exam10_keras_xor.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "exam10_keras_xor.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyN9pvPg/+/eMdixizIfKErj",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Hajinjing/jjing/blob/master/exam10_keras_xor.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "X371c9Bn3DmW"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "import matplotlib.pyplot as plt\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "training_data = np.array(\n",
        "    [[0,0],[0,1],[1,0],[1,1]], 'float32')\n",
        "\n",
        "target_data = np.array([[0],[1],[1],[0]], 'float32')\n"
      ],
      "metadata": {
        "id": "jEFdI_yH4smA"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Dense(32, input_dim=2, activation='relu'))  #두개의 값을 입력 받아서 32개의 dense로 연결  히든레이어\n",
        "model.add(Dense(1, activation='sigmoid'))  #출력레이어\n",
        "model.compile(loss='mse', optimizer='adam',\n",
        "              metrics=['binary_accuracy'])\n",
        "\n",
        "print(model.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NfPh4jup9Q4m",
        "outputId": "1d79bc1a-d650-4233-ad90-0d220811d44b"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_2 (Dense)             (None, 32)                96        \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 1)                 33        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 129\n",
            "Trainable params: 129\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fit_hist = model.fit(training_data, target_data, epochs=500, verbose=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cQN3ICB99mJU",
        "outputId": "10639ae1-a478-4ab2-aafb-f68172567c8e"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/500\n",
            "1/1 [==============================] - 0s 349ms/step - loss: 0.2519 - binary_accuracy: 0.5000\n",
            "Epoch 2/500\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.2513 - binary_accuracy: 0.5000\n",
            "Epoch 3/500\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.2508 - binary_accuracy: 0.5000\n",
            "Epoch 4/500\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.2503 - binary_accuracy: 0.5000\n",
            "Epoch 5/500\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.2498 - binary_accuracy: 0.7500\n",
            "Epoch 6/500\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.2493 - binary_accuracy: 0.7500\n",
            "Epoch 7/500\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.2488 - binary_accuracy: 0.7500\n",
            "Epoch 8/500\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.2482 - binary_accuracy: 0.7500\n",
            "Epoch 9/500\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.2477 - binary_accuracy: 0.7500\n",
            "Epoch 10/500\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.2472 - binary_accuracy: 0.7500\n",
            "Epoch 11/500\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.2467 - binary_accuracy: 0.7500\n",
            "Epoch 12/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.2462 - binary_accuracy: 1.0000\n",
            "Epoch 13/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.2456 - binary_accuracy: 1.0000\n",
            "Epoch 14/500\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.2451 - binary_accuracy: 1.0000\n",
            "Epoch 15/500\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.2446 - binary_accuracy: 1.0000\n",
            "Epoch 16/500\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.2441 - binary_accuracy: 1.0000\n",
            "Epoch 17/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.2436 - binary_accuracy: 1.0000\n",
            "Epoch 18/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.2431 - binary_accuracy: 1.0000\n",
            "Epoch 19/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.2426 - binary_accuracy: 1.0000\n",
            "Epoch 20/500\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.2421 - binary_accuracy: 1.0000\n",
            "Epoch 21/500\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.2417 - binary_accuracy: 1.0000\n",
            "Epoch 22/500\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.2412 - binary_accuracy: 1.0000\n",
            "Epoch 23/500\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.2407 - binary_accuracy: 1.0000\n",
            "Epoch 24/500\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.2402 - binary_accuracy: 1.0000\n",
            "Epoch 25/500\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.2398 - binary_accuracy: 1.0000\n",
            "Epoch 26/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.2393 - binary_accuracy: 1.0000\n",
            "Epoch 27/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.2388 - binary_accuracy: 1.0000\n",
            "Epoch 28/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.2384 - binary_accuracy: 1.0000\n",
            "Epoch 29/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.2379 - binary_accuracy: 1.0000\n",
            "Epoch 30/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.2374 - binary_accuracy: 1.0000\n",
            "Epoch 31/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.2370 - binary_accuracy: 1.0000\n",
            "Epoch 32/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.2365 - binary_accuracy: 1.0000\n",
            "Epoch 33/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.2360 - binary_accuracy: 1.0000\n",
            "Epoch 34/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.2356 - binary_accuracy: 1.0000\n",
            "Epoch 35/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.2351 - binary_accuracy: 1.0000\n",
            "Epoch 36/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.2347 - binary_accuracy: 1.0000\n",
            "Epoch 37/500\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.2342 - binary_accuracy: 1.0000\n",
            "Epoch 38/500\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.2337 - binary_accuracy: 1.0000\n",
            "Epoch 39/500\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.2333 - binary_accuracy: 1.0000\n",
            "Epoch 40/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.2328 - binary_accuracy: 1.0000\n",
            "Epoch 41/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.2324 - binary_accuracy: 1.0000\n",
            "Epoch 42/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.2321 - binary_accuracy: 1.0000\n",
            "Epoch 43/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.2317 - binary_accuracy: 1.0000\n",
            "Epoch 44/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.2313 - binary_accuracy: 1.0000\n",
            "Epoch 45/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.2309 - binary_accuracy: 1.0000\n",
            "Epoch 46/500\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.2306 - binary_accuracy: 1.0000\n",
            "Epoch 47/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.2302 - binary_accuracy: 1.0000\n",
            "Epoch 48/500\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.2298 - binary_accuracy: 1.0000\n",
            "Epoch 49/500\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.2294 - binary_accuracy: 1.0000\n",
            "Epoch 50/500\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.2290 - binary_accuracy: 1.0000\n",
            "Epoch 51/500\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.2286 - binary_accuracy: 1.0000\n",
            "Epoch 52/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.2283 - binary_accuracy: 1.0000\n",
            "Epoch 53/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.2279 - binary_accuracy: 1.0000\n",
            "Epoch 54/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.2275 - binary_accuracy: 1.0000\n",
            "Epoch 55/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.2271 - binary_accuracy: 1.0000\n",
            "Epoch 56/500\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.2268 - binary_accuracy: 1.0000\n",
            "Epoch 57/500\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.2264 - binary_accuracy: 1.0000\n",
            "Epoch 58/500\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.2260 - binary_accuracy: 1.0000\n",
            "Epoch 59/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.2257 - binary_accuracy: 1.0000\n",
            "Epoch 60/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.2253 - binary_accuracy: 1.0000\n",
            "Epoch 61/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.2250 - binary_accuracy: 1.0000\n",
            "Epoch 62/500\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.2247 - binary_accuracy: 1.0000\n",
            "Epoch 63/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.2243 - binary_accuracy: 1.0000\n",
            "Epoch 64/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.2240 - binary_accuracy: 1.0000\n",
            "Epoch 65/500\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.2236 - binary_accuracy: 1.0000\n",
            "Epoch 66/500\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.2233 - binary_accuracy: 1.0000\n",
            "Epoch 67/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.2229 - binary_accuracy: 1.0000\n",
            "Epoch 68/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.2226 - binary_accuracy: 1.0000\n",
            "Epoch 69/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.2222 - binary_accuracy: 1.0000\n",
            "Epoch 70/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.2219 - binary_accuracy: 1.0000\n",
            "Epoch 71/500\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.2215 - binary_accuracy: 1.0000\n",
            "Epoch 72/500\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.2212 - binary_accuracy: 1.0000\n",
            "Epoch 73/500\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.2208 - binary_accuracy: 1.0000\n",
            "Epoch 74/500\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.2205 - binary_accuracy: 1.0000\n",
            "Epoch 75/500\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.2201 - binary_accuracy: 1.0000\n",
            "Epoch 76/500\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.2198 - binary_accuracy: 1.0000\n",
            "Epoch 77/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.2194 - binary_accuracy: 1.0000\n",
            "Epoch 78/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.2190 - binary_accuracy: 1.0000\n",
            "Epoch 79/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.2187 - binary_accuracy: 1.0000\n",
            "Epoch 80/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.2184 - binary_accuracy: 1.0000\n",
            "Epoch 81/500\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.2180 - binary_accuracy: 1.0000\n",
            "Epoch 82/500\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.2177 - binary_accuracy: 1.0000\n",
            "Epoch 83/500\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.2174 - binary_accuracy: 1.0000\n",
            "Epoch 84/500\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.2170 - binary_accuracy: 1.0000\n",
            "Epoch 85/500\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.2167 - binary_accuracy: 1.0000\n",
            "Epoch 86/500\n",
            "1/1 [==============================] - 0s 21ms/step - loss: 0.2164 - binary_accuracy: 1.0000\n",
            "Epoch 87/500\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.2160 - binary_accuracy: 1.0000\n",
            "Epoch 88/500\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.2157 - binary_accuracy: 1.0000\n",
            "Epoch 89/500\n",
            "1/1 [==============================] - 0s 19ms/step - loss: 0.2153 - binary_accuracy: 1.0000\n",
            "Epoch 90/500\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.2150 - binary_accuracy: 1.0000\n",
            "Epoch 91/500\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.2146 - binary_accuracy: 1.0000\n",
            "Epoch 92/500\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.2143 - binary_accuracy: 1.0000\n",
            "Epoch 93/500\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.2139 - binary_accuracy: 1.0000\n",
            "Epoch 94/500\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.2136 - binary_accuracy: 1.0000\n",
            "Epoch 95/500\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.2132 - binary_accuracy: 1.0000\n",
            "Epoch 96/500\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.2129 - binary_accuracy: 1.0000\n",
            "Epoch 97/500\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.2125 - binary_accuracy: 1.0000\n",
            "Epoch 98/500\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.2121 - binary_accuracy: 1.0000\n",
            "Epoch 99/500\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.2118 - binary_accuracy: 1.0000\n",
            "Epoch 100/500\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.2114 - binary_accuracy: 1.0000\n",
            "Epoch 101/500\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.2111 - binary_accuracy: 1.0000\n",
            "Epoch 102/500\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.2107 - binary_accuracy: 1.0000\n",
            "Epoch 103/500\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.2103 - binary_accuracy: 1.0000\n",
            "Epoch 104/500\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.2100 - binary_accuracy: 1.0000\n",
            "Epoch 105/500\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.2096 - binary_accuracy: 1.0000\n",
            "Epoch 106/500\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.2092 - binary_accuracy: 1.0000\n",
            "Epoch 107/500\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.2089 - binary_accuracy: 1.0000\n",
            "Epoch 108/500\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.2085 - binary_accuracy: 1.0000\n",
            "Epoch 109/500\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.2081 - binary_accuracy: 1.0000\n",
            "Epoch 110/500\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.2078 - binary_accuracy: 1.0000\n",
            "Epoch 111/500\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.2074 - binary_accuracy: 1.0000\n",
            "Epoch 112/500\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.2070 - binary_accuracy: 1.0000\n",
            "Epoch 113/500\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 0.2066 - binary_accuracy: 1.0000\n",
            "Epoch 114/500\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.2062 - binary_accuracy: 1.0000\n",
            "Epoch 115/500\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.2059 - binary_accuracy: 1.0000\n",
            "Epoch 116/500\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.2055 - binary_accuracy: 1.0000\n",
            "Epoch 117/500\n",
            "1/1 [==============================] - 0s 13ms/step - loss: 0.2051 - binary_accuracy: 1.0000\n",
            "Epoch 118/500\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 0.2047 - binary_accuracy: 1.0000\n",
            "Epoch 119/500\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.2043 - binary_accuracy: 1.0000\n",
            "Epoch 120/500\n",
            "1/1 [==============================] - 0s 11ms/step - loss: 0.2039 - binary_accuracy: 1.0000\n",
            "Epoch 121/500\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.2035 - binary_accuracy: 1.0000\n",
            "Epoch 122/500\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.2031 - binary_accuracy: 1.0000\n",
            "Epoch 123/500\n",
            "1/1 [==============================] - 0s 15ms/step - loss: 0.2027 - binary_accuracy: 1.0000\n",
            "Epoch 124/500\n",
            "1/1 [==============================] - 0s 25ms/step - loss: 0.2023 - binary_accuracy: 1.0000\n",
            "Epoch 125/500\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.2019 - binary_accuracy: 1.0000\n",
            "Epoch 126/500\n",
            "1/1 [==============================] - 0s 21ms/step - loss: 0.2016 - binary_accuracy: 1.0000\n",
            "Epoch 127/500\n",
            "1/1 [==============================] - 0s 16ms/step - loss: 0.2012 - binary_accuracy: 1.0000\n",
            "Epoch 128/500\n",
            "1/1 [==============================] - 0s 10ms/step - loss: 0.2008 - binary_accuracy: 1.0000\n",
            "Epoch 129/500\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.2004 - binary_accuracy: 1.0000\n",
            "Epoch 130/500\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.2000 - binary_accuracy: 1.0000\n",
            "Epoch 131/500\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.1996 - binary_accuracy: 1.0000\n",
            "Epoch 132/500\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.1992 - binary_accuracy: 1.0000\n",
            "Epoch 133/500\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.1988 - binary_accuracy: 1.0000\n",
            "Epoch 134/500\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.1983 - binary_accuracy: 1.0000\n",
            "Epoch 135/500\n",
            "1/1 [==============================] - 0s 19ms/step - loss: 0.1979 - binary_accuracy: 1.0000\n",
            "Epoch 136/500\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.1975 - binary_accuracy: 1.0000\n",
            "Epoch 137/500\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.1971 - binary_accuracy: 1.0000\n",
            "Epoch 138/500\n",
            "1/1 [==============================] - 0s 12ms/step - loss: 0.1967 - binary_accuracy: 1.0000\n",
            "Epoch 139/500\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.1963 - binary_accuracy: 1.0000\n",
            "Epoch 140/500\n",
            "1/1 [==============================] - 0s 7ms/step - loss: 0.1959 - binary_accuracy: 1.0000\n",
            "Epoch 141/500\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.1955 - binary_accuracy: 1.0000\n",
            "Epoch 142/500\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.1951 - binary_accuracy: 1.0000\n",
            "Epoch 143/500\n",
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            "1/1 [==============================] - 0s 5ms/step - loss: 0.0606 - binary_accuracy: 1.0000\n",
            "Epoch 482/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.0603 - binary_accuracy: 1.0000\n",
            "Epoch 483/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.0601 - binary_accuracy: 1.0000\n",
            "Epoch 484/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.0598 - binary_accuracy: 1.0000\n",
            "Epoch 485/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.0596 - binary_accuracy: 1.0000\n",
            "Epoch 486/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.0593 - binary_accuracy: 1.0000\n",
            "Epoch 487/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.0591 - binary_accuracy: 1.0000\n",
            "Epoch 488/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.0589 - binary_accuracy: 1.0000\n",
            "Epoch 489/500\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.0586 - binary_accuracy: 1.0000\n",
            "Epoch 490/500\n",
            "1/1 [==============================] - 0s 9ms/step - loss: 0.0584 - binary_accuracy: 1.0000\n",
            "Epoch 491/500\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.0581 - binary_accuracy: 1.0000\n",
            "Epoch 492/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.0579 - binary_accuracy: 1.0000\n",
            "Epoch 493/500\n",
            "1/1 [==============================] - 0s 6ms/step - loss: 0.0576 - binary_accuracy: 1.0000\n",
            "Epoch 494/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.0574 - binary_accuracy: 1.0000\n",
            "Epoch 495/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.0572 - binary_accuracy: 1.0000\n",
            "Epoch 496/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.0569 - binary_accuracy: 1.0000\n",
            "Epoch 497/500\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.0567 - binary_accuracy: 1.0000\n",
            "Epoch 498/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.0565 - binary_accuracy: 1.0000\n",
            "Epoch 499/500\n",
            "1/1 [==============================] - 0s 5ms/step - loss: 0.0562 - binary_accuracy: 1.0000\n",
            "Epoch 500/500\n",
            "1/1 [==============================] - 0s 8ms/step - loss: 0.0560 - binary_accuracy: 1.0000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(fit_hist.history['loss'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "kDC2kYuPA1UF",
        "outputId": "00c5ffba-bd4d-4e1a-dcff-437f9e6b40fc"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(fit_hist.history['binary_accuracy'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "rUQWpLTFEkb1",
        "outputId": "4a724679-b7a0-467f-9799-4db15a9ca54b"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inp = list(map(int, input('논리값을 입력하세요:').split()))\n",
        "qwe = np.array(inp) #input 값을 array타입으로 바꿔서\n",
        "print('입력 값 : ', qwe)\n",
        "qwe = qwe.reshape(-1,2)  #행개수는 아무렇게나 만들어라 열은 2개\n",
        "print('reshape:', qwe)\n",
        "print('결과 값:', model.predict(qwe)[0][0].round())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KWyG3OvOE3jk",
        "outputId": "061b20a8-e58b-4eaf-c4a5-6958021e4439"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "논리값을 입력하세요:1 1\n",
            "입력 값 :  [1 1]\n",
            "reshape: [[1 1]]\n",
            "결과 값: 0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "nsyiSFmpFzp8"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}