03_Logistic_Regression_Classification_for_Practice_TensorFlow.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression Classification for TensorFlow Practice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_data = [[4,5], [2,4], [1,3], [3,2], [6,2], [6, 4]]\n",
    "y_data = [[1], [1], [0], [0], [1], [1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = tf.placeholder(tf.float32, shape=[None, 2], name=\"X\")\n",
    "Y = tf.placeholder(tf.float32, shape=[None, 1], name=\"Y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "W = tf.Variable(tf.random_normal([2, 1], name=\"weight\"))\n",
    "b = tf.Variable(tf.random_normal([1], name='bias'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tensor Flow에서 제공하는 sigmoid 함수를 사용"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'div_4:0' shape=(?, 1) dtype=float32>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 직접 계산할 경우에는 아래 처럼 계산\n",
    "tf.div(1., (1. + tf.exp(tf.matmul(X, W) + b)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hypothesis = tf.sigmoid(tf.matmul(X, W) + b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cost = -tf.reduce_mean(Y*tf.log(hypothesis) + (1-Y)*tf.log(1-hypothesis))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "learning_rate = 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cost)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 예측 값에 대해서 0, 1로 표시\n",
    "- 0.5 보다 큰지 여부를 Boolean 값으로 받고 이를 float로 변환하여 반환"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predicted = tf.cast(hypothesis > 0.5, dtype=tf.float32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Boolean 결과 값의 평균을 구하기 위해 tf.cast를 사용해 float로 변환함"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "accuracy = tf.reduce_mean(tf.cast(tf.equal(predicted, Y), dtype=tf.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 1.81979\n",
      "200 0.338509\n",
      "400 0.286726\n",
      "600 0.249743\n",
      "800 0.221847\n",
      "\n",
      "predict:  [[ 0.98050296]\n",
      " [ 0.71717203]\n",
      " [ 0.23860529]\n",
      " [ 0.36316991]\n",
      " [ 0.89358503]\n",
      " [ 0.98919106]] \n",
      "correct :  [[ 1.]\n",
      " [ 1.]\n",
      " [ 0.]\n",
      " [ 0.]\n",
      " [ 1.]\n",
      " [ 1.]] \n",
      "accuracy :  1.0\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    \n",
    "    for step in range(1000):\n",
    "        cost_val, _ = sess.run([cost, train], feed_dict={X:x_data, Y:y_data})\n",
    "        if step % 200 == 0:\n",
    "            print(step, cost_val)\n",
    "            \n",
    "    h, c, a = sess.run([hypothesis, predicted, accuracy], feed_dict={X:x_data, Y:y_data})\n",
    "    print(\"\\npredict: \", h, \"\\ncorrect : \", c, \"\\naccuracy : \", a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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