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Deep-Learning-Course/ComputationalGraphs.ipynb

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initial commit
2017-10-08 09:42:12 +00:00
{
"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": [
"<svg height=\"116pt\" viewBox=\"0.00 0.00 141.55 116.00\" width=\"142pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
"<g class=\"graph\" id=\"graph0\" transform=\"scale(1 1) rotate(0) translate(4 112)\">\n",
"<title>%3</title>\n",
"<polygon fill=\"#ffffff\" points=\"-4,4 -4,-112 137.5473,-112 137.5473,4 -4,4\" stroke=\"transparent\"/>\n",
"<!-- a___ -->\n",
"<g class=\"node\" id=\"node1\">\n",
"<title>a___</title>\n",
"<ellipse cx=\"30.5473\" cy=\"-90\" fill=\"none\" rx=\"30.5947\" ry=\"18\" stroke=\"#000000\"/>\n",
"<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"30.5473\" y=\"-86.3\">a___</text>\n",
"</g>\n",
"<!-- addition -->\n",
"<g class=\"node\" id=\"node3\">\n",
"<title>addition</title>\n",
"<ellipse cx=\"68.5473\" cy=\"-18\" fill=\"none\" rx=\"48.9926\" ry=\"18\" stroke=\"#000000\"/>\n",
"<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"68.5473\" y=\"-14.3\">addition</text>\n",
"</g>\n",
"<!-- a___&#45;&gt;addition -->\n",
"<g class=\"edge\" id=\"edge1\">\n",
"<title>a___-&gt;addition</title>\n",
"<path d=\"M39.746,-72.5708C44.161,-64.2055 49.5484,-53.998 54.4641,-44.6839\" fill=\"none\" stroke=\"#000000\"/>\n",
"<polygon fill=\"#000000\" points=\"57.597,-46.2464 59.1693,-35.7689 51.4063,-42.9791 57.597,-46.2464\" stroke=\"#000000\"/>\n",
"</g>\n",
"<!-- b -->\n",
"<g class=\"node\" id=\"node2\">\n",
"<title>b</title>\n",
"<ellipse cx=\"106.5473\" cy=\"-90\" fill=\"none\" rx=\"27\" ry=\"18\" stroke=\"#000000\"/>\n",
"<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"106.5473\" y=\"-86.3\">b</text>\n",
"</g>\n",
"<!-- b&#45;&gt;addition -->\n",
"<g class=\"edge\" id=\"edge2\">\n",
"<title>b-&gt;addition</title>\n",
"<path d=\"M97.5418,-72.937C93.0975,-64.5161 87.6331,-54.1627 82.6509,-44.7226\" fill=\"none\" stroke=\"#000000\"/>\n",
"<polygon fill=\"#000000\" points=\"85.6479,-42.9026 77.885,-35.6924 79.4572,-46.17 85.6479,-42.9026\" stroke=\"#000000\"/>\n",
"</g>\n",
"</g>\n",
"</svg>"
],
"text/plain": [
"<IPython.core.display.SVG object>"
]
},
"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": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7efb9463dad0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"<title>mul_by_255-&gt;to_uint8</title>\n",
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"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"
]
},
updated path reference, included tf training in notebook
2017-10-08 09:56:09 +00:00
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
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},
"metadata": {},
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},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training loss after 0 iterations: 0.693686\n",
"Training loss after 100 iterations: 0.342306\n",
"Training loss after 200 iterations: 0.210373\n",
"Training loss after 300 iterations: 0.174988\n",
"Training loss after 400 iterations: 0.152505\n",
"Training loss after 500 iterations: 0.136287\n",
"Training loss after 600 iterations: 0.123976\n",
"Training loss after 700 iterations: 0.114264\n",
"Training loss after 800 iterations: 0.106370\n",
"Training loss after 900 iterations: 0.099801\n",
"Training loss after 1000 iterations: 0.094227\n",
"Training loss after 1100 iterations: 0.089424\n",
"Training loss after 1200 iterations: 0.085229\n",
"Training loss after 1300 iterations: 0.081525\n",
"Training loss after 1400 iterations: 0.078224\n",
"Training loss after 1500 iterations: 0.075257\n",
"Training loss after 1600 iterations: 0.072572\n",
"Training loss after 1700 iterations: 0.070125\n",
"Training loss after 1800 iterations: 0.067885\n",
"Training loss after 1900 iterations: 0.065823\n",
"Accuracy of the model is 0.93\n"
]
}
],
"source": [
"%matplotlib inline\n",
"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\n",
"\n",
"import numpy as np\n",
"import scipy.io\n",
"import matplotlib.pyplot as plt\n",
"import tensorflow as tf\n",
"\n",
"mat = scipy.io.loadmat('./FaceNonFace.mat')\n",
"\n",
"faces = np.rollaxis(mat[\"face\"].astype(np.uint8),-1,0)\n",
"non_faces = np.rollaxis(mat[\"nonFace\"].astype(np.uint8),-1,0)\n",
"\n",
"rand_idx = np.arange(0,faces.shape[0])\n",
"np.random.shuffle(rand_idx)\n",
"\n",
"train_test_split = 0.8\n",
"\n",
"face_split = np.int(train_test_split*faces.shape[0])\n",
"\n",
"train_faces = faces[rand_idx[:face_split]]\n",
"test_faces = faces[rand_idx[face_split:]]\n",
"\n",
"non_face_split = np.int(train_test_split*non_faces.shape[0])\n",
"train_non_faces = non_faces[rand_idx[:non_face_split]]\n",
"test_non_faces = non_faces[rand_idx[non_face_split:]]\n",
"\n",
"train_data = np.vstack([train_faces,train_non_faces])\n",
"train_labels = np.array([0]*len(train_faces)+[1]*len(train_non_faces)).astype(np.float32).reshape(-1,1)\n",
"\n",
"test_data = np.vstack([test_faces,test_non_faces])\n",
"test_labels = np.array([0]*len(test_faces)+[1]*len(test_non_faces)).astype(np.float32).reshape(-1,1)\n",
"\n",
"train_data = train_data.reshape(train_data.shape[0],-1).astype(np.float32)\n",
"test_data = test_data.reshape(test_data.shape[0],-1).astype(np.float32)\n",
"\n",
"# plt.imshow((train_data[1000].reshape(60,60,3)*255).astype(np.float32))\n",
"# plt.show()\n",
"\n",
"# tf.reset_default_graph()\n",
"def build_graph():\n",
" g = tf.Graph()\n",
" with g.as_default():\n",
" train_x = tf.placeholder(shape=[None,train_data.shape[1]],name=\"train_data\",dtype=np.float32)\n",
" train_y = tf.placeholder(shape=[None,1],name=\"train_label\",dtype=np.float32)\n",
"\n",
"\n",
" train_x1 = tf.div(train_x,255.0)-0.5\n",
"\n",
" learning_rate = tf.constant(0.05)\n",
"\n",
" weights = tf.Variable(tf.random_normal([train_data.shape[1],1],stddev=1e-3),name=\"weights\")\n",
" bias = tf.Variable(0.1,dtype=np.float32)\n",
"\n",
" h = tf.matmul(train_x1,weights)+bias\n",
" z = tf.sigmoid(h)+1e-6\n",
"\n",
" loss = -tf.reduce_mean(train_y*tf.log(z) + (1-train_y)*tf.log(1-z))\n",
" dw,db = tf.gradients(loss,[weights,bias])\n",
"\n",
" weights_update = tf.assign_add(weights,-learning_rate*dw,name='weight_update')\n",
" bias_update = tf.assign_add(bias,-learning_rate*db,name='bias_update')\n",
"\n",
" with tf.control_dependencies([weights_update,bias_update]):\n",
" train_op = tf.no_op()\n",
" tf_to_dot(g)\n",
" return (g,loss,train_op,train_x,train_y,z)\n",
"\n",
"(g,loss,train_op,train_x,train_y,z) = build_graph()\n",
"\n",
"with g.as_default():\n",
" with tf.Session() as sess:\n",
" sess.run(tf.global_variables_initializer())\n",
" for i in xrange(2000):\n",
" l,_ = sess.run([loss,train_op],feed_dict={train_x:train_data,train_y:train_labels})\n",
" if i%100 == 0:\n",
" print 'Training loss after %d iterations: %f'%(i,l)\n",
"\n",
" y_ = sess.run(z,feed_dict = {train_x:test_data})\n",
" y_ = y_ > 0.5\n",
" accuracy = np.sum((y_ == (test_labels > 0)),0)[0]/(y_.shape[0]*1.0)\n",
" print 'Accuracy of the model is ',accuracy\n"
]
},
initial commit
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updated path reference, included tf training in notebook
2017-10-08 09:56:09 +00:00
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initial commit
2017-10-08 09:42:12 +00:00
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