDDFMYEIDl1MRDxhR1LlqSOScKojlpq6I94ZhgkkkqTCOKJEgZKMiGkAAcfl1wBHUWXOkaVdqhCBIXAGRMTZ4/fb0YtPrzNpenhSdaUnVzCN8yMwKR8lPR0OezIXfvK7tH6s+HvyYtJKARCIEbFUJ+0wEX/Swz6crMwV+umsn5+1fcmCeO/Wzy2Tf/v7r8fBWMq42Wrtru1+8vH1UTPU1DNtcce+m8/nb964dbDVKdXybt4q1QqOZ9uuGfjBaDRKkiRtv1wu16OhOthrCGlu3TjYe9CYmq1fe3WxknfPnlu8srjS3Wl3hsMPG3/SDHLlwT+0bduxPVPYXDDTEga3hBD94fhzmYivKp7Nz+eLrAebHlV9OuLF5HI2F2YU6Xp9lhvcD4defupb33rz9//wnwmGrs2C8T7iOJfHKBqtr63NlArTCwvDZP381TKJWjnveXZSKlorF5dMh7uec+nynBC5MI5MS4RqbFpOPs9bzVGY6HZXhSg1y3PL7naGwoZYQhDGKrRAQZBIyS1hIgCmNhgm0ktIqJn1SITwyAwrJE0KJkWxmNbGILA0w3f43yERCjFK5MnZSK0aA8AT5cFHU0REpLhSqBVGMSYxi2LUCqWCx6Quj5DEE9ax1qRoQvRJI9gAAJod0lzZxDZiwpCAoTAEF1oYxDkgI8Pg+iiGjBqRMK0kPjJIj17rhCae5YTsfSQKpsXh8Ry2FCYCgDBKdwBEGmki35gGXbXUJBUlCSQqrQ7icKjjdXI3k9p41zEQ0RTIhRachNBpVtU0HsvZT5LAeII1dtJ8apqQwkhjmglGQk06jWSkw5w0k1ofC488+pPB8S5k8pMIgEgTIDCZUsuBaTpOgdOnppQyylQyujt+aSBWrizt7+/ENN5ubtSL9fd/+UEylHP1xTu3N1FoA43N+1uvfv2Vl1548WHuYblQ3TnYZhyLlfxUtb67tT8aD+M41qQ0T27evFnO1ar5ut8LPFYShNTnm7c2m3rTmBteXF5Nuq31j7eMZPWsuaryecuyTMMGABnrOApCDDkzfkU/p2d4hq8MDnNvqdWhIw/YdsyHDx9qzS9dvHz3zk/3drcvX51PYoko+v2OaWEhzzk3DODdbns0Hqgg+OGf/5tf/73fbvfXo3iws7kvx7vtKes7v/Mf7+40p+fqnjPrOOVffvjh6sUzceLX6rXWftcP+7nCVELj2/e2uKbXX7mwubZRNJhSIoqUCjUSjaOYTKeEx6HXVEhCp3IHxkTx6piwighpB/jUN05dOgIEBgBSTazgo04wKFKIiAzTHj7pC0LS+vH+vqnxVglKBSphYQxJjFHE4gSRMMmKQaedCjQRgaJJES0Rok4b0k6cYDwk3woICZEZzDA5F0wYGlEhIyEIAKVWlPqsE2VH0NqBEybn6IU+lKU8pHZPHHquD5viakzb3U/Kz6JJv0Y4lOZITSMToBSTUkupleJEiMAIGaA+mkhEPLT1zLEAERgHUzBk2uDIEDVo2zrZFIFOHCqnTwEAAHk6M5PwxiFDO/XOD2/i45/iRN3zCcrVhGp3dKufvKBATJNOt1yplDWAZozTCUWUoxcyw9MVfwmxy88S4oN37yQydnOiXls0OU7NVnVVnVs+u9fYna5NHxwcMM5vP7xru+bS5YXBsP/OO++0D3oqBIcNUDNX5MueFcvIZCpOBhdfvLL2YKMf9JWPXq4yGAzQdPdhPDRvxG8WZsvzF1fLBiuXpnBwe3/UN8veajK2uGNGYjAMDwB85iRyYM3OebH9rl2iYvLC9t3awK+j03/yGZxS+TPLI8m6jKlG66eRVX+cWU98yrqxx9ua/ocjY34yj+fp3NBZdcwpK/J/PLKOMzOUlHWfnHIeThuq4uLJ44QJy5kOShkmWjJlOdwzodfYEIkqmCI3VX9492MVj86vzAsc/fgn/6aYGzYb96u18tyC9dFHt8slCILCxx9FMxeXXlucbd0fkcKSIbbGO8aZleULl3/4w19wXRz15O7uWm2mOzXLw9GumTjheHWqkG+Zveu3rueZ87ViMXTZrf1fVPlFv/2j11/92vXb9tqOA9odhn3umG2MOGfC0KYBliDTJCGQMeLgatCckQaNKBE1ATGm+6HxqDUiIgUAMSWYttED1BpBMaU0ETnOhJHEGOOcp0w0RExgIqY/IRIjACIQJDJWChOpYgmR5LFisWZaA8YWgT7uRMfSfkza0hPqLQIy5Ic9eyCKk8NhH7kuWoFWpBUlEUu7KqR3cmhJAOCc8cmOhAyOGrRhnejedpioRADTFHBodY6sLwD0x+nkTLhdR7F3FcEkKY4ER+pgAHGESiMA42zirGtCAIrViaNPb0mlAfRI+gIZA4iJCY5hSog7LpeCE+QpJKJoZD/RA0Ywj+zxSWd9MMITCeBjHpZtySc+MSdyt8eJYQAIEzw24GmOg6fX5eQox/lmxCc/d+qx5/HYy89YZzKaTGSuexnfm6nTcMp+4eKtn/6Fl3deePmqJZwo8F3HClTw4YcfTJ0teK6wEjQMMTc1PRr4D29t9bp9cIzBYDRoBuEgyTtFIYRhGLmCN2q3vWIOuFo4P723v6sUtfydQqFQrc11qbf5Tut/uP7TGbtUL5TmLs0PvMbO7mbYgmr+uVr5PEnKVTSFt965+YupWa/MZt3hWYO123ItqN3N6VfMjdfasXWqE3uGZ/giAxG1lgia8TS8hgCgZXzj1gcr518SkVufLh/s721sri0uXdg/GJQKw+cuX262Wh9++KFhGBdWzyHiYDDwPOn7wcFOY6o+fePmzZWVs5cvXf7BH/7JN797ORiME5UsLy/40ajVal27+Fy9UL314d1oLPcanYSizXY3sv2Z4uWdDb128MHKuWQwDk3bURCPfUlcOY5Q4wQ1U0SSNCNIK1oMDgESABIQ44TIGE+XV6aOOxPAyShl6mIe+8qIjKHWaY4QjpZ6xiZRbvVkjweVQq2ZpkPC8ETDBJCUPvxeSinYqI9yjZ+Gythyp9VPaVntRHdJEyKGYcIQAIlz4giAlKTReHg8xJq+CMMne34yOnLoU2fxSLFSIT6a9UzrjI+qeNIYwKQ8KXMjKGOmAblmxEEniBwYagCMoseDw+mLKNIn3zz6iY/qMRxWEB1XhT02VFYu9hl+NcTF1WVN6qMP3tUhXHnx3HRtunmwD5JKq/lyOY+FWq/d12aEiH4/tnXBKpovXn2lsdP+5Jc3W/trOadgmiYAhGFsV+2x7C+szEzt5nli+8NocWHptbm/fn9z4+baAwxiH6PtTk/nnYqLlWUz9HQht1Of5h/c/rAIelDeH5R+Ht63RovlPe3lxwW1+feyTwABAABJREFUPaBCeOlSMrtqtm987amc8NPy8LLG+eIFRLJ2dhnH/yWh759+/NPNw+nHPx04B6WUwZghWBiFHE0EPRqPwmTYbG+CgYap/aBdnypevrIi7MbW+jucV6fq9bv3u61Wy3p9tVarKaUaO+2Z2WlcwI2N9cWzs9/9jTfffeuXu5uNwD8zGGidMIOzRqPR7fRtL6fB+PCD969eeNGxizl0B/6D6xt3EzbX3M9Xposgko8+2eJmBdAlNIFhGAQQGYqB0ClhmGQClgER6NggQuKMCFNKMEOWinPAScfoONjMMe2tnuYUD2OmkEh+ZJ8OmbmECCIjckDEtQZNKW+LtCbUDGgigUREWh+pNgFA6n4/cZwMqUWm4bB1POrjCijOtOAISFoTcQQEzgARooQfnuGjhlY+bpAmIWt57NU9ihA+ZcgnkVoA0phW7cCkqwHJjG57ScARkScoOTBEztNALgz9yVGc8FwBAOPomMYFJ64Xx2MhlNSNfywlf3TWNMkZP/l4sgJG+pSs5tM+d9ke7RdrhRaebSSaDILaUul7v/mbvh88vHNfMGN/fz9f8s5eWNra3Bo1B0Gs/XDkYGE8HpfL5fmpJYrY7et3QbEkSeJ+ZHgwe7Y6e77qVHltuSD7rFKpzExNe6L+xitXS8X7P3vrRyM5fPXim5eWXup3tLDu9IMtMd0rr9SwsQ4129Kjl15aJcPBqjGW5Dfl7mY3UCM22BrOXOfwdAzwMzzDFwGMEyjF0aC0xoZp0mo8GuaKZmfQWjx7fjA4COLglZcuA+het+26brPVqlWr9Xp9NBrFccw5z+fzOw/vIuL0Qq1SL33/+9//0z/+80F7DAoZ8mLO68VRY29Hq3Bqano8jDf2H7R6jUptamNvp9MChIrmLafgXi1fm15g/d6925/cml8OTSE4M5IEkjjh2jQ4Sk3EiSQyDlFInDGfJyK1Qxw4Z0zotOGsMQnZPYlbe9wpnZCOwpjsKIusNB0xrhg8Uv579JpSno4G5MQ1GhyAFGpMU7OagBHpVExAIWE2CS4jNXPokp74IAAACItrSlW3GAFxBoDIOAUBADzhW5JHQ51HHxCfamWf/hTikJ104ksBIBUYOezBwNIEexoDeCKikCMi58AY45wYagAOqMNAwKfcViJK5NEkP1LXa/JHypZOWt+Tf378a1aKJ3P+v9x4WgZe9NrNwahfKHsrKyv9fr/V7IAwlGbjRnJr9HD+zEyYhONwLBFYTmntdzv+v/23/3amOm8zr1qtDrojrbVpGzzHVq4tPvfy2fvrdy3XaKw1S5axvbmbn7lxZam6tHxpbzfpdDoGewn8b+uuzcxkuLvbsLusfturm+fPn/vo/etMlYsXakviGxgu3Y/ecy9t5oyiEa5CY+VpKWQ984AP3/5ye8CZufyn5AGffvwn41fkjBE1Y1qj5owYQiKj8XjQbHeXly4HUbi5vePZQghrc2On3xutnJseDtb6/b7tLCwtLVmW9fDhw9FoxBi7e+8eCflf/q//y3/yT/7JwX4zHMl6vW4YhkDrYNTp9HpnLp2bmllo7o7W15qx2e0lozgStq5wbk/VBnPLRRMdE2E0cCK/nDNKlucMe5IpI2ebYUyKFKQJS6ZRpbqUwAUgkhBp4YsWmnEGyInxjPNVeFI+/bC0CcWk3/Djkcw0JP1pl4vzCfEVOTLUDNEwU6EPCQBKY6qQpTUQQ02gskidWaStk3VCJyyNJoMzognJiGnNOCelQB4qPZ3MlQIAoPXoIIfRaTZpq/eYxwxPulWISKVaypiGrCf2mJCy7ucwYpwhMhAMOQcCloYlkjijfEhnGE6BlFrP4z0BIU4UuD5tyHVGTlRn7RSytJcz8JX1gIUQ5WrVdMxOr/dgbaPbHXZ745yXX6pc/PCT9xub3Zmlaq7o6TwFfpiE4QsXXtha2w2CINE6jmMhBABIKSM1COLxwcHB3vZ+3inl3YRrsbW5FTs/tluVOn/jN771t7v9VrPb7Y37XqlswxnXvX+we+9m892Fc7XnL14rmtMUm8wvjBhHGLaChpJssfidXPFaLxbj01UnPcMzfLGhJeccgYC0YTIhIPDD0XhYLs9889t/7c6dDQZupVzd3x10WuHs9Irr9m/d3BZCTM/UoygaDodKqTt37hQ9v1arfP3rr//RH/yxiikIQq2o028LbrYP2jdufSyEefHaRa0oiimXm7618f5uozG7fLU6mnm49kvJwXPHcbitoNrvS8+dZ5B4biKMBJDnHJAnreYJE6E1MYZSQqqQpTQBEipMPuXhpTCAKVIAKYMJCCilJlnmUZD6kZVRTtraHJbv4OQzNkcATKQmAmUypTVppjTJtPpJo5JMKZJyQioOMtrqqYz6FUUnzOHxIaGUJCUwpjlDqTRnGhEQjxvO06FgxSEz+fC8HpsH45F+yUcnTvDEPrvpP6Xnkpb6UPqrztC+BkIiQI3EU3ESBFAAR2XWR6mBJ9jFR45HP348E3IWe9z0pi+yjicr1E9Zx3/aJjdfcggnlw+TcGNzLxzB1HKbSHQHfqE4LUdo6Zz0I1Pmi1Y1iEeAnTAJdnZ2kkRqjYP+GCTLeYUgCILI90qO9OnOxw/iWOdzdrVSG3ZH3Na723v+jM8riVsY2FP9KaGYbg860fuf/MA3urvNtZh1LD097vfmFmqgzbDJc3PNlvzYNbZhx2m0PwnzjhQVgOnPe64ewWftAZ+Wbfu08Fl7qM+QgkAKIZRSUmthCkSIw0glUbFW63bCODYuXXxpcS436LXioDO/cCVU70opy+VyoVDodrsAYBgGYyxS4//kb/2dd37+7o0bt1584QXPygXMbzb3tIRer9duHVy7dg1Jb2xsztavFFzrxo3ScJzMnXE726h0XPKMnJMUa+Z+ZzxO/JWls4FslAy3OOXJyDaZ7zEbDoUs0rxd6g+n7ZHSFGPaFRgAANRhWfDk59HtxIgjMAKtAQGJcWCMISPHniy4R/XE6Z8M/EfaGk46VSBYgjSBaaZmAOVhWWostdaolVaSpETUNFGVymCxxvDk5LCKJ113HnsMJrsHBaQBGWiW1tceqm1AWvyDR5qOabOjk1Y2feF6ACdUO+DwiQvDx/vswmTHkxrO9POTyLnW2YJFCBqBI6blTqn7i/S4yTx+PI+z9HgyO00ntLtPpofZ4WV6/AHPyOlm1e9SRne7p4XP2gN+aiFo07TWNjccz6nN5kdBpGRiufm9ZuvBw3u5Qo6j1dkbcjScvDEzPTszq6ve+cZuu3PQ1xbmS0UVU6vVMixxbvHCytKltb07paLZOeh324NWp2EVjPJotbs96hnbi9eskXi4t/uA+1U9qq6H7yGPB9H+VMVzhbh355Pq7FQYRXutnYX6dMnLf/eN1fu3H/z8l//8skvu4HeeTvHKMzzDFwNExBhTUjIgLjgDSGSUqOTc2ctv/eKDUnlueX5J6yjnlgO/E/iqPF26fPlykiSmab744ovnzlUHg8HVq1dffOXb9+/f+/jj647pjofByrnzfb9TrOTiOBbIa9Xqa6+9RoaxtrX+8guL3YYs8gXBXO7FZI3Prs4VvDFEIeGo53cS8OfOzHc7u2ZumGcGahQJyH4qyABKoaK0ehWJQMWU5oARFQMATFvw4PEKjY+8QMWOU5xIyIBxYIwzfmSojj5LiMhE+tdH/03KarkAng5BBEAmTOqMx1FCGrXiJEBJ1Aq0JtLoJ0/2qOS/q+nap+LDiACQOr4ARKlqPUM4bhqBJyQeSY8Ozyt9c7IyW+6nF2gCgDCcWLXHbBvipP88ESckBMxqOzEBS73v9NKgBgWAAIcSoI972JNynU875fKEh4ondK0Ze7Ihz4roZzm6X6xA8OcHcX37TtCEaSbK+ZJnhr4K+n4keO7FX/u16x/cgsBYqFzwYnft1r1mcHDu4uLH6sdnz13Mc95s9RBVrMZU7r347Rfq0+7B8EGp4rZazdZ4xynbl84s+L6/MF/Q+sGOeLj5sXzw4EGpUrQda39/f0BaB+Tkzc4g+uV7D23Xit954BUcNgdi37y190l7/tyLF79x8JCt33z/1Ss1f/g7TzyBTJZjRo4hO/TxZJzWI8xKbZw2FcL4Zy3x8hmPf8pc7FNKFWV63jrjffaUjjOrLjzr+A1kQRw5Tn48HhtxXMx577777tmVS9yZc80DjzcV4X6bBm058ruS36/Yueu3jDe++Z/trG8b0K+9Wc1N2d8Sub/4s7fefuejc+eeV1reWmsuaufq1VdgZydJQrTx2uuvbDa7hcLczMwFJ5e7eefDl3+tkrfCKSEP4p+tPnc+CJYL+bJgRvfG+hsvl1bPbu+YurWz+fy1c1vbvZjYVD0CYlpxKQUpHkakEkYaAk6HnhwHRNCYig4qVADAWOq/aiPNPKIek0RGHJEx4gIFY5wjIiAXaSCeM2CoCcngSKBL4mS5y3Hw2+bHZT+HIESMJSPEmChRGCuIE1IalCJQFjLgQpsGcKEZ04xLxojPqMlmQoOSLEkmDXkAA5w0gWBH1hQAHOMRYZDUsAKAsAaImFYw46GmChENRjYAIGjBFefaMHRKjEI40bf45Dl48tNxXSLKqkNNIkMqSpvSS3V48AAKDnPek8YG/PA3dRyNOHEirvlIEvoISiUaAIkTMA0AmqXdJjnLCOln1HuJzGUmy6U6Xd/0LM87a3R+yjrdpxURzBpHAIFXZdViLZ/Pt7vRwV7T9Upf+9rXNnYeoqXiSO4NtoqsgKaqFkpzS9MvXDzf3hm04x3bEmhqy+DT03MXr51jpkRE13XNEoZsJFXCXAKtTMeM43htba3b7dbr9Zn63O7urisK/coeDpBHFmcOJ6EosWdw6UK14cs4dl04R4OzzTtl2V50TddXpacyC8/wDF8QGIYRhkmSJEwA5zyOY8PAfM6JQ5kkNDUze9A8IHK2ttvnlhfqU+WtnZuuY87O1gySW5ufHDSKV168sPnw1vrDm6ZIOMZShYLHB7sPDR55nsd56dLli55X+clP3i6XZ+M4cD1DqThvG5zzre2Nu/fueLl8FMThOPrWG99+653ryMD341KpRNrUKnJcW44IQBEwIk7ElEIkkEoficTQYWujI9eNKBVp4oxpRKYIDA4AjHM8MsCMwaFTiDIBCZozVAw4Y4ZgilICkXpi1FRmsItBG0qRTIBS91eRlEgKGFeCp942paw3PLQ0x8SowwooRHTsSXOFx6Lfpv3Iwn20ngohjgzw5ED0pLIWQac9Ezin9CsEz9zA2Tae3PN92hg/fv8gJYqIiDSmjRNSCxgfu6InU/ZpNfAT6pVtSz/RAIcxcGJpQ0mtmeYSHueHP4KMfcIXjvT0RYN48xvf3b2/u3t/NwmTQtUrlvNxrDc21w/6u3bBIiXb/UYCoZFDzjGO44fv73bb/W6rF1OYKxgLZ2eMIq01by8uzCmlbENwlyIYxyou5fJTpeqZlaWb1281Gg0tScX64e2NB/cfnjt3Lu/lwTPCmEbjIVlusegxUu2D/qAL+42mGbpiZp68cLr42srieaWdkf95T9UzPEM2MjkBGesP5wZRnCSJEMgZRtGIMfI85+7DtUqtPj2zcP/d20kyXl29cPHC8p21d+/feLs+dfnc2Vo1h1ub49GoZRmXBML5lZnh0Fs+uxAn0d27wWDY93KwtFR95dVr9drcn/7wJ8Ph4PadG9NTS7dufDAet6bKZpKEUWwkMlpff9DrjmzDPbN8plTOc84PGq2pepELOGjsMD7luuXRMNYEmlApFiWQxFoqlBKQH3mojyzrSulJyS+w1OKqiW1WoJEYkUINpEiRRkDSiQYkwZFxtEymFJuEnBngcRFRmisFIpLqEXLQYXqS4giVYlpBnJBWqCSRIiJ0LU1IggMw4pwYV/ioZUqtLOcsZSohHplnOHKFEdHJJYfn+4ipQc2P3OWTBwaoU8loxhAZccEMgcjAyGAL8xMRr5P3Eukne2yJUGpSCQ1SAhGlv8JhudGJKSIASIc/maFPX3jucdOIkwYYAFN5SA1EijSh1hI+rRx2jM9JlDnD8j8ts/+Ze8AfvvOJIQ0ZaMpDPpc/655ttHq37908s7q4snShuz+8d/Mh59w1Xd8frt3e2VjfzOdcyxUzC+WrL6+uXF0aJ53tg41mqxOGodIguNCKIRieXSoUCt1eZ+nMomEYW+s7mw+3816hVq5vre2cW3hOkgqitmJhxNiox+Nd3VKSwvFoNMoVC7vs3k7YnsrpC2fP5cziaU8se0P22YagM8fJHP+pDP+lx2c9P09r/KyPn5Z0prUGEIhIpBF1MB4ylEShUmpqerbb62iSQZR84xtvMIx+/4/vMkHIxj/50b/yewNE38tb62t3h71GqWgKbql4WKuU6994sdfrMsZcg6rV6tRMTWOyurp848a9G63WjRsfnTt3QWkdBNG1l68oSG7duqOUjhm/d/8Wkl5ZuRyG4dbWGiN5dnV+MBaN1oiBJyUoRXEMSUKxBJmAVNo8NCRHOcv0ZyIJJAhOqVUjRoxxxnSSSETUHBggMlJMIwCgTvUetQYDIG11h1orRC6ekLMkmjQfPDITNKEXYRgBpk0JNQCA4JMehpadABJjhAwACJhOM9Z0qMmV/mSpnSQmhD6SaMaJv06MsbQdEAAcCnxMDBs7fJ3mIOiwGAkZMUDOAYXiHLkgJoAzxjNSS2KS8z72TY9urCd+Pkg5XARSaanSxLyGQ01s+hQOHfQJ/+twfHLsSSrhaLuTzqqUKdsrNe2gFAGAUkAZZUtZmsynFdw4dXlkRkg8s6NAVtnV5wSxt9nOcy8ahFG42x23phem6lO1sZ+AZI6R61MQjaXhoMsLnMxBr19wco5nxThEh0diePfh9Sj2c3mv0Ql2t/aaW8NKuRr3hZJs/XZDRrvFeuHKlavLZ5dWz174qPyJiqlcqNy6dctEoZJYU+IVXMGccS+2/KJtFGJsOLYxWzlXnjob93OOO6PB0egARJ/3XD3DM2TitB5wFKmUxkwUklZ+0CMKw6Dz/LULfqe5uXHXEDBVLw6GLaX8fN70fT0aHYy6PkRRqRaalrr+yXvxwDvY3ZybmxuNe1uDlu05jdZBEATPPXeZQN2/d+fgYMcfJ1eurDYaPcY4Y/7O1v7U3Gx9qmy6FzY2N/LTRQH2YNDzPI8x7Tg50zQLOW+qljtoDm17dtTnUoJUOkkoTiCOUem0++zjC9nR8s2YVooxpoUGRORcAwBBSoJmE0+JGIEiDcfqzJCWverUhOqUdfwpA8zQ+LR1AQCliSFHBMNAE1FqQEQGzHUnClMT0eCJgwsaGKWq1AhwSA97YjA2/ZVHT/YsSURa62Pu1UQaU3NucEaMkeDABQmOnIMQmmW179H4xFsoK2SNkw6DyBibUMsh1efKMsBHjOtHbDB9KomQfswwGBFpjVoDY8QYao2cg1QZOdqsdpBPTnl/afCZe8ClXEWPpGflnbzhFYxKpVKsVOIYmo32wcHB5uZ2q9VSOSq6Jdf1uGUO9e78ufmQeMxH3VEjHPtMiwKrnKkvDPai7l6XxqbFHZ3I9v642WzOXikDYKlY/o1f+83puen7dx744+DStVU3Z5DCB3f45noDlagVasrEbrNjeCUgm+uLVf7N2vkrrrMQSGr6Axs+Hy3oZx7wXw6e1vyc1hP9vKA0CMPgDLRmnMvQHwJFMhmVS/a4NWY8ivz2/MLsB+//xfR0xfNwMA5Aq9laycRce7AXJYOt7fUiLlSrZcPghXx+5I/W1x+WKsWLF695nvejn/zo/LnzCLrXb967f7eUrzqO1+l0TBOqM+Vmcw84Y0wrLXd3HkaBvLh6fmtryzCKQRBYRhgGAymlMM2xL0lDIimlNUlCTSnP9jjievgCAUBr0sSZJkSmNTFGUgFnZJoGMhKCcw6IIBgQKCIyDIUMDMGQgSGIISgiBke9SB6XTgQmIe2kS5MWuOk/2TZPexgwxhA1sjS2TJYxce+URqVJp4xuAK3EpE7p2FppAJQSidhhi+LUGhEi6BNM4JP3mMHDk7xlfQhDEAAwDlwQ48i44pwhMi4y2iZG/DGicvriqP/uY4gT1HpiI9NeyykLcBKIhqOK5COpSASAtJ765E8/OOKZI042ImlkG48mWBMDYIikNctuXvLZepbZHnBGCDpjGaAv2IIrPCufhBFoPR4MIyBtyL1GUxKvzVZiPY7RL03lOMFea7dUKOecHMvL+qInLRwGxJgKx1HUCO7sbFSWZRwoLVm/PVbJKPRj0mDzfBzFrVZrPB6/88u3lpeXa/Ol27f3u0FHg5f3itxCYXIhjenCXH523i+OJbNdc+rSue/mrJVB34gCM1GKZa+q2QYy48J8wS7AMzxdZHqimZ8/5fin+/ivyAFzANBaG6YwDR0EY60TIVi7vVkoMBmpsNmJwm6z2QfqDAYHS+cXunvd/Z17Dlqx2c/lLRX7a5t3Vy6d73a7+wd7zODFYvnll1/WWv3gBz/47vfeIKR8wXuhdvXO3fuvvvy1wWDw53+2NT01xzncuPlJdWoqDMdaUbPTTAJ5705o2DVhyE5zd3+7Ozs7OzV15v2PNlQ0pzQlUmsNCid6SAD85PN1ZC0AAFPlKQSaqCDp1D4ZREgMEQXjwFJHjWlStq0ZA0MgchQcOQOliQHFybEzCidsnmEeV66evBr8qGyJSURkOHHsbAGIoImUZokCTUwpzYkkCgWktdZKaa2VmmwgpBREkNrgE04wSX5Ur3yiPgfREeKIrvXI5s9ABOAsNb2EHAA1sgk//NMgso83AlofCXqM/QyWr+IagDQqSgugQWkCQnWsQY3wqCg3Puropz9H45OEMzzaYTCUmtL4OlMq3e8wANBZXdqyPPXTtpc99QN5uuoD9TnpfGR6wDubuxZZrjAtx75w+Uxlrnjj9p3d/X2ryMZ9fyyHpemyDGh/s5nomBkwf6ESwlCCPzVfNcAc7gZ+mIAwf/7Td+rT5XptWkZqb+eg2xlM16YvXrwo5oKPPvrIy3m5nCcpKVdKOwdbXs4dbSjf6THAs6sLe1uj2zc3L1WX//qb/9nuYHccxRbXndHdxkFcyp2r5hYI3Z4c/CVP2TM8w2cHwzC01lorlwshdJKEiNpxjCQZlfLOjU/WOU9a7W2G9tb2Q+4MbKeyuDx7e78LhJVqoVguakraBy3Tsy5fvnzr1q1Wu/m93/7+wsLCRx99VCgUS+ViFIfPv/B8MA5yuZzt8KtXv54kYalQSVS0vbNVKOU1qUIx/9zFi0Ci29z96KOPHHf6jW9c9Qdrd2/fLNRLjLtRQlKRTPsOIQJLRZDxKPl6aHwn5ocxkablGIOUDo2oiZiUMedcKUpdSZ22GCDNBRmccYGWKQQHDdpGoUlperJ0omUoOBaNmAAR0Uz7FBMDItI4OQYtwAAAloZgJ9JdqAk0MWKU6lURkZ40pEq7DyEAEqAmfWgOCYzkpN2aWNxDFvSRAdZaqzT+zhgCIdMMEZlO/44yq2Mg3dMQTbr86klfZQjCDI4LTjxgpZlS6VczouNvOJyix//8MSJ0GLET9vd40yO4TKdcayBgx5l+zOgCobO0gp/1d/9VEFN8OTZ8aYYql5TO1SIMoUB1q9DaaJ+5sFibLt784F4yhKmFSmWqcPWVZTlvV4rVfntcdquFXKm3AMztzMzVv5X/3rsfvLXf3clV7RevXuj3+xwMdPz+rp52VtqN9nbS7+4kfnSzXC9VvNKZ78/knTInYzzyz6z2o1flg4/eeafTNgp5VovtorH54L45Vzl74W+M9u8xuQAxqNB0sM510fdjzRLD0RGNTOk9+cQyd1JZN1DWjXJKck3G14rPvK73y42s2cmqr81CpseZ8fnThqxPW5ecBQ5+mGjbtlGPaqU4Ht9henDnzsOzS0Yz0JWK02g0dzc+IsBKZapUqDdu7y4tLc0uzd66deusPJODC71WgVX19sGB4boLZ88unjtXq8/+we//iVIqCqmUqw47ycbm5srqpVgm/f3BzHTw3V9789/8yx9InSwszRZz+ReuXOZo5dyi5+TGyYs/+/EflUtxqTh3/Uav02u8/oaOyR8r27IsihVpZll2olQcSyGEPlzpj9btNAhpCw1MM5MYQw0glZYRqIQhMxIFUYIjTDOmadJUFOtSIhEmWilGxDkiIEdCd4yIHDjARDiLiNjhc6RTxxr0JNSI2tP6UROS7gzQAEMRAAFoYofVO6gxNasWR2EKzjQCS0UTc65OFGitlQIpQSmVBmMnAdnDrkBHSd+0XyFpINJISMSIEAhDmTrigjEmEpYeJkNwvScnRSPlk0KlQEpUMdNKSAlE0B89+c59IpkLAYT9ZK5MMDKf+L4m64krHD1W7nX8efuJ42Q9Ryf3HCc/Ywvrie+DqQGAFBJR2jgrHQV52o+ZGNccNWPEGSGS0jytsGIM0qKvNMYQhbknHo+fRdrNXAeeViT1yeuYsPLCcQtmodgNDx7cf5hANOiPpJSVSrXTaxfKucp0oUej+TPTM/PT7W5rZWaF9dhCbj6Swc76XqlYvnBxtVItzzir733yVrfTz+XyZXM6Utg6aO2O2jmz6Pt+HMdEFMdhq9Xqj/qj0agwa7nTBYH27PScSvTe+gNAYgbWFkrNwVYAQWnJY8SV1yvPl7Ufw/50JJJQdULdJ1NYwmFgM5lXf8W0Q5/hqwEhBEskY8zzvOGwvb+/T0Qz88VCsfjJJ7+0LTU9PS9V0ul1oijyXLNg19vtdqvdyhdy5XIZADTJTqcz6g9rtennnnuuUCj8+Mc/vnXrlmma4/G4XKj/6Mc/+eM//lF16u1CofDyi8+PhuMP3//gxs2bz79w9dJzlxeX5j/44INuZ6+Uj2emhGG5juM4TjEIRKcbE/F8zo009tumTLiUmkiT1hxAoOafivs9th6lbhzBsZecFiMdZW3TQCciRgFHRGTEGTIkzicWnZAhIkfkDBFRAwkGisgQTINGQECalCexCRfppAN3dCRSEwIqTVIhEiRaK4Vaa6UQCNLuCQhMawQgIKZBCcY0EBFwDjQpf4LU76a00TAAUUrfSklGSGpCHp5IMRNKTojAEYFDookBpA2bwujJW81USpM0JjEoiaRAqonI1xORaTC+YCxfOFGddXJ3rB5llx2dDlcAaQYDFfLjjbNpIGOaMWKoGUt5YRog7SZ9SFCHlCHPAUBmSXN9wSC6QZspypuO7/vbm4HtGcAZQzGMu5Ywmt2xn4yXVucvXT1fKOcSHbd2u5vbG9Xpcn2mur+/v723zQWrlKu1/P1mozVTXrBV4cZbD+/dve95Xr1eb7e6YRQIwfOFXD7vjYOxH4zDINp8uKMGAhW/cOECAx7LuFwrOXmzGqxs39/fl1teWag4yQ86M7mzfi/2zJZhuBiIOCECI5FCJwkpMJ1T0taz8NTYbk9lmGeY4Kuas09PK0kSxsx2s62UOnPmzGuvvrq1vd1qx4bha7CE4QjOgKJi0d18uNnv96vVaqm8HCXRvft3pqampqamklBprYMgrlQsrfH551/0fX9/f98ynGAYRkHy8P66aZrnFpe313feeeu95kHTFFYxXw6C4MaNG9c/um0wa25m7uor18qVqufVtvfGibRdF/xgSJILnCOtGCFRAgoEQ41kHOZhH4tnTgKVE2IT6KMUKiBppPSTiPqwzQEijAcmMGKAgJpzYoiAGgENYQqOyCYUYkDSnAjJndQREaQ+xYRsQ3iiUTxNhM+ISCsJACQ1aA2kQUmUShOBUkREiQR9+E8AIDUZgggUIAmGRCCAESMAlDE7SqYeggBA60MNZ41HHhsRgUUMgTESijClHGsCgFg++X5OBUakAq2YliATptO+vxk5V54hSMkyNJaz2NRZOO3nM59T4vAk5kQqgXni18kLNgl0E2MaOSFL/4OCjQDAGAk22XUhagDgnLTWpDGWJCVXCk4ZMvucIWLyQRIMpR+E4INUSaGYr9fq3WDPyVsAUCjlzy2fKc46RJIp3VJbS8/XbMeanimvXF7ut4YP7q6//+6HcV1AzA3hdHZG3VZPjsEpu5yJWnmq2TmQKtZa2649OzurlKrVapEaN3ba7UavfdAXFgZBgMLYa21fdWf4oKijfcV4EMrI1vlKWYPahh/XnHMGO+/E00oVpQZtjgxLQvTlyDF8VQ3JZ40vC6v5tNBaT5oxSNlut0ulUq1WE0JobZ9fff7Onbc31ndyOR2GAVHc6zaWlpZu3rqBDBzPaW03Hjx4gAyklNw0x4PB9t5us9M2bKtWrw+Hw1K18uM/+1EUxq9//fUw1Ds7O3GUjIbjqWr99p0H7W6v1enMzk1PTc053tba/c3t7X3J5fd/83t+aN+9sW2YXi4nWs0dJ1c1hZFIMC2uFHBGlskZMC4gVsdG97EXcJhVpRNaGakU6BHJ5+jDo7GRJoUZckQCJM44gXZNZgpEjtwgJTQToIjgUAlZQ6rRmBYRaQBgwE98tdY6VaRiMiFFgMCkIqkINGgFRCyRoDXTWmvFkpQOTQwAGJOHfme6t6AJv5f4Ydr7kZ9RnNYFM0rpxxpT954hS4nYkgFLhyIAAJXRHUghkELSqBUqmnCwSTPkT04bZ5OMsnLGT/58VoYt6/OZildZw594fk/eIYzrJ77vcgAEjRqZZgYxoZFr5FQwDzdYQPwEIz1NUiVyIs+iNSaSKYWnNcOnXZ+f1nouFs8sIGIsQ2HxSPmJjEbj4dLKAnn5bqc3Pz9//vx5BTJQQyK6f+/B7PysYzlbW1t5uzJdmrdr+aXpczO1uZsfrA17zZ12U8U0Ozu7fH6hOl0O4nE81MVcmZm6WMx7OTcIgr2dvb3txrnV5dFgNO5Ev3z4oZHD6dk680Q8Hu4ln+jpVtTrBRjHPPZzs2Jmq9ve6+n3PdPnaqDYEmPzhvKQpCEwiZ+cA37mAX818FXduBCRbTtaaymDdrtdq9VGo9Gf/tmfVarz+XwOtIoiP+dx2zIq1eJw2EeS3W7XD30FycHBQS7vWa7VH/QLTpkzrNdr3W53aXEhjmPTEJzzW7duFUuVhYUziCbnPAzD/f39Uqm0sLgYhvGdO/eYEPWp2ZWV1V57PB6Ni8VioZgPI9IycD3Xc5Vl6rNnpjr7IUBk2znQQmllGkLKiDGWqu9/2voiHi/Rh2U8qc2dCBk+9idhgADAedq2VjNGHIEATMREkiFAaASJwiDGEFDHNgCkkWGW+tqIHFCz4zaCQMSUmhTShJFGQEWKAdOaJXLSvlBJRkSxTMPRaZ9dAuCCMQKQh3ZU60luWB63HTxJPQOpMO3WCxqBOBGlko2IHBE5w7Q/FFJKDM/sv6snRVxIRJpYSgrThGZWn90sC/k5da3JrD4g+Rh5Lf1pGvLkr0d/7piISIBIjJhBTBAKQkTjsNxI6Uc2DVEMShNoUgoTRUnCpCRNvyp6/4WCKBXKQRBorRfnlpyiGcjBQW8XuCrm80rqQX+4u7dje8aBDEiDIrXxfvNgr9Fpd3PfnRls3drYWTt3cdlyjXa7NQoGMcRGTpglGMbd4W5HqqRmLZCGfr/fudks75cMYSaxBoUb97YNdGzujfsBBXG1Xi6W84kjf7n5QzNnROao1Wtw4A1/607zvT4fF7R1sH1nML7piPmCedY1LpjJjJkU/11NTZ7hGb6gSPllQRD0+/0Xrp5pNpu7u2vnzsyNRq1SyZibmRkHHS2553mtdlupeHZ+tlwp5vP5MAo06uXlZQLd2j3o9Q6iyBoOW4jxYDBAxE6nszQ/x5nY3duxvXyu5FmCJdEIBXvh2ksEan+vYdqmZVmFfOnsmbOmYZdKlTu3bo/GwhClKExazZZhVoJgcezHSlGlzJRGFWtFQiqGjAPIx0zp8U98xE1KKUvHiyY98n/FNYO0wy4yBnpioTFUCpEUIAcUBIoAGSCw4QgJKVWgQg6p4CUAI3nSAKeGE7XWbOJ8s1iT1kAapSIijCUppbUGKVFTyqJCAB1M+vIyDQQaNaXCFkxLduT4nvSAlaY0eZymhg9fgFICcRKRRgKW6kwRqcxQ7aGUNCDRxMNlE+rYE5Bl8GRW+78MVzeT/Jjx/mnjjYY4bst4ZGgR0XMfN8Dpa8tIbyECBOSEDJAjgJbJpC0jESmNWqMmYMBSiWKtMVGkFZcSNKHWINjTIdVm4al5wL1Ov9frAVIu7zHGpmamrAqWpwv1esm+lFt/sBFKX6NutPbHw2C2tujl3TPWGYrFD3//x8jAKEFn2KzMFt787dd7vd5gMCCipcUzjd3m3VsP9rb3umGYKzvCE0E0GgWjqcp0wSsoSVEQc27qBMv5mrKCOIkszwyjYBCOV+bOIVI81J6dt6Q37o7narW4WRv0t7Q/5A5atmmZDvG8jHNPjaX2GXvAX1VP7suOzyvEzTkf+75lWVrK4XBYrVbH43Eul2u37hwcbNhm4rloCEfU8rZtG4Z1/vxyuVI0DMP1nItXLnie98ILz3PObaH7/T5jrNVq7ezsjMdjIUQYhga3gjC6ceNet98GANcywnDQ7jUce8qweZD4oYxd1yUNwrTqtRluGK32QTQm2+ZEQRy3e914f2+72ZKMGdWajcxKFKAQUms8UQj0WBqYMaZTakwqcAiT9VU96TklIoMrAOCMUiqWEIhpvz8zQUSNyBhThEpOrLqf1qrioQ+cVvgw0tHxYaTx5DQELYTShAigCZQCIlCaSKNSoHTatRAUEWhQGhBRJ6kEI+qUiwuHzijQ0a1yohD56HlHQAJiMDl61FowRAnAABmAPBT8YEbGOpCGjjkhADKdhg6AaYozDHBGDDqjKVGmYEUWntZTYZmTdoePwXHkp98EgLQ95fFhHBZWRTFojUBpviCtvGJENA6M408eHna6kzsVPq/1WQy6o2AcOa7dafZuPdxaujCz/Nzs2dXlopcru/XLK1cj8m88/KDV20finU7HyLVnqktLfMq0+NUrLxQq7lbr4bVXLw+DUX7G1K4RR/Hi+ekzy8ueURDKlkOcW542C3Bv81aYhHEcB34c+XKqPq187LS7xUreruUTNrZtc8vvr+2sV2cKChjjlm07w+HQHw5KRac69T+Lx9tVS81UpoEVB6EzlNYglsXTFno/wzN8AWAYRpL4rutyNIIgCIKg2Wz2+/2iO/THzXw91+8N8gV7qlrbbzWLpdru3jaB6vf7Qej7kT/2R3fv3XEcZ77uFIvFOI7PnDlTuHSuVqsNh0POue3mLTN39faDg2an0+nkPdsQ1Os3Bx3u5tydvc3+uNfr9TgJjiZjbO3hhonDdqMfJ/uVSt0P2hrZYMx6fc8QXqLmBRYlAUonIY0a06rPTy9bmBrQ9P3DEDQi4nGI+ERSEBBQps2COAPOiTFkTCMimSq1Z5NeeJpSExVGqRgTIiNASOPPACjDk1uBSQ0S0YSqoyEtk01FNJGIlEKiVOtYk5pEdLXGlEOrCdPqX6WAEEljVi423RcgEmgGSMAIiCHDSE3UpQhg0iqZHonAf3ogQA0EnJNOzw4IDyPYn0Z2ivO0Pu1nC9MASE8OARFShW5EcGz1aQOMiAmow1Q6kZ6E9xGYTFBq1BqUQq3ZxAkGDCMDADhPGQaac0pDMDqra/IXjFOC099wtBNcefXCcC/audGrlafNuiye5cVq7euvf21je83zPMf07t18ePeThwKt869MYWi992eftLaH9Xp95crCi29edqtibWtDYTwOR83t7mJ15cWrL2/vb/7Fu39mmPlXXnmlVq1/+N7HB5vdxlrPinNG4viq68e+nedTS2Xm6J7f/sYbX/cK3sb13d2tTl7Or86+MlWaf9i4FRfW+7B1KfyfbNyLp6avXH7xVRnl/LZhMTdMBvWqUMzsj9Uo0ACWAM5Z4lhsHJ8uOH3aHVBWve8pN5qfOU7Z/vgzR9b8PK3jpAyt2lMjo2/oaa+vIieOAs+ziKRWEpAFQWzbub4fTtW4wbbu3PjBw7s/Jhi7rhuG0vVMwzBM00TEOI5Ho9FgMPB9PwgCABBCpEGmSqWilArD0GLadV0hhJTSNM1SqeR5HiKWSk6tVqtWq6ZpMsZs2/Y8z7Isbhx39VFKpSwwrTXgaHp6GgDSfcBoNBoOh2EYal7L5c5MTz/n2tX9vaZW+Url5d19y3Cf3J7MFBkh0IyFzzQO3clHPWkFigEA00jAUiosTX6FVOKRA0cmRFqGBEryx74i/dXLZ/SvjbPYwjw1zymTS6oJtQdRE0LayklrRho0EGgmtdA0UYJMX5BmRJSVu81K3eoMRafs/uUZLOhTFqqzjO/lGdcxkchQGwy5UIahhFAGA2AkyAYARYojMKbTkAYheHn/iYbWMqMjrzdFGtvn2gUA0qAVRFJrNSkV60ZaE5DiSjIlGSlUGoEwTJwnHmdmjjxLtDoDmXYhIzWQqcyVcT+Ir3/nJadkjOPhWm8bLDx7fqkyW9huP3z5u68JMG3hdVv9PoySQHVavYJT2ryz//zFF8+dP9drfbTf2q4N85gIFtnxQIcybnX6D+9u7ulec7NrOsZMabHht95+961zS6u28JgaBePIMQqcc4GCIpKJ3t04WDg/Mz+zOBgMuAWvv/qNe8WNpGdO1esVO/dwL97fbllltte5ubR8uVLRUXTPFDNeoRbFYzMXjoyPPXspL6a18uIYiGQiQ80TAPdUE/0Mz/BZgLTUWksVA2iGKAQni5uGsk1545N393feGg1uMxoV8iLnOfm8iJNYSjkej4NDxHGslIrj2DAM27aLxSIiprwqwzCiKGSJRpn4vq/UoN0bpsYYKUybPSRJAgC2bTuOI4QolRzDMFzXzefzhUIhl8ul78/M57lwbNt23KJpmoZhAIAQYhgNTbs2GkPkj/Il3miM/Oiema+RKjyV+Tlq6vBYTFsICQCAwLhmKbVrIuI46XKICICaCIGIITJ2vOE+uWhmGRhmPDlyprQGAMZVGqZmDCmlTiuA1JHVDDkpwFS9AxWbkMEmnZGY+g/SG87aNmbt97LMyGdNPTINYMS4oYUgQyBHzoRmCIxrAmUQMKaRETtUA3WdlJL2eKjZMsXR+3Ais56k9WEaNNOAkKphAgGXglGa20AEVMAYA6URvuQkIFGvze7sbt9/uO7Yudwyv3XwfiWqmoZ16/17u60d4AqQZqZnTeZ0G4PEYuM+5+O7YRxOL1WHw+H6+vqf/aEu5IvduB3poFIrfevV7zBp3LxxZ3Nr48y5peWvzVrc6+wOHtzYZqHlMDcK4kRpacau65bL5f2DHU8UF2amGoPtulU2TbPVb5AytNs1CkXbwaiRmLbpGxv2TF0zvbk9LhUvzNe+3m0OP/rk3e9+TxWLXtItRUNHRlISGQ6ZtghHT2eCsnZAmZGkp/O1Tw1ftNRz5n7+aXnAT2eYpwZENAwkjQjEDWA84ixI4pFjhXu7H689eL9W0TMzRUNoooSBbrfbURSlLm8cxwDAGOOcG6YAIGRgGSbnXGstDO66rrLzaTmTTQZjzDTNJEn8YFjI5xhjSilggIiK2MiPkmTU7LThUX5yugjm8iKXy5mmmdp4x3FM0/Q8b3m5zLnpB1GtVpueXrTMcm9kDn3d2n/y+WZF+LKeo6MjoUfb8qRNC1jKyEkVGIABgCJkaeCZgDNAlnKflBDHucaTL2yRcWNlhJTTaiKpkEhrjWmrXQKUkpNGDaQlSQWM6UljY5k65oAKNIACzQkJf0Wo+clvZ4ZWMjwtlrkuPS3uy5OPxzIIGXFOgjEutEBI201ySxERMM0ZMYacEROACKYl4NE7LX1tmseGGR6htiWaUg0sLRQwBSlFToRcEaHmyJE0oki7M6EfZp1Wdqj/NMiez6czvvizf/VuFMaNTmd2tfT6b1+bXao39zu3Prj/+//iNghZqHl23nz+8kuXzlzZvdfZerBbNsqb9/YTNj5zcTFf9u5df3j/xmYlX8+fMVeWVy9fu7iwsAAakWOYjPP5fK/Xem513pbe1vWGP47rpWkOot/vm641U5udn1lkjA07/r1bDzZad8eyL87k723dqFarDVUZ9ZsDdsAtzOUKY3NjK/gwGRmNfX+pH0zlXySNN2/dz5doZape8c4DmMziArRWEAd06hvxGb4SeGrXPWuBO+UwpsmkNJAREgdKorDvD5thNCwUtW30Z6bs6SnTtWWv1xiNRlrrRmuYttMhIn4ChimCILAsi3OulLJt2zAMznk3CJgwmDC4JsaYMC0mDA2odMS4IYxJY900oB0n0ssV0+Bz+h0AAASIGPh6NBykJt80zSiKkiRxHMcDi5vacwzbdSMpCtXnXnzl75SqF3/FxGW8n8XOPS5POkqREgEDPOzdS5wDA4aMGAKmzY0IGNeAhKgZIgBatoYTSzxjExaYnRWSzZAuTjW7pNJAafckAo2KKI45adAEmiOXoIg0akUExsRUA0OpCIGAoSbKDHVmpa6yNigZhbdZ7+Mp79CsDWvWcyS4RAYMAdNDwDREn5alIRIojciRAEiml0ydHBAnLDVIpDq0ygCHbAEiihUnANKgNKblvJqIFKYpBk14FIogzKR2fwGROZ+tZvvMwrlatX4QrO/t7FemCxvrW7fe3ao5U/WFSj9qBf7YEpYprIJX7DRuOjKPJIRpjfsBIha9CnBT+7zf71957vvz87Pvf/TeOBo4JefcxeXxKIj9ZP3+2gurr/76d0v/6r///f3WzsqZlX4ou6PO9FzNzhnnz5/ba+6EyWBp/uzS3PL67ppXMsuLdmmWlSxrMOTt27tmHryzDLnifW5LkyszDMji5dWzV/3NZASrbm06UpbiCaCERFhgh08pNvHMA366+KvmAWudpDU4hmHE0bDX3hkONsOgMx6NOLY8Vw0HrYO97njclyk0TyPMjLFUXnESsrNNAp0v5IhoOByaliGEE8exaZqu6xqG4ft+kiSIaJom51wr0zAMREySREqJiJYtuDCFaWmtgSlQatJmAAAQuTAJJOOTAS07iePYcRztc0Vhtx/SMImVQ2YlTipKPbn4HiAtqH0CDCNrftLvx8dscFoFBICMMa0JOSAxAuSIgJoxACTG0nJP4gxM4zikeRIiw7PU7MnrA0NSmhgHIJSaSKPWKFKWMxEQJpK40EqB5kgEkpHSBERSAdOU5iwBID4lqSFDwAqIP/n4s8jOWeNkIUsYJHvDpJFAExAjkqg4YaprFplwqJIhBAgOjDFgYIojrsCkD3E6Mh1rdx9xy4mIZGITkZw4vqg1pqz1KMKJthnQRIzlV9KpvjQecOKND8LNvFOKQnn9nYcbtxvKx5X5i/2D4f52YyQ7Zy8vIOJbb721t7tfKdXiOJZax5HaDztCCFt4jMRoPBbjpH3QH4/HH374oVMyzq+uKEj2d/a9KYNHw06nZZpuba4QjKJe0BwGnfpSySxgL24yi4/jwSgYOGTuPGi4hXytWm81GruWs/raSrQ8Q1yBjVPl2VpudmzLQs69Wn7VG1rDIPzeb10OtxeK3uI4hiQCCRFhZJFjWyKKM5ouZOCrqrj0Vw1fNA9YyoQAkTFEEYXjvb0H48F9rXrh3o6MoyD0R6N+MB5ygxuGkUhtmOLQh2NH7i8iup5tWoJxkFLl8m4+7ymlhMGkBC50In2C2HEN27aklOHQNw1umsgYY5wbGhljRCQl03TcWP7kPW8JQ0rJkHPOORMSFGlIYkmmpbRod8KFxef/2m/8vZm5a5qcIBkDPDkHTJlZyF+lEEGH3eDxsOtOEnNgmgEyzjgQaURODHBS1sIIkRii4EBAiMg4hyM3i012Lb9qNcxcoAmRIYImYsAICTkCgIOkNBEBMq0UgJjUE3OuNRAp4ForDVpC2pchDk6Xjc0s5sjY0ODntNXU6lAbM+UzH3IeVSQAQDBAJGQgBAiGiCjpyaGG9FqffFonJCxpElGiIaVApwW+AKBIQconpyOp0a9CGwDxu//Tv9HYbt/8+G4wlnbiKGUXciVOBigM42jlysrzL18K4vEvP/xlY7dfMKtOzoo7ofKRiIHBISeY4NxC8M2/+MHboR6PdPviS2eCcdhpDPxu3Oo1V1bO3bx107KshQvTOTu/vbkr0Xvu5VWDm8E4SmKwC4K42z7oNHc6ppPjdqjZeLW+YmnHQdc1c0ko5W3WtPrKxPn5mfl5d7y7aeWHhbPUI9BjYmHd5payRKzCJArjyPnCuaLP8FcShoGJnBiV0ai3vfUgGN22jHEQ7YdRohQqBQpsUzimYSjlGwYdWV8hhGmaQgjOOTLFOe92u/l8/uWXXyaiDz74IIoiz3UrlbLjOL7v+76PiIwZhYKllCKiJEkYaS5QCC6lVFqZfMIaTU3dkRk2mHHUWS/9UsMwhBCKcwCPC/fs6muzc68iq0o/KDjmIGN/m7WRzdoY6ROe4lGmEACiBBkKZMQ104qYAKYRGViYAAAeGllkE8mFo3A6QCqXSJPDOSUryWDsaAxCAOBAAMCQaYGgiGyGUoFUJARTmripiUgqzTUpCdKY9BSE4Mndh7KQFYLOrLbIMD5ZWtBZ0BlfkHW9pEZMtUo0TOqe9UQpDAlirRgQY4jqsK+GfKRO9wiETyZhCcUAQKWKYBrTw1MKhRB6IvqpNQDBpNlCVhPEp4Wn5QFnhqC/8cJ33x6//Xb3w7xZtA2ntddii4yZOgrU2Ytn//rv/HqA/Xv37kVxaJji4OBg0OtXyuV6eZpiSJLEFnZCUW/ULkKtF4wUj+1qruhWCm65XkzEdN6ugoRkHAwj5YdhODezUJsuF4v5nYMtxpgMFUfDLbozU7PhMAllnGeVTmcXBTbXe/966w9kIubqi5dfurr+g/c7frN+edo5Ix42Pj7YaCas7WL42gt/P9jWnfugVCUBrhgwi0hQVtfBZ3iGv0xYloWoE6WJaDweNxp74Xin4IWGHUdhIBUXhscYSkkhaALTdSfBZyFEaobTRWo46gFAt9uZnZ35znfebDQaf/qnP9zf3y8WnMWFb166tLK9vf2zn99qNpuLCwvnz5+/fPlqt9vd29vrdrsAYNs2AIRhSHrSTi7NNE/C3lq7tomIgoFSyjYFItqm4JxrZrQaLcecma9fEXLKD3nOMvN8PMhwP07rlBA9ma6hVFoOnC7NnANoRkhIdNiwAZngKSkWAelk2QmdUGHI8hSzcqXEJhUsBCwtAdLAAMBgGoGBBqVJcEQEpUEwRKaICBVoBcg1KtCK1H9AOC0jZ5zd0P5zitiRkQaRNTFMWycQ0wAgUkqBBqY1AEMCRogYhRY8an3T1/wwYvGYATYx9XcJNNNAitILDyQFABAj0IwAVKopzjK7EH9ZIG7dubHX2EuSBEyIZGhYIvYlBBQbo+1W8JOf/OTKxef79yjcFoyz6ryZFiDGGDslRwAfjUa+71tmjgaGcqNR0rNZqbM3ittNjCy/MdgbbOatUr7mOiXGQrp165bJnfOXl3nEewcDk5u1hdIo7ERW9M3fuPzR27ccFxxrTo95417UT+7On5t+/rlrg+3mjWi/XKheWXnT7VXee+sjzkStttI8CN/3f3pp5jfyUzjepigkZrpoaIlBVt1VpsRaxuezbvSsx+KLRv76ciiiwoRw8++PLM/gqYXmTjlxWX2LozgQQpgWT5Jo+dzzf2fmf//OO+/87M//7eryQ+F0dTKykNXKZW7HxGOGDNFBxCiKCjkviiLGsN1ux3EcKc/hPGcVeqOthSXrx3/89tXVc5qGf/t3/pODg4OD3QN/6E9V6rc+ubk8v/TKi69ce/1iY7/13/2/bty8fiMME0X4yqsvX3vhhcbW9p3bd1MDPwx6y+cX55cXGu2DeJAkiZyu5rvdQbHotVsdL1eI41iGVKkF82etqTNVsrga75Ot28iE8eQ0sJIZ9aMZ9dl44rk7dFoBABgz0reUAqXgiNEh2JPLC2UGq9nOMLRZOg22yKjfzbCpUmcIdEBG0jtjnCQ5LXv5yStQlkebWRab+X6GB0wnr1eawNcIgCo1hTx9jwBkAvDExwgBACaBhsOUcKpvBgDy6DliGgGOrgZjGR5Vpg7T6Tyw7HU7K3WSJcxyyhzwIOgJi3s5d9gd5syCkJaZOP1+/7lvna/Olu7cv7m2ttbb95WP+XweQ+KJrbWOMZGRYowpqUmCklppaQmr5k0zRVv398LxOleWiqC8VIj6FAZ9K9RoaoObjmsxA01XV3jOonyn0UsgPHN2maGKrZ6WxEzDtDxDGv0DaO/1Hhrrg/5o5A9n6/OOaxVtb2phent9Z2t3u1QpNT8O8+2ua0cSIrAMJgRJUFF2TuUZnuEvEUQ0UboASCt8XnvtNcuy7r//f6AcVqZKNuWRADkapslQaC0QUUq/3e5EUeS6bpLIMIwMJz8aDCwnXl29qrW+c+/OytmLv3fl9/Y299bX169evdpqtZaXl7/3ve+9/PLLP/vZzy69uPiHf/iH1Wp1ZWXlwdr6t771Lcfz3nn7nYWpqavPX/now4/v3rtTqhZqtdpvff+3C6Xc//X/9P/4o7/4wfLy8sLS0vLZxWvXrv1//z//g23ayLwLK2evPf+Ck3d7nWG+kGMsUPJziy/FWYYqw8LEGfuxrGUVs7SUMwxnVvehZ3iGfx+ICPuDuGHkFGNifmZqe31PsZjbIDyYnq+u75njYUBMA3BORjSMiVQaLlChTgshDDCV1KZpDjp9NYiFzbyCU7AroR+hQYZ0I19G41jzxCmZrmm7rsM5t+v2g50tIwrUWJs277R6CY3nL9Wm7NU4jrQCOQZJybA/7nZ6zd7e7NSsZfIHW3fny8nifD2Ow+vXb/VGvedKlywqM3AlqEQDAgrGGUf9lEg0z/AM/z7IZsuztOwHJgE3mJmZsSyruzHfV5tGjrOYoiB2hOAGS8aq3W+nYs5xHDPGvFzesh1NoLQ0LUMYqlBwW52WUmp5eaG+UP3Bv/4jrXUYDLudhm3xv/U3//of/dG/vX//frVW9cf9B/e2f+3bv/47v/M3P/rkk48//CBfLChIdvY6V5+/kst75WoZCP/ZP/2v/1f/6D+fn1n8zpvf/bMf/fC5F54L4/F4z3/phZeuf3xzemlmdWVxYW4JhTvoDm2nFsQRAyNLbOKzjgCF8ZPfNzKCkTIjMpHVr11lsI6zgsr0V2xFyby+p6z/fmoa/l8wnNoD3muvD5J+ccaimC2uVgPoMMbYSN+7ez+Ow7xVqJ2buR9s9McB+dyUOS1ihkyT1oqIAzcEcGSYKJCMMdcthomf+GQL1IqEELvbe66RL9VLpZotHAyG4bjjr4dbrCEpZnEcR37MhZuMtQQSlthp7zCb6lPV86url84/F41lzMYbB/f6+xTL6N7a7W6798LFV2eny+vrdrvdyS27XsExbbPv8zBgQBYx0hQDZoSAntLEfdbj/JXDqcsDnsowp/+CDPwK8lGK1BUeDIacc9d1q+WZQfvAsLghWBxJpWk0SgbNoZ/EKf2KMVMIoTWTEpRCRXHesU0TXdvK5+wrVy4mSXLjxo2VC+d2d3ednB0mwcqFcxplouNYRSqJ+73u9uZaPp9fPb/ywx/+yY2b17/57TcA6eat61rq3/7t3zZt87/9f/832/ub//O///e0omK+9Pf+3t8jQevra1cuvFgv5QSaSxcul6vYOGgvnwvLJRFGfpIw0zZ0RmP5zxpJxvdmpTBkhmeclYrSGSHlrK17Zh1t1vvP1of/IHzW8/Z5XRdx65drC4vzM9P1h/fX252Wkze8gjva7WDbONjszi3MSq2ZYLmi6+/7FWdmTFFKEkmLCI+S51ESCoM7ns0V2q5ZLpcHIxbHcaWcD7pJo73fjjQzgGvLdk2LofTNXM6JKFo6N1/MFfe3D8bDIRdMAjJHSxVb3LNULo6SmI26ycHU1CXBjSRUO1ubez/ZnSkvlopWqbDYNdcj94whyiav5UURwUpkmEBs4pNZiM9u/2f4LPArHuC0qBcRpZRpqa6Xc2am5ze6t4OxH0TQaQ7zOcuxLNPKj8KWTGK76HAmDMOQiUpiaQiTMwr8sRCsVCqWyyXbMn/2Fz8eJv3nr7xYr1c8z37zzW82Gnu/+MVPX3nlFQBl28LLub/3e7/baOz93/7v/2R2duqb33y91+nMTNeC0OcGN01zOBxqrTkXmxtbxUrhvY/f/d/+7/43f/KnP1SJnp2Z291unDl7Zvn84njYv//gxvJip1bx7jwYIFZkyD4vDwYzkn5ZXYA0ZrXze/Ln6ZQssqxI21c1A5Z1fU8r6HHa8b8sOLUHHG5DzBho2xjldm+2YhXMLIg8lJRjmpbBSIz9fm2x4Ijcu+2PHW6pRGqa3OxKQ5wAETGOKDCmsBeE3IZcrmLkwWW26sullamDzU4Uxm7ZNEwBkjMDtBPS0O61eoOoc255qZyvXD+4TbE6f/48FRQiSJIHuwdRuA1ITskABru728vLy5eurjqe/eEvb+w09HRx3rOKQ7fTgw3OLWBd11rmOO0rJoSlM0JVnzW+7DfQ54an5AF/8aQ4AADSoqAjMA65fC0M9Wh/bPNC3q67rkk6FgIN04rj2LKdMAwJMIoTpcmyLcZ1opTrmfm8Qzqp1aqVanmxPD0ad1955RXLsuI4/m/+nz9aW1ubnql8441Xozj87d/6azc+vv2H/+YHnXbvW29+Z3lxcWl5IU7C1Uur51fP/ewXP+v3+9eef2G/NbWxtvn9733vo5sffPDLj4JhsjBzdm93n3PRG7Wn49Afa9IYBnu5Uo0YR7TCYCSsrAjTZ2yATy3ZeDoDnGWBTy3skBH6foYvJj43D7icK49a46bsuJBr9VpRQiMWu/ncbm8/X8iVzdz0fNksgSms+U5BDqJgUx3xyDUopaVhGLZrAZJtcbLjXNVmPN5tb+iY6QSAQxzHQRCgk4RKIwg3ZzollisW5+1Kf1xu7LX2H7T9UWB7jBelVTcFWfFQJ0lCEGpUMuJJDx3T2GvsAqf+YBirwDStcrW0NHPm4/Z7W527PbFfts5aeoiQEFWROZgp1fZkPDOcXw181kIcpx4mLYaUUiklhACAJEmCcbKzuz8axFOzsysLlxxuDoLm1s5DkrJcqWutZ2dnDw4OpJSaJIFSGm2Hl9yyl8NKuaS0OrdyZmFu2SqwJEl833/xxRd/+tOf/i//i3+4sbFhmuZrX3vFtq2vf+21997+oFwunztzbmtz3Y/Hr772iuPZL1x74ac//vkv3/1QxnI0Gl15/lK30ytPFS8+d+H2zbsqwunZBduwAxkaDg6GvWYzKBVK/eZ2ZWrVdWuBz0gbp633fVpCNzxT4jHLMztdVyvKLAc67f1wuhz5V1UI6Gl5zF/V9VnYJRYHMpQjS7jEaH5mnhms1+4qlEEyzlXsq6+vdJJdztl3Kq8FTf0n//R9KRUAAGeAQKiFxXMFDxGHSQeEvHjt7OKZhdZut7nV399s9juDKIxt286XnAjGYRhqU4uiaQsMg2G5Wko6ut3txUlcKDhv/NbLLb8l+6K1NkzCIQp0HNth+SAhBbLT6nDBCsXS/MJMOKRm+0CQ0djqDTvtYl4Ehfb5KUf2S0HE44DlzNMVwj/DM3wWIKK0nDftCai1juOYiHr9gePklxbOL0wvNfea7caQNCydObff2HIcp1KpjMfjlDsdRRHn3HVJh9FwNHQ8ZzQabW9v24YX9ga16tT1jz8Jxn6lVI7j+GuvvtZutwe9fn22uLG1+Xf/7t/9B/8Lr9sZjnw/jALgePb82WFv2GsPPKfg2m4Yh1EUXX3hKgr9/d/63v/x3f+z35WkDmzbnlmasvMiDKHV6tbOVn2/IQzpeFa/p13LlTD+XOYTMzxUzZ5MwmIZhi1LIlpmZIcxw5POLl78ahqMZ3i6EOGuDUiUc4JYSQUHvVahULA8dxR0FpbmudDv/vQdXw3cgqO1DsOQifH8Qg0FjwMVBknCY2s2hFpcNqr+g9jQbvvhiMLtymxhcbWCZrSz0YmFX1+cK9UKjU6AQPPzC6+/8Xq70/nw3Rs01O3dvgy0IwoeVpq3k5++fWN6enbUG29tbV+4sLq4sNDqNc4+v7h+c9eumfl5ATBeyNf7+0G/1+ryJB/l5WC8F4yajd1+dx160bmlYr1cTEaG0onWGjgh58hThRrF9ZOzM09vg5XxwGfWGZ8OT6sN2WnHeWoBtSxpIp6xsGbk9rLazEHGQpnZp/PJn848X8xs9/Pkt2Pt+UFoGBE3xTBMokDOzXofv/eLjeb1l1++0mq1Phl84LrWWI7cfClfqiaqEwTBw4c30y6/QRAwFhNB0EtyeVPYVjFXCoOwMpfjzKqa1U5v943vvpYkajzwOeOtZmfQG7cb/doy03q48eAAUaCpE9IMuBzDw3v7pml+7RuLb/7aqtbaMAwi6vf7fqdbKBT+4T/6W4PBoN1uT09Pzc3NWZb1yd2dV55fMCTZ1TpTrQv10qARxvki659OAiE7hHtK5awMacPM65KBTKpVVh/fU40OgBn3c+bnM9al04+foVuQpYuQESrPmocssIyMBGbMtJ0xvGFET3xfZsxPJE85b6dkZWet29mCY6e7nwXFiAKZ5iBYkiQyko5rV4vVCHKJjnJV23Mr2zujrfUtHYgzs+d/+3df/da33xiFwX//z//F9sZBuViO47HNvE6nE4R+vlKKYv/jTx6c12deeeWVUqn03BXbMIyHaw9My6hUy2N/dGZ+ubnd225ua6lN04zDZDQcIumdnb1/+S/+f0GgRZKLwxgj08a8J8qdpL95e19Ccu3a82DJYW+Ud/KtaDTs+ImvQyWNxDKCvMfnz8x+zVt+zrCne71xzvAQEVmqFA6ktVRKKcXFV5Ue8QxfRJAEBnj0GJumEfvR/v62ENjtdjvthm2ZliVc13JtM4rGqUKQlDIMw1SRI41dCyG4EIypOI65QMtyRoOg1wur1Uqr0c/lco7j9Xq9em1qdmHm3t0HSmrBjVyuSMCBJw5HIB4z0sqKx7oTxVqHSZKk8pOj0ahfll//+tcty0LEmZkZKeXa2tr8/Lzn2JaTk+E40QlAki+xYgl2er4N+c91Xp/hy42McmugU7LWv+wQtuVqreIwkRQbwrRNO0mSra2tQs3c3tkpLBnLl2defPmF8VKy+UmzYs5+7c0XrRLKAKcWK6ORnyREip1feG5Hru+MWkM21NqLwmSqMn32zLmNhxtbO00t9Z27t6vl+uWLz+WN8t7dzs2bNxeuTu1uHnRgqBWzbXs8HEQ6NE3PrlBl0SXleCPDLOtI9N0ab8fh3p39l19/wfK4Dljgh51WBzXTIYKd2EaRjYpz5RcvzX5L80rHB8NiWseABAiMMUCtiPCE8tmn8VXNMXzZcfquJqfUaD3dKKcOLJICNIlzHisCgJxnjoa7W5t35manHMd0HNOxLcaIMZ0kYeNg13XppE59mhpkjJUq5SQalirlhbmFVr+pOn3bzl+8sCLD0DV7iFiplmAZ2p2WH4zPrCyWyyXt93WIUZSEUcQtjsgB0LRQKcUYMxhzPc9xHMMw4jhu91pbm9ukoVQsdzodIUQh7+3vHUglQnOIEIf+8KCxXZ3JF4rzo4EXfcYr4mlzpV+03P9n/b1f9rLJzAjHUxrnaX3+s4ZIksS2Lc0kgKhUy7Xp6mg8XN9YQ0RbOA4W/JbKcVEv1h7Ee7c2rp99ruoFdr5amD1b3dtt0FBBbL338w+qXiUnylPlecs2u83e9p3mvcr63sFuPxzv7+4zEPtbjd7emBI82G+WC+W71x9UCjUV6H6/X8oXFxcXh8EgDgOryEdRfzz2gXC/q/pxO1dw3aKlxmzj1u75S2cq+XovHtqepRnrtQa2Z0d9OdwfzOZIUdQbdLqBzNXychhjqneGhICCMc2YIJZZUf8Z46k9MKd8/2mN8/Ru29MZyC87NUVwnbZm00nCGVkONPeanc56oaClpHzedV1TyUirmAlUSnW7I621bdu2bacFwVpry7La7Y4fds5ffpkbxvvvvf/+Lz86aPQN4VnAf/c/+l1hip/85Oe2Y07PTeVyXi2XV1p0ewGXXr0yjfYMtzgnIwmIrH7KCDtEHISjMAxtyy0Wyq1Wq9vpl4qV1C3OeQWmeVc3LMcq5WaC8bg12CiUSiyaebj3xVq4n+Hp4mkZtsyAb1af44wUVZaU5meNbIft6YwjOuODklESQmhQiYzH43EcJZZpt9utOE7u/XI7luGlq6vf+e75l159IQiCa9euGTnshd3aQjE/ZQ1GHdNxNnf2EgKOllC2GoHf0B807u497BKq5795JfG1w/NR0t/daaBiTIu8WbZz7PyZ1Yd313zfz7u5OI5lJD035/L87u1Ou9U5s3zWtapxOyRtl/OlohnceOc+xXzx7Nz8zLzJ7HvX16IktLt130/avb22vHWg3wW3LKWVyCnLqmg90fhWWiMxjoJxpjIKBp892F9MfNk9YGEg5xO/DRFBUb+7a/Kx74dJYlgmV5LG44GSviFsQ2jL8qSUaStfKaXv+3EcCyF2d7aY0LlisdnqffLJTS5sx0x+8dYHDje4MAzTuHHr+mDcm52buXjpwrnz575uPP+v/9UfPLyz77p5ZlKhUqpVpkv5aqHGGGNp0998Pm9ZlmU5rlsJhkNEvHjx4u7urpSSc24YRrFYDAd+EBjccD2nGPqhlGPPDaTZBJg55UycDs884H/Xx7/cHnDW+Wb1J6ZMctyX3AO260zyIJSSFES9oNFqyFgiYr5WoGg83Is7zZHfup2MkERsOPxgY6+y4A2oW61VLl47x02r1xhSQ3EppErW1h9ybeZ5RUrZ2w7AUDfevj8ajYKgWc5XKoW6JSzP9ba3t1967krOLoZ+lMvl3Jzrj30l9cLskjKo0+zlnPx0baaUL65vrQ1Hg6WlxVKh3Gl1N+/tjMfji1fOJ6GKfZm3SqqRO786u3IlqS/nzPlGvuw6u1KPhzouc0DSTCkgBYo0kiL46lbIP8MXEgaf9BFPtThGo87B/lohD5bJlQqThAuuEGLLEo7DAJgwLN/3kyRJ+dL9fj9lQeeKpTDsuq7XHwxv37l/Zmn13NmLtll5/71fzM7Otjvt8dC/eOHytReu9nq9d3/2y299++v7jd7uzoHnhaNkpFSiJJrcdgwTAFITa9t22vEwl8s5hSSfz//jf/yPg3H09ttvFwoFx3GUWr9yadU1puIQfcZzubqTN0xTx7iFOPu5zOcXbQF9hhSn9YCzYltf9ohXFjI94Nnz1XKxImPd3GuP+wFpZgjT4OawNajXp5fPLg1Hg0ajceeDh0EQeJ63vFrPV1yzaPZGnVKu9s1ff41Jc/Hjj41WTlKys7N7sNFhWjjokiHditXeGObyJU5RMtbaSAzPkCDtnHn7xr2XXvQ8Jye4cF23UigOh6P1tfXa4pTW2nZtpeU4GHe67XaviQLKpSmvbCutOs3u7ev3HMObyi30u9HAss6dPz+17Oian/AGcrtUmImCpOEPhRCc24gGFwASlUICndUv88sidfZXLQR9es3Jz9YDPm0Kg3PSBEprzi0A1m42dnYe5HOceAJEggPn4DiGlMiQAGBnZycIAkT0PC+1jmnnImAsCMDzvFwuJxO1ub6pIrNWm37uxatkYBjHl69cef7qtb29vXffeT+Xy5l2TmuOYCIaDA3DsZEMgSaFEhF1QiomUIoxIkfkHGNvd8svBr1u/8GDB3u7+7s7ezdv3hRCtL77zc4+/vhnH46T4de+tnrpwsrlq+rK155/sHGqaXiGp4zTrienjRx81uthNgnrdFUSp173nlZo/SlNj8iVra9/82VLuD//83c+eu+6jMBEWyvKWUUE1u23ielCOe/5BT9K/L24Wxi89dPtS986U5x2b9+/ubPTeO7C86+8fu3qzCudXuvnP/uFP/pwGCWQSN8fjVR/ofLc2B9VvFKju+vVvXwp/+DhXduzHO2trW3s7+8vn1ninDUajcFgGAfxysK1fpIgMWNYmCnPm4v5TbYxY0/vBQ+742bRrRQrUyRhbmbp1XPf+fG/eafTX++NevYwLla5kjLy+7pryrHqDw3bdG0HrbRhBCEDrjV71ij4Gf4ygaAJFBEJIaSk/qDbbh9UloG4QNuwLGFw7A/7/V7HHw+lSmIp4jg2TRMRTdMkorRxb+oHI2dxlHBuMsYajUYYyOdfuUIE09MvhePwk49u3rx+03W8uamFKIxVrAC51iRjCciUSqJEOQ5nnDNErYmETrTkwCRLCoXCxx9/3Gq1ACCO47Nnz47H41u3bt25e7diXq3nly8t537zr73eP2jdunXLWRQAZz/vqX2GryAy2yb+5R7GXxqE7tvv/fBWa7fbb4yKbNrX/jDq54u56lyZc47EXDO339nnnOtcyMw48I24a2/8aZLP6/7QWo+2Nird+aWZ5ov95XOL3/n+m7Xy/F/8/rvbtxoyBMd1YuhzR43k2MpzYjIMR9Mz9SDya7XiOBwVa67IaWQ6J5zSQqHT6YwLm8aZfq89aFJQjO3Vs+eVTAaDzrde+mawOm52W4jo2Dkjh9d33+54D4PC+lq72xqXr+H3yrlf13p2Y7tTzDmlwlS6eYkTQNSAgEaq156VY/hi5YazvvdpRdBPO87Ti9w/eSebqQSUsSHOrmM+HY8y63szqyAz/iFrnJgsABEl45rHQ7Xba34yM+VKiJOQch6RGm/v9fYbARdeqeBJ2cup6oB1xnHX9mwVY78b1KeqfjTKI9sbbi4sT+2uNc9Unosw7I5777z/bm3e+E//4T+4fvv2f/1/+eftnfFUcb4/bA2GbclGAYwDqaXPEsr5w7HnCtMVuXxFadUbHTDGLNMxkAHDjZ3dQqFQNi79+c9+/nf/89/5Z//Vf+s3rdq5pZdeWZybo4Uzf9tdkGdmqWjpndBYW4suna++fO3B9bvlhGY59bWOgNXHvrTNMcKTBXBOXwd/OiW70wYvs5/rp1Ovz06reJUlzXVKnLbeOjMUfMr67Mz5z+ySlFFfm3WFKUPbP0tR5ZQQGeOorD7EmYG20x2PqFemus1BvzcYDEamsGzbLpklSUlzv805JyLLsgaDgWVZUkrGmA5YNJY7g33b6zKTzLyIk+jDjz7oD0cz9Vl3znv99a+1N4d3P3lgegYABOOQiBQkwuGu5Tm27UcjPwn21lrCYm7BhtAYh6N+v28aVhKBiqlSrp5dWsHE2N/c7g96g7CLhurxrZkzs/bMVHO33Rs00QHHdZNSd7my6CB291v37n28NFc6f+Yc1udG/fHTujDP8Az/Y5CS/gzOCRUR3bx5s9PenJvNr69tVSv8/Ll5ZG4QRpZtKLLzecuCXNQbo5kzDKPTHYRhyJALYfjD0cLCQrFYPFhvxkkUUeR53ptvfmemVr93++Hu5l61XBu1k/6oR6iExVKDh4iccyIgEABMa90fjJRSlukxxsJYIinGmG05UeQLw9jd2YtC/fo3v712b182LIE2t+q2FV44i3k7abSpP8jniysHjXZtet4UEIcxNzgAS5QSxtMxXc/wDH91IO7cuD8ajnUErutyzk3LSCQN+gPBGOc8iiLDMLTWIBkRua7tDwIETlpzMpIodHL22cXFYVAZt/TtD9d0gqvnL1RmC27NlKH2PC/u6UTGiY4xAJVoe2RJlUSxLJqVnO3ZlsliTIYQ9bVkMQLb2zrwik4QBJwMX0bMVaKcCA+Gus1GajSIm522lOQwx7K5WzXuXV+fLpfnF2cW61M0Hqzd/8TC5Xp1odkP0jNMtyT/wY7sF80DfoYUT61O9zMuS0vVYJAhkDw42N3d3ZuqVy+sXqjXV7QejoNuu514uTkAd/+gVSnxsh23mh2nKLTGIIoFN6Wmse/PTs+y3Nh2rLE/2thcmzs79/wLLzByGjtribrfHfYXFhYEOQ/v3Rv5Qy1iwQ0tiTRpqSVJAtJaa0DTdaORTxoQlKTINIUphJLKctAu2N324GC388Krr9288YcicHd7owuXbY6t5XkvGMt7D3VvmFs+d2Zz+/3Vi9rzIExGjBtElpKJKSytnt23nzOy5/+0dTOnHf+pfOvnhtPmwrOpKaf0gMNxxJGjQSqRYRSMoxHn3HJMk0zGGElAQsd2iUgqyYgrkLblWJY1MzPTbB/4Pd8kb65S1o7xsz99+5133qnPVgSa+Yrb2u0VCoXeaCyEsLQtVSJ95cehYQpP5MMwtKU1Hid+d6i0dGxHcIOIWo2DfGmRiPrDniJtMpyaLy1dnl375eb67laoYtty8nlvMBg0ttv93ohJS4bMdko5N9/tj9qdB55tcNMBcOFR63s4NV+WW+IZ/jLwWRsMpZQtBOfIuPzkw/dLpdLFS2enZxdqdYNo9Bc/+qNq9cx/9B//F0TO7VsfCBHu3P2Z1to0LSGE5+YKtWKxnA9jP+/mWZ4BgDDYzPz08vKyKazN9b29h7tMGNu7uwZ3F5YWLIvfuPvLUdhHRCU1EiqlpFLAQDPSWo/8xLLKMowVhURJqVxYXDi7u7GpYejmbCVw7f7WN1///tmLVwvF+eT+A4QgiTpIRhBEURiSZpXy7Pb21MHBQTGfC4IkjAGZC1oTT5jgWRqPX1XD/GUvg/m88KUvo3pKEI7jIOJgNEgwtAt2GIaJktVKFUaotSaTENFzvSAIFCmVKLdsktSxDInIFHav27/zyYPBcLAwd6bT6DuRAQBTM7X55ZnQj5rtBiRGSuPkghGRAomao+YJGzO7QFyGwdhx7WIxD4RAWJ2/FFPQarWCIHBtT5gcBN27f+dc/fntxrrS7XzV8jzP72jwjWJ1xirWK6Vawavcv9feftjynMX82eVAdSzmpWf4zAP+SuLL4gFrrQkUQ61kcOvWjeefv+J59s1b6ytnL+a9omEWL15+5cL5l27d2Rr75u722kKl4nhFN+f6YRRF0qnnDdP1vGKlUpu9cCZOokiGL7z0fKzkvbv3S/npaqlWq9Q++OSjVmt9uj73jTder897jd6WVgQA+Xze4MWhP0wIgBKllZnLPXfhpX67Pw57zd6DXNG7cuXKsNVv97tOztTK+Pj9G9/5td9eOrNwsB3Ztl2pKCGEHzn7TV/JYaWgbFt5xcubmz99+eWlQgHG+5oZRcYiUrEwDP3stv1c8cwD/g/D5+YBj8dj4BQpv1grTM3Um81mY68ZKd8Axw/9tJeLIhXLWJEKoiBfsjUjfxQ0Ww0ismxTaSVV8mD9XrVaHQXDnbX9/f39YrFYLU53g6FXcoMg8KMRIqbCs2lbcpZHbiK3TA/cYjnvOM5wMBLcUFparrm8emXYGz+8tzbs+M89fynmo9vv3YrRL8x5S2cWlhbPjtrq45/f2324N8MrmC9W85fiXH+f3+l2g2QxqlSK497xdBxNCiJ+eW6JZ/jLwNPa6GQZ8lzBVnEMJDc3HhIl11544c7t23t73YLX2ksGnpubm126dXvt7r29en1hPPr/s/dfMbJkWYIgds6917S59vDQ4mmRL1VlZuku1aJazEyPwuwOljs/S4KgAEmA/OIfMQD/hl8LDBbzsSAJcLZ3MWp7Zmq7a1qUrsxKVZn5tA4d4eHh0vS99/DDPDw84oVV9+vJ6qqszoMHf+YW5teuPFoMdvbuI3Dfq4xGI03Mstwkk6ZlXTp3mZxBnlB90Am397aV5Gur524ddhzH8cvezk57Y2NjbXXx0sVLjaGvpeZczDRbpqhSh8KEslQC4e/9/t9bqF1cv/94Y+vufvdhKlMi1e8OmGmUar4MYGv76Shuz7Ss935002+VPbfmeOVRaHZ6Vhjrei0kuTPfWuvshVp1yl6lbZhJBowDIwlQVCT4VxYK98/PO2HIpwAAn/z5FIYtiKnWTKO10ASkrB0bPqvOlKhrDMMBASkNcRYlMhZCKCVViAyFySCKIs2k4bCF841fu/L69//8JwYKK3VM02SO5sDiJNKkmrP1brerB1JrzQVDRI6Cc34Y7G9v7IKhbM/0FkpENOgOfa9klUlpvbO1G/SjqjdTsSuyz3pBNIz6aZpmEkR0yHt10sbhfu+g226UwkEvlIvNpaWXKt7LG48ee6LFMuck0T1x8YmAT1Zv//Phk14PtWi9LBcGUeZY8MGH76ytLTebzR/3hkvLF1eWl374w29zpoMw3D94rJVVq7bCVn/97qFlWZVyLYqiWs1vzczu7G0xFEEQprpXq9UWlxZuf3hXStlszEZBlKlYWKzRrJESlVLlyaP1WtO+/OJFABAoXMdDMJTUUkoEtG13OIo3gl1D+OVSHTk3bdNxnFdefvW9Oz8ihIpbCfzKfnv90uWXtq+tlGfdnb3Yc5UQ0jRKceKblkjCJ65NqlLv9bZqDdMvNQe7yrVRGCzLNGJRtbFfrv38Sd9vvyj4uCTgX7b98HHB846LhVHADXbp6sUXX71eqfvcgoWluesvXqvVq7ZjGaZABkpLTUoYnHEUiZ31NSWIiAqyQdLJjPDyq2tf+MpnO4cHw/5IgLm6eG5pcXlvd5+Idg62+2FPo+YmB8w9QRRyWJxZpgwHhyPK0LfLTItRLxr1wmqjfOnyRSJq73V8u5wO9b0PHnd3gpJbrlgzFTkPO5X2B9nWBz05wpWlVeFSa3bW9WbjxDH47NL8jaW5KyWr8p85L5/Cp/CxQNCHNIot23z6+Mnq2nIYhpsb24tLK+fPLcdREIyGw+GwVqtdvnxZSjkYDIi4EHacpu12N4wSqajd6R4cHu7s7LiuS0Q7uztKqWq1ahhGlsqrN65cunZxYWnhc5//3O/8zu80680sla+8+KoQBgAopeIw7vV6QRDkEQ337t/f2Ng5d/7S2vlLSqnN7c1Hjx7V6/U0Vd1uV2ttMPbeez82DfXC5RUhktv3w3anaxqh6zlhUtHQCEa7TN5pNBrD4YHBMt91pSREtExDyk+D7D+FT+E5QBi20R8OURkLzbX2du/G9VeSNH7vw3fjJ8hQGJwzEEoqy3BAM9t0Ix2kLDUso1QqYcyifrb+094fDr7/xd9/wXKNmqgf9PfSnd4Xf/31QM0/eHcTRp5pVDzP6xwemBZ6c+ZI9sCJEokhDlpLLdd133v//dZC8/Kr527fu3XnHTVoMaKqY0Q/uffjC9dXarVauTKzd4fCwX7Z7GEFNrd6Ki6HbfPG8vnXvvx/Bu2M9j3UHinTdqqjJA2yATPO5sQ/rrq8RfCJofRFYVrq+eq8Pi8Uxt0+Z6Dxx7WKH5cglOc5fRYMC+ZmGu+/823b1jPNlQ8/evLyK6+2GtbO5jvBYJ+b9dm5ZdNiDx6/t7R60a7U4jSpVFxTYKtRhYQOd/cH3dH86pyoLly8Wg+3e7ff2gky6/H9ux7raucxdOThQfDWex+WS9Wtp23S0inrD+58cP2FOVJRf9A3xWy1Mt8NdqO07+jg/vsfnvuNud7wLlL8mStf/N4PftRe5q0X5yzeMCseeTJ9wjbf7hr/9ajywujDf7W+YJvdfZqdm2PGw8+80hyMDO6/tJsks4ODwNp159ps8GHZuqwNrzvsz/h8mDxfvOkvCj621NE8PfsPhefoY5oHfjav87x1fJ8fX/1icjJzVlDP++c83ufFM0hF9ZXPfl70ul3HcTr7B7vre67wTWb10/5MaW7f6imlpJR5Ih7IE9pqbZpWmqYkiXPueV6cRKPR6MmjJ+kf9culSrAXmaaZjqInt7db1flHYotpYpwyGXu+PUp6PNFLq0vEpU6RNDJiKlWGMC3TMg2rXCqng2xjZ10mUvF0fm7xwtrlYW+0+/Sg18+41O1RGiSUaHRAOJ6NzKHUJzAAhCbAo0VixNQnhA5+Cr/aEIdJJNKd3S0AUjor+/5olN65e9tQ22tra8Ke2d7crtRh7dxaq9V6+uSR1GpuflbrYa/XW50755VLyHilVAvDkPOZg4ODOI7LtfLMzEw2UB/evfnZqy+7nnXQ2d/Z3bvxwmcqJW9j5979249XV8tE1Gg0atVlp+tiO+10RmEQXb7y5Upt5q2fvGebYnl52XPf3dx4cOXaSrValWkEHpiWcdhrP7j/5MqV68sr/TATQdrr9vcFh/k5r9NOYhXUqg70XA4+U1aj6ezuDoPQ9P1KRqNf9Hx/Cp/CLxKeN1GJmJ9dsG17Z2P/W1t/zJBzzrMkc13XMIy8lJDWOi9QmpNhIERgiMgYswzLsV2lhkmSPPrp9vzcQgoxAKQ92r3fK11t+GUv68TINYLp17zokKVpWqlUpUrbvY5juIg8S7MkScNB4pf8cysXHj58iJJXq9U0i13bxMQY7MXDTogpb1XPjRhzLNtJB92DvmeUWq1rkFWIoSaODAG1Rp3nUvnESKK/IHj+KkMfD/yq2t6K5tMULIpGO9tb9ZofR8Mk0aaF/X6ws/u0XJlbWlrtDVMrTldWFqWUH7z/I2TKNkW3H4NWwCAIR4hiZmbJYOQ4zuMHD7a2tq7OvXjhwoXhfvDe3Z8aPqCpVs4txiFeu3w9DKOn64931rukKEulNGWaJUE4StMUkQOppeULM83lH/3g7Va9srC4Wm+Ue/3t/d0HgMb+7mbdK3s1f+tRdPODOy/feC1DBYYpk3Rz84lK1cLMPKNRr921hk4QWQxw9+motdSoNaN4IxbC6A9Sm//N8sP6RZ2jX1jVpgL4eUvAv6r4XOhE256daXnY7pIGVEgKedUES2upSREScuTj8uCKskQioiFMUJBSanCzUW/6vq+HrN3eMV0RDmI7K5uhf/fDB9WFUq+fZJQikiRTcwkK+p2BaRnRMGXIlFRxlCRS9jp90zIq9ZLhMZPsmXJzZ2e3fzDaMw+TQ6qy2YXFiwtzr4RRSXDead/fuffjcmN1fu5LnBkMUTOtmQIAIAAEKq4e+UkpuvCrCr+q80MFHK7jifZOdzQ8vH79chgODzq9udlGs15q71BvMHql2XJLRrcXhsO+1vGov+N6YjDoyiRtzcxmMj3Y2rZtf25uVSbder2+TdTpdJ48ebKyMLe4uPhb3/wtmbQTir70lc/rxDVM++mHD4c9afuObTla636/Pwq2t3a3EjUQnHmOLTMyLWdx8VzZs7NU+Z6VJLC/97Bev9Z58oSIl2ouID55sDkcxLsHO35phaO7v997/OhR2VvQytrabYeRXr50xZYzj+92hOPMzi6POlkWQaqZ/Wm1sV8o/E0jbL8ofP68xS2K8IPo7BzqmBzTdbgLCKQBBY+HKWqllCJFHHmuZ9daa9JKKcEMBjyJU01KgXLdUskrM3T3eztmxZJIllUSYB3urpdmbcMSSZIAF73gEBERcePhdrPZrDi1cBT1O4dSSsd3dQq7m/udg8PmhUp4mB0edg92O4YpEp9E7MjMWLz0xsvXfzfOZuJR2nWfJIctv9wql88xMVDEMimkEkoxQCAEZIhFVTU+BQD4VAL+uKFoPhmore2nncNdpVcHgyBNUoRyFI+klJbDHa+iAff3N//8z74NNFLpgeFIzlSaZr7vpxkE8WhmZrZWbhmGY5pmGIa9Xo82N2slr+40L1682O+SU3KE7ex2Rg8fPF3f2AMw5meXOQqORs4zAydDcEFoCKvfb8dxdPHCJSAdRSnjFpE2BF9eXuuOtrkwCLUk6nWi0TAu1/3NRweeY8dDcdhR/RDm5hZWnEWZieXzVdW379x8G5yNV16bbTbU+nZoOxVU2V/zzP9i4VMJOIdPJeC/GgjX9KN+okyKg0QphYC2bQejyOUOAAAwxhgSIiKARiQhTK11bhsGRoAUB3Fbto3UL5cqWkrbckxtaa193x8cjnjmEpFt21prwzCYFPtbB8MsLdeEjoFrQ3DbZmaWJWmWcQZpkqVxxlPuWv7s7GzDm4kStbfdDYaUpJEQaRBFc3NXSr9WB5Y45ciqbMjUioJSEpayxFaExEihQvWLYcV/VTfKxwW/bPPzc0/EQen6+qPu4f7G04ea7GqtpXS8v7shtb6wvFqp1D3f+ZxbvX/33ds3P+QwGIa9NXMxjeKA0HBcLrhhOKNAVitEREmS+L4fBMHOzk7ZqPpm+atf/apm7Ps/fve73/nB8uwLrlUJolRqnqWSMaaVJlKOY0tSWRiQoijcHw72mzMrprAtk1aWzz95/H5rdi7JFEN3NIozoUzTFNo+2D28+MKFzvaub3vuQjnNsFxpkMEsdA3TMUwWSj0Krc4eRf24XEtoL0FY+rlO5qfwscMv23n8ZYPnlYAL8UnBNAtDGnEcC0NYBpFJmUwNi4sMlFK5wJpnk8+vGWOcCaJMS5BMcoMJLpRSMpDDOPB9nyKGmmmWSsos14rD0ANuG45Awy25oBHRmG+4MtXDfqCkFtwRQpDSmsjx3Hq1drC/LrQtNK9XGnMz8w73ysKOO+ze+o+Y361U59q7LFv8jOeY3NlGL6F616d5bCvKTJ3YipgihUKJgpQ8n264T2EaPrb9UNBMv9/Z3HhSLrmGQMO0XdcKw+FgcJhmSRBG9+7fr9XWFhcWtp7yJDpkEKGBpKVtGPV6bZCkjLFqrYFkPH78+EvfeNW27YsXL95ef7C9vf3q1ddc1914vFVu1of9wWAwOBCHb3zm2jAcOH4ltxxFSZKqIXDFCLUiwYws7Tx9cnthdplz07LtxeWLacZsp+J5nu96w9GesmWlXnOt0s2P7v763/1KlD1CxLmZmYX5hmNJS4SmZclEjXr99d3Aq8yVbLO901++7JaqxmEHCooh/crCL5sE/EnBbx9fXd7na+eXTQMnslgjMAZMSklMZ1kiLL/hVvv9KB9b7gU9nccqRzZEhMQ454YpEHFAg16vZws3yxJmArMhjAPLsqp+TYNSmQKN0TC2hHH5/PXRINnq3RsMRkmc2DaaFjeYKZApqWaaLRWwwX6oFITDSDMsGQ4iOxjd/+Dh44WFhWGvnMpQa12abc9jsDa/EClpeufTfpYwDZoDgP6Yant9Cp/CfyZsbWzu7e1evrT68ssvZNLsD+Ldne1MptdfuKGUsbm93etit9O9efNmHA59VzQWWmmW1Ov1l2689PbNj5CHi0tL9Wo9TlzG2MMHDw2jsra2xrS8cvnyo/2nb37w7qXrV+rV+qVLVzefBoPhaBQH+wd7DGumaWKIirRtmpogRhRCaDV6/PjW9atvpGnEOTeEi8yWkl555TWpolu3DzKZlkol07Dv3r73a3/785kIEo0a0jTthiNZqyyuLMzrSOxGbiL3lpdfrDBre+vHi9dK5frMsPuLnu5P4VP4hUKxbfjs5wWvA2o2TIcJJQZYvl9LQp2lKcNxHBsHzgGAADKQmSbMGGOccwEGStShlokSQtjcYnaaZgEIEIZlMMOwRZZljUssypLt/S1ySrFU+53tWqMmhNWTe0M1dDx3GA8NMubm5khCcqiwJuyGNZDD84tr8S7ee7xlOQdoyEgcpn1B0njlpdWlJfno8b17Dx9sHTpRFGHadcCquKuoK1yDQGBZCrwgK3xRndGPjXM8u/2CMNFC+HnHKxfGI4rns+Fx4ADjADAEYqjHX5XIX6CP3pQ7xqmiZfk5w9iH/xkoXPUCH4KiutGWcNJEa06xSlzXHQ269WrJYPzDn/wvF9cuzMwuB7IRBP35xfmn6w9nGks3Xv5fzc/P3797Lwr7rRYtLFk7e1mGynPqB12uqLnVrXW73LV8Q8j93pPWUocBayzOkmJfvvBGkrranP3Bd350+dLC5pY3Mzu/shwBPRnEO6Nh+9K5BXBNyvxR2C/NuN1tzQm4qZwaxlJ5vvXezZ9kmTc3szzXKl8+/8rt99Z/9+/w5YXf/g9/+GTh/HZjjh08ac8uzN96+92vf2X5vfdot3vO9RfV8O2ZkhqE6ykubm0OonZHNtJwbmm/w5L9zStzfne/PYhqBieDKcEUR5REilAhmEU+GXj2ukhd4LRSEL8uC+IOP65zzYvC5guOETcK4oMLQBfsRFZwTovxw/PKHs/b/tlQPM1F+Pb56nYXvvc58TkV1PctmucigmqIovmxzr6NZ+8HkWWZbdue4yVJkiVqksumUMcNeXQSTT9GRCoh0mQKkxucCSZ1JpWUlIXpyPatL3z5s2vnLty7++h7337zzqMPqu6MxZxQx0isWq0yxsIozHXdSRaXywuLpZakOMCo2nIymY7SvoeuYZqZDG/e+8lm567myp8VzRl/4+CpI8JL8xcqpYFMvNGQKUCNyD62VA2fwl8E4/BrGiPTgpD5X23QCriBUirBuNaZKQzTEE8ePekNRpcuX3F8/86dO7OzjVKp1Ov1Fmebnuctn2s+vP/grbfe8jydRDuMMc/zBsNRFJmujwAKEV2nlMTRR5sf/NY3nY2Nx1mWuXZpNAyDKOWi3pxpCGPQml9ot7vD4fDKpQvCnOFsoVrJ0jjxvZI1iA3D8H0jHvWTOM2xUrPZ+PKXvn7QToHMmbpfqdXv31l//OjppYt/a25ubti/519e1HWj1+8+vJ987o1XPvpoKwnCasXxRDWVRpjgIB6Rjsq+9N0QWVirzj548rhas21LhJlCAgZ5SfdPFVGfwicDntum+5xQ6KTJORdCCCEMw2CM5YG/+YvPhEm38icnYKBpoi2YyRgj0MSU5YvGfDVOoq2drb29vYPugWGKxaV55lKGoQyhUZqxuB1H8Sgcokl+w0kgrNfrwMEt2WEykpBW62VhcMdxzMTp7h8eDvZj6Mesm7BD4SaVpjWUvU64vdV7t5u8F6bvAl/nRgAswU84fCyr/tcBLDe2E+ZZY/KvqIFpZBpQM9SYf0VdlEbnrwF+3uulSCFHDVKYDLR2HMMw4eaHPzUt+/U3PscY6+zvq0xubW1lWbawtOL7/re/9f29vb3XXnvNsqzt7W2lVBiGDLnWmWlAv9+Jo2RhfmFxacE0sN4olUru/Px8t9f78KOPwmCIoBBlmsZKZaPR6O7tO2kqOeeuYzab1ShJpVZZKldW1/7e3/0H3/iN3/RLlc31TS2l77pXLp+vN8qGgZpgprUUJri73bY9wzB473BkG87S6pIQ4v69hwxIoBqNtrmh/eqcxrIwZqS0R8HANFODHxisPz+3trsn+kNlm4EQWnCVM2GaEACBEcNPKfEvKXzi8c9zwvOOt4gOFraD+sx/eRWgZ/8JwzCyLEuiRCkFmnHOiQgIlTwbVwo+jgnOE3RMSLLvVNMsziDRWinKuIX1merMXDOM+ocHg7sfPHx4b7NaqVdLzWyE/XZfZJ7vOQCAAoCT49mNVl0ziUNen2lVSv7dt36QHsCFl65lgR7uDWYaTYmROyNmV2p+w1aYDUeDp5sPWssLvf3+5vadzvb+fP3F2dqrvleOYvFJZ74/KWcAj7RADHWuiGZTyqWcODOCsUIRf2Hj+vl7O0vT5JBqzrlSmWlYaZA9vH/n2qWLzWZz/dt/tLy0dOHc2tvv/Mjz/Uqt/s4772itX3rpJdtid+++rZRqtRrD/jZy4TgwP1+JIqk0VOuzs7Oz29vboCPfsyzL2tne29ruau2USrPt/Y3FpZnt7U3fKw0HvSxJTI6dg73Ll84vLizVKvXkwVbnsLtr7g56A8tymc1llgGpXm8/DodErNsfzM6vlmtLu5t7hwddYQoGZu9gODvr1Zu1ZK+zsbE1N1Pa3Tw0jNT1a91ef2a+aVtpkhmO4cZBm4uaYS7adiMYHszOuf2oBMRIcgIAYgS5SYKwKDT/Yyr39rz3nxcK+1P4/HO+t8h59hOCB35V4XlV8UVP84J1FHEcSynzTLaCjV0YWVHmSgCeW4SPIHeWVkppklwwQCNDTYjcYIwjY0z2uUNVZmK32++MRuZMKQtUEqWuU+v3+8LA5fNLxHR31IniuFarMWDDg2Gl7J07tzJwop3O+ubWlmJZZtRWLy+VW04v2k8SXFxa4CDWn27ML11hkHb6O+1Bn6eVeukq55wdZQ75y8PPe6P/qh4jBALUuew7vkBCAkBNACyXeREYgKZ8Ej4p4WGFqPXMuwpAUx60p4EDar27vRGOBp/9wt9f39zq9Xp/6/d+szU7861v/dtRONra2Z1fWJmdnUXAhw8fKqVs2yaiVquVpsqyzKXFmd29rmEYo1G8uf5kd+fRwvxna5WS7/tLS6um1fR933WMc2sL12+s3b7dXZxd+ezrb7xw9YpTahl2srq20G4fhnECTHA0Nrd2d7c2wiQyhK5VbNc2d7Y3LNP33PL2Tm919cLa+Rdv3XznC792uLq20j2ob25sA4Bhm3MzC4/uP37jjd86OAgtK9bgbG53K40VpAR4ueSV0mSEo0E/VY1quXf46PpLy+t7mcoMqZmSXCskxlDo5zdMfgp/TfA3jcAXjrfgfk7vnqf9swXXIgFAhGHIGDO4wRgDDWmaMsYMwYo6OjH65sDYOC3lMO6btjBMw2RGnpJqOBglSRIf6DBOS2W/YjeyTIb9GBGbrUbSSzIZozCGw2Ei41E0zLLMsixgqrfdzozhxSvnK59pPLq7PuCccXEY7TjWAljOqJ32eiGXvkqNWf98544cdEEPyyXhMyqD9kGZTNvPNWufwl8Zjr2uAFhOj/Ok3ApzUsWO9M5j75lfkBPWzxtQgFJKGExpaXKRyfj+vVv1qlep1t55+yfLi/OVkre98SQIgiiOkLOXXnrpww8/HPT6tarXarV2tjBN07JvEbOSOAhG+3s7u4PBYHt7L026WTq0Tba7t97r9cIokVKmabq3v0k6DKMBY9Drd2dq9ThKhtF+qeJWas7hfldpGgVxtzdIutn+7l6lxf1SpVoyfNdIgq7pmL1+N07San32/KUXbv7kPx12D6rViu+UQQ4VyVF/ZIO4f/fRV76MVy81e70winmcJkk8sHgEFJYr51QWcNtRaeA73u72OinXNJQE1IpJ4lqzXAnCGWFBWGCx987H4yz5yZGAP60f/IuEIkm36H6RUycrIsAFxjdRKpWyLAMNiAgn4o7OfrE6cmPNqS/n3DAMwzACGoDBNZNSSg3KtEySEMQxEk/SGIbkeB5wCrIhN7mwRIapcLntWu2DdhRFruumWoXdQRiM/Hmz2+5tupvmmtmcr9RaLz3eeJSy1K4KwzUY2slhuNcf+U7lpRdfffhooHrKwebiwrnFueuuvSoTl6SF4vm8EH/ZJOBP0MGbaJ5zXfSkJAYSEAIe0WDM84R+YsZVxICe/TQXKFVqWGaWxIZnDDvB3du3lpfmbt25HcVJs9l8eP/e4ycPm/VaqX6+Wq+///77aZreuHEDIfvhD/9Iaz0zM9Pvbs4vnDtob22sP9ja7lhWzTAtKbO1lQXbYX/+3e+MhiKTKomz0Ag/+Ol7nAF+cLC3w0wsHe49GfYHzcWVbv/gzj2wSrPVSs21XdOyDd/fIxoMQssUZtMmqVzHSLLwww8fVWtrwnTmFs47lvfuu+/OzDZmm3NxFDiu3Rt1swDDw97mxsMrV658cPPJYKDrNV8mByYPfHfo+5fjoGRY4C9U436PcXMUZqahgFiGCMQIWK4U+Su4vP6yabB+VeFnC1p/c6CI0H5s81Dg1c9mZ2cdx5FSJkkCALkr1iQLx7MgpVRKTSo05ATYNE23ZJuOkDobBoMoijjjnuN7jh/EA26h5Rl2yRQukimlSPrhgV9zSSiv5M7OzjYbTUMYaZhZ3P7cta+37OWgk0Eq7t9+/N//i//vD3/wY98uVVa9xSsLzcVZ1CZLSx6fbZUvrM6+8MVX//allS8ttT5z6dxXLqx9sVJaVpklk09P418X5OR2gmEnXxkBIwACIMCpf7+iwBiTWnPOlc4E41mW7u5uzrWa7U7n/PnzpiEePXrY6XTm5+cvXb704MHDnZ2dlZWV2dnZXq+Xa6HyhHGp1GEYRtGo7LtXr15dXl42TbNc9irV0g9+8IMoil588cVr164xzh4+uMeAXNca9PtpmsZhsrS09M1vfjOKw82Nx2sXzjteqdfr2bb9jW/8xje/+duLi4vhKEiSJInjmZk64/j4yUNCRGa4Xtl13Dd/8qPhcLi2uobAkiQxTdMynVTS9ubW3GzFMnWaBvW6Fw7bWdyba6HrQBwlw35vpu6HYVirLxHWEDSCLgou+hQ+hV9OKKJ3P9sZ+ayG6Mx/RSD2Hu4RkUUWEYEa2+0QjyXdSefG2uajUkj5JxFFURTHMdZ1Ekgk5plVKeWgE+kSq1arrpdIKTlaMtLRMCVitVqtVC8d7ke8Fl/55iIxPdgvP3hzs+HNMzT9Re8wwhuXXnrxhZdu37v5+m+/vjy71l0Pyh/Vdzd3jMa+1QBHL12ofy6OGIoF4Jev37iR97MfAIBCU1nF6XiKOeWzdaOFHGKRSqpgaQqj9j6uREy/KOcmyJOeHIm9NPUJZ4iRwiio2f4x1SEuPBvPrRE8ez/wgoZ0qi3EeNQvlSzg0Vtv/2m9IQwrrAnv5p2bc7OLwvPSzp0gjCy+dGH1BeQV3xclX4XDR+Foc35hJkyjKDOiwfrahQtKJhzSz7/+lY1ttrS0MDe7vbEzGg5nv/Xvb33lK+b1V65qwVob5z3LhqT0G7/1e8ur529/9O9W1la/9WcfvPt+cOPK5eHeg1v3t1cX5nXv6SjY/8Jnfz0Z7T0mox8/3hucG4SzcRT+2he/7pV4e/+d5dUrWKra8Qe7T7/lGZe39jcW0fb9ck+bHi5/73vv/tpvvhrLgyjM4kGgVfA42jJZw7z8RDH3Bz+49ebb95aXfJlsjYb27/3Dne3dr/30g4qGyPGzUcQMXRGxluL5bA+FYZZFSe35z5e3K3TGKVBFPm87ZlF97gIoPO9F+78g/rVIOfG82KRoXKrIlPCcTmdFJgziBgAgagRNRIxpRAIArRwAxZgGUIJrxogzYgi1UgYnDan5hVKKiBGBVqAUJw1aAwJLyJzu2KR7rtef3J/2o570c3ogiNiFs/GbOEXVf7ZP9pkTNL4TM8G54AYyZqKttY6DdHu4K3XGGDMZkAaTWUIIpvngcMhLUJnxq41qGI2kHigzBUp1Jt/60Q+i0mBhrWVx+/rlFxaW5/c3O8EgenJnw1sy19xVX5S9xnK9utROg0cPducXL/+M3n4Kn8JfD5iW6PZHpYonOA4H3fb+/tzcnBDi7qP7s635VqvSaUshBAAwxq5fv/Ld7777YTo83K/u7G7JNOj3M8apVHUQAFBk2ahSqe2195Rura4ueVYfCG68eOOJ3X7//fcPeu0v/tpvf+lLzs6Dm8AiITQj6HUGt8PHiZqfn1+wfDcMqdmc33zytFqD+w/uH2wNpQoPOltS71QqQ6Uz17Wc0ImiqNvtVhqDlZXG+qMDgLpl1ZqzF4J4Y27OzxBuP92dYXDnzjqQ2e93GYlet7O189R3e06pZXsryytzjJmt2aplVMPw4Dvff7ow35mfb3UOzUyBYRiCKYMMWWQE+xR+pYEVEdTnbKdQEMoFJ9IEwJgG0ETAUCOmiJohMUaCK85QCGCcDGMcZHvqM0nyEFzkDAFIEeMMlNaIavrtUxfTDAdO2B6tj+j0SQ5jYhs+1ZSYRP2eGm0RP3nmRCCiSY6BBmjIU3kIbiCwOI11BtzgKDkDYTFumzYpGvYDo46ri6sGM0bD0PdK9Zna4V43k9Ix/MbMfKs8M9gdStRxpp7c24wOCGWdJ/6Cc6NUXV40r9Tca7VKMIqHBd382KDYa+65bv+MFzzvDwqa+aTbwH7J+v+8th9C4JwJAzlSt7P/9MmjcytzWzvbQTCo1+u97ubOzpODg712m3Z2R477tldqPX3cuykkqQFAYposVWkWK6VBGYnOdKVW3d3Z9CqlanVBMCEzJYQwLH7QaYdp8PIrX11cXKx7ujKn9ju9+/futXc7phFro2I6nkRqtM594QvVe7dvMyPo9w+f3t+4eskxzajhzwPQzvYT224sLM7HSTSK40f37rcaBmNRJlWqbGEt7Ww/vHa9xIJOqVIrV62drcHq6ipjPQaW7TbdUsRx4Lh2teb75ZkwlqVypVJZvnXzvfV7WPLaC7NLYWi3B1apZBiQ6qTQqfOTDr+q4/p5w/PanrEovQ8Bw7HgyYDGEihqjglnhKgFR8Mgw0DOQAi0zfwt41aJKEfBmjRoJEIpAdX4BmeY6SzvLpyknQL5cW/puNvyhCZwerwT+fZ4+IgoYIoR+MtspulnpuORg15i+2A7pmkZuRsXY8xxXMMwAIAhR2BaqyyViAiEMko97m4/3F9/un3t8vVWXW3c39SZ9rEmI/rp27csVWo0mlbNXHAvVG4sRnONg8FToZd8tVgpXU4C1/XKljuTFqSg+8v0/1P4FIrgefdJlESVWknLBBCePn6wv7djMJklo5m5Vr+3l6ah1pFte5ZlBSE9uf3hwsI52+KSZVr2g7DLjch2Tc93Hz06LJd822Se53W68Yxn7He2luZ1Fut2uz0Kg9nZVrPV4syUmVpdW2mtYfoh+/Dhxv7Ofrky4zc4cjjsHZb8WqVqCO4EwT7wOIp7oyBqzVXiUVLyLGSJbUO9Xo0S1wxjpTTB8MUXLsaZNqyqYYth5CKaUsWRBEWmztyZxtq1a+6jR52G3wLR5PCwOdsyLOaiCQOWaZVpqzfipv2Z0WBgL+waYj7NHGSEoFKt4TnDOT6Fvxo8b5jNz935tEgjXqDRL2R76WwVOgcAIARE1Iia56kwABhTAMC5NjiYghkcLJMhB2GcIXACgKlAkSaFAJABGIhKARGJI83xKRW0ydkJPTaO25QMp9ufXOQmkmnr8vh+fnWK+uYtngmT56fvIKJpmlLKKM7Q0olM0ih1Lb9eaQA5YRjmTltxHGutLcsSQqSDdO/h4Sge7e5tNa1ZJG6Tg1LYdomiZP9gsLK4uDTzQmm25jplV5XZ+csb24/qlYpgXhRavU7guWVkDH7OdRc+lYD/muCXrP/PKwFrLQ2Tx5nM0uyD995zLLNaK1fqVUQjjgeHh9tA0nGrnlevNczW7KrjuXEw2N16ZPC4UnIcx2AGQ9LlUgOAXJubptlsNReXZrd33r5y2Ttot23bbM3WdUO8+OKrN669ePvuR083251Y+uXPM94+POxJxatzrFKr2164u7P/7rt3cl+NKImqtQrwsDXT3Ev7tm0pNQgipzeshqHMsjQYDRjXn3ntxne+/36zOVeultp79w96URxn/VGX7yTdzlpnv9uo197/8P7C/PLi8rl7t++HUSLSge2C5zqDYdI+OMykWFp4PRh9Jxh0Kn7L6ctMZpznBbp/udb344Jf1XF9XFBk2n7eWVMFSeSFIERkqBERUXMGAMAYgdKA2mTAGDAkwVFw4BwRcpXy6YXjQjMizZAzhoy0QiFQKrBw/N5TBBgQEHO6OSbC+VfTyCVjgCkbMwAAaMQ8EOSEnVfkqa/oKAMl/EWicO4YNv01/7R8axh0Y4iq5ZJl2sOBjKNRJ5Ytb0GTyuspZTJVSnHBDMNgobF9f0+j1ESbm+slt9IszVh+eXgIWtNMdfYbX/gHV6+9HKiR51aykRsn/uXLDc55EhNow3VN07bjJHzejM+fHphP4S8Dz7tPDNuKosgy+Nb6462N9QsX1l5+9YVS2R/0JYNyELQrlcrauRucVUdhUK9XqhX/3XfefOcnP2rWrdXVRcthQTSMkvSFF77w+NEDzzfK5bJwm7Wmt707LFebt3667TjOytpCGvUWZhcZ8O3Nbak2cIe9+CK3bL/erAXB8N69D89dqM2+sbK3u3Hn9oe2xZ2S190J3fK84ehREhrCDoP+xvpd5Ac7e8PhSPquI+NeuWL5JTMMD7MsvHr9i/v7Dz766b/mGFVqhsxk9zB4/PDpjZeuuLYwbLc5c/XWB06/G9Xrwhbkl+wsC56u75jC8t1at1fr9mV1Jp2pYzegVDqGaSlV4Hz3KfxC4eeND4sY2SLbcBHbqwv+wlGPCTADhsgYISMGQISADEBzBIbEEUgrBSiP6Pi0KhgAhCG1ZkyTZgTIQJBUJAzGj6tt5GRxTBwtdkwup7qDktG0ZHysXS4oRjImwNP5n59p9ARMejyh0zk9lzqO2NCvWhdfWj1/YXU4CD96/9aje09KVkViChyEELYwOeeMsTRNgbjBTGnpZrU2s1yjDDFlV2evyeWZx1u3z59/YW3pDR1XohDS0EyGIlFBrVYLBpEQVqaUYTPCiAsFBd5lf9PgE89YfEz9f17J9eMCbjCVxcIw33nrrZXluStXz1uWFYYh47Vw1I+iaGXl0ksvf9lymvfu3QuG+2GkavXWTGves1WW6ShNDMvw/fLi0srDB499v9qaWzjsq92dTcNE04JgFPV6g6Vza5VSEwDa+we9Xq9aZ436JaIScPPclYv7uxvrm3u9wxTlDDPM+dnSnVv30Qgtu8qFAdzv9DujrX4m47nFuXqrJVU66IcLrVZjqbF38CiIDjyH3br97mfe+P0LF659788zx8H585VsYJCUySi0DLrx0vmnm2Gc6LW1l5VMy57rWtpkodBRv7NfqS4aQrmlCweH65a71azPhpkXBBwFFSHcTwoU2iZ/yerRPq8K+ucOH1ORA16A5gUjAEIGjBFHyAkwIiotEPU4jkjrLCMiYEzDkS02J6WTWWFCM66BMZAMDULNeC5NqykP5ykhmONETD2pmmZ6QkNzvW9+nWXHNuNp2izyUCI4LS8/HyDizGI9PRyByCyfX7i25tqe6znIoLceE9ecc2Fyy3Jd103TtN0OGWBrsTlUXX9evPTZKzsb7Sed9tql1Tde/C//4N/8f+r1RZmYwSA1vDpDS3MHzYAxMezHMzMlpYNMSqWzUqkUh7+yoaWfwicIiMiyLGTqvffe/p1vfrVSK31w86fdweFv/9Z/8+j+geuWWq15Lhwu/CyFd9756MqVNdvxa9VGEvWyDIRlCcHjOAPiURR5njc3N/fo6cPORmdxWUmZlLzycLjVG/SaJZcz4XvVRqMhbOk5LSlFmqpKtVypnl+5UG9UnYpnV+vlL3/pc3c++nGSxJcvXwZdac7EcTukLupMMkbXr1+uz9x4+rg/26iC7HX6tlJqdW35gzs3UxnPLSzZbtWy7JlZJ2T8oL1vQLy712wtr310e99x4/PnXthY/wA0hMP+sHcYBRQFhwtzq6ah6vXVjQdblr1+vll2vWq3i0Iry/xkE+BP4a8GRTTleRmawjAwlAwQGfEj8VcwBEakGBESIVGmFDDUStEkv9SR+DslBzONmgEBZ6A1EssFOzZtrD5BgNmJO5Nuc6ZhHPYLWgNN/LMoN90e11DIhV6RYJZmKRGZpsmYUEpxzi3LigbJqTka02kUSkspVblSirMoSMJauZplWTvZOn95lZheX1//4bvfuXzl8ig77PQORlFacssvXXtp89EOSjbjzfdkd8iC3sr6zBfOn69cGA6HkUznFhYZOO/v/8CJ04tfZElngM5erbLW6SSGY0V06HE3S6LmjE8QGRzzOsUyzpBnAIDAAbjONQ/EiIgz48z1MjgQKNKaQDPUDBEZccaimE9P5eQiKwhf1Ow409b0pkFlwRSnc7yBCuIU87hSAgaaEYLWSAQEYBjxme2rorq2BcaWIn/2IpcYVRCYTAU/KDLBPzczx56vDnER4HO+9/ltvWfPv0pTr1b5k299a6beeu0zX3zzJ++QLtuWo+SDn7z13eX5tXNLL5rEb777Vvvw4Mtf/fVoeOftn/wwSvut+fqTx3dnZquu6VXc0sH+OoNwcfHcD3607pZmrsympvHQc3F55frf8y/fenDzrUeP5hf4xdk1HZUUi9rd0RxLWs7sKOyH2WZr0TjYe5TJ6/HAypRbnfvG3Zv/7oXraNrNmx/cXllosAvBsHM4HA1cs7TYXG3vPdxs78zM2q35rz96+O36gtF/508+uv3tz3/ub1+69IUffed/ev31KzMrpW6n0w033n9v+I+uX6rX6fH6d1Y/+3K9wVDoSuPCw4cDiUKY+42ZSLA/t7zPL65ce/zg4bUbpmuMPCep+pUoKchMp38xZUOf1wv3eSV4RRk8g6ARUcuCerR0du4CLIifLgrqKpaACQgZABBDAEYMEVEzeTI+eCKcmeaJN0yajWWCiIyx3L1g8jwqOxd2GWogDUyPZd+Ty3tMqPj4dZP7Y3ybnS50m3+VOnt2MgFAcK5A5dnWDEaWwU0DGcPUDJ6ZMQQA+yxnQCLSqQVIiMgYCpMQNTKFiPwo3CgnmscdLsDnlBgwFopP+B2maQoAiNMEnUEeBzwJT8xnVmt99DQ+S0g454whAIRhmMg4r2NYLpd3ehu71GYGhNTfOdj88fd/UnVmLq5dPrAPg2HY7u/ZNSOLs48evQ9cC0/MVucF2b394dbWVjaC5Tk7i+STx+u12fJK85JbtggCQyS2LS1LOq6lsrM3LnGhJRGR0hIUTg4KQoHXHJcAwARyRowzzkEwjog4piSn1QC8oBL3SZX+sdVcooKzCHARmhccgZBAaSTSiByIGDwTxz25NooqgxceybMpJz1nZcBf3RxWHw9YlpFl2dtvv/XC1WvNZnNvfzeJ42/+zm9/8N4fLy8vL86vPHz0oNdPm7MLn3vjs2iwf/MH/3Z7e3tlZXFxYabX3YvC0LVNp17P5MBxDYZwbm2lXK093XyP8dixRcw679/74NatW1C1K5XDg8NHQdIul5LdnacV/1K96UWbaRwPDXNmcWXG9cXuzuYoajoe+GWm1GBr+8F+++ncjG8Cn63WZxqtcHhw9+77SYA249FhQMDnWyscqeJa3f1t3wbPshu12UF/qDzT8aqtZtV12dbmjusoz0sOOiNhVg8OBl7JEEa5s9fV4OzsjtCqjFTHEeWZRnl/f8Nyzjcafq8zEkZBofK/SUBEhZJcAaNfSFCfl8ElBACt2YRRz8km41NfiQDGXkVpejYWsu38fs7ZT0XTaoVcQ573G/OLM/o/RXFPD3jsRawYjO2bRy5OkDs3iUk3TvhDUcYAcFztlGlSihgS8gLJ4C9kuI8VwUSQRyM9k7gDAFAX4NWCBD5mQWYZAQBCiAmtnRBggeY0FaGjiKM4joXgpmlKlZVKpTyNZZZlrulHo1gzaVdNy64MBoMgi7DCXM8Jw3BzZ6NcqpQrJRcNx7ODeBi007gthWUZqYOhaLit6moTYv7DH/6Qf945X6sYdjI7z6tVS0pSlKXp2UYASag1pSkpSUpoIs4YAGDRjjZ5whnjBppCCIFcoEAEpoWpp2f5eK7pbEla01QcGMCEwiZ4nCt7+oFMnb1gpikBgDQjrQlA6ZweQ5Gf4PNuLI1nzxsVUFQskkg+Lq/vQvhk2ICLEKJpiIcP7na73V/7ypfvP3q0t7f3wgvXX37xxve/8z88fPC4ezj6tS/95uUr5yynpEg+vvukWW/cyjLHsmSS2JYdxwkpFYUxUXD18jnOYb+9tdfe3t29/errdSLttSyjymrz1WqzWStbqezML/nXXr38+MHAMrVlazRSwCRO+ufOzb72+Sv/3X/7h/sHyShsKwokBZIgCAebu4O1hXlDpDPNapL2pW436muodb+705hbCYNelnQura3e/ejd4Tc6Zc9l4O0fbNalBdowZmq2a+zt7i+urCTp0zTNms3mBz9dr9RS1/f1fndp6Zxg2cZml+mt+XrqecZouFfzZ03hp2n6N40AnzIcTjxmCp2PCuJcn/dUFBLsvFDZ5NTnNVQACKbRFE10s2qqO9M0r2SPCSRNZVZBRAkxMM2RISMhgAEiB444iXQ9RcMmBAWOzKW5bjaTY3x7Cn8iHRFgAMgnkwgAuCAkYIwYAjICRAJNgIVFFAoED34iduj4P6VPJ+44mr/nzB1doKE8ln2zLIMjYqzUUS2bkyFNRESKAMgwDEQsl8u+72/tbAZBYFa4X3VjGoVq4Au30ihnoX6y/bDlL8okZyNgptVcPr9Qrnsa1Q+//fbTextZliHiYs1p+DNa0QbfYiSGvUGH9spOQ8leo9bk3MqybDA6VlYczwJiECmttSlIa60VatCMARAv4hBdlxgjU3DOSRia5+FZBBxPeLUdbxc8W5KWxxriE5M+OqsdAOAFHK5j5u0zrVAR6ZwAE00I9gkxmghZQcrMs5sH0GcT4KLEgEW10wvj0n7J4Bfl1Ykkb928OTffsmz77bfv1uu1hfnZjaePuoeHvX7XduoLKyuzsys3b93Jssx1jNWlhVrZszhsb27oLPZdxxRs2D0YhIclz/vo5q1eL+ZC2U7v6pXPZJm8d+eeYRsry6uW4YKUve5908Rw6CVpIOWe1mUmsuZM1RBIOs5U0B2kGh00zDBJ4lTVZ+bmlmWcbTzZ2LMsVWqUrUrLsyCMg7A/MjnWlsrdj8R+O3rl1df+p//pT+7c+bBcaxpu87D9Uau+2ulGh53B/EJlOOreePk8knX75ub87HnThCzrW4x0ejAzN+PYXkQ1ARVMI9JZv3tQmz0gbczOLoyCAo3LL5lz1se7f6Zby3dOUeuFqY8Kpq2IkBed39xEhUgMgEAjjSeeWDLp6jRw0tMq38m1Z9M0vZz8VbIEETkDxsEQnDNkIi+Ce+zeewrwKKVx3pqUUms9IfynBZhkMo+TGUMAsDgiI4aMITBGgiNjBAiGOHt+1F8iI9uR6JXTO336fj7PRXlBikx+BSa8MXbWWucFGACAMSaEQHnMuNFRlkoAcFw3n/8kSaIoEkJIKYnocNi58fkv1RdKG/uPh6OBVhSyKE2Cfm+wtbldLVcHNNjZ3rV9I1W1V19/ZWN1d29nP4lT07C67f6je0983xfc4JnZ3mw7qb9QXxkGe55tWKafJqFlGZM+TBu9pVKgmWuhJkY5G6kBQGHBgEueYIiMgxCERAQKUdGR4Dz9ivybKjgXqZCnSPV4uoSAZ3YPESXp2RvCdSVp1CSVRtKodV6aGSgSk98et4ZgFBHgggOZFkm07OwJKrahFnGUHxd8siVgmQSbTx8vrSzcvncbOV66dG5ra31396ltsq997ctcVB89ebTT7nPOz51frVZL/+b/9y0kaVosjgdpFlYqFUTV6x96vgiCoNONqpWm7UjLisol9/0P3n90a3t+dj4Nw631xyqLorTr10vivfSHbz9aXH6RiPZ2PlxcqL3w0o1+v/9k40kmS+XSYrMuH/ESaevSxZfPX/jC4ODeD77/H2MVHvRH89w0bfvWzXubj7ZfvnFZeGB43n4nvHr1FY1/+kd/8h9X1244Va+zl3qeB2TubLcP2sPdnf2vfOXq9RvzD28/1co5v1b1vISxju8e+q7XalXvrse+C6YQcWyOwtAQUa2UDEbRCP6GSsAToCmJ6lkoqjtbFP/KxNmMNSvQkAnSAMA4MdTICIFy2WPyWkRgDPMCd4wx2wzgJDIc22iT7EwCbBkaERkjzjnnSghgjHGWJ4k6DZMpyuVUIlJK5TV+JJ0WPPILk50Y7+SvjsUQGSAZHAEJUOc9FUYBviqwwedU5UT7z0q9BWbBEx0rIMBmQX9ErnOeuJbkGazY0TJOK1Lya845YwIRpcqCIEiSRCllmqZhC9PlfsNedGaztDHsjfZ5W8rMHIravO87zv5eGw5kg9Wzvfjxk/LqxdlqzVUpZCH1Ov07H92emZkRQry0+mq1VDu3duHC2vlKqaxJhlEQx4kwNZxk0+CII0PUHLkm1KT0xAnrbM0xuA7PlRUsHy4pjqRB5yaGvGGcivfSBRQ40KdtA2M25chGe2oD2dbZC2+YCjRTRFqB1qiISIEGwrP04VQcol14vyCDTFZUbq9Ig1Vg8/hlg+eVYD4ugr23vf3k6cPPf/GzaZokWWybXr+7HwajarV0+fKFdje9eefOhYvXfuMbvykYvfPWW/cf3Eqy4XB0kGQjhNQwK0kaa4oWFq5cuPhaktYYF8FokwstJf7oxz+F4ay30jQbUbDbzfShN0cLS3UL3Vq5tbR0yXXF9asLZZeXq42nuz/t90Zri2v7HQOlbpSchmfNN1uV6lq7RL3+jXb7QRylBjMXWsvtraxfalerRtDpLy3O3P2gXK80P/PyjbffuW2b/PzaQtDzh/1Dx3Fmmr5hmLv7B/fvPXn99fPNhvn4/k0ULpCuVhzLSBwzVXI0GPQoGs01Wply+4NMq2GjoUbDLuL82RP3C5KACzUZz+mcVQQT5erpnxe9t6hoQUH7Ra5rrEA0szkhADLgQnNOjBPjBKBdZ2JKI8aIMeKcGCOLn3bOyi8CCUQEPPfvzVePAIgJRETOAVELzgQnYIohwDMYciLOHRmSgYg0z/1PmT4uujDxQAIAQAefnUwiMjkAakUIqHOaQGMjYAE+LGBQGOO5W0w+IjxKmsEKvKCL8czZK4OigDHKNQBEJIQgojyTM2MMjziRU6qJyR3f90fRMAgC0zaklGW7tru1v9tbV5j5XnnUC0b90Ks6UI7mLlWZ5gOFXt00y5iq8P3bby/MNoJBAokwlFMu+/MzC+VyOQiCf/z7/43necI0kXPOTaVAS7RdByHGZwAAXIuIiDQBSKlAE0PUQIwXDBgQcKxbJoMjIQqOiPzkAaOjlYCi/AHaAYCxl/k0cH7GLgEAgAIvXw5EkhQqDUprTajleGEm+3U6nkyq4kpPZ4HBz35v7q33LBT5Ij9v4PzPWxL9uOB5CXYRvP32myXP83y32+32h8N6xS+X/F5nx3EoDMPFxVUSZc/3BqP+4/v3//AP/y3pGFm2sfUIUJoGCUNL0vMLDdN0Wq1lwKVuv2+ZabVmlaszhlF59LgtGS2tNC9eW+SmGeP67Lx3sK7+4d/9J9W5i0HQI7n77ls/3NkJh8nAda43Lfhw48Og/XTGlnqws33/zT1r43D/tsUCG9Rwp3P4cCuu7TVdBstVTA/C9u3WytzKTEkOBleX5j5487vd7bfr7iVhOA8f3VtbmjeYnWbacby799e3tweO4zx+ejNTVhAMfEebhgDm8o56cP9eMhjON88Br2RaP350xxRsafG1g8HHMs2fMMAjP2E4OhGskDAUSMbP6SVetJ8dUyIics0FCUFCEBOAjDx7rAHOkcy0ezOcJQAQo2lCAEdeq0IIznIjN3Gm88QTRJA7ZOEY6477eNQpmmBahsA4AwB38reTAuh0nqgThFyCBjI4AxyrlzUoIiokwD9LgMEx3Z0isRO8d3LUhTZdLPLdKVJB59V/ASDPyJFlmVJKCGFMec/m784XxnXdLEvjOHZcO58Xy7LSNBVk+XbZrlUSGUajdH/zMJHx3NxcZo184djCO39txfdKGxsbgIZpmoka2RUHQiM4iFQCwMG2baVUxa5Wq/Uky4ZBZPiWAj0MRqbJZqr2swQYERkkSimlSCkiIE7EGNekRQEBTjOSSAxAM9A8d8VihhhPwvS2yy/SApuBOEo5RgQ5fcz3Q647OLV7iMgs6o9GICZRM41ModSaCSAAzhg9AwCQFJQ6LlJVmUaBajo9uz/ZJ4Nu/tLB+++99/kvfr7bObzz4PbC/OyFCxc+Gna0yny/pJRaWln2a/rhk81bt27du/nB3bt3V1ciTSoJgrm5GlGmKPU88+LlC6MB39k9HAXKL5XOn1v1vY5tubPzyz8N3vveD743t1n64qtXFquJJUK3SuHDzubmgVu7mGS0t3XwnT/90dVrL1ZmnZJfe+eHf/DeW3tKqcXyIOoPf/DH/7PXWIG4d5julSwme8MPf/zWxu29lIjztDtcR+c7F9YudLbb2zdnHINbUUSYdDYyUiIYDJxzC71eZ3t/ZPleEAXrm3uGMVeqtAxrdmfn6dbW7eGgW29dLVWaV6+eG7U7tfJSY/ZGuXrl8ZM/eP+9773xxhpA4xe9RH+tgCczBk6g0Au6wJhT7Ez0fCC4AgRDABPEDTBMEgKQkSVSpZQkmZEkrbUe56dIyYMp7n8iAwjBEIFzNiFIOWnkPKe/eXjTWDAB0LkT1impEREn+Iqm9NiImKdyhGdtwFLCWfiZoSBCIEUADECSAlSaSKkiSfRnEeDxxbifJyTgaakPoDAMSWVF7Z95G3Dt6nkpZW7HPR3ahUjjQolk23alUimVSvWZ5t7eXqfTCcNQKVWtVuv1OgBUrou1i8umzyVl5Uppc2Pr7s37i3NLYA7ckmtaRhSH/UGvUW8uLy4rRfsPO/vtvd6ou7i4iIr1O8FCY8UxvNeXf/OFa6+06gsm9zKlw2RouIy4NMmCsxYSNJ8sySTGGYptJJahp9t5Vp9wauGTjMGZC88YnLmxdHZmO/o5w1yLGIIwOXuDcizYcNObZqq3o/Ds90bRcTz09KyazNYEikABKESFY/aVUv/ZzuQ9OrN900gpj4gg1DpnMhgU267oSGFFxDQh0SSUPjnz+SIVX1H8blGuWq2N3C4FR16K+X3HpoNOt1adlRkAEVJWKVt//mf/6Tv/6b9fWbmEzBOGPT8/HwYHG09vAo2C2HRd17btarVaqVS63e79+/fb7bZtbHPOpZSGYdTr9Wq16nleuVx2fTNJksXFhYsXL3KDAZJt20kab29vBGGgVGKYolRySiUHBYBWo75dK9XSML178853v/Of+qPOy69d/T/+X/63/+v/2/9j58nTOncaht2otUiUrGqzG2qLGo7jdLtdy7I455zzXq+XZZlnjJlpKTPDMsIoEEIopQwZZLbQkIqo9+pceWWpVb12dT0K3/jCFx/e3e3sJ2hZ+92nh/2d8/Mr19aupPzFIBxIKWeadcPA+/fudrvdVqv+m797/fa9aPegbLtziFkcDSqV+kEnKPuO1pq0FohKSsEYZ0wwHqmz44YL684W2UrPXl4AXeAsYxbsn6zgvUWItQDj8oK6vEqOK/kw1ICENA7Ukbmq7RkwWTT9ouP96YwjSE+huGZdTp6f/sk0npm+mORRmOb+4WSegEnkKiLazvHPT0jMPIYz8W12NhLWUxL/NO1P07O9o8/kcgAgSY7HCydQtD4W3hibIvxnNgNFglM+PdOdGWOqgrrmBPrM8Ypc5M09sGjKNXzyRO4dMJGPu91uEASMMc/z0jSVUkZRZFnW9vqu7/urlxbL5XK1UfGdksFMDkY4RJfceBhvP+2M+qOsLnjgz87MxWEajmIDzWgUVUq1y5cXZypzZb/acGuOZ2qtUkoZMyzLMm1OQqoiL8qTS/6XhAl7MbmYjLpogad31XQLkzaLeN7pyTyzJ3/55xHR4PxU344+T//qVAunNiIv8qIUk2coZ4ABNGlNWgEgA8wjFRAwL3Gp2TFnemJcBcrpyUIRnZ7qs/uT+9VpBgAMSQNQXnDs52ySxikPkelDAQyaM60wSAF5liaWAamE+w/v7O/vC9OzrYpte67DR0FnOOxqFa5dfIFzPhgM1jce8G0OAMiyWt0ru7OGYRAR59xxHCLqdDqdTuewt5+maas18+GHH0ZJqLT0fR+QWrONKIoc22zONBhC/7CbZonBueUughwkYTzTav3jf/JPmjPlfrD/+OnWP/57/8XosFcShg3CcfwoUaZflciRVXKWWgixu7ubJMnBgRPHcQlYHMfc5FmWMY6sG3GOhLxk1VLBpUqtVJRrLjGexClp3Nvbe+mll779Rz80JFtZXERIzp8/LziPsq2Vpbn19S3SVjAipWnt3OV+v7+5vlsprYaxHSUxkDI4j6PINows6yJxzhBIMESttZIyJYYfU+asIsariACTPJuQU0EYoS7g4Aol14L+kKajCENiuWIWAAAMfjbBdqyJIHFssEREy5pg78mRP7pxjOJyZJX380SKRDyq6oNTNuwT8snUvOUOQ/kzShWoiIHDxFA15epNCuA4uzJMhmCcdFb6y6CIM2HiBX0K7+XLmzeYVygYMyIF66iLBAN5PD/TCJkVrS+eIfgBwNiHGU4O8hQDlftFK6XiOFZANE6bxaSUYRgyxgzD6O6PDhuDC+cvVJyajLPDTncwGGitt+5tInJGbDRMG9V5V1UfvL+z543ACqJBsnxuqdms55lAtvc2gZExw52SKYijZlLKKE5SDcRSi9l40h97TDhpEpd2ggYXrdlk/9DUTOV4cPqHU3N6tvEfjlDz5OLor+zoYlxs8mfvnp9BgKfZgsmLmACAydrnFwAAMqVTq3vEUepTjYw/CzbW5AAQUc6AImrGiGkJgCrPuoosTyGGiJmIz6T0mSxCQAyRtAYEYgyOGD9WNA9jrMTGiAuB0TjPzNlQNNdFDFChagiJsdwrJFe+jTdebzAwDSdTKAQyjtW6f+ujd+8/uGU7drNRqVVbmdKjoHPQ3hoFw5JncpE2Go25+UoURUqpLMsGg8FoNPItQ2s9Go2iKMoDCvr9vpTStJnWOgzDzc3NvYM9rWWtViuV/Y3NJ+122zaMC+cvVUrl9s7uwcGBwblVm3ds6+DgIAqCaqV84dJyLANussOtGBFd21Uy9TwfkC0tLc3Pzz/Zf5imKWPMtm3DMeoNl7liYeFylUr9Ud+yLM6ZYYrDw0PDNlzXFUzHhKPRyIyDOQ6UprzeAGDBMIyCoWDq0sW1+ky119kLh0O7agjYM4VZq0jBh4NBYjui0agkSdLrHF64fDmKSFCWZVnJ94IoLJUdy+JaQ5ZCEqdMWKAF44KApzIqWMnnW98ir+MiAgwFknRBfoViH7KC5PtaF0jqGhE0IiIQy/k/REQU5tmaAM+lydsnfUAEIY5VpjA1LTQV3zDZz3AkWY0xyfhJAAClJrVycQrVEJ00yeXHEhFlenb4JZvyXp4+4+zoXYhI0wQS5bQsNFFqErHpRv5CkmyZcCbeC+PjdZkmGQV52gCLdCjy7PGiPntDnCLAk/vHuaDP/DMAcM6FEJxzrXUcx4N4KISwLCtTqaSMm6xU9Zuzjd764d2fPgj64etfePXSC+d9O9KK0iwdDpO9nb1GpdWqz60uXBZC7Gx09oc9p5wJtCDj0TC1bcZNpZTcb+/+pPdjpfVa61LJmZ2k7RLcUMcc02TDEeaZo57RA9BYdDsDlJoQbICpNdBHB/I0AT6KYzvVzmTnwZTsi4hIU/6M0zu6AH4GAZ7eQJP2mTo+beOujr+dYK9OdQyeWVxewKFbbFIdizQByzViBCZXGtBAVKQRmQYEhoAo+NmqHlaQACSTAgCIs3yJJrJ2MYEkAOBIDEkTag25mzkv4FhVoaLk+ZhozLkfRETMcUA+p+VypdcfNeqNJMnQIGHQd7/zbYTsa1/72tLSkud5URQNBt2Sp+dmSyXfHsYHw5FkjEVRFATBaDQaDodhGJZtQ0o5HA6VUq7r5ktmmqbv26ZptlozcRz3R31EqlarrdkZTTJNs2QYg0LfqUROMoAIUqhVG1przjkTYhCOOoPB0vLCtetX/8X/81+Ynis5bu/vOa5d8m0mVKvpf+WrnxVCtNvtfr/ved7jx4//47f+3fnz5xtO7d69e0E4qtVqy2vLw9EgTVMpsxdeeAGF0e/2MYy3kFmMi8NBalhgsizUvm/e+ug90zJ92+n3ekBaSjkc9BaWzyXRMEuHzfqMzA5aM6aAMOw9gpTVS81+b+QaKcrIMZMgCRA5ap6l0nbqkkAp1JOd8XODwv1WcC6o4H5R3G2RKaRoFxocCTCP0BAc+PhwoemcTRpcZ3x0poRgOCmgHkvG+QCOceMR6oMxI3vC3zj/Svq05H2Ebaak2yk8I6c4lBNnjc424Ykpwjy9FgrSyZ0jRJRj4LPDk4rWMfd9OYWUEDFOzyDkREQFzm5xUrBiBfkeoIAATzJaTn0CAIhc+sQp0++kr3QUK52rU/JYYTCoVPENw+j3+4qkEIJQpzIpe5XkIH1yb0NriMI4SAcbT7dqzeryylJ7ry0zmaVyZ2eHC6ZBelV7f39nfn5+b7ttmubC4vza+VV/yd3e3n68cb9SrTrcYw3Hc6qGYSACaK3kuOunNlye/+xZ2bdQAlaT9TvBARGeXtoxYR7Xjzw9rblkOb0JjiTy0xLzz94oP4MAP/sVEdlUGaz8kaP22bP802SAk9U8JuQFEjCysd0FGSHTR8W10DXG7UsNiKQnzRbEJRc5weUpgY+sv0iQU2JWqFPOU8EBUwqQdE6G856e/XwBPK8ETHC0URgg5i52pIkEMwUHmaWCgcqSe3fu7mw/+uzrLywtz6VpONjd1yoVAktlE9AI4yhJwnZ7dzQapWmaC7s5vcyCcdY5y7J8388PWq5kMgwjD0kQnAuD538ixWzhSJYpSUxzS1iWYWtUVcff29/tdrqu75RrZSK6dfv+hx/dsUzbc0ojkIpYmCYs1kE06ge9j967WSqV4jgGgLJTsblTssv1UgNR94Nef9CbmW+8cOOa67tRHA4GA98roeAHBwfrTx5/0O6oOIs1Ril8/mtfCOfiRqX++M4dmUrLc57sPG7NNff3RkR09eqVw8PDvf3d1eVF27YNw0A1GA0D16+tnb/66PEeac44zs/Wdw5FpVwvV2Y1M5eWyoBMkUoS7TjPZ2MoWt+i+NrCQvEFTjq6QKJlBTnMi30OClTWyACRISADPibAxBiMa/KcAScwT34LEXWRBH+CEE7jujMyPRGdzlR7PL1T03kkumhEnBCkU1Dky1KUYVBwPY2sTv1w+r3Py1KPu39kwnumzefzLZguL3gSzxcwcPpsBkXgkS2aToZXTx7Nb05MGp7vVqplxlgQjrhgSstevxvFIQCrlCtRZG7c2e7sddDUZMjZmXm75JiGE4ZxDwdBEJku12ZGjmjO1VzPGfRGJrfiUfrw7pPVc8uN+kzDt7yaPYh73qgrhKMUpUkax5FXKo9HflLrwgsWvugAHGkOTq9f4UZkcIrQwhS/Mrk+/qs+W1WCBf0s2kZFkutEdYyTZ4jgGZvTtNf+2Y0XEB4GioAQCRAIx/sJEcvV0z8YK5HwROKRyeuC6OxxmYYiohydaY15fnOtmSyIt5YKAUApYoI0YU6GibBINaR+tsLhLw0MaRKYIQSHMQOq41Ew22gM+qPmrB/10x/d+uDc8szLL17c29vrdPaD4V6SDNM0TZIkirM4znr9g8FgwDkvl8uMWZwxhoSIrsNyKss5NwwjTdPco0JRYppmmqZxHCutDRTD4ZALNjwMkiRlKLTSYRiOolGcRkJwGaQmCUMTA+XadrVaHQ3D+7cf1nkZbQss03ZNQGKMD4fho0frP3rrf0nT1LIsz/MQ0bKsOKY4IM6lYBZHc3/38Ic/eDOIgyzLlJIv3XjRdfy9zb29/cMkiFzTRQLONUNRK1cePLi9t7NZ92e6w06z2njx5RdufrBdbzZsg/f7fSnjfv+wWlnp99p3bt/rtLfn5mc7h+2Drnr4aLPkmS+9dNmrfF5pQymWxAqAI2cmF8ep/P/z17FAci0iwEUZqYraEQWMbGHGpQLNkAbGUBMigFYaNAFjpAl5gVNPnJwoFD91cYL7n1xwcXZGJ32W+hAAQB+HoU6/RYjjCKWTCrYTqu/JxURTSKcdjM7oJABMBMJpIZiIlDpbQCqCNJs2jU/6cwZFH7MRBa2lBQmUJgIAHdsZ84kq2j8nUhcfj1dKmTtDTrqVD37yRC740pE0HASBZVmImDtw5hiKcz4z1wqHkUyUhQ7PhDBYHAdbj/bqjWajPJcMMwsczlBYzK15paY74y/Eo9QQpgBTpzgIRoPKaG3lvON5tuHHg3CUDO0w5GgAMcb4hKKc2hD0TKD0qRGegmyKEznBNT+7BQGIiBdQ31MbC452ZE5nn22nKNdrEUz3f/otRoGtWk45TUx27fSd6UagmBPXJOFoyzKGnI/jAsvV8ZHgiIg4yelOJ+E4ZLkoI4yUWjMiScRydVn+NS3wEg8jg4gYak0IKg+bJgDSskCyKTTKnQ1FjwvOpSStiXNmmkbu8ZBlYDJm29A7iLs7Sfdwa3P9Xtmnrae3fvLevTjuI4akwsPO3jBIheEJbnuu77vccRzf9+kozl5KqVQvP0ST45ZT4iTTE+cv27Icxx6NRv1+PxlJRDQMwzRNEJTKMNGBIdykG9iWcB0ry5JOe9cwuWPa9WqNA8+Y5MANZFwwz3a5cA77gVQik4oAuICDg3ZuDH7wcHOuWSNtm6I6Gsr79za73a4Q3LIsX6z7rr/xZHs0ihkawC2tpSnYe2+/d+nCqkrCTmcPY+a6niXEj3/yY0dU4oAyzvu9g143UJIxfqCU3j4wypVzlZmm7c3+5ue+Mn/zEWfyb/3erw8Gs4ZpZgo2t/aA8SxLBKDWkjP7zHX5q8k9Z617gQnjOW3AvCAgqGgXFiF6icSIgIgxYkBAgKQBANOzCX8eHvmsBGwYEzxwQrFs0An8eXw9xhsw+cx/WBj/CvrMqePIp/szeUZietTOiYGosUvHkSQzpYDEI5h8fRab5X+apqOnIEkn1HeiMc0J7bFq/UQ7BSroIlcBpAkCPLqRk4kiE8b051SvRY4RnlV4Pssp5HQ6knI4HGqtsyybiPOWZQXxaH//wBV+s9lSKhsFgyCNpJTJQM81FlLS7f2DpBewVF+cXVlZWd56dNe1SrOzs0Evnm8tnD9/Pk7iOEhmFmYEWWGgsiyL4kCgbZiWaZpFEz1NHU9RyrMndMp2C1M7ZkI54OQy5/emxdxpYvYsmaRnRE86ubufC559LxNHrrmA+Scc0dfJAKcJYXHLZ9+feC8jIuOM8TENdsvjM8EY4wiImFuRdXZsqploUIgois/GWJyNZQNFKve9Opqws70948TI8RJp4JxAwc/dPJj3k7O8NCRjaBicGwAxV0q2d3bv3j7sdbtPHt+Lw87TJ7crvr7108On+5HMwkqFVyvCcUzXL5dLM4w7g25slXzP8zjnYRiCRiJKotQQaiJU0ZSzSaPRcF23UqkAgK89z3eFEARaZJSmKWqNDLlAYjqlWHEzCUNUBEpWyq5wHRNFqpTKpFstxXEi01jKzDCcarU205rXWvv+oNWajeO4XC7XavU8CKJarc3MLB8cjIaDFFFUSjNClJIkiaLANX1T2I7hGjU7Z8Gj4chkglnOgwcPoiiolfwsiiLiFEAn7LT87M7T+1euvcQYK5Wq1fp8fwjXrr2035u9dmXGNNLhKL36whd2Doz1Jw9S6SltMm0SkCbI7V3cQEIsTFb+nFCINwqeVwVOUkUEWD9nalgswOgKSAMhgFYqD0PKjU1xXJB4h9izSAwAkB0z6CfoEyanqCMiwoTQ5nhmgtAQ9XH7eMRO5L9SZ07ps42P+XX2zEsBEDFJjzSORHh0AQBKHYcJTQYCAFPOrTB9v2h9J2VkT2lMi7Lgq79IE3l6vMcauEk38nc8R5w3EYlcg5+T0jRN8ywcnHPkYqJ8zrODElGmNIR2vb7QaNafPn26uLBU9ss//sFbUYz/p//r/35za+N73/veQWc/y1LH9q5cudrv99OoXeV25DBW95aWLrzw4o2LF8/9+K0fJXIrTMPAyspuzTabn7vyDygrrW9suR2OiI7BOOepYopDXrCBkcEQGUfOCQkUSQBJRDJx4eTyjxeen2Agpi7O3tBCTJPIY65Q5o44gAZnhslMgxuGwTkfBv0sUWkiNSGCkSdbJU2GcUIonHRMFoQxiAJUcERoJ0t8tNhT0TvTW1DJE3Q3T2gDAAxIk9KkEUEYTJjABQKAQccsC0zRAGYyBJ1PiBBaCDINzTl3mIdj2feEsRmtcbD45NU4Vj2Fk07qqbSdUp2w/WhNREpr3axYWutUagRGhEqjUkoqbVshIhIxRFSapMxVMhDGGhE1MNIGAZOSKQ2KyAOEE0HGYzOzKAjniFMTERki5wSoiZTKpNba4dvNsteamdnZ3jo8PNzaWF9/8mhnZ2dz5+nOzg4iep7X6XQQ8bCX1uv1hflGrXZxMBgYhpGzp6ZdD4LAr5i9Xo+YyDTF2dBxHABwGI9CTOOx1J/ISAghhMU4ZxTalvHyZ1biNMpk6joewqXDgz5oFoZhHn3geV6jVbr6wuUgCEpu/fDwsHmxORgMSqWS7/vtdtu4tIqmnyu3glYzCIKZZp0zqJTLi4u1wWDgukAUMAZSBuVyuVIxSEBrcTYDORqNNGK5Uj04OJidW1TGrDJs4cUyjpXWwDj3av0gmLESDRhr0VWKDBmF3YVkQYeQ+k20rNrMuUHI/EysrMwPh8PZWZfr/v4Ofu5zn7t58+bdj95t+NnA0wKpnzAHgHFpWIQ6LLl2GCWmMAkLVCIFtrpMnyHhQbGJynxGXZR/NcXpczG+KKimwqdUqdNHkhWUn2P8hOp40luLx2c+Pw66e4a86ZSddvbMKcFU2dYJ5QQAhuZxO3DcjoBx3WI8da4FwTMeQwCg5DgM48Q0E0TqOC55mnwKHOPbk/SSkoJ8BlRkgz+ZCWtC8Ca6Wzi5arIgQJuIn8GIFGs+ZHqsG56+4PzYNyjHi0fTePa+tY0pb3OCidZaTPDpRIhRSkkp4aTWd9Ld11/8XGuputN9Wp63vvF3vtjyFzy7dG7p8tzc3OXLl3/84x/HUbqwOLe1tfn0ybomJdNufWbh6pXXapXFWm3+pZde3W9v37v1MJxPGzNVStM01XJIo4PEYM5Hb9/97K+9Apjnc2bEmNYM8h6O6+YSaWRIhAhaEKjcR2F6AcZrjwTPHAw4Gv0ZE33SfRZxvHvxSI9xamNZloUktYJM6tyOmadRlhpyZo6Oc6UCYqFKVhaw4qzAnXds53lWIYOICJwdxesAYZ5aHYGIadIEGhGIQCsA1Mj4KaF5Ql/z+AfDQCGYEMIwJveOh39qtida08k85yxdbrzIHxvvNGTT0nnuXQ8AwmCZJBNYJoGQGJICYAhKs8kaAhFnxJA4Iy05McwlY0WARIyAI01yyTKmlcpXUEOBZwQAIGrOEFmezVQDasNAQJydrTmOc9DZ+Xf/7l9tPH3MGQ4Gg8ePHxOSbdtRFB0cHOA4MVwGAFEU5ancTNPMB5UfKMbGgQb5sUqSJOd083piuVYp97oSQjDGfNsT3LDMSq0xxzgawjSEeWHN1CwbjUa5hcwwjF6vZ5qmZVmj/tD3X0qSJAiCXEFNRIPBgFI6PDy0bVsIkb80yzLbtvDSAsBC/vYoisIw9H1/ZmZmGMSWaTYbS3m3O52OIcy1tfk0jXyfj0Y6ipSU0nFEmpJt61q1Yjum0q7R4UmSGCavN6pLywtuuXnnTv+gs6kpdD0GGAtDcpHNzfs7Oxt374n2wc6jx0IppXSaZoe+1RA8E0I7nIPWqDgHA6QBBQSpCKac4unZXfosPGv7HCPT/HoSPThBG0XvHReWn3yOESsryJSEdGysxWPtKBRtUZkzEBM5NVeGQaFzFi8g/Ec+KCeChgHGxeDhGZ6jyJmrKKrrmBQdoczx83RCIzh5y+ScnnpvEeE8+TBOhHWdsx3Tn1Oay5/ZyPMBnrRFnoqCmXwWrbtSdGqk+aeYNCelnGTk0Fo/a1SHcY5oZjlWNogj7N/d+PBBdh8McenSpe9953uHvcNqpVYud54+fep5Xi4VCcscDAaH7d7Bdvbmm//+lVdeuXT1/OuvfuHffPdJKbWbC80kiu8+fSTiP7pw7oUXX75EZAOg1kg0zs2dA9cMGTGFwDTmvt4IDDhDenZCaZIw6TTHAVTkHKFPxNHS8Tk522ZsWRYS1wo0ZZkirXVeTSRPejr5h2MqjkVORkZRAHhxPM1kjDC1mfDIX31is8+jt4EUQ8YAiRigRiRElk9OHmAGAJNUaIiIHDkDzrllccPgnHPBx41Pv3d6qvO35xR3IkkbhnFKLM6JjTD4mMM7mmqllNZomoJzTDPNGKVSKwCGWoHKpIOo89RbQijGKJc5BBP59lASM6UFgkKmiJLcy04zTch57ruBWJy5j6NkyBhqAq1JCg6mMIQhKmW7Wi3Xqu5Xv/p51/7q/Fzr7t273/rWt5ZXL66srLz11ls/+clPyuVytVpdX19vNBqdw6HjWESK89ycobIs0VqORkEcB5znm0chas4ZESuXKpP9ls9bmqZElIyyJIm+/913JKVKS8/1bcupV5uGD71eL994c3NzOTFWSnkWK5W9Xv9Qa02ghMEsy0JGrdlKrepUa9U4jrXWk4W+bJ3Lj7lt27n2K8syxth+e/fFF5Zd193a2tJaVyqv5FHLUisAaLXI87x81eI4dhxnFGCeg9YtOznDUavVDMNI0+zChfmlpbkoquzv70s5ME1EjBp1RZrNtlBwa9B/kGXZ6upqtZI6dpxmCWPMFJGWgRYuQzOTRc7Lfyk4sUULznsej3u0BNM2qTE+wbGh52iHF0je3DjWlk3OIhSHJ7ET75pG3wU+DfJZwpm3UyDZmwUJQ06a5yYX094/0+JKkROrLgizEQWSq5Rn46tprdg0ZSrKpXyK+z/+ivk6jn8/+Syy6U6ATmqnqagKzTPdyC9yiXl6BXNgBRJVUsCIiImYkqPLydimc2Dm65Ff3Hz47v0dNoKuVeYffXhTJeBB5V//cf/mh3fSJP3s599wPTvNSq3WzOMnjxqNBlpqfnn+6vXrqwuv3Lvd/pM//tHjR1tf/rXP/v7n/g+GEzaX+Hvvv7mxd/vt4fe3Ok/+y//6H6u+Q0d90NMTbUlQgAgMCTnkNFgDAMhTCzOeoGcPXj7RBTYYDRpozMGxPKRnelFPzjIRMUTOuWmaSoNWahJGrDQA4ITu5G0CFVZVUkUbrggBTSEXOnGtERkiMIZKEZEGyNOha8Y4Z4LynAQEDDhnnCjhglu2laNgSGCSdpFzNE3TNLlpCs55ngoDp1whTiX3mMD0A7mL7+T+hABzYUkp8901qQaqlDINnilErpQilDxLlSaJpIiICDkHxsjgZJpgGMg5ZpEgolSilDrLSEqWotREmRIEjDE9TYN/BlgiY4xhXo5ZScHRtoXrcMZgMOhrKWeb1ZWVlXqt2mnvOBa/ePHixYsXP/zwwyiKFhcXW63WaDSam5uzTAcRlUylzOI4TpIESGVZZplgGowzEoIjmKZpGIYhZV5rYywW5zA+gIkEpP29w932zmg09Dw/DuNyqcJMGg6HuVZ5bW3t8PAQAAaDQa1kLy4ubm9vAwDnvNVq5cnpfM9ijC0vL3c6nXyZGo2GYRhBGDqOg4j1ej0IAsdx0jR1HGdxeWZra8s0TUTMLdA5FyW4cBwHGPm+r5SqVCphGNq23e+pPIZqfn7WcZwch0gpCTKi2VqtJqVcWZ3lnCdJkmXZCzeWwrBeqVSWlkthGGqtXdfd3bt7ePgwSVSpXA0jMh2wfN/gdhY+Z+JWAHYy480xoi8kwMfhLtOcN4I8dgjKLxAAQBUQniNBFCefYxVlAWPN2Ql8MjlHBdVBgfEzkBtAUdQPGGc7TU+C6o5l3+m/TvOC44P8vJ4WE5vxCV4EVIFvjdanZd/8MylyduP8iF7i9NThMxpQGGtz/+IuT6zMcHJuTwzrGWPE0W/P1gXqItX3cX9OJGgSNOU+A0eeVkIIJk7Uo51oKZ2mDgZDjoanK+mhlJhlZvvB7VtmUkGEm7c+ACTish90K81SLMPPvHJpbeWaltrzyi+//DqiU627+/uHn/n6V1vLzmHwwHG8Vmtu0Es08MPuoMzysBaYyI75ihpAgJohIieGhIA5UzrNNEwvjJziRE5s3EIV9HEjWudMMSAig+N0a5MJ0VrrTAOxXH8oM5BS5bQ2VzVMc5RjOa9gRxQtPCtQSU3fnf6lpJQBIHICIlSEWgNoAMNgQnDOuVKYpqnMNGkEYkIQMhAGF0IgA02KUp1jpVySzncCYyxXXqmpgU16npPV6bFMMNqknckA88ZtgZzxPFHcRHGtNSeSBgeTDE1MKZ2IVAdMK2CMGNOckWmCZQrLBNtinEOGSASm1GmKGddxqhkDIhrGY53zNA3WxeywEJnBODBNTBNXwuSuRbZJpmkmSZKm6Wg0arfbaRIHQWDbdhiGBwcH29vbo9FIKRWG4Wg0GgwG7YM9IsoTwwGAaXLLEpYlbEu4rptPiz6qOp6maZKE025rcMSOmIbBONq2XSqVsizNp3F1dXVjfVOQbQgUQgiyO7t9x3EMdECyzl5v2A1z/bZvV7a2thhjd4dDKWXnMGm320R0cHAwPz9fLpc3trcZY3EcLy8v93o9z/O63W6pVFpemX/48KEQwjCMVqsVBEEeK1yqOCsrK91uN47jLMtefPHFfr8/HA4rlarnefV6vdfrEVEeZ8U5F0xyzmWacM6zLDNN07ZtgzPDwGrVN03T9+eVUnmklmVZhp0Mh4lhBmjyUl36FYqzOBmmLj/bC7oIcEpJSETHfHMBw31UpSe3jEzO+5ipx5POj5PnnwU+zkw+fRbzvX22z8EkPv4UAS6KHz2Z0n6KGBR57RY4GZ0k8MdzckqSw2OLUlHKzALCfIwYTvZH0RRfciqp1hkEuNAbGQCeSV2JiHl/TmogAAAz+ZegwFNQSIAL4qEnuOvU81n2F+D50xIw4vF04FHWSc75xCafM+YTjUG7t1d2qpZ2k6ECAPSYX7Xmzl2pqoXt7e0kjfv9fhAHynBd121WaiPVXz4/79JKo9n86le+/uKLr5sWvfvBj//kvf/31ezcgye35mpzX/vabz683X/tM19uuat5kQA8kkAn/gKmqRBRjP1yCUFrrREoTY8l9WkarOWxjv5koqUCAwDhmDpjLrjS+Keo8+meMCJjgqoywUzG+CSKOu+wVidswDBWcBVmaCpWlBT084gNhGc2zUQwncidSinHHWuSIQGtIMsyJbVWZJgEqAmUJgDU+T8ClfNk+UjHjgmYZ2OWdNrJi53aVceDYizPDZsT8ol4pJSybQMRDYNNOgmAACxJpcDc8ZpJmUcJK2AaOSByxphpgOBomvkGADSV1FoDolKolWUyQCCCnAwDnaDBx5bxZ8AWinHiyBAJEIUgy5AGZ3t7Hdd1mTAVCEDBhRmnenf/8Pxlub29PRwOXddNkuTw8DCKosPDwyQOiIgzsCyR+2FxjgAQhkMASNM0/0zTlHOeJ4OczOfEz5FzbgkWRQGgXFxqWQ4O+sNM0tVrl4a9vpZZlmW+66wsLbb3dqWUo9Ho4vVXoigKh5HkEjVbWVwddIfdbrdSbZimOTe/tLt3IIThl6r1RktKadllIoKEKtVWrx8FoRyO0mrNfbq+n6ZcGM7+fheZK6UMg2Q4HFaGXr+X7u/vCyGiKIoj3N3dZYxFUa/RaJRKpXa7rbV2HKfZbA6Hw3A45Jw3m03btvOSLa1Wa3Fx8enTR5ZlCSEWFxeHw2HOvly+fNmrsr39wyjWgM5Lr+iLl6pczJtWsdH1L4IJZj/SQZ0NfFxWDxBpmgDrIzsiHmdFBipK0wCAU8440xcFpljgxtnhlEXjPSoOdFoUywp0BMWMwjEFnp6fCQGehr+KAPyM+uFI0j3b2Wpi/KapoBIA0AWq4IkP1vRvp+B03t8iVfmp3k6JE2c/OclrON1bmFq+U+Mq0rfRMy8dS8B5LO8pNnx6RqaHyhjjsUXEEkwUSL/iaq73ttth6O3HA2HwIA26UefqtSuMsfWNp699/hWjGRglURWlg+5ubxBWqrWrNy64FfaHb/2//uTt9w46+1/57G+8du6r9VJ1ZenFbi+RLEBEIEAYM6f5dDsOF4wLg5km44gatFaZUorojEQQuViRj3U8iqOBFMW/TmZ6wj6Pf3jEeUzEvzENZpj3MyctUkqGnDOuThLgozks5BwLNNO5m/cZIBie2gH5J+Njbj+/xrFtXGUyyWXZcfc1IEfOOOegtY7jeNpsPBmjUkpKQiStda40UyqjKUXIhGZMtuAp8p8/nz85ob5aa9PkuZvShKfJ95VpckWUq+S54KbFCU1hMpnyiTyNR4nApALGJQARU4xTrs3lAgmBMa01AzhBg6G4fJshFGMkhM4TbgiOjJFgcZChqTgiJglkGWptMOYI4X33u99VSq2vrwsher2eZVmmaSql6vV6nkkDEbMsmyS9UirK3aByK2yapqZpai1tuwRTvp3HA+QpE+n8YvWV114GpEcPH7/547cMU33z97/c6/UeP37c7/cvv7RYX7AGg8EPf/hDswLnrl+sL/pxHN+6dau24L3sX2u321vre5zDTMOfa1UMw9jdjetVxzTNIOgSkcnN86utcLgfhmHq4qXz83EGd+7ciaPQ85ylxYXNzc0sS3zfPbe6DABJHLqOMxwZlZLXPRSO40TDfskpCxCUgVaEJuMk9rfbFb8WDaMBizMH0xBUwjt7Q5XsBhHsbLfTNB0NKdeKDwaDXjezbHl40EtSbbszq4tfhFUbtMEkKyimVQiT3MI0QXYFuq4ccs3N9I49Ev7OttWpItUiHJd/R5xUsism/PwExj+mv0VOl2ML5TFDkBOhonrnRRnu+NhZ7BhP5i2ro+IENIX0AEAVVAYnfbaOW0wlBjjhPV2Ab4vmp4gAq5NFMibrwmFSJen4EwB0gQ3+FMmcfBYmbIHjE3qK6p/ZDtDZGzdTcKYmYMyt55qxaS4mRw3sqOjeBAmW2EyShnaJZZiOVMaIZZHSQiQUUEphFHzjN77WbDW2d7bml9+48+BWTeKw9+2vvVhaqs40ml6Wqv327tb2po6yfrtz8dKFp5tPvi9/9PKlr7eD3V4Yl/3SmFhiLpSOt7VtoRDMsgxTCGRAlGlFSlGWGfnumZCQsbCu2dF057vq6AKLWJRnUovl83CUGm1CnPL/hMhpz9h/TUop+PRjxzAWRgvQARZXRznzPheYC/Rjsf7I9pSHPUyjklxvEUXREe+GAJDrzC3TRgy11kmSwBR7AWOFMOUllhGJsTEqzKd3IrHBkVZgMmOTwU7eDkd0Jf9VToPTNMrLayGC1kprlbNZjHEkLZUGTcgUF8wCUwjBrHwteF4VI8tQ6bxwHgARMiJGgJqQoTgKxGNaa0YECPov5OQZI8ZICDQFMs4EJwStSEnJgiBLk+TBgycffnhbMNxcf3L/3qNURXnyitz6q5QyDGM0GgWhjqJoYt7OHZ1yPiP3R8sjiPLf5nQaAHJv5OmzTTRkXNoOLC01V1aXL15YHvT3bQde+/xVy7Leesv50z/9U8uXX3zhZcdxSIz2d3tf/Orf5Zzv7+9v7D3mDn3ljS+Ypvn9//T99fX1GzcufPazL2qt/+W//JcLC7UvfvGLWfb1N99884MPPrh+dfXyxcUsy773ve8xiH7rm7914eLCxsbG1tbW1Wur11849+TJkwcPHtR8d21trVH2d3Z2IDM+8+ILVc/Z3t4O/cr5cxdN0wRivV6vVCpdOH+JNO7v9VqzjcXFxcFg0OuHtlOuVqvNZnPzvZ86Tnl+rl6r1bY2D6rVqtUs12vz/f1dC2vlqtOau/DytTfOLV3ab0N/GNjV51MhnglEJAqrFY2l26PPiUB24jH8i1TQRzkbMT+ScKSxK3IiyxE6HmlQJwbIIkZ84r08RYPzn5xNCIv8/ac22LHoCScJ0jQtUQVx+YXFCdjpFI9H7R+XFJt+dZEcVASn6NwE8zCYhDkdfwKA5n+pKJLji6LUoQWJR0789kTfzk6xScfpP0/agKWUaZranh0mYW6VKZVK/X5feEygECg0asa5wQURkQYJI8FQhhkypmMCgzzLBVJYGV25eNmxfc8S77zzVjxKG9UZQ1qbH+0doGoG7y59/Vyj5vQG7b3e4Zsf/mvgOF8+X5Ur83PnXHNusB85kK7UWv1YA2omlEBliMwUYAhgjGzHBACls+hEziTDcSKtGWmWZixJUSqepFpqIGUzRoIrS2hDZJYNJZdbFmMm5siRiHLRMJ/f/X2cZkQmFtAs87XUmpBx00DLEJwBZTIdDSMEhYhSEzdNmzGtmUStnuFkc1Iki+KNjLM3tMltdQR0pKVExERNSb2MHekJUMYnqC8R5WkupBRJTJxFhkGmBfWaUSo5tm0pBb3hcNgLtOKG6TFw01RGcWq7WjDGhTYyJhgxkYfyaJKY64rzDcc5syzDNE2DSSIizPPBEuau1hxyj1kYU/SxewEAdLpgBZnjCNdF0zQQTYDcV1kicoEi93whrRhoAC2Mo3VRSikgYgyBcRaNJCJqEibiOGw4k5lSnmNLKaVMiJ2ob41o0pGn4bQHZqxTBkymZiI5IpFGLZEIuUmGad+9e/eP//iPg2CIOgHSfslJ0kwplYHa3XmaZdlkw2itOeekMsOwkyRxbFtK5JwjGoho256U0jStIIgdR0iZtWacbrfr2F6SJJxxx3EGg0GtVvPEUifcEY7JDUhGUdOvXllbARE0q9X9/f0vf/7zH73//kytNj8zQ0RvvPrqd771ZtnlXt1cXrmws/HV9tbg3MJ8qW4tLvydf/bP/hlzkhdffylN099LvvHtb3/7v7r696WU56/9/of/9zeZE6/Oz7/22msp9r773e9eOd/8/GuXut3uP/2n/3S2Lj772c8ifuEP/uAPHjy8/43f/PtKvfzo0aP/8B/+Q7UB//iLv5Mkyf/wP/6Pmd7+x//on0TRG9/97nc/+uijy9dqn3nj68Nh8K/+zb9aurR8/sIrBwdL3/vuD3xHfPXXv7i4UL5//36v1/n1r/9GtQxZlr355pu/8Y3X0yt2v3doOjVmzJTmlx+2hwQoaiNA78xzIQoSZWSgacxz6YkGChAEHdesnexGokkuYs0ZEClAzQEISD4TT8zGqSSK4onzwrCnfVYkqUkLJ1SJWgGcrgJEAJMky9NMLQAEwRkZphAR+HEVu2kCYKqzCcCURIhTn5DKMTEYz8pRa6m0zmyfwdRYpkZH/Pi8T49CnK17Qs4LUnLq4/Km059JQXW1SJ4druagAVMzNhlFkEW+7wnOg8FQSWlZlmkIRAzSkcqIM0MTBqO0WmuGo5hzUZgLuoiD+FlqlzOyW4tER2bZtMpsbWVhZrb5+MHT7a11BkL3da1WY4KFYagy8mzPMIzC2l4AtmsqkM2Z2kxj9vHjx1rrct1r7x/Mzs54Zr3Wqjqe2d4/fOe9nzSWar/3e7+3t7HHmNlqLS/PnedQyVIDyNaUGhYyIEQSQpsCbAdNgxsG189U5xiDBq010CRRFAnNUQIxhqA5Q2DAODMMblqmYQnTpgkKziWSfDoM41gNC1Ne8koRkMo0xEwTSqkMzihH5TnxO0LpY20im8pEM830FeWMLarLm2VEhEQ8X2sikBIQAXUGU9zrhDADqGcIcM7YIQMiJIJx6UatFYC0LMuKMs7jLFUyTDhjAEwIIdMEhNaaaaU0Z5gh44SIkzCtydHKSew40QECIDHGmEBAhfrEKcWTkKap1poxbZq+MAAJs0zCVMFqODKFcM4BFBEDUFqj0kCkiRAgDy9mlOu76PhFgieckWnk2u9jZ/44QRpX2sGj6ocIAAIZEJBWmZJaglaglCINNvLOwf7O9nanc8BQOpYZRWGaBEEc5Is9iRrIIU/PorWeUP3pPXaMj4SYOLiZpmmaZv6MYRi5ifSw37F8Iwz6++29vY09k9mNZotY+O7b787NzW1tbF29fPVg/0Aw4TiOZVjz8/ODweCgH1iW1ev1Ll6+eHBwIMELguTi+YtLC0uddmd2dtaxnIvnL6ZxWqvVRqPRqy+/mkTJyy++/PD+w1dffvXOrTtuVfTC/bmV2Zdevxpk3cqMEwTB3/r7v/Xf/bebjuO4rruwsPDv//2/932/2Ww2m83t3d0333zTcZxGo/G7v/u7H330UblcfuONN7a3d9/76Xvtdvsf/Rf/cDgcMhRvv/nOuXPnXn7p1adPr//zf/7P0Yj+N/+7/+rw8BBEKGHwt3//d3vdw7v3n+62AUBnSWpZHhdekSqyCPI66znrfIJmaADEaQvcROAlUCwPl0BiiBoIAHOTHDxDSApVlGdSWQACNTn74zv0rOWyEKbJ8LSQevy6Y2kPp1WahZq2IsvbVNGdidZqgl7O6PMzzY+7qs8uw6qL3LULYHKmTs0/FZkACu5rfqz4POpVvh+yOBwRkdbKskzH5kEw6na7jm8bpp2mkZLAuZBpdHCwZwizVK6d/d6CeebsbMbi2U01JsDDaOgYZrViX3ll9eLlCwmO9jrbBpqYmdVq1RRmGIZRGjmmg4iFriwA1WY5yoIoC+qt6tUbF7fX96p1X9igIE3D+MM7P614Lcfwt3c3++nhb1z+2udvfL0/CLTinNvZOMWeRk6UZqCJMcU4mSaahuna3DC5kmcTYC1BA2itiJCAkBAZcYFKSYbEmAZQ47oigpmmaVkTEwhOtIVaa8NUKImAEaBSmog05Q7DqEkJBkkmNWSkDcaZmbtYoNIakDTmpx+V1kwcZ9I54fJXlFO0YL0gTY+cI45KUY3J6ZH3Rd5/YqA1IKJGOa3JhGNLT876MiRATgCKAeR4yjRN23KzOI7SLFHEmcWYoUGSRsCMAUoxTgeNjASKaeoORFkqtSKZZTkbgxw55ygJx+Ur2CRNJuUm2aM9q7XOsiyKQAhma5uz3LWNJscepzyos4xy6qu1Vory6AWtGRIqrTnyTJHKi1AhAjHbjI+0F4gIjI0n6rCXy6k0KTA6niY0iEhJrZSWWmapUpK01qnKwjDs9g6DYJQmQaXkRMEwSiLLtuAIFQohphkLPgVCiFwmnmyz/GZeGgjGZm9zkrUDEfMU65Zl2A6dP39+cWHh6f2NUTDyy265WtnvdP7sz/5sdnb28uXLlmWNRqPvfOc7a2trv/4b39g9fLx7sCmlfPmVF2UCW9ubP3zzse241Wr1+9//fqPRyP2ZZ2dn79+/HwRBpVK5cuXKcDh85513fN9PkuTGjRv379+/cOFCr9e7evVqGIZhGCZJIqX80pe+tLOzs7i46Lru7/7u7/b7/dFo5DjO4uLi+fPn88ReCwsLly5dMk0zCILV1ZXXXn31/qO7tmX63uyLL1x/fO+RY9vVhmF7S9durKAIZxd8ZkSLK1XOZbXZKNdKvWEQp6lnG8FQqARIEbfPPjBF3lD8hM3vGO2qjAhgOvofGQJgpjNEUKDzpI9qrBTVQh1LnNMXRRRM6aNnjgIOj4TaqeJjR03lJ3i65VPk7TTVeUbjOrmJJ0njMeH/mZbvZ0GPNbdENE4blF88S31xytX0jBdNdXJ6FEUyW2E3p3LpAxzT+6JxFd1PM3UmPozikeM4gnGDUZoEo+Fhv9vZ2tpau3C+NTuLBhE3TNPJkvSgva2Uunzl1bO7WUQKeQGjcHJyJhei4lX60eEg6B0M9qxdLiFtzc/IADAVRp4T6MiP5hTLfwrCMLx+/frCwrwGyU3WDw7DNDCY4fsud/nuk92Pbr1/9epLiU427qyvnV++Pv8NE6xIJlmGBAyZBiYZQ0PESMAYGYIZgplCIQOkM1S7OUiVEyamtFQKlc5xMWmNyIjGRr5cTtapzEwaV9uY1k8qpRjTR9koxxJnPkucIQBHRhyRMeBcMwYaSXDMaRsjROSImgi1RnmUe3yaDQTAIq+8ooD6M/cVIiIjNgWTx4FOH9SpV6AQwA0UnDPGgGlESmPJGHdsL0swTqI0yYCAczIsVKSIcj9z4pxrjYwx5HTSVEZaKyk1wwwRc+rLhc59+IBpwYxppDC97XJddJqm3W5qWZbv2bZtT3yyppn9Cc76/7P2p8GSZud5GPie7dtzv/tWe1Xve6PRxCIABCmIsiiakkyNLGmCkj2SQxOyR/JE0COGFY4JzSgUtuzQODiKsEOeCZsjDrgLBEGCIAigsXQ3eq+ufbl16+735p7ffpZ3fpzMrKxCXUrw+ERFRt6szG8/592e93mMMUqDUjb0JNbBVQYUaINEazSGaE0QoBJSzpkNNI0xiDYkNWMVppm2n/HF4Y7RoJhiVFMgjFCtEBEp52HoP/PUU1c//vDG9ctaa8JItRLBTCQ0u4ZaPizbRGB7b2w3wewqNu3vmo3VyJiKxABAlmUBBSWLjY3V4XDY6/UWWmt37m4Ck0qpLC26nf7SYnbl42uEkO37u3Othbd++L1Gq1rmUmm1srrsOeG9u/ffeuutSq22sLDQqLeuX7t569atZ599ttls3rxx+6OPPqKUvvzyy0VR7O2++f777587d+6555678s7mW9+6vL+//8ILL1Sr1T/47e9sbm6urq66Id3e3n7jjTdWV1cJIc1m8/333zfGrK6vc87feOMN3/fn5+cvXLhQluXt27cdJqpR9MxTT9y9fdNxHE7huWee6XU6GkfGmC98/nNJkty6eWN+fv6JSxePjo6O2j1GSuEQxGI0OBJ0XWsQTqBo/Nh5cVKExx5JHU/BTQ8/V1NvCRGRGIJjpXQKYP+cpYiZDjh5wdX2pxOspd0nIYSyxzc8PtDxnLHNAA/Qto81Y49YYkSkOLvfB+CvkypdJ+VGH2aPesj8T8esO4IT0NaDLdhfnQAyOolb+yTsCxj22Ctwcn/tCREwor0+0/XKbkeVMkeggJxzrVWext1ud+f+FjBDCQrHr1ZqrqC+G549vabUiYQkJxn+k1LWJ/2WO8QL3FDm+t7dewiGEOI4Tu+wXXWavV5PS20bJ2zUAidDWuaa87JQP3zrnTwrKlF1aWmJc3716vWzwen1tVPNcKnuN+fmmvNzC3lZnlo/1++WSlGpOGOMCspQK10aYyKfUCCUAaVUcEYZgDHSGHnChTCaoKHaLq0KLCkkjKmMqRCEM8IFoZRKjVTrMT3hDIh8XGfVBhApACOETkgkAAAoZYxQShkdIxiprXdSajeFQBkl3Bhl0BhbK4UZd3JyY05QQzoJlc2dh1CIU3R6IIJp6RonAGylFJrHoz4YA0cwRxBHEMGJYJQxQohWkqIZF8aMAiUNIRpAcwM2qTWmAjKEUAIGkD/QpYdZj1sboEApZRypAsYYYUgIcdmDGpgxRmtjc/U2b6y1RpSEICHE+A6llLEH9bnpG2OM1qDsP2WkRGNQGwQAhUCA2tjXGKK0AaQI6LpsivqepIpx7CshEjTUVgcBpmdBKXLGCACj1GiBiAQod6FWC5cX527c+Gh76wYaJTitREFvmMyuy9NXm4K2YbGZ8JDY99P5Zmk0LLe5lVcrisJ2JSGijSC9iG1srFYq4e1bt3v9/try+cCvdIcHb7355mc+85kXX3zxa1/72pkzZ37jN37jl3/5l2/fvn3rzvWX6y873H/++Yudbufc2frt27dfeuHV+aX5f/Wv/tVf+kt/SZbqb/z1v3lwcHB4eLi8vPyJV187d+7c9773veeee25/f//FF15aW1u7t7n11IXnPvrwo1MbZxDJ3u5BVIl63cHq6rqN1/M8/+53v/vSSy/VarVut/snf/Inn/jkJy9dugQAX/va14QQTz31FGPs7bffbh8e/8SnXn/h5Wc0qrfeemt1ee3MmTNRWNm6fe/g4ODMmTNn1zfa7fbta+9VKpUXnn7t+LiPejjXrHfqOov7YbSaFAVllJ5EmHPigjj9/OEJRQEQJ4rOQAgAMUCIoHTc40goY1aTwNaQHh/hnRh7PEhsPzQvGJDZzx88bDO4L0IeGOlZQgl4rIWbsb5wcgR5gr07cZQz6OKHTvlxKGLysNjeQ8czQ0IyexYnJC5PTInjSaI4J6xvcEKpwlILTz2T6dVzRDgajeLRsNWs1+v1wA3LTCoJy0srlUoFCNdSbx9vB37VczzFsTwBHUdOOIGTDPNJgytSnjq9oZ18JLthGNYiYUosZeFk/mg0KvOSMcaFgwqVUozyk3bcihbLpPR5VBq88v51KeWrr3xidXHtzu27ncORTsnafHFq5ZLHXSgg6xdlSBC5krooJLNlVQJC8MAbx9zj/LBBadAYo06aANr2rdqObGSEAAPC0HE5p4w7lBFgbFyZVdpMG2kQcTbqsn2uAMgYmfWAgQEhhFGw3gAhSAG1UeOEE6IxSAVFpESiBpTWt57cdTLtJjyhBkxOQGm6zkOsZNPIqVpxp7G7UqosAQqNVKFyH4mA7W85M0wQxhjnhHJDGRJAY4xWqLQsclUWSilFgHLKOXcQ8/F5U86plUOmhKAxtltpGlOOT3CMHiTK5oyZ0IwxQhHZAwTEpGhqjDGUMSWVUoox9H0vDEMb/o77d2eq7/ZNKdEYY2NfY4xNeKChiGb8XaTaIAJFDQaINsIgsb/SGrUeb8cavOmYPsbSgjgMteylFMb4eddlQohKFMzPtez1VwhKj5nrZ7uwpiuvvUE2vrcUV1Nnwn7BCvFmWSaEcB0OABb2aCbknUqpufnWT7z+WqMeLa8sDzvFN/7om4vLC6fPX2o1m4yxr371q0899ZTW+qd+6qfu378fx/Hrn3rNd8Mscz6+fGV1Y1kZORj1AqcxGAzm5+d3d3ctX+bt27fTNO33+4yxxcXFTqfTbre3trb+2l/7a3fu3Ll+/XpUFeAmf/vv/p3/4pf+i9c++dpHH7719/7hf3zz5s3f/a2vfupTn2o0Gr/4i7/4L//lv7TH/4/+0T+6fOXKm2+++fTTT7/22mtnzpz54z/+Y8bY2traS8+9EEbB3s7uU88+4TrCd73dnZ0szW/f2F5aXrp9cxuMmyT59r2jJ59qpbEe9nNGpbvAWrWIsQIgUZplueT+CZHuCZHc1DQ8sgIy4BaNhQ94HJEA8T2bCQNKCSNoKXwJRaLdieenp5C9R2bW7NAPy/w9MLdTXAI8ZGsfCQCmv50+//CI4Zkm0x/Nij8mdw0AJ3Usn3T8J1Hk2rLaI7t+7GbH0/ZxBw8AJ3HgnLRNM4mkYeJkjF9PpMZ8/OdSPVg/Z4/TcSLXpbIEyiI0DqGkUl1aWNhQimjNBHelwtGooKCiqCYIqh8Ti/Bvtb+PXFVuwry5WmnMrxz0HEOUYmbhTG1uvXrve4NpNYtTLpX802173MkPj444E81m6/Qa39zcbB/0i6I4f+ZCkRtNyVxz/vy5CxuLp+vhciNaOjjs+r7PGSGEuY5nn3ifO4QUYP0XBcZeN000/ikeE9hsh02TUkoZoYSi73NCxsRQxijUBIhBw8ryQcfzdK3UWhubWxm7p2SqPkAZGGM0amoMZYzZeUJRSzs5gRCCxiBQBG0MTlMujzyIJzFbkRPaojxPU0oZo2NisskBcWEZroAAMKIdCpQz15Bh//GpNkKQAmEMrXUxxkhVGCR5bpQCWWgwYDmhCTA0iACE4VgYjgGl1LoOaIhWKKWFlphp9tvYuB80IlIOrKScc8YJcR4cxixqSZDxQ+W6zPdd3/eFEIwTnOkwni55iKi1taNaSpukQKMJABpl9Nhgo0ECZkzmmiS2u2nqR7OJ6KHBB+woD5Y/hJIQRggSSlETxPGuKQ2NloJ7fuA5DvMcryiIw7gBQSb1i+nchpk2LTORM7EAaTLDDjZGSiNOkVn2CzZZXalURqPRwsLc0tL8WAGM0m9+85vVeuPnf+Hf/0t/8c/9N//1f/PGG29UwtpLL7/08kuvvvGdN5JRdnfz9uuvfpYR51/9T//jL/2X/7DRrH/pS1/61/+v333r3e//jb/xNy49canT6fzP/+9fbTabL7300vrG+kcffvTr/9/ffOap5zbWTj3/3ItvfPt7RVH8+T/3F6oNFxF/+7d+96d/+ktJkrz22uubd7eMhj/zZ/6MJZ3++OOPT506JaUMw/Do6CiKomn1mlI6NzfnOM6dO3c+/+nPXL165drNy/Pz9ddeeUVwr3f88XDQB0Gef/mFy5cvX7728YULF37qZ34aEd98882N0y9UwuqgM0KtKC2zvBdEc4xheULo5J4gQmCmKmHwUKTIYFLTna2S2nKRzRUDMobEEEKRIFEnqHmeZDC0fqjd0f6IEIIn9PEbfHxt+EHR80fywLNL9vT9I6nX6fdPMqg/bmT2iJ86PU5C6PQIZxecB8xcOPP6pxrvx45pmxM+qrj6b8lA/MjxP/qn/aSUzPcblAah7yZJYoypVRrLK+c6neNmixOghIpKZcFxPakoGjgBe3dyRuSk8/qRmzg2wO1Ob1i0X3/+xY1y6dqtjw+7R81WIzdZr5dmWYY2t0sBzQNShcfu4P0fXq7X61nWP9g5Wl1dXZ5f9YQ/7I0Odo+q1cb83LLneWVa1Crz62un5xqLvVHou57WGo3SGhihjsMCNyzKGBHREI1G6wcqQsI5qQZgSzgEGGEMBONcUEopFxoAtDZKl1ZhghnGOS8oTg0wnXARz15N8vBANBqlkcpQAoRTIpAYMMaWB4yZruM2G4zTGvysQAdMVDv+3YfghnPqOGOpnClaOymT6XFSRoWgLnUIIcP+SY38Vj+XWSUpADQKAEiaStu+xRhzXUoAZYllWTKhLWkOJZN6LhAD41XJGpXppQMArcEY1KgNKqII59QAcrBt0g+8T2NsMIoc0fc93/cdh077qwHA3qOx00MpTtYpY2wGe/xVowkiGiATi2qFnibhA5I4ldOS6sRxoWgRerPrxeRW27CVEAZIDTGmtLvTw0GvyF3OQRZ5lmUOR4qgQbvumCLxkTjJmlXbBzwbM03Pxeodua5ro95pn6hFcgkhKpWK7/uNRsMVopT5/fv35+bmPvGJ12/fu5Om6e7u7nPPPSeEyPNcltIYs7S09K1vfWuUH37ylc+MBsknXvvE+fPnL1+5XHVbZVm22+1/8S/+xa/8P39lfn7+3XffTdN0OBz+k//bP/n6H379y//6d1Y35v/BP/gHCwsLv/7rv14UxRe/+MW/93f+/le/+tX//v/xr1566aWf+ZmfeeXlV37pl36p0+n8/C/8zKc//ekbN27803/6T1955ZVf+IVfaDQab7/9dn84/OIXv3j69OnvfOc7v/qrv/rEE088//zzp06d+vKXv3zx4oUvfOELvu87jvNHX/9G6NVee+21TloMshEPnBeefdIyVN+9e/fl11+NO87qYm1//9rcXGXt1Oq9XeQCR8nQ4f7j58UJGSOJDwhhYMbeMPL47gBVlgDAGBpCjEbBATRQCnE8FZAfr5LjdeCEeWpZeR75CfxpkdCs0Mv4m4QQgEft7qM/e3jhnqZ8pz+Z2MXH//ykLc8czINTgIlYxeznj9jjx278Ee8BEU9Ezf2p40eP9sd1IMwjEHQY07OYEozGPDeMgjHCEczz60HYHKS9wK/KUhlFo7BGiCgLSSk7iVL05PakfztaftaR4gThqXMvNMTK7Y9/cHR8dPbZtSou3vjOQXd/r1qt+oEvpZSFtMsHEIyq1aIoHMd55plnkiS5fPkyBY9oHpDmF17/s4N4sLV9LyvS4TCNk8yAyYdokrTpwOq51bXFFQbO0xfPxqO4XonyIlNADXU1Uo7aEAllxxHUcZ1SFoLRas0tiryUeVQNyuTx9953H7GYNhml8wwAAJEijlN8EhHAFC6dyd48qBsZwOlyjIhGjbuSVGEz0g4hJE4sJJkSwgqp4CG2Hit+APHg8ZEo4+VJN8YeDaWGUQCCghFCSOhVgIIQggtKCCAaA8oYE7AAEbVBYoiSRFoMGoCSts1mGkcSmyN1XG17lwoFZc6n3n0uOUzSAMKhXGBZllLqtISytAnfhwQ+Td7QiFoTY7RBBYBANQC4fEQepGSJoqTIDEDpzVMuhCMeAH2toYpqoTGGUk0I8T3hOIwQgmCmDdlWhMfaY0opUEnAADWAFJUxElATpUkBDiIaPaH7YIRRRikFFTM+tuKUGouCJoQ43C+KIk5zrYxBAsAJAQSMapoQAUBlwYoc84xrxQGokglqNuqZGzcPomgFKBgmQevBqF2tVgFAKU0IGQ6TMAzjOAkrUV6WQRDmeU65owxUao04jmWZep5njEZCSpX3hxoRlWGFZMYYaUycjk5vbCwstAzqi/4pWimcyJFSdo+P/FXxyqdO/dwvfOro6OiH7353ZWXl9U+/1Gq1hsPh1vbN3YOt51964oWn//qVjz+uNaLP/pnX61H9m1/9zoVzT/zcz/9MVKlyznfuH8zNzf2Tf/J//+/+u3++uLi4ee/Ol/7iz0lQ3//OHxs1KrLuf/5//j/+i1/5H3740eWk7Lzy+jM/decz3/3ud19sP6npk5//6de//OUv/9ZX/vDSMy8FlfnG3NrvffWNc+dfvHTpUpa6/9d//N//7ld+Z2d75HnzN27vDhJVX1idm2tduXPYHul3P978D/7yX9bSOXf2E7/1m7+ZpOzFT7z4R3/0R4SQC2cvDrrDve392zfu5EnxyosvDvOecSvnnnqOMxa09+pVWKjAQacMggAAEDHPc8ZYo9FIkqRSMYSQoih836eUWm8GAJRmUso0TTnnvu/PGFo0RhdFYRu9pJS2Ql8qsDUCg6iUIcz1fI9S6qtDq0XBGLWiEc1mM89z33PSNFVaOa5gjBVlJnVBCIlc4MyhNBglZjSQBn1KvDQrdamq1epgMAjDsZC2hekp9DgFQ2VZ5pSg67qgoSgKTqPZjDdMKh2WdgYRAR4kThCRI6GU5nlhO9mU0p7n5Xmucko5A4BClkio6wkkWJalL5zpNbHHY31HW+qaCcDHpiLL9BRrYhsw7G+l4r7v51nmuq5WJadUKxUFQT8dCSEIgL3CY6o7YwwWUkqLkLAMrJzzsiyZQ61tJsjGQg5IELEo83F6iSJhqLXWRhJCGBvbHcvqKoRI05QxpiYJj1kjh4g+q0gpKWOU0qn8mud5yGJKUXBmTO76nHPWG3aExzyuGKRu4GS5oowWhQLGlClPEJcCe9l/dOAJNfgZg42zYTuvhJWP3r3y8fvX7u/da2y4FafBMmfYjaMomnr0nudNV0agzP5Xu93WWi8uLtbr9TiO9zujN998U4NuzTejWqiNUlqmeZKLXo70KHEPR+uFzqpOJLP8eKdHm7nntQipq1IqaXwPAh98n4FRR8e7juO0FlqT7GtQFmhO8KRKOREsnDXCAEXxoKY4axHVCSgFPbZbY+jQ1GCYcpzCemT7J6ExLWHTIy4hnIgVAAoMiIFJv5BgnHOglArHJp+tniy1pM3GGJWPBYIMGm3AAEGwzTkPnakdlFIrXG/hTjCJ2xCxLB+IFNlZbWkl8mKcEEN8kDslhBRZf5KlNxMkNqGUNprRxNo9xHgQhMTigYUQsyh6+yAiAo7hb8AYe4BZfShCBQBwHUdJBKFQ2UAYFKAgmJc2GQ5gC3to0IBBQwkdQy8IRSA4ViUfd9AxyhXI8VMBQIBJiYzihGyBEKINotHg+w4hZDAaFEXR6/U8T6yuriqZB5EAgDRNhRA2H+u67uLiYqmkXbCs62BpsJRSvu8BgJX8E0L4vj+lwTKoXYeHYSBcXhSFVCVBLEvwfb9ara6urvb7fUrp0tJSmqaDXpzn+YULFzzPe/vtty0V19ra2sLi3Ob9m8KreL7X6/f2Du/HcXx0dFRv+j/3cz/3z/7ZP3vm2aeu37j2S//o/+S67n/1X/3yX//Fvy0c+s/+6//2m9/4xr3tPT8M/8p/8PMvv/Lqr/5/fu3ll18+febcT/30lwaDwdf+4Ouf/exns7zkXjQcDhu1xpf+7M+88nI7CILbN++urq2+894PwzDMi6zRanz2s5/NZYZo0jT99Gc/jYjXrlz+5p98Q3D35Rdf/ImfeN0WGubm5j788MO9vb3nnnvu4sWLQoiPPvrIvPjC2voKFaLX627e3Tk+7K2vxk9cfG7JCe2VdBynLIUxxvOBcacSsOFwWK8Gvu9tbW1RSpcWTo9GoyRLASCMuJRyOIodx7Hk1UWmrPXlIkBAygyCdlwGVDkOIILjuPamFEXOKGvNNS03EdUUCAqHF2WeZkmZ6+WVJcdxjjvHUiVB4EkJg8GgvtQaDpOyNJwJKjQjQCkIA0RqxlSl4jgO6fV6nuf5vluW5Vh/l3Df8Q1orZEB9f0gyzL6MDkrIcQY47q+tR+zthkAkFKkBCkxBCilgNQQAEaDyDP24WZCKqVR2URummU2dzU2k2Vpywd58RCXNUySvVw446zbuL5LrDGTSmdZliRJWZaeK1w/SJKkKArh+0mSU0odx0HUeV7a1cN1IuCCEgpImO2o1ISAo0ttpzsgAGgyYfhBA7a5c3wwYACYQSTMGGOmrjlM+voCTmcXjel7AwVhqpBSCEEZJagopcokViZyuu7ZyMFxnLzIEFEIIbUGoIwDp4JRrnR6wjp/UkT+46XcuUnpvRs7WZYVZRFVl5IDDWmOMbt48WKv1+v3+8YYS3hrT3uUpFJKKaVlMQyCwC43iHh4cGRQG9BcMAMmDMMkSSilzcZcs9mIi8FHN98Nea17ODzcO+Y97/TKUwvz1ciNjAAmCm0GnUF35/7W4eHh+trpeqsGAIjE9yt5WZbpSamAh9oGYPIwFelDqLwHNyk/AUwxplCYqHFN6nlTTolHdqFPSDXM2kCYBLgEgJ0kyEMBDQGCWhMhCKWUUE4Y4UwxhlaGjxBj67eEYIHaPrQKbfMsMYCIxJZAcUbI04zVXmHqc83a5qLQU/s6rUcaY6gR4/oUjuvWdh66bjE2tIxzQblDbYK3Xn0oWUcn3KXCNYwxJijlhFKCCEApMVCWcrq4SGkANOec8zE5xtROT28cpZQxVIpQaghBjQoRlDHGqJk7gtMnweXcom3QEEIpmnFKJJNy0pVApTQGNSCl1CgJyIAS22RsEDQhBgHiuJifb1y9euP4aN/zRBRFS4tzUsrBsGPZPcMwjOO4VqsVRVGv1wejofW1syxjjNnZ4bqu44x7zaeuiT0vpSWj4IXB/HyzXq1qowhlnuc9/fS5sizv3r2rlHr66aeNMe+8806e5xcvPpFlWZZlt27dOn/+vF0sKKXf/f63G3OVrEhLXRhQB0f7siGfeOKJ0Pd/4zf+l5deeWIw7Jw7vyLL0ebW1Zc/8dTu3o1Gs5Lm+RNPPz8cDvf2t5dWWkXZuXbt5tzc4vFx9/XXF9vt3q1bdz/3uZ/MsnKhEf3wh++dPn1auO65cxeOjzr379+/+MSlb37nW69/8pMN1gTafOrZZ/uj3gsvv7S2vkqBI+LVpy4mw+H9rXsXnzh/8eLF/f399y9/tLC0/LLjEsa3tnc2NjZW1zfa3d697ftE0Lm51lH7sDfqpGW6s3fv9JnTpUza7Xav18uyDADq9Xqj0QAAVQR5nmcZ7XQ6b731FqX09ddfr1arw6To9Xq+79ugbW5urtPuX758+dTG6bt374ZheObMmY2NjUoUDYZDYwaex6yMVRiGlcoyIbooYsYcpShjrNHwlVIAjFJaFHkYMs/1t7bv5nlerVWqtQhRKZM5Htk7utfrDTjzG82VqOZlaUxoWWt6qPw0HZaypMwbxUeePx9VgoODATWKICWEuK6rFGZZxh3GORcOn7KFIyLnzBijpMIyBQBCwLYJwOQhLoqSMVYqqczY1SuV1FqHHsuKhDEGhGlTYAGcc4IaOEdlDEFDENFIowghQnBUhJAxHHyyYCIiloWmdFoGGldYKKWcC0JIrVolhKBRSZLs7e0FvjfnnpIlMEYE52iI4JxSaqVfAIiSetZG2i3BBIKKs70Vk1KCDdApYQCgjARAxqgQnHNmw2ibEnCImi53iGjQjGVnBTgOjWPpupxzUpboOHZWMkInhSEKCIZQEA5nFOznlGgDyCglBKWU9N/W1/sj//H4j08aHDKOGiusXguY7MGN97aE9tOeWt+oWqpb68vb8pXjOBohz/MsyzzP01qPRqOyLKWUnuOPAzMNcZ4olI7jVKKq6cMLz//ExafOd/rt9279IM6HQcWrrkVz7EzYIp5X5nGmpDaY9oabx/17BGhY4425wHVZmpajUSmYkUoROCHk/xGy7HGEWjJ42ABPvv94Q2j7tmGCp0AERGoMUHiICm76yk4Ag9i2wmnNf/wh4okobqTj6iVFohhjQBEYUOvzaqMpI4QQoGjQomqpnjiOBhERtOXgmxjLWStLCCnKhzhaZ5zoMccsTBCPaAFYQk+v52S2IyEkCn0kyBijjNhGWyYIpdRhcupqAMCYPhpAgtJotDYM1NTAI6JRNts86boYg1amuoRm1gAbY1CDUtoGy0orK8GBiNTOlolRM5MhZ6g6yUx9DjQgWO5uOoHKEUKoklwrAFDW9SYEuUDGMfDCZr1288bVTufo4vlzSsvRqC+lTJLEzgirpLuwsGC1eG1sYQ+bc27DXCFEWaae59kQcPqh7/tocs/zwtCrV6uOK5QyqIUNuxuNxmg0unTpUhRF+/v7ly5dWl9f95wwz3ObkZqfn6/X67u7u/V6vVrzF5cWg8hLslEQeT//l3++UW2+9onXiiTlrllbW3viyYtJOrpy5Qrj+Lf/o/99fzhot4vf//1v/M2/+Xeffvrpr3/9d/qD3evXP/xP/u7fP3369Icffnh81F9e2vjZv7BGwKlWWs8///za6jpj7NqVa+koPzg4aB91Ln/08fq5U08886Tnu/uHex/fuDxKR+1+J6gFQRC4rvu5n/xMp90djPphtdJoNJpz9aBWQUTHceI4npubu3Tp0le+8pXNzc39I+f+3n0/9OuN2urGuu8PDnePb925vLi0rlQ3z4/TNImiqNWab7Xcoijax1ue5/U6PUT8iU8/nyRJb7DT7WNRqLIsl5cuAZjDw0Otuu12++jwlsPzw4N7Fy5c8L2y39vq90BrXavVDg4629vb7Xa7Xq8nySmb1UN0OsepzWB3u91arVar1bIsazab9zZvv/fee0Hov/Lqy+moGCWjWr2ytjq/eX/7qLPT6w5Pnx6dv/AUF9jpdBE9I3E06jmOUxT06tUfLCwsfO5znzt/fv7O3Q7nLqfCcYTDBaLhztiWWIDedPbZeWS7QCdIhQeAFcaJEAyIg4i270BrLRzGeKlNzoXn+Q5QhoYI1zHGAGXG8GkY7XpWfYsWij68WI3nC6VjJvPp5LKflzIBgCgMCODm5v3rV6/cuHblzJkzf/EvXzSoAABBK10KIQjFvEgrYYUQIqUxRk/Ogtp5MV2xLY0Z5YRSahQiImMCkRo9xieWZYmEIAoyhrkhpdrOfTbRIUakWmuK47mfauM4DhWCCoGESGM4IQrRd2zqjsC4WVkTgpwTQKVVWRQZGsMYB4ZKKSUlP0GY/UQD/KeqMP3o4IGoZmpYGsUMM8bs5Icu8eXQ3dzcjOPYzhnGmDW6jLFKrS6ltPa4LMs8z9M0BQBfeJlMucNWFldA4M1bN/b3DhYW519++pMXN56shEGm41A6WsKwOOgMCrcapPqwzlrEVWhMprrdeG9r9/qF8xfX1laac7WsGJUlA8MKhRS8XJ5Q9KYPMddM36N6SK9x+oaeqHw9A9+zKkdgebE4TN2aMfwBAKwY8WMGjsE+ADONgIQQlCegIsEANRSAIijQpCSEaGNQFNrqBDBGgAKZGFZlEDUxgGioUaCt12MeuJB0Qik3DigNmR7SJLEMAOA6D5itYMa3cH05G9FO/8t3CCEUCFBKCDVAStREG6PhwXUen6nVvs0VmaT0p841Ik6E0ymlFCkSQrUe+9rTVNs0NAcAaghFpAQZJdzeCovDVGgn0iS/DVqjBlOUD+HpZm6vXb+o0tpoMMaiy6jREzEo0JQQxsf9RevLyzdv3vzgg3c55+sba8fHB/v7+1JmWV7a6qN1FGx5ezAYJFlqZZHKsrSWeNbdsRxYiOh5HiHE930jWaUWeY5jUKfpSBYlEk2pWV5ePjg4+O53v/vJT34ySRJCyNra2u/+7u++/tqnbUmyWq1+5zvfefXVV9vttq23feMb3/jpL31Ra723t/cf/rW/Nhok777z7u793T/3M3+WUrp59/697c1P/cRPfOrTn/nud7+7cebM0fFeFNbv3d169eVPfOHzP729e7XbXcFy3guihaWVd955p1qtNhqNH777vh9WnnvupUajceXjK3fvbne7XSrr9igAAQAASURBVK2hWmvcvr3JI9Ef9ALtX7957c7m7Vt3bnx8/aOVlRUn4oEX/ty/9xcrQVUaPUpjgzpLc8cLRqNREHmLyzXG2PWbt+9sbm2cPjvKu8wX3WGv0qzUGiHnpFLxGrVKrelJ4yKtXGhsNBoN3/dd1+12841Ti57nuR5EUXTmzJnRaHR0dKS11gZ6vd7axvxwOIxTPjdXX1ppPPn02atXrhJeuj7OLUR5nsdx7DiO6+Pzz58PAgxDKMuy39/lfG5ubo5zvbN7e2FhwXVd4ej5+UgIurO72+vv723fiyrs2WcveR689cNvJ0ly8YkLw9FxWvaWlupFEW/eu2pM7npRmalGq3Vv8/79+/eXlpYuXLjQbHLHyYv8KM+wVEXgzxGkFCRQxjkXnKXlCCW1KRylJAAwRgkhnDPXdaZzBxEpGa9CBvTUOFmsoVIQBB5jRagpgOZcuYJJpVCXSqLC1CafLQiTMwagZVlQ6k03Pp3ChBBH2BZ2hYgEkE8CwUrF29vbax/eCwJ/NDxMk+NKxJ55+myWHo+GA8/zWq0WIV5RFJTScKliM21ANEek1DDGOGeUQr0WAUFr3KUuEA3jhAk0khoDQoBRkGWlIwinoGVGOLEt6TAJjgWnhJA4G2NCDRoNGidQHlVYprmIEK6UMoZrzQAcR7DJQj4u2wEgpSQvcyVz13WRUEKM0gaBeZ6Tl8Vj1+3ZteWhz39M0BkHakpZMGCCBKZEBZpwBSCOjo7ohC0PAJRSUsqyLA0QREsjoo0xnudZ6fIszRnnqlTdTq8+V3Mdb9ge9rr96DU2op1h91BD6XpOhde63e5glGwOriElgvuVYA1R5UlvmO0byLr9g3qz2h+24/6+y5uc1stCC07xBIFNhg+ZkAefP0waPv1PQx5PVk4eacBHHBcPH97sgyziCUQBiHSGgXHc/4BwomdkCBI9bkUiBJTWmCMhhFtFZmYNHhIKAAQI6nKMDUYDShuDBDUxMzTfUys4e8Czf9rBmZl61jBjQT3/gfGbbgcn9FWWuwDRGD0OvwGc2V0zxigipeOUOPyIlgin1iZRxtgDGDSi1sqCDMyEQnl8DMAoAKXUdZBSoZEjogHIswlKC2BigEFrKIoZTdDZ1gUqEFEbpRUqhcYQxoimxhTM0oQQYhyXui5zPcEFNbr842/84c79rSeevFQUqZQZotRK2kuhtU6SREoZxzEApGlqYfAWMDGlwVJKVSp1m4uzs8nzPFvBIUaCMVKVxkijtDGl4wrKSLVavXbt2vnz563cb6PRuH79er1eb7fb9s8PPvigXq/fuXMniqIsyw72jqqV6u7O3rPPPdPpdA72D7U0X/7Xv/WNP/zehx/c+E//s7//n//D/3Jvf/c/+j/84qc//el//s9/pd5Y8tzw2Wc/MddovffDdxdXmtc+vt3pHg36u0KILMtGSR6nxb37u2VZzs/PHx23KeNXr94wBtEQLtyo6nSOu27gehV3e2/7/Y/e7w47WZl3eu1qs7K/v8eBzS+1XnnulYWV+ahWrQShNj1g5Nr163/0jW/85E/+5EsvvXT9+nVCaaVSaWeHlaCa6nhr9+5xZ68WVZ9+4pmVtXlVmm738MaNGxcuXDCmSJJkcXFRKXXQHWVZ5rpuEPo3bl5PksT3fSllksaDYf+47XY6nSzPpAqyLAvDsNGsGFP0B23G0fVYmplK1afMHOxvVSvuM09fsJVRC5UaDoef/MRz0zSM5yJj+tyZZcdxlhcjz/PmF1qbW5u7u5ujeKgx0Vr/xKefW99YazQq9zZ3OS1drryaEwak2XI2t4ZKe5eeWD17bsHWa65fv84ECytNXRpBTZrJUmYi8MEUnITWt3fGKgXGIFJOXIfZljYAFIIzNm6IkDL3XKa1LrJC6sJOotCvBRXmuDRNcq1zLhxCSJ5LNCbwxbirwXEmW5BgDOfjTOHs6gEArkPLUo0RVfwBsw0luVG9vb07q8uLcw3ea7lGQhLvv/3W7U6n02g0nn/++SiK9vb2hBDnzp1zg0BKaUvyUkrGiBC0LMfgJgOyKDMkiVJSAzIkXPgEkQoPCGWq8IOa5/qEwP7+/UajYWkDytIkSUrGNXKPMWbxXvZo7SoUeC6nhfCJUjlQXY0YpdpzGAWjtTY2bQAAqAGREOoIDsQwioQwRJWnGWVOGIYmP6m75PHrOf1xa8ClzjWTricC4hYZNVAi0Y4HEqhtkLA1Cbt8GGPSNLWrjFUXtzQCdqUO/DDL03v37oXtwKu4p06dmpube+fKD87lZ2q1GuHk4GA/LzLGvGcvvNTr7Hb7ux9u/mCxcdoj4WBwPBoeAdEfXXlPOHxhQYKKONGucAI/cng4KrPHn8FEduoRe2geVuF4YDhPQtJPsqCzwS4AAHtow9MuwxMy0EDMAyrzh/Z+Epm7VQmkBKgBIEZrrSUBGMWEEKR83IlrjThhIMZyAqg0Kg1om7UArJAEPFzoRURCDSASMGOXYjIYU5yDEJTSB8oBhBBUY2lyMkY/jjeoHEIIsYoLFtxk95flD7XcMIacEyswCDMTe3odqMWIIAEAMelvRkRjxqxetlyK07KQ1hrG+WImxgBnoIbgw0xbFDQF5DTlD+13+kYIUUqpFUpplDEELQuupsCUNgBGOMA5dz3h+4IL+r03vnft6pVqrSIE3bxzy4Ap8jTPcyb8abrbcZzRaGRtKhNca+15HmPMVmcopYPBwB6e1QaWUiKi1ppzHvpUGyWoEMxBR2sF1aiyurpkY+izZ8/2+/2VlRXHcT788MNXX3317u2ter1erVa73e7FixcvX768sbERRZFg3pMXn9zauTMaJO/+8P1vf+vbTz/xfCWsP/nkC7dv7//6l7/23HOv7+599Xd/+5uc1+rVNc+b4wTu3tgMiHd8fLywsnxve9PxA8G8JEkQsVKpGGOIw+q1ptba931V6v39fTTQ7w3KslSljuMYBD1oH/3JG99656P38iIFh7aW5pFBVA8A4eNrl8+fu0AEubt55/XXPjU/N//9H7xrtRfjOOacR1FUluVwOMxksXu443q8N+hsbQ3WV9aWFlv1enTx4ovtfuetd98+7BwhA0rp+pkNSulXfusPtra2nnzyyc9cuMQY6/V6g8Hg7bffvnbjyvnz519cXiacd7vd7mDw0UcfRVFUjeoaCVBugN7Z3Lpz584nPvGJ9fVG4LmEkKtXr965c2d1dfXSpUsW+7a/d/zxxx+HYfjCCy+UMsmGGQDcvrPjOHx+fn53L97d26rVw/nF+sbG+sLCwvJKLR4NXE5feO5JAlQrQwijlHacslHnvq+l7JVlKSVxXbdSIQnmPNCUKFTlYNTvDLqLXlN4bLA/sE+UbbC2giVCCPsGJ/psxpgkSfI8X1msCm6SIu2197Iss4uwy40XVbmj824/SWSt2nLdgAAlhESR0+124zi21jfLMvscNpucTgbAuIfeGGO0AVSCg+M4jiOMMXGcZGky36rWKrB554MP3+s7nCbxqCiKXve+wjRNU8/z0tGdIAh2dnZc183ilxZOXbQgiTAMh8OhXSW63W466gOAgTIv00xlSpWEGspJLWoB0IpXEdyhKFx3o+4vGiwP3r1a5k1bBrVFz2az6XlezWOMWbCLLLCwVR5GmeDDJEk453mSOI7TbLWsJnc/De0ltT6BValBgoziYDDI0iKq1D2vCqC1KspSkJPsxUkg3JMoP0/4Pnc8DoXJZSYgV9IgQ2U06NyAtnk2ywlgsf5SyiiqWFiErWkVRUEIsWRG9olxjZskCXXJiy+98PnPf/53fv/L7ePe5uZWtRExRr3IQ9QGtSyz7mDYSRPNyHx1IcVhWsQySer1aH5p7tKlS0xVR20sUo6GaKlP6q+aCmA+coZIH0L34YQ31ZzETUonmDzyULw46dV7eOMntoUDxYd+YreAOCYEeMx+CRJCCCWUMEI0IhgNBlUpLaPsuMfGsmEQilXuWgOsNTEaENG+qpkjnCY/EdEyFJAJ9J0AUEIIEIMFECSUUEunMFZzIyDHDBowwV8YYxAh0yUhhDBCx9UoQKIBIM8eyIdZYV3NDaUgrHzl5DCmIbXrTjwY9gBebr84jTymNw4Rx4rFFAkhhFGg9mowz3uQ1jbGtl1RRBT8IRm16eBCaA0wxm8BEGO/QhCMQSDEpYRzzgXlgjoO/fKv/3q7fbS0tDQcDrd3t0+dOkUpdV03L1VZlrYMbK1IvV7Xtp9dKVsJppN2+aIoBoOhnSmc8zzPrZH2fb/V8jilwuVB4IIxcTKKqsHc3JyFOttY2Soi/Pk//+fjOH7hhReiKEqS5K/+1b96eHj4pS99qSgKRDy3cWln9/7zz7zse86bP3jzh2++H/fkX/nLvzDsv/Hhhx/+4de++bM/+xdOr90+ODoA4/7dv/Offv2P37p782rUCr76e//m85//fPvgME8Nd7xS6zCsOo7T6XTiOF5cXHQcP8uyubkFIYRwnTQrDo+OW805A5pyLhx+69at7/7gu71+1/c94XAA841v/vGl5880as37+zvaqP39/a///tdHw/j8uQu/9/tfNcZ0Op1f/83fGMajCxcuxGkyPz+fy/zmrRvr60uMGENULrMPPvpwb2/3uedeX15edhyn1+shYhAEeZ4fHh6urq11ul2bVSKUCscJo6jRbCKiBYoSQlZXV13X3dvbI4RUKpUwDG2bdRRFYRieOnXq9OnTDqeEkBu3bmdFubq+8dwLL9oKSBRWv/nNb0RRdPr0Rr1et1W2O3duHR62X/vki5VK5fBopywTpSii3NhY2bp/6+bNmxcvXFxbXf/ggw+uXL5y4fyll156iVATVXzPFwgKQUmp0mx03D7Y6Q444SbzUXl37uxu7d4/XawtrM1t3uwgYr1eX15ejqJIKkkpZdwxUjFGLB+SMWY4HOzu7rbbbZ+fQ8R2u/3xlSvdbtd2+LSPLpzLT4Ghuzv78ShfWz3XqC8Yja7j7ff3r127trOzAwBKqX6/TwhpNpuf+8KftTBgCwGzCHCt9fHRtgU61Go13/fTNN3f3z86OrpxfdhsNo2OtRpK4J5LfMeT+ag0g8Bzy3Jw9/Zl++jWarXR4GAeVg3mjhsFIaRZaaPPJD3+4VvfpZwY0HmZxuVQmpIJygVbWVhHjZWg6jKPgZOl/TRZybOi392Ph8f9fj/LMillvV5/6aWXauvrg84Wm3DLZ1lmm5QopY5DDw4OGGODwaBWq0Xu6dFo5DiOok9bA2yBTdN1aTQa3b+/hYaub5xZP1WPokhO+mseu26bEzA9P7YBTpM4oBGlVJrEMEMUEURwhxjtokYAIphLCAFDCSGu4LqUnnDAYJ6kWinOuSec0PO1hBLjjA35vNYmefqVJyrz9Ns//IP1i8tXrl6tLFT8wHdd1/e8vf39/p1NleVe5LiOUWkvKdiZpQtzF1+9effG7Su3dm92zzR5sz4vHYOoRlkMdFQVHgBYSQNjqLVIFFhhOfgBKDUASAmh1ACAkicYvJMsJzFkpmw7/djnxY+GcYQQaQ0SIWSGF4kQEriW+JCgsTgpAoZqxErwgNt5xrBhVsw4FoQQAM4AgJuSA4AlAzPqwf3bK+33p8lhsKaVcSBAjUEAakUpLFCLAsOHsFeTmm4YFQUpCphkidnkUXs8Fys3BWNMgOCE2eSPpSLRRky3CRqkIgAGwNSCyMKmlCoR0YAhFAkBDYQx4nCmDRit0qxkFCmBPHMREIAb1EAMogRqACDw+XT7xBiCxDYy0XFGAQEQiKEMrY8SVUvUZHx1DUFEChQAciJzk2WFRuWgpkohUGCcU1JIVTCKnlvnFEIvKov01371X4+G7bm5BiFGKTU3t1SWxg/qUkqnUlBKrZB2XPQQsTPMhRBxJ2OMJaPYhrAA4HneXLNV5t7+/r7rMGFMrnNdkLVT841WTVMz6Aw9VTnYPiLUJMnx+qnGpz/zdFid6/cGN29t3rh+I40LLTGJ01qttrCw8uxzz77/3gcHBwfLy8txHCuloiiiZvhn//xnpDmoV8/89b/yV4+2+qWUX/43v/6ln/737u5cPji61Ym3/pO//x8urywareLkWKmR8Dl6zsr58wnisMjml+fu3bunFX7xi18cDodbW3d3d3cvXjwnBM9zQyTrDwado05/0Ften8uLrLbooedsHd7b7+11Bp04iYNKmGellPLC2ScHgz6lWRRV/vW/+Y0syxzH+doHfwgf/OFHH3/oClflUilDvic3dy9ceOr05u07C/XmoR+ixFSWSSZ151i43F+o3b5/5/bdG+GCl2RJX2sE9cbb30ZDQxIsLNSzbECJvPLx5SeeeGJ/7xCxEIE47h/f37+/sLAQhmGn0yEO0Vo361WCGrWMh/1LF84d7O1kychzPalklmYrKxv7+8dFro1iBJhDvcglayvn99u7mpFMy8CPUHvPPvHanTuXA14XxE1GuZbGr/jNRqssS4ct7O2+p83OM89/0g2bh93kNDoLS+eOfvjtd25+7Pv+4tlTr7z8auAGt+7e3v7ooz/83tc5E3ONBUGdvb29/qh3Nw6qtyqYOoP+kFPxMz/9F1ZPv3jlg6tbmzu1Wu3iE+d6vd7h4VG1WvEq7sHRzvXNjz++cfmD+xvzcwudTodTfjQ4brePQ78i5gbtD67f39taXJ3zfG90eP2T858h2v/un7xnFnp3du6MRiPXdbXWEEKj0SCVdPPwq7KQWiIj/Hi/3W0PXOHKUu23t0+fPl2v14uisHmXWq22t7d30NuqVquSFoqNFldWHeHeu3uPOjQkLalK7nDCTKIz4xoT6P3R3ro+f3C0e9S+d+bMmUql0j8+TtO01ghe/9kXv/71r1sAow3wVKYggzIaOtSVdD70qzLT+3fuySs6HiVVvcwYK7KCCZapNPA91chvxVfVcaCN6g97QGxWmaKGo8Nj8HUrqtJB3j8+qHl45mxzJzWdYhCZq+nRURKnkbdSqy519vt79w85cWT80VxtpT0aNuei9fVTncMyEHMyI5lSSDDJRhqV8HhepN1+u5R5Jeyura0lSbK1tVWWpT2F+fl5jq/axCMiI0gpOKiFUZhg18qd2WSYLUkopfg01Jhgu8Eur4gEHw6qyIQnASdidtblVEoNh0MCFD3ZWmgtn5kbJJ1Bfxj3bnMi5haeOXv6PCHk6PjwqH2IYLIsW1iYLzCL81Qrfbb59IuXXqtXGofD3W568PnPfqESNpvNplEmyzKpQXDu+AJmwDUTmBEgQTCAxDDbx2nLgSfRof6p4wRoFkwuyYNN2suiJ+i7WRcJJ/zSlFIzkwWFiezX5GtgxqEpWNb+2S1PAsRpGG3fjH87w8mDM68WwT+GcM+OWe/hkRs6vafTT8hM0Xf2+4hoNAOkgGA0AoxZwIx5UGedbsq+SdPCGn5tEAEJJRSYFVCkCMYAMURJ4IYaYgBMUdrMhEHbskzGrmRZWDAXHWOeKbUukFYPgGYTfBkgImo6+QSNQYLEgCFAHd9hrMSxZSZIEA2CVlIaz6u4HisLHfhBPCrffvvtyx9diyohghnFQ8sC4VChtUrS2ADaZkSLq6KUZnHZL1IAA3LM7eA4ju/7hECaJ81qFRHjUd9o5bpurVYzxty4diNRKUM+P7eyu73n+YLSvCiCxcXFNM4/eO/Dm9dvdbt9wVzBnUFveHRwnKZlpVIZDAa9Xi9JEmvbhsPh6mIodXlqcfnevXuNRuNv/a2/9T//2q/eubf5W7/9mz/3c39hfn5+dX11fXW122u/+c47jJJWqzUYDDqdzvz8vOu61o8ZDAaeGxwcHJRlGYZhrVYbDoeMMSGEcPm1d6+1221N1OrqyoUnzh0c7v/e7/3eYdx2HKd73AvD0EiMB4nv+81Gy9GcM54M03iQWJYMQR2tNQUax7HHXRuS1hq1CxfPr6+s/tpv/1qtVsvLrN6ot+ZbRZk3m3Ujzfvvv9/rt9vtdiELS159f3NncX75f/ezvyCltHzanPN+v7+9s7OzsyNc1u4evffBO61W69SpU0IIAypORx9cfm95bfH555/zQocKOHfxTGuh0e0fFKg54aNiIEKuqNo+ut9qzFFKjwYHtYVqjP1+2lNUFqrIklKxMs6TXBcCRFSpNubmkiTeOzyst1q5TOYWm37FvXH72r3tu1E9yGX63R98+4fvv9MZtIf3R7Va7dTZU/NzC7c379zf3S7LMi3yPC5d4WVZZrRJRqnSctBPsyxjhL1/4+397v2bN27Fw2SuMd+WW8kobbfbnuc5Ps90ot1y7cLi8HBAGdnb3wcARvni8iLnPMni/e7OUeegIHGjWZ9vLgZNd6W+AQD/5q3/xYuEFzUtIqEsy96ovXt4vx55jnBLJctMxnKUmrgosjwr3IrnRG5tvr6zs7NztOv7flwmiupGo661kVpGlXB9fd32mh4cHABLFSpGSVgJXCaKMldEDfPBzc0bQoheu/fDD95mjC0vLwPA0dHR4pnm/MJclqd37tzxPM8PPEF5tVpNy54WxkKO8jIvcpmnxSiJ41H5j//xP/Yc7ytf/cr1m1dSFd/bvStNIQYNSni/39dSSynTPPV9n3HiBaxXHMjRaDA4Zl1zY/vK9qAdY9EQrTiJ0ywt94rBYEBKp1DZ1t69vCyAk6gR9NPje0c3VOFoDpryTGdhFMi8f9je9QIRVl0Jx9sH98IwVSxBxE681+v1AGB+fr4KbtjsFrlWGWEscERENBZFwjgNUCFKAqQScsY4ACBqADJVbUMhhDWoY8KNh5kjcMJba3unDAKbGGwLKqGE5UUSNJ3lheVVZ/nu3bubm/cYEcYw4QjH50KIJ08/yVxyb+uO1DmjImyERPO9zg6/8VHFrUssGbpzrSVXRIhUKWVpFBkwYui43WgSwFI2Xn8tb+I0ErTBugFQJ4CkTi6ePz405gynAe5kxQdjjDaPoq9hnIa1euCEUoJmTECJiDjbEjNOdU/7fB6T5WYzDDWzhpBKPrO3WcNMEJAABbvpcXcsmTDYTCzVZFiq3QdEFuONgDGPN8AEmQZQBAnRsxtU+iEDPNkvlKQYb4EgpUA5YYxQSgCp0cQ23DKKkqBGZIQUhQQAIFb+YZx7J4Roq5hEbIMTTqSXySzSe3pqiCitlrMCY9DoCRU+YD5Ki0wqpQhaMucxpZdBUZY2cSSrVb61tfsnf/LG/fuHUQRgn22tDCDmWRzH/X5fMC6EEEIwQmVR2nnBCCWOEUIg8jzPizKRSWYzY2nKPM8tS5FlZehH66vrnJN7o/tHw4OF5pIQIi1SDTwMwfPdpaWl/+F//M3d7f2iKJNh3usceJ5nlEnT9OVXP/Hhhx9evXrV+klJkly6dOmpp56qhPL73//+uQv//u27N3/lv/2fPvnaF15++eXjUefVV19+6ZWXfN/f3d3duX9/YWGh1Zzf2dmRcpyBPHPmTLvdtrW61dXV4SC28DFr3nx/XOr+yle+cv3GtbzIClWM4qE9qWqtendvWwihSqMdbB91O51utVo1CkSVAzCUWimlS9AAeSwRMQorslS2Q/rOnTv9Tl9K2ajWKSOBCKQuh4ORNgoJhmEYhqTT6dy8fWPvcAcoOI5DkOxs72VJ8btf/ap1EW5tbnY6nVqtdvny5f39fafK+v3+zv6253k7+9vVavXevXu9Xs/lYmVlZae9vdPebjQaWuvbO7f6/T7hpF5rXL97o9ft95POztG9c2fPu67ri8Bv8PbNw9/56m+6rru2vD7Xmg+D6Onnn46zOM5HtVb9yejp7Z3tTrf33e99n/m8n/ZKnv/R9+7ev3+/ElTLu/Ldy+/dvXeXEJLk8fXb1771xrcCN3j/gw8ODg7KQva7gzTOQq9Sq9UIIXmeKyM1KamD2pT3Dm/tdDZ7vR7nPE8HW9dvaGmKvPQ8j7sUmfYih7lEmnIQ94dJHwAqYRWpkxZJVmRhw6UuDpLeMOscHO9rrWt+6+D+UQ6J67u2DM8kxO1Be3CUpund3ZsrKyuu63mumA/qQcPRysRxfLB7WOjEDVljvtLplOcunorjmAh9eJSWsiy1BtD9eNicn3/pE68AkiRN9/f3O702EkM5Y74bVsJmq6FoEYRujUfDPBwMBseDAwCIy1G61V9YWJhbaHX7nbIsS1U4juN4otstwSW2yimVLrHUXNJAuw5Bv0CHenUWtrykGG4f3PMrztMXTp1aOrN5e/u9tz/sdrt+xalE3AjVNoOq64fLjteoleXg/RvvH4w6NHRv927lZcYY850QJNUpgBSC+pQ7/dGQ+LDd3TosjiO/VtNtShxSqjRz9vv7WwdbYeStOosFT6U4vj/a6+7se55XsrIIUkTsgTb9rPSQs4hypxmszzUaqsQ0M1HVO9w76Ha7WZZFUVSv122nog2FufXoER/0ohFCzMMGzGIFAcDxhJ2ZCpXRhhCCFKmgnDMscDAYHOwf1+cqaZIXmfQ9cfPa7agauqHwK65fcVbWF89fOqdBd64VfpONZGfr+FYvHjx/+pMXV57j6CSxNo7kNOMcHFcQBUoBShzbKoYTmJkeGwZrIQAAqYExExieDLY6CaN2Qr81MGIoGbecwkR7gBAjuHjEUOFD7bBkWgyw5sEAwiTiREAkY3YM8yM6x/Y7bAzWflRXWJRTtPaDVwDQBgkBAwYIEKtdgQiGPEKORSZtQna3P2qAtX5w2DBrgxUFAINojJ79r0LP9g3PxMFomyWM7RRiBhg3lEKWPrhKnIKZULcppWzsS9m4uYhyIASEEDNSxw/S49M66yOvhTLjFLS2Qh0akALgqEiMpJQyMERrrbRBohGMI1xlpCm177G9w/3vfPuPr9+4OTffUvrAdV03cLJMZ1la5FqD8kIqPOK6zHE4Itop5AkhhJAGEA0geo4Q2rqPwBjLy9ShTlDxh6Mek6TRapR5trGxsffBjvCEMdpxRVkWjgvzi60kid/45g+iKDp79mzVr6v8VrvdZozNzc3ZerNlHVpdXX311VeXlpZGo9Ef/P7v/r3/7BdLWbQ77SRJvva13//Zv/Tzv/zL/xeijDayyA0Yc+PWrSTJgiAYxunCwsra2hqldGNj4/LlywsLC41G4/j4uF5rPv/881LKN99800JU8jzf2dnZ3NxEMGmRSll2eu3vfu+Ne1v3jtqHutCgwJRm0BlYeEvgBGVaDtKsUqk4jgPKlKkqEikzzRhzfKKVMRwGgz5R2D46RjCXzl8AgE6vvb6+bml/FpcW6pXGM08+q3Ny49a1SqWSl/n+/n6j1gjDkFJ6a/MmY+zMmTPHx8d2OWv3j9MyuXdnj3MuhIizYZwNhRCj0SiO42oz2r2yfWXzsuM49Xp9OBz6vs85V0YvLCy0jzqHh4dlUfpecOrUqXq1sb29TQjZO9itHVaFEFt790I/opRCwgghGxsbYRgOh/2Pr1+L47gsS+2XysjWQuPw6GBra6tWrUd+lQJDioPhwHHdXBa//4e/b7QVfmCe41MSJ6NU5trzPCCY5hkrKHM5I7xU6f7BnhCiUq1E1bDT6TDGSilLKQuV0YwQDoV2XF/0RwNGOFD0XB+JOe4eooZarcY9r9asco9QSrQ2O8f3NrM7e9sHS6fm4nzIOa/VasxhpckNVXNLzZvb10uSB0EQhZXADyFUDKEaBWkZoaP7WbcyF6Y65iHFUitWVhqRUwjqkHiU7Lf3w2p0/vz5xcXF9uBYu4XxSqWVAZ1myTDvZ8fxmbNP7B5uU0rXT6+comv7BweHh4eaSKPx4HC/1Wo99/yztqptUA9HA9REK0jTXCkFDJmgYcWtsiBQzV/77V/VSjvcVbRoDw9HWoSFF/ihEzFFi2rLd6M5w1TKBoVKE9VTxmVBw68LOZAHg92tgz0iONHCArgWF+bSYbG1e+/C2qWf/7mf/fiND7YON7UskzjNRvsGwagPQr9SbwRZlnV7nVE8dApxXASEklKXRmTdbBTS0PM8NHlRFKpI+keH+6Oducriani+5vkMar3R4Lh7GOaeod1+ev/w8ND3/VVYrdVqWmvLnMr1zKATQkHzsPDybKhny9c4w5AihCAu1oMaMn37xh1vx+0PeozyWq2WiFxiIZNcQr55t+yPelEU9Ae9/qZqJNHCRq1eaWWHqhxovhA0qi0RUiE8BKK1FYIHThkBKg2hDAmhjNpgiABqRATkOOYoBmLImMfo5HFSBMxOkAucrZ7ChKJy1pI9YgDG5tbYf8YY23qLsxGnhWXZf5Yh9pFaMgBw/lAAOn2lfBztkYn/8cDwGzoFkBGKYAiccFKz25yeyPT44XGOhUIyrVvPAhAeXBsy8zrz5/SCgAZjTJra4sE4J0/IGC9tydMJJYQCpYRxK8oEDn9UXQongK/Z+zJ9rxQ1xhhLa2WsSKWhQCk6XHAkvCx0oUqFinJDGMlkz3G4NrkR7Ob1y29/8G3iJLW5ecqb46nRLwtECrQW1H3f1yLNsizLhojohLZJD43JhaZHR8dFUbRarYWleUJIlmVKqQqvJElSq0TtLiDVwqX7+8fN+UZrvlWtVUpVzM/Pd7tHQcCfeuqp/aO9NE6zJAu8YHV19eLFixp1p9MJosBWvi9evDg/P7+6ulqv1zc3N9955x3P82r1ysLCwk998YuH9wfHh8lnP/fpufn53vF+e/+40+0vLa299upre/uHl6/eDLzg2WefPXf2rBDi/v37YRj+xOuvHx4d3blzZzRM7ty5I6Xc2dk5PDy0E7zX6xVlHgRBEAZh1Erz5J1334nTESFk2M88z6tW6r7vx3FsjAFDk1Ec6xEjnFU4GOIKDwDAEMpYmRdCOIxyNLC0tBB4fhD6rfnm5u5mkiSVSqVWq21v7fhO6FA3j4vDgzbnfGNjozfodTodx3GW51fWVk9lSba/v08ZGQz7BwcHthDGOB3Fw1qtxrjLOddGyaykjDBO7avrOZzz4Wiwf7BPCGk0GpohcCJ8EedJvzeo1+ubO1sLC9luezfP8zhORnkipewM+vV6rSiK9lY3DMNhPjx79my1WjUMjzptRDw62ueCEg8kauB0lMVZXoZBBQBHceo4Shoz7A1d15+fmzOmDKOq7/mOcNFAEqdKyyQfCZfXaFMIQY1M4pgxWYsaHJxkkNXrdZkW8TBL41Rr7Ufe/FKr6jWU6jDBoyiSpYrjeDQaOdwNw7DX6yHBqhtWKhVLgp2oOMNhp0OmbeuWvjCKolarpSHPTTHsjrx46HleWZSe54Vh6FS8UZn0N29evHBhkI8+vnWVUprLfHG+qaT2I88LvDzND9q7GuTm/TvH6VG3203T1PEd13VLUmYy07keXnnPKn2VJpNSdjqdNE9t+SaO40HMVldXF1cWhsmgKAqFMvQiQkFJneUpE8QJhOPzMPTb94867S4xrNloDUeD3rDjFs7hceG67sHBgYxxffH0/Nry7fs3+nG71gqFIsfdw8PD3YX5ed8NROSxHhkMB1oRz3czOerHbYMkh1HBk+qyP7+0vNfdV1AmSTwsBlTwNC2TdHTUH9k41fFplsed3q4yklIa1UOtNTCTy/Tw8DBJkiAIHMcJMzdA0VF7IYs84uzuHV69fjnJRuvPim7c7eQdk5ohudcqWgCQJMlYed5Gt7aC9chKPTUJdhQycxzHdVybu5ZSGqO0RpWXrutSSkb9BAA4EdzlslRPvXLeio8SgKOD4+ODztzcXBzH3GOF5qcWXlu/dH5wV3KMXN+rLroq9RzHkbIEipSCybXRQNASZRDOCGeEC8PIOLlIDZMatQaliNJgDCFWdPPHa8cCfUIKWmogBJQxtvhq139EYHTCH2Nx7BNzaK+h1sRoNOOGXWIQtbJYp8eYukds+eSAKJkGvmMAMwEAix2e/HZ8i8YRNkFiEAwh1JZdbTPPo9VtewBmokuKMx8CgC2g/6gBLse5bJg6JGPDz6abHW/AbsoT480SCpQiWBgzAak0jNU6CRqiDQWKBJnDOZAJ8RZDIi0QGxg8AnYb+yhlqSYH/NATW6BGTYxBo4nRAIBgKAASxsAwq60EAMJhlFOkZmvvQ0Ihy4fVanAUbzdWuCtaaXHoUFpoVEppUThVJIQQXkqm0VGIClFJKaXJSy2EENzhTDtuRRAXeUAVLZVSmc600ZxAauJWVFk7u8yp8KpOe3AoAvrkU0+43Ds+7FUqUZoN6s3wzNlTdzevhGHo+z4SPO4czy20nn3h6YODA8fjh4eHw+FwY2Pj7Nmzg8Hge9/73tHR0eLi4vPPvbi0tHT1+tV7d+99+rM/MdfaGKSjH7z5g43F1mjUGw0HS8srrhN0eoPd3YPVlbV33333ww8/1Frfu3dvb29vf3+/0+lsb2/nWdnr9SzXAed8e3s7DMONjY0wCrZ2tqrVSr1RG20PjDGrq6vaKKD9sixdX9QaVamLXq+HRAuXBRAAQFEUtoUpCALbHEGoVlo5jNvw2vWdUTI8Oj5M0hgRb9++vbSwnGfFaBhXo/r2/b0PP/zQ9QVSXRRFrVZjjCVpWpblUe/ooHNgmOnFvfagbYyp1WqI2Kg3GWNpkgVBQClFA47jxibJ8sJ1XSC01x9IKf0gzLLs7ua9qFpVhWk2Wlmaj0YxJyIeJGUq6636YDB0mY9IynSQxQVi/+josOI3JNUf3bysiP5zf+5nVjbWf/j+e/3uwPg6qobDJGecR7VGkZcyN4NhSo1u1lpS6s5RR5aKg9s97hdF2ZxHS3AEhqRxWuoCGAruWIw9AKDGPM9HvYRoZnLSOxxqZSqiJhw/SRId68FR4tGQE8dzfc/1to+3i6xklDPKk1E2OOoY0PVGbWV9mTKURnLOwoofuhWlVJZle/cPbPlcCJHH5dLCsjEGdWxR1kVZIKA2etgubFutbb/e29trNBqO4xx2DwR3CllooiTIYTYUseN5HjAUHqeSpGmSJLFtkCnLslKt2UlqP+n1eoQQ13WlKivVqNvtlLJYWlqqN2qdTsf1HJf5hJAsTzOpi0IVRaFLrUvc29tnlKtc7mf7jDGGDkpSqzS63e5wkCSDrFTFglw4HhwMkl6c90Gr/nBUqpw7bqWCDccNg6jM5EjlUTUsyvzG7euO42qitg/v/frv/Nq6fxYCRRgZHvQ6o24YVVRpUoXd0U6tVqtWq2mWdrtdJZVtDhoOY6XUsJIEQVDmGiXNY5maQrQw95OUdAfpQbNVXVgLeklt637v6vZVIYQJTVEU+3E6MG2be+Y2AptVxp3kEjnOgHSmCUZDUKPWqK1OMGHE6qMh1XmZhSL0XL8a1guZG1BFUWx37yzOLy6vPxn5tXu3dq5/fFM6sFhdpy0Vj7KDne6FSvP8009Uw3phMjRlkijKXaWMYFQwUlAgGlFbzBOhlHJBHIcwCgQ4IZoiFYYUJVIKWICmRCmbXT2h7/YEy3wCShy0eUDhO94AWGztgxrt1PrCjAFGAwbRIEFDEEDph2TRYGKDp/Qu0zGubppZtPMD2zyhhH1wzOMI1RAgSJCMTZ39c7bwPD0BC6azxzsRP5m5LOM9AjykqGiT2wgIjExywoCIY36OGd/CvhHcZlOAMhvXjs8dgSkNnBIpQRNAQgxSRHRQoCU3IYBW44giAApuptaX0jH7FSGkLB+TmwEAZQwaMAaNAhz3ZBsAoAaMKbUiiCCE0EQOR73eoHv53tt5FguGUcUrTRwu6qJMR/lx2paETPQkPKG1GuVxHud+6dtKjVIqz3Mry+P7fpoP/KobsUApddw/tGUdAMiKgaAiU6OF1ZbreMANcKOgXFs53e8Msiwr8rIoimp10fWc/mAgtax5Ndd3Ot3jYdZdW1tZXp8DAq3K4vz8fBAEN27cuH79uiXJarVacwvNrMh29u7uHxwsPLnhec79/cH27jYWg4X5pbPnz+c5fvtbb9zd3M1yvbd/VBSm3+9Pyexu3bpFCFlcXMzSwsZGlt16d3fXdd25ublnnn+q02+Xsri/s5Xl2fzinNJyOBqsn1rf398fJaMkS+zBVGqVWq3Wz/p2FsSjUSHzUhVSSt/3K0GYZml9aalaraZpmudplqeu4CsrK8aYPCs6nU6/38/i/CbeDIPI1rmTfOT6ruM4/W4/HeV3bm0GjZAQkhSpF/nVZm04GsVFqpSquL4sZL8/UKW2y32r1SJIPSeSpRzKtN3uaa1XV1clxWRUIGSUOGCGjggCV2apopR22gPgJItlmap+b1AUBVngALQs9YgMfAgY5fvHBx9fu7J7sMs498Mg1Zkq4Hi/V61VQPM8TolhjNKiLGvVQMlMS6AolMIiz5RSlPdHgxiQjJt9AQPf91xPsmxUFlobNxQaVLfbVVJ7rj8cDTgTtWq9FpE8KeJsNIoHh3vHlKCRSKtcl+hwzxUu59xIM19fGgz7WFKTEwlKgao2oiD0q6LR7/d1nhW5SlRu+9yyYSnRI4QYA4wKSqnr+4yxYRxLRfKiiKLouN2mlBZl2el2Hcfpj44bjYZSSmujCkUpbzlQnave3tkXrlsVtSzLlNTGGMq40mZlZeXu3buHh4cLCwvNZjMIgm632+/363PV+bk5qcr+oFevVz3P8Xw3LzJg1PK2espXRpVlOSozU1KjwGgTDxKHe41GAwlhSBebq7e3r3ueF1b8ApNh3nU87kjn8OAYB0gdIL5bZCbLumVpZFqq0swttMIwiCpBWYZoQLnaSHLzznXnSVpEI+CS5oYoFWe9uJsRzUflCJCj5nEct9s9hzuVSgUMTYepUkonKdaE70cOhyzLktGo0VCaypIk24Nb2i9Wlk+1zkYxqV6/0rZNzMYYpdQoSWxjN7dT0WacLA2CXe8Y5T9qfQHAMm8opSzhgOM4tnWs3qgrpaqVqlIqS/Nur1NrVmrVWnOFhpEbd4eCurVKrVFpLdZXL5y++MNbbwHlw9Fg93hzLlxu8jmdeAwD39Ou8LUp7B4ZIZ7rUocOVDkOpzhQSrigFJAQBoYxBAIkNVZ7DggwQx4lYHowTjC0hj4+AqbIEB8Ks8YR5Al9YHoszE6tAUagth3IqgKM/9kDQUAE4Y5rydPsrt2XmAhf235cmMTHhs6cwkM46IfGNKqeBtyP3EeYZIAf+1v4key0Rm1TxOPfTv5RNuulPfi5hTsTCowB5wwojDlAmcMUKolIDSijJAUzJiMhwBCMNhqA2noqAFg2dgIEkFi6ZmODdnxwGWdPxBpd1GTmWiIFxhwGaDQYy1OfJKOtra3bmzeuH75dyHzj9HISkyQZgi6LMmu2au1el1LqUpcRoAQNNcgUEYZoVubSuq0eDyilRpq4SLvDdrVaFUJYEr4gCBAxSRJk2qsGw6xXq1VKA8NRr1qLWnONqBIOukMAyPPc8Z219bVWq7W4sPDcC8+mabqwOL9+euW4cwAMV9dXLj1xMe3Ru3fuHhwcjEaj1dVVzrntDLEqBUF08aknnz7c7n3/+99fXFs9ffo0L5MzZ08N+sm3v/Un3/vBu6tr586fP8uoWFlZjuP47t27RVFYeYO5uTml1G62b5mG7FWzjCK9Xu/K1at5nrfmmtvX7qd5gsQct4/qjVqSJYQR7nBErNQqFrR17/69hbV5y8SZF5kxRmmJYDzfVUoZhRsbp2rN2vHBfprGnHMmWL/fD8NwdXV1a/O+I1wW8du37mysn6q1Kjt7bcfnlVql2+0O+0NPBMN+XKdqcXFx2sth7bRSSiUFpVSWSitj+6cXF5bmWvO5VINOz3EcUGzQHaDcp5QGTiVPCt9R/WIYhZEKzNHBcRhEyTATLvccXzCmcyOIw6mjSi1zneui0+/Nzy3sHx/83td+P0syzgQSUvOahcz7yZBzBxH77WGrOr+4sHxwfH9ne6fMJSUsiqIorBAkWZYRbiil3OGCCFlIZZTNI7pzkMSxVmZ5cdWP3KO9DhVkfm4OuEmTLClioillzPWdVDED2uWeLBQBWovqFOhwOEzLnBDiOE46zPO0rARZaXLkMooCAjQZ5Mf7XUSs1ZpKqSRJMllyzkVSMMYII1oBEaQWNVxX6FJTFy1vxt27d+fn5y0bsVJq3MCBKATnnBdpcdw+iuP4aHBcr9Ucx7NsOg53giAAJN1Or1qpea6f5/nO9i4iam2MxiSL+ZAJlwdBkJWZNFK4/PDwsOoaC78PA4aICSR5npsSGAhllO+G1NAyVspoLUz7oEsMnSYShslgOByigizNN6pnQeiCFQQYALjc466IMeu2e3t7e2vrKxsbG/1+vz3quk4QBP5espmVaVB159aqQYMf7/SSUSocmg9UD0eouFJa5lDowihOKfVphfmMUipjLIZj0QRjqOs5fuSUJNu9f/vWzo3Tp857PNw92i8KiOPY3iDbgEQIEUKQuVMLs7lQ692TiTKuzRsgouu6Nkfh17xBf+ixIAwipSVxkLiYZokTcFf4AlyHus1WQ0JJHPPcS89oXsw3FwjQzuA4l+mta3dlj/78T/7VrWud7773B3wOP/+FL7z25OfqzhpKz+GO7yhDFXUBOCkKlcaoc1fQQImEUOkHDKgMPY8aHvezelgroEeIIMRVhhYF5IUpCjBI0JxEfHECOIs+nqADjXqsI2JlDQkCI4QRYAQIAcFIf0ZtaTaFe9J+p2CiKWfF2ADzMR/F1IiOU7vOeMuzX0bENPdhYshnK7WmfDz15knjR2sQdmiazR4PmT2yxw2Xq+njNH0dB8FjIUI9S/5cSPdH946IVX8srzT9uS2X5BNA1uxFAAAGYyZLK0Fq3UopJUDQbrerlbDUWVQn2+3rH91+f/Peda2Oe70eE1QIQSgQSlzXXV5dGvZH/V7fc/3RIE7jPPACo5FTkY3yJEmsjbE+rG22YYT3hz0rVuEGruM4aZ4IISAqqtWq9VaNMXEcM8ayLKtV/ValWY4UU1wY+sXPfeHC2XNXPv74yo17iBiGIU76DjzPW1tby4vs8Piw0+kYo6u1ihBCahlF0ec+dSaKqlqq/b0DWeqrl29GUeXll1/ez8rbt2//5m/+5trpU6ur6zt7uxcuXKjVaivVtZ2dnevXrwOSwA8559WoaowxJYxGI6VUEARWsdWaYRp4aZo6jsMFe/e9d467x9ZHT2Vq+ZkrlUoQBMPhsNPpIOLcQgDMJFl8+uyp0Wh09/bm2sq6kSAawtpLpdTi4qIlsvZ9PyAeIs7Pz9+/fz8IAs/zBoNBvV4P3Ghvb09rff78+Wq1euPGjcPDw1arxX1iobyWeKEoxplSUox5garVqm11tblovxEdHR6PhiPfDQR3sjjP0xwAonokHB4nI41K6rKQuR94BvXK+poQIonT/Z3DPCuqYc0VXp7nnNKxPKJwLYGTXUNNToK6K0nhRY4fesQwlZvDvaP1tdNa672DXcdxGs26IUYIQRgwl9gHcsr7Zp/bWrVuZwTOiIQSQjzfFULked5uty0Hp+V1cRhnlIOiWZzng7LMDDXcEb5yRzb7IlwWVsIgCmzdt7lUHQwGNmdTFEUYhtVqFREdn6dp6vv+OO5izBhTr9dHx3mv1xuNRoSQMAyDIBiDhqIxVteybSBio9EYDodY0uZcY5QMCQVHuOko1wV22t1KI2jWW0KIfm8w6A8R0Xd9x3ESPag1qpVqxDhVKKUssjJL04QULuXgBJw71PO8+eaCLmF3e799d5QViQFTqVUajQYFmgyyQXdAa9oiiimlE1nJwnVdwRwb/lBO/NDzA89xBVDYvrdj01rTK2yXjsZ8RRopBK83akHFS9J4e3d7/2CvxTbKslRKqVKXhTTa2EvXL7tBEDDKfN8/PmofHRzX63XHcWorLAiCspCeFyilkmHCmYjjWCkdeCEgkVJGUVBtRrlKDtsHD1gOZhFAAGClF8bGQAgb7AIAEqxUKlrrHGPNlYayEgWnLyyDkFv3tjm487WlQsRGoUCnc9DPeM8yCUhZFGVeqUbt7uiPvvH1wK2Bo8OwYkqTZVlISk4Y85nKjV8JuUuTMjYaPM9T6Mi8TMteEDr9wfBgb69eq57ZOOcIOhp1RcQti5UxRmtizDgX+qcisX6MQSif+WMGNEwfgIlsupYTYqV4HmvDTjJsD8K0iSEZf99M9jUNmu2zYnf7oPN18qchD94gRWMA6Uk7/V8xCFpVpof+jVPUjz8v+3BTAGIVIAhBQoBzNjlRAmDNKiKimCkZzJr1cTKDEA1jaV8z9lce5OFnAeGTu0GMhjESXIPRkEtZiepewLdu3t6/fGO7fas/2hkVXVcYQogqdZ7n9Uat1WqlWbq9tVOv1QEgSRKj0XVdRjkDgoiWgXWaGJjOHSDoOA6hUJZl1s8s/K1SjfJBSpG7riuom+fFsBdbz6DfGbnMr4bVihOGTji30Dw6Ovrh2x80F5bfeuutOI4vXLjwzDPPCCEODg6ufHxVYgEAx8dH3W6XccoEAwCl5QvPLQB1sjRrd/tRWDl1/rTLvSTLZEx/8O23s0SrXMfJqNaKOvFhzpLOQacsS+ZTl/uOELJUw2SUZ8XS3EqAqLUWQhhjpNZFUSRZFh/mCIYx5vmuISaKIsqhUqm20yMpZZqPyjiTtEpcUluIXNethz4V4MaO53lKKet8xIORI9woigglRVrGg0SXBgwxEuMytk++vZLdblcpdXR0JPN922x9cHBg1Zc9z+t0OtwnrutGUTRl2waAIAiGScoY44Izziij2uiiLMqyHOaplJIRHo+SNGmXeek5XjWqR5Ww2+0WRRFVQ6pItV6pN2q9Xq8sJKPcc/xGozGkI6UlAHDOyzynlAruGGWSJMmSjBHheV6qRiouDNEaFRgihDAGpSkt1ZQ1tNoo5rBGoyE4z9JcCMGZMBodwZzQsdTiw/7Qljwsk5pSKs3ToigarYb10T3Xt6ViexigjO8EuSrjYWxKiCpVJU3n+KC2GIZh6DiORimVHA6HjuO4rms0NurNNE339vaKvIzCCgEqZcldOpvR5JyPRqPj42NSOPbEp8GYECKKol7crtfreZajKqKwmqZpPEgDLzIMj4+P8zILQr9WrYOme+0DSqlRaL2KvMikLoQQuUp7o05rsaFKPRrG3KFe4LmuK40EIIRCXuYoeFRrCMF7w54g3tLyIpG8fWw67a5sSy217/u5ynOS1rDmMS+Xea/bS3nRaDQWWy3XdTvdtkHjOCKMQi6YLKWUkouxXLd93ixnjiWINUwiImGQZklzvukHTr1el1K2b3WNMVYk0WjDCOdUOMzLs5xRFkURGgjD8PTpkBByfHzsJT7I0hhTprEsVFmWjgBOXG0KLdEoLaUquNIKPTdcaC2OrcsslxMA2JDXHih9oN4q7X/5vp/laaZSwoykKQ2djYuLfovzhmbgrMytBqJ2dL93//ZuFufYHDnsiFJKOM4vzVXqYYcPb9y85nuuCJzl+eWN+TNVv+44VKmiUIVHIqklGHQ8QjmThdZlpsGM4oM33/zw7tadWqXymc98moJ2XJd5IjcSAJRBKUGPE5QMTtAc/F8xEB+sszjDdIEU6bhUOoZvleOs6AOvajaFe1LKetrP+tjPZ0PNyUMzMbSPGOCZMRtM/7g2+KTvU0bh5Dj+MYNQBDAIaNCgIRO9JqUfcTjGAtlWnGl6ytN9GfMQpShMa9IzKP0pGA0ApNGE2PS90caiP7TWmOcmDN3BcHj7ztWbd95OsC2hV5pEOUFZSEQsZZl7RVnIJE76/b5lPB70hqFfqYQVLQ1FSggxCo2yZX0kaCigUail4S4VDncD19NeXmZKS6UUYQRLyJIcDAk8w6nwHN913SAIkuFI5rrgGaGmVovCeq2btlVprly9YZCePXdxcWnl6Lg7HA6llHNzc1JnURQJ36s2G5xTDToIgqLI99uxBC/L8t32sCbJwvwiIezu/sGb37qqcvb80y/2s+7Vm1fDukc8EqWBSL0oitzIdQVHZUypZSnTLNvf35/ygE4XJmMMugQRFapU8UJmhhrKufB5o1IdjUYFpqXOMk2FEMQhhsvhqHQ8kef5KB46jrO+vu4KL42zMtVagAaTDHJQA2s4ZWbiYdfa0VarFQTBaDSilPb7/dCr2HXfQpaiKJqfn+90OkQYNKiV9j0/DEIlFbF8k24FAIbDIaIuVKa0okiTLGl6K14Q5DRPRp00TpnVl1XKSlNKXWrjVaKq4wpGeRhEx51O4UvP81zHiwIsyxINAEE0AIQYZYwyspCqNIQawwxxQGqppTYSZWbqzarjCxHQTqczxToVZW6kEULQnKRFEkWRJXkmhNjMIgAUueVQlJ6HjuNQwhjllKgyt/LA3HM9a5iVUoDg0pAapvJUKeX5QXOuXhYyTgc2fVKv16UueoNeIQvLvN3tdGu1mlbaEY7X8CpRxWhTFqXjc0e4jnAZY/bVaBwOh0y54xSaMRZSJ4RwXVdLkyW59WlQg1GoUfkutbBEALAZUyvswZlI49x3Sy6YRhVU/GqtooxU3VyWKk0yykhY9avVanO+0ZBF1sh63aFUnuPzeq0hhOh2e3E6CgN84oVz/e78/tbR8X6nzEsk6FecSmvh+PZA+cZxnNANsyJL43SyYBlCiMNd13G10ckoVUo5nkADeVYoqaMocl0XOdiOiSKTBnVeZN2uybJsbnHOGIMaq5WaUkpJVWRlUZSFlmColuj5vpJaSa3LnBBarVUsHGTQTinNXeGWZSlLRYCmUPpuIFERzRjlDB1dYNLLHZ8ZYPwR7vvpqm1FMS2QxBhj2fUAgDiEc44GUANowqhDlEgGxW57u5QySXppml44dREc1Y+7HN3GgiDMHLePnECsh8tlVvTjLvWAOkAMrFbWn9x4vtVYzTNVQlYWeb/sVD23ULo3aBvQvgiHebq9c7C+upCWA5knp55+4qkLT1YqlWFvpIEC52isohxIRZT6sfHPf/qYNUjT6JYQwiepYxtr60lM98ggD/cp/ejQ8vHyeQbHEOWZkJMgQAkP9TtNt6zk2BThRHNwbJ7/NwqCxxG2PcjJgdmjfvx5PTgXnI2S7RM1a2LtqyMeqILDuD0J4IGI1EMni4iyYDN7n7lugJQSg2jQ2Nh6rC8MQCkZZd3eaH+YHFMv8XzCFSWEMeDc5ZwJmevOcVdr3ag3GeGBGw1MDAZA0267iwYY4cRQ0ATo+GpoNLJQDFSBMivSAALKCHeZy5y8yBinrXCuLMsyl71OnzGmtcl1IQsVuVGaJQoKQcF1eJwMSlk2mq2RMksrKxsbG71eb/PuXQCoVCpZUSimh/mwF/eD0GeOGHRHKMCA+c6b75w6dSqKopFUWX9QAEuS5Pbt23tHvfMXzw7SQaff1aDzQlJCB9uHkeOXULjcN6qrczAKUZE8L/Lh4dRTtOGOEIJQIjynVIXDeRAF/YSiMnmZHhzlxCNaa4d5hBOKrMzGHMKLzRplDmEQJ6O6qDdadWKo6zs+9QVziqJghGtpjEallCTKxliVSsVigC3ExNqMdrtdlqXneVmWWQFy13UpI0mS9DvDPCk556gIAqSj3A0EY0yUzGYj3cB3XVd4zEhEo7M0z/NcCBGGIQD0Rh1zVLiumxd52S43Njb6vUFvs9tqtQa9UZ6WnuNZ2xmGoSxVlmUuc7XWRVGgJgJc6ghE1MoEdc9IAE6KTOYy19XI9ZzGXK2fF1JK3/MqlcoohjiL8zxngmqpZSEJEjCgjU7jVApJCKlGNXuaslBBEARBEPpR6EelKsCmWJAAEgLUHg933FE6HAwGqAnn3BDDXNpcaHQ6HXutlCmllJZKnRCiUXWOu1Yrc641Rwjp9/uyUKPRyGoh2wwzIcSKNpKysHUWu/gbYzzPq9frvhP2OwPf90XoDnuxlDIIgmSYlTpvtVpSl0kap2mqSxRCaGUYMlUqyojv+44n6q0qMHQjPtzPc5UbQ+JBylhHa40ECQVCTRQFhSo6nW6tVguCAHSa5XF7tF+UWoOyd4EIItwgqvttrz9M+yELq80KL0le5JnWnHMGXHABFEtrCaVGBFSk1WpZp3bMK0WI3eDG4npZlqBJVqbpqOjRfimL4XDEczDGSIvHVoYSRggBAw7zsiwrUmlQB0GQZVl/2IuqYecwK6V2K4wowZExxpJRSmTpVDgDQQw1pUqyLIsL7hHGbVfpw6GSfW8dGZwB8tgbaUDneU6B+RBpJSn1iw7c+OHWsOh6oatIngZyY6lY2pjv9Nr7O4ethXmZKzfjYdVPiuHhftsNxbMXn8Fcb9/eh8JZbKz4XiUdHnMqNDG7yU2s1Nuj/au3PwJiVlZWBp3Rh5c/+tsX/+PPfuqTu6dPg2RbW/fLTHPHY4yitijpsRqdNgCAhFA4oa3oxx2UPJ5oghKb7ARENDBuQ0IA2/Yz69A88sNHBpoH1/bfxVYqaXcKk2bi8X2T6kEkPWObgf5vZIAt6Yo9ZJg9nZNEMuAhkYzpqyX6IBNr+yDvMol0yaTca/+URM+e1IPtqBNq+UgtbcqY2dIgIkGkjgBgRZp2DSSKFFTnEfcpj/zK4igZGmOSNEaCDve4z6rVqpJauzodZS73iSFlpoxEQpS1EDjTIGCHFzqGKMKBUMIF8wMvqgZRNbx/b9vO8Ha7zTmvVqtWNjg2aaGzgHPmOXGW3N3eLPoShXnq6SfSNO31Ozdu3tjf3z99+rTnO8NRv6s6hJCdw91WqxXVoszkpCRFmR/3svag5/s+ABg01WpVKdUb9qqrwVB3D/oHaZ64YSBIQCXlIkqLNlDmOVorREUEcUojJSgWCMtPa9fccTqXsRLLXGb1sB5VA2/gADhSicFgMNwep4U55zrXWoGRRElMvdSW3NIsGQ6HjHKX+9VqlbHAruNhGNqQN01TxtjS0nxRFCsrK1OtnjzPbXrZHk8QBABg6fkQUUmDBpTUozK2NpIQomRe6tz3fYvfseU9e52POkPP87hgtXrVNqQimCDxeqMOULTZ1zzPkyQ5OjiWhaJMCOqggSzNtWOiSggE8yKLoEYQQBotNSKiAaOJ1pobEgSRx/xBb5RkIxu0OI6zvNzc29uzV9L3fYWKcx5GgdY+IYRR5gi3LMsszXJSuK7r0aBESaw6NTOGW1+XMDOWYSjicswWLEkRlyMxKsuyKIo8z+2ksJV7a02zLAM6RjPZr1moXZ6WkunMLzjnqjRGgSyUcnSqMlvSptTSb5YCxrIidkra6NZxnLERAmo02hAwS3MA0KSsVCqDUT+KIkTs9XqVSiXPCqIJQYIGGOVG42g0clzhOp7gJnAj13NymWVxwdhIOLzZbHJOAWi/Nzo6OgrDbq1WM6ilKomviswUZckdPjc3F1QD5kASp06Foctrc8HicpPwppRFqYuiKEzKLTIjzzMppeMKwR0ASOPUKGSEgyFFVuZ5niaZlPL2jbtaayTG9QU6JE9LJOg6fjootNZKKq01ZcQRgjFmwKS9nFDIdG57B4wxWZYJh1NgpVJ5VpRFyQgnYgwMrC/OEcOKrEyTLM9zLlhEQoc5fEJq/2ioZ7E809VwSpXFOTfacOI4jkuIX+o872d9nYVO1Dnq1RcjL/C73f7C2vzyhcbd9pVCz+eqbM43aq3KcffwoHN4qnWxtVgXCto77TQf9kfdtIj32veDoCKYv3X/9pU7/d3BVme4D0zduusbCWme3rpz66mLz6yvnClzQqlLmSCUpUXpCoJAtKbTtKsxhNATma1+3OE6anpNYMbwMErRWgJAu2tDJmHaTOA7HWM93R8Zahq9PYK1PsGuSQ2PGCT7p8ZHqStnA/R/9/GnJJlnD++Bu3bCcaKaCV0fygHYdDo8UkuWE65vQgiZAbGbyfYn+fZJX9aMaNLsMVMYh9z4sBaTUv2j7b3LV9/sJx1GJeVUS6MRImDM8CQZDvuxF7hBM9Badzs9z/GKvCSGyVyWSlFkCIYSxplglAMSRABKKGGOcD3XH6ZtqUrhRQZUVpbSFEAQqa43a5bQw9YylZEMqeCCuZyVtMhKQVmaZp1BLxKVpbX5zjA76h8CQIF5brJUJU3RECG/8cHtIPR7cT/DYslZDGqhcEU2LFrhXJIk7fa2cARQzMvMkosdJruyK9dWNo577Xv37p/eOI8SXOGVoIku3Xrgcoc6lFFuqCaCRW7FklogYpZlaZrmMqFAw2pYaBgmfU3kKBlW65VqJXIDUcbHhBBAYtuoKKWUMkpMf9Crz1XrzRrhoJQyqJmga+urd27v5nluuatwgjq0CwsAWBwNADDG0jQ1xtg+qDAMrfkBgCRJ4jgO3JBz7glfSqlLTYzmnFPKdKkUNbLUjIECAwAsEoK6yvRcv1KtN7XWeZEDQcfz6gsVt83TNG0065bmrl6r69IkSVIP65UoIoT0R/2iKBinSqlSlaUsreFRspSlRkTUiIijvgwXq37oZ0VmuM84TeLUoK6KqjEGlUHEIAgMMVYTGhFtqsAixawsoJRymCb2NF3Xc4VHkBZZqbXWqIQQ9ps2McAoF9wBbmqVih+6lsmrSAtFdZIkc0stRFRKcYeGYRiQwBrgXqcXhqEr3CRJ+t1+o9FwuJPG6fxCk3OeZZkx6LqO4ziciTwrGArra1pXpixLu/ckSRrNuta6KPMwCpIk6fW6lUqFuyzP89FoNDffstwgi60KJWzQGRhjWMkII8qUhIHne37ggSZGIyApM5nkcZIkhIGSGhjmaT4aJERynUHOSi9w6tWmLNPQC4OlWuoVZaoIITJThVLUwcX5uXqz7vjMDYUXVkfpcG9vb6m1zDkv8rLsZGgUFUyjzPNc5joMw0qlYtvA0AABqrUe9WNKGeeuQ4UqdJ7mGrVBfeb0eaVUnhZZmksptdJZlvQGuSsCL/TKskzS2HHFwvLc3HyrKIqoFlABWmmgiFQD42HVp5RqozilhBIgyDn3Pd8RDgHKbQViZu0bW1wLDbBTAnFMQE8ppSgYYwY196jj8yTuKZ6tLM2HupFvZw7ziKFHR0etflhd85761Gns4fLKou/7VEAivfnFlsvF1s69pYUqq6rD4b23r32T+/zw+PjUwtn1pbOyZMd7nZ32bqb6WTmURT7fmDu1fgYIGcapzETkzxH0u50hYYw7Y+rM8WJtqGVaIGBOsHc/9vC8ab/pg1cAoNpioYEaMAQMgAa0NBjwIwVg67s8dvuUwsP2adpbTMkkEfvAUAFoZWbqvg/KwBOVxZkuXgR42Gr+/zPohHHjkQ3qx4PHAcnsGT14pXQSk48BXJMaeenAY80/1ZOdktmzm+X9mG1/0kAAqE3Caw3GjFM427sf39269sG1t7wAkZmiVGmSEYRSHiVJIlVZZiUAdNrdsiyVlkuLy4PeME/yIi+ZEY5wFdFGQ73eAJh2ByhqxREZ9z3PMbxaq1BOtFFSyiRNhqOh7/sUiB96jVbdC1wbpQVBoBGRue2joSkMq3JZSL/mlqI83j3eOtwKgsCpOv8/1v7z2dYsvQ/DnmflN+1wwj03d5gOExEIioRAEWJSlVxiSZar7HK57P+QZfODKVsSCZgEAXAAEDOD6enpnk43n7zTG1d8/GHts/sOgv1Fu7q6bjj3nDestZ70C6f89OjhUVLpenU9O5obo7jigMQUF0YmIGmUlppzHqJXWiEHU+jNdu2Ciykgx8k5G3xKiQJ5m1ZXl3oBbpgYDUwgQWIcI8W6rju/EyQKVgghJho248p7L6Vc6mXd1JdXF5tu5b1XVvhoy7I8eXiUeSnJk+BsT67o3GKxzDNIobn3XqCcpkkwebu+EUIAI8K9p7iPLiQcx5GIzs/Pp2mqqir/NoQghV4sFlmb+gAPFkJkyHLOrnIjUWtdFEWkQAFzizXvu6bks6pRxW0g74IVimNKg+2jV0yXi+VcaUkJul2fYqqKej5bCC4pQrAJGHHgJERKKaRARG4KiBm4Txy54Cqx5JwL44SExFLkrmiULlS77SjBFrYpJQDa7XZVXRLRMAwJIhLbE6iIUQLJFVKgCNHddf6Zgog+hgz3C+QPbQnQGFzkXNRlw8tYGlPWJWNs2E7Rk3eRPMvgLymlDFwlJZTIJbgbXHABCBiw4EIKSTBBkYqiYIzlFCo3HjJcPwuIjuOIiJlr6r2fpgkZSCmFKEIIxmiCtN6srJukqXIDI4SQ4fQhBADMthnccx/3NTQLMY02+rjrdlm0riqrZtYkilVZd31r+8CSWFQzzjnFaPsw9Kt1d3P/3sO6aEY3XF3d+CkYZZQw+qgwoup3w5vzddmYk/tHgFEpxTXFZCM6XkAhDCUY+7EbWgxSKdV13TRN0zTlmxrH8Xh+Wpal0pJYiikopbLvKvHIAFTJpakQmHNufbvppl3F58EGqVXTNF3XLf388ePHb87fGC0XJ7NpmihBCokSZDT+pltxgSJwJlCQyHCwfuj3DGC6G23emc58C4TJwHR2Z/LjXTBGhOiDsPVcJz8pDd/9nXef/eSmWVb1vJQVG2H94s2zpSxO35nVJw8f33/nq6+++ubZV/Oj2fd/8L0iLe2KRn+h5nhx+eLf/+X/rEpprWca3vvg3R9++Bsff/zxB+ff+fHP//2bK//g4YP3nrxzujwxRVlWs4I1WjYhCMYdl4wJTuQBICVMB2IosBhA/m8UgLX8WzhIACAYhEQMyQP5RAjEiP4uSwe8I8/8bX/1a3qWb7+Lw799+3VQwr81AP+t5en/hp+3r+ftH0F/Jx/6EC/h7YQghF8DnR1+oZg+fM3bf56yrMrfmDQzdlcl/zpKHJEBMcgamCll+jUy9sWvfnZ583Kcds2i3m2Gq6ur5NJyfuSm1TD1mVA72OH89UVI/uzsbBrs7e0tBYqOKiNPT+8BsRDCcrnI7dO8e/N+SSktlvOu60LyPHLGmVSSehrtkDDs+ljXtU8eeLJhnLqpnzRXWqKYxslwjcRGZ6+3N+evX7WD76eOSVRK6VJVszLGeLO+Xi7n1lptVN6J6/XKOdfMasGkLGS6TZvNBnjSxVlZl42o/RSqZvb865dlWeon9cXVxf3Th++fvtcO68mO5LHrBxtGYVhVl7N59avPv0LEPH201nbTjjGmlVxvVlVVJYiJYnaIuri6ms/nVTHHlLgAWWrOy0xT8TCFEG5vbxOECEFK6by/eHPFUSSIVTPLkgPD1HPJpBYxxuVyOY7j+fl5jq+bzWbPctEmpbTdbrOiZw7Ms9ns3sm93KzOeyrdmdVDIs640UZrba0dhqHdtTHEo+Nl2+82rV0s59W80IEHClzher2bNfO+G25vb7U0dnTtrpvPFu3t2Pc9MDKFrmYVCgopHCpXZwNHPpvN6nJmrdttd5KDEsoHZ/2kq0Yb3e9GRBZDFEJMbry8vKyHSmgxDANhEmzf6geA7H+T04u6XuSg5b3PxVZmv+QSPEdBlCy6xJUodWXZthtbzYvZYqZZcXW+ckPQoiYccieDCYgxJpcyCvo77314cXGx2+2IMLg09lYIwUAM/WCMyRI44zBaZufzeWEKlnxmCebrHIYhB+NqVkzTeLY845x3XaeMnC2a29tbXagY42Kx6Ifu6uoKAJxzMSQmmNQSCYNLiGiw5EHY3pqZDC6M/ZgwVqrknA9dv13vEsRpsH5MIHlRFKYuPU3Xt1dnD+4vZgvOBOMd48gYQy/clHbTJkwJRIqQgqTtbQuYUPDXly9CSAisUEVRl9FF55nUsixmiNh3fZ6ANHWjpIohrm7XRBSisn6KFJp5XekSGXzz7GshhFamLMu6rJWuxmmQneAo2n73+OjRyYPjL5/9quu6onhXCLHtVvP5nEngnNsxTsNUCoOMLRYzBLG17TAOfgqAxAR2fSfIQ4iBKw4iTXFEBr/5W79xcXVur/d91Bx3tdZ5QQxjKyTjglOIm/Xt0LcPz07e/+jxT//oU5fGq9vNEV/Mjxa8A/eVXL8CV6/ZxG/Pr/vbgY+6GgPXPtp07/QHQ/tsiB1d7fzoF2ZRf1BoDz988PeeX3z5rBOzcnljLo4enPKyPH38joWbTVednjyKYxqnbd1Il6ZZs9y2ow+TMtXtzaqpZo0WjHxwdsSdkbMUhBsFMk3AQvKMCyEUIwCWgAghESZGAFkKBknKpCRoDVqilCQEKC4PnFV6i6LHWU8ABDIEZh06D85iCDT6yBhwJC5AcpAKM7+UYIS/EcURsdc2c2E551rrnO6EENpORk8+xBiQSBKwmCDEpDjEBCmlBClmRwqWAIB5Q29pTxzCdpAOEmMoGXHJuWCYKCaKNlFKiSBPWAnv9DR4KqdpKooiY2JTStM01XWdyB2u+e1bgL+Db11rbd2UKEnJkUMIAYA454XM4ZNLiVVlVrfXVVVZa1vbC66GYZBSITHvvVJmmibJi7/WIcgfLjBfYdaTyc3MlJIXnEVglDj6k5Nqs3n1lz/9Y2vbdfz81frrmLyzohRVzWdQwKxchBC4luDRx8iiOJodE6TtzW6sB6FZ4MFUwlR8kluttWRsE64s76kIzMfV9e1MzM6O7k+u571ODsbRKaO0UURkRDlNEzmmpZZgiNh20wkhjo5OtNaj79c3q+Rws+61rj75+gsbx3pRXn7WFfO6t9NttzLGfPqrz8Fxw+a32/O6bARj7a7drIMxRkvT3Q6ro02McQrxdt0CQPIbrYxzrpxVzk5EKiVn3SRKtKy76NqiNFLzbmz7sZdSVsW8KZtu65UoGWPRQ7sdY4yCGSLqWxuKbcTEmGKJSazWm9U0hPmcb+1NxCi0MGUtuAzeAYf7j+8ROs65g+RcWG3bEAIrOJfibHk8TZOURd/3dnR28M45wcXLZzdlWWqxAIB+lyAWdpoYM1rU2/O27/u7dUhCCBvTdbzmnGcOa+7Z7guGKN0QASC6SSg+a+YpxbZtVSEbVQohDBg2MQjASYogpCA7tX6azo6PFDd9O6TJXe3eKJydnB4DEnAYN72NtqrLmZ5ty/XmqlVk6nuz6iHUR6HynF8poQom4PZ6vb3qfUv8vilKM/lRsYJzPhczgmStTZiOj4/7scuNXMmk9x4jK02VWOpdn5KnGCFFyZnkOggOKXo7EZd5HF6WZeYX5X5koMiAD3aYNZIb5AUVnAc/MWJ2sIyxUpUJAmMMCTebTUut5LqQZd9OIES77iIGUyrbB05JomnbNsZYVVW/nbz3TFPZlIHMq1evJjsKLp1zQAiOa17eXKwJUjOrq7LMEAeJhizfXPSz2fzjd45ytB7HcbidfBebppkdLdq2zZrJs3mzPp8W6j5i7PzGWV+YQin15Zdf6lAXlUEV0CQ5x9bdjG44uj+72V2Aioqbk/vL+XL27KsXdhgFlzSkGKNgPEy0Hlrf1UVRJIrN8Uk/9dvtdhJhvkBgpEt5ev+J72h720ULZZxjZHZwY3AiaVGlYRiCj+Sp203+mtHCKGkSSgBZNI3WurcjYQJDvMbLm2+Ojo8GWNXL+2fT0WazWTSz4oOPYs9++ctfdsOkjR5HCwg9Tt77o3uL5WKpmPFTbDfdbtOFKTTVPDsZxOQTAgGgVqoq6+PlyfnNLfwtAzyoi0Zy2U9dQHt2fPzu0bumFG+eXyJiVVXEYtu2gezJ/WWM0Q9uaKe//POflWX5nfc/2K52n/3i8+P5WfLwL37n//x+/QP7wfDTn/7lm9evBJi/+otfHFX3Tu/R519+enH1SnH90bvfFwZ3w+bl9VdPHj0yM0HCgmBErt31ujBu8s2R+uKXr++dvnu6fDiNYz91BP3F5bNmeWKOKq3KFCHFmPamEQiRiCUkAEwE++iLCFJ4wUFKrjUoAULkJjYhCnjLgyFTIxhjXIgYgQjyrDzGjMzJICw8VF8xIBIysTcEfqtmQ8xYYiYYR8DEOAfM/yEgUcREiSjzfuPeeZcgBchEnoRE2XwQxOEFfdutuiu7neNEhCkwBBAMOPgYIcXeRs454zzfHYSUizlKwXufjSlzL+tuHrGP64fu+q+F4b/xORyOmSN7+Mp26pUUkokUkHv0EBMjkLScFYwxPRaCyxhj11oAy6RPpPaJxR2UOicZhdCAyIVgjCWidKdgoEBbN2gjjVHb3fUX33zy9atfENhfPPukLMuj+dJbZ509OltAwtvbG4laKlGWVcLYj13O95umObq/yD6bOQFljO1FW5nIGVgqUtM0dV03TZNSAhn3J84wOG+VUt77YRgaWY/jmFPYoigyxDSEcO/+aXTJT2tvo7VWO5EwUYLedrHzXDCOKloIwQUXIQJWHIlxzqVQKRIAJIgRAhFZa523RWnqqqmKehiGXbtd77aZg1uVNRDGkASXWmvCmMsvRMwyGojYtq3hlbU2jyT3TsB7nakdBey2vbMeABiwumqMMaouvffBRzu59bDxNlRltVwu580iDx0ZscpU+VkxxpLbA+uyED8lSCkv8G814HIDMzM1c62c0bm5UiyKwhiz2t0c8Fm0Z/87ItKqIEqMMc5FHp+13W4Yhntn9xDRjm6z2uZbU0oJIcyiSCnVdS1nWotiqAZIuN3swpistYlioODChBy1MnUl66Yq2db3CYn8FJMDRpwzMbQjFJwlxlFGS7vNThUSBXVDG0Ko67qqSynl5CdEFGKBHIQQyBASxBD6qQ0+juO4m2Ie+eX6NVFCgbrU3dg3TbNYLLTWROSTY4xJI1arPus/QwIGAiJ4F5z1UgkCii464ZATAIQQsre65D5Me7FhhMTUXn064233CCzv94Mzgt26nc/n7z5RN9e3KZIW5vz1+YMHDxIk51xMIcT9F1dVNXRdUWmKdLu+zgTusix9tFH4EMKinFXzYqLBdhF08sxG0ccEYIkI3eg3tztTqaPj5bByox+tm+a6mc/nFRXXN1d29PP6qCkWfdvfDLd1MXvy5Em7a9+8eVPV5XwxK8uyH7uu75y3ztuUEspUFIU8kcPYexeUloC03W7Xr4ZgY7TZFRUDBRttiH5pjiLGwhS84VJL731HG3CAkrnRt6x3zknDTaWFYhFCI2ez2QwA+m6oq+bm+vYv//NPfuu3fmtxb3Z8ezS87HPuCADtps/PfHOz4yiycKRUwhjj7CQIvgW/QEKKMPRD3mz5nD0QVfcnr4cJpkSxWcwePXkUor28ufzsp1+nlIZxJBZR0vvvf/fv/+5v36yuv/z6i69/+WI+W1ZNqaQ6Pbk37Karq0uK2N30jx8+bfv1//h//B+ury7/zf/zfyoW2uE0VJfVO/APfvRbjLHV9vaP/uwPn118sRrOm4Xp693F+mXBjmSlBaAQeH7+bPP6pYAq2gDABMmff/bpi6u/mh/L79Z/nzGUUjjrUySeYfyQkGUYccbLJiYIABiAMUEIVAKU5koSIiBEgJjD21+rwBCRMYmALiSikBIBJMYoJWCQII8pUwpIFCAlhhH+Bsp3H4y9g5QwJUwRKBFjueyERDm6M6KUfYUpEhCFPf4DiAgYErC9We4dY/uvDRQk1CGEuPevDzEiESCBluWhfZqdJQkROGcia1nHDFXdy05NE/K3ZuDfQqD/dtURuKMb7S8JvoWCF1WptJRCUPTEQRkjNAeu2ulWCEmMkKmUgOsAlASLwzClO9OtfJoTYUqU+3L5T3KDIp/awQcOAMnd3t6+ufrsV9/8xeXuOcGU27nGqG0YxzDoUislxcjSEAHE5EdrbTe0RWmOq+N6XgNQHonl55nuPij26FCIIOWQJYpSSqJGxthkp2madKG1ztzKYRzHaZoQsa7r+Xyeo3JKiSFTUjdNM8lJyD05dRomVYlh6BkJzU2gSER5ogwhieR1wQQq0CCVkEoCkyEFRDTGJElE1A1t9MkYs227aZqy4k9+SiEEIUSIvqoqprizPobkXUgpdW1fwmxs7W7Xee8zUxk0Y4zZ6Cj00zQFmyZhhWZCCG8D07wsqijT6mYdXNRKK6nHYco+oYxxY1QGHubl1Pe9914ro5RqmiaGFGPsu54VLH9BXmwZoMQYy8aLmY6c7yKHimma9vmBlHinpRBCSBGIEuccGQjFs9qR9z7viJxLZVpRFsHP0iuccVUYwiSE0IWiTfLBDcOQtUc4k5xjnphWVTUv2dbtJjv229HIQnD0Q+g2PfMSvRAgydOwm0IK9az0YbTWKi0bVnPOuedSSKXUbty6YDnnTCBxIEqASTJOIY52stYKK3KKoLWuZ3N348q6qGdV1sOapimFxCNPAVyYGLBS1BTIjt7bhMBTSllKJdhgKgOQKJIQYohDcGO0iYHE/XHBpZQ++Gma4A7rkx+v1rrfjN6FWb3QwvjRM8bm1ZKT3G63Usu8qHLtm8XnZydVc1SmlLbbMLW9CwQhBuZRJ+RJN2J5NvM4dW7b2g06lEtpBweBiajsFNY3myMxPz4+vncqiCjTwYHQjylZJnmxLI/IQrcZvff1w/nR0REA4AVeXJ0LxetZtTxamlKPdsjvOoYUQ1JaYYnZ3j4rqAw7i8Awog1jSgkllrWRpnY3KSGxijXzpllWLtoQvY9ud2l9CMF3MZncSmFCBPKCmzwWWa829+/fv3d69vr16/tnl0/eEfPj+nZj+ssOEmbGXWHKfjs6505OTuqi2WGrjVwsFtvtVjDGhBZSSqn5tt2OvX3xzatdu23k8u2j53CgkwdHoyhZPSsZx/X17vz5lVFls6y4YKoQLk3jOL548eL88s35xbku5fd/9L2st1IUha6+s71t+2742fM/eowPHzw5/eXl6+12Uz6El1df9z9vr//k+t7Dk3/yz/+x1oVr+5vbazu6ENK/+Z/+53/w/UGP83l5sjxZVAuznC1/8fWnv3z9l3/vo3+k02xeKK31MG2/fPmrZRQPTz84cYNWJWFiHJGzSCHGKJhkBMgIETgj5JSr4dIkKZFzFAIEB8SEmBBSno7sCQCHkg6AcRn2rr5MsESMMQZKYD8SZQNCAkopJYhAiJjoWzMGxr6Vm7DTwd+QON8nPTFG71kkSECUsqxxRAQEligbHAICSxEROMs+9eTgrf7wYZzsbWKcmVIBJsQIEYCY4tIlHkKw1mXbRAAAQmd9Pdu3c3NplYOQc26vgwYHdvKBFvy3V8D0lgJJgm+TD28BEgXuIZHwhKDsgCER51xxTUTexeASAw7AKAYOOvtOYkJEDoQpxhCSNkEIISUgEovEEyIi51RqUKa+vXn1+Rc/eXXxi60/D9gFPrz34dPry5vR9vW8SpA6u2tEc/bwdP2mRQTn3GgHZKAKpUtdluX16pLffQ6V9x0YArI1b96BgkljTLttEdEFS5CMMQw5cNDK7PpNDhuZnVIURf5Xu22bUqqaUmrBBRJQBtmePTp58+JyWE0AggiUktIwYhRtCkNiFEGC4JJzxiXjgsUhhhAYcmDJWT8NFgC0MoUu3OSCC4KJ0pSQYBzHsR+ZYIUpEXHox3EcnfVaa+8CL5kpNGCT47SUUkqBiCxxBCaFouBTSkA8hrjd7KgLZ2dnRNTuWgR2cu8eY+zy8vL2dsgoU6XQu3DIw/J0cxiGrPAjpSyKIviQF1gOwIh4kCbNBcRhGee6YRgGoXmK5ClQAiklAhNcIrD8ZTHGaZqAkTEmWxxeXl4OwxBCaJpmuVhmnFff9YuqzhDAEIKzHhIqLcvGxCn64HJaqbgCgrG1IYSxtcmnPF1WQfV85Bi67RBGmsgBMY4KOSUMucov6gIFosCcQ8SQpEAgzISxfJbm1CpCAE6LowWXPDNx8yEjtSyq4h4/zSC7RDHrMI/TYK0tTRFc8N532y6GNI0uI6oi+DzGymuVcwlglVIwg3bbB4qMM8ZYpL3CRoDwtjgU3aHbokta6qEb7GiDS8kHFnujzRC7vIzzxYQQcgCWmo+2V0odnSzK2nRdJ6VcHs/zd/NkPdnZUb0byq7risLMT6t+M7ktpA69jdGmYJOUMmFgiMRiPwxZ/iL4VM2bOOJ6s+p2Y1WXKab1ar3b7bTW2QtrvV4DQkwBORhjZrOZtXa92hhj5osZV9xHD4kJpgSlPayPYoQgGZ8fHZ09PP1ifIaEg++nm4FLxjizbhrHkXpDHJngmBhEZkdHPHgfCq5u2lshBCS8uboVQtRl8+LZS25gMV/ef3BmRzd049QPfgqVqq1NDKWRZe7c+OiQQzUrBXDgwIUQUgqO3Fm3ul73fV+eztJbn5wiMcbM3AhCm4ab69tpmta323HnzYx778dxRGGYZDc3N9t+Zf1UVObJ+08H187r2cnJou129T31/vd+0G7az378yVern77XPg0QIOHWrm673c1qrYxZNdfn/Tdswn60TAEk9uqri6cP3z1ZnN206z/683+7C6uPvvfO93/0/Uv/6vbm5Y+HPwwf4YdPygDlwyen9y6PPvn8z4/Yu0bq956UnGvESOARiIvEUkROHBE5MSSGiBw4srpiAMBYrnr3mFvGmPPxEHcPfdSUknOQEoVIiQA5Gs4SMETkG0d3jgCZihNzsZu+xVIxBgCUG9HOYUrsroG87zWkBCEiQZbUSEQEGBGRAUGCRMAIKNGdHgTmJi/9+iefXzFGzhFYJLA+TJRQsSIxBeB8HKfJMcZLUxlTElEMEGIUd2bP+WpyEMqkkUNkpf9//OZc+O5b95j4Xa8bgXubBEejJUPUWkAKHGF0iCgZMjuOkMCYMkVKhFIo4vvRe+7/7yvRor+7zqg1E0KHEIahB5W4khc3nz978bPddDmElcN+tihTSv3YSSlns1k+m5hgkWJVVZGCc26mm2pWNU3DBPro8hvPPzE/jdwdVTIaVQghMCFDnuNEjLEu6m7sok/AKIQw9iNy5Exkl1AA2Gw2ZVnmug0Rp94BQlGb/NOBkxK6UJVSqplXLAoamJ1cjKxUVa0r18WEMfkIQEhgyYUQhBS+933fp0BlUUmhk4ChG23fqsJ4FzjnMSRRyLKohn4ch6moTQ6KOdSlO6bWCJ2oxXxR52gXQghgEVFEIZhICIwiBy6YTIgJ0UbXd32K5KfIGPXdgMCmwTlvISIFOAjZ5nDeNM0wDCkm7z1FEFxm7I9ge0TSYd0e8t0D8DNLSmUFUAAMIabkGfNKRaWUEFLKPDGBGKPzdpO9iudN5nP3/UhEi7ngTAz9uNvtpsnaSVFApng2j0kpITGppFSYkUfOueD3/uhCiG7shBCeXEopOXJtYtyFECiBs4EiSS2rxpAMTKMyUhhZhkJyBRGc9Qx4cNH1gygYQUJE4DFbbO9nNABlU9TzKif6uQ0gFDdSW2t34zY/lkTJg7NpKlnBJZdSMs6ISGkxDtYFh4i5u0BEyScOnIHgKJumGXtryeWVnCD6yfloq3mVOzp5px9+ek67h2GABJxzgSI3qLNspJQypuB8ICKhuNaamBvdQCwJXRW14YqllJSRVVVN07TZbDjnDx8+PD4+VkqdnZ1Z7MDz2E0+eiAmgCdPYz9Z7JGw69vkwaiCIQ/eTb1dX236vpdaSq63q7bv+3EapJSFqvwUt27HJeOcM84oTt6G4OPUuxQo7zhAElIwzQZOzjkQUBYmUnDeTXYMLh4/mIcQNuvtzWotmGzqGUPJQgTOhVRcIiUY+rEfO8LEJZtXrKlmRFQUxXa9G4ahaZrg4uXl5ayZL5fL9qhnsPVTnHq73W4TJGPM0I8ppeCjddMtvzXGCB8tAKBHrTUSL2TFkc9KmYcBh8/htBUlU7yOrbs+X+1ue8mV5oW30W475y3w9ODJmS5Fb3ez2ez07GTeLCKFcRxxm6QUQjKu4ejB3HDz9YsvrleX//j3/6vl4uSvfvxlrY8Zqpv2wt5sX15+sziet4Mtaq13qttsv/P+Rw8fPypk9asXn/h+R9Vwtf36+dWX87J8+PBIzMZU3eoGH5qT46/vP11/9PL1s6cPnzx5/H6hK+fIeQ+YtDHkAhIwDgxRcGQCGCAykvJwr+xOIwVTwgM//a8dE8HFw/EFAMBAMGAMGU+UAIgiAWavvQSASMAzPxUR76g7hAgMs+/RW4N2IgTmfEREluWWMTFCwYExlJJixl8lSjFBopS+rXoPRcMhczo5WrTD7eXNy9vN62HstNCL+qSplqvh1TRN3kajqsX8qIlLzmQIkbGTrGmeUsoE1pwwcia/pTjf/R/ynf0dFXDaX0VADuzOPboueUyRADmnyU7e0zj0SksuZ9NkldBCKEREYHbsYwhNk18GUBZOzF4qRDFto8csLj+bzYpqMdrdxeXLIb5wzn3+2We3/UUI4+3mAot0ena02Wy0NEqpzXrbd0PTNJLLV69enc7uc8a5YMRIa80EWjflKebbUT9DgXKszStBa43AvPfTYFNKVdU458YwJkzeBS+9ZtooU2iV67mu62KMxpisFNFtRqFYIypdKJ9sjohDP0QK1awwuhh2U1y5GB1ibXQR3WCnMTgvQXLEGCDYEDjtrnfee4Y82YEzEX1kKIwsZbnvNA7DkEmcKSWllFEmhRRj1FI3VbNYLGKM0zBNfjDcCM6AKAbvos3BL9PPspqQVkBEUsmqWUzBUAQh2MMHj5z1m/XWjhYAkLEYkp1cPqaJyFk/9OPJ6bGUEiVTSrnJexcgC84X8pBUHXaZEAIjHlL/vLAZY8YYF13OhA65YI7Q3nsphZTSB3fADyuljo+Pc/qYew+ZIWOMGTsrhMiebzm0xOgShtGNHHhRFDGkabQueiBW10opxUukoIAlmQwljJS44sFlti9I5KbQvDYgItcoUFIkKRRFSJG8DW70280u87WIR8LEFCojpRKAEIIXcq8rAgBKy9wbGHZDrn1TSomQMVaUxhQ6TE4Z0dQzDsJbzzn3wU2217K0k08UeORDPyodOOcTTcrsR2n7eQ1PgOR9yEE358c5VSIi59x8MXv96s2u2xllELFoyqPFSdu27bhlggkhGGf5we5H1zxXcfKgOJYnCLb33sV+O0rsjmbODaHbDIptEwtuCEM3uCky5IgiTKlfTz1u57PFfLZAYhxFt+v7oQOAzWrDGGuaxg0ujzNijElA7p/rQi+Pl0Yb5+2wG2KMIfmyLKVQ7aYP0VdNKXgFRNW8kBVXRpRNMU7D5cXV9eVNu+7/i3/099zkU6R206UAYYyCcRm0XhhgFKJ3k4+TDylwwZqmGWhUQq9Wq6PFcfNg9sknn0S/nc/n1xc3TTHXymREW2kqrfXV5bWUkpDatiUiJXUMaeo8RiGyJGlKyQQTY1RcUSQttXWW/TpzZt9CxFExLZnWrFSgjDS532rK8l5zKg1fLpeDbVfr9Zya29tbPlX3zo53q3a3bh89fRgBLl5dK6U+OPte6FN1ov/zn/3k5PReVVXXL24asSThJzvYyc3Lo4vVs+SwWZaLp09+/uVfEdHDew+P78+OC/2j3/z49ctXQ7975+Txf/Ff/EZ9dM/HjhdNZcp3z97/4dPf/vTFX5zdP9VaMA4iMRcDQUJMACl7ygqGnANjIDjCHdwbABCBiKVEMSERpbsAfKhfc5i0PhIRg5Sy/Q4mIEYJOScChISU5RgJgXG6E8TIKT69LToW+SGu35UjAIAhJsYJgOUKk3NijAnOuElE5B0GRwEpBmIQsmnxYTPkWjPPQq6vv764ef7Nm0+uN68ihEIXjZxpUV7ZFykRBNRSl6rQsmIkiejB6T/JFq35XMvqRZhv5G4hvPX/v/OTnctSSgDEkOVDvO/7r1/8JQDUdVFV1Xp94+zY9/2DB6cffvhfrW43RVHU1czZ+ObNm+fPXyDix997kG8t0/y7rsuXVM95Simryr3zzjvvvvvu9fX1J5/89CL9fOyHaeslqqHvhnaYy2ZcOeCMM8FRdsN2u95qYVStIbK222UdxEBhcuPo+oOkba45MigmF2QHA4Dtdjt2o+Ayn+ynp6ejHRhyIYRPXjBpdCHVnuCXRXSJKJcXucCyow2R5dpXSK6UDFPquyFCKIoCReQxaeJxAh+d6xXwZOPkveeKS9LBJWsDQBh2kxKKCxF6GsaeiJp6Vug6QSh0yRjbbrejnYqigIQcBRBOo40xIjCjC62M914KJbkAD5Pbu/QgcimEZNKjF1xiwhwL7WCtp7quOUgbLHAstBYop95OyaaUlBaCSS2NUYZz7r0PzlrrVquVUkpJnYNl3w+MsWEYOOyr2xxiDyJWw647/CERZf9BAGDEFFfECLIOSkKIAOxbU5PDtIiIvPdITAntnOvbITeQhRCCyVldF0UhFOecm0JnMmgCtb0MhzUvpcTEh3Ho41icotYFL5XSUrpibJ31jpQnIGQMIRHGyLxWJomYWAg2Ousll1oURBRdmkbrnGdRCc2SJ+ctSoIIFCEkXxaaCRmSzw3zDNlDRGAglGBi3yQ7TPSnoVdSV2U5dCMIMHNVBcMHxr1ywWUPvhycpCIIMLkh53/Rg/deSVnNy0xV8t5n+m/epPk5L0/mSsvXr1+nSAgspWTjWM7Mur3dZ+Ti4DbmY4yu3YtyAwBxyElYjBEc933iUaUJr17ddl232ey2192snkVPtvOUQCgRQkhdUkxvp06iycMFAGf9hIJMLZvY2NHGGO3oXR+0LENMFOhkNh/HMfk47Zzvo/UjIRljQgqSK0zMuylRGrrRTZ5zjiCEQa4ReFJGNnXj+ri52n3x82cxxnbbRku5+g8sVFUlG0YRpm7ccw69wMC4Vr0fpJS7bdst+rOzs8KUbduWReitffnNm8xfL8vy4cOHZVNs+7XgnHHiAiU3SJhcgsSYFSJ/331ciRAoOOuDjMT3yBf4a5xUGa2bgHBZHWtuXLLEYjUr1QJ8cM+fPyceHz29PzuqispcXl+Mt+GTl5+DTvcfn7Q3IzIoiuJye/O7R98rf2guhldBuQePH7T91w++cypT8Q9/+INvrj5vynkBzesvrl6+eAX1+PB7R4/e/a6e8dlxxQqQpXzy6NGymX/0zsef/odfXF6+MUelF/xnX//Fd45/46P3fnRUHN9/v57PjgXo5CPjQgk+2mDtaJgEIAaIfO9Wi4w4shCIsXy/PBHGFHOLDu/k6fGtDwBkVFOgxFhiCDESQnbNQOAEBIgMDxIawPCtmHsQkCQC58MhAB/qVwCIiSEyYrnPHBGRM8YlSgMpYQIigiw6kigBSwiK7rS785mVt9af/9m/W+1e3fYvk/J1KRGnbrsaNgMeDZJryWSIctdhipAcQaDV1WnGcVRVdXR0lKV6tdZ2DH9rC/rv+sQYc1qDuIdiZTbnn3/6Hwqjzx7cO/LLq8vX0zTG4Kvj+LNP/+zFixfzZvnkyZPg089++leffvqplsWzc/mtP8ww5GEqY4wjSyltNpuU0jj+UKl4eXn5+ec/hafXMYKZV4bpm5trgbLRi/ZqMPf4Zr2d1bNZNbeDc5NHw85Ozp4/fz6fz5cnSyKa3LjdbYZh4JLXdZ0f5kFwMec3dnJc87IsFVeCS+99u21ztzAjP/O8PLed224bmG2aJuN4c+wZxzGlpKSe/LBrd8QDIlZlLQyzyQNAgjD6YQy9rLSW2m1j1/b1fc1HFiNDREzox9Dt+uCiYQZDtn5iShggNvaub69iPdV1vVwus5Rj9ibq+z5BxDv9qYxmQkSlFFnMBWIu/YuiqKpKGMYV37ccBQkmd/1m8L02yifnvXfWI91UVVUWFQO+Xq+1Mlm0GQgynxWBlUU1jG0IYaCx67qhG70L8/lcaz2OY9Z5SHew+VxI5d/m3AUAch2fb+TQljgcSkRU1zVjmLKI5p24UAghOsqSyLluzjE+50AppWkIWb2EMUaYGLL79++fn5/3fR9DUqIQQkBi3gXXDlikotGlKKQsxtaN0xCSlUlLJhAgQvTkIwjASJCsTavVSqCc18vkaByscx6B29FDwgTRhSgIOBNaaE7cB6dBE9Bkp2EYxCi2u633/un777w10P12IHJ0ukBgFKEb22DjrJk3y6aso18j9QQMEDFEl1JiyH1ww9Qj8bIoA6fcOD0+Pjal/vrFV9n4Ln/z/eJU6uLy4sMPPyRMXdfN6vnrV2/aYffdj7+32+245FnjLFEkolwQD3acpklyWde1t/768vrm5kZr/d7s+3G45ELywFfnGwBYFEfe++EmJEghRaUlF2wcJkpQGnr86ElOrIlosVgsj5bZU2vRsOfPXnRDx5IQTAlURJESIAkkgQQQMPgYXJJGFrKSRqRI0zQwgYUu2q4dp3VZlrtNK7UQmgnJ63JWliU/UtvYrV/2BDERVUXDJa7bdYieF1gIk9FYAGBEkYAoAHoRuU2MtDLXVzfbza5re4ZcCsWt6HeDd94Hn1JiHJtFJQ1PIRGwsi4UK8Z2DGMSTPgQsXnY5JQK3mrpMMY4yjx0yVs0r2ylVOQ+pgAAmQ0NjIQUSik7s9W8qE90wun4dC6QXV5cPX70RMXyzetL5+3irJbzNIVBcb2sTx5Mv3V//uiP/uCPv7z65bu/dU/N2WK2OK7PXn99vjyZ/fB3vvurrz97df7qFz/79MWX5//wH/zD//r3/1nq0Hbxk0//ks/cP/uX/6go5OpmHcbISb/7+LtxJ7dv0u4i/eijf3C0PBvSTdUUAGBUGQP17cBRppAsswf8ZC7ycmxY1C5zb3IOmE/8aZq4boQQ0zSllLJdedbxqVmJnLhkKCBEN00jABZFMbbyMC+Eu+ExY6wdknNDSqmuiqpUiIiMhBCXK9H3/TRYziQixkBZnyw6SwjIgHMMycXkq6ZoZpWb+ixVk4ubXKQSEYfZYfPEGOfz+Xaz+9f/+l+v/J8656wdAzpttChYpOC9r8TscBwH8rpQRVF472dq2a76hw/e+ae/98+/88730wh2CEabqzFWVTVMbhxHpQwAt9YCiFqfvE3XORwQESZImrMi+ljPxOff/Pg//qf/+8N3jj/7xU+qqsj3JQpmnQVGx/eO1m+2EPHk5GS5OFqt1ldXV84GIpJi3xuIe1MFukuCGBEBEipigp3eP+n6Nv/NxcUFJYguTcMUxsSQP3r0aBfW4zjO5/OqqrIZ7fHxsTFmvb3NrWZjTNas2W63t7e3948eOWettwAgtZRKAECIvmo45xwS2slN0+RtcNZ579fbUZf65OSECVxvVkRUNVVGHWf71YP8Qg45XLAM48oC0VVVIaL3PgbHGMtQnVzf57rk3tlpPpgyRDnrDT1//hy8mFXzqmjAsRw+QSRlFBEwyVQhiqLgTJCD6Ig8+uC0MsYUNzc3gMnBeProuKjN9lXbrQfB1DhYRDw+WYbk2mlrVGmMMaXqbb9a3xalObl3zAXPEkIHN7emaYwx1loBKlerOWXJ1y+lNEXGSEPuTs/qWbfrrLWReymllgUD1rc9Bj5r5ggspP5u9iGEUAAQXLDWLk9mWbYpFxl93wNAWZZPPr4/DIO3gQHv22G72aVEdVEziXcdeOp3PWOiqWvGmJ7x7OsACGVdjOMYoj87O3vzq3Xf9iEEijSNVgghuXbOVfdNSgmJG1E09YyDvL1en5+fq6OUceM5W82YAGMMT/vEMUPPcp0jpRRkJjeNUx/Jq0I186pZNlVVXV1fzmazuq6vr6/zVCWPV+azxWazuXfvXtYdE0Lc3t6enp5iEayblkcLrfXFm6uxs6Vsoo3IsW/7vu8hMQ48pQSEjLHl4+b+/ftd171+/Xq73R4dHX300UdFUWzW2xDCarXqdp0SioFAxLIsA7cPHjzQWudFeCgPnHNFUQBSIC8kZxJC9M459DLE4IMzpW6q2dCNl2+up97+o3/6uzc3N9vtlgHnTEyj3e120zQ1bJlx7xmdl62gp2niEqtZdXx6tNmtfXJKywTx6dOn7iKen5+vVqs8kvfe53/OSjFMndQSDjDhSCdH9+bHTUrp8vLSWpsh09nGu1/tctdqNpshYu6oCSG838/dcxQ4uDUoYYapYxqYYMMwIGGtZ5BY4uHJkydt2z578bUpzenZqXOubduHjx6M46iNEkLcrK7vnd07Ozt7/OTRz//sZ/n7H9wynHOMsb395NsDzn0jCFjuMOCditMehZTAWY8MSlUApwihaMqzs7MbdYEi3n98ogqM0RGE+++cPHzn5MPHP3j51Zt23fvkHA7vPGqacr697W+uL0wt13h+M7yBl7265c28ualvVaiAw/MXz1yc5oumaMzpw+Xt6vrf/8kf7K76y5c3Tx7fvzdf/OQ//+yD9991YxjXblYbRaUoK3ksa4ZFVXhvQcqUpHNumvoYKEUqjSJKikH0kTHU0iBiNtYmonVgjCXOOeLeGT4lEaOM1jMWpVSC877NEE3pQtBlSjFiAMYZMsa5QcQUxWgPs3MEgBgJYkRMKcGeXaAEAHnvY/IA0Hc+BeAsUnKMCSUZJQo+IDLOkYCscyhiUSopow9tWUvvh2fPn63X65RSdsIpiqK/TaXWBAkYEbP9dLVqLx1ex5ByRVsoA4KctREiF3yapgRRa6219snFGJFYXdfJpn7snJuUlsjg4voCE//+4x90Nx0Q00KiYQQMEZmuUkpjaAEB1Z54lSdMKSWtJQUK0U/WTrfTm1ev37x5E3i/WMzGcSRISqnQOhec93573T558ji6NE3T+fn5ZrPd7XZATErJGRwSmsMAPh8rXDAmGfjkJ1+0hQ+hKA0QKmGmYRr70U1BcVWYUkqpUbfbtt22DJjkUjARfdwMm7KuiCgjcbwLQogUyeii61oAQIY5J7NuEkJIJbbrXebXppQKXZ4e1d779Xr95mKVNXulNGVRWTe5yU0w3bt3r8MuIzjybrLWpki6kIwxrTVjbC9lIKUQQnDMJvM5YKc7Md6MHz4MVvM7XS6XMpYxJNtbDFwxLYwgEZlgYYKqKZpFzSR4732MCWKgyBpiLDFJSkkAYJHFAQZrp2nqp15hJMI9Egf2DQDCCABFUSxo4YPruk4Ice/evVGNbdt2bZf5PIUptNKcFCWYpik3uomIc54YxbAXls+nSowROSqjHEZkoAvpXYgUkSWXbKGKYTPs20JIMSXBJROsEHuLtpy75HlqXh4XFxfBRc545pEzxqQURVHcbm7y0F1Kbo4NQz5N0+1qVQdTVVV01I/dOIxSyaIoGQmllNMOET14IbngQkhGJF0fYoxAIcqUIjEUo++ZAi0Lo0rGmJuCx8hAIOPR0zT1hzMzZ1Q5O3TTxCU0qs78z+BSe9t3m25wQ4pECYBQcJk1io0uXr16lV/33gyKscVikVKaFbWUkjORHywAOG9TpGV5HKbkIPjoQ4opEhFxJod23OnWey+ZUlxPvb2+uFFKWW+zwzxHPg2TnWxueh2C3CH6Zkky773z9uhkWZeLyY6Mo1KKEpAXXPFcWQZy1lpKoHlxdXHtXYDIEoGUvC5rTnxHO3J7PfBDiMnpWuLovb+6upRaSqHadlcURdu2NLHct3i785Hjd1EUzbzphm4YhjyxbtuW62/duzPROeeFzoUQAhFmrMk4WuccYz4lPHRf4A6iCACjH2OKQzcKJYwxSGgnSxF1Lfu+V0rN5/Ntux2GIftpKqW22+31zdVyuXz06NGTp0+GYbi+vn785NHnn39+eXmZAfmm0Iwj51wcLC8Oj2OP+CXMW+UQffOXlbIOIUgjmnk9hWG3XYXdZObi+3//u5/+4pfXF7dloWN0s0WpjO77/vn5Vz/7+S9ffnY5DtO9hyf/zX/7z+7PHpx/+VefPftF+QCXH6nH5QkjnsUEZA2F03/wh//2O99/58l3Ht5sLut58fjx44tXl8VC1vWpUIxCvL66HqfhrHoAgAt+/6Q8WxZnBZtNgLFUhdZdOyVE52iaYkoghJBK2ujOL14byY+Pj6UU19dX2a7k7Ozs3r17mw157xmLxph8ShKRUkppmCZfVQJADIPLaWyM1qUs70GIe/o/MhqDnUI8ANborU+KnHNGmWnHUub5IWLTRCUNInobEVlezdkQJld4wFRVF8jT8xfffP3p19//wYfjOH7x1U9evHihtY60zcnjg8VvF4Ue7OjjVM2lkPirly+/efNJCl0IThvVNFUIrh96wlQ2Bd4hkxljhhed7bpt36SmLPVs2QiJLy9efvWrZ3/8R3+mmPnn/+yf/+i3/2m2tFMSpymkhMgZJGRyD4cJmfBPCTkKyaK3lAgJtWIRUEhWN6Wpi7muhtf9NE0pEiIvy4Wqdc5DOYiu67q2H8fJOSe4EkJwLuDOyuKAytknqgIZYyG5YEPXdcMwtC0/XpwopgMLFPciu8MwXF5eLs/mHHlwYRqmfGT3bd+27dnje5lmineE0b0Gb4yc8+x7P0yDD65pmmZWBz/EkBgJLY1WWisDhIWqHj54pJSiQP2ujxBjSETEBRv7cezH3GXJY0VIQJGc9ZxzrQQRxZAsOYZcGsWQ51SAM8GQM+SccSGEDzaH5HypuR01m81iK/qxj+NeoJizxDhwya9XW6NhkpZ4iuCJyIEf/aSJJ4rJphA9EPM+jlsfYwBOebqYlUZC9ImSUmqYOkScpkkZxblwydnB80rY0SGx0lSz2lMEijANNoTAySMiA4aESIiAKaTBDocTLSsutUWb2Sxlbbz3plaxDYkFyfUUBmHY3esGgJCHMlkYBAByfpDrmDxIBoBgYwiBGAhUSinOBAAyyY6OjoZhWK1WADiv50pqIqrKMtpIClOEsbOOh+OTEondXN1O0x7DFUNMKfk72DBOaq+imiClDhGB0/yoKpsy95z6viei3OpwzpGHGGOKpLWqqio30mOMt/HGFEWhCiIah2kYhqkdACDxGMeB7H7K4/su89ZyEz6Pw/PKn8/nfd97G0JIXeilEk3TKG6G3RSSH9vJDT56gsQgIYUIgBQxDrS77ohIgi5Y5SbX3Q5FQZaNxhhjDCRIIXkb8/0ySflNYWaZp7TnW1Mctr0yUinlXcAAUspptNRGbhipSDxGFyY7hSnwoK/Pb6WU0cdxHLUs5vWcRUGWheBzNpnnStmlo6qqMbU+2nbXnp2daa1TJMZYt+tTCwdL3PSWMAAyrow4uXfS+Or169fBBc21QNG3Pec8hYSE0ceUUgopYdoPkqzNjfe83wGAc3WYER7ANJzzBBRc4JIzwYZpgACGl1ywXFI/fvxYGTF8sdfpzC+rruth7PNzCyG8efMmUfz4/e+4aEc31HV9cnbMOQ9bT5DEIbL+zake/rqqQ77zYANLAoExzozUVSqERuKxMUfD2r/58jmkdLSYf/j9RsyVBZJLpcikkdk1dcp+8/mLLz7/6k/++E/f/fhpecq+8/D+4w9Pzr9ef/7zL7fb7dOPHly9utq0m9/47f9u8O2vvvpcSlXV5Uff/87x2fHVm6vfefqj2/PVy69ePf/8jbb1f/e/+5ffOf6+lHppThkpXqQoOBAAJx+AKDm7B9EkdG/OX/75z/600vSDH/ygKIqf/NVPfvWrXy2XS4/fdbA6WX4cOhtTIkaJyIYeEbWoCTCmtFqv85HHEK1z1jmPLZLUssQkc+RGROtHofQBJPz2f0opKTHPZYlTrmNCCEcLxTlSQi8hRi84MeRGsUChKsuiKm5WF988+6vnL7/54psvV6tVZy8Q8fnrX23ajfHmy2d+t9sxxn7v77OyKt68eXOzvvjOB++99533IwWheT+EGAkxOh2EEFppqWRVVLtuS0Rt29rRVVWVAo39FGyUD2RVV23b/sEf/rurV9eXr1c/+uFvHZ0sGaZCM+cTxiQ4ueBjAMalZDqllChxAMY58LsUlfnoIfqotU7otcH5spKMAJKUfByg70cgJAvJAHpxeXkxr+9ElBjTWmcuV1YGuIN7hG8HB4qIUqSYu8da6q3d9m1fsHocpml0RAQcYowRIo1J9Xt0T27+5HOcMbbbtkVR5NQ1d02n0U6jbYomJ8I5Kse0/0Fu1NNgQWNTz4jo5uo2h9L7xw99cpvNphs6bZTiGgSUZXl7e7Pv2gFyLgSXMaQUyZQmBxLv/WQnm1y2Gahrc0hociM33/thLEp3eqh5KrRab6MllrgQPIUUUuARBZcKTHDQrYfIXTEz88Ust+zafmckA3Ixxhh8HoVIxbu+i+R9wAgpxrhtPcikC91UDSIOw7DrdrkZwBhDYJQ2+7J4vpRCWWv7bpimSTOTM0tIkLtoKaXoY7frjDFFUbA5m6YpI3sBaX7c7DY7VYhhAK6Y0nxoJxumqqy99877lAJjEMHH4IkoRJUH6nniTncw71osMLmUUgxJKcUkOef6vjs+Ps6lj1JaCLHd7TabjRCiKutxcD4ETEJrbXQZfby6uKHAYowMuVBCCAFpvwZY5Bgp27wTUWKxrM1sNksWpJCePAcOAAKFQEGMSO//Ya6/8U6yRlfSGKWV9pNPkYIj8EIIYX0XIfVhwD1TkQkhgGFVVDmQbFabbtfN53OKlEKigAjobeKc5k3DaLy9XK9XGx1G51yKILkUggMySsAYo8C3qz6/Mg6aEVDgipeR+zwmyPUGleicG8dRMMxzN2P2GrcAoJRqZvXt7W3W4s5p8ThM0+CUkxPZFG0htClNWYIVyfYRAksEfoy2C5715JAS2jEQxHwG5lIk17KIeHL/pCiKeqiBQd/33oV221s3NWmWn2d6ix4ppfQ8uuCYorN7p71tz19f8MQLzbzzIJhgigmBxBkwjpKj1MpgdkolBELOBGcx95nwzhs4pyD5wB+GwSd///Te4nj+/PnzbbdTtUHAlDCnDqY0dV1nx47cNsgzkcOIpO9766YQ/b2zU2SQWwg88aI0RVHsrb4Oo7tDGMY71GtOgtid7rkUinMefWh3ra7FcnFkKmmMPn99rpUpTLi5uEo2ai1JuGpWui7ubkcMQvPCW//s2bN6UXz40fvNvXLT30b0Z8tH/oQ/N69ut7swxS+//PKDDz6oyvqLz3755OljY8zN9erJkyebdjPYXutitb6NlpbF2fff+a1/+MN/vBRnw2CDIyY0g0iMQgi6ZHHkDPdswl27WbXnz1//6svXnxnVdnTOOX/16lWfegX2y/Pxs5fhH/+2zUSRDJ/J5/5oK8AmxrharYQQT58+RY9XV1fOOa+6khf3j5825iRFiCEKyZG4kDlryU/yDjGMbNyNGUEjhDCaSymdn7quA56yjg8QEhFDXhZVVVejc68uvur77vzqzYvXz1++en5+/kZp+eO/vDbGrNdrKSWHeLnqr66ulFJ/9ov/ta5n2+2u3XURp9X11ZdffuW2A4ssxeis36zbolRlaTRXlMgnlwUQtv02hqi45kn6IZ6/vmjqxvXh5no7bqfZvL7/4KyoTaKprBpu/WTjvCzHYWq7oaoK51g+apVUB/BXjDEJF0N0wQvJnR+GoU0ptrvtxXajtRZCpBSU1PmAq8rmZmVjEdmeAMqJyE7ee9+Ou6IotNZ5JB9CyIev1HuN7pBCcMGOdhom53II3FlrlVJFpYFBVVV1XTPB6kVdFIUQYhzHDP4nou16S0RDN1hui6KQUnLkHPeE46zhpbUGVDkEehsAIIY09tM02vV6kzGrQA4ApsGyJLQouGRExIixyBVqzUyMETwQQLIELFlls07TYQPmFtzYAxAyxr0NMSSlNOd8GAYpGN4pg+ZEJE8EU0pZD9U5l0KKFJFRiD5G5kZHwgdmuWEpEkPOUfBJG1Yz5Ilb7wdTq/JI1fNKnOPQDdPWAZDSMhEDnUxpcuctQcrcj7yAFRhM2LZtsHE+n0umRj8561OgoqoYMe+8HT0AGCOk0FxKSIPY6yYyIQQysVjOZ4umanQeSSAHbZTRpm+HYeyV0FprY4wPbvJTipFxlIrb1uIdKyELZ+aOfbvrc483RXd4sHlSzjmvqirj9sUdqb1veyssISGi4BJiHgHyhKkotJJaay2ECi6M4xh93G0cETFEIgohIkJOyPpxmkbrvU+RUkrbzS7vAu9Cvp7g4zTabwd5BaUAPnk7OTv5FBJnUgrlA89nbKIkhChMWde1lHJnV8GFWT1rt+1ox+PlsZtc0zTjMKpCcpYg7UO7kLxuKr+GQ5kYQoghL2FMDsbOMsYEKMYYRu7HGAviDc+zdiSkOyqwcw7fQp3GO52yoijqWcUl2263wUVTF9basZ+U1PW87n07BcdBLOqjgoJbk+87RizYQAmyOk2KkFJUQgVyWbc1S7LQnUtjRaqsjx8/ffzs2bOLiwuO3FsvpbTWvs2xPIwzWMVsDGPoaqmrI92MZRpx3d4qZg4Ns/wP8y8EE4klIqJIBMSAcfw1HsoBbpLfl7WWS5Ranp6ezmazr7/6Zn29FqS00OM4vnz5splXSikjTCa7E6TtdpuDcU5fqqpqu91qtXr06NHx8fHr16+z3EoONN8G4MNFvN1kPxTB+Wr2qQHyGH2/G30QFRnvwu3V5tJfS6nP3jmqZkJKXhbyZrWlMZ2/urq52LVtJ4RgUgQKx0cn77/zwZvuWbseV6vV8rsPjhZHT588vb6+fvnFpWDq4vXl/+vf/L+rY/XdH37XWiu5ijHaNpwe37u5vhmG4YN3P368/OC//+f/vYpVSpB8GkdfVSpnjgmcELwoBCUmGJum6euvP//q5SctbiPvO7759PVtzkzVsWrZeru9jTH+8V/8m/zcDw3kXHip8v4h4LF6tNY+e/YsxmjD5ri5r5mYPZyVShKRMpKAB9ynaQftYkQEIHZQaSZwLjrnJjuM41jULJ8gKZCdnHfeGDOfzyPjP/6LP724umCCjNF1U8YrN0UvkghTmMLENXfJISLXvKiL9XB+cfucJX528nA2qy7O33z2i882q91sXkBibhwDBudUXl6MMVXLQpeSqU3c2sGBZCmisxFcjGMXPPHEq7Ke+vBXP/urdtf9j/+H/9PZvQdSKyFQqpiAQoKEveI1MYoxBh/CHXmp1HKITFaKYapLc3Xj+nHHJHhrYyRjSikjwCgETyl5cKTDbDaTUuYsOCMgsgj/4RQ4QHbzrBQ4CS4ggO3sdru1ox97K1AMrmOCVaZaHi2MMTGFsi6MMRzF4eUmkJC95GLI5j85tB8ovzHGPAVggnHOpZZcMM553/cMeF3XCGy93my32+ST93FzuyvKI8D8bNFPIXkeIbjJIzDOWPbNjTEWBXAmjDE+TIRvJbsEkCD6OMUkpQSOwUfvg5KaIQdC55wxpq5r733bts45zvlisfD32dANw3oa+yFF4AIRaHAuOVWaQhvjEaJPu9s2RZoGC4EBsbRXtAgEAjgJxe4/OGs33aW7xoSz2YwrzhQAp6m3nPO6rqOP0+Akk8YYIwqfpuDClKZCFzHGFBJFQsJs1X7grjjrg48AABFSAJus1NIUmgvWzOuqrrwfIYF3gQHPlumcCWttb/umacqyYBF95xJS1ZSz+awXNldCRNT3fdq7cfDtqp01jSlUjNHHoJQ0hWasiCFlgNvNza0dbF01x0dH/TB0ba8LzTiGEHxwm3YDAErJKVkppZBcSmlMEVXMsbwbHIM96yZEzzybhOvYwJBn4lae5R/EtL39Fo+dSxchhBACXAySONsz7FNKyINPk5Ccc26t5ZwxjjGFmEJyMXoCwFm9aOveWquEGYYhFdBvR8YYV9I7t93sMcP3z4qdGYZhyCbz3sVsAghRY9hXjfHOZCXj1ySPQog8d/PeZ88iIYQQmI+sQzMmb+3VetXUjTHGjs5ZP3TTOFgzL1Utxh36PkzbMCgbXArJoYjd2DnnBIqmngkh7OSij8gR476lmh/aNE3GmLOzs017NZs3s/k7TdMwYCklBmzezLfdlt7iX+SNE2M8PlskXrAi9nFVHvH3Z0+6W/vFp18zK6LbC/BB3FeSgQIHPITY3FvKbyfd+V0eDoHM2uCKl5W5Xd0Ulf7hD3/IGPvJ9qcSZQ7qbdsmCM28yZbJ4zjWTXUYL+apeV3XS7fs2t68WwguldSWOYYshjT0O3EYqr0ddBGR4b41/fZgOKW03W1UoQijz2+FOHKYrHv34ydC8uXJYqjL6EMKkZEUzvTYTrGbn9T1rJ5C3227rz97efHV5t3fOeO2CLdy9bJX2idK7WrYXjx/dP/R5fXNh99778HD092mXW/WRhZFUVTsZLW5dC5898Pv/de/8d/irkCv1ldtagCAxT38J0mp9lU7g+iBM4UQr65fffblz7GwE2sblNM0pJQk48G6XOnWdf35+acZBJsJoNmMehxHzr7OEr6IuPOvxnFcrVYZmTLZ/umDpxyjVtp7jxAIAosJAATu9ZlzWkVEbWuFAM65FMxZH0IATEopwKRNIZUe+tHHaZjcaH0/2q9efvPJZ5/GFIxRjNPN6vp2tVosF3XRpJSkUlyI3NnWxoQYK6xv1rfRJcOLRbO0adQNv18spy4wICW1YhI5y/3Soii4EDnkzJpZGBOCAEpAAYCNvXeDpYiMFBDru/FXn3/x7//of7l///67773f1POUQCjjvb+8vLq3vF+WpdIqgu13O+99ZrBc33ZVcQRJx+Rvbq8uLl6N2IcwUYKu7QGJIAF6VXBEGtz6YLeVgx8iSqFTSoXRe1g+57lnlRfqdrcWXGaZiMwUKnXJmWBFmi9mRVHMj2aM42gHxtH6wU97hvR+qYvEABgD2wcGTAlFRCzbSSbgyHOvNRfogoRUBWMsBN/UM2PMOE5+8hRISU3JB98z5CEmBE6U2l3HOWccU0oJI+dcsORtCCEUutTGzOr5BHv1g7xvM+aAMVaWOl9nBrxYa/OND348sGgO6chsNvMLKKtiJ9oY13ZyUitZckLS87k2gmsEVsYYvQ1h8H5MsgbUMVlgDClB8jCspuSpNhVyjOT9GBGRaTC1ASShZO4WVEWDYYghuSnYcZd4oIjIuR19jFEwJQudZR2/FcpI4ILPzzxEL6Usyvr4bCmN6Kc2pnhze00sehs5RQQGEa31nAkOMfporeUcE4tSCaPkbD5r5nUjl7kFxRibpomIMmJcMsWYSJFCCCjQGCOViDESg9zWms1mUz9N1mZhprIs54s6S1xxzgDAuimfeIFCGEMuuQBYpCi4WJ40iBhD6rou9pEiGzvvp81iOQv+Tmo97RubGXh8aDv/2ghvII8euSOiBAl4SkiBEjKKKUQMRVUjYte1UxjzMlBKbTab/BgzhHgcR4kqTCmkMIU+piiEMNrEJOtFJQzXRjnnptEO/Rhj5AwRSRmhtfTeUyLOefS+H1vRpTwaEFwcissYo2IqQ7hzoMrQJM55t+0O8yA7tm5ymNBbb9MQU3BTsO3Wt0QYffTVSeFH7yP5ZF2yEClE76NF3D+isizn83mM8erqinM+m80Wy0JyNdnxwYMH3a57/s3zybottpmszPmvVavWWqagbMyIuwmmoigqWZe6Gofp+vM2pv3UHChnURnIvWdw5qxxH/IIkR1gB3tCeW7mFbWWWg67/s3F6+XxQgp9dnY2DVO77Q/ZVZbKR8TdbocMlFIxha7rXLCmMIh4dnbGPfgpvH79+s3L83TH8zSy2BPm4G+gy5D2PZND/ZHX03w+Z3Ivfx9sCDyZ0syKYryJPo0Y5G63vr2+gYCC9NnD01a8mR2VTx68o5T6/PPP2l1Pnb7edl+vf/lg/nRz0W8vBlFT3dRNOZO6Xt9sHj958s/+yb/44tWnvR0fPnj44tnL0+OzWs86tn109nj1chNc+u3v/uZML3xwIU5SKx1jpOiD09oQkfcBwNkx1mVTGIbku2E1DDfROHBH4ziFEKbe5ZQCEbvt4PnAKyW46OOUUgqCrLU3tzfLZobK5QTn9uWbPVq1aJTUIU0pTpwnKdC74J11wd6hyrNtAMSYYoqU0jvvvCMEIOI09udvXjx79myzXSHiuv1msVjUVZNiipGkUEophvz169cvX74M0SslilJFCkVRnJycbLqWMQbIuVCIaIqqH6b1et1eDPnari6uMs6o79da6+1mCtEJxbRRIYR+mEIIUso8nTC8nFXzcmYYSu9Ssun29tboQstisFMIoapqDqJt23//R//r6enZf2n/y/sPHnfduFgsEOWnv/zF9M7tkydPFsePykZM3m67636ibjT/7g9//uDsvfns9Ghx8s2zr1+9fFXdS8qwJ0+eWDtKJZQSNo2AsOu2z15+8/j46dRb2INc9iA1IlIKcxw65JXWWmstKxggZVq5YKJSTbfp+74/e7g8OllKJYXkCYNQTGrpusmnPYgpUciNvoSJK8z8kEPHMqf5VVVtt9t8Eg3DwCwTkmesllJ6GMbsPT5r5ohcSs85HwYXQjClFkJNaciVXAhTCJFrwShrySIFgIgQsR3aA641/9CM4IjBBR+yYlqKyVlntNFKjwEPkNQMa8gzqoSxbirBpZtCWK+lkfWslFqiyya1do9f8zT0tt+Oj99dGiV9H1mQ01QoZnzvhsnhEdRVU5bletxu2g11cQ5zU+hptF3XSS5rM4sxhhAZ8hgoguOcM2LdtgshzOfz+WxOROub1tmQJSDyxxhTFlVMHgG2221Erwo5+UEZGWN0wSqhOQQpZIpkJyeYEExkAMt2txOaVTNTNWVRGUDKOzEv9cwYzFCaplqklLpu8MFVsyLXH+vtyshyvV5rrY+OjrHA3a4joqZpnJuEFkqpipV1UyHianPrnJNSMMZGO1KgrEaCyLnkR8sFY9xNHhEpQXSUPAzDlA09052i52GawFFQ+paXDwAhBiIysgkhBOsTeuTIJTAOiOTCnuA+W9SMsWHqsplg1u1q2za/9BhjFndcrVbtuENNyEkakckz0zRxishJ17KAohidkNxOjogYyDzVHu1IRLrQGjUiTnaX17mWWgiRAuSuvk4izzXyG8xbTwhRVdU+WBA6Zznn2ugUIIBlAiXKoXfrtpUG69Py/uNTP6bdZtd1HVF00QXwyIAoIXDvfdM077///tHR0ZdffvnNN9+cn5//49/5BxcXF1dXVx9/9N0PP/zw6uIq+mG33incw5Xyw8wVaghhtbupuXai4wySDXZ0C3Xv8bsPV19++XZD961u7n5ZHv42xzupdf6GhzeId0gL59zR0ZGL0+eff/74wdPT09NnXz/L3yFngd3QnYrTxWKRIXJFUbx81a3X6wTx9vbWWrs8WpxUR+vV5vLiahytVmocJiCsqgrvPb1Pd0Bw/pbWblXWgOS9Bw5MgLXWBZffxDANMYWiNE+fvPPsq+fjxs6a+QquFosF5/wwuq+qSkq5jVdN1Vxd3AhQtg+FqCSqQpfd2nbi+p3fPD19vIg+Xl3daC0nGn7vN/7pvbOThMEY49p0Nnt6Vj75D//Lj9/70bu//OSXP//JJx9957vH9ez/+n/5v6XgtdbrtgJMMYQEQUtujPLeT1M3Eyerzc3iZB5x/P/8p3/7Z//5P96217t+e/bgNB+1GWSPhEaWUkoOPKOZEqbJjc2sLopivV4nD8vlMvdnMu0hH9m8JIm6xNkPP/7tjz78uCjN/QenR/cWR/IeAHjvN5vNxcXFixcvrLXf+973um1cLBYA8PXXX282m/V6/ZOf/OT169e15r/zO79zfHx8fn5+dnaWNR9CCP/pxz9//vL5rttMvj95sDi6N79dXbtoUbHj4+PNZpNrzbwih2GYtj4ENz9p6rru+16CRlA35zf9VZJKhOQiBV1yFIQCtJFSi67rMlIAEe/du5d7aC+ef+O9FyiqohFc2d5PoyUi50I91/cfnz557/Fu2Nze3s6bhRK6T93TR0/Lslzdrrtd3zQNI/7s2bNgHee8ntdM4Ha7bfsdMCCi0w/nWpqhGxkJiGgHb3t38eby8f13QvBCCECa/MgYk1qklJq6Okx/i6LIMplt2+Yulu+Tt4ki7bpdP+60UacfzR4/flyW5YGlCgCZOJurzJzLT9OUbxzd3mw8B7asvhlCYF5mgmBeKpnr+dFHHwU1bjabXIgYY05OTkIIL168MNMy796MmeB36tkMWIIUY0wUkIHWuqwKY0wspxxCMm2U7qgm46bLHKSsz5WTDyJSS73ZbE5PT/Nl5yE3ANzc3Dx++IQhv7m+cWOIIdnRppCULhjxLCG5971xse97kZgy6vj42BSq61trbYh+HEdZ85N7J8ipG1o3+b4dXRckk7PqiO7gb+HOKYGIiopn2lW+kqZpcgfV9f729rYsy7qut9ttjHGxWEgpxzAg4p33WkKRRwm8WMisnEUBd+t2u+kEiLIsQcasWpXzsKIo5vO5lHK73Wa8cTZ5lFLmRnQaxDRN/+j3f/eb598IxUL0oxulFJsrF5KdH89AJGBQ6KLdtrvbrjIFIlZVNZ/PU0q5q6+UAgb5ZACADNnLKKrgQqSoNJdG7Ha7yzdX4+CUUIZXdPc51E+52Qt/2yfrorxdxuX/C66k4qBjIo+ICCL0NA62eVDmmQtlYS9EpZTW+uX1N2VZlnVR13VZlYA0DEM/doWYzxbN9erKeWt0obh+/c257bwhmVdmbiPl0pYxVurGey+VUEaklJABIQ3DkASUZckECCWUUsM0jNNQlmW21Tk5OWGMffHFF13XnZ2dVVW1nB1fvL7cXO6iQ5YEEXGJutDzeUFEu92u7/sc6XMty4QEgMVy/u6H73z/h9+7WV//px//aQghQXz//fdLU2Vm4G7bZhB7pathGJC4ZBIRpZEJQtd1xw+WIfnFyWx5Orex7+xOaCxKs/1F2Ky2XdszxrXQDKVgQik1dEPuKu3zFc6zaxZxyvnN4REBgBAie1OenC3P7t/L+lxjay8urmKM6/U6z8VTSnl9AsB3fvO91e36/PzcWW+MyYg5rfXJk+LhvSduCD//6aevX7w2lTk6netCiUPMf3v2iYjOOWR3pwlyIQQhKaX2M2NBxFLivjkuXZrW040sZbfrOOd7qCqwbMBS3uOUIAWy0YZAnvzkpnbbVXpWlGYcx2ky7733XlmWPvp6Udw7O2USnfW71rohLaqxOSl//1/8LsTav8s+++Tz2/X5rr3+jz/+ww/f++5ythh6ZgqtlPKRrLUhuLxpvbdFY4j5Fy+/ef78GxsmVUgYKYwJeGIcFRciMYoYXYwjbTabEEJRGSFEdMmraCRyJvquDTbkrWiDHccxR2K7Gu8d30cxvHz1DBk9ffp0Nq/Dm/imvbq8vDw9PR2G4csvvzw7O3v48OHr169Lffz8+fPVapUD8DiOl5eXbds2swqUc9BNaddasRlijFFKWR0pvHY02QePT+4/OPVk2UAsQiYpZZxwboDkwLlcLsexF0IAQkpp1+2CxynYyIAzQpYYJERORBQhRbjenBdFMT+phmFYr9cXty6Ht5P7R9Zabz0j0EaUZWlHOwyDWwVIbLPeJha5ZAx4t+u3qzcnj09+8cmn82bBQWw2m83NjjHetYMSLGHy3lNI3dBaa8u6NMa40ZNHbwMD8EPst4Ngcjk72m63KUVjjJA8lwI5KdztdgfUawhht9vl7ktVVuTB45glZzPSeLGcn50dxxiz5mo+6fKQKXmYpj0jkN+ZOiNj4zjmYzGP6HJf6xBXsgZLHlIAQN/3Wu27JnVdZ2gYEZ2dnV1+vsstqRzC82mOiCkkwn1hlJmpmbd9PbU5sB3C2x4BHvYertvtNvM0cizP6Xb+spxDpJSUUnVdAyMA0lq7yeeA7b0XUmcr6QOoLd55MJClzWYjeuaDizEmijFGI1WiqJWcyVmsYllUU2HjRPEtEexDpMlt/4PgVFbtz2mBIJmJpHll7tFhKRFQJhYDEueYMcMxJkiotJJSuruYRzGHhxhCUkoYXcQYg49d2zPGhmGkBEVRIrCu74ehzbIGCZzQ2I99jLEpGqI4jvb88gpJTXZECeXMlHXJGKOIOVc43FdKKZs0K6WaeXMIlvmh5XuRQqaQvPcooK5r+VhtN93QDXnQ+Hb5+3Y38W9+8jf8awGYiMyMzxa1aWQEP4yDnWziXur49mbPmh4ZgVxUpVaGAc9pojZaSimdynVUVVXSSe99iDGvAYUq3Dl4vo10ybc/TVNITAihtBRSzGaz1vbOOU6YkRkhBM72aqxElNcY53w+nzPG+r4vVHWnxsMZZHmNAAzW6ylHEyFEjnA5bdp1fcY8tu3u/Py8mpX5nBy7ERKWZUkJtDL3798Hwi+//PLV128W9ZHgsus6qcTDJ/eZYq9ev7TW5kN7GAYbB2VkVZcJAkpqlhVXOHU+hgiMhxDc6BlHxL3fRh7S52dCnHKOfoiAecAhhOCCZ7zY2dlZDsBa65cvX2bqRM66tttt27ZFUaz+7HaxWNR1nQoiIjvaSNFHf36+UayYlYvlcrlarTKIT0opDpfy9jAYETkTWcuXDrjWlFJKl7fn1aycLxrkNIRWVtik8vryBqGappHfaXdwJoKPQz9Cz5UwIYTokxJFWRU8iWmaItnJjnGVTMNjjEwi46i0+uyzXzIJZw/v1U0FFC9uX3rvC17X/dPvf/jx69/6zZcX31xcvPmTP/+Td995T2qDLbeTs1PiAsuyYiwNY9d37W67vXd24hG/evnls+dfOZiKmRZS9Ne2XCq9kIn7ECiMFGy0uwiBKyZK0RCk0U+uC14EBabg1G5by0Is75ozWnOubNe6wqPBV1fP1+3txcXFZ59+0a46gbHrut///d+PMb588c2906NHD8/s1P/kP//s5cuXq9Wq67rcYjXGnJ6eNvfi4w/vKaXacHt81gDA+fn5zfb6xfXVRC0roq7ZENpNtyaRtJYsKs6YuRNwICKtFDbNuB1dsL63PvhxHDer3TR6JYuzB/cAoJ+6yaXIPWAiokS4eNAopdSci6YSDeajeaJ+tmyKoId+tIMLzOpCSV1wDcmJolTOj1M/vfP+O48ePcKElxdXo7Pg+AfvfnR0dPzyxeury+vVauVtQhaR0PopL/cs0DpfzDZ25ccp2CgQbe9s50lyrUqgiYgppYTkFNKh0QQJvPUBQ3748c7Uz1qbD0TnJ2s94zCbzY5Oj5DYbtPmJCan2znWGl6RRzfs99gBTpzDJN6BkPNpJYQYN5tcnubyq2ma/NuQCb6cV1VlzN4T9Pj4+IK2GT66j7sp5ZPr29MZv9UAxzvD+bzVM3shpxHLqsk91WmanLdSCSFFCEQRJFfRpyz2BAmDi0ZxIfkwDAy4D957T5CKqlB+fw05pAFgnq/nn55/nHUBGQAAF7yqKlkxQGKcaaVSJCUiRFz3W/Tf1naH8RgAQMoTAIaEIYSJptwlAo6HAKy15gfZDYEQIUGGJkkpOWMsQpwGK7liSU6TTZFyop9C8skTkRTi0PPIL87fUdHgzp9NKTVrGl+EcRyvbi4IqdDFNE2CJE8CBDKOkTwwY4zBiNFFhlwKFkLIqyvGGFxIKSWWKFJGkwEARYo+UiQAMHOjuY7JHQZ43qVpmNyd+vrh8/8j+uZddviCtwNw57YygJaNKYWclz4I70IIoXvDGGMZJZARXjkfjSGmSN6FYRiGbpwtGi54DKltW+sn6yYmWHCBa9lUsyAis/u55tu1FtwR7UIMCfaOKZhQCKGFJkZ5SbvgcqyaRpsw5EzLOVdVVTbzyDz+PVbrjlMTYkiQpsHm11TXdVmW4zimkJRQ9+5VeZHvXm3avn3vg3ebplksFpRgHCY7uYwLOz4+Pj09ZRyvvll1uyHFPaS5G7pF3dx/cvri2csQQ0qJA2eMc84ZMALWnBSSq2FXXry66reWg+JM+MkRS8j37ukZ9OOcA4Lcdsr5blmWhy40ImqjnB9vb2+llCnS+ma3H/kTbbfbEEJVVXlYQEQpUPCRkss5B3JMkLqhUxCmaXp4Vr373tO23V1cnt/c3uKKBN1By97O4/bpdgayQxTA8pdJKalwjz94793vPF2v129enZNHBlJy5b3P7ZrshpGB40VRaMWqonYz3+9GyZXSsiwqY00E369XUhhE/ub8dTvuikoPtm2vxuvb6yfvPX769OlytlzfbH/+6c9Zkt89+Z36we/+3u/97m7z/Z/+5Oc//cnPPv38y+X8RMlFiI4oIMI49reryy++/uXL589JxPfee6+qzdfffH61vuCKkaiCD+11q/SRnKXEow1jcBAs2imlSELwGCBRihYCJfCsNOWInjNJlNVcnTGmKhuGLE24ulmXjTGVAYCbm+vNZTe0fjZPTdMcn8ydc0+ePpAKx6ldLOsXL1588cUXm80m13bZr7Su64eP5G/+5g8BgCh88MEHp6enP/vZz37y058yhicnx8PYQ8Tb61XXdfWsst4LAUhwQO3mAFNVVYJoKo0MkUNRFmMxpYhlWWy3O0Tw4KRm8+NaFSKyICRnhY8x2tgKIepFduxxwzAME+Occ40swOTG0AclFNPcGCMEA1CLxeLjjz/+0Y9+tJwtGXJg8urq6vGjJ48fP5Zcfvn5V//qX/2rv/zLnzhcx5QAjNSintUh+qI0xhgZtPMuebDexYkQeHBE3qpSEGXaK2OJwR0KyXCTTx84oEkZIPHJdRDRp0CQCCLnHJCcc3bl8tiMAW+7LjeuAbDvByLK81dkDCI67/u+r2blYbCUl31WP1gezzebjZ1GxhhyGm0/DAPyGVl2oOTmaiNXrplblWkV+cTJfwV37aVElHmBMYVxHNWxPKArchgOITjnEiUGTEhRsAIRGWeAwAX32VonQYp7VSnnXDTp/v37uc9sJ5cgVlVldDEMQ/LoY8i5Qkr7IpuIxmHIB5APNiNvGVdFUUR04zgG8tooBMZoDxATib1d+OLdJ/cS3i4W89/uzx3EXKDnLnqMUXIJAJj2oy4lNSCxxIa+p9BxPkUXQ0gMOUPGBGORA0MiBGCIPCWw1nPOz87uX19fD8OUs3ytMEWKkU7OlqsVWDedntyTUl68uWg3gxalbESzrGXBmWAMWIqQMVOF0Zmfkz3JBJcJE0N+IMXm/O9QLGYvPC4YYbLWUgAAKMvS9e1fewh05wnxtwbg3If4m0XwuOm8d5MbZ8dVvSirqqImxRivv7rOgSHLZeRFEkIIwWmppdSDHdu7wXYMMUbYbabRDnVTcSYKVUDBbPJ26A/Q30M6lVddXq55QuqcSy5xzolhRmZtu3Fy4/HpsTHm9vZWqSJnWkRUVVVZlqvVynuPBg8pBaSYviXXMEqAjAkueUbJ+chZWp7Ou65DBlVZVE1lreWCLRYLBqLd7aXW+r6/vLoQkk92LMtyu2q9jTmlG+1YRW1msqzKyY6THZujY8Pl4Lq+GxjHalYJLkMITGYXz2gUr8p6jO2BTJGdSfMbz5OgO9yJykdBHngbowJIgtR1HQIri8KYsuu6vLnyIs+pfF3XI41jP07TREiLxcIUmvGSiCa7ubg8R2IMWTfuXHSSyRijOCyUvBry+kgpxZBiCtM0ESPke+ZlVVUPjk4++MG733n//RffvHr+9ctx4xUWMHEvxtOTE6XUmzdviCUX7bbbFEVRF/P5bC65uorXzvptu83nhZqh0qoqa6MLUxle0tHxsqqq4rv1v/uDP3j1+sX1zdVHH35sWIGSKPpfXP7H2//4+vd++E9/+MHfPzHv3LwcX319ufp4FydjCiUVbtrVs+eff/75L15evuq6reB0fvlcSrnZrQGAImxut1070Fh27WBlwiISJkosRgjRuyEKJRhj+3Pc2pzCbzedlFJJOU0TEKvKRgq9Xq8Xi2WiPeYzFSSSockHCzbuEqbV5jYrLW/bzRdf/SrG+PLly77vtdaLxSKrhuYf8eDp06kdrbX9phu2PSxOeGK1Kh89enx9fX17u7JjHEc3DUkk6ls7u4elkSmMYz9VVWVHt9vtTk5ISlXXNbHkkwOB1ayEZDnH9aqTBmXJy5k+ejQrF3qKfYTQ3/bGmPz2vZucc1KI05Oji9e3mTDKJOeJWTv56I0yMckwBl3zsimJx92wKevi6eN3ZtXpvdMzIuLI66IZhvH8/MJ7zzUPKQCjelaXTeG954IRpNKUiusp2U23s4NHEJAopjSABSBElEr44BExG+tm+zm6A1PkOphzPsUxuBSmEANkPTwmmfVjolBV1bxZhBA67IGhYDKGuNu0mVYBCRhjkkskDDLkfDFjmvLRmX/c8fEJIe12u3Ec277NVQgTrNL7czDLOCulpmm6urpKad9dzNO1nFATEcKdlRZRoui9z9tq3pS5wXXYdLkBnuFdGULB75R6EDHgnlmYrzCXJtM0TVYiYqJo3ZQomUJrpba7jRYViX25cOhgIWJmQnvvpZTNrNZac8GMMZe79TgNjKMyUgldFQ1HbqQJ9tvCDt/Cs6QQImZXDA7sznWEoSMPd/QSzvmBwK21ZsD2ZWVIDoMQDJEprt3oU/KHNGUPLpsSEU3DtMUtY8xNLvrIgAkmvPUHzzsu+JjGaZi8L+q6lkweHx+vbjar1ardDsaYqihni1oYkSf3LIngY7Cpj30+ZPNjOTwlLt7yPYNvc462baWTRamkEfl4FEIsl8t2NRxSw7d/8XfVwYfIhL+ulKexopHaaOMIdheLRquKc8nKstxfGOd5w+b33rZBctkUTfSx73s7+qokDrJZNM5bqcVsNgshxJCGduy7EazPC/vQk6C3xNvzHYcQACljNbhU02iFDAKFUSVHLrk8ILAOgSov+LwR9t85BJaAiLjIIMF9hnHAUuTVOwwDEXnnudGc867rEkWlVHBh6Ibs0tF2u8F2Ltivv/m66weppUA9jqO3KLkMya8vr2fLJm5cTLlJjoyYCy7aICvmvHXJmUqVjYk2EktlaeKkEDFSjBRdcERESEywkpd5spOHWeLOhEZKiUhVWQBS3/eUUiF1JgFyzp8+fTqfz6+uri4uLvKT7LejUEwIUVRFVVUEKVE0hSbQ4zi2w3axWDx8ev/49MiO7vLqSrzdjkh3IiMpJcG/zXwzagkYOOdGZ6/e3GDku3Vnew+BAWApGqeGcqaNMXAVPQUthEtjGG268FKozMLMiWT+bs7G2WIOCcZu/N7977AyVVX1+NHjbtd/8PF7Qz++evHm/OWFEuZ4fsIUtzS9ufj6E7GIO3G2fO9/+Jf/+92wC5bXjenH7auLi29efv7Viy+vb84B/exIRRdv1zdt20op5/MFY2yz2aDnQvOhn1Iai4WsZgWTMkgLImbDr5iC0qKqKmunfujabofAlRaJgg+WMYaMYvIx+curVVmWTCJEnBtTFbPEiJWyWZQnJyff/d5HecMvFoujo6PcbMkzg/l8nsFB4ziO4/jVl1//yR//qbX25ubm6vL6k5//4vXr1yml2eL45csXIYTC6OhSu+po4mVZJ28ZYamL6EKpC0zQ77rkYxdaWQim90u/KAsMnBJ7/H7BJEayaJKNY5xGTxYFBRdJgnM+H+jOet2YqqiF2DrrI6WyqMpSc+aCjzFGVSgXoyyVg+n5q2eXNxdVVT198s7Tex+VZdm1vRBiGqb/x7/613/6p3/y+PET5EiOEiQumOISkAhTSJ4JpjkXTO62rQ9O/n/5+rMf27L0PhBb89rjGWOOO+WclTWpplQVi5QlSqIky5ZoQG4I0pMtoN3dAvwv+MUPRlt2Q4ANSAIMPxgwYLXplgy02iRdoqpYIlksFiszq3Ie7hQ35jjDHte8/PCds29kFq3zEIgbN+Kcvdde6xt+3+/7fQwTSmUiAsGQAZNtPwBCm6YITjjCCBBajLGQIhUpYla1BnsctHXGh+it17oLWZERxJwJWlsUCI6ob7XWevt8vQ8e6rIYYy55lqSDgxkyV0KIMiovc0xx3dZOuclkUvCCSz6kLFBtBdJ73/daE0gmoEwbYwSxYvDBkORGRCMMkMAYkuMBaYfCGCEk4TTGKKXMihQhZDdzPhCiHKjRGGFrLGecYBJ8uLq5GhUjFDGmqCzLclxY7YwxTCSAvcMRJlsR5ulspq323stETqfTPM+dt977qDF2xBpntbcsUCtJoBRJG9UQoN+2FdCdjCImGFLVGHyABAttp2FCJMG20wYxIhjH4EPXdJTTJEmk5GUxqetaKYUJxpiE+DkHBprP0BMIEPrV1RUUcRBC8BGQwVycXh/s7ydp2nXq8ePHmOLZ7phz7rGF0UB937d1m9EieBQ9dsFjjDHFkWBCKcUoIOS9t8aRW7NHEUKUUkywEDzEYIwhHFNKEcVho91BBkc+QAK3sYEvvP7C/8IYcyIRQtgRW6FV061lW06ycpLfu3dvsVhcXl4Cl3s8HpdlOR6PVzcrryIeUUlT5JDutWq1C47jlDKWJXmapn2r6nV9fXXjbCw4D1udpQHPgLDPGANhh7VWSJ5mKUIoSYv1eh1CyIsMIdQ1CmrtSvVCiDzPAdiAe7+t9hVCQNsgg1IaNEEB+RBUZ1AgwSEcKY50vV5DWae5qpRRTG4QLN9iH129rmOMxqnpfIIpYpxmM9neKNV7jBhyuF33UVqa0iSVo1i64FarFWYxz7NUpHVXexeC81LKg6NyUtp62SJPMymJQMZaGJGyrjZ4JOdMcAkhQghhMpmMx2Ow1XVdx+illFwwY4xWpm3b1arKsgyq4IDDw7gtpRRF1Bnvvc7LPM0S563plNJqOpmNRm6+O9vd25EiMb178vDpzdWC/f/bNIQQhhnnPODNXldGxRir2n64fvwwPZWJ0K3jRGivE4Flzk1QRZIlhUAI3X/xbj5OT09PV4s1DELvuk6KBBMkJBeS7z0YMSQWN8vlsu76LsvZulnaRxbRcHTvYHe6/9Hks9VlHS3emexd39zM2YEv8eXV6dXl7718/OXfePNv7KPjtlLlVJ5cXL/97k8fPnvU9osQFWKhjSoXJVTIrXYoUIwY8oLEJATvrZVSpCxPWWatJRxlk6Qx2hgdlUvSyWQ6xQwB0cBpD0BNOcqllJhE6/RoXHR+LVKesAxHXqZTZHHA7vD4YDZGQogs5X1HMHIPP/vo4WdISnl8fGytPTs7e/z4MRxaION85WtHL79yfyDaKKW6fs05bxJERn7ORpKlPlhlMuyDSFjT3gQP0kvBWTMZjwRnGOOVqpXpBeUyFYJJGgKPEmNa++skTV0k1tumr0IXCEdZntrWtqYFPXEpZTTRYNP6VoqEYRF8xAhTwtKEOeKCC/k060zkOW111Z6v0zQlmD47f3q2e51l+ZMnT7K04Ex8+uQT603TV6MJDyhAnBFR0FZBm02vWoyI5HI8GVHMgkF9ZzxyiZQheMYYJtvsAUMrTgQtIdBunUzGIHo+ylPdWdXq9U29uFp0urOIaqullH3bq04B6ApBNyFkMh8Dl2TTwBAsJZQnHAAJcIdQOoGno5WGohRgFUDIGo/HJvRAxAMgGvqjRqPRaqG7rgOOa57naZrCSDUQxN4mPQRtx4j1fQOWa/BV4K1ns8mGGRs9Qogywjg1xkRLvTWU0hiR6nSZjxKRcip4IUaTkbc++MgIT0TS1gvrTO97QSUhJPhN8QhFYoxpmqZTXQghRAeDWSIKSinTOyFSEq3WGnnSOx0siu55FD74xY0njgFoVuT2CCzvQTd4YPqU5QhIOowIynGMsVedMw5q+YwJ7DEQNr33kW3Iw0r3uSgYYUYZ3WtGGOc84uitZ4QlAjglXnWKUqp7ba0tY6kak4hMObVcL+/du1MUhRBiqdfaahtMr7q6ronkKCCCqYRZFIQwyiillDCMiMPOGEW30+HwILf0vAHGb0psRIQQII27Dc4PtYz/hA++jSUMP/TIoEAIZhRTZ1HUoY59dCi6NbA+YetWVeWck1ISTFWvVauijxTz6KNRpte905FLZp1BCJFIokeAFYPSMTyy4fKAVAXbWySsaRrAFWDo50a1w0fGqXPOKYcCERMGjNTVagXT2ACUSngWtq07BG/kPqy1MSCCqQ9BK4MRAfVHFDGMCCvLEouCCZbkEhN0cnJCvRQiMco0pElzSRnlnB8c7Ys7yds//aVbmVFeYoxV08oxPbiz33UdppgRZqx2vaWUjkbluBh7ozEiQogsKdLEU0p1bwgJEzlRSgnOm7btug4IzIwxFNEginl0dDQqSygntW1bFBlUAUajUUs60zlAF6y1JycnXdeNx+O9vb31er1cLjkTwaiu7yO+SXKZlYlMpA/O28Aka7vGnpnRaDTOp+Px+PDg6LkSFvm8FodSCjgahBBKSQghhuic26F3XWvqdRWm2DvvUKd6laZpMZdtX0/xOC9TzvlrX3rlsNrXts+YS5LEu+BdiPy5itjhnf1njy66rjOkvbq5PBzPEUIffvQBEYggqhpzdnLmNR6nM0blxbOr4I+ojHWoRtPxR1dvP7h+8f78q8d7D/78vf/4wUfvPTp53OhaCOwwqXXVt20ZtGQJp6KqGhRvGJXtutFaIxI5Y0VaTvMpwrHTHWGkGGW45+v1Wptem4SKsUwFE9RYHVOstRaYg92/vLys63pcjqdsggw22vPI+spetk8wiy+/9tLubnl9ff3ZZ59BK+Q777zz7rvvzufzelFgjG9ubpRSk8kEpqOEEI7uHzx49f50MhFC9H1/fXNzXV3d3Nwsg1q0l8EiZgRO0J37++tVXbVLIVnXdVCuoJQeHh7CgXn7w18q3VukCSu9Dd1aeR04Ta7j6f5of1xOgpdt265XVXAx9AQ7JkQaGJZccspDxBwlkmRNNFmaoohUp7U1nHLJk0gQ4RjHiEiomjWiYTwrGeVt23zw4ftHR0cXF+eEUMZYiP7+i/eTJHGoDiFoo9q2tc4Yp8uyTNKk61tn/WQ8LaZ5nhXtqm+as7bT1okYg/ceTjshhBLCGGvWDWBuGOPpdLq/vw+Teld1jSNJkiSUSHfaIy8SxjwbsETwE5AeTSYTQhGJWDIBdAkAkaCFAFwvBCIIIRA6GJVjmD60v7+/Wq2A/yWlRN7DHKqyLKFmBs1IYVEP4vXz+Xw+n2/2QNyijjEiDIJoCGNcVRVYduCgYowhXsxTDvEu+HgYPkgIaRrrvQeEYGhH4ZzXzQ3G2CjTd6pIR9a4db2y1nrfk4QCSLjx7sFCEdp6C3e6XC6bpklSGUJYXFXj6RjhED2KGDsXgsVSSGXaLzhg+Ort57OfiHGMOG4YXpAPJUkynU6TJGnbJnrMOUckQNQSUOCUJ0lSL2vvffRRGSUSIYSIKKhez0c7IQQQDR2akaD6AOsD3wN1nxCSJyNvECMMMo8sTzFHVOKRHK3WSwu93z4650iUCBPwfQP+jwYVfhtRiJFAuh8xw4gSyNiyIiM0GqOdczwXgw7D7aRl8Lv+V8hZv+qG0a1isKfW+0AcT3gmmNCu75pO6X7ddEO/eNM0IBrFGCvzcYwxBsQ5z5McUUQpRQFxIqzVy9USUTSfzPM8H49tWYx1ten3RZ8fagLWHjqanHOcMyh8LC6Wo3ERY6zruijzcTHuTe+973sD0RW0D8AjADnugc9LMA0hOGcjjhxBgXmjLYW3DfeY4uVymWbJeKcELwh9PvV576LrdRyNRnmZKqWubi6F5J7aJJciNTBVTBTIaHt9uWAJ6fueC1YURds1XdelaTKfzy+encYYke9xpNEjj41Fylg0KmaYoDRLklTe3ETvvZQ8xqj6DaeMMVbkufceBJe6rpvNJmgLxiwWS9P7shgtFos0TaH8BGqagPAzymbTeWKSul+fnZ1lTcIlS5Kk6ZqJHFln667y3guSQI2ZeWehHyN4Z8LzTgMpM2N0CIEKGq2H6pGUkiDUL3vKZHXe5vMiLeUSL6bjmSA0+rg4XTrrXn7xxbt3Dj/+rOr00laJjH69XnMmrPVYU2GTruvO318uryvVqrTIz95e6xN8cLz3pcNvfvD0vdHuOFqcy/EHbz960iyeTZfrZtWPzGQ0dcYTY0b7yfnpo4Tgp+dv/fs//nfXVzeUUmT6q/OKUupsQI632KxdJ4Qos5FSymLLJLEB5amMMdJIiKamNuraIYToTBIdkMYMMW/sxfmpzGWaJRGFZ4tzQfjueH60P//Sq2/89Gd//u7V+r2TT0ZFibHfv5tFrDt0mqQyp5mkcdm0xXT2//zv//sYY9t1xjqfpG9/8llYiqIodndnQghnnVGm7fsY0MWT5QfpZzBD4/z8/OzsLMb48z95d20sxpJhDIU0hJCQpO10Wox84z979NnXv/yVb3zjG9/+1rd2ZvPxePzxRw9/+KMf/fKXv6xPmuls7gKWheSci3AknLALhRDKiMS8CDSMstGZuqzWl3mRMclbvZIjSXO60kvKKeOUES5oEmNENFhvrNPNoyAkRxalMUUk+qVLCkFM6Lru/fff9y6kaZqm2Xg8Xi6XWmuC6GRynBfZ1eWF83oyG6vG9c2y4PtPzx7GsNo92Gn77nq9rBY9siJNgyPqzouHXpjLR0+DQvcPXrw8uaGaI+3mxXy6MyKCON+t294YncyQdZpJVObIc1Ev22g9d8zWAWdhsjd2SF/fXLOc7u3upTJr133fa4xx328UvyMJLBXBxnrdEELIiMaoMMYRBUKxQ3rVdGZpGKfT/bLtcd1UT68/3Z0eEEKMss2q51FiTxa6beou4H40lQh5Z73WteQ7jITglKCp0X2SJONy5IJbLpcuuKIoCjKlhKKOGhUZSSmlHHPGWNeGDWuJc0alswwhxChLuQk0eO+LtJgUs7PTM+fc/fv3GZbRYlU7gjgJzJnAY0I9twvX0BqiCog5dNcZpSRjkjGEEHY4wQn22LcBITSfjWIMMUSBGfaBsiBzRqlBvcUIPc/U4uarsQjIot57IHkBDyUGv7szH4/Hb731VprIzz79+OjoqFqv3vzem6enpzc3N3W7lono+/7unTur1SqXhdJ9oxuKCMNUMpGmidfuS2+8XhTFL976xWefPry+uL57975pnNHOTxFFwumOk6RvDPL9fLbXdd3yepHkyUsvPSjLAvnAGOGCPj15VJCdxcXKe6+VzVkhCW9tLaWMSGzwUo/wRrYZU0Y44oM/jtt5dgghKWW7UJA/GOIUXQ2g65D7Di+McdM06JayEvwcfBuw8zjnk8kkxgjd0r73hBDCo6cqYEwFwoFHH1GL26rriXLOFUVhlaOUCiRW62WSJFonBS9m03lVVf1aMSwlpkjT1Cf9effsos/zfD6ZtG0dE8sFxg4xJtaryhP/4N6DPM9/+fa7CKHeNL4zAXuPsUjYq6+/nKbps2fP+r63zsiMSclCgzHmKmDkaFfpUTZVSt1crMqynJYZjqbIBce4WbfOIZkKoqigMiJbVYudnZ3d3d2bm5v1es0Yc95GEjjnjJJ22VprTWXq8wYhhCK2vZuUM0EZx5JJfnV56b1/Ye9BSYo+UYEGzjlmiGoabpBPQ0rTIssTIWb5uO7Wi+sbHPwkGe3O99dXzaMPnmZpfnjnwBVmubq+UWcJzwiiAfu0SBGKhGJE4ljkL7744sXVQjWGOHp9vVxfqiRBMcbF9UpUAtgYIRCKcFM1e9N95xwNzDn37NEppMgpyxhj1WJtrM5lHrpQV52Uko2FFd5dNbCReoQ/vHoMbA82iHtB8D68hp0EBDkIdZMk0d6yScAs2LoTWXF8fAztVufPLjFFeZGhiD/9+LO+77RRlIjI2JCOOG+Hovf56QUiUXDZ1O3N4rqqZnt7e0cHdz5++lGRj4qiSMLofLx6enmeHCbF5Ki6WQcXUcDW2vV6/dmjT6+vr+tVvVysjDEoYKVUmqYxoGp9E0Is89SjEDz0E2DvQyQII1LXdVmWGOP1et11ndY6SRK/VRunNCGYOuMR0UIILth0NG2r1rtweHh0cHAAGRKKwTknJWWMMcE5Y8Gi5fXynXfe2dmf5nl+fn4eQtDGEMoIIfP5nMncWtv3rXOGS84z6jDqug7qqbAvQwgHBweHh4cuWtARharneDyGwS8hhGpZff0rX33jf/bGV9748v7e3ng81n2bSfnaKy/fv3vn8dOnP/zDH3308cf1ahHLkhVF8FE5PYwfgDQuS3yZjxCqQFrPOgP4pzXOaxssItEC1ZZxihnGGMtcGKNtp6nASSIxQXVbX99cNWtPCBEsiciprr25XjRNk6U5YjiPgrGEc9xp1TQEU8Q5t1GlaYI8Wi9X3gRrjHUmZTIgxyXr+957szPfDTaC7M718hxjjLiwUau2t9YgGnxwrx290qkWR0IwliJBBUWGWhOquuGYOG/TIj04OKjrerVamcQmIhPJhn9hrY04Wm+VUdHGzZgzgoTkMMaEMdapNkmShCQ+eGgby9IcIQRjOabTzJmgaxststYppfJSIISCC16bel1fXd7cXK6stTElTNCiKMqyVEa1bRttJISILbAZtwNeIKOChlqoJwF3CcrJujNN08xmszzPQV4ny7L1eo0yGz1qmibhqRfBWgu56SDdMDTtQKJ/u1S5Pd2AfgV0i5c7dHANghK/UqvatAjHWwN/OOdat+v1GtL3PM+B0pJl2U9+8hMom41GI2gBgORvtV63be29H41L483Nzc14PGKM/fmf//nXvvY1QAVGo1GWpkoqOKQQT0CyBUggtI644Oq6ppLAxedFvruzd3PSQGc23FSMEeDceEv4D22h9edhxue7QtCWIjd4XFjbuNVuHMDk4U9ALJNuRRzh5xD5wTGEr3ErgkSIGJw09KfBo1nerOF7AEXAoxtjWM6MMcvlEhqFB/EQeO6DSADe9tftzucxRpiSaY3TMAcXbXQkKKWc8ihikiVZVgB0UVVVCEFIXk5KQvB6va6qCksuhIA2JLgXY8xkMvE27OzseBOfqpPraqmjjo6giLnAMFQjTdPZbDZsRY2e92EDug635lzUWiMciUWd6jDbyB4sFguI8DwKtx+T7i2miAuOcBQpQxFzyhnl2pvr1U2vdFImk8k4H+e9Qagm3HNGOIk85SJBBXQKIYKQ0l23GZp5fX2tlC2KIsaY8zwRCaU0uADa1MEHa63BBm0b4eKWvOmcY5y6aBFBgQSZiJxlQjAppXZ6NJ6FEJbLpbE6z/MklW3bsqEkAK9bB3I7TZqRiP2wM1razO+O0jE3Hzc3y2tGRK2XNIuCZ1CjLse5TCXymFERTBz6wzBBwfkklVywiEJn1Wg0kqlYVQtfeRQIJZxh8dlHj6eTmcRJ2/SJTF955ZVXXnll3S7ryzYYHEJUVq/iynp7ubhYLpdSpBSzVrXehNFk4r1fr6u46b7YxJ5oOxCDcx6cFlxiRNqmgxiEYOpd4JxnOGOCYRK17WlkjLBUZISIjKXUo2q5ujg/98ZOxyMEJSIWtTK9soJxTkREwQUH1hzcJyYEYQIAWkbLqqqatvLI8CJPUkEzRjL6la9/dXd3dzwej0ajvu+hsf3p2bPddb9cLs/Pz5VSnNG93R0hRNsefO1LX/7mN77x0ksvURxR8Iwi5cy4yC4uPrlz586DB3/56Hj//Pz83fffe3pyslwufZs45yihKG5hsYi883lZwI7XVjnrnfU4Ysll06he98Ei7wKlNM0SjimnMt/J1tUqopgVaVYkMpWdaigjeSlxxBhjQpEzxvg+TeX+wQ6bstFolIpUhbZ3rTK91S6EIGmbjLiUSYxBCJEXWZOpVPK2aRkRq9XKM7N3sOdNML313kcSMCE++q5vbTBC8t39/cl03HWdaqyPjhBieutNxD4GFzDD1tuqqiLJszyLITZVSxEH/BMTIlNOGHLOBReMU9FgFJD11te+7wkU2HrVaWXwlFBKXXCMsSId5WnAGC9ulqpXaZnt7excny4effLYGlcWY0Gwcw4jQpEIFpveoUjSJEdoY7WVUp3qwFtIKcO21D0A5gPCDKKDMJaRbeeiF1khmJiOp3laOOM45WU5klJ2sfLeo4g4E5wJrwLFPJV5vWxul/oGHwAG+jZZF+x+QBtMcvirgZv9BdcSN0IcgdIIhB5KQa/fxYh3d3e11svlMs/zO3fuPHv2TCnVtq1zbtWvZrPZ4eHh22+/vb+/Px1NL88uQYYM6KY22CzLjo6OEEKPHz+BogOAQ71SbduuVqvje3fgkvC2f7qua8ZYPsphPFzbMmf9zfXCbxU2YIVBKRCqtgihoUEc3OeAJJPPtw8NjhnkRIYKGvqL4Ovbvz8kNmHb6QvXA78/kMjwtg9NO+MDCjGGGEL0HBFOCCEUb8fmoI2Wy4YGCGAlRt7zwCSnJJoAMiwOIcx5As02Qog8L7xHBNOIYsA4hI1KUlu3HeoZEdZb7wIQ5YwyIAp2enrati2lNEkl3AjotdW6J4SAVYddqpQqisIaMyrH+bSMDmVpKalEnnImrOtjjBCApmkKGIAQwhILi9B1XVVV0NXmnBuNplB7dsZHHGkkIJlg+k3NyNNACGGSEoact0bbiAJlxAXuAkckEkyjjwQj7fokT9M0jdFX7QoTVIwKZDjFDFvmAyJMZEnOOQ8oXPafLRYLbQMlXCk1Gk2/8pUdY0yMm3YGpRQwrUAKt27Xt/fJ8JRpQtIiCSEgFKjARZlxzrz30QfoxyEME4Z9dCLh5Xif3Q4Db28ma6HDlzPJfLQQpxhjuraXEzzLi3JcLquuuerWphFT9NVXX725ubm8urDWzubTbO8gzdNH+GltmqFgiRDKRymiQdnOaNt3KoTAiJiU05QnJ4/O2vWPl5fN04dnweKr02XfqoPpHVAhmY3mlNJOtUYbjLFMBKZRplxg2aimbxQEhtCNY40PLmzKVIjArGlMMRc8Hc8wolpZ7yLBDEXkXQx+e/YCChEFhzAjxLKocF3XiRQoxK7uzk7O2rblCZ+Q0njDGOWcOxtNbxvTYhcxCUByVkqt1+u8yGWSIYRiRMYqTIJMGebBEaWwIRnOi+S9j98rz8vd3V1oAk7KpG3byc74G9/87tOnT999992TkxNg+QMBkmN8+vRpu1qtVqvZZDIuR4vFwmuvVfPo4cdZlkkuX3n5weXVxccffXh++kyMBGdiVPIszd1WTFj1urVaay0TSRFXrbHmBkXMOBVcWO2ss157zLAXgQdGORUZK3DOBcuLDBEUok/TdH9/HzthtPXeS5nggDklMaCdnakvbYx23XY+mvGk9NbX67pet05oKbJpOdGu996PRqXbszQwkY24oEQgi2MqshBjpxRCaG9+APNnulpbb0nBeEym2e7Nxcemd6Blr5WxytPIg0c+2uCwjZF0KMTgvedMoIjrttrguoxJJojFxGFMkRQZIyIgF2MMKCQyJYR4FzAiRrmITERB8gRLzJjgnE8m8VJfhq1CPZw6znmw3igfQogBUcYpZtAzg1kkBBljQvTaarSdQ2y9HbKc4dCFEMDugtEZ3EOSJO2yL4qiqbrlzZoQorVu6/7g4ICkRGujO8ux5qhfLaqu7oy2cTs2+La3gPwPinCDM4CPcHaLw96ig3whF7ztgGN0hDAYYwC6C5SiJOGwV+u6Ho1GBwcHFxcX3vudnZ3pZAZp39079x49fHx4cHR8dCeR6R/8wQ9hegdjDGmU57kQ4vLyMk3TR48eRbfJGheLFcYYWrPAniCE4IOqqgJRE4SCMUZ3GiFcr1rVaWMMiQK0zO7evYcQ+uCD96HiGLa3eHv9b/vR4SusBugioS1xaWBmua2S4O21QggN7UNhO2sWdguYQehygZ/DvSBMhr8liKCAg4soBMAPoDVrNpvVdQ2UPe0cvCcANt57QRNjTMpzQkjgAehRhBDsabS4bXsgAxJEMpkLKinmCCGeCaV7QghhyFiNAlov18YYZXug+HLBtNYwX5UQkiUZCgimORtlQBZ0vVwrte47Nco1xDrjfOwNwohQNhlgA+DhA/xOCEmSBEARaBbH2+ZySimhmCacS044JjQRQvRdBxbeKpckCeeJjxassfcu+Kh7A0kwwTQ4lIySvu8Ri0Lwet00VVOWZV4UBBNJMx4T6iULCaPCW6+UOjg46Lqu1y54BEFwlo+stQRFwCoEly51wNjo+17bDUK51f8iIK0jEwbTFau2CsFH5DFhwXnGmTaaEFKWJWxjxthmHvBw6uK2lQ0hFAKGPn3OOYKkuzfW2oQl66uWUc58sjsuBU5C4wiJwYciL+BSgsGr6xo5WojxmqgkSSgjIQRE42g86vq2URX2tG1aIOBJmXodn356+ol+iCX7+JcPGZKqMUa5q6urp6ePD+/uz2YzY4z1BjiozjlMEKXUdq5eNX2nGGPrdZXIjFHRqDXx1JvgvSeREkKiQzEiihilqK5r0GoABiA0dKreaK0R2Sh/xYApVnXsjVe20XcOD1979fUsy84vLtZNXZa5xbJrW9XpGLx3zmg7m47vvXDv61/7el3XV8sF8BWRBpvLjeuJwEnCPXYOmeAxY4RT3vZt3dYPHz/88OMP5/M5sG0RQh++9/7V1dXi6lp3PQ5xvVjiEBljFamqVSVhUvSdO6vF6uzszCgTuIUbQYSFiP70z/782clp8LFaNsARkCylyFHEjTGmNwb7rm85FeW4hOhKMCmFxJT2XnsUMaGMMBopQYwgikjANBKKY4yqV9ZaLhhjzBnvehNCkElZjMpow/Jmuby8ccaa3rRtRwkr0hEjlDNOMNo/2C3KfFKOlpVfN+siK8XRflu3Vd0pazilIcTV1Tr4GA2uqqrAhXNBa2eMs9ap9do2aHnWxkzHiGQiMUFd21tnEMaE0q7viMflJI8xrtdrEqng0nTGIIUxDi6ynBNCUHAE0eCiapVHnjGGSCSECCaYYM4VDIbitT2mWAsDLUZQ9SzL0hn/5MkTVZnJZGKNq6pqbzoHg4sZyfMCPIr3nnMKaC0mG9cbcVRK2biJBgbDBBlAq2pCSJZlo9GIUgq9avEWtQewTWB89H0/HuWOBYxIcEH3tlk3bdM560tZDA51E5vTzeQof0tUcnA5QJAZXrdD+y94YviaJgmULYa8MMuy3d3d3/pbf/P8/PzZs2fe+6Ojo8vLy6dPn15dXf0v/xf/ZD6f/+7v/i4h5Bvf+EaM8d13333hhRcmk4kxCmrVIzq6e/ful7/8Rtd1GJM/+7M/a5t2MpkkSaKUkkJCIQZvxQJBEw3+uVzeJEmitWacFmkRcCCYeKfTTIKjKssSIVQURQghyzJO5QChk1tzrKFSg2/VdL+wjOiWkBljDLhIg8MevhlECslWY39YarJtOYM3hP8iVH4h84avBD+XrpzP5zAJajQaESrpLen+ISwAzlockPYYhRCE8Ka7xgQzKhhj+axIE9CfoTEGUB3GGCvTa9N3XbdYLXKRQxrmnMtYRinx3nddx7js2x4+0SiDI5ZCOuOKovAuQHCTJsUoG3mDBJeYeMh64Trrugak13gTQgBC9cHBASD8dV2fnl4ghAhlWZZlRRawD9GHEMbjcQyIkbZFHZATMRUhehgnyhNmgyaRSpZijKNHhLK265U20+mUppQgaoPvjVlWF5Nitj85ns53J/lcsqTv+6Zpzk+qPM997LtWhRC6rjMmYIyXi2u8ZZChrdQMHMlwS6J1eGrWWtC1JYQYswHqhRDa2VE5gmJBDLEYFdbap0+ePs+Ah8e8Pa7Po3LYndZZ7/1oNGrXob20ve5hQzvkYhvf+vO3Oecgwq61vjy7GqT4MCLeBRs0F2wyH+nLrhhlWAvYfCRSp+E2EMdS96atqvOTS91aifLpbHq1Nsr0NDbGmIACIgghFHzsGhVjDIoEH4UQaZo5G6y1nAlrLXZ+SOUHviLoJwAMSBBx1qlOMcYEE87522kBcsT2XimFuMukmE2mRZpmafrg/v2zy7Oqq9NJZoxp61VKEylElmUPHjz42te/sre7d3BwsG6b5XJpnfMhCiE4FyH6EL1HHtpLvPeus7o3UQrAN/oeQA5tjDk8PNRL37YtjmhvZzdJEkZova5ijIurpYfZiMvFhx9+2LZt1zR37txZVishxGgy6ZXRWt8sl1lZlOPZ6eVT53yMG7VRSinnwvtw9+j45uZGJqJIRwTRXnfRo9VyPS12oo84Uo4JJ5xiigLCkSYyhdmufVCAVgUbVTRYRdUpYwzxmDgUtNWtstZixJxztg80IXY7og5TfHZ+mlWZ9baq123fSCkJxwb141lZ13WSJNa4YBFFXGQiTbtuARrum/qWc66pW4xI1IoyjFhOKOl116qWYc2oYJzCChtj1qs1wyLhqekcTTGlNDgrmKcUGeVgP6DOGasZY5hhTCNBRKYSWjuQs1ZbTLHpLaaYMiK4ZAlRSjVV621EhpCwQRq16zwyESMUEaGIS0Y5VUoJzGIMWmulA2GEc44Ics4hRm4b6MHbQQ8xPCzA+oCwTSJZ3ayKohjlo6qqSCSZzJp1s3M0kyIkIhEkQR5FjwniYqsgfTsJA3/PN9O6yCA8Amcf8rDbmS78E0qYt10yvB7cv+uca5oG+OGYxDSTRZm9+eab77///u7uLuSIk8kEUOjjozuHh4ff/tZ39vb2rLW//OUvf/CDH/zkJz/d291tu0ZKubu345Gfz+d3794djUbr1frDDz/UnR6PJrAC4PaOjo4ACQTPB/NRIKnazCbBdDadA56fy0IkklJKKQPPenh4BNefygzSF0h3IFqCPAz/CqMKXDg8nWGMR5qmt1GKL4Qv5FYjExzwpmm6rgMcFWr5YADB90Qi/sJ1ziSt6/rq6qquazi/k8nkwYMHhGRw2VAbHtCOdVUP4DOY3yRJimL64UcrHAnFjBM5Hk2n0yko0mgLqs4gQJRbb+pmHUK4XF7Ao/ceOq8YdCE3jTHKxRgZwwSzUVGAEGPdXQouy7Ic5eNU5pIlTsdEpoSGgYoftkN6MMYqKgguN11VZNMjB+1PGOM8LybTsfFa6b6u62k2EVymMl+z2hhNCElSkWYJipSSFnMUTcAoCi4xRcbqZtXjQEWaUEq9C32nWttLkeXjnFLado3tT67INUNcKdV2jQwU3CcoVVxfL7UGGXAzBE/wsACFBswM3arRgK8xRseIhRDRxRiRNR4IqqFvm3W3iZlsrzvbdd3l5SW7XR8aDiREyiF6Y0zAIaAN5YFSurbXCHHmZNfcsImfv7SbrsjiWYNIEkLQynTtFQy+stqnMksKRCmt6iZgl5eZkBwTtLu/216Zvu+9CzBEE+gViUgoE6PJSLe2XrXpZFSWJRLh5PKxS7z3XjDJBeNE0Mh0Z73xPqI0Tb0PQgiCg+p18JpiapyjlHkfrHUYkxiRc04p7Yy9f/8+DBQ6OTlBgVDMvY3bKJLHGIMPBHOMGIrUtP3udH7v7v1UJASh/f39EMLFxdmUzUP0gkpGOaMsTZLZbDrfnUEqMB6Pt+eWgeFTTAMC7J9r0RGP/Mo0QgjKRd/3qG4opau66fRjrvhqtQoh7OzsSCm7roOutapSieBpmirtRRKNRzItOmXryjjXtV2o29YYjQnb2Z1QQlOROuesssorkAuVUkYXnzx6UlUVE2y1XBOKQRRJa12tKtUpr6JgieCEREDDQvAxBuSc8yg6FxDCwSNjLFEReeS6sOxXaq29D045HIlZO0qpjGIkSsZp5zqeUEqK5XLJCME+SsKne0f379/FBO/uTE/Or5lh1tquUanAlMTgo/e+i2tGmaPOxxBjNMEwwjIqskQiGhhnTJJinMF8CIrZ3mTfR4cJRggxypp119Zt0CglaSAoBG+JjwzbzmOMnQm5zGKMhGIppEcbE+yCM3VFCOFUWq+NMpRTxhKCqXO2bVvvfVGMXBeqRR0DKsuyMzXM0PbWGN97ZCPyITrOOWHce6+NCluyFcYYczr4uSFDijEWRQHGfUhY4ddoKmNAjPI0yWDqreAyhLBarEIIwSCDDEYkBswpJ4gliUBbRSqwHbAPwe4PJavn+S4JX8Bgt3QQ+oUMD74/2Ju3bYujy1PBOYegVjD8r/7VvwKoQGv9x3/8x4vFghBy7969f/P/+rfW2sVicffu3fV63ff9ZDS9vLxcrdcIBZCDaPrm6dOnH374gdZ6Z2f39PTUaTceTWKM9+7eRQFzzuu6RggNzg9S5+Vy2bQrKSWjPM/zoijqquGcj0cTJjksI1zh/v4+uCVnIDjx4FeggRvg0Nt3etuheu/7vl+tVsBwBj0KmNN3a7m+WFRGW3qO34q4gRPa2dlBW0Dbe+8iux0wDXlRlrIQwmKxMNvRXgDyGxUijRFHHDEjDCHkjDPGEMxQJM557zba0VKq9XrtjMcoBh+NdmWulFRt0yGEIgrKKIRQjMF5SwjR1lrjIJOD1Gu1WhGCx+Mx0BcIIU3TWGsBTYVsDzCGyWQiqKSEUkoDgTnoNTiOoXoNoQzDDGOstQazRilVSl1fX+f5aAhDGWMeOe9927ah9olMg0NN03ZdR1qc5pJxmslSKUMdUkYTjrwNIUatjGp8XmRSpjFibVxdd7p1eRI5KYzrb/pGtzZ4gFKi85aaSCl1ARvtvPeUihipcw4zQwndjBKJCEUfcIjE6V4PgezgQEMIKKB61UgpRSI4kaZ3la+d9t4HEF0oiqLr+rZtrbXBRQaB8JDpDmdPCKGNcs555BHZHGAhBCkU57lEiUjE3S9Nf/1vf/vq6frPfvBuvSQwMeb02RknqMzL9XLdu/7lV19uumZdrXzwAXltVN3W+Sgr8lIro6zOsyIRCUzgMcrIJCuzfNlcT8bThCdXl9fjvWIymdCIkSUyEZQwEinFzCmvlbHRzWZzY7quVRiRpmlw3GB6Q5EG7g4il1SK119//f79++++++7Z2RkEzhjj7XQzEkN0LljjMCLR49lstjff/e633/y1X3+zbZsPPv6QEpJlWdu2PHLvQ9VWlOCj4vD4+PiNN96YFbve+yenz4wxSmsuQI8eKddHH11w3vngECEkFakQuSHdb3z/17/+9a+//fbbf/jjH6uu2989uHf3rlnqR48eLZfLGIKztmtbUB/teuu819Y5o/OyEEJKKeqmqRtd1zXCgtIEI9z3enFdGWPysfAuWGO7vu/7PklMmiS9UoFG7z0y6OrqKkSP8REhRPfGdJ3tPfaEpCzGiCLGkQQg7ymLEMLIWeMopYxwb2J9tcqyTBKute5N55z33nMmkaZYUIIQI4IzinEfUYwxQOG8bdu6rrI8FUIgGufFtFaGEFIvGii3oK0+1MtfvleWJWMMrkRrLUWys7OzWN7YoLMyFSlTujfaUsSjR0leUk5CDEKI4MMnHzx8+OFjFCOQOJxzCU8QR8EGSinyqG1brXWSyjRNQwyAGllvmRfQFFi1UWvNImOMGWUqvUzTVJQSBWLbLk1TjEjw6P79uxDpr9c1xbQc5aNpLlLKKCvHhZTSOm2cAQg6hIAYcdvB9XiLQocQYIgn28opQ3ztnGNCcM4BLkvTtG3bpmnm87m1WsCgV8QJpnkeGBKMsQQo+oyhraYxmPuB0fMFh0r5F/UZwLCCCvcXHAxCSKsK/ms8Hs9mM6Btw1AyxtiXvvQlcBhQ6Lm5uXnvvfd2dnaMMdfX1+CQdnZ2ZrNZxIEyorWOKBS0SNPUObter997773j4+Nklsyms7Zt57OdpmpDCMpqMEQADMJCgXOF60/TNM8KZz2nIs9zLDCM3QQaCigCpWl6dXENVzjEJWQrqjVk/7e/h4WCMgG0+EOpr67rL2TM5POj3fEtyjTZDlCBEiB0tsBXKgvQywP9n6EyuMIOGr4BXASQ/PLy0igCbwtJPFQWlFKdtuDRMcbDdVZVlQjsg/c+AG6BEbm+vvYuyJQrpYwzzlnvvUxExB7GD4cQ+r4XgslMJokEeUiojDjnqqoC+Ul47tCtfnV1dXO50MqNslGwGCMSovG3BnQO7cKWbLJ2SDrRdhQEACoh+lFVZEWqbF/X9WKxyFFGaRsdsso556INyvYYx2mB2r5lkvamZZyUo8Ij17fKI6qZcd7yhEmRFHnpu8Yod/lkxTCLjjjtg0OUY4+c9TYj1HsvkpwQ0ve9EJRz0fc9IS7iGGO0zsYYrd8IzSKEh0D2dqzGedr3vVEmlSnCqFdN23Rt2093dsZ7Uyll0zSmazNRYIlXqxVjNAkhYBQIfi41F2PsVQfnhzOulLLOJUkSPBrnh/wg4PFqX+DZQXJzubh4vF4vqriUfMYmx9nF4pR3ubtKJbe9XFVmYaON1KciZUh8+PZngstP3nqayDTGCAJgta0JISxhCCFPVL1yLArbuobXJKXXlVMqlGUqKSWOF0IQSqy1UvIYvdBpfdHAM44xUotDCAFHxqi1BmMkpYgxMkaTRCKEjksyk5FGbx3utM/yzEc9KbOuEVmWXVxc3D0+rKsq6nY+H9+YzquwtztPyiQwHBia7e5ih3ezedtdU4RkXkyP5lmWpbnMEaPKI+oQQsvLm9Oz0535jmv7etmMsqyQk8urS+hSb6pKSlnOBA3cVD4L5ShOc11+efe1+e7Y+jaVYvTgeC8fGWeV9o9OTpfXyrgYCQkEsUSEGEmaaBS55LWzJEv5QfXyG/fTNPUunD2rQtUYRgMJTc27TnHO83RCcYIxliLPs6nV9fz4Aaj6YYw54gfpHmaRvxzrdYMDdcaH2J1frPNsVBQFyVMiKMbYR2eC6bomopAkiZwXV4tFCGE6nTLGdNOEgGhGx3uw7GmaC845HiNr7YRP1OlnLtP5eLz3ygt5njexpZFii/ORSIvJzsEIoSO2HRMUY4RpdMMBhikULW6cCEU+aZpm1bTj8Zhx0bZtkiRd2zvnOOc91t77g8P9nd35YrF48uTJwcEeDESr65oJvL+/m6bpJ+8+nE9mEJ9laU7piBJWluXF1ZlIRdu2hCFsMdTS2r7FDUmSZH1dc8on+SjKmMiMEDIqxsvlUhszL/ZAMve1l9+AxAv82c3Njfd+Op2CIWZUhBAIwQihXnVVs+77PiLPU+qca9sWeZyIJGWFtsY0Ta1Xm9oQokF7ljCeslY3s7xIkpQG1Hfq6uJid76bpYXkyf7eLnh3cFfOubquATEej8fj8ZhtFSKhDleWJdCVnz59GnFw2h4cHHDO2VQARgr0foB8vfdtk2h9xRgVYiTEKMtYnnuEUIigX4YpkYgxFJm1djyae7e4Xt4URREJIpwKTm1wMUar9abCZS1osGtjsOdlOqmXnZXe20gIefz4McYYQJq22eT0QxG0yDPrNpEKjGomFDNJmr6SPiERq7bnnJOIle1RiH3TIeaKScJ5KYQwvnv8bAF4+92D+wAaG2PatoVUjBCCeEiShKe4nKZUAnRMlWumo1nbtiHYpukYY8C9yvP89PIEiNND/YtSOt0tEUIYx6dnD0/OHw3QJXw0LAJ47ueopHteDnj8pAZH1Z/WOFIIQfS2ZgHBAWPM2bjdYzjG2LVrjDzCkjJMMBZCNN2qbpfWuRBCZ9HgRRgn3jsAGvs+YkIlTqMN2DLr46Krsixr1kuEUCZ5ujNDwZm+pYQEqxnjfaMr1TobaaT1qnM2RB/LSR4Cds5Z2w85XowxII8Q6mw/TCdDHgksXQ+adLS6rohDRZnPsunOq7Ozh1fD3G5YFmwx5/xicSqEaNe26zohhCBJWZY5ml7pq+VyOZ1Ox+NktWwQQkkuz8/PX7p/jyDaNX2l1l3Xe4ewxxgRxSTGGNUrtxGkXEAsJQTjnEPdAXoCAUgPaEMaGLBh8J7WGco2CneMsDItNx1iJrAsssSOMjre26VetAvj13HTpBs+3x2IEIIhpkNyTLbNPKyIB8e7IdGVWlyfrB7+7I+bC+dbLCSp2vXyk0vVa456o02SEmvc4nIlhMhkHmNUnYZgnLLnvVMQPSGEYII6YchaSwljmEPtBCMCVXFAe2CfQW0shJCKDdQ2kBrARkNQuql+CQGfhRAyBmVZluc5Alg/IkYx9NsYq4UgdVsnqbxzeFSW5YMHd68WN6vVCs4GFEVAhWdvd382me3OD/Z2DjDGq2rBOU9k2vdrnmQ7k5290eGTT5+Ny/KFe3f3dnZG851vf/vbu7u73vu33nrrvffeWy6XGOOAzNnZ2ePp46urq1VVpaUUklHG3vnsZxgTIfMOOcM7NosMC4JYJGw6nUDADiU9SFYwueuciwFJLousuL684VQYY3hKp6HctHlYC86JMeYQKYpkV4zyPG/bNsuy119/vWkaVNi6arwLzbp1Olxf3jDCOROsEBvyCMXGmV51UGdCrT/opwgh6O8ETRwppRznADex7Ux72EvT+WS4mK7roC4Cud2QfgWGCNnAoVenC7+VSkbbbhBCiNWuq3vAr6za4HtJop0JYIZAV3I6zcajSVmMxqPJdDqFxwfjwRnlfafG43GMsas7AJ8TmRJBfHBU0KZp6nYNU0vbvoWmtUwklG7nHWGKOYZSHN4W84boAczKZDKBDQMAI0Ioy7I0TVUPdpNgjBGOPjrOeUQ+SEcJCw5po/u+t8Q77ZRSO7s7txkMGONtg182ZE4bUFQkABUMdhwOnXPDcKQAZVR4KHDkV6sVZFrQWwJHDOIVtK1vwSmAY9X3PWyAGCMohYEXUdoMzgMsFEBQ8M+hZjzgUhh/rodn+Ft4HCEE6xzeSjiRbdv0FxxwCIHzzZpLKYUQTdMAtnFjluNbOxM2G8a47Vp4QLCjYLkYYw8fPtxYUhgQaS1CiGAsCz60Vsctb8taWy1q51xZlgBQQ1uOUkrmAn4zbpmtsP6DBxq+hxeUh8mWDoa202ptv4Gv8bbKC/crZeq3ryEFhwNyG8hE2z438HNgPIedAOduYBLBm8CFQTEOLhUCNW11XdcROVAfw1s9c+jxrZq1c94b0BLwVgVrvPe+7tYApYQtpQueI6aR3KKhwU0BgyxJkiSVhJG264zVabHhgcOC3N7M3vtA7UCMhyfSdR1jbDQawcmFi4f13NnZWS8rKaUQcjweBxtVb2OIzjpOP4cMDWDGsPJgUjZpHqUikUNl4fa+BVMwQCDwVoyx5XJpfJ9YTgRihBNvu9Y451hEARPMt1CVc85aB1jE7X1DtxPTHj39zLE+LSXnormsr07WOR9RwYgIwaO20kVaBu9qtSzEkTRpvWwZ01mRUcryUSEkr+u6bVtGEKwOVH/BPHHOEfacc8EkwxxFjHz0HrpcmPdO6zhsIw+sO2SsfW6VvNfea+89QYwQFoJLkmxvb8dae35+3nXd5ODO/t5ePp0SQiTfAFnee+fs4ubyr/yVv/Jf/Of/+YsvvPDw4cPz02f/6B/9o9/5nd/54Y//gDPCcCzGo5urqzxLVNt6768ubxaX9cXoKk1TTPHde3dijNqsfLC7k92//7f/s9WyfvPN79y9f2RNZ6N+/fXXJ+NxRKhpmpubm08++eRHP/rRj/7jf3A2OOtn0x3O+b37R8VIcMnP3XWWZTJJm17FCZq6OcbUOr+8vBYp4xkNITR9FUwACC50sW1bRtjd43sUs0SkmcxIQcQcQ9YyxPUY4yRJEM0gpEhTvqg7ZP2yvlqtVtdPr5xzgiWqU5KnTFApOIqoVx2lNGDPMI0oEII8ci6iNONJObLWYhqICNgF1be67/R6HWMEGX2QXYTTrlYeaIF+O98Xalo3NzcDGEu3gv7ee9UpyB4g/BoQPMEEeCPGWL/WYK+NdD56cNiw74GfiRBa3ixBNBjOOfj7uq5LMYbPzbIsy7IQwmq9vLy6iMKHEBBGgO/CIFuMMWcSY8wIo3jzT04FZ3xg8MGRGdDaq6srOJBwMiHJW6/XGFGEEKWEUmqdgYOASVyvKow26HSIARHikScUgwQxmOAsy4wxkBDs7U2gFyX4yIgAj+idW61Wt00JmD84JhChDm4PgiTwH0IIu53Hd3FxcXp6muXF0D3iNmI1FCGEUYTfBI2Xpm1DCFKIiDbFbDBAfss0TtMU0G8AruHphxDKIndbVjZcKt5OUwbDDV5/eE/wgnEL7eLn7OLng2QgXgcs1xgHFC1YgbDVr3Z+0xI2uNXNf+FmSEyHZBQhRLvNm0PYAfhB3/dQiYdoBrBuiDjbrhm8Gto2VdOt+ung84awA4BcyIPBZ2zqpgEBTD18LnwWY90XHCrci/+LFhOcBKwnVOJijIB8wJXcXpktDkoAjweCNKWUI04pbdreudC2PWx4KSVnAiOiOxMCssY544KNCJEkkYTQm8UVSLpCCqT1dtkxGXwqfAPXIKUM0SulKKciEQRTr0NjG7iLIZgjW5ETSzETDDR2lFKtao03aZqWopRSQlIHCNBmgKCP3nmZJjhja9Lg6ChlVoXbcQwQFeGsdV0DGcXARgQEHoogA2A8BCtDKeR2zIQxxhFzzhORRuK1MuvFsln0pvVMSum3mvVg6bIs45y3XTNs6LgV6PDek8iuTpaE4p29Hduh6XTy0guvXF1cX5xd+hZhz5I8a21LGMERj9l8tk861XDGe917zmWZ1KiGS4ePBuALPmU8HkMrLQp4vahCiOPxmDPedV2RpXCiKKWJlFhI6CDCOFCKKOXbtiLRNNgYk2Qp58JaM5/vvPTSi0oppVtju/l0miQJwVhwtr+/LwXWal3mCWfZ9773nb/9t37rlVdeIoQY3T169OmPf/zD73znOx99+oH3/vrmZlWtPvroo93dXcEE40gry0lirVssTiezsZTSW9/1a05wIctvf/3N89PrVEze/tm7v/jlWzy177z1Fqj+Hh4e3rlzp8zze3fuJEwKxg73j164/+JysWi69fsffvLo6cPkLlsuO9WpZbVWylAuEMLOeGxC3/aNaMCWgfmoqqogpbeecNLWXVu1lxdXMDFe3TTQZgdLFzYS+eUoT0C4QErZtu1sNvvF1cdnZ2fgJzgTbd0JLo2xFNGu60SeIoIYjJiG3CY4hJAgGJr5vPeAv0H/lW4Jxhi0kJbLZVEURVFUVWWVHXJ3sE3OdX47VSbGiLHD2/FwIQSIsggJUW4CUoQQY5glpF1ohBBNqbLWWiel9B7DZHi4TuvtarGqVtXG/WgL6hYYY5i566Rb3qygIQ9qbEqpq8XV9fX1/Giys7Obpqm11iizqcUaZ7313gcXAgqqN4FhFLCzvutbkLCHGBxtK6lAGoKMCiFUVRUcWmeBkEU55z4443SMgTLS4R5Ka4QQb71yHSSjWEQfbdM21lpte4QQE2R3NIcEzlp7dX3tTajXNcFUsOQ2OXcIzxFCiZQxRnerAwfsn9IasluQGgaw11qL0OVw9sO23YUxJqUwt8BPiKWUUjE+94K3Ice+7yFNhEwdbTtrheCD8RmeO7if0WgEAQGw3m4buNvJCvxTyg2EC/nZer2GsxYCWi6XsDkHEIgQAgIpQ9I5+FpC2BdyU7Rhg28GDFBKhbBghUEXAR7rkAwBDlHX9eBf4y2I0eQ2/grnZvMpwYBSFRApnIXRTMz74H3EGAHkEWP0PlbVYjKZwIh7qPXeTryGJz6sD8yFg0OHt2Vp7z3MvR8Q1GHPQMgVYyQURxyFEJnMKKV5mgP/IISQpmmRFYwIo5zqrXPOGeetp4TlaTadzPI8V7obIgC85ToMn0W35PYhTOFiozwTUGCMeRKt1t77LMluBxMQkadpKkajAZIZ0mu0FUIBBZi9vT2oTXjvX3vwklYGfLnRFgUkExkcAixqSAAgMiCEWKvdVn9tYOMPua+/xZi7/ayHGG44fcANTNPEOqOcNb2JMY5GI4ZwpIwgzBCO1lrnbVBeG0IpHWAfsm3MJ4Tsl3d89Mv1TRyT+XTOU/r6X3rxL8++/u//258+ffiUIoEsJoFMy6mqrCPE2S7imBSJwCJED8vEOcfoef/7sHCU0ujc/nynzEercTUuxl/7yl8aFaPLqyvVdxuMmpAkSSBiUkqpxkDqDFSdtm2Xy2XXdQ/u3QMHPxqN7t69q5Q63t9fr9f3xvn+7g7NJq+9+sq9+w/ynOtulSWCpelrr712c3P9f/2//V/6psmSNITw2WcfffrJQxJRcO702UnT1qvl4n/6d/+uNe69999llM/GO48ePv7BD37QdW1dNY8fPS1KLBNpTFet+pPHzz7+6JOHjz9uu2Uyxo8fP9Za37t3r2mat956q+u69XqdZ8XV5fWPf/zjEGLTtTyhynYB4fOzldK6Wld1XeNIORWEchIJtdg5Zzn0nHkpGWOcdNIwJ4SURKpGLxerru0Zkc70yigjAiD2EMQhhHyFl3FNKa3rmhAymUym6eH5+Xl748km3aS6iyxn2JKAkKmQ7SzGGCGHCMIkYrLBNrvoTUYgsUsSyxjrOqeUyrMRIYQGjp2ngUuSZrywzIskbrIZiwJCbjsldGdnZ4jEwQCGgEPAKFAUcMTYIxQwELBRZIEEqztLKfU0GuW8D4LigNGonAz1Y+89DMcOIQghBEbYUbwxuDF4hCzBkWllgt9kKkyy2c5EJMwxjWnkkkYU4ABGHzHHq8XKGRdCQBET1AshKGGEEOvMEMIOyRmkm7DPBz7UxuBGEmMEBxxgQi7GXLAe9dHHTVeMt4QQJhjlNOIYcUyyREaJEDLGMMHG03EupZRyYRd1XTPMnXEYkTLHXdcOlnSwDpDog+cAMzGg5W3bEkIgGoNJNUABgYGVgx2B9IVzTgj+QoaHN3X67fPdfgefDl1DgMYPbScIIaX6wVWQW/24MUYpZSI3N2u2E1jhr267xi1irAHbaJpmkwy1LSEEekIgdAOyDwQT1m5CscH/baFs+QXvu3VmG1MLuxRt0QUISmDCyv7+/nK5vLi4GEw2+Izh1iAQ+UJgNPhLeFu+fW3wAxsooYwzSHY98pTShCdszO7fuzcej29ubs7Oz+FvhzJZvIVCw+dCAzS6Nc0Xjh6s57BV/FbbK025j1Cd3KDu2OEQwnw8B4UZymme5oywptqMiIBw2XvPCPZi02+5s7MDFR+8BXLDply4UQbFW7LhBizBMckSiqm1tmu6ylWMcs55mVOojwwpPkAF83IXTplSfZJIAFqGYG6IdQCmxhgXeanVTd8p01sAWiRPoyRc0OGYDOkvQqgoCrCcEDrANQyG9Fdf7tbw8tuXQQLu+77pG+89RbQsJuOMlMWYKas2G5FhgghmWwzQb5TS4MPgOAkhuhsznY9b3iASeUrPbk7e+TD+1b/2P5rNp8/Onnb9mhGOKU5lft1eTmYTJlHX9TeVZpTlaRFjZJQ76zHB4Hdh3gvsv7ZtX3/phfl0Zo1rqxp7ZJTmE1Zk2XRUDhsL3cLW0mQzPAsAeqjBOOcSzgFgAfCVc3L37tH9+3fMxXn0HgPyxkWa8YTHhOFF13z22cfL1er1V1+az2ZWa2ttURQnn51951vfvHPn6Ozyad/Wk1HxyksvnT47++53v+tMzGQeHD7YP5zNp/PZbt/3053jpvWPnp6cn64oJ9yFlJJyPDt8bQ9qyUKIm6ur8/Pzy8vLpmkElwSzi7MrY5wLTqaSptg6Y5lLeJrFEaWJc8Fp510IODiLGRMIsV6ptukw6TcFf+GDjF3oY0B11XofrLa1qnGknrkQEIocRQ4YVN0pWtjRaEQFsda6aAnHdVdppySScFCV7andYKFVv8qLMUYY4Ygj5pQxyRBCwVrssfHaReeibZWTUkYSbDAWaYJIZ6N22iKtfEc17l0bezoE4IPFSWXq7YaGM4CWcHLyZBSDQwgFhBFC3qEYI/LRmd5qjzix2nsbY8TR44BRmmSQZoE77DsFcwwJpjEgzgS0Q2itg49GW4qpdVgHjRvkkJ3MRrOd2c7BztnN06avA/KSJ1JKZ5wPfjKarc/WbdtSwqz23nvBJeR8hKJhfw4eBWi3cF+DqvMG/hFpjJEQzBiLKLhgCCHcsUCRxd7baL0GY5TlGePUaIsxnpWzPM+rqrq4uIg4+ujrum6aFpRyd/f2uqZbLlaqM1uNqi86YLQNegafN7gxcM9Atc2y7OnTp13XcS6+UOiC28F4I4Q7NNeBPEIIn6OSDAYIUkYIRAA8hDB6tVqSW1IVw7pBHdptgUG0FWIccqnBwMFL637wPYOh994nSfbaq68WRfHo8eOTkxNCCDwRzuXtewdwCDCA24HLsDJhK2gFCwg+g3PJ2CYKmc1mr7zySl3XUsq6rrW222R9k8agrarJYLvwreQYVtJ7jxChFGQ6XAiebEU//JasA5uKEw6FT1BaxluxiMFzD58Cq7qzswPF6bitU9Jb068HGOBzBhYTIYSQHIZ8i1yqAADvkklEQVRnG2eMMc26dzYiT52LjesR6mEDCCEjiYzHyDY+FcT2Dw8PwdkjhMA1wrVF7CEkHVZ42AbOOR+9cw5FTCIPDhm3YQ9gjAH7hUdgre1Vl6YpZSRJJaW0LEtKad/3TdsKIYD2AU8cVH7ruu6aPniEPElEQiLFiFDKpNxcG9rSJmC7SsmHPQb0Asi8tTW3g6dbu+WLdmDrmoMxputUCGEyngqG6nXdti0bTKH3TspkPp8fHx/P5/Of/NFPYN+A4YBAXkrZ1G1r6myUEBmN7/u+++XPPj55cprZORqpdB4O5vPVab+6vsn2+Fe//9Ldu3c/ev+jd95+t+t7TLAxBqZXYopjjHAIYdPAY5vP51VVnZ2enz49S5Ms4VnTtFmWWdUOBwCeGVSJdNe1bTsIxQ21lsp7OCr+lhg9Y2zkvHMuobRrqtOLa8EjIw55q3HgnDurGQ7PnslEiL7tVquVbsLZxbO332M3q4tedaNy/NH7n52fXXApnQmciuXN2mjbdd2f/fTPtO0Xax8jvrlZp0L+2ne/lye0TFlVLZ6d6Pl8/vLLL19eXl5dXhCEwOS0bZtnpddWGxMxda0yvTFeJ4ko5qVMJQk9lTj4WK3XbdsSmsB5QwjJZOMsOecoIqOttRZFEn3kWEqeevt8TWD1oIzUNA2ysak65wLBTHXG2+htpJh7540zLrgQAoYx5TEQjpXuoRqEGUaIM0JccNZb1waIuwlhMUTBEpGJ6LHtHaXUa2WtxZ445TvfexsoZr+yNZ9Pmb1tiCETGnbzkH/A971pEIkRe+t1xIEyikiI2PedstaCagGU0BjlaZrWdU0wxRgDNSMG5H0w2gZNYoyMCoRQ27aYR55yicXRncOmaRgWTnuttNUuWuyEG48mwUeCWHCdsz5G5L3HEdvgbhsytIWOXnnllcVicXp6CpU8pdRoNJJSOgsgJEII+eB8tIQQhGORj7lgkcRoPTwFpXts0d7OweXlJUyihOoviOtq1yKEsiw7PJRfevVL56fnKGJGxJBZos/nr+HW4IfBtcQtOQtjPBmPhy7Vuq6tdRA0gOMcbi3G5xIiUAfZvtvnZroM1wD6POD/4EHDHFal+oFbBDHTAEjCcxysM9wC2b7irTIwIQRmMAMBB3idULQejUZ5no9Go73dXVC5gcBulI+GhHtw2FprZdVtFgLaclyR3zA9wa2CVRFC9KaFRKXv+7OzsyRJjo6OLi4uqmUD72/RJo2BLHwwYrfNNEIoSRIANoejSiihgnIuhkwaiFHwIFb18uTZs3SxGH6ygXAYGw7ObYMJ0cmQsuOtVtfwh3ErVgrLq9RGUCLLUkQQIcQ4E2MMNlLCpCDwKRjjRKZlMdriHSHGGFEAt6JtT8gx287ZBdgSNhvlfFhkvC2sYowjjkYbqy1UUoRkznkotwMkzrd/CNe5WCyg1gamA/ZY2LYzjcdjIQSw2RFCXdf1feusx5gwLAQTunOd7mJEqPe38e1hTdrWgL+Hj7bWAlXCx/CrJCy0VZoLn+/pRQgliZAisSZ0Tef1RqVca80CckywNC8ggIUBCYQ97yiHpwuryznnOVl1i8P9nd3DST5O3vzetxdXqz/6j39Cx/GbX/1yeSAfHL78099/74/+4E+P7tz59l/7svB5wLZVrem8bcPNxRIhElxAYkNJIGTD2ISV/fnP/lxrjREZFWUi09OTk/PTMyEERn7YH4NeY4yxb/vhMCCE4IwhhCZlCbzHwVUDBeCbL7505/6LJJucnp7+4t33MXZ5Qr1VKOPeakLIo4efrJfLclRyyjDGoWPG9rIQPMEhOkZFtezG48n51RWNHCNqtDXGVPX66urSY9vr6XxnmhbJarl8dv6IOptTfry388Zf+faTJ09gJ3HOgdQqhJBCjsq5MrZpOhOiDaZxDXGs65cujoK3IdoyH2VJiqKrqiXmwiNntDHOCCFwQF3XRRMf3L3jve9a5YzTve5Vzxg32qQj0bdqs7E8pRJTTgNxIibr64pSWhSFVwg7LnDGoonBY6B5IsKi8MHHwFjk1mMKlKGIJU45EsgTYp0k3LmW85RTrrXGjlMmkWWxZ4gxH0L0VHLOHMeB8BgRM7BxMXDBENp03dxKHMmWlIcQQnhD6wHFYYQj/NBaC9DikPHAgRyVk77vtbLWWkq4iyF45GxIZEYwM8ZQ4hgVjAqCAyXceYcRZYzF6Nqu1b5HBMlE3J0e7OzscCpOT866rkt4yqiQTCY7SYwRBUIwxaiVMok+xhg73dw23GRbKz04OICACdLfxWIBJV5GgWAcCSHOYxw2crJKKUIymQicRB89ojFE741vmubi4gKkDyBiBiQ5SZIYkRACIwdUkdFoNJ/uPnnyONxqUkRbiHXIvW5nYCEEqNdgjOumadpWSik4Pz46WixX0LE6UGngDPZ957ZKhyAbopTqui7L8iGVwduiXYxxd3cXhGXgNwczN+j2+S3ZakAaB4M7UMPg1A8OeDAIlFKEQpIkQ4oJYhfgzp+dnlZVVRTFiy+8UFXVuqqklJnMhjcB6wGeOMEJIMCDAxacc87XywqAZbD1AzVVZnywwg8fPtzd3T0+Pk6SJBQb7TkwQdCFHG69/OdJWGQ7hGP44ZY8/BzMv+1Q5/M51LAGLWhYqyHlvR3gQnBJtzIa6BaUOAQBA7QA+9Y577e894g3MzwQQqvVCtZnKJ9v4AFEbbDOOYjMEI6IBBTQarUC7cmh8QG+l3jDaQpb6iKojKUiYYJxzoONutfeKYopYyxJxCZCUgoPVGQhuq520fnogBYHrEwpZZZnID2mtV4sFvD7q9UqCYISJihHEW8qxzYkIun6Tc0e/hxsC0Lo5mbBGANEDXJRKJwnWTrYKDj4sHr81vCrIamglCKKYPFVp6pYp1kymUxEIlhGJs6rpbmhKXr57ssU4w9+8aHTiImYSGGMPj87heOqVX+lehuj2CX7r85e/7UHn55+GO/pl954cO4vmpur+VFxeHcvIdiyq8PXZTrRP/j3//av/d2/WiePxi+bEd+9/Li+fGKI4gWfMp05X4t9/OW//FI+Sj77+OHyvJ0WO7JETx8+JphORhky0fQ9CthT+7UvffXZs2dN04Bcu9kOQr+6eJYkCSPMGkAtRjjSEAJLKKW7cLDDVoqPUsrJKNnZ1b6bz5Ivv3FUlLkxZrG8Lscl3U4RT5Kk7/sQwmQyoQ3TzuFUOBo6VQkaxjmOxt7zLzEiPnr/k/OnV5hzVojJ3lTbXqxwX1+Osr2T1eX7H5r/7B/+z3vVv/f+u5/82//h3oNj5ZXnbufOXHdm7nY5z/JJMh5PlstlPpOgh/Xue1e66wINVbNWrpepqFZrEuju+EDmYxJp3yhjjHfe9o5xJlmCELo8W4ftFDkAb9q+p5TqxtEoMPIoIoIQTIOJyBsXm05Pd2Y9RoQxtjurHn+ywE5oxTlnQiCElqt6Czbkyq6dN7PZLMbYtb3ZEpoYCx7ZZdXleV6WJWaxVTUVOInBuT5EF1FwnhAqCOWM0RBiJmXgvO97qxRjTFDKMJ6Wk9Vq1feKc04oDdYjHwXlznoUMYrIWT9kJATTAWoDSNk5lyTJaDRaL9chBIqp8aZqKzgh3gK1Xkgu+7ZXnUrTFCFEEGESa+28JTGikk91p0/eu9zZ2UnkOoQwm83KdKITp5RCEhmq8knKEWWYt75JWV4vW0rEqJh02nGehhAgQ1XK7OxMuq774x//BDbq8fHxO++8MxvvIITyPFd2owzDGBtnGXxPKZUlopSiiAo5VrqHFhcA3w4ODiilbdumaToejw8ODqqqqoy7d+d+dOzkyemP/sOf5Mno3p0X8rx07pRSOhqV8Cfee84pNGiBCyRkU9bK89x7n6UMY/zVr35VCPHuu++en1875/f35+Vo/6233irL+XQ6vbq6grgHY4wiFZyDP+g7A/skkXme51rr4ANjFCMcfECRUEKqxco7L6UMAVlNsiwjKF8t1okUmcxDCNrpVMiN2Qoshhg8ppRhxNbrlZRyb2/v5uaGMRZ8dNb754rWDiGk+56D9q2nS1RJKa0K+/v7dd1Wy3ZxtX7ttdeMcZfnC8aYN6iLevBAMcbYdvBPmZSEyKurBVQNEEKcO++buGEFWlRvJJC2sQjSWk+n09l8/vjR448+emodo1Qsl5e3c6Cu02gjWRqdc+C9lFJ7e3s7u7sPHz7UurmdGcfnIg9fLEjDS4jk/v37QojHjx/HqAHyQQilCY23XoODR9bRW/w1yA7v3bv3+PQZDCQGDABthzxCJrd0NWNsPB4DurBcro+Pj1erlTGWEB9jnM1mlNJnz54NteTb7ifGOC7mRrub6+WAaiRJYo1aryopJeOUMJSmaV5kSS4zl1ZqAXM/81EqDa/rGsXYqsYuwfmF4bljjPtOIc+SMlEtpsJzmSQs4ZSWaYkwCtETE9t+ndOkqxWnMjVFcCFS6gkK3kLtMoTQdLUQMsbAGEMRqd5yliRp7pybjHe7rsMoSiEJJiEGRpF32PTeWotwFAlDCBmrEUGcc001T7jpjVKKIqq9Hhfj/cODanWzblcm9CzFgbm0lDs7e3mesxAdQkiwxCp9eXLDSF3XDSNCW/M8C7lFdpjujbN91pvu6dPHq2Y9qlbTfOfLX/uSvr5LKcrl6NFnT5TRb/7aNxWqguidc8cHdybp3PVoNa7JVOmVjdHeORidLdrzyyX9wL36xovf/82/zBh78ugprdkkL6MJ3/j6t978S98hAS+X64SzYDftE0O6A98URZIkieASRRRj5EwQzEIIHBRejBmSeDg2jIp1taKUvvnrX0cE9X2PcMyyrFddWZaQjcGZgXtP5Uj3HSWIc+5ipIxHTBrV5iyv1u3v/Ov/7k+7n15fX+d5/sILL3z26JM+mBjjxx9/zARDCL39zjsvvvhCluYHh8fz3Qnm8Xp5BfYUKm1W6XWsqnVNGItIOecYFXlGdexTmRGKueAuumCR9yH4WFdrxjdVNLgvyBWsUhRqR4xhwFJCIIT0qscwaZxzQpHzzlobkc9FOi1HkzTlgkxGo0NBZ15T5CpKob5CCAHKDPAXymLsnIPMUggBaWWWJZxxwZMYMIrEuxi8AxCC+M+NjsEb5otVSsFIg23hY1M7XK/XYHH6vi+KYjKZNE1zeXnJxUZ0EH6ZbUeD3YY3IfZ0zvV9D8l02LbPDSnRkItApA/2AkryfjuLFC54o2fea2OMM248HqOAmqpxiWOE+Wj7TuGou66LjnrvY3Bd30J0PBqNtNZpmkopz87OyrK0wa/XayHE8fHx0MXbNA1m0loLWoZAG4R8QhrCGKMMZy7zwWGCcMAY48vzS8hgoo9lWSYi0b2WXEYfB45SjLGqquubmxBQ27bDDoG0g20n2g4RPd0O8cUYP3nyZGdnB3KsxWJxfn7uvZdSIiwWiwXeEtpheb33lPjbfmhITMfjcV3XYNPpVuOaEBKscR5kCFFEDBjOIYTLy2vgBw3PFNJWTDEEVUmSQFoDn3J5eRlvIbdDfu+tHepNUKiDe4emR+89TLeF2YXWWh+fW7bbb3j/wZwQ4pwD8Ve4iw06eqs++tzBjEuYKQ5NwJCiJUlSVdVtHH74XtkwgN5AXQTgfSB23XbACCF0a/zt7ddAFqNb1T94uG7bbXw7A8YYY0owJYM99xh5gh1+DieQ7TAefItcBnsDDgscH8AwBrR28NlDUeMLDhgs1ZB5D6YV3hljDGMe2rYViZBShi4SQjKZzWYzRvlyuV6v17rXCG82EvpCASsS8AWUIs45phHEbaD7iCe07ZqmbaNHIuFpmrZ1Gzeoz2b1MCLxFlD0hRe0kLjt/OnB92/+djthBROEKRZCOO+yLAO9neVyCV0PWZYJRpqmsco64RgTzoXz83Pda8YE0ToKnHgT1xcN58JaiziKYdM6dvtuMcZYxpdefzB7IbFpW7tkcbMM+tOE5W3VUIqu0tXRzr0P8Sdv//IXX/u1l3cfzBbrdUZH1VJ3fZsfiZd//fDy4fr6yeKDk/rw4Ojw8IX1zfLtP/5gNp7ff+24Ccsf/c5/tMb9+vd+/Xvf/s4br77WNe1rr7xYLVc3y857v16vIQo+ODiArobxOM+STAiBEcGIUEpjiM45SxBN+FQIKK6s12uYnKWbdVVVIfqPPv7k6cnTXnWc8173O9Pd/f39siyBUD2bzUbjnHOucZOVScoQDbjvde9sHzlO03E2ratO6R5zhDlKs2Q+n7/3/rtCCEoTH22SStWrj97/aDab3Tm+8+n7n1XNZDwrjbOMcue6m5ubRKaMU5gQIpJE6U1LSd/3RGJvQgwYOcywoJwLnpR5aTtHMAku9EaBqdo0DhEUgws+Bu9JDJQCqQHjJAFkz3vvgrfeYoyFFMjqhFLbt6p1uZR1Xdddr533FocQCSdCJBRzo5wzIXJsowVTAnUKrXVVVQASQjVoAMrgkENLD7T9ASwDtgkEY4GL65yDIDRJEpjT0nXd9fU1PCmEUJ7nPmwELoA/D/gHIcT4jX8dvDJQMYFHA44EVgbOPPj+IZQcqstDYD4YBVjS4JS3/qa6ydMiEanVllOOI1adoZgKmkihTPBCCEal4KLtnmcwy+USvlmtVkf7O3Cnm3nm2/GuGCGYo5emqduOUm+axqPIGKOcEswwiZSRGBDyKPoopCiyIoSQyixkoW3boii6rluzlddRSpllfnVTAx8VVh5iKbDy8Llwm5sJChhTSkHOt65rrfU777yT5/np6Sn0Sa9WqzQba62gngohFHzlbMB+N0sK/wRTC0StgTsaQkgFDxE554xxjKeAlgN/By5yUJCA4AnmEw84x9AAepu1C095E95tRUUglhogbr+d9w4BByy1cw7T53W7wUcOYQR53g77XP4C3ypFD7sdfA/bDnKG+yqKYmhvjVuofPNP8rxDNG7LtyD2PnziELV83o1+7jV4aLj9z5fnn7+G94yIhIhD3FK+I/YB+fBFB4y3xOmw5fPDo7Fb+ZEBTAbRDL9tzRocxK86YKDN022n0+CA4xah9d4bqyOOSZK88cYbwYaqqrq2p9Q55wgigAkNDx1tScEIIYQoPNBIEHXAPNzUpzlnIMXvrCcCntGmx90HH8NmkaHU6n34gg+G64eC0XDl6Pm0QKC5bYo7EUVQwI40LhYLoGgMEUzbNjgGTnie584F1euVXqEAErOpUEoxxAWOxhrGORbYI5+lKd5S/txWZIAQcnp2OjlLTFlY3tVdG4OqrlvG2AG/8967H/zip+9/++vfW13ok8+aF17Ro1k+ycclnV3aVd00h69MXv7m12LPLh8trz4yjz54ZpvwzQe/VtXrP/n//PKnf/RzVsS/9v3f/PCD97zSru9t36u2y8Zc922SpyGEVrWEE5GIfJTnRdb1PaHcI2RciNEzyjFhIUYXUNe6m5ubi4uL8/PzTz/99NGjR1rrLMvu77z40ssvxhj/5Cd//NmjTw8ODrI8efLkyWcfPYb1Go1G8/l8f3//7t27h4dHb7x5bzrKD3Zn03KcZ4I5R5XHyi718u233765uS5HeUS+KHJokVq1FWPs4HC/bdu6rrnlJ49P5l+fT3bGMCEgT3JCyEnz7NHTR1mWpTzbQmmkrhtKqdV2tVixTGBMKCUURYyw0qpdmOVyBePDhqrV0F90PJttAuqIPEyesMo5F7k0xkQUOOeIBBgvTxneSVNEKWLUohDTrKccT2Y9WifexxittdDq57eCAG2vNpF7ynSv+76PPhplYDho9NFbH1wIITjjCCFN0wBdsKqq1Wo1mUy+8pWvvPLKK7/927+9t7cH5boYIzARkiShmM3n8xjjz3/+8//u3/ybH//4x1VV5Xnu9UYwGU4C2iqa3Y5G4edgJiATRduy1tCrCvnW7SAa/nCw+2CJwHwrpbw2IcT1ddMUXZqmtg0ORcWtjn2WFAwzzkRnau8jwc46DK4Uemy6rrt3797f//t//5e//OVbP/tT6FuQUoIOGtT+I+HAGCJbkS8w34QggjH2GEccAwoOO+ut9pKkNPCU5c65oKPASWv7aJ6rHYGbAbgCJjrcLo0Pr8ET4Ft8RkrpbDZrmub8/Bzy4N3dXYTQeDzO8kmaZsP7QEU2TVOM7PCGtxdwUHoZPh2cX57nEF9iTAnlg1sCvCHeYlCCoQfdbKjGwfMFqwq8Abyl+A73AtcHif7gVr33ALAHGJOXpsD8EkKAA/7VF6whbBhY20FE4letP8aYMYIQElLAyoNrmU6n4IMhdhxY04wxZvztYjy8IXDCbzuAwYF5b//C64Q3GZ7mEFskibzt/wanaCxAQUMqFWNE/tYwrsGJ0i3FCd8iaeOtohPAEsMRGzCVzzn7Wy+/VTsfNgm8yfZPnjPDvfd93xfp3drVVdUYsyCYRr9R3fHuOeN9eAQhBGjns9bZ4BFBJGDqMSEEUWSMQTQkSUIJi26DkG3sRogQQYFPxxiDN7+Nc6BbuG/YMtVv/xDitA14hiP22HtPJYXCM9kOgdZan5ycpEwwxrzb6uFgKoTIkoxF7AknWZpyzus6IBwRRcE7emtIw/DxhJD9nf3PPvnsyrDJQcmoZEyOJiOZ80/ffvjo/cuDw/lnHzzzFd/fOfzsnctHnzxlo/e+/vq3mOCM8bOzS0v08dHd41cPJiPPU/zwrcsf/ehHJHAsvJEtzeP/6n/9X/yd3/xbn3zyWb1s86T0Jqpe3Tm6e9OqdVUNXAzY7mmadp2hjFBCcUBdp89Wl2dnZ5dXVz/9jx88efLk9PQ0hAA0yIOD4/39/QfHdw8Oj621X/9L3/zGt761t7eHGW6a5vEnJ8vlcrlcQt728NHJz/78nfV6Pdub7uzOX3zlxa989Y2vfuXVB/eO8pzPchb57NmzZ51uXbTK9jera/+Jz9Kc7SXQ8ggbq+u6s2cXd49Xr732yuXlpVLGucAYI5ju7M53d3ex5UVZFkUh0/TqZlGWpXO+qqrJbDaejZikjBPVm6vz65vz9fXVAlH3+PHj6+traELA2IXgMSYglAjqNnQ7cSVNU884QkhKURSFSBh0FDBOfOdDCEWZc8nyND2+c7RbJB9++OHHHzzLskwptVquMEKciRBC36kiH7VtixGOHlerJsZY5mPYi4lg0WMUkNXeGKO1SZJkVOagTnD37t3vfve7v/Ebv/HGG2/M53Mw4hDVMcZmsxkhpCiKJBXeIEzRvReOf+u3fuvHP/7x/+Nf/+sf/vCHPGDwkUP7wYBtDi4kbBvevfeQUoMdAdsHJsY91z587njAfwBBBkwD1P67rmOCcS4pFkZ5RgKnCaeJ1cGHGEhUwVDC0zSlGWeUxxiXy/Wg83BwcPAP/sE/+Kf/9J++//77/8f//f/uww8/BFyaUjrsjV51AJParUgnpBTbnNJHRyIKnnirvNY2xFhXjeASY7xeVXmeY0RUr4uiyPPCduu6rtu6J5EDWwrein1eIxPsCyQuEG2gbcYJSlvAHwZTCDHN3n4BSe3g7TYXGTcSP4OJwJtiZwefNaD6YKqUUr3SkAz5AG3BlHNuwsZ8D1nmEBPcTkYHWzS4qOHNh9/HW+KS38ojQMURWiQA2QJ3Til1wf2Fjg225WDo/bYdZQBvh9d2I2HnnNEGEBrv/SCPM1zzcD0YY7BgsP7wy1BZg+39BR+GENL6L06Brd3ATrfXCm0Z6fGWHMQmj/LQxz9grRGhGLas7OEr2ubQ5BZ7Zkg94RnB+sCqxi0Jzm3H5d4+mPAIBk+Mt91c8A6EkBA3rVNSCOipe+utdxBCfaeEEJiQ6CN6ftXPHTDZguTBbzYY5UQIEbC31sQYecKtNdqRvMhMtFob3TlrreTSex9R/IKjHe59wDbgNcQ34VbPG6UUY8QYQzi6YLz3CG/EIjHG+/v7k8kESkvQV+m9pynpe220dc5JLhORxBi1MsxaywRJxoIYVFuvnQ4uYIwH9Vp/q90eIUSVVAt6+PL4hcOXl5fN6ZMr7rM8Kderbv94loi0aZqu7at29WB+KER8608eEfTul15/IytHH39yfvWsZt8YTecTI7r5/XyxEFe2ZoE64yVL7x3d+d/+b/7rX/u1Xzs62G8qv1r0ZT4JIVjjBJMkEoZZJjOKqO60L8JkMuHEW2vXi2q5WC4Xq+urm2fPnl1eXnqT6y46TSaT2auvvnp0dAQMQ1qiLtQ2mtFekWRSu071KimS17/1UtyKwocQ1uv1T3/60x/+8If1MulWJ5+8+8Ef/A//7uh4742vvfrN73zjy1/+0mRkLy4uQvQBeyZpkkrO+SsvvdIpnWUZJ7wsRicnJ3/8R3/S1t2jzx7vHI67vq8WNWciSZJxMf7e9763d7B7b++lNMs45z7Gs4vLyWSS5wWltCim5TjnCZWplFw6HZzC0aMPPvnlD37wgx/96EfL5ZJSOi43Zcurs8cvv/DiV77yleOjI0JIrxT4HiIS55wx2jkXsccUhxBCdLGUwbrZKE2RLyg5xl5mAmH/UcCcSYOd9xEhTCnHOBDChiLlcK6cc03TTKdTgFmG3UIpLYri6Gj/b/yNv/H973//61//OiRVA0wN8CZEeGDxhWQIoUGNNS3EX/+bv/nmm28uFov/+v/w33z88ccfffgR7OYQgpSyKIp6vRrOOaQWMUZoiBr618Gr0a2ELNrKW8attCraaucCt4BSCqJsXddlSSGEjxbXy7arlGoM9hQhNd0rrQ6LxQIFZIxhRAQfjTFSps65yWRS1/U3vvGN3/qt32KMvfHGG3/v7/29f/kv/yVUvkE/sigK5xwIXNweLuu9b5omS2FcIHYuxOgRjlZ707nRvOi6TitDCAG5SoRQ33Xe6CzJwLEJ4QVNYQWG0IRsa29fyIoGgBEeJXRnZVk2mWyUxgFaBK41LGbcSMB6SilnmwRiKEBscwIyQJRgNzb+NULjLCeEee2NMZw7vNWpGK52yMMg44Tq7/DzeKtYOwReG6McnkOIQ4YKcR74AMCuB6UXcMCDOx++gS0Ut5TjwfQBZjN84rAUQyo2BFLWWAgx46DmtiUZhBCUMnCO+K0+nCH4GN58uM3/xGswzsMfku20peE1eERBMcY4kg1goFFgOOLghjCU3KoBD854iAi9f97NDIG+UgqiNAgi/8IrHDwubInhaiml4FSBuAO/EF10zvnecSZijMEFh5xzDujKt7cx+nwZmBDCOJUZy4vcR9u0mzW31kaymSPQN9qqALFyvNXxjDGGzilo8Rq20PARgJdAjwBsb/gJVBhC9MZR7z2hmAAII0ie5+PxBCHEeTNUtYL2gkvOhFHGW991nbNea81QIIQhkkTKYxaE1URVliER/W15tg3W5JxbnNdsgr722jd3duf100/dCvsRUbXvVnrvcH5zc3Xv6MFkPvr5n50nYzaaJ8cvF01XnV4+ESG/+rCNHj+YB+bVH/7hj/7OX/+fvPmXD35BPsrp5Gj37sWzy08++rRMd//wD34agv+93/3Df/f//v3f/M2/+v3vf39cllzQw72jO4d3Qwgw18X0tmeqU+Hq8vqzzz47PTk1xmZpOp3sTyf7KJbz/Z2TkxNjjMfh/PoCWoBav3t8fDSfz6UkYCOklIwyRCJGiDNGGGaMHRzuTWbjozuHf/R//2/vzMe7YiyJd7i/+cXbv/P+W/86Ke4ef+Xk9CnmMWLPBS/H5bScZmm2qqqDg/0vv/Hl+3cfGOXuHt3/wz/8cdf11zdXRVqGIlodVsuKCbx/vKt0n8r08uLy2emp9X61rrMsY5xnWRYD54K6YBCKaZoxnFDM8rRs9HJ/f/8f/sN/OJvNkiS5vr5++PDh5eXll15/uSxLmRXXq2qQG0vTlEfsvQc6yXhaHh4fjsdjQvEknyWMRqeIVdM0nZbp9eFeKuVT/kQIcX5+PvS6hBCKogjRpWl68vRkvapmsxnPhHOuLIjqNSUMIxK8jxShiKWQk8n0H//jf1wUxc7ODvDa2LY9EUwzlHJBkzZNU+9i2E59IZRQjghF41kxnhX/p//zf/Peux//9Kc/ffbsmd1OFqKUPnz8CWxuMA2AVWKM1+s1vC1Qjeq6BjIR5CV+I5cYh1IlDKIAnVhCCAglUkp174ZoXWs9Hk8gUX7plQfW2pOTE4TQ8maNBtF8E6CmC/TRn//853/6p386n89PT0+HQXjHx8fT6XRTNus0QijLsq7roL9oPB577wWTPngXHUYkxBADAklCwRM+kjBe17uoeuDFWBc2RfTJZJJwEx0ZEspfTYNup6SweuDnIH0BDtF0OjXGQB4MhWRIkf32BUbKOT94i8EzxW1vLlwAaOxAkU9QIrVBCGm9AbEAMY5bKOK23R+8wm3XjreS2l9wMHiT4fkhtfqCyYK7g0BnqKYhsjGywzt8wXnjbQNJnueUUmCchC10DDun6zqMn9fCB3cFrUGDEyXkcwIGcAubfGAL/7pb+uH/ab87vMIW3B4e8a8mcMOtMe82F4kRITgAZ9LoL3wcvgUwbIJjIbTWTdO4jYQnQtsBDMNADiDQfeGtBr8OyDzIgAxAwhDNU/a8rpQkyWgy8t6vl2vnHI44+BhYiB4TRm77zsH7Msbhz4mNIQTKNopXAQeME0QDNE3hjHZeexdcdM656CPwzxAicQtI3MZX4DWsIb1FIGeMSSmhJdN5GxCjlHLBoKhhozXGrNerAZbAGJdluepXjDFOube+azprHMaYYs4EFYYo7XvMYznPkCPr2GFFI/UD/DIcM+dcQqf5ONet/ckf/uyDnz8UMS/LvlJLZ2JdN9kofXbzqMxKsYMUrb75jdd5Xjy6+giJOBJTGcb6OqxO9Lpbj/3hjB8dHB3SvjCt38kOd8TR8eiV5erq4uz02enJs6eXp0/O/v0P/vCFF+79+q//+v/47/91aILs+/7x48fPnj3DGE8mk76Lfd9X66pXKk2yVOSMsuBjh9TenZ3xbrlYLK6vr1tVU0aLWXZ1svYd5TGfzWbBci5YlmXWG+P6gbfS93Uluzwvv/nVN//qfxUnTk+r1RhHnJW/qPTvPjz75dXFf/gP/+HV118px5lHNsuy2Xw6K+e9Uutq9c47q1E5aqr2zW9/95/8k38SQ1ytqjsvzL/06htex3rdnZ+fZ4X8yl9643p5xbQ8efbspz/9KeWcUM45b4C/SgspeUQe05iILHjitMtkrmMH1gS6nOH0EkKSnC3W68V6jbfFP+1cqxTbSAcEaCe4d+/e3t4ewlEsq+ls3hulnZkfHHgUFk27mOx8982jw8NDGBg1nU6Bu1QUBaJhtVr9i3/xL37/934fHDNkdYwxIZ+Dw5BKYoz/2T/7Z1LKyWSyt7e3t7cHQqExRugLHI/HUkpoij0+PpZSnp1edF2XZdnR4SFj7PrmBt5/7+BouVzCENzj42NwlovF4ktf+hI4NkiL0zQdyEQQfMBkBeBzAZkWwlj4CYwvVUrNZjMhxPX1td1qZYNQxnrRtW0LxreqKrsZXJFyQYQQi8UiTdPlcpkkyfHx8dHR0XJRPXz4cKDS5Hk+m81ms9nf+Zu/+V/9l/9lVVXz+bzveynlarUqiqIzG62i3/u93/vn//yfn5+fA2yVitR7r43mTCCHnLchIOfc8mZZluXN1Q1kTlZvOEf7+/vj8cR2zmpveucRooQMoDraKrwOJg9t63AQZwwppnebLlLgnA+WDjLjwa3i7Vxw7/ohR0RbKhDZjmofdHvyPI8xgjg2uJm+763bTEEYkki8RVCG9wTu5PC2w89v54W381e4eCB+Q8YMRnNYB7wFqOGtjDPD3952HmLrhodegLAVMnS3xtKgbeuqEJuAwLvnEolDaDJc8GBCh9gCbWXFIO4ZLP7t+xo89K++rN1k1bfXBN+iUKHPA8I4BhwxxpgRDD3sOIboHcJ/8fs757IsK8ty6FAA5INsi9YbLiHGbNucjT4PPt92V3ELlgxOjmynL8MNaq0pp3mer9drgkgIIU9zKRLnHArEmZZs+RnxVlkaIcQFjzF2XdubSBjJigQSVuMNAElpmsYJYlic+6vrqxtOeQgBBUQg2sMY4c9d/HALcQv13QYwbvvp2z8khBBKINWBvQeVcqjIgB6IM65Rje41RiTLMhyp954h6b3VCOOmrYVgRTH65q+/3jbdn/7oz1JSUCyU6z3WVGARMxYTR67VUr797z6o67rkRZqmN+9cSyldaNPpKCG8U+34aOI0Qu307f/v2ccnn7z02v36cfvpw7eEzjJC1Sf1t9782ujVuX8Ygwl35AHLGSP8ult89Y17P/iDp3mef/UrX7t///69e/egrVAp9eE7p1964/Vg3aefPb6+vm7bcHOxOD9792B/nmT5dDrN0wlCKM1Hy+XyZ3/+8y+/fLfMMrVesdXqMOFaJEulGOf75djjUDVrS5H3vpRSEsxjqDo3Gk1hK3CcMsYykYUQ2P6b6zRZuA6ffSQXD2OJHny9/OjUvjreYTQIQq+ulq/efXVvd0/1erW6vlveI4TUp01/qczrlu+Khw8fLxaLX/v6m+eNDFJ4U//sj370te999513P3z3z/6kkrFv1OjuxJrQrnqjA+OpUtb1zXQ6Xa1q731ZBhA/klJyKqBOeXFx0bYtsOQZYwyXEKIOFS/nnJQyLYlSCgqQ0+mJa0aEnP/2b/82f73zMR6OJgihqm9/8Pu//5Of/KSu64zRYkIxHxGrv/P6V8BGd10XnJuM5csvHX90fw8GE/V9TwihePL48ePpdKo6lyX5qGDL5fLs5Mpbw3lo1+7k0ZX37zxnCcncOXf//v0XX3rx5OnJ/sH+w0e/mE6nEGIvbj45PLoqimK9XlNKl4vl4fHTvu/Pz8/BfL/66qsXFxfGmGrdTKfTg4ODZ8+ewaafTCaj0aiuqrquocOSMQatqyGEBw8ecM4fPnwI5wr87s3Nzbe+8xsHBweXl5dXV1eQ/r700kuTySTnEqGkNh4hj1BCURIMak2k0bS6pTL96OMnHpnJKD88vLezs1sU07PzC+v8er2ezWaPnzy9uLwqimL/N78/n+2OxgVjLElFjBHh0Xg8ln2rlHLO7O9N8ozfv3PY992kzAjDWnvCcIwmYXg83iWElIWUCTk4OFhcnQsh+r4v0o0oLu5Qc1XlSb66vPbeU8pj6PJsLkTgnBJiEHJpip1DzhlwCow5hJD3LaWC0uCcSv5/lP1XsKRpeh6IvZ/9XfrM4+uUr+pq3z09wwEwcMTAbigogqEVl6EI7a4oSlfakO55zzUXCkXoQjdULLmhCC2xArk0sUuAA2AADBqcHtO+u7q77Kk6Jk/6/O3nXl18mf/Jqq5mxP5A9GTl+fM3n3nt8z5vKLWOvGAVQWQJc5RrMESGkpJGGDSbzZCzXqtJ7IrssLJAEQgB59A5r5g5ZZyLuCiXjEeMsbww1mWc8zxXSSwAQFvLBJMhq1RqbJE02HxOKKfWWm21A1djoxzqLF8GYY8LQhkqXaTZnAtitQMA6gGKG0Up2jopPTEcRlEYhsF8PuOca+cMIgCUWhPOC6W8S4TwTOKzFuuUl1JEkoGMo7Is0ek4YgYLXeRbW1vD4VCKuCiKbD4Ho5tRaMEAojVVI2lrVUhBCVhwBglywauqMtYEQWCMqVTFOW/FkZftjDgecEKIrXJBnKb26woAEc1G6plsHHzdpNmrNyGEj+f5Ggr/5coQr6owDI1x6MnJrUKDyIBSMs+XhAUUUButrfE2mTHGGU0JcUYTdKEUJgpnlGhnGQHC7GI57nSTKOYyINrkWY5xIiqVEkKY5ztSlRBCSFFVyqF2ToUh14YwDhKYsRWhgOAItZwxguCMjaNYSlnkBbPUWhvywChd5kWSJGEkgQRloYUQjPOqqghCGAQ+shJEVGsdU0kpdcqWi4oQkkMZJgFjjCIdPfL1jYVTVjLOkCOgA+esM3alOzkjxijvIVijKSNAnNKlMYayJIqlsRUCxEFEKUV02pScrwwdCgStq/LKT1BbtiCHLEuXbmGtjWxojDm7d+pzTEZr3+bXWUOIJRR4u9dssdjJajh/muaAiOcn4/OTiYSQgZQ8BADfMts4Qx1tRE2llNKaWsa5oJYRQy26KEk6SV9jhZowJ1WpTxanDHjUiq3C05PzMi8P964Ww2I0nY7Gk1//5d/O8jRpxV5VnBydnRyfTYbzV199bTgcttvtd771rXa7/eTJE2vd3u7e6Hz46d1Pw0QGYbB7sH8+PB+ejYCQ3mBLSrlcLgljd+7cieLk/v37Dx/c/71f/972YLCYTkZnQ8LZ8fnobLpQyh0ti8UyZUdPW90uIhZpShEFZ06vkkxlWdZhE6215OR0OgqF3peuB9nWYa/aakTSiCa7ceNmt9sdn49v3bz9yiuvoIPHjx8PH0+uXru2u7s7mUzSYjkcnoaRzIs0ZSxHp1VljV4S9pcffTLLlrOT46paNhutIAi3t3bvvPJSlarZbL6zGweiRSndu7xdFxH5ICrgKnN5eGO/dhqMMfO5qgNcZN2vAxHBEyk7V1XV3qW9S5cP/urdd//Rf/2Prt888L3ZPerk8ePHxRw7jd3BtmDrqr6joyPOuS9qtFo3Go3f+73f+43f+I1Wq+W9zHa7/d/9t//8v/0n/8Q3HPRxOUrp7u7uYjZFRB+Sdc55/i/OuQFRlqVzOBlPp9OZEDLLMsElIdRZdA6tcVqZsqgopcZYa1BVRlWGEIuIRjtASoD5eg/vSK0oVoTwUGcfKfGVpj6Atlgs0jTd3t722NSaLqcWvoyxbre7WCx8+NoYEzbbL/QMAiG4UgYIISTP8sVsvJxPP/3kAxkkf/Znf+YZjAeDwdOnT4MgODw8fPdHf9xoNKSU3qrwPkG/3//q4ZfGmEsHl4usFJIdHl7yJAO+RryO+oZhKKVstVp5MWeM+V7CHofsxS4REIZhHMetVgsRpQh6vV6/39/a2vLQa7auhKmTuN5o84WSALBqViFoGIZCiCSOe+02ZSyJok6zyQWL4tA6s0wXiCgkbzQSKaUbTzYjq7hOmtblxXxNOIxr8mHvOoh1Fyy/XD0Xnnd53bp9PQAQuuK+95ACu9Flr54L3Eh/8nUdql9s3p4DAIALYn0AkBvNm+FZBqj6sf2NBBfdbpcLtrO7XZmiEbTDKCjKXBvlnLPOGKuttWEc1rFfsibdcyum6BU2wkebfVyawkV8m65RbIgoQlm/lNs4yAq9TJ5TzF794JoZ0N8XET0Avk5D+AcTQnC4CIrUI0kp5TKsYyR+tN26xhcA0nTVVNF3M5NSWtCI2Gq16nXlH7vdbsM6yCHXjaV9vUY9yB7BTleFf5WXLbVw8At7Mh17D5Js9LHgnEfRKrqeJBFbs1j7IHAd8LcbzGjaKLIBzPRmdxzHYC4KzMhGzN/aiyqjenYQMYnj2n33eHifCPPdtOr5qnM0Slf1s3lI7MpTF6KqKt+Ars5tMcb4yelpZ7vZ6ob9g06lSq3Vk5Pjs/vTGGIFxhHNAx7yxKEmnAVhaInVqDnjVFLllC61A0coMTnoFJdFoRZYZVaXriwqQKXzuQBoh+3LN67sNHaOlkd6qk6G58fH50Bcpe32ztbWYLudDC7tXqeETxZZs9WhlE7ny3sPHn322We+md3WoNXuNlvdZrvTcs6NRxMgePnq4eXL1y5fvvzll19+8MEH7+cfMMamo8l3vvXtL7744uzkZNDvb+3uLNKMc77V6yJlFdJLV68Zpe999eWTJ08cgnF2ulhsBQPOeavV8oR5zWZzxY/YjJaLyU6/HV66BEx/Ph5OR+dKynfeeKcoio8/utvtdimV6NjJycndu/efPHz8+ORoMBhwzr988GWj0eACXrpz494n72GYdPu9rWb47Tu3jybjdHj88qX9pSW7u7unp6fLRYr7qEGNpsM+7VvnxuPx/v4+FaQoina7HTsOAGWu4jjWerXPfT9BRNy/yj2Mtm51slq+htt1/5bXX3/9tTde+tkHP/7Z+x8++PJuUZZea4ZBkOU553x3Z+e3fuf3pZT9ft+nf8Iw3NraiqIoiSKvWWvKTy9Jv/Od7/z8/ffTNPVN3/r9vve29/f3fVGQ50D3GK4kSe4/epplWafTsdZyIRjnxtogDB8+fOg72IRRVCk1Go/9zmy1W+fnk+PjM8ZYFEXLZT6fp0VRFEXhHInj5nKZ+wY4nAdR1Hjw4NFqZxonpfQcF8vlMooiRPLo0ZHP0TYajXa7fXx8+sabWJaqyCtKaZ6Vk8lkd3ffGDc+PX+hAm7EcdJsNnvdMAyVDUnAozDgFLa3t30dVLPZ9M1FnHNXrlw5O1Pj8bjdbl/q7WfFcjabtdvtZjtxxCJ1ImTno3mh0vHsXPIVdNmDgHxezYe+vVhERJ879yNPKY2iyID2+ti3PeZMJEmSJIlHHXo4NNkorvAEHXXJFqXUt2R3dhX1bbeSSwe7uE59xY3o4HCfEOKFqQfQAcHdvd3N4K211tcK+3puWKeZ/VKklAaS1EHdzWC4Uso7B3Wgcn1rVOvWgd1OBwC0MXZN37h51JK0zkOnaaqsCuJACEEJF5xwzhuNqNWMd7b7XnUxTuvQItko2ZrMRpwEQoitwda1a9du3rrxS7/8i51+Cyw9Pj7+x//4Hz98+NA5J0ORNGPOOSEr3JNXt97m8yl5vmaIrJUQ55wyW4fBN19Br5s8PaeAlXaEEF/BiO7iT4ytVoVXY3zVO0+3Wknt2bN1JXQYhgQbdYAaNzLTDkAYEZiVx1yD3Xz8X3DuU5lBtGqRqXTls7kedVGX+flJ9ArYI+r9chJ8lZ/yg8N81ZxzWnuCydAPUW2veEXuf+JdDsZYEAQgiV+oHuTvF4mUsiixpvA06xXinONcSBkQQoyxACu8RRiGYC4K2OhGOyzGLsoUNxYhY5T43zq34jpdZSqtrg0+v/h9Yq5StjawvHtAN/qCE0K8D+DDMJxzXmbm8cPjnm1cf+PS4e09W8CDj051CXIal0VljAFGANEiAFjglWOVY9rSFTUMoZQglqZkNpqfpItqSZnMJqUubafTIYQMT06np/ObN27sb+3PzxcGq/ZWJy3Sf/kv/1XcjIXgd15+6Xu/9Muv3nmt0+pNzqdpoT0HzXK5RAc3b9zyHALf+Rtv7uwPqCBKV6PRaGd3TxW6EbX/+R/+i1/8xV/0xsXZ2ZlW6uDg4Nd//dd//uO/evT4SavZjrZaX91/pCp989oNY0xjayedTat0Sfd33XL+dDiUMtjf2dGp5iFtdOKsWjIm9w93PXXO9TfeWUyGjVC88drtsBF88OHPJ4/uXU46t27eOT8/P3p8gkg//uizRw+ezufz6WRGGJ6en50MTxqNRiSlZzxoNZrLR4/yPO+125Nu10jBrTvsDV6+dfNbv/LbYRj+wR/8s3/xL//w7Mlx0kyULpvN8Px48dlnny1vF4yxLMsuXbrko7iHh1c4C8OgcXZ2lqYpABTFeRRF3a3GcDjc2tpazCe+4rmqqp2dnSJfetrOOI5Pzx7/6Q+Xxua/+3u/Qe2qXtbLOK9Q+/2+t/U4555KtzbhvdjyNVpxHAdB4DG9APA3f/3XX3/99XqrPHjw4PPPPwe2wuJ6qQ2TSVmWlbbdTj8M4mazOZ/Pk7gZyAiQcibns+WqJBcpAYaOaGXRkSRuTdmCs4Ax1mp2o7AhRVrkqqqUMRYRCKGIHkrKwjDq9Qa+IqvZbBNC8vwkTXNKeavVYUzM58u1QZ1wLgFonMQ+6+m3lrffASDL8xcq4CzN9oKoJwQPJCwxjqNup7ndb3e8ktDap6V9+9UgCN546/Wfv//+7u7uL37vF05OTp4+fbq1tfX666/LhlBK7e3tzRezoip82LDRaIS8udrJVeUNHS+7szz1iXM/ql4+VlVFBNRIJf+9Wh+wTj3SDYhTu91m6+bnnHNftiulzLPCX1BXVb/b5YwRQpbLJQ9ou9fUWlezIkxCGXF/vjW8Br3LdYM251yRV35t1HBiL7/iiHsYlM9f6DXts/dTawe3TqoxhsaYs7OzoiybzWbNhwXkGQIQf/hH8oh3pdRoNCKErASupd7p55zvbG1du3KFEFJVVZjIOqdINrrA7qu9JGyqUgcy3NnZ6fV6169fv3L90Ocvdv/n7bRYeF/QW8AUhXPOv5rfGoyxwWBw48aNzUhGbRBrm9WO76buJxjUyqDWxIho8SKruml8lMXKw/Oj4ZzzcYJArmwRr6i8sgnDkNJn+vbAOtYt5KptVO07ekfQgxO93+YH1huCjDHfess550e4xlv4QfBeAec8iiLnnOBy8/VhXdnFBXNrcLtfKoSQNE1b7aa/iBfpbt2co91ueyI5b9/QNW1IYmK6AdzzlmJRFISvGG/q9/WGqaQBbATza2OIUuHRJN5g9TIwCALfkNwbFt6DXwXqrfYz6OfXx/mcc5UqNw0pv39xjZPwO4Vtlo0Rw6pSL6YpIoZNsXtz9/L+1ddfLv7sn/6VhrIqSwTGgCEigrPOWe04k2DXlDGEOnRa20azlS0LwYPuTrfSBSG4e2lbuwoqXMyX50ejbJznRR7JIGxGk/GU6Wh7b6vdaSFx7/3kxx999PGlncu6MoulLooiiqL9/f1rV2/ceemVZrPZ7/f/9M//eDydMklu37n58suvpYv8B3/0p599+lfGkY8+/kxw2up0D3b3rbWddivk/JWXX62q6uXXXu92u44Kiq7f650eH//5j/96OR4zawRiP5CDmzcs48PZTCVhkiRbO10aular9dJLL02n0+Vy+dYrLx8fhe0ovHH1VmtrsLt35d7nHz/45ON8WQQsaicdVGQ4GeuukzJoNTrdnZYxZj6dlWURBEGn1ZacBUEQ3rgus/Tla1da27sqaT+dL05OTqwxX3z+RafbZpTtbu0JyTjlSG2Zla14h1ixnBZ+IQYs8doukV1jJvt7e4/vHTvn9vf3HxwddbvdLMvufXVPvBkfPT6az+eHlw/Ph+cMQ+PSn/zkJ3Ec/62/9bfefvvNyWRSfZFPZjYOm2EYKqUsWqusQ4cocs2XS+ZbYJo1iZJnInTGeOHiK4yNMfP5HBEvX75cluWrr77a24mdhfd/8unTp08558o6L6RwXYuiV03XiyBIWq0WpTKOW3EcK+Uajc7Nm3dqxuMgCOK4ZVbtwJJ2u4/I/BYSIorjFgDnAY/imHIuw7DpRUkQWMQwinwwSBuDiNoYh0gJCaNIBkHSaHiZJYOAMiakLHNlYgdItdKAtNlohzLWWodx44UK2BjHGK+UFkJ0Op297X6nKWPJAeDq1at+6Qohbt265T3+45N7k8nEN073iK3t7e1Wq/X666+nadrv98ejyXg0aSZNT5d/cjRaLBZ1bSgiSik9cs0bUt6F9YNZVZWIOCGklOUq44DEK79NYqnaJVo5zau2d5DnuY9bdDqdgyuHQRCgdYN+Twoeh2EchmmaBq3G5cuXtdaj0cj/1svZ05Op777gbQWPVbHWGu08aKAsS78AVqjUNWNDDekC34kyimqWJVyzcyD6BtAwmUzyovDkaz5mThnx3gNdd+zw53ug+8qG0DrLMp+JQAdeVTjnOr329u4WAJRlGYSy1nZkI/laKpWETbRAgPp17gW6VwaMsX6/f/XqVa9dyrJEQ70D5Jzz8H4A6HQ6165ds+saJLVusmmtDeNV7rDGVHuhbNQzIKb6cLDidXrOU59NF7Uh4q/v38XZVVir0Wh4j02smCAvvGrYwHkRCptWSO2ae9yQcy5NU6+KvOVkrY3jeLFY+OXtX8EHvfz+BVh1HfB061V50Q7HW2k+O+bQem2HiN5s9R98yzX/yj53TikNgsCgoYJSoBYsUgziABG9e1ObbnX0CBEZMlMZAKBIQxECACccDfKY1+NZmy+UUmu133Q1kN5fSvBVR42aCHlli/AV3ds6IMFWJeYF9VrZy88aZuj5d/1kbU4lP3h5RwxJZhcPHj0eu+M7L78Umu7oONU8Zy0TNylYAEOsoo44wtBltNPrknX35nSRcs67yRZQ1LZqNpt7+7tn2SlN2vvXBpPF6NHPFgEL00kxGy+ilhQRnS7LRbZIkkZWpd2wY4n95MtPTp+cXb18/fLB1TxlrVYrjBvKOBnGO7u7AHA6HH34808ePX6IYP/z/+P//tLulYrg5Hxxfjq5cv365599OjofNpOIOswWC0pIq9XY2R3sXTq89+jxfD7XWktKFqPzo8eP+zvbr758p5kky9lsMBi88vrrDuCzu188Wo6vXb16586dVTOlTmexWBRFcXn72qVBc293J4qT0+HZbmcgbrz84V//9Fd/5Tfe+/FPorAZRXGnNciybDHPtNaK2uMnTwIpOEAzSphFFgR7ly5bqOBsPP5geI8Geu+wADbo9pHxNF8WVRYn0Xd/4bvWuOl0WlVVv9+fjVy/t8WZrFA3khYlXCvbbrefHj31QMHh6bDT6cRhvJwvQxkulvl4OCrTYjlbzKez7f7WbDw9e3oqIlMVRbvZ7LRazSSZT6cHe3v9fv/p6LjZj7TmbM0GRSmN41VphIfzeUDHkydPnHOT0ci7aD5V49Eft27d8tUI8/m8txMvp9XHH3/80ccfdzudIGn6difOOQQIoyj0FnHQJoQkSdLpdf0qb3c6g63B7v6eL4r1ScFuv+fXKFqyvb3nY5U+mNbtDoQQhFnvcPS6W97U9Wq+3ez6N/Jf7u5eQkQfAB8MBnHc4pz7AJoQQsp4sViGYeQN6Ha7I4QgxLegeXEVJuNivlxmVS4DtrOz8+qdG7pcZoupEOLXfu3XfBzMb0VKaaPR+Ltv/p7vEbu1teVzBF4b9bcHRVFEQeQUzCeL/b1LTx4/FUKks/e8UqRrtvNWqzWfz72+8SwrcRx7YKpb0zTaukwTiReXnvLJCz7YiNN6M86um3V6Re6cu3znpa1ebzDo66oixoSB7DQahJDDyzeyLPMdJjxps9eXWbFiGLDWFkUxm82Gw+F8Pn//5x9aa9M0XS6X3pX3UeskFl7Z1HFmWIc6zbp9fV1zYYxxTnmJvxkvRUTrLEMKhANBBAcEAZAxqo1CcA7BoaWM5EVWqUacRJwx7RxnxAIwCoHkzjkIBFAkG0zIiOjcWlAiMdY4u3LN/SDHQaPT7EUy4WQRijjgISIyEMZYH1+FNSTYrnlgzJphFNahCKWU4OCcs2DRAfHJQsopYfEKnLUidq0Ft3Fg1wygXpP5gG0URd7vr+NYXpkRYH5GPCG5p2RCROvQpylrBex/mBcLXHcB8qrdP4PYaKwkpWScllWR5zk68BYJpdRjQTwBe7vd9veia3izv0uW5nWE2dtGflgQnA8d1zla78dnaVorYLKRBq63AwDEcdxutwkhZVlOzdRLjHLNfOAF1Plw5DY6byJiYUutDLpVlKhOiKxTzrWHsAqHrOwSQETMsiz17aSk9LAMbVbdRWvhCWuildrQqS/lRVBtOMKakAAA+Nu/eefHf5Xnj+cANEur05Pz06/uPfzj+eGdpN9phLFUqZsdZ4tJbrR2Vvf3drQthqejpBkNBoOFVvN8YUTJUCK3CkpDlYhomCSNQTgxOl/mYSOJeFJCwSUEsQxiJkTIHL/78MtH54+2tgfOohXufHZelGU7utJud63F09MhJUyKYLlcfvrZZ7321hEczxfTk6ejf//uz46PTh89eLoz2C/KsqwUZZxSOpuMz54+RbRRGJycPn1yfDqezsez6f7ObicOqNGDdidqtYyQT+cLC7TTbH/y6AidfemVV15pt3Z3dw8PDz3KNMsyva0554kUOwe7QRQ9fvDoq7t3m1EjCMPO3hXOwqfHZ4yF7XafEjY8+8w5ev3a7SejJ1/effDaK3dkGA5PRsPjp9PR6OWXX/7O29fiJ/cno/Gi1QujpNS4d+lSp9c/14VWttdtAZJ0kX380Sez8+modCLp3bpzw/Ma+lYk55MzGXGjDBXgiAliwSRJi4WyJQ9onlU8oItsZlAxSZDaIBaj6bBppNEGHRIgkgvOOAEyHo3zNN/qb4UyrBFVXj7uDrpRFLVaLc8z3Gq1fEagf+vWYrHwmKbpdNpsNvf397e2tu5/OXz33Xc/+uijdrs9HA4/+fTToiiuX79+vTswxihV1G7Nyo4WEaUULZE89Npo0N9mhMVxEMq4FsGSr7AtSmkpgyiKap5n/08mwI8MY8K7I35BJ1EjDEMfvyWEdDt9zrkvPWo2m/6feZ77F+l2+s5RX6HkwVN+w8RxDBC9UAEr44zVWZYZy2czGA6Hi/HZfHLW3FU+9etD8V4KFEURBUmn3UUEAsCIYJShA3DAGG/GLQIs4PF8ku4OoMirpN/wT0LW7L7+sb0T48tzfSskH34IgoAHK2qqFWzEQe0H1G5WHacl61LaOkTsmRqNMf/4n/53Ozs7L9288fThwzLPGEAoeBQEt2+8enx8bIwZjUZenXsBxNbFzV7lLBaL8Xi8WCzOh2NYh+m8gEPE5XJJSeQdI1xDBf0JXmDVvKobEnxF2lCnBrzO0FahQUdWHF5e1Qkqlsvlyh3UChjkVZ6VWWITCkJXlfW5RmfRWWctQURnCKXgCCLZHCshIy+gq1JlWQYEvYTNFhmjjFPBCAdH8rTwDlAQrcj6/ev4VepJKryt4xWA/68xJg4TB46i5ULWgVDiyCJb4BrOYzYosi2SWgH76/gYZhQm3tTzugfWafg4WpFf1orZD5S2ura3agWMiAbLlUlEkRIKBLTRSqlFtvAZpSRJRCiAgKpUoYp+exDHseeroes0v3PON2X3OtLjjPxuLcuqfjaftPJOeZxEPj/tD1yTmXi+dD/dvm5w5QqXK8UmpVSqyrKUrds71uOm1w0t/OjhRs2YXcPK/Foya3oWWJdxMia9Be8Huc6qeA6Deikqrb3Rs0JOuFVg33/22wo26r/5mlWNEOKZUOsguSda4I/mn2cwPbxx6ZU3bp+mjyjSQXPbvhIty8fdZtzfbVVzVy1NmWpklnPxf/mHf//dd9/9kx/86a3bB7/7u787n88/+uij6XT68NNzmQTKlLPlVIuiIQMFxbKc3r56c3q+dAYIJXmeHdzYv3H1ZjHTH/z4faAYRtJaU1WVkCEVxFG7u7fnEJdpGgTBIk2/un8/TdP5YtGJW6+89CoP2OuvvDkaje599eD+lw/2dvcrppfLXHI+6G/t9LYOtna1KghA0kkckLjdeaX5WrvRVOm8n8RvvfzykJKd/YMfvffe49PT9uEV5qwtijdbnVdffvP09PT9H3/oS998QacQ4kk1CTq9agrD4akQYjIZlUjD3vYf/uG/+KN/+8eM8iiK+t3+2dmw2WwqZa5evvFx95NLl67Nxuez0ZyzAJCOh6OPH9qXjTkY9Hq3X2+8/e2/+vlHj2bLkaHaLfM8P3k6XExTXZmnj06GZ+dRGH/vt1/Z29sLgsBLlg8//DD7ZJqVYVko5cq0nBFhlcvPpycypnuHW10VNNpBqx0RPtBa7x9u9bdbs9ls0Omjg729vXark2XFeDShhHW6neF4bBUaowWtjDEkZkEgKHIfoPO6xAcnt7a2ptNpKGVNoBFF0auvvnrp0iUAOD09/cGf/Ann/NatW0VRTKfTnZ2dq1eunJ+fz2Yzj6L0wkgI0Wq13vn2rzkAH61CQpxzXrUU1Qo9iIjEOxDWOmMk96oXrSVCRGvHgpdV7hBkEAbaEMqcdVzIIAisUl6j+Epfb5X7pJ3fct5M9qHCOI7RrQkTkHhwh685JvwCbbt5UAqhjCQIAjZdLM7Ph3q5widLKbMs8+SavqIpDEOjbBCAVY5LyigDBKOsCFilUAiutWkkzUbSOti7NBqO262OF6lekPlgr6cUrVTqA26eHsHvfCmlDEWdnwMA61Yat87YwUaGFdase8YYD0zz/NXz+XxpVFQVhsCiyrUqGeJyWQVBkATHjx8/ds5NJhMvU7xvvSgyo01tWnGxApvMZjNKabfb9fFqrz7TNA1k3z9P7e/SNWyqhsrXcTmtNeeSr3kl/YuvUPRawZouw655m30uma4hvj744S9ondFWGacd+sITY+zKvtnUQ/VhrQ1kFIdJnhVSytpPQsICGXAmAIk1vkoYORNeW3v57oW+1zS1N0nXfUFWt3MEHSHAKF3lLK21xtjlclmHUjfTxpRLt8Gt4TUopdToVarV2y7+3dM0BYw9RLG+iH8ML+59/HPzxbUra+OMr0ubiqLodrvez/ZZXr+iWq1WO+lorc/OznzvE2/DMcbmGwwEfM11StaNe9maptFb+XEcL9OFt+y9QvKjt1gsHDoAqLPObg3dD5OgdpQJIX5f+7vXt/Nxcj/+gQzr0Le1loDyr6N0xdfN9HAjHa618Vg2/6Y++uJVtQcz1mp1c2+ydaOO2rhh7KIdJGxkpn1VQh0ggXWgnm9f6hcq2xscTGfnW609pdS3vn/z/K2zP/r/Pl4W89d2XlZdNSvPU5mTyr715tvBq8UvXHr92ncOnh4d3198jgi9q52j4dN0mQfWEAr5NO8NunREn/zZvJgGDRHycDldnAgpaEWiItwL9h6XT3q73cFOj3I4PnsqW2xcDIOtnf2XbsWzFReuLtOTJ4vaxrl3do9zvhVvffThjxeLxWRy1O6xXJ2dnCwODw9v3Ly2s7ezszs4ONzbO9hrtZudVlT/djN3ErLYanzy6OhnP3r36u/87u/+7u+WZTmfzx88+rSqKm2r6XzpbXyHmJeWUZqenVBKoVwUZrk0i6qqtjqdf/Lf/kEQBCKO0anR+IRQt0zHH3w0urR78Atvv3398PLPzkdaWWfcYOsACD6+P5q329evXrkSdPGjz+giPSu+2t7eSQ6vPD568uGHn3oHsbnXvf7G7bIsGeGj4XgVlskytHDj2s3pdOoUzseLaTQ7Pxn5FNpymv7sxz8PYrdcLtNu9/j4mHP+rTffevnll7XWWudx/D0hRJKIp8cPluk4CANC9bX93Z1u20tJv3sZY4043KPBPM2EqDrdiDPoBJ3GldZjfuKqvHPY9dxGSZIIIVyJzjkmZv/bv/sb82Xe7WxTJrUmxiCPQrccskDHlJ6eDk+Hpx4e3N9Jgkg/ffr0+vXrVVUtFqNOpzOdHhNCejt9VThnraDy7Oys3W0iGBETVC6MiNaLIBSMgbVKBgTAhJLEcbsscylRShqGvqtgEYZJVVXesfNRMm/z6so0Wk0grlRZox0jWEIYOCKlpkAJZaoCKdl8mYZhCITZr3H/1nsVLRJgAIxQeXSiAGIASB/PGMVWq9VyotNoxjE41JTSL+4/9BRXWutr164ppZJGYBBkwK1BGfAr1y79X/9v/wUh5Nd/85fRwZ07t/7hP/yHR0dHXkP8zu/8zuuvv/7yS7f/m//7P7p27RoQlzQibSqHxjmX5ctzU4ZhWIoKGuBj14GQZ/ZEhWoymVCCnXbHEZeli2tXrx4fH9++fjludn728fHx/IRlvNVo9dttlRb9AvWj458cnfpov2/hxzl/cPcRbIB3CCFkRgDA41dXSs4wo1dyhxJglBCnVbFAdHEc8JByJP1+4JwqioWUUikTBO2qcs45ggEHJjSJZBQFsjDVIs0NKmsDa11RoKpIUeB8XnFuwzAkzAHBIJDWWjQOLFIkkQwDZvPFlAAlyJiDa9uXAcl2PLCEU1jOp1kUh1rR6SzzWcwW6QWhSMsFUqt0hY4kYXc8nlRanaQTH/a3FAaDQVaoqoSiWGqwPArSsppneRzG8+WCEKJd7qW5KspSaedcs9mstEnBpeCAAHXGWivQMsZycCbLvH3go6CIuEIbRSuwD1JUoJhkKDBNUzVx3paqtWkcxzKU1aIKZMAYc8ZRQjnlANBMmgR4HPGyRErCqkRAWeRWSpml3oOklK7ouhw65ywzEgBarZbWupxrzrlR2Aja3DFmKQMqmXDWKlVRSsMwLFUWhqEM+NnJTHLe7fTCVpcBM1a3O01nqjxfOCCIZDJdao3ttiAUlK7Oz8ftRruZtMMwzLPCs0Ja46u6mXMOkCRxQ1eGUpoELWutNVYyGUVRIANtZmWxbDXbe9sDay1a7Pe24jg+W4x0ZWnFHHOMMU5JoWYEq9QB03Br73A5XRTpotXrZFXhQtEMAu8SGF04JLV5kcTcrpH86IjgCIBlqeJojxK6yOeIVEopAx4mwXiet5qojauqiouAizDLTFFqKaUtFOeccomIylp06JAyxuazKo5ZHHNCBCWUAAO06AhXJ5IsYh25pBH3t7uRSCTG86fl/vZ+VqY/ee+nhLtmN7m5d4Dg9m+1wNFeu/vF9OFf/NmPiIAwDFDR44ezGpmmKr2YLZVSDmxRFHE30cqEYUgZpZSen5//xZ//pVJ6b3B4vXt9/+DgpHeWlVle5Z1eJ0yj0+HMx3A89YQPsltrmw2mtU6Sx/v7++12u9HqR0mHc27Zl81+4js4WUvBBQxj7lqsDCRjlAGhAADOgivQWvvF+X0ASPN8kaY/evfd2WLh7fosndYGS30AgDf/PX9vTVijlH3jzXdqy86LVx8mWhapAZuWGZUsSEJVFJWq0Dka6jTPj4enRVmWRTUejxFJs9n8xd/+Xy9nOQPZiJq7u7u7u7s7OzuEECAXnK7W2svX9pW6o7XWlZ1Op/1+//T0tPY5wjC0uhqPx81mc3/vwBgTBtFykRJCgpgzQR1YZapSFcpUlBNl2GCrG0YCwQCxYRR61PRsPsbhV812u9vuyQbVxXJeYdRq718f8DU8k1FPneMcoLX20uXrv/nbMaU0z4qz83MPwPFMp1qZKIrzrLh8eCUIguvXrzcajc8+++z8/PzOnTsAMB6PlVInJydVVX2r/XYctb66+8Bo99FHHxGKUUN2eo1Xb7197949X5Hs87j7+/unp6fKlF4kXbt2LUmSo6MjY0wURRTlYDAQQpyenvokmbX28PBQCDmfzy0USlWLxaLb7UnZGJ+PkgbXWjMqAEBGCSHEWA0WmXixB/xNR5qm6HSe59qUabuFiIvldLlcRuGq2VG73f7ss89v3LjhISeMWl/T5cNlo9Eoz/NerzccDj3SzTvug8Fgb29vf3//V3/1V+0acuwhwascYSfVWnc6Eeech4ox1unKeXYSJdhyHBFJaDghnUZr+/qWjcxZNunGJN6KDlv7aMBUOnfLZTEJoIvPchfX+rXWvrUJ4l2QOgTHNnorlZXvGlsxThhjCHG73Wj3WiE3jUZDa+EjzEnClVIAtNCuylWuFK0EAFRKU4EypKAsWoY2I1igzcCFgkVxGBYGwyhgnAJBV1l0KGRTBoJLnpAokFGeFXlaNlvJzs7u7du3zydLINhqNwbb/SvXLitb5kU2m83Oxk8qVVa2TJKYc865UKYqirxcZpPJ5Pj4OIqi+XzuUzBZloWxsKgtKmWzvJpbKEfzM2tt3I0pUE64o66ipXGGIZGOUwE8MFVVVcpYa5VllFLrLMOgzDMA4JwTQbRSyhWWqHS+9DkFv5Z8TNsYYyuK1vmgxaoC1SFaR9yK5qzO3Xp3zf82TdNWs8nX1bGLxaJQWW1C+Z8wzrgQvda+d0N9HqpOKqdF2mq1ojiM4rAsC7eud0rTzKz7Lqh1N9I0TQlQZ0FpYiwjhCACAcY5ZKlNYhEEcatpm62W5Nw5zPK03W3aNQyNrbucaa0XallnfGvX3DlnHGk1+61WG4FJGTrtlsucs7AR9ro7vVCG1tonjx+dDZ+GjUau8lZP6kyfnD6ORLS3t4WUVqYq07yyQAgBRMo4W6OijFZBkJB1/bQH8XmHNWIzwngzyIFSoBaBVGVR5KkuIymlqmgxK9rtsJn0CQaLxaKVtL3tTjgEjBlrKWUi4Gap6tSAdwlWEI0///+9Z0X57V974+ZrV06Hx4tJPnmy/ORHj176bvPXfvOXZCTvfvVppYvBXnf/cG9nb7sZdp58efbw7pMqs72tdrvVvHblxq/+Quvf/tN3lVLGGlSQmWK2mPnwy1fjr+I46g5688WskTQOL11WheFcqFR/8em9bO66ve1uuEt1FqgOdaEQOecYBK7dvlgrlNK8GJt1T9DxtPSxuIgG7/zSO2VaTMaTzz/7cjHLrXWCSkpYN34GhFLnUY7njy5fvnx+fl6U+qt7Dx89fuoLfy8fHnjCQr+U3RpT51mCF8vKT4xf5cPz+c1bb9VBFbKGpDnn0uwcAInkva1e0og459zXsdFCCJFEzTAKrbJVpQBAijDPDKAc9Pc550bT6SRbzB9xzi9d7uMGy24dbImisCzLJEkuXd7z+UJ/QiyS4XDo++z60JBXSHl57lttobW+10SFaLXutZJKZZxzyiiCKkpzdHT06aefvt4WMoqzbH5d5Yaxdr/FKWpAcKROt9S7AhGV0SKQUnKLZme7mzSS2Wz25OhRq9XinPf7/fv371+5cqXRaLzzzjuEkPNhyujs0sHl0WhUFqrZoKoyRV4ppfZ2VtwXT548yYu002/uqa23XhFnZ2cPHz70OWlK6dWrVx8+fEgY+lBbv983xnz22WceJKxK89Zbb4Vh+PP3f+o38GKx+NVf/dWd7YN33/1RWsysNXmef+tb7+xuXfrRj95FzLUycdI4PDh89fWYMco5FzIo18bW1z3gF34vg8BotNZmWSYYLYri7t1P7969u7+z5btCXb169S9/9KO/8/u///HHHwPA3/nf/EdxHO/s7NTwmePj448//tgaPdjuJ83YI84Ig2W26PV6vXb7/Pzcd1MOgkBQ2u129/b2vjz6YjabGWMYYa1ez+ecClXs7obLZJkXJeEMBYviOGq0qJyqqsw1NFr9vbhVZsXZyRNXKk6YByfDBkeE2aChfcYDJsTL6zrk60fAnyMk5ZxT5ghBxpmjlknS7/cSOj/ca+G6WLPZbHqg3Dx3D49OHx0PHXhKwhycjkJyefvy/u7+S7cutVqtg4MDX2PDOadR7HOQVVVlWaa17vV6jDHrXvEMf0aZqtSUUl/ZPMjszt4WgiMURMD2Lu0QSobD4fnipNQZSFQEygpJzs7z89lksX9w9crlw4cPH/a6HSFEVRU//el7N25cS5JWmi8JN1RalBVrMlYap/V5OWOMccsRsagKrXXOEhVkpSo8vGhFVMe5N56yycTPbKfR4ZynOp2Xk6IorF5hczzGgjHW7/d7vd6jyVPKKACoapVfDMswLENmmI+4eiiTz3pkWebD5lqXi+UMEZvNZpxEzsnlyZStO1n55/GOtbaq0iUAcMGTJAmCgDDQWhNK4yRstZphGPoqNh+Ync3mvn+5VyT+gUejUbPTUYpUFSi1avFLCJGcERI567ShSdJpt1vWGkZIpRt+JWwOzip1EqwADWyjTpcQYnUYhkm7PUiiBmdiPB5rbe7sHX72+aMvPvvkzu2b/9l/+p9++vlH/4//538FAq9dOzwvhozQdDQVjABJtCqjMADOjHHtdjsMwzIvlou5NZoxxhqJMVBntb1UD4IgDEOVPXKWaJ1zGQSygSC0IaqCXJtOKwSUxqTpfM7BtCPZaWxVivp2hGDQER9zMJQi50CINaZ0zlmrKSVS0jAUPAjEdLH48rMvDRYfvP9J2GSXtg/f+s2rjS310reuBTw8n5x+8PMPnSLX9m5L3fj8wy/e/dOfPvz8WGKcjssiqy7v3eChkDTQ1nAQzAnrLGoEgDAOAW0cJxSYMWY+nx89eqKVJY4Adw6plSCacafZkEErrwizJOKRc44Qh4hAiEUCjhEkDmIZch+PXWZLY5wQtlQ6SGA+y4ans/PhLE0zaxARCdCss6qrW+fYV9PZ7+8jyiBoHR7e7PW2gyDo9fY8YnDd4G9Fa+yty61t7s18H5dL0zQvFnlR/dVfvefNWF9i6/MHhBDH89ls9uj46Pz0NM9zwbjgrNFoOFzV7RGgBCiFFQXr8YnRWm9vb/v94F2iVqt197O7ddjcv8UKApNwAAiCwPvlWmufEWzG5WKZAxHNZrPR7Ho13On2mpZ5BLwQIo4SZ1dmxNZ231Naaq0Wo5m1Ns0WlIHg4WyWzj/+3BB5cO1ayEIAki+zqNEDzztoV2z1FCkAtFuBNZkUgpKGazRarQ7jwXJZNWJNkKElgYjOz8aT0UzysNlsUsoePXr8wx/++cnJyXA43Nvbz/NiNBp/8vHHVWaM1oGM8iyzzizni1Z7Vd7gMy7z+dwX8yyXSxFccOX4PexDEUWVhbGUkqf5wguj49Mnpcqrqnrw8J62JePk/Pz81q1bl/bpaDSS0uZ5XpRVr9fjnBLCjXlxo7r/8FFVFaD1G9jX2gPAZDLZ6vUmk5lzjjPx5RdfOYvHx6eI6M9ptpJQhZyzbrcrpfx3/+7fcU7e+fa3ms1mHXE5PnlaVsXW1laSJJcvXx4MBkqpTz/9tNlsvvXWW8v5QmjhqTe7ve758Hw4HCZx0pLxztYuMK4Bj4fn6ayaYqVmguJ2OkMZhiLpKzUjy5hb0aK9wqw4Lvwr1HWNm8q1VsDgW6BvdJWpDwVlGIaMABAUgjtntFGUkUTS7V4TEb3WjCUh1iUhk5Q9dQVWMxI0B4NBKFocK8lsms3zXEppytLMZiTLhLe5pyUhhBwfH/vBQcQoimazWacdgm8QZFyv14uiSCm11d8qK3p6eiqEsGBPhsdlVTDGjo6OenvtqBSUUqNtwEOduy/P7udFxTk/PT29efPmb/7Wbz569Ojo6Gg+n9+9e7e/25ovZga0ZVph0QgCCIyDknALFJGhtdbyyoFBKSEwxXImhEBSOlBIRKWdq1zoQkIF5RSkVVAUlU3VPNOLQhdmQfr9fq/XzfO8zDNrrdUqT5dCrIqGKUUvFRGNNWoxy2sHw8dRvYBqt9txHO/ubXtQ5DKdF2WWZdl0NvZ86TJIZHBBBDGfnxdV5oFOlIO2lTLKOttoB2FDInVpsTTGiEBQSrMiC4JgsVh4dEid3/VRXKV0VVmlqJTEGMuoDQThPCyKDC3KMGBcEgJxEhEGJycnZVlWSsk1jZ33bZLGisTGV71e2Ls2CEQjiTtlURV5VhaGUuosa8T9nQFrNgZlafd2D379+781WZ7KSPSDJJvkbntLpUrnJWfSGquKCoFbbQzVWlVVVVmjgyDglHIusizzRqEPA3iZ8/f+k//zg0dP//1Pfnp8MsM8CMJW3Gw3OvtMjytVcsp3BtvoCqsLy2y/1RlqFkaC8so5BYCUonVGG+VLwvyuEcLT9nFKkV95c+syDsazUbpIL13buXnnan+ne/XGZZdWD756+PDLJ7PRQmU4NrNPP/n8Zz/9+XAyOb03UVMqSMgDVmT53Y++UtVn1jBnkTLiLFJK47Dh0W6NRmc+n56eHe/sbbdaLU7Fwc4OZ2JuJ51OP11UP//k/a32wcHBS0JEjAutCkIIMEbW3oZv0g2UGweIJEqarU5PrJvNEUMlDbb6Ua+7zzgRAeeCUkqD8Jl+47Un3WnsLJfL4XBojEmSxBebNxqNMte1f4MbHa+MMVxQRBQSgrATJ2WzVRRFMZnMPf6imKXjycKtm/FZNkvT1M+us1YwrrVuxLEyJSIa5ay1jHIhAkKIs8hoT2uttfORdm/JttttrKxP/te0R/6ImkEdn/FE2V4NtzuRV8ZJkngOGn+dSo/brZan5kFERmWQxIhoLAMSxEkIAMaOlVLXru+99vo7uy2plKqUHmxvRUkClACSVqMJiECAMqAE0G8MyoCQ/e3eVq8DVH5x99EXd+9bezqfFydnw+U8nU6n8/m8KvX9e59aa3/+sw8ODg5ef/Od0Wj0b/7NvxmNRtevX3fOjUaj+/fvF+VsfDo9vHRVUEbAtVvNssr0mhjSW/dpmvrCU0T05YneD/OhMx+BKIrCI018A6g6c7Zczo0xjVYcxnIyGxGKQRCEoUQsCAHrtDYVgnWIlS6Nc6t2OV87vskDVkpTslJX/nkGW4OdnZ1mM0mSSAixvbO1tT0YbPV3dracc7u7u2EYZmkxGo16vR7nvNfrvf322199ddcXznrp5st/4zgWjHda7SgMu50OIv7J8cmf/9kPP//0s9//u7/zk5+MwpZ86fXDOI6bPXbnjcvb29uJaceNBhPi6OT0n//rf/Ph558o6wghzZ3dsiwDGQaO5/NFtiwZamqFUsuVoFsHBnHNIPhCBVxXd9Q62P+pLPN1wNq7OFVGYblcNuPKGieljKPEShuGobPIKN9qxwf9ATrW7u/sXzqIA86dEtwuNfNoLx9UGA6H4/HYGDNOEQCGw6FzzmP1GWOnp6doy6SZeHDfa6+9tru30+/3f/GXf6Eq+D/7Z/9MSnlp/2B4Pux0Ojdv3tzd3W1sB0WZCyGWizSS8XS4ePr4VJLw3r27zrmTE/zLv/izw8PDL+5++vjR/WtXD//ih9n5+Pz0+KlRlaoKih3iLDoTCN8ughAHnABlNBQ8FJwAckajMAgD6a3qqqoEZ2EYeazTfDzxLA3NKG6E0dPpKAyCOIrKokDnfDVuVZZyXbcGzhFf7boKSnhUc0EIUSpY89+Vs5m2ttnpdMKQAxitldYwmYwsqFIZJEqZrHbHGWPWmUIVRVGIShSqiKKo2+02m82wCZ7Obzade7pHwWVZlrkuPGlPb9CbTCZZljkLURQBRecQHDAmJGecaEJdt8VH5+M4DAl3ZT4HojyHz9lo6PemW9fRkTUziUeleJvbrXnTCCFvvf3O7s5ev98/Ox3mWdHr9UIZhmHYbO6FgfQw1YNLW7/zO7/zw7/645998FMUi93u7t/+j//2g88e/dH/9KcUFXLRabXKNIMqV7pEY+JAkjBwANpaPy923bS03gsgb/AwjuIqSibaMhG1BttXo7iRT744PXlSqWzQaScxXyzO82x6fK7CcBAkkhCX5XPnKiFopQub6WazWVOaeIyR3188aLk7L738wc8/PDs7u3z9UpLI8ex4UMX6qfjpX3788KujZrvR7/VuvnL1ykv7Xz2+q0hbz8j9R+eGqNdef/Xk/OlXHz8yBWxFe0CQUqZ0JULRarXyMjXGBM1we3vbnOlAhG+98fZvfv+3fum73yuyqru9/ejh0X//3//z93/yKbGCaFYURRRQBFtv8k3rm7EVbbrXu75aFxG1DQGlFJRLQrkDaoE6ymA58wVkbH0pi2gQ8Yw8AABjXKPZQkJL5ebLxWyRC5rU2HS6pqqxaz4UXJWXhDIIhGxxkXd6uz6sbdad3r0k0mTqnKuKAgDQOcl5nueBkEiAAAMARoQQMggCRj1VrBwOh8fHx2l+DgCEUMJ4pY2tXKWrrLhotLkyR5y3oYRXt77TuzGm1Vn1OKtDUogYhiEw6yviPVjROecBwPfvPw2CwNMjZ5lxDrb63auXb5UwbYWhMYYyOi/L6uTMa3owrJ4Ot0GXWuYVZ7wsl+/99Wc//ukH3fYAiRwOz99+50Zz1JxNZ2++9ebpyakQYjweHxwcNBqDX/qFX3bOzWazw8PDZtK+tH85ChJghTXWQ5evXLnS7XUWy2mzE3mXt9Fo+IBto9FI0zQIgmU296zduNF0No7jRiMBQGO0ENwYba1tNBLGqAx4EAmtNdWu0YyllMqUjBNEEkURXRPbEooAwDix/wvd4CRJAE2Nh2w0GpcvX7bWtlvh9t6g1+t95zvfIRx/63e/f/3WVf8T59wXX3zx1Vdf3b59+/T01Bhz69atSmWTyWS5XDaaSafT8Yuw1+sdbPVOTk4+/fjjxw8e3L59u5nEg17XavXSzTffe/ejqqrOT5b37r2/XC5v3rw5Gebb/T6bcsp5pVTckrdeuuIQgyA4mUyVqrIie3qWqlLlei7QSsGFu6C28ALIvwtfcyPXs++3Z13riRu9dyilDIVTQDiisxZ8xwNWlaoSMi9Z3Ohu7/Z8qygPFY45v3TplVlasiAWQihdOVsK7oZzvVgsjo/Ph8Ph2dnZ6elpURSc82W14jtEREAaR42yKIq8MlVBQRhrOp3O1ctXr9241u/379y5E/KtD37+0Vtvv/na66/+4R/+4VcPvjofjh4+eJTfXS6Wi5u3bxBC5jY9Pxmfn573OoNOp/nll18GAb9//8vh8MQ5ffv2q0+fPn5w9GVZFk67XqvXajS3Otu2slVVWTA1FLYQhTcLuq2uWeoabSuEsGs62F605e2qc3tuy5XQsMZcu3bN07gul6skqPdQF0XqFS5QBIoAaNGgdb6IyRjrnDMWmUVCCKHWoZ0vxoTabq9JmUsagRCiUlmpCQBoo5SuVh6nDYQQlAEXNCKBh/FHUbS3v7O1tTUtToKYM8ZFxlcMcSJIkuSLz7/06f8kSUaj0enpqbOQJAmlWmksFZUyIo1Al7MwVNBUb7zyUrfXH01mP//wg8fH6db2HuH83sPHTSm0Mc45pTXkuRdNxtrjk6e4Llwk69ZbQojtQb/Il4+WM0oZZzA+P/NxSkeDslQEbZ7n77g3r4q9fJHFPFjktrETfuetb/fDnZ+/+8Eyz7VzSUPicNgOZBSFi0pNFoul0kApEGptBgC17cs5bzabjUbjX/7LHzLO84pESVen5Wy+pOwsirNWuBc2JanyZQmlQcpaNCyrPIsTEILl5TJNi8VyIgQyjtqoZTr3w0gpVbpk607b/MHdR82o1ek34xbv73aybDkcjtTS7HZ3D6/v9wad8WiSlkuQNukFV8IDYmasjM6+SCHnnXBQRkpHSCNapRXjlAW0VHkoZLvfhKWbTCZJknznO+8cnTz+6c/eq6rq7bfevn75+mQ2azd3HpTDmLVfvfOGlCHncnQ+qbDANU9srXVWiqe0NUagrEqtta9Rq4ocAZlgzFOWlA4BOOdOA6UUKSUbhc/OuaQlGWNZlpWFVko1Gg1KRRw1ZlO1LkhDQlbYE+fAOqCMKqUYA0qd90uK0kUJA8q5ZIRdMMoSQhywIAzS5ZIR31Qr8pRYjPruXYwCR0RrHQHKGIviAABPT0+E4J5Qxq8/EP6BL+o4vaTjTBitJeNWaW0rJLZUOQAsU63X1I911SAiImE+Eut5+QkhzWYTAJpRWIePCCFBEFy9Ojo9TplMoihKs0IIYaylnHkeBjArWiVfzFAnCAVPVFVqVX7x5RfLedZvbTVijm3xxd2vyrIMw7Aq9WCwHQRBs9lOkqTI7ct3XvWPRCllVLz+2puUUspzrbDXGTjjtrcGXPJS5UKSOOp997vfvXPnjrczfMf4X/iFXyhV7lGL3k73/ZQ455evXAoj6Zy7fOWSW/fllgGXUu7sbKfFjAsaBIEM+WIxZ4yVpRFSUCqsNdpWkkZCsCiK0mX5v0gBIyKs58irlsuXL29tbbW6HBG3trauXr16/faVfqd7eG3fWnt0/yGldDQaHR8f37hxw5dIWmtbneYyWyQQ7+3ttdvt5XKZZZkylae/Pzo68oGl27dvX7lyZTqd3v3ZUTVlAPHkSXn81Ww4HD79cpqmaYYT70N0u11jTNJq+WdrdTpEBOO8ms+naNFVqByIkFKKta6tawfIRrOgr4ega/SDHwGvbAIZA4CgxBFjrQ05D3hYZurRIl0WnzHGfNF2FEW+RI0xli9LrRGBKGOzPDVW8ZDOZ2ucRxQZY+bzqqo0Y6u64TiOnXWSBu2kc7o8Vbluxi1bgbPEKsjTCg0psuLTTz67cRhcu3y9lXSm43kjbr1y+9X+oHf54Mrxk9M/+uN/G5LmrVs3nXPj3nh+nkVBvMhmv/qrv+qt2K2tLS+U2+22CJnnQfSRyf2D/UFz11q7nA99chTWNaZJkrTb7U7c9//0IRxEtNIqpWyFFowgwcHO4eHeFUrpKkjDuO8JfeXy4cH+3mQ69Rut02p7fYwbHTUAgG/UmIGXeM75QuqyLPv9/q1bt8Ik9LgtZZWUnheHCCE82MXXcBcq87mbJEn8XO8d7A4Gg9mj43k2E1QwSaI4NJXWWnvLyTlntL1582Ycx0ePn9y/9/D8/Pzq5R4Fy7lst1utZgzIGnG5t9P81hvXKJf3H6rHT4J5uiirPEySg4PLT+5/AetysizL6lwvD1dMMnRdMk45JZw8fvRVVVXDs/Pdnf2tra3peEqABkFwNL4PjpR5eXJyMh49vnHzSlouGkEoSOfTn3/6//5//RNJAmIxCUPLSaMhbZFFSSQ4c2m6mEzmVUVkyIOAYXVBL7qmJ2OMgTuTLGo3kBCeRKooy8Ega7XY2fkiaWCcsOVyOU9zH0wllGqbEkbDMOkPdhnHspxxQTudRFc6DgWAcM5Za3xsjxHkB72rp0+Gr3zr5s3XrjhiRyfTreb+bLgsk1Gv37keHpw+Gn304ccff/jxk7NHYUPcu3tyrffyrVu3vvzJo5/9+OeM0yRpLOeZQRPwgAmCGkUguv2uI3a+mD1+/Pi73/3O/v7+u+/qyXh6dnZGkWdpfnL80XQ6bbTCIGKLxaLZDuJGwzkXxQc+BOHxzzXwKo5jzwfmS9/8olkul2EEBIAJygh3yjjNCQk4iwybA0EkDom3zQkTAgCGo2Gr1dLWEEYdoHG2yNKsyJnrEiI4lSuLHoEACEbzLCdoy1JTuiq2C0OmK1Am9fqs9gA8bmCR530RIFJCOSWOMAGorIFSa3RAqSVgfY2/s8g5L82yKIqTszOtNeXcVRVjrCiKpEGQIAGQjHEuao9csiTP86QZGVRUAFIwRlFKwXFnLGNMcgEOCYKPqBSaOse1ts5ZAGuMyXOjtQ5Z4NOruK6b/OD9L/+i99OrnddkGE3mqQWWa0TCZ8ssCGPuVhy2dk3T46XzvDRFMd8axLEsgSwl05K7Kl+qsCml3N7ens1mvV4vz/NVdTVElFLPoeG1jufQAeqEkKenp1bbw8NDn21yqDwStdVqeViQpzjY3t4WAYvj2Jf8h2H4ve99z0uoNJt7WuaDgwPfMsg3mc+z6vvf/36YcEJxPp9HYcJJ+Dd+AZOYCR7kZeEM9Hq9sqzyTJUq/6YQ9H/gqAMnnsm82YqjKBpPn4ZhqG1zPB32+/3z6bDb7Tqwly9f9gmnIAh2d3c9o6eUsksao9FoPB4fHR09ePDAFyV3Op1MGcbY2enpxx9//Bd//udKqRs3bpyfn0/zPPB9l/d6r3z3972zBQCYS2PMYjYv8/zJkycBF7PpOMuyRZYyEhjKnM0IFZarKksrjbnLPADC+zfee6vjOl+3iX2UZXP9+8BmllWIyKlwjiAiJUyXq5rj+eMzT9PtUYQr9komwQFFJqggjJbGWmohhyaPVVVYJECJMaTSTFsBVIRCZFnGiWQMe+3Bpb3L6TznZIyWBEHQ6je5oEePnnpSwx//+Me91s/CMPzxj38yX8y+851v/87v/m6j0QjCYHK8/Isf/JXJ8Pb1O1evXUEHiWjfv/9Axpe9zNnZ2Xn48GGr1UKwRZmNp5lWJkmSMivLonIlzqYLzrmgImBhBBHnXNpKORVBFLjwYHBpNpv5vJIiyqfAyrJkVHgWi2az2Ww2vQJGxHm+EEJ0u93Lly/v7u7OZjMvUlSl+brawvuIfkYCxjzB9UXsTeuyqrTW0+k0SZLDS5fKPHfODQaDK4eHQSQ3oSSwRh0rq3zixmM8GWPXrl3b3t6eqePFfMlADPrbgsj5dAlI2q3O3vaeMWa5zL799rfTNH386OhP/+SH9+7d0zrVijhwhHQ4Q85IEtFWk5+d3M1LM0/11ct73f4eyNbf/PXvR0nzRz/4n7w4rSto/RBVrvDKzz+V5+NDxJOTJ7dv3z55+vT999/bGmwRYIyxdJkOq3NORMjCViMcDk/OTo+u3Tg8vHxJBv3ZafrpB5/1W50sXSq0d9589Zf/5i99Na+Uc/NKt6Pw9vVrNoorpKU11WJliCilhBB1v/AifaJLbqwJ44CDEyQ36ixdyEa0rbUGwgQzoVSIeVHo0XjWjAaDQefalWtbO508H5+cPCiqtNWMQxn6mL9SKk1TT5sqpSTf/S+u3Lx+o9/pl3neSOIK85PzR61+Mj7N2+12d9C/f/8BsdxZ/v6PP4qjVrGs+vE2qeTsZFEuKiEEZ2CsHs8WMpSddqvIU074jcs3337jnccPHv8n/9n/4Q/+4A9+9rOfNRqNO3fufP/733/llVeyLCtyDWvUX11rRAiZTPPacfGH3w+6Cl/sebiL3lK1/e6ca6470tTxsdVBL/K7mwdQ8cLrc1wTNQDU+HhK6bKq6kvWxqlzjiEQQsi6yr7+bbWmpNmMVyNilj5xziFqpUqtlVaF0pWzlgvqB2FFJGtXTkkIja+7JogYhPFqQJ7lbZcitusD1hhXQsiydACADpwFcJRTLoQUXAb02/U5mzDXUuW1gvEWvQ/8NrhxaK2zCNrRwrEKRUWFubK3Ajf6QeNrcpk8XdU1+ggMAHg6iFdeuf3hhx8eHx9vb297ig+PtNRab21tkTV9/2Qy8T0MZMRrZHv9MIwxVZjj4+MwDHd3d2tk1mw2i8NYrFu2ecPOCzjfxcUHur3n6qGkhbqoSasTIogo131AYc1m4M2RdnsFFvMNoHwzRELIzm5TSunRy+12WwYhEArOAoAu0y8++/yTDz9Ix+NmHDGANF3+rd//Oz/4wQ/+4kc/kkHIGDs+PaWcNxqNgsp0sZhOp3m6tFr7hW6tBbTgABijQLkMozhuNJphnFy+ctUXLEUbhxDCF/i6NUF0VVWz2Wy5XJ5Ohv6lzs/PfcsH59x4PEaQHh2zmbGz1lK26ojuNqj8KaVZuqjrVj1Klq0JNRtJQggpyhIA4ihCxLwoNt3uzX3BgPtl5k/w+TMAoIJmWebLuzudzp07d3Z2dm7duvXB3S+Wy+WtW7deeeWVoij+1b/6V2dnZ//gH/yD3d3ddrP5P/4P/8N7//7d/93f+3v/+d//+0EcIcIXDx/8N//lf/mbv/EbZZHvDgbOmr/+0V+98eYbWbnURjnqjDEWyXyWnRydUiLjKO91B51eV4gIkFjnSmW11oMeH41G0+n09u3bnU5nNpsFQZCmKdCLRnU+ZebXaiBjP1BujRpZyRAbwbOs1P6/8tlG9/X4TPK03W5TwubzuS61Ne7x48d3P/9SGObAAnetdmP/8u7WTs+inS9mg+Y2pbTZbBJChsOhbyUuhFikKgiCMAkZ9/3+XBAFcRz396+kaaq13t/fR8SHDx969zoRkVJFHMleuzEajqaz/Hy0+OGf/Wi7obWlCkXpTNQVlC1+63fe/OVfeevf/X8+UgZvv/RG0uoRzhfLmZDQ6TSLvNw09WoBm1cmiiICtKoqRphzeHx8/PDBo/HTo72D/fPz8+OTk2a7/cYbb8RJ8td//ddgmdY6K1Lr9I0bN37pl39pd29nPp9bm3nrdjgceo/2ypUrzWaTcFMnWehGcFSX4E3nxWLhYcwedPLZe8f+AyL6EfNz6tndV4RCa/LOKIquvtoejUbb29vf+973tre3z8/PAeDw8PB0OiWEUKDoAACEkEmUBEHAu732PJ1//snn4/H4zTdeG2x18lwRCkjw6PhR6corVw8pkU8fD/s7PXC82+wf3zur5q7BWn4/cwZBKBPZ8bAyygOt7cnw/NpiubVz8PndL2UQvfHm2z6kfj6afPLp50VRtOJ2/fL19gYArTwtGXq+GEaBcOac4zR6oeJ0uNJGmzoYAGryH1y1EwEAJASUKmAjkrb6QAijfFMK1JpbULdp+9c3qrSuFWEtQRBxXSa7etRadpNV06sVnWl99PtXrfFB3dJa5ZyjxGev4bmt6y84H0/qh9+4L8yX080b1fedLRY1rJ8Qgg6VReecDGPfdwiQEkIFl6EMhRBg0vo1Ny2GpHnRusutQQqEaEqQUyo5p4wpB8oZZZ1xVRJ2fBeNZrPp0VJe7dnOKm5WlmVV8VoBO2eF4FEcSSmcs1rrosj9llCqMsaUVYWInu4uCCTCauW4NRO63x5lURVlAYBZltajlGVpHMcOnLbaWlupFbUhc0wZJYQIoqDRbMznc4dOBIILzuzKgnHPktfXC6zWzV7ZlNWSEBInURRFURxywRinhJDRaFRbP3SjjY91piyK0dnZ2fFxlWeSELRWq+rj/+q/ni0W8/k8SpLKmPPzcymDtCh53MyyrMyzMs+NMYDoUStBFDjfgkYptczc+TnlknP+F3/5lz6OWttSfny8FbLWzbH3oiilIhJ13rHX6/mKgCzLkqRdr+Q65IOIWb6sozI1wSGldDw6r6MjPmQFAGEY4rozRA0y98ZlPYabV7PW+pYYXq9rrX0oXmttwSjFCYHV3DprtCryzM7G0phQlyxPE8TrWz1RZvnZyYxRtZgPmvE7L93uCja6/1WUNKy1Z+/++bea4U1ijsZnR3c/OT0+XozH96bneueACJRR4NAq5c5ORw/vPa60KbJ5r9c7PDy8dOlSnESISAAFY0dHo9ls9vnnd588GV+6dMnnRAaDgXGKEEIZCTgJo43Og3ixeTd9A7/cNoXSask5u7mj61917C4h5Hw4Ojs9n88WFFiaFoyLKIiMMVRi0mhKGWmFymoAFgRJGIbdbtdaOx7P5/Nhnp9ba6NuM8Z4Uc2NMTIUcRLNJ7Pp/Wn+k488mNGneHyReq/X+5//1b9tRMHhwe7Wdv/sdDg8HStNAHggKDqnEIosnywW128O+p2t93/6IVA6Op8E0dHOJbq3t98fbFFilMoPDw7r4DmsqSI55w+fHhNCjLahDKMoMtpY40aj0aXDS0EQOES/SseTyXQ2ixrNclEYY8IwJCTylEqDwcBai8CMhUSE167f2tnZ8YO/WCwIVfUW3pSicdK11lpHaFYam+eFWqZFmhbNrUi2aFSK2pT0WyCblzIWPGT1qkZEjQottptttPjw/sNQhrdv3vZV+z0kxhgCVAhBKSuy4ujoaDya8Juv3fzgJx88eHDcaAjrzOdfflEU2a/92q+VmJ8NT8eTESEky6rxcNHf7pydjgESznkFKk1Tk/velmS5XG73XiqqgjJ0VjGGhEbIou29S+fH42bY3ursCCHKsjw9Gs5HS0LIGZvWXtozupOEeAFCJgQ4JUgZiKDxnIL051u4YOvedFb0s3WctcKmlgMAAULgYpUTQpSqmY8oIZQxoBQBgNiNzQAADhDAIXImL7YKJYgINUEFgKfy39xIYs1FB8+qf8ocQQYBZZQDWEKREUoYsrU8es6J1zcu2uRtDkU6z71C8nKtjt4nMvICl625avM8V8ouliNKKQFGgVPKECShEVLZSM7pRleyCyXHL3p+1X9yzgGJKPONQylxllrkFgzSShlR6aJUQmrhvMoB6yBuJnSjctpLdkqp0lUUh7u729vb2z7PpDd6pFRVVbfc8XlBWPdid+uGM0opVemizJ0zDq32kXlfVyP5kydHtQfsXTpYt/MMw7DVagnBz85OPcs8Y6zSZPN9L1x5SmqfrzYfjTFSKm9H+rKQ2sXXJvUf3LrK0Cu8QpVBECRJHAkpEpItl/PJNM+zR48e9/v9II7LolwsFoXSSRARhzCbKqUIYhxFAECcs1obYzJj0DhjTKW0thCEUdJoJEkSN1qenhcIazYbQoiiKOaLqZdWvrGadyu9kkNRw5jLZrMZhWEQBGVZbnW2YB04ketu9ojY3+5uxni89CSM7e3teZrATSrQOI7jeKVQ66LPOqNZx0VxzZioteZUMsaiMBRCLNP00aNHDx8+nE6n8+mQuSiOIy14wFk6m2bz2Xw8Kk5PtNYPy2Ly4L4QIp/PYT778ic/HpYVaC2taQn+8Q//9OTD9xuNBhOi61SrKh789MdFWe3v7GzfulkcXvrr9967++VjIC5uJZQBEl4VZjKeEcIWSymlnExMVZ5ygQxQqaIoire//Te+8623pmP9gx/8QMoPfffrNE239rp14McX7PrAQK+z6rLl/yrWzekMWwuajQMAArlqrgwbHRQQEU1slD05GT5+dDweTzjnjDBEAoQ7tAwEoMhTs1xOtNOEEGFHnPPZdKG1fvLkyenpqY8oHG51sjLL87xSZdJKWLAtA5E0G1/dfeApuh7xR/6BB4NBp9NpJZ3T46f3733ZbMRgIcs1Ywkg16UJo0bAQ4tAc0ORT88WDx7cvdR753T4xdPhvP/o5NZLL+3v7Wzv9KUIRqORjz/xNVuyXwa7O3vGmDzP0WIgg8wWfvscPX66vb19+/Yrv/Ebv33txvXpYnH37t39S1cPdg7u378/Gp8/OXly/+GRdrhMc2stoQbAo8NoHN+31noQTLsR1WOLm+j9ljeOWavVjeNmHb2way/xuUgbVdJX05yfn1dVFQSB5zDO8tNvvfOtK1eupGlaFIXSVkgwFrUFdEwIEcctwaVWkyI3Z2dj/tfvvatS2x00lFKffPm5VVW/M1gMCx3lSdwYjsaEEK318Pxsd7A/X0yXedWMO6SS55Oxq7DT6UShkIF4+fYrjsN0OXl6/ERpHTV782V1dDxOwoQLqQ2WlbK+q7DTQRBUaVlbzWKjR4QfuFox+/8FAC//AQkBSgAoIQiIgJQLWKffnrEc5cU3tafonJPBRagWN7CdiO6FG0CtQ82bOg8QPTQaNoK99UNvXrn+LeMXoezajAUAawkA50SICAHtioMesSorAIZICSGMXlgYQC4usva9ERFb/VXdM90oeiaE2LKoy2S11r7PRFVV47PZ5psKtqro395iXmp4nY3r6LqHmXgMvQ9m+gX68GRqNFa5LfPKFJnTubGlherug888aK4WPasUslq513V820vh1167k2UZpTQrMrpYiRuPxVBGIUEZSqAQ0IAxVqpyd2uvRp/6Ckh/qd6gFzdij9WqUWbWWsFWa8znYHygqWb/JxQoI9YZY3UcxISQkAt4xu3QRjtEbDQa6JU/EAqUIaOGMsuikI/H4+l0CgCNRsNrryzLtMk3Le56XsJWgxNSaV0U5XK5mI3H4/E4XSy73a5otghjRZZWhIhGk4WhBoANL3MlrVaY9sgok5cVSTMoNRJqHWgEEQZQFgYdI8CkEEGw6gXDKDCKlCCiBdRu5Z3HQURW9abUi2altVJqNvy89vi96eAHpNJFvWfdGgNBKeVR7H8u1z2kV8smCDbr2uu16lvZ1FZdvd5Cueps4QH/s9nMNwoLGAPBJaWEUtR6Ph5nWXpyRFuU5nk+GY+93ea7HE7GY02IK8vdVrPb66ZFlh0/6XU6vV6v4LTd7c4rzePG4NqNzta2AfhsNE3f+9BYXVSVcw4oJSCtsoyxy1feuHr5EuN4evLImrTXiZVyJ8cnyz/7ye7uk7ufHelKgOPHyxkiCiFmyxPcSIHVomB3t11nPTd3AQ+eGZZaboQy2PyyliGcNcIwqqpqdD7J0pxzLpjQ2uoqd84xYysLZJoaqx1aQkjRtXWNTVmW2jAhm1LK+bC01nIhOs22ZLKYWxfSRtzd6pZVVaUitevGXMTy+Tjb3dmHHVguAsk557yRaCGavd7e9PgjRLGYpafHJ91egyhezN1v/9rf/ujDU+DCKBiP0/KDzz///PNut6FVAc6FYdhsNn0OqG79mynFKTfGaG0Y5Vrrp4+fPnz4sNNoPj0eRtG9Rqu9u79PKZ0vlpcuXYrEPM3zvKisQUpZVer5Mk2SpMytEEJVYK0+fvp4Op1yzhuNhi1M7V3UG4oxponxcrI2KFcGd7SKHdY4di8bE9FSSmVlnldFWZbK6lJXiFhkc86/ODmeekptKaWv+K9wxVYdhTEhNFtk08nCWcKFCFr9xo2DXlosHj75godhs9N9fP843uJC8ulocfPaS/Fu++MPvpiO51KEpTE85IgafZULAOd8/2Cvv91rtBrbarvVaZWFHXS34qAlWVRWjq6ABlTKkBBirLOFZ1byxY6EAFBPGOmAbIR8N9WbMwU8e/jlSAkHAEBABCAEYeXbUio29ahzDsARgp4X8DmtiYiNJMavHQBAkG1aTLXfRum6kfXaByWrLpLyOcXsv7f64stNDQ1UMIoWgQISIIjMl0tR1sA1sBYRnAXwJGrQ8PF04g2TurF2XGyOGPqoO4LkAaWUEcYYE0xGQdxudpxzl3bX+g/NpnO2s+vbLcS+n48PmfqjZt3yaq8sy6qy+1d2qlxliyqd51JCpaixwoHpbQdkg4K7fuvx8cJHJmvfy++HyV+c+JRhnaYFACHEYDDI87wuBvPozTRNm41OfX3/5P5GoRHGGIcaYdW800PMkqRB1ilbpFZGnIrYudD7K9PFGKnNqxQYtLoNIUSRW49urQfBC1Ofma5jErV1bM0C0RmjjTGEgFKVTyMFIds0NfS6B0t3Z8ta++Tk9PjpU4+44ZQgJePFfJ5nlDKtNVAignBZFErrngz9uDmjCeIFXRCjAJRyLqPYglJKzbJ0UeSeZs+/+2y2MrZ8fzoPZPPfeCwVpTQrUz+tAOChed7AivgFI4d/0dU/6aoa7bld6a0oP4N+ABExy7LxeOzz4lmW+TBDjTit91QtDSmlkgV1jtCPs1fkINBjc0zdKdbXeTNeAeEIBojTpmLaWos6F+gEYp7neRi0QonGWKUEpfM0jRsNKaUDGM/mVgYaQcaxX1S+ck8ZYzSUZVkUi7PJl8vlUnIcjZ5ypp3bEZQ4jT9+78NG4761Noq7AFCpjFJKmfTFDr7hCGzYXmWBWjutXY3JWEknS2EjZkbWETJrs+e+9GspLR63Wq04Sowx6Cgaqq1zllbGcsGsI2maWmcYY4QCIppy4mNI3ix2DrXOAfIqtwAgJA+CQBtVVIUQotlqcm8A0Qic8VzTutJVVX0xv08JOqNLWjWSpjWsKispCqANS+ilS1s8CPuDzmI2+uG/e59D597jp9bxbnfQancopXk6B0MIkq++ul/XHCOiB1sRQhylUkpCqLMIAGiQUd5pd5O4SfJchlGaFnfvfkWZKIri9GzcikMAAIqBDC8dHCpdPfjqAQ94IGIfNPYKvixtni+Gw0kI4QsVsBUXNcd2g2PVal1jGrzI8grYulUrKp/M8jm1oij2tndH5/PxaLEOb5TpspJSLquUEEIccc5Z44y2WmtnkDtLH9x73E6y3YP+7sFOq51sNw6efH4+P8kb7WT0ePExfj7Y3gkgiXmz2ZapKzpJRy28XkBvMpRlSYISGe11o3bn9mJegOZlSkUYalUJIbzDas1KdntFBkAR0SGxzqtOQAQhnmlJXWsUjdXmOqaU+tVI2MVpmyuVbACINtWeLnFtUNbXIYQSKuQLFTyDxua6rw+1bjZi1t1YVztkbcj6n9ShclVW9TX9bf0J2hpjjNXGoiZgKRAmCCPEIdt8jPqaurjQspuvi3BRMbx5aFJvbGRrLjohKOESABwaY4xFA8RxQRlj1gpjuDHMGMYYJ2TVnnoxXfp34ZwLQSnlFCknwhmkRCZhQ5KWTrSxJWLlwDoxxQ1Hvx6K/u0d7wE8BwpTKq/BYj6KDgAeEmWcslprWwEAUIfEWtQnp0/wa8gdRCxVjoh1tqbeVJ4Zpo55euIzv7XiOG61Wnt7u0Egnz59ev/+veFwmOeFb8vYarV88wk/DnGjUcejvObwOEFiFaW03W6rdW924+l8N55t9WBCcM5Ho5FzbjqdjtZ+MyHEGNNJEl99a9EBZYQufSVJQTkAUC8vCNXGgrEEXZEtGTDCGCEMgFIRCMYJpWgv1gMCrHYXQBjHdQQeCHHrdu5CMh8255x7S8iLG0ekQ+KQ1FkbfzXrLKVktYUoq0NzRZp6rb/yRMOwXq6tZtNHI/zIeJS+74ap121iV2A9zqMg8WzwdWLFy1OjjJfUiMgpIQ4dEHCowFlCLaFEBs65AoEwzjk3VSk5s5TlDmMumQhSwkaVFlU5ffoEGTeUziZjHsWLonj65Kl1xqEBipQBJZQTAgwqU07nD/L8JAw4pRiG/PTshHNqnPXE1HESOeeyLIvicN1CNHbPHugQAdMMnSOIghD5TLBKPwMWqT/kplob22TFb4KIiDKgCFJp4hxzjhhklFIghDIUwtdEOWuQEsGZcOgajQFjRZZlQsgkaVhrPRwvDoTXIotZbp0TMgHLllNTqjNv9dZt6v34izBAilq5rEzBUMbjotJ5WoqwOZ1OGy1mHM1zK2SL5PbRgznhoYwa2uFsVmiljo8fxAFBZxbpxHcbRMS6/JIQklXKOSRAAYABM9qmabpcZEYTh8i4qzRUykSREDIpS2P1IgxDABSSR1Fkq3I2nwohmjHTWpdl6UshpJRGUUJIgZoQQzZiCV5+LrIU1uFYWLfSYoxRBM41Xbc8QkTGNGNladPntDgAWGs/+/xeu91uNpuE6Kqa+ZRZGIZUgNZaV8Zngr1vBQ54OssJyvl4mZazxi5789uvDJKDex8c3f3g3s2bN4SJh49noIJ2MEh4W2vLmFosFpTSfr+vM+NfYzabfXL3xwi00+nvbF2yOoygTwyXkBjO/P7UWleVEoIFQezWUKNNC9r/0zrvIpCVF7f2Fa325wNSpJSgQ6/DzIaIp9Rz0lACBIBdOJArgAMFAGNXsoQiUlp7zGCVreX1Zkxs0yDYfFSmlN0g4qh/W6qq/jmu22v7ROMLFSTlwlrrqLXOAFrGkDHGKF0sqs3nqcWfCFYo3M3VAwCCtF58fXEh/a0F51Y2B3MBADhk1lHrKGEIhAKh43Nk3HJeCmEZW4UZAQAtWadstV+RKwwnbzvtCeINWu0DGw40F5E22ii1MTicAGRVyhgTIQ+oJBvhehEk9bvgRhEqW3f9XIFIg4BzrrU2Vq0iY+t0sh/nqtT1fG36DdOxDwqpoiy8rN+YYlFV1ZPjx2maeo7DPM85ET7Z7CMBZM3UU65dWLJuabfyCG1VW9beJludxp+NHK7D+84pznkcRbdv3TLehTZGO4wIVlXFGC9VZYxBQouyDIIgS4uV/UQIAWf1yohpNZrWuEoro61zCIQ4B0hJHAQ+xUsp9U68z6P7Z65NMUIIZR6Lq/3C8MkFT60qhDAaPQfThWWL6JwLxSqVY2t7xKEF5/PffvC11ss0xTVp8HQ249z3YFj9sP5T7XP4qTHGdFo9L/rLsvS2mk9npMWSyKBOyVuf8LdWUOYIyauK+LZ61nLOBeeExhW6UsiMMqstBaAOjs/Om6aklGoA7XCa3psulpTzIIranU6hM6RWWaMNCh4GoWCSttrGuYW2VFBRVGSZTR1aSumgd+ns7KxScnt7m3Ecj8dAzPZOfzKeEkI4pZSLzZBypS/SXqtdjABA6uYfF94CAACIINiUk/VejsIIkCAiAcYZgOe4JUQDKEspUMpi5iwicY5zJrRy1oA1oNAqYZ1z6KjgQhtHCBWBbIWCUgoEy6ooy5ICcwZBEIKUUxHKEBHDMHx8fLK73dvd3s3zTCmllSFOhGFroRalocfnS6dpXmAgg97WjdHUjYsn6AijQRyAFMJom1tLwbQaHUS0GqtCCxYAELTE+QSERa99meCIiI5Ya4ui4jKczlLKOGWyqmwQiigKJUeH1lprCl0p7ZyLoibn3DqM4kTIgDG2XCwpc8Y4xlkcNzYDlmRdR9dqd3x0apV3q5Qx1hgXBFJZC760DwA9CavWlApjVilhAPCbkRAeJ5FDOpkufPyGritHpBR21d2EMsopYX7fcE6j5eysTKvdpCMDcb4YxbRzcOXS/Q+emBJ2ewdhHBBK55NROTdx1MjL/NHHD3qt3uu335yezY6OjhgN+4MeQpVmubIKgTbEzs721YoEIZOlQ2eREk6AACKjIWdhZSpl8loC+hFZyVNKiRfBAB637Fcofk1h+yVq3Yb3udnOiAvcyMFsuIq4uZprF03KEADQgXVgwQGsc7Tyeb69ekytddY4ZxERCCUEKCWMEEbpKjiIiAAGkSDaJEnwRSAyYxwljAsqiSSACBacQ7CNZuu5LYeIziELwDkkziPF0dXMRGrV3/Q5BSyYT54DIYCI6NBYBAClCBAHQB0yRyxFQignhAe8QYA4Syq7ibLGgCfrBVSno5hzriyUc4gWAR0Qg8RRwQgDLANiuXAB2cC4OudYfBF2q2urKKXWrNKx9VDXs+PTz9Zph05p1IZYa8NYIqKx6tnoJWmIuF5RtUYEgK2tLW/DPjcF3uadTCaHh4dlWXb7XT93ujA1OIgQUnvnylyQIZCNEHQ6H9fX90rO78myqHzQzyOYvDkshEArlFLgbBgnUkogJAiCMGmQaoXqrCqlrXFAfIC6csSnuvM0VUVlPQif8TTP0HmTRQgpgVJrnTXGF0h4+LFds/35cJz/Rq1tI6+JPfjAg5Z9oaonbzGG1puIbiR9GV9lo7FmRgMCBMhaifpx86PkvRB/U58UqK+JawBXjQ/3Z56fnyulCCG+cqa2aaQM1s+AHrcRBCEhhAFSSuu39hPBCdFc5FnGOBcOx9OZZ1cqleaByMtiNJ42221DyGyRh0kScdzPZ1meCiUAwCKJIzCojNHG+Q7wJMsdAUaZIIRWlXp8dD8MQyDm6fEjSmkQSgBzNnzaCJu1UHLO2PWSq9yKAAcAENwKT0CpI+Vq2fsxWKtgR58Reg6dx76o1CASSmkoA8ElIlrjrHMApCoqRjnnHBhVShF0gpHFsiCEIBGEcodMaVtpDCkLm8FisfBkxda4PE8ppY1mHIKv2qeUMMooZyLPc2eLXrevlJ5Ml5SYJEq0oYt5NZ/OSmbiRjvLllEgk0Z7sciazWZZ5oRQIQNVQV6WzrksKwPh4pB5akY/RB6c76eemxB9aZ+yxhhVrdgJRRA1Go3xZBqHYRQ3zobDSqlOr7tYLJ1zSSP2zjqhK3talTYIrIdHIbBud8sbnaPhqd+ztULwYo1yqTVSSn1pgB9txtj5+ZGfuLX2WO2mZtJVWhk06F0aYIGUYRiWZWmNRkQCFNAZveKHR0TOBWOMEoYOnHMUKKecfPfvXPVd/zw28urVq4eHhycnJz/8wcdFlUddfunGznZ/5y//5N+3t6P/+P/0vxp+ufziy7uT6bjf7x8/Pj19OH3zlW/duvZSZscno+NGuPWtG7/NdHs0XRLXYHqQmlPGqRAiEIIgcYY67ZxBzbJalNee0IX1tyH6a/EKG7UftfKuEZW1IQ5rwhd40aHXjZTZmiB6BR6hQS2RvfPqI3V0DVas69Xss8jk+oH9z+1aRdVO2ArlqFZS29+6luk+ELd2Lkkt0XyWrrbR6oMwCRvmcz1EhF8sqc0TwKrnTBZ/bFrlmxYJcbaWiXX6zT+h1yJkzcXhNlAk9WPXnigw89xNV5NLXtxlyLlncv/1IxF8MSckCbIXfk/ZMwn4+oMUbPOb+nsLiqxd7foghJDixUQcLnjx8yud18ugfgVCSFnmXuV4BczWnfuoXcWcfUsMH+K21mrAOg5fax1EtOj95Gdq9hCRA9ncJv6mlFKAC6zD5s5y2tXPWR8AkOv06/sOEQPB6i/tGreMiFDyegzJxmHFM9CH+nMsw/qmZn0450IePrOMazvZPJ8T/fra3nxrtxGFqv1jQohPytbgr3r/6iq7ffv2r/zKr/zrf/2vx+OxB21ZaxUl9TVhTf2mtY5IC57d6c8tpK+tB/dcLM1fUIrohefX9dD1lf0huNzcpM+d8NykE0LArga83uD1i7zwvloZeHZT+P+G9AI0+syEcvHcN6v3Ci9CbZuzI2JNCPElZFLKqqp8L4fDw56fMk9o6DkgnXMyiv2ArwJaVbWCExfPwGzXPgDUcDZv5tYo/VBEuDbuazQo53w8G22u/3rDhjL08AifOvGkBZxzZVR9ci35EdGZ1capGQ68zHcWyZosoS5FIYR8+9vfttY+evSoblXnZSnf6ezfm9w3BTRbndl0/scf/bA36Oxsb++Ehw8X95QrF+4cChW2RLm0/+M//aN2p0UoWDCj2dAJfeWVncHV5N7ZJ5JKa7AZdTBHo6mkQamM1VkgEkodWlu5ylMHWG2MMVESb8r9+rAb+VS+Ubpjvtajxi8Obx/hhpL+Dx9+pp/bG845BxeOV72s6xOe2xvkWUdzJVL9BKxVOKz9sNUEh+Ez8qUuv1HqOdHmjxqd/9zdHTyTEr4Qggw3zyQEfEEy5S94VADwDjri8065oBvAImu9ceqZIxHRw3P8zz0GwW40riebpaKwzsGvYdv+g/2GXDX7hrnz7/WC7+H5nP3q8PVF9cjU41MLVvCBu/U/KSeEEKAA1MPO3TrX8cLLo3vxc3ISrkBxDDZtxEaU+Fyax3PVrp4qC7LGblhr2ZpW8Gw0IUgJIkFKgSGAQ0cAkK6SUj5EXN+Xbqwa2NBYUiarB17/afVf8+L3MmjgxQpG4bqEd/NSTrv6/E3JqJ8dt4s9Xq2u/5y5nOdlfdP6J4QQSeXFml/d2rn1QgKCq7zo2gS01m8o///g7RxCYNDtw4b2qg3EZjOgnC6zZavTarQaNap/lhf1a/oZ8WJUp/59wYe7/AdEDMMXK9TGutyfbFRhICJ+A8l4pdSFviQXo22dqdfShdhZ/+8qOrha1YQAYUz4E5yP4aGPSoD5hvtasP6OtUihvoM6OgCgz0QcHQAsslkt7jbnfbGR+qlfFgBEpT0NAAA4ZECQcUooyHX3FET0MtAvDCjKOuXv1gUOZVmGrFnnwtfpcA8zFLj2c4TgtfyhbJ0yB2edURo9JE3rC4fEvxxjFAC01UiQCUY5BQCLVpfaOZcksdf4jFHOV0EaAFgu09WwEHRwoT7iZBWBM8ZQRoxhfmFbZ4BAGAVcMA/s9xKY//y9zz2fH6pplmXZBMDmkpSouKp0oHAxW5Z51Wg2iOFHXz05a571dzuFyjrddhKEnJGKpg/OP3/p4LVGEO8MtjEnRZbTAJDkTBICgdHGWIXgKAPKgMWGoqdZhPp96kUfx/ELFcOmFQnrJDmsFXMt8l64yJ5ZcNaStb0MG9Y0p6uyome0smedXZvt9RqtH/tiz2wCGjfuVTvlFej6zPql/Puusllr0UDW+NUXKmBlLiyA+mEIIZQ/UxZM6kbF5hmFXX+w1nqNyJ4dM2urYMOBqK8DlCqltLUAIITgUvpRMmsDqJ5HL/msNbUU21QAhD7DQ7KxGZ4Pntd/ePHX9pvOX+do14mMlf7Qz6+31fmsAkLQEUcIWHBkxQT+TSuJfIMCplTWL0KQUEopUIIkieNVrIVw6jgYupKElAEhBAgTEuiqPsTiM5P4jO5EQv3/rQXxSg7aF1NFArvwVJ4Zf37x2831nBYprFdabZwBgMMXj7MBtfmE9dW4e/H5IrlQVJvOh29q+ZwCBgDORC0HNmNOz+27GsRUliV51rCmq3qt52NU/ghlQCmdpYtWr9Nqtfy+K8syTMvN0+pQRNW8UDCbH77uGPhD6VV3NX+iTxsB4jcQ7gEnF11TYW0rOOd0pQkhvhch2WDeXn2un+fCDfCztrHIEQBA2xcbvr6r49d3XyifqSKpB6QwK/w8xYtgJKBvXwew3nFk/WDKFsCk50gwWGmnLCgkeDIc+pUWhmGMGHo5w/kyXdaSEFd14coYjczV2pcQiuD/CXo9/tZRn5X3I5PZVYTsuf0ug1V54XMRAkoYAHoVzjZoYSpVbq43QlZYnEYj8QNC1/BDf528XFH2AgEuGROrIRqOziilRZUDAGFg0RitrbX86VeTdrfhHJxOJ2EYDrpblNLlqFqwYZCECWu6tHAcwigO2wLaPatQSikkf+PVt9Jl9tOf/rRcajQQ9WgYRI1BaJ+kaTmJGpInuN1rL04LVRkorbHAiBRCMk6AOpXD5rjUw1QvaLIuMvH/3Fzom2Naa6zn1so3CfTasyQbNLbua2xH9Qz5rBj52lEvvs37AkAcx5tgHG/KIaJggRcKbk3jQDYsBtwIfdeS5blH8g9pnNn8/uIDUkYYpd7zvLBPK2PXK+y5Bfe8yF6LtMiH3GGj7AQAZBByoYJwRTRBV6i6iq1z7bVk9Ld2FX59cgGA0RfPy6YKefbzC08HdOyF31Oycvlr7ev/6ewLHsb/YgXBB3DrwQEAzl4csuPsGzx1sTbaVlde1Z+ABUIspWbTOkTEIHb1JjdrRL21Fg0CAlggjhBHKK5Y+i5Klhn4mUSHG+91EThFBAR0Xwvd+xM44bB2ZzdHQ9DnFfbq87N68eJ7HuGzvvLqBPHMFS4UhjFkw26oBXqtVDZ1OawcsBccmxroGUWbEfJsCHq9T1e/qretl5WBCJVSyqmoGTU6DU8YopxqNpv14MDalan1ATy7fjbthucOu84abm7hTfn23BFFEdmwJ2qLocjLzZ/X0sMbSV8ff7Zh4G6O52akavPwCuY5bwcRndGbXre3IZxz7Tj6ujAEAOf8e/nZvFgqnPc2k3dSyyAJAKDMM78aLLjKKFehP0dEnDnqnAfQgDCchyzUAcWVPCTP6s6aQIkQh9S4Z4MEhBD6bATCrLFEuCHAAYAzZoyplLKl3bxFVqX1u2xuAU5EvYCJvjifcerRTIQQCuAByIyxPF8SQpBZxhhw401csMg7zQ511Gkd0LgRNI0yyzwPgsAken/7MjCEkDe6oS7ceDIZHPQqVValsiVAIfSMzJ8obOo46g/zJx3Rzdz5pDgZzxZGlxgha72+e+OVKiP5GKqUWiXRcLPyXdRzSwRqs+5rCaQXLh1/bKpA2PBoGXuxgHYbcJL6ATbvezGshBBCNjfM11fe5hSu3mWdYMZ1FtlvGEehtpLqZyCE1Em++gHIhnLdvLJ/ttpA3nwdAECEtfC5oHEGACPECx/bN4h2GwBUfzSSFlzIHQSgUsgVH4JccaZ7kEJRFM4CIN18/logbvorm+LY2RfPC+KLBRx5scMMFF/8ByTrNnmr6NxaXuNaAD0bmoYNsb+5FhFeLFiJ+wYPnvlxuMj9AwAhpMYcUIqbiqGqsnrcrHVKreD06AAR0EHNObOWgLgpR+ohIrYWQM+kJNgaSHLxhF5AkIsw2qbO21R4zwgafpHrrQcMAHzE6OsKwMj8hfNoN7CQFwOPWLc13PwSAIzT8LUdV58B4AnpgOK6nIIjIQDEEUaAOqBAKCUEBLuITLCNvuDgiC+4AgBflc4YK8tS8AahFDbsY+5bj6+xFJuLGb457VVXWm/OCyLGwYtD1rW8qn/i/xvHF8RBz0UEceOof6usem7E/IdvMhSQvBgTYHX1nHDz960DT8+tilhejPPmB4orDQTrdKEXUJzwTasd6s5Itth8qdow9dt6U+TWn+Fr+mK94y7sM1gvrUaj8dwg+w9Wo1LKN7AiaxlOCJnOx/i1cCwilrmqrV4AAIJAgFBiiGaEWqfdOobhbyE4N8ZSShGsc6sun5QBtw6ts4QyRFTaGGO0sUFIA93iJpxX58m27VzuLk+rR/cXNqPRLoKh6Sx/70fvq8ySUhIp07yMtIm7O1GXQJwP9aPZ6TkNiQhMI+xwEvJImJKgAm3W8onWmuOZxmebucl6GmrF8/XjufF94Tmbx2bI5ZkdYk09i24D0LF5381p2wx9w8YGy/Lczz2uE06rF8S6M/HqUv7iPvy1eUdYk59tLqyNAfmGUOqGJtk0L4IgfuGCA2DreI4PcK0OdJ57izMpN5+zLEtjnNaaEFPkyo+AMU5rt/kYdQjXrGPvzz0qZ9+gOMHWqhER19oQEb8BDPUNMWK6rp9+TtHWkdHnFDPi857B6jnti29g6TcIso3Gzd7s9WtDyA1puHa1EVEprJ0Sa1EpNAZXubxn48n+QyQjeFbQrJLr5Jl8RD3UmwK3PgERLyySDfEKAIAvTlWU6mvjAwgARgEiIF5k41Y/MXRzHn2UFIDU1K31IDt0hADnzwSrauHL6YtBc+RFgE1CSJ2yeU7yMmCb67DeVk4TSik6Yg1maQEAGiwgNa5mWQFGgFLqrSAOzxs0/qirqp47GlFj86Vq2W2/IRfrG63WL1XPQh1ar7/xEvI5BVzPl8/lP6elyIZF/vzzQwmefRcYwIVxzDHcHP96juxGSH9zanAtVzeXEAAkcdM556GyUCKsTTpTWR+rqx+VAQMAixd6wR8rj9+9gCmMEFLD62AjkEkpRXR0jaxcSbbVMnjGnavfizLLpAwTUS8h/wxJd1VH4DYQpojoy1br54S176FsVd9uE1rrsdl+lp1zwbq6jBem8CibLM0KU8RxDAJynQOF8XC8wCn0+WQyiUQrjuPJ+fTVm9fRgM3o6HRmKpu0IpC2KjIGzSuXrhwc7JVjjB/RqSopinQ2v3f/87bYEtiBqmE0WGSEEeAO9TPaqB7ZLMvql9ncSJuW8jNi6EVu9DetNgDw1Dz1qqoDVlY/0xwD1gwpNWnw5r1wXXqx4YCuDhkE9YKotwohJBBRbV0+t0wR0ZtddeDXI55gQ6pubDVeP8wz1wEOAOiIRXRrkD0ACPm8wkMEQohSF7nbZ8YBDSWEr+tE/TIy1qZrBqjNVUUIMc9G7/3QIKJzz8QY6jmi3+jSPg92Q0Qg4Og3xaBfHFLzLujXb03J+vNab61uR9zmyQCrHfpNuc/nFlt9GHeBwCfoKFKfJBPgcJ1SfObndlWzRCl1zulq9ZlwAgiAa9bV9VNIQeuLrB7TCx2zagJdLxXnHBCorK7P2RRAjrzYgCiyCwG6GVUSGwbc5npz/BnDq/4T2hi+Po8ACFU9gKvts2IIR1jzetd/QkRC8evH5ixcjPZqr61cDcbcptwIOas9FQR0YK13mSGojGaMERDOOsaY1jqQiXbPR8X9Z85fnLz9JrQAmguDHghQgo6seGxeeH62zMnXDBEAiGVCNkBY9SzXpz03/nRdT7wpYeCb163W1YvP1xrxBaklShm86AH8fvz6+nHEaKcdYYioXYWIFphSSooaJOgAnc+nAACRF5ayT6V4sSy+QQF7WKrPNPhCS0IpZWCcdau64mdEN8EXjw9lnFDiyVIMGqdXWH3GnylCAYKEIiAG8UrO49rYXVWXVbaeLNxQzIjoiAkj6VEFNfMo55Jny7yUJSGEMmqcMc5EQVSF89G8oo7mZ/BgcXzzWtLf66R6upyleVaZ0lFGgkSIiCAzvX5y8sU4fbtMW9lsPpvOJukybSbNbJKN/v+1vVnTJMlxIObuEZlZ13d0f33PdDdmAF4gRS5IiMdyZSvSZNTuvmnN9KzfxD+xpkfJTGb7wAdRL1pJywNrAxAAMbjm6un7O+rIIyJcD57h6ZlV1cBAUFhbdX5VmZERHh5+hR/dyzT3c6KCyxg5MAMlB1zwmQLC7iU1xXDWIOXPkbSeG+I0BkP33rEzD3XoSDnXichQKfTroWqrANSai2G887VPSzg0AZMuufQTuz6ATMfJWS6WDAmIKAkT5GxVTE+4x4DJ91kO8n7QzFs+i9jDTkBEGiyvox1YVZXan2XKcv/p6UKyH7CJo+Ucpe1MdmgrYKrEo5J+BYedgEJzWAPoGaSoVfqPmY6cxaYjTiWqO4Mq0eZXzP7nuohpDBZd1nSEYB1jYNaF3XYVYqv73DIGYGQE/SdqFnlnM5rpaEEV99w/5ai8tCePylM2DNcSGo/T8n+yXpK+StFJL9brWjfmSCDGncUowj5XjIeC+9P3NKwkgMg1MvyYWHdhPxEkWafESSJhQ+idnvpnszevrq1MWscpIiwRASdw2KvnCF0anT0jIgMD8HxW1LvakfPOJ0xEVId6PpvHGCELykDEiAyQmJMhJxbg4TCZgdQGNGe6RCjOA22oD95f+QqNpju4XzACICeI+UznHdw38ySzldQCdMQEHXgIF7T7dGYKZoBJEdo1jU7fAjZlpzMiSmYibei6ruvzupS+p/B9rfOB+CueB+60f4tglnJaQClpUkYopLWcHw6LCs0o5//AVnhUppCIANg56rph/yqhQ8S26SRRPw/WJkaEohwy7yZBckZiqqrKeSpKjwFiCuSQxXy1ulhZpqIktSh9HdZYsCPnoHj49N4Hv//w7OHsnz968dOf/vTy6u3sZHbv4YN2x9989C9uzR/+7af/4fzuyb/+qz9/8fp5cxN++r3P4Wb+4f3fvB3+ZexEHEQgBB/Qx0Qxhu3t+QdzuIAEndvW4arruqpYxDAQGlUZAcAm9BdAiIp2s7m0S6U3SO405ab2BjBUyb7LopReiLt8jEOeLGXeFnWUPIV2eJfLmYqdc7nMYtIoNxmMz25yys9U41cfNHuWEEKQDqWrlJK8IoZRpKbiVtt2upkph8R575s6kImYVBjGNJzNW0z1rqJ8lpOy65D0aQVSxXVJ42G9V8CY7u0GoFwjQeFpCUEyCYfZqOB+rBkPMPdg+xl6w8Ne2YyHCSLBEcp6RPP2VbD4oG3m+6QTCSJDJJerSkNMKYXYxRhjCr2ijxy7I4JFOhI/fUQgKF3JhzQYGFsmzIR7gWlCXuGIV7O1DNn9ktwROKdwcF3qsD3Yf+Lu8DqGdvJGuejafkPZhojFvE/c0c8yf78olrITFUQZqw/HH/M74bDfiP2ET6Tsm4KHBFbIGny/fbKEElJ9YFHGdIyMxln4arLv0IgL0rnuO0Tctjd2pRTOEQ6b1rt6Yx/X+5OxbNk3ztyQS9+OIYGpJmcYuZRf3L+/jYfx31JmS4tm8+VB/EnjMDx9UWpHwLSv1ms7JCwDm6g2/dWXh9ddMunur6O3L7PTDl3sulhRQVA0u+7FZ6+T64rPeHFxdu/JeXxWA7v7d+7PT1YP3j+dn8T30+MXr55//N2fx5B8qhbx4r37X/uN935vdzknKLsWQwBETBAjJFcw0mlVlOTqBB3gmtowg5PT4u6r9nOFl8VIKSalzk2S55OZyU+ptsxCY8smiKhZTvR+3Rhs2LBuEk1Mrz1wVkEsfVF6xDmpuuq4IptD9tp3ORegvCXE1mKJMlrRiSmfJcM4PorGKUFijFLMyfJI+V4ixCnn2cccJK7PWvYJALP5NGWmTLBtWhhveGHeeqZuNwARpdSP2eIu5qRIOjwxtnP2DrV7Rh5RQNlXMzNymPRsl2NKtZnNoe/YFPxrazi+7v/pBBMkQJY6lcycUsfMiQf87O/6NTXdQf2A9Azs1zTxEdHM+84CHPZoFozXpd9HfNgpD93gHGQvMMlLkRmIRG9GRBTDmeUN/W6NAy4hIsd8eAQMETH1/g8AwMQOUkqHywLikaOTYyuGPFXU5HPiHKp7nAE4cUyDtoBAjsgXc0scBrhlj05ERJCo9V51HnWb30s0tekLHDwWAEP6LciZB8MRga9w5QgsObyeqsriw8BgUv+nwqG/v5jvYwWM7VW2ifPgfiOXBRFAZIQEMovGlKO1oypoyB9g8dMVdq0HV9w4dnLUawkzs/bRnlsh43jFM7il2yxzQP8ur3dP9pIjD+yYfUnLhG234RefrCM2cPHi4v75YjGfu/OvXfxWuSyvti9+9Pq/vH75an21DtdQ4vLy+aZ+Hs/O71NTYKxmsyUxAwfnMULnKMyXxf07py+fX6+vGgZAj4UrE9Hl+s1ytVSN08Zj5WSbqEqkTN6XOEFNyJor76lxAxnKM+WxZGpXRR+3Jg4ehwnpnSnHNTqnOjr2ngGcgCF2vXVBn5IN0IVmf5CQJXr7UmF4NjxJRih+tgiDhXD8IIExoeOQsmoI90JDtt68uXGmxJCOuSxmGewRoKcjzKkoektD23ZWTKkWCz5E63e7nX2vgp3o8JmxSiSTfvp8GnvnW/uEvv8Spyvb/3lYsTna9kfy7qYaEksVgyxOgZjQcYyNR+nPV27piNcxwS+Olf9l2mS9YICqrIh8h+bX/tP+g+Pw9FmDj32NTkAWu3bSvOhJCS+A8yONShXElACRJijEDDFKgBUhgt01kQeTtd2PdIQBHDPtYhppfoiARBNGmH+SufS0SMlR78tpnM6IBpOG23NmFOIeQ1ZwsU/okZKcBRD0+WdSfjcDsC+nvhcTIjmdbx9G2K+fMpIUpqZ+uV98PmTJ+lcCAEBpGL9wfUH+iGDBLpuCgdEfOWs/NFTm3lCVQTFkiSBy+1wfAJwr7MgHvpkOgwITMTPBEBwlLcCQGMpCNZlwWUU21Fo3B4COQOxSwx10BI6Ais47LrbPtrCsTk8WD86f3PVfo7poN/zlz7+7aa5C13Z1c7q6ded0vjo//9rT91988breEfnYdbDbbdE5KDt0qcL56Ye45mZ701J3yl2RHEO1DYt1yQ/SOFen6Lt2JpwjuIuiABrOdC1AlTpb8QTHEo02e/NkdXU/2wWzW1SRIJmEIQp97SqmBJgSD4aLHtePhLu4bBKkHBzc95M1V9SI25RSruJnTW2ygdtdr0n3B7pDNZtuNEEmB0NNGw0tV5NvWUKIbds1KSXnXEx9QHNZlgyMxA7RwUB0bIYy+6kra4FjRz5pkkZ1gs1gdvj+DpxA3r59/89fW0uD968wCPmXUjfgGRHgoO9aAp96LWh6Jv2rD2cch2pAN6ohaK+/UlMLEExNzX3H+UuY3GMXkZmPMTYCUcUytyWSZENMUxOx/OlpyJqn3wMAQokg+UtGGzMkBHCSLo4HXg4eR3lORLlExBSPMNojXvoKdksicJIVy6bGi8AJkAlZfJLJoSeklhOApJoHAPBH0J6z+ihSTV+9KoNO5B0EBGBnFg4ACGUYxv1C8ugdEdQiD9n9wBxtxPHRkkIgKwzQn7TmpRcFY9gZ2GfjAh4dEfaMOT++3yzdsMgs+a3UIKdcIIaUj8b7fxZ7R0tv+MKB/pPvnb64j7sX34JknDotniP0Xg7M4l/SL4G3r7Rtx1s3p9jx1faKyFW+orSclyfnp/fb9u22Xgdo31y9OXdPHi2++afvzT+mv7m5Xi9OK/bthncM8YvtTz59+/mHv/M7qyWHpohfXse6hFQVNCva+ecvf1THcOfpI9rML1+1N5t2PvMXt+4211NnP2Ews1zXjHOmYsmvu2vWPE5UK+OX8AA7L/3JLsmAWFp3utdbe19oKSBjnY/s2tCg5/Y7imhI92/vceRljZT7EhHmYhowoWLMVVnpaNFIwX2RuOy9os9KFakBHdFBSgxU5/EndjTSWoZsRxZj1Dodskir09FDXz2EVm9ta2qT1jTTHNS6mhZuE0zdb8nEE4OVkMwxvH3FMX5iCSIfUp1/yXaUY7FhwMYEbbgvIrEq3IgFAEit1sRSmZN/Xdz3HU130Lvm8su1CSTxnZqTENap/suMR7zcQxh8EZSMAoDHIY7WziLhSOPXC8LhTM7uI4zGkpQG46f35aR/accERDhmUeDBGcp2qN5vUwbMiOg0+a6O3/WZTfJ48kXisRe6vJOZcphQyu7lvd9yHIVN6uwiK5xHyOCPpOxCHFkcFbZVMbJADAsRxuaHzLQQsgldZdX+z1ERM5UJ+kxbey2ZeHF7IfdPiAwASODcviUmdBoImgcCAIBytKfA1w4tPlhGC8NZWLYRgEQIm5vTcP9hBszM7oQuzu+srzcv39zMVzPwsQv16epsW726aV67Ijb++vnuJ7Nby7Plw4fuwedvF3WqU+oavr5sXrztsMG3sHCfu//0Jp6V4bRz3sHtBdxalss5Fp/90/O3uzfF8pP3735YPbzovizSbsZ86twOjJOnjEc1M8plF2OMUm1NnGtUUVOMFIZt115+TSbidljm8WawoHAmL6NwHctIFHSDVYEYRSKCyMCI1HvJY098kcAhYa4AIwRI10PH76gnEHrqaV2xJmo9EQFG56gg44OXkrjbIQEgM6TBVpBS4WYmsxsxRklqul6vyVQmUPj0qS45MVMbEoQhXMoipXollOWJhb+d2jDmPVTex0abGc0eM4d2Wuxh0ol9HY8lWXvnr+3Q9UhTWyjocSUiIhaFZA6XwnCJTfjjwX6+KqvcB7t8OcR7jEX7r8r5J90O/cT+6CTvunxPz6hGmge+YwWkYLHSZcrk1Y2KTAwjkaNcBoBR1CxSgdlMLQotIgAzBERE6qOd0mA6GqXwHIaT+LBzHBxLWhrlPT38+43AkGAU1qjATIwikesplZAan4uvWLYHyqUmY8GcEGbAKNGoETnKfBUDezoG/fVkv5T+cNwz+yFHt3kvunTAVwMA2A1GWjAAKXAoFN13lxjEeU3s1f2C5UeOCYv9wg6W6x68LHgorzAaPw0WFwu/lEDemZj1a2bmpLxY7s9Qj0bayNyVmbvU9VaE/IjcL5lEJnR+xIAnO395q7q4f96kbZnwg28+nM1mX/7s9Xr3Js2vV/PqZHFy59a9+qb99Pr7z9c/jV367ONXr9fPrx9eLS+K87tn9+89iDsooPrBD//Ow+zEX0A7c2HxwXu/cfb+zNHm6v959ZMv//lt9+YbH375rQ//spxdbDepDl1XbHUkSqMR0Zouk3FvPj1fKmpaUm4LObAJuiAamTiM5tpXQILxxpYyNYMJNxuB87JNM/EC5sgKk1dSe8PeQWnIfwlHMkYhBRUXrF8VjtVH7RPd4OGpnWM+ZNVTLp17HEyjJGxYzqpPT4e6wnZqN+sr/YaN+5818vRiAaF3pN/bjcfMGoHO2UNNjQ26Xpa26NwttC0FmaDuHlEafY/jMCQAuzd/qXasf2A8rAGTBiowAouHABGVpYsxhpg96SDFGI+dR/wKDY22B2b6aRyF9Sv3P+lh2FB9akw1SOgNx3JLHjHhwpDhSAkdAJAUuM4sifUPYADNEzc8wkkodD8qxfkYk3M0LjcgvGhqEs904HDxj6OaMfME/v14YCSvmxtI/oGJ12LWM0uZnT0PnsJHLlKS83Jy2BtVIDEDe/DipqS8TRieL8vxYPOWPyJX2LNY+0jMqfKVxPVkEAedHrJGC9CfofY1UVQqA8lfNmx/6NfuqHfEkFdAuwYAYCpGir7pTCKGM0PHfiLihS63KJ2Z0HYlZYhYFpVFywEOMCQgUgJIRF0cjUc/j2RFAMCKows3zTUX6clvPzo/P7+8vvriJ68e3lk+PHvsu6Wvl1evn3364p9ev3kFAIurxfzWal4s3rx5to3XxYzWl/WiPHGvFwBUnvtyUfhZuvs7+Pj3HLi4+b9fb5o3V2/fvFld35w2ZY1AnZunGEeRiPq52+0yIQNELIqiL/0NBzQhHKfIENYllE40Y/u9gFsSdFj7sDTNeMU5iF66cqYMoqrFiJhgZPrWJAyll6oaaJ8CAJfLtzGPL3wh1mCAkZOIFSz0ZkTs88tIOkNzdK0pAC0PBoCb67XtlrME0LZDCj0VOxARkkkskIE8QT6Lc8DeWg71HslprCfNWgdUVxbGBEVmJKCwos+8Gpnu7ah4rO+yEYMOtP/fjb4q4CcExtQDRA41MPW2jQhRGPD/J6OwaWjkS/kmC0PMY7vxrzwpBfWEpuzDHwCwrx6PRgeWYb1r/LCH6oAuW1hTYuzNB8yAjIicmKh/BSECIKQB9xSrASHF3v8KelkJDyKJQu9YedNjYUgchlNVMAxYXmTNP3aNdCPomEOn+wIRgLKyJQoJ8MjHBQASRHvSpKZBGtMNJX3VvLCvUzrARzT7mEYrzv2RMOswrHEREbuW87RlSL1W2tW7EVgGi8X4DFjp6hFBx9OojKOOn6rhiMF+pnEUoQKE8oNi5lNroWALitzCrNfz+XwCZLnfuSHzGtrw1KzwjHQVRLz96Jac9glll/DTqqpOvlF87enTf/r+9+/euUfo6m39/Plz59zTD59cvr16/uz5++897uo4q+afffpZjBGI0MPZ7ZNq5ecn1eur54+ePPz9f/F77eX6088+w7o6wTs37dvVN8Lv/8WHdx7d+uxvr/7j//q/F+2tR6e/d7H8+tP3f3fX8Gw+f12v7SoqWF1g9cYasM0wGzR+wphdkxQ0CpGu66TEnhixY4xynJzSKEB72Ei5f2EAANBXlONpDmcZ83a71e/JhPZut1tr2tU94Eruuk6CpqwKy2lknNBWlXPKwUhiAZZuq6qKmtA/f0nmjHnSqmJh3axUt4YsYaTsU6a4Yuc7+d7SEezPYHrNW1cq5uSU9trlRChIZI/YFbMJphtM2nxZWdS3AdC6cGAIqDnrGlsaDkIHAKiZ3CkXuazw+OAHABMCJgBgSFIjUP5czPr6aGCoHjNzUeeFDjF1CL0Lwvr68iCBiPFwvLLET1hLw2QhYEyA3NhvU78XEZOTwITEqR4Z0XsZs66pQNtTZcEywNN7BY61DxHGCdDkIpq8MfZC6+MqaUsmx7sl0H2HMEo8Ym4YDD8WJuq0OKEhdKTMpTuSW96SGsVeHEshFhr6Xks3EHG73R6EwwTCv7AxjsoCKnzm82KCzPLZ8W4ihefwwp39UuHDY7KmgxSfG3tzT8C7I2E/hkHaX1N7ZEceSQHrjggKtMfIJ3t/0lJoD+8jRYyxf24yqYItqFveSPks55xkXBBCXbnlwf696kxsGFsIoUzn27fhzefr7esQukDgmqadzxbXb9Yp8LxaOijW9Q4CVcXs5NZpy+1mt1mv17sOq8Xd5XIFjF9+8eK7//ifP/z6Bx/+5pP1l+FHHz1/+ckn1zdXf/VXf/XhB7/7R9/udq/dKj50afV2fbPeNLR2oRmRhoHwVaTJHywmiWQ6EfqYWXPDYvZhVuirZgl5X2lVZzJNxlBmEw2Y4Fpm3m3XExEB8m7EnM9EWIuQLa3CJDRFqV7X1VbN1c0ARsnQqTGzBB2BYWOalINNAL72to928vhyPhSHsAPQuFWdlDRNyp+ytV9uOD8/t1KOIplYLOz38tN6vbbj0TWaLxaWMes4Sz+zJFt/TZtwkEBbC8do4of2NWK2Gx9puHc0c2wbIyIo2RWyYjanLrf2WRSzRBxjTFKqLoAUyp6Et1n4Hxxh1EMpO5KxGd/yHtFEFc44aSDWC5B7IAGOgx31U8M87GgVXHbWFpL73zhThMD+ZHMs64aCvbA03YASbjfMYxAsRv3vw2Ry/wSYv7Alc9ZDJvR2MsiUY/ch75HJbTZzmf0+Hsnod6xJDk8cS+0AoOE0EwQWU6ost0QhR05MYPMi2H6UHiqdkaHaFL8WsCGMlnUygP19ZM9oR/M6Yqra70G/319K1iDAvdbHGfe5zQFA9gI0g2l97LmZabj23OOhR6X5dqMJzWdjfOoZ8Gazmc/nVVWFEKQml6S8+Pi7Pzs7O9u9DPOLkqIrXUmxgg28/OKN975r4rZobt5uuipBpNJVXQgElBLGNjW7po3hy93zLz//8uz20i345faLN693t+6cUf3oZ//46u+aH//+H9+7PftgPQfeLNAtd21EVyTk5WymC6k4CgCRIxFpNXLFeKk6AhnLVQsUK7EEs2LmWCkl0RRlP0hJeQGTJOiwr5aLy8vL/Q2fcaX3mFCOzibTDeXKg5LKQ3NZ28cREf0QOK8kxk5c34hGswSzReVdmklKqaSutN0/ujGuby4tv5cviajwlXZrnxUNXh9RgHTjnLGUT75F9MOcG0tH9eDBA/u49t/mfjRAqz90T6NUHtp82Q/PCgR24RTd++/N8tlFPEpwx2fM+kgcF8kYlklDJTAXZ81YZ6m8WqicA4gxISIjoUPHMYKWSYEx1YN3EKacR2KCoseYindehSeLV7pGRASMwJRSUvOpXaxjcJNvurGpcGBvkCwkbZ+TC8jkaYLndoL7tA9weiaar6d+v5aM4lizQUQ8KKkdb2nslmFV6smY7b6221M+y3E1oaNoaWZ9+HucxvXCHpDtsxMtQtHA+RmbQzq937kif9+/hwipL1NtYxaGKh12XsNFHDYvGKp1zNJwLECAj3qnTzHKYvt+64FgmQ4eaAMpQOQ9AZQBvCsdFcO8ABGBHFByiEO+FGAgdAzshQkJx9KD1RDCrbNbROQLx4HbunUzXxYVAGw3TQdt27YlbLgFJo4x7m6aBtoUebFcsQupxaur65BCjOG9975WzovFaYEJv/7kt08WJ9/9u39exotP/n4782fbVy3yovBl08aiKplS49acBQeLrwUulElYHixOPYpGijFd11l9X5uV/RX0zjkN7xmMhNkgA4fYmNKIlE8oIbNkNE7LeqisGrminfyZsuVZH5QBeDdKnaHNskxltGOKM6VBBxGdcJoiTv6U+OP9DqUqy4RgcRYsLA2CbB7HnGpbJ6XA172hoBbN3lIK+Sl2B86eAcBD/zoyLm8wPvW07+IUFG485sHHGu7xYIAcsrJ3roy6ITHrvgaqtkP5FCkxhA6JPTgsXHQhRgc8LdO5TzRtm2h4k5HzHsObHChMhifxsggkiduAANwQrsbMaiAp3GFv0n0y11/z9E57v17rn0qI7dvZqFyK2/oaMHhrUPcA97Vjm8DnqzLg/Q5Vd5xgCOXUeJPxS9tnPJgltoPvTccYDx4+09Vc6zLJYQO2vcmRuI/BdeScc+CQmZmmZbVijDYolyU6O6UII8OYjt/jYJGy0HCOkokkVNpytFjLkZbwSOrWIxrwMUsSJmTgJElLACD70ns/wjcYrDtpuojEzOyoFxyn7kR+tE8V/v7WrVvb7Xa9XjvnLi4uzs/ONtvter1++sH93W5Xbcom7nZhm7pwUp4h4jwtY4yEHtmVfuap5Ng1TcMlhzbCHJbLk7LyBcwAWkR69smrs3vLD37jSXqMp4vVyfLk9uenlz/dnNQXtx88Ks9qhNL78nrdVPMKEKGoJ2JXv0nyaulZrHwvpg8wNLHP3JQlSouIiCgzpexdhVnTUpOXdCLWY2sQtqvIzISF3bqqHaoCJ11J/VFZDzWj6UktAEjlugnbTilRcZgBi6CgQzpAiQ7h4v6frq+uM+wu2TvoBycCMoc9q9VK5Ql7EMA5a48NoT5KHQDW6/WEOvSzGHt1Kpxtxh+7o9TkMPk8xoC1Bx7zpGPk1mrM9uKrNrt1rerJMSWOzECM5JAQgQAYQ2h4r8FxZx9fDQTXQkn3yGS+IujYQxZ5th+b2MqACHsfOjZcSsdvoTGwrncqbcegl4yz5P4s9oH5jle8o38cS12T8VvaeuwVx8avbNX2RqYYzITxo2n787Jv0aEee+/B7xGl1DtrxC0zSzkmAMN680XiLqepYuxtAIRAIQY7GJV7tOiFpUsWsScMm2iADBvC6JxHTAARslt4f6h8JDUpHVl5PFasZe+oYnIxacwiaCpiIDMBICABjgqJSiMW2W6q4DGzwSlmTgCE6EjrcPfg7+/w4nUlZYpjjDfrNTMvl8svnn8GCRkjFW62mgHAtl3HGOe4EtMuB+5i14aaiBIFCi51yWNZ+VnpyoIqTsARX/98/ebRTbdLBPTZs09OVqd+RsXMraIvPS8XGGNgTMtFJ5VTNzsCRmRy4/2QqM/t6cZNMkJP1FYAUIYxSRahTAXzea18s1qtFIJKoYjo+vo6Y0x/piv3zKpCmaWcQwvUlTHw2CKquZ3BZPkAgLIc2LyemFo11/bDzGIAYKMBSz/qtMI2xdJx78HN9mayW2QuJwtSSApktOJmzM0a2SYF23UMWgzDjlbEkX1Si4jOe2t8Vop/sPAvMzfdbgJhzgxY7wFD0Vxx2KR8rOkGmlDtCT21Q8pEbdhkMC4mAVmzBwBySOCRmDBxSpFH7vR2DHl1fgH75+P02s6iC51uAR2Pbh+pCkzoCNk5R0AxRotmujVidzij1mHI7JnuLcs5uBBhHNaiYxhSexoxS8BuJ6tCw9F60kdSnP5qTZdVd2UyXo12wGS8VfQnterZn9hY4PbbsZEjE0JO8ciSAAuYmdxhm4qjQveapaJtN+RSpr60JiEiBAZUj3pxQGNgcOTy+0Zeb5aGK8AREZjEU10SISBKXTA6UusE0B9jnEfu35NypB07A0Z0DICuT6eDwnERJAPaBF0zxRmpCtKk2hIRpcQpIYBDdIiuD4bjYZ9Kl369Xs9ms/l8nlLabrdXV1eLxeL09HS73SECR2y7joAgYUjROd/GtixLh1R3dRcbRp4tZq6ktg6r1erB3QeBw9Xby9jw++89vXX7/O7i/M43lg9OH61vNj//4tmV2z6+/RuPPrzXXkVXvS18C11IzJ4QyRO6rtvYpeJ+3Bz5tuAHGiOPqJI9BTe2YjAep7olZBufnp7qTrA6rrSUI1N1DMJIwHgd98fModS3i+wm/Ga51DqXrBJASkkSZwr7QeuezrUdLWQJI3TBylHayqLQ7+VO+V5OxyfOUCq36kx1j8n9Fj72T50yZ5ex/SpGFsXVj1rnrrDS8aRc+3ayN+SGoiylDiMAlGUpMg0zh+5wAHs3LmKhP9mztDGjPcJKD+/fw2fGiEgZzYY7B3KWOUHmxKCZuvfIQQpA5JASAqcIKaaEA/ed3MzMtlj6dKhm+vq4EvqB5ElzB2prwsC3ehO0nFCo3djev3+9/167UpwZsB2Jfk460WEncwZsAb7POPtXjDNhabdEI29bi3U09mYw6/gV2sQSw5kTp2yxm/Bgt4c/ewt94HO/HdWAYQrS/k+20xR8lqMBBBhKUPeHlIyDxgb9T5KSgsbOSlIxAozOoJOS1nSHi1tYuOlFjJGOzPdYys9jCsbRo4ojjSNOtl2PuTxsBABgVfP6xJmyfKz/OEkyLERwnrxq9pC6yaJL82VZtm3btq2UzXHOdV232WxizUXhZ1wECFU569rQhragylXCupBTLKqCClwsZ2VZNrv1vbv333vvvZ9/9vMXX74sZ8W3//Dbf/7nf76Kiy92P765fBUbulg+rKrq/YsPFs1F8YQk8DHGSOh8MSuxKnzZxZGzTwYE/5d/8EKgxWM55WJ8Gmyjiprcv9lsdMIKBWlt24YQtN6ARDepaouGYaPJHS2vQA3kDW3KfoD2++vra5dzKQ+rm73vdORsKrbqvk25yG5RFE3dWCKlrYNOud1ktx8MQ9JoAd3PPafH/h8SOY+ISEwpwayc2dtSPrgV9LAUVqfPOYEJmPCk+XyORkfXfkIIlOOAFTKcNXsxsKN6YOX4SwWREvT58tSCZaI7wh79kuou+IsU32PN8mC7KOPX5TfmvQsAMZvsFKWxNwUH55wjBCbmwMzIRB4miSkM5zh8dnU0QeIRBiwpTicMkjIN9a4gIkKH4JgZEpAx27ApBW01S8vDRkKGWS+9sESOjUVhcmGgOiCb9jORCQCAeXDysp1MrvVT+7FjFsL6lZoV1sHguXyjEqedryItGEKMv5xh5pdpk04srk42BTNzCvbtmB3OnZ8GUExgbudI4/zSaAR0Z6ohKfVAxKYRgcz0KfBxx6Z/eGGOMWDnJsUwUL8/eH/dMWKfJ0zGI7g7if9WlMY9UtB/TwCZ/auZlohCc2Tdz+7f0t7twgAP9SnVfERE5WImnTbdLkEqqkLMkn/x3/zx4689/uSnP/nZJz99+v6T89Xp+emtP/+Tf/n29SsYb1H5nC/nloQpsE5OTgCZHPnCFTM/m1ezWeUKd70umrrdXO1u3m43l/X2pmm3IXbpZUMxcAyp3sYY/axaFb50SKH6IsZI5Cu/5ATtLqUEjvzWbZiZ0JVuURYzhyUnTCkBXoogAjn7lYTn3r97V7jagwcPlsvl9fW13LbZwnw+F9eq1Wp1fX09m83evn3LkVQ1ZxMORAUlE689QQtdFb29C43d0piPk9vQx6dGkwmSiEo67EU5Xy4sH9VtMysrEUSKolgul+IEHkJ4e3lNpoHut75COam6L4LLarWKMbZtK/KQPihxh2ncmLn0czC5RRExhBBCYDfEVSuUmLkq5wfhJs53uiss4bMYZe85uPESHz5b1UxkE9YulgNlCfoWhxhTl2JESOQBEqfUxRhD3NnJ6uw8BgDoyQ0yS6UBBwV2fOhIhasjJrgwqtOuU/Y4PZLoCWU8wmE8pZQ4odRNIyAHnoh81TtDJFPFEgDAj5iZwt/x3PY6wJ+DBaZFpEkngxvEoXhT55z1QkDN9VYMBdjt28vycO5o6Kakub8gbyGvvOTYGXzTNCpNKoqiqSOexgXtQ+j0dWP8HJ0g6ABsKlYcCxAHx5NC6w7VEQcawgt1gsxMMIRropGYMe9Hu3l57ARnJfLYjUiZtjYM66gEjfdsJ8P4x5kB9YKOMGbCw3Ws6UixEKl3vt8UtlM4B69TVqpradGkf8bDJu4Ydi5H5VAOvYkx9rEiFsUFQMGYzmQ+fUiPc+L35JJLMWkAz4cffvjg0f3rt2/c524+n3/wta/FwD/+8Y8vbp1PsFkWTHP59gQbe0ektmuYGbj32i9cIYznhhqH3lN5ctudnJ609axrYmjD7Ma/fP766nJzfusEoGjqmjCGyLFd1nXddnVKdUopxNY5V5ZlJKyqCsm1bbvb7ThnJkrhRjaS6GFlWd6+fXu1WrV1XVWVMOPNZnNzc7NcLheLBRIJHJqm2e12okM3TYOS6IDAj2xfbJEVzMa2zAaMBtl2w1G0yyWEnXOpFjEZICGkPuQFABOMmLqio3gvwzgLBCJu172pX8PPZOkXyxNZL/V3Vdyygxw6vImWIUEOD43R2ZHo3BNHAIghhNhZ1UrxzbIN6h0RvZ6yK0KrV7alBczctu3kvTrNgxvjGAP2mZ7rNwpGuXYT93hzG+fq7coeLBDkwXpziYiYEB0j5UO7AJFGCV5UYzhSjEesiSOxI49h9Hg/2ZSOpX7MiQVIvAKRkXik+PK4zHvBQ5EAZiYmh46BYWzyHeCZWbhNbWbHDIbgWio/WQXZpCoG6feczzvRnDchYkiHab3NKGehF3mEhDoq6z1r7xdhHcYyEBGJ0yVmJwxLY23TtdvXzHTT7b/Xfj9hM5jrqdjxICKPcxHr5+RoSQFiM/ShsXv5nGgFJnnWDiUwmTDjg99M2sHFQsRfMmu7Pk7js3Z9bzqSesf6pij0mLmgBWcRls1Jv4WDXYJjmX0IC6VRVhfyKVtxBQN0j9lOdVUE4cqy3O12zrkEqQudGLF/8IMfnJ6fPH78+PL67Ww2W61WCO76zZXU5bUamLB8h/2CpQRAyAgcGRFiaAAAIoYuxRaAk6dUOG5W194VzrnCFYTOlegLZMbZ+en5ub+8Xlc0W9+0b15fewfrdd3WZ8RuVs6cR/QxYUcEvqDtNSyqBRHV7bZ35ImcUpr7Uuyf8/n81q1bi8UCEdu2lbjhpmnW6/Vms+m67vT0dD6fh5iKomjbtq5ra15OcSTxKWfS6kxo7DPCs9Ohs1vV8JLxPQaAFHLAHAL5ArOho223FgUVEdu2dTlFgHVJg8SSEUyIhaYYq+vaKiLa1ep0xhy60NkNCQi7pu7H7IgM5bq+3oChOzrlkFpFA6tEFsVsYi13xlkdsmioQd62W6KpOXQChF+47feb1RJ04XQ76J8qfLihTvNoT04IvWpCs/KCmRli7BXlLkFMKaUgD6JBFkLE7kgmLIGrvm4YZxoUX4uHxyysg8ZJHhEhAQfgfMSgK6U+dwUN1clstRd2R84mqWdjE8vBpHC6Nj0jtMTHQl69H5LxaWCjsfXwL2agZwf9aegAq9EIxzZw7Tnlo5b9e2QYStys3KDMCfOm2BcBeRRfflgQpPHZKhiiv89NmdnhICeh4foJOji0O44xePdLnA5YTnEwTF2eUHgenOD+I7wnUDLzMcGxr3CzJ0hNBJTh7Uf6iWbfOTOZFHh/5GjilS2jBAA+otlnmRxilPH0Q/fJ5NMHg/FWshOEbts2xuiqPoljOROLaBtjrOv6o48+evj+w2/9/n91crZqtvVqtSqL2bJa7LNzaTEOuqBzQETgARFWpysA4AShDW0TUmRCdj6d+9vMDImREYkRgQiZ4GbzcnW6cM4X3p+euXv3F1Wx+PLZ8x983HmPRVX4gshBgjKlwMxni0VVVYyRU02ziooEmLquq3B+fn4ueZRCCHVdi+KVUqrrerPZCHmaz+cnJyeYMz2JZszMq1XvHL4LO2BClqpXA/QoV3FRakg5J7Ca+NCYoBmiCi5WpD2d37brAtm6xcV0g/UXoZMOrViHhiOiUebAKB+GowAiRqgjhIQhcUJAzjl8Qz8Rh+j6aHxAZhCvctijFOj6ZN8xQtfFFKPwJZEMdCSKkMk446jR0lJwHOsWNpxssmcObox4xLtYKjdP9h6avCKKzxOLpSqFYPR+OwB5ZF7MGGN2TWp7npEgmfq4hJQAAQgB8UjSZKIRaVOYsMnZa2WIo/VrNcUgOURMnGKKKaW5nyfjTzSQ4yCcuD/q1prnm3bLe9wCAKQQsgWUZVR6G5nGRgVUSGrcuZ2aCBYoQvDYpOSLmZ2mrp0mxplsGSBvWY4OLJiC8/aGrutEIgSTJEclALtV8+c0UYlczGYHTPdofFBgvJXS+Ox52PI8OLup5J1SyglbEHGoiQQAMM5Fr/M9VubPzmt/ISY/QRYQ96fwjmZ7sELML7zZXkwSH+njx86AwaysbU3sYDy7iVVpMjY84hyXhipkgoR9n0NGezYiDxEBJ5WddZ/EGNfrddZCkJGLojg9PT07O1tW8Pnnn9+/c/H0gyfunFLT1XUd2i60I3I/DNQx58zXXRzOP+pIzlNVVWVVnqzm1awsZ6UvXPMSyIEvnCuQHEgdt8SpwyvCtnU753CxWGIiTnz7YvYbFTFzaFNTd03TNrsutTGEUIfQRY/EXWgTtoUryqpaLMuT8q4ga9M0YiVDRO/9dreTVFaiLy4Wi7Isb25u1uu1Wo9TSkVR9GUbPACkyCmGznKg1XxpQc3jZCMKdm3b3VZ/peznhYirRZ+hpuu6rhPeDCFgMRuMtBY5JABculKeSkQpRFF80bC6lNJ84XEs38nSN1kzhhw1IT9hKhg5hRiJ9cSOyIk3cjLHtzIGKkidt8UDrt9jPPjQZZRNMcbZbMGmKRZFk7Qyjf1cDl7QkY1xrE3khslPVscaCFAuWJb6oY4oCBi6zMx6BACYgBIDAzpyJBWC93kYHit7B1N/AmlWQRxRlCMMmDJ/tROcREZZ3jmxEOgAbCYv+ymhWdqtPivxF8pR2FiM2Ag3OnzrFCPfyH5p6xoyJ/ZEqJZ/o/7ai2DCS+xnCK2ar48RdzCS9CRoAnIGhn3WJV+27ZA4wq6yc94C047BkhG7CmAsTPqWFPpiJ9pDtmaNkvboWpfFtC5T/4kjzXtyMVn0yYqMIQZ2Fl+1aW/piA/HZBiT8bzjzkmzJmiL256GVMSwJ4jswweOTDPyoF+BAbhX4isiHmflo2s7JdnymNDN6+0NM89ms5QSIy+Wi1u3bj148IC79fNXzz/66CNfuttnt1LTdW2khCfLha7ZyLmmQuiLkYCyHkRsQ0uMgOAKrJbFbFa6koiwuiWA4wgxSFCVZwC8dXYaQyqc967igD/9yafrTf34vcentxqHngPUW9hchw3XN+22bbdtXDVtH7RAHgiqgnA2n282GykwLJuciLbb7du3bxezmXNutVpJtk5hWsJ91aosk9psNnVdo5tqRXLPxJSqpC2Z3NHyiGxUyl7ZChm5+er6Tb+iMarbERFRnNIL+XSFV14lMoTg0Px0Jup7VsJ64qiSPhhymVKaLWZMXNBQmkl+KtxcpyneH7L5692GjT1wYMBIyETgiKhwhcuKkfeV3qlkImUf77iXBtzlULTJxrYk0m6MYww4piOmv1zY3PamTfePpX0H2z7l6nVoJuzVWgIWcxQDRgIHCEmraAEAAgNT8QsY8LG3KxblgR4eqoSvxBiTcMrYA7aua4t+oBlY04hepwiICBGoLCzPGEa5ZxSVX5XwTbZMP6oxbHF89G77mbBSnW+veOxFne2f6cpnG7opxLJgMQGsXOhhk1VXVEmyk5VWFH7SSe5qxNqVWCdjAbLAwSNNfEH2x2Nzd2dQyLxGGccGkuWm8Zl2wPZ7+dJlpzwLNEQUxjMB2juapZlgtvOxBB2MQ05m+4gKalNUPJRXAACa3TRVcI9X1WgYCqJ0xMSNRyprMxMiAUgFTBRKAJAZsPWXEwEqhg5M+HxRFFVVFUXBDsU8G1LrnFssFuIBG5tmt9u9ffv2+fPnHt3ZYjU/XWIEh6M67ZjdUhL1uZqrqprP55KP2nu/mC1j6rWixKlt283rbV3X7z15rAQaxCnMFUTUuC2hm81WyD608dOfv3j22c8vzu49erKczxZ9tGtI4kK12Ww+fzF79uzZmzdvCledLE+LouCIu3Xz4uUrGQYA1HUtVfNkt0u5JOfcdrtNKckROKLXSoXCtzabTdu2xUxyirIko8m4yLvdUH4LzBGjOpVMclbHMGT7YuYQQu+kzVu7q3uShG67PZx5p5xVMddskFnIK6qqKstyNptJ7Q7dqG03ZIu0bTab6T3O1JsSJwXOWqYqwY4OKFLMDCyJMMm7nlXLu7rUdl0nvmzWdKZnhMLddXZozjV14ojYHYobfkc7doMl7nZPWpHUPstZ5QVA4Z69BmwMhmBMWB4rSUEAxIkDMyUIwE5tU3qcJCNxRwSIkEaEQ4etkFFMkG/oCAOOKULP6xERs6BFXdcJ2uhS9s4N0Mq5r9IjSdIZtx3vNQAIsbV8RdnbZrPBsZInsJIwNh28ztEqEBa1ijF8BvbpSsjcC41Bgo74DSij1TstR5lMirO4jMYkYNkPmC0vTRiwxSvuBZGCjWFZtdjNZgNmEfU67iWUsG8nk59LNpQYPvbfOzFx63WKB+iAQnIfbnbWSqAQUY9IJk8da3YMaM6D8VgcPI4sQNq5ru90C6fD/Qi/Q0SiSRm3aFdQadpXtaiV+SiEE3OvJ5NYa0gVFGU5lMseqDlFsMF7f+f0zrNnz4gIEghLfvHixXa7xbjd1BtM8fnz56fLk7vntx2566urwpFyTck1LdE7i/NK8UxDcp1zpSsACkd+ViwKN0sRtpt6t6kLj0VRVGU1q+aOCkgZw8p1CFz4smvTanHx9lX9n/+v73zvo+/9T9/+d2XlfUkMjI59SYx3QwifPLv7D3/397t6M6sWD+49cK548+ry8u31kydPmqYRcr9YLFarlYTZqEO/qLwCrq7rAHoGLKUMU47uVVu/tWIhYhdYDQkWOaR8od6Wsp/RenMtjFnJn4RBr06EAKUYY4i9qZyIUrfYx0JEDKkP3KyqSpc1pfTq1auiKJxz4gUdYyzLUvy9wVAfUus3EHPqLYly3icf7CC79iAjJGLAlHrNRnV0vU4hH855L/hDRADovY8mp4oouCEEOZKU6Yw08ly0Q3m/PHswBSm8K3fu4e8Re+9QS4aUIqiJXvcO7FEWXVYw+p8KKDfXW0QkByS2UsdirWMOkxXM7WhVnMlMIVtcJmbMfr8cq/MqTk8pIRARIaEjcs7FLlpqoGI6F9zzavHDjj1hWi5OBpZsBpa4PAaiMb0b4AmGU1qep+sim0KsOJDtRirO9okBpB/zKS+O47NM7XlSoFoX0ZrN7dTU616pnNqBIO9oq0yL78ihGQ0s0+KJ9f+3nEDXdDKe0g/mimjSBAlE7bz6Z8NUAJXxtGFn/9QVmSj3g5N/mmrAk6FOFv0gEk7WVy/ezbNhD6/geEpaOsLIJYGSXRpFY50ym0xNWrzHjvAdQ/VF2fPTjCcZRgs6OT2pqqpt26apEbEsK+dcgiDYoxWCZVefzFbg4vndk013tdlsKBXtOi6rs+p8M58tvv2tb92/e6+gYjmbp8ir+ayBbVmWq+Xq9OTsbHU+q+aEBIybdqWKr9RNlLigrz/+Zq9rcgypi6mPNC2o7WJKKTXyH7PYyuZQilYqNOLVq1d//dd/ffv27f/x3/93F3fuARIzXa93p+fnKWFkfr25+fLLV8++eHH1crO94dAwQVWVs3/+9AUgQ2Ik9h7KoqhK55yL3t1cXSPE7e669PjovXvO8Xa9efWqJKLFYpFSms/nzrnLy0vv/fp6Z2nKQAExyKaCsTnu+nJjuTJruYgiWGMva24N9LZ+MGThYLU8pZzQSs+wiSimoT5oMlY+ZtbUIspdEBFp6nzUMza3EJouFEGifoVrSmwJAGiUGyLebNZkmm5I5+o+8JfZ5YwliLhrBxuXJSjz8kQJ3Mjk7vrjDGUw0u0k6bleYOrLVtpFQUSBZDLkuGfzqbb36yOxq2VgZAIHQgjoRpm5FAf2M4j1zCa0+daExEjZPAvNQQINdESVpwCGPg6E0qguFp5Srg4M8+vX1y8sZIbusyBlYycAoJqVAACSOzdk1yJGKmZoGOpAy5KPOYmHLrpdwcn99kyOjYxlhw2GYet5kIU2AJSzwWUmGY+EmT/hQ83P+jrTFkSC22KhgezII/imAt+kWScgu5TiJKWul/prSENKXW3MXBVLlxsaq8/1eisnSs5UyyAigtGRv96fTNZi9etMKS3nMzCsXS/seOTLvGSHz7BLfzLBc/mzbqdhTjowfaN9r4RETvAhpVRkDVK/7+Fcv8uCtd/sfgSzyyY+p3qhufcn+0gnOHkkmbLu9hU4IUf5T7+cL1OXbnY3KSVRMZm52TZuNgBRu0BEnHW3b99OkS+fbxHp1u1VVXVNe+MoFnNXxyZhcGXJBZydn5yfni9PTquqqspZ4QpCqor5YrGYzxa+vKPKZT+9wA67H//oR5vNhjF9+OGHq5Pl1fXl5mZNHusQVARNgs3omLnhtm3bq/W1uChHSNVyPlstvvOdjx4/flxUCwDXhgBUlOU8AT+4uHdxeufDR1+7elu/eXPz6ss3n3/+5evXn3oHKSVGduTKslou5/P5vPDVpu7Sgp1zVVk6Ak+Lut7c3HQApVoLtEYmM9++uKWKWm94j0LQO10Yi7gs2cwInKfMgz0zCyHThe8VOEZyJIqjZRuIGE0ZX90AACBhYLBH0DWSB40oDQDzalpNRTetEoWkLvHOJWN5A5Nx0J7B2BrDhHXM0VDOpAxr0+jgSmllV19PSJXclrhRhFZETyntNkPVJm0AULpKYb7Pbu038l5fDOFDsJfSebK9dQy6XyytsQKWikelckhgTkl8HlNK+4Xr5fOYCfrdTUal0AMAdKPc3YoqWuVp0oNaROyM+t8YAQBjPmBlBByUAMWrPmwMUJFHoc3Z2Wp/2NfX15N1FAbQjfMT6G6S+AWLovL2kA6kQnTO7b8UxrSOjVoDACKogUmpIdKn4Lm+V7udlBfUFtpesIMsbcuz5Adrk4p9ALBdt4qWCj1mVpOh3b/MzDBy9oGMnOr8pRN0JvpUm05B4az95AF0OBbUMIu/2oP99OXIC12bvneyrWzYnnYugizu8VRmlgxxOGZvcAiT7VtGaHz8rIrHli1dNfuu/c99INhXT669dwUzoydEFJEqxhgp8TgMQzWqF5fPfeFnbvno4v0uddebN+U5PHx6e/PmclNvfvijH87n83/1J7/z8P4D5zyHeLp8VJblcr6czxdFUZRFJWe319cRkwMA4r7OwaxYLKrVf/jf/mdfuouLi4s7t5cnc+ccY3LO+XKukErm7Gd99byqqso7Zo6p2WyvADrE8Obt5cWd+3NXNE0dI6Nz5axqQ4zrbj6fnZwvzpfhwb079dNHL18+ePXy9U8++XzXtJt13QVGkgxNnFIbWoidc+CJ596RoxMHWHpumr50qzBgZUjRpBdQlRQAQtYRZHPpXLyr0FicdHm6oGUTyZEvsrPidndjc6lwVmST8am2VruD0Ycw5otglAPAITOOQpuZE5ClrTpgObVVjWeYu3dKvyzhIHIM4PJ5qsvHwAV6lfQhq5Uxxt22UYIycBFEAm/VrJStfwDOEu6BguRdkIw7CcDIOUt3OwAUxjtUaToAOEME7FZURYSNOzEiiqnKguLQcjBAn2+O8UCO3F+h2bGN6MKYiCglDXF6UNfjyfhUT/vp4xoTAOZSOghSRdg+ngbDdQ9JRLQe7MpXhpU9pAApthMNeGjtGWmvHlE/gFzSGI0khIixmcJWEV7/1AEIrioQ1KNFR6WPqKCgZ7eW3Ns/7RJzNmXv8+Dz8/MJ0ZP3+nKmmyLlNPXMLFn/7XplmjDEVdvBCD7vMwypCm/3o7TNptaxOTfscfFlRE1MNmTUmprQFYYwRu8J7bLrAgCIB/YCYu9lM3kFvDNHNIw3hX12Mhgcd27bBE9sJ2yaXQVddEsffNd1y+VyPp9TTpJMRMvlct1e6SDAMOA7Z3eBYdusT09PObQcu7OL24uTWf167gB3Nx03tChP7pw9fP/Ro6qqCnevazvNAEzo6rpumpqwFJbECRIwEwAjAlWV/+Y3f/f+g7u3bp1yilXlkVAGZmafIDHGBMzzRVFVxbC0b0JZUYh1MT+dL6qyLHf1TSu1V2PXNJ0PWHcBABjZl9Xt2+Wti/c++Pq9b//ZN1+8eP2zn3/xxRevLi+3u7rZ1U2MkLqqaxsOPsaAULW7tF2nZteDT3JKQ64wmFLabG/A0BEi8o4AoHBzK0FjDg8oXG/yShzElVQAHrqe7giT6lMl5ZzMludlabcnZPrerIgfjjtcLpcyAEUamcJ2u6Z8MG9RSs+iKGenkqGmlA6a1lNGNd2l0mLASDG5oeyE/LrZtRazlRRKgWpx3raU2nmnYLRa2unparJDpHXtQCjtTxNbAmQNWNj9ZOdMYDhhwNZep/dIIhe1umN2p+CuEegREoP4ZQjDm3KF/bn88m1CmwCgiyP5I/WJnRmOxBk7k8mLjY20KAhFA5a0uITAhIQxMANGZhabOROhR8qGHpNIHHsNYBR+A5nqSZyFbhkdgM1AhOPTgcnqyMWuGcyGyRiWxW9hInBM6KylpErQ2UiTbKRY5UlWs9yHf8hn1bp55SfvKjAUf8AlP0rpqhxUvXMmv/pDFqzJdNA0yAIKj23CYZxqURmwlgyY+LJIuI4ViaQ13Sihil4cdJYUOsOHBDLnBkueBWmKI6PLPhpM2njuA0xobGGa9LP/aRm8fZddEfs6O2xdBWb2WEDHLUV04CLGiAEAgJzkrFGCoo81V+xPccc3u83l/ffv/Nkf/cVitvybv/mbz3/w+sl7T+azk+efXP7H/+VvL2599Jf/+i//9E/+rDxdlRVEH9vQhDYwR+9m1ekidFGUBuUBKaWu637rt77xJ3/yR6uz05ury832hpG9902ziewAgACclAlnRgAGPjmtdrtd0zTirBTbm7NVAdCVJRBFxNa72LnkXQKIzI2jRdfuErIvfeRws40AkBAhhbOz8lvf+s0/+INvXl3vPv30xcc//uTZsxdIELBh7jgxMDXdbrO7aZvgKs/MckAu+bB6/qdOlBATs/oYOuiFdGJiMAy4KFKMIfWZ+VRIr2Y94QZIIbYc+j0wX5b2iBdzLknvSl0pixCTuEm7qSz1wRx8BVjpBkOjBEvezTQOoNKdibkWhU4h8iiXr1KcLnCMnBIjYmJO3Pv69uOHvmwLAjgiR31iDR1GzPm5YhpS/UkyhJ69uaksz9nkfmx3HWxKAqSpRgWjCso25GN0sKqqiSWOYCL6YkCQYCqUKkgj4nhsVF+pWaqk18QDwRr9arKz6c1sRJBJz4kRQp4sekAUP1XC/ogoxR5FC19gDsObsAQ0h4WWyCh4Fc+1FogigKKE0jL9xjJIyWms5FI5REnLg+CCsdudNg2LmJhtY07SKQNToXbC+fSzKhyMGY+0aEy4Fkoch8QmlnkIY5s8klKCNJ+uLABmq4NlbNKqYpSpTR9JMMqbqHCezYYzfjQWe5dzj4tArEsQeUSR9OIdZ+d8iAETDV7iupQWAvbiHTvICgcWdO5Qgg7eY+2/cHsKU1Pg6Lt4T9SQufhi6Zpmt92sxRUZHTJzYKjKSj190PD8Gc+9L04Wq1BuT+7M7713a7dtb7ZX9SV/2j1bFItXn19iZEr0vX/4+P/43f/zv/3Lf/v4yfsffPDB6epkW+/Wm5sUIkHVdo1nj4jiSwkIkpJ+uVo4T6Fttrt10zUxBZGJFvOaU0yh7Zq2C11oO04REn/xbCNK/GKxeP78+fe+973dzeuTkxPGTd28Zpj7wi0Lcr4tfLlaULetO+wAsfAuQWzaJqaAriBInNoYtwxYlOWTx7fu3zttQ/fZz15fXq6vr6/fvL1ibmIMMa7JE2IRc/obYcCidBbFYIq0Cxw5h/owxTToE9vtKIBBF565ZmZ1vUEAR4CuNwaIuVvUUPHWPlmVaDSM/qmMLZYESBNvzImGAQCywWAs9qaUMG3RZB5Qw5QbV4HUB9ucgWuioBSlQyKUreUc5kPf+WxKECHbya2grW+RYZDJJiavaJtpxiISiX7sXazXSixgLL0WBm6TNmEV+5uZzBHAdrtVFqL9xxjJ5r1BYhg8YOEQweJ3xvseG+SEWHDWMMDICtK6lL3bxnG6dTuED9kb2kbGj4gIiAAOHQGT58FdDhAQicA5comG+l2WvNphTFaZTZAPZ4O2hbOd2rFc344Go59O5B1wU9NRMme6mIt/KEpDlg8s/ttm5zLafWmaD0D2V9Nt9xcdAEp/QLMEAF8OBnMdDzNz7MDIeXJOTERYN/vDQ0TNzGURBhFtlAFnyz8AEI3Ims4r8SjvkMKNjnjda5Uz/Uaubd10O7B9AUIwx9Pga2LBfmyV9+0l8qCFOe9Ry/1m0dXePEx872zrYD8+Ubc4rSRGReJwyrJcLpdpi5qlyJkoQJiF9ab2AMh89frmH7/zndNby//+3//Ff9p9/POf/PTV+nI1r89PToDcd7/30Q9++P1//Pvvf/0bH/7Xf/xHf/CHf/D4yfvL1YI8shioKCXmLke+N01T1/ViPp9VFWNEZE8EDDG2IXT19ou2bZvtpt5s6s2mrZsUOo7p5VXbtu3Tp0/v3bv34oufv3z2ycXZ8unT9xPVhe+IYDFfRQZO121TJyBfrooCu8h1rGPiwIxYABCSTxwZY0oRY/QFFSUi+vce/Pab11cvXr1+9sWLtoubTRPidUqpbjDG6L2fz+fiHikHwN1Y0le0CF2fYSeZM0hE7MIWxSHCESIkDjFwSqmNDZkjq97vg6jbUVmWlhCImxWn4fhkYgCxiKXjEacVGNuNiYihtaRBTesOOg2RtMFRlliDiRRCNyUZ0qpypm+0vt9d29k7lQha5ormhEnqxqE5i5Kp1WMLlXaohNJ+cibrlvnlP2lys3weOwNWoOFYG4umoL182cOT1TkIAKcbeLLJf7W2T1MA+hzTvKcEu/F6WZJhqbDh0NznD2FCdESE4JCQyKVc+NlCxg6J81kJG8vkZOIiryjjYWaJgxfNaR+xJYJft4yOc9fc2DUaphMPw5myq4Hq39I017TijATQ60vtgSgzyxnwBPgAwDFYCOtPGvdMpiEipOFIWEHHzMvZQo0BuolijM1uoxPZFztwLOUoHPZXQRQGMCEJ0B8BoBVQhrGlwWlLlwAA0pGjjf0dav88hLo9tWHD4YhIqn7BmOROYD55rzZ720HKYNvkzgnE9DPtHULprrcTV/bst/Xm6f2nT58+TSn97Gc/29ab2eLkvcePnv34pa4ZmWQdLW0BwHVVCLCm5vs3P7j1/vzf/Q9/9eC9u1+++gJ9w5i2oT5bFuBwNit+9MN//vhHP/zOd/7+z/7Vn/6bf/tv/vDbf3BydtI0LTpAghQTY0qckJA8ksebmxugROiapqmbLRGFFG5ubrz/InZts9k2221b16nrUHZ4cs+/+BRiOytoXroPnrz3jW984+nTp1e7V0VVJsb5fLVt2mrmQtcRupvNpqoqcC50ISaqqjkidjEQeue9IyocA6aUIkJi5jeXL8mnhw9PL+6eEpRX1zfLVbHdth9/vGVm7/1sNlOf3q7rMIPbqo/MfCwMydNg8mVmCfYNISTXOucYvWg+GHq/p2ZHmk8j5gwbRVF07ZAQAPKZPSIKYeK9loxbrx5PQg5rjiaqUka7mp/IcZ24I4qPRjaSA2TRMsYohpPVamV3o76rbhrItr7+nyCiKywr7U92OXZhyMTERlsqyyqNg9d7xkZDWTrNJwUAvnR8qB000ev2sfuKe2G0v4HHDGOy+RXIIrVYgiWwitnpBhEAk5qgAQ4QDkuPfsl2iIQBmKCDPE0UTHBU4SEGbIsc5NNxQsTEhAjIhIQJABIRITA53y+3ijU9/sPAa63EM4m7VbahKyueoSklzcml6t2EhsKYwUuHemYs32g5pmV12ASqpDOZUwNE3Gw2KSczEAlYuhXJO+Xzl5S93MHwGIv/293Gjl8/JZxMf1I4iBe0DknHvzqFYGqegnrvZ+/6ZIxbmKtCKV9UFJ2VgyZqEXhW9T4f2psApGm2do46Wk+VIpsiDAAU1XyyRpyPhCyvVZQTC5wyHWX/zINlQgFFRBCdfSkbae8d62t3uu6LCQR09WGP+06+t0/FHHtteTxnsolGwJI//184Hecetwy8vgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display image with different hue.\n", + "PIL.Image.fromarray(random_hue(img_array).eval(session=session))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Distort an image by cropping it with a different aspect ratio.\n", + "def distorted_random_crop(image,\n", + " min_object_covered=0.1,\n", + " aspect_ratio_range=(3./4., 4./3.),\n", + " area_range=(0.06, 1.0),\n", + " max_attempts=100,\n", + " scope=None):\n", + "\n", + " cropbox = tf.constant([0.0, 0.0, 1.0, 1.0], dtype=tf.float32, shape=[1, 1, 4])\n", + " sample_distorted_bounding_box = tf.image.sample_distorted_bounding_box(\n", + " tf.shape(image),\n", + " bounding_boxes=cropbox,\n", + " min_object_covered=min_object_covered,\n", + " aspect_ratio_range=aspect_ratio_range,\n", + " area_range=area_range,\n", + " max_attempts=max_attempts,\n", + " use_image_if_no_bounding_boxes=True)\n", + " bbox_begin, bbox_size, distort_bbox = sample_distorted_bounding_box\n", + "\n", + " # Crop the image to the specified bounding box.\n", + " cropped_image = tf.slice(image, bbox_begin, bbox_size)\n", + " return cropped_image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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9jzv+S3kJTp5dX0N83mHsvikVisj27NlTw0Fjd2e9Xq2Eg1hRbrfVaLRba4/uBQoliabr5Va3E5QKF1Zno7CbyxUuXnyjUJ5rNDrxsFMMhIgxfQmSnPgWT/Dr4BuhBz0t/0t2es2LqAfB54RREg0QqFTMEYpJEgUYJ0YDdHuDBw8eIMqD+zd3d3fffO27i/NT/UHY73SnapXKzMoQgmGYTE3VgG0h8JUGIqcHyWhwwkxnwUuDZ60HHX89fCP0oBdaBp3g14czytwqv/fg+sLUQr8z8Ck3VZkxhivFkjGdzd29G7duvvPd39jeuQc2TuJo+dTK/cd3iPsb64N8Lf9wc7MX2nJ++u5u58yZKhWmSAkiA/PYHENUkJEjeoITfAFOZNBTwLN2hRxbbxplcj2hDQ0Ge00iT0Hey1XLVTuAfqcF0DNs4zh8771fvv7qtTOnVlZXr969e/vh+mdzUz/a2NqdlYDjhhlEucqUpdK9R3sL184hCqIgpanpCApEsuIjJ/hiPGs96AWnj57Y8F9PfH5ZI6L2ZHtnM+f5wlQu1xcXlvf39zVxPl8Ewsb+bmKi3/6dH924/ukv33u33x/milOJgZyvaiWVDPbn56avXH3t+q3HAID05LL+GvikT/Bc8KX4Qc8630TpjO3leP2esvabeYxZvB7J6DuWOZ2jx8nkZRzXXzAqwDP+oftgMvgsCskYSwiFfJCEEQJ7ObWxseU3BquvfbsvPGj2va6Kw06umitREyLbbw/f/t73i7XSJzfvRYku52YG7Z2cgnKxfPvWvaXZ4H/xn/3LIcz+t3/5cb+6CjmuB9V+O7A68XxL0VDYC5U6fj+1oyF89DiZ9Xcy19vx+pEd362eNf7x6ltlzieTv5PBU6MMXlIG3yeLT5S138zzc0ye1BM4scVeCozXerpo3GKmDFlGhEoRikvsEgQREbA8u3ymNwjzlcKZC2cgwZ3dpLqwcn5hud1pXLx4fnFhaXN3bXd3v15ZmK6VP/lkzcZxp9Mu16Z1Sd/Z2LVKk6cXpqoEAug8QYSgAAERAfHpVXk7wTcIJ7bYS4AnklFlBCQ48qU0epq0Bx6hp0lr8hQC2tVLr3eHsQkH9YKO2ztbaw8bnc52J1o5Nf/GG6/lcqVuJ7x/by2Ok2q1MjtXCYfd5eXlK69/K9L5n31898F2o1qtVotaS0xi3NIRBEFFROpkKZ3gH4RnVNP++eBFm+fTisFPhr1F0s66IpJVA1cTCpJC9D0E0ZqQULTC8sxS8PCBTXpRey8ZdHKe9nKFXpy8/saVXu/e/fvNem1+fq5nDCcmevW1K4Gv5xcq7W5nuzXoYb7AamaqVilpT4wPBoUFkcElsKJCSl6wuNjLvh6eNV6Q+Zw8vF4COEPHvYjQvZQiQjnypdBqYk+R9lTOV/mc9n2tCftxvLC0XCoUd7c2w167PlWtVupCChF7vV6z2SyVShcvXqzVaiIyNzf3xhuvz05XP/rwPWPMpVdeYdT9fpdMlCejURQKSEpSdPt93ufpBC8lfi096AWP+X2dMBllH18OysjtRBAiQmKFFgm0QmstS9Ju7E/Xp7rc3H7YTIZxTldhINp0Hz7Y3N7aieLh/Qc3RbBSqPuadnealWptdcVbnq4khaBYCNrtkBSbqBtQLUfWiUAGAmASOXEGvXTIvK+/2ml8rXzSL5qsfHr8aTtOQXW1e9yWOjOOEyGiQhBrrDWWxMRhNOxzp6lylLCw9r2cr71AE5Ty/s52c21to1SZvvvw0f5+8+zKlSuXVrud3geffmyH6sKp2bV2c/3eDb+0Wq+Uw+FGTrFGUQQABMBptqrYF02tPq6t8azXyYu2Pl8QfK1k0NceaV7WSAzpz5F0UlghYBFkZrbGIMdxHIYhmWh/bzfst1H5nvYAFdsQMRkO4vX1zbeWT1drpZ3dzWHYr1Rq1uD2zt7tj27Pz85I2Ntc68ydrjfE7u/cVz+4pDGdg6Q+8q/2RJzgawT9ZTgdWX2Cjl+v53iQ7MZUx0Lm8weP5llgJm8oq+9VBp/omHVkPCwwG2FLBIpYITAbtgkTKlJaK8/ztNbjMoYBhCLIIiJoWKywCDKA0bOKDCdhLDrkfA60iXZ6O9u7D96bXzlfruT2Y7uyUHt0865vZHqxPhTv7larcHPt0uLiUtkrLK5s9Dc+efe9f/KHf1i//K04iqgr2Gr1y8NCvvz2t7/7//2bB1fe9mGvnYdXk5wK1cD3Cn3b8LB49HnIQKY+qLN4NxlcBM7gyxxTMmZd9+PqrU9L/836ntTR43Dm/XK8ukKAGTIhkweU8TUdPc4T5+HFUp6/4SAlpJAISLFS5HkY5FQ+H+Q9ymkMFHjISgzaGEwkSWitZWZ7GMYYa22SJMYY99l9AABdEM/H3e3GmeWr1fLK3Ozp3f1t5YWep4IgGAwGhXzx4oXLg0H48cef9vv99u6tYrH0aGM4MLS0XJ6dlqtXr21uaaESYw7JF2QESyhKwLMnOvUJ/iF4odfNM48dZsnvzPzjZ50XZhEZyCKJ0uh5qLWviNAMRyYYiFh2EBbRIiwizMIs7PQgkYQThAStsQiGSaEkSaKUCqZLhXKh0x529gZhEDIE1al6saK67Wa5VJydnS+WKyxmOIjCQWTjZLbEjf1Oc6j70eCVi/XlS6t9UA83ZflsXaAoFLMFAKMBlAHFOjl5on0hnl9e4QsRg8/CCy2DvmlgMSwWwIoAoBD5fqA9RQXxYLTCmNlasFaYJQIlgsAMAAQiAsxAiGLFWKNBWBiAmNkYEwSBHyz2esmrVy4Z7gqqR/cfn7twNZc3vXZjerp+9do1Tlq//OB9LFdfe+2Nux++X4b6riFVKXlRsVQqIct2Y3uIMeglxgJpQBEEIQSyQuIlx86NeCHiMr8SLwiP5oXFr8mDe6Fl0MuiBz0tl6y1VoQRhFmsFafugKK8r8exMBFxJhgzs/FEkJitFWABtkRoRcC6zl+AiITkvMaFQoHw3Ccf/WQ6T6vna3cfbTxcfzRTvYqizq0GG/tSKJUba1trG7tLF6fOrJyJd/fWb/XP/fb51tqjqp7fb/XurN+vnXt9Zsk4n5RSytXdJqKTvs9fBs86TneiB53g14UwAigRAGKTQEJGKUWgXCn9NA4lgojWWiJCi+OYPaIrFZRuKSKkyLqUMWAng/LlubmFx/u7m/Pz9tad69XpJe0V8/n899++cufxYHNzM+qF80unCP1BN1yeX+CeVIr+3JTf2+8+XutNr7zW7ofzM8rjSGPoKURQFhCIGERlOpIzka0HvVia0ElM/ZnixIJ/gYDoIXqEPogSViaBJOE4sok17hWbZPL9CVf0GCLi+mq4Yd2fvu9v7W8tLS2QxXs314CLlcpco9Na37gX+MrzdLc3sACz80t+Lh/HppgvvfbdJU91VDzsN4d7PZk9+9rjjZZO/ED3FQ19xQQgokSphNioFytR4xsIPCae9X6/5PYnMugFAoImVESa0AdAZrIGrZXYJONXlMTjl5M4yecwlkGTUEqJF1XqxXptrrkXvXbtB2GM3W6Lgvjho/tJkpTLZd/LsYVCvlitVhFx9Vr59Jna3vp2OV8Hr7DZbIUh2a7vUagxJrQoJKAEyCIYfSKDTvAPgf4y4vBp1YU5Po6t3h8LGeVTsvs0ZcyHM2wH5Ix+Wxn1WYh76QdCpVI2ECrFCImVKDLDhCODiWgDCIASu/lMXB0EQIit0b4PvheFCYPJYSLD/ebug5mpJdFqPYq7LJ/c/PDKW98v6QVq3vvs9l6/ub6/v3/jxo1Tp06JSIkuTE1N7T3SM2e6V89WPKzPFSmKf7p4bVFXl/Pbf3/hynIYdNb5fq70Sn+vWS2oQRRolXVCjzb5LcRHfq+z+ClZyOJqZiDzutsMnk5GTYCs3T5r19ix63ll1Lf6Ak0l4+uMxwxm8Ae/nB/qxB/0AkEp5ZbXE6psYsVYtlbYAjOzAAhKdvE5IhLHXBzll1lrO52OlyvvdDvddnuqVlK5fLex041bc9TvdtX29vbMzMyVK1eazSYiFgqF6enp2/c/Cen1QQ+13t/afFCqreQrtfWNW/Nx1O0NvaCIWsVhZG3CjMr3wB6v1+MJTgAnMuiFApLG8VMOiQUtg4CEiWULsYXYWmuRgVmIxaoMdx4RWcOTfxpj9vf3k3BYnZ7L53y0w9lK3WC839rRdZXP5/v9/tmzZ4vF4u7u7pUrVxYXF3d2dmSwW+3aZjMs10wYtRoPvKCba0Inl8ttNrtQryFgHMe+zqAp/8rjfcFCaS/afF4W/JrnjZ6Fj+plx7P24X0BnAByDh3naU6SZBjLMLFhwsaiZUcOYrCZhqrj4I9VKieDer3eVDl/7uyZ6elpmwwrAXG/0Vh74IFpNpu7u7sffPBBu92+evXq0tLS3/7t3/7Zn/1ZozVstpuffPbJ7k6rWp0eDBq9XuPyxStDCLZa/aFlay0BFwt5RIzjow2rEzx1/Jo+4KeOX/NwTvSgFwiGGcZ6kJMgzADAgswoApaRmQEwLeaa7TYZrwxEdFxqz/OU8CeffLL5+L432J4u4Nrubq+5S3JKax2G4Y0bN2ZnZ99444319fV333333Llz/Z4exM3rt98b9q9evXhxYbE4u7qwtHTp4YP9XkwlDFBZESYCm5FR+MXIXL7PKeSdNZ+TCPwX49cUQ4d80id8h+eLkf002aZZAICBAJQLbwESiP3iq+7YQ+NwmLUxIs7MzKycWmo/3JtaWE62Ovt7uyVfT58/AwC1Ws3zvDfffPPs2bP3798fDAY//OEPlVJx3xQrOWPj+/ceV3Pld354ZfXKuduPdjpWS66s80UdhNFQ4jBEZM/3gU9CYy8ivhoV/h+Mk9j8EXhGOuevxFj0PLHTRMgysSCgQkQirUCp7PlYa8ehTGeIKaUWFxfv3n+IXrBw6ixo3e/3A08vLi7WZ2b39vZqtdrrr7/e6/V+8pOfbG5unj17dm1tbWP7gVa1cnm+Nl2Yni3kczUxutvdNirvF+tCHqHWHrFJ/mEn53md55dlPll4Web5JXFii71IQCWHg2KuerQwMCIgKXD/xYiHq0wfBjMjkqvrg4jMTERTU1N3P3l/9vJKqVLL5csqCdbW1qIkfP3q7zfajbfeemtvb++P/uiPRGQ4HJZKpSAItrZue6par66cWvFf/9aFbkva1x8pz+jcrM7lozhh5iAI0AoiJEkSnNS1P8HxoUt+yKBYlGVgpZ6g2LqbgZ9SvsnTsvW+TE+0L7XfjD5WmTyIDGQ+grKow1m8kpRwMjFdBADQE9NMzTVXw5CPHqiIBJYSj9iXMOoWAq8b1IPZGZrb3l67N5/fufTa8nvv28dbxY3b62989/q/+sN/81//2//Ln/zJnwYF7Wtvb6v7ib37L/7lj/vDwWef/bvZfPuV07/1aKslpdnK/Lfv3vv02qVitReVknDBj68PHvrlVUhAJMo+Q8frC5bZoyPjeLP4WZn93Y4ePZt3k/GLzOyszHpVGds/JRfIcVWhrPlkjv9s+FB6qpyPEx6EJrIxq9J4xBPf0MsMBgQQjW6ViU2SKIlDwqBWmt1Y3/C85OrF1+Yq4a1P/hQ4X8rnpqv1Sxcu5/KiVNDYjbvd/t/9/Xtvv/3tH/3w2ns/++yTjz/tmP6bv3Gx09jZ2926fHlFTCuX12pAOSqJZRDRqEFO+EEnODZ0Pa+GBB7LUGzzcHADRwy3L8l3/JV4WeRaplzPDOO8WMclCJwG5hWKEWOTYa/fo6uXXkm6W/c+e2BN5+yZqbmp6bBx18PC9trW3ubudGWqWA+ModXVs8rzH6zdPDU3OzM1tbC4cvPG1lwtVy8Ftza2FqbLdmAiflSt5PSwlKNKZEWjJfTtiQwCgOOvn6flyDm2PvJsN/+y0B4Y0hLkVEkHrd6BBjQWQE9xZy+a2+y488na/rhHdVyK/PEHEgAEIEQmUJIk4bDdbUWnV6Z3O4nv69Da9bX7S3MwM1tlmzy4fc9Xvg3Ug/v3wwRPr9bnFpfnls/UyiWTsPJL5y5dLVWEJITErq4uJQMDQadeM/6uSKwth6giw1bljmcjP6318LzGebFW8/PDr2uLAYtCQA1aedA7GHGS6X+iB6XfZ/3gRTsuEkEAERRNklgTD3vtXty+FQ6k2y7kFCGtrX3W3t/xpWm5dufuQy8olGqFO49v94Zxp9ddDoJrr1z4+z/5b3v21dv3h439frnfB+ko/0wUD/Z2WvlqOxr0FVWBjVIqn8uZWNkXTB98XjjRg44FbUgDixXgxCB6cKIHHX/748rop6UHZdmAjAygAAgBUZCTOOq3u4NIJ8OZfFFRjsgfRuHG1v0c98rvLO3145+/9+H3f+OHc4sL9/7+F9Xa4sJspdfeuXXr9uzFswMsFGdqvmysr+35tdmwRUhBGHmPHvVU3hcVeX6gvYDZvuwy6EQPei7QfUMEwJbHRflPfNIvCzLjL6NLpxBRQKxJhgOQ7unF1Tx5Udit1xYqlblkcC9q9fJehfLRo+2dc41Gbar+xreuzc0sJVFz4+EtoZxfmq4XF9AoGQz2W7fDzsOaWn7r298xa/jw/nD6zTz5YQQq6UWJTXK54/XVeNFssZcdz0sP+nVtsV5kfUIExZ8b6EAVOhFHAPDi5RZkApkBSNDF8JnZ2ETiIYi/s9VSkTmzsuwXgm7L7Edxv8vb+82V1fMP1x4Hheg/+9f/8sHdjf/b//X/XKmAbSPqnFKVtQcbU4E9feasrl488+3XPAWbA9re2J9+SwUFS5iXCH2K7EnG2AmODx0PDXnkK9AoxVxgrDWGE7bMAEiCiKiQn068I+sezupfloXj8oOyx8+qq3L0+MfloRwXWU+TzPlThm0oxbzneZ7X67TrldzavfYQi6tnz68sTt81PfaXN9Tszq3HmCjWEWszVQw37v1ifn7+7de+3dpeX3+0kc/Poidecr80CEPeb7SGj1rh2RL/4FrJK/Z/eTv4zNYuXpspRA3bVv1cEnjUHMQ1FTyV85CJjOM9Lp6a/pXBY0LJqqdzNLLKsFBGQiCTEICIRXH7YhABYBxRjidLaAJA4PvjUnYwQbNOMvqRZdXPenrG56Hzo3OBV/CVVqAA95MJctyJfvsyg5njOHYkaWauVquFQmF/d29xbr4bDVcWF/NAvR2eq11mwk6rvXr2wg9+47fyZf3ZnQeMhddfe7PRWps9M8eBDkhfuLCyeX8IUcScEymR6ZVyvlLIQH5QQFRi4qJWL5w++JJD5GhuJ7ui40CKEAQBiQQAWMY9BcccYwAAMFYOy6D09QVFzL5K6FygfK01AiC7BGtI7a+vbkE9a3n3DZSnrvSHImLmJElmZmaKxcLt99//jR+9MzdTTcLu2uOHPsTLM9MLp+e3H95dPXPx1LlXHmw+vL/ZW5ydXVw+k89DO9z2S75CGJh4bqZa4lzM5d1dsFGrXpnzPBOzeEGeLEkUF3KBSV6O8/y81tvT8rEKKEFBIYtCiApRgFHUOEaBhxUrY63L7JHR9NwMs8ouPPv75dD4pACttcM4GvTDsaSEw1lLWTlyTwvP+IC/cUBEl3PjmvAkSVKpVHzfR1S9dqsWUGdnM4qGiyunOoNhGEe/+zu/s7yyut3oN3pcnTutc5UosrPT0w8frJONVdQeNB7PTJXrC0vrXfOww9GgXa3kc762SaSU8pVWwsp+45Lmn/19wUe+GIgBgQiEGNAKiiADjpWdcTXxdKKkgTQqj7RP2kfluW+e68k7gI4Ni4ixCSeGxdUSHQuGr0g8PGsx9E0Tc0TkUue11oQ2jmOl8gBy6vz5JBpsr93rtbrJcLC712hubseXZy6/+fpP/v7e2v371qN8Ph8Owu3tEKZpZXq+YJIk6Xm2b7neSmS7b+JixYsHU77yEyEbKYoV5JkoNhaz6km/YHhZ1kNWxwmFAgAoAsgojAACjAKHjOEJnSszb+659SM7NL4OGUTAWLIyqlADaTjsJDr/ksLJIM/zRAQJXbONwNfzZ06H+7fv3PykWJkFyzev366oYY78xBiQZHFmShV0q93DfH6uXtbU/+6bb5xdnBOIHzfu3n90b3p+tZCfancavuezGC1hxcvnIVI2QhVEiXjPq/XBC4ase/u4tliW7FDAAKwIURgVkAAhAoCHMt7L5Hto7RMGh7vTUR8dQ/iKb3wdGWQgy2CdPAWA9CQijOr1vWjPjafXf/LpjP+iwdliWus4HPqBYuYwDKv5KhTz4U7U7e2XS5Xzq+cZvfb6+/391i/ff9fYYdWz+3vrrf1Gubg09Dmx7dlz86WpQqVQLdzdsHutgKRgo1ann5+Z6g+HJe4tVCqNIW8OhugVRAUAX5A6/wLhaa3nrHEyadLZ/3Hk15TRuINsgoiEoBQpBE85CSM5pSe7OY0LYGAik34Pp2mIQJIh46z9Ste/ThitgLVgGHx0ltjBf59oQy8jcNSONY5j9gIRMcZore+vP+Z2a2qqurmx7tdK337re7x/e6pYfrR2p5D3o2Hr3vX3Y2P7hfDx44fkJ5U67nVKnl+bmqmfk4oNk+7O45XyXFwud8JYMKwXvWIiksToFcEvgHk5ZNDLgmxZmSggAvAJPUXaUx4pRMlrHEsfZmZO/0QiRCSisQxy4qltMtkCz+iIjoS2YAFBaVCg2CKM8h0RABAUAKDYrPLpGXyNTH7BMfGsn1fH3f7YfZ0ytn9aOnkWopjKpTxDxApzYhZtx+w0294w7pS/PXd2/f2b52Zmt5TdCxub7Y1K+dTdXzz01cyplfNebnHz0QOvs3f6zPx/+p//kz/+s5+9q4M3Xl+8v2ZarUE1p3f7O0FOR/FleXgjKe1sd+viF/Pl+narP1+VXsbalWfcSvN56elZPCBBxol4zvidsvrQIcPIw+0khdu+pIeISIhKoSLQ6Dp3QzIwRKQVKR15hFoBkijAAHJPGGLpStMj0QOcWLEM1loRKNueFWQhwzqxZFixKCsoGX694/L4snHYH/SUBj3BCwStdZL0w7hvLVgrw+EwGvS84XBqunZ37ebM3OzC6TNb9xsPb35aK5R6fbMwf+Y//Lu/VT8ovPLKlSQZsvX+xR/+688+fa/T6UxNTRlj4sgUSmWFxiYJAvQbGwv1Cmjz3qf356+dziFP1SohxCcZVA7ZNlqWzUXwuSgbABgRJSAoiAoFjALNgCioNBK5NEUhBAJCAHSUSSdu0h26f9JeCUQg4iMYBqWJGXKSYwbDCAYEkAGYDYn6iq+jPvxMPt65y3qaPy2f3An+wRARIiqXi5gMer1eNOiVjUFpbXV2c9NlyJenquFnn90Iyh5h0GpEIF6r1Zqa8S9cPHfpwpu//MVHVsI4HA56HcswOz9/4/ZD3w6IYGtj4+x3zq9MLXa3/YePPl2+RjBssZ5pQ1yEZ8yTfsGQrX8dKkM63uwL+hWPt5yUQYkBC0AEACxICklAEJEoJyhMwAgEYAEBAVGUOppjzMaO3CrEgCgiQiKCEjCDse47yyLMwmAA/oEN4/5heCH0oKdlKz1rvCzzNMZ4CpRS+Xw+7DaMMbVabXl5eW3tYbE8tdbqVXtRfapc8qLNtXvF6u922tH5c1dKpVKn2/rxj3/3z/74Fw8ePDa2X5uyxpjd3d2Y8xubO9p0z5xauXTm/PTpWSv6estAsOhrlQe7H5tgpoSdjJyery334oueqZMCKLXFMmxq551xXjyYKJvDFhBBC7KQVqiNEBGBICIyoogVsYIKRAsiklHmYI8Tp4WIJP1OBAAZGVgAQQhEAAhAEBHQAgIggxwtg57Ref5SMihbD3o6/pETPF0QETNHSaS8qN9u53K5paWpWq3W3G0HhdpushkUqzubD+pzU/3GPcLB/v5+r5MsrUz/9m9/76c//akxsLKyst/Y2Nm6USkV91vtAWi/UNy+c184/s3f+wMsVO9u7283ehfPrra6rUJh2u6jHLPp+9cAv9LmekIPytre+Vkm/zeVQaBIMAFRjABgGRTBuAw0ARAyEWgDnkIi6qf3o4wjYO4fpQkAhJFBjICxlplFMEnIChq2iYWExTAIABNmlP9+VtAvglx4Wj7gLDytY3zWvuqnBd/3xerEEhHFcVwul4MguHnz5tmVV1o7j66snl5cWP7Jz9+9ePbU0moz7Hf8QObm67/3e7/7/od/s7e3r6m6MD9npd1tCQBXa9Mzc5cfbuy1iwVk22zselzuDzqer3LFwnZrbXphVRq5uN/xIf9MjysLL74eBF9ikpOrwn1m1IJIAgLCLARiWAhSSjSJIIki9FgSBoXiXOSjHaWpYQCQ95xEQyvALMZiwsAsJraCxAJGIBFhQEb86otAvRC22DcNz/qeca0N8/m853lRFFUqlWIx/+6t22fPvIJxZ3FhtttsYX6utPB6kEQ5VapP5975zm/97Gc/a7Z3ojAJuRPWSiun63k6R4iFciU/NTuMzLVrr63MVbTCe4+arcHu8twcijEaOCgUi1Na9TEr1vuC8cueNT4vd1K9JiOudOR6EBEkDwAEWFCEhYEBGAWEAAWNIDIoZBbUAkRgrIajZFnoAwAIijAwUGKFGRiEExRARmQQA8TOLQ2SJRSepy12gpcLURTlfBaQ4XDY6XQWC4WVleX6g4e7exs5GfCwcf3G3ZVz36ktXN56eHu6svQ//Z/95//Nf/3/AvHr0wVr5cb1z4ztv/29izvW5vP5wXDY3Ngw1k7PzCVxb3tjM/EXCGT19EKzF1I53+pHJkEKvlpm28uD8a2b2ctowk80mbBpUJGAYEoCYBlX8tIolkVQCIiB0bmJjFUwEZJ3OQ8iYq1BVIwggABoBVxSBIp2VT8siAFxrRAY8SsWChonxdBx+Z1ZtUQzev5m1eXJwhfHMo/a79HPmS+IRxxrnCwcd572Gad3YkHpxBtiqLlzpiCl0swvHm+dv/Bmzqx7fv761gbWT128dKnXXO82rudL0wnO1Wo5rXW/352Zrs/N1gi87TWzeu5qeWauvbk2o4Ifv3lmOFB31rdmFzyp4K37pjzbm6uS3UJ/er9THKx5xakka0ZZz/+jr0t2v60MPk6WX1Ky1tvR42SuK4zdaIhKAaLztwgZiOCwzeVu/tiKItIImkQRaBBkC8jWLx/mELJbaZw1TxNxeoDpAQFqAEBJBMG1ozOgDEMUAwBMdL3EiXdI3Hme7FqHAAjW9WsTAEANIwEngBl1teSY0ulL+hxOGmN+DcFswuEgSZIgCIgoDAdK4fT0dBzHnsJywctJOGys9xpbcRT2o3hjc61aKyHCK69cqZRrP/jBD69de5Utriwuzk/XyvnczHR9tl4Tjn0NxUD3u/tzU1MEFPV71YqqV5WYyIfc8z7uZwVBRaRJeUopRAJCEXHVj4Vw/D5+aaWIgBQ4wqEAiCtVyjyZ1P6VuWIlA1/N3n8lnonala03vRwx2mftr3nmPlR2edUg1kTDge9Zz9MEuL/X4FyU9PZ7SYONNxwM4mjYHSZ37twFMK12Y3BjuLmxW61OnVo52+0Oi7kg53lKbDnvdVQU9hs5jxVE7d21pVMXtHiDfvv0dCXIhzaMg1weMaOY6zH16+yzkzVQ1tdPZx2i8w07Eg46v4oAsFIHPSBGWwIAaAQkd3SjeoZAjKl+/XkRkB27yJrP04rJPB0+YBa+5Dx/LT0IM/DrjHmCXx8KpVTI53zd7/X2d3fjaCjA21ub+UIuDofRoBUkfehscmcnp5GCwvrGY6XUpUsXGo1GEAStZmdmZu67b7+zu7mxv7vd77XCXnt7+3GnuVfM63Z7t6CND7C9trW9uRGGDd+P84q0eel16qz1jIjIwszCFoURwdPoB0prUgqVQiJwL/enIiYQl5khIkbEiCQTJtik0+dX7Pdp3F/H1YO+4vv6RA96huM8r/GtjbUONKnQJvv7uyI5n2bjMKxo7MWxp3StUun1+939Xd8PEyMb61tJLJcuXRkOh0ks9fpMEKh8wb93Z3PlzIIxptPprK8/7nRa0/OV7bUdb2q53drb2trLY393r1FfiWvV6V5kJTMvPOPrl0QP8hhEBMQiikLwFWmttaKE0SWgM49lCgCApKqHMDMDuZtdRGBEoZLD7bOyztvzykPIXJ/HHP5LrvOTuNjXEGxsaEIOuBD4JolajeF0vV4OcnuPN8Nua2G6OD270OludFp7U1MBIxHm263Bzvb+wsLSo0ePzp9fbTS3792/reMwStjzC6G1YZR0hv3ecBAl4dbduysXysunFqq5qd3Gg+JG2y8taomSr2nafI6c1xsVilboeeB7oJQ4GWQtj4nODolV1uk7iMzCqchTMMpxxQk3NgDwMW2i4yJbZh1v+2eEX0sGHX+uT+fYnjUn8Gldg+fl9tOKJDE2tkrEU7pUUMVSnntWooGIeMVKUJ0RanheUK1W8/mg2Rh4nva9/UuXLly8eJHZ3Lp1c3tn88LySsIYWQkT8fKFvVYbHj4olQNrcHZ+am7lNET9jx48KO1xrqoZWwCFY83zRdODsuZTUECKNCqlRSvQxERMCIaQmS0wIzOzE0BEFKJvBI0lYRYQAGQgAAKOYWTpTH54aunoGci8L465/TOq6fHS2/An+DyCIMjlcsw8HA6Uovn5+Xql2ul0PA0iMoyl1Y+b/UEYG2buttrCXhLD1tb23t7eG2+89ulnH926/Qmi7Q+T3jBu9wfDKNK5XLPVuf/okc4H33rz26dXzxiwjWa33QbPn8rli71w/3kf97NCzqOiplLOq+SCck7lfQqINSSBgkCBT+Ih+yTjl/bIV1qrFISaSD9RZ/5Z+H2y8BX7d44LTRO5tsLHFEnZgvTorzP0gqy+XVnIPntZ+tGLVb/mqelrGfVrCslwL/HrtZl+40G+HFTmV0ql2amFiKZf617/4JVr70jt7O6tZulUksuHvuf1B80wHvz493507uLqX/zkr4Pi1PypN0CUKflL9dKm5bXN/qXzl77zm/CXf/uLj/72w//835Qv5lbuPNI/eWiGF7+liu25cCMXXaB8JkEo43iPjqOh8idPyPi0EMukEjHexgAfeV8pVJM+4PH/EilIH7+CwCjgGEMDqRCKQlYQKUjyvpTyfi5Q09QAIBASARFkS8wsAgmEhOgjoKcmZ6Vt1xJaBGMhQZ1YawVYEPLj6cnkWjWsRMTlc41q1AsAaTr6+nIGgSpTe8lYVpRBm8vaPqtWr8lQZdSXW84n/qCvIXLFQsnkiGBvb29qaipM4rUbN+Zmao1W3zXYuP3owdnTZ/KhmqHGsNc3Vr7/Gz86dfrS9nZza7szO7uwsFy6fv3G9uaGJmWMWVhYsSYu5nOWDZLy9cJmO048zqv+9NT5TnfXzgXKbz8tW0zsYb8JpMLFOjaiAOJB718REJSxO+bwOAYRCcc2WbqNRdfeXACA0urphABFCDWhIuOR9ZBznuS19RGUzgEAiutWAgigQIkIYfo4nEgQFUQEUlowsQgIYpERxCIySgY30iMCAKuALbDLxGBiBHk6rUWfObI5pV8Kh2TQU1PPjme2f+VFtL/uaHe6NlBIqt/tfOvS5b12/9H69XOnFlsApUopSaL19c0Ll6/EW7ueKGvNq99649z5y4w5IvG8KEr00sr8hSt457Pm1HS9a9QAgmJB5zRdvXxppWgY5h53USQpqt6ZmdrO2t4AlBd0EvN0ZBDLKI8cx5sJIhKqzydDAaLgiAp42N2LNtZKa60AwBjDzCCASMyCkkasGZGICBUSVaDvKe1r9DQFCrUSX4NW4PK2EBFZiIQRUImI+CjjQ5j4AIg+syiLaARAWMbqVoYioVBEUFAUGJE0I+ylEUGZ/T++pGx6IfSgZ2+aPh/T91kfV5bPVdALPA85UijlSm1tvwMAw0EH2MRxvL2x6SkMh8PNx49VZXj61DuvvGY2N3cTo6bq80Slnd1mudaanp2qvHZNax0b++6H7/6rP/hRTsmb33rd7+82B8O4WOm29kuaKn6079vWoFPUxbY93vFm5UaMq4CkB5jmEIjSh/KhxtATeRKTRlk5pzxPu/4iUWTj2DIzCgL6AiIgQoCgQAkREqEG0CRaK89DpUkhCkrCzEyIogg1KiLRxMgWAfWh6Y/lkYi4SjzMLBbBQxBki8IZNguyFQRgsCDIgCwMgkLZPoSnU08iC9m+/KOhjq10HMKJHvQ1hA4KABANu4qw2enEDJcuXQETNxqNIg+E1eKZV7e3tza31k8VqufPnvvgbmP90Xq5NEMgWuHplZW8H/Q63aunl5Mw+uzGrc9u3vxXP/7+8sLMWsfef/iosjCsn/r+3t72SqUaRru1amwGjXxhqn3MeWatN61x0hN04A+SZJJiN+FdFREBBBFxfYwJCRFrPgcB+D6KQOxRHBMzIGKCJCLGsmExbFmYwQJDBJCAxDFrId+6cUGENIaK0FMUaA4U+iRKo0L8vD8ulYNgCEAJorAGMMAKEAFshv/O7R1co0IGEUZBFgP0cqS/YGYe35fCiR70FHBcrsBxxzkuhlFM1lI0LBfynW4/X6jUSrnmg4+Fje8pjTBVrT1c3+Ak9j0VhgMwplbKzy3U49iYBGfmytaafq/voT89XVd+cPHiReHkyvkzn/zZL5rtjppSs7YrYYgzU51BfyrXLxC1hY5/HbPOW8raG9OJ3ZlRHE0KIHJWFCIhC0iaaSxASASEgFUv8TX6GpRSEijmANKLQpY5tjZMJDY2NDZOLDOEosRyZBmNIhREl+GFRc9qwpxmFFSIHgESoCtFOD4SJy4dj8haYAEBjWgRfUIUYULI6B+vCEWELRpiEVYAAkyCz6tx7bFjLBky6B+iB53g+eL41z4j/uj5gVZe4Ic5P4wSf2q60dhr7G4vzc/BoMnMnU57qlprB0EQeI8e3Ntdf2yTsLn7gEF63dbu9j0vKJTLVY1cq9VOnz4TNPo5reamqoGnZ+Zmfc8Lm/dLuYHNL4RWT3e3L54tfdgdAFSexmlIfdLOnTzZZyKHdkRKRiIiItfM2jXnHMe/iEgphYg1v6u1eIo9zxt3tkEEYiOARihhjIwaJhwnNmFu2iBJbBIbaw0AAiMggWBiCBQaAsNpcy5kBmQrPhwoaxOPEDYIRCIE5ClCJKXACpIcHTck8qwIK9DCiRVmscwiGHMWX/nZ2mLHBWWsw6zpP4ETGfQ1BJEGEMtJv99NhCr54u7j+4VcfmamtHFrszfsDjY3T587txEEpXyh1Wo1dnan5qqN/Y0wGTRa7bXHO0tL5+rlSqmQ63Q6hvnBgwf42opYUyqV+s18Pl8eNO9Xy0XxgaEWt+3pqcrH3Q7AwlOZv3U9/IiIFBEQpZKo7AdjZ5ATNNqxcMRMxuDdfxFRBRURKhKNrAiJXL8tJhgiaSBPyIuBEsbYgGGrY28YxeEQ40RESFA7/0xi0vpeACBiRZjFIsrYtnoiAwuBCUCQiEgLCiKBUpLpZVZai4hhYAalrGFgFhaB8Kmczq8AvxbJUh+urXO0fD0+byXD/58hFzN9WsdEVluzp9cXKatvd9b5eTq8p8zhM35QzAmHZrjffnzjk6WV+u7jcHG2Noi9D/7u380sz9WnqxcuTEX97v5mY+af/Mho2dleEw3V6bOfvvfBcIieXz+zsvDq+eWf/t3fgLY86BXAu/7YPmxc98PHFRokG2trZrp6+Y1kY0fv/+RO6/HDN19ZqeTv7oZMxOgLKgBQKBqNElF46F6FcZ2dWClAInLNswBT4gsp1oSBBzmfS77N+zrwRBEqjsZhr0nfc2SSySxQJ4CIqKwVomvwxzjRB9mjvBNGIqFizokrjCqGvErARhlr7aSHW8OB72mkl2kA1JZGB+X0oPQY41F/EVfyWSMDGAAoSHLk/Jn7iGhJLEtsYBhjZCQxVPbYCloGI2KEGIgFGdHPiK9lFYLO0o88RS4PDiw7qhQJIImQf3h6KRR6iKiRFAEi0KiGOGF66zEQAwqjFRaRCA9kLk30WczbiACJQKEQfNUl007wVSCOYw2EiEmS9Pv9XKne7XYbu7taa2tkOAxB6NGjR45LDYDNXuh1BuS1CwqjJDp7dvWtt7//F3/6J5dXawiq2dy/cOH83t7WvC40mq1yfmqg2euqqL3p6XyxWp+Zqz54tFGcmg70QiJsIWERItLEGkWJjB9zqccW0vecBkVIJApJoTiSDgD7FCgET0GgIdCQ88RToFAUybhgO+IBK4fcvTSCS2FHFIWCCAhMSOnzAJ98oE7en3mNzMikrIWxz5uIEOyRssPAk7U43Dtn9AR16RzjcWD0ECGlBAUAUYkV9JRYIlAYCtCIU4SSEqUU4NPKmThw9hMBiCJSgEiSI5g4w8CcvhJ0WbtECglc4TVGAcsKABhIEMAREQBEUI10EURHoEj9dIFnEVG5b4W/ETLo6dnJx8s/elo47uhKqZwf9IwJwzBJ/JLW4TAMw3BudjZBjGKMY97f311cmlVKxXHcHMbbn13/5/90dWVp9pUr50+fOXvv4aNE+cM+xwm3O8033z79p3/ylzPz5/f2Wl2FteV6f21trtL8/nfeUkptPb7303d/8S/+yY8rJJGRyBoB1kp7mjQIifEOCLaHKM45JURAIKSsRiJX7UvA1x4haAIPWZH10BIykmga03/GLwRH9p80hdIbXEhoUmocSB9kxNSF416Oh1RSyIwixKPi8O65rSf038m1NHRU60NKEIhIVgKYSWhykPFQCMTIgmABWJA1skVExJgFGQRIkFEUpOocHjM2n/X9KIqXHqki0EhIUlAJEU3IIHGvARpEVK6XGQICiwgJmIh45JuUA6EMNOIWpMFKIIUKEQPPyR9GJJBfr7fPF/zimNs/JWS68L5Zsf/BYOAXdKPRAIBKpZLL5Xb228wcRQn6hWKhtr2112w2i5Q0mnvx0HqFmgDFUfSD737H87x/9z/+6VajU6rNdPvJIIyGYRhF4fbexit8VtD77LM7r0y9ubo8/+qlU0UVdUw+onxlatZHW8tTlJhhYgW074PviQYQC9o7VKpi/KGoI2c1aQRCg6Paqz4KACi0CKLQEjKhALDn45F6kE/oEoFGF1pciwlrD6ktkMbwYeS/kNEgqXgqkZ2UZc6LjQiK1NFcAe30LxyJofSmVhnkwvBgHR6iOAmAQhRkBGQUi2QVAAMRAItCcDerBQYAQsy2/f/hSC1KEFdMX5AmTnH6IgAlggCUsi0JXNVmtCTEaNx2aVAPhUbiBQHcr1CEEB1/HRAUAn5DfNLfND0oCIJisSgi58+fv3r1nCnWkkFck7i5sbEws7Kyeu3Gzbuer7qt3Sga5rW3tr4eECjth3F878GDvf3m3PyyVyiVK14cm4Tl0dpjQTUIhzOzcx/98p72c76N6kWtwQae2tzdO7tyOorNTBWGGoYaBcD3wfdQAxAjp2rQE32vpKQtIiskItCEhAIAyIISorhHpygQJEuAgOJrBZ8ziBDRwyd9w+4ONzIhgNL73vk/AD6njACARwf+lElJp0ay6wmzC4lG8gsntaGs5TbOxxy5kNJx2LKgAIBGsEgKrQYCAq1IiNCKOOEkgigwtlm/NDL1o3Q26XEhgAVBkQGqyXDk2BbTgkKgkBAF0E2ZCMDD2HGlGYgRkVkIlQhCyilFQBAg53ZjZg4FUZMShYq+GTLomwZrbbfb3draWsp7zWaz1xkqpVQQzM7MTU/Pl4r1JOG5uVkpw+zszLDT1Ijf/e7b195446c/+dvhcPit73wHyd/db5YrhdiYQrEi6J89dzlKZHZ2pjpVnZuurd26vrOzc2HmlPbzUa9dPLfQiaMp3wYEeU2C6HmgSTSIAklwrAcdmEsAkEMkBRqBCAiZAAGZKM3zcrXjSSFiej8E6qAv++QHOrjHUh/qSFQomLgDRcRV2LCHSogdjKMm/D6T4yvCCVMLxnKHEJ6QTe5Pm/HUMIyjjeFgjgCGGQGZhAU9ZCbFCgAwQGUYABlYBFCJWGbBbBmXFZTJhkz8ZixcLQOLEKXXa2yL+YgkRIgKVOpuAwXARSUAVoDYnWNAyyySynoRSDufATupDmwVEYFoVBrVN8IWe/Y7eLH0oFwuZyOrlFpcXHy8fqsN3tULVzrtvSAIELydnYZJOE4GZ5dma7Vac2fzjasXvvfO27fvP2oNbb0+3+6H1y6fbuxsxpbyxanZmcXp+pKna2G0W6tXTq0uYtRbXV0NitX19c393sPLq4vnzp27f/fmAiZaQ6CVRSCyHooGSwj+QRLDISPCuQWcHqQQCBiAEMALUqEj5FJAnSxgbQjRdag5UFMAgDGZ1Hdcm9nxzkZ0RxEQGDGq3X+4H42YQ0B4KP394Py7yYxaMB8cBePYvksT4RFFRGfkWPjaiUgRAcYDMYRKIaIQWCREsEIiqIA85SlmRGTDlCa7IWSQrQEgKx8t0x808lu5Axz7gwI0k5rmSA1iTysiUQSeYz8gKBQA1EKMAEBODLEFCygiIZJIKr8mL33e8xSS0qQJPfXlZNAJXi7EcYxG6vX6+fOn7tz7uBuHuVzuQaNRD0zg5yLrT0/PfvTxjStnL9XqlZmZuT/4/dP/45/9SQJeuT67trXX3t+ulwvlYi5OhrV6ZRBppfNJPETS5XJxeWU+7DZmF86UKvVcoba9e2N+5RQz+5VpJYZIEYiQAFgFopA1AqlUJ4fD4XlEfyyAFCASoTAg5HxPEBCRMfV/pLEhe3R8CgjH+gwAgIijAuGEb3hSiBzwrw+PM8FjPCxrCAEAR26jMTUU7Wi0w37pLM6eQqfHuaRcdMEucbVEaDRppTxGYQUKtCgDkApoAuWElwhlyLjjuj1lnO8CqBRphR4pJCnyEEf+eBERFAtWUNBjIvIUKQV6FCIgFM8iAAmiIAmD0Wxd3zJytVOcDDuoop3zfSJSSEqhJtBH9lf7dfGcWObZWkoWX+noeUrG9vJFj6Cjhs/k9Rzv/GTZ/1nXy/NnksZ7haDVK76+Gc+9cX755i9/FtpBB2ufbGwWawuF+TPB49bC6bc/+OTD//J/8nv/+//df9XY3bh27drW1v1uu+1rff2T99955x0YtpZm54e9fQhV3I08v7syc2qjoFZe+d2Nh3d3t9fe+t7qJ/dy3WF8Ku4sSxOb/crypS6VYwCykQk75OtY+VGSOmkR0yAMgLuVLQiLAIIiAlSgyUMEoMSZV3rsk3E2UR4mLAbh0QcCH1xd50MXCJU6ECUiopQzqdCVfz6w6UbEMjtqIvjEhZ5MsZjUKVzbLjzsDAJASI6ui+QTA4CgCE36lYAlJFJCCEIM1njWGDbC3LOCwsiseJLj1koQAFhABC0IC4ogA5AKxktifNQAYDSAkHUhO0FBl5oGnvJFRKFoYI0QIGoUIkTwFYEm0Ip9AkWgEFAwIIvIiEh0eAF7Bg4LdIc46R15HoQFwI5/cqIHfQ2BwsbGiPLw/r3l5UVNkPO9Tjeaqa2sbW2WS1Ulg5xOdjceLU0XzDA5derUuXPnkiTRWi8uLsZxvLCwsL6+/k9/5535hbnrt9bLZVIe9wfdYqmwsrLYajWHw2Ghnt/bbxpj8sVSs90J9xvLZxcTE0bog/YBAFGBeNakfgpEFEnfIb2Z06U88tECMzsx5bYcRVRGf074hp/KiTrCvBp9P/mBD1MKJsXQ5+dzoG19Dk74PuE8EudCGQloZ3A53UQTiwi7Xs90MHJZKRERIJc7woAsICKJnZzGwTuAFWAEYEQStCIMoAAIFAIrAHK1kURYUKwQiTNRFRIp9ggIFCBrgLHpeui8jaX5SHtKD8c7+rTbUS6OG+PZ5M2/cMg4rszjPZ7e8aIBgQfdjrDd3dlaPXOx/eg2sNEgi9Ozj+7flrgRtcOCHjy8+8nZmUt3b92Lomh3d3d6etrzvGKxqJQKgmBxcfHM6rLv+83mvtLNYdgKw0Eulzt1evHTv/ks7LfLxcLOzk6hWJ5fXEo6uzqXDwIVmmHIpKkqFtAq1n6UWJVGxg+5e0EAyMIEncQpMojAStBFgMZCyP3J4C7NWOOQNCZ17OvyeTmCeGC6jRnP7k+TbuDusQO9TNg8Mch4qCN36uo3PiGAAECMQSRx0nd0J5OA1mZyY8SUmpBDJQyOR2kZrLAwWoBBfGja4w/uwCyIMLJKBRYjKGYkJBSdFnqzrnqI1kAEHoGvQSvySBSJGiksYykzPlFqopbbON8YEbyM/sZWuby59KBO9KBj4LgyOnPrpyTrs+ajEQbdLoTDfLlcDPzNXnfQ6xJCe3snau/HZWwPh0WV6+7vVctvPrj7eHZ2djgcFovF4XC4t7e3u7tbLpdfffXV/caOFbO3t9fpf7q9vb0wWxoOQs+nfCGAxGs2m+2hUHlhb79p+l0MI+WD4SRKYlaxtoAWlEVrlPJ+tQ3rnJeEzkqhST1IJL3/n6h9Mb7HaEIGfZlr9HkBNPpwoKdMfmA+4EzDhNHxhPrzK+eglJoceTw+EYFroCgHMpqQPMUwIdfGUKPfWgYrLpOWRUTBQex/ckradZR2MgjQphQpAYsKxzKFYeS10UoUkqfE0xQo0Ao1MSEoUQcGrHMViQCAP8G3mORecEaxWKLJZ8aJHnSc7V8WPYjEsgnDfvvUwulmYzfn0cBE/V5rc+1+Je9VA+rsNHLFqg37ntJ3Nzdv3N8+depUt9vtdrvD4bBcLv/4xz9GxM8++2Rm5nS/P9zYuT0YDC5feHvt8Y7vm821x2KSwWBQmceFqVN3H65B1OVe88LlGdaQWGtj44uQMdoTQFJ4ND+YiBwPCNy5FXF6UGIPFvSEr1noc/lQ6VAT8R05qqjrF+DwNU2VrDHhcGzRpOKQhcB5TNyfh3zeR37+/IQP1JOROeOk7ZhemVpiAL5K9w4HRhAiIiWxM9oMMQuqkTGrgkO+//EB5pzHCtF5jqy4dkNokgPNxYqLYQEzB0pIiacoUOB54pNoZ385S3kkYsaY9ClPHntWnuYTLvUTPegYeF56UJbsy5wPGzBJv9uOw2G3050pFYaBv7PTHwLOzVenq5Veq9Jp9o1hBulFg0ajce7cuVKp9NFHHyHij370I6XUu+++O1exbKtxJMaEuZz2dOHmjQcLCwW21iMcDAbD7e3S/PndVq/skU14e6uZX5gi8o0Vrcgp917O0zJ8wpmSvhORAGJqc8G4MSlnxL8O/CbjoQQANPDkxl/gkTnyxB4IhfEsBITlQBghgKQCUcAJSgQA73M5E1+83yc0rEkjayz1xgYmAHiKxtMbK2KICCZhEUTSKNYxjwEAKBjljri5jI4LOe0vJDYlgaayPBIRFAByvE0WFAIrytOiCTwFWoGHrAkUCYEIwVgAQeq5FwAxk2Jk4pQk5ujYi/YOycoTGfQ1BNuk1+30251etw2cG/aHvlZz01Nxv93uiO9RtVzffNTMF8v5cqlYLTWbzZ/+9Kff+973yuVyLpdTSv3sZz/b3t4+/e3zcSSddji3MlsoFLqdaP3h45mZi8NhH31fa33/8frM2f7S8qlTc1MY9+/e++ji1DnK5YDR8zzEBJGDQClzIHXHzgIAAHJaOacyCNOwFFuAo3IyJI1nuREO7ncrE0kZE//xZSTRpBhywfHP/+/kZ56Qgwp50jCZfD9yX9ZO+r8O5JFr++O+4PR7Nz4BwEgMOpUFEAAJCFAACZUGYEABAgA9wRef/GAFAIRBQMgCs6AICwKTBSAWawEFXEYXKSBPoUJQJJpEExIygh1l/aaSWpyXHERALKgjDznO0IOsOZT7ciKDvoZAtq1WI4qHNolL5drO7btFis+snt55fL/R3GVjTy9fGPTjSrUKhAalVCrt7e1tbm7+/u//fpIkf/EXf7GzszM1NVUu1Xtd1Wp2X/nWarVafXyn1djvejp/796982fOnDt3ri9bCwsLZy5cinqtnb397e291RhU0UMQ5RGBRs3ko5q4bycFBKM6kEgTi9i4mvaABy908eQDfWfS5nL381hTODgPx9GGAMAxrkePdxg/0w/uJDlU4tpXEzrUgZs202B38usJVSj9zYEXfBRvQlAwykcDSZmZgCCglBIEEAJSDKCQnAxSEk7OYTwTF0lnEAuuuQcyOQVErCSu4D4gEbmdiEIgBZqAXOEBYQJx3qrDpy0lWRp+UlI72IxeYTx66LifP1E/6Gi8LH6QTGCWTzQjhvrr1ab8Eng6/c7GN567J8d1vJLOw62tnfPLZy+cu/DJnbV9XVprDWvDvnjz3/vNt//83/3363v9xXOLEHeanfBxp5qjxUHnrvDm5YuFf/8//OTunYczswtb++32fsMqbPTu3X1c+9HFqx/++c99r7hvZ/2p2nf+1f/y5z//6OzZ4LWlOIpvlebO3lvzzrzzh4kZVGFH64oNIY9ohsPY0znIATi/AyKKMx2QRJn4QCQRApBjQsVJLEjCLuqMIsKOf2y8yXt+bJtoFY3PA9FBhLhI7EYeGztOqWDlpWdPAEXGzhgzwetxws9JNB0nACDoKuUAY9rAo88JpKn5Kt0pKADwOGQQw8AgLoUKFCEiWBRxabFWUvvRWUfF9IKOSNfokiCAU0r4YXJAoMd6hIEJiWPJQkqDoFH/NAIAX2t27T0sxiLGebKFGyOeklvYBESIRBiLJQZBYkYG9lBZIEAm1JPSbfzeMgcCZRI6jQOiiAi7Mw0ikKCd/PmJHvQSY1IRgFFM130IguD8+Qvzc4sPNvZ8f758evHhrY9fe+2qR4nKlXLl2VMXLpe94dTU1FSl+LNHt9/63m9+/4ff/z/91/9dfWrp6uvfanWGeR14np+Y2PcKp1bO3r3z8MKFS6V8rVKpXbpwuVSqlEulaG+j0+pWl+qDfq+/v3P29AUfQw3ie4qNU3LQimVXGYudGBJEdH8qOYgEjf3B7pY+qAQBAKk6ABZkXADIPUmdUCHRMnoUj+ouIiImcCjnYELvsOD4Nu5bTmeQ1ffdkRE5DZ2Dq/cFAII+IDirZjQYA4BmEsI0cQGIQawRAfRBj45FA/JIIqMxAAdPehy7csZHmmIkfyd4QAenDhCI0l5rcLidqBVhESNgBQ2zYUysMIjI6CymwyMziEhMqA9+ToBACCRkENzcnEAfq3XWTookGHm1QBQIp/0aXbqtC2w+IcJOZNBLjCcE0Pj5f//OnZXlU+QHH33ycT7I5fPoganWpzylPF9X5k51ra/Ks5cvTC8vLXjvvnf+2vlX3vj+ex/vru2QKnmLZ0/XQo5CBkkI/atXX1tcOH3v0cbC3Klysba+vnnp0pX2XkMLFgplESUWuq395Vpldqoct/Y0GO3rCMgTJOWxgBWT1qUATOtAMAIKOSK0O5aJRz0RCQJbRyPi8aJHobG1AgKIwCKIGIMe64CIQiRKARGSHPSAH58zSO8TSwDCaB0NEEhEbEbOpwWnNwFYYMeqFuCRG8jFkqwFtsJsRYDQT3PcCC0IM1tmZg4UgtNvgIk8UmmoC+J48oKO55lGDD9n6agR5/MJFxihCMhYZEOq5qARZKDEcsIcGzQsMbO4kkXpbkeShUWEByAaQTN6gj6JdrnyqSNJRq56GOs1Vg70mklw+jWmYU9OCRBWDnEINH45m/kEzxFZtvCk+1NEaIRmY+fCpYsEcP2jT958860kifr9PpNGsflitTq7qKAyhGBzt3l+Zcrj8Ee/84OPPn784GH30rUftXobvcRevHQ17AF33q9W68X6/Mb6biFfjSKb8+3m5ub33/mdn320llN49vRqkFO7e+0H9zcvr66WAt2wMYjVCi2SIoUEsSJjXIkrJ4AcDc894VMnNCJOdk91DBgngABo5HpAyzh6DstIWiEAGFbMaIwwC4AoBVojESK7xAIiOhQpU8ROqFkmYWRG5+zOynePrIaR7mNH2ghj6kW2VtiStdZadnoEkRLnQAanvgEziCCZ2M2CCEixUsrJSn+UDDIpUOCgVvzBk2akIqW+eRclG9MmNTvfNqb5WQwuKyQUtMKx5cRAzGwsGkYGsWPnGgAgOjHujkEheEqMZaswjYuJRHYyzjUKZh48BZ/0xFlgF3Rz2lCqEAknPI734Yke9PVE3tP5fJ4ZtB8k0bDXbSax3d1rXDoz0+l02t3huW99R0H0ySd//f1Xl9945dLf/PyDn/7tz/7Zf/Jfru1s7zY7AvHZU+fFQqEYnD23+osPb773yZ2LV14j5dnELC7OctQLO3uFoFYsFi2xKhRBN8qFYpJEbIwxBq01og0qALDgkcQggJT2RkYQsIAkxh7cbzgZs0cRZzrxgStBROyBIeCC5QjWRaaUtWJtWv9QAVlApZzkwfGNOj4/Gt0jnQyjsWhYWJRIZumLvvN3OAEkIAgWBEaxZ2ZIBRkr96hXzl8jVsQCMBKQqzmbAAA7U1QpVK6MJImlgxojYxmEiPoQH+og1i525AcUQHQtIQUxrVvo5CBbGmWrUyhgGBIriYGYIbHC4lrEHhJ5nB4FJsKM5Bw5wGhJXEXtQTLZ0+0gLUOhHc9/8mqOsuVRUq9QaqYl9pBs/VJ6UNZz+ATPF5PXZWQXsIgokkKhtL69o7SnCcNuzyCcv/zK5vZmQfH+7vbKsEM2jHvdqUoReGrr/qNvv35xaSHXHUKnq+qVsuaoXAiWp2YvXFz97//9n8ZxvL+/PzUzvd9oXn3lQt4zF07PGeP3hoPuwFRPL8Z+4d7G1lK+Fic2MRyHSWQ9IGCUyEc2CkmAARGQBF0RLAZvvCoRDz4AaGBBEnGVa5y6gQBoeJTreTgaxalBhCmDRUiEmJHSooOIiJNhCWSwAJbBMsaWEotWgAWzetGERsDxjEWsC3IDWJHEwMjwIUldQgQAHoAAo3MCk3gIgECI5GkYu6IVCKEQMkI8SpvDlHqTftByEPuHkaKR3rCpMiSIACP1RCA1vpyhJKNTlyAkVgxjbCRhsOKKbIBOCaJjVRpEEFApJD16YuBIEaXPrbfxh8/3mHKuIh65fdyDZHyuzKjOtHz52PyJvfZ8kfUMkMOc4LGbsFTIad+PEp6dX/R8FQ675em5H/7mj/79f/ffkI85Tf39LTT9cg6jfufBvfsrtZmZlflk+ODq5bqmmXKumIdkthycv3DaD1SxmL96dUkHpdhEQaAN933M14pqa6+XAO+HNhwYzpeDSq7R7OYQYoZhGBsOLEICxmBQThAAkVILAtNnMCONRYl7T/2jCCJohd1t7+IpAkI8KnYh43Pi/EFsAEDSvvPIhEAgBGZMtIZxQryICFtlBS1jzBhbihktoxXgjPhpwmkcikWssB1xKdPYs9DIKiFxYowSR6fWpBWxJlEEiBKQAhwbU4wo6OoO2nHR/0PX2lUBG2scI5ICwCg+dVCtTQQAQpaRxMCR99fZR2xEDEtiIRFgIEAFiCyH9uh0GwAo+K5/BmpiBaKRURAAPK0nRQ+MXct44EQffwAAVqmP3EUg04kJW5nQ6U5ssZcaIzt87FNM9aB8zr937x6Lunz56s33/nZjbe3VheU4Nki63W4EHpZzSlnPT3Szsd/rd3iY/OWf/tEf/Kf/eHv3djQYNte3kv2N3nTuH/3Ov17f3l1cmgtKC/ly/YOPPrh8aTWOBzP1mb1WOxy2KzNzSdS/8eCxsvLO1Uv37z708mRRR7FNmIUktLF4+VwMYxsEERAJSQCIvZQPLaN8+pFew4IgDJKmj4HTL+xIEBxShcYcRUJAQEo/CAqL+3RAq3OKl2G0AomlmCG2GFlKxHWkydD3EQHQGWJW0KaEHkwTw3GkAQkBggj4EgohafKUUkSaGMEiiCZxSlhaYlDYyVSG/PhqTt7G4oiarjgYpg2s3VkcSVMcFTlkEQlH+W6QFn1Oo/1KgWUylg2zBSWAiCRAaW2xsUtu5DwLNCCCItBAhKwEURCQg4mc2/Rfp+qocQbbAQAA0jrcKb1xFCNzlvV4DYOWjP6zh5HFZ8nIB3lKetMk32wS1n6ZOf9qZM0zS+94Wuche/yj4ergfB6hUMnP89AkwEbZIK+KAvsPHyZd6+f03NzsrY8+inu982eWMer99Z/+UYm7O9t3Zur1lZngww9v1HzpSOX9B9Hy5Zm3Tv32wzs9tpg3er+znjt1/szVK3/6Vz9FVWu2kv7e/fmF5sK0GuxteN1y+MbFuXJt229dv/EJJfmLVIUK3Xj004K6HD76yQ9W3/yok7tv8oyFYdxW5O8nkbsVfYIAxVeSVo/GAiCTCCCjGNcShoA7vpcubh4vdwsAMSeICpAAkAXZkrUsInmVOmiJSIFy0XlEtKPi8pzeZ84tDiaKraBhGzNErGKhWIgBxAYALJi2rQFyHiL2k7G8Qw0Hdf9ie3TfVOa0/mniCDfkuZUcogEAlRbOZgRRgIDsqxDGUTA4YAIGWsPhiKdbUe0kjYI7V/fYCLVjfxMKQModB4TYoAUEJKXJudYZEESSlG0wsbqEwfKAB84WU0jayTxBGHMIAFLFB9Mykj3OPSlAndpp/LFImlTZuhMcRTjpL/ZSAxHZGkAeFTZGADBRfP2z9y9cezMKC/Nz9a3+5qN790+fv7Td7FSge/Xq1b29vQ8++MDzvIsXzyFip9MpiRkMhhvrO7Oz85999tn582evXLn6H/79n/zWD672hwNjkzOnVqK4t7e3d+3CK9OVmeuf3osGsr6zk0jU6zUjHCzMXt3e51tr718sDTphmCvmpR0PQiPK5nPaxgkyWRADTATIyMDKOacBAVxlYlKjsoLWHNRvhokCHWOHyIhGhETInDZ+Fxfsco36OK3+ceRps4LshI7zujp6pwC46LwLPCMAg6CrP330+bcZzxjFiIjCyAJEyALCgoghJyiQVmJOE+Wc6+fA0TNp7IRp7i4gupqS6f8aO1brnP47TvWw+LlsfkQ0MjqXqZJEY9F+5PyNJQZU4BgSiAQkDIDRIR7QQUA2Ap78cvyOcEhGj01xgn9IbD5rm6O/f9b+o+c3/nG/P+74x4MisNZqJE9RGIcKfBTuD3pJ3N3feeTl0Fc87O3PzVSvXT2fW9958PHP1cr07OzsnY3m3t7e916/ODMzY63dWN9fXJxfXsGHDx+snl383R//6O9/9u7jRzuDN1Y7PbEJKqSdnZ1ms50rlhLQH3zw3pVLb/i5aqlc6Azv3n14i2tLW0l5frqKKvng1mO9PAVQEPKBMBwMgT2LoAVYgFkSAg8BgD0jSEIgSKhQCAhdN8IEYCJ188BfoVxwH0ZOImc9gHHdrgSQkdK4vyCCztJDQbFj6TgSNgs6z86oFyvzmIUIX6C2SoZezMjjnzppmF4vZtdEkEHUSPYgQOR8tHKYEi0wmQMxOgUAADwRVzqMcExxwBHRITXq3Fwc93pUWNtkpAMkiUJExWgICFClRArojsphjp8K7s/4QB4dvAOgkgOOqHtkpP8eDkCe6EEvMQgFrEX00sAzsljb63SrFb/T3Tubu9BpbMfJ8M1Xr4Jwc3+/UCjs7e1NT0/Pzs72er04jpVS5XL54f1biLi8MjMzW/uDP/iDP/njv2js99kioqqWC3Ey3F5ft1E4Nzvf7cdbm3f7+xvvzPzOrd1hYwgYTIG3V6kX6lOvr8xSa+P2x7evn8mH/qxW4iUCySBR6CtARlEoBlARhCgEpChRiM714Er2KUFEVKni8eQ9hmkZZki90jyWEDT2KLGVsY9+FGAa/Xz8H0gCAgxIogQ9BECLgoIWABiBRNIwmXWVTzNiAhm+gpFiMprFSB3TSo0cPAQo5Pqmogydd+hze0nwUB7VeAM9qkX7xLueKFMJE6NZTu0vERYkFoGUwH00QieDCIhIofNvKwQORY+nMakHJQdX6uDJAQC+HIrlj/HEz7/RelDW2npaetDxxz8aX+A/QmACRmCFQgBJEvV7nf1W8/S5q1ESrm2s55TWXvDw0Xqr3Ts3P9/Zut9ut/PTK6dPnw6C4N69e71ej4hu376ttPnf/G//1//23/7bra3dfs/Mzs56nqcpF/X2G63m+Svn5pdXNrZ69x/v+tzoJb3IaoapXD43N9s5tVJVtTwa6MT5gdQ9VSv6+dbAAHpF7ceOYMggiAScGjwAmgBRNIFiJMUaSCEgif+5538KxknX2yjQhqP6RONfHaxvOaqc0EHdP0ECJkCPWAQtGACwacd0YVdzB8Bm5VRmXhec2ORgMux5CtMmYYLERArFApiUZwyTfhMAAB0cHmRkpvGh+s0wKV4/NyURsU52oAigc6u7P7PWbcRELuUszSpOuVUJHIqpj/dlJgsCTQ5JI5ompE8N91TgCfVSTuJiLzesUUqBEbHsadIEwyjs97v1mYXf/PHv37zzEFShPj29sd3Za4fzS+cLfvuztTWt9UJpNoqibrdrrb1582ZZBjMzU++8871//z/8cRLLcBhaK632vtb+/u7+zeufktavvH7ZWomtlOrzD2+8t72/c+bSqz1euH/jXc1QhH40WAM93R6YQm0ZTFKgxMMESeUBQE1knk/cJCxCgIYBlaC4sJQgo4Unn/MpDI1K36TNmp2n1j+I8R+6qexEnv1YEGGajYaGWQQskdUsQlbEoOMWo2WyLIbJyYUwI2aflRduJyXCRPqFsWIACFkBGmKFaQraOOkh5fWMeyWOZDEePjQPD9VXGh+44BE1zJwbXtIyIMAueJ+2JMx4FiIKAgOq0UQcp1pG9bBTG/kod9uh+cCT85E0kntohs9EBj1rPeUEDsJGK81g2bIONCLEYZSE0UxtptEOY/Euv/LmUr3U2tsbcmPlwjW7+wtjTL1er1QqzWYTADzPIyIT9//wX/zrn/7dLz799Pobb7yRC0pEg93dTbbQarUae7uvv/46CTx89Gh2+VqhFnz6fq0bJZeXC9xGtnEl8EqUzJb9hu0nMjh95qwJdzwp1CtFgzkdDUjnAMAljLt177g/rvyOSisIcvpgFjsSFun7hA2lQAiAARHRJYURouRGVsVYyrif9JNDMmiU0AueiCA4ySWCduTaiJlZkJkti2FkK9alOGU0SIn56PisRX9yPgffO32HQRCQgYEJ0EWyANLcByIcJz3IhL4z+aGgYGRmCkzI69AqkVFa72T/xVQApernSAARZPZrAUZQoxIfLrSOE0yiJ8XcJMPIfUivF00ILBi/0+gypT7pIydxgpcCIkJEVgxY8Ug5W8xycvbC1Z/+/P363NLy+dPMUaFUHzxqDEI7X6tdvXo1SRLf97/1rW+dW5nudDqvvvrqW5d/886d2x999EngF3rd4blzF/qDRn2qFMexJm9mevrtt99mD+9t7r3xzqmdfaNoRXsFFcTK61+8sDSTRNAPre61ooaxg9ULy427G4Huln0PfdQDaBjnwgTLjmXjKnKATUQ5YgrYlDrjFAc6kCWHPphRyUAERJfoAERK4fheHW8riOhE0wFPMWVMwygp/CD3Ko1M2UQEmZQ1YAAtAKOwYJzBvTC/6lH7OUMJUQAoDU0LoLi2jhO5tTiRA8FhLz1WcF+mQi0IPr9jAYDQpjc2Ho6OIWLaBwmVgCBgVnZuCnK6CgIBCrJYQASWA+LzYf0AR416xu0nnVPcTOxl1J/KmXaHZJnWGX61L4enUwcnE1n6VMb3WT62484yS//io2O9kNXT7rjzzKq/mzV/D2mYRPlqud/vQxxXK8Vf/OIX565d8RaW/NvbqrUrHdyPZb9pBv2mCu/kCqXrTe83/vn//MGdNdhv/+jt6fqsr8pv/8Wf/+znP//w3KXXmM2dnd2kkH/11bfW19eTOERtXn/n9fu725WZpYUzl0rV0s27H/zej6YKNpwJzdb9n7xy7sIwWi6U6kTe/ocPvrtcO4trfp537j16/Z1za3utBGnOiwCIRRnRllVkJTHEDIk3dhsrJyRcMrplC+DaroJrPiMACJxogygKkFCUQo3kyvIopZ12TwAo7LYB4Fp+MgYs48vqySgWPuG3QUTDxIAJCCs0ADGIRbAsnAREoIk9Ak1MyASGUFTeHshTpsQKO3pllPYIJPc2Wga+HOqh6MQOAATQQUSl1EFbQRER6RRyAIDCGqwi9pCdnxjM0f3LhIzTgAAO6yz66HUbG89K+kiwMJq883+legumGXjuSrhe2Agw6q+d6mV0yCE1huWEERCUuMeBa5uLqA73JjjRg15ieJ4XRkmSJKRAkYrj2FNYLuTjyCRWFk4t7u5vi86vbeyfWV6ZW6iv3/gsH/iLczMyNI/e/3hnp3rttQuPNm7eu/GZ5gST2Cah4njjwT1lo2KxqFTtypXLxer0X/7Nz+qzi3E4LOY9a+Ki5yul1tYe3b59s1QuD4dxvxf94Ae/+fMPPiGCQRjXajVG38ZRoZjrRgJiBUhACZAVFAbrUtwBAFIOy/iDiAgRAAsoch2x0puAXEtkJ4PIRfIREDExAMAESAoISSOJCyore6T5wHBUvEmAxbMihoGtSz0XwygMCq1KG3JJGgRIq6EduHVxlByLgnk/zUF9wgzMqUO22/i+1aLHMsh9k3YlZUBhBEZEheJ2oRAkw0Wem8hlh0kxlGFLEosFERcEBGF0VV8hOeh7cSC4AYDw6HqYOcVHyqAQQIG7lMJCDAaEPu9GOpFBLxCy/GhZerPSnoRxkiSaUCFGwx6hFAv5Ww/uz8zNLiyv/OLnNxLsX7x46dK5M/fv/OLTD/5+7tzVs0szJcBHP+/32nt57woxXDi90K0XV8+uJEl069aw222XAji9NP3WW6/Pzi392V/8dbfbuXnj07nF059++H6/vTdX920Sx3FokujB/bvNVs/ThTNnVuu1slJqZ29vtl7VCna211V5rlCo9wYxAzCgZUoEYmYraAQmuGqj+kGAAGCZkVL3iBM6Ls1bnGfUWXPgGhAjgFjLiKIQFaKvyKaeDmeajW9F5zcBEbGHfbdj9l5k0ApZgUTEutJCLCIYICOJQiAShUJo8fDN6axApQgABQjVAWM4VYiIEDHvJaPjPXQbolXjuitwSFwyIGNaq1GUJo3ohG/Gesjob5FVWx4sA4oIuwZBJO7PAerRmUn9Sm5KypGoJrx17kNRHaqrPQYAusZIjDKqimngsKCEr1n9oONG1J/X+FnbH9cHz8xAGhFFGIWHvS6xkSS01s7PL7ZaDRYzjJLv/+AHaKI//g+3tCfE/b/8D/99a7eDZlAuBQ/u3Grt7MxW83llMR4sTNUXv/9Wq9UgoqLG6enpufkZluTyhdWPP7v12d7epx9/eO7CJWEMh/Eb37rKEl+/ftMaJlJ3bl2XhM9fuxqG4eOH99Ga89eW+0rvdHuki4bBssQWEpYEwAgYYX9Uy2rsv3DvCQowaBREVEACQqRIODEGERmBABHT6siATIBExAqAIQEEBWwYEfXnIjLuA8NkflMaFAfAyLWexrRXkML0Zs9RgihO+QIQcIxGZJnoHItpPhcJkFY8TuNCREp/S3hQXSydifuT0qYaB5UJ03psIASoEJCsIlQkikARqQz/iR6dTDj8IYMvDjBK/rXMVmFa617QwFg6H8JITRt3XkpdzzmV2tR44AgHETBAkPakFktgWQDAypPczhM96AXCcfWgKLEusCUmFGsHvZYk4bDTeO3ape7m7sMHtzyCualqt71no0E57w987jW3m/uDqBPNlsJA24/f/2XYL+5srC0tLfV67cft/Vze39nZGg6Hr7xyVcTeuXVze3N9ECavvXJpa7dJpMgM1h/tzi3Ozc7Uc/lLjx49nJ+vIua6nVaxWCTgfKHk+361UJyplHa3u7nSYneoDIMVTqy4NFHrKquP1+KEXQYAIkDAloiQNcPYg5Am0B/430jECoPShwwQax0LGmTUxxUOyyBU3udvMEh7GSoE8BQ6YYeIIFQAl8+FQgeDiAATOWeKu0oyvogTImY8uIiAndAvJj5oiph57DmSUWlwRZ6r8KFdlwtE5RxSWfzsiXpMh85qxvqhtJ4tEhGnKf+ICMr5nsdsgdGLDmJwh8SQfM6adpt5imTUippEiNB1QzNPyKATPejzOK4+8rxgBbT2FAIzKTFhrytJlAx79VKuHfUVRIPu/vLy4ns/+5v5maliDjt2CGxnKjX0S8POdhJ21h4/KOfPTU/VPE2VcrnX6zy4d7dWr16+9HqxWPyrv/7L8+cuoHCrsXv79q1adTpfLDUaDZ9keqayu71JHhEws9ncuD8cJlcuXnr8+LFXqA6Hw6JK4mHHxEZ7/qBjmMGwxCxWwGDqV5jgm8DoA4KrB4SKWBCJUQjFCCgQX3lIokkpAnS1u8SKiEeWCDQRKdAoaRsKllHo/MncAlIGQBxvRiZqVOc85VI9iQiRXXl8RPFM+pBnQAZnsIAFYNTMkFapl5RjCIiGUYRY0lwQSHUmoJG0gsNrTGE4GcniETwtAEAICoUICa1CQiSVyVdSAgcCdyzjDB/tQEoEWVIxYdwHAXAsTXfGRhasO0v8OWa2ex+aceQRMSVqjzoRjDgBjORaAjAQnPikfyWOq488LVvsuNu7nijM7HnaFx4O+mwTrWh/61ElRxHY3W4j6jZ3N9oyaHQa26fPLDR3u1sPbvsYaNUql4JkOLi/duf8lbPNZnNra4MUVOtT337rTWb7H//jf/ydH/8AUMrl4hszr968fec7b3230+n8xV88ml9aUgSffvrx3OJcOOxbK3v7O1Fo79xIVGnKK5nd9Y0901lcXJybPfX+w4eJt8QirtSqJRAAIQQaMeAAYOKGAQCkNBAjLhYpjIAC4omgECISKlJAIiAkYnOKiUATKo0KkQCYBViSUWz4CVXIpwNGy+TZVa4UBwKCQUQaPee9cXdDJAPAQJZZiRjS1gqPCkZbJ0MRDGgRcHXCUjMrLTJ70A92vFtE1FqPvdeHnn8aUUChkz4prQdHEcPPQzA31ryYecx1HNijV5AFxaNaTSzI7KqjoD3gqSNMpF/IRC7IpP+udzgtbCxkSQwjOb3Wpo0iXaXHExn0dYHneczM1hY8rYGTKETgfOAlYa+ey3+8/kBBsrezhja39uheTndyC5VTKwvNtSYJTU2X61NVtsn+dssv6qtXr16/fn1vb+cf/cHvraysfPjhh5VKtV6vxnH4+huvDfrDUqmUC9Sr338nicNadSox0fr641q9LGKr1fLVS5cFvdbWxocfflioz3//269Guw9uXf+sfLFMXiExYkUYXK8bBDVSMEaOmJH8Se9AUto1B6VUK1IILEDGxEopi0Ju8UtaoU/7opC0Rl9rhQDIyFrEij06t8BDe8A5GgERSTkHk6sFz67FEwEr9ABAgAQBU2K3y/8kSTk/zoByMghNenuiEDK7qBCLCFDyhBKBiALwhAxiZmstETERiovEYcpbABDJKnsNTqwLsyuIyMKOFjTMkEFCqR5khVxdMffnWMSNTtGT9h3iobTY0BJOYHzOtRh3xhnA1ZdLLwSbyWn8evSgFwyZ1X0yeDdZyDJPszqxHdd2yzzlx1SQVDwIDedyOQx7U7m4//gm9zo3b987XfN2kKcq+Z3N3UcbHwLh1MxctTK7fmPj9OnTy6cXr1+/XplfpeBSI6rgtKxtb3qF3MrZM6fOrc4sLP8P//HPrbWRpUp5ut1OHq4/On/5SqyS9n5nYXH427/9oz/6//2xMfHKqcVyofzq5atKBaVitVgs9/iNv/vT/7FetNXppb971Gpt7nxvlWM7CCUXBIFNrAAFQS5hGxujteZRHajx0nV1/HxhQiZfiJARjGWTgGVC8hKByCBa5z1xDhRd8wyjWEwisCSiEFGhAlGqj4gKFACgEI1usDS/LO2SyiPeI3vMLrV7dFnTGoAoniCIAIugAKWkGkRgFPEEibTyXP12AoACsAVgZstgAKy1TCgCaFPfrasFRK7Y2rg8PoMIC6MIucIAzgfvrEwthCZdPAV9ND8okoEwWlekzZBlbRhEoJNhix3p20YEraMjtx8m/pHfswoO/pi4FeTJnNvR9pib/P5ED3qJobUmNkRUzBW7rf2trS0RWa5Wq7PVjz94NwA7v7hsbdJqN6IoKgR+cXZ2f39/f3+vUinV63UAELGNRqPX7k3PzL3yyiuVSuWv/uqvrl+/7vt+v9+v12b/8q//+o///K+m5v6+Uqm89cbrvV7/g/fe//Szz157/dqVV66eOrX8/vvvN3c3a9V4QXl+oZDP5/18aWh1sx+LqHKxEPnY7vqGlWEWYWFWAppZfe7B8IRikrpY4EBXsmNXKICMi2MgRkY5HUIhEogasaWFCRFdzB4RWcRFoDQRpHTIkceDUtfs5GN8PBMGAUF2RfUZGNgKsrCFtPyQiAAQO3MDCNC6/uyC4Og8ruW7xXSXrjSIiGs3ApEFAFf+2b1QBITRkiCCQgSABIUEUMAChBmxeZdrIoKJBcvI1vF9Mh952c/Or1Qx+UbIoK+T330S7rCSJKGcv7u/b61dXV39zne+s7a2tt+LPR5ANfByedUhSaJqufBw81G73Z6enq7Xz8RJdOfOrbm5ubm5uSg0zNwfhrWpacNy7bXXB4PB1tZWEBSGgyQcmrt3Hvi+f+7U2bXHWz//+bu7u/u+l6tW6sPh8NNPP/3k4xuezi0trbz65rX69HRQndls9Y3KFQow6HeFldZLApYQhROwoBFZxOO0j8WkeeI+pOUTXezpILKU1gB1HgfmA6JKP/bBGWhp/QAEZBTU4CtEItBKFCKSKBBAyQGDpJ5WZzON6sMf8KpFwNk0wuxqgPEoXcEwWmEBsCwiYiT93nlRGEWLEzjiChBqICEBQCPkJu3CTuOyHgzjvqkoggc1SawQAJFoHlU7YwGAOKP4vqsVbwRYiC042reImAz/kcrI2KCMup3Hsya+9PZfq7hYFl6WONdxwcxaa2utMWZ/f79Wq83MzGitWeUuXn3t5kd//+DhesnnMByKjZu7O6dPn75+/VMiKBTzjbXdu3fvEoExRvtep9NZ39zYa+z7uWB2drbb7danp/7qL/8mipJ33vlePzTr6+tRFPd6g7nZhRt37u+3mnvNxuLi/NzCUv7+4/v3Hq1t7hiI/uD3/lGPcp8+XPPyxZLWe1vr+fq0F3jGgu8pi6BAfFIApBGSzzFZJlej82yMSicDjEqyjn2e4417xiMBAEZRCIIoBAqAfSGNqDR6WjSx0mBRQHCU1+6qaDgRyACAE/0emDnt0iWUpOKSrIgVYQYrIELp3c5smUZFpgkECMxIlXDidSTdWI3UukPvUeoDdiaYqwOPAECuczWjwZSP4AJiNkMPsuRqNrvcCxi1JCTEozmKWdpOlsPpi5PMvvz2T9C2T/SglxjCksvlmdmY4f7+/szMTK/X+/M///PpheVyucTGRuGgSCrwvJnpaq/bJjHNZnMYDgTM9vZ2uVzM5f12u10s1BXh7OxMs9k8fWoljmPf00qp69evV+tTKyunGD2lVBiGW1tbtVpt5dSpME5u3rqtPD23sHj+wsVWe9DvDavVaqVS7nfFDIfFeqHg2YD47PLc3lYIJsqVSoya2XpamyQiSqNTnxdAT0giGDlBxwUo0qjSqAZ+mCBM1GmmUXICIhoSbYABjUZthBABODmISZELQCOqkYXkdgoiZK0wowhEwgAoaEGIgQwwMzG44h5inF3GkNZFIkXiIliumQQwkJu5GWWQH/bEg5FR+EkQYFwHHtA611bafBElbeFsMmQQu1plzruc1ipDRvSz+ERZDsiv9pH9jZBBX2M4d/twOGy326+eXd3d3d24d391ZanX2qsVvYXCwrDXYK2KxeLe/j7beGl5cWqqVi6Xo2goYM+cOQPAO+vbncZ2Ugr67T0lcafTQcRGo3F6aVlptbm5ERSL1Voxp7UJ+0i511//FgBvbe0EOT8Igkqldnb1vOcFtfrUzes32rHWXAubyW645y9ODXunBo3YstTzxIQxs6C2rvy8NU/InfQd0oDRGHioX/MkpxEAwBIToBFOA/OpDwUtWtcGUAA1g3VV2IF64LLQCFKx5tLxR515RjRfZhJBZucOEgBiFBZgRueVNiKWmRlc29KUa2TZ9ad3/eZBkJFcuC+NOh2mKULaL0RGlV3HH8CCRhhVrZW0aw+w2AxbaVSLf+RmJwRw+VpHy6DP9wVzMBlCKDPvLENmZfVrU0/oQV9XHeGrxPOy9ZRS/cEgCAI2ptvtTk9P9/v9Uqm09+jm9vpDH5JigH4xPztVzuVy2gsuXTgzNVXzPK9YzF995WKxWHzjjdeUUoFwu90mor29vfX19X6/r7UOw9DTwTCKPr15p9XeBwAO/LDX3t/b9MtzQU5HcT+2caFQYAble9Mzczrw9na3o4SCqhIexr395m68tbG2t2ZIeTO1HHpBYgB9bYSRcNxU8AmXEFFaJAhGzW0co8YedW+IiIcWAFSaLiA6zR0FUQkCMiIhWUArqVEyBATrpBwiytg/bc3BNJwV42wxDVbIKWLOCgMr4iJQFpAF2MkRAdc92fG/mZERhFEOVJIDDtSIn+RkbvrHpAACQEZNgAaAAAnApFwGp80dhbS7mSACAgs6shOLyZBBGeNk1fDPqqOUpTd9SdvtRA96ieF5XtIbFAoF5XnD4XA4HO7u7rbb7UKv22/tztVKnVanVinMTM9s7+3Wpmc2N9cBuN1uh+FgGPYHg97t2zfz+fxiOV+tVuM4Xl1drVw8NzMz0+12lVK5UjnIlV69fXd7v9FoNMr5nIfSau42+6pYKmxuPu72mq1WC8FD9Ijo3v2HftTd32v3cb0+NZsMmxvr0GVqhUUvKCbhssaqsYAmnwDjqKnL5++ElOc28tq4b0ZdPwEOC31EBDAurVwdUIoZEUVZEHIlQcD1QWUEgIgJYBQXQ0gNMUBjJqXhqDmfCAKDBSBHnwF29TpArGvLAS6mntKIWdDVD2RAl/1uGQRRBLP8MnjARiZwZYWAEDEacY/FUWwAUjd6lgxCdEE85SoSUUoFyOr/kaWnQIbedHyv9JcC/h/+w+BXbpRRsvK5IasY99OapzytgbJ8gce8lFbn43BYzAfCho0BomEY5wqldhTOlZTXeXzzp//x1s//Cky/UCiEsSnmfc/zfN9HxDiOe71ep9MZDAbD4RAAtNadTkdEpqamrLVhGGrmQqGgtTbG+L5fq9WKxSIiztZKMzMz09PTvu8TUS6XKxaLQRAoCN3ERMRa65zizAymNz8/DwBOFPZ6vW63G4ahlGeKs6vzZ17Jl6Y3N3etLk+tfnsrDHx99NrzM563WfrmE7Vcx7eoHZVGQ3Ed310NZXbDE4FyLbRGvTisqCd24f4sBhk1zJKjb2IhJSJW0LUMMi68Juh0E2GX8EEiLupPhrSjO7IAp413SESy/DhZ+kVWn1jJdA5nxMWOaRuRZMTdMoy0RJCEPUSF1kN7oge9BBBrmNmY2DFetFLiKR9tDs2n7/5i67Of9dZvyLBXzuliPl8u6ySJjTH9fn84QhzH1to4jj3Py+Vy1WoVEZ2b2fO8aBgqyzEng8HA2k6j03XyiGzocmKTJAGAXC6Xz+e11tO1ovf/Z+/PgyTLzvtQ7DvL3XPPrH2vrq7eu2d69gEwA4DAAARJESQlPYkS9aQn0fpD7/3x7Bdhy6L9wqGQLTOCzwrJtmRZjwpb4pNIUCIhLiA5WGfD7Ev39FpVXV37lnvm3c85n/+4WdnVPXVBlNHDmQH6i4yszFs373LuOb/z+9ajabZtZ7PZXC6XyWSS7cNlh+mWaZpWJq/ruqZpAMA59+KOkas0AwyDbm6M7Xa7fmfB0CsIufvSPv3c13ts1bxnBQZKFE3SLZJRSnolQXrEBQkAUiBU3VkPaz9oEiB9jGkpte4VKCBAUSZhibRHl1Si42BvTQ2UQCgSIJgk5CfBUDQxhx/ZB5Vc5+Hb00wFaTwo7Tj3S3QKFCmjihPUCPmh7EEfN5PRD6Ki90M+bp58QkhvQQaFjAHFkAk/jroWBFuLl5avvl021HA5z0GhjAFUrVYLwzAhPlEUAQCllDGm6xwAKQVN0xljSilNY7Ztm3Y28fEj0yiluq7Hcex3OlkrQymVUiIFQohA2vHCOO5W6zU46LHaTzvIOTyTyei6nsCcZVm6rjuOMzVSZFz3grBSqQyNThjlYlPTO0RVg8PvN9UckfKA+1eCdydwJ7mdSUwOIYoCJhnnMll0mgLBZI0NTFz2nMj+vRz8oKUAAuOH89zEMyWBIFUKiVCQhFsLZEnlIyVRIFCiEk4kkxDHXpFpkKhYsrJpSr3n9Hj6o8UcpvGdNFt1uqQqdYduNSgmCz1yoIyqBzzoELlvdvq0Z3zEw+ucCtAIQUIYqDj0Wl59L/A7OUOZcWsoZw5mdZOIVn232+0qpfaanSTxGhHZAdF17vu+YRiMMSmlaZqapjHG/I7PqcaZpgAppbphcF0DSlQUMk3T9F4hnkSzi1FknHyihSXn2L8p0g1Uy20nqKfrehiGcRxblmWCYeo0YxmmbXUFFqZOP/LFXypMnvgBDZeyPc1fs++z37czA+zX+OitXoiMAAVKCNJ9cCGYFAZEAooSAkAMfk8N/J5RnKUUjuapS/5IRCKhBzGKISqiCEbIUIECUIQwBRJRoZJJuCQqBQQJkb01eJLI6qMp7WmYkqbDpm0/Kgalq3op7QaCAFAEAkjwh/OLPeBBH60oGVMCCKBpWtTtNLc32jurfrvu0i51qzaT7UZ1q9VwOy0hhBBCUUb2a4MeXKfctHQgKpfPIGKn0zFMjXMriiJd123b1jTN87w4jgkhuq4zxmSsa5pGCInjWAhBCDE557qum4ZSikqZwFCfhnBNByEY6x3QjOMoiizL8rrMVUGnFWAXpZljrBTpJckcOJxGQFrFdS3FTKf2o4HwQEUx2M+xACCUUAWJt4giIYwQAEUJACAlQJAmZlj9wPruB4Wm1RGnh683TxAVIENAul/Dnyb+9SQdlgiCjCqpQBGCCDH0csUkAQm95F4AiNJ4UIqk5jOm2NdSUllTj5MmaTGTqXMGqqSaPxJE+WC9+cPk48aDRBz3Mh05Dz13a3WpvbEoveZWeyMKwiDwup2W2+5oOtM0TQil7+dhJypYIoQQ2zZ1nVMKQshMxs5mHSkl51Qo4EyJ2AOMbEszTUMI0Qk8jTKdE0opI0xxQilFRCEosju1uA7OpZxqQghCGGOMUp4s8BVFQs8bUvD6TjAxe+7Zr/7y8Nx5xSw/doEebg/C1D6dAlrJr/YLaJH9/OwYWa/KIkkyygmhSIEYvXUg9gsVUuilkvJeHWg4oIgdcMd9QFIwgigkQAkBBUh7ufUEAAyBiiAiUKISZ5mkiEgYUQoQFQhUEkERUICIJErzTx11Dk4x/JCPaLZVat/ARhOz3QP52IvGSLxfDavbbq4tL3XXr2vCDTvbYRxLJFIB6Ca3LI1rEjyNYx+AEuLCOWeMUSoZY41GI5vNXrx4ERHfeeedMAwdyy6VipZleZ7neR4hhJpazjaklIgYxzEqxRnhnAkhlJRMs5ILIwfqBwMAJVq/DEVyUk3TOOdgMkCHGfbM+cdG5h8ldlm4fk7X2+Lw+03VEVLGvOrzpoOrrQOESChyQpAhVYiUAUVCCBiQLGjfS5KnFGjPm54YjQH200GSg/IUKEiLo9F61cr6GbEJsaCUKIKAFKkikoIE5EgVIGMKEYVSTKFUIGhSDA0hOjxPPU3S1ndJXW8m5frpEfmXSp1rU2IgkyK9vQamDzDoEyCGYRChYqUQ0XXd3Z0td3vDgcDSosDzJWGa6VBKhEJQCrhu2z0tjHOeIFGCC91uEwAajfrIyPCzzz6zu7v7rW99c3t7O29b4yOfOjl/bH19/ZVXru3t7Y2Pj8/NzZ06fbbRaGxtbSULIpqmCQBBEPRrLyRWp54CqJSp60lUipRSZ5xwojPOGKOGtrtTNfLDI2NnCB0MfGYQnYfuwfVXD0pq3EqK4IHcsYMikSblORARCWMIiiABgogUCFBFCGW9uCNCKB7MXcAD602TFF0mjSAli5QCJIv8EQBQhAIABwWEKgBFkalEIQQKhDCJiISAIkCoIgqUwkMDMv88Obw9CUk1XB39FPdDSLKoEyighFBOfwjl72MWHpSqsNLU2KrDJS0MiNyvZ5M2llKuP63OURj6nHNdZ3EcTp0895cn/4+vv/76i9/4xpS4ZRYaUdhlSItW0TIjxiJKKDKLEBKGYcZ2wjCkQGrVWhRF0nCYZKaWc5trU4PG9/74jTMzcxB1/tLP/dWdnZ3tzR2341VKA1cuX50Ym7z40COPP3lie7v6W//+ypXLV/wgRkoeffTi+YcubN1ev379ZoJxnt+aPTY+NT1ere10WnEsRLmQbTTb+aJTrdYz2VwURUGIA7Y/Nq0NT5dphsnNbS2jfIty6Rx6vwfq+N0lcUpPTLSpRPDAO00KIwLIhLPsa3JcswEOGbCCHa7rmWl1CFP6j5lSlCuN34kwJXaRaoefIOU4caqSdvhmSI0tSMXWIx0+lQeRg8/rgV/skyCI2AsCBEjc3o899phhGJf+3f/VypLSQEHDLMZAGdF1nRAugRNChPBqtXoYhrZtx7EIgtBysu1G2+LR8eNnlVI3blybPXbqF07/wvra1u3bt8+ePVutVqempp577rmLFy++/PLL589P/PEf/3G5XD527Nit27c//elP21nn9ddfHykNnjt35t13Ly0s3CiV85VK5Utf+ul8IfMv/tn/50+f/5OpqanxyYnpmYnz589/7Xe/rmsmNZxjszPnLjxsZe1mt5PLZyj4UqZoYh++pNW+SBtNUYoyk6aLkRQMTcOgP2fV0x93eYBBHyNJmzcIpYkvHHq2UhgeHjYMY+NPx2K5qplKQwxVxC3OGfW6sunWkoSvKIoopZlc1rQtJKBiYRiaRmUuY9cbVSnl9NTE4EjhG3/wvFIq8r1WvWYb+ld/7mf/5E/+ZGlxsVIpu+3WwtL6s8/81M/+7M9dev/ye2+/kyvmEMTm1sa5c2eyWadULiKS//l//rd//+//3ZHh8Wee+ex3v/vNcxdOB0HX7XoXzj986dLVydnhY3MT42OTJGu3mx0zV/GDkBINU8b8ffMJpEiQYtrWUoaDkCk15FMuM02NSqPXae3w4yr3PN8fs5zVo93Lh+3jP+qBUm2x+5IQona7wxizbbtYGt6r72iG1KgWRUIidv14b68TQ5RYoynXOecKqFAgkaCILMM0GTF1I2OZZ06diOP4ypUrx+amNjc3LVsLwu6xuSmFUSz8KPbiMGo2Gmu3l7M/kz0+d+yb33z+6tX3P/XppwjBa9euSKF++qd/2jD13/7t39rcWvmbf/OXpcRctvDLv/zLhOOt27fOnLpYKGQI6PMPnSo5ZHe3NjUSFA0exF6sqG5qKiXX4cOWOIV30JTcTnFEW6/CNP/dh6vjfELlY2fq+UmWD0am9D3EfQ+XpmmJCdZxrKHhMaoZfqTanWCv3ml1vEig4WSlxCCIGNM0zdB1M45lFAnOdcPQvK6LShQK+WKhYOr6yy9879VXXq6UixfOn3Vs85nPfGp3Z+t/+a1/N3ds5vjcrKnzTMb+ha/+/N7O1r/5N/96dGToU08/2aw3DFMLAo9rTNf1TqejlGKMr6ysFUu52yu3vvSlLxHgUuDw8GgUBzOz0zOzE9zM3l5eVe16xULXbSuqiTi9rHZ6O9yndmaHviSSQ1+KskNfacdBONpLUX7o637d78dN7nmsD3jQofJxi1IEAEg85X2hFLLFShCpruubWs6yBuysruKIa0Q3jCiKTMsKggAIieJYIZqGwaiShnQcPetYSsSVcrlUzE9MVbrtxiOPPGIYRhRF//7F7y4vLw8NlJ564tEoCr783BcvX7r+x9/4k3qj86lnPjM1MTk1OR7HwfyJuePHj73y/Zdbrda5cxd29waWb60+99xz719956233nO78djo7ObmNmO869aCKPBCpRTx61v5fAWBEd0IOl1upNhcP2TdJM3PBSm2ZJqyPb37pPmn0hK00pJQP5Q89Y+b/Nhi7Y8iH3aM4tEPQwghQggpJeccAOI49v14c3u7240GB0emxk4ypfv+3sb6LRGJUmVAKTUyMrKzsyOEQCGASgXE4swpFwsZVswXFMrZ2enxiWnDJnEce5730EMPvfTSS3/v7/03Kysruq4/9tgjpmE8/uhjr7/2TrFYnJ2dW1+9HYTdxx57xMmY589fePGF77/xxjtxJLrd7rlzp+r15uBQ/tTp+WtXb4YhGR4b17gpMDAt0m439+p+Llvo7m0PzBy37YoPFIn2A3TPQ7ffrzpNLAUj0p4XpuhWafUPMM1HnoZaqVBzNHvZR1XH6keUBxj0CRBETMJ8kgIaSqkoihCx1Wlbmezk1NxQcXJrY293r6MUTB+b3d1esyyrVCq5rpt408IwZIzZGobdsNvtOBmr2+2ur6+bhhPstSuVwfcvXfZdr1QoRlH0+KOP1Wq1drM1MpJfW1v9a3/tr/3t/9pptNyu5waBRziZOz7TbHQa9Y5lZS3TDqMgDMNz588yrr785efeeuNfNBtCqB3TNCemB02LBADVWmO2UvY6VU6EZRstT9mmLaT7kbQnSeEXSjt8ONAUP1eaMilSeVAKz0o5zk+IrZr8k+/eWavoqDn7R839P2rsWbocrcZSOqVNjSA90v5Hvd80322aRJqJQaBBwDQegBYGYrTkXPr+97/9v/z6+XNnqtUqKGmbxvr6um0ZMzMztfWFpFRQUhXI9/0wDAFAuHE+q1t6/D/8r/8+qmCvVqXM0E2nWdsslUpxLNstjxImBLRbXSHEs89Mb663qrseYZxwhVShYnEEBg+TMkMJICYmqlarVRgYy+Vy6+vr7Xa7VqsNDQ2Njo4ahnF5Zc1rmSTCYs6fmJp19bnLXSMaytPWUTOTjiZH5Qv3j//+oJySD1HwaO1JjljIKk0nTYtrS9v/HnnAgz4BggIo3CmorOta5IXbm+uckUajUdvbNTTd0LhtGaamh76brAEthAiCIAlWTJQ4zjnjnFIZRRFnxDCsdsdvdoJyobS718pkMpbtNJvNSmVwZGx4YWFJCMWYlsnlkTAgMeUEgYUBqtgII1WrR0oFcRwn+RndbrfliSeeeMIwDELI8PCwEGJ5eXlsbCxr2QU7I30/9tug4kKW5hVstDwTsh9puz6Qj4X8RGBQ+vx2f7an+1CPtj1NUAGhyBiLEIFAxtS71c3VpRsjQ4OWoVuGbhoGJUhBxVGws7Vpa0m9mp4vqZfMRWlpsBj7nUKpODY63mzv1Rst087OTx2LvcDQm4SQUqkwNQX1etX33dljE8VywXNbEkkYxUEYaowRxoAS3STJYsQapXbGsSxL07QoimqN6urqulKQzxfr9TrnPJt1trZ2JOGa1UEZdd3OzvZ62c7mtLGu74Qfslf2o+NBH5UOdVSfzH2zV/4oP7/LL3bkykVHjms44gnSz3yk7Z9MU90d4aA4AUqpimPG0NBhr7FX376dKyhBMZuxbVMXUSjjiFMipWx0u0op0zRN00yc+kopwzBq9Xrg1k+duMh17c033n7r7fd26y2uOzrRfv7nf55z+sJLr5imPjo2mMk4mXxWEd7s+hQyA4OjRFO6yQC1OCKUNBID+b5EbtANgsA07Xy+WK1WG41WoVBKyFEmk6PIQrJrFY2MMex13U5jJTdYkObw2kcUH/Tj5Qv++MoP2c4PeNB92P5h8yDOCDtYt1Bga3eTha7XCeJA0zkTFN1OWwQeN01OlOE4Qoik9I8QwvO8KIo45xtra5ypbCFfbTQvX7nKDdPQ4++/8Y7JDMZ1TWdXrlzudJujo8MnT87PHZ/V+Lmv/5c/urW8Z9kZxmWhXCiVh3LZcrlIk3KLSTlXwzAM27KzpW6zQwg5ceLE5uamEIIxpmlaPp8PWl4z1JhuW3re9wIVuw7x3WAP2PDRGuKI8oAH/Tl7f0x40H25iAfyoYpGkjWPVVKQrNuub68tZ3XQOZNREEvGUIKMDJ1bOgWgmmEk1cgSD1qr1Ur8YvlSIeg2bMdpdzo3FhYnp47Pzp3UrdK7b74+MjJSq++5rj9//NT582ebzeYrL731mU8/sVNrrm9sW042iLvyZiwkYdQ0NR0AEpQxzV5ds0wmk7PibDb7D/7BP/C98LXXXsvlcpZlSXn7zPHjmjEYBCSiNFMYyGQ1nQgSrxE+8pG05wMe9LGSnwhd7MPefvT7PZpyyAgqAKkU0w0gtLa3u7GylDEYIzECMgKMgmVogpEk439jY8P3fUKI4zgJQPTqeGjUd8FxnEwmE8dy5fZqJPRyZejMhVPAVRBFp86cOXv2/NbW1uuvv53JZAwno4Ap4EA0QjXTMgE1AnrsCUJIHGMUoZSSUrQsblna1taa5/nNZmtpaWlra3tzc+vq1auc8+pnPrXdIN97/d1Ydh6/cPzk3LHT58+dvXhqaflIzfBA7rMcFYuPyh8f6GI/PkJQIUpE5JwLia1Wo7a3M+UA49zUNUPnDEin22rW626nI2UsKE8qtBJCdF3H3urpKvRDxhhhNAxjxnVK6e7urueLRy6eQYShoSHfC99778qV969aljM0OB4EkYgVECYVRqEgnEoVx6G0dMYYU4QohZQrpYQEijTO5XKXLl2qVqsAEEXRzMyM67rXrl27sXBTz5zNZqemjmW+8IUna5vVa9euVSY4wPGPumkfyEcvnB1ck+iIPCU1GiHlOPcvGiRtHaW0YgopR0mNbzpa3ASm1RtO+0FqSanDjxNRAxgPfbdCWNDdbN66PJi3gUZBjI6G0ne3as3dts9NJ1d0hNeUUKasHsYNO2vGIWk0/cHBchh3SZe2vNWZqcH11drwwGlJgpbbePvSG2PD2t/51b995fr1f/Wvfmt70ytmx9xO1e00GO0K6EagpKRoZtq+6xjctnk2V1JKdvd2KKW6bRFCKSUbO5u5XI5bJ7/36it/9+/97L/+N/+x2jAmjk1eKE2M53H8M784uCfGTTRN5Zra8nY4t1W+WFl63yvG2RHmt5QIwRlwQ2Eql9DD6wf+kPEmB1v00K3p3fxo/DR9nr8/Dr+0uojp/fyIzpe0w6QqOGnHT6kTkNb+dx//Qc7qJ0AS55NGWbLI8NWrV1fWVltd98qNpcVbKwI51e1AUkE01O1saahQKBiGkclkNE1zXTcIAkIYZ5rv++Pj4/l83vO7cRyGYeg4zjOfeXagMnrj+q211a1yecA09U63CUTqRi8iifQKRGuccwCqlGp3u61OVzcd3XQiKYLAD4LAMKww8DSNbWxseYF64lOfbrr+TsvrhshyFROCuawssLDmRm3M5seO7VZrhmHoFDCOkhDwpLj1R93YD+QvWn7MclaPJke1Z6XJh52nk9TtIIyAFDvbm5ubWwOF8vET8wNjx1TY8duNWjfOlEdBs3da1YLFTBXtVeu5AldIwjhiXFeAru8NDY04Gde0DNftrK7enpgZPX/hPKH27uqKwMVWtzU+Pg7KWryx4HsdICFjmhSoFIpYIRWJTgdA7Jzd7XhKAQiJGOo655wLIS2daDmzVm9vbtYvPvHY5cU/DpRdb3dPUou61elctuNGt1qqjZmp2en1pbdPCOXoEMRdqmnIDalinRvqgc34o5aj+5GPtvu99YOOdtAHcph82GNGCmlqnHFCQVx+++1CoXDyxMzI5PgAahh1v/tnf1Iemf7Lf/t/hdy6dukdroKbr7ys1pWuG5xz285UyvliMRvFnm1ls1kKAJzT0bHhqakpXTNvr26t31onfGp1c0MznMmpcUNjN2+8FYQtQogQChVJiBilIBUqVN0oNrLFwI1QBSjjQiE3MTGzsrRKZMfJmJSThcW1Jz/zpRMXzuYHxsTyEol94TZoxvR9P+gGSGmpMrK+Prizs5MfzvgiDgQQ3YZIoYopZ0fWET7hknpfPxntQNNqtTyQH14+7IeklEKUFJUM/WtXr5w5c8bJl64t3RZITSuvO/lTFx6ZO/mw4o6n9KtLW7lCycnl7WzGC8MwFk4uq1u2k82XSpWTJ0/HcRiL4OGHz1uWcfPmoqHbxUKlXKo06q2rV68qpZ5++snPfe6ZkdEhKREAstlsPl80DUvjOiKRUmpO5tTZhydnTg6NTeuWmck7Z86ccZy8EMLJ6LqWeeft97mG09PjbjcyTbNkA+e8JfTtjhR+JxdVTSadiVOr6xs2iXM8lFGoqEYJYBwxqn1U7fxAEvmLHDskLZH3gRxJ7tfDSDt+JmMCIkixunwLZXz+oQuRwK3dxu5u9ebikm1nRsYmry8u37y1NjA8XhgY2dnbA8Iy2XwUSyTUMO1YCt00ZmfmHTtrGFq5XESQW9sbQoipqRnd0CzLymTtOI7X1taiKJqbO35i/pSIFWO8UhkcGBjKZDK6bhJCAMjP/MIvPP3ZL5w8c3FgYAwBIhEhyka9zTStUMzYVnZnsxb59YGCsba4aGvoZItmJuuh3pRGIJRDPGxtjZQKrudJt5ZjgjMSS6AUGH5kRaY/Qjlqf7hf/e2o5/2Q5MfKHvQJrZ/Sl7RnYRjQdmOLw6V335qenKhUKq+2O+MzcxNj469873kKyvO86q1lCUZheHBwuHWjXjcMI58v+r5fLGYGBoZ2djYo4a7rSWwWi8XxidH3L98QQpQrQ27XlzLUDVaplJBo2Uz+1q3VwYp15swcAFDCDdMBpgmhhBAAxDTtjh+sbW5zI5PNlQhnus4ty7pw4eHrN74PFDJ2Psjk63urx6fOn5qZHBqzt7zAiiQ3hWZmQ5YxHO7t3LYHURZKzb2N0pieyVTagbQp4ZzGSqWtRfNx66uf9P72UQl54Bf7xInrQeQHhqGvLN+emp7wPG99fXNsYnJmasJ33W670+l0isXi/Py8EKLdbiNhXDeDKNqrNfwwlIi1ZqPaqG9tbdm2jYjb21tSykKhoGlaFIkzZ0+eOHlsfHz0iSee+Omf/ulyqRJF4tzZhznXAEBK6blBs9l0XRcRDcO4ubCwtrE1O3d89vhxKeX6+uqtW7dKpVIUy0ajoZTSKLv0zqsGladmJjmG13a8WqOhK89xrFDPg13u1rdx63q5XO40qkzEGdsWEgkhhq4J8ZNIhX6ShROI/vy90iStBiV+uKbutPnnqOEj6QWNj3ac+zUdYspiMRqH4bHyuy8+bzI1MDx5+ebtCw8/PJAx1q++1a7u6rnS8PiEYdGlhXfGZ+esgWIkwrxt65QM5Aqhj9tru416d2J2OD89evJEqb7cfPPNLY8YtxZuaKShZZe9HbFT7bx16XI2V1hd3VNC5DLq2sKls3PDKvTb3RZ3hvKlEdfbjvyWQnfhzctTnx/u7N5APzg7//TLr35/p83OXRhmRtnIm6Yj1m+Tq292fuWXuydPd//gP6/mI313B4dGhrm39NBYpas0beb8HobGQtXg24P5vcbu5RzMK9tptFsVxrppxtiPGe+4b0sfpI3B1KXY79f6d4fDPTniQDo6P70r/u4BD/oESOCHvtvZ2t4AQCnjnJPhlN64cW1xcXF6enp27tj62maj0Zienh4dHGSICuXIyJBSqtls5vP5XCFLOcvnip7nMcaq1WoQBLlcbmBgQNO0mzdvlivZjGPU6rsLizcGh0ePz59SqF2/ttzteohYLpenpibGJ8ZKpRJjzHX9+dNniwMDb7z9zrWbCxMTE7ZprC0vCr9dKBSEUABgGFq92VxYul2uDE5Ojk/MjnBNNuo7XIZjJafElOm7o7pmZG2gGSmNcsHSsSPcOJPJiw+3rtkD+djJj0RY0vHvw9Xbf1z18LT21Bn1ve7WxkYxmwm6nTBWukZavru9tpKvDI9PTHXiKAiiyYkxIcSlN75PidQZb7UCJSRl4HldQvnA0DhVaFnW8pXFjY2N8xPnjh07Vttxr956L5Nhmq4mJ0eDmM4fP+15/urK8upaQwqMY6EpEceh53WjKCKEoZLj08cqQxMvv/JmKZsfm5wql3Kt6ub2yiLh2u5evWjls8VM/ZZ/6b3rZ88+IonQLM0Lo421FRHJgdIIut29tYZpWz4Y4JG19e7gTLlo+oEf8IzWiiNTS6t1/+MpH9U4+ujy+++ST6RN+pN4zT+MpMW2Wybf22x0GvXTJ+c9t1NtNIcHyqVsdoti2+1WhgazqDW7ntdqqTjo7G45Fm+3GqEfDQwMCRFtbG+admZ4fCpsNUql0ipirVa7ffv2xODw2NjYc889J9p7Er1Pf+ZJiY5umpcvLzU7IuNYhmEppVqtlis2t3c3hGhzSi3dFBINyxqbnHEMM4qlYxohg921pdLIqVbjNhJWKNq3CSwurrc6wW51KzMwybi9W20uL91y8qMSjO3anr+tps6coDhwY7HGM9ZQcaITxCKAiFLzL7jdH8jdctTx9SOOxwc86GMkae1JldxYX6lXt5WcanfdyI9A5AK/K4SwMszJ5yEmu7fXv/P88xh2o2bVIoIqGUVxJpOJEYKoO1AZyuUGNcfSdd3zvGazub6+XrCcjFmZm5trbWDGtphtbe52F5dW1rZ2ALWh0QkCnFFNEUSIGUdCGY0J50arvhcE/rFjx1EqP4iYbiAqjbOJyelOe5PrGgglUNXqfrsTFEuZ5fWqZZhdj9c6sqtgeGKUD48J5BPjhahrXnnrTX1p7aFHh8qmXPc9M58nQfwX3PIfrTzgQR+L6ziSfNyu+cPGRCWj1du36nu7K0tLyM3iwKASwfb6mgI1MTWVL5Qcbj2RKdy49PaVdy6D3/agqfMx3w1QEStnc51phuWGIs8QEcMwzGQyrutubW2ZvEDt3LPPPks0+uIbb73wwivDI2cMM+/7kaIsigSlFEAhSssyhZBh6EqBfnO3XdsdGJrk3NQpTkzOLl97d3BoOFaScrvrBVxKXdcBzZ3t+skzx7Za26bmjI7mYiS5Uhl1alBbo5bGqYfKA6PaQr8dFDLhmhsSGP9QG/OB3Hf5EcfjA5v0fZAPO2as1aitr9zOOjanxLFM2zI8t9Nu1uM49AL/5uJCo9keHBw0OfNbdYh8jRMlhEa1UrFIGaGUFstlBG15eVnXddM05+bmGGObm5u2bdu2vXZ7o9Vyu61Ou92uNerDo6NDoyOZQh4VYUwLw9B1O0AEpURKpFSL2rXbC9c4IOO6mclPzM7FSM1M3nEc23K6XS8I3UKpaBjZy+9fL1YKkfQC4eUL2ZGBskWEIbwClVklu/XG6vbt/NCwmZ3a2mrptJs1Qhl81I/zL1w+7P7zYcuPePsPMOgTIBtr6zs72zMzUxfOnTk+N2twrVlvCBGdOXdW07T1rc3l5ZXLl69cuXLF63QsnY8ODcZxWCqVzp45b5omoTg2MV7Il2zbppQuLS1pmjY9PT09PT0/Px+G4WuvvbG8tFwolI7Pn+z6Xsftur67W92hlCdFiBCUruvJZ865DLrLC1dbzcb29vbtlTWu21Q3hcSHHn5k5tgcY0ygymazmmbeuL4gZBQTV6AHGEVuw91btyJ3ImOOaDxr2WHUHZ2eLlRmNraqSnVzOf6TZY5+IAD8yCVZ7pK0pLqj6fMMGAAQUABAAJPyPQQUIEcCAKBI70zJ15Qwmg9d0tZRSp8JDm/ctHU7DW5FsVIMAxnatt1tNkq5rEbYm3/2J9MTxwZnJoJs2e20RiZGVleWygPjZ7/4N0dGRhZu3PQ7rcEMjleMHT0mIC2n1IiZsit1s9gImKVnNBTN+u2xTA2QlseHSkiPzT8egMVKQy988/vHKyNrrjM4OjI55oO8HXhbnfreieKEVdSD2PaiVqFk7+0qjEDXZLFIQpDZnHH56hsxdYYGJwaLudm5h955d/Vn/zobPfvlP3j59kR2c6hI127vDY+OvP/G219+eOLVFaxmZjLzY/76m4NM+u3VqDC2sd7urtfEVKTNjzfqNFzdnB3LNHCvrRU1QA0lR8mACEBJiKSgy6Ot+yZS+idPsbOII65relRhKf02bTUydsTYPZUSr5e6RmNqqtZR6xClxev9UODyseFBvVJq2OtP5Cdipe17RClgnCilOGVKxjrXdM5XVlbabndsbCzrZK5fvx5FUTabbTabtuM4jjMxUaGUvv7669/5znfW1tYopY7jdLpd3/c5I4CSEGJnsoHvv3/lkqnrayvLcRzrut7puM1m0/O7AwNlTWPj46OtRrPT6RyfPTY9NXXhwoVCoRD4oeNkDcPQNC2TyRBCAj9KBmSlXP7c5z538aGHxsZHhoaGiuVSo9NZXl6Zm5sbHh7utLuOky2VSq1WY+Hm0ujwsEaVCD3L0OxsISKaG5Nas4uRn2EiAx6PvWJ5aGG7FRLT1DgnkqCiCIDkx9X/8ED68vHAIKIIQsJyKPa/KiCKgAJQFBU58PUju8wPWSRKwohCwTUKUlmGpnG48t57hmU+9sQTlNLazq6MxcbGRhzH41OTmUzm+edf2tnZeeSRRwzD2NzclFJ6nkcIUyLWKbQaNd8PR4ZHx8dHdUbKxWw2Y4+MjDSazcvvv+92OqAkQRFHgRRxt9u9dvV6FAnGmGXolWIujCOhZBSJyenpr/7CL33huS9mC/nVlXUVi4xlnzg2WyrmNEoQYHB0PESytbnnOJqmsWazq2vW5NQ45/zmzSVQyJXs1jc1rvJDw7Ge49kBQc1ut61DxL2qFrZGxqZ3PN4JpalcThQHmcxDCggAAYL0fgUHP5D7LT+inehj4RcjCImy1cMaBJoADQLsV1yluI895CPzi33o/i8ldM4gVIwxGce6YURuvHTj+vzcXKVSef6bfzoxNn5savrNN77vZDPFcumtt95SSp0/f97k9MalN6WUg+Vya3eTadwiMFLJ+7FQBEqDQ0NDQ5ubmyr0HdMwDGNra2d9pyGplS0N7W6ujZ8Y2Nxcd6xsu9kM/ZABqe7unBydHR0dL+RLC+sbtUYjs73dbLYNyzY5E1EMUjbru0Gng4q2Ou2RyaniyPj62k612tANTkCvVTtDQ05loLizUVtb2xguZLdrdQ2iTKHYcFvDwxXTjUKiaZbt1veMfFErjJnZcqdeHRqyW5gFSlExJABAkSgAIATTn/sR1xpI3f/D1cWOvAbUffLNfxzG+A+QjwUPIoAJ2QGiABQFlbDwPglKOBEFBaDIEYs938/r/JAloXmUUoqKESBSba+vua32U5/51PrmRrPZfOzhi1MTk3vbO9V6fWNnu1gsnj59mhCytLQkpTRNExEHBwcjIQ2NjQ0NZC1d07SuH6wu395avTUyUCpks5lMZnxyeubY8UwmY+na9Pjo2TPHcxlzamLy0YuPnZo/MT46MjRcnpoc3a3WvTCkGmdU29javrl4y3P9MBIG46amb62t6QwKuVwo5NjMsdlT565cudFs1aenJ3PZ0tra5s7ONiE4NDi6cHN5bmp0vGwaEAAlm9VGFEsSh2DlsgNTYQxBq11dXy1kc/WNWyO8ZZJYA0UJAlCFRCVVrh7QoI+r/Ig86GOBQRQVRSCgCAIFIIik91URUAQUTT6joj2E+vEUwiCpqSyV0BiPo2Dh2tVCxskXiwsLC2PDI1nbWVu+7bqu7/uE0fPnz+/u7t68edMwjMHBQUJIspYh143Q97qN3a21tXa7vbm1c3PhRuR1TE63N1ebzabrB0KIKIp2t9dV6PlumxJotRqlfMn3w93d3axj53NWJGKF6PpBo9Ve29ja3N6hnGULxXw2Z+ua22iAjFrtRhCGhYGh2dNndncajUa1UMhbZk4KqlA0mjXfkzcXbpmMnJwYMJQX+O1QhGG3rYcdiLx8ZdgpjmRNm4euRVR9exXdmk6kRgQFhQAKKCIBAEbwqJie2s73af+Pm9yv+/0Llo+FLgb7TjEKKlkhi/QVLwQkQBAoAdX/+vG45h9Cjrb2NGNEiEgz9dgPNEvrNN1rV66OjwxfvX4tiMJKpbJw4+bt5aVSoTgzOFsol959990ois6ePUtk/Mp3/lQpNTA80NheH52c2dvaWL21uLldM8yiZhhCxJOjo6ZOv/ut73UjLqQMwljzvPfeeYcCXOLVzTalJLu7crvTag/PTLb2qjcr4AwO5fNF07B10ySQQcR21zM4N7K2EtLStdj3Ll+7VRiZ5qY1PDVr6M7bb789MFiulIcjcG3bbHcbvhtForF+e+nEiRPewu2Wp0q5TNiqWhhkZDNjzbsqazAYKxdau03C9G4Y644EoDEQAIqEAulNUUdu/b/YnIOfWElrtx/SdvGx4EH7XjB171eCQBBg/3Xn64+nUEoFKsaYlDGnLI6i7c31wXJlr16bnZ3VGL+1tFSr1UZGRo4fP764tLS1tTU5OTk0NNRsNj3Po5QiommaApXneb7XzVj2yZMnJyYmdF3PZZ18Pvvyyy/7vn/u3LlTp04xRhdv3qSAtmW0m60oinwvHB8f/9KXvhQE3trt5Zljs04u22w2TdP8/E994Utf/vLY2Fi344ZhGAbBQKXEGFm+vYSUEK7ZmZxl2a+9/v1OpzM1NQ1AwzDUdV3XzVjh5vrGcDmvUxUFbinneI29uFUbzRCLg++HrUazlM14nlccGid2MeG8H6Hz4YH8RQpncCeW56OaB5BRBACgP2Sn45BS5uo+1S26X7bntDggltLOKlQGkqDVyjoGoP/6979dznKDeJbpXLl+ZXhkzC45tZ3rXs03ZsePzZ4hdj5j8iyXnc1b3b310ZGBQPq+rsWN1dkTx6QXQhQ9+ugzmx4dL40Oyc2tVrfDh/7gu1efDfRzD50Ag45tzTqGGcvsF37xZyZmZq+9+PXp6ann37709qp7dv5kbWlxYWNzdGAkXFlx53Yff+qnurs7gFqns1yPpjxryPe8Tz39uazD9pbempg7kR0tBEuX1r7/DWN+fnt3zYJMJpvfJYzwiRdffftzn3tYhFW/GXfqLsTuamuDm5UZdptS++W3r7555eZ4JRPWNoK69fOf2VovfPa9Vl5J39LirqCakWeekmZaJM3hkt4b0uKG0uoWHem0qZIaL5MSd3bU4+j3a7351P6fUm8oZbb4IeHkwboaHyPRdd5odbN5h1PSaTX2dnaHh4c550uLC0ODI4OlfO224JwDAKX09KkTL7z69mW3U1stbG9tRJ7bojFjmM1bRABQHsfdfL64V91R1uDUxLhRa6GCs2fPmvbeu+++22jtPf2FL3/qU9bq+1co+pwqilCvtf1wOcyMjIyOGhnTi7AyOHJ7cUXasLi4sLHekbFX291Qna38REfJ2LYMz7L8wG80GvlOe3KkfOvNKgyVjExxcPJYt7U5PJLxSLyyts0r5PriKhK91W4g54292s7Gqm03zeKAMzg5OT5MNX2oUtAGC16r+tL7KwPnayO5wZqnxwAa1zhITdPkA2b04yh3rzf/ideHP17Xf1Q+hQQYo5wRhtjY211ZvjUzOry1s+l226VSqbm7vrVxu7q7s4e4Xe9ar77plAZXrjcvKyG9NshQJzQWUexKiSBlKKQqlgtbG+u5qWwhN0o9HseSc24YrFbbC2P3wpPPjo2NFRiWC2Lbby7cuLm9VdOMgI/mdccRRA5Ozjz1bOHa+9eY5raa9YWdtRNDlo5+bmAEFG6u3jaz5bGxkTD0u1Fw68ZC2dao9IWSUjONyvjyytJp29HcvXypmCsbGzvtqakpypugG3axYkc+Dzu2bRbzmUx5wItELpfPZyauXnrn0h55vL43mh/3mLknjKyhaXGEhH7y++fh8uN6Xz+kPOBBH6IctW/5oZ8vZFUUAoWVpcXd7S1NibDbHRodbFV3otBTkW/ajmEYnsDbVy6PTsyYnFEZC7fldRoa+LajO6Z9a7Oey2RMRh3HaUSBbWp7extjXIWB2tvb83x3aGhwYGiQMT2O5fT01NgwBpv00tW1rY3dfHmgPMIIg3qzZmeKhbLGNMt1d5kRBG7Td/3hwbznRo5pUAxNDUqlQhDbuh9IpSDunDs5F4LSswVdcJ/bkjAlI0Gk4nqMdnlo+tQZ+9ZGzSkP0lyF1pcGRwY1i9qGDj6NUcbcaEqmj1/sttqsva2xkUhZhCABGSkF7EGNxb8ISe+3H0r81AMe9CHKUXmQUkLTWOCLOIovvfWOpemFQq5YKRCmBX67vrOJUtjZgpMvlXR9cGzKcuyg3d5YvsVEkLUty9A4p0SpXL4MiKbOdF0fLFTGxoY2l948Oe9Uq3umqQ8OlsoZfu78w6dOnrtx/f3V5nqnFWfGn2TaXr3RlIQNaTRfKGUcb2tr5+23b1BKCSFh6BeLecq8SqWyvt4yDUMGbc+1Wt2CF4o4itxOm1H1+CPnv/Pq25XB4dxodm91odb1/SBqtWsMwtrk9O5Oo1Qovnt9YXRwYmxw5uZLC14Qctk2M+BoVjsI92r1GPj4/KPu9e91G7X88KClRBzFDAnQBzzox1Me8KAPUY7atzTT8H3f4GxjdXljdfXY7PSFC2eyuUzbFbSQc5t7+Xx++sRZZha6nlsq5gvZzNuvv/bG979fcoyp8THLoH7Q8aPozLmnlpcWM5aWy+XM4Uqx6GyGnUK+cuWtTcuyJmdHo3ZzaHAMkK2vbWJ3bd2ip4aZ4WQqA0XX7dy8dnlmvDg4Obm7tX7t/csGZ9mss+l5+fyIrisv8hg33XZrdekGzVa3Gp1OIDKmFbWaed3IOtRr1+PQO/nQ07vri+9/5z+TwM1nNCFEvekuLa1cOH/C1rlm25WZk1dfslptv5TnBmDWMePYXdne0rmRyRYberHhiQGIBjTSEBgxSzMMKR8sufFxlB8RQ+8ysH9IoVOfOPnRnsj//8I0mnjl33r19YmR4VPzxw3D8DyPGo4XCd/3B0cnzj/26Yuf/uzw9PFYghfJ4sDg4PBIJpuPhWq5PnAtUyiOTUwqYJlcYXhsFJTc3ljXODE0cF2/2WwapjY4WAGA3d1qs9nknJaGjqOeBV0/cWZudLTkNnaaezvK90kYDBWzgdcOA88wTa5pzM7stNqbG1vbW1uB17FMJkTUbndzmezcsWNEM12vk7O0q5ffdhzn2Pyphhv7ilRKTiGfE6EIup7FydlTs0HgBUJNn7ggUHdM2wRFA4/4fmtn19G4BtIePlaPNLe6UeGuwUkATMCRYxQ/bpL23O/Xce6X/AVfz8cjPuiBAABAsnoXAfXO228+dP78yMjIreWl9y6/OzY1y7huO9nBoRFmWMzMxBLeeu/9nd0908kUimVkeiyBaQbXrSCMgTDf9x3HGR4ebjab129cJVSKOMxmcp1Op9msCyEo5blsoVwum5ZhZQcF8iiW+WLuxMnZT3/68UcunM+b9kAx96knn1BRFIbB/Pz86OTUyPh4oTKQyRdkLCjB0yfnn/3sZ5548sn5+flSocBNU0o5NTm6cP1KFAfD4+NmrmBki8NDxUIuu7ezu7Wxsb25OljKd9pN1wtm5s5IZEqC32nXtjfcRs1t1i3GdSJLw1OtkFW3VjPo26YWSRLHR3PMP5BPivCDkWBpkEZSAiRS66GkxU2mmBTT4oKOHKdzxLpFqYc5Ym7S0e0+h9+w9CMnn//WH36jVBy8+NjTb7z9FvKcWbBkbfGNV14YGZ4enzxHY3b51df3WtVPf/6n/I3rb776Shi2hsdKtxdvDOgFhzt5K1vdWCWhNzY58/0rq3Zh4IQW6a1FWyOjx05/uTh/Y/HKa7feGznBpo1pP8pS7te63YF2mOVDHrbiaH1oUKtu3IpHTwdxRura8KOfv/Ldr59SxCxULl+7NmoOazNBt9bsdtuWnh0dmNprLq1XtwaK5uDZz9166/nSLG/d/Nb7bz7/5Gd/7vj5p178+tceO36iOJyt85rbXnv3tc5fPXW8ZOHyle9Nnb9QzlDOVXF4dmmrjTbXN3YHbJ+vfdcYe3Jy/tTi95fOMt2Ou44MC5m8H6fU08GPZipNGy/p698djTJIFffP0mcchBAVH66TUqIffp30cPhWabmyqdQGAQhFAKAEgWKSx0cFvet6cF/0uwd2/7CBDAkhlNLE1Mi1g+dLi5VK8UfgEdNHH6Qd/mAxdC2O4zfffP306VOVSmV3dzsMgi995cuXXvqziYmJkdHJW7cWW140ND76xGOPE43+3vO/v7m5OTkxNjY80Kzu+J5n67pVKsVB27Y0gjA9OZkvF1cW3tFkaGocae3qzfevXr1KCjTfrtd3boX+XjYbbq+v5AaOV4rO+k4UeB2ND0yMDdgW396+7Ydly4CcQ2XQ3lxb3NteGRjOcNDK2dJAZbBTq954/90wIiYwv+ZiiY0MT5IW5g2jsbGZoWBxs1gaarU6Tk63c4XhStnWcGNjy2bSIWG11dWyhWq7nR3SuJ2rVRtKs7bqXWLmu7WaoeXK+dzu1poxPFsuZJrNLjeMj/oRffSCiKmxjmklBX8QphxJCACoA2uJIiKC7H9PoAcACQEAjBS96+zY+2zyZHsy2SPlgP0XQ3XoK00PRIqHvo6qT37c5IgP5siSdl6Da0s3bzQajWee+fStlVs7OzvTk5MXzpxtN+uLNxeuXLnMNH7i1ImR0VGpxMKNm6VCOQ5jgxuhFxqayYDJWPpu0G3szc/OMAp7OxsL16+sLlzLOiYqVRo0nAJUhnOzE1NFyxB+bXw48+ST5+ePTZqask2l8YioMOg2xkZKjz1yYmVteXdvo9veQ+GicqXoel57e29To1nHyA4UC3HQcpt7paxdsLJxN6oMTRYro5aVmRmduv72262dmmPawJzd6l6r06013W7MpGbv7OxODuQGqRtFca5Q2aw2m25kZxxFYHxyRjNza7uNjY2NTr3qmFqntqMJV1dhFP0ICwJ/MqXfG5P3Pr2iQA59JQneh7z205zuef3g8x42MBgBdgD/FEBSbEcBKlQy+UAACeA9nI8QkrAeSmmW0SyjDgELlYWKEzyQq5F2Uepw95lM+QFN4aJJAvTHXz5sGErj6kSKq1euDA8PGpb55hs3SqXiyNDQ2vKtRq3eajesfGlidnJodPLKtetxHNu6Nj4ymrMdpmB9dS32A9u0OKGN3arn1Z0TzpWrV5vdgFNpYvPEVy7GQty4flM3tYmJKU23lSdqjQVTJ92mEwauaO0oO8dpPFApmJypOJDCbfiRKljE0L04DKUsjQ4P+yJsrN3e2LE0mS06xsCAY4Ifuu1alyEpjuUaG3yv4z/8yCNf+4NvXb96uThQsSuV2ub7lcHpRsev1dujg/luu37u9Oxcxbyytz6cm9UZxF7LIKja1YGhAct0fFbkWl51fCXiVrtWcqsotaHKaDdK490fr351f/vPwaMlPScNPtJsJmnh5Wm6Ydr47QEiIAVAUGS/3VGG/R0OClOKHFAk+58dQERUqBQqROSajO45xwclStO39cN1tNS2SDGBp+mlR5f7c6APu1ZZWjsL111fXh6fGL128xph5PixmY211e31FZ3Sz37m08wp3Lp9a6vRYozNzEwV8tnf/vY3UAhDp4HbjiMvb+cJyFa7nrG467qNyM8XKpYmjNjPOvalK+9eu7o5NDTi+96tG8sQx1HYyOSsKxi/cnVp7Pg5RNxZujw6UDx7/kyr1Vpbvx3zbK48VhkSt4wsEuP4yQuz555qr9586fk/7kRerdsd0XXdMq8u3Vy/tXn2xDy3QMs5ux3vmcceIvzbz3/rj6fmz2bKTm05chwHib65tbdX7+ys7z3z6MnTcyM3Vldk25oeKjgkpEHNjusOOIP5ws1WkDEAdB4wveu5hvKLNGwrvws/WbrYB/tJ0jOlPBxUWIrNRMrD7UE0ZT17mgJyHFXyX4qKMCSIjCAA9M9KCFBKKKWMMUqpKd3+XRx8j8P4LgziBypqp9aRw8NtYHEKeKTRHQWfjDjXo85j9wuzdjY2by8vPfnU41EUhnFgcqext+t2uoV8dv74sT0vunLj+rETp77w+S9ywLdefX1x4Wocdrqtahx1QUS6lo+CAIU/Onpi7vwjkV6knLvVdeYpocirb73nuUPWTIWC395ug3Bzdjw6NmbY2VxucHz6uG3y09OjNmOFSnl7/b1mqzs+Ml1DjcQqr1tZbgyXBgsj09sEmxfO7q0v+n6oMWN0eGKvFrfsvYKjuXut8eGBG1Yun6tcOHP2rUvXTMZmR0e7ZqZVq1uWVclnNF3f61YXFm8/enZ2MKuv3LyG3ARUpZxjgW9iJIJuu9nEdrecH5Sa3fZjDDvlsux2G8QYSWm5j1ddzbT+c9R+co8KdufnKTXqE3PxIdtTTptmyU8r0WUCJtyHMcUIUoqUIICyNbZ/eUgpUoqMIaVoRPfaqpMPHQREZASQAiJw/Yeofa/B4f4mVIcvxBKn+b/Snk1q+u4nw4h9v7j3m2+8lrUdx7EbjUar2yllMlknU9vashz0PG9sbAqdnJNz2t3W8o2FP/gvv69UQCHeWL9FUGgMNaaQq9Hhsm5YgyMTJD/eaLcMiArMKFQGNDt/6/09ouPkWOXUyXHms9APRqacnVr41V/6W0PH59xOUza3X3/pla2657db1tOnS4S+e+Vy9dbKAAHebi68+ppRXtu9dY1JVxfY3WtuXd+o5HYcQScKBdmoukvXyiPDA2a2vd2eKg6/UX1h89Kbtn9cM63lWzdnxyc0qsWRMi3nxvLq5l7bsqyllSsxMdxuO6cTXWNKs7krFxduhvVOZWCGZfIyULduXp8ndHzykap3X5r5EyaEkH1bb29E0NRatCn8KHX9jNQzHrrdVIIQQqliFDlFTpEyIAQdTtS+oESaoBMh/WvuS/KVEKT0jmrGtQM6YRpn01L0xigFI36yVuq9f/Lu2+88+fSTjVr9+sK10eGhY8eOdWs1GccZJyulHJ+ayAyrpbX1q1ev3nzv0o0bNyYdH0G6njtcKaKKUUWOpc8dn+2GfLta79ZlJpudnZrKyJpu2ENjE577zsuvvLgymn381IkxJ9Qtr5DD5fXa+la1OD0XStzern77O98/dfbcUNnOZIuXX/nD779+S0p5Ohf522vXb+3q5XEWNIOoljN00fSvvPrewrWdCNFgUdTZDZ7/3vTksd3NveXygK0x0485aXdXbkmq3FbbmibNVmtlt2HnnaDrrm7taPZotjRoFIa2NlZWF651243C+Hxer5ycm2lv1XKV8aG5s3l2YuXV337ntRcfK0wDL3/Uj+gvVPrD+B5JXzPncAxK2/+oVQgYSALAETggo6Ax5AwIQV1FUkohBAqBSinohZNGugMAiKiU6r8DAGeUEGCM9qxEL15e7d8nS2FnB71FB/W6bkrovC9Z/1cJ/iWn58REAoqAJCAZkRSQJNXJMrCP8Xc1OjmcUOkQYc9NSBRQRESgkK7H9ix5hCBQRci+ixCICg+/gRSumxbXk9ojiCalTJCdHkh3sjhW641ieUhIAIVExnnb+O63vvntr/3byZnj1HA0wxwZGXGb1ZWbVzDsBkS3bds0zUKhkM/nG43GwsLC3t6e1t1kjAkhNE0rlUqFQsFxnFwul83oYRiOjY3Ozc1xjRKCpmlGUbC5seZ5roxDXefZjJV1LK6BErIVmPlsKfSi61duvPy977ZaOxfOz/+3/93f/9X/8df2bm9mwS6bhXJxOOI6LxRcVAhly7IajYZhGIwxxliz2YzjWJOglDIMQ4jYMDXfdznnUkotdNHmgJHtBReHxyaHK4WT46th44lPP31zcXu3HmoZY3dvpb67Njs2e2ziBEycc7ttIcRAuaQxsnDjRqPRGKyUvvjM6Ws7/rbMmcVhouLAbecLpWrLzdmWUgql4kBkLDiljFBOma8Od6WljclUu0kaHU/pJ4yn9J+U9dHSlk1L4yMsxTYiJUKvIHJSkR0ISgAQ3Dp0fz32D57oTv+kEdztlUs+VAzR3//gT5J2+6DOSKnW39LnQXD3OmiUUs4JBdIf//fe+T2tcM+1shROSFl/H0xgEIhCVEpKIIRQQhkBBIIEkkLuSsBhmhem2Lb7d4P7+/Q2pPnpQAGAQgoEKKIiPQ/dh21FSDyR0OOfd3AcKFQGBz0/AsbiKDQoRAIWFq/v7u7qtmPYedNyLJ257Vqn1ZChN3fmDGOs3W6vrSxuMgYAFOJSwbGdIU3TEJExZlkWItZqtVqt1mruRlE0ODhw+fLlIPSUEplMhhAcGiz7vm/q+sBAmQDU9xpxHHLKjNKoFK3ACyqDA3/9b/2NSinTau3cXt/4a7/w1916J8NMk5iWlemGEctkUGPUykspEZFzvr29HYZhtWoFQUBcGgSBprM4jhkjjYbPGCGU5fSi1JmUkROIXDGHjIaRQCQ7Ozvnz5//02+9wkI6OTpGRDA7O6sBC9obkwPDq+sbGBquRIk4PTffarXW17fzpSkvMv0wACk1ygLPN7kWuw0CjBEChFMgSikpRQSU6PfnCafNPWkYhOJwLEN1+Jyq0sZRGn9JuR6UyPYXA02WQkomZS0FsyyWLFhCEuKShAsRQgzaJxz9Ib+/Yf8TYkLTkuvs24OSrn5n1ge46/P+b++0m1KKM57sBwAgYrwH+ZJ3te9jg7thKC2eWNtvO0RUiNgrR49ECiAEkVBCKVBFCEFKgMQqOBTs4hR/nAJKABVCEoYgk7sGmmY/2n8wKukzBGkSRJVmq0ub9tLmpdRKBwQpI0jpPgvtPYxmp60bVqwIp4RSUihkrr739sLNq5Zllgv5QmlQonLbtb2tDdftOIbOSVQulkcG877vSynjOG63291u14g1pVS32/V9PwxD3/dbrZYQwjKpUsrzvPX19b3qjlKiWCzmcpn1tdt7e3s6044dO57P5va296rVqsa4MThgaka1WvW6biGfO35sIopd3aBbWwEhxDJtJYTjOEjI+Pj4yMjI6s5iFEWUUtM0NU2rlGxO2ejocVPkOt2WYRiMUV3n9XrdMDXbtnUKIcVut6u78TCzMRKs7AAUuh3PbXeoknMz0wODhebuTrfVKeY10trhWT3PBYs6bTc0DV4u5sMwbFbrx8bmfYU8imMRZx3H9b2sZRkaUwixhDCKKDeAcGpyJCzan+d/SEl7vvKIGAQp9TPTQnpTkTKt/6fxNUkIKEJI4j4nhFAghBCesl6rw5MOCXAHZYCQO4sv38M58ICigYj9couMMUQEcgdJknfZW6KSAPQ/JATirobgiX0L909/qD+/P7bv5UEpOG0w1lPBEBUAJUQRggCUyN4yLagSBx8AAUr4/qq89xyfpqT1x8AhMcEjqn6QQvr6UwlcM0AKvfWDkiyTtJqqaYlJR/ZrqOSSCAGiEHtsCCCXzzc73XKpHIYxocgZvvDt54mMP/vZz46PjzuO4/t+u9VwmBoqZTOW6QfVTlNQSn3fd1232+12Oh3P8yzQhBCdTkdKadt28sh0Xc9kTF3XBwcHgiDodFuEYKFQGBoaQCWiKPa7ISDPOCXPlg0SyBgK+bJSijHGNN71u/Vue3Ji9PTpk//8//ybumNIRnYbO5ZlZi1T42qknP/spx/nnO/t7bVaLcdxlpeX/+QPvz47O1uwyjdv3vS8brFYnJwa73bbURQJEZ85c4ZqWqPRAjfeoLZBdV7PBRoQnfieylj6++++Yxi6xa1WowlKCSE67eb49Izvd6JOp1IaEJ3qYFanLa+zfgsELWUrzW7XYhEEvkVCL3IJZQRYHAgzWxIIUhL14RddTO1vKeMCU7T21OUb03hWyvVwIJAEKxLKCCQAxAgx2eEYZPH9oXOHCsHdYXx3+BEAANIDZuZk12QFCthHmT4uEURM9Ju7rjzZh9zRXQghPNFTsDdF03tQYP98+/YUxD4kEULS2o7QngmKUCSgKEGkAEBM3ju+Igkf2j8sP3zU85Qxn1hxFFAERCD7YESBpjjklAIAJFQiJIxsn/0ejasflQchyt4dECDJxQIqRE50TkBEEQeQcXjz/Rtbq7cev3BmYmQ4Cr3t5q6MI05JztGJ0oLQD31vb2e72+1GUZRQngQyvAgopYkJJlmLmVIqpQyCQNM0znmipmka6/1LUV2zI6qEUKioxg1NMxRRjp7Z3d2uVRuZrJUvZBHx8rUb775/VdcN28qGTMgm9eKQEum6nVa7dumdK9lsNggCAMhYeZ1ZjpkrZMsMsNNttNvNoeHS2bOnMlk7CLx2u+04WcJZtVpdXl68VOvKQAYK21H4qc8+5Q4HxXzp1tXrcSRMx1rfWh4aruzUuoh48sSJer2+u7s9MTqWEC4edbqub2ULE7Pzyxs7ChljZKhcqiqeL5Ry5SGl6eNODiiVKMNYWSnxa0d9vmlxN6k+b3b4cdLsQVQd7sVJtT+mXCfd9zZRCvsYhBSAsTSL5R0t6aCiI1UKj9s3LAAA4gEDkFQHtt+xCt1zmDvc4kBzIiLf16cQPqB/9pvg0PkfEVPtL0oiIulhXm9aIIQUrHt/0Ds55QfvoX86Vx7+kHWUPdDpLb6hkq8iBRMFJQAgESlBRUiCRAiEpMxL8j4VMafQM8ZTSjljAKCUklIFHXeoWG63u5VyxhfR9y9dmhkauHBybmdnp7a326nthF4niqIwDIMoDsK41ay2223GWC6Xo9RgnFJEQohl0wNAo0VRFEWREAIw1HU9iqIgCJRShPBOp8M5bdbdMAwJYVIqz/M8vxtFgcZ55EYcOVdAUdmmVSgUOh134eZyBnLE1Jijm5YOgJSzjuvfur36yjt/EkWRYRiO4xBCDMMIAvADYCAoMwjVt3fqL7/ymh+4cRxLKc6dOWfZmc31ne1qI3JDS7cVEsYYIbyQy99evFnd2spmSrVOrVQoX7hw5tL1zcpAWaOs1WqJKGi36sX8ZKdZv3btRn13a3hooF6v7naiW6vrjqGfPz2fm39SEk0iDUMJhBFGdZ0DU6m60lGfYwqvSY1XTvlH2nF4ynXKNOWNHq4fIKUEFBCCkDingAIiISJFnwj3QeIeS0u/3e4xMzN253rusukcQJ+739nBw/bDD9kBtz0hhHPK+mgikMDdWHDPcftUqNcUKSCUcLDkf5QSxnoJsrlMT79jQAghfTjHu6Xvw8MwBYOkUEARBRKaNFfyNS2hyFMaIlJQKgnhJgnvQ5WSyJ82H6ZJ2t6cMoGoEBmluqZRSoUQMYAO1OTQDILGZtjY3VhbvJnhuHLt6js3bgbdFhGeDLza7o4bRprlMG7adsZ2mGVZmUwGEYUQACCEkG6TECKESLSwPhiJWPVt4YZhWJbZ7XZbrZbbFYQQQ9N0XWccYuEJ5Zrc8RqeaehZyxaxX9vZ1jVm6lYxVyTIFBUMmQaUMepYNtXMWseVhAuQIIFLqFb3EsPQ0sp6JV9UxORGoeOJm0vrjUaDa8wwDIOvOnZm5fZmp+sTwjTGlBI61956850Tx2ZkGDRrVREQy7YNrr3+xqualnfryBhr1qqNlisUpXpVKrUVstzQZG6koucrX3rumfdv3GIofvZLP9VmQ5quxwjr2zvAWByHnBAlBWPmoc/lqDp16nNP0+VT7DVpkMJS+E5av1Ip1y8oUkRApAQpICAkixKn6YaR7Nuk74IhDfo4cJeGpe3fwAfcVvdagpIfHjzm3WdRB4/AKesfAsl+3PM9/vw0SRuqB+1HSYJ+AkO2pQgBCoRSygklhCQWJRFDH316kU6IiOin+BcY9giSBJmYonttk1JnLyAaAiChiMAAAUEll35/umKqMEaVIohAKdG0hNEzKcXexvaNq/Vmo3F78abXqN2+cS1D1XvN+k7oi8DLWyxvcdvSM/lctjDAdKvRCrJGxnEcxpjneQoIIvphxKWkB83/++1WLpdt287n8wAglZPJ2JxzAKVijKIIFVJKGCeIcRR3paKxFxJJiMR8NsMtTaOaL6WIRL6SDfwwigIRxZplFfLFweERpVRmrz1oDwVBkMvliuVSHMdSykKpODA4UW12225ECM8WBriZDcPQ911Dz3Bu6ppdKJpJl/A6XZ1yapiLi4uB1y1mc6Efu+hzFxterWyJhdtLx0+dpZRmc4XS8EhHwKnT56v60KnJAR2jjh+dvPjUVqit3lqMuCOJTqmOgAog6XCMk1SWfnRJGwVp3eeoGKRSdJ9U3T+NNzFUBAkmB8R9Jz0E8vCaHiApuZsB9d4T3ZMk9uM7egmosM9oDuyPdN+snVimcd+/DrB/fEIOmr4JkQeblAfyDtMR4i7oSeADAEAhokxGLueUa8AYAQAm7vAmODAMuEETAOYUOFOco84UY8yiToKI/WCZ5N0wCCJKKfun7pmN0AO4A093rpPfFZ+tFCJKpVRJN5RSAhUARSAKiJRSKGUyjxCSLMwgFQoFSimJ4MWKEKIoRaIhoQKpRJCADhK4K/iIIKGImLauWUD0xAfBCAIoVFLGQilldTYrjjM4NLC1vlFfr6+trN5eurW1tbW1tbK1tUUIcRynVqsRQppRVCqVRnPlYnGu3W5rmqaUiuPYzJVc181n9WazSYErgXHYsSwLACgwLyaB6JkSROBzzrlpMMa48G2mPXxmMop9ISLLcgCP12otQOp5nq7rlFLHccpF++Sp467rZvOVer0+PHem3W5ns9lMJqPv7RnHZ8CwhRCMscFKxXXdSqlEAXK53NhAsd1u2xwwdCmA8NxcLpe3NGbA6OQQMtHtdoGTXLFQrVaHRsdoaYiapjUcJLohZcx0iq7rlnkoAT2Emuyihr7n83A09CAql4nUy+MzXUljyifHRjqdzlDRpt3WziZ54oknrly5cv3tt0sQt4hiElucWggUhMaRhF7WMr0g1DUdMSVaNiVuOIZD5nkAYORw8NDlvUpD8lVPybFAkrbe3J3dDiofVEuxH+FdOlT/ag0V3H2bvb+oDvqy78CHFPdadBO7sEyrT2Tohx6Hinh/CyWEwL5ri3HcB+q7TiMVBwJkXzfjB8d2ctjEzJn8MslAIwiIFJNUfQKYqJugOGH9+0/gow8xiU1eY4QzyjnX9tWx/qX37vkA4vTVh/4zS3x+SSgK7LOqxOh2kKMxxpIL1jgVCkFQgQAEk+mCAsgkkQZ7sM4AKUEGqChDSpBQBJAECCJFYHcs1kBBSUi8+ApSTfBAULHEZC+TyVhpjAAjQ5miZVnV3a2v//5/Wrm1TIG02+3l5WVC0TRN3/er1SohxLbtOI4BwPd90zSllLquJzeVgC/VeLKGahKNGoYhIkZRpGkaISSJEkyM0JxzSmmGZ7mhG3a+nB1mjHCuc67PHteBxN1uN1mhTNO0ZrOp67phGO1mJ5M5G4ah67qapum6jnii3W5jDPV63TRNznly0jiOTdOk06MAo8nZfd/3PC+TyQwMDHT8wJB6xR5PLrtWq2l5fXp6JJJ+RmddS/kghRCWxaMITaVK2YJlGUo5tRoPw1DXeblcnJgYc0rl69db1d11JTzbpKACTgUj8chAZmtr7eZVXt3eWl7gUkoVRbFfz+TKXMWcKAsYSEUUY1QD1ACCwx9YitwZ8Ygf7KUflCTW46C9ojcse96ifUbQdyul2U9pcpb+e8+TnRZzS+7gzj0urcMxS/SwteeH6Yf2pOVw8BQbNulh8V3BRACY9Cj4AOym8cF7aBxX6oOqE1VKJSOfUso5RyUJoQCJf0sRgoRQQihIYIwlVyCEEEIkMMQYYQQYY4bGNM4YY5zcCdiDDzzU/vNjfae+Uoioado95CgZb5yyZDTCPh5LKZUius6ZJDFVVKBAJSkQqRBlDBZBRVASQA6SEkxmHk45AlVIJJIYFQeQQCVg2HtmVAFhBCUCAiHpBduYEpRSigqlUkpwCjrXOOd5xywUcsWs/eynnjQ/9+zw4OCNGze+8Y1vTB2bm5ycfP311994441cLlcoFFZXV8vlcr3dsSwDUTJGlFKIMo5DpUS34wa+yygQQgAlAcUYRU6zVr4/FyXtFkURInphHIY+N99SGCklbDtjGFaxULEz0Gw2AcAwjOHh4QSPpJQmpdms02zWk5NyTg3DIAQHS/lixiwUCgl/6T/o+fnpJP7bNM0kdiyOY0rp7u72ubkJ27Y3NjaUUvn8Q0k0k1ASAAYz6DhO8tSCILAsy42JYRhRFGWyZoK5xWJR07QojuemRsbHh30/v7u7K4O2QQmVfsmQKkMHHcKKRmtzMY7jqampPI9MGsRxSCnVhK8CV+k25Xr8o60EdFcXTbHXJHE6++rJQZMK7vfYu2P2UjDiYIxefyxCus+efsCKvP+eYt+UH8COZHtKAVNdS6tZeAdnIbExY3L9vRjFgyQuGcaHHkfdDcb8YFsjqsRSTCmRiesJKAACKEoZpRyxF65FCWOMgQq5xkzTSHphGEI/L4FRouu6rjE9AaF91Oxf5T1xj305uEPi9Olv72MQ0w0hRIJoCTNKQFNnTDBCpZQcmWJxLJUSRMlEn2IAFFAjqDPQGGGUBDFHREGIkCqWKIDGIBRgDBwJpagOwtAPEANjipSAkihRCg7EZNw2GCXQbrVkLCqFwuTkZDFf2NvaMhibm5ubm5u7fPmy7/tjY2ODg4Pdbnd4eFg3LEKICCMRxUEQhGGIQsZxrDPQGKWAnDEwdF3XNE0TGk8U9gSOE0kgO3IFIbi9U9+rbnW7HcfJeG6QzeZ1AzudTqJeTU9P1+t1AGi320XbHBsb29zcBADG2ODgoBDC9/2MaVBKJyYmarVa8pjK5bKmaa7vWZZFCCmVSq7rWpYVRZFlWWOjAxsbG7quE0ISa1QykeicW5ZFGWYyGSllPp/3PM80zaYrk8CCkZEhy7KSaU8IASrG8aFisSiEmBofYoyFYRjH8dn5cW+8lM/nJ4aynucppWzb3lm7UQ+Wwlhm8wUvRsMEI5fRdDOO46Pa+2ifufSGQ+9zmo886cN4IAi+14nlXXH//Q9pNY57rnxyJ14k+Ula/Np+zO0H4ulSXPAHffkk5ZgHhafZefd9SPvHuat9D06HvYGcZpK7hweRA/U0hIooAgGGgAgSQSkEhaBxyjljjElJoigSQiESAMoJUgqaxjjnlAKijCKVPJiETyWIQWnPPJR0soPaVvLIDsZfHHyoBO4UnU3+mxzcpIRxlmTS9jU4pRgqQQB1pilCpVIhjZSiSgAFpKAYQZ2BwbjBwOSU0aTPgVAqQsJQRUpRCojYwZ7ydRCGVHooGcdYQwZEISgkkjNmUzQp6roehmEURd1ud29vL/QD13VN0/Q8r1qtbm5udrtdKaXned1ut91uV/d2ENHzvOSmdI0ZOjd0bmjctu2kWZRSQgilVBRFoe8dtOLDPiJrmsYYMU0zm83GcZQ8jqmpqdXVdVQmZ4Rzjsrc3mpZlkWIhUqr7nXarTBR9By7tLGxQSm91mkKIeY74d7eHiJWq9WRkZFcLre+tUkpDYJgYmKi2Ww6jtNoNLLZ7OTEyNLSEudc07TBwUHXdZMYonzOmpycbDQaQRDEcXzu3LlWq9XpdPL5guM4pVKp2WwiYhJ8wBijSjDGoiBkjMVxrOu6aZqMUI2RQi6j63pmbERKmYQvGIZhWmGnG2rcJYRmbZGxMKBBGEW2drhfLE0I3lGg8EAccJoNmIBK6pNSQhOra9/22u/Jd31IzWvv7X/XsQFoSo4Fp4djEEnTre7afAcZ02ImIcVudXfq+p02kQf54D4cU0ox5X7vwSZ+jwdQ3V36LJlmLZNpGmOMQQRKQRzHQigpUTMQQO2TI5W8EGVCrxKVqqcTogIAJQXea/O+gy/3KGiUUilU8oEx1p8kpZSmoRFCNE77FwlAAGgUCUZoclyhKCQeB6IIACGMEqpT4JTonFJKGAFDSokKJJEgFUodaGLtoomt8W4YoqnVtsEESQEZUEIRKOEMDRCaoju7Ndu2ma4D54RxruuRUrv1+rwQm5ubnU7Htu0wDOv1uu/79Xo98FxEpACGxhOzNKMEALxuBwCSYqZJEBBjLMmW6Ldnv3AUY0zn1PddSsT42KBlklarI2I8fep4u9lSIo7jOGNbk+NjezvbQohutzt7+pTv+92OJ5hARcbHJpuNdqPRyBXKuq4PjYxv7VQ515xsoVgeFEKYmRwihhILlcGW63uR6AZRccBe2dyNkHHN2m00iG4LITwv7HQ6bc9pdKPd3V3Oue/7Xky2t7cppUGnWS6Xs9ns3t6eUsqyrEql0ul03HaHMVapVEzTrNVqnucNDg6OjY2t3L5lGAbnfGxsrNPpJAg+Pz+fy9Ldaj0QKibG+adxziqw3Ij+Iyydd1CngPQ4INbLDweyX1gnean9OOA7WhgBREzLfSUg4UD4TH9EpPERRu+ynf+51EbrG6cPUjMCccrvUrHyQHDhwfbpY9A9Cs0P6ZnkUsr+nVAKSfBB8nlfM5JxHCaMpq/lMUooYYyBUioIgoMmpD7RkVIKggRQKZWwRyni/phJJuceS9qfKO5BQCnj/p59AFJK6RpLrLbJ9j6i6RpTgAAKCGGM6hpD0LlGhWTJAXsVbUEBgACgTKBCgpIplAwVSIYECVCpFKFwNwxBeq0DDSQF5ERRRjlljBJKkYogUkQHRoCEEmJFFNGoZnHDeeGFF6SUq6urnPNms2kYhq7rUspSqZQEGSa6bT8kWkZ+YhVOLDJRFOm6rqQwzSwc0BT6N6hTobgcGy5cfOQCIbi0tPzqq68bXP7cz3662WwuLy+3Wq2zZ8aGh4x2u/3KK69YGTV3YnZg2A6C4OrVq5Uh66J1Ym9vb2OjyhgbLufHBkuapm1vxwOFjK7rvttEBM02ZkaH3Pqe53mhRucmRkPE69evB77n2Nb42Oj6+nochRnHnh6fAIDQ9SzL6nItZzsNxi3L8pptx85TwqUgUgIajIC2vVXNZgpd12c8sCziBxDFbHevE8Xbrg8bO3tRFLU9TNTDdrtdb8eWJuq1ZiDQzFWmTkUgTRAaxbTwtVSh+xaZ/dcdKnSoJPz9Li0soQBJTyG9od4b+QRkygVhz8/VB5R9MErDPtLnQXfeASDN/tIzXyOSPgz1dL00e9Ph2+/y3wHiflKH3D/+PQYjTKs7dnfMJFfqzp2wXr37O62Z8Hzf90nP3UgAgDGmcU3XTUI8pVQYhv0r6KOjUiillAQJIKW93pCAVH/ehoSwHcDmRA6eHfaHVvKrBIai0GcUQGMEQCmppEyCCChnVCmhFCikVDFGDNA55zSJP6AMgSBiLIlEUEoZFAEVSfxhSiGl/edPQSlCcT+i+tCmPPjMKCCnhFPCOGWQaLFSIHWDOArDxaXbl9+/RoGsLt++uXBLSj+J60ssQVJKTdO63a5HlO/7fVNXYvdNoDYxzydu9eS3CVQBQOKf6jcaAEDUTRaDHx+pTE1NHJueaNV2TQ2efPykYRivv259+9vfztjizGcuWJbFaXd7p/mZZ77KGNvd3d3aXjZM/NyjT+m6/r0/e2l1dfXs/PTjD51WSv2H//AfRiu5p59++qe/8Mxrr7126dKlM8cmT0yNxnH84osvstj70nPPzU2Nrq2tbWxsnJqbOjM/c/v27cXFxVImMz09Xcnltra2SBw/cu5c0XE2Nze7mdzMzDFd1xFJs9nMZrOzs3NKwdZOszJUHhsba7fb9ZanW7lCoVCpVNbee8+yckOjpUKxuLZZLRQKZTNXKI80t7cJKxYy1uDEsTOnHpuaOL7nQqvpmpn7kDKGiDwtr72XbAT770gOZF/25c/VxfZ1FkIIQUDYj75Js6n3Ig8J6fnv9kd+mtWyd17EOzCU/IQdzhXTSp4dVFn2AeEuZfMe/ogpsSz3xExywXl//pRx0gykfwJEiohC8tBF5vkaRV2DsqNlM5ZpGjKGZqfd6XgKmWY6lNtRLPwoMkFxQplSGlIukDJJEAAUymSM98gLY9Qgms505gtEBJIs4IE0AQAGiQ8F9iOPYH/mqflgiNgS3DaJrmmE6wAoETAWhDBKeDL5ECmpUqAUZ/tmFCmlAiSUAlBCPV8SQpAwDgSpVEKKWAglHW4KIYQIE6zc54NIuI77cUwHYxoCElGkQumMMCIQkShJEAmjqBnmjRs3/uzP/sztdlQYolJZx4qCWEoJKLfWVuI4Tuw7yT0yxmQca6YZhqFpmgIIY4xwjRBiWo4QQtcM1wssyoWMB3JWo9EwHScMQ8qYZVntdrtYLNrZ8VZn08rqGgev7Zeswvz4JCi3kCns7u4++eiT7775bjFbHCgOIOLD5x7+ztZrDmPFsj45dGz92Wf3dtqTAyO5vDH6N/7Sb/zGb3A9vPDw+SiKfs77/PPPP3/s+C8KIeaO//yV//1rXAsmJkceeeQRIZsvvPDC/GjliTPHG43GP/7H/3jQ5o8//jh59qnf/u3fXlxc+Pzf+Kp88tytW7f+6I/+qGCpv/6LXwrD8D9+7Xeku/lXfulv+f5jL7zwwvvvv39yuvjo2c91uu5//r3/dHxy4tixh6rV8RdeeNky+Rc+9fT0aGFhYaHZbHzp2ecqGRrH8WuvvXbhs49HE2arXtdzRVoaKB6bWPY6SAm3u0CdQ8cAT4l9jYlKmItC1SdDQIArSg4Etd2xJ5AkTk1RBFSS7Cth6gPxxLTnOzscgzTUAKC/9E3/VwLkAVvPgX8pCdDDKtzfmIBKf7wc/ODKu/ka3T8UEYdiB5eH10cVog9y5MA7COiFJirEXlAjIgBEYBx6fHr3bfLQ8xOdiCR+3wMzap+YqCREjyISJH07ixKGYRihxVgYh1L4IdMoEMo5F3EIVClFlZQqUX+SpHZ1F3wCwL5/PcF1IAQppUmBSKLuutC7dE0gURQppSgoXctwBgRJHAtAdfBRJZ2GMQYoESiAVEgkAKqE2iRhR3SfdN85EVchI6jzRA28E1IQKIIIEogivUTZ5DFwRQEBpYyFUBKUAimlUmDZrLa3u7W5WatViRSGpvuBF3puELoJiiVwdqcvalriou4DX/LfhPv0Gy0JAup5CHVd1/VkH03TEnNJs1U3Tc1tt/b2djZWd3RqlgcGUXlvvvH28PDw2urG/PGTuztVSrhlWRo3RkZG2u12s+UahtFsNueOz1WrVSEd1w9nZ+ZGR8b3dmtDQ0OGbs3OzAV+VCwWu93uhfMP+1547uyFhZtLF84/fPXK9UyOt7u7o+NDD1086YeNYtlyXfcvffW5f/n/WLcsy7bt0dHRP/zDP8xkMpVKpVKpbO5sv/baa5Zllcvlr3zlK++//34ul3vsscc2t7bffe+dvb29/+q/+sudTocQ/tqrb83MzFy48PDKysl/+S//JVHur/43f71er4PsirD5cz/3lWa9fnN5ZSsAQBWHkWE6THPS6vKkmmKxZ8jomx16fRUBkqWw+qpZ0o0pQZQUQRGVVMMCRACiUe0uOrD/q9S8s0OBBgBQ4gcilQ5+/cFyEIkOUpU7p6MHQwHulNdQabnoKSci+36kfmhh73R4518/4Dq5jBVSQNnDxbso/T4MkST9FijBJP9bJgM3GQOmYcdx4MdxGCPjBmWaEgIJAYgpEEGBUsIoJQQZ4fcAXBQJKTFWMaWUUqCMMMaIxISM4X4MJAIiBaSoFCIFkJCEEfs+cEZN02SASimyH1eenKVv845jTABIKSV7c1zi5yIKFEGWLIwESIASkNTEYN+jRwiBxDRGKa37CpNiSOqO4gkAABoiSqlkwqUiKSUqpeIo9jyv0ai73W7ouVnbctudMPJN0+j3Bs75QXRlB4RznjAjsh9alWxMksghMYHpej+gkRBiGAYhxDIM04hnZ2dHR0eXbq51vW4ma+ey+d167Tvf+c7Q0ND8/LxhGN1u93vf+9709PRP/dTnt3eX96rrQogLF86JGDY21l/5/rJp24VC4aWXXiqXy4mHa2hoaGFhwXXdfD5/4sSJTqfz1ltvZTKZMAzPnj27sLBw7NixZrN58uRJz/M8zwvDUAjxqU99amtra2xszLbtr3zlK61Wq9vtWpY1NjY2OzubhH2Pjo4eP35c13XXdacmJy8+9PDSrRuGrjtDQ2dOnV64fssyzWJOs46Pnz4xSdEbHsgw8MdHCoyJylC5WMq2PdevRY6muSGXMWCEzDh8LKUtE8N69aV6I6s/PiUgAhxc0ZNQAkBiFRMAuV+6UPac14of4B0HP0DKUFTkDpe5i9pI3NfO9okHIXgghjZR/e5ZiuIe4LuHiRzcSOhd6HBQ1Tq8gVIkqcGTcCCgJLEVASFE3QtAPUiCu07EjTv+A1QfuNa+UEI4BcYJp4xSCkQRVFEoKGWW5cSCBMKPwhhiYBw1RmQvdV4pgowxlbAJ1luAsdfEChVKIRWRMSGE8WQEql4FAiL5fuHrexoxgT8AiKKo0YgMw8hYpmmalNA+d4P9Qd4jFEiUUgJBSKWUkkgAFFEJ2ZIKiFSogEgkSCDDKedJwEFih0qIidKJREBFlCIKD3gYqa4rBUIIRiQFwghNrPOUcse2zp46fXX+vevvX5ZSMk6yWoYecJYenPqSaOkkLjRxSHPOkzn5DqvfD3o4OGOTXpSmAgDf97OUiCicnBhrt9uNRmNwYHxpeRlILITw/bBebw4P+1euXCOErK1tVCqDr73+crGYC/xYKjE2NmIYzu3l1ddeey1byA8ODhaL5evXby4sLJw7d65UKt28uXjp0iVK6SOPPBKG4ebmq++8886xY8fOnz9/+d3l7790eWtr66GHHsrlcn/8By8sLy+PjY0ZJl1bW3vxxRfHxsYIIaVS6Z133lFKjU1OcM5ffPFFy7IGBgaOHz8eRdHi4qLOtJyTOX3i5OKNm7quU4TzZ842qjUZdZRSn/vsZ13XvXnjxsDAwIn5+d3d3WqzQUSkawRE2Kntas6EVKAZtoDuoWMmrS4Ck/fqUL3+Ru/qV/0JAwUiUQQBQQEBipB8PRg9e1CxSIszkoT2B0W/vxNC6B0cvEvUvS7//b4Ed+EO3I1HH1SLKB48752wgtRyAynYdOhZ7tnnICKjgoP78Lxj9iPc0iJLGQWdMZ0TnRONEY0mFZJULCTup6UpCSJWhEkgklM4uAIaIgEkqAC0/VJeffxGAAAlFKFAkTKFVAJjLEFog97Rh5OqF4kpJrEoSSlRxgSQEKIMnVLK6B1dvf9BKSUVCIQEgGKJSvXq+hAEAJowIAVEJEcmuF8lmWEvjVZKiYQQIgEQiVL0ACASQgCBAnLKCAdGqZIJFaeaDvmsMzJQuXH10srCDSUEpzRj223PPdg1+++JLpaQo36IZvL5oPaaRM0QQqQERAzDMHHVI2LCIxzHmJwey2achZuLjWZzfHzOdrL1xvZrr776mc985uGHH/7GN74xMzPzu7/7u7/2a7+2uLi4sHD9kUce0TTrwun5eqM2O1NYXFy8ePGxgeGB3/zN3/ylX/qlOBa/8it/a3t7e2dnZ2Rk5PHHnzh27NjLL798/vz5ra2thx++OD4+fvv2yun585cuXZqamkEkm5vbmUym0WiNjU0krC0IgpdeeunixYv5fL5er3/nO995/KknT5w4AQDf+MY3NE07ffo0Y+z111+v7uw9/fRTDz18FkG89tprI8PjMzMzmUx2Zfn29vb2zMzM7PRktVpdvPl2Npt96PwTe9Wm9NqlfKHUkn6r6WTG3CgkOk3z76iUkPc7fqV7CAsFANyvAAWEABAFhGh9Hw5QRhI/nDr483uGYhoG9eUeXYzdqanaGymkFzd0xxxDyB2cIgf8Vvec/YMABKmQAinLl6VKfCDn9q5bPiwYgdxd2QQRuWPTKIIwlAQEYcY9PCgZJxxUQlI4I5QpSpD0Yn9QqDgMRRQJIQQApYRzriMGCbukhFNASoEQSggqoAqVlH1m0bsIlAokAIhEeWJcMsYIQaR3Qp72DShKKUU5E7EQQjCClmU6jmOaZqLN4b6PHw5YsiOJSinRMyUrkWAUUFRqP+mHSkQkFBUoQhTVEEgseveYKFYAkIz5vvT7ShwHAACYVPUBygAVBQBDY5qmZR27Uir3bDoAUvYS/A6GJvQ7X4JBCcvrrVWwj6fJDknhHt/3NU3TKQeAJE6vn90ihKgUKk8/8UQhmxkdG2k0wm9+6ztDwwPTcyfKpRJj7I/+6I9Onz4tpfziF7+4urra7XafevoJy3R8X798+crk1IjCuNVu2Fax1WoNDAxsbGwkCSWLi4ue5zWbTcbY0NBQrVarVqsrKyu//Mu/vLS0dP369YytAXX/7q/+/X/4D//hE088cenSa//gv/vVmzdvfv33/+hTn/pUsVj8O3/n7/yrf/Wvkuv/R//oH12+euXVV189c+bME088MTMz861vfYsxNj4+fvHcQ07G3lzfOHP2pM41yzA31td9L1hcWhseHl5cWgNiuG6wtr576lTZ82XDC2gcDxdZKZNhKoTYFRELZMxTdDGWFnPYH71378D20wnu8I6kfpPOAYAQoISw/UqnhCAxDNwPjut7MNI0DACQ/VzZu3Wrez70R69EcsjOhNAfyIDgEPXwECUOADBFWU27fpHWnh+oAX2ocEalrgElzNBJOzqccyZowAgSAhRBKSVEBEiCSAkFUSSVBMY4RwDClNx/EoTShDFRSntRW0RKjOPE0qb6JjqlEs4iEZEyYIxyzhkjhN25jINGXI32zCWGxizTsCwriQzGA5FH/aeOiaVGoZQyligTYxASAJRCJeY3lejYiMkqtm4ESt3JUwNgiX9QoeoHjyTMN5mgUESEMEKQAO2Zi1SiizlKxJpl2rapc6abZsiIxjhwjez7WcgBI30/diG504Sc3pMQkFiIELFvqE52SLS2bDbb6XQGK+XhoYGEK1FKv/3tb+eKhV/6q7/wi1/56d/4jd948cUXs5n8xYsXH7n42Isvvuh2/Vu3Fp968hnG9N/8t//m1/4P/5tSqfDlL3/5P/zW119785Vf+ZVfOXHiRK1W+3f/398qlUoXL16cmJi4dOnS137nP509c35yYurC+YdffOHlMAx/5is/l8saiPh7v/f15577suu6Tzzx1PLyilLw7LPPJolp77///tTUVBzHjuPs7u5mMpm+JYtSWqlUdF1fWlr63NOfuXr1yrXrlwfKhccfeVTTzEb1/XazCRq58OhDly9fvnz9/ePHj3/xK88h4quvvjpx4qEcz7UaHYwFxcjvNuxshXGMUmq5GOnr/eKB4Jf+c2HQt+8k02ayHYlUJOlvBCn0PhBCBDm89E3aOJTY41N4AGgIIZgSJ63gcDtR/2wfVIgOYsEB3eJwDErTVdPWmEiTe6bq/ZtCQg7WhEUKEGkaOo6Wy6UGtpNeDDQQBEQFSighkySDIAiSGJYk0A4AoiiSUu4vXEFJYlfGfkaFEkIkDukk2EdKKSQKiZGQYRwFYRxGcSTiWKpYKqFQKOx/SF6I2F/oxjDucLcPZtj3OBQmxTr2FxpCgohyn1spBQcBCxG6YdwJok4QdcM4kBghEYQJwhDoB1/QsyP3/iShpMlttpuNZrPpup3QD3zfVyIiCAgyaavE3oSIQoik5mG/EGLfW5/cDt2XJDM++Xl/XR0A6NuPstmsZVnFYtHgWhwFq6urlUrl8aee5rrmed7Gxsb58+c///nPJzkTSqnh4eG33nrr29/+JiJ22u7jjz8+Nzd35erlhHBVq9V//s//OSFkYGDgrbfe+vrXv/7d73735MmT29vbv/M7v//bv/3bQojBwcGvfe1rX/va1959991zZ55YW6n93//5b/7xH37XsQaefvILv/Xvfu//9S//XRiGTz755Pj4+D/9p//0xo0b586de+655+r1+s7Ozhe+8IVnnnmGEPJbv/VbSqkLFy58/vOf/53f+Z0oij7/+c9blqXr+je/+U3f95944onPfP7T3bBjZPRHnrx45qHTMUQ3l288/qnHdM0ZG50mwCql4vT4mMZQY+h6baLw0BdDcuir384HByoAUCUZKoaKA3LA5DNVMvaDKAhF4MdBGIWhDIM4CEUQdLtd13V930+eZn9eTJOD3TWRnmMU4NAX9DPz918H7UJpbOXguLjn7PegxpEFel7t5JX4kskBI1p/r0OvjaM0ozjBKxASEEHKPpsgjDFKKCeSKCJikAII4X2MjwiH/VwKTaPcQC2K4lh6EiIUKlJK3VnAiBAioagQpSRKSaUEACaZKUbcOaCbEKFIGCuASLdoUkqi7/pJeFCu4CilKEgCxNI1XUvcZz0XUgJGSWGt5LBAYkKTYvZUSSUESEEkEMF17Ks5sH+/lILfZYQQmjwqRfdVQm5YYRh6YSCEQiBAOSGABDO6JEwDQmPJQoGB4EJyAMoiVxHWbKubK9uZ8iglQPRYCNlpVXO5HAAIIQkh7a7rOE6362ZzmSiK7IwTBAE3dAWQLxW73W4Ue6ZpKiUJJUIG7Y5EQIUsVkyhUkR5fmdqYnJosIIoHWuaWaGR0eM43tvaHRvTHrsw8Qs/89Tu7u4br780Ojr61JMXy+Vyu91euX1zY33lwvmTD537m1cuv58vZJ75zFMZu/Anf/jC8bmTX/35r2RyOc75+vp2pVL5J//k//LP/tn/NDQ0tHx76Wd+6asCxMvf/pbyO2G7/j/89//tv/jX/++3r152vdqjj5394nOfeemllx6+eEqqU5/7/FO/8zu/8/t/+qenLl60SwPFkfE//OaLx848fOLECV8Z//if/j+//l9+b323Y+YGFlY3urEYmBirVMrXtnZqsXx7cfmv/NJflqgfO/P4f/7d/+QK9vDjDz///POEkNmZ+Xqtvba6deP6ktsNH33o4bZbh3z2+NnzTGN2dTNnQhGgFkS2bSddPwgCxlixWHRdN2sqQkgYhpZlUUoTZRYABGFxHHuexzlP1kpKJI5RKRmGYRL9kMA35zxG6CvLsVTUMBKbgFnbSVJ2GaNJbm2pVAqCwNR1z/OkEoahMcbC0I9FSAixARjXqWa7sWp1Y+QW4aYfRiIWuVwuWTgg6YGJ10LoJiegII7CgAIahgESwjBkZuag6tcHHUlgf8s+fU4ieWJCKQ2CMGEPQkjTNIMgCGLKdQYAsYyAUcPQkGAURRbT+23SG9FSSil1eidWrj/eASCMZG9+JokhuDetxsAtywp83zAMEUZcKYFAEFApQLyHESAiUkoD6EUP9hWHHgbt+976aRMJwQlkwjgA8UACKiFB2MReYLfaDwUilNJiPkMOuNL72OwYJJnhNU2j+wV01H4OGgIkWyQFxu7kGPdBty+GpguJgAKVkkqiAuBIJMaJ867H9XrBZQoVpbQH6pQmYQLJ1/1oHQ4khqStCBDCYokMMKlkTgghKFGhVGAZOiGk022FYdhoNExdGxsbi8Mg42gA4HmepmmJYmIYxtDQkJBx8swS9EwIphDCMk0ASOpjaJpmWVY/SBpR6pw7jq0bPAxDISKCGEVoWVYulxsbG2s2m5TS4eFhz/OazW4QBMePHzdN8/XXX08CtcfHxwcHK8u3b2p61rLMZrOxubXa7XZ3d3cLeeurX/3qr//6r589e/r69Wv/u//tf28Yxv/pf/y1//pX/66u0V//n/5v337+m7c3Nq2M81f/yi8++vhjv/Uf/+MjjzwyfezYF7/85Var9Y0/+7NnnnnGjyI9k2m32/lc8ctf/sqjj1Zt215cuDU2NvbmW687jhMEXqlcfOaZZ2LhIyrP85559tOIeOXS5W9/+5uaZjzy0MNPP/1UonFXKpX33ntvc3Pz/Pnz8/PzmqZdunRJPfTQ+PioZmqNZv3W7fWd3cbYeHf+xHnNcJKW1HU9ijSllKkDI7rDWbvdztm2ZZkrKyuU0sHp6U6nE0YeADgWj+O43ewmXNX3/TAQCQBp1AaJDBSgNDgjQugaIIKuG8lDCcOAAatUSgm3lZISgrrOoyjwfTfoytHRYV3Xa/W9OHRt04xjaLVaY8PldteNIsVA07gkOlAdFEIsJAORtXVdI41GwzRNyzCiKKK9etLc0i2FUkpkQC3L9j2f3p29lCjvhm0lqv1BeAIAximlhHJCGFBKkVDCgGk0Y5pKKQRgXIulkEokOpQX+EnwmqZpBCCOokSPDsWdnNX9MagAgOt6Ym/B/RQwyhilNA5lskJMFEUG17gCiUgUICJBvAMxybGSc8QAfRp4EJ7CnmWnxx6xX4xVaj2/E0KSy5tcikHDfb7DOadco4k+UjAOBHHux8IQQnRNMca4RhknlBJEIIwqBVEUU0oJJUqpOFaAknPOGcH9COyDVYcSGGWIghCaqPwgUIJCpaTo42MSEyulAACjF8PeU/YRe3RXqBj3rzGWSoEEoJQqIQEBKCrZQ2dJUAFCtxsOlItXF27sbm+ZupbJZIYGKnEct1u1JP3FcZxut5vP58MwLBQKnW6bUiql9H0/KVgBAIZh6HovdeMgc0ZEqWIK4Dj2QKVUyOWVEhSoaZpnzhyLoujWrVtCiDNnziil3nzzzSAI5udP+r7v+/7CwsLc3BwiJuD+0svfK5ayYehJFQIRu7tbcRyfPHnSMa3f/Y///uKFk61W7dj0aOx3bi9dffTh05srN0rFrB8GJ89faLfbW5troyPlyK1du36zMjC0V60/9fRQtdZYWLz12c/9lB9E+cHMG2++PT09rZvGsbnje3u11dXV+ZMnvvPCd5588skyK1FWOueea3caFx+7ODExluQ8nz493260V27fnj8xNz8/v7W19d6VS8OjI7ppMI2vbaxPTk5OTE3Wm43VjVVu0EqlXK3tdLq1MPI2N27PzExHkVutVhuNhu/7AFAoFIrFIgBEph0Egd+mtVrttddeo5Q+9dRTuVyuG4aNRsOyrGTqrlQqtd3m5cuXpyanb9265TjOzMzM5ORkJp9pt9sqbJmcJQUPHMfJjowQJcOwy3RdCMoYK+YsIQQAo5SGYeCYzNCstdVbQRDk8tlcLoMoROzrGtnZuV1vtJhmFQdHM7bpR10iorxjom55XjuOIgZmt7Vr6QNZ295utygKQEoIMQwDJQa+r+mMc67rvJ9RiIicM6VUHIso8JJZlu1XUk86cRhHDFisYhn3ZrtYxVJKi7DQdxljhDMpQhTAOSdSAucolCKoCCKqWAlCiKZxFIQkWljfuImIiFEsKe2FR6r9DC1KKecaISSfyxFCVCy4AsT9kOE+XhwEGkJIiKw/nvGgsU31HP2wbwNHRCBEYwdrTWKfQzm6RSgm7CwJwGGcUEo1FfepYzL0ExiKMZZKSiEZMnogR1wJZIztV4TqxY4R6C0Y21fZcN9HphQIJdW+9UlJmYQvJRkb/XGtVGIcV/EBL2mSQNIb9hKAEERQQFWy8h0hBKhQXCIACESQShFAzpBRtCynkMvfuHa1trc7N3tMyrjTbsZx7LpuwnGSyjuDg4NJ7Z5khkkum3OekB3t/8fan/5YnqXngdh79t96l9gzI5fasqr3IrvVZJNqakiKECVL5ljyyCMLNoyRZWAWYPTFgAFj4D/BgAFjvhiwP1iGZ4akKA43kWou3exuNpvV1VVdW2ZW7hl73PW3nv34w7n3ZlSxm7CAuUgEIiIj4v7u757znvd93ud9Hsa07pIkiYnA5ptpmnotkyTJs2Q0GArBrHYBWJpwhNB4PK7r+o033iiK4vT09I033rh586YQuZQyzqPt7u6ORqPj4+PRaDQo04OD/aJM+r4uiuQ/+af/ZFBu/czP/KxqOkr9jRs3PvOZ19u2/uCDDygJ//J//7+rmuWkUr//R9/43/6L//zzn//8v//9f7ucHt/94N3/4r/8r1966aV33333crK4dv3Wr/3HNxDmg+H2m2++eXh4kxDy4QcftY08OzubTGbvvffB7dduff6Ln01TcXZ+8tG995quni2m5TDLskwI8ct/9xcmk1n9u4tyVI7H463tUTEuQwic86ZpdnZ23njjjd/5nd95/PjxRcFPzp7lRToeD2/eupmmy+Ojy4cfv7d37YZtp3J52bVtURTbxc72gCulphdPkySZzuchhK9/7c22bZfTo8UkKG211tfeeAPAn5+fu3Y2mUwunn3MnDx7+uTOnTsJ0vOzp3OAqIJ0Nps+f/58MpmMRqN2frsoCoxx4Hwy72IpN5vNhsPhcDjs+35ra+vx4wdvv/12nqdf/epXWqeath4Oyxv7u0ePns+nR9NZdaNbvvaZz1ME08Us8MSbUM/nnHOl8Yc//Iu9vb1f/MVffO3m7sPjKWMCI8YZo5gF75mgsc6K/YrN7ov7KAotxMCK1u0dACAEMUYAeAghdqKdc4wRirR3ktEkERyAhICY4N57oMR7ukmmhIg6DVh5vM5QVs8ccSocVtOO6224ymy0agGgyDIUwuOTZ9S7qFSB/BrBRlc416u0AlZE9bAeoYnPJegL3jNc2beCmqt5zea/Eh9RFsAI4eCR18EgBy9MIDa/EmOf0w4hj7FHaFN6hBACjv6NAWOMAwREsXMOo4Aw2uScmwQNAIJH4EPs7lEMQFbIPPJhDZ/hEIIDcCE48MpeYcRexSYDQgh5wNY778GHGPewD9i7KBvgMEKErJruh3vX7t+//87bP6CU3rp14/Li7PT01Kheah2RiBgrI9S1XC572cUB+qjT+qmIHxnSIYQkSRBCaZrajgyGRcJFCK5rKqN0AIfBX7t27ezs7Nvf/vbXvva1tm0RQjdu3Pjt3/7tr33t6xGeGAwG3/rWt7761a9OJpOIbnzjG9/4+//gV5xzJycn//yf//Plon3rrR8cPzv+B//gVzHGj588e/rs8c///M9//e/8wre//e2XX3v58tFJXoweP3r6t77yM7/0y3/v+OmHs/PrhuwmRbF3/fpbb701GAzG4/Ff/fCHaVl+6ae+PB6P33//g0dPns9mMxdQORx//PBxUtLlcm5dev/+R48eP3j48N5Hd390/fr1tKBpkv/aP/qPi2zgwHWyCTPXdTLJs7quizS5NhoSQu4/fPD42dOXXn2llzORskU9H22XW+OcEVTmyXBY7myl4BOKB+Px7fF4nKapEGI2U8WN/SRJEgZFUbz88st1XV9cXDjnPMB8Pr91Y7eqqq6hOzuj6wfjz3/2lQ8++JBinYqwt1NIKZum4ZynIvzU51/LeTgSoLVeTo4Z7Ozs7FDqjp8+2NvbE0Jw7HbHBWP4+OnxYnJ69ORJmZEvfuGNhMFffe+bbdu+8Zk7zfLS9POD3ZHqm+Mnd7HXNMmUdOPtrScPj549e3ZwcHDnzp2tnPIg5fKiXwTdqWy4EwCDNRgRSikjpFN1sDgm8tYaACAEI4QoJYLwqxVMrDIAwIOjmBJOtNYUY0yRBciyhHhlDQbkKFhBiHE2GG1dsLKLVViwFgAoIeCd0QqjZPPHN1sYIcQZ9d4HZ2ORRddbqSySk5OTy+dPsjStpudUGe8BBY/8FQ3DTSC4ug+vfhkfFPlNfL0aRBL+CWW5TQ5FIod9pQoSiUIhBAecX31qQggOASMUtbjBfcIJBAAo+BiDIoa9ef3O2ohGr4GbtXI+EBQAY8xpwJh5TkMIHoFUa9A6ljYY4j+lX7x2f5VXhlnwwQXrXLAueEAEIYe89yQmWQg8Z1gwIgSjFDujv/FHf/jsydPPfvYNJTuj+uCM1SYeO865tm2NMU3TAEDXdYBCTFKSJNmQpK21ZT7CGEc8KPa/jDHGGPAWfLBWe6u9dd5qzhnGaDAYfPTRR6+99lqUBxqPx3fv3h2NRpPJJH75zjvvjEajhw8fFkXR9/3ZyUVZDo6en3zpzS9Mp9PTk3Nj/P/w3/+bb/zxd959/96/+lf/9f/x//R/OTk9/pf/8j/7+te//n/9v/23490DkeRf/Omf2R7v/OCv3j7YH7///oPZ9GLRHzPG+r5vetlK9fToWGu9u7t7MZlgSj/86J4PwQdEGC8GbHI5SzKRleL45Pk7P/rhoppKLWfzyWirfHp6QoDsH2z/9Jf+1rXru4PxIE9yD3Ps0Ed37/77b3zj7/7dv/vlL3/57t27COOyLKv+fJQNtGuOjh/NpidlPvjMG1+4cbirjZ9Nzu/du3fnzh1vVdu2+/v71trZtO77XgiR5+n9+3ejFKQxpuuaarGYXIjpdCr73uqs7/s8z7dGpbdqOZ9QHBJOeuQHRUqQP33+tEzF59+4E1GSiBxXVfW1r7y5ScZTCgT7V29d55xf2yqSJNnd3X7y9PHxs8dNUwXbOue+/rUv3bp9YzwsHz89Jl6nKMkHYpCS7SF/aiqnks+8dvjq7b0IXNy9e5cEkostGzwG32tjTM9FCkhRlsdUhAu6WrwhYIIEI9Za6yyEwCglhK7qDC0TSpxzSiujVdxE+WiYCcIZ7jrprKSYo4CkNsH5LGWrGQa6AnqMMQCeshdarlczD06w1nYF4V8h/WIjXTM/efLw2t7+OKFUKgghzi6Au0KL+EQ5RqJjmgcI67oPIYSItxQBi0KJV3h0wa27dCs8fJ1P4RX0ghGObNNItenNJ/rQJARKEQaE3KdV7ldpGollEwIAhil+MeEZNi3/mNqsgpdbkSARQpQRhKI8kkfwSR42AocgENz5Tzzv5hPGmLbG6WCst8GvWLHOISDOevCeUaCUCsHShFGKv/Pn3/nw/Q+Gw5JR/PD+x4C87DopJUvTNS3Ac87ruo5hhXEaRZoJIVHcHmO8XC7j5UUtIWNMCME5RynNKfbeYqCM8cCc0zAoysPrBzGTeuWVVxaLxfXr1znn77777le/+tWHD5+ORqPBYDCbzV5//fX33nvv1q1bRVEwmnz2zmefPnu4XLR/9f0f/tmfffPzn3uzLEaf/eJPP3h6+uv/9g++9JWfO/53v/fbv/8nNB0Od24kwx0c4OH9xynJLy8v967tPXn2OMkzQpO2bUMIZVl67zkiw9GWcy5NU6Pd6emp9zCfL7XWRrumaTDD55OLP/vzP3v7VeaVBgAAcURJREFUR28r1WGOdw92gcBwlIUAH3703quv3iEMPX788Gd/9m/v7Ox+9y9/EIVKohJ2URRa66qqpFGn50dpQhfL6fOny8NrN/Z3tkeD4s7nfnpeTd/64fcn8wvMAGP80qu3MMa/9W/+3dOnTz/72c/e+cwbhJD5fL5cLr///e/fu/fBa6+9du3GNSrobDZbNssf/ehHRVEUxShghBkFgh8/evrw4cOf+Zmfubk1TrhACH344YcPHz48PDx84403Yivg9Ozy/fffz/P8p37qp7RsIhr14P4RZ3R3d/f4qDk5fjoa5vt7o1u3bu7t7V3fG9aLJQX8pc99FgK21iNMMMYc9CijKXGmnWutDUJCiFKgXnWUauypC7quFrNqtk+3mCDL6TKuqEi8inPd0fwyZtMxY/DeR4nL/dGAeq+6bnJy0vf9imjmfXJjQImT3aLtzXC0LXgWXXqKlM9ms6ZpYgDq+z6uw61duurzk9VAu48TUdoHaykA55xz5r1vmrZr2+3dQcng4QfvvP2dBUWYGh1CWDH3AvkEufsFRQXFFteanLDyDULeKSABAcKI+FjwhACArOFXk6l1AQmeaLSaC1vFytgmk/qF/i7GgVLkgscY2JViJFwpFUV03AkvRKRiYhXhnM3k1CY7M0ptSilMMay8zUjCX9R3fmXOg0MIjH1Cc2DzoJxF3GdV22IPbnUlsUUmMIreFpRiTvGv//qvTyYXBwcHVVUdnzy/ffs2xlgIoVdNE0cIiRtpNBo55wL4mAdt+oAAoJRazquIBFFKIyErwkPbg5RizBjJUgHON01dlNnOzk5sfsWMKQ6O/sN/+A+bpvmpn/qpoijatv1n/+yfnZ+f//2///ejW8arL71xdPTszS99JUn4X/zF9/7q+z9sKvNP/+l/Wum/ePfdd//w33/z137tH7308qOzsxPA4j//L//VH/35Xz786KNiq/y9//G3f+mXfmlyPulVoEXijMuLAed8Op02TbO/v89F2vf99s4eY4wJ3vby4vJya2snIEcYZZx+/PHH3/2Lby8WszRNOKcA/o//5I+/+ObLo+HW0emR9/b09PTf/f4fVVXz6qt3fvf3f897P51Of/03f6Nq6jt37jRdu7u7q4384ON7t28eUOQBWWP7H7337snx8Re//HPXrl3jnM/n8xBClmVSyvPz88MbN6azGUROIMaM87woxltbIYQoho0QOjw8FEKcnJwghMqyzPOcMRaNj/I8v3379ksvvRTX0ccPHyijb96+9VNf/ukIBRTF4E/++BtFUbx0+9ZoNIrkr4cPPj4/mfzsV3+6LMuLsyMtW6txcObWjevPnnx8//79O6+9fnh485133nnvRx/cufPGl7/8ZYx8WaRpwiBYCNYY23f15PLsvFtiRJ3LXUgePj1+dvrspe7G3sH48f1FCGE0Gl27dq0oipg4E8K9swQjQmic+6nq5fHx8WQyoa+9GkKYTCYffPDBbDaLypwXJ3c+8+ZtCPj4+LRp5Y1br4639pwNnCeL89OPPvro6OgIAKy1i8UCIbS1tfXLf+9X4/j6xvUgLu+Lk+cR9BwOh2madl13enp6cXFx11RbW1tONrargFJaGQhrNIkCAGAfAgB2q2YZBATIkRA+wfuM2zvjhQpIKVgbJ26sfmKtBJvIEic8nVOEEAYMEEEraeTgvXeYAcRZZAAA4xA4D+ALXkQU2VodQhQYCgiBQ4hgxAlxFnywndIEAgLonQAUAGhkIIZgAHkAyFac+shX9MiijfZ+vEQIAYJf2/xAwXXwaA29rbAnANDYSNf31vnAHWBrAiZAGMVWWaMwhGQ4ogjytNCy++/+9f+3mk92tscIvLV2Z+9AW58NRsaYjCqMMQC11nZyHkKYLyVjbDnrCSF11cREBgCSJNkab0uWnJ6eckrAeS+lwei16wfjrWHAbj6ruSieH18Q7GU1uXlt9PWvfn6wszOfLz/++PHdj+61rXI2tG03HA73Dq5/8Ytf/OEP3zk7O7t27VrTNNbaoihwV/3qr/6Cac9G2y//b/6X/+ziaKGt+Y3f+fW/96v/6NGjH54d351NPv9f/B/+19eu7Xtrm+bSyVokBBJy+PorHXKVavYPtp88eWJd+JVf+ZWqqp4+eXR8fPz6nVcZo7L33pDZcjm5mC6X8xs3dpTqd/cSLvjJ+ZPJ/GS+nDZtk5d532tjzCuvfHa2XATcF0X5P/yPv9H3Pef8W+/84bfe+cN333+XM6GlsdZ/6zvm+PjO5z730qMHD3dHW+dp7kyQRne9cdNLLujw2vDxs4cPH93b2ku6vpXOcbB/8f1veo9ZyPZ3RrJd4mA++NF7n/nMZ06PzsEpkbHp4vLo9Nne3l60fiMcOedG5SBY57Sp5os7r7x68vyorWohEmtN1/fXb9w6Pb+U2llPABHK0jRHh7fuXFweYYEsMklReJR87gs/++T++1kyZpQ3jTTal2U6Hm9rrUmyd3zxtqdHb/7M1/KtrVnXesb3b73avv/Nj569n6bpS5+7/ZWvfDUV2YNHDy7v/+jPvvNHhLCt8R7B/OTkpKrnk+NsOCy7ji0WS4zpr/69f3T4xS9/8NG9p0+OhsPh62+8Op/Pz88vBoMySenZ+fNHjz+6e+/9e4e3dnb2ptMpwfT84mIymaRpXpTzrv/o+OTZ9cOdNE2e3r87/MLPgy3+9Ntvl9vzhw8f1nUthHDOYYDxaFzm3dn939PKGBMQ0POz6WSy4ExobS8nRy+99NJoNFJKxex7OByenJxMp08GgwF2ikB9ff+Qok16s55/Q1em4DY50Sb6XC2LrqYnm+9cBYCu/nwIwQMBj8HGwd+YH4H34D+pj7v5pHNqnUYFQAFjhBFZ0zDBA0BAxgEB7MADeL3yM/KA1jYmAQBAa4tecENR9ClCCDn3AnffDGGEEMJ6KDXWd+uqC3POCdHxigIghEOUS1TKJ6JMBFHapSJrWv39v/z+e+9/VJY5gG+aKhLkMGbO2a5rAIVIoYwwM8a47/RCdQDe2BXtjXOepilCIFU7GAxCCPVy4Z0VQgyHQ+/9vbsf96ZGwLZ3rj1/fpKljAWlVLq/v9808u0fvHvv3sez2YJRwRhfLuqL80kndVmWy+VyPp9HIi/nvKqqw2FurL5989qTJ0/G4/G/+Bf/4v/93/9/Hj95/Fu/9Zv/i1/7n+/u7t64eXjj+uFsNvneW28RhLa3t5fL5XQ63d3dFULEUL5cLkWSnZ2daa3zPB8Oh1VVEUIYY1zQt37w0WQyAWQPD6+/8ZlXz89Pf/d3f3faTDjnk8t5nufWhGrZpmm6Nd6WjlJC66qrlm0kEBLMnXMIcNM0nIqYmIzGw9dff+3G9cPf+K3/bjgcKt2Px6Od3W2t5dbWyBr/wx/+cLGYTCYTbVQccHvy+Gh399o/+Uf/aZTKjaZsi8Xi6Ojo6OhICDKbXbzzzlvb29u3b99mjAHYrqvfe//tGzf333zzS3nBGYfX33h5b3+8mJ1Z5DCivV6mBQVizy6fbY13MMaz5dn2XmlCXnfzgI22qmk1EN3IRlrJgBaDwc7+Tts255Pz7b1tY9v9g61yIB48/Oj50aPRODO2+4vvffMHP3xrvpw8eVYPhsOXXrm9s7P38PHD58fPtdZaybbRnCV93zvnm7pzzswWXd/3GJGP7v1gOju+d+9+VTVb451l+7iu28lkkiRJmnLrOir0q3f2Z+dLQtDp6SkAYEyvXdunlPZ9O52dTKZnATVbW6Ptrf3xVrY9vgEAf/qdf50XLC+2Ijqpta7qydn5s60iYUx4a2RvpKmMb6zq+15lpUgLvrU7PDo6Or04StO0103AdjweOeedN2WZ37x5k5IXOkarVnr080EIUEAAARD6FG9xE3HslSHhqxopf937bfUJIhAFMtBV5eng0Cdi0PoyQHq1jmsBYyAUEYRwQCFg55EPwTuEYZWooIDUyu8xYAJoLYmNEHJxtj4A8gHjsOq3rzmHn3ppIQQTfAjBuzjIBuspoaCbTkljrYXAopdCJHwHzLQDMAGcGQzo0+PjP/2zP392fF5wAABjjPMWUJCqb5pmsVgQTBljjDEEWEkd4ywCTJhnjIVApZRKt8b2q1GMQJJEaMlkr7O0uHF4k1NS10+ny7Od7WuMMak6AFoKSBJxcHDw//jXv3l8dKqUbhs5n50nSeI9dF33lZ/56rvvvvvhhx/Go6Jt2zfeeONzn/tcic13v/vdV1/6xw8e3v9v/+//r6/97V/+yle+Mm+nX/1bX/nK3/pymqbHx8fPnz7b29vb3t49Ojoy61T85ZdfnkwmR0dHQojDw8Nl1UQ0Pe7wdA17/c7v/M69ex9FWnDTVPFFDYeDxyfPGWNae87DxcVsOp0NBgNrIRtQB8QaZ63VGhBA25gQQp6XWtvInHr48OF8ujDGjAYjQhBjmXV6uay9t4BCnud5jqbT6YMH987OjxAGzjkE9Pz5Sduq3/2D34tR8tHTx9PpdDgcvvfee6enp/mALBaLk9PnSZKcnD4fDAZPnjyZz+ecsuvXr1/Mnl/Mno/HY+fcs5OPF4sFpmg4HD94dG82WzTt9PziySuvvCaE4Cwbjund++e/+3u/KYS4fu3m9vZukhVffPPznWx6VW/vjYrh54+Ons/m8+9897sip42co05+87uPnj17lmcD+8S8+8Hbj588Qgj1svn4wUff+vM/S0T2zjvvnJ2dKWXms2XT9GlSDodDhJCU0nkDSDMevNen5w8up0/m8zmltOqa+d2H1ngpdZIkXGBMXF5wJpDzum4WTbsAgCIfIMylaqXqR2PBRWjaeddPLy5PnXNZuv382ZmDNklFhOSMgclkuVhedF33/Pj+9evXuUi4YEk2Go65tb5pmpPjc+vaLCc7u+V0ql9//XbTNJS5y4tOO22DA3BNX1GKX2Q9HgJCKzd2FAChyP1B4ZMPtOmdr+Ghzcf4ifOfSJpeVHBuMzXmrv6Xwz/eu3bVF0MeY4QDJggIeBygky/IRDjAWhIcrLVR9IOQVccdE0AIWMJWscYHcC8qyg3m8qmPCvy6FouZkYOAAULdt95hjEnwyHnngg/gAvKMCeeNti6h5PTi9Ft/+sd3P76/s7NtmzMhhEh437u+75R2ztskxYyDEJhzEsLKqCdhjDFmjA1BQwhJihiP/KBACLGuw4SXg7Sp58agre2xkerWrVvnP3zGBfXeCcG0VgbD7t522zZ//s3vFUXxyiuvDMptaz6eTCaEkJ2dnYg9tW2rlDo8PPzqV796cHBQ1/W/+8Pf/q/+q//MGDWdTtq2/YM/+P1f+0/+yX/z3/yfnfTOGtn54Pz9Bx+3XZ9lWd11ezeu37hxA2N869at9957b29vbzweX15eDkdbb775pjHme9/7XgRHpZRHR0ePHz8G8FJ1xujZfPKd7/z506dPLifnWjlnwWg/my6jYr/gWd/puuvLsuScO+v7znatkb0jhGQpstZ7CsvlItgwubgE8K+/dgcAZvPJzZs35/P5YrE4ONgblOPPffaLSqKPP/6oLEul5enp6Wg4zvMcY/zw8X1CyMsvv3x5edn3fVEUs8Wl1O3pw5NoTNT1VddXjLHo3jHeKi4/eP7x4/c456PRqKqqNE0ppda7vb29y4vp+fm5UjpJstu3bw8H4+fPnyOETs+OR+cDxtjzkydZWmCMdUsQQrdu3crzvKoWH93/qGkarTVJtfdmd298cXH29OnTwWCUpQMAAjgsqyUXQhn1B3/4+ysBGSCCpwg1Td0p6ZIkQSj0sqcKU0Exotp2Z2cnjLHBoCwH+XQ6JYRoY7QxxvakR4SCdTxNWVUvMaIYByFSQH46O/cOhsOhSJLx1kAkCGPknD+/fCL7h0fPz27e3ullRSkdDoeME+clwnb/YOvx87seySzL8rxM01zklgdIikzqgnDX9LPxTq5dk+S41w6IHo6LRDHKUV2355NTiq/MegRAEPALIwAUANCmUvvrj82m3USlTVrx1zc2ADhAG4LT1Z73Rs7lr6dU8WP8mzG98t53Ol7z6soQQiuJl8hrJgh7wBgRAsgHhICvY9zmYsIa//5UabmKZQh7772LEFikj3sIGCNOOfWeKuWs0Q4sxh5hJPWcM+qcDEDu33vv+299E0E7HOzibBDnxZ3vlDaYwHBUpGlKmOr7vu8rCCErohhr8F4Shi4uLpVS29vb1w52EUJR4j6jZdu2o3IwmwLCTghyfnqxvTPe3dseDktj1e7u7nx2maXkc5/73Nn5SdfJvldZVhweHr7++uvOmel0mmVJRMFef/313d3dw8PD0Wj0+PHjt956K0mS4bDc29v7lV/5lbOT5eWs/Tv/0dd3d3cvj08nZ5fT+eLg+o2f+erPnpydf3D/fiKyL37xi6+88gpj7NmzZ3me/9zP/dzFxcXDhw+run348KEx5ujo6Pz8POofzedzrWWWZVmeFcV2L9sf/OCttqsRQotFmyRJWQ7SNG2aJgb9um6VaxAiZUm8B8YEAHgPhGAlNWMcY+o9XDvYy5I0z9Od3a1nx4/bti3LcjgcPnt6JHhOsGgbdXY2oZTeunVrsZxPp1PO+d7u9cPD213bn56eEoKqanF2dhYHEinFTVMNh0NKBaXUe9v3mhBEKY4fk4RTSut6eXZ2ihAaj8eeBEQRS1kr2/l8ORqNnh493dvrzybHUsqmaVvZGmNmy8VoNFRKnTyd5XneyOqVV14ZDAaBhIvpJIRQXZwyhmkCNjhEcdM3ndRZVgKEquk4t8b7xbwSIt3Z2fFe58UgSVLGhPfQNJ1zppe1ELTEW4yx4E3VNISYshgj4NWyH41Gfaeqqm+bzjmXF8n+wXaWjK2dchbbi7ZpmrquGRXRLQ5QECIvyzIOymnbuFBNp2hDZ8uyDCFUFMX29jaA1F41s1o0VZIkSukkSfI8T8uk0+3Hj+/fuXOnkfVHH3+IMZZG7u9uGePyIkmzpO/kxlp6JUkNKKBImYYQICCEA4S/7iG7xnc3DCCAK1Im0TstQPjURxtlVGP/DL3Y9i9si9bSCPHvs/VzYbye3AKEAKJ3sPUeAPmAogAaAkKBIgjIIeQDJgH51dQXcXrDJ8AYI7SaUdbmhWb+1VensAse+TgP6uMVYYCAMIFA4pw9ADBGMMEB+aeP3kUI+q4aZMnF+ePxwIntsu+O8oQbF6y1hKliEAljDpGecseDcyH6ICvkGGOMc+odK0rBBUozinBkGfTOu4CI801WDF965RrBrBiw6eyMC/S5z32GkuT8Yl6Wheyr0Th7+eXbjx98kOd5mqYhuMvL853drS9+6XNnZ2dc0PPz86qqbt269corryyXy+985zsXFxf7+/tvvvbTBwcHd+99+Ojhk6//ws/v7N9a9vX3vvcX10bb9XJeL5cH168Lkc2r5fHJ2fXrN37wgx+8++67zrknT56cnJycnp5Op9Pnz5/3Us/n86hYRil9/vx5nue3bt0qiuz50dPBoByPh8+eL733h4eH3luHF1rrNBXj8dA5M5/PEQpCMAIZACilYl8/y7LYY6bYO2cpoTHJSlLetNXl5XnXNSGEBw8e7O9d63tVVU1RjJ49O3n33XfTlCHslFLD4ZAQ0nWd1noyv7iYngHxy2Y+W06898PhMIQwGm0RQtq2z7IMY+w9cC68bzuphBAB4dliaYxJsrzv+4ePnxSDgVZ+PN7uOlnXDUasWrZ9Z7a2R8tlRUkaAuq7ZduoEBYXF+dZOvbYfXj/PY/cP/gH/7PDWzff+uHb89mSpK4c5MtWUkrz4VhKraRXVYe9Gw23jXGXF1OtLQIxuVwopbd3Q9M0IYTgUdt01ilMAqU8dl0BwLsgpVzMW++IlujyvLLWCzawnLdt2zVhctFSnGPEhUiFSC4vn8teY0wxpnXdn19MA7jxeHjj5jVCgvOGUlKUaSLKaMD7/NlZhNIYY22j9/euee+9a2LfTWsFELx3k4mK5KBIyzo5ORmPx5zzy9kZpVwZFZB1YFYxKIQACKxHsPIniereqy+vos6fiEHrmLGqoeBFAhNCrOU+FYlWKj1oIzobP5LN39wg3AEhRG2ckAJCECZoI28QCLEBwCPjIIqqeoQDBAIsROvLSAlapXKBeb8JQBivuNEIIb32CfjUC7TBhwDeB+9WY7eRCYQ9eK+tQyEAY8wjU1Xz+XL23o/+VPYtIzAsM+T1wT41ujNycjExCK3GbrOEOeekbGUjU51GWr21Nno6p2mapqmUfTlICMmttfPFRcStAcCqimBmbH39cJvzhFBPmQekr19/aTpZ9n0vpVZKDQZ7ScKXy6UxajgsRcKm04uqmd64cf364Q7CMB7v7+7uZll27969u3fvRgr19vb2zu6W0v3J80dn52e7O7eShD87Wx6fPNeL5d7ewSt3XpMmfPNbf/7o2bG07vT8Qhm/WCyMMVFI5OOPP0YI7e/vd72KJ2ScgDs+PhZC7OzsfOnNz80XE2PU86OnUvZ7+zvOmbpe3r598/T0tG3rvm/jxQyH5XA4rPsFAFhr67oxRlmrjTFpmpZZ3vXd9YODwWDQdZ2SnZSdYPT69eve+75X0+l0sVi0jYRwP8uKiHn1sk5SwTmfzxZNLT/++HExzhFCnerSIh1uDeu67lRnrS1EqpVZLJZGrww1t7e3IWDGC6WNNt1kMnfOHR4eIhyqWjnoAXHnK8oyIUzbWYzx5WSJKWob03d2Pl8qpfb2aACstAO0TCDDmJ5fnn3w0QcnZ8eE0jTPpOuVgtPT+XBYOkebpgueYIy91uUg06bXBkJgxoZe9tZaTBfVsgkBrfQzIaRpKkSCSC+1ss5nOQtgZ7OZMU6ItK6XhLDBYFgUw66VXd+0TX1yckERWBMGA2p0oDThTFBKrfHj0UFVLYzGWqKoPzoaF3meJmy8WCyU7KW01spI/qgr7UOCEPIeEswQxiJNCSFV0xiLpFJFUVxOJhhjpfV0NuOc1/VlNPJ2zmtl6V8LL58INOHqxMPVka5P6hL92N+Fv1amuejbgVeRa/MPX9HWXtdfCAAwCjii0RgoIZisHbUpJy5YF4L34Lz1GEIMmASAAHgPDgIOwcepEkRjooVCQDGLQWtNtfik69pzk8eheMhcISQEBIQwEoJfiYQQUrX106dPHzy69/G9HxijXnvpVtdUXVshp43ud7eHk/kMY4ywiBovCHtMLGXeO6KkiToMjGYYY2v8UnXLajIYDBhjSimMcfR3btsWE1cOsrafj4al81DX8+Gw2NkZl2U+m1YAIKUUCbtx48b29vbe3t6X3vxC13V7e7s3b12fTM4wCTdvXf/MZ16vKvzo0aOzs7O6rg8PDymlsV0ahzmL9PXPfvbzJ0fz7373u/s3D1966SW3aF9++fayar/5p3/63b/6wY2XXn3tpVcwYdevXWua5tGjR0qpOAW6s7Njre2PT8fjcZ7n8a5FsuV8Pv/www+llDs7Wx9+9EzKFpCfTC7G42HXt5ggxmkIYTAsI4b99NmTazd246iKUr333jkD4NNUWGudDbdu3R5vDS/PTvuuoZRSRqLf9OHh4ZPHzxgTRUE//vjhzZu3t7bLk5NJktLBsJzNZstFxVm2WDQO2/39/cj5jJ4Cbdtaa3WrMMZaW2t95FXt7R1sb+/2xs6mc865s2Q2W1pzijEWvOxaJbjVqsrzIsv8+dlllhV11QtBOU8JIUp6hDjGXGunpNNOTRfznZ29s8uz3/2D3+/anhDmEaTJ0Bg1bStKeQhhOqmGg+3tvf3Z5fHz50dKGoRIURR5XkJAfd9T6jHGnFOMmFLGeRvJq4MdaJrGWL+1f5gX4vRkShna3dkh1LdtL1XrHcIEp6kwtgPwlCZKWQBcFiMAXFWV1h1CiHNeV7LrdJ71zktCTVFkALhayrPTWQhhONyy1rZta42mlHatIoRggqwFxlBZjIVgRjsjQlVVAPDo0aPd3V1CSBxXXBsyB8YopRT98Q8eblCSDlJYsxOvojbuJ/gN/aTHp0qbzcNDD1dGPV48fgLiJMKqp07WLsZ4nQuFqNqxpg/Fq1VY/PVnDyHkaDWIv/n1FdOcvZitv4pbkfWoRxR4jkeiMQZoNplMBkWuTV/k6PnRBz96/7uPn9zNbT+fzymL3ELAGAkhrh8eLBf1fL4QIq2WTdPINMmcCxjTpu7bto3bLFJXYwcaIVJViyiFlmaCcy5lxxhLCxWN2JMk8d43TUMI6ft+PEjH5VZX22ApGPyLv/DLr7706gfvv//RoychhDzPw1plMUmSGzduKNVfTs6n06n3bjgsGWPOm6Io/vbnXy6KgdH25OTMGPfhB/eLovzKV75Sc/3gwYPf/M3fvPnS7euHN49Oju/cuTMcDncGN46Oju7evRsCStOcUlrkA++9Naiua2ttlmWU0ji9nSQJzljXdZxzyvDbb/9gOruMkhrSdHGGqyzLLMuqqppOpyGEw70CE9/3zcuv3K7r+uGDx9ev37QG8jGLIcNau7+/H4fd0jTlKAkh7O7uPnv2LMuyJEmWy+VoNEpEcXJy4px77bXXBoPBvXv3zs/Pt7e3kxTF5o7WOprZxpLBKGatjWSWSIGJRVkxLs7PL6uqTkRGKW8b2XUSAIajgnPatrUP1jmtjcyyJAR3/eYNxljTdMdH532vinzImJBSMrxSZWFMBA9Srjx1lfTDUQZI5QXP8iR4oqQ/Pbm4duOmc+7s7IRzvrU1BuSjXigX6FNCgHHdDgajuCPCFUUdhFCaCsaYlHIymcQhlUh55YRiTJ3FbSOrpVTSY8Q5TwNexhxcCFaWeV5kEQPaPxgsl8uYuSul8jyPrJEkpV3XpWkaN1f84dFoNL2U8/m8rmuEUJ7nWZZF4cOsWI3XRg4tDX6F5kT4Z/VJQAA4hJV6/H9QAPobHgjIX48/MVD82J/3gBEggNh/R8FHNQ2glKyvEUFAmK5mxtgVg8hPZGrrWixeBCAI0XRxbUwZ1qa+64/xPqBoQhsCRB6T1KYcjJKEPr374PT43vPnHyxmT6VaiNit1E5KOR4Pt7e3+7579vRoOBwBQNu2zgUhRJR9CSFEvcRwhfkNq+k24JxjjLTWi0Ufk77BoKiWHQQqhCBYSKkW8yYGx9m0piQt8kHG85Tne3tbFxcXf/XWO9vXrv3lX/5l0zR37tz5whe+wBg7Ozt7//0PAygAuLy8mM1mlGLGo2q1+dLLe5jxrutny0Wely+99hJnSSv7RuJv/un3m9Yp6dq23touqubck/bybKq15ilmNGWMaW2bru57tbtzPQveOccY894bZ5VSbd81512AqBOQAPJFUVCKy7KsuktjjJRt3SiEB1yg3b2BEGKcp5RB0/BoJxvjb7WsgYmiKDBGfaerZWu09x5ZE7RuNh1bAJjNZtbai4sLJU8jCevs7CyqNUUD+yRFQoiiKDYTeQCQZdm07QghjMVvY++d1kprXcvOGIMRreu2bSdKas6TohiUZT6bzZRS5SC3Fg1H5Xg8nM/nShmMqeDpeDzGuI4K4pRSLRXGmFLurG/btm17hEiSJMZ2TWMBOR+s94gx5n1wXkci8toF0zFOxuMxpazrJGOMEOZcYIzkOY/jh8tFFWv/yLO31rayU0ptbY9jC1iINMJG8TKC9YJnndVV1Wjti2JgrZ9Oz7Z20jzPOechOGtNVVVRt9O5MBptdV13cnIipc7zEgAbo7nA3vvothDnHOu6vry8NIrHF47WQumMsaIo6mYyGo1kL51VeT6g7sr2W891xSbUJ0hB/0Gx5if9fBwn+bG1249/YByRmNV5vl5k1n8yf0ErMXG+DjToyicQHRk/pbEUZaTdi6vd8AcAwEQBRgjB+TjFYq1zPkjn81Qs6+rB/Q/vf/j9tjki0ILvG54pZUIIxug+UUqZpmkXi4XWrmmaxbxK07LIS2NclH2x1lvro7NjDKXWemOcEJRzlmbCuURr6Zyx1mKCjIa2ld6jJPEYM85TIUSWZW1VK+kE7RX242Ex2hl27cQa/+HdewHhV++8fnD9+uV0VlWVMWZnZ8e6viiKpEnGeEwpDuCyLFNKTtoG0qQ3ctJWhqLdvX2EyPHl2Tf//EOpyBc+/9NNP/vo/ofDUUITNOgy1yVFUeSFYIw660E7pU3f96enp5tBmUj1Xs0PixBC8MEa2xmjMPaE4iRlohzUde1C51xvHGaMUY4wNXWtRcKklE1Tcc5v3rzJWNI2fd85zgDAV0vp7DLGDtn7tprFULK9vZ1lWV3XGOPFYpEmZVz6EcEtimJ3d3c6nVLmvQ/WuiRJsyw3xiKEyrJMRAkAVVWF4IztrbM+4K5vB8l1nmUSy7qetk0XWzSROQHgrdPeJ0UxEIJhTLOsuJxO09QkScJ5kmdBax0PM+8BIeSst9YrZbS2BCNLPONgnbHGWRNk77e2BknK0gxPp9MN9Ku10sYzxqTEUrVFUcRBMIRQNLYEACk1AGBskiQOnRCMKUI2ctAopUIkMTZZayEAxXnwRMnOWpukyfbOSCndtIuYRI9GI+fMcjnXRsXpvOl0NhwOrXWM8fE4KYrSOa+UTlLKmGBMEELiR+dCVVXeirgY4rRjPJyEEMb4tpXGGISoc0D1FT6hXX2+SoWuUof//40af+NjnV/EcIDWsDVcYRd94uHWmPcaOlp/372IKVc/crwWUkIAV7D0tXT3p0kDxpIrz/7iGqL1vPfBg49Dtys9IgQYo7qZzeen1fyME1WmxFvsEEFAuaCEMCXd5HLmnBuNtjCiiSi8b4IH59B0MvM+IESCR96Bx/E1BghOKYOA+KCU6gNkhCAuCCFcqZ5SnOc7WmslzWy6IIQ4571TWtlMFF3fBlA9BsFp2y6N1ePt7Q75a4fXb926NZ/PHz16BABlWUqtgLhGVotmkecp52w2qxEDD/47P3jr9u3bRVFIZG23dEvStu2DBw/Op/PXX3+l7pazxcyD65VhCC+en+c8daAYTa2daQnWBmeRlKqpztdB38dDjzGGMBIJs1ZTyvMia9q5tV7r7uJC0gQ55yhJGEUQiOxXc0YHW0NCOCbQtjVjo63tUfA4SbnAKSFcKYUQNcY7F6y1CNl40pZlGbtC0WUkbpvJZKK1TpIkirclSSKEIAS1bTubVl2rKaXOIgBoaplmjBCiNIlTe2mWCiFEQpQJ3ruuk1JKxlie5wBQ1/PzCyOEUEpOJvrWrVvz+XLxeLa9vT2f112nBU9i+MjzXGvb9z0lwjmnlXIuIGCCRzVxNx4l1oClqO+VMXIwKJKEb+8Me9kZY5IkLcuyaequb6SUjBFrnFYm+mU579qm08wghIpiEF+mUibLsizL0jRP09zaGJvwmmiC4/VknHVdvVwunQuUUkCeC7K7tzWdTuO98t4aY6KWHkIoBDu5nEVhmZ3tHYTQYrHQytZ1HbWTYqmFEIoKJ1arCDh47+PcbJSBFzyfTZdpmpJcLOYNdR5tEh0T/NUtukkQfrzT2n/4w79Q4XgBbwPAGmr+a4/19z8VO9zKumMdcDawkl7Fpg30E7+0xF19US/+jv/xsQ85HMWJotHSemoMcwYAqqtn3rbWtgKMoCmhBS332rb23sc5DEqTNCWDwcAYJ4Rr6p7SJHjU99qalRuqXyscXX3ePBcIOUIBY8QYybJkMMjKQf70yXPnnFJqMplQSgeDQZQZ8r6zrseUsoS3ffvs+PFyahDzn//8Z7quWyym9+/fOz09femll9KU1/WitVOE0Mn58fb29mBYaC97jbSWs3k/Xc6j32kIfjAYWGvn1Xz/MFNuNl2c9bJN8gyhzBvMWNGpScBEcBejD0Zce2PB8oxthPojkYQxSgixQWvTZ/loMMgWSw7ArWXL5fL8eRvrI0qpls7aYAwY47uky/Mky5K+b6uqwpgymg4GAyBZXMp5nsfEp+s6Qsjhwa5S6vr165upbillLKji9URV6ajrEkKIyZkxTusmhgmEkDHSOhlFFGOpG5VSnHOX0ypJEsbIaDQghJRlCeDbNqnqKcYrmwYpZdu2Z2eXSllEGMHce+g6ybkvyxyhoFRPoQwBWQPRWia6ZTrnvEdZVlCSzufLvm8j1sk5v3ZteHJyEu9kmqY+WEppUeTOpQghjAljQmvddT1CcaIlgaC9Q957TRyjHsXY4VeevW0Ti0FqDbSNcqzWWiulpJRxU0QULwaUvu8xhng34o/FzkPXaUJcmipKqdbeWtDKGu6c7SO8hTHWyspeI1hNX8ctGZswnHNrPUIEAEeXHaqv2BZHn6NPbXj48VZlf9Pjb6i2rqZUm8/DT/jx4K4kMJ+gXK+A7Bda/oAggLEvZj7iQEb80q/5R2vcZ2WtHeDFeP3Va95ExKs1KULI9ouL45P33v3eYnZKsaMUW+NtAAYkeNK27WJRZ1myvZU556bTueCJlNp7rKSxVsNKPYoQwjCmMdPEGCFEGBNCpG23sFaLpACwSmvnFUIBYTfeGkauY8Q1nDc+YEYZExQ0lr2mmNCuny3nSVLeuLk7a/vp4hwAbJDG98q2mI2TnP7wnQd5ns6bhQ4K+H42zIVgulJb+U7btpPJc84ZwkHpPlLPF+2xnpnr129N5pPHT57duPWqNYExgcB5p9koY5QLjjGmCDvKSCLKyPcLIfR933WdMa0HXAxy66BpF4BM21bDUVmURZqxunEIoRAgopzrcT68XM63dwZbW0NC4/ScowzfvHl478GxlDIym+NKiAlXzIuj8iQARB6Q9z6SA/I8jzsQANq2bZomETmllLPUGGO0C95RSjEmRluCvdaOEFgZyRSMYOH9PE3LqEOilEQoiCTZ2SsnE9p13dbWKFqfDIcjo33btoN8UBYlQqiqF0opSrG11lhtjY6zitb4VRgKKISwXMhkf5DnqVI9oZ5S3DRdCI6yPM51hxCyLAPk4yh8hBSdcxE4jxoaxpi+q+LLFEIwJkJAfa+itB5jLP5kTA8xJpQySqEsh3meRp533ymMbdu2+wc7sQ/DOc3zPEdZjEGz6TzPc85E27bz2WI8HjPK26bb3duKwtveByGiAwXrewWBxeM2RnOtdXz2tm23tkbOOa1lUWTU+ReI7IauHLcovMBo/6fJgzZcx0/9wZ9kPxnQlTh1BTPGeB0VQ/zWpve/0gz5ZOkGyLv1k6Krr27Ti38BWK/+ToTkUQjgPHgfOfLo+cfvP3r00TvvfCdhFpPQa9u2PQrQGNy2rbVG9hoAJpOZ1to5s79/bT5fdq2UUgVPGBMIOe9gNBoDoNiCCcFiTBDChNAkSbynw2FJKIppcNe1dV2laQqA8jzZ3h5lmYhndZZlLgRMxOSiMsrDgGpltoeCMf3w7PL4/GmWZdmAXyO7e9e3MPfz2eVoa5gknHKCUKCc8IQFCDzhYlXDGyE4JpCmYrmcG6u9t5ggqbW2xnvvbJDKnF/Mtka062QIHaEIwBOCfHBFUUhT2UADTimlNnRNPzPGMMa2xbgsi4uLs7qZGWMSRZ1TWZZdu74Tm7XGeEwo5xxj3DR2azSOeAQXxBiDEZNSEszm8wmlNE78dV2HMXZOe4/6vg8hnJ6eSinzPI9fWmspFaPRKM6vbRpGlFJjXMTvYkvbey+ESNM0BOss2ohYIoTyjBR5maRTH4y1inHivJeqRYZzkY3HQyGY91BXrXM+S4vBYBShYqUsxoCAUBqdfm0IQUuLVl4UgRBGCY9FiuwlBISwR0QXJU9SXi0b78HDMsaUqqqKIg8hdF0H4ENAfmXDi7wPhLCVc5+OTTdGMPMOlDOx++GD3SSnQkCcucuyIs8gTZK8yDDG1bKzJhjjgmcRC2eMWcu854zTmIj1ndbaxrQkMhgwps6FjT1JTLI2Ddw4YROl0znnsZcnpcQYGGOUptbaJBF0g/vAGqn9sWHif5LHJtdAnxRmDD/Be9eFTciAqzHRWv/JX199wqiAK8HnxQ+sgtyn0y0cNrkSuooKIcAAOM6phOCjpj1C+OMP3z0/f97Xi9HBYLmQFxcXVvvhcNzIqZRdJNpI1R0fn3pv9/f3u05Op1Nng9YuTfLd3T0I2Fo7Ho9iHRF9vuL5770fj4dN0zhvwBFCMOesbYNUHSDbtK4oCucNJt7Yvm5kLwXhgiDa95ITEQKWWk2Xk7Pjo7YzvWwoQ5zzNOPlIHPOzeaX4/FQKZUkPK7I+XymtR4MCoIZS9l06heLBSY+TffzIqO0UNJm5eDxo+dZll2/WVxcnO3sHry2+4rqaqV6a1Dbdsb2IsFFkQ2H+b17DxFCEYlQSvWywhgzzhaLWZ7nAVwILmoJnF9cDIfDJB067wmFNBOEZLF3G0Baa6fTKYANYBlj1piTkwuMaABXlINostbLljLMBXXOjcfjiIjHEBOtRAghiUh8FMnt+0jwDSEMBoPtnb1YtaErdpiEEO8DxkSIRAihlOq6rqpqa9329rhpq6pW4/FwMEwzS3ywjKPFvCrLYdN00+mUsUT2uqqawWA0mdZt22IMaZqUg5xScN5u8hetLSFsMMiLfKCUrqoqIEcpt1YbI7O8TBJRVz1G2FhHKdVanp+fd13BBe26DqEQjeYivySaX8UIOyjKiDcbY7pOxtF2a21MxGIgCAwZ7RAnicgwbru+piQdjQYEi/OzmVZe8AKhZjO/4pwL2se+2Msvv3p2dlZVVQhBa9u2PaUUALdt9J4KANB1PcZqOBwmSRq8idSZeJ1d18V4VA5SKfvReJ8Q0jQN+n/+2T0ACMgDALZJuELLe9E+xwoCxphhIAQIBhSCC8EZ/MIfFSCgNdWQoExKmaapW7sdSSmLovBBX41Bm5AU/I/3uU+o0Fp67xkjmIC1FiAQQni0Z8aYEZRnyfTyMs9zpVRPGaW86zrGOARsjOE8kVJinn6ylFs9KAkbReeIa8YvjSAYCHaBeLMzzBcXR2+/9R3V12fHf/no0SOl9Pb2tjHu4nwCAKPRVnzjCUEIh8iqAAh1XecFi41VSmme59HDB2Nsra+qxlpbV+3l5WQwGOzu7mutt3ey2D4QCU8SHoKTUkopGSeMsSzLtNbT6ZRSurW1JYTQpp1OFk2tESIHB3ujcWZdPxxld9+rBsPUBxWNyTDiRmMIxMJ5nsXWT621TZKEs8R7v7M1cM4tFtVkMgOA0XCL80RrnQ9yQshyudxYMJZlGULIsiRWW5HlNB6Py7IkhEwmE7yG8K7aCiQpz7OyqjolbVEUy2rWNIuDa3sIy9g7z7KCEGaNBwBKKUY6ztZH/s5G143ToZQyy4q2bWfTRQhIa00ITUQRiTxxVUSAA2M8SEZ1XUcHgbgOo1hKMVjBPWHNgIsxy+jVcUgIYjxqj7oQQpKyGN2i0EoUV6GUdvISIdI2UitPSVLX3XQy19oGVOzsbiEEmIBSvXWqKDJrbd3MJxc1BL63P9q/no+3MmvQ5KJlnDAqJpPF5cWsKLL9gz1KQZteG+6coxQDBKUUII8Q6vtWiLQoCoKZMQYhnCSZ975tW4ZEDEbxLYg0wrZtCV8ZYWZZtm66RynOHgGx1pflUCt3dnZhtDPGYoycc3mRZlkG4CnFhJCubyEQQljbyLZRlFKEHSCXZclgmEc+bV3Xzrmo92aM4SJkWVZV9dHRUZrkhLCqqvb2DrI0j7LFAH4wKOjK3hjRzRa9+t6sECZHQgjIWIyBYkwAnHfeuj6e15hA1F32Ph7pwVljzEaSLmJ+hBBnVqEtXJEZ+hvAo836wBhv3M0AoDMtp5RhagMyDgXiMAsUwniQYoyFSClhzrmmUQgpQozHKy/DTaK3PvoEIEQoxRj7EPya3MWRUH0nOEsSvpxffvzg/UdPPghOPrz/QZZlg+GWVkZrtbe/5T1Mp1OMOOc0y3JAvu/bGPXLstw/GEfh57B2NYmwHMY07oE09VGaL4qfMgYIoTWSojjn0XJvwIq+7+M0UJqmq6aDtQcHu0Z7JedKOaWU1hSQ9x6kanHjKMWAuFbgrNbaegdJTkLAhBBKecT1A7gANoSglDJGZVmS52WWFl3X1fVyXi0jNyfLihCQtZ4QJoRAyOG1/1JkGCKE6rqmJFdKRXgiYplrFnLlLKqWrVImppl5XiZJkheZMcYYJ6XuuoVWNsvy8Xg8KEcRgICA0ySP9wpjbPSqzxDDQaS8R7xuQ6mNMSJSAWPGFPs1MV9I0zRJkmU12cDVYWXEokMInKcheIwxITQybpqm6rpuf38PISR7PZ8t40uLmoHlKPXeF0VBBoLRNM+74NFiUbW9V0qttYU1IYjzJM9ZWeYUL7vWhICUdEYDBBI5q2lKgscIEaXcclElKSM0dN1KZK4ocsaYNhKtTL0RpRRj5Dw466RsjbF931eyigEoZjE+eEJRmommb8qyHI1G0ZTYeYMxFglbzCZxRizESSQHWhutLOMEIBjtNNWEoBjOnPV93xPClLTOeYQQCo7x1YRaFOSM4SIu+Ij0Leb1cDi8dZNfXk6dC4wmJ8en165dCxA7ZdY5Q8FBQBgFBBjFrYI+KW6PMWaksNY6b72HEKwDFAIEDyLJNnWEDdZHjWdEMLYx0MbmRVyvUsq1UuKn1T5+Ut23UTtEV0bJEEJpnnHBGKHBmkCBpwnlBDCv2ymlLISAMPcBCLUAnjLXOenX9kbrAxDFBYLWdNJ4gMT32GpLAgSjp/PpydHd+/feOj+7DyBjXZMkYmGlsX2aiZTzrqeycwBUG6mU6romy9I83x4Mi7hbItM6bMzXvKd01S/wDjYEVu99XlCMsVJSSpmkIiKLhHR930spEUJFUQyHwxiYvPcIYcZEWZaMScZWpJW+k1nO+q4NgVAi4knuvTPGGBvAmyTFGHEhgHPKOUOYRRO7JEmil0fb1db4JEkWdSOljCa38S7FnM45k+c550QpY63X2nrv67rFUNR1H+lIkcEkBGCMjdPOtlJKpRylUghCKdXKckHSNGfMTydzox3ngjHRdRJDfN9JkvCYEMXlFC0AOE8452VZWuudc03T4nTlfxkXW8RrMcZRpSTSlOKriLslSsqtqAMIRSqTtdY5CMETQjCGtWMXN8bEHRGPk9hrp5RGPFtKiTHJ0gSQp5QmKYeFN9Z0HYk5O8KEEGK0ddbneZ5nYPRSKVktPWcpIajv7GLRWkOsQQioNaGqOuf5YJBZa5RSQnCMS0IIMoRRxjnv+spaTQihFMfFBQgQZq0NSvVKKaVojJJCiMGgsBNdFNlgUMTFJqW01jtHrAVrJQLMaOZskL0xOiBE4oSw914rm+UJAMTmvXOd1lYrhxGP9yTmp9YaKSUAxKUeb68Qolv0WtuiGDGayN5gjPN8GAJZLpdcsLionHMUPEJovS2DhiuFUlg/jPGY4CTlAB4FBx6Cx4Qyz4i1VikdhZwBIASklCnzVV0TD9i4D7XW0VI2xD++QYWuiHV86hHW3WvvfYAXfXdjASBYbMAHagPCXGlkXSCYcCpCCMY4azwGAoCDswQLCC7K0iIgEJB3zlovqKWUMgYIBYxDVLkmJCQOkrKYnB3d+/CHR88+WM6fAGoZsS/duXVxPlGqHQzzAF6qmtLy+vW905MFQkhrrVSPMUpSnmYiy7LZ7IKsH5v8K57VERqIYoYhBIxpkiTLZY0QslYD+CRJECKEAOdJ0y7izokt2zRN428tl7X3vigzLiilMUwTAHz9cOf5s/PFrEeAQgDOmUgYwkEr13cuBEsZEMIIiTaTWHfOWosQwdgrZfpOAQDnSSJSJbXRlmCaJlnw0Pd91/aE4iTJEEJtu2KjCCG0tkVG0jRBCGKo2mxy6wkAjpCH9z4Eaq1bLKq6sfv7+yGEqqoB8P7eHsb4/Px8Pu3yPC/LknOktd0cRWukoxNCoLXJmjE2LrAYgxBCm9mdWIVtlnHUOe66jgviXAjBeg+MMQBMCIMrBplSSoRDkiRRD+T8/LzrOmttWZaj0TjC3k3TpnkRmXTWWqVM8EgIVpRJK7W1K8s8TngIUNe9tbaue2t8RJqs5ZT0COlq2fS9RUEDYIIFQgEhG0+vvEgJRYSiGEatdZSyECCyKNZgljXGBLCYhK2tEWMkMnRiciAEy/N0n+wRQpyzIfjYx5GyV0rlSWa01cZUy8ZaJ6UOAaVpGjWXN2s1rl7O+WAAy2UdgsVkxVyJ5MMAq8RzU99EsN9oz5lom67vldbWGu8dFiKxrovLOA6O0dhFCmtxwfDJR3wLnXUEECAXvDJahoA4STHj3mjT91JqjEma5EmShRCshVjkbwq6cEXQf4NGoyuzrD+pHIvpj4/mzsjHe+G9R4EY7QlCgjGMkODUG4sQeIeQYxhhKfvgIUky54INSAQeUEAYxctACHniPfWMt+vrdIJjSoW1tutahj1x7Pzk3pMH71bLk66fYqRHo9x73/UNY2wwGKzeHop9cHmeh+C01kIMykFeliWl2LkV2ByfMd6NWCYw5jhPKKXBI4Rw3CrOuSwtur4xxmEM1tqu7TFBGNMsy2L4XiwWWZbFjY0Q6lqNEORFEoLVWmMSGBWC55zz4TD3jsguSKmdw5wXQhRtowH5CLuEAD5oay1jVLambVtnQ5rmlApKoW36rq15msQeirU+TVma5m3bd53Mi5UUkb1iHxxC8NBmBR2MyrjhrbUAGiFEHSWYIhQgYASRvIsAIet007TOBSUdxqFpOgDcddoZ5R1ydnWuxg0Zy9uu65zzxhjvgBAWoVCCVwDtZt2uSvh1tRWP680IGwCy1nlvMDacO845pYyxFc3eOWeMWiz6KKsUeV5t24cQhkOKMW3bvqoqKZWU3FlEeGQqSe89BMw54zyCHaC1NsbGRJ5SWvUNpdQH47032re1xcRaa4NHWtsQEGMkLwRlgQskEsYSZm1GCPMOlNKxG9W1OklpLAswAYRWFShCgQAUZToY5pE2HZNBzgliiVKq66t4W3zwAYzzCuGMskiGwiEEzmnfK60lQsjZEK23rPEICABGiJRl2bZ9CCquZB+clMY5MxjmGxeGWOTGZ4+ZR9d13gMhBCMaK7U0S+LMmvfWGEsp9sEHHxCslKTD5ujY1A7bw1HTTs/Pn0+nx13XCCpG5U5ZjmfVkZTSaJeIfDTaKosxIcxah7OdaL8dUcBIXnDOEcI2E+rhinD1TxpaDS/mZy0mEIs+Y0zOiHcOCCI0qE6aLnRtKwSj+UBKxaiglCOEAHDftdbasozzYhCC9V475/zqHFw6hOIM3mAwSEejvq/Ojp53i8da63t3704vT6zulotJkiK6v71YLDhLOOfz+bJpurIsKWFHR0fDwR7GhFKCcBBCUIq1lhHRiLVeDEMRGY3hJu4ZIQQAil2MSL3TWjvbO+S1towZgYXgSSF4PNWbpnHOJUkSSXTLRc85HtI8SbnzClYwZOeDLQepEGldyfnMOWcRQkIkWjspldZWAEMIR3YZIWF+WRljECJadRhTaxxClLMsy1hcElF2J7JsOOeCJ9GtljNR5GU0Bek7qU2HSYIJDiEEa6xT6+p+BTBrbTgXIQTOWVGOjE28A0bxtWuHSpn5fCl7BQAEY2u9lDqu1BCCUqZt+93dbcYYY5hzrqTR2gJA3/dZytDaPjTG/Ri2gnvhKRoXNsY4SRLrYuXlNsdhDFLGGMYoY8xavekocc63t7fjCRoz0DiXmyRJhGZjSI27yzkNyCrdIyBpmlrrZK+10wC4KATnPM0ItwFjAC9CQM56xonR3vuYlKE0TbKCEOq4QAEx5kIE75zzShnZ68WiiiQGTDxCgXGcJJxzihA4u7r+eKgLsZp8k1UXXR689yEgjHGWJWkqjNRJQotigIAqZQghxiqpGsFzpYz3ljjStp0QnBASgkwSvgFJnHMIO4LAGBPjTkwR4mkR0/zRaHB0dFI3leAJQigv09Foq67rvq8JjV18TAihBAcHAXvrEY5v5AZXXyVUxly2j87Pnz5++v7lxVNnZZrmZbYteHYxved9gECFSLO0FCLHiIUQrr3+i1HSJb61kduKEAovyIifHo/4sY+InHsffc1WrrVt2z56+jYAFHma5/l8OtGyb9v22v7unTe/Ppst0jQtioHR7uTk5OnTZwihN169Fl+alDKqc8ZLKnLivY/4RXRruby8fP+dd+rmR33bV0uJEevauq07wYbLmcIEY0wRYl23XMyXjCZFIZxDTVPHQQEfrNZS6y5iz5HkHglasT7fECistcvlsm16Qmhc3Lu7u0r1CGFKqfMGYypEyjmN50EctAkh0LV9CgDIXjmHVxkQI5wzJX3TdAFsmqaEOuu8D1RJcM50rSYErFPGGMZJCMxop5QBMHUlKeWU0q51su9DCEVRJiIF8EKkGOPlcqlU1IcGhMiG/AaAhUg5T4wxlHJGqDPQ6NU8N0KEUkoxC8gQwqI8LgD0nTIGiqJAwKxVhCAhBEasa5X3ynvPBCWYcZYInhBCjDFGK6X0bDbjnDMmYrxo2w5j3HUdglWOE6PMhuJcVc3mmxEW8Ct3ckwJJzgAAI6Ovw48hk0jL75NMUIZYyBgRoXWuqm7WElRSglmRVGkaRo7aGkqIn8yAGfnerPmGWMIsa7r2kYNxogLnmRcCGY17zunlKTcBXAIIwQekEPYcc4wdQhbpZxShhJGaRJCMNr1vdLaYJRSiq0J2kjKwDtwLnhv81QQypw3sXKMHQyEEMLAOCV0NX++QfdU1zImsixrm55QGAwTY/OuI+ATrWU8OSLQwzmzFrRe+Uo5i4wxnLHBMI/9+xiJkiSJmzTe5+2doRDs+PjYuQCAvffOqcEgq+v52tOBEUIoxt4HFJAH8Aitmnaw7o7Fv/5X3/7j2exoOn/mfV1kHPm+mc261oi8IoQRzKym1fzMO+Q9hoBnZjfCWnmeb21txeaIEELKF5bNV2uxn/RwzsXqEKEVMh1ZHn/11jdSIfb3d7dG44uzEyl7Z22evfLue99/9uzZcDC+efOmtf7dd3/04YcfCp4+uc82k8SRlbNarIC994vFwnvf11/g2J2fn9/74J2dg0vnYDjMCc6mkwlGNBHl5KLe3hPz+bIsBkU+7DstpU4TtLuz//Tp0+FwuL0zDiFoLatq0XUdZaQoingzNxMJMcRLqYUgWZZRwgmhxpjlssYYc05jAzViZ7H+apolYFWWZezsxO0XHekYE8Z0dV1hYhFCWVagBAdvYgDXpjO2TXPBmKiXpq7bvYO861fyEcEj2ZuqarS2HCfOIu88AGI0AcB9Z9rmkuWqKIrxeBxnHeIUe9u20copvrQI7kbuiVYhpgkxAUzTNM/zJGGMk5jRUOowpm1badMlCXdeG2OUMhAmeZ6naQ6A5/M550kc7AoBIs8FAKdp3ve1tTaEvmmatum1tsPhUAgRDUJi3r2JIBFVjTc/9m5iNhdfyCY5XR2JCIUQ4lD+hquxOQiNDnFsyq7tM8l62t5733c2EjsxxoA8Rvjg4OD09LRtW2sdZxmlkSli67oTqS9KkdPUMtF3dS9b6q33DGMGgAI4H0wAipAD8Er52WyGESuKkdG+63qtDQKipAXBfQCtXAiAMWFU+ECsNQACICglu67re1pVS2PM7VduXwV3NsjAzu4otsO6vtbKleVwNC6LImuqlW5fRCdj98Na08suBJSmmTUQQkjTZHt7O8uSp88e9X2/uaVxcXLOz8/P7ty5A8g3TVMWw6Ojk7ar3njjs1VVUUYiAz4Eh37rW+/GVCWEQOhg8/vOueFwuFhU//bf/tv56bci4ArIJolIUhqCM8Ywmm9WpA82TUX0zBV8MJ/V16/d/qVf/Puvvvy5voWutUIkLXd5nndK933PRQKYKKUA0zzdudrD3twjHyQggWlqtBvk9P69v/z2n/yb6wfDu++8necpwkAISlKqtcIYdve2z0/m3sHOzs5otDWbzS8uLrSyIQROV39wQ1qJeEpsySEEjCNK8f7BTtvW8b/Ozs68B6N910nZW4Tw4eGhtlXf98PhMM/zKF6zvb2dJMlyOY01V5IkkagSrW+2t65rrY1RAMAF4zzWyWZYEkJI8EhKLaXUyiqljTGXyy7NxM7ODqV4sZiFEIoyj32oKNeyYabFXUcpjqh2HCKLXsPGGG81xjgilzHLi6fT/v5uzPti08paW9f106dPraFFPkzT0miIEYRSEAl3IVCG05SlaYoxNTpo7awBazXnSZKkk8kEIR9AXz/cyYvk/KhezBuCed8rhND29th53cs6xpQsS6Rq5/NpliW7e9uUksiajYBrCKEsyyRJlFII+EZgLCbjMZSkqYjCPbFMK4tBVTVKKUwMY4yxFAGu69ZZVJYDBAT5Pp63GFNKOQAYbZVS2zuDSOqNFOq2bQEgy7LX3jjouk4rC0CaulssKu9DlhaUoXUpGuqqxTiOO+BiQOL4K0KQF2nf986Z/f39h/fnTd1aa50Lfa8opTGNGu0nsRpiNCmLISFiOpmfnp7mAx87ifHAjvhgkiTBk3h2RiQ+0lkZYwRnUnZ93/pgkpQPh+VoXOZ5fnl5PhgMiqK4vLyM8MIaZxgtFou9vb3ISqeUrnyZUht9kIQQJycXbaM4K7VyhKC6btu29R4QEO99CIAxvn5jdHBw0DTN8fHxcrnc2tp6/fXX0zSdz5fW2tlsVlcNpTxaoGZZhom5du2aECIuwg3Oo7VO0xSh4INhjNB7H/0ozkynaVpVXlAB4DEJwau2uZjPzo25dNbHvIbzlFDQSgdwlBIpJYAXQgghnDfOuRBQURRa+b5vtZacM4ThcnLmHblx8/NBNQBYUIZSHBBGCOEk995LUwMAZrEAtNa5GCwEZyEE54zSaqrkydHxycmJ1fPRaND3fQDPOW9qGbU+J5fLWzdvxnbp6enpYrGsqgoCZox5jDd48Aa2jHeWUkIZNgakNFmdWmuzLAkBUZr0nWzbXkpNCU+SlDEGSFTLulrWCDAljGBqjZt3i7zIIwrbdVLr2L0OQqTRSH6lG6u11pJSyjldzKvIu/HeJyLb3iqMMfP5/NnZNM71MMbSNNdaKqklyL29PYQa7yEiowghpZRzIU1ZJA1FboFSakXJISjqcsWY5dcDO7GjtAFZ4ns6Ho+Dy6z1XaucRQTzNKGEBkqxkTYt89GoZAwZY5yzAN4Hm5eYYMQYcM4AwDncd14rKaXsZUuQCwGtahlYpYER8kvTNISRtTqaNe/t7XHe13Vd101scidJyrmAwL0HKeV63CkQQjAO1nq01rQLK/IHShIekMMY0pRpbUNwCCPnteBps+jiex2BW0IYoTijaVh3+mMox+vlcXZ2ZrTDmBDCYr7DGE3TdL6YRACOMcK3E4SIlHI2mxmb5HludOj6put6zlmaZhAo55wLjRACMIwRQihlOATWtdo5FwIw5rwDjGkvG0w8Y4LzBGMspUbIRDqKMU5JCWtuSjxU4gGpZEdoKAcrd3mt7WxaLxeN0p1zwXsIARHCYlIoRHp0dBTf7phdYoxHo5H3PksLxhjGNN5YADBGOReybCylRaC9M95b72JbCTd1vxC1MYZgTonoWnV+NuGca7PSM8OIdJ2UUsXSJzIYYjcz3vPIODXGGKO2d8ZZNlKqpx+9/1dOL6P63P61n05TIVWvtcwLRim6//Gzx49/5NultVokvCxzY23XtQiFoszi4Q8rmmnSqKZaNsGXWZaNxgPK0POjxw8fPPnOt7+PUfLLv/x3f/rrv6SMwYA5RVJbHxAiGDzCbIUORhwhFtKUYadVQAEQ4gSHgCjFRZElKRuKojtupZTeBYRIlg0HZRJRHgSkaZq6bvteaq0p4Ru0bNNW22TsnHNCVw1OpWzTNF3X1TUZj3YI5hhb54KzAYHruu78/Hxvf4wQ0XrVBLHW1nVb1/Xhjb1IP9kQSVZzOiuqHgGAXnbW6rIsB4OiM521HgLlLOFccJ6EgATPr127zjl3NtRVG8BZ60MIlOKu7bu2j6VohBiCB+9CxBEjZmStD0EjRJKEE0RiNMSYIkQQIhgTSqm1EUNdJeSRazcYDNqayr6VvcOIUSoAPCHAGOlncyF8yyQmAcCGEAIYbXoRKATnlXfOhICNcdVSOWcZCYQQghHBjBBinfYhOvys9ByShGNCvdd9Z/Kcyl5HFmJZGO/AO+g7Za2FYBBCCDAEFJ3unPVadZtqK/Jx07SOLd68SIwxecF9bRG2lAhrZZLQ9dsN8eJhracVP4n/G2eduq4DAK0iwwgw4pxzjCkAogxvbW11XTebzQBQWQyjFXiWZVo5wZFz0DZKEruzk4WALy6mUq4gbWtj3q3jakFAV2NG3npfR6RmNM6LMouVR9u2IYSY8GqtrYlioT5O3saK0jln3TRNU8HTEELX9V3XtbUGAESs7J1Wa1nU1kQyR6xGIzQWV/5wOGzbVilrrbe25ZyWZUlJUlfSe9PUfd9pa0Lwa/s+gOBJ37nJZRVCAGAEp0rq2bRJ0xSwir5ywUOURoqvlzIU36nIPvPex9sSglsuW5EwzrnWli6mRw/udVVVYYx//hdxlqUnJyeT6dmrr7788isvOasoCa1xzgFSTovYKRCcsyzNm6YKIdR13fcqz3Nrfdv2Stlr11hR5HVd/8mffOPifHF+NvviF35qZ2eMghcEG+eD9RSCMsYrIJwxIrz3PniCANOVhTwAABjnwTonuIBgBEPDQcawBfCMkb6Dtu0BcPDMpwyF5Pz8uCyGsSEVE4TgUViTpshalXV9xDHGIxfBxTKKM7FQy6ZuCc66ru97GULAJFIlXd+HtuUxciVJEpGFuKWXyzraP0Tg2Xvf96rvVZaWEVCIgcn71ROZXvSdEgIVxSCEcHkxjdFkZ/vAebNYLLquSRJBCMcUsiybTqcxfQVAhFBCmLXeuZBmSdxLxhilpPc6TmMOimQT02NFE1/7BiIJa63PiH0s5kulXPCYMOI9WGuJQ4RQzgpr0GLeYOLKQToaDSIJsG0rzjAE5Zyz1iCEMA6MU9k2UXnPQXDOVbWhDNJU5HmJEOq6rmmqmBJijAGw94uYHA2HY0q5UqppOiklwUlkAMXDIl6nMa6umiRJ0jQdDrGUMvZ6EApb2+VyUSUp7TpgHAtBmloaK7OsiDW+9xZjCGCMjQcyj+BaRN82jb8hHQUf44XnnDMWOVzN9vZ2rA05F5TSqqoWi0UcMek6ba0NnjIhhMiscWdnE2excw4hwjillMZbGkLAwBCgOHIRQkDY50U2GAy08owyCAYBAQCMKEaU4CBEiL8YszC0Hi3Ic5EkCedCSeMdWAMQOKVUmSWAc7ZFa2VOSinBkKU5IcRZP58t6qoZDofeBWe9swgAaeUJCYOyhNBfnM9ns0WwadRdY0xQSqPuFsbYO7SYt/Etw0ggAO8Io1nALtbLIQTOeZaFOGSDMJFSxvwrrJk6nPPBoJhOp3FejxBCAeRsdnJxccE5//5f/m5RDJbLqq5ap+vZxcWDBw91azHiHrTWdjGv04xnWUKJ8D44byI3rG2X1jpKePCk78zx8WlRlF2rJ5fLvnODQXFwbb8ok2BlXpZKGYVcxrOuk03X5SLVDoMHDJhTvsHCnXOeaOedsYZxomXXtbV3tu7nZ8sqasd5bzlP4j7Ps3K+VFnq4q3HmIQQonR80zdRt9haG0viNYuUrZ7IW61t36uuk1rrGAWUUpzzPE8xhjzPi6IgFEeDsKhUEFXsQgiL+TKE0DadJCpNU8YYRgQjglZWriFC8gjxGAWUsgBgre9a2fdqPl/ELoYKFAC6TnqPKRWMkRACBOwdxogTLJxz1gCCoJXHGBhXkcXr14M1ET3pWggBIUy0stZ6zgUhpOs6SvG6U4NiLI7ogF8POsSV55xBKFinnWNa+wAaYSMS4lyIWZWWlOFIoQzGdHmRjLeywTC/PKW86aqlBPBc0BAwF5BmCec0SRIAH4nOMUtHwIPHdV1r5YbDIcHcGqmUsTYkeQoBa236XkUImVLGGfEeorkYgvgW0/F4OByVZSn6vgfwmECScCGSpu76vs2oEEIkSWKt1kbG2o1x0tYKrRWm4mRJLF2rqo3FjnN6c2MjakYIyfM8dnI3ZLembilV0Vg8sniccxgTj3yaCsaEEIJSbrTt+94Yt2x0CAFjFEKw1tE1hij7tu+lMSaOtS0Wy7gL4hw8QsgaKXuN1jroIgVrffBGSqWU8R4IxoxxY1eltw+BUpomWZRb7NVCa1sUg+WyVqofj7el1GVZ9l2fpAxjH49qAGCMlGVezVcz5CsEw0ZCMfKO9J3BGFMSMCYImNHBWcRSEnG3mDRFRF9r7dyLmdMNiz1N03KQU4aXy6XRThQptV4jhJggeZH23WQ2PQ4e7+wcDIrs7OTk7gd3F/NqUKYARCttnYyNt5hKZAUXIiWYObfoO8UYeIe0clpr2QdrAsEiz7Hs7Y9+9KO6av7xP/1f7e9fY4JTQIy4RARnwbuWhALHHpy1m46+YEwFzCjH2Odpclnptq0wWOuMcyFJMsYcQE8p9l4ZawNi0ZQiFixRkyG+qZsbsWniRNwEk4AJRRbaRi2Xy75XbdtjRLXuKMUiybe2RlFNLi/SJEkwot5vRu0Zxl5r7ZyNY+Ixum2oQJFuGyeWCCFcsPhJ27YISFEUAHg+XyyXy6gmM5tWNBsAivcWKWms8QGclBoAYUyizo5zLk1TjEmSJM5qvxmjCwgCBA/WuN6txM+NccZYxkRsqEdFwaIojDF1XWutCSGj0Wj/ALdNt5h3XRvpZAiToLQ2JskYFyIBhOMVOue7TjqLQsBrsp8FCJgEzvG1a/vLRaP1efBoMBgwTjjHmITou1AUhTGu6yTBLEkSRhPvjdbWeylE6pyLyV1UKXDOO+cBkPdeKW2MBQDvwFrvvRKCpamgFA+GRVHkxvTBg9YWAYnYM8ZUKRVUW5ZllqXWIddoQKEss+FwMKcq5heRdRWTQULIfFaXZZmm3DlnnOWcpanAOLXWR7x/Mpn2ncrzMlZndd0mqSAEWWut1VW9AADOmfeKMcYYYYwlScq5i+Gs7iUmaIU5OG0MZlQS3KCVmZyNuN5m4M7oF5S/eE7ECTilLWOWYBbb1t57hKyxkjJMCFHKxiFO543zxmtrjAOAohgURWw48K7r0tQvly3GJeNMa71YVCGE0Wi0v5+eJ3XXdV0rYxZpbCTriOg2GtOOiGlEOL8cuzjBF/ed1hathvVWg8GblDxu7fl8VhRlkiSy10oZ6iEIznkijLMpypfzidGOkWRULpVshQgH+yPZO4wC54IDRThoZQghaZpSCnHXleVA9hYBheABHATcd1YrGwLGiAPgpu7v3fv4m3/2RwcHBy+98kpZDn0AKhJjzPnFxd74IMsywTl4FSH02Na9rJu82AIkvDOTycXZyVHfNcGq4KGpW0AhgAdkkpQiFKSax6I3nvBxwIpR4b3PknRDWY5wbAzzVbUghEUeRGyfJyLDmCQpGo0GaZqOtwaEIKk6QpAxXSdXzKl4HBHqGQDC0LU2ziKEEHkQUTmYxKIjpuIsUM5TjLG1pigGSZL0vVTSOBsYE94bY1qOqbUaAQnBV1UTe53e+8gRx5gqZay1QqRJIopiEECSK/PlcW4QY1xk4kVf2RilVHzh2vSb1vImIg8Gg3YEeZ5Sys/dVErFhUgzhlAAlIuEcoExBuecUqbvnOxdXhAuYi6GvAdj/GLWWxPyJMcEheD63iCEuMB5kSAU4ipHCKVp7iyy1ilpZV8jYp0LhKzYRhjTNGXWWq3smkOIgkfa2njPrdOMsSRL9/e3RUJ7WXvvptNLhJ1SDoIDwN4hrQzGFIGzximlCEEIO84p42w4HAyGhWDji4uL+O5ECcHYQySYR0Ecay2hKEkSzqlzLr78iJ11rYznTeSsj0YFpbTrG0Iif03G9MoH2/c2qiAAYB8cIXRru0QIWeuapnGtCx53rdFqMRoNrPHORtgFQcAQAgQcPa82yxWt55aswUZ5hGUIwfvIoXPWWsqD8w6Qy/IUIdQ0jbEyLgPO+WKxiLexqqpIZcKISWmdB2Nb7x2lVIjEezYcFSKhSSK01n0vuzamkAHhwAUVgkWMmRDivGm7GjEfa2RKVtMR8RjGeNXU8ytRTRazyMWy2QAjfV9TRAlPk9C1s/l8etYqJRljF2eXslPe+7aZCyGWS2utpgwLwa2VbdtbaxljbavatqUkyfPBYJBhxIwJ3lXT6TRJMiF410rrbZ4VEGhd1d/8s3+/u7f/c/3PHRzeaNp+NBohwj784AN5Y3rz5s3R4WGWUNmp5fyyrUOTJH/8l+9dO3x5ONrdGu88fvLo6OgoT2SW4Js3byjVM045p84rhKBpqmfPn+xt3+haCRBtMHyc2QshcE7iVozfiX0lpZRIMUIBY4i9Z8Hz5aJp23b/+s72zphzxhgBZDnHXDDXSOfVetjaRgALkGccxabpJnWPwT7P8+VyGd+MruuIwoyRCF1zLrquj6qjZTlEiDBmCCGV1NbaJOGUcum7eJ5bK621QhAIKzadsyEiuE1XbzodK4SLMSGEt9oYG5noUXVciIRzYSzaNCki/yjC0oBcWeaEMCm1n1uRsMGg4IIpja3VxqgY6azxXavaVt98aUtw0bXWWSxlSnDStVrJDrYgz8ssy/p+XtUL1IQAwzQVfa+apqGEJUmxyskROBe81oQQCLhaNtba4XA4HAxDCLNprbWN7Lj4SJIky3LnDEBYLpcIuSRl2nRJwpxzxipGBQJLKXMuSKkJphhTniTW2qqquMDlICnLLMsThELU9IttxJjXRyZkno+C903TWavLQRortcVyxlk2n8+FEFtb22mKqqqJTAKtJReUc45wVpY5Qmi+mGqtGYuzx72zIRI1ESKUkb3x1rr5hYJHziHvoGt1CPN1ozaE9ay4956QyNcLm2PPWh+Ci36n1hgfDEKB0DjqGKxdETJHowHGWMoOYCUkkCRJXdfxTXfO5XkeW3tdX3OBCAkioVHoQkoJgRGC8kLkkKZ9ylgtpQohYEQoxZSSKDgrRC4EQwgp1cd1vnJJtD6Wt96vRNriOxi3HqU0CnIjhEJAWiv0y//478QV3HVds9TW6u2dYdRbRMAQ8MnlYnEZuKDe6wBWJARhj5ATCWOCNE0TSRYIob29vZhMHj99ZozBiGZpSSlX0sXXoLUtSnFwbeull292XTWdTstySCnXvr1xeCvLsul0VlVNWZYQ8JMnT6yyhJDBsKAUL5fLtq0RhhDC7TtDzpKm6SFQ71DX6a5VJydn1w5uWWsopQgFbSTGWAjmvR8W5QYJio6mscjHBHmHutYYHUJAVbVou2WSiFdeH924cSPLsg17JfJH7Er5MMSIHhXIEEJWr/SZ4t6O4ynWWmtI5JWs+5FGCPH6669TrhaLRTyOkiSJMr3Pnj1zahiZb7GVQNYTdhhT723EreI4SJ5nSZKITMddtKKTrPuv7aKJjfnI3o7xN4SQj8Visdjd3Y2XHQEvAJhMJofXbyJELi8nsrfWetkraz0VHAKOslsYE8aY0a5tW4o453R7eztJeNNWSinnbN/3ecF293YICW1XK2nqum8bTTAri61NN8CuB0pDCGXGIhchXkkUIVJKda2ZTqdZlhVFsVwunXOj0YgxZmyHEFrrfXpCY01NBiNGMGMscRYt5vVi0SCgWZZR5iKnOR5FaZoOh0PG2HK5jB2oqIjCGIsVmeqplPLrv/C1p08fM46dM0r3jNHzS2md3NoeUgoIQyLS5bKeTasyyRFCeZ4Ph0PvfSxvOecIQ+xJRwh2M1tjtPXBCUFEQquqOj256DrNKGc0v0oZ2dwfQn68rlakjMJ6Pm4D5xHCOSeUuxAMQgiAyi50nRofJBF8iL0RhFDUJLu4fJplWV6kRVHkeYZQ6Lqu6xtCB6PRYDq7MEYJkVIinjw+bhslQupW/mhs3WbxGOM0KY0xnFMuqPceY4h/ylOfZRmlmHPKOe9lJ2WXZVn0AtrZ2cEYf/zxx6tSM8aO8Xjc9y2lFCHw3rdNZQ2SSmFKMQkIex88QlGJAryD5eIyTdPdnWHXdfP5fDY9izv84GBHKaWUgQAiIVmWKZV0Xae1hYAX8+opfsoYAcBV1cxny4MbO++//0FZDhGQxWIxnSwxJnXdCUqjhq61IYLEeZElSSJ7Yw3SygJA39lq2WJMh4Pxcrn0Po5TkXggRBivqqpNHyQejBGvSbPcmoCQiQoPsfc0Ho/293edc3Vdb9LgFbXBQHRVjusDraeT+76PKyOW637tZBK3FqU0NuaKogCAtm2HfIUrFUURkfIQwv7+/qP7i1j8xyi2WtAI+SuqcpGxEvlcrTyLe3uzw1eyOzbEDHy5XEY2UAxnsVKLPxbDaOSjFkWBcEAQhBARxY8YbcZo8GHd/ltRctadPrdYLCjFxkYKj3fOUZaE4BhnQzawuUvTvEmVkiu2V7znm80W698NHTkON8bICGElPBRX5hos9wBhLZ8QCEGxi+ScDx4xwRljbr3tvQvGGOedtZ5zGvEmY1xdtxjjruu9hzTNAHDbtl1Xr2wkgmYc9X3rnBukZQiu79Xx+YUNVKqOMjQYZFH51LkQw+XmdXnvo6gT53wwLDfxIt60+FooZcZ6YwyhUBTFjRt8sWjaprPGbQLKpuZCP3mEIP7BT8WgEEJRkuGoKEoWwPR9J6XCxBAeBUZWQj+R7hh7UlmecZ4gWJ2USSIYY1RzhHHE4LVmxhjtVnP8HHG7lrvZAKNoPQgppbQOx1YPY3QwGNSq1lqHQCJKa63FeDWuFFaSVYYQQjllIQTBBS5Rteyt1W1rrDV9389mC9kbzrP9/W0AaLtaSuO8BeRDCD7A/rUB57wc0rws8hKvV2c3HJfOirbt+04jbEUqGOeEOmdolnFt2q6VL79y+/DwMHh0dnbea2k0evmlO1tb28+fHV9cTGazmdGBYuMDMkbFPg6gkCTJaDRo1Uz2UiuHEHStahvFGBI8C0GGgDnnjBFkQ0xfnXPgQSmDkI1xwa0VMJRSznpjjNZSKYMxDAaDnd0tCHi5qCNXMMJ+MdxQkluD+s7GWYRNg2mj1rYh9cZgtFxEYWkXD+GyLOOX1q6wvTzPo+1MCGF7e/thmMeGwib0xDfv6gK9ig7EZ4nRMPLBYiQd5WUsLqSUxijOKWPU2uAdUMKt8VpZBCR4ZLQTnKwEH4DEdA3AZ3lqDQeE3LqfCoAi1hafPT6dUhbhGKRInud5ThEKhEQ7isCo8w617dzbFyf8Jp4CQIg9Q8AQkLW2DzKCmlGjK94BIcSGkUgp8g4CxFkKxhjBGAdwXacI4cEzKVVUuvEoWOudNxHH3WS+8Y3b8DMiPBH5FmVZ2tT2fX85OQMUEpHGqiR4iikQgkNwCEOSJN4hox1CmFJmrfUelDLOuaijhLH3LvgoFQPgXbDGRTfN4TDBRHiv/XqSSWvfd9KtJzQ3j78hAMHK2uyF3cMmBkm9zCwQVqYZLYaZtTSylE5OIJ6UG4W5eCTHJoDWtuu6tumHo5JSYq3v6toYpbUkFBtthaB5XjJqkVppOcXVvrnOeAo6Z31YDZZ7jyI7HOGw4jqtBI9o3ytANh42Wus8z2nXdHGP5XkO4LM8wRgRgrIsTdPeO5RlyXJZIQTG9oyjre0yTTnCjjGSpsY5511NKR2P4my37rpOytgJQtaC1n3bWko5FzhJkijuNxqN3njjjS9+8YvDwRghDJheXFzcuHHrxo0bjIkHHz/6jd/4jbff/mEI82hPKAQrB4VzJsuSJEmkFdZoY4I1SkkHQKyB4C0XNIRIh8HOr2KiMYaTJL4BsO4vRFsLrXvvkPMmSh0TihECrfVs1sUSGoDEwy1yc+q2CyF4h7xDkS4Rmf7lYKXlFt+huKYZY9vb48ViIaXCGBOClOq7riMEKYU3VJ2IEMf8JRIOoiMoWdtba60j+R0h5H2Iz+i96/t+tM02/b4Yiay1WutokMsYxThFCBGCozmtdYFS7j04t+Ica62TxB8cHMSCS0odwOV5LkTadZ0y3rsV+hgl32I87bsOoTSEYIyKvRhCIovX9n3vg0kSDoAhrPByFPDV9GfTpowZ5dWUIf5vPJZjDIpVg1nrLgCA92jdXhAIBe9x17bONoRIo130jUEIU4qtIxivHLUQIjFYEEL29w8uLy+7TsY6l3PkXHAu7O2PZzPQWu7s7DHGTk7OqmXHWcYLPBqXacoIxQiwc94Y62wgCYv7MT4FIQwhjxDZkGVgrWkb92rbtrE9ipBXSjkLAJBlWd/Vn7oJIbzAgH5sHrTZ/1dToeWiMUYr3W9t58NRlud5UXrn3MOH53GALjIJ4yKJiQlngjGhVL8BueKbvFhUSvVlmUfD2DSF4LXqlFtrH29OFISQc5asfKVDWGlme0KIwyEC1XWz1Fru7m5Hn1vO03jYhEjIjCMtVVXt7OxQxoqiwDg4byiFcpB7jwlB88uWccQ4lIP0+uF4MBLWtQFsNW2jwi4AGC211ozS3Z2t4+MpQgQjShnxHislrTOCJz5A31uRoKLMCAldVxVFduPGrbzY2ds7CCEQwop80HX96emZMUYIEtll5aAoytQYQykG8GmSUSKCl9Nm0XUaAQWEnA2d6yMZh3NqVtw5sNaCi+zMtf2Gi+0SYl2vtVfSWuswAc45ZdgY5YPN83xQjuz/r68rW3LsOK6VtdddsDSG0+R4FppWhBT2iyPkkJePtsO/wAfrlbTpoOnwkEOKs/UGXNyl9io/JIAZUZb7oRu9AY1G3cyTmSfPSYnCjMTfnPLhMNKT+ifGDlErCJHQ+g5bvHh68OF2u0cA9Xg8WmvnecRcxDkV6nQUcNVLSumcu7m5QWB/IRZgT6d+tLILUMuZ7uica/oO8/ylSsJKELvdWmv04UJ8BAAJKj+73CAzKITgnPMeNTpyCK7WYoySUh6PB8515mcNqXPFAQDIkIoxCiH6VauU4pxprQ/H984tjIHSQnBlTA/ApNAxfEjvl/wJJ9o3lswMtWEJAUqh1ojZAkfUF2KXUgqNl8ophibOKQDlTDkbS4l4txg0pZTFlVKrXdwBBkqpdyHFDIQyyoOPF8NCwdlSrF1cjCed5t1ud393eHh4OA6L1nplVpvNSmmOXbxSaIolJYJNJcz8cNa9yTkjD+sMVT6E3XEcZRBNI5XmyITknG+328N+uWTHj2/8OTR0oYPBHwt+Udp6Wx+y95ZMx9z1yrRMCIpmAfjPxAsWX/dxHBkTjelTxM2V2DQViNhs2hiDVwKd5lLK07hMkyX+VPhfkGn9aMETn3FKCec8tVYimLVOCE6BS4mEMnFpSJ/nRZI3qoEC83EusSxp0kZIRWutjLOmMTlBLfTp857SUqpXGlK21tlSPeM1hiwFiSHimfY+yl43puN88D6SWoxpm0ZRGtBbRSoZQtSNqST84edXt3fv2rZ99uzF48dfNE0zTQvn3LnwL//8r7///b89ffqMMQih1pOqNk6LSy6RcdRn4MNwTClwDhS4UqLUjDgIw2I5+8xWAEZFAZSwCQCglVTSaJ6XOZQMwZcQUq0l57gsvu0aQngIyDqntZJ59mgUVWstJZeC+RbJh0Jpc7nGLviFUuqD6/qWMpjm0bq02Wy46KQSl8SFnRfcS7TWen/iOmDLptaKC00YhhDrVELxU8QLl2yZz+RvSqkRrNaqlGo7QwhJOXhnCSGViYvJbQiRcwFAcy5397d9t6oVKCN936/WXfAphMClwCL0fMRPi1rbqyvvbc5ZKbXdbtu2zTnlnIOvOdEYkvdZ8JKiqIVSULW6S5L/GMMjawk528jcQ97JZasGB44nE8TTqgEFqDmXeVq4YFprpUTXbcZxdM5hA7DWP7qGcS8Mja6wlry9vZ2mCY8HPgQil7dv7q6vr5Uxy+J+/PFHxmD3aC2EAMiUnqS+p3HmrCmF1AKpZAAAArUAZYxRUgBNrtM5Z5yCCGOMUpBS1FpCCFwgdIV8ojXSSyz7UKj+eWOb//NbAMCZIoTUTOeRzNMi1bzeNOtN+/z584eHh5ubG5zurddr1Id8uD94l+sKGFM5EWf9MvtcUgXJOdO6McbMsz0O4+3tfYq1E6acV74vqJacnREx8sYYlRJNYwghwjTDMJQSuq4lhEzTgn0356yUsm1bhLc8x7Rdb5SQADC4QwiOMmmM4lzWwkgVACznwRhTas3ZL/Y4L4UL0rbGzTGHeZ5nBMwpVAuB5FlKTQFV04FSrjVnNKVUzEr5kJtWOH98+24wxgCwt+/+8Mkn75qm/emnnxrTCaFefv9dTH6ah6aVlRSUqiCkhOhw9mzdTAiVQm02awAWQ7VLyNlro8rJr+5yyk/z6RiTtQ4VBjabDRqT67a1S1xmf38/3N0+OL9UEmP0SqlltnZxWH3gSItSerVbI4I9z3cio0xpsdlsMLoRQnBAjqfKOY9a4tiJwP70er3OxVprEUdgAxsX38e9XZYFpx5t2xpj0Eel1g/9P9RRQpV3axeMO5fLFQPW7mpz7hEgOKJCsBBCiizFwBirldjFd+1KScOZ7Dq53qxSzDlXRoWUehofUgoxU84ktmCx/4VjsmmalmUqpZSS0BeYkOqcczYJKUmlwfuaSUm+FFrLh0LsEhpOwaicNADpR2IJOOO7AH4A6PueUjrPM2eScVy+WVDjVUrJuSwZgNBaSMqZ8dM4yXurZEcpdy5Y6ylFca8aY6aUS6lTSifOJGPW+hhjX/t5Cko2ObnDsH/+/GnXdVLK0Q8h+lyCc8s4jlpRQhilVHJBTtU0mhRwQihACsFhEiJnxVV2luUHSgnJCMEYlaUUuyy/qFIvRf3/E4Z+0ZE54aMaSKUAHAjLhcRQarU5kTaJGCPKm+Scj8cj8vIB6Jn+UwEY0hq8t9YnpURKkRBCKsVBQtu2knwYYV3+POwx4/GWik/ThOgSFXIwoeZchOApJedSKWS14TguPBwOwzBwxthnn32Gv/Pv333tvaskco6LiEvwmTGV6pt2db3pNzmreZ4Ph2NO1VtaEmfSSA5KKMFErECJ5rQhNRhj8KC7GDgTQihKqxC0VKC0TNNAWdle9YyJZZ6+++7bJ0+evH//DlcrS0kvXjzTWhOylFJCcPM8pxRi8n3fG6OtnWPM6/V2s+2aph0O8zS9nRcfk8QBjRAnLX28w2GYEHwCwHa7vb6+RmWfcRxJpVrrvi928ZVkrWXK/AKq8VLBJLnZbBgjtQLnEjkXyDM2xpSzTg3GYkJw59v2/Rr31K+vrw+Hw8m3R6mU836/L6WgVQ7Slx89ejTuh8uO32632+1233//vXOOEIrmTYgE8QQAwPF4xMONUwkAkFKWUnojMMNjmEOlDkrpMKFw1ElSE39eCDFN9wDgXVgW15hVCOk4HmKMMVejKTbITwGuxBjjPM0xBnym+/1+miZjdCnl4WFar3tCSsqVAaRKSq5SqFLGX8QgfJ/jCV6d9qEIAKlwbnhjh0trvd1utdbzPNcCQggCBQM3IYUzobU+7Ed8uUJwSqPuUrHWr1ePSim4VXOZ0GMZTk5Ldh5fQdxcbcwqRUIp7/u2aXTbGi6IVLBSq2HYo24tMhgpkQToiaB+LoTJuUOcUyW11pPVVQUOKGI7DEPbNYxVH3xKqWvlhQN9iSP44uLtP+1V/2kkIh81hiiLMZRahOANZzJlO0+L93Y/TRce2TRNaJHAOe/ada21lCqEMLqljDDGSiGSihjj4bCnjGw3u7Zt1+tN3639cYGzbhQ2gwi5dAmgaZqm1SklNPiOMd7e3K9WXa11HMe+7/pu7YPNOVsbMMHgQJn/09//7u9++9tHV7v1ev1f//3Dl19++c0339z/PG6utlDKqlNCCFqSSHJ5cIQQSVUvusLKqlm9dze3w03XNaDE7A96pUzLrN9zwYRglArOdK2VspJziMnfvipKCRoJVAO02n3qOklCWZbl22+/TakYY4xp1uv1fr/33lMmuv66bc27m/cp+6urzTDFYbpT4urd21e5wPWnj6JdHoa74zGQqoyiqdi//OIvuEyvXr2Njnz26eevf74lXtdQd/3qarcSkkL2bg4h+PUVSckrRbqWKqEP+znFTBOfxmga+OTxphJ/f39nWvbJJ4+1ao6DddYDgLUTJmFKS2NkimUYJkppv2K1OoCT+2glfpyWh30Qgj2+7hcL03S8uXt5tf2UUupdHA6WVFEy3PpxmhZG/G5jCKkxpuRmzR8LIDYGwU1wDmeCpaT9fu9L6rqO0zWjzC0suEypYoxlEJzz41wQ5QohCFM1ckIIYVyKUFjJOTem67urt2/eppRevHhBQKUI05gIEbXwEApUXbLw+5KYlTKfbdGSX1xwXnGuFCeEQAZNWyiQFkII3a1XtdZaQYKCTBgUJTljYU754iGJVyp+CIU0TQOn4e5JNUFKSUp+/Gi3Xq+/+uqrRqsfXv7PkydPxuHwD//4uzdv3tzf3y/zYLS01j57+vRwOBjVeW9HPwGhDJjk0hgdffqbv/5N13Vff/UfL1/+cPv+7tmzF3ZK3qd0RSjIGBbO9DKHWuzV1eNlWe5vH5pW/9UXn6/6ruYiOFWSvf75FaOP3r8/5Jydi5J3iup5HpVShMgzdiMpnRrtnFNCxCUknXu0gRCilBr27sz5SsPD4bKJekFAlzcAQO2XyzARv46XNw4rhBCbzabWiiwqZzOllPEKzBEATkkpouRKFnYcHaUhpdR1nfOFMsZBHoYHrbX3Sohuu706Ho/HwQJwA8p7IrM5vrPj+9dt2z7ebOd5ljoKCSkRzuXhcASaXzz/vG3br7/+T0KI8xOBAJAJgNL817/51d8a8/r1a2ttSsE0Uik5TQGA+8JyYuPRt83WOfe/VMoLm/3SkB0AAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display cropped image.\n", + "PIL.Image.fromarray(distorted_random_crop(img_array).eval(session=session))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Apply all transformations to an image.\n", + "# That is a common image augmentation technique for image datasets, such as ImageNet.\n", + "def transform_image(image):\n", + " image = distorted_random_crop(image)\n", + " image = random_flip_left_right(image)\n", + " image = random_contrast(image)\n", + " image = random_brightness(image)\n", + " image = random_hue(image)\n", + " image = random_saturation(image)\n", + " return image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display fully pre-processed image.\n", + "transformed_img = transform_image(img_array)\n", + "PIL.Image.fromarray(transformed_img.eval(session=session))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Resize transformed image to a 256x256px square image, ready for training.\n", + "def resize_image(image):\n", + " image = tf.image.resize(image, size=(256, 256), preserve_aspect_ratio=False)\n", + " image = tf.cast(image, tf.uint8)\n", + " return image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display resized image.\n", + "PIL.Image.fromarray(resize_image(transformed_img).eval(session=session))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tf1", + "language": "python", + "name": "tf1" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15+" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tensorflow_v1/notebooks/5_DataManagement/load_data.ipynb b/tensorflow_v1/notebooks/5_DataManagement/load_data.ipynb new file mode 100644 index 00000000..a6fdeec5 --- /dev/null +++ b/tensorflow_v1/notebooks/5_DataManagement/load_data.ipynb @@ -0,0 +1,577 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load and parse data with TensorFlow\n", + "\n", + "A TensorFlow example to build input pipelines for loading data efficiently.\n", + "\n", + "\n", + "- Numpy Arrays\n", + "- Images\n", + "- CSV file\n", + "- Custom data from a Generator\n", + "\n", + "For more information about creating and loading TensorFlow's `TFRecords` data format, see: [tfrecords.ipynb](tfrecords.ipynb)\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", + "import numpy as np\n", + "import random\n", + "import requests\n", + "import string\n", + "import tarfile\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load Numpy Arrays\n", + "\n", + "Build a data pipeline over numpy arrays." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a toy dataset (even and odd numbers, with respective labels of 0 and 1).\n", + "evens = np.arange(0, 100, step=2, dtype=np.int32)\n", + "evens_label = np.zeros(50, dtype=np.int32)\n", + "odds = np.arange(1, 100, step=2, dtype=np.int32)\n", + "odds_label = np.ones(50, dtype=np.int32)\n", + "# Concatenate arrays\n", + "features = np.concatenate([evens, odds])\n", + "labels = np.concatenate([evens_label, odds_label])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with tf.Graph().as_default():\n", + " # Create TF session.\n", + " sess = tf.Session()\n", + " \n", + " # Slice the numpy arrays (each row becoming a record).\n", + " data = tf.data.Dataset.from_tensor_slices((features, labels))\n", + " # Refill data indefinitely. \n", + " data = data.repeat()\n", + " # Shuffle data.\n", + " data = data.shuffle(buffer_size=100)\n", + " # Batch data (aggregate records together).\n", + " data = data.batch(batch_size=4)\n", + " # Prefetch batch (pre-load batch for faster consumption).\n", + " data = data.prefetch(buffer_size=1)\n", + " \n", + " # Create an iterator over the dataset.\n", + " iterator = data.make_initializable_iterator()\n", + " # Initialize the iterator.\n", + " sess.run(iterator.initializer)\n", + "\n", + " # Get next data batch.\n", + " d = iterator.get_next()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[82 58 80 23] [0 0 0 1]\n", + "[16 91 74 96] [0 1 0 0]\n", + "[ 4 17 32 34] [0 1 0 0]\n", + "[16 8 77 21] [0 0 1 1]\n", + "[20 99 48 18] [0 1 0 0]\n" + ] + } + ], + "source": [ + "# Display data.\n", + "for i in range(5):\n", + " x, y = sess.run(d)\n", + " print(x, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load CSV files\n", + "\n", + "Build a data pipeline from features stored in a CSV file. For this example, Titanic dataset will be used as a toy dataset stored in CSV format." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Titanic Dataset\n", + "\n", + "\n", + "\n", + "survived|pclass|name|sex|age|sibsp|parch|ticket|fare\n", + "--------|------|----|---|---|-----|-----|------|----\n", + "1|1|\"Allen, Miss. Elisabeth Walton\"|female|29|0|0|24160|211.3375\n", + "1|1|\"Allison, Master. Hudson Trevor\"|male|0.9167|1|2|113781|151.5500\n", + "0|1|\"Allison, Miss. Helen Loraine\"|female|2|1|2|113781|151.5500\n", + "0|1|\"Allison, Mr. Hudson Joshua Creighton\"|male|30|1|2|113781|151.5500\n", + "...|...|...|...|...|...|...|...|..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Download Titanic dataset (in csv format).\n", + "d = requests.get(\"https://raw.githubusercontent.com/tflearn/tflearn.github.io/master/resources/titanic_dataset.csv/")\n", + "with open(\"titanic_dataset.csv\", \"wb\") as f:\n", + " f.write(d.content)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load Titanic dataset.\n", + "# Original features: survived,pclass,name,sex,age,sibsp,parch,ticket,fare\n", + "# Select specific columns: survived,pclass,name,sex,age,fare\n", + "column_to_use = [0, 1, 2, 3, 4, 8]\n", + "record_defaults = [tf.int32, tf.int32, tf.string, tf.string, tf.float32, tf.float32]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with tf.Graph().as_default():\n", + " # Create TF session.\n", + " sess = tf.Session()\n", + " \n", + " # Load the whole dataset file, and slice each line.\n", + " data = tf.data.experimental.CsvDataset(\"titanic_dataset.csv\", record_defaults, header=True, select_cols=column_to_use)\n", + " # Refill data indefinitely. \n", + " data = data.repeat()\n", + " # Shuffle data.\n", + " data = data.shuffle(buffer_size=1000)\n", + " # Batch data (aggregate records together).\n", + " data = data.batch(batch_size=2)\n", + " # Prefetch batch (pre-load batch for faster consumption).\n", + " data = data.prefetch(buffer_size=1)\n", + " \n", + " # Create an iterator over the dataset.\n", + " iterator = data.make_initializable_iterator()\n", + " # Initialize the iterator.\n", + " sess.run(iterator.initializer)\n", + "\n", + " # Get next data batch.\n", + " d = iterator.get_next()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1 0]\n", + "[3 1]\n", + "['Lam, Mr. Ali' 'Widener, Mr. Harry Elkins']\n", + "['male' 'male']\n", + "[ 0. 27.]\n", + "[ 56.4958 211.5 ]\n", + "\n", + "[0 1]\n", + "[1 1]\n", + "['Baumann, Mr. John D' 'Daly, Mr. Peter Denis ']\n", + "['male' 'male']\n", + "[ 0. 51.]\n", + "[25.925 26.55 ]\n", + "\n", + "[0 1]\n", + "[3 1]\n", + "['Assam, Mr. Ali' 'Newell, Miss. Madeleine']\n", + "['male' 'female']\n", + "[23. 31.]\n", + "[ 7.05 113.275]\n", + "\n" + ] + } + ], + "source": [ + "# Display data.\n", + "for i in range(3):\n", + " survived, pclass, name, sex, age, fare = sess.run(d)\n", + " print(survived)\n", + " print(pclass)\n", + " print(name)\n", + " print(sex)\n", + " print(age)\n", + " print(fare)\n", + " print(\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load Images\n", + "\n", + "Build a data pipeline by loading images from disk. For this example, Oxford Flowers dataset will be used." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Download Oxford 17 flowers dataset.\n", + "d = requests.get(\"http://www.robots.ox.ac.uk/~vgg/data/flowers/17/17flowers.tgz/")\n", + "with open(\"17flowers.tgz\", \"wb\") as f:\n", + " f.write(d.content)\n", + "# Extract archive.\n", + "with tarfile.open(\"17flowers.tgz\") as t:\n", + " t.extractall()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a file to list all images path and their corresponding label.\n", + "with open('jpg/dataset.csv', 'w') as f:\n", + " c = 0\n", + " for i in range(1360):\n", + " f.write(\"jpg/image_%04i.jpg,%i\\n\" % (i+1, c))\n", + " if (i+1) % 80 == 0:\n", + " c += 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with tf.Graph().as_default():\n", + " \n", + " # Load Images.\n", + " with open(\"jpg/dataset.csv\") as f:\n", + " dataset_file = f.read().splitlines()\n", + " \n", + " # Create TF session.\n", + " sess = tf.Session()\n", + "\n", + " # Load the whole dataset file, and slice each line.\n", + " data = tf.data.Dataset.from_tensor_slices(dataset_file)\n", + " # Refill data indefinitely.\n", + " data = data.repeat()\n", + " # Shuffle data.\n", + " data = data.shuffle(buffer_size=1000)\n", + "\n", + " # Load and pre-process images.\n", + " def load_image(path):\n", + " # Read image from path.\n", + " image = tf.io.read_file(path)\n", + " # Decode the jpeg image to array [0, 255].\n", + " image = tf.image.decode_jpeg(image)\n", + " # Resize images to a common size of 256x256.\n", + " image = tf.image.resize(image, [256, 256])\n", + " # Rescale values to [-1, 1].\n", + " image = 1. - image / 127.5\n", + " return image\n", + " # Decode each line from the dataset file.\n", + " def parse_records(line):\n", + " # File is in csv format: \"image_path,label_id\".\n", + " # TensorFlow requires a default value, but it will never be used.\n", + " image_path, image_label = tf.io.decode_csv(line, [\"\", 0])\n", + " # Apply the function to load images.\n", + " image = load_image(image_path)\n", + " return image, image_label\n", + " # Use 'map' to apply the above functions in parallel.\n", + " data = data.map(parse_records, num_parallel_calls=4)\n", + "\n", + " # Batch data (aggregate images-array together).\n", + " data = data.batch(batch_size=2)\n", + " # Prefetch batch (pre-load batch for faster consumption).\n", + " data = data.prefetch(buffer_size=1)\n", + " \n", + " # Create an iterator over the dataset.\n", + " iterator = data.make_initializable_iterator()\n", + " # Initialize the iterator.\n", + " sess.run(iterator.initializer)\n", + "\n", + " # Get next data batch.\n", + " d = iterator.get_next()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[[ 0.1294117 0.05098033 0.46666664]\n", + " [ 0.1368872 0.05098033 0.48909312]\n", + " [ 0.0931372 0.0068627 0.46029407]\n", + " ...\n", + " [ 0.23480386 0.0522058 0.6102941 ]\n", + " [ 0.12696075 -0.05416667 0.38063723]\n", + " [-0.10024512 -0.28848052 0.10367644]]\n", + "\n", + " [[ 0.04120708 -0.06118262 0.36256123]\n", + " [ 0.08009624 -0.02229345 0.41640145]\n", + " [ 0.06797445 -0.04132879 0.41923058]\n", + " ...\n", + " [ 0.2495715 0.06697345 0.6251221 ]\n", + " [ 0.12058818 -0.06094813 0.37577546]\n", + " [-0.05184889 -0.24009418 0.16777915]]\n", + "\n", + " [[-0.09234071 -0.22738981 0.20484066]\n", + " [-0.03100491 -0.17312062 0.2811274 ]\n", + " [ 0.01051998 -0.13237214 0.3376838 ]\n", + " ...\n", + " [ 0.27787983 0.07494056 0.64203525]\n", + " [ 0.11533964 -0.09005249 0.3869906 ]\n", + " [-0.02704227 -0.23958337 0.19454747]]\n", + "\n", + " ...\n", + "\n", + " [[ 0.07913595 -0.13069856 0.29874384]\n", + " [ 0.10140878 -0.09445572 0.35912937]\n", + " [ 0.08869672 -0.08415675 0.41446364]\n", + " ...\n", + " [ 0.25821072 0.22463232 0.69197303]\n", + " [ 0.31636214 0.25750512 0.79362744]\n", + " [ 0.09552741 0.01709598 0.57395875]]\n", + "\n", + " [[ 0.09019601 -0.12156868 0.3098039 ]\n", + " [ 0.17446858 -0.02271283 0.43218917]\n", + " [ 0.06583172 -0.10818791 0.39230233]\n", + " ...\n", + " [ 0.27021956 0.23664117 0.70269513]\n", + " [ 0.19560927 0.1385014 0.6740407 ]\n", + " [ 0.04364848 -0.03478289 0.5220798 ]]\n", + "\n", + " [[ 0.02830875 -0.18345594 0.24791664]\n", + " [ 0.12937105 -0.06781042 0.38709164]\n", + " [ 0.01120263 -0.162817 0.33767325]\n", + " ...\n", + " [ 0.25989532 0.22631687 0.69237083]\n", + " [ 0.1200884 0.06298059 0.5985198 ]\n", + " [ 0.05961001 -0.01882136 0.53804135]]]\n", + "\n", + "\n", + " [[[ 0.3333333 0.25490195 0.05882347]\n", + " [ 0.3333333 0.25490195 0.05882347]\n", + " [ 0.3340686 0.24705875 0.03039211]\n", + " ...\n", + " [-0.5215688 -0.4599266 -0.14632356]\n", + " [-0.5100491 -0.47083342 -0.03725493]\n", + " [-0.43419123 -0.39497554 0.05992639]]\n", + "\n", + " [[ 0.34117645 0.26274508 0.0666666 ]\n", + " [ 0.35646445 0.2630821 0.0744791 ]\n", + " [ 0.3632046 0.2548713 0.04384762]\n", + " ...\n", + " [-0.9210479 -0.84267783 -0.4540485 ]\n", + " [-0.9017464 -0.8390626 -0.3507018 ]\n", + " [-0.83339334 -0.7632048 -0.2534927 ]]\n", + "\n", + " [[ 0.3646446 0.2706495 0.06678915]\n", + " [ 0.37248772 0.27837008 0.07445425]\n", + " [ 0.38033658 0.27053267 0.05950326]\n", + " ...\n", + " [-0.94302344 -0.84222686 -0.30278325]\n", + " [-0.91017747 -0.8090074 -0.18615782]\n", + " [-0.83437514 -0.7402575 -0.08192408]]\n", + "\n", + " ...\n", + "\n", + " [[ 0.64705884 0.654902 0.67058825]\n", + " [ 0.6318321 0.63967526 0.65536153]\n", + " [ 0.63128924 0.6391324 0.65481865]\n", + " ...\n", + " [ 0.6313726 0.57647055 0.51372546]\n", + " [ 0.6078431 0.53725487 0.4823529 ]\n", + " [ 0.6078431 0.53725487 0.4823529 ]]\n", + "\n", + " [[ 0.654902 0.654902 0.6704657 ]\n", + " [ 0.654902 0.654902 0.6704657 ]\n", + " [ 0.64778835 0.64778835 0.6492474 ]\n", + " ...\n", + " [ 0.6392157 0.5843137 0.5215686 ]\n", + " [ 0.6393325 0.56874424 0.5138422 ]\n", + " [ 0.63106614 0.5604779 0.50557595]]\n", + "\n", + " [[ 0.654902 0.64705884 0.6313726 ]\n", + " [ 0.6548728 0.64702964 0.63134336]\n", + " [ 0.64705884 0.63210785 0.6377451 ]\n", + " ...\n", + " [ 0.63244915 0.5775472 0.5148021 ]\n", + " [ 0.6698529 0.5992647 0.5443627 ]\n", + " [ 0.6545358 0.5839475 0.5290455 ]]]] [5 9]\n" + ] + } + ], + "source": [ + "# Display data.\n", + "for i in range(1):\n", + " batch_x, batch_y = sess.run(d)\n", + " print(batch_x, batch_y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load data from a Generator\n", + "\n", + "Build a data pipeline from a custom generator. For this example, a toy generator yielding random string, vector and it is used." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a dummy generator.\n", + "def generate_features():\n", + " # Function to generate a random string.\n", + " def random_string(length):\n", + " return ''.join(random.choice(string.ascii_letters) for m in xrange(length))\n", + " # Return a random string, a random vector, and a random int.\n", + " yield random_string(4), np.random.uniform(size=4), random.randint(0, 10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with tf.Graph().as_default():\n", + "\n", + " # Create TF session.\n", + " sess = tf.Session()\n", + "\n", + " # Create TF dataset from the generator.\n", + " data = tf.data.Dataset.from_generator(generate_features, output_types=(tf.string, tf.float32, tf.int32))\n", + " # Refill data indefinitely.\n", + " data = data.repeat()\n", + " # Shuffle data.\n", + " data = data.shuffle(buffer_size=100)\n", + " # Batch data (aggregate records together).\n", + " data = data.batch(batch_size=4)\n", + " # Prefetch batch (pre-load batch for faster consumption).\n", + " data = data.prefetch(buffer_size=1)\n", + "\n", + " # Create an iterator over the dataset.\n", + " iterator = data.make_initializable_iterator()\n", + " # Initialize the iterator.\n", + " sess.run(iterator.initializer)\n", + "\n", + " # Get next data batch.\n", + " d = iterator.get_next()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['AvCS' 'kAaI' 'QwGX' 'IWOI'] [[0.6096093 0.32192084 0.26622605 0.70250475]\n", + " [0.72534287 0.7637426 0.19977213 0.74121326]\n", + " [0.6930984 0.09409562 0.4063325 0.5002103 ]\n", + " [0.05160935 0.59411395 0.276416 0.98264974]] [1 3 5 6]\n", + "['EXjS' 'brvx' 'kwNz' 'eFOb'] [[0.34355283 0.26881003 0.70575935 0.7503411 ]\n", + " [0.9584373 0.27466875 0.27802315 0.9563204 ]\n", + " [0.19129485 0.07014314 0.0932724 0.20726128]\n", + " [0.28744072 0.81736153 0.37507302 0.8984588 ]] [1 9 7 0]\n", + "['vpSa' 'UuqW' 'xaTO' 'milw'] [[0.2942028 0.8228986 0.5793326 0.16651365]\n", + " [0.28259405 0.599063 0.2922477 0.95071274]\n", + " [0.23645316 0.00258607 0.06772221 0.7291911 ]\n", + " [0.12861755 0.31435087 0.576638 0.7333119 ]] [3 5 8 4]\n", + "['UBBb' 'MUXs' 'nLJB' 'OBGl'] [[0.2677402 0.17931737 0.02607645 0.85898155]\n", + " [0.58647937 0.727203 0.13329858 0.8898983 ]\n", + " [0.13872191 0.47390288 0.7061665 0.08478573]\n", + " [0.3786016 0.22002582 0.91989636 0.45837343]] [ 5 8 0 10]\n", + "['kiiz' 'bQYG' 'WpUU' 'AuIY'] [[0.74781317 0.13744462 0.9236441 0.63558507]\n", + " [0.23649399 0.35303807 0.0951511 0.03541444]\n", + " [0.33599988 0.6906629 0.97166294 0.55850506]\n", + " [0.90997607 0.5545979 0.43635726 0.9127501 ]] [8 1 4 4]\n" + ] + } + ], + "source": [ + "# Display data.\n", + "for i in range(5):\n", + " batch_str, batch_vector, batch_int = sess.run(d)\n", + " print(batch_str, batch_vector, batch_int)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tf1", + "language": "python", + "name": "tf1" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15+" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tensorflow_v1/notebooks/5_DataManagement/tensorflow_dataset_api.ipynb b/tensorflow_v1/notebooks/5_DataManagement/tensorflow_dataset_api.ipynb new file mode 100644 index 00000000..22c05e63 --- /dev/null +++ b/tensorflow_v1/notebooks/5_DataManagement/tensorflow_dataset_api.ipynb @@ -0,0 +1,222 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# TensorFlow Dataset API\n", + "\n", + "In this example, we will show how to load numpy array data into the new \n", + "TensorFlow 'Dataset' API. The Dataset API implements an optimized data pipeline\n", + "with queues, that make data processing and training faster (especially on GPU).\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "\n", + "# Import MNIST data (Numpy format)\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Parameters\n", + "learning_rate = 0.01\n", + "num_steps = 1000\n", + "batch_size = 128\n", + "display_step = 100\n", + "\n", + "# Network Parameters\n", + "n_input = 784 # MNIST data input (img shape: 28*28)\n", + "n_classes = 10 # MNIST total classes (0-9 digits)\n", + "dropout = 0.75 # Dropout, probability to keep units\n", + "\n", + "sess = tf.Session()\n", + "\n", + "# Create a dataset tensor from the images and the labels\n", + "dataset = tf.data.Dataset.from_tensor_slices(\n", + " (mnist.train.images, mnist.train.labels))\n", + "# Automatically refill the data queue when empty\n", + "dataset = dataset.repeat()\n", + "# Create batches of data\n", + "dataset = dataset.batch(batch_size)\n", + "# Prefetch data for faster consumption\n", + "dataset = dataset.prefetch(batch_size)\n", + "\n", + "# Create an iterator over the dataset\n", + "iterator = dataset.make_initializable_iterator()\n", + "# Initialize the iterator\n", + "sess.run(iterator.initializer)\n", + "\n", + "# Neural Net Input (images, labels)\n", + "X, Y = iterator.get_next()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# -----------------------------------------------\n", + "# THIS IS A CLASSIC CNN (see examples, section 3)\n", + "# -----------------------------------------------\n", + "# Note that a few elements have changed (usage of sess run).\n", + "\n", + "# Create model\n", + "def conv_net(x, n_classes, dropout, reuse, is_training):\n", + " # Define a scope for reusing the variables\n", + " with tf.variable_scope('ConvNet', reuse=reuse):\n", + " # MNIST data input is a 1-D vector of 784 features (28*28 pixels)\n", + " # Reshape to match picture format [Height x Width x Channel]\n", + " # Tensor input become 4-D: [Batch Size, Height, Width, Channel]\n", + " x = tf.reshape(x, shape=[-1, 28, 28, 1])\n", + "\n", + " # Convolution Layer with 32 filters and a kernel size of 5\n", + " conv1 = tf.layers.conv2d(x, 32, 5, activation=tf.nn.relu)\n", + " # Max Pooling (down-sampling) with strides of 2 and kernel size of 2\n", + " conv1 = tf.layers.max_pooling2d(conv1, 2, 2)\n", + "\n", + " # Convolution Layer with 32 filters and a kernel size of 5\n", + " conv2 = tf.layers.conv2d(conv1, 64, 3, activation=tf.nn.relu)\n", + " # Max Pooling (down-sampling) with strides of 2 and kernel size of 2\n", + " conv2 = tf.layers.max_pooling2d(conv2, 2, 2)\n", + "\n", + " # Flatten the data to a 1-D vector for the fully connected layer\n", + " fc1 = tf.contrib.layers.flatten(conv2)\n", + "\n", + " # Fully connected layer (in contrib folder for now)\n", + " fc1 = tf.layers.dense(fc1, 1024)\n", + " # Apply Dropout (if is_training is False, dropout is not applied)\n", + " fc1 = tf.layers.dropout(fc1, rate=dropout, training=is_training)\n", + "\n", + " # Output layer, class prediction\n", + " out = tf.layers.dense(fc1, n_classes)\n", + " # Because 'softmax_cross_entropy_with_logits' already apply softmax,\n", + " # we only apply softmax to testing network\n", + " out = tf.nn.softmax(out) if not is_training else out\n", + "\n", + " return out\n", + "\n", + "\n", + "# Because Dropout have different behavior at training and prediction time, we\n", + "# need to create 2 distinct computation graphs that share the same weights.\n", + "\n", + "# Create a graph for training\n", + "logits_train = conv_net(X, n_classes, dropout, reuse=False, is_training=True)\n", + "# Create another graph for testing that reuse the same weights, but has\n", + "# different behavior for 'dropout' (not applied).\n", + "logits_test = conv_net(X, n_classes, dropout, reuse=True, is_training=False)\n", + "\n", + "# Define loss and optimizer (with train logits, for dropout to take effect)\n", + "loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(\n", + " logits=logits_train, labels=Y))\n", + "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n", + "train_op = optimizer.minimize(loss_op)\n", + "\n", + "# Evaluate model (with test logits, for dropout to be disabled)\n", + "correct_pred = tf.equal(tf.argmax(logits_test, 1), tf.argmax(Y, 1))\n", + "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 1, Minibatch Loss= 7.9429, Training Accuracy= 0.070\n", + "Step 100, Minibatch Loss= 0.3491, Training Accuracy= 0.922\n", + "Step 200, Minibatch Loss= 0.2343, Training Accuracy= 0.922\n", + "Step 300, Minibatch Loss= 0.1838, Training Accuracy= 0.969\n", + "Step 400, Minibatch Loss= 0.1715, Training Accuracy= 0.953\n", + "Step 500, Minibatch Loss= 0.2730, Training Accuracy= 0.938\n", + "Step 600, Minibatch Loss= 0.3427, Training Accuracy= 0.953\n", + "Step 700, Minibatch Loss= 0.2261, Training Accuracy= 0.961\n", + "Step 800, Minibatch Loss= 0.1487, Training Accuracy= 0.953\n", + "Step 900, Minibatch Loss= 0.1438, Training Accuracy= 0.945\n", + "Step 1000, Minibatch Loss= 0.1786, Training Accuracy= 0.961\n", + "Optimization Finished!\n" + ] + } + ], + "source": [ + "# Initialize the variables (i.e. assign their default value)\n", + "init = tf.global_variables_initializer()\n", + "\n", + "# Run the initializer\n", + "sess.run(init)\n", + "\n", + "# Training cycle\n", + "for step in range(1, num_steps + 1):\n", + " \n", + " # Run optimization\n", + " sess.run(train_op)\n", + " \n", + " if step % display_step == 0 or step == 1:\n", + " # Calculate batch loss and accuracy\n", + " # (note that this consume a new batch of data)\n", + " loss, acc = sess.run([loss_op, accuracy])\n", + " print(\"Step \" + str(step) + \", Minibatch Loss= \" + \\\n", + " \"{:.4f}\".format(loss) + \", Training Accuracy= \" + \\\n", + " \"{:.3f}\".format(acc))\n", + "\n", + "print(\"Optimization Finished!\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tensorflow_v1/notebooks/5_DataManagement/tfrecords.ipynb b/tensorflow_v1/notebooks/5_DataManagement/tfrecords.ipynb new file mode 100644 index 00000000..24aa5000 --- /dev/null +++ b/tensorflow_v1/notebooks/5_DataManagement/tfrecords.ipynb @@ -0,0 +1,261 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create and Load TFRecords\n", + "\n", + "A simple TensorFlow example to parse a dataset into TFRecord format, and then read that dataset.\n", + "\n", + "In this example, the Titanic Dataset (in CSV format) will be used as a toy dataset, for parsing all the dataset features into TFRecord format, and then building an input pipeline that can be used for training models.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Titanic Dataset\n", + "\n", + "The titanic dataset is a popular dataset for ML that provides a list of all passengers onboard the Titanic, along with various features such as their age, sex, class (1st, 2nd, 3rd)... And if the passenger survived the disaster or not.\n", + "\n", + "It can be used to see that even though some luck was involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class...\n", + "\n", + "#### Overview\n", + "survived|pclass|name|sex|age|sibsp|parch|ticket|fare\n", + "--------|------|----|---|---|-----|-----|------|----\n", + "1|1|\"Allen, Miss. Elisabeth Walton\"|female|29|0|0|24160|211.3375\n", + "1|1|\"Allison, Master. Hudson Trevor\"|male|0.9167|1|2|113781|151.5500\n", + "0|1|\"Allison, Miss. Helen Loraine\"|female|2|1|2|113781|151.5500\n", + "0|1|\"Allison, Mr. Hudson Joshua Creighton\"|male|30|1|2|113781|151.5500\n", + "...|...|...|...|...|...|...|...|...\n", + "\n", + "\n", + "#### Variable Descriptions\n", + "```\n", + "survived Survived\n", + " (0 = No; 1 = Yes)\n", + "pclass Passenger Class\n", + " (1 = 1st; 2 = 2nd; 3 = 3rd)\n", + "name Name\n", + "sex Sex\n", + "age Age\n", + "sibsp Number of Siblings/Spouses Aboard\n", + "parch Number of Parents/Children Aboard\n", + "ticket Ticket Number\n", + "fare Passenger Fare\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", + "import csv\n", + "import requests\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Download Titanic dataset (in csv format).\n", + "d = requests.get(\"https://raw.githubusercontent.com/tflearn/tflearn.github.io/master/resources/titanic_dataset.csv/")\n", + "with open(\"titanic_dataset.csv\", \"wb\") as f:\n", + " f.write(d.content)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create TFRecords" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Generate Integer Features.\n", + "def build_int64_feature(data):\n", + " return tf.train.Feature(int64_list=tf.train.Int64List(value=[data]))\n", + "\n", + "# Generate Float Features.\n", + "def build_float_feature(data):\n", + " return tf.train.Feature(float_list=tf.train.FloatList(value=[data]))\n", + "\n", + "# Generate String Features.\n", + "def build_string_feature(data):\n", + " return tf.train.Feature(bytes_list=tf.train.BytesList(value=[data]))\n", + "\n", + "# Generate a TF `Example`, parsing all features of the dataset.\n", + "def convert_to_tfexample(survived, pclass, name, sex, age, sibsp, parch, ticket, fare):\n", + " return tf.train.Example(\n", + " features=tf.train.Features(\n", + " feature={\n", + " 'survived': build_int64_feature(survived),\n", + " 'pclass': build_int64_feature(pclass),\n", + " 'name': build_string_feature(name),\n", + " 'sex': build_string_feature(sex),\n", + " 'age': build_float_feature(age),\n", + " 'sibsp': build_int64_feature(sibsp),\n", + " 'parch': build_int64_feature(parch),\n", + " 'ticket': build_string_feature(ticket),\n", + " 'fare': build_float_feature(fare),\n", + " })\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Open dataset file.\n", + "with open(\"titanic_dataset.csv\") as f:\n", + " # Output TFRecord file.\n", + " with tf.io.TFRecordWriter(\"titanic_dataset.tfrecord\") as w:\n", + " # Generate a TF Example for all row in our dataset.\n", + " # CSV reader will read and parse all rows.\n", + " reader = csv.reader(f, skipinitialspace=True)\n", + " for i, record in enumerate(reader):\n", + " # Skip header.\n", + " if i == 0:\n", + " continue\n", + " survived, pclass, name, sex, age, sibsp, parch, ticket, fare = record\n", + " # Parse each csv row to TF Example using the above functions.\n", + " example = convert_to_tfexample(int(survived), int(pclass), name, sex, float(age), int(sibsp), int(parch), ticket, float(fare))\n", + " # Serialize each TF Example to string, and write to TFRecord file.\n", + " w.write(example.SerializeToString())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load TFRecords" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Build features template, with types.\n", + "features = {\n", + " 'survived': tf.io.FixedLenFeature([], tf.int64),\n", + " 'pclass': tf.io.FixedLenFeature([], tf.int64),\n", + " 'name': tf.io.FixedLenFeature([], tf.string),\n", + " 'sex': tf.io.FixedLenFeature([], tf.string),\n", + " 'age': tf.io.FixedLenFeature([], tf.float32),\n", + " 'sibsp': tf.io.FixedLenFeature([], tf.int64),\n", + " 'parch': tf.io.FixedLenFeature([], tf.int64),\n", + " 'ticket': tf.io.FixedLenFeature([], tf.string),\n", + " 'fare': tf.io.FixedLenFeature([], tf.float32),\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create TensorFlow session.\n", + "sess = tf.Session()\n", + "\n", + "# Load TFRecord data.\n", + "filenames = [\"titanic_dataset.tfrecord\"]\n", + "data = tf.data.TFRecordDataset(filenames)\n", + "\n", + "# Parse features, using the above template.\n", + "def parse_record(record):\n", + " return tf.io.parse_single_example(record, features=features)\n", + "# Apply the parsing to each record from the dataset.\n", + "data = data.map(parse_record)\n", + "\n", + "# Refill data indefinitely.\n", + "data = data.repeat()\n", + "# Shuffle data.\n", + "data = data.shuffle(buffer_size=1000)\n", + "# Batch data (aggregate records together).\n", + "data = data.batch(batch_size=4)\n", + "# Prefetch batch (pre-load batch for faster consumption).\n", + "data = data.prefetch(buffer_size=1)\n", + "\n", + "# Create an iterator over the dataset.\n", + "iterator = data.make_initializable_iterator()\n", + "# Initialize the iterator.\n", + "sess.run(iterator.initializer)\n", + "\n", + "# Get next data batch.\n", + "x = iterator.get_next()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'fare': array([ 35.5 , 73.5 , 133.65 , 19.2583], dtype=float32), 'name': array(['Sloper, Mr. William Thompson', 'Davies, Mr. Charles Henry',\n", + " 'Frauenthal, Dr. Henry William', 'Baclini, Miss. Marie Catherine'],\n", + " dtype=object), 'age': array([28., 18., 50., 5.], dtype=float32), 'parch': array([0, 0, 0, 1]), 'pclass': array([1, 2, 1, 3]), 'sex': array(['male', 'male', 'male', 'female'], dtype=object), 'survived': array([1, 0, 1, 1]), 'sibsp': array([0, 0, 2, 2]), 'ticket': array(['113788', 'S.O.C. 14879', 'PC 17611', '2666'], dtype=object)}\n", + "\n", + "{'fare': array([ 18.75 , 106.425, 78.85 , 90. ], dtype=float32), 'name': array(['Richards, Mrs. Sidney (Emily Hocking)', 'LeRoy, Miss. Bertha',\n", + " 'Cavendish, Mrs. Tyrell William (Julia Florence Siegel)',\n", + " 'Hoyt, Mrs. Frederick Maxfield (Jane Anne Forby)'], dtype=object), 'age': array([24., 30., 76., 35.], dtype=float32), 'parch': array([3, 0, 0, 0]), 'pclass': array([2, 1, 1, 1]), 'sex': array(['female', 'female', 'female', 'female'], dtype=object), 'survived': array([1, 1, 1, 1]), 'sibsp': array([2, 0, 1, 1]), 'ticket': array(['29106', 'PC 17761', '19877', '19943'], dtype=object)}\n", + "\n", + "{'fare': array([19.9667, 15.5 , 15.0458, 66.6 ], dtype=float32), 'name': array(['Hagland, Mr. Konrad Mathias Reiersen', 'Lennon, Miss. Mary',\n", + " 'Richard, Mr. Emile', 'Pears, Mr. Thomas Clinton'], dtype=object), 'age': array([ 0., 0., 23., 29.], dtype=float32), 'parch': array([0, 0, 0, 0]), 'pclass': array([3, 3, 2, 1]), 'sex': array(['male', 'female', 'male', 'male'], dtype=object), 'survived': array([0, 0, 0, 0]), 'sibsp': array([1, 1, 0, 1]), 'ticket': array(['65304', '370371', 'SC/PARIS 2133', '113776'], dtype=object)}\n", + "\n" + ] + } + ], + "source": [ + "# Dequeue data and display.\n", + "for i in range(3):\n", + " print(sess.run(x))\n", + " print(\"\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tf1", + "language": "python", + "name": "tf1" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15+" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tensorflow_v1/notebooks/6_MultiGPU/multigpu_basics.ipynb b/tensorflow_v1/notebooks/6_MultiGPU/multigpu_basics.ipynb new file mode 100644 index 00000000..1089b3e8 --- /dev/null +++ b/tensorflow_v1/notebooks/6_MultiGPU/multigpu_basics.ipynb @@ -0,0 +1,179 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Multi-GPU Basics\n", + "\n", + "Basic Multi-GPU computation example using TensorFlow library.\n", + "\n", + "This tutorial requires your machine to have 2 GPUs\n", + "\"/cpu:0\": The CPU of your machine.\n", + "\"/gpu:0\": The first GPU of your machine\n", + "\"/gpu:1\": The second GPU of your machine\n", + "For this example, we are using 2 GTX-980\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import tensorflow as tf\n", + "import datetime" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "#Processing Units logs\n", + "log_device_placement = True\n", + "\n", + "#num of multiplications to perform\n", + "n = 10" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Example: compute A^n + B^n on 2 GPUs\n", + "\n", + "# Create random large matrix\n", + "A = np.random.rand(1e4, 1e4).astype('float32')\n", + "B = np.random.rand(1e4, 1e4).astype('float32')\n", + "\n", + "# Creates a graph to store results\n", + "c1 = []\n", + "c2 = []\n", + "\n", + "# Define matrix power\n", + "def matpow(M, n):\n", + " if n < 1: #Abstract cases where n < 1\n", + " return M\n", + " else:\n", + " return tf.matmul(M, matpow(M, n-1))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Single GPU computing\n", + "\n", + "with tf.device('/gpu:0'):\n", + " a = tf.constant(A)\n", + " b = tf.constant(B)\n", + " #compute A^n and B^n and store results in c1\n", + " c1.append(matpow(a, n))\n", + " c1.append(matpow(b, n))\n", + "\n", + "with tf.device('/cpu:0'):\n", + " sum = tf.add_n(c1) #Addition of all elements in c1, i.e. A^n + B^n\n", + "\n", + "t1_1 = datetime.datetime.now()\n", + "with tf.Session(config=tf.ConfigProto(log_device_placement=log_device_placement)) as sess:\n", + " # Runs the op.\n", + " sess.run(sum)\n", + "t2_1 = datetime.datetime.now()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Multi GPU computing\n", + "# GPU:0 computes A^n\n", + "with tf.device('/gpu:0'):\n", + " #compute A^n and store result in c2\n", + " a = tf.constant(A)\n", + " c2.append(matpow(a, n))\n", + "\n", + "#GPU:1 computes B^n\n", + "with tf.device('/gpu:1'):\n", + " #compute B^n and store result in c2\n", + " b = tf.constant(B)\n", + " c2.append(matpow(b, n))\n", + "\n", + "with tf.device('/cpu:0'):\n", + " sum = tf.add_n(c2) #Addition of all elements in c2, i.e. A^n + B^n\n", + "\n", + "t1_2 = datetime.datetime.now()\n", + "with tf.Session(config=tf.ConfigProto(log_device_placement=log_device_placement)) as sess:\n", + " # Runs the op.\n", + " sess.run(sum)\n", + "t2_2 = datetime.datetime.now()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Single GPU computation time: 0:00:11.833497\n", + "Multi GPU computation time: 0:00:07.085913\n" + ] + } + ], + "source": [ + "print \"Single GPU computation time: \" + str(t2_1-t1_1)\n", + "print \"Multi GPU computation time: \" + str(t2_2-t1_2)" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/tensorflow_v1/notebooks/6_MultiGPU/multigpu_cnn.ipynb b/tensorflow_v1/notebooks/6_MultiGPU/multigpu_cnn.ipynb new file mode 100644 index 00000000..2d4746d2 --- /dev/null +++ b/tensorflow_v1/notebooks/6_MultiGPU/multigpu_cnn.ipynb @@ -0,0 +1,328 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multi-GPU Training Example\n", + "\n", + "Train a convolutional neural network on multiple GPU with TensorFlow.\n", + "\n", + "This example is using TensorFlow layers, see 'convolutional_network_raw' example\n", + "for a raw TensorFlow implementation with variables.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training with multiple GPU cards\n", + "\n", + "In this example, we are using data parallelism to split the training accross multiple GPUs. Each GPU has a full replica of the neural network model, and the weights (i.e. variables) are updated synchronously by waiting that each GPU process its batch of data.\n", + "\n", + "First, each GPU process a distinct batch of data and compute the corresponding gradients, then, all gradients are accumulated in the CPU and averaged. The model weights are finally updated with the gradients averaged, and the new model weights are sent back to each GPU, to repeat the training process.\n", + "\n", + "\"Parallelism\"\n", + "\n", + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flatten and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "from __future__ import print_function\n", + "\n", + "import numpy as np\n", + "import tensorflow as tf\n", + "import time\n", + "\n", + "# Import MNIST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)\n", + "\n", + "# Parameters\n", + "num_gpus = 2\n", + "num_steps = 200\n", + "learning_rate = 0.001\n", + "batch_size = 1024\n", + "display_step = 10\n", + "\n", + "# Network Parameters\n", + "num_input = 784 # MNIST data input (img shape: 28*28)\n", + "num_classes = 10 # MNIST total classes (0-9 digits)\n", + "dropout = 0.75 # Dropout, probability to keep units" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Build a convolutional neural network\n", + "def conv_net(x, n_classes, dropout, reuse, is_training):\n", + " # Define a scope for reusing the variables\n", + " with tf.variable_scope('ConvNet', reuse=reuse):\n", + " # MNIST data input is a 1-D vector of 784 features (28*28 pixels)\n", + " # Reshape to match picture format [Height x Width x Channel]\n", + " # Tensor input become 4-D: [Batch Size, Height, Width, Channel]\n", + " x = tf.reshape(x, shape=[-1, 28, 28, 1])\n", + "\n", + " # Convolution Layer with 64 filters and a kernel size of 5\n", + " x = tf.layers.conv2d(x, 64, 5, activation=tf.nn.relu)\n", + " # Max Pooling (down-sampling) with strides of 2 and kernel size of 2\n", + " x = tf.layers.max_pooling2d(x, 2, 2)\n", + "\n", + " # Convolution Layer with 256 filters and a kernel size of 5\n", + " x = tf.layers.conv2d(x, 256, 3, activation=tf.nn.relu)\n", + " # Convolution Layer with 512 filters and a kernel size of 5\n", + " x = tf.layers.conv2d(x, 512, 3, activation=tf.nn.relu)\n", + " # Max Pooling (down-sampling) with strides of 2 and kernel size of 2\n", + " x = tf.layers.max_pooling2d(x, 2, 2)\n", + "\n", + " # Flatten the data to a 1-D vector for the fully connected layer\n", + " x = tf.contrib.layers.flatten(x)\n", + "\n", + " # Fully connected layer (in contrib folder for now)\n", + " x = tf.layers.dense(x, 2048)\n", + " # Apply Dropout (if is_training is False, dropout is not applied)\n", + " x = tf.layers.dropout(x, rate=dropout, training=is_training)\n", + "\n", + " # Fully connected layer (in contrib folder for now)\n", + " x = tf.layers.dense(x, 1024)\n", + " # Apply Dropout (if is_training is False, dropout is not applied)\n", + " x = tf.layers.dropout(x, rate=dropout, training=is_training)\n", + "\n", + " # Output layer, class prediction\n", + " out = tf.layers.dense(x, n_classes)\n", + " # Because 'softmax_cross_entropy_with_logits' loss already apply\n", + " # softmax, we only apply softmax to testing network\n", + " out = tf.nn.softmax(out) if not is_training else out\n", + "\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Build the function to average the gradients\n", + "def average_gradients(tower_grads):\n", + " average_grads = []\n", + " for grad_and_vars in zip(*tower_grads):\n", + " # Note that each grad_and_vars looks like the following:\n", + " # ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN))\n", + " grads = []\n", + " for g, _ in grad_and_vars:\n", + " # Add 0 dimension to the gradients to represent the tower.\n", + " expanded_g = tf.expand_dims(g, 0)\n", + "\n", + " # Append on a 'tower' dimension which we will average over below.\n", + " grads.append(expanded_g)\n", + "\n", + " # Average over the 'tower' dimension.\n", + " grad = tf.concat(grads, 0)\n", + " grad = tf.reduce_mean(grad, 0)\n", + "\n", + " # Keep in mind that the Variables are redundant because they are shared\n", + " # across towers. So .. we will just return the first tower's pointer to\n", + " # the Variable.\n", + " v = grad_and_vars[0][1]\n", + " grad_and_var = (grad, v)\n", + " average_grads.append(grad_and_var)\n", + " return average_grads" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# By default, all variables will be placed on '/gpu:0'\n", + "# So we need a custom device function, to assign all variables to '/cpu:0'\n", + "# Note: If GPUs are peered, '/gpu:0' can be a faster option\n", + "PS_OPS = ['Variable', 'VariableV2', 'AutoReloadVariable']\n", + "\n", + "def assign_to_device(device, ps_device='/cpu:0'):\n", + " def _assign(op):\n", + " node_def = op if isinstance(op, tf.NodeDef) else op.node_def\n", + " if node_def.op in PS_OPS:\n", + " return \"/\" + ps_device\n", + " else:\n", + " return device\n", + "\n", + " return _assign" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 1: Minibatch Loss= 2.4077, Training Accuracy= 0.123, 682 Examples/sec\n", + "Step 10: Minibatch Loss= 1.0067, Training Accuracy= 0.765, 6528 Examples/sec\n", + "Step 20: Minibatch Loss= 0.2442, Training Accuracy= 0.945, 6803 Examples/sec\n", + "Step 30: Minibatch Loss= 0.2013, Training Accuracy= 0.951, 6741 Examples/sec\n", + "Step 40: Minibatch Loss= 0.1445, Training Accuracy= 0.962, 6700 Examples/sec\n", + "Step 50: Minibatch Loss= 0.0940, Training Accuracy= 0.971, 6746 Examples/sec\n", + "Step 60: Minibatch Loss= 0.0792, Training Accuracy= 0.977, 6627 Examples/sec\n", + "Step 70: Minibatch Loss= 0.0593, Training Accuracy= 0.979, 6749 Examples/sec\n", + "Step 80: Minibatch Loss= 0.0799, Training Accuracy= 0.984, 6368 Examples/sec\n", + "Step 90: Minibatch Loss= 0.0614, Training Accuracy= 0.988, 6762 Examples/sec\n", + "Step 100: Minibatch Loss= 0.0716, Training Accuracy= 0.983, 6338 Examples/sec\n", + "Step 110: Minibatch Loss= 0.0531, Training Accuracy= 0.986, 6504 Examples/sec\n", + "Step 120: Minibatch Loss= 0.0425, Training Accuracy= 0.990, 6721 Examples/sec\n", + "Step 130: Minibatch Loss= 0.0473, Training Accuracy= 0.986, 6735 Examples/sec\n", + "Step 140: Minibatch Loss= 0.0345, Training Accuracy= 0.991, 6636 Examples/sec\n", + "Step 150: Minibatch Loss= 0.0419, Training Accuracy= 0.993, 6777 Examples/sec\n", + "Step 160: Minibatch Loss= 0.0602, Training Accuracy= 0.984, 6392 Examples/sec\n", + "Step 170: Minibatch Loss= 0.0425, Training Accuracy= 0.990, 6855 Examples/sec\n", + "Step 180: Minibatch Loss= 0.0107, Training Accuracy= 0.998, 6804 Examples/sec\n", + "Step 190: Minibatch Loss= 0.0204, Training Accuracy= 0.995, 6645 Examples/sec\n", + "Step 200: Minibatch Loss= 0.0296, Training Accuracy= 0.993, 6747 Examples/sec\n", + "Optimization Finished!\n", + "Testing Accuracy: 0.990671\n" + ] + } + ], + "source": [ + "# Place all ops on CPU by default\n", + "with tf.device('/cpu:0'):\n", + " tower_grads = []\n", + " reuse_vars = False\n", + "\n", + " # tf Graph input\n", + " X = tf.placeholder(tf.float32, [None, num_input])\n", + " Y = tf.placeholder(tf.float32, [None, num_classes])\n", + "\n", + " # Loop over all GPUs and construct their own computation graph\n", + " for i in range(num_gpus):\n", + " with tf.device(assign_to_device('/gpu:{}'.format(i), ps_device='/cpu:0')):\n", + "\n", + " # Split data between GPUs\n", + " _x = X[i * batch_size: (i+1) * batch_size]\n", + " _y = Y[i * batch_size: (i+1) * batch_size]\n", + "\n", + " # Because Dropout have different behavior at training and prediction time, we\n", + " # need to create 2 distinct computation graphs that share the same weights.\n", + "\n", + " # Create a graph for training\n", + " logits_train = conv_net(_x, num_classes, dropout,\n", + " reuse=reuse_vars, is_training=True)\n", + " # Create another graph for testing that reuse the same weights\n", + " logits_test = conv_net(_x, num_classes, dropout,\n", + " reuse=True, is_training=False)\n", + "\n", + " # Define loss and optimizer (with train logits, for dropout to take effect)\n", + " loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(\n", + " logits=logits_train, labels=_y))\n", + " optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n", + " grads = optimizer.compute_gradients(loss_op)\n", + "\n", + " # Only first GPU compute accuracy\n", + " if i == 0:\n", + " # Evaluate model (with test logits, for dropout to be disabled)\n", + " correct_pred = tf.equal(tf.argmax(logits_test, 1), tf.argmax(_y, 1))\n", + " accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n", + "\n", + " reuse_vars = True\n", + " tower_grads.append(grads)\n", + "\n", + " tower_grads = average_gradients(tower_grads)\n", + " train_op = optimizer.apply_gradients(tower_grads)\n", + "\n", + " # Initializing the variables\n", + " init = tf.global_variables_initializer()\n", + "\n", + " # Launch the graph\n", + " with tf.Session() as sess:\n", + " sess.run(init)\n", + " step = 1\n", + " # Keep training until reach max iterations\n", + " for step in range(1, num_steps + 1):\n", + " # Get a batch for each GPU\n", + " batch_x, batch_y = mnist.train.next_batch(batch_size * num_gpus)\n", + " # Run optimization op (backprop)\n", + " ts = time.time()\n", + " sess.run(train_op, feed_dict={X: batch_x, Y: batch_y})\n", + " te = time.time() - ts\n", + " if step % display_step == 0 or step == 1:\n", + " # Calculate batch loss and accuracy\n", + " loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x,\n", + " Y: batch_y})\n", + " print(\"Step \" + str(step) + \": Minibatch Loss= \" + \\\n", + " \"{:.4f}\".format(loss) + \", Training Accuracy= \" + \\\n", + " \"{:.3f}\".format(acc) + \", %i Examples/sec\" % int(len(batch_x)/te))\n", + " step += 1\n", + " print(\"Optimization Finished!\")\n", + "\n", + " # Calculate accuracy for 1000 mnist test images\n", + " print(\"Testing Accuracy:\", \\\n", + " np.mean([sess.run(accuracy, feed_dict={X: mnist.test.images[i:i+batch_size],\n", + " Y: mnist.test.labels[i:i+batch_size]}) for i in range(0, len(mnist.test.images), batch_size)]))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} From 6d96494e7f0d5b955c3a931d46ac9a121d9b813e Mon Sep 17 00:00:00 2001 From: Hossein Sheikhi Darani <64957461+HosseinSheikhi@users.noreply.github.com> Date: Tue, 19 May 2020 20:47:14 -0700 Subject: [PATCH 08/24] Modify tf2 linear regression loss function (#371) --- .../notebooks/2_BasicModels/linear_regression.ipynb | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/tensorflow_v2/notebooks/2_BasicModels/linear_regression.ipynb b/tensorflow_v2/notebooks/2_BasicModels/linear_regression.ipynb index 17b57b8a..83ad9a53 100644 --- a/tensorflow_v2/notebooks/2_BasicModels/linear_regression.ipynb +++ b/tensorflow_v2/notebooks/2_BasicModels/linear_regression.ipynb @@ -56,8 +56,7 @@ "X = np.array([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,\n", " 7.042,10.791,5.313,7.997,5.654,9.27,3.1])\n", "Y = np.array([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,\n", - " 2.827,3.465,1.65,2.904,2.42,2.94,1.3])\n", - "n_samples = X.shape[0]" + " 2.827,3.465,1.65,2.904,2.42,2.94,1.3])\n" ] }, { @@ -76,7 +75,7 @@ "\n", "# Mean square error.\n", "def mean_square(y_pred, y_true):\n", - " return tf.reduce_sum(tf.pow(y_pred-y_true, 2)) / (2 * n_samples)\n", + " return tf.reduce_mean(tf.square(y_pred - y_true))\n", "\n", "# Stochastic Gradient Descent Optimizer.\n", "optimizer = tf.optimizers.SGD(learning_rate)" From a8ee3d2cf096f82a4bd88f0f923805c633ec84ed Mon Sep 17 00:00:00 2001 From: Hossein Sheikhi Darani <64957461+HosseinSheikhi@users.noreply.github.com> Date: Wed, 27 May 2020 10:37:09 -0700 Subject: [PATCH 09/24] Modify linear regression loss function (#373) * Modify tf2 linear regression loss function * neural_network.ipynp syntax error has been corrected --- tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb b/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb index 77926535..9ecf0f2c 100644 --- a/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb +++ b/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb @@ -116,7 +116,7 @@ " # Set forward pass.\n", " def call(self, x, is_training=False):\n", " x = self.fc1(x)\n", - " x = self.fc2(x)\n" + " x = self.fc2(x)\n", " x = self.out(x)\n", " if not is_training:\n", " # tf cross entropy expect logits without softmax, so only\n", From 4ac5751796d19b868a06136c13095228dc02acf6 Mon Sep 17 00:00:00 2001 From: Qingxu Zhu <49614979+ZQX323@users.noreply.github.com> Date: Tue, 2 Jun 2020 11:20:10 +0800 Subject: [PATCH 10/24] fix links in README of TensorFlow_v1 (#374) * Update README.md * Update README.md * Update README.md --- tensorflow_v1/README.md | 72 ++++++++++++++++++++--------------------- 1 file changed, 36 insertions(+), 36 deletions(-) diff --git a/tensorflow_v1/README.md b/tensorflow_v1/README.md index 93a8c3a9..29b188e3 100644 --- a/tensorflow_v1/README.md +++ b/tensorflow_v1/README.md @@ -5,58 +5,58 @@ All the following examples are the original TF v1 examples. *If you are using older TensorFlow version (0.11 and under), please take a [look here](https://github.com/aymericdamien/TensorFlow-Examples/tree/0.11).* #### 0 - Prerequisite -- [Introduction to Machine Learning](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/0_Prerequisite/ml_introduction.ipynb). -- [Introduction to MNIST Dataset](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/0_Prerequisite/mnist_dataset_intro.ipynb). +- [Introduction to Machine Learning](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/0_Prerequisite/ml_introduction.ipynb). +- [Introduction to MNIST Dataset](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb). #### 1 - Introduction -- **Hello World** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/1_Introduction/helloworld.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/1_Introduction/helloworld.py)). Very simple example to learn how to print "hello world" using TensorFlow. -- **Basic Operations** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/1_Introduction/basic_operations.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/1_Introduction/basic_operations.py)). A simple example that cover TensorFlow basic operations. -- **TensorFlow Eager API basics** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/1_Introduction/basic_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/1_Introduction/basic_eager_api.py)). Get started with TensorFlow's Eager API. +- **Hello World** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/1_Introduction/helloworld.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/1_Introduction/helloworld.py)). Very simple example to learn how to print "hello world" using TensorFlow. +- **Basic Operations** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/1_Introduction/basic_operations.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/1_Introduction/basic_operations.py)). A simple example that cover TensorFlow basic operations. +- **TensorFlow Eager API basics** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/1_Introduction/basic_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/1_Introduction/basic_eager_api.py)). Get started with TensorFlow's Eager API. #### 2 - Basic Models -- **Linear Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/linear_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/linear_regression.py)). Implement a Linear Regression with TensorFlow. -- **Linear Regression (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/linear_regression_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/linear_regression_eager_api.py)). Implement a Linear Regression using TensorFlow's Eager API. -- **Logistic Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/logistic_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/logistic_regression.py)). Implement a Logistic Regression with TensorFlow. -- **Logistic Regression (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/logistic_regression_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/logistic_regression_eager_api.py)). Implement a Logistic Regression using TensorFlow's Eager API. -- **Nearest Neighbor** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/nearest_neighbor.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/nearest_neighbor.py)). Implement Nearest Neighbor algorithm with TensorFlow. -- **K-Means** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/kmeans.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/kmeans.py)). Build a K-Means classifier with TensorFlow. -- **Random Forest** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/random_forest.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/random_forest.py)). Build a Random Forest classifier with TensorFlow. -- **Gradient Boosted Decision Tree (GBDT)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/gradient_boosted_decision_tree.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/gradient_boosted_decision_tree.py)). Build a Gradient Boosted Decision Tree (GBDT) with TensorFlow. -- **Word2Vec (Word Embedding)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/word2vec.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/word2vec.py)). Build a Word Embedding Model (Word2Vec) from Wikipedia data, with TensorFlow. +- **Linear Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/linear_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/linear_regression.py)). Implement a Linear Regression with TensorFlow. +- **Linear Regression (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/linear_regression_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/linear_regression_eager_api.py)). Implement a Linear Regression using TensorFlow's Eager API. +- **Logistic Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/logistic_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/logistic_regression.py)). Implement a Logistic Regression with TensorFlow. +- **Logistic Regression (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/logistic_regression_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/logistic_regression_eager_api.py)). Implement a Logistic Regression using TensorFlow's Eager API. +- **Nearest Neighbor** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/nearest_neighbor.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/nearest_neighbor.py)). Implement Nearest Neighbor algorithm with TensorFlow. +- **K-Means** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/kmeans.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/kmeans.py)). Build a K-Means classifier with TensorFlow. +- **Random Forest** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/random_forest.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/random_forest.py)). Build a Random Forest classifier with TensorFlow. +- **Gradient Boosted Decision Tree (GBDT)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/gradient_boosted_decision_tree.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/gradient_boosted_decision_tree.py)). Build a Gradient Boosted Decision Tree (GBDT) with TensorFlow. +- **Word2Vec (Word Embedding)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/word2vec.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/word2vec.py)). Build a Word Embedding Model (Word2Vec) from Wikipedia data, with TensorFlow. #### 3 - Neural Networks ##### Supervised -- **Simple Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/neural_network_raw.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/neural_network_raw.py)). Build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. Raw TensorFlow implementation. -- **Simple Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/neural_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/neural_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. -- **Simple Neural Network (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/neural_network_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/neural_network_eager_api.py)). Use TensorFlow Eager API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. -- **Convolutional Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/convolutional_network_raw.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/convolutional_network_raw.py)). Build a convolutional neural network to classify MNIST digits dataset. Raw TensorFlow implementation. -- **Convolutional Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/convolutional_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/convolutional_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a convolutional neural network to classify MNIST digits dataset. -- **Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/recurrent_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/recurrent_network.py)). Build a recurrent neural network (LSTM) to classify MNIST digits dataset. -- **Bi-directional Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/bidirectional_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/bidirectional_rnn.py)). Build a bi-directional recurrent neural network (LSTM) to classify MNIST digits dataset. -- **Dynamic Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/dynamic_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/dynamic_rnn.py)). Build a recurrent neural network (LSTM) that performs dynamic calculation to classify sequences of different length. +- **Simple Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/neural_network_raw.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/neural_network_raw.py)). Build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. Raw TensorFlow implementation. +- **Simple Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/neural_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/neural_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. +- **Simple Neural Network (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/neural_network_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/neural_network_eager_api.py)). Use TensorFlow Eager API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. +- **Convolutional Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/convolutional_network_raw.py)). Build a convolutional neural network to classify MNIST digits dataset. Raw TensorFlow implementation. +- **Convolutional Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/convolutional_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/convolutional_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a convolutional neural network to classify MNIST digits dataset. +- **Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/recurrent_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/recurrent_network.py)). Build a recurrent neural network (LSTM) to classify MNIST digits dataset. +- **Bi-directional Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/bidirectional_rnn.py)). Build a bi-directional recurrent neural network (LSTM) to classify MNIST digits dataset. +- **Dynamic Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/dynamic_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/dynamic_rnn.py)). Build a recurrent neural network (LSTM) that performs dynamic calculation to classify sequences of different length. ##### Unsupervised -- **Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/autoencoder.py)). Build an auto-encoder to encode an image to a lower dimension and re-construct it. -- **Variational Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/variational_autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/variational_autoencoder.py)). Build a variational auto-encoder (VAE), to encode and generate images from noise. -- **GAN (Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/gan.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/gan.py)). Build a Generative Adversarial Network (GAN) to generate images from noise. -- **DCGAN (Deep Convolutional Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/dcgan.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/dcgan.py)). Build a Deep Convolutional Generative Adversarial Network (DCGAN) to generate images from noise. +- **Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/autoencoder.py)). Build an auto-encoder to encode an image to a lower dimension and re-construct it. +- **Variational Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/variational_autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/variational_autoencoder.py)). Build a variational auto-encoder (VAE), to encode and generate images from noise. +- **GAN (Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/gan.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/gan.py)). Build a Generative Adversarial Network (GAN) to generate images from noise. +- **DCGAN (Deep Convolutional Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/dcgan.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/dcgan.py)). Build a Deep Convolutional Generative Adversarial Network (DCGAN) to generate images from noise. #### 4 - Utilities -- **Save and Restore a model** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/4_Utils/save_restore_model.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/4_Utils/save_restore_model.py)). Save and Restore a model with TensorFlow. -- **Tensorboard - Graph and loss visualization** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/4_Utils/tensorboard_basic.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/4_Utils/tensorboard_basic.py)). Use Tensorboard to visualize the computation Graph and plot the loss. -- **Tensorboard - Advanced visualization** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/4_Utils/tensorboard_advanced.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/4_Utils/tensorboard_advanced.py)). Going deeper into Tensorboard; visualize the variables, gradients, and more... +- **Save and Restore a model** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/4_Utils/save_restore_model.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/4_Utils/save_restore_model.py)). Save and Restore a model with TensorFlow. +- **Tensorboard - Graph and loss visualization** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/4_Utils/tensorboard_basic.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/4_Utils/tensorboard_basic.py)). Use Tensorboard to visualize the computation Graph and plot the loss. +- **Tensorboard - Advanced visualization** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/4_Utils/tensorboard_advanced.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/4_Utils/tensorboard_advanced.py)). Going deeper into Tensorboard; visualize the variables, gradients, and more... #### 5 - Data Management -- **Build an image dataset** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/build_an_image_dataset.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/5_DataManagement/build_an_image_dataset.py)). Build your own images dataset with TensorFlow data queues, from image folders or a dataset file. -- **TensorFlow Dataset API** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/tensorflow_dataset_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/5_DataManagement/tensorflow_dataset_api.py)). Introducing TensorFlow Dataset API for optimizing the input data pipeline. -- **Load and Parse data** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/load_data.ipynb)). Build efficient data pipeline (Numpy arrays, Images, CSV files, custom data, ...). -- **Build and Load TFRecords** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/tfrecords.ipynb)). Convert data into TFRecords format, and load them. -- **Image Transformation (i.e. Image Augmentation)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/image_transformation.ipynb)). Apply various image augmentation techniques, to generate distorted images for training. +- **Build an image dataset** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/5_DataManagement/build_an_image_dataset.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/5_DataManagement/build_an_image_dataset.py)). Build your own images dataset with TensorFlow data queues, from image folders or a dataset file. +- **TensorFlow Dataset API** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/5_DataManagement/tensorflow_dataset_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/5_DataManagement/tensorflow_dataset_api.py)). Introducing TensorFlow Dataset API for optimizing the input data pipeline. +- **Load and Parse data** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/5_DataManagement/load_data.ipynb)). Build efficient data pipeline (Numpy arrays, Images, CSV files, custom data, ...). +- **Build and Load TFRecords** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/5_DataManagement/tfrecords.ipynb)). Convert data into TFRecords format, and load them. +- **Image Transformation (i.e. Image Augmentation)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/5_DataManagement/image_transformation.ipynb)). Apply various image augmentation techniques, to generate distorted images for training. #### 6 - Multi GPU -- **Basic Operations on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/6_MultiGPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/6_MultiGPU/multigpu_basics.py)). A simple example to introduce multi-GPU in TensorFlow. -- **Train a Neural Network on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/6_MultiGPU/multigpu_cnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/6_MultiGPU/multigpu_cnn.py)). A clear and simple TensorFlow implementation to train a convolutional neural network on multiple GPUs. +- **Basic Operations on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/6_MultiGPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/6_MultiGPU/multigpu_basics.py)). A simple example to introduce multi-GPU in TensorFlow. +- **Train a Neural Network on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/6_MultiGPU/multigpu_cnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/6_MultiGPU/multigpu_cnn.py)). A clear and simple TensorFlow implementation to train a convolutional neural network on multiple GPUs. ## Installation From 754c3312534755246d142d43ddd5133f60866f7a Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Tue, 2 Jun 2020 01:12:28 -0700 Subject: [PATCH 11/24] Fix links --- README.md | 68 +++++++++++++++++++++++++++---------------------------- 1 file changed, 34 insertions(+), 34 deletions(-) diff --git a/README.md b/README.md index 00610dcf..673a7d0a 100644 --- a/README.md +++ b/README.md @@ -85,52 +85,52 @@ The tutorial index for TF v1 is available here: [TensorFlow v1.15 Examples](tens - [Introduction to MNIST Dataset](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/0_Prerequisite/mnist_dataset_intro.ipynb). #### 1 - Introduction -- **Hello World** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/1_Introduction/helloworld.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/1_Introduction/helloworld.py)). Very simple example to learn how to print "hello world" using TensorFlow. -- **Basic Operations** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/1_Introduction/basic_operations.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/1_Introduction/basic_operations.py)). A simple example that cover TensorFlow basic operations. -- **TensorFlow Eager API basics** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/1_Introduction/basic_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/1_Introduction/basic_eager_api.py)). Get started with TensorFlow's Eager API. +- **Hello World** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/1_Introduction/helloworld.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/1_Introduction/helloworld.py)). Very simple example to learn how to print "hello world" using TensorFlow. +- **Basic Operations** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/1_Introduction/basic_operations.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-examples/Examples/blob/master/tensorflow_v1/1_Introduction/basic_operations.py)). A simple example that cover TensorFlow basic operations. +- **TensorFlow Eager API basics** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/1_Introduction/basic_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/1_Introduction/basic_eager_api.py)). Get started with TensorFlow's Eager API. #### 2 - Basic Models -- **Linear Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/linear_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/linear_regression.py)). Implement a Linear Regression with TensorFlow. -- **Linear Regression (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/linear_regression_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/linear_regression_eager_api.py)). Implement a Linear Regression using TensorFlow's Eager API. -- **Logistic Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/logistic_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/logistic_regression.py)). Implement a Logistic Regression with TensorFlow. -- **Logistic Regression (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/logistic_regression_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/logistic_regression_eager_api.py)). Implement a Logistic Regression using TensorFlow's Eager API. -- **Nearest Neighbor** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/nearest_neighbor.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/nearest_neighbor.py)). Implement Nearest Neighbor algorithm with TensorFlow. -- **K-Means** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/kmeans.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/kmeans.py)). Build a K-Means classifier with TensorFlow. -- **Random Forest** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/random_forest.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/random_forest.py)). Build a Random Forest classifier with TensorFlow. -- **Gradient Boosted Decision Tree (GBDT)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/gradient_boosted_decision_tree.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/gradient_boosted_decision_tree.py)). Build a Gradient Boosted Decision Tree (GBDT) with TensorFlow. -- **Word2Vec (Word Embedding)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/word2vec.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/word2vec.py)). Build a Word Embedding Model (Word2Vec) from Wikipedia data, with TensorFlow. +- **Linear Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/linear_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/linear_regression.py)). Implement a Linear Regression with TensorFlow. +- **Linear Regression (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/linear_regression_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/linear_regression_eager_api.py)). Implement a Linear Regression using TensorFlow's Eager API. +- **Logistic Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/logistic_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/logistic_regression.py)). Implement a Logistic Regression with TensorFlow. +- **Logistic Regression (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/logistic_regression_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/logistic_regression_eager_api.py)). Implement a Logistic Regression using TensorFlow's Eager API. +- **Nearest Neighbor** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/nearest_neighbor.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/nearest_neighbor.py)). Implement Nearest Neighbor algorithm with TensorFlow. +- **K-Means** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/kmeans.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/kmeans.py)). Build a K-Means classifier with TensorFlow. +- **Random Forest** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/random_forest.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/random_forest.py)). Build a Random Forest classifier with TensorFlow. +- **Gradient Boosted Decision Tree (GBDT)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/gradient_boosted_decision_tree.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/gradient_boosted_decision_tree.py)). Build a Gradient Boosted Decision Tree (GBDT) with TensorFlow. +- **Word2Vec (Word Embedding)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/word2vec.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/word2vec.py)). Build a Word Embedding Model (Word2Vec) from Wikipedia data, with TensorFlow. #### 3 - Neural Networks ##### Supervised -- **Simple Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/neural_network_raw.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/neural_network_raw.py)). Build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. Raw TensorFlow implementation. -- **Simple Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/neural_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/neural_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. -- **Simple Neural Network (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/neural_network_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/neural_network_eager_api.py)). Use TensorFlow Eager API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. -- **Convolutional Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/convolutional_network_raw.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/convolutional_network_raw.py)). Build a convolutional neural network to classify MNIST digits dataset. Raw TensorFlow implementation. -- **Convolutional Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/convolutional_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/convolutional_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a convolutional neural network to classify MNIST digits dataset. -- **Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/recurrent_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/recurrent_network.py)). Build a recurrent neural network (LSTM) to classify MNIST digits dataset. -- **Bi-directional Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/bidirectional_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/bidirectional_rnn.py)). Build a bi-directional recurrent neural network (LSTM) to classify MNIST digits dataset. -- **Dynamic Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/dynamic_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/dynamic_rnn.py)). Build a recurrent neural network (LSTM) that performs dynamic calculation to classify sequences of different length. +- **Simple Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/3_NeuralNetworks/notebooks/neural_network_raw.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/neural_network_raw.py)). Build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. Raw TensorFlow implementation. +- **Simple Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/neural_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/neural_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. +- **Simple Neural Network (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/neural_network_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/neural_network_eager_api.py)). Use TensorFlow Eager API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. +- **Convolutional Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/convolutional_network_raw.py)). Build a convolutional neural network to classify MNIST digits dataset. Raw TensorFlow implementation. + - **Convolutional Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/convolutional_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/convolutional_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a convolutional neural network to classify MNIST digits dataset. +- **Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/recurrent_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/recurrent_network.py)). Build a recurrent neural network (LSTM) to classify MNIST digits dataset. +- **Bi-directional Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/bidirectional_rnn.py)). Build a bi-directional recurrent neural network (LSTM) to classify MNIST digits dataset. +- **Dynamic Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/dynamic_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/dynamic_rnn.py)). Build a recurrent neural network (LSTM) that performs dynamic calculation to classify sequences of different length. ##### Unsupervised -- **Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/autoencoder.py)). Build an auto-encoder to encode an image to a lower dimension and re-construct it. -- **Variational Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/variational_autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/variational_autoencoder.py)). Build a variational auto-encoder (VAE), to encode and generate images from noise. -- **GAN (Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/gan.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/gan.py)). Build a Generative Adversarial Network (GAN) to generate images from noise. -- **DCGAN (Deep Convolutional Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/dcgan.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/dcgan.py)). Build a Deep Convolutional Generative Adversarial Network (DCGAN) to generate images from noise. +- **Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/autoencoder.py)). Build an auto-encoder to encode an image to a lower dimension and re-construct it. +- **Variational Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/variational_autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/variational_autoencoder.py)). Build a variational auto-encoder (VAE), to encode and generate images from noise. +- **GAN (Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/gan.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/gan.py)). Build a Generative Adversarial Network (GAN) to generate images from noise. +- **DCGAN (Deep Convolutional Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/dcgan.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/dcgan.py)). Build a Deep Convolutional Generative Adversarial Network (DCGAN) to generate images from noise. #### 4 - Utilities -- **Save and Restore a model** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/4_Utils/save_restore_model.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/4_Utils/save_restore_model.py)). Save and Restore a model with TensorFlow. -- **Tensorboard - Graph and loss visualization** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/4_Utils/tensorboard_basic.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/4_Utils/tensorboard_basic.py)). Use Tensorboard to visualize the computation Graph and plot the loss. -- **Tensorboard - Advanced visualization** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/4_Utils/tensorboard_advanced.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/4_Utils/tensorboard_advanced.py)). Going deeper into Tensorboard; visualize the variables, gradients, and more... +- **Save and Restore a model** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/4_Utils/save_restore_model.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/4_Utils/save_restore_model.py)). Save and Restore a model with TensorFlow. +- **Tensorboard - Graph and loss visualization** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/4_Utils/tensorboard_basic.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/4_Utils/tensorboard_basic.py)). Use Tensorboard to visualize the computation Graph and plot the loss. +- **Tensorboard - Advanced visualization** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/4_Utils/tensorboard_advanced.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/4_Utils/tensorboard_advanced.py)). Going deeper into Tensorboard; visualize the variables, gradients, and more... #### 5 - Data Management -- **Build an image dataset** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/build_an_image_dataset.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/5_DataManagement/build_an_image_dataset.py)). Build your own images dataset with TensorFlow data queues, from image folders or a dataset file. -- **TensorFlow Dataset API** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/tensorflow_dataset_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/5_DataManagement/tensorflow_dataset_api.py)). Introducing TensorFlow Dataset API for optimizing the input data pipeline. -- **Load and Parse data** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/load_data.ipynb)). Build efficient data pipeline (Numpy arrays, Images, CSV files, custom data, ...). -- **Build and Load TFRecords** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/tfrecords.ipynb)). Convert data into TFRecords format, and load them. -- **Image Transformation (i.e. Image Augmentation)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/image_transformation.ipynb)). Apply various image augmentation techniques, to generate distorted images for training. +- **Build an image dataset** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/5_DataManagement/build_an_image_dataset.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/5_DataManagement/build_an_image_dataset.py)). Build your own images dataset with TensorFlow data queues, from image folders or a dataset file. +- **TensorFlow Dataset API** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/5_DataManagement/tensorflow_dataset_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/5_DataManagement/tensorflow_dataset_api.py)). Introducing TensorFlow Dataset API for optimizing the input data pipeline. +- **Load and Parse data** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/5_DataManagement/load_data.ipynb)). Build efficient data pipeline (Numpy arrays, Images, CSV files, custom data, ...). +- **Build and Load TFRecords** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/5_DataManagement/tfrecords.ipynb)). Convert data into TFRecords format, and load them. +- **Image Transformation (i.e. Image Augmentation)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/5_DataManagement/image_transformation.ipynb)). Apply various image augmentation techniques, to generate distorted images for training. #### 6 - Multi GPU -- **Basic Operations on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/6_MultiGPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/6_MultiGPU/multigpu_basics.py)). A simple example to introduce multi-GPU in TensorFlow. -- **Train a Neural Network on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/6_MultiGPU/multigpu_cnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/6_MultiGPU/multigpu_cnn.py)). A clear and simple TensorFlow implementation to train a convolutional neural network on multiple GPUs. +- **Basic Operations on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/6_MultiGPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/6_MultiGPU/multigpu_basics.py)). A simple example to introduce multi-GPU in TensorFlow. +- **Train a Neural Network on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/6_MultiGPU/multigpu_cnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/6_MultiGPU/multigpu_cnn.py)). A clear and simple TensorFlow implementation to train a convolutional neural network on multiple GPUs. From 0d0327544f2077786c946fb26b731bb1ef4d5103 Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Thu, 4 Jun 2020 00:05:34 -0700 Subject: [PATCH 12/24] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 673a7d0a..aea002a2 100644 --- a/README.md +++ b/README.md @@ -107,7 +107,7 @@ The tutorial index for TF v1 is available here: [TensorFlow v1.15 Examples](tens - **Simple Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/neural_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/neural_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. - **Simple Neural Network (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/neural_network_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/neural_network_eager_api.py)). Use TensorFlow Eager API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. - **Convolutional Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/convolutional_network_raw.py)). Build a convolutional neural network to classify MNIST digits dataset. Raw TensorFlow implementation. - - **Convolutional Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/convolutional_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/convolutional_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a convolutional neural network to classify MNIST digits dataset. +- **Convolutional Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/convolutional_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/convolutional_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a convolutional neural network to classify MNIST digits dataset. - **Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/recurrent_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/recurrent_network.py)). Build a recurrent neural network (LSTM) to classify MNIST digits dataset. - **Bi-directional Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/bidirectional_rnn.py)). Build a bi-directional recurrent neural network (LSTM) to classify MNIST digits dataset. - **Dynamic Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/dynamic_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/dynamic_rnn.py)). Build a recurrent neural network (LSTM) that performs dynamic calculation to classify sequences of different length. From 86fc318c24668aff7341fdace325a34181205351 Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Wed, 15 Jul 2020 00:57:36 -0700 Subject: [PATCH 13/24] add GBDT example (#379) --- README.md | 2 +- tensorflow_v2/README.md | 1 + .../gradient_boosted_trees.ipynb | 604 ++++++++++++++++++ 3 files changed, 606 insertions(+), 1 deletion(-) create mode 100644 tensorflow_v2/notebooks/2_BasicModels/gradient_boosted_trees.ipynb diff --git a/README.md b/README.md index aea002a2..da394304 100644 --- a/README.md +++ b/README.md @@ -20,6 +20,7 @@ It is suitable for beginners who want to find clear and concise examples about T - **Linear Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/linear_regression.ipynb)). Implement a Linear Regression with TensorFlow 2.0. - **Logistic Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/logistic_regression.ipynb)). Implement a Logistic Regression with TensorFlow 2.0. - **Word2Vec (Word Embedding)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/word2vec.ipynb)). Build a Word Embedding Model (Word2Vec) from Wikipedia data, with TensorFlow 2.0. +- **GBDT (Gradient Boosted Decision Trees)** ([notebooks](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/gradient_boosted_trees.ipynb)). Implement a Gradient Boosted Decision Trees with TensorFlow 2.0+ to predict house value using Boston Housing dataset. #### 3 - Neural Networks ##### Supervised @@ -133,4 +134,3 @@ The tutorial index for TF v1 is available here: [TensorFlow v1.15 Examples](tens #### 6 - Multi GPU - **Basic Operations on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/6_MultiGPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/6_MultiGPU/multigpu_basics.py)). A simple example to introduce multi-GPU in TensorFlow. - **Train a Neural Network on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/6_MultiGPU/multigpu_cnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/6_MultiGPU/multigpu_cnn.py)). A clear and simple TensorFlow implementation to train a convolutional neural network on multiple GPUs. - diff --git a/tensorflow_v2/README.md b/tensorflow_v2/README.md index ed23a174..b6cbea39 100644 --- a/tensorflow_v2/README.md +++ b/tensorflow_v2/README.md @@ -14,6 +14,7 @@ - **Linear Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/linear_regression.ipynb)). Implement a Linear Regression with TensorFlow 2.0. - **Logistic Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/logistic_regression.ipynb)). Implement a Logistic Regression with TensorFlow 2.0. - **Word2Vec (Word Embedding)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/word2vec.ipynb)). Build a Word Embedding Model (Word2Vec) from Wikipedia data, with TensorFlow 2.0. +- **GBDT (Gradient Boosted Decision Trees)** ([notebooks](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/gradient_boosted_trees.ipynb)). Implement a Gradient Boosted Decision Trees with TensorFlow 2.0+ to predict house value using Boston Housing dataset. #### 3 - Neural Networks ##### Supervised diff --git a/tensorflow_v2/notebooks/2_BasicModels/gradient_boosted_trees.ipynb b/tensorflow_v2/notebooks/2_BasicModels/gradient_boosted_trees.ipynb new file mode 100644 index 00000000..cad1d9e8 --- /dev/null +++ b/tensorflow_v2/notebooks/2_BasicModels/gradient_boosted_trees.ipynb @@ -0,0 +1,604 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Gradient Boosted Decision Tree (GBDT)\n", + "Implement a Gradient Boosted Decision Tree (GBDT) with TensorFlow. This example is using the Boston Housing Value dataset as training samples. The example supports both Classification (2 classes: value > $23000 or not) and Regression (raw home value as target).\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Boston Housing Dataset\n", + "\n", + "**Link:** https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html\n", + "\n", + "**Description:**\n", + "\n", + "The dataset contains information collected by the U.S Census Service concerning housing in the area of Boston Mass. It was obtained from the StatLib archive (http://lib.stat.cmu.edu/datasets/boston), and has been used extensively throughout the literature to benchmark algorithms. However, these comparisons were primarily done outside of Delve and are thus somewhat suspect. The dataset is small in size with only 506 cases.\n", + "\n", + "The data was originally published by Harrison, D. and Rubinfeld, D.L. `Hedonic prices and the demand for clean air', J. Environ. Economics & Management, vol.5, 81-102, 1978.`\n", + "\n", + "*For the full features list, please see the link above*" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import print_function\n", + "\n", + "# Ignore all GPUs (current TF GBDT does not support GPU).\n", + "import os\n", + "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n", + "os.environ['TF_CPP_MIN_LOG_LEVEL'] = \"1\"\n", + "\n", + "import tensorflow as tf\n", + "import numpy as np\n", + "import copy" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Dataset parameters.\n", + "num_classes = 2 # Total classes: greater or equal to $23,000, or not (See notes below).\n", + "num_features = 13 # data features size.\n", + "\n", + "# Training parameters.\n", + "max_steps = 2000\n", + "batch_size = 256\n", + "learning_rate = 1.0\n", + "l1_regul = 0.0\n", + "l2_regul = 0.1\n", + "\n", + "# GBDT parameters.\n", + "num_batches_per_layer = 1000\n", + "num_trees = 10\n", + "max_depth = 4" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Prepare Boston Housing Dataset.\n", + "from tensorflow.keras.datasets import boston_housing\n", + "(x_train, y_train), (x_test, y_test) = boston_housing.load_data()\n", + "\n", + "# For classification purpose, we build 2 classes: price greater or lower than $23,000\n", + "def to_binary_class(y):\n", + " for i, label in enumerate(y):\n", + " if label >= 23.0:\n", + " y[i] = 1\n", + " else:\n", + " y[i] = 0\n", + " return y\n", + "\n", + "y_train_binary = to_binary_class(copy.deepcopy(y_train))\n", + "y_test_binary = to_binary_class(copy.deepcopy(y_test))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### GBDT Classifier" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Build the input function.\n", + "train_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(\n", + " x={'x': x_train}, y=y_train_binary,\n", + " batch_size=batch_size, num_epochs=None, shuffle=True)\n", + "test_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(\n", + " x={'x': x_test}, y=y_test_binary,\n", + " batch_size=batch_size, num_epochs=1, shuffle=False)\n", + "test_train_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(\n", + " x={'x': x_train}, y=y_train_binary,\n", + " batch_size=batch_size, num_epochs=1, shuffle=False)\n", + "# GBDT Models from TF Estimator requires 'feature_column' data format.\n", + "feature_columns = [tf.feature_column.numeric_column(key='x', shape=(num_features,))]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Using default config.\n", + "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmp5h6BoR\n", + "INFO:tensorflow:Using config: {'_save_checkpoints_secs': 600, '_num_ps_replicas': 0, '_keep_checkpoint_max': 5, '_task_type': 'worker', '_global_id_in_cluster': 0, '_is_chief': True, '_cluster_spec': ClusterSpec({}), '_model_dir': '/tmp/tmp5h6BoR', '_protocol': None, '_save_checkpoints_steps': None, '_keep_checkpoint_every_n_hours': 10000, '_service': None, '_session_config': allow_soft_placement: true\n", + "graph_options {\n", + " rewrite_options {\n", + " meta_optimizer_iterations: ONE\n", + " }\n", + "}\n", + ", '_tf_random_seed': None, '_save_summary_steps': 100, '_device_fn': None, '_session_creation_timeout_secs': 7200, '_experimental_distribute': None, '_num_worker_replicas': 1, '_task_id': 0, '_log_step_count_steps': 100, '_experimental_max_worker_delay_secs': None, '_evaluation_master': '', '_eval_distribute': None, '_train_distribute': None, '_master': ''}\n" + ] + } + ], + "source": [ + "gbdt_classifier = tf.estimator.BoostedTreesClassifier(\n", + " n_batches_per_layer=num_batches_per_layer,\n", + " feature_columns=feature_columns, \n", + " n_classes=num_classes,\n", + " learning_rate=learning_rate, \n", + " n_trees=num_trees,\n", + " max_depth=max_depth,\n", + " l1_regularization=l1_regul, \n", + " l2_regularization=l2_regul\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /home/user/anaconda3/envs/py2/lib/python2.7/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1635: calling __init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "If using Keras pass *_constraint arguments to layers.\n", + "WARNING:tensorflow:From /home/user/anaconda3/envs/py2/lib/python2.7/site-packages/tensorflow_core/python/training/training_util.py:236: initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.\n", + "WARNING:tensorflow:From /home/user/anaconda3/envs/py2/lib/python2.7/site-packages/tensorflow_estimator/python/estimator/inputs/queues/feeding_queue_runner.py:62: __init__ (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "To construct input pipelines, use the `tf.data` module.\n", + "WARNING:tensorflow:From /home/user/anaconda3/envs/py2/lib/python2.7/site-packages/tensorflow_estimator/python/estimator/inputs/queues/feeding_functions.py:500: add_queue_runner (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "To construct input pipelines, use the `tf.data` module.\n", + "INFO:tensorflow:Calling model_fn.\n", + "INFO:tensorflow:Done calling model_fn.\n", + "INFO:tensorflow:Create CheckpointSaverHook.\n", + "WARNING:tensorflow:Issue encountered when serializing resources.\n", + "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", + "'_Resource' object has no attribute 'name'\n", + "INFO:tensorflow:Graph was finalized.\n", + "INFO:tensorflow:Running local_init_op.\n", + "INFO:tensorflow:Done running local_init_op.\n", + "WARNING:tensorflow:From /home/user/anaconda3/envs/py2/lib/python2.7/site-packages/tensorflow_core/python/training/monitored_session.py:906: start_queue_runners (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "To construct input pipelines, use the `tf.data` module.\n", + "WARNING:tensorflow:Issue encountered when serializing resources.\n", + "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", + "'_Resource' object has no attribute 'name'\n", + "INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmp5h6BoR/model.ckpt.\n", + "WARNING:tensorflow:Issue encountered when serializing resources.\n", + "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", + "'_Resource' object has no attribute 'name'\n", + "INFO:tensorflow:loss = 0.6931475, step = 0\n", + "WARNING:tensorflow:It seems that global step (tf.train.get_global_step) has not been increased. Current value (could be stable): 0 vs previous value: 0. You could increase the global step by passing tf.train.get_global_step() to Optimizer.apply_gradients or Optimizer.minimize.\n", + "WARNING:tensorflow:It seems that global step (tf.train.get_global_step) has not been increased. Current value (could be stable): 0 vs previous value: 0. You could increase the global step by passing tf.train.get_global_step() to Optimizer.apply_gradients or Optimizer.minimize.\n", + "WARNING:tensorflow:It seems that global step (tf.train.get_global_step) has not been increased. Current value (could be stable): 0 vs previous value: 0. You could increase the global step by passing tf.train.get_global_step() to Optimizer.apply_gradients or Optimizer.minimize.\n", + "WARNING:tensorflow:It seems that global step (tf.train.get_global_step) has not been increased. Current value (could be stable): 0 vs previous value: 0. You could increase the global step by passing tf.train.get_global_step() to Optimizer.apply_gradients or Optimizer.minimize.\n", + "WARNING:tensorflow:It seems that global step (tf.train.get_global_step) has not been increased. Current value (could be stable): 0 vs previous value: 0. You could increase the global step by passing tf.train.get_global_step() to Optimizer.apply_gradients or Optimizer.minimize.\n", + "INFO:tensorflow:loss = 0.6931475, step = 0 (0.406 sec)\n", + "INFO:tensorflow:loss = 0.6931475, step = 0 (0.156 sec)\n", + "INFO:tensorflow:loss = 0.6931475, step = 0 (0.167 sec)\n", + "INFO:tensorflow:loss = 0.6931475, step = 0 (0.156 sec)\n", + "INFO:tensorflow:loss = 0.6931475, step = 0 (0.161 sec)\n", + "INFO:tensorflow:loss = 0.6931475, step = 0 (0.156 sec)\n", + "INFO:tensorflow:loss = 0.6931475, step = 0 (0.154 sec)\n", + "INFO:tensorflow:loss = 0.6931475, step = 0 (0.155 sec)\n", + "INFO:tensorflow:loss = 0.6931475, step = 0 (0.158 sec)\n", + "INFO:tensorflow:loss = 0.6931475, step = 0 (0.150 sec)\n", + "INFO:tensorflow:global_step/sec: 47.2392\n", + "INFO:tensorflow:loss = 0.6931475, step = 100 (0.301 sec)\n", + "INFO:tensorflow:global_step/sec: 605.484\n", + "INFO:tensorflow:loss = 0.6931475, step = 200 (0.165 sec)\n", + "INFO:tensorflow:global_step/sec: 616.234\n", + "INFO:tensorflow:loss = 0.6931475, step = 300 (0.162 sec)\n", + "INFO:tensorflow:global_step/sec: 607.741\n", + "INFO:tensorflow:loss = 0.6931475, step = 400 (0.165 sec)\n", + "INFO:tensorflow:global_step/sec: 591.803\n", + "INFO:tensorflow:loss = 0.6931475, step = 500 (0.170 sec)\n", + "INFO:tensorflow:global_step/sec: 627.369\n", + "INFO:tensorflow:loss = 0.6931475, step = 600 (0.159 sec)\n", + "INFO:tensorflow:global_step/sec: 617.083\n", + "INFO:tensorflow:loss = 0.6931475, step = 700 (0.162 sec)\n", + "INFO:tensorflow:global_step/sec: 608.765\n", + "INFO:tensorflow:loss = 0.6931475, step = 800 (0.164 sec)\n", + "INFO:tensorflow:global_step/sec: 619.62\n", + "INFO:tensorflow:loss = 0.6931475, step = 900 (0.161 sec)\n", + "INFO:tensorflow:global_step/sec: 582.581\n", + "INFO:tensorflow:loss = 0.44474202, step = 1000 (0.172 sec)\n", + "INFO:tensorflow:global_step/sec: 587.127\n", + "INFO:tensorflow:loss = 0.46633375, step = 1100 (0.170 sec)\n", + "INFO:tensorflow:global_step/sec: 583.294\n", + "INFO:tensorflow:loss = 0.45393157, step = 1200 (0.171 sec)\n", + "INFO:tensorflow:global_step/sec: 590.375\n", + "INFO:tensorflow:loss = 0.44438446, step = 1300 (0.170 sec)\n", + "INFO:tensorflow:global_step/sec: 572.479\n", + "INFO:tensorflow:loss = 0.4523462, step = 1400 (0.175 sec)\n", + "INFO:tensorflow:global_step/sec: 580.282\n", + "INFO:tensorflow:loss = 0.4581305, step = 1500 (0.172 sec)\n", + "INFO:tensorflow:global_step/sec: 570.032\n", + "INFO:tensorflow:loss = 0.45298833, step = 1600 (0.175 sec)\n", + "INFO:tensorflow:global_step/sec: 615.6\n", + "INFO:tensorflow:loss = 0.4474975, step = 1700 (0.162 sec)\n", + "INFO:tensorflow:global_step/sec: 603.042\n", + "INFO:tensorflow:loss = 0.47046587, step = 1800 (0.166 sec)\n", + "INFO:tensorflow:global_step/sec: 598.262\n", + "INFO:tensorflow:loss = 0.46371317, step = 1900 (0.167 sec)\n", + "INFO:tensorflow:global_step/sec: 591.323\n", + "INFO:tensorflow:Saving checkpoints for 2000 into /tmp/tmp5h6BoR/model.ckpt.\n", + "WARNING:tensorflow:Issue encountered when serializing resources.\n", + "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", + "'_Resource' object has no attribute 'name'\n", + "INFO:tensorflow:Loss for final step: 0.46488184.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gbdt_classifier.train(train_input_fn, max_steps=max_steps)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Calling model_fn.\n", + "WARNING:tensorflow:From /home/user/anaconda3/envs/py2/lib/python2.7/site-packages/tensorflow_core/python/ops/metrics_impl.py:2029: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Deprecated in favor of operator or tf.math.divide.\n", + "WARNING:tensorflow:From /home/user/anaconda3/envs/py2/lib/python2.7/site-packages/tensorflow_estimator/python/estimator/canned/head.py:619: auc (from tensorflow.python.ops.metrics_impl) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "The value of AUC returned by this may race with the update so this is deprected. Please use tf.keras.metrics.AUC instead.\n", + "WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to \"careful_interpolation\" instead.\n", + "WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to \"careful_interpolation\" instead.\n", + "INFO:tensorflow:Done calling model_fn.\n", + "INFO:tensorflow:Starting evaluation at 2020-07-15T00:50:36Z\n", + "INFO:tensorflow:Graph was finalized.\n", + "INFO:tensorflow:Restoring parameters from /tmp/tmp5h6BoR/model.ckpt-2000\n", + "INFO:tensorflow:Running local_init_op.\n", + "INFO:tensorflow:Done running local_init_op.\n", + "INFO:tensorflow:Inference Time : 0.56490s\n", + "INFO:tensorflow:Finished evaluation at 2020-07-15-00:50:37\n", + "INFO:tensorflow:Saving dict for global step 2000: accuracy = 0.87376237, accuracy_baseline = 0.63118815, auc = 0.92280567, auc_precision_recall = 0.9104949, average_loss = 0.38236493, global_step = 2000, label/mean = 0.36881188, loss = 0.38619137, precision = 0.8888889, prediction/mean = 0.378958, recall = 0.7516779\n", + "WARNING:tensorflow:Issue encountered when serializing resources.\n", + "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", + "'_Resource' object has no attribute 'name'\n", + "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 2000: /tmp/tmp5h6BoR/model.ckpt-2000\n" + ] + }, + { + "data": { + "text/plain": [ + "{'accuracy': 0.87376237,\n", + " 'accuracy_baseline': 0.63118815,\n", + " 'auc': 0.92280567,\n", + " 'auc_precision_recall': 0.9104949,\n", + " 'average_loss': 0.38236493,\n", + " 'global_step': 2000,\n", + " 'label/mean': 0.36881188,\n", + " 'loss': 0.38619137,\n", + " 'precision': 0.8888889,\n", + " 'prediction/mean': 0.378958,\n", + " 'recall': 0.7516779}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gbdt_classifier.evaluate(test_train_input_fn)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Calling model_fn.\n", + "WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to \"careful_interpolation\" instead.\n", + "WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to \"careful_interpolation\" instead.\n", + "INFO:tensorflow:Done calling model_fn.\n", + "INFO:tensorflow:Starting evaluation at 2020-07-15T00:50:38Z\n", + "INFO:tensorflow:Graph was finalized.\n", + "INFO:tensorflow:Restoring parameters from /tmp/tmp5h6BoR/model.ckpt-2000\n", + "INFO:tensorflow:Running local_init_op.\n", + "INFO:tensorflow:Done running local_init_op.\n", + "INFO:tensorflow:Inference Time : 0.56883s\n", + "INFO:tensorflow:Finished evaluation at 2020-07-15-00:50:38\n", + "INFO:tensorflow:Saving dict for global step 2000: accuracy = 0.78431374, accuracy_baseline = 0.5588235, auc = 0.8458089, auc_precision_recall = 0.86285317, average_loss = 0.49404, global_step = 2000, label/mean = 0.44117647, loss = 0.49404, precision = 0.87096775, prediction/mean = 0.37467176, recall = 0.6\n", + "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 2000: /tmp/tmp5h6BoR/model.ckpt-2000\n" + ] + }, + { + "data": { + "text/plain": [ + "{'accuracy': 0.78431374,\n", + " 'accuracy_baseline': 0.5588235,\n", + " 'auc': 0.8458089,\n", + " 'auc_precision_recall': 0.86285317,\n", + " 'average_loss': 0.49404,\n", + " 'global_step': 2000,\n", + " 'label/mean': 0.44117647,\n", + " 'loss': 0.49404,\n", + " 'precision': 0.87096775,\n", + " 'prediction/mean': 0.37467176,\n", + " 'recall': 0.6}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gbdt_classifier.evaluate(test_input_fn)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### GBDT Regressor" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Build the input function.\n", + "train_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(\n", + " x={'x': x_train}, y=y_train,\n", + " batch_size=batch_size, num_epochs=None, shuffle=True)\n", + "test_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(\n", + " x={'x': x_test}, y=y_test,\n", + " batch_size=batch_size, num_epochs=1, shuffle=False)\n", + "# GBDT Models from TF Estimator requires 'feature_column' data format.\n", + "feature_columns = [tf.feature_column.numeric_column(key='x', shape=(num_features,))]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Using default config.\n", + "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpts3Kmu\n", + "INFO:tensorflow:Using config: {'_save_checkpoints_secs': 600, '_num_ps_replicas': 0, '_keep_checkpoint_max': 5, '_task_type': 'worker', '_global_id_in_cluster': 0, '_is_chief': True, '_cluster_spec': ClusterSpec({}), '_model_dir': '/tmp/tmpts3Kmu', '_protocol': None, '_save_checkpoints_steps': None, '_keep_checkpoint_every_n_hours': 10000, '_service': None, '_session_config': allow_soft_placement: true\n", + "graph_options {\n", + " rewrite_options {\n", + " meta_optimizer_iterations: ONE\n", + " }\n", + "}\n", + ", '_tf_random_seed': None, '_save_summary_steps': 100, '_device_fn': None, '_session_creation_timeout_secs': 7200, '_experimental_distribute': None, '_num_worker_replicas': 1, '_task_id': 0, '_log_step_count_steps': 100, '_experimental_max_worker_delay_secs': None, '_evaluation_master': '', '_eval_distribute': None, '_train_distribute': None, '_master': ''}\n" + ] + } + ], + "source": [ + "gbdt_regressor = tf.estimator.BoostedTreesRegressor(\n", + " n_batches_per_layer=num_batches_per_layer,\n", + " feature_columns=feature_columns, \n", + " learning_rate=learning_rate, \n", + " n_trees=num_trees,\n", + " max_depth=max_depth,\n", + " l1_regularization=l1_regul, \n", + " l2_regularization=l2_regul\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": false + }, + "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", + "WARNING:tensorflow:Issue encountered when serializing resources.\n", + "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", + "'_Resource' object has no attribute 'name'\n", + "INFO:tensorflow:Graph was finalized.\n", + "INFO:tensorflow:Running local_init_op.\n", + "INFO:tensorflow:Done running local_init_op.\n", + "WARNING:tensorflow:Issue encountered when serializing resources.\n", + "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", + "'_Resource' object has no attribute 'name'\n", + "INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmpts3Kmu/model.ckpt.\n", + "WARNING:tensorflow:Issue encountered when serializing resources.\n", + "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", + "'_Resource' object has no attribute 'name'\n", + "INFO:tensorflow:loss = 584.82294, step = 0\n", + "INFO:tensorflow:loss = 560.2794, step = 0 (0.369 sec)\n", + "INFO:tensorflow:loss = 606.68115, step = 0 (0.156 sec)\n", + "INFO:tensorflow:loss = 583.2771, step = 0 (0.155 sec)\n", + "INFO:tensorflow:loss = 603.4647, step = 0 (0.160 sec)\n", + "INFO:tensorflow:loss = 605.8213, step = 0 (0.153 sec)\n", + "INFO:tensorflow:loss = 577.5599, step = 0 (0.157 sec)\n", + "INFO:tensorflow:loss = 585.297, step = 0 (0.157 sec)\n", + "INFO:tensorflow:loss = 545.26074, step = 0 (0.156 sec)\n", + "INFO:tensorflow:loss = 597.91046, step = 0 (0.190 sec)\n", + "INFO:tensorflow:loss = 600.55396, step = 0 (0.174 sec)\n", + "INFO:tensorflow:global_step/sec: 47.5449\n", + "INFO:tensorflow:loss = 539.62646, step = 100 (0.280 sec)\n", + "INFO:tensorflow:global_step/sec: 592.267\n", + "INFO:tensorflow:loss = 573.9592, step = 200 (0.169 sec)\n", + "INFO:tensorflow:global_step/sec: 573.943\n", + "INFO:tensorflow:loss = 617.79407, step = 300 (0.175 sec)\n", + "INFO:tensorflow:global_step/sec: 583.88\n", + "INFO:tensorflow:loss = 593.62915, step = 400 (0.171 sec)\n", + "INFO:tensorflow:global_step/sec: 595.888\n", + "INFO:tensorflow:loss = 594.5435, step = 500 (0.168 sec)\n", + "INFO:tensorflow:global_step/sec: 610.997\n", + "INFO:tensorflow:loss = 579.5427, step = 600 (0.163 sec)\n", + "INFO:tensorflow:global_step/sec: 625.07\n", + "INFO:tensorflow:loss = 555.19604, step = 700 (0.160 sec)\n", + "INFO:tensorflow:global_step/sec: 674.427\n", + "INFO:tensorflow:loss = 585.61127, step = 800 (0.149 sec)\n", + "INFO:tensorflow:global_step/sec: 652.597\n", + "INFO:tensorflow:loss = 645.147, step = 900 (0.153 sec)\n", + "INFO:tensorflow:global_step/sec: 656.608\n", + "INFO:tensorflow:loss = 65.438034, step = 1000 (0.152 sec)\n", + "INFO:tensorflow:global_step/sec: 660.171\n", + "INFO:tensorflow:loss = 57.25811, step = 1100 (0.151 sec)\n", + "INFO:tensorflow:global_step/sec: 676.676\n", + "INFO:tensorflow:loss = 70.39737, step = 1200 (0.148 sec)\n", + "INFO:tensorflow:global_step/sec: 664.916\n", + "INFO:tensorflow:loss = 63.969463, step = 1300 (0.150 sec)\n", + "INFO:tensorflow:global_step/sec: 679.204\n", + "INFO:tensorflow:loss = 55.910896, step = 1400 (0.147 sec)\n", + "INFO:tensorflow:global_step/sec: 680.936\n", + "INFO:tensorflow:loss = 58.16027, step = 1500 (0.147 sec)\n", + "INFO:tensorflow:global_step/sec: 670.412\n", + "INFO:tensorflow:loss = 66.20054, step = 1600 (0.149 sec)\n", + "INFO:tensorflow:global_step/sec: 673.441\n", + "INFO:tensorflow:loss = 52.643417, step = 1700 (0.149 sec)\n", + "INFO:tensorflow:global_step/sec: 684.782\n", + "INFO:tensorflow:loss = 59.981026, step = 1800 (0.145 sec)\n", + "INFO:tensorflow:global_step/sec: 684.191\n", + "INFO:tensorflow:loss = 65.427055, step = 1900 (0.146 sec)\n", + "INFO:tensorflow:global_step/sec: 683.812\n", + "INFO:tensorflow:Saving checkpoints for 2000 into /tmp/tmpts3Kmu/model.ckpt.\n", + "WARNING:tensorflow:Issue encountered when serializing resources.\n", + "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", + "'_Resource' object has no attribute 'name'\n", + "INFO:tensorflow:Loss for final step: 42.740192.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gbdt_regressor.train(train_input_fn, max_steps=max_steps)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Calling model_fn.\n", + "INFO:tensorflow:Done calling model_fn.\n", + "INFO:tensorflow:Starting evaluation at 2020-07-15T00:50:45Z\n", + "INFO:tensorflow:Graph was finalized.\n", + "INFO:tensorflow:Restoring parameters from /tmp/tmpts3Kmu/model.ckpt-2000\n", + "INFO:tensorflow:Running local_init_op.\n", + "INFO:tensorflow:Done running local_init_op.\n", + "INFO:tensorflow:Inference Time : 0.24467s\n", + "INFO:tensorflow:Finished evaluation at 2020-07-15-00:50:45\n", + "INFO:tensorflow:Saving dict for global step 2000: average_loss = 30.202602, global_step = 2000, label/mean = 23.078432, loss = 30.202602, prediction/mean = 22.536291\n", + "WARNING:tensorflow:Issue encountered when serializing resources.\n", + "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", + "'_Resource' object has no attribute 'name'\n", + "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 2000: /tmp/tmpts3Kmu/model.ckpt-2000\n" + ] + }, + { + "data": { + "text/plain": [ + "{'average_loss': 30.202602,\n", + " 'global_step': 2000,\n", + " 'label/mean': 23.078432,\n", + " 'loss': 30.202602,\n", + " 'prediction/mean': 22.536291}" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gbdt_regressor.evaluate(test_input_fn)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.18" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From ab1b58beb18400d19e7ce06408fc180e13d78bf5 Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Sun, 26 Jul 2020 12:25:16 -0700 Subject: [PATCH 14/24] Add tensorboard example (#381) * add tensorboard example * fix desc * add tensorboard run cmd --- README.md | 1 + resources/img/tf2/tensorboard1.png | Bin 0 -> 77368 bytes resources/img/tf2/tensorboard2.png | Bin 0 -> 167312 bytes resources/img/tf2/tensorboard3.png | Bin 0 -> 137359 bytes resources/img/tf2/tensorboard4.png | Bin 0 -> 221040 bytes tensorflow_v2/README.md | 1 + .../notebooks/4_Utils/tensorboard.ipynb | 350 ++++++++++++++++++ 7 files changed, 352 insertions(+) create mode 100644 resources/img/tf2/tensorboard1.png create mode 100644 resources/img/tf2/tensorboard2.png create mode 100644 resources/img/tf2/tensorboard3.png create mode 100644 resources/img/tf2/tensorboard4.png create mode 100644 tensorflow_v2/notebooks/4_Utils/tensorboard.ipynb diff --git a/README.md b/README.md index da394304..1bba79c4 100644 --- a/README.md +++ b/README.md @@ -40,6 +40,7 @@ It is suitable for beginners who want to find clear and concise examples about T #### 4 - Utilities - **Save and Restore a model** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/save_restore_model.ipynb)). Save and Restore a model with TensorFlow 2.0. - **Build Custom Layers & Modules** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/build_custom_layers.ipynb)). Learn how to build your own layers / modules and integrate them into TensorFlow 2.0 Models. +- **Tensorboard** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/tensorboard.ipynb)). Track and visualize neural network computation graph, metrics, weights and more using TensorFlow 2.0+ tensorboard. #### 5 - Data Management - **Load and Parse data** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/5_DataManagement/load_data.ipynb)). 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zsVnna|55=WIDWohKkmu|);;aPANO7B5I2aa+YffNDS>S5Mi`?pV5yEWu@jjahKWW} zm!YFSm#X^hMI?;PA!#Vl#oYti@~w)IKYriaoj-Tr+`|JSQt5L{)y z8!o_{fAtX&!SxY14u7}*-pOaE2Y$q|pVjPa{3F=Oa=B%y9b3+p#7`s?d?2&_r-}h| zi0a`K>uY>2CU5N(6kvkl7a?g85zW_=O#jO$Sa=e;i#PGeH|+l-DJ;LHo0I-=UHd{d=(5MS4&V z<_OzIshOFx4PINtn^*o`wT;XFn*7}V?O^}Ui~cYD>;Kix{{Qdf971vbHAP8g5()=2 NRYeVjGWo|Z{s+V*BrpH~ literal 0 HcmV?d00001 diff --git a/tensorflow_v2/README.md b/tensorflow_v2/README.md index b6cbea39..83d9ad0e 100644 --- a/tensorflow_v2/README.md +++ b/tensorflow_v2/README.md @@ -34,6 +34,7 @@ #### 4 - Utilities - **Save and Restore a model** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/save_restore_model.ipynb)). Save and Restore a model with TensorFlow 2.0. - **Build Custom Layers & Modules** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/build_custom_layers.ipynb)). Learn how to build your own layers / modules and integrate them into TensorFlow 2.0 Models. +- **Tensorboard** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/tensorboard.ipynb)). Track and visualize neural network computation graph, metrics, weights and more using TensorFlow 2.0+ tensorboard. #### 5 - Data Management - **Load and Parse data** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/5_DataManagement/load_data.ipynb)). Build efficient data pipeline with TensorFlow 2.0 (Numpy arrays, Images, CSV files, custom data, ...). diff --git a/tensorflow_v2/notebooks/4_Utils/tensorboard.ipynb b/tensorflow_v2/notebooks/4_Utils/tensorboard.ipynb new file mode 100644 index 00000000..b552d0e2 --- /dev/null +++ b/tensorflow_v2/notebooks/4_Utils/tensorboard.ipynb @@ -0,0 +1,350 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tensorboard\n", + "Graph, Loss, Accuracy & Weights visualization using Tensorboard and TensorFlow v2. This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/).\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", + "import tensorflow as tf\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Path to save logs into.\n", + "logs_path = '/tmp/tensorflow_logs/example/'\n", + "\n", + "# MNIST dataset parameters.\n", + "num_classes = 10 # total classes (0-9 digits).\n", + "num_features = 784 # data features (img shape: 28*28).\n", + "\n", + "# Training parameters.\n", + "learning_rate = 0.001\n", + "training_steps = 3000\n", + "batch_size = 256\n", + "display_step = 100\n", + "\n", + "# Network parameters.\n", + "n_hidden_1 = 128 # 1st layer number of neurons.\n", + "n_hidden_2 = 256 # 2nd layer number of neurons." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Prepare MNIST data.\n", + "from tensorflow.keras.datasets import mnist\n", + "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n", + "# Convert to float32.\n", + "x_train, x_test = np.array(x_train, np.float32), np.array(x_test, np.float32)\n", + "# Flatten images to 1-D vector of 784 features (28*28).\n", + "x_train, x_test = x_train.reshape([-1, num_features]), x_test.reshape([-1, num_features])\n", + "# Normalize images value from [0, 255] to [0, 1].\n", + "x_train, x_test = x_train / 255., x_test / 255." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Use tf.data API to shuffle and batch data.\n", + "train_data = tf.data.Dataset.from_tensor_slices((x_train, y_train))\n", + "train_data = train_data.repeat().shuffle(5000).batch(batch_size).prefetch(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Store layers weight & bias\n", + "\n", + "# A random value generator to initialize weights.\n", + "random_normal = tf.initializers.RandomNormal()\n", + "\n", + "weights = {\n", + " 'h1_weights': tf.Variable(random_normal([num_features, n_hidden_1]), name='h1_weights'),\n", + " 'h2_weights': tf.Variable(random_normal([n_hidden_1, n_hidden_2]), name='h2_weights'),\n", + " 'logits_weights': tf.Variable(random_normal([n_hidden_2, num_classes]), name='logits_weights')\n", + "}\n", + "biases = {\n", + " 'h1_bias': tf.Variable(tf.zeros([n_hidden_1]), name='h1_bias'),\n", + " 'h2_bias': tf.Variable(tf.zeros([n_hidden_2]), name='h2_bias'),\n", + " 'logits_bias': tf.Variable(tf.zeros([num_classes]), name='logits_bias')\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Construct model and encapsulating all ops into scopes, making\n", + "# Tensorboard's Graph visualization more convenient.\n", + "\n", + "# The computation graph to be traced.\n", + "@tf.function\n", + "def neural_net(x):\n", + " with tf.name_scope('Model'):\n", + " with tf.name_scope('HiddenLayer1'):\n", + " # Hidden fully connected layer with 128 neurons.\n", + " layer_1 = tf.add(tf.matmul(x, weights['h1_weights']), biases['h1_bias'])\n", + " # Apply sigmoid to layer_1 output for non-linearity.\n", + " layer_1 = tf.nn.sigmoid(layer_1)\n", + " with tf.name_scope('HiddenLayer2'):\n", + " # Hidden fully connected layer with 256 neurons.\n", + " layer_2 = tf.add(tf.matmul(layer_1, weights['h2_weights']), biases['h2_bias'])\n", + " # Apply sigmoid to layer_2 output for non-linearity.\n", + " layer_2 = tf.nn.sigmoid(layer_2)\n", + " with tf.name_scope('LogitsLayer'):\n", + " # Output fully connected layer with a neuron for each class.\n", + " out_layer = tf.matmul(layer_2, weights['logits_weights']) + biases['logits_bias']\n", + " # Apply softmax to normalize the logits to a probability distribution.\n", + " out_layer = tf.nn.softmax(out_layer)\n", + " return out_layer" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Cross-Entropy loss function.\n", + "def cross_entropy(y_pred, y_true):\n", + " with tf.name_scope('CrossEntropyLoss'):\n", + " # Encode label to a one hot vector.\n", + " y_true = tf.one_hot(y_true, depth=num_classes)\n", + " # Clip prediction values to avoid log(0) error.\n", + " y_pred = tf.clip_by_value(y_pred, 1e-9, 1.)\n", + " # Compute cross-entropy.\n", + " return tf.reduce_mean(-tf.reduce_sum(y_true * tf.math.log(y_pred)))\n", + "\n", + "# Accuracy metric.\n", + "def accuracy(y_pred, y_true):\n", + " with tf.name_scope('Accuracy'):\n", + " # Predicted class is the index of highest score in prediction vector (i.e. argmax).\n", + " correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.cast(y_true, tf.int64))\n", + " return tf.reduce_mean(tf.cast(correct_prediction, tf.float32), axis=-1)\n", + "\n", + "# Stochastic gradient descent optimizer.\n", + "with tf.name_scope('Optimizer'):\n", + " optimizer = tf.optimizers.SGD(learning_rate)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Optimization process. \n", + "def run_optimization(x, y):\n", + " # Wrap computation inside a GradientTape for automatic differentiation.\n", + " with tf.GradientTape() as g:\n", + " pred = neural_net(x)\n", + " loss = cross_entropy(pred, y)\n", + " \n", + " # Variables to update, i.e. trainable variables.\n", + " trainable_variables = weights.values() + biases.values()\n", + "\n", + " # Compute gradients.\n", + " gradients = g.gradient(loss, trainable_variables)\n", + " \n", + " # Update weights/biases following gradients.\n", + " optimizer.apply_gradients(zip(gradients, trainable_variables))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Visualize weights & biases as histogram in Tensorboard.\n", + "def summarize_weights(step):\n", + " for w in weights:\n", + " tf.summary.histogram(w.replace('_', '/'), weights[w], step=step)\n", + " for b in biases:\n", + " tf.summary.histogram(b.replace('_', '/'), biases[b], step=step)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a Summary Writer to log the metrics to Tensorboad.\n", + "summary_writer = tf.summary.create_file_writer(logs_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step: 100, loss: 568.735596, accuracy: 0.140625\n", + "step: 200, loss: 413.169342, accuracy: 0.535156\n", + "step: 300, loss: 250.977036, accuracy: 0.714844\n", + "step: 400, loss: 173.749298, accuracy: 0.800781\n", + "step: 500, loss: 156.936569, accuracy: 0.839844\n", + "step: 600, loss: 137.818451, accuracy: 0.847656\n", + "step: 700, loss: 93.407814, accuracy: 0.929688\n", + "step: 800, loss: 90.832336, accuracy: 0.906250\n", + "step: 900, loss: 86.932831, accuracy: 0.914062\n", + "step: 1000, loss: 78.824707, accuracy: 0.906250\n", + "step: 1100, loss: 94.388290, accuracy: 0.902344\n", + "step: 1200, loss: 96.240608, accuracy: 0.894531\n", + "step: 1300, loss: 96.657593, accuracy: 0.898438\n", + "step: 1400, loss: 71.909309, accuracy: 0.914062\n", + "step: 1500, loss: 67.343407, accuracy: 0.941406\n", + "step: 1600, loss: 63.693596, accuracy: 0.941406\n", + "step: 1700, loss: 60.081478, accuracy: 0.914062\n", + "step: 1800, loss: 63.764942, accuracy: 0.921875\n", + "step: 1900, loss: 58.722507, accuracy: 0.921875\n", + "step: 2000, loss: 66.727455, accuracy: 0.917969\n", + "step: 2100, loss: 70.566788, accuracy: 0.949219\n", + "step: 2200, loss: 64.642334, accuracy: 0.925781\n", + "step: 2300, loss: 54.872856, accuracy: 0.941406\n", + "step: 2400, loss: 64.342377, accuracy: 0.925781\n", + "step: 2500, loss: 74.306488, accuracy: 0.921875\n", + "step: 2600, loss: 40.165890, accuracy: 0.949219\n", + "step: 2700, loss: 64.992249, accuracy: 0.925781\n", + "step: 2800, loss: 43.422794, accuracy: 0.957031\n", + "step: 2900, loss: 46.625320, accuracy: 0.937500\n", + "step: 3000, loss: 62.517433, accuracy: 0.914062\n" + ] + } + ], + "source": [ + "# Run training for the given number of steps.\n", + "for step, (batch_x, batch_y) in enumerate(train_data.take(training_steps), 1):\n", + " \n", + " # Start to trace the computation graph. The computation graph remains \n", + " # the same at each step, so we just need to export it once.\n", + " if step == 1:\n", + " tf.summary.trace_on(graph=True, profiler=True)\n", + " \n", + " # Run the optimization (computation graph).\n", + " run_optimization(batch_x, batch_y)\n", + " \n", + " # Export the computation graph to tensorboard after the first\n", + " # computation step was performed.\n", + " if step == 1:\n", + " with summary_writer.as_default():\n", + " tf.summary.trace_export(\n", + " name=\"trace\",\n", + " step=0,\n", + " profiler_outdir=logs_path)\n", + "\n", + " if step % display_step == 0:\n", + " pred = neural_net(batch_x)\n", + " loss = cross_entropy(pred, batch_y)\n", + " acc = accuracy(pred, batch_y)\n", + " print(\"step: %i, loss: %f, accuracy: %f\" % (step, loss, acc))\n", + " \n", + " # Write loss/acc metrics & weights to Tensorboard every few steps, \n", + " # to avoid storing too much data.\n", + " with summary_writer.as_default():\n", + " tf.summary.scalar('loss', loss, step=step)\n", + " tf.summary.scalar('accuracy', acc, step=step)\n", + " summarize_weights(step)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run Tensorboard\n", + "\n", + "To run tensorboard, run the following command in your terminal:\n", + "```\n", + "tensorboard --logdir=/tmp/tensorflow_logs\n", + "```\n", + "\n", + "And then connect your web browser to: [http://localhost:6006](http://localhost:6006)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![tensorboard1](../../../resources/img/tf2/tensorboard1.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![tensorboard2](../../../resources/img/tf2/tensorboard2.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![tensorboard3](../../../resources/img/tf2/tensorboard3.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![tensorboard4](../../../resources/img/tf2/tensorboard4.png)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.18" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From e3414d654d40b46549eda95245b264532ef2093c Mon Sep 17 00:00:00 2001 From: aymericdamien Date: Sun, 26 Jul 2020 12:29:43 -0700 Subject: [PATCH 15/24] fix ml intro --- .../0_Prerequisite/mnist_dataset_intro.ipynb | 16 +++++----------- 1 file changed, 5 insertions(+), 11 deletions(-) diff --git a/tensorflow_v2/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb b/tensorflow_v2/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb index f1813c85..74f8a91f 100644 --- a/tensorflow_v2/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb +++ b/tensorflow_v2/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb @@ -1,10 +1,8 @@ { "cells": [ { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ "\n", "# MNIST Dataset Introduction\n", @@ -27,12 +25,10 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": { "collapsed": true }, - "outputs": [], "source": [ "# Import MNIST\n", "from tensorflow.examples.tutorials.mnist import input_data\n", @@ -53,12 +49,10 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": { "collapsed": true }, - "outputs": [], "source": [ "# Get the next 64 images array and labels\n", "batch_X, batch_Y = mnist.train.next_batch(64)" @@ -88,9 +82,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.13" + "version": "2.7.18" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } From a1516d2303f31942b4cff615e7c39f1b548157b4 Mon Sep 17 00:00:00 2001 From: aymericdamien Date: Sun, 26 Jul 2020 12:30:48 -0700 Subject: [PATCH 16/24] fix ml intro --- .../0_Prerequisite/mnist_dataset_intro.ipynb | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/tensorflow_v2/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb b/tensorflow_v2/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb index 74f8a91f..93c9e79e 100644 --- a/tensorflow_v2/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb +++ b/tensorflow_v2/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb @@ -25,10 +25,10 @@ ] }, { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Import MNIST\n", "from tensorflow.examples.tutorials.mnist import input_data\n", @@ -49,10 +49,10 @@ ] }, { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Get the next 64 images array and labels\n", "batch_X, batch_Y = mnist.train.next_batch(64)" From 6b11799028f90f43c6591f9996ca853b7027a80c Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Sun, 26 Jul 2020 12:31:39 -0700 Subject: [PATCH 17/24] Fix ML intro notebook (#382) * fix ml intro * fix ml intro --- .../0_Prerequisite/mnist_dataset_intro.ipynb | 16 +++++----------- 1 file changed, 5 insertions(+), 11 deletions(-) diff --git a/tensorflow_v2/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb b/tensorflow_v2/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb index f1813c85..93c9e79e 100644 --- a/tensorflow_v2/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb +++ b/tensorflow_v2/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb @@ -1,10 +1,8 @@ { "cells": [ { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ "\n", "# MNIST Dataset Introduction\n", @@ -29,9 +27,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# Import MNIST\n", @@ -55,9 +51,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# Get the next 64 images array and labels\n", @@ -88,9 +82,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.13" + "version": "2.7.18" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } From fedf9e88b0fbabf6244f8811b88bfae9c9a754fb Mon Sep 17 00:00:00 2001 From: LCB0B Date: Sun, 26 Jul 2020 21:33:49 +0200 Subject: [PATCH 18/24] Update bidirectional_rnn.ipynb (#380) Replace broken link for Sepp Hochreiter & Jurgen Schmidhuber's LSTM document. --- notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb b/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb index 2435b229..9595cc50 100644 --- a/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb +++ b/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb @@ -23,7 +23,7 @@ "\"nn\"\n", "\n", "References:\n", - "- [Long Short Term Memory](http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf), Sepp Hochreiter & Jurgen Schmidhuber, Neural Computation 9(8): 1735-1780, 1997.\n", + "- [Long Short Term Memory](https://www.researchgate.net/profile/Sepp_Hochreiter/publication/13853244_Long_Short-term_Memory/links/5700e75608aea6b7746a0624/Long-Short-term-Memory.pdf), Sepp Hochreiter & Jurgen Schmidhuber, Neural Computation 9(8): 1735-1780, 1997.\n", "\n", "## MNIST Dataset Overview\n", "\n", From 26c4c7047095139f25a6ca380e9cd4d028b16460 Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Sat, 19 Sep 2020 00:53:22 -0700 Subject: [PATCH 19/24] MultiGPU Training Example (#387) * fix ml intro * fix ml intro * add multi gpu example * add multi gpu example --- README.md | 26 +- tensorflow_v2/README.md | 3 + .../6_Hardware/multigpu_training.ipynb | 371 ++++++++++++++++++ 3 files changed, 388 insertions(+), 12 deletions(-) create mode 100644 tensorflow_v2/notebooks/6_Hardware/multigpu_training.ipynb diff --git a/README.md b/README.md index 1bba79c4..e7de7049 100644 --- a/README.md +++ b/README.md @@ -13,13 +13,13 @@ It is suitable for beginners who want to find clear and concise examples about T - [Introduction to MNIST Dataset](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb). #### 1 - Introduction -- **Hello World** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/1_Introduction/helloworld.ipynb)). Very simple example to learn how to print "hello world" using TensorFlow 2.0. -- **Basic Operations** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/1_Introduction/basic_operations.ipynb)). A simple example that cover TensorFlow 2.0 basic operations. +- **Hello World** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/1_Introduction/helloworld.ipynb)). Very simple example to learn how to print "hello world" using TensorFlow 2.0+. +- **Basic Operations** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/1_Introduction/basic_operations.ipynb)). A simple example that cover TensorFlow 2.0+ basic operations. #### 2 - Basic Models -- **Linear Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/linear_regression.ipynb)). Implement a Linear Regression with TensorFlow 2.0. -- **Logistic Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/logistic_regression.ipynb)). Implement a Logistic Regression with TensorFlow 2.0. -- **Word2Vec (Word Embedding)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/word2vec.ipynb)). Build a Word Embedding Model (Word2Vec) from Wikipedia data, with TensorFlow 2.0. +- **Linear Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/linear_regression.ipynb)). Implement a Linear Regression with TensorFlow 2.0+. +- **Logistic Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/logistic_regression.ipynb)). Implement a Logistic Regression with TensorFlow 2.0+. +- **Word2Vec (Word Embedding)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/word2vec.ipynb)). Build a Word Embedding Model (Word2Vec) from Wikipedia data, with TensorFlow 2.0+. - **GBDT (Gradient Boosted Decision Trees)** ([notebooks](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/gradient_boosted_trees.ipynb)). Implement a Gradient Boosted Decision Trees with TensorFlow 2.0+ to predict house value using Boston Housing dataset. #### 3 - Neural Networks @@ -27,26 +27,28 @@ It is suitable for beginners who want to find clear and concise examples about T - **Simple Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb)). Use TensorFlow 2.0 'layers' and 'model' API to build a simple neural network to classify MNIST digits dataset. - **Simple Neural Network (low-level)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network_raw.ipynb)). Raw implementation of a simple neural network to classify MNIST digits dataset. -- **Convolutional Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network.ipynb)). Use TensorFlow 2.0 'layers' and 'model' API to build a convolutional neural network to classify MNIST digits dataset. +- **Convolutional Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network.ipynb)). Use TensorFlow 2.0+ 'layers' and 'model' API to build a convolutional neural network to classify MNIST digits dataset. - **Convolutional Neural Network (low-level)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb)). Raw implementation of a convolutional neural network to classify MNIST digits dataset. - **Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/recurrent_network.ipynb)). Build a recurrent neural network (LSTM) to classify MNIST digits dataset, using TensorFlow 2.0 'layers' and 'model' API. -- **Bi-directional Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb)). Build a bi-directional recurrent neural network (LSTM) to classify MNIST digits dataset, using TensorFlow 2.0 'layers' and 'model' API. -- **Dynamic Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/dynamic_rnn.ipynb)). Build a recurrent neural network (LSTM) that performs dynamic calculation to classify sequences of variable length, using TensorFlow 2.0 'layers' and 'model' API. +- **Bi-directional Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb)). Build a bi-directional recurrent neural network (LSTM) to classify MNIST digits dataset, using TensorFlow 2.0+ 'layers' and 'model' API. +- **Dynamic Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/dynamic_rnn.ipynb)). Build a recurrent neural network (LSTM) that performs dynamic calculation to classify sequences of variable length, using TensorFlow 2.0+ 'layers' and 'model' API. ##### Unsupervised - **Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/autoencoder.ipynb)). Build an auto-encoder to encode an image to a lower dimension and re-construct it. - **DCGAN (Deep Convolutional Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/dcgan.ipynb)). Build a Deep Convolutional Generative Adversarial Network (DCGAN) to generate images from noise. #### 4 - Utilities -- **Save and Restore a model** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/save_restore_model.ipynb)). Save and Restore a model with TensorFlow 2.0. -- **Build Custom Layers & Modules** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/build_custom_layers.ipynb)). Learn how to build your own layers / modules and integrate them into TensorFlow 2.0 Models. +- **Save and Restore a model** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/save_restore_model.ipynb)). Save and Restore a model with TensorFlow 2.0+. +- **Build Custom Layers & Modules** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/build_custom_layers.ipynb)). Learn how to build your own layers / modules and integrate them into TensorFlow 2.0+ Models. - **Tensorboard** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/tensorboard.ipynb)). Track and visualize neural network computation graph, metrics, weights and more using TensorFlow 2.0+ tensorboard. #### 5 - Data Management - **Load and Parse data** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/5_DataManagement/load_data.ipynb)). Build efficient data pipeline with TensorFlow 2.0 (Numpy arrays, Images, CSV files, custom data, ...). -- **Build and Load TFRecords** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/5_DataManagement/tfrecords.ipynb)). Convert data into TFRecords format, and load them with TensorFlow 2.0. -- **Image Transformation (i.e. Image Augmentation)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/5_DataManagement/image_transformation.ipynb)). Apply various image augmentation techniques with TensorFlow 2.0, to generate distorted images for training. +- **Build and Load TFRecords** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/5_DataManagement/tfrecords.ipynb)). Convert data into TFRecords format, and load them with TensorFlow 2.0+. +- **Image Transformation (i.e. Image Augmentation)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/5_DataManagement/image_transformation.ipynb)). Apply various image augmentation techniques with TensorFlow 2.0+, to generate distorted images for training. +#### 6 - Hardware + **Multi-GPU Training** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/6_Hardware/multigpu_training.ipynb)). Train a convolutional neural network with multiple GPUs on CIFAR-10 dataset. ## TensorFlow v1 diff --git a/tensorflow_v2/README.md b/tensorflow_v2/README.md index 83d9ad0e..ffccd7e5 100644 --- a/tensorflow_v2/README.md +++ b/tensorflow_v2/README.md @@ -41,6 +41,9 @@ - **Build and Load TFRecords** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/5_DataManagement/tfrecords.ipynb)). Convert data into TFRecords format, and load them with TensorFlow 2.0. - **Image Transformation (i.e. Image Augmentation)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/5_DataManagement/image_transformation.ipynb)). Apply various image augmentation techniques with TensorFlow 2.0, to generate distorted images for training. +#### 6 - Hardware + **Multi-GPU Training** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/6_Hardware/multigpu_training.ipynb)). Train a convolutional neural network with multiple GPUs on CIFAR-10 dataset. + ## Installation To install TensorFlow 2.0, simply run: diff --git a/tensorflow_v2/notebooks/6_Hardware/multigpu_training.ipynb b/tensorflow_v2/notebooks/6_Hardware/multigpu_training.ipynb new file mode 100644 index 00000000..46b07000 --- /dev/null +++ b/tensorflow_v2/notebooks/6_Hardware/multigpu_training.ipynb @@ -0,0 +1,371 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multi-GPU Training Example\n", + "\n", + "Train a convolutional neural network on multiple GPU with TensorFlow 2.0+.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training with multiple GPU cards\n", + "\n", + "In this example, we are using data parallelism to split the training accross multiple GPUs. Each GPU has a full replica of the neural network model, and the weights (i.e. variables) are updated synchronously by waiting that each GPU process its batch of data.\n", + "\n", + "First, each GPU process a distinct batch of data and compute the corresponding gradients, then, all gradients are accumulated in the CPU and averaged. The model weights are finally updated with the gradients averaged, and the new model weights are sent back to each GPU, to repeat the training process.\n", + "\n", + "\"Parallelism\"\n", + "\n", + "## CIFAR10 Dataset Overview\n", + "\n", + "The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.\n", + "\n", + "![CIFAR10 Dataset](https://storage.googleapis.com/kaggle-competitions/kaggle/3649/media/cifar-10.png)\n", + "\n", + "More info: https://www.cs.toronto.edu/~kriz/cifar.html" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", + "import tensorflow as tf\n", + "from tensorflow.keras import Model, layers\n", + "import time\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# MNIST dataset parameters.\n", + "num_classes = 10 # total classes (0-9 digits).\n", + "num_gpus = 4\n", + "\n", + "# Training parameters.\n", + "learning_rate = 0.001\n", + "training_steps = 1000\n", + "# Split batch size equally between GPUs.\n", + "# Note: Reduce batch size if you encounter OOM Errors.\n", + "batch_size = 1024 * num_gpus\n", + "display_step = 20\n", + "\n", + "# Network parameters.\n", + "conv1_filters = 64 # number of filters for 1st conv layer.\n", + "conv2_filters = 128 # number of filters for 2nd conv layer.\n", + "conv3_filters = 256 # number of filters for 2nd conv layer.\n", + "fc1_units = 2048 # number of neurons for 1st fully-connected layer." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Prepare MNIST data.\n", + "from tensorflow.keras.datasets import cifar10\n", + "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n", + "# Convert to float32.\n", + "x_train, x_test = np.array(x_train, np.float32), np.array(x_test, np.float32)\n", + "# Normalize images value from [0, 255] to [0, 1].\n", + "x_train, x_test = x_train / 255., x_test / 255.\n", + "y_train, y_test = np.reshape(y_train, (-1)), np.reshape(y_test, (-1))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Use tf.data API to shuffle and batch data.\n", + "train_data = tf.data.Dataset.from_tensor_slices((x_train, y_train))\n", + "train_data = train_data.repeat().shuffle(batch_size * 10).batch(batch_size).prefetch(num_gpus)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "class ConvNet(Model):\n", + " # Set layers.\n", + " def __init__(self):\n", + " super(ConvNet, self).__init__()\n", + " \n", + " # Convolution Layer with 64 filters and a kernel size of 3.\n", + " self.conv1_1 = layers.Conv2D(conv1_filters, kernel_size=3, padding='SAME', activation=tf.nn.relu)\n", + " self.conv1_2 = layers.Conv2D(conv1_filters, kernel_size=3, padding='SAME', activation=tf.nn.relu)\n", + " # Max Pooling (down-sampling) with kernel size of 2 and strides of 2. \n", + " self.maxpool1 = layers.MaxPool2D(2, strides=2)\n", + "\n", + " # Convolution Layer with 128 filters and a kernel size of 3.\n", + " self.conv2_1 = layers.Conv2D(conv2_filters, kernel_size=3, padding='SAME', activation=tf.nn.relu)\n", + " self.conv2_2 = layers.Conv2D(conv2_filters, kernel_size=3, padding='SAME', activation=tf.nn.relu)\n", + " self.conv2_3 = layers.Conv2D(conv2_filters, kernel_size=3, padding='SAME', activation=tf.nn.relu)\n", + " # Max Pooling (down-sampling) with kernel size of 2 and strides of 2. \n", + " self.maxpool2 = layers.MaxPool2D(2, strides=2)\n", + "\n", + " # Convolution Layer with 256 filters and a kernel size of 3.\n", + " self.conv3_1 = layers.Conv2D(conv3_filters, kernel_size=3, padding='SAME', activation=tf.nn.relu)\n", + " self.conv3_2 = layers.Conv2D(conv3_filters, kernel_size=3, padding='SAME', activation=tf.nn.relu)\n", + " self.conv3_3 = layers.Conv2D(conv3_filters, kernel_size=3, padding='SAME', activation=tf.nn.relu)\n", + "\n", + " # Flatten the data to a 1-D vector for the fully connected layer.\n", + " self.flatten = layers.Flatten()\n", + "\n", + " # Fully connected layer.\n", + " self.fc1 = layers.Dense(1024, activation=tf.nn.relu)\n", + " # Apply Dropout (if is_training is False, dropout is not applied).\n", + " self.dropout = layers.Dropout(rate=0.5)\n", + "\n", + " # Output layer, class prediction.\n", + " self.out = layers.Dense(num_classes)\n", + "\n", + " # Set forward pass.\n", + " @tf.function\n", + " def call(self, x, is_training=False):\n", + " x = self.conv1_1(x)\n", + " x = self.conv1_2(x)\n", + " x = self.maxpool1(x)\n", + " x = self.conv2_1(x)\n", + " x = self.conv2_2(x)\n", + " x = self.conv2_3(x)\n", + " x = self.maxpool2(x)\n", + " x = self.conv3_1(x)\n", + " x = self.conv3_2(x)\n", + " x = self.conv3_3(x)\n", + " x = self.flatten(x)\n", + " x = self.fc1(x)\n", + " x = self.dropout(x, training=is_training)\n", + " x = self.out(x)\n", + " if not is_training:\n", + " # tf cross entropy expect logits without softmax, so only\n", + " # apply softmax when not training.\n", + " x = tf.nn.softmax(x)\n", + " return x" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Cross-Entropy Loss.\n", + "# Note that this will apply 'softmax' to the logits.\n", + "@tf.function\n", + "def cross_entropy_loss(x, y):\n", + " # Convert labels to int 64 for tf cross-entropy function.\n", + " y = tf.cast(y, tf.int64)\n", + " # Apply softmax to logits and compute cross-entropy.\n", + " loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=x)\n", + " # Average loss across the batch.\n", + " return tf.reduce_mean(loss)\n", + "\n", + "# Accuracy metric.\n", + "@tf.function\n", + "def accuracy(y_pred, y_true):\n", + " # Predicted class is the index of highest score in prediction vector (i.e. argmax).\n", + " correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.cast(y_true, tf.int64))\n", + " return tf.reduce_mean(tf.cast(correct_prediction, tf.float32), axis=-1)\n", + " \n", + "\n", + "@tf.function\n", + "def backprop(batch_x, batch_y, trainable_variables):\n", + " # Wrap computation inside a GradientTape for automatic differentiation.\n", + " with tf.GradientTape() as g:\n", + " # Forward pass.\n", + " pred = conv_net(batch_x, is_training=True)\n", + " # Compute loss.\n", + " loss = cross_entropy_loss(pred, batch_y)\n", + " # Compute gradients.\n", + " gradients = g.gradient(loss, trainable_variables)\n", + " return gradients\n", + "\n", + "# Build the function to average the gradients.\n", + "@tf.function\n", + "def average_gradients(tower_grads):\n", + " avg_grads = []\n", + " for tgrads in zip(*tower_grads):\n", + " grads = []\n", + " for g in tgrads:\n", + " expanded_g = tf.expand_dims(g, 0)\n", + " grads.append(expanded_g)\n", + " \n", + " grad = tf.concat(axis=0, values=grads)\n", + " grad = tf.reduce_mean(grad, 0)\n", + " \n", + " avg_grads.append(grad)\n", + " \n", + " return avg_grads" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "with tf.device('/cpu:0'):\n", + " # Build convnet.\n", + " conv_net = ConvNet()\n", + " # Stochastic gradient descent optimizer.\n", + " optimizer = tf.optimizers.Adam(learning_rate)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Optimization process.\n", + "def run_optimization(x, y):\n", + " # Save gradients for all GPUs.\n", + " tower_grads = []\n", + " # Variables to update, i.e. trainable variables.\n", + " trainable_variables = conv_net.trainable_variables\n", + "\n", + " with tf.device('/cpu:0'):\n", + " for i in range(num_gpus):\n", + " # Split data between GPUs.\n", + " gpu_batch_size = int(batch_size/num_gpus)\n", + " batch_x = x[i * gpu_batch_size: (i+1) * gpu_batch_size]\n", + " batch_y = y[i * gpu_batch_size: (i+1) * gpu_batch_size]\n", + " \n", + " # Build the neural net on each GPU.\n", + " with tf.device('/gpu:%i' % i):\n", + " grad = backprop(batch_x, batch_y, trainable_variables)\n", + " tower_grads.append(grad)\n", + " \n", + " # Last GPU Average gradients from all GPUs.\n", + " if i == num_gpus - 1:\n", + " gradients = average_gradients(tower_grads)\n", + "\n", + " # Update vars following gradients.\n", + " optimizer.apply_gradients(zip(gradients, trainable_variables))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step: 1, loss: 2.302630, accuracy: 0.101318, speed: 16342.138481 examples/sec\n", + "step: 20, loss: 2.296755, accuracy: 0.108398, speed: 5355.197204 examples/sec\n", + "step: 40, loss: 2.216037, accuracy: 0.299072, speed: 12388.080848 examples/sec\n", + "step: 60, loss: 2.189814, accuracy: 0.362305, speed: 12033.404638 examples/sec\n", + "step: 80, loss: 2.137831, accuracy: 0.410156, speed: 12189.852065 examples/sec\n", + "step: 100, loss: 2.102876, accuracy: 0.437744, speed: 12212.349483 examples/sec\n", + "step: 120, loss: 2.077521, accuracy: 0.460693, speed: 12160.290400 examples/sec\n", + "step: 140, loss: 2.006775, accuracy: 0.545166, speed: 12202.175380 examples/sec\n", + "step: 160, loss: 1.994143, accuracy: 0.554443, speed: 12168.070368 examples/sec\n", + "step: 180, loss: 1.964281, accuracy: 0.597412, speed: 12244.148312 examples/sec\n", + "step: 200, loss: 1.893395, accuracy: 0.658203, speed: 12197.382402 examples/sec\n", + "step: 220, loss: 1.880256, accuracy: 0.672363, speed: 12178.323620 examples/sec\n", + "step: 240, loss: 1.868853, accuracy: 0.676025, speed: 12224.851444 examples/sec\n", + "step: 260, loss: 1.837151, accuracy: 0.705322, speed: 12101.154436 examples/sec\n", + "step: 280, loss: 1.799418, accuracy: 0.736816, speed: 12185.701420 examples/sec\n", + "step: 300, loss: 1.790719, accuracy: 0.755615, speed: 12126.826668 examples/sec\n", + "step: 320, loss: 1.732242, accuracy: 0.807861, speed: 12229.926783 examples/sec\n", + "step: 340, loss: 1.732089, accuracy: 0.806885, speed: 12167.651100 examples/sec\n", + "step: 360, loss: 1.693968, accuracy: 0.835693, speed: 12060.687471 examples/sec\n", + "step: 380, loss: 1.665804, accuracy: 0.862305, speed: 12130.389108 examples/sec\n", + "step: 400, loss: 1.627162, accuracy: 0.890381, speed: 12152.946766 examples/sec\n", + "step: 420, loss: 1.594189, accuracy: 0.920654, speed: 12057.401941 examples/sec\n", + "step: 440, loss: 1.575212, accuracy: 0.929688, speed: 12196.589206 examples/sec\n", + "step: 460, loss: 1.569351, accuracy: 0.942383, speed: 12147.345871 examples/sec\n", + "step: 480, loss: 1.520648, accuracy: 0.974609, speed: 11998.473978 examples/sec\n", + "step: 500, loss: 1.507439, accuracy: 0.982666, speed: 12152.490287 examples/sec\n", + "step: 520, loss: 1.495090, accuracy: 0.989746, speed: 12071.718912 examples/sec\n", + "step: 540, loss: 1.490940, accuracy: 0.989502, speed: 12049.224039 examples/sec\n", + "step: 560, loss: 1.476727, accuracy: 0.996338, speed: 12134.827424 examples/sec\n", + "step: 580, loss: 1.475038, accuracy: 0.995850, speed: 12128.228532 examples/sec\n", + "step: 600, loss: 1.469776, accuracy: 0.997559, speed: 12113.386949 examples/sec\n", + "step: 620, loss: 1.466832, accuracy: 0.999756, speed: 11939.016031 examples/sec\n", + "step: 640, loss: 1.466991, accuracy: 0.999023, speed: 12095.815773 examples/sec\n", + "step: 660, loss: 1.466177, accuracy: 0.999023, speed: 12035.037908 examples/sec\n", + "step: 680, loss: 1.465074, accuracy: 0.999512, speed: 11789.118097 examples/sec\n", + "step: 700, loss: 1.464655, accuracy: 0.999512, speed: 11965.087437 examples/sec\n", + "step: 720, loss: 1.465109, accuracy: 0.999512, speed: 11855.853520 examples/sec\n", + "step: 740, loss: 1.465021, accuracy: 0.999023, speed: 11774.901096 examples/sec\n", + "step: 760, loss: 1.463057, accuracy: 1.000000, speed: 11930.138289 examples/sec\n", + "step: 780, loss: 1.462609, accuracy: 1.000000, speed: 11766.752011 examples/sec\n", + "step: 800, loss: 1.462320, accuracy: 0.999756, speed: 11744.213314 examples/sec\n", + "step: 820, loss: 1.462975, accuracy: 1.000000, speed: 11700.815885 examples/sec\n", + "step: 840, loss: 1.462328, accuracy: 1.000000, speed: 11759.141371 examples/sec\n", + "step: 860, loss: 1.462561, accuracy: 1.000000, speed: 11650.397252 examples/sec\n", + "step: 880, loss: 1.462608, accuracy: 0.999512, speed: 11581.170575 examples/sec\n", + "step: 900, loss: 1.462178, accuracy: 0.999756, speed: 11562.545711 examples/sec\n", + "step: 920, loss: 1.461582, accuracy: 1.000000, speed: 11616.172231 examples/sec\n", + "step: 940, loss: 1.462402, accuracy: 1.000000, speed: 11709.561795 examples/sec\n", + "step: 960, loss: 1.462436, accuracy: 1.000000, speed: 11629.547741 examples/sec\n", + "step: 980, loss: 1.462415, accuracy: 1.000000, speed: 11623.658645 examples/sec\n", + "step: 1000, loss: 1.461925, accuracy: 1.000000, speed: 11579.716701 examples/sec\n" + ] + } + ], + "source": [ + "# Run training for the given number of steps.\n", + "ts = time.time()\n", + "for step, (batch_x, batch_y) in enumerate(train_data.take(training_steps), 1):\n", + " # Run the optimization to update W and b values.\n", + " run_optimization(batch_x, batch_y)\n", + " \n", + " if step % display_step == 0 or step == 1:\n", + " dt = time.time() - ts\n", + " speed = batch_size * display_step / dt\n", + " pred = conv_net(batch_x)\n", + " loss = cross_entropy_loss(pred, batch_y)\n", + " acc = accuracy(pred, batch_y)\n", + " print(\"step: %i, loss: %f, accuracy: %f, speed: %f examples/sec\" % (step, loss, acc, speed))\n", + " ts = time.time()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.18" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 35de963a4d46cff5f11f269f6d14f9ac71a5e2e2 Mon Sep 17 00:00:00 2001 From: aymericdamien Date: Sat, 19 Sep 2020 00:55:12 -0700 Subject: [PATCH 20/24] fix multigpu typo --- README.md | 2 +- tensorflow_v2/README.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index e7de7049..09431ae2 100644 --- a/README.md +++ b/README.md @@ -48,7 +48,7 @@ It is suitable for beginners who want to find clear and concise examples about T - **Image Transformation (i.e. Image Augmentation)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/5_DataManagement/image_transformation.ipynb)). Apply various image augmentation techniques with TensorFlow 2.0+, to generate distorted images for training. #### 6 - Hardware - **Multi-GPU Training** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/6_Hardware/multigpu_training.ipynb)). Train a convolutional neural network with multiple GPUs on CIFAR-10 dataset. +- **Multi-GPU Training** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/6_Hardware/multigpu_training.ipynb)). Train a convolutional neural network with multiple GPUs on CIFAR-10 dataset. ## TensorFlow v1 diff --git a/tensorflow_v2/README.md b/tensorflow_v2/README.md index ffccd7e5..ef6785f1 100644 --- a/tensorflow_v2/README.md +++ b/tensorflow_v2/README.md @@ -42,7 +42,7 @@ - **Image Transformation (i.e. Image Augmentation)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/5_DataManagement/image_transformation.ipynb)). Apply various image augmentation techniques with TensorFlow 2.0, to generate distorted images for training. #### 6 - Hardware - **Multi-GPU Training** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/6_Hardware/multigpu_training.ipynb)). Train a convolutional neural network with multiple GPUs on CIFAR-10 dataset. +- **Multi-GPU Training** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/6_Hardware/multigpu_training.ipynb)). Train a convolutional neural network with multiple GPUs on CIFAR-10 dataset. ## Installation From fe8b8122f7362b5d75bfb725ccbed4a5f0072cca Mon Sep 17 00:00:00 2001 From: ShanksAndSS <45415847+ShanksAndSS@users.noreply.github.com> Date: Mon, 30 Nov 2020 12:16:51 +0800 Subject: [PATCH 21/24] gengxi (#392) * Update README.md * Update input_data.py Co-authored-by: Aymeric Damien --- input_data.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/input_data.py b/input_data.py index d1d0d28e..0ebcbdad 100644 --- a/input_data.py +++ b/input_data.py @@ -90,7 +90,7 @@ def num_examples(self): def epochs_completed(self): return self._epochs_completed def next_batch(self, batch_size, fake_data=False): - """Return the next `batch_size` examples from this data set.""" + """Return the next `batch_size`examples from this data set.""" if fake_data: fake_image = [1.0 for _ in xrange(784)] fake_label = 0 @@ -141,4 +141,4 @@ class DataSets(object): data_sets.train = DataSet(train_images, train_labels) data_sets.validation = DataSet(validation_images, validation_labels) data_sets.test = DataSet(test_images, test_labels) - return data_sets \ No newline at end of file + return data_sets From d85fb5c279ca1312e7adeaa1ba8722874bdc45bb Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Sat, 5 Dec 2020 02:49:21 -0800 Subject: [PATCH 22/24] Update README.md --- README.md | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/README.md b/README.md index 09431ae2..9c205400 100644 --- a/README.md +++ b/README.md @@ -137,3 +137,13 @@ The tutorial index for TF v1 is available here: [TensorFlow v1.15 Examples](tens #### 6 - Multi GPU - **Basic Operations on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/6_MultiGPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/6_MultiGPU/multigpu_basics.py)). A simple example to introduce multi-GPU in TensorFlow. - **Train a Neural Network on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/6_MultiGPU/multigpu_cnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/6_MultiGPU/multigpu_cnn.py)). A clear and simple TensorFlow implementation to train a convolutional neural network on multiple GPUs. + +## More Examples +The following examples are coming from [TFLearn](https://github.com/tflearn/tflearn), a library that provides a simplified interface for TensorFlow. You can have a look, there are many [examples](https://github.com/tflearn/tflearn/tree/master/examples) and [pre-built operations and layers](http://tflearn.org/doc_index/#api). + +### Tutorials +- [TFLearn Quickstart](https://github.com/tflearn/tflearn/blob/master/tutorials/intro/quickstart.md). Learn the basics of TFLearn through a concrete machine learning task. Build and train a deep neural network classifier. + +### Examples +- [TFLearn Examples](https://github.com/tflearn/tflearn/blob/master/examples). A large collection of examples using TFLearn. + From 29df154ef110a7999d9f639228c370612dda72c8 Mon Sep 17 00:00:00 2001 From: SAJITH NANDASENA <10287973+snandasena@users.noreply.github.com> Date: Tue, 29 Dec 2020 02:42:10 +0530 Subject: [PATCH 23/24] Updated run_optimization function and tested with Python3.8 (#393) Co-authored-by: sajith --- .../3_NeuralNetworks/neural_network_raw.ipynb | 111 ++++++++++-------- 1 file changed, 64 insertions(+), 47 deletions(-) diff --git a/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network_raw.ipynb b/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network_raw.ipynb index bbec2f13..2e1032ec 100644 --- a/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network_raw.ipynb +++ b/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network_raw.ipynb @@ -180,7 +180,7 @@ " loss = cross_entropy(pred, y)\n", " \n", " # Variables to update, i.e. trainable variables.\n", - " trainable_variables = weights.values() + biases.values()\n", + " trainable_variables = list(weights.values()) + list(biases.values())\n", "\n", " # Compute gradients.\n", " gradients = g.gradient(loss, trainable_variables)\n", @@ -198,36 +198,36 @@ "name": "stdout", "output_type": "stream", "text": [ - "step: 100, loss: 567.292969, accuracy: 0.136719\n", - "step: 200, loss: 398.614929, accuracy: 0.562500\n", - "step: 300, loss: 226.743774, accuracy: 0.753906\n", - "step: 400, loss: 193.384521, accuracy: 0.777344\n", - "step: 500, loss: 138.649963, accuracy: 0.886719\n", - "step: 600, loss: 109.713669, accuracy: 0.898438\n", - "step: 700, loss: 90.397217, accuracy: 0.906250\n", - "step: 800, loss: 104.545380, accuracy: 0.894531\n", - "step: 900, loss: 94.204697, accuracy: 0.890625\n", - "step: 1000, loss: 81.660645, accuracy: 0.906250\n", - "step: 1100, loss: 81.237137, accuracy: 0.902344\n", - "step: 1200, loss: 65.776703, accuracy: 0.925781\n", - "step: 1300, loss: 94.195862, accuracy: 0.910156\n", - "step: 1400, loss: 79.425507, accuracy: 0.917969\n", - "step: 1500, loss: 93.508163, accuracy: 0.914062\n", - "step: 1600, loss: 88.912506, accuracy: 0.917969\n", - "step: 1700, loss: 79.033607, accuracy: 0.929688\n", - "step: 1800, loss: 65.788315, accuracy: 0.898438\n", - "step: 1900, loss: 73.462387, accuracy: 0.937500\n", - "step: 2000, loss: 59.309540, accuracy: 0.917969\n", - "step: 2100, loss: 67.014008, accuracy: 0.917969\n", - "step: 2200, loss: 48.297115, accuracy: 0.949219\n", - "step: 2300, loss: 64.523148, accuracy: 0.910156\n", - "step: 2400, loss: 72.989517, accuracy: 0.925781\n", - "step: 2500, loss: 57.588585, accuracy: 0.929688\n", - "step: 2600, loss: 44.957100, accuracy: 0.960938\n", - "step: 2700, loss: 59.788242, accuracy: 0.937500\n", - "step: 2800, loss: 63.581337, accuracy: 0.937500\n", - "step: 2900, loss: 53.471252, accuracy: 0.941406\n", - "step: 3000, loss: 43.869728, accuracy: 0.949219\n" + "step: 100, loss: 571.445923, accuracy: 0.222656\n", + "step: 200, loss: 405.567535, accuracy: 0.488281\n", + "step: 300, loss: 252.089172, accuracy: 0.660156\n", + "step: 400, loss: 192.252136, accuracy: 0.792969\n", + "step: 500, loss: 129.173553, accuracy: 0.855469\n", + "step: 600, loss: 125.191071, accuracy: 0.859375\n", + "step: 700, loss: 103.346634, accuracy: 0.890625\n", + "step: 800, loss: 120.199402, accuracy: 0.871094\n", + "step: 900, loss: 95.674088, accuracy: 0.890625\n", + "step: 1000, loss: 113.775406, accuracy: 0.878906\n", + "step: 1100, loss: 68.457413, accuracy: 0.941406\n", + "step: 1200, loss: 80.773163, accuracy: 0.914062\n", + "step: 1300, loss: 85.862785, accuracy: 0.902344\n", + "step: 1400, loss: 63.480415, accuracy: 0.949219\n", + "step: 1500, loss: 77.139435, accuracy: 0.910156\n", + "step: 1600, loss: 88.129692, accuracy: 0.933594\n", + "step: 1700, loss: 92.199730, accuracy: 0.906250\n", + "step: 1800, loss: 90.150421, accuracy: 0.886719\n", + "step: 1900, loss: 48.567772, accuracy: 0.949219\n", + "step: 2000, loss: 54.002838, accuracy: 0.941406\n", + "step: 2100, loss: 58.536209, accuracy: 0.933594\n", + "step: 2200, loss: 47.156784, accuracy: 0.949219\n", + "step: 2300, loss: 55.344498, accuracy: 0.949219\n", + "step: 2400, loss: 70.956612, accuracy: 0.925781\n", + "step: 2500, loss: 76.179062, accuracy: 0.917969\n", + "step: 2600, loss: 44.956696, accuracy: 0.929688\n", + "step: 2700, loss: 56.581280, accuracy: 0.941406\n", + "step: 2800, loss: 57.775612, accuracy: 0.937500\n", + "step: 2900, loss: 46.005424, accuracy: 0.960938\n", + "step: 3000, loss: 51.832504, accuracy: 0.953125\n" ] } ], @@ -253,7 +253,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Test Accuracy: 0.936800\n" + "Test Accuracy: 0.937600\n" ] } ], @@ -280,12 +280,14 @@ "outputs": [ { "data": { - "image/png": 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -297,12 +299,14 @@ }, { "data": { - "image/png": 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -314,12 +318,14 @@ }, { "data": { - "image/png": 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"text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -376,25 +386,32 @@ " plt.show()\n", " print(\"Model prediction: %i\" % np.argmax(predictions.numpy()[i]))" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.15" + "pygments_lexer": "ipython3", + "version": "3.8.0" } }, "nbformat": 4, From 6dcbe14649163814e72a22a999f20c5e247ce988 Mon Sep 17 00:00:00 2001 From: AE1020 <68134252+AE1020@users.noreply.github.com> Date: Sat, 23 Oct 2021 04:03:00 -0400 Subject: [PATCH 24/24] Update dead monkeylearn blog link (#403) --- tensorflow_v2/notebooks/0_Prerequisite/ml_introduction.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow_v2/notebooks/0_Prerequisite/ml_introduction.ipynb b/tensorflow_v2/notebooks/0_Prerequisite/ml_introduction.ipynb index 226dd66f..0ddf5419 100644 --- a/tensorflow_v2/notebooks/0_Prerequisite/ml_introduction.ipynb +++ b/tensorflow_v2/notebooks/0_Prerequisite/ml_introduction.ipynb @@ -13,7 +13,7 @@ "## Machine Learning\n", "\n", "- [An Introduction to Machine Learning Theory and Its Applications: A Visual Tutorial with Examples](https://www.toptal.com/machine-learning/machine-learning-theory-an-introductory-primer)\n", - "- [A Gentle Guide to Machine Learning](https://blog.monkeylearn.com/a-gentle-guide-to-machine-learning/)\n", + "- [A Gentle Guide to Machine Learning](https://monkeylearn.com/blog/gentle-guide-to-machine-learning/)\n", "- [A Visual Introduction to Machine Learning](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/)\n", "- [Introduction to Machine Learning](http://alex.smola.org/drafts/thebook.pdf)\n", "\n",