Lab03_keras.ipynb

{
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  "metadata": {
    "colab": {
      "name": "02_oss_keras.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jeonggunlee/DeepLearningBasics/blob/master/Lab03_keras.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3vQTVpHYADVv",
        "colab_type": "text"
      },
      "source": [
        "###한림대학교 소프트웨어 융합 대학\n",
        "\n",
        "##누구나 즐기는 딥러닝: 오픈소스 Keras를 활용하여!!!\n",
        "\n",
        "이정근 교수\n",
        "\n",
        "jeonggun.lee@hallym.ac.kr 2020년 8월\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "###Keras 딥러닝: AND 논리 함수\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oxKVohdGACKX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "import numpy as np \n",
        "\n",
        "data = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])\n",
        "outcome = np.array([[0], [0], [0], [1]])\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Dense(1, input_dim=2))\n"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vj1xRVw2BXvB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d015b840-cd7e-40f9-f7c7-0b97e0e6e5d3"
      },
      "source": [
        "model.compile(loss='mean_squared_error', optimizer='adam')\n",
        "model.fit(data, outcome, epochs=1000, batch_size=1)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/1000\n",
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            "Epoch 7/1000\n",
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            "Epoch 8/1000\n",
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            "Epoch 9/1000\n",
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            "Epoch 10/1000\n",
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            "Epoch 11/1000\n",
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            "Epoch 12/1000\n",
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            "Epoch 799/1000\n",
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            "Epoch 800/1000\n",
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            "Epoch 801/1000\n",
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            "Epoch 802/1000\n",
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            "Epoch 803/1000\n",
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            "Epoch 804/1000\n",
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          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f9b304c2b70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kbfdluIREY3_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "7078ad4f-d62e-4d5b-a7dd-d42482bdb56d"
      },
      "source": [
        "model.predict(data)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.2756664 ],\n",
              "       [ 0.25307766],\n",
              "       [ 0.23798946],\n",
              "       [ 0.7667335 ]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i0p0ZfYEPrHS",
        "colab_type": "text"
      },
      "source": [
        "## Sigmoid 활성 함수 사용\n",
        "\n",
        "좀더 성능을 끌어올릴 수 있는 방법으로 활성화 함수를 사용해보겠습니다.\n",
        "\n",
        "AND 함수의 경우 출력이 0 또는 1에 해당하기 때문에, **sigmoid** 함수를 사용하도록 하겠습니다.\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bo3a9xYfPnKA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "9da6edb0-7a4f-4d8a-8a15-66378cc5d6b2"
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Activation\n",
        "import numpy as np \n",
        "\n",
        "data = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])\n",
        "outcome = np.array([[0], [0], [0], [1]])\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Dense(1, input_dim=2, activation='sigmoid'))\n",
        "\n",
        "model.compile(loss='mean_squared_error', optimizer='adam')\n",
        "model.fit(data, outcome, epochs=1000, batch_size=1)\n",
        "\n",
        "model.predict(data)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2524\n",
            "Epoch 2/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2523\n",
            "Epoch 3/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2520\n",
            "Epoch 4/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2517\n",
            "Epoch 5/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2515\n",
            "Epoch 6/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2513\n",
            "Epoch 7/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2511\n",
            "Epoch 8/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2511\n",
            "Epoch 9/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2507\n",
            "Epoch 10/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2506\n",
            "Epoch 11/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2505\n",
            "Epoch 12/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2502\n",
            "Epoch 13/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2500\n",
            "Epoch 14/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2499\n",
            "Epoch 15/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2496\n",
            "Epoch 16/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2494\n",
            "Epoch 17/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2493\n",
            "Epoch 18/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2491\n",
            "Epoch 19/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2490\n",
            "Epoch 20/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2488\n",
            "Epoch 21/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2485\n",
            "Epoch 22/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2484\n",
            "Epoch 23/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2482\n",
            "Epoch 24/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2481\n",
            "Epoch 25/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2479\n",
            "Epoch 26/1000\n",
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            "Epoch 27/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2475\n",
            "Epoch 28/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2474\n",
            "Epoch 29/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2471\n",
            "Epoch 30/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2470\n",
            "Epoch 31/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2468\n",
            "Epoch 32/1000\n",
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            "Epoch 33/1000\n",
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            "Epoch 34/1000\n",
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            "Epoch 35/1000\n",
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            "Epoch 36/1000\n",
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            "Epoch 37/1000\n",
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            "Epoch 38/1000\n",
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            "Epoch 39/1000\n",
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            "Epoch 40/1000\n",
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            "Epoch 41/1000\n",
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            "Epoch 42/1000\n",
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            "Epoch 43/1000\n",
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            "Epoch 44/1000\n",
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            "Epoch 45/1000\n",
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            "Epoch 46/1000\n",
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            "Epoch 47/1000\n",
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            "Epoch 48/1000\n",
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            "Epoch 49/1000\n",
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            "Epoch 50/1000\n",
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            "Epoch 51/1000\n",
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            "Epoch 57/1000\n",
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            "Epoch 60/1000\n",
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            "Epoch 61/1000\n",
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            "Epoch 64/1000\n",
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            "Epoch 68/1000\n",
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            "Epoch 69/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2404\n",
            "Epoch 70/1000\n",
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            "Epoch 71/1000\n",
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            "Epoch 72/1000\n",
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            "Epoch 73/1000\n",
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            "Epoch 74/1000\n",
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            "Epoch 75/1000\n",
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            "Epoch 76/1000\n",
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            "Epoch 77/1000\n",
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            "Epoch 78/1000\n",
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            "Epoch 79/1000\n",
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            "Epoch 80/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.2386\n",
            "Epoch 81/1000\n",
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            "Epoch 82/1000\n",
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            "Epoch 83/1000\n",
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            "Epoch 84/1000\n",
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            "Epoch 85/1000\n",
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            "Epoch 86/1000\n",
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            "Epoch 87/1000\n",
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            "Epoch 88/1000\n",
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            "Epoch 89/1000\n",
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            "Epoch 90/1000\n",
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            "Epoch 99/1000\n",
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            "Epoch 100/1000\n",
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            "Epoch 101/1000\n",
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            "Epoch 109/1000\n",
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            "Epoch 118/1000\n",
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            "Epoch 119/1000\n",
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            "Epoch 120/1000\n",
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            "Epoch 121/1000\n",
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            "Epoch 122/1000\n",
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            "Epoch 123/1000\n",
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            "Epoch 124/1000\n",
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            "Epoch 125/1000\n",
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            "Epoch 126/1000\n",
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            "Epoch 127/1000\n",
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            "Epoch 128/1000\n",
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            "Epoch 129/1000\n",
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            "Epoch 130/1000\n",
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            "Epoch 131/1000\n",
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            "Epoch 132/1000\n",
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            "Epoch 147/1000\n",
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            "Epoch 150/1000\n",
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            "Epoch 151/1000\n",
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            "Epoch 157/1000\n",
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            "Epoch 158/1000\n",
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            "Epoch 159/1000\n",
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            "Epoch 160/1000\n",
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            "Epoch 161/1000\n",
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            "Epoch 162/1000\n",
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            "Epoch 163/1000\n",
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            "Epoch 164/1000\n",
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            "Epoch 165/1000\n",
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            "Epoch 166/1000\n",
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            "Epoch 167/1000\n",
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            "Epoch 168/1000\n",
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            "Epoch 169/1000\n",
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            "Epoch 170/1000\n",
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            "Epoch 171/1000\n",
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            "Epoch 172/1000\n",
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            "Epoch 173/1000\n",
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            "Epoch 174/1000\n",
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            "Epoch 175/1000\n",
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            "Epoch 176/1000\n",
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            "Epoch 177/1000\n",
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            "Epoch 178/1000\n",
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            "Epoch 179/1000\n",
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            "Epoch 180/1000\n",
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            "Epoch 181/1000\n",
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            "Epoch 182/1000\n",
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            "Epoch 185/1000\n",
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            "Epoch 186/1000\n",
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            "Epoch 187/1000\n",
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            "Epoch 188/1000\n",
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            "Epoch 813/1000\n",
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            "4/4 [==============================] - 0s 2ms/step - loss: 0.1640\n",
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            "Epoch 837/1000\n",
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            "Epoch 850/1000\n",
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            "Epoch 851/1000\n",
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            "Epoch 854/1000\n",
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            "Epoch 855/1000\n",
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            "Epoch 856/1000\n",
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            "Epoch 857/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.1609\n",
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            "Epoch 923/1000\n",
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            "4/4 [==============================] - 0s 3ms/step - loss: 0.1531\n",
            "Epoch 971/1000\n",
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            "Epoch 972/1000\n",
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            "Epoch 974/1000\n",
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            "Epoch 975/1000\n",
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            "Epoch 976/1000\n",
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            "Epoch 977/1000\n",
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            "Epoch 978/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.1526\n",
            "Epoch 979/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.1525\n",
            "Epoch 980/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.1525\n",
            "Epoch 981/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.1524\n",
            "Epoch 982/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.1524\n",
            "Epoch 983/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.1523\n",
            "Epoch 984/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.1522\n",
            "Epoch 985/1000\n",
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            "Epoch 986/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1521\n",
            "Epoch 987/1000\n",
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            "Epoch 988/1000\n",
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            "Epoch 989/1000\n",
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            "Epoch 990/1000\n",
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            "Epoch 991/1000\n",
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            "Epoch 992/1000\n",
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            "Epoch 993/1000\n",
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            "Epoch 994/1000\n",
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            "Epoch 995/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.1515\n",
            "Epoch 996/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1514\n",
            "Epoch 997/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.1513\n",
            "Epoch 998/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.1513\n",
            "Epoch 999/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.1512\n",
            "Epoch 1000/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.1511\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.25607067],\n",
              "       [0.35341036],\n",
              "       [0.3484647 ],\n",
              "       [0.4592446 ]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jwCuwaqZ-X-e",
        "colab_type": "text"
      },
      "source": [
        "## 성능 향상을 위한 다층 신경망 구성\n",
        "\n",
        "층의 수를 좀더 늘려볼까요 ?\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HbXiiSub-bi0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36125
        },
        "outputId": "ee140b58-dba4-4468-afb1-9cd49a150fd3"
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Activation\n",
        "import numpy as np \n",
        "\n",
        "data = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])\n",
        "outcome = np.array([[0], [0], [0], [1]])\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Dense(8, input_dim=2, activation='relu'))\n",
        "model.add(Dense(1, init='uniform', activation='sigmoid'))\n",
        "\n",
        "model.compile(loss='mean_squared_error', optimizer='adam')\n",
        "model.fit(data, outcome, epochs=1000, batch_size=1)\n",
        "\n",
        "model.predict(data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:10: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(1, activation=\"sigmoid\", kernel_initializer=\"uniform\")`\n",
            "  # Remove the CWD from sys.path while we load stuff.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/1000\n",
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            "4/4 [==============================] - 0s 4ms/step - loss: 0.0146\n",
            "Epoch 825/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0146\n",
            "Epoch 826/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0145\n",
            "Epoch 827/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0145\n",
            "Epoch 828/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0144\n",
            "Epoch 829/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0143\n",
            "Epoch 830/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0143\n",
            "Epoch 831/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0142\n",
            "Epoch 832/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0142\n",
            "Epoch 833/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0141\n",
            "Epoch 834/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0141\n",
            "Epoch 835/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0140\n",
            "Epoch 836/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0140\n",
            "Epoch 837/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0139\n",
            "Epoch 838/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0138\n",
            "Epoch 839/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0138\n",
            "Epoch 840/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0138\n",
            "Epoch 841/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0137\n",
            "Epoch 842/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0136\n",
            "Epoch 843/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0136\n",
            "Epoch 844/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0135\n",
            "Epoch 845/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0135\n",
            "Epoch 846/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0134\n",
            "Epoch 847/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0134\n",
            "Epoch 848/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0133\n",
            "Epoch 849/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0133\n",
            "Epoch 850/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0132\n",
            "Epoch 851/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0132\n",
            "Epoch 852/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0131\n",
            "Epoch 853/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0131\n",
            "Epoch 854/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0130\n",
            "Epoch 855/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0130\n",
            "Epoch 856/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0129\n",
            "Epoch 857/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0129\n",
            "Epoch 858/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0128\n",
            "Epoch 859/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0128\n",
            "Epoch 860/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0127\n",
            "Epoch 861/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0127\n",
            "Epoch 862/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0126\n",
            "Epoch 863/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0126\n",
            "Epoch 864/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0125\n",
            "Epoch 865/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0125\n",
            "Epoch 866/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0124\n",
            "Epoch 867/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0124\n",
            "Epoch 868/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0124\n",
            "Epoch 869/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0123\n",
            "Epoch 870/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0123\n",
            "Epoch 871/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0122\n",
            "Epoch 872/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0122\n",
            "Epoch 873/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0121\n",
            "Epoch 874/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0121\n",
            "Epoch 875/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0120\n",
            "Epoch 876/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0120\n",
            "Epoch 877/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0119\n",
            "Epoch 878/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0119\n",
            "Epoch 879/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0118\n",
            "Epoch 880/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0118\n",
            "Epoch 881/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0117\n",
            "Epoch 882/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0117\n",
            "Epoch 883/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0117\n",
            "Epoch 884/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0116\n",
            "Epoch 885/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0116\n",
            "Epoch 886/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0115\n",
            "Epoch 887/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0115\n",
            "Epoch 888/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0114\n",
            "Epoch 889/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0114\n",
            "Epoch 890/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0114\n",
            "Epoch 891/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0113\n",
            "Epoch 892/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0113\n",
            "Epoch 893/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0112\n",
            "Epoch 894/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0112\n",
            "Epoch 895/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0111\n",
            "Epoch 896/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0111\n",
            "Epoch 897/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0111\n",
            "Epoch 898/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0110\n",
            "Epoch 899/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0110\n",
            "Epoch 900/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0109\n",
            "Epoch 901/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0109\n",
            "Epoch 902/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0109\n",
            "Epoch 903/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0108\n",
            "Epoch 904/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0108\n",
            "Epoch 905/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0107\n",
            "Epoch 906/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0107\n",
            "Epoch 907/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0107\n",
            "Epoch 908/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0106\n",
            "Epoch 909/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0106\n",
            "Epoch 910/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0105\n",
            "Epoch 911/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0105\n",
            "Epoch 912/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0105\n",
            "Epoch 913/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0104\n",
            "Epoch 914/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0104\n",
            "Epoch 915/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0103\n",
            "Epoch 916/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0103\n",
            "Epoch 917/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0103\n",
            "Epoch 918/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0102\n",
            "Epoch 919/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0102\n",
            "Epoch 920/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0102\n",
            "Epoch 921/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0101\n",
            "Epoch 922/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0101\n",
            "Epoch 923/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0100\n",
            "Epoch 924/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0100\n",
            "Epoch 925/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0100\n",
            "Epoch 926/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0099\n",
            "Epoch 927/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0099\n",
            "Epoch 928/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0099\n",
            "Epoch 929/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0098\n",
            "Epoch 930/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0098\n",
            "Epoch 931/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0098\n",
            "Epoch 932/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0097\n",
            "Epoch 933/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0097\n",
            "Epoch 934/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0097\n",
            "Epoch 935/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0096\n",
            "Epoch 936/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0096\n",
            "Epoch 937/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0095\n",
            "Epoch 938/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0095\n",
            "Epoch 939/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0095\n",
            "Epoch 940/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0094\n",
            "Epoch 941/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0094\n",
            "Epoch 942/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0094\n",
            "Epoch 943/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0093\n",
            "Epoch 944/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0093\n",
            "Epoch 945/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0093\n",
            "Epoch 946/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0092\n",
            "Epoch 947/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0092\n",
            "Epoch 948/1000\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0092\n",
            "Epoch 949/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0091\n",
            "Epoch 950/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0091\n",
            "Epoch 951/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0091\n",
            "Epoch 952/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0090\n",
            "Epoch 953/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0090\n",
            "Epoch 954/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0090\n",
            "Epoch 955/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0089\n",
            "Epoch 956/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0089\n",
            "Epoch 957/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0089\n",
            "Epoch 958/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0088\n",
            "Epoch 959/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0088\n",
            "Epoch 960/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0088\n",
            "Epoch 961/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0088\n",
            "Epoch 962/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0087\n",
            "Epoch 963/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0087\n",
            "Epoch 964/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0087\n",
            "Epoch 965/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0086\n",
            "Epoch 966/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0086\n",
            "Epoch 967/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0086\n",
            "Epoch 968/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0085\n",
            "Epoch 969/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0085\n",
            "Epoch 970/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0085\n",
            "Epoch 971/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0084\n",
            "Epoch 972/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0084\n",
            "Epoch 973/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0084\n",
            "Epoch 974/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0084\n",
            "Epoch 975/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0083\n",
            "Epoch 976/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0083\n",
            "Epoch 977/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0083\n",
            "Epoch 978/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0082\n",
            "Epoch 979/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0082\n",
            "Epoch 980/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0082\n",
            "Epoch 981/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0082\n",
            "Epoch 982/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0081\n",
            "Epoch 983/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0081\n",
            "Epoch 984/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0081\n",
            "Epoch 985/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0080\n",
            "Epoch 986/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0080\n",
            "Epoch 987/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0080\n",
            "Epoch 988/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0079\n",
            "Epoch 989/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0079\n",
            "Epoch 990/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0079\n",
            "Epoch 991/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0079\n",
            "Epoch 992/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0078\n",
            "Epoch 993/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0078\n",
            "Epoch 994/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0078\n",
            "Epoch 995/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0078\n",
            "Epoch 996/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0077\n",
            "Epoch 997/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0077\n",
            "Epoch 998/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0077\n",
            "Epoch 999/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0076\n",
            "Epoch 1000/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0076\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.01967815],\n",
              "       [0.11979541],\n",
              "       [0.06792986],\n",
              "       [0.89515114]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wx7d0M5-_qXK",
        "colab_type": "text"
      },
      "source": [
        "## 성능 향상을 위한 깊은 (?) 신경망 구성\n",
        "\n",
        "층의 수를 하나 더 늘려 볼까요 ?\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Flt-S3yM_vLb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "64721b3d-bedb-42de-8d08-300485fde736"
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Activation\n",
        "import numpy as np \n",
        "\n",
        "data = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])\n",
        "outcome = np.array([[0], [0], [0], [1]])\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Dense(8, input_dim=2, activation='relu'))\n",
        "model.add(Dense(4, kernel_initializer='random_uniform', activation='relu'))\n",
        "model.add(Dense(1, kernel_initializer='random_uniform', activation='sigmoid'))\n",
        "\n",
        "model.compile(loss='mean_squared_error', optimizer='adam')\n",
        "model.fit(data, outcome, epochs=1000, batch_size=1)\n",
        "\n",
        "model.predict(data)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2501\n",
            "Epoch 2/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.2495\n",
            "Epoch 3/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2493\n",
            "Epoch 4/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2491\n",
            "Epoch 5/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2488\n",
            "Epoch 6/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2485\n",
            "Epoch 7/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2483\n",
            "Epoch 8/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.2481\n",
            "Epoch 9/1000\n",
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            "Epoch 834/1000\n",
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            "4/4 [==============================] - 0s 2ms/step - loss: 0.0212\n",
            "Epoch 839/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0211\n",
            "Epoch 840/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0210\n",
            "Epoch 841/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0210\n",
            "Epoch 842/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0209\n",
            "Epoch 843/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0209\n",
            "Epoch 844/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0208\n",
            "Epoch 845/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0208\n",
            "Epoch 846/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0207\n",
            "Epoch 847/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0206\n",
            "Epoch 848/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0206\n",
            "Epoch 849/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0205\n",
            "Epoch 850/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0205\n",
            "Epoch 851/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0204\n",
            "Epoch 852/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0204\n",
            "Epoch 853/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0203\n",
            "Epoch 854/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0203\n",
            "Epoch 855/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0202\n",
            "Epoch 856/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0202\n",
            "Epoch 857/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0201\n",
            "Epoch 858/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0201\n",
            "Epoch 859/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0200\n",
            "Epoch 860/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0200\n",
            "Epoch 861/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0199\n",
            "Epoch 862/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0199\n",
            "Epoch 863/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0198\n",
            "Epoch 864/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0198\n",
            "Epoch 865/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0197\n",
            "Epoch 866/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0197\n",
            "Epoch 867/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0196\n",
            "Epoch 868/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0196\n",
            "Epoch 869/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0195\n",
            "Epoch 870/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0195\n",
            "Epoch 871/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0194\n",
            "Epoch 872/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0194\n",
            "Epoch 873/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0193\n",
            "Epoch 874/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0193\n",
            "Epoch 875/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0192\n",
            "Epoch 876/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0192\n",
            "Epoch 877/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0191\n",
            "Epoch 878/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0191\n",
            "Epoch 879/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0190\n",
            "Epoch 880/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0190\n",
            "Epoch 881/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0189\n",
            "Epoch 882/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0189\n",
            "Epoch 883/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0188\n",
            "Epoch 884/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0188\n",
            "Epoch 885/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0187\n",
            "Epoch 886/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0187\n",
            "Epoch 887/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0186\n",
            "Epoch 888/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0186\n",
            "Epoch 889/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0185\n",
            "Epoch 890/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0185\n",
            "Epoch 891/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0184\n",
            "Epoch 892/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0184\n",
            "Epoch 893/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0184\n",
            "Epoch 894/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0183\n",
            "Epoch 895/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0183\n",
            "Epoch 896/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0182\n",
            "Epoch 897/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0182\n",
            "Epoch 898/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0181\n",
            "Epoch 899/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0181\n",
            "Epoch 900/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0180\n",
            "Epoch 901/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0180\n",
            "Epoch 902/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0179\n",
            "Epoch 903/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0179\n",
            "Epoch 904/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0178\n",
            "Epoch 905/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0178\n",
            "Epoch 906/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0177\n",
            "Epoch 907/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0177\n",
            "Epoch 908/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0176\n",
            "Epoch 909/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0176\n",
            "Epoch 910/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0176\n",
            "Epoch 911/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0175\n",
            "Epoch 912/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0175\n",
            "Epoch 913/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0174\n",
            "Epoch 914/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0174\n",
            "Epoch 915/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0174\n",
            "Epoch 916/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0173\n",
            "Epoch 917/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0173\n",
            "Epoch 918/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0172\n",
            "Epoch 919/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0172\n",
            "Epoch 920/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0171\n",
            "Epoch 921/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0171\n",
            "Epoch 922/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0171\n",
            "Epoch 923/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0170\n",
            "Epoch 924/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0170\n",
            "Epoch 925/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0169\n",
            "Epoch 926/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0169\n",
            "Epoch 927/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0168\n",
            "Epoch 928/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0168\n",
            "Epoch 929/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0168\n",
            "Epoch 930/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0167\n",
            "Epoch 931/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0167\n",
            "Epoch 932/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0166\n",
            "Epoch 933/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0166\n",
            "Epoch 934/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0166\n",
            "Epoch 935/1000\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0165\n",
            "Epoch 936/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0165\n",
            "Epoch 937/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0164\n",
            "Epoch 938/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0164\n",
            "Epoch 939/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0163\n",
            "Epoch 940/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0163\n",
            "Epoch 941/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0162\n",
            "Epoch 942/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0162\n",
            "Epoch 943/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0162\n",
            "Epoch 944/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0161\n",
            "Epoch 945/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0161\n",
            "Epoch 946/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0161\n",
            "Epoch 947/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0160\n",
            "Epoch 948/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0160\n",
            "Epoch 949/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0159\n",
            "Epoch 950/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0159\n",
            "Epoch 951/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0158\n",
            "Epoch 952/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0158\n",
            "Epoch 953/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0158\n",
            "Epoch 954/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0157\n",
            "Epoch 955/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0157\n",
            "Epoch 956/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0157\n",
            "Epoch 957/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0156\n",
            "Epoch 958/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0156\n",
            "Epoch 959/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0155\n",
            "Epoch 960/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0155\n",
            "Epoch 961/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0155\n",
            "Epoch 962/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0154\n",
            "Epoch 963/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0154\n",
            "Epoch 964/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0154\n",
            "Epoch 965/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0153\n",
            "Epoch 966/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0153\n",
            "Epoch 967/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0152\n",
            "Epoch 968/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0152\n",
            "Epoch 969/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0152\n",
            "Epoch 970/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0151\n",
            "Epoch 971/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0151\n",
            "Epoch 972/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0150\n",
            "Epoch 973/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0150\n",
            "Epoch 974/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0150\n",
            "Epoch 975/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0149\n",
            "Epoch 976/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0149\n",
            "Epoch 977/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0149\n",
            "Epoch 978/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0148\n",
            "Epoch 979/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0148\n",
            "Epoch 980/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0148\n",
            "Epoch 981/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0147\n",
            "Epoch 982/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0147\n",
            "Epoch 983/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0146\n",
            "Epoch 984/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0146\n",
            "Epoch 985/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0146\n",
            "Epoch 986/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0146\n",
            "Epoch 987/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0145\n",
            "Epoch 988/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0145\n",
            "Epoch 989/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0144\n",
            "Epoch 990/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0144\n",
            "Epoch 991/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0144\n",
            "Epoch 992/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0143\n",
            "Epoch 993/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0143\n",
            "Epoch 994/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.0143\n",
            "Epoch 995/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0142\n",
            "Epoch 996/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0142\n",
            "Epoch 997/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0142\n",
            "Epoch 998/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0141\n",
            "Epoch 999/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0141\n",
            "Epoch 1000/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0140\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.13589913],\n",
              "       [0.13589913],\n",
              "       [0.13589913],\n",
              "       [0.97319186]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "501LuExs2A0_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "8d1a100a-d630-4f4d-f549-d40730dff8f6"
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Activation\n",
        "import numpy as np \n",
        "\n",
        "data = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])\n",
        "outcome = np.array([[0], [0], [0], [1]])\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Dense(8, input_dim=2, activation='relu'))\n",
        "model.add(Dense(4, kernel_initializer='random_uniform', activation='relu'))\n",
        "model.add(Dense(1, kernel_initializer='random_uniform', activation='sigmoid'))\n",
        "\n",
        "model.compile(loss='binary_crossentropy', optimizer='adam')\n",
        "model.fit(data, outcome, epochs=1000, batch_size=1)\n",
        "\n",
        "model.predict(data)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6932\n",
            "Epoch 2/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.6926\n",
            "Epoch 3/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.6923\n",
            "Epoch 4/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6913\n",
            "Epoch 5/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.6909\n",
            "Epoch 6/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.6901\n",
            "Epoch 7/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.6899\n",
            "Epoch 8/1000\n",
            "4/4 [==============================] - 0s 4ms/step - loss: 0.6889\n",
            "Epoch 9/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.6887\n",
            "Epoch 10/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.6879\n",
            "Epoch 11/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6871\n",
            "Epoch 12/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.6862\n",
            "Epoch 13/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.6856\n",
            "Epoch 14/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.6852\n",
            "Epoch 15/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.6844\n",
            "Epoch 16/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.6835\n",
            "Epoch 17/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6830\n",
            "Epoch 18/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.6824\n",
            "Epoch 19/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6815\n",
            "Epoch 20/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.6804\n",
            "Epoch 21/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.6797\n",
            "Epoch 22/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6789\n",
            "Epoch 23/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.6778\n",
            "Epoch 24/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.6770\n",
            "Epoch 25/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6763\n",
            "Epoch 26/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.6751\n",
            "Epoch 27/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.6743\n",
            "Epoch 28/1000\n",
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            "Epoch 768/1000\n",
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            "Epoch 800/1000\n",
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            "Epoch 811/1000\n",
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            "Epoch 857/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0022\n",
            "Epoch 858/1000\n",
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            "Epoch 859/1000\n",
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            "Epoch 860/1000\n",
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            "Epoch 998/1000\n",
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0014\n",
            "Epoch 999/1000\n",
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            "Epoch 1000/1000\n",
            "4/4 [==============================] - 0s 2ms/step - loss: 0.0014\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[4.1293981e-04],\n",
              "       [6.4600841e-04],\n",
              "       [1.7485315e-03],\n",
              "       [9.9741924e-01]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    }
  ]
}