material/deep_learning/dnn_in_keras_shortly.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": " dnn_in_keras_shortly.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "p_BD2sWTprLz",
        "outputId": "42488de9-1131-481d-fb71-b19ab2e2c2b6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "%tensorflow_version 1.x\n",
        "import tensorflow\n",
        "print(tensorflow.__version__)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 1.x selected.\n",
            "1.15.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mte4_QlwwrlF"
      },
      "source": [
        "# 모듈 임포팅"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2YegBF6qExut"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "import time"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HNGWUXDmv2nF"
      },
      "source": [
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras.layers import Dense"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E18r6et9tfxU"
      },
      "source": [
        "# Keras DNN 최소 코드\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jJBqaE5oyaIi"
      },
      "source": [
        "# 데이터 준비\n",
        "\n",
        "1차원 데이터 x와 x**2의 1차원 데이타 y"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zEj2Fb6HMrnt",
        "outputId": "76fe9d3e-e466-4f1c-f111-90533342ca0e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "x = np.arange(-1,1,0.01)\n",
        "y = x**2\n",
        "\n",
        "plt.scatter(x,y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fbd58cf74e0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vbu4biIbHgd5gd737EtfTrVaK1M19A9HwONAbzPV0qxvXzUfLNfSGcz3d6sJ189HzFXoLFKmnewEvGzYvvDV6DvSW6FdP9wJeNkxeeKseHOgtUaSePnXPl1xPt9LdMXuAm3NKfuC6eZUc6C3SawEvgOdfPOn1XqxUswuL/Om+7+euMeS6ebUc6C3TawEvcH+6lafbb97r+81182o50Fuo1wJe4P50G1yRfnMvvFU9B3oL9VvAC9yfboPp128uvPDWKDjQW6g7QbpubX4ro9dPt9Xq128u4NeuuMhX5yPgQG+pmalJ9t91ldd7sVIVWaflvmsv4yMzl1Y4KutyoLec13uxsnidlvpzoLec13uxsnidlvrzWi5jwOu92KC8TkszFLpCl7Rd0iFJhyXdlrH/Q5KelvSUpK9Kurj8odogvH66rZbXN2+OvoEuaQK4H7ga2ApcJ2nrksMWgOmI+EfAI8DvlT1QG5zr6bZSrps3S5Er9MuBwxFxJCJeBh4CdqYPiIjHIuLF5OE+4IJyh2llcD3dVsp182YpEuiTwLOpx0eTbXluAL6YtUPSjZLmJc0fO3as+CitNN31XvKcjuDmh/dzx+yBCkdldTO7sMhlv/sl180bptQuF0nXA9PAvVn7I+KBiJiOiOkNGzaU+dK2Av3q6QH86b7v+0p9THXLLMdP5Ie56+b1VCTQF4ELU48vSLa9jqR3Ah8GdkTES+UMz4alXz09cPllHHUnQHuVWcB187oqEuhPAJslbZJ0FrALmEsfIGkK+GM6Yf5c+cO0shWpp3t5gPFSZAIUXDevs76BHhGngJuAvcAzwO6IOCjpHkk7ksPuBd4A/Jmk/ZLmcp7OaqTf+unQaWf0lfp46DcBCq6b152iz0/jYZmeno75+fmRvLa93h2zB3q+SQG8tuCS1+hon9mFRe6eO9izZg6w/tw13PXuS3x1PmKSvhER01n7fKeo8ZGZS5m++Dxu2f1k7q/b3YnS6YvP83/oFumWWXpdmU9Irpk3hNdyMeC18osnSsdLkTKLw7w5HOj2qqITpe5Tb74ifebgCdCmcaDb6xSZKHWferMV6TMHT4A2kQPdlpmZmuTXrriob6i7/NJMRcos689d4xuHGsiBbpk+MnMp9117mcsvLbKSMsvCnVc5zBvIgW65XH5pD5dZxoMD3Xpy+aUdXGYZDw5068vll+ZymWW8ONCtkKLll8/t+z5T93zJV+s1cMfsAW5+eL/LLGPEgW6FFSm/ADz/4kkv6jViswuLfZdzAJdZ2saBbitSpPwCfo/SUeougdsvzF1maR8Huq1YkfILdN6j1OWXanXLLP2WwHWZpZ0c6LYqKym/eLJ0+LqTn59zmWWseflcG0jRpVfBy68OS5Hlj8FLILdFr+VzHehWiql7+rfGgUOlTCv5YeolcNujV6C75GKl6PcepV2+s7QcRe/8hM4PUYf5eHCgWym6S++uW7um77G+s3RwRe78hNd+I3KYjwcHupVmZmqS/XddxfUFJkt9Z+nqFL3zEzpzFvdde5nLW2PENXQbCk+Wlmsl59PzFO3mSVEbmaIdGF0O99fMLixy795DLB4/UfhzfP7az4FuI9W9c7HfzS5p4x5MK/1BCJ07P/ffddXQxmT10CvQz6x6MDZ+uqF888P7CwdU94ak+e/9eKxKByspraT5zk8DT4paRYreWZo2bqs3Fl0dcSnf+WldvkK3ynxk5lKmLz5vxVegbb9aX+1V+biXpWw519BtJFYbYtCOIFvNhGeXu1jGmydFrbYGCXZoVrgPEuJdk+vWcuu2LY3499pwONCtEVbT2dFV92Bv87/NquVAt8YY9Iod6nMVO+i/xaUVy+JAt8YZ5Ip2qSqucNPlFMHA4/ZVueVxoFsjlXG1nucMwSux8qv5YY4JfFVu/Q0c6JK2A/8ZmAA+GREfW7L/bOAzwD8GfgRcGxF/1es5HehW1LBDtC7qUiqyehvoTlFJE8D9wD8HjgJPSJqLiKdTh90APB8R/0DSLuDjwLWDD92sc1NSN+TaFu4OcStTkRuLLgcOR8QRAEkPATuBdKDvBO5OPn4E+ENJilHVc6y10uFeZp29Sq6P27AUCfRJ4NnU46PAP8k7JiJOSXoB+Bngb9IHSboRuBHgoosuWuWQzTq6d54O2ttdFQe5DVult/5HxAPAA9CpoVf52tZOS8sxZXaalMEhblUqEuiLwIWpxxck27KOOSrpTOCNdCZHzSqTDveuKkPe4W2jViTQnwA2S9pEJ7h3Af9yyTFzwPuAx4H3Ao+6fm51kBXyMFjQO7itrvoGelITvwnYS6dt8VMRcVDSPcB8RMwB/w34rKTDwI/phL5ZbeUFvVmTFaqhR8QeYM+SbXemPv4J8KvlDs3MzFbCb3BhZtYSDnQzs5ZwoJuZtYQD3cysJUa22qKkY8D3Vvnp57PkLtQaqevYPK6V8bhWrq5ja9u4Lo6IDVk7Rhbog5A0n7fa2KjVdWwe18p4XCtX17GN07hccjEzawkHuplZSzQ10B8Y9QB6qOvYPK6V8bhWrq5jG5txNbKGbmZmyzX1Ct3MzJZwoJuZtURtA13Sr0o6KOkVSbmtPZK2Szok6bCk21LbN0n6erL9YUlnlTSu8yR9WdK3k7/XZxzzi5L2p/78RNJMsu/Tkr6b2ndZGeMqOrbkuNOp159LbR/lObtM0uPJ1/wpSdem9pV6zvK+Z1L7z07+/YeT87Exte/2ZPshSdsGGccqxvUhSU8n5+erki5O7cv8mlY0rvdLOpZ6/X+T2ve+5Ov+bUnvq3hc96XG9C1Jx1P7hnm+PiXpOUnfzNkvSX+QjPspSW9P7RvsfEVELf8APw9sAb4GTOccMwF8B3gLcBbwJLA12bcb2JV8/EfAb5Y0rt8Dbks+vg34eJ/jz6OzpPC5yeNPA+8d0jkrNDbgb3O2j+ycAW8FNicfvxn4IbCu7HPW63smdcxvAX+UfLwLeDj5eGty/NnApuR5Jioc1y+mvo9+szuuXl/Tisb1fuAPMz73POBI8vf65OP1VY1ryfG/Q2fp76Ger+S5/xnwduCbOfvfBXwREHAF8PWyzldtr9Aj4pmIONTnsFffwDoiXgYeAnZKEvBLdN6wGuB/ADMlDW1n8nxFn/e9wBcj4sWSXr+XlY7tVaM+ZxHxrYj4dvLxD4DngMy74QaU+T3TY7yPAL+cnJ+dwEMR8VJEfBc4nDxfJeOKiMdS30f76Lx72LAVOV95tgFfjogfR8TzwJeB7SMa13XAgyW9dk8R8Zd0LuLy7AQ+Ex37gHWS3kQJ56u2gV5Q1htYT9J5g+rjEXFqyfYy/GxE/DD5+P8BP9vn+F0s/0b6T8mvWvdJOrukca1kbOdImpe0r1sKokbnTNLldK66vpPaXNY5y/ueyTwmOR/dNz0v8rnDHFfaDXSu8rqyvqZVjutXkq/PI5K6b1lZi/OVlKY2AY+mNg/rfBWRN/aBz1elbxK9lKSvAH8/Y9eHI+J/Vz2erl7jSj+IiJCU2/eZ/NS9lM67PXXdTifUzqLTh/ofgHsqHtvFEbEo6S3Ao5IO0AmtVSv5nH0WeF9EvJJsHuictY2k64Fp4BdSm5d9TSPiO9nPULq/AB6MiJck/Vs6v938UkWvXcQu4JGIOJ3aNsrzNTQjDfSIeOeAT5H3BtY/ovNrzJnJFVbWG1uvalyS/lrSmyLih0n4PNfjqa4B/jwiTqaeu3ul+pKk/w78+6LjKmtsEbGY/H1E0teAKeB/MeJzJumngS/Q+YG+L/XcA52zJQZ50/MinzvMcSHpnXR+SP5CRLzU3Z7zNS0joPqOKyLSbwj/STpzJt3PfceSz/1aCWMqNK6UXcBvpzcM8XwVkTf2gc9X00sur76BtTodGbuAuejMMDxGp34NnTewLuuKv/uG2EWed1ndLgm0bs16BsicCR/W2CSt75YsJJ0PXAk8Pepzlnz9/pxObfGRJfvKPGeZ3zM9xpt+0/M5YJc6XTCbgM3A/x1gLCsal6Qp4I+BHRHxXGp75te0wnG9KfVwB/BM8vFe4KpkfOuBq3j9b6tDHVcytp+jM8H4eGrbMM9XEXPAryfdLlcALyQXLYOfr2HN9A76B/gXdGpILwF/DexNtr8Z2JM67l3At+j8dP1wavtb6PxnOwz8GXB2SeP6GeCrwLeBrwDnJdungU+mjttI5yfuGUs+/1HgAJ1Q+hzwhhLPWd+xAf80ef0nk79vqMM5A64HTgL7U38uG8Y5y/qeoVPC2ZF8fE7y7z+cnI+3pD73w8nnHQKuLvl7vt+4vpL8X+ien7l+X9OKxvVR4GDy+o8BP5f63H+dnMfDwL+qclzJ47uBjy35vGGfrwfpdGmdpJNhNwAfAD6Q7BdwfzLuA6S6+AY9X77138ysJZpecjEzs4QD3cysJRzoZmYt4UA3M2sJB7qZWUs40M3MWsKBbmbWEv8fybP/ojXiq7kAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PSlhN2bS2pwj",
        "outputId": "476b0058-5fdb-4db3-8b85-00bd1eaed567",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(len(x))\n",
        "print(x.shape)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "200\n",
            "(200,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o5Z0WwGByiBd"
      },
      "source": [
        "## 최소 코드\n",
        "\n",
        "아래의 코드는 최소 코드."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k0ti9sKoMrrE",
        "outputId": "31f6d9c9-6825-4afe-e835-c72c363846a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        }
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras.layers import Dense, Input\n",
        "\n",
        "import time\n",
        "\n",
        "\n",
        "# 모델 정의\n",
        "model = keras.Sequential()\n",
        "model.add(Input(1))\n",
        "model.add(Dense(10, activation='tanh'))\n",
        "model.add(Dense(10, activation='tanh'))\n",
        "model.add(Dense(1))\n",
        "\n",
        "\n",
        "# 컴파일 : 모델 + optimizer + loss\n",
        "model.compile(optimizer=\"SGD\", loss=\"mse\")\n",
        "\n",
        "\n",
        "# 학습\n",
        "model.fit(x, y, epochs=1000, verbose=0, batch_size=20)\n",
        "\n",
        "\n",
        "# 예측\n",
        "y_ = model.predict(x)\n",
        "\n",
        "\n",
        "# 결과 그래프로 보기\n",
        "plt.scatter(x,y) # 정답\n",
        "plt.scatter(x,y_,color='r') # 결과\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0EXLaSAcy5HI"
      },
      "source": [
        "# Keras DNN 최소 코드에 기본 정도만 추가\n",
        "\n",
        "최소는 아니고, 이정도는 매번 사용하는 코드"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0e9tc-p1NjN8"
      },
      "source": [
        "## model.evaluate() - 성능 평가하기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WAT9UZiXMrwY",
        "outputId": "7b085ef2-c6b1-43eb-a7c0-e7d43e6bce2c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        }
      },
      "source": [
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras.layers import Dense\n",
        "\n",
        "\n",
        "model = keras.Sequential()\n",
        "#model.add(Input(1))\n",
        "model.add(Dense(10, activation='tanh', input_shape=(1,) ))\n",
        "model.add(Dense(10, activation='tanh' ))\n",
        "model.add(Dense(1))\n",
        "\n",
        "\n",
        "#model.compile(optimizer=\"SGD\", loss=\"mse\")\n",
        "model.compile(optimizer=\"SGD\", loss=\"mse\", metrics=[\"mse\"])\n",
        "\n",
        "\n",
        "model.fit(x, y, epochs=1000, verbose=0, batch_size=20)\n",
        "\n",
        "loss, mse = model.evaluate(x, y) # ADD\n",
        "print(\"loss=\",loss) # ADD\n",
        "print(\"mse=\",mse) # ADD\n",
        "\n",
        "\n",
        "y_ = model.predict(x)\n",
        "\n",
        "\n",
        "plt.scatter(x,y)\n",
        "plt.scatter(x,y_,color='r')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "200/200 [==============================] - 0s 133us/sample - loss: 8.5034e-04 - mean_squared_error: 8.5034e-04\n",
            "loss= 0.0008503358345478773\n",
            "mse= 0.0008503358\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cEKtss5Oz4-L"
      },
      "source": [
        "출력에 loss와 mse가 추가되었다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jp3u-snhSL6W"
      },
      "source": [
        "## 테스트셋으로 평가하기\n",
        "\n",
        "데이터를 train과 test으로 나누어 준비한다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bd8YIGMLSvJ1",
        "outputId": "6885e21a-10a7-49b5-bf99-9337a5a634e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "x = np.arange(-1,1,0.01)\n",
        "np.random.shuffle(x)\n",
        "y = x**2\n",
        "\n",
        "split_index = int(len(x)*0.6)\n",
        "\n",
        "train_x, test_x = x[:split_index], x[split_index:]\n",
        "train_y, test_y = y[:split_index], y[split_index:]\n",
        "\n",
        "plt.scatter(train_x,train_y)\n",
        "plt.scatter(test_x,test_y)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E8M503BOSOu-",
        "outputId": "a4290e70-3ee4-4f05-99f2-2ca6aeccec65",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        }
      },
      "source": [
        "model = keras.Sequential()\n",
        "model.add(Dense(10, activation='tanh', input_shape=(1,)))\n",
        "model.add(Dense(10, activation='tanh'))\n",
        "model.add(Dense(1))\n",
        "\n",
        "\n",
        "model.compile(optimizer=\"SGD\", loss=\"mse\", metrics=[\"mse\"])\n",
        "\n",
        "\n",
        "# model.fit(x, y, epochs=1000, verbose=0, batch_size=20)\n",
        "model.fit(train_x, train_y, epochs=1000, verbose=0, batch_size=20)\n",
        "\n",
        "\n",
        "# loss, mse = model.evaluate(x, y)\n",
        "loss, mse = model.evaluate(test_x, test_y)\n",
        "print(\"loss=\",loss)\n",
        "print(\"mse=\",mse)\n",
        "\n",
        "\n",
        "# y_ = model.predict(x)\n",
        "y_ = model.predict(test_x)\n",
        "\n",
        "# plt.scatter(x,y)\n",
        "plt.scatter(test_x,test_y)\n",
        "# plt.scatter(x,y_,color='r')\n",
        "plt.scatter(test_x,y_,color='r')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "80/80 [==============================] - 0s 300us/sample - loss: 0.0013 - mean_squared_error: 0.0013\n",
            "loss= 0.0013352711335755884\n",
            "mse= 0.0013352712\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LujK_3NBz9N5"
      },
      "source": [
        "train_x, train_y로 학습하였고,\n",
        "evaluate()와 predict()에는 test_x, test_y를 사용하였다.\n",
        "\n",
        "그려진 그림을 보면 test_x의 점사이에 빈 간격이 보인다. 그 간격이 train_x에 해당한다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M0o-2M3BYsGr"
      },
      "source": [
        "## 학습 시간을 출력"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z66QeNbxYukl",
        "outputId": "954f17f0-ed39-4170-ae38-186f4a808e52",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        }
      },
      "source": [
        "model = keras.Sequential()\n",
        "model.add(Dense(10, activation='tanh', input_shape=(1,)))\n",
        "model.add(Dense(10, activation='tanh'))\n",
        "model.add(Dense(1))\n",
        "\n",
        "\n",
        "model.compile(optimizer=\"SGD\", loss=\"mse\", metrics=[\"mse\"])\n",
        "\n",
        "start_time = time.time() # ADD\n",
        "model.fit(train_x, train_y, epochs=1000, verbose=0, batch_size=20)\n",
        "print(\"elapsed : {}\".format(time.time() - start_time)) # ADD \n",
        "    \n",
        "\n",
        "loss, mse = model.evaluate(test_x, test_y)\n",
        "print(\"loss=\",loss)\n",
        "print(\"mse=\",mse)\n",
        "\n",
        "\n",
        "y_ = model.predict(test_x)\n",
        "\n",
        "plt.scatter(test_x,test_y)\n",
        "plt.scatter(test_x,y_,color='r')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "elapsed : 11.29396939277649\n",
            "80/80 [==============================] - 0s 324us/sample - loss: 0.0017 - mean_squared_error: 0.0017\n",
            "loss= 0.001717049127910286\n",
            "mse= 0.001717049\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rDrqq-7b0L1E"
      },
      "source": [
        "elapsed로 경과된 시간이 초 단위로 보인다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jQkpZHJWckjq"
      },
      "source": [
        "## model.summary() 네트웤 모양 보기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yBZdqkq7cqeO",
        "outputId": "54bb3c18-55c4-4477-bed0-55a9199743a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        }
      },
      "source": [
        "model = keras.Sequential()\n",
        "model.add(Dense(10, activation='tanh', input_shape=(1,)))\n",
        "model.add(Dense(10, activation='tanh'))\n",
        "model.add(Dense(1))\n",
        "\n",
        "\n",
        "model.compile(optimizer=\"SGD\", loss=\"mse\", metrics=[\"mse\"])\n",
        "model.summary() # ADD\n",
        "\n",
        "start_time = time.time()\n",
        "model.fit(train_x, train_y, epochs=1000, verbose=0, batch_size=20)\n",
        "print(\"elapsed : {}\".format(time.time() - start_time))\n",
        "\n",
        "\n",
        "y_ = model.predict(test_x)\n",
        "\n",
        "plt.scatter(test_x,test_y)\n",
        "plt.scatter(test_x,y_,color='r')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_12 (Dense)             (None, 10)                20        \n",
            "_________________________________________________________________\n",
            "dense_13 (Dense)             (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_14 (Dense)             (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 141\n",
            "Trainable params: 141\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "elapsed : 11.185139656066895\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BbW6vjIb0TJI"
      },
      "source": [
        "네트웤의 모양이 보인다.\n",
        "\n",
        "노드 10개, 10개를 갖는 은닉층 2개가 있다.\n",
        "\n",
        "\n",
        "모든 레이어는 activation(w\\*x+b) 의 형태로, \n",
        "\n",
        "첫 번 째 은닉층은 20개( = ((1+1)\\*10) = (입력수+1)\\*10)의 웨이트를, \n",
        "\n",
        "두 번 째 은닉층은 110개( = ( (10+1)\\*10) )의 웨이트가 있다.. \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "31Xxr4IckI9R"
      },
      "source": [
        "# Keras DNN Template\n",
        "\n",
        "본 실습에 사용되는 코든 코드의 템플릿.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eq_eO6o9kMA1",
        "outputId": "06fb622d-579a-4683-cb7b-b5f486858603",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        }
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras.layers import Dense\n",
        "\n",
        "import time\n",
        "\n",
        "\n",
        "# 모델 정의\n",
        "model = keras.Sequential()\n",
        "model.add(Dense(10, activation='tanh', input_shape=(1,)))\n",
        "model.add(Dense(10, activation='tanh'))\n",
        "model.add(Dense(1))\n",
        "\n",
        "\n",
        "# 모델 컴파일\n",
        "model.compile(optimizer=\"SGD\", loss=\"mse\", metrics=[\"mse\"])\n",
        "model.summary()\n",
        "\n",
        "\n",
        "# 학습\n",
        "start_time = time.time()\n",
        "model.fit(train_x, train_y, epochs=1000, verbose=0, batch_size=20)\n",
        "print(\"elapsed : {}\".format(time.time() - start_time))\n",
        "\n",
        "\n",
        "# 평가\n",
        "loss, mse = model.evaluate(test_x, test_y)\n",
        "print(\"loss=\",loss)\n",
        "print(\"mse=\",mse)\n",
        "\n",
        "\n",
        "# 예측\n",
        "y_ = model.predict(test_x)\n",
        "\n",
        "plt.scatter(test_x,test_y)\n",
        "plt.scatter(test_x,y_,color='r')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_8\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_22 (Dense)             (None, 10)                20        \n",
            "_________________________________________________________________\n",
            "dense_23 (Dense)             (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_24 (Dense)             (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 141\n",
            "Trainable params: 141\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hAK87lLiNmTF"
      },
      "source": [
        "# 학습 추가 진행"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Ty3h3ICmF8U"
      },
      "source": [
        "모델은 그대로 두고 데이터만 sin 데이터로 변경한다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VT4QUNhPmIej"
      },
      "source": [
        "def get_sin_data(start=0, end=10, step=0.1):\n",
        "  x = np.arange(start,end,step)\n",
        "  np.random.shuffle(x)\n",
        "  y = np.sin(x)\n",
        "\n",
        "  split_index = int(len(x)*0.6)\n",
        "\n",
        "  train_x, test_x = x[:split_index], x[split_index:]\n",
        "  train_y, test_y = y[:split_index], y[split_index:]\n",
        "\n",
        "  return (train_x, train_y), (test_x, test_y)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eIkL36cWvoCU",
        "outputId": "252aeec9-f5ae-492c-e0b0-f15e391ad573",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        }
      },
      "source": [
        "(train_x, train_y), (test_x, test_y) = get_sin_data(start=0, end=10, step=0.1)\n",
        "\n",
        "plt.scatter(train_x,train_y)\n",
        "plt.show()\n",
        "\n",
        "plt.scatter(test_x,test_y,color=\"r\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9NAK4Jg2mInV",
        "outputId": "355dae41-8bcd-4bc1-8c3d-d44631c9190f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        }
      },
      "source": [
        "model = keras.Sequential()\n",
        "model.add(Dense(10, activation='tanh', input_shape=(1,)))\n",
        "model.add(Dense(10, activation='tanh'))\n",
        "model.add(Dense(1))\n",
        "\n",
        "\n",
        "model.compile(optimizer=\"SGD\", loss=\"mse\", metrics=[\"mse\"])\n",
        "model.summary()\n",
        "\n",
        "\n",
        "start_time = time.time()\n",
        "model.fit(train_x, train_y, epochs=1000, verbose=0, batch_size=20)\n",
        "print(\"elapsed : {}\".format(time.time() - start_time))    \n",
        "\n",
        "\n",
        "loss, mse = model.evaluate(test_x, test_y)\n",
        "print(\"loss=\",loss)\n",
        "print(\"mse=\",mse)\n",
        "\n",
        "\n",
        "y_ = model.predict(test_x)\n",
        "\n",
        "plt.scatter(test_x,test_y)\n",
        "plt.scatter(test_x,y_,color='r')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_5\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_15 (Dense)             (None, 10)                20        \n",
            "_________________________________________________________________\n",
            "dense_16 (Dense)             (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_17 (Dense)             (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 141\n",
            "Trainable params: 141\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "elapsed : 6.455457925796509\n",
            "40/40 [==============================] - 0s 713us/sample - loss: 0.1887 - mean_squared_error: 0.1887\n",
            "loss= 0.18874696791172027\n",
            "mse= 0.18874696\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "prz5bv1o2MrY"
      },
      "source": [
        "학습이 충분히 되지 않은 듯 하다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fgZIrh0-nW8t"
      },
      "source": [
        "## 추가 학습"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FFzO7p3qnlKk"
      },
      "source": [
        "def fit_one_more(model, train_x, train_y, test_x, test_y, batch_size=20):\n",
        "\n",
        "  start_time = time.time()\n",
        "  model.fit(train_x, train_y, epochs=1000, verbose=0, batch_size=batch_size)\n",
        "  print(\"elapsed : {}\".format(time.time() - start_time))    \n",
        "\n",
        "  y_ = model.predict(test_x)\n",
        "\n",
        "  plt.scatter(test_x,test_y)\n",
        "  plt.scatter(test_x,y_,color='r')\n",
        "  plt.show()\n",
        "\n",
        "def fit_n_times(model, train_x, train_y, test_x, test_y, n):\n",
        "  \n",
        "  for i in range(n):\n",
        "    print(\"{} times fitting\".format(i))\n",
        "    fit_one_more(model, train_x, train_y, test_x, test_y)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cgMhQ2wMn9ct",
        "outputId": "c4ac0555-b09a-4fe8-b303-49363fd5e86c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "fit_n_times(model, train_x, train_y, test_x, test_y, 10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 times fitting\n",
            "elapsed : 6.382450103759766\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gZovMbIeZ7TSz9hz7v2hmz4SL1z9iZg1p+46a2bbwsTH7XBERGVtFJwIzqwK+DXwEmAtcbmZzsw7bCjSFi9ffC9yatm/A3eeHj0uLjUdEsnR3Q2MjTJgQPHd3Rx2RxEwpagTnAjvd/Xl3fwu4G1iSfoC7P+ru/eHmEwSL1IvIWOvuhrY22LUL3IPntjYlA8lQikRQD+xJ294bluXzGeBnadsnmVmPmT1hZi0liEdEUlavhv7+zLL+/qBcJFT04vWFMLNPAU3AB9KKG9y918zeAWw2s+3u/q85zm0D2gBmzZpVlnhFKt7u3YWVSyKVokbQC8xM2z4zLMtgZhcDq4FL3f1Qqtzde8Pn54GfAwtyfYi7d7l7k7s31dXVlSBskfGv//QZBZVLMpUiEWwBzjKz2WY2GbgMyOj9Y2YLgL8jSAL708qnmdmU8PV0oBl4pgQxiQhw6/nL6J84JaOsf+IUbj1/WUQRSRwVnQjc/QiwAtgEPAv8yN2fNrObzSzVC2gt8EfA/8rqJvouoMfMfgs8CnS6uxJBlNTDZFxZP7uZ9kUr2Du1jkGMvVPraF+0gvWzm6MOTWKkJG0E7v4A8EBW2Y1pry/Oc94vgXmliEFKINXDJNW4mOphAtDaGl1cMmozaqvZePaFbDz7wozy+trqiCKSONLIYjlOPUzGnZUL51A9qSqjrHpSFSsXzokoIomjsvYakphTD5Nxp2VB0JN77aYd7OsbYEZtNSsXzjlWLgJKBJJu1qzgdlCucom37u6g5rZ7d/D/q6Pj2O28lgX1uvDLkHRrSI7ZcvV1DEzK7GEyMGkKW66+LqKIZEQ0eliKpEQgx1xbNZcbFmb2MLlh4QqurcqeOkpiRW07sbNhay/NnZuZ3X4/zZ2b2bD1hKFVsaJbQ3LMvr4BenP0MLG+gYgikhFR206sbNjay6r7tjNw+CgAvX0DrLpvO0Bsb9GpRiDHzMjTpTBfucREvjYcte1EYu2mHceSQMrA4aOs3bQjooiGp0Qgx6irYYXq6ICamsyympqgXMpuX54adL7yOFAikGNaFtSzZuk86murMYJBR2uWzottdVZCra3Q1QUNDWAWPHd1aRBgRCqxZm3uHnUMBWtqavKenp6owxAROUF2GwEENes4/FFlZk+6e1N2uRqLRURKqBIH8SkRiIiUWKUN4lMbgYhIwikRiIgknBKBiEjCqY1AZBzYsLW3ohonJV5KUiMws0VmtsPMdppZe479U8zsnnD/r82sMW3fqrB8h5ktLEU8IkmS6q7Y2zeAc3xKg7jPbyPxUXQiMLMq4NvAR4C5wOVmlj1L2WeA19z9T4BvAreE584lWOP4bGAR8D/C9xOREarEKQ0kXkpRIzgX2Onuz7v7W8DdwJKsY5YA68PX9wIfMjMLy+9290Pu/gKwM3w/ERmhSpzSQOKlFImgHtiTtr03LMt5TLjY/evAqSM8FwAzazOzHjPrOXDgQAnCFqkA3d3Q2AgTJgTPOdYYqMQpDSReKqbXkLt3uXuTuzfV1dVFHY7I2BvhgjOaLFCKVYpE0AvMTNs+MyzLeYyZTQROBl4d4bkiyTTCBWc0WaAUqxTdR7cAZ5nZbIKL+GXAn2cdsxFYDvwK+Diw2d3dzDYC/9PMvgHMAM4C/k8JYhKpfAUsOFNpUxpIvBSdCNz9iJmtADYBVcAd7v60md0M9Lj7RuC7wF1mthM4SJAsCI/7EfAMcAT4vLsfzflBIkkza1ZwOyhXuUgJaRpqkbhKtRGk3x6qqdFaAxUuysF/+aahrpjGYpHESPUUuuIKqK6GU0/VgjPjRFwH/ykRiMRJdk+hV1+FgQG46y548UUlgQoX18F/SgQicTLCnkJSmeI6+E+JQCROCugpJJUnroP/lAhE4iRfjyD1FBoX4jr4T4lAJE46OoKeQelqaoJyqXhxHfyn9QhE4iTVGLx6dXA7aNasIAmokXjciOPgP9UIRKKWPbEcBD2EBgfVU0jKQjUCkShlDxpLTSwHSgBSNqoRiERJ3UUlBpQIRKKk7qISA0oEIlFSd1GJASUCKd4IVtGSPNRdVGJAiUCKM8JVtCSP1tZgIrmGBk0sJ5FRIpDiqLFzdNJrUatXBzUAdReViKj7qBRHjZ2FU5dRiZmiagRmdoqZPWRmz4XP03IcM9/MfmVmT5vZ78zsk2n7vm9mL5jZtvAxv5h4JAJq7CycalESM8XeGmoHHnH3s4BHwu1s/cAydz8bWATcZma1aftXuvv88LGtyHik3Do6OHJS5syJR06qVmPnUFSLkpgpNhEsAdaHr9cDLdkHuPu/uPtz4et9wH6grsjPlZjYMPeDtC9awd6pdQxi7J1aR/uiFWyY+8GoQ4sv1aIkjw1be2nu3Mzs9vtp7txctpXLim0jOM3dXwpfvwycNtTBZnYuMBn417TiDjO7kbBG4e6H8pzbBrQBzNI/mNhYu2kHvXM+wL1zPpBR/qtNO2I3sVZsdHTkXos4Ry0qyvVtpbxSy1imVjBLLWMJjPn/82FrBGb2sJk9leOxJP04d3fAh3ifM4C7gE+7+2BYvAp4J/Ae4BTghnznu3uXuze5e1NdnSoUcRHXFZdibYRdRuO6vq2MjSiXsRy2RuDuF+fbZ2avmNkZ7v5SeKHfn+e4qcD9wGp3fyLtvVO1iUNm9j3guoKil8jNqK2mN8dFP+oVl2KvtXXYHkJDXRhUKxh/ovyjqtg2go3A8vD1cuAn2QeY2WTgx8Cd7n5v1r4zwmcjaF94qsh4pMziuuLSeKDaVrJEuYxlsYmgE7jEzJ4DLg63MbMmM1sXHvMJ4ALgyhzdRLvNbDuwHZgOfK3IeKTMsldcuvKFx3nyu1fRcs5MTTdRpLiubytjI8o/qiy4tV9ZmpqavKenJ+owJFv2QCkIGkE1ZcKoZDceQnBhiMPShjI2xrpzgJk96e5NJ5QrEUjJNDYGo2SzNTQEUydIwdRrSEopXyLQFBNSOhooVXJxXN9Wxh9NOicl03/6jILKRSQelAikZG49fxn9E6dklPVPnMKt5y+LKKLKFNXoUkkuJQIpmfWzm3NON7F+dnPUoUVjFAv2aBCZREFtBFIyM2qr2Xj2hWw8+8KM8vokdncc5VTTGkQmUVCNQEpGg8vSjHKqaQ0ikygoEUjJZA8uq6+tTm6f91H2oNIgMomCEoGUVMuCeh5vv4gXOj/K4+0X0fLMz5O5sP0op5pWrUqioEQgYyfJC9t3dASjqtPlmWo6nWpVEgWNLJaxk/SRxt3dQZvA7t1BTaCjQ1NtSKQ0xYSUnU+YgOX4fbkZNjiY4wwRGUuaYkLK7pWT6zi978QlKl45uY7TI4gnrjSfkAxnrH8jaiOQMbPmvCtyjjRec94VEUUUPxpAJsMpx29EiUDGTE/z4pwjjXuaF0cdWmxEuTyhVIZy/EZ0a0jGzMqFc1j15lsZI42rJ1WxRl0hj9EAMhlOOX4jRdUIzOwUM3vIzJ4Ln6flOe5o2upkG9PKZ5vZr81sp5ndEy5rKeOEukIOTwPIZDjl+I0Ue2uoHXjE3c8CHgm3cxlw9/nh49K08luAb7r7nwCvAZ8pMh6JmRMGmCkJZNAAMhlOOX4jxSaCJcD68PV6ggXoRyRcsP4iILWgfUHni4wHqjXJcMrxGylqHIGZ9bl7bfjagNdS21nHHQG2AUeATnffYGbTgSfC2gBmNhP4mbu/O89ntQFtALNmzTpnV66BSiJR0MAxqRCjHkdgZg9Dzm7fGdMourubWb6s0uDuvWb2DmCzmW0HXh9B3Onv3wV0QTCgrJBzRcbMKKebFomTYROBu1+cb5+ZvWJmZ7j7S2Z2BnDi6KHgPXrD5+fN7OfAAuAfgFozm+juR4AzAXWelsoy1HTTQyQCDSKTOCm2jWAjsDx8vRz4SfYBZjbNzKaEr6cDzcAzHtyTehT4+FDni8TaKKab1iAyiZtiE0EncImZPQdcHG5jZk1mti485l1Aj5n9luDC3+nuz4T7bgC+aGY7gVOB7xYZj0h5jWK6aQ0ik7gpakCZu78KfChHeQ9wVfj6l8C8POc/D5xbTAwikeroyGwjgGGnm9YgMokbTTEhUozWVujqCqbWNgueu7qGbB/QIDKJGyUCkWK1tgbrKwwOBs/D9BbSIDKJG801JFJmqd5B6jUkcaFEIBKBlgX1uvBLbOjWkIhIwikRiIgknBKBiEjCKRGIiCScEoGISMKp15DEgiZhE4mOEoFELjUJW2r+ndQkbICSgUgZKBFI5NZu2sEl2x7m+l/cyYw3fs++qdO59YJlrH3b5IpOBKrlSKVQIpDINT3+AGsevJ2aI4cAOPONA3Q+eDurgGA108qjWo5UEjUWS+RWPXbXsSSQUnPkEKseuyuiiIqnqaalkigRSOROe/1AQeWVQFNNSyUpKhGY2Slm9pCZPRc+T8txzIVmti3t8Qczawn3fd/MXkjbN7+YeKQyWZ5FXPKVVwJNNS2VpNgaQTvwiLufBTwSbmdw90fdfb67zye44dsP/FPaIStT+919W5HxSCXq6AgWc0k3zOIucaeppqWSFJsIlgDrw9frgZZhjv848DN37x/mOEmSUSzuEnctC+pZs3Qe9bXVGFBfW82apfPUUCyxZMEa8qM82azP3WvD1wa8ltrOc/xm4Bvu/tNw+/vAnwGHCGsU7n4o3/kpTU1N3tPTM+q4RUSSyMyedPem7PJhu4+a2cPA6Tl2rU7fcHc3s7xZxczOIFi7eFNa8SrgZWAy0EWwmP3Nec5vA9oAZlXwvWMRkbgZNhG4+8X59pnZK2Z2hru/FF7o9w/xVp8Afuzuh9Pe+6Xw5SEz+x5w3RBxdBEkC5qamkZfjRERkQzFthFsBJaHr5cDPxni2MuBH6YXhMkjdVupBXiqyHhERKRAxSaCTuASM3sOuDjcxsyazGxd6iAzawRmAv876/xuM9sObAemA18rMh4RESlQUVNMuPurwIdylPcAV6Vtvwic0F3C3Stz/gARkXFEcw2JFEmTy0mlUyIQKYIml5PxQHMNSbJ1d0NjI0yYEDx3dxd0uiaXk/FANQJJru5uaGuD/nCg+65dwTaMeFSzJpeT8UA1AqkoG7b20ty5mdnt99PcuZkNW3tH/2arVx9PAin9/UH5CGlyORkPlAikYqTux/f2DeAcvx8/6mSwe3dh5TlocjkZD3RrSCpGyZe0nDUruB2Uq3yEUp+rXkNSyZQIpGKUfEnLjo7MNgIY1fTXLQvqdeGXiqZbQ1IxSr6k5Tic/lpkNIqahjoqmoY6mXzCBCzH79XNsMHBCCISqSz5pqFWjUAqxpBLWmaPB/jc54oaHyCSJEoEUjnyLWm5eHFwr3/XLnAPnr/znczttjYlA5E8lAikcuS7p//AAyeOB8hW4PgAkSRRG4FUvHxtBycwA7UlSIKpjUDGrVdOrhvZgVriVCQnJQKpeGvOu4L+iVMyyrLrBwOTprDl6rwroYokWlGJwMz+i5k9bWaDZnZCdSPtuEVmtsPMdppZe1r5bDP7dVh+j5lNLiYeSaae5sW0L1rB3ql1DGLsnVrHnfMXZ2zfsHAF11bNPeHcks5dJFKhih1Z/BSwFPi7fAeYWRXwbeASYC+wxcw2uvszwC3AN939bjP7W+AzwHeKjEkSZuXCOax68y02nn1hRvlNWcdZ1oygWktAJFBUjcDdn3X34SZePxfY6e7Pu/tbwN3AknDB+ouAe8Pj1hMsYC9SkJYF9axZOo/62moMqDLLeVz2jKBaS0AkUI65huqBPWnbe4H3AqcCfe5+JK08759hZtYGtAHMUqOfZEmf7yf7L33IPSOo1hIQCQxbIzCzh83sqRyPJeUIMMXdu9y9yd2b6upG2EtEEim7hlBfW82apfNOuN2jtQREAsPWCNz94iI/oxeYmbZ9Zlj2KlBrZhPDWkGqXKRoI5kRdOXCOSOqOYiMd+XoProFOCvsITQZuAzY6MFItkeBj4fHLQd+UoZ4RICR1xxExruiRhab2X8C/gaoA/qAbe6+0MxmAOvcfXF43GLgNqAKuMPdO8LydxA0Hp8CbAU+5e6HTvykTBpZLCJSuHwjizXFhIhIQmiKCRERyUmJQEQk4ZQIREQSTolARCThKrKx2MwOALuijmOMTAd+H3UQMTdE730AAALoSURBVKHv4jh9F8fpuziu0O+iwd1PGJFbkYlgPDOznlyt+kmk7+I4fRfH6bs4rlTfhW4NiYgknBKBiEjCKRHET1fUAcSIvovj9F0cp+/iuJJ8F2ojEBFJONUIREQSTolARCThlAhiwsxmmtmjZvaMmT1tZtdEHVOUzKzKzLaa2U+jjiVqZlZrZvea2f81s2fN7M+ijikKZvaF8N/GU2b2QzM7KeqYysnM7jCz/Wb2VFrZKWb2kJk9Fz5PG817KxHExxHgS+4+F3gf8HkzmxtxTFG6Bng26iBi4lvAg+7+TuA/kMDvxczqgb8Emtz93QRT2l8WbVRl931gUVZZO/CIu58FPBJuF0yJICbc/SV3/034+t8I/rEncoUUMzsT+CiwLupYomZmJwMXAN8FcPe33L0v2qgiMxGoNrOJQA2wL+J4ysrdfwEczCpeAqwPX68HWkbz3koEMWRmjcAC4NfRRhKZ24DrgcGoA4mB2cAB4HvhrbJ1Zva2qIMqN3fvBb4O7AZeAl5393+KNqpYOM3dXwpfvwycNpo3USKIGTP7I+AfgGvd/Y2o4yk3M/sYsN/dn4w6lpiYCPwp8B13XwC8ySir/5UsvPe9hCAxzgDeZmafijaqeAmX/x3VeAAlghgxs0kESaDb3e+LOp6INAOXmtmLBMuYXmRmP4g2pEjtBfa6e6p2eC9BYkiai4EX3P2Aux8G7gPeH3FMcfCKmZ0BED7vH82bKBHEhJkZwX3gZ939G1HHExV3X+XuZ7p7I0Fj4GZ3T+xffu7+MrDHzOaERR8CnokwpKjsBt5nZjXhv5UPkcBG8xw2AsvD18uBn4zmTZQI4qMZuILgL+Bt4WNx1EFJLPwF0G1mvwPmA38dcTxlF9aI7gV+A2wnuHYlaqoJM/sh8CtgjpntNbPPAJ3AJWb2HEGtqXNU760pJkREkk01AhGRhFMiEBFJOCUCEZGEUyIQEUk4JQIRkYRTIhARSTglAhGRhPv/jZJ7PMd+tTsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "1 times fitting\n",
            "elapsed : 6.1530373096466064\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "2 times fitting\n",
            "elapsed : 6.07156777381897\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "3 times fitting\n",
            "elapsed : 6.203218460083008\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "4 times fitting\n",
            "elapsed : 6.086965799331665\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "5 times fitting\n",
            "elapsed : 6.2581236362457275\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "6 times fitting\n",
            "elapsed : 6.350839376449585\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "7 times fitting\n",
            "elapsed : 6.35674524307251\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "8 times fitting\n",
            "elapsed : 6.223689079284668\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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m0e0PUnH8KIenTeeuF26A225WMpAh6bg11ApcaGYXmNkU4Dpg2OgfM7sU+FvgGnfvimsvM7Oi4PV0YCEQ38ksIinY3Xgv6564h8rj3RTgVB7vZt0T97C78d6wQ5MsknIicPd+YAWwDXgF+KG7v2Rm68zsmuCwjcC7gf87YpjoB4A2M/slsANoHDHaSERScNNTmynpPzGsraT/BDc9tTmkiCQbmXvu3W6vqanxtra2sMMQyXoDVkABo3/HBzAKfCCEiCRMZvaiu9eMbNfMYpE89vbsinG1SzQpEYjksZKNG+ifWjysrX9qMSUbN4QUkWQjJQIZprWhic6ymQxYAZ1lM2ltaAo7JElFbS2Fm++Hqiowg6qq2HZtbdiRSRZRH4EMaW1o4uLb6yg+ebpzsW9yEXvXbmJB/YoQIxORdEjWR6BEIEM6y2Yyq6drdHvpDGYdeyOEiERyU7ZO4kuWCDI6s1iy24yexEt3JGsXkdFycRKf+ghkSFdp+bjaRWS0XJzEp0QgQw7VraFvctGwtr7JRRyqWxNSRCK5Jxcn8SkRyJAF9SvYu3YTnaUzGMDoLJ2hjmKRcao4fnRc7dlAncUiImnUW1FJyZFR627SO3sOJYfbQ4joNM0sFhHJgFycxKdEICKSTjk4iU/DR0VE0q22NqtP/CPpikBEJOKUCEREIk63hkTyQLYuaSC5IS1XBGa22Mz2mdl+M1uVYH+RmT0a7H/BzKrj9q0O2veZ2aJ0xCMSJVt2dbD68T109PThQEdPH6sf38OWXaOHMIokkvIVgZlNAu4FrgLagVYz2zqi5OSXgWPu/j4zuw7YAHzezC4iVuP4g0AF8IyZ/Qd3P5VqXCJRsXHbPq7a/Qxff+57Q2vb3Hn59Wx81xRdFchZSccVwUeA/e7+mru/AzwCLBlxzBLgoeD1Y8CnzMyC9kfc/YS7/xrYH3yeiJylmuefpPGppmFr2zQ+1UTN80+GHZrkiHQkgjnAobjt9qAt4TFBsfu3gPec5XsBMLPlZtZmZm3d3VoNU2TQ6p3fT7i2zeqd3w8pIsk1OTNqyN2b3b3G3WvKy7UapsigmW8l/sMoWbvISOlIBB3A3LjtyqAt4TFmVgicB/zmLN8rImOwefPG1S4yUjoSQStwoZldYGZTiHX+bh1xzFZgWfD6WmC7x1a72wpcF4wqugC4EPjnNMQkEh0NDVBSMrytpCTWLtmnpQWqq6GgIPbc0hJ2RKmPGnL3fjNbAWwDJgEPuPtLZrYOaHP3rcB3gO+b2X7gTWLJguC4HwIvA/3AVzViSGScBpcyqK+H11+HefNiSSCHljiIjJYWWL4centj2wcPxrYh1P9fWoZaRCRTqqtjJ/+RqqrgwIEJ/3otQy0iEjJ//fVxtWeKEoGISIa8cV7iEY/J2jNFiUBEJEPWX/YleguH1wXvLSxi/WVfCimiGCUCEZEMaVt4NasWr6B9WjkDGO3Tylm1eAVtC68ONS6tPioikiErF81n9e/eYesHPznUVjx5EusXzQ8xKiUCEZGMGVwEMNuWDFciEBHJoKWXzgn9xD+S+ghERCJOiUBEJOKUCEREIk6JQEQk4pQIREQiTolARCTilAhERCJOiUBEJFuEVLRGE8pERLJBiEVrUroiMLPzzexpM3s1eC5LcMwlZvYzM3vJzP7FzD4ft+9BM/u1me0OHpekEo+ISM6qrz+dBAb19sbaJ1iqt4ZWAc+6+4XAs8H2SL3A9e7+QWAxcJeZlcbtX+nulwSP3SnGIyFobWiis2wmA1ZAZ9lMWhuawg5JJPckK06TgaI1qSaCJcBDweuHgKUjD3D3f3P3V4PXh4EuINwqDJI2rQ1NXHx7HbN6uijAmdXTxcW31ykZiIzXvHnja0+jVBPBTHc/ErzuBGaOdbCZfQSYAvwqrrkhuGX0LTMrSvJWzGy5mbWZWVt3d3eKYUu6zN10B8UnTwxrKz55grmb7ggpovyyZVcHCxu3c8Gqn7CwcTtbdnWEHZJMkNav1NE3efgpsG9yEa1fqZvw7z5jIjCzZ8xsb4LHkvjj3N0BH+NzZgPfB25094GgeTXwfmABcD5wa7L3u3uzu9e4e015uS4ossWMnsRJOVm7nL0tuzpY/fgeOnr6cKCjp4/Vj+9RMshTt0y6iFsXDS9ac+uiFdwy6aIJ/+4zjhpy9yuT7TOzN8xstrsfCU70XUmOmwb8BKh395/Hffbg1cQJM/suMPGpT9Kqq7ScWT2j/7d3lZYzK4R48snGbfu4avczfP2571Fx/CiHp03nzsuvZ+O7pmTdMsaSusM9fXR88JPDitYAWE/fhH93qreGtgLLgtfLgB+PPMDMpgA/Ar7n7o+N2Dc7eDZi/Qt7U4xHMuxQ3ZqEl7OH6taEFFH+qHn+SRqfaqLyeDcFOJXHu2l8qoma558MOzSZABWlxeNqT6dUE0EjcJWZvQpcGWxjZjVmtjk45nPA5cANCYaJtpjZHmAPMB34RorxSIYtqF/B3rWb6CydwQBGZ+kM9q7dxIL6FWGHlvNW7/w+Jf3D+19K+k+weuf3Q4pIJtLKRfMpnjxpWFvx5EmszEAZS4vd2s8tNTU13tbWFnYYIhPKCwqwBL+fboYNDCR4h+S6Lbs6JrSMpZm96O41I9s1s1gkS9m8ebHZpYnaJS+FVcZSaw1JWmlyWRo1NEBJyfC2kpJYu0gaKRFI2mhyWZrV1kJzM1RVgVnsubl5wtedkehRH4GkTWfZzIRDSTtLZzDr2BshRJSbJvo+sUSX+ghkwmlyWeoGJ5H1nTwFnJ5EBigZyIRRIpC00eSy1GkSmYRBfQSSNppcljpNIpOEJrhgjfoIJK1aG5qYu+kOZvR001VazqG6NZpcNg7qZ5FRRhasgdjosXMYOJCsj0CJQCSLaBKZjFJdnXA+CVVVcODAuD4qWSLQrSGRLJJsspgmkUVYBgrWKBHIhNJ6+uOkSWQyUgYK1igRyITZsquDnevu5tH11/GrDZ/h0fXXsXPd3UoGY9EkMhkpA38caPioTJjdjfey7ol7hlbQrDzezbon7uHOKYUsffSbIUeXPUZPIPsES8d571fyWG0trQeOjR6EkcY/DtRZLBOm/bwZVB4fPZmsfVo5lW8lrGEUOSMnkEFs6eH1n/2Q5g0IkN5/I5pZLBlXcfzouNqjSBPI5Ew2bts3LAkA9J08xcZt+9L2b0R9BDJh3p5dMa72KNIEMjmTw0lKVSZrPxcpJQIzO9/MnjazV4PnsiTHnYqrTrY1rv0CM3vBzPab2aNBWUvJEyUbN9A/dXiZvf6pxZRs3BBSRNlHVcjkTDJRwjLVK4JVwLPufiHwbLCdSJ+7XxI8rolr3wB8y93fBxwDvpxiPJJNamsp3Hz/sBEwhZvv1wiYODPfSrwgX7J2iZ5MlLBMqbPYzPYBn3D3I0Eh+p+6+6jozOy37v7uEW0GdAOz3L3fzD4O/KW7LzrT96qzWPJGGmeNSv5K19LkE9VZPNPdjwSvO4GZSY6bamZtQD/Q6O5bgPcAPe7eHxzTDiT9LzOz5cBygHmaZSn5oqEh8ToymkAmcSa6hOUZE4GZPQMJVxGuj99wdzezZJcXVe7eYWbvBbab2R7grfEE6u7NQDPErgjG816RrDV4m6y+PrZkwLx5sSSg22eSQWdMBO5+ZbJ9ZvaGmc2OuzWUcHC4u3cEz6+Z2U+BS4G/A0rNrDC4KqgENOVUImH4pf5sVv7oeQ0XldCk2lm8FVgWvF4G/HjkAWZWZmZFwevpwELgZY91TuwArh3r/SL5RktvSLZJNRE0AleZ2avAlcE2ZlZjZpuDYz4AtJnZL4md+Bvd/eVg363An5vZfmJ9Bt9JMR6RrDe49Eb83IF1T9zD7sZ7ww5NIkpLTIhkmJbekLCoHoFIltDSG5JtlAhEMkxLb0i2USIQyTAtvSHZRolAJNO09IZkGS1DLRKG2lqd+CVr6IpARCTilAhERCJOiUBEJOKUCEREIk6dxZIV0rXeuoiMnxKBhG7Lrg5WP75nqEB3R08fqx/fA6BkIJIBujUkodu4bR9X7X6GnffdyGsbPsPO+27kqt3PsHHbvrBDS0lrQxOdZTMZsAI6y2bS2tAUdkgiCemKQEJX8/yTrH+qaaiIe+XxbhqfamI1AFeEGdo5a21o4uLb6yg+GftvmtXTxXm319EKLKhfEW5wIiNo9VEJXWfZTGb1jF51s7N0BrOOvRFCRKnLx/8myX1afVSy1sy3Ri/JPFZ7LpjRkzj2ZO0iYUopEZjZ+Wb2tJm9GjyXJTjmk2a2O+7xtpktDfY9aGa/jtt3SSrxSG6yefPG1Z4LukrLx9UuEqZUrwhWAc+6+4XAs8H2MO6+w90vcfdLiN3w7QX+Me6QlYP73X13ivFILmpogJKS4W0lJbH2HHWobg19k4uGtfVNLuJQ3ZqQIhJJLtVEsAR4KHj9ELD0DMdfC/yDu/em+L2ST2probl52GqcNDfn9KJsC+pXsHftJjpLZzCA0Vk6g71rN6mjWLJSSp3FZtbj7qXBawOODW4nOX478Nfu/kSw/SDwceAEwRWFu5840/eqs1hEZPySdRafcfiomT0DzEqwqz5+w93dzJJmFTObDXwI2BbXvBroBKYAzcSK2a9L8v7lwHKAeTl871hEJNucMRG4+5XJ9pnZG2Y2292PBCf6sSpvfw74kbufjPvsI8HLE2b2XaBujDiaiSULampqcm/Mq4hIlkq1j2ArsCx4vQz48RjHfgH4QXxDkDwGbystBfamGI+IiIxTqomgEbjKzF4Frgy2MbMaM9s8eJCZVQNzgX8a8f4WM9sD7AGmA99IMR4RERmnlBKBu//G3T/l7he6+5Xu/mbQ3ubuN8Udd8Dd57j7wIj3X+HuH3L3i939i+7+21TiEQlFSwtUV0NBQey5pSXsiETGRWsNiaSipYX+m75C4dt9se2DB2PbkNPDXyVatMSESAp6V956OgkECt/uo3flrSFFJDJ+SgQiKZh65PC42kWykRKBSAoOT5s+rnaRbKREIDlly64OFjZu54JVP2Fh43a27OoINZ7Ni2+it3D4mkK9hUVsXnxTkneIZB91FkvO2LKrg53r7ubR7Q9Scfwoh6dN564XboDbbg6tpOUlq77Kbe/0c0t8TFfcwGWrvhpKPCLnQolAcsbuxntZ98Q9wyqZrXviHu6cUsjSR78ZSkxLL50Dt93M5z+6mMM9fVSUFrNy0XzVWpacogplkjPaz5tB5fHRhV3ap5VT+dZYq5uICKhCmeSBiuNHx9UuImdHiUByxtuzK8bVLiJnR4lAckbJxg30Ty0e1tY/tZiSjRtCikgkPygRSO6oraVw8/3DKpkVbr5fSzmIpEiJQHJLbS0cOAADA7HnIAm0NjTRWTaTASugs2wmrQ1NoYYpkks0fFRyXmtDExffXkfxydiw0lk9XZx3ex2toBrBImdBVwSS8+ZuumMoCQwqPnmCS9fcrCsEkbOgRCA5b0bP6LkFAIU+QAHOrJ4uLr69LnEyUC0BkdQSgZn9DzN7ycwGzGzUJIW44xab2T4z229mq+LaLzCzF4L2R81sSirxSDR1lZaf8ZjikyeYu+mO4Y1BLQEOHgT3oVoCSgYSNaleEewFPgs8l+wAM5sE3Av8EXAR8AUzuyjYvQH4lru/DzgGfDnFeCSCDtWtoW9y0RmPG3nloFoCIjGplqp8xd33neGwjwD73f01d38HeARYEhSsvwJ4LDjuIWIF7EXGZUH9Cvau3URn6QwGMPot8T/rkVcOqiUgEpOJPoI5wKG47fag7T1Aj7v3j2hPyMyWm1mbmbV1dye+JyzRtaB+BbOOvUGBD7DrjrtHXSH0TS7iUN2aYW2qJSASc8ZEYGbPmNneBI8lmQhwkLs3u3uNu9eUl5/5nrBE18grhM7SGexdu2nUUFLVEhCJOeM8Ane/MsXv6ADmxm1XBm2/AUrNrDC4KhhsF0nZgvoVEJz4ZwWPkVRLQCQmExPKWoELzewCYif664D/6e5uZjuAa4n1GywDfpyBeEQA1RIQGZRSPQIz+2/At4FyoAfY7e6LzKwC2OzuVwfHXQ3cBUwCHnD3hqD9vcSSwPnALuCL7n5i9DcNp3oEIiLjl6wegQrTiIhEhArTiJfZ1ZEAAAMSSURBVIhIQkoEIiIRp0QgIhJxSgQiIhGXk53FZtYNHAw7jgkyHVA19hj9LE7Tz+I0/SxOG+/PosrdR83IzclEkM/MrC1Rr34U6Wdxmn4Wp+lncVq6fha6NSQiEnFKBCIiEadEkH2aww4gi+hncZp+FqfpZ3FaWn4W6iMQEYk4XRGIiEScEoGISMQpEWQJM5trZjvM7GUze8nMbg47pjCZ2SQz22VmT4QdS9jMrNTMHjOzfzWzV8zs42HHFAYz+9/B78ZeM/uBmU0NO6ZMMrMHzKzLzPbGtZ1vZk+b2avBc9m5fLYSQfboB/7C3S8CPgZ81cwuCjmmMN0MvBJ2EFnibuApd38/8J+I4M/FzOYAfwbUuPvFxJa0vy7cqDLuQWDxiLZVwLPufiHwbLA9bkoEWcLdj7j7L4LX/07slz2SFVLMrBL4r8DmsGMJm5mdB1wOfAfA3d9x955wowpNIVBsZoVACXA45Hgyyt2fA94c0bwEeCh4/RCw9Fw+W4kgC5lZNXAp8EK4kYTmLuDrwEDYgWSBC4Bu4LvBrbLNZvausIPKNHfvADYBrwNHgLfc/R/DjSorzHT3I8HrTmDmuXyIEkGWMbN3A38H3OLux8OOJ9PM7I+BLnd/MexYskQh8AfAfe5+KfA7zvHyP5cF976XEEuMFcC7zOyL4UaVXTw2F+Cc5gMoEWQRM5tMLAm0uPvjYccTkoXANWZ2gFgZ0yvM7OFwQwpVO9Du7oNXh48RSwxRcyXwa3fvdveTwOPAfw45pmzwhpnNBgieu87lQ5QIsoSZGbH7wK+4+1+HHU9Y3H21u1e6ezWxzsDt7h7Zv/zcvRM4ZGbzg6ZPAS+HGFJYXgc+ZmYlwe/Kp4hgp3kCW4FlwetlwI/P5UOUCLLHQuBLxP4C3h08rg47KMkKXwNazOxfgEuAb4YcT8YFV0SPAb8A9hA7d0VqqQkz+wHwM2C+mbWb2ZeBRuAqM3uV2FVT4zl9tpaYEBGJNl0RiIhEnBKBiEjEKRGIiEScEoGISMQpEYiIRJwSgYhIxCkRiIhE3P8HAF2ILUOfqgwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "9 times fitting\n",
            "elapsed : 6.260721445083618\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "udoykOBX2SVg"
      },
      "source": [
        "학습이 더 진행되면서 거의 완벽하게 학습된 것을 볼 수 있다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Txmz0VqaqcMm"
      },
      "source": [
        "# model.fit()의 batch_size\n",
        "\n",
        "batch_size는 GPU와 관련된 옵션이다.\n",
        "\n",
        "한번에 GPU에 보내는 데이터의 수이다.  \n",
        "\n",
        "<br>\n",
        "\n",
        "batch_size가 1일 경우 1개를 보내고, 1개의 결과를 받고, 1번 웨이트를 업데이트 한다.\n",
        "\n",
        "batch_size가 10일 경우 10개를 보내고, 10개의 결과를 한 번에 받고, 1번 웨이트를 업데이트 한다.\n",
        "\n",
        "<br>\n",
        "\n",
        "GPU는 보통 수천개의 코어를 가지고 있다. 동시에 꽤 많은 연산을 처리할 수 있다. 그런데 데이터가 적으면 대부분은 사용하지 못하고 일부만 연산에 사용된다. \n",
        "\n",
        "복수의 데이터를 한번에 보내어 한번에 연산을 할수 있고, 그 결과를 반환할 수 있다. 이런 방법으로 연산 시간을 줄일 수 있다.\n",
        "\n",
        "<br>\n",
        "\n",
        "하지만, 복수의 데이터를 한번에 보내는 경우 한번에 보낸 결과가 한번에 오고 1번 업데이트 되면서 업데이트 되는 사항이 뭉개지는 단점이 있다.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RF6qxEfLriZy"
      },
      "source": [
        "def build_model():\n",
        "\n",
        "  model = keras.Sequential()\n",
        "  model.add(Dense(10, activation='tanh', input_shape=(1,)))\n",
        "  model.add(Dense(10, activation='tanh'))\n",
        "  model.add(Dense(1))\n",
        "\n",
        "  model.compile(optimizer=\"SGD\", loss=\"mse\", metrics=[\"mse\"])\n",
        "\n",
        "  return model\n",
        "\n",
        "def fit_with_batch_sizes(train_x, train_y, test_x, test_y, batch_sizes):\n",
        "\n",
        "  for batch_size in batch_sizes:\n",
        "    model = build_model()\n",
        "    print(\"batch_size={}\".format(batch_size))\n",
        "    fit_one_more(model, train_x, train_y, test_x, test_y, batch_size)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TtN9WexdvEaz"
      },
      "source": [
        "(train_x, train_y), (test_x, test_y) = get_sin_data(start=0, end=10, step=0.1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ONkh2C1GuRsg"
      },
      "source": [
        "## batch_size 적용 : [1,2,5,10,20,50,100,200,500]"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SbW3bysfrTcS",
        "outputId": "c890eb3b-fe1d-4e60-f67f-d8f3963949d0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "fit_with_batch_sizes(train_x, train_y, test_x, test_y, batch_sizes=[1,2,5,10,20,50,100,200,500])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "batch_size=1\n",
            "elapsed : 100.3012764453888\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3trjjdwAjAQP2Bcf8W3fvq0I90lNeVIQl+T/uZlhbW5IjwtUwfDRjmjqfeDeU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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "batch_size=2\n",
            "elapsed : 51.838141679763794\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wp5m9R3yI71Yz+2t/Q/JMB9DhnBv6hreb+IdB2H0B+JVzrts5dwF4Gfh3Psfk\npffNbC5A4vZMJv84zMn/EHCVmS00s8uIXwja63NMeWdmRnzs9x3n3F/4HY9XnHMbnXOVzrkFxP9b\n73fORaIX6Jw7DZw0s6pE023A2z6G5JUTwHVmVpL4u7+NCFzoTrIXuDdx/17gR5n84+KchxMQzrkB\nM1sHtBKvAtjpnHvL57C8UAPcA7xpZm8k2r7jnHvFx5gk//4z0JLo6LwLfM3nePLOOfdLM9sN/APx\nKrcjhHS2r5n9ALgZmGVmHcDjwCbgb8zs68RXQP5yRq+pGb4iItET5mEfERFJQ8lfRCSClPxFRCJI\nyV9EJIKU/EVEIkjJX0QkgpT8RUQiSMlfRCSC/j/tdSntq4qbegAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "batch_size=5\n",
            "elapsed : 22.21281623840332\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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a21PAr8IP/MQH//F+3urzwI/c/VTSex/1wLvAPwBXZfftiKRaM2s+JytTL0RP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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "batch_size=10\n",
            "elapsed : 12.134780406951904\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JIjGzaE6dEoFESktiiIhICiUGERFJocQgIiIplBhERCSFEoOIiKRQYhARkRRK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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "batch_size=20\n",
            "elapsed : 7.339964866638184\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "batch_size=50\n",
            "elapsed : 5.389750957489014\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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m3djYWIyP1y2ZMDOzBiQ9HBFjzc7zInpmZlbDgcHMzGo4MJiZWQ0HBjMzq+HA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\nwGBmZjX+P/z2NIGuoEQjAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "batch_size=100\n",
            "elapsed : 3.670841932296753\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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D9n43oE/8e5dPWX93/94LUOo5BjMzm6vsPQYzM2tQysAg6VJJeyTtlbS53+3J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\ntMQ21/MbjAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "batch_size=200\n",
            "elapsed : 3.6978063583374023\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CSfsk/UW/y1IUSQsl3SPpf0t6QtKb+12mIkj6aPo3/qikuySd3e8y9Yqk2yQ9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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "batch_size=500\n",
            "elapsed : 3.7025442123413086\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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L20zKtWltMHKRSdaHtY/LqtvA4KYkW5hqs1J1IZFG7m8onIGM5WnWTJSjtY/LyoHB0hmS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\nZmZ1/j/Ca1GhVk3TAwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SXHdl5Ie3dUd"
      },
      "source": [
        "맨 앞의 결과가 batch_size 1인 경우이다. 100개의 데이터를 매번 1개 씩 보냈고, 매번 업데이트 했다. \n",
        "\n",
        "batch_size를 키우면, 시간은 줄어들지만 학습이 빠르게 진행되지 않는다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r3YdUyA9xnZo"
      },
      "source": [
        "# 학습 진행된 내역 보기\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_FEMCvHjzs2P"
      },
      "source": [
        "## model.fit()의 반환값 history\n",
        "\n",
        "반환된 history의 loss로 진행 상황을 확인."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K69Avyero8Ee"
      },
      "source": [
        "(train_x, train_y), (test_x, test_y) = get_sin_data(start=0, end=10, step=0.1)\n",
        "\n",
        "model = keras.Sequential()\n",
        "model.add(Dense(10, activation='tanh', input_shape=(1,)))\n",
        "model.add(Dense(10, activation='tanh'))\n",
        "model.add(Dense(1))\n",
        "\n",
        "\n",
        "model.compile(optimizer=\"SGD\", loss=\"mse\", metrics=[\"mse\"])\n",
        "model.summary()\n",
        "\n",
        "\n",
        "start_time = time.time()\n",
        "# model.fit(train_x, train_y, epochs=1000, verbose=0, batch_size=20)\n",
        "history = model.fit(train_x, train_y, epochs=1000, verbose=0, batch_size=20)\n",
        "print(\"elapsed : {}\".format(time.time() - start_time))    "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SN6iFgw9o8B5",
        "outputId": "24de5c34-83bc-46fa-b10a-d86b65b3bb83",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "\n",
        "plt.plot(history.history['loss'])\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fbcd0a5f7b8>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dRYHL9fp5STU"
      },
      "source": [
        "## model.fit() verbose\n",
        "\n",
        "- 0 : X\n",
        "- 1: progress bar\n",
        "- 2 : 1 line per epoch"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E7JfvwoF4bVT",
        "outputId": "902a3c20-d5af-48e9-c47c-4226493536fb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# (train_x, train_y), (test_x, test_y) = get_sin_data(start=0, end=10, step=0.01)\n",
        "(train_x, train_y), (test_x, test_y) = get_sin_data(start=0, end=10, step=0.0001)\n",
        "\n",
        "model = keras.Sequential()\n",
        "model.add(Dense(10, activation='tanh', input_shape=(1,)))\n",
        "model.add(Dense(10, activation='tanh'))\n",
        "model.add(Dense(1))\n",
        "\n",
        "\n",
        "model.compile(optimizer=\"SGD\", loss=\"mse\", metrics=[\"mse\"])\n",
        "model.summary()\n",
        "\n",
        "print(train_x.shape)\n",
        "\n",
        "start_time = time.time()\n",
        "# history = model.fit(train_x, train_y, epochs=1000, verbose=0, batch_size=20)\n",
        "history = model.fit(train_x, train_y, epochs=5, verbose=1, batch_size=20)\n",
        "print(\"elapsed : {}\".format(time.time() - start_time))    "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_8\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_24 (Dense)             (None, 10)                20        \n",
            "_________________________________________________________________\n",
            "dense_25 (Dense)             (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_26 (Dense)             (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 141\n",
            "Trainable params: 141\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "(60000,)\n",
            "Train on 60000 samples\n",
            "Epoch 1/5\n",
            "60000/60000 [==============================] - 5s 90us/sample - loss: 0.2502 - mean_squared_error: 0.2502\n",
            "Epoch 2/5\n",
            "60000/60000 [==============================] - 6s 93us/sample - loss: 0.1525 - mean_squared_error: 0.1525\n",
            "Epoch 3/5\n",
            "60000/60000 [==============================] - 5s 91us/sample - loss: 0.1018 - mean_squared_error: 0.1018\n",
            "Epoch 4/5\n",
            "60000/60000 [==============================] - 5s 92us/sample - loss: 0.0396 - mean_squared_error: 0.0396\n",
            "Epoch 5/5\n",
            "60000/60000 [==============================] - 5s 90us/sample - loss: 0.0130 - mean_squared_error: 0.0130\n",
            "elapsed : 27.59051489830017\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hob_7N00L61C"
      },
      "source": [
        "# 학습 시에 validation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vR-m3FJ7zoiK"
      },
      "source": [
        "## model.fit()의 validation_data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r1QFN0UCzlGA",
        "outputId": "c5ff6d06-678a-47c6-d046-3f6d7588c248",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "(train_x, train_y), (test_x, test_y) = get_sin_data(start=0, end=10, step=0.1)\n",
        "\n",
        "model = keras.Sequential()\n",
        "model.add(Dense(10, activation='tanh', input_shape=(1,)))\n",
        "model.add(Dense(10, activation='tanh'))\n",
        "model.add(Dense(1))\n",
        "\n",
        "\n",
        "model.compile(optimizer=\"SGD\", loss=\"mse\", metrics=[\"mse\"])\n",
        "model.summary()\n",
        "\n",
        "\n",
        "start_time = time.time()\n",
        "# history = model.fit(train_x, train_y, epochs=1000, verbose=0, batch_size=20)\n",
        "history = model.fit(train_x, train_y, epochs=1000, verbose=1, batch_size=20, validation_data=(test_x, test_y))\n",
        "print(\"elapsed : {}\".format(time.time() - start_time))    "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_28\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_91 (Dense)             (None, 10)                20        \n",
            "_________________________________________________________________\n",
            "dense_92 (Dense)             (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_93 (Dense)             (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 141\n",
            "Trainable params: 141\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Train on 60 samples, validate on 40 samples\n",
            "Epoch 1/1000\n",
            "60/60 [==============================] - 0s 7ms/sample - loss: 1.7064 - mean_squared_error: 1.7064 - val_loss: 0.4434 - val_mean_squared_error: 0.4434\n",
            "Epoch 2/1000\n",
            "60/60 [==============================] - 0s 325us/sample - loss: 0.4610 - mean_squared_error: 0.4610 - val_loss: 0.4652 - val_mean_squared_error: 0.4652\n",
            "Epoch 3/1000\n",
            "60/60 [==============================] - 0s 229us/sample - loss: 0.3946 - mean_squared_error: 0.3946 - val_loss: 0.4812 - val_mean_squared_error: 0.4812\n",
            "Epoch 4/1000\n",
            "60/60 [==============================] - 0s 249us/sample - loss: 0.3914 - mean_squared_error: 0.3914 - val_loss: 0.4740 - val_mean_squared_error: 0.4740\n",
            "Epoch 5/1000\n",
            "60/60 [==============================] - 0s 246us/sample - loss: 0.3911 - mean_squared_error: 0.3911 - val_loss: 0.4743 - val_mean_squared_error: 0.4743\n",
            "Epoch 6/1000\n",
            "60/60 [==============================] - 0s 241us/sample - loss: 0.3918 - mean_squared_error: 0.3918 - val_loss: 0.4620 - val_mean_squared_error: 0.4620\n",
            "Epoch 7/1000\n",
            "60/60 [==============================] - 0s 254us/sample - loss: 0.3835 - mean_squared_error: 0.3835 - val_loss: 0.4804 - val_mean_squared_error: 0.4804\n",
            "Epoch 8/1000\n",
            "60/60 [==============================] - 0s 255us/sample - loss: 0.3900 - mean_squared_error: 0.3900 - val_loss: 0.4791 - val_mean_squared_error: 0.4791\n",
            "Epoch 9/1000\n",
            "60/60 [==============================] - 0s 228us/sample - loss: 0.3971 - mean_squared_error: 0.3971 - val_loss: 0.5152 - val_mean_squared_error: 0.5152\n",
            "Epoch 10/1000\n",
            "60/60 [==============================] - 0s 218us/sample - loss: 0.3817 - mean_squared_error: 0.3817 - val_loss: 0.4650 - val_mean_squared_error: 0.4650\n",
            "Epoch 11/1000\n",
            "60/60 [==============================] - 0s 215us/sample - loss: 0.3768 - mean_squared_error: 0.3768 - val_loss: 0.4663 - val_mean_squared_error: 0.4663\n",
            "Epoch 12/1000\n",
            "60/60 [==============================] - 0s 249us/sample - loss: 0.3841 - mean_squared_error: 0.3841 - val_loss: 0.4497 - val_mean_squared_error: 0.4497\n",
            "Epoch 13/1000\n",
            "60/60 [==============================] - 0s 260us/sample - loss: 0.3830 - mean_squared_error: 0.3830 - val_loss: 0.4617 - val_mean_squared_error: 0.4617\n",
            "Epoch 14/1000\n",
            "60/60 [==============================] - 0s 327us/sample - loss: 0.3759 - mean_squared_error: 0.3759 - val_loss: 0.4642 - val_mean_squared_error: 0.4642\n",
            "Epoch 15/1000\n",
            "60/60 [==============================] - 0s 247us/sample - loss: 0.3715 - mean_squared_error: 0.3715 - val_loss: 0.4776 - val_mean_squared_error: 0.4776\n",
            "Epoch 16/1000\n",
            "60/60 [==============================] - 0s 246us/sample - loss: 0.3726 - mean_squared_error: 0.3726 - val_loss: 0.4858 - val_mean_squared_error: 0.4858\n",
            "Epoch 17/1000\n",
            "60/60 [==============================] - 0s 246us/sample - loss: 0.3721 - mean_squared_error: 0.3721 - val_loss: 0.4733 - val_mean_squared_error: 0.4733\n",
            "Epoch 18/1000\n",
            "60/60 [==============================] - 0s 252us/sample - loss: 0.3667 - mean_squared_error: 0.3667 - val_loss: 0.4692 - val_mean_squared_error: 0.4692\n",
            "Epoch 19/1000\n",
            "60/60 [==============================] - 0s 235us/sample - loss: 0.3652 - mean_squared_error: 0.3652 - val_loss: 0.4629 - val_mean_squared_error: 0.4629\n",
            "Epoch 20/1000\n",
            "60/60 [==============================] - 0s 240us/sample - loss: 0.3694 - mean_squared_error: 0.3694 - val_loss: 0.4458 - val_mean_squared_error: 0.4458\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EzbvzCmNzlOI",
        "outputId": "3c061821-cfcd-43fd-9482-67cbe3d86109",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "plt.plot(history.history['loss'], label='loss')\n",
        "plt.plot(history.history['val_loss'], label='val_loss')\n",
        "plt.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f34369a3b70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3ZIyZaoxJMsYkNW/evFIV1pS7UkoFF0pwXwF0E5HOIhKH7TCd41NmJzAUQESOxwb3qjXN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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gaLsARjT1a17"
      },
      "source": [
        "## model.fit()의 validation_split\n",
        "\n",
        "따로 validation 데이터를 주지 않고, test 데이터로 준 것의 일부를 validation에 사용.\n",
        "\n",
        "validation에 사용된 데이터는 학습에 사용되지 않는다.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sjr-j0WYo7-j",
        "outputId": "014989f0-54f3-42d4-e11c-ef2a3554d8ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        }
      },
      "source": [
        "(train_x, train_y), (test_x, test_y) = get_sin_data(start=0, end=10, step=0.1)\n",
        "\n",
        "model = keras.Sequential()\n",
        "model.add(Dense(10, activation='tanh', input_shape=(1,)))\n",
        "model.add(Dense(10, activation='tanh'))\n",
        "model.add(Dense(1))\n",
        "\n",
        "\n",
        "model.compile(optimizer=\"SGD\", loss=\"mse\", metrics=[\"mse\"])\n",
        "model.summary()\n",
        "\n",
        "\n",
        "start_time = time.time()\n",
        "# history = model.fit(train_x, train_y, epochs=1000, verbose=0, batch_size=20)\n",
        "history = model.fit(train_x, train_y, epochs=1000, verbose=0, batch_size=20, validation_split=0.1)\n",
        "print(\"elapsed : {}\".format(time.time() - start_time))    "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_29\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_94 (Dense)             (None, 10)                20        \n",
            "_________________________________________________________________\n",
            "dense_95 (Dense)             (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_96 (Dense)             (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 141\n",
            "Trainable params: 141\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "elapsed : 10.968835353851318\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LbPF6kuJo77k",
        "outputId": "8233bb23-b543-449d-f544-7843a43d3852",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "plt.plot(history.history['loss'], label='loss')\n",
        "plt.plot(history.history['val_loss'], label='val_loss')\n",
        "plt.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f3436ee3550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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V/17OM7M3n+xiCCHEKeWEO1SVUiHAW8Cfgtj3XqVUolIqMSMj40Tf2mVE91b88Ngo1+Ov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\nhs9h7CTPfZTybL5xr+3neyb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aOO43hN/F28BTgGO1Y1oCudrkJwLPzxRU/qJTXFcgA/jY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\nCe5CCNEASXAXQogGSIK7EEI0QP8PaEJQGBgxsJ4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zli-EpQjamVC"
      },
      "source": [
        "# 데이터 전처리"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RaxWc4VTat58"
      },
      "source": [
        "## Normalization/Standardization\n",
        "\n",
        "입력이 여러 차원일 때 각 차원의 입력을 동일한 스케일로 맞추어주면, 학습이 빠르게 진행된다고 한다.\n",
        "\n",
        "- Normalization : 전체 데이터를 0~1로 변환해준다.\n",
        "- Standardization : 평균을 0, 표준편차를 1이되도록 변환해 준다.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NR-I6j5xajoC",
        "outputId": "35d07203-654f-48bd-98fe-92edeeb3cc2b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        }
      },
      "source": [
        "(train_x, train_y), (test_x, test_y) = get_sin_data(start=-10, end=10, step=0.1)\n",
        "\n",
        "print(\"min=\",np.min(train_x))\n",
        "print(\"max=\",np.max(train_x))\n",
        "\n",
        "plt.scatter(train_x, train_y)\n",
        "plt.scatter(test_x, test_y, color=\"r\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "min= -9.600000000000001\n",
            "max= 9.899999999999928\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f7ab01738d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fQ1dHO4vmzaSttZmt/QOsXL0plUhUJbbZo8a/FjLS3asBiiaLlIwan2iySkV6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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XH39z1Cuajfu",
        "outputId": "b64d6f86-3cf3-4a18-e708-c9fddab6be30",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        }
      },
      "source": [
        "min = -10 # ADDED\n",
        "max = 10 # ADDED\n",
        "\n",
        "train_x = (train_x-min)/(max-min) # ADDED\n",
        "test_x = (test_x-min)/(max-min) # ADDED\n",
        "\n",
        "print(\"min=\",np.min(train_x))\n",
        "print(\"max=\",np.max(train_x))\n",
        "\n",
        "plt.scatter(train_x, train_y)\n",
        "plt.scatter(test_x, test_y, color=\"r\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "min= 0.019999999999999928\n",
            "max= 0.9949999999999963\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f7ab2555ef0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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aJNHIvxZi8t5rFBRM1DVhFaDJxJl1eb17bcJo5F8LMe3tryJUShzZkNd7+Jeh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\nFEXxEBV/RVEUD/n/V+C5iToq0zAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bMzrd9hTok1X",
        "outputId": "bef0b1ae-4308-4b73-8fb3-1203cd6b5240",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        }
      },
      "source": [
        "model = keras.Sequential()\n",
        "model.add(Dense(10, activation='tanh', input_shape=(1,)))\n",
        "model.add(Dense(10, activation='tanh'))\n",
        "model.add(Dense(1))\n",
        "\n",
        "\n",
        "model.compile(optimizer=\"SGD\", loss=\"mse\", metrics=[\"mse\"])\n",
        "model.summary()\n",
        "\n",
        "\n",
        "start_time = time.time()\n",
        "model.fit(train_x, train_y, epochs=1000, verbose=0, batch_size=20)\n",
        "print(\"elapsed : {}\".format(time.time() - start_time))    \n",
        "\n",
        "\n",
        "loss, mse = model.evaluate(test_x, test_y)\n",
        "print(\"loss=\",loss)\n",
        "print(\"mse=\",mse)\n",
        "\n",
        "\n",
        "y_ = model.predict(test_x)\n",
        "\n",
        "plt.scatter(test_x,test_y)\n",
        "plt.scatter(test_x,y_,color='r')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_38\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_125 (Dense)            (None, 10)                20        \n",
            "_________________________________________________________________\n",
            "dense_126 (Dense)            (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_127 (Dense)            (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 141\n",
            "Trainable params: 141\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "elapsed : 8.89617109298706\n",
            "40/40 [==============================] - 0s 5ms/sample - loss: 0.0614 - mean_squared_error: 0.0614\n",
            "loss= 0.061400238424539566\n",
            "mse= 0.061400242\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n_X9nenlnNHH"
      },
      "source": [
        "# 모델 저장과 로딩"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nSamA7Cj7I36",
        "outputId": "acf24a0c-2227-475d-a734-b9f36bf8ca22",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 580
        }
      },
      "source": [
        "(train_x, train_y), (test_x, test_y) = get_sin_data(start=0, end=10, step=0.1)\n",
        "\n",
        "model = keras.Sequential()\n",
        "model.add(Dense(10, activation='tanh', input_shape=(1,)))\n",
        "model.add(Dense(10, activation='tanh'))\n",
        "model.add(Dense(1))\n",
        "\n",
        "\n",
        "model.compile(optimizer=\"SGD\", loss=\"mse\", metrics=[\"mse\"])\n",
        "model.summary()\n",
        "\n",
        "\n",
        "start_time = time.time()\n",
        "model.fit(train_x, train_y, epochs=1000, verbose=0, batch_size=20)\n",
        "print(\"elapsed : {}\".format(time.time() - start_time))    \n",
        "\n",
        "\n",
        "loss, mse = model.evaluate(test_x, test_y)\n",
        "print(\"loss=\",loss)\n",
        "print(\"mse=\",mse)\n",
        "\n",
        "\n",
        "y_ = model.predict(test_x)\n",
        "\n",
        "plt.scatter(test_x,test_y)\n",
        "plt.scatter(test_x,y_,color='r')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_35\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_112 (Dense)            (None, 10)                20        \n",
            "_________________________________________________________________\n",
            "dense_113 (Dense)            (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_114 (Dense)            (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 141\n",
            "Trainable params: 141\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "elapsed : 8.070200681686401\n",
            "40/40 [==============================] - 0s 3ms/sample - loss: 0.1501 - mean_squared_error: 0.1501\n",
            "loss= 0.15005545914173127\n",
            "mse= 0.15005545\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZPVD8/bfNrL8Z1wUALE7Dwd/M2iTdJelaSWslvcfM1lac9kFJP3D3n5L0aUmfbPS6iAjF\nvoBMakbP/y2SDrv78+7+I0lfknR9xTnXS9pV/PlBSVeZmTXh2milUrGv8XHJ/WyxLz4AgNRrRvDv\nkfRS2eOjxWOh57j7aUmvSTq/CddGK1HsC8isRE34mtkWMxszs7ETJ07E3RxQ7AvIrGYE/4Kki8oe\nryoeCz3HzJZKOlfSq5VP5O473H3A3Qe6u7ub0DQ0hGJfycecDBapGcH/SUkXm9lqM3uDpHdL2lNx\nzh5Jm4o/v1PSPk9qXQmcRbGvZGNOBg1oOPgXx/BvlrRX0nOS/sHdnzWzO8zsuuJpn5d0vpkdlvTH\nkuakgyKBKPaVbMzJoAEUdkP9RkeDwHLkSDD0MzzMB0GcliwJevyVzKTp6ejbg0SgsBuaiyGG5GFO\nBg0g+KM+DDEkzpMf+pim2pfNOjbVvkxPfuhjMbUIaULwR31I+0ycW9vW6uMbbtbR5d2aluno8m59\nfMPNurWtcoE9MNfSuBuAlOjtDYZ6wo4jFscmplS45ArtueSKWcdtYiqmFiFN6PmjPqR9Js7Kro4F\nHQfKEfxRH9I+E2frhjXqaG+bdayjvU1bN6yJqUVIE4Z9UL/BQYJ9gmxcF5TQ2r73kI5NTGllV4e2\nblgzcxyYD8EfSLGN63oI9lgUhn2ApKBODyJEzx9IgtIiutJaitIiOomhNrQEPX8gCVhEh4gR/IE4\nlYZ6wtZQSCyiQ8sw7APEpXKoJwyL6NAi9PyBuIQN9ZRjER1aiOAPxGW+IR0W0aHFGPYB4lKtXlJf\nn/Tii5E3B/lCzx8ttftAQetH9mn10MNaP7JPuw9Ubu+cY9RLQozo+aNldh8o6PaHntbUqTOSpMLE\nlG5/6GlJYlWqdHZIh93Rcmf3gULsZTno+aNltu89NBP4S6ZOndH2vYdialGMqq3eHRwMhnimp4Pv\nBP7MK3WKChNTcp3tFEV9V0zwR8scq1JXvtrxzGILTJRJSqeI4I+Wod58Eat3USYpnSKCP1om9/Xm\nWb2LEEnpFBH80TIb1/XozhsuVU9Xh0xST1eH7rzh0nxM9pYP9VTD6t1cSkqniGwftFRu682zehdV\nJGUTHoI/Wmt0NJ+pjLVW7+blvwNCJaFTRPBH6+S5Rj2rd5FwjPmjdfKS5RKWw8/qXdQprlXwBH+0\nTrWhjyxluVTL4ZeCwmx9fZIZhdoQKs4FX+buLb/IYgwMDPjY2FjczUAjqqU5ZmnoIw9/I1pm/cg+\nFULy+3u6OrR/6MpFPaeZPeXuA7XOo+eP1snD0Ece7m7QMnEu+CL4o3UGB7M/9FEtV58cftQhzgVf\nBH+0VtYLl+Xh7gYtE+eCL4I/UI/5qnJm/e4GLRPnKngmfIFawjZa7+wkyCORmPAFmiUv6xWQK6zw\nBWqJKKMnCbs7IT/o+QO1RJDRk5TdnZAfBH+glggyepKyuxPyo6Hgb2ZvNLOvmdl3i9/Pq3LeGTM7\nWPza08g1kQPVMmviEkFGT1J2d0J+NNrzH5L0mLtfLOmx4uMwU+5+WfHrugaviSxL6n63LV6vkJTd\nnZAfjQb/6yXtKv68S9LGBp8PeVcts+bGG5t7F5Cwu4uk7O6E/Gg02+cCdz9e/Pl7ki6oct45ZjYm\n6bSkEXff3eB1kVXzZdA0az+ABO4zkJTdnZAfNRd5mdnXJf1EyK+2Sdrl7l1l5/7A3eeM+5tZj7sX\nzOwnJe2TdJW7/3fIeVskbZGk3t7eN4/Pt/8psmm+Dc/LnX9+8P3731/4DmFU4kSG1bvIq6EVvmZ2\nSNLl7n7czC6U9C/uPu99qpndK+mr7v7gfOexwjenwlbT1sMsmCOoZ4vEJUuCc8OeY3p6YddtELn9\naLaoVvjukbSp+PMmSV8Oach5Zras+PMKSeslfafB6yKryjNrFqIUzOuZIE5IJU5y+xGnRoP/iKTf\nNLPvSrq6+FhmNmBmO4vn/IykMTP7D0nfUDDmT/BHdaXMmi98YW5+fT1qlV5ISCVOcvsRp4YmfN39\nVUlXhRwfk7S5+PO/Srq0kesgp0pDN9u21TcPUG6+iePy5z1yZOFzBk1Cbj/ixApfJNti7wJqDeEk\nYJ8BcvsRJ4I/0qFyle3555/N+DGbfW5KNlMhtx9xIvgjcXYfKGj9yD6tHnpY60f2nZ0ALe+tv/JK\n8OUu3X9/KjdTiXMjD4DNXJAopQyY8onQjvY2giJQJzZzQSqRAQNEg+CPRCEDBogGwR+JQgYMEA2C\nPxKFDBggGuzhi0ShuiUQDbJ9kGgUPgMWpt5sH3r+SKzKtM9S4TNJfAAADSL4I7HmS/tMW/DnDgZJ\nQ/BHYmUl7ZM7GCQR2T5IrKykfbJwDUlE8EdiZSXtMyt3MMgWgj8SKyuFz7JyB4NsYcwfibZxXc9M\nsC9Nmn70gYOpmjTdumFNaLG6tN3BIFsI/kiFNE6almf4nNvRrnPal2hi8lSqPriQXQR/pELa0j4r\nP6wmpk6po71Nn37XZYlsL/KHMX+kQtomTcnwQdIR/JEKaZs0TduHFfKH4I9UCEv7NAVj/7O2ekyI\ntH1YIX8I/kiF8rRPKQj8pZKEpcnfJH0AZGWNArKL4I/U2LiuR/uHrlRPV4cqa9EmbTw9K2sUkF1k\n+yB10jKeXr5GAUgaev5IHcbTgcYR/JE6aZv8BZKIYR+kTvlWj4WJqdDJ3/LzWo1a/Ugjev5IpaRM\n/pZW8hYmpuRKZuYREIbgj1SLe/KXlbxIK4I/Ui3uyd+4P3yAxSL4I9XiXkwV94cPsFgEf6Ra3Iup\n4v7wARaLbB+kXpyLqcozj8j2QZoQ/IE6zJfOyUpepBHBH6ghjbuIAbUw5g/UQDonsojgD9RAOiey\nqKHgb2a/a2bPmtm0mQ3Mc941ZnbIzA6b2VAj1wSaYfeBgtaP7NPqoYdr1gMinRNZ1GjP/xlJN0h6\nvNoJZtYm6S5J10paK+k9Zra2wesCi7bQkgykcyKLGgr+7v6cu9ca+HyLpMPu/ry7/0jSlyRd38h1\ngUYsdAw/7rUEQCtEke3TI+mlssdHJf1S2IlmtkXSFknq7e1tfcuQS4sZwyedE1lTs+dvZl83s2dC\nvpree3f3He4+4O4D3d3dzX56QBJj+IBUR8/f3a9u8BoFSReVPV5VPAbEYuuGNbPy9iXG8JE/UQz7\nPCnpYjNbrSDov1vSeyO4LhCKkgxAg8HfzH5b0t9K6pb0sJkddPcNZrZS0k53f5u7nzazmyXtldQm\n6R53f7bhlgMNYAwfeddQ8Hf3f5L0TyHHj0l6W9njRyQ90si1AADNwwpfAMghgj8A5BDBHwByiOAP\nADlE8AeAHDJ3j7sNoczshKTxGqetkPRKBM1JIv72fOJvz5+F/t197l6zREJig389zGzM3auWks4y\n/nb+9rzJ69/eqr+bYR8AyCGCPwDkUNqD/464GxAj/vZ84m/Pn5b83ake8wcALE7ae/4AgEVIbfDP\n66bwZnaRmX3DzL5jZs+a2UfiblOUzKzNzA6Y2VfjbkuUzKzLzB40s/8ys+fM7JfjblNUzOyjxdf6\nM2b2RTM7J+42tYqZ3WNmL5vZM2XH3mhmXzOz7xa/n9eMa6Uy+Od8U/jTkv7E3ddKequkD+fob5ek\nj0h6Lu5GxOBvJD3q7j8t6eeVk/8GZtYj6RZJA+7+swrKwr873la11L2Srqk4NiTpMXe/WNJjxccN\nS2XwV443hXf34+7+78Wf/1dBEMhFYXozWyXp7ZJ2xt2WKJnZuZJ+XdLnJcndf+TuE/G2KlJLJXWY\n2VJJnZKOxdyelnH3xyV9v+Lw9ZJ2FX/eJWljM66V1uAftil8LgJgOTPrl7RO0rfjbUlkPiPpNknT\ncTckYqslnZD098Uhr51m9mNxNyoK7l6Q9FeSjkg6Luk1d//neFsVuQvc/Xjx5+9JuqAZT5rW4J97\nZvbjkv5R0q3u/nrc7Wk1M3uHpJfd/am42xKDpZJ+QdLd7r5O0v+pSbf+SVcc375ewQfgSkk/ZmY3\nxtuq+HiQntmUFM20Bv9cbwpvZu0KAv+ouz8Ud3sisl7SdWb2ooJhvivN7AvxNikyRyUddffSHd6D\nCj4M8uBqSS+4+wl3PyXpIUm/EnObovY/ZnahJBW/v9yMJ01r8J/ZFN7M3qBgAmhPzG2KhJmZgrHf\n59z9r+NuT1Tc/XZ3X+Xu/Qr+f+9z91z0AN39e5JeMrM1xUNXSfpOjE2K0hFJbzWzzuJr/yrlZLK7\nzB5Jm4o/b5L05WY8aUN7+MYl55vCr5f0PklPm9nB4rE/Le6TjOz6I0mjxc7O85I+EHN7IuHu3zaz\nByX9u4JMtwPK8EpfM/uipMslrTCzo5L+XNKIpH8wsw8qqHT8e025Fit8ASB/0jrsAwBoAMEfAHKI\n4A8AOUTwB4AcIvgDQA4R/AEghwj+AJBDBH8AyKH/B0EfsCIrusjMAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HAR05QD6WNBJ"
      },
      "source": [
        "모델 저장"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EWmB-uBxWQwh"
      },
      "source": [
        "model.save('my_model.h5')\n",
        "\n",
        "!ls -al"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a02YvE4vWQIP"
      },
      "source": [
        "모델 로딩"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KuU4MnkSnKEd",
        "outputId": "d04e660b-940b-4ba6-d5ea-ff68a2f91d82",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        }
      },
      "source": [
        "new_model = keras.models.load_model('my_model.h5')\n",
        "\n",
        "y_ = new_model.predict(test_x)\n",
        "\n",
        "plt.scatter(test_x,test_y)\n",
        "plt.scatter(test_x,y_,color='r')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 52\n",
            "drwxr-xr-x 1 root root  4096 Aug 14 12:47 .\n",
            "drwxr-xr-x 1 root root  4096 Aug 14 11:06 ..\n",
            "-rw-r--r-- 1 root root 12510 Aug 14 11:30 base_result.png\n",
            "drwxr-xr-x 1 root root  4096 Aug  7 16:05 .config\n",
            "-rw-r--r-- 1 root root 19760 Aug 14 12:47 my_model.h5\n",
            "drwxr-xr-x 1 root root  4096 Aug  2 16:06 sample_data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "W0814 12:47:46.716366 139865081911168 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:97: calling GlorotUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
            "W0814 12:47:46.718369 139865081911168 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:97: calling Zeros.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rHU56sKBnLHx"
      },
      "source": [
        "# Overfitting 처리\n",
        "\n",
        "- DropOut\n",
        "- BatchNormalization\n",
        "- Regularization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JXugk5XL4Vcg"
      },
      "source": [
        "## DropOut 레이어"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-sOQzyxp3MQb"
      },
      "source": [
        "from tensorflow.keras.layers import Dropout"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zeS9ewcunK9U",
        "outputId": "1be807d3-6830-49aa-8549-c7564ef6fc79",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        }
      },
      "source": [
        "(train_x, train_y), (test_x, test_y) = get_sin_data(start=0, end=10, step=0.1)\n",
        "\n",
        "model = keras.Sequential()\n",
        "model.add(Dense(10, activation='tanh', input_shape=(1,)))\n",
        "model.add(Dropout(0.1)) # ADDED\n",
        "model.add(Dense(10, activation='tanh'))\n",
        "model.add(Dense(1))\n",
        "\n",
        "\n",
        "model.compile(optimizer=\"SGD\", loss=\"mse\", metrics=[\"mse\"])\n",
        "model.summary()\n",
        "\n",
        "\n",
        "start_time = time.time()\n",
        "model.fit(train_x, train_y, epochs=1000, verbose=0, batch_size=20)\n",
        "print(\"elapsed : {}\".format(time.time() - start_time))    \n",
        "\n",
        "\n",
        "loss, mse = model.evaluate(test_x, test_y)\n",
        "print(\"loss=\",loss)\n",
        "print(\"mse=\",mse)\n",
        "\n",
        "\n",
        "y_ = model.predict(test_x)\n",
        "\n",
        "plt.scatter(test_x,test_y)\n",
        "plt.scatter(test_x,y_,color='r')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_47\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_152 (Dense)            (None, 10)                20        \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 10)                0         \n",
            "_________________________________________________________________\n",
            "dense_153 (Dense)            (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_154 (Dense)            (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 141\n",
            "Trainable params: 141\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "elapsed : 10.028263807296753\n",
            "40/40 [==============================] - 0s 7ms/sample - loss: 0.1967 - mean_squared_error: 0.1967\n",
            "loss= 0.19667569696903228\n",
            "mse= 0.19667569\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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I0YgYXbBgQQ+KbWYzatbJDFMX0SnpimpFzAHpRVPSLuAcSctJAsIVJHf/r5F0IfAZYFVE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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "InNqbyKae5rY"
      },
      "source": [
        "## BatchNormalization 레이어"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m919TDB2e45M"
      },
      "source": [
        "from tensorflow.keras.layers import BatchNormalization"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uFiIbiccfGlK",
        "outputId": "b486656a-1a59-4eed-a4d4-568573a2a760",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        }
      },
      "source": [
        "(train_x, train_y), (test_x, test_y) = get_sin_data(start=0, end=10, step=0.1)\n",
        "\n",
        "model = keras.Sequential()\n",
        "model.add(Dense(10, activation='tanh', input_shape=(1,)))\n",
        "model.add(BatchNormalization()) # ADDED\n",
        "model.add(Dense(10, activation='tanh'))\n",
        "model.add(Dense(1))\n",
        "\n",
        "\n",
        "model.compile(optimizer=\"SGD\", loss=\"mse\", metrics=[\"mse\"])\n",
        "model.summary()\n",
        "\n",
        "\n",
        "start_time = time.time()\n",
        "model.fit(train_x, train_y, epochs=1000, verbose=0, batch_size=20)\n",
        "print(\"elapsed : {}\".format(time.time() - start_time))    \n",
        "\n",
        "\n",
        "loss, mse = model.evaluate(test_x, test_y)\n",
        "print(\"loss=\",loss)\n",
        "print(\"mse=\",mse)\n",
        "\n",
        "\n",
        "y_ = model.predict(test_x)\n",
        "\n",
        "plt.scatter(test_x,test_y)\n",
        "plt.scatter(test_x,y_,color='r')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_48\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_155 (Dense)            (None, 10)                20        \n",
            "_________________________________________________________________\n",
            "batch_normalization (BatchNo (None, 10)                40        \n",
            "_________________________________________________________________\n",
            "dense_156 (Dense)            (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_157 (Dense)            (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 181\n",
            "Trainable params: 161\n",
            "Non-trainable params: 20\n",
            "_________________________________________________________________\n",
            "elapsed : 12.237880945205688\n",
            "40/40 [==============================] - 0s 7ms/sample - loss: 0.0931 - mean_squared_error: 0.0931\n",
            "loss= 0.09305349886417388\n",
            "mse= 0.0930535\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Bh4APRsSLadpkNpGavelXjw8cOIh4ZfNzr2i2PEq1UY+kNwGHgS8DlzUKDJIm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\nrI4Dg5mZ1XFgMDOzOv8fFFRi2DoYtBMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mzj9JvpYV_f1"
      },
      "source": [
        "# 다양한 입출력"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vDlw_B29412O"
      },
      "source": [
        "## 2개의 입력, 1개의 출력\n",
        "\n",
        "x1 + x2 -> y의 함수를 학습\n",
        "\n",
        "x1과 x2는 0~10의 범위를 갖는다.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dxI99iGz4-CT",
        "outputId": "071c18bf-667f-44f7-a6ed-acf0a995a981",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 580
        }
      },
      "source": [
        "\n",
        "x1 = np.arange(0, 10, 0.1)\n",
        "np.random.shuffle(x1)\n",
        "x2 = np.arange(0, 10, 0.1)\n",
        "np.random.shuffle(x2)\n",
        "\n",
        "x = np.ones((x1.shape[0],2))\n",
        "x[:,0] = x1\n",
        "x[:,1] = x2\n",
        "np.random.shuffle(x)\n",
        "\n",
        "y = np.sum(x, axis=1)\n",
        "\n",
        "\n",
        "s = int(x.shape[0]*0.6)\n",
        "train_x, test_x = x[:s], x[s:]\n",
        "train_y, test_y = y[:s], y[s:]\n",
        "\n",
        "\n",
        "\n",
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras.layers import Dense\n",
        "\n",
        "model = keras.Sequential()\n",
        "# model.add(Dense(10, activation='tanh', input_shape=(1,)))\n",
        "model.add(Dense(10, activation='tanh', input_shape=(2,)))\n",
        "model.add(Dense(10, activation='tanh'))\n",
        "model.add(Dense(1))\n",
        "\n",
        "model.compile(optimizer=\"SGD\", loss=\"mse\", metrics=[\"mse\"])\n",
        "model.summary()\n",
        "\n",
        "start = time.time()\n",
        "model.fit(train_x, train_y, epochs=1000*5, verbose=0, batch_size=20)\n",
        "print(\"elapsed :\", (time.time() - start))\n",
        "\n",
        "loss, mse = model.evaluate(test_x, test_y)\n",
        "print(\"loss=\", loss)\n",
        "print(\"mse=\", mse)\n",
        "              \n",
        "y_ = model.predict(test_x)\n",
        "\n",
        "# plt.scatter(test_x,test_y)\n",
        "# plt.scatter(test_x,y_, color=\"r\")\n",
        "plt.scatter(test_x.T[0],test_y)\n",
        "plt.scatter(test_x.T[0],y_, color=\"r\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_36\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_115 (Dense)            (None, 10)                30        \n",
            "_________________________________________________________________\n",
            "dense_116 (Dense)            (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_117 (Dense)            (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 151\n",
            "Trainable params: 151\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "elapsed : 35.705761194229126\n",
            "40/40 [==============================] - 0s 2ms/sample - loss: 0.0065 - mean_squared_error: 0.0065\n",
            "loss= 0.006477165594696998\n",
            "mse= 0.006477165\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GRYxjnGpcxLEde1nT0xV11z4Ug6sK/eKTpItrvfdAh0SbWRvwnLtfmb//BnCdu580s6XA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\nSEoo0EVEUkKBLiKSEv8flTB0pKU3ciEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6rJYzUkaMWzN",
        "outputId": "11858359-1db7-43b0-afa5-a32d3fc05231",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        }
      },
      "source": [
        "print(x.shape)\n",
        "print(x[:5])\n",
        "print(y[:5])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(100, 2)\n",
            "[[1.5 8.4]\n",
            " [8.6 5.6]\n",
            " [4.7 9.6]\n",
            " [4.9 2.7]\n",
            " [7.5 7.1]]\n",
            "[ 9.9 14.2 14.3  7.6 14.6]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IIxLKZWs4NDz"
      },
      "source": [
        "## 1개의 입력, 2개의 출력\n",
        "\n",
        "다음과 같이 y는 2개의 값을 갖고, y1 = x%2, y2=x%3인 함수를 학습\n",
        "\n",
        "```\n",
        "x = np.arange(0,10,0.1)\n",
        "y1 = x%2\n",
        "y2 = x%3\n",
        "```\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bovsxj6f5-Bz",
        "outputId": "a52ce4f6-e1c8-45a0-eb26-8232f8e3fef3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 843
        }
      },
      "source": [
        "x = np.arange(0, 10, 0.1)\n",
        "np.random.shuffle(x)\n",
        "y1 = x%2\n",
        "y2 = x%3\n",
        "\n",
        "y = np.zeros((y.shape[0],2))\n",
        "y[:,0] = y1\n",
        "y[:,1] = y2\n",
        "\n",
        "\n",
        "s = int(x.shape[0]*0.6)\n",
        "train_x, test_x = x[:s], x[s:]\n",
        "train_y, test_y = y[:s], y[s:]\n",
        "\n",
        "\n",
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras.layers import Dense\n",
        "\n",
        "model = keras.Sequential()\n",
        "model.add(Dense(10, activation='tanh', input_shape=(1,)))\n",
        "model.add(Dense(10, activation='tanh'))\n",
        "# model.add(Dense(1))\n",
        "model.add(Dense(2))\n",
        "\n",
        "model.compile(optimizer=\"SGD\", loss=\"mse\", metrics=[\"mse\"])\n",
        "model.summary()\n",
        "\n",
        "start = time.time()\n",
        "model.fit(train_x, train_y, epochs=1000*10, verbose=0, batch_size=20)\n",
        "print(\"elapsed :\", (time.time() - start))\n",
        "\n",
        "loss, mse = model.evaluate(test_x, test_y)\n",
        "print(\"loss=\", loss)\n",
        "print(\"mse=\", mse)\n",
        "              \n",
        "y_ = model.predict(test_x)\n",
        "\n",
        "# plt.scatter(test_x,test_y)\n",
        "# plt.scatter(test_x,y_, color=\"r\")\n",
        "plt.scatter(test_x,test_y[:,0])\n",
        "plt.scatter(test_x,y_[:,0], color=\"r\")\n",
        "plt.show()\n",
        "\n",
        "plt.scatter(test_x,test_y[:,1])\n",
        "plt.scatter(test_x,y_[:,1], color=\"r\")\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_52\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_167 (Dense)            (None, 10)                20        \n",
            "_________________________________________________________________\n",
            "dense_168 (Dense)            (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_169 (Dense)            (None, 2)                 22        \n",
            "=================================================================\n",
            "Total params: 152\n",
            "Trainable params: 152\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "elapsed : 79.75155305862427\n",
            "40/40 [==============================] - 0s 7ms/sample - loss: 0.4017 - mean_squared_error: 0.4017\n",
            "loss= 0.4017043113708496\n",
            "mse= 0.4017043\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0S6+4ZM7XAqWb4IpV2xzXjL6dSpvNyn0/OAh/8ifzb2qr8WCGlvy7bpS8/xo08tFqTT2D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\nPedHPx9MAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sHtOQg8wPI-1"
      },
      "source": [
        "## 2개의 입력, 2개의 출력\n",
        "\n",
        "x1 + x2 -> z, \n",
        "\n",
        "y1 = z%2, y2=z%3인 함수를 학습\n",
        "\n",
        "x1과 x2는 0~10의 범위를 갖는다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zlXRZPTzPHZq",
        "outputId": "10e80914-17ee-4fc8-dfc7-3b3d5b6f4af5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 843
        }
      },
      "source": [
        "x1 = np.arange(0, 10, 0.1)\n",
        "np.random.shuffle(x1)\n",
        "x2 = np.arange(0, 10, 0.1)\n",
        "np.random.shuffle(x2)\n",
        "\n",
        "x = np.ones((x1.shape[0],2))\n",
        "x[:,0] = x1\n",
        "x[:,1] = x2\n",
        "\n",
        "z = x1 + x2\n",
        "y1 = z%2\n",
        "y2 = z%3\n",
        "\n",
        "y = np.zeros((y.shape[0],2))\n",
        "y[:,0] = y1\n",
        "y[:,1] = y2\n",
        "\n",
        "\n",
        "s = int(x.shape[0]*0.6)\n",
        "train_x, test_x = x[:s], x[s:]\n",
        "train_y, test_y = y[:s], y[s:]\n",
        "\n",
        "\n",
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras.layers import Dense\n",
        "\n",
        "model = keras.Sequential()\n",
        "# model.add(Dense(10, activation='tanh', input_shape=(1,)))\n",
        "model.add(Dense(10, activation='tanh', input_shape=(2,)))\n",
        "model.add(Dense(10, activation='tanh'))\n",
        "# model.add(Dense(1))\n",
        "model.add(Dense(2))\n",
        "\n",
        "model.compile(optimizer=\"SGD\", loss=\"mse\", metrics=[\"mse\"])\n",
        "model.summary()\n",
        "\n",
        "start = time.time()\n",
        "model.fit(train_x, train_y, epochs=1000*10, verbose=0, batch_size=20)\n",
        "print(\"elapsed :\", (time.time() - start))\n",
        "\n",
        "loss, mse = model.evaluate(test_x, test_y)\n",
        "print(\"loss=\", loss)\n",
        "print(\"mse=\", mse)\n",
        "              \n",
        "y_ = model.predict(test_x)\n",
        "\n",
        "# plt.scatter(test_x,test_y)\n",
        "# plt.scatter(test_x,y_, color=\"r\")\n",
        "plt.scatter(test_x.T[0],test_y[:,0])\n",
        "plt.scatter(test_x.T[0],y_[:,0], color=\"r\")\n",
        "plt.show()\n",
        "\n",
        "plt.scatter(test_x.T[1],test_y[:,1])\n",
        "plt.scatter(test_x.T[1],y_[:,1], color=\"r\")\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_55\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_176 (Dense)            (None, 10)                30        \n",
            "_________________________________________________________________\n",
            "dense_177 (Dense)            (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_178 (Dense)            (None, 2)                 22        \n",
            "=================================================================\n",
            "Total params: 162\n",
            "Trainable params: 162\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "elapsed : 80.7371563911438\n",
            "40/40 [==============================] - 0s 7ms/sample - loss: 1.0752 - mean_squared_error: 1.0752\n",
            "loss= 1.075191903114319\n",
            "mse= 1.0751919\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}