diff --git a/.gitignore b/.gitignore
index fc3ae95..9829dff 100644
--- a/.gitignore
+++ b/.gitignore
@@ -103,3 +103,5 @@ ENV/
# Rope project settings
.ropeproject
+*.pkl
+*.png
diff --git a/01_Simple_Linear_Model.ipynb b/01_Simple_Linear_Model.ipynb
index d6dacad..2c1509e 100644
--- a/01_Simple_Linear_Model.ipynb
+++ b/01_Simple_Linear_Model.ipynb
@@ -32,18 +32,60 @@
{
"cell_type": "code",
"execution_count": 1,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
+ "import peforth #debugger\n",
"import matplotlib.pyplot as plt\n",
"import tensorflow as tf\n",
"import numpy as np\n",
+ "# problem happened on the below line, read the next cell for the solution\n",
"from sklearn.metrics import confusion_matrix"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### How did I solve the problem happened here -- hcchen-2017-12-17\n",
+ "\n",
+ " from sklearn.metrics import confusion_matrix\n",
+ "\n",
+ "The above line causes this problem:\n",
+ "\n",
+ " ImportError: cannot import name 'NUMPY_MKL'\n",
+ "\n",
+ "That was expected be resolved by installing :\n",
+ "\n",
+ " Don't even try to download it! \"is not a supported wheel on this platform.\" \n",
+ " numpy‑1.14.0rc1+mkl‑cp37‑cp37m‑win_amd64.whl <-- 180MegaBytes!\n",
+ " \n",
+ "which can be found here : \n",
+ "\n",
+ " https://www.lfd.uci.edu/~gohlke/pythonlibs/#numpy\n",
+ " \n",
+ "See: \n",
+ " https://stackoverflow.com/questions/37267399/importerror-cannot-import-name-numpy-mkl\n",
+ " \n",
+ "But shit! the 180Mb whl does not work! \n",
+ "\n",
+ " c:/Users\\hcche\\Downloads>pip install numpy-1.14.0rc1+mkl-cp37-cp37m-win_amd64.whl\n",
+ " numpy-1.14.0rc1+mkl-cp37-cp37m-win_amd64.whl is not a supported wheel on this platform.\n",
+ "\n",
+ "A possible solution suggested on the above stackoverflow page is to upgrade my scipy `pip install --upgrade scipy` --> Yes! scipy 1.0.0 has resolvded the above problem. But, another problem came up then:\n",
+ "\n",
+ " ImportError: DLL load failed: 找不到指定的模組。\n",
+ "\n",
+ "My sklearn was `scikit-learn (0.18.1)` so try to make an upgrade:\n",
+ "\n",
+ " pip install --upgrade sklearn <-- Note! needs admin mode\n",
+ "\n",
+ "Now my sklearn is `scikit-learn (0.19.1)` and everything is fine, Bingo!
\n",
+ "On my T550, pip list 顯示為 sklearn (0.0) 但是照下面方法查，是對的 0.19.1：`py> sys.modules['sklearn'].__version__ . cr \\ ==> 0.19.1`\n",
+ "\n"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -54,14 +96,15 @@
{
"cell_type": "code",
"execution_count": 2,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "'0.12.0-rc1'"
+ "{'numpy': '1.13.3',\n",
+ " 'python': '3.6.0 (v3.6.0:41df79263a11, Dec 23 2016, 08:06:12) [MSC v.1900 64 bit (AMD64)]',\n",
+ " 'scikit-learn': '0.19.1',\n",
+ " 'tensorflow': '1.4.0'}"
]
},
"execution_count": 2,
@@ -70,7 +113,9 @@
}
],
"source": [
- "tf.__version__"
+ "import sys\n",
+ "# check both python and tensorflow version\n",
+ "{'python':sys.version, 'tensorflow':tf.__version__, 'numpy':np.__version__, 'scikit-learn':sys.modules['sklearn'].__version__}"
]
},
{
@@ -84,15 +129,19 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "The MNIST data-set is about 12 MB and will be downloaded automatically if it is not located in the given path."
+ "The MNIST data-set is about 12 MB and will be downloaded automatically if it is not located in the given path.\n",
+ "\n",
+ "> MNIST data 要緊的是這四個 .gz 檔，照下面程式碼期望的位置放好
\n",
+ "> c:\\Users\\hcche\\Documents\\GitHub\\TensorFlow-Tutorials\\data\\MNIST\\train-images-idx3-ubyte.gz
\n",
+ "> c:\\Users\\hcche\\Documents\\GitHub\\TensorFlow-Tutorials\\data\\MNIST\\train-labels-idx1-ubyte.gz
\n",
+ "> c:\\Users\\hcche\\Documents\\GitHub\\TensorFlow-Tutorials\\data\\MNIST\\t10k-images-idx3-ubyte.gz
\n",
+ "> c:\\Users\\hcche\\Documents\\GitHub\\TensorFlow-Tutorials\\data\\MNIST\\t10k-labels-idx1-ubyte.gz
"
]
},
{
"cell_type": "code",
"execution_count": 3,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -106,6 +155,7 @@
}
],
"source": [
+ "# 直接 ^Enter 本 cell 會出問題 on Windows 10, 用 pure python 解決。\n",
"from tensorflow.examples.tutorials.mnist import input_data\n",
"data = input_data.read_data_sets(\"data/MNIST/\", one_hot=True)"
]
@@ -120,9 +170,7 @@
{
"cell_type": "code",
"execution_count": 4,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -159,9 +207,7 @@
{
"cell_type": "code",
"execution_count": 5,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -186,18 +232,18 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "We also need the classes as single numbers for various comparisons and performance measures, so we convert the One-Hot encoded vectors to a single number by taking the index of the highest element. Note that the word 'class' is a keyword used in Python so we need to use the name 'cls' instead."
+ "We also need the classes as single numbers for various comparisons and performance measures, so we convert the One-Hot encoded vectors to a single number by taking the index of the highest element. Note that the word 'class' is a keyword used in Python so we need to use the name 'cls' instead. \n",
+ "> 分類器是 classifier 可見 class 就是說這 AI 要處裡的資料有哪些類別，也就是 0~9 的手寫數字。以上是隨便看看 MNIST test 組的 labels."
]
},
{
"cell_type": "code",
"execution_count": 6,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
- "data.test.cls = np.array([label.argmax() for label in data.test.labels])"
+ "data.test.cls = np.array([label.argmax() for label in data.test.labels])\n",
+ "# 把整串 one-hot labels 翻譯成 int "
]
},
{
@@ -210,14 +256,12 @@
{
"cell_type": "code",
"execution_count": 7,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "array([7, 2, 1, 0, 4])"
+ "array([7, 2, 1, 0, 4], dtype=int64)"
]
},
"execution_count": 7,
@@ -240,22 +284,24 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "The data dimensions are used in several places in the source-code below. In computer programming it is generally best to use variables and constants rather than having to hard-code specific numbers every time that number is used. This means the numbers only have to be changed in one single place. Ideally these would be inferred from the data that has been read, but here we just write the numbers."
+ "The data dimensions are used in several places in the source-code below. In computer programming it is generally best to use variables and constants rather than having to hard-code specific numbers every time that number is used. This means the numbers only have to be changed in one single place. Ideally these would be inferred from the data that has been read, but here we just write the numbers.\n",
+ "\n",
+ "> 這個我用 peforth 就可以從 MNIST 直接看出來"
]
},
{
"cell_type": "code",
"execution_count": 8,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"# We know that MNIST images are 28 pixels in each dimension.\n",
- "img_size = 28\n",
"\n",
"# Images are stored in one-dimensional arrays of this length.\n",
- "img_size_flat = img_size * img_size\n",
+ "img_size_flat = data.test.images.shape[1] # img_size * img_size\n",
+ "\n",
+ "import math #林老杯給你用算的\n",
+ "img_size = int(math.sqrt(img_size_flat)) #28 \n",
"\n",
"# Tuple with height and width of images used to reshape arrays.\n",
"img_shape = (img_size, img_size)\n",
@@ -275,15 +321,14 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Function used to plot 9 images in a 3x3 grid, and writing the true and predicted classes below each image."
+ "Function used to plot 9 images in a 3x3 grid, and writing the true and predicted classes below each image.\n",
+ "> 這個不必鑽牛角尖，能用就好"
]
},
{
"cell_type": "code",
"execution_count": 9,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"def plot_images(images, cls_true, cls_pred=None):\n",
@@ -310,6 +355,30 @@
" ax.set_yticks([])"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "OK "
+ ]
+ }
+ ],
+ "source": [
+ "peforth.ok(loc=locals(),cmd='''\n",
+ " __main__ :> plot_images constant plot_images // ( -- func ) \n",
+ " __main__ :> data constant data // ( -- dataset ) MNIST dataset \n",
+ " __main__ :> plt constant plt // ( -- module ) The matplotlib module\n",
+ " __main__ :> tf constant tf // ( -- module ) The tensorflow module\n",
+ " __main__ :> np constant np // ( -- module ) The numpy module \n",
+ " exit\n",
+ " ''')"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -319,16 +388,14 @@
},
{
"cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 11,
+ "metadata": {},
"outputs": [
{
"data": {
- "image/png": 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xGFs30RsuCNcxHo/R6/V4IUZZxJRoMp1O4XK52GQglUohlUohkUiwwLnd7oVeIQurx2Qy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+ "image/png": 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\n",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -354,7 +421,7 @@
"\n",
"The entire purpose of TensorFlow is to have a so-called computational graph that can be executed much more efficiently than if the same calculations were to be performed directly in Python. TensorFlow can be more efficient than NumPy because TensorFlow knows the entire computation graph that must be executed, while NumPy only knows the computation of a single mathematical operation at a time.\n",
"\n",
- "TensorFlow can also automatically calculate the gradients that are needed to optimize the variables of the graph so as to make the model perform better. This is because the graph is a combination of simple mathematical expressions so the gradient of the entire graph can be calculated using the chain-rule for derivatives.\n",
+ "TensorFlow can also automatically calculate the gradients that are needed to optimize the variables of the graph so as to make the model perform better. This is because the graph is a combination of simple mathematical expressions so the gradient of the entire graph can be calculated using the chain-rule（數學） for derivatives（導數）.\n",
"\n",
"TensorFlow can also take advantage of multi-core CPUs as well as GPUs - and Google has even built special chips just for TensorFlow which are called TPUs (Tensor Processing Units) and are even faster than GPUs.\n",
"\n",
@@ -364,7 +431,7 @@
"* Model variables that are going to be optimized so as to make the model perform better.\n",
"* The model which is essentially just a mathematical function that calculates some output given the input in the placeholder variables and the model variables.\n",
"* A cost measure that can be used to guide the optimization of the variables.\n",
- "* An optimization method which updates the variables of the model.\n",
+ "* An optimization method which updates the variables（phi 沒記錯嗎？整套 W and b） of the model.\n",
"\n",
"In addition, the TensorFlow graph may also contain various debugging statements e.g. for logging data to be displayed using TensorBoard, which is not covered in this tutorial."
]
@@ -373,7 +440,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Placeholder variables"
+ "### Placeholder variables (fed from outside)"
]
},
{
@@ -387,10 +454,8 @@
},
{
"cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "collapsed": true
- },
+ "execution_count": 12,
+ "metadata": {},
"outputs": [],
"source": [
"x = tf.placeholder(tf.float32, [None, img_size_flat])"
@@ -405,10 +470,8 @@
},
{
"cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "collapsed": true
- },
+ "execution_count": 13,
+ "metadata": {},
"outputs": [],
"source": [
"y_true = tf.placeholder(tf.float32, [None, num_classes])"
@@ -418,20 +481,37 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Finally we have the placeholder variable for the true class of each image in the placeholder variable `x`. These are integers and the dimensionality of this placeholder variable is set to `[None]` which means the placeholder variable is a one-dimensional vector of arbitrary length."
+ "Finally we have the placeholder variable for the true class of each image in the placeholder variable `x`. These are integers and the dimensionality of this placeholder variable is set to `[None]` which means the placeholder variable is a one-dimensional vector of arbitrary length.\n",
+ "> [None] 表示為 one-dimensional 我好像有過這個問題。"
]
},
{
"cell_type": "code",
- "execution_count": 13,
- "metadata": {
- "collapsed": true
- },
+ "execution_count": 14,
+ "metadata": {},
"outputs": [],
"source": [
"y_true_cls = tf.placeholder(tf.int64, [None])"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```\n",
+ "peforth.ok(cmd='cr')\n",
+ "OK .s\n",
+ "empty\n",
+ "\n",
+ "OK __main__ :> y_true_cls type . cr\n",
+ "\n",
+ "OK __main__ :> y_true_cls.shape . cr\n",
+ "(?,) <------------ one-dimension 的 shape 不是 (?,0) 我以前覺得很迷惑，原來是 None\n",
+ "OK __main__ :> y_true.shape . cr\n",
+ "(?, 10)\n",
+ "OK ```"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -443,17 +523,15 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Apart from the placeholder variables that were defined above and which serve as feeding input data into the model, there are also some model variables that must be changed by TensorFlow so as to make the model perform better on the training data.\n",
+ "Apart from the *placeholder variables* that were defined above and which serve as feeding input data into the model, there are also some model variables that must be changed by TensorFlow so as to make the model perform better on the training data.\n",
"\n",
- "The first variable that must be optimized is called `weights` and is defined here as a TensorFlow variable that must be initialized with zeros and whose shape is `[img_size_flat, num_classes]`, so it is a 2-dimensional tensor (or matrix) with `img_size_flat` rows and `num_classes` columns."
+ "The first variable that must be optimized is called `weights` and is defined here as a TensorFlow variable that must be initialized with zeros （這點我記得 Martin 的教材一開始也用零，後來改良到用上某個手段之後就要給定亂數當初值） and whose shape is `[img_size_flat, num_classes]`, so it is a 2-dimensional tensor (or matrix) with `img_size_flat` rows and `num_classes` columns."
]
},
{
"cell_type": "code",
- "execution_count": 14,
- "metadata": {
- "collapsed": true
- },
+ "execution_count": 19,
+ "metadata": {},
"outputs": [],
"source": [
"weights = tf.Variable(tf.zeros([img_size_flat, num_classes]))"
@@ -463,15 +541,13 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "The second variable that must be optimized is called `biases` and is defined as a 1-dimensional tensor (or vector) of length `num_classes`."
+ "The second variable that must be optimized is called `biases` and is defined as a 1-dimensional tensor (or vector) of length `num_classes`. 看來本教材也是只用十顆 neural cells."
]
},
{
"cell_type": "code",
- "execution_count": 15,
- "metadata": {
- "collapsed": true
- },
+ "execution_count": 18,
+ "metadata": {},
"outputs": [],
"source": [
"biases = tf.Variable(tf.zeros([num_classes]))"
@@ -490,17 +566,15 @@
"source": [
"This simple mathematical model multiplies the images in the placeholder variable `x` with the `weights` and then adds the `biases`.\n",
"\n",
- "The result is a matrix of shape `[num_images, num_classes]` because `x` has shape `[num_images, img_size_flat]` and `weights` has shape `[img_size_flat, num_classes]`, so the multiplication of those two matrices is a matrix with shape `[num_images, num_classes]` and then the `biases` vector is added to each row of that matrix.\n",
+ "The result is a matrix of shape `[num_images, num_classes]` because `x` has shape `[num_images, img_size_flat]` and `weights` has shape `[img_size_flat, num_classes]`, so the multiplication of those two matrices is a matrix with shape `[num_images, num_classes]` and then the `biases` vector is added to each row of that matrix. 這些我現在都知道了，當初開始研究時，這些都是疑點。Martin 也特別解釋過 tf.add(X*W, b) 就是把 b 加到 X*W 的每一列裡去，好像稱作 distributed addition 分發式的加法。\n",
"\n",
- "Note that the name `logits` is typical TensorFlow terminology, but other people may call the variable something else."
+ "Note that the name `logits` is typical TensorFlow terminology, but other people may call the variable something else. 哈！ logits 沒那麼簡單，Martin 的解釋好很多，花了一整節。用來解決 activation function softmax() 裡的 log(n) 出現 n 趨近於 0 的狀況, log(0) 類似除零都是禁止的， logits 法是把算式拆開來餵給 softmax() 的 logits 版，故看起來冗長多餘，背後有這個原因。"
]
},
{
"cell_type": "code",
- "execution_count": 16,
- "metadata": {
- "collapsed": true
- },
+ "execution_count": 20,
+ "metadata": {},
"outputs": [],
"source": [
"logits = tf.matmul(x, weights) + biases"
@@ -517,10 +591,8 @@
},
{
"cell_type": "code",
- "execution_count": 17,
- "metadata": {
- "collapsed": true
- },
+ "execution_count": 21,
+ "metadata": {},
"outputs": [],
"source": [
"y_pred = tf.nn.softmax(logits)"
@@ -535,13 +607,22 @@
},
{
"cell_type": "code",
- "execution_count": 18,
- "metadata": {
- "collapsed": true
- },
+ "execution_count": 22,
+ "metadata": {},
"outputs": [],
"source": [
- "y_pred_cls = tf.argmax(y_pred, dimension=1)"
+ "# y_pred_cls = tf.argmax(y_pred, dimension=1)\n",
+ "y_pred_cls = tf.argmax(y_pred, axis=1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "這老師的程式有點老了，跑出如下的 warning, 修改如上，成功：\n",
+ "> WARNING:tensorflow:From `:`1: calling `argmax (from tensorflow.python.ops.math_ops)` with dimension is deprecated and will be removed in a future version.\n",
+ "Instructions for updating:\n",
+ "Use the `axis` argument instead"
]
},
{
@@ -557,23 +638,31 @@
"source": [
"To make the model better at classifying the input images, we must somehow change the variables for `weights` and `biases`. To do this we first need to know how well the model currently performs by comparing the predicted output of the model `y_pred` to the desired output `y_true`.\n",
"\n",
- "The cross-entropy is a performance measure used in classification. The cross-entropy is a continuous function that is always positive and if the predicted output of the model exactly matches the desired output then the cross-entropy equals zero. The goal of optimization is therefore to minimize the cross-entropy so it gets as close to zero as possible by changing the `weights` and `biases` of the model.\n",
+ "The cross-entropy is a performance measure used in classification. The cross-entropy is a continuous function that is always positive (這我以前到是沒注意到過，有負的不是很好嗎？optimizer 可以有更好的方向感。) and if the predicted output of the model exactly matches the desired output then the cross-entropy equals zero. The goal of optimization is therefore to minimize the cross-entropy so it gets as close to zero as possible by changing the `weights` and `biases` of the model.\n",
"\n",
"TensorFlow has a built-in function for calculating the cross-entropy. Note that it uses the values of the `logits` because it also calculates the softmax internally."
]
},
{
"cell_type": "code",
- "execution_count": 19,
- "metadata": {
- "collapsed": true
- },
+ "execution_count": 23,
+ "metadata": {},
"outputs": [],
"source": [
"cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=logits,\n",
" labels=y_true)"
]
},
+ {
+ "cell_type": "raw",
+ "metadata": {},
+ "source": [
+ "peforth.ok()\n",
+ "__main__ :> cross_entropy . cr\n",
+ "Tensor(\"Reshape_2:0\", shape=(?,), dtype=float32)\n",
+ "看看 cross_entropy 是啥東西, 是個一維的 array 裡面都是 float32 0~1 之間的值，代表正確率，有多正確。"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -583,15 +672,20 @@
},
{
"cell_type": "code",
- "execution_count": 20,
- "metadata": {
- "collapsed": true
- },
+ "execution_count": 26,
+ "metadata": {},
"outputs": [],
"source": [
"cost = tf.reduce_mean(cross_entropy)"
]
},
+ {
+ "cell_type": "raw",
+ "metadata": {},
+ "source": [
+ "tf.reduce_mean() 是可以處裡多維度的 data 的，reduce 代表降維，mean 是取平均值。"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -605,15 +699,14 @@
"source": [
"Now that we have a cost measure that must be minimized, we can then create an optimizer. In this case it is the basic form of Gradient Descent where the step-size is set to 0.5.\n",
"\n",
- "Note that optimization is not performed at this point. In fact, nothing is calculated at all, we just add the optimizer-object to the TensorFlow graph for later execution."
+ "Note that optimization is not performed at this point. In fact, nothing is calculated at all, we just add the optimizer-object to the TensorFlow graph for later execution.\n",
+ "> 初學時，我也覺得有點茫然。後來我向同事解釋單一個 neural cell 時，我也是用式子來表達的，而不只是畫式意圖，這就對了！到此為止，這些 statements 都是在描述這個 graph -- 都是在畫圖，還沒有執行。\n"
]
},
{
"cell_type": "code",
- "execution_count": 21,
- "metadata": {
- "collapsed": true
- },
+ "execution_count": 41,
+ "metadata": {},
"outputs": [],
"source": [
"optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.5).minimize(cost)"
@@ -637,10 +730,8 @@
},
{
"cell_type": "code",
- "execution_count": 22,
- "metadata": {
- "collapsed": true
- },
+ "execution_count": 27,
+ "metadata": {},
"outputs": [],
"source": [
"correct_prediction = tf.equal(y_pred_cls, y_true_cls)"
@@ -650,15 +741,14 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "This calculates the classification accuracy by first type-casting the vector of booleans to floats, so that False becomes 0 and True becomes 1, and then calculating the average of these numbers."
+ "This calculates the classification accuracy by first type-casting the vector of booleans to floats, so that False becomes 0 and True becomes 1, and then calculating the average of these numbers.\n",
+ "> 我一直有個感覺：accuracy 跟 cost 好像同樣的東西算了兩次？這是意會它們的意義來看的。cost 是給 optimizer 看的，accuracy 是給人看的。"
]
},
{
"cell_type": "code",
- "execution_count": 23,
- "metadata": {
- "collapsed": true
- },
+ "execution_count": 28,
+ "metadata": {},
"outputs": [],
"source": [
"accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))"
@@ -682,10 +772,8 @@
},
{
"cell_type": "code",
- "execution_count": 24,
- "metadata": {
- "collapsed": true
- },
+ "execution_count": 29,
+ "metadata": {},
"outputs": [],
"source": [
"session = tf.Session()"
@@ -702,10 +790,8 @@
},
{
"cell_type": "code",
- "execution_count": 25,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 30,
+ "metadata": {},
"outputs": [],
"source": [
"session.run(tf.global_variables_initializer())"
@@ -722,15 +808,13 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "There are 50.000 images in the training-set. It takes a long time to calculate the gradient of the model using all these images. We therefore use Stochastic Gradient Descent which only uses a small batch of images in each iteration of the optimizer."
+ "There are 50,000 images in the training-set. It takes a long time to calculate the gradient of the model using all these images. We therefore use Stochastic 随机的；猜测的 Gradient Descent which only uses a small batch of images in each iteration of the optimizer."
]
},
{
"cell_type": "code",
- "execution_count": 26,
- "metadata": {
- "collapsed": true
- },
+ "execution_count": 31,
+ "metadata": {},
"outputs": [],
"source": [
"batch_size = 100"
@@ -745,10 +829,8 @@
},
{
"cell_type": "code",
- "execution_count": 27,
- "metadata": {
- "collapsed": true
- },
+ "execution_count": 32,
+ "metadata": {},
"outputs": [],
"source": [
"def optimize(num_iterations):\n",
@@ -768,7 +850,8 @@
" # Run the optimizer using this batch of training data.\n",
" # TensorFlow assigns the variables in feed_dict_train\n",
" # to the placeholder variables and then runs the optimizer.\n",
- " session.run(optimizer, feed_dict=feed_dict_train)"
+ " session.run(AdamOptimizer, feed_dict=feed_dict_train)\n",
+ " # AdamOptimizer or AdagradOptimizer"
]
},
{
@@ -787,10 +870,8 @@
},
{
"cell_type": "code",
- "execution_count": 28,
- "metadata": {
- "collapsed": true
- },
+ "execution_count": 33,
+ "metadata": {},
"outputs": [],
"source": [
"feed_dict_test = {x: data.test.images,\n",
@@ -807,10 +888,8 @@
},
{
"cell_type": "code",
- "execution_count": 29,
- "metadata": {
- "collapsed": true
- },
+ "execution_count": 34,
+ "metadata": {},
"outputs": [],
"source": [
"def print_accuracy():\n",
@@ -830,10 +909,8 @@
},
{
"cell_type": "code",
- "execution_count": 30,
- "metadata": {
- "collapsed": true
- },
+ "execution_count": 35,
+ "metadata": {},
"outputs": [],
"source": [
"def print_confusion_matrix():\n",
@@ -872,10 +949,8 @@
},
{
"cell_type": "code",
- "execution_count": 31,
- "metadata": {
- "collapsed": true
- },
+ "execution_count": 36,
+ "metadata": {},
"outputs": [],
"source": [
"def plot_example_errors():\n",
@@ -920,10 +995,8 @@
},
{
"cell_type": "code",
- "execution_count": 32,
- "metadata": {
- "collapsed": true
- },
+ "execution_count": 37,
+ "metadata": {},
"outputs": [],
"source": [
"def plot_weights():\n",
@@ -970,10 +1043,8 @@
},
{
"cell_type": "code",
- "execution_count": 33,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 38,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -989,16 +1060,14 @@
},
{
"cell_type": "code",
- "execution_count": 34,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 39,
+ "metadata": {},
"outputs": [
{
"data": {
- "image/png": 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Ix8LvkkV6Z8STxuo0m00Wz0ajwW4Im82GaDSK+/fv47333sPa2hrC4TDXLt2l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\nAElFTkSuQmCC\n",
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s2rQJSJTxAw88kHQeSLSZ5osyTRGRCPKWaY4fPx5IvNII5QdkqAk/jNzjjz8OJJ60Atx4441A9oeOkprzLzbMmjWr3LZtt90WgE6dOuU0Jqne1KlT48s9e/YEEnd0qVzXvnO8/3v27NlJ2zPZTpouZZoiIhHkPNP0wzqV/SUBGDhwYNrH9y/vr127FoAOHTrEtx155JFpH1+y69133610WybuRCQzLrvsMgBeffVVAP7973/Ht/n2aOccAJMmTar2eH7fsj1cdt99d6Cw2rGVaYqIRJDzTNMPxLBy5UogMbxXpixbtizpc8eOHTN6fMmuijJN3zaWiTsRyYz99tsPSLzRN3/+/Pi2l156CYChQ4cCsPPOOwNw7rnnVno8P/jKPvvsk7TeT5XhM85CoExTRCQCVZoiIhHk/Pbcv1zvu42EB+zwrz42atQo8nH9AAK+K5N3yCGH1ChOyS3/upzvKhbWsGFDAHbbbbecxiTV23HHHQE44ogj4uv88l133ZXycT755BMg8UDI1w9+7MxCokxTRCSCnGeafrZA/8qkf50S4PjjjwfgyiuvrPIY4VGc/YOfFStWAOW7LNSqpd+FYuDnlfGZRpgG6Ch9t956K5C4fv1DpEwML5dpqlFERCLI22uUN998M5CcWfhXsfxgxJUJ//r4XyY/K15ZfhgpKWxl26LDg6pcdNFFuQ5HciBc5o899hgA22+/PQA77bRTXmJKhTJNEZEI8pZptm/fHoCnn346vm7evHlA+Q7qZZ122mnl1vmOs35QVM+3oUph8i85lH1qHn5SnomhAqXwvPjii+XW+eca4UGNC40yTRGRCPI+3UVY586dk/6O4pe//GWF68P9QPfee++aBSZZ44eCK/vUvHfv3vkIR3IonGnWr18fgKuvvjpf4aRMmaaISASqNEVEIiio2/N0+Nu7srd5uiUvbL5Tu9e4cWMALr/88nyEIzkwatQoIHmGyaZNmwKF/QDIU6YpIhJByWSaZWfHk+Iwbdq0pM8tWrQAEoN0SOnxmWb4Wj3uuOOS9vnmm28A2LBhAwAtW7bMUXTVU6YpIhJByWSa3333XdJndWovbD/88AMAS5cuTVpft25dQDOGbmn8rLH+5ZQRI0YAiZkX/GuWhUCZpohIBCWTaY4dOxZIDPQwePDgfIYj1fBD9vlXJBcvXgxA27Zt8xaT5M/DDz8MwJgxYwDo378/ADfddFPeYqqMMk0RkQhKJtP0GcsVV1wBaI7zQle7dm0Abr/9diDxJLUY+ulJekaOHAnAH//4x/i6ww47DEjMbe+n0dh6661zHF31lGmKiERQMpnmlClT8h2C1MCuu+4KwCOPPJLnSCRXDj30UABeffXVPEdSM8o0RUQiUKUpIhKBKk0RkQhUaYqIRKBKU0QkAlWaIiIRWNlBeyN92WwtsCJz4RSFVs65JtXvVhpUxqVPZRxNWpWmiMiWRrfnIiIRqNIUEYlAlaaISARVVppmtpOZzQ/+rDazVaHPWR1+xMzqmNkCM3suhX2HhGJbaGbHp3nuN82sUzX71DWzCWa21MxmmVnhTGISQb7K2MweM7O1ZjY/xf37+/3N7AMzOz/N848zs5Oq2cfM7C9BGS+o7t9EodJ1XOU+ka/jKitN59x651wn51wnYBQwwn92zn0fnNTMLBsZ65XAogj73x3EeQbwqJWZYc3MMj04yUXAaudcG+AB4E8ZPn5O5LGMHwGiXhR/D+I8AhhqZo3DG7NQxicCLYIyHkisnIuOruMqRb6Oa/Q/yczamNkiMxsFvAe0MLOvQtv7mdmYYLmpmU00szlm9o6ZHZTC8VsBPYCxUWNzzi0CDNgxyCbuMbPXgDvMrIGZPRrEMc/MTgzOt62ZjQ9+EZ8E6qZwqt6An7jkaeDXUWMtZNkuY+fc68CXNYnNObcaWA60DLKT0Wb2CjA2yGyGB3EsMLP+QYy1gqxxiZlNARpXcQqvN/DX4JxvAruYWcl0RdJ1DNTgOk7nl6UDMMY51xlYVcV+9wFDnXNdgD6AL4QDg8KqyL3ANUDk/lBm1hX4zjnnL8jdgaOcc9cCg4GXnHMHAEcC95hZXeD/gA3OuX2Au4DOoeONrSTFbw58DhD8Wm80sx2ixlvgslnGNWZmbYBWwCfBqs7Aic65s4llDmuCMt4fGBTccp0G/ALoCAwAuoaOd7uZJc8hGxMv48DKYF0p0XUc8TpOJ9Vd5pybk8J+RwPtQln2jmZWzzn3NvB22Z0t1s70uXNuvpkdHSGea8zst8A3QN/Q+vHOuZ+D5Z7AsWb2++BzXaAlcBgwFMA5N8/MFvsvO+fOixBDqclKGafhTDPrDmwG+jvnvgrOOck556cj7Qm0N7N+weeGQFtiZfxE8G9hpZnN9Ad1zv0hgzEWG13HEaVTaW4MLf9MLJX2wmmxAQf4tpMUdAVOMbNewXG2N7PHnHPnVvO9u51z91YTpwEnOeeWhXco02ySqlVAC2C1xRrT6zvnvqrmO8UmW2VcU393zl1ewfqyZTzQOTcjvIOZnVyD8/kynh183o2qs7FipOs44nWckYbf4Bdgg5m1DRqTw/9ApwOD/IdKUuTwsa51zu3mnGsNnAW87P9Hm9lQ335RQ9OAS0Kx+PT9H8BvgnX7AnulcKzJgP8H0Ad4OY24Cl4my7gqZnaZmV1c80iZBgy04IGBmbUzs3rEyrhv0LbZHDg8hWNNBs4JjtMN+MI5tzaN2AqaruPUruNMPi27DngJmEGs7ccbBBwSNM4uAS6EGrd37QOsTiPGW4D6FuvOsBi4OVh/P7CTmS0ArgDitytVtIU8BDQzs6XE2lJuSCOuYpGxMjaz8cAbQAczWxnckgG0B9anEeNo4GNgvpktAh4kdkc1AfiM2JPckcQuMB9LZW2aU4BVZrYsOM6gCvYpNbqOq1E0755bLPd+0Tl3TL5jkewxs+eB3s65H/Mdi2ReKVzHRVNpiogUAr1GKSISgSpNEZEIVGmKiESQ1nucjRs3dq1bt85QKMVh7ty567akUb1VxqVPZRxNWpVm69atmTMnlZcJSoeZbVHTAqiMS5/KOBrdnouIRKBKU0QkAlWaIiIRqNIUEYlAlaaISASqNEVEIlClKSISQaYnKRLJug0bNgDw2WefVbpPq1atABgxYgQAHTt2BGCPPfYAYN99981miFLClGmKiESgTFMK3tSpUwGYMmUKADNnzgTg448/rvQ77dq1A2D58uUAbN68OWn7zz//XPYrIilRpikiEkFBZ5r//e9/Afj972OTzi1eHJtcbvr06fF9ttpqq9wHJhm3bFlsjqwHHngAgIceeii+bdOmTQBEGTD7ww8/zGB0IgnKNEVEIijITHPcuHEA3HjjjUD5p6Q+AwXYaaedcheYZM3KlbE5vO69t6LZW1O35557Aomn5VJ4li5dCsC6devi65599lkg0V5dq1Ysn7v44tjEpF27do3v27Zt21yEWSllmiIiERRUpumzjSuuuAJI/BKVnQT+kkviUx5z//33A9CoUaNchCg1EM4ofCbZrVs3AI45JjYp4dZbbw1Aw4YNAWjQoEH8O99++y0Av/71r4FEFnnggQcC0Llz5/i+9erVA6B+/foZ/q+Qmlq4cCGQaK+eOHEiAGvXVj+F/OzZs4HkZxe+Z4T/N/TnP/8ZSPwbyjZlmiIiEajSFBGJoKBuz4cNGwbA+vXrq9zvySefjC+/+OKLQOKhkb91z1WqLpXbuHEjAD169Iive//99wF47rnnkvY9+OCDAZg3bx4Qm4LB8w8Cd9ttNyDxkEAK04IFC4DE7fhTTz0FwNdff520ny9PgEMPPRRIlPvdd98NwH777QfA22+/Hd/X1w8vvPACkHgl1j80yjb96xMRiSDvmeaKFYn5jcaOHZu0zf+CNG3aFIBXXnml3Pf9r5fPUs8880wAdtlll8wHKyn5/vvvAfjNb34DJLJLgBtuuAGAo48+usLvVjQrYsuWLTMcoWTa7373u/iy7z5U9kGPL/O9994bgDvuuCO+rW7dukn7zpo1C4AHH3wQgPPOOy++bf78+UDiGh84cCAAp556KgBNmmR3IlFlmiIiEeQ90/S/GpDotH7YYYcB8PrrrwPw3XffAfD4448D8Kc//Sn+Hd9RdvXq1QD07t0bSLR1qitS7viuQT6D8ANshH/5r7nmGgC23XbbHEcnmeSvyaFDhwLw8MMPx7f511133nlnAAYMGAAkyj6V7mC+3fLHH38E4JZbbolv813P/GAsuaZMU0QkgrxnmuEhu3wndt+53fPtHeeffz4AEyZMiG/zAz34Xzefwejpee75J+J33nknkBgI+I033ojv4zuvS3Hzrzv6p9zhwVSaN28OJDqxH3DAAdUe76effgLg888/B+Ccc84B4PjjjwcSA09X5OyzzwZghx12SDn+dCjTFBGJIO+Z5hNPPFFu3fPPPw/ASSedVOF35syZU+nxDjroICD5NTzJjbfeeivps3+9MdwfT0qDb2usXbt2uW3+lUfft9LfGf7rX/9K2s+/8grwwQcfJP3duHFjIPGsoiK+V43vo52rYSKVaYqIRJD3TPOMM86IL0+aNAmAd999F0j8MvkX/n3/r3D7hm/H8Ov84LW+naNDhw5Zi12ShduaIdGDIfzks1evXkDyIBtSfI466igAjjjiCCC5D7Xve33ppZdW+N06dWLVjs9WK1I2wwy/BXbKKacAcN999wHQrFmzSLGnS5mmiEgEqjRFRCKwKPOulNWlSxdX1UOZVHz55Zfx5d133x1IvBrpYys7nmZ4AAg/KMAJJ5wAwEcffQTARRddBMCoUaPSiq8sM5vrnOuS0YMWsChl7MupbHmF+QcHfnAFPyam72rSpk0bAPbaa69y3/VzRPnBPbL1gEllHN1XX30VX/Zdzv75z38CidkV/Ouwvpth+PXa8IAcFfEd5CHx8kQ6XYzSKWNlmiIiEeT9QVD4Ncfx48cDcNpppwHlM07fsHzXXXfFv+M7vvvGYf+K5bRp04BE53dIZLKSHVdffTUA99xzT6X7+E7M/g7B/x2Ffz2ve/fuQPJQgZIf4azPZ5rV8R3YoXymuf322wMwfPhwAH7729/Gt1XUzSmXlGmKiESQ90wzzA8d5buu+AE6/K/YrbfeCpQfRgrgpptuAhKdY333Jf8dgMceeywbYUvAZxh9+vQBEsP0/fDDD/F9/DxQPuOsiTVr1gCJO5PwzJO+o7MULj/IR1V3CH5IOD+8YCFRpikiEkFBZZqezzgrG6i2Iv6VrL59+wKJTPO1116L7+Of1Gu4uOzwbU37778/kOjJEDZjxgwgkX3efPPNALzzzjuRz+fbuufOnRv5u5J7Y8aMAWDIkCFA8h2I5+8a/IDChUiZpohIBAWZaabDt6dNnjwZSG438XOkDx48OPeBCZB4/c7zg1D7TNMPuhCe3uDCCy8EYMSIEUCirVuKgy/bq666CoBvvvmm3D7bbbcdkGjL3GabbXIUXXTKNEVEIlClKSISQcndnvvRUK699logeX5t/9ChX79+AOyxxx65DU7K6dmzJ5CYpdI/HPCjVQF8/PHHQGK08LL8SOFSmPxcUX4OMC88V5BvTuvWrVvuAqshZZoiIhGUXKbpderUCYDbbrstvs6/5nf99dcDMG7cOCB5BGnJrfbt2wOJrmJPPfVUuX3C3cYgMR6jnz8m/FqtFA7/wMd3Zi/rrLPOii/7V2KLgTJNEZEISjbT9MKDAowePRpIzJLn28r22Wef3AcmQCLLv/fee4FEdhLusP7FF18A0Lp1ayBRpr6NWgrLt99+CyTuIr7//vuk7fvuuy+QKPNio0xTRCSCks80mzRpEl+ePn06kJiP2w8woc7S+ednFpw6dSoAf/vb3+LbZs2aBSQySz80nBSmV199FYBVq1ZVuN0P91bRwDvFQJmmiEgEJZ9phvnh9v10Gb5v2JIlSwDNXFlI/GyiZZel8PlhGsvyfaePPPLIXIaTcco0RUQi2KIyTc8Pcuyf4i1duhRQpimSCeHJEiHRBn355ZfnI5wnlQqTAAAEJUlEQVSMU6YpIhKBKk0RkQi2yNtzP9Pdp59+mudIRErPlVdemfS3fzDUrFmzvMWUSco0RUQi2CIzTRHJniuuuCLp71KjTFNEJALzM/rV6Mtma4EVmQunKLRyzjWpfrfSoDIufSrjaNKqNEVEtjS6PRcRiUCVpohIBFVWmma2k5nND/6sNrNVoc9bZysoM7vSzBYHfy5JYf/+ZrY2iOsDMzs/zfOPM7OTqtnHzOwvZrbUzBaYWad0zpkveSzjlWa2MDjP2ynsrzKuIV3HVe4TuYyr7HLknFsPdAoOfjPwrXNuWNmTEmsb/bm6k6UiCPpcoAvwI/CymU11zlXXE/3vzrnLzWwXYJGZTXbOrQsdt45z7sdMxBg4EWjhnGtjZt2AB4BDMnj8nMhHGYcc6pz7KsL+KuMa0HVcpchlXKPbczNrY2aLzGwU8B7Qwsy+Cm3vZ2ZjguWmZjbRzOaY2TtmdlA1h28PzHbObXLO/QD8Azg51dicc6uB5UBLMxtiZqPN7BVgrJnVMbPhQRwLzKx/EGOt4NdmiZlNARqncKrewF+Dc74J7GJmJfPENctlnBaVcWboOgZqUMbptGl2AMY45zoDFQ/RHHMfMNQ51wXoA/hCODAorLIWAoebWSMzqw8cC7RINSgzawO0Aj4JVnUGTnTOnQ1cBKxxzh0A7A8MMrOWwGnAL4COwACga+h4t5vZcRWcqjnweejzymBdKclWGQM44FUzm2tmF0QJSmWcUbqOI5ZxOm8ELXPOzUlhv6OBdrHsH4Adzayec+5toFxblnNukZkNB6YD3wLzgJ9SOM+ZZtYd2Az0d859FZxzknPuu2CfnkB7M+sXfG4ItAUOA54Ibk1WmtnMUDx/SOHcpSorZRw4yDm3KrgNe8XMPnDOvVXNeVTGmafrOKJ0Ks2NoeWfAQt9Dk/+YcABzrnkKemq4Jx7CHgIwMyGAktT+NrfnXMVDdgXjtOAgc65GeEdzCzl24aQVcR+OWcHn3ej6l/qYpTNMl4V/L3azCYBBwDVVZoq48zTdRyxjDPS5Sio2TeYWVszq0Vy28V0YJD/YCk8nTKznYO/WwO9gCeDz5eZ2cVphDoNGGhmdYLjtTOzesTaW/oGbSLNgcNTONZk4JzgON2AL5xza9OIraBlsozNrIGZNQiW6wM9gEXBZ5Vxnug6Tq2MM9lP8zrgJWAGsXYBbxBwSNBguwS4MAiwqvau54J9nwMuds79N1jfHlifRoyjgY+B+Wa2CHiQWLY9AfiM2IU7ktj/fII4K2sLmQKsMrNlwXEGVbBPqclUGTcD/mlm7wPvAM8656YH21TG+aXruBpF9RqlmT0P9M5wlwMpICrj0lfsZVxUlaaISL7pNUoRkQhUaYqIRKBKU0QkAlWaIiIRqNIUEYlAlaaISASqNEVEIvh/aIZ2hTii6gEAAAAASUVORK5CYII=\n",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -1020,10 +1089,8 @@
},
{
"cell_type": "code",
- "execution_count": 35,
- "metadata": {
- "collapsed": true
- },
+ "execution_count": 42,
+ "metadata": {},
"outputs": [],
"source": [
"optimize(num_iterations=1)"
@@ -1031,16 +1098,14 @@
},
{
"cell_type": "code",
- "execution_count": 36,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 43,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Accuracy on test-set: 40.7%\n"
+ "Accuracy on test-set: 22.0%\n"
]
}
],
@@ -1050,16 +1115,14 @@
},
{
"cell_type": "code",
- "execution_count": 37,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 44,
+ "metadata": {},
"outputs": [
{
"data": {
- "image/png": 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pyExyyUy3NkVROCZ+dHTE4avJZAK73Q6n0wmn0zkzA7bf76PdbrMAG5/XOImqVqsBmNpj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+ "image/png": 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\n",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -1085,16 +1148,14 @@
},
{
"cell_type": "code",
- "execution_count": 38,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 45,
+ "metadata": {},
"outputs": [
{
"data": {
- "image/png": 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u2daehCMp8ZRPzxlg10PkXDR3liG0BaTp1P7f+MCSALIUGZYI422M724K7u9d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\nFdxsID8AAAAASUVORK5CYII=\n",
+ "image/png": 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\n",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -1114,10 +1175,8 @@
},
{
"cell_type": "code",
- "execution_count": 39,
- "metadata": {
- "collapsed": true
- },
+ "execution_count": 46,
+ "metadata": {},
"outputs": [],
"source": [
"# We have already performed 1 iteration.\n",
@@ -1126,16 +1185,14 @@
},
{
"cell_type": "code",
- "execution_count": 40,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 47,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Accuracy on test-set: 78.2%\n"
+ "Accuracy on test-set: 75.3%\n"
]
}
],
@@ -1145,16 +1202,14 @@
},
{
"cell_type": "code",
- "execution_count": 41,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 48,
+ "metadata": {},
"outputs": [
{
"data": {
- "image/png": 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v1wshBDY3N9Hv9yGE4N1Qp9Ph7DGDwYBEIoG9vT08ePAA6XQa0WiUS13ow6Pd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\n1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FKemfju3Cxl7TSzTbifGIKnpdfiQcMoy/Gg7Jp7GLak03Fh1GUj5CCoNqHPoDMjqR7P\ndPU3s+y6ZU1Ri/e2rQcWxcAUdFJCn0Fnph54sNYdJ9TgzDMIgiAIWkstzjyDIAiCoFWE8wyCIAiC\nnITzDIIgCIKchPMMgiAIgpyE8wyCIAiCnITzDIIgCIKchPMMgiAIgpyE8wyCIAiCnITzDIIgCIKc\n/Ad6wg1N9mmYjwAAAABJRU5ErkJggg==\n",
+ "image/png": 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\n",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -1167,16 +1222,14 @@
},
{
"cell_type": "code",
- "execution_count": 42,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 49,
+ "metadata": {},
"outputs": [
{
"data": {
- "image/png": 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UHlgBfz1bf51DZhAhfLp5coCbHLC6E9x2a+3mppuPVMJs09MCpAxBl+DkxPGZ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CyxdsR2W9O2012p0mYDnQHbOXxn8LmLBv0JntyzXMvwcNXiCeS9xON3APxcN+pTAHwYfjokXuXW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\n/wFBsUa2QhlL0wAAAABJRU5ErkJggg==\n",
+ "image/png": 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\n",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -1198,10 +1251,8 @@
},
{
"cell_type": "code",
- "execution_count": 43,
- "metadata": {
- "collapsed": true
- },
+ "execution_count": 50,
+ "metadata": {},
"outputs": [],
"source": [
"# We have already performed 10 iterations.\n",
@@ -1210,10 +1261,8 @@
},
{
"cell_type": "code",
- "execution_count": 44,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 51,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1229,16 +1278,14 @@
},
{
"cell_type": "code",
- "execution_count": 45,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 52,
+ "metadata": {},
"outputs": [
{
"data": {
- "image/png": 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1J9L5JIzP84aRaJK7Api6PKjTEbnjPB4P4vH4bf06hAWDsrDp5FkqlVCtVrmH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\nTkSuQmCC\n",
+ "image/png": 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\n",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -1258,16 +1305,14 @@
},
{
"cell_type": "code",
- "execution_count": 46,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 53,
+ "metadata": {},
"outputs": [
{
"data": {
- "image/png": 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r4X4rrNfCag11LVxfJxtMUx1aqJsbAnm2Ile0efYCJl0TQjqn3Ep+bB4RnF+b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gBTjvv0l4dHPZfbyZGLnDw3DF+iHDlZTklwdbZprAQsASue3hvawaarnUpHYZpDSEb99lY47AyL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\ndg3rtMMAAAAASUVORK5CYII=\n",
+ "image/png": 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\n",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -1287,32 +1332,30 @@
},
{
"cell_type": "code",
- "execution_count": 47,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 54,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "[[ 957 0 3 2 0 5 11 1 1 0]\n",
- " [ 0 1108 2 2 1 2 4 2 14 0]\n",
- " [ 4 9 914 19 15 5 13 14 35 4]\n",
- " [ 1 0 16 928 0 28 2 14 13 8]\n",
- " [ 1 1 3 2 939 0 10 2 6 18]\n",
- " [ 10 3 3 33 10 784 17 6 19 7]\n",
- " [ 8 3 3 2 11 14 915 1 1 0]\n",
- " [ 3 9 21 9 7 1 0 959 2 17]\n",
- " [ 8 8 8 38 11 40 14 18 825 4]\n",
- " [ 11 7 1 13 75 13 1 39 4 845]]\n"
+ "[[ 956 0 2 2 0 5 11 1 3 0]\n",
+ " [ 0 1105 2 2 0 1 4 2 19 0]\n",
+ " [ 7 7 902 17 15 1 16 16 47 4]\n",
+ " [ 3 1 20 910 1 16 3 15 36 5]\n",
+ " [ 1 1 2 2 925 0 13 2 14 22]\n",
+ " [ 10 2 3 50 11 726 20 10 53 7]\n",
+ " [ 8 3 4 1 10 7 916 3 6 0]\n",
+ " [ 2 9 20 7 8 0 0 956 3 23]\n",
+ " [ 6 5 4 17 9 16 9 11 896 1]\n",
+ " [ 9 6 1 9 53 3 1 34 18 875]]\n"
]
},
{
"data": {
- "image/png": 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z6eecx/jxPvPLzKze2tqn0FbT+Jk/bx4fnbTbiNfSQrGx2xTGfeslhcA+D7hT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\nm5mZNYD/Dy0JOPxBG9Q8AAAAAElFTkSuQmCC\n",
+ "image/png": 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\n",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -1332,10 +1375,8 @@
},
{
"cell_type": "code",
- "execution_count": 48,
- "metadata": {
- "collapsed": true
- },
+ "execution_count": null,
+ "metadata": {},
"outputs": [],
"source": [
"# This has been commented out in case you want to modify and experiment\n",
@@ -1379,14 +1420,104 @@
"\n",
"THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE."
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import peforth"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "peforth.ok(cmd='''\n",
+ "__main__ :> data constant data // ( -- dataset ) MNIST dataset\n",
+ "data . cr\n",
+ "data :> test . cr\n",
+ "data :> test dir . cr\n",
+ "data :> test.images . cr\n",
+ "data :> test.images[0] . cr \\ it's flat\n",
+ "exit ''') "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# data.test.cls = np.array([label.argmax() for label in data.test.labels])\n",
+ "# 看看 label 的 type, , 已經有 argmax() built-in 了！\n",
+ "peforth.ok(cmd='''\n",
+ "data :> test.labels[0].argmax() . cr\n",
+ "data :> test.cls[:10] . cr\n",
+ "data :> test.labels[0] type . cr\n",
+ "__main__ :> np.array(1,) . cr\n",
+ "__main__ :> np.array(1,) dir . cr \\ 有 argmax() built-in \n",
+ "exit ''')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "peforth.ok(cmd='''\n",
+ "\\ images were read as flat instead of 28x28\n",
+ "data :> test.images[0].shape . cr \n",
+ "exit ''')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "peforth.ok(cmd='''\n",
+ "\\ play here\n",
+ "\\ play here\n",
+ "exit ''')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "peforth.ok(cmd='''\n",
+ "\\ play here\n",
+ "\\ play here\n",
+ "exit ''')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "peforth.ok(cmd='''\n",
+ "\\ play here\n",
+ "\\ play here\n",
+ "exit ''')"
+ ]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
- "display_name": "Python [conda env:tf]",
+ "display_name": "Python 3",
"language": "python",
- "name": "conda-env-tf-py"
+ "name": "python3"
},
"language_info": {
"codemirror_mode": {
@@ -1398,9 +1529,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.5.2"
+ "version": "3.6.0"
}
},
"nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
}
diff --git a/02_Convolutional_Neural_Network.ipynb b/02_Convolutional_Neural_Network.ipynb
index ee6f239..debfec1 100644
--- a/02_Convolutional_Neural_Network.ipynb
+++ b/02_Convolutional_Neural_Network.ipynb
@@ -2,10 +2,7 @@
"cells": [
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"# TensorFlow Tutorial #02\n",
"# Convolutional Neural Network\n",
@@ -16,10 +13,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Introduction\n",
"\n",
@@ -34,20 +28,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Flowchart"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The following chart shows roughly how the data flows in the Convolutional Neural Network that is implemented below."
]
@@ -56,15 +44,12 @@
"cell_type": "code",
"execution_count": 1,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": false
},
"outputs": [
{
"data": {
- "image/png": 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fRQSBVwVeQjwfexAl5q9q6WsOcDDx/E0F/gZ8OOtzHvF87pLsB/g89WtrjyZ3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\n+y1Eps78iraSJI02M4D9iWV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q8jjjI4AO8HeiBP47gQOAB3s7HEmSJElj2F4UVcyuJSbr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/lzD2OJXvT2WbN7e0sGLFChKJxBHZeqIT9HeCutaDg4NhH7GhoSEUa7XWOI5Da2trxW968Lqr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\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4gVqtxunTp3WAweTkJBMTE3iex86dO7njjjuwbZuJiQmy2SywPdvm+pCEnjyRz6hVng1+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\n4DgO+4+kSaWEtmfs7CJ79v1TY8Q5MB8b5mRul3ZckBKef97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VBHUqlaKvr49cLqdT6S5X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kfRi+pvPPHx8N2taA24BbgIlg2TeZeZDehftnnyrwZ8BJuIcg+ma4nzR8GHceb1uAY8+XNbj7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\n4Du4HubDuN6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\n",
"text/plain": [
""
]
@@ -81,10 +66,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The input image is processed in the first convolutional layer using the filter-weights. This results in 16 new images, one for each filter in the convolutional layer. The images are also down-sampled so the image resolution is decreased from 28x28 to 14x14.\n",
"\n",
@@ -101,20 +83,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Convolutional Layer"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The following chart shows the basic idea of processing an image in the first convolutional layer. The input image depicts the number 7 and four copies of the image are shown here, so we can see more clearly how the filter is being moved to different positions of the image. For each position of the filter, the dot-product is being calculated between the filter and the image pixels under the filter, which results in a single pixel in the output image. So moving the filter across the entire input image results in a new image being generated.\n",
"\n",
@@ -126,11 +102,7 @@
{
"cell_type": "code",
"execution_count": 2,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -150,10 +122,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The step-size for moving the filter across the input is called the stride. There is a stride for moving the filter horizontally (x-axis) and another stride for moving vertically (y-axis).\n",
"\n",
@@ -168,10 +137,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Imports"
]
@@ -180,9 +146,7 @@
"cell_type": "code",
"execution_count": 3,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -198,10 +162,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"This was developed using Python 3.5.2 (Anaconda) and TensorFlow version:"
]
@@ -209,11 +170,7 @@
{
"cell_type": "code",
"execution_count": 4,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -232,10 +189,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Configuration of Neural Network\n",
"\n",
@@ -246,9 +200,7 @@
"cell_type": "code",
"execution_count": 5,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -266,20 +218,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Load Data"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The MNIST data-set is about 12 MB and will be downloaded automatically if it is not located in the given path."
]
@@ -287,11 +233,7 @@
{
"cell_type": "code",
"execution_count": 6,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -311,10 +253,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The MNIST data-set has now been loaded and consists of 70,000 images and associated labels (i.e. classifications of the images). The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial."
]
@@ -322,11 +261,7 @@
{
"cell_type": "code",
"execution_count": 7,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -348,10 +283,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The class-labels are One-Hot encoded, which means that each label is a vector with 10 elements, all of which are zero except for one element. The index of this one element is the class-number, that is, the digit shown in the associated image. We also need the class-numbers as integers for the test-set, so we calculate it now."
]
@@ -360,9 +292,7 @@
"cell_type": "code",
"execution_count": 8,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -371,20 +301,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Data Dimensions"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The data dimensions are used in several places in the source-code below. They are defined once so we can use these variables instead of numbers throughout the source-code below."
]
@@ -393,9 +317,7 @@
"cell_type": "code",
"execution_count": 9,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -417,20 +339,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Helper-function for plotting images"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Function used to plot 9 images in a 3x3 grid, and writing the true and predicted classes below each image."
]
@@ -439,9 +355,7 @@
"cell_type": "code",
"execution_count": 10,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -476,10 +390,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Plot a few images to see if data is correct"
]
@@ -487,11 +398,7 @@
{
"cell_type": "code",
"execution_count": 11,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -517,10 +424,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## TensorFlow Graph\n",
"\n",
@@ -543,20 +447,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Helper-functions for creating new variables"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Functions for creating new TensorFlow variables in the given shape and initializing them with random values. Note that the initialization is not actually done at this point, it is merely being defined in the TensorFlow graph."
]
@@ -565,9 +463,7 @@
"cell_type": "code",
"execution_count": 12,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -579,9 +475,7 @@
"cell_type": "code",
"execution_count": 13,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -591,20 +485,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Helper-function for creating a new Convolutional Layer"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"This function creates a new convolutional layer in the computational graph for TensorFlow. Nothing is actually calculated here, we are just adding the mathematical formulas to the TensorFlow graph.\n",
"\n",
@@ -629,9 +517,7 @@
"cell_type": "code",
"execution_count": 14,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -696,10 +582,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Helper-function for flattening a layer\n",
"\n",
@@ -710,9 +593,7 @@
"cell_type": "code",
"execution_count": 15,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -743,20 +624,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Helper-function for creating a new Fully-Connected Layer"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"This function creates a new fully-connected layer in the computational graph for TensorFlow. Nothing is actually calculated here, we are just adding the mathematical formulas to the TensorFlow graph.\n",
"\n",
@@ -767,9 +642,7 @@
"cell_type": "code",
"execution_count": 16,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -795,20 +668,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Placeholder variables"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Placeholder variables serve as the input to the TensorFlow computational graph that we may change each time we execute the graph. We call this feeding the placeholder variables and it is demonstrated further below.\n",
"\n",
@@ -819,9 +686,7 @@
"cell_type": "code",
"execution_count": 17,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -830,10 +695,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The convolutional layers expect `x` to be encoded as a 4-dim tensor so we have to reshape it so its shape is instead `[num_images, img_height, img_width, num_channels]`. Note that `img_height == img_width == img_size` and `num_images` can be inferred automatically by using -1 for the size of the first dimension. So the reshape operation is:"
]
@@ -842,9 +704,7 @@
"cell_type": "code",
"execution_count": 18,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -853,10 +713,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Next we have the placeholder variable for the true labels associated with the images that were input in the placeholder variable `x`. The shape of this placeholder variable is `[None, num_classes]` which means it may hold an arbitrary number of labels and each label is a vector of length `num_classes` which is 10 in this case."
]
@@ -865,9 +722,7 @@
"cell_type": "code",
"execution_count": 19,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -876,10 +731,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"We could also have a placeholder variable for the class-number, but we will instead calculate it using argmax. Note that this is a TensorFlow operator so nothing is calculated at this point."
]
@@ -888,9 +740,7 @@
"cell_type": "code",
"execution_count": 20,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -899,10 +749,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Convolutional Layer 1\n",
"\n",
@@ -913,9 +760,7 @@
"cell_type": "code",
"execution_count": 21,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -929,10 +774,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Check the shape of the tensor that will be output by the convolutional layer. It is (?, 14, 14, 16) which means that there is an arbitrary number of images (this is the ?), each image is 14 pixels wide and 14 pixels high, and there are 16 different channels, one channel for each of the filters."
]
@@ -940,11 +782,7 @@
{
"cell_type": "code",
"execution_count": 22,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -963,10 +801,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Convolutional Layer 2\n",
"\n",
@@ -977,9 +812,7 @@
"cell_type": "code",
"execution_count": 23,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -993,10 +826,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Check the shape of the tensor that will be output from this convolutional layer. The shape is (?, 7, 7, 36) where the ? again means that there is an arbitrary number of images, with each image having width and height of 7 pixels, and there are 36 channels, one for each filter."
]
@@ -1004,11 +834,7 @@
{
"cell_type": "code",
"execution_count": 24,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1027,10 +853,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Flatten Layer\n",
"\n",
@@ -1040,11 +863,7 @@
{
"cell_type": "code",
"execution_count": 25,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"layer_flat, num_features = flatten_layer(layer_conv2)"
@@ -1052,10 +871,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Check that the tensors now have shape (?, 1764) which means there's an arbitrary number of images which have been flattened to vectors of length 1764 each. Note that 1764 = 7 x 7 x 36."
]
@@ -1063,11 +879,7 @@
{
"cell_type": "code",
"execution_count": 26,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1087,11 +899,7 @@
{
"cell_type": "code",
"execution_count": 27,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1110,10 +918,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Fully-Connected Layer 1\n",
"\n",
@@ -1124,9 +929,7 @@
"cell_type": "code",
"execution_count": 28,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1138,10 +941,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Check that the output of the fully-connected layer is a tensor with shape (?, 128) where the ? means there is an arbitrary number of images and `fc_size` == 128."
]
@@ -1149,11 +949,7 @@
{
"cell_type": "code",
"execution_count": 29,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1172,10 +968,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Fully-Connected Layer 2\n",
"\n",
@@ -1186,9 +979,7 @@
"cell_type": "code",
"execution_count": 30,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1201,11 +992,7 @@
{
"cell_type": "code",
"execution_count": 31,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1224,20 +1011,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Predicted Class"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The second fully-connected layer estimates how likely it is that the input image belongs to each of the 10 classes. However, these estimates are a bit rough and difficult to interpret because the numbers may be very small or large, so we want to normalize them so that each element is limited between zero and one and the 10 elements sum to one. This is calculated using the so-called softmax function and the result is stored in `y_pred`."
]
@@ -1246,9 +1027,7 @@
"cell_type": "code",
"execution_count": 32,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1257,10 +1036,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The class-number is the index of the largest element."
]
@@ -1269,9 +1045,7 @@
"cell_type": "code",
"execution_count": 33,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1280,20 +1054,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Cost-function to be optimized"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"To make the model better at classifying the input images, we must somehow change the variables for all the network layers. To do this we first need to know how well the model currently performs by comparing the predicted output of the model `y_pred` to the desired output `y_true`.\n",
"\n",
@@ -1306,9 +1074,7 @@
"cell_type": "code",
"execution_count": 34,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1318,10 +1084,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"We have now calculated the cross-entropy for each of the image classifications so we have a measure of how well the model performs on each image individually. But in order to use the cross-entropy to guide the optimization of the model's variables we need a single scalar value, so we simply take the average of the cross-entropy for all the image classifications."
]
@@ -1330,9 +1093,7 @@
"cell_type": "code",
"execution_count": 35,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1341,20 +1102,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Optimization Method"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Now that we have a cost measure that must be minimized, we can then create an optimizer. In this case it is the `AdamOptimizer` which is an advanced form of Gradient Descent.\n",
"\n",
@@ -1365,9 +1120,7 @@
"cell_type": "code",
"execution_count": 36,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1376,20 +1129,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Performance Measures"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"We need a few more performance measures to display the progress to the user.\n",
"\n",
@@ -1400,9 +1147,7 @@
"cell_type": "code",
"execution_count": 37,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1411,10 +1156,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"This calculates the classification accuracy by first type-casting the vector of booleans to floats, so that False becomes 0 and True becomes 1, and then calculating the average of these numbers."
]
@@ -1423,9 +1165,7 @@
"cell_type": "code",
"execution_count": 38,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1434,20 +1174,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## TensorFlow Run"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Create TensorFlow session\n",
"\n",
@@ -1458,9 +1192,7 @@
"cell_type": "code",
"execution_count": 39,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1469,10 +1201,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Initialize variables\n",
"\n",
@@ -1482,11 +1211,7 @@
{
"cell_type": "code",
"execution_count": 40,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"session.run(tf.global_variables_initializer())"
@@ -1494,20 +1219,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Helper-function to perform optimization iterations"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"There are 55,000 images in the training-set. It takes a long time to calculate the gradient of the model using all these images. We therefore only use a small batch of images in each iteration of the optimizer.\n",
"\n",
@@ -1518,9 +1237,7 @@
"cell_type": "code",
"execution_count": 41,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1529,10 +1246,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Function for performing a number of optimization iterations so as to gradually improve the variables of the network layers. In each iteration, a new batch of data is selected from the training-set and then TensorFlow executes the optimizer using those training samples. The progress is printed every 100 iterations."
]
@@ -1540,11 +1254,7 @@
{
"cell_type": "code",
"execution_count": 42,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"# Counter for total number of iterations performed so far.\n",
@@ -1601,20 +1311,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Helper-function to plot example errors"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Function for plotting examples of images from the test-set that have been mis-classified."
]
@@ -1623,9 +1327,7 @@
"cell_type": "code",
"execution_count": 43,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1659,10 +1361,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Helper-function to plot confusion matrix"
]
@@ -1671,9 +1370,7 @@
"cell_type": "code",
"execution_count": 44,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1711,20 +1408,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Helper-function for showing the performance"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Function for printing the classification accuracy on the test-set.\n",
"\n",
@@ -1736,11 +1427,7 @@
{
"cell_type": "code",
"execution_count": 45,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"# Split the test-set into smaller batches of this size.\n",
@@ -1815,10 +1502,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Performance before any optimization\n",
"\n",
@@ -1828,11 +1512,7 @@
{
"cell_type": "code",
"execution_count": 46,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1848,10 +1528,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Performance after 1 optimization iteration\n",
"\n",
@@ -1861,11 +1538,7 @@
{
"cell_type": "code",
"execution_count": 47,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1884,9 +1557,6 @@
"cell_type": "code",
"execution_count": 48,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": true
},
"outputs": [
@@ -1904,10 +1574,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Performance after 100 optimization iterations\n",
"\n",
@@ -1918,9 +1585,6 @@
"cell_type": "code",
"execution_count": 49,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": true
},
"outputs": [
@@ -1939,11 +1603,7 @@
{
"cell_type": "code",
"execution_count": 50,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1970,10 +1630,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Performance after 1000 optimization iterations\n",
"\n",
@@ -1984,9 +1641,6 @@
"cell_type": "code",
"execution_count": 51,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": false
},
"outputs": [
@@ -2015,9 +1669,6 @@
"cell_type": "code",
"execution_count": 52,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": true
},
"outputs": [
@@ -2046,10 +1697,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Performance after 10,000 optimization iterations\n",
"\n",
@@ -2060,9 +1708,6 @@
"cell_type": "code",
"execution_count": 53,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": true
},
"outputs": [
@@ -2172,9 +1817,6 @@
"cell_type": "code",
"execution_count": 54,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": true
},
"outputs": [
@@ -2231,10 +1873,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Visualization of Weights and Layers\n",
"\n",
@@ -2243,10 +1882,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Helper-function for plotting convolutional weights"
]
@@ -2255,9 +1891,7 @@
"cell_type": "code",
"execution_count": 55,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -2309,10 +1943,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Helper-function for plotting the output of a convolutional layer"
]
@@ -2321,9 +1952,7 @@
"cell_type": "code",
"execution_count": 56,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -2374,20 +2003,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Input Images"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Helper-function for plotting an image."
]
@@ -2396,9 +2019,7 @@
"cell_type": "code",
"execution_count": 57,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -2412,10 +2033,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Plot an image from the test-set which will be used as an example below."
]
@@ -2423,11 +2041,7 @@
{
"cell_type": "code",
"execution_count": 58,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -2447,10 +2061,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Plot another example image from the test-set."
]
@@ -2458,11 +2069,7 @@
{
"cell_type": "code",
"execution_count": 59,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -2482,20 +2089,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Convolution Layer 1"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Now plot the filter-weights for the first convolutional layer.\n",
"\n",
@@ -2506,9 +2107,6 @@
"cell_type": "code",
"execution_count": 60,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": false
},
"outputs": [
@@ -2529,10 +2127,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Applying each of these convolutional filters to the first input image gives the following output images, which are then used as input to the second convolutional layer. Note that these images are down-sampled to 14 x 14 pixels which is half the resolution of the original input image."
]
@@ -2541,9 +2136,6 @@
"cell_type": "code",
"execution_count": 61,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": true
},
"outputs": [
@@ -2564,10 +2156,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The following images are the results of applying the convolutional filters to the second image."
]
@@ -2576,9 +2165,6 @@
"cell_type": "code",
"execution_count": 62,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": false
},
"outputs": [
@@ -2599,30 +2185,21 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"It is difficult to see from these images what the purpose of the convolutional filters might be. It appears that they have merely created several variations of the input image, as if light was shining from different angles and casting shadows in the image."
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Convolution Layer 2"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Now plot the filter-weights for the second convolutional layer.\n",
"\n",
@@ -2635,9 +2212,6 @@
"cell_type": "code",
"execution_count": 63,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": false
},
"outputs": [
@@ -2658,10 +2232,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"There are 16 input channels to the second convolutional layer, so we can make another 15 plots of filter-weights like this. We just make one more with the filter-weights for the second channel. "
]
@@ -2669,11 +2240,7 @@
{
"cell_type": "code",
"execution_count": 64,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -2692,10 +2259,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"It can be difficult to understand and keep track of how these filters are applied because of the high dimensionality.\n",
"\n",
@@ -2708,9 +2272,6 @@
"cell_type": "code",
"execution_count": 65,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": false
},
"outputs": [
@@ -2731,10 +2292,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"And these are the results of applying the filter-weights to the second image."
]
@@ -2743,9 +2301,6 @@
"cell_type": "code",
"execution_count": 66,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": false
},
"outputs": [
@@ -2766,10 +2321,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"From these images, it looks like the second convolutional layer might detect lines and patterns in the input images, which are less sensitive to local variations in the original input images.\n",
"\n",
@@ -2778,20 +2330,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Close TensorFlow Session"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"We are now done using TensorFlow, so we close the session to release its resources."
]
@@ -2799,11 +2345,7 @@
{
"cell_type": "code",
"execution_count": 67,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"# This has been commented out in case you want to modify and experiment\n",
@@ -2813,10 +2355,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Conclusion\n",
"\n",
@@ -2829,10 +2368,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Exercises\n",
"\n",
@@ -2857,10 +2393,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## License (MIT)\n",
"\n",
@@ -2872,6 +2405,33 @@
"\n",
"THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE."
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import peforth"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "peforth.ok()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Image(r\"c:\\Users\\hcche\\Downloads\\55.jpg\")"
+ ]
}
],
"metadata": {
@@ -2891,9 +2451,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.6.1"
+ "version": "3.6.0"
}
},
"nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
}
diff --git a/03B_Layers_API.ipynb b/03B_Layers_API.ipynb
index dcc8f72..d9863c3 100644
--- a/03B_Layers_API.ipynb
+++ b/03B_Layers_API.ipynb
@@ -2,10 +2,7 @@
"cells": [
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"# TensorFlow Tutorial #03-B\n",
"# Layers API\n",
@@ -16,10 +13,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Introduction\n",
"\n",
@@ -34,40 +28,28 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Flowchart"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The following chart shows roughly how the data flows in the Convolutional Neural Network that is implemented below. See Tutorial #02 for a more detailed description of convolution."
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The input image is processed in the first convolutional layer using the filter-weights. This results in 16 new images, one for each filter in the convolutional layer. The images are also down-sampled using max-pooling so the image resolution is decreased from 28x28 to 14x14.\n",
"\n",
@@ -84,10 +66,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Imports"
]
@@ -96,9 +75,7 @@
"cell_type": "code",
"execution_count": 1,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -112,10 +89,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"This was developed using Python 3.6 (Anaconda) and TensorFlow version:"
]
@@ -123,11 +97,7 @@
{
"cell_type": "code",
"execution_count": 2,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -146,20 +116,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Load Data"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The MNIST data-set is about 12 MB and will be downloaded automatically if it is not located in the given path."
]
@@ -167,11 +131,7 @@
{
"cell_type": "code",
"execution_count": 3,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -191,10 +151,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The MNIST data-set has now been loaded and consists of 70,000 images and associated labels (i.e. classifications of the images). The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial."
]
@@ -202,11 +159,7 @@
{
"cell_type": "code",
"execution_count": 4,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -228,10 +181,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The class-labels are One-Hot encoded, which means that each label is a vector with 10 elements, all of which are zero except for one element. The index of this one element is the class-number, that is, the digit shown in the associated image. We also need the class-numbers as integers for the test-set, so we calculate it now."
]
@@ -240,9 +190,7 @@
"cell_type": "code",
"execution_count": 5,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -251,20 +199,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Data Dimensions"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The data dimensions are used in several places in the source-code below. They are defined once so we can use these variables instead of numbers throughout the source-code below."
]
@@ -273,9 +215,7 @@
"cell_type": "code",
"execution_count": 6,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -297,20 +237,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Helper-function for plotting images"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Function used to plot 9 images in a 3x3 grid, and writing the true and predicted classes below each image."
]
@@ -319,9 +253,7 @@
"cell_type": "code",
"execution_count": 7,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -356,10 +288,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Plot a few images to see if data is correct"
]
@@ -367,11 +296,7 @@
{
"cell_type": "code",
"execution_count": 8,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -397,10 +322,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## TensorFlow Graph\n",
"\n",
@@ -423,20 +345,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Placeholder variables"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Placeholder variables serve as the input to the TensorFlow computational graph that we may change each time we execute the graph. We call this feeding the placeholder variables and it is demonstrated further below.\n",
"\n",
@@ -447,9 +363,7 @@
"cell_type": "code",
"execution_count": 9,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -458,10 +372,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The convolutional layers expect `x` to be encoded as a 4-dim tensor so we have to reshape it so its shape is instead `[num_images, img_height, img_width, num_channels]`. Note that `img_height == img_width == img_size` and `num_images` can be inferred automatically by using -1 for the size of the first dimension. So the reshape operation is:"
]
@@ -470,9 +381,7 @@
"cell_type": "code",
"execution_count": 10,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -481,10 +390,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Next we have the placeholder variable for the true labels associated with the images that were input in the placeholder variable `x`. The shape of this placeholder variable is `[None, num_classes]` which means it may hold an arbitrary number of labels and each label is a vector of length `num_classes` which is 10 in this case."
]
@@ -492,11 +398,7 @@
{
"cell_type": "code",
"execution_count": 11,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"y_true = tf.placeholder(tf.float32, shape=[None, num_classes], name='y_true')"
@@ -504,10 +406,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"We could also have a placeholder variable for the class-number, but we will instead calculate it using argmax. Note that this is a TensorFlow operator so nothing is calculated at this point."
]
@@ -516,9 +415,7 @@
"cell_type": "code",
"execution_count": 12,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -527,10 +424,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## PrettyTensor Implementation\n",
"\n",
@@ -543,9 +437,6 @@
"cell_type": "code",
"execution_count": 13,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": true
},
"outputs": [],
@@ -566,10 +457,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Layers Implementation\n",
"\n",
@@ -582,9 +470,7 @@
"cell_type": "code",
"execution_count": 14,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -593,10 +479,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The input image is then input to the first convolutional layer, which has 16 filters each of size 5x5 pixels. The activation-function is the Rectified Linear Unit (ReLU) described in more detail in Tutorial #02."
]
@@ -605,9 +488,7 @@
"cell_type": "code",
"execution_count": 15,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -617,10 +498,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"One of the advantages of constructing neural networks in this fashion, is that we can now easily pull out a reference to a layer. This was more complicated in PrettyTensor.\n",
"\n",
@@ -631,9 +509,7 @@
"cell_type": "code",
"execution_count": 16,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -642,10 +518,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"We now do the max-pooling on the output of the convolutional layer. This was also described in more detail in Tutorial #02."
]
@@ -654,9 +527,7 @@
"cell_type": "code",
"execution_count": 17,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -665,10 +536,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"We now add the second convolutional layer which has 36 filters each with 5x5 pixels, and a ReLU activation function again."
]
@@ -677,9 +545,7 @@
"cell_type": "code",
"execution_count": 18,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -689,10 +555,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"We also want to plot the output of this convolutional layer, so we keep a reference for later use."
]
@@ -701,9 +564,7 @@
"cell_type": "code",
"execution_count": 19,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -712,10 +573,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The output of the second convolutional layer is also max-pooled for down-sampling the images."
]
@@ -723,11 +581,7 @@
{
"cell_type": "code",
"execution_count": 20,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"net = tf.layers.max_pooling2d(inputs=net, pool_size=2, strides=2)"
@@ -735,10 +589,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The tensors that are being output by this max-pooling are 4-rank, as can be seen from this:"
]
@@ -746,11 +597,7 @@
{
"cell_type": "code",
"execution_count": 21,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -769,10 +616,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Next we want to add fully-connected layers to the Neural Network, but these require 2-rank tensors as input, so we must first flatten the tensors.\n",
"\n",
@@ -783,9 +627,7 @@
"cell_type": "code",
"execution_count": 22,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -797,10 +639,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"This has now flattened the data to a 2-rank tensor, as can be seen from this:"
]
@@ -808,11 +647,7 @@
{
"cell_type": "code",
"execution_count": 23,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -831,10 +666,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"We can now add fully-connected layers to the neural network. These are called *dense* layers in the Layers API."
]
@@ -843,9 +675,7 @@
"cell_type": "code",
"execution_count": 24,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -855,10 +685,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"We need the neural network to classify the input images into 10 different classes. So the final fully-connected layer has `num_classes=10` output neurons."
]
@@ -867,9 +694,7 @@
"cell_type": "code",
"execution_count": 25,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -879,10 +704,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The output of the final fully-connected layer are sometimes called logits, so we have a convenience variable with that name."
]
@@ -891,9 +713,7 @@
"cell_type": "code",
"execution_count": 26,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -902,10 +722,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"We use the softmax function to 'squash' the outputs so they are between zero and one, and so they sum to one."
]
@@ -914,9 +731,7 @@
"cell_type": "code",
"execution_count": 27,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -925,10 +740,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"This tells us how likely the neural network thinks the input image is of each possible class. The one that has the highest value is considered the most likely so its index is taken to be the class-number."
]
@@ -937,9 +749,7 @@
"cell_type": "code",
"execution_count": 28,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -948,10 +758,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"We have now created the exact same Convolutional Neural Network in a few lines of code that required many complex lines of code in the direct TensorFlow implementation.\n",
"\n",
@@ -960,20 +767,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Loss-Function to be Optimized"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"To make the model better at classifying the input images, we must somehow change the variables of the Convolutional Neural Network.\n",
"\n",
@@ -985,11 +786,7 @@
{
"cell_type": "code",
"execution_count": 29,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_true, logits=logits)"
@@ -997,10 +794,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"We have now calculated the cross-entropy for each of the image classifications so we have a measure of how well the model performs on each image individually. But in order to use the cross-entropy to guide the optimization of the model's variables we need a single scalar value, so we simply take the average of the cross-entropy for all the image classifications."
]
@@ -1009,9 +803,7 @@
"cell_type": "code",
"execution_count": 30,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1020,10 +812,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Optimization Method\n",
"\n",
@@ -1035,11 +824,7 @@
{
"cell_type": "code",
"execution_count": 31,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(loss)"
@@ -1047,10 +832,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Classification Accuracy\n",
"\n",
@@ -1063,9 +845,7 @@
"cell_type": "code",
"execution_count": 32,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1074,10 +854,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The classification accuracy is calculated by first type-casting the vector of booleans to floats, so that False becomes 0 and True becomes 1, and then taking the average of these numbers."
]
@@ -1086,9 +863,7 @@
"cell_type": "code",
"execution_count": 33,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1097,10 +872,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Getting the Weights\n",
"\n",
@@ -1112,11 +884,7 @@
{
"cell_type": "code",
"execution_count": 34,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1158,10 +926,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Each of the convolutional layers has two variables. For the first convolutional layer they are named `layer_conv1/kernel:0` and `layer_conv1/bias:0`. The `kernel` variables are the ones we want to plot further below.\n",
"\n",
@@ -1172,9 +937,7 @@
"cell_type": "code",
"execution_count": 35,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1192,10 +955,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Using this helper-function we can retrieve the variables. These are TensorFlow objects. In order to get the contents of the variables, you must do something like: `contents = session.run(weights_conv1)` as demonstrated further below."
]
@@ -1204,9 +964,6 @@
"cell_type": "code",
"execution_count": 36,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": true
},
"outputs": [],
@@ -1217,20 +974,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## TensorFlow Run"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Create TensorFlow session\n",
"\n",
@@ -1241,9 +992,7 @@
"cell_type": "code",
"execution_count": 37,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1252,10 +1001,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Initialize variables\n",
"\n",
@@ -1265,11 +1011,7 @@
{
"cell_type": "code",
"execution_count": 38,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"session.run(tf.global_variables_initializer())"
@@ -1277,20 +1019,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Helper-function to perform optimization iterations"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"There are 55,000 images in the training-set. It takes a long time to calculate the gradient of the model using all these images. We therefore only use a small batch of images in each iteration of the optimizer.\n",
"\n",
@@ -1301,9 +1037,7 @@
"cell_type": "code",
"execution_count": 39,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1312,10 +1046,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"This function performs a number of optimization iterations so as to gradually improve the variables of the neural network layers. In each iteration, a new batch of data is selected from the training-set and then TensorFlow executes the optimizer using those training samples. The progress is printed every 100 iterations."
]
@@ -1323,11 +1054,7 @@
{
"cell_type": "code",
"execution_count": 40,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"# Counter for total number of iterations performed so far.\n",
@@ -1372,20 +1099,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Helper-function to plot example errors"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Function for plotting examples of images from the test-set that have been mis-classified."
]
@@ -1394,9 +1115,7 @@
"cell_type": "code",
"execution_count": 41,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1430,10 +1149,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Helper-function to plot confusion matrix"
]
@@ -1442,9 +1158,7 @@
"cell_type": "code",
"execution_count": 42,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1482,20 +1196,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Helper-function for showing the performance"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Below is a function for printing the classification accuracy on the test-set.\n",
"\n",
@@ -1507,11 +1215,7 @@
{
"cell_type": "code",
"execution_count": 43,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"# Split the test-set into smaller batches of this size.\n",
@@ -1586,10 +1290,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Performance before any optimization\n",
"\n",
@@ -1599,11 +1300,7 @@
{
"cell_type": "code",
"execution_count": 44,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1619,10 +1316,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Performance after 1 optimization iteration\n",
"\n",
@@ -1632,11 +1326,7 @@
{
"cell_type": "code",
"execution_count": 45,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1654,9 +1344,6 @@
"cell_type": "code",
"execution_count": 46,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": true
},
"outputs": [
@@ -1674,10 +1361,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Performance after 100 optimization iterations\n",
"\n",
@@ -1688,9 +1372,6 @@
"cell_type": "code",
"execution_count": 47,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": true
},
"outputs": [
@@ -1711,11 +1392,7 @@
{
"cell_type": "code",
"execution_count": 48,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1742,10 +1419,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Performance after 1000 optimization iterations\n",
"\n",
@@ -1756,9 +1430,6 @@
"cell_type": "code",
"execution_count": 49,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": false
},
"outputs": [
@@ -1789,9 +1460,6 @@
"cell_type": "code",
"execution_count": 50,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": true
},
"outputs": [
@@ -1820,10 +1488,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Performance after 10,000 optimization iterations\n",
"\n",
@@ -1834,9 +1499,6 @@
"cell_type": "code",
"execution_count": 51,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": true
},
"outputs": [
@@ -1948,9 +1610,6 @@
"cell_type": "code",
"execution_count": 52,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": true
},
"outputs": [
@@ -2007,20 +1666,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Visualization of Weights and Layers"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Helper-function for plotting convolutional weights"
]
@@ -2029,9 +1682,7 @@
"cell_type": "code",
"execution_count": 53,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -2083,10 +1734,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Helper-function for plotting the output of a convolutional layer"
]
@@ -2095,9 +1743,7 @@
"cell_type": "code",
"execution_count": 54,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -2146,10 +1792,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Input Images\n",
"\n",
@@ -2160,9 +1803,7 @@
"cell_type": "code",
"execution_count": 55,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -2176,10 +1817,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Plot an image from the test-set which will be used as an example below."
]
@@ -2187,11 +1825,7 @@
{
"cell_type": "code",
"execution_count": 56,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -2211,10 +1845,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Plot another example image from the test-set."
]
@@ -2222,11 +1853,7 @@
{
"cell_type": "code",
"execution_count": 57,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -2246,20 +1873,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Convolution Layer 1"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Now plot the filter-weights for the first convolutional layer.\n",
"\n",
@@ -2270,9 +1891,6 @@
"cell_type": "code",
"execution_count": 58,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": false
},
"outputs": [
@@ -2293,10 +1911,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Applying each of these convolutional filters to the first input image gives the following output images, which are then used as input to the second convolutional layer."
]
@@ -2304,11 +1919,7 @@
{
"cell_type": "code",
"execution_count": 59,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -2327,10 +1938,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The following images are the results of applying the convolutional filters to the second image."
]
@@ -2338,11 +1946,7 @@
{
"cell_type": "code",
"execution_count": 60,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -2361,20 +1965,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Convolution Layer 2"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Now plot the filter-weights for the second convolutional layer.\n",
"\n",
@@ -2387,9 +1985,6 @@
"cell_type": "code",
"execution_count": 61,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": false
},
"outputs": [
@@ -2410,10 +2005,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"There are 16 input channels to the second convolutional layer, so we can make another 15 plots of filter-weights like this. We just make one more with the filter-weights for the second channel. "
]
@@ -2421,11 +2013,7 @@
{
"cell_type": "code",
"execution_count": 62,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -2444,10 +2032,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"It can be difficult to understand and keep track of how these filters are applied because of the high dimensionality.\n",
"\n",
@@ -2459,11 +2044,7 @@
{
"cell_type": "code",
"execution_count": 63,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -2482,10 +2063,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"And these are the results of applying the filter-weights to the second image."
]
@@ -2493,11 +2071,7 @@
{
"cell_type": "code",
"execution_count": 64,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -2516,20 +2090,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Close TensorFlow Session"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"We are now done using TensorFlow, so we close the session to release its resources."
]
@@ -2537,11 +2105,7 @@
{
"cell_type": "code",
"execution_count": 65,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"# This has been commented out in case you want to modify and experiment\n",
@@ -2551,10 +2115,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Conclusion\n",
"\n",
@@ -2565,10 +2126,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Exercises\n",
"\n",
@@ -2591,10 +2149,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## License (MIT)\n",
"\n",
@@ -2625,9 +2180,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.6.1"
+ "version": "3.6.0"
}
},
"nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
}
diff --git a/03C_Keras_API.ipynb b/03C_Keras_API.ipynb
index a87cf6f..2f299c4 100644
--- a/03C_Keras_API.ipynb
+++ b/03C_Keras_API.ipynb
@@ -2,10 +2,7 @@
"cells": [
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"# TensorFlow Tutorial #03-C\n",
"# Keras API\n",
@@ -16,10 +13,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Introduction\n",
"\n",
@@ -34,20 +28,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Flowchart"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The following chart shows roughly how the data flows in the Convolutional Neural Network that is implemented below. See Tutorial #02 for a more detailed description of convolution.\n",
"\n",
@@ -56,20 +44,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Imports"
]
@@ -77,11 +59,7 @@
{
"cell_type": "code",
"execution_count": 1,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stderr",
@@ -102,10 +80,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"We need to import several things from Keras. Note the long import-statements. This might be a bug. Hopefully it will be possible to write shorter and more elegant lines in the future."
]
@@ -113,11 +88,7 @@
{
"cell_type": "code",
"execution_count": 2,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"# from tf.keras.models import Sequential # This does not work!\n",
@@ -129,10 +100,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"This was developed using Python 3.6 (Anaconda) and TensorFlow version:"
]
@@ -141,9 +109,6 @@
"cell_type": "code",
"execution_count": 3,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": true
},
"outputs": [
@@ -165,11 +130,7 @@
{
"cell_type": "code",
"execution_count": 4,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -188,20 +149,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Load Data"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The MNIST data-set is about 12 MB and will be downloaded automatically if it is not located in the given path."
]
@@ -209,11 +164,7 @@
{
"cell_type": "code",
"execution_count": 5,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -233,10 +184,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The MNIST data-set has now been loaded and consists of 70,000 images and associated labels (i.e. classifications of the images). The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial."
]
@@ -244,11 +192,7 @@
{
"cell_type": "code",
"execution_count": 6,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -270,10 +214,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The class-labels are One-Hot encoded, which means that each label is a vector with 10 elements, all of which are zero except for one element. The index of this one element is the class-number, that is, the digit shown in the associated image. We also need the class-numbers as integers for the test-set, so we calculate it now."
]
@@ -282,9 +223,7 @@
"cell_type": "code",
"execution_count": 7,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -293,20 +232,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Data Dimensions"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The data dimensions are used in several places in the source-code below. They are defined once so we can use these variables instead of numbers throughout the source-code below."
]
@@ -315,9 +248,7 @@
"cell_type": "code",
"execution_count": 8,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -344,20 +275,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Helper-function for plotting images"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Function used to plot 9 images in a 3x3 grid, and writing the true and predicted classes below each image."
]
@@ -366,9 +291,7 @@
"cell_type": "code",
"execution_count": 9,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -403,10 +326,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Plot a few images to see if data is correct"
]
@@ -414,11 +334,7 @@
{
"cell_type": "code",
"execution_count": 10,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -444,10 +360,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Helper-function to plot example errors\n",
"\n",
@@ -458,9 +371,7 @@
"cell_type": "code",
"execution_count": 11,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -489,10 +400,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## PrettyTensor API\n",
"\n",
@@ -503,9 +411,7 @@
"cell_type": "code",
"execution_count": 12,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -525,10 +431,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Sequential Model\n",
"\n",
@@ -539,9 +442,6 @@
"cell_type": "code",
"execution_count": 13,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": true
},
"outputs": [],
@@ -581,10 +481,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Model Compilation\n",
"\n",
@@ -597,9 +494,7 @@
"cell_type": "code",
"execution_count": 14,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -610,10 +505,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"For a classification-problem such as MNIST which has 10 possible classes, we need to use the loss-function called `categorical_crossentropy`. The performance metric we are interested in is the classification accuracy."
]
@@ -621,11 +513,7 @@
{
"cell_type": "code",
"execution_count": 15,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"model.compile(optimizer=optimizer,\n",
@@ -635,10 +523,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Training\n",
"\n",
@@ -648,11 +533,7 @@
{
"cell_type": "code",
"execution_count": 16,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -681,10 +562,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Evaluation\n",
"\n",
@@ -694,11 +572,7 @@
{
"cell_type": "code",
"execution_count": 17,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -715,10 +589,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"We can print all the performance metrics for the test-set."
]
@@ -726,11 +597,7 @@
{
"cell_type": "code",
"execution_count": 18,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -748,10 +615,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Or we can just print the classification accuracy."
]
@@ -759,11 +623,7 @@
{
"cell_type": "code",
"execution_count": 19,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -779,10 +639,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Prediction\n",
"\n",
@@ -793,9 +650,7 @@
"cell_type": "code",
"execution_count": 20,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -804,10 +659,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"These are the true class-number for those images. This is only used when plotting the images."
]
@@ -816,9 +668,7 @@
"cell_type": "code",
"execution_count": 21,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -827,10 +677,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Get the predicted classes as One-Hot encoded arrays."
]
@@ -838,11 +685,7 @@
{
"cell_type": "code",
"execution_count": 22,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"y_pred = model.predict(x=images)"
@@ -850,10 +693,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Get the predicted classes as integers."
]
@@ -861,11 +701,7 @@
{
"cell_type": "code",
"execution_count": 23,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"cls_pred = np.argmax(y_pred,axis=1)"
@@ -874,11 +710,7 @@
{
"cell_type": "code",
"execution_count": 24,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -899,10 +731,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Examples of Mis-Classified Images\n",
"\n",
@@ -915,9 +744,7 @@
"cell_type": "code",
"execution_count": 25,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -926,10 +753,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Then we convert the predicted class-numbers from One-Hot encoded arrays to integers."
]
@@ -938,9 +762,7 @@
"cell_type": "code",
"execution_count": 26,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -949,10 +771,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Plot some of the mis-classified images."
]
@@ -960,11 +779,7 @@
{
"cell_type": "code",
"execution_count": 27,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -983,10 +798,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Functional Model\n",
"\n",
@@ -996,11 +808,7 @@
{
"cell_type": "code",
"execution_count": 28,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"# Create an input layer which is similar to a feed_dict in TensorFlow.\n",
@@ -1040,10 +848,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Model Compilation\n",
"\n",
@@ -1054,9 +859,7 @@
"cell_type": "code",
"execution_count": 29,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1065,10 +868,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Create a new instance of the Keras Functional Model. We give it the inputs and outputs of the Convolutional Neural Network that we constructed above."
]
@@ -1076,11 +876,7 @@
{
"cell_type": "code",
"execution_count": 30,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"model2 = Model(inputs=inputs, outputs=outputs)"
@@ -1088,10 +884,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Compile the Keras model using the `rmsprop` optimizer and with a loss-function for multiple categories. The only performance metric we are interested in is the classification accuracy, but you could use a list of metrics here."
]
@@ -1100,9 +893,7 @@
"cell_type": "code",
"execution_count": 31,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1113,10 +904,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Training\n",
"\n",
@@ -1126,11 +914,7 @@
{
"cell_type": "code",
"execution_count": 32,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1159,10 +943,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Evaluation\n",
"\n",
@@ -1172,11 +953,7 @@
{
"cell_type": "code",
"execution_count": 33,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1193,10 +970,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The result is a list of values, containing the loss-value and all the metrics we defined when we compiled the model. Note that 'accuracy' is now called 'acc' which is a small inconsistency."
]
@@ -1205,9 +979,6 @@
"cell_type": "code",
"execution_count": 34,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": true
},
"outputs": [
@@ -1227,10 +998,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"We can also print the classification accuracy as a percentage:"
]
@@ -1238,11 +1006,7 @@
{
"cell_type": "code",
"execution_count": 35,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1258,10 +1022,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Examples of Mis-Classified Images\n",
"\n",
@@ -1274,9 +1035,7 @@
"cell_type": "code",
"execution_count": 36,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1285,10 +1044,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Then we convert the predicted class-numbers from One-Hot encoded arrays to integers."
]
@@ -1297,9 +1053,7 @@
"cell_type": "code",
"execution_count": 37,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1308,10 +1062,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Plot some of the mis-classified images."
]
@@ -1319,11 +1070,7 @@
{
"cell_type": "code",
"execution_count": 38,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1342,10 +1089,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Save & Load Model\n",
"\n",
@@ -1360,9 +1104,7 @@
"cell_type": "code",
"execution_count": 39,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1371,10 +1113,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Saving a Keras model with the trained weights is then just a single function call, as it should be."
]
@@ -1383,9 +1122,6 @@
"cell_type": "code",
"execution_count": 40,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": true
},
"outputs": [],
@@ -1395,10 +1131,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Delete the model from memory so we are sure it is no longer used."
]
@@ -1407,9 +1140,7 @@
"cell_type": "code",
"execution_count": 41,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1418,10 +1149,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"We need to import this Keras function for loading the model."
]
@@ -1429,11 +1157,7 @@
{
"cell_type": "code",
"execution_count": 42,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"from tensorflow.python.keras.models import load_model"
@@ -1441,10 +1165,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Loading the model is then just a single function-call, as it should be."
]
@@ -1453,9 +1174,7 @@
"cell_type": "code",
"execution_count": 43,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1464,10 +1183,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"We can then use the model again e.g. to make predictions. We get the first 9 images from the test-set and their true class-numbers."
]
@@ -1476,9 +1192,7 @@
"cell_type": "code",
"execution_count": 44,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1489,9 +1203,7 @@
"cell_type": "code",
"execution_count": 45,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1500,10 +1212,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"We then use the restored model to predict the class-numbers for those images."
]
@@ -1511,11 +1220,7 @@
{
"cell_type": "code",
"execution_count": 46,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"y_pred = model3.predict(x=images)"
@@ -1523,10 +1228,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Get the class-numbers as integers."
]
@@ -1535,9 +1237,7 @@
"cell_type": "code",
"execution_count": 47,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1546,10 +1246,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Plot the images with their true and predicted class-numbers."
]
@@ -1557,11 +1254,7 @@
{
"cell_type": "code",
"execution_count": 48,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1582,20 +1275,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Visualization of Layer Weights and Outputs"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Helper-function for plotting convolutional weights"
]
@@ -1604,9 +1291,7 @@
"cell_type": "code",
"execution_count": 49,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1651,10 +1336,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Get Layers\n",
"\n",
@@ -1664,11 +1346,7 @@
{
"cell_type": "code",
"execution_count": 50,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1708,10 +1386,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"We count the indices to get the layers we want.\n",
"\n",
@@ -1722,9 +1397,7 @@
"cell_type": "code",
"execution_count": 51,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1733,10 +1406,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The first convolutional layer has index 2."
]
@@ -1745,9 +1415,6 @@
"cell_type": "code",
"execution_count": 52,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": true
},
"outputs": [
@@ -1769,10 +1436,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The second convolutional layer has index 4."
]
@@ -1781,9 +1445,7 @@
"cell_type": "code",
"execution_count": 53,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1792,10 +1454,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Convolutional Weights\n",
"\n",
@@ -1805,11 +1464,7 @@
{
"cell_type": "code",
"execution_count": 54,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"weights_conv1 = layer_conv1.get_weights()[0]"
@@ -1817,10 +1472,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"This gives us a 4-rank tensor."
]
@@ -1829,9 +1481,6 @@
"cell_type": "code",
"execution_count": 55,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": true
},
"outputs": [
@@ -1852,10 +1501,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Plot the weights using the helper-function from above."
]
@@ -1864,9 +1510,6 @@
"cell_type": "code",
"execution_count": 56,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": true
},
"outputs": [
@@ -1887,10 +1530,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"We can also get the weights for the second convolutional layer and plot them."
]
@@ -1899,9 +1539,7 @@
"cell_type": "code",
"execution_count": 57,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1911,11 +1549,7 @@
{
"cell_type": "code",
"execution_count": 58,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1934,10 +1568,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Helper-function for plotting the output of a convolutional layer"
]
@@ -1946,9 +1577,7 @@
"cell_type": "code",
"execution_count": 59,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1984,10 +1613,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Input Image\n",
"\n",
@@ -1998,9 +1624,7 @@
"cell_type": "code",
"execution_count": 60,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -2014,10 +1638,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Plot an image from the test-set which will be used as an example below."
]
@@ -2026,9 +1647,6 @@
"cell_type": "code",
"execution_count": 61,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": true
},
"outputs": [
@@ -2050,10 +1668,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Output of Convolutional Layer - Method 1\n",
"\n",
@@ -2063,11 +1678,7 @@
{
"cell_type": "code",
"execution_count": 62,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"from tensorflow.python.keras import backend as K"
@@ -2076,11 +1687,7 @@
{
"cell_type": "code",
"execution_count": 63,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"output_conv1 = K.function(inputs=[layer_input.input],\n",
@@ -2089,10 +1696,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"We can then call this function with the input image. Note that the image is wrapped in two lists because the function expects an array of that dimensionality. Likewise, the function returns an array with one more dimensionality than we want so we just take the first element."
]
@@ -2100,11 +1704,7 @@
{
"cell_type": "code",
"execution_count": 64,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -2124,10 +1724,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"We can then plot the output of all 16 channels of the convolutional layer."
]
@@ -2136,9 +1733,6 @@
"cell_type": "code",
"execution_count": 65,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": false
},
"outputs": [
@@ -2159,10 +1753,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Output of Convolutional Layer - Method 2\n",
"\n",
@@ -2173,9 +1764,6 @@
"cell_type": "code",
"execution_count": 66,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": true
},
"outputs": [],
@@ -2186,10 +1774,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"This creates a new model-object where we can call the typical Keras functions. To get the output of the convoloutional layer we call the `predict()` function with the input image."
]
@@ -2197,11 +1782,7 @@
{
"cell_type": "code",
"execution_count": 67,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -2221,10 +1802,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"We can then plot the images for all 36 channels."
]
@@ -2232,11 +1810,7 @@
{
"cell_type": "code",
"execution_count": 68,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -2255,10 +1829,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Conclusion\n",
"\n",
@@ -2271,10 +1842,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Exercises\n",
"\n",
@@ -2298,10 +1866,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## License (MIT)\n",
"\n",
@@ -2332,9 +1897,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.6.1"
+ "version": "3.6.0"
}
},
"nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
}
diff --git a/03_PrettyTensor.ipynb b/03_PrettyTensor.ipynb
index bb3fbb8..657365e 100644
--- a/03_PrettyTensor.ipynb
+++ b/03_PrettyTensor.ipynb
@@ -2,10 +2,7 @@
"cells": [
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"# TensorFlow Tutorial #03\n",
"# PrettyTensor\n",
@@ -16,10 +13,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Introduction\n",
"\n",
@@ -34,20 +28,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Flowchart"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The following chart shows roughly how the data flows in the Convolutional Neural Network that is implemented below. See the previous tutorial for a more detailed description of convolution."
]
@@ -56,9 +44,6 @@
"cell_type": "code",
"execution_count": 1,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": false
},
"outputs": [
@@ -81,10 +66,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The input image is processed in the first convolutional layer using the filter-weights. This results in 16 new images, one for each filter in the convolutional layer. The images are also down-sampled so the image resolution is decreased from 28x28 to 14x14.\n",
"\n",
@@ -101,10 +83,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Imports"
]
@@ -113,9 +92,7 @@
"cell_type": "code",
"execution_count": 2,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -134,10 +111,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"This was developed using Python 3.5.2 (Anaconda) and TensorFlow version:"
]
@@ -145,11 +119,7 @@
{
"cell_type": "code",
"execution_count": 3,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -168,10 +138,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"PrettyTensor version:"
]
@@ -179,11 +146,7 @@
{
"cell_type": "code",
"execution_count": 4,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -202,20 +165,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Load Data"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The MNIST data-set is about 12 MB and will be downloaded automatically if it is not located in the given path."
]
@@ -223,11 +180,7 @@
{
"cell_type": "code",
"execution_count": 5,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -247,10 +200,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The MNIST data-set has now been loaded and consists of 70,000 images and associated labels (i.e. classifications of the images). The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial."
]
@@ -258,11 +208,7 @@
{
"cell_type": "code",
"execution_count": 6,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -284,10 +230,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The class-labels are One-Hot encoded, which means that each label is a vector with 10 elements, all of which are zero except for one element. The index of this one element is the class-number, that is, the digit shown in the associated image. We also need the class-numbers as integers for the test-set, so we calculate it now."
]
@@ -296,9 +239,7 @@
"cell_type": "code",
"execution_count": 7,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -307,20 +248,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Data Dimensions"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The data dimensions are used in several places in the source-code below. They are defined once so we can use these variables instead of numbers throughout the source-code below."
]
@@ -329,9 +264,7 @@
"cell_type": "code",
"execution_count": 8,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -353,20 +286,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Helper-function for plotting images"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Function used to plot 9 images in a 3x3 grid, and writing the true and predicted classes below each image."
]
@@ -375,9 +302,7 @@
"cell_type": "code",
"execution_count": 9,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -412,10 +337,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Plot a few images to see if data is correct"
]
@@ -423,11 +345,7 @@
{
"cell_type": "code",
"execution_count": 10,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -453,10 +371,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## TensorFlow Graph\n",
"\n",
@@ -479,20 +394,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Placeholder variables"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Placeholder variables serve as the input to the TensorFlow computational graph that we may change each time we execute the graph. We call this feeding the placeholder variables and it is demonstrated further below.\n",
"\n",
@@ -503,9 +412,7 @@
"cell_type": "code",
"execution_count": 11,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -514,10 +421,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The convolutional layers expect `x` to be encoded as a 4-dim tensor so we have to reshape it so its shape is instead `[num_images, img_height, img_width, num_channels]`. Note that `img_height == img_width == img_size` and `num_images` can be inferred automatically by using -1 for the size of the first dimension. So the reshape operation is:"
]
@@ -526,9 +430,7 @@
"cell_type": "code",
"execution_count": 12,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -537,10 +439,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Next we have the placeholder variable for the true labels associated with the images that were input in the placeholder variable `x`. The shape of this placeholder variable is `[None, num_classes]` which means it may hold an arbitrary number of labels and each label is a vector of length `num_classes` which is 10 in this case."
]
@@ -549,9 +448,7 @@
"cell_type": "code",
"execution_count": 13,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -560,10 +457,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"We could also have a placeholder variable for the class-number, but we will instead calculate it using argmax. Note that this is a TensorFlow operator so nothing is calculated at this point."
]
@@ -572,9 +466,7 @@
"cell_type": "code",
"execution_count": 14,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -583,20 +475,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## TensorFlow Implementation"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"This section shows the original source-code from Tutorial #02 which implements the Convolutional Neural Network directly in TensorFlow. The code is not actually used in this Notebook and is only meant for easy comparison to the PrettyTensor implementation below.\n",
"\n",
@@ -605,20 +491,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Helper-functions"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"In the direct TensorFlow implementation, we first make some helper-functions which will be used several times in the graph construction.\n",
"\n",
@@ -629,9 +509,7 @@
"cell_type": "code",
"execution_count": 15,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -643,9 +521,7 @@
"cell_type": "code",
"execution_count": 16,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -655,10 +531,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The following helper-function creates a new convolutional network. The input and output are 4-dimensional tensors (aka. 4-rank tensors). Note the low-level details of the TensorFlow API, such as the shape of the weights-variable. It is easy to make a mistake somewhere which may result in strange error-messages that are difficult to debug."
]
@@ -667,9 +540,7 @@
"cell_type": "code",
"execution_count": 17,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -734,10 +605,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The following helper-function flattens a 4-dim tensor to 2-dim so we can add fully-connected layers after the convolutional layers."
]
@@ -746,9 +614,7 @@
"cell_type": "code",
"execution_count": 18,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -779,10 +645,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The following helper-function creates a fully-connected layer."
]
@@ -791,9 +654,7 @@
"cell_type": "code",
"execution_count": 19,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -819,10 +680,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Graph Construction\n",
"\n",
@@ -837,9 +695,7 @@
"cell_type": "code",
"execution_count": 20,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -890,20 +746,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## PrettyTensor Implementation"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"This section shows how to make the exact same implementation of a Convolutional Neural Network using PrettyTensor.\n",
"\n",
@@ -914,9 +764,7 @@
"cell_type": "code",
"execution_count": 21,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -925,10 +773,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Now that we have wrapped the input image in a PrettyTensor object, we can add the convolutional and fully-connected layers in just a few lines of source-code.\n",
"\n",
@@ -939,9 +784,6 @@
"cell_type": "code",
"execution_count": 22,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": true
},
"outputs": [],
@@ -959,10 +801,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"That's it! We have now created the exact same Convolutional Neural Network in a few simple lines of code that required many complex lines of code in the direct TensorFlow implementation.\n",
"\n",
@@ -971,20 +810,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Getting the Weights"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Unfortunately, not everything is pretty when using PrettyTensor.\n",
"\n",
@@ -999,9 +832,7 @@
"cell_type": "code",
"execution_count": 23,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1019,10 +850,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Using this helper-function we can retrieve the variables. These are TensorFlow objects. In order to get the contents of the variables, you must do something like: `contents = session.run(weights_conv1)` as demonstrated further below."
]
@@ -1031,9 +859,7 @@
"cell_type": "code",
"execution_count": 24,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1043,20 +869,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Optimization Method"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"PrettyTensor gave us the predicted class-label (`y_pred`) as well as a loss-measure that must be minimized, so as to improve the ability of the Neural Network to classify the input images.\n",
"\n",
@@ -1068,11 +888,7 @@
{
"cell_type": "code",
"execution_count": 25,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(loss)"
@@ -1080,10 +896,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Performance Measures\n",
"\n",
@@ -1096,9 +909,7 @@
"cell_type": "code",
"execution_count": 26,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1107,10 +918,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Then we create a vector of booleans telling us whether the predicted class equals the true class of each image."
]
@@ -1119,9 +927,7 @@
"cell_type": "code",
"execution_count": 27,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1130,10 +936,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The classification accuracy is calculated by first type-casting the vector of booleans to floats, so that False becomes 0 and True becomes 1, and then taking the average of these numbers."
]
@@ -1142,9 +945,7 @@
"cell_type": "code",
"execution_count": 28,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1153,20 +954,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## TensorFlow Run"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Create TensorFlow session\n",
"\n",
@@ -1177,9 +972,7 @@
"cell_type": "code",
"execution_count": 29,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1188,10 +981,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Initialize variables\n",
"\n",
@@ -1201,11 +991,7 @@
{
"cell_type": "code",
"execution_count": 30,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"session.run(tf.global_variables_initializer())"
@@ -1213,20 +999,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Helper-function to perform optimization iterations"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"There are 55,000 images in the training-set. It takes a long time to calculate the gradient of the model using all these images. We therefore only use a small batch of images in each iteration of the optimizer.\n",
"\n",
@@ -1237,9 +1017,7 @@
"cell_type": "code",
"execution_count": 31,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1248,10 +1026,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Function for performing a number of optimization iterations so as to gradually improve the variables of the network layers. In each iteration, a new batch of data is selected from the training-set and then TensorFlow executes the optimizer using those training samples. The progress is printed every 100 iterations."
]
@@ -1259,11 +1034,7 @@
{
"cell_type": "code",
"execution_count": 32,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"# Counter for total number of iterations performed so far.\n",
@@ -1320,20 +1091,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Helper-function to plot example errors"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Function for plotting examples of images from the test-set that have been mis-classified."
]
@@ -1342,9 +1107,7 @@
"cell_type": "code",
"execution_count": 33,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1378,10 +1141,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Helper-function to plot confusion matrix"
]
@@ -1390,9 +1150,7 @@
"cell_type": "code",
"execution_count": 34,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1430,20 +1188,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Helper-function for showing the performance"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Function for printing the classification accuracy on the test-set.\n",
"\n",
@@ -1455,11 +1207,7 @@
{
"cell_type": "code",
"execution_count": 35,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"# Split the test-set into smaller batches of this size.\n",
@@ -1534,10 +1282,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Performance before any optimization\n",
"\n",
@@ -1547,11 +1292,7 @@
{
"cell_type": "code",
"execution_count": 36,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1567,10 +1308,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Performance after 1 optimization iteration\n",
"\n",
@@ -1580,11 +1318,7 @@
{
"cell_type": "code",
"execution_count": 37,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1603,9 +1337,6 @@
"cell_type": "code",
"execution_count": 38,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": true
},
"outputs": [
@@ -1623,10 +1354,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Performance after 100 optimization iterations\n",
"\n",
@@ -1637,9 +1365,6 @@
"cell_type": "code",
"execution_count": 39,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": true
},
"outputs": [
@@ -1658,11 +1383,7 @@
{
"cell_type": "code",
"execution_count": 40,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1689,10 +1410,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Performance after 1000 optimization iterations\n",
"\n",
@@ -1703,9 +1421,6 @@
"cell_type": "code",
"execution_count": 41,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": false
},
"outputs": [
@@ -1734,9 +1449,6 @@
"cell_type": "code",
"execution_count": 42,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": true
},
"outputs": [
@@ -1765,10 +1477,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Performance after 10,000 optimization iterations\n",
"\n",
@@ -1779,9 +1488,6 @@
"cell_type": "code",
"execution_count": 43,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": true
},
"outputs": [
@@ -1891,9 +1597,6 @@
"cell_type": "code",
"execution_count": 44,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": true
},
"outputs": [
@@ -1950,10 +1653,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Visualization of Weights and Layers\n",
"\n",
@@ -1962,10 +1662,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Helper-function for plotting convolutional weights"
]
@@ -1974,9 +1671,7 @@
"cell_type": "code",
"execution_count": 45,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -2028,20 +1723,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Convolution Layer 1"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Now plot the filter-weights for the first convolutional layer.\n",
"\n",
@@ -2052,9 +1741,6 @@
"cell_type": "code",
"execution_count": 46,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": false
},
"outputs": [
@@ -2075,20 +1761,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Convolution Layer 2"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Now plot the filter-weights for the second convolutional layer.\n",
"\n",
@@ -2101,9 +1781,6 @@
"cell_type": "code",
"execution_count": 47,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": false
},
"outputs": [
@@ -2124,10 +1801,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"There are 16 input channels to the second convolutional layer, so we can make another 15 plots of filter-weights like this. We just make one more with the filter-weights for the second channel. "
]
@@ -2135,11 +1809,7 @@
{
"cell_type": "code",
"execution_count": 48,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -2158,20 +1828,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Close TensorFlow Session"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"We are now done using TensorFlow, so we close the session to release its resources."
]
@@ -2179,11 +1843,7 @@
{
"cell_type": "code",
"execution_count": 49,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"# This has been commented out in case you want to modify and experiment\n",
@@ -2193,10 +1853,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Conclusion\n",
"\n",
@@ -2209,10 +1866,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Exercises\n",
"\n",
@@ -2236,10 +1890,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## License (MIT)\n",
"\n",
@@ -2270,9 +1921,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.6.1"
+ "version": "3.6.0"
}
},
"nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
}
diff --git a/09_Video_Data.ipynb b/09_Video_Data.ipynb
index f0e39d7..f9892dc 100644
--- a/09_Video_Data.ipynb
+++ b/09_Video_Data.ipynb
@@ -50,7 +50,6 @@
"cell_type": "code",
"execution_count": 1,
"metadata": {
- "collapsed": false,
"scrolled": true
},
"outputs": [
@@ -112,7 +111,6 @@
"cell_type": "code",
"execution_count": 3,
"metadata": {
- "collapsed": false,
"scrolled": true
},
"outputs": [
@@ -141,9 +139,7 @@
{
"cell_type": "code",
"execution_count": 4,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -243,7 +239,6 @@
"cell_type": "code",
"execution_count": 9,
"metadata": {
- "collapsed": false,
"scrolled": true
},
"outputs": [
@@ -272,7 +267,6 @@
"cell_type": "code",
"execution_count": 10,
"metadata": {
- "collapsed": false,
"scrolled": true
},
"outputs": [
@@ -303,9 +297,7 @@
{
"cell_type": "code",
"execution_count": 11,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"# This is the code you would run to load your own image-files.\n",
@@ -333,9 +325,7 @@
{
"cell_type": "code",
"execution_count": 12,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -363,9 +353,7 @@
{
"cell_type": "code",
"execution_count": 13,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"image_paths_train, cls_train, labels_train = dataset.get_training_set()"
@@ -381,9 +369,7 @@
{
"cell_type": "code",
"execution_count": 14,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -410,9 +396,7 @@
{
"cell_type": "code",
"execution_count": 15,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"image_paths_test, cls_test, labels_test = dataset.get_test_set()"
@@ -428,9 +412,7 @@
{
"cell_type": "code",
"execution_count": 16,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -458,7 +440,6 @@
"cell_type": "code",
"execution_count": 17,
"metadata": {
- "collapsed": false,
"scrolled": true
},
"outputs": [
@@ -593,9 +574,7 @@
{
"cell_type": "code",
"execution_count": 20,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -657,7 +636,6 @@
"cell_type": "code",
"execution_count": 22,
"metadata": {
- "collapsed": false,
"scrolled": true
},
"outputs": [
@@ -694,7 +672,6 @@
"cell_type": "code",
"execution_count": 23,
"metadata": {
- "collapsed": false,
"scrolled": false
},
"outputs": [],
@@ -721,9 +698,7 @@
{
"cell_type": "code",
"execution_count": 24,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"from inception import transfer_values_cache"
@@ -739,9 +714,7 @@
{
"cell_type": "code",
"execution_count": 25,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"file_path_cache_train = os.path.join(data_dir, 'inception-knifey-train.pkl')\n",
@@ -751,9 +724,7 @@
{
"cell_type": "code",
"execution_count": 26,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -777,9 +748,7 @@
{
"cell_type": "code",
"execution_count": 27,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -810,9 +779,7 @@
{
"cell_type": "code",
"execution_count": 28,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -839,9 +806,7 @@
{
"cell_type": "code",
"execution_count": 29,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -868,9 +833,7 @@
{
"cell_type": "code",
"execution_count": 30,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"def plot_transfer_values(i):\n",
@@ -896,7 +859,6 @@
"cell_type": "code",
"execution_count": 31,
"metadata": {
- "collapsed": false,
"scrolled": true
},
"outputs": [
@@ -943,7 +905,6 @@
"cell_type": "code",
"execution_count": 32,
"metadata": {
- "collapsed": false,
"scrolled": false
},
"outputs": [
@@ -1021,9 +982,7 @@
{
"cell_type": "code",
"execution_count": 34,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"pca = PCA(n_components=2)"
@@ -1039,9 +998,7 @@
{
"cell_type": "code",
"execution_count": 35,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"# transfer_values = transfer_values_train[0:3000]\n",
@@ -1078,7 +1035,6 @@
"cell_type": "code",
"execution_count": 37,
"metadata": {
- "collapsed": false,
"scrolled": true
},
"outputs": [
@@ -1126,7 +1082,6 @@
"cell_type": "code",
"execution_count": 39,
"metadata": {
- "collapsed": false,
"scrolled": true
},
"outputs": [
@@ -1155,9 +1110,7 @@
{
"cell_type": "code",
"execution_count": 40,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"def plot_scatter(values, cls):\n",
@@ -1191,7 +1144,6 @@
"cell_type": "code",
"execution_count": 41,
"metadata": {
- "collapsed": false,
"scrolled": true
},
"outputs": [
@@ -1275,9 +1227,7 @@
{
"cell_type": "code",
"execution_count": 45,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"transfer_values_reduced = tsne.fit_transform(transfer_values_50d) "
@@ -1294,7 +1244,6 @@
"cell_type": "code",
"execution_count": 46,
"metadata": {
- "collapsed": false,
"scrolled": true
},
"outputs": [
@@ -1326,7 +1275,6 @@
"cell_type": "code",
"execution_count": 47,
"metadata": {
- "collapsed": false,
"scrolled": true
},
"outputs": [
@@ -1459,9 +1407,7 @@
{
"cell_type": "code",
"execution_count": 52,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"# Wrap the transfer-values as a Pretty Tensor object.\n",
@@ -1552,9 +1498,7 @@
{
"cell_type": "code",
"execution_count": 56,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"correct_prediction = tf.equal(y_pred_cls, y_true_cls)"
@@ -1627,9 +1571,7 @@
{
"cell_type": "code",
"execution_count": 59,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"session.run(tf.global_variables_initializer())"
@@ -1782,9 +1724,7 @@
{
"cell_type": "code",
"execution_count": 63,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"def plot_example_errors(cls_pred, correct):\n",
@@ -2050,7 +1990,6 @@
"cell_type": "code",
"execution_count": 69,
"metadata": {
- "collapsed": false,
"scrolled": false
},
"outputs": [
@@ -2090,7 +2029,6 @@
"cell_type": "code",
"execution_count": 70,
"metadata": {
- "collapsed": false,
"scrolled": true
},
"outputs": [
@@ -2120,7 +2058,6 @@
"cell_type": "code",
"execution_count": 71,
"metadata": {
- "collapsed": false,
"scrolled": false
},
"outputs": [
@@ -2239,9 +2176,9 @@
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
- "display_name": "Python [conda env:tf-gpu]",
+ "display_name": "Python 3",
"language": "python",
- "name": "conda-env-tf-gpu-py"
+ "name": "python3"
},
"language_info": {
"codemirror_mode": {
@@ -2253,9 +2190,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.5.2"
+ "version": "3.6.0"
}
},
"nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
}
diff --git a/1.f b/1.f
new file mode 100644
index 0000000..34cdd43
--- /dev/null
+++ b/1.f
@@ -0,0 +1,106 @@
+import matplotlib.pyplot constant plt // ( -- module ) matplotlib.pyplot
+import tensorflow constant tf // ( -- module ) TensorFlow
+import numpy constant np // ( -- module ) numpy
+import os constant os // ( -- module ) os
+import inception constant inception // ( -- module ) Function and classes for loading and using the Inception model
+
+ok tf :> __version__ . cr \ ==> 1.4.0
+
+\ download inception model, current working directory is :
+\ c:\Users\hcche\Documents\GitHub\TensorFlow-Tutorials\
+
+ inception :> maybe_download()
+
+
+ OK inception :> maybe_download()
+ Downloading Inception v3 Model ...
+ - Download progress: 38.3%
+ ...
+ - Download progress: 100.0%
+ Download finished. Extracting files.
+ Done.
+ OK
+
+
+\ Load the inception model
+
+ inception :> Inception() constant model // ( -- model ) The Inception v3 model
+
+ \ The deprecation warning from TensorFlow
+ 2017-12-11 23:16:05.537612: I C:\tf_jenkins\home\workspace\rel
+ -win\M\windows\PY\36\tensorflow\core\platform\
+ cpu_feature_guard.cc:137] Your CPU supports instructions that
+ this TensorFlow binary was not compiled to use: AVX
+ OK
+
+\ Classify an image by using the inception model and
+\ print the classification scores.
+
+
+ cr cr
+ c:\Users\hcche\Downloads\jeforth.3we logo 2014-10-22.jpeg
+
+ model :> classify(image_path=pop().strip()) cr cr
+ model :: print_scores(pred=pop(),k=10,only_first_name=True)
+ dictate
+
+
+
+
+ OK ^D
+
+ c:\Users\hcche\Documents\GitHub\TensorFlow-Tutorials\inception\cropped_panda.jpg
+
+ model :> classify(image_path=pop().strip())
+ model :: print_scores(pred=pop(),k=10,only_first_name=True)
+ ^D
+ 2017-12-11 23:42:16.191967: W C:\tf_jenkins\home\workspace\rel-win\M\windows\PY\36\tensorflow\core\framework\op_def_util.cc:334] Op BatchNormWithGlobalNormalization is deprecated. It will cease to work in GraphDef version 9. Use tf.nn.batch_normalization().
+ 89.63% : giant panda
+ 0.77% : indri
+ 0.27% : lesser panda
+ 0.14% : custard apple
+ 0.10% : earthstar
+ 0.08% : sea urchin
+ 0.05% : forklift
+ 0.05% : go-kart
+ 0.04% : soccer ball
+ 0.04% : sports car
+ OK
+
+ char model py> tick(tos()) execute py: globals()[pop()]=pop()
+
+model :: load("saved_networks/tflearn.lstm.model.1512226129")
+import librosa constant librosa // ( -- module )
+import numpy constant np // ( -- module )
+librosa py: globals()['librosa']=pop()
+np py: globals()['np']=pop()
+char librosa glo :> [tos()] ( glo. ) py: globals()[pop()]=pop()
+char np glo :> [tos()] ( glo. ) py: globals()[pop()]=pop()
+{} value y,sr // ( -- tuple ) wave file and sampling rate from librosa.load()
+none value mfcc // ( -- obj ) mfcc
+"" value filename // ( -- pathname ) .wav file
+none value MFCC // ( -- obj ) MFCC
+s" c:\Users\hcche\Downloads\3-hc-a.wav" to filename
+char filename filename py: globals()[pop()]=pop()
+py> librosa.load(v('filename'),mono=True) to y,sr
+py> librosa.feature.mfcc(v('y,sr')[0],v('y,sr')[1]) to mfcc
+char mfcc mfcc py: globals()[pop()]=pop()
+py> np.pad(mfcc,((0,0),(0,80-len(mfcc[0]))),mode='constant',constant_values=0) to MFCC
+MFCC model :> predict([pop()]) tib.
+
+\ T550 OA in my office
+cd c:\Users\hcche\Documents\GitHub\TensorFlow-Tutorials
+import matplotlib.pyplot constant plt // ( -- module ) matplotlib.pyplot
+import tensorflow constant tf // ( -- module ) TensorFlow
+import numpy constant np // ( -- module ) numpy
+import os constant os // ( -- module ) os
+tf :> __version__ tib.
+
+\ 我也不懂為何 import inception 非要如此，否則 error : No module named 'inception'
+import sys constant sys // ( -- module )
+sys :: path.append(r'c:\Users\hcche\Documents\GitHub\TensorFlow-Tutorials')
+import inception constant inception // ( -- module ) Function and classes for loading and using the Inception model
+
+
+
+
\ No newline at end of file
diff --git a/11_Adversarial_Examples.ipynb b/11_Adversarial_Examples.ipynb
index 8dc1bbb..8a463d8 100644
--- a/11_Adversarial_Examples.ipynb
+++ b/11_Adversarial_Examples.ipynb
@@ -50,7 +50,6 @@
"cell_type": "code",
"execution_count": 1,
"metadata": {
- "collapsed": false,
"scrolled": true
},
"outputs": [
@@ -107,7 +106,6 @@
"cell_type": "code",
"execution_count": 3,
"metadata": {
- "collapsed": false,
"scrolled": true
},
"outputs": [
@@ -168,9 +166,7 @@
{
"cell_type": "code",
"execution_count": 5,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -204,9 +200,7 @@
{
"cell_type": "code",
"execution_count": 6,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"model = inception.Inception()"
@@ -689,7 +683,6 @@
"cell_type": "code",
"execution_count": 16,
"metadata": {
- "collapsed": false,
"scrolled": true
},
"outputs": [
@@ -885,7 +878,6 @@
"cell_type": "code",
"execution_count": 17,
"metadata": {
- "collapsed": false,
"scrolled": false
},
"outputs": [
@@ -975,7 +967,6 @@
"cell_type": "code",
"execution_count": 18,
"metadata": {
- "collapsed": false,
"scrolled": false
},
"outputs": [
@@ -1152,7 +1143,6 @@
"cell_type": "code",
"execution_count": 19,
"metadata": {
- "collapsed": false,
"scrolled": false
},
"outputs": [
@@ -1360,7 +1350,7 @@
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
- "display_name": "Python [default]",
+ "display_name": "Python 3",
"language": "python",
"name": "python3"
},
@@ -1374,9 +1364,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.5.2"
+ "version": "3.6.0"
}
},
"nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
}
diff --git a/12_Adversarial_Noise_MNIST.ipynb b/12_Adversarial_Noise_MNIST.ipynb
index 6a11e12..1b9b2d9 100644
--- a/12_Adversarial_Noise_MNIST.ipynb
+++ b/12_Adversarial_Noise_MNIST.ipynb
@@ -58,7 +58,6 @@
"cell_type": "code",
"execution_count": 1,
"metadata": {
- "collapsed": false,
"scrolled": false
},
"outputs": [
@@ -117,9 +116,7 @@
{
"cell_type": "code",
"execution_count": 3,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -146,9 +143,7 @@
{
"cell_type": "code",
"execution_count": 4,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -182,9 +177,7 @@
{
"cell_type": "code",
"execution_count": 5,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -212,9 +205,7 @@
{
"cell_type": "code",
"execution_count": 6,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -361,9 +352,7 @@
{
"cell_type": "code",
"execution_count": 10,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -579,9 +568,7 @@
{
"cell_type": "code",
"execution_count": 19,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"x_noise = tf.Variable(tf.zeros([img_size, img_size, num_channels]),\n",
@@ -638,9 +625,7 @@
{
"cell_type": "code",
"execution_count": 22,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"x_noisy_image = tf.clip_by_value(x_noisy_image, 0.0, 1.0)"
@@ -722,7 +707,6 @@
"cell_type": "code",
"execution_count": 25,
"metadata": {
- "collapsed": false,
"scrolled": true
},
"outputs": [
@@ -760,9 +744,7 @@
{
"cell_type": "code",
"execution_count": 26,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(loss)"
@@ -785,9 +767,7 @@
{
"cell_type": "code",
"execution_count": 27,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"adversary_variables = tf.get_collection(ADVERSARY_VARIABLES)"
@@ -804,7 +784,6 @@
"cell_type": "code",
"execution_count": 28,
"metadata": {
- "collapsed": false,
"scrolled": true
},
"outputs": [
@@ -871,9 +850,7 @@
{
"cell_type": "code",
"execution_count": 31,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"optimizer_adversary = tf.train.AdamOptimizer(learning_rate=1e-2).minimize(loss_adversary, var_list=adversary_variables)"
@@ -983,9 +960,7 @@
{
"cell_type": "code",
"execution_count": 36,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"session.run(tf.global_variables_initializer())"
@@ -1020,9 +995,7 @@
{
"cell_type": "code",
"execution_count": 38,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"init_noise()"
@@ -1067,9 +1040,7 @@
{
"cell_type": "code",
"execution_count": 40,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"def optimize(num_iterations, adversary_target_cls=None):\n",
@@ -1179,9 +1150,7 @@
{
"cell_type": "code",
"execution_count": 42,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"def plot_noise():\n",
@@ -1306,9 +1275,7 @@
{
"cell_type": "code",
"execution_count": 45,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"# Split the test-set into smaller batches of this size.\n",
@@ -1396,7 +1363,6 @@
"cell_type": "code",
"execution_count": 46,
"metadata": {
- "collapsed": false,
"scrolled": false
},
"outputs": [
@@ -1434,7 +1400,6 @@
"cell_type": "code",
"execution_count": 47,
"metadata": {
- "collapsed": false,
"scrolled": false
},
"outputs": [
@@ -1478,9 +1443,7 @@
{
"cell_type": "code",
"execution_count": 48,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"init_noise()"
@@ -1497,7 +1460,6 @@
"cell_type": "code",
"execution_count": 49,
"metadata": {
- "collapsed": false,
"scrolled": false
},
"outputs": [
@@ -1535,7 +1497,6 @@
"cell_type": "code",
"execution_count": 50,
"metadata": {
- "collapsed": false,
"scrolled": true
},
"outputs": [
@@ -1579,7 +1540,6 @@
"cell_type": "code",
"execution_count": 51,
"metadata": {
- "collapsed": false,
"scrolled": true
},
"outputs": [
@@ -1641,9 +1601,7 @@
{
"cell_type": "code",
"execution_count": 52,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"def find_all_noise(num_iterations=1000):\n",
@@ -1676,9 +1634,7 @@
{
"cell_type": "code",
"execution_count": 53,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1778,9 +1734,7 @@
{
"cell_type": "code",
"execution_count": 54,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"def plot_all_noise(all_noise): \n",
@@ -1814,7 +1768,6 @@
"cell_type": "code",
"execution_count": 55,
"metadata": {
- "collapsed": false,
"scrolled": true
},
"outputs": [
@@ -1923,9 +1876,7 @@
{
"cell_type": "code",
"execution_count": 57,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1967,9 +1918,7 @@
{
"cell_type": "code",
"execution_count": 58,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -2013,9 +1962,7 @@
{
"cell_type": "code",
"execution_count": 59,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -2246,9 +2193,7 @@
{
"cell_type": "code",
"execution_count": 60,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -2688,7 +2633,6 @@
"cell_type": "code",
"execution_count": 61,
"metadata": {
- "collapsed": false,
"scrolled": true
},
"outputs": [
@@ -2727,9 +2671,7 @@
{
"cell_type": "code",
"execution_count": 62,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -2784,9 +2726,7 @@
{
"cell_type": "code",
"execution_count": 63,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"init_noise()"
@@ -2802,9 +2742,7 @@
{
"cell_type": "code",
"execution_count": 64,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -2864,9 +2802,7 @@
{
"cell_type": "code",
"execution_count": 65,
- "metadata": {
- "collapsed": false
- },
+ "metadata": {},
"outputs": [],
"source": [
"# This has been commented out in case you want to modify and experiment\n",
@@ -2948,9 +2884,9 @@
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
- "display_name": "Python [conda env:tf-gpu]",
+ "display_name": "Python 3",
"language": "python",
- "name": "conda-env-tf-gpu-py"
+ "name": "python3"
},
"language_info": {
"codemirror_mode": {
@@ -2962,9 +2898,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.5.2"
+ "version": "3.6.0"
}
},
"nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
}
diff --git a/17_Estimator_API.ipynb b/17_Estimator_API.ipynb
index e26fcbf..e74d8a2 100644
--- a/17_Estimator_API.ipynb
+++ b/17_Estimator_API.ipynb
@@ -2,10 +2,7 @@
"cells": [
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"# TensorFlow Tutorial #17\n",
"# Estimator API\n",
@@ -16,10 +13,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Introduction\n",
"\n",
@@ -38,10 +32,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Imports"
]
@@ -49,11 +40,7 @@
{
"cell_type": "code",
"execution_count": 1,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stderr",
@@ -73,10 +60,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"This was developed using Python 3.6 (Anaconda) and TensorFlow version:"
]
@@ -84,11 +68,7 @@
{
"cell_type": "code",
"execution_count": 2,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -107,20 +87,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Load Data"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The MNIST data-set is about 12 MB and will be downloaded automatically if it is not located in the given path."
]
@@ -128,11 +102,7 @@
{
"cell_type": "code",
"execution_count": 3,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -152,10 +122,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The MNIST data-set has now been loaded and consists of 70,000 images and associated labels (i.e. classifications of the images). The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial."
]
@@ -163,11 +130,7 @@
{
"cell_type": "code",
"execution_count": 4,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -189,10 +152,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The class-labels are One-Hot encoded, which means that each label is a vector with 10 elements, all of which are zero except for one element. The index of this one element is the class-number, that is, the digit shown in the associated image. We also need the class-numbers as integers so we calculate that now."
]
@@ -201,9 +161,7 @@
"cell_type": "code",
"execution_count": 5,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -214,9 +172,7 @@
"cell_type": "code",
"execution_count": 6,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -225,10 +181,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"This is an example of one-hot encoded labels:"
]
@@ -236,11 +189,7 @@
{
"cell_type": "code",
"execution_count": 7,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -268,10 +217,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"These are the corresponding class-numbers:"
]
@@ -279,11 +225,7 @@
{
"cell_type": "code",
"execution_count": 8,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -302,20 +244,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Data Dimensions"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The data dimensions are used in several places in the source-code below. They are defined once so we can use these variables instead of numbers throughout the source-code below."
]
@@ -324,9 +260,7 @@
"cell_type": "code",
"execution_count": 9,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -348,20 +282,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Helper-function for plotting images"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Function used to plot 9 images in a 3x3 grid, and writing the true and predicted classes below each image."
]
@@ -370,9 +298,7 @@
"cell_type": "code",
"execution_count": 10,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -407,10 +333,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Plot a few images to see if data is correct"
]
@@ -418,11 +341,7 @@
{
"cell_type": "code",
"execution_count": 11,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -448,20 +367,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Input Functions for the Estimator"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Rather than providing raw data directly to the Estimator, we must provide functions that return the data. This allows for more flexibility in data-sources and how the data is randomly shuffled and iterated.\n",
"\n",
@@ -473,11 +386,7 @@
{
"cell_type": "code",
"execution_count": 12,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"train_input_fn = tf.estimator.inputs.numpy_input_fn(\n",
@@ -489,10 +398,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"This actually returns a function:"
]
@@ -501,9 +407,6 @@
"cell_type": "code",
"execution_count": 13,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": true
},
"outputs": [
@@ -524,10 +427,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Calling this function returns a tuple with TensorFlow ops for returning the input and output data:"
]
@@ -535,11 +435,7 @@
{
"cell_type": "code",
"execution_count": 14,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -559,10 +455,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Similarly we need to create a function for reading the data for the test-set. Note that we only want to process these images once so `num_epochs=1` and we do not want the images shuffled so `shuffle=False`."
]
@@ -571,9 +464,7 @@
"cell_type": "code",
"execution_count": 15,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -586,10 +477,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"An input-function is also needed for predicting the class of new data. As an example we just use a few images from the test-set."
]
@@ -598,9 +486,7 @@
"cell_type": "code",
"execution_count": 16,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -611,9 +497,7 @@
"cell_type": "code",
"execution_count": 17,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -625,10 +509,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The class-numbers are actually not used in the input-function as it is not needed for prediction. However, the true class-number is needed when we plot the images further below."
]
@@ -637,9 +518,7 @@
"cell_type": "code",
"execution_count": 18,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -648,10 +527,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Pre-Made / Canned Estimator\n",
"\n",
@@ -661,11 +537,7 @@
{
"cell_type": "code",
"execution_count": 19,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"feature_x = tf.feature_column.numeric_column(\"x\", shape=img_shape)"
@@ -673,10 +545,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"You can have several input features which would then be combined in a list:"
]
@@ -685,9 +554,7 @@
"cell_type": "code",
"execution_count": 20,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -696,10 +563,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"In this example we want to use a 3-layer DNN with 512, 256 and 128 units respectively."
]
@@ -708,9 +572,7 @@
"cell_type": "code",
"execution_count": 21,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -719,10 +581,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The `DNNClassifier` then constructs the neural network for us. We can also specify the activation function and various other parameters (see the docs). Here we just specify the number of classes and the directory where the checkpoints will be saved."
]
@@ -730,11 +589,7 @@
{
"cell_type": "code",
"execution_count": 22,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -755,10 +610,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Training\n",
"\n",
@@ -770,11 +622,7 @@
{
"cell_type": "code",
"execution_count": 23,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -842,10 +690,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Evaluation\n",
"\n",
@@ -855,11 +700,7 @@
{
"cell_type": "code",
"execution_count": 24,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -879,11 +720,7 @@
{
"cell_type": "code",
"execution_count": 25,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -906,11 +743,7 @@
{
"cell_type": "code",
"execution_count": 26,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -926,10 +759,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Predictions\n",
"\n",
@@ -943,11 +773,7 @@
{
"cell_type": "code",
"execution_count": 27,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"predictions = model.predict(input_fn=predict_input_fn)"
@@ -956,11 +782,7 @@
{
"cell_type": "code",
"execution_count": 28,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -978,9 +800,6 @@
"cell_type": "code",
"execution_count": 29,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": false
},
"outputs": [
@@ -1003,11 +822,7 @@
{
"cell_type": "code",
"execution_count": 30,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1028,20 +843,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"# New Estimator"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"If you cannot use one of the built-in Estimators, then you can create an arbitrary TensorFlow model yourself. To do this, you first need to create a function which defines the following:\n",
"\n",
@@ -1059,11 +868,7 @@
{
"cell_type": "code",
"execution_count": 31,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"def model_fn(features, labels, mode, params):\n",
@@ -1166,10 +971,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Create an Instance of the Estimator\n",
"\n",
@@ -1179,11 +981,7 @@
{
"cell_type": "code",
"execution_count": 32,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"params = {\"learning_rate\": 1e-4}"
@@ -1191,10 +989,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"We can then create an instance of the new Estimator.\n",
"\n",
@@ -1207,9 +1002,6 @@
"cell_type": "code",
"execution_count": 33,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": true
},
"outputs": [
@@ -1230,10 +1022,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Training\n",
"\n",
@@ -1244,9 +1033,6 @@
"cell_type": "code",
"execution_count": 34,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": true
},
"outputs": [
@@ -1316,10 +1102,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Evaluation\n",
"\n",
@@ -1330,9 +1113,6 @@
"cell_type": "code",
"execution_count": 35,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": true
},
"outputs": [
@@ -1354,11 +1134,7 @@
{
"cell_type": "code",
"execution_count": 36,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1378,11 +1154,7 @@
{
"cell_type": "code",
"execution_count": 37,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1398,10 +1170,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Predictions\n",
"\n",
@@ -1412,9 +1181,6 @@
"cell_type": "code",
"execution_count": 38,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": true
},
"outputs": [],
@@ -1425,11 +1191,7 @@
{
"cell_type": "code",
"execution_count": 39,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1457,11 +1219,7 @@
{
"cell_type": "code",
"execution_count": 40,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1482,10 +1240,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Conclusion\n",
"\n",
@@ -1501,10 +1256,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Exercises\n",
"\n",
@@ -1526,10 +1278,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## License (MIT)\n",
"\n",
@@ -1560,9 +1309,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.6.1"
+ "version": "3.6.0"
}
},
"nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
}
diff --git a/18_TFRecords_Dataset_API.ipynb b/18_TFRecords_Dataset_API.ipynb
index c3c6b91..f06e18e 100644
--- a/18_TFRecords_Dataset_API.ipynb
+++ b/18_TFRecords_Dataset_API.ipynb
@@ -2,10 +2,7 @@
"cells": [
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"# TensorFlow Tutorial #18\n",
"# TFRecords & Dataset API\n",
@@ -16,10 +13,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Introduction\n",
"\n",
@@ -32,10 +26,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Imports"
]
@@ -43,21 +34,8 @@
{
"cell_type": "code",
"execution_count": 1,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/importlib/_bootstrap.py:205: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
- " return f(*args, **kwds)\n"
- ]
- }
- ],
+ "metadata": {},
+ "outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
@@ -70,10 +48,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"This was developed using Python 3.6 (Anaconda) and TensorFlow version:"
]
@@ -81,11 +56,7 @@
{
"cell_type": "code",
"execution_count": 2,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -104,10 +75,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Load Data"
]
@@ -115,11 +83,7 @@
{
"cell_type": "code",
"execution_count": 3,
- "metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"import knifey"
@@ -127,10 +91,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The data dimensions have already been defined in the `knifey` module, so we just need to import the ones we need."
]
@@ -138,11 +99,7 @@
{
"cell_type": "code",
"execution_count": 4,
- "metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"from knifey import img_size, img_size_flat, img_shape, num_classes, num_channels"
@@ -150,10 +107,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Set the directory for storing the data-set on your computer."
]
@@ -162,9 +116,7 @@
"cell_type": "code",
"execution_count": 5,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -173,10 +125,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The Knifey-Spoony data-set is about 22 MB and will be downloaded automatically if it is not located in the given path."
]
@@ -184,11 +133,7 @@
{
"cell_type": "code",
"execution_count": 6,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -204,10 +149,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Now load the data-set. This scans the sub-directories for all `*.jpg` images and puts the filenames into two lists for the training-set and test-set. This does not actually load the images."
]
@@ -215,11 +157,7 @@
{
"cell_type": "code",
"execution_count": 7,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -236,10 +174,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Get the class-names."
]
@@ -247,11 +182,7 @@
{
"cell_type": "code",
"execution_count": 8,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -271,20 +202,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Training and Test-Sets"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"This function returns the file-paths for the images, the class-numbers as integers, and the class-numbers as One-Hot encoded arrays called labels.\n",
"\n",
@@ -295,9 +220,7 @@
"cell_type": "code",
"execution_count": 9,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -306,10 +229,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Print the first image-path to see if it looks OK."
]
@@ -317,11 +237,7 @@
{
"cell_type": "code",
"execution_count": 10,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -340,10 +256,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Get the test-set."
]
@@ -352,9 +265,7 @@
"cell_type": "code",
"execution_count": 11,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -363,10 +274,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Print the first image-path to see if it looks OK."
]
@@ -374,11 +282,7 @@
{
"cell_type": "code",
"execution_count": 12,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -397,10 +301,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The Knifey-Spoony data-set has now been loaded and consists of 4700 images and associated labels (i.e. classifications of the images). The data-set is split into 2 mutually exclusive sub-sets, the training-set and the test-set."
]
@@ -408,11 +309,7 @@
{
"cell_type": "code",
"execution_count": 13,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -432,20 +329,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Helper-function for plotting images"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Function used to plot 9 images in a 3x3 grid, and writing the true and predicted classes below each image."
]
@@ -454,9 +345,7 @@
"cell_type": "code",
"execution_count": 14,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -514,20 +403,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Helper-function for loading images"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"This dataset does not load the actual images, instead it has a list of the images in the training-set and another list for the images in the test-set. This helper-function loads some image-files."
]
@@ -536,9 +419,7 @@
"cell_type": "code",
"execution_count": 15,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -552,10 +433,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Plot a few images to see if data is correct"
]
@@ -564,9 +442,6 @@
"cell_type": "code",
"execution_count": 16,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": true
},
"outputs": [
@@ -594,20 +469,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Create TFRecords"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"TFRecords is the binary file-format used internally in TensorFlow which allows for high-performance reading and processing of datasets.\n",
"\n",
@@ -616,10 +485,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"File-path for the TFRecords file holding the training-set."
]
@@ -628,9 +494,6 @@
"cell_type": "code",
"execution_count": 17,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": true
},
"outputs": [
@@ -652,10 +515,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"File-path for the TFRecords file holding the test-set."
]
@@ -663,11 +523,7 @@
{
"cell_type": "code",
"execution_count": 18,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -687,10 +543,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Helper-function for printing the conversion progress."
]
@@ -699,9 +552,7 @@
"cell_type": "code",
"execution_count": 19,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -720,10 +571,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Helper-function for wrapping an integer so it can be saved to the TFRecords file."
]
@@ -732,9 +580,7 @@
"cell_type": "code",
"execution_count": 20,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -744,10 +590,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Helper-function for wrapping raw bytes so they can be saved to the TFRecords file."
]
@@ -756,9 +599,7 @@
"cell_type": "code",
"execution_count": 21,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -768,10 +609,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"This is the function for reading images from disk and writing them along with the class-labels to a TFRecords file. This loads and decodes the images to numpy-arrays and then stores the raw bytes in the TFRecords file. If the original image-files are compressed e.g. as jpeg-files, then the TFRecords file may be many times larger than the original image-files.\n",
"\n",
@@ -782,9 +620,7 @@
"cell_type": "code",
"execution_count": 22,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -836,10 +672,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Note the 4 function calls required to write the data-dict to the TFRecords file. In the original code-example from the Google Developers, these 4 function calls were actually nested. The design-philosophy for TensorFlow generally seems to be: If one function call is good, then 4 function calls are 4 times as good, and if they are nested then it is exponential goodness!\n",
"\n",
@@ -851,11 +684,7 @@
{
"cell_type": "code",
"execution_count": 23,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -874,10 +703,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Convert the test-set to a TFRecords-file:"
]
@@ -886,9 +712,6 @@
"cell_type": "code",
"execution_count": 24,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": true
},
"outputs": [
@@ -909,20 +732,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Input Functions for the Estimator"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The TFRecords files contain the data in a serialized binary format which needs to be converted back to images and labels of the correct data-type. We use a helper-function for this parsing:"
]
@@ -931,9 +748,7 @@
"cell_type": "code",
"execution_count": 25,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -971,10 +786,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"Helper-function for creating an input-function that reads from TFRecords files for use with the Estimator API."
]
@@ -983,9 +795,7 @@
"cell_type": "code",
"execution_count": 26,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1039,10 +849,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"This is the input-function for the training-set for use with the Estimator API:"
]
@@ -1051,9 +858,7 @@
"cell_type": "code",
"execution_count": 27,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1063,10 +868,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"This is the input-function for the test-set for use with the Estimator API:"
]
@@ -1075,9 +877,7 @@
"cell_type": "code",
"execution_count": 28,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1087,20 +887,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Input Function for Predicting on New Images"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"An input-function is also needed for predicting the class of new data. As an example we just use a few images from the test-set.\n",
"\n",
@@ -1111,9 +905,7 @@
"cell_type": "code",
"execution_count": 29,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1131,9 +923,7 @@
"cell_type": "code",
"execution_count": 30,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1145,10 +935,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The class-numbers are actually not used in the input-function as it is not needed for prediction. However, the true class-number is needed when we plot the images further below."
]
@@ -1157,9 +944,7 @@
"cell_type": "code",
"execution_count": 31,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1168,10 +953,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Pre-Made / Canned Estimator\n",
"\n",
@@ -1181,11 +963,7 @@
{
"cell_type": "code",
"execution_count": 32,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"feature_image = tf.feature_column.numeric_column(\"image\",\n",
@@ -1194,10 +972,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"You can have several input features which would then be combined in a list:"
]
@@ -1206,9 +981,7 @@
"cell_type": "code",
"execution_count": 33,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1217,10 +990,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"In this example we want to use a 3-layer DNN with 512, 256 and 128 units respectively."
]
@@ -1229,9 +999,7 @@
"cell_type": "code",
"execution_count": 34,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1240,10 +1008,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The `DNNClassifier` then constructs the neural network for us. We can also specify the activation function and various other parameters (see the docs). Here we just specify the number of classes and the directory where the checkpoints will be saved."
]
@@ -1251,11 +1016,7 @@
{
"cell_type": "code",
"execution_count": 35,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1276,10 +1037,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Training\n",
"\n",
@@ -1290,9 +1048,6 @@
"cell_type": "code",
"execution_count": 36,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": true
},
"outputs": [
@@ -1326,10 +1081,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Evaluation\n",
"\n",
@@ -1340,9 +1092,6 @@
"cell_type": "code",
"execution_count": 37,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": true
},
"outputs": [
@@ -1364,11 +1113,7 @@
{
"cell_type": "code",
"execution_count": 38,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1391,11 +1136,7 @@
{
"cell_type": "code",
"execution_count": 39,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1411,10 +1152,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Predictions\n",
"\n",
@@ -1428,11 +1166,7 @@
{
"cell_type": "code",
"execution_count": 40,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"predictions = model.predict(input_fn=predict_input_fn)"
@@ -1441,11 +1175,7 @@
{
"cell_type": "code",
"execution_count": 41,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1463,9 +1193,6 @@
"cell_type": "code",
"execution_count": 42,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": false
},
"outputs": [
@@ -1488,11 +1215,7 @@
{
"cell_type": "code",
"execution_count": 43,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1513,20 +1236,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Predictions for the Entire Test-Set"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"It appears that the model maybe classifies all images as 'spoony'. So let us see the predictions for the entire test-set. We can do this simply by using its input-function:"
]
@@ -1535,9 +1252,7 @@
"cell_type": "code",
"execution_count": 44,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -1548,9 +1263,6 @@
"cell_type": "code",
"execution_count": 45,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": true
},
"outputs": [
@@ -1570,11 +1282,7 @@
{
"cell_type": "code",
"execution_count": 46,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"cls_pred = np.array(cls, dtype='int').squeeze()"
@@ -1582,10 +1290,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The test-set contains 530 images in total and they have all been predicted as class 2 (spoony). So this model does not work at all for classifying the Knifey-Spoony dataset."
]
@@ -1593,11 +1298,7 @@
{
"cell_type": "code",
"execution_count": 47,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1616,20 +1317,14 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"# New Estimator"
]
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"If you cannot use one of the built-in Estimators, then you can create an arbitrary TensorFlow model yourself. To do this, you first need to create a function which defines the following:\n",
"\n",
@@ -1647,11 +1342,7 @@
{
"cell_type": "code",
"execution_count": 48,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"def model_fn(features, labels, mode, params):\n",
@@ -1753,10 +1444,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Create an Instance of the Estimator\n",
"\n",
@@ -1766,11 +1454,7 @@
{
"cell_type": "code",
"execution_count": 49,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"params = {\"learning_rate\": 1e-4}"
@@ -1778,10 +1462,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"We can then create an instance of the new Estimator.\n",
"\n",
@@ -1794,9 +1475,6 @@
"cell_type": "code",
"execution_count": 50,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": true
},
"outputs": [
@@ -1817,10 +1495,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Training\n",
"\n",
@@ -1831,9 +1506,6 @@
"cell_type": "code",
"execution_count": 51,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": false
},
"outputs": [
@@ -1867,10 +1539,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Evaluation\n",
"\n",
@@ -1881,9 +1550,6 @@
"cell_type": "code",
"execution_count": 52,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": true
},
"outputs": [
@@ -1905,11 +1571,7 @@
{
"cell_type": "code",
"execution_count": 53,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1929,11 +1591,7 @@
{
"cell_type": "code",
"execution_count": 54,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -1949,10 +1607,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Predictions\n",
"\n",
@@ -1963,9 +1618,6 @@
"cell_type": "code",
"execution_count": 55,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": true
},
"outputs": [],
@@ -1976,11 +1628,7 @@
{
"cell_type": "code",
"execution_count": 56,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -2008,11 +1656,7 @@
{
"cell_type": "code",
"execution_count": 57,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -2033,10 +1677,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"### Predictions for the Entire Test-Set\n",
"\n",
@@ -2047,9 +1688,7 @@
"cell_type": "code",
"execution_count": 58,
"metadata": {
- "collapsed": true,
- "deletable": true,
- "editable": true
+ "collapsed": true
},
"outputs": [],
"source": [
@@ -2060,9 +1699,6 @@
"cell_type": "code",
"execution_count": 59,
"metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true,
"scrolled": true
},
"outputs": [
@@ -2115,10 +1751,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"The Convolutional Neural Network predicts different classes for the images, although most have just been classified as 0 (forky), so the accuracy is horrible."
]
@@ -2126,11 +1759,7 @@
{
"cell_type": "code",
"execution_count": 60,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -2150,11 +1779,7 @@
{
"cell_type": "code",
"execution_count": 61,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -2174,11 +1799,7 @@
{
"cell_type": "code",
"execution_count": 62,
- "metadata": {
- "collapsed": false,
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"outputs": [
{
"data": {
@@ -2197,10 +1818,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Conclusion\n",
"\n",
@@ -2209,10 +1827,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## Exercises\n",
"\n",
@@ -2232,10 +1847,7 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "deletable": true,
- "editable": true
- },
+ "metadata": {},
"source": [
"## License (MIT)\n",
"\n",
@@ -2266,9 +1878,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.6.1"
+ "version": "3.6.0"
}
},
"nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
}
diff --git a/Jupyter Notebook Tutorial _ Ipython Notebook Tutorial (YouTube = EEEZX_0FMEc).ipynb b/Jupyter Notebook Tutorial _ Ipython Notebook Tutorial (YouTube = EEEZX_0FMEc).ipynb
new file mode 100644
index 0000000..78743bb
--- /dev/null
+++ b/Jupyter Notebook Tutorial _ Ipython Notebook Tutorial (YouTube = EEEZX_0FMEc).ipynb
@@ -0,0 +1,448 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Jupyter Notebook Ipython Notebook Tutorial\n",
+ "#### YouTube ID : EEEZX_0FMEc\n",
+ "\n",
+ "---\n",
+ "這個的確就是我想像中的 **jeforth** 工作環境，現在才發現人家已經做好很久了。Try google \"jupyter notebook gallery\" to find more. Like [A gallery of interesting Jupyter Notebooks](https://github.com/jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-Notebooks).\n",
+ "\n",
+ "---\n",
+ "Jupyter server 只能用 localhost:8888 登入, 若用 http://192.168.0.194:8888/login 不行.\n",
+ "\n",
+ "--- \n",
+ "登入 Jupyter server 所要求的 password 或 password 就是跑 jupyter.exe notebook 的 DOSBox 裡呈現的 `token=1458e480c30553e17f7877d33beb4a7d6cf56a7c7356956c` 如下:\n",
+ "```\n",
+ "[I 22:29:09.570 NotebookApp] Serving notebooks from local directory: c:\\Users\\hcche\\Documents\\GitHub\\TensorFlow-Tutorials\n",
+ "[I 22:29:09.570 NotebookApp] 0 active kernels\n",
+ "[I 22:29:09.571 NotebookApp] The Jupyter Notebook is running at:\n",
+ "[I 22:29:09.571 NotebookApp] http://localhost:8888/?token=1458e480c30553e17f7877d33beb4a7d6cf56a7c7356956c\n",
+ "[I 22:29:09.571 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).\n",
+ "```\n",
+ "\n",
+ "---\n",
+ "Jupyter notebook 標題右邊的符號就是該標題的 HTML Tag address \n",
+ "例如 [Jump to YouTube ID](#YouTube-ID-:-EEEZX_0FMEc) 的寫法是：``` [Jump to YouTube ID](#YouTube-ID-:-EEEZX_0FMEc) ``` 其中的 tag address 是從上面 right click 抄來的。\n",
+ "\n",
+ "---\n",
+ "\n",
+ "---\n",
+ "\n",
+ "---\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Todo\n",
+ "- [ ] How to make peforth a jupyter kernel?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Open | \n",
+ " High | \n",
+ " Low | \n",
+ " Close | \n",
+ " Adj Close | \n",
+ " Volume | \n",
+ "
\n",
+ " \n",
+ " | Date | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 2015-12-31 | \n",
+ " 107.010002 | \n",
+ " 107.029999 | \n",
+ " 104.820000 | \n",
+ " 105.260002 | \n",
+ " 101.339394 | \n",
+ " 40635300 | \n",
+ "
\n",
+ " \n",
+ " | 2016-01-04 | \n",
+ " 102.610001 | \n",
+ " 105.370003 | \n",
+ " 102.000000 | \n",
+ " 105.349998 | \n",
+ " 101.426033 | \n",
+ " 67649400 | \n",
+ "
\n",
+ " \n",
+ " | 2016-01-05 | \n",
+ " 105.750000 | \n",
+ " 105.849998 | \n",
+ " 102.410004 | \n",
+ " 102.709999 | \n",
+ " 98.884369 | \n",
+ " 55791000 | \n",
+ "
\n",
+ " \n",
+ " | 2016-01-06 | \n",
+ " 100.559998 | \n",
+ " 102.370003 | \n",
+ " 99.870003 | \n",
+ " 100.699997 | \n",
+ " 96.949242 | \n",
+ " 68457400 | \n",
+ "
\n",
+ " \n",
+ " | 2016-01-07 | \n",
+ " 98.680000 | \n",
+ " 100.129997 | \n",
+ " 96.430000 | \n",
+ " 96.449997 | \n",
+ " 92.857529 | \n",
+ " 81094400 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
hello world
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "%%HTML \n",
+ " hello world
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 128,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.26973684210526316"
+ ]
+ },
+ "execution_count": 128,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "α = 123 # \\alpha cursor 停在最後一個 a 後面，敲 key 即得 α , 或 \\Alpha 得 Α \n",
+ "β = 456\n",
+ "α / β "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 132,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "顯示檔案清單以列出目錄中的檔案及子目錄。\n",
+ "\n",
+ "DIR [drive:][path][filename] [/A[[:]attributes]] [/B] [/C] [/D] [/L] [/N]\n",
+ " [/O[[:]sortorder]] [/P] [/Q] [/R] [/S] [/T[[:]timefield]] [/W] [/X] [/4]\n",
+ "\n",
+ " [drive:][path][filename]\n",
+ " 指定要顯示的磁碟機、目錄或檔案。\n",
+ "\n",
+ " /A 依照指定的檔案屬性來顯示檔案。\n",
+ " attributes D 目錄 R 唯讀檔\n",
+ " H 隱藏檔 A 保存檔\n",
+ " S 系統檔案 - 無意義\n",
+ " L 重新分析點 - 首碼表示否定\n",
+ " /B 使用單純格式 (沒有標頭資訊或摘要)。\n",
+ " /C 顯示檔案大小千位數分隔符號。這是預設值。使用 /-C 來停用\n",
+ " 分隔符號的顯示。\n",
+ " /D 與寬的列表格式相同，但是依照欄來排序。\n",
+ " /L 使用小寫顯示。\n",
+ " /N 使用新的長列表格式，檔名會顯示在最右方。\n",
+ " /O 依照指定的排序順序來列出檔案。\n",
+ " sortorder N 依名稱 (英文字母) S 依大小 (最小的在前)\n",
+ " E 依副檔名 (英文字母) D 依照日期與時間 (日期較早的在前)\n",
+ " G 先列出子目錄 - 表示相反的順序\n",
+ " /P 當資料填滿整個螢幕時暫停顯示。\n",
+ " /Q 顯示檔案擁有者。\n",
+ " /R 顯示檔案的替代資料流。\n",
+ " /S 顯示指定目錄及所有子目錄中的檔案。\n",
+ " /T 指定用來顯示或排序的時間欄位\n",
+ " timefield C 建立\n",
+ " A 上次檔案存取時間\n",
+ " W 上次寫入檔案時間\n",
+ " /W 使用寬的列表格式。\n",
+ " /X 顯示對非 8.3 格式的檔案產生的短檔名。這個格式和 /N 相同，\n",
+ " 但是短檔名會插入在長檔名之前。如果沒有長檔名存在，該處會\n",
+ " 顯示空白。\n",
+ " /4 顯示四位數的年份\n",
+ "\n",
+ "參數可能會在 DIRCMD 環境變數預先設定。您可以在任何參數使用連字號字首(-)\n",
+ "來覆蓋預先的設定--例如: /-W。\n"
+ ]
+ }
+ ],
+ "source": [
+ "%ls /?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 136,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['', 'Microsoft Windows [版本 10.0.16299.125]']"
+ ]
+ },
+ "execution_count": 136,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%system ver"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[知乎：你为什么使用 jupyter ，进行分析，而不是用 python 脚本或仅仅利用 excel ？](https://www.zhihu.com/question/37490497)\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[Advanced Jupyter Notebook Tricks — Part I](https://blog.dominodatalab.com/lesser-known-ways-of-using-notebooks/)"
+ ]
+ }
+ ],
+ "metadata": {
+ "celltoolbar": "Raw Cell Format",
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Untitled.ipynb b/Untitled.ipynb
new file mode 100644
index 0000000..a23b713
--- /dev/null
+++ b/Untitled.ipynb
@@ -0,0 +1,99 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "str(\"sdfdsf.JPEG\").lower().endswith((\".jpeg\",'.jpg'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'sdfdsf.dsfsf'"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "(\"sdfdsf.Dsfsf\").lower()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "hello world!\n",
+ "Ooops! jpeg pictures only please. sdfsf sdlfsjdflsjdf fs dlfsjdfls fsf.jPEgg is not one.\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "results = \"hello world!\\n\"\n",
+ "def msg():\n",
+ " pass\n",
+ "msg.fileName = \"sdfsf sdlfsjdflsjdf fs dlfsjdfls fsf.jPEgg\"\n",
+ "if msg.fileName.strip().lower().endswith((\".jpeg\",'.jpg')):\n",
+ " results += 'ok!\\n'\n",
+ "else:\n",
+ " results += 'Ooops! jpeg pictures only, please. {} is not one.\\n'.format(msg.fileName)\n",
+ "print(results)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/inception.py b/inception.py
index 6194c5b..3481b9b 100644
--- a/inception.py
+++ b/inception.py
@@ -436,7 +436,7 @@ def print_scores(self, pred, k=10, only_first_name=True):
then set only_first_name=True.
:return:
- Nothing.
+ results in a text string
"""
# Get a sorted index for the pred-array.
@@ -445,6 +445,7 @@ def print_scores(self, pred, k=10, only_first_name=True):
# The index is sorted lowest-to-highest values. Take the last k.
top_k = idx[-k:]
+ results = ""
# Iterate the top-k classes in reversed order (i.e. highest first).
for cls in reversed(top_k):
# Lookup the class-name.
@@ -454,7 +455,11 @@ def print_scores(self, pred, k=10, only_first_name=True):
score = pred[cls]
# Print the score and class-name.
- print("{0:>6.2%} : {1}".format(score, name))
+ result = "{0:>6.2%} : {1}".format(score, name)
+ results += result + '\n'
+ # print("{0:>6.2%} : {1}".format(score, name))
+ print(result)
+ return results
def transfer_values(self, image_path=None, image=None):
"""
diff --git a/itchat_remote.py b/itchat_remote.py
new file mode 100644
index 0000000..78b7e5b
--- /dev/null
+++ b/itchat_remote.py
@@ -0,0 +1,111 @@
+import itchat
+from itchat.content import * # TEXT PICTURE 等 constant 的定義
+import peforth
+import matplotlib.pyplot as plt
+import tensorflow as tf
+import numpy as np
+import time
+# OK time :> ctime() . cr
+# Tue Dec 12 14:34:11 2017
+
+import inception
+inception.maybe_download()
+model = inception.Inception() # The Inception v3 model
+
+# Inhibit 'bye' command, it terminates DOSBox session immediately
+# and leaves 'bye' in msg! Only a re-login can resolve it. To avoid this,
+# decorator must return instead of doing the 'bye' command directly.
+peforth.ok(loc=locals(),cmd=":> [0] constant locals : bye locals :> ['itchat'].logout() ; exit")
+
+# Send message to friend or chatroom depends on the given 'send'
+# function. It can be itchat.send or msg.user.send up to the caller.
+def send_chunk(text, send, pcs=2000):
+ s = text
+ while True:
+ if len(s)>pcs:
+ print(s[:pcs]); send(s[:pcs])
+ else:
+ print(s); send(s)
+ break
+ s = s[pcs:]
+
+# Console is like a robot that listens and talks.
+# Used in chating with friends and in a chatroom.
+def console(msg,cmd):
+ if cmd:
+ print(cmd)
+ peforth.vm.dictate("display-off")
+ try:
+ peforth.vm.dictate(cmd)
+ except Exception as err:
+ errmsg = "Failed! : {}".format(err)
+ peforth.vm.dictate("display-on")
+ send_chunk(errmsg, msg.user.send)
+ else:
+ peforth.vm.dictate("display-on screen-buffer")
+ screen = peforth.vm.pop()[0]
+ send_chunk(screen, msg.user.send)
+ send_chunk("OK", msg.user.send)
+
+@itchat.msg_register(TEXT)
+def _(msg):
+ if peforth.vm.debug==99: peforth.ok('99> ',loc=locals(),cmd=":> [0] inport cr") # breakpoint
+ console(msg, msg.Text.strip())
+
+@itchat.msg_register([MAP, CARD, NOTE, SHARING])
+def _(msg):
+ if peforth.vm.debug==11: peforth.ok('11> ',loc=locals(),cmd=":> [0] inport cr") # breakpoint
+ send_chunk('%s: %s' % (msg.type, msg.text), msg.user.send)
+
+def predict(msg):
+ results = time.ctime() + '\n'
+ results += 'Google Inception V3 thinks it is:\n'
+ msg.download(msg.fileName) # 放在 working directory 下
+ pred = model.classify(image_path=msg.fileName.strip())
+ results += model.print_scores(pred=pred,k=10,only_first_name=True)
+ return results
+
+@itchat.msg_register(PICTURE)
+def _(msg):
+ if peforth.vm.debug==2211: peforth.ok('2211> ',loc=locals(),cmd=":> [0] constant loc2211 cr") # breakpoint
+ # msg.download(msg.fileName) # 放在 working directory 下
+ # pred = model.classify(image_path=msg.fileName.strip())
+ # results = model.print_scores(pred=pred,k=10,only_first_name=True)
+ return predict(msg)
+
+@itchat.msg_register([RECORDING, ATTACHMENT, VIDEO])
+def _(msg):
+ if peforth.vm.debug==22: peforth.ok('22> ',loc=locals(),cmd=":> [0] inport cr") # breakpoint
+ msg.download(msg.fileName)
+ typeSymbol = {
+ PICTURE: 'img',
+ VIDEO: 'vid', }.get(msg.type, 'fil')
+ return '@%s@%s' % (typeSymbol, msg.fileName)
+
+@itchat.msg_register(FRIENDS)
+def _(msg):
+ if peforth.vm.debug==33: peforth.ok('33> ',loc=locals(),cmd=":> [0] inport cr") # breakpoint
+ msg.user.verify()
+ send_chunk('Nice to meet you!', msg.user.send)
+
+@itchat.msg_register(TEXT, isGroupChat=True)
+def _(msg):
+ if peforth.vm.debug==44: peforth.ok('44> ',loc=locals(),cmd=":> [0] inport cr") # breakpoint
+ if msg.isAt:
+ cmd = msg.text.split(maxsplit=1)[1] # remove the leading @nickName
+ console(msg, cmd)
+
+@itchat.msg_register(PICTURE, isGroupChat=True)
+def _(msg):
+ if peforth.vm.debug==4411: peforth.ok('4411> ',loc=locals(),cmd=":> [0] constant loc4411 cr") # breakpoint
+ # msg.download(msg.fileName) # 放在 working directory 下
+ # pred = model.classify(image_path=msg.fileName.strip())
+ # results = model.print_scores(pred=pred,k=10,only_first_name=True)
+ send_chunk(predict(msg), msg.user.send)
+
+
+# peforth.vm.debug=99
+itchat.auto_login(True) # hotReload=True
+itchat.run(False, blockThread=True) # debug=True
+peforth.ok() # breakpoint
+
diff --git a/itchat_remote2.py b/itchat_remote2.py
new file mode 100644
index 0000000..8979443
--- /dev/null
+++ b/itchat_remote2.py
@@ -0,0 +1,125 @@
+import itchat
+from itchat.content import * # TEXT PICTURE 等 constant 的定義
+import peforth
+import matplotlib.pyplot as plt
+import tensorflow as tf
+import numpy as np
+import time
+# OK time :> ctime() . cr
+# Tue Dec 12 14:34:11 2017
+
+import inception
+inception.maybe_download()
+model = inception.Inception() # The Inception v3 model
+
+# Inhibit 'bye' command, it terminates DOSBox session immediately
+# and leaves 'bye' in msg! Only a re-login can resolve it. To avoid this,
+# decorator must return instead of doing the 'bye' command directly.
+peforth.ok(loc=locals(),cmd=":> [0] constant locals : bye locals :> ['itchat'].logout() ; exit")
+
+# Send message to friend or chatroom depends on the given 'send'
+# function. It can be itchat.send or msg.user.send up to the caller.
+def send_chunk(text, send, pcs=2000):
+ s = text
+ while True:
+ if len(s)>pcs:
+ print(s[:pcs]); send(s[:pcs])
+ else:
+ print(s); send(s)
+ break
+ s = s[pcs:]
+
+# Console is like a robot that listens and talks.
+# Used in chating with friends and in a chatroom.
+def console(msg,cmd):
+ if cmd:
+ print(cmd)
+ peforth.vm.dictate("display-off")
+ try:
+ peforth.vm.dictate(cmd)
+ except Exception as err:
+ errmsg = "Failed! : {}".format(err)
+ peforth.vm.dictate("display-on")
+ send_chunk(errmsg, msg.user.send)
+ else:
+ peforth.vm.dictate("display-on screen-buffer")
+ screen = peforth.vm.pop()[0]
+ send_chunk(screen, msg.user.send)
+ send_chunk("OK", msg.user.send)
+
+#
+# 讓 Inception V3 看照片，回答那啥。
+#
+def predict(msg):
+ results = time.ctime() + '\n'
+ results += 'Google Inception V3 thinks it is:\n'
+ msg.download(msg.fileName) # 放在 working directory 下
+ try:
+ pred = model.classify(image_path=msg.fileName.strip())
+ results += model.print_scores(pred=pred,k=10,only_first_name=True)
+ except Exception as err:
+ results += 'Ooops! ' + err + '\n'
+ return results
+
+@itchat.msg_register(TEXT)
+def _(msg):
+ if peforth.vm.debug==99: peforth.ok('99> ',loc=locals(),cmd=":> [0] inport cr") # breakpoint
+ console(msg, msg.Text.strip())
+
+@itchat.msg_register([MAP, CARD, NOTE, SHARING])
+def _(msg):
+ if peforth.vm.debug==11: peforth.ok('11> ',loc=locals(),cmd=":> [0] inport cr") # breakpoint
+ send_chunk('%s: %s' % (msg.type, msg.text), msg.user.send)
+
+#
+# 不要干擾借用帳號的同仁，只在特定的 ChatRoom 裡工作。
+#
+# @itchat.msg_register(PICTURE)
+# def _(msg):
+# if peforth.vm.debug==2211: peforth.ok('2211> ',loc=locals(),cmd=":> [0] constant loc2211 cr") # breakpoint
+# # msg.download(msg.fileName) # 放在 working directory 下
+# # pred = model.classify(image_path=msg.fileName.strip())
+# # results = model.print_scores(pred=pred,k=10,only_first_name=True)
+# return predict(msg)
+
+# @itchat.msg_register([RECORDING, ATTACHMENT, VIDEO])
+# def _(msg):
+# if peforth.vm.debug==22: peforth.ok('22> ',loc=locals(),cmd=":> [0] inport cr") # breakpoint
+# msg.download(msg.fileName)
+# typeSymbol = {
+# PICTURE: 'img',
+# VIDEO: 'vid', }.get(msg.type, 'fil')
+# return '@%s@%s' % (typeSymbol, msg.fileName)
+#
+# @itchat.msg_register(FRIENDS)
+# def _(msg):
+# if peforth.vm.debug==33: peforth.ok('33> ',loc=locals(),cmd=":> [0] inport cr") # breakpoint
+# msg.user.verify()
+# send_chunk('Nice to meet you!', msg.user.send)
+
+@itchat.msg_register(ATTACHMENT, isGroupChat=True)
+def _(msg):
+ if peforth.vm.debug==55: peforth.ok('55> ',loc=locals(),cmd=":> [0] inport cr") # breakpoint
+ msg.download(msg.fileName)
+ return 'Attachment: %s received at %s' % (msg.fileName,time.ctime())
+
+@itchat.msg_register(TEXT, isGroupChat=True)
+def _(msg):
+ if peforth.vm.debug==44: peforth.ok('44> ',loc=locals(),cmd=":> [0] inport cr") # breakpoint
+ if msg.isAt:
+ cmd = msg.text.split(maxsplit=1)[1] # remove the leading @nickName
+ console(msg, cmd)
+
+@itchat.msg_register(PICTURE, isGroupChat=True)
+def _(msg):
+ if peforth.vm.debug==4411: peforth.ok('4411> ',loc=locals(),cmd=":> [0] constant loc4411 cr") # breakpoint
+ # msg.download(msg.fileName) # 放在 working directory 下
+ # pred = model.classify(image_path=msg.fileName.strip())
+ # results = model.print_scores(pred=pred,k=10,only_first_name=True)
+ send_chunk(predict(msg), msg.user.send)
+
+# peforth.vm.debug=99
+itchat.auto_login(True) # hotReload=True
+itchat.run(debug=False, blockThread=True) # debug=True
+peforth.ok() # breakpoint
+
diff --git a/itchat_remote3.py b/itchat_remote3.py
new file mode 100644
index 0000000..ccfdd59
--- /dev/null
+++ b/itchat_remote3.py
@@ -0,0 +1,125 @@
+import itchat
+from itchat.content import * # TEXT PICTURE 等 constant 的定義
+import peforth
+import matplotlib.pyplot as plt
+import tensorflow as tf
+import numpy as np
+import time
+# OK time :> ctime() . cr
+# Tue Dec 12 14:34:11 2017
+
+import inception
+inception.maybe_download()
+model = inception.Inception() # The Inception v3 model
+
+# Inhibit 'bye' command, it terminates DOSBox session immediately
+# and leaves 'bye' in msg! Only a re-login can resolve it. To avoid this,
+# decorator must return instead of doing the 'bye' command directly.
+peforth.ok(loc=locals(),cmd=":> [0] constant locals : bye locals :> ['itchat'].logout() ; exit")
+
+# Send message to friend or chatroom depends on the given 'send'
+# function. It can be itchat.send or msg.user.send up to the caller.
+def send_chunk(text, send, pcs=2000):
+ s = text
+ while True:
+ if len(s)>pcs:
+ print(s[:pcs]); send(s[:pcs])
+ else:
+ print(s); send(s)
+ break
+ s = s[pcs:]
+
+# Console is like a robot that listens and talks.
+# Used in chating with friends and in a chatroom.
+def console(msg,cmd):
+ if cmd:
+ print(cmd)
+ peforth.vm.dictate("display-off")
+ try:
+ peforth.vm.dictate(cmd)
+ except Exception as err:
+ errmsg = "Failed! : {}".format(err)
+ peforth.vm.dictate("display-on")
+ send_chunk(errmsg, msg.user.send)
+ else:
+ peforth.vm.dictate("display-on screen-buffer")
+ screen = peforth.vm.pop()[0]
+ send_chunk(screen, msg.user.send)
+ send_chunk("OK", msg.user.send)
+
+#
+# 讓 Inception V3 看照片，回答那啥。
+#
+def predict(msg):
+ results = time.ctime() + '\n'
+ results += 'Google Inception V3 thinks it is:\n'
+ msg.download(msg.fileName) # 放在 working directory 下
+ if msg.fileName.strip().lower().endswith((".jpeg",'.jpg')):
+ pred = model.classify(image_path=msg.fileName.strip())
+ results += model.print_scores(pred=pred,k=10,only_first_name=True)
+ else:
+ results += 'Ooops! jpeg pictures only, please. {} is not one.\n'.format(msg.fileName)
+ return results
+
+@itchat.msg_register(TEXT)
+def _(msg):
+ if peforth.vm.debug==99: peforth.ok('99> ',loc=locals(),cmd=":> [0] inport cr") # breakpoint
+ console(msg, msg.Text.strip())
+
+@itchat.msg_register([MAP, CARD, NOTE, SHARING])
+def _(msg):
+ if peforth.vm.debug==11: peforth.ok('11> ',loc=locals(),cmd=":> [0] inport cr") # breakpoint
+ send_chunk('%s: %s' % (msg.type, msg.text), msg.user.send)
+
+#
+# 不要干擾借用帳號的同仁，只在特定的 ChatRoom 裡工作。
+#
+# @itchat.msg_register(PICTURE)
+# def _(msg):
+# if peforth.vm.debug==2211: peforth.ok('2211> ',loc=locals(),cmd=":> [0] constant loc2211 cr") # breakpoint
+# # msg.download(msg.fileName) # 放在 working directory 下
+# # pred = model.classify(image_path=msg.fileName.strip())
+# # results = model.print_scores(pred=pred,k=10,only_first_name=True)
+# return predict(msg)
+
+# @itchat.msg_register([RECORDING, ATTACHMENT, VIDEO])
+# def _(msg):
+# if peforth.vm.debug==22: peforth.ok('22> ',loc=locals(),cmd=":> [0] inport cr") # breakpoint
+# msg.download(msg.fileName)
+# typeSymbol = {
+# PICTURE: 'img',
+# VIDEO: 'vid', }.get(msg.type, 'fil')
+# return '@%s@%s' % (typeSymbol, msg.fileName)
+#
+# @itchat.msg_register(FRIENDS)
+# def _(msg):
+# if peforth.vm.debug==33: peforth.ok('33> ',loc=locals(),cmd=":> [0] inport cr") # breakpoint
+# msg.user.verify()
+# send_chunk('Nice to meet you!', msg.user.send)
+
+@itchat.msg_register(ATTACHMENT, isGroupChat=True)
+def _(msg):
+ if peforth.vm.debug==55: peforth.ok('55> ',loc=locals(),cmd=":> [0] inport cr") # breakpoint
+ msg.download(msg.fileName)
+ return 'Attachment: %s received at %s' % (msg.fileName,time.ctime())
+
+@itchat.msg_register(TEXT, isGroupChat=True)
+def _(msg):
+ if peforth.vm.debug==44: peforth.ok('44> ',loc=locals(),cmd=":> [0] inport cr") # breakpoint
+ if msg.isAt:
+ cmd = msg.text.split(maxsplit=1)[1] # remove the leading @nickName
+ console(msg, cmd)
+
+@itchat.msg_register(PICTURE, isGroupChat=True)
+def _(msg):
+ if peforth.vm.debug==4411: peforth.ok('4411> ',loc=locals(),cmd=":> [0] constant loc4411 cr") # breakpoint
+ # msg.download(msg.fileName) # 放在 working directory 下
+ # pred = model.classify(image_path=msg.fileName.strip())
+ # results = model.print_scores(pred=pred,k=10,only_first_name=True)
+ send_chunk(predict(msg), msg.user.send)
+
+# peforth.vm.debug=99
+itchat.auto_login(True) # hotReload=True
+itchat.run(debug=False, blockThread=True) # debug=True
+peforth.ok() # breakpoint
+
diff --git a/itchat_remote4.py b/itchat_remote4.py
new file mode 100644
index 0000000..d491b7e
--- /dev/null
+++ b/itchat_remote4.py
@@ -0,0 +1,132 @@
+import itchat
+from itchat.content import * # TEXT PICTURE 等 constant 的定義
+import peforth
+import matplotlib.pyplot as plt
+import tensorflow as tf
+import numpy as np
+import time
+# OK time :> ctime() . cr
+# Tue Dec 12 14:34:11 2017
+
+import inception
+inception.maybe_download()
+model = inception.Inception() # The Inception v3 model
+
+# Inhibit 'bye' command, it terminates DOSBox session immediately
+# and leaves 'bye' in msg! Only a re-login can resolve it. To avoid this,
+# decorator must return instead of doing the 'bye' command directly.
+peforth.ok(loc=locals(),cmd=":> [0] constant locals : bye locals :> ['itchat'].logout() ; exit")
+
+# Send message to friend or chatroom depends on the given 'send'
+# function. It can be itchat.send or msg.user.send up to the caller.
+def send_chunk(text, send, pcs=2000):
+ s = text
+ while True:
+ if len(s)>pcs:
+ print(s[:pcs]); send(s[:pcs])
+ else:
+ print(s); send(s)
+ break
+ s = s[pcs:]
+
+# Console is like a robot that listens and talks.
+# Used in chating with friends and in a chatroom.
+def console(msg,cmd):
+ if cmd:
+ print(cmd)
+ peforth.vm.dictate("display-off")
+ try:
+ peforth.vm.dictate(cmd)
+ except Exception as err:
+ errmsg = "Failed! : {}".format(err)
+ peforth.vm.dictate("display-on")
+ send_chunk(errmsg, msg.user.send)
+ else:
+ peforth.vm.dictate("display-on screen-buffer")
+ screen = peforth.vm.pop()[0]
+ send_chunk(screen, msg.user.send)
+ send_chunk("OK", msg.user.send)
+
+#
+# 讓 Inception V3 看照片，回答那啥。
+#
+def predict(msg):
+ results = time.ctime() + '\n'
+ results += 'Google Inception V3 thinks it is:\n'
+ msg.download(msg.fileName) # 放在 working directory 下
+ if msg.fileName.strip().lower().endswith((".jpeg",'.jpg','.png')):
+ pred = model.classify(image_path=msg.fileName.strip())
+ results += model.print_scores(pred=pred,k=10,only_first_name=True)
+ else:
+ results += 'Ooops! jpeg pictures only, please. {} is not one.\n'.format(msg.fileName)
+ return results
+
+#
+# 不要干擾借用帳號的同仁，只在特定的 ChatRoom 裡工作。
+#
+# @itchat.msg_register(TEXT)
+# def _(msg):
+# if peforth.vm.debug==99: peforth.ok('99> ',loc=locals(),cmd=":> [0] inport cr") # breakpoint
+# console(msg, msg.Text.strip())
+#
+# @itchat.msg_register([MAP, CARD, NOTE, SHARING])
+# def _(msg):
+# if peforth.vm.debug==11: peforth.ok('11> ',loc=locals(),cmd=":> [0] inport cr") # breakpoint
+# send_chunk('%s: %s' % (msg.type, msg.text), msg.user.send)
+#
+# @itchat.msg_register(PICTURE)
+# def _(msg):
+# if peforth.vm.debug==2211: peforth.ok('2211> ',loc=locals(),cmd=":> [0] constant loc2211 cr") # breakpoint
+# # msg.download(msg.fileName) # 放在 working directory 下
+# # pred = model.classify(image_path=msg.fileName.strip())
+# # results = model.print_scores(pred=pred,k=10,only_first_name=True)
+# return predict(msg)
+
+# @itchat.msg_register([RECORDING, ATTACHMENT, VIDEO])
+# def _(msg):
+# if peforth.vm.debug==22: peforth.ok('22> ',loc=locals(),cmd=":> [0] inport cr") # breakpoint
+# msg.download(msg.fileName)
+# typeSymbol = {
+# PICTURE: 'img',
+# VIDEO: 'vid', }.get(msg.type, 'fil')
+# return '@%s@%s' % (typeSymbol, msg.fileName)
+#
+# @itchat.msg_register(FRIENDS)
+# def _(msg):
+# if peforth.vm.debug==33: peforth.ok('33> ',loc=locals(),cmd=":> [0] inport cr") # breakpoint
+# msg.user.verify()
+# send_chunk('Nice to meet you!', msg.user.send)
+
+@itchat.msg_register(ATTACHMENT, isGroupChat=True)
+def _(msg):
+ if peforth.vm.debug==55: peforth.ok('55> ',loc=locals(),cmd=":> [0] constant loc55 cr") # breakpoint
+ if msg.user.NickName[:5]=='AILAB': # 只在 AILAB 工作，過濾掉其他的。
+ msg.download(msg.fileName)
+ return 'Attachment: %s received at %s' % (msg.fileName,time.ctime())
+
+@itchat.msg_register(TEXT, isGroupChat=True)
+def _(msg):
+ if peforth.vm.debug==44: peforth.ok('44> ',loc=locals(),cmd=":> [0] constant loc44 cr") # breakpoint
+ if msg.user.NickName[:5]=='AILAB': # 只在 AILAB 工作，過濾掉其他的。
+ if msg.isAt:
+ cmd = msg.text.split(maxsplit=1)[1] # remove the leading @nickName
+ console(msg, cmd)
+
+@itchat.msg_register(PICTURE, isGroupChat=True)
+def _(msg):
+ if peforth.vm.debug==4411: peforth.ok('4411> ',loc=locals(),cmd=":> [0] constant loc4411 cr") # breakpoint
+ if msg.user.NickName[:5]=='AILAB': # 只在 AILAB 工作，過濾掉其他的。
+ send_chunk(predict(msg), msg.user.send)
+
+# peforth.vm.debug=99
+itchat.auto_login(False) # hotReload=True
+itchat.run(debug=False, blockThread=True) # debug=True
+peforth.ok('Examine> ',loc=locals(),cmd=':> [0] value locals')
+
+# ( -- obj ) AILAB chatroom object
+# ailab = itchat.search_chatrooms('AILAB') # array
+# if len(ailab):
+# itchat.run(debug=False, blockThread=True) # debug=True
+# else:
+# peforth.ok('debug> ',loc=locals(),cmd=':> [0] value locals')
+
diff --git a/itchat_remote5.py b/itchat_remote5.py
new file mode 100644
index 0000000..6c320a9
--- /dev/null
+++ b/itchat_remote5.py
@@ -0,0 +1,160 @@
+import itchat
+from itchat.content import * # TEXT PICTURE 等 constant 的定義
+import peforth
+import matplotlib.pyplot as plt
+import tensorflow as tf
+import numpy as np
+import time
+# OK time :> ctime() . cr
+# Tue Dec 12 14:34:11 2017
+import random
+
+# Anti-Robot delay time , thanks to Rainy's great idea.
+nextDelay = random.choice(range(3,18))
+
+import inception
+inception.maybe_download()
+model = inception.Inception() # The Inception v3 model
+
+# Inhibit 'bye' command, it terminates DOSBox session immediately
+# and leaves 'bye' in msg! Only a re-login can resolve it. To avoid this,
+# decorator must return instead of doing the 'bye' command directly.
+peforth.ok(loc=locals(),cmd=":> [0] constant locals : bye locals :> ['itchat'].logout() ; exit")
+
+# Send message to friend or chatroom depends on the given 'send'
+# function. It can be itchat.send or msg.user.send up to the caller.
+# WeChat text message has a limit at about 2000 utf-8 characters so
+# we need to split a bigger string into chunks.
+def send_chunk(text, send, pcs=2000):
+ s = text
+ while True:
+ if len(s)>pcs:
+ print(s[:pcs]); send(s[:pcs])
+ else:
+ print(s); send(s)
+ break
+ s = s[pcs:]
+
+# Console is a peforth robot that listens and talks.
+# Used in chatting with friends and in a chatroom.
+def console(msg,cmd):
+ if cmd:
+ print(cmd) # already on the remote side, don't need to echo
+ # re-direct the display to peforth screen-buffer
+ peforth.vm.dictate("display-off")
+ try:
+ peforth.vm.dictate(cmd)
+ except Exception as err:
+ errmsg = "Failed! : {}".format(err)
+ peforth.vm.dictate("display-on")
+ send_chunk(errmsg, msg.user.send)
+ else:
+ peforth.vm.dictate("display-on screen-buffer")
+ screen = peforth.vm.pop()[0]
+ send_chunk(screen, msg.user.send)
+ send_chunk("OK", msg.user.send)
+
+
+#
+# 讓 Inception V3 看照片，回答那啥。
+#
+def predict(msg):
+ results = time.ctime() + '\n'
+ results += 'Google Inception V3 thinks it is:\n'
+ msg.download('download\\' + msg.fileName) # 照片放在 working directory/download 下
+ if msg.fileName.strip().lower().endswith((".jpeg",'.jpg','.png')):
+ pred = model.classify(image_path=('download\\'+ msg.fileName).strip())
+ results += model.print_scores(pred=pred,k=10,only_first_name=True)
+ else:
+ results += 'Ooops! jpeg pictures only, please. {} is not one.\n'.format(msg.fileName)
+ return results
+
+#
+# 不要干擾借用帳號的同仁，只在特定的 ChatRoom 裡工作。
+#
+# @itchat.msg_register(TEXT)
+# def _(msg):
+# if peforth.vm.debug==99: peforth.ok('99> ',loc=locals(),cmd=":> [0] inport cr") # breakpoint
+# console(msg, msg.Text.strip())
+#
+# @itchat.msg_register([MAP, CARD, NOTE, SHARING])
+# def _(msg):
+# if peforth.vm.debug==11: peforth.ok('11> ',loc=locals(),cmd=":> [0] inport cr") # breakpoint
+# send_chunk('%s: %s' % (msg.type, msg.text), msg.user.send)
+#
+# @itchat.msg_register(PICTURE)
+# def _(msg):
+# if peforth.vm.debug==2211: peforth.ok('2211> ',loc=locals(),cmd=":> [0] constant loc2211 cr") # breakpoint
+# # msg.download(msg.fileName) # 放在 working directory 下
+# # pred = model.classify(image_path=msg.fileName.strip())
+# # results = model.print_scores(pred=pred,k=10,only_first_name=True)
+# return predict(msg)
+
+# @itchat.msg_register([RECORDING, ATTACHMENT, VIDEO])
+# def _(msg):
+# if peforth.vm.debug==22: peforth.ok('22> ',loc=locals(),cmd=":> [0] inport cr") # breakpoint
+# msg.download(msg.fileName)
+# typeSymbol = {
+# PICTURE: 'img',
+# VIDEO: 'vid', }.get(msg.type, 'fil')
+# return '@%s@%s' % (typeSymbol, msg.fileName)
+#
+# @itchat.msg_register(FRIENDS)
+# def _(msg):
+# if peforth.vm.debug==33: peforth.ok('33> ',loc=locals(),cmd=":> [0] inport cr") # breakpoint
+# msg.user.verify()
+# send_chunk('Nice to meet you!', msg.user.send)
+
+@itchat.msg_register(ATTACHMENT, isGroupChat=True)
+def _(msg):
+ global nextDelay
+ time.sleep(nextDelay) # Anti-Robot delay
+ nextDelay = random.choice(range(3,18))
+ if peforth.vm.debug==55: peforth.ok('55> ',loc=locals(),cmd=":> [0] constant loc55 cr") # breakpoint
+ if msg.user.NickName[:5]=='AILAB': # 只在 AILAB 工作，過濾掉其他的。
+ msg.download('download\\' + msg.fileName)
+ send_chunk('Attachment: %s received at %s' % (msg.fileName,time.ctime()), msg.user.send)
+ send_chunk('Next anti-robot delay time: %i seconds' % (nextDelay), msg.user.send)
+
+@itchat.msg_register(TEXT, isGroupChat=True)
+def _(msg):
+ global nextDelay
+ time.sleep(nextDelay) # Anti-Robot delay
+ nextDelay = random.choice(range(3,18))
+ if peforth.vm.debug==44: peforth.ok('44> ',loc=locals(),cmd=":> [0] constant loc44 cr") # breakpoint
+ if msg.user.NickName[:5]=='AILAB': # 只在 AILAB 工作，過濾掉其他的。
+ if msg.isAt:
+ cmd = msg.text.split(maxsplit=1)[1] # remove the leading @nickName
+ console(msg, cmd)
+ send_chunk('Next anti-robot delay time: %i seconds' % (nextDelay), msg.user.send)
+
+@itchat.msg_register(PICTURE, isGroupChat=True)
+def _(msg):
+ global nextDelay
+ time.sleep(nextDelay) # Anti-Robot delay
+ nextDelay = random.choice(range(3,18))
+ if peforth.vm.debug==4411: peforth.ok('4411> ',loc=locals(),cmd=":> [0] constant loc4411 cr") # breakpoint
+ if msg.user.NickName[:5]=='AILAB': # 只在 AILAB 工作，過濾掉其他的。
+ send_chunk(predict(msg), msg.user.send)
+ send_chunk('Next anti-robot delay time: %i seconds' % (nextDelay), msg.user.send)
+
+itchat.auto_login(hotReload=False)
+itchat.run(debug=False, blockThread=True)
+peforth.ok('Examine> ',loc=locals(),cmd=':> [0] value locals')
+
+# Bug list
+# [ ] 正常對話不需 delay
+# [ ] "Next anti-robot delay time" 往上合併好再發，否則中間時間極短又被認出來是個 Bot。
+#
+#
+#
+#
+#
+#
+#
+#
+#
+#
+#
+#
+#
diff --git a/itchat_remote6.py b/itchat_remote6.py
new file mode 100644
index 0000000..0c95eb7
--- /dev/null
+++ b/itchat_remote6.py
@@ -0,0 +1,127 @@
+import itchat
+from itchat.content import * # TEXT PICTURE 等 constant 的定義
+import peforth
+import matplotlib.pyplot as plt
+import tensorflow as tf
+import numpy as np
+import time
+# OK time :> ctime() . cr
+# Tue Dec 12 14:34:11 2017
+import random
+
+# Anti-Robot delay time , thanks to Rainy's great idea.
+nextDelay = random.choice(range(3,18))
+
+import inception
+inception.maybe_download()
+model = inception.Inception() # The Inception v3 model
+
+# Inhibit 'bye' command, it terminates DOSBox session immediately
+# and leaves 'bye' in msg! Only a re-login can resolve it. To avoid this,
+# decorator must return instead of doing the 'bye' command directly.
+peforth.ok(loc=locals(),cmd=":> [0] constant main.locals : bye main.locals :> ['itchat'].logout() bye ; exit")
+
+# Send message to friend or chatroom depends on the given 'send'
+# function. It can be itchat.send or msg.user.send up to the caller.
+# WeChat text message has a limit at about 2000 utf-8 characters so
+# we need to split a bigger string into chunks.
+def send_chunk(text, send, pcs=2000):
+ s = text
+ while True:
+ if len(s)>pcs:
+ print(s[:pcs]); send(s[:pcs])
+ else:
+ print(s); send(s)
+ break
+ s = s[pcs:]
+
+# Console is a peforth robot that listens and talks.
+# Used in chatting with friends and in a chatroom.
+def console(msg,cmd):
+ if cmd:
+ print(cmd) # already on the remote side, don't need to echo
+ global nextDelay
+ nextDelay_msg = '\nNext anti-robot delay time: %i seconds\n' % (nextDelay)
+ if peforth.vm.debug==11: peforth.ok('11> ',loc=locals(),cmd=":> [0] constant loc11 cr") # breakpoint
+
+ # re-direct the display to peforth screen-buffer
+ peforth.vm.dictate("display-off")
+ try:
+ # peforth.vm.dictate(cmd)
+ peforth.ok('Console> ', loc=locals(),
+ cmd=":> [0] constant console.locals " + cmd + " exit")
+ except Exception as err:
+ errmsg = "Failed! : {}".format(err)
+ peforth.vm.dictate("display-on")
+ send_chunk(errmsg + nextDelay_msg, msg.user.send)
+ else:
+ peforth.vm.dictate("display-on screen-buffer")
+ screen = peforth.vm.pop()[0]
+ send_chunk(screen + '\nOK\n' + nextDelay_msg, msg.user.send)
+
+#
+# 讓 Inception V3 看照片，回答那啥。
+#
+def predict(msg):
+ results = time.ctime() + '\n'
+ results += 'Google Inception V3 thinks it is:\n'
+ msg.download('download\\' + msg.fileName) # 照片放在 working directory/download 下
+ if peforth.vm.debug==22: peforth.ok('22> ',loc=locals(),cmd=":> [0] constant loc22 cr") # breakpoint
+ if msg.fileName.strip().lower().endswith((".jpeg",'.jpg','.png')):
+ pred = model.classify(image_path=('download\\'+ msg.fileName).strip())
+ results += model.print_scores(pred=pred,k=10,only_first_name=True)
+ else:
+ results += 'Ooops! jpeg pictures only, please. {} is not one.\n'.format(msg.fileName)
+ return results
+
+@itchat.msg_register(ATTACHMENT, isGroupChat=True)
+def _(msg):
+ global nextDelay
+ time.sleep(nextDelay) # Anti-Robot delay
+ nextDelay = random.choice(range(3,18))
+ nextDelay_msg = '\nNext anti-robot delay time: %i seconds\n' % (nextDelay)
+ if peforth.vm.debug==33: peforth.ok('33> ',loc=locals(),cmd=":> [0] constant loc33 cr") # breakpoint
+ if msg.user.NickName[:5]=='AILAB': # 只在 AILAB 工作，過濾掉其他的。
+ msg.download('download\\' + msg.fileName)
+ send_chunk('Attachment: %s \nreceived at %s\n' % (msg.fileName,time.ctime()) + nextDelay_msg, msg.user.send)
+
+@itchat.msg_register(TEXT, isGroupChat=True)
+def _(msg):
+ if peforth.vm.debug==44: peforth.ok('44> ',loc=locals(),cmd=":> [0] constant loc44 cr") # breakpoint
+ if msg.user.NickName[:5]=='AILAB': # 只在 AILAB 工作，過濾掉其他的。
+ if msg.isAt:
+ time.sleep(nextDelay) # Anti-Robot delay
+ cmd = msg.text.split("\n",maxsplit=1)[1] # remove the first line: @nickName ...
+ console(msg, cmd) # 避免帶有空格的 nickName 惹問題
+
+@itchat.msg_register(PICTURE, isGroupChat=True)
+def _(msg):
+ global nextDelay
+ time.sleep(nextDelay) # Anti-Robot delay
+ nextDelay = random.choice(range(3,18))
+ nextDelay_msg = '\nNext anti-robot delay time: %i seconds\n' % (nextDelay)
+ if peforth.vm.debug==55: peforth.ok('55> ',loc=locals(),cmd=":> [0] constant loc55 cr") # breakpoint
+ if msg.user.NickName[:5]=='AILAB': # 只在 AILAB 工作，過濾掉其他的。
+ send_chunk(predict(msg) + nextDelay_msg, msg.user.send)
+
+itchat.auto_login(hotReload=False)
+itchat.run(debug=False, blockThread=True)
+peforth.ok('Examine> ',loc=locals(),cmd=':> [0] value locals')
+
+# Bug list
+# [x] 正常對話不需 delay --> FP @ v6
+# [x] "Next anti-robot delay time" 往上合併好再發，
+# 否則中間時間極短又被認出來是個 Bot。
+# --> FP @ v6
+#
+#
+#
+#
+#
+#
+#
+#
+#
+#
+#
+#
diff --git a/itchat_remote7.py b/itchat_remote7.py
new file mode 100644
index 0000000..f14e471
--- /dev/null
+++ b/itchat_remote7.py
@@ -0,0 +1,160 @@
+import sys
+import itchat
+from itchat.content import * # TEXT PICTURE 等 constant 的定義
+import peforth
+import matplotlib.pyplot as plt
+import tensorflow as tf
+import numpy as np
+import time
+import random
+
+# Anti-Robot delay time , thanks to Rainy's great idea.
+nextDelay = random.choice(range(3,18))
+
+import inception
+inception.maybe_download()
+model = inception.Inception() # The Inception v3 model
+
+# Inhibit 'bye' command, it terminates DOSBox session immediately
+# and leaves 'bye' in msg! Only a re-login can resolve it. To avoid this,
+# decorator must return instead of doing the 'bye' command directly.
+peforth.ok(loc=locals(),cmd='''
+ :> [0] constant main.locals // ( -- dict ) main locals
+ none value console.locals // ( -- dict ) console() locals
+ : bye main.locals :> ['itchat'].logout() bye ;
+ exit
+ ''')
+
+# Send message to friend or chatroom depends on the given 'send'
+# function. It can be itchat.send or msg.user.send up to the caller.
+# WeChat text message has a limit at about 2000 utf-8 characters so
+# we need to split a bigger string into chunks.
+def send_chunk(text, send, pcs=2000):
+ s = text
+ while True:
+ if len(s)>pcs:
+ print(s[:pcs]); send(s[:pcs])
+ else:
+ print(s); send(s)
+ break
+ s = s[pcs:]
+
+# Console is a peforth robot that listens and talks.
+# Used in chatting with friends and in a chatroom.
+def console(msg,cmd):
+ if cmd:
+ print(cmd) # already on the remote side, don't need to echo
+ global nextDelay
+ nextDelay_msg = '\nNext anti-robot delay time: %i seconds\n' % (nextDelay)
+ if peforth.vm.debug==11: peforth.ok('11> ',loc=locals(),cmd=":> [0] constant loc11 cr") # breakpoint
+
+ # re-direct the display to peforth screen-buffer
+ peforth.vm.dictate("display-off")
+ try:
+ # peforth.vm.dictate(cmd)
+ peforth.ok('OK ', loc=locals(),
+ cmd=":> [0] to console.locals " + cmd + "\n exit")
+ except Exception as err:
+ errmsg = "Failed! : {}".format(err)
+ peforth.vm.dictate("display-on")
+ send_chunk(errmsg + nextDelay_msg, msg.user.send)
+ else:
+ peforth.vm.dictate("display-on screen-buffer")
+ screen = peforth.vm.pop()[0]
+ send_chunk(screen + nextDelay_msg, msg.user.send)
+
+#
+# 讓 Inception V3 看照片，回答那啥。
+#
+def predict(msg):
+ results = time.ctime() + '\n'
+ results += 'Google Inception V3 thinks it is:\n'
+ msg.download('download\\' + msg.fileName) # 照片放在 working directory/download 下
+ if peforth.vm.debug==22: peforth.ok('22> ',loc=locals(),cmd=":> [0] constant loc22 cr") # breakpoint
+ if msg.fileName.strip().lower().endswith((".jpeg",'.jpg','.png')):
+ pred = model.classify(image_path=('download\\'+ msg.fileName).strip())
+ results += model.print_scores(pred=pred,k=10,only_first_name=True)
+ else:
+ results += 'Ooops! jpeg pictures only, please. {} is not one.\n'.format(msg.fileName)
+ return results
+
+@itchat.msg_register(ATTACHMENT, isGroupChat=True)
+def _(msg):
+ global nextDelay
+ time.sleep(nextDelay) # Anti-Robot delay
+ nextDelay = random.choice(range(3,18))
+ nextDelay_msg = '\nNext anti-robot delay time: %i seconds\n' % (nextDelay)
+ if peforth.vm.debug==33: peforth.ok('33> ',loc=locals(),cmd=":> [0] constant loc33 cr") # breakpoint
+ if msg.user.NickName[:5]=='AILAB': # 只在 AILAB 工作，過濾掉其他的。
+ msg.download('download\\' + msg.fileName)
+ send_chunk('Attachment: %s \nreceived at %s\n' % (msg.fileName,time.ctime()) + nextDelay_msg, msg.user.send)
+
+@itchat.msg_register(TEXT, isGroupChat=True)
+def _(msg):
+ if peforth.vm.debug==44: peforth.ok('44> ',loc=locals(),cmd=":> [0] constant loc44 cr") # breakpoint
+ if msg.user.NickName[:5]=='AILAB': # 只在 AILAB 工作，過濾掉其他的。
+ if msg.isAt:
+ time.sleep(nextDelay) # Anti-Robot delay
+ cmd = msg.text.split("\n",maxsplit=1)[1] # remove the first line: @nickName ...
+ console(msg, cmd) # 避免帶有空格的 nickName 惹問題
+
+@itchat.msg_register(PICTURE, isGroupChat=True)
+def _(msg):
+ global nextDelay
+ time.sleep(nextDelay) # Anti-Robot delay
+ nextDelay = random.choice(range(3,18))
+ nextDelay_msg = '\nNext anti-robot delay time: %i seconds\n' % (nextDelay)
+ if peforth.vm.debug==55: peforth.ok('55> ',loc=locals(),cmd=":> [0] constant loc55 cr") # breakpoint
+ if msg.user.NickName[:5]=='AILAB': # 只在 AILAB 工作，過濾掉其他的。
+ send_chunk(predict(msg) + nextDelay_msg, msg.user.send)
+
+itchat.auto_login(hotReload=False)
+itchat.run(debug=False, blockThread=True)
+peforth.ok('Examine> ',loc=locals(),cmd=':> [0] value locals')
+
+# Bug list
+# [x] 正常對話不需 delay --> FP @ v6
+# [x] "Next anti-robot delay time" 往上合併好再發，
+# 否則中間時間極短又被認出來是個 Bot。
+# --> FP @ v6
+#
+#
+#
+#
+#
+#
+#
+#
+#
+#
+#
+#
+'''
+\ 完整設定過程，讓 UUT 回覆它的畫面經由 itchat bot 傳給 AILAB Chatroom.
+\ 讓遠端可以來監看執行狀況。這段程式是由遠端灌過來給 UUT 的。
+ @秀。。 This line will be ignored
+ \ get itchat module object
+ py> sys.modules['itchat'] constant itchat // ( -- module ) WeChat automation
+ \ get PIL graph tool
+ import PIL.ImageGrab constant im // ( -- module ) PIL.ImageGrab
+ \ get AILAB chatroom object through partial nickName
+ itchat :> search_chatrooms('AILAB')[0] constant ailab // ( -- obj ) AILAB chatroom object
+ \ Define check command that checks the UUT desktop screenshot
+ import time constant time // ( -- module )
+ cr time :> ctime() . cr \ print recent time on UUT when making this setting
+ : check ( -- ) // check UUT
+ time :: sleep(7) \ anti-robot delay time be always 7 seconds
+ cr time :> ctime() . cr \ print the recent time on UUT
+ im :: grab().save("1.jpg") \ capture screenshot
+ ailab :> send("@img@1.jpg") \ send to AILAB chatroom
+ . cr \ shows the responsed message
+ ;
+ \ Define getfile command in case source code were modified on the UUT
+ : getfile ( "pathname" -- ) // Get source code for debugging
+ time :: sleep(7) py> str(pop()).strip() \ trim pathname
+ s" @fil@" swap + \ command string
+ cr time :> ctime() . space s" getfile: " . dup . cr
+ ailab :> send(pop()) \ send to AILAB chatroom so everybody gets it
+ . cr \ shows the responsed message
+ ;
+'''
diff --git a/requirements.txt b/requirements.txt
index 43c0e28..efe5029 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -21,6 +21,7 @@ jupyter
matplotlib
Pillow
scikit-learn
+peforth
################################################################
# TensorFlow can be installed either as CPU or GPU versions.