Assignment_4_Regularization (1).ipynb

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    "colab": {
      "name": "Assignment_4_Regularization",
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    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "<b>Name: Aishwarya Bhavsar</b> <br>\n",
        "<b>CSULB ID: 029371509 </b><br>\n",
        "<b>CECS 551 - Assignment 4</b><br>\n",
        "<b>Design a neural network and test regularization methods using keras library</b>\n"
      ],
      "metadata": {
        "id": "qlZK1pUUwD3e"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<b>(b) Design a neural network and implement it.</b>"
      ],
      "metadata": {
        "id": "gwtOKvkkwfq2"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "DzT14WN8WFpD"
      },
      "outputs": [],
      "source": [
        "import scipy.io\n",
        "import matplotlib.pyplot as plt\n",
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras import losses\n",
        "from keras import metrics\n",
        "from tensorflow.keras import optimizers\n",
        "from google.colab import drive"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drive.mount('/content/drive')\n",
        "data=scipy.io.loadmat('/content/drive/My Drive/data.mat');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-B7Pk3i9WGbp",
        "outputId": "4db943a4-0e3c-4b42-9623-fbab25920f94"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "train_X = data['X'].T\n",
        "train_y = data['y'].T\n",
        "test_X = data['Xval'].T\n",
        "test_y = data['yval'].T"
      ],
      "metadata": {
        "id": "vZ46HsOFWGpu"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# define the keras model\n",
        "model = Sequential()\n",
        "model.add(Dense(12, input_dim=2, activation='relu'))\n",
        "model.add(Dense(8, activation='relu'))\n",
        "model.add(Dense(1, activation='sigmoid'))"
      ],
      "metadata": {
        "id": "DNrVCs1hWG8H"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# compile the keras model\n",
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "4cvGIsPrWHBH"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# fit the keras model on the dataset\n",
        "history = model.fit(train_X.T, train_y.T, validation_data=(test_X.T,test_y.T),epochs=1500,  verbose = 2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-IENwe2oWHDw",
        "outputId": "cb19f982-a624-4565-900c-92bec67d53b6"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/1500\n",
            "7/7 - 0s - loss: 0.1941 - accuracy: 0.9431 - val_loss: 0.2544 - val_accuracy: 0.9350 - 40ms/epoch - 6ms/step\n",
            "Epoch 2/1500\n",
            "7/7 - 0s - loss: 0.1940 - accuracy: 0.9431 - val_loss: 0.2546 - val_accuracy: 0.9350 - 41ms/epoch - 6ms/step\n",
            "Epoch 3/1500\n",
            "7/7 - 0s - loss: 0.1940 - accuracy: 0.9431 - val_loss: 0.2545 - val_accuracy: 0.9350 - 42ms/epoch - 6ms/step\n",
            "Epoch 4/1500\n",
            "7/7 - 0s - loss: 0.1940 - accuracy: 0.9384 - val_loss: 0.2549 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 5/1500\n",
            "7/7 - 0s - loss: 0.1940 - accuracy: 0.9431 - val_loss: 0.2548 - val_accuracy: 0.9350 - 51ms/epoch - 7ms/step\n",
            "Epoch 6/1500\n",
            "7/7 - 0s - loss: 0.1938 - accuracy: 0.9431 - val_loss: 0.2539 - val_accuracy: 0.9350 - 52ms/epoch - 7ms/step\n",
            "Epoch 7/1500\n",
            "7/7 - 0s - loss: 0.1938 - accuracy: 0.9431 - val_loss: 0.2535 - val_accuracy: 0.9350 - 53ms/epoch - 8ms/step\n",
            "Epoch 8/1500\n",
            "7/7 - 0s - loss: 0.1939 - accuracy: 0.9431 - val_loss: 0.2542 - val_accuracy: 0.9350 - 41ms/epoch - 6ms/step\n",
            "Epoch 9/1500\n",
            "7/7 - 0s - loss: 0.1940 - accuracy: 0.9431 - val_loss: 0.2544 - val_accuracy: 0.9350 - 52ms/epoch - 7ms/step\n",
            "Epoch 10/1500\n",
            "7/7 - 0s - loss: 0.1938 - accuracy: 0.9431 - val_loss: 0.2548 - val_accuracy: 0.9350 - 41ms/epoch - 6ms/step\n",
            "Epoch 11/1500\n",
            "7/7 - 0s - loss: 0.1942 - accuracy: 0.9431 - val_loss: 0.2535 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 12/1500\n",
            "7/7 - 0s - loss: 0.1936 - accuracy: 0.9431 - val_loss: 0.2549 - val_accuracy: 0.9350 - 40ms/epoch - 6ms/step\n",
            "Epoch 13/1500\n",
            "7/7 - 0s - loss: 0.1939 - accuracy: 0.9384 - val_loss: 0.2553 - val_accuracy: 0.9350 - 61ms/epoch - 9ms/step\n",
            "Epoch 14/1500\n",
            "7/7 - 0s - loss: 0.1935 - accuracy: 0.9384 - val_loss: 0.2545 - val_accuracy: 0.9350 - 52ms/epoch - 7ms/step\n",
            "Epoch 15/1500\n",
            "7/7 - 0s - loss: 0.1934 - accuracy: 0.9431 - val_loss: 0.2527 - val_accuracy: 0.9350 - 54ms/epoch - 8ms/step\n",
            "Epoch 16/1500\n",
            "7/7 - 0s - loss: 0.1940 - accuracy: 0.9431 - val_loss: 0.2517 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 17/1500\n",
            "7/7 - 0s - loss: 0.1937 - accuracy: 0.9431 - val_loss: 0.2526 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 18/1500\n",
            "7/7 - 0s - loss: 0.1940 - accuracy: 0.9431 - val_loss: 0.2536 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 19/1500\n",
            "7/7 - 0s - loss: 0.1936 - accuracy: 0.9431 - val_loss: 0.2535 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 20/1500\n",
            "7/7 - 0s - loss: 0.1935 - accuracy: 0.9384 - val_loss: 0.2519 - val_accuracy: 0.9350 - 65ms/epoch - 9ms/step\n",
            "Epoch 21/1500\n",
            "7/7 - 0s - loss: 0.1939 - accuracy: 0.9384 - val_loss: 0.2516 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 22/1500\n",
            "7/7 - 0s - loss: 0.1937 - accuracy: 0.9384 - val_loss: 0.2533 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 23/1500\n",
            "7/7 - 0s - loss: 0.1933 - accuracy: 0.9431 - val_loss: 0.2533 - val_accuracy: 0.9350 - 41ms/epoch - 6ms/step\n",
            "Epoch 24/1500\n",
            "7/7 - 0s - loss: 0.1941 - accuracy: 0.9431 - val_loss: 0.2519 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 25/1500\n",
            "7/7 - 0s - loss: 0.1929 - accuracy: 0.9431 - val_loss: 0.2529 - val_accuracy: 0.9350 - 42ms/epoch - 6ms/step\n",
            "Epoch 26/1500\n",
            "7/7 - 0s - loss: 0.1939 - accuracy: 0.9384 - val_loss: 0.2553 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 27/1500\n",
            "7/7 - 0s - loss: 0.1938 - accuracy: 0.9384 - val_loss: 0.2539 - val_accuracy: 0.9350 - 53ms/epoch - 8ms/step\n",
            "Epoch 28/1500\n",
            "7/7 - 0s - loss: 0.1931 - accuracy: 0.9431 - val_loss: 0.2541 - val_accuracy: 0.9350 - 43ms/epoch - 6ms/step\n",
            "Epoch 29/1500\n",
            "7/7 - 0s - loss: 0.1932 - accuracy: 0.9431 - val_loss: 0.2541 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 30/1500\n",
            "7/7 - 0s - loss: 0.1930 - accuracy: 0.9431 - val_loss: 0.2522 - val_accuracy: 0.9350 - 53ms/epoch - 8ms/step\n",
            "Epoch 31/1500\n",
            "7/7 - 0s - loss: 0.1932 - accuracy: 0.9431 - val_loss: 0.2528 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 32/1500\n",
            "7/7 - 0s - loss: 0.1931 - accuracy: 0.9431 - val_loss: 0.2517 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 33/1500\n",
            "7/7 - 0s - loss: 0.1932 - accuracy: 0.9384 - val_loss: 0.2513 - val_accuracy: 0.9350 - 52ms/epoch - 7ms/step\n",
            "Epoch 34/1500\n",
            "7/7 - 0s - loss: 0.1927 - accuracy: 0.9431 - val_loss: 0.2530 - val_accuracy: 0.9350 - 41ms/epoch - 6ms/step\n",
            "Epoch 35/1500\n",
            "7/7 - 0s - loss: 0.1927 - accuracy: 0.9431 - val_loss: 0.2541 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 36/1500\n",
            "7/7 - 0s - loss: 0.1927 - accuracy: 0.9431 - val_loss: 0.2547 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 37/1500\n",
            "7/7 - 0s - loss: 0.1929 - accuracy: 0.9431 - val_loss: 0.2546 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 38/1500\n",
            "7/7 - 0s - loss: 0.1929 - accuracy: 0.9431 - val_loss: 0.2536 - val_accuracy: 0.9350 - 40ms/epoch - 6ms/step\n",
            "Epoch 39/1500\n",
            "7/7 - 0s - loss: 0.1925 - accuracy: 0.9431 - val_loss: 0.2530 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 40/1500\n",
            "7/7 - 0s - loss: 0.1926 - accuracy: 0.9431 - val_loss: 0.2530 - val_accuracy: 0.9350 - 42ms/epoch - 6ms/step\n",
            "Epoch 41/1500\n",
            "7/7 - 0s - loss: 0.1926 - accuracy: 0.9431 - val_loss: 0.2527 - val_accuracy: 0.9350 - 52ms/epoch - 7ms/step\n",
            "Epoch 42/1500\n",
            "7/7 - 0s - loss: 0.1928 - accuracy: 0.9384 - val_loss: 0.2523 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 43/1500\n",
            "7/7 - 0s - loss: 0.1927 - accuracy: 0.9431 - val_loss: 0.2520 - val_accuracy: 0.9350 - 43ms/epoch - 6ms/step\n",
            "Epoch 44/1500\n",
            "7/7 - 0s - loss: 0.1926 - accuracy: 0.9384 - val_loss: 0.2521 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 45/1500\n",
            "7/7 - 0s - loss: 0.1925 - accuracy: 0.9431 - val_loss: 0.2528 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 46/1500\n",
            "7/7 - 0s - loss: 0.1925 - accuracy: 0.9431 - val_loss: 0.2532 - val_accuracy: 0.9350 - 42ms/epoch - 6ms/step\n",
            "Epoch 47/1500\n",
            "7/7 - 0s - loss: 0.1923 - accuracy: 0.9431 - val_loss: 0.2530 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 48/1500\n",
            "7/7 - 0s - loss: 0.1923 - accuracy: 0.9431 - val_loss: 0.2529 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 49/1500\n",
            "7/7 - 0s - loss: 0.1922 - accuracy: 0.9431 - val_loss: 0.2525 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 50/1500\n",
            "7/7 - 0s - loss: 0.1924 - accuracy: 0.9384 - val_loss: 0.2526 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 51/1500\n",
            "7/7 - 0s - loss: 0.1924 - accuracy: 0.9431 - val_loss: 0.2528 - val_accuracy: 0.9350 - 54ms/epoch - 8ms/step\n",
            "Epoch 52/1500\n",
            "7/7 - 0s - loss: 0.1922 - accuracy: 0.9431 - val_loss: 0.2533 - val_accuracy: 0.9350 - 42ms/epoch - 6ms/step\n",
            "Epoch 53/1500\n",
            "7/7 - 0s - loss: 0.1921 - accuracy: 0.9431 - val_loss: 0.2533 - val_accuracy: 0.9350 - 65ms/epoch - 9ms/step\n",
            "Epoch 54/1500\n",
            "7/7 - 0s - loss: 0.1922 - accuracy: 0.9431 - val_loss: 0.2530 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 55/1500\n",
            "7/7 - 0s - loss: 0.1928 - accuracy: 0.9431 - val_loss: 0.2517 - val_accuracy: 0.9350 - 61ms/epoch - 9ms/step\n",
            "Epoch 56/1500\n",
            "7/7 - 0s - loss: 0.1922 - accuracy: 0.9384 - val_loss: 0.2519 - val_accuracy: 0.9350 - 40ms/epoch - 6ms/step\n",
            "Epoch 57/1500\n",
            "7/7 - 0s - loss: 0.1920 - accuracy: 0.9431 - val_loss: 0.2532 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 58/1500\n",
            "7/7 - 0s - loss: 0.1923 - accuracy: 0.9431 - val_loss: 0.2546 - val_accuracy: 0.9350 - 54ms/epoch - 8ms/step\n",
            "Epoch 59/1500\n",
            "7/7 - 0s - loss: 0.1920 - accuracy: 0.9431 - val_loss: 0.2537 - val_accuracy: 0.9350 - 53ms/epoch - 8ms/step\n",
            "Epoch 60/1500\n",
            "7/7 - 0s - loss: 0.1920 - accuracy: 0.9431 - val_loss: 0.2536 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 61/1500\n",
            "7/7 - 0s - loss: 0.1919 - accuracy: 0.9431 - val_loss: 0.2528 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 62/1500\n",
            "7/7 - 0s - loss: 0.1917 - accuracy: 0.9431 - val_loss: 0.2525 - val_accuracy: 0.9350 - 62ms/epoch - 9ms/step\n",
            "Epoch 63/1500\n",
            "7/7 - 0s - loss: 0.1919 - accuracy: 0.9431 - val_loss: 0.2528 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 64/1500\n",
            "7/7 - 0s - loss: 0.1918 - accuracy: 0.9431 - val_loss: 0.2530 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 65/1500\n",
            "7/7 - 0s - loss: 0.1916 - accuracy: 0.9431 - val_loss: 0.2525 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 66/1500\n",
            "7/7 - 0s - loss: 0.1918 - accuracy: 0.9384 - val_loss: 0.2519 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 67/1500\n",
            "7/7 - 0s - loss: 0.1916 - accuracy: 0.9431 - val_loss: 0.2522 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 68/1500\n",
            "7/7 - 0s - loss: 0.1915 - accuracy: 0.9384 - val_loss: 0.2520 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 69/1500\n",
            "7/7 - 0s - loss: 0.1916 - accuracy: 0.9384 - val_loss: 0.2523 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 70/1500\n",
            "7/7 - 0s - loss: 0.1916 - accuracy: 0.9431 - val_loss: 0.2524 - val_accuracy: 0.9350 - 54ms/epoch - 8ms/step\n",
            "Epoch 71/1500\n",
            "7/7 - 0s - loss: 0.1916 - accuracy: 0.9384 - val_loss: 0.2517 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 72/1500\n",
            "7/7 - 0s - loss: 0.1917 - accuracy: 0.9384 - val_loss: 0.2521 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 73/1500\n",
            "7/7 - 0s - loss: 0.1915 - accuracy: 0.9431 - val_loss: 0.2544 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 74/1500\n",
            "7/7 - 0s - loss: 0.1917 - accuracy: 0.9431 - val_loss: 0.2546 - val_accuracy: 0.9350 - 66ms/epoch - 9ms/step\n",
            "Epoch 75/1500\n",
            "7/7 - 0s - loss: 0.1919 - accuracy: 0.9431 - val_loss: 0.2543 - val_accuracy: 0.9350 - 53ms/epoch - 8ms/step\n",
            "Epoch 76/1500\n",
            "7/7 - 0s - loss: 0.1920 - accuracy: 0.9431 - val_loss: 0.2552 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 77/1500\n",
            "7/7 - 0s - loss: 0.1916 - accuracy: 0.9431 - val_loss: 0.2536 - val_accuracy: 0.9350 - 40ms/epoch - 6ms/step\n",
            "Epoch 78/1500\n",
            "7/7 - 0s - loss: 0.1919 - accuracy: 0.9431 - val_loss: 0.2521 - val_accuracy: 0.9350 - 43ms/epoch - 6ms/step\n",
            "Epoch 79/1500\n",
            "7/7 - 0s - loss: 0.1911 - accuracy: 0.9431 - val_loss: 0.2530 - val_accuracy: 0.9350 - 54ms/epoch - 8ms/step\n",
            "Epoch 80/1500\n",
            "7/7 - 0s - loss: 0.1914 - accuracy: 0.9431 - val_loss: 0.2538 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 81/1500\n",
            "7/7 - 0s - loss: 0.1911 - accuracy: 0.9431 - val_loss: 0.2535 - val_accuracy: 0.9350 - 54ms/epoch - 8ms/step\n",
            "Epoch 82/1500\n",
            "7/7 - 0s - loss: 0.1914 - accuracy: 0.9431 - val_loss: 0.2520 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 83/1500\n",
            "7/7 - 0s - loss: 0.1911 - accuracy: 0.9431 - val_loss: 0.2525 - val_accuracy: 0.9350 - 54ms/epoch - 8ms/step\n",
            "Epoch 84/1500\n",
            "7/7 - 0s - loss: 0.1912 - accuracy: 0.9431 - val_loss: 0.2522 - val_accuracy: 0.9350 - 43ms/epoch - 6ms/step\n",
            "Epoch 85/1500\n",
            "7/7 - 0s - loss: 0.1912 - accuracy: 0.9431 - val_loss: 0.2531 - val_accuracy: 0.9350 - 51ms/epoch - 7ms/step\n",
            "Epoch 86/1500\n",
            "7/7 - 0s - loss: 0.1911 - accuracy: 0.9431 - val_loss: 0.2535 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 87/1500\n",
            "7/7 - 0s - loss: 0.1912 - accuracy: 0.9431 - val_loss: 0.2524 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 88/1500\n",
            "7/7 - 0s - loss: 0.1911 - accuracy: 0.9431 - val_loss: 0.2518 - val_accuracy: 0.9350 - 53ms/epoch - 8ms/step\n",
            "Epoch 89/1500\n",
            "7/7 - 0s - loss: 0.1908 - accuracy: 0.9431 - val_loss: 0.2528 - val_accuracy: 0.9350 - 53ms/epoch - 8ms/step\n",
            "Epoch 90/1500\n",
            "7/7 - 0s - loss: 0.1910 - accuracy: 0.9431 - val_loss: 0.2538 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 91/1500\n",
            "7/7 - 0s - loss: 0.1910 - accuracy: 0.9431 - val_loss: 0.2542 - val_accuracy: 0.9350 - 43ms/epoch - 6ms/step\n",
            "Epoch 92/1500\n",
            "7/7 - 0s - loss: 0.1908 - accuracy: 0.9431 - val_loss: 0.2540 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 93/1500\n",
            "7/7 - 0s - loss: 0.1908 - accuracy: 0.9431 - val_loss: 0.2525 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 94/1500\n",
            "7/7 - 0s - loss: 0.1909 - accuracy: 0.9431 - val_loss: 0.2523 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 95/1500\n",
            "7/7 - 0s - loss: 0.1906 - accuracy: 0.9431 - val_loss: 0.2519 - val_accuracy: 0.9350 - 61ms/epoch - 9ms/step\n",
            "Epoch 96/1500\n",
            "7/7 - 0s - loss: 0.1906 - accuracy: 0.9431 - val_loss: 0.2520 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 97/1500\n",
            "7/7 - 0s - loss: 0.1906 - accuracy: 0.9431 - val_loss: 0.2531 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 98/1500\n",
            "7/7 - 0s - loss: 0.1905 - accuracy: 0.9431 - val_loss: 0.2535 - val_accuracy: 0.9350 - 43ms/epoch - 6ms/step\n",
            "Epoch 99/1500\n",
            "7/7 - 0s - loss: 0.1908 - accuracy: 0.9431 - val_loss: 0.2540 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 100/1500\n",
            "7/7 - 0s - loss: 0.1904 - accuracy: 0.9431 - val_loss: 0.2528 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 101/1500\n",
            "7/7 - 0s - loss: 0.1906 - accuracy: 0.9431 - val_loss: 0.2523 - val_accuracy: 0.9350 - 64ms/epoch - 9ms/step\n",
            "Epoch 102/1500\n",
            "7/7 - 0s - loss: 0.1906 - accuracy: 0.9431 - val_loss: 0.2525 - val_accuracy: 0.9350 - 52ms/epoch - 7ms/step\n",
            "Epoch 103/1500\n",
            "7/7 - 0s - loss: 0.1904 - accuracy: 0.9431 - val_loss: 0.2527 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 104/1500\n",
            "7/7 - 0s - loss: 0.1903 - accuracy: 0.9431 - val_loss: 0.2528 - val_accuracy: 0.9350 - 43ms/epoch - 6ms/step\n",
            "Epoch 105/1500\n",
            "7/7 - 0s - loss: 0.1904 - accuracy: 0.9431 - val_loss: 0.2531 - val_accuracy: 0.9350 - 42ms/epoch - 6ms/step\n",
            "Epoch 106/1500\n",
            "7/7 - 0s - loss: 0.1900 - accuracy: 0.9431 - val_loss: 0.2542 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 107/1500\n",
            "7/7 - 0s - loss: 0.1903 - accuracy: 0.9431 - val_loss: 0.2548 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 108/1500\n",
            "7/7 - 0s - loss: 0.1904 - accuracy: 0.9431 - val_loss: 0.2553 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 109/1500\n",
            "7/7 - 0s - loss: 0.1908 - accuracy: 0.9431 - val_loss: 0.2531 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 110/1500\n",
            "7/7 - 0s - loss: 0.1903 - accuracy: 0.9431 - val_loss: 0.2541 - val_accuracy: 0.9350 - 67ms/epoch - 10ms/step\n",
            "Epoch 111/1500\n",
            "7/7 - 0s - loss: 0.1902 - accuracy: 0.9431 - val_loss: 0.2532 - val_accuracy: 0.9350 - 52ms/epoch - 7ms/step\n",
            "Epoch 112/1500\n",
            "7/7 - 0s - loss: 0.1899 - accuracy: 0.9431 - val_loss: 0.2527 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 113/1500\n",
            "7/7 - 0s - loss: 0.1901 - accuracy: 0.9431 - val_loss: 0.2520 - val_accuracy: 0.9350 - 52ms/epoch - 7ms/step\n",
            "Epoch 114/1500\n",
            "7/7 - 0s - loss: 0.1903 - accuracy: 0.9431 - val_loss: 0.2529 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 115/1500\n",
            "7/7 - 0s - loss: 0.1900 - accuracy: 0.9431 - val_loss: 0.2520 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 116/1500\n",
            "7/7 - 0s - loss: 0.1901 - accuracy: 0.9431 - val_loss: 0.2523 - val_accuracy: 0.9350 - 41ms/epoch - 6ms/step\n",
            "Epoch 117/1500\n",
            "7/7 - 0s - loss: 0.1901 - accuracy: 0.9431 - val_loss: 0.2535 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 118/1500\n",
            "7/7 - 0s - loss: 0.1904 - accuracy: 0.9431 - val_loss: 0.2549 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 119/1500\n",
            "7/7 - 0s - loss: 0.1899 - accuracy: 0.9431 - val_loss: 0.2546 - val_accuracy: 0.9350 - 65ms/epoch - 9ms/step\n",
            "Epoch 120/1500\n",
            "7/7 - 0s - loss: 0.1899 - accuracy: 0.9431 - val_loss: 0.2549 - val_accuracy: 0.9350 - 41ms/epoch - 6ms/step\n",
            "Epoch 121/1500\n",
            "7/7 - 0s - loss: 0.1897 - accuracy: 0.9431 - val_loss: 0.2539 - val_accuracy: 0.9350 - 43ms/epoch - 6ms/step\n",
            "Epoch 122/1500\n",
            "7/7 - 0s - loss: 0.1900 - accuracy: 0.9431 - val_loss: 0.2528 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 123/1500\n",
            "7/7 - 0s - loss: 0.1896 - accuracy: 0.9431 - val_loss: 0.2526 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 124/1500\n",
            "7/7 - 0s - loss: 0.1896 - accuracy: 0.9431 - val_loss: 0.2534 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 125/1500\n",
            "7/7 - 0s - loss: 0.1899 - accuracy: 0.9431 - val_loss: 0.2553 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 126/1500\n",
            "7/7 - 0s - loss: 0.1896 - accuracy: 0.9431 - val_loss: 0.2560 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 127/1500\n",
            "7/7 - 0s - loss: 0.1896 - accuracy: 0.9431 - val_loss: 0.2549 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 128/1500\n",
            "7/7 - 0s - loss: 0.1896 - accuracy: 0.9431 - val_loss: 0.2546 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 129/1500\n",
            "7/7 - 0s - loss: 0.1893 - accuracy: 0.9431 - val_loss: 0.2546 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 130/1500\n",
            "7/7 - 0s - loss: 0.1895 - accuracy: 0.9431 - val_loss: 0.2551 - val_accuracy: 0.9350 - 42ms/epoch - 6ms/step\n",
            "Epoch 131/1500\n",
            "7/7 - 0s - loss: 0.1892 - accuracy: 0.9431 - val_loss: 0.2548 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 132/1500\n",
            "7/7 - 0s - loss: 0.1894 - accuracy: 0.9431 - val_loss: 0.2539 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 133/1500\n",
            "7/7 - 0s - loss: 0.1896 - accuracy: 0.9431 - val_loss: 0.2552 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 134/1500\n",
            "7/7 - 0s - loss: 0.1893 - accuracy: 0.9431 - val_loss: 0.2553 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 135/1500\n",
            "7/7 - 0s - loss: 0.1910 - accuracy: 0.9431 - val_loss: 0.2523 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 136/1500\n",
            "7/7 - 0s - loss: 0.1891 - accuracy: 0.9431 - val_loss: 0.2528 - val_accuracy: 0.9350 - 54ms/epoch - 8ms/step\n",
            "Epoch 137/1500\n",
            "7/7 - 0s - loss: 0.1892 - accuracy: 0.9431 - val_loss: 0.2552 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 138/1500\n",
            "7/7 - 0s - loss: 0.1892 - accuracy: 0.9384 - val_loss: 0.2555 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 139/1500\n",
            "7/7 - 0s - loss: 0.1893 - accuracy: 0.9384 - val_loss: 0.2553 - val_accuracy: 0.9300 - 43ms/epoch - 6ms/step\n",
            "Epoch 140/1500\n",
            "7/7 - 0s - loss: 0.1895 - accuracy: 0.9384 - val_loss: 0.2545 - val_accuracy: 0.9300 - 44ms/epoch - 6ms/step\n",
            "Epoch 141/1500\n",
            "7/7 - 0s - loss: 0.1892 - accuracy: 0.9431 - val_loss: 0.2537 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 142/1500\n",
            "7/7 - 0s - loss: 0.1892 - accuracy: 0.9431 - val_loss: 0.2538 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 143/1500\n",
            "7/7 - 0s - loss: 0.1889 - accuracy: 0.9431 - val_loss: 0.2545 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 144/1500\n",
            "7/7 - 0s - loss: 0.1895 - accuracy: 0.9431 - val_loss: 0.2538 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 145/1500\n",
            "7/7 - 0s - loss: 0.1890 - accuracy: 0.9431 - val_loss: 0.2545 - val_accuracy: 0.9350 - 43ms/epoch - 6ms/step\n",
            "Epoch 146/1500\n",
            "7/7 - 0s - loss: 0.1891 - accuracy: 0.9431 - val_loss: 0.2535 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 147/1500\n",
            "7/7 - 0s - loss: 0.1886 - accuracy: 0.9431 - val_loss: 0.2530 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 148/1500\n",
            "7/7 - 0s - loss: 0.1892 - accuracy: 0.9431 - val_loss: 0.2520 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 149/1500\n",
            "7/7 - 0s - loss: 0.1885 - accuracy: 0.9431 - val_loss: 0.2526 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 150/1500\n",
            "7/7 - 0s - loss: 0.1886 - accuracy: 0.9431 - val_loss: 0.2536 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 151/1500\n",
            "7/7 - 0s - loss: 0.1888 - accuracy: 0.9431 - val_loss: 0.2548 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 152/1500\n",
            "7/7 - 0s - loss: 0.1887 - accuracy: 0.9431 - val_loss: 0.2551 - val_accuracy: 0.9350 - 54ms/epoch - 8ms/step\n",
            "Epoch 153/1500\n",
            "7/7 - 0s - loss: 0.1895 - accuracy: 0.9431 - val_loss: 0.2535 - val_accuracy: 0.9350 - 53ms/epoch - 8ms/step\n",
            "Epoch 154/1500\n",
            "7/7 - 0s - loss: 0.1885 - accuracy: 0.9431 - val_loss: 0.2530 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 155/1500\n",
            "7/7 - 0s - loss: 0.1883 - accuracy: 0.9431 - val_loss: 0.2537 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 156/1500\n",
            "7/7 - 0s - loss: 0.1883 - accuracy: 0.9431 - val_loss: 0.2545 - val_accuracy: 0.9350 - 61ms/epoch - 9ms/step\n",
            "Epoch 157/1500\n",
            "7/7 - 0s - loss: 0.1883 - accuracy: 0.9431 - val_loss: 0.2547 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 158/1500\n",
            "7/7 - 0s - loss: 0.1884 - accuracy: 0.9431 - val_loss: 0.2528 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 159/1500\n",
            "7/7 - 0s - loss: 0.1886 - accuracy: 0.9431 - val_loss: 0.2525 - val_accuracy: 0.9350 - 52ms/epoch - 7ms/step\n",
            "Epoch 160/1500\n",
            "7/7 - 0s - loss: 0.1884 - accuracy: 0.9431 - val_loss: 0.2535 - val_accuracy: 0.9350 - 66ms/epoch - 9ms/step\n",
            "Epoch 161/1500\n",
            "7/7 - 0s - loss: 0.1880 - accuracy: 0.9431 - val_loss: 0.2544 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 162/1500\n",
            "7/7 - 0s - loss: 0.1881 - accuracy: 0.9431 - val_loss: 0.2549 - val_accuracy: 0.9350 - 53ms/epoch - 8ms/step\n",
            "Epoch 163/1500\n",
            "7/7 - 0s - loss: 0.1880 - accuracy: 0.9431 - val_loss: 0.2546 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 164/1500\n",
            "7/7 - 0s - loss: 0.1879 - accuracy: 0.9431 - val_loss: 0.2538 - val_accuracy: 0.9350 - 52ms/epoch - 7ms/step\n",
            "Epoch 165/1500\n",
            "7/7 - 0s - loss: 0.1879 - accuracy: 0.9431 - val_loss: 0.2536 - val_accuracy: 0.9350 - 41ms/epoch - 6ms/step\n",
            "Epoch 166/1500\n",
            "7/7 - 0s - loss: 0.1881 - accuracy: 0.9431 - val_loss: 0.2536 - val_accuracy: 0.9350 - 64ms/epoch - 9ms/step\n",
            "Epoch 167/1500\n",
            "7/7 - 0s - loss: 0.1878 - accuracy: 0.9431 - val_loss: 0.2535 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 168/1500\n",
            "7/7 - 0s - loss: 0.1876 - accuracy: 0.9431 - val_loss: 0.2548 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 169/1500\n",
            "7/7 - 0s - loss: 0.1878 - accuracy: 0.9431 - val_loss: 0.2550 - val_accuracy: 0.9350 - 53ms/epoch - 8ms/step\n",
            "Epoch 170/1500\n",
            "7/7 - 0s - loss: 0.1881 - accuracy: 0.9384 - val_loss: 0.2558 - val_accuracy: 0.9300 - 53ms/epoch - 8ms/step\n",
            "Epoch 171/1500\n",
            "7/7 - 0s - loss: 0.1878 - accuracy: 0.9431 - val_loss: 0.2550 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 172/1500\n",
            "7/7 - 0s - loss: 0.1879 - accuracy: 0.9431 - val_loss: 0.2550 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 173/1500\n",
            "7/7 - 0s - loss: 0.1877 - accuracy: 0.9431 - val_loss: 0.2543 - val_accuracy: 0.9350 - 43ms/epoch - 6ms/step\n",
            "Epoch 174/1500\n",
            "7/7 - 0s - loss: 0.1876 - accuracy: 0.9431 - val_loss: 0.2533 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 175/1500\n",
            "7/7 - 0s - loss: 0.1886 - accuracy: 0.9431 - val_loss: 0.2529 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 176/1500\n",
            "7/7 - 0s - loss: 0.1881 - accuracy: 0.9431 - val_loss: 0.2559 - val_accuracy: 0.9300 - 45ms/epoch - 6ms/step\n",
            "Epoch 177/1500\n",
            "7/7 - 0s - loss: 0.1890 - accuracy: 0.9336 - val_loss: 0.2580 - val_accuracy: 0.9300 - 47ms/epoch - 7ms/step\n",
            "Epoch 178/1500\n",
            "7/7 - 0s - loss: 0.1884 - accuracy: 0.9384 - val_loss: 0.2548 - val_accuracy: 0.9350 - 63ms/epoch - 9ms/step\n",
            "Epoch 179/1500\n",
            "7/7 - 0s - loss: 0.1872 - accuracy: 0.9431 - val_loss: 0.2539 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 180/1500\n",
            "7/7 - 0s - loss: 0.1874 - accuracy: 0.9431 - val_loss: 0.2533 - val_accuracy: 0.9350 - 43ms/epoch - 6ms/step\n",
            "Epoch 181/1500\n",
            "7/7 - 0s - loss: 0.1874 - accuracy: 0.9431 - val_loss: 0.2536 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 182/1500\n",
            "7/7 - 0s - loss: 0.1873 - accuracy: 0.9431 - val_loss: 0.2537 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 183/1500\n",
            "7/7 - 0s - loss: 0.1872 - accuracy: 0.9431 - val_loss: 0.2539 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 184/1500\n",
            "7/7 - 0s - loss: 0.1875 - accuracy: 0.9384 - val_loss: 0.2560 - val_accuracy: 0.9300 - 43ms/epoch - 6ms/step\n",
            "Epoch 185/1500\n",
            "7/7 - 0s - loss: 0.1875 - accuracy: 0.9336 - val_loss: 0.2558 - val_accuracy: 0.9300 - 46ms/epoch - 7ms/step\n",
            "Epoch 186/1500\n",
            "7/7 - 0s - loss: 0.1873 - accuracy: 0.9384 - val_loss: 0.2557 - val_accuracy: 0.9300 - 52ms/epoch - 7ms/step\n",
            "Epoch 187/1500\n",
            "7/7 - 0s - loss: 0.1875 - accuracy: 0.9431 - val_loss: 0.2543 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 188/1500\n",
            "7/7 - 0s - loss: 0.1878 - accuracy: 0.9431 - val_loss: 0.2559 - val_accuracy: 0.9300 - 53ms/epoch - 8ms/step\n",
            "Epoch 189/1500\n",
            "7/7 - 0s - loss: 0.1868 - accuracy: 0.9431 - val_loss: 0.2548 - val_accuracy: 0.9350 - 43ms/epoch - 6ms/step\n",
            "Epoch 190/1500\n",
            "7/7 - 0s - loss: 0.1871 - accuracy: 0.9431 - val_loss: 0.2536 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 191/1500\n",
            "7/7 - 0s - loss: 0.1869 - accuracy: 0.9431 - val_loss: 0.2541 - val_accuracy: 0.9350 - 54ms/epoch - 8ms/step\n",
            "Epoch 192/1500\n",
            "7/7 - 0s - loss: 0.1869 - accuracy: 0.9431 - val_loss: 0.2551 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 193/1500\n",
            "7/7 - 0s - loss: 0.1869 - accuracy: 0.9431 - val_loss: 0.2555 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 194/1500\n",
            "7/7 - 0s - loss: 0.1872 - accuracy: 0.9431 - val_loss: 0.2550 - val_accuracy: 0.9350 - 41ms/epoch - 6ms/step\n",
            "Epoch 195/1500\n",
            "7/7 - 0s - loss: 0.1868 - accuracy: 0.9431 - val_loss: 0.2548 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 196/1500\n",
            "7/7 - 0s - loss: 0.1872 - accuracy: 0.9431 - val_loss: 0.2545 - val_accuracy: 0.9350 - 67ms/epoch - 10ms/step\n",
            "Epoch 197/1500\n",
            "7/7 - 0s - loss: 0.1869 - accuracy: 0.9431 - val_loss: 0.2562 - val_accuracy: 0.9350 - 64ms/epoch - 9ms/step\n",
            "Epoch 198/1500\n",
            "7/7 - 0s - loss: 0.1868 - accuracy: 0.9384 - val_loss: 0.2564 - val_accuracy: 0.9300 - 46ms/epoch - 7ms/step\n",
            "Epoch 199/1500\n",
            "7/7 - 0s - loss: 0.1868 - accuracy: 0.9384 - val_loss: 0.2550 - val_accuracy: 0.9350 - 41ms/epoch - 6ms/step\n",
            "Epoch 200/1500\n",
            "7/7 - 0s - loss: 0.1867 - accuracy: 0.9431 - val_loss: 0.2546 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 201/1500\n",
            "7/7 - 0s - loss: 0.1865 - accuracy: 0.9431 - val_loss: 0.2549 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 202/1500\n",
            "7/7 - 0s - loss: 0.1865 - accuracy: 0.9431 - val_loss: 0.2548 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 203/1500\n",
            "7/7 - 0s - loss: 0.1864 - accuracy: 0.9431 - val_loss: 0.2563 - val_accuracy: 0.9350 - 42ms/epoch - 6ms/step\n",
            "Epoch 204/1500\n",
            "7/7 - 0s - loss: 0.1865 - accuracy: 0.9431 - val_loss: 0.2567 - val_accuracy: 0.9300 - 56ms/epoch - 8ms/step\n",
            "Epoch 205/1500\n",
            "7/7 - 0s - loss: 0.1864 - accuracy: 0.9431 - val_loss: 0.2568 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 206/1500\n",
            "7/7 - 0s - loss: 0.1864 - accuracy: 0.9431 - val_loss: 0.2563 - val_accuracy: 0.9350 - 62ms/epoch - 9ms/step\n",
            "Epoch 207/1500\n",
            "7/7 - 0s - loss: 0.1864 - accuracy: 0.9431 - val_loss: 0.2546 - val_accuracy: 0.9350 - 51ms/epoch - 7ms/step\n",
            "Epoch 208/1500\n",
            "7/7 - 0s - loss: 0.1863 - accuracy: 0.9431 - val_loss: 0.2551 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 209/1500\n",
            "7/7 - 0s - loss: 0.1862 - accuracy: 0.9431 - val_loss: 0.2553 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 210/1500\n",
            "7/7 - 0s - loss: 0.1864 - accuracy: 0.9431 - val_loss: 0.2565 - val_accuracy: 0.9300 - 49ms/epoch - 7ms/step\n",
            "Epoch 211/1500\n",
            "7/7 - 0s - loss: 0.1863 - accuracy: 0.9431 - val_loss: 0.2565 - val_accuracy: 0.9350 - 43ms/epoch - 6ms/step\n",
            "Epoch 212/1500\n",
            "7/7 - 0s - loss: 0.1866 - accuracy: 0.9431 - val_loss: 0.2572 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 213/1500\n",
            "7/7 - 0s - loss: 0.1858 - accuracy: 0.9431 - val_loss: 0.2557 - val_accuracy: 0.9350 - 49ms/epoch - 7ms/step\n",
            "Epoch 214/1500\n",
            "7/7 - 0s - loss: 0.1861 - accuracy: 0.9431 - val_loss: 0.2545 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 215/1500\n",
            "7/7 - 0s - loss: 0.1861 - accuracy: 0.9431 - val_loss: 0.2544 - val_accuracy: 0.9350 - 68ms/epoch - 10ms/step\n",
            "Epoch 216/1500\n",
            "7/7 - 0s - loss: 0.1863 - accuracy: 0.9431 - val_loss: 0.2551 - val_accuracy: 0.9350 - 42ms/epoch - 6ms/step\n",
            "Epoch 217/1500\n",
            "7/7 - 0s - loss: 0.1862 - accuracy: 0.9431 - val_loss: 0.2545 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 218/1500\n",
            "7/7 - 0s - loss: 0.1859 - accuracy: 0.9431 - val_loss: 0.2553 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 219/1500\n",
            "7/7 - 0s - loss: 0.1861 - accuracy: 0.9431 - val_loss: 0.2566 - val_accuracy: 0.9350 - 54ms/epoch - 8ms/step\n",
            "Epoch 220/1500\n",
            "7/7 - 0s - loss: 0.1861 - accuracy: 0.9431 - val_loss: 0.2562 - val_accuracy: 0.9350 - 41ms/epoch - 6ms/step\n",
            "Epoch 221/1500\n",
            "7/7 - 0s - loss: 0.1858 - accuracy: 0.9431 - val_loss: 0.2564 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 222/1500\n",
            "7/7 - 0s - loss: 0.1858 - accuracy: 0.9431 - val_loss: 0.2557 - val_accuracy: 0.9350 - 41ms/epoch - 6ms/step\n",
            "Epoch 223/1500\n",
            "7/7 - 0s - loss: 0.1859 - accuracy: 0.9431 - val_loss: 0.2557 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 224/1500\n",
            "7/7 - 0s - loss: 0.1857 - accuracy: 0.9431 - val_loss: 0.2555 - val_accuracy: 0.9350 - 53ms/epoch - 8ms/step\n",
            "Epoch 225/1500\n",
            "7/7 - 0s - loss: 0.1857 - accuracy: 0.9431 - val_loss: 0.2554 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 226/1500\n",
            "7/7 - 0s - loss: 0.1855 - accuracy: 0.9431 - val_loss: 0.2555 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 227/1500\n",
            "7/7 - 0s - loss: 0.1856 - accuracy: 0.9431 - val_loss: 0.2553 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 228/1500\n",
            "7/7 - 0s - loss: 0.1860 - accuracy: 0.9431 - val_loss: 0.2563 - val_accuracy: 0.9300 - 62ms/epoch - 9ms/step\n",
            "Epoch 229/1500\n",
            "7/7 - 0s - loss: 0.1855 - accuracy: 0.9431 - val_loss: 0.2559 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 230/1500\n",
            "7/7 - 0s - loss: 0.1859 - accuracy: 0.9431 - val_loss: 0.2540 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 231/1500\n",
            "7/7 - 0s - loss: 0.1854 - accuracy: 0.9431 - val_loss: 0.2548 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 232/1500\n",
            "7/7 - 0s - loss: 0.1853 - accuracy: 0.9431 - val_loss: 0.2558 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 233/1500\n",
            "7/7 - 0s - loss: 0.1852 - accuracy: 0.9431 - val_loss: 0.2568 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 234/1500\n",
            "7/7 - 0s - loss: 0.1854 - accuracy: 0.9431 - val_loss: 0.2578 - val_accuracy: 0.9300 - 59ms/epoch - 8ms/step\n",
            "Epoch 235/1500\n",
            "7/7 - 0s - loss: 0.1854 - accuracy: 0.9431 - val_loss: 0.2577 - val_accuracy: 0.9300 - 56ms/epoch - 8ms/step\n",
            "Epoch 236/1500\n",
            "7/7 - 0s - loss: 0.1857 - accuracy: 0.9431 - val_loss: 0.2569 - val_accuracy: 0.9350 - 62ms/epoch - 9ms/step\n",
            "Epoch 237/1500\n",
            "7/7 - 0s - loss: 0.1851 - accuracy: 0.9431 - val_loss: 0.2571 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 238/1500\n",
            "7/7 - 0s - loss: 0.1853 - accuracy: 0.9431 - val_loss: 0.2572 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 239/1500\n",
            "7/7 - 0s - loss: 0.1856 - accuracy: 0.9431 - val_loss: 0.2562 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 240/1500\n",
            "7/7 - 0s - loss: 0.1850 - accuracy: 0.9431 - val_loss: 0.2566 - val_accuracy: 0.9350 - 42ms/epoch - 6ms/step\n",
            "Epoch 241/1500\n",
            "7/7 - 0s - loss: 0.1852 - accuracy: 0.9431 - val_loss: 0.2577 - val_accuracy: 0.9300 - 55ms/epoch - 8ms/step\n",
            "Epoch 242/1500\n",
            "7/7 - 0s - loss: 0.1851 - accuracy: 0.9431 - val_loss: 0.2584 - val_accuracy: 0.9300 - 61ms/epoch - 9ms/step\n",
            "Epoch 243/1500\n",
            "7/7 - 0s - loss: 0.1850 - accuracy: 0.9431 - val_loss: 0.2571 - val_accuracy: 0.9350 - 41ms/epoch - 6ms/step\n",
            "Epoch 244/1500\n",
            "7/7 - 0s - loss: 0.1849 - accuracy: 0.9431 - val_loss: 0.2562 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 245/1500\n",
            "7/7 - 0s - loss: 0.1850 - accuracy: 0.9431 - val_loss: 0.2555 - val_accuracy: 0.9350 - 41ms/epoch - 6ms/step\n",
            "Epoch 246/1500\n",
            "7/7 - 0s - loss: 0.1850 - accuracy: 0.9431 - val_loss: 0.2554 - val_accuracy: 0.9350 - 54ms/epoch - 8ms/step\n",
            "Epoch 247/1500\n",
            "7/7 - 0s - loss: 0.1847 - accuracy: 0.9431 - val_loss: 0.2572 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 248/1500\n",
            "7/7 - 0s - loss: 0.1849 - accuracy: 0.9431 - val_loss: 0.2585 - val_accuracy: 0.9300 - 40ms/epoch - 6ms/step\n",
            "Epoch 249/1500\n",
            "7/7 - 0s - loss: 0.1857 - accuracy: 0.9431 - val_loss: 0.2566 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 250/1500\n",
            "7/7 - 0s - loss: 0.1847 - accuracy: 0.9431 - val_loss: 0.2570 - val_accuracy: 0.9300 - 53ms/epoch - 8ms/step\n",
            "Epoch 251/1500\n",
            "7/7 - 0s - loss: 0.1848 - accuracy: 0.9431 - val_loss: 0.2569 - val_accuracy: 0.9300 - 55ms/epoch - 8ms/step\n",
            "Epoch 252/1500\n",
            "7/7 - 0s - loss: 0.1851 - accuracy: 0.9431 - val_loss: 0.2585 - val_accuracy: 0.9300 - 55ms/epoch - 8ms/step\n",
            "Epoch 253/1500\n",
            "7/7 - 0s - loss: 0.1849 - accuracy: 0.9431 - val_loss: 0.2585 - val_accuracy: 0.9300 - 44ms/epoch - 6ms/step\n",
            "Epoch 254/1500\n",
            "7/7 - 0s - loss: 0.1848 - accuracy: 0.9384 - val_loss: 0.2595 - val_accuracy: 0.9300 - 55ms/epoch - 8ms/step\n",
            "Epoch 255/1500\n",
            "7/7 - 0s - loss: 0.1845 - accuracy: 0.9431 - val_loss: 0.2585 - val_accuracy: 0.9300 - 65ms/epoch - 9ms/step\n",
            "Epoch 256/1500\n",
            "7/7 - 0s - loss: 0.1845 - accuracy: 0.9431 - val_loss: 0.2568 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 257/1500\n",
            "7/7 - 0s - loss: 0.1845 - accuracy: 0.9431 - val_loss: 0.2569 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 258/1500\n",
            "7/7 - 0s - loss: 0.1844 - accuracy: 0.9431 - val_loss: 0.2562 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 259/1500\n",
            "7/7 - 0s - loss: 0.1844 - accuracy: 0.9431 - val_loss: 0.2562 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 260/1500\n",
            "7/7 - 0s - loss: 0.1842 - accuracy: 0.9431 - val_loss: 0.2563 - val_accuracy: 0.9350 - 40ms/epoch - 6ms/step\n",
            "Epoch 261/1500\n",
            "7/7 - 0s - loss: 0.1846 - accuracy: 0.9431 - val_loss: 0.2575 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 262/1500\n",
            "7/7 - 0s - loss: 0.1848 - accuracy: 0.9431 - val_loss: 0.2581 - val_accuracy: 0.9300 - 63ms/epoch - 9ms/step\n",
            "Epoch 263/1500\n",
            "7/7 - 0s - loss: 0.1848 - accuracy: 0.9384 - val_loss: 0.2586 - val_accuracy: 0.9300 - 43ms/epoch - 6ms/step\n",
            "Epoch 264/1500\n",
            "7/7 - 0s - loss: 0.1844 - accuracy: 0.9431 - val_loss: 0.2574 - val_accuracy: 0.9300 - 54ms/epoch - 8ms/step\n",
            "Epoch 265/1500\n",
            "7/7 - 0s - loss: 0.1842 - accuracy: 0.9431 - val_loss: 0.2566 - val_accuracy: 0.9300 - 56ms/epoch - 8ms/step\n",
            "Epoch 266/1500\n",
            "7/7 - 0s - loss: 0.1841 - accuracy: 0.9431 - val_loss: 0.2568 - val_accuracy: 0.9300 - 60ms/epoch - 9ms/step\n",
            "Epoch 267/1500\n",
            "7/7 - 0s - loss: 0.1841 - accuracy: 0.9431 - val_loss: 0.2563 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 268/1500\n",
            "7/7 - 0s - loss: 0.1840 - accuracy: 0.9479 - val_loss: 0.2564 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 269/1500\n",
            "7/7 - 0s - loss: 0.1841 - accuracy: 0.9431 - val_loss: 0.2568 - val_accuracy: 0.9350 - 62ms/epoch - 9ms/step\n",
            "Epoch 270/1500\n",
            "7/7 - 0s - loss: 0.1839 - accuracy: 0.9431 - val_loss: 0.2572 - val_accuracy: 0.9350 - 41ms/epoch - 6ms/step\n",
            "Epoch 271/1500\n",
            "7/7 - 0s - loss: 0.1842 - accuracy: 0.9431 - val_loss: 0.2576 - val_accuracy: 0.9350 - 52ms/epoch - 7ms/step\n",
            "Epoch 272/1500\n",
            "7/7 - 0s - loss: 0.1839 - accuracy: 0.9431 - val_loss: 0.2578 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 273/1500\n",
            "7/7 - 0s - loss: 0.1839 - accuracy: 0.9431 - val_loss: 0.2571 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 274/1500\n",
            "7/7 - 0s - loss: 0.1836 - accuracy: 0.9431 - val_loss: 0.2573 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 275/1500\n",
            "7/7 - 0s - loss: 0.1837 - accuracy: 0.9431 - val_loss: 0.2570 - val_accuracy: 0.9300 - 40ms/epoch - 6ms/step\n",
            "Epoch 276/1500\n",
            "7/7 - 0s - loss: 0.1835 - accuracy: 0.9431 - val_loss: 0.2575 - val_accuracy: 0.9300 - 58ms/epoch - 8ms/step\n",
            "Epoch 277/1500\n",
            "7/7 - 0s - loss: 0.1842 - accuracy: 0.9431 - val_loss: 0.2595 - val_accuracy: 0.9300 - 49ms/epoch - 7ms/step\n",
            "Epoch 278/1500\n",
            "7/7 - 0s - loss: 0.1842 - accuracy: 0.9431 - val_loss: 0.2574 - val_accuracy: 0.9300 - 44ms/epoch - 6ms/step\n",
            "Epoch 279/1500\n",
            "7/7 - 0s - loss: 0.1841 - accuracy: 0.9431 - val_loss: 0.2556 - val_accuracy: 0.9350 - 69ms/epoch - 10ms/step\n",
            "Epoch 280/1500\n",
            "7/7 - 0s - loss: 0.1839 - accuracy: 0.9431 - val_loss: 0.2570 - val_accuracy: 0.9300 - 44ms/epoch - 6ms/step\n",
            "Epoch 281/1500\n",
            "7/7 - 0s - loss: 0.1848 - accuracy: 0.9431 - val_loss: 0.2585 - val_accuracy: 0.9300 - 43ms/epoch - 6ms/step\n",
            "Epoch 282/1500\n",
            "7/7 - 0s - loss: 0.1841 - accuracy: 0.9431 - val_loss: 0.2571 - val_accuracy: 0.9350 - 62ms/epoch - 9ms/step\n",
            "Epoch 283/1500\n",
            "7/7 - 0s - loss: 0.1837 - accuracy: 0.9431 - val_loss: 0.2581 - val_accuracy: 0.9300 - 41ms/epoch - 6ms/step\n",
            "Epoch 284/1500\n",
            "7/7 - 0s - loss: 0.1837 - accuracy: 0.9431 - val_loss: 0.2570 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 285/1500\n",
            "7/7 - 0s - loss: 0.1845 - accuracy: 0.9431 - val_loss: 0.2586 - val_accuracy: 0.9300 - 41ms/epoch - 6ms/step\n",
            "Epoch 286/1500\n",
            "7/7 - 0s - loss: 0.1835 - accuracy: 0.9431 - val_loss: 0.2575 - val_accuracy: 0.9350 - 61ms/epoch - 9ms/step\n",
            "Epoch 287/1500\n",
            "7/7 - 0s - loss: 0.1838 - accuracy: 0.9431 - val_loss: 0.2557 - val_accuracy: 0.9350 - 54ms/epoch - 8ms/step\n",
            "Epoch 288/1500\n",
            "7/7 - 0s - loss: 0.1835 - accuracy: 0.9431 - val_loss: 0.2570 - val_accuracy: 0.9300 - 61ms/epoch - 9ms/step\n",
            "Epoch 289/1500\n",
            "7/7 - 0s - loss: 0.1838 - accuracy: 0.9431 - val_loss: 0.2562 - val_accuracy: 0.9350 - 40ms/epoch - 6ms/step\n",
            "Epoch 290/1500\n",
            "7/7 - 0s - loss: 0.1837 - accuracy: 0.9431 - val_loss: 0.2579 - val_accuracy: 0.9300 - 44ms/epoch - 6ms/step\n",
            "Epoch 291/1500\n",
            "7/7 - 0s - loss: 0.1832 - accuracy: 0.9431 - val_loss: 0.2572 - val_accuracy: 0.9300 - 59ms/epoch - 8ms/step\n",
            "Epoch 292/1500\n",
            "7/7 - 0s - loss: 0.1831 - accuracy: 0.9431 - val_loss: 0.2570 - val_accuracy: 0.9300 - 51ms/epoch - 7ms/step\n",
            "Epoch 293/1500\n",
            "7/7 - 0s - loss: 0.1831 - accuracy: 0.9431 - val_loss: 0.2564 - val_accuracy: 0.9350 - 40ms/epoch - 6ms/step\n",
            "Epoch 294/1500\n",
            "7/7 - 0s - loss: 0.1834 - accuracy: 0.9431 - val_loss: 0.2578 - val_accuracy: 0.9300 - 54ms/epoch - 8ms/step\n",
            "Epoch 295/1500\n",
            "7/7 - 0s - loss: 0.1830 - accuracy: 0.9431 - val_loss: 0.2574 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 296/1500\n",
            "7/7 - 0s - loss: 0.1831 - accuracy: 0.9431 - val_loss: 0.2579 - val_accuracy: 0.9300 - 58ms/epoch - 8ms/step\n",
            "Epoch 297/1500\n",
            "7/7 - 0s - loss: 0.1829 - accuracy: 0.9431 - val_loss: 0.2579 - val_accuracy: 0.9350 - 61ms/epoch - 9ms/step\n",
            "Epoch 298/1500\n",
            "7/7 - 0s - loss: 0.1829 - accuracy: 0.9431 - val_loss: 0.2573 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 299/1500\n",
            "7/7 - 0s - loss: 0.1829 - accuracy: 0.9431 - val_loss: 0.2570 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 300/1500\n",
            "7/7 - 0s - loss: 0.1831 - accuracy: 0.9431 - val_loss: 0.2568 - val_accuracy: 0.9350 - 65ms/epoch - 9ms/step\n",
            "Epoch 301/1500\n",
            "7/7 - 0s - loss: 0.1828 - accuracy: 0.9431 - val_loss: 0.2563 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 302/1500\n",
            "7/7 - 0s - loss: 0.1830 - accuracy: 0.9431 - val_loss: 0.2573 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 303/1500\n",
            "7/7 - 0s - loss: 0.1828 - accuracy: 0.9431 - val_loss: 0.2580 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 304/1500\n",
            "7/7 - 0s - loss: 0.1829 - accuracy: 0.9431 - val_loss: 0.2575 - val_accuracy: 0.9350 - 41ms/epoch - 6ms/step\n",
            "Epoch 305/1500\n",
            "7/7 - 0s - loss: 0.1829 - accuracy: 0.9431 - val_loss: 0.2586 - val_accuracy: 0.9300 - 58ms/epoch - 8ms/step\n",
            "Epoch 306/1500\n",
            "7/7 - 0s - loss: 0.1828 - accuracy: 0.9431 - val_loss: 0.2590 - val_accuracy: 0.9300 - 60ms/epoch - 9ms/step\n",
            "Epoch 307/1500\n",
            "7/7 - 0s - loss: 0.1826 - accuracy: 0.9431 - val_loss: 0.2586 - val_accuracy: 0.9300 - 58ms/epoch - 8ms/step\n",
            "Epoch 308/1500\n",
            "7/7 - 0s - loss: 0.1825 - accuracy: 0.9431 - val_loss: 0.2569 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 309/1500\n",
            "7/7 - 0s - loss: 0.1828 - accuracy: 0.9431 - val_loss: 0.2563 - val_accuracy: 0.9350 - 63ms/epoch - 9ms/step\n",
            "Epoch 310/1500\n",
            "7/7 - 0s - loss: 0.1826 - accuracy: 0.9479 - val_loss: 0.2574 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 311/1500\n",
            "7/7 - 0s - loss: 0.1827 - accuracy: 0.9431 - val_loss: 0.2585 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 312/1500\n",
            "7/7 - 0s - loss: 0.1826 - accuracy: 0.9431 - val_loss: 0.2584 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 313/1500\n",
            "7/7 - 0s - loss: 0.1825 - accuracy: 0.9431 - val_loss: 0.2580 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 314/1500\n",
            "7/7 - 0s - loss: 0.1828 - accuracy: 0.9479 - val_loss: 0.2579 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 315/1500\n",
            "7/7 - 0s - loss: 0.1823 - accuracy: 0.9431 - val_loss: 0.2587 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 316/1500\n",
            "7/7 - 0s - loss: 0.1822 - accuracy: 0.9431 - val_loss: 0.2590 - val_accuracy: 0.9350 - 51ms/epoch - 7ms/step\n",
            "Epoch 317/1500\n",
            "7/7 - 0s - loss: 0.1823 - accuracy: 0.9431 - val_loss: 0.2591 - val_accuracy: 0.9350 - 54ms/epoch - 8ms/step\n",
            "Epoch 318/1500\n",
            "7/7 - 0s - loss: 0.1821 - accuracy: 0.9431 - val_loss: 0.2577 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 319/1500\n",
            "7/7 - 0s - loss: 0.1823 - accuracy: 0.9431 - val_loss: 0.2581 - val_accuracy: 0.9350 - 53ms/epoch - 8ms/step\n",
            "Epoch 320/1500\n",
            "7/7 - 0s - loss: 0.1822 - accuracy: 0.9431 - val_loss: 0.2579 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 321/1500\n",
            "7/7 - 0s - loss: 0.1821 - accuracy: 0.9431 - val_loss: 0.2574 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 322/1500\n",
            "7/7 - 0s - loss: 0.1822 - accuracy: 0.9431 - val_loss: 0.2580 - val_accuracy: 0.9350 - 49ms/epoch - 7ms/step\n",
            "Epoch 323/1500\n",
            "7/7 - 0s - loss: 0.1821 - accuracy: 0.9431 - val_loss: 0.2575 - val_accuracy: 0.9350 - 63ms/epoch - 9ms/step\n",
            "Epoch 324/1500\n",
            "7/7 - 0s - loss: 0.1820 - accuracy: 0.9431 - val_loss: 0.2576 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 325/1500\n",
            "7/7 - 0s - loss: 0.1819 - accuracy: 0.9431 - val_loss: 0.2583 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 326/1500\n",
            "7/7 - 0s - loss: 0.1822 - accuracy: 0.9431 - val_loss: 0.2580 - val_accuracy: 0.9350 - 43ms/epoch - 6ms/step\n",
            "Epoch 327/1500\n",
            "7/7 - 0s - loss: 0.1820 - accuracy: 0.9431 - val_loss: 0.2581 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 328/1500\n",
            "7/7 - 0s - loss: 0.1821 - accuracy: 0.9431 - val_loss: 0.2601 - val_accuracy: 0.9300 - 63ms/epoch - 9ms/step\n",
            "Epoch 329/1500\n",
            "7/7 - 0s - loss: 0.1819 - accuracy: 0.9431 - val_loss: 0.2603 - val_accuracy: 0.9350 - 61ms/epoch - 9ms/step\n",
            "Epoch 330/1500\n",
            "7/7 - 0s - loss: 0.1822 - accuracy: 0.9431 - val_loss: 0.2581 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 331/1500\n",
            "7/7 - 0s - loss: 0.1819 - accuracy: 0.9431 - val_loss: 0.2574 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 332/1500\n",
            "7/7 - 0s - loss: 0.1819 - accuracy: 0.9431 - val_loss: 0.2585 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 333/1500\n",
            "7/7 - 0s - loss: 0.1817 - accuracy: 0.9431 - val_loss: 0.2589 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 334/1500\n",
            "7/7 - 0s - loss: 0.1817 - accuracy: 0.9431 - val_loss: 0.2591 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 335/1500\n",
            "7/7 - 0s - loss: 0.1816 - accuracy: 0.9431 - val_loss: 0.2580 - val_accuracy: 0.9350 - 40ms/epoch - 6ms/step\n",
            "Epoch 336/1500\n",
            "7/7 - 0s - loss: 0.1817 - accuracy: 0.9431 - val_loss: 0.2584 - val_accuracy: 0.9350 - 52ms/epoch - 7ms/step\n",
            "Epoch 337/1500\n",
            "7/7 - 0s - loss: 0.1818 - accuracy: 0.9431 - val_loss: 0.2590 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 338/1500\n",
            "7/7 - 0s - loss: 0.1822 - accuracy: 0.9431 - val_loss: 0.2578 - val_accuracy: 0.9350 - 41ms/epoch - 6ms/step\n",
            "Epoch 339/1500\n",
            "7/7 - 0s - loss: 0.1814 - accuracy: 0.9479 - val_loss: 0.2583 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 340/1500\n",
            "7/7 - 0s - loss: 0.1813 - accuracy: 0.9431 - val_loss: 0.2588 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 341/1500\n",
            "7/7 - 0s - loss: 0.1813 - accuracy: 0.9431 - val_loss: 0.2596 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 342/1500\n",
            "7/7 - 0s - loss: 0.1814 - accuracy: 0.9431 - val_loss: 0.2588 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 343/1500\n",
            "7/7 - 0s - loss: 0.1813 - accuracy: 0.9431 - val_loss: 0.2593 - val_accuracy: 0.9350 - 49ms/epoch - 7ms/step\n",
            "Epoch 344/1500\n",
            "7/7 - 0s - loss: 0.1814 - accuracy: 0.9431 - val_loss: 0.2592 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 345/1500\n",
            "7/7 - 0s - loss: 0.1823 - accuracy: 0.9431 - val_loss: 0.2572 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 346/1500\n",
            "7/7 - 0s - loss: 0.1813 - accuracy: 0.9479 - val_loss: 0.2578 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 347/1500\n",
            "7/7 - 0s - loss: 0.1811 - accuracy: 0.9479 - val_loss: 0.2593 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 348/1500\n",
            "7/7 - 0s - loss: 0.1814 - accuracy: 0.9431 - val_loss: 0.2611 - val_accuracy: 0.9300 - 48ms/epoch - 7ms/step\n",
            "Epoch 349/1500\n",
            "7/7 - 0s - loss: 0.1811 - accuracy: 0.9431 - val_loss: 0.2598 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 350/1500\n",
            "7/7 - 0s - loss: 0.1814 - accuracy: 0.9431 - val_loss: 0.2584 - val_accuracy: 0.9350 - 54ms/epoch - 8ms/step\n",
            "Epoch 351/1500\n",
            "7/7 - 0s - loss: 0.1810 - accuracy: 0.9431 - val_loss: 0.2596 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 352/1500\n",
            "7/7 - 0s - loss: 0.1810 - accuracy: 0.9431 - val_loss: 0.2595 - val_accuracy: 0.9350 - 64ms/epoch - 9ms/step\n",
            "Epoch 353/1500\n",
            "7/7 - 0s - loss: 0.1814 - accuracy: 0.9431 - val_loss: 0.2610 - val_accuracy: 0.9350 - 63ms/epoch - 9ms/step\n",
            "Epoch 354/1500\n",
            "7/7 - 0s - loss: 0.1809 - accuracy: 0.9431 - val_loss: 0.2594 - val_accuracy: 0.9350 - 64ms/epoch - 9ms/step\n",
            "Epoch 355/1500\n",
            "7/7 - 0s - loss: 0.1808 - accuracy: 0.9431 - val_loss: 0.2587 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 356/1500\n",
            "7/7 - 0s - loss: 0.1809 - accuracy: 0.9431 - val_loss: 0.2590 - val_accuracy: 0.9350 - 52ms/epoch - 7ms/step\n",
            "Epoch 357/1500\n",
            "7/7 - 0s - loss: 0.1808 - accuracy: 0.9431 - val_loss: 0.2588 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 358/1500\n",
            "7/7 - 0s - loss: 0.1807 - accuracy: 0.9431 - val_loss: 0.2589 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 359/1500\n",
            "7/7 - 0s - loss: 0.1807 - accuracy: 0.9431 - val_loss: 0.2591 - val_accuracy: 0.9350 - 54ms/epoch - 8ms/step\n",
            "Epoch 360/1500\n",
            "7/7 - 0s - loss: 0.1807 - accuracy: 0.9431 - val_loss: 0.2591 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 361/1500\n",
            "7/7 - 0s - loss: 0.1807 - accuracy: 0.9431 - val_loss: 0.2585 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 362/1500\n",
            "7/7 - 0s - loss: 0.1804 - accuracy: 0.9431 - val_loss: 0.2586 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 363/1500\n",
            "7/7 - 0s - loss: 0.1806 - accuracy: 0.9431 - val_loss: 0.2589 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 364/1500\n",
            "7/7 - 0s - loss: 0.1803 - accuracy: 0.9431 - val_loss: 0.2595 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 365/1500\n",
            "7/7 - 0s - loss: 0.1805 - accuracy: 0.9431 - val_loss: 0.2606 - val_accuracy: 0.9300 - 54ms/epoch - 8ms/step\n",
            "Epoch 366/1500\n",
            "7/7 - 0s - loss: 0.1808 - accuracy: 0.9431 - val_loss: 0.2593 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 367/1500\n",
            "7/7 - 0s - loss: 0.1802 - accuracy: 0.9431 - val_loss: 0.2586 - val_accuracy: 0.9350 - 62ms/epoch - 9ms/step\n",
            "Epoch 368/1500\n",
            "7/7 - 0s - loss: 0.1809 - accuracy: 0.9431 - val_loss: 0.2581 - val_accuracy: 0.9350 - 51ms/epoch - 7ms/step\n",
            "Epoch 369/1500\n",
            "7/7 - 0s - loss: 0.1809 - accuracy: 0.9431 - val_loss: 0.2601 - val_accuracy: 0.9350 - 54ms/epoch - 8ms/step\n",
            "Epoch 370/1500\n",
            "7/7 - 0s - loss: 0.1805 - accuracy: 0.9431 - val_loss: 0.2608 - val_accuracy: 0.9300 - 55ms/epoch - 8ms/step\n",
            "Epoch 371/1500\n",
            "7/7 - 0s - loss: 0.1802 - accuracy: 0.9431 - val_loss: 0.2590 - val_accuracy: 0.9350 - 53ms/epoch - 8ms/step\n",
            "Epoch 372/1500\n",
            "7/7 - 0s - loss: 0.1801 - accuracy: 0.9431 - val_loss: 0.2589 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 373/1500\n",
            "7/7 - 0s - loss: 0.1801 - accuracy: 0.9479 - val_loss: 0.2586 - val_accuracy: 0.9350 - 43ms/epoch - 6ms/step\n",
            "Epoch 374/1500\n",
            "7/7 - 0s - loss: 0.1803 - accuracy: 0.9479 - val_loss: 0.2591 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 375/1500\n",
            "7/7 - 0s - loss: 0.1800 - accuracy: 0.9431 - val_loss: 0.2612 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 376/1500\n",
            "7/7 - 0s - loss: 0.1800 - accuracy: 0.9431 - val_loss: 0.2619 - val_accuracy: 0.9300 - 56ms/epoch - 8ms/step\n",
            "Epoch 377/1500\n",
            "7/7 - 0s - loss: 0.1801 - accuracy: 0.9431 - val_loss: 0.2617 - val_accuracy: 0.9300 - 58ms/epoch - 8ms/step\n",
            "Epoch 378/1500\n",
            "7/7 - 0s - loss: 0.1801 - accuracy: 0.9431 - val_loss: 0.2621 - val_accuracy: 0.9300 - 40ms/epoch - 6ms/step\n",
            "Epoch 379/1500\n",
            "7/7 - 0s - loss: 0.1799 - accuracy: 0.9431 - val_loss: 0.2601 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 380/1500\n",
            "7/7 - 0s - loss: 0.1807 - accuracy: 0.9431 - val_loss: 0.2581 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 381/1500\n",
            "7/7 - 0s - loss: 0.1799 - accuracy: 0.9479 - val_loss: 0.2583 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 382/1500\n",
            "7/7 - 0s - loss: 0.1797 - accuracy: 0.9431 - val_loss: 0.2598 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 383/1500\n",
            "7/7 - 0s - loss: 0.1799 - accuracy: 0.9431 - val_loss: 0.2606 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 384/1500\n",
            "7/7 - 0s - loss: 0.1798 - accuracy: 0.9431 - val_loss: 0.2606 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 385/1500\n",
            "7/7 - 0s - loss: 0.1796 - accuracy: 0.9431 - val_loss: 0.2603 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 386/1500\n",
            "7/7 - 0s - loss: 0.1797 - accuracy: 0.9431 - val_loss: 0.2588 - val_accuracy: 0.9350 - 54ms/epoch - 8ms/step\n",
            "Epoch 387/1500\n",
            "7/7 - 0s - loss: 0.1799 - accuracy: 0.9431 - val_loss: 0.2597 - val_accuracy: 0.9350 - 54ms/epoch - 8ms/step\n",
            "Epoch 388/1500\n",
            "7/7 - 0s - loss: 0.1796 - accuracy: 0.9431 - val_loss: 0.2606 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 389/1500\n",
            "7/7 - 0s - loss: 0.1795 - accuracy: 0.9431 - val_loss: 0.2601 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 390/1500\n",
            "7/7 - 0s - loss: 0.1797 - accuracy: 0.9431 - val_loss: 0.2601 - val_accuracy: 0.9350 - 61ms/epoch - 9ms/step\n",
            "Epoch 391/1500\n",
            "7/7 - 0s - loss: 0.1794 - accuracy: 0.9431 - val_loss: 0.2606 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 392/1500\n",
            "7/7 - 0s - loss: 0.1796 - accuracy: 0.9431 - val_loss: 0.2615 - val_accuracy: 0.9350 - 42ms/epoch - 6ms/step\n",
            "Epoch 393/1500\n",
            "7/7 - 0s - loss: 0.1795 - accuracy: 0.9431 - val_loss: 0.2602 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 394/1500\n",
            "7/7 - 0s - loss: 0.1793 - accuracy: 0.9431 - val_loss: 0.2607 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 395/1500\n",
            "7/7 - 0s - loss: 0.1794 - accuracy: 0.9431 - val_loss: 0.2601 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 396/1500\n",
            "7/7 - 0s - loss: 0.1794 - accuracy: 0.9431 - val_loss: 0.2594 - val_accuracy: 0.9350 - 52ms/epoch - 7ms/step\n",
            "Epoch 397/1500\n",
            "7/7 - 0s - loss: 0.1793 - accuracy: 0.9431 - val_loss: 0.2599 - val_accuracy: 0.9350 - 64ms/epoch - 9ms/step\n",
            "Epoch 398/1500\n",
            "7/7 - 0s - loss: 0.1793 - accuracy: 0.9431 - val_loss: 0.2594 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 399/1500\n",
            "7/7 - 0s - loss: 0.1793 - accuracy: 0.9431 - val_loss: 0.2593 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 400/1500\n",
            "7/7 - 0s - loss: 0.1793 - accuracy: 0.9431 - val_loss: 0.2595 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 401/1500\n",
            "7/7 - 0s - loss: 0.1793 - accuracy: 0.9431 - val_loss: 0.2601 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 402/1500\n",
            "7/7 - 0s - loss: 0.1791 - accuracy: 0.9431 - val_loss: 0.2600 - val_accuracy: 0.9350 - 41ms/epoch - 6ms/step\n",
            "Epoch 403/1500\n",
            "7/7 - 0s - loss: 0.1793 - accuracy: 0.9431 - val_loss: 0.2588 - val_accuracy: 0.9350 - 54ms/epoch - 8ms/step\n",
            "Epoch 404/1500\n",
            "7/7 - 0s - loss: 0.1791 - accuracy: 0.9431 - val_loss: 0.2592 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 405/1500\n",
            "7/7 - 0s - loss: 0.1793 - accuracy: 0.9431 - val_loss: 0.2590 - val_accuracy: 0.9350 - 42ms/epoch - 6ms/step\n",
            "Epoch 406/1500\n",
            "7/7 - 0s - loss: 0.1789 - accuracy: 0.9431 - val_loss: 0.2585 - val_accuracy: 0.9350 - 51ms/epoch - 7ms/step\n",
            "Epoch 407/1500\n",
            "7/7 - 0s - loss: 0.1789 - accuracy: 0.9431 - val_loss: 0.2596 - val_accuracy: 0.9350 - 54ms/epoch - 8ms/step\n",
            "Epoch 408/1500\n",
            "7/7 - 0s - loss: 0.1789 - accuracy: 0.9431 - val_loss: 0.2615 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 409/1500\n",
            "7/7 - 0s - loss: 0.1788 - accuracy: 0.9431 - val_loss: 0.2618 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 410/1500\n",
            "7/7 - 0s - loss: 0.1791 - accuracy: 0.9431 - val_loss: 0.2614 - val_accuracy: 0.9350 - 62ms/epoch - 9ms/step\n",
            "Epoch 411/1500\n",
            "7/7 - 0s - loss: 0.1788 - accuracy: 0.9431 - val_loss: 0.2604 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 412/1500\n",
            "7/7 - 0s - loss: 0.1786 - accuracy: 0.9431 - val_loss: 0.2600 - val_accuracy: 0.9350 - 42ms/epoch - 6ms/step\n",
            "Epoch 413/1500\n",
            "7/7 - 0s - loss: 0.1789 - accuracy: 0.9431 - val_loss: 0.2592 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 414/1500\n",
            "7/7 - 0s - loss: 0.1791 - accuracy: 0.9431 - val_loss: 0.2608 - val_accuracy: 0.9300 - 42ms/epoch - 6ms/step\n",
            "Epoch 415/1500\n",
            "7/7 - 0s - loss: 0.1789 - accuracy: 0.9431 - val_loss: 0.2604 - val_accuracy: 0.9300 - 42ms/epoch - 6ms/step\n",
            "Epoch 416/1500\n",
            "7/7 - 0s - loss: 0.1788 - accuracy: 0.9431 - val_loss: 0.2594 - val_accuracy: 0.9300 - 44ms/epoch - 6ms/step\n",
            "Epoch 417/1500\n",
            "7/7 - 0s - loss: 0.1785 - accuracy: 0.9431 - val_loss: 0.2590 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 418/1500\n",
            "7/7 - 0s - loss: 0.1787 - accuracy: 0.9431 - val_loss: 0.2581 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 419/1500\n",
            "7/7 - 0s - loss: 0.1786 - accuracy: 0.9431 - val_loss: 0.2587 - val_accuracy: 0.9350 - 52ms/epoch - 7ms/step\n",
            "Epoch 420/1500\n",
            "7/7 - 0s - loss: 0.1786 - accuracy: 0.9431 - val_loss: 0.2585 - val_accuracy: 0.9350 - 49ms/epoch - 7ms/step\n",
            "Epoch 421/1500\n",
            "7/7 - 0s - loss: 0.1789 - accuracy: 0.9431 - val_loss: 0.2596 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 422/1500\n",
            "7/7 - 0s - loss: 0.1785 - accuracy: 0.9431 - val_loss: 0.2600 - val_accuracy: 0.9350 - 51ms/epoch - 7ms/step\n",
            "Epoch 423/1500\n",
            "7/7 - 0s - loss: 0.1790 - accuracy: 0.9431 - val_loss: 0.2601 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 424/1500\n",
            "7/7 - 0s - loss: 0.1786 - accuracy: 0.9431 - val_loss: 0.2578 - val_accuracy: 0.9350 - 49ms/epoch - 7ms/step\n",
            "Epoch 425/1500\n",
            "7/7 - 0s - loss: 0.1783 - accuracy: 0.9431 - val_loss: 0.2591 - val_accuracy: 0.9350 - 42ms/epoch - 6ms/step\n",
            "Epoch 426/1500\n",
            "7/7 - 0s - loss: 0.1783 - accuracy: 0.9431 - val_loss: 0.2602 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 427/1500\n",
            "7/7 - 0s - loss: 0.1782 - accuracy: 0.9431 - val_loss: 0.2607 - val_accuracy: 0.9350 - 43ms/epoch - 6ms/step\n",
            "Epoch 428/1500\n",
            "7/7 - 0s - loss: 0.1783 - accuracy: 0.9431 - val_loss: 0.2607 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 429/1500\n",
            "7/7 - 0s - loss: 0.1781 - accuracy: 0.9479 - val_loss: 0.2604 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 430/1500\n",
            "7/7 - 0s - loss: 0.1781 - accuracy: 0.9431 - val_loss: 0.2610 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 431/1500\n",
            "7/7 - 0s - loss: 0.1788 - accuracy: 0.9431 - val_loss: 0.2623 - val_accuracy: 0.9350 - 54ms/epoch - 8ms/step\n",
            "Epoch 432/1500\n",
            "7/7 - 0s - loss: 0.1782 - accuracy: 0.9431 - val_loss: 0.2618 - val_accuracy: 0.9300 - 59ms/epoch - 8ms/step\n",
            "Epoch 433/1500\n",
            "7/7 - 0s - loss: 0.1783 - accuracy: 0.9431 - val_loss: 0.2598 - val_accuracy: 0.9350 - 51ms/epoch - 7ms/step\n",
            "Epoch 434/1500\n",
            "7/7 - 0s - loss: 0.1781 - accuracy: 0.9431 - val_loss: 0.2592 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 435/1500\n",
            "7/7 - 0s - loss: 0.1780 - accuracy: 0.9431 - val_loss: 0.2594 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 436/1500\n",
            "7/7 - 0s - loss: 0.1779 - accuracy: 0.9431 - val_loss: 0.2598 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 437/1500\n",
            "7/7 - 0s - loss: 0.1779 - accuracy: 0.9431 - val_loss: 0.2602 - val_accuracy: 0.9350 - 53ms/epoch - 8ms/step\n",
            "Epoch 438/1500\n",
            "7/7 - 0s - loss: 0.1794 - accuracy: 0.9431 - val_loss: 0.2592 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 439/1500\n",
            "7/7 - 0s - loss: 0.1773 - accuracy: 0.9431 - val_loss: 0.2610 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 440/1500\n",
            "7/7 - 0s - loss: 0.1783 - accuracy: 0.9431 - val_loss: 0.2629 - val_accuracy: 0.9300 - 54ms/epoch - 8ms/step\n",
            "Epoch 441/1500\n",
            "7/7 - 0s - loss: 0.1782 - accuracy: 0.9431 - val_loss: 0.2613 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 442/1500\n",
            "7/7 - 0s - loss: 0.1782 - accuracy: 0.9431 - val_loss: 0.2594 - val_accuracy: 0.9350 - 49ms/epoch - 7ms/step\n",
            "Epoch 443/1500\n",
            "7/7 - 0s - loss: 0.1777 - accuracy: 0.9431 - val_loss: 0.2600 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 444/1500\n",
            "7/7 - 0s - loss: 0.1780 - accuracy: 0.9431 - val_loss: 0.2604 - val_accuracy: 0.9350 - 65ms/epoch - 9ms/step\n",
            "Epoch 445/1500\n",
            "7/7 - 0s - loss: 0.1774 - accuracy: 0.9431 - val_loss: 0.2610 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 446/1500\n",
            "7/7 - 0s - loss: 0.1775 - accuracy: 0.9431 - val_loss: 0.2616 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 447/1500\n",
            "7/7 - 0s - loss: 0.1777 - accuracy: 0.9431 - val_loss: 0.2625 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 448/1500\n",
            "7/7 - 0s - loss: 0.1775 - accuracy: 0.9431 - val_loss: 0.2619 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 449/1500\n",
            "7/7 - 0s - loss: 0.1772 - accuracy: 0.9431 - val_loss: 0.2602 - val_accuracy: 0.9350 - 61ms/epoch - 9ms/step\n",
            "Epoch 450/1500\n",
            "7/7 - 0s - loss: 0.1774 - accuracy: 0.9431 - val_loss: 0.2588 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 451/1500\n",
            "7/7 - 0s - loss: 0.1775 - accuracy: 0.9431 - val_loss: 0.2594 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 452/1500\n",
            "7/7 - 0s - loss: 0.1773 - accuracy: 0.9431 - val_loss: 0.2599 - val_accuracy: 0.9350 - 43ms/epoch - 6ms/step\n",
            "Epoch 453/1500\n",
            "7/7 - 0s - loss: 0.1776 - accuracy: 0.9431 - val_loss: 0.2610 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 454/1500\n",
            "7/7 - 0s - loss: 0.1775 - accuracy: 0.9431 - val_loss: 0.2601 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 455/1500\n",
            "7/7 - 0s - loss: 0.1774 - accuracy: 0.9431 - val_loss: 0.2610 - val_accuracy: 0.9350 - 49ms/epoch - 7ms/step\n",
            "Epoch 456/1500\n",
            "7/7 - 0s - loss: 0.1772 - accuracy: 0.9431 - val_loss: 0.2617 - val_accuracy: 0.9350 - 41ms/epoch - 6ms/step\n",
            "Epoch 457/1500\n",
            "7/7 - 0s - loss: 0.1772 - accuracy: 0.9431 - val_loss: 0.2601 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 458/1500\n",
            "7/7 - 0s - loss: 0.1770 - accuracy: 0.9431 - val_loss: 0.2601 - val_accuracy: 0.9350 - 64ms/epoch - 9ms/step\n",
            "Epoch 459/1500\n",
            "7/7 - 0s - loss: 0.1771 - accuracy: 0.9431 - val_loss: 0.2598 - val_accuracy: 0.9350 - 62ms/epoch - 9ms/step\n",
            "Epoch 460/1500\n",
            "7/7 - 0s - loss: 0.1773 - accuracy: 0.9479 - val_loss: 0.2586 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 461/1500\n",
            "7/7 - 0s - loss: 0.1770 - accuracy: 0.9479 - val_loss: 0.2596 - val_accuracy: 0.9350 - 62ms/epoch - 9ms/step\n",
            "Epoch 462/1500\n",
            "7/7 - 0s - loss: 0.1769 - accuracy: 0.9431 - val_loss: 0.2602 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 463/1500\n",
            "7/7 - 0s - loss: 0.1768 - accuracy: 0.9431 - val_loss: 0.2618 - val_accuracy: 0.9300 - 66ms/epoch - 9ms/step\n",
            "Epoch 464/1500\n",
            "7/7 - 0s - loss: 0.1773 - accuracy: 0.9479 - val_loss: 0.2616 - val_accuracy: 0.9350 - 65ms/epoch - 9ms/step\n",
            "Epoch 465/1500\n",
            "7/7 - 0s - loss: 0.1772 - accuracy: 0.9479 - val_loss: 0.2603 - val_accuracy: 0.9300 - 43ms/epoch - 6ms/step\n",
            "Epoch 466/1500\n",
            "7/7 - 0s - loss: 0.1771 - accuracy: 0.9431 - val_loss: 0.2598 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 467/1500\n",
            "7/7 - 0s - loss: 0.1768 - accuracy: 0.9431 - val_loss: 0.2587 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 468/1500\n",
            "7/7 - 0s - loss: 0.1769 - accuracy: 0.9479 - val_loss: 0.2588 - val_accuracy: 0.9350 - 43ms/epoch - 6ms/step\n",
            "Epoch 469/1500\n",
            "7/7 - 0s - loss: 0.1767 - accuracy: 0.9431 - val_loss: 0.2600 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 470/1500\n",
            "7/7 - 0s - loss: 0.1767 - accuracy: 0.9431 - val_loss: 0.2609 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 471/1500\n",
            "7/7 - 0s - loss: 0.1771 - accuracy: 0.9431 - val_loss: 0.2621 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 472/1500\n",
            "7/7 - 0s - loss: 0.1767 - accuracy: 0.9431 - val_loss: 0.2614 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 473/1500\n",
            "7/7 - 0s - loss: 0.1766 - accuracy: 0.9479 - val_loss: 0.2587 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 474/1500\n",
            "7/7 - 0s - loss: 0.1768 - accuracy: 0.9479 - val_loss: 0.2585 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 475/1500\n",
            "7/7 - 0s - loss: 0.1772 - accuracy: 0.9431 - val_loss: 0.2596 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 476/1500\n",
            "7/7 - 0s - loss: 0.1765 - accuracy: 0.9479 - val_loss: 0.2590 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 477/1500\n",
            "7/7 - 0s - loss: 0.1767 - accuracy: 0.9479 - val_loss: 0.2587 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 478/1500\n",
            "7/7 - 0s - loss: 0.1767 - accuracy: 0.9479 - val_loss: 0.2605 - val_accuracy: 0.9350 - 54ms/epoch - 8ms/step\n",
            "Epoch 479/1500\n",
            "7/7 - 0s - loss: 0.1766 - accuracy: 0.9431 - val_loss: 0.2607 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 480/1500\n",
            "7/7 - 0s - loss: 0.1766 - accuracy: 0.9431 - val_loss: 0.2614 - val_accuracy: 0.9350 - 51ms/epoch - 7ms/step\n",
            "Epoch 481/1500\n",
            "7/7 - 0s - loss: 0.1768 - accuracy: 0.9431 - val_loss: 0.2622 - val_accuracy: 0.9300 - 57ms/epoch - 8ms/step\n",
            "Epoch 482/1500\n",
            "7/7 - 0s - loss: 0.1767 - accuracy: 0.9431 - val_loss: 0.2608 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 483/1500\n",
            "7/7 - 0s - loss: 0.1767 - accuracy: 0.9431 - val_loss: 0.2592 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 484/1500\n",
            "7/7 - 0s - loss: 0.1763 - accuracy: 0.9431 - val_loss: 0.2601 - val_accuracy: 0.9350 - 53ms/epoch - 8ms/step\n",
            "Epoch 485/1500\n",
            "7/7 - 0s - loss: 0.1764 - accuracy: 0.9431 - val_loss: 0.2618 - val_accuracy: 0.9350 - 42ms/epoch - 6ms/step\n",
            "Epoch 486/1500\n",
            "7/7 - 0s - loss: 0.1761 - accuracy: 0.9431 - val_loss: 0.2617 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 487/1500\n",
            "7/7 - 0s - loss: 0.1768 - accuracy: 0.9431 - val_loss: 0.2598 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 488/1500\n",
            "7/7 - 0s - loss: 0.1763 - accuracy: 0.9479 - val_loss: 0.2604 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 489/1500\n",
            "7/7 - 0s - loss: 0.1760 - accuracy: 0.9479 - val_loss: 0.2598 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 490/1500\n",
            "7/7 - 0s - loss: 0.1762 - accuracy: 0.9431 - val_loss: 0.2598 - val_accuracy: 0.9350 - 43ms/epoch - 6ms/step\n",
            "Epoch 491/1500\n",
            "7/7 - 0s - loss: 0.1762 - accuracy: 0.9431 - val_loss: 0.2603 - val_accuracy: 0.9350 - 64ms/epoch - 9ms/step\n",
            "Epoch 492/1500\n",
            "7/7 - 0s - loss: 0.1759 - accuracy: 0.9431 - val_loss: 0.2618 - val_accuracy: 0.9300 - 64ms/epoch - 9ms/step\n",
            "Epoch 493/1500\n",
            "7/7 - 0s - loss: 0.1764 - accuracy: 0.9479 - val_loss: 0.2626 - val_accuracy: 0.9350 - 64ms/epoch - 9ms/step\n",
            "Epoch 494/1500\n",
            "7/7 - 0s - loss: 0.1764 - accuracy: 0.9431 - val_loss: 0.2610 - val_accuracy: 0.9300 - 48ms/epoch - 7ms/step\n",
            "Epoch 495/1500\n",
            "7/7 - 0s - loss: 0.1760 - accuracy: 0.9431 - val_loss: 0.2606 - val_accuracy: 0.9350 - 71ms/epoch - 10ms/step\n",
            "Epoch 496/1500\n",
            "7/7 - 0s - loss: 0.1760 - accuracy: 0.9431 - val_loss: 0.2594 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 497/1500\n",
            "7/7 - 0s - loss: 0.1760 - accuracy: 0.9431 - val_loss: 0.2600 - val_accuracy: 0.9350 - 52ms/epoch - 7ms/step\n",
            "Epoch 498/1500\n",
            "7/7 - 0s - loss: 0.1760 - accuracy: 0.9431 - val_loss: 0.2622 - val_accuracy: 0.9350 - 43ms/epoch - 6ms/step\n",
            "Epoch 499/1500\n",
            "7/7 - 0s - loss: 0.1758 - accuracy: 0.9431 - val_loss: 0.2627 - val_accuracy: 0.9300 - 49ms/epoch - 7ms/step\n",
            "Epoch 500/1500\n",
            "7/7 - 0s - loss: 0.1759 - accuracy: 0.9431 - val_loss: 0.2622 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 501/1500\n",
            "7/7 - 0s - loss: 0.1757 - accuracy: 0.9431 - val_loss: 0.2613 - val_accuracy: 0.9350 - 40ms/epoch - 6ms/step\n",
            "Epoch 502/1500\n",
            "7/7 - 0s - loss: 0.1758 - accuracy: 0.9431 - val_loss: 0.2603 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 503/1500\n",
            "7/7 - 0s - loss: 0.1758 - accuracy: 0.9431 - val_loss: 0.2612 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 504/1500\n",
            "7/7 - 0s - loss: 0.1756 - accuracy: 0.9431 - val_loss: 0.2614 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 505/1500\n",
            "7/7 - 0s - loss: 0.1755 - accuracy: 0.9431 - val_loss: 0.2613 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 506/1500\n",
            "7/7 - 0s - loss: 0.1757 - accuracy: 0.9431 - val_loss: 0.2620 - val_accuracy: 0.9350 - 51ms/epoch - 7ms/step\n",
            "Epoch 507/1500\n",
            "7/7 - 0s - loss: 0.1753 - accuracy: 0.9431 - val_loss: 0.2605 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 508/1500\n",
            "7/7 - 0s - loss: 0.1759 - accuracy: 0.9479 - val_loss: 0.2593 - val_accuracy: 0.9350 - 54ms/epoch - 8ms/step\n",
            "Epoch 509/1500\n",
            "7/7 - 0s - loss: 0.1760 - accuracy: 0.9479 - val_loss: 0.2595 - val_accuracy: 0.9350 - 53ms/epoch - 8ms/step\n",
            "Epoch 510/1500\n",
            "7/7 - 0s - loss: 0.1753 - accuracy: 0.9479 - val_loss: 0.2614 - val_accuracy: 0.9350 - 43ms/epoch - 6ms/step\n",
            "Epoch 511/1500\n",
            "7/7 - 0s - loss: 0.1754 - accuracy: 0.9431 - val_loss: 0.2633 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 512/1500\n",
            "7/7 - 0s - loss: 0.1756 - accuracy: 0.9431 - val_loss: 0.2638 - val_accuracy: 0.9300 - 58ms/epoch - 8ms/step\n",
            "Epoch 513/1500\n",
            "7/7 - 0s - loss: 0.1755 - accuracy: 0.9431 - val_loss: 0.2630 - val_accuracy: 0.9300 - 43ms/epoch - 6ms/step\n",
            "Epoch 514/1500\n",
            "7/7 - 0s - loss: 0.1752 - accuracy: 0.9431 - val_loss: 0.2603 - val_accuracy: 0.9350 - 63ms/epoch - 9ms/step\n",
            "Epoch 515/1500\n",
            "7/7 - 0s - loss: 0.1755 - accuracy: 0.9479 - val_loss: 0.2595 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 516/1500\n",
            "7/7 - 0s - loss: 0.1756 - accuracy: 0.9431 - val_loss: 0.2605 - val_accuracy: 0.9350 - 54ms/epoch - 8ms/step\n",
            "Epoch 517/1500\n",
            "7/7 - 0s - loss: 0.1754 - accuracy: 0.9431 - val_loss: 0.2611 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 518/1500\n",
            "7/7 - 0s - loss: 0.1751 - accuracy: 0.9431 - val_loss: 0.2612 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 519/1500\n",
            "7/7 - 0s - loss: 0.1753 - accuracy: 0.9479 - val_loss: 0.2623 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 520/1500\n",
            "7/7 - 0s - loss: 0.1757 - accuracy: 0.9431 - val_loss: 0.2638 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 521/1500\n",
            "7/7 - 0s - loss: 0.1755 - accuracy: 0.9431 - val_loss: 0.2618 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 522/1500\n",
            "7/7 - 0s - loss: 0.1755 - accuracy: 0.9431 - val_loss: 0.2618 - val_accuracy: 0.9350 - 53ms/epoch - 8ms/step\n",
            "Epoch 523/1500\n",
            "7/7 - 0s - loss: 0.1750 - accuracy: 0.9431 - val_loss: 0.2628 - val_accuracy: 0.9350 - 54ms/epoch - 8ms/step\n",
            "Epoch 524/1500\n",
            "7/7 - 0s - loss: 0.1752 - accuracy: 0.9431 - val_loss: 0.2640 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 525/1500\n",
            "7/7 - 0s - loss: 0.1756 - accuracy: 0.9431 - val_loss: 0.2651 - val_accuracy: 0.9300 - 46ms/epoch - 7ms/step\n",
            "Epoch 526/1500\n",
            "7/7 - 0s - loss: 0.1749 - accuracy: 0.9431 - val_loss: 0.2634 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 527/1500\n",
            "7/7 - 0s - loss: 0.1749 - accuracy: 0.9479 - val_loss: 0.2617 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 528/1500\n",
            "7/7 - 0s - loss: 0.1751 - accuracy: 0.9479 - val_loss: 0.2618 - val_accuracy: 0.9350 - 42ms/epoch - 6ms/step\n",
            "Epoch 529/1500\n",
            "7/7 - 0s - loss: 0.1753 - accuracy: 0.9431 - val_loss: 0.2624 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 530/1500\n",
            "7/7 - 0s - loss: 0.1749 - accuracy: 0.9431 - val_loss: 0.2620 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 531/1500\n",
            "7/7 - 0s - loss: 0.1749 - accuracy: 0.9479 - val_loss: 0.2604 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 532/1500\n",
            "7/7 - 0s - loss: 0.1749 - accuracy: 0.9479 - val_loss: 0.2604 - val_accuracy: 0.9350 - 43ms/epoch - 6ms/step\n",
            "Epoch 533/1500\n",
            "7/7 - 0s - loss: 0.1748 - accuracy: 0.9479 - val_loss: 0.2622 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 534/1500\n",
            "7/7 - 0s - loss: 0.1750 - accuracy: 0.9431 - val_loss: 0.2639 - val_accuracy: 0.9300 - 53ms/epoch - 8ms/step\n",
            "Epoch 535/1500\n",
            "7/7 - 0s - loss: 0.1749 - accuracy: 0.9431 - val_loss: 0.2640 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 536/1500\n",
            "7/7 - 0s - loss: 0.1749 - accuracy: 0.9431 - val_loss: 0.2624 - val_accuracy: 0.9350 - 52ms/epoch - 7ms/step\n",
            "Epoch 537/1500\n",
            "7/7 - 0s - loss: 0.1751 - accuracy: 0.9431 - val_loss: 0.2636 - val_accuracy: 0.9300 - 55ms/epoch - 8ms/step\n",
            "Epoch 538/1500\n",
            "7/7 - 0s - loss: 0.1762 - accuracy: 0.9431 - val_loss: 0.2609 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 539/1500\n",
            "7/7 - 0s - loss: 0.1751 - accuracy: 0.9431 - val_loss: 0.2636 - val_accuracy: 0.9350 - 54ms/epoch - 8ms/step\n",
            "Epoch 540/1500\n",
            "7/7 - 0s - loss: 0.1743 - accuracy: 0.9431 - val_loss: 0.2633 - val_accuracy: 0.9350 - 43ms/epoch - 6ms/step\n",
            "Epoch 541/1500\n",
            "7/7 - 0s - loss: 0.1745 - accuracy: 0.9431 - val_loss: 0.2632 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 542/1500\n",
            "7/7 - 0s - loss: 0.1747 - accuracy: 0.9431 - val_loss: 0.2616 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 543/1500\n",
            "7/7 - 0s - loss: 0.1747 - accuracy: 0.9431 - val_loss: 0.2618 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 544/1500\n",
            "7/7 - 0s - loss: 0.1747 - accuracy: 0.9431 - val_loss: 0.2634 - val_accuracy: 0.9350 - 61ms/epoch - 9ms/step\n",
            "Epoch 545/1500\n",
            "7/7 - 0s - loss: 0.1743 - accuracy: 0.9431 - val_loss: 0.2633 - val_accuracy: 0.9350 - 43ms/epoch - 6ms/step\n",
            "Epoch 546/1500\n",
            "7/7 - 0s - loss: 0.1746 - accuracy: 0.9479 - val_loss: 0.2617 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 547/1500\n",
            "7/7 - 0s - loss: 0.1747 - accuracy: 0.9479 - val_loss: 0.2613 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 548/1500\n",
            "7/7 - 0s - loss: 0.1744 - accuracy: 0.9479 - val_loss: 0.2623 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 549/1500\n",
            "7/7 - 0s - loss: 0.1746 - accuracy: 0.9431 - val_loss: 0.2649 - val_accuracy: 0.9300 - 41ms/epoch - 6ms/step\n",
            "Epoch 550/1500\n",
            "7/7 - 0s - loss: 0.1746 - accuracy: 0.9479 - val_loss: 0.2658 - val_accuracy: 0.9300 - 41ms/epoch - 6ms/step\n",
            "Epoch 551/1500\n",
            "7/7 - 0s - loss: 0.1744 - accuracy: 0.9431 - val_loss: 0.2634 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 552/1500\n",
            "7/7 - 0s - loss: 0.1740 - accuracy: 0.9431 - val_loss: 0.2632 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 553/1500\n",
            "7/7 - 0s - loss: 0.1741 - accuracy: 0.9431 - val_loss: 0.2626 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 554/1500\n",
            "7/7 - 0s - loss: 0.1742 - accuracy: 0.9431 - val_loss: 0.2628 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 555/1500\n",
            "7/7 - 0s - loss: 0.1742 - accuracy: 0.9431 - val_loss: 0.2627 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 556/1500\n",
            "7/7 - 0s - loss: 0.1740 - accuracy: 0.9431 - val_loss: 0.2640 - val_accuracy: 0.9350 - 52ms/epoch - 7ms/step\n",
            "Epoch 557/1500\n",
            "7/7 - 0s - loss: 0.1742 - accuracy: 0.9431 - val_loss: 0.2636 - val_accuracy: 0.9350 - 51ms/epoch - 7ms/step\n",
            "Epoch 558/1500\n",
            "7/7 - 0s - loss: 0.1742 - accuracy: 0.9431 - val_loss: 0.2631 - val_accuracy: 0.9350 - 77ms/epoch - 11ms/step\n",
            "Epoch 559/1500\n",
            "7/7 - 0s - loss: 0.1738 - accuracy: 0.9431 - val_loss: 0.2643 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 560/1500\n",
            "7/7 - 0s - loss: 0.1740 - accuracy: 0.9431 - val_loss: 0.2652 - val_accuracy: 0.9300 - 43ms/epoch - 6ms/step\n",
            "Epoch 561/1500\n",
            "7/7 - 0s - loss: 0.1746 - accuracy: 0.9479 - val_loss: 0.2657 - val_accuracy: 0.9300 - 55ms/epoch - 8ms/step\n",
            "Epoch 562/1500\n",
            "7/7 - 0s - loss: 0.1747 - accuracy: 0.9431 - val_loss: 0.2627 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 563/1500\n",
            "7/7 - 0s - loss: 0.1739 - accuracy: 0.9431 - val_loss: 0.2633 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 564/1500\n",
            "7/7 - 0s - loss: 0.1743 - accuracy: 0.9479 - val_loss: 0.2635 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 565/1500\n",
            "7/7 - 0s - loss: 0.1742 - accuracy: 0.9431 - val_loss: 0.2656 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 566/1500\n",
            "7/7 - 0s - loss: 0.1740 - accuracy: 0.9431 - val_loss: 0.2645 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 567/1500\n",
            "7/7 - 0s - loss: 0.1741 - accuracy: 0.9431 - val_loss: 0.2637 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 568/1500\n",
            "7/7 - 0s - loss: 0.1738 - accuracy: 0.9431 - val_loss: 0.2638 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 569/1500\n",
            "7/7 - 0s - loss: 0.1741 - accuracy: 0.9431 - val_loss: 0.2655 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 570/1500\n",
            "7/7 - 0s - loss: 0.1737 - accuracy: 0.9431 - val_loss: 0.2653 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 571/1500\n",
            "7/7 - 0s - loss: 0.1736 - accuracy: 0.9431 - val_loss: 0.2637 - val_accuracy: 0.9350 - 43ms/epoch - 6ms/step\n",
            "Epoch 572/1500\n",
            "7/7 - 0s - loss: 0.1739 - accuracy: 0.9431 - val_loss: 0.2628 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 573/1500\n",
            "7/7 - 0s - loss: 0.1738 - accuracy: 0.9479 - val_loss: 0.2626 - val_accuracy: 0.9350 - 43ms/epoch - 6ms/step\n",
            "Epoch 574/1500\n",
            "7/7 - 0s - loss: 0.1740 - accuracy: 0.9479 - val_loss: 0.2636 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 575/1500\n",
            "7/7 - 0s - loss: 0.1736 - accuracy: 0.9431 - val_loss: 0.2650 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 576/1500\n",
            "7/7 - 0s - loss: 0.1742 - accuracy: 0.9431 - val_loss: 0.2661 - val_accuracy: 0.9350 - 61ms/epoch - 9ms/step\n",
            "Epoch 577/1500\n",
            "7/7 - 0s - loss: 0.1734 - accuracy: 0.9431 - val_loss: 0.2630 - val_accuracy: 0.9350 - 64ms/epoch - 9ms/step\n",
            "Epoch 578/1500\n",
            "7/7 - 0s - loss: 0.1739 - accuracy: 0.9479 - val_loss: 0.2615 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 579/1500\n",
            "7/7 - 0s - loss: 0.1737 - accuracy: 0.9479 - val_loss: 0.2623 - val_accuracy: 0.9350 - 64ms/epoch - 9ms/step\n",
            "Epoch 580/1500\n",
            "7/7 - 0s - loss: 0.1735 - accuracy: 0.9431 - val_loss: 0.2631 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 581/1500\n",
            "7/7 - 0s - loss: 0.1736 - accuracy: 0.9431 - val_loss: 0.2649 - val_accuracy: 0.9350 - 69ms/epoch - 10ms/step\n",
            "Epoch 582/1500\n",
            "7/7 - 0s - loss: 0.1736 - accuracy: 0.9479 - val_loss: 0.2655 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 583/1500\n",
            "7/7 - 0s - loss: 0.1733 - accuracy: 0.9431 - val_loss: 0.2649 - val_accuracy: 0.9350 - 62ms/epoch - 9ms/step\n",
            "Epoch 584/1500\n",
            "7/7 - 0s - loss: 0.1734 - accuracy: 0.9431 - val_loss: 0.2637 - val_accuracy: 0.9350 - 42ms/epoch - 6ms/step\n",
            "Epoch 585/1500\n",
            "7/7 - 0s - loss: 0.1738 - accuracy: 0.9479 - val_loss: 0.2630 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 586/1500\n",
            "7/7 - 0s - loss: 0.1734 - accuracy: 0.9479 - val_loss: 0.2639 - val_accuracy: 0.9350 - 70ms/epoch - 10ms/step\n",
            "Epoch 587/1500\n",
            "7/7 - 0s - loss: 0.1734 - accuracy: 0.9431 - val_loss: 0.2653 - val_accuracy: 0.9350 - 42ms/epoch - 6ms/step\n",
            "Epoch 588/1500\n",
            "7/7 - 0s - loss: 0.1734 - accuracy: 0.9479 - val_loss: 0.2666 - val_accuracy: 0.9300 - 46ms/epoch - 7ms/step\n",
            "Epoch 589/1500\n",
            "7/7 - 0s - loss: 0.1733 - accuracy: 0.9479 - val_loss: 0.2659 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 590/1500\n",
            "7/7 - 0s - loss: 0.1738 - accuracy: 0.9431 - val_loss: 0.2645 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 591/1500\n",
            "7/7 - 0s - loss: 0.1732 - accuracy: 0.9431 - val_loss: 0.2647 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 592/1500\n",
            "7/7 - 0s - loss: 0.1733 - accuracy: 0.9431 - val_loss: 0.2642 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 593/1500\n",
            "7/7 - 0s - loss: 0.1734 - accuracy: 0.9431 - val_loss: 0.2643 - val_accuracy: 0.9350 - 69ms/epoch - 10ms/step\n",
            "Epoch 594/1500\n",
            "7/7 - 0s - loss: 0.1735 - accuracy: 0.9479 - val_loss: 0.2671 - val_accuracy: 0.9300 - 47ms/epoch - 7ms/step\n",
            "Epoch 595/1500\n",
            "7/7 - 0s - loss: 0.1731 - accuracy: 0.9479 - val_loss: 0.2661 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 596/1500\n",
            "7/7 - 0s - loss: 0.1736 - accuracy: 0.9431 - val_loss: 0.2657 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 597/1500\n",
            "7/7 - 0s - loss: 0.1731 - accuracy: 0.9479 - val_loss: 0.2664 - val_accuracy: 0.9350 - 43ms/epoch - 6ms/step\n",
            "Epoch 598/1500\n",
            "7/7 - 0s - loss: 0.1734 - accuracy: 0.9479 - val_loss: 0.2671 - val_accuracy: 0.9300 - 55ms/epoch - 8ms/step\n",
            "Epoch 599/1500\n",
            "7/7 - 0s - loss: 0.1733 - accuracy: 0.9479 - val_loss: 0.2651 - val_accuracy: 0.9350 - 51ms/epoch - 7ms/step\n",
            "Epoch 600/1500\n",
            "7/7 - 0s - loss: 0.1731 - accuracy: 0.9431 - val_loss: 0.2670 - val_accuracy: 0.9350 - 43ms/epoch - 6ms/step\n",
            "Epoch 601/1500\n",
            "7/7 - 0s - loss: 0.1730 - accuracy: 0.9431 - val_loss: 0.2670 - val_accuracy: 0.9350 - 61ms/epoch - 9ms/step\n",
            "Epoch 602/1500\n",
            "7/7 - 0s - loss: 0.1734 - accuracy: 0.9479 - val_loss: 0.2645 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 603/1500\n",
            "7/7 - 0s - loss: 0.1730 - accuracy: 0.9479 - val_loss: 0.2648 - val_accuracy: 0.9350 - 65ms/epoch - 9ms/step\n",
            "Epoch 604/1500\n",
            "7/7 - 0s - loss: 0.1729 - accuracy: 0.9479 - val_loss: 0.2651 - val_accuracy: 0.9350 - 49ms/epoch - 7ms/step\n",
            "Epoch 605/1500\n",
            "7/7 - 0s - loss: 0.1730 - accuracy: 0.9479 - val_loss: 0.2639 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 606/1500\n",
            "7/7 - 0s - loss: 0.1737 - accuracy: 0.9479 - val_loss: 0.2636 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 607/1500\n",
            "7/7 - 0s - loss: 0.1731 - accuracy: 0.9431 - val_loss: 0.2663 - val_accuracy: 0.9300 - 60ms/epoch - 9ms/step\n",
            "Epoch 608/1500\n",
            "7/7 - 0s - loss: 0.1729 - accuracy: 0.9479 - val_loss: 0.2659 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 609/1500\n",
            "7/7 - 0s - loss: 0.1728 - accuracy: 0.9431 - val_loss: 0.2659 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 610/1500\n",
            "7/7 - 0s - loss: 0.1728 - accuracy: 0.9431 - val_loss: 0.2654 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 611/1500\n",
            "7/7 - 0s - loss: 0.1734 - accuracy: 0.9431 - val_loss: 0.2639 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 612/1500\n",
            "7/7 - 0s - loss: 0.1729 - accuracy: 0.9479 - val_loss: 0.2646 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 613/1500\n",
            "7/7 - 0s - loss: 0.1728 - accuracy: 0.9431 - val_loss: 0.2650 - val_accuracy: 0.9300 - 56ms/epoch - 8ms/step\n",
            "Epoch 614/1500\n",
            "7/7 - 0s - loss: 0.1726 - accuracy: 0.9479 - val_loss: 0.2642 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 615/1500\n",
            "7/7 - 0s - loss: 0.1725 - accuracy: 0.9431 - val_loss: 0.2651 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 616/1500\n",
            "7/7 - 0s - loss: 0.1728 - accuracy: 0.9479 - val_loss: 0.2661 - val_accuracy: 0.9300 - 45ms/epoch - 6ms/step\n",
            "Epoch 617/1500\n",
            "7/7 - 0s - loss: 0.1728 - accuracy: 0.9479 - val_loss: 0.2658 - val_accuracy: 0.9350 - 43ms/epoch - 6ms/step\n",
            "Epoch 618/1500\n",
            "7/7 - 0s - loss: 0.1726 - accuracy: 0.9431 - val_loss: 0.2652 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 619/1500\n",
            "7/7 - 0s - loss: 0.1724 - accuracy: 0.9431 - val_loss: 0.2651 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 620/1500\n",
            "7/7 - 0s - loss: 0.1725 - accuracy: 0.9479 - val_loss: 0.2649 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 621/1500\n",
            "7/7 - 0s - loss: 0.1727 - accuracy: 0.9479 - val_loss: 0.2658 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 622/1500\n",
            "7/7 - 0s - loss: 0.1724 - accuracy: 0.9479 - val_loss: 0.2651 - val_accuracy: 0.9350 - 43ms/epoch - 6ms/step\n",
            "Epoch 623/1500\n",
            "7/7 - 0s - loss: 0.1726 - accuracy: 0.9479 - val_loss: 0.2651 - val_accuracy: 0.9350 - 51ms/epoch - 7ms/step\n",
            "Epoch 624/1500\n",
            "7/7 - 0s - loss: 0.1724 - accuracy: 0.9479 - val_loss: 0.2647 - val_accuracy: 0.9350 - 43ms/epoch - 6ms/step\n",
            "Epoch 625/1500\n",
            "7/7 - 0s - loss: 0.1727 - accuracy: 0.9479 - val_loss: 0.2648 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 626/1500\n",
            "7/7 - 0s - loss: 0.1724 - accuracy: 0.9479 - val_loss: 0.2650 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 627/1500\n",
            "7/7 - 0s - loss: 0.1729 - accuracy: 0.9479 - val_loss: 0.2640 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 628/1500\n",
            "7/7 - 0s - loss: 0.1721 - accuracy: 0.9479 - val_loss: 0.2655 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 629/1500\n",
            "7/7 - 0s - loss: 0.1723 - accuracy: 0.9431 - val_loss: 0.2654 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 630/1500\n",
            "7/7 - 0s - loss: 0.1730 - accuracy: 0.9431 - val_loss: 0.2675 - val_accuracy: 0.9300 - 55ms/epoch - 8ms/step\n",
            "Epoch 631/1500\n",
            "7/7 - 0s - loss: 0.1723 - accuracy: 0.9479 - val_loss: 0.2664 - val_accuracy: 0.9350 - 62ms/epoch - 9ms/step\n",
            "Epoch 632/1500\n",
            "7/7 - 0s - loss: 0.1721 - accuracy: 0.9431 - val_loss: 0.2662 - val_accuracy: 0.9350 - 62ms/epoch - 9ms/step\n",
            "Epoch 633/1500\n",
            "7/7 - 0s - loss: 0.1723 - accuracy: 0.9479 - val_loss: 0.2646 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 634/1500\n",
            "7/7 - 0s - loss: 0.1727 - accuracy: 0.9479 - val_loss: 0.2636 - val_accuracy: 0.9350 - 68ms/epoch - 10ms/step\n",
            "Epoch 635/1500\n",
            "7/7 - 0s - loss: 0.1725 - accuracy: 0.9479 - val_loss: 0.2647 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 636/1500\n",
            "7/7 - 0s - loss: 0.1723 - accuracy: 0.9479 - val_loss: 0.2654 - val_accuracy: 0.9350 - 61ms/epoch - 9ms/step\n",
            "Epoch 637/1500\n",
            "7/7 - 0s - loss: 0.1725 - accuracy: 0.9431 - val_loss: 0.2664 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 638/1500\n",
            "7/7 - 0s - loss: 0.1718 - accuracy: 0.9479 - val_loss: 0.2648 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 639/1500\n",
            "7/7 - 0s - loss: 0.1722 - accuracy: 0.9479 - val_loss: 0.2636 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 640/1500\n",
            "7/7 - 0s - loss: 0.1721 - accuracy: 0.9479 - val_loss: 0.2649 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 641/1500\n",
            "7/7 - 0s - loss: 0.1725 - accuracy: 0.9431 - val_loss: 0.2657 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 642/1500\n",
            "7/7 - 0s - loss: 0.1719 - accuracy: 0.9479 - val_loss: 0.2666 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 643/1500\n",
            "7/7 - 0s - loss: 0.1732 - accuracy: 0.9431 - val_loss: 0.2650 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 644/1500\n",
            "7/7 - 0s - loss: 0.1721 - accuracy: 0.9479 - val_loss: 0.2665 - val_accuracy: 0.9350 - 62ms/epoch - 9ms/step\n",
            "Epoch 645/1500\n",
            "7/7 - 0s - loss: 0.1721 - accuracy: 0.9479 - val_loss: 0.2675 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 646/1500\n",
            "7/7 - 0s - loss: 0.1720 - accuracy: 0.9479 - val_loss: 0.2686 - val_accuracy: 0.9300 - 62ms/epoch - 9ms/step\n",
            "Epoch 647/1500\n",
            "7/7 - 0s - loss: 0.1724 - accuracy: 0.9479 - val_loss: 0.2655 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 648/1500\n",
            "7/7 - 0s - loss: 0.1716 - accuracy: 0.9479 - val_loss: 0.2654 - val_accuracy: 0.9350 - 62ms/epoch - 9ms/step\n",
            "Epoch 649/1500\n",
            "7/7 - 0s - loss: 0.1718 - accuracy: 0.9479 - val_loss: 0.2654 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 650/1500\n",
            "7/7 - 0s - loss: 0.1715 - accuracy: 0.9479 - val_loss: 0.2657 - val_accuracy: 0.9300 - 44ms/epoch - 6ms/step\n",
            "Epoch 651/1500\n",
            "7/7 - 0s - loss: 0.1722 - accuracy: 0.9479 - val_loss: 0.2674 - val_accuracy: 0.9350 - 42ms/epoch - 6ms/step\n",
            "Epoch 652/1500\n",
            "7/7 - 0s - loss: 0.1720 - accuracy: 0.9479 - val_loss: 0.2664 - val_accuracy: 0.9350 - 51ms/epoch - 7ms/step\n",
            "Epoch 653/1500\n",
            "7/7 - 0s - loss: 0.1717 - accuracy: 0.9431 - val_loss: 0.2653 - val_accuracy: 0.9350 - 49ms/epoch - 7ms/step\n",
            "Epoch 654/1500\n",
            "7/7 - 0s - loss: 0.1718 - accuracy: 0.9479 - val_loss: 0.2666 - val_accuracy: 0.9350 - 61ms/epoch - 9ms/step\n",
            "Epoch 655/1500\n",
            "7/7 - 0s - loss: 0.1717 - accuracy: 0.9479 - val_loss: 0.2666 - val_accuracy: 0.9300 - 59ms/epoch - 8ms/step\n",
            "Epoch 656/1500\n",
            "7/7 - 0s - loss: 0.1715 - accuracy: 0.9479 - val_loss: 0.2655 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 657/1500\n",
            "7/7 - 0s - loss: 0.1723 - accuracy: 0.9479 - val_loss: 0.2673 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 658/1500\n",
            "7/7 - 0s - loss: 0.1717 - accuracy: 0.9479 - val_loss: 0.2654 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 659/1500\n",
            "7/7 - 0s - loss: 0.1718 - accuracy: 0.9479 - val_loss: 0.2661 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 660/1500\n",
            "7/7 - 0s - loss: 0.1712 - accuracy: 0.9479 - val_loss: 0.2672 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 661/1500\n",
            "7/7 - 0s - loss: 0.1723 - accuracy: 0.9479 - val_loss: 0.2696 - val_accuracy: 0.9350 - 49ms/epoch - 7ms/step\n",
            "Epoch 662/1500\n",
            "7/7 - 0s - loss: 0.1715 - accuracy: 0.9431 - val_loss: 0.2709 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 663/1500\n",
            "7/7 - 0s - loss: 0.1715 - accuracy: 0.9431 - val_loss: 0.2692 - val_accuracy: 0.9350 - 70ms/epoch - 10ms/step\n",
            "Epoch 664/1500\n",
            "7/7 - 0s - loss: 0.1717 - accuracy: 0.9479 - val_loss: 0.2681 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 665/1500\n",
            "7/7 - 0s - loss: 0.1709 - accuracy: 0.9479 - val_loss: 0.2692 - val_accuracy: 0.9350 - 41ms/epoch - 6ms/step\n",
            "Epoch 666/1500\n",
            "7/7 - 0s - loss: 0.1713 - accuracy: 0.9479 - val_loss: 0.2698 - val_accuracy: 0.9350 - 62ms/epoch - 9ms/step\n",
            "Epoch 667/1500\n",
            "7/7 - 0s - loss: 0.1712 - accuracy: 0.9479 - val_loss: 0.2685 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 668/1500\n",
            "7/7 - 0s - loss: 0.1709 - accuracy: 0.9479 - val_loss: 0.2676 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 669/1500\n",
            "7/7 - 0s - loss: 0.1712 - accuracy: 0.9479 - val_loss: 0.2678 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 670/1500\n",
            "7/7 - 0s - loss: 0.1710 - accuracy: 0.9479 - val_loss: 0.2675 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 671/1500\n",
            "7/7 - 0s - loss: 0.1714 - accuracy: 0.9479 - val_loss: 0.2662 - val_accuracy: 0.9350 - 42ms/epoch - 6ms/step\n",
            "Epoch 672/1500\n",
            "7/7 - 0s - loss: 0.1716 - accuracy: 0.9431 - val_loss: 0.2689 - val_accuracy: 0.9300 - 41ms/epoch - 6ms/step\n",
            "Epoch 673/1500\n",
            "7/7 - 0s - loss: 0.1709 - accuracy: 0.9479 - val_loss: 0.2688 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 674/1500\n",
            "7/7 - 0s - loss: 0.1713 - accuracy: 0.9526 - val_loss: 0.2672 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 675/1500\n",
            "7/7 - 0s - loss: 0.1706 - accuracy: 0.9479 - val_loss: 0.2683 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 676/1500\n",
            "7/7 - 0s - loss: 0.1708 - accuracy: 0.9479 - val_loss: 0.2702 - val_accuracy: 0.9350 - 43ms/epoch - 6ms/step\n",
            "Epoch 677/1500\n",
            "7/7 - 0s - loss: 0.1711 - accuracy: 0.9479 - val_loss: 0.2693 - val_accuracy: 0.9350 - 64ms/epoch - 9ms/step\n",
            "Epoch 678/1500\n",
            "7/7 - 0s - loss: 0.1707 - accuracy: 0.9526 - val_loss: 0.2698 - val_accuracy: 0.9300 - 49ms/epoch - 7ms/step\n",
            "Epoch 679/1500\n",
            "7/7 - 0s - loss: 0.1707 - accuracy: 0.9479 - val_loss: 0.2682 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 680/1500\n",
            "7/7 - 0s - loss: 0.1715 - accuracy: 0.9479 - val_loss: 0.2668 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 681/1500\n",
            "7/7 - 0s - loss: 0.1713 - accuracy: 0.9479 - val_loss: 0.2699 - val_accuracy: 0.9300 - 58ms/epoch - 8ms/step\n",
            "Epoch 682/1500\n",
            "7/7 - 0s - loss: 0.1707 - accuracy: 0.9479 - val_loss: 0.2706 - val_accuracy: 0.9300 - 61ms/epoch - 9ms/step\n",
            "Epoch 683/1500\n",
            "7/7 - 0s - loss: 0.1705 - accuracy: 0.9479 - val_loss: 0.2688 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 684/1500\n",
            "7/7 - 0s - loss: 0.1705 - accuracy: 0.9526 - val_loss: 0.2687 - val_accuracy: 0.9350 - 49ms/epoch - 7ms/step\n",
            "Epoch 685/1500\n",
            "7/7 - 0s - loss: 0.1706 - accuracy: 0.9479 - val_loss: 0.2683 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 686/1500\n",
            "7/7 - 0s - loss: 0.1703 - accuracy: 0.9479 - val_loss: 0.2682 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 687/1500\n",
            "7/7 - 0s - loss: 0.1706 - accuracy: 0.9479 - val_loss: 0.2696 - val_accuracy: 0.9350 - 54ms/epoch - 8ms/step\n",
            "Epoch 688/1500\n",
            "7/7 - 0s - loss: 0.1704 - accuracy: 0.9431 - val_loss: 0.2695 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 689/1500\n",
            "7/7 - 0s - loss: 0.1707 - accuracy: 0.9526 - val_loss: 0.2706 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 690/1500\n",
            "7/7 - 0s - loss: 0.1703 - accuracy: 0.9479 - val_loss: 0.2697 - val_accuracy: 0.9350 - 61ms/epoch - 9ms/step\n",
            "Epoch 691/1500\n",
            "7/7 - 0s - loss: 0.1705 - accuracy: 0.9479 - val_loss: 0.2694 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 692/1500\n",
            "7/7 - 0s - loss: 0.1702 - accuracy: 0.9479 - val_loss: 0.2678 - val_accuracy: 0.9350 - 51ms/epoch - 7ms/step\n",
            "Epoch 693/1500\n",
            "7/7 - 0s - loss: 0.1704 - accuracy: 0.9479 - val_loss: 0.2674 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 694/1500\n",
            "7/7 - 0s - loss: 0.1704 - accuracy: 0.9479 - val_loss: 0.2693 - val_accuracy: 0.9350 - 62ms/epoch - 9ms/step\n",
            "Epoch 695/1500\n",
            "7/7 - 0s - loss: 0.1702 - accuracy: 0.9479 - val_loss: 0.2709 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 696/1500\n",
            "7/7 - 0s - loss: 0.1702 - accuracy: 0.9479 - val_loss: 0.2711 - val_accuracy: 0.9350 - 66ms/epoch - 9ms/step\n",
            "Epoch 697/1500\n",
            "7/7 - 0s - loss: 0.1702 - accuracy: 0.9479 - val_loss: 0.2706 - val_accuracy: 0.9350 - 49ms/epoch - 7ms/step\n",
            "Epoch 698/1500\n",
            "7/7 - 0s - loss: 0.1708 - accuracy: 0.9479 - val_loss: 0.2695 - val_accuracy: 0.9350 - 66ms/epoch - 9ms/step\n",
            "Epoch 699/1500\n",
            "7/7 - 0s - loss: 0.1703 - accuracy: 0.9431 - val_loss: 0.2711 - val_accuracy: 0.9350 - 51ms/epoch - 7ms/step\n",
            "Epoch 700/1500\n",
            "7/7 - 0s - loss: 0.1700 - accuracy: 0.9479 - val_loss: 0.2711 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 701/1500\n",
            "7/7 - 0s - loss: 0.1702 - accuracy: 0.9479 - val_loss: 0.2704 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 702/1500\n",
            "7/7 - 0s - loss: 0.1700 - accuracy: 0.9479 - val_loss: 0.2717 - val_accuracy: 0.9300 - 46ms/epoch - 7ms/step\n",
            "Epoch 703/1500\n",
            "7/7 - 0s - loss: 0.1701 - accuracy: 0.9526 - val_loss: 0.2707 - val_accuracy: 0.9350 - 51ms/epoch - 7ms/step\n",
            "Epoch 704/1500\n",
            "7/7 - 0s - loss: 0.1700 - accuracy: 0.9479 - val_loss: 0.2706 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 705/1500\n",
            "7/7 - 0s - loss: 0.1699 - accuracy: 0.9479 - val_loss: 0.2715 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 706/1500\n",
            "7/7 - 0s - loss: 0.1697 - accuracy: 0.9479 - val_loss: 0.2716 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 707/1500\n",
            "7/7 - 0s - loss: 0.1698 - accuracy: 0.9526 - val_loss: 0.2703 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 708/1500\n",
            "7/7 - 0s - loss: 0.1697 - accuracy: 0.9526 - val_loss: 0.2707 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 709/1500\n",
            "7/7 - 0s - loss: 0.1699 - accuracy: 0.9479 - val_loss: 0.2707 - val_accuracy: 0.9350 - 49ms/epoch - 7ms/step\n",
            "Epoch 710/1500\n",
            "7/7 - 0s - loss: 0.1694 - accuracy: 0.9526 - val_loss: 0.2727 - val_accuracy: 0.9350 - 62ms/epoch - 9ms/step\n",
            "Epoch 711/1500\n",
            "7/7 - 0s - loss: 0.1700 - accuracy: 0.9479 - val_loss: 0.2735 - val_accuracy: 0.9300 - 64ms/epoch - 9ms/step\n",
            "Epoch 712/1500\n",
            "7/7 - 0s - loss: 0.1697 - accuracy: 0.9479 - val_loss: 0.2718 - val_accuracy: 0.9350 - 62ms/epoch - 9ms/step\n",
            "Epoch 713/1500\n",
            "7/7 - 0s - loss: 0.1695 - accuracy: 0.9431 - val_loss: 0.2701 - val_accuracy: 0.9350 - 66ms/epoch - 9ms/step\n",
            "Epoch 714/1500\n",
            "7/7 - 0s - loss: 0.1704 - accuracy: 0.9479 - val_loss: 0.2697 - val_accuracy: 0.9300 - 44ms/epoch - 6ms/step\n",
            "Epoch 715/1500\n",
            "7/7 - 0s - loss: 0.1701 - accuracy: 0.9479 - val_loss: 0.2703 - val_accuracy: 0.9350 - 53ms/epoch - 8ms/step\n",
            "Epoch 716/1500\n",
            "7/7 - 0s - loss: 0.1705 - accuracy: 0.9479 - val_loss: 0.2736 - val_accuracy: 0.9350 - 49ms/epoch - 7ms/step\n",
            "Epoch 717/1500\n",
            "7/7 - 0s - loss: 0.1696 - accuracy: 0.9431 - val_loss: 0.2723 - val_accuracy: 0.9350 - 76ms/epoch - 11ms/step\n",
            "Epoch 718/1500\n",
            "7/7 - 0s - loss: 0.1695 - accuracy: 0.9431 - val_loss: 0.2726 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 719/1500\n",
            "7/7 - 0s - loss: 0.1700 - accuracy: 0.9479 - val_loss: 0.2714 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 720/1500\n",
            "7/7 - 0s - loss: 0.1699 - accuracy: 0.9479 - val_loss: 0.2714 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 721/1500\n",
            "7/7 - 0s - loss: 0.1698 - accuracy: 0.9479 - val_loss: 0.2714 - val_accuracy: 0.9350 - 63ms/epoch - 9ms/step\n",
            "Epoch 722/1500\n",
            "7/7 - 0s - loss: 0.1693 - accuracy: 0.9431 - val_loss: 0.2722 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 723/1500\n",
            "7/7 - 0s - loss: 0.1693 - accuracy: 0.9431 - val_loss: 0.2731 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 724/1500\n",
            "7/7 - 0s - loss: 0.1693 - accuracy: 0.9479 - val_loss: 0.2733 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 725/1500\n",
            "7/7 - 0s - loss: 0.1697 - accuracy: 0.9479 - val_loss: 0.2720 - val_accuracy: 0.9350 - 66ms/epoch - 9ms/step\n",
            "Epoch 726/1500\n",
            "7/7 - 0s - loss: 0.1693 - accuracy: 0.9479 - val_loss: 0.2726 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 727/1500\n",
            "7/7 - 0s - loss: 0.1695 - accuracy: 0.9526 - val_loss: 0.2750 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 728/1500\n",
            "7/7 - 0s - loss: 0.1694 - accuracy: 0.9479 - val_loss: 0.2758 - val_accuracy: 0.9300 - 62ms/epoch - 9ms/step\n",
            "Epoch 729/1500\n",
            "7/7 - 0s - loss: 0.1693 - accuracy: 0.9479 - val_loss: 0.2745 - val_accuracy: 0.9350 - 63ms/epoch - 9ms/step\n",
            "Epoch 730/1500\n",
            "7/7 - 0s - loss: 0.1693 - accuracy: 0.9526 - val_loss: 0.2737 - val_accuracy: 0.9350 - 41ms/epoch - 6ms/step\n",
            "Epoch 731/1500\n",
            "7/7 - 0s - loss: 0.1694 - accuracy: 0.9431 - val_loss: 0.2740 - val_accuracy: 0.9350 - 62ms/epoch - 9ms/step\n",
            "Epoch 732/1500\n",
            "7/7 - 0s - loss: 0.1692 - accuracy: 0.9431 - val_loss: 0.2737 - val_accuracy: 0.9350 - 43ms/epoch - 6ms/step\n",
            "Epoch 733/1500\n",
            "7/7 - 0s - loss: 0.1697 - accuracy: 0.9479 - val_loss: 0.2731 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 734/1500\n",
            "7/7 - 0s - loss: 0.1690 - accuracy: 0.9479 - val_loss: 0.2750 - val_accuracy: 0.9350 - 73ms/epoch - 10ms/step\n",
            "Epoch 735/1500\n",
            "7/7 - 0s - loss: 0.1697 - accuracy: 0.9479 - val_loss: 0.2750 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 736/1500\n",
            "7/7 - 0s - loss: 0.1691 - accuracy: 0.9479 - val_loss: 0.2739 - val_accuracy: 0.9350 - 66ms/epoch - 9ms/step\n",
            "Epoch 737/1500\n",
            "7/7 - 0s - loss: 0.1690 - accuracy: 0.9479 - val_loss: 0.2730 - val_accuracy: 0.9350 - 54ms/epoch - 8ms/step\n",
            "Epoch 738/1500\n",
            "7/7 - 0s - loss: 0.1691 - accuracy: 0.9479 - val_loss: 0.2721 - val_accuracy: 0.9300 - 63ms/epoch - 9ms/step\n",
            "Epoch 739/1500\n",
            "7/7 - 0s - loss: 0.1693 - accuracy: 0.9479 - val_loss: 0.2728 - val_accuracy: 0.9300 - 45ms/epoch - 6ms/step\n",
            "Epoch 740/1500\n",
            "7/7 - 0s - loss: 0.1692 - accuracy: 0.9479 - val_loss: 0.2747 - val_accuracy: 0.9350 - 70ms/epoch - 10ms/step\n",
            "Epoch 741/1500\n",
            "7/7 - 0s - loss: 0.1690 - accuracy: 0.9479 - val_loss: 0.2750 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 742/1500\n",
            "7/7 - 0s - loss: 0.1692 - accuracy: 0.9479 - val_loss: 0.2751 - val_accuracy: 0.9350 - 63ms/epoch - 9ms/step\n",
            "Epoch 743/1500\n",
            "7/7 - 0s - loss: 0.1693 - accuracy: 0.9526 - val_loss: 0.2769 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 744/1500\n",
            "7/7 - 0s - loss: 0.1689 - accuracy: 0.9526 - val_loss: 0.2746 - val_accuracy: 0.9350 - 52ms/epoch - 7ms/step\n",
            "Epoch 745/1500\n",
            "7/7 - 0s - loss: 0.1689 - accuracy: 0.9479 - val_loss: 0.2745 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 746/1500\n",
            "7/7 - 0s - loss: 0.1699 - accuracy: 0.9479 - val_loss: 0.2721 - val_accuracy: 0.9300 - 45ms/epoch - 6ms/step\n",
            "Epoch 747/1500\n",
            "7/7 - 0s - loss: 0.1690 - accuracy: 0.9479 - val_loss: 0.2743 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 748/1500\n",
            "7/7 - 0s - loss: 0.1688 - accuracy: 0.9526 - val_loss: 0.2748 - val_accuracy: 0.9350 - 49ms/epoch - 7ms/step\n",
            "Epoch 749/1500\n",
            "7/7 - 0s - loss: 0.1691 - accuracy: 0.9479 - val_loss: 0.2766 - val_accuracy: 0.9350 - 49ms/epoch - 7ms/step\n",
            "Epoch 750/1500\n",
            "7/7 - 0s - loss: 0.1693 - accuracy: 0.9431 - val_loss: 0.2760 - val_accuracy: 0.9350 - 52ms/epoch - 7ms/step\n",
            "Epoch 751/1500\n",
            "7/7 - 0s - loss: 0.1688 - accuracy: 0.9526 - val_loss: 0.2764 - val_accuracy: 0.9350 - 53ms/epoch - 8ms/step\n",
            "Epoch 752/1500\n",
            "7/7 - 0s - loss: 0.1689 - accuracy: 0.9526 - val_loss: 0.2749 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 753/1500\n",
            "7/7 - 0s - loss: 0.1688 - accuracy: 0.9479 - val_loss: 0.2761 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 754/1500\n",
            "7/7 - 0s - loss: 0.1686 - accuracy: 0.9526 - val_loss: 0.2769 - val_accuracy: 0.9300 - 51ms/epoch - 7ms/step\n",
            "Epoch 755/1500\n",
            "7/7 - 0s - loss: 0.1689 - accuracy: 0.9479 - val_loss: 0.2783 - val_accuracy: 0.9300 - 54ms/epoch - 8ms/step\n",
            "Epoch 756/1500\n",
            "7/7 - 0s - loss: 0.1689 - accuracy: 0.9479 - val_loss: 0.2781 - val_accuracy: 0.9300 - 57ms/epoch - 8ms/step\n",
            "Epoch 757/1500\n",
            "7/7 - 0s - loss: 0.1691 - accuracy: 0.9479 - val_loss: 0.2778 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 758/1500\n",
            "7/7 - 0s - loss: 0.1687 - accuracy: 0.9526 - val_loss: 0.2761 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 759/1500\n",
            "7/7 - 0s - loss: 0.1694 - accuracy: 0.9479 - val_loss: 0.2734 - val_accuracy: 0.9300 - 48ms/epoch - 7ms/step\n",
            "Epoch 760/1500\n",
            "7/7 - 0s - loss: 0.1689 - accuracy: 0.9479 - val_loss: 0.2742 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 761/1500\n",
            "7/7 - 0s - loss: 0.1694 - accuracy: 0.9479 - val_loss: 0.2774 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 762/1500\n",
            "7/7 - 0s - loss: 0.1686 - accuracy: 0.9479 - val_loss: 0.2768 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 763/1500\n",
            "7/7 - 0s - loss: 0.1685 - accuracy: 0.9479 - val_loss: 0.2760 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 764/1500\n",
            "7/7 - 0s - loss: 0.1689 - accuracy: 0.9479 - val_loss: 0.2750 - val_accuracy: 0.9350 - 61ms/epoch - 9ms/step\n",
            "Epoch 765/1500\n",
            "7/7 - 0s - loss: 0.1686 - accuracy: 0.9526 - val_loss: 0.2762 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 766/1500\n",
            "7/7 - 0s - loss: 0.1694 - accuracy: 0.9479 - val_loss: 0.2745 - val_accuracy: 0.9350 - 61ms/epoch - 9ms/step\n",
            "Epoch 767/1500\n",
            "7/7 - 0s - loss: 0.1688 - accuracy: 0.9526 - val_loss: 0.2766 - val_accuracy: 0.9350 - 49ms/epoch - 7ms/step\n",
            "Epoch 768/1500\n",
            "7/7 - 0s - loss: 0.1685 - accuracy: 0.9479 - val_loss: 0.2764 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 769/1500\n",
            "7/7 - 0s - loss: 0.1683 - accuracy: 0.9479 - val_loss: 0.2756 - val_accuracy: 0.9350 - 52ms/epoch - 7ms/step\n",
            "Epoch 770/1500\n",
            "7/7 - 0s - loss: 0.1683 - accuracy: 0.9526 - val_loss: 0.2746 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 771/1500\n",
            "7/7 - 0s - loss: 0.1689 - accuracy: 0.9479 - val_loss: 0.2761 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 772/1500\n",
            "7/7 - 0s - loss: 0.1688 - accuracy: 0.9479 - val_loss: 0.2753 - val_accuracy: 0.9350 - 63ms/epoch - 9ms/step\n",
            "Epoch 773/1500\n",
            "7/7 - 0s - loss: 0.1686 - accuracy: 0.9479 - val_loss: 0.2758 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 774/1500\n",
            "7/7 - 0s - loss: 0.1684 - accuracy: 0.9479 - val_loss: 0.2767 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 775/1500\n",
            "7/7 - 0s - loss: 0.1683 - accuracy: 0.9479 - val_loss: 0.2764 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 776/1500\n",
            "7/7 - 0s - loss: 0.1682 - accuracy: 0.9526 - val_loss: 0.2783 - val_accuracy: 0.9350 - 42ms/epoch - 6ms/step\n",
            "Epoch 777/1500\n",
            "7/7 - 0s - loss: 0.1681 - accuracy: 0.9479 - val_loss: 0.2779 - val_accuracy: 0.9350 - 53ms/epoch - 8ms/step\n",
            "Epoch 778/1500\n",
            "7/7 - 0s - loss: 0.1682 - accuracy: 0.9526 - val_loss: 0.2778 - val_accuracy: 0.9350 - 51ms/epoch - 7ms/step\n",
            "Epoch 779/1500\n",
            "7/7 - 0s - loss: 0.1681 - accuracy: 0.9526 - val_loss: 0.2775 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 780/1500\n",
            "7/7 - 0s - loss: 0.1685 - accuracy: 0.9526 - val_loss: 0.2773 - val_accuracy: 0.9350 - 52ms/epoch - 7ms/step\n",
            "Epoch 781/1500\n",
            "7/7 - 0s - loss: 0.1680 - accuracy: 0.9526 - val_loss: 0.2778 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 782/1500\n",
            "7/7 - 0s - loss: 0.1682 - accuracy: 0.9479 - val_loss: 0.2791 - val_accuracy: 0.9350 - 66ms/epoch - 9ms/step\n",
            "Epoch 783/1500\n",
            "7/7 - 0s - loss: 0.1682 - accuracy: 0.9479 - val_loss: 0.2795 - val_accuracy: 0.9350 - 54ms/epoch - 8ms/step\n",
            "Epoch 784/1500\n",
            "7/7 - 0s - loss: 0.1681 - accuracy: 0.9526 - val_loss: 0.2771 - val_accuracy: 0.9350 - 49ms/epoch - 7ms/step\n",
            "Epoch 785/1500\n",
            "7/7 - 0s - loss: 0.1683 - accuracy: 0.9479 - val_loss: 0.2778 - val_accuracy: 0.9300 - 52ms/epoch - 7ms/step\n",
            "Epoch 786/1500\n",
            "7/7 - 0s - loss: 0.1681 - accuracy: 0.9479 - val_loss: 0.2780 - val_accuracy: 0.9350 - 61ms/epoch - 9ms/step\n",
            "Epoch 787/1500\n",
            "7/7 - 0s - loss: 0.1680 - accuracy: 0.9479 - val_loss: 0.2795 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 788/1500\n",
            "7/7 - 0s - loss: 0.1679 - accuracy: 0.9479 - val_loss: 0.2799 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 789/1500\n",
            "7/7 - 0s - loss: 0.1679 - accuracy: 0.9526 - val_loss: 0.2801 - val_accuracy: 0.9300 - 62ms/epoch - 9ms/step\n",
            "Epoch 790/1500\n",
            "7/7 - 0s - loss: 0.1680 - accuracy: 0.9479 - val_loss: 0.2797 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 791/1500\n",
            "7/7 - 0s - loss: 0.1678 - accuracy: 0.9479 - val_loss: 0.2787 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 792/1500\n",
            "7/7 - 0s - loss: 0.1681 - accuracy: 0.9479 - val_loss: 0.2793 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 793/1500\n",
            "7/7 - 0s - loss: 0.1680 - accuracy: 0.9479 - val_loss: 0.2777 - val_accuracy: 0.9350 - 49ms/epoch - 7ms/step\n",
            "Epoch 794/1500\n",
            "7/7 - 0s - loss: 0.1679 - accuracy: 0.9479 - val_loss: 0.2777 - val_accuracy: 0.9350 - 69ms/epoch - 10ms/step\n",
            "Epoch 795/1500\n",
            "7/7 - 0s - loss: 0.1683 - accuracy: 0.9479 - val_loss: 0.2774 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 796/1500\n",
            "7/7 - 0s - loss: 0.1676 - accuracy: 0.9479 - val_loss: 0.2781 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 797/1500\n",
            "7/7 - 0s - loss: 0.1678 - accuracy: 0.9479 - val_loss: 0.2797 - val_accuracy: 0.9350 - 51ms/epoch - 7ms/step\n",
            "Epoch 798/1500\n",
            "7/7 - 0s - loss: 0.1681 - accuracy: 0.9479 - val_loss: 0.2797 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 799/1500\n",
            "7/7 - 0s - loss: 0.1675 - accuracy: 0.9526 - val_loss: 0.2807 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 800/1500\n",
            "7/7 - 0s - loss: 0.1680 - accuracy: 0.9479 - val_loss: 0.2807 - val_accuracy: 0.9300 - 49ms/epoch - 7ms/step\n",
            "Epoch 801/1500\n",
            "7/7 - 0s - loss: 0.1679 - accuracy: 0.9479 - val_loss: 0.2798 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 802/1500\n",
            "7/7 - 0s - loss: 0.1680 - accuracy: 0.9479 - val_loss: 0.2799 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 803/1500\n",
            "7/7 - 0s - loss: 0.1676 - accuracy: 0.9526 - val_loss: 0.2788 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 804/1500\n",
            "7/7 - 0s - loss: 0.1682 - accuracy: 0.9479 - val_loss: 0.2777 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 805/1500\n",
            "7/7 - 0s - loss: 0.1679 - accuracy: 0.9479 - val_loss: 0.2773 - val_accuracy: 0.9300 - 55ms/epoch - 8ms/step\n",
            "Epoch 806/1500\n",
            "7/7 - 0s - loss: 0.1677 - accuracy: 0.9479 - val_loss: 0.2783 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 807/1500\n",
            "7/7 - 0s - loss: 0.1679 - accuracy: 0.9526 - val_loss: 0.2804 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 808/1500\n",
            "7/7 - 0s - loss: 0.1676 - accuracy: 0.9526 - val_loss: 0.2799 - val_accuracy: 0.9350 - 49ms/epoch - 7ms/step\n",
            "Epoch 809/1500\n",
            "7/7 - 0s - loss: 0.1675 - accuracy: 0.9526 - val_loss: 0.2791 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 810/1500\n",
            "7/7 - 0s - loss: 0.1678 - accuracy: 0.9479 - val_loss: 0.2800 - val_accuracy: 0.9350 - 62ms/epoch - 9ms/step\n",
            "Epoch 811/1500\n",
            "7/7 - 0s - loss: 0.1675 - accuracy: 0.9479 - val_loss: 0.2806 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 812/1500\n",
            "7/7 - 0s - loss: 0.1676 - accuracy: 0.9479 - val_loss: 0.2804 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 813/1500\n",
            "7/7 - 0s - loss: 0.1681 - accuracy: 0.9526 - val_loss: 0.2814 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 814/1500\n",
            "7/7 - 0s - loss: 0.1673 - accuracy: 0.9526 - val_loss: 0.2802 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 815/1500\n",
            "7/7 - 0s - loss: 0.1677 - accuracy: 0.9479 - val_loss: 0.2779 - val_accuracy: 0.9300 - 49ms/epoch - 7ms/step\n",
            "Epoch 816/1500\n",
            "7/7 - 0s - loss: 0.1676 - accuracy: 0.9479 - val_loss: 0.2793 - val_accuracy: 0.9300 - 53ms/epoch - 8ms/step\n",
            "Epoch 817/1500\n",
            "7/7 - 0s - loss: 0.1674 - accuracy: 0.9479 - val_loss: 0.2796 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 818/1500\n",
            "7/7 - 0s - loss: 0.1674 - accuracy: 0.9526 - val_loss: 0.2821 - val_accuracy: 0.9350 - 49ms/epoch - 7ms/step\n",
            "Epoch 819/1500\n",
            "7/7 - 0s - loss: 0.1673 - accuracy: 0.9526 - val_loss: 0.2824 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 820/1500\n",
            "7/7 - 0s - loss: 0.1674 - accuracy: 0.9526 - val_loss: 0.2813 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 821/1500\n",
            "7/7 - 0s - loss: 0.1673 - accuracy: 0.9526 - val_loss: 0.2817 - val_accuracy: 0.9350 - 63ms/epoch - 9ms/step\n",
            "Epoch 822/1500\n",
            "7/7 - 0s - loss: 0.1673 - accuracy: 0.9526 - val_loss: 0.2800 - val_accuracy: 0.9350 - 49ms/epoch - 7ms/step\n",
            "Epoch 823/1500\n",
            "7/7 - 0s - loss: 0.1673 - accuracy: 0.9526 - val_loss: 0.2794 - val_accuracy: 0.9350 - 67ms/epoch - 10ms/step\n",
            "Epoch 824/1500\n",
            "7/7 - 0s - loss: 0.1672 - accuracy: 0.9526 - val_loss: 0.2801 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 825/1500\n",
            "7/7 - 0s - loss: 0.1679 - accuracy: 0.9479 - val_loss: 0.2821 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 826/1500\n",
            "7/7 - 0s - loss: 0.1670 - accuracy: 0.9526 - val_loss: 0.2817 - val_accuracy: 0.9350 - 63ms/epoch - 9ms/step\n",
            "Epoch 827/1500\n",
            "7/7 - 0s - loss: 0.1670 - accuracy: 0.9526 - val_loss: 0.2801 - val_accuracy: 0.9300 - 50ms/epoch - 7ms/step\n",
            "Epoch 828/1500\n",
            "7/7 - 0s - loss: 0.1678 - accuracy: 0.9479 - val_loss: 0.2800 - val_accuracy: 0.9300 - 65ms/epoch - 9ms/step\n",
            "Epoch 829/1500\n",
            "7/7 - 0s - loss: 0.1673 - accuracy: 0.9479 - val_loss: 0.2814 - val_accuracy: 0.9300 - 57ms/epoch - 8ms/step\n",
            "Epoch 830/1500\n",
            "7/7 - 0s - loss: 0.1675 - accuracy: 0.9479 - val_loss: 0.2834 - val_accuracy: 0.9350 - 52ms/epoch - 7ms/step\n",
            "Epoch 831/1500\n",
            "7/7 - 0s - loss: 0.1671 - accuracy: 0.9526 - val_loss: 0.2830 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 832/1500\n",
            "7/7 - 0s - loss: 0.1672 - accuracy: 0.9526 - val_loss: 0.2841 - val_accuracy: 0.9350 - 71ms/epoch - 10ms/step\n",
            "Epoch 833/1500\n",
            "7/7 - 0s - loss: 0.1669 - accuracy: 0.9526 - val_loss: 0.2834 - val_accuracy: 0.9350 - 51ms/epoch - 7ms/step\n",
            "Epoch 834/1500\n",
            "7/7 - 0s - loss: 0.1671 - accuracy: 0.9479 - val_loss: 0.2831 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 835/1500\n",
            "7/7 - 0s - loss: 0.1669 - accuracy: 0.9526 - val_loss: 0.2833 - val_accuracy: 0.9350 - 49ms/epoch - 7ms/step\n",
            "Epoch 836/1500\n",
            "7/7 - 0s - loss: 0.1672 - accuracy: 0.9526 - val_loss: 0.2839 - val_accuracy: 0.9350 - 63ms/epoch - 9ms/step\n",
            "Epoch 837/1500\n",
            "7/7 - 0s - loss: 0.1668 - accuracy: 0.9526 - val_loss: 0.2839 - val_accuracy: 0.9350 - 61ms/epoch - 9ms/step\n",
            "Epoch 838/1500\n",
            "7/7 - 0s - loss: 0.1670 - accuracy: 0.9526 - val_loss: 0.2834 - val_accuracy: 0.9350 - 49ms/epoch - 7ms/step\n",
            "Epoch 839/1500\n",
            "7/7 - 0s - loss: 0.1668 - accuracy: 0.9526 - val_loss: 0.2838 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 840/1500\n",
            "7/7 - 0s - loss: 0.1669 - accuracy: 0.9526 - val_loss: 0.2839 - val_accuracy: 0.9350 - 72ms/epoch - 10ms/step\n",
            "Epoch 841/1500\n",
            "7/7 - 0s - loss: 0.1671 - accuracy: 0.9526 - val_loss: 0.2827 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 842/1500\n",
            "7/7 - 0s - loss: 0.1671 - accuracy: 0.9526 - val_loss: 0.2824 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 843/1500\n",
            "7/7 - 0s - loss: 0.1669 - accuracy: 0.9526 - val_loss: 0.2813 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 844/1500\n",
            "7/7 - 0s - loss: 0.1667 - accuracy: 0.9526 - val_loss: 0.2822 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 845/1500\n",
            "7/7 - 0s - loss: 0.1669 - accuracy: 0.9479 - val_loss: 0.2826 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 846/1500\n",
            "7/7 - 0s - loss: 0.1670 - accuracy: 0.9479 - val_loss: 0.2823 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 847/1500\n",
            "7/7 - 0s - loss: 0.1666 - accuracy: 0.9479 - val_loss: 0.2824 - val_accuracy: 0.9350 - 53ms/epoch - 8ms/step\n",
            "Epoch 848/1500\n",
            "7/7 - 0s - loss: 0.1667 - accuracy: 0.9526 - val_loss: 0.2843 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 849/1500\n",
            "7/7 - 0s - loss: 0.1666 - accuracy: 0.9526 - val_loss: 0.2848 - val_accuracy: 0.9350 - 63ms/epoch - 9ms/step\n",
            "Epoch 850/1500\n",
            "7/7 - 0s - loss: 0.1672 - accuracy: 0.9479 - val_loss: 0.2844 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 851/1500\n",
            "7/7 - 0s - loss: 0.1665 - accuracy: 0.9526 - val_loss: 0.2849 - val_accuracy: 0.9350 - 61ms/epoch - 9ms/step\n",
            "Epoch 852/1500\n",
            "7/7 - 0s - loss: 0.1667 - accuracy: 0.9526 - val_loss: 0.2861 - val_accuracy: 0.9350 - 61ms/epoch - 9ms/step\n",
            "Epoch 853/1500\n",
            "7/7 - 0s - loss: 0.1673 - accuracy: 0.9479 - val_loss: 0.2836 - val_accuracy: 0.9400 - 53ms/epoch - 8ms/step\n",
            "Epoch 854/1500\n",
            "7/7 - 0s - loss: 0.1667 - accuracy: 0.9479 - val_loss: 0.2822 - val_accuracy: 0.9350 - 64ms/epoch - 9ms/step\n",
            "Epoch 855/1500\n",
            "7/7 - 0s - loss: 0.1666 - accuracy: 0.9526 - val_loss: 0.2818 - val_accuracy: 0.9350 - 63ms/epoch - 9ms/step\n",
            "Epoch 856/1500\n",
            "7/7 - 0s - loss: 0.1666 - accuracy: 0.9526 - val_loss: 0.2820 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 857/1500\n",
            "7/7 - 0s - loss: 0.1666 - accuracy: 0.9479 - val_loss: 0.2827 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 858/1500\n",
            "7/7 - 0s - loss: 0.1665 - accuracy: 0.9479 - val_loss: 0.2839 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 859/1500\n",
            "7/7 - 0s - loss: 0.1666 - accuracy: 0.9526 - val_loss: 0.2850 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 860/1500\n",
            "7/7 - 0s - loss: 0.1665 - accuracy: 0.9526 - val_loss: 0.2861 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 861/1500\n",
            "7/7 - 0s - loss: 0.1666 - accuracy: 0.9526 - val_loss: 0.2847 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 862/1500\n",
            "7/7 - 0s - loss: 0.1665 - accuracy: 0.9479 - val_loss: 0.2850 - val_accuracy: 0.9350 - 49ms/epoch - 7ms/step\n",
            "Epoch 863/1500\n",
            "7/7 - 0s - loss: 0.1664 - accuracy: 0.9479 - val_loss: 0.2844 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 864/1500\n",
            "7/7 - 0s - loss: 0.1665 - accuracy: 0.9479 - val_loss: 0.2845 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 865/1500\n",
            "7/7 - 0s - loss: 0.1665 - accuracy: 0.9479 - val_loss: 0.2848 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 866/1500\n",
            "7/7 - 0s - loss: 0.1664 - accuracy: 0.9479 - val_loss: 0.2836 - val_accuracy: 0.9350 - 63ms/epoch - 9ms/step\n",
            "Epoch 867/1500\n",
            "7/7 - 0s - loss: 0.1664 - accuracy: 0.9479 - val_loss: 0.2838 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 868/1500\n",
            "7/7 - 0s - loss: 0.1663 - accuracy: 0.9526 - val_loss: 0.2852 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 869/1500\n",
            "7/7 - 0s - loss: 0.1663 - accuracy: 0.9526 - val_loss: 0.2853 - val_accuracy: 0.9350 - 51ms/epoch - 7ms/step\n",
            "Epoch 870/1500\n",
            "7/7 - 0s - loss: 0.1662 - accuracy: 0.9526 - val_loss: 0.2858 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 871/1500\n",
            "7/7 - 0s - loss: 0.1663 - accuracy: 0.9526 - val_loss: 0.2858 - val_accuracy: 0.9350 - 61ms/epoch - 9ms/step\n",
            "Epoch 872/1500\n",
            "7/7 - 0s - loss: 0.1664 - accuracy: 0.9479 - val_loss: 0.2862 - val_accuracy: 0.9350 - 53ms/epoch - 8ms/step\n",
            "Epoch 873/1500\n",
            "7/7 - 0s - loss: 0.1664 - accuracy: 0.9479 - val_loss: 0.2842 - val_accuracy: 0.9350 - 51ms/epoch - 7ms/step\n",
            "Epoch 874/1500\n",
            "7/7 - 0s - loss: 0.1663 - accuracy: 0.9479 - val_loss: 0.2837 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 875/1500\n",
            "7/7 - 0s - loss: 0.1666 - accuracy: 0.9479 - val_loss: 0.2829 - val_accuracy: 0.9350 - 53ms/epoch - 8ms/step\n",
            "Epoch 876/1500\n",
            "7/7 - 0s - loss: 0.1666 - accuracy: 0.9526 - val_loss: 0.2849 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 877/1500\n",
            "7/7 - 0s - loss: 0.1666 - accuracy: 0.9526 - val_loss: 0.2857 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 878/1500\n",
            "7/7 - 0s - loss: 0.1660 - accuracy: 0.9526 - val_loss: 0.2866 - val_accuracy: 0.9350 - 53ms/epoch - 8ms/step\n",
            "Epoch 879/1500\n",
            "7/7 - 0s - loss: 0.1661 - accuracy: 0.9526 - val_loss: 0.2866 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 880/1500\n",
            "7/7 - 0s - loss: 0.1662 - accuracy: 0.9479 - val_loss: 0.2867 - val_accuracy: 0.9350 - 63ms/epoch - 9ms/step\n",
            "Epoch 881/1500\n",
            "7/7 - 0s - loss: 0.1660 - accuracy: 0.9526 - val_loss: 0.2851 - val_accuracy: 0.9350 - 62ms/epoch - 9ms/step\n",
            "Epoch 882/1500\n",
            "7/7 - 0s - loss: 0.1660 - accuracy: 0.9526 - val_loss: 0.2847 - val_accuracy: 0.9350 - 63ms/epoch - 9ms/step\n",
            "Epoch 883/1500\n",
            "7/7 - 0s - loss: 0.1660 - accuracy: 0.9526 - val_loss: 0.2844 - val_accuracy: 0.9350 - 69ms/epoch - 10ms/step\n",
            "Epoch 884/1500\n",
            "7/7 - 0s - loss: 0.1661 - accuracy: 0.9526 - val_loss: 0.2844 - val_accuracy: 0.9350 - 51ms/epoch - 7ms/step\n",
            "Epoch 885/1500\n",
            "7/7 - 0s - loss: 0.1663 - accuracy: 0.9526 - val_loss: 0.2845 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 886/1500\n",
            "7/7 - 0s - loss: 0.1659 - accuracy: 0.9479 - val_loss: 0.2839 - val_accuracy: 0.9350 - 65ms/epoch - 9ms/step\n",
            "Epoch 887/1500\n",
            "7/7 - 0s - loss: 0.1661 - accuracy: 0.9479 - val_loss: 0.2836 - val_accuracy: 0.9350 - 49ms/epoch - 7ms/step\n",
            "Epoch 888/1500\n",
            "7/7 - 0s - loss: 0.1665 - accuracy: 0.9526 - val_loss: 0.2855 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 889/1500\n",
            "7/7 - 0s - loss: 0.1665 - accuracy: 0.9526 - val_loss: 0.2858 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 890/1500\n",
            "7/7 - 0s - loss: 0.1661 - accuracy: 0.9526 - val_loss: 0.2858 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 891/1500\n",
            "7/7 - 0s - loss: 0.1660 - accuracy: 0.9526 - val_loss: 0.2869 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 892/1500\n",
            "7/7 - 0s - loss: 0.1657 - accuracy: 0.9526 - val_loss: 0.2863 - val_accuracy: 0.9350 - 63ms/epoch - 9ms/step\n",
            "Epoch 893/1500\n",
            "7/7 - 0s - loss: 0.1660 - accuracy: 0.9479 - val_loss: 0.2856 - val_accuracy: 0.9350 - 49ms/epoch - 7ms/step\n",
            "Epoch 894/1500\n",
            "7/7 - 0s - loss: 0.1660 - accuracy: 0.9526 - val_loss: 0.2864 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 895/1500\n",
            "7/7 - 0s - loss: 0.1663 - accuracy: 0.9526 - val_loss: 0.2857 - val_accuracy: 0.9400 - 51ms/epoch - 7ms/step\n",
            "Epoch 896/1500\n",
            "7/7 - 0s - loss: 0.1662 - accuracy: 0.9526 - val_loss: 0.2846 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 897/1500\n",
            "7/7 - 0s - loss: 0.1658 - accuracy: 0.9526 - val_loss: 0.2850 - val_accuracy: 0.9350 - 49ms/epoch - 7ms/step\n",
            "Epoch 898/1500\n",
            "7/7 - 0s - loss: 0.1660 - accuracy: 0.9526 - val_loss: 0.2846 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 899/1500\n",
            "7/7 - 0s - loss: 0.1658 - accuracy: 0.9526 - val_loss: 0.2856 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 900/1500\n",
            "7/7 - 0s - loss: 0.1661 - accuracy: 0.9526 - val_loss: 0.2876 - val_accuracy: 0.9350 - 63ms/epoch - 9ms/step\n",
            "Epoch 901/1500\n",
            "7/7 - 0s - loss: 0.1661 - accuracy: 0.9526 - val_loss: 0.2868 - val_accuracy: 0.9350 - 61ms/epoch - 9ms/step\n",
            "Epoch 902/1500\n",
            "7/7 - 0s - loss: 0.1662 - accuracy: 0.9479 - val_loss: 0.2849 - val_accuracy: 0.9300 - 58ms/epoch - 8ms/step\n",
            "Epoch 903/1500\n",
            "7/7 - 0s - loss: 0.1660 - accuracy: 0.9479 - val_loss: 0.2852 - val_accuracy: 0.9300 - 50ms/epoch - 7ms/step\n",
            "Epoch 904/1500\n",
            "7/7 - 0s - loss: 0.1657 - accuracy: 0.9479 - val_loss: 0.2868 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 905/1500\n",
            "7/7 - 0s - loss: 0.1657 - accuracy: 0.9526 - val_loss: 0.2887 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 906/1500\n",
            "7/7 - 0s - loss: 0.1666 - accuracy: 0.9479 - val_loss: 0.2894 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 907/1500\n",
            "7/7 - 0s - loss: 0.1656 - accuracy: 0.9526 - val_loss: 0.2867 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 908/1500\n",
            "7/7 - 0s - loss: 0.1660 - accuracy: 0.9479 - val_loss: 0.2848 - val_accuracy: 0.9300 - 50ms/epoch - 7ms/step\n",
            "Epoch 909/1500\n",
            "7/7 - 0s - loss: 0.1663 - accuracy: 0.9479 - val_loss: 0.2864 - val_accuracy: 0.9300 - 56ms/epoch - 8ms/step\n",
            "Epoch 910/1500\n",
            "7/7 - 0s - loss: 0.1657 - accuracy: 0.9526 - val_loss: 0.2858 - val_accuracy: 0.9300 - 65ms/epoch - 9ms/step\n",
            "Epoch 911/1500\n",
            "7/7 - 0s - loss: 0.1657 - accuracy: 0.9526 - val_loss: 0.2865 - val_accuracy: 0.9300 - 56ms/epoch - 8ms/step\n",
            "Epoch 912/1500\n",
            "7/7 - 0s - loss: 0.1655 - accuracy: 0.9526 - val_loss: 0.2864 - val_accuracy: 0.9350 - 64ms/epoch - 9ms/step\n",
            "Epoch 913/1500\n",
            "7/7 - 0s - loss: 0.1655 - accuracy: 0.9526 - val_loss: 0.2867 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 914/1500\n",
            "7/7 - 0s - loss: 0.1657 - accuracy: 0.9526 - val_loss: 0.2853 - val_accuracy: 0.9300 - 60ms/epoch - 9ms/step\n",
            "Epoch 915/1500\n",
            "7/7 - 0s - loss: 0.1658 - accuracy: 0.9526 - val_loss: 0.2851 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 916/1500\n",
            "7/7 - 0s - loss: 0.1659 - accuracy: 0.9479 - val_loss: 0.2874 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 917/1500\n",
            "7/7 - 0s - loss: 0.1657 - accuracy: 0.9526 - val_loss: 0.2872 - val_accuracy: 0.9350 - 49ms/epoch - 7ms/step\n",
            "Epoch 918/1500\n",
            "7/7 - 0s - loss: 0.1655 - accuracy: 0.9526 - val_loss: 0.2879 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 919/1500\n",
            "7/7 - 0s - loss: 0.1662 - accuracy: 0.9526 - val_loss: 0.2894 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 920/1500\n",
            "7/7 - 0s - loss: 0.1655 - accuracy: 0.9526 - val_loss: 0.2883 - val_accuracy: 0.9350 - 62ms/epoch - 9ms/step\n",
            "Epoch 921/1500\n",
            "7/7 - 0s - loss: 0.1656 - accuracy: 0.9526 - val_loss: 0.2882 - val_accuracy: 0.9350 - 64ms/epoch - 9ms/step\n",
            "Epoch 922/1500\n",
            "7/7 - 0s - loss: 0.1656 - accuracy: 0.9526 - val_loss: 0.2866 - val_accuracy: 0.9300 - 66ms/epoch - 9ms/step\n",
            "Epoch 923/1500\n",
            "7/7 - 0s - loss: 0.1656 - accuracy: 0.9526 - val_loss: 0.2859 - val_accuracy: 0.9350 - 52ms/epoch - 7ms/step\n",
            "Epoch 924/1500\n",
            "7/7 - 0s - loss: 0.1652 - accuracy: 0.9526 - val_loss: 0.2870 - val_accuracy: 0.9350 - 42ms/epoch - 6ms/step\n",
            "Epoch 925/1500\n",
            "7/7 - 0s - loss: 0.1663 - accuracy: 0.9526 - val_loss: 0.2883 - val_accuracy: 0.9350 - 63ms/epoch - 9ms/step\n",
            "Epoch 926/1500\n",
            "7/7 - 0s - loss: 0.1659 - accuracy: 0.9526 - val_loss: 0.2883 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 927/1500\n",
            "7/7 - 0s - loss: 0.1655 - accuracy: 0.9526 - val_loss: 0.2889 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 928/1500\n",
            "7/7 - 0s - loss: 0.1656 - accuracy: 0.9526 - val_loss: 0.2882 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 929/1500\n",
            "7/7 - 0s - loss: 0.1652 - accuracy: 0.9526 - val_loss: 0.2876 - val_accuracy: 0.9300 - 62ms/epoch - 9ms/step\n",
            "Epoch 930/1500\n",
            "7/7 - 0s - loss: 0.1655 - accuracy: 0.9526 - val_loss: 0.2884 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 931/1500\n",
            "7/7 - 0s - loss: 0.1654 - accuracy: 0.9526 - val_loss: 0.2881 - val_accuracy: 0.9350 - 61ms/epoch - 9ms/step\n",
            "Epoch 932/1500\n",
            "7/7 - 0s - loss: 0.1653 - accuracy: 0.9526 - val_loss: 0.2872 - val_accuracy: 0.9300 - 47ms/epoch - 7ms/step\n",
            "Epoch 933/1500\n",
            "7/7 - 0s - loss: 0.1656 - accuracy: 0.9526 - val_loss: 0.2886 - val_accuracy: 0.9300 - 53ms/epoch - 8ms/step\n",
            "Epoch 934/1500\n",
            "7/7 - 0s - loss: 0.1655 - accuracy: 0.9526 - val_loss: 0.2905 - val_accuracy: 0.9300 - 47ms/epoch - 7ms/step\n",
            "Epoch 935/1500\n",
            "7/7 - 0s - loss: 0.1658 - accuracy: 0.9526 - val_loss: 0.2899 - val_accuracy: 0.9300 - 46ms/epoch - 7ms/step\n",
            "Epoch 936/1500\n",
            "7/7 - 0s - loss: 0.1653 - accuracy: 0.9526 - val_loss: 0.2889 - val_accuracy: 0.9350 - 52ms/epoch - 7ms/step\n",
            "Epoch 937/1500\n",
            "7/7 - 0s - loss: 0.1651 - accuracy: 0.9526 - val_loss: 0.2889 - val_accuracy: 0.9300 - 42ms/epoch - 6ms/step\n",
            "Epoch 938/1500\n",
            "7/7 - 0s - loss: 0.1651 - accuracy: 0.9526 - val_loss: 0.2884 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 939/1500\n",
            "7/7 - 0s - loss: 0.1656 - accuracy: 0.9526 - val_loss: 0.2862 - val_accuracy: 0.9300 - 61ms/epoch - 9ms/step\n",
            "Epoch 940/1500\n",
            "7/7 - 0s - loss: 0.1654 - accuracy: 0.9479 - val_loss: 0.2872 - val_accuracy: 0.9300 - 63ms/epoch - 9ms/step\n",
            "Epoch 941/1500\n",
            "7/7 - 0s - loss: 0.1650 - accuracy: 0.9479 - val_loss: 0.2888 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 942/1500\n",
            "7/7 - 0s - loss: 0.1665 - accuracy: 0.9479 - val_loss: 0.2911 - val_accuracy: 0.9350 - 52ms/epoch - 7ms/step\n",
            "Epoch 943/1500\n",
            "7/7 - 0s - loss: 0.1651 - accuracy: 0.9479 - val_loss: 0.2892 - val_accuracy: 0.9400 - 59ms/epoch - 8ms/step\n",
            "Epoch 944/1500\n",
            "7/7 - 0s - loss: 0.1653 - accuracy: 0.9479 - val_loss: 0.2863 - val_accuracy: 0.9350 - 51ms/epoch - 7ms/step\n",
            "Epoch 945/1500\n",
            "7/7 - 0s - loss: 0.1651 - accuracy: 0.9526 - val_loss: 0.2868 - val_accuracy: 0.9350 - 52ms/epoch - 7ms/step\n",
            "Epoch 946/1500\n",
            "7/7 - 0s - loss: 0.1663 - accuracy: 0.9479 - val_loss: 0.2858 - val_accuracy: 0.9300 - 57ms/epoch - 8ms/step\n",
            "Epoch 947/1500\n",
            "7/7 - 0s - loss: 0.1666 - accuracy: 0.9526 - val_loss: 0.2901 - val_accuracy: 0.9350 - 68ms/epoch - 10ms/step\n",
            "Epoch 948/1500\n",
            "7/7 - 0s - loss: 0.1656 - accuracy: 0.9526 - val_loss: 0.2889 - val_accuracy: 0.9350 - 69ms/epoch - 10ms/step\n",
            "Epoch 949/1500\n",
            "7/7 - 0s - loss: 0.1650 - accuracy: 0.9526 - val_loss: 0.2898 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 950/1500\n",
            "7/7 - 0s - loss: 0.1650 - accuracy: 0.9526 - val_loss: 0.2911 - val_accuracy: 0.9350 - 51ms/epoch - 7ms/step\n",
            "Epoch 951/1500\n",
            "7/7 - 0s - loss: 0.1654 - accuracy: 0.9526 - val_loss: 0.2889 - val_accuracy: 0.9350 - 67ms/epoch - 10ms/step\n",
            "Epoch 952/1500\n",
            "7/7 - 0s - loss: 0.1649 - accuracy: 0.9526 - val_loss: 0.2901 - val_accuracy: 0.9350 - 53ms/epoch - 8ms/step\n",
            "Epoch 953/1500\n",
            "7/7 - 0s - loss: 0.1652 - accuracy: 0.9526 - val_loss: 0.2905 - val_accuracy: 0.9350 - 52ms/epoch - 7ms/step\n",
            "Epoch 954/1500\n",
            "7/7 - 0s - loss: 0.1648 - accuracy: 0.9526 - val_loss: 0.2901 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 955/1500\n",
            "7/7 - 0s - loss: 0.1650 - accuracy: 0.9526 - val_loss: 0.2893 - val_accuracy: 0.9300 - 60ms/epoch - 9ms/step\n",
            "Epoch 956/1500\n",
            "7/7 - 0s - loss: 0.1648 - accuracy: 0.9526 - val_loss: 0.2899 - val_accuracy: 0.9350 - 62ms/epoch - 9ms/step\n",
            "Epoch 957/1500\n",
            "7/7 - 0s - loss: 0.1647 - accuracy: 0.9526 - val_loss: 0.2904 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 958/1500\n",
            "7/7 - 0s - loss: 0.1649 - accuracy: 0.9526 - val_loss: 0.2905 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 959/1500\n",
            "7/7 - 0s - loss: 0.1651 - accuracy: 0.9526 - val_loss: 0.2892 - val_accuracy: 0.9350 - 49ms/epoch - 7ms/step\n",
            "Epoch 960/1500\n",
            "7/7 - 0s - loss: 0.1648 - accuracy: 0.9526 - val_loss: 0.2895 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 961/1500\n",
            "7/7 - 0s - loss: 0.1650 - accuracy: 0.9526 - val_loss: 0.2901 - val_accuracy: 0.9350 - 62ms/epoch - 9ms/step\n",
            "Epoch 962/1500\n",
            "7/7 - 0s - loss: 0.1648 - accuracy: 0.9526 - val_loss: 0.2893 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 963/1500\n",
            "7/7 - 0s - loss: 0.1650 - accuracy: 0.9526 - val_loss: 0.2882 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 964/1500\n",
            "7/7 - 0s - loss: 0.1648 - accuracy: 0.9526 - val_loss: 0.2893 - val_accuracy: 0.9350 - 54ms/epoch - 8ms/step\n",
            "Epoch 965/1500\n",
            "7/7 - 0s - loss: 0.1650 - accuracy: 0.9526 - val_loss: 0.2906 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 966/1500\n",
            "7/7 - 0s - loss: 0.1653 - accuracy: 0.9526 - val_loss: 0.2883 - val_accuracy: 0.9300 - 48ms/epoch - 7ms/step\n",
            "Epoch 967/1500\n",
            "7/7 - 0s - loss: 0.1648 - accuracy: 0.9526 - val_loss: 0.2889 - val_accuracy: 0.9350 - 65ms/epoch - 9ms/step\n",
            "Epoch 968/1500\n",
            "7/7 - 0s - loss: 0.1652 - accuracy: 0.9479 - val_loss: 0.2886 - val_accuracy: 0.9300 - 57ms/epoch - 8ms/step\n",
            "Epoch 969/1500\n",
            "7/7 - 0s - loss: 0.1647 - accuracy: 0.9526 - val_loss: 0.2910 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 970/1500\n",
            "7/7 - 0s - loss: 0.1647 - accuracy: 0.9526 - val_loss: 0.2916 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 971/1500\n",
            "7/7 - 0s - loss: 0.1648 - accuracy: 0.9526 - val_loss: 0.2906 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 972/1500\n",
            "7/7 - 0s - loss: 0.1653 - accuracy: 0.9526 - val_loss: 0.2901 - val_accuracy: 0.9300 - 56ms/epoch - 8ms/step\n",
            "Epoch 973/1500\n",
            "7/7 - 0s - loss: 0.1646 - accuracy: 0.9526 - val_loss: 0.2903 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 974/1500\n",
            "7/7 - 0s - loss: 0.1648 - accuracy: 0.9526 - val_loss: 0.2911 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 975/1500\n",
            "7/7 - 0s - loss: 0.1646 - accuracy: 0.9526 - val_loss: 0.2899 - val_accuracy: 0.9350 - 63ms/epoch - 9ms/step\n",
            "Epoch 976/1500\n",
            "7/7 - 0s - loss: 0.1649 - accuracy: 0.9479 - val_loss: 0.2891 - val_accuracy: 0.9300 - 50ms/epoch - 7ms/step\n",
            "Epoch 977/1500\n",
            "7/7 - 0s - loss: 0.1647 - accuracy: 0.9479 - val_loss: 0.2897 - val_accuracy: 0.9350 - 64ms/epoch - 9ms/step\n",
            "Epoch 978/1500\n",
            "7/7 - 0s - loss: 0.1648 - accuracy: 0.9526 - val_loss: 0.2908 - val_accuracy: 0.9350 - 68ms/epoch - 10ms/step\n",
            "Epoch 979/1500\n",
            "7/7 - 0s - loss: 0.1646 - accuracy: 0.9526 - val_loss: 0.2888 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 980/1500\n",
            "7/7 - 0s - loss: 0.1648 - accuracy: 0.9526 - val_loss: 0.2889 - val_accuracy: 0.9350 - 68ms/epoch - 10ms/step\n",
            "Epoch 981/1500\n",
            "7/7 - 0s - loss: 0.1646 - accuracy: 0.9526 - val_loss: 0.2880 - val_accuracy: 0.9400 - 45ms/epoch - 6ms/step\n",
            "Epoch 982/1500\n",
            "7/7 - 0s - loss: 0.1646 - accuracy: 0.9526 - val_loss: 0.2891 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 983/1500\n",
            "7/7 - 0s - loss: 0.1650 - accuracy: 0.9526 - val_loss: 0.2885 - val_accuracy: 0.9300 - 50ms/epoch - 7ms/step\n",
            "Epoch 984/1500\n",
            "7/7 - 0s - loss: 0.1645 - accuracy: 0.9526 - val_loss: 0.2890 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 985/1500\n",
            "7/7 - 0s - loss: 0.1646 - accuracy: 0.9526 - val_loss: 0.2907 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 986/1500\n",
            "7/7 - 0s - loss: 0.1647 - accuracy: 0.9526 - val_loss: 0.2900 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 987/1500\n",
            "7/7 - 0s - loss: 0.1648 - accuracy: 0.9526 - val_loss: 0.2888 - val_accuracy: 0.9300 - 56ms/epoch - 8ms/step\n",
            "Epoch 988/1500\n",
            "7/7 - 0s - loss: 0.1648 - accuracy: 0.9526 - val_loss: 0.2899 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 989/1500\n",
            "7/7 - 0s - loss: 0.1645 - accuracy: 0.9526 - val_loss: 0.2902 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 990/1500\n",
            "7/7 - 0s - loss: 0.1650 - accuracy: 0.9479 - val_loss: 0.2896 - val_accuracy: 0.9300 - 61ms/epoch - 9ms/step\n",
            "Epoch 991/1500\n",
            "7/7 - 0s - loss: 0.1647 - accuracy: 0.9526 - val_loss: 0.2920 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 992/1500\n",
            "7/7 - 0s - loss: 0.1644 - accuracy: 0.9526 - val_loss: 0.2919 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 993/1500\n",
            "7/7 - 0s - loss: 0.1645 - accuracy: 0.9526 - val_loss: 0.2925 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 994/1500\n",
            "7/7 - 0s - loss: 0.1643 - accuracy: 0.9526 - val_loss: 0.2918 - val_accuracy: 0.9350 - 62ms/epoch - 9ms/step\n",
            "Epoch 995/1500\n",
            "7/7 - 0s - loss: 0.1642 - accuracy: 0.9526 - val_loss: 0.2913 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 996/1500\n",
            "7/7 - 0s - loss: 0.1649 - accuracy: 0.9526 - val_loss: 0.2902 - val_accuracy: 0.9300 - 60ms/epoch - 9ms/step\n",
            "Epoch 997/1500\n",
            "7/7 - 0s - loss: 0.1651 - accuracy: 0.9479 - val_loss: 0.2921 - val_accuracy: 0.9350 - 43ms/epoch - 6ms/step\n",
            "Epoch 998/1500\n",
            "7/7 - 0s - loss: 0.1640 - accuracy: 0.9526 - val_loss: 0.2936 - val_accuracy: 0.9350 - 62ms/epoch - 9ms/step\n",
            "Epoch 999/1500\n",
            "7/7 - 0s - loss: 0.1651 - accuracy: 0.9526 - val_loss: 0.2957 - val_accuracy: 0.9300 - 62ms/epoch - 9ms/step\n",
            "Epoch 1000/1500\n",
            "7/7 - 0s - loss: 0.1643 - accuracy: 0.9526 - val_loss: 0.2938 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 1001/1500\n",
            "7/7 - 0s - loss: 0.1658 - accuracy: 0.9526 - val_loss: 0.2908 - val_accuracy: 0.9300 - 56ms/epoch - 8ms/step\n",
            "Epoch 1002/1500\n",
            "7/7 - 0s - loss: 0.1641 - accuracy: 0.9526 - val_loss: 0.2915 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 1003/1500\n",
            "7/7 - 0s - loss: 0.1641 - accuracy: 0.9526 - val_loss: 0.2930 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 1004/1500\n",
            "7/7 - 0s - loss: 0.1643 - accuracy: 0.9526 - val_loss: 0.2935 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 1005/1500\n",
            "7/7 - 0s - loss: 0.1642 - accuracy: 0.9526 - val_loss: 0.2924 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 1006/1500\n",
            "7/7 - 0s - loss: 0.1644 - accuracy: 0.9526 - val_loss: 0.2924 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 1007/1500\n",
            "7/7 - 0s - loss: 0.1646 - accuracy: 0.9479 - val_loss: 0.2921 - val_accuracy: 0.9300 - 60ms/epoch - 9ms/step\n",
            "Epoch 1008/1500\n",
            "7/7 - 0s - loss: 0.1642 - accuracy: 0.9479 - val_loss: 0.2936 - val_accuracy: 0.9300 - 63ms/epoch - 9ms/step\n",
            "Epoch 1009/1500\n",
            "7/7 - 0s - loss: 0.1648 - accuracy: 0.9479 - val_loss: 0.2929 - val_accuracy: 0.9300 - 64ms/epoch - 9ms/step\n",
            "Epoch 1010/1500\n",
            "7/7 - 0s - loss: 0.1638 - accuracy: 0.9526 - val_loss: 0.2945 - val_accuracy: 0.9350 - 61ms/epoch - 9ms/step\n",
            "Epoch 1011/1500\n",
            "7/7 - 0s - loss: 0.1643 - accuracy: 0.9526 - val_loss: 0.2947 - val_accuracy: 0.9350 - 61ms/epoch - 9ms/step\n",
            "Epoch 1012/1500\n",
            "7/7 - 0s - loss: 0.1649 - accuracy: 0.9526 - val_loss: 0.2935 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 1013/1500\n",
            "7/7 - 0s - loss: 0.1642 - accuracy: 0.9526 - val_loss: 0.2955 - val_accuracy: 0.9350 - 53ms/epoch - 8ms/step\n",
            "Epoch 1014/1500\n",
            "7/7 - 0s - loss: 0.1651 - accuracy: 0.9431 - val_loss: 0.2956 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 1015/1500\n",
            "7/7 - 0s - loss: 0.1640 - accuracy: 0.9526 - val_loss: 0.2939 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 1016/1500\n",
            "7/7 - 0s - loss: 0.1641 - accuracy: 0.9526 - val_loss: 0.2938 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 1017/1500\n",
            "7/7 - 0s - loss: 0.1645 - accuracy: 0.9479 - val_loss: 0.2915 - val_accuracy: 0.9300 - 56ms/epoch - 8ms/step\n",
            "Epoch 1018/1500\n",
            "7/7 - 0s - loss: 0.1645 - accuracy: 0.9479 - val_loss: 0.2918 - val_accuracy: 0.9300 - 64ms/epoch - 9ms/step\n",
            "Epoch 1019/1500\n",
            "7/7 - 0s - loss: 0.1646 - accuracy: 0.9479 - val_loss: 0.2938 - val_accuracy: 0.9300 - 45ms/epoch - 6ms/step\n",
            "Epoch 1020/1500\n",
            "7/7 - 0s - loss: 0.1646 - accuracy: 0.9479 - val_loss: 0.2950 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 1021/1500\n",
            "7/7 - 0s - loss: 0.1639 - accuracy: 0.9526 - val_loss: 0.2940 - val_accuracy: 0.9350 - 63ms/epoch - 9ms/step\n",
            "Epoch 1022/1500\n",
            "7/7 - 0s - loss: 0.1639 - accuracy: 0.9526 - val_loss: 0.2935 - val_accuracy: 0.9300 - 57ms/epoch - 8ms/step\n",
            "Epoch 1023/1500\n",
            "7/7 - 0s - loss: 0.1641 - accuracy: 0.9526 - val_loss: 0.2918 - val_accuracy: 0.9300 - 60ms/epoch - 9ms/step\n",
            "Epoch 1024/1500\n",
            "7/7 - 0s - loss: 0.1648 - accuracy: 0.9479 - val_loss: 0.2928 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 1025/1500\n",
            "7/7 - 0s - loss: 0.1642 - accuracy: 0.9479 - val_loss: 0.2919 - val_accuracy: 0.9300 - 52ms/epoch - 7ms/step\n",
            "Epoch 1026/1500\n",
            "7/7 - 0s - loss: 0.1644 - accuracy: 0.9479 - val_loss: 0.2941 - val_accuracy: 0.9350 - 43ms/epoch - 6ms/step\n",
            "Epoch 1027/1500\n",
            "7/7 - 0s - loss: 0.1639 - accuracy: 0.9526 - val_loss: 0.2936 - val_accuracy: 0.9350 - 43ms/epoch - 6ms/step\n",
            "Epoch 1028/1500\n",
            "7/7 - 0s - loss: 0.1638 - accuracy: 0.9526 - val_loss: 0.2933 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 1029/1500\n",
            "7/7 - 0s - loss: 0.1641 - accuracy: 0.9526 - val_loss: 0.2943 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 1030/1500\n",
            "7/7 - 0s - loss: 0.1641 - accuracy: 0.9526 - val_loss: 0.2946 - val_accuracy: 0.9350 - 49ms/epoch - 7ms/step\n",
            "Epoch 1031/1500\n",
            "7/7 - 0s - loss: 0.1641 - accuracy: 0.9526 - val_loss: 0.2932 - val_accuracy: 0.9350 - 43ms/epoch - 6ms/step\n",
            "Epoch 1032/1500\n",
            "7/7 - 0s - loss: 0.1638 - accuracy: 0.9526 - val_loss: 0.2927 - val_accuracy: 0.9300 - 57ms/epoch - 8ms/step\n",
            "Epoch 1033/1500\n",
            "7/7 - 0s - loss: 0.1639 - accuracy: 0.9526 - val_loss: 0.2941 - val_accuracy: 0.9300 - 60ms/epoch - 9ms/step\n",
            "Epoch 1034/1500\n",
            "7/7 - 0s - loss: 0.1638 - accuracy: 0.9526 - val_loss: 0.2943 - val_accuracy: 0.9300 - 45ms/epoch - 6ms/step\n",
            "Epoch 1035/1500\n",
            "7/7 - 0s - loss: 0.1639 - accuracy: 0.9526 - val_loss: 0.2949 - val_accuracy: 0.9300 - 59ms/epoch - 8ms/step\n",
            "Epoch 1036/1500\n",
            "7/7 - 0s - loss: 0.1640 - accuracy: 0.9526 - val_loss: 0.2947 - val_accuracy: 0.9300 - 61ms/epoch - 9ms/step\n",
            "Epoch 1037/1500\n",
            "7/7 - 0s - loss: 0.1652 - accuracy: 0.9526 - val_loss: 0.2925 - val_accuracy: 0.9300 - 58ms/epoch - 8ms/step\n",
            "Epoch 1038/1500\n",
            "7/7 - 0s - loss: 0.1639 - accuracy: 0.9526 - val_loss: 0.2934 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 1039/1500\n",
            "7/7 - 0s - loss: 0.1641 - accuracy: 0.9526 - val_loss: 0.2931 - val_accuracy: 0.9350 - 53ms/epoch - 8ms/step\n",
            "Epoch 1040/1500\n",
            "7/7 - 0s - loss: 0.1638 - accuracy: 0.9526 - val_loss: 0.2933 - val_accuracy: 0.9300 - 60ms/epoch - 9ms/step\n",
            "Epoch 1041/1500\n",
            "7/7 - 0s - loss: 0.1637 - accuracy: 0.9526 - val_loss: 0.2935 - val_accuracy: 0.9300 - 54ms/epoch - 8ms/step\n",
            "Epoch 1042/1500\n",
            "7/7 - 0s - loss: 0.1641 - accuracy: 0.9479 - val_loss: 0.2943 - val_accuracy: 0.9300 - 44ms/epoch - 6ms/step\n",
            "Epoch 1043/1500\n",
            "7/7 - 0s - loss: 0.1636 - accuracy: 0.9479 - val_loss: 0.2941 - val_accuracy: 0.9300 - 49ms/epoch - 7ms/step\n",
            "Epoch 1044/1500\n",
            "7/7 - 0s - loss: 0.1637 - accuracy: 0.9526 - val_loss: 0.2941 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 1045/1500\n",
            "7/7 - 0s - loss: 0.1638 - accuracy: 0.9526 - val_loss: 0.2949 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 1046/1500\n",
            "7/7 - 0s - loss: 0.1646 - accuracy: 0.9479 - val_loss: 0.2953 - val_accuracy: 0.9350 - 52ms/epoch - 7ms/step\n",
            "Epoch 1047/1500\n",
            "7/7 - 0s - loss: 0.1644 - accuracy: 0.9526 - val_loss: 0.2937 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 1048/1500\n",
            "7/7 - 0s - loss: 0.1636 - accuracy: 0.9526 - val_loss: 0.2931 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 1049/1500\n",
            "7/7 - 0s - loss: 0.1637 - accuracy: 0.9526 - val_loss: 0.2935 - val_accuracy: 0.9350 - 43ms/epoch - 6ms/step\n",
            "Epoch 1050/1500\n",
            "7/7 - 0s - loss: 0.1637 - accuracy: 0.9526 - val_loss: 0.2945 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 1051/1500\n",
            "7/7 - 0s - loss: 0.1638 - accuracy: 0.9526 - val_loss: 0.2929 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 1052/1500\n",
            "7/7 - 0s - loss: 0.1642 - accuracy: 0.9526 - val_loss: 0.2932 - val_accuracy: 0.9300 - 59ms/epoch - 8ms/step\n",
            "Epoch 1053/1500\n",
            "7/7 - 0s - loss: 0.1640 - accuracy: 0.9479 - val_loss: 0.2941 - val_accuracy: 0.9300 - 68ms/epoch - 10ms/step\n",
            "Epoch 1054/1500\n",
            "7/7 - 0s - loss: 0.1638 - accuracy: 0.9526 - val_loss: 0.2949 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 1055/1500\n",
            "7/7 - 0s - loss: 0.1637 - accuracy: 0.9526 - val_loss: 0.2952 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 1056/1500\n",
            "7/7 - 0s - loss: 0.1632 - accuracy: 0.9526 - val_loss: 0.2936 - val_accuracy: 0.9350 - 53ms/epoch - 8ms/step\n",
            "Epoch 1057/1500\n",
            "7/7 - 0s - loss: 0.1635 - accuracy: 0.9479 - val_loss: 0.2926 - val_accuracy: 0.9300 - 62ms/epoch - 9ms/step\n",
            "Epoch 1058/1500\n",
            "7/7 - 0s - loss: 0.1636 - accuracy: 0.9479 - val_loss: 0.2938 - val_accuracy: 0.9350 - 64ms/epoch - 9ms/step\n",
            "Epoch 1059/1500\n",
            "7/7 - 0s - loss: 0.1635 - accuracy: 0.9479 - val_loss: 0.2954 - val_accuracy: 0.9350 - 62ms/epoch - 9ms/step\n",
            "Epoch 1060/1500\n",
            "7/7 - 0s - loss: 0.1636 - accuracy: 0.9526 - val_loss: 0.2970 - val_accuracy: 0.9350 - 43ms/epoch - 6ms/step\n",
            "Epoch 1061/1500\n",
            "7/7 - 0s - loss: 0.1640 - accuracy: 0.9526 - val_loss: 0.2959 - val_accuracy: 0.9350 - 53ms/epoch - 8ms/step\n",
            "Epoch 1062/1500\n",
            "7/7 - 0s - loss: 0.1637 - accuracy: 0.9526 - val_loss: 0.2954 - val_accuracy: 0.9350 - 53ms/epoch - 8ms/step\n",
            "Epoch 1063/1500\n",
            "7/7 - 0s - loss: 0.1634 - accuracy: 0.9526 - val_loss: 0.2958 - val_accuracy: 0.9350 - 54ms/epoch - 8ms/step\n",
            "Epoch 1064/1500\n",
            "7/7 - 0s - loss: 0.1635 - accuracy: 0.9526 - val_loss: 0.2958 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 1065/1500\n",
            "7/7 - 0s - loss: 0.1634 - accuracy: 0.9526 - val_loss: 0.2962 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 1066/1500\n",
            "7/7 - 0s - loss: 0.1635 - accuracy: 0.9526 - val_loss: 0.2979 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 1067/1500\n",
            "7/7 - 0s - loss: 0.1635 - accuracy: 0.9526 - val_loss: 0.2980 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 1068/1500\n",
            "7/7 - 0s - loss: 0.1636 - accuracy: 0.9526 - val_loss: 0.2957 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 1069/1500\n",
            "7/7 - 0s - loss: 0.1635 - accuracy: 0.9526 - val_loss: 0.2957 - val_accuracy: 0.9350 - 61ms/epoch - 9ms/step\n",
            "Epoch 1070/1500\n",
            "7/7 - 0s - loss: 0.1634 - accuracy: 0.9526 - val_loss: 0.2958 - val_accuracy: 0.9350 - 53ms/epoch - 8ms/step\n",
            "Epoch 1071/1500\n",
            "7/7 - 0s - loss: 0.1637 - accuracy: 0.9526 - val_loss: 0.2947 - val_accuracy: 0.9300 - 47ms/epoch - 7ms/step\n",
            "Epoch 1072/1500\n",
            "7/7 - 0s - loss: 0.1634 - accuracy: 0.9526 - val_loss: 0.2947 - val_accuracy: 0.9300 - 64ms/epoch - 9ms/step\n",
            "Epoch 1073/1500\n",
            "7/7 - 0s - loss: 0.1636 - accuracy: 0.9526 - val_loss: 0.2961 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 1074/1500\n",
            "7/7 - 0s - loss: 0.1632 - accuracy: 0.9526 - val_loss: 0.2971 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 1075/1500\n",
            "7/7 - 0s - loss: 0.1637 - accuracy: 0.9526 - val_loss: 0.2977 - val_accuracy: 0.9300 - 47ms/epoch - 7ms/step\n",
            "Epoch 1076/1500\n",
            "7/7 - 0s - loss: 0.1633 - accuracy: 0.9526 - val_loss: 0.2983 - val_accuracy: 0.9350 - 54ms/epoch - 8ms/step\n",
            "Epoch 1077/1500\n",
            "7/7 - 0s - loss: 0.1634 - accuracy: 0.9526 - val_loss: 0.2971 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 1078/1500\n",
            "7/7 - 0s - loss: 0.1634 - accuracy: 0.9479 - val_loss: 0.2954 - val_accuracy: 0.9300 - 47ms/epoch - 7ms/step\n",
            "Epoch 1079/1500\n",
            "7/7 - 0s - loss: 0.1640 - accuracy: 0.9526 - val_loss: 0.2967 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 1080/1500\n",
            "7/7 - 0s - loss: 0.1637 - accuracy: 0.9526 - val_loss: 0.2962 - val_accuracy: 0.9350 - 51ms/epoch - 7ms/step\n",
            "Epoch 1081/1500\n",
            "7/7 - 0s - loss: 0.1641 - accuracy: 0.9479 - val_loss: 0.2948 - val_accuracy: 0.9300 - 42ms/epoch - 6ms/step\n",
            "Epoch 1082/1500\n",
            "7/7 - 0s - loss: 0.1643 - accuracy: 0.9479 - val_loss: 0.2955 - val_accuracy: 0.9300 - 49ms/epoch - 7ms/step\n",
            "Epoch 1083/1500\n",
            "7/7 - 0s - loss: 0.1635 - accuracy: 0.9526 - val_loss: 0.2965 - val_accuracy: 0.9300 - 63ms/epoch - 9ms/step\n",
            "Epoch 1084/1500\n",
            "7/7 - 0s - loss: 0.1633 - accuracy: 0.9479 - val_loss: 0.2960 - val_accuracy: 0.9300 - 44ms/epoch - 6ms/step\n",
            "Epoch 1085/1500\n",
            "7/7 - 0s - loss: 0.1634 - accuracy: 0.9479 - val_loss: 0.2959 - val_accuracy: 0.9300 - 45ms/epoch - 6ms/step\n",
            "Epoch 1086/1500\n",
            "7/7 - 0s - loss: 0.1631 - accuracy: 0.9526 - val_loss: 0.2976 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 1087/1500\n",
            "7/7 - 0s - loss: 0.1631 - accuracy: 0.9526 - val_loss: 0.2969 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 1088/1500\n",
            "7/7 - 0s - loss: 0.1644 - accuracy: 0.9526 - val_loss: 0.2980 - val_accuracy: 0.9350 - 61ms/epoch - 9ms/step\n",
            "Epoch 1089/1500\n",
            "7/7 - 0s - loss: 0.1630 - accuracy: 0.9526 - val_loss: 0.2959 - val_accuracy: 0.9300 - 46ms/epoch - 7ms/step\n",
            "Epoch 1090/1500\n",
            "7/7 - 0s - loss: 0.1632 - accuracy: 0.9526 - val_loss: 0.2954 - val_accuracy: 0.9300 - 49ms/epoch - 7ms/step\n",
            "Epoch 1091/1500\n",
            "7/7 - 0s - loss: 0.1634 - accuracy: 0.9526 - val_loss: 0.2961 - val_accuracy: 0.9300 - 56ms/epoch - 8ms/step\n",
            "Epoch 1092/1500\n",
            "7/7 - 0s - loss: 0.1631 - accuracy: 0.9526 - val_loss: 0.2967 - val_accuracy: 0.9300 - 70ms/epoch - 10ms/step\n",
            "Epoch 1093/1500\n",
            "7/7 - 0s - loss: 0.1631 - accuracy: 0.9479 - val_loss: 0.2966 - val_accuracy: 0.9300 - 57ms/epoch - 8ms/step\n",
            "Epoch 1094/1500\n",
            "7/7 - 0s - loss: 0.1635 - accuracy: 0.9479 - val_loss: 0.2976 - val_accuracy: 0.9300 - 45ms/epoch - 6ms/step\n",
            "Epoch 1095/1500\n",
            "7/7 - 0s - loss: 0.1630 - accuracy: 0.9479 - val_loss: 0.2972 - val_accuracy: 0.9300 - 60ms/epoch - 9ms/step\n",
            "Epoch 1096/1500\n",
            "7/7 - 0s - loss: 0.1630 - accuracy: 0.9526 - val_loss: 0.2964 - val_accuracy: 0.9300 - 51ms/epoch - 7ms/step\n",
            "Epoch 1097/1500\n",
            "7/7 - 0s - loss: 0.1635 - accuracy: 0.9526 - val_loss: 0.2982 - val_accuracy: 0.9350 - 65ms/epoch - 9ms/step\n",
            "Epoch 1098/1500\n",
            "7/7 - 0s - loss: 0.1628 - accuracy: 0.9526 - val_loss: 0.2966 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 1099/1500\n",
            "7/7 - 0s - loss: 0.1630 - accuracy: 0.9526 - val_loss: 0.2959 - val_accuracy: 0.9300 - 57ms/epoch - 8ms/step\n",
            "Epoch 1100/1500\n",
            "7/7 - 0s - loss: 0.1636 - accuracy: 0.9479 - val_loss: 0.2963 - val_accuracy: 0.9300 - 53ms/epoch - 8ms/step\n",
            "Epoch 1101/1500\n",
            "7/7 - 0s - loss: 0.1626 - accuracy: 0.9526 - val_loss: 0.2973 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 1102/1500\n",
            "7/7 - 0s - loss: 0.1632 - accuracy: 0.9526 - val_loss: 0.2992 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 1103/1500\n",
            "7/7 - 0s - loss: 0.1635 - accuracy: 0.9479 - val_loss: 0.2981 - val_accuracy: 0.9350 - 49ms/epoch - 7ms/step\n",
            "Epoch 1104/1500\n",
            "7/7 - 0s - loss: 0.1631 - accuracy: 0.9479 - val_loss: 0.2978 - val_accuracy: 0.9350 - 63ms/epoch - 9ms/step\n",
            "Epoch 1105/1500\n",
            "7/7 - 0s - loss: 0.1631 - accuracy: 0.9526 - val_loss: 0.2969 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 1106/1500\n",
            "7/7 - 0s - loss: 0.1633 - accuracy: 0.9526 - val_loss: 0.2985 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 1107/1500\n",
            "7/7 - 0s - loss: 0.1627 - accuracy: 0.9526 - val_loss: 0.2971 - val_accuracy: 0.9300 - 62ms/epoch - 9ms/step\n",
            "Epoch 1108/1500\n",
            "7/7 - 0s - loss: 0.1628 - accuracy: 0.9526 - val_loss: 0.2974 - val_accuracy: 0.9300 - 55ms/epoch - 8ms/step\n",
            "Epoch 1109/1500\n",
            "7/7 - 0s - loss: 0.1635 - accuracy: 0.9479 - val_loss: 0.2966 - val_accuracy: 0.9300 - 46ms/epoch - 7ms/step\n",
            "Epoch 1110/1500\n",
            "7/7 - 0s - loss: 0.1630 - accuracy: 0.9479 - val_loss: 0.2964 - val_accuracy: 0.9300 - 59ms/epoch - 8ms/step\n",
            "Epoch 1111/1500\n",
            "7/7 - 0s - loss: 0.1629 - accuracy: 0.9526 - val_loss: 0.2980 - val_accuracy: 0.9300 - 48ms/epoch - 7ms/step\n",
            "Epoch 1112/1500\n",
            "7/7 - 0s - loss: 0.1629 - accuracy: 0.9526 - val_loss: 0.2980 - val_accuracy: 0.9350 - 61ms/epoch - 9ms/step\n",
            "Epoch 1113/1500\n",
            "7/7 - 0s - loss: 0.1630 - accuracy: 0.9526 - val_loss: 0.2985 - val_accuracy: 0.9400 - 55ms/epoch - 8ms/step\n",
            "Epoch 1114/1500\n",
            "7/7 - 0s - loss: 0.1633 - accuracy: 0.9526 - val_loss: 0.2978 - val_accuracy: 0.9300 - 53ms/epoch - 8ms/step\n",
            "Epoch 1115/1500\n",
            "7/7 - 0s - loss: 0.1630 - accuracy: 0.9526 - val_loss: 0.2986 - val_accuracy: 0.9300 - 50ms/epoch - 7ms/step\n",
            "Epoch 1116/1500\n",
            "7/7 - 0s - loss: 0.1629 - accuracy: 0.9526 - val_loss: 0.2985 - val_accuracy: 0.9300 - 61ms/epoch - 9ms/step\n",
            "Epoch 1117/1500\n",
            "7/7 - 0s - loss: 0.1626 - accuracy: 0.9526 - val_loss: 0.2974 - val_accuracy: 0.9300 - 57ms/epoch - 8ms/step\n",
            "Epoch 1118/1500\n",
            "7/7 - 0s - loss: 0.1632 - accuracy: 0.9526 - val_loss: 0.2961 - val_accuracy: 0.9300 - 58ms/epoch - 8ms/step\n",
            "Epoch 1119/1500\n",
            "7/7 - 0s - loss: 0.1628 - accuracy: 0.9526 - val_loss: 0.2971 - val_accuracy: 0.9300 - 49ms/epoch - 7ms/step\n",
            "Epoch 1120/1500\n",
            "7/7 - 0s - loss: 0.1633 - accuracy: 0.9526 - val_loss: 0.2992 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 1121/1500\n",
            "7/7 - 0s - loss: 0.1635 - accuracy: 0.9526 - val_loss: 0.2966 - val_accuracy: 0.9300 - 51ms/epoch - 7ms/step\n",
            "Epoch 1122/1500\n",
            "7/7 - 0s - loss: 0.1636 - accuracy: 0.9479 - val_loss: 0.2966 - val_accuracy: 0.9300 - 58ms/epoch - 8ms/step\n",
            "Epoch 1123/1500\n",
            "7/7 - 0s - loss: 0.1632 - accuracy: 0.9526 - val_loss: 0.2990 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 1124/1500\n",
            "7/7 - 0s - loss: 0.1630 - accuracy: 0.9526 - val_loss: 0.2981 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 1125/1500\n",
            "7/7 - 0s - loss: 0.1625 - accuracy: 0.9526 - val_loss: 0.2986 - val_accuracy: 0.9350 - 61ms/epoch - 9ms/step\n",
            "Epoch 1126/1500\n",
            "7/7 - 0s - loss: 0.1634 - accuracy: 0.9526 - val_loss: 0.2985 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 1127/1500\n",
            "7/7 - 0s - loss: 0.1647 - accuracy: 0.9526 - val_loss: 0.3024 - val_accuracy: 0.9350 - 51ms/epoch - 7ms/step\n",
            "Epoch 1128/1500\n",
            "7/7 - 0s - loss: 0.1637 - accuracy: 0.9431 - val_loss: 0.3009 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 1129/1500\n",
            "7/7 - 0s - loss: 0.1626 - accuracy: 0.9526 - val_loss: 0.2990 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 1130/1500\n",
            "7/7 - 0s - loss: 0.1632 - accuracy: 0.9526 - val_loss: 0.2969 - val_accuracy: 0.9300 - 49ms/epoch - 7ms/step\n",
            "Epoch 1131/1500\n",
            "7/7 - 0s - loss: 0.1626 - accuracy: 0.9526 - val_loss: 0.2986 - val_accuracy: 0.9300 - 44ms/epoch - 6ms/step\n",
            "Epoch 1132/1500\n",
            "7/7 - 0s - loss: 0.1638 - accuracy: 0.9526 - val_loss: 0.3016 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 1133/1500\n",
            "7/7 - 0s - loss: 0.1626 - accuracy: 0.9526 - val_loss: 0.2998 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 1134/1500\n",
            "7/7 - 0s - loss: 0.1627 - accuracy: 0.9526 - val_loss: 0.2990 - val_accuracy: 0.9300 - 63ms/epoch - 9ms/step\n",
            "Epoch 1135/1500\n",
            "7/7 - 0s - loss: 0.1630 - accuracy: 0.9526 - val_loss: 0.2998 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 1136/1500\n",
            "7/7 - 0s - loss: 0.1632 - accuracy: 0.9526 - val_loss: 0.2973 - val_accuracy: 0.9300 - 61ms/epoch - 9ms/step\n",
            "Epoch 1137/1500\n",
            "7/7 - 0s - loss: 0.1627 - accuracy: 0.9479 - val_loss: 0.2971 - val_accuracy: 0.9300 - 65ms/epoch - 9ms/step\n",
            "Epoch 1138/1500\n",
            "7/7 - 0s - loss: 0.1625 - accuracy: 0.9526 - val_loss: 0.2991 - val_accuracy: 0.9300 - 47ms/epoch - 7ms/step\n",
            "Epoch 1139/1500\n",
            "7/7 - 0s - loss: 0.1632 - accuracy: 0.9526 - val_loss: 0.3002 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 1140/1500\n",
            "7/7 - 0s - loss: 0.1624 - accuracy: 0.9526 - val_loss: 0.2989 - val_accuracy: 0.9300 - 62ms/epoch - 9ms/step\n",
            "Epoch 1141/1500\n",
            "7/7 - 0s - loss: 0.1627 - accuracy: 0.9526 - val_loss: 0.2981 - val_accuracy: 0.9300 - 60ms/epoch - 9ms/step\n",
            "Epoch 1142/1500\n",
            "7/7 - 0s - loss: 0.1625 - accuracy: 0.9526 - val_loss: 0.2979 - val_accuracy: 0.9300 - 54ms/epoch - 8ms/step\n",
            "Epoch 1143/1500\n",
            "7/7 - 0s - loss: 0.1626 - accuracy: 0.9526 - val_loss: 0.2988 - val_accuracy: 0.9300 - 64ms/epoch - 9ms/step\n",
            "Epoch 1144/1500\n",
            "7/7 - 0s - loss: 0.1625 - accuracy: 0.9526 - val_loss: 0.2995 - val_accuracy: 0.9350 - 65ms/epoch - 9ms/step\n",
            "Epoch 1145/1500\n",
            "7/7 - 0s - loss: 0.1632 - accuracy: 0.9479 - val_loss: 0.2982 - val_accuracy: 0.9300 - 57ms/epoch - 8ms/step\n",
            "Epoch 1146/1500\n",
            "7/7 - 0s - loss: 0.1627 - accuracy: 0.9479 - val_loss: 0.2986 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 1147/1500\n",
            "7/7 - 0s - loss: 0.1628 - accuracy: 0.9526 - val_loss: 0.3006 - val_accuracy: 0.9400 - 58ms/epoch - 8ms/step\n",
            "Epoch 1148/1500\n",
            "7/7 - 0s - loss: 0.1629 - accuracy: 0.9479 - val_loss: 0.3003 - val_accuracy: 0.9400 - 62ms/epoch - 9ms/step\n",
            "Epoch 1149/1500\n",
            "7/7 - 0s - loss: 0.1626 - accuracy: 0.9526 - val_loss: 0.3003 - val_accuracy: 0.9350 - 51ms/epoch - 7ms/step\n",
            "Epoch 1150/1500\n",
            "7/7 - 0s - loss: 0.1627 - accuracy: 0.9479 - val_loss: 0.2995 - val_accuracy: 0.9300 - 45ms/epoch - 6ms/step\n",
            "Epoch 1151/1500\n",
            "7/7 - 0s - loss: 0.1626 - accuracy: 0.9479 - val_loss: 0.3000 - val_accuracy: 0.9300 - 70ms/epoch - 10ms/step\n",
            "Epoch 1152/1500\n",
            "7/7 - 0s - loss: 0.1627 - accuracy: 0.9479 - val_loss: 0.2995 - val_accuracy: 0.9300 - 71ms/epoch - 10ms/step\n",
            "Epoch 1153/1500\n",
            "7/7 - 0s - loss: 0.1623 - accuracy: 0.9526 - val_loss: 0.3012 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 1154/1500\n",
            "7/7 - 0s - loss: 0.1630 - accuracy: 0.9526 - val_loss: 0.3029 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 1155/1500\n",
            "7/7 - 0s - loss: 0.1627 - accuracy: 0.9431 - val_loss: 0.3022 - val_accuracy: 0.9350 - 65ms/epoch - 9ms/step\n",
            "Epoch 1156/1500\n",
            "7/7 - 0s - loss: 0.1634 - accuracy: 0.9526 - val_loss: 0.2994 - val_accuracy: 0.9300 - 48ms/epoch - 7ms/step\n",
            "Epoch 1157/1500\n",
            "7/7 - 0s - loss: 0.1625 - accuracy: 0.9479 - val_loss: 0.2998 - val_accuracy: 0.9300 - 48ms/epoch - 7ms/step\n",
            "Epoch 1158/1500\n",
            "7/7 - 0s - loss: 0.1626 - accuracy: 0.9526 - val_loss: 0.3002 - val_accuracy: 0.9300 - 57ms/epoch - 8ms/step\n",
            "Epoch 1159/1500\n",
            "7/7 - 0s - loss: 0.1624 - accuracy: 0.9526 - val_loss: 0.3007 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 1160/1500\n",
            "7/7 - 0s - loss: 0.1622 - accuracy: 0.9526 - val_loss: 0.3007 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 1161/1500\n",
            "7/7 - 0s - loss: 0.1626 - accuracy: 0.9479 - val_loss: 0.3004 - val_accuracy: 0.9300 - 59ms/epoch - 8ms/step\n",
            "Epoch 1162/1500\n",
            "7/7 - 0s - loss: 0.1622 - accuracy: 0.9479 - val_loss: 0.3007 - val_accuracy: 0.9300 - 62ms/epoch - 9ms/step\n",
            "Epoch 1163/1500\n",
            "7/7 - 0s - loss: 0.1623 - accuracy: 0.9479 - val_loss: 0.3014 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 1164/1500\n",
            "7/7 - 0s - loss: 0.1622 - accuracy: 0.9526 - val_loss: 0.3011 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 1165/1500\n",
            "7/7 - 0s - loss: 0.1623 - accuracy: 0.9526 - val_loss: 0.3018 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 1166/1500\n",
            "7/7 - 0s - loss: 0.1623 - accuracy: 0.9526 - val_loss: 0.3017 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 1167/1500\n",
            "7/7 - 0s - loss: 0.1622 - accuracy: 0.9526 - val_loss: 0.3019 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 1168/1500\n",
            "7/7 - 0s - loss: 0.1622 - accuracy: 0.9526 - val_loss: 0.3008 - val_accuracy: 0.9350 - 62ms/epoch - 9ms/step\n",
            "Epoch 1169/1500\n",
            "7/7 - 0s - loss: 0.1623 - accuracy: 0.9526 - val_loss: 0.3000 - val_accuracy: 0.9300 - 67ms/epoch - 10ms/step\n",
            "Epoch 1170/1500\n",
            "7/7 - 0s - loss: 0.1627 - accuracy: 0.9479 - val_loss: 0.2995 - val_accuracy: 0.9300 - 66ms/epoch - 9ms/step\n",
            "Epoch 1171/1500\n",
            "7/7 - 0s - loss: 0.1628 - accuracy: 0.9526 - val_loss: 0.3024 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 1172/1500\n",
            "7/7 - 0s - loss: 0.1622 - accuracy: 0.9526 - val_loss: 0.3021 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 1173/1500\n",
            "7/7 - 0s - loss: 0.1620 - accuracy: 0.9526 - val_loss: 0.3003 - val_accuracy: 0.9300 - 52ms/epoch - 7ms/step\n",
            "Epoch 1174/1500\n",
            "7/7 - 0s - loss: 0.1621 - accuracy: 0.9479 - val_loss: 0.2993 - val_accuracy: 0.9300 - 61ms/epoch - 9ms/step\n",
            "Epoch 1175/1500\n",
            "7/7 - 0s - loss: 0.1627 - accuracy: 0.9479 - val_loss: 0.3002 - val_accuracy: 0.9300 - 62ms/epoch - 9ms/step\n",
            "Epoch 1176/1500\n",
            "7/7 - 0s - loss: 0.1622 - accuracy: 0.9479 - val_loss: 0.3007 - val_accuracy: 0.9300 - 46ms/epoch - 7ms/step\n",
            "Epoch 1177/1500\n",
            "7/7 - 0s - loss: 0.1623 - accuracy: 0.9526 - val_loss: 0.3015 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 1178/1500\n",
            "7/7 - 0s - loss: 0.1627 - accuracy: 0.9526 - val_loss: 0.2994 - val_accuracy: 0.9300 - 64ms/epoch - 9ms/step\n",
            "Epoch 1179/1500\n",
            "7/7 - 0s - loss: 0.1621 - accuracy: 0.9526 - val_loss: 0.3001 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 1180/1500\n",
            "7/7 - 0s - loss: 0.1629 - accuracy: 0.9526 - val_loss: 0.3023 - val_accuracy: 0.9350 - 63ms/epoch - 9ms/step\n",
            "Epoch 1181/1500\n",
            "7/7 - 0s - loss: 0.1626 - accuracy: 0.9526 - val_loss: 0.3013 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 1182/1500\n",
            "7/7 - 0s - loss: 0.1620 - accuracy: 0.9526 - val_loss: 0.3013 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 1183/1500\n",
            "7/7 - 0s - loss: 0.1622 - accuracy: 0.9526 - val_loss: 0.3001 - val_accuracy: 0.9300 - 60ms/epoch - 9ms/step\n",
            "Epoch 1184/1500\n",
            "7/7 - 0s - loss: 0.1622 - accuracy: 0.9526 - val_loss: 0.3014 - val_accuracy: 0.9350 - 54ms/epoch - 8ms/step\n",
            "Epoch 1185/1500\n",
            "7/7 - 0s - loss: 0.1621 - accuracy: 0.9526 - val_loss: 0.3019 - val_accuracy: 0.9350 - 74ms/epoch - 11ms/step\n",
            "Epoch 1186/1500\n",
            "7/7 - 0s - loss: 0.1619 - accuracy: 0.9526 - val_loss: 0.3021 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 1187/1500\n",
            "7/7 - 0s - loss: 0.1620 - accuracy: 0.9526 - val_loss: 0.3026 - val_accuracy: 0.9300 - 47ms/epoch - 7ms/step\n",
            "Epoch 1188/1500\n",
            "7/7 - 0s - loss: 0.1620 - accuracy: 0.9526 - val_loss: 0.3025 - val_accuracy: 0.9300 - 72ms/epoch - 10ms/step\n",
            "Epoch 1189/1500\n",
            "7/7 - 0s - loss: 0.1618 - accuracy: 0.9526 - val_loss: 0.3022 - val_accuracy: 0.9300 - 53ms/epoch - 8ms/step\n",
            "Epoch 1190/1500\n",
            "7/7 - 0s - loss: 0.1621 - accuracy: 0.9526 - val_loss: 0.3017 - val_accuracy: 0.9300 - 63ms/epoch - 9ms/step\n",
            "Epoch 1191/1500\n",
            "7/7 - 0s - loss: 0.1622 - accuracy: 0.9526 - val_loss: 0.3024 - val_accuracy: 0.9400 - 46ms/epoch - 7ms/step\n",
            "Epoch 1192/1500\n",
            "7/7 - 0s - loss: 0.1626 - accuracy: 0.9526 - val_loss: 0.3022 - val_accuracy: 0.9400 - 63ms/epoch - 9ms/step\n",
            "Epoch 1193/1500\n",
            "7/7 - 0s - loss: 0.1625 - accuracy: 0.9526 - val_loss: 0.3011 - val_accuracy: 0.9300 - 49ms/epoch - 7ms/step\n",
            "Epoch 1194/1500\n",
            "7/7 - 0s - loss: 0.1626 - accuracy: 0.9479 - val_loss: 0.3003 - val_accuracy: 0.9300 - 47ms/epoch - 7ms/step\n",
            "Epoch 1195/1500\n",
            "7/7 - 0s - loss: 0.1617 - accuracy: 0.9526 - val_loss: 0.3021 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 1196/1500\n",
            "7/7 - 0s - loss: 0.1620 - accuracy: 0.9526 - val_loss: 0.3029 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 1197/1500\n",
            "7/7 - 0s - loss: 0.1624 - accuracy: 0.9526 - val_loss: 0.3035 - val_accuracy: 0.9400 - 55ms/epoch - 8ms/step\n",
            "Epoch 1198/1500\n",
            "7/7 - 0s - loss: 0.1622 - accuracy: 0.9526 - val_loss: 0.3024 - val_accuracy: 0.9400 - 53ms/epoch - 8ms/step\n",
            "Epoch 1199/1500\n",
            "7/7 - 0s - loss: 0.1620 - accuracy: 0.9526 - val_loss: 0.2999 - val_accuracy: 0.9300 - 61ms/epoch - 9ms/step\n",
            "Epoch 1200/1500\n",
            "7/7 - 0s - loss: 0.1628 - accuracy: 0.9526 - val_loss: 0.3016 - val_accuracy: 0.9300 - 58ms/epoch - 8ms/step\n",
            "Epoch 1201/1500\n",
            "7/7 - 0s - loss: 0.1622 - accuracy: 0.9526 - val_loss: 0.3019 - val_accuracy: 0.9300 - 54ms/epoch - 8ms/step\n",
            "Epoch 1202/1500\n",
            "7/7 - 0s - loss: 0.1626 - accuracy: 0.9479 - val_loss: 0.3015 - val_accuracy: 0.9300 - 59ms/epoch - 8ms/step\n",
            "Epoch 1203/1500\n",
            "7/7 - 0s - loss: 0.1618 - accuracy: 0.9526 - val_loss: 0.3024 - val_accuracy: 0.9300 - 59ms/epoch - 8ms/step\n",
            "Epoch 1204/1500\n",
            "7/7 - 0s - loss: 0.1619 - accuracy: 0.9526 - val_loss: 0.3016 - val_accuracy: 0.9300 - 47ms/epoch - 7ms/step\n",
            "Epoch 1205/1500\n",
            "7/7 - 0s - loss: 0.1629 - accuracy: 0.9526 - val_loss: 0.3009 - val_accuracy: 0.9300 - 49ms/epoch - 7ms/step\n",
            "Epoch 1206/1500\n",
            "7/7 - 0s - loss: 0.1617 - accuracy: 0.9526 - val_loss: 0.3019 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 1207/1500\n",
            "7/7 - 0s - loss: 0.1619 - accuracy: 0.9526 - val_loss: 0.3032 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 1208/1500\n",
            "7/7 - 0s - loss: 0.1619 - accuracy: 0.9526 - val_loss: 0.3030 - val_accuracy: 0.9300 - 60ms/epoch - 9ms/step\n",
            "Epoch 1209/1500\n",
            "7/7 - 0s - loss: 0.1627 - accuracy: 0.9479 - val_loss: 0.3020 - val_accuracy: 0.9300 - 58ms/epoch - 8ms/step\n",
            "Epoch 1210/1500\n",
            "7/7 - 0s - loss: 0.1618 - accuracy: 0.9526 - val_loss: 0.3028 - val_accuracy: 0.9300 - 60ms/epoch - 9ms/step\n",
            "Epoch 1211/1500\n",
            "7/7 - 0s - loss: 0.1616 - accuracy: 0.9526 - val_loss: 0.3041 - val_accuracy: 0.9300 - 57ms/epoch - 8ms/step\n",
            "Epoch 1212/1500\n",
            "7/7 - 0s - loss: 0.1620 - accuracy: 0.9526 - val_loss: 0.3039 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 1213/1500\n",
            "7/7 - 0s - loss: 0.1617 - accuracy: 0.9526 - val_loss: 0.3025 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 1214/1500\n",
            "7/7 - 0s - loss: 0.1621 - accuracy: 0.9526 - val_loss: 0.3026 - val_accuracy: 0.9300 - 47ms/epoch - 7ms/step\n",
            "Epoch 1215/1500\n",
            "7/7 - 0s - loss: 0.1618 - accuracy: 0.9526 - val_loss: 0.3035 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 1216/1500\n",
            "7/7 - 0s - loss: 0.1621 - accuracy: 0.9526 - val_loss: 0.3052 - val_accuracy: 0.9350 - 53ms/epoch - 8ms/step\n",
            "Epoch 1217/1500\n",
            "7/7 - 0s - loss: 0.1617 - accuracy: 0.9526 - val_loss: 0.3039 - val_accuracy: 0.9300 - 57ms/epoch - 8ms/step\n",
            "Epoch 1218/1500\n",
            "7/7 - 0s - loss: 0.1616 - accuracy: 0.9526 - val_loss: 0.3040 - val_accuracy: 0.9300 - 47ms/epoch - 7ms/step\n",
            "Epoch 1219/1500\n",
            "7/7 - 0s - loss: 0.1619 - accuracy: 0.9526 - val_loss: 0.3032 - val_accuracy: 0.9300 - 60ms/epoch - 9ms/step\n",
            "Epoch 1220/1500\n",
            "7/7 - 0s - loss: 0.1626 - accuracy: 0.9526 - val_loss: 0.3037 - val_accuracy: 0.9400 - 62ms/epoch - 9ms/step\n",
            "Epoch 1221/1500\n",
            "7/7 - 0s - loss: 0.1616 - accuracy: 0.9526 - val_loss: 0.3035 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 1222/1500\n",
            "7/7 - 0s - loss: 0.1620 - accuracy: 0.9526 - val_loss: 0.3032 - val_accuracy: 0.9300 - 61ms/epoch - 9ms/step\n",
            "Epoch 1223/1500\n",
            "7/7 - 0s - loss: 0.1616 - accuracy: 0.9479 - val_loss: 0.3036 - val_accuracy: 0.9300 - 69ms/epoch - 10ms/step\n",
            "Epoch 1224/1500\n",
            "7/7 - 0s - loss: 0.1623 - accuracy: 0.9479 - val_loss: 0.3053 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 1225/1500\n",
            "7/7 - 0s - loss: 0.1615 - accuracy: 0.9526 - val_loss: 0.3039 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 1226/1500\n",
            "7/7 - 0s - loss: 0.1619 - accuracy: 0.9526 - val_loss: 0.3032 - val_accuracy: 0.9300 - 45ms/epoch - 6ms/step\n",
            "Epoch 1227/1500\n",
            "7/7 - 0s - loss: 0.1620 - accuracy: 0.9526 - val_loss: 0.3048 - val_accuracy: 0.9350 - 62ms/epoch - 9ms/step\n",
            "Epoch 1228/1500\n",
            "7/7 - 0s - loss: 0.1618 - accuracy: 0.9526 - val_loss: 0.3052 - val_accuracy: 0.9300 - 68ms/epoch - 10ms/step\n",
            "Epoch 1229/1500\n",
            "7/7 - 0s - loss: 0.1619 - accuracy: 0.9479 - val_loss: 0.3045 - val_accuracy: 0.9300 - 52ms/epoch - 7ms/step\n",
            "Epoch 1230/1500\n",
            "7/7 - 0s - loss: 0.1617 - accuracy: 0.9479 - val_loss: 0.3055 - val_accuracy: 0.9350 - 68ms/epoch - 10ms/step\n",
            "Epoch 1231/1500\n",
            "7/7 - 0s - loss: 0.1614 - accuracy: 0.9526 - val_loss: 0.3043 - val_accuracy: 0.9300 - 61ms/epoch - 9ms/step\n",
            "Epoch 1232/1500\n",
            "7/7 - 0s - loss: 0.1614 - accuracy: 0.9526 - val_loss: 0.3033 - val_accuracy: 0.9300 - 49ms/epoch - 7ms/step\n",
            "Epoch 1233/1500\n",
            "7/7 - 0s - loss: 0.1615 - accuracy: 0.9526 - val_loss: 0.3037 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 1234/1500\n",
            "7/7 - 0s - loss: 0.1619 - accuracy: 0.9526 - val_loss: 0.3025 - val_accuracy: 0.9300 - 61ms/epoch - 9ms/step\n",
            "Epoch 1235/1500\n",
            "7/7 - 0s - loss: 0.1619 - accuracy: 0.9526 - val_loss: 0.3025 - val_accuracy: 0.9300 - 55ms/epoch - 8ms/step\n",
            "Epoch 1236/1500\n",
            "7/7 - 0s - loss: 0.1612 - accuracy: 0.9526 - val_loss: 0.3053 - val_accuracy: 0.9400 - 50ms/epoch - 7ms/step\n",
            "Epoch 1237/1500\n",
            "7/7 - 0s - loss: 0.1618 - accuracy: 0.9526 - val_loss: 0.3071 - val_accuracy: 0.9400 - 60ms/epoch - 9ms/step\n",
            "Epoch 1238/1500\n",
            "7/7 - 0s - loss: 0.1616 - accuracy: 0.9526 - val_loss: 0.3060 - val_accuracy: 0.9400 - 61ms/epoch - 9ms/step\n",
            "Epoch 1239/1500\n",
            "7/7 - 0s - loss: 0.1616 - accuracy: 0.9526 - val_loss: 0.3058 - val_accuracy: 0.9400 - 46ms/epoch - 7ms/step\n",
            "Epoch 1240/1500\n",
            "7/7 - 0s - loss: 0.1617 - accuracy: 0.9526 - val_loss: 0.3030 - val_accuracy: 0.9300 - 60ms/epoch - 9ms/step\n",
            "Epoch 1241/1500\n",
            "7/7 - 0s - loss: 0.1627 - accuracy: 0.9479 - val_loss: 0.3016 - val_accuracy: 0.9300 - 68ms/epoch - 10ms/step\n",
            "Epoch 1242/1500\n",
            "7/7 - 0s - loss: 0.1615 - accuracy: 0.9526 - val_loss: 0.3040 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 1243/1500\n",
            "7/7 - 0s - loss: 0.1621 - accuracy: 0.9526 - val_loss: 0.3051 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 1244/1500\n",
            "7/7 - 0s - loss: 0.1616 - accuracy: 0.9526 - val_loss: 0.3046 - val_accuracy: 0.9400 - 41ms/epoch - 6ms/step\n",
            "Epoch 1245/1500\n",
            "7/7 - 0s - loss: 0.1615 - accuracy: 0.9526 - val_loss: 0.3023 - val_accuracy: 0.9300 - 48ms/epoch - 7ms/step\n",
            "Epoch 1246/1500\n",
            "7/7 - 0s - loss: 0.1613 - accuracy: 0.9526 - val_loss: 0.3031 - val_accuracy: 0.9300 - 59ms/epoch - 8ms/step\n",
            "Epoch 1247/1500\n",
            "7/7 - 0s - loss: 0.1615 - accuracy: 0.9526 - val_loss: 0.3039 - val_accuracy: 0.9350 - 70ms/epoch - 10ms/step\n",
            "Epoch 1248/1500\n",
            "7/7 - 0s - loss: 0.1614 - accuracy: 0.9526 - val_loss: 0.3038 - val_accuracy: 0.9350 - 61ms/epoch - 9ms/step\n",
            "Epoch 1249/1500\n",
            "7/7 - 0s - loss: 0.1622 - accuracy: 0.9479 - val_loss: 0.3023 - val_accuracy: 0.9300 - 65ms/epoch - 9ms/step\n",
            "Epoch 1250/1500\n",
            "7/7 - 0s - loss: 0.1615 - accuracy: 0.9479 - val_loss: 0.3045 - val_accuracy: 0.9300 - 43ms/epoch - 6ms/step\n",
            "Epoch 1251/1500\n",
            "7/7 - 0s - loss: 0.1613 - accuracy: 0.9526 - val_loss: 0.3052 - val_accuracy: 0.9300 - 46ms/epoch - 7ms/step\n",
            "Epoch 1252/1500\n",
            "7/7 - 0s - loss: 0.1613 - accuracy: 0.9526 - val_loss: 0.3053 - val_accuracy: 0.9300 - 51ms/epoch - 7ms/step\n",
            "Epoch 1253/1500\n",
            "7/7 - 0s - loss: 0.1616 - accuracy: 0.9526 - val_loss: 0.3071 - val_accuracy: 0.9350 - 74ms/epoch - 11ms/step\n",
            "Epoch 1254/1500\n",
            "7/7 - 0s - loss: 0.1616 - accuracy: 0.9526 - val_loss: 0.3058 - val_accuracy: 0.9300 - 47ms/epoch - 7ms/step\n",
            "Epoch 1255/1500\n",
            "7/7 - 0s - loss: 0.1616 - accuracy: 0.9526 - val_loss: 0.3044 - val_accuracy: 0.9300 - 49ms/epoch - 7ms/step\n",
            "Epoch 1256/1500\n",
            "7/7 - 0s - loss: 0.1616 - accuracy: 0.9526 - val_loss: 0.3041 - val_accuracy: 0.9300 - 48ms/epoch - 7ms/step\n",
            "Epoch 1257/1500\n",
            "7/7 - 0s - loss: 0.1619 - accuracy: 0.9526 - val_loss: 0.3066 - val_accuracy: 0.9400 - 62ms/epoch - 9ms/step\n",
            "Epoch 1258/1500\n",
            "7/7 - 0s - loss: 0.1613 - accuracy: 0.9526 - val_loss: 0.3044 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 1259/1500\n",
            "7/7 - 0s - loss: 0.1612 - accuracy: 0.9526 - val_loss: 0.3036 - val_accuracy: 0.9300 - 58ms/epoch - 8ms/step\n",
            "Epoch 1260/1500\n",
            "7/7 - 0s - loss: 0.1613 - accuracy: 0.9526 - val_loss: 0.3033 - val_accuracy: 0.9300 - 59ms/epoch - 8ms/step\n",
            "Epoch 1261/1500\n",
            "7/7 - 0s - loss: 0.1613 - accuracy: 0.9479 - val_loss: 0.3035 - val_accuracy: 0.9300 - 48ms/epoch - 7ms/step\n",
            "Epoch 1262/1500\n",
            "7/7 - 0s - loss: 0.1614 - accuracy: 0.9479 - val_loss: 0.3034 - val_accuracy: 0.9300 - 47ms/epoch - 7ms/step\n",
            "Epoch 1263/1500\n",
            "7/7 - 0s - loss: 0.1614 - accuracy: 0.9526 - val_loss: 0.3047 - val_accuracy: 0.9350 - 64ms/epoch - 9ms/step\n",
            "Epoch 1264/1500\n",
            "7/7 - 0s - loss: 0.1615 - accuracy: 0.9526 - val_loss: 0.3033 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 1265/1500\n",
            "7/7 - 0s - loss: 0.1619 - accuracy: 0.9526 - val_loss: 0.3045 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 1266/1500\n",
            "7/7 - 0s - loss: 0.1613 - accuracy: 0.9526 - val_loss: 0.3035 - val_accuracy: 0.9300 - 52ms/epoch - 7ms/step\n",
            "Epoch 1267/1500\n",
            "7/7 - 0s - loss: 0.1613 - accuracy: 0.9526 - val_loss: 0.3043 - val_accuracy: 0.9300 - 57ms/epoch - 8ms/step\n",
            "Epoch 1268/1500\n",
            "7/7 - 0s - loss: 0.1613 - accuracy: 0.9526 - val_loss: 0.3053 - val_accuracy: 0.9300 - 64ms/epoch - 9ms/step\n",
            "Epoch 1269/1500\n",
            "7/7 - 0s - loss: 0.1616 - accuracy: 0.9479 - val_loss: 0.3045 - val_accuracy: 0.9300 - 65ms/epoch - 9ms/step\n",
            "Epoch 1270/1500\n",
            "7/7 - 0s - loss: 0.1617 - accuracy: 0.9479 - val_loss: 0.3048 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 1271/1500\n",
            "7/7 - 0s - loss: 0.1615 - accuracy: 0.9479 - val_loss: 0.3057 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 1272/1500\n",
            "7/7 - 0s - loss: 0.1611 - accuracy: 0.9526 - val_loss: 0.3053 - val_accuracy: 0.9350 - 61ms/epoch - 9ms/step\n",
            "Epoch 1273/1500\n",
            "7/7 - 0s - loss: 0.1613 - accuracy: 0.9526 - val_loss: 0.3059 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 1274/1500\n",
            "7/7 - 0s - loss: 0.1611 - accuracy: 0.9479 - val_loss: 0.3052 - val_accuracy: 0.9300 - 54ms/epoch - 8ms/step\n",
            "Epoch 1275/1500\n",
            "7/7 - 0s - loss: 0.1612 - accuracy: 0.9479 - val_loss: 0.3054 - val_accuracy: 0.9300 - 45ms/epoch - 6ms/step\n",
            "Epoch 1276/1500\n",
            "7/7 - 0s - loss: 0.1612 - accuracy: 0.9479 - val_loss: 0.3056 - val_accuracy: 0.9300 - 47ms/epoch - 7ms/step\n",
            "Epoch 1277/1500\n",
            "7/7 - 0s - loss: 0.1614 - accuracy: 0.9479 - val_loss: 0.3068 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 1278/1500\n",
            "7/7 - 0s - loss: 0.1613 - accuracy: 0.9479 - val_loss: 0.3060 - val_accuracy: 0.9300 - 50ms/epoch - 7ms/step\n",
            "Epoch 1279/1500\n",
            "7/7 - 0s - loss: 0.1616 - accuracy: 0.9479 - val_loss: 0.3044 - val_accuracy: 0.9300 - 46ms/epoch - 7ms/step\n",
            "Epoch 1280/1500\n",
            "7/7 - 0s - loss: 0.1616 - accuracy: 0.9479 - val_loss: 0.3059 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 1281/1500\n",
            "7/7 - 0s - loss: 0.1616 - accuracy: 0.9526 - val_loss: 0.3053 - val_accuracy: 0.9300 - 47ms/epoch - 7ms/step\n",
            "Epoch 1282/1500\n",
            "7/7 - 0s - loss: 0.1610 - accuracy: 0.9526 - val_loss: 0.3066 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 1283/1500\n",
            "7/7 - 0s - loss: 0.1612 - accuracy: 0.9526 - val_loss: 0.3075 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 1284/1500\n",
            "7/7 - 0s - loss: 0.1612 - accuracy: 0.9479 - val_loss: 0.3084 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 1285/1500\n",
            "7/7 - 0s - loss: 0.1610 - accuracy: 0.9526 - val_loss: 0.3072 - val_accuracy: 0.9350 - 54ms/epoch - 8ms/step\n",
            "Epoch 1286/1500\n",
            "7/7 - 0s - loss: 0.1610 - accuracy: 0.9526 - val_loss: 0.3058 - val_accuracy: 0.9300 - 44ms/epoch - 6ms/step\n",
            "Epoch 1287/1500\n",
            "7/7 - 0s - loss: 0.1613 - accuracy: 0.9526 - val_loss: 0.3064 - val_accuracy: 0.9350 - 67ms/epoch - 10ms/step\n",
            "Epoch 1288/1500\n",
            "7/7 - 0s - loss: 0.1609 - accuracy: 0.9526 - val_loss: 0.3056 - val_accuracy: 0.9300 - 55ms/epoch - 8ms/step\n",
            "Epoch 1289/1500\n",
            "7/7 - 0s - loss: 0.1612 - accuracy: 0.9479 - val_loss: 0.3036 - val_accuracy: 0.9300 - 54ms/epoch - 8ms/step\n",
            "Epoch 1290/1500\n",
            "7/7 - 0s - loss: 0.1610 - accuracy: 0.9479 - val_loss: 0.3037 - val_accuracy: 0.9300 - 60ms/epoch - 9ms/step\n",
            "Epoch 1291/1500\n",
            "7/7 - 0s - loss: 0.1611 - accuracy: 0.9479 - val_loss: 0.3045 - val_accuracy: 0.9300 - 58ms/epoch - 8ms/step\n",
            "Epoch 1292/1500\n",
            "7/7 - 0s - loss: 0.1609 - accuracy: 0.9526 - val_loss: 0.3069 - val_accuracy: 0.9400 - 59ms/epoch - 8ms/step\n",
            "Epoch 1293/1500\n",
            "7/7 - 0s - loss: 0.1611 - accuracy: 0.9526 - val_loss: 0.3064 - val_accuracy: 0.9400 - 57ms/epoch - 8ms/step\n",
            "Epoch 1294/1500\n",
            "7/7 - 0s - loss: 0.1616 - accuracy: 0.9526 - val_loss: 0.3049 - val_accuracy: 0.9300 - 45ms/epoch - 6ms/step\n",
            "Epoch 1295/1500\n",
            "7/7 - 0s - loss: 0.1613 - accuracy: 0.9526 - val_loss: 0.3066 - val_accuracy: 0.9350 - 62ms/epoch - 9ms/step\n",
            "Epoch 1296/1500\n",
            "7/7 - 0s - loss: 0.1618 - accuracy: 0.9526 - val_loss: 0.3057 - val_accuracy: 0.9300 - 44ms/epoch - 6ms/step\n",
            "Epoch 1297/1500\n",
            "7/7 - 0s - loss: 0.1614 - accuracy: 0.9526 - val_loss: 0.3085 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 1298/1500\n",
            "7/7 - 0s - loss: 0.1612 - accuracy: 0.9479 - val_loss: 0.3101 - val_accuracy: 0.9350 - 61ms/epoch - 9ms/step\n",
            "Epoch 1299/1500\n",
            "7/7 - 0s - loss: 0.1610 - accuracy: 0.9479 - val_loss: 0.3088 - val_accuracy: 0.9350 - 43ms/epoch - 6ms/step\n",
            "Epoch 1300/1500\n",
            "7/7 - 0s - loss: 0.1614 - accuracy: 0.9479 - val_loss: 0.3070 - val_accuracy: 0.9300 - 61ms/epoch - 9ms/step\n",
            "Epoch 1301/1500\n",
            "7/7 - 0s - loss: 0.1613 - accuracy: 0.9526 - val_loss: 0.3060 - val_accuracy: 0.9300 - 55ms/epoch - 8ms/step\n",
            "Epoch 1302/1500\n",
            "7/7 - 0s - loss: 0.1608 - accuracy: 0.9526 - val_loss: 0.3072 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 1303/1500\n",
            "7/7 - 0s - loss: 0.1608 - accuracy: 0.9526 - val_loss: 0.3072 - val_accuracy: 0.9350 - 51ms/epoch - 7ms/step\n",
            "Epoch 1304/1500\n",
            "7/7 - 0s - loss: 0.1620 - accuracy: 0.9479 - val_loss: 0.3054 - val_accuracy: 0.9300 - 54ms/epoch - 8ms/step\n",
            "Epoch 1305/1500\n",
            "7/7 - 0s - loss: 0.1610 - accuracy: 0.9479 - val_loss: 0.3070 - val_accuracy: 0.9350 - 77ms/epoch - 11ms/step\n",
            "Epoch 1306/1500\n",
            "7/7 - 0s - loss: 0.1612 - accuracy: 0.9479 - val_loss: 0.3078 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 1307/1500\n",
            "7/7 - 0s - loss: 0.1610 - accuracy: 0.9479 - val_loss: 0.3080 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 1308/1500\n",
            "7/7 - 0s - loss: 0.1609 - accuracy: 0.9526 - val_loss: 0.3080 - val_accuracy: 0.9350 - 45ms/epoch - 6ms/step\n",
            "Epoch 1309/1500\n",
            "7/7 - 0s - loss: 0.1613 - accuracy: 0.9479 - val_loss: 0.3088 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 1310/1500\n",
            "7/7 - 0s - loss: 0.1609 - accuracy: 0.9526 - val_loss: 0.3072 - val_accuracy: 0.9300 - 44ms/epoch - 6ms/step\n",
            "Epoch 1311/1500\n",
            "7/7 - 0s - loss: 0.1610 - accuracy: 0.9526 - val_loss: 0.3077 - val_accuracy: 0.9300 - 49ms/epoch - 7ms/step\n",
            "Epoch 1312/1500\n",
            "7/7 - 0s - loss: 0.1608 - accuracy: 0.9526 - val_loss: 0.3077 - val_accuracy: 0.9300 - 57ms/epoch - 8ms/step\n",
            "Epoch 1313/1500\n",
            "7/7 - 0s - loss: 0.1608 - accuracy: 0.9526 - val_loss: 0.3065 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 1314/1500\n",
            "7/7 - 0s - loss: 0.1615 - accuracy: 0.9526 - val_loss: 0.3044 - val_accuracy: 0.9300 - 48ms/epoch - 7ms/step\n",
            "Epoch 1315/1500\n",
            "7/7 - 0s - loss: 0.1606 - accuracy: 0.9479 - val_loss: 0.3053 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 1316/1500\n",
            "7/7 - 0s - loss: 0.1610 - accuracy: 0.9479 - val_loss: 0.3088 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 1317/1500\n",
            "7/7 - 0s - loss: 0.1608 - accuracy: 0.9479 - val_loss: 0.3096 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 1318/1500\n",
            "7/7 - 0s - loss: 0.1610 - accuracy: 0.9479 - val_loss: 0.3092 - val_accuracy: 0.9350 - 63ms/epoch - 9ms/step\n",
            "Epoch 1319/1500\n",
            "7/7 - 0s - loss: 0.1610 - accuracy: 0.9479 - val_loss: 0.3082 - val_accuracy: 0.9350 - 72ms/epoch - 10ms/step\n",
            "Epoch 1320/1500\n",
            "7/7 - 0s - loss: 0.1612 - accuracy: 0.9479 - val_loss: 0.3060 - val_accuracy: 0.9300 - 52ms/epoch - 7ms/step\n",
            "Epoch 1321/1500\n",
            "7/7 - 0s - loss: 0.1617 - accuracy: 0.9479 - val_loss: 0.3091 - val_accuracy: 0.9300 - 46ms/epoch - 7ms/step\n",
            "Epoch 1322/1500\n",
            "7/7 - 0s - loss: 0.1608 - accuracy: 0.9479 - val_loss: 0.3078 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 1323/1500\n",
            "7/7 - 0s - loss: 0.1607 - accuracy: 0.9479 - val_loss: 0.3074 - val_accuracy: 0.9350 - 71ms/epoch - 10ms/step\n",
            "Epoch 1324/1500\n",
            "7/7 - 0s - loss: 0.1608 - accuracy: 0.9526 - val_loss: 0.3064 - val_accuracy: 0.9300 - 53ms/epoch - 8ms/step\n",
            "Epoch 1325/1500\n",
            "7/7 - 0s - loss: 0.1606 - accuracy: 0.9526 - val_loss: 0.3069 - val_accuracy: 0.9250 - 57ms/epoch - 8ms/step\n",
            "Epoch 1326/1500\n",
            "7/7 - 0s - loss: 0.1608 - accuracy: 0.9526 - val_loss: 0.3072 - val_accuracy: 0.9300 - 46ms/epoch - 7ms/step\n",
            "Epoch 1327/1500\n",
            "7/7 - 0s - loss: 0.1610 - accuracy: 0.9479 - val_loss: 0.3059 - val_accuracy: 0.9300 - 48ms/epoch - 7ms/step\n",
            "Epoch 1328/1500\n",
            "7/7 - 0s - loss: 0.1607 - accuracy: 0.9479 - val_loss: 0.3061 - val_accuracy: 0.9300 - 53ms/epoch - 8ms/step\n",
            "Epoch 1329/1500\n",
            "7/7 - 0s - loss: 0.1609 - accuracy: 0.9526 - val_loss: 0.3076 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 1330/1500\n",
            "7/7 - 0s - loss: 0.1604 - accuracy: 0.9526 - val_loss: 0.3079 - val_accuracy: 0.9300 - 49ms/epoch - 7ms/step\n",
            "Epoch 1331/1500\n",
            "7/7 - 0s - loss: 0.1604 - accuracy: 0.9526 - val_loss: 0.3077 - val_accuracy: 0.9300 - 50ms/epoch - 7ms/step\n",
            "Epoch 1332/1500\n",
            "7/7 - 0s - loss: 0.1608 - accuracy: 0.9479 - val_loss: 0.3069 - val_accuracy: 0.9300 - 54ms/epoch - 8ms/step\n",
            "Epoch 1333/1500\n",
            "7/7 - 0s - loss: 0.1609 - accuracy: 0.9479 - val_loss: 0.3062 - val_accuracy: 0.9300 - 56ms/epoch - 8ms/step\n",
            "Epoch 1334/1500\n",
            "7/7 - 0s - loss: 0.1602 - accuracy: 0.9526 - val_loss: 0.3070 - val_accuracy: 0.9300 - 55ms/epoch - 8ms/step\n",
            "Epoch 1335/1500\n",
            "7/7 - 0s - loss: 0.1611 - accuracy: 0.9526 - val_loss: 0.3085 - val_accuracy: 0.9350 - 64ms/epoch - 9ms/step\n",
            "Epoch 1336/1500\n",
            "7/7 - 0s - loss: 0.1609 - accuracy: 0.9526 - val_loss: 0.3084 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 1337/1500\n",
            "7/7 - 0s - loss: 0.1612 - accuracy: 0.9526 - val_loss: 0.3058 - val_accuracy: 0.9300 - 46ms/epoch - 7ms/step\n",
            "Epoch 1338/1500\n",
            "7/7 - 0s - loss: 0.1607 - accuracy: 0.9526 - val_loss: 0.3067 - val_accuracy: 0.9300 - 51ms/epoch - 7ms/step\n",
            "Epoch 1339/1500\n",
            "7/7 - 0s - loss: 0.1602 - accuracy: 0.9526 - val_loss: 0.3075 - val_accuracy: 0.9300 - 62ms/epoch - 9ms/step\n",
            "Epoch 1340/1500\n",
            "7/7 - 0s - loss: 0.1608 - accuracy: 0.9526 - val_loss: 0.3094 - val_accuracy: 0.9350 - 67ms/epoch - 10ms/step\n",
            "Epoch 1341/1500\n",
            "7/7 - 0s - loss: 0.1604 - accuracy: 0.9526 - val_loss: 0.3084 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 1342/1500\n",
            "7/7 - 0s - loss: 0.1602 - accuracy: 0.9526 - val_loss: 0.3078 - val_accuracy: 0.9300 - 49ms/epoch - 7ms/step\n",
            "Epoch 1343/1500\n",
            "7/7 - 0s - loss: 0.1605 - accuracy: 0.9526 - val_loss: 0.3090 - val_accuracy: 0.9350 - 53ms/epoch - 8ms/step\n",
            "Epoch 1344/1500\n",
            "7/7 - 0s - loss: 0.1603 - accuracy: 0.9526 - val_loss: 0.3085 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 1345/1500\n",
            "7/7 - 0s - loss: 0.1601 - accuracy: 0.9526 - val_loss: 0.3074 - val_accuracy: 0.9350 - 67ms/epoch - 10ms/step\n",
            "Epoch 1346/1500\n",
            "7/7 - 0s - loss: 0.1600 - accuracy: 0.9526 - val_loss: 0.3061 - val_accuracy: 0.9300 - 50ms/epoch - 7ms/step\n",
            "Epoch 1347/1500\n",
            "7/7 - 0s - loss: 0.1604 - accuracy: 0.9479 - val_loss: 0.3047 - val_accuracy: 0.9300 - 44ms/epoch - 6ms/step\n",
            "Epoch 1348/1500\n",
            "7/7 - 0s - loss: 0.1605 - accuracy: 0.9479 - val_loss: 0.3055 - val_accuracy: 0.9300 - 49ms/epoch - 7ms/step\n",
            "Epoch 1349/1500\n",
            "7/7 - 0s - loss: 0.1598 - accuracy: 0.9479 - val_loss: 0.3072 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 1350/1500\n",
            "7/7 - 0s - loss: 0.1603 - accuracy: 0.9526 - val_loss: 0.3085 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 1351/1500\n",
            "7/7 - 0s - loss: 0.1603 - accuracy: 0.9526 - val_loss: 0.3087 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 1352/1500\n",
            "7/7 - 0s - loss: 0.1612 - accuracy: 0.9526 - val_loss: 0.3064 - val_accuracy: 0.9300 - 48ms/epoch - 7ms/step\n",
            "Epoch 1353/1500\n",
            "7/7 - 0s - loss: 0.1609 - accuracy: 0.9526 - val_loss: 0.3075 - val_accuracy: 0.9300 - 68ms/epoch - 10ms/step\n",
            "Epoch 1354/1500\n",
            "7/7 - 0s - loss: 0.1607 - accuracy: 0.9526 - val_loss: 0.3062 - val_accuracy: 0.9300 - 50ms/epoch - 7ms/step\n",
            "Epoch 1355/1500\n",
            "7/7 - 0s - loss: 0.1603 - accuracy: 0.9526 - val_loss: 0.3072 - val_accuracy: 0.9300 - 48ms/epoch - 7ms/step\n",
            "Epoch 1356/1500\n",
            "7/7 - 0s - loss: 0.1603 - accuracy: 0.9526 - val_loss: 0.3090 - val_accuracy: 0.9300 - 62ms/epoch - 9ms/step\n",
            "Epoch 1357/1500\n",
            "7/7 - 0s - loss: 0.1600 - accuracy: 0.9526 - val_loss: 0.3088 - val_accuracy: 0.9300 - 66ms/epoch - 9ms/step\n",
            "Epoch 1358/1500\n",
            "7/7 - 0s - loss: 0.1600 - accuracy: 0.9526 - val_loss: 0.3073 - val_accuracy: 0.9300 - 52ms/epoch - 7ms/step\n",
            "Epoch 1359/1500\n",
            "7/7 - 0s - loss: 0.1616 - accuracy: 0.9479 - val_loss: 0.3052 - val_accuracy: 0.9300 - 49ms/epoch - 7ms/step\n",
            "Epoch 1360/1500\n",
            "7/7 - 0s - loss: 0.1604 - accuracy: 0.9479 - val_loss: 0.3078 - val_accuracy: 0.9350 - 53ms/epoch - 8ms/step\n",
            "Epoch 1361/1500\n",
            "7/7 - 0s - loss: 0.1598 - accuracy: 0.9526 - val_loss: 0.3081 - val_accuracy: 0.9350 - 51ms/epoch - 7ms/step\n",
            "Epoch 1362/1500\n",
            "7/7 - 0s - loss: 0.1601 - accuracy: 0.9526 - val_loss: 0.3088 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 1363/1500\n",
            "7/7 - 0s - loss: 0.1602 - accuracy: 0.9526 - val_loss: 0.3106 - val_accuracy: 0.9300 - 45ms/epoch - 6ms/step\n",
            "Epoch 1364/1500\n",
            "7/7 - 0s - loss: 0.1601 - accuracy: 0.9526 - val_loss: 0.3087 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 1365/1500\n",
            "7/7 - 0s - loss: 0.1601 - accuracy: 0.9526 - val_loss: 0.3077 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 1366/1500\n",
            "7/7 - 0s - loss: 0.1601 - accuracy: 0.9526 - val_loss: 0.3072 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 1367/1500\n",
            "7/7 - 0s - loss: 0.1600 - accuracy: 0.9526 - val_loss: 0.3056 - val_accuracy: 0.9400 - 58ms/epoch - 8ms/step\n",
            "Epoch 1368/1500\n",
            "7/7 - 0s - loss: 0.1600 - accuracy: 0.9526 - val_loss: 0.3073 - val_accuracy: 0.9350 - 53ms/epoch - 8ms/step\n",
            "Epoch 1369/1500\n",
            "7/7 - 0s - loss: 0.1597 - accuracy: 0.9526 - val_loss: 0.3085 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 1370/1500\n",
            "7/7 - 0s - loss: 0.1598 - accuracy: 0.9526 - val_loss: 0.3078 - val_accuracy: 0.9300 - 44ms/epoch - 6ms/step\n",
            "Epoch 1371/1500\n",
            "7/7 - 0s - loss: 0.1605 - accuracy: 0.9479 - val_loss: 0.3074 - val_accuracy: 0.9300 - 51ms/epoch - 7ms/step\n",
            "Epoch 1372/1500\n",
            "7/7 - 0s - loss: 0.1603 - accuracy: 0.9479 - val_loss: 0.3075 - val_accuracy: 0.9300 - 57ms/epoch - 8ms/step\n",
            "Epoch 1373/1500\n",
            "7/7 - 0s - loss: 0.1594 - accuracy: 0.9526 - val_loss: 0.3088 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 1374/1500\n",
            "7/7 - 0s - loss: 0.1596 - accuracy: 0.9526 - val_loss: 0.3095 - val_accuracy: 0.9350 - 51ms/epoch - 7ms/step\n",
            "Epoch 1375/1500\n",
            "7/7 - 0s - loss: 0.1606 - accuracy: 0.9479 - val_loss: 0.3106 - val_accuracy: 0.9350 - 63ms/epoch - 9ms/step\n",
            "Epoch 1376/1500\n",
            "7/7 - 0s - loss: 0.1597 - accuracy: 0.9526 - val_loss: 0.3090 - val_accuracy: 0.9300 - 62ms/epoch - 9ms/step\n",
            "Epoch 1377/1500\n",
            "7/7 - 0s - loss: 0.1597 - accuracy: 0.9526 - val_loss: 0.3094 - val_accuracy: 0.9350 - 66ms/epoch - 9ms/step\n",
            "Epoch 1378/1500\n",
            "7/7 - 0s - loss: 0.1600 - accuracy: 0.9526 - val_loss: 0.3081 - val_accuracy: 0.9300 - 53ms/epoch - 8ms/step\n",
            "Epoch 1379/1500\n",
            "7/7 - 0s - loss: 0.1603 - accuracy: 0.9526 - val_loss: 0.3104 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 1380/1500\n",
            "7/7 - 0s - loss: 0.1596 - accuracy: 0.9526 - val_loss: 0.3094 - val_accuracy: 0.9350 - 51ms/epoch - 7ms/step\n",
            "Epoch 1381/1500\n",
            "7/7 - 0s - loss: 0.1597 - accuracy: 0.9526 - val_loss: 0.3084 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 1382/1500\n",
            "7/7 - 0s - loss: 0.1596 - accuracy: 0.9526 - val_loss: 0.3084 - val_accuracy: 0.9400 - 48ms/epoch - 7ms/step\n",
            "Epoch 1383/1500\n",
            "7/7 - 0s - loss: 0.1597 - accuracy: 0.9526 - val_loss: 0.3069 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 1384/1500\n",
            "7/7 - 0s - loss: 0.1597 - accuracy: 0.9526 - val_loss: 0.3078 - val_accuracy: 0.9350 - 43ms/epoch - 6ms/step\n",
            "Epoch 1385/1500\n",
            "7/7 - 0s - loss: 0.1596 - accuracy: 0.9526 - val_loss: 0.3085 - val_accuracy: 0.9350 - 66ms/epoch - 9ms/step\n",
            "Epoch 1386/1500\n",
            "7/7 - 0s - loss: 0.1594 - accuracy: 0.9526 - val_loss: 0.3089 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 1387/1500\n",
            "7/7 - 0s - loss: 0.1593 - accuracy: 0.9526 - val_loss: 0.3078 - val_accuracy: 0.9300 - 56ms/epoch - 8ms/step\n",
            "Epoch 1388/1500\n",
            "7/7 - 0s - loss: 0.1593 - accuracy: 0.9526 - val_loss: 0.3077 - val_accuracy: 0.9300 - 65ms/epoch - 9ms/step\n",
            "Epoch 1389/1500\n",
            "7/7 - 0s - loss: 0.1595 - accuracy: 0.9479 - val_loss: 0.3066 - val_accuracy: 0.9300 - 64ms/epoch - 9ms/step\n",
            "Epoch 1390/1500\n",
            "7/7 - 0s - loss: 0.1594 - accuracy: 0.9479 - val_loss: 0.3067 - val_accuracy: 0.9300 - 43ms/epoch - 6ms/step\n",
            "Epoch 1391/1500\n",
            "7/7 - 0s - loss: 0.1595 - accuracy: 0.9526 - val_loss: 0.3094 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 1392/1500\n",
            "7/7 - 0s - loss: 0.1596 - accuracy: 0.9526 - val_loss: 0.3089 - val_accuracy: 0.9250 - 62ms/epoch - 9ms/step\n",
            "Epoch 1393/1500\n",
            "7/7 - 0s - loss: 0.1598 - accuracy: 0.9526 - val_loss: 0.3107 - val_accuracy: 0.9350 - 61ms/epoch - 9ms/step\n",
            "Epoch 1394/1500\n",
            "7/7 - 0s - loss: 0.1596 - accuracy: 0.9526 - val_loss: 0.3085 - val_accuracy: 0.9300 - 49ms/epoch - 7ms/step\n",
            "Epoch 1395/1500\n",
            "7/7 - 0s - loss: 0.1594 - accuracy: 0.9526 - val_loss: 0.3077 - val_accuracy: 0.9300 - 44ms/epoch - 6ms/step\n",
            "Epoch 1396/1500\n",
            "7/7 - 0s - loss: 0.1595 - accuracy: 0.9526 - val_loss: 0.3087 - val_accuracy: 0.9350 - 64ms/epoch - 9ms/step\n",
            "Epoch 1397/1500\n",
            "7/7 - 0s - loss: 0.1592 - accuracy: 0.9526 - val_loss: 0.3092 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 1398/1500\n",
            "7/7 - 0s - loss: 0.1592 - accuracy: 0.9526 - val_loss: 0.3084 - val_accuracy: 0.9300 - 51ms/epoch - 7ms/step\n",
            "Epoch 1399/1500\n",
            "7/7 - 0s - loss: 0.1599 - accuracy: 0.9479 - val_loss: 0.3074 - val_accuracy: 0.9300 - 67ms/epoch - 10ms/step\n",
            "Epoch 1400/1500\n",
            "7/7 - 0s - loss: 0.1593 - accuracy: 0.9479 - val_loss: 0.3095 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 1401/1500\n",
            "7/7 - 0s - loss: 0.1593 - accuracy: 0.9526 - val_loss: 0.3107 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 1402/1500\n",
            "7/7 - 0s - loss: 0.1592 - accuracy: 0.9526 - val_loss: 0.3091 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 1403/1500\n",
            "7/7 - 0s - loss: 0.1593 - accuracy: 0.9479 - val_loss: 0.3085 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 1404/1500\n",
            "7/7 - 0s - loss: 0.1593 - accuracy: 0.9479 - val_loss: 0.3074 - val_accuracy: 0.9300 - 46ms/epoch - 7ms/step\n",
            "Epoch 1405/1500\n",
            "7/7 - 0s - loss: 0.1595 - accuracy: 0.9526 - val_loss: 0.3098 - val_accuracy: 0.9350 - 62ms/epoch - 9ms/step\n",
            "Epoch 1406/1500\n",
            "7/7 - 0s - loss: 0.1590 - accuracy: 0.9526 - val_loss: 0.3089 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 1407/1500\n",
            "7/7 - 0s - loss: 0.1591 - accuracy: 0.9526 - val_loss: 0.3096 - val_accuracy: 0.9350 - 62ms/epoch - 9ms/step\n",
            "Epoch 1408/1500\n",
            "7/7 - 0s - loss: 0.1596 - accuracy: 0.9479 - val_loss: 0.3085 - val_accuracy: 0.9300 - 61ms/epoch - 9ms/step\n",
            "Epoch 1409/1500\n",
            "7/7 - 0s - loss: 0.1590 - accuracy: 0.9479 - val_loss: 0.3088 - val_accuracy: 0.9300 - 60ms/epoch - 9ms/step\n",
            "Epoch 1410/1500\n",
            "7/7 - 0s - loss: 0.1591 - accuracy: 0.9526 - val_loss: 0.3088 - val_accuracy: 0.9300 - 60ms/epoch - 9ms/step\n",
            "Epoch 1411/1500\n",
            "7/7 - 0s - loss: 0.1593 - accuracy: 0.9526 - val_loss: 0.3107 - val_accuracy: 0.9300 - 57ms/epoch - 8ms/step\n",
            "Epoch 1412/1500\n",
            "7/7 - 0s - loss: 0.1592 - accuracy: 0.9479 - val_loss: 0.3098 - val_accuracy: 0.9300 - 62ms/epoch - 9ms/step\n",
            "Epoch 1413/1500\n",
            "7/7 - 0s - loss: 0.1592 - accuracy: 0.9479 - val_loss: 0.3108 - val_accuracy: 0.9350 - 63ms/epoch - 9ms/step\n",
            "Epoch 1414/1500\n",
            "7/7 - 0s - loss: 0.1599 - accuracy: 0.9526 - val_loss: 0.3110 - val_accuracy: 0.9300 - 44ms/epoch - 6ms/step\n",
            "Epoch 1415/1500\n",
            "7/7 - 0s - loss: 0.1605 - accuracy: 0.9526 - val_loss: 0.3085 - val_accuracy: 0.9300 - 47ms/epoch - 7ms/step\n",
            "Epoch 1416/1500\n",
            "7/7 - 0s - loss: 0.1590 - accuracy: 0.9479 - val_loss: 0.3098 - val_accuracy: 0.9350 - 49ms/epoch - 7ms/step\n",
            "Epoch 1417/1500\n",
            "7/7 - 0s - loss: 0.1586 - accuracy: 0.9526 - val_loss: 0.3115 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 1418/1500\n",
            "7/7 - 0s - loss: 0.1588 - accuracy: 0.9526 - val_loss: 0.3126 - val_accuracy: 0.9350 - 70ms/epoch - 10ms/step\n",
            "Epoch 1419/1500\n",
            "7/7 - 0s - loss: 0.1594 - accuracy: 0.9526 - val_loss: 0.3129 - val_accuracy: 0.9300 - 57ms/epoch - 8ms/step\n",
            "Epoch 1420/1500\n",
            "7/7 - 0s - loss: 0.1590 - accuracy: 0.9526 - val_loss: 0.3098 - val_accuracy: 0.9250 - 54ms/epoch - 8ms/step\n",
            "Epoch 1421/1500\n",
            "7/7 - 0s - loss: 0.1597 - accuracy: 0.9526 - val_loss: 0.3071 - val_accuracy: 0.9300 - 66ms/epoch - 9ms/step\n",
            "Epoch 1422/1500\n",
            "7/7 - 0s - loss: 0.1590 - accuracy: 0.9479 - val_loss: 0.3083 - val_accuracy: 0.9300 - 62ms/epoch - 9ms/step\n",
            "Epoch 1423/1500\n",
            "7/7 - 0s - loss: 0.1591 - accuracy: 0.9526 - val_loss: 0.3086 - val_accuracy: 0.9300 - 61ms/epoch - 9ms/step\n",
            "Epoch 1424/1500\n",
            "7/7 - 0s - loss: 0.1592 - accuracy: 0.9526 - val_loss: 0.3093 - val_accuracy: 0.9300 - 58ms/epoch - 8ms/step\n",
            "Epoch 1425/1500\n",
            "7/7 - 0s - loss: 0.1597 - accuracy: 0.9479 - val_loss: 0.3078 - val_accuracy: 0.9300 - 60ms/epoch - 9ms/step\n",
            "Epoch 1426/1500\n",
            "7/7 - 0s - loss: 0.1592 - accuracy: 0.9479 - val_loss: 0.3096 - val_accuracy: 0.9300 - 52ms/epoch - 7ms/step\n",
            "Epoch 1427/1500\n",
            "7/7 - 0s - loss: 0.1592 - accuracy: 0.9526 - val_loss: 0.3106 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 1428/1500\n",
            "7/7 - 0s - loss: 0.1602 - accuracy: 0.9526 - val_loss: 0.3100 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 1429/1500\n",
            "7/7 - 0s - loss: 0.1591 - accuracy: 0.9526 - val_loss: 0.3112 - val_accuracy: 0.9350 - 44ms/epoch - 6ms/step\n",
            "Epoch 1430/1500\n",
            "7/7 - 0s - loss: 0.1594 - accuracy: 0.9526 - val_loss: 0.3106 - val_accuracy: 0.9300 - 54ms/epoch - 8ms/step\n",
            "Epoch 1431/1500\n",
            "7/7 - 0s - loss: 0.1593 - accuracy: 0.9526 - val_loss: 0.3107 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 1432/1500\n",
            "7/7 - 0s - loss: 0.1590 - accuracy: 0.9526 - val_loss: 0.3104 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 1433/1500\n",
            "7/7 - 0s - loss: 0.1596 - accuracy: 0.9479 - val_loss: 0.3078 - val_accuracy: 0.9300 - 55ms/epoch - 8ms/step\n",
            "Epoch 1434/1500\n",
            "7/7 - 0s - loss: 0.1589 - accuracy: 0.9479 - val_loss: 0.3091 - val_accuracy: 0.9300 - 69ms/epoch - 10ms/step\n",
            "Epoch 1435/1500\n",
            "7/7 - 0s - loss: 0.1588 - accuracy: 0.9526 - val_loss: 0.3092 - val_accuracy: 0.9350 - 54ms/epoch - 8ms/step\n",
            "Epoch 1436/1500\n",
            "7/7 - 0s - loss: 0.1601 - accuracy: 0.9526 - val_loss: 0.3110 - val_accuracy: 0.9400 - 44ms/epoch - 6ms/step\n",
            "Epoch 1437/1500\n",
            "7/7 - 0s - loss: 0.1589 - accuracy: 0.9526 - val_loss: 0.3081 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 1438/1500\n",
            "7/7 - 0s - loss: 0.1587 - accuracy: 0.9526 - val_loss: 0.3079 - val_accuracy: 0.9350 - 53ms/epoch - 8ms/step\n",
            "Epoch 1439/1500\n",
            "7/7 - 0s - loss: 0.1594 - accuracy: 0.9479 - val_loss: 0.3074 - val_accuracy: 0.9300 - 59ms/epoch - 8ms/step\n",
            "Epoch 1440/1500\n",
            "7/7 - 0s - loss: 0.1589 - accuracy: 0.9526 - val_loss: 0.3097 - val_accuracy: 0.9350 - 65ms/epoch - 9ms/step\n",
            "Epoch 1441/1500\n",
            "7/7 - 0s - loss: 0.1587 - accuracy: 0.9526 - val_loss: 0.3112 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 1442/1500\n",
            "7/7 - 0s - loss: 0.1586 - accuracy: 0.9526 - val_loss: 0.3112 - val_accuracy: 0.9350 - 54ms/epoch - 8ms/step\n",
            "Epoch 1443/1500\n",
            "7/7 - 0s - loss: 0.1589 - accuracy: 0.9526 - val_loss: 0.3100 - val_accuracy: 0.9300 - 71ms/epoch - 10ms/step\n",
            "Epoch 1444/1500\n",
            "7/7 - 0s - loss: 0.1591 - accuracy: 0.9526 - val_loss: 0.3106 - val_accuracy: 0.9350 - 68ms/epoch - 10ms/step\n",
            "Epoch 1445/1500\n",
            "7/7 - 0s - loss: 0.1585 - accuracy: 0.9526 - val_loss: 0.3091 - val_accuracy: 0.9300 - 56ms/epoch - 8ms/step\n",
            "Epoch 1446/1500\n",
            "7/7 - 0s - loss: 0.1586 - accuracy: 0.9526 - val_loss: 0.3084 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 1447/1500\n",
            "7/7 - 0s - loss: 0.1587 - accuracy: 0.9479 - val_loss: 0.3092 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 1448/1500\n",
            "7/7 - 0s - loss: 0.1592 - accuracy: 0.9526 - val_loss: 0.3110 - val_accuracy: 0.9350 - 55ms/epoch - 8ms/step\n",
            "Epoch 1449/1500\n",
            "7/7 - 0s - loss: 0.1587 - accuracy: 0.9526 - val_loss: 0.3099 - val_accuracy: 0.9350 - 64ms/epoch - 9ms/step\n",
            "Epoch 1450/1500\n",
            "7/7 - 0s - loss: 0.1585 - accuracy: 0.9479 - val_loss: 0.3097 - val_accuracy: 0.9350 - 62ms/epoch - 9ms/step\n",
            "Epoch 1451/1500\n",
            "7/7 - 0s - loss: 0.1591 - accuracy: 0.9479 - val_loss: 0.3092 - val_accuracy: 0.9300 - 51ms/epoch - 7ms/step\n",
            "Epoch 1452/1500\n",
            "7/7 - 0s - loss: 0.1586 - accuracy: 0.9526 - val_loss: 0.3095 - val_accuracy: 0.9300 - 51ms/epoch - 7ms/step\n",
            "Epoch 1453/1500\n",
            "7/7 - 0s - loss: 0.1586 - accuracy: 0.9479 - val_loss: 0.3094 - val_accuracy: 0.9300 - 48ms/epoch - 7ms/step\n",
            "Epoch 1454/1500\n",
            "7/7 - 0s - loss: 0.1590 - accuracy: 0.9479 - val_loss: 0.3115 - val_accuracy: 0.9300 - 48ms/epoch - 7ms/step\n",
            "Epoch 1455/1500\n",
            "7/7 - 0s - loss: 0.1594 - accuracy: 0.9479 - val_loss: 0.3131 - val_accuracy: 0.9350 - 70ms/epoch - 10ms/step\n",
            "Epoch 1456/1500\n",
            "7/7 - 0s - loss: 0.1586 - accuracy: 0.9526 - val_loss: 0.3118 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 1457/1500\n",
            "7/7 - 0s - loss: 0.1592 - accuracy: 0.9479 - val_loss: 0.3091 - val_accuracy: 0.9300 - 63ms/epoch - 9ms/step\n",
            "Epoch 1458/1500\n",
            "7/7 - 0s - loss: 0.1584 - accuracy: 0.9479 - val_loss: 0.3104 - val_accuracy: 0.9300 - 57ms/epoch - 8ms/step\n",
            "Epoch 1459/1500\n",
            "7/7 - 0s - loss: 0.1598 - accuracy: 0.9479 - val_loss: 0.3130 - val_accuracy: 0.9350 - 52ms/epoch - 7ms/step\n",
            "Epoch 1460/1500\n",
            "7/7 - 0s - loss: 0.1591 - accuracy: 0.9479 - val_loss: 0.3121 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 1461/1500\n",
            "7/7 - 0s - loss: 0.1588 - accuracy: 0.9479 - val_loss: 0.3110 - val_accuracy: 0.9350 - 68ms/epoch - 10ms/step\n",
            "Epoch 1462/1500\n",
            "7/7 - 0s - loss: 0.1584 - accuracy: 0.9479 - val_loss: 0.3103 - val_accuracy: 0.9350 - 54ms/epoch - 8ms/step\n",
            "Epoch 1463/1500\n",
            "7/7 - 0s - loss: 0.1585 - accuracy: 0.9526 - val_loss: 0.3093 - val_accuracy: 0.9350 - 62ms/epoch - 9ms/step\n",
            "Epoch 1464/1500\n",
            "7/7 - 0s - loss: 0.1583 - accuracy: 0.9526 - val_loss: 0.3105 - val_accuracy: 0.9350 - 59ms/epoch - 8ms/step\n",
            "Epoch 1465/1500\n",
            "7/7 - 0s - loss: 0.1586 - accuracy: 0.9526 - val_loss: 0.3111 - val_accuracy: 0.9350 - 53ms/epoch - 8ms/step\n",
            "Epoch 1466/1500\n",
            "7/7 - 0s - loss: 0.1587 - accuracy: 0.9526 - val_loss: 0.3099 - val_accuracy: 0.9350 - 52ms/epoch - 7ms/step\n",
            "Epoch 1467/1500\n",
            "7/7 - 0s - loss: 0.1585 - accuracy: 0.9526 - val_loss: 0.3102 - val_accuracy: 0.9350 - 56ms/epoch - 8ms/step\n",
            "Epoch 1468/1500\n",
            "7/7 - 0s - loss: 0.1584 - accuracy: 0.9479 - val_loss: 0.3098 - val_accuracy: 0.9350 - 51ms/epoch - 7ms/step\n",
            "Epoch 1469/1500\n",
            "7/7 - 0s - loss: 0.1587 - accuracy: 0.9479 - val_loss: 0.3111 - val_accuracy: 0.9350 - 62ms/epoch - 9ms/step\n",
            "Epoch 1470/1500\n",
            "7/7 - 0s - loss: 0.1585 - accuracy: 0.9526 - val_loss: 0.3111 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 1471/1500\n",
            "7/7 - 0s - loss: 0.1582 - accuracy: 0.9479 - val_loss: 0.3103 - val_accuracy: 0.9350 - 57ms/epoch - 8ms/step\n",
            "Epoch 1472/1500\n",
            "7/7 - 0s - loss: 0.1582 - accuracy: 0.9526 - val_loss: 0.3098 - val_accuracy: 0.9350 - 63ms/epoch - 9ms/step\n",
            "Epoch 1473/1500\n",
            "7/7 - 0s - loss: 0.1583 - accuracy: 0.9526 - val_loss: 0.3094 - val_accuracy: 0.9350 - 64ms/epoch - 9ms/step\n",
            "Epoch 1474/1500\n",
            "7/7 - 0s - loss: 0.1585 - accuracy: 0.9479 - val_loss: 0.3086 - val_accuracy: 0.9300 - 51ms/epoch - 7ms/step\n",
            "Epoch 1475/1500\n",
            "7/7 - 0s - loss: 0.1592 - accuracy: 0.9526 - val_loss: 0.3115 - val_accuracy: 0.9350 - 53ms/epoch - 8ms/step\n",
            "Epoch 1476/1500\n",
            "7/7 - 0s - loss: 0.1585 - accuracy: 0.9526 - val_loss: 0.3112 - val_accuracy: 0.9350 - 61ms/epoch - 9ms/step\n",
            "Epoch 1477/1500\n",
            "7/7 - 0s - loss: 0.1582 - accuracy: 0.9526 - val_loss: 0.3120 - val_accuracy: 0.9300 - 68ms/epoch - 10ms/step\n",
            "Epoch 1478/1500\n",
            "7/7 - 0s - loss: 0.1588 - accuracy: 0.9526 - val_loss: 0.3122 - val_accuracy: 0.9350 - 58ms/epoch - 8ms/step\n",
            "Epoch 1479/1500\n",
            "7/7 - 0s - loss: 0.1582 - accuracy: 0.9526 - val_loss: 0.3100 - val_accuracy: 0.9300 - 50ms/epoch - 7ms/step\n",
            "Epoch 1480/1500\n",
            "7/7 - 0s - loss: 0.1581 - accuracy: 0.9526 - val_loss: 0.3096 - val_accuracy: 0.9300 - 65ms/epoch - 9ms/step\n",
            "Epoch 1481/1500\n",
            "7/7 - 0s - loss: 0.1583 - accuracy: 0.9526 - val_loss: 0.3106 - val_accuracy: 0.9300 - 58ms/epoch - 8ms/step\n",
            "Epoch 1482/1500\n",
            "7/7 - 0s - loss: 0.1582 - accuracy: 0.9526 - val_loss: 0.3120 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 1483/1500\n",
            "7/7 - 0s - loss: 0.1582 - accuracy: 0.9526 - val_loss: 0.3118 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 1484/1500\n",
            "7/7 - 0s - loss: 0.1580 - accuracy: 0.9526 - val_loss: 0.3112 - val_accuracy: 0.9300 - 56ms/epoch - 8ms/step\n",
            "Epoch 1485/1500\n",
            "7/7 - 0s - loss: 0.1582 - accuracy: 0.9526 - val_loss: 0.3109 - val_accuracy: 0.9300 - 65ms/epoch - 9ms/step\n",
            "Epoch 1486/1500\n",
            "7/7 - 0s - loss: 0.1583 - accuracy: 0.9526 - val_loss: 0.3104 - val_accuracy: 0.9300 - 62ms/epoch - 9ms/step\n",
            "Epoch 1487/1500\n",
            "7/7 - 0s - loss: 0.1582 - accuracy: 0.9526 - val_loss: 0.3107 - val_accuracy: 0.9300 - 69ms/epoch - 10ms/step\n",
            "Epoch 1488/1500\n",
            "7/7 - 0s - loss: 0.1583 - accuracy: 0.9479 - val_loss: 0.3117 - val_accuracy: 0.9300 - 44ms/epoch - 6ms/step\n",
            "Epoch 1489/1500\n",
            "7/7 - 0s - loss: 0.1580 - accuracy: 0.9526 - val_loss: 0.3116 - val_accuracy: 0.9350 - 64ms/epoch - 9ms/step\n",
            "Epoch 1490/1500\n",
            "7/7 - 0s - loss: 0.1583 - accuracy: 0.9526 - val_loss: 0.3119 - val_accuracy: 0.9350 - 48ms/epoch - 7ms/step\n",
            "Epoch 1491/1500\n",
            "7/7 - 0s - loss: 0.1583 - accuracy: 0.9526 - val_loss: 0.3103 - val_accuracy: 0.9350 - 60ms/epoch - 9ms/step\n",
            "Epoch 1492/1500\n",
            "7/7 - 0s - loss: 0.1579 - accuracy: 0.9526 - val_loss: 0.3112 - val_accuracy: 0.9350 - 69ms/epoch - 10ms/step\n",
            "Epoch 1493/1500\n",
            "7/7 - 0s - loss: 0.1579 - accuracy: 0.9526 - val_loss: 0.3108 - val_accuracy: 0.9300 - 50ms/epoch - 7ms/step\n",
            "Epoch 1494/1500\n",
            "7/7 - 0s - loss: 0.1583 - accuracy: 0.9479 - val_loss: 0.3109 - val_accuracy: 0.9300 - 64ms/epoch - 9ms/step\n",
            "Epoch 1495/1500\n",
            "7/7 - 0s - loss: 0.1582 - accuracy: 0.9479 - val_loss: 0.3114 - val_accuracy: 0.9350 - 47ms/epoch - 7ms/step\n",
            "Epoch 1496/1500\n",
            "7/7 - 0s - loss: 0.1581 - accuracy: 0.9526 - val_loss: 0.3114 - val_accuracy: 0.9350 - 46ms/epoch - 7ms/step\n",
            "Epoch 1497/1500\n",
            "7/7 - 0s - loss: 0.1582 - accuracy: 0.9526 - val_loss: 0.3115 - val_accuracy: 0.9350 - 50ms/epoch - 7ms/step\n",
            "Epoch 1498/1500\n",
            "7/7 - 0s - loss: 0.1580 - accuracy: 0.9526 - val_loss: 0.3106 - val_accuracy: 0.9300 - 49ms/epoch - 7ms/step\n",
            "Epoch 1499/1500\n",
            "7/7 - 0s - loss: 0.1581 - accuracy: 0.9526 - val_loss: 0.3115 - val_accuracy: 0.9350 - 64ms/epoch - 9ms/step\n",
            "Epoch 1500/1500\n",
            "7/7 - 0s - loss: 0.1581 - accuracy: 0.9479 - val_loss: 0.3102 - val_accuracy: 0.9300 - 51ms/epoch - 7ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluate the keras model\n",
        "_, accuracy = model.evaluate(train_X.T, train_y.T)\n",
        "print('Accuracy: %.2f' % (accuracy*100))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-wsMVucYWHGb",
        "outputId": "7a5ec316-0ae5-4c99-9635-36be00a8b559"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7/7 [==============================] - 0s 2ms/step - loss: 0.1578 - accuracy: 0.9479\n",
            "Accuracy: 94.79\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<b>OverFitting Of The Model Due To Higher Training Accuracy</b>"
      ],
      "metadata": {
        "id": "2OuDQQOv5HHh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['loss'], label='train')\n",
        "plt.plot(history.history['val_loss'], label='test')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "3068DFOKq-4k",
        "outputId": "29880976-6887-4d04-ef3c-7684ae642d5b"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " <b>(c)Implement a function to draw decision boundary based on training dataset between two classes.</b><br>"
      ],
      "metadata": {
        "id": "rfztaY4bw-ti"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from mlxtend.plotting import plot_decision_regions\n",
        "plot_decision_regions(train_X.T, train_y.T.ravel(), clf = model, legend = 2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "qTNHWqGvq-7R",
        "outputId": "795599c9-7c93-46dc-f08a-97b29bc0044a"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/mlxtend/plotting/decision_regions.py:244: MatplotlibDeprecationWarning: Passing unsupported keyword arguments to axis() will raise a TypeError in 3.3.\n",
            "  ax.axis(xmin=xx.min(), xmax=xx.max(), y_min=yy.min(), y_max=yy.max())\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4810e083d0>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lz2WxY38Bl//un0y7qAeD06KY3tXB5vcqfH3owYIHW5dut5JhdvLwzEV8+I97fefyL6FULjvpUTYqaxTz1pUyaWQiUdrG2AFHB3Zvfm8dmReOCPi5mloHr6+vImePwmBQlFfWgMPB0DQbmYOjuesfi9iU7+Szu+o+ylJWZDnOd1UIN2nhizalKbNL1rdv8ANS/tUxoSpxvJ8KRs4qZMRL+WSvLuFPF0fx0859vv2Cbwrf/FLN7mIn958dSfbqEka+lMulfQ1EYg84p/dBqsRoA7tzj/CfKRk8cEkavxl7Pp/O/B96pcaz5vd9WTKlN+f2jmDLzoPU2GwhW/Iu7WqR91h0XtLCF21GQwOnjd031ANSJqO7XdOjwF3uGFyJ4+2jB/fgKnkbfPX23gHVyioblhL3wC1AcQVcf3IkJ6VHkXVhAvO32FlV4GLVEgD3Pi6X5kBhIb90jefV78o8g73VJMRF+2Lx3kRcLs38H6qYNCqJl78tPRFvrxCS8EXb0dDAaWP3begBKd+gaz03lFBPvnoHVK+/6pI6x5l+VTypZhOTkh3k7K5g0bMPBRzPW5lz6YWjWPn9Zt9gr7cM89qHsn3VP4XlNgyRsURH2dC2us8NbJv7EC6Xk7x9uwK2G2U9W9EEkvBFmxAq2XqfSg1u5de3b3B/uf/2R1/+gBGDegXcJGYtWsnm3UfLIf0rfG54/SCzfp3O5Rl2IqNiyPnK7ziDewV07yRGGzgz2crL76/kz3dmBsT4yq9TGb/gW24YFlvn5uT9ZLH3UBFPfm7hvIkPAjB9cmA10ba5D1F9JBdTQjcKPvq7b7tSCld1ORl9B7To70J0XJLwRZsQ3Efu3+8e3Mqvb99QE495t5cW5LFgzwFW3Z0OuG8GF85eQ3osvnN46+2z15SSZKrlzBcPEGfSvgHVP2YvoqzwEO/nFmJSLl/3jrc2PyVvoy/he2Ps3yWCyzPs4LT7zut/I9Na88SiHxg15Zl63xtnTRVGczLpE58mIjLKt91mOYDt85ekJFM0miR80Sb4P9zkr0fBzjoJ339fl0v7qm+KrWUcPBwdcAyXS3OktIiF45PIWlISML1wlLbx5OXJPPaVOwEveS7L113z0pgYrns9l5wpGQxOi2JHgY2Xv9nDrKvN/G2dgQ+ee8g3ZuBfm7/zQCEPvvgepcXFzLjMyZFSuPP0SO77pJLfXeCscyOb8+kmel0ykYioo4ncbI5n79yjC5TbrcWo6HhkYmTRXJLwRZvgP3DalH39n2D1vzF4SzZHDOpFXNFWusXYuLSvgbNm5tIlPobiimrGDTTQLcZB5sCogDGAzIEGUiNrffPgPJgWxdLtVq4fYqR3vIOLe1Bnf293zR+zF7Fn734yB5tINBkoKa9CKRjeDQY/vYdIkwEUOPTnvPCfTZRUVBOT/B0VxUeI79K1zrWaPQuVW2scmPxa90IcD0n4ot1qqKpn/tI1FB8+4OvGSTUnMj3ZXY//6p9+yz1P/Stg0DV4DKCkpJhfDTRx24clvPaDjarKSt64Nooe8QbG9nPyx5Vr64wZXD0kmpe/2UPvRBNvbari011GSms0XZMTsFYbiY104IiIZ+SDc9Fas3XZfIZeMRVjZDQbnhlP/ykz61yjf0tfiOaShC/ajKbOYV9fpY73RvDk5bEB3Tj19fUDlJaU8uDz7/q2p5rTGARkWdxz2p8aXcDgNEiMMdI32cnFPWrrHMe7IMqkM+KYvxGSEuLBYMLa5VT3lAuZ3fjVnDxqqyoo3LUF80kXYYyMbtR7Y4yO5dAb9wdss1uLZcBWNIkkfNFmNGUO+4aqerw3guBuHN/Plh8M6OsvrqgmyeRg/fZ95Fvi64wjHCk7wHpHLTk/GTAYwOWCI1UutKGGNQ4n8ze5V7wqKatgQBJc0a+GaaOiufX9cl69qQfj3lzDnWeaGZIWxbihRj5fswSrjqT7aWOBY5dcms3xYK2A6MA/V3PqABmwFU0iCV+0CU156Arglfe/5MxkK0kxicDR1rt3ArP3bo4P6Mbx1r4Hf4o4Ouga66uR11r79vFfqOTPVyT7zj/ja3fLv6zwEJmjLwBg1ddfUWKtZuU+Jxf1iyZzkIl3figPmHLhhpMimb94BX2mvuw7lrOmCmNsElGpvQGoseSiXS6cDhslFvfNp/RIPspgIjEl1fdzVmsFv7/27Hr7/uVmIIJJwhdtQlMeugJ4f+VGioqq+XhXLglxR7tFHK4NTBoeGbK8c9LV53Hl718gUVXVO+gaPMc94FmoxMSra4pxaQ24q3+iDAVkZ5p54L0V9OnZnX2Hqnnhykju+6SaV9fXYjAobJ5FUOzVlWzdW8v3B2u5oLuLlXPvRyX34pQpz9W5Nu1yEZGagXbUEmHuQv8pM9mSPY2UzAfp2XdQwL7S9y+aQhK+CLumPnR1x5PziI9UvDutT50FRK59KJsFWy11SjZ7FezEWl1LeUkRL92UwmNfhX5Q64aFa3FpzVy/qYy9C5XsqYjktHteAODgqvcZ7VzFSYOTyfxlFwcMUWRd2JVrz0pkd/XRJ3yvfSibRT/t5aPcWJwuF2XlGrPZTFRiKuWWXPbOvQ+7tRhjQldfH31tRTFGczLa6SDKnFjn/do29yGcNVUAAV1BRqOR9Iz+J+A3JDoKmTxNhF1DD12F2tdyaD+pETV1piEGd8nm+jcfZ/2bj/ObseczIDWK34w9n9f+PJmFy9cw5cxIukbaGNNfhXxQ6+Ketb5je6cy9p6ntso9GVttZTlVP3/NjcPdN4rL+5nY+vMeJo6IBQKXQFzyXBYxKb0YnvUKUUMuZuiDC+n7h3eJH/NQwHV1HfsHUjIfJG3c/xIZ34WMO16k23V/DPkJwFlTRY/JL9Bj8gu+rqCo1N44nc4W+G2IjkwSvgg7/9kqQ63t6mUptfLxF2t57CITxVabbx76nK/WsfNAYcAsmMHr2v5zwWdEaRtTTo8mMVoxdoBm/bZfeGtzDSNnFXLGzAJ6P7WH19ZVUFPrwGJ1UGy1MbafE4fTxaQz4ohyVlJbVUHBxhWMG6ToEueZOXO/nYnDTGCv9lsCUflm5yy1FJC7+RsiM07FFOO+STidTl93TYS5iyRt0SqkS0eEXWMfupq/dA0X96zl9O4mxg3RzFtXyoOXpPhKLcsKD4Xsm78ko4rXPvmWe0dGkBpnwO7SlBXX8PsLu2DMGMmDt17JjLc/Y1HO56QnRPPhHT2Z8WUR44ZE0DfZiKXUSnpKAuOGGvlqw2cU7/6BtwoLeWdrNUoZsJbYiIkw8MaWI0RHlvqWQBx6xH3D6h5Ty4H1nzPozudDXpd/yaXdWgxAVf5eQPu6axwOO06Hg/yDe5v5bovOTBK+OOGaWl9f3zFyvlrH0xdAcbWLs3spbvvQPf0xQHVVMUvu7MG0nLp988plJ8Zg590fXby7zb1/eY0Gk52hR3Zy9QXDmb14BS9eFc3vcioZ8VI+BSWVGHDx3Lc1GI2VpCXXkGexE9ljK10Gno7JcQjH0CvJuPAGtv3rIfa+9WCdaRb++YebmfrkHPomGamylWCvKiMitm6fvH+3zd6592E2x3Nw8eNEmLv4truqSilc/H8o3IPGNssBAAyR0QE3C1tqGnD0CV0h/EnCFydcU+rrGzpG5kADZw9O923LsrgHR4GARcGD++bXHazBqQ2UOyN8FT0JUdAjLZUlz2Vx/cOzyBwAI3rFkXVhbMCUyl6WUivDfvNXnBVFlH63mJfGRHPv0kXsX/UB/Xr3CojRP45IVw2/O8fMp7tKeHv2PZji3WWVJZZColN7hbzWR7MXMn1ypq/6Ztvch4jEXXrp/QQA7kHaEb/L9r3eO/c+/vpGznG9v6JzkIQvTqimLmoS/EnAuy14ARKvlNzt2GusAZU2s7P38UtuDAu22jx7RZOQGO1L8P527C9g6897mDk5jrKKSiaOSGXi4rpxzl+6hpNTDSSmxXN6vJN5W6yc1j2K4ioHN10ytE6l0dldq3l6RT5TR8bQMz6CX58Uycc/l6Oie/GnVxd7EnrdAdlQvIO04G7Zl37+KkU5M7Bbi6nytOhBWvXi2CThixOqqYuaBH8S8G7LHH1xyJ/zrlDlX2lz93ldQrbSQ3lk1mImDjPRI95IbmktjprKOtMye5P5S+NSuO71PQw91UyxtZbDFS6eHxPDkyu+Y/6y77j1VJMvjupaB0NSjTijk4jq2o104OrhR3j7+21Yy0p8M2KWHslHq6O1EwZlYPrkTFm/VpwQkvDFCXM8i5r4fxLQWh/z00FD0ypPuvq8BscOduwvYPWmXexLMLLk5zKcLk1RdSXpqUn08puW2X8GzVtOMfH+tgr+dU0MWUur6ZNkIFFZ2VdUy7/WRvPutlq01mw/aCU+2sD2NRbe+tFdeVNbXUn/BCdrl73jewrWv+vG34Znxtf7vnpb/DbLgYAHseRhK3EskvDFCXM8i5oEP/F6rE8H3i6aUN1BM97+rMGxg0dmLea20yJ44KJkSsorGJBs5NH/1hA3cAQPThzNDY+8ytO/+3XADJpX9jfy0fZatMvFTaeYeHVtDaVVTv49LpZnNsTywXMP8N6qHfy/N1bSL+vfvnPZK8soXfgAj59bzhNff8iosbdgTkyuE5OX0i5fArdbi32DtLKkoWgOSfjihGnMoibeJ2dLS4r5cKK7gmXSGXFct8D9lOv7tyT4ttX36QDqdgcda+zAUmpl/bZf2ButWbi1gJgISI42UFqjce75ntTEOEryDwYMACdGp+JyFvCb4ZF8sc/FvaNiufD1cu48PYrTu5u4uEctp9/xD0psRmqcOmAyNPu25UwcpOibbOSaQbB22TtcNmEaAPkH99apv9fK4JsPZ/rkzICWfOC7KUTjtUjCV0pdBbwIGIG5Wutngr4fBcwHzgSKgPFa630tcW7RdjWmvt775Gx6QgSp5hTA/Umga0QNp3YzBnw6GNMfRt/3PCtmPhBy8XH/5H6ssYP5S9fw23OS2XiwgrN7QI8EI49fGsdhq5Pz5lbwweff8e9fp3Lta0cHgMsra8BRS4xJkxqruGKAHSOas3soiqtdjBmgeP6bcgbe/zY75j/mmwzNWVWG4cB33DihK87yEi5JKeb1d2eyYtnHlJUUU/r+3zDGJNDz1qd98UWYu2C1VgCBK2C5nA4o3kdV7k9ExtUt8RSiIc1O+EopIzALGA3kAuuUUku01j/57XYXUKK1HqiUugX4O1B/J6XoFLyJ+vFLopjmqX83Gd0DmHmWGjbkwbJfjrZnyytrqK6xhUze/sn95cUrWfn95nrHDrznvbSnnRKrjcGJRr7e56CoykV3s5GhKYphadUMSUsNGAC+9qFsDhwu5HBxJSo6jtuW2Bg3MILuSVEMykjjlW/LGDPYxqf/foBam4N9MycBEOGs4o5TXUSVV4FSpPfuy41nFbPEeBExvUZiSOhG/tsP1/s++c96+cXCV9j+8YtYLT+TOnRCi/4+RMfXEi38UcBurfVeAKXUO8A4wD/hjwMe93y9GMhWSimtPVMPik7Jm6h/Nbw7WWVHJxyrrzzz6ENNdZO3f3K/Yu633DAs1vfpIDHawJnJVl5+fyV/vjOT+UvXcEkGrNxZyYtXRXPvshpOTjUwdGYpRoMiyqh5xD3jccDNYslzWX5LKl7Alxt3sqrAwqolUFN7mCNVLpTDBa5y+t63yHedZYv+Hx/sy+WDn90tdpP5EACuhI1E9jmb2uI8nNaSgAVOjNGxQG3A+1VRWsyOrz/kiSviycp5i93bvqaitBSXdgHufv/pk92LqMv0yCKUlkj4PYGDfq9zgbPr20dr7VBKlQEpQJ3aM6XUVGAqwOyHxzN13PktEKJoa461gEmo8sxQXTShBoZH93Hy+vpyPt7heaq2sobq6hpS8jby5zsz+XLjTn7cU864gRAZYWRULxMf7FTExkYy5drziSvaysWnHp1n39uV9M5TU/n4i7VMP8/IX1eu5YPn3F1LZdZq7pn7HRff8wyPTbkOR40j4FoTb/obNssBbJ+/BECvoKqcvH27iIzvwmlZrwRsD666WffJu1wzCM4dPog7yi3s7D6O/y5fKtMji0Zrc4O2Wus5wBwA1syUTwAdVH0VPN4FTEKVZ4a6Occ3gvYAACAASURBVIQeGI5m2AD3Q1bB0x0UlVXy2p8nc/PDL/rWtJ3e1cGn+/NJj4X3v9iAyaADjuftSvpj9iIu7llLn3gnZ6XWMDrreT598Q888tZ3jLrtLxhNDf85lRVZcGlXnZWtnA77Md8vb+v+L+PdN6IJZyQy8d0PcTlDz39YVmTxtfb9z620i6SuR59Wlk8CnUtLJPw8IMPvdS/PtlD75CqlTEAi7sFb0UnVV8HjXcAkVHmm/83hkgwYnfU8K7IfaHB+nobKPf3XtI3SNp68PJnHvlEB8+t7bxh/uyyV3yzYw8PXx9An2YSjtgpVXcX4/3uLS7P+6SuxNJvjKbHs8ZVRehmNRlza5ZsZ019V/l7s1mJ++PsEX/cMHO2iqSg+QkyE4tZBVdQUlvr+uC5MsvFmYQyhuLQroOWft28XUam9OfTG/QHb5ZNA59ISCX8dMEgp1Q93Yr8FmBi0zxLgduBb4EbgC+m/79xCVfB4k+ukM9zJ1tuSj4w215lWobiimi4RjoAyzFD9/qE+GUTFmDlSbOOtzQUUlVViMBi4brCBbjEOxvSPCKgE8t4w1uyrZvwpRrrGgtUGGw87uGawiex1O9n62P0YjEf/lLTTidPhCGjxO51OtNPZ4GLkVmtFwPw5zpoqrDUOah0OYpO6sWiPi8V78B3XYa1Aa1fIsk6Xy8m2uQ+FnE9fdF7NTviePvksYDnusszXtNbblFJPAOu11kuAfwNvKqV2A8W4bwpCBKivm4eeJwVU5QSuQ3vsfv/gTwYf7axgRfb/Mi9nNR98uhKr3cD0q7qRajYxlhJmry7i5cUrmXbDJb4bxuR3DrG/yM78H2oxGRXn9nLX7N8yzMSnKReResHRipniv92IJec5TKaIgOvTLmfIBLx37n2+ensv//lzDsy9l+SJMwACnq7dO/c+InDfTII/NRhjk3yrYgnh1SJ9+FrrZcCyoG2P+X1dA9zUEucSHVdjHtSC+sswgx+yCnU87ycD789c2CeSWls1STEG7A4XJmcNU86MZN6na0Ad7fqZe30S2Kt4dZ2NuRtsDEyJ5LdnRbHL4uCTr1ZgP2Osb+rjyISu9a4/29LM5ngOvvOngKmUAQwR0aBlMRURqM0N2orOyzvI2tD8Nw2VYXoXO/H27ft3G42aNouC0iqUw8pjo2PIev+/XDUogrX7nRws0yx4cg9aQ2yExhyp0E7N8wu/ICZCsWCrjdzCUhxOF9ZaGDfUxA0nGSi22tEuF1HVRyj+7gO6XXZHk67XXlnGoY/+7n6Y6jgFT6XslbdvF0U5M477uKJjkoQv2hRv18ysRSvZvDu3TuIP7qZJjDZweYYdnO5KF+Wyo6rLfTX3XpYKG8mnXMho5yqGDkzg+pP3oqLieeFsO93TUjh9xn6ik1N566ZkusSZKK50cNvicmxOxXevP0ja9U+RdPavidi7jtUlh7l6ifu4VcWF9E4yUrht5TETvkEZAgZJHRUWekeUUmDo1lJvn4/RaMRuLfadz79Kxz8GmVK5c5GEL9oM/ykSrntzDemx1Hmq9p8Lv0A7a3l25REAtEsTG6Hpl+xk2vldWLm7khfHxpD12bf87oZLfTcLp9PpXnj8xgTsVeXcOSKSez8p54YBUZxqNjGwi2ZERo1vndoucSbGDVK8sa6MydlfQkwiaReMhwuOdsvYK8vIzx7P324cxH1LK7FXlbFzwRPUVhTjdDgCyi+NRiOJKam+BUoqSot569FbmJXZnXtzKgOmTIbACdPqW9WqovgI0ydnUmIp5IcXf4u3DsIQEU3Xq/8ASNmlCCQJX7QJllIrV/7+BSacbCAl1ni0TPKrwInPopPSOOW3Rwc+t8x/HEd5AT9ZihmVnce4gdAlRjG6jyPgZlFbZeXmM8x0iTNRXVSNUnBGN7h+YQV9043szXfyc1El7+/O9x3bVmvHWlnF6u15VJSX1amft29bzg1DTfTQhYxNr+Ht2fdQY3NhSuhK0dIZGP0qd2rLjmAwKN/ArKPCwq2DqjDXxHHNoPiAKZMBns6agNXzoFZCpML7p2pOHVBnauX8g3uptdlQBvc+Be/+iaKcGUSn9vLNxxNw3KBtIDeGzkISvmgTXl78JeUlRYwdkMr89WXcemoEXSNtjOkf0+CiKadNehyALa/eT9/4Wt/DVH/s6giYP6e2uoq5qytZsKUao8EARAFRKGM5n878Hwbf8iSn/f4VImPdXRz5P2/Akp+Ha+f3VFZW1qmf902IdmUMPfsN5N40O6vLyjhgNTDw7uw6cW54ZjxnPPIucHSq5BvPTqa2uoBLUuy+ydQMRlOTk296Rn9fnT3ge2p329yHKLEUBlT/lFgK6Xnr06Rn9A84htTjdw6hH9MTohVZSq0sXL6GKWdGUmGt4j/byrljRBSJ0YqxA9xP2RaVVTZ4jNoqq69v32J1cPeiw1zaG9+DVpExsQxIiyF52MWo5F6cdOc/GJ71CvGp6cxfuoY+ZjsFGz6jvOAAm3PmYyWGbhffhlIq5PlsPy5n3GADyTHuP6GUuAiuGeRebPxYyn5YxrhBiuQYI8po9Eymlk7SKRfRf8rMkC3w4+GsqSL9lqfoP2Um1URirXGgNRx6/29syZ7GluxpbJv7UIucS7QP0sIXYTd/6RqiqGXxT5qZ39m48eQICqvcJYWRJjuZA6OOuQC6w1bNgq2KBVsLKa6oJslUyw8FLoaVuFe+iqWKx86NImvpRyRGw5ZXHyAmPpHEGCM5X63jr1eYufOj99i95lNiktOoObyTolULMJvjsVor6jwwZarI4x1tY9EGE4ldcn3btS3wxuR9KMrlcvq6hKp++oaF1VYWbLLiqq4gMv7oZGpcMIHSI/kBrXKviuIjxHfp6ntdYikkb98u36IoeW8/iqu2Cqe1hC3Z06itKKbgo79Tak701fVXFx7AlNSNiMgogDoPgYmOTRK+CCvvQO3nd/d2T1Q2ez8f/FzLqoJYDAZv69pWpxY/WHxqOuvffDDooawqXn/sDublrOb2kUm8vtlK5mATieZYvi+MZNGzd7s/AeRtICExkVN7KVIuuoO1a1b5WtlWawUllkLSb3kKo9EY0BWye/Y0+vbuyYSHj06tEJyovQ9FKWX0dblETZzhG5AtypnBafcGTpqmlaHeZQ/9t2/JnkZUam/fsVy1VXS//QXsloPEpPX2JfcjC/7YmF+F6AQk4YuwCi6z/OTuPsz4uqzeRchT46PY9q+63RCp8VEBxwt+KOvSnnZKKm0MTjLy/f4qLh0S6Zuo7aFzIvh0t52Xxvfl1vc+otxqYODdR5NwcGL1clWVYiooDli9yr/SBtyt8AhzF6ina6g5vJ867NZiDMqAw2mntvAXQGOzHEC7HLT8WUV7JglfhFVjn671WvvKvfUeq76Hsq4aHM0Xuyt56aoo/vCpjcv6RYLTzsLlazgtxc6yrQamjIzBduQA5yfWsP0XV8A8NP6J1VsS6XI6SNBWnrt+EPfmHF2jNniw1VtJs23uQwHdJ7VlR9AKFO4bipd7HvzG8ca3d+59/PWNHKZPzqT3wJN838/btwuTp+vGSxkM2IvzcHnm4/HW6ks9fucgCV+EVWOWQWys+ubGn7euhJtPMqIUjEg38PqmGoalg6PWztIdDhJTuvJxrrctbCY2LYlqv3loghMruFeeGnz4QwamxXDNoMqAVn4owXPobHhmPL1ufbrOpGf57/wJg2p8LcW2uQ9R46nEKbEU+m4exuhYUn51LzbLAezWYsA9D48CIqOifF1TttQ03zWJjk8Svugwvty4k9z8Gl78uoiUxDgMBoXDGUlClJOJI2IwGl3ceIri450utpcYKXMaMEWayMhaEHCcvH27qPZMS+CdtRLcreHpkzNxOR2YK3P5y+/drWnv3PTeVr5/rXtwEvZP/MGlkYDvE0RjeStxevYdFFCaeeiN+wOSutkc76vrB9iL++lb7XLUGXeQmvyOSxK+6DCWPJfFgJufoEu0i8NWiIlPoLqyjJsHGUiIMnJSH/fCH9fssrC6uh//++xC/nTHNUBgYnc47LiqK9yVLtYS+mbNA47OVGn5ZiFjSt8hJS6C/AO/4HQ6OT+xhifvHIMpPjWg1t1/6uL8d/7k699vqBUfPA7gZTKoOtvt1mJflY7RaKQqf6871vIjvsnalHZhNscHtOSfzprgHl+IT8Xqt0KXMToWWqgsVLQ9kvBFh2EpteKsKueZW/tz39Iq+t36ON+/eC8Lf3SwcEspRmMZdqemxq6JjrUF1Nj7T0fsqLVRW+xeZqTg3T/7kihod+t/+2oWFdtZkZ9LaVEpJnMyYCY6tQe9b3uWLdnTfEnevxXv330SquzSy9u6Dn4q1luS6d8Cnz4503cO/wewohK6BiyZGHyjsForSL/lqTrTKh96436IlrTQUclvVnQY85euYdxQI/1Toxk3qJoVGz7DYU6n+x3PU30kl14RFRz8eQsDLpvML//+fb3HMUVG4fQMaiqDkdj0oK6X0X/A9vlLPOQZKA1eo7Ypghcv8T4Z663/l/VqRUuShC/aPUuplTuenEdpSTF/PT8SgBuHx/Px4q/RLk81SlkhB478TPfL7wpo2Xu7T/wnKwN390jwgGpL8p7XV7bpEZ3ai/5TnguZ1L3dTt6xBHDfIPIP7g05HiBEMEn4ot2bv3QNlkP7SU+IIDkmcLbLV9eWA1BzaCc9R1xSZ6oE/66R4AVLgidLa0nBE6A1hrfbyX/Vq21zHyLv7Uep8gz2em8gtRVFAeWe3puEDMh2bpLwRbsSvECKt/b+8UuimJZTyRXzNFEJR2e8NDiqATAPOZcd86ZjiEtGKRXQSm6wBl07612D1st/wBegtvwIhxY8SqExgsSU1IC56P377lui9v0Uz6cB/7GB/lNmsiV7mm9MAo4OOPt/cjAajXUeJrNbizGnDkB0TJLwRbsSvHatt/b+V8O7k1VWxqz1dhzGKCpKLMQnp1KjHFQfySUiwoQxOo64YZeQeNb1ddaGDVUZU2IpJCatT50aeu8atOBO2iX79pB+y1MB+xxZ9iLOcvec/S7t8nXbVBMZUNff0hrqogrez7r8+To/n9F3gHwC6MAk4Yt2w3+BlGk568i8cESdJ2tzdldw6dknsXLVt2SOHs473+zC8vk/cAGOqjKctmpslgN1EmCoJOetkvFPzKVH8lEGU50qm9LPXw24MRS5nHStpz4+lOBPCd7pEjY+fRNJXdN920LFHuo6QnVR5R/cW2e6ZJC6+85EEr5oN4Lnyflj9qI6T9aO6Q+vfbqGj27ryrScdXzy7P1orbn7mbcoLS2jz9ljiUquu6Tg01kTyNv/Cy7tCtiutIte/QY12OfemPVjayy5aJeL2opiX9+63Vrsq62v8UzQ5uWdqM2/uybrmnMA94Rs/uMLZUWWRr1/TqeTCHOXOvFL1U/nIQlftAuh5smZnb2PX3JjWLDVBoDLpSkoLqdLrNF3U/DOh1+Sf5CaGnvIZA/uuvS08U+ErEtvifnptctFRGoGRnOyr2/dZjmA7fOXfPPgBLfI6x7DEfLGol3Hvwi66Fwk4Yt2IdQ8OXef1yVgVs0n/53DR598wXWnmgH3TeG6Bd9jNCjuPSeeTYsPs/mlqShDYJeIo8Li6zZpy5K6pje6Lr+i+OiTtl4ul5OIpLZ/neLEkYQv2oVQs2o6nC5KVq/k9szz0Vrz/opvyR4bw2MrK/ndBU5SzSa6RtRwajcjaw5oxg+L5H3jqUSdelXAsQvffSzkOfPefhR7RTElVgLq3v1n0gR394t31kkv/ykPXA4HGk2t5QBOazEH5rpn/FSmKFxWC9MnZza6W6ax4rt0rXNz2JI9jZSxf2jR84j2RRK+aBdCzao54+3PyFnxla/bZnQfJ11iTAzvBmfNzCUhNoryimrGDohGxacwINLBZyt/IOnySUTEJvqOY0tJDXlOV20V3cY/hcGAr7slVH99ekZ/qvymTXg6y71qVd7bj5KHu2VtjE0CgxFTcg/SbnoCgPw3H0Rrd+VO8NiBECdCsxK+UqoL8C7QF9gH3Ky1LgmxnxPY6nl5QGt9bXPOKzqH4Jr74O95K3amfrwWl9Z8OLEHqWYT07s62PxeBZeeM5w4y1a2ljiYfn4Kh/eXMm6QYsnGZaReMOG4YjIajdhK8gO6S7TTCUpx79UjSeqa7puUzDs75pbsaSSPfRBjYpp7IRSt/Y6oqT6Si3bYQnbNtOQ89cboWPLf+VOdGTllLvzOo7kt/EeA/2qtn1FKPeJ5HWo9tWqt9Yhmnkt0MsE198Hf81bsXNyzhPV5du5eVMWcm3twpKiEUxOqeH7hFyg0Tg3vbSrD5XQSmWjyrR3rz2yOJ+/dxwJa2i6XE+2yY4yI9m1Lz+hPXkQkZz7yrm+bt+zy0Bv303/KzIDXXspgwFGSByjwjCE4q0pBgzEuCadnDV/vlMXeMQWrtcL3hKzZHM/Gp29CB820aVAGns6acMzSyuCHtETn09yEPw64xPP1POBLQid8IZokuOb+9szzfa384IqdqwfCv76vpnu84qyZuVTb7ESYu2DOSIPUAXS/6l6UwcCGZ8Zz5r1vhzxfqBkqSyyFGE0ROJ3OJs9X4+3/9y4mfuQ/z6KdDlREFN1vc/f/K2WgV9Z8XPZaHCV59B54kq/LKFTppLeaRyZUE8eruQm/m9b6sOfrfCB0zRtEK6XWAw7gGa31R/UdUCk1FZgKMPvh8Uwdd34zQxTtUXDNvX8rP7hiZ0CPVLrFV/LStck89o1iT0Ukp93zAkd2b6XMrlAGd4vY+yBTQy1k/xkqvWvZAnWmIAjmqLXhcNjJ27cLp8OBs7qctJsex2CK9JVkuuw28t+sux7viVDfnPrSfdO5HTPhK6U+B0LVck33f6G11kopHWI/gD5a6zylVH/gC6XUVq31nlA7aq3nAHMAWDOzvuOJDixUzf3N7x1t5QdX7BRXVDNuoIFuMQ4yB0bx7GorWmsO7fiB7mOODvYmegZnG9tC9q5lCwSsZxtq8RKNeyplQ0I3lNYogwkMRlwup3vuHstBtMuOs7KUw2+4K2Vc9loOv3E/Gg0uJ6WmCPfiK1WlzXj33OTJWRHKMRO+1vqK+r6nlCpQSnXXWh9WSnUHCkPtp7XO8/x3r1LqS+B0IGTCFyJUzX3mQIOvle9fsWMptXLzwy8y/ap4Us0mJiU7eOnrIg79+C1xA0fVmR3zWMqKLL6nWJOuuMe3vfDdxxq1eIkyRYKj1p3EAVxONGA0mXC53KuWp137PwAcXjid9NtnoADtdBARGYW91kbe7N82KWYhGqu5XTpLgNuBZzz//Th4B6VUMlCltbYppVKB84F/NPO8ogMLVXMP0KNgZ72Dt/43h2uHGHj722UMnPISTeXSrjpP23q3ewV3l5QVWXA47aA1h+e5PxG4KktQygAGFVDWaTFGYPOsLau1C5wO96eDJkcqRNM1N+E/A7ynlLoL2A/cDKCUGgnco7WeApwEzFZKuQAD7j78n5p5XtGBhaq5r0+om8Mv+Xaq7DvY+PdbAvY1KMMJmYYgMcW9jm3XG/9CbLp7auGDr/+BiJRe2C0H6+zrPzeOq7zA9z0n4HQ4wOWs08Xk7XuXvnnRHM1K+FrrIuDyENvXA1M8X68BTm3OeYSoz2t/nhxQq6+15vbsr/jku58Y8NvsOvsHTzcQTGlXyBktlV8LP9TSg+4J0Y620w2RsRyedz9OawlK4ev/90/MiSmpIefPcXTrXm/ppPTNi+aQJ21FuxZcq7941XZ6nHsd6vvtIfc3KEODLeSmzFfjzxgdS8F7fyYqwb3QuAKMShHZtRcx1Ertu2gTJOGLdiu4Vv+2seeyZPMRLppWfymvf5dKSzplynP88PcJmKOP/kmVHsnHbjVQqwx1Vrp6NHthvd0zFcVHQg4My7z1orkk4Yt2K7hW//6XPqLPtaEXGGms5vSRB99MGnpIyv8Br1DnkYerxIkgCV+0S8G1+reOiOWf2T/zv1lDmnXc1mpBhxoHAEnq4sSShC/apeByzBW7axh7cgJrl73DZROmndBqFqmUEe2VJHzRLvmXY2qtOVBsI6VLMuba1Vw2YdoJbalLP7poryThi3bJv1b/359uYn/va8gYfFoYIxKi7ZOEL9o1u8PJl3squeiKtpfsG+r6aWidXOkyEieK0roNz08mk6eJY5i9dCOHBvyaXgNPCXcoTdJQlY50GYnm+O1F/eudqUNa+KLdqrU7+Ga/jYt+1b6SPcg4gAiPuvO8CtFOzPlkEwNH3xbuMIRoNyThi3apxmbn+0MOevRrXt29EJ2JJHzRLr26bBNDrpwc7jCEaFck4Yt2p9pWy8ZCTbfeA8IdihDtiiR80e7MyvmBob+aHO4whGh3JOGLdqWy2saWIhNde/ULdyhCtDuS8EW78tJ/NnLSmDvDHYYQ7ZIkfNFuVFTWsL0sitTuvcIdihDtkiR80W68+J+NnHL1XeEOQ4h2SxK+aBfKrNXsssbRJa1HuEMRot2ShC/ahReX/MCwTGndC9EckvBFm1dSXsUvtniSUruFOxQh2jVJ+KLNe37JDwy7ekq4wxCi3ZOEL9o0S6mVg45kErqkhjsUIdq9ZiV8pdRNSqltSimXUmpkA/tdpZTaoZTarZR6pDnnFJ3LjCU/cKr03QvRIprbwv8R+DXwdX07KKWMwCxgDHAyMEEpdXIzzys6gYLicvJJIz6pS7hDEaJDaFbC11pv11rvOMZuo4DdWuu9Wuta4B1gXHPOKzqH55ds5rRMeapWiJbSGn34PYGDfq9zPduEqNdhSxmFEd2JS0gKdyhCdBjHTPhKqc+VUj+G+HdCWulKqalKqfVKqfVzPl59Ik4h2oEZSzYzfOwd4Q5DiA7lmGvaaq2vaOY58oAMv9e9PNvqO98cYA4gi5h3UgcLSiiOyWBwfEK4QxGiQ2mNLp11wCClVD+lVCRwC7CkFc4r2qnn/7OFEVdPDncYQnQ4zS3LvF4plQucCyxVSi33bO+hlFoGoLV2AFnAcmA78J7WelvzwhYd1b7DRVTE9yU61hzuUITocI7ZpdMQrfWHwIchth8Cxvq9XgYsa865ROfwQs5Wht/yf+EOQ4gOSZ60FW3GntwjVCYNIiomNtyhCNEhScIXbcYLy7YxfMxt4Q5DiA5LEr5oE3YcKKQ29SSiomPCHYoQHZYkfNEmzPxkG8N/dWu4wxCiQ5OEL8Lux72HcXQbRkRUVLhDEaJDk4Qvwi57+c8M/9XEcIchRIcnCV+E1abdhzD0GoEpIjLcoQjR4UnCF2H1yoodnDp6fLjDEKJTkIQvwmbDzjxMfUZiMkWEOxQhOgVJ+CJsXlmxg1MvuzHcYQjRaUjCF2Hx7U8HiRl4LkZTs2b3EEI0gSR8ERZzv9jFsEuuD3cYQnQqkvBFq1v1437MQy/EYDSGOxQhOhVJ+KLVvbZyDydfdG24wxCi05GEL1rVF5v2kjTsUgwG+V9PiNYmf3Wi1WiteXPVfk664OpwhyJEpyQJX7SazzbsIXnEaJRS4Q5FiE5JEr5oFVprFn57kKHnXBnuUITotCThi1axdO0uUs8cI617IcJIEr444bTWLFp7iMFnXRbuUITo1CThixPu4293kD7qGmndCxFmkvDFCaW15oMN+Qw448JwhyJEpycJX5xQi1dtp8d510vrXog2QBK+OGFcLhdLNh9hwPDzwh2KEAJJ+OIEeufLn+h94Q3hDkMI4dGshK+UukkptU0p5VJKjWxgv31Kqa1KqU1KqfXNOadoH5xOF59uL6HvsLPDHYoQwqO5k5H/CPwamN2IfS/VWluaeT7RTrz1xY/0vvDmcIchhPDTrBa+1nq71npHSwUjOgaHw8nnO8vpc/IZ4Q5FCOGntfrwNfCZUmqDUmpqQzsqpaYqpdYrpdbP+Xh1K4UnWtK8z7fS/9KJ4Q5DCBHkmF06SqnPgfQQ35qutf64kee5QGudp5RKA1YopX7WWn8daket9RxgDgBrZupGHl+0EXaHky/3VHLRFaeGOxQhRJBjJnyt9RXNPYnWOs/z30Kl1IfAKCBkwhft278/28yAK34T7jCEECGc8C4dpVScUire+zVwJe7BXtHB1NodrN5fS88BJ4c7FCFECM0ty7xeKZULnAssVUot92zvoZRa5tmtG/CNUmozsBZYqrX+tDnnFW3TnE82MWi0tO6FaKuaVZaptf4Q+DDE9kPAWM/Xe4HhzTmPaPtqbHa+P+TgorFDwh2KEKIe8qStaBGzP9nEkCsnhzsMIUQDJOGLZquqqWVDgaZb7wHhDkUI0QBJ+KLZXl76A0OvuiPcYQghjkESvmiWymobW4tMdO3ZN9yhCCGOoblz6ZxYcWnhjkAcw8ffbuS8m+4mNT4y3KEIIY5Bad0xHmZVSk31PKXboXWG6+wM1whynR1Ne7jOjtSl0+AcPR1IZ7jOznCNINfZ0bT56+xICV8IIUQDJOELIUQn0ZESfpvuO2tBneE6O8M1glxnR9Pmr7PDDNoKIYRoWEdq4QshhGiAJHwhhOgk2m3CV0rdpJTappRyKaVGNrDfPqXUVqXUJqXU+taMsSU04TqvUkrtUErtVko90poxNpdSqotSaoVSapfnv8n17Of0/B43KaWWtHacx+tYvxulVJRS6l3P979XSvVt/SibrxHXOVkpdcTvdzglHHE2h1LqNaVUoVIq5Joeyu0lz3uwRSnVthZ21lq3y3/AScAQ4EtgZAP77QNSwx3vibxOwAjsAfoDkcBm4ORwx96Ea/wH8Ijn60eAv9eznzXcsR7HtR3zdwP8DnjV8/UtwLvhjvsEXedkIDvcsTbzOi8CzgB+rOf7Y4FPAAWcA3wf7pj9/7XbFr7WervWeke44zjRGnmdo4DdWuu9Wuta4B1g3ImPrsWMA+Z5vp4HXBfGWFpaY343/te/GLhcKaVaMcaW0N7/H2wU7V6L53wUBgAAAl9JREFUu7iBXcYB87Xbd0CSUqp760R3bO024TeBBj5TSm1QSrX5J+GOU0/goN/rXM+29qKb1vqw5+t83KukhRKtlFqvlPpOKdVebgqN+d349tFaO4AyIKVVoms5jf1/8AZPV8dipVRG64TWqtr032KbnjxNKfU5kB7iW9O11h838jAXaK3zlFJpwAql1M+eu3Sb0ULX2aY1dI3+L7TWWilVX61wH8/vsj/whVJqq9Z6T0vHKk6Y/wALtdY2pdTduD/VXBbmmDqVNp3wtdZXtMAx8jz/LVRKfYj7o2ebSvgtcJ15gH9rqZdnW5vR0DUqpQqUUt211oc9H38L6zmG93e5Vyn1JXA67n7jtqwxvxvvPrlKKROQCBS1Tngt5pjXqbX2v6a5uMduOpo2/bfYobt0lFJxSql479fAlUDI0fV2bh0wSCnVTykViXvgr91UseCO9XbP17cDdT7VKKWSlVJRnq9TgfOBn1otwuPXmN+N//XfCHyhPSOA7cgxrzOoL/taYHsrxtdalgCTPNU65wBlft2V4RfuUeNmjJZfj7t/zAYUAMs923sAyzxf98ddLbAZ2Ia7iyTssbf0dXpejwV24m7xtqvrxN1f/V9gF/A50MWzfSQw1/P1ecBWz+9yK3BXuONuwvXV+d0ATwDXer6OBhYBu4G1QP9wx3yCrvNpz9/hZmAlMDTcMR/HNS4EDgN2z9/lXcA9wD2e7ytgluc92EoDFYTh+CdTKwghRCfRobt0hBBCHCUJXwghOglJ+EII0UlIwhdCiE5CEr4QQnQSkvCFEKKTkIQvhBCdxP8HE+TBVG//9V8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "weights = model.get_weights()\n",
        "np.max(weights[0])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vCkI98hmq--H",
        "outputId": "c57fb189-4534-4a2b-9b7c-252bb3543944"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.0166342"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.max(weights[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bvIPn4hUq_Bi",
        "outputId": "313b0b4a-e761-42a1-ecbb-ac03d2165cfb"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.62905836"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " <b>(d)Implement L2 and dropout regularization and tune the hyper-parameters to get higher test accuracy.</b>"
      ],
      "metadata": {
        "id": "4glTk1z_xO8T"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<b>L2 Regularization</b>"
      ],
      "metadata": {
        "id": "a0VwmNqd0jFx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Regularized Model\n",
        "reg_model = Sequential()\n",
        "reg_model.add(Dense(500, input_dim=2, activation='relu',  kernel_regularizer='l2'))\n",
        "reg_model.add(Dense(1, activation='sigmoid', kernel_regularizer='l2'))\n",
        "reg_model.compile(loss='binary_crossentropy', \n",
        "                  optimizer='adam', \n",
        "                  metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "8Ou9ldBH6cHx"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reg_history = reg_model.fit(train_X.T, train_y.T, \n",
        "                            validation_data=(test_X.T, test_y.T), \n",
        "                            epochs=1500, verbose=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DQb98kfr6cKy",
        "outputId": "e3198fbd-44c7-466d-8e74-489d8f8de9cd"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/1500\n",
            "7/7 [==============================] - 1s 36ms/step - loss: 0.7360 - accuracy: 0.7156 - val_loss: 0.7112 - val_accuracy: 0.8300\n",
            "Epoch 2/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.7022 - accuracy: 0.8152 - val_loss: 0.6764 - val_accuracy: 0.8350\n",
            "Epoch 3/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.6733 - accuracy: 0.8294 - val_loss: 0.6484 - val_accuracy: 0.8350\n",
            "Epoch 4/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.6494 - accuracy: 0.8294 - val_loss: 0.6224 - val_accuracy: 0.8300\n",
            "Epoch 5/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.6270 - accuracy: 0.8389 - val_loss: 0.5996 - val_accuracy: 0.8300\n",
            "Epoch 6/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.6072 - accuracy: 0.8389 - val_loss: 0.5782 - val_accuracy: 0.8300\n",
            "Epoch 7/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.5881 - accuracy: 0.8389 - val_loss: 0.5590 - val_accuracy: 0.8300\n",
            "Epoch 8/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.5701 - accuracy: 0.8483 - val_loss: 0.5413 - val_accuracy: 0.8400\n",
            "Epoch 9/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.5532 - accuracy: 0.8626 - val_loss: 0.5259 - val_accuracy: 0.8500\n",
            "Epoch 10/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.5391 - accuracy: 0.8673 - val_loss: 0.5126 - val_accuracy: 0.8550\n",
            "Epoch 11/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.5265 - accuracy: 0.8673 - val_loss: 0.5020 - val_accuracy: 0.8550\n",
            "Epoch 12/1500\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.5157 - accuracy: 0.8720 - val_loss: 0.4932 - val_accuracy: 0.8750\n",
            "Epoch 13/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.5074 - accuracy: 0.8673 - val_loss: 0.4865 - val_accuracy: 0.8850\n",
            "Epoch 14/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4997 - accuracy: 0.8673 - val_loss: 0.4806 - val_accuracy: 0.8850\n",
            "Epoch 15/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4938 - accuracy: 0.8673 - val_loss: 0.4759 - val_accuracy: 0.9000\n",
            "Epoch 16/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4890 - accuracy: 0.8673 - val_loss: 0.4723 - val_accuracy: 0.9100\n",
            "Epoch 17/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4856 - accuracy: 0.8720 - val_loss: 0.4698 - val_accuracy: 0.9100\n",
            "Epoch 18/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4821 - accuracy: 0.8720 - val_loss: 0.4676 - val_accuracy: 0.9150\n",
            "Epoch 19/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4800 - accuracy: 0.8768 - val_loss: 0.4656 - val_accuracy: 0.9150\n",
            "Epoch 20/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4777 - accuracy: 0.8720 - val_loss: 0.4650 - val_accuracy: 0.9150\n",
            "Epoch 21/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4764 - accuracy: 0.8720 - val_loss: 0.4641 - val_accuracy: 0.9150\n",
            "Epoch 22/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4754 - accuracy: 0.8768 - val_loss: 0.4634 - val_accuracy: 0.9150\n",
            "Epoch 23/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4746 - accuracy: 0.8768 - val_loss: 0.4640 - val_accuracy: 0.9100\n",
            "Epoch 24/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4735 - accuracy: 0.8815 - val_loss: 0.4629 - val_accuracy: 0.9200\n",
            "Epoch 25/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4729 - accuracy: 0.8910 - val_loss: 0.4626 - val_accuracy: 0.9300\n",
            "Epoch 26/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4720 - accuracy: 0.8910 - val_loss: 0.4626 - val_accuracy: 0.9200\n",
            "Epoch 27/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4715 - accuracy: 0.8957 - val_loss: 0.4619 - val_accuracy: 0.9200\n",
            "Epoch 28/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4725 - accuracy: 0.8768 - val_loss: 0.4626 - val_accuracy: 0.9100\n",
            "Epoch 29/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4711 - accuracy: 0.8768 - val_loss: 0.4623 - val_accuracy: 0.9150\n",
            "Epoch 30/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4706 - accuracy: 0.9005 - val_loss: 0.4621 - val_accuracy: 0.9250\n",
            "Epoch 31/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4702 - accuracy: 0.8957 - val_loss: 0.4624 - val_accuracy: 0.9200\n",
            "Epoch 32/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4702 - accuracy: 0.8910 - val_loss: 0.4630 - val_accuracy: 0.9300\n",
            "Epoch 33/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4699 - accuracy: 0.8957 - val_loss: 0.4628 - val_accuracy: 0.9300\n",
            "Epoch 34/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4706 - accuracy: 0.8863 - val_loss: 0.4633 - val_accuracy: 0.9150\n",
            "Epoch 35/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4698 - accuracy: 0.9005 - val_loss: 0.4626 - val_accuracy: 0.9300\n",
            "Epoch 36/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4700 - accuracy: 0.8957 - val_loss: 0.4622 - val_accuracy: 0.9300\n",
            "Epoch 37/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4717 - accuracy: 0.8957 - val_loss: 0.4619 - val_accuracy: 0.9250\n",
            "Epoch 38/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4703 - accuracy: 0.9005 - val_loss: 0.4625 - val_accuracy: 0.9150\n",
            "Epoch 39/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4697 - accuracy: 0.8957 - val_loss: 0.4627 - val_accuracy: 0.9150\n",
            "Epoch 40/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4698 - accuracy: 0.9005 - val_loss: 0.4623 - val_accuracy: 0.9250\n",
            "Epoch 41/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4695 - accuracy: 0.9005 - val_loss: 0.4620 - val_accuracy: 0.9200\n",
            "Epoch 42/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4698 - accuracy: 0.9052 - val_loss: 0.4622 - val_accuracy: 0.9200\n",
            "Epoch 43/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4698 - accuracy: 0.9052 - val_loss: 0.4619 - val_accuracy: 0.9250\n",
            "Epoch 44/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4691 - accuracy: 0.9052 - val_loss: 0.4618 - val_accuracy: 0.9200\n",
            "Epoch 45/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4698 - accuracy: 0.9052 - val_loss: 0.4628 - val_accuracy: 0.9200\n",
            "Epoch 46/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4690 - accuracy: 0.9052 - val_loss: 0.4625 - val_accuracy: 0.9250\n",
            "Epoch 47/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4689 - accuracy: 0.9005 - val_loss: 0.4627 - val_accuracy: 0.9250\n",
            "Epoch 48/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4702 - accuracy: 0.8863 - val_loss: 0.4625 - val_accuracy: 0.9200\n",
            "Epoch 49/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4693 - accuracy: 0.8957 - val_loss: 0.4629 - val_accuracy: 0.9200\n",
            "Epoch 50/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4693 - accuracy: 0.8957 - val_loss: 0.4632 - val_accuracy: 0.9200\n",
            "Epoch 51/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4692 - accuracy: 0.8957 - val_loss: 0.4623 - val_accuracy: 0.9200\n",
            "Epoch 52/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4691 - accuracy: 0.9052 - val_loss: 0.4620 - val_accuracy: 0.9200\n",
            "Epoch 53/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4690 - accuracy: 0.9005 - val_loss: 0.4618 - val_accuracy: 0.9250\n",
            "Epoch 54/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4689 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 55/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4691 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 56/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4692 - accuracy: 0.9052 - val_loss: 0.4616 - val_accuracy: 0.9200\n",
            "Epoch 57/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4695 - accuracy: 0.9005 - val_loss: 0.4619 - val_accuracy: 0.9200\n",
            "Epoch 58/1500\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.4688 - accuracy: 0.9052 - val_loss: 0.4623 - val_accuracy: 0.9250\n",
            "Epoch 59/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4689 - accuracy: 0.9005 - val_loss: 0.4623 - val_accuracy: 0.9200\n",
            "Epoch 60/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4690 - accuracy: 0.8957 - val_loss: 0.4622 - val_accuracy: 0.9200\n",
            "Epoch 61/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4695 - accuracy: 0.8957 - val_loss: 0.4617 - val_accuracy: 0.9250\n",
            "Epoch 62/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4698 - accuracy: 0.8957 - val_loss: 0.4624 - val_accuracy: 0.9200\n",
            "Epoch 63/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4688 - accuracy: 0.8957 - val_loss: 0.4620 - val_accuracy: 0.9250\n",
            "Epoch 64/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4687 - accuracy: 0.9052 - val_loss: 0.4623 - val_accuracy: 0.9200\n",
            "Epoch 65/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4690 - accuracy: 0.8910 - val_loss: 0.4630 - val_accuracy: 0.9150\n",
            "Epoch 66/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4689 - accuracy: 0.8957 - val_loss: 0.4627 - val_accuracy: 0.9200\n",
            "Epoch 67/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4622 - val_accuracy: 0.9200\n",
            "Epoch 68/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4696 - accuracy: 0.9005 - val_loss: 0.4629 - val_accuracy: 0.9250\n",
            "Epoch 69/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4692 - accuracy: 0.9052 - val_loss: 0.4629 - val_accuracy: 0.9300\n",
            "Epoch 70/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4696 - accuracy: 0.8910 - val_loss: 0.4630 - val_accuracy: 0.9150\n",
            "Epoch 71/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4691 - accuracy: 0.9005 - val_loss: 0.4624 - val_accuracy: 0.9200\n",
            "Epoch 72/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4689 - accuracy: 0.9052 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 73/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9150\n",
            "Epoch 74/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4689 - accuracy: 0.9052 - val_loss: 0.4619 - val_accuracy: 0.9250\n",
            "Epoch 75/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4687 - accuracy: 0.9052 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 76/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4702 - accuracy: 0.9052 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 77/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4686 - accuracy: 0.8957 - val_loss: 0.4621 - val_accuracy: 0.9250\n",
            "Epoch 78/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4695 - accuracy: 0.9005 - val_loss: 0.4621 - val_accuracy: 0.9250\n",
            "Epoch 79/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4688 - accuracy: 0.8957 - val_loss: 0.4622 - val_accuracy: 0.9200\n",
            "Epoch 80/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4691 - accuracy: 0.8957 - val_loss: 0.4621 - val_accuracy: 0.9200\n",
            "Epoch 81/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4623 - val_accuracy: 0.9300\n",
            "Epoch 82/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4626 - val_accuracy: 0.9250\n",
            "Epoch 83/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4692 - accuracy: 0.8957 - val_loss: 0.4633 - val_accuracy: 0.9100\n",
            "Epoch 84/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4692 - accuracy: 0.8768 - val_loss: 0.4627 - val_accuracy: 0.9150\n",
            "Epoch 85/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4694 - accuracy: 0.9005 - val_loss: 0.4620 - val_accuracy: 0.9200\n",
            "Epoch 86/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4690 - accuracy: 0.9005 - val_loss: 0.4623 - val_accuracy: 0.9200\n",
            "Epoch 87/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4623 - val_accuracy: 0.9200\n",
            "Epoch 88/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4623 - val_accuracy: 0.9300\n",
            "Epoch 89/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4689 - accuracy: 0.8957 - val_loss: 0.4627 - val_accuracy: 0.9200\n",
            "Epoch 90/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.8957 - val_loss: 0.4625 - val_accuracy: 0.9300\n",
            "Epoch 91/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4620 - val_accuracy: 0.9200\n",
            "Epoch 92/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4700 - accuracy: 0.8768 - val_loss: 0.4629 - val_accuracy: 0.9100\n",
            "Epoch 93/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4686 - accuracy: 0.8910 - val_loss: 0.4617 - val_accuracy: 0.9250\n",
            "Epoch 94/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4723 - accuracy: 0.8910 - val_loss: 0.4617 - val_accuracy: 0.9250\n",
            "Epoch 95/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4694 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 96/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4693 - accuracy: 0.9052 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 97/1500\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.4689 - accuracy: 0.9052 - val_loss: 0.4623 - val_accuracy: 0.9200\n",
            "Epoch 98/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4692 - accuracy: 0.8910 - val_loss: 0.4624 - val_accuracy: 0.9100\n",
            "Epoch 99/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4690 - accuracy: 0.9052 - val_loss: 0.4619 - val_accuracy: 0.9200\n",
            "Epoch 100/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 101/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4701 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 102/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4688 - accuracy: 0.9052 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 103/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 104/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4616 - val_accuracy: 0.9200\n",
            "Epoch 105/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4688 - accuracy: 0.9052 - val_loss: 0.4616 - val_accuracy: 0.9200\n",
            "Epoch 106/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4623 - val_accuracy: 0.9250\n",
            "Epoch 107/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 108/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4619 - val_accuracy: 0.9250\n",
            "Epoch 109/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4692 - accuracy: 0.9005 - val_loss: 0.4620 - val_accuracy: 0.9200\n",
            "Epoch 110/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4696 - accuracy: 0.8957 - val_loss: 0.4621 - val_accuracy: 0.9250\n",
            "Epoch 111/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.8957 - val_loss: 0.4620 - val_accuracy: 0.9200\n",
            "Epoch 112/1500\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.4691 - accuracy: 0.8957 - val_loss: 0.4617 - val_accuracy: 0.9250\n",
            "Epoch 113/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4689 - accuracy: 0.9052 - val_loss: 0.4619 - val_accuracy: 0.9200\n",
            "Epoch 114/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 115/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4690 - accuracy: 0.9052 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 116/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4682 - accuracy: 0.9052 - val_loss: 0.4607 - val_accuracy: 0.9250\n",
            "Epoch 117/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4693 - accuracy: 0.8957 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 118/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4690 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 119/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 120/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4688 - accuracy: 0.9052 - val_loss: 0.4622 - val_accuracy: 0.9200\n",
            "Epoch 121/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4687 - accuracy: 0.9052 - val_loss: 0.4625 - val_accuracy: 0.9300\n",
            "Epoch 122/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4625 - val_accuracy: 0.9300\n",
            "Epoch 123/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4630 - val_accuracy: 0.9250\n",
            "Epoch 124/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4624 - val_accuracy: 0.9300\n",
            "Epoch 125/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4690 - accuracy: 0.8957 - val_loss: 0.4624 - val_accuracy: 0.9300\n",
            "Epoch 126/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4627 - val_accuracy: 0.9200\n",
            "Epoch 127/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4689 - accuracy: 0.9052 - val_loss: 0.4627 - val_accuracy: 0.9200\n",
            "Epoch 128/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4690 - accuracy: 0.8957 - val_loss: 0.4625 - val_accuracy: 0.9250\n",
            "Epoch 129/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4619 - val_accuracy: 0.9250\n",
            "Epoch 130/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4621 - val_accuracy: 0.9250\n",
            "Epoch 131/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4618 - val_accuracy: 0.9250\n",
            "Epoch 132/1500\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 133/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4687 - accuracy: 0.8957 - val_loss: 0.4619 - val_accuracy: 0.9200\n",
            "Epoch 134/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4690 - accuracy: 0.9005 - val_loss: 0.4622 - val_accuracy: 0.9300\n",
            "Epoch 135/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4691 - accuracy: 0.8957 - val_loss: 0.4624 - val_accuracy: 0.9200\n",
            "Epoch 136/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4622 - val_accuracy: 0.9250\n",
            "Epoch 137/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4622 - val_accuracy: 0.9250\n",
            "Epoch 138/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4625 - val_accuracy: 0.9300\n",
            "Epoch 139/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4622 - val_accuracy: 0.9300\n",
            "Epoch 140/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4683 - accuracy: 0.8957 - val_loss: 0.4620 - val_accuracy: 0.9250\n",
            "Epoch 141/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4620 - val_accuracy: 0.9200\n",
            "Epoch 142/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4623 - val_accuracy: 0.9200\n",
            "Epoch 143/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4623 - val_accuracy: 0.9200\n",
            "Epoch 144/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4686 - accuracy: 0.8957 - val_loss: 0.4621 - val_accuracy: 0.9250\n",
            "Epoch 145/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4618 - val_accuracy: 0.9250\n",
            "Epoch 146/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4622 - val_accuracy: 0.9250\n",
            "Epoch 147/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4693 - accuracy: 0.9005 - val_loss: 0.4619 - val_accuracy: 0.9200\n",
            "Epoch 148/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4619 - val_accuracy: 0.9200\n",
            "Epoch 149/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4682 - accuracy: 0.9052 - val_loss: 0.4617 - val_accuracy: 0.9250\n",
            "Epoch 150/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4696 - accuracy: 0.9005 - val_loss: 0.4618 - val_accuracy: 0.9250\n",
            "Epoch 151/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4688 - accuracy: 0.8957 - val_loss: 0.4618 - val_accuracy: 0.9200\n",
            "Epoch 152/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4699 - accuracy: 0.8957 - val_loss: 0.4620 - val_accuracy: 0.9200\n",
            "Epoch 153/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4621 - val_accuracy: 0.9200\n",
            "Epoch 154/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4686 - accuracy: 0.8957 - val_loss: 0.4621 - val_accuracy: 0.9250\n",
            "Epoch 155/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4690 - accuracy: 0.8957 - val_loss: 0.4619 - val_accuracy: 0.9250\n",
            "Epoch 156/1500\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9150\n",
            "Epoch 157/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4688 - accuracy: 0.9052 - val_loss: 0.4618 - val_accuracy: 0.9200\n",
            "Epoch 158/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4688 - accuracy: 0.9052 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 159/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 160/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4691 - accuracy: 0.8957 - val_loss: 0.4619 - val_accuracy: 0.9200\n",
            "Epoch 161/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4683 - accuracy: 0.8957 - val_loss: 0.4617 - val_accuracy: 0.9250\n",
            "Epoch 162/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4682 - accuracy: 0.9005 - val_loss: 0.4624 - val_accuracy: 0.9200\n",
            "Epoch 163/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4688 - accuracy: 0.8957 - val_loss: 0.4628 - val_accuracy: 0.9150\n",
            "Epoch 164/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4692 - accuracy: 0.9005 - val_loss: 0.4622 - val_accuracy: 0.9200\n",
            "Epoch 165/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4682 - accuracy: 0.9052 - val_loss: 0.4620 - val_accuracy: 0.9300\n",
            "Epoch 166/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4687 - accuracy: 0.8957 - val_loss: 0.4619 - val_accuracy: 0.9200\n",
            "Epoch 167/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4693 - accuracy: 0.9052 - val_loss: 0.4618 - val_accuracy: 0.9250\n",
            "Epoch 168/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4702 - accuracy: 0.8863 - val_loss: 0.4622 - val_accuracy: 0.9250\n",
            "Epoch 169/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4703 - accuracy: 0.8957 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 170/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 171/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 172/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4692 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9250\n",
            "Epoch 173/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4689 - accuracy: 0.8957 - val_loss: 0.4618 - val_accuracy: 0.9200\n",
            "Epoch 174/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4621 - val_accuracy: 0.9150\n",
            "Epoch 175/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4692 - accuracy: 0.8863 - val_loss: 0.4618 - val_accuracy: 0.9100\n",
            "Epoch 176/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4687 - accuracy: 0.9052 - val_loss: 0.4610 - val_accuracy: 0.9200\n",
            "Epoch 177/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4704 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 178/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 179/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 180/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 181/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4687 - accuracy: 0.9052 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 182/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 183/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 184/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 185/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4689 - accuracy: 0.9052 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 186/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 187/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 188/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4690 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 189/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4690 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 190/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 191/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9250\n",
            "Epoch 192/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 193/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4693 - accuracy: 0.9005 - val_loss: 0.4609 - val_accuracy: 0.9150\n",
            "Epoch 194/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4608 - val_accuracy: 0.9250\n",
            "Epoch 195/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4690 - accuracy: 0.9052 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 196/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4688 - accuracy: 0.9052 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 197/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4621 - val_accuracy: 0.9200\n",
            "Epoch 198/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4688 - accuracy: 0.8957 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 199/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4618 - val_accuracy: 0.9250\n",
            "Epoch 200/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4620 - val_accuracy: 0.9250\n",
            "Epoch 201/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9200\n",
            "Epoch 202/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4689 - accuracy: 0.8957 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 203/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4689 - accuracy: 0.9005 - val_loss: 0.4619 - val_accuracy: 0.9250\n",
            "Epoch 204/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4624 - val_accuracy: 0.9250\n",
            "Epoch 205/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4687 - accuracy: 0.8957 - val_loss: 0.4623 - val_accuracy: 0.9200\n",
            "Epoch 206/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4685 - accuracy: 0.8957 - val_loss: 0.4620 - val_accuracy: 0.9300\n",
            "Epoch 207/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4686 - accuracy: 0.8957 - val_loss: 0.4619 - val_accuracy: 0.9250\n",
            "Epoch 208/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 209/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4617 - val_accuracy: 0.9250\n",
            "Epoch 210/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4689 - accuracy: 0.8957 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 211/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 212/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 213/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4619 - val_accuracy: 0.9150\n",
            "Epoch 214/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4686 - accuracy: 0.8910 - val_loss: 0.4622 - val_accuracy: 0.9150\n",
            "Epoch 215/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4684 - accuracy: 0.8957 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 216/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 217/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4693 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 218/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 219/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4611 - val_accuracy: 0.9200\n",
            "Epoch 220/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4609 - val_accuracy: 0.9250\n",
            "Epoch 221/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4608 - val_accuracy: 0.9250\n",
            "Epoch 222/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4607 - val_accuracy: 0.9250\n",
            "Epoch 223/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4690 - accuracy: 0.9052 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 224/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4611 - val_accuracy: 0.9200\n",
            "Epoch 225/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4609 - val_accuracy: 0.9250\n",
            "Epoch 226/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4607 - val_accuracy: 0.9250\n",
            "Epoch 227/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4609 - val_accuracy: 0.9250\n",
            "Epoch 228/1500\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.4690 - accuracy: 0.9052 - val_loss: 0.4614 - val_accuracy: 0.9150\n",
            "Epoch 229/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 230/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4697 - accuracy: 0.9005 - val_loss: 0.4622 - val_accuracy: 0.9200\n",
            "Epoch 231/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9250\n",
            "Epoch 232/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4618 - val_accuracy: 0.9200\n",
            "Epoch 233/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4689 - accuracy: 0.9005 - val_loss: 0.4618 - val_accuracy: 0.9250\n",
            "Epoch 234/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 235/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 236/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 237/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4690 - accuracy: 0.8957 - val_loss: 0.4613 - val_accuracy: 0.9300\n",
            "Epoch 238/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 239/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4689 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9200\n",
            "Epoch 240/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4695 - accuracy: 0.8957 - val_loss: 0.4623 - val_accuracy: 0.9150\n",
            "Epoch 241/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4688 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 242/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4682 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 243/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4688 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 244/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4689 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 245/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4685 - accuracy: 0.8957 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 246/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4695 - accuracy: 0.8957 - val_loss: 0.4615 - val_accuracy: 0.9100\n",
            "Epoch 247/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4694 - accuracy: 0.9005 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 248/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 249/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4619 - val_accuracy: 0.9200\n",
            "Epoch 250/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4689 - accuracy: 0.8957 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 251/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4707 - accuracy: 0.8910 - val_loss: 0.4619 - val_accuracy: 0.9250\n",
            "Epoch 252/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4687 - accuracy: 0.8863 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 253/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 254/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4700 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 255/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4691 - accuracy: 0.9052 - val_loss: 0.4616 - val_accuracy: 0.9100\n",
            "Epoch 256/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4689 - accuracy: 0.8910 - val_loss: 0.4618 - val_accuracy: 0.9200\n",
            "Epoch 257/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4687 - accuracy: 0.9052 - val_loss: 0.4619 - val_accuracy: 0.9250\n",
            "Epoch 258/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4618 - val_accuracy: 0.9250\n",
            "Epoch 259/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4617 - val_accuracy: 0.9250\n",
            "Epoch 260/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4621 - val_accuracy: 0.9250\n",
            "Epoch 261/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4689 - accuracy: 0.9052 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 262/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4690 - accuracy: 0.9005 - val_loss: 0.4607 - val_accuracy: 0.9150\n",
            "Epoch 263/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 264/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.8957 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 265/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4696 - accuracy: 0.9005 - val_loss: 0.4620 - val_accuracy: 0.9200\n",
            "Epoch 266/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 267/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9200\n",
            "Epoch 268/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4703 - accuracy: 0.9052 - val_loss: 0.4618 - val_accuracy: 0.9200\n",
            "Epoch 269/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 270/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 271/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4618 - val_accuracy: 0.9250\n",
            "Epoch 272/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4687 - accuracy: 0.9052 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 273/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4698 - accuracy: 0.8957 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 274/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4696 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 275/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 276/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 277/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 278/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4622 - val_accuracy: 0.9250\n",
            "Epoch 279/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 280/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 281/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4687 - accuracy: 0.9052 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 282/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4618 - val_accuracy: 0.9200\n",
            "Epoch 283/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 284/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4696 - accuracy: 0.9052 - val_loss: 0.4610 - val_accuracy: 0.9200\n",
            "Epoch 285/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4690 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 286/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4686 - accuracy: 0.8957 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 287/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4696 - accuracy: 0.8957 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 288/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 289/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4692 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 290/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9200\n",
            "Epoch 291/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4694 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 292/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 293/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4609 - val_accuracy: 0.9200\n",
            "Epoch 294/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4691 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 295/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4608 - val_accuracy: 0.9250\n",
            "Epoch 296/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 297/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4693 - accuracy: 0.9052 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 298/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4618 - val_accuracy: 0.9250\n",
            "Epoch 299/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4692 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 300/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4618 - val_accuracy: 0.9200\n",
            "Epoch 301/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4690 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 302/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4689 - accuracy: 0.9052 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 303/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4693 - accuracy: 0.9005 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 304/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4689 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 305/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4687 - accuracy: 0.8957 - val_loss: 0.4617 - val_accuracy: 0.9250\n",
            "Epoch 306/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4696 - accuracy: 0.9052 - val_loss: 0.4619 - val_accuracy: 0.9150\n",
            "Epoch 307/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4693 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 308/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4688 - accuracy: 0.9052 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 309/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4690 - accuracy: 0.9005 - val_loss: 0.4622 - val_accuracy: 0.9250\n",
            "Epoch 310/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4621 - val_accuracy: 0.9250\n",
            "Epoch 311/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4619 - val_accuracy: 0.9300\n",
            "Epoch 312/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4619 - val_accuracy: 0.9250\n",
            "Epoch 313/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4620 - val_accuracy: 0.9250\n",
            "Epoch 314/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4682 - accuracy: 0.9052 - val_loss: 0.4618 - val_accuracy: 0.9200\n",
            "Epoch 315/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4689 - accuracy: 0.9052 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 316/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 317/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4688 - accuracy: 0.9052 - val_loss: 0.4609 - val_accuracy: 0.9250\n",
            "Epoch 318/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4689 - accuracy: 0.9052 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 319/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 320/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 321/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4689 - accuracy: 0.9052 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 322/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4690 - accuracy: 0.9052 - val_loss: 0.4618 - val_accuracy: 0.9250\n",
            "Epoch 323/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4695 - accuracy: 0.9005 - val_loss: 0.4625 - val_accuracy: 0.9150\n",
            "Epoch 324/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9200\n",
            "Epoch 325/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4690 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 326/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 327/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4682 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 328/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 329/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 330/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4689 - accuracy: 0.9052 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 331/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 332/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 333/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 334/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4609 - val_accuracy: 0.9250\n",
            "Epoch 335/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4703 - accuracy: 0.9052 - val_loss: 0.4606 - val_accuracy: 0.9250\n",
            "Epoch 336/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4608 - val_accuracy: 0.9200\n",
            "Epoch 337/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4690 - accuracy: 0.9005 - val_loss: 0.4618 - val_accuracy: 0.9200\n",
            "Epoch 338/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4687 - accuracy: 0.9052 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 339/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 340/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4695 - accuracy: 0.9052 - val_loss: 0.4610 - val_accuracy: 0.9200\n",
            "Epoch 341/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4688 - accuracy: 0.9052 - val_loss: 0.4616 - val_accuracy: 0.9200\n",
            "Epoch 342/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4682 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 343/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4691 - accuracy: 0.8910 - val_loss: 0.4617 - val_accuracy: 0.9250\n",
            "Epoch 344/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4689 - accuracy: 0.8910 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 345/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 346/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4690 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 347/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4682 - accuracy: 0.9052 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 348/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4694 - accuracy: 0.8957 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 349/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 350/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 351/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 352/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4618 - val_accuracy: 0.9200\n",
            "Epoch 353/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4619 - val_accuracy: 0.9250\n",
            "Epoch 354/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4693 - accuracy: 0.8910 - val_loss: 0.4623 - val_accuracy: 0.9100\n",
            "Epoch 355/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4694 - accuracy: 0.8910 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 356/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 357/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 358/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9200\n",
            "Epoch 359/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 360/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4700 - accuracy: 0.8768 - val_loss: 0.4620 - val_accuracy: 0.9100\n",
            "Epoch 361/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4689 - accuracy: 0.8863 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 362/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9300\n",
            "Epoch 363/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4692 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 364/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 365/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 366/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4693 - accuracy: 0.8957 - val_loss: 0.4617 - val_accuracy: 0.9250\n",
            "Epoch 367/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4686 - accuracy: 0.8957 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 368/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4681 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 369/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4694 - accuracy: 0.9052 - val_loss: 0.4616 - val_accuracy: 0.9100\n",
            "Epoch 370/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4685 - accuracy: 0.8957 - val_loss: 0.4608 - val_accuracy: 0.9250\n",
            "Epoch 371/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4608 - val_accuracy: 0.9150\n",
            "Epoch 372/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4604 - val_accuracy: 0.9200\n",
            "Epoch 373/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4691 - accuracy: 0.9005 - val_loss: 0.4604 - val_accuracy: 0.9250\n",
            "Epoch 374/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4691 - accuracy: 0.9005 - val_loss: 0.4604 - val_accuracy: 0.9150\n",
            "Epoch 375/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4689 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9100\n",
            "Epoch 376/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4693 - accuracy: 0.8815 - val_loss: 0.4613 - val_accuracy: 0.9150\n",
            "Epoch 377/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4690 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 378/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4687 - accuracy: 0.8957 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 379/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4689 - accuracy: 0.8957 - val_loss: 0.4610 - val_accuracy: 0.9200\n",
            "Epoch 380/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4684 - accuracy: 0.8957 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 381/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 382/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9200\n",
            "Epoch 383/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9200\n",
            "Epoch 384/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4688 - accuracy: 0.9052 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 385/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4694 - accuracy: 0.8957 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 386/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4615 - val_accuracy: 0.9300\n",
            "Epoch 387/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4687 - accuracy: 0.8910 - val_loss: 0.4616 - val_accuracy: 0.9200\n",
            "Epoch 388/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4698 - accuracy: 0.8957 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 389/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4613 - val_accuracy: 0.9300\n",
            "Epoch 390/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4619 - val_accuracy: 0.9250\n",
            "Epoch 391/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4699 - accuracy: 0.8863 - val_loss: 0.4622 - val_accuracy: 0.9150\n",
            "Epoch 392/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4682 - accuracy: 0.9052 - val_loss: 0.4617 - val_accuracy: 0.9300\n",
            "Epoch 393/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4608 - val_accuracy: 0.9250\n",
            "Epoch 394/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4689 - accuracy: 0.9005 - val_loss: 0.4600 - val_accuracy: 0.9250\n",
            "Epoch 395/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4602 - val_accuracy: 0.9200\n",
            "Epoch 396/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4687 - accuracy: 0.9052 - val_loss: 0.4607 - val_accuracy: 0.9150\n",
            "Epoch 397/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4690 - accuracy: 0.9052 - val_loss: 0.4605 - val_accuracy: 0.9250\n",
            "Epoch 398/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4609 - val_accuracy: 0.9200\n",
            "Epoch 399/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4686 - accuracy: 0.8957 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 400/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 401/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9300\n",
            "Epoch 402/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4619 - val_accuracy: 0.9200\n",
            "Epoch 403/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4687 - accuracy: 0.8957 - val_loss: 0.4620 - val_accuracy: 0.9250\n",
            "Epoch 404/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4619 - val_accuracy: 0.9250\n",
            "Epoch 405/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4619 - val_accuracy: 0.9200\n",
            "Epoch 406/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4619 - val_accuracy: 0.9250\n",
            "Epoch 407/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4625 - val_accuracy: 0.9200\n",
            "Epoch 408/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4620 - val_accuracy: 0.9250\n",
            "Epoch 409/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4619 - val_accuracy: 0.9250\n",
            "Epoch 410/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 411/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 412/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4687 - accuracy: 0.9052 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 413/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4698 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 414/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 415/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4698 - accuracy: 0.9005 - val_loss: 0.4624 - val_accuracy: 0.9100\n",
            "Epoch 416/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4692 - accuracy: 0.8863 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 417/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4607 - val_accuracy: 0.9250\n",
            "Epoch 418/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4689 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 419/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9200\n",
            "Epoch 420/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 421/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4608 - val_accuracy: 0.9200\n",
            "Epoch 422/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4689 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 423/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4693 - accuracy: 0.9052 - val_loss: 0.4609 - val_accuracy: 0.9200\n",
            "Epoch 424/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4607 - val_accuracy: 0.9150\n",
            "Epoch 425/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4611 - val_accuracy: 0.9200\n",
            "Epoch 426/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 427/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4685 - accuracy: 0.8957 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 428/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 429/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4619 - val_accuracy: 0.9150\n",
            "Epoch 430/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4692 - accuracy: 0.9052 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 431/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 432/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4690 - accuracy: 0.8957 - val_loss: 0.4610 - val_accuracy: 0.9200\n",
            "Epoch 433/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4684 - accuracy: 0.8957 - val_loss: 0.4609 - val_accuracy: 0.9250\n",
            "Epoch 434/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4690 - accuracy: 0.9005 - val_loss: 0.4620 - val_accuracy: 0.9150\n",
            "Epoch 435/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.8815 - val_loss: 0.4625 - val_accuracy: 0.9150\n",
            "Epoch 436/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4690 - accuracy: 0.8815 - val_loss: 0.4620 - val_accuracy: 0.9150\n",
            "Epoch 437/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4686 - accuracy: 0.8910 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 438/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4682 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 439/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4683 - accuracy: 0.8957 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 440/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4689 - accuracy: 0.8957 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 441/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 442/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4622 - val_accuracy: 0.9150\n",
            "Epoch 443/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4695 - accuracy: 0.8768 - val_loss: 0.4629 - val_accuracy: 0.9100\n",
            "Epoch 444/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4696 - accuracy: 0.8957 - val_loss: 0.4629 - val_accuracy: 0.9250\n",
            "Epoch 445/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4623 - val_accuracy: 0.9250\n",
            "Epoch 446/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 447/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 448/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4691 - accuracy: 0.8957 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 449/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4702 - accuracy: 0.9005 - val_loss: 0.4611 - val_accuracy: 0.9150\n",
            "Epoch 450/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4606 - val_accuracy: 0.9250\n",
            "Epoch 451/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4607 - val_accuracy: 0.9250\n",
            "Epoch 452/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4611 - val_accuracy: 0.9200\n",
            "Epoch 453/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4689 - accuracy: 0.9052 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 454/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4690 - accuracy: 0.8957 - val_loss: 0.4618 - val_accuracy: 0.9200\n",
            "Epoch 455/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4699 - accuracy: 0.9005 - val_loss: 0.4619 - val_accuracy: 0.9200\n",
            "Epoch 456/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4700 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 457/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4621 - val_accuracy: 0.9250\n",
            "Epoch 458/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4682 - accuracy: 0.8957 - val_loss: 0.4623 - val_accuracy: 0.9200\n",
            "Epoch 459/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4620 - val_accuracy: 0.9200\n",
            "Epoch 460/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4621 - val_accuracy: 0.9200\n",
            "Epoch 461/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4689 - accuracy: 0.9005 - val_loss: 0.4620 - val_accuracy: 0.9200\n",
            "Epoch 462/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4619 - val_accuracy: 0.9250\n",
            "Epoch 463/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4692 - accuracy: 0.8957 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 464/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 465/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4686 - accuracy: 0.8957 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 466/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 467/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4695 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 468/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 469/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 470/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4689 - accuracy: 0.9052 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 471/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4712 - accuracy: 0.8863 - val_loss: 0.4623 - val_accuracy: 0.9250\n",
            "Epoch 472/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4709 - accuracy: 0.8910 - val_loss: 0.4622 - val_accuracy: 0.9150\n",
            "Epoch 473/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4620 - val_accuracy: 0.9300\n",
            "Epoch 474/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4619 - val_accuracy: 0.9200\n",
            "Epoch 475/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 476/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4687 - accuracy: 0.9052 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 477/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4609 - val_accuracy: 0.9200\n",
            "Epoch 478/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4690 - accuracy: 0.9052 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 479/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4693 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 480/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4606 - val_accuracy: 0.9250\n",
            "Epoch 481/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4687 - accuracy: 0.9052 - val_loss: 0.4608 - val_accuracy: 0.9200\n",
            "Epoch 482/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4607 - val_accuracy: 0.9250\n",
            "Epoch 483/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9200\n",
            "Epoch 484/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 485/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4683 - accuracy: 0.8957 - val_loss: 0.4615 - val_accuracy: 0.9300\n",
            "Epoch 486/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4689 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 487/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 488/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 489/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4682 - accuracy: 0.9052 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 490/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4689 - accuracy: 0.8957 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 491/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4687 - accuracy: 0.8957 - val_loss: 0.4620 - val_accuracy: 0.9250\n",
            "Epoch 492/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4685 - accuracy: 0.8957 - val_loss: 0.4622 - val_accuracy: 0.9250\n",
            "Epoch 493/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4683 - accuracy: 0.8957 - val_loss: 0.4622 - val_accuracy: 0.9250\n",
            "Epoch 494/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4686 - accuracy: 0.8910 - val_loss: 0.4619 - val_accuracy: 0.9150\n",
            "Epoch 495/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4689 - accuracy: 0.8957 - val_loss: 0.4619 - val_accuracy: 0.9200\n",
            "Epoch 496/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4688 - accuracy: 0.9052 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 497/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 498/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4691 - accuracy: 0.9052 - val_loss: 0.4619 - val_accuracy: 0.9200\n",
            "Epoch 499/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4690 - accuracy: 0.9052 - val_loss: 0.4610 - val_accuracy: 0.9200\n",
            "Epoch 500/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9200\n",
            "Epoch 501/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4692 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9250\n",
            "Epoch 502/1500\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4621 - val_accuracy: 0.9300\n",
            "Epoch 503/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4700 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 504/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4690 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9200\n",
            "Epoch 505/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4689 - accuracy: 0.8957 - val_loss: 0.4616 - val_accuracy: 0.9200\n",
            "Epoch 506/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4690 - accuracy: 0.8957 - val_loss: 0.4620 - val_accuracy: 0.9300\n",
            "Epoch 507/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4688 - accuracy: 0.8957 - val_loss: 0.4624 - val_accuracy: 0.9200\n",
            "Epoch 508/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4693 - accuracy: 0.8910 - val_loss: 0.4618 - val_accuracy: 0.9200\n",
            "Epoch 509/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9250\n",
            "Epoch 510/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4703 - accuracy: 0.8957 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 511/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9200\n",
            "Epoch 512/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4678 - accuracy: 0.9052 - val_loss: 0.4617 - val_accuracy: 0.9100\n",
            "Epoch 513/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4688 - accuracy: 0.8815 - val_loss: 0.4619 - val_accuracy: 0.9100\n",
            "Epoch 514/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4691 - accuracy: 0.8768 - val_loss: 0.4620 - val_accuracy: 0.9100\n",
            "Epoch 515/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4696 - accuracy: 0.8957 - val_loss: 0.4618 - val_accuracy: 0.9250\n",
            "Epoch 516/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9300\n",
            "Epoch 517/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4685 - accuracy: 0.8957 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 518/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4609 - val_accuracy: 0.9200\n",
            "Epoch 519/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4694 - accuracy: 0.8957 - val_loss: 0.4607 - val_accuracy: 0.9250\n",
            "Epoch 520/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4682 - accuracy: 0.9005 - val_loss: 0.4609 - val_accuracy: 0.9250\n",
            "Epoch 521/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4616 - val_accuracy: 0.9100\n",
            "Epoch 522/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4688 - accuracy: 0.8910 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 523/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 524/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4619 - val_accuracy: 0.9200\n",
            "Epoch 525/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4688 - accuracy: 0.8910 - val_loss: 0.4621 - val_accuracy: 0.9200\n",
            "Epoch 526/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4679 - accuracy: 0.8957 - val_loss: 0.4618 - val_accuracy: 0.9200\n",
            "Epoch 527/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.8957 - val_loss: 0.4620 - val_accuracy: 0.9150\n",
            "Epoch 528/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4703 - accuracy: 0.8768 - val_loss: 0.4626 - val_accuracy: 0.9100\n",
            "Epoch 529/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4692 - accuracy: 0.8910 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 530/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4687 - accuracy: 0.8957 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 531/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4690 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 532/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4690 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 533/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9300\n",
            "Epoch 534/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4688 - accuracy: 0.8957 - val_loss: 0.4616 - val_accuracy: 0.9200\n",
            "Epoch 535/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4685 - accuracy: 0.8957 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 536/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4689 - accuracy: 0.8957 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 537/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4683 - accuracy: 0.8957 - val_loss: 0.4609 - val_accuracy: 0.9250\n",
            "Epoch 538/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 539/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4707 - accuracy: 0.8957 - val_loss: 0.4617 - val_accuracy: 0.9100\n",
            "Epoch 540/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4696 - accuracy: 0.9052 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 541/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4692 - accuracy: 0.8957 - val_loss: 0.4611 - val_accuracy: 0.9200\n",
            "Epoch 542/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4682 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 543/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4693 - accuracy: 0.8863 - val_loss: 0.4622 - val_accuracy: 0.9100\n",
            "Epoch 544/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4688 - accuracy: 0.8910 - val_loss: 0.4619 - val_accuracy: 0.9200\n",
            "Epoch 545/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4618 - val_accuracy: 0.9250\n",
            "Epoch 546/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4685 - accuracy: 0.8957 - val_loss: 0.4619 - val_accuracy: 0.9200\n",
            "Epoch 547/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 548/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 549/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4682 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9200\n",
            "Epoch 550/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 551/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.8957 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 552/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 553/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 554/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 555/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4682 - accuracy: 0.9052 - val_loss: 0.4608 - val_accuracy: 0.9250\n",
            "Epoch 556/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4689 - accuracy: 0.9052 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 557/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4689 - accuracy: 0.8957 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 558/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4687 - accuracy: 0.9052 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 559/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 560/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4685 - accuracy: 0.8957 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 561/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4609 - val_accuracy: 0.9250\n",
            "Epoch 562/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4682 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 563/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 564/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4704 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 565/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4693 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9150\n",
            "Epoch 566/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4696 - accuracy: 0.8957 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 567/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 568/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 569/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4694 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 570/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4609 - val_accuracy: 0.9200\n",
            "Epoch 571/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 572/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4606 - val_accuracy: 0.9250\n",
            "Epoch 573/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4606 - val_accuracy: 0.9250\n",
            "Epoch 574/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4608 - val_accuracy: 0.9250\n",
            "Epoch 575/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 576/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4694 - accuracy: 0.8957 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 577/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4607 - val_accuracy: 0.9200\n",
            "Epoch 578/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4606 - val_accuracy: 0.9200\n",
            "Epoch 579/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4688 - accuracy: 0.9052 - val_loss: 0.4609 - val_accuracy: 0.9200\n",
            "Epoch 580/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4608 - val_accuracy: 0.9250\n",
            "Epoch 581/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 582/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 583/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4618 - val_accuracy: 0.9250\n",
            "Epoch 584/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4618 - val_accuracy: 0.9200\n",
            "Epoch 585/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4620 - val_accuracy: 0.9150\n",
            "Epoch 586/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4617 - val_accuracy: 0.9250\n",
            "Epoch 587/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4620 - val_accuracy: 0.9250\n",
            "Epoch 588/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4689 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 589/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4683 - accuracy: 0.8957 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 590/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4618 - val_accuracy: 0.9200\n",
            "Epoch 591/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9200\n",
            "Epoch 592/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4687 - accuracy: 0.9052 - val_loss: 0.4609 - val_accuracy: 0.9250\n",
            "Epoch 593/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4606 - val_accuracy: 0.9250\n",
            "Epoch 594/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4605 - val_accuracy: 0.9250\n",
            "Epoch 595/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4603 - val_accuracy: 0.9200\n",
            "Epoch 596/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4689 - accuracy: 0.9052 - val_loss: 0.4602 - val_accuracy: 0.9200\n",
            "Epoch 597/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4682 - accuracy: 0.9052 - val_loss: 0.4606 - val_accuracy: 0.9250\n",
            "Epoch 598/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4688 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9200\n",
            "Epoch 599/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4618 - val_accuracy: 0.9250\n",
            "Epoch 600/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4618 - val_accuracy: 0.9250\n",
            "Epoch 601/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4713 - accuracy: 0.8910 - val_loss: 0.4624 - val_accuracy: 0.9250\n",
            "Epoch 602/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4684 - accuracy: 0.8957 - val_loss: 0.4624 - val_accuracy: 0.9200\n",
            "Epoch 603/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4684 - accuracy: 0.8863 - val_loss: 0.4627 - val_accuracy: 0.9150\n",
            "Epoch 604/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4694 - accuracy: 0.8863 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 605/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4616 - val_accuracy: 0.9200\n",
            "Epoch 606/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 607/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4689 - accuracy: 0.9052 - val_loss: 0.4613 - val_accuracy: 0.9300\n",
            "Epoch 608/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4693 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9150\n",
            "Epoch 609/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 610/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 611/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4691 - accuracy: 0.8957 - val_loss: 0.4617 - val_accuracy: 0.9250\n",
            "Epoch 612/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4682 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 613/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4688 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9200\n",
            "Epoch 614/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4696 - accuracy: 0.8863 - val_loss: 0.4617 - val_accuracy: 0.9100\n",
            "Epoch 615/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4685 - accuracy: 0.8957 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 616/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 617/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.8957 - val_loss: 0.4619 - val_accuracy: 0.9250\n",
            "Epoch 618/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4621 - val_accuracy: 0.9200\n",
            "Epoch 619/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4694 - accuracy: 0.8910 - val_loss: 0.4624 - val_accuracy: 0.9150\n",
            "Epoch 620/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 621/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4690 - accuracy: 0.8957 - val_loss: 0.4617 - val_accuracy: 0.9250\n",
            "Epoch 622/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 623/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4690 - accuracy: 0.9005 - val_loss: 0.4618 - val_accuracy: 0.9200\n",
            "Epoch 624/1500\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.4688 - accuracy: 0.9052 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 625/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 626/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 627/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4694 - accuracy: 0.9005 - val_loss: 0.4611 - val_accuracy: 0.9200\n",
            "Epoch 628/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4689 - accuracy: 0.8957 - val_loss: 0.4609 - val_accuracy: 0.9200\n",
            "Epoch 629/1500\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 0.4680 - accuracy: 0.9052 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 630/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4693 - accuracy: 0.8863 - val_loss: 0.4623 - val_accuracy: 0.9100\n",
            "Epoch 631/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4691 - accuracy: 0.8863 - val_loss: 0.4617 - val_accuracy: 0.9250\n",
            "Epoch 632/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4691 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 633/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4687 - accuracy: 0.9052 - val_loss: 0.4610 - val_accuracy: 0.9200\n",
            "Epoch 634/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 635/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4682 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9250\n",
            "Epoch 636/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4689 - accuracy: 0.8957 - val_loss: 0.4622 - val_accuracy: 0.9100\n",
            "Epoch 637/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4687 - accuracy: 0.8863 - val_loss: 0.4609 - val_accuracy: 0.9200\n",
            "Epoch 638/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4691 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 639/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4605 - val_accuracy: 0.9200\n",
            "Epoch 640/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4609 - val_accuracy: 0.9200\n",
            "Epoch 641/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 642/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 643/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4709 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9150\n",
            "Epoch 644/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 645/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4706 - accuracy: 0.8863 - val_loss: 0.4622 - val_accuracy: 0.9250\n",
            "Epoch 646/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 647/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4682 - accuracy: 0.9100 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 648/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4694 - accuracy: 0.8863 - val_loss: 0.4622 - val_accuracy: 0.9100\n",
            "Epoch 649/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4686 - accuracy: 0.8910 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 650/1500\n",
            "7/7 [==============================] - 0s 15ms/step - loss: 0.4682 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9200\n",
            "Epoch 651/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 652/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4690 - accuracy: 0.8957 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 653/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 654/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4687 - accuracy: 0.8957 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 655/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4620 - val_accuracy: 0.9250\n",
            "Epoch 656/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4693 - accuracy: 0.9005 - val_loss: 0.4619 - val_accuracy: 0.9200\n",
            "Epoch 657/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4618 - val_accuracy: 0.9250\n",
            "Epoch 658/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4690 - accuracy: 0.8957 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 659/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9200\n",
            "Epoch 660/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4681 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 661/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 662/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4689 - accuracy: 0.9052 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 663/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4695 - accuracy: 0.9052 - val_loss: 0.4608 - val_accuracy: 0.9200\n",
            "Epoch 664/1500\n",
            "7/7 [==============================] - 0s 18ms/step - loss: 0.4692 - accuracy: 0.9052 - val_loss: 0.4606 - val_accuracy: 0.9200\n",
            "Epoch 665/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4703 - accuracy: 0.8957 - val_loss: 0.4623 - val_accuracy: 0.9100\n",
            "Epoch 666/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4701 - accuracy: 0.8957 - val_loss: 0.4619 - val_accuracy: 0.9200\n",
            "Epoch 667/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4685 - accuracy: 0.8957 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 668/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4688 - accuracy: 0.8957 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 669/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4687 - accuracy: 0.8957 - val_loss: 0.4617 - val_accuracy: 0.9250\n",
            "Epoch 670/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4692 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 671/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4684 - accuracy: 0.8957 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 672/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4689 - accuracy: 0.9052 - val_loss: 0.4609 - val_accuracy: 0.9250\n",
            "Epoch 673/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4610 - val_accuracy: 0.9200\n",
            "Epoch 674/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4691 - accuracy: 0.9052 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 675/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 676/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4611 - val_accuracy: 0.9200\n",
            "Epoch 677/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4606 - val_accuracy: 0.9250\n",
            "Epoch 678/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4606 - val_accuracy: 0.9250\n",
            "Epoch 679/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4689 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9100\n",
            "Epoch 680/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.8863 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 681/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4618 - val_accuracy: 0.9200\n",
            "Epoch 682/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4686 - accuracy: 0.8957 - val_loss: 0.4623 - val_accuracy: 0.9200\n",
            "Epoch 683/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4694 - accuracy: 0.8957 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 684/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4689 - accuracy: 0.8957 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 685/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 686/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4618 - val_accuracy: 0.9250\n",
            "Epoch 687/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4686 - accuracy: 0.8957 - val_loss: 0.4615 - val_accuracy: 0.9300\n",
            "Epoch 688/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4705 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 689/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4617 - val_accuracy: 0.9250\n",
            "Epoch 690/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4689 - accuracy: 0.9005 - val_loss: 0.4619 - val_accuracy: 0.9250\n",
            "Epoch 691/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4611 - val_accuracy: 0.9200\n",
            "Epoch 692/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4609 - val_accuracy: 0.9250\n",
            "Epoch 693/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4691 - accuracy: 0.9052 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 694/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4688 - accuracy: 0.9052 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 695/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4686 - accuracy: 0.8957 - val_loss: 0.4616 - val_accuracy: 0.9150\n",
            "Epoch 696/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4710 - accuracy: 0.8815 - val_loss: 0.4623 - val_accuracy: 0.9100\n",
            "Epoch 697/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4695 - accuracy: 0.8863 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 698/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 699/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4687 - accuracy: 0.8957 - val_loss: 0.4619 - val_accuracy: 0.9250\n",
            "Epoch 700/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 701/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4609 - val_accuracy: 0.9250\n",
            "Epoch 702/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4687 - accuracy: 0.9052 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 703/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4610 - val_accuracy: 0.9200\n",
            "Epoch 704/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4691 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 705/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4611 - val_accuracy: 0.9200\n",
            "Epoch 706/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 707/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 708/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4694 - accuracy: 0.8957 - val_loss: 0.4616 - val_accuracy: 0.9100\n",
            "Epoch 709/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4687 - accuracy: 0.8957 - val_loss: 0.4608 - val_accuracy: 0.9250\n",
            "Epoch 710/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4690 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 711/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9200\n",
            "Epoch 712/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 713/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4619 - val_accuracy: 0.9250\n",
            "Epoch 714/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4693 - accuracy: 0.9052 - val_loss: 0.4618 - val_accuracy: 0.9200\n",
            "Epoch 715/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 716/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4609 - val_accuracy: 0.9250\n",
            "Epoch 717/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4609 - val_accuracy: 0.9250\n",
            "Epoch 718/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 719/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4687 - accuracy: 0.9052 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 720/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4681 - accuracy: 0.9052 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 721/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4696 - accuracy: 0.8957 - val_loss: 0.4624 - val_accuracy: 0.9250\n",
            "Epoch 722/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4690 - accuracy: 0.8910 - val_loss: 0.4627 - val_accuracy: 0.9300\n",
            "Epoch 723/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4688 - accuracy: 0.8957 - val_loss: 0.4620 - val_accuracy: 0.9200\n",
            "Epoch 724/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4619 - val_accuracy: 0.9250\n",
            "Epoch 725/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4623 - val_accuracy: 0.9250\n",
            "Epoch 726/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 727/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4687 - accuracy: 0.9052 - val_loss: 0.4617 - val_accuracy: 0.9250\n",
            "Epoch 728/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4609 - val_accuracy: 0.9200\n",
            "Epoch 729/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4690 - accuracy: 0.9005 - val_loss: 0.4622 - val_accuracy: 0.9100\n",
            "Epoch 730/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4693 - accuracy: 0.8815 - val_loss: 0.4612 - val_accuracy: 0.9150\n",
            "Epoch 731/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4605 - val_accuracy: 0.9250\n",
            "Epoch 732/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4689 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 733/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4691 - accuracy: 0.8957 - val_loss: 0.4609 - val_accuracy: 0.9250\n",
            "Epoch 734/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4690 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 735/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4689 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9200\n",
            "Epoch 736/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4682 - accuracy: 0.9005 - val_loss: 0.4619 - val_accuracy: 0.9200\n",
            "Epoch 737/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4689 - accuracy: 0.8957 - val_loss: 0.4620 - val_accuracy: 0.9100\n",
            "Epoch 738/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4691 - accuracy: 0.8910 - val_loss: 0.4616 - val_accuracy: 0.9200\n",
            "Epoch 739/1500\n",
            "7/7 [==============================] - 0s 15ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4618 - val_accuracy: 0.9250\n",
            "Epoch 740/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4620 - val_accuracy: 0.9300\n",
            "Epoch 741/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4686 - accuracy: 0.8957 - val_loss: 0.4623 - val_accuracy: 0.9300\n",
            "Epoch 742/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4625 - val_accuracy: 0.9200\n",
            "Epoch 743/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4619 - val_accuracy: 0.9200\n",
            "Epoch 744/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 745/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4619 - val_accuracy: 0.9250\n",
            "Epoch 746/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 747/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 748/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4687 - accuracy: 0.9052 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 749/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4692 - accuracy: 0.8957 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 750/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 751/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4688 - accuracy: 0.9052 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 752/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4618 - val_accuracy: 0.9250\n",
            "Epoch 753/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4687 - accuracy: 0.9052 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 754/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4692 - accuracy: 0.8957 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 755/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 756/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 757/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 758/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4690 - accuracy: 0.9052 - val_loss: 0.4617 - val_accuracy: 0.9250\n",
            "Epoch 759/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 760/1500\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4609 - val_accuracy: 0.9250\n",
            "Epoch 761/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4697 - accuracy: 0.9005 - val_loss: 0.4606 - val_accuracy: 0.9150\n",
            "Epoch 762/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4682 - accuracy: 0.9005 - val_loss: 0.4609 - val_accuracy: 0.9200\n",
            "Epoch 763/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 764/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4689 - accuracy: 0.8910 - val_loss: 0.4619 - val_accuracy: 0.9150\n",
            "Epoch 765/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 766/1500\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.4681 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 767/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4687 - accuracy: 0.8957 - val_loss: 0.4622 - val_accuracy: 0.9200\n",
            "Epoch 768/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4685 - accuracy: 0.8957 - val_loss: 0.4622 - val_accuracy: 0.9200\n",
            "Epoch 769/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4691 - accuracy: 0.8957 - val_loss: 0.4624 - val_accuracy: 0.9250\n",
            "Epoch 770/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 771/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4618 - val_accuracy: 0.9250\n",
            "Epoch 772/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 773/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4681 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 774/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 775/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4692 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9250\n",
            "Epoch 776/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9200\n",
            "Epoch 777/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4688 - accuracy: 0.9052 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 778/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 779/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4688 - accuracy: 0.8957 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 780/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4700 - accuracy: 0.8957 - val_loss: 0.4609 - val_accuracy: 0.9250\n",
            "Epoch 781/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 782/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.8957 - val_loss: 0.4618 - val_accuracy: 0.9200\n",
            "Epoch 783/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4682 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9250\n",
            "Epoch 784/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4689 - accuracy: 0.9052 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 785/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4687 - accuracy: 0.9052 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 786/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 787/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9200\n",
            "Epoch 788/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9200\n",
            "Epoch 789/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 790/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4693 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9100\n",
            "Epoch 791/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 792/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4618 - val_accuracy: 0.9250\n",
            "Epoch 793/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4611 - val_accuracy: 0.9200\n",
            "Epoch 794/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4690 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 795/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 796/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4687 - accuracy: 0.8957 - val_loss: 0.4616 - val_accuracy: 0.9200\n",
            "Epoch 797/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 798/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4620 - val_accuracy: 0.9200\n",
            "Epoch 799/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4618 - val_accuracy: 0.9250\n",
            "Epoch 800/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4616 - val_accuracy: 0.9200\n",
            "Epoch 801/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 802/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4688 - accuracy: 0.9052 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 803/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 804/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4702 - accuracy: 0.9052 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 805/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 806/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4693 - accuracy: 0.9052 - val_loss: 0.4622 - val_accuracy: 0.9150\n",
            "Epoch 807/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4619 - val_accuracy: 0.9250\n",
            "Epoch 808/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 809/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4686 - accuracy: 0.8957 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 810/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4689 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 811/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4695 - accuracy: 0.8957 - val_loss: 0.4618 - val_accuracy: 0.9200\n",
            "Epoch 812/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4694 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 813/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4685 - accuracy: 0.8957 - val_loss: 0.4620 - val_accuracy: 0.9300\n",
            "Epoch 814/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4620 - val_accuracy: 0.9200\n",
            "Epoch 815/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4682 - accuracy: 0.9005 - val_loss: 0.4618 - val_accuracy: 0.9300\n",
            "Epoch 816/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4682 - accuracy: 0.8957 - val_loss: 0.4619 - val_accuracy: 0.9200\n",
            "Epoch 817/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4691 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 818/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4698 - accuracy: 0.8957 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 819/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 820/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 821/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 822/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4619 - val_accuracy: 0.9300\n",
            "Epoch 823/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9300\n",
            "Epoch 824/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4687 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9200\n",
            "Epoch 825/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 826/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 827/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 828/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 829/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 830/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4688 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9200\n",
            "Epoch 831/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 832/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4689 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 833/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4690 - accuracy: 0.8957 - val_loss: 0.4618 - val_accuracy: 0.9200\n",
            "Epoch 834/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4682 - accuracy: 0.8957 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 835/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4619 - val_accuracy: 0.9100\n",
            "Epoch 836/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4688 - accuracy: 0.8957 - val_loss: 0.4616 - val_accuracy: 0.9200\n",
            "Epoch 837/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 838/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 839/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4678 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 840/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4693 - accuracy: 0.8957 - val_loss: 0.4619 - val_accuracy: 0.9250\n",
            "Epoch 841/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 842/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4679 - accuracy: 0.8957 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 843/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4685 - accuracy: 0.8910 - val_loss: 0.4619 - val_accuracy: 0.9100\n",
            "Epoch 844/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4690 - accuracy: 0.8815 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 845/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 846/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4686 - accuracy: 0.8957 - val_loss: 0.4617 - val_accuracy: 0.9250\n",
            "Epoch 847/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4618 - val_accuracy: 0.9200\n",
            "Epoch 848/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 849/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4691 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 850/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 851/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4692 - accuracy: 0.8910 - val_loss: 0.4617 - val_accuracy: 0.9100\n",
            "Epoch 852/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 853/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4696 - accuracy: 0.8910 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 854/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4609 - val_accuracy: 0.9200\n",
            "Epoch 855/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4703 - accuracy: 0.9052 - val_loss: 0.4616 - val_accuracy: 0.9100\n",
            "Epoch 856/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4687 - accuracy: 0.8957 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 857/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 858/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4689 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 859/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4690 - accuracy: 0.9052 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 860/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4618 - val_accuracy: 0.9200\n",
            "Epoch 861/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4619 - val_accuracy: 0.9250\n",
            "Epoch 862/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4688 - accuracy: 0.8957 - val_loss: 0.4619 - val_accuracy: 0.9200\n",
            "Epoch 863/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 864/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 865/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 866/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 867/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4609 - val_accuracy: 0.9200\n",
            "Epoch 868/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4691 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9200\n",
            "Epoch 869/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4607 - val_accuracy: 0.9250\n",
            "Epoch 870/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4609 - val_accuracy: 0.9200\n",
            "Epoch 871/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4609 - val_accuracy: 0.9250\n",
            "Epoch 872/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 873/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4608 - val_accuracy: 0.9250\n",
            "Epoch 874/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4698 - accuracy: 0.8910 - val_loss: 0.4621 - val_accuracy: 0.9100\n",
            "Epoch 875/1500\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.4689 - accuracy: 0.8910 - val_loss: 0.4615 - val_accuracy: 0.9300\n",
            "Epoch 876/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4698 - accuracy: 0.8957 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 877/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4683 - accuracy: 0.8957 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 878/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9150\n",
            "Epoch 879/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4688 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 880/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4688 - accuracy: 0.9052 - val_loss: 0.4607 - val_accuracy: 0.9250\n",
            "Epoch 881/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4697 - accuracy: 0.9005 - val_loss: 0.4608 - val_accuracy: 0.9250\n",
            "Epoch 882/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4691 - accuracy: 0.9052 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 883/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 884/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4690 - accuracy: 0.9052 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 885/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4695 - accuracy: 0.9052 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 886/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4691 - accuracy: 0.8957 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 887/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 888/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4691 - accuracy: 0.8957 - val_loss: 0.4616 - val_accuracy: 0.9200\n",
            "Epoch 889/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 890/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4608 - val_accuracy: 0.9200\n",
            "Epoch 891/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4693 - accuracy: 0.9052 - val_loss: 0.4609 - val_accuracy: 0.9200\n",
            "Epoch 892/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9150\n",
            "Epoch 893/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4695 - accuracy: 0.8768 - val_loss: 0.4620 - val_accuracy: 0.9150\n",
            "Epoch 894/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4681 - accuracy: 0.8957 - val_loss: 0.4613 - val_accuracy: 0.9300\n",
            "Epoch 895/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 896/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4688 - accuracy: 0.8957 - val_loss: 0.4607 - val_accuracy: 0.9250\n",
            "Epoch 897/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4693 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9200\n",
            "Epoch 898/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 899/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 900/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4608 - val_accuracy: 0.9250\n",
            "Epoch 901/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4611 - val_accuracy: 0.9200\n",
            "Epoch 902/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4684 - accuracy: 0.9100 - val_loss: 0.4607 - val_accuracy: 0.9250\n",
            "Epoch 903/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 904/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4609 - val_accuracy: 0.9250\n",
            "Epoch 905/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 906/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 907/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4618 - val_accuracy: 0.9300\n",
            "Epoch 908/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4618 - val_accuracy: 0.9250\n",
            "Epoch 909/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4691 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 910/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 911/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 912/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4608 - val_accuracy: 0.9250\n",
            "Epoch 913/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4688 - accuracy: 0.9052 - val_loss: 0.4607 - val_accuracy: 0.9200\n",
            "Epoch 914/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4687 - accuracy: 0.9052 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 915/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4609 - val_accuracy: 0.9250\n",
            "Epoch 916/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 917/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4609 - val_accuracy: 0.9200\n",
            "Epoch 918/1500\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 919/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4694 - accuracy: 0.9052 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 920/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4682 - accuracy: 0.9052 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 921/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 922/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 923/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4688 - accuracy: 0.9052 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 924/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4695 - accuracy: 0.8957 - val_loss: 0.4616 - val_accuracy: 0.9200\n",
            "Epoch 925/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.8957 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 926/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 927/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 928/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.8957 - val_loss: 0.4618 - val_accuracy: 0.9150\n",
            "Epoch 929/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4694 - accuracy: 0.8910 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 930/1500\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.4678 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 931/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4690 - accuracy: 0.8957 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 932/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 933/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4712 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9200\n",
            "Epoch 934/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4687 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9200\n",
            "Epoch 935/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 936/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4690 - accuracy: 0.9005 - val_loss: 0.4619 - val_accuracy: 0.9200\n",
            "Epoch 937/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4688 - accuracy: 0.8957 - val_loss: 0.4618 - val_accuracy: 0.9250\n",
            "Epoch 938/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4689 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 939/1500\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 940/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 941/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4690 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 942/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 943/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 944/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4688 - accuracy: 0.8957 - val_loss: 0.4620 - val_accuracy: 0.9100\n",
            "Epoch 945/1500\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.4687 - accuracy: 0.8957 - val_loss: 0.4618 - val_accuracy: 0.9200\n",
            "Epoch 946/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9200\n",
            "Epoch 947/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4701 - accuracy: 0.8957 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 948/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4693 - accuracy: 0.8910 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 949/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 950/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 951/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9200\n",
            "Epoch 952/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 953/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4692 - accuracy: 0.8957 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 954/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4683 - accuracy: 0.8957 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 955/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4690 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 956/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4694 - accuracy: 0.8957 - val_loss: 0.4619 - val_accuracy: 0.9100\n",
            "Epoch 957/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4687 - accuracy: 0.8910 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 958/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4693 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9200\n",
            "Epoch 959/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4690 - accuracy: 0.9052 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 960/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 961/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4689 - accuracy: 0.9005 - val_loss: 0.4619 - val_accuracy: 0.9200\n",
            "Epoch 962/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4609 - val_accuracy: 0.9250\n",
            "Epoch 963/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4609 - val_accuracy: 0.9200\n",
            "Epoch 964/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4607 - val_accuracy: 0.9200\n",
            "Epoch 965/1500\n",
            "7/7 [==============================] - 0s 15ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4607 - val_accuracy: 0.9250\n",
            "Epoch 966/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 967/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4687 - accuracy: 0.9052 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 968/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4689 - accuracy: 0.9052 - val_loss: 0.4616 - val_accuracy: 0.9200\n",
            "Epoch 969/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9200\n",
            "Epoch 970/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4607 - val_accuracy: 0.9250\n",
            "Epoch 971/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4687 - accuracy: 0.9052 - val_loss: 0.4607 - val_accuracy: 0.9250\n",
            "Epoch 972/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4699 - accuracy: 0.9052 - val_loss: 0.4610 - val_accuracy: 0.9200\n",
            "Epoch 973/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4680 - accuracy: 0.9052 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 974/1500\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 975/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4688 - accuracy: 0.9052 - val_loss: 0.4608 - val_accuracy: 0.9250\n",
            "Epoch 976/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 977/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4688 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9200\n",
            "Epoch 978/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4689 - accuracy: 0.9052 - val_loss: 0.4618 - val_accuracy: 0.9300\n",
            "Epoch 979/1500\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.4687 - accuracy: 0.8957 - val_loss: 0.4616 - val_accuracy: 0.9200\n",
            "Epoch 980/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4609 - val_accuracy: 0.9200\n",
            "Epoch 981/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4607 - val_accuracy: 0.9200\n",
            "Epoch 982/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4607 - val_accuracy: 0.9250\n",
            "Epoch 983/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 984/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4609 - val_accuracy: 0.9250\n",
            "Epoch 985/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4609 - val_accuracy: 0.9250\n",
            "Epoch 986/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4609 - val_accuracy: 0.9200\n",
            "Epoch 987/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4693 - accuracy: 0.8957 - val_loss: 0.4620 - val_accuracy: 0.9200\n",
            "Epoch 988/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4681 - accuracy: 0.8957 - val_loss: 0.4620 - val_accuracy: 0.9250\n",
            "Epoch 989/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4688 - accuracy: 0.8957 - val_loss: 0.4619 - val_accuracy: 0.9100\n",
            "Epoch 990/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4687 - accuracy: 0.8910 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 991/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 992/1500\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.4678 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 993/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4690 - accuracy: 0.8910 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 994/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4689 - accuracy: 0.8910 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 995/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4680 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 996/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.8957 - val_loss: 0.4623 - val_accuracy: 0.9150\n",
            "Epoch 997/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4689 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 998/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4692 - accuracy: 0.9052 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 999/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 1000/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 1001/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4686 - accuracy: 0.8957 - val_loss: 0.4621 - val_accuracy: 0.9250\n",
            "Epoch 1002/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 1003/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4691 - accuracy: 0.9005 - val_loss: 0.4608 - val_accuracy: 0.9200\n",
            "Epoch 1004/1500\n",
            "7/7 [==============================] - 0s 15ms/step - loss: 0.4688 - accuracy: 0.9052 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 1005/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4697 - accuracy: 0.9052 - val_loss: 0.4606 - val_accuracy: 0.9200\n",
            "Epoch 1006/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4682 - accuracy: 0.9052 - val_loss: 0.4605 - val_accuracy: 0.9250\n",
            "Epoch 1007/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4688 - accuracy: 0.9052 - val_loss: 0.4606 - val_accuracy: 0.9200\n",
            "Epoch 1008/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4690 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9200\n",
            "Epoch 1009/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4687 - accuracy: 0.9052 - val_loss: 0.4609 - val_accuracy: 0.9200\n",
            "Epoch 1010/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 1011/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.8957 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 1012/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4688 - accuracy: 0.8957 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 1013/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 1014/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 1015/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4687 - accuracy: 0.8957 - val_loss: 0.4621 - val_accuracy: 0.9250\n",
            "Epoch 1016/1500\n",
            "7/7 [==============================] - 0s 15ms/step - loss: 0.4686 - accuracy: 0.8957 - val_loss: 0.4621 - val_accuracy: 0.9200\n",
            "Epoch 1017/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.8957 - val_loss: 0.4618 - val_accuracy: 0.9250\n",
            "Epoch 1018/1500\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.4689 - accuracy: 0.8957 - val_loss: 0.4617 - val_accuracy: 0.9300\n",
            "Epoch 1019/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 1020/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 1021/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4605 - val_accuracy: 0.9200\n",
            "Epoch 1022/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4606 - val_accuracy: 0.9200\n",
            "Epoch 1023/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4604 - val_accuracy: 0.9150\n",
            "Epoch 1024/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4689 - accuracy: 0.9052 - val_loss: 0.4600 - val_accuracy: 0.9200\n",
            "Epoch 1025/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4693 - accuracy: 0.9052 - val_loss: 0.4605 - val_accuracy: 0.9250\n",
            "Epoch 1026/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9200\n",
            "Epoch 1027/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9200\n",
            "Epoch 1028/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.8957 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 1029/1500\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.4686 - accuracy: 0.8957 - val_loss: 0.4619 - val_accuracy: 0.9200\n",
            "Epoch 1030/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4682 - accuracy: 0.8957 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 1031/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9150\n",
            "Epoch 1032/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4689 - accuracy: 0.8957 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 1033/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4680 - accuracy: 0.9052 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 1034/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4620 - val_accuracy: 0.9200\n",
            "Epoch 1035/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.8957 - val_loss: 0.4623 - val_accuracy: 0.9200\n",
            "Epoch 1036/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4690 - accuracy: 0.8957 - val_loss: 0.4623 - val_accuracy: 0.9250\n",
            "Epoch 1037/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4693 - accuracy: 0.8910 - val_loss: 0.4619 - val_accuracy: 0.9300\n",
            "Epoch 1038/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4693 - accuracy: 0.8957 - val_loss: 0.4621 - val_accuracy: 0.9150\n",
            "Epoch 1039/1500\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 1040/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9300\n",
            "Epoch 1041/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4689 - accuracy: 0.8957 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 1042/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4690 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 1043/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4690 - accuracy: 0.8957 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 1044/1500\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.4713 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 1045/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4677 - accuracy: 0.9052 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 1046/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4692 - accuracy: 0.8863 - val_loss: 0.4623 - val_accuracy: 0.9100\n",
            "Epoch 1047/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4688 - accuracy: 0.8815 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 1048/1500\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.4682 - accuracy: 0.9052 - val_loss: 0.4610 - val_accuracy: 0.9200\n",
            "Epoch 1049/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4688 - accuracy: 0.8957 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 1050/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4687 - accuracy: 0.8910 - val_loss: 0.4607 - val_accuracy: 0.9200\n",
            "Epoch 1051/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4690 - accuracy: 0.9052 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 1052/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4696 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 1053/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 1054/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4682 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 1055/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4687 - accuracy: 0.9052 - val_loss: 0.4610 - val_accuracy: 0.9200\n",
            "Epoch 1056/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4608 - val_accuracy: 0.9250\n",
            "Epoch 1057/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4606 - val_accuracy: 0.9250\n",
            "Epoch 1058/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4687 - accuracy: 0.9052 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 1059/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 1060/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4699 - accuracy: 0.8957 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 1061/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4696 - accuracy: 0.9052 - val_loss: 0.4610 - val_accuracy: 0.9200\n",
            "Epoch 1062/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4692 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 1063/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4703 - accuracy: 0.8957 - val_loss: 0.4625 - val_accuracy: 0.9250\n",
            "Epoch 1064/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4687 - accuracy: 0.8910 - val_loss: 0.4618 - val_accuracy: 0.9250\n",
            "Epoch 1065/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4682 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 1066/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 1067/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4689 - accuracy: 0.8957 - val_loss: 0.4613 - val_accuracy: 0.9100\n",
            "Epoch 1068/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4693 - accuracy: 0.8957 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 1069/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4690 - accuracy: 0.9052 - val_loss: 0.4610 - val_accuracy: 0.9200\n",
            "Epoch 1070/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4681 - accuracy: 0.9052 - val_loss: 0.4607 - val_accuracy: 0.9200\n",
            "Epoch 1071/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 1072/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4690 - accuracy: 0.8957 - val_loss: 0.4609 - val_accuracy: 0.9250\n",
            "Epoch 1073/1500\n",
            "7/7 [==============================] - 0s 9ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 1074/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 1075/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4682 - accuracy: 0.9052 - val_loss: 0.4619 - val_accuracy: 0.9200\n",
            "Epoch 1076/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4621 - val_accuracy: 0.9200\n",
            "Epoch 1077/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4689 - accuracy: 0.8815 - val_loss: 0.4624 - val_accuracy: 0.9150\n",
            "Epoch 1078/1500\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.4682 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9300\n",
            "Epoch 1079/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4683 - accuracy: 0.8957 - val_loss: 0.4611 - val_accuracy: 0.9200\n",
            "Epoch 1080/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4606 - val_accuracy: 0.9200\n",
            "Epoch 1081/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4605 - val_accuracy: 0.9200\n",
            "Epoch 1082/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4601 - val_accuracy: 0.9250\n",
            "Epoch 1083/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4606 - val_accuracy: 0.9250\n",
            "Epoch 1084/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4609 - val_accuracy: 0.9200\n",
            "Epoch 1085/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4690 - accuracy: 0.9052 - val_loss: 0.4609 - val_accuracy: 0.9200\n",
            "Epoch 1086/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4694 - accuracy: 0.9005 - val_loss: 0.4607 - val_accuracy: 0.9250\n",
            "Epoch 1087/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4606 - val_accuracy: 0.9150\n",
            "Epoch 1088/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 1089/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4694 - accuracy: 0.9005 - val_loss: 0.4607 - val_accuracy: 0.9250\n",
            "Epoch 1090/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 1091/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4610 - val_accuracy: 0.9200\n",
            "Epoch 1092/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4682 - accuracy: 0.9005 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 1093/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4619 - val_accuracy: 0.9250\n",
            "Epoch 1094/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4696 - accuracy: 0.8863 - val_loss: 0.4624 - val_accuracy: 0.9150\n",
            "Epoch 1095/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4697 - accuracy: 0.8957 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 1096/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4683 - accuracy: 0.8957 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 1097/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4687 - accuracy: 0.9052 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 1098/1500\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4609 - val_accuracy: 0.9250\n",
            "Epoch 1099/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4688 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9200\n",
            "Epoch 1100/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4608 - val_accuracy: 0.9200\n",
            "Epoch 1101/1500\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.4681 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 1102/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 1103/1500\n",
            "7/7 [==============================] - 0s 15ms/step - loss: 0.4692 - accuracy: 0.8910 - val_loss: 0.4618 - val_accuracy: 0.9150\n",
            "Epoch 1104/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4681 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 1105/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4696 - accuracy: 0.8957 - val_loss: 0.4617 - val_accuracy: 0.9250\n",
            "Epoch 1106/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4689 - accuracy: 0.9005 - val_loss: 0.4618 - val_accuracy: 0.9250\n",
            "Epoch 1107/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4694 - accuracy: 0.8957 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 1108/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4608 - val_accuracy: 0.9200\n",
            "Epoch 1109/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4609 - val_accuracy: 0.9250\n",
            "Epoch 1110/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 1111/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 1112/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4682 - accuracy: 0.9052 - val_loss: 0.4618 - val_accuracy: 0.9250\n",
            "Epoch 1113/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4688 - accuracy: 0.8957 - val_loss: 0.4619 - val_accuracy: 0.9200\n",
            "Epoch 1114/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 1115/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4680 - accuracy: 0.9052 - val_loss: 0.4620 - val_accuracy: 0.9200\n",
            "Epoch 1116/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4689 - accuracy: 0.8957 - val_loss: 0.4626 - val_accuracy: 0.9150\n",
            "Epoch 1117/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4690 - accuracy: 0.8863 - val_loss: 0.4620 - val_accuracy: 0.9150\n",
            "Epoch 1118/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4695 - accuracy: 0.9005 - val_loss: 0.4618 - val_accuracy: 0.9200\n",
            "Epoch 1119/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4691 - accuracy: 0.9005 - val_loss: 0.4609 - val_accuracy: 0.9250\n",
            "Epoch 1120/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4693 - accuracy: 0.8957 - val_loss: 0.4609 - val_accuracy: 0.9150\n",
            "Epoch 1121/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4690 - accuracy: 0.8910 - val_loss: 0.4611 - val_accuracy: 0.9150\n",
            "Epoch 1122/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4605 - val_accuracy: 0.9150\n",
            "Epoch 1123/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4695 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 1124/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4689 - accuracy: 0.8957 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 1125/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.8957 - val_loss: 0.4610 - val_accuracy: 0.9200\n",
            "Epoch 1126/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4607 - val_accuracy: 0.9250\n",
            "Epoch 1127/1500\n",
            "7/7 [==============================] - 0s 15ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 1128/1500\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 1129/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4682 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 1130/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 1131/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 1132/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4687 - accuracy: 0.9052 - val_loss: 0.4616 - val_accuracy: 0.9200\n",
            "Epoch 1133/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 1134/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 1135/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4691 - accuracy: 0.9052 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 1136/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4611 - val_accuracy: 0.9200\n",
            "Epoch 1137/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4687 - accuracy: 0.9052 - val_loss: 0.4610 - val_accuracy: 0.9200\n",
            "Epoch 1138/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 1139/1500\n",
            "7/7 [==============================] - 0s 15ms/step - loss: 0.4687 - accuracy: 0.9052 - val_loss: 0.4618 - val_accuracy: 0.9200\n",
            "Epoch 1140/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4688 - accuracy: 0.8957 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 1141/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4692 - accuracy: 0.8910 - val_loss: 0.4617 - val_accuracy: 0.9250\n",
            "Epoch 1142/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4694 - accuracy: 0.8957 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 1143/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4682 - accuracy: 0.9052 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 1144/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 1145/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 1146/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 1147/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4681 - accuracy: 0.9052 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 1148/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4688 - accuracy: 0.9052 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 1149/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4690 - accuracy: 0.9052 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 1150/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4681 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 1151/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4694 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9100\n",
            "Epoch 1152/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 1153/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4687 - accuracy: 0.9052 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 1154/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 1155/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 1156/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9250\n",
            "Epoch 1157/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4693 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 1158/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9300\n",
            "Epoch 1159/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4692 - accuracy: 0.8957 - val_loss: 0.4616 - val_accuracy: 0.9150\n",
            "Epoch 1160/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4689 - accuracy: 0.9005 - val_loss: 0.4619 - val_accuracy: 0.9200\n",
            "Epoch 1161/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 1162/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4687 - accuracy: 0.9052 - val_loss: 0.4608 - val_accuracy: 0.9200\n",
            "Epoch 1163/1500\n",
            "7/7 [==============================] - 0s 16ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 1164/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 1165/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 1166/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4607 - val_accuracy: 0.9200\n",
            "Epoch 1167/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4693 - accuracy: 0.8957 - val_loss: 0.4609 - val_accuracy: 0.9250\n",
            "Epoch 1168/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4689 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 1169/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4620 - val_accuracy: 0.9250\n",
            "Epoch 1170/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 1171/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 1172/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4683 - accuracy: 0.8957 - val_loss: 0.4610 - val_accuracy: 0.9200\n",
            "Epoch 1173/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.8957 - val_loss: 0.4608 - val_accuracy: 0.9250\n",
            "Epoch 1174/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4609 - val_accuracy: 0.9250\n",
            "Epoch 1175/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4692 - accuracy: 0.9052 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 1176/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 1177/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4689 - accuracy: 0.8957 - val_loss: 0.4616 - val_accuracy: 0.9200\n",
            "Epoch 1178/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9250\n",
            "Epoch 1179/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4618 - val_accuracy: 0.9300\n",
            "Epoch 1180/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9200\n",
            "Epoch 1181/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4622 - val_accuracy: 0.9150\n",
            "Epoch 1182/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4687 - accuracy: 0.9052 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 1183/1500\n",
            "7/7 [==============================] - 0s 30ms/step - loss: 0.4686 - accuracy: 0.8957 - val_loss: 0.4616 - val_accuracy: 0.9200\n",
            "Epoch 1184/1500\n",
            "7/7 [==============================] - 0s 46ms/step - loss: 0.4682 - accuracy: 0.9005 - val_loss: 0.4611 - val_accuracy: 0.9200\n",
            "Epoch 1185/1500\n",
            "7/7 [==============================] - 0s 31ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 1186/1500\n",
            "7/7 [==============================] - 0s 28ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4609 - val_accuracy: 0.9250\n",
            "Epoch 1187/1500\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.4692 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9200\n",
            "Epoch 1188/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4687 - accuracy: 0.9100 - val_loss: 0.4609 - val_accuracy: 0.9250\n",
            "Epoch 1189/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4693 - accuracy: 0.9005 - val_loss: 0.4608 - val_accuracy: 0.9250\n",
            "Epoch 1190/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 1191/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 1192/1500\n",
            "7/7 [==============================] - 0s 51ms/step - loss: 0.4687 - accuracy: 0.8957 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 1193/1500\n",
            "7/7 [==============================] - 0s 46ms/step - loss: 0.4687 - accuracy: 0.8957 - val_loss: 0.4609 - val_accuracy: 0.9200\n",
            "Epoch 1194/1500\n",
            "7/7 [==============================] - 0s 19ms/step - loss: 0.4688 - accuracy: 0.8957 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 1195/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4690 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 1196/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4621 - val_accuracy: 0.9150\n",
            "Epoch 1197/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4708 - accuracy: 0.8720 - val_loss: 0.4624 - val_accuracy: 0.9100\n",
            "Epoch 1198/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4696 - accuracy: 0.8863 - val_loss: 0.4620 - val_accuracy: 0.9250\n",
            "Epoch 1199/1500\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4619 - val_accuracy: 0.9300\n",
            "Epoch 1200/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4687 - accuracy: 0.8957 - val_loss: 0.4617 - val_accuracy: 0.9250\n",
            "Epoch 1201/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 1202/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 1203/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4618 - val_accuracy: 0.9300\n",
            "Epoch 1204/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4618 - val_accuracy: 0.9300\n",
            "Epoch 1205/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4686 - accuracy: 0.8957 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 1206/1500\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.4682 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 1207/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 1208/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 1209/1500\n",
            "7/7 [==============================] - 0s 19ms/step - loss: 0.4682 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 1210/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4696 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 1211/1500\n",
            "7/7 [==============================] - 0s 15ms/step - loss: 0.4691 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 1212/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4686 - accuracy: 0.8957 - val_loss: 0.4609 - val_accuracy: 0.9250\n",
            "Epoch 1213/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4689 - accuracy: 0.9052 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 1214/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4687 - accuracy: 0.8957 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 1215/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4608 - val_accuracy: 0.9250\n",
            "Epoch 1216/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4609 - val_accuracy: 0.9200\n",
            "Epoch 1217/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4681 - accuracy: 0.9052 - val_loss: 0.4608 - val_accuracy: 0.9250\n",
            "Epoch 1218/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4608 - val_accuracy: 0.9200\n",
            "Epoch 1219/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4609 - val_accuracy: 0.9250\n",
            "Epoch 1220/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 1221/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 1222/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4687 - accuracy: 0.9052 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 1223/1500\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.4690 - accuracy: 0.9052 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 1224/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4687 - accuracy: 0.8957 - val_loss: 0.4609 - val_accuracy: 0.9250\n",
            "Epoch 1225/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4691 - accuracy: 0.9052 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 1226/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4618 - val_accuracy: 0.9200\n",
            "Epoch 1227/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4692 - accuracy: 0.9005 - val_loss: 0.4620 - val_accuracy: 0.9200\n",
            "Epoch 1228/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 1229/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4691 - accuracy: 0.8910 - val_loss: 0.4627 - val_accuracy: 0.9150\n",
            "Epoch 1230/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4684 - accuracy: 0.8957 - val_loss: 0.4626 - val_accuracy: 0.9300\n",
            "Epoch 1231/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4681 - accuracy: 0.8957 - val_loss: 0.4618 - val_accuracy: 0.9250\n",
            "Epoch 1232/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4690 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9200\n",
            "Epoch 1233/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4701 - accuracy: 0.8957 - val_loss: 0.4607 - val_accuracy: 0.9250\n",
            "Epoch 1234/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4703 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9150\n",
            "Epoch 1235/1500\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.4690 - accuracy: 0.8910 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 1236/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 1237/1500\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9250\n",
            "Epoch 1238/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4622 - val_accuracy: 0.9250\n",
            "Epoch 1239/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4619 - val_accuracy: 0.9250\n",
            "Epoch 1240/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 1241/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4689 - accuracy: 0.8957 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 1242/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4681 - accuracy: 0.8957 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 1243/1500\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.4694 - accuracy: 0.9005 - val_loss: 0.4624 - val_accuracy: 0.9150\n",
            "Epoch 1244/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4698 - accuracy: 0.9052 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 1245/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 1246/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4694 - accuracy: 0.8957 - val_loss: 0.4615 - val_accuracy: 0.9100\n",
            "Epoch 1247/1500\n",
            "7/7 [==============================] - 0s 15ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4609 - val_accuracy: 0.9250\n",
            "Epoch 1248/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4610 - val_accuracy: 0.9200\n",
            "Epoch 1249/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 1250/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4609 - val_accuracy: 0.9250\n",
            "Epoch 1251/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4680 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 1252/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4616 - val_accuracy: 0.9100\n",
            "Epoch 1253/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4690 - accuracy: 0.9005 - val_loss: 0.4618 - val_accuracy: 0.9200\n",
            "Epoch 1254/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4621 - val_accuracy: 0.9250\n",
            "Epoch 1255/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 1256/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.8957 - val_loss: 0.4619 - val_accuracy: 0.9250\n",
            "Epoch 1257/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4619 - val_accuracy: 0.9200\n",
            "Epoch 1258/1500\n",
            "7/7 [==============================] - 0s 18ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9200\n",
            "Epoch 1259/1500\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 1260/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 1261/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4611 - val_accuracy: 0.9200\n",
            "Epoch 1262/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4696 - accuracy: 0.8910 - val_loss: 0.4617 - val_accuracy: 0.9250\n",
            "Epoch 1263/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4683 - accuracy: 0.8957 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 1264/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4681 - accuracy: 0.9052 - val_loss: 0.4612 - val_accuracy: 0.9100\n",
            "Epoch 1265/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4692 - accuracy: 0.8768 - val_loss: 0.4617 - val_accuracy: 0.9100\n",
            "Epoch 1266/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4688 - accuracy: 0.8957 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 1267/1500\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.4688 - accuracy: 0.8957 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 1268/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4692 - accuracy: 0.8957 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 1269/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4688 - accuracy: 0.8957 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 1270/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4690 - accuracy: 0.8957 - val_loss: 0.4617 - val_accuracy: 0.9250\n",
            "Epoch 1271/1500\n",
            "7/7 [==============================] - 0s 15ms/step - loss: 0.4680 - accuracy: 0.8957 - val_loss: 0.4619 - val_accuracy: 0.9200\n",
            "Epoch 1272/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4690 - accuracy: 0.8910 - val_loss: 0.4628 - val_accuracy: 0.9100\n",
            "Epoch 1273/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4701 - accuracy: 0.8957 - val_loss: 0.4621 - val_accuracy: 0.9250\n",
            "Epoch 1274/1500\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.4684 - accuracy: 0.8957 - val_loss: 0.4619 - val_accuracy: 0.9250\n",
            "Epoch 1275/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4687 - accuracy: 0.8957 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 1276/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4681 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 1277/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4619 - val_accuracy: 0.9150\n",
            "Epoch 1278/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4607 - val_accuracy: 0.9250\n",
            "Epoch 1279/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4609 - val_accuracy: 0.9200\n",
            "Epoch 1280/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4704 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 1281/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4694 - accuracy: 0.8957 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 1282/1500\n",
            "7/7 [==============================] - 0s 20ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9200\n",
            "Epoch 1283/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 1284/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 1285/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4695 - accuracy: 0.8910 - val_loss: 0.4614 - val_accuracy: 0.9100\n",
            "Epoch 1286/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4683 - accuracy: 0.8957 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 1287/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 1288/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4692 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9250\n",
            "Epoch 1289/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 1290/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 1291/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4689 - accuracy: 0.8957 - val_loss: 0.4617 - val_accuracy: 0.9100\n",
            "Epoch 1292/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 1293/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4689 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9200\n",
            "Epoch 1294/1500\n",
            "7/7 [==============================] - 0s 16ms/step - loss: 0.4691 - accuracy: 0.9005 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 1295/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4721 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9100\n",
            "Epoch 1296/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4680 - accuracy: 0.9005 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 1297/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.8957 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 1298/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 1299/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4703 - accuracy: 0.9005 - val_loss: 0.4611 - val_accuracy: 0.9200\n",
            "Epoch 1300/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4688 - accuracy: 0.9052 - val_loss: 0.4608 - val_accuracy: 0.9200\n",
            "Epoch 1301/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4690 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9200\n",
            "Epoch 1302/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 1303/1500\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4603 - val_accuracy: 0.9250\n",
            "Epoch 1304/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4688 - accuracy: 0.9100 - val_loss: 0.4602 - val_accuracy: 0.9200\n",
            "Epoch 1305/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4604 - val_accuracy: 0.9250\n",
            "Epoch 1306/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4605 - val_accuracy: 0.9200\n",
            "Epoch 1307/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4609 - val_accuracy: 0.9250\n",
            "Epoch 1308/1500\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 1309/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 1310/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4623 - val_accuracy: 0.9200\n",
            "Epoch 1311/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 1312/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 1313/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4609 - val_accuracy: 0.9200\n",
            "Epoch 1314/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4688 - accuracy: 0.8957 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 1315/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 1316/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 1317/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4694 - accuracy: 0.9005 - val_loss: 0.4618 - val_accuracy: 0.9200\n",
            "Epoch 1318/1500\n",
            "7/7 [==============================] - 0s 15ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 1319/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 1320/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4619 - val_accuracy: 0.9250\n",
            "Epoch 1321/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4619 - val_accuracy: 0.9300\n",
            "Epoch 1322/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4684 - accuracy: 0.8957 - val_loss: 0.4621 - val_accuracy: 0.9250\n",
            "Epoch 1323/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4704 - accuracy: 0.8957 - val_loss: 0.4624 - val_accuracy: 0.9200\n",
            "Epoch 1324/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4682 - accuracy: 0.8957 - val_loss: 0.4622 - val_accuracy: 0.9150\n",
            "Epoch 1325/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4685 - accuracy: 0.8957 - val_loss: 0.4617 - val_accuracy: 0.9150\n",
            "Epoch 1326/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4691 - accuracy: 0.8863 - val_loss: 0.4613 - val_accuracy: 0.9150\n",
            "Epoch 1327/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4605 - val_accuracy: 0.9250\n",
            "Epoch 1328/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4606 - val_accuracy: 0.9200\n",
            "Epoch 1329/1500\n",
            "7/7 [==============================] - 0s 19ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 1330/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4687 - accuracy: 0.9052 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 1331/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4689 - accuracy: 0.9005 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 1332/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4608 - val_accuracy: 0.9250\n",
            "Epoch 1333/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4692 - accuracy: 0.9052 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 1334/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4692 - accuracy: 0.9052 - val_loss: 0.4605 - val_accuracy: 0.9250\n",
            "Epoch 1335/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 1336/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 1337/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9250\n",
            "Epoch 1338/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4692 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9200\n",
            "Epoch 1339/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4684 - accuracy: 0.8957 - val_loss: 0.4621 - val_accuracy: 0.9250\n",
            "Epoch 1340/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4691 - accuracy: 0.8910 - val_loss: 0.4622 - val_accuracy: 0.9150\n",
            "Epoch 1341/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4690 - accuracy: 0.8815 - val_loss: 0.4620 - val_accuracy: 0.9150\n",
            "Epoch 1342/1500\n",
            "7/7 [==============================] - 0s 19ms/step - loss: 0.4694 - accuracy: 0.9005 - val_loss: 0.4619 - val_accuracy: 0.9250\n",
            "Epoch 1343/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4692 - accuracy: 0.9005 - val_loss: 0.4618 - val_accuracy: 0.9200\n",
            "Epoch 1344/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.8957 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 1345/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4688 - accuracy: 0.8957 - val_loss: 0.4609 - val_accuracy: 0.9200\n",
            "Epoch 1346/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4688 - accuracy: 0.9052 - val_loss: 0.4606 - val_accuracy: 0.9200\n",
            "Epoch 1347/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4607 - val_accuracy: 0.9250\n",
            "Epoch 1348/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 1349/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4688 - accuracy: 0.9052 - val_loss: 0.4605 - val_accuracy: 0.9250\n",
            "Epoch 1350/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4691 - accuracy: 0.9052 - val_loss: 0.4601 - val_accuracy: 0.9200\n",
            "Epoch 1351/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4605 - val_accuracy: 0.9250\n",
            "Epoch 1352/1500\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.4687 - accuracy: 0.9052 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 1353/1500\n",
            "7/7 [==============================] - 0s 17ms/step - loss: 0.4689 - accuracy: 0.9052 - val_loss: 0.4619 - val_accuracy: 0.9200\n",
            "Epoch 1354/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 1355/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4684 - accuracy: 0.8957 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 1356/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4619 - val_accuracy: 0.9250\n",
            "Epoch 1357/1500\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9250\n",
            "Epoch 1358/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 1359/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9250\n",
            "Epoch 1360/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4695 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 1361/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9200\n",
            "Epoch 1362/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 1363/1500\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 1364/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4705 - accuracy: 0.8768 - val_loss: 0.4617 - val_accuracy: 0.9100\n",
            "Epoch 1365/1500\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9200\n",
            "Epoch 1366/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4609 - val_accuracy: 0.9200\n",
            "Epoch 1367/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4609 - val_accuracy: 0.9250\n",
            "Epoch 1368/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4683 - accuracy: 0.8957 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 1369/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4698 - accuracy: 0.8910 - val_loss: 0.4619 - val_accuracy: 0.9250\n",
            "Epoch 1370/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 1371/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4620 - val_accuracy: 0.9200\n",
            "Epoch 1372/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 1373/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4690 - accuracy: 0.9052 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 1374/1500\n",
            "7/7 [==============================] - 0s 18ms/step - loss: 0.4683 - accuracy: 0.8957 - val_loss: 0.4617 - val_accuracy: 0.9150\n",
            "Epoch 1375/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 1376/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9250\n",
            "Epoch 1377/1500\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.4688 - accuracy: 0.8957 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 1378/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 1379/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4609 - val_accuracy: 0.9200\n",
            "Epoch 1380/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4692 - accuracy: 0.9052 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 1381/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4690 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 1382/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4693 - accuracy: 0.9005 - val_loss: 0.4608 - val_accuracy: 0.9200\n",
            "Epoch 1383/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4684 - accuracy: 0.9100 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 1384/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 1385/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 1386/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4687 - accuracy: 0.9052 - val_loss: 0.4616 - val_accuracy: 0.9200\n",
            "Epoch 1387/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4614 - val_accuracy: 0.9300\n",
            "Epoch 1388/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4684 - accuracy: 0.8957 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 1389/1500\n",
            "7/7 [==============================] - 0s 19ms/step - loss: 0.4700 - accuracy: 0.8957 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 1390/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4616 - val_accuracy: 0.9200\n",
            "Epoch 1391/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4690 - accuracy: 0.9052 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 1392/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 1393/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 1394/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4687 - accuracy: 0.9052 - val_loss: 0.4619 - val_accuracy: 0.9200\n",
            "Epoch 1395/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4688 - accuracy: 0.8910 - val_loss: 0.4623 - val_accuracy: 0.9150\n",
            "Epoch 1396/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4689 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 1397/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4692 - accuracy: 0.8957 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 1398/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4709 - accuracy: 0.8910 - val_loss: 0.4618 - val_accuracy: 0.9100\n",
            "Epoch 1399/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4687 - accuracy: 0.8815 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 1400/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4681 - accuracy: 0.9052 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 1401/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9200\n",
            "Epoch 1402/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4694 - accuracy: 0.8957 - val_loss: 0.4618 - val_accuracy: 0.9200\n",
            "Epoch 1403/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4682 - accuracy: 0.9005 - val_loss: 0.4620 - val_accuracy: 0.9150\n",
            "Epoch 1404/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4705 - accuracy: 0.8768 - val_loss: 0.4627 - val_accuracy: 0.9100\n",
            "Epoch 1405/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4693 - accuracy: 0.8768 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 1406/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 1407/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 1408/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4686 - accuracy: 0.8957 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 1409/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4690 - accuracy: 0.9052 - val_loss: 0.4604 - val_accuracy: 0.9200\n",
            "Epoch 1410/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4697 - accuracy: 0.9005 - val_loss: 0.4604 - val_accuracy: 0.9200\n",
            "Epoch 1411/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4605 - val_accuracy: 0.9250\n",
            "Epoch 1412/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4605 - val_accuracy: 0.9200\n",
            "Epoch 1413/1500\n",
            "7/7 [==============================] - 0s 15ms/step - loss: 0.4682 - accuracy: 0.9005 - val_loss: 0.4606 - val_accuracy: 0.9250\n",
            "Epoch 1414/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4682 - accuracy: 0.9052 - val_loss: 0.4607 - val_accuracy: 0.9200\n",
            "Epoch 1415/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4612 - val_accuracy: 0.9150\n",
            "Epoch 1416/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4607 - val_accuracy: 0.9250\n",
            "Epoch 1417/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4687 - accuracy: 0.9052 - val_loss: 0.4608 - val_accuracy: 0.9250\n",
            "Epoch 1418/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4609 - val_accuracy: 0.9200\n",
            "Epoch 1419/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4692 - accuracy: 0.8957 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 1420/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 1421/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4619 - val_accuracy: 0.9200\n",
            "Epoch 1422/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4691 - accuracy: 0.8957 - val_loss: 0.4619 - val_accuracy: 0.9200\n",
            "Epoch 1423/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4687 - accuracy: 0.9052 - val_loss: 0.4623 - val_accuracy: 0.9200\n",
            "Epoch 1424/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4690 - accuracy: 0.8910 - val_loss: 0.4622 - val_accuracy: 0.9150\n",
            "Epoch 1425/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4691 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 1426/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.8957 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 1427/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4696 - accuracy: 0.8910 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 1428/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9300\n",
            "Epoch 1429/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9200\n",
            "Epoch 1430/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 1431/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4691 - accuracy: 0.8910 - val_loss: 0.4619 - val_accuracy: 0.9150\n",
            "Epoch 1432/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4694 - accuracy: 0.9052 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 1433/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.8957 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 1434/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4702 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9200\n",
            "Epoch 1435/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4610 - val_accuracy: 0.9250\n",
            "Epoch 1436/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 1437/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9200\n",
            "Epoch 1438/1500\n",
            "7/7 [==============================] - 0s 14ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9200\n",
            "Epoch 1439/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4690 - accuracy: 0.8957 - val_loss: 0.4623 - val_accuracy: 0.9150\n",
            "Epoch 1440/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.8863 - val_loss: 0.4624 - val_accuracy: 0.9150\n",
            "Epoch 1441/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4688 - accuracy: 0.8957 - val_loss: 0.4624 - val_accuracy: 0.9300\n",
            "Epoch 1442/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4695 - accuracy: 0.9005 - val_loss: 0.4618 - val_accuracy: 0.9250\n",
            "Epoch 1443/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4680 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9200\n",
            "Epoch 1444/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9200\n",
            "Epoch 1445/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4608 - val_accuracy: 0.9250\n",
            "Epoch 1446/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 1447/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4610 - val_accuracy: 0.9200\n",
            "Epoch 1448/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4608 - val_accuracy: 0.9150\n",
            "Epoch 1449/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4696 - accuracy: 0.8957 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 1450/1500\n",
            "7/7 [==============================] - 0s 19ms/step - loss: 0.4682 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 1451/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4689 - accuracy: 0.9005 - val_loss: 0.4622 - val_accuracy: 0.9150\n",
            "Epoch 1452/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4686 - accuracy: 0.8957 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 1453/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4605 - val_accuracy: 0.9250\n",
            "Epoch 1454/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4607 - val_accuracy: 0.9200\n",
            "Epoch 1455/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9200\n",
            "Epoch 1456/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4607 - val_accuracy: 0.9200\n",
            "Epoch 1457/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4682 - accuracy: 0.9052 - val_loss: 0.4609 - val_accuracy: 0.9250\n",
            "Epoch 1458/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 1459/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 1460/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4617 - val_accuracy: 0.9250\n",
            "Epoch 1461/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 1462/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4702 - accuracy: 0.8863 - val_loss: 0.4621 - val_accuracy: 0.9250\n",
            "Epoch 1463/1500\n",
            "7/7 [==============================] - 0s 21ms/step - loss: 0.4687 - accuracy: 0.8910 - val_loss: 0.4620 - val_accuracy: 0.9200\n",
            "Epoch 1464/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4618 - val_accuracy: 0.9200\n",
            "Epoch 1465/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4689 - accuracy: 0.9005 - val_loss: 0.4612 - val_accuracy: 0.9250\n",
            "Epoch 1466/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 1467/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4698 - accuracy: 0.9005 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 1468/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4703 - accuracy: 0.8957 - val_loss: 0.4621 - val_accuracy: 0.9200\n",
            "Epoch 1469/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4683 - accuracy: 0.8957 - val_loss: 0.4615 - val_accuracy: 0.9300\n",
            "Epoch 1470/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4683 - accuracy: 0.9005 - val_loss: 0.4615 - val_accuracy: 0.9200\n",
            "Epoch 1471/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4617 - val_accuracy: 0.9200\n",
            "Epoch 1472/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.9052 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 1473/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4619 - val_accuracy: 0.9200\n",
            "Epoch 1474/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4686 - accuracy: 0.9052 - val_loss: 0.4616 - val_accuracy: 0.9250\n",
            "Epoch 1475/1500\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 0.4693 - accuracy: 0.9052 - val_loss: 0.4619 - val_accuracy: 0.9200\n",
            "Epoch 1476/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4694 - accuracy: 0.8957 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 1477/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9200\n",
            "Epoch 1478/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4689 - accuracy: 0.9005 - val_loss: 0.4610 - val_accuracy: 0.9200\n",
            "Epoch 1479/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4684 - accuracy: 0.9052 - val_loss: 0.4605 - val_accuracy: 0.9250\n",
            "Epoch 1480/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4604 - val_accuracy: 0.9200\n",
            "Epoch 1481/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4608 - val_accuracy: 0.9250\n",
            "Epoch 1482/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4706 - accuracy: 0.9005 - val_loss: 0.4607 - val_accuracy: 0.9250\n",
            "Epoch 1483/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4680 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9200\n",
            "Epoch 1484/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4685 - accuracy: 0.9005 - val_loss: 0.4622 - val_accuracy: 0.9100\n",
            "Epoch 1485/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4691 - accuracy: 0.8815 - val_loss: 0.4616 - val_accuracy: 0.9100\n",
            "Epoch 1486/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4688 - accuracy: 0.9052 - val_loss: 0.4611 - val_accuracy: 0.9200\n",
            "Epoch 1487/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4690 - accuracy: 0.9005 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 1488/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4696 - accuracy: 0.8957 - val_loss: 0.4615 - val_accuracy: 0.9250\n",
            "Epoch 1489/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 1490/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4618 - val_accuracy: 0.9250\n",
            "Epoch 1491/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4686 - accuracy: 0.9005 - val_loss: 0.4623 - val_accuracy: 0.9250\n",
            "Epoch 1492/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4688 - accuracy: 0.9005 - val_loss: 0.4619 - val_accuracy: 0.9300\n",
            "Epoch 1493/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4686 - accuracy: 0.8957 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 1494/1500\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 0.4684 - accuracy: 0.9005 - val_loss: 0.4614 - val_accuracy: 0.9250\n",
            "Epoch 1495/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4687 - accuracy: 0.9005 - val_loss: 0.4611 - val_accuracy: 0.9250\n",
            "Epoch 1496/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4683 - accuracy: 0.9052 - val_loss: 0.4613 - val_accuracy: 0.9250\n",
            "Epoch 1497/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4690 - accuracy: 0.8957 - val_loss: 0.4619 - val_accuracy: 0.9200\n",
            "Epoch 1498/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4683 - accuracy: 0.8957 - val_loss: 0.4617 - val_accuracy: 0.9300\n",
            "Epoch 1499/1500\n",
            "7/7 [==============================] - 0s 13ms/step - loss: 0.4689 - accuracy: 0.8910 - val_loss: 0.4621 - val_accuracy: 0.9150\n",
            "Epoch 1500/1500\n",
            "7/7 [==============================] - 0s 12ms/step - loss: 0.4691 - accuracy: 0.8910 - val_loss: 0.4613 - val_accuracy: 0.9250\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluate the keras model\n",
        "_, accuracy = reg_model.evaluate(train_X.T, train_y.T)\n",
        "print('Accuracy: %.2f' % (accuracy*100))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EmEBFtf3Ftej",
        "outputId": "29d4da47-9dbd-46ae-97b4-332994e05297"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7/7 [==============================] - 0s 3ms/step - loss: 0.4681 - accuracy: 0.9005\n",
            "Accuracy: 90.05\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(reg_history.history['loss'], label='train')\n",
        "plt.plot(reg_history.history['val_loss'], label='test')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "FU8Nl1P66cOV",
        "outputId": "2ca2e3e9-bc1a-4a14-cc46-12c4f931a570"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_decision_regions(train_X.T, train_y.T.ravel(), clf = reg_model, legend = 2)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "XW99o4STu9bl",
        "outputId": "c2fcc300-3fc0-43a3-f65d-98e5408c97e0"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/mlxtend/plotting/decision_regions.py:244: MatplotlibDeprecationWarning: Passing unsupported keyword arguments to axis() will raise a TypeError in 3.3.\n",
            "  ax.axis(xmin=xx.min(), xmax=xx.max(), y_min=yy.min(), y_max=yy.max())\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<b>Dropout Regularization</b>"
      ],
      "metadata": {
        "id": "PyTpat4n0ci0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.constraints import maxnorm\n",
        "from keras.layers import Dropout\n",
        "from tensorflow.keras.optimizers import SGD\n",
        "#from keras.optimizers import RMSprop\n",
        "DT_model = Sequential()\n",
        "DT_model.add(Dense(60, input_dim=2, activation='relu', kernel_constraint=maxnorm(3)))\n",
        "DT_model.add(Dropout(0.5))\n",
        "DT_model.add(Dense(30, activation='relu', kernel_constraint=maxnorm(3)))\n",
        "DT_model.add(Dropout(0.5))\n",
        "DT_model.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "sgd = SGD(lr=0.1, momentum=0.9)\n",
        "DT_model.compile(loss='binary_crossentropy', optimizer=sgd, metrics=['accuracy'])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fGdXCGCb6cXT",
        "outputId": "3dcc9478-d470-4c37-ea26-49f9dd26ad36"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(SGD, self).__init__(name, **kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "DT_history = DT_model.fit(train_X.T, train_y.T, \n",
        "                            validation_data=(test_X.T, test_y.T), \n",
        "                            epochs=1500, verbose=0)"
      ],
      "metadata": {
        "id": "arh4laF_EgcE"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluate the keras model\n",
        "_, accuracy = DT_model.evaluate(train_X.T, train_y.T)\n",
        "print('Accuracy: %.2f' % (accuracy*100))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bbuLL3UeF7h_",
        "outputId": "168ca39b-6dee-4a84-cfee-62ccc075e7e6"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7/7 [==============================] - 0s 2ms/step - loss: 0.1923 - accuracy: 0.9336\n",
            "Accuracy: 93.36\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(DT_history.history['loss'], label='train')\n",
        "plt.plot(DT_history.history['val_loss'], label='test')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "j-QfViw-FF7o",
        "outputId": "d2b59d89-4faf-43f6-ae93-763348cde102"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_decision_regions(train_X.T, train_y.T.ravel(), clf = DT_model, legend = 2)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "8h29z8NavUih",
        "outputId": "78865bf5-fe62-4086-a29c-c26cb449a860"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/mlxtend/plotting/decision_regions.py:244: MatplotlibDeprecationWarning: Passing unsupported keyword arguments to axis() will raise a TypeError in 3.3.\n",
            "  ax.axis(xmin=xx.min(), xmax=xx.max(), y_min=yy.min(), y_max=yy.max())\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(train_X[0, :], train_X[1, :], c=train_y[0, :], s=40, cmap=plt.cm.Spectral);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "rnafwuTGFGPp",
        "outputId": "615e7798-2310-4443-ae7d-44b7192723e4"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<b>(e)Compare the decision boundaries between non-regularization and each regularization.</b>\n"
      ],
      "metadata": {
        "id": "zIRCtzupxfCy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<b>Non-regularization</b><br>\n",
        "<b>Accuracy: 94.79% </b><br>"
      ],
      "metadata": {
        "id": "ryl98zZOz0wg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_decision_regions(train_X.T, train_y.T.ravel(), clf = model, legend = 2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "UiA0o9kAyqr-",
        "outputId": "c34ba902-6a05-4905-9e8d-73b6279a0c37"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/mlxtend/plotting/decision_regions.py:244: MatplotlibDeprecationWarning: Passing unsupported keyword arguments to axis() will raise a TypeError in 3.3.\n",
            "  ax.axis(xmin=xx.min(), xmax=xx.max(), y_min=yy.min(), y_max=yy.max())\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4811458550>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<b>L2 Regularization</b><br>\n",
        "<b>Accuracy : 90.05% </b>"
      ],
      "metadata": {
        "id": "0udPlDpb4Em9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_decision_regions(train_X.T, train_y.T.ravel(), clf = reg_model, legend = 2)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "EkjyQc3h4yB0",
        "outputId": "523f431a-80b8-47b6-8217-686771fc3def"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/mlxtend/plotting/decision_regions.py:244: MatplotlibDeprecationWarning: Passing unsupported keyword arguments to axis() will raise a TypeError in 3.3.\n",
            "  ax.axis(xmin=xx.min(), xmax=xx.max(), y_min=yy.min(), y_max=yy.max())\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RhYVFhIWF0un4HooLLZ73LHz9EWYv38jyVetIuvpyn5vGpE9WerYDkLqtpKO2iHcXreHnfSnV0t/w1pItHGlyEb2vl7wJUZbSWge6DOXbNLkOF07URZM+WQmp21zt5bOPMu26MIYtyueWwYMqrOW733eqc7X8NvppSeGMW17AoD/1IMK80/MeS9MLWLv5F8/v3TV37/fd9z8LRoNi8R1NiI80YbbYuWpWGlFBmpuuHVhh233fcVMx5xWX2R4fFcIPU8fx7McbsXcbLCtUNXL3X9Gx3HkyZKStaFDWbt/L3J3F9J2SSo/m4NBOBrU3sHj1tnLfU7qN/rquoUyau4p9R9N99vNOoRzYBhZ8uZE1e3PIyHcwslcE877exOCOqsyAKe/3NQsqYkBLq6edPzrUwJVtbPRrF3Ta/gZzXjHn3/9mmT8nc4t4eMYaTH1GSLAXFZLeHFGnnEk6pb99l72ZzO+H07jygTcYd0VLzkkIYWIzO798mudpQy+tdGfrij0W2kQ6eGLyQpb8+0HPubxTKJXTRmJIMflFitlbshnZJ5oQXcyQTqc6dm//dAtJ/Xv6vK/IaueDrQUsP6AwGBS5+UVgt9MtoZikc0KZvXwj87/b57cmfzwzj/NLbbNbizBnZtNi8EM0a9W+0p+zaJwk4Is65UzSKcvb13uA1GMJIT7ZMSOvu6zMTcK7jd7ucJKbZ2Hy4BAeW3nIc5MofVP47o9C9mc6eGdwOI+szOKdTXncdI6BYGzAqYwc90Cq6FAD+1NO8vnoNryzMQ9a9WbkdZd50jjdzTu3f7qFtLxgLvzbf8pcb+qr9/u8tubn8uu3iwiJSZBgLypFAr6oMyrqOK3svv4GSJmMrpbLlmmudMfSN4llbyZ7jutuy3fn27s7VPMLijFnuW4KAJl5cNN5wZybGEJy/ybM2WFjQ5qTDcsAXPs4nZoj6en80SyK937IKensLaRJRKinLP5G1b6+0XLaz6ogK53fN6wg8eoxHPm4zPRVQvglAV/UGb7TDBRVWMsvb9+KBkh5Ok/LuaH4G/l61azvy3Souo8z8VpXzXxkrJ3l+/NY+PoEn+O5M3MG9e/Lmh9/YVpSc5/O3BsmTPE8WXizFxeWud6NM5/D4XSy8480rJmp5O39geiLh2I+mVa1D100KhLwRZ3gL9i6R6WWruWXt2/p9nLv7U+/+xk9u7T2uUlMXbiGX/afSod030QAbvngKFNvTuTKNjaCQ8JYvs7rOOe09qmZR4ca6B1r4d3Fa/jnvUk+ZZx2czzD5n7PLd3Dy9ycvJ8svHX86ySf1xtnPkfuyWMYmzQn9dMXcNoKMYTHYF3+Fs7CXNq071RN34Jo6CTgizqhdBu5d7t76Vp+efuWN/HYk1MWkp2WytwDR9gwNhFw3Qz6T99EYjiec7jb8qdsyibGZKX320eIMGlPh+qTUxaSk36MxSnpmJTTUzN35+bHpW73BHx3GTs2DeLKNjZw2DznLe9GVh5rUSGmyKY06XsjOj+TJn2GopSi2HyE4m/e4ekp86r46YvGQgK+qBO8O069+Zs0zHtfp1N7OlYzLTkcPR7qcwynU3MyO4N5w2JIXpbls5RfiC7mxStjeXadKwAvezPZ01zzzuAwbvwgheWj23BOQgi/pxXz7ncHmHpdJK9sMfDZmxNK5di7mmv2Hknnsbc/JTszk0l/dnAyG+69KJjxX+bzQD9HhTcyt/ioEHbPnOB5bbPk4tAKZ3EBMRffWB0ft2ikJOCLOqG85o3T7es9gtU7gLpTNnt2aU1Exk6ahxUzqL2Biyen0DQqjMy8QoZ2NtA8zE5S5xCfPoCkzgbig62eeXAeSwhhxR4LN3U10jbKzoCWlNnf3Vzz5JSFHDh4mKRzTESbDGTlFqAU9GgO57x6gGCTARTY9TdM+XKXp7wnM3No1jS6zLXGRQaTlQMFOpiYi649y09XCBcJ+KLeqiirZ86KTWQeP+JpxomPjGZirCsf/71n7udvL8306XQt3QeQlZXJXzqbuGtJFu//VExBfj4f3hBCyygDQzo4eHLN5jJ9Btd1DeXd7w7QNtrExz8X8NU+I9lFmmaxTbAUGgkPtmMPiqLPY2XX/1n56v2cf/+bPtu008GGN8ZgCIvGSHDNf6CiwZOAL+qMM53DvrxMHfeN4MUrw32accpr6wfIzsrmsbcWeLbHRybQBUg2u+a0vyA0jXMSIDrMSPtYBwNaWsscx70gysheEczZDjFNosBg8ppyoTl/mZGKtSCP4PCoCq/NYbPy68r5GCOaYgoOwU4wxz58xGcfmyVTOmzFGZGAL+qMMxl0VVFWj/tGULoZx/Pe3KM+bf2ZeYXEmOxs3XOIE+aoMv0IJ3OOsNVuZfmvBgwGcDrhZIETbShik93BnJ9daZRZOXl0ioGrOhQxrm8ody7O5b3bWjL0o03c2zuSrgkhDO1mZN22lbTpf4vn+N4plwBOayFZW5YR3WswpmN7iYyMAksehPr+d42M7yQdtuKMSMAXdcKZDLoCmLZ4Lb1jLcSEudq93bX3qQvXsHbzLyWjV08143hPZOb9FFF6QrSFrz+C1tqzj/dCJf+8KtZz/knrXTX/nPRjJF3dD4AN69eRZSlkzSEHV3QIJamLifk/5fpMuXDLucF8uWY91t7XeGr51qJCjOExhDVrjd2SyfGNC4jqdT3OoBCyzK6bT/bJEyiDiei4eE8ZLJY8HrrhEqKaNivz+URGRsnNQJQhAV/UCWcy6Apg8ZrtZGQUsnRfCk0iQj3b7c5tjOwR7De9c+R1l3HNQ/8hWhWU2+laeo57oGShEhPvbcrEWTK7rNOpCTGkMSUpkkc/XUW7Vi04dKyQ/1wTzPgvC3lvqxWDQVFcsgiKrTCfnQetRARprknIZPaUxwiKbcnl9//LU/bijFSytn1B1MU3E5LYCW23EhTZlI6jJ7Njyjjikh6jVfsuPp/DtteG0XH05DKfz8FZ48/ymxANmQR8EXBnOujqnhdnExWsWDCuXZkFRG6YMIW5O81lUjZbp+3FUmglNyuDd26L49l1/gdq3TJvM06tmXVzPPf970eMBuVZqOSA1xw3Rzcs5mrHBs49J5akP/ZxxBBCcv9m3HBxNPsLT43wvWHCFBb+epD/pYQDYLMbgCBCouPJMx9j98wJ2Cy5EBrF0Y+fxhQVT95vmzBGxqIddkIiy2bu7J41AUdRAQBOp4PUQ/sAMBqNJLbpWP1fkGgwJOCLgDvTQVfmY4dJbBJE14S4Mk8D5aVsjrzuMvrf/wqjewfTLLiYwR3D/A7UGtDKys40B10T4mgWZOaC5ka6JjQlqXORZ44ba34uBb+t59ZbmwBwZQcTj319gGnJ7QDfG9ayN5Pp+NdJZTJw9hxJJ++TiQA47TYi2vckssdfMJlMpC99nZaj/kOx+UiZGj2Ao6iAlqNcN54jsx4kJL4tAMXmI1X4FkRjIPPhi4Bzz2F/urVdzdkWlq7ezLNXmMi0FHvmoV++bgt7j6T7rEdbel3bN+auJEQXM/qiUKJDFUM6abbu/oOPfymiz9R0ek1Oo+1LB3h/Sx5FVjtmi51MSzFDOjiwO5yM7BVBiCMfa0EeadtXMbSLomlEycyZh22M6G4CW6HXEoiK2cs3Ys62kG1Ow1qQ53MtdocmKDKW6L43o4LCiL9qDKHN2uFwOGrnQxeNktTwRcBVdtDVnBWbGNDKykUtTAztqpm9JZvHBsZ5Ui1z0o/5bZsf2KaA97/8ngf7BBEfYcDm1ORkFvFQ/6YY2/ThsTuvYdInK1m4/BsSm4Sy5J5WTFqbwdCuQbSPNWLOtpAY18STYZO5/yc+Tk9n/s5ClDJgySomLMjAhztOEhqc7VkCsdtJ1w2rXaSNtFKZOQC2/BwKDOGExLXwpFzaLJkAFJw4CGhPc43dbsNht3Pi6MFq+tRFYyQBX9S4M82vL+8Yy9dt4dV+kFno5JLWiruWuKY/BigsyGTZvS0Zt7xs27xy2ggz2Fiwy8mC3a79c4s0mGx0O7mX6/r1YPqiVbx9bSgPLM+n5zsnSMvKx4CTN78vwmjMJyG2iFSzjeCWO2na+SJM9mPYu11Dm/63sHvmBA5+/FiZaRbeePh2/vbSTP41KJy/f38qM0c7nWRv/wJMIcR2H0hs94Ge6zw4azyRkVEcXfQ8QZFNPdudBdmkL/oXClensbv5xhAc6nOzKI5PAFxZOkKUJgFf1Lgzya+v6BhJnQ1cck6iZ1uy2dU5CvgsCl66bX7L0SIc2kCuI8iT0dMkBFomxLPszWRuemIqSZ2gZ+sIkvuHl7uebfe/vowjL4PsHxbxzuBQHlyxkMMbPqND29Y+ZSxdji7xQQxOzODT9x4lNLIJGVk5FNg0Yc3b+73Wp6fMY+KoJE/2ze5ZEwjGlXrpfgIAVydtzwemeF4fnDWelz9cflafr2gcJOCLGnWmi5qUfhJwbyu9AIlbXMoebEUWn0yb6VMO8UdKGHN3upcJDKVJdKgnwHv7/XAaO387wORREeTk5TOiZzwjFpUt55wVmzgv3kB0QhQXRTmYvcPChS1CyCywc9vAbmUyjS5pVsjba9O497oI7A4Tt5wbzJI9FnJz4alpi/nPxGQ6jvbtyC2PdydtsfkI2d+8R8bySdgsmRSU1OhBavXi9CTgixp1poualH4ScG9LunqA3/e5V6jyzrQZe1lTv7V0f56auogR3U20jDKSkm3FXpRfJkPIHczfGRrHjR8coNsFkWRarBzPc/LW4DBeXPUDc774gTsvMHnKERnk5M4eIXxvjqDbOQmE5NtpEn6YzEwLYZHRREZGcXDWeLJPnkCrU7kTBmVg4qgkcjLMZ/ZBC1EJEvBFjTmbRU28nwS01qd9OqhoWmV/69d6+/1wGht/3sehJkaW/ZaDw6nJKMwnMT6G1l7TMnvPoHnH+SYW785j5vVhJK8opF2MgWhl4VCGlZmbQ1mw2wrA0TQLBoPCqjP58GcbufkFhAYZ6BztZPMX8z2jYL2bbrxte21YuZ+ru8ZfOm1TBluJ05GAL2rM2SxqUnrE6+meDtxNNP6agyZ9srLCvoOnpi7irguDePSKWLJy8+gUa+Tpb4uI6NyTx0ZczS1PvcerD9zsM4PmNR2N/G+PFe10ctv5Jt7bXER2gYP/Dg3ntW3hfPbmo8RFR3hy73NOHOaPn76jy6W3k7vwCZ6/NJcX1i+h75A7iIyOLVMmN6WdngBus2R6OmmNRuPZfBVCABLwRQ2qzKIm7pGz2VmZLBnhGlU6slcEN851jXJdfEcTz7aKVooq3Rx0ur4Dc7aFrbv/4GCoZt7ONMKCIDbUQHaRxnHgR+KjI8g6cdSnAzg6NB6nI42/9ghm9SEnD/YNp/8Hudx7UQgXtTAxoKWVvve/iQ6OJNWcS8Gm7yg8tpeoXkM4/t18RnRRtI81cn0X2PzFfP48fBwAJ44eLJN/r5XBMx/OxFFJPjV5309TiMqrloCvlLoWeBswArO01q+V+n0IMAfoDWQAw7TWh6rj3KLuqkx+vffI2fjIOMD1JNAsqIgLmht9ng4Gd4Srx7/FqsmP+l183Du4n67vYM6KTdz/p1i2H83jkpbQsomR5wdFcNzi4LJZeXz2zQ/89+Z4bnj/VAdwbn4R2K2EmTTx4YqrOtkwormkpSKz0Ml1nQ3M2Z5L13ve4NjUp3AUWYi/ZhzOwlyMR37g1uHNcORmMTAukw8WTGbVF0vJycoke/ErGMOa0OrOVz3lC4psisXiGqzlbu8HcDrskHmIgpRfCY4oO+2CEBWpcsBXShmBqcDVQAqwRSm1TGv9q9du9wFZWuvOSqk7gP8Dym+kFI2CO1A/PzCEcSX57yajqwMz1VzEtlT44o9T9dnc/CIKi4r9Bm/v4P7uojWs+fGXcvsO3Ocd1MpGlqWYc6KNrD9kJ6PASYtII93iFN0TCumaEO/TAXzDhCkcOZ7O8cx8VGgEdy0rZmjnIFrEhNCljStbZmi3w8z8z0PY7E6yfviMrB8+I8hRwD0XOAnJLQClSGzbnlsvzmSZ8QrCWvfB0KQ5Jz55otzPyXvWy9XzprFn6dtYzL8R3214tX4fouGrjhp+X2C/1voggFJqPjAU8A74Q4HnS35eBExRSimtSwln7wAAACAASURBVKYeFI2SO1D/pUcLknNOTThWXnrmqUFNZYO3d3C/atb33NI93PN0EB1qoHeshXcXr+Gf9yYxZ8UmBraBNXvzefvaUB78oojz4g10m5yN0aAIMWqecs14XGZenFPz8/Rj7fa9bEgzs2EZQDpaa/YdKwblpMOjn3muM2fhP/jsUAqf/eaqsZsijwHgbLKd4HaXYM1MxWHJ8lngxBgaDlh9Pq+87Ex+X7+EF66KInn5x+zfvZ687Gyc2gm42v0njnItoi7TIwt/qiPgtwKOer1OAS4pbx+ttV0plQPEAWVyz5RSY4AxANOfGMaYoZdXQxFFXXO6BUz8pWf6a6Lx1zF8dTsHH2zNZenvJaNq84soLCwiLnU7/7w3ibXb97LrQC5DO0NwkJG+rU18tlcRHh7M6BsuJyJjJwMuODXPvrspaf5LY1i6ejMTLzPy8prNng5aAIfDyeMfrOPgV9uxl1qOMPq2Vyg2H6H4m3cAaF0qKyf10D6Co5pyYfI0n+2ls262fLmA67vApT26cE+umb0thvLt1ytkemRRaXWu01ZrPQOYAcCmyfIE0ECVl8HjXsDEX3qmv5uD/47hULp3cg2yKj3dQUZOPu//cxS3P/G2Z03bic3sfHX4BInhsHj1NkwG7XM8d1PSk1MWMqCVlXZRDi6OL+Lq5LdYNeVRIkKDeWjmWlpfO46gtRMoLrL7veacDDNO7fTMj+PmsNtO+3m5a/fPDXPdiIb3imbEgiU4Hf7nP8zJMHtq+97nVtpJTLNTo5XlSaBxqY6Anwq08XrdumSbv31SlFImIBpX561opMrL4HEvYOIvPdP75jCwDZ6AW9H8PBWle3qvaRuii3nxylie/U75zK/vvmG88ud4/jr3AE/cFEa7WBN2awGqsIA3564kpSiS7sOeJDahBZGRUWSZD5SZqthoNOLUToIim3qmM3YrOHEQmyWTn/5vuKd5Bk410eRlniQsSHFnlwKK0rM9/7n6xxTzUXoY/ji106fmn3poHyHxbTn24SM+2+VJoHGpjoC/BeiilOqAK7DfAYwotc8y4G7ge+BWYLW03zdu/jJ43MF1ZC9XsHXX5INDI8tMq5CZV0jTILtPGqa/dn9/TwYhYZGczCzm41/SyMjJx2AwcOM5BpqH2RncMcgnE8h9w9h0qJBh5xtpFg6WYth+3M7E/sHcvXAdQfHt+Wrb/Z6yaYcDh92O0XTqv5fD4UA7HBhDw8tdjNxiyfOZP8dRVIClyI7Vbic8pjkLDzhZdADPce2WPLR2+k3rdDod7J41gfMrOX2DaByqHPBL2uSTga9xpWW+r7XerZR6AdiqtV4G/Bf4SCm1H8jEdVMQwkd5zTy0OtcnK8d3HdrTt/uXfjL43948Vk15nNnLN/LZV2uw2AxMvLY58ZEmhpDF9I0ZvLtoDeNuGei5YYyaf4zDGTbm/GTFZFQMaGtk0a827rzAxDfNBhHf71TGTOYrt2Je/iYmU5DP9Wmnw28APjhrvCff3q30IiexIyYB+IyuPThrPEG4bialnxqM4TGeVbGEcKuWNnyt9RfAF6W2Pev1cxFwW3WcSzRclRmoBeWnYZYeZOXveO4nA/d7+rcLxlpcSEyYAZvdiclRxOjewcz+ahOoU00/s26KAVsBM7Za+fiXYpqEGZnQL4wDGXZWrVuFrdcQgsJd7evBTZqVu/5sdYuMjOLo/Gd8plIGMASFgpbFVISvOtdpKxovdydrRfPfVJSG6V7sxN22791s1HfcVNKyC1B2C89eHUby4m+5tksQmw87OJqjmfviAbSG8CBNZLBCOzRvzVtNWJBi7s5iUtKzQWvyrZruzQ3c29NIdr4N7XQSUniSzB8+o/mf7zmj67Xl53Dsf//nGkx1lkpPpeyWemgfGcsnnfVxRcMkAV/UKe6mmakL1/DL/pQygb90M010qIEr29jA4cp0UU4bqjDXk3PvZs4rJvb8/lzt2EC3zk246byDqJAo/nOJjRYJcVw06TChsfF8fFssTSNMZObbuWtRLsUOxdaPHqfjXycR3fdmDq37lKNOG0mfu45bkJlO2xgj6bvXnDbgG5TBp5PUnmembVA2aYbm1fXxeRiNRmyWTM/5vLN0vMsgUyo3LhLwRZ3hPUXCjR9tIjGcMqNq35i3Gu2w8vqakwBopyY8SNMh1sG4y5uyZn8+bw8JI3nl9zxwyyCvPHmHZ+FxW0Eu9/YM5sEvc7mlUwgXRJro3FTTs02RZ53aphEmhnZRfLzdtXB5Tm4eQYZwuo7/0FMWW34OJ6YM45VbuzB+RT62ghz2zn0Ba14mDrvdJ/3SaDQSHRfvWaAkLzuTj5++g6lJLXhweT6WnCyfKRS8J0wrb1WrvMyTTByVRJY5nZ/evh93HoQhKJRm1z0MSNql8CUBX9QJ5mwL1zz0H4afZyAu3HgqTXKd78RnoTEJnH//qY7PHXOex56bxq/mTPpOSWVoZ2gapri6nd3nZmEtsHB7r0iaRpgozChEKejVHG6al0f7RCMHTzj4LSOfxftP+JTLVlRAy1teJKfARnhkKwq8grht99fc0s1ES53OkMQiPpn+N4qKnZiaNCNjxSSMxlP/vaw5JzEYlKdj1p5n5s4uBUQWRXB9lyifKZMBXk0ejqVkoFaTYIX7v2pkfKcyUyufOHoQa3ExyuDaJ23BM2Qsn0RofGvPfDw+xy21DeTG0FhIwBd1wruL1pKblcGQTvHM2ZrDnRcE0Sy4mMEdwypcNOXCkc8DsOO9R2gfZfUMpnqymd1n/hxrYQGzNuYzd0chRoMBCAFCUMZcvpr8d86540UufGgaweGnmjicDjsb3hiLwxBBcEyMTyaMoyAHw5EfuPWaMFp16MyDCTY25uRwxGKg89gplLbttWH0emoB4HoyyJ73KLdeEou1MI2BcTbPZGoGo+mMg29im46ePHvAM2p396wJZJnTfbJ/sszptLrzVRLbdPQ5huTjNw7+h+kJUYvM2Rbmfb2J0b2DybMU8PnuXO7pGUJ0qGJIJ9co24yc/AqPYS2weNr2zRY7YxceZ1BbPAOtgsPC6ZQQRmz3AajY1px777/pkTyNqPhE5qzYRLtIG2nbVnqOZy8uZNcXHxEUFUdQSNnBTcW7vmboOQZiw1z/heIigri+i2ux8dPJ+ekLhnZRxIYZUUZjyWRqicScfwUdR0/2WwM/G46iAhLveImOoydTSDCWIjtaw7HFr7Bjyjh2TBnH7lkTquVcon6QGr4IuDkrNhGClUW/aib/UMyt5wWRXuBKKQw22UjqHHLaBdDtxYXM3amYuzOdzLxCYkxWfkpz0j3LtfJVOAU8e2kIySv+R3Qo7HjvUcKiookOM7J83RZeviqSB5YvImv3BgAysnMJjUkgOjYOiyWvzIApU14q83UxC7eZiG6a4tmui31vTO5BUU6nw9OmX/Drd8wrtDD3ZwvOwjyCo05Npka/4WSfPOFTK3fLyzxJVNNmntdZ5nRSD+3zLIqS+snTOK0FOCxZ7JgyDmteJmn/+z+yI6M9ef2F6UcwxTQnKDgEoMwgMNGwScAXAeXuqP1mbFvXRGXTD/PZb1Y2pIVjMKiSvYrL5OKXFhWfyNaPHis1KKuAD569h9nLN3J3nxg++MVC0jkmoiPD+TE9mIWvj3U9AaRuY0jPRB7KzSEjqgvvfbmD0NjmGAxGLJY8sszpJN7xEkaj0acpZP/0cbRv24rhT7zhWb2qdKB2D4pSyuhpcgkZMcnTIZuxfBIXPug7aZpWhnKXPfTevmPKOELi23qO5bQW0OLu/2AzHyUsoa0nuJ+c+2Qlvw3R0EnAFwFVOs3yy7HtmLQ+p9xFyOOjQtg9s2wzRHxUiM/xSg/KGtTKRlZ+MefEGPnxcAGDugZ7Jmpz5/T3ahnEiE+3QngCnce86zl26cDq5izIxpSW6bN6lXemDbhq4UGRTUEpqpv7qcNmycSgDNgdNqzpfwCaYvMRtNNO9Z9V1GcS8EVAVXZ0rdvmaQ+We6zyBmVde04oq/fn8861ITz8VTF/7hAMDhvzvt7Ete2dHE+38vlPNn5MdXBzF8V/f0n1mYfGO7C6UyKdDjtNtIU3b+rCg8tPrVFburPVnUmze9YEn+YTa85JtAKF64bi5poHv3Lc5Ts4azwvf7iciaOSaNv5XM/vUw/tw1TSdOOmDAZsmak4S+bjcefqSz5+4yABXwRUZZZBrKzy5safvSWL2881ohT0TDTwwc9FdE+EEOzM2+Hgoz1B2B2KiIgolFKEJ8RQ6DUPTenACq6Vp845voTOCWFc3yXfp5bvT+k5dLa9NozWd75aZtKzE/OfwaAqn0uxe9YEikoycbLM6Z6bhzE0nLi/PEix+Qg2SybgmodHAcEhIZ6mqeL4BM81iYZPAr5oMNZu30vKiSLeXp9BXHQEBoPC7gimSYiDET3DMBqd3Hq+YuleJ0cKgjGFhmLJyKPl4LE07XnqaSL10D4KS6YlcM9aCa7a8MRRSTgddiLzU3juIVdt2j03vbuW753rXjoIewf+0qmRgOcJorLcmTit2nfxSc089uEjPkE9MjLKk9cPcBDX6FvttJfpd5Cc/IZLAr5oMJa9mUyn21+gaaiT4xYIi2pCYX4Ot3cx0CTEyLntXAt/JJtz0C17UWCMYv/C72ja8xqfwG6323AW5rkyXSxZtE+eDZyaqdL83TwGZ88nLiKIE0f+wOFwcHl0ES/eOxhTVLxPrrv31MUn5j/jad+vqBZfuh/AzWRQZbbbLJmeLB2j0UjBiYOusuae9EzWprSTyMgon5r8q8nDXf0LUfFYvBZsMYaGQzWlhYq6RwK+aDDM2RYcBbm8dmdHxq8ooMOdz/Pj2w8yb5edeTuyMRpzANBaY7Gt5JFJHxEc/gvgOx2x3VqMNdO1zEjagn96gihoV+1/z0YWZtpYdSKF7IxsTJGxQCSh8S1pe9fr7JgyzhPkvWvx3s0n/tIu3dy169KjYt0pmd418Imjkjzn8B6AFdKkmc+SiaVvFBZLHol3vFRmWuVjHz4CoRIWGir5ZkWDMWfFJoZ2M9IxPpShXQpZtW0l9shEWtzzFoUnU7igQ3McNiu/rpyH9dAu2p1/sd/jmIJDcJR0aiqDkfDEUk0vVz9M8TfvMKGko7T0GrVnovTiJe6RsZGRUT4LoniTUbHibEnAF/WeOdvCPS/OJjsrk5cvdy0gfmuPKJYuWo92nvonbivMZ/eqBcRf8VcKjr0AnGo+8Z6sDFzNI6U7VKuT+7yetM0SofGt6Tj6Tb9B3d3s5O5LANcN4sTRg377A4QoTQK+qPfmrNiE+dhhEpsEERvmO9vle5tzAbDnZ7Nr1QoSrxqNKexUCqJ300jpBUtKLzZenUpPgFYZ7mYn71Wvds+aQOonT1NQ0tnrvoFY8zJ80j3dNwnpkG3cJOCLeqX0Ainu3PvnB4Ywbnk+V83WhDQ5NeOlwV5IcUYKOTtWofPMZP73CQCfWnKFOejaUe4atG7eHb4A1tyTHJv7NOnGIKLj4n3movduu6+O3PfzS54GvPsGOo6ezI4p4zx9EnCqw9n7ycFoNJYZTGazZBIZ3wnRMEnAF/VK6bVr3bn3f+nRguScHKZutWE3hpCXZSYqNp4CZxH6h6U0vex2Tn4+yRMES68N6y8zJsucTlhCuzI59O41aMEVtLMOHSDxjpd89jn5xds4cl1z9ju109NsU0iwT15/dauoiar0fpav3yrz/jbtO8kTQAMmAV/UG94LpIxbvoWk/j3LjKxdvj+PQZecy5oN39Oxa2uWbnVCzmEKV73jEwRLB0B/Qc6dJeMdmLNPnkAZTGWybLK/ec/nxpDhdNCsnPx4f0o/JbinS9j+6m3ENEv0bCs2HylTdn/X4a+J6sTRg2WmSwbJu29MJOCLeqP0PDlPTllYZmTt4I7w/lebuLd3OFO37uUfM75Ga8381x/H6bCXCYJuryYPJ/XwHzi102e70k5ad+hSYZt7ZdaPLTKnoJ1OrHmZnrZ1myXTk1tfVDJBm5t7ojbv5prk6/8EuCZk8+5fyMkwn/azc78vKLJpmfJL1k/jIQFf1Av+5smZPuUQf6SEMXdnMQBOpyYtMxetoVlsInf31Wz+Yj4AprSdOAvKH+xkseSRMOwFv3np1TE/vXY6CYpvgzEy1qdZqfibdzzz4JR3Mzp1DLvfG4t2nv0i6KJxkYAv6gV/8+SMvaypz6ya/5r1OdMXfsvAbrFcd2EcGfk2bv9kEUYDvHtTK259bze/vDMGZfBtErHnmT3NJnVZTLPESufl52WeGmnr5nQ6CIqp+9cpao4EfFEv+JtV0+5wkrVxDXcnXU6x1cbURet49apw5uwoIDPfRlxEEK2Dc+ne3EjnhObc2C2IhYYLCLngWp9jpy941u85Uz95GlteJlkWfPLevWfSBFfzi3vWSTfvKQ+cdjsajdV8BIclkyOzXDN+KlMITouZiaOSKt0sU1lRTZuVuTnsmDKOuCEPV+t5RP0iAV/UC/5m1Zz0yUqWr1rHjCXrWPLjHyR1DeKSViZ2pjm4cvJ+QsPDceTlMfHyaABu7R7GV2t+IubKkQSFR3uOUxwX7/ecTmsBzYe9hMGAp7nFX3t9YpuOFHhNm/BqsmvVqtRPniYVV83aGB4DBiOm2JYk3OYa9HXio8fQ2pW5U7rvQIiaUKWAr5RqCiwA2gOHgNu11ll+9nMAO0teHtFa31CV84rGoXTOfenfLV+3hZevjeWmjzbQtGlTnri7K3ERQfwtwcb6nBza972WC7K+4pwuroAeG2ZgaBfFsu1fEN9v+FmVyWg0Upx1wqe5RDscoBQPXteHmGaJnknJ3LNj7pgyjtghj2GMTnAthKK11xE1hSdT0PZiv00z1TlPvTE0nBPznykzI6fMhd94VLWG/xTwrdb6NaXUUyWv/a2nVqi17lnFc4lGpnTOfenfXdrSyZzthdzRI5RdaXk8vjCPN27riC0jhcuj8/no0xlsMimmrzkKgNPhIDja5Fk71ltkZBSpC571qWk7nQ6004YxKNSzLbFNR1KDgun91ALPNnfa5bEPH6Hj6Mk+r92UwYA9KxVQUNKH4CjIBg3GiBgcJWv4uqcsdvcpWCx5nhGykZFRbH/1NnSpmTYNysCrycNPm1pZepCWaHyqGvCHAgNLfp4NrMV/wBfijJTOub876XJPLd+cbWH+19/To7mB529oz6E//mD+tlxaRBq4cvJ+7NYiTJGxhLd1zV7ptu21YfR+8BO/5/M3Q2WWOR2jKQiHw3HG89W42//di4mf/Px1tMOOCgqhxV2u9n+lDLROnoPTZsWelUrbzud6moz8pU66s3lkQjVxtqoa8JtrrY+X/HwCaF7OfqFKqa2AHXhNa/2/8g6olBoDjAGY/sQwxgy9vIpFFPVR6Zx771r+MzNXEGly8NKNnTAYFHEt25IQlcvrNyTwxIYQjlgMtB87rcwx3QOZKqohe89Q6V7LFigzBUFpdmsxdruN1EP7cNjtOApzSbjteQymYE9KptNWzImPyq7HWxPKm1Nfmm8at9MGfKXUN4C/XK6J3i+01loppf3sB9BOa52qlOoIrFZK7dRaH/C3o9Z6BjADgE2TyzueaMD85dzf/qmrlr/6lyN8/dNRQgwRDJjmmrPekpfHrV2MtAyzcn2XECZvzPZ73OiSztnK1pDda9kCPuvZ+lu8ROOaStnQpDlKa5TBBAYjTqcDpRQ281G004YjP5vjH7oyZZw2K8c/fASNBqeDbFOQa/GVAv/lPxMyclb4c9qAr7W+qrzfKaXSlFIttNbHlVItgHR/+2mtU0v+PqiUWgtcBPgN+EL4y7lP6mxgzOsLadnvdv4xZ41n37zsTD5++g7+dm00cRFBDI+18cH6FGwFOT6ZOJWVk2H2jGKNuepvnu3pC56t1OIlyhQMdqsriAM4HWjAaDLhdLpWLU+44e8AHJ83kcS7J6EA7bATFByCzVpM6vT7z7jcQlRGVZt0lgF3A6+V/L209A5KqVigQGtdrJSKBy4H/l3F84oGrHTOvdaaEznFRDZrwrVX3eaz75YvF3B9F4iLCAJcfw/tZuLLs8zEcWpnmdG27u1upZtLcjLM2B020Jrjs11PBM78LJQygEH5pHWajUEUl6wtq7UTHHbX08EZl1SIM1fVgP8a8KlS6j7gMHA7gFKqD/A3rfVo4FxgulLKCRhwteH/WsXzigbMO+fe4XDy9/fXEXnJHbQ9r0+Zfff9tJGf0otYsCPFsy0n046leDaHv/PtKjIoQ41MQxAd51rHttmtzxGe6Jpa+OgHDxMU1xqb+WiZfb3nxnHmpnl+5wAcdjs4HWWamNxt79I2L6qiSgFfa50BXOln+1ZgdMnPm4ALqnIe0TgVFlt5aMY62g5+gMT25/jdZ8Q/3mH+648z/Ik3iIyO9WyfOCqJ8/201ZeebqA0pZ1+Z7RUXjV8f0sPuiZEO1VPNwSHc3z2IzgsWSiFp/3fOzBHx8X7nT/H3rxFuamT0jYvqkJG2oo6KTuvgPEzv+OC4U8TW8E8N1u+XIApbSebv5jPn4ePK3c/N4MyVFhDPpP5arwZQ8NJ+/SfhDRxLTSuAKNSBDdrTRhWyX0XdYIEfFHnpJ7MZsJH2/jTPS8QEVV+x2tedia/r1/C1Jta8eDyJfQdcodPLd8f7yaV6nT+6Df56f+GExl66r9U9skT2CwGrMpQZqWrp6fMK7d5Ji/zpN+OYZm3XlSVBHxRp+w5nM6/lv5O/7GvEBwSWuG+7g7bzglhXN8lv9K1/IpUpY289M2kokFS3gO8/J1HBleJmiABX9QZ3+9JYeqGdAaOeQmjqeJ/mu7a/XPDXE8Aw3tFM2JB5Wr5FamtGrS/fgCQoC5qlgR8USd8/sM+luxXXHHPMyh1+iRFf+mY13fBU8uvyWwWyZQR9ZUEfBFwH6zawZbCVlx6x8hKv8dfOiZA5ImN/Hn4uBqtqUs7uqivJOCLgNFa88bizZyI78tFg8ofverP2H9/XEOlEqLhkoAvAsLpdDJxzneoC26gW4+GOUFeRU0/Fa2TK01GoqYorevw/GQyeVqDZLXZeXTWOhIG3UvLzt0DXZyAqChLR5qMRFXcf0XHcjvBpIYvalVefhHjZ66n6y0TiG9Rds6axkKCuggECfii1pzMyuPhD36g913/pEms/3VkhRA1RwK+qBUHj2Xwj093cvnolwkNjzj9G4QQ1U4CvqhxP+07xuurjjJg7MuYgoIDXRwhGi0J+KJGffPTH3z0cwEDRj+PwVB2pSghRO2RgC9qzIL1e/g2LYp+dz0R6KIIIZCAL2rIu8u3s8fUjYtvuu30OwshaoUEfFGttNa8OH8TlnZ/5sK+5S6HLIQIAAn4otrY7Q4e/2A9kX8aTpdzewe6OEKIUiTgi2pRWGxl/Iy1tBuSTGK7ssv2CSECTwK+qLKs3ALG//c7Lryj4uUIhRCBJQFfVEnqyWz+/vE2/jTqRcKjmgS6OEKICkjAF2ft18NpvLB0L/3HnH45QiFE4EnAF2dl4+6jTNt4kkFjX8ZgNAa6OEKISpCAL87Y5z/sY8kBQ6WXIxRC1A1VGuuulLpNKbVbKeVUSvWpYL9rlVK/K6X2K6Weqso5RWB9sPIXvkyP49JhD0mwF6KeqerkJruAm4H15e2glDICU4HBwHnAcKXUeVU8r6hlWmv+vehHdgVfyEVD7gp0cYQQZ6FKTTpa6z3A6Wp6fYH9WuuDJfvOB4YCv1bl3KL2OJ1O/jFnA4YLhjbY5QiFaAxqY/rCVsBRr9cpJdtEPWC12UmevpqwS++mgwR7Ieq10wZ8pdQ3Sqldfv4MrYkCKaXGKKW2KqW2zli6sSZOISopL7+IMVNX0ybpUVp2Oj/QxRFCVNFpm3S01lWdASsVaOP1unXJtvLONwOYAcgi5gGUnpXHIx/+QJ+7niUqJi7QxRFCVIPaaNLZAnRRSnVQSgUDdwDLauG84iwdPJbB+NnbuPS+lyXYC9GAVDUt8yalVApwKbBCKfV1yfaWSqkvALTWdiAZ+BrYA3yqtd5dtWKLmrJt7zGe+d9+Bo6VtWeFaGiU1nW41USadGqVeznCy+78uyxHKEQ9df8VHctNm5SRtgKA+et+ZXV6DP3uSg50UYQQNUQCvmDq59v5PfhcLr7p1kAXRQhRgyTgN2LeyxFeIMsRCtHgScBvpOx2B39/fx1NLr2TLuf2CnRxhBC1QAJ+I1RYbGX89LW0TxpP87adA10cIUQtkYDfyLiXI+wx/B/ExDcPdHGEELVIAn4jkpKexd8/3s6l98hyhEI0RhLwG4ndh9J46fN99B8ryxEK0VhJwG8E3MsRDhzzkixHKEQjJgG/gVv2/V6W/mGS5QiFEBLwG7IPVv7C1uK2/On2vwa6KEKIOkACfgOktebfizeTFn8JPf98XaCLI4SoIyTgNzDu5QiNF9zIuT0uC3RxhBB1iAT8BsRqs/PwzLUkXnU/LTvKOvFCCF8S8BuIvPwixs9cT7db/k5cizanf4MQotGRgN8AyHKEQojKkIBfzx1INfOPhbvod5+sUCWEqJgE/Hps295jvPHNUQaOfRlTUHCgiyOEqOMk4NdTq7Yf5OMdRQwc/bwsRyiEqBQJ+PXQ/HW/suZkLP3+Oj7QRRFC1CMS8OuZKZ9vY1/wefS5UZYjFEKcGQn49YTWmn/N3Uhhx6vofvGVgS6OEKIekoBfD3gvR9hZliMUQpwlCfh1XEGRlYdmyHKEQoiqk4Bfh2XlFjB+1nf0GCHLEQohqq5K+XxKqduUUruVUk6lVJ8K9juklNqplPpZKbW1KudsLFLSsxg363suvudFCfZCiGpR1Rr+LuBmYHol9h2ktTZX8XyNgns5wivGvkpQSEigiyOEaCCqFPC11nsAWUmpGn236wjTv8+Q5QiFENWutoZoamClUmqbUmpMRTsqpcYopbYqpbbOWLqxlopX6h21TwAABYlJREFUNyzdtJcPdtjpP2qiBHshRLU7bQ1fKfUNkOjnVxO11ksreZ5+WutUpVQCsEop9ZvWer2/HbXWM4AZAGyarCt5/Hrvvyt/YbssRyiEqEGnDfha66uqehKtdWrJ3+lKqSVAX8BvwG9sZDlCIURtqfEmHaVUhFIqyv0zcA2uzt5Gz+l08vTs9eR0GMy5/STYCyFqVlXTMm9SSqUAlwIrlFJfl2xvqZT6omS35sB3SqlfgM3ACq31V1U5b0Ngtdl5cNpqwi4bRYcelwa6OEKIRqCqWTpLgCV+th8DhpT8fBDoUZXzNDR5+UUkz1zPubIcoRCiFslI21qWlpnLox9ups/I54iKaRro4gghGhEJ+LXoQKqZZxbt5vL7XyYkLDzQxRFCNDIS8GvJ1r3HmPTNUa4Y+zImU1CgiyOEaIQk4NeCldsO8MlOKwNGy3KEQojAqdsBPyIh0CWosp/2HuWrY1HcOG5soIsihGjklNYNYzCrUmpMySjdBq0xXGdjuEaQ62xo6sN1NqT2hQrn6GlAGsN1NoZrBLnOhqbOX2dDCvhCCCEqIAFfCCEaiYYU8Ot021k1agzX2RiuEeQ6G5o6f50NptNWCCFExRpSDV8IIUQFJOALIUQjUW8DvlLqNqXUbqWUUynVp4L9DimldiqlflZKba3NMlaHM7jOa5VSvyul9iulnqrNMlaVUqqpUmqVUmpfyd+x5eznKPkef1ZKLavtcp6t0303SqkQpdSCkt//qJRqX/ulrLpKXOcopdRJr+9wdCDKWRVKqfeVUulKKb9reiiXd0o+gx1KqV61XcYKaa3r5R/gXKArsBboU8F+h4D4QJe3Jq8TMAIHgI5AMPALcF6gy34G1/hv4KmSn58C/q+c/SyBLutZXNtpvxvgAeC9kp/vABYEutw1dJ2jgCmBLmsVr/MKoBewq5zfDwG+BBTwJ+DHQJfZ+0+9reFrrfdorX8PdDlqWiWvsy+wX2t9UGttBeYDQ2u+dNVmKDC75OfZwI0BLEt1q8x34339i4ArlVKqFstYHer7v8FK0a61uDMr2GUoMEe7/ADEKKVa1E7pTq/eBvwzoIGVSqltSqk6PxLuLLUCjnq9TinZVl8011ofL/n5BK5V0vwJVUptVUr9oJSqLzeFynw3nn201nYgB4irldJVn8r+G7ylpKljkVKqIa7+U6f/L9bpydOUUt8AiX5+NVFrvbSSh+mntU5VSiUAq5RSv5XcpeuMarrOOq2ia/R+obXWSqnycoXblXyXHYHVSqmdWusD1V1WUWM+B+ZprYuVUmNxPdX8OcBlalTqdMDXWl9VDcdILfk7XSm1BNejZ50K+NVwnamAd22pdcm2OqOia1RKpSmlWmitj5c8/qaXcwz3d3lQKbUWuAhXu3FdVpnvxr1PilLKBEQDGbVTvGpz2uvUWntf0yxcfTcNTZ3+v9igm3SUUhFKqSj3z8A1gN/e9XpuC9BFKdVBKRWMq+Ov3mSx4Crr3SU/3w2UeapRSsUqpUJKfo4HLgd+rbUSnr3KfDfe138rsFqX9ADWI6e9zlJt2TcAe2qxfLVlGTDy/9u5X5UKgigOwJ/NbDTe5AMYxEcwCD7DLfc5LD6BzW6wC4Jit10Wi9rFaBSLYQ4oCIL/GHR+HyzMzpY5O8xhOTNsndbZwuObcmV/vXeNv7FbvqfVx57wgLPqX8dptWfaaYElrrUSSfex/3Scdb+DG+2L90/FqdWrL3CLc6xV/yaOqr2NqeZywrz3uD8R37u5wT52q72KE9zhCrPeY/6lOA9qHS5xiY3eY/5CjMe4x3OtyzkWWNTzFRzWO5h8cIKwx5VfK0REDOJfl3QiIuJVEn5ExCCS8CMiBpGEHxExiCT8iIhBJOFHRAwiCT8iYhAvs4sdQAfqBf4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<b>Dropout Regularization</b><br>\n",
        "<b>Accuracy : 93.36%</b>"
      ],
      "metadata": {
        "id": "gVG6QJ-Q5c3f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_decision_regions(train_X.T, train_y.T.ravel(), clf = DT_model, legend = 2)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "8Yxqw93W6IEn",
        "outputId": "32e7a4c9-601e-4a63-9d00-b9008e19a939"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/mlxtend/plotting/decision_regions.py:244: MatplotlibDeprecationWarning: Passing unsupported keyword arguments to axis() will raise a TypeError in 3.3.\n",
            "  ax.axis(xmin=xx.min(), xmax=xx.max(), y_min=yy.min(), y_max=yy.max())\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<b>Conclusion: </b>Amongst L2 and Dropout, Dropout Regularisation gives best performance and model fits accurately when the hyperparameter dropout rate is tune to 0.5"
      ],
      "metadata": {
        "id": "WG72H0Fv8kcO"
      }
    }
  ]
}