Mnist_Dataset.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow import keras"
      ],
      "metadata": {
        "id": "92njFZ06V3LR"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mnist = keras.datasets.mnist\n"
      ],
      "metadata": {
        "id": "pCPSp90CWliH"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "(train_features, train_labels), (test_features, test_labels)= mnist.load_data()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2a60vE88X4hK",
        "outputId": "bc567abc-a01c-446a-e339-7b57abd524ae"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11490434/11490434 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_features.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YWHlFj4MYx9T",
        "outputId": "677ced6a-a774-43ce-b439-62e6117d3ab3"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_features.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I_s8a1LLY4f1",
        "outputId": "68919bea-fd9c-404d-b77a-1e32b7be0f2c"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "idx=0\n",
        "img = train_features[idx]\n",
        "\n",
        "print(train_labels[idx])\n",
        "\n",
        "plt.gray()\n",
        "plt.imshow(img)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "2yQD1_PMZEdl",
        "outputId": "65b2a5a0-f95a-44dd-e733-463cec98061d"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7bd644398ac0>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Preprocessing\n"
      ],
      "metadata": {
        "id": "mfMW24-ypmve"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.min(img), np.max(img)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qHvy8qYan_Js",
        "outputId": "1ef76614-549e-406d-e14a-df84017b38c8"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 255)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_features= train_features/255.0\n",
        "test_features = test_features/255.0"
      ],
      "metadata": {
        "id": "gmblmJHWqa8U"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model definition\n"
      ],
      "metadata": {
        "id": "CA6i8vBjJb8k"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "K2qvvblmJZRV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model= keras.Sequential()\n",
        "model.add(keras.layers.Flatten())\n",
        "model.add (keras.layers.Dense(units=128, activation='relu'))\n",
        "model.add(keras.layers.Dense(units=128, activation='relu'))\n",
        "model.add(keras.layers.Dense(units=10, activation='softmax'))\n"
      ],
      "metadata": {
        "id": "Gj5NhYG4uIAp"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer=tf.optimizers.Adam(), loss=tf.losses.sparse_categorical_crossentropy, metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "BHCs6RN5wR0o"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.build(input_shape=(None, 28, 28))\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vD-zlieV0mgr",
        "outputId": "d5f6511b-c96e-4748-8c9f-a7c66ea3959f"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " flatten (Flatten)           (None, 784)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 128)               100480    \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 128)               16512     \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 10)                1290      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 118282 (462.04 KB)\n",
            "Trainable params: 118282 (462.04 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hist= model.fit(train_features, train_labels, epochs=1000, batch_size=256, validation_data=(test_features, test_labels))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lmqchtgj2v_h",
        "outputId": "5e3a7279-0224-4789-a8ab-def754756d6a"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/1000\n",
            "235/235 [==============================] - 3s 11ms/step - loss: 0.3934 - accuracy: 0.8900 - val_loss: 0.1738 - val_accuracy: 0.9473\n",
            "Epoch 2/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 0.1494 - accuracy: 0.9568 - val_loss: 0.1223 - val_accuracy: 0.9635\n",
            "Epoch 3/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 0.1072 - accuracy: 0.9681 - val_loss: 0.1015 - val_accuracy: 0.9698\n",
            "Epoch 4/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 0.0807 - accuracy: 0.9764 - val_loss: 0.0881 - val_accuracy: 0.9727\n",
            "Epoch 5/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 0.0641 - accuracy: 0.9814 - val_loss: 0.0809 - val_accuracy: 0.9750\n",
            "Epoch 6/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 0.0520 - accuracy: 0.9846 - val_loss: 0.0718 - val_accuracy: 0.9783\n",
            "Epoch 7/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 0.0421 - accuracy: 0.9876 - val_loss: 0.0726 - val_accuracy: 0.9782\n",
            "Epoch 8/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 0.0352 - accuracy: 0.9898 - val_loss: 0.0715 - val_accuracy: 0.9780\n",
            "Epoch 9/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 0.0292 - accuracy: 0.9915 - val_loss: 0.0729 - val_accuracy: 0.9791\n",
            "Epoch 10/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 0.0242 - accuracy: 0.9930 - val_loss: 0.0760 - val_accuracy: 0.9762\n",
            "Epoch 11/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 0.0205 - accuracy: 0.9942 - val_loss: 0.0779 - val_accuracy: 0.9781\n",
            "Epoch 12/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 0.0156 - accuracy: 0.9956 - val_loss: 0.0753 - val_accuracy: 0.9800\n",
            "Epoch 13/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 0.0148 - accuracy: 0.9959 - val_loss: 0.0776 - val_accuracy: 0.9798\n",
            "Epoch 14/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 0.0117 - accuracy: 0.9970 - val_loss: 0.0817 - val_accuracy: 0.9775\n",
            "Epoch 15/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 0.0097 - accuracy: 0.9974 - val_loss: 0.0802 - val_accuracy: 0.9801\n",
            "Epoch 16/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 0.0093 - accuracy: 0.9975 - val_loss: 0.0871 - val_accuracy: 0.9785\n",
            "Epoch 17/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 0.0072 - accuracy: 0.9980 - val_loss: 0.0883 - val_accuracy: 0.9795\n",
            "Epoch 18/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 0.0070 - accuracy: 0.9982 - val_loss: 0.0953 - val_accuracy: 0.9780\n",
            "Epoch 19/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 0.0051 - accuracy: 0.9986 - val_loss: 0.0914 - val_accuracy: 0.9781\n",
            "Epoch 20/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 0.0045 - accuracy: 0.9990 - val_loss: 0.0974 - val_accuracy: 0.9781\n",
            "Epoch 21/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 0.0053 - accuracy: 0.9986 - val_loss: 0.0937 - val_accuracy: 0.9782\n",
            "Epoch 22/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 0.0059 - accuracy: 0.9983 - val_loss: 0.1178 - val_accuracy: 0.9730\n",
            "Epoch 23/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 0.0140 - accuracy: 0.9952 - val_loss: 0.0936 - val_accuracy: 0.9779\n",
            "Epoch 24/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 0.0043 - accuracy: 0.9989 - val_loss: 0.0952 - val_accuracy: 0.9789\n",
            "Epoch 25/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 0.0035 - accuracy: 0.9991 - val_loss: 0.1028 - val_accuracy: 0.9784\n",
            "Epoch 26/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 0.0043 - accuracy: 0.9986 - val_loss: 0.1020 - val_accuracy: 0.9795\n",
            "Epoch 27/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 0.0040 - accuracy: 0.9988 - val_loss: 0.0976 - val_accuracy: 0.9785\n",
            "Epoch 28/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 0.0062 - accuracy: 0.9983 - val_loss: 0.1166 - val_accuracy: 0.9767\n",
            "Epoch 29/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 0.0063 - accuracy: 0.9979 - val_loss: 0.1258 - val_accuracy: 0.9760\n",
            "Epoch 30/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 0.0097 - accuracy: 0.9970 - val_loss: 0.0960 - val_accuracy: 0.9802\n",
            "Epoch 31/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 0.0016 - accuracy: 0.9996 - val_loss: 0.1026 - val_accuracy: 0.9802\n",
            "Epoch 32/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.1083 - val_accuracy: 0.9790\n",
            "Epoch 33/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 4.3613e-04 - accuracy: 0.9999 - val_loss: 0.1013 - val_accuracy: 0.9813\n",
            "Epoch 34/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 1.7916e-04 - accuracy: 1.0000 - val_loss: 0.0998 - val_accuracy: 0.9811\n",
            "Epoch 35/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 1.3569e-04 - accuracy: 1.0000 - val_loss: 0.1006 - val_accuracy: 0.9813\n",
            "Epoch 36/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.1398e-04 - accuracy: 1.0000 - val_loss: 0.1016 - val_accuracy: 0.9812\n",
            "Epoch 37/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.0504e-04 - accuracy: 1.0000 - val_loss: 0.1022 - val_accuracy: 0.9811\n",
            "Epoch 38/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 9.0929e-05 - accuracy: 1.0000 - val_loss: 0.1033 - val_accuracy: 0.9812\n",
            "Epoch 39/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 8.6780e-05 - accuracy: 1.0000 - val_loss: 0.1034 - val_accuracy: 0.9807\n",
            "Epoch 40/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 0.0282 - accuracy: 0.9913 - val_loss: 0.1372 - val_accuracy: 0.9742\n",
            "Epoch 41/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 0.0144 - accuracy: 0.9953 - val_loss: 0.1037 - val_accuracy: 0.9785\n",
            "Epoch 42/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 0.0030 - accuracy: 0.9992 - val_loss: 0.0975 - val_accuracy: 0.9817\n",
            "Epoch 43/1000\n",
            "235/235 [==============================] - 2s 11ms/step - loss: 8.5498e-04 - accuracy: 0.9999 - val_loss: 0.0983 - val_accuracy: 0.9816\n",
            "Epoch 44/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 2.7665e-04 - accuracy: 1.0000 - val_loss: 0.0979 - val_accuracy: 0.9828\n",
            "Epoch 45/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.5056e-04 - accuracy: 1.0000 - val_loss: 0.0993 - val_accuracy: 0.9821\n",
            "Epoch 46/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.2195e-04 - accuracy: 1.0000 - val_loss: 0.0992 - val_accuracy: 0.9825\n",
            "Epoch 47/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.0373e-04 - accuracy: 1.0000 - val_loss: 0.1005 - val_accuracy: 0.9826\n",
            "Epoch 48/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 9.0300e-05 - accuracy: 1.0000 - val_loss: 0.1014 - val_accuracy: 0.9827\n",
            "Epoch 49/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 8.0488e-05 - accuracy: 1.0000 - val_loss: 0.1024 - val_accuracy: 0.9826\n",
            "Epoch 50/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 7.0812e-05 - accuracy: 1.0000 - val_loss: 0.1032 - val_accuracy: 0.9825\n",
            "Epoch 51/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 6.3423e-05 - accuracy: 1.0000 - val_loss: 0.1039 - val_accuracy: 0.9828\n",
            "Epoch 52/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 5.7283e-05 - accuracy: 1.0000 - val_loss: 0.1050 - val_accuracy: 0.9824\n",
            "Epoch 53/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 5.2170e-05 - accuracy: 1.0000 - val_loss: 0.1061 - val_accuracy: 0.9824\n",
            "Epoch 54/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 4.6519e-05 - accuracy: 1.0000 - val_loss: 0.1074 - val_accuracy: 0.9825\n",
            "Epoch 55/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 4.1614e-05 - accuracy: 1.0000 - val_loss: 0.1084 - val_accuracy: 0.9824\n",
            "Epoch 56/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 3.7661e-05 - accuracy: 1.0000 - val_loss: 0.1089 - val_accuracy: 0.9823\n",
            "Epoch 57/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 3.5284e-05 - accuracy: 1.0000 - val_loss: 0.1092 - val_accuracy: 0.9827\n",
            "Epoch 58/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 3.1028e-05 - accuracy: 1.0000 - val_loss: 0.1106 - val_accuracy: 0.9830\n",
            "Epoch 59/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 2.7878e-05 - accuracy: 1.0000 - val_loss: 0.1111 - val_accuracy: 0.9825\n",
            "Epoch 60/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.5330e-05 - accuracy: 1.0000 - val_loss: 0.1122 - val_accuracy: 0.9826\n",
            "Epoch 61/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.2986e-05 - accuracy: 1.0000 - val_loss: 0.1135 - val_accuracy: 0.9825\n",
            "Epoch 62/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.1139e-05 - accuracy: 1.0000 - val_loss: 0.1133 - val_accuracy: 0.9825\n",
            "Epoch 63/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.9015e-05 - accuracy: 1.0000 - val_loss: 0.1152 - val_accuracy: 0.9827\n",
            "Epoch 64/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.7170e-05 - accuracy: 1.0000 - val_loss: 0.1160 - val_accuracy: 0.9827\n",
            "Epoch 65/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.5707e-05 - accuracy: 1.0000 - val_loss: 0.1171 - val_accuracy: 0.9828\n",
            "Epoch 66/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.4297e-05 - accuracy: 1.0000 - val_loss: 0.1183 - val_accuracy: 0.9827\n",
            "Epoch 67/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 1.2596e-05 - accuracy: 1.0000 - val_loss: 0.1199 - val_accuracy: 0.9824\n",
            "Epoch 68/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.1698e-05 - accuracy: 1.0000 - val_loss: 0.1189 - val_accuracy: 0.9825\n",
            "Epoch 69/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.0154e-05 - accuracy: 1.0000 - val_loss: 0.1203 - val_accuracy: 0.9825\n",
            "Epoch 70/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.1141e-05 - accuracy: 1.0000 - val_loss: 0.1202 - val_accuracy: 0.9828\n",
            "Epoch 71/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 0.0381 - accuracy: 0.9898 - val_loss: 0.1037 - val_accuracy: 0.9767\n",
            "Epoch 72/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 0.0095 - accuracy: 0.9970 - val_loss: 0.1065 - val_accuracy: 0.9787\n",
            "Epoch 73/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 0.0028 - accuracy: 0.9991 - val_loss: 0.1064 - val_accuracy: 0.9803\n",
            "Epoch 74/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 9.2752e-04 - accuracy: 0.9998 - val_loss: 0.1016 - val_accuracy: 0.9814\n",
            "Epoch 75/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 2.0898e-04 - accuracy: 1.0000 - val_loss: 0.1029 - val_accuracy: 0.9814\n",
            "Epoch 76/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 1.2841e-04 - accuracy: 1.0000 - val_loss: 0.1047 - val_accuracy: 0.9817\n",
            "Epoch 77/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.0155e-04 - accuracy: 1.0000 - val_loss: 0.1056 - val_accuracy: 0.9817\n",
            "Epoch 78/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 8.5438e-05 - accuracy: 1.0000 - val_loss: 0.1067 - val_accuracy: 0.9817\n",
            "Epoch 79/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 7.2379e-05 - accuracy: 1.0000 - val_loss: 0.1079 - val_accuracy: 0.9816\n",
            "Epoch 80/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 6.2471e-05 - accuracy: 1.0000 - val_loss: 0.1091 - val_accuracy: 0.9816\n",
            "Epoch 81/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 5.4800e-05 - accuracy: 1.0000 - val_loss: 0.1100 - val_accuracy: 0.9817\n",
            "Epoch 82/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 4.8435e-05 - accuracy: 1.0000 - val_loss: 0.1108 - val_accuracy: 0.9817\n",
            "Epoch 83/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 4.2931e-05 - accuracy: 1.0000 - val_loss: 0.1120 - val_accuracy: 0.9816\n",
            "Epoch 84/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 3.8077e-05 - accuracy: 1.0000 - val_loss: 0.1129 - val_accuracy: 0.9815\n",
            "Epoch 85/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 3.4007e-05 - accuracy: 1.0000 - val_loss: 0.1141 - val_accuracy: 0.9813\n",
            "Epoch 86/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 3.0038e-05 - accuracy: 1.0000 - val_loss: 0.1145 - val_accuracy: 0.9818\n",
            "Epoch 87/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.7128e-05 - accuracy: 1.0000 - val_loss: 0.1157 - val_accuracy: 0.9815\n",
            "Epoch 88/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.4273e-05 - accuracy: 1.0000 - val_loss: 0.1171 - val_accuracy: 0.9813\n",
            "Epoch 89/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.1565e-05 - accuracy: 1.0000 - val_loss: 0.1175 - val_accuracy: 0.9813\n",
            "Epoch 90/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.9332e-05 - accuracy: 1.0000 - val_loss: 0.1193 - val_accuracy: 0.9815\n",
            "Epoch 91/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 1.7081e-05 - accuracy: 1.0000 - val_loss: 0.1204 - val_accuracy: 0.9813\n",
            "Epoch 92/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 1.5186e-05 - accuracy: 1.0000 - val_loss: 0.1216 - val_accuracy: 0.9814\n",
            "Epoch 93/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.3580e-05 - accuracy: 1.0000 - val_loss: 0.1227 - val_accuracy: 0.9814\n",
            "Epoch 94/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.2263e-05 - accuracy: 1.0000 - val_loss: 0.1245 - val_accuracy: 0.9814\n",
            "Epoch 95/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.0930e-05 - accuracy: 1.0000 - val_loss: 0.1251 - val_accuracy: 0.9814\n",
            "Epoch 96/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 9.6319e-06 - accuracy: 1.0000 - val_loss: 0.1263 - val_accuracy: 0.9816\n",
            "Epoch 97/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 8.6795e-06 - accuracy: 1.0000 - val_loss: 0.1273 - val_accuracy: 0.9815\n",
            "Epoch 98/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 7.7520e-06 - accuracy: 1.0000 - val_loss: 0.1283 - val_accuracy: 0.9813\n",
            "Epoch 99/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 6.9609e-06 - accuracy: 1.0000 - val_loss: 0.1300 - val_accuracy: 0.9815\n",
            "Epoch 100/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 6.2191e-06 - accuracy: 1.0000 - val_loss: 0.1308 - val_accuracy: 0.9813\n",
            "Epoch 101/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 5.6718e-06 - accuracy: 1.0000 - val_loss: 0.1306 - val_accuracy: 0.9816\n",
            "Epoch 102/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 5.0129e-06 - accuracy: 1.0000 - val_loss: 0.1325 - val_accuracy: 0.9818\n",
            "Epoch 103/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 4.5138e-06 - accuracy: 1.0000 - val_loss: 0.1335 - val_accuracy: 0.9814\n",
            "Epoch 104/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 4.0090e-06 - accuracy: 1.0000 - val_loss: 0.1351 - val_accuracy: 0.9818\n",
            "Epoch 105/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 3.6053e-06 - accuracy: 1.0000 - val_loss: 0.1351 - val_accuracy: 0.9814\n",
            "Epoch 106/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 3.3079e-06 - accuracy: 1.0000 - val_loss: 0.1380 - val_accuracy: 0.9817\n",
            "Epoch 107/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.8859e-06 - accuracy: 1.0000 - val_loss: 0.1379 - val_accuracy: 0.9821\n",
            "Epoch 108/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 2.6132e-06 - accuracy: 1.0000 - val_loss: 0.1397 - val_accuracy: 0.9815\n",
            "Epoch 109/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 2.3786e-06 - accuracy: 1.0000 - val_loss: 0.1412 - val_accuracy: 0.9817\n",
            "Epoch 110/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.0737e-06 - accuracy: 1.0000 - val_loss: 0.1418 - val_accuracy: 0.9817\n",
            "Epoch 111/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.8731e-06 - accuracy: 1.0000 - val_loss: 0.1421 - val_accuracy: 0.9817\n",
            "Epoch 112/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.6975e-06 - accuracy: 1.0000 - val_loss: 0.1442 - val_accuracy: 0.9814\n",
            "Epoch 113/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.5152e-06 - accuracy: 1.0000 - val_loss: 0.1452 - val_accuracy: 0.9816\n",
            "Epoch 114/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.3451e-06 - accuracy: 1.0000 - val_loss: 0.1452 - val_accuracy: 0.9818\n",
            "Epoch 115/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.1859e-06 - accuracy: 1.0000 - val_loss: 0.1465 - val_accuracy: 0.9817\n",
            "Epoch 116/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 1.0857e-06 - accuracy: 1.0000 - val_loss: 0.1488 - val_accuracy: 0.9818\n",
            "Epoch 117/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 9.7039e-07 - accuracy: 1.0000 - val_loss: 0.1497 - val_accuracy: 0.9816\n",
            "Epoch 118/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 8.8494e-07 - accuracy: 1.0000 - val_loss: 0.1513 - val_accuracy: 0.9815\n",
            "Epoch 119/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 7.9591e-07 - accuracy: 1.0000 - val_loss: 0.1520 - val_accuracy: 0.9816\n",
            "Epoch 120/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 6.9700e-07 - accuracy: 1.0000 - val_loss: 0.1520 - val_accuracy: 0.9818\n",
            "Epoch 121/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 6.4812e-07 - accuracy: 1.0000 - val_loss: 0.1538 - val_accuracy: 0.9817\n",
            "Epoch 122/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 5.6394e-07 - accuracy: 1.0000 - val_loss: 0.1565 - val_accuracy: 0.9816\n",
            "Epoch 123/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 4.9045e-07 - accuracy: 1.0000 - val_loss: 0.1562 - val_accuracy: 0.9813\n",
            "Epoch 124/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 4.2905e-07 - accuracy: 1.0000 - val_loss: 0.1581 - val_accuracy: 0.9815\n",
            "Epoch 125/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 3.9966e-07 - accuracy: 1.0000 - val_loss: 0.1592 - val_accuracy: 0.9817\n",
            "Epoch 126/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 3.5744e-07 - accuracy: 1.0000 - val_loss: 0.1598 - val_accuracy: 0.9816\n",
            "Epoch 127/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 3.1636e-07 - accuracy: 1.0000 - val_loss: 0.1614 - val_accuracy: 0.9812\n",
            "Epoch 128/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.7788e-07 - accuracy: 1.0000 - val_loss: 0.1615 - val_accuracy: 0.9812\n",
            "Epoch 129/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.4684e-07 - accuracy: 1.0000 - val_loss: 0.1632 - val_accuracy: 0.9812\n",
            "Epoch 130/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.2117e-07 - accuracy: 1.0000 - val_loss: 0.1660 - val_accuracy: 0.9817\n",
            "Epoch 131/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.0027e-07 - accuracy: 1.0000 - val_loss: 0.1646 - val_accuracy: 0.9815\n",
            "Epoch 132/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 1.7961e-07 - accuracy: 1.0000 - val_loss: 0.1661 - val_accuracy: 0.9816\n",
            "Epoch 133/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 1.6614e-07 - accuracy: 1.0000 - val_loss: 0.1680 - val_accuracy: 0.9817\n",
            "Epoch 134/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.4274e-07 - accuracy: 1.0000 - val_loss: 0.1688 - val_accuracy: 0.9811\n",
            "Epoch 135/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.2604e-07 - accuracy: 1.0000 - val_loss: 0.1707 - val_accuracy: 0.9815\n",
            "Epoch 136/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.1431e-07 - accuracy: 1.0000 - val_loss: 0.1723 - val_accuracy: 0.9814\n",
            "Epoch 137/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.0296e-07 - accuracy: 1.0000 - val_loss: 0.1722 - val_accuracy: 0.9811\n",
            "Epoch 138/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 9.2331e-08 - accuracy: 1.0000 - val_loss: 0.1734 - val_accuracy: 0.9814\n",
            "Epoch 139/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 8.3109e-08 - accuracy: 1.0000 - val_loss: 0.1739 - val_accuracy: 0.9811\n",
            "Epoch 140/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 7.3681e-08 - accuracy: 1.0000 - val_loss: 0.1756 - val_accuracy: 0.9814\n",
            "Epoch 141/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 6.6302e-08 - accuracy: 1.0000 - val_loss: 0.1762 - val_accuracy: 0.9817\n",
            "Epoch 142/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 5.9519e-08 - accuracy: 1.0000 - val_loss: 0.1769 - val_accuracy: 0.9818\n",
            "Epoch 143/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 5.3183e-08 - accuracy: 1.0000 - val_loss: 0.1786 - val_accuracy: 0.9818\n",
            "Epoch 144/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 4.9607e-08 - accuracy: 1.0000 - val_loss: 0.1806 - val_accuracy: 0.9814\n",
            "Epoch 145/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 4.4517e-08 - accuracy: 1.0000 - val_loss: 0.1803 - val_accuracy: 0.9817\n",
            "Epoch 146/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 4.0291e-08 - accuracy: 1.0000 - val_loss: 0.1811 - val_accuracy: 0.9818\n",
            "Epoch 147/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 3.6015e-08 - accuracy: 1.0000 - val_loss: 0.1827 - val_accuracy: 0.9815\n",
            "Epoch 148/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 3.2872e-08 - accuracy: 1.0000 - val_loss: 0.1832 - val_accuracy: 0.9815\n",
            "Epoch 149/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 2.9880e-08 - accuracy: 1.0000 - val_loss: 0.1845 - val_accuracy: 0.9818\n",
            "Epoch 150/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.6745e-08 - accuracy: 1.0000 - val_loss: 0.1855 - val_accuracy: 0.9818\n",
            "Epoch 151/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.4567e-08 - accuracy: 1.0000 - val_loss: 0.1859 - val_accuracy: 0.9815\n",
            "Epoch 152/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.2415e-08 - accuracy: 1.0000 - val_loss: 0.1865 - val_accuracy: 0.9818\n",
            "Epoch 153/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.0719e-08 - accuracy: 1.0000 - val_loss: 0.1869 - val_accuracy: 0.9818\n",
            "Epoch 154/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.8630e-08 - accuracy: 1.0000 - val_loss: 0.1894 - val_accuracy: 0.9821\n",
            "Epoch 155/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.7307e-08 - accuracy: 1.0000 - val_loss: 0.1897 - val_accuracy: 0.9817\n",
            "Epoch 156/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.6139e-08 - accuracy: 1.0000 - val_loss: 0.1903 - val_accuracy: 0.9820\n",
            "Epoch 157/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 1.4722e-08 - accuracy: 1.0000 - val_loss: 0.1909 - val_accuracy: 0.9820\n",
            "Epoch 158/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.3481e-08 - accuracy: 1.0000 - val_loss: 0.1918 - val_accuracy: 0.9817\n",
            "Epoch 159/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.2573e-08 - accuracy: 1.0000 - val_loss: 0.1925 - val_accuracy: 0.9816\n",
            "Epoch 160/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.1814e-08 - accuracy: 1.0000 - val_loss: 0.1934 - val_accuracy: 0.9817\n",
            "Epoch 161/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.0884e-08 - accuracy: 1.0000 - val_loss: 0.1936 - val_accuracy: 0.9817\n",
            "Epoch 162/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.0188e-08 - accuracy: 1.0000 - val_loss: 0.1942 - val_accuracy: 0.9819\n",
            "Epoch 163/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 9.4314e-09 - accuracy: 1.0000 - val_loss: 0.1946 - val_accuracy: 0.9818\n",
            "Epoch 164/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 8.8493e-09 - accuracy: 1.0000 - val_loss: 0.1963 - val_accuracy: 0.9820\n",
            "Epoch 165/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 8.4142e-09 - accuracy: 1.0000 - val_loss: 0.1965 - val_accuracy: 0.9818\n",
            "Epoch 166/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 7.8241e-09 - accuracy: 1.0000 - val_loss: 0.1965 - val_accuracy: 0.9818\n",
            "Epoch 167/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 7.3493e-09 - accuracy: 1.0000 - val_loss: 0.1976 - val_accuracy: 0.9818\n",
            "Epoch 168/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 6.9300e-09 - accuracy: 1.0000 - val_loss: 0.1980 - val_accuracy: 0.9818\n",
            "Epoch 169/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 6.5307e-09 - accuracy: 1.0000 - val_loss: 0.1984 - val_accuracy: 0.9817\n",
            "Epoch 170/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 6.2386e-09 - accuracy: 1.0000 - val_loss: 0.1993 - val_accuracy: 0.9816\n",
            "Epoch 171/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 5.8909e-09 - accuracy: 1.0000 - val_loss: 0.1999 - val_accuracy: 0.9818\n",
            "Epoch 172/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 5.5492e-09 - accuracy: 1.0000 - val_loss: 0.1997 - val_accuracy: 0.9816\n",
            "Epoch 173/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 5.2691e-09 - accuracy: 1.0000 - val_loss: 0.1998 - val_accuracy: 0.9817\n",
            "Epoch 174/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 5.1061e-09 - accuracy: 1.0000 - val_loss: 0.1999 - val_accuracy: 0.9817\n",
            "Epoch 175/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 4.8677e-09 - accuracy: 1.0000 - val_loss: 0.2010 - val_accuracy: 0.9815\n",
            "Epoch 176/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 4.6412e-09 - accuracy: 1.0000 - val_loss: 0.2012 - val_accuracy: 0.9816\n",
            "Epoch 177/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 4.4862e-09 - accuracy: 1.0000 - val_loss: 0.2020 - val_accuracy: 0.9815\n",
            "Epoch 178/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 4.3154e-09 - accuracy: 1.0000 - val_loss: 0.2020 - val_accuracy: 0.9817\n",
            "Epoch 179/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 4.0948e-09 - accuracy: 1.0000 - val_loss: 0.2021 - val_accuracy: 0.9816\n",
            "Epoch 180/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 3.9518e-09 - accuracy: 1.0000 - val_loss: 0.2026 - val_accuracy: 0.9816\n",
            "Epoch 181/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 3.7531e-09 - accuracy: 1.0000 - val_loss: 0.2024 - val_accuracy: 0.9818\n",
            "Epoch 182/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 3.6995e-09 - accuracy: 1.0000 - val_loss: 0.2031 - val_accuracy: 0.9816\n",
            "Epoch 183/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 3.5226e-09 - accuracy: 1.0000 - val_loss: 0.2031 - val_accuracy: 0.9817\n",
            "Epoch 184/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 3.4451e-09 - accuracy: 1.0000 - val_loss: 0.2035 - val_accuracy: 0.9817\n",
            "Epoch 185/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 3.2723e-09 - accuracy: 1.0000 - val_loss: 0.2039 - val_accuracy: 0.9816\n",
            "Epoch 186/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 3.2147e-09 - accuracy: 1.0000 - val_loss: 0.2047 - val_accuracy: 0.9819\n",
            "Epoch 187/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 3.1213e-09 - accuracy: 1.0000 - val_loss: 0.2045 - val_accuracy: 0.9815\n",
            "Epoch 188/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.9882e-09 - accuracy: 1.0000 - val_loss: 0.2044 - val_accuracy: 0.9818\n",
            "Epoch 189/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 2.9246e-09 - accuracy: 1.0000 - val_loss: 0.2048 - val_accuracy: 0.9816\n",
            "Epoch 190/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 2.8372e-09 - accuracy: 1.0000 - val_loss: 0.2050 - val_accuracy: 0.9817\n",
            "Epoch 191/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.7915e-09 - accuracy: 1.0000 - val_loss: 0.2049 - val_accuracy: 0.9817\n",
            "Epoch 192/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.7140e-09 - accuracy: 1.0000 - val_loss: 0.2052 - val_accuracy: 0.9816\n",
            "Epoch 193/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 2.6623e-09 - accuracy: 1.0000 - val_loss: 0.2057 - val_accuracy: 0.9820\n",
            "Epoch 194/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.5789e-09 - accuracy: 1.0000 - val_loss: 0.2063 - val_accuracy: 0.9818\n",
            "Epoch 195/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.5252e-09 - accuracy: 1.0000 - val_loss: 0.2064 - val_accuracy: 0.9817\n",
            "Epoch 196/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.4358e-09 - accuracy: 1.0000 - val_loss: 0.2062 - val_accuracy: 0.9816\n",
            "Epoch 197/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 2.4378e-09 - accuracy: 1.0000 - val_loss: 0.2064 - val_accuracy: 0.9814\n",
            "Epoch 198/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 2.3444e-09 - accuracy: 1.0000 - val_loss: 0.2068 - val_accuracy: 0.9815\n",
            "Epoch 199/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.2988e-09 - accuracy: 1.0000 - val_loss: 0.2073 - val_accuracy: 0.9815\n",
            "Epoch 200/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.2610e-09 - accuracy: 1.0000 - val_loss: 0.2073 - val_accuracy: 0.9815\n",
            "Epoch 201/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.2113e-09 - accuracy: 1.0000 - val_loss: 0.2076 - val_accuracy: 0.9815\n",
            "Epoch 202/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.1835e-09 - accuracy: 1.0000 - val_loss: 0.2074 - val_accuracy: 0.9813\n",
            "Epoch 203/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.1696e-09 - accuracy: 1.0000 - val_loss: 0.2081 - val_accuracy: 0.9818\n",
            "Epoch 204/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 2.1398e-09 - accuracy: 1.0000 - val_loss: 0.2081 - val_accuracy: 0.9814\n",
            "Epoch 205/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 2.0901e-09 - accuracy: 1.0000 - val_loss: 0.2083 - val_accuracy: 0.9815\n",
            "Epoch 206/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 2.0703e-09 - accuracy: 1.0000 - val_loss: 0.2086 - val_accuracy: 0.9817\n",
            "Epoch 207/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.0623e-09 - accuracy: 1.0000 - val_loss: 0.2089 - val_accuracy: 0.9814\n",
            "Epoch 208/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.9868e-09 - accuracy: 1.0000 - val_loss: 0.2089 - val_accuracy: 0.9813\n",
            "Epoch 209/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 2.0067e-09 - accuracy: 1.0000 - val_loss: 0.2092 - val_accuracy: 0.9813\n",
            "Epoch 210/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.9848e-09 - accuracy: 1.0000 - val_loss: 0.2095 - val_accuracy: 0.9814\n",
            "Epoch 211/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.9709e-09 - accuracy: 1.0000 - val_loss: 0.2095 - val_accuracy: 0.9813\n",
            "Epoch 212/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.9729e-09 - accuracy: 1.0000 - val_loss: 0.2095 - val_accuracy: 0.9813\n",
            "Epoch 213/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 1.9570e-09 - accuracy: 1.0000 - val_loss: 0.2103 - val_accuracy: 0.9812\n",
            "Epoch 214/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 1.9113e-09 - accuracy: 1.0000 - val_loss: 0.2097 - val_accuracy: 0.9814\n",
            "Epoch 215/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.9272e-09 - accuracy: 1.0000 - val_loss: 0.2101 - val_accuracy: 0.9813\n",
            "Epoch 216/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.8835e-09 - accuracy: 1.0000 - val_loss: 0.2103 - val_accuracy: 0.9814\n",
            "Epoch 217/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.8835e-09 - accuracy: 1.0000 - val_loss: 0.2108 - val_accuracy: 0.9813\n",
            "Epoch 218/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.8418e-09 - accuracy: 1.0000 - val_loss: 0.2107 - val_accuracy: 0.9811\n",
            "Epoch 219/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.8736e-09 - accuracy: 1.0000 - val_loss: 0.2114 - val_accuracy: 0.9815\n",
            "Epoch 220/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.8716e-09 - accuracy: 1.0000 - val_loss: 0.2114 - val_accuracy: 0.9815\n",
            "Epoch 221/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 1.8358e-09 - accuracy: 1.0000 - val_loss: 0.2117 - val_accuracy: 0.9813\n",
            "Epoch 222/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 1.8239e-09 - accuracy: 1.0000 - val_loss: 0.2117 - val_accuracy: 0.9812\n",
            "Epoch 223/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.8636e-09 - accuracy: 1.0000 - val_loss: 0.2122 - val_accuracy: 0.9814\n",
            "Epoch 224/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.8617e-09 - accuracy: 1.0000 - val_loss: 0.2123 - val_accuracy: 0.9814\n",
            "Epoch 225/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 1.8696e-09 - accuracy: 1.0000 - val_loss: 0.2130 - val_accuracy: 0.9814\n",
            "Epoch 226/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.8120e-09 - accuracy: 1.0000 - val_loss: 0.2123 - val_accuracy: 0.9815\n",
            "Epoch 227/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.8020e-09 - accuracy: 1.0000 - val_loss: 0.2129 - val_accuracy: 0.9814\n",
            "Epoch 228/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.7782e-09 - accuracy: 1.0000 - val_loss: 0.2135 - val_accuracy: 0.9816\n",
            "Epoch 229/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 1.8219e-09 - accuracy: 1.0000 - val_loss: 0.2138 - val_accuracy: 0.9814\n",
            "Epoch 230/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 1.8040e-09 - accuracy: 1.0000 - val_loss: 0.2140 - val_accuracy: 0.9817\n",
            "Epoch 231/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.7901e-09 - accuracy: 1.0000 - val_loss: 0.2139 - val_accuracy: 0.9814\n",
            "Epoch 232/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.8259e-09 - accuracy: 1.0000 - val_loss: 0.2143 - val_accuracy: 0.9816\n",
            "Epoch 233/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.8279e-09 - accuracy: 1.0000 - val_loss: 0.2147 - val_accuracy: 0.9815\n",
            "Epoch 234/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.8160e-09 - accuracy: 1.0000 - val_loss: 0.2153 - val_accuracy: 0.9817\n",
            "Epoch 235/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.8438e-09 - accuracy: 1.0000 - val_loss: 0.2150 - val_accuracy: 0.9816\n",
            "Epoch 236/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.8100e-09 - accuracy: 1.0000 - val_loss: 0.2152 - val_accuracy: 0.9815\n",
            "Epoch 237/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.8438e-09 - accuracy: 1.0000 - val_loss: 0.2161 - val_accuracy: 0.9816\n",
            "Epoch 238/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 1.7901e-09 - accuracy: 1.0000 - val_loss: 0.2156 - val_accuracy: 0.9815\n",
            "Epoch 239/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 1.8378e-09 - accuracy: 1.0000 - val_loss: 0.2163 - val_accuracy: 0.9813\n",
            "Epoch 240/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.8537e-09 - accuracy: 1.0000 - val_loss: 0.2168 - val_accuracy: 0.9816\n",
            "Epoch 241/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.7782e-09 - accuracy: 1.0000 - val_loss: 0.2165 - val_accuracy: 0.9814\n",
            "Epoch 242/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.8259e-09 - accuracy: 1.0000 - val_loss: 0.2168 - val_accuracy: 0.9815\n",
            "Epoch 243/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.7941e-09 - accuracy: 1.0000 - val_loss: 0.2173 - val_accuracy: 0.9815\n",
            "Epoch 244/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.8438e-09 - accuracy: 1.0000 - val_loss: 0.2177 - val_accuracy: 0.9815\n",
            "Epoch 245/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.8338e-09 - accuracy: 1.0000 - val_loss: 0.2178 - val_accuracy: 0.9816\n",
            "Epoch 246/1000\n",
            "235/235 [==============================] - 3s 11ms/step - loss: 1.8100e-09 - accuracy: 1.0000 - val_loss: 0.2182 - val_accuracy: 0.9814\n",
            "Epoch 247/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.8855e-09 - accuracy: 1.0000 - val_loss: 0.2184 - val_accuracy: 0.9815\n",
            "Epoch 248/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.8458e-09 - accuracy: 1.0000 - val_loss: 0.2191 - val_accuracy: 0.9813\n",
            "Epoch 249/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.8438e-09 - accuracy: 1.0000 - val_loss: 0.2191 - val_accuracy: 0.9814\n",
            "Epoch 250/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.8438e-09 - accuracy: 1.0000 - val_loss: 0.2199 - val_accuracy: 0.9814\n",
            "Epoch 251/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.8775e-09 - accuracy: 1.0000 - val_loss: 0.2201 - val_accuracy: 0.9816\n",
            "Epoch 252/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 1.8477e-09 - accuracy: 1.0000 - val_loss: 0.2204 - val_accuracy: 0.9814\n",
            "Epoch 253/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 1.8557e-09 - accuracy: 1.0000 - val_loss: 0.2208 - val_accuracy: 0.9814\n",
            "Epoch 254/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 1.8497e-09 - accuracy: 1.0000 - val_loss: 0.2207 - val_accuracy: 0.9814\n",
            "Epoch 255/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.8736e-09 - accuracy: 1.0000 - val_loss: 0.2212 - val_accuracy: 0.9817\n",
            "Epoch 256/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.8974e-09 - accuracy: 1.0000 - val_loss: 0.2216 - val_accuracy: 0.9815\n",
            "Epoch 257/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 1.8477e-09 - accuracy: 1.0000 - val_loss: 0.2222 - val_accuracy: 0.9818\n",
            "Epoch 258/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.8060e-09 - accuracy: 1.0000 - val_loss: 0.2229 - val_accuracy: 0.9814\n",
            "Epoch 259/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 1.9034e-09 - accuracy: 1.0000 - val_loss: 0.2227 - val_accuracy: 0.9815\n",
            "Epoch 260/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 1.8815e-09 - accuracy: 1.0000 - val_loss: 0.2234 - val_accuracy: 0.9817\n",
            "Epoch 261/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 1.8795e-09 - accuracy: 1.0000 - val_loss: 0.2239 - val_accuracy: 0.9819\n",
            "Epoch 262/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 1.8915e-09 - accuracy: 1.0000 - val_loss: 0.2243 - val_accuracy: 0.9816\n",
            "Epoch 263/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.8994e-09 - accuracy: 1.0000 - val_loss: 0.2244 - val_accuracy: 0.9816\n",
            "Epoch 264/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.8994e-09 - accuracy: 1.0000 - val_loss: 0.2253 - val_accuracy: 0.9816\n",
            "Epoch 265/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.8795e-09 - accuracy: 1.0000 - val_loss: 0.2255 - val_accuracy: 0.9816\n",
            "Epoch 266/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 1.9372e-09 - accuracy: 1.0000 - val_loss: 0.2259 - val_accuracy: 0.9817\n",
            "Epoch 267/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 1.8855e-09 - accuracy: 1.0000 - val_loss: 0.2256 - val_accuracy: 0.9815\n",
            "Epoch 268/1000\n",
            "235/235 [==============================] - 3s 11ms/step - loss: 1.9113e-09 - accuracy: 1.0000 - val_loss: 0.2266 - val_accuracy: 0.9814\n",
            "Epoch 269/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 1.9590e-09 - accuracy: 1.0000 - val_loss: 0.2268 - val_accuracy: 0.9816\n",
            "Epoch 270/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.9372e-09 - accuracy: 1.0000 - val_loss: 0.2281 - val_accuracy: 0.9815\n",
            "Epoch 271/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.9471e-09 - accuracy: 1.0000 - val_loss: 0.2284 - val_accuracy: 0.9817\n",
            "Epoch 272/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.9372e-09 - accuracy: 1.0000 - val_loss: 0.2282 - val_accuracy: 0.9816\n",
            "Epoch 273/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.9332e-09 - accuracy: 1.0000 - val_loss: 0.2289 - val_accuracy: 0.9813\n",
            "Epoch 274/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.9550e-09 - accuracy: 1.0000 - val_loss: 0.2298 - val_accuracy: 0.9815\n",
            "Epoch 275/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.9530e-09 - accuracy: 1.0000 - val_loss: 0.2301 - val_accuracy: 0.9815\n",
            "Epoch 276/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.9729e-09 - accuracy: 1.0000 - val_loss: 0.2307 - val_accuracy: 0.9815\n",
            "Epoch 277/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 1.9272e-09 - accuracy: 1.0000 - val_loss: 0.2303 - val_accuracy: 0.9814\n",
            "Epoch 278/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.9391e-09 - accuracy: 1.0000 - val_loss: 0.2312 - val_accuracy: 0.9815\n",
            "Epoch 279/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.9511e-09 - accuracy: 1.0000 - val_loss: 0.2317 - val_accuracy: 0.9815\n",
            "Epoch 280/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.9352e-09 - accuracy: 1.0000 - val_loss: 0.2320 - val_accuracy: 0.9814\n",
            "Epoch 281/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.9769e-09 - accuracy: 1.0000 - val_loss: 0.2323 - val_accuracy: 0.9813\n",
            "Epoch 282/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.9332e-09 - accuracy: 1.0000 - val_loss: 0.2327 - val_accuracy: 0.9813\n",
            "Epoch 283/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.0087e-09 - accuracy: 1.0000 - val_loss: 0.2327 - val_accuracy: 0.9813\n",
            "Epoch 284/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 2.0087e-09 - accuracy: 1.0000 - val_loss: 0.2343 - val_accuracy: 0.9813\n",
            "Epoch 285/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 2.0345e-09 - accuracy: 1.0000 - val_loss: 0.2339 - val_accuracy: 0.9813\n",
            "Epoch 286/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 2.0226e-09 - accuracy: 1.0000 - val_loss: 0.2335 - val_accuracy: 0.9812\n",
            "Epoch 287/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.9868e-09 - accuracy: 1.0000 - val_loss: 0.2347 - val_accuracy: 0.9815\n",
            "Epoch 288/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 2.1001e-09 - accuracy: 1.0000 - val_loss: 0.2351 - val_accuracy: 0.9813\n",
            "Epoch 289/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.0365e-09 - accuracy: 1.0000 - val_loss: 0.2352 - val_accuracy: 0.9810\n",
            "Epoch 290/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.0663e-09 - accuracy: 1.0000 - val_loss: 0.2358 - val_accuracy: 0.9815\n",
            "Epoch 291/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.0762e-09 - accuracy: 1.0000 - val_loss: 0.2365 - val_accuracy: 0.9814\n",
            "Epoch 292/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 2.0862e-09 - accuracy: 1.0000 - val_loss: 0.2363 - val_accuracy: 0.9813\n",
            "Epoch 293/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 2.0822e-09 - accuracy: 1.0000 - val_loss: 0.2371 - val_accuracy: 0.9815\n",
            "Epoch 294/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 2.0961e-09 - accuracy: 1.0000 - val_loss: 0.2378 - val_accuracy: 0.9817\n",
            "Epoch 295/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 2.0842e-09 - accuracy: 1.0000 - val_loss: 0.2379 - val_accuracy: 0.9815\n",
            "Epoch 296/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.1180e-09 - accuracy: 1.0000 - val_loss: 0.2391 - val_accuracy: 0.9813\n",
            "Epoch 297/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.1100e-09 - accuracy: 1.0000 - val_loss: 0.2384 - val_accuracy: 0.9816\n",
            "Epoch 298/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.1418e-09 - accuracy: 1.0000 - val_loss: 0.2397 - val_accuracy: 0.9815\n",
            "Epoch 299/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.1319e-09 - accuracy: 1.0000 - val_loss: 0.2390 - val_accuracy: 0.9814\n",
            "Epoch 300/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 2.1398e-09 - accuracy: 1.0000 - val_loss: 0.2403 - val_accuracy: 0.9814\n",
            "Epoch 301/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 2.1418e-09 - accuracy: 1.0000 - val_loss: 0.2402 - val_accuracy: 0.9815\n",
            "Epoch 302/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 2.1299e-09 - accuracy: 1.0000 - val_loss: 0.2413 - val_accuracy: 0.9815\n",
            "Epoch 303/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.1577e-09 - accuracy: 1.0000 - val_loss: 0.2411 - val_accuracy: 0.9818\n",
            "Epoch 304/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 2.1895e-09 - accuracy: 1.0000 - val_loss: 0.2423 - val_accuracy: 0.9816\n",
            "Epoch 305/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.1756e-09 - accuracy: 1.0000 - val_loss: 0.2423 - val_accuracy: 0.9816\n",
            "Epoch 306/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.2749e-09 - accuracy: 1.0000 - val_loss: 0.2428 - val_accuracy: 0.9815\n",
            "Epoch 307/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.2113e-09 - accuracy: 1.0000 - val_loss: 0.2440 - val_accuracy: 0.9815\n",
            "Epoch 308/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 2.1855e-09 - accuracy: 1.0000 - val_loss: 0.2434 - val_accuracy: 0.9815\n",
            "Epoch 309/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 2.1815e-09 - accuracy: 1.0000 - val_loss: 0.2449 - val_accuracy: 0.9819\n",
            "Epoch 310/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 2.2133e-09 - accuracy: 1.0000 - val_loss: 0.2443 - val_accuracy: 0.9814\n",
            "Epoch 311/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 2.3027e-09 - accuracy: 1.0000 - val_loss: 0.2456 - val_accuracy: 0.9818\n",
            "Epoch 312/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 2.2511e-09 - accuracy: 1.0000 - val_loss: 0.2465 - val_accuracy: 0.9813\n",
            "Epoch 313/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.2292e-09 - accuracy: 1.0000 - val_loss: 0.2461 - val_accuracy: 0.9817\n",
            "Epoch 314/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.3206e-09 - accuracy: 1.0000 - val_loss: 0.2474 - val_accuracy: 0.9819\n",
            "Epoch 315/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 2.2471e-09 - accuracy: 1.0000 - val_loss: 0.2473 - val_accuracy: 0.9815\n",
            "Epoch 316/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 2.3266e-09 - accuracy: 1.0000 - val_loss: 0.2486 - val_accuracy: 0.9816\n",
            "Epoch 317/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 2.2868e-09 - accuracy: 1.0000 - val_loss: 0.2495 - val_accuracy: 0.9816\n",
            "Epoch 318/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 2.3246e-09 - accuracy: 1.0000 - val_loss: 0.2489 - val_accuracy: 0.9815\n",
            "Epoch 319/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.3047e-09 - accuracy: 1.0000 - val_loss: 0.2505 - val_accuracy: 0.9815\n",
            "Epoch 320/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.3921e-09 - accuracy: 1.0000 - val_loss: 0.2505 - val_accuracy: 0.9815\n",
            "Epoch 321/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 2.2749e-09 - accuracy: 1.0000 - val_loss: 0.2510 - val_accuracy: 0.9811\n",
            "Epoch 322/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 2.2908e-09 - accuracy: 1.0000 - val_loss: 0.2507 - val_accuracy: 0.9816\n",
            "Epoch 323/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 2.2888e-09 - accuracy: 1.0000 - val_loss: 0.2534 - val_accuracy: 0.9817\n",
            "Epoch 324/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 2.3981e-09 - accuracy: 1.0000 - val_loss: 0.2530 - val_accuracy: 0.9816\n",
            "Epoch 325/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 2.5491e-09 - accuracy: 1.0000 - val_loss: 0.2535 - val_accuracy: 0.9817\n",
            "Epoch 326/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.3703e-09 - accuracy: 1.0000 - val_loss: 0.2545 - val_accuracy: 0.9815\n",
            "Epoch 327/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 2.3206e-09 - accuracy: 1.0000 - val_loss: 0.2564 - val_accuracy: 0.9816\n",
            "Epoch 328/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.5411e-09 - accuracy: 1.0000 - val_loss: 0.2564 - val_accuracy: 0.9814\n",
            "Epoch 329/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.5670e-09 - accuracy: 1.0000 - val_loss: 0.2559 - val_accuracy: 0.9814\n",
            "Epoch 330/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 2.4398e-09 - accuracy: 1.0000 - val_loss: 0.2572 - val_accuracy: 0.9812\n",
            "Epoch 331/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 2.5471e-09 - accuracy: 1.0000 - val_loss: 0.2573 - val_accuracy: 0.9813\n",
            "Epoch 332/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 2.5193e-09 - accuracy: 1.0000 - val_loss: 0.2581 - val_accuracy: 0.9810\n",
            "Epoch 333/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 2.5809e-09 - accuracy: 1.0000 - val_loss: 0.2578 - val_accuracy: 0.9814\n",
            "Epoch 334/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.5113e-09 - accuracy: 1.0000 - val_loss: 0.2613 - val_accuracy: 0.9814\n",
            "Epoch 335/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 2.6822e-09 - accuracy: 1.0000 - val_loss: 0.2595 - val_accuracy: 0.9813\n",
            "Epoch 336/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.5789e-09 - accuracy: 1.0000 - val_loss: 0.2605 - val_accuracy: 0.9813\n",
            "Epoch 337/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 0.0648 - accuracy: 0.9928 - val_loss: 0.2201 - val_accuracy: 0.9803\n",
            "Epoch 338/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 0.0102 - accuracy: 0.9975 - val_loss: 0.2296 - val_accuracy: 0.9797\n",
            "Epoch 339/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 0.0061 - accuracy: 0.9986 - val_loss: 0.2191 - val_accuracy: 0.9798\n",
            "Epoch 340/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 8.9837e-04 - accuracy: 0.9997 - val_loss: 0.2111 - val_accuracy: 0.9805\n",
            "Epoch 341/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 4.9283e-04 - accuracy: 0.9998 - val_loss: 0.2065 - val_accuracy: 0.9807\n",
            "Epoch 342/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 3.4283e-04 - accuracy: 0.9999 - val_loss: 0.2111 - val_accuracy: 0.9815\n",
            "Epoch 343/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.3612e-05 - accuracy: 1.0000 - val_loss: 0.2061 - val_accuracy: 0.9812\n",
            "Epoch 344/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 7.7403e-06 - accuracy: 1.0000 - val_loss: 0.2060 - val_accuracy: 0.9815\n",
            "Epoch 345/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 4.8783e-06 - accuracy: 1.0000 - val_loss: 0.2059 - val_accuracy: 0.9817\n",
            "Epoch 346/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 4.1016e-06 - accuracy: 1.0000 - val_loss: 0.2059 - val_accuracy: 0.9817\n",
            "Epoch 347/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 3.5809e-06 - accuracy: 1.0000 - val_loss: 0.2058 - val_accuracy: 0.9816\n",
            "Epoch 348/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 3.1930e-06 - accuracy: 1.0000 - val_loss: 0.2057 - val_accuracy: 0.9816\n",
            "Epoch 349/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 2.8730e-06 - accuracy: 1.0000 - val_loss: 0.2058 - val_accuracy: 0.9816\n",
            "Epoch 350/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.6004e-06 - accuracy: 1.0000 - val_loss: 0.2057 - val_accuracy: 0.9815\n",
            "Epoch 351/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 2.3708e-06 - accuracy: 1.0000 - val_loss: 0.2057 - val_accuracy: 0.9815\n",
            "Epoch 352/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.1689e-06 - accuracy: 1.0000 - val_loss: 0.2057 - val_accuracy: 0.9815\n",
            "Epoch 353/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.9902e-06 - accuracy: 1.0000 - val_loss: 0.2057 - val_accuracy: 0.9814\n",
            "Epoch 354/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.8350e-06 - accuracy: 1.0000 - val_loss: 0.2057 - val_accuracy: 0.9814\n",
            "Epoch 355/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.6915e-06 - accuracy: 1.0000 - val_loss: 0.2057 - val_accuracy: 0.9814\n",
            "Epoch 356/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 1.5639e-06 - accuracy: 1.0000 - val_loss: 0.2058 - val_accuracy: 0.9814\n",
            "Epoch 357/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 1.4409e-06 - accuracy: 1.0000 - val_loss: 0.2059 - val_accuracy: 0.9813\n",
            "Epoch 358/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.3343e-06 - accuracy: 1.0000 - val_loss: 0.2059 - val_accuracy: 0.9812\n",
            "Epoch 359/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.2328e-06 - accuracy: 1.0000 - val_loss: 0.2061 - val_accuracy: 0.9812\n",
            "Epoch 360/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.1442e-06 - accuracy: 1.0000 - val_loss: 0.2063 - val_accuracy: 0.9813\n",
            "Epoch 361/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.0613e-06 - accuracy: 1.0000 - val_loss: 0.2064 - val_accuracy: 0.9813\n",
            "Epoch 362/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 9.8318e-07 - accuracy: 1.0000 - val_loss: 0.2065 - val_accuracy: 0.9813\n",
            "Epoch 363/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 9.1280e-07 - accuracy: 1.0000 - val_loss: 0.2066 - val_accuracy: 0.9813\n",
            "Epoch 364/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 8.4713e-07 - accuracy: 1.0000 - val_loss: 0.2067 - val_accuracy: 0.9813\n",
            "Epoch 365/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 7.8315e-07 - accuracy: 1.0000 - val_loss: 0.2069 - val_accuracy: 0.9813\n",
            "Epoch 366/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 7.2462e-07 - accuracy: 1.0000 - val_loss: 0.2071 - val_accuracy: 0.9812\n",
            "Epoch 367/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 6.7128e-07 - accuracy: 1.0000 - val_loss: 0.2073 - val_accuracy: 0.9814\n",
            "Epoch 368/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 6.2190e-07 - accuracy: 1.0000 - val_loss: 0.2075 - val_accuracy: 0.9814\n",
            "Epoch 369/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 5.7628e-07 - accuracy: 1.0000 - val_loss: 0.2077 - val_accuracy: 0.9814\n",
            "Epoch 370/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 5.3356e-07 - accuracy: 1.0000 - val_loss: 0.2080 - val_accuracy: 0.9814\n",
            "Epoch 371/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.9301e-07 - accuracy: 1.0000 - val_loss: 0.2082 - val_accuracy: 0.9816\n",
            "Epoch 372/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 4.5691e-07 - accuracy: 1.0000 - val_loss: 0.2085 - val_accuracy: 0.9816\n",
            "Epoch 373/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 4.2318e-07 - accuracy: 1.0000 - val_loss: 0.2087 - val_accuracy: 0.9815\n",
            "Epoch 374/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 3.9133e-07 - accuracy: 1.0000 - val_loss: 0.2090 - val_accuracy: 0.9815\n",
            "Epoch 375/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 3.6342e-07 - accuracy: 1.0000 - val_loss: 0.2093 - val_accuracy: 0.9815\n",
            "Epoch 376/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 3.3524e-07 - accuracy: 1.0000 - val_loss: 0.2097 - val_accuracy: 0.9816\n",
            "Epoch 377/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 3.1002e-07 - accuracy: 1.0000 - val_loss: 0.2099 - val_accuracy: 0.9815\n",
            "Epoch 378/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 2.8649e-07 - accuracy: 1.0000 - val_loss: 0.2103 - val_accuracy: 0.9816\n",
            "Epoch 379/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 2.6429e-07 - accuracy: 1.0000 - val_loss: 0.2106 - val_accuracy: 0.9815\n",
            "Epoch 380/1000\n",
            "235/235 [==============================] - 3s 11ms/step - loss: 2.4353e-07 - accuracy: 1.0000 - val_loss: 0.2109 - val_accuracy: 0.9814\n",
            "Epoch 381/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 2.2515e-07 - accuracy: 1.0000 - val_loss: 0.2112 - val_accuracy: 0.9814\n",
            "Epoch 382/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 2.0744e-07 - accuracy: 1.0000 - val_loss: 0.2116 - val_accuracy: 0.9815\n",
            "Epoch 383/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.9140e-07 - accuracy: 1.0000 - val_loss: 0.2121 - val_accuracy: 0.9814\n",
            "Epoch 384/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.7596e-07 - accuracy: 1.0000 - val_loss: 0.2124 - val_accuracy: 0.9815\n",
            "Epoch 385/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.6237e-07 - accuracy: 1.0000 - val_loss: 0.2128 - val_accuracy: 0.9814\n",
            "Epoch 386/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.4944e-07 - accuracy: 1.0000 - val_loss: 0.2132 - val_accuracy: 0.9815\n",
            "Epoch 387/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.3720e-07 - accuracy: 1.0000 - val_loss: 0.2135 - val_accuracy: 0.9815\n",
            "Epoch 388/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 1.2590e-07 - accuracy: 1.0000 - val_loss: 0.2137 - val_accuracy: 0.9815\n",
            "Epoch 389/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.1620e-07 - accuracy: 1.0000 - val_loss: 0.2142 - val_accuracy: 0.9815\n",
            "Epoch 390/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 1.0641e-07 - accuracy: 1.0000 - val_loss: 0.2146 - val_accuracy: 0.9815\n",
            "Epoch 391/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 9.7420e-08 - accuracy: 1.0000 - val_loss: 0.2149 - val_accuracy: 0.9815\n",
            "Epoch 392/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 8.9514e-08 - accuracy: 1.0000 - val_loss: 0.2155 - val_accuracy: 0.9816\n",
            "Epoch 393/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 8.2185e-08 - accuracy: 1.0000 - val_loss: 0.2158 - val_accuracy: 0.9815\n",
            "Epoch 394/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 7.5163e-08 - accuracy: 1.0000 - val_loss: 0.2164 - val_accuracy: 0.9815\n",
            "Epoch 395/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 6.9336e-08 - accuracy: 1.0000 - val_loss: 0.2168 - val_accuracy: 0.9815\n",
            "Epoch 396/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 6.3217e-08 - accuracy: 1.0000 - val_loss: 0.2171 - val_accuracy: 0.9816\n",
            "Epoch 397/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 5.8275e-08 - accuracy: 1.0000 - val_loss: 0.2179 - val_accuracy: 0.9815\n",
            "Epoch 398/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 5.3227e-08 - accuracy: 1.0000 - val_loss: 0.2184 - val_accuracy: 0.9815\n",
            "Epoch 399/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 4.8735e-08 - accuracy: 1.0000 - val_loss: 0.2189 - val_accuracy: 0.9815\n",
            "Epoch 400/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 4.4568e-08 - accuracy: 1.0000 - val_loss: 0.2193 - val_accuracy: 0.9815\n",
            "Epoch 401/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 4.0841e-08 - accuracy: 1.0000 - val_loss: 0.2196 - val_accuracy: 0.9815\n",
            "Epoch 402/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 3.7364e-08 - accuracy: 1.0000 - val_loss: 0.2202 - val_accuracy: 0.9815\n",
            "Epoch 403/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 3.4239e-08 - accuracy: 1.0000 - val_loss: 0.2208 - val_accuracy: 0.9815\n",
            "Epoch 404/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 3.1374e-08 - accuracy: 1.0000 - val_loss: 0.2214 - val_accuracy: 0.9815\n",
            "Epoch 405/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 2.8729e-08 - accuracy: 1.0000 - val_loss: 0.2222 - val_accuracy: 0.9815\n",
            "Epoch 406/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.6313e-08 - accuracy: 1.0000 - val_loss: 0.2227 - val_accuracy: 0.9814\n",
            "Epoch 407/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 2.3989e-08 - accuracy: 1.0000 - val_loss: 0.2232 - val_accuracy: 0.9815\n",
            "Epoch 408/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 2.1791e-08 - accuracy: 1.0000 - val_loss: 0.2237 - val_accuracy: 0.9813\n",
            "Epoch 409/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.0109e-08 - accuracy: 1.0000 - val_loss: 0.2243 - val_accuracy: 0.9816\n",
            "Epoch 410/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.8424e-08 - accuracy: 1.0000 - val_loss: 0.2251 - val_accuracy: 0.9815\n",
            "Epoch 411/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 1.6878e-08 - accuracy: 1.0000 - val_loss: 0.2257 - val_accuracy: 0.9818\n",
            "Epoch 412/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 1.5434e-08 - accuracy: 1.0000 - val_loss: 0.2262 - val_accuracy: 0.9817\n",
            "Epoch 413/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 1.4265e-08 - accuracy: 1.0000 - val_loss: 0.2270 - val_accuracy: 0.9817\n",
            "Epoch 414/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 1.3101e-08 - accuracy: 1.0000 - val_loss: 0.2275 - val_accuracy: 0.9817\n",
            "Epoch 415/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 1.2068e-08 - accuracy: 1.0000 - val_loss: 0.2280 - val_accuracy: 0.9816\n",
            "Epoch 416/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.1043e-08 - accuracy: 1.0000 - val_loss: 0.2287 - val_accuracy: 0.9817\n",
            "Epoch 417/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.0250e-08 - accuracy: 1.0000 - val_loss: 0.2293 - val_accuracy: 0.9817\n",
            "Epoch 418/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 9.4195e-09 - accuracy: 1.0000 - val_loss: 0.2301 - val_accuracy: 0.9817\n",
            "Epoch 419/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 8.7341e-09 - accuracy: 1.0000 - val_loss: 0.2307 - val_accuracy: 0.9818\n",
            "Epoch 420/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 8.0029e-09 - accuracy: 1.0000 - val_loss: 0.2314 - val_accuracy: 0.9818\n",
            "Epoch 421/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 7.4228e-09 - accuracy: 1.0000 - val_loss: 0.2324 - val_accuracy: 0.9817\n",
            "Epoch 422/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 6.8426e-09 - accuracy: 1.0000 - val_loss: 0.2325 - val_accuracy: 0.9817\n",
            "Epoch 423/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 6.3161e-09 - accuracy: 1.0000 - val_loss: 0.2336 - val_accuracy: 0.9818\n",
            "Epoch 424/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 5.8631e-09 - accuracy: 1.0000 - val_loss: 0.2342 - val_accuracy: 0.9818\n",
            "Epoch 425/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 5.4518e-09 - accuracy: 1.0000 - val_loss: 0.2347 - val_accuracy: 0.9815\n",
            "Epoch 426/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 5.0366e-09 - accuracy: 1.0000 - val_loss: 0.2357 - val_accuracy: 0.9816\n",
            "Epoch 427/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 4.7207e-09 - accuracy: 1.0000 - val_loss: 0.2361 - val_accuracy: 0.9817\n",
            "Epoch 428/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 4.4107e-09 - accuracy: 1.0000 - val_loss: 0.2376 - val_accuracy: 0.9815\n",
            "Epoch 429/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.0472e-09 - accuracy: 1.0000 - val_loss: 0.2384 - val_accuracy: 0.9815\n",
            "Epoch 430/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 3.7948e-09 - accuracy: 1.0000 - val_loss: 0.2385 - val_accuracy: 0.9815\n",
            "Epoch 431/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 3.6041e-09 - accuracy: 1.0000 - val_loss: 0.2392 - val_accuracy: 0.9815\n",
            "Epoch 432/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 3.3915e-09 - accuracy: 1.0000 - val_loss: 0.2400 - val_accuracy: 0.9814\n",
            "Epoch 433/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 3.2127e-09 - accuracy: 1.0000 - val_loss: 0.2407 - val_accuracy: 0.9813\n",
            "Epoch 434/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 3.0001e-09 - accuracy: 1.0000 - val_loss: 0.2417 - val_accuracy: 0.9816\n",
            "Epoch 435/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 2.8471e-09 - accuracy: 1.0000 - val_loss: 0.2427 - val_accuracy: 0.9815\n",
            "Epoch 436/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 2.7100e-09 - accuracy: 1.0000 - val_loss: 0.2432 - val_accuracy: 0.9816\n",
            "Epoch 437/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.5650e-09 - accuracy: 1.0000 - val_loss: 0.2433 - val_accuracy: 0.9816\n",
            "Epoch 438/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.4219e-09 - accuracy: 1.0000 - val_loss: 0.2449 - val_accuracy: 0.9817\n",
            "Epoch 439/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 2.3504e-09 - accuracy: 1.0000 - val_loss: 0.2457 - val_accuracy: 0.9816\n",
            "Epoch 440/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.2431e-09 - accuracy: 1.0000 - val_loss: 0.2464 - val_accuracy: 0.9814\n",
            "Epoch 441/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 2.1279e-09 - accuracy: 1.0000 - val_loss: 0.2474 - val_accuracy: 0.9815\n",
            "Epoch 442/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 2.0683e-09 - accuracy: 1.0000 - val_loss: 0.2481 - val_accuracy: 0.9813\n",
            "Epoch 443/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 1.9928e-09 - accuracy: 1.0000 - val_loss: 0.2489 - val_accuracy: 0.9815\n",
            "Epoch 444/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 1.8895e-09 - accuracy: 1.0000 - val_loss: 0.2498 - val_accuracy: 0.9813\n",
            "Epoch 445/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 1.8239e-09 - accuracy: 1.0000 - val_loss: 0.2510 - val_accuracy: 0.9812\n",
            "Epoch 446/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.8020e-09 - accuracy: 1.0000 - val_loss: 0.2520 - val_accuracy: 0.9814\n",
            "Epoch 447/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 1.7802e-09 - accuracy: 1.0000 - val_loss: 0.2525 - val_accuracy: 0.9813\n",
            "Epoch 448/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 1.6987e-09 - accuracy: 1.0000 - val_loss: 0.2536 - val_accuracy: 0.9811\n",
            "Epoch 449/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 1.6232e-09 - accuracy: 1.0000 - val_loss: 0.2539 - val_accuracy: 0.9813\n",
            "Epoch 450/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 1.5994e-09 - accuracy: 1.0000 - val_loss: 0.2550 - val_accuracy: 0.9816\n",
            "Epoch 451/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 1.6073e-09 - accuracy: 1.0000 - val_loss: 0.2566 - val_accuracy: 0.9813\n",
            "Epoch 452/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 1.5835e-09 - accuracy: 1.0000 - val_loss: 0.2565 - val_accuracy: 0.9814\n",
            "Epoch 453/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.5557e-09 - accuracy: 1.0000 - val_loss: 0.2570 - val_accuracy: 0.9816\n",
            "Epoch 454/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.5279e-09 - accuracy: 1.0000 - val_loss: 0.2583 - val_accuracy: 0.9814\n",
            "Epoch 455/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.4961e-09 - accuracy: 1.0000 - val_loss: 0.2587 - val_accuracy: 0.9816\n",
            "Epoch 456/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.4583e-09 - accuracy: 1.0000 - val_loss: 0.2598 - val_accuracy: 0.9812\n",
            "Epoch 457/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.4464e-09 - accuracy: 1.0000 - val_loss: 0.2606 - val_accuracy: 0.9815\n",
            "Epoch 458/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.4623e-09 - accuracy: 1.0000 - val_loss: 0.2609 - val_accuracy: 0.9815\n",
            "Epoch 459/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 1.4683e-09 - accuracy: 1.0000 - val_loss: 0.2624 - val_accuracy: 0.9817\n",
            "Epoch 460/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 1.4603e-09 - accuracy: 1.0000 - val_loss: 0.2623 - val_accuracy: 0.9817\n",
            "Epoch 461/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.4246e-09 - accuracy: 1.0000 - val_loss: 0.2632 - val_accuracy: 0.9818\n",
            "Epoch 462/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.4206e-09 - accuracy: 1.0000 - val_loss: 0.2638 - val_accuracy: 0.9815\n",
            "Epoch 463/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 1.4325e-09 - accuracy: 1.0000 - val_loss: 0.2643 - val_accuracy: 0.9815\n",
            "Epoch 464/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.3848e-09 - accuracy: 1.0000 - val_loss: 0.2643 - val_accuracy: 0.9816\n",
            "Epoch 465/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.4067e-09 - accuracy: 1.0000 - val_loss: 0.2654 - val_accuracy: 0.9817\n",
            "Epoch 466/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 1.4166e-09 - accuracy: 1.0000 - val_loss: 0.2660 - val_accuracy: 0.9817\n",
            "Epoch 467/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 1.4226e-09 - accuracy: 1.0000 - val_loss: 0.2658 - val_accuracy: 0.9815\n",
            "Epoch 468/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 1.3928e-09 - accuracy: 1.0000 - val_loss: 0.2673 - val_accuracy: 0.9812\n",
            "Epoch 469/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.4047e-09 - accuracy: 1.0000 - val_loss: 0.2677 - val_accuracy: 0.9813\n",
            "Epoch 470/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 1.3570e-09 - accuracy: 1.0000 - val_loss: 0.2687 - val_accuracy: 0.9814\n",
            "Epoch 471/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.3888e-09 - accuracy: 1.0000 - val_loss: 0.2698 - val_accuracy: 0.9812\n",
            "Epoch 472/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 1.3669e-09 - accuracy: 1.0000 - val_loss: 0.2698 - val_accuracy: 0.9814\n",
            "Epoch 473/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.3530e-09 - accuracy: 1.0000 - val_loss: 0.2704 - val_accuracy: 0.9814\n",
            "Epoch 474/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.3987e-09 - accuracy: 1.0000 - val_loss: 0.2706 - val_accuracy: 0.9814\n",
            "Epoch 475/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 1.4067e-09 - accuracy: 1.0000 - val_loss: 0.2720 - val_accuracy: 0.9810\n",
            "Epoch 476/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 1.3947e-09 - accuracy: 1.0000 - val_loss: 0.2722 - val_accuracy: 0.9811\n",
            "Epoch 477/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.4047e-09 - accuracy: 1.0000 - val_loss: 0.2729 - val_accuracy: 0.9808\n",
            "Epoch 478/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.4146e-09 - accuracy: 1.0000 - val_loss: 0.2740 - val_accuracy: 0.9806\n",
            "Epoch 479/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.4524e-09 - accuracy: 1.0000 - val_loss: 0.2747 - val_accuracy: 0.9810\n",
            "Epoch 480/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.4444e-09 - accuracy: 1.0000 - val_loss: 0.2749 - val_accuracy: 0.9808\n",
            "Epoch 481/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 1.4325e-09 - accuracy: 1.0000 - val_loss: 0.2757 - val_accuracy: 0.9805\n",
            "Epoch 482/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 1.4166e-09 - accuracy: 1.0000 - val_loss: 0.2760 - val_accuracy: 0.9808\n",
            "Epoch 483/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 1.4226e-09 - accuracy: 1.0000 - val_loss: 0.2777 - val_accuracy: 0.9809\n",
            "Epoch 484/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.4206e-09 - accuracy: 1.0000 - val_loss: 0.2780 - val_accuracy: 0.9809\n",
            "Epoch 485/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.4305e-09 - accuracy: 1.0000 - val_loss: 0.2785 - val_accuracy: 0.9806\n",
            "Epoch 486/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.4484e-09 - accuracy: 1.0000 - val_loss: 0.2786 - val_accuracy: 0.9810\n",
            "Epoch 487/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 1.5001e-09 - accuracy: 1.0000 - val_loss: 0.2795 - val_accuracy: 0.9808\n",
            "Epoch 488/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.5597e-09 - accuracy: 1.0000 - val_loss: 0.2803 - val_accuracy: 0.9811\n",
            "Epoch 489/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 1.5001e-09 - accuracy: 1.0000 - val_loss: 0.2814 - val_accuracy: 0.9807\n",
            "Epoch 490/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 1.5120e-09 - accuracy: 1.0000 - val_loss: 0.2817 - val_accuracy: 0.9810\n",
            "Epoch 491/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 1.5120e-09 - accuracy: 1.0000 - val_loss: 0.2826 - val_accuracy: 0.9808\n",
            "Epoch 492/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.4901e-09 - accuracy: 1.0000 - val_loss: 0.2823 - val_accuracy: 0.9806\n",
            "Epoch 493/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.5775e-09 - accuracy: 1.0000 - val_loss: 0.2840 - val_accuracy: 0.9806\n",
            "Epoch 494/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.5477e-09 - accuracy: 1.0000 - val_loss: 0.2843 - val_accuracy: 0.9807\n",
            "Epoch 495/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.5279e-09 - accuracy: 1.0000 - val_loss: 0.2859 - val_accuracy: 0.9804\n",
            "Epoch 496/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.5954e-09 - accuracy: 1.0000 - val_loss: 0.2849 - val_accuracy: 0.9807\n",
            "Epoch 497/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.7007e-09 - accuracy: 1.0000 - val_loss: 0.2871 - val_accuracy: 0.9805\n",
            "Epoch 498/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 1.8597e-09 - accuracy: 1.0000 - val_loss: 0.2898 - val_accuracy: 0.9805\n",
            "Epoch 499/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 1.7226e-09 - accuracy: 1.0000 - val_loss: 0.2898 - val_accuracy: 0.9806\n",
            "Epoch 500/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 1.5914e-09 - accuracy: 1.0000 - val_loss: 0.2880 - val_accuracy: 0.9809\n",
            "Epoch 501/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 1.5736e-09 - accuracy: 1.0000 - val_loss: 0.2888 - val_accuracy: 0.9804\n",
            "Epoch 502/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 0.0506 - accuracy: 0.9943 - val_loss: 0.3481 - val_accuracy: 0.9701\n",
            "Epoch 503/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 0.0178 - accuracy: 0.9962 - val_loss: 0.2295 - val_accuracy: 0.9793\n",
            "Epoch 504/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 0.0049 - accuracy: 0.9988 - val_loss: 0.2350 - val_accuracy: 0.9794\n",
            "Epoch 505/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 7.0894e-04 - accuracy: 0.9997 - val_loss: 0.2401 - val_accuracy: 0.9807\n",
            "Epoch 506/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 3.2070e-04 - accuracy: 0.9999 - val_loss: 0.2231 - val_accuracy: 0.9809\n",
            "Epoch 507/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 1.3925e-04 - accuracy: 0.9999 - val_loss: 0.2285 - val_accuracy: 0.9818\n",
            "Epoch 508/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 2.7538e-04 - accuracy: 0.9999 - val_loss: 0.2324 - val_accuracy: 0.9818\n",
            "Epoch 509/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 0.0045 - accuracy: 0.9988 - val_loss: 0.2563 - val_accuracy: 0.9774\n",
            "Epoch 510/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 0.0070 - accuracy: 0.9984 - val_loss: 0.2769 - val_accuracy: 0.9748\n",
            "Epoch 511/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 0.0054 - accuracy: 0.9986 - val_loss: 0.2489 - val_accuracy: 0.9778\n",
            "Epoch 512/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 0.0043 - accuracy: 0.9988 - val_loss: 0.2352 - val_accuracy: 0.9802\n",
            "Epoch 513/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 0.0028 - accuracy: 0.9993 - val_loss: 0.2483 - val_accuracy: 0.9790\n",
            "Epoch 514/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 0.0057 - accuracy: 0.9988 - val_loss: 0.2405 - val_accuracy: 0.9791\n",
            "Epoch 515/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 0.0041 - accuracy: 0.9989 - val_loss: 0.2497 - val_accuracy: 0.9786\n",
            "Epoch 516/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 0.0023 - accuracy: 0.9993 - val_loss: 0.2387 - val_accuracy: 0.9791\n",
            "Epoch 517/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 0.0024 - accuracy: 0.9992 - val_loss: 0.2419 - val_accuracy: 0.9780\n",
            "Epoch 518/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 0.0022 - accuracy: 0.9994 - val_loss: 0.2436 - val_accuracy: 0.9790\n",
            "Epoch 519/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 0.0071 - accuracy: 0.9983 - val_loss: 0.2454 - val_accuracy: 0.9789\n",
            "Epoch 520/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 0.0053 - accuracy: 0.9987 - val_loss: 0.2864 - val_accuracy: 0.9774\n",
            "Epoch 521/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 0.0035 - accuracy: 0.9990 - val_loss: 0.2556 - val_accuracy: 0.9780\n",
            "Epoch 522/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 0.0044 - accuracy: 0.9988 - val_loss: 0.2370 - val_accuracy: 0.9803\n",
            "Epoch 523/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 0.0022 - accuracy: 0.9994 - val_loss: 0.2614 - val_accuracy: 0.9783\n",
            "Epoch 524/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 9.8373e-04 - accuracy: 0.9997 - val_loss: 0.2495 - val_accuracy: 0.9805\n",
            "Epoch 525/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 0.0033 - accuracy: 0.9992 - val_loss: 0.2490 - val_accuracy: 0.9785\n",
            "Epoch 526/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 0.0028 - accuracy: 0.9992 - val_loss: 0.2695 - val_accuracy: 0.9768\n",
            "Epoch 527/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 0.0042 - accuracy: 0.9989 - val_loss: 0.2553 - val_accuracy: 0.9782\n",
            "Epoch 528/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 0.0040 - accuracy: 0.9990 - val_loss: 0.2617 - val_accuracy: 0.9775\n",
            "Epoch 529/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 0.0051 - accuracy: 0.9988 - val_loss: 0.2362 - val_accuracy: 0.9800\n",
            "Epoch 530/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 0.0031 - accuracy: 0.9991 - val_loss: 0.2381 - val_accuracy: 0.9782\n",
            "Epoch 531/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 0.0018 - accuracy: 0.9994 - val_loss: 0.2467 - val_accuracy: 0.9792\n",
            "Epoch 532/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 0.0023 - accuracy: 0.9993 - val_loss: 0.2608 - val_accuracy: 0.9783\n",
            "Epoch 533/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 0.0025 - accuracy: 0.9992 - val_loss: 0.2525 - val_accuracy: 0.9782\n",
            "Epoch 534/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 0.0047 - accuracy: 0.9988 - val_loss: 0.2531 - val_accuracy: 0.9797\n",
            "Epoch 535/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 0.0014 - accuracy: 0.9995 - val_loss: 0.2581 - val_accuracy: 0.9798\n",
            "Epoch 536/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 0.0026 - accuracy: 0.9993 - val_loss: 0.2459 - val_accuracy: 0.9801\n",
            "Epoch 537/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 6.1546e-04 - accuracy: 0.9998 - val_loss: 0.2611 - val_accuracy: 0.9783\n",
            "Epoch 538/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 2.9584e-04 - accuracy: 0.9999 - val_loss: 0.2858 - val_accuracy: 0.9773\n",
            "Epoch 539/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 0.0071 - accuracy: 0.9984 - val_loss: 0.2808 - val_accuracy: 0.9763\n",
            "Epoch 540/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 0.0069 - accuracy: 0.9986 - val_loss: 0.2387 - val_accuracy: 0.9787\n",
            "Epoch 541/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 0.0031 - accuracy: 0.9991 - val_loss: 0.2522 - val_accuracy: 0.9787\n",
            "Epoch 542/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.8273e-04 - accuracy: 0.9999 - val_loss: 0.2484 - val_accuracy: 0.9796\n",
            "Epoch 543/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.8670e-04 - accuracy: 0.9999 - val_loss: 0.2419 - val_accuracy: 0.9808\n",
            "Epoch 544/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 5.0869e-05 - accuracy: 1.0000 - val_loss: 0.2353 - val_accuracy: 0.9805\n",
            "Epoch 545/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 6.7894e-06 - accuracy: 1.0000 - val_loss: 0.2338 - val_accuracy: 0.9807\n",
            "Epoch 546/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 3.5695e-06 - accuracy: 1.0000 - val_loss: 0.2328 - val_accuracy: 0.9806\n",
            "Epoch 547/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 2.8774e-06 - accuracy: 1.0000 - val_loss: 0.2324 - val_accuracy: 0.9808\n",
            "Epoch 548/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 2.4588e-06 - accuracy: 1.0000 - val_loss: 0.2321 - val_accuracy: 0.9808\n",
            "Epoch 549/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 2.1465e-06 - accuracy: 1.0000 - val_loss: 0.2317 - val_accuracy: 0.9807\n",
            "Epoch 550/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 1.9082e-06 - accuracy: 1.0000 - val_loss: 0.2316 - val_accuracy: 0.9807\n",
            "Epoch 551/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.7155e-06 - accuracy: 1.0000 - val_loss: 0.2315 - val_accuracy: 0.9807\n",
            "Epoch 552/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.5550e-06 - accuracy: 1.0000 - val_loss: 0.2315 - val_accuracy: 0.9806\n",
            "Epoch 553/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 1.4113e-06 - accuracy: 1.0000 - val_loss: 0.2314 - val_accuracy: 0.9807\n",
            "Epoch 554/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 1.2875e-06 - accuracy: 1.0000 - val_loss: 0.2314 - val_accuracy: 0.9807\n",
            "Epoch 555/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 1.1777e-06 - accuracy: 1.0000 - val_loss: 0.2313 - val_accuracy: 0.9807\n",
            "Epoch 556/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.0803e-06 - accuracy: 1.0000 - val_loss: 0.2313 - val_accuracy: 0.9807\n",
            "Epoch 557/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 9.9414e-07 - accuracy: 1.0000 - val_loss: 0.2313 - val_accuracy: 0.9808\n",
            "Epoch 558/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 9.1561e-07 - accuracy: 1.0000 - val_loss: 0.2313 - val_accuracy: 0.9809\n",
            "Epoch 559/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 8.4360e-07 - accuracy: 1.0000 - val_loss: 0.2313 - val_accuracy: 0.9810\n",
            "Epoch 560/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 7.7878e-07 - accuracy: 1.0000 - val_loss: 0.2313 - val_accuracy: 0.9810\n",
            "Epoch 561/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 7.1717e-07 - accuracy: 1.0000 - val_loss: 0.2313 - val_accuracy: 0.9810\n",
            "Epoch 562/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 6.6273e-07 - accuracy: 1.0000 - val_loss: 0.2314 - val_accuracy: 0.9811\n",
            "Epoch 563/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 6.1214e-07 - accuracy: 1.0000 - val_loss: 0.2314 - val_accuracy: 0.9812\n",
            "Epoch 564/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 5.6577e-07 - accuracy: 1.0000 - val_loss: 0.2315 - val_accuracy: 0.9811\n",
            "Epoch 565/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 5.2114e-07 - accuracy: 1.0000 - val_loss: 0.2315 - val_accuracy: 0.9811\n",
            "Epoch 566/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 4.8027e-07 - accuracy: 1.0000 - val_loss: 0.2316 - val_accuracy: 0.9811\n",
            "Epoch 567/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 4.4364e-07 - accuracy: 1.0000 - val_loss: 0.2316 - val_accuracy: 0.9811\n",
            "Epoch 568/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.0840e-07 - accuracy: 1.0000 - val_loss: 0.2317 - val_accuracy: 0.9810\n",
            "Epoch 569/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 3.7654e-07 - accuracy: 1.0000 - val_loss: 0.2318 - val_accuracy: 0.9810\n",
            "Epoch 570/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 3.4618e-07 - accuracy: 1.0000 - val_loss: 0.2319 - val_accuracy: 0.9811\n",
            "Epoch 571/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 3.1910e-07 - accuracy: 1.0000 - val_loss: 0.2319 - val_accuracy: 0.9811\n",
            "Epoch 572/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 2.9397e-07 - accuracy: 1.0000 - val_loss: 0.2321 - val_accuracy: 0.9812\n",
            "Epoch 573/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 2.7056e-07 - accuracy: 1.0000 - val_loss: 0.2322 - val_accuracy: 0.9811\n",
            "Epoch 574/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 2.4864e-07 - accuracy: 1.0000 - val_loss: 0.2324 - val_accuracy: 0.9810\n",
            "Epoch 575/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 2.2848e-07 - accuracy: 1.0000 - val_loss: 0.2325 - val_accuracy: 0.9811\n",
            "Epoch 576/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 2.1046e-07 - accuracy: 1.0000 - val_loss: 0.2328 - val_accuracy: 0.9810\n",
            "Epoch 577/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 1.9236e-07 - accuracy: 1.0000 - val_loss: 0.2329 - val_accuracy: 0.9811\n",
            "Epoch 578/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 1.7701e-07 - accuracy: 1.0000 - val_loss: 0.2332 - val_accuracy: 0.9811\n",
            "Epoch 579/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.6228e-07 - accuracy: 1.0000 - val_loss: 0.2333 - val_accuracy: 0.9810\n",
            "Epoch 580/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 1.4885e-07 - accuracy: 1.0000 - val_loss: 0.2336 - val_accuracy: 0.9812\n",
            "Epoch 581/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.3686e-07 - accuracy: 1.0000 - val_loss: 0.2337 - val_accuracy: 0.9811\n",
            "Epoch 582/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.2549e-07 - accuracy: 1.0000 - val_loss: 0.2339 - val_accuracy: 0.9810\n",
            "Epoch 583/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.1490e-07 - accuracy: 1.0000 - val_loss: 0.2342 - val_accuracy: 0.9810\n",
            "Epoch 584/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.0529e-07 - accuracy: 1.0000 - val_loss: 0.2344 - val_accuracy: 0.9809\n",
            "Epoch 585/1000\n",
            "235/235 [==============================] - 3s 11ms/step - loss: 9.6485e-08 - accuracy: 1.0000 - val_loss: 0.2347 - val_accuracy: 0.9810\n",
            "Epoch 586/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 8.8214e-08 - accuracy: 1.0000 - val_loss: 0.2350 - val_accuracy: 0.9809\n",
            "Epoch 587/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 8.0627e-08 - accuracy: 1.0000 - val_loss: 0.2353 - val_accuracy: 0.9808\n",
            "Epoch 588/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 7.3790e-08 - accuracy: 1.0000 - val_loss: 0.2356 - val_accuracy: 0.9809\n",
            "Epoch 589/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 6.7470e-08 - accuracy: 1.0000 - val_loss: 0.2359 - val_accuracy: 0.9809\n",
            "Epoch 590/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 6.1693e-08 - accuracy: 1.0000 - val_loss: 0.2363 - val_accuracy: 0.9808\n",
            "Epoch 591/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 5.6352e-08 - accuracy: 1.0000 - val_loss: 0.2368 - val_accuracy: 0.9810\n",
            "Epoch 592/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 5.1431e-08 - accuracy: 1.0000 - val_loss: 0.2371 - val_accuracy: 0.9808\n",
            "Epoch 593/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 4.7105e-08 - accuracy: 1.0000 - val_loss: 0.2374 - val_accuracy: 0.9808\n",
            "Epoch 594/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.2844e-08 - accuracy: 1.0000 - val_loss: 0.2378 - val_accuracy: 0.9808\n",
            "Epoch 595/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 3.9164e-08 - accuracy: 1.0000 - val_loss: 0.2382 - val_accuracy: 0.9808\n",
            "Epoch 596/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 3.5769e-08 - accuracy: 1.0000 - val_loss: 0.2387 - val_accuracy: 0.9808\n",
            "Epoch 597/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 3.2687e-08 - accuracy: 1.0000 - val_loss: 0.2391 - val_accuracy: 0.9809\n",
            "Epoch 598/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 2.9735e-08 - accuracy: 1.0000 - val_loss: 0.2396 - val_accuracy: 0.9809\n",
            "Epoch 599/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 2.7076e-08 - accuracy: 1.0000 - val_loss: 0.2400 - val_accuracy: 0.9809\n",
            "Epoch 600/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 2.4688e-08 - accuracy: 1.0000 - val_loss: 0.2403 - val_accuracy: 0.9810\n",
            "Epoch 601/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 2.2632e-08 - accuracy: 1.0000 - val_loss: 0.2409 - val_accuracy: 0.9809\n",
            "Epoch 602/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 2.0552e-08 - accuracy: 1.0000 - val_loss: 0.2413 - val_accuracy: 0.9811\n",
            "Epoch 603/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.8801e-08 - accuracy: 1.0000 - val_loss: 0.2418 - val_accuracy: 0.9811\n",
            "Epoch 604/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.7128e-08 - accuracy: 1.0000 - val_loss: 0.2422 - val_accuracy: 0.9813\n",
            "Epoch 605/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.5632e-08 - accuracy: 1.0000 - val_loss: 0.2428 - val_accuracy: 0.9811\n",
            "Epoch 606/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.4244e-08 - accuracy: 1.0000 - val_loss: 0.2433 - val_accuracy: 0.9812\n",
            "Epoch 607/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 1.3018e-08 - accuracy: 1.0000 - val_loss: 0.2437 - val_accuracy: 0.9811\n",
            "Epoch 608/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 1.1895e-08 - accuracy: 1.0000 - val_loss: 0.2442 - val_accuracy: 0.9813\n",
            "Epoch 609/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.0870e-08 - accuracy: 1.0000 - val_loss: 0.2447 - val_accuracy: 0.9813\n",
            "Epoch 610/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 9.9262e-09 - accuracy: 1.0000 - val_loss: 0.2454 - val_accuracy: 0.9813\n",
            "Epoch 611/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 9.0877e-09 - accuracy: 1.0000 - val_loss: 0.2457 - val_accuracy: 0.9814\n",
            "Epoch 612/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 8.3069e-09 - accuracy: 1.0000 - val_loss: 0.2464 - val_accuracy: 0.9813\n",
            "Epoch 613/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 7.6234e-09 - accuracy: 1.0000 - val_loss: 0.2468 - val_accuracy: 0.9812\n",
            "Epoch 614/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 6.9718e-09 - accuracy: 1.0000 - val_loss: 0.2475 - val_accuracy: 0.9814\n",
            "Epoch 615/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 6.3737e-09 - accuracy: 1.0000 - val_loss: 0.2479 - val_accuracy: 0.9812\n",
            "Epoch 616/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 5.8949e-09 - accuracy: 1.0000 - val_loss: 0.2486 - val_accuracy: 0.9814\n",
            "Epoch 617/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 5.3803e-09 - accuracy: 1.0000 - val_loss: 0.2492 - val_accuracy: 0.9813\n",
            "Epoch 618/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 5.0227e-09 - accuracy: 1.0000 - val_loss: 0.2499 - val_accuracy: 0.9812\n",
            "Epoch 619/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.5955e-09 - accuracy: 1.0000 - val_loss: 0.2505 - val_accuracy: 0.9813\n",
            "Epoch 620/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.2021e-09 - accuracy: 1.0000 - val_loss: 0.2510 - val_accuracy: 0.9813\n",
            "Epoch 621/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 3.8763e-09 - accuracy: 1.0000 - val_loss: 0.2515 - val_accuracy: 0.9813\n",
            "Epoch 622/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 3.6379e-09 - accuracy: 1.0000 - val_loss: 0.2520 - val_accuracy: 0.9812\n",
            "Epoch 623/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 3.3696e-09 - accuracy: 1.0000 - val_loss: 0.2527 - val_accuracy: 0.9811\n",
            "Epoch 624/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 3.1114e-09 - accuracy: 1.0000 - val_loss: 0.2533 - val_accuracy: 0.9811\n",
            "Epoch 625/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 2.9027e-09 - accuracy: 1.0000 - val_loss: 0.2540 - val_accuracy: 0.9813\n",
            "Epoch 626/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 2.6802e-09 - accuracy: 1.0000 - val_loss: 0.2541 - val_accuracy: 0.9810\n",
            "Epoch 627/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 2.5193e-09 - accuracy: 1.0000 - val_loss: 0.2548 - val_accuracy: 0.9811\n",
            "Epoch 628/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 2.3067e-09 - accuracy: 1.0000 - val_loss: 0.2554 - val_accuracy: 0.9812\n",
            "Epoch 629/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 2.1756e-09 - accuracy: 1.0000 - val_loss: 0.2559 - val_accuracy: 0.9813\n",
            "Epoch 630/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 2.0504e-09 - accuracy: 1.0000 - val_loss: 0.2564 - val_accuracy: 0.9813\n",
            "Epoch 631/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 1.9133e-09 - accuracy: 1.0000 - val_loss: 0.2567 - val_accuracy: 0.9811\n",
            "Epoch 632/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.7703e-09 - accuracy: 1.0000 - val_loss: 0.2572 - val_accuracy: 0.9813\n",
            "Epoch 633/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.6769e-09 - accuracy: 1.0000 - val_loss: 0.2577 - val_accuracy: 0.9811\n",
            "Epoch 634/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.5716e-09 - accuracy: 1.0000 - val_loss: 0.2582 - val_accuracy: 0.9811\n",
            "Epoch 635/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.4603e-09 - accuracy: 1.0000 - val_loss: 0.2586 - val_accuracy: 0.9811\n",
            "Epoch 636/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.3967e-09 - accuracy: 1.0000 - val_loss: 0.2593 - val_accuracy: 0.9811\n",
            "Epoch 637/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.2934e-09 - accuracy: 1.0000 - val_loss: 0.2597 - val_accuracy: 0.9809\n",
            "Epoch 638/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 1.2318e-09 - accuracy: 1.0000 - val_loss: 0.2605 - val_accuracy: 0.9810\n",
            "Epoch 639/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 1.1623e-09 - accuracy: 1.0000 - val_loss: 0.2609 - val_accuracy: 0.9809\n",
            "Epoch 640/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.0987e-09 - accuracy: 1.0000 - val_loss: 0.2614 - val_accuracy: 0.9809\n",
            "Epoch 641/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.0490e-09 - accuracy: 1.0000 - val_loss: 0.2619 - val_accuracy: 0.9809\n",
            "Epoch 642/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 9.8745e-10 - accuracy: 1.0000 - val_loss: 0.2623 - val_accuracy: 0.9810\n",
            "Epoch 643/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 9.2586e-10 - accuracy: 1.0000 - val_loss: 0.2628 - val_accuracy: 0.9809\n",
            "Epoch 644/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 9.1195e-10 - accuracy: 1.0000 - val_loss: 0.2633 - val_accuracy: 0.9809\n",
            "Epoch 645/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 8.4639e-10 - accuracy: 1.0000 - val_loss: 0.2636 - val_accuracy: 0.9809\n",
            "Epoch 646/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 8.3049e-10 - accuracy: 1.0000 - val_loss: 0.2642 - val_accuracy: 0.9810\n",
            "Epoch 647/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 7.8877e-10 - accuracy: 1.0000 - val_loss: 0.2646 - val_accuracy: 0.9810\n",
            "Epoch 648/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 7.8082e-10 - accuracy: 1.0000 - val_loss: 0.2649 - val_accuracy: 0.9810\n",
            "Epoch 649/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 7.5301e-10 - accuracy: 1.0000 - val_loss: 0.2655 - val_accuracy: 0.9809\n",
            "Epoch 650/1000\n",
            "235/235 [==============================] - 2s 6ms/step - loss: 6.9340e-10 - accuracy: 1.0000 - val_loss: 0.2661 - val_accuracy: 0.9809\n",
            "Epoch 651/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 6.7949e-10 - accuracy: 1.0000 - val_loss: 0.2663 - val_accuracy: 0.9811\n",
            "Epoch 652/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 6.6161e-10 - accuracy: 1.0000 - val_loss: 0.2669 - val_accuracy: 0.9809\n",
            "Epoch 653/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 6.3380e-10 - accuracy: 1.0000 - val_loss: 0.2671 - val_accuracy: 0.9811\n",
            "Epoch 654/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 6.3578e-10 - accuracy: 1.0000 - val_loss: 0.2676 - val_accuracy: 0.9811\n",
            "Epoch 655/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 6.0598e-10 - accuracy: 1.0000 - val_loss: 0.2682 - val_accuracy: 0.9811\n",
            "Epoch 656/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 5.7618e-10 - accuracy: 1.0000 - val_loss: 0.2685 - val_accuracy: 0.9811\n",
            "Epoch 657/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 5.7022e-10 - accuracy: 1.0000 - val_loss: 0.2688 - val_accuracy: 0.9811\n",
            "Epoch 658/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 5.6227e-10 - accuracy: 1.0000 - val_loss: 0.2693 - val_accuracy: 0.9812\n",
            "Epoch 659/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 5.5631e-10 - accuracy: 1.0000 - val_loss: 0.2698 - val_accuracy: 0.9812\n",
            "Epoch 660/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 5.3247e-10 - accuracy: 1.0000 - val_loss: 0.2701 - val_accuracy: 0.9812\n",
            "Epoch 661/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 5.2055e-10 - accuracy: 1.0000 - val_loss: 0.2706 - val_accuracy: 0.9813\n",
            "Epoch 662/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 5.2849e-10 - accuracy: 1.0000 - val_loss: 0.2710 - val_accuracy: 0.9812\n",
            "Epoch 663/1000\n",
            "235/235 [==============================] - 1s 6ms/step - loss: 5.1856e-10 - accuracy: 1.0000 - val_loss: 0.2713 - val_accuracy: 0.9811\n",
            "Epoch 664/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.9273e-10 - accuracy: 1.0000 - val_loss: 0.2716 - val_accuracy: 0.9810\n",
            "Epoch 665/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.8876e-10 - accuracy: 1.0000 - val_loss: 0.2722 - val_accuracy: 0.9812\n",
            "Epoch 666/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 5.0465e-10 - accuracy: 1.0000 - val_loss: 0.2726 - val_accuracy: 0.9812\n",
            "Epoch 667/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.8280e-10 - accuracy: 1.0000 - val_loss: 0.2730 - val_accuracy: 0.9812\n",
            "Epoch 668/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.6889e-10 - accuracy: 1.0000 - val_loss: 0.2735 - val_accuracy: 0.9810\n",
            "Epoch 669/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 4.5498e-10 - accuracy: 1.0000 - val_loss: 0.2740 - val_accuracy: 0.9810\n",
            "Epoch 670/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 4.9074e-10 - accuracy: 1.0000 - val_loss: 0.2744 - val_accuracy: 0.9810\n",
            "Epoch 671/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.5697e-10 - accuracy: 1.0000 - val_loss: 0.2748 - val_accuracy: 0.9809\n",
            "Epoch 672/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.9273e-10 - accuracy: 1.0000 - val_loss: 0.2752 - val_accuracy: 0.9808\n",
            "Epoch 673/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.7286e-10 - accuracy: 1.0000 - val_loss: 0.2756 - val_accuracy: 0.9809\n",
            "Epoch 674/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.6690e-10 - accuracy: 1.0000 - val_loss: 0.2761 - val_accuracy: 0.9809\n",
            "Epoch 675/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.7684e-10 - accuracy: 1.0000 - val_loss: 0.2765 - val_accuracy: 0.9808\n",
            "Epoch 676/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 4.6889e-10 - accuracy: 1.0000 - val_loss: 0.2769 - val_accuracy: 0.9809\n",
            "Epoch 677/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 4.5498e-10 - accuracy: 1.0000 - val_loss: 0.2773 - val_accuracy: 0.9807\n",
            "Epoch 678/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 4.9273e-10 - accuracy: 1.0000 - val_loss: 0.2777 - val_accuracy: 0.9809\n",
            "Epoch 679/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.5300e-10 - accuracy: 1.0000 - val_loss: 0.2783 - val_accuracy: 0.9810\n",
            "Epoch 680/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.3909e-10 - accuracy: 1.0000 - val_loss: 0.2786 - val_accuracy: 0.9809\n",
            "Epoch 681/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.7286e-10 - accuracy: 1.0000 - val_loss: 0.2791 - val_accuracy: 0.9808\n",
            "Epoch 682/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.6094e-10 - accuracy: 1.0000 - val_loss: 0.2795 - val_accuracy: 0.9808\n",
            "Epoch 683/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.5498e-10 - accuracy: 1.0000 - val_loss: 0.2801 - val_accuracy: 0.9808\n",
            "Epoch 684/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 4.5101e-10 - accuracy: 1.0000 - val_loss: 0.2804 - val_accuracy: 0.9807\n",
            "Epoch 685/1000\n",
            "235/235 [==============================] - 2s 11ms/step - loss: 4.5498e-10 - accuracy: 1.0000 - val_loss: 0.2810 - val_accuracy: 0.9808\n",
            "Epoch 686/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.7088e-10 - accuracy: 1.0000 - val_loss: 0.2813 - val_accuracy: 0.9808\n",
            "Epoch 687/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.5697e-10 - accuracy: 1.0000 - val_loss: 0.2818 - val_accuracy: 0.9810\n",
            "Epoch 688/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.5896e-10 - accuracy: 1.0000 - val_loss: 0.2823 - val_accuracy: 0.9809\n",
            "Epoch 689/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.4703e-10 - accuracy: 1.0000 - val_loss: 0.2828 - val_accuracy: 0.9810\n",
            "Epoch 690/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.3114e-10 - accuracy: 1.0000 - val_loss: 0.2833 - val_accuracy: 0.9809\n",
            "Epoch 691/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.2717e-10 - accuracy: 1.0000 - val_loss: 0.2836 - val_accuracy: 0.9810\n",
            "Epoch 692/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 4.4703e-10 - accuracy: 1.0000 - val_loss: 0.2842 - val_accuracy: 0.9810\n",
            "Epoch 693/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 4.4902e-10 - accuracy: 1.0000 - val_loss: 0.2845 - val_accuracy: 0.9812\n",
            "Epoch 694/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.4703e-10 - accuracy: 1.0000 - val_loss: 0.2854 - val_accuracy: 0.9812\n",
            "Epoch 695/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.4505e-10 - accuracy: 1.0000 - val_loss: 0.2856 - val_accuracy: 0.9814\n",
            "Epoch 696/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.4306e-10 - accuracy: 1.0000 - val_loss: 0.2864 - val_accuracy: 0.9812\n",
            "Epoch 697/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.5498e-10 - accuracy: 1.0000 - val_loss: 0.2867 - val_accuracy: 0.9814\n",
            "Epoch 698/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.4505e-10 - accuracy: 1.0000 - val_loss: 0.2873 - val_accuracy: 0.9814\n",
            "Epoch 699/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 4.5101e-10 - accuracy: 1.0000 - val_loss: 0.2878 - val_accuracy: 0.9814\n",
            "Epoch 700/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 4.4107e-10 - accuracy: 1.0000 - val_loss: 0.2884 - val_accuracy: 0.9814\n",
            "Epoch 701/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.4505e-10 - accuracy: 1.0000 - val_loss: 0.2890 - val_accuracy: 0.9814\n",
            "Epoch 702/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.7485e-10 - accuracy: 1.0000 - val_loss: 0.2897 - val_accuracy: 0.9812\n",
            "Epoch 703/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.5896e-10 - accuracy: 1.0000 - val_loss: 0.2901 - val_accuracy: 0.9813\n",
            "Epoch 704/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.7882e-10 - accuracy: 1.0000 - val_loss: 0.2905 - val_accuracy: 0.9813\n",
            "Epoch 705/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.6094e-10 - accuracy: 1.0000 - val_loss: 0.2911 - val_accuracy: 0.9813\n",
            "Epoch 706/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.7286e-10 - accuracy: 1.0000 - val_loss: 0.2916 - val_accuracy: 0.9814\n",
            "Epoch 707/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 4.5101e-10 - accuracy: 1.0000 - val_loss: 0.2920 - val_accuracy: 0.9814\n",
            "Epoch 708/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 4.5101e-10 - accuracy: 1.0000 - val_loss: 0.2926 - val_accuracy: 0.9812\n",
            "Epoch 709/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.7088e-10 - accuracy: 1.0000 - val_loss: 0.2930 - val_accuracy: 0.9813\n",
            "Epoch 710/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.7088e-10 - accuracy: 1.0000 - val_loss: 0.2937 - val_accuracy: 0.9812\n",
            "Epoch 711/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.7485e-10 - accuracy: 1.0000 - val_loss: 0.2945 - val_accuracy: 0.9813\n",
            "Epoch 712/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.9273e-10 - accuracy: 1.0000 - val_loss: 0.2948 - val_accuracy: 0.9813\n",
            "Epoch 713/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.8876e-10 - accuracy: 1.0000 - val_loss: 0.2952 - val_accuracy: 0.9811\n",
            "Epoch 714/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.8677e-10 - accuracy: 1.0000 - val_loss: 0.2959 - val_accuracy: 0.9810\n",
            "Epoch 715/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 4.7882e-10 - accuracy: 1.0000 - val_loss: 0.2965 - val_accuracy: 0.9809\n",
            "Epoch 716/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 4.9869e-10 - accuracy: 1.0000 - val_loss: 0.2969 - val_accuracy: 0.9810\n",
            "Epoch 717/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.8081e-10 - accuracy: 1.0000 - val_loss: 0.2972 - val_accuracy: 0.9812\n",
            "Epoch 718/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 5.0267e-10 - accuracy: 1.0000 - val_loss: 0.2979 - val_accuracy: 0.9813\n",
            "Epoch 719/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.8280e-10 - accuracy: 1.0000 - val_loss: 0.2984 - val_accuracy: 0.9809\n",
            "Epoch 720/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 5.0465e-10 - accuracy: 1.0000 - val_loss: 0.2990 - val_accuracy: 0.9812\n",
            "Epoch 721/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.8478e-10 - accuracy: 1.0000 - val_loss: 0.2996 - val_accuracy: 0.9813\n",
            "Epoch 722/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 5.0863e-10 - accuracy: 1.0000 - val_loss: 0.2999 - val_accuracy: 0.9813\n",
            "Epoch 723/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 5.1459e-10 - accuracy: 1.0000 - val_loss: 0.3005 - val_accuracy: 0.9813\n",
            "Epoch 724/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 4.9472e-10 - accuracy: 1.0000 - val_loss: 0.3010 - val_accuracy: 0.9811\n",
            "Epoch 725/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.8478e-10 - accuracy: 1.0000 - val_loss: 0.3017 - val_accuracy: 0.9813\n",
            "Epoch 726/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 5.2452e-10 - accuracy: 1.0000 - val_loss: 0.3022 - val_accuracy: 0.9811\n",
            "Epoch 727/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 5.1061e-10 - accuracy: 1.0000 - val_loss: 0.3027 - val_accuracy: 0.9811\n",
            "Epoch 728/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 5.3247e-10 - accuracy: 1.0000 - val_loss: 0.3031 - val_accuracy: 0.9811\n",
            "Epoch 729/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 5.1856e-10 - accuracy: 1.0000 - val_loss: 0.3037 - val_accuracy: 0.9811\n",
            "Epoch 730/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 5.2253e-10 - accuracy: 1.0000 - val_loss: 0.3040 - val_accuracy: 0.9810\n",
            "Epoch 731/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 5.2452e-10 - accuracy: 1.0000 - val_loss: 0.3047 - val_accuracy: 0.9811\n",
            "Epoch 732/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 5.2452e-10 - accuracy: 1.0000 - val_loss: 0.3052 - val_accuracy: 0.9811\n",
            "Epoch 733/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 5.2253e-10 - accuracy: 1.0000 - val_loss: 0.3060 - val_accuracy: 0.9808\n",
            "Epoch 734/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 5.4638e-10 - accuracy: 1.0000 - val_loss: 0.3063 - val_accuracy: 0.9810\n",
            "Epoch 735/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 5.4240e-10 - accuracy: 1.0000 - val_loss: 0.3068 - val_accuracy: 0.9809\n",
            "Epoch 736/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 5.1657e-10 - accuracy: 1.0000 - val_loss: 0.3075 - val_accuracy: 0.9809\n",
            "Epoch 737/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 5.4240e-10 - accuracy: 1.0000 - val_loss: 0.3082 - val_accuracy: 0.9809\n",
            "Epoch 738/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 5.5432e-10 - accuracy: 1.0000 - val_loss: 0.3088 - val_accuracy: 0.9806\n",
            "Epoch 739/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 5.8413e-10 - accuracy: 1.0000 - val_loss: 0.3089 - val_accuracy: 0.9809\n",
            "Epoch 740/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 5.7817e-10 - accuracy: 1.0000 - val_loss: 0.3093 - val_accuracy: 0.9808\n",
            "Epoch 741/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 5.4240e-10 - accuracy: 1.0000 - val_loss: 0.3101 - val_accuracy: 0.9808\n",
            "Epoch 742/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 5.8810e-10 - accuracy: 1.0000 - val_loss: 0.3103 - val_accuracy: 0.9807\n",
            "Epoch 743/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 5.6426e-10 - accuracy: 1.0000 - val_loss: 0.3111 - val_accuracy: 0.9808\n",
            "Epoch 744/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 5.5830e-10 - accuracy: 1.0000 - val_loss: 0.3114 - val_accuracy: 0.9808\n",
            "Epoch 745/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 5.6823e-10 - accuracy: 1.0000 - val_loss: 0.3123 - val_accuracy: 0.9809\n",
            "Epoch 746/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 6.1393e-10 - accuracy: 1.0000 - val_loss: 0.3128 - val_accuracy: 0.9806\n",
            "Epoch 747/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 6.0995e-10 - accuracy: 1.0000 - val_loss: 0.3134 - val_accuracy: 0.9807\n",
            "Epoch 748/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 5.8015e-10 - accuracy: 1.0000 - val_loss: 0.3141 - val_accuracy: 0.9807\n",
            "Epoch 749/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 6.0201e-10 - accuracy: 1.0000 - val_loss: 0.3142 - val_accuracy: 0.9806\n",
            "Epoch 750/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 6.1194e-10 - accuracy: 1.0000 - val_loss: 0.3151 - val_accuracy: 0.9806\n",
            "Epoch 751/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 6.0399e-10 - accuracy: 1.0000 - val_loss: 0.3155 - val_accuracy: 0.9806\n",
            "Epoch 752/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 6.1790e-10 - accuracy: 1.0000 - val_loss: 0.3163 - val_accuracy: 0.9807\n",
            "Epoch 753/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 6.0201e-10 - accuracy: 1.0000 - val_loss: 0.3168 - val_accuracy: 0.9807\n",
            "Epoch 754/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 5.9605e-10 - accuracy: 1.0000 - val_loss: 0.3177 - val_accuracy: 0.9805\n",
            "Epoch 755/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 6.1989e-10 - accuracy: 1.0000 - val_loss: 0.3182 - val_accuracy: 0.9805\n",
            "Epoch 756/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 6.3976e-10 - accuracy: 1.0000 - val_loss: 0.3190 - val_accuracy: 0.9805\n",
            "Epoch 757/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 6.2784e-10 - accuracy: 1.0000 - val_loss: 0.3191 - val_accuracy: 0.9805\n",
            "Epoch 758/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 6.3976e-10 - accuracy: 1.0000 - val_loss: 0.3198 - val_accuracy: 0.9804\n",
            "Epoch 759/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 6.2585e-10 - accuracy: 1.0000 - val_loss: 0.3205 - val_accuracy: 0.9805\n",
            "Epoch 760/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 6.1194e-10 - accuracy: 1.0000 - val_loss: 0.3209 - val_accuracy: 0.9804\n",
            "Epoch 761/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 6.2585e-10 - accuracy: 1.0000 - val_loss: 0.3212 - val_accuracy: 0.9804\n",
            "Epoch 762/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 6.3976e-10 - accuracy: 1.0000 - val_loss: 0.3221 - val_accuracy: 0.9804\n",
            "Epoch 763/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 6.4969e-10 - accuracy: 1.0000 - val_loss: 0.3224 - val_accuracy: 0.9804\n",
            "Epoch 764/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 6.2982e-10 - accuracy: 1.0000 - val_loss: 0.3232 - val_accuracy: 0.9803\n",
            "Epoch 765/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 6.5565e-10 - accuracy: 1.0000 - val_loss: 0.3237 - val_accuracy: 0.9802\n",
            "Epoch 766/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 6.7155e-10 - accuracy: 1.0000 - val_loss: 0.3244 - val_accuracy: 0.9803\n",
            "Epoch 767/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 6.7552e-10 - accuracy: 1.0000 - val_loss: 0.3248 - val_accuracy: 0.9804\n",
            "Epoch 768/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 6.2585e-10 - accuracy: 1.0000 - val_loss: 0.3253 - val_accuracy: 0.9802\n",
            "Epoch 769/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 6.9539e-10 - accuracy: 1.0000 - val_loss: 0.3259 - val_accuracy: 0.9802\n",
            "Epoch 770/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 6.8347e-10 - accuracy: 1.0000 - val_loss: 0.3266 - val_accuracy: 0.9801\n",
            "Epoch 771/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 6.8347e-10 - accuracy: 1.0000 - val_loss: 0.3269 - val_accuracy: 0.9802\n",
            "Epoch 772/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 6.7949e-10 - accuracy: 1.0000 - val_loss: 0.3274 - val_accuracy: 0.9802\n",
            "Epoch 773/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 6.5764e-10 - accuracy: 1.0000 - val_loss: 0.3283 - val_accuracy: 0.9802\n",
            "Epoch 774/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 6.7751e-10 - accuracy: 1.0000 - val_loss: 0.3283 - val_accuracy: 0.9802\n",
            "Epoch 775/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 6.7552e-10 - accuracy: 1.0000 - val_loss: 0.3291 - val_accuracy: 0.9802\n",
            "Epoch 776/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 7.0731e-10 - accuracy: 1.0000 - val_loss: 0.3299 - val_accuracy: 0.9801\n",
            "Epoch 777/1000\n",
            "235/235 [==============================] - 2s 11ms/step - loss: 7.1724e-10 - accuracy: 1.0000 - val_loss: 0.3307 - val_accuracy: 0.9801\n",
            "Epoch 778/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 7.1327e-10 - accuracy: 1.0000 - val_loss: 0.3312 - val_accuracy: 0.9800\n",
            "Epoch 779/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 7.3711e-10 - accuracy: 1.0000 - val_loss: 0.3314 - val_accuracy: 0.9800\n",
            "Epoch 780/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 7.2320e-10 - accuracy: 1.0000 - val_loss: 0.3322 - val_accuracy: 0.9800\n",
            "Epoch 781/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 7.1128e-10 - accuracy: 1.0000 - val_loss: 0.3322 - val_accuracy: 0.9798\n",
            "Epoch 782/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 7.1128e-10 - accuracy: 1.0000 - val_loss: 0.3330 - val_accuracy: 0.9800\n",
            "Epoch 783/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 6.8744e-10 - accuracy: 1.0000 - val_loss: 0.3329 - val_accuracy: 0.9800\n",
            "Epoch 784/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 7.2916e-10 - accuracy: 1.0000 - val_loss: 0.3340 - val_accuracy: 0.9800\n",
            "Epoch 785/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 7.4506e-10 - accuracy: 1.0000 - val_loss: 0.3347 - val_accuracy: 0.9799\n",
            "Epoch 786/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 7.6890e-10 - accuracy: 1.0000 - val_loss: 0.3345 - val_accuracy: 0.9801\n",
            "Epoch 787/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 7.5897e-10 - accuracy: 1.0000 - val_loss: 0.3353 - val_accuracy: 0.9801\n",
            "Epoch 788/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 7.1128e-10 - accuracy: 1.0000 - val_loss: 0.3362 - val_accuracy: 0.9800\n",
            "Epoch 789/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 7.6890e-10 - accuracy: 1.0000 - val_loss: 0.3362 - val_accuracy: 0.9801\n",
            "Epoch 790/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 7.5499e-10 - accuracy: 1.0000 - val_loss: 0.3361 - val_accuracy: 0.9800\n",
            "Epoch 791/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 7.5897e-10 - accuracy: 1.0000 - val_loss: 0.3374 - val_accuracy: 0.9804\n",
            "Epoch 792/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 8.0466e-10 - accuracy: 1.0000 - val_loss: 0.3374 - val_accuracy: 0.9799\n",
            "Epoch 793/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 7.7883e-10 - accuracy: 1.0000 - val_loss: 0.3374 - val_accuracy: 0.9799\n",
            "Epoch 794/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 7.7287e-10 - accuracy: 1.0000 - val_loss: 0.3385 - val_accuracy: 0.9799\n",
            "Epoch 795/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 7.3314e-10 - accuracy: 1.0000 - val_loss: 0.3392 - val_accuracy: 0.9799\n",
            "Epoch 796/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 7.9870e-10 - accuracy: 1.0000 - val_loss: 0.3397 - val_accuracy: 0.9802\n",
            "Epoch 797/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 8.0864e-10 - accuracy: 1.0000 - val_loss: 0.3401 - val_accuracy: 0.9801\n",
            "Epoch 798/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 7.9672e-10 - accuracy: 1.0000 - val_loss: 0.3403 - val_accuracy: 0.9798\n",
            "Epoch 799/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 8.1658e-10 - accuracy: 1.0000 - val_loss: 0.3413 - val_accuracy: 0.9798\n",
            "Epoch 800/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 8.2056e-10 - accuracy: 1.0000 - val_loss: 0.3412 - val_accuracy: 0.9800\n",
            "Epoch 801/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 8.5632e-10 - accuracy: 1.0000 - val_loss: 0.3408 - val_accuracy: 0.9799\n",
            "Epoch 802/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 7.9672e-10 - accuracy: 1.0000 - val_loss: 0.3423 - val_accuracy: 0.9800\n",
            "Epoch 803/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 7.9075e-10 - accuracy: 1.0000 - val_loss: 0.3428 - val_accuracy: 0.9801\n",
            "Epoch 804/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 7.8678e-10 - accuracy: 1.0000 - val_loss: 0.3432 - val_accuracy: 0.9803\n",
            "Epoch 805/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 8.2056e-10 - accuracy: 1.0000 - val_loss: 0.3430 - val_accuracy: 0.9800\n",
            "Epoch 806/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 8.3446e-10 - accuracy: 1.0000 - val_loss: 0.3440 - val_accuracy: 0.9803\n",
            "Epoch 807/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 8.2453e-10 - accuracy: 1.0000 - val_loss: 0.3439 - val_accuracy: 0.9802\n",
            "Epoch 808/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 8.1658e-10 - accuracy: 1.0000 - val_loss: 0.3442 - val_accuracy: 0.9803\n",
            "Epoch 809/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 8.0268e-10 - accuracy: 1.0000 - val_loss: 0.3449 - val_accuracy: 0.9803\n",
            "Epoch 810/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 8.2254e-10 - accuracy: 1.0000 - val_loss: 0.3450 - val_accuracy: 0.9803\n",
            "Epoch 811/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 8.1261e-10 - accuracy: 1.0000 - val_loss: 0.3459 - val_accuracy: 0.9804\n",
            "Epoch 812/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 8.6427e-10 - accuracy: 1.0000 - val_loss: 0.3466 - val_accuracy: 0.9803\n",
            "Epoch 813/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 9.1990e-10 - accuracy: 1.0000 - val_loss: 0.3472 - val_accuracy: 0.9803\n",
            "Epoch 814/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 8.3446e-10 - accuracy: 1.0000 - val_loss: 0.3472 - val_accuracy: 0.9803\n",
            "Epoch 815/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 8.4837e-10 - accuracy: 1.0000 - val_loss: 0.3475 - val_accuracy: 0.9801\n",
            "Epoch 816/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 9.1592e-10 - accuracy: 1.0000 - val_loss: 0.3482 - val_accuracy: 0.9802\n",
            "Epoch 817/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 8.7023e-10 - accuracy: 1.0000 - val_loss: 0.3482 - val_accuracy: 0.9801\n",
            "Epoch 818/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 9.1791e-10 - accuracy: 1.0000 - val_loss: 0.3493 - val_accuracy: 0.9800\n",
            "Epoch 819/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 9.5566e-10 - accuracy: 1.0000 - val_loss: 0.3489 - val_accuracy: 0.9802\n",
            "Epoch 820/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 8.7818e-10 - accuracy: 1.0000 - val_loss: 0.3503 - val_accuracy: 0.9803\n",
            "Epoch 821/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 9.3579e-10 - accuracy: 1.0000 - val_loss: 0.3500 - val_accuracy: 0.9801\n",
            "Epoch 822/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 9.2983e-10 - accuracy: 1.0000 - val_loss: 0.3518 - val_accuracy: 0.9799\n",
            "Epoch 823/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 9.3182e-10 - accuracy: 1.0000 - val_loss: 0.3510 - val_accuracy: 0.9800\n",
            "Epoch 824/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 8.7221e-10 - accuracy: 1.0000 - val_loss: 0.3526 - val_accuracy: 0.9799\n",
            "Epoch 825/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 9.1394e-10 - accuracy: 1.0000 - val_loss: 0.3507 - val_accuracy: 0.9800\n",
            "Epoch 826/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 8.9804e-10 - accuracy: 1.0000 - val_loss: 0.3529 - val_accuracy: 0.9801\n",
            "Epoch 827/1000\n",
            "235/235 [==============================] - 2s 11ms/step - loss: 9.3977e-10 - accuracy: 1.0000 - val_loss: 0.3533 - val_accuracy: 0.9800\n",
            "Epoch 828/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 9.2387e-10 - accuracy: 1.0000 - val_loss: 0.3530 - val_accuracy: 0.9803\n",
            "Epoch 829/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 8.7818e-10 - accuracy: 1.0000 - val_loss: 0.3548 - val_accuracy: 0.9800\n",
            "Epoch 830/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 9.5963e-10 - accuracy: 1.0000 - val_loss: 0.3551 - val_accuracy: 0.9800\n",
            "Epoch 831/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 9.3977e-10 - accuracy: 1.0000 - val_loss: 0.3552 - val_accuracy: 0.9801\n",
            "Epoch 832/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 9.7156e-10 - accuracy: 1.0000 - val_loss: 0.3563 - val_accuracy: 0.9800\n",
            "Epoch 833/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.0033e-09 - accuracy: 1.0000 - val_loss: 0.3560 - val_accuracy: 0.9800\n",
            "Epoch 834/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 9.8546e-10 - accuracy: 1.0000 - val_loss: 0.3562 - val_accuracy: 0.9799\n",
            "Epoch 835/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 9.8149e-10 - accuracy: 1.0000 - val_loss: 0.3570 - val_accuracy: 0.9800\n",
            "Epoch 836/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 9.9540e-10 - accuracy: 1.0000 - val_loss: 0.3584 - val_accuracy: 0.9798\n",
            "Epoch 837/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 1.0014e-09 - accuracy: 1.0000 - val_loss: 0.3589 - val_accuracy: 0.9799\n",
            "Epoch 838/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 9.8745e-10 - accuracy: 1.0000 - val_loss: 0.3587 - val_accuracy: 0.9800\n",
            "Epoch 839/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.0033e-09 - accuracy: 1.0000 - val_loss: 0.3592 - val_accuracy: 0.9799\n",
            "Epoch 840/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 9.6758e-10 - accuracy: 1.0000 - val_loss: 0.3583 - val_accuracy: 0.9799\n",
            "Epoch 841/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.0947e-09 - accuracy: 1.0000 - val_loss: 0.3598 - val_accuracy: 0.9802\n",
            "Epoch 842/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.0908e-09 - accuracy: 1.0000 - val_loss: 0.3606 - val_accuracy: 0.9802\n",
            "Epoch 843/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.1484e-09 - accuracy: 1.0000 - val_loss: 0.3618 - val_accuracy: 0.9802\n",
            "Epoch 844/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 1.1961e-09 - accuracy: 1.0000 - val_loss: 0.3604 - val_accuracy: 0.9801\n",
            "Epoch 845/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 1.0530e-09 - accuracy: 1.0000 - val_loss: 0.3624 - val_accuracy: 0.9799\n",
            "Epoch 846/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 1.0908e-09 - accuracy: 1.0000 - val_loss: 0.3620 - val_accuracy: 0.9803\n",
            "Epoch 847/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.0729e-09 - accuracy: 1.0000 - val_loss: 0.3627 - val_accuracy: 0.9801\n",
            "Epoch 848/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.1007e-09 - accuracy: 1.0000 - val_loss: 0.3624 - val_accuracy: 0.9801\n",
            "Epoch 849/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.0987e-09 - accuracy: 1.0000 - val_loss: 0.3630 - val_accuracy: 0.9802\n",
            "Epoch 850/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.1186e-09 - accuracy: 1.0000 - val_loss: 0.3666 - val_accuracy: 0.9801\n",
            "Epoch 851/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 0.0503 - accuracy: 0.9932 - val_loss: 0.3503 - val_accuracy: 0.9778\n",
            "Epoch 852/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 0.0086 - accuracy: 0.9983 - val_loss: 0.2959 - val_accuracy: 0.9792\n",
            "Epoch 853/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 4.8445e-04 - accuracy: 0.9998 - val_loss: 0.2916 - val_accuracy: 0.9806\n",
            "Epoch 854/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.6304e-04 - accuracy: 0.9999 - val_loss: 0.3147 - val_accuracy: 0.9786\n",
            "Epoch 855/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 8.1496e-04 - accuracy: 0.9998 - val_loss: 0.3045 - val_accuracy: 0.9803\n",
            "Epoch 856/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 0.0025 - accuracy: 0.9995 - val_loss: 0.3135 - val_accuracy: 0.9794\n",
            "Epoch 857/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 0.0043 - accuracy: 0.9991 - val_loss: 0.3163 - val_accuracy: 0.9793\n",
            "Epoch 858/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 0.0029 - accuracy: 0.9993 - val_loss: 0.3077 - val_accuracy: 0.9793\n",
            "Epoch 859/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 0.0027 - accuracy: 0.9993 - val_loss: 0.2879 - val_accuracy: 0.9789\n",
            "Epoch 860/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 0.0042 - accuracy: 0.9991 - val_loss: 0.2806 - val_accuracy: 0.9797\n",
            "Epoch 861/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 0.0018 - accuracy: 0.9996 - val_loss: 0.3085 - val_accuracy: 0.9795\n",
            "Epoch 862/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 0.0040 - accuracy: 0.9991 - val_loss: 0.3362 - val_accuracy: 0.9777\n",
            "Epoch 863/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 0.0084 - accuracy: 0.9984 - val_loss: 0.2570 - val_accuracy: 0.9809\n",
            "Epoch 864/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 0.0033 - accuracy: 0.9993 - val_loss: 0.2840 - val_accuracy: 0.9785\n",
            "Epoch 865/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 0.0014 - accuracy: 0.9997 - val_loss: 0.2661 - val_accuracy: 0.9796\n",
            "Epoch 866/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 5.0575e-05 - accuracy: 1.0000 - val_loss: 0.2641 - val_accuracy: 0.9792\n",
            "Epoch 867/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 8.4484e-06 - accuracy: 1.0000 - val_loss: 0.2632 - val_accuracy: 0.9793\n",
            "Epoch 868/1000\n",
            "235/235 [==============================] - 3s 11ms/step - loss: 1.7600e-06 - accuracy: 1.0000 - val_loss: 0.2627 - val_accuracy: 0.9793\n",
            "Epoch 869/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 1.4040e-06 - accuracy: 1.0000 - val_loss: 0.2623 - val_accuracy: 0.9794\n",
            "Epoch 870/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.2137e-06 - accuracy: 1.0000 - val_loss: 0.2620 - val_accuracy: 0.9797\n",
            "Epoch 871/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.0752e-06 - accuracy: 1.0000 - val_loss: 0.2617 - val_accuracy: 0.9796\n",
            "Epoch 872/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 9.6428e-07 - accuracy: 1.0000 - val_loss: 0.2615 - val_accuracy: 0.9799\n",
            "Epoch 873/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 8.7503e-07 - accuracy: 1.0000 - val_loss: 0.2613 - val_accuracy: 0.9800\n",
            "Epoch 874/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 7.9965e-07 - accuracy: 1.0000 - val_loss: 0.2611 - val_accuracy: 0.9800\n",
            "Epoch 875/1000\n",
            "235/235 [==============================] - 2s 11ms/step - loss: 7.3300e-07 - accuracy: 1.0000 - val_loss: 0.2609 - val_accuracy: 0.9800\n",
            "Epoch 876/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 6.7480e-07 - accuracy: 1.0000 - val_loss: 0.2607 - val_accuracy: 0.9799\n",
            "Epoch 877/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 6.2314e-07 - accuracy: 1.0000 - val_loss: 0.2606 - val_accuracy: 0.9799\n",
            "Epoch 878/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 5.7645e-07 - accuracy: 1.0000 - val_loss: 0.2604 - val_accuracy: 0.9798\n",
            "Epoch 879/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 5.3338e-07 - accuracy: 1.0000 - val_loss: 0.2603 - val_accuracy: 0.9798\n",
            "Epoch 880/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.9461e-07 - accuracy: 1.0000 - val_loss: 0.2602 - val_accuracy: 0.9798\n",
            "Epoch 881/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.5842e-07 - accuracy: 1.0000 - val_loss: 0.2600 - val_accuracy: 0.9798\n",
            "Epoch 882/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 4.2523e-07 - accuracy: 1.0000 - val_loss: 0.2599 - val_accuracy: 0.9799\n",
            "Epoch 883/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 3.9428e-07 - accuracy: 1.0000 - val_loss: 0.2598 - val_accuracy: 0.9798\n",
            "Epoch 884/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 3.6596e-07 - accuracy: 1.0000 - val_loss: 0.2597 - val_accuracy: 0.9799\n",
            "Epoch 885/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 3.4029e-07 - accuracy: 1.0000 - val_loss: 0.2596 - val_accuracy: 0.9800\n",
            "Epoch 886/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 3.1554e-07 - accuracy: 1.0000 - val_loss: 0.2595 - val_accuracy: 0.9799\n",
            "Epoch 887/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 2.9305e-07 - accuracy: 1.0000 - val_loss: 0.2594 - val_accuracy: 0.9799\n",
            "Epoch 888/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 2.7177e-07 - accuracy: 1.0000 - val_loss: 0.2593 - val_accuracy: 0.9798\n",
            "Epoch 889/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 2.5199e-07 - accuracy: 1.0000 - val_loss: 0.2592 - val_accuracy: 0.9798\n",
            "Epoch 890/1000\n",
            "235/235 [==============================] - 3s 11ms/step - loss: 2.3331e-07 - accuracy: 1.0000 - val_loss: 0.2591 - val_accuracy: 0.9798\n",
            "Epoch 891/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 2.1609e-07 - accuracy: 1.0000 - val_loss: 0.2590 - val_accuracy: 0.9798\n",
            "Epoch 892/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 2.0023e-07 - accuracy: 1.0000 - val_loss: 0.2589 - val_accuracy: 0.9800\n",
            "Epoch 893/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 1.8558e-07 - accuracy: 1.0000 - val_loss: 0.2589 - val_accuracy: 0.9800\n",
            "Epoch 894/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.7181e-07 - accuracy: 1.0000 - val_loss: 0.2588 - val_accuracy: 0.9800\n",
            "Epoch 895/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.5906e-07 - accuracy: 1.0000 - val_loss: 0.2587 - val_accuracy: 0.9799\n",
            "Epoch 896/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.4726e-07 - accuracy: 1.0000 - val_loss: 0.2587 - val_accuracy: 0.9800\n",
            "Epoch 897/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 1.3621e-07 - accuracy: 1.0000 - val_loss: 0.2586 - val_accuracy: 0.9800\n",
            "Epoch 898/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 1.2566e-07 - accuracy: 1.0000 - val_loss: 0.2586 - val_accuracy: 0.9800\n",
            "Epoch 899/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.1644e-07 - accuracy: 1.0000 - val_loss: 0.2585 - val_accuracy: 0.9801\n",
            "Epoch 900/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.0733e-07 - accuracy: 1.0000 - val_loss: 0.2585 - val_accuracy: 0.9801\n",
            "Epoch 901/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 9.8792e-08 - accuracy: 1.0000 - val_loss: 0.2585 - val_accuracy: 0.9802\n",
            "Epoch 902/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 9.1115e-08 - accuracy: 1.0000 - val_loss: 0.2585 - val_accuracy: 0.9802\n",
            "Epoch 903/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 8.3929e-08 - accuracy: 1.0000 - val_loss: 0.2584 - val_accuracy: 0.9802\n",
            "Epoch 904/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 7.7330e-08 - accuracy: 1.0000 - val_loss: 0.2584 - val_accuracy: 0.9803\n",
            "Epoch 905/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 7.1195e-08 - accuracy: 1.0000 - val_loss: 0.2584 - val_accuracy: 0.9803\n",
            "Epoch 906/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 6.5674e-08 - accuracy: 1.0000 - val_loss: 0.2584 - val_accuracy: 0.9803\n",
            "Epoch 907/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 6.0425e-08 - accuracy: 1.0000 - val_loss: 0.2584 - val_accuracy: 0.9803\n",
            "Epoch 908/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 5.5577e-08 - accuracy: 1.0000 - val_loss: 0.2583 - val_accuracy: 0.9803\n",
            "Epoch 909/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 5.1089e-08 - accuracy: 1.0000 - val_loss: 0.2585 - val_accuracy: 0.9803\n",
            "Epoch 910/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.7012e-08 - accuracy: 1.0000 - val_loss: 0.2585 - val_accuracy: 0.9803\n",
            "Epoch 911/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.3205e-08 - accuracy: 1.0000 - val_loss: 0.2585 - val_accuracy: 0.9803\n",
            "Epoch 912/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 3.9768e-08 - accuracy: 1.0000 - val_loss: 0.2586 - val_accuracy: 0.9803\n",
            "Epoch 913/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 3.6498e-08 - accuracy: 1.0000 - val_loss: 0.2586 - val_accuracy: 0.9803\n",
            "Epoch 914/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 3.3512e-08 - accuracy: 1.0000 - val_loss: 0.2587 - val_accuracy: 0.9804\n",
            "Epoch 915/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 3.0734e-08 - accuracy: 1.0000 - val_loss: 0.2590 - val_accuracy: 0.9804\n",
            "Epoch 916/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 2.8201e-08 - accuracy: 1.0000 - val_loss: 0.2591 - val_accuracy: 0.9806\n",
            "Epoch 917/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 2.5874e-08 - accuracy: 1.0000 - val_loss: 0.2592 - val_accuracy: 0.9806\n",
            "Epoch 918/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 2.3661e-08 - accuracy: 1.0000 - val_loss: 0.2594 - val_accuracy: 0.9804\n",
            "Epoch 919/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 2.1787e-08 - accuracy: 1.0000 - val_loss: 0.2595 - val_accuracy: 0.9804\n",
            "Epoch 920/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 1.9906e-08 - accuracy: 1.0000 - val_loss: 0.2597 - val_accuracy: 0.9804\n",
            "Epoch 921/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 1.8217e-08 - accuracy: 1.0000 - val_loss: 0.2599 - val_accuracy: 0.9804\n",
            "Epoch 922/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.6648e-08 - accuracy: 1.0000 - val_loss: 0.2601 - val_accuracy: 0.9806\n",
            "Epoch 923/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.5263e-08 - accuracy: 1.0000 - val_loss: 0.2603 - val_accuracy: 0.9807\n",
            "Epoch 924/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.3957e-08 - accuracy: 1.0000 - val_loss: 0.2606 - val_accuracy: 0.9807\n",
            "Epoch 925/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.2755e-08 - accuracy: 1.0000 - val_loss: 0.2610 - val_accuracy: 0.9809\n",
            "Epoch 926/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.1708e-08 - accuracy: 1.0000 - val_loss: 0.2612 - val_accuracy: 0.9811\n",
            "Epoch 927/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 1.0671e-08 - accuracy: 1.0000 - val_loss: 0.2615 - val_accuracy: 0.9811\n",
            "Epoch 928/1000\n",
            "235/235 [==============================] - 3s 12ms/step - loss: 9.7513e-09 - accuracy: 1.0000 - val_loss: 0.2618 - val_accuracy: 0.9812\n",
            "Epoch 929/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 8.9367e-09 - accuracy: 1.0000 - val_loss: 0.2623 - val_accuracy: 0.9812\n",
            "Epoch 930/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 8.2056e-09 - accuracy: 1.0000 - val_loss: 0.2627 - val_accuracy: 0.9812\n",
            "Epoch 931/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 7.4844e-09 - accuracy: 1.0000 - val_loss: 0.2630 - val_accuracy: 0.9814\n",
            "Epoch 932/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 6.8148e-09 - accuracy: 1.0000 - val_loss: 0.2635 - val_accuracy: 0.9814\n",
            "Epoch 933/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 6.2486e-09 - accuracy: 1.0000 - val_loss: 0.2639 - val_accuracy: 0.9815\n",
            "Epoch 934/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 5.6903e-09 - accuracy: 1.0000 - val_loss: 0.2643 - val_accuracy: 0.9814\n",
            "Epoch 935/1000\n",
            "235/235 [==============================] - 3s 11ms/step - loss: 5.2472e-09 - accuracy: 1.0000 - val_loss: 0.2648 - val_accuracy: 0.9814\n",
            "Epoch 936/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 4.8141e-09 - accuracy: 1.0000 - val_loss: 0.2651 - val_accuracy: 0.9814\n",
            "Epoch 937/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 4.3988e-09 - accuracy: 1.0000 - val_loss: 0.2657 - val_accuracy: 0.9815\n",
            "Epoch 938/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.0253e-09 - accuracy: 1.0000 - val_loss: 0.2662 - val_accuracy: 0.9816\n",
            "Epoch 939/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 3.7193e-09 - accuracy: 1.0000 - val_loss: 0.2665 - val_accuracy: 0.9816\n",
            "Epoch 940/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 3.3617e-09 - accuracy: 1.0000 - val_loss: 0.2670 - val_accuracy: 0.9816\n",
            "Epoch 941/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 3.1312e-09 - accuracy: 1.0000 - val_loss: 0.2674 - val_accuracy: 0.9816\n",
            "Epoch 942/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 2.8412e-09 - accuracy: 1.0000 - val_loss: 0.2680 - val_accuracy: 0.9817\n",
            "Epoch 943/1000\n",
            "235/235 [==============================] - 3s 11ms/step - loss: 2.6067e-09 - accuracy: 1.0000 - val_loss: 0.2686 - val_accuracy: 0.9815\n",
            "Epoch 944/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 2.4319e-09 - accuracy: 1.0000 - val_loss: 0.2691 - val_accuracy: 0.9816\n",
            "Epoch 945/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 2.2133e-09 - accuracy: 1.0000 - val_loss: 0.2696 - val_accuracy: 0.9815\n",
            "Epoch 946/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 2.0583e-09 - accuracy: 1.0000 - val_loss: 0.2703 - val_accuracy: 0.9815\n",
            "Epoch 947/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.9073e-09 - accuracy: 1.0000 - val_loss: 0.2707 - val_accuracy: 0.9816\n",
            "Epoch 948/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.7385e-09 - accuracy: 1.0000 - val_loss: 0.2712 - val_accuracy: 0.9817\n",
            "Epoch 949/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 1.6431e-09 - accuracy: 1.0000 - val_loss: 0.2720 - val_accuracy: 0.9816\n",
            "Epoch 950/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 1.5100e-09 - accuracy: 1.0000 - val_loss: 0.2726 - val_accuracy: 0.9815\n",
            "Epoch 951/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 1.4087e-09 - accuracy: 1.0000 - val_loss: 0.2731 - val_accuracy: 0.9817\n",
            "Epoch 952/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 1.3113e-09 - accuracy: 1.0000 - val_loss: 0.2736 - val_accuracy: 0.9813\n",
            "Epoch 953/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 1.2199e-09 - accuracy: 1.0000 - val_loss: 0.2744 - val_accuracy: 0.9814\n",
            "Epoch 954/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 1.1365e-09 - accuracy: 1.0000 - val_loss: 0.2751 - val_accuracy: 0.9815\n",
            "Epoch 955/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 1.0550e-09 - accuracy: 1.0000 - val_loss: 0.2760 - val_accuracy: 0.9815\n",
            "Epoch 956/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 9.7950e-10 - accuracy: 1.0000 - val_loss: 0.2766 - val_accuracy: 0.9815\n",
            "Epoch 957/1000\n",
            "235/235 [==============================] - 3s 11ms/step - loss: 9.0003e-10 - accuracy: 1.0000 - val_loss: 0.2775 - val_accuracy: 0.9815\n",
            "Epoch 958/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 8.3844e-10 - accuracy: 1.0000 - val_loss: 0.2785 - val_accuracy: 0.9815\n",
            "Epoch 959/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 8.0069e-10 - accuracy: 1.0000 - val_loss: 0.2793 - val_accuracy: 0.9816\n",
            "Epoch 960/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 7.7486e-10 - accuracy: 1.0000 - val_loss: 0.2802 - val_accuracy: 0.9816\n",
            "Epoch 961/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 7.0333e-10 - accuracy: 1.0000 - val_loss: 0.2811 - val_accuracy: 0.9815\n",
            "Epoch 962/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 6.9936e-10 - accuracy: 1.0000 - val_loss: 0.2820 - val_accuracy: 0.9815\n",
            "Epoch 963/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 6.3181e-10 - accuracy: 1.0000 - val_loss: 0.2830 - val_accuracy: 0.9815\n",
            "Epoch 964/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 6.0797e-10 - accuracy: 1.0000 - val_loss: 0.2839 - val_accuracy: 0.9814\n",
            "Epoch 965/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 6.0797e-10 - accuracy: 1.0000 - val_loss: 0.2845 - val_accuracy: 0.9815\n",
            "Epoch 966/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 5.7419e-10 - accuracy: 1.0000 - val_loss: 0.2856 - val_accuracy: 0.9815\n",
            "Epoch 967/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 5.5234e-10 - accuracy: 1.0000 - val_loss: 0.2863 - val_accuracy: 0.9814\n",
            "Epoch 968/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 5.3247e-10 - accuracy: 1.0000 - val_loss: 0.2871 - val_accuracy: 0.9814\n",
            "Epoch 969/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 5.2055e-10 - accuracy: 1.0000 - val_loss: 0.2881 - val_accuracy: 0.9813\n",
            "Epoch 970/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 5.0267e-10 - accuracy: 1.0000 - val_loss: 0.2888 - val_accuracy: 0.9814\n",
            "Epoch 971/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.9472e-10 - accuracy: 1.0000 - val_loss: 0.2900 - val_accuracy: 0.9813\n",
            "Epoch 972/1000\n",
            "235/235 [==============================] - 3s 11ms/step - loss: 4.8478e-10 - accuracy: 1.0000 - val_loss: 0.2907 - val_accuracy: 0.9813\n",
            "Epoch 973/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 4.8280e-10 - accuracy: 1.0000 - val_loss: 0.2916 - val_accuracy: 0.9812\n",
            "Epoch 974/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 4.6690e-10 - accuracy: 1.0000 - val_loss: 0.2924 - val_accuracy: 0.9813\n",
            "Epoch 975/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.6293e-10 - accuracy: 1.0000 - val_loss: 0.2930 - val_accuracy: 0.9813\n",
            "Epoch 976/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 4.4902e-10 - accuracy: 1.0000 - val_loss: 0.2940 - val_accuracy: 0.9812\n",
            "Epoch 977/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 4.4505e-10 - accuracy: 1.0000 - val_loss: 0.2946 - val_accuracy: 0.9810\n",
            "Epoch 978/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 4.4703e-10 - accuracy: 1.0000 - val_loss: 0.2957 - val_accuracy: 0.9811\n",
            "Epoch 979/1000\n",
            "235/235 [==============================] - 2s 10ms/step - loss: 4.3909e-10 - accuracy: 1.0000 - val_loss: 0.2963 - val_accuracy: 0.9810\n",
            "Epoch 980/1000\n",
            "235/235 [==============================] - 2s 11ms/step - loss: 4.3313e-10 - accuracy: 1.0000 - val_loss: 0.2971 - val_accuracy: 0.9810\n",
            "Epoch 981/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 4.3114e-10 - accuracy: 1.0000 - val_loss: 0.2978 - val_accuracy: 0.9808\n",
            "Epoch 982/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 4.3909e-10 - accuracy: 1.0000 - val_loss: 0.2989 - val_accuracy: 0.9808\n",
            "Epoch 983/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.1525e-10 - accuracy: 1.0000 - val_loss: 0.2993 - val_accuracy: 0.9808\n",
            "Epoch 984/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.1127e-10 - accuracy: 1.0000 - val_loss: 0.3003 - val_accuracy: 0.9809\n",
            "Epoch 985/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 4.2915e-10 - accuracy: 1.0000 - val_loss: 0.3008 - val_accuracy: 0.9809\n",
            "Epoch 986/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 3.9538e-10 - accuracy: 1.0000 - val_loss: 0.3020 - val_accuracy: 0.9809\n",
            "Epoch 987/1000\n",
            "235/235 [==============================] - 3s 11ms/step - loss: 4.0332e-10 - accuracy: 1.0000 - val_loss: 0.3024 - val_accuracy: 0.9809\n",
            "Epoch 988/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 4.1326e-10 - accuracy: 1.0000 - val_loss: 0.3033 - val_accuracy: 0.9809\n",
            "Epoch 989/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 4.1922e-10 - accuracy: 1.0000 - val_loss: 0.3039 - val_accuracy: 0.9809\n",
            "Epoch 990/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.2717e-10 - accuracy: 1.0000 - val_loss: 0.3043 - val_accuracy: 0.9809\n",
            "Epoch 991/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.0332e-10 - accuracy: 1.0000 - val_loss: 0.3051 - val_accuracy: 0.9809\n",
            "Epoch 992/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 4.2717e-10 - accuracy: 1.0000 - val_loss: 0.3063 - val_accuracy: 0.9809\n",
            "Epoch 993/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 4.1525e-10 - accuracy: 1.0000 - val_loss: 0.3067 - val_accuracy: 0.9809\n",
            "Epoch 994/1000\n",
            "235/235 [==============================] - 3s 11ms/step - loss: 4.1922e-10 - accuracy: 1.0000 - val_loss: 0.3077 - val_accuracy: 0.9808\n",
            "Epoch 995/1000\n",
            "235/235 [==============================] - 2s 9ms/step - loss: 4.0929e-10 - accuracy: 1.0000 - val_loss: 0.3081 - val_accuracy: 0.9808\n",
            "Epoch 996/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 4.1922e-10 - accuracy: 1.0000 - val_loss: 0.3088 - val_accuracy: 0.9808\n",
            "Epoch 997/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 3.9935e-10 - accuracy: 1.0000 - val_loss: 0.3091 - val_accuracy: 0.9807\n",
            "Epoch 998/1000\n",
            "235/235 [==============================] - 2s 7ms/step - loss: 3.8743e-10 - accuracy: 1.0000 - val_loss: 0.3102 - val_accuracy: 0.9807\n",
            "Epoch 999/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 3.9736e-10 - accuracy: 1.0000 - val_loss: 0.3107 - val_accuracy: 0.9808\n",
            "Epoch 1000/1000\n",
            "235/235 [==============================] - 2s 8ms/step - loss: 4.1127e-10 - accuracy: 1.0000 - val_loss: 0.3117 - val_accuracy: 0.9807\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(test_features, test_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tbwsQf5X3dZk",
        "outputId": "d6a3349a-f835-4f2d-80ea-77ff026c0b54"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step - loss: 0.3117 - accuracy: 0.9807\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.31167516112327576, 0.9807000160217285]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "idx=100\n",
        "img= test_features[idx]\n",
        "print(test_labels[idx])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iXck0hSq4kCO",
        "outputId": "32d719d3-d9a0-4206-fc0f-1c757706cc82"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.gray()\n",
        "plt.imshow(img)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "evhSznMc5C42",
        "outputId": "f685ec65-3274-4e10-cb74-8ca0f0c6183b"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7bd620116b60>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict(np.reshape(img,(-1,784)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hJhWZH8D5not",
        "outputId": "d4116531-c5df-460f-f562-04a7d1dcf28d"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 154ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.2956449e-26, 4.3583427e-37, 2.9413080e-36, 0.0000000e+00,\n",
              "        0.0000000e+00, 3.2926295e-34, 1.0000000e+00, 0.0000000e+00,\n",
              "        2.1269408e-37, 0.0000000e+00]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "acc= hist.history['accuracy']\n",
        "val_acc =hist.history['val_accuracy']\n",
        "\n",
        "plt.plot(acc,color='blue')\n",
        "plt.plot(val_acc, color='red')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "gN7PXuQZ50nY",
        "outputId": "fc812791-32f1-4acb-cbb9-4bffb6c56bd5"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7bd61b790d30>]"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "AUmJHrC86i-A"
      },
      "execution_count": 19,
      "outputs": []
    }
  ]
}