Deep Learning TimeSeries Birth Dataset.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Assignment17_RickyGunawan_Birth.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "iha5nnDjwBXU"
      },
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sb\n",
        "from datetime import datetime\n",
        "import numpy as np\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "from tensorflow.keras.layers import LSTM\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from math import sqrt"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "alIdsUE-U7pT"
      },
      "source": [
        "def parser(x):\n",
        "\treturn datetime.strptime( x, '%Y-%m-%d')\n",
        " \n",
        "def timeseries_to_supervised(data, lag=1):\n",
        "\tdf = pd.DataFrame(data)\n",
        "\tcolumns = [df.shift(i) for i in range(1, lag+1)]\n",
        "\tcolumns.append(df)\n",
        "\tdf = pd.concat(columns, axis=1)\n",
        "\treturn df\n",
        "\n",
        "def difference(dataset, interval=1):\n",
        "\tdiff = list()\n",
        "\tfor i in range(interval, len(dataset)):\n",
        "\t\tvalue = dataset[i] - dataset[i - interval]\n",
        "\t\tdiff.append(value)\n",
        "\treturn pd.Series(diff)\n",
        " \n",
        "def plot_loss(history):\n",
        "  plt.plot(history.history['loss'], label='loss')\n",
        "  plt.plot(history.history['val_loss'], label='val_loss')\n",
        "  plt.xlabel('Epoch')\n",
        "  plt.ylabel('Loss (MSE)')\n",
        "  plt.legend()\n",
        "  plt.grid(True)"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LqI_mUGP0OEA"
      },
      "source": [
        "# **Dataset Birth**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rG0W63lzXHXf",
        "outputId": "8927cb0d-5103-4631-debe-158c8b269fc5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "dataset = pd.read_csv('/content/drive/My Drive/BCML/datasets/birth.csv', header=0, parse_dates=[0], index_col=0, squeeze=True, date_parser=parser)\n",
        "dataset.head()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date\n",
              "1959-01-01    35\n",
              "1959-01-02    32\n",
              "1959-01-03    30\n",
              "1959-01-04    31\n",
              "1959-01-05    44\n",
              "Name: Births, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G8LOc5U5XdLL",
        "outputId": "9e4d0f16-8a49-4ccb-ca72-d4415d374c05",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 291
        }
      },
      "source": [
        "dataset.plot()\n",
        "plt.show()"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IjJqMXjIX9P4",
        "outputId": "b2f7c051-84c7-47dc-bbe3-42d820392017",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "lag = 1\n",
        "\n",
        "raw_values = dataset.values\n",
        "diff_values = difference(raw_values, 1)\n",
        "\n",
        "diff_values"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      -3\n",
              "1      -2\n",
              "2       1\n",
              "3      13\n",
              "4     -15\n",
              "       ..\n",
              "359     3\n",
              "360    15\n",
              "361    -4\n",
              "362     7\n",
              "363    -5\n",
              "Length: 364, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hqrf2FT9YLzV",
        "outputId": "c23bec4a-217d-44f6-c69b-0ce69d1d96d5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "supervised = timeseries_to_supervised(diff_values, lag)\n",
        "supervised"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NaN</td>\n",
              "      <td>-3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-3.0</td>\n",
              "      <td>-2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-2.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>13.0</td>\n",
              "      <td>-15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>359</th>\n",
              "      <td>-10.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>360</th>\n",
              "      <td>3.0</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>361</th>\n",
              "      <td>15.0</td>\n",
              "      <td>-4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>362</th>\n",
              "      <td>-4.0</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>363</th>\n",
              "      <td>7.0</td>\n",
              "      <td>-5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>364 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        0   0\n",
              "0     NaN  -3\n",
              "1    -3.0  -2\n",
              "2    -2.0   1\n",
              "3     1.0  13\n",
              "4    13.0 -15\n",
              "..    ...  ..\n",
              "359 -10.0   3\n",
              "360   3.0  15\n",
              "361  15.0  -4\n",
              "362  -4.0   7\n",
              "363   7.0  -5\n",
              "\n",
              "[364 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DPRtOw5QYQld",
        "outputId": "43990da5-9881-4ad1-bd56-945396dd95c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "supervised_values = supervised.values[lag:,:]\n",
        "supervised_values"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       [-14.,   1.],\n",
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              "       [-10.,   3.],\n",
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              "       [ 15.,  -4.],\n",
              "       [ -4.,   7.],\n",
              "       [  7.,  -5.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kdBliluYYWOh"
      },
      "source": [
        "split_percentage = 0.75\n",
        "\n",
        "train_size = int(split_percentage * len(supervised_values))\n",
        "\n",
        "train, test = supervised_values[0:train_size], supervised_values[train_size:len(supervised_values)]"
      ],
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "behU8o0PYZg7"
      },
      "source": [
        "scaler = MinMaxScaler(feature_range=(-1, 1)) # Range hasil scaling menjadi angka diantara -1 hingga 1\n",
        "scaler = scaler.fit(train)\n",
        "\n",
        "train_scaled = scaler.transform(train)\n",
        "test_scaled = scaler.transform(test)"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cpdkWwehYH8n"
      },
      "source": [
        "# Baseline model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dsJGBXaQQwFB",
        "outputId": "1c46fb3f-aa18-4ffd-d4fe-7b848974fa54",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "neurons = 1\n",
        "epoch = 1000\n",
        "batch_size = 32\n",
        "\n",
        "feature_train, label_train = train_scaled[:, 0:-1], train_scaled[:, -1]\n",
        "feature_test, label_test = test_scaled[:, 0:-1], test_scaled[:, -1]\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Dense(neurons, activation='relu', input_dim=feature_train.shape[1]))\n",
        "model.add(Dense(1))\n",
        "model.compile(loss='mean_squared_error', optimizer='adam')\n",
        "history = model.fit(feature_train, label_train, epochs=epoch, batch_size=batch_size, validation_data=(feature_test, label_test))"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/1000\n",
            "9/9 [==============================] - 0s 33ms/step - loss: 0.2888 - val_loss: 0.1612\n",
            "Epoch 2/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2751 - val_loss: 0.1516\n",
            "Epoch 3/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2619 - val_loss: 0.1436\n",
            "Epoch 4/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2502 - val_loss: 0.1365\n",
            "Epoch 5/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2404 - val_loss: 0.1301\n",
            "Epoch 6/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.2305 - val_loss: 0.1248\n",
            "Epoch 7/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2217 - val_loss: 0.1204\n",
            "Epoch 8/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.2156 - val_loss: 0.1159\n",
            "Epoch 9/1000\n",
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            "Epoch 127/1000\n",
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            "Epoch 128/1000\n",
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            "Epoch 129/1000\n",
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            "Epoch 130/1000\n",
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            "Epoch 131/1000\n",
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            "Epoch 132/1000\n",
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            "Epoch 133/1000\n",
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            "Epoch 134/1000\n",
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            "Epoch 135/1000\n",
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            "Epoch 136/1000\n",
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            "Epoch 139/1000\n",
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            "Epoch 140/1000\n",
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            "Epoch 141/1000\n",
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            "Epoch 144/1000\n",
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            "Epoch 145/1000\n",
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            "Epoch 150/1000\n",
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            "Epoch 153/1000\n",
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            "Epoch 158/1000\n",
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            "Epoch 159/1000\n",
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            "Epoch 160/1000\n",
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            "Epoch 161/1000\n",
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            "Epoch 162/1000\n",
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            "Epoch 163/1000\n",
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            "Epoch 164/1000\n",
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            "Epoch 165/1000\n",
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            "Epoch 166/1000\n",
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            "Epoch 167/1000\n",
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            "Epoch 168/1000\n",
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            "Epoch 169/1000\n",
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            "Epoch 170/1000\n",
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            "Epoch 171/1000\n",
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            "Epoch 172/1000\n",
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            "Epoch 173/1000\n",
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            "Epoch 174/1000\n",
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            "Epoch 175/1000\n",
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            "Epoch 176/1000\n",
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            "Epoch 177/1000\n",
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            "Epoch 684/1000\n",
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            "Epoch 685/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 686/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 687/1000\n",
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            "Epoch 688/1000\n",
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            "Epoch 689/1000\n",
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            "Epoch 690/1000\n",
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            "Epoch 691/1000\n",
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            "Epoch 692/1000\n",
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            "Epoch 693/1000\n",
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            "Epoch 694/1000\n",
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            "Epoch 695/1000\n",
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            "Epoch 696/1000\n",
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            "Epoch 697/1000\n",
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            "Epoch 698/1000\n",
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            "Epoch 699/1000\n",
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            "Epoch 700/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 701/1000\n",
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            "Epoch 702/1000\n",
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            "Epoch 703/1000\n",
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            "Epoch 704/1000\n",
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            "Epoch 705/1000\n",
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            "Epoch 706/1000\n",
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            "Epoch 707/1000\n",
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            "Epoch 708/1000\n",
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            "Epoch 709/1000\n",
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            "Epoch 710/1000\n",
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            "Epoch 711/1000\n",
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            "Epoch 712/1000\n",
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            "Epoch 713/1000\n",
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            "Epoch 714/1000\n",
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            "Epoch 715/1000\n",
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            "Epoch 716/1000\n",
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            "Epoch 717/1000\n",
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            "Epoch 718/1000\n",
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            "Epoch 719/1000\n",
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            "Epoch 720/1000\n",
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            "Epoch 721/1000\n",
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            "Epoch 722/1000\n",
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            "Epoch 723/1000\n",
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            "Epoch 724/1000\n",
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            "Epoch 725/1000\n",
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            "Epoch 726/1000\n",
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            "Epoch 727/1000\n",
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            "Epoch 728/1000\n",
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            "Epoch 729/1000\n",
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            "Epoch 730/1000\n",
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            "Epoch 731/1000\n",
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            "Epoch 732/1000\n",
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            "Epoch 733/1000\n",
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            "Epoch 734/1000\n",
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            "Epoch 735/1000\n",
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            "Epoch 736/1000\n",
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            "Epoch 737/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 738/1000\n",
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            "Epoch 739/1000\n",
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            "Epoch 740/1000\n",
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            "Epoch 741/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 742/1000\n",
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            "Epoch 743/1000\n",
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            "Epoch 744/1000\n",
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            "Epoch 745/1000\n",
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            "Epoch 746/1000\n",
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            "Epoch 747/1000\n",
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            "Epoch 748/1000\n",
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            "Epoch 749/1000\n",
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            "Epoch 750/1000\n",
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            "Epoch 751/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 752/1000\n",
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            "Epoch 753/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 754/1000\n",
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            "Epoch 755/1000\n",
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            "Epoch 756/1000\n",
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            "Epoch 757/1000\n",
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            "Epoch 758/1000\n",
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            "Epoch 759/1000\n",
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            "Epoch 760/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 761/1000\n",
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            "Epoch 762/1000\n",
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            "Epoch 763/1000\n",
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            "Epoch 764/1000\n",
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            "Epoch 765/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 766/1000\n",
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            "Epoch 767/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 768/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 769/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 770/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 771/1000\n",
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            "Epoch 772/1000\n",
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            "Epoch 773/1000\n",
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            "Epoch 774/1000\n",
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            "Epoch 775/1000\n",
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            "Epoch 776/1000\n",
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            "Epoch 777/1000\n",
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            "Epoch 778/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 779/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 780/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 781/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 782/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1109 - val_loss: 0.0694\n",
            "Epoch 783/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 784/1000\n",
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            "Epoch 785/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 786/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 787/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 788/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 789/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 790/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 791/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 792/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 793/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 794/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 795/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 796/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 797/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 798/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 799/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 800/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 801/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 802/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 803/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 804/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 805/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 806/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 807/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 808/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 809/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 810/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 811/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 812/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 813/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 814/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 815/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 816/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 817/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 818/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 819/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 820/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 821/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 822/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 823/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 824/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 825/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 826/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 827/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 828/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 829/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 830/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 831/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 832/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 833/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 834/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 835/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 836/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 837/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 838/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 839/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 840/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 841/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 842/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 843/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 844/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 845/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 846/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 847/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 848/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 849/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 850/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 851/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 852/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 853/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 854/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 855/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 856/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 857/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1109 - val_loss: 0.0694\n",
            "Epoch 858/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 859/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 860/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 861/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 862/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 863/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 864/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 865/1000\n",
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            "Epoch 866/1000\n",
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            "Epoch 867/1000\n",
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            "Epoch 868/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 869/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 870/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 871/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 872/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 873/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 874/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 875/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 876/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 877/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 878/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 879/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 880/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 881/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 882/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 883/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 884/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 885/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 886/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 887/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 888/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 889/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 890/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 891/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 892/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 893/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 894/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 895/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 896/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 897/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 898/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 899/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 900/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 901/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 902/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 903/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 904/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 905/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 906/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 907/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 908/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 909/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 910/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 911/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 912/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 913/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 914/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 915/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 916/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 917/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 918/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 919/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 920/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 921/1000\n",
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            "Epoch 922/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 923/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 924/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 925/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 926/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 927/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 928/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 929/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 930/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 931/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 932/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 933/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 934/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 935/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 936/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 937/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 938/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 939/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 940/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 941/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 942/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 943/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 944/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 945/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 946/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 947/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 948/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 949/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 950/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 951/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 952/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 953/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 954/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 955/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 956/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 957/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 958/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 959/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 960/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 961/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 962/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 963/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 964/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 965/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 966/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 967/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 968/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 969/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 970/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 971/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 972/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 973/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 974/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 975/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 976/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 977/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 978/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 979/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 980/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 981/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 982/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 983/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 984/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 985/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 986/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 987/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 988/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 989/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 990/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 991/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 992/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 993/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 994/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 995/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 996/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 997/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 998/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 999/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 1000/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1108 - val_loss: 0.0694\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xJuCimMs0Xlw",
        "outputId": "5f8bc0dc-b4c6-40fe-a3d8-aa70fe552c2d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "loss = model.evaluate(feature_test, label_test, verbose=2)\n",
        "\n",
        "print(\"Test loss:\", loss)"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3/3 - 0s - loss: 0.0694\n",
            "Test loss: 0.06940607726573944\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xgx1IuNdcfY6",
        "outputId": "9f275d88-e941-499e-ae1c-0f6770f74924",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "history_dataframe = pd.DataFrame(history.history)\n",
        "history_dataframe['epoch'] = history.epoch\n",
        "history_dataframe.sort_values(by='val_loss', ascending=True)"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>loss</th>\n",
              "      <th>val_loss</th>\n",
              "      <th>epoch</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>732</th>\n",
              "      <td>0.110770</td>\n",
              "      <td>0.069397</td>\n",
              "      <td>732</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>858</th>\n",
              "      <td>0.110767</td>\n",
              "      <td>0.069397</td>\n",
              "      <td>858</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>785</th>\n",
              "      <td>0.110775</td>\n",
              "      <td>0.069397</td>\n",
              "      <td>785</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>751</th>\n",
              "      <td>0.110765</td>\n",
              "      <td>0.069397</td>\n",
              "      <td>751</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>859</th>\n",
              "      <td>0.110763</td>\n",
              "      <td>0.069397</td>\n",
              "      <td>859</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.240365</td>\n",
              "      <td>0.130101</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.250212</td>\n",
              "      <td>0.136457</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.261920</td>\n",
              "      <td>0.143645</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.275056</td>\n",
              "      <td>0.151645</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.288845</td>\n",
              "      <td>0.161220</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         loss  val_loss  epoch\n",
              "732  0.110770  0.069397    732\n",
              "858  0.110767  0.069397    858\n",
              "785  0.110775  0.069397    785\n",
              "751  0.110765  0.069397    751\n",
              "859  0.110763  0.069397    859\n",
              "..        ...       ...    ...\n",
              "4    0.240365  0.130101      4\n",
              "3    0.250212  0.136457      3\n",
              "2    0.261920  0.143645      2\n",
              "1    0.275056  0.151645      1\n",
              "0    0.288845  0.161220      0\n",
              "\n",
              "[1000 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RUuHpMPfchpG",
        "outputId": "69ab039e-bccd-4c5f-abd0-988122fe2a4b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "plot_loss(history) # epoch vs loss graph"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1hGMfAR5pvQjpVVJcoHmpRURow3MQ7r4yfH8NsMbdJwIfB76Z0ciypKS4gL0H1cQkItJagqhNev9Z4A8A7v5hxiLKsv69Yuw+oAQhItJagthlZp8LO5M/CfwJwMwKgF6ZDi4bSvsUsnOfEoSISGt3Mf1P4H5gKHBLUs3hM8CzmQwsWwb2LmTn/trWdxQRyXGt3cW0hhRDXLj7fIK7k3JOaZ9C9tfGqamLUxzL2akvRERa1dpdTPcfabu735TecLJvYJ9CAHbur2VY/5xsRRMRaZPWmpj+AVgBzAU20fr4Sz1eae8gQezYpwQhIvmttQQxDLgUuAyoJ5j74Ul335XpwLKlsQahjmoRyXNHvIvJ3be7+4Pu/mmC5yAGAG+b2Ze6JLosGNgnmBNi+76DrewpIpLbWqtBAGBmpwJXEDwL8UfgjUwGlU2D+xYBsK1adzKJSH5rrZP6LuBvgVUEE/7c5u45PQ5F/14xCqMRtuytyXYoIiJZ1VoN4nbgfeDk8PWDcFA9AzycKjSnmBlHlRSxdY+amEQkv7WWIFqb8yEnHVVSxJa9ShAikt9aSxAb3f2IM8eZmbW2T08zpKSI9dv3ZTsMEZGsam0spgVmdqOZHZu80swKzWyGmf0KmJm58LJjSD/VIEREWqtBnAv8PfDbcGa4XUAxwQRAzwM/dvc3Mxti1xtSUsyu/XUcrI9TVKDhNkQkP7U2FlMN8G/Av4WzyQ0GDuTyg3IQNDEBbN17kPLS3lmORkQkO1prYmrk7nXuvjnXkwMETUyAmplEJK+1OUHkkyElxQB8tFvPQohI/lKCSGH4gGCQvqpdB7IciYhI9rQpQZhZHzOLhO9PMLMLwz6JnDSgd4zehVE27VINQkTyV1trEC8DxWY2nODupS8Bv8xUUNlmZgwf0IuqXfuzHYqISNa0NUGYu+8HLgb+zd0vBU7MXFjZN7y0l5qYRCSvtTlBmNnpwJUcmos6px8QOHpAL6p2KkGISP5qa4K4BbgN+L27rzSz0cCCzIWVfcMH9GLn/jr21+b04LUiIi1q03wQ7r4QWAgQdlZvy8X5qJOVlwZ3Mm3adYDjh5RkORoRka7X1ruYHjezfmbWh2CO6rfN7BuZDS27jhkYPEG9Ybs6qkUkP7W1iWm8u+8B/o5gRrlRBHcy5azRg/sA8P42jeoqIvmprQkiFj738HfAPHevA3JjiO+aPfD87fTbvarJ6gG9CyntHWOdEoSI5Km2JoiHgPVAH+BlMxsB7MlUUF0qUQ9//ikle9cetmnU4D68v1UJQkTyU5sShLvf7+7D3f18D2wAPp3h2LpGLOiMjsYPH5hv5OA+amISkbzV1k7q/mZ2n5ktDl//h6A20fMVBAPzRRKHJ4jRg/vw4Z4a3eoqInmprU1MjwB7gf8RvvYAj2YqqC5lBrHeROO1h20aNbgvAOu36U4mEck/bXoOAjjO3b+QtPxPZrY0EwFlRaxXyhrEqKQ7mcYf3a+roxIRyaq21iAOmNmnGhbM7JNA7oxDEevdYoIwgzUf7c1CUCIi2dXWGsQ/AL82s/7h8k5gZmZCyoKC4pSd1L0Ko4wa1IfVHypBiEj+aetdTG+5+8nAScBJ7j4JmNHacWZ2rpmtNrO1ZjYrxfYzzGyJmdWb2SXNtsXNbGn4mtfG8nRMrBeRxOF9EADjhpWw6sPcuKNXRKQ92jWjnLvvCZ+oBvj6kfY1syjwAHAeMB64wszGN9ttI3A18HiKUxxw91PC14XtibPdYr1T1iAAPja0Hxu272ffQd3JJCL5pTNTjlor26cCa919nbvXAk8AFyXv4O7r3X0ZkOhEHJ3XQic1wLhhQef0avVDiEieaWsfRCqtDbUxHPggabkSOK0d5y82s8VAPfBDd/9D8x3M7HrgeoCysjIqKiracfpDJuzeT6zuQMrjd+0Pctd/VCxmz7G5NctqdXV1h7+znirfypxv5QWVOZ2OmCDMbC+pE4EBvdIeTVMj3L0qnHviJTNb7u7vJe/g7g8DDwNMnjzZp0+f3rFP2vZr9r/3AamOd3fuev15Ev2GMX36hI6dv5uqqKhIWeZclm9lzrfygsqcTkdMEO7emYkQqoBjkpbLw3Vt4u5V4c91ZlYBTALeO+JBHRXr1WIfhJkFHdWb1VEtIvmlM30QrVkEjDGzUWZWCFwOtOluJDMrNbOi8P1g4JPA2xmLtIXnIBp8bFg/3vlwL+65MYCtiEhbZCxBuHs98DVgPrAKmBtOV3qXmV0IYGZTzKwSuBR4yMxWhod/DFhsZm8RTG36Q3fPYIJouQYBMG5oP6oP1lOpOapFJI90ppO6Ve7+HPBcs3XfS3q/iKDpqflxfwYmZjK2JmK9iXgdJOIQiR62uWGYjeVVuxtnmhMRyXWZbGLqOWLhL/261IPyjR/Wj6KCCEs27OzCoEREsksJAqAwHLn8YHXqzQURTirvz5KNShAikj+UIACKwpu1alMnCIBTR5SyomoPNXXxLgpKRCS7lCAACoN5HzjY8tPSHz+2lNp4gpWbdndRUCIi2aUEAVAUJojalqcXPXVEKQCL1quZSUTygxIEHOqDOEIT0+C+RZxQ1pf/Wruti4ISEckuJQiAwrAPooVO6gafOv4o/vr+DvVDiEheUIKApCamI4/YOm3MYA7WJ1i0fkcXBCUikl1KEJDUSX3kGsRpowcSixqvvKtmJhHJfUoQkNQH0XInNUDvwgJOGzWIF97+SOMyiUjOU4IAiESJR4qO2End4G9PGsb72/axcpNGdxWR3KYEEYpHex3xOYgG5544lIKI8cyyzV0QlYhI9ihBhOoLesPB1msFpX0K+dSYwfznW5tIJNTMJCK5SwkiVBcrgQNtewju4lPLqdp1gP96T53VIpK7lCBC9QV925wgzjmxjNLeMZ746wet7ywi0kMpQYTaU4MoKohy8anlPP/2h3y0pybDkYmIZIcSRKi+oAQO7Grz/jNPH4k7/OyltRmMSkQke5QgQnWxvkEndbyuTfsfO6g3l005hicWbeSDHaknGhIR6cmUIEJ1sXA8pnbUIm6cMYaIGfc+vzpDUYmIZI8SRKi+oCFBtH0476H9i7n+jNH8x9JNGuVVRHKOEkSoLhaOx3SgfQPxffXTxzNqcB9umbOULeqwFpEcogQR6kgNAqA4FuXBqz7OvoP13PDYEmrrExmITkSk6ylBhA71QbR/xrixQ0uYfcnJvLFhJ3c9szLNkYmIZEdBtgPoLjqTICAYxG951XE8uPA9Rg7qw7WfGoWZpTFCEZGupRpEKB7tDRaF/R2fDOgb54zl7PFl3P3sKm6ds5Stew+mMUIRka6lBNHADPocBdUfdfgU0Yjx4FUf5+bPjOGZZZuZcW8Fv3hlHXVx9UuISM+jBJGs39GwZ1OnThGJGLd+9gTm33oGp44o5e5nV3HeT15hzqKN7NxXm6ZARUQyT30QyfoPh23vpuVUxx3Vl19eM4WX3tnCD55bxbeeWs63f7+CvzluEOdPHMb4Yf0Y2r+YQX0KKYgqT4tI96MEkazfcFi3MG2nMzM+87EyZowbwspNe3h2+WaeW76Z255e3rhPxOCokiIG9CqkV2GUXrEovQujFMUiRMyIRoyoGbFohOJYhIJohIKoEYuEP6MRCiJGQTRCLGoUNK4P3jddF+zb0Hn+7s44JRt2AIYZRMwwgta24F3wvmmZwp8ptqc6zpod1/BZB+sS9CqM4u40n1XDwu/OgIQ78YQTiRgRMyLhZ5iFn2dGPO44TkE0EpzPg5pc1IL9GmaHdZwdNQk27ToQLndP6by1YfuBQ+XtUCzd7D4La8O3s7MmwYe7u+aZpO7y/eytzcy/ZiWIZP2GB+Mx1eyB4n5pO62ZMWF4fyYM7883zxnLu1uq2bh9Px/uqWHLnho+3FPD7gN1HKhLcKC2no/21lFTlyDhTiLhxN2prU9QU5egPp6gLuHUxxOkZb6i119Lw0l6mIqXsh1B11qYZ+UFqHgx2xF0qdH9I1xwdvrPqwSRrN/Rwc89m9KaIJKZGSeUlXBCWUmnz5VIOHWJBPVxpz5+6H1dPEF9mETq4k59IvwZrofgr+qlb73FxJNOOvRXvAd/sR/6izv8Ga44tNwQwaEM5X749oa6QfL5Gs4VjRj1cQ+/k6blCs4VbiOoRSXcG2NLOI0xuzvRSAQD6hOJxtpFPBFsT7g3rgNYs3o1Y8eOPawm1F0cXp/qnHdWr2bc2LEdi6WbVbHaGs7q8BpnWnf6fjavX5OR8ypBJOtfHvzcUwlDxmU3ljaIRIyiSJSiDl7F+qooZ55wVHqD6uYq9q9j+tRjsx1Gl6nYt47pU/KnvJB/1xig4sC6jJxXvaPJkmsQIiJ5TgkiWckwiBTAzvXZjkREJOuUIJJFYzBwNGzV/A4iIkoQzQ0+AbZlpsNHRKQnyWiCMLNzzWy1ma01s1kptp9hZkvMrN7MLmm2baaZvRu+ZmYyziYGnwA71rV56lERkVyVsQRhZlHgAeA8YDxwhZmNb7bbRuBq4PFmxw4E7gBOA6YCd5hZaaZibeKosZCoD5KEiEgey2QNYiqw1t3XuXst8ARwUfIO7r7e3ZcBzUezOwd4wd13uPtO4AXg3AzGesjgE4KfW9/pko8TEemuMvkcxHDgg6TlSoIaQUePHd58JzO7HrgeoKysjIqKig4FClBdXU1FRQWR+EGmEWHjX+bx/pb+HT5fT9BQ5nySb2XOt/KCypxOPfpBOXd/GHgYYPLkyT59+vQOn6uiooLG49+dwIjoFkZ04nw9QZMy54l8K3O+lRdU5nTKZBNTFXBM0nJ5uC7Tx3Ze+WSoehMS8S77SBGR7iaTCWIRMMbMRplZIXA5MK+Nx84Hzjaz0rBz+uxwXdcYPhlq9+p2VxHJaxlLEO5eD3yN4Bf7KmCuu680s7vM7EIAM5tiZpXApcBDZrYyPHYH8H2CJLMIuCtc1zWOCbtKNvxXl32kiEh3k9E+CHd/Dniu2brvJb1fRNB8lOrYR4BHMhlfiwYdB/2PhbUvwZSvZCUEEZFs05PUqZjB8TPg/Zf1wJyI5C0liJYc/9mgHyKNM8yJiPQkShAtGfNZ6D0YFmenlUtEJNuUIFpSUASTroI1f4RdH7S+v4hIjlGCOJIp1wIGf30o25GIiHQ5JYgjGXAsjL8I3vgV1OzJdjQiIl1KCaI1p38NDu6BN/9ftiMREelSShCtKf84HHs6/OXnEK/PdjQiIl1GCaItTv8a7N4Iy3+X7UhERLqMEkRbjD0fhp0MC34A9QezHY2ISJdQgmiLSATOujOoRSz692xHIyLSJZQg2uq4GTB6Orw8G2p2ZzsaEZGMU4Joj7PuhAM74M8/zXYkIiIZpwTRHkdPghMvhtcegD2bsx2NiEhGKUG014zbwR1+f71mnBORnKYE0V6DjoPzZwdDgb/0/WxHIyKSMRmdMChnnfolqFoMr/4ISkfCx6/OdkQiImmnBNFR590TjPL6nzfDzg0w47vB7bAiIjlCv9E6qqAIvjgHTp0Jr94Hj18KO97PdlQiImmjBNEZ0Rhc8BM4/17Y8Br8bAo8903Y+1G2IxMR6TQ1MXWWGUy9DsZ9Dhb+Kyz6BbzxaDBM+LBTgk7tASOgz2Ao7g8WSXpZtqMXEWmREkS69BsGF/wY/ubGYOTXlU+3cXA/a5owGt43WU8L65vvn3ROSEpAqZenHjgAy3un2N4svm7Lmy16y9tCU/fvD8vcU7XveqS1vN3xDxo//DofKnPqfwOHa+H/S9MP6kBwbZAi/o4YHxkC06en5VzJlCDSbdBx8Lf3Bq/9O2DHOti1EfZvD4bocAccPJH0Slpu3OYtrE+1f7gMSf/gvNXlvVu20Puoo5puT5amf7zp4xz2n/ewX1p2hG0EZR4yJO2RdYkOXI/qtJU3i/8W3FtJTk23NSlza0mtxf8fqT4mUwmy8+c9sDeahjgOpwSRSb0HBq/yydmOJKVVFRWUZeCvju4s38r8dkUFQ/KovJCfZX6/ooIRGTivOqlFRCQlJQgREUlJCUJERFJSghARkZSUIEREJCUlCBERSUkJQkREUlKCEBGRlMy73dOyHWNmW4ENnTjFYGBbmsLpKVTm3Jdv5QWVub1GuPtRqTbkTILoLDNb7KxRBkgAAAWdSURBVO7d85HnDFGZc1++lRdU5nRSE5OIiKSkBCEiIikpQRzycLYDyAKVOfflW3lBZU4b9UGIiEhKqkGIiEhKShAiIpJS3icIMzvXzFab2Vozm5XteNLFzI4xswVm9raZrTSzm8P1A83sBTN7N/xZGq43M7s//B6Wmdmp2S1Bx5lZ1MzeNLNnwuVRZvZ6WLY5ZlYYri8Kl9eG20dmM+6OMrMBZvakmb1jZqvM7PRcv85mdmv473qFmf3WzIpz7Tqb2SNmtsXMViSta/d1NbOZ4f7vmtnM9sSQ1wnCzKLAA8B5wHjgCjMbn92o0qYe+F/uPh74BPDVsGyzgBfdfQzwYrgMwXcwJnxdD/y860NOm5uBVUnL/wr8yN2PB3YC14brrwV2hut/FO7XE/0E+JO7jwNOJih7zl5nMxsO3ARMdvcJQBS4nNy7zr8Ezm22rl3X1cwGAncApwFTgTsakkqbuHvevoDTgflJy7cBt2U7rgyV9T+AzwKrgWHhumHA6vD9Q8AVSfs37teTXkB5+B9nBvAMwYS/24CC5tccmA+cHr4vCPezbJehneXtD7zfPO5cvs7AcOADYGB43Z4BzsnF6wyMBFZ09LoCVwAPJa1vsl9rr7yuQXDoH1qDynBdTgmr1JOA14Eyd98cbvoQKAvf58p38WPgm0AiXB4E7HL3+nA5uVyNZQ637w7370lGAVuBR8NmtV+YWR9y+Dq7exVwL7AR2Exw3d4gt69zg/Ze105d73xPEDnPzPoCTwG3uPue5G0e/EmRM/c5m9nngC3u/ka2Y+lCBcCpwM/dfRKwj0PNDkBOXudS4CKC5Hg00IfDm2JyXldc13xPEFXAMUnL5eG6nGBmMYLk8Ji7Px2u/sjMhoXbhwFbwvW58F18ErjQzNYDTxA0M/0EGGBmBeE+yeVqLHO4vT+wvSsDToNKoNLdXw+XnyRIGLl8nc8C3nf3re5eBzxNcO1z+To3aO917dT1zvcEsQgYE979UEjQ0TUvyzGlhZkZ8O/AKne/L2nTPKDhToaZBH0TDeu/HN4N8Qlgd1JVtkdw99vcvdzdRxJcy5fc/UpgAXBJuFvzMjd8F5eE+/eov7Td/UPgAzMbG676DPA2OXydCZqWPmFmvcN/5w1lztnrnKS913U+cLaZlYY1r7PDdW2T7U6YbL+A84E1wHvAd7IdTxrL9SmC6ucyYGn4Op+g7fVF4F3g/wMDw/2N4I6u94DlBHeIZL0cnSj/dOCZ8P1o4K/AWuB3QFG4vjhcXhtuH53tuDtY1lOAxeG1/gNQmuvXGfgn4B1gBfAboCjXrjPwW4I+ljqCmuK1HbmuwN+HZV8LXNOeGDTUhoiIpJTvTUwiItICJQgREUlJCUJERFJSghARkZSUIEREJCUlCJF2MLO4mS1NeqVtBGAzG5k8cqdIthW0vouIJDng7qdkOwiRrqAahEgamNl6M7vHzJab2V/N7Phw/Ugzeykco/9FMzs2XF9mZr83s7fC19+Ep4qa2f8N5zp43sx6Za1QkveUIETap1ezJqbLkrbtdveJwM8IRpUF+CnwK3c/CXgMuD9cfz+w0N1PJhg7aWW4fgzwgLufCOwCvpDh8oi0SE9Si7SDmVW7e98U69cDM9x9XThI4ofuPsjMthGM318Xrt/s7oPNbCtQ7u4Hk84xEnjBg8lgMLNvATF3vzvzJRM5nGoQIunjLbxvj4NJ7+Oon1CySAlCJH0uS/r5Wvj+zwQjywJcCbwSvn8RuAEa59Du31VBirSV/joRaZ9eZrY0aflP7t5wq2upmS0jqAVcEa67kWC2t28QzPx2Tbj+ZuBhM7uWoKZwA8HInSLdhvogRNIg7IOY7O7bsh2LSLqoiUlERFJSDUJERFJSDUJERFJSghARkZSUIEREJCUlCBERSUkJQkREUvpvLPxv4bJXrZkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GkeaQDxbZHYI"
      },
      "source": [
        "# Deeper model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nRjbIlSnZIVu",
        "outputId": "ef2786f1-179c-4675-a2b1-9c7fbdcf6de4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "neurons = 1\n",
        "epoch = 1000\n",
        "batch_size = 32\n",
        "\n",
        "deeper_model = Sequential()\n",
        "deeper_model.add(Dense(neurons, activation='relu', input_dim=feature_train.shape[1]))\n",
        "deeper_model.add(Dense(5, activation='relu'))\n",
        "deeper_model.add(Dense(1))\n",
        "deeper_model.compile(loss='mean_squared_error', optimizer='adam')\n",
        "deeper_model_history = deeper_model.fit(feature_train, label_train, epochs=epoch, batch_size=batch_size, validation_data=(feature_test, label_test))"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/1000\n",
            "9/9 [==============================] - 0s 11ms/step - loss: 0.1190 - val_loss: 0.0725\n",
            "Epoch 2/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1165 - val_loss: 0.0714\n",
            "Epoch 3/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1146 - val_loss: 0.0709\n",
            "Epoch 4/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1133 - val_loss: 0.0706\n",
            "Epoch 5/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1124 - val_loss: 0.0702\n",
            "Epoch 6/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1120 - val_loss: 0.0700\n",
            "Epoch 7/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1115 - val_loss: 0.0698\n",
            "Epoch 8/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1112 - val_loss: 0.0696\n",
            "Epoch 9/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1110 - val_loss: 0.0695\n",
            "Epoch 10/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1109 - val_loss: 0.0695\n",
            "Epoch 11/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1108 - val_loss: 0.0694\n",
            "Epoch 12/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1107 - val_loss: 0.0694\n",
            "Epoch 13/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1106 - val_loss: 0.0694\n",
            "Epoch 14/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1106 - val_loss: 0.0693\n",
            "Epoch 15/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1106 - val_loss: 0.0693\n",
            "Epoch 16/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1105 - val_loss: 0.0693\n",
            "Epoch 17/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1105 - val_loss: 0.0693\n",
            "Epoch 18/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1105 - val_loss: 0.0693\n",
            "Epoch 19/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1105 - val_loss: 0.0692\n",
            "Epoch 20/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1104 - val_loss: 0.0692\n",
            "Epoch 21/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1104 - val_loss: 0.0692\n",
            "Epoch 22/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1103 - val_loss: 0.0692\n",
            "Epoch 23/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1103 - val_loss: 0.0692\n",
            "Epoch 24/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1103 - val_loss: 0.0692\n",
            "Epoch 25/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.1102 - val_loss: 0.0692\n",
            "Epoch 26/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1102 - val_loss: 0.0692\n",
            "Epoch 27/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1101 - val_loss: 0.0691\n",
            "Epoch 28/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1100 - val_loss: 0.0691\n",
            "Epoch 29/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1100 - val_loss: 0.0690\n",
            "Epoch 30/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1098 - val_loss: 0.0690\n",
            "Epoch 31/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1097 - val_loss: 0.0689\n",
            "Epoch 32/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1096 - val_loss: 0.0688\n",
            "Epoch 33/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.1094 - val_loss: 0.0687\n",
            "Epoch 34/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1093 - val_loss: 0.0686\n",
            "Epoch 35/1000\n",
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            "Epoch 149/1000\n",
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            "Epoch 153/1000\n",
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            "Epoch 158/1000\n",
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            "Epoch 159/1000\n",
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            "Epoch 160/1000\n",
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            "Epoch 161/1000\n",
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            "Epoch 162/1000\n",
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            "Epoch 163/1000\n",
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            "Epoch 164/1000\n",
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            "Epoch 169/1000\n",
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            "Epoch 171/1000\n",
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            "Epoch 172/1000\n",
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            "Epoch 178/1000\n",
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            "Epoch 180/1000\n",
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            "Epoch 181/1000\n",
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            "Epoch 183/1000\n",
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            "Epoch 184/1000\n",
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            "Epoch 185/1000\n",
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            "Epoch 186/1000\n",
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            "Epoch 188/1000\n",
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            "Epoch 191/1000\n",
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            "Epoch 192/1000\n",
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            "Epoch 193/1000\n",
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            "Epoch 195/1000\n",
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            "Epoch 196/1000\n",
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            "Epoch 197/1000\n",
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            "Epoch 198/1000\n",
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            "Epoch 199/1000\n",
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            "Epoch 200/1000\n",
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            "Epoch 201/1000\n",
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            "Epoch 202/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0830 - val_loss: 0.0544\n",
            "Epoch 203/1000\n",
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            "Epoch 542/1000\n",
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            "Epoch 561/1000\n",
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            "Epoch 564/1000\n",
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            "Epoch 567/1000\n",
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            "Epoch 569/1000\n",
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            "Epoch 570/1000\n",
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            "Epoch 571/1000\n",
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            "Epoch 572/1000\n",
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            "Epoch 573/1000\n",
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            "Epoch 574/1000\n",
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            "Epoch 575/1000\n",
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            "Epoch 576/1000\n",
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            "Epoch 577/1000\n",
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            "Epoch 578/1000\n",
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            "Epoch 579/1000\n",
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            "Epoch 580/1000\n",
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            "Epoch 582/1000\n",
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            "Epoch 583/1000\n",
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            "Epoch 584/1000\n",
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            "Epoch 585/1000\n",
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            "Epoch 586/1000\n",
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            "Epoch 587/1000\n",
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            "Epoch 588/1000\n",
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            "Epoch 592/1000\n",
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            "Epoch 595/1000\n",
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            "Epoch 596/1000\n",
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            "Epoch 597/1000\n",
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            "Epoch 598/1000\n",
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            "Epoch 599/1000\n",
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            "Epoch 600/1000\n",
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            "Epoch 601/1000\n",
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            "Epoch 602/1000\n",
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            "Epoch 603/1000\n",
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            "Epoch 604/1000\n",
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            "Epoch 605/1000\n",
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            "Epoch 607/1000\n",
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            "Epoch 608/1000\n",
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            "Epoch 609/1000\n",
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            "Epoch 610/1000\n",
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            "Epoch 612/1000\n",
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            "Epoch 613/1000\n",
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            "Epoch 615/1000\n",
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            "Epoch 616/1000\n",
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            "Epoch 617/1000\n",
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            "Epoch 619/1000\n",
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            "Epoch 620/1000\n",
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            "Epoch 621/1000\n",
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            "Epoch 622/1000\n",
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            "Epoch 623/1000\n",
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            "Epoch 624/1000\n",
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            "Epoch 625/1000\n",
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            "Epoch 626/1000\n",
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            "Epoch 628/1000\n",
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            "Epoch 629/1000\n",
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            "Epoch 630/1000\n",
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            "Epoch 631/1000\n",
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            "Epoch 632/1000\n",
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            "Epoch 633/1000\n",
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            "Epoch 634/1000\n",
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            "Epoch 635/1000\n",
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            "Epoch 636/1000\n",
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            "Epoch 637/1000\n",
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            "Epoch 638/1000\n",
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            "Epoch 639/1000\n",
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            "Epoch 640/1000\n",
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            "Epoch 641/1000\n",
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            "Epoch 642/1000\n",
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            "Epoch 643/1000\n",
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            "Epoch 644/1000\n",
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            "Epoch 645/1000\n",
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            "Epoch 646/1000\n",
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            "Epoch 647/1000\n",
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            "Epoch 648/1000\n",
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            "Epoch 649/1000\n",
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            "Epoch 650/1000\n",
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            "Epoch 651/1000\n",
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            "Epoch 652/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0815 - val_loss: 0.0550\n",
            "Epoch 653/1000\n",
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            "Epoch 654/1000\n",
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            "Epoch 655/1000\n",
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            "Epoch 656/1000\n",
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            "Epoch 657/1000\n",
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            "Epoch 658/1000\n",
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            "Epoch 659/1000\n",
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            "Epoch 660/1000\n",
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            "Epoch 661/1000\n",
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            "Epoch 662/1000\n",
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            "Epoch 663/1000\n",
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            "Epoch 664/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0542\n",
            "Epoch 665/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0543\n",
            "Epoch 666/1000\n",
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            "Epoch 667/1000\n",
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            "Epoch 668/1000\n",
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            "Epoch 669/1000\n",
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            "Epoch 670/1000\n",
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            "Epoch 671/1000\n",
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            "Epoch 672/1000\n",
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            "Epoch 673/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0813 - val_loss: 0.0547\n",
            "Epoch 674/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0816 - val_loss: 0.0542\n",
            "Epoch 675/1000\n",
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            "Epoch 676/1000\n",
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            "Epoch 677/1000\n",
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            "Epoch 678/1000\n",
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            "Epoch 679/1000\n",
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            "Epoch 680/1000\n",
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            "Epoch 681/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0545\n",
            "Epoch 682/1000\n",
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            "Epoch 683/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0813 - val_loss: 0.0544\n",
            "Epoch 684/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0543\n",
            "Epoch 685/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0813 - val_loss: 0.0542\n",
            "Epoch 686/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0544\n",
            "Epoch 687/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0548\n",
            "Epoch 688/1000\n",
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            "Epoch 689/1000\n",
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            "Epoch 690/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0813 - val_loss: 0.0541\n",
            "Epoch 691/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0542\n",
            "Epoch 692/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0544\n",
            "Epoch 693/1000\n",
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            "Epoch 694/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0813 - val_loss: 0.0550\n",
            "Epoch 695/1000\n",
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            "Epoch 696/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0544\n",
            "Epoch 697/1000\n",
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            "Epoch 698/1000\n",
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            "Epoch 699/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0546\n",
            "Epoch 700/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0546\n",
            "Epoch 701/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0812 - val_loss: 0.0547\n",
            "Epoch 702/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0547\n",
            "Epoch 703/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0814 - val_loss: 0.0544\n",
            "Epoch 704/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0546\n",
            "Epoch 705/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0813 - val_loss: 0.0547\n",
            "Epoch 706/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0547\n",
            "Epoch 707/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0545\n",
            "Epoch 708/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0543\n",
            "Epoch 709/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0542\n",
            "Epoch 710/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0542\n",
            "Epoch 711/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0543\n",
            "Epoch 712/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0546\n",
            "Epoch 713/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0812 - val_loss: 0.0548\n",
            "Epoch 714/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0813 - val_loss: 0.0548\n",
            "Epoch 715/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0814 - val_loss: 0.0546\n",
            "Epoch 716/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0813 - val_loss: 0.0547\n",
            "Epoch 717/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0813 - val_loss: 0.0550\n",
            "Epoch 718/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0813 - val_loss: 0.0545\n",
            "Epoch 719/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0545\n",
            "Epoch 720/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0545\n",
            "Epoch 721/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0813 - val_loss: 0.0549\n",
            "Epoch 722/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0814 - val_loss: 0.0545\n",
            "Epoch 723/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0545\n",
            "Epoch 724/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0813 - val_loss: 0.0547\n",
            "Epoch 725/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0545\n",
            "Epoch 726/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0544\n",
            "Epoch 727/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0544\n",
            "Epoch 728/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0545\n",
            "Epoch 729/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0544\n",
            "Epoch 730/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0545\n",
            "Epoch 731/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0546\n",
            "Epoch 732/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0813 - val_loss: 0.0550\n",
            "Epoch 733/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0812 - val_loss: 0.0548\n",
            "Epoch 734/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0546\n",
            "Epoch 735/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0546\n",
            "Epoch 736/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0545\n",
            "Epoch 737/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0812 - val_loss: 0.0547\n",
            "Epoch 738/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0813 - val_loss: 0.0547\n",
            "Epoch 739/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0812 - val_loss: 0.0548\n",
            "Epoch 740/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0813 - val_loss: 0.0549\n",
            "Epoch 741/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0812 - val_loss: 0.0549\n",
            "Epoch 742/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0813 - val_loss: 0.0547\n",
            "Epoch 743/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0548\n",
            "Epoch 744/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0812 - val_loss: 0.0547\n",
            "Epoch 745/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0813 - val_loss: 0.0542\n",
            "Epoch 746/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0543\n",
            "Epoch 747/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0545\n",
            "Epoch 748/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0812 - val_loss: 0.0549\n",
            "Epoch 749/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0549\n",
            "Epoch 750/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0546\n",
            "Epoch 751/1000\n",
            "9/9 [==============================] - 0s 5ms/step - loss: 0.0812 - val_loss: 0.0545\n",
            "Epoch 752/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0546\n",
            "Epoch 753/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0544\n",
            "Epoch 754/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0813 - val_loss: 0.0542\n",
            "Epoch 755/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0544\n",
            "Epoch 756/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0546\n",
            "Epoch 757/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0547\n",
            "Epoch 758/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0813 - val_loss: 0.0550\n",
            "Epoch 759/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0813 - val_loss: 0.0550\n",
            "Epoch 760/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0813 - val_loss: 0.0547\n",
            "Epoch 761/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0546\n",
            "Epoch 762/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0547\n",
            "Epoch 763/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0547\n",
            "Epoch 764/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0546\n",
            "Epoch 765/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0545\n",
            "Epoch 766/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0543\n",
            "Epoch 767/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0541\n",
            "Epoch 768/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0542\n",
            "Epoch 769/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0543\n",
            "Epoch 770/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0812 - val_loss: 0.0543\n",
            "Epoch 771/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0544\n",
            "Epoch 772/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0545\n",
            "Epoch 773/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0545\n",
            "Epoch 774/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0547\n",
            "Epoch 775/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0546\n",
            "Epoch 776/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0812 - val_loss: 0.0542\n",
            "Epoch 777/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0544\n",
            "Epoch 778/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0545\n",
            "Epoch 779/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0813 - val_loss: 0.0547\n",
            "Epoch 780/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0544\n",
            "Epoch 781/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0545\n",
            "Epoch 782/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0544\n",
            "Epoch 783/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0547\n",
            "Epoch 784/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0546\n",
            "Epoch 785/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0546\n",
            "Epoch 786/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0544\n",
            "Epoch 787/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0544\n",
            "Epoch 788/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0812 - val_loss: 0.0546\n",
            "Epoch 789/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0545\n",
            "Epoch 790/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0546\n",
            "Epoch 791/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0546\n",
            "Epoch 792/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0547\n",
            "Epoch 793/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0545\n",
            "Epoch 794/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0545\n",
            "Epoch 795/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0812 - val_loss: 0.0547\n",
            "Epoch 796/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0550\n",
            "Epoch 797/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0816 - val_loss: 0.0544\n",
            "Epoch 798/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0546\n",
            "Epoch 799/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0547\n",
            "Epoch 800/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0546\n",
            "Epoch 801/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0812 - val_loss: 0.0546\n",
            "Epoch 802/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0545\n",
            "Epoch 803/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0812 - val_loss: 0.0546\n",
            "Epoch 804/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0813 - val_loss: 0.0543\n",
            "Epoch 805/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0545\n",
            "Epoch 806/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0546\n",
            "Epoch 807/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0544\n",
            "Epoch 808/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0811 - val_loss: 0.0545\n",
            "Epoch 809/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0546\n",
            "Epoch 810/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0547\n",
            "Epoch 811/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0813 - val_loss: 0.0546\n",
            "Epoch 812/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0546\n",
            "Epoch 813/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0548\n",
            "Epoch 814/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0547\n",
            "Epoch 815/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0547\n",
            "Epoch 816/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0544\n",
            "Epoch 817/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0543\n",
            "Epoch 818/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0813 - val_loss: 0.0542\n",
            "Epoch 819/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0545\n",
            "Epoch 820/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0547\n",
            "Epoch 821/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0547\n",
            "Epoch 822/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0547\n",
            "Epoch 823/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0812 - val_loss: 0.0546\n",
            "Epoch 824/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0541\n",
            "Epoch 825/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0540\n",
            "Epoch 826/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0813 - val_loss: 0.0541\n",
            "Epoch 827/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0545\n",
            "Epoch 828/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0547\n",
            "Epoch 829/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0547\n",
            "Epoch 830/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0545\n",
            "Epoch 831/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0544\n",
            "Epoch 832/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0545\n",
            "Epoch 833/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0546\n",
            "Epoch 834/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0544\n",
            "Epoch 835/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0813 - val_loss: 0.0541\n",
            "Epoch 836/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0541\n",
            "Epoch 837/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0541\n",
            "Epoch 838/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0543\n",
            "Epoch 839/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0545\n",
            "Epoch 840/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0544\n",
            "Epoch 841/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0545\n",
            "Epoch 842/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0544\n",
            "Epoch 843/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0545\n",
            "Epoch 844/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0546\n",
            "Epoch 845/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0815 - val_loss: 0.0543\n",
            "Epoch 846/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0811 - val_loss: 0.0547\n",
            "Epoch 847/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0546\n",
            "Epoch 848/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0549\n",
            "Epoch 849/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0546\n",
            "Epoch 850/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0542\n",
            "Epoch 851/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0542\n",
            "Epoch 852/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0544\n",
            "Epoch 853/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0543\n",
            "Epoch 854/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0542\n",
            "Epoch 855/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0543\n",
            "Epoch 856/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0542\n",
            "Epoch 857/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0544\n",
            "Epoch 858/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0545\n",
            "Epoch 859/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0545\n",
            "Epoch 860/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0545\n",
            "Epoch 861/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0543\n",
            "Epoch 862/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0542\n",
            "Epoch 863/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0543\n",
            "Epoch 864/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0813 - val_loss: 0.0541\n",
            "Epoch 865/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0812 - val_loss: 0.0541\n",
            "Epoch 866/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0543\n",
            "Epoch 867/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0542\n",
            "Epoch 868/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0546\n",
            "Epoch 869/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0813 - val_loss: 0.0544\n",
            "Epoch 870/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0810 - val_loss: 0.0547\n",
            "Epoch 871/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0549\n",
            "Epoch 872/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0550\n",
            "Epoch 873/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0546\n",
            "Epoch 874/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0813 - val_loss: 0.0543\n",
            "Epoch 875/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0547\n",
            "Epoch 876/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0545\n",
            "Epoch 877/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0543\n",
            "Epoch 878/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0813 - val_loss: 0.0547\n",
            "Epoch 879/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0543\n",
            "Epoch 880/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0543\n",
            "Epoch 881/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0543\n",
            "Epoch 882/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0542\n",
            "Epoch 883/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0543\n",
            "Epoch 884/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0547\n",
            "Epoch 885/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0547\n",
            "Epoch 886/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0546\n",
            "Epoch 887/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0542\n",
            "Epoch 888/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0543\n",
            "Epoch 889/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0811 - val_loss: 0.0543\n",
            "Epoch 890/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0544\n",
            "Epoch 891/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0548\n",
            "Epoch 892/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0548\n",
            "Epoch 893/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0545\n",
            "Epoch 894/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0812 - val_loss: 0.0547\n",
            "Epoch 895/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0544\n",
            "Epoch 896/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0543\n",
            "Epoch 897/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0543\n",
            "Epoch 898/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0541\n",
            "Epoch 899/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0811 - val_loss: 0.0543\n",
            "Epoch 900/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0548\n",
            "Epoch 901/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0547\n",
            "Epoch 902/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0811 - val_loss: 0.0546\n",
            "Epoch 903/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0544\n",
            "Epoch 904/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0542\n",
            "Epoch 905/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0813 - val_loss: 0.0541\n",
            "Epoch 906/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0545\n",
            "Epoch 907/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0546\n",
            "Epoch 908/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0549\n",
            "Epoch 909/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0544\n",
            "Epoch 910/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0544\n",
            "Epoch 911/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0543\n",
            "Epoch 912/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0811 - val_loss: 0.0545\n",
            "Epoch 913/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0813 - val_loss: 0.0542\n",
            "Epoch 914/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0814 - val_loss: 0.0547\n",
            "Epoch 915/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0812 - val_loss: 0.0545\n",
            "Epoch 916/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0548\n",
            "Epoch 917/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0548\n",
            "Epoch 918/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0546\n",
            "Epoch 919/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0544\n",
            "Epoch 920/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0811 - val_loss: 0.0544\n",
            "Epoch 921/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0543\n",
            "Epoch 922/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0541\n",
            "Epoch 923/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0542\n",
            "Epoch 924/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0546\n",
            "Epoch 925/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0811 - val_loss: 0.0545\n",
            "Epoch 926/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0544\n",
            "Epoch 927/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0545\n",
            "Epoch 928/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0811 - val_loss: 0.0549\n",
            "Epoch 929/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0549\n",
            "Epoch 930/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0547\n",
            "Epoch 931/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0545\n",
            "Epoch 932/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0546\n",
            "Epoch 933/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0544\n",
            "Epoch 934/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0542\n",
            "Epoch 935/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0542\n",
            "Epoch 936/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0541\n",
            "Epoch 937/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0545\n",
            "Epoch 938/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0814 - val_loss: 0.0542\n",
            "Epoch 939/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0544\n",
            "Epoch 940/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0546\n",
            "Epoch 941/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0547\n",
            "Epoch 942/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0547\n",
            "Epoch 943/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0812 - val_loss: 0.0548\n",
            "Epoch 944/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0813 - val_loss: 0.0544\n",
            "Epoch 945/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0811 - val_loss: 0.0543\n",
            "Epoch 946/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0811 - val_loss: 0.0543\n",
            "Epoch 947/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0545\n",
            "Epoch 948/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0811 - val_loss: 0.0544\n",
            "Epoch 949/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0543\n",
            "Epoch 950/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0544\n",
            "Epoch 951/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0546\n",
            "Epoch 952/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0811 - val_loss: 0.0545\n",
            "Epoch 953/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0811 - val_loss: 0.0544\n",
            "Epoch 954/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0543\n",
            "Epoch 955/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0544\n",
            "Epoch 956/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0547\n",
            "Epoch 957/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0545\n",
            "Epoch 958/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0544\n",
            "Epoch 959/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0543\n",
            "Epoch 960/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0544\n",
            "Epoch 961/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0546\n",
            "Epoch 962/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0547\n",
            "Epoch 963/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0811 - val_loss: 0.0544\n",
            "Epoch 964/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0544\n",
            "Epoch 965/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0544\n",
            "Epoch 966/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0545\n",
            "Epoch 967/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0810 - val_loss: 0.0547\n",
            "Epoch 968/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0547\n",
            "Epoch 969/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0545\n",
            "Epoch 970/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0545\n",
            "Epoch 971/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0811 - val_loss: 0.0546\n",
            "Epoch 972/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0811 - val_loss: 0.0548\n",
            "Epoch 973/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0547\n",
            "Epoch 974/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0545\n",
            "Epoch 975/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0541\n",
            "Epoch 976/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0542\n",
            "Epoch 977/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0544\n",
            "Epoch 978/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0544\n",
            "Epoch 979/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0811 - val_loss: 0.0545\n",
            "Epoch 980/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0545\n",
            "Epoch 981/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0813 - val_loss: 0.0548\n",
            "Epoch 982/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0813 - val_loss: 0.0542\n",
            "Epoch 983/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0543\n",
            "Epoch 984/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0543\n",
            "Epoch 985/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0811 - val_loss: 0.0544\n",
            "Epoch 986/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0811 - val_loss: 0.0544\n",
            "Epoch 987/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0543\n",
            "Epoch 988/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0546\n",
            "Epoch 989/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0545\n",
            "Epoch 990/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0811 - val_loss: 0.0544\n",
            "Epoch 991/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0547\n",
            "Epoch 992/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0546\n",
            "Epoch 993/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0543\n",
            "Epoch 994/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0542\n",
            "Epoch 995/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0811 - val_loss: 0.0542\n",
            "Epoch 996/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0544\n",
            "Epoch 997/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0545\n",
            "Epoch 998/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0545\n",
            "Epoch 999/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0811 - val_loss: 0.0545\n",
            "Epoch 1000/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0811 - val_loss: 0.0544\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BxksLfsjZlK2",
        "outputId": "a9bc5464-c9d5-4296-a015-d1d7b237307c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "deeper_history_dataframe = pd.DataFrame(deeper_model_history.history)\n",
        "deeper_history_dataframe['epoch'] = deeper_model_history.epoch\n",
        "deeper_history_dataframe.sort_values(by='val_loss', ascending=True)"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>loss</th>\n",
              "      <th>val_loss</th>\n",
              "      <th>epoch</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>420</th>\n",
              "      <td>0.081421</td>\n",
              "      <td>0.053958</td>\n",
              "      <td>420</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>339</th>\n",
              "      <td>0.081896</td>\n",
              "      <td>0.053965</td>\n",
              "      <td>339</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>824</th>\n",
              "      <td>0.081237</td>\n",
              "      <td>0.053996</td>\n",
              "      <td>824</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>318</th>\n",
              "      <td>0.081682</td>\n",
              "      <td>0.054019</td>\n",
              "      <td>318</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>419</th>\n",
              "      <td>0.081503</td>\n",
              "      <td>0.054025</td>\n",
              "      <td>419</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.112438</td>\n",
              "      <td>0.070247</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.113302</td>\n",
              "      <td>0.070555</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.114648</td>\n",
              "      <td>0.070867</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.116467</td>\n",
              "      <td>0.071374</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.118951</td>\n",
              "      <td>0.072463</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         loss  val_loss  epoch\n",
              "420  0.081421  0.053958    420\n",
              "339  0.081896  0.053965    339\n",
              "824  0.081237  0.053996    824\n",
              "318  0.081682  0.054019    318\n",
              "419  0.081503  0.054025    419\n",
              "..        ...       ...    ...\n",
              "4    0.112438  0.070247      4\n",
              "3    0.113302  0.070555      3\n",
              "2    0.114648  0.070867      2\n",
              "1    0.116467  0.071374      1\n",
              "0    0.118951  0.072463      0\n",
              "\n",
              "[1000 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Uv9EnMgt--_",
        "outputId": "e39a48f4-4c87-4f17-8890-8949f5e5ab6a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "plot_loss(deeper_model_history) # epoch vs loss graph"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cqcjp3avYLgU"
      },
      "source": [
        "# Wider model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "82rebSGUYNK2",
        "outputId": "0ecb8855-c610-4850-b210-7aff106fd095",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "neurons = 50\n",
        "epoch = 1000\n",
        "batch_size = 32\n",
        "\n",
        "wider_model = Sequential()\n",
        "wider_model.add(Dense(neurons, activation='relu', input_dim=feature_train.shape[1]))\n",
        "wider_model.add(Dense(1))\n",
        "wider_model.compile(loss='mean_squared_error', optimizer='adam')\n",
        "wider_model_history = wider_model.fit(feature_train, label_train, epochs=epoch, batch_size=batch_size, validation_data=(feature_test, label_test))"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/1000\n",
            "9/9 [==============================] - 0s 10ms/step - loss: 0.0886 - val_loss: 0.0565\n",
            "Epoch 2/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0852 - val_loss: 0.0557\n",
            "Epoch 3/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0841 - val_loss: 0.0553\n",
            "Epoch 4/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0836 - val_loss: 0.0551\n",
            "Epoch 5/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0830 - val_loss: 0.0548\n",
            "Epoch 6/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0824 - val_loss: 0.0547\n",
            "Epoch 7/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0822 - val_loss: 0.0547\n",
            "Epoch 8/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0821 - val_loss: 0.0542\n",
            "Epoch 9/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0816 - val_loss: 0.0542\n",
            "Epoch 10/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0818 - val_loss: 0.0546\n",
            "Epoch 11/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0813 - val_loss: 0.0542\n",
            "Epoch 12/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0813 - val_loss: 0.0542\n",
            "Epoch 13/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0810 - val_loss: 0.0543\n",
            "Epoch 14/1000\n",
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            "Epoch 128/1000\n",
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            "Epoch 168/1000\n",
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            "Epoch 169/1000\n",
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            "Epoch 170/1000\n",
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            "Epoch 171/1000\n",
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            "Epoch 172/1000\n",
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            "Epoch 173/1000\n",
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            "Epoch 174/1000\n",
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            "Epoch 175/1000\n",
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            "Epoch 176/1000\n",
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            "Epoch 177/1000\n",
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            "Epoch 178/1000\n",
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            "Epoch 179/1000\n",
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            "Epoch 180/1000\n",
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            "Epoch 181/1000\n",
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            "Epoch 182/1000\n",
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            "Epoch 628/1000\n",
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            "Epoch 631/1000\n",
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            "Epoch 632/1000\n",
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            "Epoch 633/1000\n",
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            "Epoch 634/1000\n",
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            "Epoch 635/1000\n",
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            "Epoch 636/1000\n",
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            "Epoch 637/1000\n",
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            "Epoch 638/1000\n",
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            "Epoch 639/1000\n",
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            "Epoch 640/1000\n",
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            "Epoch 641/1000\n",
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            "Epoch 642/1000\n",
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            "Epoch 643/1000\n",
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            "Epoch 644/1000\n",
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            "Epoch 645/1000\n",
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            "Epoch 646/1000\n",
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            "Epoch 647/1000\n",
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            "Epoch 648/1000\n",
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            "Epoch 649/1000\n",
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            "Epoch 650/1000\n",
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            "Epoch 651/1000\n",
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            "Epoch 652/1000\n",
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            "Epoch 653/1000\n",
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            "Epoch 654/1000\n",
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            "Epoch 655/1000\n",
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            "Epoch 656/1000\n",
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            "Epoch 657/1000\n",
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            "Epoch 658/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0561\n",
            "Epoch 659/1000\n",
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            "Epoch 660/1000\n",
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            "Epoch 661/1000\n",
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            "Epoch 662/1000\n",
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            "Epoch 663/1000\n",
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            "Epoch 664/1000\n",
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            "Epoch 665/1000\n",
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            "Epoch 666/1000\n",
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            "Epoch 667/1000\n",
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            "Epoch 668/1000\n",
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            "Epoch 669/1000\n",
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            "Epoch 670/1000\n",
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            "Epoch 671/1000\n",
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            "Epoch 672/1000\n",
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            "Epoch 673/1000\n",
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            "Epoch 674/1000\n",
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            "Epoch 675/1000\n",
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            "Epoch 676/1000\n",
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            "Epoch 677/1000\n",
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            "Epoch 678/1000\n",
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            "Epoch 679/1000\n",
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            "Epoch 680/1000\n",
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            "Epoch 681/1000\n",
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            "Epoch 682/1000\n",
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            "Epoch 683/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0553\n",
            "Epoch 684/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0557\n",
            "Epoch 685/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0552\n",
            "Epoch 686/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0553\n",
            "Epoch 687/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0555\n",
            "Epoch 688/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0556\n",
            "Epoch 689/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0554\n",
            "Epoch 690/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0549\n",
            "Epoch 691/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0553\n",
            "Epoch 692/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0555\n",
            "Epoch 693/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0803 - val_loss: 0.0552\n",
            "Epoch 694/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0555\n",
            "Epoch 695/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0556\n",
            "Epoch 696/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0556\n",
            "Epoch 697/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0556\n",
            "Epoch 698/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0554\n",
            "Epoch 699/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0553\n",
            "Epoch 700/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0802 - val_loss: 0.0554\n",
            "Epoch 701/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0802 - val_loss: 0.0552\n",
            "Epoch 702/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0554\n",
            "Epoch 703/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0555\n",
            "Epoch 704/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0553\n",
            "Epoch 705/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0802 - val_loss: 0.0552\n",
            "Epoch 706/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0552\n",
            "Epoch 707/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0550\n",
            "Epoch 708/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0555\n",
            "Epoch 709/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0556\n",
            "Epoch 710/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0558\n",
            "Epoch 711/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0559\n",
            "Epoch 712/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0556\n",
            "Epoch 713/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0555\n",
            "Epoch 714/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0555\n",
            "Epoch 715/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0553\n",
            "Epoch 716/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0802 - val_loss: 0.0553\n",
            "Epoch 717/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0554\n",
            "Epoch 718/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0554\n",
            "Epoch 719/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0556\n",
            "Epoch 720/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0557\n",
            "Epoch 721/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0556\n",
            "Epoch 722/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0553\n",
            "Epoch 723/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0554\n",
            "Epoch 724/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0554\n",
            "Epoch 725/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0556\n",
            "Epoch 726/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0556\n",
            "Epoch 727/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0557\n",
            "Epoch 728/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0553\n",
            "Epoch 729/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0552\n",
            "Epoch 730/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0550\n",
            "Epoch 731/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0552\n",
            "Epoch 732/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0552\n",
            "Epoch 733/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0553\n",
            "Epoch 734/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0551\n",
            "Epoch 735/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0802 - val_loss: 0.0553\n",
            "Epoch 736/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0555\n",
            "Epoch 737/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0802 - val_loss: 0.0552\n",
            "Epoch 738/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0554\n",
            "Epoch 739/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0803 - val_loss: 0.0554\n",
            "Epoch 740/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0553\n",
            "Epoch 741/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0551\n",
            "Epoch 742/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0552\n",
            "Epoch 743/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0555\n",
            "Epoch 744/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0560\n",
            "Epoch 745/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0555\n",
            "Epoch 746/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0552\n",
            "Epoch 747/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0553\n",
            "Epoch 748/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0556\n",
            "Epoch 749/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0553\n",
            "Epoch 750/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0553\n",
            "Epoch 751/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0552\n",
            "Epoch 752/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0552\n",
            "Epoch 753/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0553\n",
            "Epoch 754/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0553\n",
            "Epoch 755/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0555\n",
            "Epoch 756/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0801 - val_loss: 0.0556\n",
            "Epoch 757/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0553\n",
            "Epoch 758/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0554\n",
            "Epoch 759/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0552\n",
            "Epoch 760/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0552\n",
            "Epoch 761/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0554\n",
            "Epoch 762/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0551\n",
            "Epoch 763/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0551\n",
            "Epoch 764/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0557\n",
            "Epoch 765/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0553\n",
            "Epoch 766/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0552\n",
            "Epoch 767/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0550\n",
            "Epoch 768/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0801 - val_loss: 0.0555\n",
            "Epoch 769/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0555\n",
            "Epoch 770/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0556\n",
            "Epoch 771/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0556\n",
            "Epoch 772/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0557\n",
            "Epoch 773/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0558\n",
            "Epoch 774/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0560\n",
            "Epoch 775/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0556\n",
            "Epoch 776/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0551\n",
            "Epoch 777/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0552\n",
            "Epoch 778/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0553\n",
            "Epoch 779/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0551\n",
            "Epoch 780/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0801 - val_loss: 0.0554\n",
            "Epoch 781/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0561\n",
            "Epoch 782/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0557\n",
            "Epoch 783/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0554\n",
            "Epoch 784/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0553\n",
            "Epoch 785/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0550\n",
            "Epoch 786/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0801 - val_loss: 0.0555\n",
            "Epoch 787/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0557\n",
            "Epoch 788/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0559\n",
            "Epoch 789/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0560\n",
            "Epoch 790/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0561\n",
            "Epoch 791/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0556\n",
            "Epoch 792/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0552\n",
            "Epoch 793/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0801 - val_loss: 0.0558\n",
            "Epoch 794/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0557\n",
            "Epoch 795/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0557\n",
            "Epoch 796/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0556\n",
            "Epoch 797/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0556\n",
            "Epoch 798/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0559\n",
            "Epoch 799/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0551\n",
            "Epoch 800/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0550\n",
            "Epoch 801/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0552\n",
            "Epoch 802/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0554\n",
            "Epoch 803/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0560\n",
            "Epoch 804/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0555\n",
            "Epoch 805/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0552\n",
            "Epoch 806/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0554\n",
            "Epoch 807/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0553\n",
            "Epoch 808/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0554\n",
            "Epoch 809/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0554\n",
            "Epoch 810/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0554\n",
            "Epoch 811/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0554\n",
            "Epoch 812/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0559\n",
            "Epoch 813/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0555\n",
            "Epoch 814/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0805 - val_loss: 0.0551\n",
            "Epoch 815/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0802 - val_loss: 0.0554\n",
            "Epoch 816/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0558\n",
            "Epoch 817/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0556\n",
            "Epoch 818/1000\n",
            "9/9 [==============================] - 0s 2ms/step - loss: 0.0803 - val_loss: 0.0559\n",
            "Epoch 819/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0556\n",
            "Epoch 820/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0556\n",
            "Epoch 821/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0556\n",
            "Epoch 822/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0557\n",
            "Epoch 823/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0560\n",
            "Epoch 824/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0556\n",
            "Epoch 825/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0553\n",
            "Epoch 826/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0553\n",
            "Epoch 827/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0553\n",
            "Epoch 828/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0552\n",
            "Epoch 829/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0559\n",
            "Epoch 830/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0803 - val_loss: 0.0559\n",
            "Epoch 831/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0560\n",
            "Epoch 832/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0806 - val_loss: 0.0552\n",
            "Epoch 833/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0556\n",
            "Epoch 834/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0555\n",
            "Epoch 835/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0554\n",
            "Epoch 836/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0555\n",
            "Epoch 837/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0559\n",
            "Epoch 838/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0559\n",
            "Epoch 839/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0558\n",
            "Epoch 840/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0553\n",
            "Epoch 841/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0552\n",
            "Epoch 842/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0554\n",
            "Epoch 843/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0555\n",
            "Epoch 844/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0556\n",
            "Epoch 845/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0559\n",
            "Epoch 846/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0555\n",
            "Epoch 847/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0555\n",
            "Epoch 848/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0555\n",
            "Epoch 849/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0555\n",
            "Epoch 850/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0554\n",
            "Epoch 851/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0555\n",
            "Epoch 852/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0554\n",
            "Epoch 853/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0554\n",
            "Epoch 854/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0550\n",
            "Epoch 855/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0551\n",
            "Epoch 856/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0554\n",
            "Epoch 857/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0801 - val_loss: 0.0555\n",
            "Epoch 858/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0556\n",
            "Epoch 859/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0555\n",
            "Epoch 860/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0558\n",
            "Epoch 861/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0559\n",
            "Epoch 862/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0804 - val_loss: 0.0553\n",
            "Epoch 863/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0558\n",
            "Epoch 864/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0558\n",
            "Epoch 865/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0555\n",
            "Epoch 866/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0551\n",
            "Epoch 867/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0552\n",
            "Epoch 868/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0801 - val_loss: 0.0556\n",
            "Epoch 869/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0558\n",
            "Epoch 870/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0556\n",
            "Epoch 871/1000\n",
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            "Epoch 872/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0555\n",
            "Epoch 873/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0551\n",
            "Epoch 874/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0554\n",
            "Epoch 875/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0559\n",
            "Epoch 876/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0560\n",
            "Epoch 877/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0556\n",
            "Epoch 878/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0557\n",
            "Epoch 879/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0560\n",
            "Epoch 880/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0560\n",
            "Epoch 881/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0556\n",
            "Epoch 882/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0555\n",
            "Epoch 883/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0558\n",
            "Epoch 884/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0806 - val_loss: 0.0553\n",
            "Epoch 885/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0555\n",
            "Epoch 886/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0557\n",
            "Epoch 887/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0561\n",
            "Epoch 888/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0806 - val_loss: 0.0554\n",
            "Epoch 889/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0801 - val_loss: 0.0556\n",
            "Epoch 890/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0560\n",
            "Epoch 891/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0556\n",
            "Epoch 892/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0554\n",
            "Epoch 893/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0555\n",
            "Epoch 894/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0803 - val_loss: 0.0559\n",
            "Epoch 895/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0559\n",
            "Epoch 896/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0557\n",
            "Epoch 897/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0552\n",
            "Epoch 898/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0553\n",
            "Epoch 899/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0558\n",
            "Epoch 900/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0559\n",
            "Epoch 901/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0556\n",
            "Epoch 902/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0555\n",
            "Epoch 903/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0557\n",
            "Epoch 904/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0554\n",
            "Epoch 905/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0554\n",
            "Epoch 906/1000\n",
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            "Epoch 907/1000\n",
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            "Epoch 908/1000\n",
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            "Epoch 909/1000\n",
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            "Epoch 910/1000\n",
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            "Epoch 911/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0554\n",
            "Epoch 912/1000\n",
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            "Epoch 913/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0554\n",
            "Epoch 914/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0553\n",
            "Epoch 915/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0557\n",
            "Epoch 916/1000\n",
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            "Epoch 917/1000\n",
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            "Epoch 918/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0552\n",
            "Epoch 919/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0551\n",
            "Epoch 920/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0554\n",
            "Epoch 921/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0555\n",
            "Epoch 922/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0555\n",
            "Epoch 923/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0553\n",
            "Epoch 924/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0554\n",
            "Epoch 925/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0559\n",
            "Epoch 926/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0801 - val_loss: 0.0556\n",
            "Epoch 927/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0801 - val_loss: 0.0556\n",
            "Epoch 928/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0556\n",
            "Epoch 929/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0556\n",
            "Epoch 930/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0554\n",
            "Epoch 931/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0801 - val_loss: 0.0558\n",
            "Epoch 932/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0561\n",
            "Epoch 933/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0556\n",
            "Epoch 934/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0553\n",
            "Epoch 935/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0556\n",
            "Epoch 936/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0560\n",
            "Epoch 937/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0557\n",
            "Epoch 938/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0801 - val_loss: 0.0559\n",
            "Epoch 939/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0557\n",
            "Epoch 940/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0555\n",
            "Epoch 941/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0553\n",
            "Epoch 942/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0564\n",
            "Epoch 943/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0803 - val_loss: 0.0559\n",
            "Epoch 944/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0554\n",
            "Epoch 945/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0554\n",
            "Epoch 946/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0552\n",
            "Epoch 947/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0555\n",
            "Epoch 948/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0554\n",
            "Epoch 949/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0560\n",
            "Epoch 950/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0560\n",
            "Epoch 951/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0558\n",
            "Epoch 952/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0554\n",
            "Epoch 953/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0555\n",
            "Epoch 954/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0557\n",
            "Epoch 955/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0801 - val_loss: 0.0559\n",
            "Epoch 956/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0559\n",
            "Epoch 957/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0554\n",
            "Epoch 958/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0558\n",
            "Epoch 959/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0801 - val_loss: 0.0557\n",
            "Epoch 960/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0801 - val_loss: 0.0555\n",
            "Epoch 961/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0556\n",
            "Epoch 962/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0553\n",
            "Epoch 963/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0554\n",
            "Epoch 964/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0555\n",
            "Epoch 965/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0554\n",
            "Epoch 966/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0556\n",
            "Epoch 967/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0556\n",
            "Epoch 968/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0554\n",
            "Epoch 969/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0554\n",
            "Epoch 970/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0555\n",
            "Epoch 971/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0554\n",
            "Epoch 972/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0558\n",
            "Epoch 973/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0557\n",
            "Epoch 974/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0554\n",
            "Epoch 975/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0562\n",
            "Epoch 976/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0557\n",
            "Epoch 977/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0555\n",
            "Epoch 978/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0553\n",
            "Epoch 979/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0556\n",
            "Epoch 980/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0558\n",
            "Epoch 981/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0558\n",
            "Epoch 982/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0551\n",
            "Epoch 983/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0554\n",
            "Epoch 984/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0553\n",
            "Epoch 985/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0553\n",
            "Epoch 986/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0552\n",
            "Epoch 987/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0559\n",
            "Epoch 988/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0801 - val_loss: 0.0556\n",
            "Epoch 989/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0801 - val_loss: 0.0554\n",
            "Epoch 990/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0558\n",
            "Epoch 991/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0555\n",
            "Epoch 992/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0553\n",
            "Epoch 993/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0553\n",
            "Epoch 994/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0555\n",
            "Epoch 995/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0555\n",
            "Epoch 996/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0554\n",
            "Epoch 997/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0556\n",
            "Epoch 998/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0558\n",
            "Epoch 999/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0556\n",
            "Epoch 1000/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0802 - val_loss: 0.0555\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UmrJSX3NYYh5",
        "outputId": "2fc02269-ee01-4424-e01b-ea4841df75c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "wider_history_dataframe = pd.DataFrame(wider_model_history.history)\n",
        "wider_history_dataframe['epoch'] = wider_model_history.epoch\n",
        "wider_history_dataframe.sort_values(by='val_loss', ascending=True)"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>loss</th>\n",
              "      <th>val_loss</th>\n",
              "      <th>epoch</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0.080894</td>\n",
              "      <td>0.054137</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.081331</td>\n",
              "      <td>0.054159</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.081266</td>\n",
              "      <td>0.054173</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.082119</td>\n",
              "      <td>0.054185</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.080846</td>\n",
              "      <td>0.054208</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>666</th>\n",
              "      <td>0.080348</td>\n",
              "      <td>0.056086</td>\n",
              "      <td>666</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>657</th>\n",
              "      <td>0.080353</td>\n",
              "      <td>0.056143</td>\n",
              "      <td>657</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>974</th>\n",
              "      <td>0.080392</td>\n",
              "      <td>0.056205</td>\n",
              "      <td>974</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>941</th>\n",
              "      <td>0.080483</td>\n",
              "      <td>0.056399</td>\n",
              "      <td>941</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.088578</td>\n",
              "      <td>0.056486</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         loss  val_loss  epoch\n",
              "19   0.080894  0.054137     19\n",
              "10   0.081331  0.054159     10\n",
              "11   0.081266  0.054173     11\n",
              "7    0.082119  0.054185      7\n",
              "15   0.080846  0.054208     15\n",
              "..        ...       ...    ...\n",
              "666  0.080348  0.056086    666\n",
              "657  0.080353  0.056143    657\n",
              "974  0.080392  0.056205    974\n",
              "941  0.080483  0.056399    941\n",
              "0    0.088578  0.056486      0\n",
              "\n",
              "[1000 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uIb7GxpGhnS8"
      },
      "source": [
        "# **LSTM**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3NiKK3E-Z_Ke",
        "outputId": "ed9fc11e-d763-490c-90b1-d4bca34bdcea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "from tensorflow.keras.layers import LSTM\n",
        "\n",
        "epoch = 1000\n",
        "batch_size = 32\n",
        "\n",
        "# Reshape menjadi (jumlah sample, time steps, jumlah feature)\n",
        "# Time steps: jumlah lag, gunakan default 1\n",
        "# https://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/\n",
        "feature_train_reshaped = np.reshape(feature_train, (feature_train.shape[0], 1, feature_train.shape[1]))\n",
        "feature_test_reshaped = np.reshape(feature_test, (feature_test.shape[0], 1, feature_test.shape[1]))\n",
        "\n",
        "lstm_model = Sequential()\n",
        "lstm_model.add(LSTM(50, activation='relu', input_dim=feature_train.shape[1])) # 50 LSTM Block\n",
        "lstm_model.add(Dense(1))\n",
        "lstm_model.compile(optimizer='adam', loss='mean_squared_error')\n",
        "\n",
        "lstm_model_history = lstm_model.fit(feature_train_reshaped, label_train, epochs=epoch, batch_size=batch_size, validation_data=(feature_test_reshaped, label_test))"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/1000\n",
            "9/9 [==============================] - 0s 22ms/step - loss: 0.1084 - val_loss: 0.0674\n",
            "Epoch 2/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1054 - val_loss: 0.0656\n",
            "Epoch 3/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.1030 - val_loss: 0.0643\n",
            "Epoch 4/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.1009 - val_loss: 0.0631\n",
            "Epoch 5/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0992 - val_loss: 0.0621\n",
            "Epoch 6/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0972 - val_loss: 0.0613\n",
            "Epoch 7/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0957 - val_loss: 0.0605\n",
            "Epoch 8/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0942 - val_loss: 0.0598\n",
            "Epoch 9/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0929 - val_loss: 0.0592\n",
            "Epoch 10/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0917 - val_loss: 0.0586\n",
            "Epoch 11/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0905 - val_loss: 0.0581\n",
            "Epoch 12/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0893 - val_loss: 0.0577\n",
            "Epoch 13/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0883 - val_loss: 0.0573\n",
            "Epoch 14/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0875 - val_loss: 0.0570\n",
            "Epoch 15/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0868 - val_loss: 0.0568\n",
            "Epoch 16/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0862 - val_loss: 0.0566\n",
            "Epoch 17/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0856 - val_loss: 0.0564\n",
            "Epoch 18/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0851 - val_loss: 0.0564\n",
            "Epoch 19/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0846 - val_loss: 0.0563\n",
            "Epoch 20/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0842 - val_loss: 0.0562\n",
            "Epoch 21/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0840 - val_loss: 0.0560\n",
            "Epoch 22/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0836 - val_loss: 0.0560\n",
            "Epoch 23/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0833 - val_loss: 0.0560\n",
            "Epoch 24/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0830 - val_loss: 0.0558\n",
            "Epoch 25/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0828 - val_loss: 0.0558\n",
            "Epoch 26/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0826 - val_loss: 0.0557\n",
            "Epoch 27/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0824 - val_loss: 0.0555\n",
            "Epoch 28/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0822 - val_loss: 0.0555\n",
            "Epoch 29/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0821 - val_loss: 0.0554\n",
            "Epoch 30/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0820 - val_loss: 0.0554\n",
            "Epoch 31/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0818 - val_loss: 0.0552\n",
            "Epoch 32/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0817 - val_loss: 0.0550\n",
            "Epoch 33/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0816 - val_loss: 0.0549\n",
            "Epoch 34/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0815 - val_loss: 0.0549\n",
            "Epoch 35/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0814 - val_loss: 0.0548\n",
            "Epoch 36/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0813 - val_loss: 0.0547\n",
            "Epoch 37/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0547\n",
            "Epoch 38/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0812 - val_loss: 0.0546\n",
            "Epoch 39/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0810 - val_loss: 0.0547\n",
            "Epoch 40/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0810 - val_loss: 0.0546\n",
            "Epoch 41/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0809 - val_loss: 0.0546\n",
            "Epoch 42/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0809 - val_loss: 0.0547\n",
            "Epoch 43/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0808 - val_loss: 0.0546\n",
            "Epoch 44/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0808 - val_loss: 0.0544\n",
            "Epoch 45/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0807 - val_loss: 0.0545\n",
            "Epoch 46/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0807 - val_loss: 0.0547\n",
            "Epoch 47/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0808 - val_loss: 0.0546\n",
            "Epoch 48/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0807 - val_loss: 0.0547\n",
            "Epoch 49/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0807 - val_loss: 0.0547\n",
            "Epoch 50/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0806 - val_loss: 0.0546\n",
            "Epoch 51/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0807 - val_loss: 0.0544\n",
            "Epoch 52/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0806 - val_loss: 0.0544\n",
            "Epoch 53/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0807 - val_loss: 0.0543\n",
            "Epoch 54/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0806 - val_loss: 0.0544\n",
            "Epoch 55/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0806 - val_loss: 0.0544\n",
            "Epoch 56/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0806 - val_loss: 0.0544\n",
            "Epoch 57/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0544\n",
            "Epoch 58/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0806 - val_loss: 0.0542\n",
            "Epoch 59/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0806 - val_loss: 0.0544\n",
            "Epoch 60/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0805 - val_loss: 0.0544\n",
            "Epoch 61/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0544\n",
            "Epoch 62/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0806 - val_loss: 0.0545\n",
            "Epoch 63/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0805 - val_loss: 0.0544\n",
            "Epoch 64/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0807 - val_loss: 0.0543\n",
            "Epoch 65/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0805 - val_loss: 0.0544\n",
            "Epoch 66/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0545\n",
            "Epoch 67/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0546\n",
            "Epoch 68/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0806 - val_loss: 0.0544\n",
            "Epoch 69/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0806 - val_loss: 0.0547\n",
            "Epoch 70/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0546\n",
            "Epoch 71/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0545\n",
            "Epoch 72/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0547\n",
            "Epoch 73/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0546\n",
            "Epoch 74/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0546\n",
            "Epoch 75/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0548\n",
            "Epoch 76/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0546\n",
            "Epoch 77/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 78/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0545\n",
            "Epoch 79/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0810 - val_loss: 0.0542\n",
            "Epoch 80/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0807 - val_loss: 0.0545\n",
            "Epoch 81/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0546\n",
            "Epoch 82/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0806 - val_loss: 0.0547\n",
            "Epoch 83/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 84/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 85/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0543\n",
            "Epoch 86/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0544\n",
            "Epoch 87/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 88/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0543\n",
            "Epoch 89/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0804 - val_loss: 0.0544\n",
            "Epoch 90/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 91/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 92/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0546\n",
            "Epoch 93/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0806 - val_loss: 0.0548\n",
            "Epoch 94/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0546\n",
            "Epoch 95/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0545\n",
            "Epoch 96/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 97/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0545\n",
            "Epoch 98/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 99/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0545\n",
            "Epoch 100/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0544\n",
            "Epoch 101/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 102/1000\n",
            "9/9 [==============================] - 0s 5ms/step - loss: 0.0805 - val_loss: 0.0547\n",
            "Epoch 103/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0545\n",
            "Epoch 104/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 105/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0549\n",
            "Epoch 106/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0550\n",
            "Epoch 107/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0804 - val_loss: 0.0548\n",
            "Epoch 108/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0545\n",
            "Epoch 109/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 110/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0805 - val_loss: 0.0546\n",
            "Epoch 111/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 112/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0545\n",
            "Epoch 113/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0544\n",
            "Epoch 114/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 115/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0546\n",
            "Epoch 116/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0545\n",
            "Epoch 117/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 118/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 119/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0546\n",
            "Epoch 120/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0546\n",
            "Epoch 121/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 122/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 123/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0546\n",
            "Epoch 124/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0547\n",
            "Epoch 125/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0548\n",
            "Epoch 126/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0804 - val_loss: 0.0548\n",
            "Epoch 127/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0549\n",
            "Epoch 128/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0805 - val_loss: 0.0545\n",
            "Epoch 129/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 130/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0544\n",
            "Epoch 131/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0544\n",
            "Epoch 132/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 133/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0543\n",
            "Epoch 134/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0543\n",
            "Epoch 135/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 136/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 137/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 138/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0547\n",
            "Epoch 139/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 140/1000\n",
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            "Epoch 600/1000\n",
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            "Epoch 627/1000\n",
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            "Epoch 628/1000\n",
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            "Epoch 630/1000\n",
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            "Epoch 631/1000\n",
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            "Epoch 632/1000\n",
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            "Epoch 633/1000\n",
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            "Epoch 634/1000\n",
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            "Epoch 635/1000\n",
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            "Epoch 636/1000\n",
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            "Epoch 637/1000\n",
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            "Epoch 638/1000\n",
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            "Epoch 639/1000\n",
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            "Epoch 640/1000\n",
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            "Epoch 641/1000\n",
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            "Epoch 642/1000\n",
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            "Epoch 643/1000\n",
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            "Epoch 644/1000\n",
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            "Epoch 645/1000\n",
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            "Epoch 646/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 647/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 648/1000\n",
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            "Epoch 649/1000\n",
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            "Epoch 650/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0544\n",
            "Epoch 651/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 652/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0545\n",
            "Epoch 653/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 654/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0548\n",
            "Epoch 655/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0547\n",
            "Epoch 656/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 657/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 658/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 659/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 660/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0806 - val_loss: 0.0548\n",
            "Epoch 661/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 662/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 663/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0547\n",
            "Epoch 664/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0545\n",
            "Epoch 665/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 666/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0806 - val_loss: 0.0548\n",
            "Epoch 667/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 668/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0546\n",
            "Epoch 669/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0544\n",
            "Epoch 670/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 671/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0547\n",
            "Epoch 672/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0804 - val_loss: 0.0548\n",
            "Epoch 673/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0548\n",
            "Epoch 674/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0548\n",
            "Epoch 675/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0548\n",
            "Epoch 676/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0548\n",
            "Epoch 677/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0548\n",
            "Epoch 678/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0547\n",
            "Epoch 679/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0548\n",
            "Epoch 680/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0548\n",
            "Epoch 681/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0804 - val_loss: 0.0551\n",
            "Epoch 682/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0550\n",
            "Epoch 683/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0550\n",
            "Epoch 684/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0547\n",
            "Epoch 685/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0547\n",
            "Epoch 686/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 687/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0547\n",
            "Epoch 688/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0546\n",
            "Epoch 689/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0546\n",
            "Epoch 690/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0546\n",
            "Epoch 691/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0546\n",
            "Epoch 692/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 693/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0803 - val_loss: 0.0547\n",
            "Epoch 694/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 695/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 696/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0805 - val_loss: 0.0546\n",
            "Epoch 697/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0546\n",
            "Epoch 698/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0544\n",
            "Epoch 699/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 700/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0544\n",
            "Epoch 701/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 702/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 703/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 704/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0549\n",
            "Epoch 705/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0804 - val_loss: 0.0550\n",
            "Epoch 706/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0804 - val_loss: 0.0551\n",
            "Epoch 707/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0547\n",
            "Epoch 708/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0804 - val_loss: 0.0548\n",
            "Epoch 709/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 710/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0806 - val_loss: 0.0544\n",
            "Epoch 711/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0547\n",
            "Epoch 712/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0547\n",
            "Epoch 713/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 714/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0546\n",
            "Epoch 715/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 716/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0548\n",
            "Epoch 717/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0547\n",
            "Epoch 718/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0547\n",
            "Epoch 719/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0804 - val_loss: 0.0547\n",
            "Epoch 720/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0544\n",
            "Epoch 721/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 722/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 723/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0545\n",
            "Epoch 724/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 725/1000\n",
            "9/9 [==============================] - 0s 5ms/step - loss: 0.0803 - val_loss: 0.0545\n",
            "Epoch 726/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 727/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 728/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 729/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0544\n",
            "Epoch 730/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0545\n",
            "Epoch 731/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0547\n",
            "Epoch 732/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0547\n",
            "Epoch 733/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0548\n",
            "Epoch 734/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0549\n",
            "Epoch 735/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0547\n",
            "Epoch 736/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0805 - val_loss: 0.0546\n",
            "Epoch 737/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0547\n",
            "Epoch 738/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0547\n",
            "Epoch 739/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0548\n",
            "Epoch 740/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 741/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0546\n",
            "Epoch 742/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0547\n",
            "Epoch 743/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 744/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 745/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 746/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 747/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0547\n",
            "Epoch 748/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0547\n",
            "Epoch 749/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 750/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 751/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0546\n",
            "Epoch 752/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0545\n",
            "Epoch 753/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 754/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0548\n",
            "Epoch 755/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0548\n",
            "Epoch 756/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0547\n",
            "Epoch 757/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0546\n",
            "Epoch 758/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0547\n",
            "Epoch 759/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 760/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0546\n",
            "Epoch 761/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0803 - val_loss: 0.0546\n",
            "Epoch 762/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 763/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0548\n",
            "Epoch 764/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0549\n",
            "Epoch 765/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0804 - val_loss: 0.0547\n",
            "Epoch 766/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 767/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0548\n",
            "Epoch 768/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 769/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0547\n",
            "Epoch 770/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0806 - val_loss: 0.0549\n",
            "Epoch 771/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0548\n",
            "Epoch 772/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0547\n",
            "Epoch 773/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0545\n",
            "Epoch 774/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0548\n",
            "Epoch 775/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0548\n",
            "Epoch 776/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0546\n",
            "Epoch 777/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0545\n",
            "Epoch 778/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0544\n",
            "Epoch 779/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 780/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0544\n",
            "Epoch 781/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0545\n",
            "Epoch 782/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0547\n",
            "Epoch 783/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0548\n",
            "Epoch 784/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0804 - val_loss: 0.0549\n",
            "Epoch 785/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0804 - val_loss: 0.0547\n",
            "Epoch 786/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0547\n",
            "Epoch 787/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0548\n",
            "Epoch 788/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0547\n",
            "Epoch 789/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0548\n",
            "Epoch 790/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0547\n",
            "Epoch 791/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 792/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0547\n",
            "Epoch 793/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0805 - val_loss: 0.0545\n",
            "Epoch 794/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 795/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0547\n",
            "Epoch 796/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0547\n",
            "Epoch 797/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0547\n",
            "Epoch 798/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0548\n",
            "Epoch 799/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0550\n",
            "Epoch 800/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0547\n",
            "Epoch 801/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 802/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 803/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0546\n",
            "Epoch 804/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0544\n",
            "Epoch 805/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0544\n",
            "Epoch 806/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 807/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 808/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 809/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 810/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 811/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 812/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0547\n",
            "Epoch 813/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0548\n",
            "Epoch 814/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0548\n",
            "Epoch 815/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 816/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 817/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0545\n",
            "Epoch 818/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 819/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0546\n",
            "Epoch 820/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0548\n",
            "Epoch 821/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0547\n",
            "Epoch 822/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 823/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 824/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 825/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0548\n",
            "Epoch 826/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 827/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 828/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0547\n",
            "Epoch 829/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0548\n",
            "Epoch 830/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 831/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0547\n",
            "Epoch 832/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 833/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0550\n",
            "Epoch 834/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0548\n",
            "Epoch 835/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0548\n",
            "Epoch 836/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 837/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 838/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 839/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0546\n",
            "Epoch 840/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 841/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 842/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 843/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 844/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 845/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0547\n",
            "Epoch 846/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0547\n",
            "Epoch 847/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0549\n",
            "Epoch 848/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0549\n",
            "Epoch 849/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0548\n",
            "Epoch 850/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 851/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0547\n",
            "Epoch 852/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0546\n",
            "Epoch 853/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 854/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 855/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0803 - val_loss: 0.0547\n",
            "Epoch 856/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0549\n",
            "Epoch 857/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0804 - val_loss: 0.0548\n",
            "Epoch 858/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0548\n",
            "Epoch 859/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 860/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 861/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0545\n",
            "Epoch 862/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0803 - val_loss: 0.0544\n",
            "Epoch 863/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0545\n",
            "Epoch 864/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0547\n",
            "Epoch 865/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0548\n",
            "Epoch 866/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0549\n",
            "Epoch 867/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0804 - val_loss: 0.0547\n",
            "Epoch 868/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 869/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0547\n",
            "Epoch 870/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0547\n",
            "Epoch 871/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0548\n",
            "Epoch 872/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0547\n",
            "Epoch 873/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 874/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0546\n",
            "Epoch 875/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0547\n",
            "Epoch 876/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0546\n",
            "Epoch 877/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 878/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 879/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0804 - val_loss: 0.0547\n",
            "Epoch 880/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0548\n",
            "Epoch 881/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0548\n",
            "Epoch 882/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 883/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 884/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0547\n",
            "Epoch 885/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0547\n",
            "Epoch 886/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 887/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0546\n",
            "Epoch 888/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 889/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 890/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0548\n",
            "Epoch 891/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0549\n",
            "Epoch 892/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0549\n",
            "Epoch 893/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0548\n",
            "Epoch 894/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0546\n",
            "Epoch 895/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 896/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0545\n",
            "Epoch 897/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0546\n",
            "Epoch 898/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0547\n",
            "Epoch 899/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0548\n",
            "Epoch 900/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0549\n",
            "Epoch 901/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0550\n",
            "Epoch 902/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0550\n",
            "Epoch 903/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0548\n",
            "Epoch 904/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0547\n",
            "Epoch 905/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0547\n",
            "Epoch 906/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0803 - val_loss: 0.0547\n",
            "Epoch 907/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0549\n",
            "Epoch 908/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0803 - val_loss: 0.0547\n",
            "Epoch 909/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0548\n",
            "Epoch 910/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0549\n",
            "Epoch 911/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0549\n",
            "Epoch 912/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0806 - val_loss: 0.0546\n",
            "Epoch 913/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0548\n",
            "Epoch 914/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0549\n",
            "Epoch 915/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0548\n",
            "Epoch 916/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0547\n",
            "Epoch 917/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0546\n",
            "Epoch 918/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0546\n",
            "Epoch 919/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0546\n",
            "Epoch 920/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0546\n",
            "Epoch 921/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0548\n",
            "Epoch 922/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0804 - val_loss: 0.0548\n",
            "Epoch 923/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0546\n",
            "Epoch 924/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 925/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0547\n",
            "Epoch 926/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0548\n",
            "Epoch 927/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0550\n",
            "Epoch 928/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0548\n",
            "Epoch 929/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0547\n",
            "Epoch 930/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0547\n",
            "Epoch 931/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0547\n",
            "Epoch 932/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 933/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0803 - val_loss: 0.0546\n",
            "Epoch 934/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0804 - val_loss: 0.0547\n",
            "Epoch 935/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0805 - val_loss: 0.0548\n",
            "Epoch 936/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0803 - val_loss: 0.0547\n",
            "Epoch 937/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0546\n",
            "Epoch 938/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 939/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0803 - val_loss: 0.0545\n",
            "Epoch 940/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 941/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 942/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0546\n",
            "Epoch 943/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0547\n",
            "Epoch 944/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0547\n",
            "Epoch 945/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0547\n",
            "Epoch 946/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0548\n",
            "Epoch 947/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0548\n",
            "Epoch 948/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0546\n",
            "Epoch 949/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0548\n",
            "Epoch 950/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0549\n",
            "Epoch 951/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0803 - val_loss: 0.0548\n",
            "Epoch 952/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0805 - val_loss: 0.0545\n",
            "Epoch 953/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0548\n",
            "Epoch 954/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0548\n",
            "Epoch 955/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0549\n",
            "Epoch 956/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0550\n",
            "Epoch 957/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0547\n",
            "Epoch 958/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0547\n",
            "Epoch 959/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 960/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0547\n",
            "Epoch 961/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0549\n",
            "Epoch 962/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0550\n",
            "Epoch 963/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0547\n",
            "Epoch 964/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0804 - val_loss: 0.0547\n",
            "Epoch 965/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0547\n",
            "Epoch 966/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0548\n",
            "Epoch 967/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0546\n",
            "Epoch 968/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0545\n",
            "Epoch 969/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0546\n",
            "Epoch 970/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0546\n",
            "Epoch 971/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 972/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0547\n",
            "Epoch 973/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0547\n",
            "Epoch 974/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0549\n",
            "Epoch 975/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0549\n",
            "Epoch 976/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0548\n",
            "Epoch 977/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0548\n",
            "Epoch 978/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0547\n",
            "Epoch 979/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0547\n",
            "Epoch 980/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0547\n",
            "Epoch 981/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0549\n",
            "Epoch 982/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0548\n",
            "Epoch 983/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0803 - val_loss: 0.0547\n",
            "Epoch 984/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0548\n",
            "Epoch 985/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0548\n",
            "Epoch 986/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 987/1000\n",
            "9/9 [==============================] - 0s 5ms/step - loss: 0.0804 - val_loss: 0.0549\n",
            "Epoch 988/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0549\n",
            "Epoch 989/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0548\n",
            "Epoch 990/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0547\n",
            "Epoch 991/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0549\n",
            "Epoch 992/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0803 - val_loss: 0.0548\n",
            "Epoch 993/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0547\n",
            "Epoch 994/1000\n",
            "9/9 [==============================] - 0s 4ms/step - loss: 0.0804 - val_loss: 0.0546\n",
            "Epoch 995/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0547\n",
            "Epoch 996/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0547\n",
            "Epoch 997/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0547\n",
            "Epoch 998/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0548\n",
            "Epoch 999/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0803 - val_loss: 0.0548\n",
            "Epoch 1000/1000\n",
            "9/9 [==============================] - 0s 3ms/step - loss: 0.0804 - val_loss: 0.0548\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zwE413j8bCIr",
        "outputId": "db142331-41cb-408f-da29-56d658511216",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "lstm_history_dataframe = pd.DataFrame(lstm_model_history.history)\n",
        "lstm_history_dataframe['epoch'] = lstm_model_history.epoch\n",
        "lstm_history_dataframe.sort_values(by='val_loss', ascending=True)"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>loss</th>\n",
              "      <th>val_loss</th>\n",
              "      <th>epoch</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>0.080961</td>\n",
              "      <td>0.054192</td>\n",
              "      <td>78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>0.080617</td>\n",
              "      <td>0.054216</td>\n",
              "      <td>57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>0.080516</td>\n",
              "      <td>0.054277</td>\n",
              "      <td>87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>210</th>\n",
              "      <td>0.080444</td>\n",
              "      <td>0.054277</td>\n",
              "      <td>210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>0.080698</td>\n",
              "      <td>0.054279</td>\n",
              "      <td>63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.099154</td>\n",
              "      <td>0.062077</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.100930</td>\n",
              "      <td>0.063094</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.102957</td>\n",
              "      <td>0.064289</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.105391</td>\n",
              "      <td>0.065582</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.108446</td>\n",
              "      <td>0.067406</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         loss  val_loss  epoch\n",
              "78   0.080961  0.054192     78\n",
              "57   0.080617  0.054216     57\n",
              "87   0.080516  0.054277     87\n",
              "210  0.080444  0.054277    210\n",
              "63   0.080698  0.054279     63\n",
              "..        ...       ...    ...\n",
              "4    0.099154  0.062077      4\n",
              "3    0.100930  0.063094      3\n",
              "2    0.102957  0.064289      2\n",
              "1    0.105391  0.065582      1\n",
              "0    0.108446  0.067406      0\n",
              "\n",
              "[1000 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qjQM8Yvqdjrx",
        "outputId": "8d6199fa-4296-4ccd-b587-4fef6d76e041",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "plot_loss(lstm_model_history)"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wSYnJgj964Wk"
      },
      "source": [
        "# **Kesimpulan**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s4v7qpOP7Vq5"
      },
      "source": [
        "Berdasarkan perhitungan diatas didapatkan hasil:\n",
        "\n",
        "```\n",
        "Baseline Model: Val_loss 0.069397 Epoch 732\n",
        "Deeper Model: Val_loss 0.053958 Epoch 420\n",
        "Wider Model: Val_loss 0.054137 Epoch 19\n",
        "LSTM: Val_loss 0.054192 Epoch 78\n",
        "```\n",
        "melihat dari hasil val_loss yang ada maka dapat di simpulkan **Deeper model** mendapatkan hasil yang **terbaik** meskipun perbedaannya hanya sedikit tetapi karena val_loss semakin kecil semakin baik jadi kita ambil yang paling kecil.\n"
      ]
    }
  ]
}