GTZAN_Dataset_Music_Genre_Classification.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MS-H2020/Open/blob/main/GTZAN_Dataset_Music_Genre_Classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iTk6K05zgK4V"
      },
      "source": [
        "# [GTZAN Dataset - Music Genre Classification](https://www.kaggle.com/datasets/andradaolteanu/gtzan-dataset-music-genre-classification)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2cMFdSF7t6Sj",
        "outputId": "79a3a25b-d451-4f84-bbfe-c03b6ac75cb4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "Qe3HxlqQuQhG"
      },
      "outputs": [],
      "source": [
        "import shutil\n",
        "shutil.unpack_archive('/content/drive/MyDrive/archive.zip', './data')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4YsuqlaIhGRZ"
      },
      "source": [
        "## Reffering to [Kaggle, Music Genre Classification using CNN](https://www.kaggle.com/code/soumyasharma20/music-genre-classification-using-cnn)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eGcP4FVbHu-3",
        "outputId": "1c7050f8-e8f2-48d4-dcb1-fd360c76959d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "ffmpeg is already the newest version (7:4.4.2-0ubuntu0.22.04.1).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 49 not upgraded.\n",
            "Requirement already satisfied: sox in /usr/local/lib/python3.10/dist-packages (1.5.0)\n",
            "Requirement already satisfied: numpy>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from sox) (1.26.4)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.2 in /usr/local/lib/python3.10/dist-packages (from sox) (4.12.2)\n"
          ]
        }
      ],
      "source": [
        "# This Python 3 environment comes with many helpful analytics libraries installed\n",
        "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
        "# For example, here's several helpful packages to load\n",
        "\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import os\n",
        "import json\n",
        "import librosa\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "import tensorflow as tf\n",
        "\n",
        "# make sure ffmpeg is installed as backend.\n",
        "!apt install -y ffmpeg\n",
        "!pip install sox"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "bXglVPGnIA8h"
      },
      "outputs": [],
      "source": [
        "# Dataset location\n",
        "SOURCE_PATH = '/content/data/Data/genres_original'\n",
        "\n",
        "# Path to labels and processed data file, json format.\n",
        "JSON_PATH = './data.json'\n",
        "\n",
        "# Sampling rate.\n",
        "sr = 22050\n",
        "\n",
        "# Let's make sure all files have the same amount of samples and pick a duration right under 30 seconds.\n",
        "TOTAL_SAMPLES = 29 * sr\n",
        "\n",
        "# The dataset contains 999 files. Lets make it bigger.\n",
        "# X amount of slices => X times more training examples.\n",
        "NUM_SLICES = 10\n",
        "SAMPLES_PER_SLICE = int(TOTAL_SAMPLES / NUM_SLICES)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7phaViwPYCDV"
      },
      "source": [
        "#### [【機械学習】音声データ水増し（オーギュメンテーション）チートシート！実践的内容網羅＆海外事例も紹介【Python】](https://hanadamaya.net/audio_data_aug_python/)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "tuuOM_5IXgtI"
      },
      "outputs": [],
      "source": [
        "def white_noise(data, rate=0.002):\n",
        "    return data + rate*np.random.randn(len(data))\n",
        "#data_whitenoise = white_noise(data)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def time_shift(data, shift):\n",
        "    data_roll = np.roll(data, shift)\n",
        "    return data_roll\n",
        "#data_timeshift = time_shift(data,2**10)"
      ],
      "metadata": {
        "id": "mD85M9vjisrZ"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def stretch(data, rate=1.0):\n",
        "    input_length = len(data)\n",
        "    data = librosa.effects.time_stretch(data, rate=rate)\n",
        "    if len(data)>input_length:\n",
        "        data = data[:input_length]\n",
        "    else:\n",
        "        data = np.pad(data, (0, max(0, input_length - len(data))), \"constant\")\n",
        "    return data\n",
        "#data_stretch = stretch(data, rate=1.5)"
      ],
      "metadata": {
        "id": "3pm1j6q9jJJR"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def pitch_shift(data, sample_rate, shift):\n",
        "    ret = librosa.effects.pitch_shift(data, sr=sample_rate, n_steps=shift, bins_per_octave=12, res_type='kaiser_best')\n",
        "    return ret\n",
        "#data_pitch = pitch_shift(data, 44100, 12)"
      ],
      "metadata": {
        "id": "x60oAibujjmg"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def reverb(data, sample_rate, rate):\n",
        "    import sox\n",
        "    tfm = sox.Transformer()\n",
        "    tfm.reverb(rate)\n",
        "    return tfm.build_array(input_array=data, sample_rate_in=sample_rate)\n",
        "#data_reverb = reverb(data, 44100, 35)"
      ],
      "metadata": {
        "id": "sbHzrzTnjrQX"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def eq(data, sample_rate):\n",
        "    import sox\n",
        "    tfm = sox.Transformer()\n",
        "    tfm.equalizer(2000, 2.0, 5.0)\n",
        "    return tfm.build_array(input_array=data, sample_rate_in=sample_rate)\n",
        "#data_eq = eq(data, 44100, 35)"
      ],
      "metadata": {
        "id": "bPUdOS-ej4nF"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "ESB5zq_0JaUV"
      },
      "outputs": [],
      "source": [
        "def preprocess_data(source_path, json_path):\n",
        "\n",
        "    # Let's create a dictionary of labels and processed data.\n",
        "    mydict = {\n",
        "        \"labels\": [],\n",
        "        \"mfcc\": []\n",
        "        }\n",
        "    flag_white_noise = True\n",
        "    flag_time_shift = True\n",
        "    flag_stretch = False\n",
        "    flag_pitch_shift = False\n",
        "    flag_reverb = False\n",
        "    flag_eq = False\n",
        "\n",
        "    # Let's browse each file, slice it and generate the 13 band mfcc for each slice.\n",
        "    for i, (dirpath, dirnames, filenames) in enumerate(os.walk(source_path)):\n",
        "        for file in filenames:\n",
        "            # exclude a corrupted wav file that makes everything crash.\n",
        "            if os.path.join(dirpath, file) != '/content/data/Data/genres_original/jazz/jazz.00054.wav':\n",
        "                song, sr = librosa.load(os.path.join(dirpath, file), duration=29)\n",
        "                #noised_song = white_noise(song)\n",
        "                for s in range(NUM_SLICES):\n",
        "                    start_sample = SAMPLES_PER_SLICE * s\n",
        "                    end_sample = start_sample + SAMPLES_PER_SLICE\n",
        "                    if flag_white_noise:\n",
        "                        noised_data = white_noise(song)\n",
        "                        mfcc = librosa.feature.mfcc(y=noised_data[start_sample:end_sample], sr=sr, n_mfcc=13)\n",
        "                        mfcc = mfcc.T\n",
        "                        mydict[\"labels\"].append(i-1)\n",
        "                        mydict[\"mfcc\"].append(mfcc.tolist())\n",
        "                    if flag_time_shift:\n",
        "                        timeshifted_data = time_shift(song,2**10)\n",
        "                        mfcc = librosa.feature.mfcc(y=timeshifted_data[start_sample:end_sample], sr=sr, n_mfcc=13)\n",
        "                        mfcc = mfcc.T\n",
        "                        mydict[\"labels\"].append(i-1)\n",
        "                        mydict[\"mfcc\"].append(mfcc.tolist())\n",
        "                    if flag_stretch:\n",
        "                        stretched_data = stretch(song, rate=1.5)\n",
        "                        mfcc = librosa.feature.mfcc(y=stretched_data[start_sample:end_sample], sr=sr, n_mfcc=13)\n",
        "                        mfcc = mfcc.T\n",
        "                        mydict[\"labels\"].append(i-1)\n",
        "                        mydict[\"mfcc\"].append(mfcc.tolist())\n",
        "                    if flag_pitch_shift:\n",
        "                        pitchshifted_data = pitch_shift(song, 44100, 12)\n",
        "                        mfcc = librosa.feature.mfcc(y=pitchshifted_data[start_sample:end_sample], sr=sr, n_mfcc=13)\n",
        "                        mfcc = mfcc.T\n",
        "                        mydict[\"labels\"].append(i-1)\n",
        "                        mydict[\"mfcc\"].append(mfcc.tolist())\n",
        "                    if flag_reverb:\n",
        "                        reverb_data = reverb(song, 44100, 35)\n",
        "                        mfcc = librosa.feature.mfcc(y=reverb_data[start_sample:end_sample], sr=sr, n_mfcc=13)\n",
        "                        mfcc = mfcc.T\n",
        "                        mydict[\"labels\"].append(i-1)\n",
        "                        mydict[\"mfcc\"].append(mfcc.tolist())\n",
        "                    if flag_eq:\n",
        "                        eq_data = eq(song, 44100, 35)\n",
        "                        mfcc = librosa.feature.mfcc(y=eq_data[start_sample:end_sample], sr=sr, n_mfcc=13)\n",
        "                        mfcc = mfcc.T\n",
        "                        mydict[\"labels\"].append(i-1)\n",
        "                        mydict[\"mfcc\"].append(mfcc.tolist())\n",
        "                    mfcc = librosa.feature.mfcc(y=song[start_sample:end_sample], sr=sr, n_mfcc=13)\n",
        "                    mfcc = mfcc.T\n",
        "                    mydict[\"labels\"].append(i-1)\n",
        "                    mydict[\"mfcc\"].append(mfcc.tolist())\n",
        "            else:\n",
        "                pass\n",
        "\n",
        "    # Let's write the dictionary in a json file.\n",
        "    with open(json_path, 'w') as f:\n",
        "        json.dump(mydict, f)\n",
        "    f.close()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "BIVd4Oz1JpTI"
      },
      "outputs": [],
      "source": [
        "def load_data(json_path):\n",
        "\n",
        "    with open(json_path, 'r') as f:\n",
        "        data = json.load(f)\n",
        "    f.close()\n",
        "\n",
        "    # Let's load our data into numpy arrays for TensorFlow compatibility.\n",
        "    X = np.array(data[\"mfcc\"])\n",
        "    y = np.array(data[\"labels\"])\n",
        "\n",
        "    return X, y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "k6hECTlBIjJt"
      },
      "outputs": [],
      "source": [
        "def prepare_datasets(inputs, targets, split_size):\n",
        "\n",
        "    # Creating a validation set and a test set.\n",
        "    inputs_train, inputs_val, targets_train, targets_val = train_test_split(inputs, targets, test_size=split_size, stratify=targets)\n",
        "    inputs_train, inputs_test, targets_train, targets_test = train_test_split(inputs_train, targets_train, test_size=split_size, stratify=targets_train)\n",
        "\n",
        "    # Our CNN model expects 3D input shape.\n",
        "    inputs_train = inputs_train[..., np.newaxis]\n",
        "    inputs_val = inputs_val[..., np.newaxis]\n",
        "    inputs_test = inputs_test[..., np.newaxis]\n",
        "\n",
        "    return inputs_train, inputs_val, inputs_test, targets_train, targets_val, targets_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "XI7NstnKu7QH"
      },
      "outputs": [],
      "source": [
        "def design_cnn_model(input_shape):\n",
        "\n",
        "    # Let's design the model architecture.\n",
        "    model = tf.keras.models.Sequential([\n",
        "\n",
        "        tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=input_shape),\n",
        "        tf.keras.layers.MaxPooling2D((3,3), strides=(2,2), padding='same'),\n",
        "        tf.keras.layers.BatchNormalization(),\n",
        "\n",
        "        tf.keras.layers.Conv2D(32, (3,3), activation='relu'),\n",
        "        tf.keras.layers.MaxPooling2D((3,3), strides=(2,2), padding='same'),\n",
        "        tf.keras.layers.BatchNormalization(),\n",
        "\n",
        "        tf.keras.layers.Conv2D(32, (2,2), activation='relu'),\n",
        "        tf.keras.layers.MaxPooling2D((3,3), strides=(2,2), padding='same'),\n",
        "        tf.keras.layers.BatchNormalization(),\n",
        "        tf.keras.layers.Dropout(0.3),\n",
        "\n",
        "        tf.keras.layers.Flatten(),\n",
        "        tf.keras.layers.Dense(64, activation='relu'),\n",
        "        tf.keras.layers.Dense(len(np.unique(targets)), activation='softmax')\n",
        "    ])\n",
        "\n",
        "    return model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "08bPc795hsty"
      },
      "source": [
        "### Reffering to RNN model : [Kaggle, intro to audio with Deep Learning](https://www.kaggle.com/code/riadalmadani/intro-to-audio-with-deep-learning)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "46JZkhgviHjw"
      },
      "outputs": [],
      "source": [
        "def design_rnn_model(input_shape):\n",
        "\n",
        "    # Let's design the model architecture.\n",
        "    model = tf.keras.models.Sequential([\n",
        "\n",
        "        # 64 represnets the number of units NOT cells\n",
        "        # the number of cells equal to the numper of steps which is 130 here\n",
        "\n",
        "        tf.keras.layers.LSTM(64,input_shape=input_shape, return_sequences=True),\n",
        "        tf.keras.layers.LSTM(64),\n",
        "        tf.keras.layers.Dense(64, activation='relu'),\n",
        "        tf.keras.layers.Dropout(0.3),\n",
        "        tf.keras.layers.Dense(len(np.unique(targets)), activation='softmax')\n",
        "    ])\n",
        "\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "Aoio99WaGpem"
      },
      "outputs": [],
      "source": [
        "def make_prediction(model, X, y, idx):\n",
        "\n",
        "    genre_dict = {\n",
        "        0 : \"blues\",\n",
        "        1 : \"classical\",\n",
        "        2 : \"country\",\n",
        "        3 : \"disco\",\n",
        "        4 : \"hiphop\",\n",
        "        5 : \"jazz\",\n",
        "        6 : \"metal\",\n",
        "        7 : \"pop\",\n",
        "        8 : \"reggae\",\n",
        "        9 : \"rock\",\n",
        "        }\n",
        "\n",
        "    predictions = model.predict(X)\n",
        "    genre = np.argmax(predictions[idx])\n",
        "\n",
        "    print(\"\\n---Now testing the model for one audio file---\\nThe model predicts: {}, and ground truth is: {}.\\n\".format(genre_dict[genre], genre_dict[y[idx]]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "UOeUAPfmGwEW"
      },
      "outputs": [],
      "source": [
        "def plot_performance(hist):\n",
        "\n",
        "    acc = hist.history['accuracy']\n",
        "    val_acc = hist.history['val_accuracy']\n",
        "    loss = hist.history['loss']\n",
        "    val_loss = hist.history['val_loss']\n",
        "\n",
        "    epochs = range(len(acc))\n",
        "\n",
        "    plt.plot(epochs, acc, 'r', label='Training accuracy')\n",
        "    plt.plot(epochs, val_acc, 'b', label='Validation accuracy')\n",
        "    plt.title('Training and validation accuracy')\n",
        "    plt.legend()\n",
        "    plt.figure()\n",
        "\n",
        "    plt.plot(epochs, loss, 'r', label='Training Loss')\n",
        "    plt.plot(epochs, val_loss, 'b', label='Validation Loss')\n",
        "    plt.title('Training and validation loss')\n",
        "    plt.legend()\n",
        "\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "RMJaFyzNdNxh"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "\n",
        "def plot_confusion_matrix(labels, predictions, threshold=0.5):\n",
        "  cm = confusion_matrix(labels, predictions > threshold)\n",
        "  plt.figure(figsize=(5,5))\n",
        "  sns.heatmap(cm, annot=True, fmt=\"d\")\n",
        "  plt.title('Confusion matrix @{:.2f}'.format(threshold))\n",
        "  plt.ylabel('Actual label')\n",
        "  plt.xlabel('Predicted label')\n",
        "\n",
        "  print('Legitimate Transactions Detected (True Negatives): ', cm[0][0])\n",
        "  print('Legitimate Transactions Incorrectly Detected (False Positives): ', cm[0][1])\n",
        "  print('Fraudulent Transactions Missed (False Negatives): ', cm[1][0])\n",
        "  print('Fraudulent Transactions Detected (True Positives): ', cm[1][1])\n",
        "  print('Total Fraudulent Transactions: ', np.sum(cm[1]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "LWEwVdqyG5Jy",
        "outputId": "cc0ec32b-4d60-4944-e213-67232778a019"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
              "</pre>\n"
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          },
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          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m123\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m32\u001b[0m)         │             \u001b[38;5;34m320\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m32\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m62\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m32\u001b[0m)           │             \u001b[38;5;34m128\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m60\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m32\u001b[0m)           │           \u001b[38;5;34m9,248\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m32\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_1                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m32\u001b[0m)           │             \u001b[38;5;34m128\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m29\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m32\u001b[0m)           │           \u001b[38;5;34m4,128\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m32\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_2                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m32\u001b[0m)           │             \u001b[38;5;34m128\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout (\u001b[38;5;33mDropout\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m32\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m480\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │          \u001b[38;5;34m30,784\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)                  │             \u001b[38;5;34m650\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">123</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">11</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)         │             <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">62</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">62</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">60</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)           │           <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_1                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">29</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)           │           <span style=\"color: #00af00; text-decoration-color: #00af00\">4,128</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_2                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">480</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">30,784</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)                  │             <span style=\"color: #00af00; text-decoration-color: #00af00\">650</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m45,514\u001b[0m (177.79 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">45,514</span> (177.79 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m45,322\u001b[0m (177.04 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">45,322</span> (177.04 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m192\u001b[0m (768.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">192</span> (768.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 120ms/step - accuracy: 0.4620 - loss: 1.6018 - val_accuracy: 0.6587 - val_loss: 0.9547\n",
            "Epoch 2/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m71s\u001b[0m 102ms/step - accuracy: 0.6770 - loss: 0.9017 - val_accuracy: 0.7301 - val_loss: 0.7746\n",
            "Epoch 3/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 104ms/step - accuracy: 0.7531 - loss: 0.7112 - val_accuracy: 0.7551 - val_loss: 0.7085\n",
            "Epoch 4/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m66s\u001b[0m 109ms/step - accuracy: 0.7817 - loss: 0.6198 - val_accuracy: 0.7629 - val_loss: 0.6912\n",
            "Epoch 5/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m72s\u001b[0m 120ms/step - accuracy: 0.8052 - loss: 0.5648 - val_accuracy: 0.8205 - val_loss: 0.5228\n",
            "Epoch 6/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m74s\u001b[0m 124ms/step - accuracy: 0.8277 - loss: 0.4924 - val_accuracy: 0.8217 - val_loss: 0.5290\n",
            "Epoch 7/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m73s\u001b[0m 110ms/step - accuracy: 0.8454 - loss: 0.4498 - val_accuracy: 0.8292 - val_loss: 0.5024\n",
            "Epoch 8/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m89s\u001b[0m 121ms/step - accuracy: 0.8547 - loss: 0.4071 - val_accuracy: 0.8292 - val_loss: 0.5348\n",
            "Epoch 9/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m71s\u001b[0m 119ms/step - accuracy: 0.8700 - loss: 0.3703 - val_accuracy: 0.8700 - val_loss: 0.3921\n",
            "Epoch 10/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 117ms/step - accuracy: 0.8752 - loss: 0.3496 - val_accuracy: 0.8597 - val_loss: 0.4204\n",
            "Epoch 11/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m77s\u001b[0m 108ms/step - accuracy: 0.8776 - loss: 0.3418 - val_accuracy: 0.8679 - val_loss: 0.4081\n",
            "Epoch 12/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m87s\u001b[0m 117ms/step - accuracy: 0.8925 - loss: 0.3131 - val_accuracy: 0.8956 - val_loss: 0.3314\n",
            "Epoch 13/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m74s\u001b[0m 123ms/step - accuracy: 0.8955 - loss: 0.3010 - val_accuracy: 0.8515 - val_loss: 0.4278\n",
            "Epoch 14/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m69s\u001b[0m 114ms/step - accuracy: 0.9033 - loss: 0.2738 - val_accuracy: 0.8704 - val_loss: 0.3910\n",
            "Epoch 15/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m77s\u001b[0m 106ms/step - accuracy: 0.9095 - loss: 0.2627 - val_accuracy: 0.8800 - val_loss: 0.3576\n",
            "Epoch 16/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m90s\u001b[0m 119ms/step - accuracy: 0.9069 - loss: 0.2613 - val_accuracy: 0.8997 - val_loss: 0.3176\n",
            "Epoch 17/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 117ms/step - accuracy: 0.9122 - loss: 0.2506 - val_accuracy: 0.8810 - val_loss: 0.3648\n",
            "Epoch 18/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m71s\u001b[0m 99ms/step - accuracy: 0.9112 - loss: 0.2456 - val_accuracy: 0.9199 - val_loss: 0.2668\n",
            "Epoch 19/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m93s\u001b[0m 117ms/step - accuracy: 0.9259 - loss: 0.2122 - val_accuracy: 0.8877 - val_loss: 0.3448\n",
            "Epoch 20/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 115ms/step - accuracy: 0.9264 - loss: 0.2086 - val_accuracy: 0.8854 - val_loss: 0.3570\n",
            "Epoch 21/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m69s\u001b[0m 116ms/step - accuracy: 0.9238 - loss: 0.2158 - val_accuracy: 0.9064 - val_loss: 0.3017\n",
            "Epoch 22/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m65s\u001b[0m 108ms/step - accuracy: 0.9299 - loss: 0.2018 - val_accuracy: 0.9166 - val_loss: 0.2702\n",
            "Epoch 23/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m87s\u001b[0m 116ms/step - accuracy: 0.9292 - loss: 0.2070 - val_accuracy: 0.9216 - val_loss: 0.2613\n",
            "Epoch 24/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 116ms/step - accuracy: 0.9321 - loss: 0.1987 - val_accuracy: 0.9261 - val_loss: 0.2276\n",
            "Epoch 25/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m68s\u001b[0m 92ms/step - accuracy: 0.9389 - loss: 0.1788 - val_accuracy: 0.9116 - val_loss: 0.2982\n",
            "Epoch 26/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m97s\u001b[0m 117ms/step - accuracy: 0.9310 - loss: 0.1955 - val_accuracy: 0.9017 - val_loss: 0.3158\n",
            "Epoch 27/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 116ms/step - accuracy: 0.9364 - loss: 0.1861 - val_accuracy: 0.9226 - val_loss: 0.2500\n",
            "Epoch 28/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 109ms/step - accuracy: 0.9350 - loss: 0.1853 - val_accuracy: 0.9324 - val_loss: 0.2217\n",
            "Epoch 29/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m79s\u001b[0m 104ms/step - accuracy: 0.9412 - loss: 0.1743 - val_accuracy: 0.8722 - val_loss: 0.4210\n",
            "Epoch 30/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m91s\u001b[0m 119ms/step - accuracy: 0.9404 - loss: 0.1698 - val_accuracy: 0.9313 - val_loss: 0.2222\n",
            "Epoch 31/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 118ms/step - accuracy: 0.9397 - loss: 0.1716 - val_accuracy: 0.9253 - val_loss: 0.2342\n",
            "Epoch 32/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m57s\u001b[0m 95ms/step - accuracy: 0.9387 - loss: 0.1722 - val_accuracy: 0.9181 - val_loss: 0.2804\n",
            "Epoch 33/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m95s\u001b[0m 118ms/step - accuracy: 0.9467 - loss: 0.1633 - val_accuracy: 0.8887 - val_loss: 0.3490\n",
            "Epoch 34/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m69s\u001b[0m 115ms/step - accuracy: 0.9425 - loss: 0.1667 - val_accuracy: 0.9246 - val_loss: 0.2486\n",
            "Epoch 35/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 125ms/step - accuracy: 0.9403 - loss: 0.1647 - val_accuracy: 0.9333 - val_loss: 0.2203\n",
            "Epoch 36/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m72s\u001b[0m 109ms/step - accuracy: 0.9446 - loss: 0.1550 - val_accuracy: 0.9266 - val_loss: 0.2395\n",
            "Epoch 37/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m87s\u001b[0m 116ms/step - accuracy: 0.9488 - loss: 0.1462 - val_accuracy: 0.9388 - val_loss: 0.1980\n",
            "Epoch 38/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m72s\u001b[0m 120ms/step - accuracy: 0.9447 - loss: 0.1569 - val_accuracy: 0.9371 - val_loss: 0.1968\n",
            "Epoch 39/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m93s\u001b[0m 137ms/step - accuracy: 0.9488 - loss: 0.1466 - val_accuracy: 0.8952 - val_loss: 0.3312\n",
            "Epoch 40/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m133s\u001b[0m 122ms/step - accuracy: 0.9445 - loss: 0.1578 - val_accuracy: 0.9268 - val_loss: 0.2485\n",
            "Epoch 41/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m79s\u001b[0m 117ms/step - accuracy: 0.9483 - loss: 0.1411 - val_accuracy: 0.9283 - val_loss: 0.2423\n",
            "Epoch 42/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m76s\u001b[0m 107ms/step - accuracy: 0.9508 - loss: 0.1438 - val_accuracy: 0.9349 - val_loss: 0.2215\n",
            "Epoch 43/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m93s\u001b[0m 126ms/step - accuracy: 0.9491 - loss: 0.1426 - val_accuracy: 0.9369 - val_loss: 0.2216\n",
            "Epoch 44/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 118ms/step - accuracy: 0.9479 - loss: 0.1473 - val_accuracy: 0.9218 - val_loss: 0.2595\n",
            "Epoch 45/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m72s\u001b[0m 102ms/step - accuracy: 0.9521 - loss: 0.1418 - val_accuracy: 0.9386 - val_loss: 0.2041\n",
            "Epoch 46/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m89s\u001b[0m 114ms/step - accuracy: 0.9508 - loss: 0.1345 - val_accuracy: 0.9253 - val_loss: 0.2458\n",
            "Epoch 47/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 117ms/step - accuracy: 0.9575 - loss: 0.1221 - val_accuracy: 0.9238 - val_loss: 0.2525\n",
            "Epoch 48/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 117ms/step - accuracy: 0.9550 - loss: 0.1291 - val_accuracy: 0.9449 - val_loss: 0.1938\n",
            "Epoch 49/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m63s\u001b[0m 105ms/step - accuracy: 0.9503 - loss: 0.1375 - val_accuracy: 0.9147 - val_loss: 0.2913\n",
            "Epoch 50/50\n",
            "\u001b[1m600/600\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m89s\u001b[0m 117ms/step - accuracy: 0.9575 - loss: 0.1226 - val_accuracy: 0.9358 - val_loss: 0.2183\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 28ms/step\n",
            "\n",
            "---Now testing the model for one audio file---\n",
            "The model predicts: classical, and ground truth is: classical.\n",
            "\n",
            "\u001b[1m150/150\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - accuracy: 0.9322 - loss: 0.1982\n",
            "Accuracy on test is 0.9288991093635559\n",
            "Test error is 0.21029821038246155\n"
          ]
        }
      ],
      "source": [
        "if __name__ == \"__main__\":\n",
        "\n",
        "    preprocess_data(source_path=SOURCE_PATH, json_path=JSON_PATH)\n",
        "\n",
        "    inputs, targets = load_data(json_path=JSON_PATH)\n",
        "\n",
        "    Xtrain, Xval, Xtest, ytrain, yval, ytest = prepare_datasets(inputs, targets, 0.2)\n",
        "\n",
        "    input_shape = (Xtrain.shape[1], Xtrain.shape[2], 1)\n",
        "\n",
        "    model = design_cnn_model(input_shape)\n",
        "\n",
        "    # Selection of the optimizer, loss type and metrics for performance evaluation.\n",
        "    model.compile(optimizer = tf.keras.optimizers.AdamW(learning_rate=0.001),\n",
        "                  loss='sparse_categorical_crossentropy',\n",
        "                  metrics = ['accuracy']\n",
        "                  )\n",
        "\n",
        "    model.summary()\n",
        "\n",
        "    #Training the model.\n",
        "    history = model.fit(Xtrain, ytrain,\n",
        "                        validation_data=(Xval, yval),\n",
        "                        epochs=100,\n",
        "                        batch_size=32,\n",
        "                         callbacks=[tf.keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=1)],\n",
        "                        )\n",
        "\n",
        "    plot_performance(history)\n",
        "\n",
        "    # Testing the model on never seen before data.\n",
        "    make_prediction(model, Xtest, ytest, 24)\n",
        "\n",
        "    test_error , test_accuracy = model.evaluate(Xtest,ytest,verbose=1)\n",
        "    print(\"Accuracy on test is {}\".format(test_accuracy))\n",
        "    print(\"Test error is {}\".format(test_error))\n",
        "\n",
        "    model.save('/content/drive/MyDrive/my_model')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Aa9-b6UWo1tD",
        "outputId": "1f19a1ed-6d8e-4195-a7ee-b75c58679ff9"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(**kwargs)\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">125</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │          <span style=\"color: #00af00; text-decoration-color: #00af00\">19,968</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ lstm_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">33,024</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">4,160</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)                  │             <span style=\"color: #00af00; text-decoration-color: #00af00\">650</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ],
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m125\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │          \u001b[38;5;34m19,968\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │          \u001b[38;5;34m33,024\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │           \u001b[38;5;34m4,160\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)                  │             \u001b[38;5;34m650\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">57,802</span> (225.79 KB)\n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m57,802\u001b[0m (225.79 KB)\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">57,802</span> (225.79 KB)\n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m57,802\u001b[0m (225.79 KB)\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "\u001b[1m200/200\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 184ms/step - accuracy: 0.3000 - loss: 1.9889 - val_accuracy: 0.4620 - val_loss: 1.4965\n",
            "Epoch 2/50\n",
            "\u001b[1m200/200\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 235ms/step - accuracy: 0.4741 - loss: 1.4554 - val_accuracy: 0.5155 - val_loss: 1.3621\n",
            "Epoch 3/50\n",
            "\u001b[1m200/200\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m70s\u001b[0m 178ms/step - accuracy: 0.5243 - loss: 1.3223 - val_accuracy: 0.5480 - val_loss: 1.2556\n",
            "Epoch 4/50\n",
            "\u001b[1m200/200\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m37s\u001b[0m 183ms/step - accuracy: 0.5815 - loss: 1.2126 - val_accuracy: 0.5906 - val_loss: 1.1941\n",
            "Epoch 5/50\n",
            "\u001b[1m200/200\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 188ms/step - accuracy: 0.5755 - loss: 1.2184 - val_accuracy: 0.5631 - val_loss: 1.2245\n",
            "Epoch 6/50\n",
            "\u001b[1m200/200\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m51s\u001b[0m 241ms/step - accuracy: 0.6067 - loss: 1.1367 - val_accuracy: 0.6061 - val_loss: 1.1319\n",
            "Epoch 7/50\n",
            "\u001b[1m200/200\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 219ms/step - accuracy: 0.6506 - loss: 1.0291 - val_accuracy: 0.6286 - val_loss: 1.0817\n",
            "Epoch 8/50\n",
            "\u001b[1m200/200\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m77s\u001b[0m 194ms/step - accuracy: 0.6760 - loss: 0.9472 - val_accuracy: 0.6517 - val_loss: 1.0254\n",
            "Epoch 9/50\n",
            "\u001b[1m200/200\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 209ms/step - accuracy: 0.7087 - loss: 0.8956 - val_accuracy: 0.6471 - val_loss: 1.0554\n",
            "Epoch 10/50\n",
            "\u001b[1m200/200\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 173ms/step - accuracy: 0.7082 - loss: 0.8821 - val_accuracy: 0.6657 - val_loss: 0.9726\n",
            "Epoch 11/50\n",
            "\u001b[1m200/200\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 189ms/step - accuracy: 0.7298 - loss: 0.8136 - val_accuracy: 0.6807 - val_loss: 0.9352\n",
            "Epoch 12/50\n",
            "\u001b[1m200/200\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 216ms/step - accuracy: 0.7466 - loss: 0.7712 - val_accuracy: 0.6872 - val_loss: 0.9407\n",
            "Epoch 13/50\n",
            "\u001b[1m200/200\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m86s\u001b[0m 233ms/step - accuracy: 0.7577 - loss: 0.7144 - val_accuracy: 0.6957 - val_loss: 0.9329\n",
            "Epoch 14/50\n",
            "\u001b[1m200/200\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m77s\u001b[0m 209ms/step - accuracy: 0.7698 - loss: 0.6868 - val_accuracy: 0.7017 - val_loss: 0.8810\n",
            "Epoch 15/50\n",
            "\u001b[1m200/200\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 198ms/step - accuracy: 0.7788 - loss: 0.6528 - val_accuracy: 0.6882 - val_loss: 0.9185\n",
            "Epoch 16/50\n",
            "\u001b[1m200/200\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 171ms/step - accuracy: 0.8040 - loss: 0.6107 - val_accuracy: 0.7367 - val_loss: 0.8144\n",
            "Epoch 17/50\n",
            "\u001b[1m200/200\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m55s\u001b[0m 241ms/step - accuracy: 0.8031 - loss: 0.5984 - val_accuracy: 0.7257 - val_loss: 0.8222\n",
            "Epoch 18/50\n",
            "\u001b[1m200/200\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m70s\u001b[0m 183ms/step - accuracy: 0.8164 - loss: 0.5628 - val_accuracy: 0.7292 - val_loss: 0.8391\n",
            "Epoch 19/50\n",
            "\u001b[1m200/200\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 179ms/step - accuracy: 0.8210 - loss: 0.5298 - val_accuracy: 0.7367 - val_loss: 0.8544\n",
            "Epoch 20/50\n",
            "\u001b[1m200/200\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 179ms/step - accuracy: 0.8340 - loss: 0.4886 - val_accuracy: 0.7422 - val_loss: 0.8400\n",
            "Epoch 21/50\n",
            "\u001b[1m200/200\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m51s\u001b[0m 232ms/step - accuracy: 0.8460 - loss: 0.4584 - val_accuracy: 0.7508 - val_loss: 0.7805\n",
            "Epoch 22/50\n",
            "\u001b[1m200/200\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 228ms/step - accuracy: 0.8521 - loss: 0.4677 - val_accuracy: 0.7197 - val_loss: 0.8793\n",
            "Epoch 23/50\n",
            "\u001b[1m200/200\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 182ms/step - accuracy: 0.8442 - loss: 0.4829 - val_accuracy: 0.7407 - val_loss: 0.8147\n",
            "Epoch 24/50\n",
            "\u001b[1m200/200\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 207ms/step - accuracy: 0.8669 - loss: 0.4128 - val_accuracy: 0.7628 - val_loss: 0.8284\n",
            "Epoch 25/50\n",
            "\u001b[1m200/200\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 211ms/step - accuracy: 0.8802 - loss: 0.3810 - val_accuracy: 0.7457 - val_loss: 0.8573\n",
            "Epoch 26/50\n",
            "\u001b[1m200/200\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m76s\u001b[0m 181ms/step - accuracy: 0.8690 - loss: 0.3888 - val_accuracy: 0.7497 - val_loss: 0.8400\n",
            "Epoch 27/50\n",
            "\u001b[1m200/200\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 205ms/step - accuracy: 0.8648 - loss: 0.3966 - val_accuracy: 0.7628 - val_loss: 0.7887\n",
            "Epoch 28/50\n",
            "\u001b[1m200/200\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 201ms/step - accuracy: 0.8881 - loss: 0.3293 - val_accuracy: 0.7718 - val_loss: 0.8063\n",
            "Epoch 29/50\n",
            "\u001b[1m200/200\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m55s\u001b[0m 270ms/step - accuracy: 0.8913 - loss: 0.3313 - val_accuracy: 0.7628 - val_loss: 0.8449\n",
            "Epoch 30/50\n",
            "\u001b[1m200/200\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m64s\u001b[0m 182ms/step - accuracy: 0.8861 - loss: 0.3536 - val_accuracy: 0.7397 - val_loss: 0.9117\n",
            "Epoch 31/50\n",
            "\u001b[1m200/200\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 170ms/step - accuracy: 0.8934 - loss: 0.3246 - val_accuracy: 0.7688 - val_loss: 0.8282\n",
            "Epoch 31: early stopping\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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h7NixWLRoEeLi4lCnTh389ddf6tYJfZk8eTJsbW2xePFijBgxAsWLF8eAAQMwY8YM9Tw4Li4u8PT0xF9//YWHDx/CwsICLi4u2LNnj3okUfv27XH//n38+uuveP78OUqWLIlmzZphypQp6tE4RNmlEnnpT0Ai0ljHjh1x7dq1DOsZiIjyE9aMEOUD707dfufOHezevRvNmzdXJiAiIh1iywhRPuDg4KBeLyUkJARLly5FfHw8Ll68iMqVKysdHhFRjrBmhCgfaNWqFdavX4/Hjx/D1NQU7u7umDFjBhMRIioQ2DJCREREimLNCBERESmKyQgREREpKl/UjCQnJ+PRo0coWrSoVlMwExERkXKEEHj9+jUcHR2znNwvXyQjjx49grOzs9JhEBERUTY8ePAApUuXzvT5fJGMFC1aFIB8MVZWVgpHQ0RERJqIjIyEs7Oz+ns8M/kiGUnpmrGysmIyQkRElM+8r8SCBaxERESkKCYjREREpCgmI0RERKSofFEzQkRE2SeEQGJiIpKSkpQOhQoYQ0NDGBkZ5XjaDSYjREQFWEJCAsLCwhATE6N0KFRAWVhYwMHBASYmJtk+h9bJSEBAAGbPno3AwECEhYVh27Zt6NixY5bHrF27FrNmzcKdO3dgbW2N1q1bY/bs2ShRokR24yYiovdITk5GcHAwDA0N4ejoCBMTE04cSTojhEBCQgKePXuG4OBgVK5cOcuJzbKidTISHR0NFxcX9O3bF506dXrv/idPnoSPjw/mz58PLy8vPHz4EAMHDkT//v2xdevWbAVNRETvl5CQgOTkZDg7O8PCwkLpcKgAMjc3h7GxMUJCQpCQkAAzM7NsnUfrZKR169Zo3bq1xvufPn0a5cqVw9ChQwEA5cuXx1dffYUffvhB20sTEVE2ZPevVSJN6OLzpfdPqLu7Ox48eIDdu3dDCIEnT55gy5YtaNOmTabHxMfHIzIyMs2NiIiICia9JyONGzfG2rVr4e3tDRMTE9jb28Pa2hpLlizJ9JiZM2fC2tpafeO6NERERAWX3pOR69evY9iwYfD390dgYCD27t2L+/fvY+DAgZkeM27cOERERKhvDx480HeYRERUwJUrVw4LFizQeP+jR49CpVIhPDxcbzGRpPehvTNnzkTjxo0xZswYAECdOnVQpEgRNGnSBN999x0cHBzSHWNqagpTU1N9h0ZERHnQ+0b8TJo0CZMnT9b6vOfOnUORIkU03r9Ro0YICwuDtbW11tfSxtGjR/Hxxx/j1atXsLGx0eu18iq9JyMxMTEwMkp7GUNDQwByWJCiNmwADh4EvvoKqF9f2ViIiAgAEBYWpr6/ceNG+Pv749atW+ptlpaW6vtCCCQlJaX7nsmIra2tVnGklBaQ/mndTRMVFYWgoCAEBQUBAIKDgxEUFITQ0FAAsovFx8dHvb+Xlxe2bt2KpUuX4t69ezh58iSGDh2KBg0awNHRUTevIrv+/BNYuRI4elTZOIiIcosQQHS0MjcN/wC1t7dX36ytraFSqdSPb968iaJFi2LPnj1wc3ODqakpTpw4gX///RcdOnSAnZ0dLC0tUb9+fRw8eDDNed/tplGpVPjll1/w2WefwcLCApUrV8bOnTvVz7/bTbN69WrY2Nhg3759qF69OiwtLdGqVas0yVNiYiKGDh0KGxsblChRAmPHjoWvr+975+PKyqtXr+Dj44NixYrBwsICrVu3xp07d9TPh4SEwMvLC8WKFUORIkVQs2ZN7N69W31sjx49YGtrC3Nzc1SuXBmrVq3Kdiz6onUycv78ebi6usLV1RUAMHLkSLi6usLf3x+AzGhTEhMA6N27N+bNm4fFixejVq1a6NKlC6pWrZo35hipV0/+PH9e2TiIiHJLTAxgaanMTYezwH777bf4/vvvcePGDdSpUwdRUVFo06YNDh06hIsXL6JVq1bw8vJK832UkSlTpqBr1664fPky2rRpgx49euDly5dZvH0xmDNnDtasWYOAgACEhoZi9OjR6ud/+OEHrF27FqtWrcLJkycRGRmJ7du35+i19u7dG+fPn8fOnTtx+vRpCCHQpk0bvHnzBgDg5+eH+Ph4BAQE4MqVK/jhhx/UrUcTJ07E9evXsWfPHty4cQNLly5FyZIlcxSPXoh8ICIiQgAQERERuj3xgQNCAEJUqKDb8xIR5QGxsbHi+vXrIjY2NnVjVJT8vafELSpK69ewatUqYW1trX585MgRAUBs3779vcfWrFlTLFq0SP24bNmyYv78+erHAMSECRPeemuiBACxZ8+eNNd69eqVOhYA4u7du+pjlixZIuzs7NSP7ezsxOzZs9WPExMTRZkyZUSHDh0yjfPd67zt9u3bAoA4efKketvz58+Fubm52LRpkxBCiNq1a4vJkydneG4vLy/Rp0+fTK+tCxl+zv6fpt/fhXttGjc3+fPePeDlS6B4cWXjISLSNwsLICpKuWvrSL2Ulu3/FxUVhcmTJ2PXrl0ICwtDYmIiYmNj39syUqdOHfX9IkWKwMrKCk+fPs10fwsLC1SsWFH92MHBQb1/REQEnjx5ggYNGqifNzQ0hJubG5KTk7V6fSlu3LgBIyMjNGzYUL2tRIkSqFq1Km7cuAEAGDp0KAYNGoT9+/fDw8MDnTt3Vr+uQYMGoXPnzrhw4QI+/fRTdOzYEY0aNcpWLPpUuKflK1YMSPlQBQYqGwsRUW5QqYAiRZS56XBdnHdHxYwePRrbtm3DjBkzcPz4cQQFBaF27dpISEjI8jzGxsbvvD2qLBOHjPYXCg/G6NevH+7du4devXrhypUrqFevHhYtWgRAzpoeEhKCESNG4NGjR2jZsmWabqW8onAnI0Bq3QiTESKifOvkyZPo3bs3PvvsM9SuXRv29va4f/9+rsZgbW0NOzs7nDt3Tr0tKSkJFy5cyPY5q1evjsTERJw5c0a97cWLF7h16xZq1Kih3ubs7IyBAwdi69atGDVqFFasWKF+ztbWFr6+vvjjjz+wYMECLF++PNvx6Evh7qYBZDKycSOLWImI8rHKlStj69at8PLygkqlwsSJE7PdNZITQ4YMwcyZM1GpUiVUq1YNixYtwqtXrzRaLfnKlSsoWrSo+rFKpYKLiws6dOiA/v374+eff0bRokXx7bffwsnJCR06dAAADB8+HK1bt0aVKlXw6tUrHDlyBNWrVwcA+Pv7w83NDTVr1kR8fDz+/vtv9XN5CZMRjqghIsr35s2bh759+6JRo0YoWbIkxo4dq8i6ZmPHjsXjx4/h4+MDQ0NDDBgwAJ6enur5tbLStGnTNI8NDQ2RmJiIVatWYdiwYWjXrh0SEhLQtGlT7N69W91llJSUBD8/P/z333+wsrJCq1atMH/+fAByrpRx48bh/v37MDc3R5MmTbBhwwbdv/AcUgmlO7s0EBkZCWtra0RERMDKykq3J4+IAFJmvHv6FNByUhwiorwqLi4OwcHBKF++fLaXdqecSU5ORvXq1dG1a1dMmzZN6XD0IqvPmabf36wZsbYGqlSR91k3QkREORASEoIVK1bg9u3buHLlCgYNGoTg4GB0795d6dDyNCYjALtqiIhIJwwMDLB69WrUr18fjRs3xpUrV3Dw4ME8WaeRl7BmBJDJyLp1bBkhIqIccXZ2xsmTJ5UOI99hywjAlhEiIiIFMRkBAFdXORnPf/8Bjx8rHQ0REVGhwmQEkAs4Vasm77OrhoiIKFcxGUnBrhoiIiJFMBlJwWSEiIhIEUxGUnCNGiIiIkUwGUlRty5gYACEhQGPHikdDRER5VDz5s0xfPhw9eNy5cphwYIFWR6jUqmwffv2HF9bV+cpLJiMpLCwAFJWQGRXDRGRYry8vNCqVasMnzt+/DhUKhUuX76s9XnPnTuHAQMG5DS8NCZPnoy6deum2x4WFobWrVvr9FrvWr16NWxSljPJ55iMvI11I0REivvyyy9x4MAB/Pfff+meW7VqFerVq4c6depofV5bW1tYWFjoIsT3sre3h6mpaa5cqyBgMvI2JiNEVMAJAURHK3PTdFnWdu3awdbWFqtXr06zPSoqCps3b8aXX36JFy9e4IsvvoCTkxMsLCxQu3ZtrF+/PsvzvttNc+fOHTRt2hRmZmaoUaMGDhw4kO6YsWPHokqVKrCwsECFChUwceJEvHnzBoBsmZgyZQouXboElUoFlUqljvndbporV66gRYsWMDc3R4kSJTBgwABERUWpn+/duzc6duyIOXPmwMHBASVKlICfn5/6WtkRGhqKDh06wNLSElZWVujatSuePHmifv7SpUv4+OOPUbRoUVhZWcHNzQ3n///7LyQkBF5eXihWrBiKFCmCmjVrYvfu3dmO5X04Hfzb3i5iFUJOhEZEVIDExMiplZQQFQUUKfL+/YyMjODj44PVq1dj/PjxUP3/7+LNmzcjKSkJX3zxBaKiouDm5oaxY8fCysoKu3btQq9evVCxYkU0aNDgvddITk5Gp06dYGdnhzNnziAiIiJNfUmKokWLYvXq1XB0dMSVK1fQv39/FC1aFN988w28vb1x9epV7N27FwcPHgQAWFtbpztHdHQ0PD094e7ujnPnzuHp06fo168fBg8enCbhOnLkCBwcHHDkyBHcvXsX3t7eqFu3Lvr37//+Ny2D15eSiBw7dgyJiYnw8/ODt7c3jh49CgDo0aMHXF1dsXTpUhgaGiIoKAjGxsYAAD8/PyQkJCAgIABFihTB9evXYanPD47IByIiIgQAERERod8LxcQIYWQkBCBEaKh+r0VEpGexsbHi+vXrIjY2Vr0tKkr+ilPiFhWleew3btwQAMSRI0fU25o0aSJ69uyZ6TFt27YVo0aNUj9u1qyZGDZsmPpx2bJlxfz584UQQuzbt08YGRmJhw8fqp/fs2ePACC2bduW6TVmz54t3Nzc1I8nTZokXFxc0u339nmWL18uihUrJqLeegN27dolDAwMxOPHj4UQQvj6+oqyZcuKxMRE9T5dunQR3t7emcayatUqYW1tneFz+/fvF4aGhiL0re+ya9euCQDi7NmzQgghihYtKlavXp3h8bVr1xaTJ0/O9Npvy+hzlkLT72+2jLzN3ByoVQsICpJdNc7OSkdERKRTFhayhUKpa2uqWrVqaNSoEX799Vc0b94cd+/exfHjxzF16lQAQFJSEmbMmIFNmzbh4cOHSEhIQHx8vMY1ITdu3ICzszMcHR3V29zd3dPtt3HjRixcuBD//vsvoqKikJiYCCsrK81fyP9fy8XFBUXeahZq3LgxkpOTcevWLdjZ2QEAatasCUNDQ/U+Dg4OuHLlilbXevuazs7OcH7re6xGjRqwsbHBjRs3UL9+fYwcORL9+vXDmjVr4OHhgS5duqBixYoAgKFDh2LQoEHYv38/PDw80Llz52zV6WiKNSPvcnOTP1k3QkQFkEolu0qUuGnb8/3ll1/izz//xOvXr7Fq1SpUrFgRzZo1AwDMnj0bP/74I8aOHYsjR44gKCgInp6eSEhI0Nl7dfr0afTo0QNt2rTB33//jYsXL2L8+PE6vcbbUrpIUqhUKiQnJ+vlWoAcCXTt2jW0bdsWhw8fRo0aNbBt2zYAQL9+/XDv3j306tULV65cQb169bBo0SK9xcJk5F0sYiUiyhO6du0KAwMDrFu3Dr///jv69u2rrh85efIkOnTogJ49e8LFxQUVKlTA7du3NT539erV8eDBA4SFham3/fPPP2n2OXXqFMqWLYvx48ejXr16qFy5MkJCQtLsY2JigqSkpPde69KlS4iOjlZvO3nyJAwMDFC1alWNY9ZGyut78OCBetv169cRHh6OGinTWACoUqUKRowYgf3796NTp05YtWqV+jlnZ2cMHDgQW7duxahRo7BixQq9xAowGUnv7WRE09JvIiLSOUtLS3h7e2PcuHEICwtD79691c9VrlwZBw4cwKlTp3Djxg189dVXaUaKvI+HhweqVKkCX19fXLp0CcePH8f48ePT7FO5cmWEhoZiw4YN+Pfff7Fw4UJ1y0GKcuXKITg4GEFBQXj+/Dni4+PTXatHjx4wMzODr68vrl69iiNHjmDIkCHo1auXuosmu5KSkhAUFJTmduPGDXh4eKB27dro0aMHLly4gLNnz8LHxwfNmjVDvXr1EBsbi8GDB+Po0aMICQnByZMnce7cOVSvXh0AMHz4cOzbtw/BwcG4cOECjhw5on5OH5iMvKt2bcDYGHj5EngnAyYiotz15Zdf4tWrV/D09ExT3zFhwgR88MEH8PT0RPPmzWFvb4+OHTtqfF4DAwNs27YNsbGxaNCgAfr164fp06en2ad9+/YYMWIEBg8ejLp16+LUqVOYOHFimn06d+6MVq1a4eOPP4atrW2Gw4stLCywb98+vHz5EvXr18fnn3+Oli1bYvHixdq9GRmIioqCq6trmpuXlxdUKhV27NiBYsWKoWnTpvDw8ECFChWwceNGAIChoSFevHgBHx8fVKlSBV27dkXr1q0xZcoUADLJ8fPzQ/Xq1dGqVStUqVIFP/30U47jzYxKiLz/539kZCSsra0RERGhdeFQttSrJ4f3bt4MfP65/q9HRKQHcXFxCA4ORvny5WFmZqZ0OFRAZfU50/T7my0jGWHdCBERUa5hMpIRjqghIiLKNUxGMsIiViIiolzDZCQjNWsCpqZARATw779KR0NERFSgMRnJiIkJ4OIi7wcGKhsLEVEO5YNxCpSP6eLzxWQkMyxiJaJ8LmVGz5iYGIUjoYIs5fP17gyy2uDaNJlhMkJE+ZyhoSFsbGzw9OlTAHK+CxVXIycdEUIgJiYGT58+hY2NTZp1dbTFZCQzKSNqAgOB5GTAgI1IRJT/2NvbA4A6ISHSNRsbG/XnLLu0TkYCAgIwe/ZsBAYGIiwsDNu2bXvvrHfx8fGYOnUq/vjjDzx+/BgODg7w9/dH3759sxu3/tWoAZiZAa9fA3fuAHpaP4CISJ9UKhUcHBxQqlQpvHnzRulwqIAxNjbOUYtICq2TkejoaLi4uKBv377o1KmTRsd07doVT548wcqVK1GpUiWEhYXpdSVCnTAyAlxdgdOnZesIkxEiyscMDQ118qVBpA9aJyOtW7dG69atNd5/7969OHbsGO7du4fixYsDkAsL5Qv16slk5Px5oHt3paMhIiIqkPReCLFz507Uq1cPs2bNgpOTE6pUqYLRo0cjNjY202Pi4+MRGRmZ5qYIFrESERHpnd4LWO/du4cTJ07AzMwM27Ztw/Pnz/H111/jxYsXWLVqVYbHzJw5U71yoKJSilgvXACSkgA2cRIREemc3ltGkpOToVKpsHbtWjRo0ABt2rTBvHnz8Ntvv2XaOjJu3DhERESobw8ePNB3mBmrVg2wsACio4Fbt5SJgYiIqIDTezLi4OAAJycnWFtbq7dVr14dQgj8999/GR5jamoKKyurNDdFGBoCH3wg77OrhoiISC/0now0btwYjx49QlRUlHrb7du3YWBggNKlS+v78jmXUjfCaeGJiIj0QutkJCoqCkFBQQgKCgIABAcHIygoCKGhoQBkF4uPj496/+7du6NEiRLo06cPrl+/joCAAIwZMwZ9+/aFubm5bl6FPrGIlYiISK+0TkbOnz8PV1dXuLq6AgBGjhwJV1dX+Pv7AwDCwsLUiQkAWFpa4sCBAwgPD0e9evXQo0cPeHl5YeHChTp6CXqWkoxcvAgkJiobCxERUQGkEvlgOcfIyEhYW1sjIiIi9+tHkpMBGxs5E+vly0Dt2rl7fSIionxK0+9vLrjyPgYGLGIlIiLSIyYjmmDdCBERkd4wGdEER9QQERHpDZMRTaQkI0FBAFe9JCIi0ikmI5qoWBGwtgbi44Fr15SOhoiIqEBhMqIJlSp1nRrWjRAREekUkxFNsYiViIhIL5iMaIpFrERERHrBZERTKcnIpUuydoSIiIh0gsmIpsqVA4oXl6Nprl5VOhoiIqICg8mIplQq1o0QERHpAZMRbXBEDRERkc4xGdEGW0aIiIh0jsmINlKSkatXgbg4ZWMhIiIqIJiMaMPZGbC1BRITgcuXlY6GiIioQCjUycj9+8DQoUB4uIYHsIiViIhI5wp1MtKpE7BoEbBwoRYHsYiViIhIpwp1MvLNN/LnggXA69caHsSWESIiIp0q1MlIly5A1arAq1fATz9peFBKMnL9OhATo7fYiIiICotCnYwYGgL/+5+8P3cuEB2twUGOjoC9PZCUJKeGJyIiohwp1MkIAHTvDlSoADx7BixfrsEBLGIlIiLSqUKfjBgZAePGyfuzZ2s4fQiTESIiIp0p9MkIAPj4AGXKAGFhwMqVGhzAETVEREQ6w2QEgIkJMHasvP/DD0BCwnsOSElGbtwAoqL0GhsREVFBx2Tk//XtCzg4AA8eAL/99p6dHRwAJydACCAoKDfCIyIiKrCYjPw/M7PUeUdmzgTevHnPASl1I8eO6TUuIiKigo7JyFsGDJBLzwQHA+vXv2fnDh3kz2XLNMhciIiIKDNMRt5iYQGMGiXvT58upxLJ1BdfAKVKAf/9B2zZkivxERERFURMRt7x9ddA8eLA7dvA5s1Z7GhmBvj5yfvz58v6ESIiItIak5F3FC0KDB8u73/3HZCcnMXOAwcCpqbAuXPAqVO5ER4REVGBw2QkA0OGAFZWwLVrwPbtWexYqhTQs6e8P29eboRGRERU4DAZyYCNDTB0qLz/3Xfv6YEZMUL+3L5dVr4SERGRVpiMZGL4cMDSErh4Edi1K4sda9YEPv1U9ucsXJhb4RERERUYTEYyUaKELGYFgGnTNGwdWbkSiIjQe2xEREQFCZORLIwaBZibA2fPAgcOZLGjpydQvTrw+rWGi9sQERFRCq2TkYCAAHh5ecHR0REqlQrbs6zwTOvkyZMwMjJC3bp1tb2sIkqVAr76St7/7rssdlSpUltHFi4EEhP1HhsREVFBoXUyEh0dDRcXFyxZskSr48LDw+Hj44OWLVtqe0lFjRkjR+8eP/6emd979gRKlgRCQt4zBIeIiIjepnUy0rp1a3z33Xf47LPPtDpu4MCB6N69O9zd3bW9pKIcHYEvv5T3p03LYkdzcznvCMBhvkRERFrIlZqRVatW4d69e5g0aZJG+8fHxyMyMjLNTUljxwJGRsChQ8Dp01ns6OcHmJjInc6cybX4iIiI8jO9JyN37tzBt99+iz/++ANGRkYaHTNz5kxYW1urb87OznqOMmtlygC+vvJ+lq0j9vZyzRpAThFPRERE76XXZCQpKQndu3fHlClTUKVKFY2PGzduHCIiItS3Bw8e6DFKTWMCDA2BPXuA8+ez2DGlkHXLFiA0NFdiIyIiys/0moy8fv0a58+fx+DBg2FkZAQjIyNMnToVly5dgpGREQ4fPpzhcaamprCyskpzU1rFikD37vJ+liNrXFyAFi3kkr+LFuVKbERERPmZXpMRKysrXLlyBUFBQerbwIEDUbVqVQQFBaFhw4b6vLzO/e9/chTvjh3A5ctZ7JjSOrJiBRAVlSuxERER5VdaJyNRUVHqxAIAgoODERQUhND/75IYN24cfHx85MkNDFCrVq00t1KlSsHMzAy1atVCkSJFdPdKckG1akDXrvJ+lq0jbdoAVarI2VhXrcqV2IiIiPIrrZOR8+fPw9XVFa6urgCAkSNHwtXVFf7+/gCAsLAwdWJSEI0fL39u2QLcuJHJTgYGcnEbAFiwQHbZEBERUYZUQmS56kqeEBkZCWtra0REROSJ+pFOnYBt2+Q8Z2vWZLJTdDTg7Ay8eiV37tgxN0MkIiJSnKbf31ybJhsmTJA/160D7t7NZKciRVLnkucwXyIiokwxGcmGDz4A2rYFkpOB0aOz2HHwYDlbWkAAEBiYa/ERERHlJ0xGsumHH2SesWMHsHNnJjs5OQHe3vI+W0eIiIgyxGQkm2rWTG0VGTJElohkKGWY78aNwMOHuRIbERFRfsJkJAcmTgTKlpUTrU6dmslObm5AkyZAYiKweHGuxkdERJQfMBnJAQuL1Pxi3jzg6tVMdhw5Uv78+ecsmlCIiIgKJyYjOdSuHfDZZ7LhY9AgWdSajpcXUKGCHOb7+++5HiMREVFexmREB378UY7kPXECWL06gx0MDYFhw+T9BQsyyViIiIgKJyYjOuDsDEyZIu+PGQM8f57BTn36ANbWwO3bwO7duRofERFRXsZkREeGDgXq1AFevgTGjs1gh6JFgf795X0O8yUiIlJjMqIjxsbAsmXy/q+/AsePZ7DTkCGyy+bwYeDSpVyNj4iIKK9iMqJD7u6pjR+DBgFv3ryzQ5kyQOfO8j5bR4iIiAAwGdG5778HSpYErl3LJN9IGea7fj3w+HGuxkZERJQXMRnRseLFgblz5f3Jk4H799/ZoWFD2YSSkABMm5bL0REREeU9TEb0oFcvoFkzIDZWlokI8c4OKUnITz8Bx47lenxERER5CZMRPVCpgKVLZVHr33/LxfTSaNkSGDBA3u/bl7OyEhFRocZkRE+qV5dzjgBy2G9U1Ds7zJ4tJyi5dw/43/9yPT4iIqK8gsmIHo0fD5QvDzx4kDopmpqVFfDLL/L+woVAQECux0dERJQXMBnRo7cX0ps/H7h8+Z0dPv0U+PJLeb9vXyAmJlfjIyIiyguYjOhZmzZyapGkpEwW0ps7FyhdGvj3X2DCBEViJCIiUhKTkVywYAFgaQmcOiVnZ03D2hpYvjx1x5Mnczk6IiIiZTEZyQWlSwNTp8r733wDPHv2zg6tW8uF9ISQP2Njcz1GIiIipTAZySVDhgAuLsCrVzIhSWfePMDREbhzB5g4MdfjIyIiUgqTkVxiZAT8/LOcg2T16gzmOrOxSe2umT8fOH06lyMkIiJSBpORXNSwIfDVV/L+oEFyRvg02rYFfHxklSu7a4iIqJBgMpLLZswASpUCbtyQiUlS0js7LFgAODgAt27JxW2IiIgKOCYjuaxYMdldY2gou2u6dQPi4zPYAQDmzAHOnFEiTCIiolzDZEQBHTsCW7YAJibyZ4cO7yxP4+UF9OyZ2l0TF6ebC795A1y6lMHKfURERMphMqKQjh2BXbuAIkWAffsAT08gPPytHX78EbCzk/05KeOCs+vNG2DlSqBKFaBuXU6uRkREeQqTEQV5eAAHDsiBNCdPAh9/DDx9+v9PFi8OLFsm7//wA3DunPYXSEwEVq0CqlUD+vUD7t+X2+fNAx490sErICIiyjkmIwpzd5fDfO3sgKAgoEkTIDT0/5/s2BH44ovU7po0xSVZSEwEfv9dJiF9+8qVgUuVklPPN2oku31mzNDTKyIiItIOk5E8oE4d4PhxoEwZ4PZt4KOP5E8AckXfUqWAa9eAadOyPlFSEvDHH0CNGoCvr1zvxtYWmD1bJiQjR+LN5Oly3+XLgZAQvb4uIiIiTTAZySMqVwZOnACqVgUePJAtJEFBAEqWBJYulTt9/z1w4UL6g5OSgHXrgJo1gV695CyuJUrI7p179yBGjcahf4rA0xMw9WyOhZUXyTqS777LzZdIRESUIZUQeX9oRWRkJKytrREREQErKyulw9GrZ89kMevFi3INvV27gMaNIccAb9wI1K4NnD8vh+IkJwObNskC1xs35AmKFwdGjwYGD0aieVFs3QrMmgUEBqZew8gwGf8k1Yeb4SXg5k2gUiVFXisRERVsmn5/s2Ukj7G1BY4ckV01ERHAp58C+/cDWLRIPnnliuyu2bRJJiZffCETkWLFZEtHcDBih4/D0j+KompVwNtbJiLm5nJ9HC8vIDHJAD2KbEd0kikwZYrSL5mIiAo5rZORgIAAeHl5wdHRESqVCtu3b89y/61bt+KTTz6Bra0trKys4O7ujn379mU33kLB2loO923VCoiJAdq1A/4MsAWWLJE7fPedzDKuX5dDcaZOBYKD8XLQeHy30AplywJffy3LREqUkBO5hobK8pNVq+R6fLeinTEKc4G1a+V5iIiIFKJ1MhIdHQ0XFxcsSflifI+AgAB88skn2L17NwIDA/Hxxx/Dy8sLFy9e1DrYwsTCAtixA+jaVZZ3dO0KrIrqAnz+udzB2lpmGcHBCPWdiBGTrVGmjFzw99kzoFw52ZgSEgJMmiRLTwCZnPz2m7z/MwZip2jHaeeJiEhROaoZUalU2LZtGzp27KjVcTVr1oS3tzf8/f012r8w1Yy8KykJGDgQ+OUX+Xj+rDcYXmU30KwZrjywwezZwPr1cjQvALi4AN98I5MXI6PMzzt6tBzpWxLPcAW1YX9xr5wQjYiISEfybM1IcnIyXr9+jeLFi2e6T3x8PCIjI9PcCitDQzkKd9Qo+XjEN8YYcrAD2nS3QZ06wJo1MhFp0QLYu1cWvnbvnnUiAgDTp8vE5Tls0QerICZqlhgSERHpWq4nI3PmzEFUVBS6du2a6T4zZ86EtbW1+ubs7JyLEeY9KpWcKiRlJO7ixcCePYCBAdClC3D2LHDokByFo1Jpdk5TU1kuYmaajL1ojcV/l+WifEREpIhcTUbWrVuHKVOmYNOmTShVqlSm+40bNw4RERHq24MHD3IxyrxJpQLGjwd++gkoXVp23dy6JQfV1K+fvXPWrAnMniM/AmMwG9dG/KLDiImIiDSTa8nIhg0b0K9fP2zatAkeHh5Z7mtqagorK6s0N5IGDZKToi1dqpvpQfz8gNbNYhAPM3Q/PRjxB4/n/KRERERayJVkZP369ejTpw/Wr1+Ptm3b5sYlSUMqFfDrBgvYmr3GZbjgf70fAXl/HjwiIipAtE5GoqKiEBQUhKCgIABAcHAwgoKCEPr/q7uNGzcOPj4+6v3XrVsHHx8fzJ07Fw0bNsTjx4/x+PFjRERE6OYVUI7Z2wO/LpWL8M176I2DszKYcp6IiEhPtE5Gzp8/D1dXV7i6ugIARo4cCVdXV/Uw3bCwMHViAgDLly9HYmIi/Pz84ODgoL4NGzZMRy+BdKFd75IYWPskAMB3ojNePGfrCBER5Q6uTUNqMcFP8EHFcNwSVdHJPQxbTjpoPDqHiIjoXXl2nhHKuyzK22Gdzz4YIwFbTztg1cpkpUMiIqJCgMkIpfHB3B6YZiInNBk6OBl37igcEBERFXhMRiitEiUweqwhmuMIouON0LOHwJs3SgdFREQFGZMRSsdw1HD8bj0UNniFs+dUmDZN6YiIiKggYzJC6Vlbw3lsdyzDQADA9OkCJ04oHBMRERVYTEYoY0OGwNv2CHzwG5KTVejVC+DUMEREpA9MRihjlpbAuHFYhCEobxiK+/eBIUOUDoqIiAoiJiOUuYEDYeVYFH8kdYOBKhlr1gALFnC2eCIi0i0mI5Q5c3Ng/Hg0wmn4F5kLABgxAujVC4iKUjg2IiIqMJiMUNa+/BIoWxb+Ud9gdrtjMDQE1q4FGjYEbtxQOjgiIioImIxQ1kxNAX9/qACM/udzHNkVAwcH4Pp1oH59YP16pQMkIqL8jskIvZ+PD1CpEvD8OZocmYqLF4EWLYDoaKB7d8DPD4iPVzpIIiLKr5iM0PsZGQHffy/v//AD7M79jf37gQkT5KaffgI++gi4f1+xCImIKB9jMkKa6dxZNoEAQM+eMAy+i2nTgF27gOLFgfPngQ8+kI+JiIi0wWSENDdvHuDuLmc/69QJiIlBmzbAhQtAgwbAq1dAu3bA//4HJCYqHSwREeUXTEZIcyYmwObNQKlSwJUrwIABgBAoWxY4fjx1UrSZM4FPPgEeP1Y2XCIiyh+YjJB2nJyATZugHuO7ZAkAmacsXAhs2CAnbz16FHB1BQIClA2XiIjyPiYjpL1mzYBZs+T9ESOAU6fUT3l7A+fOATVrypaRFi3krsnJCsVKRER5HpMRyp4RI4CuXWVxyOefp+mTqVYNOHNGztSalASMHQt07ChrSoiIiN7FZISyR6UCVq4EqlcHwsJkk8ibN+qnixQBfvsNWL5czpv2119Ao0bAvXsKxkxERHkSkxHKPktLYNs2oGhRWRwydmyap1UqoH9/2Yvj7AzcvCmnkX+rV4eIiIjJCOVQ1aqyCQQA5s8HNm5Mt8sHH8huGzc34PlzWUeSwW5ERFRIMRmhnPvss9RWkS+/BK5dS7eLgwNw7BjQoYOcOr5bN2DGDECIXI6ViIjyHCYjpBvffQe0bCkXrOnUSU6M9o4iRYA//5S1rwAwfrzMXRIScjlWIiLKU5iMkG4YGcklfEuXBm7fBnr3zrDZw9BQTuS6ZAlgYACsWgW0bq2fkTbx8cDBg0BUlO7PTUREusNkhHTH1lY2fZiYANu3Az/8kOmuX38N/P23rIE9fFi3I21evZKzwJYrJ2eC9fDgqsJERHkZkxHSrQYNgEWL5P3x42XTRCZatwZOnJCNKTdvAh9+CPzzT/YvHRIiu4CcneX6OClTn5w5Awwdmv3zEhGRfjEZId3r3x/o00dOu/rFF0BoaKa7urjIZMHVFXj2DPj4Y7n8jTYuXgR69AAqVgQWLJBlK7VrA7//DuzcKYcYL18O/PJLzl4WERHpB5MR0j2VShaFfPCBHMvbuTMQF5fp7o6OcpoSLy+5W9euwPffZz3SRghg/37ZDfPBB8C6dXK215Ytgb17gUuX5AywXl7AtGnyGD8/4OxZHb9WIiLKMSYjpB/m5rJ+pHhx4Px5YNiwLHdPmT9t+HD5eNw42cDy1qSuAOTjP/6QLSmenrIXyNBQNsAEBsrHnp4yH0oxbpycjj4hQeZFT5/q9JUSEVEOMRkh/SlXTjZZpPSTzJ2b5e6GhnLetMWL5UiblStlXUl4OBAZKQ+vUEG2eFy6JIcKDxsG3L0rL/PBBxmf18BAzstWtSrw33+y5eXdJIeIiJSjEiLvTzsVGRkJa2trREREwMrKSulwSFvTpwMTJsj7fn6ysMPIKMtDdu+Wy91ERQHlywMvX6ZOXWJnJwtSBw6UDS+aunFD1tdGRckWmPnzs/VqiIhIQ5p+f7NlhPTvf/+Tw3xTakm8vDKcFO1tbdoAx48DTk5AcLDcvWpVYMUK4P59eUptEhFArun3++/y/oIFsjWFiIiUx5YRyj3btslhL7GxQM2acqKRcuWyPOThQzlSuFEjoF072eWSU+PHy6nozc2B06fliB4iItI9Tb+/mYxQ7goMBNq3Bx49kpOk7dgBuLvnaghJSUDbtsC+fbIL6Px57VtZiIjo/fTWTRMQEAAvLy84OjpCpVJh+/bt7z3m6NGj+OCDD2BqaopKlSph9erV2l6WCgo3Nzm+9u2JRdavz9UQDA1lF0358rILqHt3maAQEZEytE5GoqOj4eLigiVLlmi0f3BwMNq2bYuPP/4YQUFBGD58OPr164d9+/ZpHSwVEE5OcmKRlCV8u3cHpkzJ1SV8ixeXvUbm5rKFZNKkXLs0ERG9I0fdNCqVCtu2bUPHjh0z3Wfs2LHYtWsXrl69qt7WrVs3hIeHY+/evRkeEx8fj/i3FhOJjIyEs7Mzu2kKmqQk4NtvgTlz5OPu3eV4XjOzXAth3TpZxgLI5CSLjzIREWkpz4ymOX36NDw8PNJs8/T0xOnTpzM9ZubMmbC2tlbfnJ2d9R0mKcHQEJg9Ww6RMTKSmUHLlrk6K1n37qkTrfn4yDVyiIgod+k9GXn8+DHs7OzSbLOzs0NkZCRiY2MzPGbcuHGIiIhQ3x48eKDvMElJ/frJvhIbG+DUKaBhQ+DatVy7/KxZQLNmwOvXsmUkMjLXLk1ERMij84yYmprCysoqzY0KuBYt5JK9FSvKiUQaNZIJSi4wNgY2bpSlLLduAb6+co0/IiLKHXpPRuzt7fHkyZM02548eQIrKyuYm5vr+/KUn1StKpfwbdpUNk+0bQv89FOuXNrODti6FTAxAbZvlwv1ERFR7tB7MuLu7o5Dhw6l2XbgwAG45/LcEpRPlCghl+P19ZUFrn5+cgGaXBh726CBnCAWkLPXZ1JfTUREOqZ1MhIVFYWgoCAEBQUBkEN3g4KCEBoaCkDWe/j4+Kj3HzhwIO7du4dvvvkGN2/exE8//YRNmzZhxIgRunkFVPCYmgKrVgEzZ8rHCxfK1fFyoe+kXz9gwAA5yrh7d7kIHxER6ZfWycj58+fh6uoKV1dXAMDIkSPh6uoKf39/AEBYWJg6MQGA8uXLY9euXThw4ABcXFwwd+5c/PLLL/D09NTRS6ACSaWSw343b5ZFHevXAyNG5MpcJAsXAh9+CLx6BdStK6eOz6TWmoiIdIDTwVPet369bKYAZGYwbpzeL/noEfD553LtGgAoW1aOuunSReZJRET0fnlmnhGiHPviC7nMLiCX6125Uu+XdHQETp6UU584OwMhIYC3N9CkiVzLhoiIdIfJCOUPw4altogMGADs3Kn3S6pUMg+6eROYOhWwsJAJSv36sr724UO9h0BEVCgwGaH8Y/p0oG9fWcjq7Q2cOJErl7WwACZOBG7flrO0AsDvvwNVqgDTpgExMbkSBhFRgcVkhPIPlQr4+WfAywuIi5M/r1zJtcs7OQG//SYXHW7USCYh/v5AtWqyrCXvV18REeVNTEYofzEyAjZsABo3BsLDgVatZEFHLqpfXzbKbNgAlCkDPHgg62sbN5ZztuVUXBwTGyIqXDiahvKnV69kNem1a3Lm1hMngJIlcz2M2Fhg3jw5JUp0tNzWs6d8XLq0fBwTAzx7Jm9Pn77/fnS0XLjY2VkmO2XLyp9v35ydc3VxYyKibNH0+5vJCOVfDx/K/pLQUDl96qFDgKWlIqE8egSMHw+sXi0fm5vLKeZTkgt9KFUq42SlZUvA2lo/1yQi0gaTESocbt6U/SMvXwKennKUjYmJYuEEBgLDh6evrTUxkcmDra28ZXbf1lbOiB8eLnufQkPT30JCsi6adXIC/vxTLn5MRKQkJiNUeJw5I1f9jYkBevSQQ10MlCuHEkLORZKYmJpoFC2qu8nShJC5V0aJyj//yJ8mJsDixUD//rq5JhFRdjAZocJl7145uiYxUU4bP3duoZwqNTIS6N0b2LZNPu7fH1i0SC73Q0SU2zgDKxUurVoBv/4q78+fD8yerWw8CrGykl00M2bIXGzFCqBZM+C///RzvaQkOSFu5crAV19xFBARZQ+TESo4evUC5syR98eOTa0mLWRUKjlZ7Z49QLFishfLzQ0ICNDtdQICgHr15ErHd+8Cy5cDW7fq9hpEpJmHD+X/+VxY3FwvmIxQwTJqFDBmjLzfrx/w99/KxqMgT09Zu+LiIocOt2wpVyTOaetFcLBcMLBZMyAoSI7c+fRT+dyQIUBERI5DJyItXL8OuLoCbdrIpSvyIyYjVPB8/72ctz0pCejaFTh2TOmIFFOhAnDqlJyULTFRLvHj45O9Kexfv5brFFavDmzZImuEBw0C7twBduyQXTVhYXIfIsodN2/K+v1nz+TjKVOAXbuUjSk7mIxQwWNgAPzyi/wzITYW+OQTOY18IS1osLAA/vhDltIYGsr7jRvLFg5NJCcDq1bJtXhmzgTi42UrS1AQ8NNPcrSQmZl8iwFg6VLg9Gm9vRwi+n937shE5MkT2QLap4/c3rMn8O+/ysamLSYjVDAZGwObNwOffw68eQMMHChX+42PVzoyRahUcv6Tgwdl8hAUJOs99u/P+rjjx+X09337Ao8fAxUrAtu3AwcOALVrp93344/lSB4h5Fv95o1+XgsRyWTj449la2StWvL/9rJlgLu7nKeoU6f8tYgnkxEquCwsgE2bZLeNSiVbS5o1k5VehVTz5sCFC3LC2pcvgdat5dvzbqPR/ftyYeSmTeX+VlZygNK1a0CHDpmPmp4zR87Kf/Vqai0xEelWcLBMRB4+BGrUkJNPlywp5xfavFnOb3T5svyjIL80CDMZoYJNpZIja94dWnL8uNKRKaZ0aVlG06+f7IIZN04WpL5+DURFARMmyJWIN22Sb9+AAbI5ePTo989XUqKE7A4CZN/13bv6fz1EhUloqOyaefBALst16JBMPlI4Ocn/u4aGwNq1cvLD/ICTnlHhce8e8Nln8k8GIyNgwQLg668L5eRoKVasAAYPBhIS5C+2yEjZ7AvIVpQFC2RftDaEkCN5DhyQtSUHDhTqt5hIZ/77Tzbu3rsnC8aPHgUcHTPed/58YORI+avuyBHgo49yNVQ1TnpG9K6UoSXdusmhJYMHy4qv2FilI1NM//6ylcTJCbh1SyYiFSrI+UIOH9Y+EQFk4rF0qSxqPXRIFsySsuLiZB33hx/Kv6gp/3n0SLaI3Lsn/48ePpx5IgLIGjFvb/mrrkuX1D8y8iomI1S4FCkCrFsnCxoMDIDffgOaNJFtn4XUhx/KBf7695dvy7VrsgEpJ60ZFSsC/v7y/siRwPPnuomVsmfKFFngeOaM/Ms6JETpiEgbjx/LROTOHblK9+HDsrs1KyllcjVryuO7ds3bReXspqHC6+BB2Ury4oWs/tq8WfZNkE68eQN88IEsZvX1LbQT4iru3DmZcCYny9qCp0+BcuVk0325ckpHR+/z9KksVr1+HXB2li2Z5ctrfvydO3LkXGQkMHQo8OOP+os1I+ymIXofDw85RWnduvJPdw8PWSSR9/PzfMHYWNakqFSyAerwYaUjKnzi4+Vw6+RkOfFdYKCsNbh/P7X2gPKulF9L16/LrtQjR7RLRAD5771mjby/cKFsGM6LmIxQ4VauHHDyJNCjh5yxdcQIucZNfhqgn4d9+KGcpRWQC+kV4vIcRUydKr/ISpWSX0SlS8uixypVZM9ks2Yc8ZRXvXwp63yuXAEcHGQyX7Fi9s7Vvj0wfry836+frOHPa5iMEFlYyD8dFixIHQ/XuLH885FybMYM+cv07l1g+nSlo8m5+HhZexEYmLf74M+fB374Qd5ftkwOuwZk0ePRo3L49n//yZ7J27eVijJjiYnAxo2ynKtCBdm6s2aNLOIsDMLD5XpPQUGAnZ1MRKpUydk5p0yRo9xiY+WEaOHhOghUh1gzQvS2o0dlpdezZ0Dx4jJB6dZN9jlQtm3dCnTuLIcZBgXJorr8IjxcDsI6cULezp5NncjXzEz2x3/4oZz58sMPsx7hkFvi42VcV6/KERUbNqTf58kTWRR5/XrqX97VquV+rG8LD5dde4sWZT7qp3p12XXh4SFbdqytczVEvYuIkInI2bNytuQjR3T3/+XFC/m5uH8faNdOrilloOcmCY2/v0U+EBERIQCIiIgIpUOhwiAkRAg3NyFk9YgQTk5CfP+9EC9fKh1ZvpWcLET79vLtbNRIiKQkpSPKXEiIEGvXCjFokBC1awuhUqV+FFJuJUsKYWOTfjsghLOzEF27CjFvnhCnTgkRG5v7r2HCBBmLra0QT59mvt+TJ/I1AkLY2Qlx7Vruxfi2O3eEGDxYiCJFUt9HW1sh/P2F2L1biLFj5X/Jd/8tDA2F+PBDIcaPF+LIESHi4pSJX1ciI4Vwd5evrUQJIS5f1v01AgOFMDOT15gyRffnf5em399MRogyEhsrxLRp8jd0ym8+Cwsh/PyEuH1b6ejypdBQISwt5Vu5bJnS0UiJiUJcuiTEkiVCfPGFTCQySjAqVxaiTx8hVq4U4tYtmVwlJQlx86YQq1cL8dVXQri4CGFgkP5YExMhGjYUYtgwIdavF+L+fXm8vgQGyi9pQIjNm9+//7NnMnZAiFKlhLhyRX+xvS05WSYQ7dunTTJq1ZLvc0ZJ3IsXQmzZIhPFypXTv9fm5kJ8+qkQs2bJ9yEvJ73vOnhQiOrV5esoVkyIixf1d61Vq+R1VCqZ7OkTkxEiXYiLk/9z69RJ/Y2nUgnRoYMQx47p91ulAFqwQL6F1tZCPHqU+9dPShLiwgUh5swRok0bGce7X2iGhkLUry/EiBFC/PmnEI8fa37+16/lF+yMGfJLtlSpjJObBg308/rj41NbOrp00fy458+FcHVNbfW5dEn3sb0d42+/pV4v5damjRAHDmj3XyokRIhffxWiR4+0fzek3MqUEeL4cf29Fl24f1+Izp1TYy5VSojz5/V/3a++ktezsRHi33/1dx0mI0S6lJws/3Rp2zbtb7sPPhDijz/kb1h6r8REIerV0/7LMruSk4W4fl2IxYuF6NRJiOLF039hWVoK8cknssn60CEhoqJ0e/1792S3z5AhMskxMpLXLVdOtrLo0qRJqQnFkyfaHfvyZWrvZIkSuv/L/Nkz2dhob5+2JWPgQCFu3Mj5+ZOTZavOggVCtGsnRNGi8hpGRnJbXvu7ISZGiMmTU7tMDA2FGDpUiFevcuf6cXGyxQ6QLWPR0fq5DpMRIn25cUP+WZHyWySlrmTmTNmOTFm6cCG1G+Gvv3R//nv3hPjlFyG6d0/7xfd28tG2rRBz58q/QN+80X0M74uvUqXUpOHsWd2c9+LF1ERn48bsnePVK9lqk9JVEBiYs5hiY4U4eVKI/v3T/ndxdJStR8+f5+z8WXn9Wghv79RrfvGFbhPN7EpOFmLrVpmMpsTWvLl+6kPe58EDWZsDCNGrl34SNiYjRPr27JkQ332X9huPdSUaGT06tRn99eucnevhQ9k41bdv2l/wKTczMyFathRi+nQhTp8WIiFBN68hJ548SW2FKFJEiP37c3a+hITUuo/OnXP2pRIeLotCU5rwNU2W3ryRX6i//CJzdVfX1OQo5ebmlrsNicnJslUkJY5atZT9r3njhqxpSXk/SpeWiaOSrTaHD6fWOi1ZovvzMxkhyi1xcbITPOXbIKWupGtXJiWZiIoSomxZ+VaNGJH1vtHR8pf4vn1CrFghxMSJQvj4yL8mM0o+jIyEaNxY7nfkiDKjWTQRGSmTJEAIY2Mh1q3L/rmmTEntXtGmxiUzERFy1FNKfc8//6R9PjlZ1hmsXy/EyJFCfPSRzMMzqo8pVUr+VwgIUO5LNyAg9W8GKyshtm/P3etHRAgxalRqUmRiIkcA5YWWGiGEmD1biPLlZaulrmn6/c15Roh0RQg5T8m8ecDff8ttRkZyBTp/f8DeXtHw8po9e4A2beQ8Bxs2yGnjQ0NTbyEh8uf7FtlTqeQaOC1ayNtHHwGWlrnzGnIqPl6u27Nxo3y8YAEwbJh257h0Sc4dkZgop/r+4gvdxPb6NdC2LXD8OFC0qPxYh4bKtW7OnZNzVryraFEZS/36QIMG8qezc84WXdSVsDA5hdCJE/LxuHHAtGlynkN9SU6Wq1aPHSsXqwMALy9g/vzsz6aqD0LIf299fL3qdZ6RxYsXi7JlywpTU1PRoEEDcebMmSz3nz9/vqhSpYowMzMTpUuXFsOHDxexWvy5wpYRyncuXUpb7GphISd/4Gc4jW7dMv5rOqM6j5o1hWjdWhY8zpghm/sDAvJ/mU5SkixuTXmt48Zp3oKQkJA6KqVjR923PLx+LUSzZhn/m5iYyPoSPz85vPn69bw/lDYhQQ6xTnkNHh6yt1UfAgNT5wwB5FBkfQ+jzYv01k2zYcMGYWJiIn799Vdx7do10b9/f2FjYyOeZFK6vXbtWmFqairWrl0rgoODxb59+4SDg4MY8b622bcwGaF869ix1A74lHb0+fPz/+xMOvL4sRyK6uQkuwW6dRPim2/k6JedO2VO9+pV3hsJoWvJybKmJeVj0revZoW106alFpuGhekntqgoIT7/XNZb9O4t6wrOncvfH+F161K7lcqU0V0RsRByPp0BA1LnTilSRM6ZmJ/fr5zQWzLSoEED4efnp36clJQkHB0dxcyZMzPc38/PT7Ro0SLNtpEjR4rGjRtrfE0mI5SvpZTPV6uW+m1TtqysM0lMVDo6ykNWrEgtJvTyynq45eXLstYEkK1EpJ0rV1InTjMxEWL58uydJyJCiB07ZOtWyqRlKbfu3YX47z/dxp3faPr9rdWs9AkJCQgMDISHh4d6m4GBATw8PHD69OkMj2nUqBECAwNx9uxZAMC9e/ewe/dutGnTJtPrxMfHIzIyMs2NKN9SqYDPPpPLb65YIdcCDwmRxQKursCuXfJ3FxV6/frJdXzMzIC//pJrlLx6lX6/N2/k4nFv3sgVWbt3z/VQ871atWTtS4cOQEICMGCAfP/j4rI+7s0budD35MlyPc3ixeU5Fi0CbtyQNVBNmgABAXLNTSenXHk5+Z82Gc7Dhw8FAHHq1Kk028eMGSMaNGiQ6XE//vijMDY2FkZGRgKAGDhwYJbXmTRpkgCQ7saWESoQoqNlu+3bi5s0bSrHnRIJWQuTMjtszZpyPoi3pXTp2NgoM5NtQZKUJGuQUlqk3NyECA5OfT5l4rwff5StVSmTqb19q1xZTlG/dSuXsHqXXrppspOMHDlyRNjZ2YkVK1aIy5cvi61btwpnZ2cxderUTK8TFxcnIiIi1LcHDx4wGaGC58ULIcaMEcLUNPW3WseO8jcfFXqXLwvh4CA/Fs7OqR+Lq1dltwIgxO+/KxtjQXLggCzpAuRMvXPmCOHrK+uZ3k0+SpaUE6r98ouczp0yp5dkJD4+XhgaGopt27al2e7j4yPat2+f4TEfffSRGD16dJpta9asEebm5iJJw9Jr1oxQgRYaKisWU/40MzCQs4Ll9tSglOcEBwtRpUrqF+SJE3JKeUBOeV7QC3tzW0hI6nIF706c98knQvzwg5yLI6+PGspL9FIzYmJiAjc3Nxw6dEi9LTk5GYcOHYK7u3uGx8TExMDAIO1lDP9/YLdgPzmRnIhh5UpZU9Kxo5ycYM4coHVr4OVLpaMjBZUrJ+fFaNBAfhSaNJF1DtbWwLJleWP+joKkTBk5r8qYMUDTpnJ+kIMHZd3O/v3AN9/IMi8Drb45SRNG2h4wcuRI+Pr6ol69emjQoAEWLFiA6Oho9OnTBwDg4+MDJycnzJw5EwDg5eWFefPmwdXVFQ0bNsTdu3cxceJEeHl5qZMSIgJQowawbRuwZYssbj14UM4atXMnULOm0tGRQmxtgUOHgM8/B/btk9sWLGBhpL6YmQGzZikdReGjdTLi7e2NZ8+ewd/fH48fP0bdunWxd+9e2NnZAQBCQ0PTtIRMmDABKpUKEyZMwMOHD2FrawsvLy9Mnz5dd6+CqCD5/HOgShVZon/vHvDhh8CaNbLVhAolS0uZk86YARgby1yVqCDhdPBEedXz53L+6iNH5OMpU4AJE9hGTET5hqbf3/ytRpRXlSwp2+WHDJGPJ00CunQBoqKUjYuISMeYjBDlZcbGwMKFssDVxETOiOXuLrtviIgKCCYjRPlB375yRWB7e+DqVVnY+taoNiKi/IzJCFF+4e4OnD8vE5GXLwFPT+DHHzmVPBHle0xGiPITJye56EWvXkBSEjB8uGw1ed+CGkREeRiTEaL8xswM+O03YN48ObJm9WqgeXPg0SOlIyMiyhYmI0T5kUoFjBgB7N0LFCsGnDkD1KsnfxIR5TNMRojys08+Ac6elbO3hoXJOax//FGuiU5ElE8wGSHK7ypVAv75R87YmpAg60iqVQPWrpXr3BAR5XFMRogKgqJF5RwkS5fK4b/BwUDPnnJVr127OOKGiPI0JiNEBYWBATBwIHD3rlzExNoauHwZaNdOdt+cOKF0hEREGWIyQlTQFCkCjBsnZ2n95hs5+ubECbn+fLt2MkEhIspDmIwQFVTFiwM//CBbSr76CjA0lF02devKLhxOKU9EeQSTEaKCzskJWLYMuH4d8PaW9SNr1wJVqwKDBwOPHysdIREVckxGiAqLKlWADRuAwEA5lXxiIrBkCVCxIjB+PBAernSERFRIMRkhKmw++EBOlnbkCPDhh0BMjCx4rVABWLBAJilERLmIyQhRYdW8OXDqFLB9u5w07dUrOatrvXpy3hIiolzCZISoMFOp5GRply8DP/8sp5a/dAlo1EgOE371SukIiagQYDJCRHKkzYABwK1bQO/essj1559lkeuaNZw0jYj0iskIEaWytQVWrQKOHZNdN8+eAT4+QIsWwM2bSkdHRAUUkxEiSq9pU+DiRWDmTMDcHDh6FKhTB5gwAYiNVTo6IipgmIwQUcZMTIBvv5Xzk7RrB7x5A0yfDtSsCezerXR0RFSAMBkhoqyVKwfs3Als2waULi0X4WvbFvj8c+C//5SOjogKACYjRPR+KhXQsSNw4wYwerQseP3zT6B6dWD+fM5NQkQ5wmSEiDRnaQnMng1cuAC4uwNRUcDIkXJukpMnlY6OiPIpJiNEpL06deRKwCtWpM5N8tFHQKdOHHVDRFpjMkJE2WNgAPTrJ+cm6ddPPt62DahVS64SHBamdIRElE8wGSGinLG1lS0kV64A7dsDSUnA8uVApUrAxIlAZKTSERJRHsdkhIh0o0YNYMcOICAgdQG+776TqwIvWgQkJCgdIRHlUUxGiEi3mjSRC/D9+SdQpQrw/DkwdKgcebNhA5CcrHSERJTHMBkhIt1TqWQx69WrwNKlgJ0dcO8e8MUXQIMGwOHDSkdIRHkIkxEi0h9jY7n67927wJQpcmhwYCDQsiXQurVcLZiICj0mI0Skf5aWgL8/8O+/wODBgJERsHcvULcu4OsL3L+vdIREpCAmI0SUe0qVksWsN24AXbsCQgC//w5UrixXB756VekIiUgBTEaIKPdVqgRs3AicPQt4eMjp5NesAWrXlsODT51SOkIiykXZSkaWLFmCcuXKwczMDA0bNsTZs2ez3D88PBx+fn5wcHCAqakpqlSpgt1c9ZOI6tcHDhyQSUnnzrLw9a+/gMaNgaZNgV27ZOsJERVoWicjGzduxMiRIzFp0iRcuHABLi4u8PT0xNOnTzPcPyEhAZ988gnu37+PLVu24NatW1ixYgWcnJxyHDwRFRD16wNbtsjum379ZOHr8eNAu3aAiwuwdi0X4yMqwFRCaPdnR8OGDVG/fn0sXrwYAJCcnAxnZ2cMGTIE3377bbr9ly1bhtmzZ+PmzZswNjbOVpCRkZGwtrZGREQErKyssnUOIspHHj4EFiwAli2Ti/EBQLlycsXgPn0ACwsloyMiDWn6/a1Vy0hCQgICAwPh4eGRegIDA3h4eOD06dMZHrNz5064u7vDz88PdnZ2qFWrFmbMmIGkpKRMrxMfH4/IyMg0NyIqRJyc5OrAoaFyFldbWzniZvBgmZRMnw68eqV0lESkI1olI8+fP0dSUhLs7OzSbLezs8Pjx48zPObevXvYsmULkpKSsHv3bkycOBFz587Fd999l+l1Zs6cCWtra/XN2dlZmzCJqKAoVgwYPx4ICQGWLJGJyLNnwIQJQJkysqUkOFjpKIkoh/Q+miY5ORmlSpXC8uXL4ebmBm9vb4wfPx7Lli3L9Jhx48YhIiJCfXvw4IG+wySivMzcHPj6a+DOHVk/Uru27L6ZOxeoUAGoWRMYMwY4coRr4BDlQ1olIyVLloShoSGePHmSZvuTJ09gb2+f4TEODg6oUqUKDA0N1duqV6+Ox48fIyGTXxqmpqawsrJKcyMigpER0L07cOmSHGnTsiVgaAhcvw7MmQO0aAGULClH5qxcCTx6pHTERKQBrZIRExMTuLm54dChQ+ptycnJOHToENzd3TM8pnHjxrh79y6S31oc6/bt23BwcICJiUk2wyaiQk2lAtq0AQ4elN02GzfKmVxLlQJevwa2bpWjcpycAFdX2dVz6hSQRa0aESlH69E0GzduhK+vL37++Wc0aNAACxYswKZNm3Dz5k3Y2dnBx8cHTk5OmDlzJgDgwYMHqFmzJnx9fTFkyBDcuXMHffv2xdChQzF+/HiNrsnRNESkkeRk4MIFYPdueTt7Nu08JcWLA56eMpHx9JSFsUSkN5p+fxtpe2Jvb288e/YM/v7+ePz4MerWrYu9e/eqi1pDQ0NhYJDa4OLs7Ix9+/ZhxIgRqFOnDpycnDBs2DCMHTs2Gy+LiCgLBgZAvXry5u8vW0327pWJyb59wMuXwPr18qZSAR9+KFcX7tRJ1p4QkSK0bhlRAltGiCjHEhOBM2dSW02CgtI+X7duamJSo4ZMVogoRzT9/mYyQkSF08OHwI4dsr7k6NG09SRVqqQmJvXqMTEhyiYmI0REmnr+XK6Js3UrsH9/2uHBzs6piUnjxnL0DhFphMkIEVF2REbKbpytW+XP6OjU52xtgY4dZWLSogXAEYFEWWIyQkSUU7GxclXhP/8Edu4EwsNTnytfHvj9d+CjjxQLjyiv08vaNEREhYq5OdC+PfDbb8DTp7ILZ+BA2UISHAw0ayanpn/zRulIifI1JiNERJowNgY++QRYulROS+/jI+c1mT4daNQIuHVL6QiJ8i0mI0RE2rK2lq0lmzbJxfzOn5czvS5blnaSNSLSCJMRIqLs6tIFuHIF8PCQ9SWDBgFeXsA763cRUdaYjBAR5YSTk5zddf58wNRULuBXu7YcKkxEGmEyQkSUUwYGwPDhwLlzMhF59kwWvg4cmHZoMBFliMkIEZGu1K4tF+cbNUo+/vlnWUty7pyycRHlcUxGiIh0ycwMmDMHOHRIduHcuSNH23z3nVwfh4jSYTJCRKQPLVrI4tauXWUSMnGinJfk3j2lIyPKc5iMEBHpS7FiwIYNwJo1gJUVcOoU4OIiu2/YSkKkxmSEiEifVCqgZ0/g8mWgaVMgKkoWtlarJucqYVJCxGSEiChXlC0LHD4MLFggp5P/91+gd28mJURgMkJElHsMDYFhw+S6NrNmASVLpiYl1avLhfeYlFAhxGSEiCi3FSkCjBmTNim5exfw9WVSQoUSkxEiIqVYWqYmJT/8kDYpqVFDFr4yKaFCgMkIEZHSLC2Bb76RScn33wMlSqSuDMykhAoBJiNERHmFpSUwdixw/376pKRmTeCPP5iUUIGkEiLvr3cdGRkJa2trREREwMrKSulwiIhyx+vXwJIlckbXFy/ktpIlgbp1gTp15JwlLi6yzsTERNFQiTKi6fc3kxEiorwuJSmZPRt4+TL980ZGMiF5O0GpUwewt9fuOrGxwJMn8vb4cdr7lSsDgwfLaxFpiMkIEVFBExcnp5i/dElOonbpkrxFRGS8f6lSqQlKnTpyRti3E413E47IyKyv37IlsGkTULy47l8bFUhMRoiICgMhgAcP0icod+7I57RlYiJbVOzsUn9aWwPLlgHR0UClSsDOnbIlhug9mIwQERVmMTHA1atpk5SYmPSJRkaJh0qV/nyXLwPt2wMhIUDRonLNnTZtcv91Ub7CZISIiHTr2TOgc2fg+HGZsMyaBYwalXHyQgTNv785tJeIiDRjawscPAj07y+7gMaMkVPZx8UpHRnlc0xGiIhIcyYmwM8/AwsXyrV2fv8d+PhjICxM6cgoH2MyQkRE2lGpgCFDgL175Qidf/4B6tcHAgOVjozyKSYjRESUPR4ewJkzQLVqwMOHQJMmwMaNSkdF+RCTESIiyr7KlWXLSOvWctK0bt2ACROA5GSlI6N8hMkIERHljLU18NdfwOjR8vH06XLUTVSUsnFRvsFkhIiIcs7QUE5Xv3q1LHLdvh1o1EiuREz0HlxkgIiIdMfXF6haFejYUU5d36CBXFendGm5ro2xsbyl3H/359v3DQw4h0khka2WkSVLlqBcuXIwMzNDw4YNcfbsWY2O27BhA1QqFTp27JidyxIRUX7w4YfA+fPABx8Az58D3t5A48ZAw4ZyW+3acjr5SpWAcuUAJyc5+2vx4nJ2V3NzmZAYGMj1dTp3lkOJL19mLUoBpfUMrBs3boSPjw+WLVuGhg0bYsGCBdi8eTNu3bqFUqVKZXrc/fv38dFHH6FChQooXrw4tm/frvE1OQMrEVE+FBMj60gOHwYSE4E3bzL/mZSk2TmLFweaNgWaNQOaN5cLABqw4iCv0tt08A0bNkT9+vWxePFiAEBycjKcnZ0xZMgQfPvttxkek5SUhKZNm6Jv3744fvw4wsPDmYwQEVEqIWRi8m6Scu8ecPQocOwYcOKEXKzvbTY2ckhx8+YyQalbV9avUJ6g6fe3VjUjCQkJCAwMxLhx49TbDAwM4OHhgdOnT2d63NSpU1GqVCl8+eWXOH78+HuvEx8fj/j4ePXjyPcta01ERPmbSpVaM2Junrrdzg5wdwfGjZPJyYULqcnJ8eNAeLgcyfPXX3J/KyuZnDRrJltNkpNTk5ysbikJUMrN2Rno1Ut2F5HeafUuP3/+HElJSbCzs0uz3c7ODjdv3szwmBMnTmDlypUICgrS+DozZ87ElClTtAmNiIgKOmNjWXfSsCEwdqxMGi5eTJucREYCu3bJW06tXg2sXw84Oub8XJQlvaZ8r1+/Rq9evbBixQqULFlS4+PGjRuHkSNHqh9HRkbC2dlZHyESEVF+ZWQkp6GvX18u2peUBAQFycTk6FEgJETuk9ktZeTOuzeVSs4kGxAgu33WrZOzzZLeaJWMlCxZEoaGhnjy5Ema7U+ePIG9vX26/f/991/cv38fXl5e6m3J/18JbWRkhFu3bqFixYrpjjM1NYWpqak2oRERUWFnaAi4ucnbW3/QZsuYMUCXLnIEz6efAv7+wMSJebceJShIrg3Uq5ec5yWf0aoE2cTEBG5ubjh06JB6W3JyMg4dOgR3d/d0+1erVg1XrlxBUFCQ+ta+fXt8/PHHCAoKYmsHERHlTVWqyGnu+/eXxbVTpgCensA7f4wrLjkZ+P572TrUr59MRjQdmZSHaN1NM3LkSPj6+qJevXpo0KABFixYgOjoaPTp0wcA4OPjAycnJ8ycORNmZmaoVatWmuNtbGwAIN12IiKiPMXcHFi+XA4l/uor4NAh2W2zfr0cvaO0R48AHx8ZV4pNm+RcLStW5KsJ47QenO3t7Y05c+bA398fdevWRVBQEPbu3asuag0NDUVYWJjOAyUiIlJEz55yEreaNYHHj4GWLeX6O0pOwPb334CLi0xELCyAlSuBzZvlnCsrVwKjRskWnXxC63lGlMB5RoiISHHR0cDgwXKUDSC7bdasAWxtcy+GuDg5kmjhQvm4bl1gwwY5BT8gY/v/ngpMngxMmpR7sWVA0+9vTltHRESkiSJFgFWrgF9/lV04+/YBrq5yMrbccOOGnGo/JREZPlzWtaQkIgDQuzfw44/y/uTJwPz5uRNbDjEZISIi0kafPsDZszIJePhQ1o/MmqW/bhshgF9+kaOELl2SLTG7dslEI6ORp0OHAtOmyfsjR8pumzyOyQgREZG2atWSdSTdu8vRK2PHAu3bAy9e6PY64eFyocH+/YHYWDnfyaVLQJs2WR83frxcFwiQx27cqNu4dIzJCBERUXZYWgJ//AH8/LNsodi1S65KfOKEbopHT56URaqbN8vJ2GbNkl1DDg7vP1alkvsPGCBj6dlTN7PS6gkLWImIiHLq4kWga1fg7l352MoKqFFDjsCpVUv+rFlTJhLvG3KblATMmCFrPpKTgYoV5XDi+vW1jyspSQ7/XbcOMDMD9u6V6/bkEr2t2qsEJiNERJTnRUYCfn5ydEtiYsb7FCuWmpikJCm1aqWOyHnwQLZiBATIx716AUuWyLlDsuvNG6BzZ7mYoKWlHA7coEH2z6cFJiNERERKSEgA7twBrl4Frl2Tt6tXZatJZkWutrYyMbl0CXj1SiYNS5fKxEQX4uKAtm2Bw4dlQhQQIJMgPWMyQkRElJfExQG3bqVNUq5dA+7dS1tjUr++7FapVEm314+KkgWwZ84A9vZylWNdX+MdTEaIiIjyg5gYOYfItWtyBtWuXfW32N3Ll8DHH8sFAMuWlQmJHteJYzJCRERE6T15AjRpIruSqlaVXTalSunlUpyBlYiIiNKzswMOHpQtIrduyWntw8MVDYnJCBERUWFTpoxMSEqVAoKCZHFrdLRi4TAZISIiKoyqVAEOHABsbIBTp+RKxAphMkJERFRY1akD7Nkjp5yfOFGxMIwUuzIREREp78MP5URtCmLLCBERESmKyQgREREpiskIERERKYrJCBERESmKyQgREREpiskIERERKYrJCBERESmKyQgREREpiskIERERKYrJCBERESmKyQgREREpiskIERERKYrJCBERESkqX6zaK4QAAERGRiocCREREWkq5Xs75Xs8M/kiGXn9+jUAwNnZWeFIiIiISFuvX7+GtbV1ps+rxPvSlTwgOTkZjx49QtGiRaFSqXR23sjISDg7O+PBgwewsrLS2XkLKr5fmuN7pTm+V5rje6U5vlea0+d7JYTA69ev4ejoCAODzCtD8kXLiIGBAUqXLq2381tZWfHDqgW+X5rje6U5vlea43ulOb5XmtPXe5VVi0gKFrASERGRopiMEBERkaIKdTJiamqKSZMmwdTUVOlQ8gW+X5rje6U5vlea43ulOb5XmssL71W+KGAlIiKigqtQt4wQERGR8piMEBERkaKYjBAREZGimIwQERGRopiMEBERkaIKdTKyZMkSlCtXDmZmZmjYsCHOnj2rdEh5zuTJk6FSqdLcqlWrpnRYeUJAQAC8vLzg6OgIlUqF7du3p3leCAF/f384ODjA3NwcHh4euHPnjjLB5gHve7969+6d7rPWqlUrZYJV0MyZM1G/fn0ULVoUpUqVQseOHXHr1q00+8TFxcHPzw8lSpSApaUlOnfujCdPnigUsXI0ea+aN2+e7nM1cOBAhSJW1tKlS1GnTh31TKvu7u7Ys2eP+nklP1eFNhnZuHEjRo4ciUmTJuHChQtwcXGBp6cnnj59qnRoeU7NmjURFhamvp04cULpkPKE6OhouLi4YMmSJRk+P2vWLCxcuBDLli3DmTNnUKRIEXh6eiIuLi6XI80b3vd+AUCrVq3SfNbWr1+fixHmDceOHYOfnx/++ecfHDhwAG/evMGnn36K6Oho9T4jRozAX3/9hc2bN+PYsWN49OgROnXqpGDUytDkvQKA/v37p/lczZo1S6GIlVW6dGl8//33CAwMxPnz59GiRQt06NAB165dA6Dw50oUUg0aNBB+fn7qx0lJScLR0VHMnDlTwajynkmTJgkXFxelw8jzAIht27apHycnJwt7e3sxe/Zs9bbw8HBhamoq1q9fr0CEecu775cQQvj6+ooOHTooEk9e9vTpUwFAHDt2TAghP0fGxsZi8+bN6n1u3LghAIjTp08rFWae8O57JYQQzZo1E8OGDVMuqDyuWLFi4pdfflH8c1UoW0YSEhIQGBgIDw8P9TYDAwN4eHjg9OnTCkaWN925cweOjo6oUKECevTogdDQUKVDyvOCg4Px+PHjNJ8xa2trNGzYkJ+xLBw9ehSlSpVC1apVMWjQILx48ULpkBQXEREBAChevDgAIDAwEG/evEnz2apWrRrKlClT6D9b775XKdauXYuSJUuiVq1aGDduHGJiYpQIL09JSkrChg0bEB0dDXd3d8U/V/li1V5de/78OZKSkmBnZ5dmu52dHW7evKlQVHlTw4YNsXr1alStWhVhYWGYMmUKmjRpgqtXr6Jo0aJKh5dnPX78GAAy/IylPEdptWrVCp06dUL58uXx77//4n//+x9at26N06dPw9DQUOnwFJGcnIzhw4ejcePGqFWrFgD52TIxMYGNjU2afQv7Zyuj9woAunfvjrJly8LR0RGXL1/G2LFjcevWLWzdulXBaJVz5coVuLu7Iy4uDpaWlti2bRtq1KiBoKAgRT9XhTIZIc21bt1afb9OnTpo2LAhypYti02bNuHLL79UMDIqaLp166a+X7t2bdSpUwcVK1bE0aNH0bJlSwUjU46fnx+uXr3KOi0NZPZeDRgwQH2/du3acHBwQMuWLfHvv/+iYsWKuR2m4qpWrYqgoCBERERgy5Yt8PX1xbFjx5QOq3AWsJYsWRKGhobpqoSfPHkCe3t7haLKH2xsbFClShXcvXtX6VDytJTPET9j2VehQgWULFmy0H7WBg8ejL///htHjhxB6dKl1dvt7e2RkJCA8PDwNPsX5s9WZu9VRho2bAgAhfZzZWJigkqVKsHNzQ0zZ86Ei4sLfvzxR8U/V4UyGTExMYGbmxsOHTqk3pacnIxDhw7B3d1dwcjyvqioKPz7779wcHBQOpQ8rXz58rC3t0/zGYuMjMSZM2f4GdPQf//9hxcvXhS6z5oQAoMHD8a2bdtw+PBhlC9fPs3zbm5uMDY2TvPZunXrFkJDQwvdZ+t971VGgoKCAKDQfa4yk5ycjPj4eOU/V3ovkc2jNmzYIExNTcXq1avF9evXxYABA4SNjY14/Pix0qHlKaNGjRJHjx4VwcHB4uTJk8LDw0OULFlSPH36VOnQFPf69Wtx8eJFcfHiRQFAzJs3T1y8eFGEhIQIIYT4/vvvhY2NjdixY4e4fPmy6NChgyhfvryIjY1VOHJlZPV+vX79WowePVqcPn1aBAcHi4MHD4oPPvhAVK5cWcTFxSkdeq4aNGiQsLa2FkePHhVhYWHqW0xMjHqfgQMHijJlyojDhw+L8+fPC3d3d+Hu7q5g1Mp433t19+5dMXXqVHH+/HkRHBwsduzYISpUqCCaNm2qcOTK+Pbbb8WxY8dEcHCwuHz5svj222+FSqUS+/fvF0Io+7kqtMmIEEIsWrRIlClTRpiYmIgGDRqIf/75R+mQ8hxvb2/h4OAgTExMhJOTk/D29hZ3795VOqw84ciRIwJAupuvr68QQg7vnThxorCzsxOmpqaiZcuW4tatW8oGraCs3q+YmBjx6aefCltbW2FsbCzKli0r+vfvXyj/OMjoPQIgVq1apd4nNjZWfP3116JYsWLCwsJCfPbZZyIsLEy5oBXyvvcqNDRUNG3aVBQvXlyYmpqKSpUqiTFjxoiIiAhlA1dI3759RdmyZYWJiYmwtbUVLVu2VCciQij7uVIJIYT+21+IiIiIMlYoa0aIiIgo72AyQkRERIpiMkJERESKYjJCREREimIyQkRERIpiMkJERESKYjJCREREimIyQkRERIpiMkJERESKYjJCREREimIyQkRERIr6PzDwd6flq9/BAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 69ms/step\n",
            "\n",
            "---Now testing the model for one audio file---\n",
            "The model predicts: disco, and ground truth is: disco.\n",
            "\n",
            "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 61ms/step - accuracy: 0.7876 - loss: 0.8178\n",
            "Accuracy on test is 0.7711069583892822\n",
            "Test error is 0.8545954823493958\n"
          ]
        }
      ],
      "source": [
        "if __name__ == \"__main__\":\n",
        "\n",
        "    preprocess_data(source_path=SOURCE_PATH, json_path=JSON_PATH)\n",
        "\n",
        "    inputs, targets = load_data(json_path=JSON_PATH)\n",
        "\n",
        "    Xtrain, Xval, Xtest, ytrain, yval, ytest = prepare_datasets(inputs, targets, 0.2)\n",
        "\n",
        "    input_shape = (Xtrain.shape[1], Xtrain.shape[2])\n",
        "\n",
        "    model = design_rnn_model(input_shape)\n",
        "\n",
        "    # Selection of the optimizer, loss type and metrics for performance evaluation.\n",
        "    model.compile(optimizer = tf.keras.optimizers.AdamW(),\n",
        "                     loss='sparse_categorical_crossentropy',\n",
        "                     metrics = ['accuracy']\n",
        "                  )\n",
        "\n",
        "    model.summary()\n",
        "\n",
        "    #Training the model.\n",
        "    history = model.fit(Xtrain, ytrain,\n",
        "                        validation_data=(Xval, yval),\n",
        "                        epochs=50,\n",
        "                        batch_size=32,\n",
        "                        callbacks=[tf.keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=1)],\n",
        "                      )\n",
        "\n",
        "    plot_performance(history)\n",
        "\n",
        "    # Testing the model on never seen before data.\n",
        "    make_prediction(model, Xtest, ytest, 24)\n",
        "\n",
        "    test_error, test_accuracy = model.evaluate(Xtest,ytest,verbose=1)\n",
        "    print(\"Accuracy on test is {}\".format(test_accuracy))\n",
        "    print(\"Test error is {}\".format(test_error))"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOthGLJvq4CGQ5bVhNuRyyF",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}