Lab09/Lab09-2_JijoongHong.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab 09 XOR - Neural Network - Eager Excuetion\n",
    "- XOR 문제를 Neural Network을 활용해 풀어보도록 하겠습니다. \n",
    "\n",
    "## 기본 Library 선언 및 Tensorflow 버전 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.6.0\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import tensorflow as tf\n",
    "\n",
    "tf.random.set_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": {},
   "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))\n",
    "\n",
    "def preprocess_data(features, labels):\n",
    "    features = tf.cast(features, tf.float32)\n",
    "    labels = tf.cast(labels, tf.float32)\n",
    "    return features, labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Neural Network를 통해 XOR해결\n",
    "\n",
    "- 위의 Data를 3Layer의 Neural Network를 통해 학습시킨 후 모델을 생성합니다.(강의에서 설명한 구조)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "W1 = tf.Variable(tf.random.normal((2, 1)), name='weight1')\n",
    "b1 = tf.Variable(tf.random.normal((1,)), name='bias1')\n",
    "\n",
    "W2 = tf.Variable(tf.random.normal((2, 1)), name='weight2')\n",
    "b2 = tf.Variable(tf.random.normal((1,)), name='bias2')\n",
    "\n",
    "W3 = tf.Variable(tf.random.normal((2, 1)), name='weight3')\n",
    "b3 = tf.Variable(tf.random.normal((1,)), name='bias3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def neural_net(features):\n",
    "    layer1 = tf.sigmoid(tf.matmul(features, W1) + b1)\n",
    "    layer2 = tf.sigmoid(tf.matmul(features, W2) + b2)\n",
    "    layer3 = tf.concat([layer1, layer2],-1)\n",
    "    layer3 = tf.reshape(layer3, shape = [-1,2])\n",
    "    hypothesis = tf.sigmoid(tf.matmul(layer3, W3) + b3)\n",
    "    return hypothesis\n",
    "\n",
    "def loss_fn(hypothesis, labels):\n",
    "    cost = -tf.reduce_mean(labels * tf.math.log(hypothesis) + (1 - labels) * tf.math.log(1 - hypothesis))\n",
    "    return cost\n",
    "\n",
    "optimizer = tf.keras.optimizers.SGD(learning_rate=0.01)\n",
    "\n",
    "def accuracy_fn(hypothesis, labels):\n",
    "    predicted = tf.cast(hypothesis > 0.5, dtype=tf.float32)\n",
    "    accuracy = tf.reduce_mean(tf.cast(tf.equal(predicted, labels), dtype=tf.float32))\n",
    "    return accuracy\n",
    "\n",
    "def grad(hypothesis, features, labels):\n",
    "    with tf.GradientTape() as tape:\n",
    "        loss_value = loss_fn(neural_net(features),labels)\n",
    "    return tape.gradient(loss_value, [W1, W2, W3, b1, b2, b3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter: 0, Loss: 0.8487\n",
      "Iter: 5000, Loss: 0.6847\n",
      "Iter: 10000, Loss: 0.6610\n",
      "Iter: 15000, Loss: 0.6154\n",
      "Iter: 20000, Loss: 0.5722\n",
      "Iter: 25000, Loss: 0.5433\n",
      "Iter: 30000, Loss: 0.5211\n",
      "Iter: 35000, Loss: 0.4911\n",
      "Iter: 40000, Loss: 0.4416\n",
      "Iter: 45000, Loss: 0.3313\n",
      "Testset Accuracy: 1.0000\n"
     ]
    }
   ],
   "source": [
    "EPOCHS = 50000\n",
    "\n",
    "for step in range(EPOCHS):\n",
    "    for features, labels  in dataset:\n",
    "        features, labels = preprocess_data(features, labels)\n",
    "        grads = grad(neural_net(features), features, labels)\n",
    "        optimizer.apply_gradients(grads_and_vars=zip(grads,[W1, W2, W3, b1, b2, b3]))\n",
    "        if step % 5000 == 0:\n",
    "            print(\"Iter: {}, Loss: {:.4f}\".format(step, loss_fn(neural_net(features),labels)))\n",
    "x_data, y_data = preprocess_data(x_data, y_data)\n",
    "test_acc = accuracy_fn(neural_net(x_data),y_data)\n",
    "print(\"Testset Accuracy: {:.4f}\".format(test_acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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