Keras_FunctionalAPI.ipynb

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    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMd0LzE9XPk6JpT/tKg7EBo",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/yuji-sgs/Machine-Learning/blob/main/Keras_FunctionalAPI.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "8xwqV7mdH0za"
      },
      "outputs": [],
      "source": [
        "# 使用するライブラリをインポート\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import io\n",
        "from google.colab import files\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras.layers import Dense, Activation\n",
        "from keras.optimizers import Adam\n",
        "from keras.callbacks import EarlyStopping, LearningRateScheduler\n",
        "from keras.models import Model\n",
        "from keras.layers import Input, Dense\n",
        "\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from math import sqrt\n",
        "from sklearn.metrics import r2_score"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Google Colaboratoryでファイルをアップロード\n",
        "uploaded = files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 78
        },
        "id": "h5akEkc4Isea",
        "outputId": "5ff30dee-24e4-4695-cb78-9be1fe9b7581"
      },
      "execution_count": 3,
      "outputs": [
        {
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              "\n",
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              "        style=\"border:none\" />\n",
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              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
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              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
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              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
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              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving Multiple-Outputs.csv to Multiple-Outputs.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# データフレームとして、変数dfに4列×10000行のデータを格納・確認\n",
        "df = pd.read_csv(io.BytesIO(uploaded['Multiple-Outputs.csv']))\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "OyfGzklhI4Rf",
        "outputId": "9e47c5fd-f7a0-4ba3-d4d9-772013be9f44"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         x1    x2            y1            y2\n",
              "0      0.15  0.01  3.255800e-02  6.521200e-02\n",
              "1      0.30  0.01  2.749700e-02  5.505700e-02\n",
              "2      0.45  0.01  2.075500e-02  4.153700e-02\n",
              "3      0.60  0.01  1.433700e-02  2.868200e-02\n",
              "4      0.75  0.01  8.508700e-03  1.701600e-02\n",
              "...     ...   ...           ...           ...\n",
              "9995  14.40  1.00  1.780000e-14  1.240000e-14\n",
              "9996  14.55  1.00  1.820000e-14  1.510000e-14\n",
              "9997  14.70  1.00  3.110000e-14  3.640000e-14\n",
              "9998  14.85  1.00  2.180000e-14  4.170000e-14\n",
              "9999  15.00  1.00  6.220000e-15  2.040000e-14\n",
              "\n",
              "[10000 rows x 4 columns]"
            ],
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>x1</th>\n",
              "      <th>x2</th>\n",
              "      <th>y1</th>\n",
              "      <th>y2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.15</td>\n",
              "      <td>0.01</td>\n",
              "      <td>3.255800e-02</td>\n",
              "      <td>6.521200e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.30</td>\n",
              "      <td>0.01</td>\n",
              "      <td>2.749700e-02</td>\n",
              "      <td>5.505700e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.45</td>\n",
              "      <td>0.01</td>\n",
              "      <td>2.075500e-02</td>\n",
              "      <td>4.153700e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.60</td>\n",
              "      <td>0.01</td>\n",
              "      <td>1.433700e-02</td>\n",
              "      <td>2.868200e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.75</td>\n",
              "      <td>0.01</td>\n",
              "      <td>8.508700e-03</td>\n",
              "      <td>1.701600e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>14.40</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.780000e-14</td>\n",
              "      <td>1.240000e-14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>14.55</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.820000e-14</td>\n",
              "      <td>1.510000e-14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>14.70</td>\n",
              "      <td>1.00</td>\n",
              "      <td>3.110000e-14</td>\n",
              "      <td>3.640000e-14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>14.85</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2.180000e-14</td>\n",
              "      <td>4.170000e-14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>15.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>6.220000e-15</td>\n",
              "      <td>2.040000e-14</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10000 rows × 4 columns</p>\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9938954c-ccf9-42a8-a92d-fc0c5e950db4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# データの分割\n",
        "(train, test) = train_test_split(df, test_size=0.2, shuffle=True)"
      ],
      "metadata": {
        "id": "blQXtOuSJDS4"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# x_train, x_testではcsvファイルの1列目から2列目を，y_train, y_testでは3, 4列目のデータを活用\n",
        "x_train = train.iloc[:, [0, 1]]\n",
        "y_train = train.iloc[:, [2,3]]\n",
        "\n",
        "x_test = test.iloc[:, [0, 1]]\n",
        "y_test = test.iloc[:, [2,3]]"
      ],
      "metadata": {
        "id": "crPkUJomJJOt"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# データの正規化\n",
        "x_train = (x_train - x_train.min()) / (x_train.max() - x_train.min())\n",
        "x_test = (x_test - x_test.min()) / (x_test.max() - x_test.min())"
      ],
      "metadata": {
        "id": "qA5L81iwJLhz"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# モデルの構築\n",
        "# inputの数:x1, x2\n",
        "inputs = Input(shape=(2,))\n",
        "\n",
        "# 学習回数\n",
        "epochs = 100\n",
        "batch_size = 64\n",
        "\n",
        "# ノード数\n",
        "node = 64\n",
        "\n",
        "x1 = Dense(node, activation='relu')(inputs)\n",
        "x2 = Dense(node, activation='relu')(x1)\n",
        "x3 = Dense(node, activation='relu')(x2)\n",
        "\n",
        "\n",
        "x4 = Dense(node, activation='relu')(x3) # アウトプット直前の層\n",
        "output1 = Dense(1, name='output1')(x4)\n",
        "\n",
        "x5 = Dense(node, activation='relu')(x3) # アウトプット直前の層\n",
        "output2 = Dense(1, name='output2')(x5)\n",
        "\n",
        "model = Model(inputs=inputs, outputs=[output1, output2])\n",
        "\n",
        "optimizer = Adam(lr=0.001, beta_1=0.9, beta_2=0.999)\n",
        "model.compile(\n",
        "    optimizer=optimizer,\n",
        "    loss=['mse', 'mse']\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hMNwBPkmJNrJ",
        "outputId": "321e0b28-9786-4e16-acfe-b1100dc07794"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 学習オプション\n",
        "history = model.fit(x_train,\n",
        "                    {\"output1\":y_train.iloc[:, [0]], \"output2\":y_train.iloc[:, [1]]},\n",
        "                    epochs=epochs,\n",
        "                    batch_size=batch_size,\n",
        "                    validation_data=(x_test, {\"output1\":y_test.iloc[:, [0]], \"output2\":y_test.iloc[:, [1]]})\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dsh4xaP8JpwR",
        "outputId": "c3651004-56de-47a0-dd42-5568299d1d67"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "125/125 [==============================] - 3s 5ms/step - loss: 3.4828 - output1_loss: 1.6893 - output2_loss: 1.7934 - val_loss: 1.8802 - val_output1_loss: 0.8263 - val_output2_loss: 1.0539\n",
            "Epoch 2/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 1.5599 - output1_loss: 0.5025 - output2_loss: 1.0574 - val_loss: 1.1721 - val_output1_loss: 0.2264 - val_output2_loss: 0.9457\n",
            "Epoch 3/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.9979 - output1_loss: 0.1202 - output2_loss: 0.8777 - val_loss: 0.7772 - val_output1_loss: 0.0464 - val_output2_loss: 0.7308\n",
            "Epoch 4/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.6307 - output1_loss: 0.0389 - output2_loss: 0.5918 - val_loss: 0.3567 - val_output1_loss: 0.0286 - val_output2_loss: 0.3281\n",
            "Epoch 5/100\n",
            "125/125 [==============================] - 0s 4ms/step - loss: 0.1895 - output1_loss: 0.0236 - output2_loss: 0.1659 - val_loss: 0.0894 - val_output1_loss: 0.0184 - val_output2_loss: 0.0710\n",
            "Epoch 6/100\n",
            "125/125 [==============================] - 1s 4ms/step - loss: 0.0544 - output1_loss: 0.0151 - output2_loss: 0.0394 - val_loss: 0.0373 - val_output1_loss: 0.0123 - val_output2_loss: 0.0251\n",
            "Epoch 7/100\n",
            "125/125 [==============================] - 1s 4ms/step - loss: 0.0347 - output1_loss: 0.0124 - output2_loss: 0.0223 - val_loss: 0.0276 - val_output1_loss: 0.0099 - val_output2_loss: 0.0176\n",
            "Epoch 8/100\n",
            "125/125 [==============================] - 1s 4ms/step - loss: 0.0283 - output1_loss: 0.0110 - output2_loss: 0.0173 - val_loss: 0.0252 - val_output1_loss: 0.0093 - val_output2_loss: 0.0159\n",
            "Epoch 9/100\n",
            "125/125 [==============================] - 1s 5ms/step - loss: 0.0266 - output1_loss: 0.0107 - output2_loss: 0.0159 - val_loss: 0.0214 - val_output1_loss: 0.0081 - val_output2_loss: 0.0133\n",
            "Epoch 10/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0228 - output1_loss: 0.0095 - output2_loss: 0.0133 - val_loss: 0.0208 - val_output1_loss: 0.0085 - val_output2_loss: 0.0123\n",
            "Epoch 11/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0205 - output1_loss: 0.0090 - output2_loss: 0.0115 - val_loss: 0.0172 - val_output1_loss: 0.0072 - val_output2_loss: 0.0100\n",
            "Epoch 12/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0194 - output1_loss: 0.0087 - output2_loss: 0.0107 - val_loss: 0.0158 - val_output1_loss: 0.0068 - val_output2_loss: 0.0089\n",
            "Epoch 13/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0188 - output1_loss: 0.0085 - output2_loss: 0.0103 - val_loss: 0.0164 - val_output1_loss: 0.0073 - val_output2_loss: 0.0091\n",
            "Epoch 14/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0172 - output1_loss: 0.0081 - output2_loss: 0.0091 - val_loss: 0.0146 - val_output1_loss: 0.0067 - val_output2_loss: 0.0080\n",
            "Epoch 15/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0160 - output1_loss: 0.0078 - output2_loss: 0.0082 - val_loss: 0.0133 - val_output1_loss: 0.0062 - val_output2_loss: 0.0072\n",
            "Epoch 16/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0161 - output1_loss: 0.0079 - output2_loss: 0.0082 - val_loss: 0.0145 - val_output1_loss: 0.0063 - val_output2_loss: 0.0082\n",
            "Epoch 17/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0157 - output1_loss: 0.0078 - output2_loss: 0.0079 - val_loss: 0.0135 - val_output1_loss: 0.0060 - val_output2_loss: 0.0075\n",
            "Epoch 18/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0146 - output1_loss: 0.0075 - output2_loss: 0.0070 - val_loss: 0.0121 - val_output1_loss: 0.0057 - val_output2_loss: 0.0064\n",
            "Epoch 19/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0159 - output1_loss: 0.0079 - output2_loss: 0.0079 - val_loss: 0.0307 - val_output1_loss: 0.0101 - val_output2_loss: 0.0207\n",
            "Epoch 20/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0158 - output1_loss: 0.0079 - output2_loss: 0.0079 - val_loss: 0.0132 - val_output1_loss: 0.0058 - val_output2_loss: 0.0074\n",
            "Epoch 21/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0140 - output1_loss: 0.0074 - output2_loss: 0.0066 - val_loss: 0.0113 - val_output1_loss: 0.0057 - val_output2_loss: 0.0055\n",
            "Epoch 22/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0123 - output1_loss: 0.0069 - output2_loss: 0.0055 - val_loss: 0.0103 - val_output1_loss: 0.0057 - val_output2_loss: 0.0045\n",
            "Epoch 23/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0130 - output1_loss: 0.0071 - output2_loss: 0.0060 - val_loss: 0.0090 - val_output1_loss: 0.0050 - val_output2_loss: 0.0040\n",
            "Epoch 24/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0124 - output1_loss: 0.0068 - output2_loss: 0.0056 - val_loss: 0.0143 - val_output1_loss: 0.0066 - val_output2_loss: 0.0077\n",
            "Epoch 25/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0121 - output1_loss: 0.0066 - output2_loss: 0.0055 - val_loss: 0.0086 - val_output1_loss: 0.0047 - val_output2_loss: 0.0039\n",
            "Epoch 26/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0116 - output1_loss: 0.0064 - output2_loss: 0.0052 - val_loss: 0.0115 - val_output1_loss: 0.0057 - val_output2_loss: 0.0058\n",
            "Epoch 27/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0118 - output1_loss: 0.0064 - output2_loss: 0.0054 - val_loss: 0.0139 - val_output1_loss: 0.0055 - val_output2_loss: 0.0084\n",
            "Epoch 28/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0104 - output1_loss: 0.0057 - output2_loss: 0.0047 - val_loss: 0.0113 - val_output1_loss: 0.0062 - val_output2_loss: 0.0051\n",
            "Epoch 29/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0107 - output1_loss: 0.0056 - output2_loss: 0.0051 - val_loss: 0.0067 - val_output1_loss: 0.0035 - val_output2_loss: 0.0031\n",
            "Epoch 30/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0084 - output1_loss: 0.0047 - output2_loss: 0.0036 - val_loss: 0.0078 - val_output1_loss: 0.0036 - val_output2_loss: 0.0042\n",
            "Epoch 31/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0098 - output1_loss: 0.0051 - output2_loss: 0.0047 - val_loss: 0.0074 - val_output1_loss: 0.0034 - val_output2_loss: 0.0040\n",
            "Epoch 32/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0085 - output1_loss: 0.0045 - output2_loss: 0.0040 - val_loss: 0.0074 - val_output1_loss: 0.0033 - val_output2_loss: 0.0041\n",
            "Epoch 33/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0086 - output1_loss: 0.0043 - output2_loss: 0.0043 - val_loss: 0.0079 - val_output1_loss: 0.0034 - val_output2_loss: 0.0045\n",
            "Epoch 34/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0073 - output1_loss: 0.0037 - output2_loss: 0.0035 - val_loss: 0.0055 - val_output1_loss: 0.0027 - val_output2_loss: 0.0029\n",
            "Epoch 35/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0069 - output1_loss: 0.0035 - output2_loss: 0.0034 - val_loss: 0.0058 - val_output1_loss: 0.0025 - val_output2_loss: 0.0034\n",
            "Epoch 36/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0073 - output1_loss: 0.0035 - output2_loss: 0.0038 - val_loss: 0.0054 - val_output1_loss: 0.0022 - val_output2_loss: 0.0031\n",
            "Epoch 37/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0094 - output1_loss: 0.0040 - output2_loss: 0.0055 - val_loss: 0.0147 - val_output1_loss: 0.0068 - val_output2_loss: 0.0079\n",
            "Epoch 38/100\n",
            "125/125 [==============================] - 0s 4ms/step - loss: 0.0071 - output1_loss: 0.0033 - output2_loss: 0.0039 - val_loss: 0.0158 - val_output1_loss: 0.0082 - val_output2_loss: 0.0076\n",
            "Epoch 39/100\n",
            "125/125 [==============================] - 1s 5ms/step - loss: 0.0063 - output1_loss: 0.0029 - output2_loss: 0.0034 - val_loss: 0.0153 - val_output1_loss: 0.0080 - val_output2_loss: 0.0074\n",
            "Epoch 40/100\n",
            "125/125 [==============================] - 1s 5ms/step - loss: 0.0069 - output1_loss: 0.0029 - output2_loss: 0.0040 - val_loss: 0.0071 - val_output1_loss: 0.0020 - val_output2_loss: 0.0051\n",
            "Epoch 41/100\n",
            "125/125 [==============================] - 0s 4ms/step - loss: 0.0070 - output1_loss: 0.0030 - output2_loss: 0.0040 - val_loss: 0.0056 - val_output1_loss: 0.0016 - val_output2_loss: 0.0040\n",
            "Epoch 42/100\n",
            "125/125 [==============================] - 1s 5ms/step - loss: 0.0071 - output1_loss: 0.0028 - output2_loss: 0.0043 - val_loss: 0.0043 - val_output1_loss: 0.0014 - val_output2_loss: 0.0029\n",
            "Epoch 43/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0051 - output1_loss: 0.0021 - output2_loss: 0.0030 - val_loss: 0.0061 - val_output1_loss: 0.0018 - val_output2_loss: 0.0043\n",
            "Epoch 44/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0055 - output1_loss: 0.0022 - output2_loss: 0.0033 - val_loss: 0.0068 - val_output1_loss: 0.0014 - val_output2_loss: 0.0055\n",
            "Epoch 45/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0098 - output1_loss: 0.0036 - output2_loss: 0.0062 - val_loss: 0.0062 - val_output1_loss: 0.0021 - val_output2_loss: 0.0041\n",
            "Epoch 46/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0042 - output1_loss: 0.0016 - output2_loss: 0.0026 - val_loss: 0.0039 - val_output1_loss: 0.0013 - val_output2_loss: 0.0026\n",
            "Epoch 47/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0045 - output1_loss: 0.0017 - output2_loss: 0.0028 - val_loss: 0.0049 - val_output1_loss: 0.0018 - val_output2_loss: 0.0031\n",
            "Epoch 48/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0072 - output1_loss: 0.0026 - output2_loss: 0.0046 - val_loss: 0.0057 - val_output1_loss: 0.0027 - val_output2_loss: 0.0030\n",
            "Epoch 49/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0047 - output1_loss: 0.0018 - output2_loss: 0.0030 - val_loss: 0.0038 - val_output1_loss: 0.0011 - val_output2_loss: 0.0027\n",
            "Epoch 50/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0038 - output1_loss: 0.0014 - output2_loss: 0.0024 - val_loss: 0.0065 - val_output1_loss: 0.0019 - val_output2_loss: 0.0045\n",
            "Epoch 51/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0040 - output1_loss: 0.0014 - output2_loss: 0.0026 - val_loss: 0.0037 - val_output1_loss: 0.0012 - val_output2_loss: 0.0025\n",
            "Epoch 52/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0055 - output1_loss: 0.0018 - output2_loss: 0.0036 - val_loss: 0.0112 - val_output1_loss: 0.0049 - val_output2_loss: 0.0063\n",
            "Epoch 53/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0052 - output1_loss: 0.0018 - output2_loss: 0.0034 - val_loss: 0.0039 - val_output1_loss: 0.0013 - val_output2_loss: 0.0026\n",
            "Epoch 54/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0066 - output1_loss: 0.0023 - output2_loss: 0.0042 - val_loss: 0.0029 - val_output1_loss: 7.4192e-04 - val_output2_loss: 0.0022\n",
            "Epoch 55/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0032 - output1_loss: 0.0011 - output2_loss: 0.0021 - val_loss: 0.0046 - val_output1_loss: 0.0017 - val_output2_loss: 0.0029\n",
            "Epoch 56/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0032 - output1_loss: 0.0010 - output2_loss: 0.0022 - val_loss: 0.0039 - val_output1_loss: 0.0010 - val_output2_loss: 0.0028\n",
            "Epoch 57/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0050 - output1_loss: 0.0016 - output2_loss: 0.0034 - val_loss: 0.0068 - val_output1_loss: 0.0029 - val_output2_loss: 0.0039\n",
            "Epoch 58/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0057 - output1_loss: 0.0020 - output2_loss: 0.0037 - val_loss: 0.0032 - val_output1_loss: 7.4928e-04 - val_output2_loss: 0.0025\n",
            "Epoch 59/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0039 - output1_loss: 0.0013 - output2_loss: 0.0026 - val_loss: 0.0028 - val_output1_loss: 7.8375e-04 - val_output2_loss: 0.0020\n",
            "Epoch 60/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0031 - output1_loss: 0.0011 - output2_loss: 0.0021 - val_loss: 0.0088 - val_output1_loss: 0.0033 - val_output2_loss: 0.0055\n",
            "Epoch 61/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0044 - output1_loss: 0.0014 - output2_loss: 0.0030 - val_loss: 0.0044 - val_output1_loss: 0.0012 - val_output2_loss: 0.0032\n",
            "Epoch 62/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0072 - output1_loss: 0.0025 - output2_loss: 0.0047 - val_loss: 0.0164 - val_output1_loss: 0.0071 - val_output2_loss: 0.0093\n",
            "Epoch 63/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0045 - output1_loss: 0.0015 - output2_loss: 0.0030 - val_loss: 0.0028 - val_output1_loss: 6.2999e-04 - val_output2_loss: 0.0021\n",
            "Epoch 64/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0030 - output1_loss: 9.5548e-04 - output2_loss: 0.0021 - val_loss: 0.0044 - val_output1_loss: 0.0014 - val_output2_loss: 0.0031\n",
            "Epoch 65/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0031 - output1_loss: 0.0010 - output2_loss: 0.0021 - val_loss: 0.0028 - val_output1_loss: 7.0387e-04 - val_output2_loss: 0.0021\n",
            "Epoch 66/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0042 - output1_loss: 0.0014 - output2_loss: 0.0029 - val_loss: 0.0028 - val_output1_loss: 7.7208e-04 - val_output2_loss: 0.0020\n",
            "Epoch 67/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0027 - output1_loss: 8.1106e-04 - output2_loss: 0.0019 - val_loss: 0.0046 - val_output1_loss: 0.0016 - val_output2_loss: 0.0031\n",
            "Epoch 68/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0033 - output1_loss: 9.8668e-04 - output2_loss: 0.0023 - val_loss: 0.0034 - val_output1_loss: 0.0012 - val_output2_loss: 0.0023\n",
            "Epoch 69/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0034 - output1_loss: 0.0010 - output2_loss: 0.0024 - val_loss: 0.0045 - val_output1_loss: 8.5716e-04 - val_output2_loss: 0.0036\n",
            "Epoch 70/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0032 - output1_loss: 9.9036e-04 - output2_loss: 0.0022 - val_loss: 0.0038 - val_output1_loss: 9.4511e-04 - val_output2_loss: 0.0029\n",
            "Epoch 71/100\n",
            "125/125 [==============================] - 0s 4ms/step - loss: 0.0031 - output1_loss: 9.4526e-04 - output2_loss: 0.0021 - val_loss: 0.0033 - val_output1_loss: 0.0011 - val_output2_loss: 0.0023\n",
            "Epoch 72/100\n",
            "125/125 [==============================] - 1s 4ms/step - loss: 0.0029 - output1_loss: 9.1892e-04 - output2_loss: 0.0020 - val_loss: 0.0037 - val_output1_loss: 7.8041e-04 - val_output2_loss: 0.0029\n",
            "Epoch 73/100\n",
            "125/125 [==============================] - 1s 5ms/step - loss: 0.0037 - output1_loss: 0.0011 - output2_loss: 0.0026 - val_loss: 0.0036 - val_output1_loss: 0.0012 - val_output2_loss: 0.0023\n",
            "Epoch 74/100\n",
            "125/125 [==============================] - 1s 4ms/step - loss: 0.0042 - output1_loss: 0.0013 - output2_loss: 0.0028 - val_loss: 0.0079 - val_output1_loss: 0.0057 - val_output2_loss: 0.0022\n",
            "Epoch 75/100\n",
            "125/125 [==============================] - 1s 5ms/step - loss: 0.0059 - output1_loss: 0.0020 - output2_loss: 0.0039 - val_loss: 0.0028 - val_output1_loss: 0.0011 - val_output2_loss: 0.0018\n",
            "Epoch 76/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0043 - output1_loss: 0.0014 - output2_loss: 0.0029 - val_loss: 0.0027 - val_output1_loss: 8.6249e-04 - val_output2_loss: 0.0019\n",
            "Epoch 77/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0033 - output1_loss: 9.9382e-04 - output2_loss: 0.0023 - val_loss: 0.0027 - val_output1_loss: 6.5781e-04 - val_output2_loss: 0.0020\n",
            "Epoch 78/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0024 - output1_loss: 7.1239e-04 - output2_loss: 0.0017 - val_loss: 0.0030 - val_output1_loss: 7.4839e-04 - val_output2_loss: 0.0022\n",
            "Epoch 79/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0037 - output1_loss: 0.0012 - output2_loss: 0.0025 - val_loss: 0.0045 - val_output1_loss: 0.0014 - val_output2_loss: 0.0031\n",
            "Epoch 80/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0032 - output1_loss: 0.0010 - output2_loss: 0.0022 - val_loss: 0.0032 - val_output1_loss: 4.3694e-04 - val_output2_loss: 0.0027\n",
            "Epoch 81/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0039 - output1_loss: 0.0012 - output2_loss: 0.0027 - val_loss: 0.0095 - val_output1_loss: 0.0022 - val_output2_loss: 0.0073\n",
            "Epoch 82/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0043 - output1_loss: 0.0013 - output2_loss: 0.0030 - val_loss: 0.0025 - val_output1_loss: 8.4410e-04 - val_output2_loss: 0.0016\n",
            "Epoch 83/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0043 - output1_loss: 0.0014 - output2_loss: 0.0029 - val_loss: 0.0033 - val_output1_loss: 0.0010 - val_output2_loss: 0.0023\n",
            "Epoch 84/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0039 - output1_loss: 0.0012 - output2_loss: 0.0028 - val_loss: 0.0031 - val_output1_loss: 9.0537e-04 - val_output2_loss: 0.0022\n",
            "Epoch 85/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0027 - output1_loss: 8.1338e-04 - output2_loss: 0.0019 - val_loss: 0.0020 - val_output1_loss: 5.0916e-04 - val_output2_loss: 0.0015\n",
            "Epoch 86/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0024 - output1_loss: 6.7218e-04 - output2_loss: 0.0017 - val_loss: 0.0035 - val_output1_loss: 8.9267e-04 - val_output2_loss: 0.0026\n",
            "Epoch 87/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0029 - output1_loss: 8.4667e-04 - output2_loss: 0.0020 - val_loss: 0.0035 - val_output1_loss: 9.9977e-04 - val_output2_loss: 0.0025\n",
            "Epoch 88/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0039 - output1_loss: 0.0012 - output2_loss: 0.0027 - val_loss: 0.0068 - val_output1_loss: 0.0027 - val_output2_loss: 0.0041\n",
            "Epoch 89/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0035 - output1_loss: 0.0011 - output2_loss: 0.0024 - val_loss: 0.0019 - val_output1_loss: 5.8050e-04 - val_output2_loss: 0.0013\n",
            "Epoch 90/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0040 - output1_loss: 0.0013 - output2_loss: 0.0027 - val_loss: 0.0071 - val_output1_loss: 0.0012 - val_output2_loss: 0.0059\n",
            "Epoch 91/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0039 - output1_loss: 0.0013 - output2_loss: 0.0026 - val_loss: 0.0024 - val_output1_loss: 7.7514e-04 - val_output2_loss: 0.0016\n",
            "Epoch 92/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0020 - output1_loss: 5.9737e-04 - output2_loss: 0.0014 - val_loss: 0.0045 - val_output1_loss: 0.0021 - val_output2_loss: 0.0024\n",
            "Epoch 93/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0051 - output1_loss: 0.0017 - output2_loss: 0.0033 - val_loss: 0.0077 - val_output1_loss: 0.0023 - val_output2_loss: 0.0054\n",
            "Epoch 94/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0028 - output1_loss: 8.4986e-04 - output2_loss: 0.0019 - val_loss: 0.0021 - val_output1_loss: 3.4544e-04 - val_output2_loss: 0.0017\n",
            "Epoch 95/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0029 - output1_loss: 8.7735e-04 - output2_loss: 0.0020 - val_loss: 0.0023 - val_output1_loss: 4.5770e-04 - val_output2_loss: 0.0018\n",
            "Epoch 96/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0033 - output1_loss: 9.8424e-04 - output2_loss: 0.0024 - val_loss: 0.0046 - val_output1_loss: 8.8229e-04 - val_output2_loss: 0.0037\n",
            "Epoch 97/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0039 - output1_loss: 0.0012 - output2_loss: 0.0027 - val_loss: 0.0020 - val_output1_loss: 5.0806e-04 - val_output2_loss: 0.0015\n",
            "Epoch 98/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0022 - output1_loss: 5.9501e-04 - output2_loss: 0.0016 - val_loss: 0.0019 - val_output1_loss: 3.7643e-04 - val_output2_loss: 0.0015\n",
            "Epoch 99/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0022 - output1_loss: 6.5130e-04 - output2_loss: 0.0016 - val_loss: 0.0054 - val_output1_loss: 0.0018 - val_output2_loss: 0.0037\n",
            "Epoch 100/100\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.0022 - output1_loss: 6.1004e-04 - output2_loss: 0.0015 - val_loss: 0.0022 - val_output1_loss: 5.1700e-04 - val_output2_loss: 0.0017\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 構築したモデルで予測\n",
        "pred1, pred2 = model.predict(x_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "drnGbk8gJwWm",
        "outputId": "4dac46b5-f173-4d94-ec95-d0cdf3e0be4d"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "63/63 [==============================] - 0s 3ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#精度評価指標\n",
        "# Output1 ====================================================================\n",
        "print(\"=======  Output1  =======\")\n",
        "# MAE\n",
        "test_MAE = mean_absolute_error(y_test.iloc[:, 0], pred1)\n",
        "print('MAE：', test_MAE)\n",
        "\n",
        "# RMSE\n",
        "test_RMSE = sqrt(mean_squared_error(y_test.iloc[:, 0], pred1))\n",
        "print('RMSE：', test_RMSE)\n",
        "\n",
        "# 決定係数\n",
        "R2 = r2_score(y_test.iloc[:, 0], pred1)\n",
        "print('R2：', R2)\n",
        "\n",
        "print(\"\")\n",
        "\n",
        "# Output2 ====================================================================\n",
        "print(\"=======  Output2  =======\")\n",
        "# MAE\n",
        "test_MAE = mean_absolute_error(y_test.iloc[:, 1], pred2)\n",
        "print('MAE：', test_MAE)\n",
        "\n",
        "# RMSE\n",
        "test_RMSE = sqrt(mean_squared_error(y_test.iloc[:, 1], pred2))\n",
        "print('RMSE：', test_RMSE)\n",
        "\n",
        "# 決定係数\n",
        "R2 = r2_score(y_test.iloc[:, 1], pred2)\n",
        "print('R2：', R2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EqDNWkDcKEGu",
        "outputId": "cd900b32-bf51-4b66-cd55-e46844603839"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=======  Output1  =======\n",
            "MAE： 0.015728550443867676\n",
            "RMSE： 0.0227376189428299\n",
            "R2： 0.9997757278312376\n",
            "\n",
            "=======  Output2  =======\n",
            "MAE： 0.02770679601081276\n",
            "RMSE： 0.04106129343864195\n",
            "R2： 0.9991440073301294\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Figureを追加\n",
        "fig = plt.figure(figsize = (15, 5))\n",
        "\n",
        "# Output1 ====================================================================\n",
        "# 1×2 の1枚目に描画する\n",
        "loss_graph1 = fig.add_subplot(1, 2, 1)\n",
        "\n",
        "# x,yに要素追加\n",
        "loss1 = history.history['output1_loss']\n",
        "val_loss1 = history.history['val_output1_loss']\n",
        "epochs1 = len(loss1)\n",
        "\n",
        "# グラフ設定\n",
        "loss_graph1.plot(range(epochs), loss1, marker='.', label='loss')\n",
        "loss_graph1.plot(range(epochs), val_loss1, marker='.', label='val_loss')\n",
        "loss_graph1.set_title(\"Loss_Output1\", size = 18)\n",
        "loss_graph1.legend(loc='best')\n",
        "loss_graph1.grid()\n",
        "loss_graph1.set_xlabel(\"epochs\", size = 14, color = \"r\")\n",
        "loss_graph1.set_ylabel(\"loss\", size = 14, color = \"r\")\n",
        "\n",
        "#==============================================================================\n",
        "# Output2 ====================================================================\n",
        "# 1×2 の2枚目に描画する\n",
        "loss_graph2 = fig.add_subplot(1, 2, 2)\n",
        "\n",
        "# x,yに要素追加\n",
        "loss2 = history.history['output2_loss']\n",
        "val_loss2 = history.history['val_output2_loss']\n",
        "epochs2 = len(loss2)\n",
        "\n",
        "# グラフ設定\n",
        "loss_graph2.plot(range(epochs), loss2, marker='.', label='loss')\n",
        "loss_graph2.plot(range(epochs), val_loss2, marker='.', label='val_loss')\n",
        "loss_graph2.set_title(\"Loss_Output2\", size = 18)\n",
        "loss_graph2.legend(loc='best')\n",
        "loss_graph2.grid()\n",
        "loss_graph2.set_xlabel(\"epochs\", size = 14, color = \"r\")\n",
        "loss_graph2.set_ylabel(\"loss\", size = 14, color = \"r\")\n",
        "#=============================================================================="
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "id": "LjjCot-PKGfk",
        "outputId": "0f1c7da8-dec0-4eeb-f359-beaca489367d"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'loss')"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Observed-Predicted Plot\n",
        "# Figureを追加\n",
        "fig = plt.figure(figsize = (15, 5))\n",
        "\n",
        "# Output1 ====================================================================\n",
        "# 1×2 の1枚目に描画する\n",
        "yy1 = fig.add_subplot(1, 2, 1)\n",
        "\n",
        "yy1.scatter(y_test.iloc[:, 0], pred1, c='blue', alpha=0.8)\n",
        "\n",
        "# グラフ設定\n",
        "yy1.set_title(\"observed_predicted1\", size = 18)\n",
        "yy1.grid()\n",
        "yy1.set_xlabel(\"observed_data\", size = 14, color = \"r\")\n",
        "yy1.set_ylabel(\"predicted_data\", size = 14, color = \"r\")\n",
        "\n",
        "# Output2 ====================================================================\n",
        "# 1×2 の2枚目に描画する\n",
        "yy2 = fig.add_subplot(1, 2, 2)\n",
        "\n",
        "yy2.scatter(y_test.iloc[:, 1], pred2, c='blue', alpha=0.8)\n",
        "\n",
        "# グラフ設定\n",
        "yy2.set_title(\"observed_predicted2\", size = 18)\n",
        "yy2.grid()\n",
        "yy2.set_xlabel(\"observed_data\", size = 14, color = \"r\")\n",
        "yy2.set_ylabel(\"predicted_data\", size = 14, color = \"r\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "id": "6mNMPTQXKJxS",
        "outputId": "2cc9e855-9e1b-4593-e02d-6cca572bca5b"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'predicted_data')"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 2 Axes>"
            ],
            "image/png": 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      ]
    }
  ]
}