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‎lab-09-1-XOR-eager.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Lab 09 XOR - Logistic Regression - Eager Excuetion\n",
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"* XOR 문제를 Logistic Regression을 활용해 풀어보도록 하겠습니다.\n",
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"\n",
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"### 기본 Library 선언 및 Tensorflow 버전 확인"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"1.12.0\n"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"%matplotlib inline\n",
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"import tensorflow as tf\n",
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"import tensorflow.contrib.eager as tfe\n",
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"\n",
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"tf.enable_eager_execution()\n",
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"tf.set_random_seed(777) # for reproducibility\n",
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"\n",
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"print(tf.__version__)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 강의에 설명할 Data입니다\n",
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"* x_data가 2차원 배열이기에 2차원 공간에 표현하여 x1과 x2를 기준으로 y_data 0과 1로 구분하는 예제입니다\n",
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"* 붉은색과 푸른색으로 0과 1을 표시해 보도록 하겠습니다."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"data": {
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"image/png": 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\n",
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": [
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"x_data = [[0, 0],\n",
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" [0, 1],\n",
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" [1, 0],\n",
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" [1, 1]]\n",
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"y_data = [[0],\n",
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" [1],\n",
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" [1],\n",
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" [0]]\n",
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"\n",
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"plt.scatter(x_data[0][0],x_data[0][1], c='red' , marker='^')\n",
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"plt.scatter(x_data[3][0],x_data[3][1], c='red' , marker='^')\n",
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"plt.scatter(x_data[1][0],x_data[1][1], c='blue' , marker='^')\n",
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"plt.scatter(x_data[2][0],x_data[2][1], c='blue' , marker='^')\n",
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"\n",
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"plt.xlabel(\"x1\")\n",
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"plt.ylabel(\"x2\")\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Tensorflow Eager\n",
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"### 위 Data를 기준으로 XOR처리를 위한 모델을 만들도록 하겠습니다\n",
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"* Tensorflow data API를 통해 학습시킬 값들을 담는다 (Batch Size는 한번에 학습시킬 Size로 정한다)\n",
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"* preprocess function으로 features,labels는 실재 학습에 쓰일 Data 연산을 위해 Type를 맞춰준다"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"dataset = tf.data.Dataset.from_tensor_slices((x_data, y_data)).batch(len(x_data))\n",
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"\n",
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"def preprocess_data(features, labels):\n",
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" features = tf.cast(features, tf.float32)\n",
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" labels = tf.cast(labels, tf.float32)\n",
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" return features, labels"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 1) Logistic Regression으로 XOR모델을 만들어 보겠습니다\n",
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"### W와 b은 학습을 통해 생성되는 모델에 쓰이는 Wegith와 Bias (초기값을 variable : 0이나 Random값으로 가능 tf.random_normal([2, 1]) )"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"W = [[0.]\n",
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" [0.]], B = [0.]\n"
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]
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}
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],
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"source": [
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"W = tf.Variable(tf.zeros([2,1]), name='weight')\n",
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"b = tf.Variable(tf.zeros([1]), name='bias')\n",
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"print(\"W = {}, B = {}\".format(W.numpy(), b.numpy()))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Sigmoid 함수를 가설로 선언합니다\n",
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"* Sigmoid는 아래 그래프와 같이 0과 1의 값만을 리턴합니다 tf.sigmoid(tf.matmul(X, W) + b)와 같습니다\n",
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"\n",
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"$$\n",
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"\\begin{align}\n",
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"sigmoid(x) & = \\frac{1}{1+e^{-x}} \\\\\\\\\\\n",
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"\\end{align}\n",
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"$$"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"def logistic_regression(features):\n",
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" hypothesis = tf.div(1., 1. + tf.exp(tf.matmul(features, W) + b))\n",
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" return hypothesis"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 가설을 검증할 Cost 함수를 정의합니다\n",
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"$$\n",
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"\\begin{align}\n",
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"cost(h(x),y) & = −log(h(x)) & if & y=1 \\\\\\\\\\\n",
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"cost(h(x),y) & = -log(1−h(x)) & if & y=0\n",
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"\\end{align}\n",
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"$$"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"* 위 두수식을 합치면 아래과 같습니다\n",
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"$$\n",
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"\\begin{align}\n",
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"cost(h(x),y) & = −y log(h(x))−(1−y)log(1−h(x))\n",
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"\\end{align}\n",
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"$$"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"def loss_fn(hypothesis, features, labels):\n",
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" cost = -tf.reduce_mean(labels * tf.log(logistic_regression(features)) + (1 - labels) * tf.log(1 - hypothesis))\n",
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" return cost\n",
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"\n",
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"optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 추론한 값은 0.5를 기준(Sigmoid 그래프 참조)로 0과 1의 값을 리턴합니다.\n",
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"* Sigmoid 함수를 통해 예측값이 0.5보다 크면 1을 반환하고 0.5보다 작으면 0으로 반환합니다."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"def accuracy_fn(hypothesis, labels):\n",
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" predicted = tf.cast(hypothesis > 0.5, dtype=tf.float32)\n",
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" accuracy = tf.reduce_mean(tf.cast(tf.equal(predicted, labels), dtype=tf.float32))\n",
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" return accuracy"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### GradientTape를 통해 경사값을 계산합니다."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"def grad(hypothesis, features, labels):\n",
239+
" with tf.GradientTape() as tape:\n",
240+
" loss_value = loss_fn(logistic_regression(features),features,labels)\n",
241+
" return tape.gradient(loss_value, [W,b])"
242+
]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
248+
"### Tensorflow를 통한 실행을 위해 Session를 선언합니다.\n",
249+
"* 위의 Data를 Cost함수를 통해 학습시킨 후 모델을 생성합니다. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Iter: 0, Loss: 0.6931\n",
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"Iter: 100, Loss: 0.6931\n",
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"Iter: 200, Loss: 0.6931\n",
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"Iter: 300, Loss: 0.6931\n",
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"Iter: 400, Loss: 0.6931\n",
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"Iter: 500, Loss: 0.6931\n",
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"Iter: 600, Loss: 0.6931\n",
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"Iter: 700, Loss: 0.6931\n",
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"Iter: 800, Loss: 0.6931\n",
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"Iter: 900, Loss: 0.6931\n",
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"Iter: 1000, Loss: 0.6931\n",
274+
"W = [[0.]\n",
275+
" [0.]], B = [0.]\n",
276+
"Testset Accuracy: 0.5000\n"
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]
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}
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],
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"source": [
281+
"EPOCHS = 1001\n",
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"\n",
283+
"for step in range(EPOCHS):\n",
284+
" for features, labels in tfe.Iterator(dataset):\n",
285+
" features, labels = preprocess_data(features, labels)\n",
286+
" grads = grad(logistic_regression(features), features, labels)\n",
287+
" optimizer.apply_gradients(grads_and_vars=zip(grads,[W,b]))\n",
288+
" if step % 100 == 0:\n",
289+
" print(\"Iter: {}, Loss: {:.4f}\".format(step, loss_fn(logistic_regression(features),features,labels)))\n",
290+
"print(\"W = {}, B = {}\".format(W.numpy(), b.numpy()))\n",
291+
"x_data, y_data = preprocess_data(x_data, y_data)\n",
292+
"test_acc = accuracy_fn(logistic_regression(x_data),y_data)\n",
293+
"print(\"Testset Accuracy: {:.4f}\".format(test_acc))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎lab-09-1-XOR.ipynb renamed to ‎lab-09-1-XOR-session.ipynb

+1-1
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@@ -4,7 +4,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
7-
"# Lab 09 XOR - Logistic Regression\n",
7+
"# Lab 09 XOR - Logistic Regression - Session\n",
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"* XOR 문제를 Logistic Regression을 활용해 풀어보도록 하겠습니다.\n",
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"\n",
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"### 기본 Library 선언 및 Tensorflow 버전 확인"

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