training_v1.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/omerfguzel/global-ai-hub-dl-bootcamp/blob/main/training_v1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
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      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bMLHSMvHHkN5",
        "outputId": "136d9852-e922-4320-89ce-0bd28d464868"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Thu Oct  6 15:42:12 2022       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   33C    P0    27W / 250W |      0MiB / 16280MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "# To see which GPU has being used. Colab Pro offers Tesla-P100 as the best processor.\n",
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Imports\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import cv2\n",
        "import os\n",
        "from zipfile import ZipFile\n",
        "import time\n",
        "from datetime import datetime\n",
        "import itertools\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import random\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.models import load_model\n",
        "from tensorflow.keras.layers import Conv2D, AveragePooling2D, GlobalAveragePooling2D, Dense, Dropout, BatchNormalization\n",
        "from tensorflow.keras import utils\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint"
      ],
      "metadata": {
        "id": "uhyZb7kCH8JN"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Unzipping the dataset file preprocessed_spectograms.zip\n",
        "\n",
        "combined_faces_zip_path = \"/content/drive/MyDrive/global-ai-hub-bootcamp/preprocessed_spectograms.zip\"\n",
        "\n",
        "with ZipFile(combined_faces_zip_path, 'r') as myzip:\n",
        "    myzip.extractall()\n",
        "    print('Done unzipping preprocessed_spectograms.zip')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qC22BfzdIGHT",
        "outputId": "8df845bb-4ff6-44cb-e209-ae4b15848587"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done unzipping preprocessed_spectograms.zip\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/drive/MyDrive/global-ai-hub-bootcamp/cleaned_metadata.csv\")\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "_KZ54NLcIaYc",
        "outputId": "a5080624-2842-4a24-af02-c8e7802b7dc6"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   fold  classID                                           filename\n",
              "0     5        3    /content/content/spectograms/3/100032-3-0-0.png\n",
              "1     5        2  /content/content/spectograms/2/100263-2-0-117.png\n",
              "2     5        2  /content/content/spectograms/2/100263-2-0-121.png\n",
              "3     5        2  /content/content/spectograms/2/100263-2-0-126.png\n",
              "4     5        2  /content/content/spectograms/2/100263-2-0-137.png"
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    {
      "cell_type": "code",
      "source": [
        "def model_generator(dim=256):\n",
        "  # Defining the architecture of the sequential neural network.\n",
        "\n",
        "  model = Sequential()\n",
        "\n",
        "  # Input layer with 32 filters, followed by an AveragePooling2D layer.\n",
        "  model.add(Conv2D(filters=32, kernel_size=3, activation='relu', input_shape=(dim, dim, 1)))    # 3rd dim = 1 for grayscale images.\n",
        "  model.add(AveragePooling2D(pool_size=(2,2)))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(filters=64, kernel_size=3, activation='relu'))\n",
        "  model.add(AveragePooling2D(pool_size=(2,2)))\n",
        "  model.add(Dropout(0.125))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(filters=128, kernel_size=3, activation='relu'))\n",
        "  model.add(AveragePooling2D(pool_size=(2,2)))\n",
        "  model.add(Dropout(0.25))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Conv2D(filters=256, kernel_size=3, activation='relu'))\n",
        "  model.add(AveragePooling2D(pool_size=(2,2)))\n",
        "  model.add(Dropout(0.5))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(GlobalAveragePooling2D())\n",
        "\n",
        "  # One Dense layer with 132 nodes so as to taper down the no. of nodes from no. of outputs of GlobalAveragePooling2D layer above towards no. of nodes in output layer below (7).\n",
        "  model.add(Dense(128, activation='relu'))\n",
        "  model.add(Dropout(0.5))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  model.add(Dense(32, activation='relu'))\n",
        "  model.add(Dropout(0.25))\n",
        "  model.add(BatchNormalization())\n",
        "\n",
        "  # Output layer with 7 nodes (equal to the no. of classes).\n",
        "  model.add(Dense(10, activation='softmax'))\n",
        "\n",
        "  return model"
      ],
      "metadata": {
        "id": "lOkhs5UeJ2W6"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Defining a function to read the image, decode the image from given tensor and one-hot encode the image label class.\n",
        "\n",
        "num_classes = 10\n",
        "\n",
        "def _parse_function(filename, label):\n",
        "    \n",
        "    image_string = tf.io.read_file(filename)\n",
        "    image_decoded = tf.io.decode_jpeg(image_string, channels=1)    # channels=1 to convert to grayscale.\n",
        "    image_resized = tf.image.resize(image_decoded, [256, 256])\n",
        "    # image_normalized = tf.image.per_image_standardization(image_resized)\n",
        "    label = tf.one_hot(label, num_classes)\n",
        "\n",
        "    return image_resized, label"
      ],
      "metadata": {
        "id": "H1Z60wygMJ_v"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def dataset_generator(i, batch_size):\n",
        "  train_set = df[df['fold'] != i]\n",
        "  X_train, y_train = train_set.filename, train_set.classID\n",
        "  temp_set = df[df['fold'] == i]\n",
        "\n",
        "  X_val, X_test, y_val, y_test = train_test_split(temp_set.filename, temp_set.classID,\n",
        "                                                  test_size=0.4, random_state=42, stratify=temp_set.classID)\n",
        "  \n",
        "  train_filenames_tensor = tf.constant(list(X_train))\n",
        "  train_labels_tensor = tf.constant(list(y_train))\n",
        "\n",
        "  val_filenames_tensor = tf.constant(list(X_val))\n",
        "  val_labels_tensor = tf.constant(list(y_val))\n",
        "\n",
        "  test_filenames_tensor = tf.constant(list(X_test))\n",
        "  test_labels_tensor = tf.constant(list(y_test))\n",
        "\n",
        "    # Getting the dataset ready for the neural network.\n",
        "  # Using the tensor vectors defined above, accessing the images in the dataset and passing them through the function defined above.\n",
        "\n",
        "  train_dataset = tf.data.Dataset.from_tensor_slices((train_filenames_tensor, train_labels_tensor))\n",
        "  train_dataset = train_dataset.map(_parse_function)\n",
        "  train_dataset = train_dataset.batch(batch_size)    # Same as batch_size hyperparameter in model.fit() below.\n",
        "\n",
        "  val_dataset = tf.data.Dataset.from_tensor_slices((val_filenames_tensor, val_labels_tensor))\n",
        "  val_dataset = val_dataset.map(_parse_function)\n",
        "  val_dataset = val_dataset.batch(batch_size)    # Same as batch_size hyperparameter in model.fit() below.\n",
        "\n",
        "\n",
        "  test_dataset = tf.data.Dataset.from_tensor_slices((test_filenames_tensor, test_labels_tensor))\n",
        "  test_dataset = test_dataset.map(_parse_function)\n",
        "  test_dataset = test_dataset.batch(batch_size)    # Same as batch_size hyperparameter in model.fit() below.\n",
        "\n",
        "  return train_dataset, val_dataset, test_dataset, y_test"
      ],
      "metadata": {
        "id": "lUPCZveJKIeT"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fitting the above created CNN model for 50 epochs.\n",
        "def train_test(i,model,epochs,batch_size):\n",
        "  checkpoint = ModelCheckpoint(filepath=f\"/content/drive/My Drive/512-gray-urbansounds-fold{str(i)}-best_model.h5\",\n",
        "                  monitor='val_accuracy',\n",
        "                  save_best_only=True,\n",
        "                  save_weights_only=False,\n",
        "                  verbose=2\n",
        "                  )\n",
        "  \n",
        "  model_history = model.fit(train_dataset,\n",
        "                                    batch_size=batch_size,\n",
        "                                    validation_data=val_dataset,\n",
        "                                    epochs=epochs,\n",
        "                                    callbacks=[checkpoint],\n",
        "                                    shuffle=False,\n",
        "                                    verbose=2    # shuffle=False to reduce randomness and increase reproducibility\n",
        "                                  )\n",
        "  \n",
        "  best_model = load_model(f\"/content/drive/My Drive/512-gray-urbansounds-fold{str(i)}-best_model.h5\")\n",
        "  model_score = best_model.evaluate(test_dataset, verbose=0)\n",
        "  best_loss = round(model_score[0], 3)\n",
        "  best_accuracy = round(model_score[1], 3)\n",
        "\n",
        "  model_pred = model.predict(test_dataset)\n",
        "  model_pred = model_pred.argmax(axis=-1)\n",
        "  conf_mat = confusion_matrix(y_test, model_pred)\n",
        "\n",
        "  return model_history, best_accuracy, best_loss, conf_mat"
      ],
      "metadata": {
        "id": "7o6QHf71NImr"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Defining a function to plot the confusion matrix in a grid for easier visualization.\n",
        "\n",
        "def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion Matrix', export_as='confusion_matrix', cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    This function prints and plots the confusion matrix.\n",
        "    Normalization can be applied by setting `normalize=True`.\n",
        "    \"\"\"\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        print(\"Normalized confusion matrix\")\n",
        "    else:\n",
        "        print('Confusion matrix, without normalization')\n",
        "\n",
        "    # print(cm)\n",
        "\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title, fontsize=16)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "\n",
        "    fmt = '.2f' if normalize else 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, format(cm[i, j], fmt), horizontalalignment=\"center\", color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('Gerçek etiket', fontsize=14)\n",
        "    plt.xlabel('Tahmin edilen etiket', fontsize=14)"
      ],
      "metadata": {
        "id": "GBoX2TQeUJHA"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracies = []\n",
        "losses = []\n",
        "conf_matrices = []\n",
        "histories = []\n",
        "for i in range(1,11):\n",
        "  print(f\"Training model on Fold-{i}\")\n",
        "  train_dataset, val_dataset, test_dataset, y_test= dataset_generator(i,batch_size=128)\n",
        "  model = model_generator()\n",
        "  model.compile(optimizer=\"adam\", loss=\"categorical_crossentropy\", metrics=[\"accuracy\"])\n",
        "  model_history, best_accuracy, best_loss, conf_mat = train_test(i,model,epochs=100,batch_size=128)\n",
        "  \n",
        "  histories.append(model_history)\n",
        "  accuracies.append(best_accuracy)\n",
        "  losses.append(best_loss)\n",
        "  conf_matrices.append(conf_mat)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ETVkOA9JFdcX",
        "outputId": "704ab6ee-5fb3-45a4-83c1-d0ad590c3cef"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training model on Fold-1\n",
            "Epoch 1/100\n",
            "\n",
            "Epoch 1: val_accuracy improved from -inf to 0.27342, saving model to /content/drive/My Drive/512-gray-urbansounds-fold1-best_model.h5\n",
            "62/62 - 12s - loss: 2.3841 - accuracy: 0.2050 - val_loss: 2.1332 - val_accuracy: 0.2734 - 12s/epoch - 186ms/step\n",
            "Epoch 2/100\n",
            "\n",
            "Epoch 2: val_accuracy did not improve from 0.27342\n",
            "62/62 - 9s - loss: 2.1116 - accuracy: 0.2661 - val_loss: 2.3898 - val_accuracy: 0.1950 - 9s/epoch - 147ms/step\n",
            "Epoch 3/100\n",
            "\n",
            "Epoch 3: val_accuracy did not improve from 0.27342\n",
            "62/62 - 9s - loss: 1.9864 - accuracy: 0.2952 - val_loss: 1.8420 - val_accuracy: 0.2696 - 9s/epoch - 147ms/step\n",
            "Epoch 4/100\n",
            "\n",
            "Epoch 4: val_accuracy did not improve from 0.27342\n",
            "62/62 - 9s - loss: 1.8989 - accuracy: 0.3223 - val_loss: 2.3001 - val_accuracy: 0.2447 - 9s/epoch - 147ms/step\n",
            "Epoch 5/100\n",
            "\n",
            "Epoch 5: val_accuracy improved from 0.27342 to 0.36520, saving model to /content/drive/My Drive/512-gray-urbansounds-fold1-best_model.h5\n",
            "62/62 - 10s - loss: 1.8258 - accuracy: 0.3404 - val_loss: 1.7351 - val_accuracy: 0.3652 - 10s/epoch - 155ms/step\n",
            "Epoch 6/100\n",
            "\n",
            "Epoch 6: val_accuracy improved from 0.36520 to 0.39388, saving model to /content/drive/My Drive/512-gray-urbansounds-fold1-best_model.h5\n",
            "62/62 - 9s - loss: 1.7484 - accuracy: 0.3927 - val_loss: 1.6610 - val_accuracy: 0.3939 - 9s/epoch - 149ms/step\n",
            "Epoch 7/100\n",
            "\n",
            "Epoch 7: val_accuracy improved from 0.39388 to 0.47610, saving model to /content/drive/My Drive/512-gray-urbansounds-fold1-best_model.h5\n",
            "62/62 - 10s - loss: 1.6958 - accuracy: 0.3961 - val_loss: 1.3581 - val_accuracy: 0.4761 - 10s/epoch - 155ms/step\n",
            "Epoch 8/100\n",
            "\n",
            "Epoch 8: val_accuracy improved from 0.47610 to 0.50669, saving model to /content/drive/My Drive/512-gray-urbansounds-fold1-best_model.h5\n",
            "62/62 - 9s - loss: 1.6733 - accuracy: 0.4154 - val_loss: 1.3867 - val_accuracy: 0.5067 - 9s/epoch - 149ms/step\n",
            "Epoch 9/100\n",
            "\n",
            "Epoch 9: val_accuracy improved from 0.50669 to 0.51052, saving model to /content/drive/My Drive/512-gray-urbansounds-fold1-best_model.h5\n",
            "62/62 - 10s - loss: 1.6300 - accuracy: 0.4278 - val_loss: 1.3879 - val_accuracy: 0.5105 - 10s/epoch - 159ms/step\n",
            "Epoch 10/100\n",
            "\n",
            "Epoch 10: val_accuracy did not improve from 0.51052\n",
            "62/62 - 9s - loss: 1.5703 - accuracy: 0.4490 - val_loss: 1.5665 - val_accuracy: 0.4532 - 9s/epoch - 147ms/step\n",
            "Epoch 11/100\n",
            "\n",
            "Epoch 11: val_accuracy did not improve from 0.51052\n",
            "62/62 - 9s - loss: 1.5564 - accuracy: 0.4479 - val_loss: 1.5394 - val_accuracy: 0.4780 - 9s/epoch - 148ms/step\n",
            "Epoch 12/100\n",
            "\n",
            "Epoch 12: val_accuracy did not improve from 0.51052\n",
            "62/62 - 9s - loss: 1.5424 - accuracy: 0.4649 - val_loss: 1.5997 - val_accuracy: 0.4187 - 9s/epoch - 147ms/step\n",
            "Epoch 13/100\n",
            "\n",
            "Epoch 13: val_accuracy improved from 0.51052 to 0.55258, saving model to /content/drive/My Drive/512-gray-urbansounds-fold1-best_model.h5\n",
            "62/62 - 10s - loss: 1.5055 - accuracy: 0.4723 - val_loss: 1.2548 - val_accuracy: 0.5526 - 10s/epoch - 155ms/step\n",
            "Epoch 14/100\n",
            "\n",
            "Epoch 14: val_accuracy did not improve from 0.55258\n",
            "62/62 - 9s - loss: 1.4735 - accuracy: 0.4891 - val_loss: 1.1576 - val_accuracy: 0.5430 - 9s/epoch - 147ms/step\n",
            "Epoch 15/100\n",
            "\n",
            "Epoch 15: val_accuracy improved from 0.55258 to 0.57935, saving model to /content/drive/My Drive/512-gray-urbansounds-fold1-best_model.h5\n",
            "62/62 - 10s - loss: 1.4647 - accuracy: 0.4847 - val_loss: 1.1477 - val_accuracy: 0.5793 - 10s/epoch - 154ms/step\n",
            "Epoch 16/100\n",
            "\n",
            "Epoch 16: val_accuracy improved from 0.57935 to 0.61950, saving model to /content/drive/My Drive/512-gray-urbansounds-fold1-best_model.h5\n",
            "62/62 - 9s - loss: 1.4280 - accuracy: 0.5027 - val_loss: 1.1039 - val_accuracy: 0.6195 - 9s/epoch - 149ms/step\n",
            "Epoch 17/100\n",
            "\n",
            "Epoch 17: val_accuracy improved from 0.61950 to 0.65392, saving model to /content/drive/My Drive/512-gray-urbansounds-fold1-best_model.h5\n",
            "62/62 - 9s - loss: 1.3943 - accuracy: 0.5237 - val_loss: 1.0316 - val_accuracy: 0.6539 - 9s/epoch - 153ms/step\n",
            "Epoch 18/100\n",
            "\n",
            "Epoch 18: val_accuracy did not improve from 0.65392\n",
            "62/62 - 9s - loss: 1.3801 - accuracy: 0.5162 - val_loss: 1.0774 - val_accuracy: 0.5564 - 9s/epoch - 147ms/step\n",
            "Epoch 19/100\n",
            "\n",
            "Epoch 19: val_accuracy did not improve from 0.65392\n",
            "62/62 - 9s - loss: 1.3561 - accuracy: 0.5279 - val_loss: 1.0515 - val_accuracy: 0.5698 - 9s/epoch - 148ms/step\n",
            "Epoch 20/100\n",
            "\n",
            "Epoch 20: val_accuracy improved from 0.65392 to 0.70363, saving model to /content/drive/My Drive/512-gray-urbansounds-fold1-best_model.h5\n",
            "62/62 - 10s - loss: 1.3291 - accuracy: 0.5408 - val_loss: 1.0662 - val_accuracy: 0.7036 - 10s/epoch - 154ms/step\n",
            "Epoch 21/100\n",
            "\n",
            "Epoch 21: val_accuracy did not improve from 0.70363\n",
            "62/62 - 9s - loss: 1.3170 - accuracy: 0.5427 - val_loss: 0.9424 - val_accuracy: 0.6539 - 9s/epoch - 147ms/step\n",
            "Epoch 22/100\n",
            "\n",
            "Epoch 22: val_accuracy did not improve from 0.70363\n",
            "62/62 - 9s - loss: 1.2745 - accuracy: 0.5641 - val_loss: 1.1597 - val_accuracy: 0.6348 - 9s/epoch - 147ms/step\n",
            "Epoch 23/100\n",
            "\n",
            "Epoch 23: val_accuracy did not improve from 0.70363\n",
            "62/62 - 9s - loss: 1.2496 - accuracy: 0.5727 - val_loss: 1.2105 - val_accuracy: 0.5583 - 9s/epoch - 147ms/step\n",
            "Epoch 24/100\n",
            "\n",
            "Epoch 24: val_accuracy did not improve from 0.70363\n",
            "62/62 - 9s - loss: 1.2514 - accuracy: 0.5720 - val_loss: 1.1361 - val_accuracy: 0.5927 - 9s/epoch - 147ms/step\n",
            "Epoch 25/100\n",
            "\n",
            "Epoch 25: val_accuracy did not improve from 0.70363\n",
            "62/62 - 9s - loss: 1.2222 - accuracy: 0.5806 - val_loss: 0.9692 - val_accuracy: 0.6482 - 9s/epoch - 147ms/step\n",
            "Epoch 26/100\n",
            "\n",
            "Epoch 26: val_accuracy did not improve from 0.70363\n",
            "62/62 - 9s - loss: 1.1754 - accuracy: 0.6068 - val_loss: 1.1441 - val_accuracy: 0.5946 - 9s/epoch - 147ms/step\n",
            "Epoch 27/100\n",
            "\n",
            "Epoch 27: val_accuracy did not improve from 0.70363\n",
            "62/62 - 9s - loss: 1.1414 - accuracy: 0.6232 - val_loss: 1.0780 - val_accuracy: 0.5889 - 9s/epoch - 147ms/step\n",
            "Epoch 28/100\n",
            "\n",
            "Epoch 28: val_accuracy did not improve from 0.70363\n",
            "62/62 - 9s - loss: 1.1214 - accuracy: 0.6231 - val_loss: 1.3591 - val_accuracy: 0.5392 - 9s/epoch - 147ms/step\n",
            "Epoch 29/100\n",
            "\n",
            "Epoch 29: val_accuracy did not improve from 0.70363\n",
            "62/62 - 9s - loss: 1.1071 - accuracy: 0.6356 - val_loss: 1.1580 - val_accuracy: 0.5793 - 9s/epoch - 147ms/step\n",
            "Epoch 30/100\n",
            "\n",
            "Epoch 30: val_accuracy did not improve from 0.70363\n",
            "62/62 - 10s - loss: 1.0932 - accuracy: 0.6366 - val_loss: 1.3622 - val_accuracy: 0.4933 - 10s/epoch - 154ms/step\n",
            "Epoch 31/100\n",
            "\n",
            "Epoch 31: val_accuracy did not improve from 0.70363\n",
            "62/62 - 9s - loss: 1.0636 - accuracy: 0.6514 - val_loss: 0.9270 - val_accuracy: 0.6348 - 9s/epoch - 148ms/step\n",
            "Epoch 32/100\n",
            "\n",
            "Epoch 32: val_accuracy did not improve from 0.70363\n",
            "62/62 - 9s - loss: 1.0386 - accuracy: 0.6549 - val_loss: 0.8771 - val_accuracy: 0.5946 - 9s/epoch - 147ms/step\n",
            "Epoch 33/100\n",
            "\n",
            "Epoch 33: val_accuracy did not improve from 0.70363\n",
            "62/62 - 9s - loss: 1.0066 - accuracy: 0.6603 - val_loss: 0.9326 - val_accuracy: 0.6405 - 9s/epoch - 148ms/step\n",
            "Epoch 34/100\n",
            "\n",
            "Epoch 34: val_accuracy did not improve from 0.70363\n",
            "62/62 - 9s - loss: 0.9879 - accuracy: 0.6687 - val_loss: 1.4276 - val_accuracy: 0.5889 - 9s/epoch - 147ms/step\n",
            "Epoch 35/100\n",
            "\n",
            "Epoch 35: val_accuracy did not improve from 0.70363\n",
            "62/62 - 9s - loss: 0.9615 - accuracy: 0.6786 - val_loss: 1.8348 - val_accuracy: 0.5985 - 9s/epoch - 147ms/step\n",
            "Epoch 36/100\n",
            "\n",
            "Epoch 36: val_accuracy did not improve from 0.70363\n",
            "62/62 - 9s - loss: 0.9478 - accuracy: 0.6908 - val_loss: 2.0326 - val_accuracy: 0.5239 - 9s/epoch - 147ms/step\n",
            "Epoch 37/100\n",
            "\n",
            "Epoch 37: val_accuracy did not improve from 0.70363\n",
            "62/62 - 9s - loss: 0.9343 - accuracy: 0.6921 - val_loss: 1.7728 - val_accuracy: 0.5411 - 9s/epoch - 147ms/step\n",
            "Epoch 38/100\n",
            "\n",
            "Epoch 38: val_accuracy did not improve from 0.70363\n",
            "62/62 - 9s - loss: 0.9137 - accuracy: 0.7019 - val_loss: 1.6249 - val_accuracy: 0.5430 - 9s/epoch - 147ms/step\n",
            "Epoch 39/100\n",
            "\n",
            "Epoch 39: val_accuracy did not improve from 0.70363\n",
            "62/62 - 9s - loss: 0.9029 - accuracy: 0.6944 - val_loss: 2.2877 - val_accuracy: 0.5182 - 9s/epoch - 147ms/step\n",
            "Epoch 40/100\n",
            "\n",
            "Epoch 40: val_accuracy did not improve from 0.70363\n",
            "62/62 - 9s - loss: 0.8882 - accuracy: 0.7133 - val_loss: 1.3196 - val_accuracy: 0.6310 - 9s/epoch - 147ms/step\n",
            "Epoch 41/100\n",
            "\n",
            "Epoch 41: val_accuracy did not improve from 0.70363\n",
            "62/62 - 9s - loss: 0.8888 - accuracy: 0.7140 - val_loss: 1.2565 - val_accuracy: 0.6539 - 9s/epoch - 147ms/step\n",
            "Epoch 42/100\n",
            "\n",
            "Epoch 42: val_accuracy did not improve from 0.70363\n",
            "62/62 - 9s - loss: 0.8472 - accuracy: 0.7227 - val_loss: 3.7136 - val_accuracy: 0.3595 - 9s/epoch - 147ms/step\n",
            "Epoch 43/100\n",
            "\n",
            "Epoch 43: val_accuracy did not improve from 0.70363\n",
            "62/62 - 9s - loss: 0.8353 - accuracy: 0.7295 - val_loss: 0.8289 - val_accuracy: 0.6979 - 9s/epoch - 147ms/step\n",
            "Epoch 44/100\n",
            "\n",
            "Epoch 44: val_accuracy did not improve from 0.70363\n",
            "62/62 - 9s - loss: 0.7944 - accuracy: 0.7397 - val_loss: 0.9889 - val_accuracy: 0.6769 - 9s/epoch - 147ms/step\n",
            "Epoch 45/100\n",
            "\n",
            "Epoch 45: val_accuracy did not improve from 0.70363\n",
            "62/62 - 9s - loss: 0.7678 - accuracy: 0.7509 - val_loss: 1.2800 - val_accuracy: 0.6195 - 9s/epoch - 147ms/step\n",
            "Epoch 46/100\n",
            "\n",
            "Epoch 46: val_accuracy did not improve from 0.70363\n",
            "62/62 - 9s - loss: 0.7454 - accuracy: 0.7556 - val_loss: 1.0188 - val_accuracy: 0.6233 - 9s/epoch - 147ms/step\n",
            "Epoch 47/100\n",
            "\n",
            "Epoch 47: val_accuracy did not improve from 0.70363\n",
            "62/62 - 9s - loss: 0.7386 - accuracy: 0.7645 - val_loss: 1.0301 - val_accuracy: 0.6520 - 9s/epoch - 147ms/step\n",
            "Epoch 48/100\n",
            "\n",
            "Epoch 48: val_accuracy did not improve from 0.70363\n",
            "62/62 - 9s - loss: 0.7050 - accuracy: 0.7725 - val_loss: 1.4759 - val_accuracy: 0.5966 - 9s/epoch - 148ms/step\n",
            "Epoch 49/100\n",
            "\n",
            "Epoch 49: val_accuracy did not improve from 0.70363\n",
            "62/62 - 9s - loss: 0.6912 - accuracy: 0.7787 - val_loss: 1.5313 - val_accuracy: 0.6138 - 9s/epoch - 147ms/step\n",
            "Epoch 50/100\n",
            "\n",
            "Epoch 50: val_accuracy did not improve from 0.70363\n",
            "62/62 - 9s - loss: 0.6773 - accuracy: 0.7834 - val_loss: 3.5667 - val_accuracy: 0.4799 - 9s/epoch - 148ms/step\n",
            "Epoch 51/100\n",
            "\n",
            "Epoch 51: val_accuracy did not improve from 0.70363\n",
            "62/62 - 10s - loss: 0.6724 - accuracy: 0.7815 - val_loss: 2.7798 - val_accuracy: 0.4818 - 10s/epoch - 154ms/step\n",
            "Epoch 52/100\n",
            "\n",
            "Epoch 52: val_accuracy did not improve from 0.70363\n",
            "62/62 - 9s - loss: 0.6761 - accuracy: 0.7799 - val_loss: 2.3574 - val_accuracy: 0.5048 - 9s/epoch - 147ms/step\n",
            "Epoch 53/100\n",
            "\n",
            "Epoch 53: val_accuracy did not improve from 0.70363\n",
            "62/62 - 9s - loss: 0.6568 - accuracy: 0.7893 - val_loss: 4.5326 - val_accuracy: 0.4283 - 9s/epoch - 147ms/step\n",
            "Epoch 54/100\n",
            "\n",
            "Epoch 54: val_accuracy did not improve from 0.70363\n",
            "62/62 - 9s - loss: 0.6642 - accuracy: 0.7804 - val_loss: 3.1991 - val_accuracy: 0.3996 - 9s/epoch - 147ms/step\n",
            "Epoch 55/100\n",
            "\n",
            "Epoch 55: val_accuracy did not improve from 0.70363\n",
            "62/62 - 9s - loss: 0.6693 - accuracy: 0.7808 - val_loss: 1.3370 - val_accuracy: 0.6310 - 9s/epoch - 148ms/step\n",
            "Epoch 56/100\n",
            "\n",
            "Epoch 56: val_accuracy improved from 0.70363 to 0.72275, saving model to /content/drive/My Drive/512-gray-urbansounds-fold1-best_model.h5\n",
            "62/62 - 10s - loss: 0.6595 - accuracy: 0.7926 - val_loss: 0.9087 - val_accuracy: 0.7228 - 10s/epoch - 154ms/step\n",
            "Epoch 57/100\n",
            "\n",
            "Epoch 57: val_accuracy did not improve from 0.72275\n",
            "62/62 - 9s - loss: 0.6103 - accuracy: 0.8048 - val_loss: 1.0702 - val_accuracy: 0.6845 - 9s/epoch - 147ms/step\n",
            "Epoch 58/100\n",
            "\n",
            "Epoch 58: val_accuracy did not improve from 0.72275\n",
            "62/62 - 9s - loss: 0.6008 - accuracy: 0.8054 - val_loss: 0.9415 - val_accuracy: 0.7189 - 9s/epoch - 147ms/step\n",
            "Epoch 59/100\n",
            "\n",
            "Epoch 59: val_accuracy did not improve from 0.72275\n",
            "62/62 - 9s - loss: 0.5741 - accuracy: 0.8142 - val_loss: 1.0499 - val_accuracy: 0.6941 - 9s/epoch - 147ms/step\n",
            "Epoch 60/100\n",
            "\n",
            "Epoch 60: val_accuracy did not improve from 0.72275\n",
            "62/62 - 9s - loss: 0.5745 - accuracy: 0.8130 - val_loss: 1.1825 - val_accuracy: 0.6673 - 9s/epoch - 147ms/step\n",
            "Epoch 61/100\n",
            "\n",
            "Epoch 61: val_accuracy did not improve from 0.72275\n",
            "62/62 - 9s - loss: 0.5293 - accuracy: 0.8310 - val_loss: 1.0784 - val_accuracy: 0.7036 - 9s/epoch - 147ms/step\n",
            "Epoch 62/100\n",
            "\n",
            "Epoch 62: val_accuracy improved from 0.72275 to 0.73614, saving model to /content/drive/My Drive/512-gray-urbansounds-fold1-best_model.h5\n",
            "62/62 - 10s - loss: 0.5351 - accuracy: 0.8276 - val_loss: 0.8568 - val_accuracy: 0.7361 - 10s/epoch - 155ms/step\n",
            "Epoch 63/100\n",
            "\n",
            "Epoch 63: val_accuracy did not improve from 0.73614\n",
            "62/62 - 9s - loss: 0.5205 - accuracy: 0.8342 - val_loss: 1.0583 - val_accuracy: 0.6902 - 9s/epoch - 147ms/step\n",
            "Epoch 64/100\n",
            "\n",
            "Epoch 64: val_accuracy improved from 0.73614 to 0.73996, saving model to /content/drive/My Drive/512-gray-urbansounds-fold1-best_model.h5\n",
            "62/62 - 10s - loss: 0.5093 - accuracy: 0.8387 - val_loss: 0.9206 - val_accuracy: 0.7400 - 10s/epoch - 156ms/step\n",
            "Epoch 65/100\n",
            "\n",
            "Epoch 65: val_accuracy did not improve from 0.73996\n",
            "62/62 - 9s - loss: 0.4990 - accuracy: 0.8369 - val_loss: 0.9822 - val_accuracy: 0.7017 - 9s/epoch - 147ms/step\n",
            "Epoch 66/100\n",
            "\n",
            "Epoch 66: val_accuracy did not improve from 0.73996\n",
            "62/62 - 9s - loss: 0.4868 - accuracy: 0.8430 - val_loss: 1.0117 - val_accuracy: 0.7247 - 9s/epoch - 148ms/step\n",
            "Epoch 67/100\n",
            "\n",
            "Epoch 67: val_accuracy improved from 0.73996 to 0.75717, saving model to /content/drive/My Drive/512-gray-urbansounds-fold1-best_model.h5\n",
            "62/62 - 10s - loss: 0.4648 - accuracy: 0.8527 - val_loss: 0.7511 - val_accuracy: 0.7572 - 10s/epoch - 155ms/step\n",
            "Epoch 68/100\n",
            "\n",
            "Epoch 68: val_accuracy did not improve from 0.75717\n",
            "62/62 - 9s - loss: 0.4608 - accuracy: 0.8516 - val_loss: 1.0042 - val_accuracy: 0.6960 - 9s/epoch - 147ms/step\n",
            "Epoch 69/100\n",
            "\n",
            "Epoch 69: val_accuracy did not improve from 0.75717\n",
            "62/62 - 9s - loss: 0.4316 - accuracy: 0.8618 - val_loss: 1.0644 - val_accuracy: 0.6960 - 9s/epoch - 147ms/step\n",
            "Epoch 70/100\n",
            "\n",
            "Epoch 70: val_accuracy did not improve from 0.75717\n",
            "62/62 - 9s - loss: 0.4374 - accuracy: 0.8630 - val_loss: 0.8841 - val_accuracy: 0.7457 - 9s/epoch - 147ms/step\n",
            "Epoch 71/100\n",
            "\n",
            "Epoch 71: val_accuracy did not improve from 0.75717\n",
            "62/62 - 9s - loss: 0.4433 - accuracy: 0.8524 - val_loss: 1.1793 - val_accuracy: 0.7113 - 9s/epoch - 150ms/step\n",
            "Epoch 72/100\n",
            "\n",
            "Epoch 72: val_accuracy did not improve from 0.75717\n",
            "62/62 - 9s - loss: 0.4234 - accuracy: 0.8633 - val_loss: 1.1037 - val_accuracy: 0.7285 - 9s/epoch - 148ms/step\n",
            "Epoch 73/100\n",
            "\n",
            "Epoch 73: val_accuracy did not improve from 0.75717\n",
            "62/62 - 9s - loss: 0.4184 - accuracy: 0.8614 - val_loss: 0.8573 - val_accuracy: 0.7247 - 9s/epoch - 148ms/step\n",
            "Epoch 74/100\n",
            "\n",
            "Epoch 74: val_accuracy did not improve from 0.75717\n",
            "62/62 - 9s - loss: 0.4118 - accuracy: 0.8628 - val_loss: 1.2223 - val_accuracy: 0.6386 - 9s/epoch - 147ms/step\n",
            "Epoch 75/100\n",
            "\n",
            "Epoch 75: val_accuracy did not improve from 0.75717\n",
            "62/62 - 9s - loss: 0.4112 - accuracy: 0.8745 - val_loss: 1.2062 - val_accuracy: 0.6501 - 9s/epoch - 147ms/step\n",
            "Epoch 76/100\n",
            "\n",
            "Epoch 76: val_accuracy did not improve from 0.75717\n",
            "62/62 - 9s - loss: 0.4043 - accuracy: 0.8665 - val_loss: 1.5825 - val_accuracy: 0.5946 - 9s/epoch - 147ms/step\n",
            "Epoch 77/100\n",
            "\n",
            "Epoch 77: val_accuracy did not improve from 0.75717\n",
            "62/62 - 9s - loss: 0.4075 - accuracy: 0.8658 - val_loss: 1.9720 - val_accuracy: 0.6348 - 9s/epoch - 148ms/step\n",
            "Epoch 78/100\n",
            "\n",
            "Epoch 78: val_accuracy did not improve from 0.75717\n",
            "62/62 - 9s - loss: 0.4014 - accuracy: 0.8689 - val_loss: 1.2838 - val_accuracy: 0.6061 - 9s/epoch - 147ms/step\n",
            "Epoch 79/100\n",
            "\n",
            "Epoch 79: val_accuracy did not improve from 0.75717\n",
            "62/62 - 9s - loss: 0.3960 - accuracy: 0.8701 - val_loss: 2.4614 - val_accuracy: 0.5698 - 9s/epoch - 147ms/step\n",
            "Epoch 80/100\n",
            "\n",
            "Epoch 80: val_accuracy did not improve from 0.75717\n",
            "62/62 - 9s - loss: 0.3835 - accuracy: 0.8750 - val_loss: 1.3971 - val_accuracy: 0.6252 - 9s/epoch - 147ms/step\n",
            "Epoch 81/100\n",
            "\n",
            "Epoch 81: val_accuracy did not improve from 0.75717\n",
            "62/62 - 9s - loss: 0.3912 - accuracy: 0.8730 - val_loss: 1.7739 - val_accuracy: 0.5813 - 9s/epoch - 147ms/step\n",
            "Epoch 82/100\n",
            "\n",
            "Epoch 82: val_accuracy did not improve from 0.75717\n",
            "62/62 - 9s - loss: 0.3756 - accuracy: 0.8749 - val_loss: 1.8788 - val_accuracy: 0.6195 - 9s/epoch - 147ms/step\n",
            "Epoch 83/100\n",
            "\n",
            "Epoch 83: val_accuracy did not improve from 0.75717\n",
            "62/62 - 9s - loss: 0.3595 - accuracy: 0.8801 - val_loss: 1.2948 - val_accuracy: 0.6195 - 9s/epoch - 147ms/step\n",
            "Epoch 84/100\n",
            "\n",
            "Epoch 84: val_accuracy did not improve from 0.75717\n",
            "62/62 - 9s - loss: 0.3513 - accuracy: 0.8866 - val_loss: 1.2221 - val_accuracy: 0.6233 - 9s/epoch - 147ms/step\n",
            "Epoch 85/100\n",
            "\n",
            "Epoch 85: val_accuracy did not improve from 0.75717\n",
            "62/62 - 9s - loss: 0.3600 - accuracy: 0.8854 - val_loss: 1.7112 - val_accuracy: 0.6463 - 9s/epoch - 147ms/step\n",
            "Epoch 86/100\n",
            "\n",
            "Epoch 86: val_accuracy did not improve from 0.75717\n",
            "62/62 - 9s - loss: 0.3518 - accuracy: 0.8864 - val_loss: 1.2743 - val_accuracy: 0.6635 - 9s/epoch - 148ms/step\n",
            "Epoch 87/100\n",
            "\n",
            "Epoch 87: val_accuracy did not improve from 0.75717\n",
            "62/62 - 9s - loss: 0.3252 - accuracy: 0.8958 - val_loss: 0.9248 - val_accuracy: 0.7247 - 9s/epoch - 147ms/step\n",
            "Epoch 88/100\n",
            "\n",
            "Epoch 88: val_accuracy did not improve from 0.75717\n",
            "62/62 - 9s - loss: 0.3312 - accuracy: 0.8925 - val_loss: 1.3280 - val_accuracy: 0.6558 - 9s/epoch - 147ms/step\n",
            "Epoch 89/100\n",
            "\n",
            "Epoch 89: val_accuracy did not improve from 0.75717\n",
            "62/62 - 9s - loss: 0.3189 - accuracy: 0.8983 - val_loss: 0.9602 - val_accuracy: 0.7514 - 9s/epoch - 147ms/step\n",
            "Epoch 90/100\n",
            "\n",
            "Epoch 90: val_accuracy did not improve from 0.75717\n",
            "62/62 - 9s - loss: 0.3186 - accuracy: 0.8941 - val_loss: 1.0436 - val_accuracy: 0.7342 - 9s/epoch - 147ms/step\n",
            "Epoch 91/100\n",
            "\n",
            "Epoch 91: val_accuracy did not improve from 0.75717\n",
            "62/62 - 9s - loss: 0.3406 - accuracy: 0.8927 - val_loss: 1.0078 - val_accuracy: 0.7342 - 9s/epoch - 147ms/step\n",
            "Epoch 92/100\n",
            "\n",
            "Epoch 92: val_accuracy did not improve from 0.75717\n",
            "62/62 - 9s - loss: 0.2974 - accuracy: 0.9014 - val_loss: 0.9770 - val_accuracy: 0.7400 - 9s/epoch - 153ms/step\n",
            "Epoch 93/100\n",
            "\n",
            "Epoch 93: val_accuracy did not improve from 0.75717\n",
            "62/62 - 9s - loss: 0.3164 - accuracy: 0.8953 - val_loss: 1.1046 - val_accuracy: 0.7247 - 9s/epoch - 148ms/step\n",
            "Epoch 94/100\n",
            "\n",
            "Epoch 94: val_accuracy did not improve from 0.75717\n",
            "62/62 - 9s - loss: 0.2970 - accuracy: 0.9046 - val_loss: 1.1437 - val_accuracy: 0.6998 - 9s/epoch - 148ms/step\n",
            "Epoch 95/100\n",
            "\n",
            "Epoch 95: val_accuracy did not improve from 0.75717\n",
            "62/62 - 9s - loss: 0.2884 - accuracy: 0.9084 - val_loss: 1.0572 - val_accuracy: 0.7247 - 9s/epoch - 147ms/step\n",
            "Epoch 96/100\n",
            "\n",
            "Epoch 96: val_accuracy did not improve from 0.75717\n",
            "62/62 - 9s - loss: 0.2837 - accuracy: 0.9076 - val_loss: 1.1168 - val_accuracy: 0.6864 - 9s/epoch - 147ms/step\n",
            "Epoch 97/100\n",
            "\n",
            "Epoch 97: val_accuracy did not improve from 0.75717\n",
            "62/62 - 9s - loss: 0.2812 - accuracy: 0.9145 - val_loss: 1.4166 - val_accuracy: 0.6482 - 9s/epoch - 147ms/step\n",
            "Epoch 98/100\n",
            "\n",
            "Epoch 98: val_accuracy did not improve from 0.75717\n",
            "62/62 - 9s - loss: 0.2772 - accuracy: 0.9117 - val_loss: 1.1520 - val_accuracy: 0.7208 - 9s/epoch - 147ms/step\n",
            "Epoch 99/100\n",
            "\n",
            "Epoch 99: val_accuracy did not improve from 0.75717\n",
            "62/62 - 9s - loss: 0.2778 - accuracy: 0.9062 - val_loss: 0.8971 - val_accuracy: 0.7304 - 9s/epoch - 147ms/step\n",
            "Epoch 100/100\n",
            "\n",
            "Epoch 100: val_accuracy did not improve from 0.75717\n",
            "62/62 - 9s - loss: 0.2812 - accuracy: 0.9070 - val_loss: 1.4378 - val_accuracy: 0.6730 - 9s/epoch - 147ms/step\n",
            "Training model on Fold-2\n",
            "Epoch 1/100\n",
            "\n",
            "Epoch 1: val_accuracy improved from -inf to 0.12218, saving model to /content/drive/My Drive/512-gray-urbansounds-fold2-best_model.h5\n",
            "62/62 - 11s - loss: 2.3975 - accuracy: 0.1843 - val_loss: 2.8389 - val_accuracy: 0.1222 - 11s/epoch - 176ms/step\n",
            "Epoch 2/100\n",
            "\n",
            "Epoch 2: val_accuracy improved from 0.12218 to 0.23872, saving model to /content/drive/My Drive/512-gray-urbansounds-fold2-best_model.h5\n",
            "62/62 - 10s - loss: 2.1143 - accuracy: 0.2611 - val_loss: 2.3756 - val_accuracy: 0.2387 - 10s/epoch - 155ms/step\n",
            "Epoch 3/100\n",
            "\n",
            "Epoch 3: val_accuracy did not improve from 0.23872\n",
            "62/62 - 9s - loss: 1.9883 - accuracy: 0.2923 - val_loss: 2.4343 - val_accuracy: 0.2011 - 9s/epoch - 147ms/step\n",
            "Epoch 4/100\n",
            "\n",
            "Epoch 4: val_accuracy improved from 0.23872 to 0.31015, saving model to /content/drive/My Drive/512-gray-urbansounds-fold2-best_model.h5\n",
            "62/62 - 10s - loss: 1.8879 - accuracy: 0.3322 - val_loss: 2.1048 - val_accuracy: 0.3102 - 10s/epoch - 155ms/step\n",
            "Epoch 5/100\n",
            "\n",
            "Epoch 5: val_accuracy improved from 0.31015 to 0.32143, saving model to /content/drive/My Drive/512-gray-urbansounds-fold2-best_model.h5\n",
            "62/62 - 10s - loss: 1.8148 - accuracy: 0.3593 - val_loss: 1.8577 - val_accuracy: 0.3214 - 10s/epoch - 154ms/step\n",
            "Epoch 6/100\n",
            "\n",
            "Epoch 6: val_accuracy improved from 0.32143 to 0.36090, saving model to /content/drive/My Drive/512-gray-urbansounds-fold2-best_model.h5\n",
            "62/62 - 10s - loss: 1.7749 - accuracy: 0.3771 - val_loss: 1.6704 - val_accuracy: 0.3609 - 10s/epoch - 154ms/step\n",
            "Epoch 7/100\n",
            "\n",
            "Epoch 7: val_accuracy improved from 0.36090 to 0.40414, saving model to /content/drive/My Drive/512-gray-urbansounds-fold2-best_model.h5\n",
            "62/62 - 10s - loss: 1.7052 - accuracy: 0.4034 - val_loss: 1.5360 - val_accuracy: 0.4041 - 10s/epoch - 154ms/step\n",
            "Epoch 8/100\n",
            "\n",
            "Epoch 8: val_accuracy did not improve from 0.40414\n",
            "62/62 - 9s - loss: 1.6615 - accuracy: 0.4206 - val_loss: 1.6705 - val_accuracy: 0.3477 - 9s/epoch - 147ms/step\n",
            "Epoch 9/100\n",
            "\n",
            "Epoch 9: val_accuracy improved from 0.40414 to 0.46053, saving model to /content/drive/My Drive/512-gray-urbansounds-fold2-best_model.h5\n",
            "62/62 - 10s - loss: 1.6125 - accuracy: 0.4341 - val_loss: 1.3696 - val_accuracy: 0.4605 - 10s/epoch - 155ms/step\n",
            "Epoch 10/100\n",
            "\n",
            "Epoch 10: val_accuracy did not improve from 0.46053\n",
            "62/62 - 9s - loss: 1.5990 - accuracy: 0.4477 - val_loss: 1.8315 - val_accuracy: 0.3722 - 9s/epoch - 147ms/step\n",
            "Epoch 11/100\n",
            "\n",
            "Epoch 11: val_accuracy did not improve from 0.46053\n",
            "62/62 - 9s - loss: 1.5563 - accuracy: 0.4623 - val_loss: 1.4324 - val_accuracy: 0.4605 - 9s/epoch - 147ms/step\n",
            "Epoch 12/100\n",
            "\n",
            "Epoch 12: val_accuracy improved from 0.46053 to 0.50000, saving model to /content/drive/My Drive/512-gray-urbansounds-fold2-best_model.h5\n",
            "62/62 - 10s - loss: 1.5324 - accuracy: 0.4713 - val_loss: 1.2659 - val_accuracy: 0.5000 - 10s/epoch - 160ms/step\n",
            "Epoch 13/100\n",
            "\n",
            "Epoch 13: val_accuracy improved from 0.50000 to 0.61466, saving model to /content/drive/My Drive/512-gray-urbansounds-fold2-best_model.h5\n",
            "62/62 - 9s - loss: 1.5038 - accuracy: 0.4865 - val_loss: 1.2251 - val_accuracy: 0.6147 - 9s/epoch - 149ms/step\n",
            "Epoch 14/100\n",
            "\n",
            "Epoch 14: val_accuracy did not improve from 0.61466\n",
            "62/62 - 9s - loss: 1.4660 - accuracy: 0.5024 - val_loss: 1.2030 - val_accuracy: 0.5564 - 9s/epoch - 147ms/step\n",
            "Epoch 15/100\n",
            "\n",
            "Epoch 15: val_accuracy did not improve from 0.61466\n",
            "62/62 - 9s - loss: 1.4392 - accuracy: 0.5193 - val_loss: 1.3063 - val_accuracy: 0.5883 - 9s/epoch - 147ms/step\n",
            "Epoch 16/100\n",
            "\n",
            "Epoch 16: val_accuracy did not improve from 0.61466\n",
            "62/62 - 9s - loss: 1.4327 - accuracy: 0.5166 - val_loss: 1.1747 - val_accuracy: 0.5977 - 9s/epoch - 147ms/step\n",
            "Epoch 17/100\n",
            "\n",
            "Epoch 17: val_accuracy did not improve from 0.61466\n",
            "62/62 - 9s - loss: 1.4064 - accuracy: 0.5310 - val_loss: 1.3882 - val_accuracy: 0.5188 - 9s/epoch - 147ms/step\n",
            "Epoch 18/100\n",
            "\n",
            "Epoch 18: val_accuracy did not improve from 0.61466\n",
            "62/62 - 9s - loss: 1.3759 - accuracy: 0.5363 - val_loss: 1.2347 - val_accuracy: 0.5545 - 9s/epoch - 147ms/step\n",
            "Epoch 19/100\n",
            "\n",
            "Epoch 19: val_accuracy did not improve from 0.61466\n",
            "62/62 - 9s - loss: 1.3383 - accuracy: 0.5589 - val_loss: 1.2000 - val_accuracy: 0.5282 - 9s/epoch - 148ms/step\n",
            "Epoch 20/100\n",
            "\n",
            "Epoch 20: val_accuracy did not improve from 0.61466\n",
            "62/62 - 9s - loss: 1.3148 - accuracy: 0.5683 - val_loss: 1.2832 - val_accuracy: 0.5169 - 9s/epoch - 147ms/step\n",
            "Epoch 21/100\n",
            "\n",
            "Epoch 21: val_accuracy did not improve from 0.61466\n",
            "62/62 - 9s - loss: 1.2895 - accuracy: 0.5753 - val_loss: 1.5730 - val_accuracy: 0.4944 - 9s/epoch - 147ms/step\n",
            "Epoch 22/100\n",
            "\n",
            "Epoch 22: val_accuracy did not improve from 0.61466\n",
            "62/62 - 9s - loss: 1.2798 - accuracy: 0.5751 - val_loss: 1.4720 - val_accuracy: 0.5470 - 9s/epoch - 147ms/step\n",
            "Epoch 23/100\n",
            "\n",
            "Epoch 23: val_accuracy did not improve from 0.61466\n",
            "62/62 - 9s - loss: 1.2487 - accuracy: 0.5927 - val_loss: 1.3713 - val_accuracy: 0.5733 - 9s/epoch - 147ms/step\n",
            "Epoch 24/100\n",
            "\n",
            "Epoch 24: val_accuracy did not improve from 0.61466\n",
            "62/62 - 9s - loss: 1.2295 - accuracy: 0.5998 - val_loss: 1.2281 - val_accuracy: 0.5320 - 9s/epoch - 147ms/step\n",
            "Epoch 25/100\n",
            "\n",
            "Epoch 25: val_accuracy did not improve from 0.61466\n",
            "62/62 - 9s - loss: 1.1857 - accuracy: 0.6137 - val_loss: 1.1026 - val_accuracy: 0.5789 - 9s/epoch - 147ms/step\n",
            "Epoch 26/100\n",
            "\n",
            "Epoch 26: val_accuracy did not improve from 0.61466\n",
            "62/62 - 9s - loss: 1.1516 - accuracy: 0.6318 - val_loss: 1.2425 - val_accuracy: 0.5526 - 9s/epoch - 147ms/step\n",
            "Epoch 27/100\n",
            "\n",
            "Epoch 27: val_accuracy did not improve from 0.61466\n",
            "62/62 - 9s - loss: 1.1460 - accuracy: 0.6261 - val_loss: 1.1328 - val_accuracy: 0.5865 - 9s/epoch - 147ms/step\n",
            "Epoch 28/100\n",
            "\n",
            "Epoch 28: val_accuracy did not improve from 0.61466\n",
            "62/62 - 9s - loss: 1.1080 - accuracy: 0.6438 - val_loss: 1.3484 - val_accuracy: 0.5301 - 9s/epoch - 148ms/step\n",
            "Epoch 29/100\n",
            "\n",
            "Epoch 29: val_accuracy improved from 0.61466 to 0.61654, saving model to /content/drive/My Drive/512-gray-urbansounds-fold2-best_model.h5\n",
            "62/62 - 10s - loss: 1.0828 - accuracy: 0.6501 - val_loss: 1.1207 - val_accuracy: 0.6165 - 10s/epoch - 155ms/step\n",
            "Epoch 30/100\n",
            "\n",
            "Epoch 30: val_accuracy did not improve from 0.61654\n",
            "62/62 - 9s - loss: 1.0638 - accuracy: 0.6602 - val_loss: 1.5081 - val_accuracy: 0.4962 - 9s/epoch - 148ms/step\n",
            "Epoch 31/100\n",
            "\n",
            "Epoch 31: val_accuracy did not improve from 0.61654\n",
            "62/62 - 9s - loss: 1.0414 - accuracy: 0.6694 - val_loss: 1.0748 - val_accuracy: 0.6071 - 9s/epoch - 147ms/step\n",
            "Epoch 32/100\n",
            "\n",
            "Epoch 32: val_accuracy improved from 0.61654 to 0.65602, saving model to /content/drive/My Drive/512-gray-urbansounds-fold2-best_model.h5\n",
            "62/62 - 10s - loss: 1.0235 - accuracy: 0.6717 - val_loss: 0.9884 - val_accuracy: 0.6560 - 10s/epoch - 154ms/step\n",
            "Epoch 33/100\n",
            "\n",
            "Epoch 33: val_accuracy did not improve from 0.65602\n",
            "62/62 - 9s - loss: 0.9941 - accuracy: 0.6800 - val_loss: 1.6624 - val_accuracy: 0.5376 - 9s/epoch - 153ms/step\n",
            "Epoch 34/100\n",
            "\n",
            "Epoch 34: val_accuracy did not improve from 0.65602\n",
            "62/62 - 9s - loss: 0.9931 - accuracy: 0.6772 - val_loss: 1.2359 - val_accuracy: 0.5865 - 9s/epoch - 147ms/step\n",
            "Epoch 35/100\n",
            "\n",
            "Epoch 35: val_accuracy did not improve from 0.65602\n",
            "62/62 - 9s - loss: 0.9527 - accuracy: 0.6982 - val_loss: 1.5658 - val_accuracy: 0.5451 - 9s/epoch - 147ms/step\n",
            "Epoch 36/100\n",
            "\n",
            "Epoch 36: val_accuracy did not improve from 0.65602\n",
            "62/62 - 9s - loss: 0.9231 - accuracy: 0.7059 - val_loss: 1.1418 - val_accuracy: 0.5865 - 9s/epoch - 147ms/step\n",
            "Epoch 37/100\n",
            "\n",
            "Epoch 37: val_accuracy did not improve from 0.65602\n",
            "62/62 - 9s - loss: 0.8975 - accuracy: 0.7161 - val_loss: 1.0468 - val_accuracy: 0.6523 - 9s/epoch - 148ms/step\n",
            "Epoch 38/100\n",
            "\n",
            "Epoch 38: val_accuracy did not improve from 0.65602\n",
            "62/62 - 9s - loss: 0.8718 - accuracy: 0.7236 - val_loss: 1.1246 - val_accuracy: 0.5921 - 9s/epoch - 147ms/step\n",
            "Epoch 39/100\n",
            "\n",
            "Epoch 39: val_accuracy improved from 0.65602 to 0.69173, saving model to /content/drive/My Drive/512-gray-urbansounds-fold2-best_model.h5\n",
            "62/62 - 10s - loss: 0.8459 - accuracy: 0.7346 - val_loss: 0.9154 - val_accuracy: 0.6917 - 10s/epoch - 156ms/step\n",
            "Epoch 40/100\n",
            "\n",
            "Epoch 40: val_accuracy did not improve from 0.69173\n",
            "62/62 - 9s - loss: 0.8270 - accuracy: 0.7375 - val_loss: 1.1168 - val_accuracy: 0.6410 - 9s/epoch - 148ms/step\n",
            "Epoch 41/100\n",
            "\n",
            "Epoch 41: val_accuracy did not improve from 0.69173\n",
            "62/62 - 9s - loss: 0.8041 - accuracy: 0.7475 - val_loss: 1.0137 - val_accuracy: 0.6823 - 9s/epoch - 147ms/step\n",
            "Epoch 42/100\n",
            "\n",
            "Epoch 42: val_accuracy did not improve from 0.69173\n",
            "62/62 - 9s - loss: 0.8038 - accuracy: 0.7503 - val_loss: 1.0191 - val_accuracy: 0.6635 - 9s/epoch - 147ms/step\n",
            "Epoch 43/100\n",
            "\n",
            "Epoch 43: val_accuracy did not improve from 0.69173\n",
            "62/62 - 9s - loss: 0.7846 - accuracy: 0.7515 - val_loss: 1.0409 - val_accuracy: 0.6203 - 9s/epoch - 147ms/step\n",
            "Epoch 44/100\n",
            "\n",
            "Epoch 44: val_accuracy did not improve from 0.69173\n",
            "62/62 - 9s - loss: 0.7715 - accuracy: 0.7543 - val_loss: 1.3330 - val_accuracy: 0.5752 - 9s/epoch - 147ms/step\n",
            "Epoch 45/100\n",
            "\n",
            "Epoch 45: val_accuracy did not improve from 0.69173\n",
            "62/62 - 9s - loss: 0.7490 - accuracy: 0.7601 - val_loss: 1.1074 - val_accuracy: 0.6673 - 9s/epoch - 147ms/step\n",
            "Epoch 46/100\n",
            "\n",
            "Epoch 46: val_accuracy did not improve from 0.69173\n",
            "62/62 - 9s - loss: 0.7495 - accuracy: 0.7627 - val_loss: 1.1996 - val_accuracy: 0.6184 - 9s/epoch - 147ms/step\n",
            "Epoch 47/100\n",
            "\n",
            "Epoch 47: val_accuracy did not improve from 0.69173\n",
            "62/62 - 9s - loss: 0.7264 - accuracy: 0.7659 - val_loss: 0.9542 - val_accuracy: 0.6447 - 9s/epoch - 147ms/step\n",
            "Epoch 48/100\n",
            "\n",
            "Epoch 48: val_accuracy improved from 0.69173 to 0.74812, saving model to /content/drive/My Drive/512-gray-urbansounds-fold2-best_model.h5\n",
            "62/62 - 10s - loss: 0.7161 - accuracy: 0.7777 - val_loss: 0.7790 - val_accuracy: 0.7481 - 10s/epoch - 155ms/step\n",
            "Epoch 49/100\n",
            "\n",
            "Epoch 49: val_accuracy did not improve from 0.74812\n",
            "62/62 - 9s - loss: 0.6899 - accuracy: 0.7833 - val_loss: 0.8788 - val_accuracy: 0.7312 - 9s/epoch - 147ms/step\n",
            "Epoch 50/100\n",
            "\n",
            "Epoch 50: val_accuracy did not improve from 0.74812\n",
            "62/62 - 9s - loss: 0.6884 - accuracy: 0.7847 - val_loss: 1.1182 - val_accuracy: 0.6805 - 9s/epoch - 147ms/step\n",
            "Epoch 51/100\n",
            "\n",
            "Epoch 51: val_accuracy did not improve from 0.74812\n",
            "62/62 - 9s - loss: 0.6791 - accuracy: 0.7852 - val_loss: 0.9222 - val_accuracy: 0.6880 - 9s/epoch - 147ms/step\n",
            "Epoch 52/100\n",
            "\n",
            "Epoch 52: val_accuracy did not improve from 0.74812\n",
            "62/62 - 9s - loss: 0.6387 - accuracy: 0.7978 - val_loss: 1.3632 - val_accuracy: 0.5883 - 9s/epoch - 147ms/step\n",
            "Epoch 53/100\n",
            "\n",
            "Epoch 53: val_accuracy did not improve from 0.74812\n",
            "62/62 - 9s - loss: 0.6444 - accuracy: 0.7988 - val_loss: 0.9893 - val_accuracy: 0.6767 - 9s/epoch - 152ms/step\n",
            "Epoch 54/100\n",
            "\n",
            "Epoch 54: val_accuracy did not improve from 0.74812\n",
            "62/62 - 9s - loss: 0.6238 - accuracy: 0.8025 - val_loss: 1.1307 - val_accuracy: 0.6410 - 9s/epoch - 148ms/step\n",
            "Epoch 55/100\n",
            "\n",
            "Epoch 55: val_accuracy did not improve from 0.74812\n",
            "62/62 - 9s - loss: 0.5954 - accuracy: 0.8135 - val_loss: 0.9479 - val_accuracy: 0.6992 - 9s/epoch - 147ms/step\n",
            "Epoch 56/100\n",
            "\n",
            "Epoch 56: val_accuracy did not improve from 0.74812\n",
            "62/62 - 9s - loss: 0.5926 - accuracy: 0.8165 - val_loss: 0.9507 - val_accuracy: 0.6974 - 9s/epoch - 147ms/step\n",
            "Epoch 57/100\n",
            "\n",
            "Epoch 57: val_accuracy did not improve from 0.74812\n",
            "62/62 - 9s - loss: 0.5780 - accuracy: 0.8154 - val_loss: 1.1042 - val_accuracy: 0.6936 - 9s/epoch - 147ms/step\n",
            "Epoch 58/100\n",
            "\n",
            "Epoch 58: val_accuracy did not improve from 0.74812\n",
            "62/62 - 9s - loss: 0.5679 - accuracy: 0.8191 - val_loss: 0.9719 - val_accuracy: 0.6842 - 9s/epoch - 147ms/step\n",
            "Epoch 59/100\n",
            "\n",
            "Epoch 59: val_accuracy did not improve from 0.74812\n",
            "62/62 - 9s - loss: 0.5352 - accuracy: 0.8329 - val_loss: 1.2346 - val_accuracy: 0.5977 - 9s/epoch - 148ms/step\n",
            "Epoch 60/100\n",
            "\n",
            "Epoch 60: val_accuracy did not improve from 0.74812\n",
            "62/62 - 9s - loss: 0.5327 - accuracy: 0.8311 - val_loss: 1.1522 - val_accuracy: 0.6711 - 9s/epoch - 147ms/step\n",
            "Epoch 61/100\n",
            "\n",
            "Epoch 61: val_accuracy did not improve from 0.74812\n",
            "62/62 - 9s - loss: 0.5187 - accuracy: 0.8372 - val_loss: 1.5663 - val_accuracy: 0.5620 - 9s/epoch - 147ms/step\n",
            "Epoch 62/100\n",
            "\n",
            "Epoch 62: val_accuracy did not improve from 0.74812\n",
            "62/62 - 9s - loss: 0.5370 - accuracy: 0.8320 - val_loss: 0.9312 - val_accuracy: 0.6767 - 9s/epoch - 147ms/step\n",
            "Epoch 63/100\n",
            "\n",
            "Epoch 63: val_accuracy did not improve from 0.74812\n",
            "62/62 - 9s - loss: 0.5173 - accuracy: 0.8359 - val_loss: 0.9830 - val_accuracy: 0.6842 - 9s/epoch - 147ms/step\n",
            "Epoch 64/100\n",
            "\n",
            "Epoch 64: val_accuracy did not improve from 0.74812\n",
            "62/62 - 9s - loss: 0.5110 - accuracy: 0.8375 - val_loss: 0.9659 - val_accuracy: 0.6880 - 9s/epoch - 147ms/step\n",
            "Epoch 65/100\n",
            "\n",
            "Epoch 65: val_accuracy did not improve from 0.74812\n",
            "62/62 - 9s - loss: 0.4955 - accuracy: 0.8445 - val_loss: 1.1031 - val_accuracy: 0.6579 - 9s/epoch - 148ms/step\n",
            "Epoch 66/100\n",
            "\n",
            "Epoch 66: val_accuracy did not improve from 0.74812\n",
            "62/62 - 9s - loss: 0.5018 - accuracy: 0.8409 - val_loss: 0.9205 - val_accuracy: 0.7086 - 9s/epoch - 148ms/step\n",
            "Epoch 67/100\n",
            "\n",
            "Epoch 67: val_accuracy improved from 0.74812 to 0.75000, saving model to /content/drive/My Drive/512-gray-urbansounds-fold2-best_model.h5\n",
            "62/62 - 10s - loss: 0.4706 - accuracy: 0.8510 - val_loss: 0.7819 - val_accuracy: 0.7500 - 10s/epoch - 155ms/step\n",
            "Epoch 68/100\n",
            "\n",
            "Epoch 68: val_accuracy did not improve from 0.75000\n",
            "62/62 - 9s - loss: 0.4629 - accuracy: 0.8492 - val_loss: 0.9707 - val_accuracy: 0.6786 - 9s/epoch - 147ms/step\n",
            "Epoch 69/100\n",
            "\n",
            "Epoch 69: val_accuracy did not improve from 0.75000\n",
            "62/62 - 9s - loss: 0.4490 - accuracy: 0.8565 - val_loss: 0.8312 - val_accuracy: 0.7312 - 9s/epoch - 147ms/step\n",
            "Epoch 70/100\n",
            "\n",
            "Epoch 70: val_accuracy did not improve from 0.75000\n",
            "62/62 - 9s - loss: 0.4539 - accuracy: 0.8568 - val_loss: 0.9987 - val_accuracy: 0.6898 - 9s/epoch - 147ms/step\n",
            "Epoch 71/100\n",
            "\n",
            "Epoch 71: val_accuracy did not improve from 0.75000\n",
            "62/62 - 9s - loss: 0.4713 - accuracy: 0.8510 - val_loss: 1.0511 - val_accuracy: 0.6635 - 9s/epoch - 148ms/step\n",
            "Epoch 72/100\n",
            "\n",
            "Epoch 72: val_accuracy did not improve from 0.75000\n",
            "62/62 - 9s - loss: 0.4559 - accuracy: 0.8524 - val_loss: 1.1478 - val_accuracy: 0.6466 - 9s/epoch - 147ms/step\n",
            "Epoch 73/100\n",
            "\n",
            "Epoch 73: val_accuracy did not improve from 0.75000\n",
            "62/62 - 9s - loss: 0.4492 - accuracy: 0.8580 - val_loss: 0.9157 - val_accuracy: 0.7350 - 9s/epoch - 147ms/step\n",
            "Epoch 74/100\n",
            "\n",
            "Epoch 74: val_accuracy did not improve from 0.75000\n",
            "62/62 - 10s - loss: 0.4270 - accuracy: 0.8609 - val_loss: 1.0437 - val_accuracy: 0.6729 - 10s/epoch - 155ms/step\n",
            "Epoch 75/100\n",
            "\n",
            "Epoch 75: val_accuracy did not improve from 0.75000\n",
            "62/62 - 9s - loss: 0.4189 - accuracy: 0.8624 - val_loss: 0.8589 - val_accuracy: 0.7256 - 9s/epoch - 147ms/step\n",
            "Epoch 76/100\n",
            "\n",
            "Epoch 76: val_accuracy did not improve from 0.75000\n",
            "62/62 - 9s - loss: 0.4018 - accuracy: 0.8747 - val_loss: 0.9718 - val_accuracy: 0.7049 - 9s/epoch - 147ms/step\n",
            "Epoch 77/100\n",
            "\n",
            "Epoch 77: val_accuracy did not improve from 0.75000\n",
            "62/62 - 9s - loss: 0.4057 - accuracy: 0.8721 - val_loss: 1.0964 - val_accuracy: 0.6786 - 9s/epoch - 147ms/step\n",
            "Epoch 78/100\n",
            "\n",
            "Epoch 78: val_accuracy did not improve from 0.75000\n",
            "62/62 - 9s - loss: 0.4015 - accuracy: 0.8686 - val_loss: 1.0941 - val_accuracy: 0.6617 - 9s/epoch - 147ms/step\n",
            "Epoch 79/100\n",
            "\n",
            "Epoch 79: val_accuracy did not improve from 0.75000\n",
            "62/62 - 9s - loss: 0.4012 - accuracy: 0.8720 - val_loss: 0.9657 - val_accuracy: 0.6880 - 9s/epoch - 147ms/step\n",
            "Epoch 80/100\n",
            "\n",
            "Epoch 80: val_accuracy did not improve from 0.75000\n",
            "62/62 - 9s - loss: 0.3796 - accuracy: 0.8770 - val_loss: 1.1660 - val_accuracy: 0.6767 - 9s/epoch - 148ms/step\n",
            "Epoch 81/100\n",
            "\n",
            "Epoch 81: val_accuracy did not improve from 0.75000\n",
            "62/62 - 9s - loss: 0.3684 - accuracy: 0.8846 - val_loss: 1.0314 - val_accuracy: 0.7312 - 9s/epoch - 147ms/step\n",
            "Epoch 82/100\n",
            "\n",
            "Epoch 82: val_accuracy did not improve from 0.75000\n",
            "62/62 - 9s - loss: 0.3634 - accuracy: 0.8799 - val_loss: 1.3947 - val_accuracy: 0.6541 - 9s/epoch - 147ms/step\n",
            "Epoch 83/100\n",
            "\n",
            "Epoch 83: val_accuracy did not improve from 0.75000\n",
            "62/62 - 9s - loss: 0.3501 - accuracy: 0.8907 - val_loss: 1.1771 - val_accuracy: 0.6823 - 9s/epoch - 147ms/step\n",
            "Epoch 84/100\n",
            "\n",
            "Epoch 84: val_accuracy improved from 0.75000 to 0.75376, saving model to /content/drive/My Drive/512-gray-urbansounds-fold2-best_model.h5\n",
            "62/62 - 10s - loss: 0.3688 - accuracy: 0.8822 - val_loss: 0.9111 - val_accuracy: 0.7538 - 10s/epoch - 155ms/step\n",
            "Epoch 85/100\n",
            "\n",
            "Epoch 85: val_accuracy did not improve from 0.75376\n",
            "62/62 - 9s - loss: 0.3413 - accuracy: 0.8878 - val_loss: 0.8643 - val_accuracy: 0.7538 - 9s/epoch - 147ms/step\n",
            "Epoch 86/100\n",
            "\n",
            "Epoch 86: val_accuracy did not improve from 0.75376\n",
            "62/62 - 9s - loss: 0.3661 - accuracy: 0.8802 - val_loss: 1.1130 - val_accuracy: 0.6955 - 9s/epoch - 148ms/step\n",
            "Epoch 87/100\n",
            "\n",
            "Epoch 87: val_accuracy did not improve from 0.75376\n",
            "62/62 - 9s - loss: 0.3368 - accuracy: 0.8958 - val_loss: 0.9618 - val_accuracy: 0.7068 - 9s/epoch - 147ms/step\n",
            "Epoch 88/100\n",
            "\n",
            "Epoch 88: val_accuracy did not improve from 0.75376\n",
            "62/62 - 9s - loss: 0.3298 - accuracy: 0.8943 - val_loss: 1.0808 - val_accuracy: 0.6823 - 9s/epoch - 147ms/step\n",
            "Epoch 89/100\n",
            "\n",
            "Epoch 89: val_accuracy did not improve from 0.75376\n",
            "62/62 - 9s - loss: 0.3483 - accuracy: 0.8901 - val_loss: 1.1493 - val_accuracy: 0.6917 - 9s/epoch - 147ms/step\n",
            "Epoch 90/100\n",
            "\n",
            "Epoch 90: val_accuracy did not improve from 0.75376\n",
            "62/62 - 9s - loss: 0.3225 - accuracy: 0.8944 - val_loss: 1.3143 - val_accuracy: 0.6617 - 9s/epoch - 148ms/step\n",
            "Epoch 91/100\n",
            "\n",
            "Epoch 91: val_accuracy did not improve from 0.75376\n",
            "62/62 - 9s - loss: 0.3210 - accuracy: 0.8978 - val_loss: 1.1146 - val_accuracy: 0.6917 - 9s/epoch - 147ms/step\n",
            "Epoch 92/100\n",
            "\n",
            "Epoch 92: val_accuracy did not improve from 0.75376\n",
            "62/62 - 9s - loss: 0.3212 - accuracy: 0.8976 - val_loss: 1.0204 - val_accuracy: 0.7237 - 9s/epoch - 147ms/step\n",
            "Epoch 93/100\n",
            "\n",
            "Epoch 93: val_accuracy improved from 0.75376 to 0.76692, saving model to /content/drive/My Drive/512-gray-urbansounds-fold2-best_model.h5\n",
            "62/62 - 10s - loss: 0.3191 - accuracy: 0.8974 - val_loss: 0.8771 - val_accuracy: 0.7669 - 10s/epoch - 157ms/step\n",
            "Epoch 94/100\n",
            "\n",
            "Epoch 94: val_accuracy did not improve from 0.76692\n",
            "62/62 - 9s - loss: 0.3165 - accuracy: 0.8976 - val_loss: 0.9497 - val_accuracy: 0.7331 - 9s/epoch - 150ms/step\n",
            "Epoch 95/100\n",
            "\n",
            "Epoch 95: val_accuracy did not improve from 0.76692\n",
            "62/62 - 9s - loss: 0.2972 - accuracy: 0.9067 - val_loss: 1.6170 - val_accuracy: 0.6429 - 9s/epoch - 153ms/step\n",
            "Epoch 96/100\n",
            "\n",
            "Epoch 96: val_accuracy did not improve from 0.76692\n",
            "62/62 - 9s - loss: 0.2992 - accuracy: 0.9027 - val_loss: 1.2375 - val_accuracy: 0.6485 - 9s/epoch - 148ms/step\n",
            "Epoch 97/100\n",
            "\n",
            "Epoch 97: val_accuracy did not improve from 0.76692\n",
            "62/62 - 9s - loss: 0.2955 - accuracy: 0.9044 - val_loss: 1.0861 - val_accuracy: 0.7086 - 9s/epoch - 147ms/step\n",
            "Epoch 98/100\n",
            "\n",
            "Epoch 98: val_accuracy did not improve from 0.76692\n",
            "62/62 - 9s - loss: 0.2949 - accuracy: 0.9067 - val_loss: 1.1209 - val_accuracy: 0.7162 - 9s/epoch - 147ms/step\n",
            "Epoch 99/100\n",
            "\n",
            "Epoch 99: val_accuracy did not improve from 0.76692\n",
            "62/62 - 9s - loss: 0.2941 - accuracy: 0.9085 - val_loss: 1.1594 - val_accuracy: 0.6748 - 9s/epoch - 147ms/step\n",
            "Epoch 100/100\n",
            "\n",
            "Epoch 100: val_accuracy did not improve from 0.76692\n",
            "62/62 - 9s - loss: 0.2883 - accuracy: 0.9083 - val_loss: 1.2570 - val_accuracy: 0.6748 - 9s/epoch - 147ms/step\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:5 out of the last 14 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f6f3ebf7dd0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training model on Fold-3\n",
            "Epoch 1/100\n",
            "\n",
            "Epoch 1: val_accuracy improved from -inf to 0.23243, saving model to /content/drive/My Drive/512-gray-urbansounds-fold3-best_model.h5\n",
            "61/61 - 12s - loss: 2.3378 - accuracy: 0.2058 - val_loss: 2.3466 - val_accuracy: 0.2324 - 12s/epoch - 196ms/step\n",
            "Epoch 2/100\n",
            "\n",
            "Epoch 2: val_accuracy improved from 0.23243 to 0.28288, saving model to /content/drive/My Drive/512-gray-urbansounds-fold3-best_model.h5\n",
            "61/61 - 10s - loss: 2.1192 - accuracy: 0.2635 - val_loss: 2.0517 - val_accuracy: 0.2829 - 10s/epoch - 156ms/step\n",
            "Epoch 3/100\n",
            "\n",
            "Epoch 3: val_accuracy improved from 0.28288 to 0.39279, saving model to /content/drive/My Drive/512-gray-urbansounds-fold3-best_model.h5\n",
            "61/61 - 9s - loss: 1.9946 - accuracy: 0.2988 - val_loss: 1.7307 - val_accuracy: 0.3928 - 9s/epoch - 151ms/step\n",
            "Epoch 4/100\n",
            "\n",
            "Epoch 4: val_accuracy did not improve from 0.39279\n",
            "61/61 - 9s - loss: 1.9237 - accuracy: 0.3204 - val_loss: 1.8594 - val_accuracy: 0.2829 - 9s/epoch - 149ms/step\n",
            "Epoch 5/100\n",
            "\n",
            "Epoch 5: val_accuracy did not improve from 0.39279\n",
            "61/61 - 9s - loss: 1.8780 - accuracy: 0.3383 - val_loss: 1.9471 - val_accuracy: 0.3225 - 9s/epoch - 149ms/step\n",
            "Epoch 6/100\n",
            "\n",
            "Epoch 6: val_accuracy did not improve from 0.39279\n",
            "61/61 - 9s - loss: 1.8206 - accuracy: 0.3560 - val_loss: 1.8218 - val_accuracy: 0.3892 - 9s/epoch - 149ms/step\n",
            "Epoch 7/100\n",
            "\n",
            "Epoch 7: val_accuracy did not improve from 0.39279\n",
            "61/61 - 9s - loss: 1.7776 - accuracy: 0.3713 - val_loss: 1.7601 - val_accuracy: 0.3568 - 9s/epoch - 149ms/step\n",
            "Epoch 8/100\n",
            "\n",
            "Epoch 8: val_accuracy improved from 0.39279 to 0.45586, saving model to /content/drive/My Drive/512-gray-urbansounds-fold3-best_model.h5\n",
            "61/61 - 10s - loss: 1.7290 - accuracy: 0.3891 - val_loss: 1.6850 - val_accuracy: 0.4559 - 10s/epoch - 156ms/step\n",
            "Epoch 9/100\n",
            "\n",
            "Epoch 9: val_accuracy improved from 0.45586 to 0.48108, saving model to /content/drive/My Drive/512-gray-urbansounds-fold3-best_model.h5\n",
            "61/61 - 9s - loss: 1.6813 - accuracy: 0.4141 - val_loss: 1.4105 - val_accuracy: 0.4811 - 9s/epoch - 151ms/step\n",
            "Epoch 10/100\n",
            "\n",
            "Epoch 10: val_accuracy did not improve from 0.48108\n",
            "61/61 - 9s - loss: 1.6403 - accuracy: 0.4269 - val_loss: 1.9470 - val_accuracy: 0.4306 - 9s/epoch - 149ms/step\n",
            "Epoch 11/100\n",
            "\n",
            "Epoch 11: val_accuracy did not improve from 0.48108\n",
            "61/61 - 9s - loss: 1.6207 - accuracy: 0.4356 - val_loss: 1.5057 - val_accuracy: 0.4180 - 9s/epoch - 149ms/step\n",
            "Epoch 12/100\n",
            "\n",
            "Epoch 12: val_accuracy did not improve from 0.48108\n",
            "61/61 - 9s - loss: 1.5838 - accuracy: 0.4481 - val_loss: 1.6033 - val_accuracy: 0.4360 - 9s/epoch - 149ms/step\n",
            "Epoch 13/100\n",
            "\n",
            "Epoch 13: val_accuracy did not improve from 0.48108\n",
            "61/61 - 9s - loss: 1.5501 - accuracy: 0.4688 - val_loss: 2.3081 - val_accuracy: 0.2901 - 9s/epoch - 149ms/step\n",
            "Epoch 14/100\n",
            "\n",
            "Epoch 14: val_accuracy improved from 0.48108 to 0.51532, saving model to /content/drive/My Drive/512-gray-urbansounds-fold3-best_model.h5\n",
            "61/61 - 10s - loss: 1.5254 - accuracy: 0.4906 - val_loss: 1.4136 - val_accuracy: 0.5153 - 10s/epoch - 162ms/step\n",
            "Epoch 15/100\n",
            "\n",
            "Epoch 15: val_accuracy did not improve from 0.51532\n",
            "61/61 - 9s - loss: 1.5160 - accuracy: 0.4848 - val_loss: 1.5160 - val_accuracy: 0.4901 - 9s/epoch - 150ms/step\n",
            "Epoch 16/100\n",
            "\n",
            "Epoch 16: val_accuracy did not improve from 0.51532\n",
            "61/61 - 9s - loss: 1.4890 - accuracy: 0.4976 - val_loss: 1.5937 - val_accuracy: 0.4847 - 9s/epoch - 149ms/step\n",
            "Epoch 17/100\n",
            "\n",
            "Epoch 17: val_accuracy did not improve from 0.51532\n",
            "61/61 - 9s - loss: 1.4588 - accuracy: 0.5040 - val_loss: 1.7802 - val_accuracy: 0.4414 - 9s/epoch - 149ms/step\n",
            "Epoch 18/100\n",
            "\n",
            "Epoch 18: val_accuracy did not improve from 0.51532\n",
            "61/61 - 9s - loss: 1.4472 - accuracy: 0.5161 - val_loss: 1.9043 - val_accuracy: 0.3856 - 9s/epoch - 149ms/step\n",
            "Epoch 19/100\n",
            "\n",
            "Epoch 19: val_accuracy did not improve from 0.51532\n",
            "61/61 - 9s - loss: 1.4385 - accuracy: 0.5080 - val_loss: 1.6256 - val_accuracy: 0.4378 - 9s/epoch - 149ms/step\n",
            "Epoch 20/100\n",
            "\n",
            "Epoch 20: val_accuracy did not improve from 0.51532\n",
            "61/61 - 9s - loss: 1.3891 - accuracy: 0.5250 - val_loss: 1.3538 - val_accuracy: 0.5027 - 9s/epoch - 152ms/step\n",
            "Epoch 21/100\n",
            "\n",
            "Epoch 21: val_accuracy did not improve from 0.51532\n",
            "61/61 - 9s - loss: 1.3652 - accuracy: 0.5393 - val_loss: 1.8303 - val_accuracy: 0.4468 - 9s/epoch - 149ms/step\n",
            "Epoch 22/100\n",
            "\n",
            "Epoch 22: val_accuracy did not improve from 0.51532\n",
            "61/61 - 9s - loss: 1.3369 - accuracy: 0.5517 - val_loss: 1.9162 - val_accuracy: 0.4883 - 9s/epoch - 149ms/step\n",
            "Epoch 23/100\n",
            "\n",
            "Epoch 23: val_accuracy did not improve from 0.51532\n",
            "61/61 - 9s - loss: 1.3066 - accuracy: 0.5572 - val_loss: 1.5920 - val_accuracy: 0.5081 - 9s/epoch - 149ms/step\n",
            "Epoch 24/100\n",
            "\n",
            "Epoch 24: val_accuracy improved from 0.51532 to 0.52973, saving model to /content/drive/My Drive/512-gray-urbansounds-fold3-best_model.h5\n",
            "61/61 - 10s - loss: 1.3121 - accuracy: 0.5663 - val_loss: 1.3552 - val_accuracy: 0.5297 - 10s/epoch - 158ms/step\n",
            "Epoch 25/100\n",
            "\n",
            "Epoch 25: val_accuracy did not improve from 0.52973\n",
            "61/61 - 9s - loss: 1.3035 - accuracy: 0.5627 - val_loss: 1.7293 - val_accuracy: 0.4901 - 9s/epoch - 149ms/step\n",
            "Epoch 26/100\n",
            "\n",
            "Epoch 26: val_accuracy did not improve from 0.52973\n",
            "61/61 - 9s - loss: 1.2857 - accuracy: 0.5653 - val_loss: 2.1693 - val_accuracy: 0.4486 - 9s/epoch - 149ms/step\n",
            "Epoch 27/100\n",
            "\n",
            "Epoch 27: val_accuracy improved from 0.52973 to 0.53514, saving model to /content/drive/My Drive/512-gray-urbansounds-fold3-best_model.h5\n",
            "61/61 - 10s - loss: 1.2398 - accuracy: 0.5902 - val_loss: 1.5670 - val_accuracy: 0.5351 - 10s/epoch - 157ms/step\n",
            "Epoch 28/100\n",
            "\n",
            "Epoch 28: val_accuracy did not improve from 0.53514\n",
            "61/61 - 9s - loss: 1.2008 - accuracy: 0.6027 - val_loss: 1.8032 - val_accuracy: 0.4793 - 9s/epoch - 149ms/step\n",
            "Epoch 29/100\n",
            "\n",
            "Epoch 29: val_accuracy improved from 0.53514 to 0.55676, saving model to /content/drive/My Drive/512-gray-urbansounds-fold3-best_model.h5\n",
            "61/61 - 10s - loss: 1.1828 - accuracy: 0.6127 - val_loss: 1.3954 - val_accuracy: 0.5568 - 10s/epoch - 157ms/step\n",
            "Epoch 30/100\n",
            "\n",
            "Epoch 30: val_accuracy did not improve from 0.55676\n",
            "61/61 - 9s - loss: 1.1675 - accuracy: 0.6173 - val_loss: 2.4510 - val_accuracy: 0.4234 - 9s/epoch - 149ms/step\n",
            "Epoch 31/100\n",
            "\n",
            "Epoch 31: val_accuracy did not improve from 0.55676\n",
            "61/61 - 9s - loss: 1.1429 - accuracy: 0.6278 - val_loss: 1.4406 - val_accuracy: 0.5405 - 9s/epoch - 149ms/step\n",
            "Epoch 32/100\n",
            "\n",
            "Epoch 32: val_accuracy improved from 0.55676 to 0.65586, saving model to /content/drive/My Drive/512-gray-urbansounds-fold3-best_model.h5\n",
            "61/61 - 10s - loss: 1.1264 - accuracy: 0.6333 - val_loss: 1.1188 - val_accuracy: 0.6559 - 10s/epoch - 157ms/step\n",
            "Epoch 33/100\n",
            "\n",
            "Epoch 33: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 1.1095 - accuracy: 0.6419 - val_loss: 1.3990 - val_accuracy: 0.6288 - 9s/epoch - 150ms/step\n",
            "Epoch 34/100\n",
            "\n",
            "Epoch 34: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 1.0629 - accuracy: 0.6565 - val_loss: 1.1010 - val_accuracy: 0.6523 - 9s/epoch - 152ms/step\n",
            "Epoch 35/100\n",
            "\n",
            "Epoch 35: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 1.0317 - accuracy: 0.6707 - val_loss: 1.1034 - val_accuracy: 0.6432 - 9s/epoch - 153ms/step\n",
            "Epoch 36/100\n",
            "\n",
            "Epoch 36: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 1.0129 - accuracy: 0.6754 - val_loss: 1.3577 - val_accuracy: 0.5351 - 9s/epoch - 150ms/step\n",
            "Epoch 37/100\n",
            "\n",
            "Epoch 37: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 1.0039 - accuracy: 0.6791 - val_loss: 1.8639 - val_accuracy: 0.4991 - 9s/epoch - 149ms/step\n",
            "Epoch 38/100\n",
            "\n",
            "Epoch 38: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.9767 - accuracy: 0.6935 - val_loss: 2.1668 - val_accuracy: 0.4919 - 9s/epoch - 149ms/step\n",
            "Epoch 39/100\n",
            "\n",
            "Epoch 39: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.9608 - accuracy: 0.7021 - val_loss: 2.0818 - val_accuracy: 0.4631 - 9s/epoch - 149ms/step\n",
            "Epoch 40/100\n",
            "\n",
            "Epoch 40: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.9425 - accuracy: 0.7135 - val_loss: 1.6587 - val_accuracy: 0.5063 - 9s/epoch - 149ms/step\n",
            "Epoch 41/100\n",
            "\n",
            "Epoch 41: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.9344 - accuracy: 0.7090 - val_loss: 1.0713 - val_accuracy: 0.6342 - 9s/epoch - 149ms/step\n",
            "Epoch 42/100\n",
            "\n",
            "Epoch 42: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.9272 - accuracy: 0.7085 - val_loss: 1.2927 - val_accuracy: 0.5694 - 9s/epoch - 149ms/step\n",
            "Epoch 43/100\n",
            "\n",
            "Epoch 43: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.8834 - accuracy: 0.7267 - val_loss: 1.3756 - val_accuracy: 0.5982 - 9s/epoch - 150ms/step\n",
            "Epoch 44/100\n",
            "\n",
            "Epoch 44: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.8691 - accuracy: 0.7287 - val_loss: 1.0535 - val_accuracy: 0.6450 - 9s/epoch - 149ms/step\n",
            "Epoch 45/100\n",
            "\n",
            "Epoch 45: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.8414 - accuracy: 0.7406 - val_loss: 1.2924 - val_accuracy: 0.5766 - 9s/epoch - 149ms/step\n",
            "Epoch 46/100\n",
            "\n",
            "Epoch 46: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.8264 - accuracy: 0.7464 - val_loss: 1.1984 - val_accuracy: 0.6180 - 9s/epoch - 149ms/step\n",
            "Epoch 47/100\n",
            "\n",
            "Epoch 47: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.8090 - accuracy: 0.7514 - val_loss: 1.1442 - val_accuracy: 0.6342 - 9s/epoch - 149ms/step\n",
            "Epoch 48/100\n",
            "\n",
            "Epoch 48: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.8084 - accuracy: 0.7448 - val_loss: 1.1507 - val_accuracy: 0.6180 - 9s/epoch - 150ms/step\n",
            "Epoch 49/100\n",
            "\n",
            "Epoch 49: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.7733 - accuracy: 0.7641 - val_loss: 1.3411 - val_accuracy: 0.5838 - 9s/epoch - 149ms/step\n",
            "Epoch 50/100\n",
            "\n",
            "Epoch 50: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.7625 - accuracy: 0.7688 - val_loss: 1.3937 - val_accuracy: 0.5712 - 9s/epoch - 150ms/step\n",
            "Epoch 51/100\n",
            "\n",
            "Epoch 51: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.7332 - accuracy: 0.7755 - val_loss: 1.3201 - val_accuracy: 0.5964 - 9s/epoch - 149ms/step\n",
            "Epoch 52/100\n",
            "\n",
            "Epoch 52: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.7215 - accuracy: 0.7829 - val_loss: 1.1492 - val_accuracy: 0.6234 - 9s/epoch - 149ms/step\n",
            "Epoch 53/100\n",
            "\n",
            "Epoch 53: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.6989 - accuracy: 0.7902 - val_loss: 1.1747 - val_accuracy: 0.6216 - 9s/epoch - 149ms/step\n",
            "Epoch 54/100\n",
            "\n",
            "Epoch 54: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.6878 - accuracy: 0.7952 - val_loss: 1.1230 - val_accuracy: 0.5856 - 9s/epoch - 149ms/step\n",
            "Epoch 55/100\n",
            "\n",
            "Epoch 55: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.6723 - accuracy: 0.7898 - val_loss: 2.6449 - val_accuracy: 0.4703 - 9s/epoch - 155ms/step\n",
            "Epoch 56/100\n",
            "\n",
            "Epoch 56: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.6447 - accuracy: 0.8082 - val_loss: 1.0873 - val_accuracy: 0.6162 - 9s/epoch - 149ms/step\n",
            "Epoch 57/100\n",
            "\n",
            "Epoch 57: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.6366 - accuracy: 0.8067 - val_loss: 1.1439 - val_accuracy: 0.6162 - 9s/epoch - 149ms/step\n",
            "Epoch 58/100\n",
            "\n",
            "Epoch 58: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.6478 - accuracy: 0.8018 - val_loss: 1.4949 - val_accuracy: 0.5802 - 9s/epoch - 149ms/step\n",
            "Epoch 59/100\n",
            "\n",
            "Epoch 59: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.6152 - accuracy: 0.8117 - val_loss: 1.4686 - val_accuracy: 0.5495 - 9s/epoch - 149ms/step\n",
            "Epoch 60/100\n",
            "\n",
            "Epoch 60: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.6003 - accuracy: 0.8199 - val_loss: 1.4722 - val_accuracy: 0.5946 - 9s/epoch - 149ms/step\n",
            "Epoch 61/100\n",
            "\n",
            "Epoch 61: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.6079 - accuracy: 0.8190 - val_loss: 1.5033 - val_accuracy: 0.5820 - 9s/epoch - 149ms/step\n",
            "Epoch 62/100\n",
            "\n",
            "Epoch 62: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.5799 - accuracy: 0.8255 - val_loss: 1.2938 - val_accuracy: 0.6144 - 9s/epoch - 149ms/step\n",
            "Epoch 63/100\n",
            "\n",
            "Epoch 63: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.5727 - accuracy: 0.8276 - val_loss: 1.1674 - val_accuracy: 0.6126 - 9s/epoch - 149ms/step\n",
            "Epoch 64/100\n",
            "\n",
            "Epoch 64: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.5592 - accuracy: 0.8270 - val_loss: 1.5906 - val_accuracy: 0.5910 - 9s/epoch - 149ms/step\n",
            "Epoch 65/100\n",
            "\n",
            "Epoch 65: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.5656 - accuracy: 0.8257 - val_loss: 1.6314 - val_accuracy: 0.5459 - 9s/epoch - 149ms/step\n",
            "Epoch 66/100\n",
            "\n",
            "Epoch 66: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.5571 - accuracy: 0.8287 - val_loss: 1.8125 - val_accuracy: 0.5784 - 9s/epoch - 149ms/step\n",
            "Epoch 67/100\n",
            "\n",
            "Epoch 67: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.5478 - accuracy: 0.8366 - val_loss: 1.3866 - val_accuracy: 0.6054 - 9s/epoch - 149ms/step\n",
            "Epoch 68/100\n",
            "\n",
            "Epoch 68: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.5372 - accuracy: 0.8368 - val_loss: 1.7025 - val_accuracy: 0.5387 - 9s/epoch - 149ms/step\n",
            "Epoch 69/100\n",
            "\n",
            "Epoch 69: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.5274 - accuracy: 0.8385 - val_loss: 1.3517 - val_accuracy: 0.5694 - 9s/epoch - 149ms/step\n",
            "Epoch 70/100\n",
            "\n",
            "Epoch 70: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.4989 - accuracy: 0.8473 - val_loss: 1.3420 - val_accuracy: 0.6342 - 9s/epoch - 149ms/step\n",
            "Epoch 71/100\n",
            "\n",
            "Epoch 71: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.5050 - accuracy: 0.8464 - val_loss: 1.4183 - val_accuracy: 0.5946 - 9s/epoch - 149ms/step\n",
            "Epoch 72/100\n",
            "\n",
            "Epoch 72: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.4976 - accuracy: 0.8458 - val_loss: 1.3882 - val_accuracy: 0.6270 - 9s/epoch - 153ms/step\n",
            "Epoch 73/100\n",
            "\n",
            "Epoch 73: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.4993 - accuracy: 0.8519 - val_loss: 1.5369 - val_accuracy: 0.6000 - 9s/epoch - 149ms/step\n",
            "Epoch 74/100\n",
            "\n",
            "Epoch 74: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.4582 - accuracy: 0.8627 - val_loss: 1.1986 - val_accuracy: 0.6324 - 9s/epoch - 150ms/step\n",
            "Epoch 75/100\n",
            "\n",
            "Epoch 75: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.4488 - accuracy: 0.8646 - val_loss: 1.3367 - val_accuracy: 0.6360 - 9s/epoch - 153ms/step\n",
            "Epoch 76/100\n",
            "\n",
            "Epoch 76: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.4476 - accuracy: 0.8667 - val_loss: 1.1332 - val_accuracy: 0.6541 - 9s/epoch - 153ms/step\n",
            "Epoch 77/100\n",
            "\n",
            "Epoch 77: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.4350 - accuracy: 0.8678 - val_loss: 1.3927 - val_accuracy: 0.6216 - 9s/epoch - 150ms/step\n",
            "Epoch 78/100\n",
            "\n",
            "Epoch 78: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.4212 - accuracy: 0.8755 - val_loss: 1.2432 - val_accuracy: 0.6468 - 9s/epoch - 149ms/step\n",
            "Epoch 79/100\n",
            "\n",
            "Epoch 79: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.4241 - accuracy: 0.8705 - val_loss: 1.3345 - val_accuracy: 0.6432 - 9s/epoch - 149ms/step\n",
            "Epoch 80/100\n",
            "\n",
            "Epoch 80: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.4209 - accuracy: 0.8692 - val_loss: 1.2694 - val_accuracy: 0.6090 - 9s/epoch - 149ms/step\n",
            "Epoch 81/100\n",
            "\n",
            "Epoch 81: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.4114 - accuracy: 0.8774 - val_loss: 1.3648 - val_accuracy: 0.6180 - 9s/epoch - 149ms/step\n",
            "Epoch 82/100\n",
            "\n",
            "Epoch 82: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.4084 - accuracy: 0.8766 - val_loss: 1.2537 - val_accuracy: 0.6144 - 9s/epoch - 149ms/step\n",
            "Epoch 83/100\n",
            "\n",
            "Epoch 83: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.4131 - accuracy: 0.8737 - val_loss: 1.5650 - val_accuracy: 0.5964 - 9s/epoch - 149ms/step\n",
            "Epoch 84/100\n",
            "\n",
            "Epoch 84: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.3920 - accuracy: 0.8799 - val_loss: 1.1832 - val_accuracy: 0.6162 - 9s/epoch - 149ms/step\n",
            "Epoch 85/100\n",
            "\n",
            "Epoch 85: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.3806 - accuracy: 0.8831 - val_loss: 1.4439 - val_accuracy: 0.6198 - 9s/epoch - 150ms/step\n",
            "Epoch 86/100\n",
            "\n",
            "Epoch 86: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.3782 - accuracy: 0.8819 - val_loss: 1.2337 - val_accuracy: 0.6234 - 9s/epoch - 150ms/step\n",
            "Epoch 87/100\n",
            "\n",
            "Epoch 87: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.3766 - accuracy: 0.8824 - val_loss: 1.4800 - val_accuracy: 0.6396 - 9s/epoch - 149ms/step\n",
            "Epoch 88/100\n",
            "\n",
            "Epoch 88: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.3780 - accuracy: 0.8892 - val_loss: 1.4628 - val_accuracy: 0.5748 - 9s/epoch - 149ms/step\n",
            "Epoch 89/100\n",
            "\n",
            "Epoch 89: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.3630 - accuracy: 0.8887 - val_loss: 1.5101 - val_accuracy: 0.6288 - 9s/epoch - 149ms/step\n",
            "Epoch 90/100\n",
            "\n",
            "Epoch 90: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.3593 - accuracy: 0.8913 - val_loss: 1.2147 - val_accuracy: 0.6378 - 9s/epoch - 149ms/step\n",
            "Epoch 91/100\n",
            "\n",
            "Epoch 91: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.3561 - accuracy: 0.8888 - val_loss: 1.5652 - val_accuracy: 0.6018 - 9s/epoch - 149ms/step\n",
            "Epoch 92/100\n",
            "\n",
            "Epoch 92: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.3471 - accuracy: 0.8907 - val_loss: 1.3373 - val_accuracy: 0.6144 - 9s/epoch - 149ms/step\n",
            "Epoch 93/100\n",
            "\n",
            "Epoch 93: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.3434 - accuracy: 0.8936 - val_loss: 1.3072 - val_accuracy: 0.6018 - 9s/epoch - 149ms/step\n",
            "Epoch 94/100\n",
            "\n",
            "Epoch 94: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.3372 - accuracy: 0.8956 - val_loss: 1.3828 - val_accuracy: 0.6036 - 9s/epoch - 150ms/step\n",
            "Epoch 95/100\n",
            "\n",
            "Epoch 95: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.3412 - accuracy: 0.8947 - val_loss: 1.3370 - val_accuracy: 0.6054 - 9s/epoch - 150ms/step\n",
            "Epoch 96/100\n",
            "\n",
            "Epoch 96: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.3382 - accuracy: 0.8955 - val_loss: 1.4131 - val_accuracy: 0.5964 - 9s/epoch - 156ms/step\n",
            "Epoch 97/100\n",
            "\n",
            "Epoch 97: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.3304 - accuracy: 0.8970 - val_loss: 1.2595 - val_accuracy: 0.6252 - 9s/epoch - 149ms/step\n",
            "Epoch 98/100\n",
            "\n",
            "Epoch 98: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.3305 - accuracy: 0.8994 - val_loss: 1.2560 - val_accuracy: 0.6505 - 9s/epoch - 150ms/step\n",
            "Epoch 99/100\n",
            "\n",
            "Epoch 99: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.3387 - accuracy: 0.8978 - val_loss: 1.4966 - val_accuracy: 0.6234 - 9s/epoch - 149ms/step\n",
            "Epoch 100/100\n",
            "\n",
            "Epoch 100: val_accuracy did not improve from 0.65586\n",
            "61/61 - 9s - loss: 0.3182 - accuracy: 0.9012 - val_loss: 1.5806 - val_accuracy: 0.6270 - 9s/epoch - 149ms/step\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:5 out of the last 13 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f6f5838fd40> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training model on Fold-4\n",
            "Epoch 1/100\n",
            "\n",
            "Epoch 1: val_accuracy improved from -inf to 0.18519, saving model to /content/drive/My Drive/512-gray-urbansounds-fold4-best_model.h5\n",
            "61/61 - 13s - loss: 2.3949 - accuracy: 0.1932 - val_loss: 3.0119 - val_accuracy: 0.1852 - 13s/epoch - 217ms/step\n",
            "Epoch 2/100\n",
            "\n",
            "Epoch 2: val_accuracy improved from 0.18519 to 0.24916, saving model to /content/drive/My Drive/512-gray-urbansounds-fold4-best_model.h5\n",
            "61/61 - 9s - loss: 2.1261 - accuracy: 0.2605 - val_loss: 2.0093 - val_accuracy: 0.2492 - 9s/epoch - 150ms/step\n",
            "Epoch 3/100\n",
            "\n",
            "Epoch 3: val_accuracy improved from 0.24916 to 0.28451, saving model to /content/drive/My Drive/512-gray-urbansounds-fold4-best_model.h5\n",
            "61/61 - 10s - loss: 1.9915 - accuracy: 0.2954 - val_loss: 2.1695 - val_accuracy: 0.2845 - 10s/epoch - 157ms/step\n",
            "Epoch 4/100\n",
            "\n",
            "Epoch 4: val_accuracy did not improve from 0.28451\n",
            "61/61 - 9s - loss: 1.9143 - accuracy: 0.3153 - val_loss: 2.5296 - val_accuracy: 0.2357 - 9s/epoch - 149ms/step\n",
            "Epoch 5/100\n",
            "\n",
            "Epoch 5: val_accuracy did not improve from 0.28451\n",
            "61/61 - 9s - loss: 1.8552 - accuracy: 0.3460 - val_loss: 2.1139 - val_accuracy: 0.2744 - 9s/epoch - 148ms/step\n",
            "Epoch 6/100\n",
            "\n",
            "Epoch 6: val_accuracy did not improve from 0.28451\n",
            "61/61 - 9s - loss: 1.7848 - accuracy: 0.3652 - val_loss: 2.4463 - val_accuracy: 0.2205 - 9s/epoch - 149ms/step\n",
            "Epoch 7/100\n",
            "\n",
            "Epoch 7: val_accuracy improved from 0.28451 to 0.32828, saving model to /content/drive/My Drive/512-gray-urbansounds-fold4-best_model.h5\n",
            "61/61 - 10s - loss: 1.7262 - accuracy: 0.3850 - val_loss: 1.9947 - val_accuracy: 0.3283 - 10s/epoch - 156ms/step\n",
            "Epoch 8/100\n",
            "\n",
            "Epoch 8: val_accuracy did not improve from 0.32828\n",
            "61/61 - 9s - loss: 1.6780 - accuracy: 0.4004 - val_loss: 2.5507 - val_accuracy: 0.2155 - 9s/epoch - 149ms/step\n",
            "Epoch 9/100\n",
            "\n",
            "Epoch 9: val_accuracy improved from 0.32828 to 0.36364, saving model to /content/drive/My Drive/512-gray-urbansounds-fold4-best_model.h5\n",
            "61/61 - 9s - loss: 1.6272 - accuracy: 0.4235 - val_loss: 2.0390 - val_accuracy: 0.3636 - 9s/epoch - 156ms/step\n",
            "Epoch 10/100\n",
            "\n",
            "Epoch 10: val_accuracy did not improve from 0.36364\n",
            "61/61 - 9s - loss: 1.5978 - accuracy: 0.4376 - val_loss: 2.5281 - val_accuracy: 0.2896 - 9s/epoch - 148ms/step\n",
            "Epoch 11/100\n",
            "\n",
            "Epoch 11: val_accuracy improved from 0.36364 to 0.41077, saving model to /content/drive/My Drive/512-gray-urbansounds-fold4-best_model.h5\n",
            "61/61 - 10s - loss: 1.5668 - accuracy: 0.4385 - val_loss: 1.9853 - val_accuracy: 0.4108 - 10s/epoch - 157ms/step\n",
            "Epoch 12/100\n",
            "\n",
            "Epoch 12: val_accuracy improved from 0.41077 to 0.43771, saving model to /content/drive/My Drive/512-gray-urbansounds-fold4-best_model.h5\n",
            "61/61 - 9s - loss: 1.5551 - accuracy: 0.4482 - val_loss: 1.6641 - val_accuracy: 0.4377 - 9s/epoch - 151ms/step\n",
            "Epoch 13/100\n",
            "\n",
            "Epoch 13: val_accuracy did not improve from 0.43771\n",
            "61/61 - 9s - loss: 1.5396 - accuracy: 0.4601 - val_loss: 1.8036 - val_accuracy: 0.4158 - 9s/epoch - 149ms/step\n",
            "Epoch 14/100\n",
            "\n",
            "Epoch 14: val_accuracy improved from 0.43771 to 0.50168, saving model to /content/drive/My Drive/512-gray-urbansounds-fold4-best_model.h5\n",
            "61/61 - 10s - loss: 1.5211 - accuracy: 0.4644 - val_loss: 1.3766 - val_accuracy: 0.5017 - 10s/epoch - 157ms/step\n",
            "Epoch 15/100\n",
            "\n",
            "Epoch 15: val_accuracy did not improve from 0.50168\n",
            "61/61 - 9s - loss: 1.4845 - accuracy: 0.4800 - val_loss: 2.6832 - val_accuracy: 0.2879 - 9s/epoch - 154ms/step\n",
            "Epoch 16/100\n",
            "\n",
            "Epoch 16: val_accuracy improved from 0.50168 to 0.50673, saving model to /content/drive/My Drive/512-gray-urbansounds-fold4-best_model.h5\n",
            "61/61 - 10s - loss: 1.4306 - accuracy: 0.5052 - val_loss: 1.4311 - val_accuracy: 0.5067 - 10s/epoch - 157ms/step\n",
            "Epoch 17/100\n",
            "\n",
            "Epoch 17: val_accuracy improved from 0.50673 to 0.58249, saving model to /content/drive/My Drive/512-gray-urbansounds-fold4-best_model.h5\n",
            "61/61 - 9s - loss: 1.4023 - accuracy: 0.5075 - val_loss: 1.2553 - val_accuracy: 0.5825 - 9s/epoch - 151ms/step\n",
            "Epoch 18/100\n",
            "\n",
            "Epoch 18: val_accuracy did not improve from 0.58249\n",
            "61/61 - 9s - loss: 1.3784 - accuracy: 0.5254 - val_loss: 1.4118 - val_accuracy: 0.5051 - 9s/epoch - 149ms/step\n",
            "Epoch 19/100\n",
            "\n",
            "Epoch 19: val_accuracy did not improve from 0.58249\n",
            "61/61 - 9s - loss: 1.3676 - accuracy: 0.5200 - val_loss: 1.7955 - val_accuracy: 0.3687 - 9s/epoch - 149ms/step\n",
            "Epoch 20/100\n",
            "\n",
            "Epoch 20: val_accuracy did not improve from 0.58249\n",
            "61/61 - 9s - loss: 1.3409 - accuracy: 0.5347 - val_loss: 1.7610 - val_accuracy: 0.4646 - 9s/epoch - 149ms/step\n",
            "Epoch 21/100\n",
            "\n",
            "Epoch 21: val_accuracy did not improve from 0.58249\n",
            "61/61 - 9s - loss: 1.3210 - accuracy: 0.5456 - val_loss: 1.4111 - val_accuracy: 0.5421 - 9s/epoch - 149ms/step\n",
            "Epoch 22/100\n",
            "\n",
            "Epoch 22: val_accuracy did not improve from 0.58249\n",
            "61/61 - 9s - loss: 1.2837 - accuracy: 0.5559 - val_loss: 2.1052 - val_accuracy: 0.4226 - 9s/epoch - 149ms/step\n",
            "Epoch 23/100\n",
            "\n",
            "Epoch 23: val_accuracy did not improve from 0.58249\n",
            "61/61 - 9s - loss: 1.2783 - accuracy: 0.5646 - val_loss: 2.0735 - val_accuracy: 0.4747 - 9s/epoch - 149ms/step\n",
            "Epoch 24/100\n",
            "\n",
            "Epoch 24: val_accuracy did not improve from 0.58249\n",
            "61/61 - 9s - loss: 1.2406 - accuracy: 0.5685 - val_loss: 2.4876 - val_accuracy: 0.4074 - 9s/epoch - 148ms/step\n",
            "Epoch 25/100\n",
            "\n",
            "Epoch 25: val_accuracy did not improve from 0.58249\n",
            "61/61 - 9s - loss: 1.1988 - accuracy: 0.5969 - val_loss: 2.3984 - val_accuracy: 0.3569 - 9s/epoch - 149ms/step\n",
            "Epoch 26/100\n",
            "\n",
            "Epoch 26: val_accuracy did not improve from 0.58249\n",
            "61/61 - 9s - loss: 1.2190 - accuracy: 0.5882 - val_loss: 1.7813 - val_accuracy: 0.4226 - 9s/epoch - 148ms/step\n",
            "Epoch 27/100\n",
            "\n",
            "Epoch 27: val_accuracy did not improve from 0.58249\n",
            "61/61 - 9s - loss: 1.1812 - accuracy: 0.5971 - val_loss: 1.4152 - val_accuracy: 0.5774 - 9s/epoch - 149ms/step\n",
            "Epoch 28/100\n",
            "\n",
            "Epoch 28: val_accuracy did not improve from 0.58249\n",
            "61/61 - 9s - loss: 1.1308 - accuracy: 0.6223 - val_loss: 1.7728 - val_accuracy: 0.5067 - 9s/epoch - 148ms/step\n",
            "Epoch 29/100\n",
            "\n",
            "Epoch 29: val_accuracy did not improve from 0.58249\n",
            "61/61 - 9s - loss: 1.1149 - accuracy: 0.6266 - val_loss: 1.5049 - val_accuracy: 0.5404 - 9s/epoch - 149ms/step\n",
            "Epoch 30/100\n",
            "\n",
            "Epoch 30: val_accuracy did not improve from 0.58249\n",
            "61/61 - 9s - loss: 1.0727 - accuracy: 0.6516 - val_loss: 1.2095 - val_accuracy: 0.5825 - 9s/epoch - 149ms/step\n",
            "Epoch 31/100\n",
            "\n",
            "Epoch 31: val_accuracy did not improve from 0.58249\n",
            "61/61 - 9s - loss: 1.0582 - accuracy: 0.6522 - val_loss: 1.6126 - val_accuracy: 0.4966 - 9s/epoch - 148ms/step\n",
            "Epoch 32/100\n",
            "\n",
            "Epoch 32: val_accuracy did not improve from 0.58249\n",
            "61/61 - 9s - loss: 1.0419 - accuracy: 0.6518 - val_loss: 2.4911 - val_accuracy: 0.4074 - 9s/epoch - 149ms/step\n",
            "Epoch 33/100\n",
            "\n",
            "Epoch 33: val_accuracy did not improve from 0.58249\n",
            "61/61 - 9s - loss: 1.0258 - accuracy: 0.6649 - val_loss: 4.1682 - val_accuracy: 0.4613 - 9s/epoch - 149ms/step\n",
            "Epoch 34/100\n",
            "\n",
            "Epoch 34: val_accuracy did not improve from 0.58249\n",
            "61/61 - 9s - loss: 1.0046 - accuracy: 0.6749 - val_loss: 2.1929 - val_accuracy: 0.5471 - 9s/epoch - 149ms/step\n",
            "Epoch 35/100\n",
            "\n",
            "Epoch 35: val_accuracy did not improve from 0.58249\n",
            "61/61 - 9s - loss: 0.9624 - accuracy: 0.6879 - val_loss: 6.2124 - val_accuracy: 0.3519 - 9s/epoch - 148ms/step\n",
            "Epoch 36/100\n",
            "\n",
            "Epoch 36: val_accuracy did not improve from 0.58249\n",
            "61/61 - 9s - loss: 0.9679 - accuracy: 0.6883 - val_loss: 6.6245 - val_accuracy: 0.3502 - 9s/epoch - 155ms/step\n",
            "Epoch 37/100\n",
            "\n",
            "Epoch 37: val_accuracy did not improve from 0.58249\n",
            "61/61 - 9s - loss: 0.9262 - accuracy: 0.7001 - val_loss: 3.3444 - val_accuracy: 0.4175 - 9s/epoch - 149ms/step\n",
            "Epoch 38/100\n",
            "\n",
            "Epoch 38: val_accuracy did not improve from 0.58249\n",
            "61/61 - 9s - loss: 0.9041 - accuracy: 0.7081 - val_loss: 2.4539 - val_accuracy: 0.5152 - 9s/epoch - 149ms/step\n",
            "Epoch 39/100\n",
            "\n",
            "Epoch 39: val_accuracy did not improve from 0.58249\n",
            "61/61 - 9s - loss: 0.8881 - accuracy: 0.7140 - val_loss: 2.3472 - val_accuracy: 0.5253 - 9s/epoch - 149ms/step\n",
            "Epoch 40/100\n",
            "\n",
            "Epoch 40: val_accuracy did not improve from 0.58249\n",
            "61/61 - 9s - loss: 0.8519 - accuracy: 0.7298 - val_loss: 3.1607 - val_accuracy: 0.5067 - 9s/epoch - 149ms/step\n",
            "Epoch 41/100\n",
            "\n",
            "Epoch 41: val_accuracy did not improve from 0.58249\n",
            "61/61 - 9s - loss: 0.8488 - accuracy: 0.7325 - val_loss: 4.2523 - val_accuracy: 0.4158 - 9s/epoch - 149ms/step\n",
            "Epoch 42/100\n",
            "\n",
            "Epoch 42: val_accuracy did not improve from 0.58249\n",
            "61/61 - 9s - loss: 0.8271 - accuracy: 0.7344 - val_loss: 4.6903 - val_accuracy: 0.4966 - 9s/epoch - 149ms/step\n",
            "Epoch 43/100\n",
            "\n",
            "Epoch 43: val_accuracy improved from 0.58249 to 0.63973, saving model to /content/drive/My Drive/512-gray-urbansounds-fold4-best_model.h5\n",
            "61/61 - 10s - loss: 0.8035 - accuracy: 0.7454 - val_loss: 1.2410 - val_accuracy: 0.6397 - 10s/epoch - 156ms/step\n",
            "Epoch 44/100\n",
            "\n",
            "Epoch 44: val_accuracy improved from 0.63973 to 0.64141, saving model to /content/drive/My Drive/512-gray-urbansounds-fold4-best_model.h5\n",
            "61/61 - 10s - loss: 0.7816 - accuracy: 0.7492 - val_loss: 1.0903 - val_accuracy: 0.6414 - 10s/epoch - 157ms/step\n",
            "Epoch 45/100\n",
            "\n",
            "Epoch 45: val_accuracy did not improve from 0.64141\n",
            "61/61 - 9s - loss: 0.7925 - accuracy: 0.7427 - val_loss: 1.2834 - val_accuracy: 0.6077 - 9s/epoch - 149ms/step\n",
            "Epoch 46/100\n",
            "\n",
            "Epoch 46: val_accuracy improved from 0.64141 to 0.67340, saving model to /content/drive/My Drive/512-gray-urbansounds-fold4-best_model.h5\n",
            "61/61 - 10s - loss: 0.7552 - accuracy: 0.7548 - val_loss: 1.0172 - val_accuracy: 0.6734 - 10s/epoch - 156ms/step\n",
            "Epoch 47/100\n",
            "\n",
            "Epoch 47: val_accuracy improved from 0.67340 to 0.71212, saving model to /content/drive/My Drive/512-gray-urbansounds-fold4-best_model.h5\n",
            "61/61 - 9s - loss: 0.7606 - accuracy: 0.7592 - val_loss: 0.8980 - val_accuracy: 0.7121 - 9s/epoch - 150ms/step\n",
            "Epoch 48/100\n",
            "\n",
            "Epoch 48: val_accuracy did not improve from 0.71212\n",
            "61/61 - 9s - loss: 0.7588 - accuracy: 0.7578 - val_loss: 1.5229 - val_accuracy: 0.5539 - 9s/epoch - 149ms/step\n",
            "Epoch 49/100\n",
            "\n",
            "Epoch 49: val_accuracy did not improve from 0.71212\n",
            "61/61 - 9s - loss: 0.7275 - accuracy: 0.7688 - val_loss: 1.1366 - val_accuracy: 0.6347 - 9s/epoch - 149ms/step\n",
            "Epoch 50/100\n",
            "\n",
            "Epoch 50: val_accuracy did not improve from 0.71212\n",
            "61/61 - 9s - loss: 0.7316 - accuracy: 0.7669 - val_loss: 2.2658 - val_accuracy: 0.4276 - 9s/epoch - 149ms/step\n",
            "Epoch 51/100\n",
            "\n",
            "Epoch 51: val_accuracy did not improve from 0.71212\n",
            "61/61 - 9s - loss: 0.7034 - accuracy: 0.7749 - val_loss: 1.0175 - val_accuracy: 0.6785 - 9s/epoch - 148ms/step\n",
            "Epoch 52/100\n",
            "\n",
            "Epoch 52: val_accuracy did not improve from 0.71212\n",
            "61/61 - 9s - loss: 0.6721 - accuracy: 0.7821 - val_loss: 1.0488 - val_accuracy: 0.6633 - 9s/epoch - 149ms/step\n",
            "Epoch 53/100\n",
            "\n",
            "Epoch 53: val_accuracy did not improve from 0.71212\n",
            "61/61 - 9s - loss: 0.6401 - accuracy: 0.8025 - val_loss: 1.7137 - val_accuracy: 0.5370 - 9s/epoch - 149ms/step\n",
            "Epoch 54/100\n",
            "\n",
            "Epoch 54: val_accuracy did not improve from 0.71212\n",
            "61/61 - 9s - loss: 0.6385 - accuracy: 0.7945 - val_loss: 1.4542 - val_accuracy: 0.5690 - 9s/epoch - 149ms/step\n",
            "Epoch 55/100\n",
            "\n",
            "Epoch 55: val_accuracy did not improve from 0.71212\n",
            "61/61 - 9s - loss: 0.6655 - accuracy: 0.7887 - val_loss: 1.4235 - val_accuracy: 0.6044 - 9s/epoch - 149ms/step\n",
            "Epoch 56/100\n",
            "\n",
            "Epoch 56: val_accuracy did not improve from 0.71212\n",
            "61/61 - 9s - loss: 0.6384 - accuracy: 0.7977 - val_loss: 2.3747 - val_accuracy: 0.5135 - 9s/epoch - 155ms/step\n",
            "Epoch 57/100\n",
            "\n",
            "Epoch 57: val_accuracy did not improve from 0.71212\n",
            "61/61 - 9s - loss: 0.6091 - accuracy: 0.8033 - val_loss: 1.6856 - val_accuracy: 0.6077 - 9s/epoch - 149ms/step\n",
            "Epoch 58/100\n",
            "\n",
            "Epoch 58: val_accuracy did not improve from 0.71212\n",
            "61/61 - 9s - loss: 0.6234 - accuracy: 0.8030 - val_loss: 1.4796 - val_accuracy: 0.5791 - 9s/epoch - 148ms/step\n",
            "Epoch 59/100\n",
            "\n",
            "Epoch 59: val_accuracy improved from 0.71212 to 0.72391, saving model to /content/drive/My Drive/512-gray-urbansounds-fold4-best_model.h5\n",
            "61/61 - 10s - loss: 0.6078 - accuracy: 0.8090 - val_loss: 1.0081 - val_accuracy: 0.7239 - 10s/epoch - 156ms/step\n",
            "Epoch 60/100\n",
            "\n",
            "Epoch 60: val_accuracy did not improve from 0.72391\n",
            "61/61 - 9s - loss: 0.6040 - accuracy: 0.8103 - val_loss: 1.0437 - val_accuracy: 0.6886 - 9s/epoch - 149ms/step\n",
            "Epoch 61/100\n",
            "\n",
            "Epoch 61: val_accuracy improved from 0.72391 to 0.75253, saving model to /content/drive/My Drive/512-gray-urbansounds-fold4-best_model.h5\n",
            "61/61 - 10s - loss: 0.5799 - accuracy: 0.8152 - val_loss: 0.8226 - val_accuracy: 0.7525 - 10s/epoch - 157ms/step\n",
            "Epoch 62/100\n",
            "\n",
            "Epoch 62: val_accuracy did not improve from 0.75253\n",
            "61/61 - 9s - loss: 0.5764 - accuracy: 0.8163 - val_loss: 1.1398 - val_accuracy: 0.6313 - 9s/epoch - 149ms/step\n",
            "Epoch 63/100\n",
            "\n",
            "Epoch 63: val_accuracy did not improve from 0.75253\n",
            "61/61 - 9s - loss: 0.5502 - accuracy: 0.8233 - val_loss: 1.2509 - val_accuracy: 0.6145 - 9s/epoch - 149ms/step\n",
            "Epoch 64/100\n",
            "\n",
            "Epoch 64: val_accuracy improved from 0.75253 to 0.76431, saving model to /content/drive/My Drive/512-gray-urbansounds-fold4-best_model.h5\n",
            "61/61 - 10s - loss: 0.5325 - accuracy: 0.8308 - val_loss: 0.7935 - val_accuracy: 0.7643 - 10s/epoch - 157ms/step\n",
            "Epoch 65/100\n",
            "\n",
            "Epoch 65: val_accuracy did not improve from 0.76431\n",
            "61/61 - 9s - loss: 0.5065 - accuracy: 0.8383 - val_loss: 0.8242 - val_accuracy: 0.7290 - 9s/epoch - 148ms/step\n",
            "Epoch 66/100\n",
            "\n",
            "Epoch 66: val_accuracy did not improve from 0.76431\n",
            "61/61 - 9s - loss: 0.5020 - accuracy: 0.8424 - val_loss: 0.9335 - val_accuracy: 0.7256 - 9s/epoch - 149ms/step\n",
            "Epoch 67/100\n",
            "\n",
            "Epoch 67: val_accuracy improved from 0.76431 to 0.77609, saving model to /content/drive/My Drive/512-gray-urbansounds-fold4-best_model.h5\n",
            "61/61 - 10s - loss: 0.4887 - accuracy: 0.8398 - val_loss: 0.7931 - val_accuracy: 0.7761 - 10s/epoch - 156ms/step\n",
            "Epoch 68/100\n",
            "\n",
            "Epoch 68: val_accuracy did not improve from 0.77609\n",
            "61/61 - 9s - loss: 0.4674 - accuracy: 0.8547 - val_loss: 0.8255 - val_accuracy: 0.7458 - 9s/epoch - 149ms/step\n",
            "Epoch 69/100\n",
            "\n",
            "Epoch 69: val_accuracy did not improve from 0.77609\n",
            "61/61 - 9s - loss: 0.4708 - accuracy: 0.8513 - val_loss: 0.8888 - val_accuracy: 0.7357 - 9s/epoch - 148ms/step\n",
            "Epoch 70/100\n",
            "\n",
            "Epoch 70: val_accuracy did not improve from 0.77609\n",
            "61/61 - 9s - loss: 0.4564 - accuracy: 0.8528 - val_loss: 0.9115 - val_accuracy: 0.7357 - 9s/epoch - 148ms/step\n",
            "Epoch 71/100\n",
            "\n",
            "Epoch 71: val_accuracy did not improve from 0.77609\n",
            "61/61 - 9s - loss: 0.4334 - accuracy: 0.8652 - val_loss: 0.8471 - val_accuracy: 0.7441 - 9s/epoch - 148ms/step\n",
            "Epoch 72/100\n",
            "\n",
            "Epoch 72: val_accuracy did not improve from 0.77609\n",
            "61/61 - 9s - loss: 0.4296 - accuracy: 0.8635 - val_loss: 0.9782 - val_accuracy: 0.6936 - 9s/epoch - 149ms/step\n",
            "Epoch 73/100\n",
            "\n",
            "Epoch 73: val_accuracy did not improve from 0.77609\n",
            "61/61 - 9s - loss: 0.4353 - accuracy: 0.8602 - val_loss: 0.9801 - val_accuracy: 0.7138 - 9s/epoch - 149ms/step\n",
            "Epoch 74/100\n",
            "\n",
            "Epoch 74: val_accuracy did not improve from 0.77609\n",
            "61/61 - 9s - loss: 0.4041 - accuracy: 0.8704 - val_loss: 1.8277 - val_accuracy: 0.6431 - 9s/epoch - 149ms/step\n",
            "Epoch 75/100\n",
            "\n",
            "Epoch 75: val_accuracy did not improve from 0.77609\n",
            "61/61 - 9s - loss: 0.4039 - accuracy: 0.8697 - val_loss: 1.6817 - val_accuracy: 0.5943 - 9s/epoch - 149ms/step\n",
            "Epoch 76/100\n",
            "\n",
            "Epoch 76: val_accuracy did not improve from 0.77609\n",
            "61/61 - 9s - loss: 0.4202 - accuracy: 0.8608 - val_loss: 1.0500 - val_accuracy: 0.6717 - 9s/epoch - 156ms/step\n",
            "Epoch 77/100\n",
            "\n",
            "Epoch 77: val_accuracy did not improve from 0.77609\n",
            "61/61 - 9s - loss: 0.4206 - accuracy: 0.8658 - val_loss: 1.7255 - val_accuracy: 0.5640 - 9s/epoch - 150ms/step\n",
            "Epoch 78/100\n",
            "\n",
            "Epoch 78: val_accuracy did not improve from 0.77609\n",
            "61/61 - 9s - loss: 0.4023 - accuracy: 0.8743 - val_loss: 1.6059 - val_accuracy: 0.6128 - 9s/epoch - 149ms/step\n",
            "Epoch 79/100\n",
            "\n",
            "Epoch 79: val_accuracy did not improve from 0.77609\n",
            "61/61 - 9s - loss: 0.3853 - accuracy: 0.8747 - val_loss: 1.0713 - val_accuracy: 0.6953 - 9s/epoch - 148ms/step\n",
            "Epoch 80/100\n",
            "\n",
            "Epoch 80: val_accuracy did not improve from 0.77609\n",
            "61/61 - 9s - loss: 0.3745 - accuracy: 0.8816 - val_loss: 2.7614 - val_accuracy: 0.5202 - 9s/epoch - 149ms/step\n",
            "Epoch 81/100\n",
            "\n",
            "Epoch 81: val_accuracy did not improve from 0.77609\n",
            "61/61 - 9s - loss: 0.3814 - accuracy: 0.8763 - val_loss: 1.1101 - val_accuracy: 0.6717 - 9s/epoch - 149ms/step\n",
            "Epoch 82/100\n",
            "\n",
            "Epoch 82: val_accuracy did not improve from 0.77609\n",
            "61/61 - 9s - loss: 0.3703 - accuracy: 0.8838 - val_loss: 1.1611 - val_accuracy: 0.6734 - 9s/epoch - 149ms/step\n",
            "Epoch 83/100\n",
            "\n",
            "Epoch 83: val_accuracy did not improve from 0.77609\n",
            "61/61 - 9s - loss: 0.3757 - accuracy: 0.8782 - val_loss: 1.2623 - val_accuracy: 0.6700 - 9s/epoch - 149ms/step\n",
            "Epoch 84/100\n",
            "\n",
            "Epoch 84: val_accuracy did not improve from 0.77609\n",
            "61/61 - 9s - loss: 0.3686 - accuracy: 0.8848 - val_loss: 1.1357 - val_accuracy: 0.6768 - 9s/epoch - 149ms/step\n",
            "Epoch 85/100\n",
            "\n",
            "Epoch 85: val_accuracy did not improve from 0.77609\n",
            "61/61 - 9s - loss: 0.3622 - accuracy: 0.8803 - val_loss: 1.5411 - val_accuracy: 0.6246 - 9s/epoch - 149ms/step\n",
            "Epoch 86/100\n",
            "\n",
            "Epoch 86: val_accuracy did not improve from 0.77609\n",
            "61/61 - 9s - loss: 0.3534 - accuracy: 0.8905 - val_loss: 1.2748 - val_accuracy: 0.6768 - 9s/epoch - 149ms/step\n",
            "Epoch 87/100\n",
            "\n",
            "Epoch 87: val_accuracy did not improve from 0.77609\n",
            "61/61 - 9s - loss: 0.3455 - accuracy: 0.8893 - val_loss: 1.2501 - val_accuracy: 0.6902 - 9s/epoch - 149ms/step\n",
            "Epoch 88/100\n",
            "\n",
            "Epoch 88: val_accuracy did not improve from 0.77609\n",
            "61/61 - 9s - loss: 0.3523 - accuracy: 0.8884 - val_loss: 2.0311 - val_accuracy: 0.6313 - 9s/epoch - 148ms/step\n",
            "Epoch 89/100\n",
            "\n",
            "Epoch 89: val_accuracy did not improve from 0.77609\n",
            "61/61 - 9s - loss: 0.3369 - accuracy: 0.8924 - val_loss: 1.1393 - val_accuracy: 0.7239 - 9s/epoch - 149ms/step\n",
            "Epoch 90/100\n",
            "\n",
            "Epoch 90: val_accuracy improved from 0.77609 to 0.78114, saving model to /content/drive/My Drive/512-gray-urbansounds-fold4-best_model.h5\n",
            "61/61 - 10s - loss: 0.3063 - accuracy: 0.9058 - val_loss: 0.7333 - val_accuracy: 0.7811 - 10s/epoch - 157ms/step\n",
            "Epoch 91/100\n",
            "\n",
            "Epoch 91: val_accuracy did not improve from 0.78114\n",
            "61/61 - 9s - loss: 0.3143 - accuracy: 0.8987 - val_loss: 1.0329 - val_accuracy: 0.7424 - 9s/epoch - 149ms/step\n",
            "Epoch 92/100\n",
            "\n",
            "Epoch 92: val_accuracy did not improve from 0.78114\n",
            "61/61 - 9s - loss: 0.3048 - accuracy: 0.9016 - val_loss: 1.0081 - val_accuracy: 0.7003 - 9s/epoch - 149ms/step\n",
            "Epoch 93/100\n",
            "\n",
            "Epoch 93: val_accuracy did not improve from 0.78114\n",
            "61/61 - 9s - loss: 0.3091 - accuracy: 0.9027 - val_loss: 1.2041 - val_accuracy: 0.7071 - 9s/epoch - 149ms/step\n",
            "Epoch 94/100\n",
            "\n",
            "Epoch 94: val_accuracy did not improve from 0.78114\n",
            "61/61 - 9s - loss: 0.3031 - accuracy: 0.9040 - val_loss: 0.9089 - val_accuracy: 0.7323 - 9s/epoch - 149ms/step\n",
            "Epoch 95/100\n",
            "\n",
            "Epoch 95: val_accuracy did not improve from 0.78114\n",
            "61/61 - 9s - loss: 0.2883 - accuracy: 0.9084 - val_loss: 0.8498 - val_accuracy: 0.7660 - 9s/epoch - 148ms/step\n",
            "Epoch 96/100\n",
            "\n",
            "Epoch 96: val_accuracy did not improve from 0.78114\n",
            "61/61 - 9s - loss: 0.2907 - accuracy: 0.9084 - val_loss: 0.8478 - val_accuracy: 0.7424 - 9s/epoch - 154ms/step\n",
            "Epoch 97/100\n",
            "\n",
            "Epoch 97: val_accuracy did not improve from 0.78114\n",
            "61/61 - 9s - loss: 0.2871 - accuracy: 0.9045 - val_loss: 0.9285 - val_accuracy: 0.7576 - 9s/epoch - 149ms/step\n",
            "Epoch 98/100\n",
            "\n",
            "Epoch 98: val_accuracy did not improve from 0.78114\n",
            "61/61 - 9s - loss: 0.2829 - accuracy: 0.9089 - val_loss: 0.9180 - val_accuracy: 0.7492 - 9s/epoch - 149ms/step\n",
            "Epoch 99/100\n",
            "\n",
            "Epoch 99: val_accuracy did not improve from 0.78114\n",
            "61/61 - 9s - loss: 0.2787 - accuracy: 0.9136 - val_loss: 0.9207 - val_accuracy: 0.7710 - 9s/epoch - 149ms/step\n",
            "Epoch 100/100\n",
            "\n",
            "Epoch 100: val_accuracy did not improve from 0.78114\n",
            "61/61 - 9s - loss: 0.2731 - accuracy: 0.9089 - val_loss: 0.9817 - val_accuracy: 0.7290 - 9s/epoch - 149ms/step\n",
            "Training model on Fold-5\n",
            "Epoch 1/100\n",
            "\n",
            "Epoch 1: val_accuracy improved from -inf to 0.15508, saving model to /content/drive/My Drive/512-gray-urbansounds-fold5-best_model.h5\n",
            "61/61 - 12s - loss: 2.3957 - accuracy: 0.1979 - val_loss: 3.3688 - val_accuracy: 0.1551 - 12s/epoch - 196ms/step\n",
            "Epoch 2/100\n",
            "\n",
            "Epoch 2: val_accuracy improved from 0.15508 to 0.30481, saving model to /content/drive/My Drive/512-gray-urbansounds-fold5-best_model.h5\n",
            "61/61 - 9s - loss: 2.0927 - accuracy: 0.2732 - val_loss: 2.0484 - val_accuracy: 0.3048 - 9s/epoch - 150ms/step\n",
            "Epoch 3/100\n",
            "\n",
            "Epoch 3: val_accuracy improved from 0.30481 to 0.36364, saving model to /content/drive/My Drive/512-gray-urbansounds-fold5-best_model.h5\n",
            "61/61 - 9s - loss: 1.9541 - accuracy: 0.3139 - val_loss: 1.7640 - val_accuracy: 0.3636 - 9s/epoch - 155ms/step\n",
            "Epoch 4/100\n",
            "\n",
            "Epoch 4: val_accuracy did not improve from 0.36364\n",
            "61/61 - 9s - loss: 1.8356 - accuracy: 0.3448 - val_loss: 1.6789 - val_accuracy: 0.3619 - 9s/epoch - 149ms/step\n",
            "Epoch 5/100\n",
            "\n",
            "Epoch 5: val_accuracy improved from 0.36364 to 0.37077, saving model to /content/drive/My Drive/512-gray-urbansounds-fold5-best_model.h5\n",
            "61/61 - 10s - loss: 1.7759 - accuracy: 0.3633 - val_loss: 1.7066 - val_accuracy: 0.3708 - 10s/epoch - 159ms/step\n",
            "Epoch 6/100\n",
            "\n",
            "Epoch 6: val_accuracy improved from 0.37077 to 0.40820, saving model to /content/drive/My Drive/512-gray-urbansounds-fold5-best_model.h5\n",
            "61/61 - 10s - loss: 1.6999 - accuracy: 0.3969 - val_loss: 1.4694 - val_accuracy: 0.4082 - 10s/epoch - 168ms/step\n",
            "Epoch 7/100\n",
            "\n",
            "Epoch 7: val_accuracy did not improve from 0.40820\n",
            "61/61 - 9s - loss: 1.6429 - accuracy: 0.4183 - val_loss: 2.0682 - val_accuracy: 0.3048 - 9s/epoch - 149ms/step\n",
            "Epoch 8/100\n",
            "\n",
            "Epoch 8: val_accuracy did not improve from 0.40820\n",
            "61/61 - 9s - loss: 1.6056 - accuracy: 0.4343 - val_loss: 1.7048 - val_accuracy: 0.3850 - 9s/epoch - 149ms/step\n",
            "Epoch 9/100\n",
            "\n",
            "Epoch 9: val_accuracy improved from 0.40820 to 0.42424, saving model to /content/drive/My Drive/512-gray-urbansounds-fold5-best_model.h5\n",
            "61/61 - 10s - loss: 1.5646 - accuracy: 0.4497 - val_loss: 1.5214 - val_accuracy: 0.4242 - 10s/epoch - 157ms/step\n",
            "Epoch 10/100\n",
            "\n",
            "Epoch 10: val_accuracy improved from 0.42424 to 0.48307, saving model to /content/drive/My Drive/512-gray-urbansounds-fold5-best_model.h5\n",
            "61/61 - 9s - loss: 1.5341 - accuracy: 0.4659 - val_loss: 1.5323 - val_accuracy: 0.4831 - 9s/epoch - 150ms/step\n",
            "Epoch 11/100\n",
            "\n",
            "Epoch 11: val_accuracy did not improve from 0.48307\n",
            "61/61 - 9s - loss: 1.5005 - accuracy: 0.4718 - val_loss: 1.5844 - val_accuracy: 0.4278 - 9s/epoch - 149ms/step\n",
            "Epoch 12/100\n",
            "\n",
            "Epoch 12: val_accuracy did not improve from 0.48307\n",
            "61/61 - 9s - loss: 1.4765 - accuracy: 0.4850 - val_loss: 1.4938 - val_accuracy: 0.4439 - 9s/epoch - 149ms/step\n",
            "Epoch 13/100\n",
            "\n",
            "Epoch 13: val_accuracy did not improve from 0.48307\n",
            "61/61 - 9s - loss: 1.4429 - accuracy: 0.4999 - val_loss: 1.9352 - val_accuracy: 0.4688 - 9s/epoch - 149ms/step\n",
            "Epoch 14/100\n",
            "\n",
            "Epoch 14: val_accuracy did not improve from 0.48307\n",
            "61/61 - 9s - loss: 1.4190 - accuracy: 0.5063 - val_loss: 4.5499 - val_accuracy: 0.2513 - 9s/epoch - 149ms/step\n",
            "Epoch 15/100\n",
            "\n",
            "Epoch 15: val_accuracy did not improve from 0.48307\n",
            "61/61 - 9s - loss: 1.3991 - accuracy: 0.5146 - val_loss: 2.4225 - val_accuracy: 0.4367 - 9s/epoch - 155ms/step\n",
            "Epoch 16/100\n",
            "\n",
            "Epoch 16: val_accuracy improved from 0.48307 to 0.51872, saving model to /content/drive/My Drive/512-gray-urbansounds-fold5-best_model.h5\n",
            "61/61 - 10s - loss: 1.3610 - accuracy: 0.5289 - val_loss: 1.3926 - val_accuracy: 0.5187 - 10s/epoch - 157ms/step\n",
            "Epoch 17/100\n",
            "\n",
            "Epoch 17: val_accuracy did not improve from 0.51872\n",
            "61/61 - 9s - loss: 1.3102 - accuracy: 0.5522 - val_loss: 1.5972 - val_accuracy: 0.4724 - 9s/epoch - 149ms/step\n",
            "Epoch 18/100\n",
            "\n",
            "Epoch 18: val_accuracy improved from 0.51872 to 0.56328, saving model to /content/drive/My Drive/512-gray-urbansounds-fold5-best_model.h5\n",
            "61/61 - 10s - loss: 1.3110 - accuracy: 0.5549 - val_loss: 1.5077 - val_accuracy: 0.5633 - 10s/epoch - 157ms/step\n",
            "Epoch 19/100\n",
            "\n",
            "Epoch 19: val_accuracy did not improve from 0.56328\n",
            "61/61 - 9s - loss: 1.2678 - accuracy: 0.5702 - val_loss: 1.2683 - val_accuracy: 0.4439 - 9s/epoch - 149ms/step\n",
            "Epoch 20/100\n",
            "\n",
            "Epoch 20: val_accuracy did not improve from 0.56328\n",
            "61/61 - 9s - loss: 1.2359 - accuracy: 0.5811 - val_loss: 1.5016 - val_accuracy: 0.5490 - 9s/epoch - 149ms/step\n",
            "Epoch 21/100\n",
            "\n",
            "Epoch 21: val_accuracy did not improve from 0.56328\n",
            "61/61 - 9s - loss: 1.2101 - accuracy: 0.5902 - val_loss: 2.6618 - val_accuracy: 0.4563 - 9s/epoch - 149ms/step\n",
            "Epoch 22/100\n",
            "\n",
            "Epoch 22: val_accuracy did not improve from 0.56328\n",
            "61/61 - 9s - loss: 1.2074 - accuracy: 0.5868 - val_loss: 1.3823 - val_accuracy: 0.5223 - 9s/epoch - 149ms/step\n",
            "Epoch 23/100\n",
            "\n",
            "Epoch 23: val_accuracy did not improve from 0.56328\n",
            "61/61 - 9s - loss: 1.1803 - accuracy: 0.5967 - val_loss: 1.8085 - val_accuracy: 0.5116 - 9s/epoch - 149ms/step\n",
            "Epoch 24/100\n",
            "\n",
            "Epoch 24: val_accuracy did not improve from 0.56328\n",
            "61/61 - 9s - loss: 1.1462 - accuracy: 0.6120 - val_loss: 1.1207 - val_accuracy: 0.5579 - 9s/epoch - 149ms/step\n",
            "Epoch 25/100\n",
            "\n",
            "Epoch 25: val_accuracy improved from 0.56328 to 0.63636, saving model to /content/drive/My Drive/512-gray-urbansounds-fold5-best_model.h5\n",
            "61/61 - 10s - loss: 1.1153 - accuracy: 0.6243 - val_loss: 1.0230 - val_accuracy: 0.6364 - 10s/epoch - 157ms/step\n",
            "Epoch 26/100\n",
            "\n",
            "Epoch 26: val_accuracy improved from 0.63636 to 0.66132, saving model to /content/drive/My Drive/512-gray-urbansounds-fold5-best_model.h5\n",
            "61/61 - 9s - loss: 1.0906 - accuracy: 0.6342 - val_loss: 0.9563 - val_accuracy: 0.6613 - 9s/epoch - 151ms/step\n",
            "Epoch 27/100\n",
            "\n",
            "Epoch 27: val_accuracy improved from 0.66132 to 0.72014, saving model to /content/drive/My Drive/512-gray-urbansounds-fold5-best_model.h5\n",
            "61/61 - 10s - loss: 1.0842 - accuracy: 0.6425 - val_loss: 0.9701 - val_accuracy: 0.7201 - 10s/epoch - 158ms/step\n",
            "Epoch 28/100\n",
            "\n",
            "Epoch 28: val_accuracy did not improve from 0.72014\n",
            "61/61 - 9s - loss: 1.0875 - accuracy: 0.6321 - val_loss: 1.3555 - val_accuracy: 0.5348 - 9s/epoch - 149ms/step\n",
            "Epoch 29/100\n",
            "\n",
            "Epoch 29: val_accuracy did not improve from 0.72014\n",
            "61/61 - 9s - loss: 1.0538 - accuracy: 0.6533 - val_loss: 0.9486 - val_accuracy: 0.6471 - 9s/epoch - 149ms/step\n",
            "Epoch 30/100\n",
            "\n",
            "Epoch 30: val_accuracy did not improve from 0.72014\n",
            "61/61 - 9s - loss: 1.0332 - accuracy: 0.6566 - val_loss: 1.1914 - val_accuracy: 0.5989 - 9s/epoch - 149ms/step\n",
            "Epoch 31/100\n",
            "\n",
            "Epoch 31: val_accuracy did not improve from 0.72014\n",
            "61/61 - 9s - loss: 0.9920 - accuracy: 0.6709 - val_loss: 1.6455 - val_accuracy: 0.5205 - 9s/epoch - 149ms/step\n",
            "Epoch 32/100\n",
            "\n",
            "Epoch 32: val_accuracy did not improve from 0.72014\n",
            "61/61 - 9s - loss: 0.9858 - accuracy: 0.6751 - val_loss: 0.8912 - val_accuracy: 0.6916 - 9s/epoch - 149ms/step\n",
            "Epoch 33/100\n",
            "\n",
            "Epoch 33: val_accuracy did not improve from 0.72014\n",
            "61/61 - 9s - loss: 0.9846 - accuracy: 0.6710 - val_loss: 0.9763 - val_accuracy: 0.6934 - 9s/epoch - 149ms/step\n",
            "Epoch 34/100\n",
            "\n",
            "Epoch 34: val_accuracy did not improve from 0.72014\n",
            "61/61 - 9s - loss: 0.9743 - accuracy: 0.6756 - val_loss: 1.2794 - val_accuracy: 0.5490 - 9s/epoch - 149ms/step\n",
            "Epoch 35/100\n",
            "\n",
            "Epoch 35: val_accuracy did not improve from 0.72014\n",
            "61/61 - 9s - loss: 0.8944 - accuracy: 0.7138 - val_loss: 1.0181 - val_accuracy: 0.6061 - 9s/epoch - 155ms/step\n",
            "Epoch 36/100\n",
            "\n",
            "Epoch 36: val_accuracy did not improve from 0.72014\n",
            "61/61 - 9s - loss: 0.8961 - accuracy: 0.7025 - val_loss: 1.0757 - val_accuracy: 0.6595 - 9s/epoch - 149ms/step\n",
            "Epoch 37/100\n",
            "\n",
            "Epoch 37: val_accuracy did not improve from 0.72014\n",
            "61/61 - 9s - loss: 0.8785 - accuracy: 0.7066 - val_loss: 1.1425 - val_accuracy: 0.6613 - 9s/epoch - 149ms/step\n",
            "Epoch 38/100\n",
            "\n",
            "Epoch 38: val_accuracy did not improve from 0.72014\n",
            "61/61 - 9s - loss: 0.8707 - accuracy: 0.7183 - val_loss: 0.9899 - val_accuracy: 0.6595 - 9s/epoch - 149ms/step\n",
            "Epoch 39/100\n",
            "\n",
            "Epoch 39: val_accuracy did not improve from 0.72014\n",
            "61/61 - 9s - loss: 0.8587 - accuracy: 0.7199 - val_loss: 1.0976 - val_accuracy: 0.6043 - 9s/epoch - 149ms/step\n",
            "Epoch 40/100\n",
            "\n",
            "Epoch 40: val_accuracy did not improve from 0.72014\n",
            "61/61 - 9s - loss: 0.8248 - accuracy: 0.7320 - val_loss: 1.1778 - val_accuracy: 0.6417 - 9s/epoch - 149ms/step\n",
            "Epoch 41/100\n",
            "\n",
            "Epoch 41: val_accuracy did not improve from 0.72014\n",
            "61/61 - 9s - loss: 0.7848 - accuracy: 0.7459 - val_loss: 0.8690 - val_accuracy: 0.6898 - 9s/epoch - 149ms/step\n",
            "Epoch 42/100\n",
            "\n",
            "Epoch 42: val_accuracy did not improve from 0.72014\n",
            "61/61 - 9s - loss: 0.7728 - accuracy: 0.7486 - val_loss: 1.1823 - val_accuracy: 0.5847 - 9s/epoch - 149ms/step\n",
            "Epoch 43/100\n",
            "\n",
            "Epoch 43: val_accuracy improved from 0.72014 to 0.73262, saving model to /content/drive/My Drive/512-gray-urbansounds-fold5-best_model.h5\n",
            "61/61 - 10s - loss: 0.7509 - accuracy: 0.7614 - val_loss: 0.8482 - val_accuracy: 0.7326 - 10s/epoch - 157ms/step\n",
            "Epoch 44/100\n",
            "\n",
            "Epoch 44: val_accuracy improved from 0.73262 to 0.76827, saving model to /content/drive/My Drive/512-gray-urbansounds-fold5-best_model.h5\n",
            "61/61 - 9s - loss: 0.7486 - accuracy: 0.7574 - val_loss: 0.6532 - val_accuracy: 0.7683 - 9s/epoch - 150ms/step\n",
            "Epoch 45/100\n",
            "\n",
            "Epoch 45: val_accuracy did not improve from 0.76827\n",
            "61/61 - 9s - loss: 0.7105 - accuracy: 0.7713 - val_loss: 0.9185 - val_accuracy: 0.6595 - 9s/epoch - 149ms/step\n",
            "Epoch 46/100\n",
            "\n",
            "Epoch 46: val_accuracy did not improve from 0.76827\n",
            "61/61 - 9s - loss: 0.6915 - accuracy: 0.7753 - val_loss: 3.6155 - val_accuracy: 0.5258 - 9s/epoch - 149ms/step\n",
            "Epoch 47/100\n",
            "\n",
            "Epoch 47: val_accuracy did not improve from 0.76827\n",
            "61/61 - 9s - loss: 0.6760 - accuracy: 0.7842 - val_loss: 1.8941 - val_accuracy: 0.5508 - 9s/epoch - 149ms/step\n",
            "Epoch 48/100\n",
            "\n",
            "Epoch 48: val_accuracy did not improve from 0.76827\n",
            "61/61 - 9s - loss: 0.6738 - accuracy: 0.7844 - val_loss: 3.2485 - val_accuracy: 0.4902 - 9s/epoch - 149ms/step\n",
            "Epoch 49/100\n",
            "\n",
            "Epoch 49: val_accuracy did not improve from 0.76827\n",
            "61/61 - 9s - loss: 0.6702 - accuracy: 0.7831 - val_loss: 1.2324 - val_accuracy: 0.6542 - 9s/epoch - 149ms/step\n",
            "Epoch 50/100\n",
            "\n",
            "Epoch 50: val_accuracy did not improve from 0.76827\n",
            "61/61 - 9s - loss: 0.6600 - accuracy: 0.7930 - val_loss: 0.9809 - val_accuracy: 0.6488 - 9s/epoch - 149ms/step\n",
            "Epoch 51/100\n",
            "\n",
            "Epoch 51: val_accuracy did not improve from 0.76827\n",
            "61/61 - 9s - loss: 0.6332 - accuracy: 0.8013 - val_loss: 2.2725 - val_accuracy: 0.6560 - 9s/epoch - 149ms/step\n",
            "Epoch 52/100\n",
            "\n",
            "Epoch 52: val_accuracy did not improve from 0.76827\n",
            "61/61 - 9s - loss: 0.6031 - accuracy: 0.8105 - val_loss: 2.3615 - val_accuracy: 0.6061 - 9s/epoch - 149ms/step\n",
            "Epoch 53/100\n",
            "\n",
            "Epoch 53: val_accuracy did not improve from 0.76827\n",
            "61/61 - 9s - loss: 0.5874 - accuracy: 0.8179 - val_loss: 1.9062 - val_accuracy: 0.6292 - 9s/epoch - 149ms/step\n",
            "Epoch 54/100\n",
            "\n",
            "Epoch 54: val_accuracy did not improve from 0.76827\n",
            "61/61 - 9s - loss: 0.5851 - accuracy: 0.8143 - val_loss: 2.3376 - val_accuracy: 0.6025 - 9s/epoch - 149ms/step\n",
            "Epoch 55/100\n",
            "\n",
            "Epoch 55: val_accuracy did not improve from 0.76827\n",
            "61/61 - 9s - loss: 0.5732 - accuracy: 0.8189 - val_loss: 2.1524 - val_accuracy: 0.5383 - 9s/epoch - 154ms/step\n",
            "Epoch 56/100\n",
            "\n",
            "Epoch 56: val_accuracy did not improve from 0.76827\n",
            "61/61 - 9s - loss: 0.5579 - accuracy: 0.8217 - val_loss: 2.9904 - val_accuracy: 0.5187 - 9s/epoch - 150ms/step\n",
            "Epoch 57/100\n",
            "\n",
            "Epoch 57: val_accuracy did not improve from 0.76827\n",
            "61/61 - 9s - loss: 0.5522 - accuracy: 0.8285 - val_loss: 2.1734 - val_accuracy: 0.5205 - 9s/epoch - 149ms/step\n",
            "Epoch 58/100\n",
            "\n",
            "Epoch 58: val_accuracy did not improve from 0.76827\n",
            "61/61 - 9s - loss: 0.5542 - accuracy: 0.8247 - val_loss: 3.8165 - val_accuracy: 0.5989 - 9s/epoch - 149ms/step\n",
            "Epoch 59/100\n",
            "\n",
            "Epoch 59: val_accuracy did not improve from 0.76827\n",
            "61/61 - 9s - loss: 0.5423 - accuracy: 0.8299 - val_loss: 2.1615 - val_accuracy: 0.6096 - 9s/epoch - 149ms/step\n",
            "Epoch 60/100\n",
            "\n",
            "Epoch 60: val_accuracy did not improve from 0.76827\n",
            "61/61 - 9s - loss: 0.5126 - accuracy: 0.8374 - val_loss: 3.8932 - val_accuracy: 0.5348 - 9s/epoch - 149ms/step\n",
            "Epoch 61/100\n",
            "\n",
            "Epoch 61: val_accuracy did not improve from 0.76827\n",
            "61/61 - 9s - loss: 0.5005 - accuracy: 0.8450 - val_loss: 1.2715 - val_accuracy: 0.6756 - 9s/epoch - 149ms/step\n",
            "Epoch 62/100\n",
            "\n",
            "Epoch 62: val_accuracy did not improve from 0.76827\n",
            "61/61 - 9s - loss: 0.4996 - accuracy: 0.8393 - val_loss: 1.2105 - val_accuracy: 0.7380 - 9s/epoch - 149ms/step\n",
            "Epoch 63/100\n",
            "\n",
            "Epoch 63: val_accuracy did not improve from 0.76827\n",
            "61/61 - 9s - loss: 0.5299 - accuracy: 0.8330 - val_loss: 0.7097 - val_accuracy: 0.7326 - 9s/epoch - 148ms/step\n",
            "Epoch 64/100\n",
            "\n",
            "Epoch 64: val_accuracy did not improve from 0.76827\n",
            "61/61 - 9s - loss: 0.4959 - accuracy: 0.8420 - val_loss: 0.7940 - val_accuracy: 0.7380 - 9s/epoch - 149ms/step\n",
            "Epoch 65/100\n",
            "\n",
            "Epoch 65: val_accuracy did not improve from 0.76827\n",
            "61/61 - 9s - loss: 0.4820 - accuracy: 0.8484 - val_loss: 0.7109 - val_accuracy: 0.7522 - 9s/epoch - 149ms/step\n",
            "Epoch 66/100\n",
            "\n",
            "Epoch 66: val_accuracy improved from 0.76827 to 0.78788, saving model to /content/drive/My Drive/512-gray-urbansounds-fold5-best_model.h5\n",
            "61/61 - 10s - loss: 0.4727 - accuracy: 0.8547 - val_loss: 0.6654 - val_accuracy: 0.7879 - 10s/epoch - 156ms/step\n",
            "Epoch 67/100\n",
            "\n",
            "Epoch 67: val_accuracy did not improve from 0.78788\n",
            "61/61 - 9s - loss: 0.4593 - accuracy: 0.8536 - val_loss: 0.9477 - val_accuracy: 0.7148 - 9s/epoch - 149ms/step\n",
            "Epoch 68/100\n",
            "\n",
            "Epoch 68: val_accuracy did not improve from 0.78788\n",
            "61/61 - 9s - loss: 0.4410 - accuracy: 0.8622 - val_loss: 0.6655 - val_accuracy: 0.7790 - 9s/epoch - 149ms/step\n",
            "Epoch 69/100\n",
            "\n",
            "Epoch 69: val_accuracy did not improve from 0.78788\n",
            "61/61 - 9s - loss: 0.4157 - accuracy: 0.8687 - val_loss: 0.6933 - val_accuracy: 0.7629 - 9s/epoch - 148ms/step\n",
            "Epoch 70/100\n",
            "\n",
            "Epoch 70: val_accuracy improved from 0.78788 to 0.78966, saving model to /content/drive/My Drive/512-gray-urbansounds-fold5-best_model.h5\n",
            "61/61 - 10s - loss: 0.4290 - accuracy: 0.8625 - val_loss: 0.6566 - val_accuracy: 0.7897 - 10s/epoch - 156ms/step\n",
            "Epoch 71/100\n",
            "\n",
            "Epoch 71: val_accuracy did not improve from 0.78966\n",
            "61/61 - 9s - loss: 0.4153 - accuracy: 0.8693 - val_loss: 0.8590 - val_accuracy: 0.7094 - 9s/epoch - 149ms/step\n",
            "Epoch 72/100\n",
            "\n",
            "Epoch 72: val_accuracy did not improve from 0.78966\n",
            "61/61 - 9s - loss: 0.4216 - accuracy: 0.8639 - val_loss: 0.8810 - val_accuracy: 0.7166 - 9s/epoch - 149ms/step\n",
            "Epoch 73/100\n",
            "\n",
            "Epoch 73: val_accuracy did not improve from 0.78966\n",
            "61/61 - 9s - loss: 0.4005 - accuracy: 0.8717 - val_loss: 0.7899 - val_accuracy: 0.7237 - 9s/epoch - 149ms/step\n",
            "Epoch 74/100\n",
            "\n",
            "Epoch 74: val_accuracy did not improve from 0.78966\n",
            "61/61 - 9s - loss: 0.3908 - accuracy: 0.8758 - val_loss: 0.9095 - val_accuracy: 0.7166 - 9s/epoch - 149ms/step\n",
            "Epoch 75/100\n",
            "\n",
            "Epoch 75: val_accuracy did not improve from 0.78966\n",
            "61/61 - 9s - loss: 0.3867 - accuracy: 0.8812 - val_loss: 1.0702 - val_accuracy: 0.6684 - 9s/epoch - 155ms/step\n",
            "Epoch 76/100\n",
            "\n",
            "Epoch 76: val_accuracy did not improve from 0.78966\n",
            "61/61 - 9s - loss: 0.3666 - accuracy: 0.8893 - val_loss: 1.0019 - val_accuracy: 0.7130 - 9s/epoch - 149ms/step\n",
            "Epoch 77/100\n",
            "\n",
            "Epoch 77: val_accuracy did not improve from 0.78966\n",
            "61/61 - 9s - loss: 0.3596 - accuracy: 0.8830 - val_loss: 0.8409 - val_accuracy: 0.7130 - 9s/epoch - 149ms/step\n",
            "Epoch 78/100\n",
            "\n",
            "Epoch 78: val_accuracy did not improve from 0.78966\n",
            "61/61 - 9s - loss: 0.3628 - accuracy: 0.8858 - val_loss: 1.0430 - val_accuracy: 0.6720 - 9s/epoch - 149ms/step\n",
            "Epoch 79/100\n",
            "\n",
            "Epoch 79: val_accuracy did not improve from 0.78966\n",
            "61/61 - 9s - loss: 0.3571 - accuracy: 0.8889 - val_loss: 0.9730 - val_accuracy: 0.7308 - 9s/epoch - 149ms/step\n",
            "Epoch 80/100\n",
            "\n",
            "Epoch 80: val_accuracy did not improve from 0.78966\n",
            "61/61 - 9s - loss: 0.3631 - accuracy: 0.8855 - val_loss: 1.6994 - val_accuracy: 0.6292 - 9s/epoch - 149ms/step\n",
            "Epoch 81/100\n",
            "\n",
            "Epoch 81: val_accuracy did not improve from 0.78966\n",
            "61/61 - 9s - loss: 0.3293 - accuracy: 0.8960 - val_loss: 1.0272 - val_accuracy: 0.7005 - 9s/epoch - 149ms/step\n",
            "Epoch 82/100\n",
            "\n",
            "Epoch 82: val_accuracy did not improve from 0.78966\n",
            "61/61 - 9s - loss: 0.3501 - accuracy: 0.8864 - val_loss: 1.1602 - val_accuracy: 0.6417 - 9s/epoch - 149ms/step\n",
            "Epoch 83/100\n",
            "\n",
            "Epoch 83: val_accuracy did not improve from 0.78966\n",
            "61/61 - 9s - loss: 0.3424 - accuracy: 0.8917 - val_loss: 1.6131 - val_accuracy: 0.6364 - 9s/epoch - 149ms/step\n",
            "Epoch 84/100\n",
            "\n",
            "Epoch 84: val_accuracy did not improve from 0.78966\n",
            "61/61 - 9s - loss: 0.3492 - accuracy: 0.8896 - val_loss: 1.9855 - val_accuracy: 0.5704 - 9s/epoch - 148ms/step\n",
            "Epoch 85/100\n",
            "\n",
            "Epoch 85: val_accuracy did not improve from 0.78966\n",
            "61/61 - 9s - loss: 0.3212 - accuracy: 0.8989 - val_loss: 2.2001 - val_accuracy: 0.4973 - 9s/epoch - 149ms/step\n",
            "Epoch 86/100\n",
            "\n",
            "Epoch 86: val_accuracy did not improve from 0.78966\n",
            "61/61 - 9s - loss: 0.3264 - accuracy: 0.8962 - val_loss: 1.5309 - val_accuracy: 0.6275 - 9s/epoch - 149ms/step\n",
            "Epoch 87/100\n",
            "\n",
            "Epoch 87: val_accuracy did not improve from 0.78966\n",
            "61/61 - 9s - loss: 0.3346 - accuracy: 0.8920 - val_loss: 1.7863 - val_accuracy: 0.5989 - 9s/epoch - 149ms/step\n",
            "Epoch 88/100\n",
            "\n",
            "Epoch 88: val_accuracy did not improve from 0.78966\n",
            "61/61 - 9s - loss: 0.3321 - accuracy: 0.8938 - val_loss: 1.6770 - val_accuracy: 0.5865 - 9s/epoch - 149ms/step\n",
            "Epoch 89/100\n",
            "\n",
            "Epoch 89: val_accuracy did not improve from 0.78966\n",
            "61/61 - 9s - loss: 0.3204 - accuracy: 0.8985 - val_loss: 1.1988 - val_accuracy: 0.6542 - 9s/epoch - 149ms/step\n",
            "Epoch 90/100\n",
            "\n",
            "Epoch 90: val_accuracy did not improve from 0.78966\n",
            "61/61 - 9s - loss: 0.2863 - accuracy: 0.9107 - val_loss: 0.7746 - val_accuracy: 0.7380 - 9s/epoch - 149ms/step\n",
            "Epoch 91/100\n",
            "\n",
            "Epoch 91: val_accuracy did not improve from 0.78966\n",
            "61/61 - 9s - loss: 0.3051 - accuracy: 0.8960 - val_loss: 0.6985 - val_accuracy: 0.7736 - 9s/epoch - 149ms/step\n",
            "Epoch 92/100\n",
            "\n",
            "Epoch 92: val_accuracy did not improve from 0.78966\n",
            "61/61 - 9s - loss: 0.2923 - accuracy: 0.9066 - val_loss: 0.9733 - val_accuracy: 0.7308 - 9s/epoch - 149ms/step\n",
            "Epoch 93/100\n",
            "\n",
            "Epoch 93: val_accuracy did not improve from 0.78966\n",
            "61/61 - 9s - loss: 0.2801 - accuracy: 0.9132 - val_loss: 0.9289 - val_accuracy: 0.7576 - 9s/epoch - 149ms/step\n",
            "Epoch 94/100\n",
            "\n",
            "Epoch 94: val_accuracy did not improve from 0.78966\n",
            "61/61 - 9s - loss: 0.2712 - accuracy: 0.9129 - val_loss: 0.9834 - val_accuracy: 0.7148 - 9s/epoch - 149ms/step\n",
            "Epoch 95/100\n",
            "\n",
            "Epoch 95: val_accuracy improved from 0.78966 to 0.82888, saving model to /content/drive/My Drive/512-gray-urbansounds-fold5-best_model.h5\n",
            "61/61 - 10s - loss: 0.2625 - accuracy: 0.9206 - val_loss: 0.6967 - val_accuracy: 0.8289 - 10s/epoch - 163ms/step\n",
            "Epoch 96/100\n",
            "\n",
            "Epoch 96: val_accuracy did not improve from 0.82888\n",
            "61/61 - 9s - loss: 0.2668 - accuracy: 0.9142 - val_loss: 1.9658 - val_accuracy: 0.5793 - 9s/epoch - 149ms/step\n",
            "Epoch 97/100\n",
            "\n",
            "Epoch 97: val_accuracy did not improve from 0.82888\n",
            "61/61 - 9s - loss: 0.2695 - accuracy: 0.9169 - val_loss: 0.7688 - val_accuracy: 0.7772 - 9s/epoch - 149ms/step\n",
            "Epoch 98/100\n",
            "\n",
            "Epoch 98: val_accuracy did not improve from 0.82888\n",
            "61/61 - 9s - loss: 0.2663 - accuracy: 0.9120 - val_loss: 0.6978 - val_accuracy: 0.8182 - 9s/epoch - 149ms/step\n",
            "Epoch 99/100\n",
            "\n",
            "Epoch 99: val_accuracy did not improve from 0.82888\n",
            "61/61 - 9s - loss: 0.2658 - accuracy: 0.9166 - val_loss: 0.8036 - val_accuracy: 0.7647 - 9s/epoch - 149ms/step\n",
            "Epoch 100/100\n",
            "\n",
            "Epoch 100: val_accuracy did not improve from 0.82888\n",
            "61/61 - 9s - loss: 0.2429 - accuracy: 0.9237 - val_loss: 0.9543 - val_accuracy: 0.7201 - 9s/epoch - 149ms/step\n",
            "Training model on Fold-6\n",
            "Epoch 1/100\n",
            "\n",
            "Epoch 1: val_accuracy improved from -inf to 0.17039, saving model to /content/drive/My Drive/512-gray-urbansounds-fold6-best_model.h5\n",
            "62/62 - 12s - loss: 2.3592 - accuracy: 0.2000 - val_loss: 3.0925 - val_accuracy: 0.1704 - 12s/epoch - 200ms/step\n",
            "Epoch 2/100\n",
            "\n",
            "Epoch 2: val_accuracy improved from 0.17039 to 0.21501, saving model to /content/drive/My Drive/512-gray-urbansounds-fold6-best_model.h5\n",
            "62/62 - 10s - loss: 2.1093 - accuracy: 0.2692 - val_loss: 2.9029 - val_accuracy: 0.2150 - 10s/epoch - 154ms/step\n",
            "Epoch 3/100\n",
            "\n",
            "Epoch 3: val_accuracy did not improve from 0.21501\n",
            "62/62 - 9s - loss: 1.9780 - accuracy: 0.3031 - val_loss: 3.2101 - val_accuracy: 0.1988 - 9s/epoch - 148ms/step\n",
            "Epoch 4/100\n",
            "\n",
            "Epoch 4: val_accuracy improved from 0.21501 to 0.25761, saving model to /content/drive/My Drive/512-gray-urbansounds-fold6-best_model.h5\n",
            "62/62 - 10s - loss: 1.8705 - accuracy: 0.3348 - val_loss: 2.6070 - val_accuracy: 0.2576 - 10s/epoch - 156ms/step\n",
            "Epoch 5/100\n",
            "\n",
            "Epoch 5: val_accuracy improved from 0.25761 to 0.36714, saving model to /content/drive/My Drive/512-gray-urbansounds-fold6-best_model.h5\n",
            "62/62 - 9s - loss: 1.7879 - accuracy: 0.3702 - val_loss: 1.7355 - val_accuracy: 0.3671 - 9s/epoch - 150ms/step\n",
            "Epoch 6/100\n",
            "\n",
            "Epoch 6: val_accuracy improved from 0.36714 to 0.43813, saving model to /content/drive/My Drive/512-gray-urbansounds-fold6-best_model.h5\n",
            "62/62 - 10s - loss: 1.7300 - accuracy: 0.3859 - val_loss: 1.5664 - val_accuracy: 0.4381 - 10s/epoch - 155ms/step\n",
            "Epoch 7/100\n",
            "\n",
            "Epoch 7: val_accuracy did not improve from 0.43813\n",
            "62/62 - 9s - loss: 1.6786 - accuracy: 0.4049 - val_loss: 2.0968 - val_accuracy: 0.2860 - 9s/epoch - 148ms/step\n",
            "Epoch 8/100\n",
            "\n",
            "Epoch 8: val_accuracy did not improve from 0.43813\n",
            "62/62 - 9s - loss: 1.6504 - accuracy: 0.4126 - val_loss: 1.6107 - val_accuracy: 0.4280 - 9s/epoch - 148ms/step\n",
            "Epoch 9/100\n",
            "\n",
            "Epoch 9: val_accuracy did not improve from 0.43813\n",
            "62/62 - 9s - loss: 1.6102 - accuracy: 0.4304 - val_loss: 1.5959 - val_accuracy: 0.4381 - 9s/epoch - 148ms/step\n",
            "Epoch 10/100\n",
            "\n",
            "Epoch 10: val_accuracy did not improve from 0.43813\n",
            "62/62 - 9s - loss: 1.5526 - accuracy: 0.4530 - val_loss: 1.7443 - val_accuracy: 0.4199 - 9s/epoch - 148ms/step\n",
            "Epoch 11/100\n",
            "\n",
            "Epoch 11: val_accuracy did not improve from 0.43813\n",
            "62/62 - 9s - loss: 1.5386 - accuracy: 0.4687 - val_loss: 1.5708 - val_accuracy: 0.4300 - 9s/epoch - 148ms/step\n",
            "Epoch 12/100\n",
            "\n",
            "Epoch 12: val_accuracy did not improve from 0.43813\n",
            "62/62 - 9s - loss: 1.5204 - accuracy: 0.4657 - val_loss: 1.7701 - val_accuracy: 0.3793 - 9s/epoch - 148ms/step\n",
            "Epoch 13/100\n",
            "\n",
            "Epoch 13: val_accuracy improved from 0.43813 to 0.45233, saving model to /content/drive/My Drive/512-gray-urbansounds-fold6-best_model.h5\n",
            "62/62 - 10s - loss: 1.4845 - accuracy: 0.4893 - val_loss: 1.8505 - val_accuracy: 0.4523 - 10s/epoch - 156ms/step\n",
            "Epoch 14/100\n",
            "\n",
            "Epoch 14: val_accuracy improved from 0.45233 to 0.47262, saving model to /content/drive/My Drive/512-gray-urbansounds-fold6-best_model.h5\n",
            "62/62 - 9s - loss: 1.4665 - accuracy: 0.4950 - val_loss: 1.7664 - val_accuracy: 0.4726 - 9s/epoch - 153ms/step\n",
            "Epoch 15/100\n",
            "\n",
            "Epoch 15: val_accuracy did not improve from 0.47262\n",
            "62/62 - 9s - loss: 1.4592 - accuracy: 0.4991 - val_loss: 1.5224 - val_accuracy: 0.4665 - 9s/epoch - 150ms/step\n",
            "Epoch 16/100\n",
            "\n",
            "Epoch 16: val_accuracy did not improve from 0.47262\n",
            "62/62 - 9s - loss: 1.4103 - accuracy: 0.5195 - val_loss: 1.8053 - val_accuracy: 0.4199 - 9s/epoch - 148ms/step\n",
            "Epoch 17/100\n",
            "\n",
            "Epoch 17: val_accuracy did not improve from 0.47262\n",
            "62/62 - 9s - loss: 1.3883 - accuracy: 0.5303 - val_loss: 1.9108 - val_accuracy: 0.4239 - 9s/epoch - 148ms/step\n",
            "Epoch 18/100\n",
            "\n",
            "Epoch 18: val_accuracy did not improve from 0.47262\n",
            "62/62 - 9s - loss: 1.3734 - accuracy: 0.5283 - val_loss: 2.1682 - val_accuracy: 0.4037 - 9s/epoch - 148ms/step\n",
            "Epoch 19/100\n",
            "\n",
            "Epoch 19: val_accuracy improved from 0.47262 to 0.52130, saving model to /content/drive/My Drive/512-gray-urbansounds-fold6-best_model.h5\n",
            "62/62 - 10s - loss: 1.3490 - accuracy: 0.5413 - val_loss: 1.2185 - val_accuracy: 0.5213 - 10s/epoch - 156ms/step\n",
            "Epoch 20/100\n",
            "\n",
            "Epoch 20: val_accuracy improved from 0.52130 to 0.58215, saving model to /content/drive/My Drive/512-gray-urbansounds-fold6-best_model.h5\n",
            "62/62 - 9s - loss: 1.3236 - accuracy: 0.5529 - val_loss: 1.2107 - val_accuracy: 0.5822 - 9s/epoch - 149ms/step\n",
            "Epoch 21/100\n",
            "\n",
            "Epoch 21: val_accuracy did not improve from 0.58215\n",
            "62/62 - 9s - loss: 1.3124 - accuracy: 0.5504 - val_loss: 1.4348 - val_accuracy: 0.5456 - 9s/epoch - 148ms/step\n",
            "Epoch 22/100\n",
            "\n",
            "Epoch 22: val_accuracy did not improve from 0.58215\n",
            "62/62 - 9s - loss: 1.2953 - accuracy: 0.5661 - val_loss: 1.4599 - val_accuracy: 0.5051 - 9s/epoch - 148ms/step\n",
            "Epoch 23/100\n",
            "\n",
            "Epoch 23: val_accuracy did not improve from 0.58215\n",
            "62/62 - 9s - loss: 1.2764 - accuracy: 0.5663 - val_loss: 1.7426 - val_accuracy: 0.4361 - 9s/epoch - 148ms/step\n",
            "Epoch 24/100\n",
            "\n",
            "Epoch 24: val_accuracy did not improve from 0.58215\n",
            "62/62 - 9s - loss: 1.2606 - accuracy: 0.5748 - val_loss: 1.1577 - val_accuracy: 0.5598 - 9s/epoch - 147ms/step\n",
            "Epoch 25/100\n",
            "\n",
            "Epoch 25: val_accuracy improved from 0.58215 to 0.62272, saving model to /content/drive/My Drive/512-gray-urbansounds-fold6-best_model.h5\n",
            "62/62 - 10s - loss: 1.2303 - accuracy: 0.5888 - val_loss: 1.1091 - val_accuracy: 0.6227 - 10s/epoch - 156ms/step\n",
            "Epoch 26/100\n",
            "\n",
            "Epoch 26: val_accuracy did not improve from 0.62272\n",
            "62/62 - 9s - loss: 1.2166 - accuracy: 0.5944 - val_loss: 1.2610 - val_accuracy: 0.5456 - 9s/epoch - 148ms/step\n",
            "Epoch 27/100\n",
            "\n",
            "Epoch 27: val_accuracy did not improve from 0.62272\n",
            "62/62 - 9s - loss: 1.1810 - accuracy: 0.6061 - val_loss: 1.2488 - val_accuracy: 0.5822 - 9s/epoch - 148ms/step\n",
            "Epoch 28/100\n",
            "\n",
            "Epoch 28: val_accuracy did not improve from 0.62272\n",
            "62/62 - 9s - loss: 1.1562 - accuracy: 0.6168 - val_loss: 1.5240 - val_accuracy: 0.5416 - 9s/epoch - 148ms/step\n",
            "Epoch 29/100\n",
            "\n",
            "Epoch 29: val_accuracy did not improve from 0.62272\n",
            "62/62 - 9s - loss: 1.1188 - accuracy: 0.6260 - val_loss: 1.0693 - val_accuracy: 0.6146 - 9s/epoch - 148ms/step\n",
            "Epoch 30/100\n",
            "\n",
            "Epoch 30: val_accuracy did not improve from 0.62272\n",
            "62/62 - 9s - loss: 1.0950 - accuracy: 0.6399 - val_loss: 1.4580 - val_accuracy: 0.5497 - 9s/epoch - 148ms/step\n",
            "Epoch 31/100\n",
            "\n",
            "Epoch 31: val_accuracy did not improve from 0.62272\n",
            "62/62 - 9s - loss: 1.0671 - accuracy: 0.6433 - val_loss: 1.0924 - val_accuracy: 0.6146 - 9s/epoch - 148ms/step\n",
            "Epoch 32/100\n",
            "\n",
            "Epoch 32: val_accuracy improved from 0.62272 to 0.62475, saving model to /content/drive/My Drive/512-gray-urbansounds-fold6-best_model.h5\n",
            "62/62 - 10s - loss: 1.0520 - accuracy: 0.6610 - val_loss: 1.1434 - val_accuracy: 0.6247 - 10s/epoch - 154ms/step\n",
            "Epoch 33/100\n",
            "\n",
            "Epoch 33: val_accuracy did not improve from 0.62475\n",
            "62/62 - 9s - loss: 1.0287 - accuracy: 0.6699 - val_loss: 2.1082 - val_accuracy: 0.5538 - 9s/epoch - 148ms/step\n",
            "Epoch 34/100\n",
            "\n",
            "Epoch 34: val_accuracy did not improve from 0.62475\n",
            "62/62 - 10s - loss: 1.0588 - accuracy: 0.6522 - val_loss: 1.2245 - val_accuracy: 0.5700 - 10s/epoch - 154ms/step\n",
            "Epoch 35/100\n",
            "\n",
            "Epoch 35: val_accuracy did not improve from 0.62475\n",
            "62/62 - 9s - loss: 1.0013 - accuracy: 0.6729 - val_loss: 1.7063 - val_accuracy: 0.5538 - 9s/epoch - 148ms/step\n",
            "Epoch 36/100\n",
            "\n",
            "Epoch 36: val_accuracy did not improve from 0.62475\n",
            "62/62 - 9s - loss: 0.9573 - accuracy: 0.6891 - val_loss: 1.7738 - val_accuracy: 0.5923 - 9s/epoch - 148ms/step\n",
            "Epoch 37/100\n",
            "\n",
            "Epoch 37: val_accuracy did not improve from 0.62475\n",
            "62/62 - 9s - loss: 0.9405 - accuracy: 0.6877 - val_loss: 2.2578 - val_accuracy: 0.5355 - 9s/epoch - 148ms/step\n",
            "Epoch 38/100\n",
            "\n",
            "Epoch 38: val_accuracy did not improve from 0.62475\n",
            "62/62 - 9s - loss: 0.9055 - accuracy: 0.7032 - val_loss: 2.2739 - val_accuracy: 0.5355 - 9s/epoch - 148ms/step\n",
            "Epoch 39/100\n",
            "\n",
            "Epoch 39: val_accuracy did not improve from 0.62475\n",
            "62/62 - 9s - loss: 0.9089 - accuracy: 0.7082 - val_loss: 2.0645 - val_accuracy: 0.5132 - 9s/epoch - 148ms/step\n",
            "Epoch 40/100\n",
            "\n",
            "Epoch 40: val_accuracy did not improve from 0.62475\n",
            "62/62 - 9s - loss: 0.8792 - accuracy: 0.7172 - val_loss: 2.2995 - val_accuracy: 0.5071 - 9s/epoch - 148ms/step\n",
            "Epoch 41/100\n",
            "\n",
            "Epoch 41: val_accuracy did not improve from 0.62475\n",
            "62/62 - 9s - loss: 0.8616 - accuracy: 0.7251 - val_loss: 1.8070 - val_accuracy: 0.5943 - 9s/epoch - 148ms/step\n",
            "Epoch 42/100\n",
            "\n",
            "Epoch 42: val_accuracy did not improve from 0.62475\n",
            "62/62 - 9s - loss: 0.8318 - accuracy: 0.7346 - val_loss: 1.6106 - val_accuracy: 0.5720 - 9s/epoch - 148ms/step\n",
            "Epoch 43/100\n",
            "\n",
            "Epoch 43: val_accuracy improved from 0.62475 to 0.62880, saving model to /content/drive/My Drive/512-gray-urbansounds-fold6-best_model.h5\n",
            "62/62 - 10s - loss: 0.8409 - accuracy: 0.7337 - val_loss: 1.1400 - val_accuracy: 0.6288 - 10s/epoch - 157ms/step\n",
            "Epoch 44/100\n",
            "\n",
            "Epoch 44: val_accuracy did not improve from 0.62880\n",
            "62/62 - 9s - loss: 0.8358 - accuracy: 0.7335 - val_loss: 1.4268 - val_accuracy: 0.5152 - 9s/epoch - 148ms/step\n",
            "Epoch 45/100\n",
            "\n",
            "Epoch 45: val_accuracy did not improve from 0.62880\n",
            "62/62 - 9s - loss: 0.8370 - accuracy: 0.7290 - val_loss: 1.3908 - val_accuracy: 0.5051 - 9s/epoch - 148ms/step\n",
            "Epoch 46/100\n",
            "\n",
            "Epoch 46: val_accuracy did not improve from 0.62880\n",
            "62/62 - 9s - loss: 0.8344 - accuracy: 0.7294 - val_loss: 1.9587 - val_accuracy: 0.4990 - 9s/epoch - 148ms/step\n",
            "Epoch 47/100\n",
            "\n",
            "Epoch 47: val_accuracy did not improve from 0.62880\n",
            "62/62 - 9s - loss: 0.7816 - accuracy: 0.7485 - val_loss: 1.7212 - val_accuracy: 0.5213 - 9s/epoch - 148ms/step\n",
            "Epoch 48/100\n",
            "\n",
            "Epoch 48: val_accuracy did not improve from 0.62880\n",
            "62/62 - 9s - loss: 0.7468 - accuracy: 0.7665 - val_loss: 1.2273 - val_accuracy: 0.6085 - 9s/epoch - 148ms/step\n",
            "Epoch 49/100\n",
            "\n",
            "Epoch 49: val_accuracy did not improve from 0.62880\n",
            "62/62 - 9s - loss: 0.7494 - accuracy: 0.7553 - val_loss: 1.1806 - val_accuracy: 0.6024 - 9s/epoch - 148ms/step\n",
            "Epoch 50/100\n",
            "\n",
            "Epoch 50: val_accuracy did not improve from 0.62880\n",
            "62/62 - 9s - loss: 0.7382 - accuracy: 0.7684 - val_loss: 1.1494 - val_accuracy: 0.6146 - 9s/epoch - 148ms/step\n",
            "Epoch 51/100\n",
            "\n",
            "Epoch 51: val_accuracy improved from 0.62880 to 0.70588, saving model to /content/drive/My Drive/512-gray-urbansounds-fold6-best_model.h5\n",
            "62/62 - 10s - loss: 0.7139 - accuracy: 0.7676 - val_loss: 0.9650 - val_accuracy: 0.7059 - 10s/epoch - 154ms/step\n",
            "Epoch 52/100\n",
            "\n",
            "Epoch 52: val_accuracy did not improve from 0.70588\n",
            "62/62 - 9s - loss: 0.6788 - accuracy: 0.7866 - val_loss: 0.9972 - val_accuracy: 0.6491 - 9s/epoch - 148ms/step\n",
            "Epoch 53/100\n",
            "\n",
            "Epoch 53: val_accuracy did not improve from 0.70588\n",
            "62/62 - 9s - loss: 0.6849 - accuracy: 0.7809 - val_loss: 1.3752 - val_accuracy: 0.5862 - 9s/epoch - 150ms/step\n",
            "Epoch 54/100\n",
            "\n",
            "Epoch 54: val_accuracy did not improve from 0.70588\n",
            "62/62 - 9s - loss: 0.6523 - accuracy: 0.7923 - val_loss: 1.6288 - val_accuracy: 0.6552 - 9s/epoch - 151ms/step\n",
            "Epoch 55/100\n",
            "\n",
            "Epoch 55: val_accuracy did not improve from 0.70588\n",
            "62/62 - 9s - loss: 0.6578 - accuracy: 0.7948 - val_loss: 1.2590 - val_accuracy: 0.6613 - 9s/epoch - 148ms/step\n",
            "Epoch 56/100\n",
            "\n",
            "Epoch 56: val_accuracy did not improve from 0.70588\n",
            "62/62 - 9s - loss: 0.6288 - accuracy: 0.8017 - val_loss: 1.0719 - val_accuracy: 0.6917 - 9s/epoch - 148ms/step\n",
            "Epoch 57/100\n",
            "\n",
            "Epoch 57: val_accuracy did not improve from 0.70588\n",
            "62/62 - 9s - loss: 0.6035 - accuracy: 0.8134 - val_loss: 2.2764 - val_accuracy: 0.5558 - 9s/epoch - 148ms/step\n",
            "Epoch 58/100\n",
            "\n",
            "Epoch 58: val_accuracy did not improve from 0.70588\n",
            "62/62 - 9s - loss: 0.6235 - accuracy: 0.7997 - val_loss: 1.7744 - val_accuracy: 0.5903 - 9s/epoch - 148ms/step\n",
            "Epoch 59/100\n",
            "\n",
            "Epoch 59: val_accuracy did not improve from 0.70588\n",
            "62/62 - 9s - loss: 0.6056 - accuracy: 0.8088 - val_loss: 4.2212 - val_accuracy: 0.4239 - 9s/epoch - 148ms/step\n",
            "Epoch 60/100\n",
            "\n",
            "Epoch 60: val_accuracy did not improve from 0.70588\n",
            "62/62 - 9s - loss: 0.6029 - accuracy: 0.8044 - val_loss: 1.7808 - val_accuracy: 0.5538 - 9s/epoch - 148ms/step\n",
            "Epoch 61/100\n",
            "\n",
            "Epoch 61: val_accuracy did not improve from 0.70588\n",
            "62/62 - 9s - loss: 0.5910 - accuracy: 0.8103 - val_loss: 2.5989 - val_accuracy: 0.5254 - 9s/epoch - 148ms/step\n",
            "Epoch 62/100\n",
            "\n",
            "Epoch 62: val_accuracy did not improve from 0.70588\n",
            "62/62 - 9s - loss: 0.5626 - accuracy: 0.8221 - val_loss: 1.5944 - val_accuracy: 0.5903 - 9s/epoch - 148ms/step\n",
            "Epoch 63/100\n",
            "\n",
            "Epoch 63: val_accuracy did not improve from 0.70588\n",
            "62/62 - 9s - loss: 0.5546 - accuracy: 0.8240 - val_loss: 2.7822 - val_accuracy: 0.5396 - 9s/epoch - 148ms/step\n",
            "Epoch 64/100\n",
            "\n",
            "Epoch 64: val_accuracy did not improve from 0.70588\n",
            "62/62 - 9s - loss: 0.5279 - accuracy: 0.8296 - val_loss: 3.2616 - val_accuracy: 0.5335 - 9s/epoch - 148ms/step\n",
            "Epoch 65/100\n",
            "\n",
            "Epoch 65: val_accuracy did not improve from 0.70588\n",
            "62/62 - 9s - loss: 0.5229 - accuracy: 0.8317 - val_loss: 3.4661 - val_accuracy: 0.5233 - 9s/epoch - 148ms/step\n",
            "Epoch 66/100\n",
            "\n",
            "Epoch 66: val_accuracy did not improve from 0.70588\n",
            "62/62 - 9s - loss: 0.5048 - accuracy: 0.8409 - val_loss: 3.0722 - val_accuracy: 0.5538 - 9s/epoch - 148ms/step\n",
            "Epoch 67/100\n",
            "\n",
            "Epoch 67: val_accuracy did not improve from 0.70588\n",
            "62/62 - 9s - loss: 0.5004 - accuracy: 0.8379 - val_loss: 2.0306 - val_accuracy: 0.6389 - 9s/epoch - 148ms/step\n",
            "Epoch 68/100\n",
            "\n",
            "Epoch 68: val_accuracy did not improve from 0.70588\n",
            "62/62 - 9s - loss: 0.4777 - accuracy: 0.8500 - val_loss: 1.7379 - val_accuracy: 0.6207 - 9s/epoch - 148ms/step\n",
            "Epoch 69/100\n",
            "\n",
            "Epoch 69: val_accuracy did not improve from 0.70588\n",
            "62/62 - 9s - loss: 0.4835 - accuracy: 0.8460 - val_loss: 1.3103 - val_accuracy: 0.6755 - 9s/epoch - 148ms/step\n",
            "Epoch 70/100\n",
            "\n",
            "Epoch 70: val_accuracy improved from 0.70588 to 0.71400, saving model to /content/drive/My Drive/512-gray-urbansounds-fold6-best_model.h5\n",
            "62/62 - 10s - loss: 0.4669 - accuracy: 0.8543 - val_loss: 1.0510 - val_accuracy: 0.7140 - 10s/epoch - 155ms/step\n",
            "Epoch 71/100\n",
            "\n",
            "Epoch 71: val_accuracy did not improve from 0.71400\n",
            "62/62 - 9s - loss: 0.4608 - accuracy: 0.8517 - val_loss: 1.3906 - val_accuracy: 0.6552 - 9s/epoch - 148ms/step\n",
            "Epoch 72/100\n",
            "\n",
            "Epoch 72: val_accuracy improved from 0.71400 to 0.75862, saving model to /content/drive/My Drive/512-gray-urbansounds-fold6-best_model.h5\n",
            "62/62 - 10s - loss: 0.4351 - accuracy: 0.8619 - val_loss: 0.9325 - val_accuracy: 0.7586 - 10s/epoch - 154ms/step\n",
            "Epoch 73/100\n",
            "\n",
            "Epoch 73: val_accuracy did not improve from 0.75862\n",
            "62/62 - 10s - loss: 0.4206 - accuracy: 0.8700 - val_loss: 1.0406 - val_accuracy: 0.7383 - 10s/epoch - 154ms/step\n",
            "Epoch 74/100\n",
            "\n",
            "Epoch 74: val_accuracy did not improve from 0.75862\n",
            "62/62 - 9s - loss: 0.4255 - accuracy: 0.8660 - val_loss: 1.1792 - val_accuracy: 0.7404 - 9s/epoch - 148ms/step\n",
            "Epoch 75/100\n",
            "\n",
            "Epoch 75: val_accuracy did not improve from 0.75862\n",
            "62/62 - 9s - loss: 0.3985 - accuracy: 0.8739 - val_loss: 1.2692 - val_accuracy: 0.6978 - 9s/epoch - 148ms/step\n",
            "Epoch 76/100\n",
            "\n",
            "Epoch 76: val_accuracy did not improve from 0.75862\n",
            "62/62 - 9s - loss: 0.4112 - accuracy: 0.8733 - val_loss: 1.4582 - val_accuracy: 0.6937 - 9s/epoch - 148ms/step\n",
            "Epoch 77/100\n",
            "\n",
            "Epoch 77: val_accuracy did not improve from 0.75862\n",
            "62/62 - 9s - loss: 0.3929 - accuracy: 0.8743 - val_loss: 1.4402 - val_accuracy: 0.6876 - 9s/epoch - 148ms/step\n",
            "Epoch 78/100\n",
            "\n",
            "Epoch 78: val_accuracy did not improve from 0.75862\n",
            "62/62 - 9s - loss: 0.3959 - accuracy: 0.8715 - val_loss: 1.2218 - val_accuracy: 0.7302 - 9s/epoch - 149ms/step\n",
            "Epoch 79/100\n",
            "\n",
            "Epoch 79: val_accuracy did not improve from 0.75862\n",
            "62/62 - 9s - loss: 0.3923 - accuracy: 0.8744 - val_loss: 1.1760 - val_accuracy: 0.7201 - 9s/epoch - 148ms/step\n",
            "Epoch 80/100\n",
            "\n",
            "Epoch 80: val_accuracy did not improve from 0.75862\n",
            "62/62 - 9s - loss: 0.3828 - accuracy: 0.8775 - val_loss: 1.1965 - val_accuracy: 0.7018 - 9s/epoch - 148ms/step\n",
            "Epoch 81/100\n",
            "\n",
            "Epoch 81: val_accuracy did not improve from 0.75862\n",
            "62/62 - 9s - loss: 0.3694 - accuracy: 0.8844 - val_loss: 1.3779 - val_accuracy: 0.6917 - 9s/epoch - 148ms/step\n",
            "Epoch 82/100\n",
            "\n",
            "Epoch 82: val_accuracy did not improve from 0.75862\n",
            "62/62 - 9s - loss: 0.3403 - accuracy: 0.8916 - val_loss: 1.8259 - val_accuracy: 0.6673 - 9s/epoch - 148ms/step\n",
            "Epoch 83/100\n",
            "\n",
            "Epoch 83: val_accuracy did not improve from 0.75862\n",
            "62/62 - 9s - loss: 0.3573 - accuracy: 0.8884 - val_loss: 1.5882 - val_accuracy: 0.7160 - 9s/epoch - 148ms/step\n",
            "Epoch 84/100\n",
            "\n",
            "Epoch 84: val_accuracy did not improve from 0.75862\n",
            "62/62 - 9s - loss: 0.3349 - accuracy: 0.8909 - val_loss: 0.9720 - val_accuracy: 0.7424 - 9s/epoch - 148ms/step\n",
            "Epoch 85/100\n",
            "\n",
            "Epoch 85: val_accuracy improved from 0.75862 to 0.79513, saving model to /content/drive/My Drive/512-gray-urbansounds-fold6-best_model.h5\n",
            "62/62 - 10s - loss: 0.3156 - accuracy: 0.8986 - val_loss: 0.7467 - val_accuracy: 0.7951 - 10s/epoch - 156ms/step\n",
            "Epoch 86/100\n",
            "\n",
            "Epoch 86: val_accuracy did not improve from 0.79513\n",
            "62/62 - 9s - loss: 0.3110 - accuracy: 0.8983 - val_loss: 0.9644 - val_accuracy: 0.7262 - 9s/epoch - 148ms/step\n",
            "Epoch 87/100\n",
            "\n",
            "Epoch 87: val_accuracy did not improve from 0.79513\n",
            "62/62 - 9s - loss: 0.3248 - accuracy: 0.8975 - val_loss: 1.5007 - val_accuracy: 0.7383 - 9s/epoch - 148ms/step\n",
            "Epoch 88/100\n",
            "\n",
            "Epoch 88: val_accuracy did not improve from 0.79513\n",
            "62/62 - 9s - loss: 0.3028 - accuracy: 0.9007 - val_loss: 1.2985 - val_accuracy: 0.7424 - 9s/epoch - 147ms/step\n",
            "Epoch 89/100\n",
            "\n",
            "Epoch 89: val_accuracy did not improve from 0.79513\n",
            "62/62 - 9s - loss: 0.3051 - accuracy: 0.9033 - val_loss: 1.0498 - val_accuracy: 0.7221 - 9s/epoch - 148ms/step\n",
            "Epoch 90/100\n",
            "\n",
            "Epoch 90: val_accuracy did not improve from 0.79513\n",
            "62/62 - 9s - loss: 0.2890 - accuracy: 0.9098 - val_loss: 1.2747 - val_accuracy: 0.7099 - 9s/epoch - 148ms/step\n",
            "Epoch 91/100\n",
            "\n",
            "Epoch 91: val_accuracy did not improve from 0.79513\n",
            "62/62 - 9s - loss: 0.3034 - accuracy: 0.9043 - val_loss: 1.2532 - val_accuracy: 0.7363 - 9s/epoch - 148ms/step\n",
            "Epoch 92/100\n",
            "\n",
            "Epoch 92: val_accuracy did not improve from 0.79513\n",
            "62/62 - 9s - loss: 0.2847 - accuracy: 0.9087 - val_loss: 1.2854 - val_accuracy: 0.7221 - 9s/epoch - 151ms/step\n",
            "Epoch 93/100\n",
            "\n",
            "Epoch 93: val_accuracy did not improve from 0.79513\n",
            "62/62 - 9s - loss: 0.2796 - accuracy: 0.9172 - val_loss: 1.1801 - val_accuracy: 0.7485 - 9s/epoch - 150ms/step\n",
            "Epoch 94/100\n",
            "\n",
            "Epoch 94: val_accuracy did not improve from 0.79513\n",
            "62/62 - 9s - loss: 0.3073 - accuracy: 0.9042 - val_loss: 1.4636 - val_accuracy: 0.6795 - 9s/epoch - 148ms/step\n",
            "Epoch 95/100\n",
            "\n",
            "Epoch 95: val_accuracy did not improve from 0.79513\n",
            "62/62 - 9s - loss: 0.2895 - accuracy: 0.9073 - val_loss: 1.3498 - val_accuracy: 0.7079 - 9s/epoch - 148ms/step\n",
            "Epoch 96/100\n",
            "\n",
            "Epoch 96: val_accuracy did not improve from 0.79513\n",
            "62/62 - 9s - loss: 0.2774 - accuracy: 0.9117 - val_loss: 1.4271 - val_accuracy: 0.6613 - 9s/epoch - 147ms/step\n",
            "Epoch 97/100\n",
            "\n",
            "Epoch 97: val_accuracy did not improve from 0.79513\n",
            "62/62 - 9s - loss: 0.2654 - accuracy: 0.9158 - val_loss: 0.9879 - val_accuracy: 0.7424 - 9s/epoch - 148ms/step\n",
            "Epoch 98/100\n",
            "\n",
            "Epoch 98: val_accuracy did not improve from 0.79513\n",
            "62/62 - 9s - loss: 0.2782 - accuracy: 0.9107 - val_loss: 0.9041 - val_accuracy: 0.7606 - 9s/epoch - 148ms/step\n",
            "Epoch 99/100\n",
            "\n",
            "Epoch 99: val_accuracy did not improve from 0.79513\n",
            "62/62 - 9s - loss: 0.2682 - accuracy: 0.9166 - val_loss: 1.1363 - val_accuracy: 0.7606 - 9s/epoch - 148ms/step\n",
            "Epoch 100/100\n",
            "\n",
            "Epoch 100: val_accuracy did not improve from 0.79513\n",
            "62/62 - 9s - loss: 0.2707 - accuracy: 0.9138 - val_loss: 1.3248 - val_accuracy: 0.7383 - 9s/epoch - 148ms/step\n",
            "Training model on Fold-7\n",
            "Epoch 1/100\n",
            "\n",
            "Epoch 1: val_accuracy improved from -inf to 0.12351, saving model to /content/drive/My Drive/512-gray-urbansounds-fold7-best_model.h5\n",
            "62/62 - 14s - loss: 2.4095 - accuracy: 0.2018 - val_loss: 3.3267 - val_accuracy: 0.1235 - 14s/epoch - 233ms/step\n",
            "Epoch 2/100\n",
            "\n",
            "Epoch 2: val_accuracy improved from 0.12351 to 0.20916, saving model to /content/drive/My Drive/512-gray-urbansounds-fold7-best_model.h5\n",
            "62/62 - 9s - loss: 2.0917 - accuracy: 0.2727 - val_loss: 2.1140 - val_accuracy: 0.2092 - 9s/epoch - 149ms/step\n",
            "Epoch 3/100\n",
            "\n",
            "Epoch 3: val_accuracy improved from 0.20916 to 0.30478, saving model to /content/drive/My Drive/512-gray-urbansounds-fold7-best_model.h5\n",
            "62/62 - 10s - loss: 1.9479 - accuracy: 0.3181 - val_loss: 1.8444 - val_accuracy: 0.3048 - 10s/epoch - 156ms/step\n",
            "Epoch 4/100\n",
            "\n",
            "Epoch 4: val_accuracy improved from 0.30478 to 0.42430, saving model to /content/drive/My Drive/512-gray-urbansounds-fold7-best_model.h5\n",
            "62/62 - 9s - loss: 1.8607 - accuracy: 0.3339 - val_loss: 1.6903 - val_accuracy: 0.4243 - 9s/epoch - 149ms/step\n",
            "Epoch 5/100\n",
            "\n",
            "Epoch 5: val_accuracy improved from 0.42430 to 0.43426, saving model to /content/drive/My Drive/512-gray-urbansounds-fold7-best_model.h5\n",
            "62/62 - 10s - loss: 1.7716 - accuracy: 0.3686 - val_loss: 1.6071 - val_accuracy: 0.4343 - 10s/epoch - 155ms/step\n",
            "Epoch 6/100\n",
            "\n",
            "Epoch 6: val_accuracy improved from 0.43426 to 0.44024, saving model to /content/drive/My Drive/512-gray-urbansounds-fold7-best_model.h5\n",
            "62/62 - 10s - loss: 1.7223 - accuracy: 0.3949 - val_loss: 1.6813 - val_accuracy: 0.4402 - 10s/epoch - 154ms/step\n",
            "Epoch 7/100\n",
            "\n",
            "Epoch 7: val_accuracy improved from 0.44024 to 0.48805, saving model to /content/drive/My Drive/512-gray-urbansounds-fold7-best_model.h5\n",
            "62/62 - 10s - loss: 1.6468 - accuracy: 0.4227 - val_loss: 1.6871 - val_accuracy: 0.4880 - 10s/epoch - 155ms/step\n",
            "Epoch 8/100\n",
            "\n",
            "Epoch 8: val_accuracy did not improve from 0.48805\n",
            "62/62 - 9s - loss: 1.6082 - accuracy: 0.4425 - val_loss: 1.6763 - val_accuracy: 0.4382 - 9s/epoch - 148ms/step\n",
            "Epoch 9/100\n",
            "\n",
            "Epoch 9: val_accuracy improved from 0.48805 to 0.56773, saving model to /content/drive/My Drive/512-gray-urbansounds-fold7-best_model.h5\n",
            "62/62 - 10s - loss: 1.5550 - accuracy: 0.4631 - val_loss: 1.4388 - val_accuracy: 0.5677 - 10s/epoch - 156ms/step\n",
            "Epoch 10/100\n",
            "\n",
            "Epoch 10: val_accuracy did not improve from 0.56773\n",
            "62/62 - 9s - loss: 1.5111 - accuracy: 0.4767 - val_loss: 1.4845 - val_accuracy: 0.5080 - 9s/epoch - 148ms/step\n",
            "Epoch 11/100\n",
            "\n",
            "Epoch 11: val_accuracy improved from 0.56773 to 0.57171, saving model to /content/drive/My Drive/512-gray-urbansounds-fold7-best_model.h5\n",
            "62/62 - 10s - loss: 1.4883 - accuracy: 0.4930 - val_loss: 1.4207 - val_accuracy: 0.5717 - 10s/epoch - 162ms/step\n",
            "Epoch 12/100\n",
            "\n",
            "Epoch 12: val_accuracy did not improve from 0.57171\n",
            "62/62 - 9s - loss: 1.4512 - accuracy: 0.4995 - val_loss: 1.3880 - val_accuracy: 0.5339 - 9s/epoch - 148ms/step\n",
            "Epoch 13/100\n",
            "\n",
            "Epoch 13: val_accuracy did not improve from 0.57171\n",
            "62/62 - 9s - loss: 1.4061 - accuracy: 0.5258 - val_loss: 1.3069 - val_accuracy: 0.5538 - 9s/epoch - 148ms/step\n",
            "Epoch 14/100\n",
            "\n",
            "Epoch 14: val_accuracy did not improve from 0.57171\n",
            "62/62 - 9s - loss: 1.3944 - accuracy: 0.5250 - val_loss: 1.3012 - val_accuracy: 0.5319 - 9s/epoch - 148ms/step\n",
            "Epoch 15/100\n",
            "\n",
            "Epoch 15: val_accuracy did not improve from 0.57171\n",
            "62/62 - 9s - loss: 1.3698 - accuracy: 0.5362 - val_loss: 1.6086 - val_accuracy: 0.4402 - 9s/epoch - 147ms/step\n",
            "Epoch 16/100\n",
            "\n",
            "Epoch 16: val_accuracy did not improve from 0.57171\n",
            "62/62 - 9s - loss: 1.3563 - accuracy: 0.5498 - val_loss: 1.2573 - val_accuracy: 0.5279 - 9s/epoch - 148ms/step\n",
            "Epoch 17/100\n",
            "\n",
            "Epoch 17: val_accuracy improved from 0.57171 to 0.58765, saving model to /content/drive/My Drive/512-gray-urbansounds-fold7-best_model.h5\n",
            "62/62 - 10s - loss: 1.3303 - accuracy: 0.5533 - val_loss: 1.3373 - val_accuracy: 0.5876 - 10s/epoch - 155ms/step\n",
            "Epoch 18/100\n",
            "\n",
            "Epoch 18: val_accuracy did not improve from 0.58765\n",
            "62/62 - 9s - loss: 1.2907 - accuracy: 0.5655 - val_loss: 1.7493 - val_accuracy: 0.4203 - 9s/epoch - 148ms/step\n",
            "Epoch 19/100\n",
            "\n",
            "Epoch 19: val_accuracy did not improve from 0.58765\n",
            "62/62 - 9s - loss: 1.2601 - accuracy: 0.5813 - val_loss: 1.4112 - val_accuracy: 0.4920 - 9s/epoch - 148ms/step\n",
            "Epoch 20/100\n",
            "\n",
            "Epoch 20: val_accuracy did not improve from 0.58765\n",
            "62/62 - 9s - loss: 1.2344 - accuracy: 0.5882 - val_loss: 1.6282 - val_accuracy: 0.5060 - 9s/epoch - 147ms/step\n",
            "Epoch 21/100\n",
            "\n",
            "Epoch 21: val_accuracy did not improve from 0.58765\n",
            "62/62 - 9s - loss: 1.2165 - accuracy: 0.6035 - val_loss: 1.6666 - val_accuracy: 0.4303 - 9s/epoch - 148ms/step\n",
            "Epoch 22/100\n",
            "\n",
            "Epoch 22: val_accuracy did not improve from 0.58765\n",
            "62/62 - 9s - loss: 1.1936 - accuracy: 0.6013 - val_loss: 1.8953 - val_accuracy: 0.4661 - 9s/epoch - 148ms/step\n",
            "Epoch 23/100\n",
            "\n",
            "Epoch 23: val_accuracy did not improve from 0.58765\n",
            "62/62 - 9s - loss: 1.1812 - accuracy: 0.5959 - val_loss: 2.1264 - val_accuracy: 0.4980 - 9s/epoch - 148ms/step\n",
            "Epoch 24/100\n",
            "\n",
            "Epoch 24: val_accuracy did not improve from 0.58765\n",
            "62/62 - 9s - loss: 1.1672 - accuracy: 0.6089 - val_loss: 1.4437 - val_accuracy: 0.5558 - 9s/epoch - 148ms/step\n",
            "Epoch 25/100\n",
            "\n",
            "Epoch 25: val_accuracy did not improve from 0.58765\n",
            "62/62 - 9s - loss: 1.1234 - accuracy: 0.6369 - val_loss: 1.2862 - val_accuracy: 0.5817 - 9s/epoch - 148ms/step\n",
            "Epoch 26/100\n",
            "\n",
            "Epoch 26: val_accuracy improved from 0.58765 to 0.59960, saving model to /content/drive/My Drive/512-gray-urbansounds-fold7-best_model.h5\n",
            "62/62 - 10s - loss: 1.0871 - accuracy: 0.6423 - val_loss: 1.1595 - val_accuracy: 0.5996 - 10s/epoch - 156ms/step\n",
            "Epoch 27/100\n",
            "\n",
            "Epoch 27: val_accuracy did not improve from 0.59960\n",
            "62/62 - 9s - loss: 1.0559 - accuracy: 0.6535 - val_loss: 1.2774 - val_accuracy: 0.5797 - 9s/epoch - 148ms/step\n",
            "Epoch 28/100\n",
            "\n",
            "Epoch 28: val_accuracy did not improve from 0.59960\n",
            "62/62 - 9s - loss: 1.0318 - accuracy: 0.6627 - val_loss: 1.6699 - val_accuracy: 0.4920 - 9s/epoch - 148ms/step\n",
            "Epoch 29/100\n",
            "\n",
            "Epoch 29: val_accuracy did not improve from 0.59960\n",
            "62/62 - 9s - loss: 1.0145 - accuracy: 0.6647 - val_loss: 1.7633 - val_accuracy: 0.5299 - 9s/epoch - 148ms/step\n",
            "Epoch 30/100\n",
            "\n",
            "Epoch 30: val_accuracy did not improve from 0.59960\n",
            "62/62 - 9s - loss: 0.9966 - accuracy: 0.6752 - val_loss: 1.1913 - val_accuracy: 0.5996 - 9s/epoch - 152ms/step\n",
            "Epoch 31/100\n",
            "\n",
            "Epoch 31: val_accuracy did not improve from 0.59960\n",
            "62/62 - 9s - loss: 0.9772 - accuracy: 0.6758 - val_loss: 1.2798 - val_accuracy: 0.5677 - 9s/epoch - 148ms/step\n",
            "Epoch 32/100\n",
            "\n",
            "Epoch 32: val_accuracy improved from 0.59960 to 0.62351, saving model to /content/drive/My Drive/512-gray-urbansounds-fold7-best_model.h5\n",
            "62/62 - 10s - loss: 0.9642 - accuracy: 0.6814 - val_loss: 1.2390 - val_accuracy: 0.6235 - 10s/epoch - 155ms/step\n",
            "Epoch 33/100\n",
            "\n",
            "Epoch 33: val_accuracy improved from 0.62351 to 0.70916, saving model to /content/drive/My Drive/512-gray-urbansounds-fold7-best_model.h5\n",
            "62/62 - 9s - loss: 0.9409 - accuracy: 0.6942 - val_loss: 0.9304 - val_accuracy: 0.7092 - 9s/epoch - 149ms/step\n",
            "Epoch 34/100\n",
            "\n",
            "Epoch 34: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.9102 - accuracy: 0.7059 - val_loss: 1.2188 - val_accuracy: 0.6056 - 9s/epoch - 148ms/step\n",
            "Epoch 35/100\n",
            "\n",
            "Epoch 35: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.8918 - accuracy: 0.7083 - val_loss: 0.9819 - val_accuracy: 0.6793 - 9s/epoch - 148ms/step\n",
            "Epoch 36/100\n",
            "\n",
            "Epoch 36: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.8723 - accuracy: 0.7143 - val_loss: 1.2418 - val_accuracy: 0.6574 - 9s/epoch - 148ms/step\n",
            "Epoch 37/100\n",
            "\n",
            "Epoch 37: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.8399 - accuracy: 0.7321 - val_loss: 1.0369 - val_accuracy: 0.6853 - 9s/epoch - 148ms/step\n",
            "Epoch 38/100\n",
            "\n",
            "Epoch 38: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.8513 - accuracy: 0.7235 - val_loss: 1.0745 - val_accuracy: 0.6673 - 9s/epoch - 148ms/step\n",
            "Epoch 39/100\n",
            "\n",
            "Epoch 39: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.8312 - accuracy: 0.7283 - val_loss: 1.3162 - val_accuracy: 0.6355 - 9s/epoch - 148ms/step\n",
            "Epoch 40/100\n",
            "\n",
            "Epoch 40: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.8131 - accuracy: 0.7350 - val_loss: 0.9438 - val_accuracy: 0.6912 - 9s/epoch - 148ms/step\n",
            "Epoch 41/100\n",
            "\n",
            "Epoch 41: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.7704 - accuracy: 0.7518 - val_loss: 0.9916 - val_accuracy: 0.6833 - 9s/epoch - 147ms/step\n",
            "Epoch 42/100\n",
            "\n",
            "Epoch 42: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.7681 - accuracy: 0.7601 - val_loss: 0.9732 - val_accuracy: 0.6813 - 9s/epoch - 148ms/step\n",
            "Epoch 43/100\n",
            "\n",
            "Epoch 43: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.7611 - accuracy: 0.7560 - val_loss: 1.0908 - val_accuracy: 0.6853 - 9s/epoch - 148ms/step\n",
            "Epoch 44/100\n",
            "\n",
            "Epoch 44: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.7387 - accuracy: 0.7641 - val_loss: 1.3100 - val_accuracy: 0.6614 - 9s/epoch - 148ms/step\n",
            "Epoch 45/100\n",
            "\n",
            "Epoch 45: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.7064 - accuracy: 0.7734 - val_loss: 1.2637 - val_accuracy: 0.6255 - 9s/epoch - 148ms/step\n",
            "Epoch 46/100\n",
            "\n",
            "Epoch 46: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.6999 - accuracy: 0.7779 - val_loss: 1.1986 - val_accuracy: 0.5976 - 9s/epoch - 148ms/step\n",
            "Epoch 47/100\n",
            "\n",
            "Epoch 47: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.6851 - accuracy: 0.7834 - val_loss: 1.0366 - val_accuracy: 0.6454 - 9s/epoch - 147ms/step\n",
            "Epoch 48/100\n",
            "\n",
            "Epoch 48: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.6625 - accuracy: 0.7897 - val_loss: 1.1396 - val_accuracy: 0.6494 - 9s/epoch - 148ms/step\n",
            "Epoch 49/100\n",
            "\n",
            "Epoch 49: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.6397 - accuracy: 0.7962 - val_loss: 1.2809 - val_accuracy: 0.5876 - 9s/epoch - 151ms/step\n",
            "Epoch 50/100\n",
            "\n",
            "Epoch 50: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.6328 - accuracy: 0.7990 - val_loss: 1.3793 - val_accuracy: 0.6295 - 9s/epoch - 151ms/step\n",
            "Epoch 51/100\n",
            "\n",
            "Epoch 51: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.6121 - accuracy: 0.8062 - val_loss: 1.1662 - val_accuracy: 0.6673 - 9s/epoch - 148ms/step\n",
            "Epoch 52/100\n",
            "\n",
            "Epoch 52: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.5963 - accuracy: 0.8116 - val_loss: 1.3359 - val_accuracy: 0.6016 - 9s/epoch - 148ms/step\n",
            "Epoch 53/100\n",
            "\n",
            "Epoch 53: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.5824 - accuracy: 0.8185 - val_loss: 1.0776 - val_accuracy: 0.6036 - 9s/epoch - 148ms/step\n",
            "Epoch 54/100\n",
            "\n",
            "Epoch 54: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.5727 - accuracy: 0.8207 - val_loss: 1.1653 - val_accuracy: 0.6534 - 9s/epoch - 148ms/step\n",
            "Epoch 55/100\n",
            "\n",
            "Epoch 55: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.5455 - accuracy: 0.8295 - val_loss: 1.3661 - val_accuracy: 0.5637 - 9s/epoch - 148ms/step\n",
            "Epoch 56/100\n",
            "\n",
            "Epoch 56: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.5407 - accuracy: 0.8263 - val_loss: 1.4830 - val_accuracy: 0.6135 - 9s/epoch - 148ms/step\n",
            "Epoch 57/100\n",
            "\n",
            "Epoch 57: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.5347 - accuracy: 0.8314 - val_loss: 1.9579 - val_accuracy: 0.4203 - 9s/epoch - 148ms/step\n",
            "Epoch 58/100\n",
            "\n",
            "Epoch 58: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.5306 - accuracy: 0.8296 - val_loss: 0.9807 - val_accuracy: 0.6713 - 9s/epoch - 148ms/step\n",
            "Epoch 59/100\n",
            "\n",
            "Epoch 59: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.5141 - accuracy: 0.8405 - val_loss: 1.1897 - val_accuracy: 0.6434 - 9s/epoch - 148ms/step\n",
            "Epoch 60/100\n",
            "\n",
            "Epoch 60: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.5035 - accuracy: 0.8429 - val_loss: 1.2536 - val_accuracy: 0.6554 - 9s/epoch - 148ms/step\n",
            "Epoch 61/100\n",
            "\n",
            "Epoch 61: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.4797 - accuracy: 0.8472 - val_loss: 1.2773 - val_accuracy: 0.6315 - 9s/epoch - 147ms/step\n",
            "Epoch 62/100\n",
            "\n",
            "Epoch 62: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.4701 - accuracy: 0.8498 - val_loss: 1.2861 - val_accuracy: 0.6155 - 9s/epoch - 148ms/step\n",
            "Epoch 63/100\n",
            "\n",
            "Epoch 63: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.4632 - accuracy: 0.8542 - val_loss: 1.4814 - val_accuracy: 0.5837 - 9s/epoch - 148ms/step\n",
            "Epoch 64/100\n",
            "\n",
            "Epoch 64: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.4579 - accuracy: 0.8556 - val_loss: 1.4843 - val_accuracy: 0.6215 - 9s/epoch - 148ms/step\n",
            "Epoch 65/100\n",
            "\n",
            "Epoch 65: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.4450 - accuracy: 0.8571 - val_loss: 1.3665 - val_accuracy: 0.6235 - 9s/epoch - 148ms/step\n",
            "Epoch 66/100\n",
            "\n",
            "Epoch 66: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.4355 - accuracy: 0.8591 - val_loss: 1.5945 - val_accuracy: 0.5956 - 9s/epoch - 148ms/step\n",
            "Epoch 67/100\n",
            "\n",
            "Epoch 67: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.4250 - accuracy: 0.8656 - val_loss: 2.2244 - val_accuracy: 0.5518 - 9s/epoch - 148ms/step\n",
            "Epoch 68/100\n",
            "\n",
            "Epoch 68: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.4477 - accuracy: 0.8574 - val_loss: 1.7683 - val_accuracy: 0.5797 - 9s/epoch - 148ms/step\n",
            "Epoch 69/100\n",
            "\n",
            "Epoch 69: val_accuracy did not improve from 0.70916\n",
            "62/62 - 10s - loss: 0.4195 - accuracy: 0.8666 - val_loss: 1.4734 - val_accuracy: 0.6454 - 10s/epoch - 155ms/step\n",
            "Epoch 70/100\n",
            "\n",
            "Epoch 70: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.4208 - accuracy: 0.8626 - val_loss: 1.5820 - val_accuracy: 0.6116 - 9s/epoch - 148ms/step\n",
            "Epoch 71/100\n",
            "\n",
            "Epoch 71: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.4012 - accuracy: 0.8740 - val_loss: 1.8704 - val_accuracy: 0.5857 - 9s/epoch - 148ms/step\n",
            "Epoch 72/100\n",
            "\n",
            "Epoch 72: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.3922 - accuracy: 0.8721 - val_loss: 1.7103 - val_accuracy: 0.6255 - 9s/epoch - 148ms/step\n",
            "Epoch 73/100\n",
            "\n",
            "Epoch 73: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.3800 - accuracy: 0.8808 - val_loss: 1.4385 - val_accuracy: 0.6355 - 9s/epoch - 148ms/step\n",
            "Epoch 74/100\n",
            "\n",
            "Epoch 74: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.3882 - accuracy: 0.8775 - val_loss: 1.5848 - val_accuracy: 0.5896 - 9s/epoch - 148ms/step\n",
            "Epoch 75/100\n",
            "\n",
            "Epoch 75: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.3754 - accuracy: 0.8807 - val_loss: 1.4913 - val_accuracy: 0.6275 - 9s/epoch - 148ms/step\n",
            "Epoch 76/100\n",
            "\n",
            "Epoch 76: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.3537 - accuracy: 0.8898 - val_loss: 1.8842 - val_accuracy: 0.5498 - 9s/epoch - 148ms/step\n",
            "Epoch 77/100\n",
            "\n",
            "Epoch 77: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.3607 - accuracy: 0.8835 - val_loss: 1.7257 - val_accuracy: 0.5558 - 9s/epoch - 148ms/step\n",
            "Epoch 78/100\n",
            "\n",
            "Epoch 78: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.3589 - accuracy: 0.8829 - val_loss: 1.5739 - val_accuracy: 0.6076 - 9s/epoch - 148ms/step\n",
            "Epoch 79/100\n",
            "\n",
            "Epoch 79: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.3531 - accuracy: 0.8883 - val_loss: 1.4802 - val_accuracy: 0.6076 - 9s/epoch - 148ms/step\n",
            "Epoch 80/100\n",
            "\n",
            "Epoch 80: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.3356 - accuracy: 0.8964 - val_loss: 1.8380 - val_accuracy: 0.5976 - 9s/epoch - 148ms/step\n",
            "Epoch 81/100\n",
            "\n",
            "Epoch 81: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.3284 - accuracy: 0.8951 - val_loss: 1.4337 - val_accuracy: 0.5896 - 9s/epoch - 148ms/step\n",
            "Epoch 82/100\n",
            "\n",
            "Epoch 82: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.3233 - accuracy: 0.8923 - val_loss: 1.5497 - val_accuracy: 0.6355 - 9s/epoch - 148ms/step\n",
            "Epoch 83/100\n",
            "\n",
            "Epoch 83: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.3215 - accuracy: 0.8998 - val_loss: 1.2704 - val_accuracy: 0.6813 - 9s/epoch - 148ms/step\n",
            "Epoch 84/100\n",
            "\n",
            "Epoch 84: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.3093 - accuracy: 0.9012 - val_loss: 1.2608 - val_accuracy: 0.6853 - 9s/epoch - 147ms/step\n",
            "Epoch 85/100\n",
            "\n",
            "Epoch 85: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.3025 - accuracy: 0.9070 - val_loss: 1.3459 - val_accuracy: 0.6992 - 9s/epoch - 148ms/step\n",
            "Epoch 86/100\n",
            "\n",
            "Epoch 86: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.3043 - accuracy: 0.9055 - val_loss: 1.3219 - val_accuracy: 0.6733 - 9s/epoch - 148ms/step\n",
            "Epoch 87/100\n",
            "\n",
            "Epoch 87: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.2919 - accuracy: 0.9083 - val_loss: 1.3673 - val_accuracy: 0.6534 - 9s/epoch - 148ms/step\n",
            "Epoch 88/100\n",
            "\n",
            "Epoch 88: val_accuracy did not improve from 0.70916\n",
            "62/62 - 10s - loss: 0.3036 - accuracy: 0.9031 - val_loss: 1.4094 - val_accuracy: 0.6355 - 10s/epoch - 153ms/step\n",
            "Epoch 89/100\n",
            "\n",
            "Epoch 89: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.2900 - accuracy: 0.9069 - val_loss: 1.4304 - val_accuracy: 0.6434 - 9s/epoch - 148ms/step\n",
            "Epoch 90/100\n",
            "\n",
            "Epoch 90: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.2896 - accuracy: 0.9092 - val_loss: 1.4165 - val_accuracy: 0.6673 - 9s/epoch - 148ms/step\n",
            "Epoch 91/100\n",
            "\n",
            "Epoch 91: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.2778 - accuracy: 0.9122 - val_loss: 1.4457 - val_accuracy: 0.6912 - 9s/epoch - 148ms/step\n",
            "Epoch 92/100\n",
            "\n",
            "Epoch 92: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.2852 - accuracy: 0.9131 - val_loss: 2.5544 - val_accuracy: 0.5837 - 9s/epoch - 148ms/step\n",
            "Epoch 93/100\n",
            "\n",
            "Epoch 93: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.2898 - accuracy: 0.9061 - val_loss: 1.4531 - val_accuracy: 0.6594 - 9s/epoch - 148ms/step\n",
            "Epoch 94/100\n",
            "\n",
            "Epoch 94: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.2685 - accuracy: 0.9099 - val_loss: 1.2444 - val_accuracy: 0.6673 - 9s/epoch - 148ms/step\n",
            "Epoch 95/100\n",
            "\n",
            "Epoch 95: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.2672 - accuracy: 0.9144 - val_loss: 1.2119 - val_accuracy: 0.6813 - 9s/epoch - 148ms/step\n",
            "Epoch 96/100\n",
            "\n",
            "Epoch 96: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.2699 - accuracy: 0.9147 - val_loss: 1.3204 - val_accuracy: 0.6614 - 9s/epoch - 148ms/step\n",
            "Epoch 97/100\n",
            "\n",
            "Epoch 97: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.2538 - accuracy: 0.9164 - val_loss: 1.5013 - val_accuracy: 0.6534 - 9s/epoch - 148ms/step\n",
            "Epoch 98/100\n",
            "\n",
            "Epoch 98: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.2649 - accuracy: 0.9150 - val_loss: 1.5210 - val_accuracy: 0.6434 - 9s/epoch - 148ms/step\n",
            "Epoch 99/100\n",
            "\n",
            "Epoch 99: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.2609 - accuracy: 0.9169 - val_loss: 1.4822 - val_accuracy: 0.6494 - 9s/epoch - 148ms/step\n",
            "Epoch 100/100\n",
            "\n",
            "Epoch 100: val_accuracy did not improve from 0.70916\n",
            "62/62 - 9s - loss: 0.2467 - accuracy: 0.9223 - val_loss: 1.4018 - val_accuracy: 0.6733 - 9s/epoch - 147ms/step\n",
            "Training model on Fold-8\n",
            "Epoch 1/100\n",
            "\n",
            "Epoch 1: val_accuracy improved from -inf to 0.11387, saving model to /content/drive/My Drive/512-gray-urbansounds-fold8-best_model.h5\n",
            "62/62 - 14s - loss: 2.3765 - accuracy: 0.1896 - val_loss: 5.8276 - val_accuracy: 0.1139 - 14s/epoch - 228ms/step\n",
            "Epoch 2/100\n",
            "\n",
            "Epoch 2: val_accuracy improved from 0.11387 to 0.30435, saving model to /content/drive/My Drive/512-gray-urbansounds-fold8-best_model.h5\n",
            "62/62 - 9s - loss: 2.0964 - accuracy: 0.2655 - val_loss: 2.1769 - val_accuracy: 0.3043 - 9s/epoch - 150ms/step\n",
            "Epoch 3/100\n",
            "\n",
            "Epoch 3: val_accuracy did not improve from 0.30435\n",
            "62/62 - 9s - loss: 1.9942 - accuracy: 0.2933 - val_loss: 2.4843 - val_accuracy: 0.2712 - 9s/epoch - 148ms/step\n",
            "Epoch 4/100\n",
            "\n",
            "Epoch 4: val_accuracy did not improve from 0.30435\n",
            "62/62 - 9s - loss: 1.8783 - accuracy: 0.3364 - val_loss: 1.9965 - val_accuracy: 0.2940 - 9s/epoch - 148ms/step\n",
            "Epoch 5/100\n",
            "\n",
            "Epoch 5: val_accuracy did not improve from 0.30435\n",
            "62/62 - 9s - loss: 1.8244 - accuracy: 0.3540 - val_loss: 3.1426 - val_accuracy: 0.1781 - 9s/epoch - 148ms/step\n",
            "Epoch 6/100\n",
            "\n",
            "Epoch 6: val_accuracy improved from 0.30435 to 0.45342, saving model to /content/drive/My Drive/512-gray-urbansounds-fold8-best_model.h5\n",
            "62/62 - 10s - loss: 1.7534 - accuracy: 0.3793 - val_loss: 1.6501 - val_accuracy: 0.4534 - 10s/epoch - 162ms/step\n",
            "Epoch 7/100\n",
            "\n",
            "Epoch 7: val_accuracy did not improve from 0.45342\n",
            "62/62 - 9s - loss: 1.7020 - accuracy: 0.4018 - val_loss: 1.5089 - val_accuracy: 0.4099 - 9s/epoch - 148ms/step\n",
            "Epoch 8/100\n",
            "\n",
            "Epoch 8: val_accuracy did not improve from 0.45342\n",
            "62/62 - 9s - loss: 1.6450 - accuracy: 0.4189 - val_loss: 1.6881 - val_accuracy: 0.3644 - 9s/epoch - 148ms/step\n",
            "Epoch 9/100\n",
            "\n",
            "Epoch 9: val_accuracy did not improve from 0.45342\n",
            "62/62 - 9s - loss: 1.6049 - accuracy: 0.4304 - val_loss: 2.4112 - val_accuracy: 0.3188 - 9s/epoch - 149ms/step\n",
            "Epoch 10/100\n",
            "\n",
            "Epoch 10: val_accuracy did not improve from 0.45342\n",
            "62/62 - 9s - loss: 1.5765 - accuracy: 0.4444 - val_loss: 1.9403 - val_accuracy: 0.3395 - 9s/epoch - 148ms/step\n",
            "Epoch 11/100\n",
            "\n",
            "Epoch 11: val_accuracy improved from 0.45342 to 0.45549, saving model to /content/drive/My Drive/512-gray-urbansounds-fold8-best_model.h5\n",
            "62/62 - 10s - loss: 1.5486 - accuracy: 0.4621 - val_loss: 1.8958 - val_accuracy: 0.4555 - 10s/epoch - 156ms/step\n",
            "Epoch 12/100\n",
            "\n",
            "Epoch 12: val_accuracy did not improve from 0.45549\n",
            "62/62 - 9s - loss: 1.5279 - accuracy: 0.4657 - val_loss: 1.6681 - val_accuracy: 0.4410 - 9s/epoch - 148ms/step\n",
            "Epoch 13/100\n",
            "\n",
            "Epoch 13: val_accuracy improved from 0.45549 to 0.48033, saving model to /content/drive/My Drive/512-gray-urbansounds-fold8-best_model.h5\n",
            "62/62 - 10s - loss: 1.5213 - accuracy: 0.4712 - val_loss: 1.4883 - val_accuracy: 0.4803 - 10s/epoch - 156ms/step\n",
            "Epoch 14/100\n",
            "\n",
            "Epoch 14: val_accuracy did not improve from 0.48033\n",
            "62/62 - 9s - loss: 1.4762 - accuracy: 0.4889 - val_loss: 1.6338 - val_accuracy: 0.4555 - 9s/epoch - 148ms/step\n",
            "Epoch 15/100\n",
            "\n",
            "Epoch 15: val_accuracy did not improve from 0.48033\n",
            "62/62 - 9s - loss: 1.4553 - accuracy: 0.4932 - val_loss: 2.5899 - val_accuracy: 0.3644 - 9s/epoch - 148ms/step\n",
            "Epoch 16/100\n",
            "\n",
            "Epoch 16: val_accuracy did not improve from 0.48033\n",
            "62/62 - 9s - loss: 1.4274 - accuracy: 0.5092 - val_loss: 1.5043 - val_accuracy: 0.4741 - 9s/epoch - 148ms/step\n",
            "Epoch 17/100\n",
            "\n",
            "Epoch 17: val_accuracy improved from 0.48033 to 0.49896, saving model to /content/drive/My Drive/512-gray-urbansounds-fold8-best_model.h5\n",
            "62/62 - 10s - loss: 1.3995 - accuracy: 0.5265 - val_loss: 1.6697 - val_accuracy: 0.4990 - 10s/epoch - 156ms/step\n",
            "Epoch 18/100\n",
            "\n",
            "Epoch 18: val_accuracy did not improve from 0.49896\n",
            "62/62 - 9s - loss: 1.3816 - accuracy: 0.5309 - val_loss: 2.6791 - val_accuracy: 0.4451 - 9s/epoch - 149ms/step\n",
            "Epoch 19/100\n",
            "\n",
            "Epoch 19: val_accuracy improved from 0.49896 to 0.50311, saving model to /content/drive/My Drive/512-gray-urbansounds-fold8-best_model.h5\n",
            "62/62 - 10s - loss: 1.3665 - accuracy: 0.5343 - val_loss: 2.1113 - val_accuracy: 0.5031 - 10s/epoch - 154ms/step\n",
            "Epoch 20/100\n",
            "\n",
            "Epoch 20: val_accuracy improved from 0.50311 to 0.54451, saving model to /content/drive/My Drive/512-gray-urbansounds-fold8-best_model.h5\n",
            "62/62 - 10s - loss: 1.3261 - accuracy: 0.5498 - val_loss: 1.3664 - val_accuracy: 0.5445 - 10s/epoch - 155ms/step\n",
            "Epoch 21/100\n",
            "\n",
            "Epoch 21: val_accuracy did not improve from 0.54451\n",
            "62/62 - 9s - loss: 1.3324 - accuracy: 0.5419 - val_loss: 1.8604 - val_accuracy: 0.4762 - 9s/epoch - 148ms/step\n",
            "Epoch 22/100\n",
            "\n",
            "Epoch 22: val_accuracy did not improve from 0.54451\n",
            "62/62 - 9s - loss: 1.3069 - accuracy: 0.5596 - val_loss: 2.8418 - val_accuracy: 0.4700 - 9s/epoch - 148ms/step\n",
            "Epoch 23/100\n",
            "\n",
            "Epoch 23: val_accuracy did not improve from 0.54451\n",
            "62/62 - 9s - loss: 1.2706 - accuracy: 0.5701 - val_loss: 4.3283 - val_accuracy: 0.4472 - 9s/epoch - 148ms/step\n",
            "Epoch 24/100\n",
            "\n",
            "Epoch 24: val_accuracy improved from 0.54451 to 0.54865, saving model to /content/drive/My Drive/512-gray-urbansounds-fold8-best_model.h5\n",
            "62/62 - 10s - loss: 1.2399 - accuracy: 0.5797 - val_loss: 1.9520 - val_accuracy: 0.5487 - 10s/epoch - 155ms/step\n",
            "Epoch 25/100\n",
            "\n",
            "Epoch 25: val_accuracy did not improve from 0.54865\n",
            "62/62 - 10s - loss: 1.2168 - accuracy: 0.5918 - val_loss: 3.9125 - val_accuracy: 0.4679 - 10s/epoch - 153ms/step\n",
            "Epoch 26/100\n",
            "\n",
            "Epoch 26: val_accuracy did not improve from 0.54865\n",
            "62/62 - 9s - loss: 1.1751 - accuracy: 0.6024 - val_loss: 2.8257 - val_accuracy: 0.4803 - 9s/epoch - 148ms/step\n",
            "Epoch 27/100\n",
            "\n",
            "Epoch 27: val_accuracy did not improve from 0.54865\n",
            "62/62 - 9s - loss: 1.1497 - accuracy: 0.6175 - val_loss: 4.1008 - val_accuracy: 0.4120 - 9s/epoch - 148ms/step\n",
            "Epoch 28/100\n",
            "\n",
            "Epoch 28: val_accuracy did not improve from 0.54865\n",
            "62/62 - 9s - loss: 1.1320 - accuracy: 0.6196 - val_loss: 3.8134 - val_accuracy: 0.4596 - 9s/epoch - 148ms/step\n",
            "Epoch 29/100\n",
            "\n",
            "Epoch 29: val_accuracy improved from 0.54865 to 0.55694, saving model to /content/drive/My Drive/512-gray-urbansounds-fold8-best_model.h5\n",
            "62/62 - 10s - loss: 1.1060 - accuracy: 0.6341 - val_loss: 2.1987 - val_accuracy: 0.5569 - 10s/epoch - 156ms/step\n",
            "Epoch 30/100\n",
            "\n",
            "Epoch 30: val_accuracy did not improve from 0.55694\n",
            "62/62 - 9s - loss: 1.0799 - accuracy: 0.6419 - val_loss: 1.8642 - val_accuracy: 0.5176 - 9s/epoch - 148ms/step\n",
            "Epoch 31/100\n",
            "\n",
            "Epoch 31: val_accuracy did not improve from 0.55694\n",
            "62/62 - 9s - loss: 1.0754 - accuracy: 0.6379 - val_loss: 3.3316 - val_accuracy: 0.4990 - 9s/epoch - 148ms/step\n",
            "Epoch 32/100\n",
            "\n",
            "Epoch 32: val_accuracy did not improve from 0.55694\n",
            "62/62 - 9s - loss: 1.0593 - accuracy: 0.6477 - val_loss: 2.2472 - val_accuracy: 0.4700 - 9s/epoch - 148ms/step\n",
            "Epoch 33/100\n",
            "\n",
            "Epoch 33: val_accuracy did not improve from 0.55694\n",
            "62/62 - 9s - loss: 1.0265 - accuracy: 0.6587 - val_loss: 5.4973 - val_accuracy: 0.3313 - 9s/epoch - 148ms/step\n",
            "Epoch 34/100\n",
            "\n",
            "Epoch 34: val_accuracy did not improve from 0.55694\n",
            "62/62 - 9s - loss: 1.0068 - accuracy: 0.6650 - val_loss: 4.1066 - val_accuracy: 0.5321 - 9s/epoch - 148ms/step\n",
            "Epoch 35/100\n",
            "\n",
            "Epoch 35: val_accuracy improved from 0.55694 to 0.56936, saving model to /content/drive/My Drive/512-gray-urbansounds-fold8-best_model.h5\n",
            "62/62 - 10s - loss: 0.9772 - accuracy: 0.6793 - val_loss: 2.0486 - val_accuracy: 0.5694 - 10s/epoch - 155ms/step\n",
            "Epoch 36/100\n",
            "\n",
            "Epoch 36: val_accuracy did not improve from 0.56936\n",
            "62/62 - 9s - loss: 0.9716 - accuracy: 0.6800 - val_loss: 3.9119 - val_accuracy: 0.5217 - 9s/epoch - 148ms/step\n",
            "Epoch 37/100\n",
            "\n",
            "Epoch 37: val_accuracy improved from 0.56936 to 0.57971, saving model to /content/drive/My Drive/512-gray-urbansounds-fold8-best_model.h5\n",
            "62/62 - 10s - loss: 0.9464 - accuracy: 0.6875 - val_loss: 2.0607 - val_accuracy: 0.5797 - 10s/epoch - 155ms/step\n",
            "Epoch 38/100\n",
            "\n",
            "Epoch 38: val_accuracy improved from 0.57971 to 0.64596, saving model to /content/drive/My Drive/512-gray-urbansounds-fold8-best_model.h5\n",
            "62/62 - 9s - loss: 0.9291 - accuracy: 0.6925 - val_loss: 1.2083 - val_accuracy: 0.6460 - 9s/epoch - 150ms/step\n",
            "Epoch 39/100\n",
            "\n",
            "Epoch 39: val_accuracy did not improve from 0.64596\n",
            "62/62 - 9s - loss: 0.9156 - accuracy: 0.7021 - val_loss: 1.4165 - val_accuracy: 0.6253 - 9s/epoch - 148ms/step\n",
            "Epoch 40/100\n",
            "\n",
            "Epoch 40: val_accuracy did not improve from 0.64596\n",
            "62/62 - 9s - loss: 0.8692 - accuracy: 0.7204 - val_loss: 1.6481 - val_accuracy: 0.6004 - 9s/epoch - 148ms/step\n",
            "Epoch 41/100\n",
            "\n",
            "Epoch 41: val_accuracy did not improve from 0.64596\n",
            "62/62 - 9s - loss: 0.8486 - accuracy: 0.7303 - val_loss: 1.5759 - val_accuracy: 0.5611 - 9s/epoch - 148ms/step\n",
            "Epoch 42/100\n",
            "\n",
            "Epoch 42: val_accuracy did not improve from 0.64596\n",
            "62/62 - 9s - loss: 0.8099 - accuracy: 0.7431 - val_loss: 1.9042 - val_accuracy: 0.5197 - 9s/epoch - 148ms/step\n",
            "Epoch 43/100\n",
            "\n",
            "Epoch 43: val_accuracy did not improve from 0.64596\n",
            "62/62 - 9s - loss: 0.7989 - accuracy: 0.7406 - val_loss: 2.4050 - val_accuracy: 0.5424 - 9s/epoch - 148ms/step\n",
            "Epoch 44/100\n",
            "\n",
            "Epoch 44: val_accuracy did not improve from 0.64596\n",
            "62/62 - 9s - loss: 0.7729 - accuracy: 0.7502 - val_loss: 1.6017 - val_accuracy: 0.6128 - 9s/epoch - 153ms/step\n",
            "Epoch 45/100\n",
            "\n",
            "Epoch 45: val_accuracy did not improve from 0.64596\n",
            "62/62 - 9s - loss: 0.7658 - accuracy: 0.7517 - val_loss: 2.8773 - val_accuracy: 0.5487 - 9s/epoch - 148ms/step\n",
            "Epoch 46/100\n",
            "\n",
            "Epoch 46: val_accuracy did not improve from 0.64596\n",
            "62/62 - 9s - loss: 0.7521 - accuracy: 0.7628 - val_loss: 2.6879 - val_accuracy: 0.5611 - 9s/epoch - 148ms/step\n",
            "Epoch 47/100\n",
            "\n",
            "Epoch 47: val_accuracy did not improve from 0.64596\n",
            "62/62 - 9s - loss: 0.7233 - accuracy: 0.7663 - val_loss: 1.6459 - val_accuracy: 0.6356 - 9s/epoch - 148ms/step\n",
            "Epoch 48/100\n",
            "\n",
            "Epoch 48: val_accuracy did not improve from 0.64596\n",
            "62/62 - 9s - loss: 0.7035 - accuracy: 0.7764 - val_loss: 1.6488 - val_accuracy: 0.5652 - 9s/epoch - 148ms/step\n",
            "Epoch 49/100\n",
            "\n",
            "Epoch 49: val_accuracy did not improve from 0.64596\n",
            "62/62 - 9s - loss: 0.6939 - accuracy: 0.7759 - val_loss: 1.1214 - val_accuracy: 0.6398 - 9s/epoch - 148ms/step\n",
            "Epoch 50/100\n",
            "\n",
            "Epoch 50: val_accuracy did not improve from 0.64596\n",
            "62/62 - 9s - loss: 0.6819 - accuracy: 0.7766 - val_loss: 1.1530 - val_accuracy: 0.6315 - 9s/epoch - 148ms/step\n",
            "Epoch 51/100\n",
            "\n",
            "Epoch 51: val_accuracy improved from 0.64596 to 0.68530, saving model to /content/drive/My Drive/512-gray-urbansounds-fold8-best_model.h5\n",
            "62/62 - 10s - loss: 0.6807 - accuracy: 0.7812 - val_loss: 1.3020 - val_accuracy: 0.6853 - 10s/epoch - 156ms/step\n",
            "Epoch 52/100\n",
            "\n",
            "Epoch 52: val_accuracy did not improve from 0.68530\n",
            "62/62 - 9s - loss: 0.6546 - accuracy: 0.7869 - val_loss: 1.5567 - val_accuracy: 0.6501 - 9s/epoch - 148ms/step\n",
            "Epoch 53/100\n",
            "\n",
            "Epoch 53: val_accuracy did not improve from 0.68530\n",
            "62/62 - 9s - loss: 0.6365 - accuracy: 0.8028 - val_loss: 1.3928 - val_accuracy: 0.5901 - 9s/epoch - 148ms/step\n",
            "Epoch 54/100\n",
            "\n",
            "Epoch 54: val_accuracy did not improve from 0.68530\n",
            "62/62 - 9s - loss: 0.6413 - accuracy: 0.7956 - val_loss: 1.4853 - val_accuracy: 0.6211 - 9s/epoch - 148ms/step\n",
            "Epoch 55/100\n",
            "\n",
            "Epoch 55: val_accuracy did not improve from 0.68530\n",
            "62/62 - 9s - loss: 0.6419 - accuracy: 0.7985 - val_loss: 1.5565 - val_accuracy: 0.6087 - 9s/epoch - 148ms/step\n",
            "Epoch 56/100\n",
            "\n",
            "Epoch 56: val_accuracy did not improve from 0.68530\n",
            "62/62 - 9s - loss: 0.6117 - accuracy: 0.8057 - val_loss: 1.5300 - val_accuracy: 0.5776 - 9s/epoch - 148ms/step\n",
            "Epoch 57/100\n",
            "\n",
            "Epoch 57: val_accuracy did not improve from 0.68530\n",
            "62/62 - 9s - loss: 0.5783 - accuracy: 0.8191 - val_loss: 1.6511 - val_accuracy: 0.6025 - 9s/epoch - 148ms/step\n",
            "Epoch 58/100\n",
            "\n",
            "Epoch 58: val_accuracy did not improve from 0.68530\n",
            "62/62 - 9s - loss: 0.5729 - accuracy: 0.8149 - val_loss: 1.2375 - val_accuracy: 0.6066 - 9s/epoch - 148ms/step\n",
            "Epoch 59/100\n",
            "\n",
            "Epoch 59: val_accuracy did not improve from 0.68530\n",
            "62/62 - 9s - loss: 0.5613 - accuracy: 0.8230 - val_loss: 2.8838 - val_accuracy: 0.4886 - 9s/epoch - 148ms/step\n",
            "Epoch 60/100\n",
            "\n",
            "Epoch 60: val_accuracy did not improve from 0.68530\n",
            "62/62 - 9s - loss: 0.5610 - accuracy: 0.8193 - val_loss: 1.8466 - val_accuracy: 0.5445 - 9s/epoch - 148ms/step\n",
            "Epoch 61/100\n",
            "\n",
            "Epoch 61: val_accuracy did not improve from 0.68530\n",
            "62/62 - 9s - loss: 0.5541 - accuracy: 0.8249 - val_loss: 1.3220 - val_accuracy: 0.6812 - 9s/epoch - 148ms/step\n",
            "Epoch 62/100\n",
            "\n",
            "Epoch 62: val_accuracy did not improve from 0.68530\n",
            "62/62 - 9s - loss: 0.5246 - accuracy: 0.8323 - val_loss: 1.3746 - val_accuracy: 0.6128 - 9s/epoch - 148ms/step\n",
            "Epoch 63/100\n",
            "\n",
            "Epoch 63: val_accuracy did not improve from 0.68530\n",
            "62/62 - 10s - loss: 0.5281 - accuracy: 0.8332 - val_loss: 1.8370 - val_accuracy: 0.5983 - 10s/epoch - 154ms/step\n",
            "Epoch 64/100\n",
            "\n",
            "Epoch 64: val_accuracy did not improve from 0.68530\n",
            "62/62 - 9s - loss: 0.5279 - accuracy: 0.8333 - val_loss: 1.6926 - val_accuracy: 0.6087 - 9s/epoch - 148ms/step\n",
            "Epoch 65/100\n",
            "\n",
            "Epoch 65: val_accuracy did not improve from 0.68530\n",
            "62/62 - 9s - loss: 0.5104 - accuracy: 0.8385 - val_loss: 1.5815 - val_accuracy: 0.6294 - 9s/epoch - 148ms/step\n",
            "Epoch 66/100\n",
            "\n",
            "Epoch 66: val_accuracy did not improve from 0.68530\n",
            "62/62 - 9s - loss: 0.4769 - accuracy: 0.8510 - val_loss: 1.2543 - val_accuracy: 0.6770 - 9s/epoch - 148ms/step\n",
            "Epoch 67/100\n",
            "\n",
            "Epoch 67: val_accuracy improved from 0.68530 to 0.70600, saving model to /content/drive/My Drive/512-gray-urbansounds-fold8-best_model.h5\n",
            "62/62 - 10s - loss: 0.4842 - accuracy: 0.8489 - val_loss: 1.2468 - val_accuracy: 0.7060 - 10s/epoch - 156ms/step\n",
            "Epoch 68/100\n",
            "\n",
            "Epoch 68: val_accuracy did not improve from 0.70600\n",
            "62/62 - 9s - loss: 0.4618 - accuracy: 0.8483 - val_loss: 1.5945 - val_accuracy: 0.6211 - 9s/epoch - 148ms/step\n",
            "Epoch 69/100\n",
            "\n",
            "Epoch 69: val_accuracy did not improve from 0.70600\n",
            "62/62 - 9s - loss: 0.4528 - accuracy: 0.8543 - val_loss: 1.2046 - val_accuracy: 0.6853 - 9s/epoch - 148ms/step\n",
            "Epoch 70/100\n",
            "\n",
            "Epoch 70: val_accuracy did not improve from 0.70600\n",
            "62/62 - 9s - loss: 0.4545 - accuracy: 0.8518 - val_loss: 1.2806 - val_accuracy: 0.6542 - 9s/epoch - 148ms/step\n",
            "Epoch 71/100\n",
            "\n",
            "Epoch 71: val_accuracy did not improve from 0.70600\n",
            "62/62 - 9s - loss: 0.4389 - accuracy: 0.8624 - val_loss: 1.4500 - val_accuracy: 0.6253 - 9s/epoch - 148ms/step\n",
            "Epoch 72/100\n",
            "\n",
            "Epoch 72: val_accuracy did not improve from 0.70600\n",
            "62/62 - 9s - loss: 0.4401 - accuracy: 0.8622 - val_loss: 1.6586 - val_accuracy: 0.6149 - 9s/epoch - 148ms/step\n",
            "Epoch 73/100\n",
            "\n",
            "Epoch 73: val_accuracy did not improve from 0.70600\n",
            "62/62 - 9s - loss: 0.4191 - accuracy: 0.8694 - val_loss: 1.5643 - val_accuracy: 0.6563 - 9s/epoch - 148ms/step\n",
            "Epoch 74/100\n",
            "\n",
            "Epoch 74: val_accuracy did not improve from 0.70600\n",
            "62/62 - 9s - loss: 0.4053 - accuracy: 0.8717 - val_loss: 1.4287 - val_accuracy: 0.6335 - 9s/epoch - 148ms/step\n",
            "Epoch 75/100\n",
            "\n",
            "Epoch 75: val_accuracy did not improve from 0.70600\n",
            "62/62 - 9s - loss: 0.4016 - accuracy: 0.8719 - val_loss: 1.4057 - val_accuracy: 0.6874 - 9s/epoch - 148ms/step\n",
            "Epoch 76/100\n",
            "\n",
            "Epoch 76: val_accuracy did not improve from 0.70600\n",
            "62/62 - 9s - loss: 0.3971 - accuracy: 0.8740 - val_loss: 1.5222 - val_accuracy: 0.6770 - 9s/epoch - 148ms/step\n",
            "Epoch 77/100\n",
            "\n",
            "Epoch 77: val_accuracy did not improve from 0.70600\n",
            "62/62 - 9s - loss: 0.3932 - accuracy: 0.8721 - val_loss: 1.5001 - val_accuracy: 0.6687 - 9s/epoch - 148ms/step\n",
            "Epoch 78/100\n",
            "\n",
            "Epoch 78: val_accuracy did not improve from 0.70600\n",
            "62/62 - 9s - loss: 0.3936 - accuracy: 0.8712 - val_loss: 1.4741 - val_accuracy: 0.6418 - 9s/epoch - 148ms/step\n",
            "Epoch 79/100\n",
            "\n",
            "Epoch 79: val_accuracy did not improve from 0.70600\n",
            "62/62 - 9s - loss: 0.3777 - accuracy: 0.8791 - val_loss: 1.5392 - val_accuracy: 0.6584 - 9s/epoch - 148ms/step\n",
            "Epoch 80/100\n",
            "\n",
            "Epoch 80: val_accuracy did not improve from 0.70600\n",
            "62/62 - 9s - loss: 0.3727 - accuracy: 0.8827 - val_loss: 1.7185 - val_accuracy: 0.6066 - 9s/epoch - 148ms/step\n",
            "Epoch 81/100\n",
            "\n",
            "Epoch 81: val_accuracy did not improve from 0.70600\n",
            "62/62 - 9s - loss: 0.3712 - accuracy: 0.8785 - val_loss: 1.8499 - val_accuracy: 0.6749 - 9s/epoch - 149ms/step\n",
            "Epoch 82/100\n",
            "\n",
            "Epoch 82: val_accuracy did not improve from 0.70600\n",
            "62/62 - 10s - loss: 0.3603 - accuracy: 0.8859 - val_loss: 1.7857 - val_accuracy: 0.5983 - 10s/epoch - 154ms/step\n",
            "Epoch 83/100\n",
            "\n",
            "Epoch 83: val_accuracy did not improve from 0.70600\n",
            "62/62 - 9s - loss: 0.3582 - accuracy: 0.8875 - val_loss: 3.1450 - val_accuracy: 0.5859 - 9s/epoch - 148ms/step\n",
            "Epoch 84/100\n",
            "\n",
            "Epoch 84: val_accuracy did not improve from 0.70600\n",
            "62/62 - 9s - loss: 0.3597 - accuracy: 0.8837 - val_loss: 2.3643 - val_accuracy: 0.5673 - 9s/epoch - 148ms/step\n",
            "Epoch 85/100\n",
            "\n",
            "Epoch 85: val_accuracy did not improve from 0.70600\n",
            "62/62 - 9s - loss: 0.3481 - accuracy: 0.8921 - val_loss: 2.6920 - val_accuracy: 0.6004 - 9s/epoch - 148ms/step\n",
            "Epoch 86/100\n",
            "\n",
            "Epoch 86: val_accuracy did not improve from 0.70600\n",
            "62/62 - 9s - loss: 0.3475 - accuracy: 0.8866 - val_loss: 2.7020 - val_accuracy: 0.5611 - 9s/epoch - 148ms/step\n",
            "Epoch 87/100\n",
            "\n",
            "Epoch 87: val_accuracy did not improve from 0.70600\n",
            "62/62 - 9s - loss: 0.3591 - accuracy: 0.8858 - val_loss: 1.6349 - val_accuracy: 0.6460 - 9s/epoch - 148ms/step\n",
            "Epoch 88/100\n",
            "\n",
            "Epoch 88: val_accuracy did not improve from 0.70600\n",
            "62/62 - 9s - loss: 0.3398 - accuracy: 0.8851 - val_loss: 1.6100 - val_accuracy: 0.6894 - 9s/epoch - 148ms/step\n",
            "Epoch 89/100\n",
            "\n",
            "Epoch 89: val_accuracy did not improve from 0.70600\n",
            "62/62 - 9s - loss: 0.3189 - accuracy: 0.8969 - val_loss: 2.8348 - val_accuracy: 0.5818 - 9s/epoch - 148ms/step\n",
            "Epoch 90/100\n",
            "\n",
            "Epoch 90: val_accuracy did not improve from 0.70600\n",
            "62/62 - 9s - loss: 0.3326 - accuracy: 0.8915 - val_loss: 1.6754 - val_accuracy: 0.6211 - 9s/epoch - 149ms/step\n",
            "Epoch 91/100\n",
            "\n",
            "Epoch 91: val_accuracy did not improve from 0.70600\n",
            "62/62 - 9s - loss: 0.3160 - accuracy: 0.8991 - val_loss: 1.6860 - val_accuracy: 0.6232 - 9s/epoch - 149ms/step\n",
            "Epoch 92/100\n",
            "\n",
            "Epoch 92: val_accuracy did not improve from 0.70600\n",
            "62/62 - 9s - loss: 0.3016 - accuracy: 0.8984 - val_loss: 1.7031 - val_accuracy: 0.5880 - 9s/epoch - 148ms/step\n",
            "Epoch 93/100\n",
            "\n",
            "Epoch 93: val_accuracy did not improve from 0.70600\n",
            "62/62 - 9s - loss: 0.2963 - accuracy: 0.9034 - val_loss: 1.6043 - val_accuracy: 0.6625 - 9s/epoch - 148ms/step\n",
            "Epoch 94/100\n",
            "\n",
            "Epoch 94: val_accuracy did not improve from 0.70600\n",
            "62/62 - 9s - loss: 0.2885 - accuracy: 0.9094 - val_loss: 1.5584 - val_accuracy: 0.6584 - 9s/epoch - 148ms/step\n",
            "Epoch 95/100\n",
            "\n",
            "Epoch 95: val_accuracy did not improve from 0.70600\n",
            "62/62 - 9s - loss: 0.3027 - accuracy: 0.9035 - val_loss: 1.4828 - val_accuracy: 0.6418 - 9s/epoch - 148ms/step\n",
            "Epoch 96/100\n",
            "\n",
            "Epoch 96: val_accuracy did not improve from 0.70600\n",
            "62/62 - 9s - loss: 0.2906 - accuracy: 0.9036 - val_loss: 1.9418 - val_accuracy: 0.5735 - 9s/epoch - 148ms/step\n",
            "Epoch 97/100\n",
            "\n",
            "Epoch 97: val_accuracy did not improve from 0.70600\n",
            "62/62 - 9s - loss: 0.2799 - accuracy: 0.9079 - val_loss: 2.3579 - val_accuracy: 0.6046 - 9s/epoch - 148ms/step\n",
            "Epoch 98/100\n",
            "\n",
            "Epoch 98: val_accuracy did not improve from 0.70600\n",
            "62/62 - 9s - loss: 0.2873 - accuracy: 0.9042 - val_loss: 2.3290 - val_accuracy: 0.5714 - 9s/epoch - 148ms/step\n",
            "Epoch 99/100\n",
            "\n",
            "Epoch 99: val_accuracy did not improve from 0.70600\n",
            "62/62 - 9s - loss: 0.2745 - accuracy: 0.9097 - val_loss: 1.9336 - val_accuracy: 0.6563 - 9s/epoch - 148ms/step\n",
            "Epoch 100/100\n",
            "\n",
            "Epoch 100: val_accuracy did not improve from 0.70600\n",
            "62/62 - 9s - loss: 0.2675 - accuracy: 0.9172 - val_loss: 1.9464 - val_accuracy: 0.6501 - 9s/epoch - 148ms/step\n",
            "Training model on Fold-9\n",
            "Epoch 1/100\n",
            "\n",
            "Epoch 1: val_accuracy improved from -inf to 0.23926, saving model to /content/drive/My Drive/512-gray-urbansounds-fold9-best_model.h5\n",
            "62/62 - 13s - loss: 2.4185 - accuracy: 0.1841 - val_loss: 2.4325 - val_accuracy: 0.2393 - 13s/epoch - 203ms/step\n",
            "Epoch 2/100\n",
            "\n",
            "Epoch 2: val_accuracy improved from 0.23926 to 0.38037, saving model to /content/drive/My Drive/512-gray-urbansounds-fold9-best_model.h5\n",
            "62/62 - 9s - loss: 2.0729 - accuracy: 0.2688 - val_loss: 2.2708 - val_accuracy: 0.3804 - 9s/epoch - 150ms/step\n",
            "Epoch 3/100\n",
            "\n",
            "Epoch 3: val_accuracy did not improve from 0.38037\n",
            "62/62 - 9s - loss: 1.9466 - accuracy: 0.3220 - val_loss: 2.1334 - val_accuracy: 0.3313 - 9s/epoch - 148ms/step\n",
            "Epoch 4/100\n",
            "\n",
            "Epoch 4: val_accuracy improved from 0.38037 to 0.40900, saving model to /content/drive/My Drive/512-gray-urbansounds-fold9-best_model.h5\n",
            "62/62 - 10s - loss: 1.8437 - accuracy: 0.3522 - val_loss: 2.1012 - val_accuracy: 0.4090 - 10s/epoch - 157ms/step\n",
            "Epoch 5/100\n",
            "\n",
            "Epoch 5: val_accuracy did not improve from 0.40900\n",
            "62/62 - 9s - loss: 1.7778 - accuracy: 0.3804 - val_loss: 1.8885 - val_accuracy: 0.3681 - 9s/epoch - 148ms/step\n",
            "Epoch 6/100\n",
            "\n",
            "Epoch 6: val_accuracy did not improve from 0.40900\n",
            "62/62 - 9s - loss: 1.7136 - accuracy: 0.4093 - val_loss: 2.0238 - val_accuracy: 0.3436 - 9s/epoch - 148ms/step\n",
            "Epoch 7/100\n",
            "\n",
            "Epoch 7: val_accuracy improved from 0.40900 to 0.46626, saving model to /content/drive/My Drive/512-gray-urbansounds-fold9-best_model.h5\n",
            "62/62 - 10s - loss: 1.6728 - accuracy: 0.4222 - val_loss: 1.5029 - val_accuracy: 0.4663 - 10s/epoch - 156ms/step\n",
            "Epoch 8/100\n",
            "\n",
            "Epoch 8: val_accuracy improved from 0.46626 to 0.53988, saving model to /content/drive/My Drive/512-gray-urbansounds-fold9-best_model.h5\n",
            "62/62 - 9s - loss: 1.6058 - accuracy: 0.4488 - val_loss: 1.3547 - val_accuracy: 0.5399 - 9s/epoch - 150ms/step\n",
            "Epoch 9/100\n",
            "\n",
            "Epoch 9: val_accuracy did not improve from 0.53988\n",
            "62/62 - 9s - loss: 1.5761 - accuracy: 0.4622 - val_loss: 1.8198 - val_accuracy: 0.3947 - 9s/epoch - 148ms/step\n",
            "Epoch 10/100\n",
            "\n",
            "Epoch 10: val_accuracy did not improve from 0.53988\n",
            "62/62 - 9s - loss: 1.5223 - accuracy: 0.4756 - val_loss: 1.8340 - val_accuracy: 0.5010 - 9s/epoch - 148ms/step\n",
            "Epoch 11/100\n",
            "\n",
            "Epoch 11: val_accuracy improved from 0.53988 to 0.55215, saving model to /content/drive/My Drive/512-gray-urbansounds-fold9-best_model.h5\n",
            "62/62 - 10s - loss: 1.5093 - accuracy: 0.4828 - val_loss: 1.7908 - val_accuracy: 0.5521 - 10s/epoch - 155ms/step\n",
            "Epoch 12/100\n",
            "\n",
            "Epoch 12: val_accuracy did not improve from 0.55215\n",
            "62/62 - 9s - loss: 1.4697 - accuracy: 0.4995 - val_loss: 1.6421 - val_accuracy: 0.5256 - 9s/epoch - 148ms/step\n",
            "Epoch 13/100\n",
            "\n",
            "Epoch 13: val_accuracy did not improve from 0.55215\n",
            "62/62 - 9s - loss: 1.4593 - accuracy: 0.4946 - val_loss: 1.9149 - val_accuracy: 0.5092 - 9s/epoch - 148ms/step\n",
            "Epoch 14/100\n",
            "\n",
            "Epoch 14: val_accuracy improved from 0.55215 to 0.56442, saving model to /content/drive/My Drive/512-gray-urbansounds-fold9-best_model.h5\n",
            "62/62 - 10s - loss: 1.4348 - accuracy: 0.5153 - val_loss: 1.4001 - val_accuracy: 0.5644 - 10s/epoch - 155ms/step\n",
            "Epoch 15/100\n",
            "\n",
            "Epoch 15: val_accuracy did not improve from 0.56442\n",
            "62/62 - 9s - loss: 1.4037 - accuracy: 0.5234 - val_loss: 1.6579 - val_accuracy: 0.5337 - 9s/epoch - 148ms/step\n",
            "Epoch 16/100\n",
            "\n",
            "Epoch 16: val_accuracy did not improve from 0.56442\n",
            "62/62 - 9s - loss: 1.4003 - accuracy: 0.5239 - val_loss: 1.3258 - val_accuracy: 0.5644 - 9s/epoch - 148ms/step\n",
            "Epoch 17/100\n",
            "\n",
            "Epoch 17: val_accuracy improved from 0.56442 to 0.61350, saving model to /content/drive/My Drive/512-gray-urbansounds-fold9-best_model.h5\n",
            "62/62 - 10s - loss: 1.3613 - accuracy: 0.5363 - val_loss: 1.2886 - val_accuracy: 0.6135 - 10s/epoch - 156ms/step\n",
            "Epoch 18/100\n",
            "\n",
            "Epoch 18: val_accuracy improved from 0.61350 to 0.62781, saving model to /content/drive/My Drive/512-gray-urbansounds-fold9-best_model.h5\n",
            "62/62 - 10s - loss: 1.3325 - accuracy: 0.5493 - val_loss: 1.4203 - val_accuracy: 0.6278 - 10s/epoch - 156ms/step\n",
            "Epoch 19/100\n",
            "\n",
            "Epoch 19: val_accuracy did not improve from 0.62781\n",
            "62/62 - 9s - loss: 1.3241 - accuracy: 0.5494 - val_loss: 1.2418 - val_accuracy: 0.5746 - 9s/epoch - 153ms/step\n",
            "Epoch 20/100\n",
            "\n",
            "Epoch 20: val_accuracy improved from 0.62781 to 0.63190, saving model to /content/drive/My Drive/512-gray-urbansounds-fold9-best_model.h5\n",
            "62/62 - 10s - loss: 1.2989 - accuracy: 0.5651 - val_loss: 1.4013 - val_accuracy: 0.6319 - 10s/epoch - 155ms/step\n",
            "Epoch 21/100\n",
            "\n",
            "Epoch 21: val_accuracy did not improve from 0.63190\n",
            "62/62 - 9s - loss: 1.2910 - accuracy: 0.5686 - val_loss: 1.3466 - val_accuracy: 0.5869 - 9s/epoch - 148ms/step\n",
            "Epoch 22/100\n",
            "\n",
            "Epoch 22: val_accuracy did not improve from 0.63190\n",
            "62/62 - 9s - loss: 1.2636 - accuracy: 0.5824 - val_loss: 1.4159 - val_accuracy: 0.5521 - 9s/epoch - 148ms/step\n",
            "Epoch 23/100\n",
            "\n",
            "Epoch 23: val_accuracy improved from 0.63190 to 0.65440, saving model to /content/drive/My Drive/512-gray-urbansounds-fold9-best_model.h5\n",
            "62/62 - 10s - loss: 1.2535 - accuracy: 0.5768 - val_loss: 1.1520 - val_accuracy: 0.6544 - 10s/epoch - 155ms/step\n",
            "Epoch 24/100\n",
            "\n",
            "Epoch 24: val_accuracy did not improve from 0.65440\n",
            "62/62 - 9s - loss: 1.1999 - accuracy: 0.6002 - val_loss: 1.4066 - val_accuracy: 0.6033 - 9s/epoch - 148ms/step\n",
            "Epoch 25/100\n",
            "\n",
            "Epoch 25: val_accuracy did not improve from 0.65440\n",
            "62/62 - 9s - loss: 1.1578 - accuracy: 0.6134 - val_loss: 1.1641 - val_accuracy: 0.6094 - 9s/epoch - 148ms/step\n",
            "Epoch 26/100\n",
            "\n",
            "Epoch 26: val_accuracy did not improve from 0.65440\n",
            "62/62 - 9s - loss: 1.1663 - accuracy: 0.6064 - val_loss: 1.4570 - val_accuracy: 0.6299 - 9s/epoch - 148ms/step\n",
            "Epoch 27/100\n",
            "\n",
            "Epoch 27: val_accuracy did not improve from 0.65440\n",
            "62/62 - 9s - loss: 1.1269 - accuracy: 0.6220 - val_loss: 1.2127 - val_accuracy: 0.6380 - 9s/epoch - 148ms/step\n",
            "Epoch 28/100\n",
            "\n",
            "Epoch 28: val_accuracy did not improve from 0.65440\n",
            "62/62 - 9s - loss: 1.1054 - accuracy: 0.6350 - val_loss: 1.2493 - val_accuracy: 0.5930 - 9s/epoch - 148ms/step\n",
            "Epoch 29/100\n",
            "\n",
            "Epoch 29: val_accuracy improved from 0.65440 to 0.66053, saving model to /content/drive/My Drive/512-gray-urbansounds-fold9-best_model.h5\n",
            "62/62 - 10s - loss: 1.1010 - accuracy: 0.6374 - val_loss: 1.2647 - val_accuracy: 0.6605 - 10s/epoch - 155ms/step\n",
            "Epoch 30/100\n",
            "\n",
            "Epoch 30: val_accuracy did not improve from 0.66053\n",
            "62/62 - 9s - loss: 1.0577 - accuracy: 0.6584 - val_loss: 1.0741 - val_accuracy: 0.6380 - 9s/epoch - 148ms/step\n",
            "Epoch 31/100\n",
            "\n",
            "Epoch 31: val_accuracy did not improve from 0.66053\n",
            "62/62 - 9s - loss: 1.0451 - accuracy: 0.6611 - val_loss: 1.1032 - val_accuracy: 0.6074 - 9s/epoch - 149ms/step\n",
            "Epoch 32/100\n",
            "\n",
            "Epoch 32: val_accuracy improved from 0.66053 to 0.68507, saving model to /content/drive/My Drive/512-gray-urbansounds-fold9-best_model.h5\n",
            "62/62 - 10s - loss: 1.0310 - accuracy: 0.6721 - val_loss: 1.0050 - val_accuracy: 0.6851 - 10s/epoch - 155ms/step\n",
            "Epoch 33/100\n",
            "\n",
            "Epoch 33: val_accuracy did not improve from 0.68507\n",
            "62/62 - 9s - loss: 0.9955 - accuracy: 0.6761 - val_loss: 0.9767 - val_accuracy: 0.6564 - 9s/epoch - 148ms/step\n",
            "Epoch 34/100\n",
            "\n",
            "Epoch 34: val_accuracy did not improve from 0.68507\n",
            "62/62 - 9s - loss: 0.9753 - accuracy: 0.6867 - val_loss: 1.1628 - val_accuracy: 0.6258 - 9s/epoch - 148ms/step\n",
            "Epoch 35/100\n",
            "\n",
            "Epoch 35: val_accuracy did not improve from 0.68507\n",
            "62/62 - 9s - loss: 0.9681 - accuracy: 0.6906 - val_loss: 1.2495 - val_accuracy: 0.5624 - 9s/epoch - 148ms/step\n",
            "Epoch 36/100\n",
            "\n",
            "Epoch 36: val_accuracy did not improve from 0.68507\n",
            "62/62 - 9s - loss: 0.9260 - accuracy: 0.7006 - val_loss: 1.1128 - val_accuracy: 0.6380 - 9s/epoch - 148ms/step\n",
            "Epoch 37/100\n",
            "\n",
            "Epoch 37: val_accuracy did not improve from 0.68507\n",
            "62/62 - 9s - loss: 0.9077 - accuracy: 0.7107 - val_loss: 1.1294 - val_accuracy: 0.6585 - 9s/epoch - 148ms/step\n",
            "Epoch 38/100\n",
            "\n",
            "Epoch 38: val_accuracy improved from 0.68507 to 0.70757, saving model to /content/drive/My Drive/512-gray-urbansounds-fold9-best_model.h5\n",
            "62/62 - 10s - loss: 0.9048 - accuracy: 0.7112 - val_loss: 0.9485 - val_accuracy: 0.7076 - 10s/epoch - 163ms/step\n",
            "Epoch 39/100\n",
            "\n",
            "Epoch 39: val_accuracy did not improve from 0.70757\n",
            "62/62 - 9s - loss: 0.8975 - accuracy: 0.7126 - val_loss: 1.1389 - val_accuracy: 0.6687 - 9s/epoch - 148ms/step\n",
            "Epoch 40/100\n",
            "\n",
            "Epoch 40: val_accuracy did not improve from 0.70757\n",
            "62/62 - 9s - loss: 0.8491 - accuracy: 0.7333 - val_loss: 1.0884 - val_accuracy: 0.6953 - 9s/epoch - 148ms/step\n",
            "Epoch 41/100\n",
            "\n",
            "Epoch 41: val_accuracy did not improve from 0.70757\n",
            "62/62 - 10s - loss: 0.8438 - accuracy: 0.7307 - val_loss: 1.2622 - val_accuracy: 0.6217 - 10s/epoch - 154ms/step\n",
            "Epoch 42/100\n",
            "\n",
            "Epoch 42: val_accuracy did not improve from 0.70757\n",
            "62/62 - 9s - loss: 0.8227 - accuracy: 0.7353 - val_loss: 1.0059 - val_accuracy: 0.6953 - 9s/epoch - 149ms/step\n",
            "Epoch 43/100\n",
            "\n",
            "Epoch 43: val_accuracy did not improve from 0.70757\n",
            "62/62 - 9s - loss: 0.8024 - accuracy: 0.7470 - val_loss: 1.1331 - val_accuracy: 0.6851 - 9s/epoch - 148ms/step\n",
            "Epoch 44/100\n",
            "\n",
            "Epoch 44: val_accuracy did not improve from 0.70757\n",
            "62/62 - 9s - loss: 0.7746 - accuracy: 0.7616 - val_loss: 1.3433 - val_accuracy: 0.6933 - 9s/epoch - 148ms/step\n",
            "Epoch 45/100\n",
            "\n",
            "Epoch 45: val_accuracy improved from 0.70757 to 0.72393, saving model to /content/drive/My Drive/512-gray-urbansounds-fold9-best_model.h5\n",
            "62/62 - 10s - loss: 0.7394 - accuracy: 0.7684 - val_loss: 0.9576 - val_accuracy: 0.7239 - 10s/epoch - 156ms/step\n",
            "Epoch 46/100\n",
            "\n",
            "Epoch 46: val_accuracy improved from 0.72393 to 0.73415, saving model to /content/drive/My Drive/512-gray-urbansounds-fold9-best_model.h5\n",
            "62/62 - 10s - loss: 0.7280 - accuracy: 0.7797 - val_loss: 0.8464 - val_accuracy: 0.7342 - 10s/epoch - 156ms/step\n",
            "Epoch 47/100\n",
            "\n",
            "Epoch 47: val_accuracy did not improve from 0.73415\n",
            "62/62 - 9s - loss: 0.7060 - accuracy: 0.7828 - val_loss: 0.8926 - val_accuracy: 0.7321 - 9s/epoch - 148ms/step\n",
            "Epoch 48/100\n",
            "\n",
            "Epoch 48: val_accuracy did not improve from 0.73415\n",
            "62/62 - 9s - loss: 0.6806 - accuracy: 0.7880 - val_loss: 1.0308 - val_accuracy: 0.6953 - 9s/epoch - 148ms/step\n",
            "Epoch 49/100\n",
            "\n",
            "Epoch 49: val_accuracy did not improve from 0.73415\n",
            "62/62 - 9s - loss: 0.6678 - accuracy: 0.7930 - val_loss: 0.9806 - val_accuracy: 0.7239 - 9s/epoch - 148ms/step\n",
            "Epoch 50/100\n",
            "\n",
            "Epoch 50: val_accuracy did not improve from 0.73415\n",
            "62/62 - 9s - loss: 0.6545 - accuracy: 0.7989 - val_loss: 1.0900 - val_accuracy: 0.7014 - 9s/epoch - 148ms/step\n",
            "Epoch 51/100\n",
            "\n",
            "Epoch 51: val_accuracy did not improve from 0.73415\n",
            "62/62 - 9s - loss: 0.6574 - accuracy: 0.7941 - val_loss: 0.9306 - val_accuracy: 0.7096 - 9s/epoch - 148ms/step\n",
            "Epoch 52/100\n",
            "\n",
            "Epoch 52: val_accuracy did not improve from 0.73415\n",
            "62/62 - 9s - loss: 0.6251 - accuracy: 0.8042 - val_loss: 1.9368 - val_accuracy: 0.6401 - 9s/epoch - 148ms/step\n",
            "Epoch 53/100\n",
            "\n",
            "Epoch 53: val_accuracy did not improve from 0.73415\n",
            "62/62 - 9s - loss: 0.6185 - accuracy: 0.8036 - val_loss: 1.1993 - val_accuracy: 0.6851 - 9s/epoch - 148ms/step\n",
            "Epoch 54/100\n",
            "\n",
            "Epoch 54: val_accuracy did not improve from 0.73415\n",
            "62/62 - 9s - loss: 0.6080 - accuracy: 0.8084 - val_loss: 1.3935 - val_accuracy: 0.6483 - 9s/epoch - 148ms/step\n",
            "Epoch 55/100\n",
            "\n",
            "Epoch 55: val_accuracy did not improve from 0.73415\n",
            "62/62 - 9s - loss: 0.5788 - accuracy: 0.8218 - val_loss: 1.8529 - val_accuracy: 0.6339 - 9s/epoch - 148ms/step\n",
            "Epoch 56/100\n",
            "\n",
            "Epoch 56: val_accuracy did not improve from 0.73415\n",
            "62/62 - 9s - loss: 0.5558 - accuracy: 0.8250 - val_loss: 1.0821 - val_accuracy: 0.6994 - 9s/epoch - 148ms/step\n",
            "Epoch 57/100\n",
            "\n",
            "Epoch 57: val_accuracy did not improve from 0.73415\n",
            "62/62 - 9s - loss: 0.5386 - accuracy: 0.8343 - val_loss: 2.6840 - val_accuracy: 0.4683 - 9s/epoch - 148ms/step\n",
            "Epoch 58/100\n",
            "\n",
            "Epoch 58: val_accuracy did not improve from 0.73415\n",
            "62/62 - 9s - loss: 0.5314 - accuracy: 0.8363 - val_loss: 4.7988 - val_accuracy: 0.4192 - 9s/epoch - 148ms/step\n",
            "Epoch 59/100\n",
            "\n",
            "Epoch 59: val_accuracy did not improve from 0.73415\n",
            "62/62 - 9s - loss: 0.5341 - accuracy: 0.8401 - val_loss: 2.6082 - val_accuracy: 0.4417 - 9s/epoch - 148ms/step\n",
            "Epoch 60/100\n",
            "\n",
            "Epoch 60: val_accuracy did not improve from 0.73415\n",
            "62/62 - 9s - loss: 0.5157 - accuracy: 0.8391 - val_loss: 1.9688 - val_accuracy: 0.6237 - 9s/epoch - 148ms/step\n",
            "Epoch 61/100\n",
            "\n",
            "Epoch 61: val_accuracy did not improve from 0.73415\n",
            "62/62 - 9s - loss: 0.5065 - accuracy: 0.8375 - val_loss: 2.5078 - val_accuracy: 0.4438 - 9s/epoch - 148ms/step\n",
            "Epoch 62/100\n",
            "\n",
            "Epoch 62: val_accuracy did not improve from 0.73415\n",
            "62/62 - 9s - loss: 0.4992 - accuracy: 0.8459 - val_loss: 2.5548 - val_accuracy: 0.5317 - 9s/epoch - 148ms/step\n",
            "Epoch 63/100\n",
            "\n",
            "Epoch 63: val_accuracy did not improve from 0.73415\n",
            "62/62 - 10s - loss: 0.4929 - accuracy: 0.8531 - val_loss: 2.3940 - val_accuracy: 0.4724 - 10s/epoch - 154ms/step\n",
            "Epoch 64/100\n",
            "\n",
            "Epoch 64: val_accuracy did not improve from 0.73415\n",
            "62/62 - 9s - loss: 0.4806 - accuracy: 0.8499 - val_loss: 3.9808 - val_accuracy: 0.4029 - 9s/epoch - 148ms/step\n",
            "Epoch 65/100\n",
            "\n",
            "Epoch 65: val_accuracy did not improve from 0.73415\n",
            "62/62 - 9s - loss: 0.4592 - accuracy: 0.8541 - val_loss: 1.1499 - val_accuracy: 0.6728 - 9s/epoch - 148ms/step\n",
            "Epoch 66/100\n",
            "\n",
            "Epoch 66: val_accuracy improved from 0.73415 to 0.74847, saving model to /content/drive/My Drive/512-gray-urbansounds-fold9-best_model.h5\n",
            "62/62 - 12s - loss: 0.4611 - accuracy: 0.8555 - val_loss: 0.8034 - val_accuracy: 0.7485 - 12s/epoch - 199ms/step\n",
            "Epoch 67/100\n",
            "\n",
            "Epoch 67: val_accuracy did not improve from 0.74847\n",
            "62/62 - 9s - loss: 0.4652 - accuracy: 0.8559 - val_loss: 1.1012 - val_accuracy: 0.6462 - 9s/epoch - 148ms/step\n",
            "Epoch 68/100\n",
            "\n",
            "Epoch 68: val_accuracy did not improve from 0.74847\n",
            "62/62 - 9s - loss: 0.4442 - accuracy: 0.8610 - val_loss: 1.3461 - val_accuracy: 0.6012 - 9s/epoch - 148ms/step\n",
            "Epoch 69/100\n",
            "\n",
            "Epoch 69: val_accuracy did not improve from 0.74847\n",
            "62/62 - 9s - loss: 0.4165 - accuracy: 0.8724 - val_loss: 1.6412 - val_accuracy: 0.5562 - 9s/epoch - 148ms/step\n",
            "Epoch 70/100\n",
            "\n",
            "Epoch 70: val_accuracy did not improve from 0.74847\n",
            "62/62 - 9s - loss: 0.4278 - accuracy: 0.8656 - val_loss: 2.0737 - val_accuracy: 0.5235 - 9s/epoch - 148ms/step\n",
            "Epoch 71/100\n",
            "\n",
            "Epoch 71: val_accuracy did not improve from 0.74847\n",
            "62/62 - 9s - loss: 0.4120 - accuracy: 0.8739 - val_loss: 1.9477 - val_accuracy: 0.6544 - 9s/epoch - 148ms/step\n",
            "Epoch 72/100\n",
            "\n",
            "Epoch 72: val_accuracy did not improve from 0.74847\n",
            "62/62 - 9s - loss: 0.4087 - accuracy: 0.8699 - val_loss: 1.2787 - val_accuracy: 0.6605 - 9s/epoch - 148ms/step\n",
            "Epoch 73/100\n",
            "\n",
            "Epoch 73: val_accuracy did not improve from 0.74847\n",
            "62/62 - 9s - loss: 0.4055 - accuracy: 0.8741 - val_loss: 1.1441 - val_accuracy: 0.7137 - 9s/epoch - 148ms/step\n",
            "Epoch 74/100\n",
            "\n",
            "Epoch 74: val_accuracy did not improve from 0.74847\n",
            "62/62 - 9s - loss: 0.4040 - accuracy: 0.8722 - val_loss: 1.1973 - val_accuracy: 0.7280 - 9s/epoch - 148ms/step\n",
            "Epoch 75/100\n",
            "\n",
            "Epoch 75: val_accuracy did not improve from 0.74847\n",
            "62/62 - 9s - loss: 0.4049 - accuracy: 0.8761 - val_loss: 1.3282 - val_accuracy: 0.6912 - 9s/epoch - 148ms/step\n",
            "Epoch 76/100\n",
            "\n",
            "Epoch 76: val_accuracy improved from 0.74847 to 0.77096, saving model to /content/drive/My Drive/512-gray-urbansounds-fold9-best_model.h5\n",
            "62/62 - 10s - loss: 0.4170 - accuracy: 0.8674 - val_loss: 0.8896 - val_accuracy: 0.7710 - 10s/epoch - 156ms/step\n",
            "Epoch 77/100\n",
            "\n",
            "Epoch 77: val_accuracy did not improve from 0.77096\n",
            "62/62 - 9s - loss: 0.3773 - accuracy: 0.8799 - val_loss: 1.6877 - val_accuracy: 0.6626 - 9s/epoch - 148ms/step\n",
            "Epoch 78/100\n",
            "\n",
            "Epoch 78: val_accuracy did not improve from 0.77096\n",
            "62/62 - 9s - loss: 0.3751 - accuracy: 0.8820 - val_loss: 1.3525 - val_accuracy: 0.6953 - 9s/epoch - 148ms/step\n",
            "Epoch 79/100\n",
            "\n",
            "Epoch 79: val_accuracy improved from 0.77096 to 0.80573, saving model to /content/drive/My Drive/512-gray-urbansounds-fold9-best_model.h5\n",
            "62/62 - 10s - loss: 0.3660 - accuracy: 0.8873 - val_loss: 0.7657 - val_accuracy: 0.8057 - 10s/epoch - 155ms/step\n",
            "Epoch 80/100\n",
            "\n",
            "Epoch 80: val_accuracy did not improve from 0.80573\n",
            "62/62 - 9s - loss: 0.3558 - accuracy: 0.8877 - val_loss: 1.0263 - val_accuracy: 0.7566 - 9s/epoch - 148ms/step\n",
            "Epoch 81/100\n",
            "\n",
            "Epoch 81: val_accuracy did not improve from 0.80573\n",
            "62/62 - 9s - loss: 0.3544 - accuracy: 0.8933 - val_loss: 1.0880 - val_accuracy: 0.7423 - 9s/epoch - 152ms/step\n",
            "Epoch 82/100\n",
            "\n",
            "Epoch 82: val_accuracy did not improve from 0.80573\n",
            "62/62 - 9s - loss: 0.3434 - accuracy: 0.8914 - val_loss: 1.2339 - val_accuracy: 0.7076 - 9s/epoch - 151ms/step\n",
            "Epoch 83/100\n",
            "\n",
            "Epoch 83: val_accuracy did not improve from 0.80573\n",
            "62/62 - 9s - loss: 0.3476 - accuracy: 0.8919 - val_loss: 1.2239 - val_accuracy: 0.7403 - 9s/epoch - 148ms/step\n",
            "Epoch 84/100\n",
            "\n",
            "Epoch 84: val_accuracy did not improve from 0.80573\n",
            "62/62 - 9s - loss: 0.3423 - accuracy: 0.8916 - val_loss: 1.2113 - val_accuracy: 0.7219 - 9s/epoch - 148ms/step\n",
            "Epoch 85/100\n",
            "\n",
            "Epoch 85: val_accuracy did not improve from 0.80573\n",
            "62/62 - 9s - loss: 0.3386 - accuracy: 0.8968 - val_loss: 1.6175 - val_accuracy: 0.6503 - 9s/epoch - 148ms/step\n",
            "Epoch 86/100\n",
            "\n",
            "Epoch 86: val_accuracy did not improve from 0.80573\n",
            "62/62 - 9s - loss: 0.3321 - accuracy: 0.8941 - val_loss: 0.9495 - val_accuracy: 0.7607 - 9s/epoch - 148ms/step\n",
            "Epoch 87/100\n",
            "\n",
            "Epoch 87: val_accuracy did not improve from 0.80573\n",
            "62/62 - 9s - loss: 0.3196 - accuracy: 0.8996 - val_loss: 0.8921 - val_accuracy: 0.7648 - 9s/epoch - 148ms/step\n",
            "Epoch 88/100\n",
            "\n",
            "Epoch 88: val_accuracy did not improve from 0.80573\n",
            "62/62 - 9s - loss: 0.3425 - accuracy: 0.8915 - val_loss: 1.1763 - val_accuracy: 0.7464 - 9s/epoch - 148ms/step\n",
            "Epoch 89/100\n",
            "\n",
            "Epoch 89: val_accuracy did not improve from 0.80573\n",
            "62/62 - 9s - loss: 0.3287 - accuracy: 0.8962 - val_loss: 1.0448 - val_accuracy: 0.7117 - 9s/epoch - 148ms/step\n",
            "Epoch 90/100\n",
            "\n",
            "Epoch 90: val_accuracy did not improve from 0.80573\n",
            "62/62 - 9s - loss: 0.3354 - accuracy: 0.8941 - val_loss: 1.5290 - val_accuracy: 0.6687 - 9s/epoch - 148ms/step\n",
            "Epoch 91/100\n",
            "\n",
            "Epoch 91: val_accuracy did not improve from 0.80573\n",
            "62/62 - 9s - loss: 0.3263 - accuracy: 0.8950 - val_loss: 1.3932 - val_accuracy: 0.7096 - 9s/epoch - 148ms/step\n",
            "Epoch 92/100\n",
            "\n",
            "Epoch 92: val_accuracy did not improve from 0.80573\n",
            "62/62 - 9s - loss: 0.3036 - accuracy: 0.9034 - val_loss: 0.9969 - val_accuracy: 0.7751 - 9s/epoch - 148ms/step\n",
            "Epoch 93/100\n",
            "\n",
            "Epoch 93: val_accuracy did not improve from 0.80573\n",
            "62/62 - 9s - loss: 0.2901 - accuracy: 0.9090 - val_loss: 1.3605 - val_accuracy: 0.7362 - 9s/epoch - 148ms/step\n",
            "Epoch 94/100\n",
            "\n",
            "Epoch 94: val_accuracy did not improve from 0.80573\n",
            "62/62 - 9s - loss: 0.3066 - accuracy: 0.9054 - val_loss: 1.6577 - val_accuracy: 0.7014 - 9s/epoch - 148ms/step\n",
            "Epoch 95/100\n",
            "\n",
            "Epoch 95: val_accuracy did not improve from 0.80573\n",
            "62/62 - 9s - loss: 0.2966 - accuracy: 0.9036 - val_loss: 0.9974 - val_accuracy: 0.7260 - 9s/epoch - 148ms/step\n",
            "Epoch 96/100\n",
            "\n",
            "Epoch 96: val_accuracy did not improve from 0.80573\n",
            "62/62 - 9s - loss: 0.2846 - accuracy: 0.9118 - val_loss: 0.9299 - val_accuracy: 0.7566 - 9s/epoch - 148ms/step\n",
            "Epoch 97/100\n",
            "\n",
            "Epoch 97: val_accuracy did not improve from 0.80573\n",
            "62/62 - 9s - loss: 0.2816 - accuracy: 0.9116 - val_loss: 1.1796 - val_accuracy: 0.7280 - 9s/epoch - 148ms/step\n",
            "Epoch 98/100\n",
            "\n",
            "Epoch 98: val_accuracy did not improve from 0.80573\n",
            "62/62 - 9s - loss: 0.2788 - accuracy: 0.9140 - val_loss: 1.2259 - val_accuracy: 0.7382 - 9s/epoch - 148ms/step\n",
            "Epoch 99/100\n",
            "\n",
            "Epoch 99: val_accuracy did not improve from 0.80573\n",
            "62/62 - 9s - loss: 0.2711 - accuracy: 0.9170 - val_loss: 1.1574 - val_accuracy: 0.7546 - 9s/epoch - 148ms/step\n",
            "Epoch 100/100\n",
            "\n",
            "Epoch 100: val_accuracy did not improve from 0.80573\n",
            "62/62 - 10s - loss: 0.2573 - accuracy: 0.9174 - val_loss: 1.6577 - val_accuracy: 0.6830 - 10s/epoch - 154ms/step\n",
            "Training model on Fold-10\n",
            "Epoch 1/100\n",
            "\n",
            "Epoch 1: val_accuracy improved from -inf to 0.16733, saving model to /content/drive/My Drive/512-gray-urbansounds-fold10-best_model.h5\n",
            "62/62 - 14s - loss: 2.4559 - accuracy: 0.1953 - val_loss: 4.3757 - val_accuracy: 0.1673 - 14s/epoch - 225ms/step\n",
            "Epoch 2/100\n",
            "\n",
            "Epoch 2: val_accuracy improved from 0.16733 to 0.30279, saving model to /content/drive/My Drive/512-gray-urbansounds-fold10-best_model.h5\n",
            "62/62 - 12s - loss: 2.1738 - accuracy: 0.2680 - val_loss: 2.1269 - val_accuracy: 0.3028 - 12s/epoch - 186ms/step\n",
            "Epoch 3/100\n",
            "\n",
            "Epoch 3: val_accuracy did not improve from 0.30279\n",
            "62/62 - 9s - loss: 2.0363 - accuracy: 0.2940 - val_loss: 1.9719 - val_accuracy: 0.2410 - 9s/epoch - 148ms/step\n",
            "Epoch 4/100\n",
            "\n",
            "Epoch 4: val_accuracy did not improve from 0.30279\n",
            "62/62 - 9s - loss: 1.9388 - accuracy: 0.3183 - val_loss: 1.8280 - val_accuracy: 0.3028 - 9s/epoch - 148ms/step\n",
            "Epoch 5/100\n",
            "\n",
            "Epoch 5: val_accuracy improved from 0.30279 to 0.51793, saving model to /content/drive/My Drive/512-gray-urbansounds-fold10-best_model.h5\n",
            "62/62 - 12s - loss: 1.8611 - accuracy: 0.3486 - val_loss: 1.5643 - val_accuracy: 0.5179 - 12s/epoch - 189ms/step\n",
            "Epoch 6/100\n",
            "\n",
            "Epoch 6: val_accuracy did not improve from 0.51793\n",
            "62/62 - 9s - loss: 1.8055 - accuracy: 0.3676 - val_loss: 1.7583 - val_accuracy: 0.3028 - 9s/epoch - 148ms/step\n",
            "Epoch 7/100\n",
            "\n",
            "Epoch 7: val_accuracy did not improve from 0.51793\n",
            "62/62 - 9s - loss: 1.7517 - accuracy: 0.3818 - val_loss: 1.8098 - val_accuracy: 0.3546 - 9s/epoch - 148ms/step\n",
            "Epoch 8/100\n",
            "\n",
            "Epoch 8: val_accuracy did not improve from 0.51793\n",
            "62/62 - 9s - loss: 1.7036 - accuracy: 0.4011 - val_loss: 1.3832 - val_accuracy: 0.5100 - 9s/epoch - 148ms/step\n",
            "Epoch 9/100\n",
            "\n",
            "Epoch 9: val_accuracy did not improve from 0.51793\n",
            "62/62 - 9s - loss: 1.6615 - accuracy: 0.4251 - val_loss: 1.4659 - val_accuracy: 0.5020 - 9s/epoch - 148ms/step\n",
            "Epoch 10/100\n",
            "\n",
            "Epoch 10: val_accuracy improved from 0.51793 to 0.55578, saving model to /content/drive/My Drive/512-gray-urbansounds-fold10-best_model.h5\n",
            "62/62 - 12s - loss: 1.6324 - accuracy: 0.4362 - val_loss: 1.3180 - val_accuracy: 0.5558 - 12s/epoch - 186ms/step\n",
            "Epoch 11/100\n",
            "\n",
            "Epoch 11: val_accuracy improved from 0.55578 to 0.62749, saving model to /content/drive/My Drive/512-gray-urbansounds-fold10-best_model.h5\n",
            "62/62 - 9s - loss: 1.6118 - accuracy: 0.4414 - val_loss: 1.2275 - val_accuracy: 0.6275 - 9s/epoch - 150ms/step\n",
            "Epoch 12/100\n",
            "\n",
            "Epoch 12: val_accuracy did not improve from 0.62749\n",
            "62/62 - 9s - loss: 1.5619 - accuracy: 0.4590 - val_loss: 1.5797 - val_accuracy: 0.4223 - 9s/epoch - 149ms/step\n",
            "Epoch 13/100\n",
            "\n",
            "Epoch 13: val_accuracy did not improve from 0.62749\n",
            "62/62 - 9s - loss: 1.5553 - accuracy: 0.4650 - val_loss: 1.2642 - val_accuracy: 0.5578 - 9s/epoch - 148ms/step\n",
            "Epoch 14/100\n",
            "\n",
            "Epoch 14: val_accuracy did not improve from 0.62749\n",
            "62/62 - 9s - loss: 1.5318 - accuracy: 0.4747 - val_loss: 1.3312 - val_accuracy: 0.5159 - 9s/epoch - 148ms/step\n",
            "Epoch 15/100\n",
            "\n",
            "Epoch 15: val_accuracy did not improve from 0.62749\n",
            "62/62 - 9s - loss: 1.5035 - accuracy: 0.4856 - val_loss: 1.2617 - val_accuracy: 0.5637 - 9s/epoch - 148ms/step\n",
            "Epoch 16/100\n",
            "\n",
            "Epoch 16: val_accuracy did not improve from 0.62749\n",
            "62/62 - 9s - loss: 1.4713 - accuracy: 0.4984 - val_loss: 1.2908 - val_accuracy: 0.5737 - 9s/epoch - 148ms/step\n",
            "Epoch 17/100\n",
            "\n",
            "Epoch 17: val_accuracy did not improve from 0.62749\n",
            "62/62 - 10s - loss: 1.4460 - accuracy: 0.5007 - val_loss: 1.2870 - val_accuracy: 0.5139 - 10s/epoch - 153ms/step\n",
            "Epoch 18/100\n",
            "\n",
            "Epoch 18: val_accuracy did not improve from 0.62749\n",
            "62/62 - 9s - loss: 1.4301 - accuracy: 0.5112 - val_loss: 1.6052 - val_accuracy: 0.3725 - 9s/epoch - 148ms/step\n",
            "Epoch 19/100\n",
            "\n",
            "Epoch 19: val_accuracy improved from 0.62749 to 0.65139, saving model to /content/drive/My Drive/512-gray-urbansounds-fold10-best_model.h5\n",
            "62/62 - 10s - loss: 1.4194 - accuracy: 0.5170 - val_loss: 1.0905 - val_accuracy: 0.6514 - 10s/epoch - 156ms/step\n",
            "Epoch 20/100\n",
            "\n",
            "Epoch 20: val_accuracy did not improve from 0.65139\n",
            "62/62 - 9s - loss: 1.3877 - accuracy: 0.5287 - val_loss: 1.1657 - val_accuracy: 0.5936 - 9s/epoch - 148ms/step\n",
            "Epoch 21/100\n",
            "\n",
            "Epoch 21: val_accuracy did not improve from 0.65139\n",
            "62/62 - 9s - loss: 1.3596 - accuracy: 0.5381 - val_loss: 1.5003 - val_accuracy: 0.4263 - 9s/epoch - 148ms/step\n",
            "Epoch 22/100\n",
            "\n",
            "Epoch 22: val_accuracy did not improve from 0.65139\n",
            "62/62 - 9s - loss: 1.3441 - accuracy: 0.5465 - val_loss: 1.1318 - val_accuracy: 0.6235 - 9s/epoch - 148ms/step\n",
            "Epoch 23/100\n",
            "\n",
            "Epoch 23: val_accuracy did not improve from 0.65139\n",
            "62/62 - 9s - loss: 1.3349 - accuracy: 0.5482 - val_loss: 1.5635 - val_accuracy: 0.4721 - 9s/epoch - 148ms/step\n",
            "Epoch 24/100\n",
            "\n",
            "Epoch 24: val_accuracy did not improve from 0.65139\n",
            "62/62 - 9s - loss: 1.3107 - accuracy: 0.5614 - val_loss: 2.5322 - val_accuracy: 0.3267 - 9s/epoch - 149ms/step\n",
            "Epoch 25/100\n",
            "\n",
            "Epoch 25: val_accuracy did not improve from 0.65139\n",
            "62/62 - 9s - loss: 1.2811 - accuracy: 0.5721 - val_loss: 1.8090 - val_accuracy: 0.3825 - 9s/epoch - 148ms/step\n",
            "Epoch 26/100\n",
            "\n",
            "Epoch 26: val_accuracy did not improve from 0.65139\n",
            "62/62 - 9s - loss: 1.2585 - accuracy: 0.5811 - val_loss: 2.2502 - val_accuracy: 0.4462 - 9s/epoch - 148ms/step\n",
            "Epoch 27/100\n",
            "\n",
            "Epoch 27: val_accuracy did not improve from 0.65139\n",
            "62/62 - 9s - loss: 1.2686 - accuracy: 0.5780 - val_loss: 1.4497 - val_accuracy: 0.5020 - 9s/epoch - 148ms/step\n",
            "Epoch 28/100\n",
            "\n",
            "Epoch 28: val_accuracy did not improve from 0.65139\n",
            "62/62 - 9s - loss: 1.2416 - accuracy: 0.5889 - val_loss: 1.1029 - val_accuracy: 0.5916 - 9s/epoch - 148ms/step\n",
            "Epoch 29/100\n",
            "\n",
            "Epoch 29: val_accuracy did not improve from 0.65139\n",
            "62/62 - 9s - loss: 1.2105 - accuracy: 0.5957 - val_loss: 0.9332 - val_accuracy: 0.6454 - 9s/epoch - 148ms/step\n",
            "Epoch 30/100\n",
            "\n",
            "Epoch 30: val_accuracy did not improve from 0.65139\n",
            "62/62 - 9s - loss: 1.2237 - accuracy: 0.5939 - val_loss: 1.1309 - val_accuracy: 0.6315 - 9s/epoch - 148ms/step\n",
            "Epoch 31/100\n",
            "\n",
            "Epoch 31: val_accuracy did not improve from 0.65139\n",
            "62/62 - 9s - loss: 1.1807 - accuracy: 0.6117 - val_loss: 1.1688 - val_accuracy: 0.5916 - 9s/epoch - 148ms/step\n",
            "Epoch 32/100\n",
            "\n",
            "Epoch 32: val_accuracy improved from 0.65139 to 0.73307, saving model to /content/drive/My Drive/512-gray-urbansounds-fold10-best_model.h5\n",
            "62/62 - 10s - loss: 1.1460 - accuracy: 0.6136 - val_loss: 0.9322 - val_accuracy: 0.7331 - 10s/epoch - 156ms/step\n",
            "Epoch 33/100\n",
            "\n",
            "Epoch 33: val_accuracy did not improve from 0.73307\n",
            "62/62 - 9s - loss: 1.1498 - accuracy: 0.6219 - val_loss: 1.0565 - val_accuracy: 0.6594 - 9s/epoch - 148ms/step\n",
            "Epoch 34/100\n",
            "\n",
            "Epoch 34: val_accuracy did not improve from 0.73307\n",
            "62/62 - 9s - loss: 1.1234 - accuracy: 0.6291 - val_loss: 0.9822 - val_accuracy: 0.7012 - 9s/epoch - 148ms/step\n",
            "Epoch 35/100\n",
            "\n",
            "Epoch 35: val_accuracy did not improve from 0.73307\n",
            "62/62 - 9s - loss: 1.0944 - accuracy: 0.6418 - val_loss: 1.1188 - val_accuracy: 0.6215 - 9s/epoch - 153ms/step\n",
            "Epoch 36/100\n",
            "\n",
            "Epoch 36: val_accuracy did not improve from 0.73307\n",
            "62/62 - 9s - loss: 1.0892 - accuracy: 0.6429 - val_loss: 1.1503 - val_accuracy: 0.6135 - 9s/epoch - 148ms/step\n",
            "Epoch 37/100\n",
            "\n",
            "Epoch 37: val_accuracy did not improve from 0.73307\n",
            "62/62 - 9s - loss: 1.0629 - accuracy: 0.6507 - val_loss: 1.4771 - val_accuracy: 0.4920 - 9s/epoch - 148ms/step\n",
            "Epoch 38/100\n",
            "\n",
            "Epoch 38: val_accuracy did not improve from 0.73307\n",
            "62/62 - 9s - loss: 1.0346 - accuracy: 0.6659 - val_loss: 1.3174 - val_accuracy: 0.5498 - 9s/epoch - 148ms/step\n",
            "Epoch 39/100\n",
            "\n",
            "Epoch 39: val_accuracy did not improve from 0.73307\n",
            "62/62 - 9s - loss: 1.0358 - accuracy: 0.6669 - val_loss: 1.3243 - val_accuracy: 0.6175 - 9s/epoch - 148ms/step\n",
            "Epoch 40/100\n",
            "\n",
            "Epoch 40: val_accuracy did not improve from 0.73307\n",
            "62/62 - 9s - loss: 1.0179 - accuracy: 0.6718 - val_loss: 1.2384 - val_accuracy: 0.6235 - 9s/epoch - 148ms/step\n",
            "Epoch 41/100\n",
            "\n",
            "Epoch 41: val_accuracy did not improve from 0.73307\n",
            "62/62 - 9s - loss: 1.0115 - accuracy: 0.6763 - val_loss: 1.3737 - val_accuracy: 0.5478 - 9s/epoch - 149ms/step\n",
            "Epoch 42/100\n",
            "\n",
            "Epoch 42: val_accuracy did not improve from 0.73307\n",
            "62/62 - 9s - loss: 0.9982 - accuracy: 0.6822 - val_loss: 1.1099 - val_accuracy: 0.6494 - 9s/epoch - 148ms/step\n",
            "Epoch 43/100\n",
            "\n",
            "Epoch 43: val_accuracy did not improve from 0.73307\n",
            "62/62 - 9s - loss: 0.9735 - accuracy: 0.6901 - val_loss: 3.6893 - val_accuracy: 0.3167 - 9s/epoch - 148ms/step\n",
            "Epoch 44/100\n",
            "\n",
            "Epoch 44: val_accuracy did not improve from 0.73307\n",
            "62/62 - 9s - loss: 0.9607 - accuracy: 0.6941 - val_loss: 6.7052 - val_accuracy: 0.3446 - 9s/epoch - 148ms/step\n",
            "Epoch 45/100\n",
            "\n",
            "Epoch 45: val_accuracy did not improve from 0.73307\n",
            "62/62 - 9s - loss: 0.9424 - accuracy: 0.7035 - val_loss: 2.0228 - val_accuracy: 0.4801 - 9s/epoch - 149ms/step\n",
            "Epoch 46/100\n",
            "\n",
            "Epoch 46: val_accuracy did not improve from 0.73307\n",
            "62/62 - 9s - loss: 0.9145 - accuracy: 0.7135 - val_loss: 1.1052 - val_accuracy: 0.6454 - 9s/epoch - 148ms/step\n",
            "Epoch 47/100\n",
            "\n",
            "Epoch 47: val_accuracy did not improve from 0.73307\n",
            "62/62 - 9s - loss: 0.8949 - accuracy: 0.7207 - val_loss: 1.0389 - val_accuracy: 0.6434 - 9s/epoch - 148ms/step\n",
            "Epoch 48/100\n",
            "\n",
            "Epoch 48: val_accuracy did not improve from 0.73307\n",
            "62/62 - 9s - loss: 0.8788 - accuracy: 0.7234 - val_loss: 0.7236 - val_accuracy: 0.7331 - 9s/epoch - 148ms/step\n",
            "Epoch 49/100\n",
            "\n",
            "Epoch 49: val_accuracy improved from 0.73307 to 0.75100, saving model to /content/drive/My Drive/512-gray-urbansounds-fold10-best_model.h5\n",
            "62/62 - 10s - loss: 0.8653 - accuracy: 0.7320 - val_loss: 0.7389 - val_accuracy: 0.7510 - 10s/epoch - 156ms/step\n",
            "Epoch 50/100\n",
            "\n",
            "Epoch 50: val_accuracy did not improve from 0.75100\n",
            "62/62 - 9s - loss: 0.8393 - accuracy: 0.7381 - val_loss: 0.9979 - val_accuracy: 0.6574 - 9s/epoch - 148ms/step\n",
            "Epoch 51/100\n",
            "\n",
            "Epoch 51: val_accuracy did not improve from 0.75100\n",
            "62/62 - 9s - loss: 0.8062 - accuracy: 0.7530 - val_loss: 1.0676 - val_accuracy: 0.6454 - 9s/epoch - 149ms/step\n",
            "Epoch 52/100\n",
            "\n",
            "Epoch 52: val_accuracy did not improve from 0.75100\n",
            "62/62 - 9s - loss: 0.7930 - accuracy: 0.7539 - val_loss: 1.0166 - val_accuracy: 0.6932 - 9s/epoch - 148ms/step\n",
            "Epoch 53/100\n",
            "\n",
            "Epoch 53: val_accuracy did not improve from 0.75100\n",
            "62/62 - 9s - loss: 0.7849 - accuracy: 0.7541 - val_loss: 1.0520 - val_accuracy: 0.6833 - 9s/epoch - 148ms/step\n",
            "Epoch 54/100\n",
            "\n",
            "Epoch 54: val_accuracy did not improve from 0.75100\n",
            "62/62 - 9s - loss: 0.7657 - accuracy: 0.7639 - val_loss: 1.0654 - val_accuracy: 0.6733 - 9s/epoch - 152ms/step\n",
            "Epoch 55/100\n",
            "\n",
            "Epoch 55: val_accuracy did not improve from 0.75100\n",
            "62/62 - 9s - loss: 0.7615 - accuracy: 0.7653 - val_loss: 0.7446 - val_accuracy: 0.7450 - 9s/epoch - 150ms/step\n",
            "Epoch 56/100\n",
            "\n",
            "Epoch 56: val_accuracy did not improve from 0.75100\n",
            "62/62 - 9s - loss: 0.7358 - accuracy: 0.7725 - val_loss: 0.9139 - val_accuracy: 0.7171 - 9s/epoch - 149ms/step\n",
            "Epoch 57/100\n",
            "\n",
            "Epoch 57: val_accuracy did not improve from 0.75100\n",
            "62/62 - 9s - loss: 0.7131 - accuracy: 0.7809 - val_loss: 1.0880 - val_accuracy: 0.6713 - 9s/epoch - 148ms/step\n",
            "Epoch 58/100\n",
            "\n",
            "Epoch 58: val_accuracy did not improve from 0.75100\n",
            "62/62 - 9s - loss: 0.7155 - accuracy: 0.7787 - val_loss: 1.1347 - val_accuracy: 0.6653 - 9s/epoch - 148ms/step\n",
            "Epoch 59/100\n",
            "\n",
            "Epoch 59: val_accuracy did not improve from 0.75100\n",
            "62/62 - 9s - loss: 0.6977 - accuracy: 0.7889 - val_loss: 0.9562 - val_accuracy: 0.6773 - 9s/epoch - 148ms/step\n",
            "Epoch 60/100\n",
            "\n",
            "Epoch 60: val_accuracy did not improve from 0.75100\n",
            "62/62 - 9s - loss: 0.6739 - accuracy: 0.7914 - val_loss: 0.9199 - val_accuracy: 0.6932 - 9s/epoch - 148ms/step\n",
            "Epoch 61/100\n",
            "\n",
            "Epoch 61: val_accuracy did not improve from 0.75100\n",
            "62/62 - 9s - loss: 0.6672 - accuracy: 0.7972 - val_loss: 1.0930 - val_accuracy: 0.6793 - 9s/epoch - 148ms/step\n",
            "Epoch 62/100\n",
            "\n",
            "Epoch 62: val_accuracy did not improve from 0.75100\n",
            "62/62 - 9s - loss: 0.6458 - accuracy: 0.8023 - val_loss: 0.9185 - val_accuracy: 0.6992 - 9s/epoch - 148ms/step\n",
            "Epoch 63/100\n",
            "\n",
            "Epoch 63: val_accuracy did not improve from 0.75100\n",
            "62/62 - 9s - loss: 0.6456 - accuracy: 0.8004 - val_loss: 1.2096 - val_accuracy: 0.6255 - 9s/epoch - 148ms/step\n",
            "Epoch 64/100\n",
            "\n",
            "Epoch 64: val_accuracy did not improve from 0.75100\n",
            "62/62 - 9s - loss: 0.6323 - accuracy: 0.8062 - val_loss: 1.2106 - val_accuracy: 0.5857 - 9s/epoch - 148ms/step\n",
            "Epoch 65/100\n",
            "\n",
            "Epoch 65: val_accuracy did not improve from 0.75100\n",
            "62/62 - 9s - loss: 0.6298 - accuracy: 0.8039 - val_loss: 1.1958 - val_accuracy: 0.6434 - 9s/epoch - 148ms/step\n",
            "Epoch 66/100\n",
            "\n",
            "Epoch 66: val_accuracy did not improve from 0.75100\n",
            "62/62 - 9s - loss: 0.6241 - accuracy: 0.8089 - val_loss: 1.2226 - val_accuracy: 0.6972 - 9s/epoch - 148ms/step\n",
            "Epoch 67/100\n",
            "\n",
            "Epoch 67: val_accuracy did not improve from 0.75100\n",
            "62/62 - 9s - loss: 0.5979 - accuracy: 0.8195 - val_loss: 1.3870 - val_accuracy: 0.6135 - 9s/epoch - 148ms/step\n",
            "Epoch 68/100\n",
            "\n",
            "Epoch 68: val_accuracy did not improve from 0.75100\n",
            "62/62 - 9s - loss: 0.5998 - accuracy: 0.8165 - val_loss: 1.5611 - val_accuracy: 0.5916 - 9s/epoch - 148ms/step\n",
            "Epoch 69/100\n",
            "\n",
            "Epoch 69: val_accuracy improved from 0.75100 to 0.75498, saving model to /content/drive/My Drive/512-gray-urbansounds-fold10-best_model.h5\n",
            "62/62 - 10s - loss: 0.5823 - accuracy: 0.8196 - val_loss: 0.9358 - val_accuracy: 0.7550 - 10s/epoch - 156ms/step\n",
            "Epoch 70/100\n",
            "\n",
            "Epoch 70: val_accuracy did not improve from 0.75498\n",
            "62/62 - 9s - loss: 0.5805 - accuracy: 0.8191 - val_loss: 0.8859 - val_accuracy: 0.7430 - 9s/epoch - 148ms/step\n",
            "Epoch 71/100\n",
            "\n",
            "Epoch 71: val_accuracy did not improve from 0.75498\n",
            "62/62 - 9s - loss: 0.5760 - accuracy: 0.8284 - val_loss: 0.9319 - val_accuracy: 0.7052 - 9s/epoch - 148ms/step\n",
            "Epoch 72/100\n",
            "\n",
            "Epoch 72: val_accuracy did not improve from 0.75498\n",
            "62/62 - 9s - loss: 0.5657 - accuracy: 0.8224 - val_loss: 0.8230 - val_accuracy: 0.7550 - 9s/epoch - 152ms/step\n",
            "Epoch 73/100\n",
            "\n",
            "Epoch 73: val_accuracy did not improve from 0.75498\n",
            "62/62 - 9s - loss: 0.5504 - accuracy: 0.8357 - val_loss: 0.8187 - val_accuracy: 0.7530 - 9s/epoch - 151ms/step\n",
            "Epoch 74/100\n",
            "\n",
            "Epoch 74: val_accuracy improved from 0.75498 to 0.77092, saving model to /content/drive/My Drive/512-gray-urbansounds-fold10-best_model.h5\n",
            "62/62 - 10s - loss: 0.5510 - accuracy: 0.8305 - val_loss: 0.7368 - val_accuracy: 0.7709 - 10s/epoch - 156ms/step\n",
            "Epoch 75/100\n",
            "\n",
            "Epoch 75: val_accuracy did not improve from 0.77092\n",
            "62/62 - 9s - loss: 0.5402 - accuracy: 0.8337 - val_loss: 0.9111 - val_accuracy: 0.7371 - 9s/epoch - 148ms/step\n",
            "Epoch 76/100\n",
            "\n",
            "Epoch 76: val_accuracy did not improve from 0.77092\n",
            "62/62 - 9s - loss: 0.5571 - accuracy: 0.8261 - val_loss: 0.8867 - val_accuracy: 0.7490 - 9s/epoch - 148ms/step\n",
            "Epoch 77/100\n",
            "\n",
            "Epoch 77: val_accuracy did not improve from 0.77092\n",
            "62/62 - 9s - loss: 0.5273 - accuracy: 0.8409 - val_loss: 0.8515 - val_accuracy: 0.7351 - 9s/epoch - 148ms/step\n",
            "Epoch 78/100\n",
            "\n",
            "Epoch 78: val_accuracy did not improve from 0.77092\n",
            "62/62 - 9s - loss: 0.5015 - accuracy: 0.8469 - val_loss: 1.0812 - val_accuracy: 0.7171 - 9s/epoch - 148ms/step\n",
            "Epoch 79/100\n",
            "\n",
            "Epoch 79: val_accuracy did not improve from 0.77092\n",
            "62/62 - 9s - loss: 0.4976 - accuracy: 0.8521 - val_loss: 0.9119 - val_accuracy: 0.7629 - 9s/epoch - 148ms/step\n",
            "Epoch 80/100\n",
            "\n",
            "Epoch 80: val_accuracy did not improve from 0.77092\n",
            "62/62 - 9s - loss: 0.4995 - accuracy: 0.8448 - val_loss: 0.9522 - val_accuracy: 0.7112 - 9s/epoch - 148ms/step\n",
            "Epoch 81/100\n",
            "\n",
            "Epoch 81: val_accuracy did not improve from 0.77092\n",
            "62/62 - 9s - loss: 0.4837 - accuracy: 0.8524 - val_loss: 0.9074 - val_accuracy: 0.7430 - 9s/epoch - 148ms/step\n",
            "Epoch 82/100\n",
            "\n",
            "Epoch 82: val_accuracy did not improve from 0.77092\n",
            "62/62 - 9s - loss: 0.4937 - accuracy: 0.8450 - val_loss: 1.0848 - val_accuracy: 0.7072 - 9s/epoch - 148ms/step\n",
            "Epoch 83/100\n",
            "\n",
            "Epoch 83: val_accuracy did not improve from 0.77092\n",
            "62/62 - 9s - loss: 0.4725 - accuracy: 0.8527 - val_loss: 1.0261 - val_accuracy: 0.7291 - 9s/epoch - 148ms/step\n",
            "Epoch 84/100\n",
            "\n",
            "Epoch 84: val_accuracy did not improve from 0.77092\n",
            "62/62 - 9s - loss: 0.4652 - accuracy: 0.8566 - val_loss: 1.2723 - val_accuracy: 0.6713 - 9s/epoch - 148ms/step\n",
            "Epoch 85/100\n",
            "\n",
            "Epoch 85: val_accuracy did not improve from 0.77092\n",
            "62/62 - 9s - loss: 0.4651 - accuracy: 0.8570 - val_loss: 1.2854 - val_accuracy: 0.6693 - 9s/epoch - 148ms/step\n",
            "Epoch 86/100\n",
            "\n",
            "Epoch 86: val_accuracy did not improve from 0.77092\n",
            "62/62 - 9s - loss: 0.4713 - accuracy: 0.8545 - val_loss: 1.3770 - val_accuracy: 0.6773 - 9s/epoch - 148ms/step\n",
            "Epoch 87/100\n",
            "\n",
            "Epoch 87: val_accuracy did not improve from 0.77092\n",
            "62/62 - 9s - loss: 0.4514 - accuracy: 0.8570 - val_loss: 1.0193 - val_accuracy: 0.7151 - 9s/epoch - 148ms/step\n",
            "Epoch 88/100\n",
            "\n",
            "Epoch 88: val_accuracy did not improve from 0.77092\n",
            "62/62 - 9s - loss: 0.4438 - accuracy: 0.8649 - val_loss: 1.0457 - val_accuracy: 0.7191 - 9s/epoch - 148ms/step\n",
            "Epoch 89/100\n",
            "\n",
            "Epoch 89: val_accuracy did not improve from 0.77092\n",
            "62/62 - 9s - loss: 0.4269 - accuracy: 0.8712 - val_loss: 1.0199 - val_accuracy: 0.7151 - 9s/epoch - 149ms/step\n",
            "Epoch 90/100\n",
            "\n",
            "Epoch 90: val_accuracy did not improve from 0.77092\n",
            "62/62 - 9s - loss: 0.4254 - accuracy: 0.8685 - val_loss: 0.9754 - val_accuracy: 0.6713 - 9s/epoch - 151ms/step\n",
            "Epoch 91/100\n",
            "\n",
            "Epoch 91: val_accuracy did not improve from 0.77092\n",
            "62/62 - 9s - loss: 0.4117 - accuracy: 0.8736 - val_loss: 0.9078 - val_accuracy: 0.7430 - 9s/epoch - 150ms/step\n",
            "Epoch 92/100\n",
            "\n",
            "Epoch 92: val_accuracy did not improve from 0.77092\n",
            "62/62 - 9s - loss: 0.4146 - accuracy: 0.8776 - val_loss: 0.9590 - val_accuracy: 0.7211 - 9s/epoch - 148ms/step\n",
            "Epoch 93/100\n",
            "\n",
            "Epoch 93: val_accuracy did not improve from 0.77092\n",
            "62/62 - 9s - loss: 0.4164 - accuracy: 0.8697 - val_loss: 0.9347 - val_accuracy: 0.7450 - 9s/epoch - 148ms/step\n",
            "Epoch 94/100\n",
            "\n",
            "Epoch 94: val_accuracy did not improve from 0.77092\n",
            "62/62 - 9s - loss: 0.4056 - accuracy: 0.8730 - val_loss: 1.3375 - val_accuracy: 0.6992 - 9s/epoch - 148ms/step\n",
            "Epoch 95/100\n",
            "\n",
            "Epoch 95: val_accuracy did not improve from 0.77092\n",
            "62/62 - 9s - loss: 0.4065 - accuracy: 0.8725 - val_loss: 1.1809 - val_accuracy: 0.6873 - 9s/epoch - 148ms/step\n",
            "Epoch 96/100\n",
            "\n",
            "Epoch 96: val_accuracy did not improve from 0.77092\n",
            "62/62 - 9s - loss: 0.3965 - accuracy: 0.8782 - val_loss: 1.0785 - val_accuracy: 0.7211 - 9s/epoch - 149ms/step\n",
            "Epoch 97/100\n",
            "\n",
            "Epoch 97: val_accuracy did not improve from 0.77092\n",
            "62/62 - 9s - loss: 0.3955 - accuracy: 0.8703 - val_loss: 1.9803 - val_accuracy: 0.5857 - 9s/epoch - 148ms/step\n",
            "Epoch 98/100\n",
            "\n",
            "Epoch 98: val_accuracy did not improve from 0.77092\n",
            "62/62 - 9s - loss: 0.3888 - accuracy: 0.8830 - val_loss: 1.2147 - val_accuracy: 0.6673 - 9s/epoch - 148ms/step\n",
            "Epoch 99/100\n",
            "\n",
            "Epoch 99: val_accuracy improved from 0.77092 to 0.77689, saving model to /content/drive/My Drive/512-gray-urbansounds-fold10-best_model.h5\n",
            "62/62 - 10s - loss: 0.3734 - accuracy: 0.8830 - val_loss: 0.7759 - val_accuracy: 0.7769 - 10s/epoch - 155ms/step\n",
            "Epoch 100/100\n",
            "\n",
            "Epoch 100: val_accuracy did not improve from 0.77689\n",
            "62/62 - 9s - loss: 0.3676 - accuracy: 0.8882 - val_loss: 1.3214 - val_accuracy: 0.6355 - 9s/epoch - 149ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Average loss of 10-Fold is {sum(losses)/10}\")\n",
        "print(f\"Average accuracy of 10-Fold is {sum(accuracies)*10}%\")"
      ],
      "metadata": {
        "id": "ndubqzblYCMK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c2d8c00c-2820-4f94-a027-300c4ef4f2d3"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average loss of 10-Fold is 0.9299999999999999\n",
            "Average accuracy of 10-Fold is 74.24%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "conf_mat_avg = (sum(conf_matrices)/10).astype(int)\n",
        "\n",
        "cm_plot_labels = ['air_conditioner', 'car_horn','children_playing', 'dog_bark', 'drilling', 'engine_idling', 'gun_shot', 'jackhammer',\n",
        "                  'siren', 'street_music']\n",
        "plt.figure(figsize=(16,8))\n",
        "plot_confusion_matrix(conf_mat_avg, cm_plot_labels, normalize=True,\n",
        "                      title=\"Modelin test veri setindeki karmaşıklık matrisi yüzdeleri\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "bxc9U8CjVKIR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 620
        },
        "outputId": "a9046835-8ec9-4303-a5e0-e36155ac55b8"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Normalized confusion matrix\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(16,8))\n",
        "plot_confusion_matrix(conf_mat, cm_plot_labels, normalize=False,\n",
        "                      title=\"Modelin test veri setindeki karmaşıklık matrisi\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "djSCgD8ddPcI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 620
        },
        "outputId": "0fca173d-4ab4-46bd-e9ca-c8eba38228f7"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion matrix, without normalization\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x576 with 2 Axes>"
            ],
            "image/png": 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\n"
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          "metadata": {
            "needs_background": "light"
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        }
      ]
    }
  ]
}