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Add TensorFlow fullyconnected notebook.
This commit is contained in:
Donne Martin
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b6caa1368b
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@ -134,6 +134,7 @@ IPython Notebook(s) demonstrating deep learning functionality.
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| [deep dream](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/deep-dream/dream.ipynb) | Caffe-based computer vision program which uses a convolutional neural network to find and enhance patterns in images. |
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| [ts-not-mnist](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-exercises/1_notmnist.ipynb) | Learn simple data curation by creating a pickle with formatted datasets for training, development and testing in TensorFlow. |
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| [ts-fully-connected](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-exercises/2_fullyconnected.ipynb) | Progressively train deeper and more accurate models using logistic regression and neural networks in TensorFlow. |
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<br/>
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<p align="center">
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611
deep-learning/tensor-flow-exercises/2_fullyconnected.ipynb
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611
deep-learning/tensor-flow-exercises/2_fullyconnected.ipynb
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@ -0,0 +1,611 @@
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{
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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": "kR-4eNdK6lYS"
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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 2\n",
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"------------\n",
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"\n",
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"Previously in `1_notmnist.ipynb`, we created a pickle with formatted datasets for training, development and testing on the [notMNIST dataset](http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html).\n",
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"\n",
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"The goal of this exercise is to progressively train deeper and more accurate models using TensorFlow."
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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": "JLpLa8Jt7Vu4"
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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": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "1HrCK6e17WzV"
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},
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"source": [
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"First reload the data we generated in `1_notmist.ipynb`."
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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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"elapsed": 19456,
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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 shape that's more adapted to the models we're going to train:\n",
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"- data as a flat matrix,\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": 19723,
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"status": "ok",
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"timestamp": 1449847956364,
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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, 784) (200000, 10)\n",
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"Validation set (10000, 784) (10000, 10)\n",
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"Test set (18724, 784) (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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"\n",
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"def reformat(dataset, labels):\n",
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" dataset = dataset.reshape((-1, image_size * image_size)).astype(np.float32)\n",
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" # Map 0 to [1.0, 0.0, 0.0 ...], 1 to [0.0, 1.0, 0.0 ...]\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": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "nCLVqyQ5vPPH"
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},
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"source": [
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"We're first going to train a multinomial logistic regression using simple gradient descent.\n",
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"\n",
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"TensorFlow works like this:\n",
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"* First you describe the computation that you want to see performed: what the inputs, the variables, and the operations look like. These get created as nodes over a computation graph. This description is all contained within the block below:\n",
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"\n",
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" with graph.as_default():\n",
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" ...\n",
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"\n",
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"* Then you can run the operations on this graph as many times as you want by calling `session.run()`, providing it outputs to fetch from the graph that get returned. This runtime operation is all contained in the block below:\n",
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"\n",
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" with tf.Session(graph=graph) as session:\n",
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" ...\n",
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"\n",
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"Let's load all the data into TensorFlow and build the computation graph corresponding to our training:"
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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": "Nfv39qvtvOl_"
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},
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"outputs": [],
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"source": [
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"# With gradient descent training, even this much data is prohibitive.\n",
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"# Subset the training data for faster turnaround.\n",
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"train_subset = 10000\n",
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"\n",
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"graph = tf.Graph()\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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" # Load the training, validation and test data into constants that are\n",
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" # attached to the graph.\n",
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" tf_train_dataset = tf.constant(train_dataset[:train_subset, :])\n",
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" tf_train_labels = tf.constant(train_labels[:train_subset])\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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" # These are the parameters that we are going to be training. The weight\n",
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" # matrix will be initialized using random valued following a (truncated)\n",
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" # normal distribution. The biases get initialized to zero.\n",
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" weights = tf.Variable(\n",
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" tf.truncated_normal([image_size * image_size, num_labels]))\n",
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" biases = tf.Variable(tf.zeros([num_labels]))\n",
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" \n",
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" # Training computation.\n",
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" # We multiply the inputs with the weight matrix, and add biases. We compute\n",
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" # the softmax and cross-entropy (it's one operation in TensorFlow, because\n",
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" # it's very common, and it can be optimized). We take the average of this\n",
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" # cross-entropy across all training examples: that's our loss.\n",
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" logits = tf.matmul(tf_train_dataset, weights) + biases\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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" # We are going to find the minimum of this loss using gradient descent.\n",
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" optimizer = tf.train.GradientDescentOptimizer(0.5).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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" # These are not part of training, but merely here so that we can report\n",
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" # accuracy figures as we train.\n",
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" train_prediction = tf.nn.softmax(logits)\n",
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" valid_prediction = tf.nn.softmax(\n",
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" tf.matmul(tf_valid_dataset, weights) + biases)\n",
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" test_prediction = tf.nn.softmax(tf.matmul(tf_test_dataset, weights) + biases)"
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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": "KQcL4uqISHjP"
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},
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"source": [
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"Let's run this computation and iterate:"
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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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"autoexec": {
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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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"Loss at step 0 : 17.2939\n",
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"Training accuracy: 10.8%\n",
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"Validation accuracy: 13.8%\n",
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"Loss at step 100 : 2.26903\n",
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"Training accuracy: 72.3%\n",
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"Validation accuracy: 71.6%\n",
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"Loss at step 200 : 1.84895\n",
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"Training accuracy: 74.9%\n",
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"Validation accuracy: 73.9%\n",
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"Loss at step 300 : 1.60701\n",
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"Training accuracy: 76.0%\n",
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"Validation accuracy: 74.5%\n",
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"Loss at step 400 : 1.43912\n",
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"Training accuracy: 76.8%\n",
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"Validation accuracy: 74.8%\n",
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"Loss at step 500 : 1.31349\n",
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"Training accuracy: 77.5%\n",
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"Validation accuracy: 75.0%\n",
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"Loss at step 600 : 1.21501\n",
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"Training accuracy: 78.1%\n",
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"Validation accuracy: 75.4%\n",
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"Loss at step 700 : 1.13515\n",
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"Training accuracy: 78.6%\n",
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"Validation accuracy: 75.4%\n",
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"Loss at step 800 : 1.0687\n",
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"Training accuracy: 79.2%\n",
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"Validation accuracy: 75.6%\n",
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"Test accuracy: 82.9%\n"
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]
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}
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],
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"source": [
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"num_steps = 801\n",
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"\n",
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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])\n",
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"\n",
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"with tf.Session(graph=graph) as session:\n",
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" # This is a one-time operation which ensures the parameters get initialized as\n",
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" # we described in the graph: random weights for the matrix, zeros for the\n",
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" # biases. \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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" # Run the computations. We tell .run() that we want to run the optimizer,\n",
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" # and get the loss value and the training predictions returned as numpy\n",
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" # arrays.\n",
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" _, l, predictions = session.run([optimizer, loss, train_prediction])\n",
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" if (step % 100 == 0):\n",
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" print 'Loss at step', step, ':', l\n",
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" print 'Training accuracy: %.1f%%' % accuracy(\n",
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" predictions, train_labels[:train_subset, :])\n",
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" # Calling .eval() on valid_prediction is basically like calling run(), but\n",
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" # just to get that one numpy array. Note that it recomputes all its graph\n",
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" # dependencies.\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": "x68f-hxRGm3H"
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},
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"source": [
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"Let's now switch to stochastic gradient descent training instead, which is much faster.\n",
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"\n",
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"The graph will be similar, except that instead of holding all the training data into a constant node, we create a `Placeholder` node which will be fed actual data at every call of `sesion.run()`."
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]
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},
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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": "qhPMzWYRGrzM"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"batch_size = 128\n",
|
||||
"\n",
|
||||
"graph = tf.Graph()\n",
|
||||
"with graph.as_default():\n",
|
||||
"\n",
|
||||
" # Input data. For the training data, we use a placeholder that will be fed\n",
|
||||
" # at run time with a training minibatch.\n",
|
||||
" tf_train_dataset = tf.placeholder(tf.float32,\n",
|
||||
" shape=(batch_size, image_size * image_size))\n",
|
||||
" tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))\n",
|
||||
" tf_valid_dataset = tf.constant(valid_dataset)\n",
|
||||
" tf_test_dataset = tf.constant(test_dataset)\n",
|
||||
" \n",
|
||||
" # Variables.\n",
|
||||
" weights = tf.Variable(\n",
|
||||
" tf.truncated_normal([image_size * image_size, num_labels]))\n",
|
||||
" biases = tf.Variable(tf.zeros([num_labels]))\n",
|
||||
" \n",
|
||||
" # Training computation.\n",
|
||||
" logits = tf.matmul(tf_train_dataset, weights) + biases\n",
|
||||
" loss = tf.reduce_mean(\n",
|
||||
" tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels))\n",
|
||||
" \n",
|
||||
" # Optimizer.\n",
|
||||
" optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)\n",
|
||||
" \n",
|
||||
" # Predictions for the training, validation, and test data.\n",
|
||||
" train_prediction = tf.nn.softmax(logits)\n",
|
||||
" valid_prediction = tf.nn.softmax(\n",
|
||||
" tf.matmul(tf_valid_dataset, weights) + biases)\n",
|
||||
" test_prediction = tf.nn.softmax(tf.matmul(tf_test_dataset, weights) + biases)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"colab_type": "text",
|
||||
"id": "XmVZESmtG4JH"
|
||||
},
|
||||
"source": [
|
||||
"Let's run it:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"cellView": "both",
|
||||
"colab": {
|
||||
"autoexec": {
|
||||
"startup": false,
|
||||
"wait_interval": 0
|
||||
},
|
||||
"output_extras": [
|
||||
{
|
||||
"item_id": 6
|
||||
}
|
||||
]
|
||||
},
|
||||
"colab_type": "code",
|
||||
"collapsed": false,
|
||||
"executionInfo": {
|
||||
"elapsed": 66292,
|
||||
"status": "ok",
|
||||
"timestamp": 1449848003013,
|
||||
"user": {
|
||||
"color": "",
|
||||
"displayName": "",
|
||||
"isAnonymous": false,
|
||||
"isMe": true,
|
||||
"permissionId": "",
|
||||
"photoUrl": "",
|
||||
"sessionId": "0",
|
||||
"userId": ""
|
||||
},
|
||||
"user_tz": 480
|
||||
},
|
||||
"id": "FoF91pknG_YW",
|
||||
"outputId": "d255c80e-954d-4183-ca1c-c7333ce91d0a"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Initialized\n",
|
||||
"Minibatch loss at step 0 : 16.8091\n",
|
||||
"Minibatch accuracy: 12.5%\n",
|
||||
"Validation accuracy: 14.0%\n",
|
||||
"Minibatch loss at step 500 : 1.75256\n",
|
||||
"Minibatch accuracy: 77.3%\n",
|
||||
"Validation accuracy: 75.0%\n",
|
||||
"Minibatch loss at step 1000 : 1.32283\n",
|
||||
"Minibatch accuracy: 77.3%\n",
|
||||
"Validation accuracy: 76.6%\n",
|
||||
"Minibatch loss at step 1500 : 0.944533\n",
|
||||
"Minibatch accuracy: 83.6%\n",
|
||||
"Validation accuracy: 76.5%\n",
|
||||
"Minibatch loss at step 2000 : 1.03795\n",
|
||||
"Minibatch accuracy: 78.9%\n",
|
||||
"Validation accuracy: 77.8%\n",
|
||||
"Minibatch loss at step 2500 : 1.10219\n",
|
||||
"Minibatch accuracy: 80.5%\n",
|
||||
"Validation accuracy: 78.0%\n",
|
||||
"Minibatch loss at step 3000 : 0.758874\n",
|
||||
"Minibatch accuracy: 82.8%\n",
|
||||
"Validation accuracy: 78.8%\n",
|
||||
"Test accuracy: 86.1%\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"num_steps = 3001\n",
|
||||
"\n",
|
||||
"with tf.Session(graph=graph) as session:\n",
|
||||
" tf.initialize_all_variables().run()\n",
|
||||
" print \"Initialized\"\n",
|
||||
" for step in xrange(num_steps):\n",
|
||||
" # Pick an offset within the training data, which has been randomized.\n",
|
||||
" # Note: we could use better randomization across epochs.\n",
|
||||
" offset = (step * batch_size) % (train_labels.shape[0] - batch_size)\n",
|
||||
" # Generate a minibatch.\n",
|
||||
" batch_data = train_dataset[offset:(offset + batch_size), :]\n",
|
||||
" batch_labels = train_labels[offset:(offset + batch_size), :]\n",
|
||||
" # Prepare a dictionary telling the session where to feed the minibatch.\n",
|
||||
" # The key of the dictionary is the placeholder node of the graph to be fed,\n",
|
||||
" # and the value is the numpy array to feed to it.\n",
|
||||
" feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels}\n",
|
||||
" _, l, predictions = session.run(\n",
|
||||
" [optimizer, loss, train_prediction], feed_dict=feed_dict)\n",
|
||||
" if (step % 500 == 0):\n",
|
||||
" print \"Minibatch loss at step\", step, \":\", l\n",
|
||||
" print \"Minibatch accuracy: %.1f%%\" % accuracy(predictions, batch_labels)\n",
|
||||
" print \"Validation accuracy: %.1f%%\" % accuracy(\n",
|
||||
" valid_prediction.eval(), valid_labels)\n",
|
||||
" print \"Test accuracy: %.1f%%\" % accuracy(test_prediction.eval(), test_labels)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"colab_type": "text",
|
||||
"id": "7omWxtvLLxik"
|
||||
},
|
||||
"source": [
|
||||
"---\n",
|
||||
"Problem\n",
|
||||
"-------\n",
|
||||
"\n",
|
||||
"Turn the logistic regression example with SGD into a 1-hidden layer neural network with rectified linear units (nn.relu()) and 1024 hidden nodes. This model should improve your validation / test accuracy.\n",
|
||||
"\n",
|
||||
"---"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"colabVersion": "0.3.2",
|
||||
"colab_default_view": {},
|
||||
"colab_views": {},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.4.3"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user