TensorFlow - Linear Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Plotting Graphs\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "\n",
    "from sklearn import datasets\n",
    "\n",
    "\n",
    "from tensorflow.python.framework import ops\n",
    "ops.reset_default_graph()\n",
    "\n",
    "#show graph in ipython notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], \n",
       "      dtype='<U10')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "150"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(iris.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_vals = np.array([x[3] for x in iris.data])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_vals = np.array([x[0] for x in iris.data])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.2,  0.2,  0.2,  0.2,  0.2,  0.4,  0.3,  0.2,  0.2,  0.1,  0.2,\n",
       "        0.2,  0.1,  0.1,  0.2,  0.4,  0.4,  0.3,  0.3,  0.3,  0.2,  0.4,\n",
       "        0.2,  0.5,  0.2,  0.2,  0.4,  0.2,  0.2,  0.2,  0.2,  0.4,  0.1,\n",
       "        0.2,  0.1,  0.2,  0.2,  0.1,  0.2,  0.2,  0.3,  0.3,  0.2,  0.6,\n",
       "        0.4,  0.3,  0.2,  0.2,  0.2,  0.2,  1.4,  1.5,  1.5,  1.3,  1.5,\n",
       "        1.3,  1.6,  1. ,  1.3,  1.4,  1. ,  1.5,  1. ,  1.4,  1.3,  1.4,\n",
       "        1.5,  1. ,  1.5,  1.1,  1.8,  1.3,  1.5,  1.2,  1.3,  1.4,  1.4,\n",
       "        1.7,  1.5,  1. ,  1.1,  1. ,  1.2,  1.6,  1.5,  1.6,  1.5,  1.3,\n",
       "        1.3,  1.3,  1.2,  1.4,  1.2,  1. ,  1.3,  1.2,  1.3,  1.3,  1.1,\n",
       "        1.3,  2.5,  1.9,  2.1,  1.8,  2.2,  2.1,  1.7,  1.8,  1.8,  2.5,\n",
       "        2. ,  1.9,  2.1,  2. ,  2.4,  2.3,  1.8,  2.2,  2.3,  1.5,  2.3,\n",
       "        2. ,  2. ,  1.8,  2.1,  1.8,  1.8,  1.8,  2.1,  1.6,  1.9,  2. ,\n",
       "        2.2,  1.5,  1.4,  2.3,  2.4,  1.8,  1.8,  2.1,  2.4,  2.3,  1.9,\n",
       "        2.3,  2.5,  2.3,  1.9,  2. ,  2.3,  1.8])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5.1,  4.9,  4.7,  4.6,  5. ,  5.4,  4.6,  5. ,  4.4,  4.9,  5.4,\n",
       "        4.8,  4.8,  4.3,  5.8,  5.7,  5.4,  5.1,  5.7,  5.1,  5.4,  5.1,\n",
       "        4.6,  5.1,  4.8,  5. ,  5. ,  5.2,  5.2,  4.7,  4.8,  5.4,  5.2,\n",
       "        5.5,  4.9,  5. ,  5.5,  4.9,  4.4,  5.1,  5. ,  4.5,  4.4,  5. ,\n",
       "        5.1,  4.8,  5.1,  4.6,  5.3,  5. ,  7. ,  6.4,  6.9,  5.5,  6.5,\n",
       "        5.7,  6.3,  4.9,  6.6,  5.2,  5. ,  5.9,  6. ,  6.1,  5.6,  6.7,\n",
       "        5.6,  5.8,  6.2,  5.6,  5.9,  6.1,  6.3,  6.1,  6.4,  6.6,  6.8,\n",
       "        6.7,  6. ,  5.7,  5.5,  5.5,  5.8,  6. ,  5.4,  6. ,  6.7,  6.3,\n",
       "        5.6,  5.5,  5.5,  6.1,  5.8,  5. ,  5.6,  5.7,  5.7,  6.2,  5.1,\n",
       "        5.7,  6.3,  5.8,  7.1,  6.3,  6.5,  7.6,  4.9,  7.3,  6.7,  7.2,\n",
       "        6.5,  6.4,  6.8,  5.7,  5.8,  6.4,  6.5,  7.7,  7.7,  6. ,  6.9,\n",
       "        5.6,  7.7,  6.3,  6.7,  7.2,  6.2,  6.1,  6.4,  7.2,  7.4,  7.9,\n",
       "        6.4,  6.3,  6.1,  7.7,  6.3,  6.4,  6. ,  6.9,  6.7,  6.9,  5.8,\n",
       "        6.8,  6.7,  6.7,  6.3,  6.5,  6.2,  5.9])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Initialize placeholders\n",
    "x_data = tf.placeholder(shape=[None, 1], dtype=tf.float32)\n",
    "y_target = tf.placeholder(shape=[None, 1], dtype=tf.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create variables for linear regression, y = Ax + b\n",
    "A = tf.Variable(tf.random_normal(shape=[1,1]))\n",
    "b = tf.Variable(tf.random_normal(shape=[1,1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Declare model operations\n",
    "model_output = tf.add(tf.matmul(x_data, A), b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Declare loss function (L2 loss)\n",
    "loss = tf.reduce_mean(tf.square(y_target - model_output))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize variables\n",
    "init = tf.global_variables_initializer()\n",
    "sess = tf.Session()\n",
    "sess.run(init)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Using GD find the case which has min loss \n",
    "my_opt = tf.train.GradientDescentOptimizer(0.05)\n",
    "train_step = my_opt.minimize(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step #25 A = [[ 1.0110389]] b = [[ 4.52075243]]\n",
      "Loss = 0.278333\n",
      "Step #50 A = [[ 0.97072154]] b = [[ 4.65077209]]\n",
      "Loss = 0.109885\n",
      "Step #75 A = [[ 0.95730346]] b = [[ 4.68907738]]\n",
      "Loss = 0.139451\n",
      "Step #100 A = [[ 0.93071383]] b = [[ 4.72984409]]\n",
      "Loss = 0.208288\n"
     ]
    },
    {
     "data": {
      "image/png": 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s26lpTM6U3kfRsi6pCeLz+RgwYACqis/n45FHHokqZXWwhx9+mIkTJ9KiRYt68+bNm8fk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\nVmuooXnKmL51nmgG631kjMk8SbtSUNW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      "text/plain": [
       "<matplotlib.figure.Figure at 0x186688d3320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss_vec = []\n",
    "batch_size = 25\n",
    "for i in range(100):\n",
    "    #getting 25 random index out of 150 data\n",
    "    rand_index = np.random.choice(len(x_vals), size=batch_size)\n",
    "    rand_x = np.transpose([x_vals[rand_index]])\n",
    "    rand_y = np.transpose([y_vals[rand_index]])\n",
    "    #feed_dict to pass variables\n",
    "    sess.run(train_step, feed_dict={x_data:rand_x, y_target:rand_y})\n",
    "    temp_loss = sess.run(loss,feed_dict={x_data:rand_x, y_target:rand_y})\n",
    "    loss_vec.append(temp_loss)\n",
    "    if (i+1)%25 ==0:\n",
    "        print('Step #' + str(i+1) + ' A = ' + str(sess.run(A)) + ' b = ' + str(sess.run(b)))\n",
    "        print('Loss = ' + str(temp_loss)) \n",
    "        \n",
    "# Get the optimal coefficients\n",
    "[slope] = sess.run(A)\n",
    "[y_intercept] = sess.run(b)\n",
    "\n",
    "# Get best fit line\n",
    "best_fit = []\n",
    "for i in x_vals:\n",
    "    best_fit.append(slope*i+y_intercept)\n",
    "\n",
    "# Plot the result\n",
    "plt.plot(x_vals, y_vals, 'o', label='Data Points')\n",
    "plt.plot(x_vals, best_fit, 'y-', label='Best fit line', linewidth=3)\n",
    "plt.legend(loc='upper left')\n",
    "plt.title('Sepal Length vs Pedal Width')\n",
    "plt.xlabel('Pedal Width')\n",
    "plt.ylabel('Sepal Length')\n",
    "plt.show()\n",
    "\n",
    "    "
   ]
  },
  {
   "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
}