{ "metadata": { "name": "Digit Recognition using nolearn" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": "This IPython notebook is amalgamation of codes available on these 2 resources ->\n1. [Problem migrating from DBN to lasagne NeuralNet: NaN for each epoch #35](https://github.com/dnouri/nolearn/issues/35)\n2. [Getting Started with Deep Learning and Python](http://www.pyimagesearch.com/2014/09/22/getting-started-deep-learning-python/)\n\nInstall the following packages - \n1. Theono (`pip install Theono`)\n2. [Lasagne](https://github.com/Lasagne/Lasagne) (Clone and install)\n3. [nolearn](https://github.com/dnouri/nolearn) (Clone and install)\n4. sklearn, numpy, matplotlib\n\n# IMPORT the required modules" }, { "cell_type": "code", "collapsed": false, "input": "# Import the required modules\nfrom sklearn.cross_validation import train_test_split\nfrom sklearn.metrics import classification_report\nfrom sklearn import datasets\nfrom lasagne import layers\nfrom lasagne import init\nfrom lasagne import nonlinearities\nfrom lasagne.updates import sgd,nesterov_momentum\n\nfrom nolearn.lasagne import NeuralNet\nimport numpy as np\n%pylab inline", "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": "Populating the interactive namespace from numpy and matplotlib\n" } ], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": "# Load the DATASET" }, { "cell_type": "code", "collapsed": false, "input": "# Load the training and testing set\ndataset = datasets.fetch_mldata(\"MNIST Original\")\n(trainX, testX, trainY, testY) = train_test_split((dataset.data / 255.0).astype(np.float32), dataset.target.astype(np.int32), test_size = 0.33)", "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": "# Create the CLASSIFIER" }, { "cell_type": "code", "collapsed": false, "input": "# Create the classifier\nclf = NeuralNet(\n layers=[\n ('input', layers.InputLayer),\n ('hidden', layers.DenseLayer),\n ('output', layers.DenseLayer),\n ],\n input_shape = (None, trainX.shape[1]),\n hidden_num_units=100,\n output_num_units=10,\n output_nonlinearity=nonlinearities.softmax,\n update=nesterov_momentum,\n #update=sgd,\n update_learning_rate=0.01,\n update_momentum=0.9,\n regression=False,\n max_epochs=20,\n verbose=1,\n #W=init.Uniform()\n )", "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": "# Perform the TRAINING " }, { "cell_type": "code", "collapsed": false, "input": "# Perform the training\nclf.fit(trainX, trainY)", "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": " input \t(None, 784) \tproduces 784 outputs\n hidden \t(None, 100) \tproduces 100 outputs\n output \t(None, 10) \tproduces 10 outputs\n\n Epoch | Train loss | Valid loss | Train / Val | Valid acc | Dur\n--------|--------------|--------------|---------------|-------------|-------" }, { "output_type": "stream", "stream": "stdout", "text": "\n 1 | \u001b[94m 0.584614\u001b[0m | \u001b[32m 0.344075\u001b[0m | 1.699087 | 90.41% | 2.2s" }, { "output_type": "stream", "stream": "stdout", "text": "\n 2 | \u001b[94m 0.304825\u001b[0m | \u001b[32m 0.285069\u001b[0m | 1.069301 | 91.85% | 1.8s" }, { "output_type": "stream", "stream": "stdout", "text": "\n 3 | \u001b[94m 0.257520\u001b[0m | \u001b[32m 0.250602\u001b[0m | 1.027604 | 92.88% | 1.8s" }, { "output_type": "stream", "stream": "stdout", "text": "\n 4 | \u001b[94m 0.225423\u001b[0m | \u001b[32m 0.225860\u001b[0m | 0.998066 | 93.61% | 1.8s" }, { "output_type": "stream", "stream": "stdout", "text": "\n 5 | \u001b[94m 0.200749\u001b[0m | \u001b[32m 0.207713\u001b[0m | 0.966475 | 94.25% | 1.8s" }, { "output_type": "stream", "stream": "stdout", "text": "\n 6 | \u001b[94m 0.181049\u001b[0m | \u001b[32m 0.193451\u001b[0m | 0.935888 | 94.59% | 1.8s" }, { "output_type": "stream", "stream": "stdout", "text": "\n 7 | \u001b[94m 0.164904\u001b[0m | \u001b[32m 0.181389\u001b[0m | 0.909117 | 94.96% | 1.8s" }, { "output_type": "stream", "stream": "stdout", "text": "\n 8 | \u001b[94m 0.151395\u001b[0m | \u001b[32m 0.171604\u001b[0m | 0.882234 | 95.22% | 1.7s" }, { "output_type": "stream", "stream": "stdout", "text": "\n 9 | \u001b[94m 0.139916\u001b[0m | \u001b[32m 0.163222\u001b[0m | 0.857209 | 95.41% | 2.9s" }, { "output_type": "stream", "stream": "stdout", "text": "\n 10 | \u001b[94m 0.129993\u001b[0m | \u001b[32m 0.156027\u001b[0m | 0.833147 | 95.65% | 2.4s" }, { "output_type": "stream", "stream": "stdout", "text": "\n 11 | \u001b[94m 0.121252\u001b[0m | \u001b[32m 0.149896\u001b[0m | 0.808904 | 95.76% | 3.7s" }, { "output_type": "stream", "stream": "stdout", "text": "\n 12 | \u001b[94m 0.113535\u001b[0m | \u001b[32m 0.144447\u001b[0m | 0.785995 | 95.91% | 2.3s" }, { "output_type": "stream", "stream": "stdout", "text": "\n 13 | \u001b[94m 0.106624\u001b[0m | \u001b[32m 0.139752\u001b[0m | 0.762946 | 95.99% | 2.2s" }, { "output_type": "stream", "stream": "stdout", "text": "\n 14 | \u001b[94m 0.100366\u001b[0m | \u001b[32m 0.135412\u001b[0m | 0.741190 | 96.11% | 2.3s" }, { "output_type": "stream", "stream": "stdout", "text": "\n 15 | \u001b[94m 0.094659\u001b[0m | \u001b[32m 0.131647\u001b[0m | 0.719040 | 96.26% | 2.2s" }, { "output_type": "stream", "stream": "stdout", "text": "\n 16 | \u001b[94m 0.089504\u001b[0m | \u001b[32m 0.128271\u001b[0m | 0.697774 | 96.33% | 2.3s" }, { "output_type": "stream", "stream": "stdout", "text": "\n 17 | \u001b[94m 0.084747\u001b[0m | \u001b[32m 0.125208\u001b[0m | 0.676852 | 96.43% | 2.4s" }, { "output_type": "stream", "stream": "stdout", "text": "\n 18 | \u001b[94m 0.080371\u001b[0m | \u001b[32m 0.122598\u001b[0m | 0.655569 | 96.46% | 2.3s" }, { "output_type": "stream", "stream": "stdout", "text": "\n 19 | \u001b[94m 0.076373\u001b[0m | \u001b[32m 0.120191\u001b[0m | 0.635432 | 96.48% | 2.2s" }, { "output_type": "stream", "stream": "stdout", "text": "\n 20 | \u001b[94m 0.072641\u001b[0m | \u001b[32m 0.118045\u001b[0m | 0.615364 | 96.55% | 2.2s" }, { "output_type": "stream", "stream": "stdout", "text": "\n" }, { "output_type": "stream", "stream": "stderr", "text": "/usr/local/lib/python2.7/dist-packages/Lasagne-0.1dev-py2.7.egg/lasagne/init.py:30: UserWarning: The uniform initializer no longer uses Glorot et al.'s approach to determine the bounds, but defaults to the range (-0.01, 0.01) instead. Please use the new GlorotUniform initializer to get the old behavior. GlorotUniform is now the default for all layers.\n warnings.warn(\"The uniform initializer no longer uses Glorot et al.'s \"\n" }, { "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": "NeuralNet(X_tensor_type=,\n batch_iterator_test=,\n batch_iterator_train=,\n eval_size=0.2, hidden_num_units=100, input_shape=(None, 784),\n layers=[('input', ), ('hidden', ), ('output', )],\n loss=None, max_epochs=20, more_params={},\n objective=,\n objective_loss_function=,\n on_epoch_finished=(), on_training_finished=(),\n output_nonlinearity=,\n output_num_units=10, regression=False,\n update=,\n update_learning_rate=0.01, update_momentum=0.9,\n use_label_encoder=False, verbose=1,\n y_tensor_type=TensorType(int32, vector))" } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": "# Perform the PREDICTIONS" }, { "cell_type": "code", "collapsed": false, "input": "# Perform the predictions\npreds = clf.predict(testX)\n# and show the precision, recall and f1-score\nprint classification_report(testY, preds)", "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": " precision recall f1-score support\n\n 0 0.97 0.98 0.97 2274\n 1 0.98 0.98 0.98 2600\n 2 0.97 0.96 0.96 2277\n 3 0.96 0.96 0.96 2323\n 4 0.95 0.97 0.96 2233\n 5 0.97 0.95 0.96 2136\n 6 0.97 0.98 0.98 2279\n 7 0.98 0.96 0.97 2409\n 8 0.95 0.97 0.96 2279\n 9 0.96 0.94 0.95 2290\n\navg / total 0.97 0.97 0.97 23100\n\n" } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": "# Sidekick Function to display the image in python notebook\ndef imshow(im_title, im):\n ''' This is function to display the image'''\n plt.figure() \n plt.title(im_title)\n plt.axis(\"off\")\n if len(im.shape) == 2:\n plt.imshow(im, cmap = \"gray\")\n else:\n im_display = cv2.cvtColor(im, cv2.COLOR_RGB2BGR)\n plt.imshow(im_display)\n plt.show()", "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": "# Show some RESULTS" }, { "cell_type": "code", "collapsed": false, "input": "# randomly select a few of the test instances\nfor i in np.random.choice(np.arange(0, len(testY)), size = (10,)):\n\t# classify the digit\n\tpred = clf.predict(np.atleast_2d(testX[i]))\n \n\t# reshape the feature vector to be a 28x28 pixel image, then change\n\t# the data type to be an unsigned 8-bit integer\n\timage = (testX[i] * 255).reshape((28, 28)).astype(\"uint8\")\n \n\t# show the image and prediction\n\timshow(\"Actual digit is {0}, predicted {1}\".format(testY[i], pred[0]), image)", "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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R7gSpzvPzSc/92NiY57UncmqhOP6attQUf03r2Mqr8/gEMDY2VhbaM+JPHUb4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\nXXUG1UxI3IaXK8C6JHxvby/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hUMh+nXrdaviOMLxhhakoRi7LmSroZkLfeOUjVPAeI0VO8KU30zZPsVgM6XQa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\nKm7hN4Tu7u6aQYJPp9OOraL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TvV6vB643s8IPF0b4IaFf8IoEnriqucgD3c8PHqUya6OcywrvM9b1M9qJhd41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\nhCvR6DY5OYnx8XGUy2WUSqV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mKcLLe9RDLGtra5HL5RCLxQLP61ps4bUijxbt5IrCmpoa5HI55/9J1C4CRvgZ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\nNTIy4v+tDo5GBUb4WQQtbBE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GiWKck9BZ7hij/eyaTLwV0nDPN11DngNsJLiG3W6bAYvsOrTXyt5rNpsRlZ37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\nJLz18FjrPyf85sKS8Jrw/Ft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