Lab09/Lab 09-2 minkiKang.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 모두를 위한 딥러닝 시즌2 - TensorFlow\n",
    "\n",
    "## LAB09 - 2 Neural nets XOR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "XOR문제를 Neural Network를 이용해 풀기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.5.0\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)\n",
    "\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "XOR 문제를 로지스틱 회귀로 풀 때와 동일한 데이터!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
    "XOR처리를 위한 모델 만들기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = tf.data.Dataset.from_tensor_slices((x_data, y_data)).batch(len(x_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "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 Network 를 통해 XOR 해결\n",
    "\n",
    "- 3 Layer 의 뉴럴 네트워크를 통해 학습시킨 후 모델 생성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#뉴럴넷\n",
    "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#손실함수\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#옵티마이저는 케라스의 SGD(확률적 경사하강)를 사용\n",
    "optimizer = tf.keras.optimizers.SGD(learning_rate = 0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter : 0, Loss : 1.8251\n",
      "Iter : 5000, Loss : 0.6820\n",
      "Iter : 10000, Loss : 0.6531\n",
      "Iter : 15000, Loss : 0.6061\n",
      "Iter : 20000, Loss : 0.5565\n",
      "Iter : 25000, Loss : 0.5135\n",
      "Iter : 30000, Loss : 0.4735\n",
      "Iter : 35000, Loss : 0.4191\n",
      "Iter : 40000, Loss : 0.3102\n",
      "Iter : 45000, Loss : 0.2131\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'accuracy_fn' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-13-354c5b483b6f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      9\u001b[0m             \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Iter : {}, Loss : {:.4f}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloss_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mneural_net\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[0mx_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpreprocess_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_data\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m \u001b[0mtest_acc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maccuracy_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mneural_net\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     12\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Testset Accuracy: {:.4f}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest_acc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'accuracy_fn' is not defined"
     ]
    }
   ],
   "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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