LSTM_Model.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "LSTM Model.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyM2i7UtMmeqjfmSTtaNBl74",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/abhinav9629/MusicGenreClassification/blob/main/LSTM_Model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Em0V-qB8eqtt",
        "outputId": "c9abe4e0-5591-49f7-e718-7fcac87ea2c7"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XN4SJtvVe3Ap"
      },
      "source": [
        "import json\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow.keras as keras\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z_7IbzkCfiIK"
      },
      "source": [
        "DATASET_PATH = '/content/drive/MyDrive/data.json'"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZVDsOj9bfv4M"
      },
      "source": [
        "def load_data(DATASET_PATH):\n",
        "  with open(DATASET_PATH,'r') as fp:\n",
        "    data = json.load(fp)\n",
        "    inputs = np.array(data['mfcc'])\n",
        "    targets = np.array(data['labels'])\n",
        "    return inputs,targets"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5aBMsDcPfw2O"
      },
      "source": [
        "def dataset(test_size,val_size):\n",
        "  inputs,targets = load_data(DATASET_PATH=DATASET_PATH)\n",
        "  train_inputs,test_inputs,train_targets,test_targets = train_test_split(inputs,targets,test_size=test_size)\n",
        "  train_inputs,val_inputs,train_targets,val_targets = train_test_split(train_inputs,train_targets,test_size=val_size)\n",
        "  return train_inputs,val_inputs,test_inputs,train_targets,val_targets,test_targets"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ig31xC24hN0T"
      },
      "source": [
        "train_inputs,val_inputs,test_inputs,train_targets,val_targets,test_targets = dataset(0.25,0.2)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RxZMFGG8hROc"
      },
      "source": [
        "#MODEL ARCHITECTURE"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KQ2FAmFmkg_Z"
      },
      "source": [
        "model = keras.Sequential([\n",
        "  keras.layers.LSTM(64,input_shape = (train_inputs.shape[1],train_inputs.shape[2]),return_sequences=True),\n",
        "  keras.layers.LSTM(64),\n",
        "\n",
        "  keras.layers.Dense(256,activation = 'relu'),\n",
        "  keras.layers.Dropout(0.2),\n",
        "  keras.layers.Dense(128,activation = 'relu'),\n",
        "  keras.layers.Dropout(0.2),\n",
        "  keras.layers.Dense(64,activation = 'relu'),\n",
        "  keras.layers.Dropout(0.2),\n",
        "\n",
        "  keras.layers.Dense(10,activation = 'softmax')\n",
        "\n",
        "])"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vvxQt837sWbD"
      },
      "source": [
        "optimiser = keras.optimizers.Adam(learning_rate=0.001)"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RPuQj9x6sg23"
      },
      "source": [
        "model.compile(optimizer=optimiser,loss='sparse_categorical_crossentropy',metrics=['accuracy'])"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gl9UcZeYuwow",
        "outputId": "2fed536d-fe10-4ff4-b9f9-9d9cb7261743"
      },
      "source": [
        "model.fit(train_inputs,train_targets,validation_data=(val_inputs,val_targets),epochs = 400)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/400\n",
            "188/188 [==============================] - 26s 139ms/step - loss: 0.0804 - accuracy: 0.9778 - val_loss: 1.3117 - val_accuracy: 0.7787\n",
            "Epoch 2/400\n",
            "188/188 [==============================] - 26s 139ms/step - loss: 0.0719 - accuracy: 0.9803 - val_loss: 1.4207 - val_accuracy: 0.7787\n",
            "Epoch 3/400\n",
            "188/188 [==============================] - 26s 141ms/step - loss: 0.0474 - accuracy: 0.9867 - val_loss: 1.4035 - val_accuracy: 0.7907\n",
            "Epoch 4/400\n",
            "188/188 [==============================] - 26s 139ms/step - loss: 0.0656 - accuracy: 0.9802 - val_loss: 1.3782 - val_accuracy: 0.7887\n",
            "Epoch 5/400\n",
            "188/188 [==============================] - 26s 139ms/step - loss: 0.0302 - accuracy: 0.9907 - val_loss: 1.5885 - val_accuracy: 0.7760\n",
            "Epoch 6/400\n",
            "188/188 [==============================] - 26s 139ms/step - loss: 0.0563 - accuracy: 0.9843 - val_loss: 1.4595 - val_accuracy: 0.7800\n",
            "Epoch 7/400\n",
            "188/188 [==============================] - 26s 140ms/step - loss: 0.0284 - accuracy: 0.9912 - val_loss: 1.4264 - val_accuracy: 0.7920\n",
            "Epoch 8/400\n",
            "188/188 [==============================] - 26s 140ms/step - loss: 0.0386 - accuracy: 0.9883 - val_loss: 1.5510 - val_accuracy: 0.7733\n",
            "Epoch 9/400\n",
            "188/188 [==============================] - 26s 140ms/step - loss: 0.0775 - accuracy: 0.9740 - val_loss: 1.3047 - val_accuracy: 0.7967\n",
            "Epoch 10/400\n",
            "188/188 [==============================] - 26s 141ms/step - loss: 0.0527 - accuracy: 0.9847 - val_loss: 1.5890 - val_accuracy: 0.7580\n",
            "Epoch 11/400\n",
            "188/188 [==============================] - 26s 140ms/step - loss: 0.0666 - accuracy: 0.9803 - val_loss: 1.3733 - val_accuracy: 0.8073\n",
            "Epoch 12/400\n",
            "188/188 [==============================] - 26s 140ms/step - loss: 0.0383 - accuracy: 0.9880 - val_loss: 1.5118 - val_accuracy: 0.7807\n",
            "Epoch 13/400\n",
            "188/188 [==============================] - 26s 140ms/step - loss: 0.0553 - accuracy: 0.9842 - val_loss: 1.5131 - val_accuracy: 0.7767\n",
            "Epoch 14/400\n",
            "188/188 [==============================] - 26s 140ms/step - loss: 0.0429 - accuracy: 0.9870 - val_loss: 1.3194 - val_accuracy: 0.7927\n",
            "Epoch 15/400\n",
            "188/188 [==============================] - 26s 141ms/step - loss: 0.0362 - accuracy: 0.9898 - val_loss: 1.4418 - val_accuracy: 0.7807\n",
            "Epoch 16/400\n",
            "188/188 [==============================] - 26s 140ms/step - loss: 0.0335 - accuracy: 0.9892 - val_loss: 1.5838 - val_accuracy: 0.7700\n",
            "Epoch 17/400\n",
            "188/188 [==============================] - 26s 139ms/step - loss: 0.0555 - accuracy: 0.9847 - val_loss: 1.4688 - val_accuracy: 0.7793\n",
            "Epoch 18/400\n",
            "188/188 [==============================] - 26s 140ms/step - loss: 0.0639 - accuracy: 0.9823 - val_loss: 1.3935 - val_accuracy: 0.7733\n",
            "Epoch 19/400\n",
            "188/188 [==============================] - 26s 140ms/step - loss: 0.0583 - accuracy: 0.9820 - val_loss: 1.5101 - val_accuracy: 0.7820\n",
            "Epoch 20/400\n",
            "188/188 [==============================] - 26s 140ms/step - loss: 0.0373 - accuracy: 0.9897 - val_loss: 1.4622 - val_accuracy: 0.7873\n",
            "Epoch 21/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0378 - accuracy: 0.9888 - val_loss: 1.4794 - val_accuracy: 0.7887\n",
            "Epoch 22/400\n",
            "188/188 [==============================] - 26s 140ms/step - loss: 0.0205 - accuracy: 0.9932 - val_loss: 1.5757 - val_accuracy: 0.7853\n",
            "Epoch 23/400\n",
            "188/188 [==============================] - 26s 140ms/step - loss: 0.0654 - accuracy: 0.9825 - val_loss: 1.3463 - val_accuracy: 0.7847\n",
            "Epoch 24/400\n",
            "188/188 [==============================] - 27s 141ms/step - loss: 0.0574 - accuracy: 0.9828 - val_loss: 1.3643 - val_accuracy: 0.7720\n",
            "Epoch 25/400\n",
            "188/188 [==============================] - 26s 141ms/step - loss: 0.0937 - accuracy: 0.9750 - val_loss: 1.2766 - val_accuracy: 0.7820\n",
            "Epoch 26/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0429 - accuracy: 0.9875 - val_loss: 1.4123 - val_accuracy: 0.7873\n",
            "Epoch 27/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0223 - accuracy: 0.9932 - val_loss: 1.3812 - val_accuracy: 0.7893\n",
            "Epoch 28/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0478 - accuracy: 0.9892 - val_loss: 1.5790 - val_accuracy: 0.7580\n",
            "Epoch 29/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0514 - accuracy: 0.9867 - val_loss: 1.3684 - val_accuracy: 0.7807\n",
            "Epoch 30/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0269 - accuracy: 0.9925 - val_loss: 1.4130 - val_accuracy: 0.7960\n",
            "Epoch 31/400\n",
            "188/188 [==============================] - 26s 140ms/step - loss: 0.0359 - accuracy: 0.9885 - val_loss: 1.4245 - val_accuracy: 0.7787\n",
            "Epoch 32/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0370 - accuracy: 0.9912 - val_loss: 1.4989 - val_accuracy: 0.7793\n",
            "Epoch 33/400\n",
            "188/188 [==============================] - 27s 141ms/step - loss: 0.0659 - accuracy: 0.9828 - val_loss: 1.5229 - val_accuracy: 0.7593\n",
            "Epoch 34/400\n",
            "188/188 [==============================] - 26s 141ms/step - loss: 0.0530 - accuracy: 0.9845 - val_loss: 1.3343 - val_accuracy: 0.7760\n",
            "Epoch 35/400\n",
            "188/188 [==============================] - 26s 140ms/step - loss: 0.0161 - accuracy: 0.9953 - val_loss: 1.6897 - val_accuracy: 0.7687\n",
            "Epoch 36/400\n",
            "188/188 [==============================] - 26s 141ms/step - loss: 0.0309 - accuracy: 0.9910 - val_loss: 1.4687 - val_accuracy: 0.7860\n",
            "Epoch 37/400\n",
            "188/188 [==============================] - 27s 141ms/step - loss: 0.0214 - accuracy: 0.9933 - val_loss: 1.5619 - val_accuracy: 0.7940\n",
            "Epoch 38/400\n",
            "188/188 [==============================] - 26s 141ms/step - loss: 0.0203 - accuracy: 0.9928 - val_loss: 1.7896 - val_accuracy: 0.7780\n",
            "Epoch 39/400\n",
            "188/188 [==============================] - 27s 141ms/step - loss: 0.0690 - accuracy: 0.9797 - val_loss: 1.5825 - val_accuracy: 0.7627\n",
            "Epoch 40/400\n",
            "188/188 [==============================] - 26s 140ms/step - loss: 0.1180 - accuracy: 0.9655 - val_loss: 1.2089 - val_accuracy: 0.7820\n",
            "Epoch 41/400\n",
            "188/188 [==============================] - 26s 141ms/step - loss: 0.0235 - accuracy: 0.9922 - val_loss: 1.5741 - val_accuracy: 0.7847\n",
            "Epoch 42/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0160 - accuracy: 0.9962 - val_loss: 1.6205 - val_accuracy: 0.7880\n",
            "Epoch 43/400\n",
            "188/188 [==============================] - 26s 140ms/step - loss: 0.0329 - accuracy: 0.9908 - val_loss: 1.6264 - val_accuracy: 0.7767\n",
            "Epoch 44/400\n",
            "188/188 [==============================] - 26s 140ms/step - loss: 0.0623 - accuracy: 0.9817 - val_loss: 1.4944 - val_accuracy: 0.7733\n",
            "Epoch 45/400\n",
            "188/188 [==============================] - 26s 141ms/step - loss: 0.0347 - accuracy: 0.9898 - val_loss: 1.7017 - val_accuracy: 0.7773\n",
            "Epoch 46/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0371 - accuracy: 0.9887 - val_loss: 1.4554 - val_accuracy: 0.7867\n",
            "Epoch 47/400\n",
            "188/188 [==============================] - 26s 140ms/step - loss: 0.0961 - accuracy: 0.9778 - val_loss: 1.2664 - val_accuracy: 0.7787\n",
            "Epoch 48/400\n",
            "188/188 [==============================] - 26s 140ms/step - loss: 0.0280 - accuracy: 0.9923 - val_loss: 1.4129 - val_accuracy: 0.7907\n",
            "Epoch 49/400\n",
            "188/188 [==============================] - 26s 140ms/step - loss: 0.0097 - accuracy: 0.9967 - val_loss: 1.6266 - val_accuracy: 0.7947\n",
            "Epoch 50/400\n",
            "188/188 [==============================] - 26s 141ms/step - loss: 0.0040 - accuracy: 0.9988 - val_loss: 1.6826 - val_accuracy: 0.7973\n",
            "Epoch 51/400\n",
            "188/188 [==============================] - 26s 140ms/step - loss: 0.0519 - accuracy: 0.9862 - val_loss: 1.4468 - val_accuracy: 0.7613\n",
            "Epoch 52/400\n",
            "188/188 [==============================] - 26s 141ms/step - loss: 0.0610 - accuracy: 0.9848 - val_loss: 1.3902 - val_accuracy: 0.7820\n",
            "Epoch 53/400\n",
            "188/188 [==============================] - 26s 141ms/step - loss: 0.0360 - accuracy: 0.9882 - val_loss: 1.5059 - val_accuracy: 0.7900\n",
            "Epoch 54/400\n",
            "188/188 [==============================] - 26s 140ms/step - loss: 0.0708 - accuracy: 0.9820 - val_loss: 1.5835 - val_accuracy: 0.7507\n",
            "Epoch 55/400\n",
            "188/188 [==============================] - 26s 140ms/step - loss: 0.0703 - accuracy: 0.9820 - val_loss: 1.3074 - val_accuracy: 0.7940\n",
            "Epoch 56/400\n",
            "188/188 [==============================] - 26s 140ms/step - loss: 0.0442 - accuracy: 0.9898 - val_loss: 1.5769 - val_accuracy: 0.7647\n",
            "Epoch 57/400\n",
            "188/188 [==============================] - 26s 141ms/step - loss: 0.0445 - accuracy: 0.9860 - val_loss: 1.5577 - val_accuracy: 0.7720\n",
            "Epoch 58/400\n",
            "188/188 [==============================] - 26s 140ms/step - loss: 0.0508 - accuracy: 0.9845 - val_loss: 1.5467 - val_accuracy: 0.7700\n",
            "Epoch 59/400\n",
            "188/188 [==============================] - 27s 141ms/step - loss: 0.0597 - accuracy: 0.9832 - val_loss: 1.5203 - val_accuracy: 0.7767\n",
            "Epoch 60/400\n",
            "188/188 [==============================] - 27s 141ms/step - loss: 0.0305 - accuracy: 0.9898 - val_loss: 1.5603 - val_accuracy: 0.7873\n",
            "Epoch 61/400\n",
            "188/188 [==============================] - 26s 141ms/step - loss: 0.0721 - accuracy: 0.9785 - val_loss: 1.5310 - val_accuracy: 0.7800\n",
            "Epoch 62/400\n",
            "188/188 [==============================] - 26s 140ms/step - loss: 0.0687 - accuracy: 0.9825 - val_loss: 1.3580 - val_accuracy: 0.7813\n",
            "Epoch 63/400\n",
            "188/188 [==============================] - 26s 140ms/step - loss: 0.0268 - accuracy: 0.9923 - val_loss: 1.5056 - val_accuracy: 0.7833\n",
            "Epoch 64/400\n",
            "188/188 [==============================] - 26s 141ms/step - loss: 0.0520 - accuracy: 0.9873 - val_loss: 1.2882 - val_accuracy: 0.7840\n",
            "Epoch 65/400\n",
            "188/188 [==============================] - 26s 141ms/step - loss: 0.0240 - accuracy: 0.9938 - val_loss: 1.4753 - val_accuracy: 0.8073\n",
            "Epoch 66/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0047 - accuracy: 0.9987 - val_loss: 1.6154 - val_accuracy: 0.7947\n",
            "Epoch 67/400\n",
            "188/188 [==============================] - 27s 141ms/step - loss: 0.0084 - accuracy: 0.9978 - val_loss: 1.6018 - val_accuracy: 0.7927\n",
            "Epoch 68/400\n",
            "188/188 [==============================] - 26s 141ms/step - loss: 0.0543 - accuracy: 0.9847 - val_loss: 1.5067 - val_accuracy: 0.7593\n",
            "Epoch 69/400\n",
            "188/188 [==============================] - 27s 141ms/step - loss: 0.0645 - accuracy: 0.9825 - val_loss: 1.4131 - val_accuracy: 0.7753\n",
            "Epoch 70/400\n",
            "188/188 [==============================] - 27s 141ms/step - loss: 0.0167 - accuracy: 0.9942 - val_loss: 1.5980 - val_accuracy: 0.7867\n",
            "Epoch 71/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0223 - accuracy: 0.9927 - val_loss: 1.6446 - val_accuracy: 0.7973\n",
            "Epoch 72/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0675 - accuracy: 0.9808 - val_loss: 1.4646 - val_accuracy: 0.7787\n",
            "Epoch 73/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0660 - accuracy: 0.9843 - val_loss: 1.4611 - val_accuracy: 0.7920\n",
            "Epoch 74/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0193 - accuracy: 0.9947 - val_loss: 1.8024 - val_accuracy: 0.7807\n",
            "Epoch 75/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0299 - accuracy: 0.9895 - val_loss: 1.5648 - val_accuracy: 0.7873\n",
            "Epoch 76/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0089 - accuracy: 0.9973 - val_loss: 1.7531 - val_accuracy: 0.8020\n",
            "Epoch 77/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0067 - accuracy: 0.9978 - val_loss: 1.7315 - val_accuracy: 0.7987\n",
            "Epoch 78/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0720 - accuracy: 0.9802 - val_loss: 1.6343 - val_accuracy: 0.7687\n",
            "Epoch 79/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0761 - accuracy: 0.9803 - val_loss: 1.3557 - val_accuracy: 0.7893\n",
            "Epoch 80/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0578 - accuracy: 0.9840 - val_loss: 1.3171 - val_accuracy: 0.8013\n",
            "Epoch 81/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0236 - accuracy: 0.9932 - val_loss: 1.5022 - val_accuracy: 0.7967\n",
            "Epoch 82/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0362 - accuracy: 0.9902 - val_loss: 1.5578 - val_accuracy: 0.7660\n",
            "Epoch 83/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0355 - accuracy: 0.9893 - val_loss: 1.6916 - val_accuracy: 0.7807\n",
            "Epoch 84/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0294 - accuracy: 0.9908 - val_loss: 1.5248 - val_accuracy: 0.7907\n",
            "Epoch 85/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0434 - accuracy: 0.9873 - val_loss: 1.6025 - val_accuracy: 0.7813\n",
            "Epoch 86/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0257 - accuracy: 0.9927 - val_loss: 1.6631 - val_accuracy: 0.7913\n",
            "Epoch 87/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0455 - accuracy: 0.9892 - val_loss: 1.5737 - val_accuracy: 0.7880\n",
            "Epoch 88/400\n",
            "188/188 [==============================] - 27s 145ms/step - loss: 0.0516 - accuracy: 0.9857 - val_loss: 1.4748 - val_accuracy: 0.7800\n",
            "Epoch 89/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0224 - accuracy: 0.9938 - val_loss: 1.6034 - val_accuracy: 0.7740\n",
            "Epoch 90/400\n",
            "188/188 [==============================] - 27s 141ms/step - loss: 0.0077 - accuracy: 0.9973 - val_loss: 1.7317 - val_accuracy: 0.7847\n",
            "Epoch 91/400\n",
            "188/188 [==============================] - 26s 141ms/step - loss: 0.0427 - accuracy: 0.9883 - val_loss: 1.7780 - val_accuracy: 0.7720\n",
            "Epoch 92/400\n",
            "188/188 [==============================] - 26s 141ms/step - loss: 0.0191 - accuracy: 0.9945 - val_loss: 1.7432 - val_accuracy: 0.7727\n",
            "Epoch 93/400\n",
            "188/188 [==============================] - 26s 141ms/step - loss: 0.0739 - accuracy: 0.9827 - val_loss: 1.4661 - val_accuracy: 0.7800\n",
            "Epoch 94/400\n",
            "188/188 [==============================] - 26s 140ms/step - loss: 0.0592 - accuracy: 0.9832 - val_loss: 1.5548 - val_accuracy: 0.7547\n",
            "Epoch 95/400\n",
            "188/188 [==============================] - 26s 141ms/step - loss: 0.0647 - accuracy: 0.9820 - val_loss: 1.4730 - val_accuracy: 0.7840\n",
            "Epoch 96/400\n",
            "188/188 [==============================] - 27s 141ms/step - loss: 0.0215 - accuracy: 0.9935 - val_loss: 1.5829 - val_accuracy: 0.7860\n",
            "Epoch 97/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0132 - accuracy: 0.9952 - val_loss: 1.7969 - val_accuracy: 0.7820\n",
            "Epoch 98/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0313 - accuracy: 0.9913 - val_loss: 1.7508 - val_accuracy: 0.7820\n",
            "Epoch 99/400\n",
            "188/188 [==============================] - 27s 141ms/step - loss: 0.0449 - accuracy: 0.9877 - val_loss: 1.6704 - val_accuracy: 0.7853\n",
            "Epoch 100/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0509 - accuracy: 0.9857 - val_loss: 1.5281 - val_accuracy: 0.7813\n",
            "Epoch 101/400\n",
            "188/188 [==============================] - 26s 141ms/step - loss: 0.0286 - accuracy: 0.9927 - val_loss: 1.8087 - val_accuracy: 0.7913\n",
            "Epoch 102/400\n",
            "188/188 [==============================] - 27s 141ms/step - loss: 0.0322 - accuracy: 0.9912 - val_loss: 1.6683 - val_accuracy: 0.7773\n",
            "Epoch 103/400\n",
            "188/188 [==============================] - 27s 141ms/step - loss: 0.0323 - accuracy: 0.9918 - val_loss: 1.6753 - val_accuracy: 0.7833\n",
            "Epoch 104/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0709 - accuracy: 0.9822 - val_loss: 1.3141 - val_accuracy: 0.7827\n",
            "Epoch 105/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0434 - accuracy: 0.9867 - val_loss: 1.4984 - val_accuracy: 0.7813\n",
            "Epoch 106/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0120 - accuracy: 0.9970 - val_loss: 1.6765 - val_accuracy: 0.7927\n",
            "Epoch 107/400\n",
            "188/188 [==============================] - 27s 145ms/step - loss: 0.0100 - accuracy: 0.9973 - val_loss: 1.6641 - val_accuracy: 0.7993\n",
            "Epoch 108/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0040 - accuracy: 0.9990 - val_loss: 1.7638 - val_accuracy: 0.7973\n",
            "Epoch 109/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0032 - accuracy: 0.9987 - val_loss: 1.8967 - val_accuracy: 0.7953\n",
            "Epoch 110/400\n",
            "188/188 [==============================] - 27s 141ms/step - loss: 0.0188 - accuracy: 0.9945 - val_loss: 1.8415 - val_accuracy: 0.7760\n",
            "Epoch 111/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0932 - accuracy: 0.9732 - val_loss: 1.2911 - val_accuracy: 0.7747\n",
            "Epoch 112/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0421 - accuracy: 0.9878 - val_loss: 1.3718 - val_accuracy: 0.7827\n",
            "Epoch 113/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0340 - accuracy: 0.9903 - val_loss: 1.4427 - val_accuracy: 0.7733\n",
            "Epoch 114/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0244 - accuracy: 0.9937 - val_loss: 1.6200 - val_accuracy: 0.7873\n",
            "Epoch 115/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0260 - accuracy: 0.9928 - val_loss: 1.6614 - val_accuracy: 0.7873\n",
            "Epoch 116/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0360 - accuracy: 0.9900 - val_loss: 1.5744 - val_accuracy: 0.7900\n",
            "Epoch 117/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0228 - accuracy: 0.9938 - val_loss: 1.6484 - val_accuracy: 0.7713\n",
            "Epoch 118/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0565 - accuracy: 0.9863 - val_loss: 1.5283 - val_accuracy: 0.7840\n",
            "Epoch 119/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0591 - accuracy: 0.9862 - val_loss: 1.5391 - val_accuracy: 0.7760\n",
            "Epoch 120/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0255 - accuracy: 0.9922 - val_loss: 1.6139 - val_accuracy: 0.7827\n",
            "Epoch 121/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0172 - accuracy: 0.9948 - val_loss: 1.7611 - val_accuracy: 0.7687\n",
            "Epoch 122/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0128 - accuracy: 0.9957 - val_loss: 1.8310 - val_accuracy: 0.7880\n",
            "Epoch 123/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0442 - accuracy: 0.9870 - val_loss: 1.7849 - val_accuracy: 0.7780\n",
            "Epoch 124/400\n",
            "188/188 [==============================] - 28s 146ms/step - loss: 0.0824 - accuracy: 0.9778 - val_loss: 1.4900 - val_accuracy: 0.7747\n",
            "Epoch 125/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0322 - accuracy: 0.9912 - val_loss: 1.5334 - val_accuracy: 0.7920\n",
            "Epoch 126/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0226 - accuracy: 0.9942 - val_loss: 1.5994 - val_accuracy: 0.7807\n",
            "Epoch 127/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0349 - accuracy: 0.9903 - val_loss: 1.5545 - val_accuracy: 0.7833\n",
            "Epoch 128/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0211 - accuracy: 0.9947 - val_loss: 1.6658 - val_accuracy: 0.7900\n",
            "Epoch 129/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0136 - accuracy: 0.9960 - val_loss: 1.8357 - val_accuracy: 0.7747\n",
            "Epoch 130/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0169 - accuracy: 0.9953 - val_loss: 1.9818 - val_accuracy: 0.7867\n",
            "Epoch 131/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0511 - accuracy: 0.9862 - val_loss: 1.5684 - val_accuracy: 0.7647\n",
            "Epoch 132/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0711 - accuracy: 0.9797 - val_loss: 1.4687 - val_accuracy: 0.7753\n",
            "Epoch 133/400\n",
            "188/188 [==============================] - 28s 148ms/step - loss: 0.0324 - accuracy: 0.9902 - val_loss: 1.4752 - val_accuracy: 0.7873\n",
            "Epoch 134/400\n",
            "188/188 [==============================] - 27s 146ms/step - loss: 0.0126 - accuracy: 0.9965 - val_loss: 1.7067 - val_accuracy: 0.7900\n",
            "Epoch 135/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0425 - accuracy: 0.9903 - val_loss: 1.4898 - val_accuracy: 0.7940\n",
            "Epoch 136/400\n",
            "188/188 [==============================] - 27s 145ms/step - loss: 0.0414 - accuracy: 0.9893 - val_loss: 1.4983 - val_accuracy: 0.7927\n",
            "Epoch 137/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0122 - accuracy: 0.9955 - val_loss: 1.5488 - val_accuracy: 0.8007\n",
            "Epoch 138/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0143 - accuracy: 0.9958 - val_loss: 1.8012 - val_accuracy: 0.7907\n",
            "Epoch 139/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0226 - accuracy: 0.9940 - val_loss: 1.8330 - val_accuracy: 0.7793\n",
            "Epoch 140/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0513 - accuracy: 0.9855 - val_loss: 1.9061 - val_accuracy: 0.7607\n",
            "Epoch 141/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0618 - accuracy: 0.9850 - val_loss: 1.5823 - val_accuracy: 0.7780\n",
            "Epoch 142/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0395 - accuracy: 0.9888 - val_loss: 1.5394 - val_accuracy: 0.7720\n",
            "Epoch 143/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0506 - accuracy: 0.9838 - val_loss: 1.5538 - val_accuracy: 0.7887\n",
            "Epoch 144/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0248 - accuracy: 0.9935 - val_loss: 1.6711 - val_accuracy: 0.7753\n",
            "Epoch 145/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0120 - accuracy: 0.9982 - val_loss: 1.8088 - val_accuracy: 0.7667\n",
            "Epoch 146/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0057 - accuracy: 0.9982 - val_loss: 1.9050 - val_accuracy: 0.7873\n",
            "Epoch 147/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0057 - accuracy: 0.9975 - val_loss: 1.9491 - val_accuracy: 0.7853\n",
            "Epoch 148/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0070 - accuracy: 0.9977 - val_loss: 1.9202 - val_accuracy: 0.7847\n",
            "Epoch 149/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.1051 - accuracy: 0.9740 - val_loss: 1.4017 - val_accuracy: 0.7793\n",
            "Epoch 150/400\n",
            "188/188 [==============================] - 27s 141ms/step - loss: 0.0300 - accuracy: 0.9910 - val_loss: 1.7796 - val_accuracy: 0.7620\n",
            "Epoch 151/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0339 - accuracy: 0.9895 - val_loss: 1.6337 - val_accuracy: 0.7833\n",
            "Epoch 152/400\n",
            "188/188 [==============================] - 27s 141ms/step - loss: 0.0233 - accuracy: 0.9935 - val_loss: 1.6794 - val_accuracy: 0.7807\n",
            "Epoch 153/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0366 - accuracy: 0.9890 - val_loss: 1.5804 - val_accuracy: 0.7840\n",
            "Epoch 154/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0252 - accuracy: 0.9943 - val_loss: 1.6090 - val_accuracy: 0.7860\n",
            "Epoch 155/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0349 - accuracy: 0.9915 - val_loss: 1.5965 - val_accuracy: 0.7867\n",
            "Epoch 156/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0201 - accuracy: 0.9938 - val_loss: 1.6954 - val_accuracy: 0.7827\n",
            "Epoch 157/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0115 - accuracy: 0.9972 - val_loss: 1.7627 - val_accuracy: 0.7853\n",
            "Epoch 158/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0088 - accuracy: 0.9967 - val_loss: 1.8329 - val_accuracy: 0.7900\n",
            "Epoch 159/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0391 - accuracy: 0.9898 - val_loss: 1.8347 - val_accuracy: 0.7793\n",
            "Epoch 160/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0607 - accuracy: 0.9848 - val_loss: 1.5774 - val_accuracy: 0.7800\n",
            "Epoch 161/400\n",
            "188/188 [==============================] - 27s 141ms/step - loss: 0.0319 - accuracy: 0.9910 - val_loss: 1.6708 - val_accuracy: 0.7873\n",
            "Epoch 162/400\n",
            "188/188 [==============================] - 26s 141ms/step - loss: 0.0113 - accuracy: 0.9967 - val_loss: 1.7607 - val_accuracy: 0.7840\n",
            "Epoch 163/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0121 - accuracy: 0.9972 - val_loss: 1.7825 - val_accuracy: 0.7840\n",
            "Epoch 164/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0347 - accuracy: 0.9897 - val_loss: 2.0369 - val_accuracy: 0.7587\n",
            "Epoch 165/400\n",
            "188/188 [==============================] - 27s 141ms/step - loss: 0.0409 - accuracy: 0.9887 - val_loss: 1.7379 - val_accuracy: 0.7953\n",
            "Epoch 166/400\n",
            "188/188 [==============================] - 27s 141ms/step - loss: 0.0125 - accuracy: 0.9962 - val_loss: 1.8604 - val_accuracy: 0.7860\n",
            "Epoch 167/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0236 - accuracy: 0.9937 - val_loss: 1.8144 - val_accuracy: 0.7847\n",
            "Epoch 168/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0293 - accuracy: 0.9917 - val_loss: 1.7360 - val_accuracy: 0.7753\n",
            "Epoch 169/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0320 - accuracy: 0.9908 - val_loss: 1.8212 - val_accuracy: 0.7793\n",
            "Epoch 170/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0532 - accuracy: 0.9847 - val_loss: 1.8033 - val_accuracy: 0.7720\n",
            "Epoch 171/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0409 - accuracy: 0.9877 - val_loss: 1.6627 - val_accuracy: 0.7807\n",
            "Epoch 172/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0229 - accuracy: 0.9942 - val_loss: 1.8160 - val_accuracy: 0.7793\n",
            "Epoch 173/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0525 - accuracy: 0.9872 - val_loss: 1.7595 - val_accuracy: 0.7820\n",
            "Epoch 174/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0769 - accuracy: 0.9827 - val_loss: 1.4416 - val_accuracy: 0.7587\n",
            "Epoch 175/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0542 - accuracy: 0.9845 - val_loss: 1.5819 - val_accuracy: 0.7833\n",
            "Epoch 176/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0237 - accuracy: 0.9928 - val_loss: 1.6030 - val_accuracy: 0.7920\n",
            "Epoch 177/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0124 - accuracy: 0.9965 - val_loss: 1.7454 - val_accuracy: 0.7933\n",
            "Epoch 178/400\n",
            "188/188 [==============================] - 27s 141ms/step - loss: 0.0105 - accuracy: 0.9972 - val_loss: 1.8326 - val_accuracy: 0.7827\n",
            "Epoch 179/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0097 - accuracy: 0.9972 - val_loss: 1.9678 - val_accuracy: 0.7760\n",
            "Epoch 180/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0291 - accuracy: 0.9918 - val_loss: 1.7749 - val_accuracy: 0.7767\n",
            "Epoch 181/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0615 - accuracy: 0.9827 - val_loss: 1.5175 - val_accuracy: 0.7853\n",
            "Epoch 182/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0224 - accuracy: 0.9928 - val_loss: 1.6552 - val_accuracy: 0.7853\n",
            "Epoch 183/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0116 - accuracy: 0.9965 - val_loss: 1.7750 - val_accuracy: 0.7873\n",
            "Epoch 184/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0084 - accuracy: 0.9977 - val_loss: 1.9053 - val_accuracy: 0.7833\n",
            "Epoch 185/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0065 - accuracy: 0.9983 - val_loss: 2.0285 - val_accuracy: 0.7853\n",
            "Epoch 186/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0144 - accuracy: 0.9973 - val_loss: 1.7469 - val_accuracy: 0.7860\n",
            "Epoch 187/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0492 - accuracy: 0.9877 - val_loss: 1.7514 - val_accuracy: 0.7713\n",
            "Epoch 188/400\n",
            "188/188 [==============================] - 27s 141ms/step - loss: 0.0431 - accuracy: 0.9882 - val_loss: 1.5766 - val_accuracy: 0.7613\n",
            "Epoch 189/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0662 - accuracy: 0.9825 - val_loss: 1.4076 - val_accuracy: 0.7840\n",
            "Epoch 190/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0483 - accuracy: 0.9857 - val_loss: 1.5222 - val_accuracy: 0.7813\n",
            "Epoch 191/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0329 - accuracy: 0.9908 - val_loss: 1.6848 - val_accuracy: 0.7760\n",
            "Epoch 192/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0494 - accuracy: 0.9867 - val_loss: 1.4768 - val_accuracy: 0.7820\n",
            "Epoch 193/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0256 - accuracy: 0.9913 - val_loss: 1.5495 - val_accuracy: 0.7793\n",
            "Epoch 194/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0064 - accuracy: 0.9983 - val_loss: 1.6976 - val_accuracy: 0.7820\n",
            "Epoch 195/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0028 - accuracy: 0.9992 - val_loss: 1.7578 - val_accuracy: 0.7827\n",
            "Epoch 196/400\n",
            "188/188 [==============================] - 27s 145ms/step - loss: 0.0026 - accuracy: 0.9993 - val_loss: 1.8864 - val_accuracy: 0.7853\n",
            "Epoch 197/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0022 - accuracy: 0.9995 - val_loss: 1.9126 - val_accuracy: 0.7913\n",
            "Epoch 198/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0341 - accuracy: 0.9923 - val_loss: 1.8862 - val_accuracy: 0.7580\n",
            "Epoch 199/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0679 - accuracy: 0.9807 - val_loss: 1.6571 - val_accuracy: 0.7740\n",
            "Epoch 200/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0501 - accuracy: 0.9867 - val_loss: 1.6863 - val_accuracy: 0.7727\n",
            "Epoch 201/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0418 - accuracy: 0.9883 - val_loss: 1.6724 - val_accuracy: 0.7627\n",
            "Epoch 202/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0206 - accuracy: 0.9950 - val_loss: 1.7838 - val_accuracy: 0.7700\n",
            "Epoch 203/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0166 - accuracy: 0.9955 - val_loss: 1.8793 - val_accuracy: 0.7780\n",
            "Epoch 204/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0133 - accuracy: 0.9960 - val_loss: 1.9308 - val_accuracy: 0.7793\n",
            "Epoch 205/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0168 - accuracy: 0.9947 - val_loss: 1.9817 - val_accuracy: 0.7720\n",
            "Epoch 206/400\n",
            "188/188 [==============================] - 27s 141ms/step - loss: 0.0617 - accuracy: 0.9850 - val_loss: 1.6906 - val_accuracy: 0.7733\n",
            "Epoch 207/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0193 - accuracy: 0.9938 - val_loss: 1.7633 - val_accuracy: 0.7767\n",
            "Epoch 208/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0157 - accuracy: 0.9960 - val_loss: 1.8404 - val_accuracy: 0.7873\n",
            "Epoch 209/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0456 - accuracy: 0.9883 - val_loss: 1.7593 - val_accuracy: 0.7740\n",
            "Epoch 210/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0262 - accuracy: 0.9933 - val_loss: 1.7424 - val_accuracy: 0.7860\n",
            "Epoch 211/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0194 - accuracy: 0.9947 - val_loss: 1.7205 - val_accuracy: 0.7667\n",
            "Epoch 212/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0127 - accuracy: 0.9967 - val_loss: 1.9781 - val_accuracy: 0.7747\n",
            "Epoch 213/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0169 - accuracy: 0.9962 - val_loss: 2.0863 - val_accuracy: 0.7647\n",
            "Epoch 214/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0520 - accuracy: 0.9883 - val_loss: 1.6573 - val_accuracy: 0.7740\n",
            "Epoch 215/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0126 - accuracy: 0.9968 - val_loss: 1.8256 - val_accuracy: 0.7807\n",
            "Epoch 216/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0235 - accuracy: 0.9950 - val_loss: 1.8209 - val_accuracy: 0.7867\n",
            "Epoch 217/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0283 - accuracy: 0.9918 - val_loss: 1.9488 - val_accuracy: 0.7840\n",
            "Epoch 218/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0263 - accuracy: 0.9927 - val_loss: 1.8697 - val_accuracy: 0.7713\n",
            "Epoch 219/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0249 - accuracy: 0.9928 - val_loss: 1.7112 - val_accuracy: 0.7813\n",
            "Epoch 220/400\n",
            "188/188 [==============================] - 27s 145ms/step - loss: 0.0430 - accuracy: 0.9888 - val_loss: 1.6480 - val_accuracy: 0.7780\n",
            "Epoch 221/400\n",
            "188/188 [==============================] - 27s 145ms/step - loss: 0.0326 - accuracy: 0.9903 - val_loss: 1.7444 - val_accuracy: 0.7813\n",
            "Epoch 222/400\n",
            "188/188 [==============================] - 27s 145ms/step - loss: 0.0066 - accuracy: 0.9977 - val_loss: 1.8724 - val_accuracy: 0.7807\n",
            "Epoch 223/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0026 - accuracy: 0.9992 - val_loss: 1.9728 - val_accuracy: 0.7907\n",
            "Epoch 224/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0994 - accuracy: 0.9773 - val_loss: 1.6160 - val_accuracy: 0.7767\n",
            "Epoch 225/400\n",
            "188/188 [==============================] - 27s 145ms/step - loss: 0.0308 - accuracy: 0.9925 - val_loss: 1.7056 - val_accuracy: 0.7780\n",
            "Epoch 226/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0204 - accuracy: 0.9953 - val_loss: 1.8316 - val_accuracy: 0.7633\n",
            "Epoch 227/400\n",
            "188/188 [==============================] - 27s 145ms/step - loss: 0.0090 - accuracy: 0.9972 - val_loss: 1.9408 - val_accuracy: 0.7753\n",
            "Epoch 228/400\n",
            "188/188 [==============================] - 27s 142ms/step - loss: 0.0102 - accuracy: 0.9977 - val_loss: 1.9010 - val_accuracy: 0.7833\n",
            "Epoch 229/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0056 - accuracy: 0.9987 - val_loss: 2.0372 - val_accuracy: 0.7840\n",
            "Epoch 230/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0057 - accuracy: 0.9973 - val_loss: 2.1352 - val_accuracy: 0.7800\n",
            "Epoch 231/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0351 - accuracy: 0.9908 - val_loss: 1.9257 - val_accuracy: 0.7740\n",
            "Epoch 232/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0566 - accuracy: 0.9845 - val_loss: 1.7114 - val_accuracy: 0.7627\n",
            "Epoch 233/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0615 - accuracy: 0.9810 - val_loss: 1.7611 - val_accuracy: 0.7693\n",
            "Epoch 234/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0374 - accuracy: 0.9880 - val_loss: 1.7730 - val_accuracy: 0.7880\n",
            "Epoch 235/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0106 - accuracy: 0.9968 - val_loss: 1.7948 - val_accuracy: 0.7860\n",
            "Epoch 236/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0220 - accuracy: 0.9942 - val_loss: 1.7939 - val_accuracy: 0.7800\n",
            "Epoch 237/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0676 - accuracy: 0.9843 - val_loss: 1.5921 - val_accuracy: 0.7740\n",
            "Epoch 238/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0361 - accuracy: 0.9898 - val_loss: 1.6269 - val_accuracy: 0.7773\n",
            "Epoch 239/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0141 - accuracy: 0.9962 - val_loss: 1.7588 - val_accuracy: 0.7987\n",
            "Epoch 240/400\n",
            "188/188 [==============================] - 27s 145ms/step - loss: 0.0320 - accuracy: 0.9910 - val_loss: 1.7203 - val_accuracy: 0.7847\n",
            "Epoch 241/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0274 - accuracy: 0.9927 - val_loss: 1.7287 - val_accuracy: 0.7907\n",
            "Epoch 242/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0098 - accuracy: 0.9973 - val_loss: 1.9577 - val_accuracy: 0.7907\n",
            "Epoch 243/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0094 - accuracy: 0.9967 - val_loss: 1.9574 - val_accuracy: 0.7960\n",
            "Epoch 244/400\n",
            "188/188 [==============================] - 27s 146ms/step - loss: 0.0120 - accuracy: 0.9972 - val_loss: 1.9601 - val_accuracy: 0.7880\n",
            "Epoch 245/400\n",
            "188/188 [==============================] - 27s 145ms/step - loss: 0.0563 - accuracy: 0.9872 - val_loss: 1.7051 - val_accuracy: 0.7913\n",
            "Epoch 246/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0423 - accuracy: 0.9893 - val_loss: 1.4995 - val_accuracy: 0.7927\n",
            "Epoch 247/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0310 - accuracy: 0.9908 - val_loss: 1.6365 - val_accuracy: 0.7980\n",
            "Epoch 248/400\n",
            "188/188 [==============================] - 27s 143ms/step - loss: 0.0160 - accuracy: 0.9958 - val_loss: 1.9113 - val_accuracy: 0.7853\n",
            "Epoch 249/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0502 - accuracy: 0.9890 - val_loss: 1.5425 - val_accuracy: 0.7860\n",
            "Epoch 250/400\n",
            "188/188 [==============================] - 27s 146ms/step - loss: 0.0155 - accuracy: 0.9962 - val_loss: 1.7533 - val_accuracy: 0.7987\n",
            "Epoch 251/400\n",
            "188/188 [==============================] - 27s 146ms/step - loss: 0.0254 - accuracy: 0.9932 - val_loss: 1.8360 - val_accuracy: 0.7940\n",
            "Epoch 252/400\n",
            "188/188 [==============================] - 27s 145ms/step - loss: 0.0180 - accuracy: 0.9948 - val_loss: 1.7505 - val_accuracy: 0.7967\n",
            "Epoch 253/400\n",
            "188/188 [==============================] - 27s 145ms/step - loss: 0.0239 - accuracy: 0.9945 - val_loss: 1.7319 - val_accuracy: 0.7893\n",
            "Epoch 254/400\n",
            "188/188 [==============================] - 27s 146ms/step - loss: 0.0259 - accuracy: 0.9927 - val_loss: 1.8487 - val_accuracy: 0.7847\n",
            "Epoch 255/400\n",
            "188/188 [==============================] - 28s 146ms/step - loss: 0.0411 - accuracy: 0.9882 - val_loss: 1.6203 - val_accuracy: 0.7980\n",
            "Epoch 256/400\n",
            "188/188 [==============================] - 28s 146ms/step - loss: 0.0339 - accuracy: 0.9895 - val_loss: 1.7021 - val_accuracy: 0.7953\n",
            "Epoch 257/400\n",
            "188/188 [==============================] - 27s 146ms/step - loss: 0.0455 - accuracy: 0.9875 - val_loss: 1.4750 - val_accuracy: 0.7927\n",
            "Epoch 258/400\n",
            "188/188 [==============================] - 27s 146ms/step - loss: 0.0344 - accuracy: 0.9937 - val_loss: 1.4771 - val_accuracy: 0.7907\n",
            "Epoch 259/400\n",
            "188/188 [==============================] - 27s 145ms/step - loss: 0.0196 - accuracy: 0.9948 - val_loss: 1.5769 - val_accuracy: 0.7993\n",
            "Epoch 260/400\n",
            "188/188 [==============================] - 27s 146ms/step - loss: 0.0273 - accuracy: 0.9925 - val_loss: 1.6476 - val_accuracy: 0.7913\n",
            "Epoch 261/400\n",
            "188/188 [==============================] - 27s 146ms/step - loss: 0.0347 - accuracy: 0.9902 - val_loss: 1.6920 - val_accuracy: 0.7907\n",
            "Epoch 262/400\n",
            "188/188 [==============================] - 27s 145ms/step - loss: 0.0135 - accuracy: 0.9962 - val_loss: 1.8067 - val_accuracy: 0.7887\n",
            "Epoch 263/400\n",
            "188/188 [==============================] - 27s 145ms/step - loss: 0.0107 - accuracy: 0.9967 - val_loss: 1.9706 - val_accuracy: 0.7787\n",
            "Epoch 264/400\n",
            "188/188 [==============================] - 27s 145ms/step - loss: 0.0243 - accuracy: 0.9925 - val_loss: 1.8879 - val_accuracy: 0.7760\n",
            "Epoch 265/400\n",
            "188/188 [==============================] - 27s 146ms/step - loss: 0.0259 - accuracy: 0.9918 - val_loss: 1.7237 - val_accuracy: 0.7987\n",
            "Epoch 266/400\n",
            "188/188 [==============================] - 28s 147ms/step - loss: 0.0106 - accuracy: 0.9962 - val_loss: 1.8833 - val_accuracy: 0.7860\n",
            "Epoch 267/400\n",
            "188/188 [==============================] - 28s 149ms/step - loss: 0.0135 - accuracy: 0.9957 - val_loss: 1.9558 - val_accuracy: 0.7847\n",
            "Epoch 268/400\n",
            "188/188 [==============================] - 28s 147ms/step - loss: 0.0211 - accuracy: 0.9945 - val_loss: 2.0263 - val_accuracy: 0.7793\n",
            "Epoch 269/400\n",
            "188/188 [==============================] - 27s 146ms/step - loss: 0.0734 - accuracy: 0.9830 - val_loss: 1.5668 - val_accuracy: 0.7807\n",
            "Epoch 270/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0382 - accuracy: 0.9890 - val_loss: 1.5527 - val_accuracy: 0.7867\n",
            "Epoch 271/400\n",
            "188/188 [==============================] - 27s 145ms/step - loss: 0.0365 - accuracy: 0.9902 - val_loss: 1.6054 - val_accuracy: 0.7967\n",
            "Epoch 272/400\n",
            "188/188 [==============================] - 27s 145ms/step - loss: 0.0128 - accuracy: 0.9952 - val_loss: 1.8206 - val_accuracy: 0.7920\n",
            "Epoch 273/400\n",
            "188/188 [==============================] - 28s 147ms/step - loss: 0.0373 - accuracy: 0.9920 - val_loss: 1.6533 - val_accuracy: 0.7780\n",
            "Epoch 274/400\n",
            "188/188 [==============================] - 28s 147ms/step - loss: 0.0256 - accuracy: 0.9937 - val_loss: 1.7869 - val_accuracy: 0.7947\n",
            "Epoch 275/400\n",
            "188/188 [==============================] - 28s 147ms/step - loss: 0.0043 - accuracy: 0.9987 - val_loss: 1.9116 - val_accuracy: 0.7893\n",
            "Epoch 276/400\n",
            "188/188 [==============================] - 28s 147ms/step - loss: 0.0022 - accuracy: 0.9995 - val_loss: 1.9979 - val_accuracy: 0.7967\n",
            "Epoch 277/400\n",
            "188/188 [==============================] - 28s 147ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 2.0698 - val_accuracy: 0.7967\n",
            "Epoch 278/400\n",
            "188/188 [==============================] - 28s 146ms/step - loss: 0.0020 - accuracy: 0.9993 - val_loss: 2.3245 - val_accuracy: 0.7900\n",
            "Epoch 279/400\n",
            "188/188 [==============================] - 28s 147ms/step - loss: 0.0545 - accuracy: 0.9870 - val_loss: 1.8188 - val_accuracy: 0.7807\n",
            "Epoch 280/400\n",
            "188/188 [==============================] - 28s 149ms/step - loss: 0.0433 - accuracy: 0.9868 - val_loss: 1.7920 - val_accuracy: 0.7787\n",
            "Epoch 281/400\n",
            "188/188 [==============================] - 27s 145ms/step - loss: 0.0307 - accuracy: 0.9912 - val_loss: 1.8197 - val_accuracy: 0.7853\n",
            "Epoch 282/400\n",
            "188/188 [==============================] - 27s 146ms/step - loss: 0.0566 - accuracy: 0.9862 - val_loss: 1.5841 - val_accuracy: 0.7913\n",
            "Epoch 283/400\n",
            "188/188 [==============================] - 27s 146ms/step - loss: 0.0189 - accuracy: 0.9945 - val_loss: 1.8394 - val_accuracy: 0.7807\n",
            "Epoch 284/400\n",
            "188/188 [==============================] - 28s 146ms/step - loss: 0.0112 - accuracy: 0.9965 - val_loss: 1.8092 - val_accuracy: 0.7880\n",
            "Epoch 285/400\n",
            "188/188 [==============================] - 27s 146ms/step - loss: 0.0651 - accuracy: 0.9855 - val_loss: 1.6506 - val_accuracy: 0.7813\n",
            "Epoch 286/400\n",
            "188/188 [==============================] - 27s 146ms/step - loss: 0.0400 - accuracy: 0.9902 - val_loss: 1.4643 - val_accuracy: 0.7947\n",
            "Epoch 287/400\n",
            "188/188 [==============================] - 28s 148ms/step - loss: 0.0094 - accuracy: 0.9970 - val_loss: 1.6318 - val_accuracy: 0.7920\n",
            "Epoch 288/400\n",
            "188/188 [==============================] - 28s 148ms/step - loss: 0.0088 - accuracy: 0.9967 - val_loss: 1.8737 - val_accuracy: 0.7827\n",
            "Epoch 289/400\n",
            "188/188 [==============================] - 28s 148ms/step - loss: 0.0239 - accuracy: 0.9950 - val_loss: 1.6825 - val_accuracy: 0.7813\n",
            "Epoch 290/400\n",
            "188/188 [==============================] - 28s 147ms/step - loss: 0.0232 - accuracy: 0.9927 - val_loss: 1.7031 - val_accuracy: 0.7847\n",
            "Epoch 291/400\n",
            "188/188 [==============================] - 28s 147ms/step - loss: 0.0289 - accuracy: 0.9935 - val_loss: 1.6963 - val_accuracy: 0.7840\n",
            "Epoch 292/400\n",
            "188/188 [==============================] - 28s 147ms/step - loss: 0.0084 - accuracy: 0.9970 - val_loss: 1.7627 - val_accuracy: 0.8087\n",
            "Epoch 293/400\n",
            "188/188 [==============================] - 28s 147ms/step - loss: 0.0038 - accuracy: 0.9988 - val_loss: 1.7774 - val_accuracy: 0.8020\n",
            "Epoch 294/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0281 - accuracy: 0.9948 - val_loss: 1.8029 - val_accuracy: 0.7807\n",
            "Epoch 295/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0280 - accuracy: 0.9912 - val_loss: 1.7633 - val_accuracy: 0.7907\n",
            "Epoch 296/400\n",
            "188/188 [==============================] - 27s 145ms/step - loss: 0.0115 - accuracy: 0.9972 - val_loss: 1.8108 - val_accuracy: 0.7967\n",
            "Epoch 297/400\n",
            "188/188 [==============================] - 27s 145ms/step - loss: 0.0118 - accuracy: 0.9968 - val_loss: 2.1424 - val_accuracy: 0.7840\n",
            "Epoch 298/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0356 - accuracy: 0.9912 - val_loss: 1.7140 - val_accuracy: 0.8007\n",
            "Epoch 299/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0336 - accuracy: 0.9905 - val_loss: 1.5910 - val_accuracy: 0.7907\n",
            "Epoch 300/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0384 - accuracy: 0.9902 - val_loss: 1.6401 - val_accuracy: 0.7913\n",
            "Epoch 301/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0350 - accuracy: 0.9902 - val_loss: 1.6549 - val_accuracy: 0.8020\n",
            "Epoch 302/400\n",
            "188/188 [==============================] - 27s 146ms/step - loss: 0.0116 - accuracy: 0.9963 - val_loss: 1.7392 - val_accuracy: 0.8007\n",
            "Epoch 303/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0031 - accuracy: 0.9990 - val_loss: 1.8815 - val_accuracy: 0.8053\n",
            "Epoch 304/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0139 - accuracy: 0.9967 - val_loss: 1.8368 - val_accuracy: 0.7993\n",
            "Epoch 305/400\n",
            "188/188 [==============================] - 27s 145ms/step - loss: 0.0146 - accuracy: 0.9955 - val_loss: 1.9465 - val_accuracy: 0.7980\n",
            "Epoch 306/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0375 - accuracy: 0.9902 - val_loss: 1.7403 - val_accuracy: 0.7860\n",
            "Epoch 307/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0328 - accuracy: 0.9935 - val_loss: 1.6403 - val_accuracy: 0.7800\n",
            "Epoch 308/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0292 - accuracy: 0.9932 - val_loss: 1.7249 - val_accuracy: 0.7887\n",
            "Epoch 309/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0094 - accuracy: 0.9967 - val_loss: 1.8401 - val_accuracy: 0.8027\n",
            "Epoch 310/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0027 - accuracy: 0.9993 - val_loss: 1.9813 - val_accuracy: 0.7987\n",
            "Epoch 311/400\n",
            "188/188 [==============================] - 27s 145ms/step - loss: 0.0081 - accuracy: 0.9972 - val_loss: 2.1057 - val_accuracy: 0.7853\n",
            "Epoch 312/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0881 - accuracy: 0.9817 - val_loss: 1.5530 - val_accuracy: 0.7687\n",
            "Epoch 313/400\n",
            "188/188 [==============================] - 27s 145ms/step - loss: 0.0500 - accuracy: 0.9847 - val_loss: 1.5578 - val_accuracy: 0.7953\n",
            "Epoch 314/400\n",
            "188/188 [==============================] - 27s 146ms/step - loss: 0.0314 - accuracy: 0.9932 - val_loss: 1.6338 - val_accuracy: 0.7853\n",
            "Epoch 315/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0251 - accuracy: 0.9930 - val_loss: 1.7405 - val_accuracy: 0.7987\n",
            "Epoch 316/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0284 - accuracy: 0.9948 - val_loss: 1.6064 - val_accuracy: 0.8080\n",
            "Epoch 317/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0059 - accuracy: 0.9985 - val_loss: 1.7763 - val_accuracy: 0.8013\n",
            "Epoch 318/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0072 - accuracy: 0.9980 - val_loss: 1.8313 - val_accuracy: 0.7900\n",
            "Epoch 319/400\n",
            "188/188 [==============================] - 27s 145ms/step - loss: 0.0392 - accuracy: 0.9893 - val_loss: 1.7207 - val_accuracy: 0.7933\n",
            "Epoch 320/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0218 - accuracy: 0.9950 - val_loss: 1.9017 - val_accuracy: 0.7880\n",
            "Epoch 321/400\n",
            "188/188 [==============================] - 27s 145ms/step - loss: 0.0405 - accuracy: 0.9897 - val_loss: 1.7509 - val_accuracy: 0.7773\n",
            "Epoch 322/400\n",
            "188/188 [==============================] - 28s 147ms/step - loss: 0.0178 - accuracy: 0.9950 - val_loss: 1.7725 - val_accuracy: 0.7907\n",
            "Epoch 323/400\n",
            "188/188 [==============================] - 27s 146ms/step - loss: 0.0227 - accuracy: 0.9943 - val_loss: 1.7221 - val_accuracy: 0.7947\n",
            "Epoch 324/400\n",
            "188/188 [==============================] - 28s 147ms/step - loss: 0.0197 - accuracy: 0.9950 - val_loss: 1.8704 - val_accuracy: 0.7933\n",
            "Epoch 325/400\n",
            "188/188 [==============================] - 27s 145ms/step - loss: 0.0054 - accuracy: 0.9985 - val_loss: 2.0002 - val_accuracy: 0.7980\n",
            "Epoch 326/400\n",
            "188/188 [==============================] - 28s 147ms/step - loss: 0.0097 - accuracy: 0.9982 - val_loss: 1.8953 - val_accuracy: 0.8067\n",
            "Epoch 327/400\n",
            "188/188 [==============================] - 27s 145ms/step - loss: 0.0033 - accuracy: 0.9988 - val_loss: 1.9675 - val_accuracy: 0.8040\n",
            "Epoch 328/400\n",
            "188/188 [==============================] - 27s 146ms/step - loss: 0.0025 - accuracy: 0.9993 - val_loss: 2.1504 - val_accuracy: 0.8053\n",
            "Epoch 329/400\n",
            "188/188 [==============================] - 27s 145ms/step - loss: 0.0156 - accuracy: 0.9968 - val_loss: 2.1602 - val_accuracy: 0.7713\n",
            "Epoch 330/400\n",
            "188/188 [==============================] - 27s 145ms/step - loss: 0.0569 - accuracy: 0.9862 - val_loss: 1.5547 - val_accuracy: 0.7753\n",
            "Epoch 331/400\n",
            "188/188 [==============================] - 27s 145ms/step - loss: 0.0553 - accuracy: 0.9848 - val_loss: 1.5298 - val_accuracy: 0.7927\n",
            "Epoch 332/400\n",
            "188/188 [==============================] - 27s 145ms/step - loss: 0.0410 - accuracy: 0.9897 - val_loss: 1.5511 - val_accuracy: 0.7940\n",
            "Epoch 333/400\n",
            "188/188 [==============================] - 28s 149ms/step - loss: 0.0216 - accuracy: 0.9947 - val_loss: 1.5865 - val_accuracy: 0.8060\n",
            "Epoch 334/400\n",
            "188/188 [==============================] - 27s 146ms/step - loss: 0.0094 - accuracy: 0.9977 - val_loss: 1.7985 - val_accuracy: 0.8060\n",
            "Epoch 335/400\n",
            "188/188 [==============================] - 27s 146ms/step - loss: 0.0100 - accuracy: 0.9973 - val_loss: 1.7474 - val_accuracy: 0.7967\n",
            "Epoch 336/400\n",
            "188/188 [==============================] - 27s 146ms/step - loss: 0.0253 - accuracy: 0.9945 - val_loss: 1.8231 - val_accuracy: 0.8027\n",
            "Epoch 337/400\n",
            "188/188 [==============================] - 28s 148ms/step - loss: 0.0124 - accuracy: 0.9963 - val_loss: 1.9242 - val_accuracy: 0.7913\n",
            "Epoch 338/400\n",
            "188/188 [==============================] - 28s 147ms/step - loss: 0.0349 - accuracy: 0.9912 - val_loss: 1.9352 - val_accuracy: 0.7913\n",
            "Epoch 339/400\n",
            "188/188 [==============================] - 28s 147ms/step - loss: 0.0210 - accuracy: 0.9928 - val_loss: 1.9709 - val_accuracy: 0.8047\n",
            "Epoch 340/400\n",
            "188/188 [==============================] - 28s 147ms/step - loss: 0.0072 - accuracy: 0.9975 - val_loss: 1.9278 - val_accuracy: 0.8060\n",
            "Epoch 341/400\n",
            "188/188 [==============================] - 27s 146ms/step - loss: 0.0191 - accuracy: 0.9953 - val_loss: 1.9063 - val_accuracy: 0.7987\n",
            "Epoch 342/400\n",
            "188/188 [==============================] - 27s 145ms/step - loss: 0.0268 - accuracy: 0.9923 - val_loss: 1.9284 - val_accuracy: 0.7947\n",
            "Epoch 343/400\n",
            "188/188 [==============================] - 27s 145ms/step - loss: 0.0267 - accuracy: 0.9930 - val_loss: 1.8815 - val_accuracy: 0.7920\n",
            "Epoch 344/400\n",
            "188/188 [==============================] - 28s 146ms/step - loss: 0.0120 - accuracy: 0.9960 - val_loss: 1.8973 - val_accuracy: 0.8067\n",
            "Epoch 345/400\n",
            "188/188 [==============================] - 28s 146ms/step - loss: 0.0174 - accuracy: 0.9955 - val_loss: 1.9670 - val_accuracy: 0.7853\n",
            "Epoch 346/400\n",
            "188/188 [==============================] - 28s 147ms/step - loss: 0.0567 - accuracy: 0.9860 - val_loss: 1.7142 - val_accuracy: 0.7880\n",
            "Epoch 347/400\n",
            "188/188 [==============================] - 27s 146ms/step - loss: 0.0095 - accuracy: 0.9975 - val_loss: 1.8261 - val_accuracy: 0.7980\n",
            "Epoch 348/400\n",
            "188/188 [==============================] - 28s 147ms/step - loss: 0.0044 - accuracy: 0.9983 - val_loss: 1.9917 - val_accuracy: 0.7980\n",
            "Epoch 349/400\n",
            "188/188 [==============================] - 28s 147ms/step - loss: 0.0023 - accuracy: 0.9992 - val_loss: 2.0220 - val_accuracy: 0.8047\n",
            "Epoch 350/400\n",
            "188/188 [==============================] - 27s 146ms/step - loss: 0.0040 - accuracy: 0.9992 - val_loss: 2.3476 - val_accuracy: 0.7940\n",
            "Epoch 351/400\n",
            "188/188 [==============================] - 27s 146ms/step - loss: 0.0418 - accuracy: 0.9902 - val_loss: 1.8309 - val_accuracy: 0.7973\n",
            "Epoch 352/400\n",
            "188/188 [==============================] - 28s 146ms/step - loss: 0.0887 - accuracy: 0.9815 - val_loss: 1.7041 - val_accuracy: 0.7753\n",
            "Epoch 353/400\n",
            "188/188 [==============================] - 28s 147ms/step - loss: 0.0234 - accuracy: 0.9930 - val_loss: 1.8277 - val_accuracy: 0.7973\n",
            "Epoch 354/400\n",
            "188/188 [==============================] - 28s 147ms/step - loss: 0.0240 - accuracy: 0.9947 - val_loss: 1.8425 - val_accuracy: 0.7840\n",
            "Epoch 355/400\n",
            "188/188 [==============================] - 28s 147ms/step - loss: 0.0248 - accuracy: 0.9923 - val_loss: 1.9476 - val_accuracy: 0.7820\n",
            "Epoch 356/400\n",
            "188/188 [==============================] - 27s 146ms/step - loss: 0.0180 - accuracy: 0.9948 - val_loss: 1.9848 - val_accuracy: 0.7847\n",
            "Epoch 357/400\n",
            "188/188 [==============================] - 28s 148ms/step - loss: 0.0245 - accuracy: 0.9950 - val_loss: 1.9094 - val_accuracy: 0.7887\n",
            "Epoch 358/400\n",
            "188/188 [==============================] - 27s 146ms/step - loss: 0.0175 - accuracy: 0.9963 - val_loss: 2.1391 - val_accuracy: 0.7580\n",
            "Epoch 359/400\n",
            "188/188 [==============================] - 28s 147ms/step - loss: 0.0654 - accuracy: 0.9823 - val_loss: 1.5605 - val_accuracy: 0.7947\n",
            "Epoch 360/400\n",
            "188/188 [==============================] - 28s 146ms/step - loss: 0.0124 - accuracy: 0.9963 - val_loss: 1.7267 - val_accuracy: 0.8007\n",
            "Epoch 361/400\n",
            "188/188 [==============================] - 28s 147ms/step - loss: 0.0068 - accuracy: 0.9987 - val_loss: 1.7780 - val_accuracy: 0.8020\n",
            "Epoch 362/400\n",
            "188/188 [==============================] - 27s 145ms/step - loss: 0.0016 - accuracy: 0.9997 - val_loss: 1.8552 - val_accuracy: 0.8047\n",
            "Epoch 363/400\n",
            "188/188 [==============================] - 27s 144ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 1.9510 - val_accuracy: 0.8060\n",
            "Epoch 364/400\n",
            "188/188 [==============================] - 27s 145ms/step - loss: 0.0014 - accuracy: 0.9993 - val_loss: 2.0280 - val_accuracy: 0.8073\n",
            "Epoch 365/400\n",
            "188/188 [==============================] - 28s 146ms/step - loss: 0.0305 - accuracy: 0.9940 - val_loss: 1.8795 - val_accuracy: 0.7873\n",
            "Epoch 366/400\n",
            "188/188 [==============================] - 27s 146ms/step - loss: 0.0299 - accuracy: 0.9910 - val_loss: 1.9618 - val_accuracy: 0.7800\n",
            "Epoch 367/400\n",
            "188/188 [==============================] - 27s 146ms/step - loss: 0.0413 - accuracy: 0.9882 - val_loss: 1.7734 - val_accuracy: 0.7867\n",
            "Epoch 368/400\n",
            "188/188 [==============================] - 27s 146ms/step - loss: 0.0219 - accuracy: 0.9930 - val_loss: 1.8979 - val_accuracy: 0.7860\n",
            "Epoch 369/400\n",
            "188/188 [==============================] - 27s 146ms/step - loss: 0.0492 - accuracy: 0.9878 - val_loss: 1.6940 - val_accuracy: 0.7673\n",
            "Epoch 370/400\n",
            "188/188 [==============================] - 27s 145ms/step - loss: 0.0172 - accuracy: 0.9958 - val_loss: 1.9885 - val_accuracy: 0.7887\n",
            "Epoch 371/400\n",
            "188/188 [==============================] - 27s 146ms/step - loss: 0.0328 - accuracy: 0.9915 - val_loss: 1.7685 - val_accuracy: 0.7853\n",
            "Epoch 372/400\n",
            "188/188 [==============================] - 28s 147ms/step - loss: 0.0150 - accuracy: 0.9950 - val_loss: 1.9780 - val_accuracy: 0.8007\n",
            "Epoch 373/400\n",
            "188/188 [==============================] - 27s 146ms/step - loss: 0.0163 - accuracy: 0.9955 - val_loss: 2.1911 - val_accuracy: 0.7947\n",
            "Epoch 374/400\n",
            "188/188 [==============================] - 27s 145ms/step - loss: 0.0204 - accuracy: 0.9953 - val_loss: 1.9027 - val_accuracy: 0.7847\n",
            "Epoch 375/400\n",
            "188/188 [==============================] - 27s 145ms/step - loss: 0.0132 - accuracy: 0.9963 - val_loss: 2.1339 - val_accuracy: 0.7953\n",
            "Epoch 376/400\n",
            "188/188 [==============================] - 27s 146ms/step - loss: 0.0077 - accuracy: 0.9973 - val_loss: 2.3826 - val_accuracy: 0.7867\n",
            "Epoch 377/400\n",
            "188/188 [==============================] - 27s 146ms/step - loss: 0.0049 - accuracy: 0.9985 - val_loss: 2.0652 - val_accuracy: 0.8067\n",
            "Epoch 378/400\n",
            "188/188 [==============================] - 28s 147ms/step - loss: 0.0019 - accuracy: 0.9993 - val_loss: 2.2312 - val_accuracy: 0.8033\n",
            "Epoch 379/400\n",
            "188/188 [==============================] - 28s 146ms/step - loss: 0.0013 - accuracy: 0.9993 - val_loss: 2.3092 - val_accuracy: 0.8053\n",
            "Epoch 380/400\n",
            "188/188 [==============================] - 28s 146ms/step - loss: 9.3175e-04 - accuracy: 0.9995 - val_loss: 2.3643 - val_accuracy: 0.8027\n",
            "Epoch 381/400\n",
            "188/188 [==============================] - 27s 145ms/step - loss: 0.0015 - accuracy: 0.9995 - val_loss: 2.3795 - val_accuracy: 0.8087\n",
            "Epoch 382/400\n",
            "188/188 [==============================] - 27s 146ms/step - loss: 0.0430 - accuracy: 0.9908 - val_loss: 2.1738 - val_accuracy: 0.7600\n",
            "Epoch 383/400\n",
            "188/188 [==============================] - 27s 146ms/step - loss: 0.1067 - accuracy: 0.9732 - val_loss: 1.5104 - val_accuracy: 0.7880\n",
            "Epoch 384/400\n",
            "188/188 [==============================] - 28s 147ms/step - loss: 0.0194 - accuracy: 0.9942 - val_loss: 1.8261 - val_accuracy: 0.7953\n",
            "Epoch 385/400\n",
            "188/188 [==============================] - 28s 148ms/step - loss: 0.0087 - accuracy: 0.9975 - val_loss: 1.8323 - val_accuracy: 0.7953\n",
            "Epoch 386/400\n",
            "188/188 [==============================] - 27s 145ms/step - loss: 0.0135 - accuracy: 0.9963 - val_loss: 2.0179 - val_accuracy: 0.7920\n",
            "Epoch 387/400\n",
            "188/188 [==============================] - 27s 145ms/step - loss: 0.0190 - accuracy: 0.9963 - val_loss: 1.9352 - val_accuracy: 0.7900\n",
            "Epoch 388/400\n",
            "188/188 [==============================] - 27s 146ms/step - loss: 0.0114 - accuracy: 0.9968 - val_loss: 1.9240 - val_accuracy: 0.7960\n",
            "Epoch 389/400\n",
            "188/188 [==============================] - 28s 148ms/step - loss: 0.0022 - accuracy: 0.9993 - val_loss: 2.1254 - val_accuracy: 0.7900\n",
            "Epoch 390/400\n",
            "188/188 [==============================] - 28s 147ms/step - loss: 0.0014 - accuracy: 0.9995 - val_loss: 2.2615 - val_accuracy: 0.7953\n",
            "Epoch 391/400\n",
            "188/188 [==============================] - 28s 148ms/step - loss: 0.0011 - accuracy: 0.9995 - val_loss: 2.2499 - val_accuracy: 0.7980\n",
            "Epoch 392/400\n",
            "188/188 [==============================] - 28s 148ms/step - loss: 9.1465e-04 - accuracy: 0.9992 - val_loss: 2.2537 - val_accuracy: 0.7960\n",
            "Epoch 393/400\n",
            "188/188 [==============================] - 28s 148ms/step - loss: 0.0015 - accuracy: 0.9993 - val_loss: 2.3459 - val_accuracy: 0.8020\n",
            "Epoch 394/400\n",
            "188/188 [==============================] - 28s 148ms/step - loss: 6.6655e-04 - accuracy: 0.9995 - val_loss: 2.3882 - val_accuracy: 0.8013\n",
            "Epoch 395/400\n",
            "188/188 [==============================] - 28s 148ms/step - loss: 0.0990 - accuracy: 0.9785 - val_loss: 1.6792 - val_accuracy: 0.7767\n",
            "Epoch 396/400\n",
            "188/188 [==============================] - 28s 151ms/step - loss: 0.0653 - accuracy: 0.9838 - val_loss: 1.7059 - val_accuracy: 0.7900\n",
            "Epoch 397/400\n",
            "188/188 [==============================] - 28s 148ms/step - loss: 0.0598 - accuracy: 0.9875 - val_loss: 1.5625 - val_accuracy: 0.7880\n",
            "Epoch 398/400\n",
            "188/188 [==============================] - 28s 150ms/step - loss: 0.0303 - accuracy: 0.9918 - val_loss: 1.7928 - val_accuracy: 0.7907\n",
            "Epoch 399/400\n",
            "188/188 [==============================] - 28s 147ms/step - loss: 0.0452 - accuracy: 0.9897 - val_loss: 1.5499 - val_accuracy: 0.7833\n",
            "Epoch 400/400\n",
            "188/188 [==============================] - 28s 147ms/step - loss: 0.0119 - accuracy: 0.9962 - val_loss: 1.7903 - val_accuracy: 0.7907\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f9cb4cfac90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3T6jOVySvKuo",
        "outputId": "5557f20c-073e-4096-dea7-53f1cfe1e1f5"
      },
      "source": [
        "model.evaluate(test_inputs,test_targets)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "79/79 [==============================] - 3s 35ms/step - loss: 1.6257 - accuracy: 0.8011\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.6257128715515137, 0.8011204600334167]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CUT1RXzX1Kbn"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}