{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "kR-4eNdK6lYS" }, "source": [ "Deep Learning with TensorFlow\n", "=============\n", "\n", "Credits: Forked from [TensorFlow](https://github.com/tensorflow/tensorflow) by Google\n", "\n", "Setup\n", "------------\n", "\n", "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", "\n", "Exercise 3\n", "------------\n", "\n", "Previously in `2_fullyconnected.ipynb`, you trained a logistic regression and a neural network model.\n", "\n", "The goal of this exercise is to explore regularization techniques." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "collapsed": true, "id": "JLpLa8Jt7Vu4" }, "outputs": [], "source": [ "# These are all the modules we'll be using later. Make sure you can import them\n", "# before proceeding further.\n", "import cPickle as pickle\n", "import numpy as np\n", "import tensorflow as tf" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "1HrCK6e17WzV" }, "source": [ "First reload the data we generated in _notmist.ipynb_." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 11777, "status": "ok", "timestamp": 1449849322348, "user": { "color": "", "displayName": "", "isAnonymous": false, "isMe": true, "permissionId": "", "photoUrl": "", "sessionId": "0", "userId": "" }, "user_tz": 480 }, "id": "y3-cj1bpmuxc", "outputId": "e03576f1-ebbe-4838-c388-f1777bcc9873" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training set (200000, 28, 28) (200000,)\n", "Validation set (10000, 28, 28) (10000,)\n", "Test set (18724, 28, 28) (18724,)\n" ] } ], "source": [ "pickle_file = 'notMNIST.pickle'\n", "\n", "with open(pickle_file, 'rb') as f:\n", " save = pickle.load(f)\n", " train_dataset = save['train_dataset']\n", " train_labels = save['train_labels']\n", " valid_dataset = save['valid_dataset']\n", " valid_labels = save['valid_labels']\n", " test_dataset = save['test_dataset']\n", " test_labels = save['test_labels']\n", " del save # hint to help gc free up memory\n", " print 'Training set', train_dataset.shape, train_labels.shape\n", " print 'Validation set', valid_dataset.shape, valid_labels.shape\n", " print 'Test set', test_dataset.shape, test_labels.shape" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "L7aHrm6nGDMB" }, "source": [ "Reformat into a shape that's more adapted to the models we're going to train:\n", "- data as a flat matrix,\n", "- labels as float 1-hot encodings." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 11728, "status": "ok", "timestamp": 1449849322356, "user": { "color": "", "displayName": "", "isAnonymous": false, "isMe": true, "permissionId": "", "photoUrl": "", "sessionId": "0", "userId": "" }, "user_tz": 480 }, "id": "IRSyYiIIGIzS", "outputId": "3f8996ee-3574-4f44-c953-5c8a04636582" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training set (200000, 784) (200000, 10)\n", "Validation set (10000, 784) (10000, 10)\n", "Test set (18724, 784) (18724, 10)\n" ] } ], "source": [ "image_size = 28\n", "num_labels = 10\n", "\n", "def reformat(dataset, labels):\n", " dataset = dataset.reshape((-1, image_size * image_size)).astype(np.float32)\n", " # Map 2 to [0.0, 1.0, 0.0 ...], 3 to [0.0, 0.0, 1.0 ...]\n", " labels = (np.arange(num_labels) == labels[:,None]).astype(np.float32)\n", " return dataset, labels\n", "train_dataset, train_labels = reformat(train_dataset, train_labels)\n", "valid_dataset, valid_labels = reformat(valid_dataset, valid_labels)\n", "test_dataset, test_labels = reformat(test_dataset, test_labels)\n", "print 'Training set', train_dataset.shape, train_labels.shape\n", "print 'Validation set', valid_dataset.shape, valid_labels.shape\n", "print 'Test set', test_dataset.shape, test_labels.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "collapsed": true, "id": "RajPLaL_ZW6w" }, "outputs": [], "source": [ "def accuracy(predictions, labels):\n", " return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1))\n", " / predictions.shape[0])" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "sgLbUAQ1CW-1" }, "source": [ "---\n", "Problem 1\n", "---------\n", "\n", "Introduce and tune L2 regularization for both logistic and neural network models. Remember that L2 amounts to adding a penalty on the norm of the weights to the loss. In TensorFlow, you can compue the L2 loss for a tensor `t` using `nn.l2_loss(t)`. The right amount of regularization should improve your validation / test accuracy.\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "na8xX2yHZzNF" }, "source": [ "---\n", "Problem 2\n", "---------\n", "Let's demonstrate an extreme case of overfitting. Restrict your training data to just a few batches. What happens?\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "ww3SCBUdlkRc" }, "source": [ "---\n", "Problem 3\n", "---------\n", "Introduce Dropout on the hidden layer of the neural network. Remember: Dropout should only be introduced during training, not evaluation, otherwise your evaluation results would be stochastic as well. TensorFlow provides `nn.dropout()` for that, but you have to make sure it's only inserted during training.\n", "\n", "What happens to our extreme overfitting case?\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "-b1hTz3VWZjw" }, "source": [ "---\n", "Problem 4\n", "---------\n", "\n", "Try to get the best performance you can using a multi-layer model! The best reported test accuracy using a deep network is [97.1%](http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html?showComment=1391023266211#c8758720086795711595).\n", "\n", "One avenue you can explore is to add multiple layers.\n", "\n", "Another one is to use learning rate decay:\n", "\n", " global_step = tf.Variable(0) # count the number of steps taken.\n", " learning_rate = tf.train.exponential_decay(0.5, step, ...)\n", " optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)\n", " \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 }