code_session_version/lab-09-1-XOR-session.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab 09 XOR - Logistic Regression - Session\n",
    "* XOR 문제를 Logistic Regression을 활용해 풀어보도록 하겠습니다.\n",
    "\n",
    "### 기본 Library 선언 및 Tensorflow 버전 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.12.0\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import tensorflow as tf\n",
    "\n",
    "tf.set_random_seed(777)  # for reproducibility\n",
    "\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 강의에 설명할 Data입니다\n",
    "* x_data가 2차원 배열이기에 2차원 공간에 표현하여 x1과 x2를 기준으로 y_data 0과 1로 구분하는 예제입니다\n",
    "* 붉은색과 푸른색으로 0과 1을 표시해 보도록 하겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_data = [[0, 0],\n",
    "          [0, 1],\n",
    "          [1, 0],\n",
    "          [1, 1]]\n",
    "y_data = [[0],\n",
    "          [1],\n",
    "          [1],\n",
    "          [0]]\n",
    "\n",
    "plt.scatter(x_data[0][0],x_data[0][1], c='red' , marker='^')\n",
    "plt.scatter(x_data[3][0],x_data[3][1], c='red' , marker='^')\n",
    "plt.scatter(x_data[1][0],x_data[1][1], c='blue' , marker='^')\n",
    "plt.scatter(x_data[2][0],x_data[2][1], c='blue' , marker='^')\n",
    "\n",
    "plt.xlabel(\"x1\")\n",
    "plt.ylabel(\"x2\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Tensorflow Session\n",
    "### 위 Data를 기준으로 XOR처리를 위한 모델을 만들도록 하겠습니다\n",
    "* Tensorflow data API를 통해 학습시킬 값들을 담는다 (Batch Size는 한번에 학습시킬 Size로 정한다)\n",
    "* features,labels는 실재 학습에 쓰일 Data (연산을 위해 Type를 맞춰준다)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = tf.data.Dataset.from_tensor_slices((x_data, y_data)).batch(len(x_data)).repeat()\n",
    "\n",
    "iter = dataset.make_initializable_iterator()\n",
    "features, labels = iter.get_next()\n",
    "\n",
    "features = tf.cast(features, tf.float32)\n",
    "labels = tf.cast(labels, tf.float32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1) Logistic Regression으로 XOR모델을 만들어 보겠습니다\n",
    "### W와 b은 학습을 통해 생성되는 모델에 쓰이는 Wegith와 Bias (초기값을 variable : 0이나 Random값으로 가능 tf.random_normal([2, 1]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "W = tf.Variable(tf.zeros([2,1]), name='weight')\n",
    "b = tf.Variable(tf.zeros([1]), name='bias')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sigmoid 함수를 가설로 선언합니다\n",
    "* Sigmoid는 아래 그래프와 같이 0과 1의 값만을 리턴합니다 tf.sigmoid(tf.matmul(X, W) + b)와 같습니다\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "sigmoid(x) & = \\frac{1}{1+e^{-x}}  \\\\\\\\\\\n",
    "\\end{align}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "hypothesis  = tf.div(1., 1. + tf.exp(tf.matmul(features, W) + b))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 가설을 검증할 Cost 함수를 정의합니다\n",
    "$$\n",
    "\\begin{align}\n",
    "cost(h(x),y) & = −log(h(x))  &  if  &  y=1 \\\\\\\\\\\n",
    "cost(h(x),y) & = -log(1−h(x))  &  if  &  y=0\n",
    "\\end{align}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 위 두수식을 합치면 아래과 같습니다\n",
    "$$\n",
    "\\begin{align}\n",
    "cost(h(x),y) & = −y log(h(x))−(1−y)log(1−h(x))\n",
    "\\end{align}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "cost = -tf.reduce_mean(labels * tf.log(hypothesis) + (1 - labels) * tf.log(1 - hypothesis))\n",
    "\n",
    "learning_rate = 0.01\n",
    "train = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cost)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 추론한 값은 0.5를 기준(Sigmoid 그래프 참조)로 0과 1의 값을 리턴합니다.\n",
    "* Sigmoid 함수를 통해 예측값이 0.5보다 크면 1을 반환하고 0.5보다 작으면 0으로 반환합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted = tf.cast(hypothesis > 0.5, dtype=tf.float32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 가설을 통해 실재 값과 비교한 정확도를 측정합니다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy = tf.reduce_mean(tf.cast(tf.equal(predicted, labels), dtype=tf.float32))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tensorflow를 통한 실행을 위해 Session를 선언합니다.\n",
    "* 위의 Data를 Cost함수를 통해 학습시킨 후 모델을 생성합니다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter: 0, Loss: 0.6931\n",
      "Iter: 1000, Loss: 0.6931\n",
      "Iter: 2000, Loss: 0.6931\n",
      "Iter: 3000, Loss: 0.6931\n",
      "Iter: 4000, Loss: 0.6931\n",
      "Iter: 5000, Loss: 0.6931\n",
      "Iter: 6000, Loss: 0.6931\n",
      "Iter: 7000, Loss: 0.6931\n",
      "Iter: 8000, Loss: 0.6931\n",
      "Iter: 9000, Loss: 0.6931\n",
      "Iter: 10000, Loss: 0.6931\n",
      "\n",
      "Hypothesis:  [[0.5]\n",
      " [0.5]\n",
      " [0.5]\n",
      " [0.5]] \n",
      "Correct (Y):  [[0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]] \n",
      "Accuracy:  0.5\n"
     ]
    }
   ],
   "source": [
    "EPOCHS = 10001\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    for step in range(EPOCHS):\n",
    "        sess.run(iter.initializer)\n",
    "        _, loss_value = sess.run([train, cost])\n",
    "        if step % 1000 == 0:\n",
    "            print(\"Iter: {}, Loss: {:.4f}\".format(step, loss_value))\n",
    "    h, c, a = sess.run([hypothesis, predicted, accuracy])\n",
    "    print(\"\\nHypothesis: \", h, \"\\nCorrect (Y): \", c, \"\\nAccuracy: \", a)"
   ]
  }
 ],
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