1431 lines
39 KiB
Plaintext
1431 lines
39 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 14 – Deep Computer Vision Using 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 in chapter 14._"
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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 import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20 and TensorFlow ≥2.0-preview."
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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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"outputs": [],
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"source": [
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"# Python ≥3.5 is required\n",
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"import sys\n",
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"assert sys.version_info >= (3, 5)\n",
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"\n",
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"# Scikit-Learn ≥0.20 is required\n",
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"import sklearn\n",
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"assert sklearn.__version__ >= \"0.20\"\n",
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"\n",
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"# TensorFlow ≥2.0-preview is required\n",
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"import tensorflow as tf\n",
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"from tensorflow import keras\n",
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"assert tf.__version__ >= \"2.0\"\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 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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"np.random.seed(42)\n",
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"tf.random.set_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 as mpl\n",
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"import matplotlib.pyplot as plt\n",
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"mpl.rc('axes', labelsize=14)\n",
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"mpl.rc('xtick', labelsize=12)\n",
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"mpl.rc('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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"IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
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"os.makedirs(IMAGES_PATH, exist_ok=True)\n",
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"\n",
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"def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
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" path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\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=fig_extension, dpi=resolution)"
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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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"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, 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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"# What is a Convolution?"
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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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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from sklearn.datasets import load_sample_image\n",
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"\n",
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"# Load sample images\n",
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"china = load_sample_image(\"china.jpg\") / 255\n",
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"flower = load_sample_image(\"flower.jpg\") / 255\n",
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"images = np.array([china, flower])\n",
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"batch_size, height, width, channels = images.shape\n",
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"\n",
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"# Create 2 filters\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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"outputs = tf.nn.conv2d(images, filters, strides=1, padding=\"SAME\")\n",
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"\n",
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"plt.imshow(outputs[0, :, :, 1], cmap=\"gray\") # plot 1st image's 2nd feature map\n",
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"plt.axis(\"off\") # Not shown in the book\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": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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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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" plt.subplot(2, 2, image_index * 2 + feature_map_index + 1)\n",
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" plot_image(outputs[image_index, :, :, feature_map_index])\n",
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"\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": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"def crop(images):\n",
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" return images[150:220, 130:250]"
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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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"outputs": [],
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"source": [
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"plot_image(crop(images[0, :, :, 0]))\n",
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"save_fig(\"china_original\", tight_layout=False)\n",
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"plt.show()\n",
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"\n",
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"for feature_map_index, filename in enumerate([\"china_vertical\", \"china_horizontal\"]):\n",
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" plot_image(crop(outputs[0, :, :, feature_map_index]))\n",
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" save_fig(filename, 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": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"plot_image(filters[:, :, 0, 0])\n",
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"plt.show()\n",
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"plot_image(filters[:, :, 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": "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": "markdown",
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"metadata": {},
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"source": [
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"Using `keras.layers.Conv2D()`:"
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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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"outputs": [],
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"source": [
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"conv = keras.layers.Conv2D(filters=32, kernel_size=3, strides=1,\n",
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" padding=\"SAME\", activation=\"relu\")"
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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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"outputs": [],
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"source": [
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"plot_image(crop(outputs[0, :, :, 0]))\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": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"def feature_map_size(input_size, kernel_size, strides=1, padding=\"SAME\"):\n",
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" if padding == \"SAME\":\n",
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" return (input_size - 1) // strides + 1\n",
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" else:\n",
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" return (input_size - kernel_size) // strides + 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": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"def pad_before_and_padded_size(input_size, kernel_size, strides=1):\n",
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" fmap_size = feature_map_size(input_size, kernel_size, strides)\n",
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" padded_size = max((fmap_size - 1) * strides + kernel_size, input_size)\n",
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" pad_before = (padded_size - input_size) // 2\n",
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" return pad_before, padded_size"
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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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"outputs": [],
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"source": [
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"def manual_same_padding(images, kernel_size, strides=1):\n",
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" if kernel_size == 1:\n",
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" return images.astype(np.float32)\n",
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" batch_size, height, width, channels = images.shape\n",
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" top_pad, padded_height = pad_before_and_padded_size(height, kernel_size, strides)\n",
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" left_pad, padded_width = pad_before_and_padded_size(width, kernel_size, strides)\n",
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" padded_shape = [batch_size, padded_height, padded_width, channels]\n",
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" padded_images = np.zeros(padded_shape, dtype=np.float32)\n",
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" padded_images[:, top_pad:height+top_pad, left_pad:width+left_pad, :] = images\n",
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" return padded_images"
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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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"Using `\"SAME\"` padding is equivalent to padding manually using `manual_same_padding()` then using `\"VALID\"` padding (confusingly, `\"VALID\"` padding means no padding at all):"
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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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"outputs": [],
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"source": [
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"kernel_size = 7\n",
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"strides = 2\n",
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"\n",
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"conv_valid = keras.layers.Conv2D(filters=1, kernel_size=kernel_size, strides=strides, padding=\"VALID\")\n",
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"conv_same = keras.layers.Conv2D(filters=1, kernel_size=kernel_size, strides=strides, padding=\"SAME\")\n",
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"\n",
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"valid_output = conv_valid(manual_same_padding(images, kernel_size, strides))\n",
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"\n",
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"# Need to call build() so conv_same's weights get created\n",
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"conv_same.build(tf.TensorShape(images.shape))\n",
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"\n",
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"# Copy the weights from conv_valid to conv_same\n",
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"conv_same.set_weights(conv_valid.get_weights())\n",
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"\n",
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"same_output = conv_same(images.astype(np.float32))\n",
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"\n",
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"assert np.allclose(valid_output.numpy(), same_output.numpy())"
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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": "markdown",
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"metadata": {},
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"source": [
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"## Max pooling"
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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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"outputs": [],
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"source": [
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"max_pool = keras.layers.MaxPool2D(pool_size=2)"
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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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"outputs": [],
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"source": [
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"cropped_images = np.array([crop(image) for image in images])\n",
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"output = max_pool(cropped_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": 16,
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"metadata": {},
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"outputs": [],
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"source": [
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"fig = plt.figure(figsize=(12, 8))\n",
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"gs = mpl.gridspec.GridSpec(nrows=1, ncols=2, width_ratios=[2, 1])\n",
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"\n",
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"ax1 = fig.add_subplot(gs[0, 0])\n",
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"ax1.set_title(\"Input\", fontsize=14)\n",
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"ax1.imshow(cropped_images[0]) # plot the 1st image\n",
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"ax1.axis(\"off\")\n",
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"ax2 = fig.add_subplot(gs[0, 1])\n",
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"ax2.set_title(\"Output\", fontsize=14)\n",
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"ax2.imshow(output[0]) # plot the output for the 1st image\n",
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"ax2.axis(\"off\")\n",
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"save_fig(\"china_max_pooling\")\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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"## Depth-wise pooling"
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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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"outputs": [],
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"source": [
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||
"class DepthMaxPool(keras.layers.Layer):\n",
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" def __init__(self, pool_size, strides=None, padding=\"VALID\", **kwargs):\n",
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" super().__init__(**kwargs)\n",
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" if strides is None:\n",
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" strides = pool_size\n",
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" self.pool_size = pool_size\n",
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" self.strides = strides\n",
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" self.padding = padding\n",
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" def call(self, inputs):\n",
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" return tf.nn.max_pool(inputs,\n",
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" ksize=(1, 1, 1, self.pool_size),\n",
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" strides=(1, 1, 1, self.pool_size),\n",
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" padding=self.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": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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"depth_pool = DepthMaxPool(3)\n",
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"with tf.device(\"/cpu:0\"): # there is no GPU-kernel yet\n",
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" depth_output = depth_pool(cropped_images)\n",
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"depth_output.shape"
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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": [
|
||
"Or just use a `Lambda` 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": 19,
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||
"metadata": {},
|
||
"outputs": [],
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||
"source": [
|
||
"depth_pool = keras.layers.Lambda(lambda X: tf.nn.max_pool(\n",
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" X, ksize=(1, 1, 1, 3), strides=(1, 1, 1, 3), padding=\"VALID\"))\n",
|
||
"with tf.device(\"/cpu:0\"): # there is no GPU-kernel yet\n",
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||
" depth_output = depth_pool(cropped_images)\n",
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||
"depth_output.shape"
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||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
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||
"execution_count": 20,
|
||
"metadata": {},
|
||
"outputs": [],
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||
"source": [
|
||
"plt.figure(figsize=(12, 8))\n",
|
||
"plt.subplot(1, 2, 1)\n",
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||
"plt.title(\"Input\", fontsize=14)\n",
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||
"plot_color_image(cropped_images[0]) # plot the 1st image\n",
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||
"plt.subplot(1, 2, 2)\n",
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||
"plt.title(\"Output\", fontsize=14)\n",
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||
"plot_image(depth_output[0, ..., 0]) # plot the output for the 1st image\n",
|
||
"plt.axis(\"off\")\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Average pooling"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"avg_pool = keras.layers.AvgPool2D(pool_size=2)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"output_avg = avg_pool(cropped_images)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"fig = plt.figure(figsize=(12, 8))\n",
|
||
"gs = mpl.gridspec.GridSpec(nrows=1, ncols=2, width_ratios=[2, 1])\n",
|
||
"\n",
|
||
"ax1 = fig.add_subplot(gs[0, 0])\n",
|
||
"ax1.set_title(\"Input\", fontsize=14)\n",
|
||
"ax1.imshow(cropped_images[0]) # plot the 1st image\n",
|
||
"ax1.axis(\"off\")\n",
|
||
"ax2 = fig.add_subplot(gs[0, 1])\n",
|
||
"ax2.set_title(\"Output\", fontsize=14)\n",
|
||
"ax2.imshow(output_avg[0]) # plot the output for the 1st image\n",
|
||
"ax2.axis(\"off\")\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Global Average Pooling"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 24,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"global_avg_pool = keras.layers.GlobalAvgPool2D()\n",
|
||
"global_avg_pool(cropped_images)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 25,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"output_global_avg2 = keras.layers.Lambda(lambda X: tf.reduce_mean(X, axis=[1, 2]))\n",
|
||
"output_global_avg2(cropped_images)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# Tackling Fashion MNIST With a CNN"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 26,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"(X_train_full, y_train_full), (X_test, y_test) = keras.datasets.fashion_mnist.load_data()\n",
|
||
"X_train, X_valid = X_train_full[:-5000], X_train_full[-5000:]\n",
|
||
"y_train, y_valid = y_train_full[:-5000], y_train_full[-5000:]\n",
|
||
"\n",
|
||
"X_mean = X_train.mean(axis=0, keepdims=True)\n",
|
||
"X_std = X_train.std(axis=0, keepdims=True) + 1e-7\n",
|
||
"X_train = (X_train - X_mean) / X_std\n",
|
||
"X_valid = (X_valid - X_mean) / X_std\n",
|
||
"X_test = (X_test - X_mean) / X_std\n",
|
||
"\n",
|
||
"X_train = X_train[..., np.newaxis]\n",
|
||
"X_valid = X_valid[..., np.newaxis]\n",
|
||
"X_test = X_test[..., np.newaxis]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 27,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from functools import partial\n",
|
||
"\n",
|
||
"DefaultConv2D = partial(keras.layers.Conv2D,\n",
|
||
" kernel_size=3, activation='relu', padding=\"SAME\")\n",
|
||
"\n",
|
||
"model = keras.models.Sequential([\n",
|
||
" DefaultConv2D(filters=64, kernel_size=7, input_shape=[28, 28, 1]),\n",
|
||
" keras.layers.MaxPooling2D(pool_size=2),\n",
|
||
" DefaultConv2D(filters=128),\n",
|
||
" DefaultConv2D(filters=128),\n",
|
||
" keras.layers.MaxPooling2D(pool_size=2),\n",
|
||
" DefaultConv2D(filters=256),\n",
|
||
" DefaultConv2D(filters=256),\n",
|
||
" keras.layers.MaxPooling2D(pool_size=2),\n",
|
||
" keras.layers.Flatten(),\n",
|
||
" keras.layers.Dense(units=128, activation='relu'),\n",
|
||
" keras.layers.Dropout(0.5),\n",
|
||
" keras.layers.Dense(units=64, activation='relu'),\n",
|
||
" keras.layers.Dropout(0.5),\n",
|
||
" keras.layers.Dense(units=10, activation='softmax'),\n",
|
||
"])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 28,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"nadam\", metrics=[\"accuracy\"])\n",
|
||
"history = model.fit(X_train, y_train, epochs=10, validation_data=[X_valid, y_valid])\n",
|
||
"score = model.evaluate(X_test, y_test)\n",
|
||
"X_new = X_test[:10] # pretend we have new images\n",
|
||
"y_pred = model.predict(X_new)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## ResNet-34"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 29,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"DefaultConv2D = partial(keras.layers.Conv2D, kernel_size=3, strides=1,\n",
|
||
" padding=\"SAME\", use_bias=False)\n",
|
||
"\n",
|
||
"class ResidualUnit(keras.layers.Layer):\n",
|
||
" def __init__(self, filters, strides=1, activation=\"relu\", **kwargs):\n",
|
||
" super().__init__(**kwargs)\n",
|
||
" self.activation = keras.activations.get(activation)\n",
|
||
" self.main_layers = [\n",
|
||
" DefaultConv2D(filters, strides=strides),\n",
|
||
" keras.layers.BatchNormalization(),\n",
|
||
" self.activation,\n",
|
||
" DefaultConv2D(filters),\n",
|
||
" keras.layers.BatchNormalization()]\n",
|
||
" self.skip_layers = []\n",
|
||
" if strides > 1:\n",
|
||
" self.skip_layers = [\n",
|
||
" DefaultConv2D(filters, kernel_size=1, strides=strides),\n",
|
||
" keras.layers.BatchNormalization()]\n",
|
||
"\n",
|
||
" def call(self, inputs):\n",
|
||
" Z = inputs\n",
|
||
" for layer in self.main_layers:\n",
|
||
" Z = layer(Z)\n",
|
||
" skip_Z = inputs\n",
|
||
" for layer in self.skip_layers:\n",
|
||
" skip_Z = layer(skip_Z)\n",
|
||
" return self.activation(Z + skip_Z)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 30,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"model = keras.models.Sequential()\n",
|
||
"model.add(DefaultConv2D(64, kernel_size=7, strides=2,\n",
|
||
" input_shape=[224, 224, 3]))\n",
|
||
"model.add(keras.layers.BatchNormalization())\n",
|
||
"model.add(keras.layers.Activation(\"relu\"))\n",
|
||
"model.add(keras.layers.MaxPool2D(pool_size=3, strides=2, padding=\"SAME\"))\n",
|
||
"prev_filters = 64\n",
|
||
"for filters in [64] * 3 + [128] * 4 + [256] * 6 + [512] * 3:\n",
|
||
" strides = 1 if filters == prev_filters else 2\n",
|
||
" model.add(ResidualUnit(filters, strides=strides))\n",
|
||
" prev_filters = filters\n",
|
||
"model.add(keras.layers.GlobalAvgPool2D())\n",
|
||
"model.add(keras.layers.Flatten())\n",
|
||
"model.add(keras.layers.Dense(10, activation=\"softmax\"))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 31,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"model.summary()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Using a Pretrained Model"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 32,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"model = keras.applications.resnet50.ResNet50(weights=\"imagenet\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 33,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"images_resized = tf.image.resize(images, [224, 224])\n",
|
||
"plot_color_image(images_resized[0])\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 34,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"images_resized = tf.image.resize_with_pad(images, 224, 224, antialias=True)\n",
|
||
"plot_color_image(images_resized[0])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 35,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"images_resized = tf.image.resize_with_crop_or_pad(images, 224, 224)\n",
|
||
"plot_color_image(images_resized[0])\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 36,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"china_box = [0, 0.03, 1, 0.68]\n",
|
||
"flower_box = [0.19, 0.26, 0.86, 0.7]\n",
|
||
"images_resized = tf.image.crop_and_resize(images, [china_box, flower_box], [0, 1], [224, 224])\n",
|
||
"plot_color_image(images_resized[0])\n",
|
||
"plt.show()\n",
|
||
"plot_color_image(images_resized[1])\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 37,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"inputs = keras.applications.resnet50.preprocess_input(images_resized * 255)\n",
|
||
"Y_proba = model.predict(inputs)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 38,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"Y_proba.shape"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 39,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"top_K = keras.applications.resnet50.decode_predictions(Y_proba, top=3)\n",
|
||
"for image_index in range(len(images)):\n",
|
||
" print(\"Image #{}\".format(image_index))\n",
|
||
" for class_id, name, y_proba in top_K[image_index]:\n",
|
||
" print(\" {} - {:12s} {:.2f}%\".format(class_id, name, y_proba * 100))\n",
|
||
" print()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Pretrained Models for Transfer Learning"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 40,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import tensorflow_datasets as tfds\n",
|
||
"\n",
|
||
"dataset, info = tfds.load(\"tf_flowers\", as_supervised=True, with_info=True)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 41,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"info.splits"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 42,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"info.splits[\"train\"]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 43,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"class_names = info.features[\"label\"].names\n",
|
||
"class_names"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 44,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"n_classes = info.features[\"label\"].num_classes"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 45,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"dataset_size = info.splits[\"train\"].num_examples\n",
|
||
"dataset_size"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 46,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"test_split, valid_split, train_split = tfds.Split.TRAIN.subsplit([10, 15, 75])\n",
|
||
"\n",
|
||
"test_set_raw = tfds.load(\"tf_flowers\", split=test_split, as_supervised=True)\n",
|
||
"valid_set_raw = tfds.load(\"tf_flowers\", split=valid_split, as_supervised=True)\n",
|
||
"train_set_raw = tfds.load(\"tf_flowers\", split=train_split, as_supervised=True)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 47,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"plt.figure(figsize=(12, 10))\n",
|
||
"index = 0\n",
|
||
"for image, label in train_set_raw.take(9):\n",
|
||
" index += 1\n",
|
||
" plt.subplot(3, 3, index)\n",
|
||
" plt.imshow(image)\n",
|
||
" plt.title(\"Class: {}\".format(class_names[label]))\n",
|
||
" plt.axis(\"off\")\n",
|
||
"\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Basic preprocessing:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 48,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def preprocess(image, label):\n",
|
||
" resized_image = tf.image.resize(image, [224, 224])\n",
|
||
" final_image = keras.applications.xception.preprocess_input(resized_image)\n",
|
||
" return final_image, label"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Slightly fancier preprocessing (but you could add much more data augmentation):"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 49,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def central_crop(image):\n",
|
||
" shape = tf.shape(image)\n",
|
||
" min_dim = tf.reduce_min([shape[0], shape[1]])\n",
|
||
" top_crop = (shape[0] - min_dim) // 4\n",
|
||
" bottom_crop = shape[0] - top_crop\n",
|
||
" left_crop = (shape[1] - min_dim) // 4\n",
|
||
" right_crop = shape[1] - left_crop\n",
|
||
" return image[top_crop:bottom_crop, left_crop:right_crop]\n",
|
||
"\n",
|
||
"def random_crop(image):\n",
|
||
" shape = tf.shape(image)\n",
|
||
" min_dim = tf.reduce_min([shape[0], shape[1]]) * 90 // 100\n",
|
||
" return tf.image.random_crop(image, [min_dim, min_dim, 3])\n",
|
||
"\n",
|
||
"def preprocess(image, label, randomize=False):\n",
|
||
" if randomize:\n",
|
||
" cropped_image = random_crop(image)\n",
|
||
" cropped_image = tf.image.random_flip_left_right(cropped_image)\n",
|
||
" else:\n",
|
||
" cropped_image = central_crop(image)\n",
|
||
" resized_image = tf.image.resize(cropped_image, [224, 224])\n",
|
||
" final_image = keras.applications.xception.preprocess_input(resized_image)\n",
|
||
" return final_image, label\n",
|
||
"\n",
|
||
"batch_size = 32\n",
|
||
"train_set = train_set_raw.shuffle(1000).repeat()\n",
|
||
"train_set = train_set.map(partial(preprocess, randomize=True)).batch(batch_size).prefetch(1)\n",
|
||
"valid_set = valid_set_raw.map(preprocess).batch(batch_size).prefetch(1)\n",
|
||
"test_set = test_set_raw.map(preprocess).batch(batch_size).prefetch(1)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 50,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"plt.figure(figsize=(12, 12))\n",
|
||
"for X_batch, y_batch in train_set.take(1):\n",
|
||
" for index in range(9):\n",
|
||
" plt.subplot(3, 3, index + 1)\n",
|
||
" plt.imshow(X_batch[index] / 2 + 0.5)\n",
|
||
" plt.title(\"Class: {}\".format(class_names[y_batch[index]]))\n",
|
||
" plt.axis(\"off\")\n",
|
||
"\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 51,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"plt.figure(figsize=(12, 12))\n",
|
||
"for X_batch, y_batch in test_set.take(1):\n",
|
||
" for index in range(9):\n",
|
||
" plt.subplot(3, 3, index + 1)\n",
|
||
" plt.imshow(X_batch[index] / 2 + 0.5)\n",
|
||
" plt.title(\"Class: {}\".format(class_names[y_batch[index]]))\n",
|
||
" plt.axis(\"off\")\n",
|
||
"\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 52,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"base_model = keras.applications.xception.Xception(weights=\"imagenet\",\n",
|
||
" include_top=False)\n",
|
||
"avg = keras.layers.GlobalAveragePooling2D()(base_model.output)\n",
|
||
"output = keras.layers.Dense(n_classes, activation=\"softmax\")(avg)\n",
|
||
"model = keras.models.Model(inputs=base_model.input, outputs=output)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 53,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"for index, layer in enumerate(base_model.layers):\n",
|
||
" print(index, layer.name)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 54,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"for layer in base_model.layers:\n",
|
||
" layer.trainable = False\n",
|
||
"\n",
|
||
"optimizer = keras.optimizers.SGD(lr=0.2, momentum=0.9, decay=0.01)\n",
|
||
"model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=optimizer,\n",
|
||
" metrics=[\"accuracy\"])\n",
|
||
"history = model.fit(train_set,\n",
|
||
" steps_per_epoch=int(0.75 * dataset_size / batch_size),\n",
|
||
" validation_data=valid_set,\n",
|
||
" validation_steps=int(0.15 * dataset_size / batch_size),\n",
|
||
" epochs=5)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 55,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"for layer in base_model.layers:\n",
|
||
" layer.trainable = True\n",
|
||
"\n",
|
||
"optimizer = keras.optimizers.SGD(learning_rate=0.01, momentum=0.9,\n",
|
||
" nesterov=True, decay=0.001)\n",
|
||
"model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=optimizer,\n",
|
||
" metrics=[\"accuracy\"])\n",
|
||
"history = model.fit(train_set,\n",
|
||
" steps_per_epoch=int(0.75 * dataset_size / batch_size),\n",
|
||
" validation_data=valid_set,\n",
|
||
" validation_steps=int(0.15 * dataset_size / batch_size),\n",
|
||
" epochs=40)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# Classification and Localization"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 56,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"base_model = keras.applications.xception.Xception(weights=\"imagenet\",\n",
|
||
" include_top=False)\n",
|
||
"avg = keras.layers.GlobalAveragePooling2D()(base_model.output)\n",
|
||
"class_output = keras.layers.Dense(n_classes, activation=\"softmax\")(avg)\n",
|
||
"loc_output = keras.layers.Dense(4)(avg)\n",
|
||
"model = keras.models.Model(inputs=base_model.input,\n",
|
||
" outputs=[class_output, loc_output])\n",
|
||
"model.compile(loss=[\"sparse_categorical_crossentropy\", \"mse\"],\n",
|
||
" loss_weights=[0.8, 0.2], # depends on what you care most about\n",
|
||
" optimizer=optimizer, metrics=[\"accuracy\"])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 57,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def add_random_bounding_boxes(images, labels):\n",
|
||
" fake_bboxes = tf.random.uniform([tf.shape(images)[0], 4])\n",
|
||
" return images, (labels, fake_bboxes)\n",
|
||
"\n",
|
||
"fake_train_set = train_set.take(5).repeat(2).map(add_random_bounding_boxes)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 58,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"model.fit(fake_train_set, steps_per_epoch=5, epochs=2)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Mean Average Precision (mAP)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 59,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def maximum_precisions(precisions):\n",
|
||
" return np.flip(np.maximum.accumulate(np.flip(precisions)))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 60,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"recalls = np.linspace(0, 1, 11)\n",
|
||
"\n",
|
||
"precisions = [0.91, 0.94, 0.96, 0.94, 0.95, 0.92, 0.80, 0.60, 0.45, 0.20, 0.10]\n",
|
||
"max_precisions = maximum_precisions(precisions)\n",
|
||
"mAP = max_precisions.mean()\n",
|
||
"plt.plot(recalls, precisions, \"ro--\", label=\"Precision\")\n",
|
||
"plt.plot(recalls, max_precisions, \"bo-\", label=\"Max Precision\")\n",
|
||
"plt.xlabel(\"Recall\")\n",
|
||
"plt.ylabel(\"Precision\")\n",
|
||
"plt.plot([0, 1], [mAP, mAP], \"g:\", linewidth=3, label=\"mAP\")\n",
|
||
"plt.grid(True)\n",
|
||
"plt.axis([0, 1, 0, 1])\n",
|
||
"plt.legend(loc=\"lower center\", fontsize=14)\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Transpose convolutions:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 61,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"tf.random.set_seed(42)\n",
|
||
"X = images_resized.numpy()\n",
|
||
"\n",
|
||
"conv_transpose = keras.layers.Conv2DTranspose(filters=5, kernel_size=3, strides=2, padding=\"VALID\")\n",
|
||
"output = conv_transpose(X)\n",
|
||
"output.shape"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 62,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def normalize(X):\n",
|
||
" return (X - tf.reduce_min(X)) / (tf.reduce_max(X) - tf.reduce_min(X))\n",
|
||
"\n",
|
||
"fig = plt.figure(figsize=(12, 8))\n",
|
||
"gs = mpl.gridspec.GridSpec(nrows=1, ncols=2, width_ratios=[1, 2])\n",
|
||
"\n",
|
||
"ax1 = fig.add_subplot(gs[0, 0])\n",
|
||
"ax1.set_title(\"Input\", fontsize=14)\n",
|
||
"ax1.imshow(X[0]) # plot the 1st image\n",
|
||
"ax1.axis(\"off\")\n",
|
||
"ax2 = fig.add_subplot(gs[0, 1])\n",
|
||
"ax2.set_title(\"Output\", fontsize=14)\n",
|
||
"ax2.imshow(normalize(output[0, ..., :3]), interpolation=\"bicubic\") # plot the output for the 1st image\n",
|
||
"ax2.axis(\"off\")\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 63,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def upscale_images(images, stride, kernel_size):\n",
|
||
" batch_size, height, width, channels = images.shape\n",
|
||
" upscaled = np.zeros((batch_size,\n",
|
||
" (height - 1) * stride + 2 * kernel_size - 1,\n",
|
||
" (width - 1) * stride + 2 * kernel_size - 1,\n",
|
||
" channels))\n",
|
||
" upscaled[:,\n",
|
||
" kernel_size - 1:(height - 1) * stride + kernel_size:stride,\n",
|
||
" kernel_size - 1:(width - 1) * stride + kernel_size:stride,\n",
|
||
" :] = images\n",
|
||
" return upscaled"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 64,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"upscaled = upscale_images(X, stride=2, kernel_size=3)\n",
|
||
"weights, biases = conv_transpose.weights\n",
|
||
"reversed_filters = np.flip(weights.numpy(), axis=[0, 1])\n",
|
||
"reversed_filters = np.transpose(reversed_filters, [0, 1, 3, 2])\n",
|
||
"manual_output = tf.nn.conv2d(upscaled, reversed_filters, strides=1, padding=\"VALID\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 65,
|
||
"metadata": {
|
||
"scrolled": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def normalize(X):\n",
|
||
" return (X - tf.reduce_min(X)) / (tf.reduce_max(X) - tf.reduce_min(X))\n",
|
||
"\n",
|
||
"fig = plt.figure(figsize=(12, 8))\n",
|
||
"gs = mpl.gridspec.GridSpec(nrows=1, ncols=3, width_ratios=[1, 2, 2])\n",
|
||
"\n",
|
||
"ax1 = fig.add_subplot(gs[0, 0])\n",
|
||
"ax1.set_title(\"Input\", fontsize=14)\n",
|
||
"ax1.imshow(X[0]) # plot the 1st image\n",
|
||
"ax1.axis(\"off\")\n",
|
||
"ax2 = fig.add_subplot(gs[0, 1])\n",
|
||
"ax2.set_title(\"Upscaled\", fontsize=14)\n",
|
||
"ax2.imshow(upscaled[0], interpolation=\"bicubic\")\n",
|
||
"ax2.axis(\"off\")\n",
|
||
"ax3 = fig.add_subplot(gs[0, 2])\n",
|
||
"ax3.set_title(\"Output\", fontsize=14)\n",
|
||
"ax3.imshow(normalize(manual_output[0, ..., :3]), interpolation=\"bicubic\") # plot the output for the 1st image\n",
|
||
"ax3.axis(\"off\")\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 66,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"np.allclose(output, manual_output.numpy(), atol=1e-7)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"source": [
|
||
"# Exercises"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 1. to 8."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"See appendix A."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 9. High Accuracy CNN for MNIST\n",
|
||
"Exercise: Build your own CNN from scratch and try to achieve the highest possible accuracy on MNIST."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"source": [
|
||
"## 10. Use transfer learning for large image classification\n",
|
||
"\n",
|
||
"### 10.1)\n",
|
||
"Create a training set containing at least 100 images per class. For example, you could classify your own pictures based on the location (beach, mountain, city, etc.), or alternatively you can just use an existing dataset (e.g., from TensorFlow Datasets)."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 10.2)\n",
|
||
"Split it into a training set, a validation set and a test set."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 10.3)\n",
|
||
"Build the input pipeline, including the appropriate preprocessing operations, and optionally add data augmentation."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 10.4)\n",
|
||
"Fine-tune a pretrained model on this dataset."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 11.\n",
|
||
"Exercise: Go through TensorFlow's [DeepDream tutorial](https://goo.gl/4b2s6g). It is a fun way to familiarize yourself with various ways of visualizing the patterns learned by a CNN, and to generate art using Deep Learning.\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"source": [
|
||
"Simply download the notebook and follow its instructions. For extra fun, you can produce a series of images, by repeatedly zooming in and running the DeepDream algorithm: using a tool such as [ffmpeg](https://ffmpeg.org/) you can then create a video from these images. For example, here is a [DeepDream video](https://www.youtube.com/watch?v=l6i_fDg30p0) I made... as you will see, it quickly turns into a nightmare. ;-) You can find hundreds of [similar videos](https://www.youtube.com/results?search_query=+deepdream) (often much more artistic) on the web."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.6.8"
|
||
},
|
||
"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": 1
|
||
}
|