Predicting__Diabetes.ipynb

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  "metadata": {
    "colab": {
      "name": "1479_Predicting _Diabetes.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "myXv2LUMn1s4"
      },
      "source": [
        "import csv\n",
        "import sklearn\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-aYvHpN-oXCx",
        "outputId": "195396cc-764b-47e3-cf47-0e55e4064e14"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mI0o_BQ-pHF0",
        "outputId": "bdb04434-30eb-4fad-8d13-55d187ae6676"
      },
      "source": [
        "%cd /content/drive/MyDrive/Colab Notebooks/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/Colab Notebooks\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pn3SdvlPpqas",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6e4622e5-e72b-4ff0-ab8d-b512fb485261"
      },
      "source": [
        "dataset = pd.read_csv('data.tsv', sep='\\t')\n",
        "# It consists of 768 observation units and 9 variables.\n",
        "print(np.array(dataset.shape))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[768   9]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "Ay-VsJZSORBj",
        "outputId": "f1e9546e-485f-4fe4-d26c-a85243c8e6d1"
      },
      "source": [
        "# The first 5 observation units of the data set were accessed.\n",
        "dataset.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\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>Preg</th>\n",
              "      <th>GLU</th>\n",
              "      <th>BP</th>\n",
              "      <th>ST</th>\n",
              "      <th>INS</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DPF</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6</td>\n",
              "      <td>148</td>\n",
              "      <td>72</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>33.6</td>\n",
              "      <td>0.627</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>85</td>\n",
              "      <td>66</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>26.6</td>\n",
              "      <td>0.351</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8</td>\n",
              "      <td>183</td>\n",
              "      <td>64</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>23.3</td>\n",
              "      <td>0.672</td>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>89</td>\n",
              "      <td>66</td>\n",
              "      <td>23</td>\n",
              "      <td>94</td>\n",
              "      <td>28.1</td>\n",
              "      <td>0.167</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>137</td>\n",
              "      <td>40</td>\n",
              "      <td>35</td>\n",
              "      <td>168</td>\n",
              "      <td>43.1</td>\n",
              "      <td>2.288</td>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "   Preg  GLU  BP  ST  INS   BMI    DPF  Age  Outcome\n",
              "0     6  148  72  35    0  33.6  0.627   50        1\n",
              "1     1   85  66  29    0  26.6  0.351   31        0\n",
              "2     8  183  64   0    0  23.3  0.672   32        1\n",
              "3     1   89  66  23   94  28.1  0.167   21        0\n",
              "4     0  137  40  35  168  43.1  2.288   33        1"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1mW9Q1W8m6YT",
        "outputId": "b882028b-5196-4da6-cec9-7bfe1ca3581a"
      },
      "source": [
        "#Feature information\n",
        "dataset.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 768 entries, 0 to 767\n",
            "Data columns (total 9 columns):\n",
            " #   Column   Non-Null Count  Dtype  \n",
            "---  ------   --------------  -----  \n",
            " 0   Preg     768 non-null    int64  \n",
            " 1   GLU      768 non-null    int64  \n",
            " 2   BP       768 non-null    int64  \n",
            " 3   ST       768 non-null    int64  \n",
            " 4   INS      768 non-null    int64  \n",
            " 5   BMI      768 non-null    float64\n",
            " 6   DPF      768 non-null    float64\n",
            " 7   Age      768 non-null    int64  \n",
            " 8   Outcome  768 non-null    int64  \n",
            "dtypes: float64(2), int64(7)\n",
            "memory usage: 54.1 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GgPnc5oJV8Ew",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "00f014c2-a4ea-4f71-8e6e-ec7f88c86302"
      },
      "source": [
        "print(np.array(dataset))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[  6.    148.     72.    ...   0.627  50.      1.   ]\n",
            " [  1.     85.     66.    ...   0.351  31.      0.   ]\n",
            " [  8.    183.     64.    ...   0.672  32.      1.   ]\n",
            " ...\n",
            " [  5.    121.     72.    ...   0.245  30.      0.   ]\n",
            " [  1.    126.     60.    ...   0.349  47.      1.   ]\n",
            " [  1.     93.     70.    ...   0.315  23.      0.   ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tneQGK-pV_te"
      },
      "source": [
        "# drop columns number 9\n",
        "train_data_x = dataset.drop(columns = 'Outcome', axis = 1)\n",
        "train_data_y = dataset['Outcome']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EX5s5DG-WFA-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7ab83aa2-7acd-4858-d3d5-43f8f0b71c66"
      },
      "source": [
        "# split into 80% for train and 20% for test\n",
        "# random number generator 10\n",
        "train_Data, test_Data, train_Out, test_Out = train_test_split(train_data_x,train_data_y,test_size=0.2, random_state=10)\n",
        "print(train_Out)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "120    1\n",
            "172    0\n",
            "307    0\n",
            "7      0\n",
            "448    1\n",
            "      ..\n",
            "369    1\n",
            "320    0\n",
            "527    0\n",
            "125    1\n",
            "265    0\n",
            "Name: Outcome, Length: 614, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "geJ0YNKjKBT0",
        "outputId": "fe8d1923-6933-4986-a6d6-71f772724e68"
      },
      "source": [
        "# create model\n",
        "# using model keras\n",
        "# using activations functions sigmoid\n",
        "data_model = keras.Sequential(\n",
        "    [\n",
        "      #  keras.layers.Flatten()\n",
        "      keras.layers.Dense(units=100, input_dim=8, name='hidden_layer_1'),\n",
        "      keras.layers.Dense(units=90, name='hidden_layer_2'),\n",
        "      keras.layers.Dense(units=80, name='hidden_layer_3'),\n",
        "      keras.layers.Dense(units=70, name='hidden_layer_4'),\n",
        "      keras.layers.Dense(units=60, name='hidden_layer_5'),\n",
        "      keras.layers.Dense(units=50, name='hidden_layer_6'),\n",
        "      keras.layers.Dense(units=40, name='hidden_layer_7'),\n",
        "      keras.layers.Dense(units=30, name='hidden_layer_8'),\n",
        "      keras.layers.Dense(units=1, activation=tf.keras.activations.sigmoid, name='output_layer')\n",
        "    ]\n",
        ")\n",
        "\n",
        "print(data_model.summary())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "hidden_layer_1 (Dense)       (None, 100)               900       \n",
            "_________________________________________________________________\n",
            "hidden_layer_2 (Dense)       (None, 90)                9090      \n",
            "_________________________________________________________________\n",
            "hidden_layer_3 (Dense)       (None, 80)                7280      \n",
            "_________________________________________________________________\n",
            "hidden_layer_4 (Dense)       (None, 70)                5670      \n",
            "_________________________________________________________________\n",
            "hidden_layer_5 (Dense)       (None, 60)                4260      \n",
            "_________________________________________________________________\n",
            "hidden_layer_6 (Dense)       (None, 50)                3050      \n",
            "_________________________________________________________________\n",
            "hidden_layer_7 (Dense)       (None, 40)                2040      \n",
            "_________________________________________________________________\n",
            "hidden_layer_8 (Dense)       (None, 30)                1230      \n",
            "_________________________________________________________________\n",
            "output_layer (Dense)         (None, 1)                 31        \n",
            "=================================================================\n",
            "Total params: 33,551\n",
            "Trainable params: 33,551\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9s5imtwgqQ0z"
      },
      "source": [
        "# Compile model\n",
        "# using opimizer Adam\n",
        "# using loss binary_crossentropy\n",
        "data_model.compile(optimizer=tf.keras.optimizers.Adam(), loss=tf.keras.losses.binary_crossentropy, metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XPrlW9rePrN0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "37375363-9cf8-4174-9b18-7b5e07bdc943"
      },
      "source": [
        "data_model.fit(train_Data, train_Out, validation_data=(test_Data, test_Out), epochs=1000, verbose=1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/1000\n",
            "20/20 [==============================] - 1s 14ms/step - loss: 10.4838 - accuracy: 0.5423 - val_loss: 0.8015 - val_accuracy: 0.6623\n",
            "Epoch 2/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.9461 - accuracy: 0.5945 - val_loss: 0.7379 - val_accuracy: 0.4805\n",
            "Epoch 3/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.7388 - accuracy: 0.6205 - val_loss: 0.7893 - val_accuracy: 0.6753\n",
            "Epoch 4/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.8839 - accuracy: 0.5831 - val_loss: 0.6762 - val_accuracy: 0.6558\n",
            "Epoch 5/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.7033 - accuracy: 0.6531 - val_loss: 0.6457 - val_accuracy: 0.6494\n",
            "Epoch 6/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.6712 - accuracy: 0.6287 - val_loss: 0.8205 - val_accuracy: 0.6753\n",
            "Epoch 7/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.7492 - accuracy: 0.6336 - val_loss: 0.8197 - val_accuracy: 0.6234\n",
            "Epoch 8/1000\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.6804 - accuracy: 0.6450 - val_loss: 0.6480 - val_accuracy: 0.6753\n",
            "Epoch 9/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.6695 - accuracy: 0.6498 - val_loss: 0.6675 - val_accuracy: 0.6494\n",
            "Epoch 10/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.6652 - accuracy: 0.6482 - val_loss: 0.8567 - val_accuracy: 0.6364\n",
            "Epoch 11/1000\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.7621 - accuracy: 0.6238 - val_loss: 0.6129 - val_accuracy: 0.6688\n",
            "Epoch 12/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.6320 - accuracy: 0.6629 - val_loss: 0.6241 - val_accuracy: 0.6558\n",
            "Epoch 13/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.6289 - accuracy: 0.6645 - val_loss: 0.6457 - val_accuracy: 0.6818\n",
            "Epoch 14/1000\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.6468 - accuracy: 0.6612 - val_loss: 0.6596 - val_accuracy: 0.6558\n",
            "Epoch 15/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.6645 - accuracy: 0.6612 - val_loss: 0.6084 - val_accuracy: 0.7013\n",
            "Epoch 16/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.6330 - accuracy: 0.6808 - val_loss: 0.6407 - val_accuracy: 0.7013\n",
            "Epoch 17/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.6156 - accuracy: 0.6857 - val_loss: 0.6295 - val_accuracy: 0.6494\n",
            "Epoch 18/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.6017 - accuracy: 0.6938 - val_loss: 0.6320 - val_accuracy: 0.6688\n",
            "Epoch 19/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.6131 - accuracy: 0.6678 - val_loss: 0.6340 - val_accuracy: 0.6429\n",
            "Epoch 20/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5987 - accuracy: 0.6840 - val_loss: 0.6098 - val_accuracy: 0.6688\n",
            "Epoch 21/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5936 - accuracy: 0.6889 - val_loss: 0.6332 - val_accuracy: 0.6883\n",
            "Epoch 22/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5889 - accuracy: 0.6938 - val_loss: 0.6072 - val_accuracy: 0.6688\n",
            "Epoch 23/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.6044 - accuracy: 0.7052 - val_loss: 0.6149 - val_accuracy: 0.6818\n",
            "Epoch 24/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.6078 - accuracy: 0.6759 - val_loss: 0.6126 - val_accuracy: 0.6753\n",
            "Epoch 25/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.6109 - accuracy: 0.6922 - val_loss: 0.5991 - val_accuracy: 0.6818\n",
            "Epoch 26/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5916 - accuracy: 0.7003 - val_loss: 0.6267 - val_accuracy: 0.6558\n",
            "Epoch 27/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5753 - accuracy: 0.7085 - val_loss: 0.5940 - val_accuracy: 0.6948\n",
            "Epoch 28/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5794 - accuracy: 0.7215 - val_loss: 0.5934 - val_accuracy: 0.6818\n",
            "Epoch 29/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5735 - accuracy: 0.7101 - val_loss: 0.6029 - val_accuracy: 0.6558\n",
            "Epoch 30/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5839 - accuracy: 0.7231 - val_loss: 0.6021 - val_accuracy: 0.6688\n",
            "Epoch 31/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5680 - accuracy: 0.7068 - val_loss: 0.6010 - val_accuracy: 0.6688\n",
            "Epoch 32/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5771 - accuracy: 0.7003 - val_loss: 0.6229 - val_accuracy: 0.6623\n",
            "Epoch 33/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5881 - accuracy: 0.7036 - val_loss: 0.6025 - val_accuracy: 0.6948\n",
            "Epoch 34/1000\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.5727 - accuracy: 0.7199 - val_loss: 0.5806 - val_accuracy: 0.7078\n",
            "Epoch 35/1000\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.5614 - accuracy: 0.7085 - val_loss: 0.6032 - val_accuracy: 0.7013\n",
            "Epoch 36/1000\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.5687 - accuracy: 0.7068 - val_loss: 0.6155 - val_accuracy: 0.6623\n",
            "Epoch 37/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5590 - accuracy: 0.7248 - val_loss: 0.6371 - val_accuracy: 0.6558\n",
            "Epoch 38/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5755 - accuracy: 0.7150 - val_loss: 0.5824 - val_accuracy: 0.7143\n",
            "Epoch 39/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5670 - accuracy: 0.7101 - val_loss: 0.5913 - val_accuracy: 0.6948\n",
            "Epoch 40/1000\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.5648 - accuracy: 0.7199 - val_loss: 0.6132 - val_accuracy: 0.6883\n",
            "Epoch 41/1000\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.5548 - accuracy: 0.7166 - val_loss: 0.5745 - val_accuracy: 0.6948\n",
            "Epoch 42/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5564 - accuracy: 0.7492 - val_loss: 0.5860 - val_accuracy: 0.7078\n",
            "Epoch 43/1000\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.5664 - accuracy: 0.7166 - val_loss: 0.6310 - val_accuracy: 0.6558\n",
            "Epoch 44/1000\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.6060 - accuracy: 0.7215 - val_loss: 0.6125 - val_accuracy: 0.7078\n",
            "Epoch 45/1000\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.5535 - accuracy: 0.7248 - val_loss: 0.5988 - val_accuracy: 0.6818\n",
            "Epoch 46/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5678 - accuracy: 0.7166 - val_loss: 0.5810 - val_accuracy: 0.7013\n",
            "Epoch 47/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5711 - accuracy: 0.7166 - val_loss: 0.5915 - val_accuracy: 0.6948\n",
            "Epoch 48/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.6146 - accuracy: 0.6808 - val_loss: 0.5858 - val_accuracy: 0.6818\n",
            "Epoch 49/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5661 - accuracy: 0.7199 - val_loss: 0.5803 - val_accuracy: 0.6883\n",
            "Epoch 50/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5735 - accuracy: 0.7362 - val_loss: 0.6094 - val_accuracy: 0.6883\n",
            "Epoch 51/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5669 - accuracy: 0.7134 - val_loss: 0.5936 - val_accuracy: 0.7078\n",
            "Epoch 52/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5593 - accuracy: 0.7199 - val_loss: 0.5819 - val_accuracy: 0.7013\n",
            "Epoch 53/1000\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.5474 - accuracy: 0.7199 - val_loss: 0.5628 - val_accuracy: 0.7078\n",
            "Epoch 54/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5325 - accuracy: 0.7345 - val_loss: 0.5698 - val_accuracy: 0.7143\n",
            "Epoch 55/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5375 - accuracy: 0.7313 - val_loss: 0.5706 - val_accuracy: 0.7078\n",
            "Epoch 56/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5479 - accuracy: 0.7280 - val_loss: 0.5832 - val_accuracy: 0.7208\n",
            "Epoch 57/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5549 - accuracy: 0.7280 - val_loss: 0.6012 - val_accuracy: 0.6753\n",
            "Epoch 58/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5422 - accuracy: 0.7264 - val_loss: 0.5713 - val_accuracy: 0.7208\n",
            "Epoch 59/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5296 - accuracy: 0.7557 - val_loss: 0.5700 - val_accuracy: 0.6948\n",
            "Epoch 60/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5263 - accuracy: 0.7427 - val_loss: 0.5775 - val_accuracy: 0.7078\n",
            "Epoch 61/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5438 - accuracy: 0.7296 - val_loss: 0.5734 - val_accuracy: 0.7143\n",
            "Epoch 62/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5691 - accuracy: 0.7134 - val_loss: 0.5554 - val_accuracy: 0.7403\n",
            "Epoch 63/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5410 - accuracy: 0.7410 - val_loss: 0.5811 - val_accuracy: 0.7078\n",
            "Epoch 64/1000\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.5198 - accuracy: 0.7573 - val_loss: 0.5584 - val_accuracy: 0.7403\n",
            "Epoch 65/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5602 - accuracy: 0.7378 - val_loss: 0.5820 - val_accuracy: 0.6883\n",
            "Epoch 66/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5594 - accuracy: 0.7329 - val_loss: 0.6193 - val_accuracy: 0.6623\n",
            "Epoch 67/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5607 - accuracy: 0.7362 - val_loss: 0.5741 - val_accuracy: 0.6818\n",
            "Epoch 68/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5671 - accuracy: 0.7117 - val_loss: 0.5717 - val_accuracy: 0.7078\n",
            "Epoch 69/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5532 - accuracy: 0.7101 - val_loss: 0.5723 - val_accuracy: 0.7143\n",
            "Epoch 70/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5641 - accuracy: 0.7166 - val_loss: 0.6219 - val_accuracy: 0.6688\n",
            "Epoch 71/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5316 - accuracy: 0.7410 - val_loss: 0.5621 - val_accuracy: 0.6883\n",
            "Epoch 72/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.5406 - accuracy: 0.7362 - val_loss: 0.5741 - val_accuracy: 0.7273\n",
            "Epoch 73/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5347 - accuracy: 0.7378 - val_loss: 0.5595 - val_accuracy: 0.7143\n",
            "Epoch 74/1000\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.5336 - accuracy: 0.7508 - val_loss: 0.5924 - val_accuracy: 0.6753\n",
            "Epoch 75/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5362 - accuracy: 0.7459 - val_loss: 0.5689 - val_accuracy: 0.7403\n",
            "Epoch 76/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5456 - accuracy: 0.7329 - val_loss: 0.5981 - val_accuracy: 0.6883\n",
            "Epoch 77/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5203 - accuracy: 0.7410 - val_loss: 0.5910 - val_accuracy: 0.7013\n",
            "Epoch 78/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5331 - accuracy: 0.7492 - val_loss: 0.5456 - val_accuracy: 0.7208\n",
            "Epoch 79/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5216 - accuracy: 0.7459 - val_loss: 0.5459 - val_accuracy: 0.7208\n",
            "Epoch 80/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5391 - accuracy: 0.7394 - val_loss: 0.6268 - val_accuracy: 0.6558\n",
            "Epoch 81/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5301 - accuracy: 0.7410 - val_loss: 0.5399 - val_accuracy: 0.6883\n",
            "Epoch 82/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5242 - accuracy: 0.7524 - val_loss: 0.6152 - val_accuracy: 0.6688\n",
            "Epoch 83/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5361 - accuracy: 0.7362 - val_loss: 0.5778 - val_accuracy: 0.6948\n",
            "Epoch 84/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5068 - accuracy: 0.7492 - val_loss: 0.6926 - val_accuracy: 0.6364\n",
            "Epoch 85/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5199 - accuracy: 0.7410 - val_loss: 0.5400 - val_accuracy: 0.6948\n",
            "Epoch 86/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5227 - accuracy: 0.7557 - val_loss: 0.5455 - val_accuracy: 0.7078\n",
            "Epoch 87/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5138 - accuracy: 0.7769 - val_loss: 0.6339 - val_accuracy: 0.6429\n",
            "Epoch 88/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5426 - accuracy: 0.7280 - val_loss: 0.5769 - val_accuracy: 0.7013\n",
            "Epoch 89/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5397 - accuracy: 0.7345 - val_loss: 0.5674 - val_accuracy: 0.7468\n",
            "Epoch 90/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5691 - accuracy: 0.7085 - val_loss: 0.5637 - val_accuracy: 0.7143\n",
            "Epoch 91/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5294 - accuracy: 0.7508 - val_loss: 0.6253 - val_accuracy: 0.6948\n",
            "Epoch 92/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5314 - accuracy: 0.7573 - val_loss: 0.5750 - val_accuracy: 0.7078\n",
            "Epoch 93/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5117 - accuracy: 0.7541 - val_loss: 0.5841 - val_accuracy: 0.6753\n",
            "Epoch 94/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5112 - accuracy: 0.7655 - val_loss: 0.7102 - val_accuracy: 0.6234\n",
            "Epoch 95/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5517 - accuracy: 0.7231 - val_loss: 0.6736 - val_accuracy: 0.6623\n",
            "Epoch 96/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5832 - accuracy: 0.7166 - val_loss: 0.5995 - val_accuracy: 0.7078\n",
            "Epoch 97/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5427 - accuracy: 0.7573 - val_loss: 0.6544 - val_accuracy: 0.6948\n",
            "Epoch 98/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.6166 - accuracy: 0.7020 - val_loss: 0.6527 - val_accuracy: 0.6883\n",
            "Epoch 99/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5602 - accuracy: 0.7166 - val_loss: 0.5673 - val_accuracy: 0.6948\n",
            "Epoch 100/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5247 - accuracy: 0.7524 - val_loss: 0.5620 - val_accuracy: 0.7143\n",
            "Epoch 101/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5175 - accuracy: 0.7524 - val_loss: 0.5352 - val_accuracy: 0.7273\n",
            "Epoch 102/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5091 - accuracy: 0.7541 - val_loss: 0.5353 - val_accuracy: 0.7273\n",
            "Epoch 103/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5264 - accuracy: 0.7345 - val_loss: 0.5771 - val_accuracy: 0.7013\n",
            "Epoch 104/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5129 - accuracy: 0.7541 - val_loss: 0.5502 - val_accuracy: 0.7143\n",
            "Epoch 105/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5180 - accuracy: 0.7541 - val_loss: 0.5608 - val_accuracy: 0.7143\n",
            "Epoch 106/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5109 - accuracy: 0.7443 - val_loss: 0.5628 - val_accuracy: 0.7013\n",
            "Epoch 107/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5006 - accuracy: 0.7655 - val_loss: 0.5532 - val_accuracy: 0.7013\n",
            "Epoch 108/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5010 - accuracy: 0.7687 - val_loss: 0.5565 - val_accuracy: 0.7143\n",
            "Epoch 109/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5117 - accuracy: 0.7443 - val_loss: 0.5497 - val_accuracy: 0.6948\n",
            "Epoch 110/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5064 - accuracy: 0.7524 - val_loss: 0.5254 - val_accuracy: 0.7013\n",
            "Epoch 111/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5075 - accuracy: 0.7541 - val_loss: 0.6571 - val_accuracy: 0.6364\n",
            "Epoch 112/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5207 - accuracy: 0.7427 - val_loss: 0.6041 - val_accuracy: 0.6753\n",
            "Epoch 113/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5278 - accuracy: 0.7427 - val_loss: 0.5390 - val_accuracy: 0.6948\n",
            "Epoch 114/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5184 - accuracy: 0.7492 - val_loss: 0.5440 - val_accuracy: 0.7468\n",
            "Epoch 115/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5187 - accuracy: 0.7443 - val_loss: 0.5452 - val_accuracy: 0.7013\n",
            "Epoch 116/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5042 - accuracy: 0.7557 - val_loss: 0.5279 - val_accuracy: 0.7143\n",
            "Epoch 117/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5080 - accuracy: 0.7622 - val_loss: 0.5310 - val_accuracy: 0.6948\n",
            "Epoch 118/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5063 - accuracy: 0.7573 - val_loss: 0.5304 - val_accuracy: 0.7143\n",
            "Epoch 119/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4958 - accuracy: 0.7687 - val_loss: 0.5212 - val_accuracy: 0.7403\n",
            "Epoch 120/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5132 - accuracy: 0.7573 - val_loss: 0.5243 - val_accuracy: 0.7078\n",
            "Epoch 121/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5019 - accuracy: 0.7736 - val_loss: 0.5654 - val_accuracy: 0.7013\n",
            "Epoch 122/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5045 - accuracy: 0.7655 - val_loss: 0.5507 - val_accuracy: 0.7143\n",
            "Epoch 123/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5133 - accuracy: 0.7362 - val_loss: 0.5659 - val_accuracy: 0.7078\n",
            "Epoch 124/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5074 - accuracy: 0.7573 - val_loss: 0.5294 - val_accuracy: 0.7078\n",
            "Epoch 125/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4994 - accuracy: 0.7720 - val_loss: 0.5282 - val_accuracy: 0.7338\n",
            "Epoch 126/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.5005 - accuracy: 0.7590 - val_loss: 0.5328 - val_accuracy: 0.7078\n",
            "Epoch 127/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5170 - accuracy: 0.7394 - val_loss: 0.5304 - val_accuracy: 0.7013\n",
            "Epoch 128/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5084 - accuracy: 0.7573 - val_loss: 0.5428 - val_accuracy: 0.7143\n",
            "Epoch 129/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5324 - accuracy: 0.7362 - val_loss: 0.5360 - val_accuracy: 0.6948\n",
            "Epoch 130/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5029 - accuracy: 0.7573 - val_loss: 0.5740 - val_accuracy: 0.7013\n",
            "Epoch 131/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4995 - accuracy: 0.7785 - val_loss: 0.5259 - val_accuracy: 0.7208\n",
            "Epoch 132/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5002 - accuracy: 0.7573 - val_loss: 0.5309 - val_accuracy: 0.7143\n",
            "Epoch 133/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5155 - accuracy: 0.7524 - val_loss: 0.5619 - val_accuracy: 0.6948\n",
            "Epoch 134/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4923 - accuracy: 0.7622 - val_loss: 0.5471 - val_accuracy: 0.7143\n",
            "Epoch 135/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5138 - accuracy: 0.7541 - val_loss: 0.5321 - val_accuracy: 0.7013\n",
            "Epoch 136/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5000 - accuracy: 0.7524 - val_loss: 0.5657 - val_accuracy: 0.7208\n",
            "Epoch 137/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4949 - accuracy: 0.7752 - val_loss: 0.5383 - val_accuracy: 0.7013\n",
            "Epoch 138/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4883 - accuracy: 0.7752 - val_loss: 0.5351 - val_accuracy: 0.7143\n",
            "Epoch 139/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5273 - accuracy: 0.7394 - val_loss: 0.5455 - val_accuracy: 0.7338\n",
            "Epoch 140/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5054 - accuracy: 0.7541 - val_loss: 0.5389 - val_accuracy: 0.7273\n",
            "Epoch 141/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4930 - accuracy: 0.7687 - val_loss: 0.5190 - val_accuracy: 0.7143\n",
            "Epoch 142/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4930 - accuracy: 0.7638 - val_loss: 0.5237 - val_accuracy: 0.7208\n",
            "Epoch 143/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.5012 - accuracy: 0.7704 - val_loss: 0.6289 - val_accuracy: 0.6688\n",
            "Epoch 144/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5103 - accuracy: 0.7557 - val_loss: 0.6558 - val_accuracy: 0.6623\n",
            "Epoch 145/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5980 - accuracy: 0.7020 - val_loss: 0.6443 - val_accuracy: 0.6364\n",
            "Epoch 146/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5124 - accuracy: 0.7622 - val_loss: 0.5794 - val_accuracy: 0.7013\n",
            "Epoch 147/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5162 - accuracy: 0.7345 - val_loss: 0.5334 - val_accuracy: 0.7143\n",
            "Epoch 148/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5112 - accuracy: 0.7590 - val_loss: 0.5427 - val_accuracy: 0.7143\n",
            "Epoch 149/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5170 - accuracy: 0.7524 - val_loss: 0.5286 - val_accuracy: 0.7078\n",
            "Epoch 150/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5089 - accuracy: 0.7541 - val_loss: 0.5218 - val_accuracy: 0.7013\n",
            "Epoch 151/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5076 - accuracy: 0.7459 - val_loss: 0.5404 - val_accuracy: 0.7273\n",
            "Epoch 152/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5097 - accuracy: 0.7508 - val_loss: 0.5388 - val_accuracy: 0.7143\n",
            "Epoch 153/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4925 - accuracy: 0.7785 - val_loss: 0.5471 - val_accuracy: 0.7273\n",
            "Epoch 154/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4915 - accuracy: 0.7671 - val_loss: 0.5117 - val_accuracy: 0.7403\n",
            "Epoch 155/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5135 - accuracy: 0.7459 - val_loss: 0.5812 - val_accuracy: 0.7013\n",
            "Epoch 156/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5004 - accuracy: 0.7541 - val_loss: 0.5287 - val_accuracy: 0.7532\n",
            "Epoch 157/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5073 - accuracy: 0.7622 - val_loss: 0.5494 - val_accuracy: 0.7143\n",
            "Epoch 158/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5229 - accuracy: 0.7573 - val_loss: 0.6018 - val_accuracy: 0.6948\n",
            "Epoch 159/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5398 - accuracy: 0.7280 - val_loss: 0.5412 - val_accuracy: 0.7143\n",
            "Epoch 160/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5042 - accuracy: 0.7655 - val_loss: 0.5195 - val_accuracy: 0.7208\n",
            "Epoch 161/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5042 - accuracy: 0.7655 - val_loss: 0.6645 - val_accuracy: 0.6364\n",
            "Epoch 162/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5270 - accuracy: 0.7427 - val_loss: 0.5971 - val_accuracy: 0.6818\n",
            "Epoch 163/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5363 - accuracy: 0.7345 - val_loss: 0.5277 - val_accuracy: 0.7013\n",
            "Epoch 164/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5259 - accuracy: 0.7590 - val_loss: 0.5664 - val_accuracy: 0.7013\n",
            "Epoch 165/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5087 - accuracy: 0.7622 - val_loss: 0.5265 - val_accuracy: 0.7273\n",
            "Epoch 166/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5115 - accuracy: 0.7362 - val_loss: 0.5283 - val_accuracy: 0.7143\n",
            "Epoch 167/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5063 - accuracy: 0.7590 - val_loss: 0.5259 - val_accuracy: 0.7273\n",
            "Epoch 168/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4999 - accuracy: 0.7671 - val_loss: 0.5311 - val_accuracy: 0.7078\n",
            "Epoch 169/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5017 - accuracy: 0.7541 - val_loss: 0.5784 - val_accuracy: 0.7143\n",
            "Epoch 170/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5162 - accuracy: 0.7443 - val_loss: 0.5656 - val_accuracy: 0.7208\n",
            "Epoch 171/1000\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.5110 - accuracy: 0.7557 - val_loss: 0.5483 - val_accuracy: 0.7208\n",
            "Epoch 172/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5063 - accuracy: 0.7606 - val_loss: 0.5429 - val_accuracy: 0.7468\n",
            "Epoch 173/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5125 - accuracy: 0.7541 - val_loss: 0.5361 - val_accuracy: 0.7208\n",
            "Epoch 174/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4950 - accuracy: 0.7736 - val_loss: 0.5198 - val_accuracy: 0.7338\n",
            "Epoch 175/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5012 - accuracy: 0.7622 - val_loss: 0.5286 - val_accuracy: 0.7208\n",
            "Epoch 176/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4867 - accuracy: 0.7671 - val_loss: 0.6114 - val_accuracy: 0.6558\n",
            "Epoch 177/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5282 - accuracy: 0.7378 - val_loss: 0.5270 - val_accuracy: 0.7013\n",
            "Epoch 178/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4846 - accuracy: 0.7704 - val_loss: 0.5663 - val_accuracy: 0.7078\n",
            "Epoch 179/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5071 - accuracy: 0.7622 - val_loss: 0.5448 - val_accuracy: 0.7208\n",
            "Epoch 180/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5178 - accuracy: 0.7541 - val_loss: 0.5351 - val_accuracy: 0.7143\n",
            "Epoch 181/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5187 - accuracy: 0.7459 - val_loss: 0.7277 - val_accuracy: 0.6234\n",
            "Epoch 182/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5429 - accuracy: 0.7280 - val_loss: 0.6498 - val_accuracy: 0.6364\n",
            "Epoch 183/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5202 - accuracy: 0.7427 - val_loss: 0.6252 - val_accuracy: 0.6494\n",
            "Epoch 184/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5340 - accuracy: 0.7443 - val_loss: 0.5293 - val_accuracy: 0.7013\n",
            "Epoch 185/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5026 - accuracy: 0.7606 - val_loss: 0.5370 - val_accuracy: 0.7208\n",
            "Epoch 186/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5385 - accuracy: 0.7427 - val_loss: 0.5819 - val_accuracy: 0.6883\n",
            "Epoch 187/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5354 - accuracy: 0.7427 - val_loss: 0.5483 - val_accuracy: 0.7143\n",
            "Epoch 188/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5063 - accuracy: 0.7557 - val_loss: 0.5403 - val_accuracy: 0.7013\n",
            "Epoch 189/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5088 - accuracy: 0.7590 - val_loss: 0.6105 - val_accuracy: 0.6883\n",
            "Epoch 190/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5012 - accuracy: 0.7687 - val_loss: 0.5451 - val_accuracy: 0.7013\n",
            "Epoch 191/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5245 - accuracy: 0.7410 - val_loss: 0.5587 - val_accuracy: 0.7013\n",
            "Epoch 192/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5115 - accuracy: 0.7557 - val_loss: 0.5342 - val_accuracy: 0.7208\n",
            "Epoch 193/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4983 - accuracy: 0.7622 - val_loss: 0.5448 - val_accuracy: 0.7143\n",
            "Epoch 194/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4990 - accuracy: 0.7459 - val_loss: 0.5504 - val_accuracy: 0.7143\n",
            "Epoch 195/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4939 - accuracy: 0.7752 - val_loss: 0.5248 - val_accuracy: 0.7208\n",
            "Epoch 196/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4863 - accuracy: 0.7671 - val_loss: 0.6301 - val_accuracy: 0.6753\n",
            "Epoch 197/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5084 - accuracy: 0.7606 - val_loss: 0.5235 - val_accuracy: 0.7013\n",
            "Epoch 198/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5055 - accuracy: 0.7541 - val_loss: 0.5803 - val_accuracy: 0.7078\n",
            "Epoch 199/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5086 - accuracy: 0.7410 - val_loss: 0.6402 - val_accuracy: 0.6494\n",
            "Epoch 200/1000\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.5417 - accuracy: 0.7410 - val_loss: 0.6320 - val_accuracy: 0.6558\n",
            "Epoch 201/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4998 - accuracy: 0.7557 - val_loss: 0.5244 - val_accuracy: 0.7143\n",
            "Epoch 202/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5118 - accuracy: 0.7508 - val_loss: 0.5247 - val_accuracy: 0.7403\n",
            "Epoch 203/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4970 - accuracy: 0.7573 - val_loss: 0.5826 - val_accuracy: 0.7013\n",
            "Epoch 204/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5135 - accuracy: 0.7394 - val_loss: 0.5412 - val_accuracy: 0.7143\n",
            "Epoch 205/1000\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.5019 - accuracy: 0.7508 - val_loss: 0.5173 - val_accuracy: 0.7143\n",
            "Epoch 206/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4946 - accuracy: 0.7638 - val_loss: 0.5248 - val_accuracy: 0.7273\n",
            "Epoch 207/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5106 - accuracy: 0.7557 - val_loss: 0.5271 - val_accuracy: 0.7662\n",
            "Epoch 208/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5329 - accuracy: 0.7362 - val_loss: 0.5517 - val_accuracy: 0.7273\n",
            "Epoch 209/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5141 - accuracy: 0.7606 - val_loss: 0.5393 - val_accuracy: 0.7208\n",
            "Epoch 210/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5026 - accuracy: 0.7622 - val_loss: 0.6434 - val_accuracy: 0.6364\n",
            "Epoch 211/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5061 - accuracy: 0.7687 - val_loss: 0.5422 - val_accuracy: 0.7143\n",
            "Epoch 212/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5005 - accuracy: 0.7638 - val_loss: 0.5220 - val_accuracy: 0.7013\n",
            "Epoch 213/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5038 - accuracy: 0.7655 - val_loss: 0.5903 - val_accuracy: 0.6688\n",
            "Epoch 214/1000\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.4983 - accuracy: 0.7557 - val_loss: 0.5148 - val_accuracy: 0.7273\n",
            "Epoch 215/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4976 - accuracy: 0.7524 - val_loss: 0.5470 - val_accuracy: 0.7208\n",
            "Epoch 216/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5090 - accuracy: 0.7492 - val_loss: 0.5204 - val_accuracy: 0.7273\n",
            "Epoch 217/1000\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.5073 - accuracy: 0.7671 - val_loss: 0.5783 - val_accuracy: 0.7013\n",
            "Epoch 218/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5110 - accuracy: 0.7362 - val_loss: 0.5239 - val_accuracy: 0.7013\n",
            "Epoch 219/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5080 - accuracy: 0.7508 - val_loss: 0.5588 - val_accuracy: 0.7273\n",
            "Epoch 220/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4878 - accuracy: 0.7801 - val_loss: 0.5227 - val_accuracy: 0.7338\n",
            "Epoch 221/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5012 - accuracy: 0.7492 - val_loss: 0.5915 - val_accuracy: 0.6883\n",
            "Epoch 222/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4911 - accuracy: 0.7557 - val_loss: 0.5143 - val_accuracy: 0.7403\n",
            "Epoch 223/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5062 - accuracy: 0.7524 - val_loss: 0.5448 - val_accuracy: 0.7078\n",
            "Epoch 224/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5040 - accuracy: 0.7524 - val_loss: 0.5325 - val_accuracy: 0.7338\n",
            "Epoch 225/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5252 - accuracy: 0.7329 - val_loss: 0.5377 - val_accuracy: 0.7273\n",
            "Epoch 226/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4929 - accuracy: 0.7671 - val_loss: 0.5555 - val_accuracy: 0.7078\n",
            "Epoch 227/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5100 - accuracy: 0.7492 - val_loss: 0.5340 - val_accuracy: 0.7143\n",
            "Epoch 228/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4891 - accuracy: 0.7720 - val_loss: 0.5159 - val_accuracy: 0.7143\n",
            "Epoch 229/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4985 - accuracy: 0.7410 - val_loss: 0.5570 - val_accuracy: 0.7273\n",
            "Epoch 230/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5621 - accuracy: 0.7296 - val_loss: 0.6193 - val_accuracy: 0.6818\n",
            "Epoch 231/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5165 - accuracy: 0.7590 - val_loss: 0.5287 - val_accuracy: 0.7143\n",
            "Epoch 232/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4913 - accuracy: 0.7622 - val_loss: 0.5185 - val_accuracy: 0.7143\n",
            "Epoch 233/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5174 - accuracy: 0.7459 - val_loss: 0.5385 - val_accuracy: 0.7078\n",
            "Epoch 234/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5102 - accuracy: 0.7524 - val_loss: 0.5270 - val_accuracy: 0.7662\n",
            "Epoch 235/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5280 - accuracy: 0.7345 - val_loss: 0.6401 - val_accuracy: 0.6623\n",
            "Epoch 236/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5088 - accuracy: 0.7704 - val_loss: 0.5207 - val_accuracy: 0.7013\n",
            "Epoch 237/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5039 - accuracy: 0.7622 - val_loss: 0.5173 - val_accuracy: 0.7013\n",
            "Epoch 238/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4991 - accuracy: 0.7606 - val_loss: 0.5111 - val_accuracy: 0.7208\n",
            "Epoch 239/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4936 - accuracy: 0.7508 - val_loss: 0.5214 - val_accuracy: 0.7143\n",
            "Epoch 240/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5100 - accuracy: 0.7638 - val_loss: 0.5129 - val_accuracy: 0.7143\n",
            "Epoch 241/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4994 - accuracy: 0.7638 - val_loss: 0.5305 - val_accuracy: 0.7078\n",
            "Epoch 242/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5079 - accuracy: 0.7459 - val_loss: 0.5225 - val_accuracy: 0.7208\n",
            "Epoch 243/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5043 - accuracy: 0.7573 - val_loss: 0.5758 - val_accuracy: 0.7013\n",
            "Epoch 244/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4964 - accuracy: 0.7476 - val_loss: 0.5437 - val_accuracy: 0.7208\n",
            "Epoch 245/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5114 - accuracy: 0.7345 - val_loss: 0.5485 - val_accuracy: 0.7143\n",
            "Epoch 246/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4916 - accuracy: 0.7687 - val_loss: 0.5186 - val_accuracy: 0.7143\n",
            "Epoch 247/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4920 - accuracy: 0.7736 - val_loss: 0.5563 - val_accuracy: 0.7143\n",
            "Epoch 248/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5080 - accuracy: 0.7443 - val_loss: 0.5293 - val_accuracy: 0.7078\n",
            "Epoch 249/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4875 - accuracy: 0.7687 - val_loss: 0.5394 - val_accuracy: 0.7143\n",
            "Epoch 250/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4887 - accuracy: 0.7687 - val_loss: 0.5318 - val_accuracy: 0.7208\n",
            "Epoch 251/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4918 - accuracy: 0.7736 - val_loss: 0.5054 - val_accuracy: 0.7403\n",
            "Epoch 252/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4938 - accuracy: 0.7704 - val_loss: 0.5289 - val_accuracy: 0.7273\n",
            "Epoch 253/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5002 - accuracy: 0.7557 - val_loss: 0.5320 - val_accuracy: 0.7338\n",
            "Epoch 254/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4978 - accuracy: 0.7638 - val_loss: 0.5255 - val_accuracy: 0.7273\n",
            "Epoch 255/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4917 - accuracy: 0.7541 - val_loss: 0.5507 - val_accuracy: 0.7208\n",
            "Epoch 256/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5072 - accuracy: 0.7590 - val_loss: 0.5361 - val_accuracy: 0.7208\n",
            "Epoch 257/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5114 - accuracy: 0.7492 - val_loss: 0.5729 - val_accuracy: 0.7078\n",
            "Epoch 258/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5024 - accuracy: 0.7622 - val_loss: 0.5209 - val_accuracy: 0.7143\n",
            "Epoch 259/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4870 - accuracy: 0.7671 - val_loss: 0.5034 - val_accuracy: 0.7338\n",
            "Epoch 260/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4931 - accuracy: 0.7671 - val_loss: 0.5207 - val_accuracy: 0.7403\n",
            "Epoch 261/1000\n",
            "20/20 [==============================] - 0s 3ms/step - loss: 0.5133 - accuracy: 0.7459 - val_loss: 0.5166 - val_accuracy: 0.7273\n",
            "Epoch 262/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4891 - accuracy: 0.7671 - val_loss: 0.5487 - val_accuracy: 0.7208\n",
            "Epoch 263/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4917 - accuracy: 0.7736 - val_loss: 0.5201 - val_accuracy: 0.7078\n",
            "Epoch 264/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4874 - accuracy: 0.7622 - val_loss: 0.5184 - val_accuracy: 0.7338\n",
            "Epoch 265/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5026 - accuracy: 0.7541 - val_loss: 0.5209 - val_accuracy: 0.7078\n",
            "Epoch 266/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4836 - accuracy: 0.7671 - val_loss: 0.6297 - val_accuracy: 0.6558\n",
            "Epoch 267/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4966 - accuracy: 0.7671 - val_loss: 0.5067 - val_accuracy: 0.7338\n",
            "Epoch 268/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4932 - accuracy: 0.7606 - val_loss: 0.5343 - val_accuracy: 0.7273\n",
            "Epoch 269/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4966 - accuracy: 0.7573 - val_loss: 0.5272 - val_accuracy: 0.7143\n",
            "Epoch 270/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4861 - accuracy: 0.7687 - val_loss: 0.5096 - val_accuracy: 0.7208\n",
            "Epoch 271/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4915 - accuracy: 0.7590 - val_loss: 0.5766 - val_accuracy: 0.6948\n",
            "Epoch 272/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4931 - accuracy: 0.7769 - val_loss: 0.5287 - val_accuracy: 0.7273\n",
            "Epoch 273/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5467 - accuracy: 0.7329 - val_loss: 0.5326 - val_accuracy: 0.7208\n",
            "Epoch 274/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.5124 - accuracy: 0.7541 - val_loss: 0.5254 - val_accuracy: 0.7403\n",
            "Epoch 275/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4970 - accuracy: 0.7671 - val_loss: 0.5113 - val_accuracy: 0.7208\n",
            "Epoch 276/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4884 - accuracy: 0.7606 - val_loss: 0.5453 - val_accuracy: 0.7468\n",
            "Epoch 277/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4906 - accuracy: 0.7704 - val_loss: 0.5246 - val_accuracy: 0.7078\n",
            "Epoch 278/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4946 - accuracy: 0.7720 - val_loss: 0.5220 - val_accuracy: 0.7338\n",
            "Epoch 279/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5081 - accuracy: 0.7557 - val_loss: 0.6036 - val_accuracy: 0.6623\n",
            "Epoch 280/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5064 - accuracy: 0.7557 - val_loss: 0.5439 - val_accuracy: 0.7143\n",
            "Epoch 281/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5420 - accuracy: 0.7296 - val_loss: 0.5237 - val_accuracy: 0.7208\n",
            "Epoch 282/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5023 - accuracy: 0.7557 - val_loss: 0.5295 - val_accuracy: 0.6948\n",
            "Epoch 283/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4910 - accuracy: 0.7573 - val_loss: 0.6231 - val_accuracy: 0.6558\n",
            "Epoch 284/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4985 - accuracy: 0.7590 - val_loss: 0.5607 - val_accuracy: 0.7208\n",
            "Epoch 285/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4862 - accuracy: 0.7573 - val_loss: 0.5496 - val_accuracy: 0.7208\n",
            "Epoch 286/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5051 - accuracy: 0.7638 - val_loss: 0.5824 - val_accuracy: 0.6948\n",
            "Epoch 287/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5244 - accuracy: 0.7427 - val_loss: 0.5950 - val_accuracy: 0.6818\n",
            "Epoch 288/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4960 - accuracy: 0.7720 - val_loss: 0.5436 - val_accuracy: 0.7143\n",
            "Epoch 289/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4895 - accuracy: 0.7655 - val_loss: 0.5341 - val_accuracy: 0.7273\n",
            "Epoch 290/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5139 - accuracy: 0.7459 - val_loss: 0.5161 - val_accuracy: 0.7143\n",
            "Epoch 291/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4957 - accuracy: 0.7557 - val_loss: 0.5566 - val_accuracy: 0.7078\n",
            "Epoch 292/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4982 - accuracy: 0.7508 - val_loss: 0.5114 - val_accuracy: 0.7403\n",
            "Epoch 293/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5274 - accuracy: 0.7329 - val_loss: 0.5624 - val_accuracy: 0.7208\n",
            "Epoch 294/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5029 - accuracy: 0.7541 - val_loss: 0.6031 - val_accuracy: 0.6948\n",
            "Epoch 295/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5106 - accuracy: 0.7573 - val_loss: 0.5278 - val_accuracy: 0.7143\n",
            "Epoch 296/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5172 - accuracy: 0.7590 - val_loss: 0.5259 - val_accuracy: 0.7143\n",
            "Epoch 297/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.5019 - accuracy: 0.7638 - val_loss: 0.5475 - val_accuracy: 0.7273\n",
            "Epoch 298/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.5086 - accuracy: 0.7541 - val_loss: 0.5120 - val_accuracy: 0.7338\n",
            "Epoch 299/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5056 - accuracy: 0.7622 - val_loss: 0.5254 - val_accuracy: 0.7208\n",
            "Epoch 300/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5383 - accuracy: 0.7296 - val_loss: 0.5207 - val_accuracy: 0.7273\n",
            "Epoch 301/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5070 - accuracy: 0.7541 - val_loss: 0.5376 - val_accuracy: 0.7208\n",
            "Epoch 302/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5211 - accuracy: 0.7443 - val_loss: 0.5434 - val_accuracy: 0.7208\n",
            "Epoch 303/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4946 - accuracy: 0.7752 - val_loss: 0.5119 - val_accuracy: 0.7078\n",
            "Epoch 304/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5045 - accuracy: 0.7508 - val_loss: 0.6084 - val_accuracy: 0.6688\n",
            "Epoch 305/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4929 - accuracy: 0.7557 - val_loss: 0.5194 - val_accuracy: 0.7143\n",
            "Epoch 306/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4999 - accuracy: 0.7524 - val_loss: 0.5693 - val_accuracy: 0.7013\n",
            "Epoch 307/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5068 - accuracy: 0.7410 - val_loss: 0.5210 - val_accuracy: 0.7468\n",
            "Epoch 308/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5031 - accuracy: 0.7638 - val_loss: 0.5210 - val_accuracy: 0.7143\n",
            "Epoch 309/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4899 - accuracy: 0.7655 - val_loss: 0.5417 - val_accuracy: 0.7273\n",
            "Epoch 310/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4839 - accuracy: 0.7638 - val_loss: 0.5150 - val_accuracy: 0.7468\n",
            "Epoch 311/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4849 - accuracy: 0.7655 - val_loss: 0.5126 - val_accuracy: 0.7468\n",
            "Epoch 312/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4934 - accuracy: 0.7638 - val_loss: 0.5298 - val_accuracy: 0.7208\n",
            "Epoch 313/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5162 - accuracy: 0.7655 - val_loss: 0.5720 - val_accuracy: 0.7078\n",
            "Epoch 314/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5036 - accuracy: 0.7541 - val_loss: 0.5140 - val_accuracy: 0.7403\n",
            "Epoch 315/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4841 - accuracy: 0.7720 - val_loss: 0.5361 - val_accuracy: 0.7208\n",
            "Epoch 316/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5108 - accuracy: 0.7492 - val_loss: 0.5107 - val_accuracy: 0.7468\n",
            "Epoch 317/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4887 - accuracy: 0.7736 - val_loss: 0.5184 - val_accuracy: 0.7078\n",
            "Epoch 318/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4886 - accuracy: 0.7671 - val_loss: 0.5591 - val_accuracy: 0.7273\n",
            "Epoch 319/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4854 - accuracy: 0.7687 - val_loss: 0.5788 - val_accuracy: 0.7013\n",
            "Epoch 320/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4980 - accuracy: 0.7655 - val_loss: 0.5057 - val_accuracy: 0.7468\n",
            "Epoch 321/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4854 - accuracy: 0.7704 - val_loss: 0.6190 - val_accuracy: 0.6623\n",
            "Epoch 322/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5052 - accuracy: 0.7541 - val_loss: 0.5105 - val_accuracy: 0.7338\n",
            "Epoch 323/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4823 - accuracy: 0.7655 - val_loss: 0.5043 - val_accuracy: 0.7468\n",
            "Epoch 324/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4852 - accuracy: 0.7622 - val_loss: 0.6689 - val_accuracy: 0.6364\n",
            "Epoch 325/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5011 - accuracy: 0.7524 - val_loss: 0.5107 - val_accuracy: 0.7338\n",
            "Epoch 326/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4938 - accuracy: 0.7671 - val_loss: 0.5325 - val_accuracy: 0.7208\n",
            "Epoch 327/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4935 - accuracy: 0.7671 - val_loss: 0.5102 - val_accuracy: 0.7338\n",
            "Epoch 328/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4948 - accuracy: 0.7573 - val_loss: 0.5176 - val_accuracy: 0.7403\n",
            "Epoch 329/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4908 - accuracy: 0.7606 - val_loss: 0.5234 - val_accuracy: 0.7338\n",
            "Epoch 330/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4886 - accuracy: 0.7638 - val_loss: 0.5015 - val_accuracy: 0.7597\n",
            "Epoch 331/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4983 - accuracy: 0.7557 - val_loss: 0.5131 - val_accuracy: 0.7597\n",
            "Epoch 332/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4997 - accuracy: 0.7410 - val_loss: 0.5711 - val_accuracy: 0.7143\n",
            "Epoch 333/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4994 - accuracy: 0.7606 - val_loss: 0.5113 - val_accuracy: 0.7468\n",
            "Epoch 334/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4864 - accuracy: 0.7606 - val_loss: 0.5786 - val_accuracy: 0.7143\n",
            "Epoch 335/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4910 - accuracy: 0.7785 - val_loss: 0.5255 - val_accuracy: 0.7143\n",
            "Epoch 336/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4833 - accuracy: 0.7687 - val_loss: 0.5155 - val_accuracy: 0.7078\n",
            "Epoch 337/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5015 - accuracy: 0.7492 - val_loss: 0.5313 - val_accuracy: 0.7143\n",
            "Epoch 338/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4859 - accuracy: 0.7720 - val_loss: 0.5086 - val_accuracy: 0.7468\n",
            "Epoch 339/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4900 - accuracy: 0.7573 - val_loss: 0.5658 - val_accuracy: 0.7013\n",
            "Epoch 340/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4859 - accuracy: 0.7736 - val_loss: 0.5110 - val_accuracy: 0.7208\n",
            "Epoch 341/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5057 - accuracy: 0.7655 - val_loss: 0.5307 - val_accuracy: 0.7208\n",
            "Epoch 342/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4844 - accuracy: 0.7720 - val_loss: 0.5356 - val_accuracy: 0.7208\n",
            "Epoch 343/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4881 - accuracy: 0.7704 - val_loss: 0.5109 - val_accuracy: 0.7468\n",
            "Epoch 344/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4923 - accuracy: 0.7622 - val_loss: 0.5516 - val_accuracy: 0.7078\n",
            "Epoch 345/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4974 - accuracy: 0.7655 - val_loss: 0.5043 - val_accuracy: 0.7208\n",
            "Epoch 346/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4930 - accuracy: 0.7671 - val_loss: 0.5259 - val_accuracy: 0.7013\n",
            "Epoch 347/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4995 - accuracy: 0.7704 - val_loss: 0.5514 - val_accuracy: 0.7208\n",
            "Epoch 348/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4913 - accuracy: 0.7736 - val_loss: 0.5217 - val_accuracy: 0.7532\n",
            "Epoch 349/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5123 - accuracy: 0.7622 - val_loss: 0.5066 - val_accuracy: 0.7468\n",
            "Epoch 350/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5068 - accuracy: 0.7573 - val_loss: 0.5383 - val_accuracy: 0.7143\n",
            "Epoch 351/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4980 - accuracy: 0.7671 - val_loss: 0.5378 - val_accuracy: 0.7273\n",
            "Epoch 352/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5067 - accuracy: 0.7345 - val_loss: 0.5152 - val_accuracy: 0.7468\n",
            "Epoch 353/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4991 - accuracy: 0.7606 - val_loss: 0.5117 - val_accuracy: 0.7013\n",
            "Epoch 354/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4970 - accuracy: 0.7459 - val_loss: 0.5246 - val_accuracy: 0.7208\n",
            "Epoch 355/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5016 - accuracy: 0.7622 - val_loss: 0.5194 - val_accuracy: 0.7403\n",
            "Epoch 356/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4861 - accuracy: 0.7638 - val_loss: 0.5186 - val_accuracy: 0.7273\n",
            "Epoch 357/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4902 - accuracy: 0.7671 - val_loss: 0.5278 - val_accuracy: 0.7208\n",
            "Epoch 358/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4879 - accuracy: 0.7671 - val_loss: 0.5381 - val_accuracy: 0.7273\n",
            "Epoch 359/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4982 - accuracy: 0.7720 - val_loss: 0.5481 - val_accuracy: 0.7208\n",
            "Epoch 360/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4856 - accuracy: 0.7671 - val_loss: 0.5432 - val_accuracy: 0.7208\n",
            "Epoch 361/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4981 - accuracy: 0.7622 - val_loss: 0.5540 - val_accuracy: 0.7013\n",
            "Epoch 362/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5101 - accuracy: 0.7557 - val_loss: 0.5074 - val_accuracy: 0.7468\n",
            "Epoch 363/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4878 - accuracy: 0.7606 - val_loss: 0.5409 - val_accuracy: 0.7273\n",
            "Epoch 364/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5070 - accuracy: 0.7590 - val_loss: 0.5267 - val_accuracy: 0.7273\n",
            "Epoch 365/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4909 - accuracy: 0.7769 - val_loss: 0.5127 - val_accuracy: 0.7532\n",
            "Epoch 366/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4948 - accuracy: 0.7655 - val_loss: 0.5338 - val_accuracy: 0.7208\n",
            "Epoch 367/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4965 - accuracy: 0.7671 - val_loss: 0.5177 - val_accuracy: 0.7078\n",
            "Epoch 368/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4969 - accuracy: 0.7687 - val_loss: 0.5167 - val_accuracy: 0.7403\n",
            "Epoch 369/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4903 - accuracy: 0.7671 - val_loss: 0.5266 - val_accuracy: 0.7208\n",
            "Epoch 370/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5127 - accuracy: 0.7508 - val_loss: 0.5153 - val_accuracy: 0.7273\n",
            "Epoch 371/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4811 - accuracy: 0.7801 - val_loss: 0.5051 - val_accuracy: 0.7403\n",
            "Epoch 372/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4971 - accuracy: 0.7541 - val_loss: 0.5750 - val_accuracy: 0.7143\n",
            "Epoch 373/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5276 - accuracy: 0.7557 - val_loss: 0.5269 - val_accuracy: 0.7078\n",
            "Epoch 374/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.5142 - accuracy: 0.7492 - val_loss: 0.5165 - val_accuracy: 0.7078\n",
            "Epoch 375/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4899 - accuracy: 0.7655 - val_loss: 0.5169 - val_accuracy: 0.7468\n",
            "Epoch 376/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5199 - accuracy: 0.7410 - val_loss: 0.5567 - val_accuracy: 0.7208\n",
            "Epoch 377/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4923 - accuracy: 0.7687 - val_loss: 0.5096 - val_accuracy: 0.7403\n",
            "Epoch 378/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.5104 - accuracy: 0.7720 - val_loss: 0.5500 - val_accuracy: 0.7208\n",
            "Epoch 379/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4871 - accuracy: 0.7736 - val_loss: 0.5169 - val_accuracy: 0.7208\n",
            "Epoch 380/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4859 - accuracy: 0.7687 - val_loss: 0.5587 - val_accuracy: 0.7078\n",
            "Epoch 381/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4873 - accuracy: 0.7524 - val_loss: 0.5067 - val_accuracy: 0.7273\n",
            "Epoch 382/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4796 - accuracy: 0.7736 - val_loss: 0.5053 - val_accuracy: 0.7338\n",
            "Epoch 383/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.5155 - accuracy: 0.7606 - val_loss: 0.6244 - val_accuracy: 0.6688\n",
            "Epoch 384/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5011 - accuracy: 0.7476 - val_loss: 0.5080 - val_accuracy: 0.7208\n",
            "Epoch 385/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4897 - accuracy: 0.7476 - val_loss: 0.5140 - val_accuracy: 0.7468\n",
            "Epoch 386/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5124 - accuracy: 0.7622 - val_loss: 0.5037 - val_accuracy: 0.7273\n",
            "Epoch 387/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4923 - accuracy: 0.7590 - val_loss: 0.5057 - val_accuracy: 0.7532\n",
            "Epoch 388/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4846 - accuracy: 0.7638 - val_loss: 0.5351 - val_accuracy: 0.7273\n",
            "Epoch 389/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4898 - accuracy: 0.7687 - val_loss: 0.5040 - val_accuracy: 0.7338\n",
            "Epoch 390/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4851 - accuracy: 0.7769 - val_loss: 0.5110 - val_accuracy: 0.7403\n",
            "Epoch 391/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5016 - accuracy: 0.7541 - val_loss: 0.5072 - val_accuracy: 0.7403\n",
            "Epoch 392/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4913 - accuracy: 0.7671 - val_loss: 0.5423 - val_accuracy: 0.7208\n",
            "Epoch 393/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4860 - accuracy: 0.7622 - val_loss: 0.5132 - val_accuracy: 0.7532\n",
            "Epoch 394/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4937 - accuracy: 0.7622 - val_loss: 0.6387 - val_accuracy: 0.6364\n",
            "Epoch 395/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5109 - accuracy: 0.7508 - val_loss: 0.5244 - val_accuracy: 0.7338\n",
            "Epoch 396/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.5323 - accuracy: 0.7313 - val_loss: 0.6693 - val_accuracy: 0.6364\n",
            "Epoch 397/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5511 - accuracy: 0.7362 - val_loss: 0.5166 - val_accuracy: 0.7013\n",
            "Epoch 398/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4855 - accuracy: 0.7622 - val_loss: 0.5744 - val_accuracy: 0.7078\n",
            "Epoch 399/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5110 - accuracy: 0.7443 - val_loss: 0.5121 - val_accuracy: 0.7338\n",
            "Epoch 400/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4905 - accuracy: 0.7671 - val_loss: 0.5140 - val_accuracy: 0.7078\n",
            "Epoch 401/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5011 - accuracy: 0.7606 - val_loss: 0.5088 - val_accuracy: 0.7338\n",
            "Epoch 402/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4952 - accuracy: 0.7573 - val_loss: 0.5101 - val_accuracy: 0.7403\n",
            "Epoch 403/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4937 - accuracy: 0.7541 - val_loss: 0.5292 - val_accuracy: 0.7273\n",
            "Epoch 404/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4844 - accuracy: 0.7769 - val_loss: 0.5304 - val_accuracy: 0.7208\n",
            "Epoch 405/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4886 - accuracy: 0.7704 - val_loss: 0.5362 - val_accuracy: 0.7273\n",
            "Epoch 406/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4910 - accuracy: 0.7704 - val_loss: 0.5104 - val_accuracy: 0.7468\n",
            "Epoch 407/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4831 - accuracy: 0.7720 - val_loss: 0.5406 - val_accuracy: 0.7273\n",
            "Epoch 408/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4814 - accuracy: 0.7785 - val_loss: 0.5112 - val_accuracy: 0.7273\n",
            "Epoch 409/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4857 - accuracy: 0.7638 - val_loss: 0.5113 - val_accuracy: 0.7532\n",
            "Epoch 410/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4936 - accuracy: 0.7541 - val_loss: 0.5276 - val_accuracy: 0.7208\n",
            "Epoch 411/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4998 - accuracy: 0.7508 - val_loss: 0.5336 - val_accuracy: 0.7468\n",
            "Epoch 412/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5219 - accuracy: 0.7345 - val_loss: 0.5130 - val_accuracy: 0.7273\n",
            "Epoch 413/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4812 - accuracy: 0.7638 - val_loss: 0.5143 - val_accuracy: 0.7143\n",
            "Epoch 414/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4785 - accuracy: 0.7785 - val_loss: 0.5095 - val_accuracy: 0.7532\n",
            "Epoch 415/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.5211 - accuracy: 0.7427 - val_loss: 0.5850 - val_accuracy: 0.6818\n",
            "Epoch 416/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5157 - accuracy: 0.7362 - val_loss: 0.6484 - val_accuracy: 0.6429\n",
            "Epoch 417/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.5029 - accuracy: 0.7671 - val_loss: 0.5062 - val_accuracy: 0.7338\n",
            "Epoch 418/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5073 - accuracy: 0.7590 - val_loss: 0.5724 - val_accuracy: 0.7468\n",
            "Epoch 419/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.5100 - accuracy: 0.7476 - val_loss: 0.5637 - val_accuracy: 0.7208\n",
            "Epoch 420/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4810 - accuracy: 0.7769 - val_loss: 0.5444 - val_accuracy: 0.7143\n",
            "Epoch 421/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4863 - accuracy: 0.7818 - val_loss: 0.5359 - val_accuracy: 0.7338\n",
            "Epoch 422/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4931 - accuracy: 0.7524 - val_loss: 0.5373 - val_accuracy: 0.7273\n",
            "Epoch 423/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4842 - accuracy: 0.7687 - val_loss: 0.5035 - val_accuracy: 0.7338\n",
            "Epoch 424/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4845 - accuracy: 0.7736 - val_loss: 0.5080 - val_accuracy: 0.7338\n",
            "Epoch 425/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4791 - accuracy: 0.7785 - val_loss: 0.5088 - val_accuracy: 0.7403\n",
            "Epoch 426/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4818 - accuracy: 0.7720 - val_loss: 0.5367 - val_accuracy: 0.7273\n",
            "Epoch 427/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5010 - accuracy: 0.7573 - val_loss: 0.5653 - val_accuracy: 0.7143\n",
            "Epoch 428/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5049 - accuracy: 0.7524 - val_loss: 0.5620 - val_accuracy: 0.7208\n",
            "Epoch 429/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4854 - accuracy: 0.7573 - val_loss: 0.5580 - val_accuracy: 0.7208\n",
            "Epoch 430/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4972 - accuracy: 0.7606 - val_loss: 0.5066 - val_accuracy: 0.7273\n",
            "Epoch 431/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4829 - accuracy: 0.7704 - val_loss: 0.5223 - val_accuracy: 0.7208\n",
            "Epoch 432/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4937 - accuracy: 0.7752 - val_loss: 0.5095 - val_accuracy: 0.7338\n",
            "Epoch 433/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4844 - accuracy: 0.7720 - val_loss: 0.5022 - val_accuracy: 0.7403\n",
            "Epoch 434/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4866 - accuracy: 0.7524 - val_loss: 0.5822 - val_accuracy: 0.6948\n",
            "Epoch 435/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4881 - accuracy: 0.7671 - val_loss: 0.5178 - val_accuracy: 0.7273\n",
            "Epoch 436/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4920 - accuracy: 0.7655 - val_loss: 0.5324 - val_accuracy: 0.7273\n",
            "Epoch 437/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4802 - accuracy: 0.7687 - val_loss: 0.5044 - val_accuracy: 0.7468\n",
            "Epoch 438/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4909 - accuracy: 0.7638 - val_loss: 0.5116 - val_accuracy: 0.7532\n",
            "Epoch 439/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.5043 - accuracy: 0.7508 - val_loss: 0.5299 - val_accuracy: 0.7208\n",
            "Epoch 440/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4941 - accuracy: 0.7655 - val_loss: 0.5209 - val_accuracy: 0.7403\n",
            "Epoch 441/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4825 - accuracy: 0.7720 - val_loss: 0.5035 - val_accuracy: 0.7338\n",
            "Epoch 442/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4862 - accuracy: 0.7590 - val_loss: 0.5217 - val_accuracy: 0.7208\n",
            "Epoch 443/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4908 - accuracy: 0.7655 - val_loss: 0.5099 - val_accuracy: 0.7403\n",
            "Epoch 444/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4897 - accuracy: 0.7557 - val_loss: 0.5217 - val_accuracy: 0.7273\n",
            "Epoch 445/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4945 - accuracy: 0.7590 - val_loss: 0.5108 - val_accuracy: 0.7338\n",
            "Epoch 446/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4856 - accuracy: 0.7573 - val_loss: 0.5257 - val_accuracy: 0.7273\n",
            "Epoch 447/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5067 - accuracy: 0.7704 - val_loss: 0.5066 - val_accuracy: 0.7273\n",
            "Epoch 448/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5042 - accuracy: 0.7524 - val_loss: 0.5166 - val_accuracy: 0.7273\n",
            "Epoch 449/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4937 - accuracy: 0.7638 - val_loss: 0.5036 - val_accuracy: 0.7403\n",
            "Epoch 450/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4838 - accuracy: 0.7752 - val_loss: 0.5441 - val_accuracy: 0.7078\n",
            "Epoch 451/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4910 - accuracy: 0.7590 - val_loss: 0.5081 - val_accuracy: 0.7532\n",
            "Epoch 452/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4861 - accuracy: 0.7736 - val_loss: 0.5101 - val_accuracy: 0.7273\n",
            "Epoch 453/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4870 - accuracy: 0.7590 - val_loss: 0.5471 - val_accuracy: 0.7208\n",
            "Epoch 454/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4785 - accuracy: 0.7818 - val_loss: 0.5120 - val_accuracy: 0.7208\n",
            "Epoch 455/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4824 - accuracy: 0.7736 - val_loss: 0.5262 - val_accuracy: 0.7208\n",
            "Epoch 456/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4853 - accuracy: 0.7736 - val_loss: 0.5147 - val_accuracy: 0.7143\n",
            "Epoch 457/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4923 - accuracy: 0.7638 - val_loss: 0.5342 - val_accuracy: 0.7208\n",
            "Epoch 458/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4793 - accuracy: 0.7720 - val_loss: 0.5360 - val_accuracy: 0.7338\n",
            "Epoch 459/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4941 - accuracy: 0.7573 - val_loss: 0.5433 - val_accuracy: 0.7273\n",
            "Epoch 460/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4902 - accuracy: 0.7524 - val_loss: 0.5207 - val_accuracy: 0.7338\n",
            "Epoch 461/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4836 - accuracy: 0.7687 - val_loss: 0.5206 - val_accuracy: 0.7273\n",
            "Epoch 462/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4869 - accuracy: 0.7736 - val_loss: 0.5166 - val_accuracy: 0.7338\n",
            "Epoch 463/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4775 - accuracy: 0.7801 - val_loss: 0.5136 - val_accuracy: 0.7273\n",
            "Epoch 464/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4935 - accuracy: 0.7590 - val_loss: 0.5031 - val_accuracy: 0.7403\n",
            "Epoch 465/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4989 - accuracy: 0.7704 - val_loss: 0.5688 - val_accuracy: 0.7078\n",
            "Epoch 466/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5000 - accuracy: 0.7508 - val_loss: 0.5145 - val_accuracy: 0.7273\n",
            "Epoch 467/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5110 - accuracy: 0.7459 - val_loss: 0.6054 - val_accuracy: 0.6948\n",
            "Epoch 468/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4826 - accuracy: 0.7687 - val_loss: 0.5336 - val_accuracy: 0.7273\n",
            "Epoch 469/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4827 - accuracy: 0.7801 - val_loss: 0.5334 - val_accuracy: 0.7208\n",
            "Epoch 470/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5040 - accuracy: 0.7573 - val_loss: 0.5155 - val_accuracy: 0.7403\n",
            "Epoch 471/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4971 - accuracy: 0.7573 - val_loss: 0.5456 - val_accuracy: 0.7208\n",
            "Epoch 472/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4939 - accuracy: 0.7752 - val_loss: 0.5072 - val_accuracy: 0.7468\n",
            "Epoch 473/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4840 - accuracy: 0.7720 - val_loss: 0.5077 - val_accuracy: 0.7208\n",
            "Epoch 474/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5007 - accuracy: 0.7541 - val_loss: 0.6183 - val_accuracy: 0.7143\n",
            "Epoch 475/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.5171 - accuracy: 0.7622 - val_loss: 0.5549 - val_accuracy: 0.7143\n",
            "Epoch 476/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4903 - accuracy: 0.7687 - val_loss: 0.5270 - val_accuracy: 0.7208\n",
            "Epoch 477/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.5055 - accuracy: 0.7638 - val_loss: 0.5245 - val_accuracy: 0.7078\n",
            "Epoch 478/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4806 - accuracy: 0.7736 - val_loss: 0.5260 - val_accuracy: 0.7403\n",
            "Epoch 479/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4842 - accuracy: 0.7655 - val_loss: 0.5110 - val_accuracy: 0.7532\n",
            "Epoch 480/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4772 - accuracy: 0.7720 - val_loss: 0.5121 - val_accuracy: 0.7597\n",
            "Epoch 481/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.5072 - accuracy: 0.7427 - val_loss: 0.5420 - val_accuracy: 0.7208\n",
            "Epoch 482/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.5032 - accuracy: 0.7622 - val_loss: 0.5183 - val_accuracy: 0.7273\n",
            "Epoch 483/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.5001 - accuracy: 0.7524 - val_loss: 0.5159 - val_accuracy: 0.7403\n",
            "Epoch 484/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5011 - accuracy: 0.7394 - val_loss: 0.5657 - val_accuracy: 0.7078\n",
            "Epoch 485/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4852 - accuracy: 0.7752 - val_loss: 0.5122 - val_accuracy: 0.7532\n",
            "Epoch 486/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4926 - accuracy: 0.7671 - val_loss: 0.5070 - val_accuracy: 0.7468\n",
            "Epoch 487/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4941 - accuracy: 0.7590 - val_loss: 0.5143 - val_accuracy: 0.7338\n",
            "Epoch 488/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4791 - accuracy: 0.7720 - val_loss: 0.5100 - val_accuracy: 0.7273\n",
            "Epoch 489/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4864 - accuracy: 0.7655 - val_loss: 0.5424 - val_accuracy: 0.7143\n",
            "Epoch 490/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4885 - accuracy: 0.7557 - val_loss: 0.5129 - val_accuracy: 0.7273\n",
            "Epoch 491/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4776 - accuracy: 0.7752 - val_loss: 0.5159 - val_accuracy: 0.7208\n",
            "Epoch 492/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4769 - accuracy: 0.7850 - val_loss: 0.5093 - val_accuracy: 0.7597\n",
            "Epoch 493/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4994 - accuracy: 0.7606 - val_loss: 0.5827 - val_accuracy: 0.7013\n",
            "Epoch 494/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4901 - accuracy: 0.7606 - val_loss: 0.5313 - val_accuracy: 0.7273\n",
            "Epoch 495/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4750 - accuracy: 0.7752 - val_loss: 0.5083 - val_accuracy: 0.7468\n",
            "Epoch 496/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4838 - accuracy: 0.7752 - val_loss: 0.5185 - val_accuracy: 0.7273\n",
            "Epoch 497/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4828 - accuracy: 0.7736 - val_loss: 0.6018 - val_accuracy: 0.6883\n",
            "Epoch 498/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4839 - accuracy: 0.7720 - val_loss: 0.5031 - val_accuracy: 0.7273\n",
            "Epoch 499/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5130 - accuracy: 0.7524 - val_loss: 0.5169 - val_accuracy: 0.7273\n",
            "Epoch 500/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4784 - accuracy: 0.7769 - val_loss: 0.5084 - val_accuracy: 0.7273\n",
            "Epoch 501/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4770 - accuracy: 0.7704 - val_loss: 0.5151 - val_accuracy: 0.7338\n",
            "Epoch 502/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4928 - accuracy: 0.7655 - val_loss: 0.5080 - val_accuracy: 0.7338\n",
            "Epoch 503/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.5123 - accuracy: 0.7410 - val_loss: 0.4993 - val_accuracy: 0.7532\n",
            "Epoch 504/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4994 - accuracy: 0.7541 - val_loss: 0.5278 - val_accuracy: 0.7338\n",
            "Epoch 505/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4884 - accuracy: 0.7866 - val_loss: 0.5316 - val_accuracy: 0.7208\n",
            "Epoch 506/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5000 - accuracy: 0.7573 - val_loss: 0.5513 - val_accuracy: 0.7208\n",
            "Epoch 507/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.5010 - accuracy: 0.7443 - val_loss: 0.5075 - val_accuracy: 0.7662\n",
            "Epoch 508/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4859 - accuracy: 0.7801 - val_loss: 0.5220 - val_accuracy: 0.7143\n",
            "Epoch 509/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4886 - accuracy: 0.7622 - val_loss: 0.5152 - val_accuracy: 0.7468\n",
            "Epoch 510/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4908 - accuracy: 0.7671 - val_loss: 0.5393 - val_accuracy: 0.7273\n",
            "Epoch 511/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4844 - accuracy: 0.7801 - val_loss: 0.5122 - val_accuracy: 0.7273\n",
            "Epoch 512/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4852 - accuracy: 0.7638 - val_loss: 0.5092 - val_accuracy: 0.7403\n",
            "Epoch 513/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4863 - accuracy: 0.7736 - val_loss: 0.5055 - val_accuracy: 0.7338\n",
            "Epoch 514/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4877 - accuracy: 0.7769 - val_loss: 0.5046 - val_accuracy: 0.7532\n",
            "Epoch 515/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4778 - accuracy: 0.7704 - val_loss: 0.5255 - val_accuracy: 0.7273\n",
            "Epoch 516/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4848 - accuracy: 0.7655 - val_loss: 0.5129 - val_accuracy: 0.7208\n",
            "Epoch 517/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.5158 - accuracy: 0.7541 - val_loss: 0.5073 - val_accuracy: 0.7403\n",
            "Epoch 518/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5092 - accuracy: 0.7622 - val_loss: 0.5158 - val_accuracy: 0.7208\n",
            "Epoch 519/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4934 - accuracy: 0.7769 - val_loss: 0.5102 - val_accuracy: 0.7403\n",
            "Epoch 520/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4787 - accuracy: 0.7687 - val_loss: 0.5086 - val_accuracy: 0.7338\n",
            "Epoch 521/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4836 - accuracy: 0.7606 - val_loss: 0.5054 - val_accuracy: 0.7468\n",
            "Epoch 522/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4771 - accuracy: 0.7720 - val_loss: 0.5021 - val_accuracy: 0.7662\n",
            "Epoch 523/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5097 - accuracy: 0.7492 - val_loss: 0.5291 - val_accuracy: 0.7273\n",
            "Epoch 524/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.5007 - accuracy: 0.7573 - val_loss: 0.5121 - val_accuracy: 0.7143\n",
            "Epoch 525/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4826 - accuracy: 0.7736 - val_loss: 0.5177 - val_accuracy: 0.7338\n",
            "Epoch 526/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4864 - accuracy: 0.7720 - val_loss: 0.5007 - val_accuracy: 0.7403\n",
            "Epoch 527/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4892 - accuracy: 0.7557 - val_loss: 0.5045 - val_accuracy: 0.7273\n",
            "Epoch 528/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4776 - accuracy: 0.7736 - val_loss: 0.5678 - val_accuracy: 0.7078\n",
            "Epoch 529/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4807 - accuracy: 0.7704 - val_loss: 0.5331 - val_accuracy: 0.7273\n",
            "Epoch 530/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4863 - accuracy: 0.7671 - val_loss: 0.5301 - val_accuracy: 0.7208\n",
            "Epoch 531/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4853 - accuracy: 0.7752 - val_loss: 0.5089 - val_accuracy: 0.7273\n",
            "Epoch 532/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4867 - accuracy: 0.7655 - val_loss: 0.5120 - val_accuracy: 0.7403\n",
            "Epoch 533/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4790 - accuracy: 0.7687 - val_loss: 0.5088 - val_accuracy: 0.7532\n",
            "Epoch 534/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4859 - accuracy: 0.7704 - val_loss: 0.5352 - val_accuracy: 0.7273\n",
            "Epoch 535/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4872 - accuracy: 0.7720 - val_loss: 0.5036 - val_accuracy: 0.7338\n",
            "Epoch 536/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4778 - accuracy: 0.7590 - val_loss: 0.5720 - val_accuracy: 0.7078\n",
            "Epoch 537/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4935 - accuracy: 0.7622 - val_loss: 0.5049 - val_accuracy: 0.7468\n",
            "Epoch 538/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4941 - accuracy: 0.7606 - val_loss: 0.5410 - val_accuracy: 0.7338\n",
            "Epoch 539/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5053 - accuracy: 0.7557 - val_loss: 0.5222 - val_accuracy: 0.7338\n",
            "Epoch 540/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4778 - accuracy: 0.7850 - val_loss: 0.5264 - val_accuracy: 0.7273\n",
            "Epoch 541/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5021 - accuracy: 0.7638 - val_loss: 0.5123 - val_accuracy: 0.7338\n",
            "Epoch 542/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5009 - accuracy: 0.7541 - val_loss: 0.5841 - val_accuracy: 0.7013\n",
            "Epoch 543/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5001 - accuracy: 0.7590 - val_loss: 0.5017 - val_accuracy: 0.7338\n",
            "Epoch 544/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4796 - accuracy: 0.7752 - val_loss: 0.5002 - val_accuracy: 0.7338\n",
            "Epoch 545/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4949 - accuracy: 0.7704 - val_loss: 0.5132 - val_accuracy: 0.7143\n",
            "Epoch 546/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4913 - accuracy: 0.7590 - val_loss: 0.5223 - val_accuracy: 0.7338\n",
            "Epoch 547/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4924 - accuracy: 0.7752 - val_loss: 0.5124 - val_accuracy: 0.7273\n",
            "Epoch 548/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4884 - accuracy: 0.7687 - val_loss: 0.5026 - val_accuracy: 0.7597\n",
            "Epoch 549/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4815 - accuracy: 0.7687 - val_loss: 0.5060 - val_accuracy: 0.7532\n",
            "Epoch 550/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4814 - accuracy: 0.7671 - val_loss: 0.5036 - val_accuracy: 0.7532\n",
            "Epoch 551/1000\n",
            "20/20 [==============================] - 0s 8ms/step - loss: 0.4799 - accuracy: 0.7671 - val_loss: 0.5232 - val_accuracy: 0.7273\n",
            "Epoch 552/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4940 - accuracy: 0.7590 - val_loss: 0.4992 - val_accuracy: 0.7338\n",
            "Epoch 553/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4992 - accuracy: 0.7541 - val_loss: 0.5045 - val_accuracy: 0.7532\n",
            "Epoch 554/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4800 - accuracy: 0.7720 - val_loss: 0.5094 - val_accuracy: 0.7468\n",
            "Epoch 555/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4874 - accuracy: 0.7785 - val_loss: 0.5140 - val_accuracy: 0.7208\n",
            "Epoch 556/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4775 - accuracy: 0.7720 - val_loss: 0.5074 - val_accuracy: 0.7468\n",
            "Epoch 557/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4887 - accuracy: 0.7687 - val_loss: 0.5023 - val_accuracy: 0.7338\n",
            "Epoch 558/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4977 - accuracy: 0.7638 - val_loss: 0.5207 - val_accuracy: 0.7338\n",
            "Epoch 559/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4967 - accuracy: 0.7524 - val_loss: 0.5771 - val_accuracy: 0.7143\n",
            "Epoch 560/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4745 - accuracy: 0.7769 - val_loss: 0.6098 - val_accuracy: 0.6753\n",
            "Epoch 561/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4966 - accuracy: 0.7541 - val_loss: 0.5180 - val_accuracy: 0.7208\n",
            "Epoch 562/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4941 - accuracy: 0.7638 - val_loss: 0.5813 - val_accuracy: 0.7143\n",
            "Epoch 563/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4915 - accuracy: 0.7671 - val_loss: 0.5064 - val_accuracy: 0.7273\n",
            "Epoch 564/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4849 - accuracy: 0.7752 - val_loss: 0.4978 - val_accuracy: 0.7338\n",
            "Epoch 565/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4900 - accuracy: 0.7606 - val_loss: 0.5131 - val_accuracy: 0.7338\n",
            "Epoch 566/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4816 - accuracy: 0.7785 - val_loss: 0.5047 - val_accuracy: 0.7273\n",
            "Epoch 567/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4995 - accuracy: 0.7606 - val_loss: 0.5077 - val_accuracy: 0.7532\n",
            "Epoch 568/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4771 - accuracy: 0.7720 - val_loss: 0.5067 - val_accuracy: 0.7597\n",
            "Epoch 569/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4778 - accuracy: 0.7834 - val_loss: 0.5015 - val_accuracy: 0.7532\n",
            "Epoch 570/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4916 - accuracy: 0.7638 - val_loss: 0.5132 - val_accuracy: 0.7403\n",
            "Epoch 571/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4968 - accuracy: 0.7606 - val_loss: 0.5025 - val_accuracy: 0.7597\n",
            "Epoch 572/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4836 - accuracy: 0.7671 - val_loss: 0.5521 - val_accuracy: 0.7208\n",
            "Epoch 573/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5104 - accuracy: 0.7573 - val_loss: 0.5370 - val_accuracy: 0.7403\n",
            "Epoch 574/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.5095 - accuracy: 0.7394 - val_loss: 0.5109 - val_accuracy: 0.7532\n",
            "Epoch 575/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4832 - accuracy: 0.7704 - val_loss: 0.5244 - val_accuracy: 0.7403\n",
            "Epoch 576/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4831 - accuracy: 0.7720 - val_loss: 0.5730 - val_accuracy: 0.7078\n",
            "Epoch 577/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4837 - accuracy: 0.7736 - val_loss: 0.5169 - val_accuracy: 0.7468\n",
            "Epoch 578/1000\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.4825 - accuracy: 0.7736 - val_loss: 0.5165 - val_accuracy: 0.7143\n",
            "Epoch 579/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4778 - accuracy: 0.7704 - val_loss: 0.5030 - val_accuracy: 0.7468\n",
            "Epoch 580/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4850 - accuracy: 0.7687 - val_loss: 0.5034 - val_accuracy: 0.7338\n",
            "Epoch 581/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4872 - accuracy: 0.7622 - val_loss: 0.5458 - val_accuracy: 0.7208\n",
            "Epoch 582/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5155 - accuracy: 0.7476 - val_loss: 0.4986 - val_accuracy: 0.7403\n",
            "Epoch 583/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5302 - accuracy: 0.7606 - val_loss: 0.5185 - val_accuracy: 0.7338\n",
            "Epoch 584/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5015 - accuracy: 0.7492 - val_loss: 0.5359 - val_accuracy: 0.7273\n",
            "Epoch 585/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4969 - accuracy: 0.7606 - val_loss: 0.5438 - val_accuracy: 0.7143\n",
            "Epoch 586/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4911 - accuracy: 0.7492 - val_loss: 0.5178 - val_accuracy: 0.7273\n",
            "Epoch 587/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4843 - accuracy: 0.7720 - val_loss: 0.5093 - val_accuracy: 0.7597\n",
            "Epoch 588/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4881 - accuracy: 0.7655 - val_loss: 0.5441 - val_accuracy: 0.7273\n",
            "Epoch 589/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5091 - accuracy: 0.7590 - val_loss: 0.5096 - val_accuracy: 0.7597\n",
            "Epoch 590/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4874 - accuracy: 0.7606 - val_loss: 0.5649 - val_accuracy: 0.7143\n",
            "Epoch 591/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4929 - accuracy: 0.7736 - val_loss: 0.5334 - val_accuracy: 0.7338\n",
            "Epoch 592/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4825 - accuracy: 0.7752 - val_loss: 0.5105 - val_accuracy: 0.7208\n",
            "Epoch 593/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4845 - accuracy: 0.7704 - val_loss: 0.5055 - val_accuracy: 0.7532\n",
            "Epoch 594/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4892 - accuracy: 0.7671 - val_loss: 0.5024 - val_accuracy: 0.7403\n",
            "Epoch 595/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4757 - accuracy: 0.7769 - val_loss: 0.5109 - val_accuracy: 0.7338\n",
            "Epoch 596/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4879 - accuracy: 0.7687 - val_loss: 0.5034 - val_accuracy: 0.7338\n",
            "Epoch 597/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4938 - accuracy: 0.7671 - val_loss: 0.5178 - val_accuracy: 0.7338\n",
            "Epoch 598/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4825 - accuracy: 0.7687 - val_loss: 0.5024 - val_accuracy: 0.7338\n",
            "Epoch 599/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4842 - accuracy: 0.7638 - val_loss: 0.5255 - val_accuracy: 0.7273\n",
            "Epoch 600/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4771 - accuracy: 0.7704 - val_loss: 0.5506 - val_accuracy: 0.7143\n",
            "Epoch 601/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4898 - accuracy: 0.7671 - val_loss: 0.5490 - val_accuracy: 0.7078\n",
            "Epoch 602/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4907 - accuracy: 0.7590 - val_loss: 0.5049 - val_accuracy: 0.7338\n",
            "Epoch 603/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4822 - accuracy: 0.7687 - val_loss: 0.5137 - val_accuracy: 0.7273\n",
            "Epoch 604/1000\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.4944 - accuracy: 0.7508 - val_loss: 0.5899 - val_accuracy: 0.6948\n",
            "Epoch 605/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4888 - accuracy: 0.7687 - val_loss: 0.5094 - val_accuracy: 0.7468\n",
            "Epoch 606/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4887 - accuracy: 0.7785 - val_loss: 0.5191 - val_accuracy: 0.7273\n",
            "Epoch 607/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4830 - accuracy: 0.7736 - val_loss: 0.5198 - val_accuracy: 0.7273\n",
            "Epoch 608/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4810 - accuracy: 0.7785 - val_loss: 0.5202 - val_accuracy: 0.7338\n",
            "Epoch 609/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4770 - accuracy: 0.7785 - val_loss: 0.5358 - val_accuracy: 0.7208\n",
            "Epoch 610/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4792 - accuracy: 0.7671 - val_loss: 0.5002 - val_accuracy: 0.7468\n",
            "Epoch 611/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4819 - accuracy: 0.7687 - val_loss: 0.5099 - val_accuracy: 0.7468\n",
            "Epoch 612/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4929 - accuracy: 0.7573 - val_loss: 0.5015 - val_accuracy: 0.7403\n",
            "Epoch 613/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4853 - accuracy: 0.7590 - val_loss: 0.5505 - val_accuracy: 0.7273\n",
            "Epoch 614/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5146 - accuracy: 0.7508 - val_loss: 0.5068 - val_accuracy: 0.7468\n",
            "Epoch 615/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4890 - accuracy: 0.7655 - val_loss: 0.5358 - val_accuracy: 0.7273\n",
            "Epoch 616/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4994 - accuracy: 0.7704 - val_loss: 0.5071 - val_accuracy: 0.7403\n",
            "Epoch 617/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4939 - accuracy: 0.7557 - val_loss: 0.5473 - val_accuracy: 0.7143\n",
            "Epoch 618/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4818 - accuracy: 0.7801 - val_loss: 0.5428 - val_accuracy: 0.7208\n",
            "Epoch 619/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4831 - accuracy: 0.7720 - val_loss: 0.5542 - val_accuracy: 0.7013\n",
            "Epoch 620/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4964 - accuracy: 0.7704 - val_loss: 0.5027 - val_accuracy: 0.7403\n",
            "Epoch 621/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4776 - accuracy: 0.7752 - val_loss: 0.5486 - val_accuracy: 0.7078\n",
            "Epoch 622/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5197 - accuracy: 0.7378 - val_loss: 0.5327 - val_accuracy: 0.7273\n",
            "Epoch 623/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5013 - accuracy: 0.7573 - val_loss: 0.5670 - val_accuracy: 0.7143\n",
            "Epoch 624/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4735 - accuracy: 0.7687 - val_loss: 0.5064 - val_accuracy: 0.7143\n",
            "Epoch 625/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4861 - accuracy: 0.7818 - val_loss: 0.5370 - val_accuracy: 0.7273\n",
            "Epoch 626/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4850 - accuracy: 0.7671 - val_loss: 0.4968 - val_accuracy: 0.7338\n",
            "Epoch 627/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4739 - accuracy: 0.7883 - val_loss: 0.5188 - val_accuracy: 0.7338\n",
            "Epoch 628/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4771 - accuracy: 0.7769 - val_loss: 0.4952 - val_accuracy: 0.7273\n",
            "Epoch 629/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4848 - accuracy: 0.7720 - val_loss: 0.4996 - val_accuracy: 0.7532\n",
            "Epoch 630/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4785 - accuracy: 0.7801 - val_loss: 0.5008 - val_accuracy: 0.7532\n",
            "Epoch 631/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4781 - accuracy: 0.7720 - val_loss: 0.5164 - val_accuracy: 0.7273\n",
            "Epoch 632/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4888 - accuracy: 0.7606 - val_loss: 0.5689 - val_accuracy: 0.7078\n",
            "Epoch 633/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.5022 - accuracy: 0.7606 - val_loss: 0.5292 - val_accuracy: 0.7273\n",
            "Epoch 634/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4837 - accuracy: 0.7671 - val_loss: 0.5796 - val_accuracy: 0.7013\n",
            "Epoch 635/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4804 - accuracy: 0.7785 - val_loss: 0.4994 - val_accuracy: 0.7532\n",
            "Epoch 636/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4766 - accuracy: 0.7704 - val_loss: 0.5125 - val_accuracy: 0.7403\n",
            "Epoch 637/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4742 - accuracy: 0.7752 - val_loss: 0.5848 - val_accuracy: 0.7143\n",
            "Epoch 638/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4933 - accuracy: 0.7769 - val_loss: 0.5674 - val_accuracy: 0.7013\n",
            "Epoch 639/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4901 - accuracy: 0.7590 - val_loss: 0.5039 - val_accuracy: 0.7532\n",
            "Epoch 640/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4847 - accuracy: 0.7769 - val_loss: 0.4979 - val_accuracy: 0.7468\n",
            "Epoch 641/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4830 - accuracy: 0.7785 - val_loss: 0.4995 - val_accuracy: 0.7403\n",
            "Epoch 642/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4878 - accuracy: 0.7769 - val_loss: 0.4968 - val_accuracy: 0.7403\n",
            "Epoch 643/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5084 - accuracy: 0.7443 - val_loss: 0.6005 - val_accuracy: 0.6818\n",
            "Epoch 644/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5253 - accuracy: 0.7394 - val_loss: 0.5414 - val_accuracy: 0.7208\n",
            "Epoch 645/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4857 - accuracy: 0.7622 - val_loss: 0.5168 - val_accuracy: 0.7273\n",
            "Epoch 646/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4866 - accuracy: 0.7720 - val_loss: 0.5174 - val_accuracy: 0.7273\n",
            "Epoch 647/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4885 - accuracy: 0.7638 - val_loss: 0.5232 - val_accuracy: 0.7403\n",
            "Epoch 648/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4780 - accuracy: 0.7687 - val_loss: 0.5007 - val_accuracy: 0.7338\n",
            "Epoch 649/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4800 - accuracy: 0.7704 - val_loss: 0.5197 - val_accuracy: 0.7403\n",
            "Epoch 650/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4796 - accuracy: 0.7704 - val_loss: 0.4995 - val_accuracy: 0.7662\n",
            "Epoch 651/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4856 - accuracy: 0.7769 - val_loss: 0.5027 - val_accuracy: 0.7597\n",
            "Epoch 652/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4888 - accuracy: 0.7720 - val_loss: 0.5039 - val_accuracy: 0.7597\n",
            "Epoch 653/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4786 - accuracy: 0.7752 - val_loss: 0.5001 - val_accuracy: 0.7468\n",
            "Epoch 654/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4797 - accuracy: 0.7687 - val_loss: 0.5205 - val_accuracy: 0.7273\n",
            "Epoch 655/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4846 - accuracy: 0.7638 - val_loss: 0.5051 - val_accuracy: 0.7403\n",
            "Epoch 656/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5022 - accuracy: 0.7573 - val_loss: 0.5332 - val_accuracy: 0.7338\n",
            "Epoch 657/1000\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.4798 - accuracy: 0.7769 - val_loss: 0.5476 - val_accuracy: 0.7208\n",
            "Epoch 658/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4815 - accuracy: 0.7801 - val_loss: 0.5072 - val_accuracy: 0.7597\n",
            "Epoch 659/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5075 - accuracy: 0.7720 - val_loss: 0.5043 - val_accuracy: 0.7468\n",
            "Epoch 660/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4820 - accuracy: 0.7655 - val_loss: 0.5273 - val_accuracy: 0.7273\n",
            "Epoch 661/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.5036 - accuracy: 0.7655 - val_loss: 0.5309 - val_accuracy: 0.7273\n",
            "Epoch 662/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4884 - accuracy: 0.7687 - val_loss: 0.5052 - val_accuracy: 0.7468\n",
            "Epoch 663/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4919 - accuracy: 0.7476 - val_loss: 0.5278 - val_accuracy: 0.7338\n",
            "Epoch 664/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4786 - accuracy: 0.7671 - val_loss: 0.5103 - val_accuracy: 0.7338\n",
            "Epoch 665/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4800 - accuracy: 0.7704 - val_loss: 0.5159 - val_accuracy: 0.7208\n",
            "Epoch 666/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4751 - accuracy: 0.7834 - val_loss: 0.5091 - val_accuracy: 0.7338\n",
            "Epoch 667/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4825 - accuracy: 0.7801 - val_loss: 0.5399 - val_accuracy: 0.7143\n",
            "Epoch 668/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4848 - accuracy: 0.7720 - val_loss: 0.5080 - val_accuracy: 0.7468\n",
            "Epoch 669/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4757 - accuracy: 0.7736 - val_loss: 0.5296 - val_accuracy: 0.7208\n",
            "Epoch 670/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4811 - accuracy: 0.7704 - val_loss: 0.5469 - val_accuracy: 0.7143\n",
            "Epoch 671/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4814 - accuracy: 0.7769 - val_loss: 0.5033 - val_accuracy: 0.7403\n",
            "Epoch 672/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4775 - accuracy: 0.7752 - val_loss: 0.4992 - val_accuracy: 0.7532\n",
            "Epoch 673/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4829 - accuracy: 0.7720 - val_loss: 0.5257 - val_accuracy: 0.7273\n",
            "Epoch 674/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4748 - accuracy: 0.7704 - val_loss: 0.5110 - val_accuracy: 0.7338\n",
            "Epoch 675/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5000 - accuracy: 0.7573 - val_loss: 0.5049 - val_accuracy: 0.7662\n",
            "Epoch 676/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4795 - accuracy: 0.7671 - val_loss: 0.5034 - val_accuracy: 0.7403\n",
            "Epoch 677/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4794 - accuracy: 0.7785 - val_loss: 0.5012 - val_accuracy: 0.7532\n",
            "Epoch 678/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4766 - accuracy: 0.7638 - val_loss: 0.5056 - val_accuracy: 0.7468\n",
            "Epoch 679/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4760 - accuracy: 0.7736 - val_loss: 0.5049 - val_accuracy: 0.7597\n",
            "Epoch 680/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4903 - accuracy: 0.7752 - val_loss: 0.5223 - val_accuracy: 0.7403\n",
            "Epoch 681/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4771 - accuracy: 0.7769 - val_loss: 0.5171 - val_accuracy: 0.7273\n",
            "Epoch 682/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4824 - accuracy: 0.7622 - val_loss: 0.5669 - val_accuracy: 0.7013\n",
            "Epoch 683/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4797 - accuracy: 0.7687 - val_loss: 0.5416 - val_accuracy: 0.7273\n",
            "Epoch 684/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4913 - accuracy: 0.7720 - val_loss: 0.5042 - val_accuracy: 0.7532\n",
            "Epoch 685/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4783 - accuracy: 0.7785 - val_loss: 0.5407 - val_accuracy: 0.7208\n",
            "Epoch 686/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4820 - accuracy: 0.7704 - val_loss: 0.4973 - val_accuracy: 0.7338\n",
            "Epoch 687/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4799 - accuracy: 0.7834 - val_loss: 0.5071 - val_accuracy: 0.7532\n",
            "Epoch 688/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4821 - accuracy: 0.7687 - val_loss: 0.4980 - val_accuracy: 0.7338\n",
            "Epoch 689/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4775 - accuracy: 0.7671 - val_loss: 0.5025 - val_accuracy: 0.7532\n",
            "Epoch 690/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4851 - accuracy: 0.7671 - val_loss: 0.4981 - val_accuracy: 0.7468\n",
            "Epoch 691/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4803 - accuracy: 0.7704 - val_loss: 0.5079 - val_accuracy: 0.7532\n",
            "Epoch 692/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4886 - accuracy: 0.7622 - val_loss: 0.5168 - val_accuracy: 0.7338\n",
            "Epoch 693/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4737 - accuracy: 0.7720 - val_loss: 0.5260 - val_accuracy: 0.7273\n",
            "Epoch 694/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4773 - accuracy: 0.7785 - val_loss: 0.5589 - val_accuracy: 0.7013\n",
            "Epoch 695/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4858 - accuracy: 0.7638 - val_loss: 0.5041 - val_accuracy: 0.7403\n",
            "Epoch 696/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.5142 - accuracy: 0.7443 - val_loss: 0.5296 - val_accuracy: 0.7208\n",
            "Epoch 697/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4871 - accuracy: 0.7736 - val_loss: 0.5040 - val_accuracy: 0.7468\n",
            "Epoch 698/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4749 - accuracy: 0.7801 - val_loss: 0.5082 - val_accuracy: 0.7468\n",
            "Epoch 699/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4925 - accuracy: 0.7687 - val_loss: 0.5177 - val_accuracy: 0.7338\n",
            "Epoch 700/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4939 - accuracy: 0.7590 - val_loss: 0.5127 - val_accuracy: 0.7403\n",
            "Epoch 701/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4914 - accuracy: 0.7769 - val_loss: 0.5361 - val_accuracy: 0.7143\n",
            "Epoch 702/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4842 - accuracy: 0.7638 - val_loss: 0.5538 - val_accuracy: 0.7208\n",
            "Epoch 703/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4848 - accuracy: 0.7606 - val_loss: 0.5218 - val_accuracy: 0.7273\n",
            "Epoch 704/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4848 - accuracy: 0.7704 - val_loss: 0.5014 - val_accuracy: 0.7338\n",
            "Epoch 705/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4888 - accuracy: 0.7622 - val_loss: 0.5138 - val_accuracy: 0.7338\n",
            "Epoch 706/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4763 - accuracy: 0.7752 - val_loss: 0.5144 - val_accuracy: 0.7403\n",
            "Epoch 707/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4748 - accuracy: 0.7704 - val_loss: 0.4939 - val_accuracy: 0.7403\n",
            "Epoch 708/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4755 - accuracy: 0.7752 - val_loss: 0.4987 - val_accuracy: 0.7597\n",
            "Epoch 709/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4828 - accuracy: 0.7752 - val_loss: 0.5004 - val_accuracy: 0.7532\n",
            "Epoch 710/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4719 - accuracy: 0.7769 - val_loss: 0.4986 - val_accuracy: 0.7403\n",
            "Epoch 711/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4795 - accuracy: 0.7687 - val_loss: 0.4974 - val_accuracy: 0.7338\n",
            "Epoch 712/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4753 - accuracy: 0.7671 - val_loss: 0.5967 - val_accuracy: 0.7078\n",
            "Epoch 713/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5040 - accuracy: 0.7655 - val_loss: 0.5063 - val_accuracy: 0.7468\n",
            "Epoch 714/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4810 - accuracy: 0.7736 - val_loss: 0.5238 - val_accuracy: 0.7208\n",
            "Epoch 715/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4754 - accuracy: 0.7720 - val_loss: 0.5619 - val_accuracy: 0.7078\n",
            "Epoch 716/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4797 - accuracy: 0.7606 - val_loss: 0.5079 - val_accuracy: 0.7338\n",
            "Epoch 717/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4865 - accuracy: 0.7801 - val_loss: 0.4964 - val_accuracy: 0.7403\n",
            "Epoch 718/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4754 - accuracy: 0.7785 - val_loss: 0.5285 - val_accuracy: 0.7273\n",
            "Epoch 719/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4777 - accuracy: 0.7801 - val_loss: 0.4992 - val_accuracy: 0.7468\n",
            "Epoch 720/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4754 - accuracy: 0.7671 - val_loss: 0.5025 - val_accuracy: 0.7662\n",
            "Epoch 721/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4776 - accuracy: 0.7752 - val_loss: 0.4966 - val_accuracy: 0.7532\n",
            "Epoch 722/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4766 - accuracy: 0.7720 - val_loss: 0.4999 - val_accuracy: 0.7662\n",
            "Epoch 723/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4801 - accuracy: 0.7736 - val_loss: 0.5391 - val_accuracy: 0.7273\n",
            "Epoch 724/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4800 - accuracy: 0.7720 - val_loss: 0.4974 - val_accuracy: 0.7338\n",
            "Epoch 725/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4794 - accuracy: 0.7801 - val_loss: 0.4976 - val_accuracy: 0.7597\n",
            "Epoch 726/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4783 - accuracy: 0.7736 - val_loss: 0.5827 - val_accuracy: 0.7078\n",
            "Epoch 727/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4767 - accuracy: 0.7818 - val_loss: 0.5278 - val_accuracy: 0.7338\n",
            "Epoch 728/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.5244 - accuracy: 0.7345 - val_loss: 0.4999 - val_accuracy: 0.7662\n",
            "Epoch 729/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4965 - accuracy: 0.7524 - val_loss: 0.5251 - val_accuracy: 0.7403\n",
            "Epoch 730/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4934 - accuracy: 0.7541 - val_loss: 0.5078 - val_accuracy: 0.7468\n",
            "Epoch 731/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4891 - accuracy: 0.7704 - val_loss: 0.5172 - val_accuracy: 0.7468\n",
            "Epoch 732/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4902 - accuracy: 0.7785 - val_loss: 0.5134 - val_accuracy: 0.7273\n",
            "Epoch 733/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4964 - accuracy: 0.7622 - val_loss: 0.4982 - val_accuracy: 0.7338\n",
            "Epoch 734/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4909 - accuracy: 0.7769 - val_loss: 0.5027 - val_accuracy: 0.7597\n",
            "Epoch 735/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4858 - accuracy: 0.7704 - val_loss: 0.4975 - val_accuracy: 0.7468\n",
            "Epoch 736/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4978 - accuracy: 0.7590 - val_loss: 0.5392 - val_accuracy: 0.7208\n",
            "Epoch 737/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4747 - accuracy: 0.7655 - val_loss: 0.5306 - val_accuracy: 0.7403\n",
            "Epoch 738/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4845 - accuracy: 0.7866 - val_loss: 0.4978 - val_accuracy: 0.7403\n",
            "Epoch 739/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4748 - accuracy: 0.7769 - val_loss: 0.4996 - val_accuracy: 0.7338\n",
            "Epoch 740/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4839 - accuracy: 0.7622 - val_loss: 0.5040 - val_accuracy: 0.7403\n",
            "Epoch 741/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4833 - accuracy: 0.7704 - val_loss: 0.4995 - val_accuracy: 0.7403\n",
            "Epoch 742/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4841 - accuracy: 0.7752 - val_loss: 0.5199 - val_accuracy: 0.7338\n",
            "Epoch 743/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4815 - accuracy: 0.7736 - val_loss: 0.5586 - val_accuracy: 0.7078\n",
            "Epoch 744/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4921 - accuracy: 0.7769 - val_loss: 0.5008 - val_accuracy: 0.7532\n",
            "Epoch 745/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4893 - accuracy: 0.7687 - val_loss: 0.4966 - val_accuracy: 0.7403\n",
            "Epoch 746/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4826 - accuracy: 0.7687 - val_loss: 0.5608 - val_accuracy: 0.7013\n",
            "Epoch 747/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4889 - accuracy: 0.7752 - val_loss: 0.5121 - val_accuracy: 0.7273\n",
            "Epoch 748/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4843 - accuracy: 0.7769 - val_loss: 0.5027 - val_accuracy: 0.7403\n",
            "Epoch 749/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4791 - accuracy: 0.7883 - val_loss: 0.5230 - val_accuracy: 0.7273\n",
            "Epoch 750/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4806 - accuracy: 0.7655 - val_loss: 0.5083 - val_accuracy: 0.7532\n",
            "Epoch 751/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4825 - accuracy: 0.7752 - val_loss: 0.5370 - val_accuracy: 0.7208\n",
            "Epoch 752/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4806 - accuracy: 0.7687 - val_loss: 0.5236 - val_accuracy: 0.7338\n",
            "Epoch 753/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4807 - accuracy: 0.7687 - val_loss: 0.5077 - val_accuracy: 0.7532\n",
            "Epoch 754/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4859 - accuracy: 0.7655 - val_loss: 0.5274 - val_accuracy: 0.7338\n",
            "Epoch 755/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4880 - accuracy: 0.7638 - val_loss: 0.5887 - val_accuracy: 0.7013\n",
            "Epoch 756/1000\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.4871 - accuracy: 0.7687 - val_loss: 0.5175 - val_accuracy: 0.7273\n",
            "Epoch 757/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4746 - accuracy: 0.7834 - val_loss: 0.5058 - val_accuracy: 0.7532\n",
            "Epoch 758/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4777 - accuracy: 0.7720 - val_loss: 0.5539 - val_accuracy: 0.7013\n",
            "Epoch 759/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.5062 - accuracy: 0.7752 - val_loss: 0.5150 - val_accuracy: 0.7273\n",
            "Epoch 760/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4892 - accuracy: 0.7769 - val_loss: 0.5171 - val_accuracy: 0.7338\n",
            "Epoch 761/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4919 - accuracy: 0.7638 - val_loss: 0.6374 - val_accuracy: 0.6494\n",
            "Epoch 762/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.5147 - accuracy: 0.7443 - val_loss: 0.5202 - val_accuracy: 0.7273\n",
            "Epoch 763/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4863 - accuracy: 0.7622 - val_loss: 0.6030 - val_accuracy: 0.6883\n",
            "Epoch 764/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4789 - accuracy: 0.7590 - val_loss: 0.5383 - val_accuracy: 0.7338\n",
            "Epoch 765/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4810 - accuracy: 0.7655 - val_loss: 0.4975 - val_accuracy: 0.7338\n",
            "Epoch 766/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4871 - accuracy: 0.7736 - val_loss: 0.5054 - val_accuracy: 0.7532\n",
            "Epoch 767/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4821 - accuracy: 0.7704 - val_loss: 0.5092 - val_accuracy: 0.7727\n",
            "Epoch 768/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4819 - accuracy: 0.7638 - val_loss: 0.5068 - val_accuracy: 0.7532\n",
            "Epoch 769/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4873 - accuracy: 0.7704 - val_loss: 0.5159 - val_accuracy: 0.7338\n",
            "Epoch 770/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4903 - accuracy: 0.7736 - val_loss: 0.5129 - val_accuracy: 0.7273\n",
            "Epoch 771/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4791 - accuracy: 0.7850 - val_loss: 0.5091 - val_accuracy: 0.7338\n",
            "Epoch 772/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4965 - accuracy: 0.7687 - val_loss: 0.5179 - val_accuracy: 0.7403\n",
            "Epoch 773/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4761 - accuracy: 0.7704 - val_loss: 0.5379 - val_accuracy: 0.7273\n",
            "Epoch 774/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4911 - accuracy: 0.7655 - val_loss: 0.5007 - val_accuracy: 0.7662\n",
            "Epoch 775/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4897 - accuracy: 0.7785 - val_loss: 0.5118 - val_accuracy: 0.7403\n",
            "Epoch 776/1000\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.4821 - accuracy: 0.7720 - val_loss: 0.5392 - val_accuracy: 0.7208\n",
            "Epoch 777/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4839 - accuracy: 0.7736 - val_loss: 0.5922 - val_accuracy: 0.7143\n",
            "Epoch 778/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5016 - accuracy: 0.7557 - val_loss: 0.5069 - val_accuracy: 0.7597\n",
            "Epoch 779/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4950 - accuracy: 0.7687 - val_loss: 0.5005 - val_accuracy: 0.7468\n",
            "Epoch 780/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4767 - accuracy: 0.7736 - val_loss: 0.4965 - val_accuracy: 0.7338\n",
            "Epoch 781/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4788 - accuracy: 0.7704 - val_loss: 0.5001 - val_accuracy: 0.7468\n",
            "Epoch 782/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4788 - accuracy: 0.7720 - val_loss: 0.4997 - val_accuracy: 0.7468\n",
            "Epoch 783/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4789 - accuracy: 0.7801 - val_loss: 0.5007 - val_accuracy: 0.7792\n",
            "Epoch 784/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4844 - accuracy: 0.7655 - val_loss: 0.4943 - val_accuracy: 0.7273\n",
            "Epoch 785/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4839 - accuracy: 0.7622 - val_loss: 0.5363 - val_accuracy: 0.7273\n",
            "Epoch 786/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4793 - accuracy: 0.7736 - val_loss: 0.4980 - val_accuracy: 0.7662\n",
            "Epoch 787/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4734 - accuracy: 0.7818 - val_loss: 0.4959 - val_accuracy: 0.7403\n",
            "Epoch 788/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4784 - accuracy: 0.7752 - val_loss: 0.5075 - val_accuracy: 0.7597\n",
            "Epoch 789/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4825 - accuracy: 0.7769 - val_loss: 0.4963 - val_accuracy: 0.7597\n",
            "Epoch 790/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4979 - accuracy: 0.7720 - val_loss: 0.5046 - val_accuracy: 0.7597\n",
            "Epoch 791/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4968 - accuracy: 0.7785 - val_loss: 0.5209 - val_accuracy: 0.7338\n",
            "Epoch 792/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4924 - accuracy: 0.7655 - val_loss: 0.5760 - val_accuracy: 0.7013\n",
            "Epoch 793/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4981 - accuracy: 0.7687 - val_loss: 0.5494 - val_accuracy: 0.7143\n",
            "Epoch 794/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4834 - accuracy: 0.7736 - val_loss: 0.5001 - val_accuracy: 0.7338\n",
            "Epoch 795/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4765 - accuracy: 0.7720 - val_loss: 0.4990 - val_accuracy: 0.7338\n",
            "Epoch 796/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4814 - accuracy: 0.7720 - val_loss: 0.5319 - val_accuracy: 0.7273\n",
            "Epoch 797/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4769 - accuracy: 0.7834 - val_loss: 0.5525 - val_accuracy: 0.7143\n",
            "Epoch 798/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4881 - accuracy: 0.7769 - val_loss: 0.5008 - val_accuracy: 0.7403\n",
            "Epoch 799/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4875 - accuracy: 0.7590 - val_loss: 0.5010 - val_accuracy: 0.7597\n",
            "Epoch 800/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4842 - accuracy: 0.7687 - val_loss: 0.6095 - val_accuracy: 0.6753\n",
            "Epoch 801/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4944 - accuracy: 0.7638 - val_loss: 0.5320 - val_accuracy: 0.7273\n",
            "Epoch 802/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4774 - accuracy: 0.7638 - val_loss: 0.5032 - val_accuracy: 0.7597\n",
            "Epoch 803/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4791 - accuracy: 0.7850 - val_loss: 0.5332 - val_accuracy: 0.7273\n",
            "Epoch 804/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4929 - accuracy: 0.7606 - val_loss: 0.5152 - val_accuracy: 0.7403\n",
            "Epoch 805/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4820 - accuracy: 0.7752 - val_loss: 0.5144 - val_accuracy: 0.7338\n",
            "Epoch 806/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4814 - accuracy: 0.7752 - val_loss: 0.5338 - val_accuracy: 0.7273\n",
            "Epoch 807/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4792 - accuracy: 0.7769 - val_loss: 0.5068 - val_accuracy: 0.7468\n",
            "Epoch 808/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4755 - accuracy: 0.7850 - val_loss: 0.5388 - val_accuracy: 0.7208\n",
            "Epoch 809/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4825 - accuracy: 0.7818 - val_loss: 0.4993 - val_accuracy: 0.7532\n",
            "Epoch 810/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4786 - accuracy: 0.7704 - val_loss: 0.4972 - val_accuracy: 0.7338\n",
            "Epoch 811/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4817 - accuracy: 0.7720 - val_loss: 0.5016 - val_accuracy: 0.7403\n",
            "Epoch 812/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4801 - accuracy: 0.7687 - val_loss: 0.5218 - val_accuracy: 0.7208\n",
            "Epoch 813/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4881 - accuracy: 0.7573 - val_loss: 0.5176 - val_accuracy: 0.7273\n",
            "Epoch 814/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4815 - accuracy: 0.7720 - val_loss: 0.5371 - val_accuracy: 0.7273\n",
            "Epoch 815/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4801 - accuracy: 0.7801 - val_loss: 0.5054 - val_accuracy: 0.7662\n",
            "Epoch 816/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4744 - accuracy: 0.7704 - val_loss: 0.5010 - val_accuracy: 0.7403\n",
            "Epoch 817/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4773 - accuracy: 0.7704 - val_loss: 0.5040 - val_accuracy: 0.7468\n",
            "Epoch 818/1000\n",
            "20/20 [==============================] - 0s 4ms/step - loss: 0.4719 - accuracy: 0.7720 - val_loss: 0.5070 - val_accuracy: 0.7662\n",
            "Epoch 819/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4731 - accuracy: 0.7704 - val_loss: 0.4976 - val_accuracy: 0.7338\n",
            "Epoch 820/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4831 - accuracy: 0.7801 - val_loss: 0.5040 - val_accuracy: 0.7468\n",
            "Epoch 821/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4747 - accuracy: 0.7704 - val_loss: 0.5041 - val_accuracy: 0.7403\n",
            "Epoch 822/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4942 - accuracy: 0.7541 - val_loss: 0.5121 - val_accuracy: 0.7468\n",
            "Epoch 823/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4894 - accuracy: 0.7834 - val_loss: 0.5434 - val_accuracy: 0.7208\n",
            "Epoch 824/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4779 - accuracy: 0.7671 - val_loss: 0.5145 - val_accuracy: 0.7403\n",
            "Epoch 825/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4762 - accuracy: 0.7704 - val_loss: 0.5178 - val_accuracy: 0.7532\n",
            "Epoch 826/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4792 - accuracy: 0.7704 - val_loss: 0.5207 - val_accuracy: 0.7273\n",
            "Epoch 827/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4840 - accuracy: 0.7850 - val_loss: 0.6050 - val_accuracy: 0.7013\n",
            "Epoch 828/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4958 - accuracy: 0.7557 - val_loss: 0.5014 - val_accuracy: 0.7662\n",
            "Epoch 829/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4910 - accuracy: 0.7573 - val_loss: 0.4985 - val_accuracy: 0.7338\n",
            "Epoch 830/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4791 - accuracy: 0.7769 - val_loss: 0.4978 - val_accuracy: 0.7338\n",
            "Epoch 831/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4833 - accuracy: 0.7606 - val_loss: 0.5542 - val_accuracy: 0.7143\n",
            "Epoch 832/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4808 - accuracy: 0.7720 - val_loss: 0.5003 - val_accuracy: 0.7468\n",
            "Epoch 833/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4751 - accuracy: 0.7752 - val_loss: 0.4971 - val_accuracy: 0.7468\n",
            "Epoch 834/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4845 - accuracy: 0.7736 - val_loss: 0.5010 - val_accuracy: 0.7468\n",
            "Epoch 835/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4741 - accuracy: 0.7752 - val_loss: 0.4986 - val_accuracy: 0.7338\n",
            "Epoch 836/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4818 - accuracy: 0.7785 - val_loss: 0.4983 - val_accuracy: 0.7403\n",
            "Epoch 837/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4820 - accuracy: 0.7655 - val_loss: 0.5031 - val_accuracy: 0.7403\n",
            "Epoch 838/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4742 - accuracy: 0.7720 - val_loss: 0.5124 - val_accuracy: 0.7403\n",
            "Epoch 839/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4763 - accuracy: 0.7720 - val_loss: 0.5042 - val_accuracy: 0.7662\n",
            "Epoch 840/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4739 - accuracy: 0.7818 - val_loss: 0.4975 - val_accuracy: 0.7338\n",
            "Epoch 841/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4743 - accuracy: 0.7720 - val_loss: 0.5257 - val_accuracy: 0.7273\n",
            "Epoch 842/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4901 - accuracy: 0.7704 - val_loss: 0.5403 - val_accuracy: 0.7208\n",
            "Epoch 843/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4756 - accuracy: 0.7752 - val_loss: 0.5100 - val_accuracy: 0.7403\n",
            "Epoch 844/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4725 - accuracy: 0.7687 - val_loss: 0.5056 - val_accuracy: 0.7532\n",
            "Epoch 845/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4775 - accuracy: 0.7915 - val_loss: 0.5116 - val_accuracy: 0.7338\n",
            "Epoch 846/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4805 - accuracy: 0.7785 - val_loss: 0.4987 - val_accuracy: 0.7403\n",
            "Epoch 847/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4779 - accuracy: 0.7769 - val_loss: 0.5156 - val_accuracy: 0.7338\n",
            "Epoch 848/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4932 - accuracy: 0.7557 - val_loss: 0.5114 - val_accuracy: 0.7468\n",
            "Epoch 849/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4833 - accuracy: 0.7655 - val_loss: 0.4995 - val_accuracy: 0.7532\n",
            "Epoch 850/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4748 - accuracy: 0.7785 - val_loss: 0.4975 - val_accuracy: 0.7338\n",
            "Epoch 851/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4760 - accuracy: 0.7720 - val_loss: 0.4976 - val_accuracy: 0.7403\n",
            "Epoch 852/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4824 - accuracy: 0.7655 - val_loss: 0.4975 - val_accuracy: 0.7338\n",
            "Epoch 853/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4767 - accuracy: 0.7752 - val_loss: 0.4992 - val_accuracy: 0.7403\n",
            "Epoch 854/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4818 - accuracy: 0.7736 - val_loss: 0.5051 - val_accuracy: 0.7597\n",
            "Epoch 855/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4745 - accuracy: 0.7606 - val_loss: 0.5051 - val_accuracy: 0.7597\n",
            "Epoch 856/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4798 - accuracy: 0.7704 - val_loss: 0.5535 - val_accuracy: 0.7078\n",
            "Epoch 857/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4886 - accuracy: 0.7736 - val_loss: 0.5247 - val_accuracy: 0.7338\n",
            "Epoch 858/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4914 - accuracy: 0.7687 - val_loss: 0.5027 - val_accuracy: 0.7662\n",
            "Epoch 859/1000\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.4871 - accuracy: 0.7687 - val_loss: 0.5002 - val_accuracy: 0.7532\n",
            "Epoch 860/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4842 - accuracy: 0.7492 - val_loss: 0.5713 - val_accuracy: 0.7078\n",
            "Epoch 861/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4938 - accuracy: 0.7622 - val_loss: 0.5052 - val_accuracy: 0.7597\n",
            "Epoch 862/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4802 - accuracy: 0.7508 - val_loss: 0.5610 - val_accuracy: 0.6948\n",
            "Epoch 863/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4944 - accuracy: 0.7720 - val_loss: 0.5690 - val_accuracy: 0.7013\n",
            "Epoch 864/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4914 - accuracy: 0.7671 - val_loss: 0.5062 - val_accuracy: 0.7597\n",
            "Epoch 865/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5041 - accuracy: 0.7573 - val_loss: 0.5358 - val_accuracy: 0.7143\n",
            "Epoch 866/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.5002 - accuracy: 0.7622 - val_loss: 0.5503 - val_accuracy: 0.7078\n",
            "Epoch 867/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4828 - accuracy: 0.7671 - val_loss: 0.5349 - val_accuracy: 0.7273\n",
            "Epoch 868/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4860 - accuracy: 0.7736 - val_loss: 0.5326 - val_accuracy: 0.7143\n",
            "Epoch 869/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4894 - accuracy: 0.7687 - val_loss: 0.5164 - val_accuracy: 0.7403\n",
            "Epoch 870/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4748 - accuracy: 0.7769 - val_loss: 0.5050 - val_accuracy: 0.7532\n",
            "Epoch 871/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4870 - accuracy: 0.7671 - val_loss: 0.5245 - val_accuracy: 0.7273\n",
            "Epoch 872/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4890 - accuracy: 0.7606 - val_loss: 0.5120 - val_accuracy: 0.7338\n",
            "Epoch 873/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4806 - accuracy: 0.7720 - val_loss: 0.4984 - val_accuracy: 0.7597\n",
            "Epoch 874/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5033 - accuracy: 0.7524 - val_loss: 0.5030 - val_accuracy: 0.7338\n",
            "Epoch 875/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4817 - accuracy: 0.7720 - val_loss: 0.4988 - val_accuracy: 0.7532\n",
            "Epoch 876/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4801 - accuracy: 0.7785 - val_loss: 0.5005 - val_accuracy: 0.7662\n",
            "Epoch 877/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4829 - accuracy: 0.7687 - val_loss: 0.5144 - val_accuracy: 0.7338\n",
            "Epoch 878/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4768 - accuracy: 0.7704 - val_loss: 0.5336 - val_accuracy: 0.7338\n",
            "Epoch 879/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4759 - accuracy: 0.7720 - val_loss: 0.5054 - val_accuracy: 0.7468\n",
            "Epoch 880/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4789 - accuracy: 0.7818 - val_loss: 0.5025 - val_accuracy: 0.7403\n",
            "Epoch 881/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4777 - accuracy: 0.7752 - val_loss: 0.4988 - val_accuracy: 0.7468\n",
            "Epoch 882/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4768 - accuracy: 0.7769 - val_loss: 0.5126 - val_accuracy: 0.7338\n",
            "Epoch 883/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4867 - accuracy: 0.7704 - val_loss: 0.5172 - val_accuracy: 0.7403\n",
            "Epoch 884/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4776 - accuracy: 0.7704 - val_loss: 0.5024 - val_accuracy: 0.7468\n",
            "Epoch 885/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4770 - accuracy: 0.7801 - val_loss: 0.5114 - val_accuracy: 0.7532\n",
            "Epoch 886/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4856 - accuracy: 0.7704 - val_loss: 0.4995 - val_accuracy: 0.7403\n",
            "Epoch 887/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4848 - accuracy: 0.7736 - val_loss: 0.5335 - val_accuracy: 0.7273\n",
            "Epoch 888/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4815 - accuracy: 0.7704 - val_loss: 0.5266 - val_accuracy: 0.7403\n",
            "Epoch 889/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4818 - accuracy: 0.7687 - val_loss: 0.5557 - val_accuracy: 0.7143\n",
            "Epoch 890/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4914 - accuracy: 0.7638 - val_loss: 0.5003 - val_accuracy: 0.7532\n",
            "Epoch 891/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4751 - accuracy: 0.7720 - val_loss: 0.5215 - val_accuracy: 0.7338\n",
            "Epoch 892/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4822 - accuracy: 0.7785 - val_loss: 0.5011 - val_accuracy: 0.7532\n",
            "Epoch 893/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4797 - accuracy: 0.7752 - val_loss: 0.5036 - val_accuracy: 0.7532\n",
            "Epoch 894/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4947 - accuracy: 0.7655 - val_loss: 0.5535 - val_accuracy: 0.7078\n",
            "Epoch 895/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4929 - accuracy: 0.7606 - val_loss: 0.4984 - val_accuracy: 0.7468\n",
            "Epoch 896/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4852 - accuracy: 0.7687 - val_loss: 0.5029 - val_accuracy: 0.7338\n",
            "Epoch 897/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4776 - accuracy: 0.7720 - val_loss: 0.5103 - val_accuracy: 0.7468\n",
            "Epoch 898/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4725 - accuracy: 0.7785 - val_loss: 0.5172 - val_accuracy: 0.7468\n",
            "Epoch 899/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4756 - accuracy: 0.7736 - val_loss: 0.5019 - val_accuracy: 0.7403\n",
            "Epoch 900/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4711 - accuracy: 0.7801 - val_loss: 0.4953 - val_accuracy: 0.7597\n",
            "Epoch 901/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4753 - accuracy: 0.7687 - val_loss: 0.4958 - val_accuracy: 0.7403\n",
            "Epoch 902/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4716 - accuracy: 0.7720 - val_loss: 0.4973 - val_accuracy: 0.7338\n",
            "Epoch 903/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4788 - accuracy: 0.7785 - val_loss: 0.4966 - val_accuracy: 0.7403\n",
            "Epoch 904/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4758 - accuracy: 0.7720 - val_loss: 0.5056 - val_accuracy: 0.7597\n",
            "Epoch 905/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4732 - accuracy: 0.7736 - val_loss: 0.4972 - val_accuracy: 0.7532\n",
            "Epoch 906/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4745 - accuracy: 0.7720 - val_loss: 0.5062 - val_accuracy: 0.7662\n",
            "Epoch 907/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4758 - accuracy: 0.7671 - val_loss: 0.5043 - val_accuracy: 0.7468\n",
            "Epoch 908/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4798 - accuracy: 0.7785 - val_loss: 0.4985 - val_accuracy: 0.7468\n",
            "Epoch 909/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4729 - accuracy: 0.7785 - val_loss: 0.4993 - val_accuracy: 0.7403\n",
            "Epoch 910/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4766 - accuracy: 0.7687 - val_loss: 0.4990 - val_accuracy: 0.7273\n",
            "Epoch 911/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4857 - accuracy: 0.7704 - val_loss: 0.5467 - val_accuracy: 0.7143\n",
            "Epoch 912/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4977 - accuracy: 0.7622 - val_loss: 0.5007 - val_accuracy: 0.7273\n",
            "Epoch 913/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4831 - accuracy: 0.7687 - val_loss: 0.5014 - val_accuracy: 0.7338\n",
            "Epoch 914/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4793 - accuracy: 0.7736 - val_loss: 0.4970 - val_accuracy: 0.7403\n",
            "Epoch 915/1000\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.4884 - accuracy: 0.7573 - val_loss: 0.4963 - val_accuracy: 0.7403\n",
            "Epoch 916/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4863 - accuracy: 0.7785 - val_loss: 0.5076 - val_accuracy: 0.7597\n",
            "Epoch 917/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4759 - accuracy: 0.7736 - val_loss: 0.5078 - val_accuracy: 0.7662\n",
            "Epoch 918/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4780 - accuracy: 0.7671 - val_loss: 0.5007 - val_accuracy: 0.7403\n",
            "Epoch 919/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4808 - accuracy: 0.7866 - val_loss: 0.5317 - val_accuracy: 0.7338\n",
            "Epoch 920/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4871 - accuracy: 0.7687 - val_loss: 0.5026 - val_accuracy: 0.7403\n",
            "Epoch 921/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4812 - accuracy: 0.7752 - val_loss: 0.5304 - val_accuracy: 0.7338\n",
            "Epoch 922/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4767 - accuracy: 0.7736 - val_loss: 0.5123 - val_accuracy: 0.7403\n",
            "Epoch 923/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4751 - accuracy: 0.7704 - val_loss: 0.5039 - val_accuracy: 0.7597\n",
            "Epoch 924/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.5173 - accuracy: 0.7394 - val_loss: 0.5092 - val_accuracy: 0.7403\n",
            "Epoch 925/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4923 - accuracy: 0.7671 - val_loss: 0.6805 - val_accuracy: 0.6688\n",
            "Epoch 926/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.5043 - accuracy: 0.7524 - val_loss: 0.5254 - val_accuracy: 0.7403\n",
            "Epoch 927/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4942 - accuracy: 0.7769 - val_loss: 0.5102 - val_accuracy: 0.7143\n",
            "Epoch 928/1000\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.4926 - accuracy: 0.7606 - val_loss: 0.4998 - val_accuracy: 0.7532\n",
            "Epoch 929/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4878 - accuracy: 0.7720 - val_loss: 0.4959 - val_accuracy: 0.7338\n",
            "Epoch 930/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4779 - accuracy: 0.7671 - val_loss: 0.5522 - val_accuracy: 0.7078\n",
            "Epoch 931/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4915 - accuracy: 0.7687 - val_loss: 0.5103 - val_accuracy: 0.7468\n",
            "Epoch 932/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4724 - accuracy: 0.7671 - val_loss: 0.5272 - val_accuracy: 0.7273\n",
            "Epoch 933/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4786 - accuracy: 0.7704 - val_loss: 0.5085 - val_accuracy: 0.7532\n",
            "Epoch 934/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4978 - accuracy: 0.7590 - val_loss: 0.5501 - val_accuracy: 0.7208\n",
            "Epoch 935/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4813 - accuracy: 0.7622 - val_loss: 0.4977 - val_accuracy: 0.7597\n",
            "Epoch 936/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4865 - accuracy: 0.7769 - val_loss: 0.4997 - val_accuracy: 0.7273\n",
            "Epoch 937/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4761 - accuracy: 0.7818 - val_loss: 0.4991 - val_accuracy: 0.7403\n",
            "Epoch 938/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4796 - accuracy: 0.7687 - val_loss: 0.4987 - val_accuracy: 0.7338\n",
            "Epoch 939/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4783 - accuracy: 0.7785 - val_loss: 0.5153 - val_accuracy: 0.7338\n",
            "Epoch 940/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4719 - accuracy: 0.7687 - val_loss: 0.5058 - val_accuracy: 0.7468\n",
            "Epoch 941/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4843 - accuracy: 0.7720 - val_loss: 0.5712 - val_accuracy: 0.7078\n",
            "Epoch 942/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4905 - accuracy: 0.7671 - val_loss: 0.5274 - val_accuracy: 0.7338\n",
            "Epoch 943/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4774 - accuracy: 0.7655 - val_loss: 0.5027 - val_accuracy: 0.7532\n",
            "Epoch 944/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4867 - accuracy: 0.7720 - val_loss: 0.5056 - val_accuracy: 0.7532\n",
            "Epoch 945/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4804 - accuracy: 0.7557 - val_loss: 0.5068 - val_accuracy: 0.7597\n",
            "Epoch 946/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4807 - accuracy: 0.7720 - val_loss: 0.4945 - val_accuracy: 0.7338\n",
            "Epoch 947/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4739 - accuracy: 0.7622 - val_loss: 0.5344 - val_accuracy: 0.7273\n",
            "Epoch 948/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4756 - accuracy: 0.7769 - val_loss: 0.5267 - val_accuracy: 0.7338\n",
            "Epoch 949/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4799 - accuracy: 0.7704 - val_loss: 0.5272 - val_accuracy: 0.7338\n",
            "Epoch 950/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4786 - accuracy: 0.7687 - val_loss: 0.5022 - val_accuracy: 0.7403\n",
            "Epoch 951/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4808 - accuracy: 0.7655 - val_loss: 0.4999 - val_accuracy: 0.7403\n",
            "Epoch 952/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4741 - accuracy: 0.7720 - val_loss: 0.5194 - val_accuracy: 0.7468\n",
            "Epoch 953/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4896 - accuracy: 0.7573 - val_loss: 0.5021 - val_accuracy: 0.7403\n",
            "Epoch 954/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4823 - accuracy: 0.7524 - val_loss: 0.5229 - val_accuracy: 0.7338\n",
            "Epoch 955/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4934 - accuracy: 0.7687 - val_loss: 0.4976 - val_accuracy: 0.7403\n",
            "Epoch 956/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4857 - accuracy: 0.7687 - val_loss: 0.5250 - val_accuracy: 0.7338\n",
            "Epoch 957/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4730 - accuracy: 0.7752 - val_loss: 0.4991 - val_accuracy: 0.7532\n",
            "Epoch 958/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4755 - accuracy: 0.7752 - val_loss: 0.5121 - val_accuracy: 0.7403\n",
            "Epoch 959/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4912 - accuracy: 0.7785 - val_loss: 0.5034 - val_accuracy: 0.7403\n",
            "Epoch 960/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4744 - accuracy: 0.7769 - val_loss: 0.5136 - val_accuracy: 0.7403\n",
            "Epoch 961/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4751 - accuracy: 0.7785 - val_loss: 0.5107 - val_accuracy: 0.7597\n",
            "Epoch 962/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4930 - accuracy: 0.7638 - val_loss: 0.5604 - val_accuracy: 0.7078\n",
            "Epoch 963/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4877 - accuracy: 0.7720 - val_loss: 0.5194 - val_accuracy: 0.7273\n",
            "Epoch 964/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4833 - accuracy: 0.7736 - val_loss: 0.5410 - val_accuracy: 0.7208\n",
            "Epoch 965/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4801 - accuracy: 0.7687 - val_loss: 0.5098 - val_accuracy: 0.7597\n",
            "Epoch 966/1000\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.4921 - accuracy: 0.7590 - val_loss: 0.5525 - val_accuracy: 0.7078\n",
            "Epoch 967/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4859 - accuracy: 0.7687 - val_loss: 0.5366 - val_accuracy: 0.7273\n",
            "Epoch 968/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4768 - accuracy: 0.7834 - val_loss: 0.5044 - val_accuracy: 0.7597\n",
            "Epoch 969/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4743 - accuracy: 0.7704 - val_loss: 0.5121 - val_accuracy: 0.7468\n",
            "Epoch 970/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4739 - accuracy: 0.7769 - val_loss: 0.5176 - val_accuracy: 0.7273\n",
            "Epoch 971/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4793 - accuracy: 0.7769 - val_loss: 0.4990 - val_accuracy: 0.7338\n",
            "Epoch 972/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4862 - accuracy: 0.7704 - val_loss: 0.5000 - val_accuracy: 0.7338\n",
            "Epoch 973/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4829 - accuracy: 0.7736 - val_loss: 0.5041 - val_accuracy: 0.7662\n",
            "Epoch 974/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4797 - accuracy: 0.7638 - val_loss: 0.5011 - val_accuracy: 0.7403\n",
            "Epoch 975/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4839 - accuracy: 0.7736 - val_loss: 0.5026 - val_accuracy: 0.7273\n",
            "Epoch 976/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4731 - accuracy: 0.7736 - val_loss: 0.5016 - val_accuracy: 0.7727\n",
            "Epoch 977/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4822 - accuracy: 0.7687 - val_loss: 0.5067 - val_accuracy: 0.7662\n",
            "Epoch 978/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4773 - accuracy: 0.7704 - val_loss: 0.5076 - val_accuracy: 0.7662\n",
            "Epoch 979/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4739 - accuracy: 0.7769 - val_loss: 0.5043 - val_accuracy: 0.7468\n",
            "Epoch 980/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4818 - accuracy: 0.7736 - val_loss: 0.5281 - val_accuracy: 0.7273\n",
            "Epoch 981/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4826 - accuracy: 0.7573 - val_loss: 0.5070 - val_accuracy: 0.7532\n",
            "Epoch 982/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4943 - accuracy: 0.7590 - val_loss: 0.5118 - val_accuracy: 0.7532\n",
            "Epoch 983/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4786 - accuracy: 0.7638 - val_loss: 0.5184 - val_accuracy: 0.7273\n",
            "Epoch 984/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4739 - accuracy: 0.7704 - val_loss: 0.5202 - val_accuracy: 0.7403\n",
            "Epoch 985/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4760 - accuracy: 0.7785 - val_loss: 0.5043 - val_accuracy: 0.7597\n",
            "Epoch 986/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4780 - accuracy: 0.7736 - val_loss: 0.5098 - val_accuracy: 0.7532\n",
            "Epoch 987/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4758 - accuracy: 0.7638 - val_loss: 0.5026 - val_accuracy: 0.7403\n",
            "Epoch 988/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4782 - accuracy: 0.7655 - val_loss: 0.5044 - val_accuracy: 0.7597\n",
            "Epoch 989/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4764 - accuracy: 0.7720 - val_loss: 0.5131 - val_accuracy: 0.7403\n",
            "Epoch 990/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4709 - accuracy: 0.7736 - val_loss: 0.4993 - val_accuracy: 0.7403\n",
            "Epoch 991/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4734 - accuracy: 0.7752 - val_loss: 0.5205 - val_accuracy: 0.7338\n",
            "Epoch 992/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4758 - accuracy: 0.7818 - val_loss: 0.5180 - val_accuracy: 0.7403\n",
            "Epoch 993/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4819 - accuracy: 0.7720 - val_loss: 0.4970 - val_accuracy: 0.7468\n",
            "Epoch 994/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4820 - accuracy: 0.7622 - val_loss: 0.4988 - val_accuracy: 0.7662\n",
            "Epoch 995/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4750 - accuracy: 0.7655 - val_loss: 0.4989 - val_accuracy: 0.7338\n",
            "Epoch 996/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4729 - accuracy: 0.7752 - val_loss: 0.4997 - val_accuracy: 0.7338\n",
            "Epoch 997/1000\n",
            "20/20 [==============================] - 0s 5ms/step - loss: 0.4732 - accuracy: 0.7655 - val_loss: 0.5488 - val_accuracy: 0.7143\n",
            "Epoch 998/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4736 - accuracy: 0.7704 - val_loss: 0.4980 - val_accuracy: 0.7403\n",
            "Epoch 999/1000\n",
            "20/20 [==============================] - 0s 6ms/step - loss: 0.4732 - accuracy: 0.7736 - val_loss: 0.4949 - val_accuracy: 0.7597\n",
            "Epoch 1000/1000\n",
            "20/20 [==============================] - 0s 7ms/step - loss: 0.4785 - accuracy: 0.7638 - val_loss: 0.5121 - val_accuracy: 0.7532\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f6e0fe74850>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SIWa32mCWbbw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6650352c-d2d0-48e9-b405-4206c694e344"
      },
      "source": [
        "# Final Prediction\n",
        "print(data_model.predict(test_Data))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.51022726]\n",
            " [0.19823965]\n",
            " [0.41735175]\n",
            " [0.09965181]\n",
            " [0.09464279]\n",
            " [0.04902756]\n",
            " [0.08382353]\n",
            " [0.28592914]\n",
            " [0.06817454]\n",
            " [0.32918575]\n",
            " [0.07548138]\n",
            " [0.22722542]\n",
            " [0.87176514]\n",
            " [0.30081874]\n",
            " [0.10610759]\n",
            " [0.6535546 ]\n",
            " [0.72781456]\n",
            " [0.05108386]\n",
            " [0.10158905]\n",
            " [0.89931524]\n",
            " [0.31170642]\n",
            " [0.46357304]\n",
            " [0.29949987]\n",
            " [0.20578882]\n",
            " [0.16067022]\n",
            " [0.62627363]\n",
            " [0.28214008]\n",
            " [0.01378658]\n",
            " [0.60071075]\n",
            " [0.7786457 ]\n",
            " [0.21071133]\n",
            " [0.04344276]\n",
            " [0.07636774]\n",
            " [0.13687906]\n",
            " [0.3792286 ]\n",
            " [0.12277865]\n",
            " [0.28292918]\n",
            " [0.26827663]\n",
            " [0.68239546]\n",
            " [0.0643976 ]\n",
            " [0.38077638]\n",
            " [0.24778974]\n",
            " [0.39056364]\n",
            " [0.29267544]\n",
            " [0.4079241 ]\n",
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            " [0.10110292]\n",
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            " [0.25098318]\n",
            " [0.17334735]\n",
            " [0.0504435 ]\n",
            " [0.1038194 ]\n",
            " [0.09779871]\n",
            " [0.05184093]\n",
            " [0.43195814]\n",
            " [0.11412746]\n",
            " [0.37830657]\n",
            " [0.5173272 ]\n",
            " [0.274088  ]\n",
            " [0.1561875 ]\n",
            " [0.519541  ]\n",
            " [0.6251128 ]\n",
            " [0.16863951]\n",
            " [0.4524543 ]\n",
            " [0.511105  ]\n",
            " [0.18976375]\n",
            " [0.12807786]\n",
            " [0.5920047 ]\n",
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            " [0.25925526]\n",
            " [0.07138821]\n",
            " [0.7444372 ]\n",
            " [0.05460998]\n",
            " [0.1400837 ]\n",
            " [0.2953899 ]\n",
            " [0.702335  ]\n",
            " [0.44388172]\n",
            " [0.38110036]\n",
            " [0.90527916]\n",
            " [0.23199752]\n",
            " [0.17657155]\n",
            " [0.39374134]\n",
            " [0.05152941]\n",
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            " [0.5290692 ]\n",
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            " [0.10996619]\n",
            " [0.48926526]\n",
            " [0.07784542]]\n"
          ]
        }
      ]
    }
  ]
}