wehub-resource-sync
1413 lines
29 KiB
Plaintext
1413 lines
29 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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"colab_type": "text"
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},
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"source": [
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"This is a companion notebook for the book [Deep Learning with Python, Second Edition](https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras&a_bid=76564dff). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\n\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\n\nThis notebook was generated for TensorFlow 2.6."
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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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"colab_type": "text"
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},
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"source": [
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"# Getting started with neural networks: Classification and regression"
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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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"colab_type": "text"
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},
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"source": [
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"## Classifying movie reviews: A binary classification example"
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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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"colab_type": "text"
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},
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"source": [
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"### The IMDB dataset"
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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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"colab_type": "text"
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},
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"source": [
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"**Loading the IMDB dataset**"
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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": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"from tensorflow.keras.datasets import imdb\n",
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"(train_data, train_labels), (test_data, test_labels) = imdb.load_data(\n",
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" num_words=10000)"
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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": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"train_data[0]"
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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": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"train_labels[0]"
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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": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"max([max(sequence) for sequence in train_data])"
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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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"colab_type": "text"
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},
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"source": [
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"**Decoding reviews back to text**"
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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": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"word_index = imdb.get_word_index()\n",
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"reverse_word_index = dict(\n",
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" [(value, key) for (key, value) in word_index.items()])\n",
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"decoded_review = \" \".join(\n",
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" [reverse_word_index.get(i - 3, \"?\") for i in train_data[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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"colab_type": "text"
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},
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"source": [
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"### Preparing the data"
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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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"colab_type": "text"
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},
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"source": [
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"**Encoding the integer sequences via multi-hot encoding**"
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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": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"def vectorize_sequences(sequences, dimension=10000):\n",
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" results = np.zeros((len(sequences), dimension))\n",
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" for i, sequence in enumerate(sequences):\n",
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" for j in sequence:\n",
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" results[i, j] = 1.\n",
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" return results\n",
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"x_train = vectorize_sequences(train_data)\n",
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"x_test = vectorize_sequences(test_data)"
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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": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"x_train[0]"
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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": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"y_train = np.asarray(train_labels).astype(\"float32\")\n",
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"y_test = np.asarray(test_labels).astype(\"float32\")"
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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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"colab_type": "text"
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},
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"source": [
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"### Building your model"
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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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"colab_type": "text"
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},
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"source": [
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"**Model definition**"
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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": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"from tensorflow import keras\n",
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"from tensorflow.keras import layers\n",
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"\n",
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"model = keras.Sequential([\n",
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" layers.Dense(16, activation=\"relu\"),\n",
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" layers.Dense(16, activation=\"relu\"),\n",
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" layers.Dense(1, activation=\"sigmoid\")\n",
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"])"
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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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"colab_type": "text"
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},
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"source": [
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"**Compiling the model**"
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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": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"model.compile(optimizer=\"rmsprop\",\n",
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" loss=\"binary_crossentropy\",\n",
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" metrics=[\"accuracy\"])"
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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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"colab_type": "text"
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},
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"source": [
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"### Validating your approach"
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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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"colab_type": "text"
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},
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"source": [
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"**Setting aside a validation set**"
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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": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"x_val = x_train[:10000]\n",
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"partial_x_train = x_train[10000:]\n",
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"y_val = y_train[:10000]\n",
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"partial_y_train = y_train[10000:]"
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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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"colab_type": "text"
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},
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"source": [
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"**Training your model**"
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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": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"history = model.fit(partial_x_train,\n",
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" partial_y_train,\n",
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" epochs=20,\n",
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" batch_size=512,\n",
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" validation_data=(x_val, y_val))"
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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": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"history_dict = history.history\n",
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"history_dict.keys()"
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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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"colab_type": "text"
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},
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"source": [
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"**Plotting the training and validation loss**"
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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": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"history_dict = history.history\n",
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"loss_values = history_dict[\"loss\"]\n",
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"val_loss_values = history_dict[\"val_loss\"]\n",
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"epochs = range(1, len(loss_values) + 1)\n",
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"plt.plot(epochs, loss_values, \"bo\", label=\"Training loss\")\n",
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"plt.plot(epochs, val_loss_values, \"b\", label=\"Validation loss\")\n",
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"plt.title(\"Training and validation loss\")\n",
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"plt.xlabel(\"Epochs\")\n",
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"plt.ylabel(\"Loss\")\n",
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"plt.legend()\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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"colab_type": "text"
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},
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"source": [
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"**Plotting the training and validation accuracy**"
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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": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"plt.clf()\n",
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"acc = history_dict[\"accuracy\"]\n",
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"val_acc = history_dict[\"val_accuracy\"]\n",
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"plt.plot(epochs, acc, \"bo\", label=\"Training acc\")\n",
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"plt.plot(epochs, val_acc, \"b\", label=\"Validation acc\")\n",
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"plt.title(\"Training and validation accuracy\")\n",
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"plt.xlabel(\"Epochs\")\n",
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"plt.ylabel(\"Accuracy\")\n",
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"plt.legend()\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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"colab_type": "text"
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},
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"source": [
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"**Retraining a model from scratch**"
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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": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"model = keras.Sequential([\n",
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" layers.Dense(16, activation=\"relu\"),\n",
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" layers.Dense(16, activation=\"relu\"),\n",
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" layers.Dense(1, activation=\"sigmoid\")\n",
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"])\n",
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"model.compile(optimizer=\"rmsprop\",\n",
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" loss=\"binary_crossentropy\",\n",
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" metrics=[\"accuracy\"])\n",
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"model.fit(x_train, y_train, epochs=4, batch_size=512)\n",
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"results = model.evaluate(x_test, y_test)"
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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": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"results"
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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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"colab_type": "text"
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},
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"source": [
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"### Using a trained model to generate predictions on new data"
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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": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"model.predict(x_test)"
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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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"colab_type": "text"
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},
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"source": [
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"### Further experiments"
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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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"colab_type": "text"
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},
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"source": [
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"### Wrapping up"
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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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"colab_type": "text"
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},
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"source": [
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"## Classifying newswires: A multiclass classification example"
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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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"colab_type": "text"
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},
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"source": [
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"### The Reuters dataset"
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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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"colab_type": "text"
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},
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"source": [
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"**Loading the Reuters dataset**"
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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": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"from tensorflow.keras.datasets import reuters\n",
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"(train_data, train_labels), (test_data, test_labels) = reuters.load_data(\n",
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" num_words=10000)"
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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": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"len(train_data)"
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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": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"len(test_data)"
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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": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"train_data[10]"
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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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"colab_type": "text"
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|
},
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"source": [
|
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"**Decoding newswires back to text**"
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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": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"word_index = reuters.get_word_index()\n",
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"reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])\n",
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"decoded_newswire = \" \".join([reverse_word_index.get(i - 3, \"?\") for i in\n",
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" train_data[0]])"
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]
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},
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{
|
|
"cell_type": "code",
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|
"execution_count": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"train_labels[10]"
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]
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},
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{
|
|
"cell_type": "markdown",
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|
"metadata": {
|
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"colab_type": "text"
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|
},
|
|
"source": [
|
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"### Preparing the data"
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]
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},
|
|
{
|
|
"cell_type": "markdown",
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|
"metadata": {
|
|
"colab_type": "text"
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|
},
|
|
"source": [
|
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"**Encoding the input data**"
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|
]
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|
},
|
|
{
|
|
"cell_type": "code",
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|
"execution_count": 0,
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"metadata": {
|
|
"colab_type": "code"
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|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"x_train = vectorize_sequences(train_data)\n",
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|
"x_test = vectorize_sequences(test_data)"
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|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
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|
},
|
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"source": [
|
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"**Encoding the labels**"
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|
]
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},
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{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def to_one_hot(labels, dimension=46):\n",
|
|
" results = np.zeros((len(labels), dimension))\n",
|
|
" for i, label in enumerate(labels):\n",
|
|
" results[i, label] = 1.\n",
|
|
" return results\n",
|
|
"y_train = to_one_hot(train_labels)\n",
|
|
"y_test = to_one_hot(test_labels)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"from tensorflow.keras.utils import to_categorical\n",
|
|
"y_train = to_categorical(train_labels)\n",
|
|
"y_test = to_categorical(test_labels)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"### Building your model"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"**Model definition**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"model = keras.Sequential([\n",
|
|
" layers.Dense(64, activation=\"relu\"),\n",
|
|
" layers.Dense(64, activation=\"relu\"),\n",
|
|
" layers.Dense(46, activation=\"softmax\")\n",
|
|
"])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"**Compiling the model**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"model.compile(optimizer=\"rmsprop\",\n",
|
|
" loss=\"categorical_crossentropy\",\n",
|
|
" metrics=[\"accuracy\"])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"### Validating your approach"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"**Setting aside a validation set**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"x_val = x_train[:1000]\n",
|
|
"partial_x_train = x_train[1000:]\n",
|
|
"y_val = y_train[:1000]\n",
|
|
"partial_y_train = y_train[1000:]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"**Training the model**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"history = model.fit(partial_x_train,\n",
|
|
" partial_y_train,\n",
|
|
" epochs=20,\n",
|
|
" batch_size=512,\n",
|
|
" validation_data=(x_val, y_val))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"**Plotting the training and validation loss**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"loss = history.history[\"loss\"]\n",
|
|
"val_loss = history.history[\"val_loss\"]\n",
|
|
"epochs = range(1, len(loss) + 1)\n",
|
|
"plt.plot(epochs, loss, \"bo\", label=\"Training loss\")\n",
|
|
"plt.plot(epochs, val_loss, \"b\", label=\"Validation loss\")\n",
|
|
"plt.title(\"Training and validation loss\")\n",
|
|
"plt.xlabel(\"Epochs\")\n",
|
|
"plt.ylabel(\"Loss\")\n",
|
|
"plt.legend()\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"**Plotting the training and validation accuracy**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"plt.clf()\n",
|
|
"acc = history.history[\"accuracy\"]\n",
|
|
"val_acc = history.history[\"val_accuracy\"]\n",
|
|
"plt.plot(epochs, acc, \"bo\", label=\"Training accuracy\")\n",
|
|
"plt.plot(epochs, val_acc, \"b\", label=\"Validation accuracy\")\n",
|
|
"plt.title(\"Training and validation accuracy\")\n",
|
|
"plt.xlabel(\"Epochs\")\n",
|
|
"plt.ylabel(\"Accuracy\")\n",
|
|
"plt.legend()\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"**Retraining a model from scratch**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"model = keras.Sequential([\n",
|
|
" layers.Dense(64, activation=\"relu\"),\n",
|
|
" layers.Dense(64, activation=\"relu\"),\n",
|
|
" layers.Dense(46, activation=\"softmax\")\n",
|
|
"])\n",
|
|
"model.compile(optimizer=\"rmsprop\",\n",
|
|
" loss=\"categorical_crossentropy\",\n",
|
|
" metrics=[\"accuracy\"])\n",
|
|
"model.fit(x_train,\n",
|
|
" y_train,\n",
|
|
" epochs=9,\n",
|
|
" batch_size=512)\n",
|
|
"results = model.evaluate(x_test, y_test)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"results"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"import copy\n",
|
|
"test_labels_copy = copy.copy(test_labels)\n",
|
|
"np.random.shuffle(test_labels_copy)\n",
|
|
"hits_array = np.array(test_labels) == np.array(test_labels_copy)\n",
|
|
"hits_array.mean()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"### Generating predictions on new data"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"predictions = model.predict(x_test)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"predictions[0].shape"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"np.sum(predictions[0])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"np.argmax(predictions[0])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"### A different way to handle the labels and the loss"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"y_train = np.array(train_labels)\n",
|
|
"y_test = np.array(test_labels)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"model.compile(optimizer=\"rmsprop\",\n",
|
|
" loss=\"sparse_categorical_crossentropy\",\n",
|
|
" metrics=[\"accuracy\"])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"### The importance of having sufficiently large intermediate layers"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"**A model with an information bottleneck**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"model = keras.Sequential([\n",
|
|
" layers.Dense(64, activation=\"relu\"),\n",
|
|
" layers.Dense(4, activation=\"relu\"),\n",
|
|
" layers.Dense(46, activation=\"softmax\")\n",
|
|
"])\n",
|
|
"model.compile(optimizer=\"rmsprop\",\n",
|
|
" loss=\"categorical_crossentropy\",\n",
|
|
" metrics=[\"accuracy\"])\n",
|
|
"model.fit(partial_x_train,\n",
|
|
" partial_y_train,\n",
|
|
" epochs=20,\n",
|
|
" batch_size=128,\n",
|
|
" validation_data=(x_val, y_val))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"### Further experiments"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"### Wrapping up"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"## Predicting house prices: A regression example"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"### The Boston Housing Price dataset"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"**Loading the Boston housing dataset**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"from tensorflow.keras.datasets import boston_housing\n",
|
|
"(train_data, train_targets), (test_data, test_targets) = boston_housing.load_data()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"train_data.shape"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"test_data.shape"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"train_targets"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"### Preparing the data"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"**Normalizing the data**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"mean = train_data.mean(axis=0)\n",
|
|
"train_data -= mean\n",
|
|
"std = train_data.std(axis=0)\n",
|
|
"train_data /= std\n",
|
|
"test_data -= mean\n",
|
|
"test_data /= std"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"### Building your model"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"**Model definition**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def build_model():\n",
|
|
" model = keras.Sequential([\n",
|
|
" layers.Dense(64, activation=\"relu\"),\n",
|
|
" layers.Dense(64, activation=\"relu\"),\n",
|
|
" layers.Dense(1)\n",
|
|
" ])\n",
|
|
" model.compile(optimizer=\"rmsprop\", loss=\"mse\", metrics=[\"mae\"])\n",
|
|
" return model"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"### Validating your approach using K-fold validation"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"**K-fold validation**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"k = 4\n",
|
|
"num_val_samples = len(train_data) // k\n",
|
|
"num_epochs = 100\n",
|
|
"all_scores = []\n",
|
|
"for i in range(k):\n",
|
|
" print(f\"Processing fold #{i}\")\n",
|
|
" val_data = train_data[i * num_val_samples: (i + 1) * num_val_samples]\n",
|
|
" val_targets = train_targets[i * num_val_samples: (i + 1) * num_val_samples]\n",
|
|
" partial_train_data = np.concatenate(\n",
|
|
" [train_data[:i * num_val_samples],\n",
|
|
" train_data[(i + 1) * num_val_samples:]],\n",
|
|
" axis=0)\n",
|
|
" partial_train_targets = np.concatenate(\n",
|
|
" [train_targets[:i * num_val_samples],\n",
|
|
" train_targets[(i + 1) * num_val_samples:]],\n",
|
|
" axis=0)\n",
|
|
" model = build_model()\n",
|
|
" model.fit(partial_train_data, partial_train_targets,\n",
|
|
" epochs=num_epochs, batch_size=16, verbose=0)\n",
|
|
" val_mse, val_mae = model.evaluate(val_data, val_targets, verbose=0)\n",
|
|
" all_scores.append(val_mae)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"all_scores"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"np.mean(all_scores)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"**Saving the validation logs at each fold**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"num_epochs = 500\n",
|
|
"all_mae_histories = []\n",
|
|
"for i in range(k):\n",
|
|
" print(f\"Processing fold #{i}\")\n",
|
|
" val_data = train_data[i * num_val_samples: (i + 1) * num_val_samples]\n",
|
|
" val_targets = train_targets[i * num_val_samples: (i + 1) * num_val_samples]\n",
|
|
" partial_train_data = np.concatenate(\n",
|
|
" [train_data[:i * num_val_samples],\n",
|
|
" train_data[(i + 1) * num_val_samples:]],\n",
|
|
" axis=0)\n",
|
|
" partial_train_targets = np.concatenate(\n",
|
|
" [train_targets[:i * num_val_samples],\n",
|
|
" train_targets[(i + 1) * num_val_samples:]],\n",
|
|
" axis=0)\n",
|
|
" model = build_model()\n",
|
|
" history = model.fit(partial_train_data, partial_train_targets,\n",
|
|
" validation_data=(val_data, val_targets),\n",
|
|
" epochs=num_epochs, batch_size=16, verbose=0)\n",
|
|
" mae_history = history.history[\"val_mae\"]\n",
|
|
" all_mae_histories.append(mae_history)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"**Building the history of successive mean K-fold validation scores**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"average_mae_history = [\n",
|
|
" np.mean([x[i] for x in all_mae_histories]) for i in range(num_epochs)]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"**Plotting validation scores**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"plt.plot(range(1, len(average_mae_history) + 1), average_mae_history)\n",
|
|
"plt.xlabel(\"Epochs\")\n",
|
|
"plt.ylabel(\"Validation MAE\")\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"**Plotting validation scores, excluding the first 10 data points**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"truncated_mae_history = average_mae_history[10:]\n",
|
|
"plt.plot(range(1, len(truncated_mae_history) + 1), truncated_mae_history)\n",
|
|
"plt.xlabel(\"Epochs\")\n",
|
|
"plt.ylabel(\"Validation MAE\")\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"**Training the final model**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"model = build_model()\n",
|
|
"model.fit(train_data, train_targets,\n",
|
|
" epochs=130, batch_size=16, verbose=0)\n",
|
|
"test_mse_score, test_mae_score = model.evaluate(test_data, test_targets)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"test_mae_score"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"### Generating predictions on new data"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"predictions = model.predict(test_data)\n",
|
|
"predictions[0]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"### Wrapping up"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"## Summary"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"colab": {
|
|
"collapsed_sections": [],
|
|
"name": "chapter04_getting-started-with-neural-networks.i",
|
|
"private_outputs": false,
|
|
"provenance": [],
|
|
"toc_visible": true
|
|
},
|
|
"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": "3.7.0"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 0
|
|
} |