diff --git a/0. Preamble.ipynb b/0. Preamble.ipynb index 38e0f56..c16923c 100644 --- a/0. Preamble.ipynb +++ b/0. Preamble.ipynb @@ -69,8 +69,8 @@ "1. _Multi-layer Fully Connected Networks (and the `backends`)_\n", "2. _Hidden Layers features and Embeddings_\n", "3. _Convolutional Networks_\n", - "4. _Transfer Learning and Fine Tuning_\n", - "5. _Residual Networks_\n", + "4. _Deep CNN and Residual Networks_\n", + "5. _Transfer Learning and Fine Tuning_\n", "6. _Recursive Neural Networks_\n", "7. _[Variational] AutoEncoders and Adversarials_\n", "8. _Multi-Modal Networks_\n", diff --git a/2.2.2 CNN HandsOn - MNIST & CN Nets.ipynb b/2.2.2 CNN HandsOn - MNIST & CN Nets.ipynb deleted file mode 100644 index 19f4cc9..0000000 --- a/2.2.2 CNN HandsOn - MNIST & CN Nets.ipynb +++ /dev/null @@ -1,1028 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true, - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Convolution Nets for MNIST" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true, - "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": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [], - "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": null, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true, - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [], - "source": [ - "from keras.models import Sequential\n", - "from keras.layers.core import Dense, Dropout, Activation, Flatten" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [], - "source": [ - "from keras.layers.convolutional import Conv2D\n", - "from keras.layers.pooling import MaxPooling2D" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true, - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [], - "source": [ - "from keras.utils import np_utils\n", - "from keras.optimizers import SGD" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true, - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "## Loading Data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [], - "source": [ - "#Load the training and testing data\n", - "(X_train, y_train), (X_test, y_test) = mnist.load_data()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true, - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [], - "source": [ - "X_test_orig = X_test" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true, - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "## Data Preparation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], - "source": [ - "from keras import backend as K" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Very Important: \n", - "When dealing with images & convolutions, it is paramount to handle `image_data_format` properly" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, - "outputs": [], - "source": [ - "img_rows, img_cols = 28, 28\n", - "\n", - "if K.image_data_format() == 'channels_first':\n", - " shape_ord = (1, img_rows, img_cols)\n", - "else: # channel_last\n", - " shape_ord = (img_rows, img_cols, 1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Preprocess and Normalise Data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [], - "source": [ - "X_train = X_train.reshape((X_train.shape[0],) + shape_ord)\n", - "X_test = X_test.reshape((X_test.shape[0],) + shape_ord)\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": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [], - "source": [ - "np.random.seed(1338) # for reproducibilty!!\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": null, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": 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],) + shape_ord)\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": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [], - "source": [ - "print(X_train.shape, Y_labels.shape, X_test.shape, Y_test.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": 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": { - "deletable": true, - "editable": true, - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# A simple CNN" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true, - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [], - "source": [ - "# -- Initializing the values for the convolution neural network\n", - "\n", - "nb_epoch = 2 # kept very low! Please increase if you have GPU\n", - "\n", - "batch_size = 64\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": { - "deletable": true, - "editable": true, - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "#### Step 1: Model Definition" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], - "source": [ - "model = Sequential()\n", - "\n", - "model.add(Conv2D(nb_filters, (nb_conv, nb_conv), padding='valid', \n", - " input_shape=shape_ord)) # note: the very first layer **must** always specify the input_shape\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": { - "deletable": true, - "editable": true, - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "#### Step 2: Compile" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, - "outputs": [], - "source": [ - "model.compile(loss='categorical_crossentropy',\n", - " optimizer='sgd',\n", - " metrics=['accuracy'])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true, - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "#### Step 3: Fit" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], - "source": [ - "hist = model.fit(X_train, Y_train, batch_size=batch_size, \n", - " epochs=nb_epoch, verbose=1, \n", - " validation_data=(X_test, Y_test))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "\n", - "plt.figure()\n", - "plt.xlabel('Epochs')\n", - "plt.ylabel('Loss')\n", - "plt.plot(hist.history['loss'])\n", - "plt.plot(hist.history['val_loss'])\n", - "plt.legend(['Training', 'Validation'])\n", - "\n", - "plt.figure()\n", - "plt.xlabel('Epochs')\n", - "plt.ylabel('Accuracy')\n", - "plt.plot(hist.history['acc'])\n", - "plt.plot(hist.history['val_acc'])\n", - "plt.legend(['Training', 'Validation'], loc='lower right')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true, - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "### Step 4: Evaluate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "print('Available Metrics in Model: {}'.format(model.metrics_names))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], - "source": [ - "# Evaluating the model on the test data \n", - "loss, accuracy = model.evaluate(X_test, Y_test, verbose=0)\n", - "print('Test Loss:', loss)\n", - "print('Test Accuracy:', accuracy)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true, - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "### Let's plot our model Predictions!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true, - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [], - "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": { - "deletable": true, - "editable": true, - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Adding more Dense Layers" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true, - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [], - "source": [ - "model = Sequential()\n", - "model.add(Conv2D(nb_filters, (nb_conv, nb_conv),\n", - " padding='valid', input_shape=shape_ord))\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": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [], - "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", - " epochs=nb_epoch,verbose=1,\n", - " validation_data=(X_test, Y_test))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [], - "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": { - "deletable": true, - "editable": true, - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Adding Dropout" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true, - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [], - "source": [ - "model = Sequential()\n", - "\n", - "model.add(Conv2D(nb_filters, (nb_conv, nb_conv),\n", - " padding='valid',\n", - " input_shape=shape_ord))\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": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [], - "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", - " epochs=nb_epoch,verbose=1,\n", - " validation_data=(X_test, Y_test))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [], - "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": { - "deletable": true, - "editable": true, - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Adding more Convolution Layers" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true, - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [], - "source": [ - "model = Sequential()\n", - "model.add(Conv2D(nb_filters, (nb_conv, nb_conv),\n", - " padding='valid', input_shape=shape_ord))\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": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [], - "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", - " epochs=nb_epoch,verbose=1,\n", - " validation_data=(X_test, Y_test))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], - "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": { - "deletable": true, - "editable": true - }, - "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": null, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": 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(Conv2D(nb_filters, (nb_conv, nb_conv), \n", - " padding='valid',\n", - " input_shape=shape_ord))\n", - " model.add(Activation('relu'))\n", - " model.add(Conv2D(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", - " epochs=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": null, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], - "source": [ - "#Timing how long it takes to build the model and test it.\n", - "%timeit -n1 -r1 build_model()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true, - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Batch Normalisation" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, - "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": { - "deletable": true, - "editable": true, - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "## How to BatchNorm in Keras" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```python\n", - "from keras.layers.normalization import BatchNormalization\n", - "\n", - "BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, \n", - " beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros',\n", - " moving_variance_initializer='ones', beta_regularizer=None, gamma_regularizer=None,\n", - " beta_constraint=None, gamma_constraint=None)\n", - "```\n", - "\n", - "#### Arguments\n", - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Excercise" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true, - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [], - "source": [ - "# Try to add a new BatchNormalization layer to the Model \n", - "# (after the Dropout layer) - before or after the ReLU Activation" - ] - } - ], - "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": "3.5.2" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/3. Convolutional Neural Networks.ipynb b/3. Convolutional Neural Networks.ipynb new file mode 100644 index 0000000..004c16f --- /dev/null +++ b/3. Convolutional Neural Networks.ipynb @@ -0,0 +1,1439 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Convolution Nets for MNIST" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### References:\n", + "\n", + "Some of the images and the content I used came from this great couple of blog posts \\[1\\] [https://adeshpande3.github.io/adeshpande3.github.io/]() and \\[2\\] the terrific book, [\"Neural Networks and Deep Learning\"](http://neuralnetworksanddeeplearning.com/) by Michael Nielsen. (**Strongly recommend**) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A convolutional neural network (CNN, or ConvNet) is a type of **feed-forward** artificial neural network in which the connectivity pattern between its neurons is inspired by the organization of the animal visual cortex." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "> source: https://flickrcode.files.wordpress.com/2014/10/conv-net2.png" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Structure of a CNN\n", + "\n", + "> A more detailed overview of what CNNs do would be that you take the image, pass it through a series of convolutional, nonlinear, pooling (downsampling), and fully connected layers, and get an output. As we said earlier, the output can be a single class or a probability of classes that best describes the image. \n", + "\n", + "source: [1]\n", + "\n", + "## Convolutional Layer\n", + "\n", + "The first layer in a CNN is always a **Convolutional Layer**.\n", + "\n", + "\n", + "\n", + "**Reference**: [http://deeplearning.net/software/theano/tutorial/conv_arithmetic.html](http://deeplearning.net/software/theano/tutorial/conv_arithmetic.html)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Typical CNN Structure" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A traditional **convolutional neural network** architecture, there are other layers that are interspersed between these conv layers.\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " After some ReLU layers, it is customary to apply a **pooling layer** (aka *downsampling layer*).\n", + " \n", + " Example of a MaxPooling filter\n", + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Other options for pooling layers are average pooling and L2-norm pooling. \n", + "\n", + "This kind of layer serves two main purposes: reduce the amount of parameters; controlling overfitting. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "# CNN in Keras" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Keras** has an extensive support for Convolutional Layers:\n", + "\n", + "- 1D Convolutional Layers;\n", + "- 2D Convolutional Layers;\n", + "- 3D Convolutional Layers;\n", + "- Depthwise Convolution;\n", + "- Transpose Convolution;\n", + "- ....\n", + "\n", + "The corresponding `keras` package is `keras.layers.convolutional`.\n", + "\n", + "Take a look at the [Convolutional Layers](https://keras.io/layers/convolutional/) documentation to know more about Conv Layers that are missing in this notebook." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Convolution1D\n", + "\n", + "```python\n", + "from keras.layers.convolutional import Conv1D\n", + "\n", + "Conv1D(filters, kernel_size, strides=1, padding='valid', \n", + " dilation_rate=1, activation=None, use_bias=True, \n", + " kernel_initializer='glorot_uniform', bias_initializer='zeros', \n", + " kernel_regularizer=None, bias_regularizer=None, \n", + " activity_regularizer=None, kernel_constraint=None, \n", + " bias_constraint=None)\n", + "```\n", + "\n", + "#### Arguments:\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + ">Convolution operator for filtering neighborhoods of **one-dimensional inputs**. When using this layer as the first layer in a model, either provide the keyword argument `input_dim` (int, e.g. 128 for sequences of 128-dimensional vectors), or `input_shape` (tuple of integers, e.g. (10, 128) for sequences of 10 vectors of 128-dimensional vectors)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Example\n", + "\n", + "```python\n", + "\n", + "# apply a convolution 1d of length 3 to a sequence with 10 timesteps,\n", + "# with 64 output filters\n", + "model = Sequential()\n", + "model.add(Conv1D(64, 3, padding='same', input_shape=(10, 32)))\n", + "# now model.output_shape == (None, 10, 64)\n", + "\n", + "# add a new conv1d on top\n", + "model.add(Conv1D(32, 3, padding='same'))\n", + "# now model.output_shape == (None, 10, 32)\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Convolution2D\n", + "\n", + "```python\n", + "from keras.layers.convolutional import Conv2D\n", + "\n", + "Conv2D(filters, kernel_size, strides=(1, 1), padding='valid', \n", + " data_format=None, dilation_rate=(1, 1), activation=None, \n", + " use_bias=True, kernel_initializer='glorot_uniform', \n", + " bias_initializer='zeros', kernel_regularizer=None, \n", + " bias_regularizer=None, activity_regularizer=None, \n", + " kernel_constraint=None, bias_constraint=None)\n", + "```\n", + "\n", + "#### Arguments:\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Example\n", + "Assuming \n", + "``keras.backend.image_data_format == \"channels_last\"``\n", + "```python\n", + "\n", + "# apply a 3x3 convolution with 64 output filters on a 256x256 image:\n", + "model = Sequential()\n", + "model.add(Conv2D(64, (3, 3), padding='same', \n", + " input_shape=(3, 256, 256)))\n", + "# now model.output_shape == (None, 256, 256, 64)\n", + "\n", + "# add a 3x3 convolution on top, with 32 output filters:\n", + "model.add(Conv2D(32, (3, 3), padding='same'))\n", + "# now model.output_shape == (None, 256, 256, 32)\n", + "\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Dimensions of Conv Filters in Keras\n", + "\n", + "The complex structure of ConvNets *may* lead to a representation that is challenging to understand.\n", + "\n", + "Of course, the dimensions vary according to the dimension of the Convolutional filters (e.g. 1D, 2D)\n", + "\n", + "### Convolution1D\n", + "\n", + "**Input Shape**:\n", + "\n", + "**3D** tensor with shape: (`batch_size`, `steps`, `input_dim`).\n", + "\n", + "**Output Shape**:\n", + "\n", + "**3D** tensor with shape: (`batch_size`, `new_steps`, `filters`).\n", + "\n", + "### Convolution2D\n", + "\n", + "**Input Shape**:\n", + "\n", + "**4D** tensor with shape: \n", + "\n", + "- (`batch_size`, `channels`, `rows`, `cols`) if `image_data_format='channels_last'`\n", + "- (`batch_size`, `rows`, `cols`, `channels`) if `image_data_format='channels_first'`\n", + "\n", + "**Output Shape**:\n", + "\n", + "**4D** tensor with shape:\n", + "\n", + "- (`batch_size`, `filters`, `new_rows`, `new_cols`) \n", + "if `image_data_format='channels_first'`\n", + "- (`batch_size`, `new_rows`, `new_cols`, `filters`) if `image_data_format='channels_last'`\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "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": 2, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\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": 3, + "metadata": { + "collapsed": true, + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "from keras.models import Sequential\n", + "from keras.layers.core import Dense, Dropout, Activation, Flatten" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true, + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "from keras.layers.convolutional import Conv2D\n", + "from keras.layers.pooling import MaxPooling2D" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "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": 6, + "metadata": { + "collapsed": true, + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [], + "source": [ + "#Load the training and testing data\n", + "(X_train, y_train), (X_test, y_test) = mnist.load_data()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "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": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from keras import backend as K" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Very Important: \n", + "When dealing with images & convolutions, it is paramount to handle `image_data_format` properly" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "img_rows, img_cols = 28, 28\n", + "\n", + "if K.image_data_format() == 'channels_first':\n", + " shape_ord = (1, img_rows, img_cols)\n", + "else: # channel_last\n", + " shape_ord = (img_rows, img_cols, 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Preprocess and Normalise Data" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true, + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [], + "source": [ + "X_train = X_train.reshape((X_train.shape[0],) + shape_ord)\n", + "X_test = X_test.reshape((X_test.shape[0],) + shape_ord)\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": 11, + "metadata": { + "collapsed": true, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [], + "source": [ + "np.random.seed(1338) # for reproducibilty!!\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": 12, + "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],) + shape_ord)\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": 13, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(11918, 28, 28, 1) (11918,) (10000, 28, 28, 1) (10000,)\n" + ] + } + ], + "source": [ + "print(X_train.shape, Y_labels.shape, X_test.shape, Y_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "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": 15, + "metadata": { + "collapsed": true, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [], + "source": [ + "# -- Initializing the values for the convolution neural network\n", + "\n", + "nb_epoch = 2 # kept very low! Please increase if you have GPU\n", + "\n", + "batch_size = 64\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": 16, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model = Sequential()\n", + "\n", + "model.add(Conv2D(nb_filters, (nb_conv, nb_conv), padding='valid', \n", + " input_shape=shape_ord)) # note: the very first layer **must** always specify the input_shape\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": 17, + "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": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train on 11918 samples, validate on 10000 samples\n", + "Epoch 1/2\n", + "11918/11918 [==============================] - 9s - loss: 0.2321 - acc: 0.9491 - val_loss: 0.1276 - val_acc: 0.9616\n", + "Epoch 2/2\n", + "11918/11918 [==============================] - 1s - loss: 0.1065 - acc: 0.9666 - val_loss: 0.0933 - val_acc: 0.9685\n" + ] + } + ], + "source": [ + "hist = model.fit(X_train, Y_train, batch_size=batch_size, \n", + " epochs=nb_epoch, verbose=1, \n", + " validation_data=(X_test, Y_test))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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EZJoxZrPNsKPAc8DdRdhXKeVuRCC0guUR2zb/a5cbONpMuu9fD2nT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\nUnbRhKGUUsoumjCUUkrZ5f8B7mYrlyR2uvYAAAAASUVORK5CYII=\n", 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/Fz5FpD1wSFULRaQH0As3WBkTbEdy8njyo2/4uxX/MnVIIAHmdeATEXkVEGAy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\n3Q1vb/KLyGgR2Swi6SLynQe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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "plt.figure()\n", + "plt.xlabel('Epochs')\n", + "plt.ylabel('Loss')\n", + "plt.plot(hist.history['loss'])\n", + "plt.plot(hist.history['val_loss'])\n", + "plt.legend(['Training', 'Validation'])\n", + "\n", + "plt.figure()\n", + "plt.xlabel('Epochs')\n", + "plt.ylabel('Accuracy')\n", + "plt.plot(hist.history['acc'])\n", + "plt.plot(hist.history['val_acc'])\n", + "plt.legend(['Training', 'Validation'], loc='lower right')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Step 4: Evaluate" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Available Metrics in Model: ['loss', 'acc']\n" + ] + } + ], + "source": [ + "print('Available Metrics in Model: {}'.format(model.metrics_names))" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test Loss: 0.0933376396745\n", + "Test Accuracy: 0.9685\n" + ] + } + ], + "source": [ + "# Evaluating the model on the test data \n", + "loss, accuracy = model.evaluate(X_test, Y_test, verbose=0)\n", + "print('Test Loss:', loss)\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": 22, + "metadata": { + "collapsed": true, + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "image/png": 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rTzj8fjonqMz/RnHl2CQGk0MO2BzHE9asfjzvPaBJ1YMUJxWkMe3Lpo4y9r3j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\nrkJggg==\n", + "text/plain": [ + "" + ] + }, + "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": 24, + "metadata": { + "collapsed": true, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(Conv2D(nb_filters, (nb_conv, nb_conv),\n", + " padding='valid', input_shape=shape_ord))\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": 25, + "metadata": { + "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 [==============================] - 2s - loss: 0.1922 - acc: 0.9503 - val_loss: 0.0864 - val_acc: 0.9721\n", + "Epoch 2/2\n", + "11918/11918 [==============================] - 1s - loss: 0.0902 - acc: 0.9705 - val_loss: 0.0898 - val_acc: 0.9676\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "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", + " epochs=nb_epoch,verbose=1,\n", + " validation_data=(X_test, Y_test))" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test score: 0.0898334540322\n", + "Test accuracy: 0.9676\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": 27, + "metadata": { + "collapsed": true, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [], + "source": [ + "model = Sequential()\n", + "\n", + "model.add(Conv2D(nb_filters, (nb_conv, nb_conv),\n", + " padding='valid',\n", + " input_shape=shape_ord))\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": 28, + "metadata": { + "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 [==============================] - 2s - loss: 0.2394 - acc: 0.9329 - val_loss: 0.1882 - val_acc: 0.9355\n", + "Epoch 2/2\n", + "11918/11918 [==============================] - 1s - loss: 0.1038 - acc: 0.9656 - val_loss: 0.0900 - val_acc: 0.9679\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 28, + "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", + " epochs=nb_epoch,verbose=1,\n", + " validation_data=(X_test, Y_test))" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test score: 0.090035862723\n", + "Test accuracy: 0.9679\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": 31, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(Conv2D(nb_filters, (nb_conv, nb_conv),\n", + " padding='valid', input_shape=shape_ord))\n", + "model.add(Activation('relu'))\n", + "model.add(Conv2D(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": 32, + "metadata": { + "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 [==============================] - 2s - loss: 0.3656 - acc: 0.8706 - val_loss: 0.1640 - val_acc: 0.9468\n", + "Epoch 2/2\n", + "11918/11918 [==============================] - 2s - loss: 0.1345 - acc: 0.9528 - val_loss: 0.0693 - val_acc: 0.9764\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 32, + "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", + " epochs=nb_epoch,verbose=1,\n", + " validation_data=(X_test, Y_test))" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test score: 0.0693048403405\n", + "Test accuracy: 0.9764\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": 34, + "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(Conv2D(nb_filters, (nb_conv, nb_conv), \n", + " padding='valid',\n", + " input_shape=shape_ord))\n", + " model.add(Activation('relu'))\n", + " model.add(Conv2D(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", + " epochs=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": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train on 11918 samples, validate on 10000 samples\n", + "Epoch 1/2\n", + "11918/11918 [==============================] - 2s - loss: 0.3752 - acc: 0.8673 - val_loss: 0.1513 - val_acc: 0.9505\n", + "Epoch 2/2\n", + "11918/11918 [==============================] - 2s - loss: 0.1384 - acc: 0.9530 - val_loss: 0.0672 - val_acc: 0.9774\n", + "Test score: 0.0671841920182\n", + "Test accuracy: 0.9774\n", + "5.84 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\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" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "from keras.layers.normalization import BatchNormalization\n", + "\n", + "BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, \n", + " beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros',\n", + " moving_variance_initializer='ones', beta_regularizer=None, gamma_regularizer=None,\n", + " beta_constraint=None, gamma_constraint=None)\n", + "```\n", + "\n", + "#### Arguments\n", + "\n", + "
    \n", + "
  • axis: Integer, the axis that should be normalized\n", + " (typically the features axis).\n", + " For instance, after a Conv2D layer with\n", + " data_format=\"channels_first\",\n", + " set axis=1 in BatchNormalization.
    • \n", + "
  • momentum: Momentum for the moving average.
    • \n", + "
  • epsilon: Small float added to variance to avoid dividing by zero.
    • \n", + "
  • center: If True, add offset of beta to normalized tensor.\n", + " If False, beta is ignored.
    • \n", + "
  • scale: If True, multiply by gamma.\n", + " If False, gamma is not used.\n", + " When the next layer is linear (also e.g. nn.relu),\n", + " this can be disabled since the scaling\n", + " will be done by the next layer.
    • \n", + "
  • beta_initializer: Initializer for the beta weight.
    • \n", + "
  • gamma_initializer: Initializer for the gamma weight.
    • \n", + "
  • moving_mean_initializer: Initializer for the moving mean.
    • \n", + "
  • moving_variance_initializer: Initializer for the moving variance.
    • \n", + "
  • beta_regularizer: Optional regularizer for the beta weight.
    • \n", + "
  • gamma_regularizer: Optional regularizer for the gamma weight.
    • \n", + "
  • beta_constraint: Optional constraint for the beta weight.
    • \n", + "
  • gamma_constraint: Optional constraint for the gamma weight.
    • \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Excercise" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true, + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [], + "source": [ + "# Try to add a new BatchNormalization layer to the Model \n", + "# (after the Dropout layer) - before or after the ReLU Activation" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.3" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/2.3 Deep Convolutional Neural Networks.ipynb b/4. Deep Networks Models and ResNets.ipynb similarity index 86% rename from 2.3 Deep Convolutional Neural Networks.ipynb rename to 4. Deep Networks Models and ResNets.ipynb index df7cbaa..724a210 100644 --- a/2.3 Deep Convolutional Neural Networks.ipynb +++ b/4. Deep Networks Models and ResNets.ipynb @@ -3,8 +3,6 @@ { "cell_type": "markdown", "metadata": { - "deletable": true, - "editable": true, "slideshow": { "slide_type": "slide" } @@ -16,8 +14,6 @@ { "cell_type": "markdown", "metadata": { - "deletable": true, - "editable": true, "slideshow": { "slide_type": "subslide" } @@ -31,8 +27,6 @@ { "cell_type": "markdown", "metadata": { - "deletable": true, - "editable": true, "slideshow": { "slide_type": "slide" } @@ -44,8 +38,6 @@ { "cell_type": "markdown", "metadata": { - "deletable": true, - "editable": true, "slideshow": { "slide_type": "subslide" } @@ -64,8 +56,6 @@ { "cell_type": "markdown", "metadata": { - "deletable": true, - "editable": true, "slideshow": { "slide_type": "skip" } @@ -81,8 +71,6 @@ { "cell_type": "markdown", "metadata": { - "deletable": true, - "editable": true, "slideshow": { "slide_type": "subslide" } @@ -96,8 +84,6 @@ { "cell_type": "markdown", "metadata": { - "deletable": true, - "editable": true, "slideshow": { "slide_type": "slide" } @@ -108,10 +94,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "" ] @@ -125,10 +108,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "" ] @@ -136,9 +116,7 @@ { "cell_type": "markdown", "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "source": [ "# `keras.applications`" @@ -148,9 +126,6 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "slideshow": { "slide_type": "subslide" } @@ -173,11 +148,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# -- Jupyter/IPython way to see documentation\n", @@ -188,11 +159,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -234,11 +201,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -255,10 +218,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "" ] @@ -267,9 +227,6 @@ "cell_type": "code", "execution_count": 13, "metadata": { - "collapsed": false, - "deletable": true, - "editable": true, "slideshow": { "slide_type": "skip" } @@ -302,10 +259,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "" ] @@ -313,11 +267,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -342,10 +292,7 @@ }, { "cell_type": "markdown", - "metadata": { - "deletable": true, - "editable": true - }, + "metadata": {}, "source": [ "" ] @@ -353,11 +300,7 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -383,8 +326,6 @@ { "cell_type": "markdown", "metadata": { - "deletable": true, - "editable": true, "slideshow": { "slide_type": "slide" } @@ -399,9 +340,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": true, - "deletable": true, - "editable": true + "collapsed": true }, "outputs": [], "source": [ @@ -459,7 +398,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python [default]", "language": "python", "name": "python3" }, @@ -473,9 +412,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.5.3" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/2.4 Transfer Learning & Fine-Tuning.ipynb b/5 Transfer Learning & Fine-Tuning.ipynb similarity index 99% rename from 2.4 Transfer Learning & Fine-Tuning.ipynb rename to 5 Transfer Learning & Fine-Tuning.ipynb index 9b75068..361d53c 100644 --- a/2.4 Transfer Learning & Fine-Tuning.ipynb +++ b/5 Transfer Learning & Fine-Tuning.ipynb @@ -341,7 +341,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python [default]", "language": "python", "name": "python3" }, diff --git a/Intro cnn.ipynb b/Intro cnn.ipynb deleted file mode 100644 index 53358e9..0000000 --- a/Intro cnn.ipynb +++ /dev/null @@ -1,1023 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Convolutional Neural Network" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "### References:\n", - "\n", - "Some of the images and the content I used came from this great couple of blog posts \\[1\\] [https://adeshpande3.github.io/adeshpande3.github.io/]() and \\[2\\] the terrific book, [\"Neural Networks and Deep Learning\"](http://neuralnetworksanddeeplearning.com/) by Michael Nielsen. (**Strongly recommend**) " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "A convolutional neural network (CNN, or ConvNet) is a type of **feed-forward** artificial neural network in which the connectivity pattern between its neurons is inspired by the organization of the animal visual cortex." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "The networks consist of multiple layers of small neuron collections which process portions of the input image, called **receptive fields**. \n", - "\n", - "The outputs of these collections are then tiled so that their input regions overlap, to obtain a _better representation_ of the original image; this is repeated for every such layer." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "## How does it look like?" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true, - "slideshow": { - "slide_type": "-" - } - }, - "source": [ - "\n", - "\n", - "> source: https://flickrcode.files.wordpress.com/2014/10/conv-net2.png" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# The Problem Space \n", - "\n", - "## Image Classification" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "Image classification is the task of taking an input image and outputting a class (a cat, dog, etc) or a probability of classes that best describes the image. \n", - "\n", - "For humans, this task of recognition is one of the first skills we learn from the moment we are born and is one that comes naturally and effortlessly as adults." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "These skills of being able to quickly recognize patterns, *generalize* from prior knowledge, and adapt to different image environments are ones that we do not share with machines." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "## Inputs and Outputs" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "source: [http://www.pawbuzz.com/wp-content/uploads/sites/551/2014/11/corgi-puppies-21.jpg]()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "When a computer sees an image (takes an image as input), it will see an array of pixel values. \n", - "\n", - "Depending on the resolution and size of the image, it will see a 32 x 32 x 3 array of numbers (The 3 refers to RGB values).\n", - "\n", - "let's say we have a color image in JPG form and its size is 480 x 480. The representative array will be 480 x 480 x 3. Each of these numbers is given a value from 0 to 255 which describes the pixel intensity at that point." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "## Goal" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What we want the computer to do is to be able to differentiate between all the images it’s given and figure out the unique features that make a dog a dog or that make a cat a cat. " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "When we look at a picture of a dog, we can classify it as such if the picture has identifiable features such as paws or 4 legs. \n", - "\n", - "In a similar way, the computer should be able to perform image classification by looking for *low level* features such as edges and curves, and then building up to more abstract concepts through a series of **convolutional layers**." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "## Structure of a CNN" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> A more detailed overview of what CNNs do would be that you take the image, pass it through a series of convolutional, nonlinear, pooling (downsampling), and fully connected layers, and get an output. As we said earlier, the output can be a single class or a probability of classes that best describes the image. \n", - "\n", - "source: [1]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Convolutional Layer" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "The first layer in a CNN is always a **Convolutional Layer**." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "### Convolutional filters\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A Convolutional Filter much like a **kernel** in image recognition is a small matrix useful for blurring, sharpening, embossing, edge detection, and more. \n", - "\n", - "This is accomplished by means of convolution between a kernel and an image.\n", - "\n", - "#### The main difference _here_ is that the conv matrices are **learned**." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "As the filter is sliding, or **convolving**, around the input image, it is multiplying the values in the filter with the original pixel values of the image
\n", - "(a.k.a. computing **element wise multiplications**)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "Now, we repeat this process for every location on the input volume. (Next step would be moving the filter to the right by 1 unit, then right again by 1, and so on)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "After sliding the filter over all the locations, we are left with an array of numbers usually called an **activation map** or **feature map**." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "## High Level Perspective\n", - "\n", - "Let’s talk about briefly what this convolution is actually doing from a high level. " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "Each of these filters can be thought of as **feature identifiers** (e.g. *straight edges, simple colors, curves*)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "### Visualisation of the Receptive Field" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "The value is much lower! This is because there wasn’t anything in the image section that responded to the curve detector filter. Remember, the output of this conv layer is an activation map. \n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Going Deeper Through the Network" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "Now in a traditional **convolutional neural network** architecture, there are other layers that are interspersed between these conv layers.\n", - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "## ReLU (Rectified Linear Units) Layer" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - " After each conv layer, it is convention to apply a *nonlinear layer* (or **activation layer**) immediately afterward.\n", - "\n", - "\n", - "The purpose of this layer is to introduce nonlinearity to a system that basically has just been computing linear operations during the conv layers (just element wise multiplications and summations)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "In the past, nonlinear functions like tanh and sigmoid were used, but researchers found out that **ReLU layers** work far better because the network is able to train a lot faster (because of the computational efficiency) without making a significant difference to the accuracy." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "It also helps to alleviate the **vanishing gradient problem**, which is the issue where the lower layers of the network train very slowly because the gradient decreases exponentially through the layers" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "(**very briefly**)\n", - "\n", - "Vanishing gradient problem depends on the choice of the activation function. \n", - "\n", - "Many common activation functions (e.g `sigmoid` or `tanh`) *squash* their input into a very small output range in a very non-linear fashion. \n", - "\n", - "For example, sigmoid maps the real number line onto a \"small\" range of [0, 1]." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "As a result, there are large regions of the input space which are mapped to an extremely small range. \n", - "\n", - "In these regions of the input space, even a large change in the input will produce a small change in the output - hence the **gradient is small**." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "### ReLu\n", - "\n", - "The **ReLu** function is defined as $f(x) = \\max(0, x),$ [2]\n", - "\n", - "A smooth approximation to the rectifier is the *analytic function*: $f(x) = \\ln(1 + e^x)$\n", - "\n", - "which is called the **softplus** function.\n", - "\n", - "The derivative of softplus is $f'(x) = e^x / (e^x + 1) = 1 / (1 + e^{-x})$, i.e. the **logistic function**.\n", - "\n", - "[2] [http://www.cs.toronto.edu/~fritz/absps/reluICML.pdf]() by G. E. Hinton " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "## Pooling Layers" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - " After some ReLU layers, it is customary to apply a **pooling layer** (aka *downsampling layer*)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "In this category, there are also several layer options, with **maxpooling** being the most popular. " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "Example of a MaxPooling filter" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "Other options for pooling layers are average pooling and L2-norm pooling. " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "The intuition behind this Pooling layer is that once we know that a specific feature is in the original input volume (there will be a high activation value), its exact location is not as important as its relative location to the other features. \n", - "\n", - "Therefore this layer drastically reduces the spatial dimension (the length and the width but not the depth) of the input volume.\n", - "\n", - "This serves two main purposes: reduce the amount of parameters; controlling overfitting. " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "An intuitive explanation for the usefulness of pooling could be explained by an example: \n", - "\n", - "Lets assume that we have a filter that is used for detecting faces. The exact pixel location of the face is less relevant then the fact that there is a face \"somewhere at the top\"" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "## Dropout Layer" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "The **dropout layers** have the very specific function to *drop out* a random set of activations in that layers by setting them to zero in the forward pass. Simple as that. \n", - "\n", - "It allows to avoid *overfitting* but has to be used **only** at training time and **not** at test time. " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "## Fully Connected Layer" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "The last layer, however, is an important one, namely the **Fully Connected Layer**." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "Basically, a FC layer looks at what high level features most strongly correlate to a particular class and has particular weights so that when you compute the products between the weights and the previous layer, you get the correct probabilities for the different classes." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Going further: Convolution Arithmetic" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you want to go further with Convolution and you want to fully understand how convolution works with all the details we omitted in this notebook, I strongly suggest to read this **terrific** paper: [A guide to convolution arithmetic for deep learning](https://arxiv.org/abs/1603.07285).\n", - "\n", - "This paper is also referenced (with animations) in the `theano` main documentation: [convnet tutorial](http://deeplearning.net/software/theano/tutorial/conv_arithmetic.html)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# CNN in Keras" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "**Keras** has an extensive support for Convolutional Layers:\n", - "\n", - "- 1D Convolutional Layers;\n", - "- 2D Convolutional Layers;\n", - "- 3D Convolutional Layers;\n", - "- Depthwise Convolution;\n", - "- Transpose Convolution;\n", - "- ....\n", - "\n", - "The corresponding `keras` package is `keras.layers.convolutional`.\n", - "\n", - "Take a look at the [Convolutional Layers](https://keras.io/layers/convolutional/) documentation to know more about Conv Layers that are missing in this notebook." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "#### Convolution1D\n", - "\n", - "```python\n", - "from keras.layers.convolutional import Conv1D\n", - "\n", - "Conv1D(filters, kernel_size, strides=1, padding='valid', \n", - " dilation_rate=1, activation=None, use_bias=True, \n", - " kernel_initializer='glorot_uniform', bias_initializer='zeros', \n", - " kernel_regularizer=None, bias_regularizer=None, \n", - " activity_regularizer=None, kernel_constraint=None, \n", - " bias_constraint=None)\n", - "```\n", - "\n", - "#### Arguments:\n", - "\n", - "
    \n", - "
  • filters: Integer, the dimensionality of the output space\n", - " (i.e. the number output of filters in the convolution).
    • \n", - "
  • kernel_size: An integer or tuple/list of a single integer,\n", - " specifying the length of the 1D convolution window.
    • \n", - "
  • strides: An integer or tuple/list of a single integer,\n", - " specifying the stride length of the convolution.\n", - " Specifying any stride value != 1 is incompatible with specifying\n", - " any dilation_rate value != 1.
    • \n", - "
  • padding: One of \"valid\", \"causal\" or \"same\" (case-insensitive).\n", - " \"causal\" results in causal (dilated) convolutions, e.g. output[t]\n", - " does not depend on input[t+1:]. Useful when modeling temporal data\n", - " where the model should not violate the temporal order.\n", - " See WaveNet: A Generative Model for Raw Audio, section 2.1.
    • \n", - "
  • dilation_rate: an integer or tuple/list of a single integer, specifying\n", - " the dilation rate to use for dilated convolution.\n", - " Currently, specifying any dilation_rate value != 1 is\n", - " incompatible with specifying any strides value != 1.
    • \n", - "
  • activation: Activation function to use\n", - " (see activations).\n", - " If you don't specify anything, no activation is applied\n", - " (ie. \"linear\" activation: a(x) = x).
    • \n", - "
  • use_bias: Boolean, whether the layer uses a bias vector.
    • \n", - "
  • kernel_initializer: Initializer for the kernel weights matrix\n", - " (see initializers).
    • \n", - "
  • bias_initializer: Initializer for the bias vector\n", - " (see initializers).
    • \n", - "
  • kernel_regularizer: Regularizer function applied to\n", - " the kernel weights matrix\n", - " (see regularizer).
    • \n", - "
  • bias_regularizer: Regularizer function applied to the bias vector\n", - " (see regularizer).
    • \n", - "
  • activity_regularizer: Regularizer function applied to\n", - " the output of the layer (its \"activation\").\n", - " (see regularizer).
    • \n", - "
  • kernel_constraint: Constraint function applied to the kernel matrix\n", - " (see constraints).
    • \n", - "
  • bias_constraint: Constraint function applied to the bias vector\n", - " (see constraints).
    • \n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - ">Convolution operator for filtering neighborhoods of **one-dimensional inputs**. When using this layer as the first layer in a model, either provide the keyword argument `input_dim` (int, e.g. 128 for sequences of 128-dimensional vectors), or `input_shape` (tuple of integers, e.g. (10, 128) for sequences of 10 vectors of 128-dimensional vectors)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "#### Example\n", - "\n", - "```python\n", - "\n", - "# apply a convolution 1d of length 3 to a sequence with 10 timesteps,\n", - "# with 64 output filters\n", - "model = Sequential()\n", - "model.add(Conv1D(64, 3, padding='same', input_shape=(10, 32)))\n", - "# now model.output_shape == (None, 10, 64)\n", - "\n", - "# add a new conv1d on top\n", - "model.add(Conv1D(32, 3, padding='same'))\n", - "# now model.output_shape == (None, 10, 32)\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "#### Convolution2D\n", - "\n", - "```python\n", - "from keras.layers.convolutional import Conv2D\n", - "\n", - "Conv2D(filters, kernel_size, strides=(1, 1), padding='valid', \n", - " data_format=None, dilation_rate=(1, 1), activation=None, \n", - " use_bias=True, kernel_initializer='glorot_uniform', \n", - " bias_initializer='zeros', kernel_regularizer=None, \n", - " bias_regularizer=None, activity_regularizer=None, \n", - " kernel_constraint=None, bias_constraint=None)\n", - "```\n", - "\n", - "#### Arguments:\n", - "\n", - "
    \n", - "
  • filters: Integer, the dimensionality of the output space\n", - " (i.e. the number output of filters in the convolution).
    • \n", - "
  • kernel_size: An integer or tuple/list of 2 integers, specifying the\n", - " width and height of the 2D convolution window.\n", - " Can be a single integer to specify the same value for\n", - " all spatial dimensions.
    • \n", - "
  • strides: An integer or tuple/list of 2 integers,\n", - " specifying the strides of the convolution along the width and height.\n", - " Can be a single integer to specify the same value for\n", - " all spatial dimensions.\n", - " Specifying any stride value != 1 is incompatible with specifying\n", - " any dilation_rate value != 1.
    • \n", - "
  • padding: one of \"valid\" or \"same\" (case-insensitive).
    • \n", - "
  • data_format: A string,\n", - " one of channels_last (default) or channels_first.\n", - " The ordering of the dimensions in the inputs.\n", - " channels_last corresponds to inputs with shape\n", - " (batch, height, width, channels) while channels_first\n", - " corresponds to inputs with shape\n", - " (batch, channels, height, width).\n", - " It defaults to the image_data_format value found in your\n", - " Keras config file at ~/.keras/keras.json.\n", - " If you never set it, then it will be \"channels_last\".
    • \n", - "
  • dilation_rate: an integer or tuple/list of 2 integers, specifying\n", - " the dilation rate to use for dilated convolution.\n", - " Can be a single integer to specify the same value for\n", - " all spatial dimensions.\n", - " Currently, specifying any dilation_rate value != 1 is\n", - " incompatible with specifying any stride value != 1.
    • \n", - "
  • activation: Activation function to use\n", - " (see activations).\n", - " If you don't specify anything, no activation is applied\n", - " (ie. \"linear\" activation: a(x) = x).
    • \n", - "
  • use_bias: Boolean, whether the layer uses a bias vector.
    • \n", - "
  • kernel_initializer: Initializer for the kernel weights matrix\n", - " (see initializers).
    • \n", - "
  • bias_initializer: Initializer for the bias vector\n", - " (see initializers).
    • \n", - "
  • kernel_regularizer: Regularizer function applied to\n", - " the kernel weights matrix\n", - " (see regularizer).
    • \n", - "
  • bias_regularizer: Regularizer function applied to the bias vector\n", - " (see regularizer).
    • \n", - "
  • activity_regularizer: Regularizer function applied to\n", - " the output of the layer (its \"activation\").\n", - " (see regularizer).
    • \n", - "
  • kernel_constraint: Constraint function applied to the kernel matrix\n", - " (see constraints).
    • \n", - "
  • bias_constraint: Constraint function applied to the bias vector\n", - " (see constraints).
    • \n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "#### Example\n", - "Assuming \n", - "``keras.backend.image_data_format == \"channels_last\"``\n", - "```python\n", - "\n", - "# apply a 3x3 convolution with 64 output filters on a 256x256 image:\n", - "model = Sequential()\n", - "model.add(Conv2D(64, (3, 3), padding='same', \n", - " input_shape=(3, 256, 256)))\n", - "# now model.output_shape == (None, 256, 256, 64)\n", - "\n", - "# add a 3x3 convolution on top, with 32 output filters:\n", - "model.add(Conv2D(32, (3, 3), padding='same'))\n", - "# now model.output_shape == (None, 256, 256, 32)\n", - "\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "## Dimensions of Conv filters in Keras" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "The complex structure of ConvNets *may* lead to a representation that is challenging to understand." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "Of course, the dimensions vary according to the dimension of the Convolutional filters (e.g. 1D, 2D)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "### Convolution1D\n", - "\n", - "**Input Shape**:\n", - "\n", - "**3D** tensor with shape: (`batch_size`, `steps`, `input_dim`).\n", - "\n", - "**Output Shape**:\n", - "\n", - "**3D** tensor with shape: (`batch_size`, `new_steps`, `filters`)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "### Convolution2D\n", - "\n", - "**Input Shape**:\n", - "\n", - "**4D** tensor with shape: \n", - "\n", - "- (`batch_size`, `channels`, `rows`, `cols`) if `image_data_format='channels_last'`\n", - "- (`batch_size`, `rows`, `cols`, `channels`) if `image_data_format='channels_first'`\n", - "\n", - "**Output Shape**:\n", - "\n", - "**4D** tensor with shape:\n", - "\n", - "- (`batch_size`, `filters`, `new_rows`, `new_cols`) \n", - "if `image_data_format='channels_first'`\n", - "- (`batch_size`, `new_rows`, `new_cols`, `filters`) if `image_data_format='channels_last'`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "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": "3.5.3" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/to remove/1.1 Introduction - Deep Learning and ANN.ipynb b/to remove/1.1 Introduction - Deep Learning and ANN.ipynb deleted file mode 100644 index c75b7e0..0000000 --- a/to remove/1.1 Introduction - Deep Learning and ANN.ipynb +++ /dev/null @@ -1,1797 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "nbpresent": { - "id": "24ca50a0-b9ad-425d-a28f-6bd3ca3ac378" - }, - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Introduction to Deep Learning" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nbpresent": { - "id": "3896ecb2-47fa-4dbc-a2ff-9c76e9dfe93e" - }, - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "Deep learning allows computational models that are composed of multiple processing **layers** to learn representations of data with multiple levels of abstraction." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nbpresent": { - "id": "8c3060aa-fee9-438c-bc60-4685c0eb4750" - }, - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nbpresent": { - "id": "6287766f-972f-4b4d-bee7-5418f0af74de" - }, - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "**Deep learning** is one of the leading tools in data analysis these days and one of the most common frameworks for deep learning is **Keras**. " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nbpresent": { - "id": "2f1c6299-954a-461a-b5c1-a86a1d12ad15" - }, - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "The Tutorial will provide an introduction to deep learning using `keras` with practical code examples." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## This Section will cover:\n", - "\n", - "* Getting a conceptual understanding of multi-layer neural networks\n", - "* Training neural networks for image classification\n", - "* Implementing the powerful backpropagation algorithm\n", - "* Debugging neural network implementations" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nbpresent": { - "id": "5e13607b-3ec5-4a95-a2d8-f898f20748da" - }, - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Building Blocks: Artificial Neural Networks (ANN)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nbpresent": { - "id": "4fa2e86a-be32-4e78-96d9-f511a07e3908" - }, - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "In machine learning and cognitive science, an artificial neural network (ANN) is a network inspired by biological neural networks which are used to estimate or approximate functions that can depend on a large number of inputs that are generally unknown" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nbpresent": { - "id": "df0121bc-10f1-4ace-840e-6fc89c6fdc7f" - }, - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "An ANN is built from nodes (neurons) stacked in layers between the feature vector and the target vector. " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nbpresent": { - "id": "c25d7194-10bd-4196-9d4c-592bf6e188f9" - }, - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "A node in a neural network is built from Weights and Activation function" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nbpresent": { - "id": "15260f90-13d1-4fcc-afc6-379c507cb950" - }, - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "An early version of ANN built from one node was called the **Perceptron**" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "nbpresent": { - "id": "92d4603e-7e39-4156-818c-785df6189fe8" - }, - "slideshow": { - "slide_type": "-" - } - }, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true, - "nbpresent": { - "id": "356d5ec7-3392-4daa-9671-4cc7111c5c91" - }, - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "The Perceptron is an algorithm for supervised learning of binary classifiers. functions that can decide whether an input (represented by a vector of numbers) belongs to one class or another.\n", - "\n", - "Much like logistic regression, the weights in a neural net are being multiplied by the input vertor summed up and feeded into the activation function's input." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "A Perceptron Network can be designed to have *multiple layers*, leading to the **Multi-Layer Perceptron** (aka `MLP`)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Single Layer Neural Network" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "_(Source: Python Machine Learning, S. Raschka)_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Weights Update Rule" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "- We use a **gradient descent** optimization algorithm to learn the _Weights Coefficients_ of the model.\n", - "

\n", - "- In every **epoch** (pass over the training set), we update the weight vector $w$ using the following update rule:\n", - "\n", - "$$\n", - "w = w + \\Delta w, \\text{where } \\Delta w = - \\eta \\nabla J(w)\n", - "$$\n", - "\n", - "

\n", - "\n", - "In other words, we computed the gradient based on the whole training set and updated the weights of the model by taking a step into the **opposite direction** of the gradient $ \\nabla J(w)$. \n", - "\n", - "In order to fin the **optimal weights of the model**, we optimized an objective function (e.g. the Sum of Squared Errors (SSE)) cost function $J(w)$. \n", - "\n", - "Furthermore, we multiply the gradient by a factor, the learning rate $\\eta$ , which we choose carefully to balance the **speed of learning** against the risk of overshooting the global minimum of the cost function." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Gradient Descent" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In **gradient descent optimization**, we update all the **weights simultaneously** after each epoch, and we define the _partial derivative_ for each weight $w_j$ in the weight vector $w$ as follows:\n", - "\n", - "$$\n", - "\\frac{\\partial}{\\partial w_j} J(w) = \\sum_{i} ( y^{(i)} - a^{(i)} ) x^{(i)}_j\n", - "$$\n", - "\n", - "**Note**: _The superscript $(i)$ refers to the ith sample. The subscript $j$ refers to the jth dimension/feature_\n", - "\n", - "\n", - "Here $y^{(i)}$ is the target class label of a particular sample $x^{(i)}$ , and $a^{(i)}$ is the **activation** of the neuron \n", - "\n", - "(which is a linear function in the special case of _Perceptron_)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We define the **activation function** $\\phi(\\cdot)$ as follows:\n", - "\n", - "$$\n", - "\\phi(z) = z = a = \\sum_{j} w_j x_j = \\mathbf{w}^T \\mathbf{x}\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Binary Classification" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "While we used the **activation** $\\phi(z)$ to compute the gradient update, we may use a **threshold function** _(Heaviside function)_ to squash the continuous-valued output into binary class labels for prediction:\n", - "\n", - "$$\n", - "\\hat{y} = \n", - "\\begin{cases}\n", - " 1 & \\text{if } \\phi(z) \\geq 0 \\\\\n", - " 0 & \\text{otherwise}\n", - "\\end{cases}\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## Building Neural Nets from scratch \n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "### Idea:\n", - "\n", - "We will build the neural networks from first principles. \n", - "We will create a very simple model and understand how it works. We will also be implementing backpropagation algorithm. \n", - "\n", - "**Please note that this code is not optimized and not to be used in production**. \n", - "\n", - "This is for instructive purpose - for us to understand how ANN works. \n", - "\n", - "Libraries like `theano` have highly optimized code." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Perceptron and Adaline Models\n", - "\n", - "Take a look at this notebook : Perceptron and Adaline " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you want a sneak peek of alternate (production ready) implementation of _Perceptron_ for instance try:\n", - "```python\n", - "from sklearn.linear_model import Perceptron\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introducing the multi-layer neural network architecture" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "_(Source: Python Machine Learning, S. Raschka)_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we will see how to connect **multiple single neurons** to a **multi-layer feedforward neural network**; this special type of network is also called a **multi-layer perceptron** (MLP). \n", - "\n", - "The figure shows the concept of an **MLP** consisting of three layers: one _input_ layer, one _hidden_ layer, and one _output_ layer. \n", - "\n", - "The units in the hidden layer are fully connected to the input layer, and the output layer is fully connected to the hidden layer, respectively. \n", - "\n", - "If such a network has **more than one hidden layer**, we also call it a **deep artificial neural network**.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Notation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "we denote the `ith` activation unit in the `lth` layer as $a_i^{(l)}$ , and the activation units $a_0^{(1)}$ and \n", - "$a_0^{(2)}$ are the **bias units**, respectively, which we set equal to $1$. \n", - "

\n", - "The _activation_ of the units in the **input layer** is just its input plus the bias unit:\n", - "\n", - "$$\n", - "\\mathbf{a}^{(1)} = [a_0^{(1)}, a_1^{(1)}, \\ldots, a_m^{(1)}]^T = [1, x_1^{(i)}, \\ldots, x_m^{(i)}]^T\n", - "$$\n", - "

\n", - "**Note**: $x_j^{(i)}$ refers to the jth feature/dimension of the ith sample" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Notes on Notation (usually) Adopted\n", - "\n", - "The terminology around the indices (subscripts and superscripts) may look a little bit confusing at first. \n", - "

\n", - "\n", - "You may wonder why we wrote $w_{j,k}^{(l)}$ and not $w_{k,j}^{(l)}$ to refer to \n", - "the **weight coefficient** that connects the *kth* unit in layer $l$ to the jth unit in layer $l+1$. \n", - "

\n", - "\n", - "What may seem a little bit quirky at first will make much more sense later when we **vectorize** the neural network representation. \n", - "

\n", - "\n", - "For example, we will summarize the weights that connect the input and hidden layer by a matrix \n", - "$$ W^{(1)} \\in \\mathbb{R}^{h×[m+1]}$$\n", - "\n", - "where $h$ is the number of hidden units and $m + 1$ is the number of hidden units plus bias unit. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "_(Source: Python Machine Learning, S. Raschka)_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Forward Propagation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* Starting at the input layer, we forward propagate the patterns of the training data through the network to generate an output.\n", - "\n", - "* Based on the network's output, we calculate the error that we want to minimize using a cost function that we will describe later.\n", - "\n", - "* We backpropagate the error, find its derivative with respect to each weight in the network, and update the model." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Sigmoid Activation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "_(Source: Python Machine Learning, S. Raschka)_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "_(Source: Python Machine Learning, S. Raschka)_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "_(Source: Python Machine Learning, S. Raschka)_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Backward Propagation" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true, - "nbpresent": { - "id": "5678486b-caf4-440b-be62-2f1286982c71" - }, - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "The weights of each neuron are learned by **gradient descent**, where each neuron's error is derived with respect to it's weight." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "_(Source: Python Machine Learning, S. Raschka)_" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "Optimization is done for each layer with respect to the previous layer in a technique known as **BackPropagation**." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "(*The following code is inspired from [these](https://github.com/dennybritz/nn-from-scratch) terrific notebooks*)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true, - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [], - "source": [ - "# Import the required packages\n", - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib\n", - "import matplotlib.pyplot as plt\n", - "import scipy" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true, - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [], - "source": [ - "# Display plots in notebook \n", - "%matplotlib inline\n", - "# Define plot's default figure size\n", - "matplotlib.rcParams['figure.figsize'] = (10.0, 8.0)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true, - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [], - "source": [ - "#read the datasets\n", - "train = pd.read_csv(\"data/intro_to_ann.csv\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": true, - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [], - "source": [ - "X, y = np.array(train.ix[:,0:2]), np.array(train.ix[:,2])" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(500, 2)" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(500,)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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xE3D7zh20atUKO3bswA8/Tpb6nBaWlujbr5/kInLGGPtUfM4W+2pZW1vj2tWr\nKCgokDzxBwDhYWGwsbH57P43btyI4cOHo665GaysrBEVdQ8eHh749ddfAQBPnjxBjx49sG7jRnTt\n1h0AEHz8OHr37o2rV6/C3Nz8s2MYP3487O3tYWFpgYGDhyAnJwfLfv0Vz54+Re/evSX1hg4ditat\nW6Nvv36wtbMDAMTGxOBIwGHk5ubi6dOnMDIyktQ3MDRE5O3bcGjVSlImFotx+9ZtZGdn4cxfF1Cp\nUiUAwIBBg5GdnQPfhQtx+NAhuWN/+vQp6lnVkymvZ2WFJ0+efLRtYmIi6tSpC3V1dUnZvbt3ERoS\ngoi79yTlrp07Y/TIEQgJDoZ6TXWZfmrWVEd2drbcMTPG2HvJM/1VWi/wMiIrZa5ubjRk6FB69vIV\niQoK6erNCKpTty798ccfJTZGUlIS/fXXX/Ts2TOp8jlz5tCosWNllu4m/DCJpk+fXmLj37p1i1zd\n3EhFRYWqVKlCbdu1o9WrV1NSUpJUvd27d5OWlha1d3Ghjp06kYaGBm3dupVmzZpFzZu3oEuXr1B2\nXj4Fh54iXV1dsmvUSPIAQIboDU2dPoOqV69Offv1k/lM0fEJZGxsXKy4w8LCqHadOjLLvMtWrqK+\nnp4fbftuGff5q1RJu4W/LiGf0WNkYjt38RJpamnJxJ2amUUWlpYUHh5e7O+cMfZ1gJzLiDyzxb5q\ne/fswbhx42BlaQENDQ3k5+dj1qxZGDBggExdIsLFixfx4MED2NnZyX2SuImJCUxMTGTK4xMS8O17\n7gds2rwZ1q5ciYhbt1CjRg0MHDAA7u7uxVo+fGfnzp1Yvnw5YmJiYGxsjNdpaahQoQIuXrqEuXPn\nYsyYMZg/fz4EQUC/fv3g7u6O06dPo7CwEPv37UPNmjVBRNDR0cGAfp548OABGjRogBUrViA6OhpN\nbRvCytoaMTEx0NbSwuxf5iPg4AGZOOJiY2FoaFis2B0cHFC7dm0MGTgA8+YvgKGREQ7u34fFvgtw\n8uTJj7bV09NDr169MHLYUKzbsBG6urrIyclG2uvXMnVzsrNhaWmJyNu3MXTQIAwcPBhp6WlYtWw5\nnFq3RsuWLYsVN2OMyZAnIyutF3hmi/1HYWFhsTaEf0hBQQEFBgbS1KlTaenSpTKzTGlpaRQfH//B\nIxkeP35MTZs2JZv69cmzf38yNTUlt86dKTMz85NjWrhwIQ0ZOlRmpmXI0KFUv0ED2nfoEG3w8yNr\nGxupzex6PJPxAAAgAElEQVTyWrt2LdWzsqITJ0Mp7sFDUldXp1PnzkvGSXr+gmzq16eDBw/K3ed/\nfxavX7+mSZMmkXPbtiQqKKT0HBGZ1qpFW3fskGxqf/wimZo0bUqbN28u9mfIzs6myZMnk46ODlWs\nWJHat29Pf//9t1xtRSIRjRo1imrUqEFGRkakqqpKampqdCc6RvIdZL7JJZeOHWnJ8hX07OUrmjd/\nAbW0tycNDQ3atm3be59cZIyxd8BPI7LyLCQkhOzt7UkQBNLR0aHp06fTmzdvPqmvjIwMcnR0pBYt\nWtKceb/QYG9v0tLSomPHjsndh7OzM82aPUeSQGS+yaX+331HPj4+nxQT0dulLgMDA/pt40bKEL2h\nzDe5tGnLVqpRowbFP0qSJATPXr4iAwMDunv3rtx95+bmkr6+vuR8rVVr11E/Ly+ZxO73XbvI1c1N\n0k4kElF6enqxPoe3tzf9tnGjpM93S7E29evTt85tSUNDg6ZMmfLZSfOntA8KCiI9PT1atnIlJT5+\nQj9Om0Y1atSgMeO+p3kLFlDtOnWok6srpeeIJPFn5+VTlSpVKCsr67PiZYx9+eRNtkpkGVEQhK0A\n3AG8ICK7D9RZA8ANQDaAIUQUURJjsy/P6dOnMWjQICxbtQoqKipITEjA6dBQDB48GHv27CmyfVJS\nEtavX4/bkZEwNzNDTk4OzCwssHXH75KluCFDh6FPzx54+PAhqlat+tH+4uLiEB0djSMngiXtVVRU\nsGjJUthaW2H16tVQVVUt9ufU09PDyZMnMW7cOMyYOhWCIKBy5cpYsmKF1GZ0DQ0N9OrTB0ePHkX9\n+vXl6vvhw4dQq1oV9f+54uVlSgpq1TKTqVerlhlepqTgxYsXmDhxIo4cOYLCwkJUr14dtnZ2+HXx\n4iKX0QwNDREb8/+nNxva2uLW3XsYN2oUnj19gqioKBgYGMgV98d8yjLqnDlzsMFvMzq7uwMA5vsu\nRLcePdGpXVt4eXnh+bNnCAoOQeXKlSVtgo8fh42NDapVq/bZMTPGGFBy1/VsB9DpQ28KguAGoDYR\n1QXgA+Djz22zr5qvry+GDBuGHydOxKb1G3Dj+nXcuH4dISEhiIqK+mjbiIgING/eHFk5IngPHw4d\nPX0cOHgQLe0dpP6ydmjVCnaNGhW59wd4e5WPiYmp5Om6d/T09CAIgszTakSE/fv3o1v37nBxccGv\nv/6K9PT09/Zta2uL8+fPIz4+Hvfv30ftOnVQy7SWTL03IpFUQlAUHR0dvE5NRUZGBgCglaMjjgUd\nlbkX8WhgIFq1aoUOHTpA39AI8Y+S8CojE6vWrcPNmzfh5uaGkJCQj441dOhQ/Lnzd1z/19EOMdHR\nOH4sCAsXLiyRROvp06dISkp6NwMul4KCAty8eROunTtLlbdo0QIOrRzRp08fzJw5E726dUXw8eN4\n/Pgx/tz5O8b4jISvr+9nx8wYYxLyTH/J8wJgBuD2B97bCMDzX7+PAqD/nnqKmedj5Ur16tVJW1ub\njoeclCztPElOobr16tGYMWM+2rZt27ZSS1qigkI689cFMjA0pAzRG6nyHj17kr+/f5HxZGRkkKam\nJsUkJEq1Dw49RdbW1jLLWz4+PtSocWPatnMnHT5ylPp4elKDBg0oNTW1yLFWrFhBHTp2lJzWLioo\npOi4eNLS0qKHDx8W2f7fvLy8aPjIkZT5Jpdy8guobbt21KVrVwq/dp1iEx/Q3F/mk6GhIW3atIla\nOTpKlkjfvRYsWkwdOnYkOzu7IpfwDh06RDo6OuTU5ltq2649aWlp0e+//16seMPDw8mjd2+ytrYm\nVzc3Cg4Optu3b5OjoyNpa2uTnp4eNWnSRO4DUsViMWlra9PdmFipz5Wdl0/mFhYUERFBYrGYdu3a\nRQ4ODmRoaEiubm50/vz5YsXNGPt6obT3bBWRbB0F4Piv358C0PQ99RT4lbDywtDQkNp8+y2N8PGh\nzl260MyfZ1Pi4ye0e/9+at6ixQfbZWZmkpqamtT+m3evuvXq0V9hf8uc5P7ixQu5YvL19aUGDRvS\n8ZCT9CQ5hfz37SNjY2Pav3+/VL0bN26QiYmJzMnxXgMG0Lx584oc582bN9SxUydq3KQJ+S7+lSZP\nmUp6enq0du1aueL8t9evX1PHTp3IxMSEuvfoQcbGxlSnbl0yt7AgPT09GjhwIN2/f5/mzp1L0376\nSeY7u/B3ODVp2vTtSfhy3EmYk5NDx48fp6NHjxb7wYHg4GDS09Oj1evW0Y3bkbTt99+pVq1apKGh\nQRv8/CjzTS5l5+WT/759pKOjQwkJCXL1O336dOri7i45GT4nv4AWLFpMLVu2LJEHLxhjXzd5k63S\nOvrhfZst3rseMHfuXMmvnZ2d4ezsrJiIWJll+M+BmS4dOsKlY0ecOnkSji1bYNWatRCJRB9sV6HC\n21Xx/Px8qSU3IkJ2djbWrVmNocOG48GDRCxZvBhTp06Fnp6eXDHNmDEDhoaG+GnaVMnRD5s3b4ab\nm5tUvePHj8OjT1/UqFFDqnyI91DM+mkGZs+e/dFxVFVVcfzYMZw4cQInT55EjRo1cO7cuU86ZFVD\nQwMhwcGIjIxETEwMfpk3D3Z2slsqzc3NsXfffpny27ciYGRsjPuxsZIT7z9GTU1N5vuQBxFh6tSp\n2Lh5C9y6dAEA2NSvD7tGjdHWqTX6eX0nOXS2Zy8PXL18BRs2bMCSJUuK7Hvu3Lnw9vaGlaUFWjs5\nISoqCqqVKyMgIOCT9oAxxr5u586dw7lz54rfUJ6MTJ4XireMGA1eRmTvkZ2dTRoaGnTlxk2Zu/wa\n2trSyJEjP9revWtX+sV3oVTbA4cDyMLCgqZMmUKtnZyol4cHnThxQiHxL1u2jEb4+MjMEh0KPEKt\nnZzK5FECWVlZZGxsTBu3bJEsJV69GUHGJibUu2/fEr9c+r9SUlJIXV2dsvPyZb43K2truhh+Waps\n78GD1LVbt2KNERMTQ3v27KGLFy/yjBZjrMRAzpktgYqx4fRjBEEwB3CUiGzf815nAGOJqIsgCA4A\nVhGRw3vqUUnFw8qn0NBQzJ33C07/635AAMjMzISBjjaio6JQt27dD7ZPSEhA27Zt0bxFC7Rxbovb\ntyJwJCAAhw8fRuvWrRUdPh4+fIimTZvi0uUrMLewAADk5eWhY7u2uHrlCqpVq4bvvvsOv/76K2rW\nrKnweOQVGRkJT09PvEpNhYaGBp4/ewYdHR3UqFEDgYGBOHXqFI6fOAG1KlXg5eUFNze3EpsZys7O\nhoGBAeIePpK6XqewsBBmxkY4+9cF1K33/2t7Zs2YAYgLsWzZshIZnzHGPpUgCCCiIv8wLJGnEQVB\n8AcQBqCeIAiPBEHwFgTBRxCEkQBARMcBJAqCEAdgE4AxJTEu+/KoqKggPy9PpjwvLw+VK1VCnTp1\nPtre0tISkZGRaN+uHaLuRKLOP78vjUQLeHvZ8fz58+HUygFTJ0/GIt8FsKtvAyLCy/QM3LoXhWyR\nCN26dSvyybrnz5/jxo0byMrKKnYcd+7cQS8PD6irq8PU1BQzZsyQXIj9Pra2trh79y6OHzuGgQMG\noG/fvrC3t0efPn3Qp08f+O/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C8HYWrGwQBIHKUjxMsV6+fIlNmzbh2vXrMDYywsiRI2Fn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\nykcV+1mUBgaDAaUo4LXmdGMwGPN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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#Let's plot the dataset and see how it is\n", - "plt.scatter(X[:,0], X[:,1], s=40, c=y, cmap=plt.cm.BuGn)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true, - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "## Start Building our MLP building blocks\n", - "\n", - "Note: This process will eventually result in our own Neural Networks class" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "### A look at the details" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": true, - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [], - "source": [ - "import random\n", - "random.seed(123)\n", - "\n", - "# calculate a random number where: a <= rand < b\n", - "def rand(a, b):\n", - " return (b-a)*random.random() + a" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "##### Function to generate a random number, given two numbers\n", - "\n", - "**Where will it be used?**: When we initialize the neural networks, the weights have to be randomly assigned." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": true, - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [], - "source": [ - "# Make a matrix \n", - "def makeMatrix(I, J, fill=0.0):\n", - " return np.zeros([I,J])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "### Define our activation function. Let's use sigmoid function" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# our sigmoid function\n", - "def sigmoid(x):\n", - " #return math.tanh(x)\n", - " return 1/(1+np.exp(-x))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "### Derivative of our activation function. \n", - "\n", - "Note: We need this when we run the backpropagation algorithm\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# derivative of our sigmoid function, in terms of the output (i.e. y)\n", - "def dsigmoid(y):\n", - " return y - y**2" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "### Our neural networks class\n", - "\n", - "When we first create a neural networks architecture, we need to know the number of inputs, number of hidden layers and number of outputs.\n", - "\n", - "The weights have to be randomly initialized." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "```python\n", - "class MLP:\n", - " def __init__(self, ni, nh, no):\n", - " # number of input, hidden, and output nodes\n", - " self.ni = ni + 1 # +1 for bias node\n", - " self.nh = nh\n", - " self.no = no\n", - "\n", - " # activations for nodes\n", - " self.ai = [1.0]*self.ni\n", - " self.ah = [1.0]*self.nh\n", - " self.ao = [1.0]*self.no\n", - " \n", - " # create weights\n", - " self.wi = makeMatrix(self.ni, self.nh)\n", - " self.wo = makeMatrix(self.nh, self.no)\n", - " \n", - " # set them to random vaules\n", - " self.wi = rand(-0.2, 0.2, size=self.wi.shape)\n", - " self.wo = rand(-2.0, 2.0, size=self.wo.shape)\n", - "\n", - " # last change in weights for momentum \n", - " self.ci = makeMatrix(self.ni, self.nh)\n", - " self.co = makeMatrix(self.nh, self.no)\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "### Activation Function" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```python\n", - "def activate(self, inputs):\n", - " \n", - " if len(inputs) != self.ni-1:\n", - " print(inputs)\n", - " raise ValueError('wrong number of inputs')\n", - "\n", - " # input activations\n", - " for i in range(self.ni-1):\n", - " self.ai[i] = inputs[i]\n", - "\n", - " # hidden activations\n", - " for j in range(self.nh):\n", - " sum_h = 0.0\n", - " for i in range(self.ni):\n", - " sum_h += self.ai[i] * self.wi[i][j]\n", - " self.ah[j] = sigmoid(sum_h)\n", - "\n", - " # output activations\n", - " for k in range(self.no):\n", - " sum_o = 0.0\n", - " for j in range(self.nh):\n", - " sum_o += self.ah[j] * self.wo[j][k]\n", - " self.ao[k] = sigmoid(sum_o)\n", - "\n", - " return self.ao[:]\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "### BackPropagation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```python\n", - "def backPropagate(self, targets, N, M):\n", - " \n", - " if len(targets) != self.no:\n", - " print(targets)\n", - " raise ValueError('wrong number of target values')\n", - "\n", - " # calculate error terms for output\n", - " output_deltas = np.zeros(self.no)\n", - " for k in range(self.no):\n", - " error = targets[k]-self.ao[k]\n", - " output_deltas[k] = dsigmoid(self.ao[k]) * error\n", - "\n", - " # calculate error terms for hidden\n", - " hidden_deltas = np.zeros(self.nh)\n", - " for j in range(self.nh):\n", - " error = 0.0\n", - " for k in range(self.no):\n", - " error += output_deltas[k]*self.wo[j][k]\n", - " hidden_deltas[j] = dsigmoid(self.ah[j]) * error\n", - "\n", - " # update output weights\n", - " for j in range(self.nh):\n", - " for k in range(self.no):\n", - " change = output_deltas[k] * self.ah[j]\n", - " self.wo[j][k] += N*change + \n", - " M*self.co[j][k]\n", - " self.co[j][k] = change\n", - "\n", - " # update input weights\n", - " for i in range(self.ni):\n", - " for j in range(self.nh):\n", - " change = hidden_deltas[j]*self.ai[i]\n", - " self.wi[i][j] += N*change + \n", - " M*self.ci[i][j]\n", - " self.ci[i][j] = change\n", - "\n", - " # calculate error\n", - " error = 0.0\n", - " for k in range(len(targets)):\n", - " error += 0.5*(targets[k]-self.ao[k])**2\n", - " return error\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [], - "source": [ - "# Putting all together\n", - "\n", - "class MLP:\n", - " def __init__(self, ni, nh, no):\n", - " # number of input, hidden, and output nodes\n", - " self.ni = ni + 1 # +1 for bias node\n", - " self.nh = nh\n", - " self.no = no\n", - "\n", - " # activations for nodes\n", - " self.ai = [1.0]*self.ni\n", - " self.ah = [1.0]*self.nh\n", - " self.ao = [1.0]*self.no\n", - " \n", - " # create weights\n", - " self.wi = makeMatrix(self.ni, self.nh)\n", - " self.wo = makeMatrix(self.nh, self.no)\n", - " \n", - " # set them to random vaules\n", - " for i in range(self.ni):\n", - " for j in range(self.nh):\n", - " self.wi[i][j] = rand(-0.2, 0.2)\n", - " for j in range(self.nh):\n", - " for k in range(self.no):\n", - " self.wo[j][k] = rand(-2.0, 2.0)\n", - "\n", - " # last change in weights for momentum \n", - " self.ci = makeMatrix(self.ni, self.nh)\n", - " self.co = makeMatrix(self.nh, self.no)\n", - " \n", - "\n", - " def backPropagate(self, targets, N, M):\n", - " \n", - " if len(targets) != self.no:\n", - " print(targets)\n", - " raise ValueError('wrong number of target values')\n", - "\n", - " # calculate error terms for output\n", - " output_deltas = np.zeros(self.no)\n", - " for k in range(self.no):\n", - " error = targets[k]-self.ao[k]\n", - " output_deltas[k] = dsigmoid(self.ao[k]) * error\n", - "\n", - " # calculate error terms for hidden\n", - " hidden_deltas = np.zeros(self.nh)\n", - " for j in range(self.nh):\n", - " error = 0.0\n", - " for k in range(self.no):\n", - " error += output_deltas[k]*self.wo[j][k]\n", - " hidden_deltas[j] = dsigmoid(self.ah[j]) * error\n", - "\n", - " # update output weights\n", - " for j in range(self.nh):\n", - " for k in range(self.no):\n", - " change = output_deltas[k] * self.ah[j]\n", - " self.wo[j][k] += N*change + M*self.co[j][k]\n", - " self.co[j][k] = change\n", - "\n", - " # update input weights\n", - " for i in range(self.ni):\n", - " for j in range(self.nh):\n", - " change = hidden_deltas[j]*self.ai[i]\n", - " self.wi[i][j] += N*change + M*self.ci[i][j]\n", - " self.ci[i][j] = change\n", - "\n", - " # calculate error\n", - " error = 0.0\n", - " for k in range(len(targets)):\n", - " error += 0.5*(targets[k]-self.ao[k])**2\n", - " return error\n", - "\n", - "\n", - " def test(self, patterns):\n", - " self.predict = np.empty([len(patterns), self.no])\n", - " for i, p in enumerate(patterns):\n", - " self.predict[i] = self.activate(p)\n", - " #self.predict[i] = self.activate(p[0])\n", - " \n", - " def activate(self, inputs):\n", - " \n", - " if len(inputs) != self.ni-1:\n", - " print(inputs)\n", - " raise ValueError('wrong number of inputs')\n", - "\n", - " # input activations\n", - " for i in range(self.ni-1):\n", - " self.ai[i] = inputs[i]\n", - "\n", - " # hidden activations\n", - " for j in range(self.nh):\n", - " sum_h = 0.0\n", - " for i in range(self.ni):\n", - " sum_h += self.ai[i] * self.wi[i][j]\n", - " self.ah[j] = sigmoid(sum_h)\n", - "\n", - " # output activations\n", - " for k in range(self.no):\n", - " sum_o = 0.0\n", - " for j in range(self.nh):\n", - " sum_o += self.ah[j] * self.wo[j][k]\n", - " self.ao[k] = sigmoid(sum_o)\n", - "\n", - " return self.ao[:]\n", - " \n", - "\n", - " def train(self, patterns, iterations=1000, N=0.5, M=0.1):\n", - " # N: learning rate\n", - " # M: momentum factor\n", - " patterns = list(patterns)\n", - " for i in range(iterations):\n", - " error = 0.0\n", - " for p in patterns:\n", - " inputs = p[0]\n", - " targets = p[1]\n", - " self.activate(inputs)\n", - " error += self.backPropagate([targets], N, M)\n", - " if i % 5 == 0:\n", - " print('error in interation %d : %-.5f' % (i,error))\n", - " print('Final training error: %-.5f' % error)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "### Running the model on our dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "error in interation 0 : 53.62995\n", - "Final training error: 53.62995\n", - "Final training error: 47.35136\n", - "1 loop, best of 1: 36.7 ms per loop\n" - ] - } - ], - "source": [ - "# create a network with two inputs, one hidden, and one output nodes\n", - "ann = MLP(2, 1, 1)\n", - "\n", - "%timeit -n 1 -r 1 ann.train(zip(X,y), iterations=2)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "### Predicting on training dataset and measuring in-sample accuracy" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1 loop, best of 1: 11.8 ms per loop\n" - ] - } - ], - "source": [ - "%timeit -n 1 -r 1 ann.test(X)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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actualprediction
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" - ], - "text/plain": [ - " actual prediction\n", - "0 1.0 0.491100\n", - "1 1.0 0.495469\n", - "2 0.0 0.097362\n", - "3 0.0 0.400006\n", - "4 1.0 0.489664" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prediction = pd.DataFrame(data=np.array([y, np.ravel(ann.predict)]).T, \n", - " columns=[\"actual\", \"prediction\"])\n", - "prediction.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.076553078113180129" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.min(prediction.prediction)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "### Let's visualize and observe the results" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": true, - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [], - "source": [ - "# Helper function to plot a decision boundary.\n", - "# This generates the contour plot to show the decision boundary visually\n", - "def plot_decision_boundary(nn_model):\n", - " # Set min and max values and give it some padding\n", - " x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n", - " y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n", - " h = 0.01\n", - " # Generate a grid of points with distance h between them\n", - " xx, yy = np.meshgrid(np.arange(x_min, x_max, h), \n", - " np.arange(y_min, y_max, h))\n", - " # Predict the function value for the whole gid\n", - " nn_model.test(np.c_[xx.ravel(), yy.ravel()])\n", - " Z = nn_model.predict\n", - " Z[Z>=0.5] = 1\n", - " Z[Z<0.5] = 0\n", - " Z = Z.reshape(xx.shape)\n", - " # Plot the contour and training examples\n", - " plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)\n", - " plt.scatter(X[:, 0], X[:, 1], s=40, c=y, cmap=plt.cm.BuGn)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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RSxcv8vr1a/5YvoyJ40bz86DaiV2eEELPZJsG8d3zeRNA2rRpSJUqlU67ubk56dKlxedN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0Nbvv4SwREqXvkHpV/Xwf37adNzLL4P5uZYHb6/6dLKhYX7rpJqaZBhaMmU6MWk/XAR8exS2cke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\nndfe+YmtO06j1ap59sm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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_decision_boundary(ann)\n", - "plt.title(\"Our initial model\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "**Exercise**: \n", - "\n", - "Create Neural networks with 10 hidden nodes on the above code. \n", - "\n", - "What's the impact on accuracy?" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Put your code here \n", - "#(or load the solution if you wanna cheat :-)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [], - "source": [ - "# %load solutions/sol_111.py\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "**Exercise:**\n", - "\n", - "Train the neural networks by increasing the epochs. \n", - "\n", - "What's the impact on accuracy?" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "#Put your code here" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "scrolled": false, - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [], - "source": [ - "# %load solutions/sol_112.py" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "# Addendum\n", - "\n", - "There is an additional notebook in the repo, i.e. [MLP and MNIST](extra/1.1.2 MLP and MNIST.ipynb) for a more complete (but still *naive* implementation) of **SGD** and **MLP** applied on **MNIST** dataset.\n", - "\n", - "Another terrific reference to start is the online book http://neuralnetworksanddeeplearning.com/. Highly recommended! 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Tensorflow.ipynb deleted file mode 100644 index c631dff..0000000 --- a/to remove/1.2 Introduction - Tensorflow.ipynb +++ /dev/null @@ -1,1447 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "# Tensorflow\n", - "\n", - ">**TensorFlow** (https://www.tensorflow.org/) is a software library, developed by Google Brain Team within Google's Machine Learning Intelligence research organization, for the purposes of conducting machine learning and deep neural network research. \n", - "\n", - ">TensorFlow combines the computational algebra of compilation optimization techniques, making easy the calculation of many mathematical expressions that would be difficult to calculate, instead.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "## Tensorflow Main Features" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* Defining, optimizing, and efficiently calculating mathematical expressions involving multi-dimensional arrays (tensors).\n", - "\n", - "* Programming support of **deep neural networks** and machine learning techniques.\n", - "\n", - "* Transparent use of GPU computing, automating management and optimization of the same memory and the data used. You can write the same code and run it either on CPUs or GPUs. More specifically, TensorFlow will figure out which parts of the computation should be moved to the GPU.\n", - "\n", - "* High scalability of computation across machines and huge data sets.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - ">TensorFlow is available with Python and C++ support, but the **Python API** is better supported and much easier to learn." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Very Preliminary Example" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "11\n" - ] - } - ], - "source": [ - "# A simple calculation in Python\n", - "x = 1\n", - "y = x + 10\n", - "print(y)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [], - "source": [ - "import tensorflow as tf" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Tensor(\"y/read:0\", shape=(), dtype=int32)\n" - ] - } - ], - "source": [ - "# The ~same simple calculation in Tensorflow\n", - "x = tf.constant(1, name='x')\n", - "y = tf.Variable(x+10, name='y')\n", - "print(y)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "**Meaning**: \"When the variable `y` is computed, take the value of the constant `x` and add `10` to it\"" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## Sessions and Models\n", - "\n", - "To actually calculate the value of the `y` variable and to evaluate expressions, we need to **initialise** the variables, and then create a **session** where the actual computation happens" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [], - "source": [ - "model = tf.global_variables_initializer() # model is used by convention" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "11\n" - ] - } - ], - "source": [ - "with tf.Session() as session:\n", - " session.run(model)\n", - " print(session.run(y))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "## Data Flow Graph" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "* (**IDEA**) \n", - "_A Machine Learning application is the result of the repeated computation of complex mathematical expressions, thus \n", - "we could describe this computation by using a **Data Flow Graph**" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "* **Data Flow Graph**: a graph where:\n", - " - each Node represents the _instance_ of a mathematical operation \n", - " - `multiply`, `add`, `divide`\n", - " - each Edge is a multi-dimensional data set (`tensors`) on which the operations are performed." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "## Tensorflow Graph Model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* **Node**: In TensorFlow, each node represents the instantion of an operation. \n", - " - Each operation has inputs (`>= 2`) and outputs `>= 0`.\n", - " \n", - "* **Edges**: In TensorFlow, there are two types of edge:\n", - " - Data Edges: \n", - " They are carriers of data structures (`tensors`), where an output of one operation (from one node) becomes the input for another operation.\n", - " - Dependency Edges: These edges indicate a _control dependency_ between two nodes (i.e. \"happens before\" relationship). \n", - " + Let's suppose we have two nodes `A` and `B` and a dependency edge connecting `A` to `B`. This means that `B` will start its operation only when the operation in `A` ends. " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "## Tensorflow Graph Model (cont.)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* **Operation**: This represents an abstract computation, such as adding or multiplying matrices. \n", - " - An operation manages tensors, and It can just be polymorphic: the same operation can manipulate different tensor element types. \n", - " + For example, the addition of two int32 tensors, the addition of two float tensors, and so on.\n", - "\n", - "* **Kernel**: This represents the concrete implementation of that operation. \n", - " - A kernel defines the implementation of the operation on a particular device. \n", - " + For example, an `add matrix` operation can have a CPU implementation and a GPU one." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "## Tensorflow Graph Model Session" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Session**: When the client program has to establish communication with the TensorFlow runtime system, a session must be created. \n", - " \n", - "As soon as the session is created for a client, an initial graph is created and is empty. It has two fundamental methods:\n", - "\n", - "* `session.extend`: To be used during a computation, requesting to add more operations (nodes) and edges (data). The execution graph is then extended accordingly.\n", - "\n", - "* `session.run`: The execution graphs are executed to get the outputs (sometimes, subgraphs are executed thousands/millions of times using run invocations)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Tensorboard" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**TensorBoard** is a visualization tool, devoted to analyzing Data Flow Graph and also to better understand the machine learning models. \n", - "\n", - "It can view different types of statistics about the parameters and details of any part of a computer graph graphically. It often happens that a graph of computation can be very complex." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "## Tensorboard Example" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "Run the **TensorBoard** Server:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```shell\n", - "tensorboard --logdir=/tmp/tf_logs\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "[Open TensorBoard](http://localhost:6006)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Example" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "95\n" - ] - } - ], - "source": [ - "a = tf.constant(5, name=\"a\")\n", - "b = tf.constant(45, name=\"b\")\n", - "y = tf.Variable(a+b*2, name='y')\n", - "model = tf.global_variables_initializer()\n", - "\n", - "with tf.Session() as session:\n", - " # Merge all the summaries collected in the default graph.\n", - " merged = tf.summary.merge_all() \n", - " \n", - " # Then we create `SummaryWriter`. \n", - " # It will write all the summaries (in this case the execution graph) \n", - " # obtained from the code's execution into the specified path”\n", - " writer = tf.summary.FileWriter(\"tmp/tf_logs_simple\", session.graph)\n", - " session.run(model)\n", - " print(session.run(y))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Data Types (Tensors)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "## One Dimensional Tensor (Vector)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "tensor_1d = np.array([1, 2.5, 4.6, 5.75, 9.7])\n", - "tf_tensor=tf.convert_to_tensor(tensor_1d,dtype=tf.float64)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 1. 2.5 4.6 5.75 9.7 ]\n", - "1.0\n", - "4.6\n" - ] - } - ], - "source": [ - "with tf.Session() as sess: \n", - " print(sess.run(tf_tensor))\n", - " print(sess.run(tf_tensor[0]))\n", - " print(sess.run(tf_tensor[2]))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "## Two Dimensional Tensor (Matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ 0 1 2 3]\n", - " [ 4 5 6 7]\n", - " [ 8 9 10 11]\n", - " [12 13 14 15]]\n", - "[[ 0. 1. 2. 3.]\n", - " [ 4. 5. 6. 7.]\n", - " [ 8. 9. 10. 11.]\n", - " [ 12. 13. 14. 15.]]\n" - ] - } - ], - "source": [ - "tensor_2d = np.arange(16).reshape(4, 4)\n", - "print(tensor_2d)\n", - "tf_tensor = tf.placeholder(tf.float32, shape=(4, 4))\n", - "with tf.Session() as sess:\n", - " print(sess.run(tf_tensor, feed_dict={tf_tensor: tensor_2d}))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "# Basic Operations (Examples)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [], - "source": [ - "matrix1 = np.array([(2,2,2),(2,2,2),(2,2,2)],dtype='float32') \n", - "matrix2 = np.array([(1,1,1),(1,1,1),(1,1,1)],dtype='float32')" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "tf_mat1 = tf.constant(matrix1) \n", - "tf_mat2 = tf.constant(matrix2)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [], - "source": [ - "matrix_product = tf.matmul(tf_mat1, tf_mat2)\n", - "matrix_sum = tf.add(tf_mat1, tf_mat2)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [], - "source": [ - "matrix_det = tf.matrix_determinant(matrix2)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": true, - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [], - "source": [ - "with tf.Session() as sess: \n", - " prod_res = sess.run(matrix_product) \n", - " sum_res = sess.run(matrix_sum) \n", - " det_res = sess.run(matrix_det)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "matrix1*matrix2 : \n", - " [[ 6. 6. 6.]\n", - " [ 6. 6. 6.]\n", - " [ 6. 6. 6.]]\n", - "matrix1+matrix2 : \n", - " [[ 3. 3. 3.]\n", - " [ 3. 3. 3.]\n", - " [ 3. 3. 3.]]\n", - "det(matrix2) : \n", - " 0.0\n" - ] - } - ], - "source": [ - "print(\"matrix1*matrix2 : \\n\", prod_res)\n", - "print(\"matrix1+matrix2 : \\n\", sum_res)\n", - "print(\"det(matrix2) : \\n\", det_res)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "# Handling Tensors" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.image as mp_image\n", - "filename = \"imgs/keras-logo-small.jpg\"\n", - "input_image = mp_image.imread(filename)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "input dim = 3\n", - "input shape = (300, 300, 3)\n" - ] - } - ], - "source": [ - "#dimension\n", - "print('input dim = {}'.format(input_image.ndim))\n", - "#shape\n", - "print('input shape = {}'.format(input_image.shape))" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [ - { - "data": { - "image/png": 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xD4QAKgRAYolSgA8wcSKzv3wOE194ADVVsijkNtwY1ZhwjGc6HmflV7/Mwx8+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\nJBtDn2zpYD1VGwNxTIypZWT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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "plt.imshow(input_image)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Slicing" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": true, - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [], - "source": [ - "my_image = tf.placeholder(\"uint8\",[None,None,3])\n", - "slice = tf.slice(my_image,[10,0,0],[16,-1,-1])" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(16, 300, 3)\n" - ] - } - ], - "source": [ - "with tf.Session() as session:\n", - " result = session.run(slice,feed_dict={my_image: input_image})\n", - " print(result.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.imshow(result)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "## Transpose" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "x = tf.Variable(input_image,name='x')\n", - "model = tf.global_variables_initializer()\n", - "\n", - "with tf.Session() as session:\n", - " x = tf.transpose(x, perm=[1,0,2])\n", - " session.run(model)\n", - " result=session.run(x)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [ - { - "data": { - "image/png": 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5cgdjX/Eqpp79DcZM3YlSsgGNnkRANLLm5jtZcMz7qC9aTF03EHZtEk9VuCUG\nMoGoqWxhTVMGbFFfxeofXs38WW/i8dmzCXF1KlRcJYkpjUVpAyseToW694CisU7uHLWXvJhJ557H\nbld/D9luGporGYFc1A7+fmD7rqUoZV5j/L6vY6cfX4ZsP5noSjLigEZPXL2TVT/5BfMPOgBWrSGP\nVUZoq+pEq4DLUNJVPaOqTC6kTrZlytEsVi3jqRM/waIPfRQemENdAkEFdb6abhjgCIeLZCV4POJq\naFScRrJaG+2HHcaOv76WrQ58A8X48ZRqGR79wYRjM+h93VQh1YjA47IaE956OFMuPpe2bacRJRDF\nIbQyepImG2N107KTFb+6gUUnnUQsulEijphCri0qmaBAvWruFCQFULyC17WB3UJjsq9jBU/PvozH\njvoAa668Eu1eQ1RBUSKRoErUAsq0hiaoptT4FiAoMUsiRqXR6rPUsIrI+Be+mJ0vm83UM75K7QUv\n7KnGLqSFcz2NsgGV9CsF1/i9bU1ub0w4NoNUIEuoA04dXlPdg/zAA9n5zM+RTd8ppY4jZEFJy7da\nET1JolFHUQIFJWv+PJd5Rx9DXDi/IRfVUKh1h3lahJ9c+0aBMOgdfYmplkZPhkjJirl/YcH7P8j8\n//k4Rb0TUQcakqhpRnBCjGV6j1Z5IpKRU+1jcXh8sjk6xGkqWbjtJLb+wHvZ+ZpLaH/1gURy1MWq\nRkWqPxIQggg1HCFCyFyqmWL0YLtjc1FlnGtDJFJvb2OrNx/KrpddQDlpOj6mNSKx8ipaVOYRqhJ6\neSiReknHL37FvP1nkT+9lLiOdzHwMwrrpwaM7yqhu5MVF1zMY/u9jhU3XkfZ1YVWC9oc4B04DQPf\n49YJpTrURSRGvGvD7b4nu1z3AyadeQZtO+6KuirvRiM10pCsC6WGMLaM1GK0gU0vTDg2AyXlEnRr\nN0WeMeHNRzD5vLPw2+1AroJ6RTX1EinFtXz6PjphxfW/YtHxHyauXtOT1DRcDuxu5+gEGmlX4dY/\nsOC97+PJ/z0Dli+r0sqStIVBULggkKcy0ASf2i7UFFxtAtt/5FimfvcS/uWwI8jJyVC6fVpUB4qK\n0I1QiCWM9caEYzMQBKcZeQ61V+3PtHPPYuykGYAQXEBIPUQiVUi0hUs6lEDH3X9j0TvfSdfCRQhC\n7OOIHlIh0UiGQJajopQIbYueZPmXv8Rf/30/OubehQboIkvF7QbY5fCxqIZw6YfwKEgd75UyqzHm\nFa9m++84bNuFAAAVUUlEQVR9m23OPxO2msC4AJl4StdI4lN8+kZGhQnHZqAoZVuN8a97G7v8aDb5\nNs8heBBKnApOPVEzouRpwk5bs/xciy5W/eyXzN9vFvrMarKypKwWdCW71pm4bcmnNk+mQkTxZSp0\n7IAulxamufvu5bH9D+OJC88hX7qsyvsY2IwKjZ4Q02SnR1GJKDlQdcQTcL7Gc475ADv+8ie4gw4i\n1MZSU08hSg0otDSPoxcmHBuhZwdJqiwVHWR+HFu/9W1MOvsM2rfenkgg1fVyOPEgPk2WKuSAa7LC\nsKJojGndSXXT0MnK63/N4hNPJD6zklRlQpFeEV151nsMHYFqrQ7VcA1BNFKGiHcZYcnjLP3kqTx8\n9HvouPNWgoYUACrTmptuCkpSxTFCIC3CicQYN2/uJnPreH2u59b41yGMcRlbv2oWu1x+MZO/eDpu\nxo6UZNTFUUPBebR6q8Kl6e5UhX70YcKxAZTUytBLRtRUkSpGodz3Nezy9S+QV9ETL4KPlSvcGCNU\nx6ZU6efNfnBj3QlApM6qOffx6FHvpnxsXk/Nzb6GI7KBxwYL12s9jFThYamizzGW5FIjrH6G7v/7\nJfcffgyrvvtD6F5B3Uc0Qi04fIQ6geBS1/sUMZJUwrDJU9WRikKLCEgSCanC6FKtd5Eq5CUiZNtO\nZbsTT2THn11N/oJ/xWukcCAxkMX0unoMjCGS6fCZWxpMTDg2QDr/HYWW6YKlgTbveMFXP0XxnKm4\nkMJ0kZji/i2MnpQItRIouuj41a95/KCD8auXD6voSbMkLwRiTCqSAbUnHuDh/3ov80/8BF1//xta\nhiqeGsnJqmI+nlIcElJIdcAnU7O0jH/cC1/Mi269gW0/8xnapu6Mc47CQ4lnK8noBuriR8z+byUm\nHBtAScvMGyenE6EMwuqtJqbyfj4lL7kIocXRkxQBEJ753W9ZeNJJdC1f2ithaWSSVTVOAyWZKiop\nJyUrC1ZcdAlL3vFenvrB94ES1eRdSFpoi6ik5C4NA/79HQEJJUEccfwkpnz6k8z41mzGzdqfGGt4\njQTvKADfusKrIwoTjg3Qu9htLp5Ck8vcXrYRXFwbPXEpeqK+VZmaSpA6xd/+wRNH/idrHn4Eon9W\nIujaocDIEZJCHF1AowpoRPAxR73Do6ycO4d5//VuHvnAh2DFM5QSWZXViT721GENA7zCFlJSX3eW\nIVHwIVLmjny/17Dz9Vcx5fOforPWTizrtOOrxLfRhwnHRlCXcjZUJWV7upSj4TWFZKNmRM3TuL5V\nXePLOh2/+g2PHXgQ5fKnqZWxZzqvTxtb86kDjyi5pP0ZRAiqOB+RoJQi5KXSJo41s7/N3DccQsfP\nr2d8Z51QTZQ2ho4D/Y1VIzUiWYwUTsmo0RY9pZvAc048nu2OeCNZ9R2KUZqKbsKxERprQ0oCooqL\nqWhuip6kAv65NArMbEb0RLWnSpY2oid/+COLTziRziVPUNKYbAwj/sdyMa2qRUnp3wAhdZmRShik\nTK5/edttLPjQB5l/8mdwSxZRuli9rvKxNBK1WFv5rJWzPb5aveyFrNFs21Gls9fIfVuqN9KiMPtI\nZKQfiwNPdSzGaml47/BsT/RENi96IioQA3Wpp2GIFnTOfZSHDj+KjoceRFF8lbG47nVW1nN/uJOy\nRtd+n2fnnTRO/5TeXS5azFPnns0DB76BMOcvKYXfpSbUEUFiBgiiAQ2xz0S4zUHw6fd0KT3eC1VE\nJg1bI9oobJZaN7TmY0cUGz3SRWSaiNwkInNF5F4ROb7aPlFEbhCR+0XkehGZ0Os1Z4vIgyIyR0T2\nGMgvMNJRcWTBEcs6q667kXkHHEC2fAmo4mTtCteRFD1pFTkFPir1uffyyJveylNfP4viiUVITBGs\nKJGgBdEpKqPzBB4qNuUSWQIfVdUXAK8EjhOR3YGTgRtVdTfgJuAUABF5HTBTVZ8LfAC4cEAs32Jw\nlE5YfcttLDjhROpLFwHge4kGVFe8UYZWtVtFlfD4fJb9z6dZ8P5jWTXndqBM3oBmaWWrJO/DGBw2\nKhyqulhV51T3VwP3AdOAQ4HLq6ddXv1N9f8rquffAUwQkckttnuLIEVPSuI/HmLRkf9B57xHyGKe\n8kEaY/5qqBJaVFtjJKEoOTWyCN0CXRl0/OJa5r/2dTz14+9TFqnRo2qJYCnhg0lTg3IR2QnYA7gd\nmKyqSyCJCzCpetpUYEGvlz1ebTPWJQQ6bvoD817/erqWLaEWAmV1JW1MoegoFIwGqX9KnSA+1dao\n1yk8FGtW8uQR7+fJDx7HM/f+mRDT+qDRGd8YGjZZOERkPHANcELleazvV+rLXxzVv6iqEmJIha7K\nVFRYQxer7ryTJ074MGvmP9p4ZhX6XTunkYryjM5ZbK+pNQMa8DFFlfKQIliFdrP0u99h/pHvYsV3\nL4VyNdqrMlqsglVltRMbAqyqz7oZm8cmtf8UkYwkGt9R1WurzUtEZLKqLhGRKcCT1faFwPReL58G\nLOrrfU//7OkgSrFgATNjZM/N+gojAUFCpPQl6mu4WNJ1/6M8ePiR6OKFSFSyVGAQNCWT+Z5XGg0a\na1Qc0JE58qKgeOg+njj6ozx1671s/7lPM37SpJS0FxydXhkTtactQ0MoRGRUicZdMXLV7Nnk111P\nq9ZFyKbsQBG5Alimqh/tte0M4GlVPUNETga2UdWTReQQ4DhVfb2IvAL4hqq+oo/31BgUXKTj1tt4\neNZ+aNHdki/VSrQqRlMV/ccDOz3wAOOf+9xNfw9NXcx8hBgjz9z8e5Z84IN0PvwwVO/ZKPkHa1eX\nGn0TSO0dCwI5Veq65ox71SuY+omP0fba/fHjx4HUCdGTVWHVvo71piadFYrODp449liWX355dUz0\n/uWGBw4oq5VBHtC8jV1v/i1j93oliFZlEvtXr3FTwrGvAt4B7CsifxGRu0XkYOAM4AARuR/YD/gy\ngKr+EnhURB4CLgKO7Y+BWwqiqYBu551/YtHxJ9H1yCMg6/4Ajf6oxobwQOkiglAKdDqoSUHXH//A\no+/9AI+feir15Y8jRUZW5daMJg9jMNjoUEVV/8j6a1jtv57XfLg/Rm1pCOC0oPPv9zP/LW8hPvkU\nntRrpeE5prUb6Y8B7gE/4smAMkYKST5gjtClJe0I4aklPP3Nr9N91x1Mm/112ma+CJe32ZCvxZhH\nPAjEGFjx+1uY/5a3Ui5bTCkldQJE11P/qrHG0g7wjVMnCW1Nq9J+oSQT6BbFSY44R/3WP/LIq17P\nkrPPonvpEvM4WowJx0ZopJKrpF4bpQheHRojhIBqTPc1UCdQqkIsqWus8rfqdPx1LktPOp41Dz9E\no4GB71kjajRLY8FfrP4T0pyfUyi0QGJIHtzypSw9/Ys8+p9HUf/rnQSNEKEuZeqLrZLCL1W6eiS1\nu1zf71Ly7BYXAFnrirCMKEw4NkBKvEpl64KCdz4dbD6iokRSDw4VQQPksZoqc5DXBcqSNQ88wGNv\nPZSVf5+buqxHxWmq7UGv+hojaXn8cKARpl53zU61dGht2YE1a4i//j337XMIS799GeWaNdTqGR6l\nWwo0ptKGQQuC1nFBqoV0/4w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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.imshow(result)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## Computing the Gradient\n", - "\n", - "- Gradients are free!" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.5]\n" - ] - } - ], - "source": [ - "x = tf.placeholder(tf.float32)\n", - "y = tf.log(x) \n", - "var_grad = tf.gradients(y, x)\n", - "with tf.Session() as session:\n", - " var_grad_val = session.run(var_grad, feed_dict={x:2})\n", - " print(var_grad_val)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Warming up: Logistic Regression" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [], - "source": [ - "import tensorflow as tf\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### Kaggle Challenge Data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The Otto Group is one of the world’s biggest e-commerce companies, A consistent analysis of the performance of products is crucial. However, due to diverse global infrastructure, many identical products get classified differently.\n", - "For this competition, we have provided a dataset with 93 features for more than 200,000 products. The objective is to build a predictive model which is able to distinguish between our main product categories. \n", - "Each row corresponds to a single product. There are a total of 93 numerical features, which represent counts of different events. All features have been obfuscated and will not be defined any further.\n", - "\n", - "https://www.kaggle.com/c/otto-group-product-classification-challenge/data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### For this section we will use the Kaggle Otto Group Challenge Data. You will find these data in \n", - "`data/kaggle_ottogroup/` folder." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "## Logistic Regression" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This algorithm has nothing to do with the canonical linear regression, but it is an algorithm that allows us to solve problems of classification(supervised learning). \n", - "\n", - "In fact, to estimate the dependent variable, now we make use of the so-called **logistic function** or **sigmoid**. \n", - "\n", - "It is precisely because of this feature we call this algorithm logistic regression.\n", - "\n", - "![](imgs/sigmoid.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Data Preparation" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using TensorFlow backend.\n" - ] - } - ], - "source": [ - "from kaggle_data import load_data, preprocess_data, preprocess_labels" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "9 classes\n", - "93 dims\n" - ] - } - ], - "source": [ - "X_train, labels = load_data('data/kaggle_ottogroup/train.csv', train=True)\n", - "X_train, scaler = preprocess_data(X_train)\n", - "Y_train, encoder = preprocess_labels(labels)\n", - "\n", - "X_test, ids = load_data('data/kaggle_ottogroup/test.csv', train=False)\n", - "\n", - "X_test, _ = preprocess_data(X_test, scaler)\n", - "\n", - "nb_classes = Y_train.shape[1]\n", - "print(nb_classes, 'classes')\n", - "\n", - "dims = X_train.shape[1]\n", - "print(dims, 'dims')" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['Class_1', 'Class_2', 'Class_3', 'Class_4', 'Class_5', 'Class_6',\n", - " 'Class_7', 'Class_8', 'Class_9'], dtype=object)" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.unique(labels)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "#### Hands On - Logistic Regression" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "collapsed": true, - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [], - "source": [ - "# Parameters\n", - "learning_rate = 0.01\n", - "training_epochs = 25\n", - "display_step = 1" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "collapsed": true, - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [], - "source": [ - "# tf Graph Input\n", - "x = tf.placeholder(\"float\", [None, dims]) \n", - "y = tf.placeholder(\"float\", [None, nb_classes])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## The Model" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Set model weights\n", - "W = tf.Variable(tf.zeros([dims, nb_classes]))\n", - "b = tf.Variable(tf.zeros([nb_classes]))" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "collapsed": true, - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [], - "source": [ - "# Construct model\n", - "activation = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": { - "collapsed": true, - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [], - "source": [ - "# Minimize error using cross entropy\n", - "cross_entropy = y*tf.log(activation)\n", - "cost = tf.reduce_mean(-tf.reduce_sum(cross_entropy,reduction_indices=1))" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": { - "collapsed": true, - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [], - "source": [ - "# Set the Optimizer\n", - "optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [], - "source": [ - "# Initializing the variables\n", - "init = tf.global_variables_initializer()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "## Learning" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def training_phase(X, Y):\n", - " cost_epochs = []\n", - " # Training cycle\n", - " for epoch in range(training_epochs):\n", - " _, c = sess.run([optimizer, cost], feed_dict={x: X, y: Y})\n", - " cost_epochs.append(c)\n", - " return cost_epochs" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "## Prediction" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def testing_phase(X, Y):\n", - " # Test model\n", - " correct_prediction = tf.equal(tf.argmax(activation, 1), tf.argmax(y, 1))\n", - " # Calculate accuracy\n", - " accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n", - " print(\"Model accuracy:\", accuracy.eval({x: X, y: Y}))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "## TF Session" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training phase finished\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1 5 5 ..., 2 1 1]\n" - ] - } - ], - "source": [ - "# Launch the graph\n", - "with tf.Session() as sess:\n", - " # Plug TensorBoard Visualisation\n", - " merged = tf.summary.merge_all() \n", - " writer = tf.summary.FileWriter(\"/tmp/logistic_logs\", session.graph)\n", - " \n", - " sess.run(init)\n", - " cost_epochs = training_phase(X_train, Y_train)\n", - " print(\"Training phase finished\")\n", - " \n", - " #plotting\n", - " plt.plot(range(len(cost_epochs)), cost_epochs, 'o', label='Logistic Regression Training phase')\n", - " plt.ylabel('cost')\n", - " plt.xlabel('epoch')\n", - " plt.legend()\n", - " plt.show()\n", - " \n", - " prediction = tf.argmax(activation, 1)\n", - " print(prediction.eval({x: X_test}))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "[Open TensorBoard](http://localhost:6006)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Why Tensorflow ?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "On a typical system, there are multiple computing devices. \n", - "\n", - "In TensorFlow, the supported device types are **CPU** and **GPU**. \n", - "\n", - "They are represented as strings. For example:\n", - "\n", - "* `\"/cpu:0\"`: The CPU of your machine.\n", - "* `\"/gpu:0\"`: The GPU of your machine, if you have one.\n", - "* `\"/gpu:1\"`: The second GPU of your machine, etc." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "If a TensorFlow operation has both **CPU** and **GPU** implementations, the GPU devices will be given priority when the operation is assigned to a device. \n", - "\n", - "For example, `matmul` has both CPU and GPU kernels. On a system with devices `cpu:0` and `gpu:0`, `gpu:0` will be selected to run `matmul`." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "### Example 1. Logging Device Placement\n", - "\n", - "`tf.Session(config=tf.ConfigProto(log_device_placement=True))`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```python\n", - "# Creates a graph.\n", - "a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')\n", - "b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')\n", - "c = tf.matmul(a, b)\n", - "# Creates a session with log_device_placement set to True.\n", - "sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))\n", - "# Runs the op.\n", - "print(sess.run(c))\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "```\n", - "Device mapping:\n", - "/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce GTX 760, pci bus\n", - "id: 0000:05:00.0\n", - "b: /job:localhost/replica:0/task:0/gpu:0\n", - "a: /job:localhost/replica:0/task:0/gpu:0\n", - "MatMul: /job:localhost/replica:0/task:0/gpu:0\n", - "[[ 22. 28.]\n", - " [ 49. 64.]]\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "## Using Multiple GPUs" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```python\n", - "# Creates a graph.\n", - "c = []\n", - "for d in ['/gpu:0', '/gpu:1']:\n", - " with tf.device(d):\n", - " a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3])\n", - " b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2])\n", - " c.append(tf.matmul(a, b))\n", - "with tf.device('/cpu:0'):\n", - " sum = tf.add_n(c)\n", - "# Creates a session with log_device_placement set to True.\n", - "sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))\n", - "# Runs the op.\n", - "print sess.run(sum)\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "```\n", - "Device mapping:\n", - "/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce GTX 760, pci bus\n", - "id: 0000:02:00.0\n", - "/job:localhost/replica:0/task:0/gpu:1 -> device: 1, name: GeForce GTX 760, pci bus\n", - "id: 0000:03:00.0\n", - "Const_3: /job:localhost/replica:0/task:0/gpu:0\n", - "Const_2: /job:localhost/replica:0/task:0/gpu:0\n", - "MatMul_1: /job:localhost/replica:0/task:0/gpu:0\n", - "Const_1: /job:localhost/replica:0/task:0/gpu:1\n", - "Const: /job:localhost/replica:0/task:0/gpu:1\n", - "MatMul: /job:localhost/replica:0/task:0/gpu:1\n", - "AddN: /job:localhost/replica:0/task:0/cpu:0\n", - "[[ 44. 56.]\n", - " [ 98. 128.]]\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## More on Tensorflow\n", - "\n", - "[Official Documentation](https://www.tensorflow.org/versions/r0.10/get_started/)" - ] - } - ], - "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": "3.5.3" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/to remove/1.5.1 Introduction - Theano.ipynb b/to remove/1.5.1 Introduction - Theano.ipynb deleted file mode 100644 index 6631871..0000000 --- a/to remove/1.5.1 Introduction - Theano.ipynb +++ /dev/null @@ -1,949 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "Theano \n", - "===\n", - "A language in a language" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Dealing with weights matrices and gradients can be tricky and sometimes not trivial.\n", - "Theano is a great framework for handling vectors, matrices and high dimensional tensor algebra. \n", - "Most of this tutorial will refer to Theano however TensorFlow is another great framework capable of providing an incredible abstraction for complex algebra.\n", - "More on TensorFlow in the next chapters." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [], - "source": [ - "import theano\n", - "import theano.tensor as T" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "Symbolic variables\n", - "==========" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Theano has it's own variables and functions, defined the following" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [], - "source": [ - "x = T.scalar()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "Variables can be used in expressions" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [], - "source": [ - "y = 3*(x**2) + 1" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "y is an expression now " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "Result is symbolic as well" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Shape.0" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(y)\n", - "y.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "#####printing" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "As we are about to see, normal printing isn't the best when it comes to theano" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Elemwise{add,no_inplace}.0\n" - ] - } - ], - "source": [ - "print(y)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'((TensorConstant{3} * ( ** TensorConstant{2})) + TensorConstant{1})'" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "theano.pprint(y)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Elemwise{add,no_inplace} [id A] '' \n", - " |Elemwise{mul,no_inplace} [id B] '' \n", - " | |TensorConstant{3} [id C]\n", - " | |Elemwise{pow,no_inplace} [id D] '' \n", - " | | [id E]\n", - " | |TensorConstant{2} [id F]\n", - " |TensorConstant{1} [id G]\n" - ] - } - ], - "source": [ - "theano.printing.debugprint(y)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "Evaluating expressions\n", - "============\n", - "\n", - "Supply a `dict` mapping variables to values" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(13.0)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y.eval({x: 2})" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "Or compile a function" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "f = theano.function([x], y)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array(13.0)" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "f(2)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "Other tensor types\n", - "==========" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": true, - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [], - "source": [ - "X = T.vector()\n", - "X = T.matrix()\n", - "X = T.tensor3()\n", - "X = T.tensor4()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "Automatic differention\n", - "============\n", - "- Gradients are free!" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "x = T.scalar()\n", - "y = T.log(x)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Elemwise{true_div}.0\n", - "0.5\n", - "Elemwise{mul,no_inplace}.0\n" - ] - } - ], - "source": [ - "gradient = T.grad(y, x)\n", - "print(gradient)\n", - "print(gradient.eval({x: 2}))\n", - "print((2 * gradient))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Shared Variables\n", - "\n", - "- Symbolic + Storage" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "x = theano.shared(np.zeros((2, 3), dtype=theano.config.floatX))" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "We can get and set the variable's value" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(2, 3)\n", - "[[ 0. 0. 0.]\n", - " [ 0. 0. 0.]]\n" - ] - } - ], - "source": [ - "values = x.get_value()\n", - "print(values.shape)\n", - "print(values)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "x.set_value(values)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "Shared variables can be used in expressions as well" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Elemwise{pow,no_inplace}.0" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(x + 2) ** 2" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "Their value is used as input when evaluating" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 4., 4., 4.],\n", - " [ 4., 4., 4.]])" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "((x + 2) ** 2).eval()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 4., 4., 4.],\n", - " [ 4., 4., 4.]])" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "theano.function([], (x + 2) ** 2)()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Updates\n", - "\n", - "- Store results of function evalution\n", - "- `dict` mapping shared variables to new values" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [], - "source": [ - "count = theano.shared(0)\n", - "new_count = count + 1\n", - "updates = {count: new_count}\n", - "\n", - "f = theano.function([], count, updates=updates)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array(0)" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "f()" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array(1)" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "f()" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array(2)" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "f()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Warming up! Logistic Regression" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import theano\n", - "import theano.tensor as T\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Kaggle Challenge Data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The Otto Group is one of the world’s biggest e-commerce companies, A consistent analysis of the performance of products is crucial. However, due to diverse global infrastructure, many identical products get classified differently.\n", - "For this competition, we have provided a dataset with 93 features for more than 200,000 products. The objective is to build a predictive model which is able to distinguish between our main product categories. \n", - "Each row corresponds to a single product. There are a total of 93 numerical features, which represent counts of different events. All features have been obfuscated and will not be defined any further.\n", - "\n", - "https://www.kaggle.com/c/otto-group-product-classification-challenge/data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### For this section we will use the Kaggle Otto Group Challenge Data. You will find these data in \n", - "`../data/kaggle_ottogroup/` folder.\n", - "\n", - "**Note** We already used this dataset in the [1.2 Introduction - Tensorflow](../1.2 Introduction - Tensorflow.ipynb) notebook, as well as [1.3 Introduction - Keras](../1.3 Introduction - Keras.ipynb) notebook." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import sys\n", - "nb_dir = os.path.abspath('..')\n", - "if nb_dir not in sys.path:\n", - " sys.path.append(nb_dir)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using TensorFlow backend.\n" - ] - } - ], - "source": [ - "from kaggle_data import load_data, preprocess_data, preprocess_labels" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading data...\n", - "[[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 3. 0. 0. 0. 3.\n", - " 2. 1. 0. 0. 0. 0. 0. 0. 0. 5. 3. 1. 1. 0.\n", - " 0. 0. 0. 0. 1. 0. 0. 1. 0. 1. 0. 1. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 0. 0. 0. 0. 0. 0. 3. 0. 0. 0. 0. 1. 1.\n", - " 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", - " 0. 11. 1. 20. 0. 0. 0. 0. 0.]]\n", - "9 classes\n", - "93 dims\n" - ] - } - ], - "source": [ - "print(\"Loading data...\")\n", - "X, labels = load_data('../data/kaggle_ottogroup/train.csv', train=True)\n", - "X, scaler = preprocess_data(X)\n", - "Y, encoder = preprocess_labels(labels)\n", - "\n", - "\n", - "X_test, ids = load_data('../data/kaggle_ottogroup/test.csv', train=False)\n", - "X_test, ids = X_test[:1000], ids[:1000]\n", - "\n", - "#Plotting the data\n", - "print(X_test[:1])\n", - "\n", - "X_test, _ = preprocess_data(X_test, scaler)\n", - "\n", - "nb_classes = Y.shape[1]\n", - "print(nb_classes, 'classes')\n", - "\n", - "dims = X.shape[1]\n", - "print(dims, 'dims')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now lets create and train a logistic regression model." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Hands On - Logistic Regression" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1\n", - "Epoch 2\n", - "Epoch 3\n", - "Epoch 4\n", - "Epoch 5\n", - "Epoch 6\n", - "Epoch 7\n", - "Epoch 8\n", - "Epoch 9\n", - "Epoch 10\n", - "target values for Data:\n", - "[ 0. 0. 0. ..., 0. 0. 0.]\n", - "prediction on training set:\n", - "[False False True ..., False False False]\n" - ] - } - ], - "source": [ - "#Based on example from DeepLearning.net\n", - "rng = np.random\n", - "N = 400\n", - "feats = 93\n", - "training_steps = 10\n", - "\n", - "# Declare Theano symbolic variables\n", - "x = T.matrix(\"x\")\n", - "y = T.vector(\"y\")\n", - "w = theano.shared(rng.randn(feats), name=\"w\")\n", - "b = theano.shared(0., name=\"b\")\n", - "\n", - "# Construct Theano expression graph\n", - "p_1 = 1 / (1 + T.exp(-T.dot(x, w) - b)) # Probability that target = 1\n", - "prediction = p_1 > 0.5 # The prediction thresholded\n", - "xent = -y * T.log(p_1) - (1-y) * T.log(1-p_1) # Cross-entropy loss function\n", - "cost = xent.mean() + 0.01 * (w ** 2).sum()# The cost to minimize\n", - "gw, gb = T.grad(cost, [w, b]) # Compute the gradient of the cost\n", - " # (we shall return to this in a\n", - " # following sections of this tutorial\n", - " # See: Intro to tf & Keras)\n", - "\n", - "# Compile\n", - "train = theano.function(\n", - " inputs=[x,y],\n", - " outputs=[prediction, xent],\n", - " updates=((w, w - 0.1 * gw), (b, b - 0.1 * gb)),\n", - " allow_input_downcast=True)\n", - "predict = theano.function(inputs=[x], outputs=prediction, allow_input_downcast=True)\n", - "\n", - "#Transform for class1\n", - "y_class1 = []\n", - "for i in Y:\n", - " y_class1.append(i[0])\n", - "y_class1 = np.array(y_class1)\n", - "\n", - "# Train\n", - "for i in range(training_steps):\n", - " print('Epoch %s' % (i+1,))\n", - " pred, err = train(X, y_class1)\n", - "\n", - "print(\"target values for Data:\")\n", - "print(y_class1)\n", - "print(\"prediction on training set:\")\n", - "print(predict(X))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "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": "3.5.3" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -}