1. Linear Regression using TensorFlow.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x226cd34f710>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAELCAYAAADURYGZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRAAAkft/FlVWALgHnikAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x226cd338dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.read_csv('LR.csv', names=['x', 'y'])\n",
    "data.plot(x='x', y='y', kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preparing Training data\n",
    "train_x = data.x\n",
    "train_y = data.y\n",
    "\n",
    "X = tf.placeholder(dtype=tf.float32)\n",
    "y_ = tf.placeholder(dtype=tf.float32)\n",
    "\n",
    "# Parameters\n",
    "W = tf.Variable(1, dtype=tf.float32, name='Weights')\n",
    "b = tf.Variable(1, dtype=tf.float32, name='Bias')\n",
    "\n",
    "# Constants\n",
    "learning_rate = 0.001\n",
    "epochs = 1000\n",
    "display_batch = 50\n",
    "n_samples = data.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Computing the logits\n",
    "y = (W * X + b)\n",
    "\n",
    "# Defining the loss function\n",
    "loss_fn = tf.reduce_sum(tf.pow((y - y_), 2)) / (2 * n_samples)\n",
    "\n",
    "# Defining Gradient Descent Optimizer and the loss operation\n",
    "optimizer = tf.train.GradientDescentOptimizer(learning_rate)\n",
    "loss_op = optimizer.minimize(loss_fn)\n",
    "\n",
    "# Defining the initializer operation\n",
    "init_op = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.94181\n",
      "1.888\n",
      "1.86592\n",
      "1.85265\n",
      "1.84201\n",
      "1.83233\n",
      "1.82317\n",
      "1.81439\n",
      "1.80593\n",
      "1.79778\n",
      "1.78994\n",
      "1.78238\n",
      "1.7751\n",
      "1.76809\n",
      "1.76132\n",
      "1.75481\n",
      "1.74854\n",
      "1.74249\n",
      "1.73666\n",
      "1.73105\n"
     ]
    }
   ],
   "source": [
    "# Starting the Session\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init_op)\n",
    "    \n",
    "    # Training loop\n",
    "    for epoch in range(epochs):\n",
    "        count = 0\n",
    "        while count < n_samples:\n",
    "            sess.run(loss_op, feed_dict={X:train_x[count], y_:train_y[count]})\n",
    "            count += 1\n",
    "        \n",
    "        if epoch % display_batch == 0:\n",
    "            print(loss_fn.eval(feed_dict={X:train_x, y_:train_y}))\n",
    "    \n",
    "    # Learnt parameters\n",
    "    weight_learnt = W.eval()\n",
    "    bias_learnt = b.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x226ce2e3940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_x = np.array([min(train_x), max(train_x)])\n",
    "plot_y = np.multiply(weight_learnt, plot_x) + bias_learnt\n",
    "\n",
    "plt.plot(train_x, train_y, 'r+', label='Data Points')\n",
    "plt.plot(plot_x, plot_y, '-', label='Regression Line')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}