mirror of
https://github.com/donnemartin/data-science-ipython-notebooks.git
synced 2024-03-22 13:30:56 +08:00
490 lines
15 KiB
Python
490 lines
15 KiB
Python
{
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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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"id": "4embtkV0pNxM"
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},
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"source": [
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"Deep Learning with TensorFlow\n",
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"=============\n",
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"\n",
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"Credits: Forked from [TensorFlow](https://github.com/tensorflow/tensorflow) by Google\n",
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"\n",
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"Setup\n",
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"------------\n",
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"\n",
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"Refer to the [setup instructions](https://github.com/donnemartin/data-science-ipython-notebooks/tree/feature/deep-learning/deep-learning/tensor-flow-exercises/README.md).\n",
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"\n",
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"Exercise 4\n",
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"------------\n",
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"\n",
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"Previously in `2_fullyconnected.ipynb` and `3_regularization.ipynb`, we trained fully connected networks to classify [notMNIST](http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html) characters.\n",
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"\n",
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"The goal of this exercise is make the neural network convolutional."
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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": null,
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"metadata": {
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"cellView": "both",
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"colab": {
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"autoexec": {
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"startup": false,
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"wait_interval": 0
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}
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},
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"colab_type": "code",
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"collapsed": true,
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"id": "tm2CQN_Cpwj0"
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},
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"outputs": [],
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"source": [
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"# These are all the modules we'll be using later. Make sure you can import them\n",
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"# before proceeding further.\n",
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"import cPickle as pickle\n",
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"import numpy as np\n",
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"import tensorflow as tf"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"cellView": "both",
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"colab": {
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"autoexec": {
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"startup": false,
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"wait_interval": 0
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},
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"output_extras": [
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{
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"item_id": 1
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}
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]
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},
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"colab_type": "code",
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"collapsed": false,
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"executionInfo": {
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"elapsed": 11948,
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"status": "ok",
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"timestamp": 1446658914837,
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"user": {
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"color": "",
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"displayName": "",
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"isAnonymous": false,
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"isMe": true,
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"permissionId": "",
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"photoUrl": "",
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"sessionId": "0",
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"userId": ""
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},
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"user_tz": 480
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},
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"id": "y3-cj1bpmuxc",
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"outputId": "016b1a51-0290-4b08-efdb-8c95ffc3cd01"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Training set (200000, 28, 28) (200000,)\n",
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"Validation set (10000, 28, 28) (10000,)\n",
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"Test set (18724, 28, 28) (18724,)\n"
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]
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}
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],
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"source": [
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"pickle_file = 'notMNIST.pickle'\n",
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"\n",
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"with open(pickle_file, 'rb') as f:\n",
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" save = pickle.load(f)\n",
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" train_dataset = save['train_dataset']\n",
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" train_labels = save['train_labels']\n",
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" valid_dataset = save['valid_dataset']\n",
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" valid_labels = save['valid_labels']\n",
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" test_dataset = save['test_dataset']\n",
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" test_labels = save['test_labels']\n",
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" del save # hint to help gc free up memory\n",
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" print 'Training set', train_dataset.shape, train_labels.shape\n",
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" print 'Validation set', valid_dataset.shape, valid_labels.shape\n",
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" print 'Test set', test_dataset.shape, test_labels.shape"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "L7aHrm6nGDMB"
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},
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"source": [
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"Reformat into a TensorFlow-friendly shape:\n",
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"- convolutions need the image data formatted as a cube (width by height by #channels)\n",
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"- labels as float 1-hot encodings."
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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": null,
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"metadata": {
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"cellView": "both",
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"colab": {
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"autoexec": {
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"startup": false,
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"wait_interval": 0
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},
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"output_extras": [
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{
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"item_id": 1
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}
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]
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},
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"colab_type": "code",
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"collapsed": false,
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"executionInfo": {
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"elapsed": 11952,
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"status": "ok",
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"timestamp": 1446658914857,
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"user": {
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"color": "",
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"displayName": "",
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"isAnonymous": false,
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"isMe": true,
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"permissionId": "",
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"photoUrl": "",
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"sessionId": "0",
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"userId": ""
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},
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"user_tz": 480
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},
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"id": "IRSyYiIIGIzS",
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"outputId": "650a208c-8359-4852-f4f5-8bf10e80ef6c"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Training set (200000, 28, 28, 1) (200000, 10)\n",
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"Validation set (10000, 28, 28, 1) (10000, 10)\n",
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"Test set (18724, 28, 28, 1) (18724, 10)\n"
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]
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}
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],
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"source": [
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"image_size = 28\n",
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"num_labels = 10\n",
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"num_channels = 1 # grayscale\n",
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"\n",
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"import numpy as np\n",
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"\n",
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"def reformat(dataset, labels):\n",
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" dataset = dataset.reshape(\n",
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" (-1, image_size, image_size, num_channels)).astype(np.float32)\n",
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" labels = (np.arange(num_labels) == labels[:,None]).astype(np.float32)\n",
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" return dataset, labels\n",
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"train_dataset, train_labels = reformat(train_dataset, train_labels)\n",
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"valid_dataset, valid_labels = reformat(valid_dataset, valid_labels)\n",
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"test_dataset, test_labels = reformat(test_dataset, test_labels)\n",
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"print 'Training set', train_dataset.shape, train_labels.shape\n",
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"print 'Validation set', valid_dataset.shape, valid_labels.shape\n",
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"print 'Test set', test_dataset.shape, test_labels.shape"
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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": null,
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"metadata": {
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"cellView": "both",
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"colab": {
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"autoexec": {
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"startup": false,
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"wait_interval": 0
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}
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},
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"colab_type": "code",
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"collapsed": true,
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"id": "AgQDIREv02p1"
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},
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"outputs": [],
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"source": [
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"def accuracy(predictions, labels):\n",
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" return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1))\n",
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" / predictions.shape[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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"id": "5rhgjmROXu2O"
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},
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"source": [
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"Let's build a small network with two convolutional layers, followed by one fully connected layer. Convolutional networks are more expensive computationally, so we'll limit its depth and number of fully connected nodes."
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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": null,
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"metadata": {
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"cellView": "both",
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"colab": {
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"autoexec": {
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"startup": false,
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"wait_interval": 0
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}
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},
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"colab_type": "code",
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"collapsed": true,
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"id": "IZYv70SvvOan"
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},
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"outputs": [],
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"source": [
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"batch_size = 16\n",
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"patch_size = 5\n",
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"depth = 16\n",
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"num_hidden = 64\n",
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"\n",
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"graph = tf.Graph()\n",
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"\n",
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"with graph.as_default():\n",
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"\n",
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" # Input data.\n",
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" tf_train_dataset = tf.placeholder(\n",
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" tf.float32, shape=(batch_size, image_size, image_size, num_channels))\n",
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" tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))\n",
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" tf_valid_dataset = tf.constant(valid_dataset)\n",
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" tf_test_dataset = tf.constant(test_dataset)\n",
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" \n",
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" # Variables.\n",
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" layer1_weights = tf.Variable(tf.truncated_normal(\n",
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" [patch_size, patch_size, num_channels, depth], stddev=0.1))\n",
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" layer1_biases = tf.Variable(tf.zeros([depth]))\n",
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" layer2_weights = tf.Variable(tf.truncated_normal(\n",
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" [patch_size, patch_size, depth, depth], stddev=0.1))\n",
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" layer2_biases = tf.Variable(tf.constant(1.0, shape=[depth]))\n",
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" layer3_weights = tf.Variable(tf.truncated_normal(\n",
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" [image_size / 4 * image_size / 4 * depth, num_hidden], stddev=0.1))\n",
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" layer3_biases = tf.Variable(tf.constant(1.0, shape=[num_hidden]))\n",
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" layer4_weights = tf.Variable(tf.truncated_normal(\n",
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" [num_hidden, num_labels], stddev=0.1))\n",
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" layer4_biases = tf.Variable(tf.constant(1.0, shape=[num_labels]))\n",
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" \n",
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" # Model.\n",
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" def model(data):\n",
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" conv = tf.nn.conv2d(data, layer1_weights, [1, 2, 2, 1], padding='SAME')\n",
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" hidden = tf.nn.relu(conv + layer1_biases)\n",
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" conv = tf.nn.conv2d(hidden, layer2_weights, [1, 2, 2, 1], padding='SAME')\n",
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" hidden = tf.nn.relu(conv + layer2_biases)\n",
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" shape = hidden.get_shape().as_list()\n",
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" reshape = tf.reshape(hidden, [shape[0], shape[1] * shape[2] * shape[3]])\n",
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" hidden = tf.nn.relu(tf.matmul(reshape, layer3_weights) + layer3_biases)\n",
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" return tf.matmul(hidden, layer4_weights) + layer4_biases\n",
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" \n",
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" # Training computation.\n",
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" logits = model(tf_train_dataset)\n",
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" loss = tf.reduce_mean(\n",
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" tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels))\n",
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" \n",
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" # Optimizer.\n",
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" optimizer = tf.train.GradientDescentOptimizer(0.05).minimize(loss)\n",
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" \n",
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" # Predictions for the training, validation, and test data.\n",
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" train_prediction = tf.nn.softmax(logits)\n",
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" valid_prediction = tf.nn.softmax(model(tf_valid_dataset))\n",
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" test_prediction = tf.nn.softmax(model(tf_test_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": null,
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"metadata": {
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"cellView": "both",
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"colab": {
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"autoexec": {
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"startup": false,
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"wait_interval": 0
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},
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"output_extras": [
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{
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"item_id": 37
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}
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]
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},
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"colab_type": "code",
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"collapsed": false,
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"executionInfo": {
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"elapsed": 63292,
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"status": "ok",
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"timestamp": 1446658966251,
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"user": {
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"color": "",
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"displayName": "",
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"isAnonymous": false,
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"isMe": true,
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"permissionId": "",
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"photoUrl": "",
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"sessionId": "0",
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"userId": ""
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},
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"user_tz": 480
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},
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"id": "noKFb2UovVFR",
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"outputId": "28941338-2ef9-4088-8bd1-44295661e628"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Initialized\n",
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"Minibatch loss at step 0 : 3.51275\n",
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"Minibatch accuracy: 6.2%\n",
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"Validation accuracy: 12.8%\n",
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"Minibatch loss at step 50 : 1.48703\n",
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"Minibatch accuracy: 43.8%\n",
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"Validation accuracy: 50.4%\n",
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"Minibatch loss at step 100 : 1.04377\n",
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"Minibatch accuracy: 68.8%\n",
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"Validation accuracy: 67.4%\n",
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"Minibatch loss at step 150 : 0.601682\n",
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"Minibatch accuracy: 68.8%\n",
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"Validation accuracy: 73.0%\n",
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"Minibatch loss at step 200 : 0.898649\n",
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"Minibatch accuracy: 75.0%\n",
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"Validation accuracy: 77.8%\n",
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"Minibatch loss at step 250 : 1.3637\n",
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"Minibatch accuracy: 56.2%\n",
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"Validation accuracy: 75.4%\n",
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"Minibatch loss at step 300 : 1.41968\n",
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"Minibatch accuracy: 62.5%\n",
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"Validation accuracy: 76.0%\n",
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"Minibatch loss at step 350 : 0.300648\n",
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"Minibatch accuracy: 81.2%\n",
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"Validation accuracy: 80.2%\n",
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"Minibatch loss at step 400 : 1.32092\n",
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"Minibatch accuracy: 56.2%\n",
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"Validation accuracy: 80.4%\n",
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"Minibatch loss at step 450 : 0.556701\n",
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"Minibatch accuracy: 81.2%\n",
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"Validation accuracy: 79.4%\n",
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"Minibatch loss at step 500 : 1.65595\n",
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"Minibatch accuracy: 43.8%\n",
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"Validation accuracy: 79.6%\n",
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"Minibatch loss at step 550 : 1.06995\n",
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"Minibatch accuracy: 75.0%\n",
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"Validation accuracy: 81.2%\n",
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"Minibatch loss at step 600 : 0.223684\n",
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"Minibatch accuracy: 100.0%\n",
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"Validation accuracy: 82.3%\n",
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"Minibatch loss at step 650 : 0.619602\n",
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"Minibatch accuracy: 87.5%\n",
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"Validation accuracy: 81.8%\n",
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"Minibatch loss at step 700 : 0.812091\n",
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"Minibatch accuracy: 75.0%\n",
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"Validation accuracy: 82.4%\n",
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"Minibatch loss at step 750 : 0.276302\n",
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"Minibatch accuracy: 87.5%\n",
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"Validation accuracy: 82.3%\n",
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"Minibatch loss at step 800 : 0.450241\n",
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"Minibatch accuracy: 81.2%\n",
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"Validation accuracy: 82.3%\n",
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"Minibatch loss at step 850 : 0.137139\n",
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"Minibatch accuracy: 93.8%\n",
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"Validation accuracy: 82.3%\n",
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"Minibatch loss at step 900 : 0.52664\n",
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"Minibatch accuracy: 75.0%\n",
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"Validation accuracy: 82.2%\n",
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"Minibatch loss at step 950 : 0.623835\n",
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"Minibatch accuracy: 87.5%\n",
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"Validation accuracy: 82.1%\n",
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"Minibatch loss at step 1000 : 0.243114\n",
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"Minibatch accuracy: 93.8%\n",
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"Validation accuracy: 82.9%\n",
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"Test accuracy: 90.0%\n"
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]
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}
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],
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"source": [
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"num_steps = 1001\n",
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"\n",
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"with tf.Session(graph=graph) as session:\n",
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" tf.initialize_all_variables().run()\n",
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" print \"Initialized\"\n",
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" for step in xrange(num_steps):\n",
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" offset = (step * batch_size) % (train_labels.shape[0] - batch_size)\n",
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" batch_data = train_dataset[offset:(offset + batch_size), :, :, :]\n",
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" batch_labels = train_labels[offset:(offset + batch_size), :]\n",
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" feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels}\n",
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" _, l, predictions = session.run(\n",
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" [optimizer, loss, train_prediction], feed_dict=feed_dict)\n",
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" if (step % 50 == 0):\n",
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" print \"Minibatch loss at step\", step, \":\", l\n",
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" print \"Minibatch accuracy: %.1f%%\" % accuracy(predictions, batch_labels)\n",
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" print \"Validation accuracy: %.1f%%\" % accuracy(\n",
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" valid_prediction.eval(), valid_labels)\n",
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" print \"Test accuracy: %.1f%%\" % accuracy(test_prediction.eval(), test_labels)"
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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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"id": "KedKkn4EutIK"
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},
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"source": [
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"---\n",
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"Problem 1\n",
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"---------\n",
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"\n",
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"The convolutional model above uses convolutions with stride 2 to reduce the dimensionality. Replace the strides a max pooling operation (`nn.max_pool()`) of stride 2 and kernel size 2.\n",
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"\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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"id": "klf21gpbAgb-"
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},
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"source": [
|
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"---\n",
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"Problem 2\n",
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"---------\n",
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"\n",
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"Try to get the best performance you can using a convolutional net. Look for example at the classic [LeNet5](http://yann.lecun.com/exdb/lenet/) architecture, adding Dropout, and/or adding learning rate decay.\n",
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"\n",
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"---"
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]
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}
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],
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"metadata": {
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"colabVersion": "0.3.2",
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"colab_default_view": {},
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