Music_genre_classification.ipynb

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  "metadata": {
    "colab": {
      "name": "Music_genre_classification.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t4V4chfOtGsr",
        "outputId": "8aaf667b-5e9f-4a40-f87e-b6bad3a5b367"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive # import drive from google colab\n",
        "\n",
        "ROOT = \"/content/drive\" \n",
        "drive.mount(ROOT, force_remount=True)  "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import librosa\n",
        "import matplotlib.pyplot as plt\n",
        "import librosa.display\n",
        "import os\n",
        "\n",
        "def func_spec(cls):\n",
        "    '''\n",
        "    Function to create spectrogram images from audio files.\n",
        "    First 60 audio files in given class are used for training set.\n",
        "    Last 40 audio files in given class are used for test set.\n",
        "    '''\n",
        "    img_names = os.listdir('/content/drive/MyDrive/Data/genres_original/'+cls)\n",
        "    if not os.path.exists('/content/drive/MyDrive/Data/spectrogram/train/'+cls):\n",
        "      os.makedirs('/content/drive/MyDrive/Data/spectrogram/train/'+cls)\n",
        "    if not os.path.exists('/content/drive/MyDrive/Data/spectrogram/test/'+cls):\n",
        "      os.makedirs('/content/drive/MyDrive/Data/spectrogram/test/'+cls)\n",
        "    print(cls)\n",
        "    train_names = img_names[:60]\n",
        "    test_names = img_names[60:]\n",
        "    c1 = 0\n",
        "    for i in train_names:\n",
        "        c1+=1\n",
        "        x , sr = librosa.load('/content/drive/MyDrive/Data/genres_original/'+cls+'/'+i)\n",
        "        X = librosa.stft(x)\n",
        "        Xdb = librosa.amplitude_to_db(abs(X))\n",
        "        librosa.display.specshow(Xdb)\n",
        "        plt.savefig('/content/drive/MyDrive/Data/spectrogram/train/'+cls+'/'+str(c1)+'.png')\n",
        "        plt.close()\n",
        "  \n",
        "    c2 = 0\n",
        "    for i in test_names:\n",
        "        c2+=1\n",
        "        x , sr = librosa.load('/content/drive/MyDrive/Data/genres_original/'+cls+'/'+i)\n",
        "        X = librosa.stft(x)\n",
        "        Xdb = librosa.amplitude_to_db(abs(X))\n",
        "        librosa.display.specshow(Xdb)\n",
        "        plt.savefig('/content/drive/MyDrive/Data/spectrogram/test/'+cls+'/'+str(c2)+'.png')\n",
        "        plt.close()"
      ],
      "metadata": {
        "id": "PHX9qGw-tV3W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "def func_wavelet(cls):\n",
        "    '''\n",
        "    Function to create wavelet images from audio files.\n",
        "    First 60 audio files in given class are used for training set.\n",
        "    Last 40 audio files in given class are used for test set.\n",
        "    '''\n",
        "    img_names= os.listdir('/content/drive/MyDrive/Data/genres_original/'+cls)\n",
        "    if not os.path.exists('/content/drive/MyDrive/Data/wavelets/train/'+cls):\n",
        "      os.makedirs('/content/drive/MyDrive/Data/wavelets/train/'+cls)\n",
        "    if not os.path.exists('/content/drive/MyDrive/Data/wavelets/test/'+cls):\n",
        "      os.makedirs('/content/drive/MyDrive/Data/wavelets/test/'+cls)\n",
        "    print(cls)\n",
        "    train_names = img_names[:60]\n",
        "    test_names = img_names[60:]\n",
        "    c=0\n",
        "    for i in train_names:\n",
        "      print(c)\n",
        "      c+=1\n",
        "      x , sr =librosa.load('/content/drive/MyDrive/Data/genres_original/'+cls+'/'+i)\n",
        "      librosa.display.waveplot(x)\n",
        "      plt.savefig('/content/drive/MyDrive/Data/wavelets/train/'+cls+'/'+str(c)+'.png')\n",
        "      plt.close()\n",
        "    c=0\n",
        "    for i in test_names:    \n",
        "        print(c)\n",
        "        c+=1\n",
        "        x , sr=librosa.load('/content/drive/MyDrive/Data/genres_original/'+cls+'/'+i)\n",
        "        librosa.display.waveplot(x)\n",
        "        plt.savefig('/content/drive/MyDrive/Data/wavelets/test/'+cls+'/'+str(c)+'.png')\n",
        "        plt.close()"
      ],
      "metadata": {
        "id": "Yo2po5EitvIo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "classes = [a for a in os.listdir('/content/drive/MyDrive/Data/genres_original')]\n",
        "print(classes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "frv7xyNP6egI",
        "outputId": "94814832-3b31-4c02-d33c-542bfcc31850"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['pop', 'hiphop', 'disco', 'reggae', 'blues', 'classical', 'country', 'rock', 'metal', 'jazz']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the number of training and test files for each category\n",
        "for cls in classes:\n",
        "  l = len(os.listdir('/content/drive/MyDrive/Data/spectrogram/train/'+cls))\n",
        "  print(\"Number of training spectrogram files for {}: {}\".format(cls, l))\n",
        "  l = len(os.listdir('/content/drive/MyDrive/Data/spectrogram/test/'+cls))\n",
        "  print(\"Number of testing spectrogram files for {}: {}\".format(cls, l))\n",
        "\n",
        "  l = len(os.listdir('/content/drive/MyDrive/Data/wavelets/train/'+cls))\n",
        "  print(\"Number of training wavelets files for {}: {}\".format(cls, l))\n",
        "  l = len(os.listdir('/content/drive/MyDrive/Data/wavelets/test/'+cls))\n",
        "  print(\"Number of testing wavelets files for {}: {}\".format(cls, l))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rd3wm1NP4TQJ",
        "outputId": "8455e208-9bc0-443d-a644-2b76ebe1a749"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of training spectrogram files for pop: 60\n",
            "Number of testing spectrogram files for pop: 40\n",
            "Number of training wavelets files for pop: 60\n",
            "Number of testing wavelets files for pop: 40\n",
            "Number of training spectrogram files for hiphop: 60\n",
            "Number of testing spectrogram files for hiphop: 40\n",
            "Number of training wavelets files for hiphop: 60\n",
            "Number of testing wavelets files for hiphop: 40\n",
            "Number of training spectrogram files for disco: 60\n",
            "Number of testing spectrogram files for disco: 40\n",
            "Number of training wavelets files for disco: 60\n",
            "Number of testing wavelets files for disco: 40\n",
            "Number of training spectrogram files for reggae: 60\n",
            "Number of testing spectrogram files for reggae: 40\n",
            "Number of training wavelets files for reggae: 60\n",
            "Number of testing wavelets files for reggae: 40\n",
            "Number of training spectrogram files for blues: 60\n",
            "Number of testing spectrogram files for blues: 40\n",
            "Number of training wavelets files for blues: 60\n",
            "Number of testing wavelets files for blues: 40\n",
            "Number of training spectrogram files for classical: 60\n",
            "Number of testing spectrogram files for classical: 40\n",
            "Number of training wavelets files for classical: 60\n",
            "Number of testing wavelets files for classical: 40\n",
            "Number of training spectrogram files for country: 60\n",
            "Number of testing spectrogram files for country: 40\n",
            "Number of training wavelets files for country: 60\n",
            "Number of testing wavelets files for country: 40\n",
            "Number of training spectrogram files for rock: 60\n",
            "Number of testing spectrogram files for rock: 40\n",
            "Number of training wavelets files for rock: 60\n",
            "Number of testing wavelets files for rock: 40\n",
            "Number of training spectrogram files for metal: 60\n",
            "Number of testing spectrogram files for metal: 40\n",
            "Number of training wavelets files for metal: 60\n",
            "Number of testing wavelets files for metal: 40\n",
            "Number of training spectrogram files for jazz: 60\n",
            "Number of testing spectrogram files for jazz: 40\n",
            "Number of training wavelets files for jazz: 60\n",
            "Number of testing wavelets files for jazz: 40\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#func_wavelet('reggae')\n",
        "#func_wavelet('pop')\n",
        "#func_wavelet('metal')\n",
        "#func_wavelet('jazz')\n",
        "#func_wavelet('hiphop')\n",
        "#func_wavelet('disco')\n",
        "#func_wavelet('country')\n",
        "#func_wavelet('classical')\n",
        "#func_wavelet('blues')\n",
        "#func_wavelet('rock')"
      ],
      "metadata": {
        "id": "dFGe4zLHuClL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#func_spec('reggae')\n",
        "#func_spec('pop')\n",
        "#func_spec('metal')\n",
        "#func_spec('jazz')\n",
        "#func_spec('hiphop')\n",
        "#func_spec('disco')\n",
        "#func_spec('country')\n",
        "#func_spec('classical')\n",
        "#func_spec('blues')\n",
        "#func_spec('rock')"
      ],
      "metadata": {
        "id": "TRBvHlPcSmvD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Model Training"
      ],
      "metadata": {
        "id": "YciROdax5W8m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import tensorflow as tf\n",
        "\n",
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Conv2D , MaxPool2D , Flatten , Dropout \n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "\n",
        "from sklearn.metrics import classification_report,confusion_matrix\n",
        "\n",
        "import cv2\n",
        "import os\n",
        "\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "BMPZ1pAX5MVt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "labels = ['blues', 'classical', 'country', 'disco', 'hiphop', 'jazz', 'metal', 'pop', 'reggae', 'rock']\n",
        "img_size = 256\n",
        "def get_data(data_dir):\n",
        "    '''\n",
        "    Read all image files present in the given directory.\n",
        "    The images are read into a numpy array using cv2.imread()\n",
        "    Returns an array containing the images and their corresponding labels\n",
        "    '''\n",
        "    data = [] \n",
        "    for label in labels: \n",
        "        c = 0\n",
        "        path = os.path.join(data_dir, label)\n",
        "        class_num = labels.index(label)\n",
        "        for img in os.listdir(path):\n",
        "            c += 1\n",
        "            try:\n",
        "                img_arr = cv2.imread(os.path.join(path, img))[...,::-1] #convert BGR to RGB format\n",
        "                resized_arr = cv2.resize(img_arr, (img_size, img_size)) # Reshaping images to preferred size\n",
        "                data.append([resized_arr, class_num])\n",
        "            except Exception as e:\n",
        "                print(e)\n",
        "        print(label, c)\n",
        "    return np.array(data)"
      ],
      "metadata": {
        "id": "vz7MId235ZKe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# train = get_data('/content/drive/MyDrive/Data/spectrogram/train')\n",
        "# val = get_data('/content/drive/MyDrive/Data/spectrogram/test')"
      ],
      "metadata": {
        "id": "2MPPDW-m5wj8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "outputId": "c65361fc-5386-48cf-861a-2621006f1c25"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-10-a58e1420d90d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive/MyDrive/Data/spectrogram/train'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive/MyDrive/Data/spectrogram/test'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-9-4b6cdbd5c0d3>\u001b[0m in \u001b[0;36mget_data\u001b[0;34m(data_dir)\u001b[0m\n\u001b[1;32m      9\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m                 \u001b[0mimg_arr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m...\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m#convert BGR to RGB format\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m                 \u001b[0mresized_arr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg_arr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mimg_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Reshaping images to preferred size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m                 \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mresized_arr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclass_num\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# x_train = []\n",
        "# y_train = []\n",
        "# x_val = []\n",
        "# y_val = []\n",
        "\n",
        "# for feature, label in train:\n",
        "#   x_train.append(feature)\n",
        "#   y_train.append(label)\n",
        "\n",
        "# for feature, label in val:\n",
        "#   x_val.append(feature)\n",
        "#   y_val.append(label)\n",
        "\n",
        "# # Normalize the data\n",
        "# x_train = np.array(x_train) / 255\n",
        "# x_val = np.array(x_val) / 255\n",
        "\n",
        "# x_train.reshape(-1, img_size, img_size, 1)\n",
        "# y_train = np.array(y_train)\n",
        "\n",
        "# x_val.reshape(-1, img_size, img_size, 1)\n",
        "# y_val = np.array(y_val)"
      ],
      "metadata": {
        "id": "bfuvJ-UtXhTN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# datagen = ImageDataGenerator(\n",
        "#         featurewise_center=False,  # set input mean to 0 over the dataset\n",
        "#         samplewise_center=False,  # set each sample mean to 0\n",
        "#         featurewise_std_normalization=False,  # divide inputs by std of the dataset\n",
        "#         samplewise_std_normalization=False,  # divide each input by its std\n",
        "#         zca_whitening=False,  # apply ZCA whitening\n",
        "#         #rotation_range = 30,  # randomly rotate images in the range (degrees, 0 to 180)\n",
        "#         zoom_range = 0.2, # Randomly zoom image \n",
        "#         width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)\n",
        "#         height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)\n",
        "#         #horizontal_flip = True,  # randomly flip images\n",
        "#         vertical_flip=False)  # randomly flip images\n",
        "\n",
        "\n",
        "# datagen.fit(x_train)"
      ],
      "metadata": {
        "id": "Mo2n8D5YXk4F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# model = Sequential()\n",
        "# model.add(Conv2D(32,3,padding=\"same\", activation=\"relu\", input_shape=(256,256,3)))\n",
        "# model.add(MaxPool2D())\n",
        "\n",
        "# model.add(Conv2D(32, 3, padding=\"same\", activation=\"relu\"))\n",
        "# model.add(MaxPool2D())\n",
        "\n",
        "# model.add(Conv2D(64, 3, padding=\"same\", activation=\"relu\"))\n",
        "# model.add(MaxPool2D())\n",
        "# model.add(Dropout(0.4))\n",
        "\n",
        "# model.add(Flatten())\n",
        "# model.add(Dense(128,activation=\"relu\"))\n",
        "# model.add(Dense(10, activation=\"softmax\"))\n",
        "\n",
        "# model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hn1qIvrZXney",
        "outputId": "c0287bab-e181-415b-c03d-577a6f2351dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 256, 256, 32)      896       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 128, 128, 32)     0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 128, 128, 32)      9248      \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 64, 64, 32)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 64, 64, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 32, 32, 64)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 32, 32, 64)        0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 65536)             0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 128)               8388736   \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 10)                1290      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 8,418,666\n",
            "Trainable params: 8,418,666\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# opt = Adam(lr=0.0001)\n",
        "# model.compile(optimizer = opt , loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) , metrics = ['accuracy'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pLZU_oN4XqvF",
        "outputId": "f3f37c94-8e79-483a-f0bf-c9ed0e788c2c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(Adam, self).__init__(name, **kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# history = model.fit(x_train,y_train,epochs = 500, validation_data = (x_val, y_val))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SwwRKvEuX2gQ",
        "outputId": "f3834e82-c283-43bc-ceb6-92b5718484c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1096: UserWarning: \"`sparse_categorical_crossentropy` received `from_logits=True`, but the `output` argument was produced by a sigmoid or softmax activation and thus does not represent logits. Was this intended?\"\n",
            "  return dispatch_target(*args, **kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "19/19 [==============================] - 13s 129ms/step - loss: 2.2579 - accuracy: 0.1517 - val_loss: 2.0966 - val_accuracy: 0.2149\n",
            "Epoch 2/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 1.9739 - accuracy: 0.3017 - val_loss: 2.0632 - val_accuracy: 0.2176\n",
            "Epoch 3/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 1.7987 - accuracy: 0.3167 - val_loss: 2.1245 - val_accuracy: 0.2259\n",
            "Epoch 4/500\n",
            "19/19 [==============================] - 1s 59ms/step - loss: 1.7208 - accuracy: 0.3483 - val_loss: 1.9553 - val_accuracy: 0.2810\n",
            "Epoch 5/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 1.6580 - accuracy: 0.3733 - val_loss: 1.9750 - val_accuracy: 0.2700\n",
            "Epoch 6/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 1.5978 - accuracy: 0.4283 - val_loss: 2.0453 - val_accuracy: 0.3003\n",
            "Epoch 7/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 1.5562 - accuracy: 0.4733 - val_loss: 1.9758 - val_accuracy: 0.2810\n",
            "Epoch 8/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 1.4979 - accuracy: 0.4783 - val_loss: 1.9796 - val_accuracy: 0.2865\n",
            "Epoch 9/500\n",
            "19/19 [==============================] - 1s 59ms/step - loss: 1.4511 - accuracy: 0.4983 - val_loss: 1.9514 - val_accuracy: 0.2590\n",
            "Epoch 10/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 1.4184 - accuracy: 0.4767 - val_loss: 1.9920 - val_accuracy: 0.2562\n",
            "Epoch 11/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 1.3972 - accuracy: 0.5133 - val_loss: 1.9548 - val_accuracy: 0.2534\n",
            "Epoch 12/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 1.4081 - accuracy: 0.4950 - val_loss: 1.9489 - val_accuracy: 0.2755\n",
            "Epoch 13/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 1.3285 - accuracy: 0.5333 - val_loss: 1.9001 - val_accuracy: 0.2727\n",
            "Epoch 14/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 1.2594 - accuracy: 0.5533 - val_loss: 1.9330 - val_accuracy: 0.3058\n",
            "Epoch 15/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 1.2144 - accuracy: 0.5750 - val_loss: 1.8347 - val_accuracy: 0.3085\n",
            "Epoch 16/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 1.1937 - accuracy: 0.5833 - val_loss: 1.8852 - val_accuracy: 0.3113\n",
            "Epoch 17/500\n",
            "19/19 [==============================] - 1s 65ms/step - loss: 1.1535 - accuracy: 0.6100 - val_loss: 1.8752 - val_accuracy: 0.3196\n",
            "Epoch 18/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 1.1423 - accuracy: 0.6050 - val_loss: 1.8672 - val_accuracy: 0.3196\n",
            "Epoch 19/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 1.0847 - accuracy: 0.6383 - val_loss: 1.9238 - val_accuracy: 0.3223\n",
            "Epoch 20/500\n",
            "19/19 [==============================] - 1s 59ms/step - loss: 1.1271 - accuracy: 0.6017 - val_loss: 1.8799 - val_accuracy: 0.3251\n",
            "Epoch 21/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 1.0234 - accuracy: 0.6450 - val_loss: 1.9600 - val_accuracy: 0.3526\n",
            "Epoch 22/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 1.0003 - accuracy: 0.6650 - val_loss: 1.9510 - val_accuracy: 0.3003\n",
            "Epoch 23/500\n",
            "19/19 [==============================] - 1s 59ms/step - loss: 0.9884 - accuracy: 0.6533 - val_loss: 1.8715 - val_accuracy: 0.3140\n",
            "Epoch 24/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.9160 - accuracy: 0.7067 - val_loss: 1.9141 - val_accuracy: 0.3278\n",
            "Epoch 25/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.9093 - accuracy: 0.6883 - val_loss: 2.0172 - val_accuracy: 0.3003\n",
            "Epoch 26/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.9300 - accuracy: 0.6933 - val_loss: 1.9056 - val_accuracy: 0.3747\n",
            "Epoch 27/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.8728 - accuracy: 0.7033 - val_loss: 1.8765 - val_accuracy: 0.3719\n",
            "Epoch 28/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.8947 - accuracy: 0.7033 - val_loss: 1.7980 - val_accuracy: 0.3581\n",
            "Epoch 29/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.8586 - accuracy: 0.7183 - val_loss: 1.9707 - val_accuracy: 0.3361\n",
            "Epoch 30/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.7984 - accuracy: 0.7550 - val_loss: 1.7831 - val_accuracy: 0.3994\n",
            "Epoch 31/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.7240 - accuracy: 0.7950 - val_loss: 1.7847 - val_accuracy: 0.3719\n",
            "Epoch 32/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.7163 - accuracy: 0.7700 - val_loss: 1.8628 - val_accuracy: 0.3306\n",
            "Epoch 33/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.7201 - accuracy: 0.7517 - val_loss: 1.8473 - val_accuracy: 0.3747\n",
            "Epoch 34/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.6622 - accuracy: 0.8000 - val_loss: 1.8500 - val_accuracy: 0.3636\n",
            "Epoch 35/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.6394 - accuracy: 0.8083 - val_loss: 1.7639 - val_accuracy: 0.3829\n",
            "Epoch 36/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.6208 - accuracy: 0.8150 - val_loss: 1.8126 - val_accuracy: 0.3829\n",
            "Epoch 37/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.6231 - accuracy: 0.7917 - val_loss: 1.8226 - val_accuracy: 0.4022\n",
            "Epoch 38/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.5910 - accuracy: 0.8333 - val_loss: 2.0124 - val_accuracy: 0.3196\n",
            "Epoch 39/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.5768 - accuracy: 0.8317 - val_loss: 1.9588 - val_accuracy: 0.3416\n",
            "Epoch 40/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.5685 - accuracy: 0.8350 - val_loss: 1.8363 - val_accuracy: 0.4105\n",
            "Epoch 41/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.5057 - accuracy: 0.8500 - val_loss: 2.0041 - val_accuracy: 0.3774\n",
            "Epoch 42/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.5186 - accuracy: 0.8433 - val_loss: 1.9518 - val_accuracy: 0.3554\n",
            "Epoch 43/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.4923 - accuracy: 0.8567 - val_loss: 1.9477 - val_accuracy: 0.3939\n",
            "Epoch 44/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.4805 - accuracy: 0.8517 - val_loss: 1.8572 - val_accuracy: 0.3939\n",
            "Epoch 45/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.4601 - accuracy: 0.8567 - val_loss: 2.0366 - val_accuracy: 0.3884\n",
            "Epoch 46/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.4568 - accuracy: 0.8483 - val_loss: 2.0615 - val_accuracy: 0.3939\n",
            "Epoch 47/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.4714 - accuracy: 0.8517 - val_loss: 2.0957 - val_accuracy: 0.3554\n",
            "Epoch 48/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.4154 - accuracy: 0.8833 - val_loss: 1.8985 - val_accuracy: 0.3994\n",
            "Epoch 49/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.3752 - accuracy: 0.8867 - val_loss: 2.0384 - val_accuracy: 0.4077\n",
            "Epoch 50/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.4010 - accuracy: 0.8800 - val_loss: 1.9852 - val_accuracy: 0.4105\n",
            "Epoch 51/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.3571 - accuracy: 0.8967 - val_loss: 1.9904 - val_accuracy: 0.3857\n",
            "Epoch 52/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.3279 - accuracy: 0.9200 - val_loss: 1.8556 - val_accuracy: 0.4353\n",
            "Epoch 53/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.3300 - accuracy: 0.9167 - val_loss: 1.9920 - val_accuracy: 0.3967\n",
            "Epoch 54/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.3425 - accuracy: 0.9067 - val_loss: 1.8974 - val_accuracy: 0.4353\n",
            "Epoch 55/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.3229 - accuracy: 0.9050 - val_loss: 2.0334 - val_accuracy: 0.4132\n",
            "Epoch 56/500\n",
            "19/19 [==============================] - 1s 59ms/step - loss: 0.2869 - accuracy: 0.9283 - val_loss: 2.0751 - val_accuracy: 0.3802\n",
            "Epoch 57/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.2783 - accuracy: 0.9267 - val_loss: 2.2283 - val_accuracy: 0.3967\n",
            "Epoch 58/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.2717 - accuracy: 0.9200 - val_loss: 2.0113 - val_accuracy: 0.3939\n",
            "Epoch 59/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.2430 - accuracy: 0.9400 - val_loss: 2.0762 - val_accuracy: 0.4132\n",
            "Epoch 60/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.2446 - accuracy: 0.9317 - val_loss: 1.9694 - val_accuracy: 0.4325\n",
            "Epoch 61/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.2373 - accuracy: 0.9350 - val_loss: 1.9929 - val_accuracy: 0.4160\n",
            "Epoch 62/500\n",
            "19/19 [==============================] - 1s 59ms/step - loss: 0.2447 - accuracy: 0.9300 - val_loss: 2.1701 - val_accuracy: 0.3939\n",
            "Epoch 63/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.2161 - accuracy: 0.9450 - val_loss: 2.0541 - val_accuracy: 0.4380\n",
            "Epoch 64/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.2073 - accuracy: 0.9483 - val_loss: 2.2717 - val_accuracy: 0.4022\n",
            "Epoch 65/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.1963 - accuracy: 0.9550 - val_loss: 2.0868 - val_accuracy: 0.4077\n",
            "Epoch 66/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.1942 - accuracy: 0.9600 - val_loss: 2.0277 - val_accuracy: 0.4656\n",
            "Epoch 67/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.1963 - accuracy: 0.9533 - val_loss: 2.1774 - val_accuracy: 0.3912\n",
            "Epoch 68/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.1695 - accuracy: 0.9750 - val_loss: 2.1707 - val_accuracy: 0.3939\n",
            "Epoch 69/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.1662 - accuracy: 0.9717 - val_loss: 2.0405 - val_accuracy: 0.4490\n",
            "Epoch 70/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.1429 - accuracy: 0.9683 - val_loss: 2.1584 - val_accuracy: 0.4463\n",
            "Epoch 71/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.1519 - accuracy: 0.9683 - val_loss: 2.2911 - val_accuracy: 0.4077\n",
            "Epoch 72/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.1623 - accuracy: 0.9600 - val_loss: 2.2756 - val_accuracy: 0.4270\n",
            "Epoch 73/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.1649 - accuracy: 0.9583 - val_loss: 2.7014 - val_accuracy: 0.3636\n",
            "Epoch 74/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.1576 - accuracy: 0.9617 - val_loss: 2.4337 - val_accuracy: 0.3884\n",
            "Epoch 75/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.1480 - accuracy: 0.9700 - val_loss: 2.1371 - val_accuracy: 0.4408\n",
            "Epoch 76/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.1232 - accuracy: 0.9700 - val_loss: 2.4299 - val_accuracy: 0.4242\n",
            "Epoch 77/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.1147 - accuracy: 0.9717 - val_loss: 2.1574 - val_accuracy: 0.4711\n",
            "Epoch 78/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.1112 - accuracy: 0.9867 - val_loss: 2.4153 - val_accuracy: 0.4160\n",
            "Epoch 79/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.1038 - accuracy: 0.9833 - val_loss: 2.2937 - val_accuracy: 0.4353\n",
            "Epoch 80/500\n",
            "19/19 [==============================] - 1s 66ms/step - loss: 0.1031 - accuracy: 0.9900 - val_loss: 2.2187 - val_accuracy: 0.4298\n",
            "Epoch 81/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.1038 - accuracy: 0.9867 - val_loss: 2.3023 - val_accuracy: 0.4215\n",
            "Epoch 82/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.1013 - accuracy: 0.9833 - val_loss: 2.2895 - val_accuracy: 0.4325\n",
            "Epoch 83/500\n",
            "19/19 [==============================] - 1s 66ms/step - loss: 0.0938 - accuracy: 0.9817 - val_loss: 2.3604 - val_accuracy: 0.4160\n",
            "Epoch 84/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.1102 - accuracy: 0.9767 - val_loss: 2.2601 - val_accuracy: 0.4408\n",
            "Epoch 85/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0761 - accuracy: 0.9917 - val_loss: 2.2928 - val_accuracy: 0.4353\n",
            "Epoch 86/500\n",
            "19/19 [==============================] - 1s 66ms/step - loss: 0.1000 - accuracy: 0.9717 - val_loss: 2.3912 - val_accuracy: 0.3994\n",
            "Epoch 87/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.0966 - accuracy: 0.9750 - val_loss: 2.4705 - val_accuracy: 0.4077\n",
            "Epoch 88/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.0834 - accuracy: 0.9900 - val_loss: 2.3904 - val_accuracy: 0.4242\n",
            "Epoch 89/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0730 - accuracy: 0.9900 - val_loss: 2.6504 - val_accuracy: 0.3912\n",
            "Epoch 90/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0702 - accuracy: 0.9933 - val_loss: 2.3501 - val_accuracy: 0.4435\n",
            "Epoch 91/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0645 - accuracy: 0.9883 - val_loss: 2.4775 - val_accuracy: 0.4353\n",
            "Epoch 92/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0595 - accuracy: 0.9967 - val_loss: 2.3817 - val_accuracy: 0.4766\n",
            "Epoch 93/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0682 - accuracy: 0.9950 - val_loss: 2.3721 - val_accuracy: 0.4325\n",
            "Epoch 94/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.0530 - accuracy: 1.0000 - val_loss: 2.3471 - val_accuracy: 0.4380\n",
            "Epoch 95/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0483 - accuracy: 0.9967 - val_loss: 2.4040 - val_accuracy: 0.4187\n",
            "Epoch 96/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0538 - accuracy: 0.9950 - val_loss: 2.5131 - val_accuracy: 0.4270\n",
            "Epoch 97/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0678 - accuracy: 0.9900 - val_loss: 2.5028 - val_accuracy: 0.4105\n",
            "Epoch 98/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0501 - accuracy: 0.9967 - val_loss: 2.6168 - val_accuracy: 0.4160\n",
            "Epoch 99/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.0515 - accuracy: 0.9950 - val_loss: 2.4982 - val_accuracy: 0.4215\n",
            "Epoch 100/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.0496 - accuracy: 0.9967 - val_loss: 2.3839 - val_accuracy: 0.4490\n",
            "Epoch 101/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0394 - accuracy: 0.9967 - val_loss: 2.5270 - val_accuracy: 0.4628\n",
            "Epoch 102/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0463 - accuracy: 0.9967 - val_loss: 2.6834 - val_accuracy: 0.3939\n",
            "Epoch 103/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0462 - accuracy: 0.9950 - val_loss: 2.5076 - val_accuracy: 0.4463\n",
            "Epoch 104/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0542 - accuracy: 0.9917 - val_loss: 2.5453 - val_accuracy: 0.4132\n",
            "Epoch 105/500\n",
            "19/19 [==============================] - 1s 67ms/step - loss: 0.0376 - accuracy: 0.9967 - val_loss: 2.5964 - val_accuracy: 0.4325\n",
            "Epoch 106/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0416 - accuracy: 0.9917 - val_loss: 2.6055 - val_accuracy: 0.4518\n",
            "Epoch 107/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0658 - accuracy: 0.9817 - val_loss: 2.9805 - val_accuracy: 0.4105\n",
            "Epoch 108/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0694 - accuracy: 0.9917 - val_loss: 2.6538 - val_accuracy: 0.4380\n",
            "Epoch 109/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.0509 - accuracy: 0.9933 - val_loss: 2.5736 - val_accuracy: 0.4215\n",
            "Epoch 110/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0376 - accuracy: 0.9983 - val_loss: 2.6189 - val_accuracy: 0.4325\n",
            "Epoch 111/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0312 - accuracy: 0.9967 - val_loss: 2.6374 - val_accuracy: 0.4298\n",
            "Epoch 112/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0298 - accuracy: 0.9983 - val_loss: 2.5243 - val_accuracy: 0.4408\n",
            "Epoch 113/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0362 - accuracy: 0.9917 - val_loss: 2.6608 - val_accuracy: 0.4242\n",
            "Epoch 114/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.0300 - accuracy: 0.9967 - val_loss: 2.5369 - val_accuracy: 0.4545\n",
            "Epoch 115/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0255 - accuracy: 0.9967 - val_loss: 2.7173 - val_accuracy: 0.4298\n",
            "Epoch 116/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0309 - accuracy: 0.9983 - val_loss: 2.6295 - val_accuracy: 0.4463\n",
            "Epoch 117/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0281 - accuracy: 0.9983 - val_loss: 2.6674 - val_accuracy: 0.4435\n",
            "Epoch 118/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0221 - accuracy: 1.0000 - val_loss: 2.6479 - val_accuracy: 0.4490\n",
            "Epoch 119/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0338 - accuracy: 0.9967 - val_loss: 2.9355 - val_accuracy: 0.4187\n",
            "Epoch 120/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0366 - accuracy: 0.9967 - val_loss: 2.6705 - val_accuracy: 0.4463\n",
            "Epoch 121/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0388 - accuracy: 0.9933 - val_loss: 2.6420 - val_accuracy: 0.4105\n",
            "Epoch 122/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0282 - accuracy: 0.9983 - val_loss: 2.6367 - val_accuracy: 0.4380\n",
            "Epoch 123/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0395 - accuracy: 0.9950 - val_loss: 2.9110 - val_accuracy: 0.4132\n",
            "Epoch 124/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.0401 - accuracy: 0.9950 - val_loss: 2.6914 - val_accuracy: 0.4683\n",
            "Epoch 125/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0277 - accuracy: 0.9967 - val_loss: 2.6480 - val_accuracy: 0.4545\n",
            "Epoch 126/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0477 - accuracy: 0.9917 - val_loss: 2.7525 - val_accuracy: 0.4325\n",
            "Epoch 127/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.0466 - accuracy: 0.9883 - val_loss: 2.6112 - val_accuracy: 0.4545\n",
            "Epoch 128/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0648 - accuracy: 0.9850 - val_loss: 2.8691 - val_accuracy: 0.3884\n",
            "Epoch 129/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0359 - accuracy: 0.9950 - val_loss: 2.6861 - val_accuracy: 0.4545\n",
            "Epoch 130/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0259 - accuracy: 0.9967 - val_loss: 2.6912 - val_accuracy: 0.4380\n",
            "Epoch 131/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0256 - accuracy: 0.9967 - val_loss: 2.7817 - val_accuracy: 0.4435\n",
            "Epoch 132/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0494 - accuracy: 0.9833 - val_loss: 2.9443 - val_accuracy: 0.3939\n",
            "Epoch 133/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0296 - accuracy: 0.9967 - val_loss: 2.7801 - val_accuracy: 0.4601\n",
            "Epoch 134/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0330 - accuracy: 0.9917 - val_loss: 2.6339 - val_accuracy: 0.4628\n",
            "Epoch 135/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0153 - accuracy: 0.9983 - val_loss: 2.7252 - val_accuracy: 0.4242\n",
            "Epoch 136/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.0151 - accuracy: 0.9983 - val_loss: 2.7030 - val_accuracy: 0.4463\n",
            "Epoch 137/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.0176 - accuracy: 0.9983 - val_loss: 2.7139 - val_accuracy: 0.4545\n",
            "Epoch 138/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0320 - accuracy: 0.9900 - val_loss: 2.7387 - val_accuracy: 0.4215\n",
            "Epoch 139/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.0395 - accuracy: 0.9933 - val_loss: 2.8311 - val_accuracy: 0.4077\n",
            "Epoch 140/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0303 - accuracy: 0.9967 - val_loss: 3.1359 - val_accuracy: 0.3967\n",
            "Epoch 141/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0189 - accuracy: 0.9967 - val_loss: 2.8933 - val_accuracy: 0.4270\n",
            "Epoch 142/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0171 - accuracy: 0.9967 - val_loss: 2.8137 - val_accuracy: 0.4435\n",
            "Epoch 143/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0177 - accuracy: 0.9983 - val_loss: 2.9372 - val_accuracy: 0.4270\n",
            "Epoch 144/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.0158 - accuracy: 0.9983 - val_loss: 2.7576 - val_accuracy: 0.4490\n",
            "Epoch 145/500\n",
            "19/19 [==============================] - 1s 68ms/step - loss: 0.0141 - accuracy: 0.9983 - val_loss: 2.8140 - val_accuracy: 0.4325\n",
            "Epoch 146/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0178 - accuracy: 0.9983 - val_loss: 2.8364 - val_accuracy: 0.4601\n",
            "Epoch 147/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0321 - accuracy: 0.9967 - val_loss: 2.8993 - val_accuracy: 0.4435\n",
            "Epoch 148/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.0120 - accuracy: 1.0000 - val_loss: 2.8207 - val_accuracy: 0.4435\n",
            "Epoch 149/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0118 - accuracy: 1.0000 - val_loss: 2.7844 - val_accuracy: 0.4325\n",
            "Epoch 150/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0153 - accuracy: 0.9967 - val_loss: 3.0347 - val_accuracy: 0.4132\n",
            "Epoch 151/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.0182 - accuracy: 0.9983 - val_loss: 2.9858 - val_accuracy: 0.4187\n",
            "Epoch 152/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0118 - accuracy: 0.9983 - val_loss: 2.7736 - val_accuracy: 0.4573\n",
            "Epoch 153/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0151 - accuracy: 0.9967 - val_loss: 2.8320 - val_accuracy: 0.4408\n",
            "Epoch 154/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0252 - accuracy: 0.9967 - val_loss: 2.9957 - val_accuracy: 0.4490\n",
            "Epoch 155/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0594 - accuracy: 0.9800 - val_loss: 2.9041 - val_accuracy: 0.4738\n",
            "Epoch 156/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.0493 - accuracy: 0.9883 - val_loss: 2.8393 - val_accuracy: 0.4490\n",
            "Epoch 157/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0272 - accuracy: 0.9950 - val_loss: 2.7363 - val_accuracy: 0.4490\n",
            "Epoch 158/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0227 - accuracy: 0.9983 - val_loss: 2.9011 - val_accuracy: 0.4105\n",
            "Epoch 159/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0314 - accuracy: 0.9967 - val_loss: 3.1144 - val_accuracy: 0.4050\n",
            "Epoch 160/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0159 - accuracy: 0.9983 - val_loss: 3.0289 - val_accuracy: 0.4132\n",
            "Epoch 161/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0161 - accuracy: 0.9967 - val_loss: 2.8016 - val_accuracy: 0.4463\n",
            "Epoch 162/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0161 - accuracy: 0.9983 - val_loss: 3.1586 - val_accuracy: 0.4105\n",
            "Epoch 163/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0147 - accuracy: 0.9967 - val_loss: 2.9201 - val_accuracy: 0.4463\n",
            "Epoch 164/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0130 - accuracy: 0.9967 - val_loss: 2.9866 - val_accuracy: 0.4463\n",
            "Epoch 165/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0117 - accuracy: 0.9983 - val_loss: 3.1260 - val_accuracy: 0.4077\n",
            "Epoch 166/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0133 - accuracy: 0.9983 - val_loss: 3.0706 - val_accuracy: 0.4242\n",
            "Epoch 167/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0095 - accuracy: 0.9983 - val_loss: 2.9109 - val_accuracy: 0.4353\n",
            "Epoch 168/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0265 - accuracy: 0.9967 - val_loss: 2.9956 - val_accuracy: 0.4270\n",
            "Epoch 169/500\n",
            "19/19 [==============================] - 1s 67ms/step - loss: 0.0100 - accuracy: 1.0000 - val_loss: 2.8909 - val_accuracy: 0.4298\n",
            "Epoch 170/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.0215 - accuracy: 0.9967 - val_loss: 2.9101 - val_accuracy: 0.4435\n",
            "Epoch 171/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0113 - accuracy: 0.9983 - val_loss: 3.1131 - val_accuracy: 0.4242\n",
            "Epoch 172/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.0219 - accuracy: 0.9967 - val_loss: 3.1072 - val_accuracy: 0.4132\n",
            "Epoch 173/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0377 - accuracy: 0.9900 - val_loss: 3.1371 - val_accuracy: 0.4353\n",
            "Epoch 174/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.0293 - accuracy: 0.9967 - val_loss: 2.9760 - val_accuracy: 0.4298\n",
            "Epoch 175/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0226 - accuracy: 0.9950 - val_loss: 3.1001 - val_accuracy: 0.4298\n",
            "Epoch 176/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0140 - accuracy: 0.9983 - val_loss: 2.8803 - val_accuracy: 0.4408\n",
            "Epoch 177/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0120 - accuracy: 0.9967 - val_loss: 2.9075 - val_accuracy: 0.4408\n",
            "Epoch 178/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0100 - accuracy: 0.9967 - val_loss: 3.0027 - val_accuracy: 0.4353\n",
            "Epoch 179/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.0094 - accuracy: 0.9983 - val_loss: 3.1744 - val_accuracy: 0.4380\n",
            "Epoch 180/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0146 - accuracy: 0.9967 - val_loss: 2.9864 - val_accuracy: 0.4022\n",
            "Epoch 181/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0507 - accuracy: 0.9917 - val_loss: 3.5435 - val_accuracy: 0.3967\n",
            "Epoch 182/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0240 - accuracy: 0.9967 - val_loss: 3.2573 - val_accuracy: 0.4298\n",
            "Epoch 183/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0119 - accuracy: 0.9967 - val_loss: 2.8051 - val_accuracy: 0.4463\n",
            "Epoch 184/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.0108 - accuracy: 0.9983 - val_loss: 2.9648 - val_accuracy: 0.4435\n",
            "Epoch 185/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0102 - accuracy: 0.9983 - val_loss: 3.1399 - val_accuracy: 0.4435\n",
            "Epoch 186/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0183 - accuracy: 0.9967 - val_loss: 2.9468 - val_accuracy: 0.4298\n",
            "Epoch 187/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0089 - accuracy: 0.9983 - val_loss: 3.0669 - val_accuracy: 0.4160\n",
            "Epoch 188/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.0180 - accuracy: 0.9983 - val_loss: 2.9265 - val_accuracy: 0.4518\n",
            "Epoch 189/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0461 - accuracy: 0.9900 - val_loss: 3.0588 - val_accuracy: 0.4435\n",
            "Epoch 190/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0416 - accuracy: 0.9917 - val_loss: 3.2872 - val_accuracy: 0.4022\n",
            "Epoch 191/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0318 - accuracy: 0.9917 - val_loss: 2.9867 - val_accuracy: 0.4105\n",
            "Epoch 192/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0238 - accuracy: 0.9967 - val_loss: 3.2801 - val_accuracy: 0.4215\n",
            "Epoch 193/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0099 - accuracy: 0.9983 - val_loss: 2.8995 - val_accuracy: 0.4380\n",
            "Epoch 194/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0069 - accuracy: 0.9983 - val_loss: 2.8622 - val_accuracy: 0.4573\n",
            "Epoch 195/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0095 - accuracy: 0.9983 - val_loss: 2.9979 - val_accuracy: 0.4601\n",
            "Epoch 196/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0084 - accuracy: 0.9967 - val_loss: 2.9964 - val_accuracy: 0.4408\n",
            "Epoch 197/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0109 - accuracy: 0.9967 - val_loss: 3.1318 - val_accuracy: 0.4380\n",
            "Epoch 198/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0092 - accuracy: 0.9983 - val_loss: 3.0042 - val_accuracy: 0.4408\n",
            "Epoch 199/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0058 - accuracy: 1.0000 - val_loss: 3.1586 - val_accuracy: 0.4435\n",
            "Epoch 200/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0363 - accuracy: 0.9967 - val_loss: 3.1523 - val_accuracy: 0.4270\n",
            "Epoch 201/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.0146 - accuracy: 0.9983 - val_loss: 2.8750 - val_accuracy: 0.4573\n",
            "Epoch 202/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0208 - accuracy: 0.9967 - val_loss: 3.1481 - val_accuracy: 0.4242\n",
            "Epoch 203/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0252 - accuracy: 0.9950 - val_loss: 3.0153 - val_accuracy: 0.4298\n",
            "Epoch 204/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0496 - accuracy: 0.9900 - val_loss: 3.2773 - val_accuracy: 0.4050\n",
            "Epoch 205/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0245 - accuracy: 0.9967 - val_loss: 3.3529 - val_accuracy: 0.4105\n",
            "Epoch 206/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0531 - accuracy: 0.9817 - val_loss: 3.1155 - val_accuracy: 0.4050\n",
            "Epoch 207/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.0965 - accuracy: 0.9650 - val_loss: 2.9123 - val_accuracy: 0.4545\n",
            "Epoch 208/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.1258 - accuracy: 0.9617 - val_loss: 3.3652 - val_accuracy: 0.4105\n",
            "Epoch 209/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.1747 - accuracy: 0.9383 - val_loss: 3.1800 - val_accuracy: 0.3939\n",
            "Epoch 210/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0464 - accuracy: 0.9883 - val_loss: 2.7908 - val_accuracy: 0.4242\n",
            "Epoch 211/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0250 - accuracy: 0.9967 - val_loss: 2.7582 - val_accuracy: 0.4380\n",
            "Epoch 212/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0196 - accuracy: 0.9950 - val_loss: 2.7232 - val_accuracy: 0.4242\n",
            "Epoch 213/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0063 - accuracy: 1.0000 - val_loss: 2.7652 - val_accuracy: 0.4325\n",
            "Epoch 214/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0129 - accuracy: 0.9967 - val_loss: 2.8780 - val_accuracy: 0.4215\n",
            "Epoch 215/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0213 - accuracy: 0.9967 - val_loss: 2.8152 - val_accuracy: 0.4380\n",
            "Epoch 216/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0146 - accuracy: 0.9983 - val_loss: 2.9454 - val_accuracy: 0.4353\n",
            "Epoch 217/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.0125 - accuracy: 0.9983 - val_loss: 2.9472 - val_accuracy: 0.4518\n",
            "Epoch 218/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0122 - accuracy: 0.9983 - val_loss: 3.1079 - val_accuracy: 0.3939\n",
            "Epoch 219/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0061 - accuracy: 1.0000 - val_loss: 2.8588 - val_accuracy: 0.4242\n",
            "Epoch 220/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0176 - accuracy: 0.9967 - val_loss: 2.9486 - val_accuracy: 0.4601\n",
            "Epoch 221/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0060 - accuracy: 0.9983 - val_loss: 2.8965 - val_accuracy: 0.4380\n",
            "Epoch 222/500\n",
            "19/19 [==============================] - 1s 66ms/step - loss: 0.0068 - accuracy: 0.9983 - val_loss: 3.0385 - val_accuracy: 0.4380\n",
            "Epoch 223/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0115 - accuracy: 0.9983 - val_loss: 3.0908 - val_accuracy: 0.4270\n",
            "Epoch 224/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0127 - accuracy: 0.9967 - val_loss: 2.9368 - val_accuracy: 0.4435\n",
            "Epoch 225/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0081 - accuracy: 0.9983 - val_loss: 2.9636 - val_accuracy: 0.4353\n",
            "Epoch 226/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0102 - accuracy: 0.9967 - val_loss: 2.9332 - val_accuracy: 0.4298\n",
            "Epoch 227/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0045 - accuracy: 1.0000 - val_loss: 2.9473 - val_accuracy: 0.4380\n",
            "Epoch 228/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0098 - accuracy: 0.9967 - val_loss: 2.9777 - val_accuracy: 0.4490\n",
            "Epoch 229/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.0180 - accuracy: 0.9983 - val_loss: 3.1196 - val_accuracy: 0.4270\n",
            "Epoch 230/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0191 - accuracy: 0.9983 - val_loss: 3.1372 - val_accuracy: 0.4435\n",
            "Epoch 231/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0070 - accuracy: 0.9983 - val_loss: 3.1701 - val_accuracy: 0.4187\n",
            "Epoch 232/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0110 - accuracy: 0.9967 - val_loss: 3.0905 - val_accuracy: 0.4215\n",
            "Epoch 233/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0102 - accuracy: 0.9983 - val_loss: 2.9859 - val_accuracy: 0.4435\n",
            "Epoch 234/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0139 - accuracy: 0.9983 - val_loss: 2.9463 - val_accuracy: 0.4187\n",
            "Epoch 235/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0064 - accuracy: 0.9983 - val_loss: 3.0272 - val_accuracy: 0.4490\n",
            "Epoch 236/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0122 - accuracy: 0.9983 - val_loss: 2.9531 - val_accuracy: 0.4270\n",
            "Epoch 237/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0140 - accuracy: 0.9967 - val_loss: 2.9576 - val_accuracy: 0.4463\n",
            "Epoch 238/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0187 - accuracy: 0.9967 - val_loss: 2.9200 - val_accuracy: 0.4050\n",
            "Epoch 239/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 2.8885 - val_accuracy: 0.4435\n",
            "Epoch 240/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.0047 - accuracy: 0.9983 - val_loss: 2.9775 - val_accuracy: 0.4490\n",
            "Epoch 241/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0127 - accuracy: 0.9967 - val_loss: 2.9977 - val_accuracy: 0.4380\n",
            "Epoch 242/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0031 - accuracy: 1.0000 - val_loss: 3.0519 - val_accuracy: 0.4380\n",
            "Epoch 243/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0049 - accuracy: 0.9983 - val_loss: 3.0764 - val_accuracy: 0.4408\n",
            "Epoch 244/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0091 - accuracy: 0.9983 - val_loss: 3.0820 - val_accuracy: 0.4463\n",
            "Epoch 245/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0122 - accuracy: 0.9983 - val_loss: 3.0849 - val_accuracy: 0.4132\n",
            "Epoch 246/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0044 - accuracy: 0.9983 - val_loss: 3.0425 - val_accuracy: 0.4518\n",
            "Epoch 247/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0101 - accuracy: 0.9983 - val_loss: 3.1263 - val_accuracy: 0.4325\n",
            "Epoch 248/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0195 - accuracy: 0.9950 - val_loss: 2.9336 - val_accuracy: 0.4573\n",
            "Epoch 249/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0136 - accuracy: 0.9983 - val_loss: 3.1619 - val_accuracy: 0.4353\n",
            "Epoch 250/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0131 - accuracy: 0.9983 - val_loss: 3.0677 - val_accuracy: 0.4187\n",
            "Epoch 251/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0118 - accuracy: 0.9983 - val_loss: 3.0905 - val_accuracy: 0.4325\n",
            "Epoch 252/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0118 - accuracy: 0.9983 - val_loss: 3.1330 - val_accuracy: 0.4215\n",
            "Epoch 253/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0065 - accuracy: 0.9983 - val_loss: 3.1195 - val_accuracy: 0.4518\n",
            "Epoch 254/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0050 - accuracy: 0.9983 - val_loss: 3.2175 - val_accuracy: 0.4132\n",
            "Epoch 255/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0080 - accuracy: 0.9983 - val_loss: 3.2570 - val_accuracy: 0.4380\n",
            "Epoch 256/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0249 - accuracy: 0.9983 - val_loss: 3.1687 - val_accuracy: 0.4242\n",
            "Epoch 257/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0117 - accuracy: 0.9967 - val_loss: 2.9659 - val_accuracy: 0.4298\n",
            "Epoch 258/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0176 - accuracy: 0.9967 - val_loss: 3.0345 - val_accuracy: 0.4380\n",
            "Epoch 259/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0068 - accuracy: 0.9983 - val_loss: 3.1278 - val_accuracy: 0.4325\n",
            "Epoch 260/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0081 - accuracy: 0.9983 - val_loss: 3.2031 - val_accuracy: 0.4353\n",
            "Epoch 261/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0275 - accuracy: 0.9933 - val_loss: 3.3938 - val_accuracy: 0.4408\n",
            "Epoch 262/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0169 - accuracy: 0.9983 - val_loss: 3.0815 - val_accuracy: 0.4160\n",
            "Epoch 263/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0169 - accuracy: 0.9967 - val_loss: 3.1996 - val_accuracy: 0.4242\n",
            "Epoch 264/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0192 - accuracy: 0.9983 - val_loss: 3.1886 - val_accuracy: 0.4325\n",
            "Epoch 265/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0184 - accuracy: 0.9967 - val_loss: 2.8746 - val_accuracy: 0.4545\n",
            "Epoch 266/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0134 - accuracy: 0.9967 - val_loss: 3.0894 - val_accuracy: 0.4050\n",
            "Epoch 267/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0105 - accuracy: 0.9967 - val_loss: 2.9480 - val_accuracy: 0.4435\n",
            "Epoch 268/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0058 - accuracy: 0.9967 - val_loss: 3.0558 - val_accuracy: 0.4298\n",
            "Epoch 269/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0045 - accuracy: 1.0000 - val_loss: 3.0002 - val_accuracy: 0.4408\n",
            "Epoch 270/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0074 - accuracy: 0.9983 - val_loss: 3.1376 - val_accuracy: 0.4325\n",
            "Epoch 271/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0136 - accuracy: 0.9983 - val_loss: 3.0067 - val_accuracy: 0.4628\n",
            "Epoch 272/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.0061 - accuracy: 0.9983 - val_loss: 3.2922 - val_accuracy: 0.4050\n",
            "Epoch 273/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0051 - accuracy: 1.0000 - val_loss: 3.3200 - val_accuracy: 0.4353\n",
            "Epoch 274/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0085 - accuracy: 0.9983 - val_loss: 3.2701 - val_accuracy: 0.4270\n",
            "Epoch 275/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0159 - accuracy: 0.9967 - val_loss: 3.1233 - val_accuracy: 0.4325\n",
            "Epoch 276/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0109 - accuracy: 0.9983 - val_loss: 3.0636 - val_accuracy: 0.4518\n",
            "Epoch 277/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0314 - accuracy: 0.9950 - val_loss: 2.9497 - val_accuracy: 0.4353\n",
            "Epoch 278/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0239 - accuracy: 0.9967 - val_loss: 3.2258 - val_accuracy: 0.4187\n",
            "Epoch 279/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0180 - accuracy: 0.9983 - val_loss: 3.0098 - val_accuracy: 0.4187\n",
            "Epoch 280/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0108 - accuracy: 0.9967 - val_loss: 3.2522 - val_accuracy: 0.4463\n",
            "Epoch 281/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0197 - accuracy: 0.9967 - val_loss: 3.1136 - val_accuracy: 0.4242\n",
            "Epoch 282/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0121 - accuracy: 0.9983 - val_loss: 3.1079 - val_accuracy: 0.4408\n",
            "Epoch 283/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0094 - accuracy: 0.9967 - val_loss: 3.1293 - val_accuracy: 0.4187\n",
            "Epoch 284/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0095 - accuracy: 0.9967 - val_loss: 3.0495 - val_accuracy: 0.4270\n",
            "Epoch 285/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0061 - accuracy: 0.9983 - val_loss: 3.0017 - val_accuracy: 0.4242\n",
            "Epoch 286/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0130 - accuracy: 0.9983 - val_loss: 3.0505 - val_accuracy: 0.4435\n",
            "Epoch 287/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0063 - accuracy: 0.9967 - val_loss: 3.2419 - val_accuracy: 0.4215\n",
            "Epoch 288/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0130 - accuracy: 0.9983 - val_loss: 3.0760 - val_accuracy: 0.4490\n",
            "Epoch 289/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0149 - accuracy: 0.9967 - val_loss: 3.2191 - val_accuracy: 0.4490\n",
            "Epoch 290/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0087 - accuracy: 0.9983 - val_loss: 3.0904 - val_accuracy: 0.4187\n",
            "Epoch 291/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0110 - accuracy: 0.9967 - val_loss: 3.0390 - val_accuracy: 0.4380\n",
            "Epoch 292/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0025 - accuracy: 1.0000 - val_loss: 3.0757 - val_accuracy: 0.4242\n",
            "Epoch 293/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0174 - accuracy: 0.9967 - val_loss: 3.0886 - val_accuracy: 0.4353\n",
            "Epoch 294/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0130 - accuracy: 0.9983 - val_loss: 3.0558 - val_accuracy: 0.4270\n",
            "Epoch 295/500\n",
            "19/19 [==============================] - 1s 65ms/step - loss: 0.0120 - accuracy: 0.9983 - val_loss: 3.0405 - val_accuracy: 0.4353\n",
            "Epoch 296/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0045 - accuracy: 0.9983 - val_loss: 3.1848 - val_accuracy: 0.4353\n",
            "Epoch 297/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0111 - accuracy: 0.9983 - val_loss: 3.1606 - val_accuracy: 0.4187\n",
            "Epoch 298/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0183 - accuracy: 0.9983 - val_loss: 3.3231 - val_accuracy: 0.4270\n",
            "Epoch 299/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.0072 - accuracy: 0.9983 - val_loss: 3.2532 - val_accuracy: 0.4463\n",
            "Epoch 300/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0167 - accuracy: 0.9967 - val_loss: 3.0941 - val_accuracy: 0.4132\n",
            "Epoch 301/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0032 - accuracy: 0.9983 - val_loss: 3.0425 - val_accuracy: 0.4435\n",
            "Epoch 302/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0070 - accuracy: 0.9983 - val_loss: 3.1931 - val_accuracy: 0.4105\n",
            "Epoch 303/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0064 - accuracy: 0.9983 - val_loss: 3.3556 - val_accuracy: 0.4545\n",
            "Epoch 304/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0081 - accuracy: 0.9983 - val_loss: 3.5142 - val_accuracy: 0.4160\n",
            "Epoch 305/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0109 - accuracy: 0.9967 - val_loss: 2.9445 - val_accuracy: 0.4518\n",
            "Epoch 306/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0059 - accuracy: 0.9983 - val_loss: 3.3603 - val_accuracy: 0.4215\n",
            "Epoch 307/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0189 - accuracy: 0.9967 - val_loss: 3.1138 - val_accuracy: 0.4242\n",
            "Epoch 308/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0101 - accuracy: 0.9983 - val_loss: 3.0041 - val_accuracy: 0.4408\n",
            "Epoch 309/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0113 - accuracy: 0.9983 - val_loss: 3.2218 - val_accuracy: 0.4298\n",
            "Epoch 310/500\n",
            "19/19 [==============================] - 1s 59ms/step - loss: 0.0126 - accuracy: 0.9967 - val_loss: 3.0343 - val_accuracy: 0.4408\n",
            "Epoch 311/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0033 - accuracy: 1.0000 - val_loss: 2.9867 - val_accuracy: 0.4490\n",
            "Epoch 312/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0096 - accuracy: 0.9983 - val_loss: 3.0922 - val_accuracy: 0.4270\n",
            "Epoch 313/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0129 - accuracy: 0.9983 - val_loss: 3.2485 - val_accuracy: 0.4353\n",
            "Epoch 314/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0056 - accuracy: 0.9983 - val_loss: 3.0483 - val_accuracy: 0.4601\n",
            "Epoch 315/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0182 - accuracy: 0.9983 - val_loss: 3.2494 - val_accuracy: 0.4105\n",
            "Epoch 316/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0067 - accuracy: 0.9967 - val_loss: 3.0026 - val_accuracy: 0.4325\n",
            "Epoch 317/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0055 - accuracy: 0.9967 - val_loss: 3.1048 - val_accuracy: 0.4380\n",
            "Epoch 318/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0211 - accuracy: 0.9967 - val_loss: 3.0029 - val_accuracy: 0.4463\n",
            "Epoch 319/500\n",
            "19/19 [==============================] - 1s 67ms/step - loss: 0.0164 - accuracy: 0.9967 - val_loss: 3.1401 - val_accuracy: 0.4160\n",
            "Epoch 320/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0083 - accuracy: 0.9983 - val_loss: 3.1551 - val_accuracy: 0.4408\n",
            "Epoch 321/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0069 - accuracy: 0.9983 - val_loss: 3.1303 - val_accuracy: 0.4353\n",
            "Epoch 322/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0148 - accuracy: 0.9967 - val_loss: 3.0530 - val_accuracy: 0.4628\n",
            "Epoch 323/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0144 - accuracy: 0.9967 - val_loss: 2.9747 - val_accuracy: 0.4380\n",
            "Epoch 324/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0091 - accuracy: 0.9983 - val_loss: 3.1508 - val_accuracy: 0.4242\n",
            "Epoch 325/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0081 - accuracy: 0.9983 - val_loss: 3.0613 - val_accuracy: 0.4711\n",
            "Epoch 326/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0146 - accuracy: 0.9967 - val_loss: 3.1066 - val_accuracy: 0.4187\n",
            "Epoch 327/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0026 - accuracy: 1.0000 - val_loss: 3.2367 - val_accuracy: 0.4435\n",
            "Epoch 328/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0043 - accuracy: 0.9983 - val_loss: 3.1801 - val_accuracy: 0.4298\n",
            "Epoch 329/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0067 - accuracy: 0.9983 - val_loss: 3.0900 - val_accuracy: 0.4656\n",
            "Epoch 330/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0136 - accuracy: 0.9983 - val_loss: 3.1100 - val_accuracy: 0.4353\n",
            "Epoch 331/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0044 - accuracy: 0.9967 - val_loss: 3.0725 - val_accuracy: 0.4463\n",
            "Epoch 332/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0044 - accuracy: 0.9983 - val_loss: 3.0829 - val_accuracy: 0.4380\n",
            "Epoch 333/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0062 - accuracy: 0.9967 - val_loss: 3.0416 - val_accuracy: 0.4408\n",
            "Epoch 334/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0098 - accuracy: 0.9983 - val_loss: 3.2825 - val_accuracy: 0.4408\n",
            "Epoch 335/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0073 - accuracy: 0.9967 - val_loss: 3.1490 - val_accuracy: 0.4298\n",
            "Epoch 336/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.0067 - accuracy: 0.9983 - val_loss: 3.1141 - val_accuracy: 0.4187\n",
            "Epoch 337/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0154 - accuracy: 0.9967 - val_loss: 3.0251 - val_accuracy: 0.4573\n",
            "Epoch 338/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0052 - accuracy: 0.9983 - val_loss: 3.1535 - val_accuracy: 0.4380\n",
            "Epoch 339/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0112 - accuracy: 0.9967 - val_loss: 3.0824 - val_accuracy: 0.4408\n",
            "Epoch 340/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0137 - accuracy: 0.9967 - val_loss: 3.0922 - val_accuracy: 0.4298\n",
            "Epoch 341/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0126 - accuracy: 0.9967 - val_loss: 3.2463 - val_accuracy: 0.4298\n",
            "Epoch 342/500\n",
            "19/19 [==============================] - 1s 65ms/step - loss: 0.0156 - accuracy: 0.9983 - val_loss: 3.2166 - val_accuracy: 0.4105\n",
            "Epoch 343/500\n",
            "19/19 [==============================] - 1s 65ms/step - loss: 0.0107 - accuracy: 0.9983 - val_loss: 3.0919 - val_accuracy: 0.4160\n",
            "Epoch 344/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0092 - accuracy: 0.9983 - val_loss: 3.3491 - val_accuracy: 0.4435\n",
            "Epoch 345/500\n",
            "19/19 [==============================] - 1s 66ms/step - loss: 0.0110 - accuracy: 0.9983 - val_loss: 3.5133 - val_accuracy: 0.4132\n",
            "Epoch 346/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0101 - accuracy: 0.9983 - val_loss: 3.1965 - val_accuracy: 0.4738\n",
            "Epoch 347/500\n",
            "19/19 [==============================] - 1s 65ms/step - loss: 0.0114 - accuracy: 0.9983 - val_loss: 3.3598 - val_accuracy: 0.4077\n",
            "Epoch 348/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0094 - accuracy: 0.9983 - val_loss: 3.2161 - val_accuracy: 0.4545\n",
            "Epoch 349/500\n",
            "19/19 [==============================] - 1s 65ms/step - loss: 0.0127 - accuracy: 0.9967 - val_loss: 3.1753 - val_accuracy: 0.4132\n",
            "Epoch 350/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0141 - accuracy: 0.9983 - val_loss: 3.3152 - val_accuracy: 0.4160\n",
            "Epoch 351/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0106 - accuracy: 0.9967 - val_loss: 3.0381 - val_accuracy: 0.4077\n",
            "Epoch 352/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0106 - accuracy: 0.9983 - val_loss: 3.1780 - val_accuracy: 0.4545\n",
            "Epoch 353/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0108 - accuracy: 0.9983 - val_loss: 3.2261 - val_accuracy: 0.4215\n",
            "Epoch 354/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0081 - accuracy: 0.9983 - val_loss: 3.1069 - val_accuracy: 0.4408\n",
            "Epoch 355/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0091 - accuracy: 0.9983 - val_loss: 3.1037 - val_accuracy: 0.4353\n",
            "Epoch 356/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0080 - accuracy: 0.9983 - val_loss: 3.2874 - val_accuracy: 0.4270\n",
            "Epoch 357/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0100 - accuracy: 0.9983 - val_loss: 3.3508 - val_accuracy: 0.4132\n",
            "Epoch 358/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0136 - accuracy: 0.9967 - val_loss: 3.1514 - val_accuracy: 0.4325\n",
            "Epoch 359/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0068 - accuracy: 0.9983 - val_loss: 3.2154 - val_accuracy: 0.4353\n",
            "Epoch 360/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0092 - accuracy: 0.9983 - val_loss: 3.1983 - val_accuracy: 0.4187\n",
            "Epoch 361/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0069 - accuracy: 0.9983 - val_loss: 3.1997 - val_accuracy: 0.4408\n",
            "Epoch 362/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0068 - accuracy: 0.9983 - val_loss: 3.2833 - val_accuracy: 0.4050\n",
            "Epoch 363/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0339 - accuracy: 0.9933 - val_loss: 3.3828 - val_accuracy: 0.4022\n",
            "Epoch 364/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0576 - accuracy: 0.9833 - val_loss: 3.7744 - val_accuracy: 0.3967\n",
            "Epoch 365/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.1126 - accuracy: 0.9683 - val_loss: 3.0210 - val_accuracy: 0.4242\n",
            "Epoch 366/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0661 - accuracy: 0.9850 - val_loss: 2.8629 - val_accuracy: 0.4160\n",
            "Epoch 367/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0163 - accuracy: 0.9967 - val_loss: 3.0894 - val_accuracy: 0.4270\n",
            "Epoch 368/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0149 - accuracy: 0.9967 - val_loss: 3.2779 - val_accuracy: 0.4463\n",
            "Epoch 369/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0103 - accuracy: 0.9983 - val_loss: 3.1454 - val_accuracy: 0.4215\n",
            "Epoch 370/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0114 - accuracy: 0.9967 - val_loss: 3.2219 - val_accuracy: 0.4408\n",
            "Epoch 371/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0156 - accuracy: 0.9983 - val_loss: 2.8914 - val_accuracy: 0.4518\n",
            "Epoch 372/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0105 - accuracy: 0.9983 - val_loss: 3.0540 - val_accuracy: 0.4298\n",
            "Epoch 373/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0045 - accuracy: 0.9983 - val_loss: 2.8813 - val_accuracy: 0.4353\n",
            "Epoch 374/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0129 - accuracy: 0.9967 - val_loss: 2.9856 - val_accuracy: 0.4463\n",
            "Epoch 375/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0060 - accuracy: 0.9983 - val_loss: 2.9959 - val_accuracy: 0.4298\n",
            "Epoch 376/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0067 - accuracy: 0.9983 - val_loss: 3.0220 - val_accuracy: 0.4545\n",
            "Epoch 377/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0128 - accuracy: 0.9983 - val_loss: 2.8738 - val_accuracy: 0.4601\n",
            "Epoch 378/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.0070 - accuracy: 0.9983 - val_loss: 3.0474 - val_accuracy: 0.4215\n",
            "Epoch 379/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0074 - accuracy: 0.9983 - val_loss: 3.0679 - val_accuracy: 0.4545\n",
            "Epoch 380/500\n",
            "19/19 [==============================] - 1s 65ms/step - loss: 0.0107 - accuracy: 0.9983 - val_loss: 3.0908 - val_accuracy: 0.4270\n",
            "Epoch 381/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0088 - accuracy: 0.9967 - val_loss: 3.1015 - val_accuracy: 0.4187\n",
            "Epoch 382/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0131 - accuracy: 0.9983 - val_loss: 3.1459 - val_accuracy: 0.4518\n",
            "Epoch 383/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0069 - accuracy: 0.9983 - val_loss: 2.9143 - val_accuracy: 0.4353\n",
            "Epoch 384/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0097 - accuracy: 0.9983 - val_loss: 2.9778 - val_accuracy: 0.4490\n",
            "Epoch 385/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0117 - accuracy: 0.9967 - val_loss: 2.9882 - val_accuracy: 0.4518\n",
            "Epoch 386/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0088 - accuracy: 0.9983 - val_loss: 3.0044 - val_accuracy: 0.4380\n",
            "Epoch 387/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0075 - accuracy: 0.9983 - val_loss: 3.0891 - val_accuracy: 0.4215\n",
            "Epoch 388/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0109 - accuracy: 0.9967 - val_loss: 3.0727 - val_accuracy: 0.4242\n",
            "Epoch 389/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0050 - accuracy: 0.9983 - val_loss: 3.0334 - val_accuracy: 0.4628\n",
            "Epoch 390/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0083 - accuracy: 0.9983 - val_loss: 2.9467 - val_accuracy: 0.4353\n",
            "Epoch 391/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0115 - accuracy: 0.9967 - val_loss: 3.0101 - val_accuracy: 0.4463\n",
            "Epoch 392/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0047 - accuracy: 0.9967 - val_loss: 3.1205 - val_accuracy: 0.4270\n",
            "Epoch 393/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0037 - accuracy: 0.9983 - val_loss: 3.0934 - val_accuracy: 0.4573\n",
            "Epoch 394/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0084 - accuracy: 0.9983 - val_loss: 3.0324 - val_accuracy: 0.4435\n",
            "Epoch 395/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0022 - accuracy: 1.0000 - val_loss: 3.1610 - val_accuracy: 0.4160\n",
            "Epoch 396/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0074 - accuracy: 0.9967 - val_loss: 3.0787 - val_accuracy: 0.4380\n",
            "Epoch 397/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0096 - accuracy: 0.9983 - val_loss: 3.0331 - val_accuracy: 0.4435\n",
            "Epoch 398/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0110 - accuracy: 0.9967 - val_loss: 3.0663 - val_accuracy: 0.4490\n",
            "Epoch 399/500\n",
            "19/19 [==============================] - 1s 69ms/step - loss: 0.0091 - accuracy: 0.9967 - val_loss: 3.0501 - val_accuracy: 0.4435\n",
            "Epoch 400/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0037 - accuracy: 0.9983 - val_loss: 3.1012 - val_accuracy: 0.4298\n",
            "Epoch 401/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0130 - accuracy: 0.9967 - val_loss: 3.2567 - val_accuracy: 0.4518\n",
            "Epoch 402/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0073 - accuracy: 0.9983 - val_loss: 3.1551 - val_accuracy: 0.4298\n",
            "Epoch 403/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0041 - accuracy: 0.9983 - val_loss: 3.1084 - val_accuracy: 0.4298\n",
            "Epoch 404/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0046 - accuracy: 0.9983 - val_loss: 3.1031 - val_accuracy: 0.4353\n",
            "Epoch 405/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0100 - accuracy: 0.9983 - val_loss: 3.0543 - val_accuracy: 0.4380\n",
            "Epoch 406/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0056 - accuracy: 0.9983 - val_loss: 3.0274 - val_accuracy: 0.4353\n",
            "Epoch 407/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0029 - accuracy: 0.9983 - val_loss: 3.1596 - val_accuracy: 0.4353\n",
            "Epoch 408/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0125 - accuracy: 0.9967 - val_loss: 3.0619 - val_accuracy: 0.4408\n",
            "Epoch 409/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0062 - accuracy: 0.9967 - val_loss: 3.0912 - val_accuracy: 0.4270\n",
            "Epoch 410/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0052 - accuracy: 0.9983 - val_loss: 3.0812 - val_accuracy: 0.4463\n",
            "Epoch 411/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0097 - accuracy: 0.9967 - val_loss: 3.0530 - val_accuracy: 0.4325\n",
            "Epoch 412/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0026 - accuracy: 0.9983 - val_loss: 3.1108 - val_accuracy: 0.4325\n",
            "Epoch 413/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0067 - accuracy: 0.9983 - val_loss: 3.0978 - val_accuracy: 0.4325\n",
            "Epoch 414/500\n",
            "19/19 [==============================] - 1s 65ms/step - loss: 0.0234 - accuracy: 0.9950 - val_loss: 3.5621 - val_accuracy: 0.4132\n",
            "Epoch 415/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0048 - accuracy: 0.9983 - val_loss: 3.2031 - val_accuracy: 0.4325\n",
            "Epoch 416/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0117 - accuracy: 0.9967 - val_loss: 3.2043 - val_accuracy: 0.4215\n",
            "Epoch 417/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0063 - accuracy: 0.9967 - val_loss: 3.0747 - val_accuracy: 0.4518\n",
            "Epoch 418/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0057 - accuracy: 0.9983 - val_loss: 3.1853 - val_accuracy: 0.4215\n",
            "Epoch 419/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0144 - accuracy: 0.9967 - val_loss: 3.1462 - val_accuracy: 0.4215\n",
            "Epoch 420/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 3.1439 - val_accuracy: 0.4215\n",
            "Epoch 421/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0068 - accuracy: 0.9967 - val_loss: 3.1294 - val_accuracy: 0.4545\n",
            "Epoch 422/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0108 - accuracy: 0.9983 - val_loss: 3.0679 - val_accuracy: 0.4518\n",
            "Epoch 423/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0086 - accuracy: 0.9983 - val_loss: 3.1407 - val_accuracy: 0.4298\n",
            "Epoch 424/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0084 - accuracy: 0.9983 - val_loss: 3.0633 - val_accuracy: 0.4270\n",
            "Epoch 425/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0079 - accuracy: 0.9967 - val_loss: 3.0297 - val_accuracy: 0.4380\n",
            "Epoch 426/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0097 - accuracy: 0.9967 - val_loss: 3.0456 - val_accuracy: 0.4325\n",
            "Epoch 427/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0065 - accuracy: 0.9983 - val_loss: 3.0917 - val_accuracy: 0.4270\n",
            "Epoch 428/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0062 - accuracy: 0.9983 - val_loss: 3.0751 - val_accuracy: 0.4270\n",
            "Epoch 429/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0079 - accuracy: 0.9983 - val_loss: 3.0639 - val_accuracy: 0.4435\n",
            "Epoch 430/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0049 - accuracy: 0.9967 - val_loss: 3.2230 - val_accuracy: 0.4187\n",
            "Epoch 431/500\n",
            "19/19 [==============================] - 1s 65ms/step - loss: 0.0085 - accuracy: 0.9983 - val_loss: 2.9517 - val_accuracy: 0.4518\n",
            "Epoch 432/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0116 - accuracy: 0.9967 - val_loss: 3.0159 - val_accuracy: 0.4270\n",
            "Epoch 433/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0082 - accuracy: 0.9983 - val_loss: 3.1398 - val_accuracy: 0.4298\n",
            "Epoch 434/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0046 - accuracy: 0.9983 - val_loss: 3.3300 - val_accuracy: 0.4132\n",
            "Epoch 435/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0092 - accuracy: 0.9983 - val_loss: 3.1382 - val_accuracy: 0.4628\n",
            "Epoch 436/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0127 - accuracy: 0.9983 - val_loss: 3.1000 - val_accuracy: 0.4380\n",
            "Epoch 437/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0081 - accuracy: 0.9983 - val_loss: 3.0816 - val_accuracy: 0.4408\n",
            "Epoch 438/500\n",
            "19/19 [==============================] - 1s 70ms/step - loss: 0.0063 - accuracy: 0.9983 - val_loss: 3.1591 - val_accuracy: 0.4242\n",
            "Epoch 439/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0046 - accuracy: 0.9967 - val_loss: 3.1153 - val_accuracy: 0.4408\n",
            "Epoch 440/500\n",
            "19/19 [==============================] - 1s 65ms/step - loss: 0.0072 - accuracy: 0.9967 - val_loss: 3.2006 - val_accuracy: 0.4380\n",
            "Epoch 441/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0108 - accuracy: 0.9983 - val_loss: 3.0602 - val_accuracy: 0.4353\n",
            "Epoch 442/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 3.1858 - val_accuracy: 0.4298\n",
            "Epoch 443/500\n",
            "19/19 [==============================] - 1s 65ms/step - loss: 0.0081 - accuracy: 0.9967 - val_loss: 3.2103 - val_accuracy: 0.4242\n",
            "Epoch 444/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0037 - accuracy: 0.9983 - val_loss: 3.1512 - val_accuracy: 0.4683\n",
            "Epoch 445/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0096 - accuracy: 0.9983 - val_loss: 3.4112 - val_accuracy: 0.4270\n",
            "Epoch 446/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0055 - accuracy: 0.9983 - val_loss: 3.1381 - val_accuracy: 0.4463\n",
            "Epoch 447/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0110 - accuracy: 0.9983 - val_loss: 3.0443 - val_accuracy: 0.4160\n",
            "Epoch 448/500\n",
            "19/19 [==============================] - 1s 65ms/step - loss: 0.0108 - accuracy: 0.9983 - val_loss: 3.1915 - val_accuracy: 0.4380\n",
            "Epoch 449/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0063 - accuracy: 0.9983 - val_loss: 3.1353 - val_accuracy: 0.4298\n",
            "Epoch 450/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0139 - accuracy: 0.9967 - val_loss: 3.0120 - val_accuracy: 0.4298\n",
            "Epoch 451/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0030 - accuracy: 0.9983 - val_loss: 3.0739 - val_accuracy: 0.4353\n",
            "Epoch 452/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0047 - accuracy: 0.9983 - val_loss: 3.2005 - val_accuracy: 0.4545\n",
            "Epoch 453/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0092 - accuracy: 0.9983 - val_loss: 3.3288 - val_accuracy: 0.4187\n",
            "Epoch 454/500\n",
            "19/19 [==============================] - 1s 66ms/step - loss: 0.0139 - accuracy: 0.9967 - val_loss: 2.9801 - val_accuracy: 0.4683\n",
            "Epoch 455/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0117 - accuracy: 0.9967 - val_loss: 3.0618 - val_accuracy: 0.4160\n",
            "Epoch 456/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0109 - accuracy: 0.9983 - val_loss: 3.0896 - val_accuracy: 0.4490\n",
            "Epoch 457/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0116 - accuracy: 0.9983 - val_loss: 3.1556 - val_accuracy: 0.4270\n",
            "Epoch 458/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0197 - accuracy: 0.9950 - val_loss: 3.6524 - val_accuracy: 0.4160\n",
            "Epoch 459/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0123 - accuracy: 0.9983 - val_loss: 3.0348 - val_accuracy: 0.4270\n",
            "Epoch 460/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0093 - accuracy: 0.9967 - val_loss: 3.2656 - val_accuracy: 0.4270\n",
            "Epoch 461/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0109 - accuracy: 0.9983 - val_loss: 3.0246 - val_accuracy: 0.4435\n",
            "Epoch 462/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0099 - accuracy: 0.9967 - val_loss: 3.1411 - val_accuracy: 0.4132\n",
            "Epoch 463/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0079 - accuracy: 0.9983 - val_loss: 2.9957 - val_accuracy: 0.4573\n",
            "Epoch 464/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0106 - accuracy: 0.9967 - val_loss: 2.8670 - val_accuracy: 0.4518\n",
            "Epoch 465/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0093 - accuracy: 0.9983 - val_loss: 3.0148 - val_accuracy: 0.4353\n",
            "Epoch 466/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0074 - accuracy: 0.9983 - val_loss: 3.0247 - val_accuracy: 0.4353\n",
            "Epoch 467/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0025 - accuracy: 0.9983 - val_loss: 3.0224 - val_accuracy: 0.4270\n",
            "Epoch 468/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0064 - accuracy: 0.9983 - val_loss: 3.0526 - val_accuracy: 0.4518\n",
            "Epoch 469/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0073 - accuracy: 0.9967 - val_loss: 3.3329 - val_accuracy: 0.4656\n",
            "Epoch 470/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0059 - accuracy: 0.9983 - val_loss: 3.1629 - val_accuracy: 0.4242\n",
            "Epoch 471/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0166 - accuracy: 0.9967 - val_loss: 3.0790 - val_accuracy: 0.4353\n",
            "Epoch 472/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0124 - accuracy: 0.9967 - val_loss: 2.9289 - val_accuracy: 0.4353\n",
            "Epoch 473/500\n",
            "19/19 [==============================] - 1s 65ms/step - loss: 0.0047 - accuracy: 0.9967 - val_loss: 3.1644 - val_accuracy: 0.4380\n",
            "Epoch 474/500\n",
            "19/19 [==============================] - 1s 60ms/step - loss: 0.0068 - accuracy: 0.9967 - val_loss: 3.0479 - val_accuracy: 0.4628\n",
            "Epoch 475/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0131 - accuracy: 0.9967 - val_loss: 2.9845 - val_accuracy: 0.4683\n",
            "Epoch 476/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0113 - accuracy: 0.9967 - val_loss: 3.0985 - val_accuracy: 0.4132\n",
            "Epoch 477/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0085 - accuracy: 0.9967 - val_loss: 3.1579 - val_accuracy: 0.4242\n",
            "Epoch 478/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0185 - accuracy: 0.9950 - val_loss: 2.8881 - val_accuracy: 0.4601\n",
            "Epoch 479/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0721 - accuracy: 0.9800 - val_loss: 3.0786 - val_accuracy: 0.4160\n",
            "Epoch 480/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.1253 - accuracy: 0.9650 - val_loss: 2.6667 - val_accuracy: 0.4518\n",
            "Epoch 481/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0256 - accuracy: 0.9917 - val_loss: 2.8682 - val_accuracy: 0.4408\n",
            "Epoch 482/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0171 - accuracy: 0.9983 - val_loss: 2.7545 - val_accuracy: 0.4573\n",
            "Epoch 483/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0088 - accuracy: 0.9983 - val_loss: 2.8091 - val_accuracy: 0.4545\n",
            "Epoch 484/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0095 - accuracy: 0.9967 - val_loss: 2.7837 - val_accuracy: 0.4628\n",
            "Epoch 485/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0091 - accuracy: 0.9967 - val_loss: 2.8560 - val_accuracy: 0.4408\n",
            "Epoch 486/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0191 - accuracy: 0.9967 - val_loss: 2.8408 - val_accuracy: 0.4601\n",
            "Epoch 487/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0051 - accuracy: 0.9983 - val_loss: 3.0334 - val_accuracy: 0.4325\n",
            "Epoch 488/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0086 - accuracy: 0.9967 - val_loss: 2.8891 - val_accuracy: 0.4463\n",
            "Epoch 489/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0097 - accuracy: 0.9983 - val_loss: 2.9793 - val_accuracy: 0.4408\n",
            "Epoch 490/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0099 - accuracy: 0.9983 - val_loss: 2.8727 - val_accuracy: 0.4408\n",
            "Epoch 491/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0055 - accuracy: 0.9967 - val_loss: 2.9217 - val_accuracy: 0.4408\n",
            "Epoch 492/500\n",
            "19/19 [==============================] - 1s 62ms/step - loss: 0.0082 - accuracy: 0.9967 - val_loss: 2.8853 - val_accuracy: 0.4463\n",
            "Epoch 493/500\n",
            "19/19 [==============================] - 1s 61ms/step - loss: 0.0043 - accuracy: 0.9983 - val_loss: 2.8662 - val_accuracy: 0.4435\n",
            "Epoch 494/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0065 - accuracy: 0.9983 - val_loss: 2.9637 - val_accuracy: 0.4353\n",
            "Epoch 495/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0138 - accuracy: 0.9967 - val_loss: 2.9646 - val_accuracy: 0.4380\n",
            "Epoch 496/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0065 - accuracy: 0.9983 - val_loss: 2.9417 - val_accuracy: 0.4408\n",
            "Epoch 497/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0024 - accuracy: 1.0000 - val_loss: 2.9851 - val_accuracy: 0.4518\n",
            "Epoch 498/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 2.9915 - val_accuracy: 0.4463\n",
            "Epoch 499/500\n",
            "19/19 [==============================] - 1s 64ms/step - loss: 0.0035 - accuracy: 0.9983 - val_loss: 3.0036 - val_accuracy: 0.4518\n",
            "Epoch 500/500\n",
            "19/19 [==============================] - 1s 63ms/step - loss: 0.0068 - accuracy: 0.9983 - val_loss: 3.0169 - val_accuracy: 0.4353\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import pickle\n",
        "# model.save_weights('/content/drive/MyDrive/Data/M500_epoch_simple_lr.cpkt')\n",
        "\n",
        "# pickle.dump(history.history, open('/content/drive/MyDrive/Data/history_500_epoch_simple.pkl','wb'))"
      ],
      "metadata": {
        "id": "lu7vIehPYA7z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# acc = history.history['accuracy']\n",
        "# val_acc = history.history['val_accuracy']\n",
        "# loss = history.history['loss']\n",
        "# val_loss = history.history['val_loss']\n",
        "\n",
        "# epochs_range = range(500)\n",
        "\n",
        "# plt.figure(figsize=(25, 15))\n",
        "# plt.subplot(2, 2, 1)\n",
        "# plt.plot(epochs_range, acc, label='Training Accuracy')\n",
        "# plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
        "# plt.legend(loc='lower right')\n",
        "# plt.title('Training and Validation Accuracy')\n",
        "\n",
        "# plt.subplot(2, 2, 2)\n",
        "# plt.plot(epochs_range, loss, label='Training Loss')\n",
        "# plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
        "# plt.legend(loc='upper right')\n",
        "# plt.title('Training and Validation Loss')\n",
        "# plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "id": "s96KI3yfYROU",
        "outputId": "177b000e-e04b-43d9-c74b-79b519771996"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# pred = model.predict(x_val)\n",
        "# predictions = np.argmax(pred, axis=1)\n",
        "# print(predictions)\n",
        "# print(classfication_report(y_val, predictions, target_names=labels))\n",
        "# # predictions = model.predict_classes(x_val)\n",
        "# # predictions = predictions.reshape(1,-1)[0]\n",
        "# # print(classification_report(y_val, predictions, target_names = labels))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234
        },
        "id": "pHiD7rPlYfW6",
        "outputId": "7b9f320b-08f6-49aa-9b01-9de46c80f759"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-ac1d438d93d4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_val\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredictions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclassfication_report\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_val\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_names\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# predictions = model.predict_classes(x_val)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import seaborn as sn\n",
        "# import pandas as pd\n",
        "# import matplotlib.pyplot as plt\n",
        "# cm1 = confusion_matrix(y_val, predictions)\n",
        "# df_cm = pd.DataFrame(cm1, index = [i f or i in labels],\n",
        "#               columns = [i for i in labels])\n",
        "# plt.figure(figsize = (10,7))\n",
        "# sn.heatmap(df_cm, annot=True,cmap=\"RdPu\")\n",
        "# plt.savefig('confusion_mrtx2.png',bbox_inches = 'tight')"
      ],
      "metadata": {
        "id": "oz7zhnBkYfva"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Transfer Learning Based Model"
      ],
      "metadata": {
        "id": "perPBRTXI_uG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# base_model = tf.keras.applications.DenseNet201(input_shape = (256, 256, 3), include_top = False, weights = \"imagenet\")"
      ],
      "metadata": {
        "id": "4m1cp-KSJCQq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a010c5c8-aaad-4f3d-a998-dda54daae24e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/densenet/densenet201_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "74842112/74836368 [==============================] - 3s 0us/step\n",
            "74850304/74836368 [==============================] - 3s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# base_model.trainable = False\n",
        "# model = tf.keras.Sequential([base_model,\n",
        "#                                  tf.keras.layers.GlobalAveragePooling2D(),\n",
        "#                                  tf.keras.layers.Dropout(0.2),\n",
        "#                                  tf.keras.layers.Dense(10, activation=\"softmax\")                                     \n",
        "#                                 ])"
      ],
      "metadata": {
        "id": "sED3kjpOJFMF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# base_learning_rate = 0.0001\n",
        "# model.compile(optimizer=tf.keras.optimizers.Adam(lr=base_learning_rate),\n",
        "#               loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "#               metrics=['accuracy'])\n",
        "\n",
        "# history1 = model.fit(x_train,y_train,epochs = 500 , validation_data = (x_val, y_val))"
      ],
      "metadata": {
        "id": "catMB-QeJG0f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9fcdf94f-1b29-47f5-b1bf-60345b8dbf7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(Adam, self).__init__(name, **kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/500\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1096: UserWarning: \"`sparse_categorical_crossentropy` received `from_logits=True`, but the `output` argument was produced by a sigmoid or softmax activation and thus does not represent logits. Was this intended?\"\n",
            "  return dispatch_target(*args, **kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "19/19 [==============================] - 48s 1s/step - loss: 2.5556 - accuracy: 0.0850 - val_loss: 2.3481 - val_accuracy: 0.1102\n",
            "Epoch 2/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 2.4135 - accuracy: 0.1067 - val_loss: 2.2842 - val_accuracy: 0.1047\n",
            "Epoch 3/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 2.3579 - accuracy: 0.0933 - val_loss: 2.2433 - val_accuracy: 0.1267\n",
            "Epoch 4/500\n",
            "19/19 [==============================] - 13s 725ms/step - loss: 2.3105 - accuracy: 0.1550 - val_loss: 2.2079 - val_accuracy: 0.1598\n",
            "Epoch 5/500\n",
            "19/19 [==============================] - 13s 724ms/step - loss: 2.2418 - accuracy: 0.1633 - val_loss: 2.1794 - val_accuracy: 0.1873\n",
            "Epoch 6/500\n",
            "19/19 [==============================] - 13s 725ms/step - loss: 2.1722 - accuracy: 0.2267 - val_loss: 2.1467 - val_accuracy: 0.1928\n",
            "Epoch 7/500\n",
            "19/19 [==============================] - 13s 725ms/step - loss: 2.1656 - accuracy: 0.2150 - val_loss: 2.1126 - val_accuracy: 0.2039\n",
            "Epoch 8/500\n",
            "19/19 [==============================] - 13s 724ms/step - loss: 2.0815 - accuracy: 0.2400 - val_loss: 2.0843 - val_accuracy: 0.2287\n",
            "Epoch 9/500\n",
            "19/19 [==============================] - 13s 723ms/step - loss: 2.0525 - accuracy: 0.2867 - val_loss: 2.0602 - val_accuracy: 0.2287\n",
            "Epoch 10/500\n",
            "19/19 [==============================] - 13s 723ms/step - loss: 1.9829 - accuracy: 0.3167 - val_loss: 2.0393 - val_accuracy: 0.2369\n",
            "Epoch 11/500\n",
            "19/19 [==============================] - 13s 723ms/step - loss: 1.9593 - accuracy: 0.3300 - val_loss: 2.0146 - val_accuracy: 0.2645\n",
            "Epoch 12/500\n",
            "19/19 [==============================] - 13s 723ms/step - loss: 1.9271 - accuracy: 0.3250 - val_loss: 1.9885 - val_accuracy: 0.2672\n",
            "Epoch 13/500\n",
            "19/19 [==============================] - 13s 724ms/step - loss: 1.9127 - accuracy: 0.3267 - val_loss: 1.9730 - val_accuracy: 0.2837\n",
            "Epoch 14/500\n",
            "19/19 [==============================] - 13s 724ms/step - loss: 1.8633 - accuracy: 0.3700 - val_loss: 1.9559 - val_accuracy: 0.2810\n",
            "Epoch 15/500\n",
            "19/19 [==============================] - 13s 724ms/step - loss: 1.8270 - accuracy: 0.3933 - val_loss: 1.9298 - val_accuracy: 0.2975\n",
            "Epoch 16/500\n",
            "19/19 [==============================] - 13s 723ms/step - loss: 1.8314 - accuracy: 0.3683 - val_loss: 1.9112 - val_accuracy: 0.3113\n",
            "Epoch 17/500\n",
            "19/19 [==============================] - 13s 723ms/step - loss: 1.7828 - accuracy: 0.3817 - val_loss: 1.8972 - val_accuracy: 0.3113\n",
            "Epoch 18/500\n",
            "19/19 [==============================] - 13s 722ms/step - loss: 1.7483 - accuracy: 0.4200 - val_loss: 1.8858 - val_accuracy: 0.3140\n",
            "Epoch 19/500\n",
            "19/19 [==============================] - 13s 724ms/step - loss: 1.7240 - accuracy: 0.4333 - val_loss: 1.8697 - val_accuracy: 0.3113\n",
            "Epoch 20/500\n",
            "19/19 [==============================] - 13s 723ms/step - loss: 1.6991 - accuracy: 0.4367 - val_loss: 1.8597 - val_accuracy: 0.3168\n",
            "Epoch 21/500\n",
            "19/19 [==============================] - 13s 724ms/step - loss: 1.6684 - accuracy: 0.4250 - val_loss: 1.8415 - val_accuracy: 0.3196\n",
            "Epoch 22/500\n",
            "19/19 [==============================] - 13s 723ms/step - loss: 1.6399 - accuracy: 0.4667 - val_loss: 1.8308 - val_accuracy: 0.3251\n",
            "Epoch 23/500\n",
            "19/19 [==============================] - 13s 724ms/step - loss: 1.6119 - accuracy: 0.4617 - val_loss: 1.8204 - val_accuracy: 0.3388\n",
            "Epoch 24/500\n",
            "19/19 [==============================] - 13s 722ms/step - loss: 1.6064 - accuracy: 0.4767 - val_loss: 1.8072 - val_accuracy: 0.3444\n",
            "Epoch 25/500\n",
            "19/19 [==============================] - 13s 724ms/step - loss: 1.5733 - accuracy: 0.5150 - val_loss: 1.7982 - val_accuracy: 0.3388\n",
            "Epoch 26/500\n",
            "19/19 [==============================] - 13s 721ms/step - loss: 1.5954 - accuracy: 0.4633 - val_loss: 1.7885 - val_accuracy: 0.3388\n",
            "Epoch 27/500\n",
            "19/19 [==============================] - 13s 722ms/step - loss: 1.5548 - accuracy: 0.4800 - val_loss: 1.7794 - val_accuracy: 0.3526\n",
            "Epoch 28/500\n",
            "19/19 [==============================] - 13s 720ms/step - loss: 1.5460 - accuracy: 0.4800 - val_loss: 1.7651 - val_accuracy: 0.3554\n",
            "Epoch 29/500\n",
            "19/19 [==============================] - 13s 722ms/step - loss: 1.5284 - accuracy: 0.5000 - val_loss: 1.7578 - val_accuracy: 0.3526\n",
            "Epoch 30/500\n",
            "19/19 [==============================] - 13s 723ms/step - loss: 1.5064 - accuracy: 0.4950 - val_loss: 1.7497 - val_accuracy: 0.3609\n",
            "Epoch 31/500\n",
            "19/19 [==============================] - 13s 722ms/step - loss: 1.4858 - accuracy: 0.5133 - val_loss: 1.7454 - val_accuracy: 0.3581\n",
            "Epoch 32/500\n",
            "19/19 [==============================] - 13s 721ms/step - loss: 1.4992 - accuracy: 0.4867 - val_loss: 1.7365 - val_accuracy: 0.3554\n",
            "Epoch 33/500\n",
            "19/19 [==============================] - 13s 722ms/step - loss: 1.4809 - accuracy: 0.5117 - val_loss: 1.7282 - val_accuracy: 0.3691\n",
            "Epoch 34/500\n",
            "19/19 [==============================] - 13s 722ms/step - loss: 1.4183 - accuracy: 0.5550 - val_loss: 1.7185 - val_accuracy: 0.3636\n",
            "Epoch 35/500\n",
            "19/19 [==============================] - 13s 723ms/step - loss: 1.4466 - accuracy: 0.5283 - val_loss: 1.7111 - val_accuracy: 0.3691\n",
            "Epoch 36/500\n",
            "19/19 [==============================] - 13s 721ms/step - loss: 1.4438 - accuracy: 0.5133 - val_loss: 1.7052 - val_accuracy: 0.3774\n",
            "Epoch 37/500\n",
            "19/19 [==============================] - 13s 721ms/step - loss: 1.3897 - accuracy: 0.5517 - val_loss: 1.6995 - val_accuracy: 0.3581\n",
            "Epoch 38/500\n",
            "19/19 [==============================] - 13s 723ms/step - loss: 1.4038 - accuracy: 0.5450 - val_loss: 1.6918 - val_accuracy: 0.3774\n",
            "Epoch 39/500\n",
            "19/19 [==============================] - 13s 721ms/step - loss: 1.3746 - accuracy: 0.5483 - val_loss: 1.6855 - val_accuracy: 0.3719\n",
            "Epoch 40/500\n",
            "19/19 [==============================] - 13s 722ms/step - loss: 1.3972 - accuracy: 0.5533 - val_loss: 1.6788 - val_accuracy: 0.3774\n",
            "Epoch 41/500\n",
            "19/19 [==============================] - 13s 722ms/step - loss: 1.3833 - accuracy: 0.5350 - val_loss: 1.6736 - val_accuracy: 0.3719\n",
            "Epoch 42/500\n",
            "19/19 [==============================] - 13s 722ms/step - loss: 1.3553 - accuracy: 0.5533 - val_loss: 1.6667 - val_accuracy: 0.3747\n",
            "Epoch 43/500\n",
            "19/19 [==============================] - 13s 723ms/step - loss: 1.3508 - accuracy: 0.5533 - val_loss: 1.6627 - val_accuracy: 0.3802\n",
            "Epoch 44/500\n",
            "19/19 [==============================] - 13s 722ms/step - loss: 1.3557 - accuracy: 0.5600 - val_loss: 1.6618 - val_accuracy: 0.3829\n",
            "Epoch 45/500\n",
            "19/19 [==============================] - 13s 723ms/step - loss: 1.3256 - accuracy: 0.5600 - val_loss: 1.6568 - val_accuracy: 0.3691\n",
            "Epoch 46/500\n",
            "19/19 [==============================] - 13s 723ms/step - loss: 1.3244 - accuracy: 0.5600 - val_loss: 1.6507 - val_accuracy: 0.3774\n",
            "Epoch 47/500\n",
            "19/19 [==============================] - 13s 722ms/step - loss: 1.3141 - accuracy: 0.5633 - val_loss: 1.6454 - val_accuracy: 0.3829\n",
            "Epoch 48/500\n",
            "19/19 [==============================] - 13s 722ms/step - loss: 1.3095 - accuracy: 0.5850 - val_loss: 1.6354 - val_accuracy: 0.3884\n",
            "Epoch 49/500\n",
            "19/19 [==============================] - 13s 723ms/step - loss: 1.2957 - accuracy: 0.5850 - val_loss: 1.6352 - val_accuracy: 0.3802\n",
            "Epoch 50/500\n",
            "19/19 [==============================] - 13s 721ms/step - loss: 1.2895 - accuracy: 0.5850 - val_loss: 1.6303 - val_accuracy: 0.3912\n",
            "Epoch 51/500\n",
            "19/19 [==============================] - 13s 722ms/step - loss: 1.2874 - accuracy: 0.5683 - val_loss: 1.6246 - val_accuracy: 0.3967\n",
            "Epoch 52/500\n",
            "19/19 [==============================] - 13s 723ms/step - loss: 1.2880 - accuracy: 0.5683 - val_loss: 1.6183 - val_accuracy: 0.3967\n",
            "Epoch 53/500\n",
            "19/19 [==============================] - 13s 722ms/step - loss: 1.2513 - accuracy: 0.6067 - val_loss: 1.6160 - val_accuracy: 0.3967\n",
            "Epoch 54/500\n",
            "19/19 [==============================] - 13s 723ms/step - loss: 1.2691 - accuracy: 0.5917 - val_loss: 1.6152 - val_accuracy: 0.3884\n",
            "Epoch 55/500\n",
            "19/19 [==============================] - 13s 721ms/step - loss: 1.2647 - accuracy: 0.5733 - val_loss: 1.6091 - val_accuracy: 0.4105\n",
            "Epoch 56/500\n",
            "19/19 [==============================] - 13s 722ms/step - loss: 1.2476 - accuracy: 0.5850 - val_loss: 1.6013 - val_accuracy: 0.4050\n",
            "Epoch 57/500\n",
            "19/19 [==============================] - 13s 725ms/step - loss: 1.2240 - accuracy: 0.6100 - val_loss: 1.6028 - val_accuracy: 0.4077\n",
            "Epoch 58/500\n",
            "19/19 [==============================] - 13s 722ms/step - loss: 1.2249 - accuracy: 0.6000 - val_loss: 1.5988 - val_accuracy: 0.4160\n",
            "Epoch 59/500\n",
            "19/19 [==============================] - 13s 722ms/step - loss: 1.2337 - accuracy: 0.5917 - val_loss: 1.5913 - val_accuracy: 0.4132\n",
            "Epoch 60/500\n",
            "19/19 [==============================] - 13s 722ms/step - loss: 1.2330 - accuracy: 0.5933 - val_loss: 1.5885 - val_accuracy: 0.4105\n",
            "Epoch 61/500\n",
            "19/19 [==============================] - 13s 722ms/step - loss: 1.2386 - accuracy: 0.5917 - val_loss: 1.5888 - val_accuracy: 0.4215\n",
            "Epoch 62/500\n",
            "19/19 [==============================] - 13s 721ms/step - loss: 1.2196 - accuracy: 0.5783 - val_loss: 1.5817 - val_accuracy: 0.4298\n",
            "Epoch 63/500\n",
            "19/19 [==============================] - 13s 724ms/step - loss: 1.2379 - accuracy: 0.5717 - val_loss: 1.5811 - val_accuracy: 0.4325\n",
            "Epoch 64/500\n",
            "19/19 [==============================] - 13s 723ms/step - loss: 1.1929 - accuracy: 0.5883 - val_loss: 1.5703 - val_accuracy: 0.4242\n",
            "Epoch 65/500\n",
            "19/19 [==============================] - 13s 724ms/step - loss: 1.1736 - accuracy: 0.6233 - val_loss: 1.5713 - val_accuracy: 0.4353\n",
            "Epoch 66/500\n",
            "19/19 [==============================] - 13s 723ms/step - loss: 1.1930 - accuracy: 0.5900 - val_loss: 1.5731 - val_accuracy: 0.4270\n",
            "Epoch 67/500\n",
            "19/19 [==============================] - 13s 722ms/step - loss: 1.1950 - accuracy: 0.6133 - val_loss: 1.5691 - val_accuracy: 0.4298\n",
            "Epoch 68/500\n",
            "19/19 [==============================] - 13s 721ms/step - loss: 1.1708 - accuracy: 0.6067 - val_loss: 1.5627 - val_accuracy: 0.4325\n",
            "Epoch 69/500\n",
            "19/19 [==============================] - 13s 724ms/step - loss: 1.1395 - accuracy: 0.6200 - val_loss: 1.5596 - val_accuracy: 0.4298\n",
            "Epoch 70/500\n",
            "19/19 [==============================] - 13s 723ms/step - loss: 1.1478 - accuracy: 0.6283 - val_loss: 1.5563 - val_accuracy: 0.4298\n",
            "Epoch 71/500\n",
            "19/19 [==============================] - 13s 724ms/step - loss: 1.1594 - accuracy: 0.6183 - val_loss: 1.5527 - val_accuracy: 0.4325\n",
            "Epoch 72/500\n",
            "19/19 [==============================] - 13s 721ms/step - loss: 1.1496 - accuracy: 0.6050 - val_loss: 1.5515 - val_accuracy: 0.4463\n",
            "Epoch 73/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 1.1616 - accuracy: 0.6083 - val_loss: 1.5451 - val_accuracy: 0.4408\n",
            "Epoch 74/500\n",
            "19/19 [==============================] - 13s 722ms/step - loss: 1.1324 - accuracy: 0.6283 - val_loss: 1.5494 - val_accuracy: 0.4353\n",
            "Epoch 75/500\n",
            "19/19 [==============================] - 13s 721ms/step - loss: 1.1551 - accuracy: 0.6100 - val_loss: 1.5407 - val_accuracy: 0.4463\n",
            "Epoch 76/500\n",
            "19/19 [==============================] - 13s 723ms/step - loss: 1.1295 - accuracy: 0.6350 - val_loss: 1.5406 - val_accuracy: 0.4490\n",
            "Epoch 77/500\n",
            "19/19 [==============================] - 14s 726ms/step - loss: 1.1563 - accuracy: 0.6133 - val_loss: 1.5344 - val_accuracy: 0.4380\n",
            "Epoch 78/500\n",
            "19/19 [==============================] - 13s 722ms/step - loss: 1.1153 - accuracy: 0.6217 - val_loss: 1.5319 - val_accuracy: 0.4325\n",
            "Epoch 79/500\n",
            "19/19 [==============================] - 13s 721ms/step - loss: 1.1189 - accuracy: 0.6317 - val_loss: 1.5278 - val_accuracy: 0.4463\n",
            "Epoch 80/500\n",
            "19/19 [==============================] - 13s 721ms/step - loss: 1.1038 - accuracy: 0.6667 - val_loss: 1.5272 - val_accuracy: 0.4463\n",
            "Epoch 81/500\n",
            "19/19 [==============================] - 13s 722ms/step - loss: 1.1179 - accuracy: 0.6183 - val_loss: 1.5259 - val_accuracy: 0.4353\n",
            "Epoch 82/500\n",
            "19/19 [==============================] - 13s 721ms/step - loss: 1.1019 - accuracy: 0.6450 - val_loss: 1.5253 - val_accuracy: 0.4435\n",
            "Epoch 83/500\n",
            "19/19 [==============================] - 13s 721ms/step - loss: 1.1160 - accuracy: 0.6133 - val_loss: 1.5216 - val_accuracy: 0.4545\n",
            "Epoch 84/500\n",
            "19/19 [==============================] - 13s 721ms/step - loss: 1.0820 - accuracy: 0.6517 - val_loss: 1.5190 - val_accuracy: 0.4490\n",
            "Epoch 85/500\n",
            "19/19 [==============================] - 13s 722ms/step - loss: 1.0772 - accuracy: 0.6650 - val_loss: 1.5180 - val_accuracy: 0.4573\n",
            "Epoch 86/500\n",
            "19/19 [==============================] - 13s 722ms/step - loss: 1.0889 - accuracy: 0.6350 - val_loss: 1.5145 - val_accuracy: 0.4628\n",
            "Epoch 87/500\n",
            "19/19 [==============================] - 13s 722ms/step - loss: 1.0698 - accuracy: 0.6500 - val_loss: 1.5117 - val_accuracy: 0.4490\n",
            "Epoch 88/500\n",
            "19/19 [==============================] - 13s 722ms/step - loss: 1.0874 - accuracy: 0.6350 - val_loss: 1.5104 - val_accuracy: 0.4601\n",
            "Epoch 89/500\n",
            "19/19 [==============================] - 13s 722ms/step - loss: 1.0788 - accuracy: 0.6567 - val_loss: 1.5048 - val_accuracy: 0.4490\n",
            "Epoch 90/500\n",
            "19/19 [==============================] - 13s 722ms/step - loss: 1.0676 - accuracy: 0.6450 - val_loss: 1.5020 - val_accuracy: 0.4408\n",
            "Epoch 91/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 1.0576 - accuracy: 0.6700 - val_loss: 1.5018 - val_accuracy: 0.4545\n",
            "Epoch 92/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 1.0695 - accuracy: 0.6500 - val_loss: 1.4976 - val_accuracy: 0.4601\n",
            "Epoch 93/500\n",
            "19/19 [==============================] - 13s 723ms/step - loss: 1.0593 - accuracy: 0.6567 - val_loss: 1.4963 - val_accuracy: 0.4601\n",
            "Epoch 94/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 1.0572 - accuracy: 0.6433 - val_loss: 1.4900 - val_accuracy: 0.4601\n",
            "Epoch 95/500\n",
            "19/19 [==============================] - 13s 725ms/step - loss: 1.0538 - accuracy: 0.6300 - val_loss: 1.4915 - val_accuracy: 0.4545\n",
            "Epoch 96/500\n",
            "19/19 [==============================] - 14s 726ms/step - loss: 1.0556 - accuracy: 0.6433 - val_loss: 1.4925 - val_accuracy: 0.4545\n",
            "Epoch 97/500\n",
            "19/19 [==============================] - 13s 725ms/step - loss: 1.0343 - accuracy: 0.6600 - val_loss: 1.4903 - val_accuracy: 0.4518\n",
            "Epoch 98/500\n",
            "19/19 [==============================] - 14s 726ms/step - loss: 1.0445 - accuracy: 0.6733 - val_loss: 1.4825 - val_accuracy: 0.4601\n",
            "Epoch 99/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 1.0129 - accuracy: 0.6700 - val_loss: 1.4817 - val_accuracy: 0.4573\n",
            "Epoch 100/500\n",
            "19/19 [==============================] - 13s 725ms/step - loss: 1.0108 - accuracy: 0.6700 - val_loss: 1.4838 - val_accuracy: 0.4628\n",
            "Epoch 101/500\n",
            "19/19 [==============================] - 14s 726ms/step - loss: 1.0196 - accuracy: 0.6583 - val_loss: 1.4807 - val_accuracy: 0.4573\n",
            "Epoch 102/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 1.0192 - accuracy: 0.6600 - val_loss: 1.4747 - val_accuracy: 0.4601\n",
            "Epoch 103/500\n",
            "19/19 [==============================] - 14s 726ms/step - loss: 1.0275 - accuracy: 0.6867 - val_loss: 1.4791 - val_accuracy: 0.4628\n",
            "Epoch 104/500\n",
            "19/19 [==============================] - 14s 726ms/step - loss: 0.9983 - accuracy: 0.6733 - val_loss: 1.4744 - val_accuracy: 0.4573\n",
            "Epoch 105/500\n",
            "19/19 [==============================] - 13s 725ms/step - loss: 1.0363 - accuracy: 0.6483 - val_loss: 1.4696 - val_accuracy: 0.4601\n",
            "Epoch 106/500\n",
            "19/19 [==============================] - 14s 726ms/step - loss: 0.9992 - accuracy: 0.6767 - val_loss: 1.4701 - val_accuracy: 0.4490\n",
            "Epoch 107/500\n",
            "19/19 [==============================] - 14s 726ms/step - loss: 1.0047 - accuracy: 0.6667 - val_loss: 1.4667 - val_accuracy: 0.4490\n",
            "Epoch 108/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 1.0026 - accuracy: 0.6633 - val_loss: 1.4644 - val_accuracy: 0.4573\n",
            "Epoch 109/500\n",
            "19/19 [==============================] - 14s 726ms/step - loss: 1.0253 - accuracy: 0.6683 - val_loss: 1.4625 - val_accuracy: 0.4490\n",
            "Epoch 110/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.9957 - accuracy: 0.6783 - val_loss: 1.4598 - val_accuracy: 0.4601\n",
            "Epoch 111/500\n",
            "19/19 [==============================] - 14s 726ms/step - loss: 1.0012 - accuracy: 0.6800 - val_loss: 1.4591 - val_accuracy: 0.4601\n",
            "Epoch 112/500\n",
            "19/19 [==============================] - 13s 726ms/step - loss: 0.9823 - accuracy: 0.6767 - val_loss: 1.4586 - val_accuracy: 0.4545\n",
            "Epoch 113/500\n",
            "19/19 [==============================] - 13s 725ms/step - loss: 0.9744 - accuracy: 0.6917 - val_loss: 1.4580 - val_accuracy: 0.4545\n",
            "Epoch 114/500\n",
            "19/19 [==============================] - 14s 731ms/step - loss: 0.9777 - accuracy: 0.6667 - val_loss: 1.4566 - val_accuracy: 0.4545\n",
            "Epoch 115/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.9843 - accuracy: 0.6533 - val_loss: 1.4547 - val_accuracy: 0.4490\n",
            "Epoch 116/500\n",
            "19/19 [==============================] - 13s 725ms/step - loss: 0.9861 - accuracy: 0.6700 - val_loss: 1.4476 - val_accuracy: 0.4628\n",
            "Epoch 117/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.9705 - accuracy: 0.6917 - val_loss: 1.4487 - val_accuracy: 0.4545\n",
            "Epoch 118/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.9873 - accuracy: 0.6617 - val_loss: 1.4491 - val_accuracy: 0.4518\n",
            "Epoch 119/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.9723 - accuracy: 0.6783 - val_loss: 1.4458 - val_accuracy: 0.4573\n",
            "Epoch 120/500\n",
            "19/19 [==============================] - 13s 726ms/step - loss: 0.9595 - accuracy: 0.7017 - val_loss: 1.4434 - val_accuracy: 0.4628\n",
            "Epoch 121/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.9370 - accuracy: 0.6950 - val_loss: 1.4417 - val_accuracy: 0.4601\n",
            "Epoch 122/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.9533 - accuracy: 0.6833 - val_loss: 1.4380 - val_accuracy: 0.4628\n",
            "Epoch 123/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.9527 - accuracy: 0.7033 - val_loss: 1.4430 - val_accuracy: 0.4573\n",
            "Epoch 124/500\n",
            "19/19 [==============================] - 13s 725ms/step - loss: 0.9712 - accuracy: 0.6883 - val_loss: 1.4422 - val_accuracy: 0.4545\n",
            "Epoch 125/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.9278 - accuracy: 0.6950 - val_loss: 1.4367 - val_accuracy: 0.4518\n",
            "Epoch 126/500\n",
            "19/19 [==============================] - 14s 726ms/step - loss: 0.9558 - accuracy: 0.6950 - val_loss: 1.4365 - val_accuracy: 0.4545\n",
            "Epoch 127/500\n",
            "19/19 [==============================] - 13s 725ms/step - loss: 0.9298 - accuracy: 0.7083 - val_loss: 1.4335 - val_accuracy: 0.4656\n",
            "Epoch 128/500\n",
            "19/19 [==============================] - 14s 726ms/step - loss: 0.9416 - accuracy: 0.6933 - val_loss: 1.4333 - val_accuracy: 0.4573\n",
            "Epoch 129/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.9460 - accuracy: 0.6950 - val_loss: 1.4327 - val_accuracy: 0.4463\n",
            "Epoch 130/500\n",
            "19/19 [==============================] - 14s 726ms/step - loss: 0.9217 - accuracy: 0.7050 - val_loss: 1.4279 - val_accuracy: 0.4518\n",
            "Epoch 131/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.9281 - accuracy: 0.7267 - val_loss: 1.4251 - val_accuracy: 0.4573\n",
            "Epoch 132/500\n",
            "19/19 [==============================] - 13s 725ms/step - loss: 0.9446 - accuracy: 0.6800 - val_loss: 1.4234 - val_accuracy: 0.4601\n",
            "Epoch 133/500\n",
            "19/19 [==============================] - 13s 726ms/step - loss: 0.9151 - accuracy: 0.7150 - val_loss: 1.4250 - val_accuracy: 0.4545\n",
            "Epoch 134/500\n",
            "19/19 [==============================] - 13s 726ms/step - loss: 0.9551 - accuracy: 0.6917 - val_loss: 1.4254 - val_accuracy: 0.4545\n",
            "Epoch 135/500\n",
            "19/19 [==============================] - 13s 726ms/step - loss: 0.9377 - accuracy: 0.7050 - val_loss: 1.4223 - val_accuracy: 0.4573\n",
            "Epoch 136/500\n",
            "19/19 [==============================] - 13s 725ms/step - loss: 0.9344 - accuracy: 0.6817 - val_loss: 1.4234 - val_accuracy: 0.4518\n",
            "Epoch 137/500\n",
            "19/19 [==============================] - 14s 726ms/step - loss: 0.9147 - accuracy: 0.7083 - val_loss: 1.4190 - val_accuracy: 0.4518\n",
            "Epoch 138/500\n",
            "19/19 [==============================] - 14s 726ms/step - loss: 0.9431 - accuracy: 0.6933 - val_loss: 1.4164 - val_accuracy: 0.4573\n",
            "Epoch 139/500\n",
            "19/19 [==============================] - 14s 726ms/step - loss: 0.9119 - accuracy: 0.6850 - val_loss: 1.4131 - val_accuracy: 0.4545\n",
            "Epoch 140/500\n",
            "19/19 [==============================] - 14s 726ms/step - loss: 0.8941 - accuracy: 0.6983 - val_loss: 1.4184 - val_accuracy: 0.4601\n",
            "Epoch 141/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.9189 - accuracy: 0.7083 - val_loss: 1.4150 - val_accuracy: 0.4573\n",
            "Epoch 142/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.9015 - accuracy: 0.7000 - val_loss: 1.4143 - val_accuracy: 0.4628\n",
            "Epoch 143/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.8827 - accuracy: 0.7200 - val_loss: 1.4103 - val_accuracy: 0.4573\n",
            "Epoch 144/500\n",
            "19/19 [==============================] - 13s 725ms/step - loss: 0.9240 - accuracy: 0.7067 - val_loss: 1.4122 - val_accuracy: 0.4490\n",
            "Epoch 145/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.9243 - accuracy: 0.7133 - val_loss: 1.4115 - val_accuracy: 0.4683\n",
            "Epoch 146/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.9012 - accuracy: 0.7050 - val_loss: 1.4052 - val_accuracy: 0.4601\n",
            "Epoch 147/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.8738 - accuracy: 0.7217 - val_loss: 1.4035 - val_accuracy: 0.4628\n",
            "Epoch 148/500\n",
            "19/19 [==============================] - 14s 726ms/step - loss: 0.9127 - accuracy: 0.6950 - val_loss: 1.4112 - val_accuracy: 0.4545\n",
            "Epoch 149/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.8970 - accuracy: 0.7333 - val_loss: 1.4045 - val_accuracy: 0.4628\n",
            "Epoch 150/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.8831 - accuracy: 0.7150 - val_loss: 1.3994 - val_accuracy: 0.4683\n",
            "Epoch 151/500\n",
            "19/19 [==============================] - 14s 726ms/step - loss: 0.8661 - accuracy: 0.7317 - val_loss: 1.4049 - val_accuracy: 0.4628\n",
            "Epoch 152/500\n",
            "19/19 [==============================] - 14s 726ms/step - loss: 0.8854 - accuracy: 0.7333 - val_loss: 1.4056 - val_accuracy: 0.4683\n",
            "Epoch 153/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.9023 - accuracy: 0.7067 - val_loss: 1.4043 - val_accuracy: 0.4711\n",
            "Epoch 154/500\n",
            "19/19 [==============================] - 14s 726ms/step - loss: 0.8721 - accuracy: 0.7117 - val_loss: 1.3976 - val_accuracy: 0.4683\n",
            "Epoch 155/500\n",
            "19/19 [==============================] - 13s 726ms/step - loss: 0.8826 - accuracy: 0.7250 - val_loss: 1.3990 - val_accuracy: 0.4656\n",
            "Epoch 156/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.8850 - accuracy: 0.7017 - val_loss: 1.3958 - val_accuracy: 0.4738\n",
            "Epoch 157/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.9005 - accuracy: 0.6883 - val_loss: 1.3936 - val_accuracy: 0.4628\n",
            "Epoch 158/500\n",
            "19/19 [==============================] - 14s 726ms/step - loss: 0.8810 - accuracy: 0.7333 - val_loss: 1.3969 - val_accuracy: 0.4601\n",
            "Epoch 159/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.8883 - accuracy: 0.7233 - val_loss: 1.3975 - val_accuracy: 0.4711\n",
            "Epoch 160/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.8881 - accuracy: 0.7183 - val_loss: 1.3936 - val_accuracy: 0.4738\n",
            "Epoch 161/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.8666 - accuracy: 0.7200 - val_loss: 1.3937 - val_accuracy: 0.4545\n",
            "Epoch 162/500\n",
            "19/19 [==============================] - 14s 731ms/step - loss: 0.8736 - accuracy: 0.7233 - val_loss: 1.3931 - val_accuracy: 0.4793\n",
            "Epoch 163/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.8662 - accuracy: 0.7150 - val_loss: 1.3903 - val_accuracy: 0.4656\n",
            "Epoch 164/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.8651 - accuracy: 0.7383 - val_loss: 1.3875 - val_accuracy: 0.4711\n",
            "Epoch 165/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.8436 - accuracy: 0.7317 - val_loss: 1.3865 - val_accuracy: 0.4628\n",
            "Epoch 166/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.8615 - accuracy: 0.7283 - val_loss: 1.3897 - val_accuracy: 0.4573\n",
            "Epoch 167/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.8763 - accuracy: 0.7217 - val_loss: 1.3872 - val_accuracy: 0.4656\n",
            "Epoch 168/500\n",
            "19/19 [==============================] - 14s 726ms/step - loss: 0.8626 - accuracy: 0.7267 - val_loss: 1.3845 - val_accuracy: 0.4738\n",
            "Epoch 169/500\n",
            "19/19 [==============================] - 13s 725ms/step - loss: 0.8558 - accuracy: 0.7183 - val_loss: 1.3845 - val_accuracy: 0.4628\n",
            "Epoch 170/500\n",
            "19/19 [==============================] - 14s 726ms/step - loss: 0.8483 - accuracy: 0.7133 - val_loss: 1.3850 - val_accuracy: 0.4821\n",
            "Epoch 171/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.8713 - accuracy: 0.7167 - val_loss: 1.3821 - val_accuracy: 0.4683\n",
            "Epoch 172/500\n",
            "19/19 [==============================] - 13s 726ms/step - loss: 0.8401 - accuracy: 0.7300 - val_loss: 1.3789 - val_accuracy: 0.4656\n",
            "Epoch 173/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.8442 - accuracy: 0.7383 - val_loss: 1.3773 - val_accuracy: 0.4683\n",
            "Epoch 174/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.8276 - accuracy: 0.7467 - val_loss: 1.3773 - val_accuracy: 0.4821\n",
            "Epoch 175/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.8368 - accuracy: 0.7433 - val_loss: 1.3787 - val_accuracy: 0.4683\n",
            "Epoch 176/500\n",
            "19/19 [==============================] - 14s 726ms/step - loss: 0.8411 - accuracy: 0.7400 - val_loss: 1.3724 - val_accuracy: 0.4656\n",
            "Epoch 177/500\n",
            "19/19 [==============================] - 14s 726ms/step - loss: 0.8416 - accuracy: 0.7083 - val_loss: 1.3713 - val_accuracy: 0.4711\n",
            "Epoch 178/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.8392 - accuracy: 0.7467 - val_loss: 1.3758 - val_accuracy: 0.4738\n",
            "Epoch 179/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.8444 - accuracy: 0.7150 - val_loss: 1.3761 - val_accuracy: 0.4821\n",
            "Epoch 180/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.8441 - accuracy: 0.7483 - val_loss: 1.3725 - val_accuracy: 0.4683\n",
            "Epoch 181/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.8282 - accuracy: 0.7483 - val_loss: 1.3698 - val_accuracy: 0.4711\n",
            "Epoch 182/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.8298 - accuracy: 0.7267 - val_loss: 1.3667 - val_accuracy: 0.4821\n",
            "Epoch 183/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.8339 - accuracy: 0.7483 - val_loss: 1.3681 - val_accuracy: 0.4738\n",
            "Epoch 184/500\n",
            "19/19 [==============================] - 14s 726ms/step - loss: 0.8374 - accuracy: 0.7367 - val_loss: 1.3670 - val_accuracy: 0.4766\n",
            "Epoch 185/500\n",
            "19/19 [==============================] - 13s 726ms/step - loss: 0.8171 - accuracy: 0.7433 - val_loss: 1.3657 - val_accuracy: 0.4711\n",
            "Epoch 186/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.8320 - accuracy: 0.7450 - val_loss: 1.3656 - val_accuracy: 0.4766\n",
            "Epoch 187/500\n",
            "19/19 [==============================] - 14s 726ms/step - loss: 0.8044 - accuracy: 0.7450 - val_loss: 1.3658 - val_accuracy: 0.4821\n",
            "Epoch 188/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.8115 - accuracy: 0.7400 - val_loss: 1.3644 - val_accuracy: 0.4793\n",
            "Epoch 189/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.8208 - accuracy: 0.7317 - val_loss: 1.3652 - val_accuracy: 0.4766\n",
            "Epoch 190/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.8329 - accuracy: 0.7167 - val_loss: 1.3635 - val_accuracy: 0.4766\n",
            "Epoch 191/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.8106 - accuracy: 0.7483 - val_loss: 1.3608 - val_accuracy: 0.4848\n",
            "Epoch 192/500\n",
            "19/19 [==============================] - 13s 725ms/step - loss: 0.8224 - accuracy: 0.7367 - val_loss: 1.3594 - val_accuracy: 0.4766\n",
            "Epoch 193/500\n",
            "19/19 [==============================] - 13s 725ms/step - loss: 0.8103 - accuracy: 0.7583 - val_loss: 1.3593 - val_accuracy: 0.4793\n",
            "Epoch 194/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.8055 - accuracy: 0.7483 - val_loss: 1.3640 - val_accuracy: 0.4766\n",
            "Epoch 195/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.8088 - accuracy: 0.7583 - val_loss: 1.3617 - val_accuracy: 0.4904\n",
            "Epoch 196/500\n",
            "19/19 [==============================] - 14s 726ms/step - loss: 0.7791 - accuracy: 0.7767 - val_loss: 1.3602 - val_accuracy: 0.4876\n",
            "Epoch 197/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.7831 - accuracy: 0.7567 - val_loss: 1.3601 - val_accuracy: 0.4766\n",
            "Epoch 198/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.8047 - accuracy: 0.7333 - val_loss: 1.3576 - val_accuracy: 0.4793\n",
            "Epoch 199/500\n",
            "19/19 [==============================] - 14s 726ms/step - loss: 0.7939 - accuracy: 0.7550 - val_loss: 1.3572 - val_accuracy: 0.4931\n",
            "Epoch 200/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.7917 - accuracy: 0.7467 - val_loss: 1.3581 - val_accuracy: 0.4986\n",
            "Epoch 201/500\n",
            "19/19 [==============================] - 14s 726ms/step - loss: 0.7840 - accuracy: 0.7517 - val_loss: 1.3538 - val_accuracy: 0.4848\n",
            "Epoch 202/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.7888 - accuracy: 0.7650 - val_loss: 1.3499 - val_accuracy: 0.5014\n",
            "Epoch 203/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.7803 - accuracy: 0.7550 - val_loss: 1.3515 - val_accuracy: 0.4904\n",
            "Epoch 204/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.7890 - accuracy: 0.7500 - val_loss: 1.3516 - val_accuracy: 0.4821\n",
            "Epoch 205/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.7984 - accuracy: 0.7500 - val_loss: 1.3499 - val_accuracy: 0.4904\n",
            "Epoch 206/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.7930 - accuracy: 0.7483 - val_loss: 1.3476 - val_accuracy: 0.4959\n",
            "Epoch 207/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.7828 - accuracy: 0.7617 - val_loss: 1.3491 - val_accuracy: 0.4904\n",
            "Epoch 208/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.7781 - accuracy: 0.7583 - val_loss: 1.3494 - val_accuracy: 0.5069\n",
            "Epoch 209/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.7862 - accuracy: 0.7533 - val_loss: 1.3509 - val_accuracy: 0.4986\n",
            "Epoch 210/500\n",
            "19/19 [==============================] - 13s 725ms/step - loss: 0.7937 - accuracy: 0.7500 - val_loss: 1.3466 - val_accuracy: 0.5041\n",
            "Epoch 211/500\n",
            "19/19 [==============================] - 13s 722ms/step - loss: 0.7902 - accuracy: 0.7783 - val_loss: 1.3498 - val_accuracy: 0.4821\n",
            "Epoch 212/500\n",
            "19/19 [==============================] - 13s 724ms/step - loss: 0.7837 - accuracy: 0.7700 - val_loss: 1.3421 - val_accuracy: 0.5014\n",
            "Epoch 213/500\n",
            "19/19 [==============================] - 13s 722ms/step - loss: 0.7830 - accuracy: 0.7400 - val_loss: 1.3428 - val_accuracy: 0.5041\n",
            "Epoch 214/500\n",
            "19/19 [==============================] - 13s 724ms/step - loss: 0.7987 - accuracy: 0.7533 - val_loss: 1.3438 - val_accuracy: 0.5014\n",
            "Epoch 215/500\n",
            "19/19 [==============================] - 14s 726ms/step - loss: 0.7787 - accuracy: 0.7500 - val_loss: 1.3442 - val_accuracy: 0.4986\n",
            "Epoch 216/500\n",
            "19/19 [==============================] - 14s 725ms/step - loss: 0.7673 - accuracy: 0.7617 - val_loss: 1.3462 - val_accuracy: 0.4876\n",
            "Epoch 217/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.7621 - accuracy: 0.7550 - val_loss: 1.3419 - val_accuracy: 0.5014\n",
            "Epoch 218/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.7723 - accuracy: 0.7517 - val_loss: 1.3401 - val_accuracy: 0.5014\n",
            "Epoch 219/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.7752 - accuracy: 0.7483 - val_loss: 1.3381 - val_accuracy: 0.4931\n",
            "Epoch 220/500\n",
            "19/19 [==============================] - 14s 726ms/step - loss: 0.7769 - accuracy: 0.7650 - val_loss: 1.3397 - val_accuracy: 0.4959\n",
            "Epoch 221/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.7722 - accuracy: 0.7583 - val_loss: 1.3409 - val_accuracy: 0.4986\n",
            "Epoch 222/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.7784 - accuracy: 0.7667 - val_loss: 1.3416 - val_accuracy: 0.5069\n",
            "Epoch 223/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.7790 - accuracy: 0.7383 - val_loss: 1.3339 - val_accuracy: 0.4986\n",
            "Epoch 224/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.7531 - accuracy: 0.7750 - val_loss: 1.3357 - val_accuracy: 0.4959\n",
            "Epoch 225/500\n",
            "19/19 [==============================] - 14s 732ms/step - loss: 0.7805 - accuracy: 0.7483 - val_loss: 1.3400 - val_accuracy: 0.4959\n",
            "Epoch 226/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.7783 - accuracy: 0.7500 - val_loss: 1.3342 - val_accuracy: 0.5096\n",
            "Epoch 227/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.7769 - accuracy: 0.7350 - val_loss: 1.3322 - val_accuracy: 0.4986\n",
            "Epoch 228/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.7511 - accuracy: 0.7700 - val_loss: 1.3338 - val_accuracy: 0.4959\n",
            "Epoch 229/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.7704 - accuracy: 0.7600 - val_loss: 1.3354 - val_accuracy: 0.4986\n",
            "Epoch 230/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.7417 - accuracy: 0.7850 - val_loss: 1.3359 - val_accuracy: 0.4931\n",
            "Epoch 231/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.7587 - accuracy: 0.7317 - val_loss: 1.3316 - val_accuracy: 0.4986\n",
            "Epoch 232/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.7459 - accuracy: 0.7733 - val_loss: 1.3354 - val_accuracy: 0.5014\n",
            "Epoch 233/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.7624 - accuracy: 0.7633 - val_loss: 1.3352 - val_accuracy: 0.4986\n",
            "Epoch 234/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.7360 - accuracy: 0.7667 - val_loss: 1.3319 - val_accuracy: 0.4959\n",
            "Epoch 235/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.7473 - accuracy: 0.7783 - val_loss: 1.3352 - val_accuracy: 0.4931\n",
            "Epoch 236/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.7487 - accuracy: 0.7733 - val_loss: 1.3300 - val_accuracy: 0.4904\n",
            "Epoch 237/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.7623 - accuracy: 0.7467 - val_loss: 1.3326 - val_accuracy: 0.4931\n",
            "Epoch 238/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.7505 - accuracy: 0.7583 - val_loss: 1.3297 - val_accuracy: 0.5014\n",
            "Epoch 239/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.7510 - accuracy: 0.7717 - val_loss: 1.3263 - val_accuracy: 0.4986\n",
            "Epoch 240/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.7448 - accuracy: 0.7783 - val_loss: 1.3262 - val_accuracy: 0.5014\n",
            "Epoch 241/500\n",
            "19/19 [==============================] - 14s 726ms/step - loss: 0.7419 - accuracy: 0.7733 - val_loss: 1.3273 - val_accuracy: 0.4986\n",
            "Epoch 242/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.7545 - accuracy: 0.7517 - val_loss: 1.3320 - val_accuracy: 0.4959\n",
            "Epoch 243/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.7342 - accuracy: 0.7817 - val_loss: 1.3281 - val_accuracy: 0.4959\n",
            "Epoch 244/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.7603 - accuracy: 0.7700 - val_loss: 1.3278 - val_accuracy: 0.5014\n",
            "Epoch 245/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.7421 - accuracy: 0.7750 - val_loss: 1.3272 - val_accuracy: 0.5096\n",
            "Epoch 246/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.7157 - accuracy: 0.7883 - val_loss: 1.3268 - val_accuracy: 0.5041\n",
            "Epoch 247/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.7342 - accuracy: 0.7717 - val_loss: 1.3225 - val_accuracy: 0.5124\n",
            "Epoch 248/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.7258 - accuracy: 0.7800 - val_loss: 1.3265 - val_accuracy: 0.5014\n",
            "Epoch 249/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.7221 - accuracy: 0.7783 - val_loss: 1.3222 - val_accuracy: 0.5041\n",
            "Epoch 250/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.7383 - accuracy: 0.7800 - val_loss: 1.3212 - val_accuracy: 0.4931\n",
            "Epoch 251/500\n",
            "19/19 [==============================] - 14s 726ms/step - loss: 0.7403 - accuracy: 0.7633 - val_loss: 1.3231 - val_accuracy: 0.4931\n",
            "Epoch 252/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.7367 - accuracy: 0.7683 - val_loss: 1.3198 - val_accuracy: 0.5096\n",
            "Epoch 253/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.7320 - accuracy: 0.7733 - val_loss: 1.3217 - val_accuracy: 0.5069\n",
            "Epoch 254/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.7252 - accuracy: 0.7783 - val_loss: 1.3196 - val_accuracy: 0.5096\n",
            "Epoch 255/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.7237 - accuracy: 0.7717 - val_loss: 1.3185 - val_accuracy: 0.4986\n",
            "Epoch 256/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.7191 - accuracy: 0.7933 - val_loss: 1.3220 - val_accuracy: 0.5069\n",
            "Epoch 257/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.7360 - accuracy: 0.7600 - val_loss: 1.3262 - val_accuracy: 0.5041\n",
            "Epoch 258/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.7312 - accuracy: 0.7517 - val_loss: 1.3267 - val_accuracy: 0.4986\n",
            "Epoch 259/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.7148 - accuracy: 0.7833 - val_loss: 1.3215 - val_accuracy: 0.4986\n",
            "Epoch 260/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.7294 - accuracy: 0.7733 - val_loss: 1.3207 - val_accuracy: 0.5014\n",
            "Epoch 261/500\n",
            "19/19 [==============================] - 14s 731ms/step - loss: 0.7301 - accuracy: 0.7650 - val_loss: 1.3186 - val_accuracy: 0.5014\n",
            "Epoch 262/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.7137 - accuracy: 0.7683 - val_loss: 1.3160 - val_accuracy: 0.5041\n",
            "Epoch 263/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.7112 - accuracy: 0.7750 - val_loss: 1.3199 - val_accuracy: 0.4986\n",
            "Epoch 264/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.7171 - accuracy: 0.7833 - val_loss: 1.3132 - val_accuracy: 0.5069\n",
            "Epoch 265/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.7067 - accuracy: 0.7600 - val_loss: 1.3124 - val_accuracy: 0.5014\n",
            "Epoch 266/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.7221 - accuracy: 0.7767 - val_loss: 1.3153 - val_accuracy: 0.5014\n",
            "Epoch 267/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.7090 - accuracy: 0.7783 - val_loss: 1.3190 - val_accuracy: 0.5041\n",
            "Epoch 268/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.7053 - accuracy: 0.7800 - val_loss: 1.3177 - val_accuracy: 0.5014\n",
            "Epoch 269/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.7199 - accuracy: 0.7733 - val_loss: 1.3201 - val_accuracy: 0.5096\n",
            "Epoch 270/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.7233 - accuracy: 0.7650 - val_loss: 1.3097 - val_accuracy: 0.5069\n",
            "Epoch 271/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.7168 - accuracy: 0.7700 - val_loss: 1.3088 - val_accuracy: 0.5096\n",
            "Epoch 272/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.7279 - accuracy: 0.7800 - val_loss: 1.3164 - val_accuracy: 0.5014\n",
            "Epoch 273/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.6925 - accuracy: 0.7950 - val_loss: 1.3179 - val_accuracy: 0.5041\n",
            "Epoch 274/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.7045 - accuracy: 0.7883 - val_loss: 1.3119 - val_accuracy: 0.5014\n",
            "Epoch 275/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.6992 - accuracy: 0.7850 - val_loss: 1.3110 - val_accuracy: 0.5124\n",
            "Epoch 276/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.7193 - accuracy: 0.7650 - val_loss: 1.3110 - val_accuracy: 0.5096\n",
            "Epoch 277/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.7045 - accuracy: 0.7683 - val_loss: 1.3128 - val_accuracy: 0.5096\n",
            "Epoch 278/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.6932 - accuracy: 0.7867 - val_loss: 1.3123 - val_accuracy: 0.5096\n",
            "Epoch 279/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.7018 - accuracy: 0.7783 - val_loss: 1.3078 - val_accuracy: 0.5014\n",
            "Epoch 280/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.6840 - accuracy: 0.8033 - val_loss: 1.3091 - val_accuracy: 0.5096\n",
            "Epoch 281/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.6793 - accuracy: 0.7917 - val_loss: 1.3119 - val_accuracy: 0.5124\n",
            "Epoch 282/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.7063 - accuracy: 0.7750 - val_loss: 1.3085 - val_accuracy: 0.5096\n",
            "Epoch 283/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.6954 - accuracy: 0.7883 - val_loss: 1.3086 - val_accuracy: 0.5152\n",
            "Epoch 284/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.7038 - accuracy: 0.8017 - val_loss: 1.3089 - val_accuracy: 0.5069\n",
            "Epoch 285/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.7067 - accuracy: 0.7817 - val_loss: 1.3076 - val_accuracy: 0.5124\n",
            "Epoch 286/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.7115 - accuracy: 0.7917 - val_loss: 1.3076 - val_accuracy: 0.5069\n",
            "Epoch 287/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.6990 - accuracy: 0.7883 - val_loss: 1.3074 - val_accuracy: 0.5152\n",
            "Epoch 288/500\n",
            "19/19 [==============================] - 14s 731ms/step - loss: 0.6761 - accuracy: 0.7983 - val_loss: 1.3054 - val_accuracy: 0.5096\n",
            "Epoch 289/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.6777 - accuracy: 0.7917 - val_loss: 1.3028 - val_accuracy: 0.5069\n",
            "Epoch 290/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.6736 - accuracy: 0.7867 - val_loss: 1.3069 - val_accuracy: 0.5152\n",
            "Epoch 291/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.7047 - accuracy: 0.7817 - val_loss: 1.3076 - val_accuracy: 0.5124\n",
            "Epoch 292/500\n",
            "19/19 [==============================] - 14s 731ms/step - loss: 0.6813 - accuracy: 0.7967 - val_loss: 1.3084 - val_accuracy: 0.5096\n",
            "Epoch 293/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.6862 - accuracy: 0.7933 - val_loss: 1.3061 - val_accuracy: 0.5069\n",
            "Epoch 294/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.6726 - accuracy: 0.7867 - val_loss: 1.3063 - val_accuracy: 0.5207\n",
            "Epoch 295/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.6590 - accuracy: 0.7967 - val_loss: 1.3033 - val_accuracy: 0.5096\n",
            "Epoch 296/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.6748 - accuracy: 0.7817 - val_loss: 1.3059 - val_accuracy: 0.5124\n",
            "Epoch 297/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.6771 - accuracy: 0.7783 - val_loss: 1.3062 - val_accuracy: 0.5096\n",
            "Epoch 298/500\n",
            "19/19 [==============================] - 14s 734ms/step - loss: 0.6974 - accuracy: 0.7833 - val_loss: 1.3062 - val_accuracy: 0.5124\n",
            "Epoch 299/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.6909 - accuracy: 0.7850 - val_loss: 1.3076 - val_accuracy: 0.5069\n",
            "Epoch 300/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.6912 - accuracy: 0.7883 - val_loss: 1.3014 - val_accuracy: 0.5179\n",
            "Epoch 301/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.6620 - accuracy: 0.7850 - val_loss: 1.3029 - val_accuracy: 0.5152\n",
            "Epoch 302/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.6666 - accuracy: 0.8050 - val_loss: 1.3025 - val_accuracy: 0.5069\n",
            "Epoch 303/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.6591 - accuracy: 0.8033 - val_loss: 1.2998 - val_accuracy: 0.5152\n",
            "Epoch 304/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.6675 - accuracy: 0.8017 - val_loss: 1.3007 - val_accuracy: 0.5152\n",
            "Epoch 305/500\n",
            "19/19 [==============================] - 14s 731ms/step - loss: 0.6528 - accuracy: 0.8017 - val_loss: 1.2982 - val_accuracy: 0.5124\n",
            "Epoch 306/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.6761 - accuracy: 0.7767 - val_loss: 1.3005 - val_accuracy: 0.5124\n",
            "Epoch 307/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.6556 - accuracy: 0.7833 - val_loss: 1.3009 - val_accuracy: 0.5124\n",
            "Epoch 308/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.6565 - accuracy: 0.8050 - val_loss: 1.3000 - val_accuracy: 0.5096\n",
            "Epoch 309/500\n",
            "19/19 [==============================] - 14s 731ms/step - loss: 0.6579 - accuracy: 0.8000 - val_loss: 1.2979 - val_accuracy: 0.5124\n",
            "Epoch 310/500\n",
            "19/19 [==============================] - 14s 731ms/step - loss: 0.6734 - accuracy: 0.7933 - val_loss: 1.2994 - val_accuracy: 0.5069\n",
            "Epoch 311/500\n",
            "19/19 [==============================] - 14s 731ms/step - loss: 0.6689 - accuracy: 0.7867 - val_loss: 1.3066 - val_accuracy: 0.5124\n",
            "Epoch 312/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.6627 - accuracy: 0.7917 - val_loss: 1.3003 - val_accuracy: 0.5096\n",
            "Epoch 313/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.6446 - accuracy: 0.8083 - val_loss: 1.2958 - val_accuracy: 0.5152\n",
            "Epoch 314/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.6549 - accuracy: 0.8083 - val_loss: 1.2995 - val_accuracy: 0.5124\n",
            "Epoch 315/500\n",
            "19/19 [==============================] - 14s 731ms/step - loss: 0.6589 - accuracy: 0.7933 - val_loss: 1.2974 - val_accuracy: 0.5124\n",
            "Epoch 316/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.6539 - accuracy: 0.7950 - val_loss: 1.3003 - val_accuracy: 0.5152\n",
            "Epoch 317/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.6670 - accuracy: 0.7750 - val_loss: 1.3003 - val_accuracy: 0.5069\n",
            "Epoch 318/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.6612 - accuracy: 0.7983 - val_loss: 1.2978 - val_accuracy: 0.5041\n",
            "Epoch 319/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.6643 - accuracy: 0.7883 - val_loss: 1.2999 - val_accuracy: 0.5069\n",
            "Epoch 320/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.6671 - accuracy: 0.7750 - val_loss: 1.2951 - val_accuracy: 0.5179\n",
            "Epoch 321/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.6657 - accuracy: 0.7800 - val_loss: 1.2992 - val_accuracy: 0.5124\n",
            "Epoch 322/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.6617 - accuracy: 0.8017 - val_loss: 1.2995 - val_accuracy: 0.5096\n",
            "Epoch 323/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.6645 - accuracy: 0.7917 - val_loss: 1.2999 - val_accuracy: 0.5124\n",
            "Epoch 324/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.6648 - accuracy: 0.8000 - val_loss: 1.3017 - val_accuracy: 0.5069\n",
            "Epoch 325/500\n",
            "19/19 [==============================] - 14s 732ms/step - loss: 0.6863 - accuracy: 0.7900 - val_loss: 1.3014 - val_accuracy: 0.5069\n",
            "Epoch 326/500\n",
            "19/19 [==============================] - 14s 737ms/step - loss: 0.6555 - accuracy: 0.7900 - val_loss: 1.2981 - val_accuracy: 0.5096\n",
            "Epoch 327/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.6510 - accuracy: 0.8083 - val_loss: 1.3002 - val_accuracy: 0.5069\n",
            "Epoch 328/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.6435 - accuracy: 0.7933 - val_loss: 1.2981 - val_accuracy: 0.5124\n",
            "Epoch 329/500\n",
            "19/19 [==============================] - 14s 740ms/step - loss: 0.6573 - accuracy: 0.7983 - val_loss: 1.2949 - val_accuracy: 0.5152\n",
            "Epoch 330/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.6500 - accuracy: 0.8050 - val_loss: 1.2966 - val_accuracy: 0.5124\n",
            "Epoch 331/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.6380 - accuracy: 0.7983 - val_loss: 1.2934 - val_accuracy: 0.5124\n",
            "Epoch 332/500\n",
            "19/19 [==============================] - 14s 731ms/step - loss: 0.6366 - accuracy: 0.7967 - val_loss: 1.2914 - val_accuracy: 0.5152\n",
            "Epoch 333/500\n",
            "19/19 [==============================] - 14s 734ms/step - loss: 0.6285 - accuracy: 0.8117 - val_loss: 1.2940 - val_accuracy: 0.5041\n",
            "Epoch 334/500\n",
            "19/19 [==============================] - 14s 732ms/step - loss: 0.6818 - accuracy: 0.7767 - val_loss: 1.2966 - val_accuracy: 0.5069\n",
            "Epoch 335/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.6382 - accuracy: 0.7967 - val_loss: 1.2958 - val_accuracy: 0.5096\n",
            "Epoch 336/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.6318 - accuracy: 0.8117 - val_loss: 1.2940 - val_accuracy: 0.5124\n",
            "Epoch 337/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.6259 - accuracy: 0.8133 - val_loss: 1.2929 - val_accuracy: 0.5069\n",
            "Epoch 338/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.6319 - accuracy: 0.8183 - val_loss: 1.2898 - val_accuracy: 0.5124\n",
            "Epoch 339/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.6403 - accuracy: 0.8050 - val_loss: 1.2894 - val_accuracy: 0.5152\n",
            "Epoch 340/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.6569 - accuracy: 0.7850 - val_loss: 1.2928 - val_accuracy: 0.5096\n",
            "Epoch 341/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.6418 - accuracy: 0.7950 - val_loss: 1.2941 - val_accuracy: 0.5152\n",
            "Epoch 342/500\n",
            "19/19 [==============================] - 14s 726ms/step - loss: 0.6420 - accuracy: 0.8150 - val_loss: 1.2946 - val_accuracy: 0.5096\n",
            "Epoch 343/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.6332 - accuracy: 0.8133 - val_loss: 1.2933 - val_accuracy: 0.5124\n",
            "Epoch 344/500\n",
            "19/19 [==============================] - 14s 731ms/step - loss: 0.6325 - accuracy: 0.8067 - val_loss: 1.2897 - val_accuracy: 0.5096\n",
            "Epoch 345/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.6362 - accuracy: 0.8217 - val_loss: 1.2889 - val_accuracy: 0.5096\n",
            "Epoch 346/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.6286 - accuracy: 0.8167 - val_loss: 1.2941 - val_accuracy: 0.5096\n",
            "Epoch 347/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.6338 - accuracy: 0.8017 - val_loss: 1.2919 - val_accuracy: 0.5069\n",
            "Epoch 348/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.6513 - accuracy: 0.7783 - val_loss: 1.2908 - val_accuracy: 0.5234\n",
            "Epoch 349/500\n",
            "19/19 [==============================] - 14s 735ms/step - loss: 0.6200 - accuracy: 0.8050 - val_loss: 1.2877 - val_accuracy: 0.5152\n",
            "Epoch 350/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.6248 - accuracy: 0.8167 - val_loss: 1.2850 - val_accuracy: 0.5179\n",
            "Epoch 351/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.6320 - accuracy: 0.8100 - val_loss: 1.2943 - val_accuracy: 0.5069\n",
            "Epoch 352/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.6173 - accuracy: 0.8250 - val_loss: 1.2858 - val_accuracy: 0.5096\n",
            "Epoch 353/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.6381 - accuracy: 0.8050 - val_loss: 1.2906 - val_accuracy: 0.5152\n",
            "Epoch 354/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.6071 - accuracy: 0.8300 - val_loss: 1.2916 - val_accuracy: 0.5069\n",
            "Epoch 355/500\n",
            "19/19 [==============================] - 14s 731ms/step - loss: 0.6194 - accuracy: 0.8067 - val_loss: 1.2867 - val_accuracy: 0.5152\n",
            "Epoch 356/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.6388 - accuracy: 0.7917 - val_loss: 1.2876 - val_accuracy: 0.5152\n",
            "Epoch 357/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.6309 - accuracy: 0.7950 - val_loss: 1.2886 - val_accuracy: 0.5124\n",
            "Epoch 358/500\n",
            "19/19 [==============================] - 14s 734ms/step - loss: 0.6226 - accuracy: 0.8100 - val_loss: 1.2906 - val_accuracy: 0.5069\n",
            "Epoch 359/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.6203 - accuracy: 0.8067 - val_loss: 1.2872 - val_accuracy: 0.5069\n",
            "Epoch 360/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.6241 - accuracy: 0.8133 - val_loss: 1.2890 - val_accuracy: 0.5069\n",
            "Epoch 361/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.6275 - accuracy: 0.8050 - val_loss: 1.2884 - val_accuracy: 0.5069\n",
            "Epoch 362/500\n",
            "19/19 [==============================] - 14s 735ms/step - loss: 0.6320 - accuracy: 0.8033 - val_loss: 1.2873 - val_accuracy: 0.5179\n",
            "Epoch 363/500\n",
            "19/19 [==============================] - 14s 734ms/step - loss: 0.6228 - accuracy: 0.8050 - val_loss: 1.2857 - val_accuracy: 0.5152\n",
            "Epoch 364/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.6365 - accuracy: 0.7950 - val_loss: 1.2898 - val_accuracy: 0.5152\n",
            "Epoch 365/500\n",
            "19/19 [==============================] - 14s 726ms/step - loss: 0.6125 - accuracy: 0.7983 - val_loss: 1.2828 - val_accuracy: 0.5234\n",
            "Epoch 366/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.6270 - accuracy: 0.8133 - val_loss: 1.2804 - val_accuracy: 0.5179\n",
            "Epoch 367/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.6280 - accuracy: 0.8033 - val_loss: 1.2824 - val_accuracy: 0.5124\n",
            "Epoch 368/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.6260 - accuracy: 0.7983 - val_loss: 1.2843 - val_accuracy: 0.5124\n",
            "Epoch 369/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.5957 - accuracy: 0.8283 - val_loss: 1.2827 - val_accuracy: 0.5152\n",
            "Epoch 370/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.6220 - accuracy: 0.8083 - val_loss: 1.2864 - val_accuracy: 0.5262\n",
            "Epoch 371/500\n",
            "19/19 [==============================] - 14s 731ms/step - loss: 0.6208 - accuracy: 0.8150 - val_loss: 1.2800 - val_accuracy: 0.5207\n",
            "Epoch 372/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.6107 - accuracy: 0.8050 - val_loss: 1.2798 - val_accuracy: 0.5234\n",
            "Epoch 373/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.5929 - accuracy: 0.8317 - val_loss: 1.2797 - val_accuracy: 0.5152\n",
            "Epoch 374/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.6159 - accuracy: 0.8100 - val_loss: 1.2799 - val_accuracy: 0.5152\n",
            "Epoch 375/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.5969 - accuracy: 0.8083 - val_loss: 1.2787 - val_accuracy: 0.5179\n",
            "Epoch 376/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.6271 - accuracy: 0.8050 - val_loss: 1.2754 - val_accuracy: 0.5234\n",
            "Epoch 377/500\n",
            "19/19 [==============================] - 14s 731ms/step - loss: 0.6110 - accuracy: 0.8100 - val_loss: 1.2781 - val_accuracy: 0.5152\n",
            "Epoch 378/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.5965 - accuracy: 0.8200 - val_loss: 1.2806 - val_accuracy: 0.5152\n",
            "Epoch 379/500\n",
            "19/19 [==============================] - 14s 732ms/step - loss: 0.6376 - accuracy: 0.7850 - val_loss: 1.2837 - val_accuracy: 0.5179\n",
            "Epoch 380/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.5976 - accuracy: 0.8283 - val_loss: 1.2780 - val_accuracy: 0.5207\n",
            "Epoch 381/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.6191 - accuracy: 0.8083 - val_loss: 1.2789 - val_accuracy: 0.5179\n",
            "Epoch 382/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.6054 - accuracy: 0.8083 - val_loss: 1.2792 - val_accuracy: 0.5124\n",
            "Epoch 383/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.6137 - accuracy: 0.8167 - val_loss: 1.2735 - val_accuracy: 0.5207\n",
            "Epoch 384/500\n",
            "19/19 [==============================] - 14s 733ms/step - loss: 0.5915 - accuracy: 0.8233 - val_loss: 1.2762 - val_accuracy: 0.5179\n",
            "Epoch 385/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.5967 - accuracy: 0.8133 - val_loss: 1.2749 - val_accuracy: 0.5234\n",
            "Epoch 386/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.6078 - accuracy: 0.8083 - val_loss: 1.2765 - val_accuracy: 0.5179\n",
            "Epoch 387/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.6091 - accuracy: 0.8167 - val_loss: 1.2762 - val_accuracy: 0.5179\n",
            "Epoch 388/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.6086 - accuracy: 0.8117 - val_loss: 1.2784 - val_accuracy: 0.5179\n",
            "Epoch 389/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.5996 - accuracy: 0.8183 - val_loss: 1.2805 - val_accuracy: 0.5207\n",
            "Epoch 390/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.6132 - accuracy: 0.8117 - val_loss: 1.2749 - val_accuracy: 0.5207\n",
            "Epoch 391/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.6198 - accuracy: 0.8067 - val_loss: 1.2747 - val_accuracy: 0.5234\n",
            "Epoch 392/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.5936 - accuracy: 0.7983 - val_loss: 1.2757 - val_accuracy: 0.5262\n",
            "Epoch 393/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.5867 - accuracy: 0.8267 - val_loss: 1.2764 - val_accuracy: 0.5234\n",
            "Epoch 394/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.5882 - accuracy: 0.8150 - val_loss: 1.2788 - val_accuracy: 0.5207\n",
            "Epoch 395/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.6149 - accuracy: 0.8100 - val_loss: 1.2731 - val_accuracy: 0.5262\n",
            "Epoch 396/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.5884 - accuracy: 0.8183 - val_loss: 1.2790 - val_accuracy: 0.5152\n",
            "Epoch 397/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.5893 - accuracy: 0.8317 - val_loss: 1.2761 - val_accuracy: 0.5234\n",
            "Epoch 398/500\n",
            "19/19 [==============================] - 14s 731ms/step - loss: 0.5804 - accuracy: 0.8250 - val_loss: 1.2767 - val_accuracy: 0.5124\n",
            "Epoch 399/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.5990 - accuracy: 0.8217 - val_loss: 1.2747 - val_accuracy: 0.5234\n",
            "Epoch 400/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.6000 - accuracy: 0.8050 - val_loss: 1.2709 - val_accuracy: 0.5234\n",
            "Epoch 401/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.5813 - accuracy: 0.8200 - val_loss: 1.2768 - val_accuracy: 0.5289\n",
            "Epoch 402/500\n",
            "19/19 [==============================] - 14s 734ms/step - loss: 0.5627 - accuracy: 0.8467 - val_loss: 1.2721 - val_accuracy: 0.5289\n",
            "Epoch 403/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.5736 - accuracy: 0.8367 - val_loss: 1.2722 - val_accuracy: 0.5289\n",
            "Epoch 404/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.5868 - accuracy: 0.8117 - val_loss: 1.2810 - val_accuracy: 0.5179\n",
            "Epoch 405/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.6034 - accuracy: 0.8033 - val_loss: 1.2763 - val_accuracy: 0.5207\n",
            "Epoch 406/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.5875 - accuracy: 0.8183 - val_loss: 1.2743 - val_accuracy: 0.5317\n",
            "Epoch 407/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.5941 - accuracy: 0.8283 - val_loss: 1.2706 - val_accuracy: 0.5262\n",
            "Epoch 408/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.5903 - accuracy: 0.8267 - val_loss: 1.2769 - val_accuracy: 0.5207\n",
            "Epoch 409/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.5723 - accuracy: 0.8233 - val_loss: 1.2764 - val_accuracy: 0.5262\n",
            "Epoch 410/500\n",
            "19/19 [==============================] - 14s 731ms/step - loss: 0.5836 - accuracy: 0.8400 - val_loss: 1.2736 - val_accuracy: 0.5262\n",
            "Epoch 411/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.5872 - accuracy: 0.8200 - val_loss: 1.2805 - val_accuracy: 0.5262\n",
            "Epoch 412/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.5708 - accuracy: 0.8167 - val_loss: 1.2796 - val_accuracy: 0.5234\n",
            "Epoch 413/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.5933 - accuracy: 0.8267 - val_loss: 1.2738 - val_accuracy: 0.5262\n",
            "Epoch 414/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.5883 - accuracy: 0.8183 - val_loss: 1.2740 - val_accuracy: 0.5317\n",
            "Epoch 415/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.5814 - accuracy: 0.8217 - val_loss: 1.2737 - val_accuracy: 0.5262\n",
            "Epoch 416/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.5690 - accuracy: 0.8383 - val_loss: 1.2698 - val_accuracy: 0.5234\n",
            "Epoch 417/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.5954 - accuracy: 0.8217 - val_loss: 1.2702 - val_accuracy: 0.5317\n",
            "Epoch 418/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.5933 - accuracy: 0.8233 - val_loss: 1.2745 - val_accuracy: 0.5262\n",
            "Epoch 419/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.5793 - accuracy: 0.8300 - val_loss: 1.2739 - val_accuracy: 0.5289\n",
            "Epoch 420/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.5893 - accuracy: 0.8333 - val_loss: 1.2729 - val_accuracy: 0.5234\n",
            "Epoch 421/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.5846 - accuracy: 0.8300 - val_loss: 1.2718 - val_accuracy: 0.5207\n",
            "Epoch 422/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.5782 - accuracy: 0.8317 - val_loss: 1.2718 - val_accuracy: 0.5234\n",
            "Epoch 423/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.5634 - accuracy: 0.8383 - val_loss: 1.2728 - val_accuracy: 0.5262\n",
            "Epoch 424/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.5779 - accuracy: 0.8350 - val_loss: 1.2760 - val_accuracy: 0.5289\n",
            "Epoch 425/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.5448 - accuracy: 0.8333 - val_loss: 1.2745 - val_accuracy: 0.5289\n",
            "Epoch 426/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.5611 - accuracy: 0.8283 - val_loss: 1.2736 - val_accuracy: 0.5317\n",
            "Epoch 427/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.5835 - accuracy: 0.8083 - val_loss: 1.2728 - val_accuracy: 0.5262\n",
            "Epoch 428/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.5696 - accuracy: 0.8283 - val_loss: 1.2743 - val_accuracy: 0.5344\n",
            "Epoch 429/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.5705 - accuracy: 0.8250 - val_loss: 1.2718 - val_accuracy: 0.5344\n",
            "Epoch 430/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.5726 - accuracy: 0.8267 - val_loss: 1.2671 - val_accuracy: 0.5344\n",
            "Epoch 431/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.5703 - accuracy: 0.8350 - val_loss: 1.2721 - val_accuracy: 0.5152\n",
            "Epoch 432/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.5891 - accuracy: 0.8067 - val_loss: 1.2715 - val_accuracy: 0.5317\n",
            "Epoch 433/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.5540 - accuracy: 0.8300 - val_loss: 1.2737 - val_accuracy: 0.5262\n",
            "Epoch 434/500\n",
            "19/19 [==============================] - 14s 727ms/step - loss: 0.5706 - accuracy: 0.8167 - val_loss: 1.2718 - val_accuracy: 0.5262\n",
            "Epoch 435/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.5616 - accuracy: 0.8250 - val_loss: 1.2690 - val_accuracy: 0.5317\n",
            "Epoch 436/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.5581 - accuracy: 0.8383 - val_loss: 1.2725 - val_accuracy: 0.5372\n",
            "Epoch 437/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.5500 - accuracy: 0.8500 - val_loss: 1.2685 - val_accuracy: 0.5317\n",
            "Epoch 438/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.5604 - accuracy: 0.8283 - val_loss: 1.2712 - val_accuracy: 0.5234\n",
            "Epoch 439/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.5597 - accuracy: 0.8333 - val_loss: 1.2712 - val_accuracy: 0.5427\n",
            "Epoch 440/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.5681 - accuracy: 0.8350 - val_loss: 1.2751 - val_accuracy: 0.5317\n",
            "Epoch 441/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.5586 - accuracy: 0.8150 - val_loss: 1.2747 - val_accuracy: 0.5262\n",
            "Epoch 442/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.5503 - accuracy: 0.8283 - val_loss: 1.2747 - val_accuracy: 0.5289\n",
            "Epoch 443/500\n",
            "19/19 [==============================] - 14s 731ms/step - loss: 0.5515 - accuracy: 0.8567 - val_loss: 1.2710 - val_accuracy: 0.5372\n",
            "Epoch 444/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.5531 - accuracy: 0.8400 - val_loss: 1.2721 - val_accuracy: 0.5289\n",
            "Epoch 445/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.5732 - accuracy: 0.8150 - val_loss: 1.2692 - val_accuracy: 0.5344\n",
            "Epoch 446/500\n",
            "19/19 [==============================] - 14s 732ms/step - loss: 0.5894 - accuracy: 0.8217 - val_loss: 1.2728 - val_accuracy: 0.5399\n",
            "Epoch 447/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.5702 - accuracy: 0.8283 - val_loss: 1.2706 - val_accuracy: 0.5344\n",
            "Epoch 448/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.5547 - accuracy: 0.8483 - val_loss: 1.2712 - val_accuracy: 0.5344\n",
            "Epoch 449/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.5693 - accuracy: 0.8267 - val_loss: 1.2701 - val_accuracy: 0.5289\n",
            "Epoch 450/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.5554 - accuracy: 0.8300 - val_loss: 1.2775 - val_accuracy: 0.5207\n",
            "Epoch 451/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.5725 - accuracy: 0.8100 - val_loss: 1.2774 - val_accuracy: 0.5234\n",
            "Epoch 452/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.5689 - accuracy: 0.8383 - val_loss: 1.2733 - val_accuracy: 0.5399\n",
            "Epoch 453/500\n",
            "19/19 [==============================] - 14s 731ms/step - loss: 0.5478 - accuracy: 0.8250 - val_loss: 1.2716 - val_accuracy: 0.5372\n",
            "Epoch 454/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.5617 - accuracy: 0.8367 - val_loss: 1.2780 - val_accuracy: 0.5234\n",
            "Epoch 455/500\n",
            "19/19 [==============================] - 14s 734ms/step - loss: 0.5469 - accuracy: 0.8367 - val_loss: 1.2708 - val_accuracy: 0.5317\n",
            "Epoch 456/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.5561 - accuracy: 0.8300 - val_loss: 1.2697 - val_accuracy: 0.5262\n",
            "Epoch 457/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.5625 - accuracy: 0.8217 - val_loss: 1.2722 - val_accuracy: 0.5262\n",
            "Epoch 458/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.5722 - accuracy: 0.8283 - val_loss: 1.2740 - val_accuracy: 0.5317\n",
            "Epoch 459/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.5577 - accuracy: 0.8283 - val_loss: 1.2769 - val_accuracy: 0.5372\n",
            "Epoch 460/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.5601 - accuracy: 0.8317 - val_loss: 1.2734 - val_accuracy: 0.5372\n",
            "Epoch 461/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.5585 - accuracy: 0.8200 - val_loss: 1.2750 - val_accuracy: 0.5317\n",
            "Epoch 462/500\n",
            "19/19 [==============================] - 14s 731ms/step - loss: 0.5580 - accuracy: 0.8317 - val_loss: 1.2741 - val_accuracy: 0.5234\n",
            "Epoch 463/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.5383 - accuracy: 0.8333 - val_loss: 1.2707 - val_accuracy: 0.5262\n",
            "Epoch 464/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.5540 - accuracy: 0.8367 - val_loss: 1.2685 - val_accuracy: 0.5289\n",
            "Epoch 465/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.5446 - accuracy: 0.8283 - val_loss: 1.2712 - val_accuracy: 0.5207\n",
            "Epoch 466/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.5508 - accuracy: 0.8100 - val_loss: 1.2709 - val_accuracy: 0.5207\n",
            "Epoch 467/500\n",
            "19/19 [==============================] - 14s 731ms/step - loss: 0.5399 - accuracy: 0.8417 - val_loss: 1.2701 - val_accuracy: 0.5317\n",
            "Epoch 468/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.5307 - accuracy: 0.8417 - val_loss: 1.2666 - val_accuracy: 0.5399\n",
            "Epoch 469/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.5489 - accuracy: 0.8467 - val_loss: 1.2718 - val_accuracy: 0.5317\n",
            "Epoch 470/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.5461 - accuracy: 0.8333 - val_loss: 1.2720 - val_accuracy: 0.5289\n",
            "Epoch 471/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.5637 - accuracy: 0.8317 - val_loss: 1.2725 - val_accuracy: 0.5234\n",
            "Epoch 472/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.5680 - accuracy: 0.8367 - val_loss: 1.2728 - val_accuracy: 0.5207\n",
            "Epoch 473/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.5507 - accuracy: 0.8300 - val_loss: 1.2717 - val_accuracy: 0.5262\n",
            "Epoch 474/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.5536 - accuracy: 0.8233 - val_loss: 1.2681 - val_accuracy: 0.5317\n",
            "Epoch 475/500\n",
            "19/19 [==============================] - 14s 732ms/step - loss: 0.5335 - accuracy: 0.8550 - val_loss: 1.2686 - val_accuracy: 0.5399\n",
            "Epoch 476/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.5358 - accuracy: 0.8350 - val_loss: 1.2695 - val_accuracy: 0.5289\n",
            "Epoch 477/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.5377 - accuracy: 0.8450 - val_loss: 1.2694 - val_accuracy: 0.5262\n",
            "Epoch 478/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.5564 - accuracy: 0.8367 - val_loss: 1.2689 - val_accuracy: 0.5372\n",
            "Epoch 479/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.5265 - accuracy: 0.8483 - val_loss: 1.2736 - val_accuracy: 0.5427\n",
            "Epoch 480/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.5433 - accuracy: 0.8267 - val_loss: 1.2697 - val_accuracy: 0.5344\n",
            "Epoch 481/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.5438 - accuracy: 0.8433 - val_loss: 1.2723 - val_accuracy: 0.5262\n",
            "Epoch 482/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.5255 - accuracy: 0.8433 - val_loss: 1.2641 - val_accuracy: 0.5372\n",
            "Epoch 483/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.5283 - accuracy: 0.8517 - val_loss: 1.2659 - val_accuracy: 0.5344\n",
            "Epoch 484/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.5278 - accuracy: 0.8450 - val_loss: 1.2685 - val_accuracy: 0.5372\n",
            "Epoch 485/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.5290 - accuracy: 0.8433 - val_loss: 1.2685 - val_accuracy: 0.5262\n",
            "Epoch 486/500\n",
            "19/19 [==============================] - 14s 732ms/step - loss: 0.5302 - accuracy: 0.8550 - val_loss: 1.2692 - val_accuracy: 0.5317\n",
            "Epoch 487/500\n",
            "19/19 [==============================] - 14s 732ms/step - loss: 0.5462 - accuracy: 0.8400 - val_loss: 1.2740 - val_accuracy: 0.5152\n",
            "Epoch 488/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.5244 - accuracy: 0.8517 - val_loss: 1.2708 - val_accuracy: 0.5207\n",
            "Epoch 489/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.5316 - accuracy: 0.8350 - val_loss: 1.2666 - val_accuracy: 0.5399\n",
            "Epoch 490/500\n",
            "19/19 [==============================] - 14s 731ms/step - loss: 0.5239 - accuracy: 0.8667 - val_loss: 1.2670 - val_accuracy: 0.5234\n",
            "Epoch 491/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.5320 - accuracy: 0.8500 - val_loss: 1.2760 - val_accuracy: 0.5207\n",
            "Epoch 492/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.5429 - accuracy: 0.8450 - val_loss: 1.2747 - val_accuracy: 0.5262\n",
            "Epoch 493/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.5419 - accuracy: 0.8450 - val_loss: 1.2721 - val_accuracy: 0.5317\n",
            "Epoch 494/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.5300 - accuracy: 0.8383 - val_loss: 1.2729 - val_accuracy: 0.5207\n",
            "Epoch 495/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.5578 - accuracy: 0.8283 - val_loss: 1.2709 - val_accuracy: 0.5234\n",
            "Epoch 496/500\n",
            "19/19 [==============================] - 14s 731ms/step - loss: 0.5456 - accuracy: 0.8317 - val_loss: 1.2685 - val_accuracy: 0.5289\n",
            "Epoch 497/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.5222 - accuracy: 0.8450 - val_loss: 1.2648 - val_accuracy: 0.5372\n",
            "Epoch 498/500\n",
            "19/19 [==============================] - 14s 729ms/step - loss: 0.5153 - accuracy: 0.8633 - val_loss: 1.2659 - val_accuracy: 0.5372\n",
            "Epoch 499/500\n",
            "19/19 [==============================] - 14s 730ms/step - loss: 0.5312 - accuracy: 0.8417 - val_loss: 1.2654 - val_accuracy: 0.5399\n",
            "Epoch 500/500\n",
            "19/19 [==============================] - 14s 728ms/step - loss: 0.5317 - accuracy: 0.8383 - val_loss: 1.2686 - val_accuracy: 0.5372\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# model.summary()"
      ],
      "metadata": {
        "id": "vI_enc9DJI1V",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "031a4c73-3681-4bf0-b227-25edf9efed77"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " densenet201 (Functional)    (None, 8, 8, 1920)        18321984  \n",
            "                                                                 \n",
            " global_average_pooling2d (G  (None, 1920)             0         \n",
            " lobalAveragePooling2D)                                          \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 1920)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 10)                19210     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 18,341,194\n",
            "Trainable params: 19,210\n",
            "Non-trainable params: 18,321,984\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# acc = history1.history['accuracy']\n",
        "# val_acc = history1.history['val_accuracy']\n",
        "# loss = history1.history['loss']\n",
        "# val_loss = history1.history['val_loss']\n",
        "\n",
        "# epochs_range = range(500)\n",
        "\n",
        "# plt.figure(figsize=(25, 15))\n",
        "# plt.subplot(2, 2, 1)\n",
        "# plt.plot(epochs_range, acc, label='Training Accuracy')\n",
        "# plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
        "# plt.legend(loc='lower right')\n",
        "# plt.title('Training and Validation Accuracy')\n",
        "\n",
        "# plt.subplot(2, 2, 2)\n",
        "# plt.plot(epochs_range, loss, label='Training Loss')\n",
        "# plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
        "# plt.legend(loc='upper right')\n",
        "# plt.title('Training and Validation Loss')\n",
        "# plt.show()"
      ],
      "metadata": {
        "id": "29J-tY_EJKTY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "a2b37e94-f51f-4727-f39f-347ca5558f56"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# predictions = model.predict_classes(x_val)\n",
        "# predictions = predictions.reshape(1,-1)[0]\n",
        "# print(classification_report(y_val, predictions, target_names = labels))"
      ],
      "metadata": {
        "id": "y7A-J63UJTrW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# import seaborn as sn\n",
        "# import pandas as pd\n",
        "# import matplotlib.pyplot as plt\n",
        "# cm1 = confusion_matrix(y_val, predictions)\n",
        "# df_cm = pd.DataFrame(cm1, index = [i for i in labels],\n",
        "#               columns = [i for i in labels])\n",
        "# plt.figure(figsize = (10,7))\n",
        "# sn.heatmap(df_cm, annot=True,cmap=\"RdPu\")\n",
        "# plt.savefig('confusion_mrtx2.png',bbox_inches = 'tight')"
      ],
      "metadata": {
        "id": "mZeH2PJEJXa3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# model.save_weights('/content/drive/MyDrive/Data/500_epoch_transfer_lr.cpkt')\n",
        "# pickle.dump(history1.history, open('/content/drive/MyDrive/Data/history_500_epoch_tr.pkl','wb'))"
      ],
      "metadata": {
        "id": "t_uurCfFJY1f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# import pickle\n",
        "# model = pickle.load(open('/content/drive/MyDrive/Data/M500_epoch_simple_lr.cpkt.index', 'rb'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 182
        },
        "id": "6xPgOHc3qxqt",
        "outputId": "39051d59-011c-4647-821c-990f9ab7230c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "UnpicklingError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mUnpicklingError\u001b[0m                           Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-13-b2a0ca62f035>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive/MyDrive/Data/M500_epoch_simple_lr.cpkt.index'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mUnpicklingError\u001b[0m: invalid load key, '\\x00'."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# model.save('/content/drive/MyDrive/Data/MyModel.h5')"
      ],
      "metadata": {
        "id": "ZYqIM3CWq5Bg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "oMpdycLKp2c-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Combining both"
      ],
      "metadata": {
        "id": "elJd2TtWELhK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get spectrogram and wavelet data\n",
        "\n",
        "sp_train = get_data('/content/drive/MyDrive/Data/spectrogram/train')\n",
        "sp_val = get_data('/content/drive/MyDrive/Data/spectrogram/test')\n",
        "\n",
        "wv_train = get_data('/content/drive/MyDrive/Data/wavelets/train')\n",
        "wv_val = get_data('/content/drive/MyDrive/Data/wavelets/test')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ODiv4qnHEN-I",
        "outputId": "0634bbb2-0b13-4587-a31a-24cf37ae7dc9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "blues 60\n",
            "classical 60\n",
            "country 60\n",
            "disco 60\n",
            "hiphop 60\n",
            "jazz 60\n",
            "metal 60\n",
            "pop 60\n",
            "reggae 60\n",
            "rock 60\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:18: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "blues 40\n",
            "classical 40\n",
            "country 40\n",
            "disco 40\n",
            "hiphop 40\n",
            "jazz 40\n",
            "metal 40\n",
            "pop 40\n",
            "reggae 40\n",
            "rock 40\n",
            "blues 60\n",
            "classical 60\n",
            "country 60\n",
            "disco 60\n",
            "hiphop 60\n",
            "jazz 60\n",
            "metal 60\n",
            "pop 60\n",
            "reggae 60\n",
            "rock 60\n",
            "blues 40\n",
            "classical 40\n",
            "country 40\n",
            "disco 40\n",
            "hiphop 40\n",
            "jazz 40\n",
            "metal 40\n",
            "pop 40\n",
            "reggae 40\n",
            "rock 40\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_sp_train = []\n",
        "y_sp_train = []\n",
        "x_sp_val = []\n",
        "y_sp_val = []\n",
        "print(len(sp_val))\n",
        "for feature, label in sp_train:\n",
        "  x_sp_train.append(feature)\n",
        "  y_sp_train.append(label)\n",
        "\n",
        "for feature, label in sp_val:\n",
        "  x_sp_val.append(feature)\n",
        "  y_sp_val.append(label)\n",
        "\n",
        "\n",
        "# Normalize the data\n",
        "x_sp_train = np.array(x_sp_train) / 255\n",
        "x_sp_val = np.array(x_sp_val) / 255\n",
        "\n",
        "# Resize data so that it can be fed into the model\n",
        "x_sp_train.reshape(-1, img_size, img_size, 1)\n",
        "y_sp_train = np.array(y_sp_train)\n",
        "\n",
        "x_sp_val.reshape(-1, img_size, img_size, 1)\n",
        "y_sp_val = np.array(y_sp_val)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9EDIECyaEXxU",
        "outputId": "f25899a4-f16e-45f4-c9fe-e75239b85a7e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "400\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_wv_train = []\n",
        "y_wv_train = []\n",
        "x_wv_val = []\n",
        "y_wv_val = []\n",
        "\n",
        "for feature, label in wv_train:\n",
        "  x_wv_train.append(feature)\n",
        "  y_wv_train.append(label)\n",
        "\n",
        "for feature, label in wv_val:\n",
        "  x_wv_val.append(feature)\n",
        "  y_wv_val.append(label)\n",
        "\n",
        "# Normalize the data\n",
        "x_wv_train = np.array(x_wv_train) / 255\n",
        "x_wv_val = np.array(x_wv_val) / 255\n",
        "\n",
        "# Resize data so that it can be fed into the model\n",
        "x_wv_train.reshape(-1, img_size, img_size, 1)\n",
        "y_wv_train = np.array(y_wv_train)\n",
        "\n",
        "x_wv_val.reshape(-1, img_size, img_size, 1)\n",
        "y_wv_val = np.array(y_wv_val)"
      ],
      "metadata": {
        "id": "ZByW9grXEaD-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "datagen_sp = ImageDataGenerator(\n",
        "        featurewise_center=False,  # set input mean to 0 over the dataset\n",
        "        samplewise_center=False,  # set each sample mean to 0\n",
        "        featurewise_std_normalization=False,  # divide inputs by std of the dataset\n",
        "        samplewise_std_normalization=False,  # divide each input by its std\n",
        "        zca_whitening=False,  # apply ZCA whitening\n",
        "        #rotation_range = 30,  # randomly rotate images in the range (degrees, 0 to 180)\n",
        "        zoom_range = 0.2, # Randomly zoom image \n",
        "        width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)\n",
        "        height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)\n",
        "        #horizontal_flip = True,  # randomly flip images\n",
        "        vertical_flip=False)  # randomly flip images\n",
        "\n",
        "\n",
        "datagen_sp.fit(x_sp_train)\n",
        "\n",
        "datagen_wv = ImageDataGenerator(\n",
        "        featurewise_center=False,  # set input mean to 0 over the dataset\n",
        "        samplewise_center=False,  # set each sample mean to 0\n",
        "        featurewise_std_normalization=False,  # divide inputs by std of the dataset\n",
        "        samplewise_std_normalization=False,  # divide each input by its std\n",
        "        zca_whitening=False,  # apply ZCA whitening\n",
        "        #rotation_range = 30,  # randomly rotate images in the range (degrees, 0 to 180)\n",
        "        zoom_range = 0.2, # Randomly zoom image \n",
        "        width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)\n",
        "        height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)\n",
        "        #horizontal_flip = True,  # randomly flip images\n",
        "        vertical_flip=False)  # randomly flip images\n",
        "\n",
        "\n",
        "datagen_wv.fit(x_wv_train)"
      ],
      "metadata": {
        "id": "pFLyAZyVEct7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(x_sp_train.shape)\n",
        "print(x_wv_train.shape)\n",
        "print(y_sp_train.shape)\n",
        "print(x_sp_val.shape)\n",
        "print(x_wv_val.shape)\n",
        "print(y_sp_val.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mdfU9kLmmLVZ",
        "outputId": "3d41fd20-f807-48c6-ce17-da9e762b9b2d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(600, 256, 256, 3)\n",
            "(600, 256, 256, 3)\n",
            "(600,)\n",
            "(400, 256, 256, 3)\n",
            "(400, 256, 256, 3)\n",
            "(400,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_model = tf.keras.applications.MobileNetV2(input_shape=(256, 256, 3), include_top = False, weights = \"imagenet\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_mPFU0AjGf0S",
        "outputId": "b02bdfb1-177c-471f-e805-09e4bbf3a479"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_sp = keras.Input(shape=(256,256,3))\n",
        "input_wv = keras.Input(shape=(256,256,3))\n",
        "\n",
        "x = base_model(input_sp, training=False)\n",
        "x = tf.keras.layers.GlobalAveragePooling2D()(x)\n",
        "x = tf.keras.layers.Dropout(0.2)(x)\n",
        "x = tf.keras.layers.Dense(128, activation=\"relu\")(x)\n",
        "\n",
        "y = base_model(input_sp, training=False)\n",
        "y = tf.keras.layers.GlobalAveragePooling2D()(y)\n",
        "y = tf.keras.layers.Dropout(0.2)(y)\n",
        "y = tf.keras.layers.Dense(128, activation=\"relu\")(y)\n",
        "\n",
        "from tensorflow.keras.layers import concatenate\n",
        "combined = concatenate([x, y])\n",
        "\n",
        "z = Dense(32, activation=\"relu\")(combined)\n",
        "z = Dense(10, activation=\"softmax\")(z)\n",
        "\n",
        "model = keras.Model(inputs=[input_sp, input_wv], outputs=z)"
      ],
      "metadata": {
        "id": "hBsTpDa3EfPk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s00KzIOvEnob",
        "outputId": "afac7cb5-b52f-4a35-9fc4-9a35f2e26367"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_2 (InputLayer)           [(None, 256, 256, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " mobilenetv2_1.00_224 (Function  (None, 8, 8, 1280)  2257984     ['input_2[0][0]',                \n",
            " al)                                                              'input_2[0][0]']                \n",
            "                                                                                                  \n",
            " global_average_pooling2d (Glob  (None, 1280)        0           ['mobilenetv2_1.00_224[0][0]']   \n",
            " alAveragePooling2D)                                                                              \n",
            "                                                                                                  \n",
            " global_average_pooling2d_1 (Gl  (None, 1280)        0           ['mobilenetv2_1.00_224[1][0]']   \n",
            " obalAveragePooling2D)                                                                            \n",
            "                                                                                                  \n",
            " dropout (Dropout)              (None, 1280)         0           ['global_average_pooling2d[0][0]'\n",
            "                                                                 ]                                \n",
            "                                                                                                  \n",
            " dropout_1 (Dropout)            (None, 1280)         0           ['global_average_pooling2d_1[0][0\n",
            "                                                                 ]']                              \n",
            "                                                                                                  \n",
            " dense (Dense)                  (None, 128)          163968      ['dropout[0][0]']                \n",
            "                                                                                                  \n",
            " dense_1 (Dense)                (None, 128)          163968      ['dropout_1[0][0]']              \n",
            "                                                                                                  \n",
            " concatenate (Concatenate)      (None, 256)          0           ['dense[0][0]',                  \n",
            "                                                                  'dense_1[0][0]']                \n",
            "                                                                                                  \n",
            " dense_2 (Dense)                (None, 32)           8224        ['concatenate[0][0]']            \n",
            "                                                                                                  \n",
            " input_3 (InputLayer)           [(None, 256, 256, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " dense_3 (Dense)                (None, 10)           330         ['dense_2[0][0]']                \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 2,594,474\n",
            "Trainable params: 2,560,362\n",
            "Non-trainable params: 34,112\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "opt = Adam(lr=0.0001)\n",
        "model.compile(optimizer = opt , loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) , metrics = ['accuracy'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FwlNSiKSEoM8",
        "outputId": "250bd0d8-0393-47f4-a7d0-fd6300635ede"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(Adam, self).__init__(name, **kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history2 = model.fit([np.array(x_sp_train),np.array(x_wv_train)],np.array(y_sp_train),epochs = 100, validation_data = ([x_sp_val,x_wv_val], y_sp_val))\n",
        "import pickle\n",
        "# model.save_weights('500_epoch_multi_lr.cpkt')\n",
        "# pickle.dump(history2.history, open('history_500_epoch_multi.pkl','wb'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ij-5GbWdEqG1",
        "outputId": "6bc8744f-4748-4716-8f70-95a6d1972039"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1096: UserWarning: \"`sparse_categorical_crossentropy` received `from_logits=True`, but the `output` argument was produced by a sigmoid or softmax activation and thus does not represent logits. Was this intended?\"\n",
            "  return dispatch_target(*args, **kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "19/19 [==============================] - 33s 846ms/step - loss: 2.2764 - accuracy: 0.1433 - val_loss: 2.1036 - val_accuracy: 0.2325\n",
            "Epoch 2/100\n",
            "19/19 [==============================] - 12s 614ms/step - loss: 2.0827 - accuracy: 0.1983 - val_loss: 2.0642 - val_accuracy: 0.2625\n",
            "Epoch 3/100\n",
            "19/19 [==============================] - 12s 619ms/step - loss: 1.9152 - accuracy: 0.2667 - val_loss: 2.0383 - val_accuracy: 0.2325\n",
            "Epoch 4/100\n",
            "19/19 [==============================] - 12s 613ms/step - loss: 1.7173 - accuracy: 0.3467 - val_loss: 1.8368 - val_accuracy: 0.3575\n",
            "Epoch 5/100\n",
            "19/19 [==============================] - 12s 627ms/step - loss: 1.4815 - accuracy: 0.4683 - val_loss: 1.7665 - val_accuracy: 0.3450\n",
            "Epoch 6/100\n",
            "19/19 [==============================] - 12s 622ms/step - loss: 1.3938 - accuracy: 0.5017 - val_loss: 1.7060 - val_accuracy: 0.4000\n",
            "Epoch 7/100\n",
            "19/19 [==============================] - 12s 619ms/step - loss: 1.2283 - accuracy: 0.5833 - val_loss: 1.7598 - val_accuracy: 0.3025\n",
            "Epoch 8/100\n",
            "19/19 [==============================] - 12s 624ms/step - loss: 1.2215 - accuracy: 0.5633 - val_loss: 1.6313 - val_accuracy: 0.3825\n",
            "Epoch 9/100\n",
            "19/19 [==============================] - 12s 625ms/step - loss: 1.0659 - accuracy: 0.6300 - val_loss: 1.5093 - val_accuracy: 0.4450\n",
            "Epoch 10/100\n",
            "19/19 [==============================] - 12s 631ms/step - loss: 1.0099 - accuracy: 0.6233 - val_loss: 1.6517 - val_accuracy: 0.4500\n",
            "Epoch 11/100\n",
            "19/19 [==============================] - 12s 625ms/step - loss: 0.8644 - accuracy: 0.6850 - val_loss: 1.6851 - val_accuracy: 0.3925\n",
            "Epoch 12/100\n",
            "19/19 [==============================] - 12s 624ms/step - loss: 0.8673 - accuracy: 0.6833 - val_loss: 1.5828 - val_accuracy: 0.4825\n",
            "Epoch 13/100\n",
            "19/19 [==============================] - 12s 631ms/step - loss: 0.6984 - accuracy: 0.7467 - val_loss: 1.5989 - val_accuracy: 0.4525\n",
            "Epoch 14/100\n",
            "19/19 [==============================] - 12s 628ms/step - loss: 0.6540 - accuracy: 0.7533 - val_loss: 1.6464 - val_accuracy: 0.4475\n",
            "Epoch 15/100\n",
            "19/19 [==============================] - 12s 624ms/step - loss: 0.6011 - accuracy: 0.7683 - val_loss: 1.5105 - val_accuracy: 0.5225\n",
            "Epoch 16/100\n",
            "19/19 [==============================] - 12s 628ms/step - loss: 0.6398 - accuracy: 0.7617 - val_loss: 1.7594 - val_accuracy: 0.4300\n",
            "Epoch 17/100\n",
            "19/19 [==============================] - 12s 628ms/step - loss: 0.5699 - accuracy: 0.7817 - val_loss: 1.5587 - val_accuracy: 0.5075\n",
            "Epoch 18/100\n",
            "19/19 [==============================] - 12s 625ms/step - loss: 0.5533 - accuracy: 0.7933 - val_loss: 2.0046 - val_accuracy: 0.4625\n",
            "Epoch 19/100\n",
            "19/19 [==============================] - 12s 628ms/step - loss: 0.5564 - accuracy: 0.8050 - val_loss: 1.5830 - val_accuracy: 0.5100\n",
            "Epoch 20/100\n",
            "19/19 [==============================] - 12s 630ms/step - loss: 0.3445 - accuracy: 0.8883 - val_loss: 1.7232 - val_accuracy: 0.5200\n",
            "Epoch 21/100\n",
            "19/19 [==============================] - 12s 631ms/step - loss: 0.3946 - accuracy: 0.8550 - val_loss: 1.6297 - val_accuracy: 0.5475\n",
            "Epoch 22/100\n",
            "19/19 [==============================] - 12s 629ms/step - loss: 0.2720 - accuracy: 0.9083 - val_loss: 1.5939 - val_accuracy: 0.5650\n",
            "Epoch 23/100\n",
            "19/19 [==============================] - 12s 630ms/step - loss: 0.2236 - accuracy: 0.9267 - val_loss: 1.8919 - val_accuracy: 0.5250\n",
            "Epoch 24/100\n",
            "19/19 [==============================] - 13s 702ms/step - loss: 0.3062 - accuracy: 0.8833 - val_loss: 1.8510 - val_accuracy: 0.5000\n",
            "Epoch 25/100\n",
            "19/19 [==============================] - 13s 705ms/step - loss: 0.2447 - accuracy: 0.9250 - val_loss: 1.7767 - val_accuracy: 0.5350\n",
            "Epoch 26/100\n",
            "19/19 [==============================] - 12s 629ms/step - loss: 0.2729 - accuracy: 0.9050 - val_loss: 1.9708 - val_accuracy: 0.5075\n",
            "Epoch 27/100\n",
            "19/19 [==============================] - 12s 627ms/step - loss: 0.4140 - accuracy: 0.8567 - val_loss: 1.7718 - val_accuracy: 0.5225\n",
            "Epoch 28/100\n",
            "19/19 [==============================] - 12s 637ms/step - loss: 0.2765 - accuracy: 0.9050 - val_loss: 1.8475 - val_accuracy: 0.5650\n",
            "Epoch 29/100\n",
            "19/19 [==============================] - 12s 634ms/step - loss: 0.1584 - accuracy: 0.9467 - val_loss: 1.7563 - val_accuracy: 0.5950\n",
            "Epoch 30/100\n",
            "19/19 [==============================] - 12s 633ms/step - loss: 0.1462 - accuracy: 0.9567 - val_loss: 1.7525 - val_accuracy: 0.5800\n",
            "Epoch 31/100\n",
            "19/19 [==============================] - 12s 628ms/step - loss: 0.1412 - accuracy: 0.9550 - val_loss: 1.9765 - val_accuracy: 0.5400\n",
            "Epoch 32/100\n",
            "19/19 [==============================] - 12s 631ms/step - loss: 0.1147 - accuracy: 0.9617 - val_loss: 1.8917 - val_accuracy: 0.5550\n",
            "Epoch 33/100\n",
            "19/19 [==============================] - 12s 629ms/step - loss: 0.0738 - accuracy: 0.9717 - val_loss: 1.8825 - val_accuracy: 0.5850\n",
            "Epoch 34/100\n",
            "19/19 [==============================] - 12s 632ms/step - loss: 0.0655 - accuracy: 0.9800 - val_loss: 1.8731 - val_accuracy: 0.5650\n",
            "Epoch 35/100\n",
            "19/19 [==============================] - 12s 634ms/step - loss: 0.1142 - accuracy: 0.9667 - val_loss: 2.0863 - val_accuracy: 0.5275\n",
            "Epoch 36/100\n",
            "19/19 [==============================] - 12s 631ms/step - loss: 0.0761 - accuracy: 0.9750 - val_loss: 2.0611 - val_accuracy: 0.5625\n",
            "Epoch 37/100\n",
            "19/19 [==============================] - 12s 635ms/step - loss: 0.0704 - accuracy: 0.9717 - val_loss: 2.1866 - val_accuracy: 0.5375\n",
            "Epoch 38/100\n",
            "19/19 [==============================] - 12s 632ms/step - loss: 0.0349 - accuracy: 0.9917 - val_loss: 2.2979 - val_accuracy: 0.5550\n",
            "Epoch 39/100\n",
            "19/19 [==============================] - 12s 633ms/step - loss: 0.0338 - accuracy: 0.9867 - val_loss: 2.2306 - val_accuracy: 0.5600\n",
            "Epoch 40/100\n",
            "19/19 [==============================] - 12s 633ms/step - loss: 0.0386 - accuracy: 0.9850 - val_loss: 2.2026 - val_accuracy: 0.5700\n",
            "Epoch 41/100\n",
            "19/19 [==============================] - 12s 632ms/step - loss: 0.1583 - accuracy: 0.9550 - val_loss: 2.0270 - val_accuracy: 0.4825\n",
            "Epoch 42/100\n",
            "19/19 [==============================] - 12s 635ms/step - loss: 0.1940 - accuracy: 0.9400 - val_loss: 2.3510 - val_accuracy: 0.4875\n",
            "Epoch 43/100\n",
            "19/19 [==============================] - 12s 628ms/step - loss: 0.2097 - accuracy: 0.9383 - val_loss: 2.1336 - val_accuracy: 0.5350\n",
            "Epoch 44/100\n",
            "19/19 [==============================] - 12s 634ms/step - loss: 0.1367 - accuracy: 0.9567 - val_loss: 2.1010 - val_accuracy: 0.5800\n",
            "Epoch 45/100\n",
            "19/19 [==============================] - 12s 631ms/step - loss: 0.0448 - accuracy: 0.9917 - val_loss: 2.0265 - val_accuracy: 0.5475\n",
            "Epoch 46/100\n",
            "19/19 [==============================] - 12s 633ms/step - loss: 0.0139 - accuracy: 0.9933 - val_loss: 2.2181 - val_accuracy: 0.5700\n",
            "Epoch 47/100\n",
            "19/19 [==============================] - 12s 629ms/step - loss: 0.0077 - accuracy: 0.9983 - val_loss: 2.1949 - val_accuracy: 0.5900\n",
            "Epoch 48/100\n",
            "19/19 [==============================] - 12s 636ms/step - loss: 0.0102 - accuracy: 0.9967 - val_loss: 2.1621 - val_accuracy: 0.5900\n",
            "Epoch 49/100\n",
            "19/19 [==============================] - 12s 635ms/step - loss: 0.0067 - accuracy: 0.9967 - val_loss: 2.2303 - val_accuracy: 0.5575\n",
            "Epoch 50/100\n",
            "19/19 [==============================] - 12s 636ms/step - loss: 0.0048 - accuracy: 0.9983 - val_loss: 2.2900 - val_accuracy: 0.5675\n",
            "Epoch 51/100\n",
            "19/19 [==============================] - 12s 631ms/step - loss: 0.0069 - accuracy: 0.9967 - val_loss: 2.2611 - val_accuracy: 0.5800\n",
            "Epoch 52/100\n",
            "19/19 [==============================] - 12s 633ms/step - loss: 0.0064 - accuracy: 0.9983 - val_loss: 2.2740 - val_accuracy: 0.6100\n",
            "Epoch 53/100\n",
            "19/19 [==============================] - 12s 633ms/step - loss: 0.0037 - accuracy: 0.9983 - val_loss: 2.2000 - val_accuracy: 0.6025\n",
            "Epoch 54/100\n",
            "19/19 [==============================] - 12s 630ms/step - loss: 0.0052 - accuracy: 0.9983 - val_loss: 2.2293 - val_accuracy: 0.6050\n",
            "Epoch 55/100\n",
            "19/19 [==============================] - 12s 635ms/step - loss: 0.0076 - accuracy: 0.9967 - val_loss: 2.2773 - val_accuracy: 0.5900\n",
            "Epoch 56/100\n",
            "19/19 [==============================] - 12s 631ms/step - loss: 0.0029 - accuracy: 0.9983 - val_loss: 2.2854 - val_accuracy: 0.5975\n",
            "Epoch 57/100\n",
            "19/19 [==============================] - 12s 634ms/step - loss: 0.0026 - accuracy: 1.0000 - val_loss: 2.3198 - val_accuracy: 0.5975\n",
            "Epoch 58/100\n",
            "19/19 [==============================] - 12s 634ms/step - loss: 0.0045 - accuracy: 0.9967 - val_loss: 2.3081 - val_accuracy: 0.6000\n",
            "Epoch 59/100\n",
            "19/19 [==============================] - 12s 633ms/step - loss: 0.0028 - accuracy: 0.9983 - val_loss: 2.3510 - val_accuracy: 0.5925\n",
            "Epoch 60/100\n",
            "19/19 [==============================] - 12s 633ms/step - loss: 0.0048 - accuracy: 0.9983 - val_loss: 2.3223 - val_accuracy: 0.6000\n",
            "Epoch 61/100\n",
            "19/19 [==============================] - 12s 637ms/step - loss: 0.0069 - accuracy: 0.9967 - val_loss: 2.3655 - val_accuracy: 0.5875\n",
            "Epoch 62/100\n",
            "19/19 [==============================] - 12s 634ms/step - loss: 0.0067 - accuracy: 0.9967 - val_loss: 2.3410 - val_accuracy: 0.5900\n",
            "Epoch 63/100\n",
            "19/19 [==============================] - 12s 632ms/step - loss: 0.0060 - accuracy: 0.9967 - val_loss: 2.2769 - val_accuracy: 0.5950\n",
            "Epoch 64/100\n",
            "19/19 [==============================] - 12s 632ms/step - loss: 0.0061 - accuracy: 0.9967 - val_loss: 2.2934 - val_accuracy: 0.5950\n",
            "Epoch 65/100\n",
            "19/19 [==============================] - 12s 633ms/step - loss: 0.0047 - accuracy: 0.9983 - val_loss: 2.3172 - val_accuracy: 0.5975\n",
            "Epoch 66/100\n",
            "19/19 [==============================] - 12s 626ms/step - loss: 0.0042 - accuracy: 0.9967 - val_loss: 2.2970 - val_accuracy: 0.6050\n",
            "Epoch 67/100\n",
            "19/19 [==============================] - 12s 634ms/step - loss: 0.0036 - accuracy: 0.9983 - val_loss: 2.3344 - val_accuracy: 0.6025\n",
            "Epoch 68/100\n",
            "19/19 [==============================] - 12s 634ms/step - loss: 0.0035 - accuracy: 0.9983 - val_loss: 2.3486 - val_accuracy: 0.6025\n",
            "Epoch 69/100\n",
            "19/19 [==============================] - 12s 637ms/step - loss: 0.0034 - accuracy: 0.9983 - val_loss: 2.3607 - val_accuracy: 0.5975\n",
            "Epoch 70/100\n",
            "19/19 [==============================] - 12s 635ms/step - loss: 0.0028 - accuracy: 0.9983 - val_loss: 2.3897 - val_accuracy: 0.6000\n",
            "Epoch 71/100\n",
            "19/19 [==============================] - 12s 636ms/step - loss: 0.0057 - accuracy: 0.9967 - val_loss: 2.3929 - val_accuracy: 0.5925\n",
            "Epoch 72/100\n",
            "19/19 [==============================] - 12s 631ms/step - loss: 0.0058 - accuracy: 0.9983 - val_loss: 2.3797 - val_accuracy: 0.6025\n",
            "Epoch 73/100\n",
            "19/19 [==============================] - 12s 633ms/step - loss: 0.0023 - accuracy: 1.0000 - val_loss: 2.3842 - val_accuracy: 0.6050\n",
            "Epoch 74/100\n",
            "19/19 [==============================] - 12s 637ms/step - loss: 0.0056 - accuracy: 0.9983 - val_loss: 2.3338 - val_accuracy: 0.6075\n",
            "Epoch 75/100\n",
            "19/19 [==============================] - 12s 636ms/step - loss: 0.0025 - accuracy: 0.9983 - val_loss: 2.3766 - val_accuracy: 0.6025\n",
            "Epoch 76/100\n",
            "19/19 [==============================] - 12s 635ms/step - loss: 0.0018 - accuracy: 1.0000 - val_loss: 2.3827 - val_accuracy: 0.6125\n",
            "Epoch 77/100\n",
            "19/19 [==============================] - 12s 632ms/step - loss: 0.0023 - accuracy: 1.0000 - val_loss: 2.3989 - val_accuracy: 0.6125\n",
            "Epoch 78/100\n",
            "19/19 [==============================] - 12s 634ms/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 2.4208 - val_accuracy: 0.6125\n",
            "Epoch 79/100\n",
            "19/19 [==============================] - 12s 633ms/step - loss: 0.0039 - accuracy: 0.9967 - val_loss: 2.4717 - val_accuracy: 0.5975\n",
            "Epoch 80/100\n",
            "19/19 [==============================] - 12s 637ms/step - loss: 0.0036 - accuracy: 0.9983 - val_loss: 2.4511 - val_accuracy: 0.6025\n",
            "Epoch 81/100\n",
            "19/19 [==============================] - 12s 632ms/step - loss: 0.0032 - accuracy: 0.9983 - val_loss: 2.4633 - val_accuracy: 0.5950\n",
            "Epoch 82/100\n",
            "19/19 [==============================] - 12s 638ms/step - loss: 0.0028 - accuracy: 0.9983 - val_loss: 2.4787 - val_accuracy: 0.5950\n",
            "Epoch 83/100\n",
            "19/19 [==============================] - 12s 636ms/step - loss: 0.0021 - accuracy: 0.9983 - val_loss: 2.4853 - val_accuracy: 0.6000\n",
            "Epoch 84/100\n",
            "19/19 [==============================] - 12s 633ms/step - loss: 0.0030 - accuracy: 0.9983 - val_loss: 2.4874 - val_accuracy: 0.5975\n",
            "Epoch 85/100\n",
            "19/19 [==============================] - 12s 633ms/step - loss: 0.0034 - accuracy: 0.9967 - val_loss: 2.4658 - val_accuracy: 0.6025\n",
            "Epoch 86/100\n",
            "19/19 [==============================] - 12s 637ms/step - loss: 0.0045 - accuracy: 0.9983 - val_loss: 2.4844 - val_accuracy: 0.6000\n",
            "Epoch 87/100\n",
            "19/19 [==============================] - 13s 700ms/step - loss: 0.0041 - accuracy: 0.9983 - val_loss: 2.5196 - val_accuracy: 0.5850\n",
            "Epoch 88/100\n",
            "19/19 [==============================] - 12s 636ms/step - loss: 0.0031 - accuracy: 0.9967 - val_loss: 2.5113 - val_accuracy: 0.5925\n",
            "Epoch 89/100\n",
            "19/19 [==============================] - 12s 638ms/step - loss: 0.0025 - accuracy: 0.9983 - val_loss: 2.5026 - val_accuracy: 0.5975\n",
            "Epoch 90/100\n",
            "19/19 [==============================] - 12s 635ms/step - loss: 0.0038 - accuracy: 0.9967 - val_loss: 2.5160 - val_accuracy: 0.5925\n",
            "Epoch 91/100\n",
            "19/19 [==============================] - 12s 636ms/step - loss: 0.0045 - accuracy: 0.9967 - val_loss: 2.5218 - val_accuracy: 0.5975\n",
            "Epoch 92/100\n",
            "19/19 [==============================] - 12s 634ms/step - loss: 0.0034 - accuracy: 0.9967 - val_loss: 2.4966 - val_accuracy: 0.5975\n",
            "Epoch 93/100\n",
            "19/19 [==============================] - 12s 631ms/step - loss: 0.0033 - accuracy: 0.9983 - val_loss: 2.4778 - val_accuracy: 0.6050\n",
            "Epoch 94/100\n",
            "19/19 [==============================] - 12s 634ms/step - loss: 0.0031 - accuracy: 0.9983 - val_loss: 2.4934 - val_accuracy: 0.5975\n",
            "Epoch 95/100\n",
            "19/19 [==============================] - 12s 631ms/step - loss: 0.0035 - accuracy: 0.9983 - val_loss: 2.5051 - val_accuracy: 0.6000\n",
            "Epoch 96/100\n",
            "19/19 [==============================] - 12s 635ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 2.5366 - val_accuracy: 0.6125\n",
            "Epoch 97/100\n",
            "19/19 [==============================] - 12s 634ms/step - loss: 0.0032 - accuracy: 0.9983 - val_loss: 2.5544 - val_accuracy: 0.6125\n",
            "Epoch 98/100\n",
            "19/19 [==============================] - 12s 637ms/step - loss: 0.0041 - accuracy: 0.9983 - val_loss: 2.5146 - val_accuracy: 0.6050\n",
            "Epoch 99/100\n",
            "19/19 [==============================] - 12s 632ms/step - loss: 0.0060 - accuracy: 0.9967 - val_loss: 2.5076 - val_accuracy: 0.6050\n",
            "Epoch 100/100\n",
            "19/19 [==============================] - 12s 632ms/step - loss: 0.0052 - accuracy: 0.9983 - val_loss: 2.5631 - val_accuracy: 0.6000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "acc = history2.history['accuracy']\n",
        "val_acc = history2.history['val_accuracy']\n",
        "loss = history2.history['loss']\n",
        "val_loss = history2.history['val_loss']\n",
        "\n",
        "epochs_range = range(100)\n",
        "\n",
        "plt.figure(figsize=(25, 15))\n",
        "plt.subplot(2, 2, 1)\n",
        "plt.plot(epochs_range, acc, label='Training Accuracy')\n",
        "plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.title('Training and Validation Accuracy')\n",
        "\n",
        "plt.subplot(2, 2, 2)\n",
        "plt.plot(epochs_range, loss, label='Training Loss')\n",
        "plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "jMFAxSGNEsfy",
        "outputId": "58361561-0b7a-4ec7-ab96-2bdc13e15e52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = model.predict([x_sp_val,x_wv_val])\n",
        "print(classification_report(y_wv_val, np.argmax(predictions, axis=1), target_names = labels))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lTXceaoPEyhB",
        "outputId": "494fb866-db39-477b-90a5-a5147a4d684e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "       blues       0.45      0.38      0.41        40\n",
            "   classical       0.86      0.80      0.83        40\n",
            "     country       0.58      0.62      0.60        40\n",
            "       disco       0.43      0.50      0.46        40\n",
            "      hiphop       0.56      0.75      0.64        40\n",
            "        jazz       0.70      0.75      0.72        40\n",
            "       metal       0.60      0.72      0.66        40\n",
            "         pop       0.80      0.70      0.75        40\n",
            "      reggae       0.46      0.47      0.47        40\n",
            "        rock       0.63      0.30      0.41        40\n",
            "\n",
            "    accuracy                           0.60       400\n",
            "   macro avg       0.61      0.60      0.59       400\n",
            "weighted avg       0.61      0.60      0.59       400\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sn\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "print(predictions.shape)\n",
        "print(predictions[0:120])\n",
        "#predictions = np.argmax(predictions, axis=1)\n",
        "cm1 = confusion_matrix(y_wv_val, predictions)\n",
        "df_cm = pd.DataFrame(cm1, index = [i for i in labels],\n",
        "              columns = [i for i in labels])\n",
        "plt.figure(figsize = (10,7))\n",
        "sn.heatmap(df_cm, annot=True,cmap=\"RdPu\")\n",
        "plt.savefig('confusion_mrtx3.png',bbox_inches = 'tight')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "id": "yLPYt9dZEy0f",
        "outputId": "5c4826fa-8d88-41c7-ff11-8c82709d907b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(400,)\n",
            "[0 6 0 6 0 6 6 0 6 4 8 0 6 6 0 6 6 3 0 3 6 8 6 6 3 5 8 0 0 5 0 8 8 0 5 0 8\n",
            " 0 0 0 1 1 5 1 5 1 1 1 5 1 1 1 5 5 1 1 1 1 1 1 1 1 1 5 5 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 5 2 3 9 2 2 7 2 2 2 0 5 5 0 2 9 0 2 3 2 2 3 2 2 2 3 2 2 2 6 2 0\n",
            " 2 2 2 2 2 2 2 9 2]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save(\"/content/drive/MyDrive/Data/MobileNetV2Combined-2.h5\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "POiIwHnusOrs",
        "outputId": "a415c224-a388-435b-acb4-81a4e09e956f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/engine/functional.py:1410: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
            "  layer_config = serialize_layer_fn(layer)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# model1 = keras.models.load_model('/content/drive/MyDrive/Data/MyModel.h5')\n",
        "# model2 = keras.models.load_model('/content/drive/MyDrive/Data/MobileNetV2Combined-2.h5')"
      ],
      "metadata": {
        "id": "X5-aLjanGGhq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# model2.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TW0jAa1ZOCWW",
        "outputId": "170d9cbf-7ac8-4065-a74e-5900d88ee28d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_2 (InputLayer)           [(None, 256, 256, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " mobilenetv2_1.00_224 (Function  (None, 8, 8, 1280)  2257984     ['input_2[0][0]',                \n",
            " al)                                                              'input_2[0][0]']                \n",
            "                                                                                                  \n",
            " global_average_pooling2d (Glob  (None, 1280)        0           ['mobilenetv2_1.00_224[0][0]']   \n",
            " alAveragePooling2D)                                                                              \n",
            "                                                                                                  \n",
            " global_average_pooling2d_1 (Gl  (None, 1280)        0           ['mobilenetv2_1.00_224[1][0]']   \n",
            " obalAveragePooling2D)                                                                            \n",
            "                                                                                                  \n",
            " dropout (Dropout)              (None, 1280)         0           ['global_average_pooling2d[0][0]'\n",
            "                                                                 ]                                \n",
            "                                                                                                  \n",
            " dropout_1 (Dropout)            (None, 1280)         0           ['global_average_pooling2d_1[0][0\n",
            "                                                                 ]']                              \n",
            "                                                                                                  \n",
            " dense (Dense)                  (None, 128)          163968      ['dropout[0][0]']                \n",
            "                                                                                                  \n",
            " dense_1 (Dense)                (None, 128)          163968      ['dropout_1[0][0]']              \n",
            "                                                                                                  \n",
            " concatenate (Concatenate)      (None, 256)          0           ['dense[0][0]',                  \n",
            "                                                                  'dense_1[0][0]']                \n",
            "                                                                                                  \n",
            " dense_2 (Dense)                (None, 32)           8224        ['concatenate[0][0]']            \n",
            "                                                                                                  \n",
            " input_3 (InputLayer)           [(None, 256, 256, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " dense_3 (Dense)                (None, 10)           330         ['dense_2[0][0]']                \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 2,594,474\n",
            "Trainable params: 2,560,362\n",
            "Non-trainable params: 34,112\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "QsYsXRItOIai"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}