Repository: incubator-systemml Updated Branches: refs/heads/master a27663946 -> 7fe372b9a
[SYSTEMML-1606] Update notebook samples with latest code Project: http://git-wip-us.apache.org/repos/asf/incubator-systemml/repo Commit: http://git-wip-us.apache.org/repos/asf/incubator-systemml/commit/7fe372b9 Tree: http://git-wip-us.apache.org/repos/asf/incubator-systemml/tree/7fe372b9 Diff: http://git-wip-us.apache.org/repos/asf/incubator-systemml/diff/7fe372b9 Branch: refs/heads/master Commit: 7fe372b9adcb91d6ac5fb22646f33e488d29cf2a Parents: a276639 Author: Arvind Surve <[email protected]> Authored: Fri May 12 01:11:51 2017 -0700 Committer: Arvind Surve <[email protected]> Committed: Fri May 12 01:11:51 2017 -0700 ---------------------------------------------------------------------- .../Deep Learning Image Classification.ipynb | 489 ------------------- .../Deep_Learning_Image_Classification.ipynb | 406 +++++++++++++++ 2 files changed, 406 insertions(+), 489 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/7fe372b9/samples/jupyter-notebooks/Deep Learning Image Classification.ipynb ---------------------------------------------------------------------- diff --git a/samples/jupyter-notebooks/Deep Learning Image Classification.ipynb b/samples/jupyter-notebooks/Deep Learning Image Classification.ipynb deleted file mode 100644 index eac20fd..0000000 --- a/samples/jupyter-notebooks/Deep Learning Image Classification.ipynb +++ /dev/null @@ -1,489 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Deep Learning Image Classification\n", - "\n", - "This notebook shows SystemML Deep Learning functionality to map images of single digit numbers to their corresponding numeric representations. See [Getting Started with Deep Learning and Python](http://www.pyimagesearch.com/2014/09/22/getting-started-deep-learning-python/) for an explanation of the used deep learning concepts and assumptions.\n", - "\n", - "The downloaded MNIST dataset contains labeled images of handwritten digits, where each example is a 28x28 pixel image of grayscale values in the range [0,255] stretched out as 784 pixels, and each label is one of 10 possible digits in [0,9]. We download 60,000 training examples, and 10,000 test examples, where the format is \"label, pixel_1, pixel_2, ..., pixel_n\". We train a SystemML LeNet model. The results of the learning algorithms have an accuracy of 98 percent.\n", - "\n", - "1. [Install and load SystemML and other libraries](#load_systemml)\n", - "1. [Download and Access MNIST data](#access_data)\n", - "1. [Train a CNN classifier for MNIST handwritten digits](#train)\n", - "1. [Detect handwritten Digits](#predict)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "<div style=\"text-align:center\" markdown=\"1\">\n", - "\n", - "Mapping images of numbers to numbers\n", - "</div>" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "<a id=\"load_systemml\"></a>\n", - "## Install and load SystemML and other libraries" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [], - "source": [ - "#!pip install --user systemml>0.13.0\n", - "!pip install ~/git/incubator-systemml/target/systemml-0.15.0-incubating-SNAPSHOT-python.tgz" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [], - "source": [ - "!pip show systemml" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [], - "source": [ - "# Create symbolic link in ~/data/libs to use installed site-packages SystemML jar as opposed to DSX platform SystemML jar\n", - "# !ln -s -f ~/.local/lib/python2.7/site-packages/systemml/systemml-java/*.jar ~/data/libs/" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from systemml import MLContext, dml\n", - "\n", - "ml = MLContext(sc)\n", - "\n", - "print \"Spark Version:\", sc.version\n", - "print \"SystemML Version:\", ml.version()\n", - "print \"SystemML Built-Time:\", ml.buildTime()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings(\"ignore\")\n", - "from sklearn import datasets\n", - "from sklearn.cross_validation import train_test_split\n", - "from sklearn.metrics import classification_report\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "#import matplotlib.image as mpimg\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "<a id=\"access_data\"></a>\n", - "## Download and Access MNIST data\n", - "\n", - "Download the [MNIST data from the MLData repository](http://mldata.org/repository/data/viewslug/mnist-original/), and then split and save." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "scrolled": true - }, - "outputs": [], - "source": [ - "mnist = datasets.fetch_mldata(\"MNIST Original\")\n", - "\n", - "print \"Mnist data features:\", mnist.data.shape\n", - "print \"Mnist data label:\", mnist.target.shape\n", - "\n", - "trainX, testX, trainY, testY = train_test_split(mnist.data, mnist.target.astype(\"int0\"), test_size = 0.142857)\n", - "\n", - "trainD = np.concatenate((trainY.reshape(trainY.size, 1), trainX),axis=1)\n", - "testD = np.concatenate((testY.reshape (testY.size, 1), testX),axis=1)\n", - "\n", - "print \"Images for training:\", trainD.shape\n", - "print \"Images used for testing:\", testD.shape\n", - "pix = int(np.sqrt(trainD.shape[1]))\n", - "print \"Each image is:\", pix, \"by\", pix, \"pixels\"\n", - "\n", - "np.savetxt('data/mnist/mnist_train.csv', trainD, fmt='%u', delimiter=\",\")\n", - "np.savetxt('data/mnist/mnist_test.csv', testD, fmt='%u', delimiter=\",\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Alternatively get the data from here." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "scrolled": true - }, - "outputs": [], - "source": [ - "%%sh\n", - "mkdir -p data/mnist/\n", - "cd data/mnist/\n", - "curl -O https://pjreddie.com/media/files/mnist_train.csv\n";, - "curl -O https://pjreddie.com/media/files/mnist_test.csv\n";, - "wc -l mnist*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Read the data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "trainData = np.genfromtxt('data/mnist/mnist_train.csv', delimiter=\",\")\n", - "testData = np.genfromtxt('data/mnist/mnist_test.csv', delimiter=\",\")\n", - "\n", - "print \"Training data: \", trainData.shape\n", - "print \"Test data: \", testData.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pd.set_option('display.max_columns', 200)\n", - "pd.DataFrame(testData[1:10,],dtype='uint')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "<a id=\"train\"></a>\n", - "## Develop LeNet CNN classifier on Training Data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "<div style=\"text-align:center\" markdown=\"1\">\n", - "\n", - "MNIST digit recognition â LeNet architecture\n", - "</div>" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Download the SystemML LeNet Implementation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "scrolled": false - }, - "outputs": [], - "source": [ - "!wget -N -q 'https://raw.githubusercontent.com/apache/incubator-systemml/master/scripts/staging/SystemML-NN/examples/mnist_lenet.dml'\n", - "#!cat mnist_lenet.dml" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "scrolled": true - }, - "outputs": [], - "source": [ - "#import requests\n", - "#get = requests.get('https://raw.githubusercontent.com/apache/incubator-systemml/master/scripts/staging/SystemML-NN/examples/mnist_lenet.dml')\n", - "#mnist_lenet = get.text.replace(\"epochs = 10\", \"epochs = 1\")\n", - "#with open('mnist_lenet.dml','wb') as out:\n", - "# out.write(mnist_lenet)\n", - "#print mnist_lenet " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Download SystemML neural network library" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "!svn --force export https://github.com/apache/incubator-systemml/trunk/scripts/staging/SystemML-NN/nn"; - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Train Model using SystemML LeNet CNN." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "(on a Mac Book, this takes approx. 5-6 mins for 1 epoch)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "scrolled": true - }, - "outputs": [], - "source": [ - "script = \"\"\"\n", - " source(\"mnist_lenet.dml\") as mnist_lenet\n", - "\n", - " # Bind training data\n", - " n = nrow(data)\n", - "\n", - " # Extract images and labels\n", - " images = data[,2:ncol(data)]\n", - " labels = data[,1]\n", - "\n", - " # Scale images to [-1,1], and one-hot encode the labels\n", - " images = (images / 255.0) * 2 - 1\n", - " labels = table(seq(1, n), labels+1, n, 10)\n", - "\n", - " # Split into training (55,000 examples) and validation (5,000 examples)\n", - " X = images[5001:nrow(images),]\n", - " X_val = images[1:5000,]\n", - " y = labels[5001:nrow(images),]\n", - " y_val = labels[1:5000,]\n", - "\n", - " # Train the model using channel, height, and width to produce weights/biases.\n", - " [W1, b1, W2, b2, W3, b3, W4, b4] = mnist_lenet::train(X, y, X_val, y_val, C, Hin, Win, epochs)\n", - "\"\"\"\n", - "rets = ('W1', 'b1','W2','b2','W3','b3','W4','b4')\n", - "\n", - "script = (dml(script).input(data=trainData, epochs=1, C=1, Hin=28, Win=28)\n", - " .output(*rets)) \n", - "\n", - "W1, b1, W2, b2, W3, b3, W4, b4 = (ml.execute(script).get(*rets))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "Use trained model and predict on test data, and evaluate the quality of the predictions for each digit." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "scriptPredict = \"\"\"\n", - " source(\"mnist_lenet.dml\") as mnist_lenet\n", - "\n", - " # Separate images from lables and scale images to [-1,1]\n", - " X_test = data[,2:ncol(data)]\n", - " X_test = (X_test / 255.0) * 2 - 1\n", - "\n", - " # Predict\n", - " probs = mnist_lenet::predict(X_test, C, Hin, Win, W1, b1, W2, b2, W3, b3, W4, b4)\n", - " predictions = rowIndexMax(probs) - 1\n", - "\"\"\"\n", - "script = (dml(scriptPredict).input(data=testData, C=1, Hin=28, Win=28, W1=W1, b1=b1, W2=W2, b2=b2, W3=W3, b3=b3, W4=W4, b4=b4)\n", - " .output(\"predictions\"))\n", - "\n", - "predictions = ml.execute(script).get(\"predictions\").toNumPy()\n", - "\n", - "print classification_report(testData[:,0], predictions)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "<a id=\"predict\"></a>\n", - "## Detect handwritten Digits" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Define a function that randomly selects a test image, display the image, and scores it." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "img_size = np.sqrt(testData.shape[1] - 1)\n", - "\n", - "def displayImage(i):\n", - " image = (testData[i,1:]).reshape((img_size, img_size)).astype(\"uint8\")\n", - " imgplot = plt.imshow(image, cmap='gray') " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def predictImage(i):\n", - " image = testData[i,:].reshape(1,testData.shape[1])\n", - " prog = dml(scriptPredict).input(data=image, C=1, Hin=28, Win=28, W1=W1, b1=b1, W2=W2, b2=b2, W3=W3, b3=b3, W4=W4, b4=b4) \\\n", - " .output(\"predictions\")\n", - " result = ml.execute(prog)\n", - " return (result.get(\"predictions\").toNumPy())[0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "scrolled": true - }, - "outputs": [], - "source": [ - "i = np.random.choice(np.arange(0, len(testData)), size = (1,))\n", - "\n", - "p = predictImage(i)\n", - "\n", - "print \"Image\", i, \"\\nPredicted digit:\", p, \"\\nActual digit: \", testData[i,0], \"\\nResult: \", (p == testData[i,0])\n", - "\n", - "displayImage(i)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pd.set_option('display.max_columns', 28)\n", - "pd.DataFrame((testData[i,1:]).reshape(img_size, img_size),dtype='uint')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/7fe372b9/samples/jupyter-notebooks/Deep_Learning_Image_Classification.ipynb ---------------------------------------------------------------------- diff --git a/samples/jupyter-notebooks/Deep_Learning_Image_Classification.ipynb b/samples/jupyter-notebooks/Deep_Learning_Image_Classification.ipynb new file mode 100644 index 0000000..d7f00c9 --- /dev/null +++ b/samples/jupyter-notebooks/Deep_Learning_Image_Classification.ipynb @@ -0,0 +1,406 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Deep Learning Image Classification\n", + "\n", + "This notebook shows SystemML Deep Learning functionality to map images of single digit numbers to their corresponding numeric representations. See [Getting Started with Deep Learning and Python](http://www.pyimagesearch.com/2014/09/22/getting-started-deep-learning-python/) for an explanation of the used deep learning concepts and assumptions.\n", + "\n", + "The downloaded MNIST dataset contains labeled images of handwritten digits, where each example is a 28x28 pixel image of grayscale values in the range [0,255] stretched out as 784 pixels, and each label is one of 10 possible digits in [0,9]. We download 60,000 training examples, and 10,000 test examples, where the format is \"label, pixel_1, pixel_2, ..., pixel_n\". We train a SystemML LeNet model. The results of the learning algorithms have an accuracy of 98 percent.\n", + "\n", + "1. [Install and load SystemML and other libraries](#load_systemml)\n", + "1. [Download and Access MNIST data](#access_data)\n", + "1. [Train a CNN classifier for MNIST handwritten digits](#train)\n", + "1. [Detect handwritten Digits](#predict)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "<div style=\"text-align:center\" markdown=\"1\">\n", + "\n", + "Mapping images of numbers to numbers\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<a id=\"load_systemml\"></a>\n", + "## Install and load SystemML and other libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "#!pip install --user systemml>=0.14.0\n", + "!pip install ~/git/systemml_scala/incubator-systemml/target/systemml-1.0.0-incubating-SNAPSHOT-python.tgz" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "!pip show systemml" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from systemml import MLContext, dml\n", + "\n", + "ml = MLContext(sc)\n", + "\n", + "print \"Spark Version:\", sc.version\n", + "print \"SystemML Version:\", ml.version()\n", + "print \"SystemML Built-Time:\", ml.buildTime()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "from sklearn import datasets\n", + "from sklearn.cross_validation import train_test_split\n", + "from sklearn.metrics import classification_report\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "#import matplotlib.image as mpimg\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create data directory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%%sh\n", + "mkdir -p data/mnist/\n", + "cd data/mnist/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<a id=\"access_data\"></a>\n", + "## Download and Access MNIST data\n", + "\n", + "Download the [MNIST data from the MLData repository](http://mldata.org/repository/data/viewslug/mnist-original/), and then split and save." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "mnist = datasets.fetch_mldata(\"MNIST Original\")\n", + "\n", + "print \"Mnist data features:\", mnist.data.shape\n", + "print \"Mnist data label:\", mnist.target.shape\n", + "\n", + "trainX, testX, trainY, testY = train_test_split(mnist.data, mnist.target.astype(\"int0\"), test_size = 0.142857)\n", + "\n", + "trainD = np.concatenate((trainY.reshape(trainY.size, 1), trainX),axis=1)\n", + "testD = np.concatenate((testY.reshape (testY.size, 1), testX),axis=1)\n", + "\n", + "print \"Images for training:\", trainD.shape\n", + "print \"Images used for testing:\", testD.shape\n", + "pix = int(np.sqrt(trainD.shape[1]))\n", + "print \"Each image is:\", pix, \"by\", pix, \"pixels\"\n", + "\n", + "np.savetxt('data/mnist/mnist_train.csv', trainD, fmt='%u', delimiter=\",\")\n", + "np.savetxt('data/mnist/mnist_test.csv', testD, fmt='%u', delimiter=\",\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively get the data from here." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "%%sh\n", + "curl -O https://pjreddie.com/media/files/mnist_train.csv\n";, + "curl -O https://pjreddie.com/media/files/mnist_test.csv\n";, + "wc -l mnist*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Read the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "trainData = np.genfromtxt('data/mnist/mnist_train.csv', delimiter=\",\")\n", + "testData = np.genfromtxt('data/mnist/mnist_test.csv', delimiter=\",\")\n", + "\n", + "print \"Training data: \", trainData.shape\n", + "print \"Test data: \", testData.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pd.set_option('display.max_columns', 200)\n", + "pd.DataFrame(testData[1:10,],dtype='uint')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<a id=\"train\"></a>\n", + "## Develop LeNet CNN classifier on Training Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<div style=\"text-align:center\" markdown=\"1\">\n", + "\n", + "MNIST digit recognition â LeNet architecture\n", + "</div>" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train Model using SystemML LeNet CNN." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "(on a Mac Book, this takes approx. 5-6 mins for 1 epoch)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true, + "scrolled": true + }, + "outputs": [], + "source": [ + "script = \"\"\"\n", + " source(\"nn/examples/mnist_lenet.dml\") as mnist_lenet\n", + "\n", + " # Bind training data\n", + " n = nrow(data)\n", + "\n", + " # Extract images and labels\n", + " images = data[,2:ncol(data)]\n", + " labels = data[,1]\n", + "\n", + " # Scale images to [-1,1], and one-hot encode the labels\n", + " images = (images / 255.0) * 2 - 1\n", + " labels = table(seq(1, n), labels+1, n, 10)\n", + "\n", + " # Split into training (55,000 examples) and validation (5,000 examples)\n", + " X = images[5001:nrow(images),]\n", + " X_val = images[1:5000,]\n", + " y = labels[5001:nrow(images),]\n", + " y_val = labels[1:5000,]\n", + "\n", + " # Train the model using channel, height, and width to produce weights/biases.\n", + " [W1, b1, W2, b2, W3, b3, W4, b4] = mnist_lenet::train(X, y, X_val, y_val, C, Hin, Win, epochs)\n", + "\"\"\"\n", + "rets = ('W1', 'b1','W2','b2','W3','b3','W4','b4')\n", + "\n", + "script = (dml(script).input(data=trainData, epochs=1, C=1, Hin=28, Win=28)\n", + " .output(*rets)) \n", + "\n", + "W1, b1, W2, b2, W3, b3, W4, b4 = (ml.execute(script).get(*rets))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "Use trained model and predict on test data, and evaluate the quality of the predictions for each digit." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "scriptPredict = \"\"\"\n", + " source(\"nn/examples/mnist_lenet.dml\") as mnist_lenet\n", + "\n", + " # Separate images from lables and scale images to [-1,1]\n", + " X_test = data[,2:ncol(data)]\n", + " X_test = (X_test / 255.0) * 2 - 1\n", + "\n", + " # Predict\n", + " probs = mnist_lenet::predict(X_test, C, Hin, Win, W1, b1, W2, b2, W3, b3, W4, b4)\n", + " predictions = rowIndexMax(probs) - 1\n", + "\"\"\"\n", + "script = (dml(scriptPredict).input(data=testData, C=1, Hin=28, Win=28, W1=W1, b1=b1, W2=W2, b2=b2, W3=W3, b3=b3, W4=W4, b4=b4)\n", + " .output(\"predictions\"))\n", + "\n", + "predictions = ml.execute(script).get(\"predictions\").toNumPy()\n", + "\n", + "print classification_report(testData[:,0], predictions)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "<a id=\"predict\"></a>\n", + "## Detect handwritten Digits" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Define a function that randomly selects a test image, display the image, and scores it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "img_size = np.sqrt(testData.shape[1] - 1)\n", + "\n", + "def displayImage(i):\n", + " image = (testData[i,1:]).reshape((img_size, img_size)).astype(\"uint8\")\n", + " imgplot = plt.imshow(image, cmap='gray') " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def predictImage(i):\n", + " image = testData[i,:].reshape(1,testData.shape[1])\n", + " prog = dml(scriptPredict).input(data=image, C=1, Hin=28, Win=28, W1=W1, b1=b1, W2=W2, b2=b2, W3=W3, b3=b3, W4=W4, b4=b4) \\\n", + " .output(\"predictions\")\n", + " result = ml.execute(prog)\n", + " return (result.get(\"predictions\").toNumPy())[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "i = np.random.choice(np.arange(0, len(testData)), size = (1,))\n", + "\n", + "p = predictImage(i)\n", + "\n", + "print \"Image\", i, \"\\nPredicted digit:\", p, \"\\nActual digit: \", testData[i,0], \"\\nResult: \", (p == testData[i,0])\n", + "\n", + "displayImage(i)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pd.set_option('display.max_columns', 28)\n", + "pd.DataFrame((testData[i,1:]).reshape(img_size, img_size),dtype='uint')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +}
