From 02360d5104e63bb17ce9ed34e22e1b5ced1ca6e8 Mon Sep 17 00:00:00 2001 From: Malar Kannan Date: Sun, 8 Oct 2017 15:12:12 +0530 Subject: [PATCH] initial commit --- ComputationalGraphs.ipynb | 397 +++++++++++ LinearAlgebra.ipynb | 1079 ++++++++++++++++++++++++++++++ LinearAlgebra.md | 332 +++++++++ linear_regression_tf_lowlevel.py | 97 +++ 4 files changed, 1905 insertions(+) create mode 100644 ComputationalGraphs.ipynb create mode 100644 LinearAlgebra.ipynb create mode 100644 LinearAlgebra.md create mode 100644 linear_regression_tf_lowlevel.py diff --git a/ComputationalGraphs.ipynb b/ComputationalGraphs.ipynb new file mode 100644 index 0000000..4eaef5f --- /dev/null +++ b/ComputationalGraphs.ipynb @@ -0,0 +1,397 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Computational Graphs (Basics)\n", + "\n", + "## What is a computational graph?\n", + "\n", + "Computational graph is a graphical representation of numerical computations. The mathematical operations are captured as nodes. The data flowing as input and output of the nodes is represented by the edges connecting them. \n", + "\n", + "## Why computational graphs?\n", + "\n", + "Computational graphs provide a way to *define* the computations that need to be done. They are like a visual programming language (declarative language). Once the computation is described, the libraries like Tensorflow and Theano, can execute them depending on the device configurations where the computation is executed. Thus once defined the computation can be run on CPUs, a distributed network of CPUs, GPUs, mobile phones etc.\n", + "\n", + "## Computational graph in Tensorflow\n", + "\n", + "In Tensorflow, you can use Python language to programmatically build a computational graph. Inputs for the graph are defined as placeholders and they are supplied at the time of executing the computations. To compute on the graph, a Tensorflow session is created and input data is provided. If you execute the run and provide the nodes whose values you seek as output. Tensorflow finds all *dependent* computation nodes necessary to compute the values of output nodes, and executes the computation only for the necessary paths. \n", + "\n", + "We will see in later sessions how more advanced computing is done in the context of neural networks, and also understand how Tensorflow computational graph provides automatic differentiation, necessary for learning algorithms like backpropagation." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from graphviz import Digraph\n", + "from IPython.core.display import display, SVG\n", + "\n", + "def tf_to_dot(graph):\n", + " dot = Digraph()\n", + "\n", + " for n in graph.as_graph_def().node:\n", + " name = n.name.split('/')[0]\n", + " dot.node(name, label=name)\n", + "\n", + " for src in n.input:\n", + " src = src.split('/')[0]\n", + " if src != name:\n", + " dot.edge(src, name)\n", + " display(SVG(dot._repr_svg_())) \n", + " return dot" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "%3\n", + "\n", + "\n", + "\n", + "a___\n", + "\n", + "a___\n", + "\n", + "\n", + "\n", + "addition\n", + "\n", + "addition\n", + "\n", + "\n", + "\n", + "a___->addition\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "b\n", + "\n", + "b\n", + "\n", + "\n", + "\n", + "b->addition\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "16.0\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "\n", + "tf.reset_default_graph()\n", + "\n", + "node_a = tf.placeholder(dtype=tf.float32,name=\"a___\")\n", + "node_b = tf.constant(6.0,dtype=tf.float32, name=\"b\")\n", + "\n", + "adder_node = tf.add(node_a,node_b, name=\"addition\")\n", + "\n", + "\n", + "sess = tf.Session()\n", + "output = sess.run(adder_node,{node_a:10.0})\n", + "tf_to_dot(tf.get_default_graph())\n", + "sess.close()\n", + "\n", + "print output" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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s+1xFEAwVQRFsS66YPyCNluBSEdiSQS928DO7yFfGwPOQUYR5rgScNRtahd1OuBiUFx/v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RjfbwqZ//bVQTWFSKr1yquGUxZzELrA0LcqCwGcbswslkBrRLPDUJ4m57Bq5Le3clv/I9qnPq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z/Xm55a0GaX/+/WLvXHYnJYps8egt15Nv+VbZ/SlDltL05gHjeCfJZda+eRuBu+HxboHI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\nlGsF0sEEv1jAK9WJpxPCYhE/LCHRxAY6G3dpzrYwQY1B54h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P4HV6PH29xur6EsWaB7LA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35I9kMmlZMgAwgUX2leGZAFCynjygUl+f7tpoNDQcDlUulydAEwPO/+Vy2eJE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5m77roLvb29OHHiBLLZHAd+aWkJCwsL2Lp1q1jjnZ2deOaZZ1BRUQGfz4e5uTk5hKurq9i8eTPi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\n7MWvrKwwM4VYK0rZBY/Hg8OHD+PRRx+FyWRCNpvlRDAZY2qxKcs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W2OjoKLRaLQunEbzo\ndrvx7LPPYvfu3TCZTKhWq5iYmGBYs7e3FyMjI9y8Z3V1lTnv4r1MEOzc3BxLkBCVMRwOs4CYyWTi\nc93T08MTK41arYbu7m5UKhVUq1XOG5CeDk28AFiyI5vNcq3M5OQkgsEg01M7Oztx66234vz582xD\ngI3JeWVlhbs+6fV67N+/H48++ij0ej327NmD+fl5ZDIZ9Pb2olQq4ezZs5ifn3/Vlawv5XW9RoO4\n66T9QgU009PTGBwcxMmTJ7F7927odDrk83ls376dtc41Gg2Gh4dRq9WQSqU4MShJEp9U8nwNBgN0\nOh0XMqytrXETj0gkwqyYRCIBn8/H+0U3Mj1UDoeDG36TsaLMeLVaxfLyMvbs2QNJkpg/S8lVk8kE\nSZKwd+9ehMNh2Gy2Lap4pDRXrVZZS4M6RAFXVR5FlocSc1UaaBpKHFz8/lqG4eUmAEmSWKWSoi7a\nV8pDkMdF0hH0B2w8OMViEQ8//DAeeeQR7Nu3D/feey8zEG6//XYcPHiQKXTFYhHhcBiLi4sIh8N8\nLuh80Dkh3W4yaDQRKHno9J5+S9WhVP5O11SSrtI3XS4XrFYrAoEAQqEQi21Rod3Y2BgWFxdfIuNA\nCVFZljlBWi6XWf6BPG6K3CjforxmtO8U6rdKQiuvV6t7QZZl5tJT7gkAt5MzGAxYWlri6MPr9bKa\nK/2+q6sLNpuNazv279/PlbZEWTSbzXC73Xj44Ydx7733IpVKYWlpiZkwNCFLkoTx8XFMTk7yxEKS\nvbFYjOUYqNVmZ2cnN5wn+Faj2WjBt7q6Cp/Ph7m5OZYRWF1d5a5RNDGsra2xU2E0GlGpVODxeDhq\nLhQKTGWkqEWl2qjQ7e7uhiRJTBPu7+/H+Pg4FwD29fVhaWkJlUqFmXQdHR2wWCy4ePEiO56kfun1\nemGz2ZDL5Zj6+WrH68aD/+hHPwqXy4Xu7m4Wxp+fn8eBAwdw9OhRHDhwgLG3/v5+hMNhPP/88wgG\ng3j729+O73znO0w9I7yd4Bp6cAiDIy1qUpkkFUm6CISb0gxOISU9iDqdDgC26LBTc97l5WV4vV48\n/vjjuP/++7mQAwDm5+dZuVKSJGZS1Go1lMtlrK+v48yZM+yRx2IxnD17lhuRE9UK2CrJq/TelPK4\nymtMBkLp2bda9uUMxrW8fKKbUXGOUkmxvb2di0rEQQ8onX/CVbu6utDf3w+Px4POzk4MDAwwm4Mg\nC3rAG40G6+FTU/Zms7mlYIu8OkoaAxsTH4XspHZIuQX6jBpB5PN5JJNJhMNhzM3NoVgsco/fZrO5\nJUKh/ylRWKvVsLq6uoUBQ1ENTQZ0bpXeeCvYTHldxAS2cmJQXkuCu0RaLk1wAwMDmJmZwf79+3Hu\n3Dk+ttXVVWaJUVHg2toaQqEQAoEAms0misUigI2cDfG+f/jDH2LPnj2o1+ucgCee/+LiIkZGRth5\nowY7JJsbi8XYmRJzCdTIgww9XYN0Og2Xy8WRBgCmHTqdTiZy0DMej8dZ0dFqtbJ8ATmIsiyzlz8w\nMIDl5WV0dHRw74VCoYCdO3eira0NiUQClUoFfr8f8XgcwAbk63K5UK/XYbfbAYCF1IaHh3Hp0iWY\nzWauOZmamoIkSTh58iRKpdKbA6L50z/9U+zZswe5XA6rq6tcPTc3N4eBgQGcO3cOu3btYk0LokiN\njo7iH/7hHzA0NIT29nY4HA4sLi7i8OHD3J6s0WjAbDYzPlitVjE4OAiVSoVisYhcLge9Xg+1Ws3t\nu6igoVQqobu7m1tvLS0toa+vD7Iss0Enlcu1tTWMjY3hrW99Ky5duoRKpYKbb76ZbxBSr/P5fBwF\ntLe3szbF5cuX4fV6sbi4yDoYsVgM0WiUQzh6yAAwDCF6b8BVD56wauAq35mWVcIsytHK0Cu/V3r+\nygSfCKUQj5h44MSaAa6yVYimpsTZaZIWVRppYhQ1U3Q6Hex2O7q7u5k/TZEEJQ8JDiGslpgZ1WqV\ntUsI76Y+wbQvZIDJ86UqXvLwRRydOO6UQ6HfkYe+vr7OxlKEbOj15Qy0mIOh897q2lG0Kf5PmLwY\nqRDLhyZjn8/HfX4JsqzX61uaexCV0Gg0wuFwwGKxYHR0FHNzc5xcp4rTer0On8+Hrq4uZLNZlEol\n9PT0oL29neVJVCoVdu7ciUAggJmZGW7LNzU1xUJcGs1GezzKA1gsFn62xImxo6ODRQOpybXJZOJi\npLe//e04duwYd20iI+/1epmiTF2qxC5OwWAQFy5c4GeLGG8Uud12223cW4LsBfVXUKlU2LFjB5rN\nJmZnZ7ly/OTJk/B4PHA6nfD7/Xjuuef4nj927NirTrK+LiAatVqNG2+8EVNTUzh37hxGRka46mvH\njh34xje+gQ984ANYWFhAPB6HwWCA3W5Hf38/HnroIYyMjKCnpwf1eh2Tk5Nwu91YWFjA0NAQSqUS\nXC4XJ/tsNhvMZjOSyST3fyT6pVar5Qw7SRX4/X488cQT2LFjB9RqNZLJJGfCJWmjAIhkdQ0GA/bu\n3YtKpYJdu3Yx9Y1uBp/Ph8uXL6NSqXBoOz8/D5fLhWg0ire97W2Eu8HtdrMHMT8/z3gviZNRL1gy\nGEpjLSZexWQgsLWNXitPEXgprZCGMkknbk/JVBGTpATLkLEjhgNh2iqViic6pYY66Y/TvUIGkrw5\ngkdqtRqWlpbYcxJplrROsZBJTEIqDalIKxSxatJ6Ic+VJgB6T14+GW2KtES9HhHbpWuknKTpVWT3\nKI2/MnJrlSsR1yWygcS8A02YsizD7XYjlUqh0WhwdEPLE03QYrFgZGQE8Xicdd4JzqHiv0wmg8XF\nRfh8PobHiA3kcDi4n8PIyAgAsPy3Xq9naW2CW6vVKpxOJxYWFnjioc5H9NyRaiwVKe7cuRNjY2PQ\narUMg1C9yve+9z0cOHAAwEaj60ajAa/XCwDs6JGelCxv1L4QwwW4qkNFrQinpqbYlhCkqtfrkclk\nGOLbs2cPTp8+zTUFgUAA586dQ2dnJ3p7e+H1evH000/zsyEqfr6a8brw4IeGhuQ/+qM/wqVLl3Dn\nnXcyFS0ajaJQKGD//v2sAJfL5TA0NIRGo8F9GX0+3xYjQIkvlepqA2DCvs1mM86cOcOKiuVymUPw\narXKeDGwwSRYWFhgAbFsNguTycThXaPRQLFYhNFo5O1TmTbpY5OnR1giefKHDx/mgguSRgiHw+jr\n68Njjz0Gk8nE5ekkt5pOpzEzM4NYLMZJOMJzga3YLHBVMoA8VeAqZEDYq9Jg0HkTvUrg2olZkb2j\nXEb0IOk9LU9eL7A1sSlJEht8UXRMNMpiZatYIAVcLZii34jRAEUM9Bkdw+rqKicx6fhF6ISMM73S\nfpDhFmETWk78DY1rsXyUSVM6n6JBVhp38bNrXTPaprgO8tipmxT9tqOjAw6Hg50Rt9uNbDbLre/o\nWIissG3bNt6Xvr4+NBoNxuuJo05NZIhqWS6X4XQ6cerUKe6Rur6+jnvuuQePPvoo9u7di56eHrjd\nbhw9epQlALRaLVZWVvi5JHhH1JUCwM+ixWLBxMQE2trasHPnTkxPT6Orq4sjcLfbzUSLYrHIfVRJ\n8z0SifD5LJfLGBoaQjQahdfrRSAQwJEjR6DT6WA0GhGNRlnLKR6P8/mzWq18PQ8dOoTx8XFOHHd3\nd2Nqaoq9+GazyXU0hCLk83mcOnXqzSEX3NvbKz/wwAPYvn0743Pz8/Pw+XzcNJeSJqTQRvBEMpnE\nW97yFkxMTHCv1vb2dpjNZkQiET6pRIWcmZmBzWaDXq/nh4o47cQjNxqNiEQiXDFHw2Aw4Ny5c0y/\npJnW4/FwSEd0SvJMS6USC55Ro2Gqrv3N3/xNnjjIsGUyGTgcDjz77LMwmUw8iel0OiSTSczPzzNk\nQ+G90piIRlX0dMmo0eQEtG6yQQ++sipUvFdED7cVRq80ZiILhT4Ti4BoEDYufk7nUuSli3x0paet\nHMr9EycLcRnxHJDhFr1w8TwQxELfiTkR0egq4Sull6007MpJUtw3UWHy5SAz5TGLEwNNgAT1kWYM\n5YlKpRJCoRBSqRRrsBCOTYWChEV3dnbC5XIhkUgwHj8/P4/h4WHG9nt7e1miYH19HdFoFB6Ph5lw\nzWYTDzzwAKanp/mZpqpXk8mE9vZ2zMzMcO6LCgALhQI7TCTRQBWn6XQabW1tXJBIzz555oTzHz58\nGHNzc9xMiHJs1NiEov9sNguVSoUbb7wRP/rRj1iM8Pz582wLiIhRr9dZY4aaec/OzqJYLHJuY3Jy\nEh0dHbDb7ZiamoLFYmGHrbu7m+sCjh8//qox+NeFFg1VydXrdaysrGB2dhYDAwOsNaFWb0iU9vX1\nIRaLsVBRvV7nmZioiER9JJ0O0smWZZk5s1arlb1GSvhJkoRsNsvl4BQWEn5L6x4cHITb7WY+u9Pp\n3GI8m80ma4GQIRkeHmZP3ePxwO/344477uCu8tTHVavVwuFwwGw244477kAqlcK+fftQr9eZReD1\netHX1weDwcDboLBOrFSkB17pBVKYTf8rYR3x96KBE/9XevKt4BolQ4UmBCXPXjT49FvC1EW8WdTJ\noVCfdFcoeUqJSsK2CS4hj5rgLFGSmRK04vLE7xYZP6KeSD6f56QbJUuvZaiVk4uSwSSeC+X1UEY+\nSshMuX5xHXRN6J4QowWCpcgI2e12ZpSo1RsNrGVZxsDAAF9/YpAQG2h9fZ0bYHi9XhQKBZjNZuzZ\nswfBYJCjQyI0mM1m2Gw2Vl+lmhev14t4PI4rV65w1zEA/BxGo1Gsr2+01lxYWIDH44FGo2EtKCId\nUIekQqGAQqHAmL/FYoHJZGIhMVLepA5OpClPMgo02ZGUQKVS4RwSFTGp1Wp2xBqNBmv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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "%3\n", + "\n", + "\n", + "\n", + "to_float\n", + "\n", + "to_float\n", + "\n", + "\n", + "\n", + "div_by_255\n", + "\n", + "div_by_255\n", + "\n", + "\n", + "\n", + "to_float->div_by_255\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "dot\n", + "\n", + "dot\n", + "\n", + "\n", + "\n", + "div_by_255->dot\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "weights\n", + "\n", + "weights\n", + "\n", + "\n", + "\n", + "weights->dot\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "mul_by_255\n", + "\n", + "mul_by_255\n", + "\n", + "\n", + "\n", + "dot->mul_by_255\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "to_uint8\n", + "\n", + "to_uint8\n", + "\n", + "\n", + "\n", + "mul_by_255->to_uint8\n", + "\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from skimage import data as imgdata\n", + "import skimage.color as color\n", + "import matplotlib.pyplot as plt\n", + "import scipy.ndimage as ndimage\n", + "import numpy as np\n", + "\n", + "coffee_cup = imgdata.coffee()\n", + "plt.imshow(coffee_cup)\n", + "plt.show()\n", + "\n", + "def to_gray(img):\n", + " #img = tf.placeholder(tf.uint8,name=\"input____\")\n", + " img_float = tf.div(tf.cast(img,dtype=tf.float64,name=\"to_float\"),255.0,name='div_by_255')\n", + " weights = tf.constant([0.2125,0.7154,0.0721],dtype=tf.float64,name=\"weights\")\n", + " \n", + "# R = tf.slice(img_float,[0,0,0],[-1,-1,1],name='extract_red')\n", + "# G = tf.slice(img_float,[0,0,1],[-1,-1,1],name='extract_green')\n", + "# B = tf.slice(img_float,[0,0,2],[-1,-1,1],name='extract_blue')\n", + " \n", + " \n", + "# gray = 0.2125*R + 0.7154*G + 0.0721*B\n", + " gray = tf.tensordot(img_float,weights,axes=1,name=\"dot\")\n", + "# weighted = tf.multiply(img_float,weights,name=\"weighting\")\n", + "# gray = tf.reduce_sum(weighted,axis=2, name =\"weighted_sum\") \n", + " \n", + " output = tf.cast(tf.multiply(gray,255.0,name='mul_by_255'), tf.uint8, name=\"to_uint8\")\n", + " return output\n", + "\n", + "tf.reset_default_graph()\n", + "gray_graph = to_gray(coffee_cup)\n", + "#gray_graph = to_gray(coffee_cup)\n", + "\n", + "I = np.uint8(color.rgb2gray(coffee_cup)*255)\n", + "\n", + "with tf.Session() as sess:\n", + " gray_image = np.squeeze(sess.run(gray_graph))\n", + "# print gray_image.shape\n", + "# print I.shape\n", + "# print np.max(gray_image),np.min(gray_image),np.mean(gray_image)\n", + "# print np.max(I),np.min(I),np.mean(I)\n", + "# print np.max(np.abs(gray_image-I))\n", + " plt.imshow(np.squeeze(gray_image),'gray')\n", + " plt.show()\n", + "dot=tf_to_dot(tf.get_default_graph())\n", + "\n", + "\n", + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0 0]\n", + " [ 0 1]\n", + " [ 0 2]\n", + " ..., \n", + " [327 397]\n", + " [327 398]\n", + " [327 399]] [[ True True True ..., True True True]\n", + " [ True True True ..., True True True]\n", + " [ True True True ..., True True True]\n", + " ..., \n", + " [ True True True ..., True True True]\n", + " [ True True True ..., True True True]\n", + " [ True True True ..., True True True]]\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "import tensorflow as tf\n", + "import skimage.data as imgdata\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "horse = imgdata.horse().astype(np.uint8)\n", + "\n", + "def horse_color(img):\n", + "# horse_idx = tf.constant(img.)\n", + " horse = tf.constant(img.astype(bool),name='horse')\n", + "# white = tf.constant(np.ones((img.shape[0],img.shape[1],3),dtype=np.float64)*255)\n", + "# color = tf.constant(np.full((img.shape[0],img.shape[1],3),np.array([127.0,255.0,128.0]),dtype=np.float64))\n", + "# white = tf.constant(np.zeros(img.shape,dtype=np.uint8))\n", + "# color = tf.constant(np.ones(img.shape,dtype=np.uint8)*128)\n", + " horse_idx = tf.where(horse)\n", + "# colored_horse = tf.boolean_mask(color,horse)\n", + "# zero = tf.constant(0, dtype=tf.uint8)\n", + "# non_empty = tf.not_equal(horse, zero,name='horse_nonempty')\n", + "# unit8_nonempty_horse = tf.cast(tf.not_equal(horse, zero,name='horse_nonempty'),dtype=tf.uint8,name=\"to_uint8\")\n", + " return horse_idx\n", + "\n", + "tf.reset_default_graph()\n", + "horse_data = imgdata.horse()\n", + "horse_graph = horse_color(horse_data)\n", + "\n", + "with tf.Session() as sess:\n", + " horse_image = sess.run(horse_graph)\n", + " print horse_image,horse_data\n", + "# plt.imshow(horse_image)\n", + "# plt.show()\n", + "# dot=tf_to_dot(tf.get_default_graph())\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python2", + "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": "2.7.14" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/LinearAlgebra.ipynb b/LinearAlgebra.ipynb new file mode 100644 index 0000000..8049e9a --- /dev/null +++ b/LinearAlgebra.ipynb @@ -0,0 +1,1079 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Linear Algebra \n", + "\n", + "### Motivation\n", + "\n", + "Linear algebra deals with vectors, matrices and tensors. Before using machine learning to solve a problem, the first step is usually to represent the real-world input in the form of vectors/matrices of numbers. Following are the key reasons why linear algebra will be found everywhere throughout this course:\n", + "\n", + "**Compact Notation** Linear algebra provides a convenient language for compactly representing computations which may otherwise require more verbose expressions. Getting familiar with these notations will give you access to several books and literatures in the domain of machine learning and deep learning.\n", + "\n", + "**Standard Representation** In many important domains, data is naturally available in digital form. Speech, audio/video, images from social network to medical scans, etc. This kind of data can be readily be represented in vector or matrix forms, thus making vectors and matrices the most preferred input formats. Most machine learning libraries assume the input data structure to be matrices or tensors. \n", + "\n", + "**Fast Computation** Representing computations in the form of linear algebra equations enables underlying machine learning libraries to take advantage of fast matrix computation routines. Further more, frameworks like Tensorflow, can leverage distributed systems and GPUs to run matrix computations much faster. While what can be done with matrix operations can also be done using for loops, the speed difference between the two options is extremely significant.\n", + "\n", + "\n", + "```python\n", + "from sympy import *\n", + "import numpy as np\n", + "\n", + "r = r'$%s$'%latex(Matrix(np.arange(3).reshape(1,-1)))\n", + "c = r'$%s$'%latex(Matrix(np.arange(3).reshape(-1,1)))\n", + "m = r'$%s$'%latex(Matrix(np.arange(12).reshape(3,4)))\n", + "\n", + "```\n", + "\n", + "### Terms & Notations\n", + "\n", + "**Scalars** are 0 dimensional. They are just numbers. For example, height of a person, temperature, stock price etc. They are represented using lower case letters as $x,y,x_1,w_5$ etc.\n", + "\n", + "**Vectors** are 1 dimensional. Meaning, you can represent them as a collection of numbers. For example, to represent a color of pixel in an image, we will need three numbers, r, g and b. That is single color, $\\mathbf{c} = [r,g,b]$. Vectors are denoted by boldface, lower case letters, like $\\mathbf{x,y,z,w,v}$. While one dimensional array of numbers can be either row vector, {{r}} or column vector {{c}}, by convention, vector is taken to be a column vector.\n", + "\n", + "**Matrices** Matrices are 2 dimensional array of numbers. For example a matrix {{m}} is a $\\mathrm{3x4}$ array of numbers. That is, it has 3 rows and 4 columns. A gray scale image, for example, is represented as a matrix of size $\\mathrm{width\\ x\\ height}$. A collection of document vectors can be represented as a matrix.\n", + "\n", + "**Tensors** Higher dimensional arrays are called Tensors. They are generalisation of vectors and matrices. However, note that a whole lot of linear algebra computations like matrix multiplication, SVD, determinants etc. are not defined or are not used with Tensors. A color image is represented as $\\mathrm{w x h x c}$ array, which is a tensor. A training data may involve 1000s of such arrays in a single bigger tensor of dimensions $\\mathrm{N x w x h x c}$. In Tensorflow, the data is represented as generic tensors. We will see more of that soon.\n", + "\n", + "### Numpy\n", + "\n", + "Numpy is a python library for numerical computations, with rich support for linear algebra computations among lot of other things. It is essential to have a deep working expertise with numpy. Most numpy operations have equivalent operations in Tensorflow as well.\n", + "\n", + "\n", + "### Matrix operations in Action 1 - Slicing an Image to extract R,G,B channels\n", + "\n", + "Try the following code.\n", + "\n", + "```python\n", + "%matplotlib inline\n", + "import skimage.data as imgdata\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "astronaut = imgdata.astronaut()\n", + "\n", + "#Please take the opportunity to get familiar with matplotlib and numpy operations used in sample codes.\n", + "R = astronaut[:,:,0] #0th channel is R, 1st channel is G, and 2nd channel will be red\n", + "G = astronaut[:,:,1]\n", + "B = astronaut[:,:,2]\n", + "\n", + "plt.subplot(1,4,1) #We want to show the images as 1 row, 4 columns, the last number indicating that we are about to draw the first image\n", + "plt.imshow(astronaut)\n", + "plt.title('Color')\n", + "plt.axis('off') #When showing images, we don't need axes. They clutter the display with axis labels.\n", + "\n", + "plt.subplot(1,4,2) #Now we are setting the context to draw the R channel of the image\n", + "plt.imshow(R,'gray')\n", + "plt.title('Red Levels')\n", + "plt.axis('off')\n", + "\n", + "plt.subplot(1,4,3)\n", + "plt.imshow(G,'gray')\n", + "plt.title('Green Levels')\n", + "plt.axis('off')\n", + "\n", + "plt.subplot(1,4,4)\n", + "plt.imshow(B,'gray')\n", + "plt.title('Blue Levels')\n", + "plt.axis('off')\n", + "\n", + "plt.suptitle('Image and its Color Channels')\n", + "plt.show()\n", + "```\n", + "\n", + "### Matrix operations in Action 2 - Weighted average of pixels to convert a color image into grayscale\n", + "```python\n", + "%matplotlib inline\n", + "import skimage.data as imgdata\n", + "import matplotlib.pyplot as plt\n", + "\n", + "coffee_cup = imgdata.coffee()\n", + "\n", + "#Please take the opportunity to get familiar with matplotlib and numpy operations used in sample codes.\n", + "R = coffee_cup[:,:,0] #0th channel is R, 1st channel is G, and 2nd channel will be red\n", + "G = coffee_cup[:,:,1]\n", + "B = coffee_cup[:,:,2]\n", + "\n", + "I = 0.2125*R + 0.7154*G + 0.0721*B #Gray scale image is a weighted average of R, G and B values of the pixels. All pixels of I are simultaneously computed with this elementwise addition\n", + "\n", + "plt.subplot(1,2,1)\n", + "plt.imshow(coffee_cup)\n", + "plt.title('Color Image')\n", + "plt.axis('off')\n", + "\n", + "plt.subplot(1,2,2)\n", + "plt.imshow(I,'gray') #Even though I is a grayscale image, we have to set the colormap to \"gray\". Otherwise matplotlib will show the gray values using multicolor pallete, chosing color based on the intensity value\n", + "plt.title('Grayscale Image')\n", + "plt.axis('off')\n", + "\n", + "plt.show()\n", + "```\n", + "\n", + "### Matrix operations in Action 3 - Finding the mean of 1000 images in one numpy operation\n", + "Surprisingly, the mean face, which is an average of random faces appears to have very symmetric features. Try this code that averages 1000 different faces:\n", + "```python\n", + "%matplotlib inline\n", + "\n", + "import numpy as np\n", + "import pickle\n", + "\n", + "faces = pickle.load(open('faces.pkl'))\n", + "\n", + "(num_of_images,height,width,clr_channels) = faces.shape #First dimension shows the number of face images we have.\n", + "\n", + "#Lets select 30 images randomly, and display them in 3x10 plot. You may want to understand this code\n", + "\n", + "idxList = np.random.randint(0,num_of_images,30) #Please check the documentation of np.random.randint for help. Type 'np.random.randint?' in iPython \n", + "\n", + "for i,idx in enumerate(idxList): #Check if sampled images change everytime you run the cell\n", + " plt.subplot(3,10,i+1)\n", + " plt.imshow(faces[idx])\n", + " plt.axis('off')\n", + "plt.suptitle('Sample Images from the Dataset')\n", + "plt.show()\n", + "\n", + "m = np.mean(faces,0)\n", + "m = m.astype(np.uint8) #Matplotlib expects the images to be of uint8 type, meaning RGB values should be integers in the range of 0 to 255. Else the image displayed looks like garbage\n", + "plt.imshow(m)\n", + "plt.axis('off')\n", + "plt.title('Mean of 1000 Faces - Surprising?')\n", + "plt.show()\n", + "```\n", + "\n", + "\n", + "### Matrix operations in Action 4 - Datatype checking, Casting, Counting etc.\n", + "```python\n", + "import numpy as np\n", + "\n", + "A = np.random.rand(3,6)\n", + "print \"Shape of A:\", A.shape #3x6\n", + "print\n", + "print \"Uniform Random Numbers\"\n", + "print A #array of 3 rows, 6 columns, uniform random numbers\n", + "print\n", + "print \"Sometimes it is clumsy to inspect arrays, with so many decimal places printed on the screen\"\n", + "print \"We can control the numpy printing options as below\"\n", + "np.set_printoptions(precision=2)\n", + "print A\n", + "print \"Pleas note! It doesn't round the numbers, but only printing is controlled!\"\n", + "print A.dtype #64 bit floating point number\n", + "\n", + "#How to generate an array of random integers between 5 to 20, of size 5x10?\n", + "\n", + "#Method 1. We can use uniform random numbers between 0 to 1 and scale them to the required range.\n", + "#Then we can convert the scaled array to integers\n", + "\n", + "A = np.random.rand(5,10) #Uniform random numbers between 0 to 1, of size 5x10\n", + "A = A*(20-5) + 5 #Scale and shift the values to fit in the range of 5 to 20\n", + "print\n", + "print \"Values are now between 5 to 20, but floating point.\"\n", + "print \"Note that A is still printed upto 2 decimal places. np.set_printoptions is a global setting.\"\n", + "print A #A is in the required range, but it is of type floating point\n", + "print \"Datatype of A is \", A.dtype\n", + "print \"Casting A to 16 bit integer values\"\n", + "A = A.astype(np.int16)\n", + "print A #Now A is the desired output\n", + "print A.dtype #This should be np.int16\n", + "print\n", + "print \"Are they really in 5 to 20 range?\"\n", + "print \"The unique values in A are:\", np.unique(A) #This will tell us what are the unique values present in the array\n", + "print \"Are they really unform? We can count how many times each number is appearing. That should be roughly equal.\"\n", + "counts = np.bincount(A.flatten()) #np.bincount takes only one dimension array. A.flatten() will flatten n-dimension array into a 1d array\n", + "print \"counts will be a 1d array of size np.max(A). counts[i] tells us how many times the number i has appeared in the input\"\n", + "print counts[5:] #We are interested in counts of numbers between 5 to 20 only.\n", + "print \"We can plot the counts and check. The numbers are not roughly equal! Why?\"\n", + "print \"Change the code to check if it helps if you generate much bigger sample.\"\n", + "```\n", + "\n", + "### Matrix operations in Action 5 - Zeros, Ones, Linspace,Elementwise computations\n", + "\n", + "```python\n", + "import numpy as np\n", + "\n", + "A = np.zeros(shape=(5,5))\n", + "print A\n", + "print\n", + "B = np.ones((3,5))\n", + "print B\n", + "print\n", + "x = np.linspace(-2*np.pi,2*np.pi,100) #x is a linearly spaced, 100 numbers between -2*pi to +2*pi\n", + "y = np.sin(x) #All scalar math functions in numpy apply to every element in the input, element wise. No need for for loop to call sin function on every value.\n", + "print 'x values:',x[:10] #Show only first 10 values.\n", + "print 'y values:',y[:10]\n", + "print 'You can zip x and y values together: '\n", + "points = zip(x,y) #Useful python function to combine corresponding elements in two 1d arrays into list of tuples.\n", + "print points[:10] #Note, the values are now printed beyond 2 decimals. Can you reason why?\n", + "print\n", + "print \"Plot:\"\n", + "plt.plot(x,y)\n", + "plt.show()\n", + "```\n", + "\n", + "\n", + "\n", + "### Matrix operations in Action 6- Broadcasting\n", + "\n", + "In math, two matrices can be added only if they both are of same dimension. Numpy does allow adding matrices of different dimensions under certain conditions. This is called broadcasting. It is important to understand how broadcasting in numpy works, one to avoid unintended effects causing bugs, two, to achieve computational efficiency. \n", + "\n", + "You can learn about broadcasting works on [this page](http://scipy.github.io/old-wiki/pages/EricsBroadcastingDoc)\n", + "\n", + "Run the following code to see the benefits of broadcasting.\n", + "\n", + "```python\n", + "import numpy as np\n", + "import time\n", + "\n", + "A = np.random.rand(10000,100) #10000x100 array of random numbers\n", + "B = np.ones((1,100))*10 #B is a a 1x100 array of 10s\n", + "\n", + "#Suppose we want to add B to every row of A.\n", + "#In matrix algebra, A+B is forbidden. We need to replicate B 10000 times and make an array of size compatible to A, and then add\n", + "\n", + "#Lets see how fast this code is. We will run this 1000 times and average the time.\n", + "start = time.time()\n", + "for i in range(1000):\n", + " B1 = np.repeat(B,10000,axis=0) #Repeat 10000 times along rows (axis=0)\n", + " S = A+B1 #desired output\n", + "stop = time.time()\n", + "\n", + "total_time1 = stop-start\n", + "print \"Average execution time to compute the desired output: \", total_time1/1000\n", + "\n", + "print \"With Numpy broadcasting, we save memory and time.\"\n", + "start = time.time()\n", + "for i in range(1000):\n", + " S = A+B #Numpy will automatically broadcast the values in a compatible way. Important to understand the rules to avoid unintended bugs\n", + "stop = time.time()\n", + "total_time2 = stop-start\n", + "print \"Total time taken for 1000 executions of A+B with broadcasting is: \",total_time2/100\n", + "\n", + "print \"Broadcasting is %s times faster for this case.\"%(total_time1/total_time2)\n", + "```\n", + "\n", + "### Matrix operations in Action 7 - Boolean Indexing \n", + "```python\n", + "%matplotlib inline\n", + "import skimage.data as imgdata\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "horse = imgdata.horse()\n", + "horse = horse[:,:,:3] #Drop the A channel\n", + "#This is a RGBA image. Convert it into Binary\n", + "horse = np.max(horse,2)\n", + "#Horse is a binary image, with values 0 an 1. You can inspect the values of the image\n", + "print 'Min and Max values in the horse image'\n", + "np.min(horse),np.max(horse)\n", + "#Let's make the horse red and background black, using boolean indexing to operate on the image\n", + "I,J = np.nonzero(horse==0) #Boolean indexing finds all (i,j)s in the image where the pixels are black(0), giving us the indices of horse pixels\n", + "#We will make R, G, and B panels separately and put them together to make color image\n", + "R = np.zeros_like(horse) #Make zeros of same type and shape as the horse array\n", + "R[I,J] = 255 #Red panel we have set\n", + "output = np.zeros((horse.shape[0],horse.shape[1],3),dtype=horse.dtype)\n", + "output[:,:,0] = R\n", + "#G and B channels are zeros. So we get a red horse and black background.\n", + "\n", + "plt.subplot(1,2,1)\n", + "plt.title('Input')\n", + "plt.axis('off')\n", + "plt.imshow(horse,'gray')\n", + "\n", + "\n", + "plt.subplot(1,2,2)\n", + "plt.title('output')\n", + "plt.axis('off')\n", + "plt.imshow(output,'gray')\n", + "\n", + "plt.show()\n", + "```\n", + "\n", + "### Matrix operations in Action 8 - Dot Product, Least Squares Error\n", + "```python\n", + "import numpy as np\n", + "from sklearn.datasets import load_digits\n", + "\n", + "digits = load_digits()\n", + "images = digits['images']\n", + "num_images = images.shape[0]\n", + "\n", + "print \"Shape of images array is: \", images.shape\n", + "\n", + "#The images array contains N number of 8x8 binary digit images, this is a 3 dimensional array\n", + "#We will flatten 8x8 images into 64 dimensional vector for each image, stacked as image vectors\n", + "image_vectors = images.reshape(-1,64)\n", + "#image_vectors will be of shape N x 64\n", + "\n", + "rand_idx = np.random.randint(0,num_images,1)[0]\n", + "sample = images[rand_idx,:].flatten() #Radomly select a sample image\n", + "\n", + "#Let's take a random digit image, and find top 30 digits from the images that are closest to this.\n", + "#To measure closeness, we will use euclidean distance.\n", + "images_diff = image_vectors - sample #Check the shapes of image_vectors and sample, and understand how broadcasting is at work here\n", + "distances = np.sum(images_diff**2,1) #Elementwise square all the differeneces and add them across columns to get distances\n", + "\n", + "#Find indices of smallest distances. We can use argsort, which gives you sorted indices.\n", + "sorted_idxes = np.argsort(distances)\n", + "#these indices can be used to select the corresponding images from the original images \n", + "\n", + "nearest_images = images[sorted_idxes,:,:][:20] #Last line truncates selects the nearest 20\n", + "\n", + "plt.subplot(5,5,1) #1 row for the input image, and 5 rows for 50 output images\n", + "plt.imshow(images[rand_idx],'gray',interpolation='nearest')\n", + "plt.axis('off')\n", + "plt.title('Input Sample')\n", + "\n", + "loc = 6 #Start from the second row\n", + "for i,img in enumerate(nearest_images):\n", + " plt.subplot(5,5,loc+i)\n", + " plt.imshow(img,'gray',interpolation='nearest')\n", + " plt.title('d = %0.0f'%distances[sorted_idxes[i]]) #Make sure you understand how we are reading the corresponding distance\n", + " plt.axis('off')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from sympy import *\n", + "import numpy as np\n", + "\n", + "r = r'$%s$'%latex(Matrix(np.arange(3).reshape(1,-1)))\n", + "c = r'$%s$'%latex(Matrix(np.arange(3).reshape(-1,1)))\n", + "m = r'$%s$'%latex(Matrix(np.arange(12).reshape(3,4)))" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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yX3nllUycOJFly5aRTqfZsWMHixcvprW1FaUUqVQKz/P43e9+Rzab5ayzzqKh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uwYM2bM4Ac/+AEfffQRJpOJlStX8vDDD0uiZGVlJatWreK8885jy5YtbNy4kebmZhRFIT8/n+bm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\ni8RIELPbhZJJk0LH3b/ezp6hb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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import skimage.data as imgdata\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "astronaut = imgdata.astronaut()\n", + "\n", + "#Please take the opportunity to get familiar with matplotlib and numpy operations used in sample codes.\n", + "R = astronaut[:,:,0] #0th channel is R, 1st channel is G, and 2nd channel will be red\n", + "G = astronaut[:,:,1]\n", + "B = astronaut[:,:,2]\n", + "\n", + "plt.subplot(1,4,1) #We want to show the images as 1 row, 4 columns, the last number indicating that we are about to draw the first image\n", + "plt.imshow(astronaut)\n", + "plt.title('Color')\n", + "plt.axis('off') #When showing images, we don't need axes. They clutter the display with axis labels.\n", + "\n", + "plt.subplot(1,4,2) #Now we are setting the context to draw the R channel of the image\n", + "plt.imshow(R,'gray')\n", + "plt.title('Red Levels')\n", + "plt.axis('off')\n", + "\n", + "plt.subplot(1,4,3)\n", + "plt.imshow(G,'gray')\n", + "plt.title('Green Levels')\n", + "plt.axis('off')\n", + "\n", + "plt.subplot(1,4,4)\n", + "plt.imshow(B,'gray')\n", + "plt.title('Blue Levels')\n", + "plt.axis('off')\n", + "\n", + "plt.suptitle('Image and its Color Channels')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Nx+NasYgIzWaTJElwHIeiKIjjmCAIODw8rJ9PdS1rLdPplHa7jdYaESFJEjY3N8nznOFw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05z7iTHvns/X/qLrzMjJA4SR0GIk1XzN7x2nMW1ZYw2hM0uH5nf4OFqlapKkFKlOwspiIMIz5Gp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\nCSHwhaCCoFZpU8xl8eoV5OZb8cuLnHjgj/nhH/4+t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DdPALxzhpIQkpKP/4e/IxKbYm6l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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import skimage.data as imgdata\n", + "import matplotlib.pyplot as plt\n", + "\n", + "coffee_cup = imgdata.coffee()\n", + "\n", + "#Please take the opportunity to get familiar with matplotlib and numpy operations used in sample codes.\n", + "R = coffee_cup[:,:,0] #0th channel is R, 1st channel is G, and 2nd channel will be red\n", + "G = coffee_cup[:,:,1]\n", + "B = coffee_cup[:,:,2]\n", + "\n", + "I = 0.2125*R + 0.7154*G + 0.0721*B #Gray scale image is a weighted average of R, G and B values of the pixels. All pixels of I are simultaneously computed with this elementwise addition\n", + "\n", + "plt.subplot(1,2,1)\n", + "plt.imshow(coffee_cup)\n", + "plt.title('Color Image')\n", + "plt.axis('off')\n", + "\n", + "plt.subplot(1,2,2)\n", + "plt.imshow(I,'gray') #Even though I is a grayscale image, we have to set the colormap to \"gray\". Otherwise matplotlib will show the gray values using multicolor pallete, chosing color based on the intensity value\n", + "plt.title('Grayscale Image')\n", + "plt.axis('off')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Y0Hms9vz580S9NpZJCDyXZJAPGBfXB8wvLNPrJ+zef4hD1x8mSmIuXVpkfX2N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\nESytre1peqRMTSJTXNfl1tIG8xsbnL/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eeeQRfN/HtASu63Lx4kWEEDz00EM7zUSWZWEI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0x2tyknhwibwVGknGUa30rIM3SZklcljnC4tLVcUm2THIoKW2kKbchKQ5pXZLleZMzCouRi2iNJ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\nzc/E0U9OxhiGXvsUxVM2NtfQNEmahigKpKCGU1QO1M6XSoHp6JDVdOWP7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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "import numpy as np\n", + "import pickle\n", + "\n", + "faces = pickle.load(open('Linear Algebra KB v1.0/faces.pkl'))\n", + "\n", + "(num_of_images,height,width,clr_channels) = faces.shape #First dimension shows the number of face images we have.\n", + "\n", + "#Lets select 30 images randomly, and display them in 3x10 plot. You may want to understand this code\n", + "\n", + "idxList = np.random.randint(0,num_of_images,30) #Please check the documentation of np.random.randint for help. Type 'np.random.randint?' in iPython \n", + "\n", + "for i,idx in enumerate(idxList): #Check if sampled images change everytime you run the cell\n", + " plt.subplot(3,10,i+1)\n", + " plt.imshow(faces[idx])\n", + " plt.axis('off')\n", + "plt.suptitle('Sample Images from the Dataset')\n", + "plt.show()\n", + "\n", + "m = np.mean(faces,0)\n", + "m = m.astype(np.uint8) #Matplotlib expects the images to be of uint8 type, meaning RGB values should be integers in the range of 0 to 255. Else the image displayed looks like garbage\n", + "plt.imshow(m)\n", + "plt.axis('off')\n", + "plt.title('Mean of 1000 Faces - Surprising?')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape of A: (3, 6)\n", + "\n", + "Uniform Random Numbers\n", + "[[ 0.53026842 0.85568589 0.59917602 0.13160372 0.82012804 0.36597499]\n", + " [ 0.68449891 0.25387986 0.55686847 0.55368587 0.85834321 0.48071346]\n", + " [ 0.26787755 0.25289669 0.21857296 0.83156644 0.56501215 0.57388012]]\n", + "\n", + "Sometimes it is clumsy to inspect arrays, with so many decimal places printed on the screen\n", + "We can control the numpy printing options as below\n", + "[[ 0.53 0.86 0.6 0.13 0.82 0.37]\n", + " [ 0.68 0.25 0.56 0.55 0.86 0.48]\n", + " [ 0.27 0.25 0.22 0.83 0.57 0.57]]\n", + "Pleas note! It doesn't round the numbers, but only printing is controlled!\n", + "float64\n", + "\n", + "Values are now between 5 to 20, but floating point.\n", + "Note that A is still printed upto 2 decimal places. np.set_printoptions is a global setting.\n", + "[[ 7.81 18.93 6.21 ..., 19.72 17.44 18.95]\n", + " [ 11.42 18.6 14.93 ..., 7.79 8.07 10.11]\n", + " [ 16.4 17.86 14.55 ..., 18.49 15.98 14.67]\n", + " ..., \n", + " [ 18.67 6.67 8.71 ..., 10.98 10.91 13.49]\n", + " [ 15.5 8.98 18.79 ..., 8.52 10.42 16.95]\n", + " [ 17.4 10.77 16.5 ..., 9.47 10.77 18.58]]\n", + "Datatype of A is float64\n", + "Casting A to 16 bit integer values\n", + "[[ 7 18 6 ..., 19 17 18]\n", + " [11 18 14 ..., 7 8 10]\n", + " [16 17 14 ..., 18 15 14]\n", + " ..., \n", + " [18 6 8 ..., 10 10 13]\n", + " [15 8 18 ..., 8 10 16]\n", + " [17 10 16 ..., 9 10 18]]\n", + "int16\n", + "\n", + "Are they really in 5 to 20 range?\n", + "The unique values in A are: [ 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19]\n", + "Are they really unform? We can count how many times each number is appearing. That should be roughly equal.\n", + "counts will be a 1d array of size np.max(A). counts[i] tells us how many times the number i has appeared in the input\n", + "[33144 33358 33020 33266 33069 33198 33593 33313 33538 33421 33617 33221\n", + " 33235 33378 33629]\n", + "We can plot the counts and check. The numbers are not roughly equal! Why?\n", + "Change the code to check if it helps if you generate much bigger sample.\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "A = np.random.rand(3,6)\n", + "print \"Shape of A:\", A.shape #3x6\n", + "print\n", + "print \"Uniform Random Numbers\"\n", + "print A #array of 3 rows, 6 columns, uniform random numbers\n", + "print\n", + "print \"Sometimes it is clumsy to inspect arrays, with so many decimal places printed on the screen\"\n", + "print \"We can control the numpy printing options as below\"\n", + "np.set_printoptions(precision=2)\n", + "print A\n", + "print \"Pleas note! It doesn't round the numbers, but only printing is controlled!\"\n", + "print A.dtype #64 bit floating point number\n", + "\n", + "#How to generate an array of random integers between 5 to 20, of size 5x10?\n", + "\n", + "#Method 1. We can use uniform random numbers between 0 to 1 and scale them to the required range.\n", + "#Then we can convert the scaled array to integers\n", + "\n", + "A = np.random.rand(500,1000) #Uniform random numbers between 0 to 1, of size 5x10\n", + "A = A*(20-5) + 5 #Scale and shift the values to fit in the range of 5 to 20\n", + "print\n", + "print \"Values are now between 5 to 20, but floating point.\"\n", + "print \"Note that A is still printed upto 2 decimal places. np.set_printoptions is a global setting.\"\n", + "print A #A is in the required range, but it is of type floating point\n", + "print \"Datatype of A is \", A.dtype\n", + "print \"Casting A to 16 bit integer values\"\n", + "A = A.astype(np.int16)\n", + "print A #Now A is the desired output\n", + "print A.dtype #This should be np.int16\n", + "print\n", + "print \"Are they really in 5 to 20 range?\"\n", + "print \"The unique values in A are:\", np.unique(A) #This will tell us what are the unique values present in the array\n", + "print \"Are they really unform? We can count how many times each number is appearing. That should be roughly equal.\"\n", + "counts = np.bincount(A.flatten()) #np.bincount takes only one dimension array. A.flatten() will flatten n-dimension array into a 1d array\n", + "print \"counts will be a 1d array of size np.max(A). counts[i] tells us how many times the number i has appeared in the input\"\n", + "print counts[5:] #We are interested in counts of numbers between 5 to 20 only.\n", + "print \"We can plot the counts and check. The numbers are not roughly equal! Why?\"\n", + "print \"Change the code to check if it helps if you generate much bigger sample.\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0. 0. 0. 0. 0.]\n", + " [ 0. 0. 0. 0. 0.]\n", + " [ 0. 0. 0. 0. 0.]\n", + " [ 0. 0. 0. 0. 0.]\n", + " [ 0. 0. 0. 0. 0.]]\n", + "\n", + "[[ 1. 1. 1. 1. 1.]\n", + " [ 1. 1. 1. 1. 1.]\n", + " [ 1. 1. 1. 1. 1.]]\n", + "\n", + "x values: [-6.28 -6.16 -6.03 -5.9 -5.78 -5.65 -5.52 -5.39 -5.27 -5.14]\n", + "y values: [ 2.45e-16 1.27e-01 2.51e-01 3.72e-01 4.86e-01 5.93e-01\n", + " 6.90e-01 7.76e-01 8.50e-01 9.10e-01]\n", + "You can zip x and y values together: \n", + "[(-6.2831853071795862, 2.4492935982947064e-16), (-6.1562522706709073, 0.12659245357374993), (-6.0293192341622293, 0.25114798718107939), (-5.9023861976535503, 0.37166245566032807), (-5.7754531611448723, 0.48619673610046882), (-5.6485201246361934, 0.59290792905464096), (-5.5215870881275153, 0.69007901148211204), (-5.3946540516188364, 0.77614646429175715), (-5.2677210151101583, 0.84972542994951439), (-5.1407879786014794, 0.90963199535451855)]\n", + "\n", + "Plot:\n" + ] + }, + { + "data": { + "image/png": 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+Xt74hWwE5ox4nOveNu4+IhIBJAPtUzwWAGPMk8aYMmNMWUZGxjUF+u0PLuGJ\nT1x3TceGovLCNAaGdGGVqTDGUFHbTnmBLvw0VZ4uXe1OmpqKug7mZSWQFh/l8/fyRmLYB5SISIGI\nRDFcTN48Zp/NwMPu+w8AbxpjjHv7g+5RSwVACbDXCzEpL9CFVabubHsPLV39en3MNMxKjiUvLU4L\n0FPgHHKx/0yH37opZ9wmMcY4ReRLwKtAOPC0MeaYiHwTqDTGbAZ+CPy3iFQDHQwnD9z7/Rw4DjiB\nPzbGaL9FgNCFVabO82+kK7ZNT3lBGltPNONyGcJ0CPmEjp6/TPfAkN8Gznils8oY8zLw8phtXx9x\nvw/4yATHfhv4tjfiUN5XXuDg2T1n6XcOER3hs5HEtldR20F6QhRFGTox43SsKkjjF/sbON3SpRed\nXoWnVeWvFqlWYdVV6cIqU+O5cFLrC9PjmZpcR79dXUVdB4UZ/lv4SRODuipdWGVy9R09NHb2sipf\nu5GmKzc1ltnJMVrHuoohl2HfGf9OzKiJQV2VLqwyOc+/zeoivX5hukSE8kIHFXXtDI9HUWN5Fn7y\n5/UxmhjUpFYXOth/9iKDOuHZuCpq20mJi2ReZqLVodhSeUEabVcGqGnttjqUgFRhwYwNmhjUpMoL\n0ugdHOJwg9YZxrPHvfCTjqq5Np6Cqo5+G19FbTt5aXHMSo7123tqYlCT0i/uxM539lLf0avrO89A\nQXo8mYnRWmcYh8tl2Ovn+gJoYlBT4EiIpiQzQb+44/AkS52Y8dp56gx7arXOMFZVyxU6ewYp9/OJ\nhyYGNSXlhWlU6sIq76MLP3nH6sI0Wrr6OeOHCeLs5L0TD20xqEBUXuCge2CIY7qwyih7attZVeDQ\nhZ9myNMVt0eHRY+yp7adnJRY5qT5d2JGTQxqSn4/4Zl+cT2aL/dxpr1Hp8HwgsL0eNITovV6mRGM\nMeyp7bCkfqWJQU1JZmIMhenxWmcYwXN2q+svzJyIsLowjT21HVpncKtquUJH94AlJx6aGNSUlRc6\n2KsLq7xnT20HidERlM7W+oI3lBc6aLrcx7kOrTPA7088tMWgAtqaouGFVY5rnQEY7la7viBN6wte\nssZ9Zqx1hmFW1RdAE4OahtXukRG7a9ssjsR6LV191LZ26/rOXlSUkUB6QpR2V/L7+oJVw6A1Magp\ny0yKoTAj/r21jUOZ599gjc6P5DUiQnmBXs8AI+sL1ny+NDGoaVnjrjOE+vUMu2vah+sLs7S+4E3l\nhWmcv9S0cfv4AAAa60lEQVRHw8Veq0OxlKc7bY0dE4OIpInIVhGpcv83dZx9lovIbhE5JiKHReRj\nI577sYjUichB9235TOJRvre60MGVfmfIX88wfP1CGhHhem7lTZ4z5N0hXmeoqO0gJyWW3FT/zY80\n0kw/1Y8CbxhjSoA33I/H6gH+0BizCNgIfE9EUkY8/1fGmOXu28EZxqN8rFwLhDRd6qOurVu7kXyg\nJDMBR3wUe2pC9/M1XF9op7zQuoWfZpoYNgHPuO8/A9w/dgdjzGljTJX7/nmgBciY4fsqi2QmxlCc\nmRDSZ3Se4rtOnOd9IsLqIge7Q7jOUN1yhXYL6wsw88SQZYy54L7fBGRdbWcRWQVEATUjNn/b3cX0\nXRGJnmE8yg9WF6axL4TrDHtqOkiOjdT6go+sKXRw4VJfyM6btNvi+gJMITGIyOsicnSc26aR+5nh\n9D5hiheRWcB/A582xnh+UR4DFgDXA2nAX1/l+EdEpFJEKltbWyf/y5TPrC4cnjfpaIjWGXa76wu6\n/oJveLrododod9LumnZL6wswhcRgjNlgjFk8zu1FoNn9g+/54W8Z7zVEJAn4HfA1Y8yeEa99wQzr\nB34ErLpKHE8aY8qMMWUZGdoTZaX3CoQh+MVt7OzlXEePpWdzwa4wPZ6spGh21YTe9TIul2F3bTtr\nixyW1Rdg5l1Jm4GH3fcfBl4cu4OIRAG/Bn5ijHlhzHOepCIM1yeOzjAe5Qfp7vUZQrHO4EmGWnj2\nHRFhTaEjJOdNOtF0mc6eQdYWW/v5mmlieBy4XUSqgA3ux4hImYg85d7no8BNwKfGGZb6UxE5AhwB\n0oG/m2E8yk/WFDnYV9fBgDO06gy7a9pJjYtkfpau7+xLa4vSabvST3XLFatD8av3TjwK0y2NI2Im\nBxtj2oHbxtleCXzOff9Z4NkJjr91Ju+vrLO2KJ2f7D7LoYZOrs8PjWkhPMMIVxc6tL7gY54W2a6a\ndkpCKAnvqmmnMCOe7OQYS+PQq3PUNVlT6EAEdlaHTj9wfUcvjZ26vrM/zEmLIyclNqTqWINDLirc\n9QWraWJQ1yQ5LpLFs5PZVR06X1xPMTQQvrihYG2Rgz117bhCZJr3ww2X6B4YYm2Rtd1IoIlBzcDa\nYgfv1l+kZ8BpdSh+saO6jczEaIozE6wOJSSsKXLQ2TPIiabQGBa9uyZwLpzUxKCu2bqidAaHDHvr\ngn+2VZfLsKumnRuK0y0dRhhKQu16hl017SyclURafJTVoWhiUNfu+vw0osLD2BUCX9yTTV10dA+w\nttj6Zn6omJUcS2F6PDtCoI7VNzhE5dmLAdNNqYlBXbPYqHBW5KWExIVIniL7OovHl4eaG0rSqagN\n/mHRB85dZMDp0sSggsPaonSOnb9MZ8+A1aH41M6aNooy4pmVbN00BaFoXXE6vYNDHDh30epQfGpX\ndTvhYcKqAFkRUBODmpF1xQ6MCe5+4AGni4raDtZpN5LfrSlyEBYCw6K3V7exNDeZxJhIq0MBNDGo\nGVo2J4X4qHB2BnF30sH6TnoHhzQxWCApJpJlc1LYXhW8n6/OngGONHRyY0ngzAGniUHNSGR4GKsK\n0tgZxNcz7KhuI0wCYxhhKLqxOJ3DDZ1c6h20OhSf2FXTjsvAjSWBc+KhiUHN2A0lGdS1dVPfEZzz\n5++sbmNJbgrJsYHRzA8164rTcQVxd+X2qjYSoiNYPidl8p39RBODmrGb5w2f6QRjc7+rb5CD9Z3c\noKORLLMiL5W4qPCgrDMYY9he1crqQgeRAbR+eOBEomyrKCOBWckxbDsdfAsoVdR2MOQyrAuAaQpC\nVVREGKsLHUGZGM6299BwsZeb5gXW50sTg5oxEeGmkgx21rQF3XKf26paiY0MZ2V+qtWhhLR1xenU\ntnXT2NlrdShetd2d7G4IsIENmhiUV9w0L4OuPieHGjqtDsWr3jndytoiB9ER4VaHEtI8hdmdQdZd\nuf10KzkpsRSkx1sdyiiaGJRXrCseHm++7XTwfHHPtHVztr2Hm+cHzjDCUFWSmUBmYjTvVAVPd6Vz\nyMXumnZumhd482/NKDGISJqIbBWRKvd/x21vi8jQiNXbNo/YXiAiFSJSLSLPu5cBVTaUEhfF0twU\ntgXRF9fzt9w8TxOD1USEm+dlsP10a9B0Vx5q6KSr38kNxYH3+Zppi+FR4A1jTAnwhvvxeHqNMcvd\nt/tGbP8H4LvGmGLgIvDZGcajLHTTvAwO1XdyqSc4xpu/c6qVfEcccx2B1cwPVevnZ3K5z8nB+uDo\nrtxe1YZIYM6/NdPEsAl4xn3/GeD+qR4ow22nW4EXruV4FXhuKhkebx4MV0H3O4fYVdOurYUAckNJ\nOuFhwtungqNV+vapVpbmppASF3gdJTNNDFnGmAvu+01A1gT7xYhIpYjsERHPj78D6DTGeFZ5aQBy\nZhiPstDyOSkkRkcExbDVyjMX6R0c0vpCAEmOjeS6vBTePt1idSgz1n6ln0MNndwSoJ+viMl2EJHX\ngexxnvrayAfGGCMiE63BN9cY0ygihcCbInIEuDSdQEXkEeARgLy8vOkcqvwkIjyMtcUOtp1uxRgT\ncAW16XjndCtR4WE6DUaAWT8/k++8eoqWrj4yE2OsDueavXO6FWPg1gWZVocyrklbDMaYDcaYxePc\nXgSaRWQWgPu/46ZyY0yj+7+1wNvACqAdSBERT3LKBRqvEseTxpgyY0xZRkZgZlkFt8zP5PylPk41\nd1kdyoxsO93K9QWpxEVNeu6k/Gi9+wz7HZt3J715soX0hGgWz062OpRxzbQraTPwsPv+w8CLY3cQ\nkVQRiXbfTwfWAceNMQZ4C3jgascre7nFfQb0xgn7NvebLvVxsqlL6wsBqHRWEpmJ0bxt4+5K55CL\nbadbWT8/g7CwwGxVzzQxPA7cLiJVwAb3Y0SkTESecu+zEKgUkUMMJ4LHjTHH3c/9NfDnIlLNcM3h\nhzOMR1ksKymGJTnJvHnSvonh7VPDsd+kiSHgBMOw1QPnOrnc5wzYbiSYQo3haowx7cBt42yvBD7n\nvr8LWDLB8bXAqpnEoALPrQsy+dc3q+joHgiIhc2n6/UTLeSkxDI/K9HqUNQ41s/P5Bf7GzhY30lZ\nfmCseDYdb55sISJMuCGAptkeS698Vl5328JMjPn9mbed9A4MsaO6ldtLs2xdPA9mnmGrb9nw8wXD\n34uy/FSSAmS1tvFoYlBet3h2MpmJ0basM+ysbqNv0MWGhRONvFZWS46NZOXcVFt+vho7eznZ1BXQ\n3UigiUH5QFiYcOuCTLadbmXAaa9+4NdPNJMYHREwi7Kr8d1RmsXJpi7Otdtrcai33LW3W+ZrYlAh\n6NYFmXT1O6k802F1KFPmchleP9HCzfMziIrQr0Ygu710uEX32vEmiyOZnrdOtpCbGktxZoLVoVyV\nfvqVT6wrTicqIow3bDQ66VBDJ21X+t/70VGBa64jngXZibx2vNnqUKbsSr+T7dVttqhfaWJQPhEf\nHcGaQgdvnGhm+JKVwPf6iWbCw4T18wK7ma+G3VGaReWZDtqv9FsdypS8faqFAaeLjYvGm0gisGhi\nUD6zYWEmZ9p7qGq5YnUoU/L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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "\n", + "A = np.zeros(shape=(5,5))\n", + "print A\n", + "print\n", + "B = np.ones((3,5))\n", + "print B\n", + "print\n", + "x = np.linspace(-2*np.pi,2*np.pi,100) #x is a linearly spaced, 100 numbers between -2*pi to +2*pi\n", + "y = np.sin(x) #All scalar math functions in numpy apply to every element in the input, element wise. No need for for loop to call sin function on every value.\n", + "print 'x values:',x[:10] #Show only first 10 values.\n", + "print 'y values:',y[:10]\n", + "print 'You can zip x and y values together: '\n", + "points = zip(x,y) #Useful python function to combine corresponding elements in two 1d arrays into list of tuples.\n", + "print points[:10] #Note, the values are now printed beyond 2 decimals. Can you reason why?\n", + "print\n", + "print \"Plot:\"\n", + "plt.plot(x,y)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average execution time to compute the desired output: 0.0118508088589\n", + "With Numpy broadcasting, we save memory and time.\n", + "Total time taken for 1000 executions of A+B with broadcasting is: 0.00538574504852\n", + "Broadcasting is 2.20040286945 times faster for this case.\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import time\n", + "\n", + "A = np.random.rand(10000,100) #10000x100 array of random numbers\n", + "B = np.ones((1,100))*10 #B is a a 1x100 array of 10s\n", + "\n", + "#Suppose we want to add B to every row of A.\n", + "#In matrix algebra, A+B is forbidden. We need to replicate B 10000 times and make an array of size compatible to A, and then add\n", + "\n", + "#Lets see how fast this code is. We will run this 1000 times and average the time.\n", + "start = time.time()\n", + "for i in range(1000):\n", + " B1 = np.repeat(B,10000,axis=0) #Repeat 10000 times along rows (axis=0)\n", + " S = A+B1 #desired output\n", + "stop = time.time()\n", + "\n", + "total_time1 = stop-start\n", + "print \"Average execution time to compute the desired output: \", total_time1/1000\n", + "\n", + "print \"With Numpy broadcasting, we save memory and time.\"\n", + "start = time.time()\n", + "for i in range(1000):\n", + " S = A+B #Numpy will automatically broadcast the values in a compatible way. Important to understand the rules to avoid unintended bugs\n", + "stop = time.time()\n", + "total_time2 = stop-start\n", + "print \"Total time taken for 1000 executions of A+B with broadcasting is: \",total_time2/1000\n", + "\n", + "print \"Broadcasting is %s times faster for this case.\"%(total_time1/total_time2)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Min and Max values in the horse image\n", + "0 1\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import skimage.data as imgdata\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "horse = imgdata.horse().astype(np.uint8)\n", + "# horse = horse[:,:,:3] #Drop the A channel\n", + "#This is a RGBA image. Convert it into Binary\n", + "# horse = np.max(horse,2)\n", + "#Horse is a binary image, with values 0 an 1. You can inspect the values of the image\n", + "print 'Min and Max values in the horse image'\n", + "print np.min(horse),np.max(horse)\n", + "#Let's make the horse red and background black, using boolean indexing to operate on the image\n", + "I,J = np.nonzero(horse==0) #Boolean indexing finds all (i,j)s in the image where the pixels are black(0), giving us the indices of horse pixels\n", + "#We will make R, G, and B panels separately and put them together to make color image\n", + "# R = np.zeros_like(horse) #Make zeros of same type and shape as the horse array\n", + "# R[I,J] = 255#ed panel we have set\n", + "# R.shape\n", + "# B = np.zeros_like(horse) #Make zeros of same type and shape as the horse array\n", + "# B[I,J] = 128 #Red panel we have set\n", + "colored_horse = np.ones((horse.shape[0],horse.shape[1],3),dtype=horse.dtype)*255\n", + "colored_horse[I,J] = np.array([127,255,128])#ed panel we have set\n", + "output = colored_horse\n", + "# output[:,:,0] = R\n", + "#G and B channels are zeros. So we get a red horse and black background.\n", + "\n", + "plt.subplot(1,2,1)\n", + "plt.title('Input')\n", + "plt.axis('off')\n", + "plt.imshow(horse,'gray')\n", + "\n", + "\n", + "plt.subplot(1,2,2)\n", + "plt.title('output')\n", + "plt.axis('off')\n", + "plt.imshow(output,'gray')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape of images array is: (1797, 8, 8)\n" + ] + }, + { + "data": { + "image/png": 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8JL1W0h2SpiU9JGlY0prS/KskPSDpUUl3Snpjw/pvlHSXpF2SviTpmPTvon0sIM9dDY+n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\nFsdnrVZj69at9fm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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "from sklearn.datasets import load_digits\n", + "\n", + "digits = load_digits()\n", + "images = digits['images']\n", + "num_images = images.shape[0]\n", + "\n", + "print \"Shape of images array is: \", images.shape\n", + "\n", + "#The images array contains N number of 8x8 binary digit images, this is a 3 dimensional array\n", + "#We will flatten 8x8 images into 64 dimensional vector for each image, stacked as image vectors\n", + "image_vectors = images.reshape(-1,64)\n", + "#image_vectors will be of shape N x 64\n", + "\n", + "rand_idx = np.random.randint(0,num_images,1)[0]\n", + "sample = images[rand_idx,:].flatten() #Radomly select a sample image\n", + "\n", + "#Let's take a random digit image, and find top 30 digits from the images that are closest to this.\n", + "#To measure closeness, we will use euclidean distance.\n", + "images_diff = image_vectors - sample #Check the shapes of image_vectors and sample, and understand how broadcasting is at work here\n", + "distances = np.sum(images_diff**2,1) #Elementwise square all the differeneces and add them across columns to get distances\n", + "\n", + "#Find indices of smallest distances. We can use argsort, which gives you sorted indices.\n", + "sorted_idxes = np.argsort(distances)\n", + "#these indices can be used to select the corresponding images from the original images \n", + "\n", + "nearest_images = images[sorted_idxes,:,:][:20] #Last line truncates selects the nearest 20\n", + "\n", + "plt.subplot(5,5,1) #1 row for the input image, and 5 rows for 50 output images\n", + "plt.imshow(images[rand_idx],'gray',interpolation='nearest')\n", + "plt.axis('off')\n", + "plt.title('Input Sample')\n", + "\n", + "loc = 6 #Start from the second row\n", + "for i,img in enumerate(nearest_images):\n", + " plt.subplot(5,5,loc+i)\n", + " plt.imshow(img,'gray',interpolation='nearest')\n", + " plt.title('d = %0.0f'%distances[sorted_idxes[i]]) #Make sure you understand how we are reading the corresponding distance\n", + " plt.axis('off')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1000, 60, 60)\n" + ] + }, + { + "data": { + "text/plain": [ + "(-0.5, 59.5, 59.5, -0.5)" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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7lF1ToVJRhnZlZdsuK4m2eH0fx01P79j3kdWrHZ/PqGjId6tiybFq4JYaDvie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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "import numpy as np\n", + "import pickle\n", + "import matplotlib.pyplot as plt\n", + "\n", + "faces = pickle.load(open('Linear Algebra KB v1.0/faces.pkl'))\n", + "\n", + "(num_of_images,height,width,clr_channels) = faces.shape \n", + "\n", + "#grayscaler = lambda x : 0.2125*x[:,:,:,0] + 0.7154*x[:,:,:,1] + 0.0721*x[:,:,:,2]\n", + "# face_channels = faces.reshape(num_of_images*height*width,clr_channels)\n", + "#gray_faces = grayscaler(faces)#.reshape(num_of_images,height,width)\n", + "gray_faces = np.dot(faces,np.array([0.2125,0.7154,0.0721]))\n", + "print gray_faces.shape\n", + "plt.subplot(1,2,1)\n", + "plt.imshow(gray_faces[np.random.randint(num_of_images)],'gray')\n", + "plt.title('Grayscale Image')\n", + "plt.axis('off')" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape of images array is: (1797, 8, 8)\n", + "[773]\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "from sklearn.datasets import load_digits\n", + "\n", + "digits = load_digits()\n", + "images = digits['images']\n", + "num_images = images.shape[0]\n", + "\n", + "print \"Shape of images array is: \", images.shape\n", + "\n", + "#The images array contains N number of 8x8 binary digit images, this is a 3 dimensional array\n", + "#We will flatten 8x8 images into 64 dimensional vector for each image, stacked as image vectors\n", + "image_vectors = images.reshape(-1,64)\n", + "#image_vectors will be of shape N x 64\n", + "\n", + "rand_idx = np.random.randint(0,num_images,1)[0]\n", + "print(np.random.randint(0,num_images,1))\n", + "sample = images[rand_idx,:].flatten() #Radomly select a sample image\n", + "samples = images[np.random.choice(images.shape[0],5)].shape\n", + "\n", + "#Let's take a random digit image, and find top 30 digits from the images that are closest to this.\n", + "#To measure closeness, we will use euclidean distance.\n", + "images_diff = image_vectors - sample #Check the shapes of image_vectors and sample, and understand how broadcasting is at work here\n", + "distances = np.sum(images_diff**2,1) #Elementwise square all the differeneces and add them across columns to get distances\n", + "\n", + "#Find indices of smallest distances. We can use argsort, which gives you sorted indices.\n", + "sorted_idxes = np.argsort(distances)\n", + "#these indices can be used to select the corresponding images from the original images \n", + "\n", + "nearest_images = images[sorted_idxes,:,:][:20] #Last line truncates selects the nearest 20\n", + "\n", + "plt.subplot(5,5,1) #1 row for the input image, and 5 rows for 50 output images\n", + "plt.imshow(images[rand_idx],'gray',interpolation='nearest')\n", + "plt.axis('off')\n", + "plt.title('Input Sample')\n", + "\n", + "loc = 6 #Start from the second row\n", + "for i,img in enumerate(nearest_images):\n", + " plt.subplot(5,5,loc+i)\n", + " plt.imshow(img,'gray',interpolation='nearest')\n", + " plt.title('d = %0.0f'%distances[sorted_idxes[i]]) #Make sure you understand how we are reading the corresponding distance\n", + " plt.axis('off')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape of images array is: (1797, 8, 8)\n" + ] + }, + { + "data": { + "image/png": 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CCO8PIbw6yD8q3ICs4tV5LP+3QgilEMJiZKuivUNy/nGig/Od/3Lmz1cZMXwB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\nN3e0/0KHR0AOh8Ph6AoW5Dsgh8PhcPzfh0dADof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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "from sklearn.datasets import load_digits\n", + "\n", + "digits = load_digits()\n", + "images = digits['images']\n", + "num_images = images.shape[0]\n", + "\n", + "print \"Shape of images array is: \", images.shape\n", + "\n", + "#The images array contains N number of 8x8 binary digit images, this is a 3 dimensional array\n", + "#We will flatten 8x8 images into 64 dimensional vector for each image, stacked as image vectors\n", + "sample_idx = np.random.choice(images.shape[0],5)\n", + "samples = images[sample_idx].reshape(-1,1,64)\n", + "square_dist = np.sum((image_vectors - samples)**2,2)\n", + "ss = 20\n", + "image_nearest = zip(images[sample_idx],images[np.argsort(square_dist)[:,:ss]])\n", + "for i,(img,nearest) in enumerate(image_nearest):\n", + " plt.subplot(5,ss+1,(i*(ss+1))+1)\n", + " plt.imshow(img,'gray',interpolation='nearest')\n", + " plt.title('Input Image')\n", + " plt.axis('off')\n", + " for (j,nimg) in enumerate(nearest):\n", + " plt.subplot(5,ss+1,(i*(ss+1)+j+2))\n", + " plt.imshow(nimg,'gray',interpolation='nearest')\n", + " plt.title('Nearest Image')\n", + " plt.axis('off')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python2", + "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": "2.7.14" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/LinearAlgebra.md b/LinearAlgebra.md new file mode 100644 index 0000000..215eaaa --- /dev/null +++ b/LinearAlgebra.md @@ -0,0 +1,332 @@ + +# Linear Algebra + +### Motivation + +Linear algebra deals with vectors, matrices and tensors. Before using machine learning to solve a problem, the first step is usually to represent the real-world input in the form of vectors/matrices of numbers. Following are the key reasons why linear algebra will be found everywhere throughout this course: + +**Compact Notation** Linear algebra provides a convenient language for compactly representing computations which may otherwise require more verbose expressions. Getting familiar with these notations will give you access to several books and literatures in the domain of machine learning and deep learning. + +**Standard Representation** In many important domains, data is naturally available in digital form. Speech, audio/video, images from social network to medical scans, etc. This kind of data can be readily be represented in vector or matrix forms, thus making vectors and matrices the most preferred input formats. Most machine learning libraries assume the input data structure to be matrices or tensors. + +**Fast Computation** Representing computations in the form of linear algebra equations enables underlying machine learning libraries to take advantage of fast matrix computation routines. Further more, frameworks like Tensorflow, can leverage distributed systems and GPUs to run matrix computations much faster. While what can be done with matrix operations can also be done using for loops, the speed difference between the two options is extremely significant. + + +```python +from sympy import * +import numpy as np + +r = r'$%s$'%latex(Matrix(np.arange(3).reshape(1,-1))) +c = r'$%s$'%latex(Matrix(np.arange(3).reshape(-1,1))) +m = r'$%s$'%latex(Matrix(np.arange(12).reshape(3,4))) + +``` + +### Terms & Notations + +**Scalars** are 0 dimensional. They are just numbers. For example, height of a person, temperature, stock price etc. They are represented using lower case letters as $x,y,x_1,w_5$ etc. + +**Vectors** are 1 dimensional. Meaning, you can represent them as a collection of numbers. For example, to represent a color of pixel in an image, we will need three numbers, r, g and b. That is single color, $\mathbf{c} = [r,g,b]$. Vectors are denoted by boldface, lower case letters, like $\mathbf{x,y,z,w,v}$. While one dimensional array of numbers can be either row vector, {{r}} or column vector {{c}}, by convention, vector is taken to be a column vector. + +**Matrices** Matrices are 2 dimensional array of numbers. For example a matrix {{m}} is a $\mathrm{3x4}$ array of numbers. That is, it has 3 rows and 4 columns. A gray scale image, for example, is represented as a matrix of size $\mathrm{width\ x\ height}$. A collection of document vectors can be represented as a matrix. + +**Tensors** Higher dimensional arrays are called Tensors. They are generalisation of vectors and matrices. However, note that a whole lot of linear algebra computations like matrix multiplication, SVD, determinants etc. are not defined or are not used with Tensors. A color image is represented as $\mathrm{w x h x c}$ array, which is a tensor. A training data may involve 1000s of such arrays in a single bigger tensor of dimensions $\mathrm{N x w x h x c}$. In Tensorflow, the data is represented as generic tensors. We will see more of that soon. + +### Numpy + +Numpy is a python library for numerical computations, with rich support for linear algebra computations among lot of other things. It is essential to have a deep working expertise with numpy. Most numpy operations have equivalent operations in Tensorflow as well. + + +### Matrix operations in Action 1 - Slicing an Image to extract R,G,B channels + +Try the following code. + +```python +%matplotlib inline +import skimage.data as imgdata +import matplotlib.pyplot as plt +import numpy as np + +astronaut = imgdata.astronaut() + +#Please take the opportunity to get familiar with matplotlib and numpy operations used in sample codes. +R = astronaut[:,:,0] #0th channel is R, 1st channel is G, and 2nd channel will be red +G = astronaut[:,:,1] +B = astronaut[:,:,2] + +plt.subplot(1,4,1) #We want to show the images as 1 row, 4 columns, the last number indicating that we are about to draw the first image +plt.imshow(astronaut) +plt.title('Color') +plt.axis('off') #When showing images, we don't need axes. They clutter the display with axis labels. + +plt.subplot(1,4,2) #Now we are setting the context to draw the R channel of the image +plt.imshow(R,'gray') +plt.title('Red Levels') +plt.axis('off') + +plt.subplot(1,4,3) +plt.imshow(G,'gray') +plt.title('Green Levels') +plt.axis('off') + +plt.subplot(1,4,4) +plt.imshow(B,'gray') +plt.title('Blue Levels') +plt.axis('off') + +plt.suptitle('Image and its Color Channels') +plt.show() +``` + +### Matrix operations in Action 2 - Weighted average of pixels to convert a color image into grayscale +```python +%matplotlib inline +import skimage.data as imgdata +import matplotlib.pyplot as plt + +coffee_cup = imgdata.coffee() + +#Please take the opportunity to get familiar with matplotlib and numpy operations used in sample codes. +R = coffee_cup[:,:,0] #0th channel is R, 1st channel is G, and 2nd channel will be red +G = coffee_cup[:,:,1] +B = coffee_cup[:,:,2] + +I = 0.2125*R + 0.7154*G + 0.0721*B #Gray scale image is a weighted average of R, G and B values of the pixels. All pixels of I are simultaneously computed with this elementwise addition + +plt.subplot(1,2,1) +plt.imshow(coffee_cup) +plt.title('Color Image') +plt.axis('off') + +plt.subplot(1,2,2) +plt.imshow(I,'gray') #Even though I is a grayscale image, we have to set the colormap to "gray". Otherwise matplotlib will show the gray values using multicolor pallete, chosing color based on the intensity value +plt.title('Grayscale Image') +plt.axis('off') + +plt.show() +``` + +### Matrix operations in Action 3 - Finding the mean of 1000 images in one numpy operation +Surprisingly, the mean face, which is an average of random faces appears to have very symmetric features. Try this code that averages 1000 different faces: +```python +%matplotlib inline + +import numpy as np +import pickle + +faces = pickle.load(open('faces.pkl')) + +(num_of_images,height,width,clr_channels) = faces.shape #First dimension shows the number of face images we have. + +#Lets select 30 images randomly, and display them in 3x10 plot. You may want to understand this code + +idxList = np.random.randint(0,num_of_images,30) #Please check the documentation of np.random.randint for help. Type 'np.random.randint?' in iPython + +for i,idx in enumerate(idxList): #Check if sampled images change everytime you run the cell + plt.subplot(3,10,i+1) + plt.imshow(faces[idx]) + plt.axis('off') +plt.suptitle('Sample Images from the Dataset') +plt.show() + +m = np.mean(faces,0) +m = m.astype(np.uint8) #Matplotlib expects the images to be of uint8 type, meaning RGB values should be integers in the range of 0 to 255. Else the image displayed looks like garbage +plt.imshow(m) +plt.axis('off') +plt.title('Mean of 1000 Faces - Surprising?') +plt.show() +``` + + +### Matrix operations in Action 4 - Datatype checking, Casting, Counting etc. +```python +import numpy as np + +A = np.random.rand(3,6) +print "Shape of A:", A.shape #3x6 +print +print "Uniform Random Numbers" +print A #array of 3 rows, 6 columns, uniform random numbers +print +print "Sometimes it is clumsy to inspect arrays, with so many decimal places printed on the screen" +print "We can control the numpy printing options as below" +np.set_printoptions(precision=2) +print A +print "Pleas note! It doesn't round the numbers, but only printing is controlled!" +print A.dtype #64 bit floating point number + +#How to generate an array of random integers between 5 to 20, of size 5x10? + +#Method 1. We can use uniform random numbers between 0 to 1 and scale them to the required range. +#Then we can convert the scaled array to integers + +A = np.random.rand(5,10) #Uniform random numbers between 0 to 1, of size 5x10 +A = A*(20-5) + 5 #Scale and shift the values to fit in the range of 5 to 20 +print +print "Values are now between 5 to 20, but floating point." +print "Note that A is still printed upto 2 decimal places. np.set_printoptions is a global setting." +print A #A is in the required range, but it is of type floating point +print "Datatype of A is ", A.dtype +print "Casting A to 16 bit integer values" +A = A.astype(np.int16) +print A #Now A is the desired output +print A.dtype #This should be np.int16 +print +print "Are they really in 5 to 20 range?" +print "The unique values in A are:", np.unique(A) #This will tell us what are the unique values present in the array +print "Are they really unform? We can count how many times each number is appearing. That should be roughly equal." +counts = np.bincount(A.flatten()) #np.bincount takes only one dimension array. A.flatten() will flatten n-dimension array into a 1d array +print "counts will be a 1d array of size np.max(A). counts[i] tells us how many times the number i has appeared in the input" +print counts[5:] #We are interested in counts of numbers between 5 to 20 only. +print "We can plot the counts and check. The numbers are not roughly equal! Why?" +print "Change the code to check if it helps if you generate much bigger sample." +``` + +### Matrix operations in Action 5 - Zeros, Ones, Linspace,Elementwise computations + +```python +import numpy as np + +A = np.zeros(shape=(5,5)) +print A +print +B = np.ones((3,5)) +print B +print +x = np.linspace(-2*np.pi,2*np.pi,100) #x is a linearly spaced, 100 numbers between -2*pi to +2*pi +y = np.sin(x) #All scalar math functions in numpy apply to every element in the input, element wise. No need for for loop to call sin function on every value. +print 'x values:',x[:10] #Show only first 10 values. +print 'y values:',y[:10] +print 'You can zip x and y values together: ' +points = zip(x,y) #Useful python function to combine corresponding elements in two 1d arrays into list of tuples. +print points[:10] #Note, the values are now printed beyond 2 decimals. Can you reason why? +print +print "Plot:" +plt.plot(x,y) +plt.show() +``` + + + +### Matrix operations in Action 6- Broadcasting + +In math, two matrices can be added only if they both are of same dimension. Numpy does allow adding matrices of different dimensions under certain conditions. This is called broadcasting. It is important to understand how broadcasting in numpy works, one to avoid unintended effects causing bugs, two, to achieve computational efficiency. + +You can learn about broadcasting works on [this page](http://scipy.github.io/old-wiki/pages/EricsBroadcastingDoc) + +Run the following code to see the benefits of broadcasting. + +```python +import numpy as np +import time + +A = np.random.rand(10000,100) #10000x100 array of random numbers +B = np.ones((1,100))*10 #B is a a 1x100 array of 10s + +#Suppose we want to add B to every row of A. +#In matrix algebra, A+B is forbidden. We need to replicate B 10000 times and make an array of size compatible to A, and then add + +#Lets see how fast this code is. We will run this 1000 times and average the time. +start = time.time() +for i in range(1000): + B1 = np.repeat(B,10000,axis=0) #Repeat 10000 times along rows (axis=0) + S = A+B1 #desired output +stop = time.time() + +total_time1 = stop-start +print "Average execution time to compute the desired output: ", total_time1/1000 + +print "With Numpy broadcasting, we save memory and time." +start = time.time() +for i in range(1000): + S = A+B #Numpy will automatically broadcast the values in a compatible way. Important to understand the rules to avoid unintended bugs +stop = time.time() +total_time2 = stop-start +print "Total time taken for 1000 executions of A+B with broadcasting is: ",total_time2/100 + +print "Broadcasting is %s times faster for this case."%(total_time1/total_time2) +``` + +### Matrix operations in Action 7 - Boolean Indexing +```python +%matplotlib inline +import skimage.data as imgdata +import matplotlib.pyplot as plt +import numpy as np + +horse = imgdata.horse() +horse = horse[:,:,:3] #Drop the A channel +#This is a RGBA image. Convert it into Binary +horse = np.max(horse,2) +#Horse is a binary image, with values 0 an 1. You can inspect the values of the image +print 'Min and Max values in the horse image' +np.min(horse),np.max(horse) +#Let's make the horse red and background black, using boolean indexing to operate on the image +I,J = np.nonzero(horse==0) #Boolean indexing finds all (i,j)s in the image where the pixels are black(0), giving us the indices of horse pixels +#We will make R, G, and B panels separately and put them together to make color image +R = np.zeros_like(horse) #Make zeros of same type and shape as the horse array +R[I,J] = 255 #Red panel we have set +output = np.zeros((horse.shape[0],horse.shape[1],3),dtype=horse.dtype) +output[:,:,0] = R +#G and B channels are zeros. So we get a red horse and black background. + +plt.subplot(1,2,1) +plt.title('Input') +plt.axis('off') +plt.imshow(horse,'gray') + + +plt.subplot(1,2,2) +plt.title('output') +plt.axis('off') +plt.imshow(output,'gray') + +plt.show() +``` + +### Matrix operations in Action 8 - Dot Product, Least Squares Error +```python +import numpy as np +from sklearn.datasets import load_digits + +digits = load_digits() +images = digits['images'] +num_images = images.shape[0] + +print "Shape of images array is: ", images.shape + +#The images array contains N number of 8x8 binary digit images, this is a 3 dimensional array +#We will flatten 8x8 images into 64 dimensional vector for each image, stacked as image vectors +image_vectors = images.reshape(-1,64) +#image_vectors will be of shape N x 64 + +rand_idx = np.random.randint(0,num_images,1)[0] +sample = images[rand_idx,:].flatten() #Radomly select a sample image + +#Let's take a random digit image, and find top 30 digits from the images that are closest to this. +#To measure closeness, we will use euclidean distance. +images_diff = image_vectors - sample #Check the shapes of image_vectors and sample, and understand how broadcasting is at work here +distances = np.sum(images_diff**2,1) #Elementwise square all the differeneces and add them across columns to get distances + +#Find indices of smallest distances. We can use argsort, which gives you sorted indices. +sorted_idxes = np.argsort(distances) +#these indices can be used to select the corresponding images from the original images + +nearest_images = images[sorted_idxes,:,:][:20] #Last line truncates selects the nearest 20 + +plt.subplot(5,5,1) #1 row for the input image, and 5 rows for 50 output images +plt.imshow(images[rand_idx],'gray',interpolation='nearest') +plt.axis('off') +plt.title('Input Sample') + +loc = 6 #Start from the second row +for i,img in enumerate(nearest_images): + plt.subplot(5,5,loc+i) + plt.imshow(img,'gray',interpolation='nearest') + plt.title('d = %0.0f'%distances[sorted_idxes[i]]) #Make sure you understand how we are reading the corresponding distance + plt.axis('off') + +plt.tight_layout() +plt.show() + +``` \ No newline at end of file diff --git a/linear_regression_tf_lowlevel.py b/linear_regression_tf_lowlevel.py new file mode 100644 index 0000000..1b4ff11 --- /dev/null +++ b/linear_regression_tf_lowlevel.py @@ -0,0 +1,97 @@ +%matplotlib inline +from graphviz import Digraph +from IPython.core.display import display, SVG + +def tf_to_dot(graph): + dot = Digraph() + + for n in graph.as_graph_def().node: + name = n.name.split('/')[0] + dot.node(name, label=name) + + for src in n.input: + src = src.split('/')[0] + if src != name: + dot.edge(src, name) + display(SVG(dot._repr_svg_())) + return dot + +import numpy as np +import scipy.io +import matplotlib.pyplot as plt +import tensorflow as tf + +mat = scipy.io.loadmat('./FaceNonFace.mat') + +faces = np.rollaxis(mat["face"].astype(np.uint8),-1,0) +non_faces = np.rollaxis(mat["nonFace"].astype(np.uint8),-1,0) + +rand_idx = np.arange(0,faces.shape[0]) +np.random.shuffle(rand_idx) + +train_test_split = 0.8 + +face_split = np.int(train_test_split*faces.shape[0]) + +train_faces = faces[rand_idx[:face_split]] +test_faces = faces[rand_idx[face_split:]] + +non_face_split = np.int(train_test_split*non_faces.shape[0]) +train_non_faces = non_faces[rand_idx[:non_face_split]] +test_non_faces = non_faces[rand_idx[non_face_split:]] + +train_data = np.vstack([train_faces,train_non_faces]) +train_labels = np.array([0]*len(train_faces)+[1]*len(train_non_faces)).astype(np.float32).reshape(-1,1) + +test_data = np.vstack([test_faces,test_non_faces]) +test_labels = np.array([0]*len(test_faces)+[1]*len(test_non_faces)).astype(np.float32).reshape(-1,1) + +train_data = train_data.reshape(train_data.shape[0],-1).astype(np.float32) +test_data = test_data.reshape(test_data.shape[0],-1).astype(np.float32) + +# plt.imshow((train_data[1000].reshape(60,60,3)*255).astype(np.float32)) +# plt.show() + +# tf.reset_default_graph() +def build_graph(): + g = tf.Graph() + with g.as_default(): + train_x = tf.placeholder(shape=[None,train_data.shape[1]],name="train_data",dtype=np.float32) + train_y = tf.placeholder(shape=[None,1],name="train_label",dtype=np.float32) + + + train_x1 = tf.div(train_x,255.0)-0.5 + + learning_rate = tf.constant(0.05) + + weights = tf.Variable(tf.random_normal([train_data.shape[1],1],stddev=1e-3),name="weights") + bias = tf.Variable(0.1,dtype=np.float32) + + h = tf.matmul(train_x1,weights)+bias + z = tf.sigmoid(h)+1e-6 + + loss = -tf.reduce_mean(train_y*tf.log(z) + (1-train_y)*tf.log(1-z)) + dw,db = tf.gradients(loss,[weights,bias]) + + weights_update = tf.assign_add(weights,-learning_rate*dw,name='weight_update') + bias_update = tf.assign_add(bias,-learning_rate*db,name='bias_update') + + with tf.control_dependencies([weights_update,bias_update]): + train_op = tf.no_op() + tf_to_dot(g) + return (g,loss,train_op,train_x,train_y,z) + +(g,loss,train_op,train_x,train_y,z) = build_graph() + +with g.as_default(): + with tf.Session() as sess: + sess.run(tf.global_variables_initializer()) + for i in xrange(2000): + l,_ = sess.run([loss,train_op],feed_dict={train_x:train_data,train_y:train_labels}) + if i%100 == 0: + print 'Training loss after %d iterations: %f'%(i,l) + + y_ = sess.run(z,feed_dict = {train_x:test_data}) + y_ = y_ > 0.5 + accuracy = np.sum((y_ == (test_labels > 0)),0)[0]/(y_.shape[0]*1.0) + print 'Accuracy of the model is ',accuracy