{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Computer vision: AlexNet, VGG-19, GoogLeNet\n", "Import various modules that we need for this notebook." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using Theano backend.\n", "/Users/taylor/anaconda3/lib/python3.5/site-packages/theano/tensor/signal/downsample.py:5: UserWarning: downsample module has been moved to the pool module.\n", " warnings.warn(\"downsample module has been moved to the pool module.\")\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline\n", "\n", "import copy\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "from keras.datasets import mnist, cifar10\n", "from keras.models import Sequential, Graph\n", "from keras.layers.core import Dense, Dropout, Activation, Flatten, Reshape\n", "from keras.optimizers import SGD, RMSprop\n", "from keras.utils import np_utils\n", "from keras.regularizers import l2\n", "from keras.layers.convolutional import Convolution2D, MaxPooling2D, ZeroPadding2D, AveragePooling2D\n", "from keras.callbacks import EarlyStopping\n", "from keras.preprocessing.image import ImageDataGenerator\n", "from keras.layers.normalization import BatchNormalization\n", "\n", "from PIL import Image" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load the MNIST dataset, flatten the images, convert the class labels, and scale the data." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n", "X_train = X_train.reshape(X_train.shape[0], 1, 28, 28).astype('float32') / 255\n", "X_test = X_test.reshape(X_test.shape[0], 1, 28, 28).astype('float32') / 255\n", "Y_train = np_utils.to_categorical(y_train, 10)\n", "Y_test = np_utils.to_categorical(y_test, 10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### I. OverFeat adaptation of AlexNet (2012)\n", "An adaptation of the 'fast' model from AlexNet applied to MNIST-10." ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [], "source": [ "model = Sequential()\n", "\n", "# Layer 1\n", "model.add(Convolution2D(96, 11, 11, input_shape = (1,28,28), border_mode='same'))\n", "model.add(Activation('relu'))\n", "model.add(MaxPooling2D(pool_size=(2, 2)))\n", "\n", "# Layer 2\n", "model.add(Convolution2D(256, 5, 5, border_mode='same'))\n", "model.add(Activation('relu'))\n", "model.add(MaxPooling2D(pool_size=(2, 2)))\n", "\n", "# Layer 3\n", "model.add(ZeroPadding2D((1,1)))\n", "model.add(Convolution2D(512, 3, 3, border_mode='same'))\n", "model.add(Activation('relu'))\n", "\n", "# Layer 4\n", "model.add(ZeroPadding2D((1,1)))\n", "model.add(Convolution2D(1024, 3, 3, border_mode='same'))\n", "model.add(Activation('relu'))\n", "\n", "# Layer 5\n", "model.add(ZeroPadding2D((1,1)))\n", "model.add(Convolution2D(1024, 3, 3, border_mode='same'))\n", "model.add(Activation('relu'))\n", "model.add(MaxPooling2D(pool_size=(2, 2)))\n", "\n", "# Layer 6\n", "model.add(Flatten())\n", "model.add(Dense(3072, init='glorot_normal'))\n", "model.add(Activation('relu'))\n", "model.add(Dropout(0.5))\n", "\n", "# Layer 7\n", "model.add(Dense(4096, init='glorot_normal'))\n", "model.add(Activation('relu'))\n", "model.add(Dropout(0.5))\n", "\n", "# Layer 8\n", "model.add(Dense(10, init='glorot_normal'))\n", "model.add(Activation('softmax'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can imagine, training this model (even on MNIST-10) is quite time consuming. I'll run just one Epoch with 10 samples to show how it works." ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/1\n", "10/10 [==============================] - 95s - loss: nan - acc: 0.1000 \n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.compile(loss='categorical_crossentropy', optimizer=RMSprop())\n", "model.fit(X_train[:10], Y_train[:10], batch_size=1, nb_epoch=1,\n", " verbose=1, show_accuracy=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The true power of this model really comes out when it is used on a larger corpus of images, such as ILSVRC and MS COCO, with images having a larger spatial size." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### II. VGG-19 Model\n", "Now, let's load the VGG-19 model using pre-trained weights. First, we'll create a keras model as normal:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model = Sequential()\n", "model.add(ZeroPadding2D((1,1),input_shape=(3,224,224)))\n", "model.add(Convolution2D(64, 3, 3, activation='relu'))\n", "model.add(ZeroPadding2D((1,1)))\n", "model.add(Convolution2D(64, 3, 3, activation='relu'))\n", "model.add(MaxPooling2D((2,2), strides=(2,2)))\n", "\n", "model.add(ZeroPadding2D((1,1)))\n", "model.add(Convolution2D(128, 3, 3, activation='relu'))\n", "model.add(ZeroPadding2D((1,1)))\n", "model.add(Convolution2D(128, 3, 3, activation='relu'))\n", "model.add(MaxPooling2D((2,2), strides=(2,2)))\n", "\n", "model.add(ZeroPadding2D((1,1)))\n", "model.add(Convolution2D(256, 3, 3, activation='relu'))\n", "model.add(ZeroPadding2D((1,1)))\n", "model.add(Convolution2D(256, 3, 3, activation='relu'))\n", "model.add(ZeroPadding2D((1,1)))\n", "model.add(Convolution2D(256, 3, 3, activation='relu'))\n", "model.add(ZeroPadding2D((1,1)))\n", "model.add(Convolution2D(256, 3, 3, activation='relu'))\n", "model.add(MaxPooling2D((2,2), strides=(2,2)))\n", "\n", "model.add(ZeroPadding2D((1,1)))\n", "model.add(Convolution2D(512, 3, 3, activation='relu'))\n", "model.add(ZeroPadding2D((1,1)))\n", "model.add(Convolution2D(512, 3, 3, activation='relu'))\n", "model.add(ZeroPadding2D((1,1)))\n", "model.add(Convolution2D(512, 3, 3, activation='relu'))\n", "model.add(ZeroPadding2D((1,1)))\n", "model.add(Convolution2D(512, 3, 3, activation='relu'))\n", "model.add(MaxPooling2D((2,2), strides=(2,2)))\n", "\n", "model.add(ZeroPadding2D((1,1)))\n", "model.add(Convolution2D(512, 3, 3, activation='relu'))\n", "model.add(ZeroPadding2D((1,1)))\n", "model.add(Convolution2D(512, 3, 3, activation='relu'))\n", "model.add(ZeroPadding2D((1,1)))\n", "model.add(Convolution2D(512, 3, 3, activation='relu'))\n", "model.add(ZeroPadding2D((1,1)))\n", "model.add(Convolution2D(512, 3, 3, activation='relu'))\n", "model.add(MaxPooling2D((2,2), strides=(2,2)))\n", "\n", "model.add(Flatten())\n", "model.add(Dense(4096, activation='relu'))\n", "model.add(Dropout(0.5))\n", "model.add(Dense(4096, activation='relu'))\n", "model.add(Dropout(0.5))\n", "model.add(Dense(1000, activation='softmax'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We then load the weights of the model from a file (you can download this from the course website; it is not small, coming in at about half a gigabyte). We then have to compile the model, even though we have no intention of actually training it. This is because the compilation in part sets the forward propigation code, which we will need to do predictions." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model.load_weights(\"../../../class_data/keras/vgg19_weights.h5\")\n", "\n", "sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)\n", "model.compile(optimizer=sgd, loss='categorical_crossentropy')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will also load some metadata, that gives class labels to the output:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "synsets = []\n", "with open(\"../../../class_data/keras/synset_words.txt\", \"r\") as f:\n", " synsets += f.readlines()\n", "synsets = [x.replace(\"\\n\",\"\") for x in synsets]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now lets read in an image of a lion:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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swTh5Sg0E33O8VlbhkloLIRRODWwW/59cE/AVAoI1gtCcOMYofRfoukhOuXnN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SKayd8SrOHQtjjnaScisZ90Bt5bbq6viJOE/f4gEubpGeimPIk7qEvTNLRtSznZlw/7wzGk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\nut8Q7uu+7utY/zcPU7wDuUgUGwAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "im = Image.open('img/lion.jpg').resize((224, 224), Image.ANTIALIAS)\n", "plt.figure(figsize=(4, 4))\n", "plt.axis(\"off\")\n", "plt.imshow(im)\n", "im = np.array(im).astype(np.float32)\n", "\n", "# scale the image, according to the format used in training\n", "im[:,:,0] -= 103.939\n", "im[:,:,1] -= 116.779\n", "im[:,:,2] -= 123.68\n", "im = im.transpose((2,0,1))\n", "im = np.expand_dims(im, axis=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And now predict the class label from the VGG-19 model:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.3274 - n02129165 lion, king of beasts, Panthera leo\n", "0.2489 - n02125311 cougar, puma, catamount, mountain lion, painter, panther, Felis concolor\n", "0.2208 - n02128757 snow leopard, ounce, Panthera uncia\n", "0.0753 - n02128385 leopard, Panthera pardus\n", "0.0631 - n02128925 jaguar, panther, Panthera onca, Felis onca\n", "0.0360 - n02117135 hyena, hyaena\n", "0.0091 - n02127052 lynx, catamount\n", "0.0063 - n01882714 koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus\n", "0.0024 - n02129604 tiger, Panthera tigris\n", "0.0020 - n01883070 wombat\n" ] } ], "source": [ "out = model.predict(im)\n", "for index in np.argsort(out)[0][::-1][:10]:\n", " print(\"%01.4f - %s\" % (out[0][index], synsets[index].replace(\"\\n\",\"\")))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A relatively impressive result for an out of sample image!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### III. GoogLeNet - Inception Module\n", "An implementation of the Inception module, the basic building block of GoogLeNet (2014). As with OverFeat, I don't have enough compute power here to actually traing the model, but this does serve as a nice example of how to use the graph interface in keras." ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [], "source": [ "model = Graph()\n", "model.add_input(name='n00', input_shape=(1,28,28))\n", "\n", "# layer 1\n", "model.add_node(Convolution2D(64,1,1, activation='relu'), name='n11', input='n00')\n", "model.add_node(Flatten(), name='n11_f', input='n11')\n", "\n", "model.add_node(Convolution2D(96,1,1, activation='relu'), name='n12', input='n00')\n", "\n", "model.add_node(Convolution2D(16,1,1, activation='relu'), name='n13', input='n00')\n", "\n", "model.add_node(MaxPooling2D((3,3),strides=(2,2)), name='n14', input='n00')\n", "\n", "# layer 2\n", "model.add_node(Convolution2D(128,3,3, activation='relu'), name='n22', input='n12')\n", "model.add_node(Flatten(), name='n22_f', input='n22')\n", "\n", "model.add_node(Convolution2D(32,5,5, activation='relu'), name='n23', input='n13')\n", "model.add_node(Flatten(), name='n23_f', input='n23')\n", "\n", "model.add_node(Convolution2D(32,1,1, activation='relu'), name='n24', input='n14')\n", "model.add_node(Flatten(), name='n24_f', input='n24')\n", "\n", "# output layer\n", "model.add_node(Dense(1024, activation='relu'), name='layer4',\n", " inputs=['n11_f', 'n22_f', 'n23_f', 'n24_f'], merge_mode='concat')\n", "model.add_node(Dense(10, activation='softmax'), name='layer5', input='layer4')\n", "model.add_output(name='output1',input='layer5')" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/1\n", "100/100 [==============================] - 24s - loss: 7.0162\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.compile(loss={'output1':'categorical_crossentropy'}, optimizer=RMSprop())\n", "model.fit({'n00':X_train[:100], 'output1':Y_train[:100]}, nb_epoch=1, verbose=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### IV. Batch Normalization\n", "Use the Batch Normalization of: Ioffe, Sergey, and Christian Szegedy. \"Batch normalization: Accelerating deep network training by reducing internal covariate shift.\" arXiv preprint arXiv:1502.03167 (2015). We'll re-train LeNet-5, but use relu units." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "model = Sequential()\n", "\n", "model.add(Convolution2D(6, 5, 5, border_mode='valid', input_shape = (1, 28, 28)))\n", "model.add(MaxPooling2D(pool_size=(2, 2)))\n", "model.add(BatchNormalization())\n", "model.add(Activation(\"relu\"))\n", "\n", "model.add(Convolution2D(16, 5, 5, border_mode='valid'))\n", "model.add(MaxPooling2D(pool_size=(2, 2)))\n", "model.add(BatchNormalization())\n", "model.add(Activation(\"relu\"))\n", "model.add(Dropout(0.5))\n", "\n", "\n", "model.add(Convolution2D(120, 1, 1, border_mode='valid'))\n", "\n", "model.add(Flatten())\n", "model.add(Dense(84))\n", "model.add(Activation(\"relu\"))\n", "model.add(Dense(10))\n", "model.add(Activation('softmax'))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 60000 samples, validate on 10000 samples\n", "Epoch 1/20\n", "60000/60000 [==============================] - 69s - loss: 0.3758 - acc: 0.8819 - val_loss: 0.1097 - val_acc: 0.9652\n", "Epoch 2/20\n", "12896/60000 [=====>........................] - ETA: 54s - loss: 0.1846 - acc: 0.9418" ] } ], "source": [ "model.compile(loss='categorical_crossentropy', optimizer=RMSprop())\n", "model.fit(X_train, Y_train, batch_size=32, nb_epoch=20,\n", " verbose=1, show_accuracy=True, validation_data=(X_test, Y_test))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### V. Residual block - as in ResNet (2015)\n", "An example of the residual block used in the pre-print: \"Deep Residual Learning for Image Recognition.\" (2015)." ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [], "source": [ "model = Graph()\n", "model.add_input(name='input0', input_shape=(1,28,28))\n", "model.add_node(Flatten(), name='input1', input='input0')\n", "model.add_node(Dense(50), name='input2', input='input1')\n", "\n", "model.add_node(Dense(50, activation='relu'), name='middle1', input='input2')\n", "model.add_node(Dense(50, activation='relu'), name='middle2', input='middle1')\n", "\n", "model.add_node(Dense(512, activation='relu'), name='top1',\n", " inputs=['input2', 'middle2'], merge_mode='sum')\n", "model.add_node(Dense(10, activation='softmax'), name='top2', input='top1')\n", "model.add_output(name='top3',input='top2')" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 60000 samples, validate on 10000 samples\n", "Epoch 1/25\n", "60000/60000 [==============================] - 3s - loss: 0.3205 - val_loss: 0.1624\n", "Epoch 2/25\n", "60000/60000 [==============================] - 2s - loss: 0.1416 - val_loss: 0.1197\n", "Epoch 3/25\n", "60000/60000 [==============================] - 2s - loss: 0.1025 - val_loss: 0.1044\n", "Epoch 4/25\n", "60000/60000 [==============================] - 2s - loss: 0.0812 - val_loss: 0.0978\n", "Epoch 5/25\n", "60000/60000 [==============================] - 2s - loss: 0.0679 - val_loss: 0.0857\n", "Epoch 6/25\n", "60000/60000 [==============================] - 2s - loss: 0.0574 - val_loss: 0.0819\n", "Epoch 7/25\n", "60000/60000 [==============================] - 2s - loss: 0.0493 - val_loss: 0.1023\n", "Epoch 8/25\n", "60000/60000 [==============================] - 2s - loss: 0.0428 - val_loss: 0.0861\n", "Epoch 9/25\n", "60000/60000 [==============================] - 3s - loss: 0.0373 - val_loss: 0.0948\n", "Epoch 10/25\n", "60000/60000 [==============================] - 2s - loss: 0.0316 - val_loss: 0.0789\n", "Epoch 11/25\n", "60000/60000 [==============================] - 3s - loss: 0.0277 - val_loss: 0.0882\n", "Epoch 12/25\n", "60000/60000 [==============================] - 3s - loss: 0.0241 - val_loss: 0.0995\n", "Epoch 13/25\n", "60000/60000 [==============================] - 3s - loss: 0.0230 - val_loss: 0.0865\n", "Epoch 14/25\n", "60000/60000 [==============================] - 2s - loss: 0.0203 - val_loss: 0.0958\n", "Epoch 15/25\n", "60000/60000 [==============================] - 2s - loss: 0.0180 - val_loss: 0.1060\n", "Epoch 16/25\n", "60000/60000 [==============================] - 2s - loss: 0.0158 - val_loss: 0.0942\n", "Epoch 17/25\n", "60000/60000 [==============================] - 2s - loss: 0.0152 - val_loss: 0.0940\n", "Epoch 18/25\n", "60000/60000 [==============================] - 3s - loss: 0.0138 - val_loss: 0.0969\n", "Epoch 19/25\n", "60000/60000 [==============================] - 2s - loss: 0.0128 - val_loss: 0.1041\n", "Epoch 20/25\n", "60000/60000 [==============================] - 2s - loss: 0.0106 - val_loss: 0.0998\n", "Epoch 21/25\n", "60000/60000 [==============================] - 2s - loss: 0.0109 - val_loss: 0.1075\n", "Epoch 22/25\n", "60000/60000 [==============================] - 2s - loss: 0.0103 - val_loss: 0.1018\n", "Epoch 23/25\n", "60000/60000 [==============================] - 2s - loss: 0.0088 - val_loss: 0.1103\n", "Epoch 24/25\n", "60000/60000 [==============================] - 2s - loss: 0.0079 - val_loss: 0.1218\n", "Epoch 25/25\n", "60000/60000 [==============================] - 2s - loss: 0.0081 - val_loss: 0.1210\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.compile(loss={'top3':'categorical_crossentropy'}, optimizer=RMSprop())\n", "model.fit({'input0':X_train, 'top3':Y_train}, nb_epoch=25, verbose=1,\n", " validation_data={'input0':X_test, 'top3':Y_test})" ] } ], "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": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }