Multiple_output_model_with_UCI_dataset.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Multiple_output_model_with_UCI_dataset.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPrLEA+FbPyflJ4u6W36tzB",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/armeh429/A-journey-for-learning-advanced-tensorflow/blob/main/Multiple_output_model_with_UCI_dataset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "bFXXObY207Bk"
      },
      "outputs": [],
      "source": [
        "try:\n",
        "  # %tensorflow_version only exists in Colab.\n",
        "  %tensorflow_version 2.x\n",
        "except Exception:\n",
        "  pass\n",
        "\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.layers import Dense, Input\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.python.keras.utils.vis_utils import plot_model"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Get data from UCI "
      ],
      "metadata": {
        "id": "kgjwjANc1SBL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def format_output(data):\n",
        "    y1 = data.pop('Y1')\n",
        "    y1 = np.array(y1)\n",
        "    y2 = data.pop('Y2')\n",
        "    y2 = np.array(y2)\n",
        "    return y1, y2\n",
        "\n",
        "\n",
        "def norm(x):\n",
        "    return (x - train_stats['mean']) / train_stats['std']\n",
        "\n",
        "\n",
        "def plot_diff(y_true, y_pred, title=''):\n",
        "    plt.scatter(y_true, y_pred)\n",
        "    plt.title(title)\n",
        "    plt.xlabel('True Values')\n",
        "    plt.ylabel('Predictions')\n",
        "    plt.axis('equal')\n",
        "    plt.axis('square')\n",
        "    plt.xlim(plt.xlim())\n",
        "    plt.ylim(plt.ylim())\n",
        "    plt.plot([-100, 100], [-100, 100])\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "def plot_metrics(metric_name, title, ylim=5):\n",
        "    plt.title(title)\n",
        "    plt.ylim(0, ylim)\n",
        "    plt.plot(history.history[metric_name], color='blue', label=metric_name)\n",
        "    plt.plot(history.history['val_' + metric_name], color='green', label='val_' + metric_name)\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "aOneTg6g1apz"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the data from UCI dataset\n",
        "URL = 'https://archive.ics.uci.edu/ml/machine-learning-databases/00242/ENB2012_data.xlsx'\n",
        "\n",
        "# Use pandas excel reader\n",
        "df = pd.read_excel(URL)\n",
        "df = df.sample(frac=1).reset_index(drop=True)\n",
        "\n",
        "# Split the data into train and test with 80 train / 20 test\n",
        "train, test = train_test_split(df, test_size=0.2)\n",
        "train_stats = train.describe()\n",
        "\n",
        "# Get Y1 and Y2 as the 2 outputs and format them as np arrays\n",
        "train_stats.pop('Y1')\n",
        "train_stats.pop('Y2')\n",
        "train_stats = train_stats.transpose()\n",
        "train_Y = format_output(train)\n",
        "test_Y = format_output(test)\n",
        "\n",
        "# Normalize the training and test data\n",
        "norm_train_X = norm(train)\n",
        "norm_test_X = norm(test)"
      ],
      "metadata": {
        "id": "0HV19Zlt1EIt"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "design the model"
      ],
      "metadata": {
        "id": "HV-231pE1qan"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define model layers.\n",
        "input_layer = Input(shape=(len(train .columns),))\n",
        "first_dense = Dense(units='128', activation='relu')(input_layer)\n",
        "second_dense = Dense(units='128', activation='relu')(first_dense)\n",
        "\n",
        "# Y1 output will be fed directly from the second dense\n",
        "y1_output = Dense(units='1', name='y1_output')(second_dense)\n",
        "third_dense = Dense(units='64', activation='relu')(second_dense)\n",
        "\n",
        "# Y2 output will come via the third dense\n",
        "y2_output = Dense(units='1', name='y2_output')(third_dense)\n",
        "\n",
        "# Define the model with the input layer and a list of output layers\n",
        "model = Model(inputs=input_layer, outputs=[y1_output, y2_output])\n",
        "\n",
        "print(model.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bAw_iqu11XrW",
        "outputId": "ab50fb83-ca1b-4469-e683-f193eb7cff07"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)           [(None, 8)]          0           []                               \n",
            "                                                                                                  \n",
            " dense (Dense)                  (None, 128)          1152        ['input_1[0][0]']                \n",
            "                                                                                                  \n",
            " dense_1 (Dense)                (None, 128)          16512       ['dense[0][0]']                  \n",
            "                                                                                                  \n",
            " dense_2 (Dense)                (None, 64)           8256        ['dense_1[0][0]']                \n",
            "                                                                                                  \n",
            " y1_output (Dense)              (None, 1)            129         ['dense_1[0][0]']                \n",
            "                                                                                                  \n",
            " y2_output (Dense)              (None, 1)            65          ['dense_2[0][0]']                \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 26,114\n",
            "Trainable params: 26,114\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_model(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "fJCZ7WXO4nBR",
        "outputId": "9ce9a5c9-af80-48ec-ba5b-89418a00657f"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Optimizer and loss!"
      ],
      "metadata": {
        "id": "Iqoyse7h2H7K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Specify the optimizer, and compile the model with loss functions for both outputs\n",
        "optimizer = tf.keras.optimizers.SGD(lr=0.001)\n",
        "model.compile(optimizer=optimizer,\n",
        "              loss={'y1_output': 'mse', 'y2_output': 'mse'},\n",
        "              metrics={'y1_output': tf.keras.metrics.RootMeanSquaredError(),\n",
        "                       'y2_output': tf.keras.metrics.RootMeanSquaredError()})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8v_F6o6Q1skB",
        "outputId": "833069cf-3c10-4db6-ce66-af7eaba02129"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(SGD, self).__init__(name, **kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model for 500 epochs\n",
        "history = model.fit(norm_train_X, train_Y,\n",
        "                    epochs=500, batch_size=10, validation_data=(norm_test_X, test_Y))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KpibFAoN2MYS",
        "outputId": "acbc29a6-c5ec-4996-f7c3-629d7b7504c9"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/500\n",
            "62/62 [==============================] - 4s 26ms/step - loss: 208.9368 - y1_output_loss: 105.3044 - y2_output_loss: 103.6323 - y1_output_root_mean_squared_error: 10.2618 - y2_output_root_mean_squared_error: 10.1800 - val_loss: 27.3376 - val_y1_output_loss: 12.3188 - val_y2_output_loss: 15.0188 - val_y1_output_root_mean_squared_error: 3.5098 - val_y2_output_root_mean_squared_error: 3.8754\n",
            "Epoch 2/500\n",
            "62/62 [==============================] - 1s 8ms/step - loss: 29.2713 - y1_output_loss: 12.1314 - y2_output_loss: 17.1399 - y1_output_root_mean_squared_error: 3.4830 - y2_output_root_mean_squared_error: 4.1400 - val_loss: 20.6453 - val_y1_output_loss: 8.2401 - val_y2_output_loss: 12.4053 - val_y1_output_root_mean_squared_error: 2.8705 - val_y2_output_root_mean_squared_error: 3.5221\n",
            "Epoch 3/500\n",
            "62/62 [==============================] - 0s 7ms/step - loss: 26.6495 - y1_output_loss: 10.1589 - y2_output_loss: 16.4907 - y1_output_root_mean_squared_error: 3.1873 - y2_output_root_mean_squared_error: 4.0609 - val_loss: 18.7076 - val_y1_output_loss: 8.0711 - val_y2_output_loss: 10.6365 - val_y1_output_root_mean_squared_error: 2.8410 - val_y2_output_root_mean_squared_error: 3.2614\n",
            "Epoch 4/500\n",
            "62/62 [==============================] - 0s 8ms/step - loss: 25.0648 - y1_output_loss: 9.6180 - y2_output_loss: 15.4468 - y1_output_root_mean_squared_error: 3.1013 - y2_output_root_mean_squared_error: 3.9302 - val_loss: 46.7512 - val_y1_output_loss: 10.1402 - val_y2_output_loss: 36.6110 - val_y1_output_root_mean_squared_error: 3.1844 - val_y2_output_root_mean_squared_error: 6.0507\n",
            "Epoch 5/500\n",
            "62/62 [==============================] - 1s 11ms/step - loss: 25.6174 - y1_output_loss: 9.6036 - y2_output_loss: 16.0138 - y1_output_root_mean_squared_error: 3.0990 - y2_output_root_mean_squared_error: 4.0017 - val_loss: 20.7874 - val_y1_output_loss: 8.4820 - val_y2_output_loss: 12.3054 - val_y1_output_root_mean_squared_error: 2.9124 - val_y2_output_root_mean_squared_error: 3.5079\n",
            "Epoch 6/500\n",
            "62/62 [==============================] - 0s 7ms/step - loss: 27.6830 - y1_output_loss: 9.8523 - y2_output_loss: 17.8307 - y1_output_root_mean_squared_error: 3.1388 - y2_output_root_mean_squared_error: 4.2226 - val_loss: 21.5076 - val_y1_output_loss: 9.3626 - val_y2_output_loss: 12.1450 - val_y1_output_root_mean_squared_error: 3.0598 - val_y2_output_root_mean_squared_error: 3.4850\n",
            "Epoch 7/500\n",
            "62/62 [==============================] - 1s 10ms/step - loss: 24.3104 - y1_output_loss: 9.0247 - y2_output_loss: 15.2858 - y1_output_root_mean_squared_error: 3.0041 - y2_output_root_mean_squared_error: 3.9097 - val_loss: 16.8257 - val_y1_output_loss: 7.4700 - val_y2_output_loss: 9.3557 - val_y1_output_root_mean_squared_error: 2.7331 - val_y2_output_root_mean_squared_error: 3.0587\n",
            "Epoch 8/500\n",
            "62/62 [==============================] - 1s 8ms/step - loss: 21.3348 - y1_output_loss: 8.1022 - y2_output_loss: 13.2325 - y1_output_root_mean_squared_error: 2.8464 - y2_output_root_mean_squared_error: 3.6377 - val_loss: 50.4843 - val_y1_output_loss: 17.8790 - val_y2_output_loss: 32.6053 - val_y1_output_root_mean_squared_error: 4.2284 - val_y2_output_root_mean_squared_error: 5.7101\n",
            "Epoch 9/500\n",
            "62/62 [==============================] - 1s 13ms/step - loss: 23.5006 - y1_output_loss: 8.8285 - y2_output_loss: 14.6721 - y1_output_root_mean_squared_error: 2.9713 - y2_output_root_mean_squared_error: 3.8304 - val_loss: 18.2093 - val_y1_output_loss: 7.1500 - val_y2_output_loss: 11.0594 - val_y1_output_root_mean_squared_error: 2.6739 - val_y2_output_root_mean_squared_error: 3.3256\n",
            "Epoch 10/500\n",
            "62/62 [==============================] - 1s 13ms/step - loss: 23.7369 - y1_output_loss: 8.6246 - y2_output_loss: 15.1122 - y1_output_root_mean_squared_error: 2.9368 - y2_output_root_mean_squared_error: 3.8874 - val_loss: 15.1930 - val_y1_output_loss: 6.5290 - val_y2_output_loss: 8.6640 - val_y1_output_root_mean_squared_error: 2.5552 - val_y2_output_root_mean_squared_error: 2.9435\n",
            "Epoch 11/500\n",
            "62/62 [==============================] - 1s 11ms/step - loss: 23.1473 - y1_output_loss: 8.2455 - y2_output_loss: 14.9018 - y1_output_root_mean_squared_error: 2.8715 - y2_output_root_mean_squared_error: 3.8603 - val_loss: 18.6625 - val_y1_output_loss: 7.3137 - val_y2_output_loss: 11.3488 - val_y1_output_root_mean_squared_error: 2.7044 - val_y2_output_root_mean_squared_error: 3.3688\n",
            "Epoch 12/500\n",
            "62/62 [==============================] - 0s 8ms/step - loss: 19.0632 - y1_output_loss: 7.5317 - y2_output_loss: 11.5315 - y1_output_root_mean_squared_error: 2.7444 - y2_output_root_mean_squared_error: 3.3958 - val_loss: 15.6260 - val_y1_output_loss: 6.5667 - val_y2_output_loss: 9.0593 - val_y1_output_root_mean_squared_error: 2.5626 - val_y2_output_root_mean_squared_error: 3.0099\n",
            "Epoch 13/500\n",
            "62/62 [==============================] - 1s 9ms/step - loss: 23.2984 - y1_output_loss: 8.2335 - y2_output_loss: 15.0649 - y1_output_root_mean_squared_error: 2.8694 - y2_output_root_mean_squared_error: 3.8814 - val_loss: 15.6576 - val_y1_output_loss: 6.8949 - val_y2_output_loss: 8.7627 - val_y1_output_root_mean_squared_error: 2.6258 - val_y2_output_root_mean_squared_error: 2.9602\n",
            "Epoch 14/500\n",
            "62/62 [==============================] - 0s 7ms/step - loss: 17.1778 - y1_output_loss: 6.7341 - y2_output_loss: 10.4437 - y1_output_root_mean_squared_error: 2.5950 - y2_output_root_mean_squared_error: 3.2317 - val_loss: 13.6512 - val_y1_output_loss: 6.4448 - val_y2_output_loss: 7.2064 - val_y1_output_root_mean_squared_error: 2.5387 - val_y2_output_root_mean_squared_error: 2.6845\n",
            "Epoch 15/500\n",
            "62/62 [==============================] - 0s 7ms/step - loss: 17.2732 - y1_output_loss: 6.4524 - y2_output_loss: 10.8209 - y1_output_root_mean_squared_error: 2.5401 - y2_output_root_mean_squared_error: 3.2895 - val_loss: 31.7193 - val_y1_output_loss: 10.7182 - val_y2_output_loss: 21.0010 - val_y1_output_root_mean_squared_error: 3.2739 - val_y2_output_root_mean_squared_error: 4.5827\n",
            "Epoch 16/500\n",
            "62/62 [==============================] - 0s 8ms/step - loss: 17.1504 - y1_output_loss: 6.5166 - y2_output_loss: 10.6338 - y1_output_root_mean_squared_error: 2.5528 - y2_output_root_mean_squared_error: 3.2610 - val_loss: 12.8312 - val_y1_output_loss: 5.6415 - val_y2_output_loss: 7.1897 - val_y1_output_root_mean_squared_error: 2.3752 - val_y2_output_root_mean_squared_error: 2.6814\n",
            "Epoch 17/500\n",
            "62/62 [==============================] - 0s 7ms/step - loss: 15.1245 - y1_output_loss: 5.9641 - y2_output_loss: 9.1604 - y1_output_root_mean_squared_error: 2.4422 - y2_output_root_mean_squared_error: 3.0266 - val_loss: 15.2630 - val_y1_output_loss: 6.7618 - val_y2_output_loss: 8.5012 - val_y1_output_root_mean_squared_error: 2.6003 - val_y2_output_root_mean_squared_error: 2.9157\n",
            "Epoch 18/500\n",
            "62/62 [==============================] - 0s 7ms/step - loss: 21.6137 - y1_output_loss: 7.1010 - y2_output_loss: 14.5127 - y1_output_root_mean_squared_error: 2.6648 - y2_output_root_mean_squared_error: 3.8096 - val_loss: 22.3151 - val_y1_output_loss: 7.7159 - val_y2_output_loss: 14.5992 - val_y1_output_root_mean_squared_error: 2.7777 - val_y2_output_root_mean_squared_error: 3.8209\n",
            "Epoch 19/500\n",
            "62/62 [==============================] - 0s 6ms/step - loss: 15.2228 - y1_output_loss: 5.7027 - y2_output_loss: 9.5200 - y1_output_root_mean_squared_error: 2.3880 - y2_output_root_mean_squared_error: 3.0855 - val_loss: 26.9170 - val_y1_output_loss: 5.7700 - val_y2_output_loss: 21.1469 - val_y1_output_root_mean_squared_error: 2.4021 - val_y2_output_root_mean_squared_error: 4.5986\n",
            "Epoch 20/500\n",
            "62/62 [==============================] - 0s 6ms/step - loss: 15.8352 - y1_output_loss: 5.7818 - y2_output_loss: 10.0534 - y1_output_root_mean_squared_error: 2.4045 - y2_output_root_mean_squared_error: 3.1707 - val_loss: 23.6652 - val_y1_output_loss: 7.0728 - val_y2_output_loss: 16.5924 - val_y1_output_root_mean_squared_error: 2.6595 - val_y2_output_root_mean_squared_error: 4.0734\n",
            "Epoch 21/500\n",
            "62/62 [==============================] - 0s 6ms/step - loss: 13.9766 - y1_output_loss: 5.0552 - y2_output_loss: 8.9214 - y1_output_root_mean_squared_error: 2.2484 - y2_output_root_mean_squared_error: 2.9869 - val_loss: 32.2625 - val_y1_output_loss: 7.6800 - val_y2_output_loss: 24.5825 - val_y1_output_root_mean_squared_error: 2.7713 - val_y2_output_root_mean_squared_error: 4.9581\n",
            "Epoch 22/500\n",
            "62/62 [==============================] - 0s 8ms/step - loss: 14.9026 - y1_output_loss: 5.1648 - y2_output_loss: 9.7378 - y1_output_root_mean_squared_error: 2.2726 - y2_output_root_mean_squared_error: 3.1205 - val_loss: 11.3492 - val_y1_output_loss: 4.5808 - val_y2_output_loss: 6.7684 - val_y1_output_root_mean_squared_error: 2.1403 - val_y2_output_root_mean_squared_error: 2.6016\n",
            "Epoch 23/500\n",
            "62/62 [==============================] - 0s 5ms/step - loss: 13.1931 - y1_output_loss: 4.8525 - y2_output_loss: 8.3406 - y1_output_root_mean_squared_error: 2.2028 - y2_output_root_mean_squared_error: 2.8880 - val_loss: 10.6366 - val_y1_output_loss: 4.2781 - val_y2_output_loss: 6.3585 - val_y1_output_root_mean_squared_error: 2.0684 - val_y2_output_root_mean_squared_error: 2.5216\n",
            "Epoch 24/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 13.9292 - y1_output_loss: 5.1040 - y2_output_loss: 8.8252 - y1_output_root_mean_squared_error: 2.2592 - y2_output_root_mean_squared_error: 2.9707 - val_loss: 19.7766 - val_y1_output_loss: 7.3609 - val_y2_output_loss: 12.4158 - val_y1_output_root_mean_squared_error: 2.7131 - val_y2_output_root_mean_squared_error: 3.5236\n",
            "Epoch 25/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 14.5174 - y1_output_loss: 4.9850 - y2_output_loss: 9.5325 - y1_output_root_mean_squared_error: 2.2327 - y2_output_root_mean_squared_error: 3.0875 - val_loss: 14.2853 - val_y1_output_loss: 5.7073 - val_y2_output_loss: 8.5780 - val_y1_output_root_mean_squared_error: 2.3890 - val_y2_output_root_mean_squared_error: 2.9288\n",
            "Epoch 26/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 13.8587 - y1_output_loss: 4.9094 - y2_output_loss: 8.9493 - y1_output_root_mean_squared_error: 2.2157 - y2_output_root_mean_squared_error: 2.9915 - val_loss: 23.2959 - val_y1_output_loss: 6.5910 - val_y2_output_loss: 16.7049 - val_y1_output_root_mean_squared_error: 2.5673 - val_y2_output_root_mean_squared_error: 4.0872\n",
            "Epoch 27/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 11.7858 - y1_output_loss: 4.2266 - y2_output_loss: 7.5592 - y1_output_root_mean_squared_error: 2.0559 - y2_output_root_mean_squared_error: 2.7494 - val_loss: 9.3334 - val_y1_output_loss: 3.4596 - val_y2_output_loss: 5.8739 - val_y1_output_root_mean_squared_error: 1.8600 - val_y2_output_root_mean_squared_error: 2.4236\n",
            "Epoch 28/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 10.1553 - y1_output_loss: 3.5547 - y2_output_loss: 6.6006 - y1_output_root_mean_squared_error: 1.8854 - y2_output_root_mean_squared_error: 2.5692 - val_loss: 9.3688 - val_y1_output_loss: 4.0941 - val_y2_output_loss: 5.2747 - val_y1_output_root_mean_squared_error: 2.0234 - val_y2_output_root_mean_squared_error: 2.2967\n",
            "Epoch 29/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 10.6337 - y1_output_loss: 3.7697 - y2_output_loss: 6.8640 - y1_output_root_mean_squared_error: 1.9416 - y2_output_root_mean_squared_error: 2.6199 - val_loss: 9.4484 - val_y1_output_loss: 4.2419 - val_y2_output_loss: 5.2065 - val_y1_output_root_mean_squared_error: 2.0596 - val_y2_output_root_mean_squared_error: 2.2818\n",
            "Epoch 30/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 10.7758 - y1_output_loss: 3.6843 - y2_output_loss: 7.0915 - y1_output_root_mean_squared_error: 1.9195 - y2_output_root_mean_squared_error: 2.6630 - val_loss: 10.4656 - val_y1_output_loss: 3.2317 - val_y2_output_loss: 7.2340 - val_y1_output_root_mean_squared_error: 1.7977 - val_y2_output_root_mean_squared_error: 2.6896\n",
            "Epoch 31/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 10.3880 - y1_output_loss: 3.5190 - y2_output_loss: 6.8690 - y1_output_root_mean_squared_error: 1.8759 - y2_output_root_mean_squared_error: 2.6209 - val_loss: 8.2251 - val_y1_output_loss: 3.1891 - val_y2_output_loss: 5.0360 - val_y1_output_root_mean_squared_error: 1.7858 - val_y2_output_root_mean_squared_error: 2.2441\n",
            "Epoch 32/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 10.3903 - y1_output_loss: 3.4251 - y2_output_loss: 6.9653 - y1_output_root_mean_squared_error: 1.8507 - y2_output_root_mean_squared_error: 2.6392 - val_loss: 12.0178 - val_y1_output_loss: 5.0481 - val_y2_output_loss: 6.9697 - val_y1_output_root_mean_squared_error: 2.2468 - val_y2_output_root_mean_squared_error: 2.6400\n",
            "Epoch 33/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 9.4223 - y1_output_loss: 3.3814 - y2_output_loss: 6.0408 - y1_output_root_mean_squared_error: 1.8389 - y2_output_root_mean_squared_error: 2.4578 - val_loss: 8.2024 - val_y1_output_loss: 2.9342 - val_y2_output_loss: 5.2682 - val_y1_output_root_mean_squared_error: 1.7130 - val_y2_output_root_mean_squared_error: 2.2953\n",
            "Epoch 34/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 8.5428 - y1_output_loss: 3.0430 - y2_output_loss: 5.4998 - y1_output_root_mean_squared_error: 1.7444 - y2_output_root_mean_squared_error: 2.3452 - val_loss: 9.0541 - val_y1_output_loss: 3.9979 - val_y2_output_loss: 5.0562 - val_y1_output_root_mean_squared_error: 1.9995 - val_y2_output_root_mean_squared_error: 2.2486\n",
            "Epoch 35/500\n",
            "62/62 [==============================] - 0s 2ms/step - loss: 9.0720 - y1_output_loss: 3.2619 - y2_output_loss: 5.8102 - y1_output_root_mean_squared_error: 1.8061 - y2_output_root_mean_squared_error: 2.4104 - val_loss: 7.5480 - val_y1_output_loss: 2.8423 - val_y2_output_loss: 4.7058 - val_y1_output_root_mean_squared_error: 1.6859 - val_y2_output_root_mean_squared_error: 2.1693\n",
            "Epoch 36/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 9.6601 - y1_output_loss: 3.1776 - y2_output_loss: 6.4826 - y1_output_root_mean_squared_error: 1.7826 - y2_output_root_mean_squared_error: 2.5461 - val_loss: 50.9709 - val_y1_output_loss: 14.5090 - val_y2_output_loss: 36.4620 - val_y1_output_root_mean_squared_error: 3.8091 - val_y2_output_root_mean_squared_error: 6.0384\n",
            "Epoch 37/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 8.3917 - y1_output_loss: 3.0605 - y2_output_loss: 5.3312 - y1_output_root_mean_squared_error: 1.7494 - y2_output_root_mean_squared_error: 2.3089 - val_loss: 9.5180 - val_y1_output_loss: 3.6436 - val_y2_output_loss: 5.8743 - val_y1_output_root_mean_squared_error: 1.9088 - val_y2_output_root_mean_squared_error: 2.4237\n",
            "Epoch 38/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 10.4589 - y1_output_loss: 3.6207 - y2_output_loss: 6.8382 - y1_output_root_mean_squared_error: 1.9028 - y2_output_root_mean_squared_error: 2.6150 - val_loss: 7.2857 - val_y1_output_loss: 2.7488 - val_y2_output_loss: 4.5369 - val_y1_output_root_mean_squared_error: 1.6580 - val_y2_output_root_mean_squared_error: 2.1300\n",
            "Epoch 39/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 7.6923 - y1_output_loss: 2.5913 - y2_output_loss: 5.1010 - y1_output_root_mean_squared_error: 1.6097 - y2_output_root_mean_squared_error: 2.2585 - val_loss: 20.4938 - val_y1_output_loss: 8.3901 - val_y2_output_loss: 12.1037 - val_y1_output_root_mean_squared_error: 2.8966 - val_y2_output_root_mean_squared_error: 3.4790\n",
            "Epoch 40/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 10.5333 - y1_output_loss: 3.4044 - y2_output_loss: 7.1289 - y1_output_root_mean_squared_error: 1.8451 - y2_output_root_mean_squared_error: 2.6700 - val_loss: 10.5661 - val_y1_output_loss: 3.9716 - val_y2_output_loss: 6.5945 - val_y1_output_root_mean_squared_error: 1.9929 - val_y2_output_root_mean_squared_error: 2.5680\n",
            "Epoch 41/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 8.6823 - y1_output_loss: 2.7576 - y2_output_loss: 5.9247 - y1_output_root_mean_squared_error: 1.6606 - y2_output_root_mean_squared_error: 2.4341 - val_loss: 6.6086 - val_y1_output_loss: 2.3087 - val_y2_output_loss: 4.2999 - val_y1_output_root_mean_squared_error: 1.5194 - val_y2_output_root_mean_squared_error: 2.0736\n",
            "Epoch 42/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 9.4325 - y1_output_loss: 2.8720 - y2_output_loss: 6.5605 - y1_output_root_mean_squared_error: 1.6947 - y2_output_root_mean_squared_error: 2.5614 - val_loss: 7.3978 - val_y1_output_loss: 2.5207 - val_y2_output_loss: 4.8771 - val_y1_output_root_mean_squared_error: 1.5877 - val_y2_output_root_mean_squared_error: 2.2084\n",
            "Epoch 43/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 7.8884 - y1_output_loss: 2.3151 - y2_output_loss: 5.5733 - y1_output_root_mean_squared_error: 1.5216 - y2_output_root_mean_squared_error: 2.3608 - val_loss: 13.7999 - val_y1_output_loss: 3.8020 - val_y2_output_loss: 9.9980 - val_y1_output_root_mean_squared_error: 1.9499 - val_y2_output_root_mean_squared_error: 3.1620\n",
            "Epoch 44/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 8.5970 - y1_output_loss: 2.6766 - y2_output_loss: 5.9204 - y1_output_root_mean_squared_error: 1.6360 - y2_output_root_mean_squared_error: 2.4332 - val_loss: 8.2993 - val_y1_output_loss: 2.4099 - val_y2_output_loss: 5.8894 - val_y1_output_root_mean_squared_error: 1.5524 - val_y2_output_root_mean_squared_error: 2.4268\n",
            "Epoch 45/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 8.0510 - y1_output_loss: 2.4843 - y2_output_loss: 5.5667 - y1_output_root_mean_squared_error: 1.5762 - y2_output_root_mean_squared_error: 2.3594 - val_loss: 16.3570 - val_y1_output_loss: 5.2367 - val_y2_output_loss: 11.1203 - val_y1_output_root_mean_squared_error: 2.2884 - val_y2_output_root_mean_squared_error: 3.3347\n",
            "Epoch 46/500\n",
            "62/62 [==============================] - 0s 2ms/step - loss: 7.2263 - y1_output_loss: 2.3853 - y2_output_loss: 4.8410 - y1_output_root_mean_squared_error: 1.5444 - y2_output_root_mean_squared_error: 2.2002 - val_loss: 6.6418 - val_y1_output_loss: 2.3597 - val_y2_output_loss: 4.2821 - val_y1_output_root_mean_squared_error: 1.5361 - val_y2_output_root_mean_squared_error: 2.0693\n",
            "Epoch 47/500\n",
            "62/62 [==============================] - 0s 2ms/step - loss: 7.2064 - y1_output_loss: 2.2273 - y2_output_loss: 4.9791 - y1_output_root_mean_squared_error: 1.4924 - y2_output_root_mean_squared_error: 2.2314 - val_loss: 25.9885 - val_y1_output_loss: 5.3958 - val_y2_output_loss: 20.5926 - val_y1_output_root_mean_squared_error: 2.3229 - val_y2_output_root_mean_squared_error: 4.5379\n",
            "Epoch 48/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 9.8414 - y1_output_loss: 2.9417 - y2_output_loss: 6.8997 - y1_output_root_mean_squared_error: 1.7152 - y2_output_root_mean_squared_error: 2.6267 - val_loss: 10.0121 - val_y1_output_loss: 4.3259 - val_y2_output_loss: 5.6863 - val_y1_output_root_mean_squared_error: 2.0799 - val_y2_output_root_mean_squared_error: 2.3846\n",
            "Epoch 49/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 8.3158 - y1_output_loss: 2.7676 - y2_output_loss: 5.5482 - y1_output_root_mean_squared_error: 1.6636 - y2_output_root_mean_squared_error: 2.3555 - val_loss: 55.6233 - val_y1_output_loss: 19.6663 - val_y2_output_loss: 35.9570 - val_y1_output_root_mean_squared_error: 4.4347 - val_y2_output_root_mean_squared_error: 5.9964\n",
            "Epoch 50/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 9.1722 - y1_output_loss: 2.8637 - y2_output_loss: 6.3086 - y1_output_root_mean_squared_error: 1.6922 - y2_output_root_mean_squared_error: 2.5117 - val_loss: 12.4996 - val_y1_output_loss: 2.7653 - val_y2_output_loss: 9.7343 - val_y1_output_root_mean_squared_error: 1.6629 - val_y2_output_root_mean_squared_error: 3.1200\n",
            "Epoch 51/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 9.6672 - y1_output_loss: 3.0820 - y2_output_loss: 6.5851 - y1_output_root_mean_squared_error: 1.7556 - y2_output_root_mean_squared_error: 2.5662 - val_loss: 5.9587 - val_y1_output_loss: 2.0536 - val_y2_output_loss: 3.9051 - val_y1_output_root_mean_squared_error: 1.4330 - val_y2_output_root_mean_squared_error: 1.9761\n",
            "Epoch 52/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 10.3839 - y1_output_loss: 3.1331 - y2_output_loss: 7.2508 - y1_output_root_mean_squared_error: 1.7701 - y2_output_root_mean_squared_error: 2.6927 - val_loss: 9.1314 - val_y1_output_loss: 3.6812 - val_y2_output_loss: 5.4502 - val_y1_output_root_mean_squared_error: 1.9187 - val_y2_output_root_mean_squared_error: 2.3346\n",
            "Epoch 53/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 7.4338 - y1_output_loss: 2.4322 - y2_output_loss: 5.0016 - y1_output_root_mean_squared_error: 1.5596 - y2_output_root_mean_squared_error: 2.2364 - val_loss: 6.9472 - val_y1_output_loss: 1.8144 - val_y2_output_loss: 5.1328 - val_y1_output_root_mean_squared_error: 1.3470 - val_y2_output_root_mean_squared_error: 2.2656\n",
            "Epoch 54/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 9.2772 - y1_output_loss: 2.8253 - y2_output_loss: 6.4518 - y1_output_root_mean_squared_error: 1.6809 - y2_output_root_mean_squared_error: 2.5400 - val_loss: 8.0640 - val_y1_output_loss: 2.5321 - val_y2_output_loss: 5.5318 - val_y1_output_root_mean_squared_error: 1.5913 - val_y2_output_root_mean_squared_error: 2.3520\n",
            "Epoch 55/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 6.3382 - y1_output_loss: 2.0407 - y2_output_loss: 4.2976 - y1_output_root_mean_squared_error: 1.4285 - y2_output_root_mean_squared_error: 2.0731 - val_loss: 6.7239 - val_y1_output_loss: 2.8786 - val_y2_output_loss: 3.8453 - val_y1_output_root_mean_squared_error: 1.6967 - val_y2_output_root_mean_squared_error: 1.9609\n",
            "Epoch 56/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 6.5134 - y1_output_loss: 2.0810 - y2_output_loss: 4.4324 - y1_output_root_mean_squared_error: 1.4426 - y2_output_root_mean_squared_error: 2.1053 - val_loss: 9.3079 - val_y1_output_loss: 2.2847 - val_y2_output_loss: 7.0232 - val_y1_output_root_mean_squared_error: 1.5115 - val_y2_output_root_mean_squared_error: 2.6501\n",
            "Epoch 57/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 6.6862 - y1_output_loss: 2.0665 - y2_output_loss: 4.6196 - y1_output_root_mean_squared_error: 1.4375 - y2_output_root_mean_squared_error: 2.1493 - val_loss: 5.5699 - val_y1_output_loss: 1.9500 - val_y2_output_loss: 3.6199 - val_y1_output_root_mean_squared_error: 1.3964 - val_y2_output_root_mean_squared_error: 1.9026\n",
            "Epoch 58/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 7.1511 - y1_output_loss: 2.1266 - y2_output_loss: 5.0245 - y1_output_root_mean_squared_error: 1.4583 - y2_output_root_mean_squared_error: 2.2415 - val_loss: 7.2664 - val_y1_output_loss: 2.6767 - val_y2_output_loss: 4.5897 - val_y1_output_root_mean_squared_error: 1.6361 - val_y2_output_root_mean_squared_error: 2.1424\n",
            "Epoch 59/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 5.5107 - y1_output_loss: 1.7704 - y2_output_loss: 3.7402 - y1_output_root_mean_squared_error: 1.3306 - y2_output_root_mean_squared_error: 1.9340 - val_loss: 5.6255 - val_y1_output_loss: 1.7062 - val_y2_output_loss: 3.9194 - val_y1_output_root_mean_squared_error: 1.3062 - val_y2_output_root_mean_squared_error: 1.9797\n",
            "Epoch 60/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 6.2630 - y1_output_loss: 1.8687 - y2_output_loss: 4.3943 - y1_output_root_mean_squared_error: 1.3670 - y2_output_root_mean_squared_error: 2.0963 - val_loss: 6.0261 - val_y1_output_loss: 2.1850 - val_y2_output_loss: 3.8411 - val_y1_output_root_mean_squared_error: 1.4782 - val_y2_output_root_mean_squared_error: 1.9599\n",
            "Epoch 61/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 5.3251 - y1_output_loss: 1.5740 - y2_output_loss: 3.7511 - y1_output_root_mean_squared_error: 1.2546 - y2_output_root_mean_squared_error: 1.9368 - val_loss: 8.0346 - val_y1_output_loss: 2.3570 - val_y2_output_loss: 5.6777 - val_y1_output_root_mean_squared_error: 1.5352 - val_y2_output_root_mean_squared_error: 2.3828\n",
            "Epoch 62/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 5.7122 - y1_output_loss: 1.6561 - y2_output_loss: 4.0561 - y1_output_root_mean_squared_error: 1.2869 - y2_output_root_mean_squared_error: 2.0140 - val_loss: 6.6304 - val_y1_output_loss: 1.9822 - val_y2_output_loss: 4.6482 - val_y1_output_root_mean_squared_error: 1.4079 - val_y2_output_root_mean_squared_error: 2.1560\n",
            "Epoch 63/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 6.8942 - y1_output_loss: 1.9708 - y2_output_loss: 4.9233 - y1_output_root_mean_squared_error: 1.4039 - y2_output_root_mean_squared_error: 2.2189 - val_loss: 7.3688 - val_y1_output_loss: 1.6254 - val_y2_output_loss: 5.7433 - val_y1_output_root_mean_squared_error: 1.2749 - val_y2_output_root_mean_squared_error: 2.3965\n",
            "Epoch 64/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 11.4894 - y1_output_loss: 3.8407 - y2_output_loss: 7.6487 - y1_output_root_mean_squared_error: 1.9598 - y2_output_root_mean_squared_error: 2.7656 - val_loss: 4.7812 - val_y1_output_loss: 1.4855 - val_y2_output_loss: 3.2957 - val_y1_output_root_mean_squared_error: 1.2188 - val_y2_output_root_mean_squared_error: 1.8154\n",
            "Epoch 65/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 6.9499 - y1_output_loss: 2.1332 - y2_output_loss: 4.8166 - y1_output_root_mean_squared_error: 1.4606 - y2_output_root_mean_squared_error: 2.1947 - val_loss: 4.1157 - val_y1_output_loss: 1.2703 - val_y2_output_loss: 2.8454 - val_y1_output_root_mean_squared_error: 1.1271 - val_y2_output_root_mean_squared_error: 1.6868\n",
            "Epoch 66/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 5.6462 - y1_output_loss: 1.7418 - y2_output_loss: 3.9044 - y1_output_root_mean_squared_error: 1.3198 - y2_output_root_mean_squared_error: 1.9760 - val_loss: 12.5148 - val_y1_output_loss: 2.3636 - val_y2_output_loss: 10.1512 - val_y1_output_root_mean_squared_error: 1.5374 - val_y2_output_root_mean_squared_error: 3.1861\n",
            "Epoch 67/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 5.5158 - y1_output_loss: 1.5283 - y2_output_loss: 3.9874 - y1_output_root_mean_squared_error: 1.2363 - y2_output_root_mean_squared_error: 1.9969 - val_loss: 5.7272 - val_y1_output_loss: 2.1688 - val_y2_output_loss: 3.5584 - val_y1_output_root_mean_squared_error: 1.4727 - val_y2_output_root_mean_squared_error: 1.8864\n",
            "Epoch 68/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 6.6251 - y1_output_loss: 1.9699 - y2_output_loss: 4.6553 - y1_output_root_mean_squared_error: 1.4035 - y2_output_root_mean_squared_error: 2.1576 - val_loss: 4.6659 - val_y1_output_loss: 1.4034 - val_y2_output_loss: 3.2625 - val_y1_output_root_mean_squared_error: 1.1846 - val_y2_output_root_mean_squared_error: 1.8062\n",
            "Epoch 69/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 6.1990 - y1_output_loss: 1.7869 - y2_output_loss: 4.4122 - y1_output_root_mean_squared_error: 1.3367 - y2_output_root_mean_squared_error: 2.1005 - val_loss: 6.7061 - val_y1_output_loss: 1.5350 - val_y2_output_loss: 5.1711 - val_y1_output_root_mean_squared_error: 1.2389 - val_y2_output_root_mean_squared_error: 2.2740\n",
            "Epoch 70/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 5.3040 - y1_output_loss: 1.5306 - y2_output_loss: 3.7734 - y1_output_root_mean_squared_error: 1.2372 - y2_output_root_mean_squared_error: 1.9425 - val_loss: 17.9768 - val_y1_output_loss: 4.2864 - val_y2_output_loss: 13.6904 - val_y1_output_root_mean_squared_error: 2.0704 - val_y2_output_root_mean_squared_error: 3.7001\n",
            "Epoch 71/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 4.8092 - y1_output_loss: 1.2442 - y2_output_loss: 3.5651 - y1_output_root_mean_squared_error: 1.1154 - y2_output_root_mean_squared_error: 1.8881 - val_loss: 3.4158 - val_y1_output_loss: 1.0076 - val_y2_output_loss: 2.4082 - val_y1_output_root_mean_squared_error: 1.0038 - val_y2_output_root_mean_squared_error: 1.5518\n",
            "Epoch 72/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 4.8103 - y1_output_loss: 1.2827 - y2_output_loss: 3.5276 - y1_output_root_mean_squared_error: 1.1325 - y2_output_root_mean_squared_error: 1.8782 - val_loss: 4.8668 - val_y1_output_loss: 1.4939 - val_y2_output_loss: 3.3728 - val_y1_output_root_mean_squared_error: 1.2223 - val_y2_output_root_mean_squared_error: 1.8365\n",
            "Epoch 73/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 4.6202 - y1_output_loss: 1.2595 - y2_output_loss: 3.3608 - y1_output_root_mean_squared_error: 1.1223 - y2_output_root_mean_squared_error: 1.8332 - val_loss: 5.2070 - val_y1_output_loss: 1.4174 - val_y2_output_loss: 3.7896 - val_y1_output_root_mean_squared_error: 1.1905 - val_y2_output_root_mean_squared_error: 1.9467\n",
            "Epoch 74/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 4.8136 - y1_output_loss: 1.2005 - y2_output_loss: 3.6131 - y1_output_root_mean_squared_error: 1.0957 - y2_output_root_mean_squared_error: 1.9008 - val_loss: 3.5110 - val_y1_output_loss: 0.9971 - val_y2_output_loss: 2.5139 - val_y1_output_root_mean_squared_error: 0.9985 - val_y2_output_root_mean_squared_error: 1.5855\n",
            "Epoch 75/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 4.1841 - y1_output_loss: 1.1499 - y2_output_loss: 3.0343 - y1_output_root_mean_squared_error: 1.0723 - y2_output_root_mean_squared_error: 1.7419 - val_loss: 4.8832 - val_y1_output_loss: 1.7959 - val_y2_output_loss: 3.0874 - val_y1_output_root_mean_squared_error: 1.3401 - val_y2_output_root_mean_squared_error: 1.7571\n",
            "Epoch 76/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 6.9975 - y1_output_loss: 1.9131 - y2_output_loss: 5.0845 - y1_output_root_mean_squared_error: 1.3831 - y2_output_root_mean_squared_error: 2.2549 - val_loss: 6.6210 - val_y1_output_loss: 0.9034 - val_y2_output_loss: 5.7177 - val_y1_output_root_mean_squared_error: 0.9504 - val_y2_output_root_mean_squared_error: 2.3912\n",
            "Epoch 77/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 5.2177 - y1_output_loss: 1.3695 - y2_output_loss: 3.8483 - y1_output_root_mean_squared_error: 1.1702 - y2_output_root_mean_squared_error: 1.9617 - val_loss: 5.3508 - val_y1_output_loss: 1.7769 - val_y2_output_loss: 3.5739 - val_y1_output_root_mean_squared_error: 1.3330 - val_y2_output_root_mean_squared_error: 1.8905\n",
            "Epoch 78/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 4.0227 - y1_output_loss: 1.1174 - y2_output_loss: 2.9052 - y1_output_root_mean_squared_error: 1.0571 - y2_output_root_mean_squared_error: 1.7045 - val_loss: 4.8474 - val_y1_output_loss: 0.9298 - val_y2_output_loss: 3.9176 - val_y1_output_root_mean_squared_error: 0.9642 - val_y2_output_root_mean_squared_error: 1.9793\n",
            "Epoch 79/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 4.5003 - y1_output_loss: 1.1179 - y2_output_loss: 3.3824 - y1_output_root_mean_squared_error: 1.0573 - y2_output_root_mean_squared_error: 1.8391 - val_loss: 6.2211 - val_y1_output_loss: 1.5728 - val_y2_output_loss: 4.6483 - val_y1_output_root_mean_squared_error: 1.2541 - val_y2_output_root_mean_squared_error: 2.1560\n",
            "Epoch 80/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 3.9848 - y1_output_loss: 0.9806 - y2_output_loss: 3.0042 - y1_output_root_mean_squared_error: 0.9902 - y2_output_root_mean_squared_error: 1.7333 - val_loss: 2.8115 - val_y1_output_loss: 0.8434 - val_y2_output_loss: 1.9681 - val_y1_output_root_mean_squared_error: 0.9184 - val_y2_output_root_mean_squared_error: 1.4029\n",
            "Epoch 81/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 4.2639 - y1_output_loss: 1.0757 - y2_output_loss: 3.1882 - y1_output_root_mean_squared_error: 1.0371 - y2_output_root_mean_squared_error: 1.7856 - val_loss: 4.3813 - val_y1_output_loss: 0.7583 - val_y2_output_loss: 3.6231 - val_y1_output_root_mean_squared_error: 0.8708 - val_y2_output_root_mean_squared_error: 1.9034\n",
            "Epoch 82/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 3.7363 - y1_output_loss: 0.8808 - y2_output_loss: 2.8555 - y1_output_root_mean_squared_error: 0.9385 - y2_output_root_mean_squared_error: 1.6898 - val_loss: 4.9753 - val_y1_output_loss: 0.7632 - val_y2_output_loss: 4.2121 - val_y1_output_root_mean_squared_error: 0.8736 - val_y2_output_root_mean_squared_error: 2.0523\n",
            "Epoch 83/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 3.7361 - y1_output_loss: 0.9301 - y2_output_loss: 2.8060 - y1_output_root_mean_squared_error: 0.9644 - y2_output_root_mean_squared_error: 1.6751 - val_loss: 4.5860 - val_y1_output_loss: 1.6348 - val_y2_output_loss: 2.9512 - val_y1_output_root_mean_squared_error: 1.2786 - val_y2_output_root_mean_squared_error: 1.7179\n",
            "Epoch 84/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 3.5156 - y1_output_loss: 0.9264 - y2_output_loss: 2.5892 - y1_output_root_mean_squared_error: 0.9625 - y2_output_root_mean_squared_error: 1.6091 - val_loss: 4.1177 - val_y1_output_loss: 1.0029 - val_y2_output_loss: 3.1148 - val_y1_output_root_mean_squared_error: 1.0014 - val_y2_output_root_mean_squared_error: 1.7649\n",
            "Epoch 85/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 4.1802 - y1_output_loss: 0.9752 - y2_output_loss: 3.2050 - y1_output_root_mean_squared_error: 0.9875 - y2_output_root_mean_squared_error: 1.7902 - val_loss: 5.7461 - val_y1_output_loss: 1.1123 - val_y2_output_loss: 4.6338 - val_y1_output_root_mean_squared_error: 1.0547 - val_y2_output_root_mean_squared_error: 2.1526\n",
            "Epoch 86/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 3.8394 - y1_output_loss: 1.0049 - y2_output_loss: 2.8344 - y1_output_root_mean_squared_error: 1.0025 - y2_output_root_mean_squared_error: 1.6836 - val_loss: 4.7470 - val_y1_output_loss: 0.8305 - val_y2_output_loss: 3.9165 - val_y1_output_root_mean_squared_error: 0.9113 - val_y2_output_root_mean_squared_error: 1.9790\n",
            "Epoch 87/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 3.4156 - y1_output_loss: 0.9967 - y2_output_loss: 2.4189 - y1_output_root_mean_squared_error: 0.9983 - y2_output_root_mean_squared_error: 1.5553 - val_loss: 2.3861 - val_y1_output_loss: 0.6204 - val_y2_output_loss: 1.7657 - val_y1_output_root_mean_squared_error: 0.7877 - val_y2_output_root_mean_squared_error: 1.3288\n",
            "Epoch 88/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 3.5080 - y1_output_loss: 0.8734 - y2_output_loss: 2.6346 - y1_output_root_mean_squared_error: 0.9346 - y2_output_root_mean_squared_error: 1.6231 - val_loss: 11.2853 - val_y1_output_loss: 1.3024 - val_y2_output_loss: 9.9829 - val_y1_output_root_mean_squared_error: 1.1412 - val_y2_output_root_mean_squared_error: 3.1596\n",
            "Epoch 89/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 4.5637 - y1_output_loss: 1.1411 - y2_output_loss: 3.4226 - y1_output_root_mean_squared_error: 1.0682 - y2_output_root_mean_squared_error: 1.8500 - val_loss: 11.7746 - val_y1_output_loss: 4.4048 - val_y2_output_loss: 7.3697 - val_y1_output_root_mean_squared_error: 2.0988 - val_y2_output_root_mean_squared_error: 2.7147\n",
            "Epoch 90/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 3.6420 - y1_output_loss: 0.9840 - y2_output_loss: 2.6581 - y1_output_root_mean_squared_error: 0.9920 - y2_output_root_mean_squared_error: 1.6304 - val_loss: 5.8503 - val_y1_output_loss: 1.4330 - val_y2_output_loss: 4.4173 - val_y1_output_root_mean_squared_error: 1.1971 - val_y2_output_root_mean_squared_error: 2.1017\n",
            "Epoch 91/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 3.6884 - y1_output_loss: 0.9784 - y2_output_loss: 2.7100 - y1_output_root_mean_squared_error: 0.9891 - y2_output_root_mean_squared_error: 1.6462 - val_loss: 3.7499 - val_y1_output_loss: 0.7369 - val_y2_output_loss: 3.0130 - val_y1_output_root_mean_squared_error: 0.8584 - val_y2_output_root_mean_squared_error: 1.7358\n",
            "Epoch 92/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 2.8477 - y1_output_loss: 0.7098 - y2_output_loss: 2.1379 - y1_output_root_mean_squared_error: 0.8425 - y2_output_root_mean_squared_error: 1.4622 - val_loss: 2.6153 - val_y1_output_loss: 0.6245 - val_y2_output_loss: 1.9908 - val_y1_output_root_mean_squared_error: 0.7902 - val_y2_output_root_mean_squared_error: 1.4110\n",
            "Epoch 93/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 2.9428 - y1_output_loss: 0.6919 - y2_output_loss: 2.2509 - y1_output_root_mean_squared_error: 0.8318 - y2_output_root_mean_squared_error: 1.5003 - val_loss: 2.0544 - val_y1_output_loss: 0.5319 - val_y2_output_loss: 1.5225 - val_y1_output_root_mean_squared_error: 0.7293 - val_y2_output_root_mean_squared_error: 1.2339\n",
            "Epoch 94/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 2.5647 - y1_output_loss: 0.6412 - y2_output_loss: 1.9234 - y1_output_root_mean_squared_error: 0.8008 - y2_output_root_mean_squared_error: 1.3869 - val_loss: 9.7426 - val_y1_output_loss: 3.6706 - val_y2_output_loss: 6.0721 - val_y1_output_root_mean_squared_error: 1.9159 - val_y2_output_root_mean_squared_error: 2.4642\n",
            "Epoch 95/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 3.7369 - y1_output_loss: 0.9903 - y2_output_loss: 2.7466 - y1_output_root_mean_squared_error: 0.9951 - y2_output_root_mean_squared_error: 1.6573 - val_loss: 2.0909 - val_y1_output_loss: 0.5006 - val_y2_output_loss: 1.5903 - val_y1_output_root_mean_squared_error: 0.7075 - val_y2_output_root_mean_squared_error: 1.2611\n",
            "Epoch 96/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 3.7408 - y1_output_loss: 0.9256 - y2_output_loss: 2.8152 - y1_output_root_mean_squared_error: 0.9621 - y2_output_root_mean_squared_error: 1.6779 - val_loss: 4.3770 - val_y1_output_loss: 1.8027 - val_y2_output_loss: 2.5743 - val_y1_output_root_mean_squared_error: 1.3426 - val_y2_output_root_mean_squared_error: 1.6045\n",
            "Epoch 97/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 4.5619 - y1_output_loss: 1.0848 - y2_output_loss: 3.4771 - y1_output_root_mean_squared_error: 1.0415 - y2_output_root_mean_squared_error: 1.8647 - val_loss: 3.7015 - val_y1_output_loss: 1.2294 - val_y2_output_loss: 2.4722 - val_y1_output_root_mean_squared_error: 1.1088 - val_y2_output_root_mean_squared_error: 1.5723\n",
            "Epoch 98/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 4.8066 - y1_output_loss: 1.5525 - y2_output_loss: 3.2541 - y1_output_root_mean_squared_error: 1.2460 - y2_output_root_mean_squared_error: 1.8039 - val_loss: 3.6287 - val_y1_output_loss: 0.7110 - val_y2_output_loss: 2.9177 - val_y1_output_root_mean_squared_error: 0.8432 - val_y2_output_root_mean_squared_error: 1.7081\n",
            "Epoch 99/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 2.7746 - y1_output_loss: 0.5478 - y2_output_loss: 2.2268 - y1_output_root_mean_squared_error: 0.7401 - y2_output_root_mean_squared_error: 1.4923 - val_loss: 2.4831 - val_y1_output_loss: 0.7877 - val_y2_output_loss: 1.6954 - val_y1_output_root_mean_squared_error: 0.8875 - val_y2_output_root_mean_squared_error: 1.3021\n",
            "Epoch 100/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 2.8716 - y1_output_loss: 0.6881 - y2_output_loss: 2.1834 - y1_output_root_mean_squared_error: 0.8295 - y2_output_root_mean_squared_error: 1.4776 - val_loss: 4.3271 - val_y1_output_loss: 1.3513 - val_y2_output_loss: 2.9758 - val_y1_output_root_mean_squared_error: 1.1625 - val_y2_output_root_mean_squared_error: 1.7250\n",
            "Epoch 101/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 3.0491 - y1_output_loss: 0.7026 - y2_output_loss: 2.3465 - y1_output_root_mean_squared_error: 0.8382 - y2_output_root_mean_squared_error: 1.5318 - val_loss: 2.0303 - val_y1_output_loss: 0.5115 - val_y2_output_loss: 1.5187 - val_y1_output_root_mean_squared_error: 0.7152 - val_y2_output_root_mean_squared_error: 1.2324\n",
            "Epoch 102/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 2.1581 - y1_output_loss: 0.4820 - y2_output_loss: 1.6761 - y1_output_root_mean_squared_error: 0.6942 - y2_output_root_mean_squared_error: 1.2947 - val_loss: 1.6867 - val_y1_output_loss: 0.3963 - val_y2_output_loss: 1.2904 - val_y1_output_root_mean_squared_error: 0.6295 - val_y2_output_root_mean_squared_error: 1.1360\n",
            "Epoch 103/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 2.3696 - y1_output_loss: 0.5218 - y2_output_loss: 1.8478 - y1_output_root_mean_squared_error: 0.7224 - y2_output_root_mean_squared_error: 1.3593 - val_loss: 2.0176 - val_y1_output_loss: 0.4447 - val_y2_output_loss: 1.5729 - val_y1_output_root_mean_squared_error: 0.6669 - val_y2_output_root_mean_squared_error: 1.2542\n",
            "Epoch 104/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 2.6216 - y1_output_loss: 0.6228 - y2_output_loss: 1.9988 - y1_output_root_mean_squared_error: 0.7891 - y2_output_root_mean_squared_error: 1.4138 - val_loss: 2.8326 - val_y1_output_loss: 0.8554 - val_y2_output_loss: 1.9773 - val_y1_output_root_mean_squared_error: 0.9249 - val_y2_output_root_mean_squared_error: 1.4061\n",
            "Epoch 105/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 3.2497 - y1_output_loss: 0.6651 - y2_output_loss: 2.5846 - y1_output_root_mean_squared_error: 0.8155 - y2_output_root_mean_squared_error: 1.6077 - val_loss: 2.3453 - val_y1_output_loss: 0.6405 - val_y2_output_loss: 1.7047 - val_y1_output_root_mean_squared_error: 0.8003 - val_y2_output_root_mean_squared_error: 1.3056\n",
            "Epoch 106/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 2.6405 - y1_output_loss: 0.6745 - y2_output_loss: 1.9660 - y1_output_root_mean_squared_error: 0.8213 - y2_output_root_mean_squared_error: 1.4021 - val_loss: 3.2195 - val_y1_output_loss: 1.2231 - val_y2_output_loss: 1.9964 - val_y1_output_root_mean_squared_error: 1.1059 - val_y2_output_root_mean_squared_error: 1.4129\n",
            "Epoch 107/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 3.8900 - y1_output_loss: 1.0066 - y2_output_loss: 2.8833 - y1_output_root_mean_squared_error: 1.0033 - y2_output_root_mean_squared_error: 1.6980 - val_loss: 2.3349 - val_y1_output_loss: 0.5776 - val_y2_output_loss: 1.7573 - val_y1_output_root_mean_squared_error: 0.7600 - val_y2_output_root_mean_squared_error: 1.3256\n",
            "Epoch 108/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 2.3452 - y1_output_loss: 0.5238 - y2_output_loss: 1.8214 - y1_output_root_mean_squared_error: 0.7237 - y2_output_root_mean_squared_error: 1.3496 - val_loss: 1.8349 - val_y1_output_loss: 0.3771 - val_y2_output_loss: 1.4577 - val_y1_output_root_mean_squared_error: 0.6141 - val_y2_output_root_mean_squared_error: 1.2074\n",
            "Epoch 109/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 2.5168 - y1_output_loss: 0.5631 - y2_output_loss: 1.9537 - y1_output_root_mean_squared_error: 0.7504 - y2_output_root_mean_squared_error: 1.3977 - val_loss: 6.4110 - val_y1_output_loss: 1.3262 - val_y2_output_loss: 5.0848 - val_y1_output_root_mean_squared_error: 1.1516 - val_y2_output_root_mean_squared_error: 2.2549\n",
            "Epoch 110/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 2.6545 - y1_output_loss: 0.7112 - y2_output_loss: 1.9434 - y1_output_root_mean_squared_error: 0.8433 - y2_output_root_mean_squared_error: 1.3940 - val_loss: 2.6873 - val_y1_output_loss: 0.6582 - val_y2_output_loss: 2.0292 - val_y1_output_root_mean_squared_error: 0.8113 - val_y2_output_root_mean_squared_error: 1.4245\n",
            "Epoch 111/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 4.2325 - y1_output_loss: 1.1517 - y2_output_loss: 3.0808 - y1_output_root_mean_squared_error: 1.0732 - y2_output_root_mean_squared_error: 1.7552 - val_loss: 2.6253 - val_y1_output_loss: 0.4218 - val_y2_output_loss: 2.2036 - val_y1_output_root_mean_squared_error: 0.6494 - val_y2_output_root_mean_squared_error: 1.4844\n",
            "Epoch 112/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 3.3247 - y1_output_loss: 0.8542 - y2_output_loss: 2.4705 - y1_output_root_mean_squared_error: 0.9242 - y2_output_root_mean_squared_error: 1.5718 - val_loss: 10.4729 - val_y1_output_loss: 4.2970 - val_y2_output_loss: 6.1759 - val_y1_output_root_mean_squared_error: 2.0729 - val_y2_output_root_mean_squared_error: 2.4851\n",
            "Epoch 113/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 3.2638 - y1_output_loss: 0.6847 - y2_output_loss: 2.5791 - y1_output_root_mean_squared_error: 0.8275 - y2_output_root_mean_squared_error: 1.6060 - val_loss: 5.7962 - val_y1_output_loss: 0.6511 - val_y2_output_loss: 5.1451 - val_y1_output_root_mean_squared_error: 0.8069 - val_y2_output_root_mean_squared_error: 2.2683\n",
            "Epoch 114/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 2.8023 - y1_output_loss: 0.6390 - y2_output_loss: 2.1633 - y1_output_root_mean_squared_error: 0.7994 - y2_output_root_mean_squared_error: 1.4708 - val_loss: 2.2509 - val_y1_output_loss: 0.4267 - val_y2_output_loss: 1.8242 - val_y1_output_root_mean_squared_error: 0.6533 - val_y2_output_root_mean_squared_error: 1.3506\n",
            "Epoch 115/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 2.6181 - y1_output_loss: 0.6596 - y2_output_loss: 1.9585 - y1_output_root_mean_squared_error: 0.8121 - y2_output_root_mean_squared_error: 1.3995 - val_loss: 4.5875 - val_y1_output_loss: 1.2488 - val_y2_output_loss: 3.3387 - val_y1_output_root_mean_squared_error: 1.1175 - val_y2_output_root_mean_squared_error: 1.8272\n",
            "Epoch 116/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 2.1825 - y1_output_loss: 0.5255 - y2_output_loss: 1.6569 - y1_output_root_mean_squared_error: 0.7249 - y2_output_root_mean_squared_error: 1.2872 - val_loss: 1.7269 - val_y1_output_loss: 0.4255 - val_y2_output_loss: 1.3014 - val_y1_output_root_mean_squared_error: 0.6523 - val_y2_output_root_mean_squared_error: 1.1408\n",
            "Epoch 117/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.9863 - y1_output_loss: 0.4622 - y2_output_loss: 1.5241 - y1_output_root_mean_squared_error: 0.6799 - y2_output_root_mean_squared_error: 1.2345 - val_loss: 1.8938 - val_y1_output_loss: 0.3865 - val_y2_output_loss: 1.5074 - val_y1_output_root_mean_squared_error: 0.6217 - val_y2_output_root_mean_squared_error: 1.2277\n",
            "Epoch 118/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.8973 - y1_output_loss: 0.4556 - y2_output_loss: 1.4417 - y1_output_root_mean_squared_error: 0.6750 - y2_output_root_mean_squared_error: 1.2007 - val_loss: 3.4385 - val_y1_output_loss: 0.9136 - val_y2_output_loss: 2.5249 - val_y1_output_root_mean_squared_error: 0.9558 - val_y2_output_root_mean_squared_error: 1.5890\n",
            "Epoch 119/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 2.0643 - y1_output_loss: 0.4785 - y2_output_loss: 1.5859 - y1_output_root_mean_squared_error: 0.6917 - y2_output_root_mean_squared_error: 1.2593 - val_loss: 2.1383 - val_y1_output_loss: 0.5428 - val_y2_output_loss: 1.5955 - val_y1_output_root_mean_squared_error: 0.7368 - val_y2_output_root_mean_squared_error: 1.2631\n",
            "Epoch 120/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.9077 - y1_output_loss: 0.4506 - y2_output_loss: 1.4570 - y1_output_root_mean_squared_error: 0.6713 - y2_output_root_mean_squared_error: 1.2071 - val_loss: 3.1036 - val_y1_output_loss: 0.6119 - val_y2_output_loss: 2.4917 - val_y1_output_root_mean_squared_error: 0.7823 - val_y2_output_root_mean_squared_error: 1.5785\n",
            "Epoch 121/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 3.2051 - y1_output_loss: 0.8075 - y2_output_loss: 2.3976 - y1_output_root_mean_squared_error: 0.8986 - y2_output_root_mean_squared_error: 1.5484 - val_loss: 3.4713 - val_y1_output_loss: 0.8473 - val_y2_output_loss: 2.6241 - val_y1_output_root_mean_squared_error: 0.9205 - val_y2_output_root_mean_squared_error: 1.6199\n",
            "Epoch 122/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 2.2180 - y1_output_loss: 0.5358 - y2_output_loss: 1.6823 - y1_output_root_mean_squared_error: 0.7320 - y2_output_root_mean_squared_error: 1.2970 - val_loss: 1.9649 - val_y1_output_loss: 0.3503 - val_y2_output_loss: 1.6146 - val_y1_output_root_mean_squared_error: 0.5918 - val_y2_output_root_mean_squared_error: 1.2707\n",
            "Epoch 123/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 2.3174 - y1_output_loss: 0.5275 - y2_output_loss: 1.7899 - y1_output_root_mean_squared_error: 0.7263 - y2_output_root_mean_squared_error: 1.3379 - val_loss: 3.8485 - val_y1_output_loss: 0.5472 - val_y2_output_loss: 3.3014 - val_y1_output_root_mean_squared_error: 0.7397 - val_y2_output_root_mean_squared_error: 1.8170\n",
            "Epoch 124/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 2.5730 - y1_output_loss: 0.5406 - y2_output_loss: 2.0324 - y1_output_root_mean_squared_error: 0.7353 - y2_output_root_mean_squared_error: 1.4256 - val_loss: 2.7643 - val_y1_output_loss: 0.3809 - val_y2_output_loss: 2.3834 - val_y1_output_root_mean_squared_error: 0.6172 - val_y2_output_root_mean_squared_error: 1.5438\n",
            "Epoch 125/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 2.7473 - y1_output_loss: 0.6807 - y2_output_loss: 2.0666 - y1_output_root_mean_squared_error: 0.8251 - y2_output_root_mean_squared_error: 1.4376 - val_loss: 2.5839 - val_y1_output_loss: 0.3432 - val_y2_output_loss: 2.2407 - val_y1_output_root_mean_squared_error: 0.5858 - val_y2_output_root_mean_squared_error: 1.4969\n",
            "Epoch 126/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 2.0440 - y1_output_loss: 0.4951 - y2_output_loss: 1.5489 - y1_output_root_mean_squared_error: 0.7036 - y2_output_root_mean_squared_error: 1.2445 - val_loss: 1.5709 - val_y1_output_loss: 0.3233 - val_y2_output_loss: 1.2476 - val_y1_output_root_mean_squared_error: 0.5686 - val_y2_output_root_mean_squared_error: 1.1170\n",
            "Epoch 127/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 2.0996 - y1_output_loss: 0.5008 - y2_output_loss: 1.5988 - y1_output_root_mean_squared_error: 0.7077 - y2_output_root_mean_squared_error: 1.2644 - val_loss: 1.4695 - val_y1_output_loss: 0.3241 - val_y2_output_loss: 1.1454 - val_y1_output_root_mean_squared_error: 0.5693 - val_y2_output_root_mean_squared_error: 1.0702\n",
            "Epoch 128/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.9465 - y1_output_loss: 0.4155 - y2_output_loss: 1.5310 - y1_output_root_mean_squared_error: 0.6446 - y2_output_root_mean_squared_error: 1.2373 - val_loss: 20.1713 - val_y1_output_loss: 4.0009 - val_y2_output_loss: 16.1704 - val_y1_output_root_mean_squared_error: 2.0002 - val_y2_output_root_mean_squared_error: 4.0212\n",
            "Epoch 129/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 2.4349 - y1_output_loss: 0.5503 - y2_output_loss: 1.8846 - y1_output_root_mean_squared_error: 0.7418 - y2_output_root_mean_squared_error: 1.3728 - val_loss: 2.7542 - val_y1_output_loss: 0.7973 - val_y2_output_loss: 1.9568 - val_y1_output_root_mean_squared_error: 0.8929 - val_y2_output_root_mean_squared_error: 1.3989\n",
            "Epoch 130/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 2.1099 - y1_output_loss: 0.4761 - y2_output_loss: 1.6338 - y1_output_root_mean_squared_error: 0.6900 - y2_output_root_mean_squared_error: 1.2782 - val_loss: 1.5849 - val_y1_output_loss: 0.3596 - val_y2_output_loss: 1.2253 - val_y1_output_root_mean_squared_error: 0.5997 - val_y2_output_root_mean_squared_error: 1.1069\n",
            "Epoch 131/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.9364 - y1_output_loss: 0.4647 - y2_output_loss: 1.4717 - y1_output_root_mean_squared_error: 0.6817 - y2_output_root_mean_squared_error: 1.2131 - val_loss: 1.8634 - val_y1_output_loss: 0.3033 - val_y2_output_loss: 1.5600 - val_y1_output_root_mean_squared_error: 0.5507 - val_y2_output_root_mean_squared_error: 1.2490\n",
            "Epoch 132/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 2.4072 - y1_output_loss: 0.5326 - y2_output_loss: 1.8746 - y1_output_root_mean_squared_error: 0.7298 - y2_output_root_mean_squared_error: 1.3691 - val_loss: 4.2092 - val_y1_output_loss: 0.7071 - val_y2_output_loss: 3.5021 - val_y1_output_root_mean_squared_error: 0.8409 - val_y2_output_root_mean_squared_error: 1.8714\n",
            "Epoch 133/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.7089 - y1_output_loss: 0.4278 - y2_output_loss: 1.2811 - y1_output_root_mean_squared_error: 0.6540 - y2_output_root_mean_squared_error: 1.1319 - val_loss: 2.8694 - val_y1_output_loss: 0.5629 - val_y2_output_loss: 2.3065 - val_y1_output_root_mean_squared_error: 0.7503 - val_y2_output_root_mean_squared_error: 1.5187\n",
            "Epoch 134/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 2.7324 - y1_output_loss: 0.6277 - y2_output_loss: 2.1046 - y1_output_root_mean_squared_error: 0.7923 - y2_output_root_mean_squared_error: 1.4507 - val_loss: 6.8698 - val_y1_output_loss: 2.4681 - val_y2_output_loss: 4.4017 - val_y1_output_root_mean_squared_error: 1.5710 - val_y2_output_root_mean_squared_error: 2.0980\n",
            "Epoch 135/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 2.2001 - y1_output_loss: 0.4902 - y2_output_loss: 1.7099 - y1_output_root_mean_squared_error: 0.7001 - y2_output_root_mean_squared_error: 1.3076 - val_loss: 2.7438 - val_y1_output_loss: 0.6771 - val_y2_output_loss: 2.0667 - val_y1_output_root_mean_squared_error: 0.8229 - val_y2_output_root_mean_squared_error: 1.4376\n",
            "Epoch 136/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.9329 - y1_output_loss: 0.4592 - y2_output_loss: 1.4738 - y1_output_root_mean_squared_error: 0.6776 - y2_output_root_mean_squared_error: 1.2140 - val_loss: 1.4028 - val_y1_output_loss: 0.2989 - val_y2_output_loss: 1.1039 - val_y1_output_root_mean_squared_error: 0.5467 - val_y2_output_root_mean_squared_error: 1.0507\n",
            "Epoch 137/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.6300 - y1_output_loss: 0.3756 - y2_output_loss: 1.2544 - y1_output_root_mean_squared_error: 0.6129 - y2_output_root_mean_squared_error: 1.1200 - val_loss: 2.2940 - val_y1_output_loss: 0.4053 - val_y2_output_loss: 1.8887 - val_y1_output_root_mean_squared_error: 0.6366 - val_y2_output_root_mean_squared_error: 1.3743\n",
            "Epoch 138/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 2.9603 - y1_output_loss: 0.7849 - y2_output_loss: 2.1754 - y1_output_root_mean_squared_error: 0.8860 - y2_output_root_mean_squared_error: 1.4749 - val_loss: 3.7608 - val_y1_output_loss: 0.4972 - val_y2_output_loss: 3.2636 - val_y1_output_root_mean_squared_error: 0.7051 - val_y2_output_root_mean_squared_error: 1.8065\n",
            "Epoch 139/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.8851 - y1_output_loss: 0.3863 - y2_output_loss: 1.4988 - y1_output_root_mean_squared_error: 0.6216 - y2_output_root_mean_squared_error: 1.2243 - val_loss: 1.5382 - val_y1_output_loss: 0.3980 - val_y2_output_loss: 1.1403 - val_y1_output_root_mean_squared_error: 0.6309 - val_y2_output_root_mean_squared_error: 1.0678\n",
            "Epoch 140/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 2.5454 - y1_output_loss: 0.7092 - y2_output_loss: 1.8362 - y1_output_root_mean_squared_error: 0.8421 - y2_output_root_mean_squared_error: 1.3550 - val_loss: 3.4224 - val_y1_output_loss: 0.5080 - val_y2_output_loss: 2.9145 - val_y1_output_root_mean_squared_error: 0.7127 - val_y2_output_root_mean_squared_error: 1.7072\n",
            "Epoch 141/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.8030 - y1_output_loss: 0.4168 - y2_output_loss: 1.3861 - y1_output_root_mean_squared_error: 0.6456 - y2_output_root_mean_squared_error: 1.1773 - val_loss: 1.6975 - val_y1_output_loss: 0.3743 - val_y2_output_loss: 1.3231 - val_y1_output_root_mean_squared_error: 0.6118 - val_y2_output_root_mean_squared_error: 1.1503\n",
            "Epoch 142/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.8556 - y1_output_loss: 0.3937 - y2_output_loss: 1.4619 - y1_output_root_mean_squared_error: 0.6275 - y2_output_root_mean_squared_error: 1.2091 - val_loss: 1.9629 - val_y1_output_loss: 0.4906 - val_y2_output_loss: 1.4724 - val_y1_output_root_mean_squared_error: 0.7004 - val_y2_output_root_mean_squared_error: 1.2134\n",
            "Epoch 143/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.8643 - y1_output_loss: 0.4795 - y2_output_loss: 1.3848 - y1_output_root_mean_squared_error: 0.6924 - y2_output_root_mean_squared_error: 1.1768 - val_loss: 3.3044 - val_y1_output_loss: 0.9459 - val_y2_output_loss: 2.3585 - val_y1_output_root_mean_squared_error: 0.9726 - val_y2_output_root_mean_squared_error: 1.5357\n",
            "Epoch 144/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 2.3416 - y1_output_loss: 0.5898 - y2_output_loss: 1.7518 - y1_output_root_mean_squared_error: 0.7680 - y2_output_root_mean_squared_error: 1.3236 - val_loss: 2.2123 - val_y1_output_loss: 0.3957 - val_y2_output_loss: 1.8166 - val_y1_output_root_mean_squared_error: 0.6291 - val_y2_output_root_mean_squared_error: 1.3478\n",
            "Epoch 145/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.7388 - y1_output_loss: 0.3634 - y2_output_loss: 1.3754 - y1_output_root_mean_squared_error: 0.6028 - y2_output_root_mean_squared_error: 1.1728 - val_loss: 13.9748 - val_y1_output_loss: 1.8680 - val_y2_output_loss: 12.1068 - val_y1_output_root_mean_squared_error: 1.3667 - val_y2_output_root_mean_squared_error: 3.4795\n",
            "Epoch 146/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 3.0895 - y1_output_loss: 0.7013 - y2_output_loss: 2.3882 - y1_output_root_mean_squared_error: 0.8375 - y2_output_root_mean_squared_error: 1.5454 - val_loss: 1.7672 - val_y1_output_loss: 0.4306 - val_y2_output_loss: 1.3366 - val_y1_output_root_mean_squared_error: 0.6562 - val_y2_output_root_mean_squared_error: 1.1561\n",
            "Epoch 147/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 2.4758 - y1_output_loss: 0.5252 - y2_output_loss: 1.9506 - y1_output_root_mean_squared_error: 0.7247 - y2_output_root_mean_squared_error: 1.3967 - val_loss: 1.4285 - val_y1_output_loss: 0.2796 - val_y2_output_loss: 1.1488 - val_y1_output_root_mean_squared_error: 0.5288 - val_y2_output_root_mean_squared_error: 1.0718\n",
            "Epoch 148/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.5398 - y1_output_loss: 0.3090 - y2_output_loss: 1.2307 - y1_output_root_mean_squared_error: 0.5559 - y2_output_root_mean_squared_error: 1.1094 - val_loss: 8.8319 - val_y1_output_loss: 1.8704 - val_y2_output_loss: 6.9615 - val_y1_output_root_mean_squared_error: 1.3676 - val_y2_output_root_mean_squared_error: 2.6385\n",
            "Epoch 149/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.9560 - y1_output_loss: 0.4445 - y2_output_loss: 1.5114 - y1_output_root_mean_squared_error: 0.6667 - y2_output_root_mean_squared_error: 1.2294 - val_loss: 3.9047 - val_y1_output_loss: 1.4486 - val_y2_output_loss: 2.4561 - val_y1_output_root_mean_squared_error: 1.2036 - val_y2_output_root_mean_squared_error: 1.5672\n",
            "Epoch 150/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.6534 - y1_output_loss: 0.4128 - y2_output_loss: 1.2406 - y1_output_root_mean_squared_error: 0.6425 - y2_output_root_mean_squared_error: 1.1138 - val_loss: 3.7346 - val_y1_output_loss: 0.5258 - val_y2_output_loss: 3.2088 - val_y1_output_root_mean_squared_error: 0.7251 - val_y2_output_root_mean_squared_error: 1.7913\n",
            "Epoch 151/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.7387 - y1_output_loss: 0.3708 - y2_output_loss: 1.3679 - y1_output_root_mean_squared_error: 0.6089 - y2_output_root_mean_squared_error: 1.1696 - val_loss: 5.2868 - val_y1_output_loss: 0.6287 - val_y2_output_loss: 4.6581 - val_y1_output_root_mean_squared_error: 0.7929 - val_y2_output_root_mean_squared_error: 2.1583\n",
            "Epoch 152/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.5108 - y1_output_loss: 0.3321 - y2_output_loss: 1.1788 - y1_output_root_mean_squared_error: 0.5762 - y2_output_root_mean_squared_error: 1.0857 - val_loss: 1.2603 - val_y1_output_loss: 0.3339 - val_y2_output_loss: 0.9263 - val_y1_output_root_mean_squared_error: 0.5779 - val_y2_output_root_mean_squared_error: 0.9625\n",
            "Epoch 153/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.4842 - y1_output_loss: 0.3936 - y2_output_loss: 1.0907 - y1_output_root_mean_squared_error: 0.6273 - y2_output_root_mean_squared_error: 1.0443 - val_loss: 1.7372 - val_y1_output_loss: 0.3797 - val_y2_output_loss: 1.3576 - val_y1_output_root_mean_squared_error: 0.6162 - val_y2_output_root_mean_squared_error: 1.1651\n",
            "Epoch 154/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 2.5457 - y1_output_loss: 0.6233 - y2_output_loss: 1.9224 - y1_output_root_mean_squared_error: 0.7895 - y2_output_root_mean_squared_error: 1.3865 - val_loss: 3.8515 - val_y1_output_loss: 0.4648 - val_y2_output_loss: 3.3866 - val_y1_output_root_mean_squared_error: 0.6818 - val_y2_output_root_mean_squared_error: 1.8403\n",
            "Epoch 155/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.7428 - y1_output_loss: 0.3921 - y2_output_loss: 1.3507 - y1_output_root_mean_squared_error: 0.6262 - y2_output_root_mean_squared_error: 1.1622 - val_loss: 2.6531 - val_y1_output_loss: 1.0265 - val_y2_output_loss: 1.6267 - val_y1_output_root_mean_squared_error: 1.0131 - val_y2_output_root_mean_squared_error: 1.2754\n",
            "Epoch 156/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.3678 - y1_output_loss: 0.3031 - y2_output_loss: 1.0648 - y1_output_root_mean_squared_error: 0.5505 - y2_output_root_mean_squared_error: 1.0319 - val_loss: 1.6045 - val_y1_output_loss: 0.3427 - val_y2_output_loss: 1.2618 - val_y1_output_root_mean_squared_error: 0.5854 - val_y2_output_root_mean_squared_error: 1.1233\n",
            "Epoch 157/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.6507 - y1_output_loss: 0.3570 - y2_output_loss: 1.2937 - y1_output_root_mean_squared_error: 0.5975 - y2_output_root_mean_squared_error: 1.1374 - val_loss: 1.8968 - val_y1_output_loss: 0.5116 - val_y2_output_loss: 1.3852 - val_y1_output_root_mean_squared_error: 0.7153 - val_y2_output_root_mean_squared_error: 1.1770\n",
            "Epoch 158/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.4506 - y1_output_loss: 0.3143 - y2_output_loss: 1.1363 - y1_output_root_mean_squared_error: 0.5606 - y2_output_root_mean_squared_error: 1.0660 - val_loss: 1.7951 - val_y1_output_loss: 0.4646 - val_y2_output_loss: 1.3305 - val_y1_output_root_mean_squared_error: 0.6816 - val_y2_output_root_mean_squared_error: 1.1535\n",
            "Epoch 159/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 2.1236 - y1_output_loss: 0.5720 - y2_output_loss: 1.5516 - y1_output_root_mean_squared_error: 0.7563 - y2_output_root_mean_squared_error: 1.2456 - val_loss: 4.9070 - val_y1_output_loss: 0.3402 - val_y2_output_loss: 4.5668 - val_y1_output_root_mean_squared_error: 0.5833 - val_y2_output_root_mean_squared_error: 2.1370\n",
            "Epoch 160/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.2523 - y1_output_loss: 0.2841 - y2_output_loss: 0.9683 - y1_output_root_mean_squared_error: 0.5330 - y2_output_root_mean_squared_error: 0.9840 - val_loss: 3.0979 - val_y1_output_loss: 0.3401 - val_y2_output_loss: 2.7578 - val_y1_output_root_mean_squared_error: 0.5832 - val_y2_output_root_mean_squared_error: 1.6607\n",
            "Epoch 161/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.4065 - y1_output_loss: 0.3017 - y2_output_loss: 1.1048 - y1_output_root_mean_squared_error: 0.5493 - y2_output_root_mean_squared_error: 1.0511 - val_loss: 4.7690 - val_y1_output_loss: 1.1451 - val_y2_output_loss: 3.6239 - val_y1_output_root_mean_squared_error: 1.0701 - val_y2_output_root_mean_squared_error: 1.9037\n",
            "Epoch 162/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 3.2799 - y1_output_loss: 0.9529 - y2_output_loss: 2.3271 - y1_output_root_mean_squared_error: 0.9761 - y2_output_root_mean_squared_error: 1.5255 - val_loss: 1.7794 - val_y1_output_loss: 0.4952 - val_y2_output_loss: 1.2842 - val_y1_output_root_mean_squared_error: 0.7037 - val_y2_output_root_mean_squared_error: 1.1332\n",
            "Epoch 163/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 2.3170 - y1_output_loss: 0.6405 - y2_output_loss: 1.6765 - y1_output_root_mean_squared_error: 0.8003 - y2_output_root_mean_squared_error: 1.2948 - val_loss: 5.6412 - val_y1_output_loss: 1.1141 - val_y2_output_loss: 4.5271 - val_y1_output_root_mean_squared_error: 1.0555 - val_y2_output_root_mean_squared_error: 2.1277\n",
            "Epoch 164/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.6210 - y1_output_loss: 0.3427 - y2_output_loss: 1.2782 - y1_output_root_mean_squared_error: 0.5854 - y2_output_root_mean_squared_error: 1.1306 - val_loss: 1.1945 - val_y1_output_loss: 0.2814 - val_y2_output_loss: 0.9131 - val_y1_output_root_mean_squared_error: 0.5305 - val_y2_output_root_mean_squared_error: 0.9556\n",
            "Epoch 165/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 2.5272 - y1_output_loss: 0.6583 - y2_output_loss: 1.8690 - y1_output_root_mean_squared_error: 0.8113 - y2_output_root_mean_squared_error: 1.3671 - val_loss: 2.6448 - val_y1_output_loss: 0.4108 - val_y2_output_loss: 2.2340 - val_y1_output_root_mean_squared_error: 0.6409 - val_y2_output_root_mean_squared_error: 1.4946\n",
            "Epoch 166/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.7279 - y1_output_loss: 0.4240 - y2_output_loss: 1.3039 - y1_output_root_mean_squared_error: 0.6511 - y2_output_root_mean_squared_error: 1.1419 - val_loss: 1.9773 - val_y1_output_loss: 0.4726 - val_y2_output_loss: 1.5048 - val_y1_output_root_mean_squared_error: 0.6874 - val_y2_output_root_mean_squared_error: 1.2267\n",
            "Epoch 167/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 2.1530 - y1_output_loss: 0.5449 - y2_output_loss: 1.6081 - y1_output_root_mean_squared_error: 0.7382 - y2_output_root_mean_squared_error: 1.2681 - val_loss: 1.5151 - val_y1_output_loss: 0.3877 - val_y2_output_loss: 1.1273 - val_y1_output_root_mean_squared_error: 0.6227 - val_y2_output_root_mean_squared_error: 1.0618\n",
            "Epoch 168/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.7265 - y1_output_loss: 0.3934 - y2_output_loss: 1.3331 - y1_output_root_mean_squared_error: 0.6272 - y2_output_root_mean_squared_error: 1.1546 - val_loss: 2.3925 - val_y1_output_loss: 0.4812 - val_y2_output_loss: 1.9113 - val_y1_output_root_mean_squared_error: 0.6937 - val_y2_output_root_mean_squared_error: 1.3825\n",
            "Epoch 169/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.1894 - y1_output_loss: 0.2611 - y2_output_loss: 0.9284 - y1_output_root_mean_squared_error: 0.5109 - y2_output_root_mean_squared_error: 0.9635 - val_loss: 1.2425 - val_y1_output_loss: 0.2973 - val_y2_output_loss: 0.9452 - val_y1_output_root_mean_squared_error: 0.5452 - val_y2_output_root_mean_squared_error: 0.9722\n",
            "Epoch 170/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.2316 - y1_output_loss: 0.2664 - y2_output_loss: 0.9651 - y1_output_root_mean_squared_error: 0.5162 - y2_output_root_mean_squared_error: 0.9824 - val_loss: 1.7916 - val_y1_output_loss: 0.2807 - val_y2_output_loss: 1.5109 - val_y1_output_root_mean_squared_error: 0.5298 - val_y2_output_root_mean_squared_error: 1.2292\n",
            "Epoch 171/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.3632 - y1_output_loss: 0.2600 - y2_output_loss: 1.1031 - y1_output_root_mean_squared_error: 0.5099 - y2_output_root_mean_squared_error: 1.0503 - val_loss: 1.7416 - val_y1_output_loss: 0.2588 - val_y2_output_loss: 1.4828 - val_y1_output_root_mean_squared_error: 0.5087 - val_y2_output_root_mean_squared_error: 1.2177\n",
            "Epoch 172/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.7231 - y1_output_loss: 0.4252 - y2_output_loss: 1.2979 - y1_output_root_mean_squared_error: 0.6520 - y2_output_root_mean_squared_error: 1.1392 - val_loss: 2.8363 - val_y1_output_loss: 0.3760 - val_y2_output_loss: 2.4603 - val_y1_output_root_mean_squared_error: 0.6132 - val_y2_output_root_mean_squared_error: 1.5685\n",
            "Epoch 173/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.6122 - y1_output_loss: 0.3125 - y2_output_loss: 1.2997 - y1_output_root_mean_squared_error: 0.5590 - y2_output_root_mean_squared_error: 1.1401 - val_loss: 1.5552 - val_y1_output_loss: 0.3727 - val_y2_output_loss: 1.1825 - val_y1_output_root_mean_squared_error: 0.6105 - val_y2_output_root_mean_squared_error: 1.0874\n",
            "Epoch 174/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.2495 - y1_output_loss: 0.2743 - y2_output_loss: 0.9752 - y1_output_root_mean_squared_error: 0.5237 - y2_output_root_mean_squared_error: 0.9875 - val_loss: 1.2435 - val_y1_output_loss: 0.2519 - val_y2_output_loss: 0.9917 - val_y1_output_root_mean_squared_error: 0.5019 - val_y2_output_root_mean_squared_error: 0.9958\n",
            "Epoch 175/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.2103 - y1_output_loss: 0.2644 - y2_output_loss: 0.9459 - y1_output_root_mean_squared_error: 0.5142 - y2_output_root_mean_squared_error: 0.9726 - val_loss: 1.2189 - val_y1_output_loss: 0.2926 - val_y2_output_loss: 0.9263 - val_y1_output_root_mean_squared_error: 0.5409 - val_y2_output_root_mean_squared_error: 0.9624\n",
            "Epoch 176/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 2.0317 - y1_output_loss: 0.4805 - y2_output_loss: 1.5512 - y1_output_root_mean_squared_error: 0.6932 - y2_output_root_mean_squared_error: 1.2455 - val_loss: 1.3981 - val_y1_output_loss: 0.3843 - val_y2_output_loss: 1.0138 - val_y1_output_root_mean_squared_error: 0.6199 - val_y2_output_root_mean_squared_error: 1.0069\n",
            "Epoch 177/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.8701 - y1_output_loss: 0.4701 - y2_output_loss: 1.4000 - y1_output_root_mean_squared_error: 0.6857 - y2_output_root_mean_squared_error: 1.1832 - val_loss: 2.4974 - val_y1_output_loss: 0.3914 - val_y2_output_loss: 2.1060 - val_y1_output_root_mean_squared_error: 0.6256 - val_y2_output_root_mean_squared_error: 1.4512\n",
            "Epoch 178/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.2496 - y1_output_loss: 0.3137 - y2_output_loss: 0.9359 - y1_output_root_mean_squared_error: 0.5601 - y2_output_root_mean_squared_error: 0.9674 - val_loss: 11.5114 - val_y1_output_loss: 3.8454 - val_y2_output_loss: 7.6659 - val_y1_output_root_mean_squared_error: 1.9610 - val_y2_output_root_mean_squared_error: 2.7687\n",
            "Epoch 179/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.5838 - y1_output_loss: 0.4060 - y2_output_loss: 1.1778 - y1_output_root_mean_squared_error: 0.6372 - y2_output_root_mean_squared_error: 1.0853 - val_loss: 1.7612 - val_y1_output_loss: 0.5148 - val_y2_output_loss: 1.2464 - val_y1_output_root_mean_squared_error: 0.7175 - val_y2_output_root_mean_squared_error: 1.1164\n",
            "Epoch 180/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.9776 - y1_output_loss: 0.4193 - y2_output_loss: 1.5583 - y1_output_root_mean_squared_error: 0.6476 - y2_output_root_mean_squared_error: 1.2483 - val_loss: 5.1364 - val_y1_output_loss: 1.3029 - val_y2_output_loss: 3.8335 - val_y1_output_root_mean_squared_error: 1.1414 - val_y2_output_root_mean_squared_error: 1.9579\n",
            "Epoch 181/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.2712 - y1_output_loss: 0.3183 - y2_output_loss: 0.9529 - y1_output_root_mean_squared_error: 0.5641 - y2_output_root_mean_squared_error: 0.9762 - val_loss: 2.8747 - val_y1_output_loss: 0.9696 - val_y2_output_loss: 1.9051 - val_y1_output_root_mean_squared_error: 0.9847 - val_y2_output_root_mean_squared_error: 1.3802\n",
            "Epoch 182/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.1148 - y1_output_loss: 0.2662 - y2_output_loss: 0.8486 - y1_output_root_mean_squared_error: 0.5159 - y2_output_root_mean_squared_error: 0.9212 - val_loss: 4.0062 - val_y1_output_loss: 0.4457 - val_y2_output_loss: 3.5605 - val_y1_output_root_mean_squared_error: 0.6676 - val_y2_output_root_mean_squared_error: 1.8869\n",
            "Epoch 183/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 2.1688 - y1_output_loss: 0.4678 - y2_output_loss: 1.7010 - y1_output_root_mean_squared_error: 0.6840 - y2_output_root_mean_squared_error: 1.3042 - val_loss: 3.7851 - val_y1_output_loss: 0.9624 - val_y2_output_loss: 2.8227 - val_y1_output_root_mean_squared_error: 0.9810 - val_y2_output_root_mean_squared_error: 1.6801\n",
            "Epoch 184/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.2657 - y1_output_loss: 0.3370 - y2_output_loss: 0.9288 - y1_output_root_mean_squared_error: 0.5805 - y2_output_root_mean_squared_error: 0.9637 - val_loss: 1.4890 - val_y1_output_loss: 0.4202 - val_y2_output_loss: 1.0688 - val_y1_output_root_mean_squared_error: 0.6482 - val_y2_output_root_mean_squared_error: 1.0338\n",
            "Epoch 185/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.4518 - y1_output_loss: 0.3270 - y2_output_loss: 1.1248 - y1_output_root_mean_squared_error: 0.5719 - y2_output_root_mean_squared_error: 1.0606 - val_loss: 3.5386 - val_y1_output_loss: 0.7863 - val_y2_output_loss: 2.7523 - val_y1_output_root_mean_squared_error: 0.8867 - val_y2_output_root_mean_squared_error: 1.6590\n",
            "Epoch 186/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.5806 - y1_output_loss: 0.3773 - y2_output_loss: 1.2033 - y1_output_root_mean_squared_error: 0.6143 - y2_output_root_mean_squared_error: 1.0970 - val_loss: 5.9263 - val_y1_output_loss: 0.6279 - val_y2_output_loss: 5.2984 - val_y1_output_root_mean_squared_error: 0.7924 - val_y2_output_root_mean_squared_error: 2.3018\n",
            "Epoch 187/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.2337 - y1_output_loss: 0.3044 - y2_output_loss: 0.9294 - y1_output_root_mean_squared_error: 0.5517 - y2_output_root_mean_squared_error: 0.9640 - val_loss: 8.6769 - val_y1_output_loss: 1.7829 - val_y2_output_loss: 6.8939 - val_y1_output_root_mean_squared_error: 1.3353 - val_y2_output_root_mean_squared_error: 2.6256\n",
            "Epoch 188/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 1.5519 - y1_output_loss: 0.3595 - y2_output_loss: 1.1923 - y1_output_root_mean_squared_error: 0.5996 - y2_output_root_mean_squared_error: 1.0919 - val_loss: 1.6268 - val_y1_output_loss: 0.5138 - val_y2_output_loss: 1.1130 - val_y1_output_root_mean_squared_error: 0.7168 - val_y2_output_root_mean_squared_error: 1.0550\n",
            "Epoch 189/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.3485 - y1_output_loss: 0.2871 - y2_output_loss: 1.0614 - y1_output_root_mean_squared_error: 0.5358 - y2_output_root_mean_squared_error: 1.0302 - val_loss: 1.6191 - val_y1_output_loss: 0.4061 - val_y2_output_loss: 1.2130 - val_y1_output_root_mean_squared_error: 0.6373 - val_y2_output_root_mean_squared_error: 1.1013\n",
            "Epoch 190/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.3584 - y1_output_loss: 0.3074 - y2_output_loss: 1.0510 - y1_output_root_mean_squared_error: 0.5545 - y2_output_root_mean_squared_error: 1.0252 - val_loss: 2.0635 - val_y1_output_loss: 0.3614 - val_y2_output_loss: 1.7021 - val_y1_output_root_mean_squared_error: 0.6012 - val_y2_output_root_mean_squared_error: 1.3047\n",
            "Epoch 191/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.4697 - y1_output_loss: 0.2880 - y2_output_loss: 1.1817 - y1_output_root_mean_squared_error: 0.5367 - y2_output_root_mean_squared_error: 1.0870 - val_loss: 2.8257 - val_y1_output_loss: 0.4705 - val_y2_output_loss: 2.3552 - val_y1_output_root_mean_squared_error: 0.6859 - val_y2_output_root_mean_squared_error: 1.5347\n",
            "Epoch 192/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.5510 - y1_output_loss: 0.3261 - y2_output_loss: 1.2249 - y1_output_root_mean_squared_error: 0.5710 - y2_output_root_mean_squared_error: 1.1068 - val_loss: 2.0451 - val_y1_output_loss: 0.7318 - val_y2_output_loss: 1.3133 - val_y1_output_root_mean_squared_error: 0.8554 - val_y2_output_root_mean_squared_error: 1.1460\n",
            "Epoch 193/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.2857 - y1_output_loss: 0.2663 - y2_output_loss: 1.0195 - y1_output_root_mean_squared_error: 0.5160 - y2_output_root_mean_squared_error: 1.0097 - val_loss: 1.2049 - val_y1_output_loss: 0.2591 - val_y2_output_loss: 0.9458 - val_y1_output_root_mean_squared_error: 0.5090 - val_y2_output_root_mean_squared_error: 0.9725\n",
            "Epoch 194/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.4663 - y1_output_loss: 0.3761 - y2_output_loss: 1.0902 - y1_output_root_mean_squared_error: 0.6133 - y2_output_root_mean_squared_error: 1.0441 - val_loss: 1.2417 - val_y1_output_loss: 0.2485 - val_y2_output_loss: 0.9932 - val_y1_output_root_mean_squared_error: 0.4985 - val_y2_output_root_mean_squared_error: 0.9966\n",
            "Epoch 195/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.1232 - y1_output_loss: 0.2215 - y2_output_loss: 0.9017 - y1_output_root_mean_squared_error: 0.4706 - y2_output_root_mean_squared_error: 0.9496 - val_loss: 1.3047 - val_y1_output_loss: 0.3158 - val_y2_output_loss: 0.9889 - val_y1_output_root_mean_squared_error: 0.5619 - val_y2_output_root_mean_squared_error: 0.9944\n",
            "Epoch 196/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.1811 - y1_output_loss: 0.2583 - y2_output_loss: 0.9228 - y1_output_root_mean_squared_error: 0.5082 - y2_output_root_mean_squared_error: 0.9606 - val_loss: 1.3204 - val_y1_output_loss: 0.2133 - val_y2_output_loss: 1.1072 - val_y1_output_root_mean_squared_error: 0.4618 - val_y2_output_root_mean_squared_error: 1.0522\n",
            "Epoch 197/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.3650 - y1_output_loss: 0.3003 - y2_output_loss: 1.0648 - y1_output_root_mean_squared_error: 0.5480 - y2_output_root_mean_squared_error: 1.0319 - val_loss: 2.8290 - val_y1_output_loss: 0.5218 - val_y2_output_loss: 2.3072 - val_y1_output_root_mean_squared_error: 0.7224 - val_y2_output_root_mean_squared_error: 1.5189\n",
            "Epoch 198/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.2590 - y1_output_loss: 0.2850 - y2_output_loss: 0.9740 - y1_output_root_mean_squared_error: 0.5338 - y2_output_root_mean_squared_error: 0.9869 - val_loss: 1.2361 - val_y1_output_loss: 0.2612 - val_y2_output_loss: 0.9750 - val_y1_output_root_mean_squared_error: 0.5110 - val_y2_output_root_mean_squared_error: 0.9874\n",
            "Epoch 199/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.2632 - y1_output_loss: 0.2708 - y2_output_loss: 0.9924 - y1_output_root_mean_squared_error: 0.5204 - y2_output_root_mean_squared_error: 0.9962 - val_loss: 2.6859 - val_y1_output_loss: 1.0297 - val_y2_output_loss: 1.6562 - val_y1_output_root_mean_squared_error: 1.0147 - val_y2_output_root_mean_squared_error: 1.2869\n",
            "Epoch 200/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.4280 - y1_output_loss: 0.2998 - y2_output_loss: 1.1283 - y1_output_root_mean_squared_error: 0.5475 - y2_output_root_mean_squared_error: 1.0622 - val_loss: 19.4758 - val_y1_output_loss: 5.5470 - val_y2_output_loss: 13.9288 - val_y1_output_root_mean_squared_error: 2.3552 - val_y2_output_root_mean_squared_error: 3.7321\n",
            "Epoch 201/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.7733 - y1_output_loss: 0.4640 - y2_output_loss: 1.3093 - y1_output_root_mean_squared_error: 0.6812 - y2_output_root_mean_squared_error: 1.1443 - val_loss: 1.7448 - val_y1_output_loss: 0.3248 - val_y2_output_loss: 1.4200 - val_y1_output_root_mean_squared_error: 0.5699 - val_y2_output_root_mean_squared_error: 1.1916\n",
            "Epoch 202/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.2419 - y1_output_loss: 0.2864 - y2_output_loss: 0.9555 - y1_output_root_mean_squared_error: 0.5352 - y2_output_root_mean_squared_error: 0.9775 - val_loss: 0.9767 - val_y1_output_loss: 0.2392 - val_y2_output_loss: 0.7375 - val_y1_output_root_mean_squared_error: 0.4891 - val_y2_output_root_mean_squared_error: 0.8588\n",
            "Epoch 203/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.2521 - y1_output_loss: 0.2431 - y2_output_loss: 1.0090 - y1_output_root_mean_squared_error: 0.4930 - y2_output_root_mean_squared_error: 1.0045 - val_loss: 1.9448 - val_y1_output_loss: 0.5648 - val_y2_output_loss: 1.3800 - val_y1_output_root_mean_squared_error: 0.7516 - val_y2_output_root_mean_squared_error: 1.1747\n",
            "Epoch 204/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.5119 - y1_output_loss: 0.3069 - y2_output_loss: 1.2050 - y1_output_root_mean_squared_error: 0.5540 - y2_output_root_mean_squared_error: 1.0977 - val_loss: 1.1323 - val_y1_output_loss: 0.2904 - val_y2_output_loss: 0.8419 - val_y1_output_root_mean_squared_error: 0.5389 - val_y2_output_root_mean_squared_error: 0.9176\n",
            "Epoch 205/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.1232 - y1_output_loss: 0.2514 - y2_output_loss: 0.8718 - y1_output_root_mean_squared_error: 0.5014 - y2_output_root_mean_squared_error: 0.9337 - val_loss: 1.8155 - val_y1_output_loss: 0.4728 - val_y2_output_loss: 1.3428 - val_y1_output_root_mean_squared_error: 0.6876 - val_y2_output_root_mean_squared_error: 1.1588\n",
            "Epoch 206/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.4109 - y1_output_loss: 0.2594 - y2_output_loss: 1.1515 - y1_output_root_mean_squared_error: 0.5093 - y2_output_root_mean_squared_error: 1.0731 - val_loss: 1.3160 - val_y1_output_loss: 0.3796 - val_y2_output_loss: 0.9364 - val_y1_output_root_mean_squared_error: 0.6161 - val_y2_output_root_mean_squared_error: 0.9677\n",
            "Epoch 207/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.7175 - y1_output_loss: 0.3793 - y2_output_loss: 1.3382 - y1_output_root_mean_squared_error: 0.6159 - y2_output_root_mean_squared_error: 1.1568 - val_loss: 5.4125 - val_y1_output_loss: 1.4075 - val_y2_output_loss: 4.0050 - val_y1_output_root_mean_squared_error: 1.1864 - val_y2_output_root_mean_squared_error: 2.0012\n",
            "Epoch 208/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.1359 - y1_output_loss: 0.2607 - y2_output_loss: 0.8752 - y1_output_root_mean_squared_error: 0.5105 - y2_output_root_mean_squared_error: 0.9355 - val_loss: 3.7379 - val_y1_output_loss: 1.0198 - val_y2_output_loss: 2.7181 - val_y1_output_root_mean_squared_error: 1.0098 - val_y2_output_root_mean_squared_error: 1.6487\n",
            "Epoch 209/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 1.3176 - y1_output_loss: 0.3340 - y2_output_loss: 0.9836 - y1_output_root_mean_squared_error: 0.5780 - y2_output_root_mean_squared_error: 0.9918 - val_loss: 1.3964 - val_y1_output_loss: 0.3489 - val_y2_output_loss: 1.0475 - val_y1_output_root_mean_squared_error: 0.5907 - val_y2_output_root_mean_squared_error: 1.0235\n",
            "Epoch 210/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.5530 - y1_output_loss: 0.3343 - y2_output_loss: 1.2187 - y1_output_root_mean_squared_error: 0.5782 - y2_output_root_mean_squared_error: 1.1040 - val_loss: 6.0478 - val_y1_output_loss: 0.8443 - val_y2_output_loss: 5.2035 - val_y1_output_root_mean_squared_error: 0.9189 - val_y2_output_root_mean_squared_error: 2.2811\n",
            "Epoch 211/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.1917 - y1_output_loss: 0.2615 - y2_output_loss: 0.9303 - y1_output_root_mean_squared_error: 0.5113 - y2_output_root_mean_squared_error: 0.9645 - val_loss: 1.0827 - val_y1_output_loss: 0.2013 - val_y2_output_loss: 0.8814 - val_y1_output_root_mean_squared_error: 0.4487 - val_y2_output_root_mean_squared_error: 0.9388\n",
            "Epoch 212/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.1664 - y1_output_loss: 0.2492 - y2_output_loss: 0.9172 - y1_output_root_mean_squared_error: 0.4992 - y2_output_root_mean_squared_error: 0.9577 - val_loss: 1.3052 - val_y1_output_loss: 0.2533 - val_y2_output_loss: 1.0519 - val_y1_output_root_mean_squared_error: 0.5033 - val_y2_output_root_mean_squared_error: 1.0256\n",
            "Epoch 213/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.9238 - y1_output_loss: 0.2283 - y2_output_loss: 0.6955 - y1_output_root_mean_squared_error: 0.4778 - y2_output_root_mean_squared_error: 0.8340 - val_loss: 1.9463 - val_y1_output_loss: 0.6288 - val_y2_output_loss: 1.3175 - val_y1_output_root_mean_squared_error: 0.7929 - val_y2_output_root_mean_squared_error: 1.1478\n",
            "Epoch 214/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.3844 - y1_output_loss: 0.3303 - y2_output_loss: 1.0540 - y1_output_root_mean_squared_error: 0.5748 - y2_output_root_mean_squared_error: 1.0267 - val_loss: 1.5590 - val_y1_output_loss: 0.4027 - val_y2_output_loss: 1.1562 - val_y1_output_root_mean_squared_error: 0.6346 - val_y2_output_root_mean_squared_error: 1.0753\n",
            "Epoch 215/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.1954 - y1_output_loss: 0.2651 - y2_output_loss: 0.9302 - y1_output_root_mean_squared_error: 0.5149 - y2_output_root_mean_squared_error: 0.9645 - val_loss: 0.9141 - val_y1_output_loss: 0.2200 - val_y2_output_loss: 0.6940 - val_y1_output_root_mean_squared_error: 0.4691 - val_y2_output_root_mean_squared_error: 0.8331\n",
            "Epoch 216/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.9751 - y1_output_loss: 0.2480 - y2_output_loss: 0.7271 - y1_output_root_mean_squared_error: 0.4980 - y2_output_root_mean_squared_error: 0.8527 - val_loss: 1.2539 - val_y1_output_loss: 0.2917 - val_y2_output_loss: 0.9622 - val_y1_output_root_mean_squared_error: 0.5401 - val_y2_output_root_mean_squared_error: 0.9809\n",
            "Epoch 217/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.1694 - y1_output_loss: 0.2406 - y2_output_loss: 0.9288 - y1_output_root_mean_squared_error: 0.4905 - y2_output_root_mean_squared_error: 0.9637 - val_loss: 1.0470 - val_y1_output_loss: 0.2156 - val_y2_output_loss: 0.8314 - val_y1_output_root_mean_squared_error: 0.4643 - val_y2_output_root_mean_squared_error: 0.9118\n",
            "Epoch 218/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.0195 - y1_output_loss: 0.2225 - y2_output_loss: 0.7970 - y1_output_root_mean_squared_error: 0.4717 - y2_output_root_mean_squared_error: 0.8927 - val_loss: 1.2644 - val_y1_output_loss: 0.2593 - val_y2_output_loss: 1.0051 - val_y1_output_root_mean_squared_error: 0.5092 - val_y2_output_root_mean_squared_error: 1.0026\n",
            "Epoch 219/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.0639 - y1_output_loss: 0.2555 - y2_output_loss: 0.8084 - y1_output_root_mean_squared_error: 0.5054 - y2_output_root_mean_squared_error: 0.8991 - val_loss: 1.5627 - val_y1_output_loss: 0.2083 - val_y2_output_loss: 1.3544 - val_y1_output_root_mean_squared_error: 0.4564 - val_y2_output_root_mean_squared_error: 1.1638\n",
            "Epoch 220/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.5469 - y1_output_loss: 0.3713 - y2_output_loss: 1.1756 - y1_output_root_mean_squared_error: 0.6094 - y2_output_root_mean_squared_error: 1.0842 - val_loss: 1.7449 - val_y1_output_loss: 0.4847 - val_y2_output_loss: 1.2602 - val_y1_output_root_mean_squared_error: 0.6962 - val_y2_output_root_mean_squared_error: 1.1226\n",
            "Epoch 221/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.3713 - y1_output_loss: 0.4155 - y2_output_loss: 0.9558 - y1_output_root_mean_squared_error: 0.6446 - y2_output_root_mean_squared_error: 0.9776 - val_loss: 1.1633 - val_y1_output_loss: 0.2558 - val_y2_output_loss: 0.9075 - val_y1_output_root_mean_squared_error: 0.5058 - val_y2_output_root_mean_squared_error: 0.9526\n",
            "Epoch 222/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.0791 - y1_output_loss: 0.2227 - y2_output_loss: 0.8564 - y1_output_root_mean_squared_error: 0.4719 - y2_output_root_mean_squared_error: 0.9254 - val_loss: 1.4404 - val_y1_output_loss: 0.2719 - val_y2_output_loss: 1.1685 - val_y1_output_root_mean_squared_error: 0.5215 - val_y2_output_root_mean_squared_error: 1.0810\n",
            "Epoch 223/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.0151 - y1_output_loss: 0.2193 - y2_output_loss: 0.7957 - y1_output_root_mean_squared_error: 0.4683 - y2_output_root_mean_squared_error: 0.8920 - val_loss: 6.9592 - val_y1_output_loss: 2.0310 - val_y2_output_loss: 4.9283 - val_y1_output_root_mean_squared_error: 1.4251 - val_y2_output_root_mean_squared_error: 2.2200\n",
            "Epoch 224/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 2.0703 - y1_output_loss: 0.5116 - y2_output_loss: 1.5587 - y1_output_root_mean_squared_error: 0.7153 - y2_output_root_mean_squared_error: 1.2485 - val_loss: 1.4986 - val_y1_output_loss: 0.2783 - val_y2_output_loss: 1.2202 - val_y1_output_root_mean_squared_error: 0.5276 - val_y2_output_root_mean_squared_error: 1.1046\n",
            "Epoch 225/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.1698 - y1_output_loss: 0.2438 - y2_output_loss: 0.9260 - y1_output_root_mean_squared_error: 0.4937 - y2_output_root_mean_squared_error: 0.9623 - val_loss: 1.0185 - val_y1_output_loss: 0.2297 - val_y2_output_loss: 0.7888 - val_y1_output_root_mean_squared_error: 0.4793 - val_y2_output_root_mean_squared_error: 0.8882\n",
            "Epoch 226/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.0680 - y1_output_loss: 0.2212 - y2_output_loss: 0.8468 - y1_output_root_mean_squared_error: 0.4703 - y2_output_root_mean_squared_error: 0.9202 - val_loss: 1.0685 - val_y1_output_loss: 0.2421 - val_y2_output_loss: 0.8265 - val_y1_output_root_mean_squared_error: 0.4920 - val_y2_output_root_mean_squared_error: 0.9091\n",
            "Epoch 227/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.9054 - y1_output_loss: 0.1952 - y2_output_loss: 0.7102 - y1_output_root_mean_squared_error: 0.4418 - y2_output_root_mean_squared_error: 0.8427 - val_loss: 1.1118 - val_y1_output_loss: 0.2081 - val_y2_output_loss: 0.9036 - val_y1_output_root_mean_squared_error: 0.4562 - val_y2_output_root_mean_squared_error: 0.9506\n",
            "Epoch 228/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.8368 - y1_output_loss: 0.1810 - y2_output_loss: 0.6558 - y1_output_root_mean_squared_error: 0.4254 - y2_output_root_mean_squared_error: 0.8098 - val_loss: 1.5011 - val_y1_output_loss: 0.5001 - val_y2_output_loss: 1.0010 - val_y1_output_root_mean_squared_error: 0.7071 - val_y2_output_root_mean_squared_error: 1.0005\n",
            "Epoch 229/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.1456 - y1_output_loss: 0.2778 - y2_output_loss: 0.8679 - y1_output_root_mean_squared_error: 0.5270 - y2_output_root_mean_squared_error: 0.9316 - val_loss: 2.9985 - val_y1_output_loss: 0.5989 - val_y2_output_loss: 2.3996 - val_y1_output_root_mean_squared_error: 0.7739 - val_y2_output_root_mean_squared_error: 1.5491\n",
            "Epoch 230/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.9930 - y1_output_loss: 0.2166 - y2_output_loss: 0.7763 - y1_output_root_mean_squared_error: 0.4654 - y2_output_root_mean_squared_error: 0.8811 - val_loss: 2.7287 - val_y1_output_loss: 0.3391 - val_y2_output_loss: 2.3896 - val_y1_output_root_mean_squared_error: 0.5824 - val_y2_output_root_mean_squared_error: 1.5458\n",
            "Epoch 231/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.7983 - y1_output_loss: 0.1930 - y2_output_loss: 0.6054 - y1_output_root_mean_squared_error: 0.4393 - y2_output_root_mean_squared_error: 0.7781 - val_loss: 1.1730 - val_y1_output_loss: 0.3254 - val_y2_output_loss: 0.8476 - val_y1_output_root_mean_squared_error: 0.5704 - val_y2_output_root_mean_squared_error: 0.9207\n",
            "Epoch 232/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.1042 - y1_output_loss: 0.2387 - y2_output_loss: 0.8655 - y1_output_root_mean_squared_error: 0.4885 - y2_output_root_mean_squared_error: 0.9303 - val_loss: 1.2885 - val_y1_output_loss: 0.3438 - val_y2_output_loss: 0.9447 - val_y1_output_root_mean_squared_error: 0.5863 - val_y2_output_root_mean_squared_error: 0.9720\n",
            "Epoch 233/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.8553 - y1_output_loss: 0.1939 - y2_output_loss: 0.6614 - y1_output_root_mean_squared_error: 0.4403 - y2_output_root_mean_squared_error: 0.8133 - val_loss: 0.9912 - val_y1_output_loss: 0.2002 - val_y2_output_loss: 0.7910 - val_y1_output_root_mean_squared_error: 0.4475 - val_y2_output_root_mean_squared_error: 0.8894\n",
            "Epoch 234/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.9540 - y1_output_loss: 0.2237 - y2_output_loss: 0.7303 - y1_output_root_mean_squared_error: 0.4730 - y2_output_root_mean_squared_error: 0.8546 - val_loss: 1.0816 - val_y1_output_loss: 0.2642 - val_y2_output_loss: 0.8175 - val_y1_output_root_mean_squared_error: 0.5140 - val_y2_output_root_mean_squared_error: 0.9041\n",
            "Epoch 235/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.9738 - y1_output_loss: 0.2217 - y2_output_loss: 0.7521 - y1_output_root_mean_squared_error: 0.4709 - y2_output_root_mean_squared_error: 0.8673 - val_loss: 1.1251 - val_y1_output_loss: 0.3761 - val_y2_output_loss: 0.7489 - val_y1_output_root_mean_squared_error: 0.6133 - val_y2_output_root_mean_squared_error: 0.8654\n",
            "Epoch 236/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 3.0186 - y1_output_loss: 0.8775 - y2_output_loss: 2.1412 - y1_output_root_mean_squared_error: 0.9367 - y2_output_root_mean_squared_error: 1.4633 - val_loss: 2.1680 - val_y1_output_loss: 0.5253 - val_y2_output_loss: 1.6427 - val_y1_output_root_mean_squared_error: 0.7247 - val_y2_output_root_mean_squared_error: 1.2817\n",
            "Epoch 237/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.9010 - y1_output_loss: 0.2161 - y2_output_loss: 0.6850 - y1_output_root_mean_squared_error: 0.4648 - y2_output_root_mean_squared_error: 0.8276 - val_loss: 1.1603 - val_y1_output_loss: 0.2517 - val_y2_output_loss: 0.9087 - val_y1_output_root_mean_squared_error: 0.5017 - val_y2_output_root_mean_squared_error: 0.9532\n",
            "Epoch 238/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.9131 - y1_output_loss: 0.2208 - y2_output_loss: 0.6923 - y1_output_root_mean_squared_error: 0.4699 - y2_output_root_mean_squared_error: 0.8320 - val_loss: 1.4060 - val_y1_output_loss: 0.2623 - val_y2_output_loss: 1.1437 - val_y1_output_root_mean_squared_error: 0.5122 - val_y2_output_root_mean_squared_error: 1.0694\n",
            "Epoch 239/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.9130 - y1_output_loss: 0.2093 - y2_output_loss: 0.7036 - y1_output_root_mean_squared_error: 0.4575 - y2_output_root_mean_squared_error: 0.8388 - val_loss: 1.0857 - val_y1_output_loss: 0.2537 - val_y2_output_loss: 0.8320 - val_y1_output_root_mean_squared_error: 0.5037 - val_y2_output_root_mean_squared_error: 0.9122\n",
            "Epoch 240/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 1.1338 - y1_output_loss: 0.2693 - y2_output_loss: 0.8645 - y1_output_root_mean_squared_error: 0.5190 - y2_output_root_mean_squared_error: 0.9298 - val_loss: 1.0961 - val_y1_output_loss: 0.2292 - val_y2_output_loss: 0.8668 - val_y1_output_root_mean_squared_error: 0.4788 - val_y2_output_root_mean_squared_error: 0.9310\n",
            "Epoch 241/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.8780 - y1_output_loss: 0.2236 - y2_output_loss: 0.6545 - y1_output_root_mean_squared_error: 0.4728 - y2_output_root_mean_squared_error: 0.8090 - val_loss: 1.3006 - val_y1_output_loss: 0.3301 - val_y2_output_loss: 0.9705 - val_y1_output_root_mean_squared_error: 0.5746 - val_y2_output_root_mean_squared_error: 0.9852\n",
            "Epoch 242/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.1188 - y1_output_loss: 0.2549 - y2_output_loss: 0.8640 - y1_output_root_mean_squared_error: 0.5048 - y2_output_root_mean_squared_error: 0.9295 - val_loss: 0.9099 - val_y1_output_loss: 0.2160 - val_y2_output_loss: 0.6938 - val_y1_output_root_mean_squared_error: 0.4648 - val_y2_output_root_mean_squared_error: 0.8330\n",
            "Epoch 243/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.9177 - y1_output_loss: 0.1971 - y2_output_loss: 0.7206 - y1_output_root_mean_squared_error: 0.4439 - y2_output_root_mean_squared_error: 0.8489 - val_loss: 1.0190 - val_y1_output_loss: 0.2022 - val_y2_output_loss: 0.8168 - val_y1_output_root_mean_squared_error: 0.4496 - val_y2_output_root_mean_squared_error: 0.9038\n",
            "Epoch 244/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.7757 - y1_output_loss: 0.1914 - y2_output_loss: 0.5843 - y1_output_root_mean_squared_error: 0.4375 - y2_output_root_mean_squared_error: 0.7644 - val_loss: 1.4313 - val_y1_output_loss: 0.4285 - val_y2_output_loss: 1.0028 - val_y1_output_root_mean_squared_error: 0.6546 - val_y2_output_root_mean_squared_error: 1.0014\n",
            "Epoch 245/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.8861 - y1_output_loss: 0.2152 - y2_output_loss: 0.6710 - y1_output_root_mean_squared_error: 0.4638 - y2_output_root_mean_squared_error: 0.8191 - val_loss: 2.3006 - val_y1_output_loss: 0.5559 - val_y2_output_loss: 1.7446 - val_y1_output_root_mean_squared_error: 0.7456 - val_y2_output_root_mean_squared_error: 1.3208\n",
            "Epoch 246/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.7385 - y1_output_loss: 0.1924 - y2_output_loss: 0.5460 - y1_output_root_mean_squared_error: 0.4387 - y2_output_root_mean_squared_error: 0.7389 - val_loss: 0.9123 - val_y1_output_loss: 0.2478 - val_y2_output_loss: 0.6645 - val_y1_output_root_mean_squared_error: 0.4978 - val_y2_output_root_mean_squared_error: 0.8152\n",
            "Epoch 247/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.8388 - y1_output_loss: 0.1915 - y2_output_loss: 0.6473 - y1_output_root_mean_squared_error: 0.4376 - y2_output_root_mean_squared_error: 0.8046 - val_loss: 16.3395 - val_y1_output_loss: 4.0712 - val_y2_output_loss: 12.2683 - val_y1_output_root_mean_squared_error: 2.0177 - val_y2_output_root_mean_squared_error: 3.5026\n",
            "Epoch 248/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.3427 - y1_output_loss: 0.3029 - y2_output_loss: 1.0398 - y1_output_root_mean_squared_error: 0.5504 - y2_output_root_mean_squared_error: 1.0197 - val_loss: 0.9554 - val_y1_output_loss: 0.1842 - val_y2_output_loss: 0.7712 - val_y1_output_root_mean_squared_error: 0.4292 - val_y2_output_root_mean_squared_error: 0.8782\n",
            "Epoch 249/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.3114 - y1_output_loss: 0.3219 - y2_output_loss: 0.9894 - y1_output_root_mean_squared_error: 0.5674 - y2_output_root_mean_squared_error: 0.9947 - val_loss: 1.0017 - val_y1_output_loss: 0.2813 - val_y2_output_loss: 0.7204 - val_y1_output_root_mean_squared_error: 0.5304 - val_y2_output_root_mean_squared_error: 0.8488\n",
            "Epoch 250/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.1127 - y1_output_loss: 0.2353 - y2_output_loss: 0.8774 - y1_output_root_mean_squared_error: 0.4851 - y2_output_root_mean_squared_error: 0.9367 - val_loss: 4.4271 - val_y1_output_loss: 0.2725 - val_y2_output_loss: 4.1546 - val_y1_output_root_mean_squared_error: 0.5220 - val_y2_output_root_mean_squared_error: 2.0383\n",
            "Epoch 251/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.2561 - y1_output_loss: 0.2809 - y2_output_loss: 0.9752 - y1_output_root_mean_squared_error: 0.5300 - y2_output_root_mean_squared_error: 0.9875 - val_loss: 2.6289 - val_y1_output_loss: 0.5407 - val_y2_output_loss: 2.0882 - val_y1_output_root_mean_squared_error: 0.7354 - val_y2_output_root_mean_squared_error: 1.4450\n",
            "Epoch 252/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.9495 - y1_output_loss: 0.2131 - y2_output_loss: 0.7364 - y1_output_root_mean_squared_error: 0.4617 - y2_output_root_mean_squared_error: 0.8581 - val_loss: 1.7232 - val_y1_output_loss: 0.4360 - val_y2_output_loss: 1.2872 - val_y1_output_root_mean_squared_error: 0.6603 - val_y2_output_root_mean_squared_error: 1.1345\n",
            "Epoch 253/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.1858 - y1_output_loss: 0.3174 - y2_output_loss: 0.8684 - y1_output_root_mean_squared_error: 0.5634 - y2_output_root_mean_squared_error: 0.9319 - val_loss: 1.5600 - val_y1_output_loss: 0.4549 - val_y2_output_loss: 1.1051 - val_y1_output_root_mean_squared_error: 0.6745 - val_y2_output_root_mean_squared_error: 1.0512\n",
            "Epoch 254/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.9813 - y1_output_loss: 0.2419 - y2_output_loss: 0.7394 - y1_output_root_mean_squared_error: 0.4918 - y2_output_root_mean_squared_error: 0.8599 - val_loss: 1.0492 - val_y1_output_loss: 0.2074 - val_y2_output_loss: 0.8418 - val_y1_output_root_mean_squared_error: 0.4554 - val_y2_output_root_mean_squared_error: 0.9175\n",
            "Epoch 255/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.8425 - y1_output_loss: 0.2045 - y2_output_loss: 0.6380 - y1_output_root_mean_squared_error: 0.4522 - y2_output_root_mean_squared_error: 0.7988 - val_loss: 0.9873 - val_y1_output_loss: 0.1684 - val_y2_output_loss: 0.8189 - val_y1_output_root_mean_squared_error: 0.4104 - val_y2_output_root_mean_squared_error: 0.9049\n",
            "Epoch 256/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.3879 - y1_output_loss: 0.3338 - y2_output_loss: 1.0541 - y1_output_root_mean_squared_error: 0.5778 - y2_output_root_mean_squared_error: 1.0267 - val_loss: 0.9989 - val_y1_output_loss: 0.2198 - val_y2_output_loss: 0.7791 - val_y1_output_root_mean_squared_error: 0.4688 - val_y2_output_root_mean_squared_error: 0.8827\n",
            "Epoch 257/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.6965 - y1_output_loss: 0.1743 - y2_output_loss: 0.5223 - y1_output_root_mean_squared_error: 0.4174 - y2_output_root_mean_squared_error: 0.7227 - val_loss: 1.3314 - val_y1_output_loss: 0.2706 - val_y2_output_loss: 1.0609 - val_y1_output_root_mean_squared_error: 0.5202 - val_y2_output_root_mean_squared_error: 1.0300\n",
            "Epoch 258/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.8617 - y1_output_loss: 0.2294 - y2_output_loss: 0.6323 - y1_output_root_mean_squared_error: 0.4790 - y2_output_root_mean_squared_error: 0.7952 - val_loss: 1.2141 - val_y1_output_loss: 0.2954 - val_y2_output_loss: 0.9187 - val_y1_output_root_mean_squared_error: 0.5435 - val_y2_output_root_mean_squared_error: 0.9585\n",
            "Epoch 259/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.7175 - y1_output_loss: 0.1936 - y2_output_loss: 0.5239 - y1_output_root_mean_squared_error: 0.4400 - y2_output_root_mean_squared_error: 0.7238 - val_loss: 0.9804 - val_y1_output_loss: 0.2306 - val_y2_output_loss: 0.7499 - val_y1_output_root_mean_squared_error: 0.4802 - val_y2_output_root_mean_squared_error: 0.8660\n",
            "Epoch 260/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.9381 - y1_output_loss: 0.2343 - y2_output_loss: 0.7038 - y1_output_root_mean_squared_error: 0.4841 - y2_output_root_mean_squared_error: 0.8389 - val_loss: 2.8513 - val_y1_output_loss: 0.5431 - val_y2_output_loss: 2.3082 - val_y1_output_root_mean_squared_error: 0.7370 - val_y2_output_root_mean_squared_error: 1.5193\n",
            "Epoch 261/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.8886 - y1_output_loss: 0.2000 - y2_output_loss: 0.6886 - y1_output_root_mean_squared_error: 0.4472 - y2_output_root_mean_squared_error: 0.8298 - val_loss: 1.1774 - val_y1_output_loss: 0.2786 - val_y2_output_loss: 0.8988 - val_y1_output_root_mean_squared_error: 0.5278 - val_y2_output_root_mean_squared_error: 0.9481\n",
            "Epoch 262/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.8462 - y1_output_loss: 0.2178 - y2_output_loss: 0.6284 - y1_output_root_mean_squared_error: 0.4667 - y2_output_root_mean_squared_error: 0.7927 - val_loss: 1.2668 - val_y1_output_loss: 0.2619 - val_y2_output_loss: 1.0049 - val_y1_output_root_mean_squared_error: 0.5117 - val_y2_output_root_mean_squared_error: 1.0025\n",
            "Epoch 263/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.9728 - y1_output_loss: 0.2436 - y2_output_loss: 0.7292 - y1_output_root_mean_squared_error: 0.4935 - y2_output_root_mean_squared_error: 0.8540 - val_loss: 2.9640 - val_y1_output_loss: 0.2525 - val_y2_output_loss: 2.7115 - val_y1_output_root_mean_squared_error: 0.5025 - val_y2_output_root_mean_squared_error: 1.6467\n",
            "Epoch 264/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.8390 - y1_output_loss: 0.1881 - y2_output_loss: 0.6510 - y1_output_root_mean_squared_error: 0.4337 - y2_output_root_mean_squared_error: 0.8068 - val_loss: 0.9289 - val_y1_output_loss: 0.2224 - val_y2_output_loss: 0.7065 - val_y1_output_root_mean_squared_error: 0.4716 - val_y2_output_root_mean_squared_error: 0.8405\n",
            "Epoch 265/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.9748 - y1_output_loss: 0.2182 - y2_output_loss: 0.7566 - y1_output_root_mean_squared_error: 0.4671 - y2_output_root_mean_squared_error: 0.8698 - val_loss: 2.1389 - val_y1_output_loss: 0.2064 - val_y2_output_loss: 1.9325 - val_y1_output_root_mean_squared_error: 0.4544 - val_y2_output_root_mean_squared_error: 1.3901\n",
            "Epoch 266/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.0678 - y1_output_loss: 0.2546 - y2_output_loss: 0.8132 - y1_output_root_mean_squared_error: 0.5046 - y2_output_root_mean_squared_error: 0.9018 - val_loss: 0.9617 - val_y1_output_loss: 0.1943 - val_y2_output_loss: 0.7674 - val_y1_output_root_mean_squared_error: 0.4408 - val_y2_output_root_mean_squared_error: 0.8760\n",
            "Epoch 267/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.8822 - y1_output_loss: 0.2106 - y2_output_loss: 0.6716 - y1_output_root_mean_squared_error: 0.4589 - y2_output_root_mean_squared_error: 0.8195 - val_loss: 1.0731 - val_y1_output_loss: 0.1967 - val_y2_output_loss: 0.8765 - val_y1_output_root_mean_squared_error: 0.4435 - val_y2_output_root_mean_squared_error: 0.9362\n",
            "Epoch 268/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.3103 - y1_output_loss: 0.3517 - y2_output_loss: 0.9586 - y1_output_root_mean_squared_error: 0.5930 - y2_output_root_mean_squared_error: 0.9791 - val_loss: 1.4289 - val_y1_output_loss: 0.2280 - val_y2_output_loss: 1.2008 - val_y1_output_root_mean_squared_error: 0.4775 - val_y2_output_root_mean_squared_error: 1.0958\n",
            "Epoch 269/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.7183 - y1_output_loss: 0.1768 - y2_output_loss: 0.5415 - y1_output_root_mean_squared_error: 0.4205 - y2_output_root_mean_squared_error: 0.7358 - val_loss: 1.3012 - val_y1_output_loss: 0.2843 - val_y2_output_loss: 1.0170 - val_y1_output_root_mean_squared_error: 0.5332 - val_y2_output_root_mean_squared_error: 1.0085\n",
            "Epoch 270/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.9861 - y1_output_loss: 0.1993 - y2_output_loss: 0.7868 - y1_output_root_mean_squared_error: 0.4465 - y2_output_root_mean_squared_error: 0.8870 - val_loss: 1.3867 - val_y1_output_loss: 0.3295 - val_y2_output_loss: 1.0571 - val_y1_output_root_mean_squared_error: 0.5740 - val_y2_output_root_mean_squared_error: 1.0282\n",
            "Epoch 271/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.6760 - y1_output_loss: 0.1750 - y2_output_loss: 0.5010 - y1_output_root_mean_squared_error: 0.4184 - y2_output_root_mean_squared_error: 0.7078 - val_loss: 0.7957 - val_y1_output_loss: 0.1937 - val_y2_output_loss: 0.6020 - val_y1_output_root_mean_squared_error: 0.4401 - val_y2_output_root_mean_squared_error: 0.7759\n",
            "Epoch 272/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.8189 - y1_output_loss: 0.1873 - y2_output_loss: 0.6317 - y1_output_root_mean_squared_error: 0.4328 - y2_output_root_mean_squared_error: 0.7948 - val_loss: 0.7834 - val_y1_output_loss: 0.1781 - val_y2_output_loss: 0.6054 - val_y1_output_root_mean_squared_error: 0.4220 - val_y2_output_root_mean_squared_error: 0.7781\n",
            "Epoch 273/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.6769 - y1_output_loss: 0.1689 - y2_output_loss: 0.5080 - y1_output_root_mean_squared_error: 0.4110 - y2_output_root_mean_squared_error: 0.7128 - val_loss: 5.5365 - val_y1_output_loss: 0.8104 - val_y2_output_loss: 4.7261 - val_y1_output_root_mean_squared_error: 0.9002 - val_y2_output_root_mean_squared_error: 2.1740\n",
            "Epoch 274/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.0404 - y1_output_loss: 0.2132 - y2_output_loss: 0.8272 - y1_output_root_mean_squared_error: 0.4618 - y2_output_root_mean_squared_error: 0.9095 - val_loss: 0.9872 - val_y1_output_loss: 0.2598 - val_y2_output_loss: 0.7274 - val_y1_output_root_mean_squared_error: 0.5097 - val_y2_output_root_mean_squared_error: 0.8529\n",
            "Epoch 275/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.8641 - y1_output_loss: 0.2436 - y2_output_loss: 0.6205 - y1_output_root_mean_squared_error: 0.4935 - y2_output_root_mean_squared_error: 0.7877 - val_loss: 1.5613 - val_y1_output_loss: 0.4598 - val_y2_output_loss: 1.1015 - val_y1_output_root_mean_squared_error: 0.6781 - val_y2_output_root_mean_squared_error: 1.0495\n",
            "Epoch 276/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.7586 - y1_output_loss: 0.2101 - y2_output_loss: 0.5485 - y1_output_root_mean_squared_error: 0.4584 - y2_output_root_mean_squared_error: 0.7406 - val_loss: 2.0368 - val_y1_output_loss: 0.2002 - val_y2_output_loss: 1.8366 - val_y1_output_root_mean_squared_error: 0.4474 - val_y2_output_root_mean_squared_error: 1.3552\n",
            "Epoch 277/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.0180 - y1_output_loss: 0.2217 - y2_output_loss: 0.7963 - y1_output_root_mean_squared_error: 0.4708 - y2_output_root_mean_squared_error: 0.8924 - val_loss: 1.0116 - val_y1_output_loss: 0.2080 - val_y2_output_loss: 0.8036 - val_y1_output_root_mean_squared_error: 0.4561 - val_y2_output_root_mean_squared_error: 0.8964\n",
            "Epoch 278/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.6505 - y1_output_loss: 0.1863 - y2_output_loss: 0.4642 - y1_output_root_mean_squared_error: 0.4316 - y2_output_root_mean_squared_error: 0.6813 - val_loss: 1.0801 - val_y1_output_loss: 0.2400 - val_y2_output_loss: 0.8401 - val_y1_output_root_mean_squared_error: 0.4899 - val_y2_output_root_mean_squared_error: 0.9166\n",
            "Epoch 279/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.7420 - y1_output_loss: 0.1837 - y2_output_loss: 0.5583 - y1_output_root_mean_squared_error: 0.4286 - y2_output_root_mean_squared_error: 0.7472 - val_loss: 0.9044 - val_y1_output_loss: 0.2425 - val_y2_output_loss: 0.6619 - val_y1_output_root_mean_squared_error: 0.4925 - val_y2_output_root_mean_squared_error: 0.8136\n",
            "Epoch 280/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.7127 - y1_output_loss: 0.1959 - y2_output_loss: 0.5168 - y1_output_root_mean_squared_error: 0.4426 - y2_output_root_mean_squared_error: 0.7189 - val_loss: 2.1620 - val_y1_output_loss: 0.2694 - val_y2_output_loss: 1.8926 - val_y1_output_root_mean_squared_error: 0.5190 - val_y2_output_root_mean_squared_error: 1.3757\n",
            "Epoch 281/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.8857 - y1_output_loss: 0.2049 - y2_output_loss: 0.6808 - y1_output_root_mean_squared_error: 0.4526 - y2_output_root_mean_squared_error: 0.8251 - val_loss: 1.0922 - val_y1_output_loss: 0.2048 - val_y2_output_loss: 0.8874 - val_y1_output_root_mean_squared_error: 0.4526 - val_y2_output_root_mean_squared_error: 0.9420\n",
            "Epoch 282/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.7308 - y1_output_loss: 0.1714 - y2_output_loss: 0.5594 - y1_output_root_mean_squared_error: 0.4140 - y2_output_root_mean_squared_error: 0.7479 - val_loss: 0.8987 - val_y1_output_loss: 0.2394 - val_y2_output_loss: 0.6592 - val_y1_output_root_mean_squared_error: 0.4893 - val_y2_output_root_mean_squared_error: 0.8119\n",
            "Epoch 283/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.7535 - y1_output_loss: 0.2119 - y2_output_loss: 0.5416 - y1_output_root_mean_squared_error: 0.4603 - y2_output_root_mean_squared_error: 0.7359 - val_loss: 0.9207 - val_y1_output_loss: 0.2238 - val_y2_output_loss: 0.6969 - val_y1_output_root_mean_squared_error: 0.4731 - val_y2_output_root_mean_squared_error: 0.8348\n",
            "Epoch 284/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.7638 - y1_output_loss: 0.2068 - y2_output_loss: 0.5570 - y1_output_root_mean_squared_error: 0.4548 - y2_output_root_mean_squared_error: 0.7463 - val_loss: 3.0635 - val_y1_output_loss: 0.3921 - val_y2_output_loss: 2.6714 - val_y1_output_root_mean_squared_error: 0.6262 - val_y2_output_root_mean_squared_error: 1.6344\n",
            "Epoch 285/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.7634 - y1_output_loss: 0.1800 - y2_output_loss: 0.5834 - y1_output_root_mean_squared_error: 0.4242 - y2_output_root_mean_squared_error: 0.7638 - val_loss: 0.9107 - val_y1_output_loss: 0.1994 - val_y2_output_loss: 0.7113 - val_y1_output_root_mean_squared_error: 0.4465 - val_y2_output_root_mean_squared_error: 0.8434\n",
            "Epoch 286/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.6873 - y1_output_loss: 0.1731 - y2_output_loss: 0.5143 - y1_output_root_mean_squared_error: 0.4160 - y2_output_root_mean_squared_error: 0.7171 - val_loss: 1.1737 - val_y1_output_loss: 0.2530 - val_y2_output_loss: 0.9207 - val_y1_output_root_mean_squared_error: 0.5030 - val_y2_output_root_mean_squared_error: 0.9595\n",
            "Epoch 287/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.6459 - y1_output_loss: 0.1516 - y2_output_loss: 0.4943 - y1_output_root_mean_squared_error: 0.3894 - y2_output_root_mean_squared_error: 0.7031 - val_loss: 1.0957 - val_y1_output_loss: 0.2250 - val_y2_output_loss: 0.8708 - val_y1_output_root_mean_squared_error: 0.4743 - val_y2_output_root_mean_squared_error: 0.9331\n",
            "Epoch 288/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.5810 - y1_output_loss: 0.1520 - y2_output_loss: 0.4290 - y1_output_root_mean_squared_error: 0.3898 - y2_output_root_mean_squared_error: 0.6550 - val_loss: 0.9476 - val_y1_output_loss: 0.2589 - val_y2_output_loss: 0.6887 - val_y1_output_root_mean_squared_error: 0.5088 - val_y2_output_root_mean_squared_error: 0.8299\n",
            "Epoch 289/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.7611 - y1_output_loss: 0.1921 - y2_output_loss: 0.5690 - y1_output_root_mean_squared_error: 0.4383 - y2_output_root_mean_squared_error: 0.7543 - val_loss: 1.3969 - val_y1_output_loss: 0.3911 - val_y2_output_loss: 1.0058 - val_y1_output_root_mean_squared_error: 0.6254 - val_y2_output_root_mean_squared_error: 1.0029\n",
            "Epoch 290/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.7458 - y1_output_loss: 0.1912 - y2_output_loss: 0.5546 - y1_output_root_mean_squared_error: 0.4372 - y2_output_root_mean_squared_error: 0.7447 - val_loss: 1.4466 - val_y1_output_loss: 0.1978 - val_y2_output_loss: 1.2488 - val_y1_output_root_mean_squared_error: 0.4448 - val_y2_output_root_mean_squared_error: 1.1175\n",
            "Epoch 291/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.8244 - y1_output_loss: 0.1916 - y2_output_loss: 0.6327 - y1_output_root_mean_squared_error: 0.4378 - y2_output_root_mean_squared_error: 0.7954 - val_loss: 1.0398 - val_y1_output_loss: 0.2188 - val_y2_output_loss: 0.8210 - val_y1_output_root_mean_squared_error: 0.4677 - val_y2_output_root_mean_squared_error: 0.9061\n",
            "Epoch 292/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.7463 - y1_output_loss: 0.2083 - y2_output_loss: 0.5380 - y1_output_root_mean_squared_error: 0.4564 - y2_output_root_mean_squared_error: 0.7335 - val_loss: 1.1881 - val_y1_output_loss: 0.2926 - val_y2_output_loss: 0.8955 - val_y1_output_root_mean_squared_error: 0.5409 - val_y2_output_root_mean_squared_error: 0.9463\n",
            "Epoch 293/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.5992 - y1_output_loss: 0.1758 - y2_output_loss: 0.4234 - y1_output_root_mean_squared_error: 0.4193 - y2_output_root_mean_squared_error: 0.6507 - val_loss: 1.4916 - val_y1_output_loss: 0.2666 - val_y2_output_loss: 1.2250 - val_y1_output_root_mean_squared_error: 0.5163 - val_y2_output_root_mean_squared_error: 1.1068\n",
            "Epoch 294/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.7221 - y1_output_loss: 0.1791 - y2_output_loss: 0.5430 - y1_output_root_mean_squared_error: 0.4232 - y2_output_root_mean_squared_error: 0.7369 - val_loss: 3.9029 - val_y1_output_loss: 0.3069 - val_y2_output_loss: 3.5960 - val_y1_output_root_mean_squared_error: 0.5539 - val_y2_output_root_mean_squared_error: 1.8963\n",
            "Epoch 295/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.9560 - y1_output_loss: 0.2038 - y2_output_loss: 0.7522 - y1_output_root_mean_squared_error: 0.4515 - y2_output_root_mean_squared_error: 0.8673 - val_loss: 1.0498 - val_y1_output_loss: 0.2028 - val_y2_output_loss: 0.8470 - val_y1_output_root_mean_squared_error: 0.4504 - val_y2_output_root_mean_squared_error: 0.9203\n",
            "Epoch 296/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.5797 - y1_output_loss: 0.1490 - y2_output_loss: 0.4306 - y1_output_root_mean_squared_error: 0.3861 - y2_output_root_mean_squared_error: 0.6562 - val_loss: 0.9686 - val_y1_output_loss: 0.2557 - val_y2_output_loss: 0.7128 - val_y1_output_root_mean_squared_error: 0.5057 - val_y2_output_root_mean_squared_error: 0.8443\n",
            "Epoch 297/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.6671 - y1_output_loss: 0.1613 - y2_output_loss: 0.5057 - y1_output_root_mean_squared_error: 0.4016 - y2_output_root_mean_squared_error: 0.7112 - val_loss: 0.9865 - val_y1_output_loss: 0.2401 - val_y2_output_loss: 0.7464 - val_y1_output_root_mean_squared_error: 0.4900 - val_y2_output_root_mean_squared_error: 0.8640\n",
            "Epoch 298/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.5501 - y1_output_loss: 0.1516 - y2_output_loss: 0.3985 - y1_output_root_mean_squared_error: 0.3894 - y2_output_root_mean_squared_error: 0.6312 - val_loss: 0.9129 - val_y1_output_loss: 0.1812 - val_y2_output_loss: 0.7317 - val_y1_output_root_mean_squared_error: 0.4256 - val_y2_output_root_mean_squared_error: 0.8554\n",
            "Epoch 299/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.6207 - y1_output_loss: 0.1522 - y2_output_loss: 0.4686 - y1_output_root_mean_squared_error: 0.3901 - y2_output_root_mean_squared_error: 0.6845 - val_loss: 0.9491 - val_y1_output_loss: 0.2034 - val_y2_output_loss: 0.7457 - val_y1_output_root_mean_squared_error: 0.4510 - val_y2_output_root_mean_squared_error: 0.8635\n",
            "Epoch 300/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.8169 - y1_output_loss: 0.1934 - y2_output_loss: 0.6234 - y1_output_root_mean_squared_error: 0.4398 - y2_output_root_mean_squared_error: 0.7896 - val_loss: 1.0419 - val_y1_output_loss: 0.2036 - val_y2_output_loss: 0.8383 - val_y1_output_root_mean_squared_error: 0.4512 - val_y2_output_root_mean_squared_error: 0.9156\n",
            "Epoch 301/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.6790 - y1_output_loss: 0.1727 - y2_output_loss: 0.5063 - y1_output_root_mean_squared_error: 0.4155 - y2_output_root_mean_squared_error: 0.7116 - val_loss: 1.0683 - val_y1_output_loss: 0.2017 - val_y2_output_loss: 0.8666 - val_y1_output_root_mean_squared_error: 0.4492 - val_y2_output_root_mean_squared_error: 0.9309\n",
            "Epoch 302/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.5863 - y1_output_loss: 0.1499 - y2_output_loss: 0.4364 - y1_output_root_mean_squared_error: 0.3871 - y2_output_root_mean_squared_error: 0.6606 - val_loss: 0.8379 - val_y1_output_loss: 0.1948 - val_y2_output_loss: 0.6431 - val_y1_output_root_mean_squared_error: 0.4414 - val_y2_output_root_mean_squared_error: 0.8019\n",
            "Epoch 303/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.8443 - y1_output_loss: 0.2437 - y2_output_loss: 0.6007 - y1_output_root_mean_squared_error: 0.4936 - y2_output_root_mean_squared_error: 0.7750 - val_loss: 1.9495 - val_y1_output_loss: 0.3974 - val_y2_output_loss: 1.5521 - val_y1_output_root_mean_squared_error: 0.6304 - val_y2_output_root_mean_squared_error: 1.2458\n",
            "Epoch 304/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.9065 - y1_output_loss: 0.2369 - y2_output_loss: 0.6695 - y1_output_root_mean_squared_error: 0.4868 - y2_output_root_mean_squared_error: 0.8182 - val_loss: 0.8712 - val_y1_output_loss: 0.1876 - val_y2_output_loss: 0.6836 - val_y1_output_root_mean_squared_error: 0.4331 - val_y2_output_root_mean_squared_error: 0.8268\n",
            "Epoch 305/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.7219 - y1_output_loss: 0.1925 - y2_output_loss: 0.5294 - y1_output_root_mean_squared_error: 0.4388 - y2_output_root_mean_squared_error: 0.7276 - val_loss: 0.7752 - val_y1_output_loss: 0.1870 - val_y2_output_loss: 0.5882 - val_y1_output_root_mean_squared_error: 0.4325 - val_y2_output_root_mean_squared_error: 0.7669\n",
            "Epoch 306/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.3271 - y1_output_loss: 0.3006 - y2_output_loss: 1.0265 - y1_output_root_mean_squared_error: 0.5482 - y2_output_root_mean_squared_error: 1.0132 - val_loss: 4.6893 - val_y1_output_loss: 1.9878 - val_y2_output_loss: 2.7014 - val_y1_output_root_mean_squared_error: 1.4099 - val_y2_output_root_mean_squared_error: 1.6436\n",
            "Epoch 307/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.6619 - y1_output_loss: 0.1775 - y2_output_loss: 0.4844 - y1_output_root_mean_squared_error: 0.4213 - y2_output_root_mean_squared_error: 0.6960 - val_loss: 2.7873 - val_y1_output_loss: 0.4454 - val_y2_output_loss: 2.3419 - val_y1_output_root_mean_squared_error: 0.6674 - val_y2_output_root_mean_squared_error: 1.5303\n",
            "Epoch 308/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.6461 - y1_output_loss: 0.1712 - y2_output_loss: 0.4749 - y1_output_root_mean_squared_error: 0.4138 - y2_output_root_mean_squared_error: 0.6891 - val_loss: 0.7636 - val_y1_output_loss: 0.1747 - val_y2_output_loss: 0.5889 - val_y1_output_root_mean_squared_error: 0.4180 - val_y2_output_root_mean_squared_error: 0.7674\n",
            "Epoch 309/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.5242 - y1_output_loss: 0.1508 - y2_output_loss: 0.3733 - y1_output_root_mean_squared_error: 0.3884 - y2_output_root_mean_squared_error: 0.6110 - val_loss: 1.2689 - val_y1_output_loss: 0.2537 - val_y2_output_loss: 1.0152 - val_y1_output_root_mean_squared_error: 0.5037 - val_y2_output_root_mean_squared_error: 1.0076\n",
            "Epoch 310/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.5528 - y1_output_loss: 0.1511 - y2_output_loss: 0.4017 - y1_output_root_mean_squared_error: 0.3887 - y2_output_root_mean_squared_error: 0.6338 - val_loss: 0.9198 - val_y1_output_loss: 0.2301 - val_y2_output_loss: 0.6897 - val_y1_output_root_mean_squared_error: 0.4797 - val_y2_output_root_mean_squared_error: 0.8305\n",
            "Epoch 311/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.8403 - y1_output_loss: 0.1919 - y2_output_loss: 0.6484 - y1_output_root_mean_squared_error: 0.4381 - y2_output_root_mean_squared_error: 0.8052 - val_loss: 1.2538 - val_y1_output_loss: 0.2020 - val_y2_output_loss: 1.0518 - val_y1_output_root_mean_squared_error: 0.4495 - val_y2_output_root_mean_squared_error: 1.0256\n",
            "Epoch 312/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.6250 - y1_output_loss: 0.1620 - y2_output_loss: 0.4630 - y1_output_root_mean_squared_error: 0.4025 - y2_output_root_mean_squared_error: 0.6804 - val_loss: 2.1669 - val_y1_output_loss: 0.6279 - val_y2_output_loss: 1.5391 - val_y1_output_root_mean_squared_error: 0.7924 - val_y2_output_root_mean_squared_error: 1.2406\n",
            "Epoch 313/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.6669 - y1_output_loss: 0.1775 - y2_output_loss: 0.4894 - y1_output_root_mean_squared_error: 0.4213 - y2_output_root_mean_squared_error: 0.6996 - val_loss: 0.9770 - val_y1_output_loss: 0.2037 - val_y2_output_loss: 0.7733 - val_y1_output_root_mean_squared_error: 0.4513 - val_y2_output_root_mean_squared_error: 0.8794\n",
            "Epoch 314/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.5949 - y1_output_loss: 0.1603 - y2_output_loss: 0.4346 - y1_output_root_mean_squared_error: 0.4004 - y2_output_root_mean_squared_error: 0.6592 - val_loss: 1.2333 - val_y1_output_loss: 0.4129 - val_y2_output_loss: 0.8204 - val_y1_output_root_mean_squared_error: 0.6426 - val_y2_output_root_mean_squared_error: 0.9058\n",
            "Epoch 315/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.5259 - y1_output_loss: 0.1401 - y2_output_loss: 0.3858 - y1_output_root_mean_squared_error: 0.3743 - y2_output_root_mean_squared_error: 0.6211 - val_loss: 1.4180 - val_y1_output_loss: 0.3671 - val_y2_output_loss: 1.0510 - val_y1_output_root_mean_squared_error: 0.6059 - val_y2_output_root_mean_squared_error: 1.0252\n",
            "Epoch 316/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.5471 - y1_output_loss: 0.1439 - y2_output_loss: 0.4031 - y1_output_root_mean_squared_error: 0.3794 - y2_output_root_mean_squared_error: 0.6349 - val_loss: 0.9936 - val_y1_output_loss: 0.2848 - val_y2_output_loss: 0.7088 - val_y1_output_root_mean_squared_error: 0.5336 - val_y2_output_root_mean_squared_error: 0.8419\n",
            "Epoch 317/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.4927 - y1_output_loss: 0.1430 - y2_output_loss: 0.3497 - y1_output_root_mean_squared_error: 0.3781 - y2_output_root_mean_squared_error: 0.5914 - val_loss: 1.4934 - val_y1_output_loss: 0.3919 - val_y2_output_loss: 1.1016 - val_y1_output_root_mean_squared_error: 0.6260 - val_y2_output_root_mean_squared_error: 1.0496\n",
            "Epoch 318/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.8163 - y1_output_loss: 0.2251 - y2_output_loss: 0.5912 - y1_output_root_mean_squared_error: 0.4745 - y2_output_root_mean_squared_error: 0.7689 - val_loss: 2.9655 - val_y1_output_loss: 0.3156 - val_y2_output_loss: 2.6498 - val_y1_output_root_mean_squared_error: 0.5618 - val_y2_output_root_mean_squared_error: 1.6278\n",
            "Epoch 319/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.6120 - y1_output_loss: 0.1563 - y2_output_loss: 0.4558 - y1_output_root_mean_squared_error: 0.3953 - y2_output_root_mean_squared_error: 0.6751 - val_loss: 1.2187 - val_y1_output_loss: 0.2217 - val_y2_output_loss: 0.9970 - val_y1_output_root_mean_squared_error: 0.4709 - val_y2_output_root_mean_squared_error: 0.9985\n",
            "Epoch 320/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.6600 - y1_output_loss: 0.1561 - y2_output_loss: 0.5039 - y1_output_root_mean_squared_error: 0.3950 - y2_output_root_mean_squared_error: 0.7099 - val_loss: 0.9745 - val_y1_output_loss: 0.2383 - val_y2_output_loss: 0.7362 - val_y1_output_root_mean_squared_error: 0.4882 - val_y2_output_root_mean_squared_error: 0.8580\n",
            "Epoch 321/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 2.7999 - y1_output_loss: 0.8648 - y2_output_loss: 1.9351 - y1_output_root_mean_squared_error: 0.9300 - y2_output_root_mean_squared_error: 1.3911 - val_loss: 7.7883 - val_y1_output_loss: 0.5452 - val_y2_output_loss: 7.2430 - val_y1_output_root_mean_squared_error: 0.7384 - val_y2_output_root_mean_squared_error: 2.6913\n",
            "Epoch 322/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.0502 - y1_output_loss: 0.1996 - y2_output_loss: 0.8506 - y1_output_root_mean_squared_error: 0.4468 - y2_output_root_mean_squared_error: 0.9223 - val_loss: 1.1755 - val_y1_output_loss: 0.2933 - val_y2_output_loss: 0.8821 - val_y1_output_root_mean_squared_error: 0.5416 - val_y2_output_root_mean_squared_error: 0.9392\n",
            "Epoch 323/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.1264 - y1_output_loss: 0.2690 - y2_output_loss: 0.8575 - y1_output_root_mean_squared_error: 0.5186 - y2_output_root_mean_squared_error: 0.9260 - val_loss: 1.1355 - val_y1_output_loss: 0.3854 - val_y2_output_loss: 0.7501 - val_y1_output_root_mean_squared_error: 0.6208 - val_y2_output_root_mean_squared_error: 0.8661\n",
            "Epoch 324/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 1.0089 - y1_output_loss: 0.2543 - y2_output_loss: 0.7547 - y1_output_root_mean_squared_error: 0.5043 - y2_output_root_mean_squared_error: 0.8687 - val_loss: 0.9249 - val_y1_output_loss: 0.2869 - val_y2_output_loss: 0.6380 - val_y1_output_root_mean_squared_error: 0.5356 - val_y2_output_root_mean_squared_error: 0.7988\n",
            "Epoch 325/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.4772 - y1_output_loss: 0.1297 - y2_output_loss: 0.3475 - y1_output_root_mean_squared_error: 0.3602 - y2_output_root_mean_squared_error: 0.5895 - val_loss: 0.9326 - val_y1_output_loss: 0.1969 - val_y2_output_loss: 0.7357 - val_y1_output_root_mean_squared_error: 0.4437 - val_y2_output_root_mean_squared_error: 0.8578\n",
            "Epoch 326/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.6510 - y1_output_loss: 0.1664 - y2_output_loss: 0.4846 - y1_output_root_mean_squared_error: 0.4080 - y2_output_root_mean_squared_error: 0.6961 - val_loss: 1.9382 - val_y1_output_loss: 0.5332 - val_y2_output_loss: 1.4050 - val_y1_output_root_mean_squared_error: 0.7302 - val_y2_output_root_mean_squared_error: 1.1853\n",
            "Epoch 327/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.9384 - y1_output_loss: 0.2495 - y2_output_loss: 0.6890 - y1_output_root_mean_squared_error: 0.4995 - y2_output_root_mean_squared_error: 0.8300 - val_loss: 2.2535 - val_y1_output_loss: 0.6262 - val_y2_output_loss: 1.6273 - val_y1_output_root_mean_squared_error: 0.7913 - val_y2_output_root_mean_squared_error: 1.2756\n",
            "Epoch 328/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 2.3918 - y1_output_loss: 0.8875 - y2_output_loss: 1.5043 - y1_output_root_mean_squared_error: 0.9421 - y2_output_root_mean_squared_error: 1.2265 - val_loss: 0.8406 - val_y1_output_loss: 0.2259 - val_y2_output_loss: 0.6147 - val_y1_output_root_mean_squared_error: 0.4753 - val_y2_output_root_mean_squared_error: 0.7840\n",
            "Epoch 329/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.6329 - y1_output_loss: 0.1620 - y2_output_loss: 0.4709 - y1_output_root_mean_squared_error: 0.4025 - y2_output_root_mean_squared_error: 0.6863 - val_loss: 2.0081 - val_y1_output_loss: 0.3672 - val_y2_output_loss: 1.6409 - val_y1_output_root_mean_squared_error: 0.6060 - val_y2_output_root_mean_squared_error: 1.2810\n",
            "Epoch 330/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.7349 - y1_output_loss: 0.1599 - y2_output_loss: 0.5750 - y1_output_root_mean_squared_error: 0.3999 - y2_output_root_mean_squared_error: 0.7583 - val_loss: 0.7532 - val_y1_output_loss: 0.1893 - val_y2_output_loss: 0.5639 - val_y1_output_root_mean_squared_error: 0.4351 - val_y2_output_root_mean_squared_error: 0.7510\n",
            "Epoch 331/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.8961 - y1_output_loss: 0.2150 - y2_output_loss: 0.6812 - y1_output_root_mean_squared_error: 0.4637 - y2_output_root_mean_squared_error: 0.8253 - val_loss: 0.8142 - val_y1_output_loss: 0.2088 - val_y2_output_loss: 0.6054 - val_y1_output_root_mean_squared_error: 0.4570 - val_y2_output_root_mean_squared_error: 0.7781\n",
            "Epoch 332/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.5382 - y1_output_loss: 0.1399 - y2_output_loss: 0.3983 - y1_output_root_mean_squared_error: 0.3740 - y2_output_root_mean_squared_error: 0.6311 - val_loss: 0.7951 - val_y1_output_loss: 0.2372 - val_y2_output_loss: 0.5578 - val_y1_output_root_mean_squared_error: 0.4871 - val_y2_output_root_mean_squared_error: 0.7469\n",
            "Epoch 333/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.5276 - y1_output_loss: 0.1494 - y2_output_loss: 0.3783 - y1_output_root_mean_squared_error: 0.3865 - y2_output_root_mean_squared_error: 0.6150 - val_loss: 1.0448 - val_y1_output_loss: 0.3287 - val_y2_output_loss: 0.7161 - val_y1_output_root_mean_squared_error: 0.5734 - val_y2_output_root_mean_squared_error: 0.8462\n",
            "Epoch 334/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.8246 - y1_output_loss: 0.2061 - y2_output_loss: 0.6185 - y1_output_root_mean_squared_error: 0.4540 - y2_output_root_mean_squared_error: 0.7864 - val_loss: 6.3881 - val_y1_output_loss: 0.5259 - val_y2_output_loss: 5.8622 - val_y1_output_root_mean_squared_error: 0.7252 - val_y2_output_root_mean_squared_error: 2.4212\n",
            "Epoch 335/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.6106 - y1_output_loss: 0.1486 - y2_output_loss: 0.4620 - y1_output_root_mean_squared_error: 0.3855 - y2_output_root_mean_squared_error: 0.6797 - val_loss: 0.9261 - val_y1_output_loss: 0.2404 - val_y2_output_loss: 0.6857 - val_y1_output_root_mean_squared_error: 0.4903 - val_y2_output_root_mean_squared_error: 0.8281\n",
            "Epoch 336/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.6752 - y1_output_loss: 0.1875 - y2_output_loss: 0.4877 - y1_output_root_mean_squared_error: 0.4331 - y2_output_root_mean_squared_error: 0.6983 - val_loss: 1.4772 - val_y1_output_loss: 0.2239 - val_y2_output_loss: 1.2533 - val_y1_output_root_mean_squared_error: 0.4732 - val_y2_output_root_mean_squared_error: 1.1195\n",
            "Epoch 337/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.4620 - y1_output_loss: 0.1314 - y2_output_loss: 0.3307 - y1_output_root_mean_squared_error: 0.3624 - y2_output_root_mean_squared_error: 0.5751 - val_loss: 1.0204 - val_y1_output_loss: 0.2383 - val_y2_output_loss: 0.7820 - val_y1_output_root_mean_squared_error: 0.4882 - val_y2_output_root_mean_squared_error: 0.8843\n",
            "Epoch 338/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.8996 - y1_output_loss: 0.1886 - y2_output_loss: 0.7110 - y1_output_root_mean_squared_error: 0.4343 - y2_output_root_mean_squared_error: 0.8432 - val_loss: 0.9947 - val_y1_output_loss: 0.2973 - val_y2_output_loss: 0.6974 - val_y1_output_root_mean_squared_error: 0.5453 - val_y2_output_root_mean_squared_error: 0.8351\n",
            "Epoch 339/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.6877 - y1_output_loss: 0.2141 - y2_output_loss: 0.4736 - y1_output_root_mean_squared_error: 0.4627 - y2_output_root_mean_squared_error: 0.6882 - val_loss: 2.0158 - val_y1_output_loss: 0.6583 - val_y2_output_loss: 1.3576 - val_y1_output_root_mean_squared_error: 0.8113 - val_y2_output_root_mean_squared_error: 1.1651\n",
            "Epoch 340/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.7421 - y1_output_loss: 0.1936 - y2_output_loss: 0.5485 - y1_output_root_mean_squared_error: 0.4400 - y2_output_root_mean_squared_error: 0.7406 - val_loss: 0.8104 - val_y1_output_loss: 0.1622 - val_y2_output_loss: 0.6482 - val_y1_output_root_mean_squared_error: 0.4027 - val_y2_output_root_mean_squared_error: 0.8051\n",
            "Epoch 341/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.5346 - y1_output_loss: 0.1520 - y2_output_loss: 0.3826 - y1_output_root_mean_squared_error: 0.3899 - y2_output_root_mean_squared_error: 0.6185 - val_loss: 0.8785 - val_y1_output_loss: 0.2665 - val_y2_output_loss: 0.6120 - val_y1_output_root_mean_squared_error: 0.5162 - val_y2_output_root_mean_squared_error: 0.7823\n",
            "Epoch 342/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.7142 - y1_output_loss: 0.1965 - y2_output_loss: 0.5177 - y1_output_root_mean_squared_error: 0.4433 - y2_output_root_mean_squared_error: 0.7195 - val_loss: 0.7501 - val_y1_output_loss: 0.1944 - val_y2_output_loss: 0.5557 - val_y1_output_root_mean_squared_error: 0.4409 - val_y2_output_root_mean_squared_error: 0.7455\n",
            "Epoch 343/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.4597 - y1_output_loss: 0.1367 - y2_output_loss: 0.3229 - y1_output_root_mean_squared_error: 0.3698 - y2_output_root_mean_squared_error: 0.5683 - val_loss: 0.8029 - val_y1_output_loss: 0.1975 - val_y2_output_loss: 0.6054 - val_y1_output_root_mean_squared_error: 0.4445 - val_y2_output_root_mean_squared_error: 0.7781\n",
            "Epoch 344/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.5802 - y1_output_loss: 0.1607 - y2_output_loss: 0.4195 - y1_output_root_mean_squared_error: 0.4008 - y2_output_root_mean_squared_error: 0.6477 - val_loss: 3.2791 - val_y1_output_loss: 0.3919 - val_y2_output_loss: 2.8872 - val_y1_output_root_mean_squared_error: 0.6261 - val_y2_output_root_mean_squared_error: 1.6992\n",
            "Epoch 345/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.4828 - y1_output_loss: 0.1396 - y2_output_loss: 0.3433 - y1_output_root_mean_squared_error: 0.3736 - y2_output_root_mean_squared_error: 0.5859 - val_loss: 0.7507 - val_y1_output_loss: 0.1903 - val_y2_output_loss: 0.5603 - val_y1_output_root_mean_squared_error: 0.4363 - val_y2_output_root_mean_squared_error: 0.7485\n",
            "Epoch 346/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.6067 - y1_output_loss: 0.1582 - y2_output_loss: 0.4484 - y1_output_root_mean_squared_error: 0.3978 - y2_output_root_mean_squared_error: 0.6696 - val_loss: 0.9685 - val_y1_output_loss: 0.2568 - val_y2_output_loss: 0.7117 - val_y1_output_root_mean_squared_error: 0.5067 - val_y2_output_root_mean_squared_error: 0.8436\n",
            "Epoch 347/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.5903 - y1_output_loss: 0.1818 - y2_output_loss: 0.4085 - y1_output_root_mean_squared_error: 0.4264 - y2_output_root_mean_squared_error: 0.6391 - val_loss: 2.7881 - val_y1_output_loss: 0.3062 - val_y2_output_loss: 2.4819 - val_y1_output_root_mean_squared_error: 0.5533 - val_y2_output_root_mean_squared_error: 1.5754\n",
            "Epoch 348/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.5591 - y1_output_loss: 0.1616 - y2_output_loss: 0.3975 - y1_output_root_mean_squared_error: 0.4020 - y2_output_root_mean_squared_error: 0.6305 - val_loss: 2.9344 - val_y1_output_loss: 0.3955 - val_y2_output_loss: 2.5390 - val_y1_output_root_mean_squared_error: 0.6289 - val_y2_output_root_mean_squared_error: 1.5934\n",
            "Epoch 349/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.5995 - y1_output_loss: 0.1575 - y2_output_loss: 0.4420 - y1_output_root_mean_squared_error: 0.3968 - y2_output_root_mean_squared_error: 0.6648 - val_loss: 1.2600 - val_y1_output_loss: 0.2972 - val_y2_output_loss: 0.9628 - val_y1_output_root_mean_squared_error: 0.5452 - val_y2_output_root_mean_squared_error: 0.9812\n",
            "Epoch 350/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.5860 - y1_output_loss: 0.1698 - y2_output_loss: 0.4161 - y1_output_root_mean_squared_error: 0.4121 - y2_output_root_mean_squared_error: 0.6451 - val_loss: 0.8572 - val_y1_output_loss: 0.1961 - val_y2_output_loss: 0.6612 - val_y1_output_root_mean_squared_error: 0.4428 - val_y2_output_root_mean_squared_error: 0.8131\n",
            "Epoch 351/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.5381 - y1_output_loss: 0.1639 - y2_output_loss: 0.3742 - y1_output_root_mean_squared_error: 0.4049 - y2_output_root_mean_squared_error: 0.6117 - val_loss: 0.8536 - val_y1_output_loss: 0.1978 - val_y2_output_loss: 0.6558 - val_y1_output_root_mean_squared_error: 0.4448 - val_y2_output_root_mean_squared_error: 0.8098\n",
            "Epoch 352/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.6281 - y1_output_loss: 0.1544 - y2_output_loss: 0.4736 - y1_output_root_mean_squared_error: 0.3930 - y2_output_root_mean_squared_error: 0.6882 - val_loss: 0.8075 - val_y1_output_loss: 0.1747 - val_y2_output_loss: 0.6328 - val_y1_output_root_mean_squared_error: 0.4180 - val_y2_output_root_mean_squared_error: 0.7955\n",
            "Epoch 353/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.4272 - y1_output_loss: 0.1293 - y2_output_loss: 0.2979 - y1_output_root_mean_squared_error: 0.3596 - y2_output_root_mean_squared_error: 0.5458 - val_loss: 0.7748 - val_y1_output_loss: 0.2010 - val_y2_output_loss: 0.5738 - val_y1_output_root_mean_squared_error: 0.4484 - val_y2_output_root_mean_squared_error: 0.7575\n",
            "Epoch 354/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.4711 - y1_output_loss: 0.1311 - y2_output_loss: 0.3400 - y1_output_root_mean_squared_error: 0.3620 - y2_output_root_mean_squared_error: 0.5831 - val_loss: 4.1659 - val_y1_output_loss: 0.3550 - val_y2_output_loss: 3.8108 - val_y1_output_root_mean_squared_error: 0.5958 - val_y2_output_root_mean_squared_error: 1.9521\n",
            "Epoch 355/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.5587 - y1_output_loss: 0.1459 - y2_output_loss: 0.4128 - y1_output_root_mean_squared_error: 0.3820 - y2_output_root_mean_squared_error: 0.6425 - val_loss: 0.9983 - val_y1_output_loss: 0.2243 - val_y2_output_loss: 0.7740 - val_y1_output_root_mean_squared_error: 0.4736 - val_y2_output_root_mean_squared_error: 0.8798\n",
            "Epoch 356/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.6527 - y1_output_loss: 0.1965 - y2_output_loss: 0.4562 - y1_output_root_mean_squared_error: 0.4433 - y2_output_root_mean_squared_error: 0.6754 - val_loss: 0.9735 - val_y1_output_loss: 0.1912 - val_y2_output_loss: 0.7823 - val_y1_output_root_mean_squared_error: 0.4373 - val_y2_output_root_mean_squared_error: 0.8845\n",
            "Epoch 357/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.5569 - y1_output_loss: 0.1389 - y2_output_loss: 0.4180 - y1_output_root_mean_squared_error: 0.3727 - y2_output_root_mean_squared_error: 0.6465 - val_loss: 0.8128 - val_y1_output_loss: 0.2232 - val_y2_output_loss: 0.5896 - val_y1_output_root_mean_squared_error: 0.4724 - val_y2_output_root_mean_squared_error: 0.7679\n",
            "Epoch 358/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.4733 - y1_output_loss: 0.1440 - y2_output_loss: 0.3293 - y1_output_root_mean_squared_error: 0.3794 - y2_output_root_mean_squared_error: 0.5738 - val_loss: 2.6492 - val_y1_output_loss: 1.1189 - val_y2_output_loss: 1.5303 - val_y1_output_root_mean_squared_error: 1.0578 - val_y2_output_root_mean_squared_error: 1.2370\n",
            "Epoch 359/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.6880 - y1_output_loss: 0.2075 - y2_output_loss: 0.4805 - y1_output_root_mean_squared_error: 0.4555 - y2_output_root_mean_squared_error: 0.6932 - val_loss: 0.8221 - val_y1_output_loss: 0.1800 - val_y2_output_loss: 0.6421 - val_y1_output_root_mean_squared_error: 0.4243 - val_y2_output_root_mean_squared_error: 0.8013\n",
            "Epoch 360/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.5036 - y1_output_loss: 0.1561 - y2_output_loss: 0.3475 - y1_output_root_mean_squared_error: 0.3951 - y2_output_root_mean_squared_error: 0.5895 - val_loss: 0.8299 - val_y1_output_loss: 0.1858 - val_y2_output_loss: 0.6441 - val_y1_output_root_mean_squared_error: 0.4310 - val_y2_output_root_mean_squared_error: 0.8026\n",
            "Epoch 361/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.6255 - y1_output_loss: 0.1755 - y2_output_loss: 0.4501 - y1_output_root_mean_squared_error: 0.4189 - y2_output_root_mean_squared_error: 0.6709 - val_loss: 2.1990 - val_y1_output_loss: 0.2359 - val_y2_output_loss: 1.9631 - val_y1_output_root_mean_squared_error: 0.4856 - val_y2_output_root_mean_squared_error: 1.4011\n",
            "Epoch 362/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.5678 - y1_output_loss: 0.1498 - y2_output_loss: 0.4179 - y1_output_root_mean_squared_error: 0.3871 - y2_output_root_mean_squared_error: 0.6465 - val_loss: 1.1864 - val_y1_output_loss: 0.2573 - val_y2_output_loss: 0.9291 - val_y1_output_root_mean_squared_error: 0.5073 - val_y2_output_root_mean_squared_error: 0.9639\n",
            "Epoch 363/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.4360 - y1_output_loss: 0.1347 - y2_output_loss: 0.3013 - y1_output_root_mean_squared_error: 0.3671 - y2_output_root_mean_squared_error: 0.5489 - val_loss: 1.1965 - val_y1_output_loss: 0.2671 - val_y2_output_loss: 0.9293 - val_y1_output_root_mean_squared_error: 0.5168 - val_y2_output_root_mean_squared_error: 0.9640\n",
            "Epoch 364/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.5478 - y1_output_loss: 0.1620 - y2_output_loss: 0.3859 - y1_output_root_mean_squared_error: 0.4024 - y2_output_root_mean_squared_error: 0.6212 - val_loss: 0.9183 - val_y1_output_loss: 0.2341 - val_y2_output_loss: 0.6842 - val_y1_output_root_mean_squared_error: 0.4839 - val_y2_output_root_mean_squared_error: 0.8272\n",
            "Epoch 365/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.5870 - y1_output_loss: 0.1735 - y2_output_loss: 0.4135 - y1_output_root_mean_squared_error: 0.4166 - y2_output_root_mean_squared_error: 0.6430 - val_loss: 0.7698 - val_y1_output_loss: 0.2054 - val_y2_output_loss: 0.5643 - val_y1_output_root_mean_squared_error: 0.4532 - val_y2_output_root_mean_squared_error: 0.7512\n",
            "Epoch 366/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.4366 - y1_output_loss: 0.1257 - y2_output_loss: 0.3109 - y1_output_root_mean_squared_error: 0.3546 - y2_output_root_mean_squared_error: 0.5576 - val_loss: 0.9032 - val_y1_output_loss: 0.2461 - val_y2_output_loss: 0.6571 - val_y1_output_root_mean_squared_error: 0.4961 - val_y2_output_root_mean_squared_error: 0.8106\n",
            "Epoch 367/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.4569 - y1_output_loss: 0.1320 - y2_output_loss: 0.3248 - y1_output_root_mean_squared_error: 0.3634 - y2_output_root_mean_squared_error: 0.5700 - val_loss: 0.8522 - val_y1_output_loss: 0.1880 - val_y2_output_loss: 0.6642 - val_y1_output_root_mean_squared_error: 0.4336 - val_y2_output_root_mean_squared_error: 0.8150\n",
            "Epoch 368/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.4413 - y1_output_loss: 0.1321 - y2_output_loss: 0.3091 - y1_output_root_mean_squared_error: 0.3635 - y2_output_root_mean_squared_error: 0.5560 - val_loss: 0.7026 - val_y1_output_loss: 0.1850 - val_y2_output_loss: 0.5176 - val_y1_output_root_mean_squared_error: 0.4301 - val_y2_output_root_mean_squared_error: 0.7194\n",
            "Epoch 369/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.4117 - y1_output_loss: 0.1261 - y2_output_loss: 0.2856 - y1_output_root_mean_squared_error: 0.3551 - y2_output_root_mean_squared_error: 0.5344 - val_loss: 0.9854 - val_y1_output_loss: 0.2679 - val_y2_output_loss: 0.7175 - val_y1_output_root_mean_squared_error: 0.5176 - val_y2_output_root_mean_squared_error: 0.8471\n",
            "Epoch 370/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.4378 - y1_output_loss: 0.1264 - y2_output_loss: 0.3115 - y1_output_root_mean_squared_error: 0.3555 - y2_output_root_mean_squared_error: 0.5581 - val_loss: 0.7911 - val_y1_output_loss: 0.1774 - val_y2_output_loss: 0.6137 - val_y1_output_root_mean_squared_error: 0.4211 - val_y2_output_root_mean_squared_error: 0.7834\n",
            "Epoch 371/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.5278 - y1_output_loss: 0.1532 - y2_output_loss: 0.3747 - y1_output_root_mean_squared_error: 0.3914 - y2_output_root_mean_squared_error: 0.6121 - val_loss: 1.1907 - val_y1_output_loss: 0.3663 - val_y2_output_loss: 0.8243 - val_y1_output_root_mean_squared_error: 0.6053 - val_y2_output_root_mean_squared_error: 0.9079\n",
            "Epoch 372/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.4850 - y1_output_loss: 0.1401 - y2_output_loss: 0.3449 - y1_output_root_mean_squared_error: 0.3743 - y2_output_root_mean_squared_error: 0.5873 - val_loss: 0.8537 - val_y1_output_loss: 0.1757 - val_y2_output_loss: 0.6781 - val_y1_output_root_mean_squared_error: 0.4191 - val_y2_output_root_mean_squared_error: 0.8235\n",
            "Epoch 373/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.5500 - y1_output_loss: 0.1414 - y2_output_loss: 0.4087 - y1_output_root_mean_squared_error: 0.3760 - y2_output_root_mean_squared_error: 0.6393 - val_loss: 1.5179 - val_y1_output_loss: 0.2164 - val_y2_output_loss: 1.3015 - val_y1_output_root_mean_squared_error: 0.4651 - val_y2_output_root_mean_squared_error: 1.1408\n",
            "Epoch 374/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.5070 - y1_output_loss: 0.1386 - y2_output_loss: 0.3684 - y1_output_root_mean_squared_error: 0.3723 - y2_output_root_mean_squared_error: 0.6070 - val_loss: 2.3381 - val_y1_output_loss: 0.3043 - val_y2_output_loss: 2.0338 - val_y1_output_root_mean_squared_error: 0.5516 - val_y2_output_root_mean_squared_error: 1.4261\n",
            "Epoch 375/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.6667 - y1_output_loss: 0.1736 - y2_output_loss: 0.4932 - y1_output_root_mean_squared_error: 0.4166 - y2_output_root_mean_squared_error: 0.7023 - val_loss: 1.3162 - val_y1_output_loss: 0.1768 - val_y2_output_loss: 1.1394 - val_y1_output_root_mean_squared_error: 0.4205 - val_y2_output_root_mean_squared_error: 1.0674\n",
            "Epoch 376/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.5052 - y1_output_loss: 0.1282 - y2_output_loss: 0.3770 - y1_output_root_mean_squared_error: 0.3580 - y2_output_root_mean_squared_error: 0.6140 - val_loss: 0.8084 - val_y1_output_loss: 0.2239 - val_y2_output_loss: 0.5845 - val_y1_output_root_mean_squared_error: 0.4732 - val_y2_output_root_mean_squared_error: 0.7645\n",
            "Epoch 377/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.4915 - y1_output_loss: 0.1327 - y2_output_loss: 0.3588 - y1_output_root_mean_squared_error: 0.3643 - y2_output_root_mean_squared_error: 0.5990 - val_loss: 0.7090 - val_y1_output_loss: 0.1951 - val_y2_output_loss: 0.5139 - val_y1_output_root_mean_squared_error: 0.4417 - val_y2_output_root_mean_squared_error: 0.7169\n",
            "Epoch 378/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.4907 - y1_output_loss: 0.1381 - y2_output_loss: 0.3526 - y1_output_root_mean_squared_error: 0.3716 - y2_output_root_mean_squared_error: 0.5938 - val_loss: 0.8108 - val_y1_output_loss: 0.2111 - val_y2_output_loss: 0.5997 - val_y1_output_root_mean_squared_error: 0.4595 - val_y2_output_root_mean_squared_error: 0.7744\n",
            "Epoch 379/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.9278 - y1_output_loss: 0.2331 - y2_output_loss: 0.6947 - y1_output_root_mean_squared_error: 0.4828 - y2_output_root_mean_squared_error: 0.8335 - val_loss: 1.3141 - val_y1_output_loss: 0.4322 - val_y2_output_loss: 0.8819 - val_y1_output_root_mean_squared_error: 0.6574 - val_y2_output_root_mean_squared_error: 0.9391\n",
            "Epoch 380/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.5235 - y1_output_loss: 0.1585 - y2_output_loss: 0.3650 - y1_output_root_mean_squared_error: 0.3981 - y2_output_root_mean_squared_error: 0.6042 - val_loss: 1.3153 - val_y1_output_loss: 0.2985 - val_y2_output_loss: 1.0168 - val_y1_output_root_mean_squared_error: 0.5463 - val_y2_output_root_mean_squared_error: 1.0084\n",
            "Epoch 381/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.5067 - y1_output_loss: 0.1559 - y2_output_loss: 0.3508 - y1_output_root_mean_squared_error: 0.3949 - y2_output_root_mean_squared_error: 0.5923 - val_loss: 0.9436 - val_y1_output_loss: 0.1692 - val_y2_output_loss: 0.7745 - val_y1_output_root_mean_squared_error: 0.4113 - val_y2_output_root_mean_squared_error: 0.8800\n",
            "Epoch 382/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.4587 - y1_output_loss: 0.1355 - y2_output_loss: 0.3232 - y1_output_root_mean_squared_error: 0.3682 - y2_output_root_mean_squared_error: 0.5685 - val_loss: 0.7506 - val_y1_output_loss: 0.1588 - val_y2_output_loss: 0.5918 - val_y1_output_root_mean_squared_error: 0.3985 - val_y2_output_root_mean_squared_error: 0.7693\n",
            "Epoch 383/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.5360 - y1_output_loss: 0.1507 - y2_output_loss: 0.3853 - y1_output_root_mean_squared_error: 0.3882 - y2_output_root_mean_squared_error: 0.6207 - val_loss: 0.8661 - val_y1_output_loss: 0.2085 - val_y2_output_loss: 0.6576 - val_y1_output_root_mean_squared_error: 0.4566 - val_y2_output_root_mean_squared_error: 0.8109\n",
            "Epoch 384/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.4540 - y1_output_loss: 0.1243 - y2_output_loss: 0.3297 - y1_output_root_mean_squared_error: 0.3526 - y2_output_root_mean_squared_error: 0.5742 - val_loss: 1.1692 - val_y1_output_loss: 0.3062 - val_y2_output_loss: 0.8630 - val_y1_output_root_mean_squared_error: 0.5533 - val_y2_output_root_mean_squared_error: 0.9290\n",
            "Epoch 385/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.5391 - y1_output_loss: 0.1641 - y2_output_loss: 0.3750 - y1_output_root_mean_squared_error: 0.4051 - y2_output_root_mean_squared_error: 0.6124 - val_loss: 2.1660 - val_y1_output_loss: 0.2338 - val_y2_output_loss: 1.9322 - val_y1_output_root_mean_squared_error: 0.4835 - val_y2_output_root_mean_squared_error: 1.3900\n",
            "Epoch 386/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.5154 - y1_output_loss: 0.1572 - y2_output_loss: 0.3583 - y1_output_root_mean_squared_error: 0.3964 - y2_output_root_mean_squared_error: 0.5986 - val_loss: 0.7333 - val_y1_output_loss: 0.2168 - val_y2_output_loss: 0.5165 - val_y1_output_root_mean_squared_error: 0.4656 - val_y2_output_root_mean_squared_error: 0.7187\n",
            "Epoch 387/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.4428 - y1_output_loss: 0.1291 - y2_output_loss: 0.3137 - y1_output_root_mean_squared_error: 0.3593 - y2_output_root_mean_squared_error: 0.5601 - val_loss: 0.7522 - val_y1_output_loss: 0.1935 - val_y2_output_loss: 0.5587 - val_y1_output_root_mean_squared_error: 0.4398 - val_y2_output_root_mean_squared_error: 0.7475\n",
            "Epoch 388/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.4662 - y1_output_loss: 0.1337 - y2_output_loss: 0.3325 - y1_output_root_mean_squared_error: 0.3656 - y2_output_root_mean_squared_error: 0.5766 - val_loss: 0.7502 - val_y1_output_loss: 0.1702 - val_y2_output_loss: 0.5800 - val_y1_output_root_mean_squared_error: 0.4125 - val_y2_output_root_mean_squared_error: 0.7616\n",
            "Epoch 389/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.4027 - y1_output_loss: 0.1179 - y2_output_loss: 0.2848 - y1_output_root_mean_squared_error: 0.3433 - y2_output_root_mean_squared_error: 0.5337 - val_loss: 0.8483 - val_y1_output_loss: 0.1630 - val_y2_output_loss: 0.6853 - val_y1_output_root_mean_squared_error: 0.4038 - val_y2_output_root_mean_squared_error: 0.8278\n",
            "Epoch 390/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.3997 - y1_output_loss: 0.1204 - y2_output_loss: 0.2793 - y1_output_root_mean_squared_error: 0.3470 - y2_output_root_mean_squared_error: 0.5285 - val_loss: 4.4712 - val_y1_output_loss: 0.8045 - val_y2_output_loss: 3.6667 - val_y1_output_root_mean_squared_error: 0.8969 - val_y2_output_root_mean_squared_error: 1.9149\n",
            "Epoch 391/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.6270 - y1_output_loss: 0.1552 - y2_output_loss: 0.4718 - y1_output_root_mean_squared_error: 0.3940 - y2_output_root_mean_squared_error: 0.6869 - val_loss: 0.7745 - val_y1_output_loss: 0.2252 - val_y2_output_loss: 0.5493 - val_y1_output_root_mean_squared_error: 0.4746 - val_y2_output_root_mean_squared_error: 0.7412\n",
            "Epoch 392/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.3996 - y1_output_loss: 0.1223 - y2_output_loss: 0.2773 - y1_output_root_mean_squared_error: 0.3498 - y2_output_root_mean_squared_error: 0.5266 - val_loss: 0.7573 - val_y1_output_loss: 0.1666 - val_y2_output_loss: 0.5907 - val_y1_output_root_mean_squared_error: 0.4082 - val_y2_output_root_mean_squared_error: 0.7686\n",
            "Epoch 393/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.4119 - y1_output_loss: 0.1282 - y2_output_loss: 0.2837 - y1_output_root_mean_squared_error: 0.3580 - y2_output_root_mean_squared_error: 0.5327 - val_loss: 2.7571 - val_y1_output_loss: 0.4290 - val_y2_output_loss: 2.3281 - val_y1_output_root_mean_squared_error: 0.6549 - val_y2_output_root_mean_squared_error: 1.5258\n",
            "Epoch 394/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.4633 - y1_output_loss: 0.1343 - y2_output_loss: 0.3290 - y1_output_root_mean_squared_error: 0.3664 - y2_output_root_mean_squared_error: 0.5736 - val_loss: 0.7520 - val_y1_output_loss: 0.1898 - val_y2_output_loss: 0.5622 - val_y1_output_root_mean_squared_error: 0.4357 - val_y2_output_root_mean_squared_error: 0.7498\n",
            "Epoch 395/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.3844 - y1_output_loss: 0.1227 - y2_output_loss: 0.2618 - y1_output_root_mean_squared_error: 0.3502 - y2_output_root_mean_squared_error: 0.5116 - val_loss: 0.7481 - val_y1_output_loss: 0.1884 - val_y2_output_loss: 0.5597 - val_y1_output_root_mean_squared_error: 0.4340 - val_y2_output_root_mean_squared_error: 0.7481\n",
            "Epoch 396/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.5883 - y1_output_loss: 0.1608 - y2_output_loss: 0.4274 - y1_output_root_mean_squared_error: 0.4011 - y2_output_root_mean_squared_error: 0.6538 - val_loss: 0.8176 - val_y1_output_loss: 0.1699 - val_y2_output_loss: 0.6477 - val_y1_output_root_mean_squared_error: 0.4122 - val_y2_output_root_mean_squared_error: 0.8048\n",
            "Epoch 397/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.4433 - y1_output_loss: 0.1207 - y2_output_loss: 0.3226 - y1_output_root_mean_squared_error: 0.3475 - y2_output_root_mean_squared_error: 0.5680 - val_loss: 3.0774 - val_y1_output_loss: 0.6311 - val_y2_output_loss: 2.4464 - val_y1_output_root_mean_squared_error: 0.7944 - val_y2_output_root_mean_squared_error: 1.5641\n",
            "Epoch 398/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.7677 - y1_output_loss: 0.2275 - y2_output_loss: 0.5402 - y1_output_root_mean_squared_error: 0.4769 - y2_output_root_mean_squared_error: 0.7350 - val_loss: 0.7256 - val_y1_output_loss: 0.2214 - val_y2_output_loss: 0.5042 - val_y1_output_root_mean_squared_error: 0.4706 - val_y2_output_root_mean_squared_error: 0.7101\n",
            "Epoch 399/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.4519 - y1_output_loss: 0.1390 - y2_output_loss: 0.3128 - y1_output_root_mean_squared_error: 0.3729 - y2_output_root_mean_squared_error: 0.5593 - val_loss: 0.6777 - val_y1_output_loss: 0.1627 - val_y2_output_loss: 0.5150 - val_y1_output_root_mean_squared_error: 0.4033 - val_y2_output_root_mean_squared_error: 0.7176\n",
            "Epoch 400/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.4452 - y1_output_loss: 0.1324 - y2_output_loss: 0.3129 - y1_output_root_mean_squared_error: 0.3638 - y2_output_root_mean_squared_error: 0.5593 - val_loss: 0.8640 - val_y1_output_loss: 0.2031 - val_y2_output_loss: 0.6609 - val_y1_output_root_mean_squared_error: 0.4506 - val_y2_output_root_mean_squared_error: 0.8130\n",
            "Epoch 401/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.4302 - y1_output_loss: 0.1380 - y2_output_loss: 0.2922 - y1_output_root_mean_squared_error: 0.3715 - y2_output_root_mean_squared_error: 0.5406 - val_loss: 1.4946 - val_y1_output_loss: 0.2493 - val_y2_output_loss: 1.2453 - val_y1_output_root_mean_squared_error: 0.4993 - val_y2_output_root_mean_squared_error: 1.1159\n",
            "Epoch 402/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.4493 - y1_output_loss: 0.1264 - y2_output_loss: 0.3229 - y1_output_root_mean_squared_error: 0.3555 - y2_output_root_mean_squared_error: 0.5682 - val_loss: 0.8094 - val_y1_output_loss: 0.1931 - val_y2_output_loss: 0.6163 - val_y1_output_root_mean_squared_error: 0.4395 - val_y2_output_root_mean_squared_error: 0.7850\n",
            "Epoch 403/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.4195 - y1_output_loss: 0.1300 - y2_output_loss: 0.2895 - y1_output_root_mean_squared_error: 0.3606 - y2_output_root_mean_squared_error: 0.5380 - val_loss: 0.7322 - val_y1_output_loss: 0.1619 - val_y2_output_loss: 0.5702 - val_y1_output_root_mean_squared_error: 0.4024 - val_y2_output_root_mean_squared_error: 0.7551\n",
            "Epoch 404/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.5333 - y1_output_loss: 0.1378 - y2_output_loss: 0.3955 - y1_output_root_mean_squared_error: 0.3712 - y2_output_root_mean_squared_error: 0.6289 - val_loss: 1.0401 - val_y1_output_loss: 0.2064 - val_y2_output_loss: 0.8336 - val_y1_output_root_mean_squared_error: 0.4543 - val_y2_output_root_mean_squared_error: 0.9130\n",
            "Epoch 405/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.4339 - y1_output_loss: 0.1281 - y2_output_loss: 0.3058 - y1_output_root_mean_squared_error: 0.3580 - y2_output_root_mean_squared_error: 0.5530 - val_loss: 0.8230 - val_y1_output_loss: 0.2183 - val_y2_output_loss: 0.6047 - val_y1_output_root_mean_squared_error: 0.4672 - val_y2_output_root_mean_squared_error: 0.7776\n",
            "Epoch 406/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.3839 - y1_output_loss: 0.1221 - y2_output_loss: 0.2617 - y1_output_root_mean_squared_error: 0.3495 - y2_output_root_mean_squared_error: 0.5116 - val_loss: 0.7031 - val_y1_output_loss: 0.1835 - val_y2_output_loss: 0.5196 - val_y1_output_root_mean_squared_error: 0.4284 - val_y2_output_root_mean_squared_error: 0.7208\n",
            "Epoch 407/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.3764 - y1_output_loss: 0.1134 - y2_output_loss: 0.2630 - y1_output_root_mean_squared_error: 0.3367 - y2_output_root_mean_squared_error: 0.5129 - val_loss: 0.9690 - val_y1_output_loss: 0.2305 - val_y2_output_loss: 0.7385 - val_y1_output_root_mean_squared_error: 0.4801 - val_y2_output_root_mean_squared_error: 0.8594\n",
            "Epoch 408/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.3904 - y1_output_loss: 0.1273 - y2_output_loss: 0.2631 - y1_output_root_mean_squared_error: 0.3568 - y2_output_root_mean_squared_error: 0.5130 - val_loss: 1.0278 - val_y1_output_loss: 0.2864 - val_y2_output_loss: 0.7415 - val_y1_output_root_mean_squared_error: 0.5351 - val_y2_output_root_mean_squared_error: 0.8611\n",
            "Epoch 409/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.3427 - y1_output_loss: 0.1131 - y2_output_loss: 0.2297 - y1_output_root_mean_squared_error: 0.3362 - y2_output_root_mean_squared_error: 0.4792 - val_loss: 0.7556 - val_y1_output_loss: 0.1783 - val_y2_output_loss: 0.5773 - val_y1_output_root_mean_squared_error: 0.4223 - val_y2_output_root_mean_squared_error: 0.7598\n",
            "Epoch 410/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.5851 - y1_output_loss: 0.1509 - y2_output_loss: 0.4341 - y1_output_root_mean_squared_error: 0.3885 - y2_output_root_mean_squared_error: 0.6589 - val_loss: 1.0504 - val_y1_output_loss: 0.3033 - val_y2_output_loss: 0.7471 - val_y1_output_root_mean_squared_error: 0.5507 - val_y2_output_root_mean_squared_error: 0.8644\n",
            "Epoch 411/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.3969 - y1_output_loss: 0.1184 - y2_output_loss: 0.2785 - y1_output_root_mean_squared_error: 0.3441 - y2_output_root_mean_squared_error: 0.5277 - val_loss: 0.7992 - val_y1_output_loss: 0.1748 - val_y2_output_loss: 0.6245 - val_y1_output_root_mean_squared_error: 0.4180 - val_y2_output_root_mean_squared_error: 0.7902\n",
            "Epoch 412/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.3705 - y1_output_loss: 0.1044 - y2_output_loss: 0.2662 - y1_output_root_mean_squared_error: 0.3231 - y2_output_root_mean_squared_error: 0.5159 - val_loss: 0.6167 - val_y1_output_loss: 0.1502 - val_y2_output_loss: 0.4666 - val_y1_output_root_mean_squared_error: 0.3875 - val_y2_output_root_mean_squared_error: 0.6831\n",
            "Epoch 413/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.4501 - y1_output_loss: 0.1312 - y2_output_loss: 0.3189 - y1_output_root_mean_squared_error: 0.3622 - y2_output_root_mean_squared_error: 0.5647 - val_loss: 1.0427 - val_y1_output_loss: 0.2341 - val_y2_output_loss: 0.8086 - val_y1_output_root_mean_squared_error: 0.4838 - val_y2_output_root_mean_squared_error: 0.8992\n",
            "Epoch 414/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.3921 - y1_output_loss: 0.1179 - y2_output_loss: 0.2742 - y1_output_root_mean_squared_error: 0.3433 - y2_output_root_mean_squared_error: 0.5237 - val_loss: 3.6659 - val_y1_output_loss: 0.7630 - val_y2_output_loss: 2.9029 - val_y1_output_root_mean_squared_error: 0.8735 - val_y2_output_root_mean_squared_error: 1.7038\n",
            "Epoch 415/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.4822 - y1_output_loss: 0.1585 - y2_output_loss: 0.3237 - y1_output_root_mean_squared_error: 0.3982 - y2_output_root_mean_squared_error: 0.5690 - val_loss: 0.8215 - val_y1_output_loss: 0.2207 - val_y2_output_loss: 0.6008 - val_y1_output_root_mean_squared_error: 0.4698 - val_y2_output_root_mean_squared_error: 0.7751\n",
            "Epoch 416/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.5497 - y1_output_loss: 0.1370 - y2_output_loss: 0.4127 - y1_output_root_mean_squared_error: 0.3702 - y2_output_root_mean_squared_error: 0.6424 - val_loss: 0.6539 - val_y1_output_loss: 0.1755 - val_y2_output_loss: 0.4784 - val_y1_output_root_mean_squared_error: 0.4190 - val_y2_output_root_mean_squared_error: 0.6916\n",
            "Epoch 417/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.5970 - y1_output_loss: 0.1696 - y2_output_loss: 0.4274 - y1_output_root_mean_squared_error: 0.4119 - y2_output_root_mean_squared_error: 0.6538 - val_loss: 1.6076 - val_y1_output_loss: 0.1746 - val_y2_output_loss: 1.4330 - val_y1_output_root_mean_squared_error: 0.4178 - val_y2_output_root_mean_squared_error: 1.1971\n",
            "Epoch 418/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.7506 - y1_output_loss: 0.1476 - y2_output_loss: 0.6030 - y1_output_root_mean_squared_error: 0.3842 - y2_output_root_mean_squared_error: 0.7765 - val_loss: 1.1082 - val_y1_output_loss: 0.1939 - val_y2_output_loss: 0.9143 - val_y1_output_root_mean_squared_error: 0.4404 - val_y2_output_root_mean_squared_error: 0.9562\n",
            "Epoch 419/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.5224 - y1_output_loss: 0.1326 - y2_output_loss: 0.3898 - y1_output_root_mean_squared_error: 0.3641 - y2_output_root_mean_squared_error: 0.6243 - val_loss: 1.1801 - val_y1_output_loss: 0.2944 - val_y2_output_loss: 0.8857 - val_y1_output_root_mean_squared_error: 0.5426 - val_y2_output_root_mean_squared_error: 0.9411\n",
            "Epoch 420/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.5163 - y1_output_loss: 0.1635 - y2_output_loss: 0.3528 - y1_output_root_mean_squared_error: 0.4043 - y2_output_root_mean_squared_error: 0.5940 - val_loss: 0.6751 - val_y1_output_loss: 0.1774 - val_y2_output_loss: 0.4977 - val_y1_output_root_mean_squared_error: 0.4212 - val_y2_output_root_mean_squared_error: 0.7055\n",
            "Epoch 421/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.7198 - y1_output_loss: 0.2019 - y2_output_loss: 0.5179 - y1_output_root_mean_squared_error: 0.4493 - y2_output_root_mean_squared_error: 0.7197 - val_loss: 2.4713 - val_y1_output_loss: 0.8213 - val_y2_output_loss: 1.6500 - val_y1_output_root_mean_squared_error: 0.9063 - val_y2_output_root_mean_squared_error: 1.2845\n",
            "Epoch 422/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.4911 - y1_output_loss: 0.1653 - y2_output_loss: 0.3258 - y1_output_root_mean_squared_error: 0.4066 - y2_output_root_mean_squared_error: 0.5707 - val_loss: 2.3889 - val_y1_output_loss: 0.3191 - val_y2_output_loss: 2.0699 - val_y1_output_root_mean_squared_error: 0.5649 - val_y2_output_root_mean_squared_error: 1.4387\n",
            "Epoch 423/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.3881 - y1_output_loss: 0.1201 - y2_output_loss: 0.2680 - y1_output_root_mean_squared_error: 0.3465 - y2_output_root_mean_squared_error: 0.5177 - val_loss: 0.6601 - val_y1_output_loss: 0.1931 - val_y2_output_loss: 0.4670 - val_y1_output_root_mean_squared_error: 0.4394 - val_y2_output_root_mean_squared_error: 0.6834\n",
            "Epoch 424/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.4728 - y1_output_loss: 0.1278 - y2_output_loss: 0.3450 - y1_output_root_mean_squared_error: 0.3574 - y2_output_root_mean_squared_error: 0.5874 - val_loss: 1.3228 - val_y1_output_loss: 0.2333 - val_y2_output_loss: 1.0895 - val_y1_output_root_mean_squared_error: 0.4830 - val_y2_output_root_mean_squared_error: 1.0438\n",
            "Epoch 425/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.5669 - y1_output_loss: 0.1452 - y2_output_loss: 0.4217 - y1_output_root_mean_squared_error: 0.3811 - y2_output_root_mean_squared_error: 0.6494 - val_loss: 0.9093 - val_y1_output_loss: 0.2201 - val_y2_output_loss: 0.6892 - val_y1_output_root_mean_squared_error: 0.4692 - val_y2_output_root_mean_squared_error: 0.8302\n",
            "Epoch 426/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.3958 - y1_output_loss: 0.1293 - y2_output_loss: 0.2665 - y1_output_root_mean_squared_error: 0.3596 - y2_output_root_mean_squared_error: 0.5163 - val_loss: 0.7574 - val_y1_output_loss: 0.1944 - val_y2_output_loss: 0.5630 - val_y1_output_root_mean_squared_error: 0.4409 - val_y2_output_root_mean_squared_error: 0.7503\n",
            "Epoch 427/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.3816 - y1_output_loss: 0.1189 - y2_output_loss: 0.2627 - y1_output_root_mean_squared_error: 0.3448 - y2_output_root_mean_squared_error: 0.5125 - val_loss: 0.7153 - val_y1_output_loss: 0.2181 - val_y2_output_loss: 0.4972 - val_y1_output_root_mean_squared_error: 0.4670 - val_y2_output_root_mean_squared_error: 0.7051\n",
            "Epoch 428/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.4265 - y1_output_loss: 0.1291 - y2_output_loss: 0.2975 - y1_output_root_mean_squared_error: 0.3592 - y2_output_root_mean_squared_error: 0.5454 - val_loss: 1.0972 - val_y1_output_loss: 0.2715 - val_y2_output_loss: 0.8256 - val_y1_output_root_mean_squared_error: 0.5211 - val_y2_output_root_mean_squared_error: 0.9086\n",
            "Epoch 429/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.3050 - y1_output_loss: 0.1139 - y2_output_loss: 0.1911 - y1_output_root_mean_squared_error: 0.3375 - y2_output_root_mean_squared_error: 0.4371 - val_loss: 1.1969 - val_y1_output_loss: 0.3649 - val_y2_output_loss: 0.8320 - val_y1_output_root_mean_squared_error: 0.6040 - val_y2_output_root_mean_squared_error: 0.9122\n",
            "Epoch 430/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.4750 - y1_output_loss: 0.1274 - y2_output_loss: 0.3476 - y1_output_root_mean_squared_error: 0.3570 - y2_output_root_mean_squared_error: 0.5895 - val_loss: 0.7396 - val_y1_output_loss: 0.1809 - val_y2_output_loss: 0.5588 - val_y1_output_root_mean_squared_error: 0.4253 - val_y2_output_root_mean_squared_error: 0.7475\n",
            "Epoch 431/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.4604 - y1_output_loss: 0.1328 - y2_output_loss: 0.3276 - y1_output_root_mean_squared_error: 0.3644 - y2_output_root_mean_squared_error: 0.5724 - val_loss: 0.7476 - val_y1_output_loss: 0.2183 - val_y2_output_loss: 0.5293 - val_y1_output_root_mean_squared_error: 0.4672 - val_y2_output_root_mean_squared_error: 0.7275\n",
            "Epoch 432/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.4418 - y1_output_loss: 0.1255 - y2_output_loss: 0.3162 - y1_output_root_mean_squared_error: 0.3543 - y2_output_root_mean_squared_error: 0.5623 - val_loss: 1.0547 - val_y1_output_loss: 0.2788 - val_y2_output_loss: 0.7759 - val_y1_output_root_mean_squared_error: 0.5281 - val_y2_output_root_mean_squared_error: 0.8809\n",
            "Epoch 433/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.6770 - y1_output_loss: 0.1759 - y2_output_loss: 0.5011 - y1_output_root_mean_squared_error: 0.4194 - y2_output_root_mean_squared_error: 0.7079 - val_loss: 0.7585 - val_y1_output_loss: 0.1641 - val_y2_output_loss: 0.5944 - val_y1_output_root_mean_squared_error: 0.4051 - val_y2_output_root_mean_squared_error: 0.7710\n",
            "Epoch 434/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.3863 - y1_output_loss: 0.1224 - y2_output_loss: 0.2638 - y1_output_root_mean_squared_error: 0.3499 - y2_output_root_mean_squared_error: 0.5137 - val_loss: 0.6743 - val_y1_output_loss: 0.1901 - val_y2_output_loss: 0.4843 - val_y1_output_root_mean_squared_error: 0.4360 - val_y2_output_root_mean_squared_error: 0.6959\n",
            "Epoch 435/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.3646 - y1_output_loss: 0.1114 - y2_output_loss: 0.2532 - y1_output_root_mean_squared_error: 0.3338 - y2_output_root_mean_squared_error: 0.5032 - val_loss: 0.6997 - val_y1_output_loss: 0.2236 - val_y2_output_loss: 0.4761 - val_y1_output_root_mean_squared_error: 0.4729 - val_y2_output_root_mean_squared_error: 0.6900\n",
            "Epoch 436/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.4093 - y1_output_loss: 0.1296 - y2_output_loss: 0.2796 - y1_output_root_mean_squared_error: 0.3600 - y2_output_root_mean_squared_error: 0.5288 - val_loss: 0.7246 - val_y1_output_loss: 0.2336 - val_y2_output_loss: 0.4909 - val_y1_output_root_mean_squared_error: 0.4834 - val_y2_output_root_mean_squared_error: 0.7007\n",
            "Epoch 437/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.3910 - y1_output_loss: 0.1315 - y2_output_loss: 0.2595 - y1_output_root_mean_squared_error: 0.3627 - y2_output_root_mean_squared_error: 0.5094 - val_loss: 0.8046 - val_y1_output_loss: 0.2094 - val_y2_output_loss: 0.5951 - val_y1_output_root_mean_squared_error: 0.4576 - val_y2_output_root_mean_squared_error: 0.7715\n",
            "Epoch 438/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.4834 - y1_output_loss: 0.1497 - y2_output_loss: 0.3337 - y1_output_root_mean_squared_error: 0.3869 - y2_output_root_mean_squared_error: 0.5776 - val_loss: 1.4565 - val_y1_output_loss: 0.4462 - val_y2_output_loss: 1.0103 - val_y1_output_root_mean_squared_error: 0.6680 - val_y2_output_root_mean_squared_error: 1.0051\n",
            "Epoch 439/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.7760 - y1_output_loss: 0.2249 - y2_output_loss: 0.5511 - y1_output_root_mean_squared_error: 0.4742 - y2_output_root_mean_squared_error: 0.7424 - val_loss: 0.6912 - val_y1_output_loss: 0.1671 - val_y2_output_loss: 0.5241 - val_y1_output_root_mean_squared_error: 0.4087 - val_y2_output_root_mean_squared_error: 0.7240\n",
            "Epoch 440/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.4206 - y1_output_loss: 0.1330 - y2_output_loss: 0.2876 - y1_output_root_mean_squared_error: 0.3647 - y2_output_root_mean_squared_error: 0.5362 - val_loss: 0.6905 - val_y1_output_loss: 0.1831 - val_y2_output_loss: 0.5074 - val_y1_output_root_mean_squared_error: 0.4279 - val_y2_output_root_mean_squared_error: 0.7123\n",
            "Epoch 441/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.3348 - y1_output_loss: 0.1145 - y2_output_loss: 0.2203 - y1_output_root_mean_squared_error: 0.3384 - y2_output_root_mean_squared_error: 0.4694 - val_loss: 0.7779 - val_y1_output_loss: 0.2148 - val_y2_output_loss: 0.5631 - val_y1_output_root_mean_squared_error: 0.4634 - val_y2_output_root_mean_squared_error: 0.7504\n",
            "Epoch 442/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.4066 - y1_output_loss: 0.1240 - y2_output_loss: 0.2826 - y1_output_root_mean_squared_error: 0.3522 - y2_output_root_mean_squared_error: 0.5316 - val_loss: 0.8872 - val_y1_output_loss: 0.2330 - val_y2_output_loss: 0.6543 - val_y1_output_root_mean_squared_error: 0.4827 - val_y2_output_root_mean_squared_error: 0.8089\n",
            "Epoch 443/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.3568 - y1_output_loss: 0.1076 - y2_output_loss: 0.2491 - y1_output_root_mean_squared_error: 0.3281 - y2_output_root_mean_squared_error: 0.4991 - val_loss: 0.7292 - val_y1_output_loss: 0.2090 - val_y2_output_loss: 0.5202 - val_y1_output_root_mean_squared_error: 0.4572 - val_y2_output_root_mean_squared_error: 0.7213\n",
            "Epoch 444/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.4610 - y1_output_loss: 0.1472 - y2_output_loss: 0.3138 - y1_output_root_mean_squared_error: 0.3836 - y2_output_root_mean_squared_error: 0.5602 - val_loss: 2.3683 - val_y1_output_loss: 0.4839 - val_y2_output_loss: 1.8844 - val_y1_output_root_mean_squared_error: 0.6956 - val_y2_output_root_mean_squared_error: 1.3727\n",
            "Epoch 445/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.6179 - y1_output_loss: 0.1559 - y2_output_loss: 0.4620 - y1_output_root_mean_squared_error: 0.3948 - y2_output_root_mean_squared_error: 0.6797 - val_loss: 0.7194 - val_y1_output_loss: 0.1874 - val_y2_output_loss: 0.5320 - val_y1_output_root_mean_squared_error: 0.4329 - val_y2_output_root_mean_squared_error: 0.7294\n",
            "Epoch 446/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.3890 - y1_output_loss: 0.1197 - y2_output_loss: 0.2692 - y1_output_root_mean_squared_error: 0.3460 - y2_output_root_mean_squared_error: 0.5189 - val_loss: 1.1711 - val_y1_output_loss: 0.2253 - val_y2_output_loss: 0.9458 - val_y1_output_root_mean_squared_error: 0.4747 - val_y2_output_root_mean_squared_error: 0.9725\n",
            "Epoch 447/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.4060 - y1_output_loss: 0.1110 - y2_output_loss: 0.2950 - y1_output_root_mean_squared_error: 0.3332 - y2_output_root_mean_squared_error: 0.5431 - val_loss: 1.3929 - val_y1_output_loss: 0.3015 - val_y2_output_loss: 1.0914 - val_y1_output_root_mean_squared_error: 0.5491 - val_y2_output_root_mean_squared_error: 1.0447\n",
            "Epoch 448/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.3486 - y1_output_loss: 0.1141 - y2_output_loss: 0.2345 - y1_output_root_mean_squared_error: 0.3378 - y2_output_root_mean_squared_error: 0.4842 - val_loss: 0.5892 - val_y1_output_loss: 0.1571 - val_y2_output_loss: 0.4321 - val_y1_output_root_mean_squared_error: 0.3964 - val_y2_output_root_mean_squared_error: 0.6573\n",
            "Epoch 449/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.3392 - y1_output_loss: 0.1179 - y2_output_loss: 0.2213 - y1_output_root_mean_squared_error: 0.3434 - y2_output_root_mean_squared_error: 0.4704 - val_loss: 1.1758 - val_y1_output_loss: 0.2422 - val_y2_output_loss: 0.9336 - val_y1_output_root_mean_squared_error: 0.4922 - val_y2_output_root_mean_squared_error: 0.9662\n",
            "Epoch 450/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.3478 - y1_output_loss: 0.1121 - y2_output_loss: 0.2357 - y1_output_root_mean_squared_error: 0.3348 - y2_output_root_mean_squared_error: 0.4855 - val_loss: 0.7030 - val_y1_output_loss: 0.1561 - val_y2_output_loss: 0.5469 - val_y1_output_root_mean_squared_error: 0.3951 - val_y2_output_root_mean_squared_error: 0.7395\n",
            "Epoch 451/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.3657 - y1_output_loss: 0.1221 - y2_output_loss: 0.2435 - y1_output_root_mean_squared_error: 0.3495 - y2_output_root_mean_squared_error: 0.4935 - val_loss: 0.7355 - val_y1_output_loss: 0.1634 - val_y2_output_loss: 0.5721 - val_y1_output_root_mean_squared_error: 0.4043 - val_y2_output_root_mean_squared_error: 0.7563\n",
            "Epoch 452/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.4557 - y1_output_loss: 0.1478 - y2_output_loss: 0.3079 - y1_output_root_mean_squared_error: 0.3844 - y2_output_root_mean_squared_error: 0.5549 - val_loss: 0.7604 - val_y1_output_loss: 0.1575 - val_y2_output_loss: 0.6029 - val_y1_output_root_mean_squared_error: 0.3969 - val_y2_output_root_mean_squared_error: 0.7765\n",
            "Epoch 453/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.3619 - y1_output_loss: 0.1183 - y2_output_loss: 0.2435 - y1_output_root_mean_squared_error: 0.3440 - y2_output_root_mean_squared_error: 0.4935 - val_loss: 0.7056 - val_y1_output_loss: 0.1985 - val_y2_output_loss: 0.5071 - val_y1_output_root_mean_squared_error: 0.4456 - val_y2_output_root_mean_squared_error: 0.7121\n",
            "Epoch 454/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.4851 - y1_output_loss: 0.1393 - y2_output_loss: 0.3458 - y1_output_root_mean_squared_error: 0.3733 - y2_output_root_mean_squared_error: 0.5880 - val_loss: 0.7789 - val_y1_output_loss: 0.1531 - val_y2_output_loss: 0.6258 - val_y1_output_root_mean_squared_error: 0.3913 - val_y2_output_root_mean_squared_error: 0.7911\n",
            "Epoch 455/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.3420 - y1_output_loss: 0.1028 - y2_output_loss: 0.2392 - y1_output_root_mean_squared_error: 0.3206 - y2_output_root_mean_squared_error: 0.4891 - val_loss: 0.6663 - val_y1_output_loss: 0.1779 - val_y2_output_loss: 0.4883 - val_y1_output_root_mean_squared_error: 0.4218 - val_y2_output_root_mean_squared_error: 0.6988\n",
            "Epoch 456/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.3401 - y1_output_loss: 0.1110 - y2_output_loss: 0.2291 - y1_output_root_mean_squared_error: 0.3332 - y2_output_root_mean_squared_error: 0.4786 - val_loss: 0.8793 - val_y1_output_loss: 0.1746 - val_y2_output_loss: 0.7047 - val_y1_output_root_mean_squared_error: 0.4178 - val_y2_output_root_mean_squared_error: 0.8395\n",
            "Epoch 457/500\n",
            "62/62 [==============================] - 1s 9ms/step - loss: 0.3135 - y1_output_loss: 0.1081 - y2_output_loss: 0.2054 - y1_output_root_mean_squared_error: 0.3288 - y2_output_root_mean_squared_error: 0.4532 - val_loss: 0.7778 - val_y1_output_loss: 0.1653 - val_y2_output_loss: 0.6125 - val_y1_output_root_mean_squared_error: 0.4065 - val_y2_output_root_mean_squared_error: 0.7826\n",
            "Epoch 458/500\n",
            "62/62 [==============================] - 1s 13ms/step - loss: 0.3531 - y1_output_loss: 0.1133 - y2_output_loss: 0.2398 - y1_output_root_mean_squared_error: 0.3366 - y2_output_root_mean_squared_error: 0.4897 - val_loss: 0.6142 - val_y1_output_loss: 0.1775 - val_y2_output_loss: 0.4367 - val_y1_output_root_mean_squared_error: 0.4213 - val_y2_output_root_mean_squared_error: 0.6608\n",
            "Epoch 459/500\n",
            "62/62 [==============================] - 0s 8ms/step - loss: 0.3909 - y1_output_loss: 0.1158 - y2_output_loss: 0.2750 - y1_output_root_mean_squared_error: 0.3404 - y2_output_root_mean_squared_error: 0.5244 - val_loss: 1.6778 - val_y1_output_loss: 0.3090 - val_y2_output_loss: 1.3688 - val_y1_output_root_mean_squared_error: 0.5559 - val_y2_output_root_mean_squared_error: 1.1699\n",
            "Epoch 460/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.4684 - y1_output_loss: 0.1767 - y2_output_loss: 0.2917 - y1_output_root_mean_squared_error: 0.4204 - y2_output_root_mean_squared_error: 0.5401 - val_loss: 1.6016 - val_y1_output_loss: 0.2828 - val_y2_output_loss: 1.3188 - val_y1_output_root_mean_squared_error: 0.5318 - val_y2_output_root_mean_squared_error: 1.1484\n",
            "Epoch 461/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.3828 - y1_output_loss: 0.1234 - y2_output_loss: 0.2594 - y1_output_root_mean_squared_error: 0.3513 - y2_output_root_mean_squared_error: 0.5093 - val_loss: 0.6258 - val_y1_output_loss: 0.1441 - val_y2_output_loss: 0.4817 - val_y1_output_root_mean_squared_error: 0.3797 - val_y2_output_root_mean_squared_error: 0.6940\n",
            "Epoch 462/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.3523 - y1_output_loss: 0.1050 - y2_output_loss: 0.2474 - y1_output_root_mean_squared_error: 0.3240 - y2_output_root_mean_squared_error: 0.4973 - val_loss: 1.3283 - val_y1_output_loss: 0.2373 - val_y2_output_loss: 1.0911 - val_y1_output_root_mean_squared_error: 0.4871 - val_y2_output_root_mean_squared_error: 1.0445\n",
            "Epoch 463/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.3393 - y1_output_loss: 0.1168 - y2_output_loss: 0.2225 - y1_output_root_mean_squared_error: 0.3417 - y2_output_root_mean_squared_error: 0.4717 - val_loss: 1.0596 - val_y1_output_loss: 0.2355 - val_y2_output_loss: 0.8241 - val_y1_output_root_mean_squared_error: 0.4853 - val_y2_output_root_mean_squared_error: 0.9078\n",
            "Epoch 464/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.2915 - y1_output_loss: 0.0976 - y2_output_loss: 0.1940 - y1_output_root_mean_squared_error: 0.3124 - y2_output_root_mean_squared_error: 0.4404 - val_loss: 1.2276 - val_y1_output_loss: 0.2075 - val_y2_output_loss: 1.0200 - val_y1_output_root_mean_squared_error: 0.4556 - val_y2_output_root_mean_squared_error: 1.0100\n",
            "Epoch 465/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.5708 - y1_output_loss: 0.1462 - y2_output_loss: 0.4246 - y1_output_root_mean_squared_error: 0.3823 - y2_output_root_mean_squared_error: 0.6516 - val_loss: 0.6739 - val_y1_output_loss: 0.1644 - val_y2_output_loss: 0.5095 - val_y1_output_root_mean_squared_error: 0.4055 - val_y2_output_root_mean_squared_error: 0.7138\n",
            "Epoch 466/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.3180 - y1_output_loss: 0.1020 - y2_output_loss: 0.2160 - y1_output_root_mean_squared_error: 0.3194 - y2_output_root_mean_squared_error: 0.4647 - val_loss: 1.4655 - val_y1_output_loss: 0.3261 - val_y2_output_loss: 1.1394 - val_y1_output_root_mean_squared_error: 0.5710 - val_y2_output_root_mean_squared_error: 1.0674\n",
            "Epoch 467/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.3822 - y1_output_loss: 0.1244 - y2_output_loss: 0.2578 - y1_output_root_mean_squared_error: 0.3527 - y2_output_root_mean_squared_error: 0.5077 - val_loss: 0.9191 - val_y1_output_loss: 0.1882 - val_y2_output_loss: 0.7309 - val_y1_output_root_mean_squared_error: 0.4338 - val_y2_output_root_mean_squared_error: 0.8549\n",
            "Epoch 468/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.3136 - y1_output_loss: 0.1068 - y2_output_loss: 0.2068 - y1_output_root_mean_squared_error: 0.3268 - y2_output_root_mean_squared_error: 0.4548 - val_loss: 0.7343 - val_y1_output_loss: 0.1904 - val_y2_output_loss: 0.5438 - val_y1_output_root_mean_squared_error: 0.4364 - val_y2_output_root_mean_squared_error: 0.7374\n",
            "Epoch 469/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.2929 - y1_output_loss: 0.1076 - y2_output_loss: 0.1853 - y1_output_root_mean_squared_error: 0.3280 - y2_output_root_mean_squared_error: 0.4305 - val_loss: 0.9956 - val_y1_output_loss: 0.2267 - val_y2_output_loss: 0.7689 - val_y1_output_root_mean_squared_error: 0.4761 - val_y2_output_root_mean_squared_error: 0.8769\n",
            "Epoch 470/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.3460 - y1_output_loss: 0.1072 - y2_output_loss: 0.2388 - y1_output_root_mean_squared_error: 0.3274 - y2_output_root_mean_squared_error: 0.4887 - val_loss: 0.9587 - val_y1_output_loss: 0.2252 - val_y2_output_loss: 0.7334 - val_y1_output_root_mean_squared_error: 0.4746 - val_y2_output_root_mean_squared_error: 0.8564\n",
            "Epoch 471/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.4992 - y1_output_loss: 0.1511 - y2_output_loss: 0.3481 - y1_output_root_mean_squared_error: 0.3887 - y2_output_root_mean_squared_error: 0.5900 - val_loss: 1.0721 - val_y1_output_loss: 0.2129 - val_y2_output_loss: 0.8592 - val_y1_output_root_mean_squared_error: 0.4614 - val_y2_output_root_mean_squared_error: 0.9269\n",
            "Epoch 472/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.5207 - y1_output_loss: 0.1240 - y2_output_loss: 0.3967 - y1_output_root_mean_squared_error: 0.3522 - y2_output_root_mean_squared_error: 0.6298 - val_loss: 0.8265 - val_y1_output_loss: 0.1591 - val_y2_output_loss: 0.6674 - val_y1_output_root_mean_squared_error: 0.3989 - val_y2_output_root_mean_squared_error: 0.8169\n",
            "Epoch 473/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.4330 - y1_output_loss: 0.1171 - y2_output_loss: 0.3159 - y1_output_root_mean_squared_error: 0.3422 - y2_output_root_mean_squared_error: 0.5620 - val_loss: 0.7187 - val_y1_output_loss: 0.2155 - val_y2_output_loss: 0.5032 - val_y1_output_root_mean_squared_error: 0.4642 - val_y2_output_root_mean_squared_error: 0.7094\n",
            "Epoch 474/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.4117 - y1_output_loss: 0.1348 - y2_output_loss: 0.2768 - y1_output_root_mean_squared_error: 0.3672 - y2_output_root_mean_squared_error: 0.5262 - val_loss: 1.4146 - val_y1_output_loss: 0.3998 - val_y2_output_loss: 1.0148 - val_y1_output_root_mean_squared_error: 0.6323 - val_y2_output_root_mean_squared_error: 1.0074\n",
            "Epoch 475/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.3947 - y1_output_loss: 0.1247 - y2_output_loss: 0.2700 - y1_output_root_mean_squared_error: 0.3531 - y2_output_root_mean_squared_error: 0.5196 - val_loss: 0.7160 - val_y1_output_loss: 0.1693 - val_y2_output_loss: 0.5467 - val_y1_output_root_mean_squared_error: 0.4114 - val_y2_output_root_mean_squared_error: 0.7394\n",
            "Epoch 476/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.4059 - y1_output_loss: 0.1213 - y2_output_loss: 0.2846 - y1_output_root_mean_squared_error: 0.3482 - y2_output_root_mean_squared_error: 0.5335 - val_loss: 0.6231 - val_y1_output_loss: 0.1682 - val_y2_output_loss: 0.4548 - val_y1_output_root_mean_squared_error: 0.4102 - val_y2_output_root_mean_squared_error: 0.6744\n",
            "Epoch 477/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.4125 - y1_output_loss: 0.1237 - y2_output_loss: 0.2889 - y1_output_root_mean_squared_error: 0.3516 - y2_output_root_mean_squared_error: 0.5375 - val_loss: 1.3668 - val_y1_output_loss: 0.3971 - val_y2_output_loss: 0.9697 - val_y1_output_root_mean_squared_error: 0.6302 - val_y2_output_root_mean_squared_error: 0.9847\n",
            "Epoch 478/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.2993 - y1_output_loss: 0.1041 - y2_output_loss: 0.1952 - y1_output_root_mean_squared_error: 0.3226 - y2_output_root_mean_squared_error: 0.4419 - val_loss: 0.6528 - val_y1_output_loss: 0.1606 - val_y2_output_loss: 0.4922 - val_y1_output_root_mean_squared_error: 0.4008 - val_y2_output_root_mean_squared_error: 0.7016\n",
            "Epoch 479/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.2787 - y1_output_loss: 0.1058 - y2_output_loss: 0.1729 - y1_output_root_mean_squared_error: 0.3253 - y2_output_root_mean_squared_error: 0.4158 - val_loss: 0.6967 - val_y1_output_loss: 0.1842 - val_y2_output_loss: 0.5125 - val_y1_output_root_mean_squared_error: 0.4292 - val_y2_output_root_mean_squared_error: 0.7159\n",
            "Epoch 480/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.4343 - y1_output_loss: 0.1324 - y2_output_loss: 0.3019 - y1_output_root_mean_squared_error: 0.3638 - y2_output_root_mean_squared_error: 0.5495 - val_loss: 0.6489 - val_y1_output_loss: 0.1958 - val_y2_output_loss: 0.4531 - val_y1_output_root_mean_squared_error: 0.4425 - val_y2_output_root_mean_squared_error: 0.6731\n",
            "Epoch 481/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.3377 - y1_output_loss: 0.1088 - y2_output_loss: 0.2290 - y1_output_root_mean_squared_error: 0.3298 - y2_output_root_mean_squared_error: 0.4785 - val_loss: 0.6692 - val_y1_output_loss: 0.1671 - val_y2_output_loss: 0.5021 - val_y1_output_root_mean_squared_error: 0.4088 - val_y2_output_root_mean_squared_error: 0.7086\n",
            "Epoch 482/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.3219 - y1_output_loss: 0.1033 - y2_output_loss: 0.2187 - y1_output_root_mean_squared_error: 0.3214 - y2_output_root_mean_squared_error: 0.4676 - val_loss: 0.7387 - val_y1_output_loss: 0.1746 - val_y2_output_loss: 0.5641 - val_y1_output_root_mean_squared_error: 0.4179 - val_y2_output_root_mean_squared_error: 0.7511\n",
            "Epoch 483/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.3079 - y1_output_loss: 0.1073 - y2_output_loss: 0.2007 - y1_output_root_mean_squared_error: 0.3275 - y2_output_root_mean_squared_error: 0.4479 - val_loss: 0.8065 - val_y1_output_loss: 0.1613 - val_y2_output_loss: 0.6452 - val_y1_output_root_mean_squared_error: 0.4016 - val_y2_output_root_mean_squared_error: 0.8032\n",
            "Epoch 484/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.3445 - y1_output_loss: 0.1147 - y2_output_loss: 0.2298 - y1_output_root_mean_squared_error: 0.3387 - y2_output_root_mean_squared_error: 0.4794 - val_loss: 1.8236 - val_y1_output_loss: 0.3815 - val_y2_output_loss: 1.4420 - val_y1_output_root_mean_squared_error: 0.6177 - val_y2_output_root_mean_squared_error: 1.2008\n",
            "Epoch 485/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.3283 - y1_output_loss: 0.1092 - y2_output_loss: 0.2191 - y1_output_root_mean_squared_error: 0.3305 - y2_output_root_mean_squared_error: 0.4680 - val_loss: 0.6788 - val_y1_output_loss: 0.1772 - val_y2_output_loss: 0.5016 - val_y1_output_root_mean_squared_error: 0.4209 - val_y2_output_root_mean_squared_error: 0.7083\n",
            "Epoch 486/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.4085 - y1_output_loss: 0.1273 - y2_output_loss: 0.2812 - y1_output_root_mean_squared_error: 0.3567 - y2_output_root_mean_squared_error: 0.5303 - val_loss: 1.4795 - val_y1_output_loss: 0.4753 - val_y2_output_loss: 1.0042 - val_y1_output_root_mean_squared_error: 0.6894 - val_y2_output_root_mean_squared_error: 1.0021\n",
            "Epoch 487/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.3118 - y1_output_loss: 0.1076 - y2_output_loss: 0.2042 - y1_output_root_mean_squared_error: 0.3280 - y2_output_root_mean_squared_error: 0.4519 - val_loss: 0.8099 - val_y1_output_loss: 0.2429 - val_y2_output_loss: 0.5670 - val_y1_output_root_mean_squared_error: 0.4928 - val_y2_output_root_mean_squared_error: 0.7530\n",
            "Epoch 488/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.3493 - y1_output_loss: 0.1128 - y2_output_loss: 0.2365 - y1_output_root_mean_squared_error: 0.3359 - y2_output_root_mean_squared_error: 0.4863 - val_loss: 0.6592 - val_y1_output_loss: 0.1528 - val_y2_output_loss: 0.5064 - val_y1_output_root_mean_squared_error: 0.3909 - val_y2_output_root_mean_squared_error: 0.7116\n",
            "Epoch 489/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.3605 - y1_output_loss: 0.1284 - y2_output_loss: 0.2320 - y1_output_root_mean_squared_error: 0.3584 - y2_output_root_mean_squared_error: 0.4817 - val_loss: 0.6515 - val_y1_output_loss: 0.1642 - val_y2_output_loss: 0.4872 - val_y1_output_root_mean_squared_error: 0.4053 - val_y2_output_root_mean_squared_error: 0.6980\n",
            "Epoch 490/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.3644 - y1_output_loss: 0.1040 - y2_output_loss: 0.2604 - y1_output_root_mean_squared_error: 0.3225 - y2_output_root_mean_squared_error: 0.5103 - val_loss: 0.8009 - val_y1_output_loss: 0.2607 - val_y2_output_loss: 0.5403 - val_y1_output_root_mean_squared_error: 0.5105 - val_y2_output_root_mean_squared_error: 0.7350\n",
            "Epoch 491/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.4210 - y1_output_loss: 0.1345 - y2_output_loss: 0.2865 - y1_output_root_mean_squared_error: 0.3668 - y2_output_root_mean_squared_error: 0.5352 - val_loss: 0.6500 - val_y1_output_loss: 0.1628 - val_y2_output_loss: 0.4872 - val_y1_output_root_mean_squared_error: 0.4035 - val_y2_output_root_mean_squared_error: 0.6980\n",
            "Epoch 492/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.3371 - y1_output_loss: 0.1045 - y2_output_loss: 0.2326 - y1_output_root_mean_squared_error: 0.3233 - y2_output_root_mean_squared_error: 0.4822 - val_loss: 0.8939 - val_y1_output_loss: 0.3166 - val_y2_output_loss: 0.5773 - val_y1_output_root_mean_squared_error: 0.5627 - val_y2_output_root_mean_squared_error: 0.7598\n",
            "Epoch 493/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.3538 - y1_output_loss: 0.1121 - y2_output_loss: 0.2417 - y1_output_root_mean_squared_error: 0.3348 - y2_output_root_mean_squared_error: 0.4917 - val_loss: 1.1654 - val_y1_output_loss: 0.2006 - val_y2_output_loss: 0.9648 - val_y1_output_root_mean_squared_error: 0.4479 - val_y2_output_root_mean_squared_error: 0.9822\n",
            "Epoch 494/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.3373 - y1_output_loss: 0.1059 - y2_output_loss: 0.2313 - y1_output_root_mean_squared_error: 0.3255 - y2_output_root_mean_squared_error: 0.4809 - val_loss: 1.7241 - val_y1_output_loss: 0.2779 - val_y2_output_loss: 1.4462 - val_y1_output_root_mean_squared_error: 0.5272 - val_y2_output_root_mean_squared_error: 1.2026\n",
            "Epoch 495/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.3277 - y1_output_loss: 0.1114 - y2_output_loss: 0.2163 - y1_output_root_mean_squared_error: 0.3338 - y2_output_root_mean_squared_error: 0.4651 - val_loss: 3.2011 - val_y1_output_loss: 0.7864 - val_y2_output_loss: 2.4147 - val_y1_output_root_mean_squared_error: 0.8868 - val_y2_output_root_mean_squared_error: 1.5539\n",
            "Epoch 496/500\n",
            "62/62 [==============================] - 0s 3ms/step - loss: 0.3752 - y1_output_loss: 0.1207 - y2_output_loss: 0.2544 - y1_output_root_mean_squared_error: 0.3475 - y2_output_root_mean_squared_error: 0.5044 - val_loss: 1.6157 - val_y1_output_loss: 0.3104 - val_y2_output_loss: 1.3053 - val_y1_output_root_mean_squared_error: 0.5571 - val_y2_output_root_mean_squared_error: 1.1425\n",
            "Epoch 497/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.3814 - y1_output_loss: 0.1197 - y2_output_loss: 0.2617 - y1_output_root_mean_squared_error: 0.3460 - y2_output_root_mean_squared_error: 0.5116 - val_loss: 0.6268 - val_y1_output_loss: 0.1674 - val_y2_output_loss: 0.4594 - val_y1_output_root_mean_squared_error: 0.4092 - val_y2_output_root_mean_squared_error: 0.6778\n",
            "Epoch 498/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.3643 - y1_output_loss: 0.1098 - y2_output_loss: 0.2545 - y1_output_root_mean_squared_error: 0.3313 - y2_output_root_mean_squared_error: 0.5045 - val_loss: 0.9162 - val_y1_output_loss: 0.2189 - val_y2_output_loss: 0.6973 - val_y1_output_root_mean_squared_error: 0.4679 - val_y2_output_root_mean_squared_error: 0.8350\n",
            "Epoch 499/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.3171 - y1_output_loss: 0.1008 - y2_output_loss: 0.2163 - y1_output_root_mean_squared_error: 0.3175 - y2_output_root_mean_squared_error: 0.4651 - val_loss: 0.6693 - val_y1_output_loss: 0.1718 - val_y2_output_loss: 0.4975 - val_y1_output_root_mean_squared_error: 0.4145 - val_y2_output_root_mean_squared_error: 0.7054\n",
            "Epoch 500/500\n",
            "62/62 [==============================] - 0s 4ms/step - loss: 0.3930 - y1_output_loss: 0.1115 - y2_output_loss: 0.2815 - y1_output_root_mean_squared_error: 0.3339 - y2_output_root_mean_squared_error: 0.5306 - val_loss: 0.6492 - val_y1_output_loss: 0.1496 - val_y2_output_loss: 0.4996 - val_y1_output_root_mean_squared_error: 0.3868 - val_y2_output_root_mean_squared_error: 0.7068\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the loss and mse\n",
        "Y_pred = model.predict(norm_test_X)\n",
        "plot_diff(test_Y[0], Y_pred[0], title='Y1')\n",
        "plot_diff(test_Y[1], Y_pred[1], title='Y2')\n",
        "plot_metrics(metric_name='y1_output_root_mean_squared_error', title='Y1 RMSE', ylim=6)\n",
        "plot_metrics(metric_name='y2_output_root_mean_squared_error', title='Y2 RMSE', ylim=7)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "bQifQuhu2UTR",
        "outputId": "b280c10b-2643-430d-a412-3eaad955cd87"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mym2U8mGt9lXpxOWUtJ4nABGxSdLLyrxnLslBsFV55zw+DZwtaRbJztungA9W12RrReXmIHxYq31VGiT2SJoREb+FZPhB8h95URHxC14ciub7YXVNtFbXPzBYMkD09sg7KNtYpUHiM8AvJP2U5D/8vwAuzKxV1jb6Bwa56OYyB4LLT3tZC6toTiIi/p1kT8RNwPeA10ZEyTkJ6w6f+f4qdpcJAkN7gkVL1jSmQVZ35fZJHBMR/y0pt2nq9+nvGenww4lwu8jwhDHzZh3C1p2VHf32xGX7KjfcuAi4gMJ7GZwIt4sUShjzlfser/j9nrhsX+XOblyQ/nYi3C5XTcKY4Xz0u72VG26UzGEZEbfXtznWqkrtg+jtEUN5ExM9o8T++43muW1DHOIq322v3HDjtPT3y0jOcNyTPn8T8B8kxXqsw/UPDCIKL1JMT4NA/lyFg0JnKTfc+BsAST8Gjk1PduZ2Td6QeeusJSxasqZggMglq51/4nQHhQ5W6bbsw3IBIrUeKF3y2TpGsaGGk9V2h0o3U92dntW4MX3+PuAn2TTJWsmytRtLDjWs81WaCPcjkk4H/jK99PWI+H52zbJWkKvu/fKJY9m0dSfbd72YPsYrFt2j0p4EwK+AP0XETySNk7R/RPwpq4ZZc+UCxMzJ4/nuBSfx88c2enKyS1UUJCRdQHJWYzJJwpjpwL8Bb86uadYswwPEQRP28+RkF6t04vLDJEe/twBExGMky6LWYQoFCOtulQaJHRGxM/dE0mh8tq/jOEBYIZUGiZ9K+jTQJ+ktwC3Andk1yxrNAcKKqTRIfArYAKwiyST1Q+CyrBpljeUAYaWUnbiU1AOsjohjgG9k3yRrJAcIK6dsTyIidgNrJFW1w7JEmb/JkpZKeiz9fWCNbbcRcoCwSlQ63DgQWC3pbkl35H7KvCdX5u9YYA7wYUnHApcAd0fE0cDd6XNrMAcIq1Slm6k+W+0fLlHmbx5wSnrbYuA+kjkPaxAHCKtGuXwSY4EPAUeRTFpeFxG7qv2QYWX+Ds47LPYH4OAi77mQNNnujBk+S1YvDhBWrXLDjcXAbJIA8XaKlOQrpUCZv70iKR9WcL9FRHw9ImZHxOypU6dW+7FWgAOE1aLccOPYiDgeQNJ1wH9V88fTMn+3Ad/Jy2K1XtK0iFiX5qV4ptpGW/UcIKxW5XoSQ7kH1Q4zipX5A+4Azksfnwf8oJq/a9VzgLCRKNeTOEFSboggkh2XW9LHEREHlHhvsTJ/1wA3Szof+A1wVs2tt5foHxhk4R2r2bwtie8T9uthx649HDFlggOE1aRc+rqeWv9wiTJ/4NOjmegfGOTiWx5iaM+L0zzP70gyXJ87Z6YDhNWk0n0S1gYWLVmzT4DI99WfVl4jwyyfg0QHKVUlyxW0rFYOEh2kVJUsV9CyWjlIdJB5sw4peL23R85HaTVzkOgQy9Zu5PplTzJt4lgOGPvifPSB43pZdOYJTj1nNasmEa61KO+DsCy5J9HmHCAsa+5JtKn+gUE+d9cjPLt1J6NHyfsgLDMOEm2if2CQRUvWFCy5t2tPcNUPH2XC2NGee7C683CjDfQPDHLp7auK1uQE2Da0m0VL1jSwVdYtHCTawKIla9g2tLvsfd4wZVlwkGgDpXoQ+bxhyrLgINEhBN4wZZnwxGWLyU1Q5hfmrUSAJy0tEw4SLWJ4HghIhhmfvHVlRe+f7qGGZcRBogXkVi8KTU7u3L0HCaJE5dW+3h4PNSwznpNoAeVWLyJgwZwZ9CjJ4SPBuN5RiKQHcfUZx3uoYZlxT6IFlFu9mD6pjyvnH8+V849vUIvMXpRZT0LS9ZKekfRw3rWFkgYlrUh/3pHV57eD/oFB5l5zT8l7ekb5mLc1V5bDjRuAtxW4fm1EzEp/fpjh57e0SnZRAuzZEx5KWFNlNtyIiJ+llbuMly5tbt2xq6JdlCXmK80aohkTlx+RtDIdjhStKC7pQknLJS3fsGFDI9tXd/m9hiCZg8hf6iwlN1lp1iyNDhJfBY4EZpEUEy5aNrCTyvxVevaikLNPOqzOrTGrTkNXNyJife6xpG8AdzXy8xuh0I7JWg9eHf2y8V7RsKZraE8irf2ZczrwcLF721GhYcU/3LSCcWOqr3F09MvGs/Tjp9S9jWbVyqwnIelG4BRgiqSngcuBUyTNIpmPewr4YFaf3wyFhhUBbN1Z3VBjwZwZ7kFYy8hydePsApevy+rzWkE98jl8+X2zvORpLcXbsutopPkcxvWOcoCwluMgUUcXv/WVRSsklyPgn854dT2bY1YXDhJ1Vm6S8uUHjGXBnBnkb38Y1zuKaz3MsBblA151Uuq4d771W7b7sJa1Ffck6mThHasr2jDlPJTWbhwk6uCy/lUVb7P2iU5rNx5ujEAyxFjJtqE9Fb/H8w7WbhwkatQ/MMjFtzzE0B6f07TO5uFGjRYtWVN1gBhfw/Zss2ZzkKhB/8BgxQVzcnpGiatO94qGtR8PN6qUW+qsxvT0NKjnI6wdOUhU6dNVTFRK8OTV78y4RWbZ8nCjQv0Dg/zZZ3/EC1WsZJxz0owMW2TWGO5JVKCWlYy5R072rkrrCA4SFfj07SurChA+7m2dxMONMi7rX1XVEAO8Yco6i4NECf0Dg3z7l79tdjPMmspBooQr7lxd9XsOHNebQUvMmqfRZf4mS1oq6bH0d9G6G61g0wuVHdrK6e0Rl5/2qoxaY9YcjS7zdwlwd0QcDdydPm9r0yf17a3uvejMEzwfYR2n0WX+5pFk0AZYDNwHfCqrNmRt+qQ+ll1yarObYZapRs9JHBwR69LHfwAOLnZjs8v8LVu7seTrfb09zg1hXaFpE5cREZSoh9vMMn/L1m7k/MUPMHpU4bS2PRJXn3G8hxbWFRodJNbnqnilv59p8OeXlQsQMyePZ+Fpr6Kvd9/j3X29PXzxLM89WPdo9I7LO4DzgGvS3z9o8OeXlB8gvnvBSRw0YT8mjB39ktqeDhDWTRpd5u8a4GZJ5wO/Ac7K6vOrVShAQLJ70kHBulmjy/wBvDmrz6xGfvXvyePHsGX7EEdMmbBPgDCzLj3gNbxGxrNbdyLg3DkzHSDMhunKbdnFqn9/9aePN6dBZi2sK4NEserf9agKbtZpujJITB4/puB1V9cye6muCxLL1m5ky/ahl1T/9g5Ks8K6KkjkljmPmDKBz807bp/DWd5BaVZY16xuFNoHseDkmc1ullnL64qeRLGNUmZWXscHCQcIs5HpyOFGbjfl4OZtCHj5xLEOEGY16rieRG43Za5WZwCbtu7k54+Vzg9hZoV1VJDoHxjkopsfesluyu279rBoyZomtcqsvXXEcKN/YJAr7lxdMnGtd1Oa1abtg8Tww1rFeDelWW3afrhR6LDWcN5NaVa7tg8S5YYRzkdpNjJtHySKHdYC56M0q4e2DhLFDmsBTOrrdQ/CrA6aMnEp6SngT8BuYFdEzK72b+Qf1jp3zky++tPHnazWLAPNXN14U0TUtMPJh7XMGqfthhs+i2HWWM0KEgH8WNKDki4sdEOhMn8OEGaN16wg8YaIeA3wduDDkv5y+A3Dy/w5QJg1R1OCREQMpr+fAb4PvL7U/c/v2OUAYdYkDQ8SksZL2j/3GPgr4OFS73nq2a0OEGZNoqS4dwM/UDqCpPcAyerKdyPiqjLv2UBSFrAZpgCdeM7c36t9NOI7zYyIqYVeaHiQaDeSlteyj6PV+Xu1j2Z/p7ZbAjWzxnKQMLOSHCTK+3qzG5ARf6/20dTv5DkJMyvJPQkzK8lBwsxKcpDII+l6Sc9Iejjv2mRJSyU9lv4+sJltrJakwyTdK+kRSaslfTS93u7fa6yk/5L0UPq9rkivv0LS/ZLWSrpJUvGsRC1MUo+kAUl3pc+b9r0cJPZ1A/C2YdcuAe6OiKOBu9Pn7WQXcFFEHAvMITkrcyzt/712AKdGxAnALOBtkuYAnweujYijgE3A+U1s40h8FHg073nTvpeDRJ6I+Bnwx2GX5wGL08eLgfkNbdQIRcS6iPhV+vhPJP/Hm077f6+IiOfTp73pTwCnArem19vuewFIOhR4J/DN9Llo4vdykCjv4IhYlz7+A3BwMxszEpIOB04E7qcDvlfaJV8BPAMsBR4HNkfErvSWp0kCYrv5MvBJYE/6/CCa+L0cJKoQyXpxW64ZS5oA3AZ8LCK25L/Wrt8rInZHxCzgUJKTxMc0uUkjJuldwDMR8WCz25LT9sV5GmC9pGkRsU7SNJJ/tdqKpF6SAPGdiLg9vdz23ysnIjZLuhc4GZgkaXT6r+6hwGBzW1e1ucC7Jb0DGAscAPwLTfxe7kmUdwdwXvr4POAHTWxL1dLx7HXAoxHxpbyX2v17TZU0KX3cB7yFZL7lXuDM9La2+14RcWlEHBoRhwN/DdwTEefQxO/lHZd5JN0InEJyNHc9cDnQD9wMzCA5rn5WRAyf3GxZkt4A/BxYxYtj3E+TzEu08/d6NckEXg/JP3Y3R8Q/pqkIvgdMBgaABRGxo3ktrZ2kU4BPRMS7mvm9HCTMrCQPN8ysJAcJMyvJQcLMSnKQMLOSHCTMrCQHiS4g6SBJK9KfP0gazHs+4tOEki6XdPWwa7MkPVriPQslfWKkn23Z847LLhARz5KclETSQuD5iPhC7vW8nXy1uhH4d+DSvGt/nV63NueeRJeSdIOkf5N0P/DPw/9ll/RweiAMSQvS3A0rJH1NUk/+34qIXwObJJ2Ud/ks4EZJF0h6IM37cJukcQXacp+k2enjKZKeSh/3SFqUvn+lpA+m16dJ+lnanocl/UVd/8exfThIdLdDgT+PiI8Xu0HSnwHvA+amh6l2A+cUuPVGkt4DaV6HP0bEY8DtEfG6NO/Do1SXB+F84LmIeB3wOuACSa8A3g8sSdtzArCiir9pVfJwo7vdEhG7y9zzZuC1wAPJMRD6KHwY7CbgPyRdxL5DjeMkXQlMAiYAS6po318Br5aUO7MwETgaeAC4Pj241h8RDhIZcpDoblvzHu9i357l2PS3gMURkT/f8BIR8TtJTwJvBN5DciITkmxf8yPiIUkfIDkbM1z+Z4/Nuy7g7yLiJYFFSSX6dwI3SPpSRPyfUu2z2nm4YTlPAa8BkPQa4BXp9buBMyW9LH1tsqSZRf7GjcC1wBMR8XR6bX9gXfqvfqFhSu6zX5s+PjPv+hLgb9P3Iul/KCk4PRNYHxHfIMne9JpqvqhVx0HCcm4DJktaDXwE+DVARDwCXAb8WNJKkgxQ04r8jVuAV7HvqsZnSU6cLgP+u8j7vkASDAZITuDmfBN4BPiVkuTEXyPp/Z4CPJTe/z6SfAuWEZ8CNbOS3JMws5IcJMysJAcJMyvJQcLMSnKQMLOSHCTMrCQHCTMr6f8D7l3meltlZyIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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",
      "source": [
        ""
      ],
      "metadata": {
        "id": "KMT9vlmg295Q"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}