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data-science-ipython-notebooks/deep-learning/keras-tutorial/2.2.2 Supervised Learning - ConvNet HandsOn Part II.ipynb
Donne Martin 565136cd4a Fix #49: Keras tutorial links (#52)
2017-08-26 08:47:27 -04:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Credits: Forked from [deep-learning-keras-tensorflow](https://github.com/leriomaggio/deep-learning-keras-tensorflow) by Valerio Maggio"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Convolution Nets for MNIST"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Deep Learning models can take quite a bit of time to run, particularly if GPU isn't used. \n",
"\n",
"In the interest of time, you could sample a subset of observations (e.g. $1000$) that are a particular number of your choice (e.g. $6$) and $1000$ observations that aren't that particular number (i.e. $\\neq 6$). \n",
"\n",
"We will build a model using that and see how it performs on the test dataset"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using Theano backend.\n",
"Using gpu device 0: GeForce GTX 760 (CNMeM is enabled with initial size: 90.0% of memory, cuDNN 4007)\n"
]
}
],
"source": [
"#Import the required libraries\n",
"import numpy as np\n",
"np.random.seed(1338)\n",
"\n",
"from keras.datasets import mnist"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"from keras.models import Sequential"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"from keras.layers.core import Dense, Dropout, Activation, Flatten"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"from keras.layers.convolutional import Convolution2D\n",
"from keras.layers.pooling import MaxPooling2D"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"from keras.utils import np_utils\n",
"from keras.optimizers import SGD"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## Loading Data"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "-"
}
},
"outputs": [],
"source": [
"path_to_dataset = \"euroscipy_2016_dl-keras/data/mnist.pkl.gz\"\n",
"\n",
"#Load the training and testing data\n",
"(X_train, y_train), (X_test, y_test) = mnist.load_data(path_to_dataset)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"X_test_orig = X_test"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## Data Preparation"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "-"
}
},
"outputs": [],
"source": [
"img_rows, img_cols = 28, 28\n",
"\n",
"X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols)\n",
"X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)\n",
"\n",
"X_train = X_train.astype('float32')\n",
"X_test = X_test.astype('float32')\n",
"\n",
"X_train /= 255\n",
"X_test /= 255"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"# Seed for reproducibilty\n",
"np.random.seed(1338)\n",
"\n",
"# Test data\n",
"X_test = X_test.copy()\n",
"Y = y_test.copy()\n",
"\n",
"# Converting the output to binary classification(Six=1,Not Six=0)\n",
"Y_test = Y == 6\n",
"Y_test = Y_test.astype(int)\n",
"\n",
"# Selecting the 5918 examples where the output is 6\n",
"X_six = X_train[y_train == 6].copy()\n",
"Y_six = y_train[y_train == 6].copy()\n",
"\n",
"# Selecting the examples where the output is not 6\n",
"X_not_six = X_train[y_train != 6].copy()\n",
"Y_not_six = y_train[y_train != 6].copy()\n",
"\n",
"# Selecting 6000 random examples from the data that \n",
"# only contains the data where the output is not 6\n",
"random_rows = np.random.randint(0,X_six.shape[0],6000)\n",
"X_not_six = X_not_six[random_rows]\n",
"Y_not_six = Y_not_six[random_rows]"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"# Appending the data with output as 6 and data with output as <> 6\n",
"X_train = np.append(X_six,X_not_six)\n",
"\n",
"# Reshaping the appended data to appropraite form\n",
"X_train = X_train.reshape(X_six.shape[0] + X_not_six.shape[0], \n",
" 1, img_rows, img_cols)\n",
"\n",
"# Appending the labels and converting the labels to \n",
"# binary classification(Six=1,Not Six=0)\n",
"Y_labels = np.append(Y_six,Y_not_six)\n",
"Y_train = Y_labels == 6 \n",
"Y_train = Y_train.astype(int)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(11918, 1, 28, 28) (11918,) (10000, 1, 28, 28) (10000, 2)\n"
]
}
],
"source": [
"print(X_train.shape, Y_labels.shape, X_test.shape, Y_test.shape)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"# Converting the classes to its binary categorical form\n",
"nb_classes = 2\n",
"Y_train = np_utils.to_categorical(Y_train, nb_classes)\n",
"Y_test = np_utils.to_categorical(Y_test, nb_classes)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# A simple CNN"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"#Initializing the values for the convolution neural network\n",
"nb_epoch = 2\n",
"batch_size = 128\n",
"# number of convolutional filters to use\n",
"nb_filters = 32\n",
"# size of pooling area for max pooling\n",
"nb_pool = 2\n",
"# convolution kernel size\n",
"nb_conv = 3\n",
"\n",
"sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Step 1: Model Definition"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"model = Sequential()\n",
"\n",
"model.add(Convolution2D(nb_filters, nb_conv, nb_conv,\n",
" border_mode='valid',\n",
" input_shape=(1, img_rows, img_cols)))\n",
"model.add(Activation('relu'))\n",
"\n",
"model.add(Flatten())\n",
"model.add(Dense(nb_classes))\n",
"model.add(Activation('softmax'))"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Step 2: Compile"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model.compile(loss='categorical_crossentropy',\n",
" optimizer='sgd',\n",
" metrics=['accuracy'])"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Step 3: Fit"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 11918 samples, validate on 10000 samples\n",
"Epoch 1/2\n",
"11918/11918 [==============================] - 0s - loss: 0.2890 - acc: 0.9326 - val_loss: 0.1251 - val_acc: 0.9722\n",
"Epoch 2/2\n",
"11918/11918 [==============================] - 0s - loss: 0.1341 - acc: 0.9612 - val_loss: 0.1298 - val_acc: 0.9599\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x7f6ccb68f630>"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.fit(X_train, Y_train, batch_size=batch_size, \n",
" nb_epoch=nb_epoch,verbose=1,\n",
" validation_data=(X_test, Y_test))"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Step 4: Evaluate"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test score: 0.129807630396\n",
"Test accuracy: 0.9599\n"
]
}
],
"source": [
"# Evaluating the model on the test data \n",
"score, accuracy = model.evaluate(X_test, Y_test, verbose=0)\n",
"print('Test score:', score)\n",
"print('Test accuracy:', accuracy)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Let's plot our model Predictions!"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
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l6w3R6+GE5rxpkk/4XFw8Gpeyg1s2b2xyflbdaO0tI0ewTTjRPjRJ9hXdVB+m2fUu/ihw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\nWbumY8f6AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f6cab246898>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"slice = 15\n",
"predicted = model.predict(X_test[:slice]).argmax(-1)\n",
"\n",
"plt.figure(figsize=(16,8))\n",
"for i in range(slice):\n",
" plt.subplot(1, slice, i+1)\n",
" plt.imshow(X_test_orig[i], interpolation='nearest')\n",
" plt.text(0, 0, predicted[i], color='black', \n",
" bbox=dict(facecolor='white', alpha=1))\n",
" plt.axis('off')"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Adding more Dense Layers"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"model = Sequential()\n",
"model.add(Convolution2D(nb_filters, nb_conv, nb_conv,\n",
" border_mode='valid',\n",
" input_shape=(1, img_rows, img_cols)))\n",
"model.add(Activation('relu'))\n",
"\n",
"model.add(Flatten())\n",
"model.add(Dense(128))\n",
"model.add(Activation('relu'))\n",
"\n",
"model.add(Dense(nb_classes))\n",
"model.add(Activation('softmax'))"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 11918 samples, validate on 10000 samples\n",
"Epoch 1/2\n",
"11918/11918 [==============================] - 0s - loss: 0.3044 - acc: 0.9379 - val_loss: 0.1469 - val_acc: 0.9625\n",
"Epoch 2/2\n",
"11918/11918 [==============================] - 0s - loss: 0.1189 - acc: 0.9640 - val_loss: 0.1058 - val_acc: 0.9655\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x7f6cf59f7358>"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.compile(loss='categorical_crossentropy',\n",
" optimizer='sgd',\n",
" metrics=['accuracy'])\n",
"\n",
"model.fit(X_train, Y_train, batch_size=batch_size, \n",
" nb_epoch=nb_epoch,verbose=1,\n",
" validation_data=(X_test, Y_test))"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test score: 0.105762729073\n",
"Test accuracy: 0.9655\n"
]
}
],
"source": [
"#Evaluating the model on the test data \n",
"score, accuracy = model.evaluate(X_test, Y_test, verbose=0)\n",
"print('Test score:', score)\n",
"print('Test accuracy:', accuracy)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Adding Dropout"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"model = Sequential()\n",
"\n",
"model.add(Convolution2D(nb_filters, nb_conv, nb_conv,\n",
" border_mode='valid',\n",
" input_shape=(1, img_rows, img_cols)))\n",
"model.add(Activation('relu'))\n",
"\n",
"model.add(Flatten())\n",
"model.add(Dense(128))\n",
"model.add(Activation('relu'))\n",
"model.add(Dropout(0.5))\n",
"model.add(Dense(nb_classes))\n",
"model.add(Activation('softmax'))"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 11918 samples, validate on 10000 samples\n",
"Epoch 1/2\n",
"11918/11918 [==============================] - 0s - loss: 0.3128 - acc: 0.9097 - val_loss: 0.1438 - val_acc: 0.9624\n",
"Epoch 2/2\n",
"11918/11918 [==============================] - 0s - loss: 0.1362 - acc: 0.9580 - val_loss: 0.1145 - val_acc: 0.9628\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x7f6ccb180208>"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.compile(loss='categorical_crossentropy',\n",
" optimizer='sgd',\n",
" metrics=['accuracy'])\n",
"\n",
"model.fit(X_train, Y_train, batch_size=batch_size, \n",
" nb_epoch=nb_epoch,verbose=1,\n",
" validation_data=(X_test, Y_test))"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test score: 0.11448907243\n",
"Test accuracy: 0.9628\n"
]
}
],
"source": [
"#Evaluating the model on the test data \n",
"score, accuracy = model.evaluate(X_test, Y_test, verbose=0)\n",
"print('Test score:', score)\n",
"print('Test accuracy:', accuracy)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Adding more Convolution Layers"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"model = Sequential()\n",
"model.add(Convolution2D(nb_filters, nb_conv, nb_conv,\n",
" border_mode='valid',\n",
" input_shape=(1, img_rows, img_cols)))\n",
"model.add(Activation('relu'))\n",
"model.add(Convolution2D(nb_filters, nb_conv, nb_conv))\n",
"model.add(Activation('relu'))\n",
"model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))\n",
"model.add(Dropout(0.25))\n",
" \n",
"model.add(Flatten())\n",
"model.add(Dense(128))\n",
"model.add(Activation('relu'))\n",
"model.add(Dropout(0.5))\n",
"model.add(Dense(nb_classes))\n",
"model.add(Activation('softmax'))"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 11918 samples, validate on 10000 samples\n",
"Epoch 1/2\n",
"11918/11918 [==============================] - 1s - loss: 0.4707 - acc: 0.8288 - val_loss: 0.2307 - val_acc: 0.9399\n",
"Epoch 2/2\n",
"11918/11918 [==============================] - 1s - loss: 0.1882 - acc: 0.9383 - val_loss: 0.1195 - val_acc: 0.9621\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x7f6cc97b8748>"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.compile(loss='categorical_crossentropy',\n",
" optimizer='sgd',\n",
" metrics=['accuracy'])\n",
"\n",
"model.fit(X_train, Y_train, batch_size=batch_size, \n",
" nb_epoch=nb_epoch,verbose=1,\n",
" validation_data=(X_test, Y_test))"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test score: 0.11954063682\n",
"Test accuracy: 0.9621\n"
]
}
],
"source": [
"#Evaluating the model on the test data \n",
"score, accuracy = model.evaluate(X_test, Y_test, verbose=0)\n",
"print('Test score:', score)\n",
"print('Test accuracy:', accuracy)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exercise\n",
"\n",
"The above code has been written as a function. \n",
"\n",
"Change some of the **hyperparameters** and see what happens. "
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"# Function for constructing the convolution neural network\n",
"# Feel free to add parameters, if you want\n",
"\n",
"def build_model():\n",
" \"\"\"\"\"\"\n",
" model = Sequential()\n",
" model.add(Convolution2D(nb_filters, nb_conv, nb_conv,\n",
" border_mode='valid',\n",
" input_shape=(1, img_rows, img_cols)))\n",
" model.add(Activation('relu'))\n",
" model.add(Convolution2D(nb_filters, nb_conv, nb_conv))\n",
" model.add(Activation('relu'))\n",
" model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))\n",
" model.add(Dropout(0.25))\n",
" \n",
" model.add(Flatten())\n",
" model.add(Dense(128))\n",
" model.add(Activation('relu'))\n",
" model.add(Dropout(0.5))\n",
" model.add(Dense(nb_classes))\n",
" model.add(Activation('softmax'))\n",
" \n",
" model.compile(loss='categorical_crossentropy',\n",
" optimizer='sgd',\n",
" metrics=['accuracy'])\n",
"\n",
" model.fit(X_train, Y_train, batch_size=batch_size, \n",
" nb_epoch=nb_epoch,verbose=1,\n",
" validation_data=(X_test, Y_test))\n",
" \n",
"\n",
" #Evaluating the model on the test data \n",
" score, accuracy = model.evaluate(X_test, Y_test, verbose=0)\n",
" print('Test score:', score)\n",
" print('Test accuracy:', accuracy)"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 11918 samples, validate on 10000 samples\n",
"Epoch 1/2\n",
"11918/11918 [==============================] - 1s - loss: 0.5634 - acc: 0.7860 - val_loss: 0.3574 - val_acc: 0.9363\n",
"Epoch 2/2\n",
"11918/11918 [==============================] - 1s - loss: 0.2372 - acc: 0.9292 - val_loss: 0.2253 - val_acc: 0.9190\n",
"Test score: 0.225333989978\n",
"Test accuracy: 0.919\n",
"1 loop, best of 1: 5.45 s per loop\n"
]
}
],
"source": [
"#Timing how long it takes to build the model and test it.\n",
"%timeit -n1 -r1 build_model()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Batch Normalisation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Normalize the activations of the previous layer at each batch, i.e. applies a transformation that maintains the mean activation close to 0 and the activation standard deviation close to 1."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## How to BatchNorm in Keras\n",
"\n",
"```python\n",
"from keras.layers.normalization import BatchNormalization\n",
"\n",
"BatchNormalization(epsilon=1e-06, mode=0, \n",
" axis=-1, momentum=0.99, \n",
" weights=None, beta_init='zero', \n",
" gamma_init='one')\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"# Try to add a new BatchNormalization layer to the Model \n",
"# (after the Dropout layer)"
]
}
],
"metadata": {
"celltoolbar": "Slideshow",
"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": "3.4.3"
}
},
"nbformat": 4,
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