material/deep_learning/template_attribute_data_regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "template_attribute_data_regression.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "giMgHzNY8861"
      },
      "source": [
        "# 속성 데이터 회귀 Template"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qDu8wpKXq7kV"
      },
      "source": [
        "# 데이터 준비"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ROMsisKxw8MC"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gwMz9NaYwx5e",
        "outputId": "cb4b6d89-1cee-4e79-d5b2-f36e9f72b08d"
      },
      "source": [
        "!wget https://raw.githubusercontent.com/dhrim/2022_WISET/main/material/deep_learning/car_regression.csv"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2021-09-24 16:26:31--  https://raw.githubusercontent.com/dhrim/2022_WISET/main/material/deep_learning/car_regression.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 58697 (57K) [text/plain]\n",
            "Saving to: ‘car_regression.csv’\n",
            "\n",
            "car_regression.csv  100%[===================>]  57.32K  --.-KB/s    in 0.006s  \n",
            "\n",
            "2021-09-24 16:26:31 (8.83 MB/s) - ‘car_regression.csv’ saved [58697/58697]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VckhCYiwxAZj"
      },
      "source": [
        "raw_data = pd.read_csv(\"car_regression.csv\")\n",
        "data = raw_data.to_numpy()\n",
        "np.random.shuffle(data)\n",
        "\n",
        "x = data[:,1:]\n",
        "y = data[:,:1]\n",
        "\n",
        "split_index = int(len(x)*0.8)\n",
        "train_x, test_x = x[:split_index], x[split_index:]\n",
        "train_y, test_y = y[:split_index], y[split_index:]"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JskspNe2Z5F5",
        "outputId": "4644fb02-ff9c-4b58-e10e-b3fbd85fcaac"
      },
      "source": [
        "print(train_x[:5])\n",
        "print(train_y[:5])"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 0.30957112 -0.20461746  0.27358521 -0.32679933 -0.95739638  0.81993056\n",
            "   1.          0.          0.        ]\n",
            " [-0.86291079 -0.36707301  0.0137853  -0.20906952 -0.41369436  1.09139405\n",
            "   1.          0.          0.        ]\n",
            " [-0.86291079 -0.51997236 -0.47983453 -0.2208425   0.02126726  1.63432105\n",
            "   1.          0.          0.        ]\n",
            " [-0.86291079 -0.93088936 -0.42787455 -1.03317817  0.34748847 -1.08031391\n",
            "   0.          0.          1.        ]\n",
            " [ 1.48205303  1.07591459  0.66328508  1.31906339 -0.55868157 -1.08031391\n",
            "   1.          0.          0.        ]]\n",
            "[[ 0.42973458]\n",
            " [ 0.57066975]\n",
            " [ 0.45535916]\n",
            " [ 0.45535916]\n",
            " [-1.3383611 ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XR8Ck1Er6HmY",
        "outputId": "e6758ce6-64db-4502-b669-ff071453c11f"
      },
      "source": [
        "print(train_x.shape)\n",
        "print(train_y.shape)\n",
        "print(test_x.shape)\n",
        "print(test_y.shape)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(313, 9)\n",
            "(313, 1)\n",
            "(79, 9)\n",
            "(79, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PvaZhPiSq5Gc"
      },
      "source": [
        "# Template"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S-wBUWBH_mTQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 822
        },
        "outputId": "46887960-da3c-4f82-d0a5-34a30603873e"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import tensorflow as tf\n",
        "\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras.layers import Dense\n",
        "\n",
        "model = keras.Sequential()\n",
        "model.add(Dense(10, activation='relu', input_shape=(9,)))\n",
        "model.add(Dense(10, activation='relu'))\n",
        "model.add(Dense(1))\n",
        "\n",
        "model.compile(optimizer=\"SGD\", loss=\"mse\", metrics=[\"mape\"])\n",
        "model.summary()\n",
        "\n",
        "history = model.fit(train_x, train_y, epochs=1000, verbose=1, batch_size=128, validation_split=0.1)\n",
        "\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.show()\n",
        "\n",
        "\n",
        "\n",
        "loss, mape = model.evaluate(test_x, test_y)\n",
        "print(\"loss=\", loss)\n",
        "print(\"mape=\", mape)\n",
        "\n",
        "y_ = model.predict(test_x)\n",
        "\n",
        "plt.scatter(test_y, y_)\n",
        "plt.show()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 10)                100       \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 221\n",
            "Trainable params: 221\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IyLQwnkDfAKwws+VmlgRuAdaVFjrnepxzrc65Zc65ZcAzwE3OuY2TUvEo5jSkGcoV6RnMQcM8mH8pbP/OVK1eRGRaGDPQnXN54GPAemAb8JBzbouZ3W1mN012geOxuKUWgNeODPgZq270nxjtm7hxehGR6W5cY+jOuUedcyudc+c75/4snHeXc27dKG2vncreOcDS2T7Qd3eFgb7yBsDBzsemsgwRkYqK/CdFAZaM7KHPvwQa5sNL361gVSIiU6sqAr0uFae1Psnurn4/wwxWroUdj8NQT2WLExGZIlUR6AAXzKln+8G+EzPe+GHI9cMLX6tYTSIiU6lqAn31/FlsP3CMQjG80+KCS2HJVfDsF6FYqGxxIiJToHoCfUEjQ7kiuw73n5h55a9D926NpYvIjFA9gT6/EYCt+4+dmPm6d8GsxfDMvRWqSkRk6lRNoF8wp55EzNhWHuixOLzpDnj1B3BwS+WKExGZAlUT6Ml4wAVzGti679jwBZd/EOI1fixdRKSKVU2gA1y8sJEX93STKxRPzKxtgTf8Emx6CAaPVq44EZFJVlWBft2Fc+kZzPHjXUeGL7ji1yA/CD/4XGUKExGZAlUV6G9Z0UY6EbB+y4HhC+ZdBJd9AH74BfjRPZUpTkRkklVVoNckY1y7cg7rtxygWLoeveTn/xoufDd877/D3ucrU6CIyCSqqkAHePclCzh4LHNyLz2ehJv+N9TPhYc/Ctn+0Z9ARCSiqi7Q1140j/Pb6vj84ztO7qXXNMF7vghdL8N3P1WZAkVEJknVBXosMD5+/Uq2H+zlkZ/uP7nB8rfANb8Dz38Ftn576gsUEZkkVRfoAD9/8XwunN/I3f9v64kvjy73tj+EBZfDtz8Gh3dMfYEiIpOgKgM9Fhiff/+l9A7l+OQ3fnLihl3HGyTgfX8PsSQ8eAsMdlemUBGRCVSVgQ6wal4Dd717NU+/1MmnH9508nh60xJ4/9fg6G745q/qjowiEnlVG+gAt125lN++bgUPbezgj769GedGhPrSq+DnPwsvPwFfuVnfQSoikRavdAGT7XevX0GuUOTep14mGQu4612rCQI70eCNH4Z8Bh67C776C3DbQ9C4oGL1ioicraruoQOYGb9/wyo++ublPPDDV7nty88Ov2c6wJW/Brf8AxzdBV+6Dg78tDLFioicg6oPdPCh/t9uvJC/eM/FbN7Xww2ff5p7ntxJNl92E68Lrodf/a7/PtL718LOxytXsIjIWRhXoJvZWjPbbmY7zeykT+SY2SfMbKuZbTKzJ8xs6cSXem7MjFuuWMITn3gr1184h79av513/69/54XXyu7AOO9iuOMJ/6UYX/tFePBWyA1WrmgRkTMwZqCbWQy4B3gnsBq41cxWj2j2AtDunHsD8E3gf050oRNlTmOav73tjXzpg+30DOZ4z70/5I/XbaEvk/cNGufDHY/BdXfB9u/ANz4CI0+miohMQ+PpoV8B7HTOveKcywJfB24ub+Cce9I5NxBOPgMsmtgyJ97bV8/lsU+8hQ+uWcrf/+hV3vQ/HueeJ3fSPZCFVAO8+ZOw9i/gpe/At34DckOVLllE5LTGE+gLgT1l0x3hvFO5HfjOaAvM7E4z22hmGzs7K3+JYEM6wZ/cfBH/8htXc8XyFv5q/XbW/PkT/ME3N/HSwV5/snTNb8BP/hG+fD288lSlSxYROSU76drskQ3M3gusdc7dEU7/CnClc+5jo7T9APAx4K3OuVE+c39Ce3u727hx41kXPhm27T/GV360m2+9sJdMvsDbV8/ljjefR3vfv2GP3QU9r8GKG+At/xUWX1HpckVkBjKz55xz7aMtG8916HuBxWXTi8J5I1dyPfCHjCPMp6sL5zfy5++5mN+7YRVf+sErfO2Z3azfcpDz2pr5yBXf5L35R6j5tz+FHevhvLfBjZ+F1gsqXbaICDC+HnoceAm4Dh/kG4Bfds5tKWtzGf5k6Frn3LjudjUde+gj9Wfy/Oum/fzjj1/jxT3dxAPjlsVH+b2+v2JW/y4IEv7OjZf+MrScV+lyRWQGOF0PfcxAD5/gRuDzQAy43zn3Z2Z2N7DRObfOzB4HLgZK96t9zTl30+meMwqBXm5TRzeP/vQA3928n1e7BljAYe5u+Beuzz0JQHHhmwje+vuw8h0VrlREqtk5B/pkiFqglzjneLmzn6e2H+KxrQcZeu15rrMf877Y08yzIwzGZ9G34Grq3n8ftXUNlS5XRKqMAn0S9WXybNh1hGd3HmTO1vt5V//DzLFuulwjP6h7BxsWfIDFi5ew5rzZrJrbQE0yVumSRSTCFOhT6NhQjl0b1pN6/su87qgfjulxtfyo+Hr+KH87TW0LuHB+I6vnN7J6QSNLW2pZOrsWMxvjmUVEFOiVs2cDbP0Wxee/SpDpYSjeyHPpNTyeuZBv9F5MH7UANNUmWDW3geWtdSwOA35Rcy0LmtK01acU9iJynAJ9Otj3Anzjw3D0VQBcvIbuOe283LiGhxPvZtvBPvYcGeBwX3bYP0vFAxa31NJSl2TV3AbmzUrTWp+kpS7FgqY0zbVJWuqSpBMayhGZCRTo00nfIX8nx46N8OI/QH4I6ufBxe+FhW8k09tFx6zL2cUi9nYPsufIAB1HB+nsy/DSgV56S/ecGWFhUw2t9Uma65K01CZpqk3SUpeguS5Jc22SppoEDekEC5rSzKpJEI/NiBttilQdBfp05Rw89wBs/mfY/R/gym7nO+8N8OZPQLwGDmyCVe+EeRczmC3Q1Z/hcF+WvUcHOTKQ5dCxIfYcGeDIQI6j/VmODmQ52p+lP3vqr9VrTMdpqk0yqyZBTTJGc22Clrok9ak46USM+lScBU011Kfj1Kf8TyIW0JiOU5OMkU7ESOigIDLlFOhRUMhBxwb/5RoHt8CO70Hv/rIGBsuu8d+mtOzNsOLtYDGoaYbY6B/4zeQLdA/kOBKGfPdAjkPHhjg6kKN7IEvPYI6ewRz9GX+Q6BnM05/JM5gb+/tVA4NZNQlqk2Hgh8EfD4zGmgSzahJk8gUa0wkc/gDSWp8iEQtIJQJaapOkEgGBGelEjMaaBPHASMQCEjH/mIwFw79dSkQU6JFUyMOrT/u7PKYa4LH/7sfhR4ql/Jdz1M3293FvXQndu6FxoR/GOQvFoqN7MMeR/gy9Q3n6Mnn6hvJk8kV6M3mGsgV6h3J0D+boy/iDQF8mT+9QnqJzHOnL0pfJc2zIDw+l4gGZ8i8TOQOxwIYFfCIWkIiPmC4tjwcnHRBGb+/nDZuOWdm/D6djAYn4iOnj84xULEYqEZCKBzpxLVNGgV4tnPNfuHHkFT8OH0/DrqdhzzMwdAyKueHtU7MA50N+3sVwaJv/Rqa2VTBrETQtgwuug9qW4f+uv8v3/INxDKkUCxCc+oSscw4zYyhXoKs/S75QZChX5OhAlky+SKFYZDBbpC+To1CEXKFIrlAkWyiSy7vh02Xzjk8Xwun8iOnTtC8UJ/5vPh4YscBOPMaC4dPHH8P54UEnFQ9IJ2LEAiMwCMwIAvOPBsaJeYmYf45c3tFclyQwSMYDUvEYgcGR/izxmNFSlyIVDxi5lYFBXdIPnRWco1D0r0ddMk5D2g+1ld4QWVhLeb1+2tdwYv9C0bnwB6D0CE01CWbXp4a1l3OnQJ8JBrt90GeOQduF8IPPQufPINkAr/0QBo/6+81kB6DvwPB/u7AdDu+AZB2kZ0HnNv/vWpbBvEugpgn2vQjNy6D/ECy6Arp2QH8n7PkxrLzBz2tcANsfhSVXweGXoK4V9v8Eju2DpVeDBTDU7YeOzr8OcgNQPzf8AhHn31kc2QUN82CgC1rOh0LG153PQiwBe5/zB6TUGXwK1zmfUKFC8cSB4rQHhHyRQqafDEmyZQebXN6RyefJFmAoVyCTK1BwjnzRUSiEj8XSY3H4dLg8X/TPlckVGcwVKDp/8CsWCuSx40F5PDCLjlzpeQoFBrJFXLgt5QKDSThenbPSASIw/+1h/mB14iBmBkFg5AsO5xzJeEBtMk6+WKQmESObLx5/F+ScI5MvMqcxTTJmmNmwA2Lp4FNan39+IxZwfH55PaXlJw6oDJuOU6Ah30Vfat4p2hsxMxJx/zhyu1PxGKl4QDwWEBjkC45V8xq4ZHHTWb6WCnTJZ/zNxIp5cAUfmP/xN9C1E47t973spiWQ7feBOdQDe5/3y4O4X54f8gHcd/DE89bN8SE/XrEkFIZfmunryo3eHvxBZqjHnyDOD/p3Joka/y5i2TX+vEOy3h/M0k1+HbMvgJ490NMBndth+VsgkYaXvgepen/QOO9tcGirf+7GBf4gMdgNvQf8QSjX75elZsFlt8GSNf4qpf0vwpZv+3c3DfP8wTCf8c9byMKc1XBsr39tghgsuNxvx66noHk5zD7fX9mU7fPPP9QDBzfDjsf9LZoXtsPc18OCS/1BNDcE9XN8bc//PWxb5w+4a/4LzhXJpZqI/ewRYh3P4OrmkF11E5k5l5BvOZ/09nXkFv0cQd8+UtsepmfVL1HIDVFsWEh+/huxdCO5wy/jDmwln8vStP8HDM46n3yshvSxV+lrOI8D869jMNHM7H3fJygMcaR+BQsO/hvx/AAH5l1LvJiBIM6KHV/m8NyrefWCD1MEBrsPMNR9kMahvcw/tomCJdjdcDnpfA9ZS9EfbyJrKRozBylgtAztZnnv87zQ9p/JF3Kc1/0MfYnZ9NgsWopd7Ki9jJbcPo4mF/JS6vUUhnqpzR5hkDTmClDMM6vQRbYYkCeg22aRKAyxJ1jIivx20sVBVuW3U+v66aGBAgF7bS7PBpfypvyLXFl4njRDHKSFogvYw1y2s5QPuW9zPc+ywa3mMfcmXnELmE8nSXL8tLiMhMuyyjpYaXtIW5afFM/nfNvHAGma6aXGsuxy81hgXewuzuUYtZx3yTV88H3vH///mzIKdDl7xaI/AMQSJ3q6h3f4HvjSn/OhemyfHwrq2ODDqPNnvrc/9/X+AGEB7HseFl/pQ27TP/l3AYk6H46NC31gpRr8OgpZH3DNy/zQ0pzX++fID/lwzxzzbfe9cOKdQ7bPB14x78O9kPFtGxf6g1d+yPfy84P+vEO2FzA/9GTmT0rDiXA/uhuWXgVdr/h/O3D45NcmXuNfm9IBqvzANPLANxUaFvihtR3rR1+erPev0zAGJw3OjGKsg+5IpYPvZLGYf+3H1TYYfgXZqWpL1IbfIXyK1+MMXwMXJLBTtM+s+TiptXeP+7nKKdClehXyo1/lU/q7PtXJyqFjvpcfS4xvPZk+fxBzRVj4xuHPm+nzvfPeA/6A0DDPH0z2bPDDVV0vQzzph6IGuuC1Z/yBLjcIcy707RM1PnDB9+qLBX+10+BR/y5ioMu/c7rkl/0BJIj5zzLkh/wlrulG/+/N/LuFA5v9QbRtlR/GStTA6951/INtHNoK/Yf9lVSpBjj/bZBq9OGXG/AHh2N7/TuSV5+GTK8PvAuuO/Fuad5F8PL3w5rj/iDevcfXnA/fVSRq/eP8S30N+170Q4O9+2DuxTB4xL9WFvO1Z/v9u8K5F/l3RAc2+YNjts9/x2/TEt+JOLzDD+mlGn2Nda2+jpomX1PrSv830HvAr8MV/cHuol/0B+z+Lv9a9O6Dg1v9tqx4h9/O3KB/vq6dvt7a2dCyHGpb/fBlf6evNzfgOwQAdW2+MzPQ5Zc3LvB1mvlOUf+h4R2WWNL/zZwFBbqISJU4XaDrkyEiIlVCgS4iUiUU6CIiVUKBLiJSJRToIiJVQoEuIlIlFOgiIlVCgS4iUiUq9sEiM+sEdp/lP28FRvksdlXTNs8M2uaZ4Vy2ealzrm20BRUL9HNhZhtP9UmpaqVtnhm0zTPDZG2zhlxERKqEAl1EpEpENdDvq3QBFaBtnhm0zTPDpGxzJMfQRUTkZFHtoYuIyAgKdBGRKhG5QDeztWa23cx2mtmnKl3PRDGzxWb2pJltNbMtZvbxcH6LmT1mZjvCx+ZwvpnZF8LXYZOZXV7ZLTg7ZhYzsxfM7JFwermZPRtu1z+ZWTKcnwqnd4bLl1Wy7nNhZk1m9k0z+5mZbTOzq6p5P5vZ74Z/05vN7EEzS1fjfjaz+83skJltLpt3xvvVzD4UtqZJfToAAANdSURBVN9hZh86kxoiFehmFgPuAd4JrAZuNbPVla1qwuSBTzrnVgNrgN8Mt+1TwBPOuRXAE+E0+NdgRfhzJ3Dv1Jc8IT4ObCub/kvgr51zFwBHgdvD+bcDR8P5fx22i6q/Ab7rnHsdcAl++6tyP5vZQuC3gXbn3EVADLiF6tzPDwBrR8w7o/1qZi3AZ4ArgSuAz5QOAuPinIvMD3AVsL5s+tPApytd1yRt67eBtwPbgfnhvPnA9vD3LwK3lrU/3i4qP8Ci8I/8PwGP4L+x+DAQH7m/gfXAVeHv8bCdVXobzmKbZwG7RtZerfsZWAjsAVrC/fYIcEO17mdgGbD5bPcrcCvwxbL5w9qN9ROpHjon/jhKOsJ5VSV8m3kZ8Cww1zm3P1x0AJgb/l4Nr8Xngd8HSl/JPhvods7lw+nybTq+veHynrB91CwHOoH/Gw41fdnM6qjS/eyc2wt8FngN2I/fb89R/fu55Ez36znt76gFetUzs3rgn4Hfcc4dK1/m/CG7Kq4zNbN3AYecc89VupYpFgcuB+51zl0G9HPibThQdfu5GbgZfyBbANRx8rDEjDAV+zVqgb4XWFw2vSicVxXMLIEP839wzj0czj5oZvPD5fOBQ+H8qL8WVwM3mdmrwNfxwy5/AzSZWTxsU75Nx7c3XD4L6JrKgidIB9DhnHs2nP4mPuCrdT9fD+xyznU653LAw/h9X+37ueRM9+s57e+oBfoGYEV4hjyJP7myrsI1TQgzM+D/ANucc58rW7QOKJ3p/hB+bL00/4Ph2fI1QE/ZW7tpzzn3aefcIufcMvx+/L5z7jbgSeC9YbOR21t6Hd4bto9cL9Y5dwDYY2arwlnXAVup0v2MH2pZY2a14d94aXurej+XOdP9uh54h5k1h+9u3hHOG59Kn0Q4i5MONwIvAS8Df1jpeiZwu67Bvx3bBLwY/tyIHz98AtgBPA60hO0Nf8XPy8BP8VcRVHw7znLbrwUeCX8/D/gxsBP4BpAK56fD6Z3h8vMqXfc5bO+lwMZwX38LaK7m/Qz8CfAzYDPwVSBVjfsZeBB/niCHfyd2+9nsV+BXw+3fCXzkTGrQR/9FRKpE1IZcRETkFBToIiJVQoEuIlIlFOgiIlVCgS4iUiUU6CIiVUKBLiJSJf4/Vp4ULZflz+YAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3/3 [==============================] - 0s 4ms/step - loss: 0.1229 - mape: 203.4032\n",
            "loss= 0.12286374717950821\n",
            "mape= 203.4031524658203\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-OlstKv7zLpI"
      },
      "source": [
        ""
      ],
      "execution_count": 6,
      "outputs": []
    }
  ]
}