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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from IPython.display import Image\n",
    "from IPython.core.display import HTML \n",
    "PATH = \"images/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting Started with Tensorflow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tensors\n",
    "\n",
    "The central unit of data in TensorFlow is the tensor. A tensor consists of a set of primitive values shaped into an array of any number of dimensions. A tensor's rank is its number of dimensions. Here are some examples of tensors:\n",
    "\n",
    "    + 3 # a rank 0 tensor; this is a scalar with shape []\n",
    "\n",
    "    + [1., 2., 3.] # a rank 1 tensor; this is a vector with shape [3]\n",
    "\n",
    "    + [[1., 2., 3.], [4., 5., 6.]] # a rank 2 tensor; a matrix with shape [2, 3]\n",
    "\n",
    "    + [[[1., 2., 3.]], [[7., 8., 9.]]] # a rank 3 tensor with shape [2, 1, 3]\n",
    "\n",
    "\n",
    "#### Importing TensorFlow\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "You might think of TensorFlow Core programs as consisting of two discrete sections:\n",
    "\n",
    "    + Building the computational graph.\n",
    "    + Running the computational graph.\n",
    "\n",
    "A computational graph is a series of TensorFlow operations arranged into a graph of nodes. Let's build a simple computational graph. Each node takes zero or more tensors as inputs and produces a tensor as an output. One type of node is a constant. Like all TensorFlow constants, it takes no inputs, and it outputs a value it stores internally. We can create two floating point Tensors node1 and node2 as follows:\n",
    "\n",
    "   \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"Const_2:0\", shape=(), dtype=float32) Tensor(\"Const_3:0\", shape=(), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "node1 = tf.constant(3.0, dtype=tf.float32)\n",
    "node2 = tf.constant(4.0) # also tf.float32 implicitly\n",
    "print(node1, node2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that printing the nodes does not output the values 3.0 and 4.0 as you might expect. Instead, they are nodes that, when evaluated, would produce 3.0 and 4.0, respectively. To actually evaluate the nodes, we must run the computational graph within a session. A session encapsulates the control and state of the TensorFlow runtime.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Session\n",
    "\n",
    "The following code creates a Session object and then invokes its run method to run enough of the computational graph to evaluate node1 and node2. By running the computational graph in a session as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3.0, 4.0]\n"
     ]
    }
   ],
   "source": [
    "sess = tf.Session()\n",
    "print(sess.run([node1, node2]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can build more complicated computations by combining Tensor nodes with operations (Operations are also nodes). For example, we can add our two constant nodes and produce a new graph as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "node3: Tensor(\"Add:0\", shape=(), dtype=float32)\n",
      "sess.run(node3): 7.0\n"
     ]
    }
   ],
   "source": [
    "node3 = tf.add(node1, node2)\n",
    "print(\"node3:\", node3)\n",
    "print(\"sess.run(node3):\", sess.run(node3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TensorFlow provides a utility called TensorBoard that can display a picture of the computational graph. Here is a screenshot showing how TensorBoard visualizes the graph:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Wlhadbj3Sk6KoHGz9c/GR/NvsUWS7Do4LP+f/PTd/wq3B4H4+r5NvFdIiRCKR8GGRw8PD\nwkb/66+//izTnc+XtFAagiAaGhrQk73PxMTETz/9tPnzoqKi+vr6Z2lULC4uTk9PB4PBLHpslgQ2\npU0CAKVSqdVqd7eFE4lEfvzxx7QogaamphwPXUFPnpV4PO73+7M3bKqqqqqrq39VUeBC7mmajkaj\naZvKy8v5lV5bSZLl2pRKpVqtfvEzKlzESloEsUwmy7geAT3JHRif0+mVGm2WjMsbo952zdtXYhUd\n/oCTlG4tyeDg4E5+mVKpbGxs3Ha3zUu4eORyeWVlJbeEKy1eOKMk/Akzxp6JxeKysjJuseFve5cZ\nhmGYcDi8OVT01Vdfra2tzf0gSNjX8/FJxuPs7gEwZ/Ik6rWar2w/our3+9P+8WVlZTKZbGlpKRaL\nCePJI5HI1NTUVj0EYfhWRj3SlgS/+eabvCdZJOG2NjQ0bH6Wc+NgoVBILBYfOnRILpcrlUqZTPbs\npZZbexOJRB49erTVUvuioiIud2a+FBaMW8lE1GczX56TqyA2l2WviYkJXgOxWPzmm2/yTf/i4mKl\nUllVVeX3+3/55Rd+/82LPWZnZzlD0sKBt00xIZFIWJbNLgkP19DK2PJZWVnhHvncW04VuVwukUiE\nWVSEc4IgyMYCa4sZWZaNxWJZljTyhmi12hyMo8lOrq3TUtWZrA6nzbj+oEkG3Habg1s6Jdc2m20O\nh3m1HcS49GUfBM4MeA0em901OL0M8roWm9MlSHMU9TmtNqf7++llAFVdy+q6q6jHcPBdYXOpqX/e\nZ1o7KDli0Z24RHR9Rfa9dwm2bnd98803/OssYXyjo6N8KeQDYx8+fMgNXmVcXrKTDCx37tyZnZ19\nivXxXCuIYZinS6rylM9jsZggCK6ll49PzlxZ96tqOms16SDg7bt0Y3rTut/CujarWU8wIy7nlbHl\nus7bIw69dNWT7wsLC6WkyWwiCdrnvHRjTt7ST3tMCuATa1c0d1iMmtVzy1u+CnjaYcRLMQGn+ZMb\n0pbePouGII16Ys0Sq+5Ej6Jr3GfsI+t7tvJE2DN59dVXs0z/sSzr9Xq511ykfcblivyC+F9x76LR\nZ0lo9AKEyXc9csUTLmd8tG2AXssjEfWZdacuKzrHAw4dBBy6mvN0U2+AT41N9xl05wY1neOUQyfl\nPOFKPvdv4A5oGWA8RgUkR8zEictE1/haehVuQXxdb5iyEBny1XMXZCPru6HTTzl0Aatua0+EYeHb\nRq0PDg5yLbRUKrW8vCGpUnl5uUaj+VV6PHcSiQQ/8/PscVZcP4dbar9nkt9hPsiN2jrN3RPajtt2\nUgrJ7coW/3rb0iCTyThP+ODFbdOpvEiKi4uLi4uFqnO2xGIxvlnI9c6FfxF/5Xx/Zg9/ERLmgxRa\n6zSfH6s4e9uh3zbp0YZcqduuyBV2bUmSfMH57J4Crq+FX0HBs8vxXS8iH2TY399rNWmiPue5t2sU\netuG730QEOgznx+Ttzismig3ABRNJgGSySjDMJsPkcvl/OttY3L5HWQy2WuvvZbjkiA554lCRwAw\nI4ywjZOkR0ZGAlEAqYYkAGgfvXHrr8kHGQjQSYI0WRx9nhE6On6xCb7vtvQFMu084rB/DxC79l5N\nGUflqUtzAHOXTlWWlZk86aYI2xhTU1NZqhRhkhH8ikb05CmQ6tpNFTDtFny3T5KyG0+caO+jkwA6\nc7sW5tw2N80fsZoP0rTDfJA1NXorf24uHyRw+SDXd1vNjaqzeQY20N/bIgeQt/T2D9xy8jm/1pBI\nJHyzZGVlRbjUWwiX7Y5/W1ZWhmUuH9ntPNykve+M5+3Lb5PRjfkgbaQUAHR2V6f3RPcHpN63ng9y\nWdvhspPSHZzbam/u++Cy0ZBczwc5DdrOVcekhF4DPYMuq5Xg8kEajBt6PEnKqwAAjd5ozPzbSJIc\nHBzk6gqGYQYHB9O+1Dftq4BfQJAIsjc9AVAYXdQt0mp3us6fW4bCiuNnLt5azwcp1Tt8fp3d5nB1\nf3IZClVaQ8eX6/kgt0Nj9gQUdpvD7fzkCpcP8kyvx2lZK/WEqa+3xWS9dul8gNGZzTtQbyPFxcVa\nrZZfRMHNqHDfbQCbFrKKxWKSJLHA5SkYL/yspKXJyvw02hjVgqAn+5FIJOL3+7PknM6L1KAIevKb\nw2UEjUQiiUSCm4/jp6WxT4KeIMi+APNIIAh6giDoCYKgJwiCniAIeoIg6AmCoCcIgqAnCIKeIAh6\ngiDoCYKgJwiCniAIeoIg6AmCIOgJgqAnCIKeIAh6giDoCYKgJwiCniAIgp4gCHqCIOgJgqAnCIKe\nIAh6giDoCYKgJwiCoCcIgp4gCHqCIOgJgqAnCIKeIAh6giAIeoIgO+H/Afg7UzczvJIBAAAAAElF\nTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image (filename= PATH + \"getting_started_add.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Placeholders\n",
    "\n",
    "As it stands, this graph is not especially interesting because it always produces a constant result. A graph can be parameterized to accept external inputs, known as placeholders. A placeholder is a promise to provide a value later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a = tf.placeholder(tf.float32)\n",
    "b = tf.placeholder(tf.float32)\n",
    "adder_node = a + b  # + provides a shortcut for tf.add(a, b)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The preceding three lines are a bit like a function or a lambda in which we define two input parameters (a and b) and then an operation on them. We can evaluate this graph with multiple inputs by using the feed_dict argument to the run method to feed concrete values to the placeholders:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.5\n",
      "[ 3.  7.]\n"
     ]
    }
   ],
   "source": [
    "print(sess.run(adder_node, feed_dict= {a: 3, b: 4.5}))\n",
    "print(sess.run(adder_node, feed_dict={a:[1, 3], b: [2, 4]}))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In TensorBoard, the graph looks like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+P5qNrJG/+WwZiqLk6TZoNlI4ZsOLemQ4HC6eYRvNLgrk2UDFE2qj2XGRL83Z0SgvLyd3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\nRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image (filename= PATH + \"getting_started_adder.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can make the computational graph more complex by adding another operation. For example,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22.5\n"
     ]
    }
   ],
   "source": [
    "add_and_triple = adder_node * 3.\n",
    "print(sess.run(add_and_triple, {a: 3, b: 4.5}))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The preceding computational graph would look as follows in TensorBoard:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SlspCpEQxev7o0RNtX39x8q9L/7n12tGKGZZ/r9fr9XpHR0czMzONRqNC1sZOX6cJaklL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\nrkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image (filename= PATH + \"getting_started_triple.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variables\n",
    "\n",
    "In machine learning we will typically want a model that can take arbitrary inputs, such as the one above. To make the model trainable, we need to be able to modify the graph to get new outputs with the same input. Variables allow us to add trainable parameters to a graph. They are constructed with a type and initial value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "W = tf.Variable([.3], dtype=tf.float32)\n",
    "b = tf.Variable([-.3], dtype=tf.float32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = tf.placeholder(tf.float32)\n",
    "linear_model = W * x + b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initializing Variables\n",
    "\n",
    "Constants are initialized when you call tf.constant, and their value can never change. By contrast, variables are not initialized when you call tf.Variable. To initialize all the variables in a TensorFlow program, you must explicitly call a special operation as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "init = tf.global_variables_initializer()\n",
    "sess.run(init)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is important to realize init is a handle to the TensorFlow sub-graph that initializes all the global variables. Until we call sess.run, the variables are uninitialized.\n",
    "\n",
    "Since x is a placeholder, we can evaluate linear_model for several values of x simultaneously as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.          0.30000001  0.60000002  0.90000004]\n"
     ]
    }
   ],
   "source": [
    "print(sess.run(linear_model, {x: [1, 2, 3, 4]}))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've created a model, but we don't know how good it is yet. To evaluate the model on training data, we need a y placeholder to provide the desired values, and we need to write a loss function.\n",
    "\n",
    "### Cost-function to be optimized¶\n",
    "\n",
    "A loss function measures how far apart the current model is from the provided data. We'll use a standard loss model for linear regression, which sums the squares of the deltas between the current model and the provided data. linear_model - y creates a vector where each element is the corresponding example's error delta. We call tf.square to square that error. Then, we sum all the squared errors to create a single scalar that abstracts the error of all examples using tf.reduce_sum:\n",
    "\\begin{eqnarray}  C(w,b) \\equiv\n",
    "   \\sum_x \\| LinearModel - y\\|^2.\n",
    "\\tag{1}\\end{eqnarray}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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W8tGwBtWrV1drOxsChoAhEB4CabKAe5MmgM3/CA+33biOKoFoM1J+fj6uvvpq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\niqCcPRPsOOImy6bDdQY6xcD/AUZeTyDHeHlqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image (filename= PATH  + \"lin_reg_error.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23.66\n"
     ]
    }
   ],
   "source": [
    "y = tf.placeholder(tf.float32)\n",
    "squared_deltas = tf.square(linear_model - y)\n",
    "loss = tf.reduce_sum(squared_deltas)\n",
    "print(sess.run(loss, {x: [1, 2, 3, 4], y: [0, -1, -2, -3]}))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could improve this manually by reassigning the values of W and b to the perfect values of -1 and 1. A variable is initialized to the value provided to tf.Variable but can be changed using operations like tf.assign. For example, W=-1 and b=1 are the optimal parameters for our model. We can change W and b accordingly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.  0.  0.  0.]\n"
     ]
    }
   ],
   "source": [
    "fixW = tf.assign(W, [-1.])\n",
    "fixb = tf.assign(b, [1.])\n",
    "sess.run([fixW, fixb])\n",
    "print(sess.run(squared_deltas, {x: [1, 2, 3, 4], y: [0, -1, -2, -3]}))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## tf.train API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A complete discussion of machine learning is out of the scope of this tutorial. However, TensorFlow provides optimizers that slowly change each variable in order to minimize the loss function. The simplest optimizer is gradient descent. It modifies each variable according to the magnitude of the derivative of loss with respect to that variable. In general, computing symbolic derivatives manually is tedious and error-prone. Consequently, TensorFlow can automatically produce derivatives given only a description of the model using the function tf.gradients. For simplicity, optimizers typically do this for you. For example,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W: [-0.84079814] b: [ 0.53192717] loss: 0.146364\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "# Model parameters\n",
    "W = tf.Variable([.3], dtype=tf.float32)\n",
    "b = tf.Variable([-.3], dtype=tf.float32)\n",
    "# Model input and output\n",
    "x = tf.placeholder(tf.float32)\n",
    "linear_model = W * x + b\n",
    "y = tf.placeholder(tf.float32)\n",
    "\n",
    "# loss\n",
    "loss = tf.reduce_sum(tf.square(linear_model - y)) # sum of the squares\n",
    "# optimizer\n",
    "optimizer = tf.train.GradientDescentOptimizer(0.01)\n",
    "train = optimizer.minimize(loss)\n",
    "\n",
    "# training data\n",
    "x_train = [1, 2, 3, 4]\n",
    "y_train = [0, -1, -2, -3]\n",
    "# training loop\n",
    "init = tf.global_variables_initializer()\n",
    "sess = tf.Session()\n",
    "sess.run(init) # reset values to wrong\n",
    "for i in range(100):\n",
    "    sess.run(train, {x: x_train, y: y_train})\n",
    "\n",
    "# evaluate training accuracy\n",
    "curr_W, curr_b, curr_loss = sess.run([W, b, loss], {x: x_train, y: y_train})\n",
    "print(\"W: %s b: %s loss: %s\"%(curr_W, curr_b, curr_loss))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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b7RpFzNNzzh+TEH0Snwqqv/Twxq4fdPziO4+eu7t5jSI00HHknXNzdDAVbfjG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\nTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image (filename= PATH + \"getting_started_final.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

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