mirror of
https://github.com/ArthurDanjou/handson-ml3.git
synced 2026-01-14 20:19:29 +01:00
611 lines
15 KiB
Plaintext
611 lines
15 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Chapter 13 – Convolutional Neural Networks**"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"_This notebook contains all the sample code and solutions to the exercices in chapter 13._"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Setup"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# To support both python 2 and python 3\n",
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"from __future__ import division, print_function, unicode_literals\n",
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"\n",
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"# Common imports\n",
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"import numpy as np\n",
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"import numpy.random as rnd\n",
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"import os\n",
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"\n",
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"# to make this notebook's output stable across runs\n",
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"rnd.seed(42)\n",
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"\n",
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"# To plot pretty figures\n",
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"%matplotlib inline\n",
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"import matplotlib\n",
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"import matplotlib.pyplot as plt\n",
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"plt.rcParams['axes.labelsize'] = 14\n",
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"plt.rcParams['xtick.labelsize'] = 12\n",
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"plt.rcParams['ytick.labelsize'] = 12\n",
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"\n",
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"# Where to save the figures\n",
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"PROJECT_ROOT_DIR = \".\"\n",
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"CHAPTER_ID = \"cnn\"\n",
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"\n",
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"def save_fig(fig_id, tight_layout=True):\n",
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" path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\n",
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" print(\"Saving figure\", fig_id)\n",
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" if tight_layout:\n",
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" plt.tight_layout()\n",
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" plt.savefig(path, format='png', dpi=300)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"A couple utility functions to plot grayscale and RGB images:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"def plot_image(image):\n",
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" plt.imshow(image, cmap=\"gray\", interpolation=\"nearest\")\n",
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" plt.axis(\"off\")\n",
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"\n",
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"def plot_color_image(image):\n",
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" plt.imshow(image.astype(np.uint8),interpolation=\"nearest\")\n",
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" plt.axis(\"off\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"And of course we will need TensorFlow:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import tensorflow as tf"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Convolutional layer"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"from sklearn.datasets import load_sample_image\n",
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"china = load_sample_image(\"china.jpg\")\n",
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"flower = load_sample_image(\"flower.jpg\")\n",
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"image = china[150:220, 130:250]\n",
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"height, width, channels = image.shape\n",
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"image_grayscale = image.mean(axis=2).astype(np.float32)\n",
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"images = image_grayscale.reshape(1, height, width, 1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"fmap = np.zeros(shape=(7, 7, 1, 2), dtype=np.float32)\n",
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"fmap[:, 3, 0, 0] = 1\n",
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"fmap[3, :, 0, 1] = 1\n",
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"fmap[:, :, 0, 0]\n",
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"plot_image(fmap[:, :, 0, 0])\n",
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"plt.show()\n",
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"plot_image(fmap[:, :, 0, 1])\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"tf.reset_default_graph()\n",
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"\n",
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"X = tf.placeholder(tf.float32, shape=(None, height, width, 1))\n",
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"feature_maps = tf.constant(fmap)\n",
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"convolution = tf.nn.conv2d(X, feature_maps, strides=[1,1,1,1], padding=\"SAME\", use_cudnn_on_gpu=False)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"with tf.Session() as sess:\n",
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" output = convolution.eval(feed_dict={X: images})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"plot_image(images[0, :, :, 0])\n",
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"save_fig(\"china_original\", tight_layout=False)\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"plot_image(output[0, :, :, 0])\n",
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"save_fig(\"china_vertical\", tight_layout=False)\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"plot_image(output[0, :, :, 1])\n",
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"save_fig(\"china_horizontal\", tight_layout=False)\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Simple example"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"dataset = np.array([china, flower], dtype=np.float32)\n",
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"batch_size, height, width, channels = dataset.shape\n",
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"\n",
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"filters = np.zeros(shape=(7, 7, channels, 2), dtype=np.float32)\n",
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"filters[:, 3, :, 0] = 1 # vertical line\n",
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"filters[3, :, :, 1] = 1 # horizontal line\n",
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"\n",
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"X = tf.placeholder(tf.float32, shape=(None, height, width, channels))\n",
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"convolution = tf.nn.conv2d(X, filters, strides=[1,2,2,1], padding=\"SAME\")\n",
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"\n",
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"with tf.Session() as sess:\n",
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" output = sess.run(convolution, feed_dict={X: dataset})\n",
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"\n",
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"for image_index in (0, 1):\n",
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" for feature_map_index in (0, 1):\n",
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" plot_image(output[image_index, :, :, feature_map_index])\n",
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" plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## VALID vs SAME padding"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"tf.reset_default_graph()\n",
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"\n",
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"filter_primes = np.array([2., 3., 5., 7., 11., 13.], dtype=np.float32)\n",
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"x = tf.constant(np.arange(1, 13+1, dtype=np.float32).reshape([1, 1, 13, 1]))\n",
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"filters = tf.constant(filter_primes.reshape(1, 6, 1, 1))\n",
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"\n",
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"valid_conv = tf.nn.conv2d(x, filters, strides=[1, 1, 5, 1], padding='VALID')\n",
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"same_conv = tf.nn.conv2d(x, filters, strides=[1, 1, 5, 1], padding='SAME')\n",
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"\n",
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"with tf.Session() as sess:\n",
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" print(\"VALID:\\n\", valid_conv.eval())\n",
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" print(\"SAME:\\n\", same_conv.eval())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"print(\"VALID:\")\n",
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"print(np.array([1,2,3,4,5,6]).T.dot(filter_primes))\n",
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"print(np.array([6,7,8,9,10,11]).T.dot(filter_primes))\n",
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"print(\"SAME:\")\n",
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"print(np.array([0,1,2,3,4,5]).T.dot(filter_primes))\n",
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"print(np.array([5,6,7,8,9,10]).T.dot(filter_primes))\n",
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"print(np.array([10,11,12,13,0,0]).T.dot(filter_primes))\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Pooling layer"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"batch_size, height, width, channels = dataset.shape\n",
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"\n",
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"filters = np.zeros(shape=(7, 7, channels, 2), dtype=np.float32)\n",
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"filters[:, 3, :, 0] = 1 # vertical line\n",
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"filters[3, :, :, 1] = 1 # horizontal line\n",
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"\n",
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"X = tf.placeholder(tf.float32, shape=(None, height, width, channels))\n",
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"max_pool = tf.nn.max_pool(X, ksize=[1, 2, 2, 1], strides=[1,2,2,1], padding=\"VALID\")\n",
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"\n",
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"with tf.Session() as sess:\n",
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" output = sess.run(max_pool, feed_dict={X: dataset})\n",
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"\n",
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"plot_color_image(dataset[0])\n",
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"save_fig(\"china_original\")\n",
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"plt.show()\n",
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" \n",
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"plot_color_image(output[0])\n",
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"save_fig(\"china_max_pool\")\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# MNIST"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"from sklearn.datasets import fetch_mldata\n",
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"\n",
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"mnist = fetch_mldata('MNIST original')\n",
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"X_train, X_test = mnist[\"data\"][:60000].astype(np.float64), mnist[\"data\"][60000:].astype(np.float64)\n",
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"y_train, y_test = mnist[\"target\"][:60000].astype(np.int64), mnist[\"target\"][60000:].astype(np.int64)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"height, width = 28, 28\n",
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"images = X_test[5000].reshape(1, height, width, 1)\n",
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"plot_image(images[0, :, :, 0])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Inception v3"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import os\n",
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"import sys\n",
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"import tarfile\n",
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"import urllib.request\n",
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"\n",
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"TF_MODELS_URL = \"http://download.tensorflow.org/models\"\n",
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"INCEPTION_V3_URL = TF_MODELS_URL + \"/inception_v3_2016_08_28.tar.gz\"\n",
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"INCEPTION_PATH = os.path.join(\"datasets\", \"inception\")\n",
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"INCEPTION_V3_CHECKPOINT_PATH = os.path.join(INCEPTION_PATH, \"inception_v3.ckpt\")\n",
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"\n",
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"def download_progress(count, block_size, total_size):\n",
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" percent = count * block_size * 100 // total_size\n",
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" sys.stdout.write(\"\\rDownloading: {}%\".format(percent))\n",
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" sys.stdout.flush()\n",
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"\n",
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"def fetch_pretrained_inception_v3(url=INCEPTION_V3_URL, path=INCEPTION_PATH):\n",
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" if os.path.exists(INCEPTION_V3_CHECKPOINT_PATH):\n",
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" return\n",
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" os.makedirs(path, exist_ok=True)\n",
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" tgz_path = os.path.join(path, \"inception_v3.tgz\")\n",
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" urllib.request.urlretrieve(url, tgz_path, reporthook=download_progress)\n",
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" inception_tgz = tarfile.open(tgz_path)\n",
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" inception_tgz.extractall(path=path)\n",
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" inception_tgz.close()\n",
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" os.remove(tgz_path)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"fetch_pretrained_inception_v3()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import re\n",
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"\n",
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"CLASS_NAME_REGEX = re.compile(r\"^n\\d+\\s+(.*)\\s*$\", re.M | re.U)\n",
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"\n",
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"def load_class_names():\n",
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" with open(os.path.join(\"datasets\",\"inception\",\"imagenet_class_names.txt\"), \"rb\") as f:\n",
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" content = f.read().decode(\"utf-8\")\n",
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" return CLASS_NAME_REGEX.findall(content)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"class_names = load_class_names()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"width = 299\n",
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"height = 299\n",
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"channels = 3"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import matplotlib.image as mpimg\n",
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"test_image = mpimg.imread(os.path.join(\"images\",\"cnn\",\"test_image.png\"))[:, :, :channels]\n",
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"plt.imshow(test_image)\n",
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"plt.axis(\"off\")\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import tensorflow as tf\n",
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"from nets.inception_v3 import inception_v3, inception_v3_arg_scope\n",
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"import tensorflow.contrib.slim as slim\n",
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"\n",
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"tf.reset_default_graph()\n",
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"\n",
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"X = tf.placeholder(tf.float32, shape=[None, height, width, channels], name=\"X\")\n",
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"with slim.arg_scope(inception_v3_arg_scope()):\n",
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" logits, end_points = inception_v3(X, num_classes=1001, is_training=False)\n",
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"predictions = end_points[\"Predictions\"]\n",
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"saver = tf.train.Saver()"
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]
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},
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{
|
||
"cell_type": "code",
|
||
"execution_count": 24,
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"X_test = test_image.reshape(-1, height, width, channels)\n",
|
||
"\n",
|
||
"with tf.Session() as sess:\n",
|
||
" saver.restore(sess, INCEPTION_V3_CHECKPOINT_PATH)\n",
|
||
" predictions_val = predictions.eval(feed_dict={X: X_test})"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 25,
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"class_names[np.argmax(predictions_val[0])]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 26,
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"np.argmax(predictions_val, axis=1)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 27,
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"top_5 = np.argpartition(predictions_val[0], -5)[-5:]\n",
|
||
"top_5 = top_5[np.argsort(predictions_val[0][top_5])]\n",
|
||
"for i in top_5:\n",
|
||
" print(\"{0}: {1:.2f}%\".format(class_names[i], 100*predictions_val[0][i]))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"source": [
|
||
"# Exercise solutions"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"**Coming soon**"
|
||
]
|
||
},
|
||
{
|
||
"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": "3.5.1"
|
||
},
|
||
"nav_menu": {},
|
||
"toc": {
|
||
"navigate_menu": true,
|
||
"number_sections": true,
|
||
"sideBar": true,
|
||
"threshold": 6,
|
||
"toc_cell": false,
|
||
"toc_section_display": "block",
|
||
"toc_window_display": false
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 0
|
||
}
|