Replace test set with validation set in code example from page 269
Aurélien Geron
parent
7d7ccce9d3
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72a747ed74
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@ -2,40 +2,28 @@
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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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"deletable": true,
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"editable": true
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},
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"metadata": {},
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"source": [
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"**Chapter 10 – Introduction to Artificial 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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"deletable": true,
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"editable": true
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},
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"metadata": {},
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"source": [
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"_This notebook contains all the sample code and solutions to the exercises in chapter 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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"deletable": true,
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"editable": true
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},
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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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"deletable": true,
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"editable": true
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},
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"metadata": {},
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"source": [
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"First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:"
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]
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@ -43,11 +31,7 @@
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"# To support both python 2 and python 3\n",
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@ -85,10 +69,7 @@
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"deletable": true,
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"editable": true
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},
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"metadata": {},
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"source": [
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"# Perceptrons"
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]
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@ -97,9 +78,7 @@
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": true,
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"deletable": true,
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"editable": true
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"collapsed": true
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},
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"outputs": [],
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"source": [
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@ -120,11 +99,7 @@
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"y_pred"
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@ -133,11 +108,7 @@
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"a = -per_clf.coef_[0][0] / per_clf.coef_[0][1]\n",
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@ -173,10 +144,7 @@
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"deletable": true,
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"editable": true
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},
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"metadata": {},
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"source": [
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"# Activation functions"
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]
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@ -185,9 +153,7 @@
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"collapsed": true,
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"deletable": true,
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"editable": true
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"collapsed": true
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},
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"outputs": [],
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"source": [
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@ -204,11 +170,7 @@
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"z = np.linspace(-5, 5, 200)\n",
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@ -245,9 +207,7 @@
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"collapsed": true,
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"deletable": true,
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"editable": true
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"collapsed": true
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},
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"outputs": [],
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"source": [
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@ -264,11 +224,7 @@
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"x1s = np.linspace(-0.2, 1.2, 100)\n",
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@ -297,20 +253,14 @@
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"deletable": true,
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"editable": true
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},
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"metadata": {},
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"source": [
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"# FNN for MNIST"
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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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"deletable": true,
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"editable": true
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},
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"metadata": {},
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"source": [
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"## using tf.learn"
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]
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@ -318,11 +268,7 @@
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"from tensorflow.examples.tutorials.mnist import input_data\n",
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@ -334,9 +280,7 @@
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {
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"collapsed": true,
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"deletable": true,
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"editable": true
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"collapsed": true
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},
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"outputs": [],
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"source": [
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@ -349,11 +293,7 @@
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"import tensorflow as tf\n",
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@ -370,11 +310,7 @@
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.metrics import accuracy_score\n",
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@ -386,11 +322,7 @@
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.metrics import log_loss\n",
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@ -402,9 +334,7 @@
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": true,
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"deletable": true,
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"editable": true
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"collapsed": true
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},
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"source": [
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"## Using plain TensorFlow"
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@ -413,11 +343,7 @@
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"import tensorflow as tf\n",
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@ -431,11 +357,7 @@
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"reset_graph()\n",
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@ -447,11 +369,7 @@
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {
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"collapsed": true,
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"deletable": true,
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"editable": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"def neuron_layer(X, n_neurons, name, activation=None):\n",
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@ -471,11 +389,7 @@
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"with tf.name_scope(\"dnn\"):\n",
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@ -489,11 +403,7 @@
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {
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"collapsed": true,
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"deletable": true,
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"editable": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"with tf.name_scope(\"loss\"):\n",
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@ -505,11 +415,7 @@
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {
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"collapsed": true,
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"deletable": true,
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"editable": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"learning_rate = 0.01\n",
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@ -522,11 +428,7 @@
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {
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"collapsed": true,
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"deletable": true,
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"editable": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"with tf.name_scope(\"eval\"):\n",
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@ -537,11 +439,7 @@
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{
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {
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"collapsed": true,
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"deletable": true,
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"editable": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"init = tf.global_variables_initializer()\n",
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@ -551,11 +449,7 @@
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{
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {
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"collapsed": true,
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"deletable": true,
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"editable": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"n_epochs = 40\n",
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@ -565,11 +459,7 @@
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{
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"cell_type": "code",
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"execution_count": 23,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"with tf.Session() as sess:\n",
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@ -579,9 +469,9 @@
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" X_batch, y_batch = mnist.train.next_batch(batch_size)\n",
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" sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n",
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" acc_train = accuracy.eval(feed_dict={X: X_batch, y: y_batch})\n",
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" acc_test = accuracy.eval(feed_dict={X: mnist.test.images,\n",
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" y: mnist.test.labels})\n",
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" print(epoch, \"Train accuracy:\", acc_train, \"Test accuracy:\", acc_test)\n",
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" acc_val = accuracy.eval(feed_dict={X: mnist.validation.images,\n",
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" y: mnist.validation.labels})\n",
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" print(epoch, \"Train accuracy:\", acc_train, \"Val accuracy:\", acc_val)\n",
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"\n",
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" save_path = saver.save(sess, \"./my_model_final.ckpt\")"
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]
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@ -589,11 +479,7 @@
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{
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"cell_type": "code",
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"execution_count": 24,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"with tf.Session() as sess:\n",
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@ -606,11 +492,7 @@
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{
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"cell_type": "code",
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"execution_count": 25,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"print(\"Predicted classes:\", y_pred)\n",
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@ -621,9 +503,7 @@
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"cell_type": "code",
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"execution_count": 26,
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"metadata": {
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"collapsed": true,
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"deletable": true,
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"editable": true
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"collapsed": true
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},
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"outputs": [],
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"source": [
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{
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"cell_type": "code",
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"execution_count": 27,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"show_graph(tf.get_default_graph())"
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@ -680,20 +556,14 @@
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"deletable": true,
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"editable": true
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},
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"metadata": {},
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"source": [
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"## Using `dense()` instead of `neuron_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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"deletable": true,
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"editable": true
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},
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"metadata": {},
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"source": [
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"Note: the book uses `tensorflow.contrib.layers.fully_connected()` rather than `tf.layers.dense()` (which did not exist when this chapter was written). It is now preferable to use `tf.layers.dense()`, because anything in the contrib module may change or be deleted without notice. The `dense()` function is almost identical to the `fully_connected()` function, except for a few minor differences:\n",
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"* several parameters are renamed: `scope` becomes `name`, `activation_fn` becomes `activation` (and similarly the `_fn` suffix is removed from other parameters such as `normalizer_fn`), `weights_initializer` becomes `kernel_initializer`, etc.\n",
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@ -704,11 +574,7 @@
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{
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"cell_type": "code",
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"execution_count": 28,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"n_inputs = 28*28 # MNIST\n",
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@ -721,9 +587,7 @@
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"cell_type": "code",
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"execution_count": 29,
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"metadata": {
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"collapsed": true,
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"deletable": true,
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"editable": true
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"collapsed": true
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},
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"outputs": [],
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"source": [
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@ -736,11 +600,7 @@
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{
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"cell_type": "code",
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"execution_count": 30,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"with tf.name_scope(\"dnn\"):\n",
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@ -755,9 +615,7 @@
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"cell_type": "code",
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"execution_count": 31,
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"metadata": {
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"collapsed": true,
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"deletable": true,
|
||||
"editable": true
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -770,9 +628,7 @@
|
|||
"cell_type": "code",
|
||||
"execution_count": 32,
|
||||
"metadata": {
|
||||
"collapsed": true,
|
||||
"deletable": true,
|
||||
"editable": true
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -787,9 +643,7 @@
|
|||
"cell_type": "code",
|
||||
"execution_count": 33,
|
||||
"metadata": {
|
||||
"collapsed": true,
|
||||
"deletable": true,
|
||||
"editable": true
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -802,9 +656,7 @@
|
|||
"cell_type": "code",
|
||||
"execution_count": 34,
|
||||
"metadata": {
|
||||
"collapsed": true,
|
||||
"deletable": true,
|
||||
"editable": true
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -815,11 +667,7 @@
|
|||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 35,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"deletable": true,
|
||||
"editable": true
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"n_epochs = 20\n",
|
||||
|
|
@ -841,11 +689,7 @@
|
|||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 36,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"deletable": true,
|
||||
"editable": true
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"show_graph(tf.get_default_graph())"
|
||||
|
|
@ -854,9 +698,7 @@
|
|||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"collapsed": true,
|
||||
"deletable": true,
|
||||
"editable": true
|
||||
"collapsed": true
|
||||
},
|
||||
"source": [
|
||||
"# Exercise solutions"
|
||||
|
|
@ -864,10 +706,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"deletable": true,
|
||||
"editable": true
|
||||
},
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 1. to 8."
|
||||
]
|
||||
|
|
@ -875,9 +714,7 @@
|
|||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"collapsed": true,
|
||||
"deletable": true,
|
||||
"editable": true
|
||||
"collapsed": true
|
||||
},
|
||||
"source": [
|
||||
"See appendix A."
|
||||
|
|
@ -885,30 +722,21 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"deletable": true,
|
||||
"editable": true
|
||||
},
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 9."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"deletable": true,
|
||||
"editable": true
|
||||
},
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"_Train a deep MLP on the MNIST dataset and see if you can get over 98% precision. Just like in the last exercise of chapter 9, try adding all the bells and whistles (i.e., save checkpoints, restore the last checkpoint in case of an interruption, add summaries, plot learning curves using TensorBoard, and so on)._"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"deletable": true,
|
||||
"editable": true
|
||||
},
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"First let's create the deep net. It's exactly the same as earlier, with just one addition: we add a `tf.summary.scalar()` to track the loss and the accuracy during training, so we can view nice learning curves using TensorBoard."
|
||||
]
|
||||
|
|
@ -916,11 +744,7 @@
|
|||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 37,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"deletable": true,
|
||||
"editable": true
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"n_inputs = 28*28 # MNIST\n",
|
||||
|
|
@ -933,9 +757,7 @@
|
|||
"cell_type": "code",
|
||||
"execution_count": 38,
|
||||
"metadata": {
|
||||
"collapsed": true,
|
||||
"deletable": true,
|
||||
"editable": true
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -948,11 +770,7 @@
|
|||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 39,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"deletable": true,
|
||||
"editable": true
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"with tf.name_scope(\"dnn\"):\n",
|
||||
|
|
@ -967,9 +785,7 @@
|
|||
"cell_type": "code",
|
||||
"execution_count": 40,
|
||||
"metadata": {
|
||||
"collapsed": true,
|
||||
"deletable": true,
|
||||
"editable": true
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -983,9 +799,7 @@
|
|||
"cell_type": "code",
|
||||
"execution_count": 41,
|
||||
"metadata": {
|
||||
"collapsed": true,
|
||||
"deletable": true,
|
||||
"editable": true
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1000,9 +814,7 @@
|
|||
"cell_type": "code",
|
||||
"execution_count": 42,
|
||||
"metadata": {
|
||||
"collapsed": true,
|
||||
"deletable": true,
|
||||
"editable": true
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1016,9 +828,7 @@
|
|||
"cell_type": "code",
|
||||
"execution_count": 43,
|
||||
"metadata": {
|
||||
"collapsed": true,
|
||||
"deletable": true,
|
||||
"editable": true
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1028,10 +838,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"deletable": true,
|
||||
"editable": true
|
||||
},
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Now we need to define the directory to write the TensorBoard logs to:"
|
||||
]
|
||||
|
|
@ -1040,9 +847,7 @@
|
|||
"cell_type": "code",
|
||||
"execution_count": 44,
|
||||
"metadata": {
|
||||
"collapsed": true,
|
||||
"deletable": true,
|
||||
"editable": true
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1061,9 +866,7 @@
|
|||
"cell_type": "code",
|
||||
"execution_count": 45,
|
||||
"metadata": {
|
||||
"collapsed": true,
|
||||
"deletable": true,
|
||||
"editable": true
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1072,10 +875,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"deletable": true,
|
||||
"editable": true
|
||||
},
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Now we can create the `FileWriter` that we will use to write the TensorBoard logs:"
|
||||
]
|
||||
|
|
@ -1084,9 +884,7 @@
|
|||
"cell_type": "code",
|
||||
"execution_count": 46,
|
||||
"metadata": {
|
||||
"collapsed": true,
|
||||
"deletable": true,
|
||||
"editable": true
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1095,10 +893,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"deletable": true,
|
||||
"editable": true
|
||||
},
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Hey! Why don't we implement early stopping? For this, we are going to need a validation set. Luckily, the dataset returned by TensorFlow's `input_data()` function (see above) is already split into a training set (60,000 instances, already shuffled for us), a validation set (5,000 instances) and a test set (5,000 instances). So we can easily define `X_valid` and `y_valid`:"
|
||||
]
|
||||
|
|
@ -1106,11 +901,7 @@
|
|||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 47,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"deletable": true,
|
||||
"editable": true
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"X_valid = mnist.validation.images\n",
|
||||
|
|
@ -1121,9 +912,7 @@
|
|||
"cell_type": "code",
|
||||
"execution_count": 48,
|
||||
"metadata": {
|
||||
"collapsed": true,
|
||||
"deletable": true,
|
||||
"editable": true
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1133,11 +922,7 @@
|
|||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 49,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"deletable": true,
|
||||
"editable": true
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"n_epochs = 10001\n",
|
||||
|
|
@ -1190,11 +975,7 @@
|
|||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 50,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"deletable": true,
|
||||
"editable": true
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"os.remove(checkpoint_epoch_path)"
|
||||
|
|
@ -1203,11 +984,7 @@
|
|||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 51,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"deletable": true,
|
||||
"editable": true
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"with tf.Session() as sess:\n",
|
||||
|
|
@ -1218,11 +995,7 @@
|
|||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 52,
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"deletable": true,
|
||||
"editable": true
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"accuracy_val"
|
||||
|
|
@ -1232,9 +1005,7 @@
|
|||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true,
|
||||
"deletable": true,
|
||||
"editable": true
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
|
|
@ -1256,7 +1027,7 @@
|
|||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.5.3"
|
||||
"version": "3.6.2"
|
||||
},
|
||||
"nav_menu": {
|
||||
"height": "264px",
|
||||
|
|
@ -1273,5 +1044,5 @@
|
|||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
"nbformat_minor": 1
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in New Issue