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Replace test set with validation set in code example from page 269

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Aurélien Geron 2017-10-27 12:58:21 +02:00
parent 7d7ccce9d3
commit 72a747ed74
1 changed files with 78 additions and 307 deletions
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10_introduction_to_artificial_neural_networks.ipynb
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@ -2,40 +2,28 @@
"cells": [ "cells": [
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {},
"deletable": true,
"editable": true
},
"source": [ "source": [
"**Chapter 10 – Introduction to Artificial Neural Networks**" "**Chapter 10 – Introduction to Artificial Neural Networks**"
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {},
"deletable": true,
"editable": true
},
"source": [ "source": [
"_This notebook contains all the sample code and solutions to the exercises in chapter 10._" "_This notebook contains all the sample code and solutions to the exercises in chapter 10._"
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {},
"deletable": true,
"editable": true
},
"source": [ "source": [
"# Setup" "# Setup"
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {},
"deletable": true,
"editable": true
},
"source": [ "source": [
"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:" "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:"
] ]
@ -43,11 +31,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 1, "execution_count": 1,
"metadata": { "metadata": {},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"# To support both python 2 and python 3\n", "# To support both python 2 and python 3\n",
@ -85,10 +69,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {},
"deletable": true,
"editable": true
},
"source": [ "source": [
"# Perceptrons" "# Perceptrons"
] ]
@ -97,9 +78,7 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": 2,
"metadata": { "metadata": {
"collapsed": true, "collapsed": true
"deletable": true,
"editable": true
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
@ -120,11 +99,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 3,
"metadata": { "metadata": {},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"y_pred" "y_pred"
@ -133,11 +108,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 4,
"metadata": { "metadata": {},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"a = -per_clf.coef_[0][0] / per_clf.coef_[0][1]\n", "a = -per_clf.coef_[0][0] / per_clf.coef_[0][1]\n",
@ -173,10 +144,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {},
"deletable": true,
"editable": true
},
"source": [ "source": [
"# Activation functions" "# Activation functions"
] ]
@ -185,9 +153,7 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 5,
"metadata": { "metadata": {
"collapsed": true, "collapsed": true
"deletable": true,
"editable": true
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
@ -204,11 +170,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": 6,
"metadata": { "metadata": {},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"z = np.linspace(-5, 5, 200)\n", "z = np.linspace(-5, 5, 200)\n",
@ -245,9 +207,7 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 7,
"metadata": { "metadata": {
"collapsed": true, "collapsed": true
"deletable": true,
"editable": true
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
@ -264,11 +224,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": 8,
"metadata": { "metadata": {},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"x1s = np.linspace(-0.2, 1.2, 100)\n", "x1s = np.linspace(-0.2, 1.2, 100)\n",
@ -297,20 +253,14 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {},
"deletable": true,
"editable": true
},
"source": [ "source": [
"# FNN for MNIST" "# FNN for MNIST"
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {},
"deletable": true,
"editable": true
},
"source": [ "source": [
"## using tf.learn" "## using tf.learn"
] ]
@ -318,11 +268,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 9, "execution_count": 9,
"metadata": { "metadata": {},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"from tensorflow.examples.tutorials.mnist import input_data\n", "from tensorflow.examples.tutorials.mnist import input_data\n",
@ -334,9 +280,7 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 10, "execution_count": 10,
"metadata": { "metadata": {
"collapsed": true, "collapsed": true
"deletable": true,
"editable": true
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
@ -349,11 +293,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 11, "execution_count": 11,
"metadata": { "metadata": {},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"import tensorflow as tf\n", "import tensorflow as tf\n",
@ -370,11 +310,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 12, "execution_count": 12,
"metadata": { "metadata": {},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"from sklearn.metrics import accuracy_score\n", "from sklearn.metrics import accuracy_score\n",
@ -386,11 +322,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 13, "execution_count": 13,
"metadata": { "metadata": {},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"from sklearn.metrics import log_loss\n", "from sklearn.metrics import log_loss\n",
@ -402,9 +334,7 @@
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"collapsed": true, "collapsed": true
"deletable": true,
"editable": true
}, },
"source": [ "source": [
"## Using plain TensorFlow" "## Using plain TensorFlow"
@ -413,11 +343,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 14, "execution_count": 14,
"metadata": { "metadata": {},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"import tensorflow as tf\n", "import tensorflow as tf\n",
@ -431,11 +357,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 15, "execution_count": 15,
"metadata": { "metadata": {},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"reset_graph()\n", "reset_graph()\n",
@ -447,11 +369,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 16, "execution_count": 16,
"metadata": { "metadata": {},
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"def neuron_layer(X, n_neurons, name, activation=None):\n", "def neuron_layer(X, n_neurons, name, activation=None):\n",
@ -471,11 +389,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 17, "execution_count": 17,
"metadata": { "metadata": {},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"with tf.name_scope(\"dnn\"):\n", "with tf.name_scope(\"dnn\"):\n",
@ -489,11 +403,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 18, "execution_count": 18,
"metadata": { "metadata": {},
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"with tf.name_scope(\"loss\"):\n", "with tf.name_scope(\"loss\"):\n",
@ -505,11 +415,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 19, "execution_count": 19,
"metadata": { "metadata": {},
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"learning_rate = 0.01\n", "learning_rate = 0.01\n",
@ -522,11 +428,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 20, "execution_count": 20,
"metadata": { "metadata": {},
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"with tf.name_scope(\"eval\"):\n", "with tf.name_scope(\"eval\"):\n",
@ -537,11 +439,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 21, "execution_count": 21,
"metadata": { "metadata": {},
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"init = tf.global_variables_initializer()\n", "init = tf.global_variables_initializer()\n",
@ -551,11 +449,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 22, "execution_count": 22,
"metadata": { "metadata": {},
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"n_epochs = 40\n", "n_epochs = 40\n",
@ -565,11 +459,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 23, "execution_count": 23,
"metadata": { "metadata": {},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"with tf.Session() as sess:\n", "with tf.Session() as sess:\n",
@ -579,9 +469,9 @@
" X_batch, y_batch = mnist.train.next_batch(batch_size)\n", " X_batch, y_batch = mnist.train.next_batch(batch_size)\n",
" sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n", " sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n",
" acc_train = accuracy.eval(feed_dict={X: X_batch, y: y_batch})\n", " acc_train = accuracy.eval(feed_dict={X: X_batch, y: y_batch})\n",
" acc_test = accuracy.eval(feed_dict={X: mnist.test.images,\n", " acc_val = accuracy.eval(feed_dict={X: mnist.validation.images,\n",
" y: mnist.test.labels})\n", " y: mnist.validation.labels})\n",
" print(epoch, \"Train accuracy:\", acc_train, \"Test accuracy:\", acc_test)\n", " print(epoch, \"Train accuracy:\", acc_train, \"Val accuracy:\", acc_val)\n",
"\n", "\n",
" save_path = saver.save(sess, \"./my_model_final.ckpt\")" " save_path = saver.save(sess, \"./my_model_final.ckpt\")"
] ]
@ -589,11 +479,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 24, "execution_count": 24,
"metadata": { "metadata": {},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"with tf.Session() as sess:\n", "with tf.Session() as sess:\n",
@ -606,11 +492,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 25, "execution_count": 25,
"metadata": { "metadata": {},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"print(\"Predicted classes:\", y_pred)\n", "print(\"Predicted classes:\", y_pred)\n",
@ -621,9 +503,7 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 26, "execution_count": 26,
"metadata": { "metadata": {
"collapsed": true, "collapsed": true
"deletable": true,
"editable": true
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
@ -668,11 +548,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 27, "execution_count": 27,
"metadata": { "metadata": {},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"show_graph(tf.get_default_graph())" "show_graph(tf.get_default_graph())"
@ -680,20 +556,14 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {},
"deletable": true,
"editable": true
},
"source": [ "source": [
"## Using `dense()` instead of `neuron_layer()`" "## Using `dense()` instead of `neuron_layer()`"
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {},
"deletable": true,
"editable": true
},
"source": [ "source": [
"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", "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",
"* 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", "* 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",
@ -704,11 +574,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 28, "execution_count": 28,
"metadata": { "metadata": {},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"n_inputs = 28*28 # MNIST\n", "n_inputs = 28*28 # MNIST\n",
@ -721,9 +587,7 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 29, "execution_count": 29,
"metadata": { "metadata": {
"collapsed": true, "collapsed": true
"deletable": true,
"editable": true
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
@ -736,11 +600,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 30, "execution_count": 30,
"metadata": { "metadata": {},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"with tf.name_scope(\"dnn\"):\n", "with tf.name_scope(\"dnn\"):\n",
@ -755,9 +615,7 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 31, "execution_count": 31,
"metadata": { "metadata": {
"collapsed": true, "collapsed": true
"deletable": true,
"editable": true
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
@ -770,9 +628,7 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 32, "execution_count": 32,
"metadata": { "metadata": {
"collapsed": true, "collapsed": true
"deletable": true,
"editable": true
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
@ -787,9 +643,7 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 33, "execution_count": 33,
"metadata": { "metadata": {
"collapsed": true, "collapsed": true
"deletable": true,
"editable": true
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
@ -802,9 +656,7 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 34, "execution_count": 34,
"metadata": { "metadata": {
"collapsed": true, "collapsed": true
"deletable": true,
"editable": true
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
@ -815,11 +667,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 35, "execution_count": 35,
"metadata": { "metadata": {},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"n_epochs = 20\n", "n_epochs = 20\n",
@ -841,11 +689,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 36, "execution_count": 36,
"metadata": { "metadata": {},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"show_graph(tf.get_default_graph())" "show_graph(tf.get_default_graph())"
@ -854,9 +698,7 @@
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"collapsed": true, "collapsed": true
"deletable": true,
"editable": true
}, },
"source": [ "source": [
"# Exercise solutions" "# Exercise solutions"
@ -864,10 +706,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {},
"deletable": true,
"editable": true
},
"source": [ "source": [
"## 1. to 8." "## 1. to 8."
] ]
@ -875,9 +714,7 @@
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"collapsed": true, "collapsed": true
"deletable": true,
"editable": true
}, },
"source": [ "source": [
"See appendix A." "See appendix A."
@ -885,30 +722,21 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {},
"deletable": true,
"editable": true
},
"source": [ "source": [
"## 9." "## 9."
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {},
"deletable": true,
"editable": true
},
"source": [ "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)._" "_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", "cell_type": "markdown",
"metadata": { "metadata": {},
"deletable": true,
"editable": true
},
"source": [ "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." "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", "cell_type": "code",
"execution_count": 37, "execution_count": 37,
"metadata": { "metadata": {},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"n_inputs = 28*28 # MNIST\n", "n_inputs = 28*28 # MNIST\n",
@ -933,9 +757,7 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 38, "execution_count": 38,
"metadata": { "metadata": {
"collapsed": true, "collapsed": true
"deletable": true,
"editable": true
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
@ -948,11 +770,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 39, "execution_count": 39,
"metadata": { "metadata": {},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"with tf.name_scope(\"dnn\"):\n", "with tf.name_scope(\"dnn\"):\n",
@ -967,9 +785,7 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 40, "execution_count": 40,
"metadata": { "metadata": {
"collapsed": true, "collapsed": true
"deletable": true,
"editable": true
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
@ -983,9 +799,7 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 41, "execution_count": 41,
"metadata": { "metadata": {
"collapsed": true, "collapsed": true
"deletable": true,
"editable": true
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1000,9 +814,7 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 42, "execution_count": 42,
"metadata": { "metadata": {
"collapsed": true, "collapsed": true
"deletable": true,
"editable": true
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1016,9 +828,7 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 43, "execution_count": 43,
"metadata": { "metadata": {
"collapsed": true, "collapsed": true
"deletable": true,
"editable": true
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1028,10 +838,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {},
"deletable": true,
"editable": true
},
"source": [ "source": [
"Now we need to define the directory to write the TensorBoard logs to:" "Now we need to define the directory to write the TensorBoard logs to:"
] ]
@ -1040,9 +847,7 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 44, "execution_count": 44,
"metadata": { "metadata": {
"collapsed": true, "collapsed": true
"deletable": true,
"editable": true
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1061,9 +866,7 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 45, "execution_count": 45,
"metadata": { "metadata": {
"collapsed": true, "collapsed": true
"deletable": true,
"editable": true
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1072,10 +875,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {},
"deletable": true,
"editable": true
},
"source": [ "source": [
"Now we can create the `FileWriter` that we will use to write the TensorBoard logs:" "Now we can create the `FileWriter` that we will use to write the TensorBoard logs:"
] ]
@ -1084,9 +884,7 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 46, "execution_count": 46,
"metadata": { "metadata": {
"collapsed": true, "collapsed": true
"deletable": true,
"editable": true
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1095,10 +893,7 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {},
"deletable": true,
"editable": true
},
"source": [ "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`:" "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", "cell_type": "code",
"execution_count": 47, "execution_count": 47,
"metadata": { "metadata": {},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"X_valid = mnist.validation.images\n", "X_valid = mnist.validation.images\n",
@ -1121,9 +912,7 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 48, "execution_count": 48,
"metadata": { "metadata": {
"collapsed": true, "collapsed": true
"deletable": true,
"editable": true
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1133,11 +922,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 49, "execution_count": 49,
"metadata": { "metadata": {},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"n_epochs = 10001\n", "n_epochs = 10001\n",
@ -1190,11 +975,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 50, "execution_count": 50,
"metadata": { "metadata": {},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"os.remove(checkpoint_epoch_path)" "os.remove(checkpoint_epoch_path)"
@ -1203,11 +984,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 51, "execution_count": 51,
"metadata": { "metadata": {},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"with tf.Session() as sess:\n", "with tf.Session() as sess:\n",
@ -1218,11 +995,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 52, "execution_count": 52,
"metadata": { "metadata": {},
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"accuracy_val" "accuracy_val"
@ -1232,9 +1005,7 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"collapsed": true, "collapsed": true
"deletable": true,
"editable": true
}, },
"outputs": [], "outputs": [],
"source": [] "source": []
@ -1256,7 +1027,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.5.3" "version": "3.6.2"
}, },
"nav_menu": { "nav_menu": {
"height": "264px", "height": "264px",
@ -1273,5 +1044,5 @@
} }
}, },
"nbformat": 4, "nbformat": 4,
"nbformat_minor": 0 "nbformat_minor": 1
} }