2. Logistic Classifier using TensorFlow.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "import pandas as pd\n",
    "pd.options.mode.chained_assignment = None\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2200297cef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.read_csv('svm_data.csv', names=['x1', 'x2', 'y'])\n",
    "data_pos = data[data.y == 1]\n",
    "data_neg = data[data.y == -1]\n",
    "\n",
    "plt.plot(data_pos.x1, data_pos.x2, 'b+', label='Positive Samples')\n",
    "plt.plot(data_neg.x1, data_neg.x2, 'rx', label='Negative Samples')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Converting all -1 values to 0 for logistic regression\n",
    "data.y[data.y == -1] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Preaparing training data\n",
    "train_x = data[['x1', 'x2']]\n",
    "train_y = data['y']\n",
    "\n",
    "X = tf.placeholder(dtype=tf.float32, shape=[None, 2])\n",
    "labels = tf.placeholder(dtype=tf.float32, shape=[None, 1])\n",
    "\n",
    "# Parameters\n",
    "W = tf.Variable(tf.truncated_normal(shape=[2, 1], stddev=0.01), dtype=tf.float32, name='Weights')\n",
    "b = tf.Variable(tf.zeros([1]), dtype=tf.float32, name='Bias')\n",
    "\n",
    "# Constants\n",
    "learning_rate = 0.01\n",
    "epochs = 1500\n",
    "display_batch = 50\n",
    "n_samples = data.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Computing the logits\n",
    "logits = tf.matmul(X, W) + b\n",
    "hypothesis = tf.nn.sigmoid(logits)\n",
    "\n",
    "# Defining the logistic regression loss function ~ Cross Entropy\n",
    "loss_fn = tf.reduce_mean((-labels * tf.log(hypothesis)) - ((1 - labels) * tf.log(1 - hypothesis)))\n",
    "\n",
    "# Defining the Gradient Descent Optimizer and the training operation\n",
    "optimizer = tf.train.GradientDescentOptimizer(learning_rate)\n",
    "train_op = optimizer.minimize(loss_fn)\n",
    "\n",
    "# Defining the initializer operation\n",
    "init_op = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss at epoch 0: 0.622004\n",
      "Loss at epoch 50: 0.390254\n",
      "Loss at epoch 100: 0.297842\n",
      "Loss at epoch 150: 0.245838\n",
      "Loss at epoch 200: 0.212694\n",
      "Loss at epoch 250: 0.189496\n",
      "Loss at epoch 300: 0.171883\n",
      "Loss at epoch 350: 0.157688\n",
      "Loss at epoch 400: 0.145804\n",
      "Loss at epoch 450: 0.135615\n",
      "Loss at epoch 500: 0.126748\n",
      "Loss at epoch 550: 0.118948\n",
      "Loss at epoch 600: 0.112035\n",
      "Loss at epoch 650: 0.105868\n",
      "Loss at epoch 700: 0.100338\n",
      "Loss at epoch 750: 0.095354\n",
      "Loss at epoch 800: 0.0908428\n",
      "Loss at epoch 850: 0.086743\n",
      "Loss at epoch 900: 0.0830026\n",
      "Loss at epoch 950: 0.0795785\n",
      "Loss at epoch 1000: 0.0764332\n",
      "Loss at epoch 1050: 0.0735341\n",
      "Loss at epoch 1100: 0.0708547\n",
      "Loss at epoch 1150: 0.0683713\n",
      "Loss at epoch 1200: 0.0660642\n",
      "Loss at epoch 1250: 0.0639154\n",
      "Loss at epoch 1300: 0.0619092\n",
      "Loss at epoch 1350: 0.0600314\n",
      "Loss at epoch 1400: 0.0582708\n",
      "Loss at epoch 1450: 0.0566158\n"
     ]
    }
   ],
   "source": [
    "# Starting a 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",
    "            feed_dict = {\n",
    "                X: train_x.iloc[count, :].values.reshape(1, 2),\n",
    "                labels: train_y.iloc[count].reshape(1, 1)\n",
    "            }\n",
    "            sess.run(train_op, feed_dict=feed_dict)\n",
    "            count += 1\n",
    "        \n",
    "        if epoch % display_batch == 0:\n",
    "            feed_dict = {\n",
    "                X: train_x,\n",
    "                labels: train_y.values.reshape(n_samples, 1)\n",
    "            }\n",
    "            \n",
    "            print('Loss at epoch %d: %g' % (epoch, sess.run(loss_fn, feed_dict=feed_dict)))\n",
    "    \n",
    "    weights_learnt = W.eval()\n",
    "    bias_learnt = b.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RV4F3VXVuZc+pr0HnCop+5NNv84MthkAZqWu7pozp5ZSRTurVjpZJVkbykp/HrvfzmP5+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\nAloAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22004745940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_x = [min(train_x.iloc[:, 0])-1,  max(train_x.iloc[:, 0])+1]\n",
    "plot_y = (-1 / weights_learnt[1]) * (np.multiply(weights_learnt[0], plot_x) + bias_learnt[0])\n",
    "\n",
    "plt.plot(data_pos.x1, data_pos.x2, 'b+', label='Positive Samples')\n",
    "plt.plot(data_neg.x1, data_neg.x2, 'rx', label='Negative Samples')\n",
    "plt.plot(plot_x, plot_y, '-', label='Decision Boundary')\n",
    "plt.title('Logistic Regression - Decision Boundary')\n",
    "plt.xlabel('x1')\n",
    "plt.ylabel('x2')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "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
}