TensorFlow - Linear Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 1.1 Import tensorflow and other libraries.\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline\n",
    "import pylab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2c587f33c50>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2c587dfef28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1.2 Create input data using NumPy. y = x * 0.1 + 0.3 + noise\n",
    "x_data = np.random.rand(100).astype(np.float32)\n",
    "noise = np.random.normal(scale=0.01, size=len(x_data))\n",
    "y_data = x_data * 0.1 + 0.3 + noise\n",
    "\n",
    "# Uncomment the following line to plot our input data.\n",
    "pylab.plot(x_data, y_data, '.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"Variable_2/read:0\", shape=(1,), dtype=float32)\n",
      "Tensor(\"Variable_3/read:0\", shape=(1,), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "# 1.3 Buld inference graph.\n",
    "# Create Variables W and b that compute y_data = W * x_data + b\n",
    "W = tf.Variable(tf.random_uniform([1], 0.0, 1.0))\n",
    "b = tf.Variable(tf.zeros([1]))\n",
    "y = W * x_data + b\n",
    "\n",
    "# The following lines to see W and b are.\n",
    "print(W)\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1.4 Build training graph.\n",
    "loss = tf.reduce_mean(tf.square(y - y_data))  # Create an operation that calculates loss.\n",
    "optimizer = tf.train.GradientDescentOptimizer(0.5)  # Create an optimizer.\n",
    "train = optimizer.minimize(loss)  # Create an operation that minimizes loss.\n",
    "init = tf.global_variables_initializer()  # Create an operation initializes all the variables.\n",
    "\n",
    "# Uncomment the following 3 lines to see what 'loss', 'optimizer' and 'train' are.\n",
    "#print(\"loss:\", loss)\n",
    "#print(\"optimizer:\", optimizer)\n",
    "#print(\"train:\", train)\n",
    "#print(init)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 1.6 Create a session and launch the graph.\n",
    "sess = tf.Session()\n",
    "sess.run(init)\n",
    "y_initial_values = sess.run(y)  # Save initial values for plotting later.\n",
    "\n",
    "# Uncomment the following line to see the initial W and b values.\n",
    "# print(sess.run([W, b]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([ 0.10520767], dtype=float32), array([ 0.29671186], dtype=float32)]\n"
     ]
    }
   ],
   "source": [
    "# 1.7 Perform training.\n",
    "for step in range(201):\n",
    "    sess.run(train)\n",
    "    # Uncomment the following two lines to watch training happen real time.\n",
    "    # if step % 20 == 0:\n",
    "    #    print(step, sess.run([W, b]))\n",
    "\n",
    "print(sess.run([W, b]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 1.0)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8UKCf07I9T61bz5jDV4R7nUX7iviP9b9wShmHF+A9NxAM7GX8aZ3TC46sa4em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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2c58827b7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# 1.8 Uncomment the following lines to compare.\n",
    "pylab.plot(x_data, y_data, '.', label=\"target_values\")\n",
    "pylab.plot(x_data, y_initial_values, \".\", label=\"initial_values\")\n",
    "pylab.plot(x_data, sess.run(y), \".\", label=\"trained_values\")\n",
    "pylab.legend()\n",
    "pylab.ylim(0, 1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'random_uniform_4:0' shape=(1,) dtype=float32>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.random_uniform([1], 0.0, 1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'random_uniform_5:0' shape=(1,) dtype=float32>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.random_uniform([1], 0.0, 1.0)"
   ]
  },
  {
   "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": "3.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}