Music_Genre_Recognition_Spotify.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "55bf55eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import librosa\n",
    "import random\n",
    "import shutil\n",
    "import optuna\n",
    "import os\n",
    "\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from collections import Counter\n",
    "from pydub import AudioSegment\n",
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bbf7945",
   "metadata": {},
   "source": [
    "<H1>Spotify 3-Sec Dataset</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "36ae6bbb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 35840 images belonging to 14 classes.\n",
      "Found 4480 images belonging to 14 classes.\n",
      "Found 4480 images belonging to 14 classes.\n"
     ]
    }
   ],
   "source": [
    "#Load dataset into training/validation/testing at 80-10-10\n",
    "\n",
    "train_dir = \"dataset/training/\"\n",
    "train_datagen = tf.keras.preprocessing.image.ImageDataGenerator(rescale = 1./255)\n",
    "train_generator = train_datagen.flow_from_directory(train_dir, target_size = (515, 389), color_mode = \"rgb\", class_mode = \"categorical\", batch_size = 32, seed = 767)\n",
    "\n",
    "validation_dir = \"dataset/validation/\"\n",
    "vali_datagen = tf.keras.preprocessing.image.ImageDataGenerator(rescale = 1./255)\n",
    "vali_generator = vali_datagen.flow_from_directory(validation_dir, target_size = (515, 389), color_mode = \"rgb\", class_mode = \"categorical\", batch_size = 32, seed = 767)\n",
    "\n",
    "testing_dir = \"dataset/testing/\"\n",
    "testing_datagen = tf.keras.preprocessing.image.ImageDataGenerator(rescale = 1./255)\n",
    "testing_generator = vali_datagen.flow_from_directory(testing_dir, target_size = (515, 389), color_mode = \"rgb\", class_mode = \"categorical\", shuffle = False, batch_size = 32, seed = 767)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3838bf8f",
   "metadata": {},
   "source": [
    "<H1>Spotify 9-Sec Dataset</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "0214eef8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 11200 images belonging to 14 classes.\n",
      "Found 1400 images belonging to 14 classes.\n",
      "Found 1400 images belonging to 14 classes.\n"
     ]
    }
   ],
   "source": [
    "#Load dataset into training/validation/testing at 80-10-10\n",
    "\n",
    "train_dir = \"9_sec_dataset/training/\"\n",
    "train_datagen = tf.keras.preprocessing.image.ImageDataGenerator(rescale = 1./255)\n",
    "train_generator = train_datagen.flow_from_directory(train_dir, target_size = (515, 389), color_mode = \"rgb\", class_mode = \"categorical\", batch_size = 32, seed = 767)\n",
    "\n",
    "validation_dir = \"9_sec_dataset/validation/\"\n",
    "vali_datagen = tf.keras.preprocessing.image.ImageDataGenerator(rescale = 1./255)\n",
    "vali_generator = vali_datagen.flow_from_directory(validation_dir, target_size = (515, 389), color_mode = \"rgb\", class_mode = \"categorical\", batch_size = 32, seed = 767)\n",
    "\n",
    "testing_dir = \"9_sec_dataset/testing/\"\n",
    "testing_datagen = tf.keras.preprocessing.image.ImageDataGenerator(rescale = 1./255)\n",
    "testing_generator = vali_datagen.flow_from_directory(testing_dir, target_size = (515, 389), color_mode = \"rgb\", class_mode = \"categorical\", shuffle = False, batch_size = 32, seed = 767)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3513ac9",
   "metadata": {},
   "source": [
    "<H1>Custom GTZAN + Rap, Brazilian Funk Dataset</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c6a6da7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load dataset into training/validation/testing at 80-10-10\n",
    "\n",
    "train_dir = \"custom_dataset/training/\"\n",
    "train_datagen = tf.keras.preprocessing.image.ImageDataGenerator(rescale = 1./255)\n",
    "train_generator = train_datagen.flow_from_directory(train_dir, target_size = (515, 389), color_mode = \"rgb\", class_mode = \"categorical\", batch_size = 32, seed = 767)\n",
    "\n",
    "validation_dir = \"custom_dataset/validation/\"\n",
    "vali_datagen = tf.keras.preprocessing.image.ImageDataGenerator(rescale = 1./255)\n",
    "vali_generator = vali_datagen.flow_from_directory(validation_dir, target_size = (515, 389), color_mode = \"rgb\", class_mode = \"categorical\", batch_size = 32, seed = 767)\n",
    "\n",
    "testing_dir = \"custom_dataset/testing/\"\n",
    "testing_datagen = tf.keras.preprocessing.image.ImageDataGenerator(rescale = 1./255)\n",
    "testing_generator = vali_datagen.flow_from_directory(testing_dir, target_size = (515, 389), color_mode = \"rgb\", class_mode = \"categorical\", shuffle = False, batch_size = 32, seed = 767)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc65bbe6",
   "metadata": {},
   "source": [
    "<H1>GTZAN Dataset</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31dbf9ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load dataset into training/validation/testing at 80-10-10\n",
    "\n",
    "train_dir = \"original_dataset/training/\"\n",
    "train_datagen = tf.keras.preprocessing.image.ImageDataGenerator(rescale = 1./255)\n",
    "train_generator = train_datagen.flow_from_directory(train_dir, target_size = (515, 389), color_mode = \"rgb\", class_mode = \"categorical\", batch_size = 32, seed = 767)\n",
    "\n",
    "validation_dir = \"original_dataset/validation/\"\n",
    "vali_datagen = tf.keras.preprocessing.image.ImageDataGenerator(rescale = 1./255)\n",
    "vali_generator = vali_datagen.flow_from_directory(validation_dir, target_size = (515, 389), color_mode = \"rgb\", class_mode = \"categorical\", batch_size = 32, seed = 767)\n",
    "\n",
    "testing_dir = \"original_dataset/testing/\"\n",
    "testing_datagen = tf.keras.preprocessing.image.ImageDataGenerator(rescale = 1./255)\n",
    "testing_generator = vali_datagen.flow_from_directory(testing_dir, target_size = (515, 389), color_mode = \"rgb\", class_mode = \"categorical\", shuffle = False, batch_size = 32, seed = 767)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "d1059b7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Number of genres\n",
    "n_classes = len(list(train_generator.class_indices.keys()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e648d2fb",
   "metadata": {},
   "source": [
    "<H1>Load Models</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4596ed0",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.models.load_model(\"GTZAN_Dataset_Model.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c7d8abc",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.models.load_model(\"Custom_GTZAN_Model.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "a693b4ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.models.load_model(\"Final_Model.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af41dc63",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.models.load_model(\"Final_Model_9_Sec.h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "191d8271",
   "metadata": {},
   "source": [
    "<H1>Custom Functions</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "09164899",
   "metadata": {},
   "outputs": [],
   "source": [
    "def precision_per_class(model, testing_generator):\n",
    "    predictions = model.predict(testing_generator)\n",
    "    y_pred = np.argmax(predictions, axis = 1)\n",
    "    true_classes = testing_generator.classes\n",
    "    class_labels = list(testing_generator.class_indices.keys())   \n",
    "    report = classification_report(true_classes, y_pred, target_names = class_labels)\n",
    "    print(report)   \n",
    "    \n",
    "def multiclass_confusion_matrix(model, testing_generator):\n",
    "    predictions = model.predict(testing_generator)\n",
    "    y_pred = np.argmax(predictions, axis = 1)\n",
    "    true_classes = testing_generator.classes\n",
    "    class_labels = list(testing_generator.class_indices.keys())   \n",
    "    report = confusion_matrix(true_classes, y_pred)\n",
    "    plt.figure(figsize = (12, 9))\n",
    "    ax = plt.axes()\n",
    "    plt.tick_params(axis = \"both\", which = \"both\", labelsize = 10, labelbottom = False, bottom = False, top = True, labeltop = True)\n",
    "    sns.heatmap(report, linewidths = 1, ax = ax, annot = True, fmt = \"g\", cmap = \"Greens\", xticklabels = class_labels, yticklabels = class_labels)\n",
    "    ax.set_xlabel(\"Predicted Labels\")\n",
    "    ax.set_ylabel(\"True Labels\")\n",
    "    ax.set_title(\"Confusion Matrix\")\n",
    "    ax.xaxis.set_label_position(\"top\")\n",
    "    \n",
    "def process_wav(song):\n",
    "    name, ext = os.path.splitext(song)\n",
    "    sound = AudioSegment.from_wav(song)\n",
    "    \n",
    "    #Stereo to Mono like original dataset\n",
    "    sound = sound.set_channels(1)\n",
    "    \n",
    "    #Change Hz to 22050Hz like original dataset\n",
    "    sound = sound.set_frame_rate(22050)\n",
    "    \n",
    "    sound.export(\"{0}_resampled.wav\".format(name), format = \"wav\")\n",
    "    \n",
    "    song = \"{0}_resampled.wav\".format(name)\n",
    "    \n",
    "    i = 0\n",
    "    to_return = []\n",
    "    \n",
    "    total_duration = len(AudioSegment.from_wav(song))\n",
    "    \n",
    "    intervals =  int(total_duration / 3000)\n",
    "    \n",
    "    for w in range(0, intervals):\n",
    "        i = i + 1\n",
    "        t1 = 3 * (w) * 1000\n",
    "        t2 = 3 * (w + 1) * 1000\n",
    "        newAudio = AudioSegment.from_wav(song)\n",
    "        new = newAudio[t1:t2]\n",
    "        new.export(str(w) + \".wav\", format = \"wav\")\n",
    "        to_return.append(str(w) + \".wav\")\n",
    "        \n",
    "    return to_return, intervals\n",
    "\n",
    "def generate_spectogram_for_song(song):\n",
    "    x, sr = librosa.load(song, sr = None)\n",
    "    X = librosa.stft(x)\n",
    "    Xdb = librosa.amplitude_to_db(abs(X))\n",
    "    librosa.display.specshow(Xdb, sr = sr, x_axis = \"time\", y_axis = \"log\")\n",
    "    plt.axis(\"off\")\n",
    "    plt.savefig(song.replace(\".wav\", \".jpg\"), bbox_inches = \"tight\")\n",
    "    plt.cla()\n",
    "    \n",
    "    return song.replace(\".wav\", \".jpg\")\n",
    "    \n",
    "def test_song(song, model):\n",
    "    name, ext = os.path.splitext(song)\n",
    "    to_delete, intervals = process_wav(song)\n",
    "    files_to_delete = [\"{0}_resampled.wav\".format(name)]\n",
    "    files_to_delete = files_to_delete + to_delete\n",
    "    \n",
    "    probabilities = np.zeros(len(list(train_generator.class_indices.keys())))\n",
    "    \n",
    "    images_to_delete = []\n",
    "    \n",
    "    for mini_part in files_to_delete:\n",
    "        spectogram = generate_spectogram_for_song(mini_part)\n",
    "        images_to_delete.append(spectogram)\n",
    "        image = Image.open(spectogram)\n",
    "        im_arr = np.frombuffer(image.tobytes(), dtype = np.uint8)\n",
    "        im_arr = im_arr.reshape((image.size[0], image.size[1], 3))                                   \n",
    "        image = im_arr / 255.0  #Normalize the image\n",
    "        image = np.expand_dims(image, axis = 0)\n",
    "        random_test = image\n",
    "        y_proba = model.predict(random_test, verbose = 0)\n",
    "        np.set_printoptions(suppress = True) \n",
    "        probabilities = probabilities + y_proba[0]\n",
    "        \n",
    "    files_to_delete = files_to_delete + images_to_delete   \n",
    "    for file_to_delete in files_to_delete:\n",
    "        os.remove(file_to_delete)\n",
    "    \n",
    "    hexadecimal_alphabets = \"0123456789ABCDEF\"\n",
    "\n",
    "    colors = [\"#\" + \"\".join([random.choice(hexadecimal_alphabets) for j in range(6)]) for i in range(9)]\n",
    "    \n",
    "    plt.clf()\n",
    "\n",
    "    fig = plt.figure(figsize = (10, 5))\n",
    "\n",
    "    # Create the bar plot with random colors for each\n",
    "    plt.bar(list(train_generator.class_indices.keys()), probabilities / intervals, color = colors, width = 0.4)\n",
    "\n",
    "    plt.xlabel(\"Genres\")\n",
    "    plt.ylabel(\"Probability\")\n",
    "    plt.title(\"Model prediction for the song {}\".format(song.replace(\".wav\", \"\")))\n",
    "    plt.show()\n",
    "    \n",
    "    return probabilities / intervals\n",
    "\n",
    "def piecewise(epoch, lr):\n",
    "    if epoch < 5:\n",
    "        return lr\n",
    "    elif epoch < 15:\n",
    "        return lr * 0.5\n",
    "    elif epoch < 20:\n",
    "        return lr * 0.5\n",
    "    else:\n",
    "        return lr * 0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58c00b93",
   "metadata": {},
   "source": [
    "<H1>Final Model (100 Epochs)</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "93c53001",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metal device set to: Apple M2 Max\n",
      "Epoch 1/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-05-05 10:19:41.229933: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1120/1120 [==============================] - 101s 90ms/step - loss: 2.0041 - accuracy: 0.3938 - val_loss: 1.6212 - val_accuracy: 0.4634 - lr: 0.0015\n",
      "Epoch 2/100\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 1.2775 - accuracy: 0.5857 - val_loss: 1.6667 - val_accuracy: 0.4911 - lr: 0.0015\n",
      "Epoch 3/100\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 1.0463 - accuracy: 0.6619 - val_loss: 1.0486 - val_accuracy: 0.6451 - lr: 0.0015\n",
      "Epoch 4/100\n",
      "1120/1120 [==============================] - 99s 89ms/step - loss: 0.8679 - accuracy: 0.7158 - val_loss: 0.9164 - val_accuracy: 0.6969 - lr: 0.0015\n",
      "Epoch 5/100\n",
      "1120/1120 [==============================] - 100s 90ms/step - loss: 0.7252 - accuracy: 0.7653 - val_loss: 1.1171 - val_accuracy: 0.6455 - lr: 0.0015\n",
      "Epoch 6/100\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.5487 - accuracy: 0.8209 - val_loss: 1.5723 - val_accuracy: 0.5446 - lr: 0.0012\n",
      "Epoch 7/100\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.4014 - accuracy: 0.8694 - val_loss: 0.8634 - val_accuracy: 0.7208 - lr: 9.6000e-04\n",
      "Epoch 8/100\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.3220 - accuracy: 0.8934 - val_loss: 1.2090 - val_accuracy: 0.6732 - lr: 9.6000e-04\n",
      "Epoch 9/100\n",
      "1120/1120 [==============================] - 101s 91ms/step - loss: 0.2314 - accuracy: 0.9248 - val_loss: 0.9107 - val_accuracy: 0.7270 - lr: 7.6800e-04\n",
      "Epoch 10/100\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.1869 - accuracy: 0.9409 - val_loss: 0.9457 - val_accuracy: 0.7435 - lr: 7.6800e-04\n",
      "Epoch 11/100\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.1620 - accuracy: 0.9487 - val_loss: 0.9817 - val_accuracy: 0.7435 - lr: 7.6800e-04\n",
      "Epoch 12/100\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.1186 - accuracy: 0.9633 - val_loss: 1.0556 - val_accuracy: 0.7315 - lr: 6.1440e-04\n",
      "Epoch 13/100\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.0885 - accuracy: 0.9728 - val_loss: 1.0280 - val_accuracy: 0.7614 - lr: 4.9152e-04\n",
      "Epoch 14/100\n",
      "1120/1120 [==============================] - 99s 89ms/step - loss: 0.0806 - accuracy: 0.9765 - val_loss: 1.1267 - val_accuracy: 0.7377 - lr: 4.9152e-04\n",
      "Epoch 15/100\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.0655 - accuracy: 0.9819 - val_loss: 1.1145 - val_accuracy: 0.7473 - lr: 3.9322e-04\n",
      "Epoch 16/100\n",
      "1120/1120 [==============================] - 99s 89ms/step - loss: 0.0534 - accuracy: 0.9863 - val_loss: 1.1152 - val_accuracy: 0.7603 - lr: 3.1457e-04\n",
      "Epoch 17/100\n",
      "1120/1120 [==============================] - 99s 89ms/step - loss: 0.0441 - accuracy: 0.9889 - val_loss: 1.1342 - val_accuracy: 0.7603 - lr: 2.5166e-04\n",
      "Epoch 18/100\n",
      "1120/1120 [==============================] - 99s 89ms/step - loss: 0.0348 - accuracy: 0.9910 - val_loss: 1.1812 - val_accuracy: 0.7549 - lr: 2.0133e-04\n",
      "Epoch 19/100\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.0326 - accuracy: 0.9922 - val_loss: 1.1363 - val_accuracy: 0.7616 - lr: 1.6106e-04\n",
      "Epoch 20/100\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.0321 - accuracy: 0.9926 - val_loss: 1.1642 - val_accuracy: 0.7580 - lr: 1.6106e-04\n",
      "Epoch 21/100\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.0283 - accuracy: 0.9931 - val_loss: 1.1389 - val_accuracy: 0.7629 - lr: 1.2885e-04\n",
      "Epoch 22/100\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.0251 - accuracy: 0.9936 - val_loss: 1.0947 - val_accuracy: 0.7712 - lr: 1.2885e-04\n",
      "Epoch 23/100\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.0267 - accuracy: 0.9935 - val_loss: 1.1366 - val_accuracy: 0.7692 - lr: 1.2885e-04\n",
      "Epoch 24/100\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.0236 - accuracy: 0.9941 - val_loss: 1.1225 - val_accuracy: 0.7763 - lr: 1.0308e-04\n",
      "Epoch 25/100\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.0214 - accuracy: 0.9946 - val_loss: 1.1763 - val_accuracy: 0.7712 - lr: 1.0308e-04\n",
      "Epoch 26/100\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.0215 - accuracy: 0.9945 - val_loss: 1.1563 - val_accuracy: 0.7746 - lr: 8.2463e-05\n",
      "Epoch 27/100\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.0202 - accuracy: 0.9948 - val_loss: 1.1781 - val_accuracy: 0.7723 - lr: 6.5971e-05\n",
      "Epoch 28/100\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.0182 - accuracy: 0.9953 - val_loss: 1.1542 - val_accuracy: 0.7728 - lr: 5.2777e-05\n",
      "Epoch 29/100\n",
      "1120/1120 [==============================] - 100s 90ms/step - loss: 0.0158 - accuracy: 0.9958 - val_loss: 1.1747 - val_accuracy: 0.7739 - lr: 4.2221e-05\n",
      "Epoch 30/100\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.0163 - accuracy: 0.9956 - val_loss: 1.1770 - val_accuracy: 0.7779 - lr: 3.3777e-05\n",
      "Epoch 31/100\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.0158 - accuracy: 0.9954 - val_loss: 1.1988 - val_accuracy: 0.7750 - lr: 3.3777e-05\n",
      "Epoch 32/100\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.0149 - accuracy: 0.9957 - val_loss: 1.1825 - val_accuracy: 0.7777 - lr: 2.7022e-05\n",
      "Epoch 33/100\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.0151 - accuracy: 0.9956 - val_loss: 1.1857 - val_accuracy: 0.7761 - lr: 2.1617e-05\n",
      "Epoch 34/100\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.0143 - accuracy: 0.9959 - val_loss: 1.1899 - val_accuracy: 0.7766 - lr: 1.7294e-05\n",
      "Epoch 35/100\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.0140 - accuracy: 0.9958 - val_loss: 1.1899 - val_accuracy: 0.7770 - lr: 1.3835e-05\n",
      "Epoch 36/100\n",
      "1120/1120 [==============================] - 100s 90ms/step - loss: 0.0131 - accuracy: 0.9964 - val_loss: 1.2062 - val_accuracy: 0.7768 - lr: 1.1068e-05\n",
      "Epoch 37/100\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.0126 - accuracy: 0.9963 - val_loss: 1.2108 - val_accuracy: 0.7779 - lr: 8.8544e-06\n",
      "Epoch 38/100\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.0126 - accuracy: 0.9963 - val_loss: 1.2146 - val_accuracy: 0.7779 - lr: 7.0836e-06\n",
      "Epoch 39/100\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.0133 - accuracy: 0.9960 - val_loss: 1.2138 - val_accuracy: 0.7768 - lr: 5.6668e-06\n",
      "Epoch 40/100\n",
      "1120/1120 [==============================] - 99s 89ms/step - loss: 0.0123 - accuracy: 0.9963 - val_loss: 1.2156 - val_accuracy: 0.7768 - lr: 4.5335e-06\n"
     ]
    }
   ],
   "source": [
    "#Main model - BatchNormalization + 5x5\n",
    "\n",
    "#Best hyper-parameters with Optuna (lr = 0.0015, dropout = 0.2)\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Conv2D(8, (5, 5), input_shape = (515, 389, 3)),\n",
    "    tf.keras.layers.BatchNormalization(axis = 3),\n",
    "    tf.keras.layers.ReLU(),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(16, (5, 5)),\n",
    "    tf.keras.layers.BatchNormalization(axis = 3),\n",
    "    tf.keras.layers.ReLU(),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(32, (5, 5)),\n",
    "    tf.keras.layers.BatchNormalization(axis = 3),\n",
    "    tf.keras.layers.ReLU(),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(64, (5, 5)),\n",
    "    tf.keras.layers.BatchNormalization(axis = -1),\n",
    "    tf.keras.layers.ReLU(),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(128, (5, 5)),\n",
    "    tf.keras.layers.BatchNormalization(axis = -1),\n",
    "    tf.keras.layers.ReLU(),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Dropout(0.2),\n",
    "    tf.keras.layers.Flatten(),\n",
    "    tf.keras.layers.Dense(n_classes, activation = \"softmax\")\n",
    "])\n",
    "\n",
    "#Callbacks\n",
    "reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor = \"val_accuracy\", factor = 0.8, patience = 1)\n",
    "early_stopping = tf.keras.callbacks.EarlyStopping(monitor = \"val_accuracy\", patience = 10)\n",
    "\n",
    "model.compile(loss = \"categorical_crossentropy\", optimizer = tf.keras.optimizers.legacy.Adam(learning_rate = 0.0015), metrics = [\"accuracy\"])\n",
    "history = model.fit(train_generator, epochs = 100, validation_data = vali_generator, callbacks = [reduce_lr, early_stopping])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "3702d151",
   "metadata": {},
   "outputs": [],
   "source": [
    "#We save the model\n",
    "model.save(\"Final_Model.h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f324fc73",
   "metadata": {},
   "source": [
    "<H1>Final Model (100 Epochs) for 9-Sec Dataset</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "55c6f197",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "350/350 [==============================] - 35s 97ms/step - loss: 2.9175 - accuracy: 0.2464 - val_loss: 2.8214 - val_accuracy: 0.2007 - lr: 0.0015\n",
      "Epoch 2/100\n",
      "350/350 [==============================] - 34s 96ms/step - loss: 1.6854 - accuracy: 0.4514 - val_loss: 2.5186 - val_accuracy: 0.3043 - lr: 0.0015\n",
      "Epoch 3/100\n",
      "350/350 [==============================] - 33s 94ms/step - loss: 1.3906 - accuracy: 0.5466 - val_loss: 2.2433 - val_accuracy: 0.3521 - lr: 0.0015\n",
      "Epoch 4/100\n",
      "350/350 [==============================] - 33s 94ms/step - loss: 1.1990 - accuracy: 0.6062 - val_loss: 1.5704 - val_accuracy: 0.4871 - lr: 0.0015\n",
      "Epoch 5/100\n",
      "350/350 [==============================] - 33s 95ms/step - loss: 1.0481 - accuracy: 0.6561 - val_loss: 1.4765 - val_accuracy: 0.5064 - lr: 0.0015\n",
      "Epoch 6/100\n",
      "350/350 [==============================] - 33s 94ms/step - loss: 0.9281 - accuracy: 0.6929 - val_loss: 2.4648 - val_accuracy: 0.3271 - lr: 0.0015\n",
      "Epoch 7/100\n",
      "350/350 [==============================] - 32s 92ms/step - loss: 0.7316 - accuracy: 0.7609 - val_loss: 1.3393 - val_accuracy: 0.5893 - lr: 0.0012\n",
      "Epoch 8/100\n",
      "350/350 [==============================] - 32s 91ms/step - loss: 0.6419 - accuracy: 0.7905 - val_loss: 1.2268 - val_accuracy: 0.6129 - lr: 0.0012\n",
      "Epoch 9/100\n",
      "350/350 [==============================] - 33s 95ms/step - loss: 0.5199 - accuracy: 0.8265 - val_loss: 2.1097 - val_accuracy: 0.4414 - lr: 0.0012\n",
      "Epoch 10/100\n",
      "350/350 [==============================] - 34s 96ms/step - loss: 0.3708 - accuracy: 0.8745 - val_loss: 2.0178 - val_accuracy: 0.4957 - lr: 9.6000e-04\n",
      "Epoch 11/100\n",
      "350/350 [==============================] - 34s 96ms/step - loss: 0.2446 - accuracy: 0.9207 - val_loss: 1.2213 - val_accuracy: 0.6721 - lr: 7.6800e-04\n",
      "Epoch 12/100\n",
      "350/350 [==============================] - 34s 96ms/step - loss: 0.1861 - accuracy: 0.9397 - val_loss: 1.3195 - val_accuracy: 0.6629 - lr: 7.6800e-04\n",
      "Epoch 13/100\n",
      "350/350 [==============================] - 33s 93ms/step - loss: 0.1287 - accuracy: 0.9592 - val_loss: 1.3629 - val_accuracy: 0.6964 - lr: 6.1440e-04\n",
      "Epoch 14/100\n",
      "350/350 [==============================] - 32s 92ms/step - loss: 0.1080 - accuracy: 0.9662 - val_loss: 1.4453 - val_accuracy: 0.6671 - lr: 6.1440e-04\n",
      "Epoch 15/100\n",
      "350/350 [==============================] - 32s 91ms/step - loss: 0.0888 - accuracy: 0.9750 - val_loss: 1.5346 - val_accuracy: 0.6471 - lr: 4.9152e-04\n",
      "Epoch 16/100\n",
      "350/350 [==============================] - 32s 91ms/step - loss: 0.0541 - accuracy: 0.9864 - val_loss: 1.3600 - val_accuracy: 0.7000 - lr: 3.9322e-04\n",
      "Epoch 17/100\n",
      "350/350 [==============================] - 32s 90ms/step - loss: 0.0479 - accuracy: 0.9870 - val_loss: 2.2405 - val_accuracy: 0.6307 - lr: 3.9322e-04\n",
      "Epoch 18/100\n",
      "350/350 [==============================] - 32s 90ms/step - loss: 0.0380 - accuracy: 0.9918 - val_loss: 1.3889 - val_accuracy: 0.7207 - lr: 3.1457e-04\n",
      "Epoch 19/100\n",
      "350/350 [==============================] - 32s 90ms/step - loss: 0.0350 - accuracy: 0.9921 - val_loss: 1.4283 - val_accuracy: 0.7157 - lr: 3.1457e-04\n",
      "Epoch 20/100\n",
      "350/350 [==============================] - 32s 90ms/step - loss: 0.0332 - accuracy: 0.9927 - val_loss: 1.2679 - val_accuracy: 0.7407 - lr: 2.5166e-04\n",
      "Epoch 21/100\n",
      "350/350 [==============================] - 32s 90ms/step - loss: 0.0309 - accuracy: 0.9945 - val_loss: 1.3066 - val_accuracy: 0.7371 - lr: 2.5166e-04\n",
      "Epoch 22/100\n",
      "350/350 [==============================] - 32s 90ms/step - loss: 0.0253 - accuracy: 0.9945 - val_loss: 1.2507 - val_accuracy: 0.7336 - lr: 2.0133e-04\n",
      "Epoch 23/100\n",
      "350/350 [==============================] - 32s 91ms/step - loss: 0.0250 - accuracy: 0.9941 - val_loss: 1.2709 - val_accuracy: 0.7286 - lr: 1.6106e-04\n",
      "Epoch 24/100\n",
      "350/350 [==============================] - 32s 91ms/step - loss: 0.0205 - accuracy: 0.9955 - val_loss: 1.1880 - val_accuracy: 0.7493 - lr: 1.2885e-04\n",
      "Epoch 25/100\n",
      "350/350 [==============================] - 32s 91ms/step - loss: 0.0206 - accuracy: 0.9954 - val_loss: 1.2851 - val_accuracy: 0.7400 - lr: 1.2885e-04\n",
      "Epoch 26/100\n",
      "350/350 [==============================] - 32s 90ms/step - loss: 0.0193 - accuracy: 0.9953 - val_loss: 1.5254 - val_accuracy: 0.7036 - lr: 1.0308e-04\n",
      "Epoch 27/100\n",
      "350/350 [==============================] - 32s 91ms/step - loss: 0.0212 - accuracy: 0.9954 - val_loss: 1.2996 - val_accuracy: 0.7407 - lr: 8.2463e-05\n",
      "Epoch 28/100\n",
      "350/350 [==============================] - 32s 91ms/step - loss: 0.0158 - accuracy: 0.9961 - val_loss: 1.2200 - val_accuracy: 0.7493 - lr: 6.5971e-05\n",
      "Epoch 29/100\n",
      "350/350 [==============================] - 33s 93ms/step - loss: 0.0133 - accuracy: 0.9962 - val_loss: 1.2432 - val_accuracy: 0.7436 - lr: 5.2777e-05\n",
      "Epoch 30/100\n",
      "350/350 [==============================] - 32s 91ms/step - loss: 0.0135 - accuracy: 0.9962 - val_loss: 1.3352 - val_accuracy: 0.7300 - lr: 4.2221e-05\n",
      "Epoch 31/100\n",
      "350/350 [==============================] - 32s 91ms/step - loss: 0.0114 - accuracy: 0.9972 - val_loss: 1.2819 - val_accuracy: 0.7450 - lr: 3.3777e-05\n",
      "Epoch 32/100\n",
      "350/350 [==============================] - 33s 94ms/step - loss: 0.0128 - accuracy: 0.9962 - val_loss: 1.3308 - val_accuracy: 0.7443 - lr: 2.7022e-05\n",
      "Epoch 33/100\n",
      "350/350 [==============================] - 32s 91ms/step - loss: 0.0101 - accuracy: 0.9971 - val_loss: 1.2632 - val_accuracy: 0.7493 - lr: 2.1617e-05\n",
      "Epoch 34/100\n",
      "350/350 [==============================] - 33s 93ms/step - loss: 0.0109 - accuracy: 0.9965 - val_loss: 1.2633 - val_accuracy: 0.7493 - lr: 1.7294e-05\n"
     ]
    }
   ],
   "source": [
    "#Main model - BatchNormalization + 5x5\n",
    "\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Conv2D(8, (5, 5), input_shape = (515, 389, 3)),\n",
    "    tf.keras.layers.BatchNormalization(axis = 3),\n",
    "    tf.keras.layers.ReLU(),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(16, (5, 5)),\n",
    "    tf.keras.layers.BatchNormalization(axis = 3),\n",
    "    tf.keras.layers.ReLU(),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(32, (5, 5)),\n",
    "    tf.keras.layers.BatchNormalization(axis = 3),\n",
    "    tf.keras.layers.ReLU(),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(64, (5, 5)),\n",
    "    tf.keras.layers.BatchNormalization(axis = -1),\n",
    "    tf.keras.layers.ReLU(),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(128, (5, 5)),\n",
    "    tf.keras.layers.BatchNormalization(axis = -1),\n",
    "    tf.keras.layers.ReLU(),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Dropout(0.2),\n",
    "    tf.keras.layers.Flatten(),\n",
    "    tf.keras.layers.Dense(n_classes, activation = \"softmax\")\n",
    "])\n",
    "\n",
    "#Callbacks\n",
    "reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor = \"val_accuracy\", factor = 0.8, patience = 1)\n",
    "early_stopping = tf.keras.callbacks.EarlyStopping(monitor = \"val_accuracy\", patience = 10)\n",
    "\n",
    "model.compile(loss = \"categorical_crossentropy\", optimizer = tf.keras.optimizers.legacy.Adam(learning_rate = 0.0015), metrics = [\"accuracy\"])\n",
    "history = model.fit(train_generator, epochs = 100, validation_data = vali_generator, callbacks = [reduce_lr, early_stopping])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea91f3d3",
   "metadata": {},
   "source": [
    "<H1>Hyper-parameter Tuning with Optuna</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28af4945",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-05-04 19:28:17,148]\u001b[0m A new study created in memory with name: no-name-9ad80337-5e39-4b1f-9b81-f8f86ee030af\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 107s 95ms/step - loss: 2.4882 - accuracy: 0.2443 - val_loss: 1.9353 - val_accuracy: 0.3533 - lr: 1.5451e-05\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 107s 95ms/step - loss: 1.9480 - accuracy: 0.3679 - val_loss: 1.7369 - val_accuracy: 0.4277 - lr: 1.5451e-05\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 108s 97ms/step - loss: 1.7442 - accuracy: 0.4351 - val_loss: 1.6147 - val_accuracy: 0.4701 - lr: 1.5451e-05\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 105s 93ms/step - loss: 1.6063 - accuracy: 0.4816 - val_loss: 1.5113 - val_accuracy: 0.5078 - lr: 1.5451e-05\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 104s 93ms/step - loss: 1.5034 - accuracy: 0.5120 - val_loss: 1.4673 - val_accuracy: 0.5228 - lr: 1.5451e-05\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 104s 93ms/step - loss: 1.4272 - accuracy: 0.5359 - val_loss: 1.4324 - val_accuracy: 0.5330 - lr: 1.5451e-05\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 1.3606 - accuracy: 0.5630 - val_loss: 1.3762 - val_accuracy: 0.5464 - lr: 1.5451e-05\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 101s 91ms/step - loss: 1.2953 - accuracy: 0.5802 - val_loss: 1.3491 - val_accuracy: 0.5480 - lr: 1.5451e-05\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 1.2385 - accuracy: 0.5986 - val_loss: 1.2917 - val_accuracy: 0.5725 - lr: 1.5451e-05\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 1.1885 - accuracy: 0.6140 - val_loss: 1.2722 - val_accuracy: 0.5801 - lr: 1.5451e-05\n",
      "140/140 [==============================] - 11s 81ms/step - loss: 1.2821 - accuracy: 0.5862\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-05-04 19:45:48,854]\u001b[0m Trial 0 finished with value: 0.5861607193946838 and parameters: {'lr': 1.5450722452802812e-05, 'dropout': 0.45000000000000007}. Best is trial 0 with value: 0.5861607193946838.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 2.3014 - accuracy: 0.3633 - val_loss: 1.6308 - val_accuracy: 0.4525 - lr: 0.0036\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 100s 90ms/step - loss: 1.3902 - accuracy: 0.5492 - val_loss: 1.2768 - val_accuracy: 0.5750 - lr: 0.0036\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 100s 90ms/step - loss: 1.1810 - accuracy: 0.6167 - val_loss: 3.1704 - val_accuracy: 0.3098 - lr: 0.0036\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.9941 - accuracy: 0.6761 - val_loss: 1.3109 - val_accuracy: 0.5627 - lr: 0.0029\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.8574 - accuracy: 0.7189 - val_loss: 1.5444 - val_accuracy: 0.5056 - lr: 0.0023\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.7238 - accuracy: 0.7605 - val_loss: 1.8356 - val_accuracy: 0.4717 - lr: 0.0018\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.6153 - accuracy: 0.7953 - val_loss: 0.8523 - val_accuracy: 0.7152 - lr: 0.0015\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 99s 89ms/step - loss: 0.5488 - accuracy: 0.8188 - val_loss: 0.8138 - val_accuracy: 0.7270 - lr: 0.0015\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.4938 - accuracy: 0.8350 - val_loss: 0.8995 - val_accuracy: 0.7141 - lr: 0.0015\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.4183 - accuracy: 0.8604 - val_loss: 0.9203 - val_accuracy: 0.7045 - lr: 0.0012\n",
      "140/140 [==============================] - 11s 79ms/step - loss: 0.8872 - accuracy: 0.7170\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-05-04 20:02:43,956]\u001b[0m Trial 1 finished with value: 0.7169643044471741 and parameters: {'lr': 0.0035894444208110854, 'dropout': 0.5}. Best is trial 1 with value: 0.7169643044471741.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 3.2687 - accuracy: 0.2656 - val_loss: 2.2700 - val_accuracy: 0.2688 - lr: 0.0201\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 99s 88ms/step - loss: 1.9766 - accuracy: 0.3424 - val_loss: 5.1408 - val_accuracy: 0.1723 - lr: 0.0201\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 1.8314 - accuracy: 0.3906 - val_loss: 2.1081 - val_accuracy: 0.3319 - lr: 0.0161\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 1.7024 - accuracy: 0.4388 - val_loss: 3.4114 - val_accuracy: 0.2549 - lr: 0.0161\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 1.5558 - accuracy: 0.4847 - val_loss: 2.6132 - val_accuracy: 0.3136 - lr: 0.0129\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 1.4473 - accuracy: 0.5232 - val_loss: 2.1013 - val_accuracy: 0.3650 - lr: 0.0103\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 100s 90ms/step - loss: 1.3782 - accuracy: 0.5475 - val_loss: 1.7223 - val_accuracy: 0.4371 - lr: 0.0103\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 1.3264 - accuracy: 0.5612 - val_loss: 1.8910 - val_accuracy: 0.3688 - lr: 0.0103\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 1.2470 - accuracy: 0.5898 - val_loss: 1.3243 - val_accuracy: 0.5627 - lr: 0.0082\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 99s 89ms/step - loss: 1.2051 - accuracy: 0.6049 - val_loss: 1.3283 - val_accuracy: 0.5674 - lr: 0.0082\n",
      "140/140 [==============================] - 11s 79ms/step - loss: 1.3422 - accuracy: 0.5645\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-05-04 20:19:38,278]\u001b[0m Trial 2 finished with value: 0.5645089149475098 and parameters: {'lr': 0.02011062208951294, 'dropout': 0.25}. Best is trial 1 with value: 0.7169643044471741.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 2.2313 - accuracy: 0.2883 - val_loss: 1.8148 - val_accuracy: 0.4022 - lr: 1.8043e-05\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 100s 90ms/step - loss: 1.7447 - accuracy: 0.4314 - val_loss: 1.6374 - val_accuracy: 0.4618 - lr: 1.8043e-05\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 1.5541 - accuracy: 0.4939 - val_loss: 1.5142 - val_accuracy: 0.5049 - lr: 1.8043e-05\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 100s 90ms/step - loss: 1.4223 - accuracy: 0.5428 - val_loss: 1.4265 - val_accuracy: 0.5386 - lr: 1.8043e-05\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 1.3165 - accuracy: 0.5741 - val_loss: 1.3502 - val_accuracy: 0.5580 - lr: 1.8043e-05\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 100s 90ms/step - loss: 1.2311 - accuracy: 0.6047 - val_loss: 1.3132 - val_accuracy: 0.5694 - lr: 1.8043e-05\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 1.1579 - accuracy: 0.6261 - val_loss: 1.3260 - val_accuracy: 0.5708 - lr: 1.8043e-05\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 1.0891 - accuracy: 0.6504 - val_loss: 1.2771 - val_accuracy: 0.5750 - lr: 1.8043e-05\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 1.0310 - accuracy: 0.6693 - val_loss: 1.1938 - val_accuracy: 0.6098 - lr: 1.8043e-05\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 0.9750 - accuracy: 0.6914 - val_loss: 1.1605 - val_accuracy: 0.6201 - lr: 1.8043e-05\n",
      "140/140 [==============================] - 11s 79ms/step - loss: 1.1679 - accuracy: 0.6161\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-05-04 20:36:39,781]\u001b[0m Trial 3 finished with value: 0.6160714030265808 and parameters: {'lr': 1.804293584940275e-05, 'dropout': 0.30000000000000004}. Best is trial 1 with value: 0.7169643044471741.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 104s 92ms/step - loss: 1.7860 - accuracy: 0.4390 - val_loss: 2.3188 - val_accuracy: 0.2801 - lr: 3.2119e-04\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 99s 89ms/step - loss: 1.2780 - accuracy: 0.5840 - val_loss: 1.5504 - val_accuracy: 0.5103 - lr: 3.2119e-04\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 99s 89ms/step - loss: 1.0557 - accuracy: 0.6519 - val_loss: 1.1121 - val_accuracy: 0.6272 - lr: 3.2119e-04\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 99s 88ms/step - loss: 0.9053 - accuracy: 0.7008 - val_loss: 1.2721 - val_accuracy: 0.5911 - lr: 3.2119e-04\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 99s 89ms/step - loss: 0.7427 - accuracy: 0.7563 - val_loss: 1.2330 - val_accuracy: 0.6013 - lr: 2.5696e-04\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 99s 89ms/step - loss: 0.5665 - accuracy: 0.8137 - val_loss: 1.0256 - val_accuracy: 0.6721 - lr: 2.0556e-04\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 99s 89ms/step - loss: 0.4874 - accuracy: 0.8369 - val_loss: 1.0316 - val_accuracy: 0.6797 - lr: 2.0556e-04\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 99s 89ms/step - loss: 0.3864 - accuracy: 0.8708 - val_loss: 1.0911 - val_accuracy: 0.6980 - lr: 2.0556e-04\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 99s 89ms/step - loss: 0.3153 - accuracy: 0.8937 - val_loss: 1.1468 - val_accuracy: 0.6768 - lr: 2.0556e-04\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 99s 88ms/step - loss: 0.2173 - accuracy: 0.9302 - val_loss: 0.9110 - val_accuracy: 0.7350 - lr: 1.6445e-04\n",
      "140/140 [==============================] - 11s 81ms/step - loss: 0.9240 - accuracy: 0.7391\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-05-04 20:53:28,206]\u001b[0m Trial 4 finished with value: 0.739062488079071 and parameters: {'lr': 0.0003211945430643472, 'dropout': 0.2}. Best is trial 4 with value: 0.739062488079071.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 2.5040 - accuracy: 0.3323 - val_loss: 1.9990 - val_accuracy: 0.3415 - lr: 0.0058\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 99s 89ms/step - loss: 1.5511 - accuracy: 0.4926 - val_loss: 1.4714 - val_accuracy: 0.5116 - lr: 0.0058\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 99s 88ms/step - loss: 1.3659 - accuracy: 0.5540 - val_loss: 2.9871 - val_accuracy: 0.3337 - lr: 0.0058\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 100s 90ms/step - loss: 1.2166 - accuracy: 0.6053 - val_loss: 2.5110 - val_accuracy: 0.3989 - lr: 0.0046\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 1.0867 - accuracy: 0.6428 - val_loss: 1.9797 - val_accuracy: 0.4739 - lr: 0.0037\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.9900 - accuracy: 0.6765 - val_loss: 1.0129 - val_accuracy: 0.6607 - lr: 0.0029\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.9236 - accuracy: 0.6958 - val_loss: 0.9934 - val_accuracy: 0.6734 - lr: 0.0029\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.8746 - accuracy: 0.7110 - val_loss: 1.0594 - val_accuracy: 0.6522 - lr: 0.0029\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 99s 89ms/step - loss: 0.7924 - accuracy: 0.7379 - val_loss: 0.9080 - val_accuracy: 0.6987 - lr: 0.0024\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 99s 89ms/step - loss: 0.7497 - accuracy: 0.7487 - val_loss: 0.9023 - val_accuracy: 0.6989 - lr: 0.0024\n",
      "140/140 [==============================] - 11s 79ms/step - loss: 0.9293 - accuracy: 0.6888\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-05-04 21:10:17,307]\u001b[0m Trial 5 finished with value: 0.6888392567634583 and parameters: {'lr': 0.005750349825334339, 'dropout': 0.5}. Best is trial 4 with value: 0.739062488079071.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 2.1162 - accuracy: 0.3202 - val_loss: 1.7679 - val_accuracy: 0.4190 - lr: 2.7032e-05\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 1.6475 - accuracy: 0.4627 - val_loss: 1.5094 - val_accuracy: 0.5067 - lr: 2.7032e-05\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 1.4572 - accuracy: 0.5275 - val_loss: 1.3882 - val_accuracy: 0.5453 - lr: 2.7032e-05\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 1.3219 - accuracy: 0.5710 - val_loss: 1.3086 - val_accuracy: 0.5717 - lr: 2.7032e-05\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 1.2194 - accuracy: 0.6061 - val_loss: 1.3204 - val_accuracy: 0.5545 - lr: 2.7032e-05\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 1.1181 - accuracy: 0.6378 - val_loss: 1.3505 - val_accuracy: 0.5676 - lr: 2.1625e-05\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 1.0481 - accuracy: 0.6600 - val_loss: 1.1788 - val_accuracy: 0.6127 - lr: 1.7300e-05\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 1.0006 - accuracy: 0.6785 - val_loss: 1.1315 - val_accuracy: 0.6252 - lr: 1.7300e-05\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.9539 - accuracy: 0.6912 - val_loss: 1.1074 - val_accuracy: 0.6379 - lr: 1.7300e-05\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.9150 - accuracy: 0.7062 - val_loss: 1.1252 - val_accuracy: 0.6221 - lr: 1.7300e-05\n",
      "140/140 [==============================] - 11s 80ms/step - loss: 1.1308 - accuracy: 0.6375\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-05-04 21:27:11,979]\u001b[0m Trial 6 finished with value: 0.637499988079071 and parameters: {'lr': 2.703152401936026e-05, 'dropout': 0.35}. Best is trial 4 with value: 0.739062488079071.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 1.9724 - accuracy: 0.3858 - val_loss: 1.5768 - val_accuracy: 0.4824 - lr: 1.5180e-04\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 1.4845 - accuracy: 0.5259 - val_loss: 1.4546 - val_accuracy: 0.5362 - lr: 1.5180e-04\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 100s 90ms/step - loss: 1.2747 - accuracy: 0.5905 - val_loss: 1.4097 - val_accuracy: 0.5547 - lr: 1.5180e-04\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 1.1313 - accuracy: 0.6345 - val_loss: 1.4534 - val_accuracy: 0.5257 - lr: 1.5180e-04\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.9719 - accuracy: 0.6822 - val_loss: 1.0704 - val_accuracy: 0.6491 - lr: 1.2144e-04\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.8780 - accuracy: 0.7122 - val_loss: 0.9659 - val_accuracy: 0.6844 - lr: 1.2144e-04\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 100s 90ms/step - loss: 0.7900 - accuracy: 0.7424 - val_loss: 1.0245 - val_accuracy: 0.6645 - lr: 1.2144e-04\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.6809 - accuracy: 0.7737 - val_loss: 0.9338 - val_accuracy: 0.7004 - lr: 9.7153e-05\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 99s 88ms/step - loss: 0.6154 - accuracy: 0.7955 - val_loss: 0.8764 - val_accuracy: 0.7076 - lr: 9.7153e-05\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 99s 89ms/step - loss: 0.5513 - accuracy: 0.8173 - val_loss: 1.0422 - val_accuracy: 0.6681 - lr: 9.7153e-05\n",
      "140/140 [==============================] - 11s 79ms/step - loss: 1.0040 - accuracy: 0.6826\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-05-04 21:44:05,861]\u001b[0m Trial 7 finished with value: 0.6825892925262451 and parameters: {'lr': 0.00015180100836113437, 'dropout': 0.5}. Best is trial 4 with value: 0.739062488079071.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 1.8730 - accuracy: 0.4108 - val_loss: 2.9543 - val_accuracy: 0.2306 - lr: 1.4199e-04\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 1.3824 - accuracy: 0.5526 - val_loss: 1.5336 - val_accuracy: 0.5125 - lr: 1.4199e-04\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 1.1852 - accuracy: 0.6143 - val_loss: 1.7356 - val_accuracy: 0.4933 - lr: 1.4199e-04\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.9966 - accuracy: 0.6747 - val_loss: 1.0402 - val_accuracy: 0.6511 - lr: 1.1359e-04\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.8854 - accuracy: 0.7100 - val_loss: 1.1760 - val_accuracy: 0.6199 - lr: 1.1359e-04\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.7543 - accuracy: 0.7520 - val_loss: 0.8977 - val_accuracy: 0.6982 - lr: 9.0873e-05\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.6714 - accuracy: 0.7782 - val_loss: 0.9332 - val_accuracy: 0.6960 - lr: 9.0873e-05\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.5728 - accuracy: 0.8112 - val_loss: 0.9563 - val_accuracy: 0.6835 - lr: 7.2698e-05\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.4854 - accuracy: 0.8447 - val_loss: 0.9161 - val_accuracy: 0.6944 - lr: 5.8158e-05\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 101s 91ms/step - loss: 0.4242 - accuracy: 0.8657 - val_loss: 0.8502 - val_accuracy: 0.7243 - lr: 4.6527e-05\n",
      "140/140 [==============================] - 11s 80ms/step - loss: 0.8541 - accuracy: 0.7205\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-05-04 22:01:10,046]\u001b[0m Trial 8 finished with value: 0.7205356955528259 and parameters: {'lr': 0.00014198837051209056, 'dropout': 0.4}. Best is trial 4 with value: 0.739062488079071.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 2.5517 - accuracy: 0.3071 - val_loss: 2.2392 - val_accuracy: 0.2795 - lr: 0.0071\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 1.5766 - accuracy: 0.4858 - val_loss: 1.5742 - val_accuracy: 0.4701 - lr: 0.0071\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 1.3610 - accuracy: 0.5549 - val_loss: 1.4960 - val_accuracy: 0.5089 - lr: 0.0071\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 1.2303 - accuracy: 0.5993 - val_loss: 1.4321 - val_accuracy: 0.5431 - lr: 0.0071\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 1.1385 - accuracy: 0.6283 - val_loss: 1.3632 - val_accuracy: 0.5587 - lr: 0.0071\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 1.0676 - accuracy: 0.6529 - val_loss: 1.5823 - val_accuracy: 0.5049 - lr: 0.0071\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.9797 - accuracy: 0.6805 - val_loss: 1.0826 - val_accuracy: 0.6324 - lr: 0.0057\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.9065 - accuracy: 0.7051 - val_loss: 1.1862 - val_accuracy: 0.6152 - lr: 0.0057\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.8305 - accuracy: 0.7270 - val_loss: 0.9870 - val_accuracy: 0.6681 - lr: 0.0046\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 100s 90ms/step - loss: 0.7922 - accuracy: 0.7386 - val_loss: 0.9446 - val_accuracy: 0.6900 - lr: 0.0046\n",
      "140/140 [==============================] - 11s 79ms/step - loss: 0.9906 - accuracy: 0.6853\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-05-04 22:18:10,009]\u001b[0m Trial 9 finished with value: 0.6852678656578064 and parameters: {'lr': 0.007125562236544208, 'dropout': 0.4}. Best is trial 4 with value: 0.739062488079071.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 102s 90ms/step - loss: 1.9124 - accuracy: 0.4083 - val_loss: 2.1720 - val_accuracy: 0.3366 - lr: 8.1784e-04\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 1.2397 - accuracy: 0.5953 - val_loss: 1.6614 - val_accuracy: 0.4232 - lr: 8.1784e-04\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 1.0101 - accuracy: 0.6731 - val_loss: 1.6412 - val_accuracy: 0.4915 - lr: 8.1784e-04\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.8477 - accuracy: 0.7261 - val_loss: 1.0383 - val_accuracy: 0.6643 - lr: 8.1784e-04\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.6982 - accuracy: 0.7744 - val_loss: 0.8641 - val_accuracy: 0.7105 - lr: 8.1784e-04\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.5695 - accuracy: 0.8148 - val_loss: 1.3996 - val_accuracy: 0.5786 - lr: 8.1784e-04\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.4131 - accuracy: 0.8653 - val_loss: 0.9306 - val_accuracy: 0.7138 - lr: 6.5428e-04\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.3212 - accuracy: 0.8954 - val_loss: 1.0089 - val_accuracy: 0.7103 - lr: 6.5428e-04\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.2224 - accuracy: 0.9282 - val_loss: 0.9513 - val_accuracy: 0.7268 - lr: 5.2342e-04\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.1715 - accuracy: 0.9453 - val_loss: 1.1078 - val_accuracy: 0.6987 - lr: 5.2342e-04\n",
      "140/140 [==============================] - 11s 79ms/step - loss: 1.1103 - accuracy: 0.7114\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-05-04 22:35:14,651]\u001b[0m Trial 10 finished with value: 0.7113839387893677 and parameters: {'lr': 0.0008178440404399496, 'dropout': 0.1}. Best is trial 4 with value: 0.739062488079071.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 103s 91ms/step - loss: 1.7460 - accuracy: 0.4512 - val_loss: 2.5508 - val_accuracy: 0.3458 - lr: 2.8483e-04\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 1.2723 - accuracy: 0.5914 - val_loss: 2.8086 - val_accuracy: 0.3105 - lr: 2.8483e-04\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 1.0049 - accuracy: 0.6711 - val_loss: 1.2014 - val_accuracy: 0.5982 - lr: 2.2786e-04\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.8631 - accuracy: 0.7169 - val_loss: 1.3687 - val_accuracy: 0.5605 - lr: 2.2786e-04\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.6828 - accuracy: 0.7777 - val_loss: 1.2873 - val_accuracy: 0.6299 - lr: 1.8229e-04\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.5690 - accuracy: 0.8121 - val_loss: 1.0320 - val_accuracy: 0.6725 - lr: 1.8229e-04\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 101s 91ms/step - loss: 0.4628 - accuracy: 0.8469 - val_loss: 0.9843 - val_accuracy: 0.6955 - lr: 1.8229e-04\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.3853 - accuracy: 0.8706 - val_loss: 1.0579 - val_accuracy: 0.6817 - lr: 1.8229e-04\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.2713 - accuracy: 0.9143 - val_loss: 0.9085 - val_accuracy: 0.7143 - lr: 1.4583e-04\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.2257 - accuracy: 0.9297 - val_loss: 1.2009 - val_accuracy: 0.6395 - lr: 1.4583e-04\n",
      "140/140 [==============================] - 11s 79ms/step - loss: 1.2382 - accuracy: 0.6406\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-05-04 22:52:25,103]\u001b[0m Trial 11 finished with value: 0.640625 and parameters: {'lr': 0.00028482689607528647, 'dropout': 0.2}. Best is trial 4 with value: 0.739062488079071.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 1.8026 - accuracy: 0.4321 - val_loss: 1.8864 - val_accuracy: 0.3967 - lr: 2.3622e-04\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 1.3075 - accuracy: 0.5784 - val_loss: 1.9797 - val_accuracy: 0.4353 - lr: 2.3622e-04\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 1.0649 - accuracy: 0.6518 - val_loss: 1.2042 - val_accuracy: 0.6087 - lr: 2.3622e-04\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 0.8945 - accuracy: 0.7084 - val_loss: 1.9133 - val_accuracy: 0.4616 - lr: 2.3622e-04\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 0.7021 - accuracy: 0.7688 - val_loss: 1.2626 - val_accuracy: 0.5967 - lr: 1.8898e-04\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 0.5274 - accuracy: 0.8274 - val_loss: 1.1558 - val_accuracy: 0.6467 - lr: 1.5118e-04\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.4409 - accuracy: 0.8545 - val_loss: 1.0291 - val_accuracy: 0.6817 - lr: 1.5118e-04\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 0.3603 - accuracy: 0.8849 - val_loss: 1.2053 - val_accuracy: 0.6516 - lr: 1.5118e-04\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 0.2578 - accuracy: 0.9212 - val_loss: 0.9979 - val_accuracy: 0.6906 - lr: 1.2095e-04\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.2186 - accuracy: 0.9362 - val_loss: 1.0992 - val_accuracy: 0.6743 - lr: 1.2095e-04\n",
      "140/140 [==============================] - 11s 80ms/step - loss: 1.0994 - accuracy: 0.6723\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-05-04 23:09:42,962]\u001b[0m Trial 12 finished with value: 0.6723214387893677 and parameters: {'lr': 0.0002362230970269334, 'dropout': 0.15000000000000002}. Best is trial 4 with value: 0.739062488079071.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 104s 92ms/step - loss: 1.8820 - accuracy: 0.3955 - val_loss: 1.5207 - val_accuracy: 0.4953 - lr: 8.1376e-05\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 1.4197 - accuracy: 0.5374 - val_loss: 1.4089 - val_accuracy: 0.5395 - lr: 8.1376e-05\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 1.2016 - accuracy: 0.6096 - val_loss: 1.2475 - val_accuracy: 0.5808 - lr: 8.1376e-05\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 1.0302 - accuracy: 0.6662 - val_loss: 1.2769 - val_accuracy: 0.5806 - lr: 8.1376e-05\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.8701 - accuracy: 0.7150 - val_loss: 1.1315 - val_accuracy: 0.6261 - lr: 6.5101e-05\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.7749 - accuracy: 0.7500 - val_loss: 1.0436 - val_accuracy: 0.6473 - lr: 6.5101e-05\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.6829 - accuracy: 0.7784 - val_loss: 1.3570 - val_accuracy: 0.5931 - lr: 6.5101e-05\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 104s 93ms/step - loss: 0.5847 - accuracy: 0.8150 - val_loss: 1.0889 - val_accuracy: 0.6538 - lr: 5.2081e-05\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.5204 - accuracy: 0.8375 - val_loss: 0.9868 - val_accuracy: 0.6824 - lr: 5.2081e-05\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.4702 - accuracy: 0.8550 - val_loss: 1.0564 - val_accuracy: 0.6578 - lr: 5.2081e-05\n",
      "140/140 [==============================] - 11s 80ms/step - loss: 1.0645 - accuracy: 0.6636\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-05-04 23:26:59,419]\u001b[0m Trial 13 finished with value: 0.6636160612106323 and parameters: {'lr': 8.137603249317074e-05, 'dropout': 0.30000000000000004}. Best is trial 4 with value: 0.739062488079071.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 103s 91ms/step - loss: 1.8984 - accuracy: 0.4160 - val_loss: 1.5346 - val_accuracy: 0.4942 - lr: 9.4767e-04\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 1.2443 - accuracy: 0.5947 - val_loss: 1.5103 - val_accuracy: 0.5123 - lr: 9.4767e-04\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 1.0187 - accuracy: 0.6697 - val_loss: 1.0499 - val_accuracy: 0.6542 - lr: 9.4767e-04\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 0.8485 - accuracy: 0.7260 - val_loss: 0.9942 - val_accuracy: 0.6685 - lr: 9.4767e-04\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.7067 - accuracy: 0.7717 - val_loss: 1.0549 - val_accuracy: 0.6592 - lr: 9.4767e-04\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 0.5351 - accuracy: 0.8247 - val_loss: 1.1902 - val_accuracy: 0.6362 - lr: 7.5813e-04\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.3943 - accuracy: 0.8716 - val_loss: 0.8482 - val_accuracy: 0.7230 - lr: 6.0651e-04\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.3141 - accuracy: 0.8970 - val_loss: 0.9122 - val_accuracy: 0.7228 - lr: 6.0651e-04\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 103s 91ms/step - loss: 0.2209 - accuracy: 0.9290 - val_loss: 1.0261 - val_accuracy: 0.7194 - lr: 4.8521e-04\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 0.1576 - accuracy: 0.9505 - val_loss: 0.9909 - val_accuracy: 0.7324 - lr: 3.8816e-04\n",
      "140/140 [==============================] - 11s 79ms/step - loss: 1.0247 - accuracy: 0.7357\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-05-04 23:44:15,191]\u001b[0m Trial 14 finished with value: 0.7357142567634583 and parameters: {'lr': 0.0009476665954129145, 'dropout': 0.2}. Best is trial 4 with value: 0.739062488079071.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 104s 92ms/step - loss: 1.9643 - accuracy: 0.4004 - val_loss: 1.6104 - val_accuracy: 0.4799 - lr: 0.0011\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 104s 93ms/step - loss: 1.2660 - accuracy: 0.5860 - val_loss: 1.6851 - val_accuracy: 0.4547 - lr: 0.0011\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 1.0072 - accuracy: 0.6708 - val_loss: 1.1082 - val_accuracy: 0.6348 - lr: 8.8258e-04\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 0.8359 - accuracy: 0.7271 - val_loss: 1.2111 - val_accuracy: 0.6176 - lr: 8.8258e-04\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 104s 93ms/step - loss: 0.6562 - accuracy: 0.7873 - val_loss: 1.0980 - val_accuracy: 0.6502 - lr: 7.0606e-04\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 0.5334 - accuracy: 0.8262 - val_loss: 1.1031 - val_accuracy: 0.6496 - lr: 7.0606e-04\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 104s 92ms/step - loss: 0.4007 - accuracy: 0.8698 - val_loss: 0.9985 - val_accuracy: 0.6886 - lr: 5.6485e-04\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 104s 93ms/step - loss: 0.3186 - accuracy: 0.8948 - val_loss: 0.9072 - val_accuracy: 0.7181 - lr: 5.6485e-04\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 0.2642 - accuracy: 0.9138 - val_loss: 0.8687 - val_accuracy: 0.7473 - lr: 5.6485e-04\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 105s 93ms/step - loss: 0.2188 - accuracy: 0.9277 - val_loss: 1.2637 - val_accuracy: 0.6580 - lr: 5.6485e-04\n",
      "140/140 [==============================] - 11s 80ms/step - loss: 1.2474 - accuracy: 0.6661\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-05-05 00:01:43,015]\u001b[0m Trial 15 finished with value: 0.6660714149475098 and parameters: {'lr': 0.0011032192275082869, 'dropout': 0.2}. Best is trial 4 with value: 0.739062488079071.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 105s 93ms/step - loss: 4.2456 - accuracy: 0.0731 - val_loss: 2.6577 - val_accuracy: 0.0714 - lr: 0.0845\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 104s 93ms/step - loss: 2.6530 - accuracy: 0.0707 - val_loss: 2.6565 - val_accuracy: 0.0714 - lr: 0.0845\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 105s 94ms/step - loss: 2.6494 - accuracy: 0.0722 - val_loss: 2.6497 - val_accuracy: 0.0714 - lr: 0.0676\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 106s 94ms/step - loss: 2.6475 - accuracy: 0.0714 - val_loss: 2.6475 - val_accuracy: 0.0714 - lr: 0.0541\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 105s 94ms/step - loss: 2.6469 - accuracy: 0.0713 - val_loss: 2.6527 - val_accuracy: 0.0714 - lr: 0.0433\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 104s 93ms/step - loss: 2.6447 - accuracy: 0.0702 - val_loss: 2.6422 - val_accuracy: 0.0714 - lr: 0.0346\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 105s 93ms/step - loss: 2.6439 - accuracy: 0.0718 - val_loss: 2.6416 - val_accuracy: 0.0714 - lr: 0.0277\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 105s 93ms/step - loss: 2.6427 - accuracy: 0.0694 - val_loss: 2.6432 - val_accuracy: 0.0714 - lr: 0.0222\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 104s 93ms/step - loss: 2.6420 - accuracy: 0.0708 - val_loss: 2.6410 - val_accuracy: 0.0714 - lr: 0.0177\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 104s 93ms/step - loss: 2.6417 - accuracy: 0.0699 - val_loss: 2.6405 - val_accuracy: 0.0714 - lr: 0.0142\n",
      "140/140 [==============================] - 11s 80ms/step - loss: 2.6405 - accuracy: 0.0714\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-05-05 00:19:21,826]\u001b[0m Trial 16 finished with value: 0.0714285746216774 and parameters: {'lr': 0.08453237160778611, 'dropout': 0.1}. Best is trial 4 with value: 0.739062488079071.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 104s 92ms/step - loss: 1.9275 - accuracy: 0.4103 - val_loss: 1.4054 - val_accuracy: 0.5339 - lr: 7.5477e-04\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 104s 92ms/step - loss: 1.2745 - accuracy: 0.5824 - val_loss: 1.5327 - val_accuracy: 0.5051 - lr: 7.5477e-04\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 1.0148 - accuracy: 0.6718 - val_loss: 1.2400 - val_accuracy: 0.5779 - lr: 6.0381e-04\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 0.8617 - accuracy: 0.7178 - val_loss: 0.9907 - val_accuracy: 0.6717 - lr: 6.0381e-04\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 0.7144 - accuracy: 0.7686 - val_loss: 0.9529 - val_accuracy: 0.6748 - lr: 6.0381e-04\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 0.5916 - accuracy: 0.8081 - val_loss: 0.8927 - val_accuracy: 0.7051 - lr: 6.0381e-04\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 105s 94ms/step - loss: 0.4834 - accuracy: 0.8394 - val_loss: 0.9721 - val_accuracy: 0.6810 - lr: 6.0381e-04\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 105s 94ms/step - loss: 0.3520 - accuracy: 0.8847 - val_loss: 1.0173 - val_accuracy: 0.6964 - lr: 4.8305e-04\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 107s 95ms/step - loss: 0.2486 - accuracy: 0.9181 - val_loss: 1.0245 - val_accuracy: 0.7279 - lr: 3.8644e-04\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 105s 94ms/step - loss: 0.2024 - accuracy: 0.9336 - val_loss: 1.0795 - val_accuracy: 0.7196 - lr: 3.8644e-04\n",
      "140/140 [==============================] - 11s 80ms/step - loss: 1.1481 - accuracy: 0.7172\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-05-05 00:36:56,172]\u001b[0m Trial 17 finished with value: 0.7171875238418579 and parameters: {'lr': 0.0007547655482736487, 'dropout': 0.2}. Best is trial 4 with value: 0.739062488079071.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 105s 94ms/step - loss: 1.9395 - accuracy: 0.3729 - val_loss: 1.6093 - val_accuracy: 0.4737 - lr: 5.8426e-05\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 105s 94ms/step - loss: 1.4688 - accuracy: 0.5225 - val_loss: 1.8178 - val_accuracy: 0.4504 - lr: 5.8426e-05\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 106s 94ms/step - loss: 1.2424 - accuracy: 0.5979 - val_loss: 1.2808 - val_accuracy: 0.5859 - lr: 4.6740e-05\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 105s 94ms/step - loss: 1.1048 - accuracy: 0.6408 - val_loss: 1.2342 - val_accuracy: 0.5862 - lr: 4.6740e-05\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 105s 93ms/step - loss: 0.9863 - accuracy: 0.6773 - val_loss: 1.2411 - val_accuracy: 0.5893 - lr: 4.6740e-05\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 104s 93ms/step - loss: 0.8766 - accuracy: 0.7178 - val_loss: 1.1917 - val_accuracy: 0.6076 - lr: 4.6740e-05\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.7828 - accuracy: 0.7492 - val_loss: 1.2114 - val_accuracy: 0.6098 - lr: 4.6740e-05\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.7023 - accuracy: 0.7763 - val_loss: 1.0636 - val_accuracy: 0.6547 - lr: 4.6740e-05\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.6355 - accuracy: 0.7990 - val_loss: 1.0423 - val_accuracy: 0.6594 - lr: 4.6740e-05\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.5685 - accuracy: 0.8273 - val_loss: 1.0274 - val_accuracy: 0.6694 - lr: 4.6740e-05\n",
      "140/140 [==============================] - 11s 79ms/step - loss: 1.0416 - accuracy: 0.6721\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-05-05 00:54:26,214]\u001b[0m Trial 18 finished with value: 0.6720982193946838 and parameters: {'lr': 5.84255513583829e-05, 'dropout': 0.25}. Best is trial 4 with value: 0.739062488079071.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 1.7757 - accuracy: 0.4444 - val_loss: 1.8954 - val_accuracy: 0.4002 - lr: 4.1436e-04\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 1.2474 - accuracy: 0.5936 - val_loss: 1.3232 - val_accuracy: 0.5598 - lr: 4.1436e-04\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 101s 91ms/step - loss: 1.0263 - accuracy: 0.6645 - val_loss: 1.3809 - val_accuracy: 0.5643 - lr: 4.1436e-04\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.8628 - accuracy: 0.7194 - val_loss: 1.2550 - val_accuracy: 0.6009 - lr: 4.1436e-04\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.7271 - accuracy: 0.7612 - val_loss: 0.9672 - val_accuracy: 0.6779 - lr: 4.1436e-04\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.5992 - accuracy: 0.8034 - val_loss: 0.9384 - val_accuracy: 0.6837 - lr: 4.1436e-04\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.4867 - accuracy: 0.8390 - val_loss: 1.0180 - val_accuracy: 0.6627 - lr: 4.1436e-04\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.3352 - accuracy: 0.8913 - val_loss: 0.9587 - val_accuracy: 0.7145 - lr: 3.3149e-04\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.2667 - accuracy: 0.9110 - val_loss: 0.9774 - val_accuracy: 0.7125 - lr: 3.3149e-04\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.1733 - accuracy: 0.9444 - val_loss: 1.0067 - val_accuracy: 0.7254 - lr: 2.6519e-04\n",
      "140/140 [==============================] - 11s 78ms/step - loss: 1.0128 - accuracy: 0.7252\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-05-05 01:11:33,962]\u001b[0m Trial 19 finished with value: 0.7252232432365417 and parameters: {'lr': 0.00041436402797946555, 'dropout': 0.15000000000000002}. Best is trial 4 with value: 0.739062488079071.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 2.2534 - accuracy: 0.3723 - val_loss: 5.1329 - val_accuracy: 0.2080 - lr: 0.0021\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 1.3177 - accuracy: 0.5703 - val_loss: 1.9884 - val_accuracy: 0.4085 - lr: 0.0021\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 105s 94ms/step - loss: 1.0850 - accuracy: 0.6480 - val_loss: 1.1165 - val_accuracy: 0.6321 - lr: 0.0021\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 105s 94ms/step - loss: 0.9151 - accuracy: 0.7025 - val_loss: 1.6485 - val_accuracy: 0.5330 - lr: 0.0021\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 105s 93ms/step - loss: 0.7209 - accuracy: 0.7647 - val_loss: 1.1988 - val_accuracy: 0.6107 - lr: 0.0017\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 105s 94ms/step - loss: 0.5479 - accuracy: 0.8204 - val_loss: 0.9092 - val_accuracy: 0.7065 - lr: 0.0014\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 104s 93ms/step - loss: 0.4487 - accuracy: 0.8524 - val_loss: 1.3630 - val_accuracy: 0.6152 - lr: 0.0014\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.3260 - accuracy: 0.8919 - val_loss: 1.0151 - val_accuracy: 0.6917 - lr: 0.0011\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.2339 - accuracy: 0.9224 - val_loss: 0.9429 - val_accuracy: 0.7257 - lr: 8.6687e-04\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.1950 - accuracy: 0.9362 - val_loss: 0.9647 - val_accuracy: 0.7388 - lr: 8.6687e-04\n",
      "140/140 [==============================] - 11s 79ms/step - loss: 0.9672 - accuracy: 0.7335\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-05-05 01:28:53,387]\u001b[0m Trial 20 finished with value: 0.7334821224212646 and parameters: {'lr': 0.0021163731707962864, 'dropout': 0.25}. Best is trial 4 with value: 0.739062488079071.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 2.0372 - accuracy: 0.3824 - val_loss: 1.6513 - val_accuracy: 0.4538 - lr: 0.0012\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 99s 89ms/step - loss: 1.3068 - accuracy: 0.5728 - val_loss: 1.1792 - val_accuracy: 0.6049 - lr: 0.0012\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 99s 89ms/step - loss: 1.0669 - accuracy: 0.6528 - val_loss: 1.0649 - val_accuracy: 0.6417 - lr: 0.0012\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.8991 - accuracy: 0.7083 - val_loss: 1.1143 - val_accuracy: 0.6350 - lr: 0.0012\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.7090 - accuracy: 0.7724 - val_loss: 0.9698 - val_accuracy: 0.6759 - lr: 9.9426e-04\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 99s 89ms/step - loss: 0.5923 - accuracy: 0.8060 - val_loss: 0.8507 - val_accuracy: 0.7056 - lr: 9.9426e-04\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 99s 89ms/step - loss: 0.4898 - accuracy: 0.8406 - val_loss: 0.7970 - val_accuracy: 0.7306 - lr: 9.9426e-04\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 99s 89ms/step - loss: 0.3958 - accuracy: 0.8705 - val_loss: 1.0485 - val_accuracy: 0.6761 - lr: 9.9426e-04\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 99s 89ms/step - loss: 0.2872 - accuracy: 0.9057 - val_loss: 1.0402 - val_accuracy: 0.7027 - lr: 7.9541e-04\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 99s 89ms/step - loss: 0.2075 - accuracy: 0.9318 - val_loss: 1.1669 - val_accuracy: 0.6770 - lr: 6.3632e-04\n",
      "140/140 [==============================] - 10s 72ms/step - loss: 1.1794 - accuracy: 0.6797\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-05-05 01:45:38,622]\u001b[0m Trial 21 finished with value: 0.6796875 and parameters: {'lr': 0.0012428212142409673, 'dropout': 0.25}. Best is trial 4 with value: 0.739062488079071.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 2.1347 - accuracy: 0.3869 - val_loss: 1.4172 - val_accuracy: 0.5359 - lr: 0.0022\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 1.2803 - accuracy: 0.5851 - val_loss: 2.0709 - val_accuracy: 0.3819 - lr: 0.0022\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 1.0060 - accuracy: 0.6734 - val_loss: 1.2333 - val_accuracy: 0.5891 - lr: 0.0018\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 99s 89ms/step - loss: 0.8539 - accuracy: 0.7242 - val_loss: 1.2289 - val_accuracy: 0.6163 - lr: 0.0018\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 99s 89ms/step - loss: 0.7130 - accuracy: 0.7681 - val_loss: 1.1040 - val_accuracy: 0.6446 - lr: 0.0018\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.5853 - accuracy: 0.8097 - val_loss: 1.1998 - val_accuracy: 0.6442 - lr: 0.0018\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.4216 - accuracy: 0.8625 - val_loss: 0.9292 - val_accuracy: 0.7109 - lr: 0.0014\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.3307 - accuracy: 0.8922 - val_loss: 1.0029 - val_accuracy: 0.7116 - lr: 0.0014\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.2578 - accuracy: 0.9157 - val_loss: 1.1051 - val_accuracy: 0.7009 - lr: 0.0014\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 0.1878 - accuracy: 0.9399 - val_loss: 1.3695 - val_accuracy: 0.6576 - lr: 0.0011\n",
      "140/140 [==============================] - 11s 79ms/step - loss: 1.3845 - accuracy: 0.6547\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-05-05 02:02:30,051]\u001b[0m Trial 22 finished with value: 0.6546875238418579 and parameters: {'lr': 0.002204959658941327, 'dropout': 0.15000000000000002}. Best is trial 4 with value: 0.739062488079071.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 105s 93ms/step - loss: 1.8904 - accuracy: 0.4147 - val_loss: 1.5174 - val_accuracy: 0.5098 - lr: 4.6631e-04\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 1.2821 - accuracy: 0.5818 - val_loss: 2.3322 - val_accuracy: 0.3393 - lr: 4.6631e-04\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 101s 91ms/step - loss: 1.0433 - accuracy: 0.6592 - val_loss: 1.3151 - val_accuracy: 0.5562 - lr: 3.7305e-04\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 101s 91ms/step - loss: 0.9015 - accuracy: 0.7067 - val_loss: 1.0975 - val_accuracy: 0.6333 - lr: 3.7305e-04\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.7755 - accuracy: 0.7465 - val_loss: 1.0198 - val_accuracy: 0.6600 - lr: 3.7305e-04\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.6550 - accuracy: 0.7848 - val_loss: 0.9833 - val_accuracy: 0.6897 - lr: 3.7305e-04\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 100s 90ms/step - loss: 0.5581 - accuracy: 0.8170 - val_loss: 0.9920 - val_accuracy: 0.6804 - lr: 3.7305e-04\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.4226 - accuracy: 0.8620 - val_loss: 1.1463 - val_accuracy: 0.6708 - lr: 2.9844e-04\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.3139 - accuracy: 0.8951 - val_loss: 0.8469 - val_accuracy: 0.7402 - lr: 2.3875e-04\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.2603 - accuracy: 0.9129 - val_loss: 0.9997 - val_accuracy: 0.7172 - lr: 2.3875e-04\n",
      "140/140 [==============================] - 11s 79ms/step - loss: 1.0256 - accuracy: 0.7205\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-05-05 02:19:35,116]\u001b[0m Trial 23 finished with value: 0.7205356955528259 and parameters: {'lr': 0.00046630771132175763, 'dropout': 0.30000000000000004}. Best is trial 4 with value: 0.739062488079071.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 2.2165 - accuracy: 0.3668 - val_loss: 1.7435 - val_accuracy: 0.4395 - lr: 0.0015\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 1.3291 - accuracy: 0.5659 - val_loss: 1.5787 - val_accuracy: 0.4879 - lr: 0.0015\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 1.0745 - accuracy: 0.6508 - val_loss: 1.3706 - val_accuracy: 0.5482 - lr: 0.0015\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.8860 - accuracy: 0.7130 - val_loss: 2.8532 - val_accuracy: 0.2924 - lr: 0.0015\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.6898 - accuracy: 0.7760 - val_loss: 0.8541 - val_accuracy: 0.7225 - lr: 0.0012\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.5608 - accuracy: 0.8195 - val_loss: 1.0991 - val_accuracy: 0.6520 - lr: 0.0012\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.4148 - accuracy: 0.8633 - val_loss: 1.0167 - val_accuracy: 0.6855 - lr: 9.7974e-04\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.2938 - accuracy: 0.9041 - val_loss: 1.0440 - val_accuracy: 0.6839 - lr: 7.8379e-04\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.2063 - accuracy: 0.9343 - val_loss: 1.0564 - val_accuracy: 0.7118 - lr: 6.2703e-04\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.1471 - accuracy: 0.9537 - val_loss: 0.8661 - val_accuracy: 0.7549 - lr: 5.0163e-04\n",
      "140/140 [==============================] - 11s 79ms/step - loss: 0.9147 - accuracy: 0.7529\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-05-05 02:36:36,188]\u001b[0m Trial 24 finished with value: 0.7529017925262451 and parameters: {'lr': 0.0015308392905864121, 'dropout': 0.2}. Best is trial 24 with value: 0.7529017925262451.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 1.8272 - accuracy: 0.4350 - val_loss: 1.4315 - val_accuracy: 0.5337 - lr: 4.0555e-04\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 1.2503 - accuracy: 0.5923 - val_loss: 1.3158 - val_accuracy: 0.5598 - lr: 4.0555e-04\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 1.0395 - accuracy: 0.6587 - val_loss: 1.4504 - val_accuracy: 0.5455 - lr: 4.0555e-04\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.8463 - accuracy: 0.7228 - val_loss: 1.0656 - val_accuracy: 0.6491 - lr: 3.2444e-04\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 103s 91ms/step - loss: 0.7146 - accuracy: 0.7641 - val_loss: 0.9859 - val_accuracy: 0.6833 - lr: 3.2444e-04\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.5931 - accuracy: 0.8039 - val_loss: 0.9948 - val_accuracy: 0.6761 - lr: 3.2444e-04\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.4425 - accuracy: 0.8521 - val_loss: 1.0196 - val_accuracy: 0.6859 - lr: 2.5955e-04\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.3598 - accuracy: 0.8787 - val_loss: 0.9774 - val_accuracy: 0.7154 - lr: 2.5955e-04\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.2847 - accuracy: 0.9052 - val_loss: 0.9561 - val_accuracy: 0.7321 - lr: 2.5955e-04\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.2402 - accuracy: 0.9189 - val_loss: 1.0547 - val_accuracy: 0.7143 - lr: 2.5955e-04\n",
      "140/140 [==============================] - 11s 79ms/step - loss: 1.0757 - accuracy: 0.7199\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-05-05 02:53:47,874]\u001b[0m Trial 25 finished with value: 0.7198660969734192 and parameters: {'lr': 0.0004055474100053558, 'dropout': 0.2}. Best is trial 24 with value: 0.7529017925262451.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 104s 92ms/step - loss: 1.9035 - accuracy: 0.4149 - val_loss: 1.5889 - val_accuracy: 0.5000 - lr: 6.0127e-04\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 1.2608 - accuracy: 0.5879 - val_loss: 1.4669 - val_accuracy: 0.5275 - lr: 6.0127e-04\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 1.0412 - accuracy: 0.6588 - val_loss: 1.2703 - val_accuracy: 0.5938 - lr: 6.0127e-04\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.8689 - accuracy: 0.7163 - val_loss: 1.3631 - val_accuracy: 0.5598 - lr: 6.0127e-04\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.6929 - accuracy: 0.7733 - val_loss: 0.9052 - val_accuracy: 0.6973 - lr: 4.8101e-04\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.5749 - accuracy: 0.8103 - val_loss: 1.0458 - val_accuracy: 0.6578 - lr: 4.8101e-04\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.4314 - accuracy: 0.8581 - val_loss: 0.9317 - val_accuracy: 0.7018 - lr: 3.8481e-04\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.3410 - accuracy: 0.8875 - val_loss: 1.2098 - val_accuracy: 0.6699 - lr: 3.8481e-04\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.2388 - accuracy: 0.9239 - val_loss: 1.1325 - val_accuracy: 0.7196 - lr: 3.0785e-04\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.1907 - accuracy: 0.9379 - val_loss: 0.9631 - val_accuracy: 0.7413 - lr: 3.0785e-04\n",
      "140/140 [==============================] - 11s 79ms/step - loss: 0.9461 - accuracy: 0.7464\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-05-05 03:11:01,689]\u001b[0m Trial 26 finished with value: 0.7464285492897034 and parameters: {'lr': 0.000601266546830139, 'dropout': 0.15000000000000002}. Best is trial 24 with value: 0.7529017925262451.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 103s 91ms/step - loss: 1.7290 - accuracy: 0.4461 - val_loss: 2.0455 - val_accuracy: 0.3788 - lr: 1.6123e-04\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 1.2421 - accuracy: 0.5990 - val_loss: 1.4563 - val_accuracy: 0.5350 - lr: 1.6123e-04\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 1.0123 - accuracy: 0.6689 - val_loss: 1.2925 - val_accuracy: 0.5786 - lr: 1.6123e-04\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.8363 - accuracy: 0.7269 - val_loss: 1.0789 - val_accuracy: 0.6484 - lr: 1.6123e-04\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.6824 - accuracy: 0.7760 - val_loss: 1.3321 - val_accuracy: 0.5868 - lr: 1.6123e-04\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.5018 - accuracy: 0.8387 - val_loss: 0.9794 - val_accuracy: 0.6837 - lr: 1.2899e-04\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.4100 - accuracy: 0.8709 - val_loss: 0.8879 - val_accuracy: 0.7165 - lr: 1.2899e-04\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 101s 91ms/step - loss: 0.3306 - accuracy: 0.8981 - val_loss: 1.4473 - val_accuracy: 0.6085 - lr: 1.2899e-04\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.2284 - accuracy: 0.9384 - val_loss: 0.9957 - val_accuracy: 0.6958 - lr: 1.0319e-04\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.1651 - accuracy: 0.9621 - val_loss: 0.9162 - val_accuracy: 0.7228 - lr: 8.2552e-05\n",
      "140/140 [==============================] - 11s 79ms/step - loss: 0.9168 - accuracy: 0.7188\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-05-05 03:28:14,050]\u001b[0m Trial 27 finished with value: 0.71875 and parameters: {'lr': 0.00016123406641481452, 'dropout': 0.1}. Best is trial 24 with value: 0.7529017925262451.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 104s 92ms/step - loss: 1.8864 - accuracy: 0.4160 - val_loss: 2.0092 - val_accuracy: 0.3667 - lr: 5.5015e-04\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 1.2682 - accuracy: 0.5854 - val_loss: 1.7086 - val_accuracy: 0.4585 - lr: 5.5015e-04\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 1.0467 - accuracy: 0.6569 - val_loss: 2.6511 - val_accuracy: 0.3145 - lr: 5.5015e-04\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 0.8420 - accuracy: 0.7258 - val_loss: 1.4345 - val_accuracy: 0.5489 - lr: 4.4012e-04\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 0.7026 - accuracy: 0.7721 - val_loss: 1.0124 - val_accuracy: 0.6710 - lr: 4.4012e-04\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.5898 - accuracy: 0.8073 - val_loss: 1.1693 - val_accuracy: 0.6359 - lr: 4.4012e-04\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.4378 - accuracy: 0.8545 - val_loss: 1.2103 - val_accuracy: 0.6487 - lr: 3.5210e-04\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 104s 93ms/step - loss: 0.3218 - accuracy: 0.8946 - val_loss: 1.0149 - val_accuracy: 0.6967 - lr: 2.8168e-04\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 0.2535 - accuracy: 0.9162 - val_loss: 1.1398 - val_accuracy: 0.7105 - lr: 2.8168e-04\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 104s 92ms/step - loss: 0.2100 - accuracy: 0.9292 - val_loss: 0.9802 - val_accuracy: 0.7071 - lr: 2.8168e-04\n",
      "140/140 [==============================] - 11s 79ms/step - loss: 1.0016 - accuracy: 0.7058\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-05-05 03:45:34,876]\u001b[0m Trial 28 finished with value: 0.7058035731315613 and parameters: {'lr': 0.000550152005594306, 'dropout': 0.15000000000000002}. Best is trial 24 with value: 0.7529017925262451.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 104s 92ms/step - loss: 1.7439 - accuracy: 0.4485 - val_loss: 1.9192 - val_accuracy: 0.4027 - lr: 2.7943e-04\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 1.2458 - accuracy: 0.5951 - val_loss: 1.2865 - val_accuracy: 0.5792 - lr: 2.7943e-04\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 1.0208 - accuracy: 0.6701 - val_loss: 1.4355 - val_accuracy: 0.5205 - lr: 2.7943e-04\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 0.8002 - accuracy: 0.7387 - val_loss: 1.0387 - val_accuracy: 0.6540 - lr: 2.2354e-04\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 0.6664 - accuracy: 0.7833 - val_loss: 1.1742 - val_accuracy: 0.6266 - lr: 2.2354e-04\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 0.4970 - accuracy: 0.8376 - val_loss: 1.1011 - val_accuracy: 0.6511 - lr: 1.7883e-04\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.3552 - accuracy: 0.8860 - val_loss: 1.0410 - val_accuracy: 0.6904 - lr: 1.4307e-04\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.2904 - accuracy: 0.9080 - val_loss: 1.0899 - val_accuracy: 0.6777 - lr: 1.4307e-04\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 103s 91ms/step - loss: 0.2009 - accuracy: 0.9418 - val_loss: 1.0523 - val_accuracy: 0.7002 - lr: 1.1445e-04\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.1680 - accuracy: 0.9543 - val_loss: 0.9594 - val_accuracy: 0.7161 - lr: 1.1445e-04\n",
      "140/140 [==============================] - 11s 79ms/step - loss: 0.9603 - accuracy: 0.7219\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-05-05 04:02:53,428]\u001b[0m Trial 29 finished with value: 0.721875011920929 and parameters: {'lr': 0.0002794253594087899, 'dropout': 0.15000000000000002}. Best is trial 24 with value: 0.7529017925262451.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 104s 92ms/step - loss: 1.8321 - accuracy: 0.4087 - val_loss: 1.5617 - val_accuracy: 0.4750 - lr: 6.7105e-05\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 1.3513 - accuracy: 0.5609 - val_loss: 1.3641 - val_accuracy: 0.5542 - lr: 6.7105e-05\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 1.1256 - accuracy: 0.6340 - val_loss: 1.1959 - val_accuracy: 0.5946 - lr: 6.7105e-05\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 0.9481 - accuracy: 0.6917 - val_loss: 1.2955 - val_accuracy: 0.5848 - lr: 6.7105e-05\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 0.7878 - accuracy: 0.7485 - val_loss: 1.1649 - val_accuracy: 0.6223 - lr: 5.3684e-05\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 0.6877 - accuracy: 0.7834 - val_loss: 1.0670 - val_accuracy: 0.6406 - lr: 5.3684e-05\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 0.5997 - accuracy: 0.8158 - val_loss: 1.1423 - val_accuracy: 0.6257 - lr: 5.3684e-05\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 0.5006 - accuracy: 0.8539 - val_loss: 1.0101 - val_accuracy: 0.6583 - lr: 4.2947e-05\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 0.4422 - accuracy: 0.8755 - val_loss: 0.9893 - val_accuracy: 0.6650 - lr: 4.2947e-05\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 0.3916 - accuracy: 0.8950 - val_loss: 0.9990 - val_accuracy: 0.6730 - lr: 4.2947e-05\n",
      "140/140 [==============================] - 11s 79ms/step - loss: 1.0343 - accuracy: 0.6694\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-05-05 04:20:17,520]\u001b[0m Trial 30 finished with value: 0.6694196462631226 and parameters: {'lr': 6.710458551309241e-05, 'dropout': 0.1}. Best is trial 24 with value: 0.7529017925262451.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 105s 93ms/step - loss: 2.0349 - accuracy: 0.3847 - val_loss: 1.5402 - val_accuracy: 0.4826 - lr: 0.0014\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 104s 92ms/step - loss: 1.2981 - accuracy: 0.5757 - val_loss: 1.6959 - val_accuracy: 0.4467 - lr: 0.0014\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 104s 92ms/step - loss: 0.9913 - accuracy: 0.6795 - val_loss: 2.3999 - val_accuracy: 0.3969 - lr: 0.0011\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 104s 93ms/step - loss: 0.7785 - accuracy: 0.7484 - val_loss: 1.2145 - val_accuracy: 0.6121 - lr: 8.7568e-04\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 104s 93ms/step - loss: 0.6518 - accuracy: 0.7889 - val_loss: 1.3519 - val_accuracy: 0.6172 - lr: 8.7568e-04\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 104s 93ms/step - loss: 0.5238 - accuracy: 0.8282 - val_loss: 1.0587 - val_accuracy: 0.6670 - lr: 8.7568e-04\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 104s 92ms/step - loss: 0.4308 - accuracy: 0.8596 - val_loss: 0.8969 - val_accuracy: 0.7214 - lr: 8.7568e-04\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 104s 93ms/step - loss: 0.3512 - accuracy: 0.8841 - val_loss: 0.9884 - val_accuracy: 0.6980 - lr: 8.7568e-04\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 104s 92ms/step - loss: 0.2469 - accuracy: 0.9199 - val_loss: 0.9551 - val_accuracy: 0.7272 - lr: 7.0054e-04\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 104s 93ms/step - loss: 0.2032 - accuracy: 0.9332 - val_loss: 1.0857 - val_accuracy: 0.7105 - lr: 7.0054e-04\n",
      "140/140 [==============================] - 11s 79ms/step - loss: 1.0858 - accuracy: 0.7080\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-05-05 04:37:48,163]\u001b[0m Trial 31 finished with value: 0.7080357074737549 and parameters: {'lr': 0.0013682437569830005, 'dropout': 0.2}. Best is trial 24 with value: 0.7529017925262451.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 105s 93ms/step - loss: 1.8677 - accuracy: 0.4178 - val_loss: 1.4859 - val_accuracy: 0.5016 - lr: 6.9311e-04\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 104s 93ms/step - loss: 1.2608 - accuracy: 0.5871 - val_loss: 1.3209 - val_accuracy: 0.5549 - lr: 6.9311e-04\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 1.0323 - accuracy: 0.6618 - val_loss: 1.3551 - val_accuracy: 0.5571 - lr: 6.9311e-04\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 104s 93ms/step - loss: 0.8705 - accuracy: 0.7171 - val_loss: 1.1938 - val_accuracy: 0.6083 - lr: 6.9311e-04\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 104s 93ms/step - loss: 0.7302 - accuracy: 0.7624 - val_loss: 1.1333 - val_accuracy: 0.6259 - lr: 6.9311e-04\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 104s 93ms/step - loss: 0.6075 - accuracy: 0.8010 - val_loss: 1.4912 - val_accuracy: 0.5473 - lr: 6.9311e-04\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 104s 93ms/step - loss: 0.4552 - accuracy: 0.8512 - val_loss: 0.8397 - val_accuracy: 0.7190 - lr: 5.5449e-04\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 105s 93ms/step - loss: 0.3691 - accuracy: 0.8786 - val_loss: 0.8907 - val_accuracy: 0.7243 - lr: 5.5449e-04\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 104s 93ms/step - loss: 0.2970 - accuracy: 0.9016 - val_loss: 0.9291 - val_accuracy: 0.7297 - lr: 5.5449e-04\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 104s 93ms/step - loss: 0.2449 - accuracy: 0.9191 - val_loss: 1.0854 - val_accuracy: 0.7047 - lr: 5.5449e-04\n",
      "140/140 [==============================] - 11s 79ms/step - loss: 1.0872 - accuracy: 0.7033\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-05-05 04:55:20,548]\u001b[0m Trial 32 finished with value: 0.7033482193946838 and parameters: {'lr': 0.0006931087626705095, 'dropout': 0.2}. Best is trial 24 with value: 0.7529017925262451.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 106s 94ms/step - loss: 2.1079 - accuracy: 0.3997 - val_loss: 2.2556 - val_accuracy: 0.2792 - lr: 0.0023\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 105s 94ms/step - loss: 1.2870 - accuracy: 0.5832 - val_loss: 1.4408 - val_accuracy: 0.5158 - lr: 0.0023\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 105s 94ms/step - loss: 1.0609 - accuracy: 0.6545 - val_loss: 1.1523 - val_accuracy: 0.6078 - lr: 0.0023\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 105s 94ms/step - loss: 0.9093 - accuracy: 0.7058 - val_loss: 1.0810 - val_accuracy: 0.6424 - lr: 0.0023\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 105s 94ms/step - loss: 0.7745 - accuracy: 0.7485 - val_loss: 0.9179 - val_accuracy: 0.6958 - lr: 0.0023\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 105s 94ms/step - loss: 0.6378 - accuracy: 0.7909 - val_loss: 0.9033 - val_accuracy: 0.7020 - lr: 0.0023\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 105s 94ms/step - loss: 0.5354 - accuracy: 0.8242 - val_loss: 0.9320 - val_accuracy: 0.6998 - lr: 0.0023\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 105s 94ms/step - loss: 0.3975 - accuracy: 0.8695 - val_loss: 1.2087 - val_accuracy: 0.6484 - lr: 0.0018\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 105s 93ms/step - loss: 0.2891 - accuracy: 0.9040 - val_loss: 0.9644 - val_accuracy: 0.7234 - lr: 0.0014\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 105s 94ms/step - loss: 0.2349 - accuracy: 0.9244 - val_loss: 0.9040 - val_accuracy: 0.7379 - lr: 0.0014\n",
      "140/140 [==============================] - 11s 79ms/step - loss: 0.9562 - accuracy: 0.7310\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-05-05 05:13:03,609]\u001b[0m Trial 33 finished with value: 0.7310267686843872 and parameters: {'lr': 0.0022532909391380657, 'dropout': 0.25}. Best is trial 24 with value: 0.7529017925262451.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 105s 94ms/step - loss: 1.9060 - accuracy: 0.4192 - val_loss: 1.7047 - val_accuracy: 0.4571 - lr: 9.7924e-04\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 105s 93ms/step - loss: 1.2292 - accuracy: 0.5997 - val_loss: 1.2830 - val_accuracy: 0.5844 - lr: 9.7924e-04\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 105s 94ms/step - loss: 1.0027 - accuracy: 0.6734 - val_loss: 1.1167 - val_accuracy: 0.6246 - lr: 9.7924e-04\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 105s 94ms/step - loss: 0.8348 - accuracy: 0.7285 - val_loss: 1.0115 - val_accuracy: 0.6690 - lr: 9.7924e-04\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 105s 94ms/step - loss: 0.6948 - accuracy: 0.7723 - val_loss: 0.9472 - val_accuracy: 0.6788 - lr: 9.7924e-04\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 105s 94ms/step - loss: 0.5630 - accuracy: 0.8162 - val_loss: 1.1971 - val_accuracy: 0.6326 - lr: 9.7924e-04\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 105s 94ms/step - loss: 0.3985 - accuracy: 0.8670 - val_loss: 1.4140 - val_accuracy: 0.6062 - lr: 7.8339e-04\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 105s 94ms/step - loss: 0.2692 - accuracy: 0.9141 - val_loss: 1.7119 - val_accuracy: 0.6022 - lr: 6.2672e-04\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 105s 94ms/step - loss: 0.1814 - accuracy: 0.9422 - val_loss: 1.3287 - val_accuracy: 0.6908 - lr: 5.0137e-04\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 105s 93ms/step - loss: 0.1455 - accuracy: 0.9552 - val_loss: 1.0007 - val_accuracy: 0.7225 - lr: 5.0137e-04\n",
      "140/140 [==============================] - 11s 80ms/step - loss: 1.0532 - accuracy: 0.7129\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-05-05 05:30:44,596]\u001b[0m Trial 34 finished with value: 0.7129464149475098 and parameters: {'lr': 0.000979242444659313, 'dropout': 0.15000000000000002}. Best is trial 24 with value: 0.7529017925262451.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 106s 94ms/step - loss: 2.4960 - accuracy: 0.3574 - val_loss: 1.6841 - val_accuracy: 0.4299 - lr: 0.0042\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 105s 94ms/step - loss: 1.3939 - accuracy: 0.5443 - val_loss: 2.4662 - val_accuracy: 0.3138 - lr: 0.0042\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 105s 94ms/step - loss: 1.1681 - accuracy: 0.6210 - val_loss: 1.1931 - val_accuracy: 0.6025 - lr: 0.0034\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 105s 94ms/step - loss: 1.0340 - accuracy: 0.6610 - val_loss: 1.1547 - val_accuracy: 0.6277 - lr: 0.0034\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 105s 94ms/step - loss: 0.9151 - accuracy: 0.7044 - val_loss: 1.0727 - val_accuracy: 0.6518 - lr: 0.0034\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 105s 93ms/step - loss: 0.8144 - accuracy: 0.7324 - val_loss: 1.1005 - val_accuracy: 0.6424 - lr: 0.0034\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 105s 93ms/step - loss: 0.6720 - accuracy: 0.7783 - val_loss: 0.9285 - val_accuracy: 0.6980 - lr: 0.0027\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 105s 93ms/step - loss: 0.5859 - accuracy: 0.8054 - val_loss: 1.0780 - val_accuracy: 0.6603 - lr: 0.0027\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 105s 94ms/step - loss: 0.4924 - accuracy: 0.8367 - val_loss: 0.9388 - val_accuracy: 0.7083 - lr: 0.0021\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 105s 94ms/step - loss: 0.4319 - accuracy: 0.8554 - val_loss: 1.2301 - val_accuracy: 0.6516 - lr: 0.0021\n",
      "140/140 [==============================] - 11s 79ms/step - loss: 1.1896 - accuracy: 0.6587\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-05-05 05:48:26,257]\u001b[0m Trial 35 finished with value: 0.6587053537368774 and parameters: {'lr': 0.004192920131247175, 'dropout': 0.35}. Best is trial 24 with value: 0.7529017925262451.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 104s 92ms/step - loss: 2.1367 - accuracy: 0.3686 - val_loss: 1.4144 - val_accuracy: 0.5344 - lr: 0.0017\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 104s 92ms/step - loss: 1.3247 - accuracy: 0.5712 - val_loss: 1.2840 - val_accuracy: 0.5783 - lr: 0.0017\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 1.0840 - accuracy: 0.6461 - val_loss: 1.4626 - val_accuracy: 0.5261 - lr: 0.0017\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 0.8721 - accuracy: 0.7173 - val_loss: 1.2286 - val_accuracy: 0.5975 - lr: 0.0014\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 0.7289 - accuracy: 0.7643 - val_loss: 0.9287 - val_accuracy: 0.6828 - lr: 0.0014\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 0.6010 - accuracy: 0.8049 - val_loss: 0.8589 - val_accuracy: 0.7254 - lr: 0.0014\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 0.4897 - accuracy: 0.8390 - val_loss: 1.1524 - val_accuracy: 0.6379 - lr: 0.0014\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 0.3482 - accuracy: 0.8843 - val_loss: 1.5850 - val_accuracy: 0.5475 - lr: 0.0011\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 0.2504 - accuracy: 0.9174 - val_loss: 0.8506 - val_accuracy: 0.7478 - lr: 8.6817e-04\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 0.2086 - accuracy: 0.9316 - val_loss: 0.9731 - val_accuracy: 0.7212 - lr: 8.6817e-04\n",
      "140/140 [==============================] - 11s 79ms/step - loss: 0.9881 - accuracy: 0.7221\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-05-05 06:05:50,919]\u001b[0m Trial 36 finished with value: 0.7220982313156128 and parameters: {'lr': 0.0016956523777115436, 'dropout': 0.25}. Best is trial 24 with value: 0.7529017925262451.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 104s 92ms/step - loss: 2.4009 - accuracy: 0.3642 - val_loss: 1.7097 - val_accuracy: 0.4353 - lr: 0.0033\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 1.3717 - accuracy: 0.5541 - val_loss: 1.3703 - val_accuracy: 0.5527 - lr: 0.0033\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 1.1533 - accuracy: 0.6231 - val_loss: 1.1835 - val_accuracy: 0.6002 - lr: 0.0033\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 0.9784 - accuracy: 0.6817 - val_loss: 1.0891 - val_accuracy: 0.6446 - lr: 0.0033\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 104s 92ms/step - loss: 0.8400 - accuracy: 0.7238 - val_loss: 1.4945 - val_accuracy: 0.5263 - lr: 0.0033\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 104s 93ms/step - loss: 0.6686 - accuracy: 0.7824 - val_loss: 2.2745 - val_accuracy: 0.4344 - lr: 0.0026\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 0.5211 - accuracy: 0.8271 - val_loss: 1.0809 - val_accuracy: 0.6636 - lr: 0.0021\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 0.4346 - accuracy: 0.8555 - val_loss: 1.2249 - val_accuracy: 0.6594 - lr: 0.0021\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 0.3306 - accuracy: 0.8895 - val_loss: 1.0522 - val_accuracy: 0.6996 - lr: 0.0017\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 0.2785 - accuracy: 0.9063 - val_loss: 0.9263 - val_accuracy: 0.7379 - lr: 0.0017\n",
      "140/140 [==============================] - 11s 78ms/step - loss: 0.9887 - accuracy: 0.7295\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2023-05-05 06:23:17,072]\u001b[0m Trial 37 finished with value: 0.7294642925262451 and parameters: {'lr': 0.0032937791765257833, 'dropout': 0.2}. Best is trial 24 with value: 0.7529017925262451.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 105s 93ms/step - loss: 1.7977 - accuracy: 0.4346 - val_loss: 3.6570 - val_accuracy: 0.2069 - lr: 5.2672e-04\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 104s 93ms/step - loss: 1.2440 - accuracy: 0.5944 - val_loss: 1.2120 - val_accuracy: 0.5989 - lr: 5.2672e-04\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 104s 92ms/step - loss: 1.0175 - accuracy: 0.6687 - val_loss: 1.1140 - val_accuracy: 0.6377 - lr: 5.2672e-04\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 104s 93ms/step - loss: 0.8492 - accuracy: 0.7217 - val_loss: 1.0949 - val_accuracy: 0.6379 - lr: 5.2672e-04\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 104s 93ms/step - loss: 0.7005 - accuracy: 0.7710 - val_loss: 1.5583 - val_accuracy: 0.5355 - lr: 5.2672e-04\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.5817 - accuracy: 0.8107 - val_loss: 0.8393 - val_accuracy: 0.7250 - lr: 6.2137e-04\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - ETA: 0s - loss: 0.4280 - accuracy: 0.8594"
     ]
    }
   ],
   "source": [
    "def objective(trial):\n",
    "    #What we want to fine-tune\n",
    "    lr = trial.suggest_float(\"lr\", 1e-5, 1e-1, log = True)\n",
    "    dropout = trial.suggest_float(\"dropout\", 0.1, 0.5, step = 0.05)\n",
    "    \n",
    "    #Main model - BatchNormalization + 5x5\n",
    "    model = tf.keras.Sequential([\n",
    "        tf.keras.layers.Conv2D(8, (5, 5), input_shape = (515, 389, 3)),\n",
    "        tf.keras.layers.BatchNormalization(axis = 3),\n",
    "        tf.keras.layers.ReLU(),\n",
    "        tf.keras.layers.MaxPooling2D(),\n",
    "        tf.keras.layers.Conv2D(16, (5, 5)),\n",
    "        tf.keras.layers.BatchNormalization(axis = 3),\n",
    "        tf.keras.layers.ReLU(),\n",
    "        tf.keras.layers.MaxPooling2D(),\n",
    "        tf.keras.layers.Conv2D(32, (5, 5)),\n",
    "        tf.keras.layers.BatchNormalization(axis = 3),\n",
    "        tf.keras.layers.ReLU(),\n",
    "        tf.keras.layers.MaxPooling2D(),\n",
    "        tf.keras.layers.Conv2D(64, (5, 5)),\n",
    "        tf.keras.layers.BatchNormalization(axis = -1),\n",
    "        tf.keras.layers.ReLU(),\n",
    "        tf.keras.layers.MaxPooling2D(),\n",
    "        tf.keras.layers.Conv2D(128, (5, 5)),\n",
    "        tf.keras.layers.BatchNormalization(axis = -1),\n",
    "        tf.keras.layers.ReLU(),\n",
    "        tf.keras.layers.MaxPooling2D(),\n",
    "        tf.keras.layers.Dropout(dropout),\n",
    "        tf.keras.layers.Flatten(),\n",
    "        tf.keras.layers.Dense(n_classes, activation = \"softmax\")\n",
    "    ])\n",
    "    callback = tf.keras.callbacks.ReduceLROnPlateau(monitor = \"val_accuracy\", factor = 0.8, patience = 1)\n",
    "    model.compile(loss = \"categorical_crossentropy\", optimizer = tf.keras.optimizers.legacy.Adam(learning_rate = lr), metrics = [\"accuracy\"])\n",
    "    model.fit(train_generator, epochs = 10, validation_data = vali_generator, callbacks = [callback])\n",
    "    score = model.evaluate(testing_generator)\n",
    "    return score[1]\n",
    "\n",
    "#We run 50 trials to find the best dropout/lr values after 10 epochs\n",
    "study = optuna.create_study(direction = 'maximize')\n",
    "study.optimize(objective, n_trials = 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cd400b1",
   "metadata": {},
   "source": [
    "<H1>Model Building (20 Epochs)</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bb678004",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 1.7135 - accuracy: 0.4345 - val_loss: 1.3639 - val_accuracy: 0.5502 - lr: 0.0010\n",
      "Epoch 2/20\n",
      "1120/1120 [==============================] - 105s 94ms/step - loss: 1.1982 - accuracy: 0.6088 - val_loss: 1.1296 - val_accuracy: 0.6170 - lr: 0.0010\n",
      "Epoch 3/20\n",
      "1120/1120 [==============================] - 104s 93ms/step - loss: 0.9826 - accuracy: 0.6753 - val_loss: 1.0347 - val_accuracy: 0.6502 - lr: 0.0010\n",
      "Epoch 4/20\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 0.8384 - accuracy: 0.7225 - val_loss: 1.0026 - val_accuracy: 0.6674 - lr: 0.0010\n",
      "Epoch 5/20\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 0.7267 - accuracy: 0.7566 - val_loss: 1.1193 - val_accuracy: 0.6525 - lr: 0.0010\n",
      "Epoch 6/20\n",
      "1120/1120 [==============================] - 96s 86ms/step - loss: 0.5895 - accuracy: 0.8023 - val_loss: 1.0027 - val_accuracy: 0.6868 - lr: 8.0000e-04\n",
      "Epoch 7/20\n",
      "1120/1120 [==============================] - 95s 85ms/step - loss: 0.5184 - accuracy: 0.8244 - val_loss: 1.0342 - val_accuracy: 0.6830 - lr: 8.0000e-04\n",
      "Epoch 8/20\n",
      "1120/1120 [==============================] - 98s 88ms/step - loss: 0.4279 - accuracy: 0.8548 - val_loss: 1.0869 - val_accuracy: 0.6859 - lr: 6.4000e-04\n",
      "Epoch 9/20\n",
      "1120/1120 [==============================] - 99s 88ms/step - loss: 0.3536 - accuracy: 0.8786 - val_loss: 1.0812 - val_accuracy: 0.6964 - lr: 5.1200e-04\n",
      "Epoch 10/20\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.3175 - accuracy: 0.8917 - val_loss: 1.0944 - val_accuracy: 0.7083 - lr: 5.1200e-04\n",
      "Epoch 11/20\n",
      "1120/1120 [==============================] - 98s 87ms/step - loss: 0.2880 - accuracy: 0.9006 - val_loss: 1.1388 - val_accuracy: 0.7083 - lr: 5.1200e-04\n",
      "Epoch 12/20\n",
      "1120/1120 [==============================] - 97s 87ms/step - loss: 0.2390 - accuracy: 0.9160 - val_loss: 1.1878 - val_accuracy: 0.7025 - lr: 4.0960e-04\n",
      "Epoch 13/20\n",
      "1120/1120 [==============================] - 97s 87ms/step - loss: 0.2018 - accuracy: 0.9306 - val_loss: 1.2895 - val_accuracy: 0.7083 - lr: 3.2768e-04\n",
      "Epoch 14/20\n",
      "1120/1120 [==============================] - 99s 88ms/step - loss: 0.1764 - accuracy: 0.9378 - val_loss: 1.2761 - val_accuracy: 0.7083 - lr: 2.6214e-04\n",
      "Epoch 15/20\n",
      "1120/1120 [==============================] - 98s 87ms/step - loss: 0.1475 - accuracy: 0.9491 - val_loss: 1.3780 - val_accuracy: 0.7065 - lr: 2.0972e-04\n",
      "Epoch 16/20\n",
      "1120/1120 [==============================] - 95s 85ms/step - loss: 0.1312 - accuracy: 0.9561 - val_loss: 1.3696 - val_accuracy: 0.7094 - lr: 1.6777e-04\n",
      "Epoch 17/20\n",
      "1120/1120 [==============================] - 96s 86ms/step - loss: 0.1291 - accuracy: 0.9546 - val_loss: 1.3950 - val_accuracy: 0.7112 - lr: 1.6777e-04\n",
      "Epoch 18/20\n",
      "1120/1120 [==============================] - 97s 86ms/step - loss: 0.1196 - accuracy: 0.9594 - val_loss: 1.4140 - val_accuracy: 0.7138 - lr: 1.6777e-04\n",
      "Epoch 19/20\n",
      "1120/1120 [==============================] - 94s 84ms/step - loss: 0.1127 - accuracy: 0.9609 - val_loss: 1.4576 - val_accuracy: 0.7074 - lr: 1.6777e-04\n",
      "Epoch 20/20\n",
      "1120/1120 [==============================] - 95s 85ms/step - loss: 0.1000 - accuracy: 0.9662 - val_loss: 1.5396 - val_accuracy: 0.7103 - lr: 1.3422e-04\n"
     ]
    }
   ],
   "source": [
    "#Main model - GTZAN\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Conv2D(8, (3, 3), activation = \"relu\", input_shape = (515, 389, 3)),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(16, (3, 3), activation = \"relu\"),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(32, (3, 3), activation = \"relu\"),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(64, (3, 3), activation = \"relu\"),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(128, (3, 3), activation = \"relu\"),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Dropout(0.3),\n",
    "    tf.keras.layers.Flatten(),\n",
    "    tf.keras.layers.Dense(n_classes, activation = \"softmax\")\n",
    "])\n",
    "callback = tf.keras.callbacks.ReduceLROnPlateau(monitor = \"val_accuracy\", factor = 0.8, patience = 1)\n",
    "model.compile(loss = \"categorical_crossentropy\", optimizer = tf.keras.optimizers.legacy.Adam(learning_rate = 0.001), metrics = [\"accuracy\"])\n",
    "history = model.fit(train_generator, epochs = 20, validation_data = vali_generator, callbacks = [callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "aed85a21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 2.0455 - accuracy: 0.3772 - val_loss: 2.0225 - val_accuracy: 0.3364 - lr: 0.0010\n",
      "Epoch 2/20\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 1.3292 - accuracy: 0.5640 - val_loss: 1.8515 - val_accuracy: 0.4335 - lr: 0.0010\n",
      "Epoch 3/20\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 1.0708 - accuracy: 0.6523 - val_loss: 1.8884 - val_accuracy: 0.3938 - lr: 0.0010\n",
      "Epoch 4/20\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.8602 - accuracy: 0.7203 - val_loss: 1.2299 - val_accuracy: 0.5935 - lr: 8.0000e-04\n",
      "Epoch 5/20\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.7282 - accuracy: 0.7620 - val_loss: 1.1515 - val_accuracy: 0.6232 - lr: 8.0000e-04\n",
      "Epoch 6/20\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.6008 - accuracy: 0.8043 - val_loss: 1.6244 - val_accuracy: 0.4864 - lr: 8.0000e-04\n",
      "Epoch 7/20\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.4615 - accuracy: 0.8489 - val_loss: 0.9535 - val_accuracy: 0.6790 - lr: 6.4000e-04\n",
      "Epoch 8/20\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.3790 - accuracy: 0.8754 - val_loss: 1.0414 - val_accuracy: 0.6964 - lr: 6.4000e-04\n",
      "Epoch 9/20\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.3232 - accuracy: 0.8919 - val_loss: 0.8221 - val_accuracy: 0.7422 - lr: 6.4000e-04\n",
      "Epoch 10/20\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.2716 - accuracy: 0.9099 - val_loss: 0.8381 - val_accuracy: 0.7545 - lr: 6.4000e-04\n",
      "Epoch 11/20\n",
      "1120/1120 [==============================] - 100s 90ms/step - loss: 0.2335 - accuracy: 0.9218 - val_loss: 0.8368 - val_accuracy: 0.7417 - lr: 6.4000e-04\n",
      "Epoch 12/20\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.1687 - accuracy: 0.9449 - val_loss: 0.9068 - val_accuracy: 0.7442 - lr: 5.1200e-04\n",
      "Epoch 13/20\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.1235 - accuracy: 0.9612 - val_loss: 0.9649 - val_accuracy: 0.7609 - lr: 4.0960e-04\n",
      "Epoch 14/20\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.1125 - accuracy: 0.9640 - val_loss: 0.8878 - val_accuracy: 0.7652 - lr: 4.0960e-04\n",
      "Epoch 15/20\n",
      "1120/1120 [==============================] - 100s 90ms/step - loss: 0.1080 - accuracy: 0.9663 - val_loss: 0.9518 - val_accuracy: 0.7672 - lr: 4.0960e-04\n",
      "Epoch 16/20\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.0968 - accuracy: 0.9703 - val_loss: 0.9739 - val_accuracy: 0.7542 - lr: 4.0960e-04\n",
      "Epoch 17/20\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.0792 - accuracy: 0.9769 - val_loss: 0.9407 - val_accuracy: 0.7719 - lr: 3.2768e-04\n",
      "Epoch 18/20\n",
      "1120/1120 [==============================] - 99s 89ms/step - loss: 0.0724 - accuracy: 0.9786 - val_loss: 0.9823 - val_accuracy: 0.7571 - lr: 3.2768e-04\n",
      "Epoch 19/20\n",
      "1120/1120 [==============================] - 105s 94ms/step - loss: 0.0631 - accuracy: 0.9830 - val_loss: 0.9356 - val_accuracy: 0.7864 - lr: 2.6214e-04\n",
      "Epoch 20/20\n",
      "1120/1120 [==============================] - 104s 93ms/step - loss: 0.0567 - accuracy: 0.9849 - val_loss: 0.9646 - val_accuracy: 0.7737 - lr: 2.6214e-04\n"
     ]
    }
   ],
   "source": [
    "#Main model - BatchNormalization + 5x5\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Conv2D(8, (5, 5), input_shape = (515, 389, 3)),\n",
    "    tf.keras.layers.BatchNormalization(axis = 3),\n",
    "    tf.keras.layers.ReLU(),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(16, (5, 5)),\n",
    "    tf.keras.layers.BatchNormalization(axis = 3),\n",
    "    tf.keras.layers.ReLU(),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(32, (5, 5)),\n",
    "    tf.keras.layers.BatchNormalization(axis = 3),\n",
    "    tf.keras.layers.ReLU(),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(64, (5, 5)),\n",
    "    tf.keras.layers.BatchNormalization(axis = -1),\n",
    "    tf.keras.layers.ReLU(),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(128, (5, 5)),\n",
    "    tf.keras.layers.BatchNormalization(axis = -1),\n",
    "    tf.keras.layers.ReLU(),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Dropout(0.3),\n",
    "    tf.keras.layers.Flatten(),\n",
    "    tf.keras.layers.Dense(n_classes, activation = \"softmax\")\n",
    "])\n",
    "callback = tf.keras.callbacks.ReduceLROnPlateau(monitor = \"val_accuracy\", factor = 0.8, patience = 1)\n",
    "model.compile(loss = \"categorical_crossentropy\", optimizer = tf.keras.optimizers.legacy.Adam(learning_rate = 0.001), metrics = [\"accuracy\"])\n",
    "history = model.fit(train_generator, epochs = 20, validation_data = vali_generator, callbacks = [callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a8e2b0ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "1120/1120 [==============================] - 96s 86ms/step - loss: 1.7079 - accuracy: 0.4324 - val_loss: 1.3681 - val_accuracy: 0.5533 - lr: 0.0010\n",
      "Epoch 2/20\n",
      "1120/1120 [==============================] - 96s 86ms/step - loss: 1.2308 - accuracy: 0.5987 - val_loss: 1.1940 - val_accuracy: 0.6000 - lr: 0.0010\n",
      "Epoch 3/20\n",
      "1120/1120 [==============================] - 96s 86ms/step - loss: 1.0386 - accuracy: 0.6596 - val_loss: 1.1017 - val_accuracy: 0.6346 - lr: 0.0010\n",
      "Epoch 4/20\n",
      "1120/1120 [==============================] - 97s 86ms/step - loss: 0.9046 - accuracy: 0.7024 - val_loss: 1.0606 - val_accuracy: 0.6545 - lr: 0.0010\n",
      "Epoch 5/20\n",
      "1120/1120 [==============================] - 97s 87ms/step - loss: 0.8021 - accuracy: 0.7331 - val_loss: 1.0197 - val_accuracy: 0.6621 - lr: 0.0010\n",
      "Epoch 6/20\n",
      "1120/1120 [==============================] - 99s 88ms/step - loss: 0.7215 - accuracy: 0.7601 - val_loss: 1.0331 - val_accuracy: 0.6609 - lr: 0.0010\n",
      "Epoch 7/20\n",
      "1120/1120 [==============================] - 98s 88ms/step - loss: 0.6125 - accuracy: 0.7949 - val_loss: 0.9852 - val_accuracy: 0.6846 - lr: 8.0000e-04\n",
      "Epoch 8/20\n",
      "1120/1120 [==============================] - 98s 88ms/step - loss: 0.5602 - accuracy: 0.8098 - val_loss: 1.0685 - val_accuracy: 0.6810 - lr: 8.0000e-04\n",
      "Epoch 9/20\n",
      "1120/1120 [==============================] - 98s 88ms/step - loss: 0.4940 - accuracy: 0.8319 - val_loss: 1.0522 - val_accuracy: 0.6833 - lr: 6.4000e-04\n",
      "Epoch 10/20\n",
      "1120/1120 [==============================] - 97s 87ms/step - loss: 0.4276 - accuracy: 0.8515 - val_loss: 1.0449 - val_accuracy: 0.6980 - lr: 5.1200e-04\n",
      "Epoch 11/20\n",
      "1120/1120 [==============================] - 97s 86ms/step - loss: 0.4027 - accuracy: 0.8611 - val_loss: 1.0835 - val_accuracy: 0.6850 - lr: 5.1200e-04\n",
      "Epoch 12/20\n",
      "1120/1120 [==============================] - 97s 86ms/step - loss: 0.3571 - accuracy: 0.8786 - val_loss: 1.1087 - val_accuracy: 0.6967 - lr: 4.0960e-04\n",
      "Epoch 13/20\n",
      "1120/1120 [==============================] - 97s 86ms/step - loss: 0.3244 - accuracy: 0.8869 - val_loss: 1.1382 - val_accuracy: 0.6924 - lr: 3.2768e-04\n",
      "Epoch 14/20\n",
      "1120/1120 [==============================] - 98s 87ms/step - loss: 0.2911 - accuracy: 0.8986 - val_loss: 1.1649 - val_accuracy: 0.6975 - lr: 2.6214e-04\n",
      "Epoch 15/20\n",
      "1120/1120 [==============================] - 99s 88ms/step - loss: 0.2679 - accuracy: 0.9069 - val_loss: 1.2232 - val_accuracy: 0.6929 - lr: 2.0972e-04\n",
      "Epoch 16/20\n",
      "1120/1120 [==============================] - 98s 88ms/step - loss: 0.2478 - accuracy: 0.9121 - val_loss: 1.1950 - val_accuracy: 0.6964 - lr: 1.6777e-04\n",
      "Epoch 17/20\n",
      "1120/1120 [==============================] - 98s 88ms/step - loss: 0.2374 - accuracy: 0.9192 - val_loss: 1.2254 - val_accuracy: 0.6949 - lr: 1.3422e-04\n",
      "Epoch 18/20\n",
      "1120/1120 [==============================] - 98s 88ms/step - loss: 0.2193 - accuracy: 0.9238 - val_loss: 1.2685 - val_accuracy: 0.7009 - lr: 1.0737e-04\n",
      "Epoch 19/20\n",
      "1120/1120 [==============================] - 97s 86ms/step - loss: 0.2152 - accuracy: 0.9255 - val_loss: 1.2718 - val_accuracy: 0.6958 - lr: 1.0737e-04\n",
      "Epoch 20/20\n",
      "1120/1120 [==============================] - 98s 87ms/step - loss: 0.2041 - accuracy: 0.9294 - val_loss: 1.2909 - val_accuracy: 0.6971 - lr: 8.5899e-05\n"
     ]
    }
   ],
   "source": [
    "#Main model - Less filters\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Conv2D(4, (3, 3), activation = \"relu\", input_shape = (515, 389, 3)),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(8, (3, 3), activation = \"relu\"),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(16, (3, 3), activation = \"relu\"),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(32, (3, 3), activation = \"relu\"),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(64, (3, 3), activation = \"relu\"),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Dropout(0.3),\n",
    "    tf.keras.layers.Flatten(),\n",
    "    tf.keras.layers.Dense(n_classes, activation = \"softmax\")\n",
    "])\n",
    "callback = tf.keras.callbacks.ReduceLROnPlateau(monitor = \"val_accuracy\", factor = 0.8, patience = 1)\n",
    "model.compile(loss = \"categorical_crossentropy\", optimizer = tf.keras.optimizers.legacy.Adam(learning_rate = 0.001), metrics = [\"accuracy\"])\n",
    "history = model.fit(train_generator, epochs = 20, validation_data = vali_generator, callbacks = [callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0e1a8ce7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 1.8228 - accuracy: 0.3919 - val_loss: 1.4072 - val_accuracy: 0.5411 - lr: 0.0010\n",
      "Epoch 2/20\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 1.2837 - accuracy: 0.5781 - val_loss: 1.1922 - val_accuracy: 0.6109 - lr: 0.0010\n",
      "Epoch 3/20\n",
      "1120/1120 [==============================] - 99s 88ms/step - loss: 1.0790 - accuracy: 0.6439 - val_loss: 1.0665 - val_accuracy: 0.6511 - lr: 0.0010\n",
      "Epoch 4/20\n",
      "1120/1120 [==============================] - 98s 88ms/step - loss: 0.9510 - accuracy: 0.6870 - val_loss: 1.0039 - val_accuracy: 0.6699 - lr: 0.0010\n",
      "Epoch 5/20\n",
      "1120/1120 [==============================] - 98s 88ms/step - loss: 0.8427 - accuracy: 0.7180 - val_loss: 0.9450 - val_accuracy: 0.6873 - lr: 0.0010\n",
      "Epoch 6/20\n",
      "1120/1120 [==============================] - 98s 87ms/step - loss: 0.7576 - accuracy: 0.7461 - val_loss: 0.9557 - val_accuracy: 0.6891 - lr: 0.0010\n",
      "Epoch 7/20\n",
      "1120/1120 [==============================] - 98s 87ms/step - loss: 0.6934 - accuracy: 0.7669 - val_loss: 0.9791 - val_accuracy: 0.6871 - lr: 0.0010\n",
      "Epoch 8/20\n",
      "1120/1120 [==============================] - 99s 89ms/step - loss: 0.5791 - accuracy: 0.8041 - val_loss: 0.9943 - val_accuracy: 0.6866 - lr: 8.0000e-04\n",
      "Epoch 9/20\n",
      "1120/1120 [==============================] - 98s 88ms/step - loss: 0.4798 - accuracy: 0.8366 - val_loss: 1.0438 - val_accuracy: 0.6817 - lr: 6.4000e-04\n",
      "Epoch 10/20\n",
      "1120/1120 [==============================] - 98s 88ms/step - loss: 0.4004 - accuracy: 0.8636 - val_loss: 1.0233 - val_accuracy: 0.6987 - lr: 5.1200e-04\n",
      "Epoch 11/20\n",
      "1120/1120 [==============================] - 98s 88ms/step - loss: 0.3602 - accuracy: 0.8746 - val_loss: 1.0456 - val_accuracy: 0.7036 - lr: 5.1200e-04\n",
      "Epoch 12/20\n",
      "1120/1120 [==============================] - 99s 88ms/step - loss: 0.3273 - accuracy: 0.8862 - val_loss: 1.0986 - val_accuracy: 0.7049 - lr: 5.1200e-04\n",
      "Epoch 13/20\n",
      "1120/1120 [==============================] - 99s 88ms/step - loss: 0.3001 - accuracy: 0.8961 - val_loss: 1.2054 - val_accuracy: 0.6895 - lr: 5.1200e-04\n",
      "Epoch 14/20\n",
      "1120/1120 [==============================] - 98s 88ms/step - loss: 0.2467 - accuracy: 0.9136 - val_loss: 1.1529 - val_accuracy: 0.7009 - lr: 4.0960e-04\n",
      "Epoch 15/20\n",
      "1120/1120 [==============================] - 98s 88ms/step - loss: 0.2053 - accuracy: 0.9295 - val_loss: 1.2286 - val_accuracy: 0.7007 - lr: 3.2768e-04\n",
      "Epoch 16/20\n",
      "1120/1120 [==============================] - 98s 88ms/step - loss: 0.1675 - accuracy: 0.9419 - val_loss: 1.3049 - val_accuracy: 0.6971 - lr: 2.6214e-04\n",
      "Epoch 17/20\n",
      "1120/1120 [==============================] - 98s 88ms/step - loss: 0.1489 - accuracy: 0.9494 - val_loss: 1.3118 - val_accuracy: 0.7040 - lr: 2.0972e-04\n",
      "Epoch 18/20\n",
      "1120/1120 [==============================] - 98s 87ms/step - loss: 0.1285 - accuracy: 0.9568 - val_loss: 1.3575 - val_accuracy: 0.7089 - lr: 1.6777e-04\n",
      "Epoch 19/20\n",
      "1120/1120 [==============================] - 99s 88ms/step - loss: 0.1169 - accuracy: 0.9617 - val_loss: 1.4061 - val_accuracy: 0.7004 - lr: 1.6777e-04\n",
      "Epoch 20/20\n",
      "1120/1120 [==============================] - 99s 88ms/step - loss: 0.1077 - accuracy: 0.9619 - val_loss: 1.4281 - val_accuracy: 0.7076 - lr: 1.3422e-04\n"
     ]
    }
   ],
   "source": [
    "#Main model - 5x5 Kernel\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Conv2D(8, (5, 5), activation = \"relu\", input_shape = (515, 389, 3)),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(16, (5, 5), activation = \"relu\"),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(32, (5, 5), activation = \"relu\"),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(64, (5, 5), activation = \"relu\"),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(128, (5, 5), activation = \"relu\"),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Dropout(0.3),\n",
    "    tf.keras.layers.Flatten(),\n",
    "    tf.keras.layers.Dense(n_classes, activation = \"softmax\")\n",
    "])\n",
    "callback = tf.keras.callbacks.ReduceLROnPlateau(monitor = \"val_accuracy\", factor = 0.8, patience = 1)\n",
    "model.compile(loss = \"categorical_crossentropy\", optimizer = tf.keras.optimizers.legacy.Adam(learning_rate = 0.001), metrics = [\"accuracy\"])\n",
    "history = model.fit(train_generator, epochs = 20, validation_data = vali_generator, callbacks = [callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f5d74b80",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "1120/1120 [==============================] - 99s 88ms/step - loss: 1.9965 - accuracy: 0.4213 - val_loss: 1.4988 - val_accuracy: 0.5143 - lr: 0.0010\n",
      "Epoch 2/20\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 1.2940 - accuracy: 0.5782 - val_loss: 1.3190 - val_accuracy: 0.5879 - lr: 0.0010\n",
      "Epoch 3/20\n",
      "1120/1120 [==============================] - 99s 89ms/step - loss: 1.0778 - accuracy: 0.6484 - val_loss: 1.0364 - val_accuracy: 0.6520 - lr: 0.0010\n",
      "Epoch 4/20\n",
      "1120/1120 [==============================] - 99s 88ms/step - loss: 0.8980 - accuracy: 0.7063 - val_loss: 1.0904 - val_accuracy: 0.6317 - lr: 0.0010\n",
      "Epoch 5/20\n",
      "1120/1120 [==============================] - 99s 89ms/step - loss: 0.7072 - accuracy: 0.7711 - val_loss: 0.9488 - val_accuracy: 0.6844 - lr: 8.0000e-04\n",
      "Epoch 6/20\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.5942 - accuracy: 0.8061 - val_loss: 1.1764 - val_accuracy: 0.6268 - lr: 8.0000e-04\n",
      "Epoch 7/20\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.4514 - accuracy: 0.8504 - val_loss: 0.8473 - val_accuracy: 0.7281 - lr: 6.4000e-04\n",
      "Epoch 8/20\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.3656 - accuracy: 0.8785 - val_loss: 1.0231 - val_accuracy: 0.7002 - lr: 6.4000e-04\n",
      "Epoch 9/20\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.2748 - accuracy: 0.9085 - val_loss: 0.9684 - val_accuracy: 0.7252 - lr: 5.1200e-04\n",
      "Epoch 10/20\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.2064 - accuracy: 0.9328 - val_loss: 0.9426 - val_accuracy: 0.7310 - lr: 4.0960e-04\n",
      "Epoch 11/20\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.1835 - accuracy: 0.9414 - val_loss: 1.0233 - val_accuracy: 0.7306 - lr: 4.0960e-04\n",
      "Epoch 12/20\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 0.1428 - accuracy: 0.9536 - val_loss: 1.1809 - val_accuracy: 0.6973 - lr: 3.2768e-04\n",
      "Epoch 13/20\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.1139 - accuracy: 0.9642 - val_loss: 0.9993 - val_accuracy: 0.7413 - lr: 2.6214e-04\n",
      "Epoch 14/20\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.1049 - accuracy: 0.9662 - val_loss: 1.1480 - val_accuracy: 0.7248 - lr: 2.6214e-04\n",
      "Epoch 15/20\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.0865 - accuracy: 0.9728 - val_loss: 1.0824 - val_accuracy: 0.7393 - lr: 2.0972e-04\n",
      "Epoch 16/20\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.0720 - accuracy: 0.9784 - val_loss: 1.0281 - val_accuracy: 0.7502 - lr: 1.6777e-04\n",
      "Epoch 17/20\n",
      "1120/1120 [==============================] - 99s 89ms/step - loss: 0.0706 - accuracy: 0.9792 - val_loss: 1.0839 - val_accuracy: 0.7516 - lr: 1.6777e-04\n",
      "Epoch 18/20\n",
      "1120/1120 [==============================] - 99s 88ms/step - loss: 0.0652 - accuracy: 0.9803 - val_loss: 1.2127 - val_accuracy: 0.7237 - lr: 1.6777e-04\n",
      "Epoch 19/20\n",
      "1120/1120 [==============================] - 99s 88ms/step - loss: 0.0577 - accuracy: 0.9831 - val_loss: 1.1117 - val_accuracy: 0.7491 - lr: 1.3422e-04\n",
      "Epoch 20/20\n",
      "1120/1120 [==============================] - 99s 88ms/step - loss: 0.0506 - accuracy: 0.9845 - val_loss: 1.1969 - val_accuracy: 0.7520 - lr: 1.0737e-04\n"
     ]
    }
   ],
   "source": [
    "#Main model - BatchNormalization\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Conv2D(8, (3, 3), input_shape = (515, 389, 3)),\n",
    "    tf.keras.layers.BatchNormalization(axis = 3),\n",
    "    tf.keras.layers.ReLU(),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(16, (3, 3)),\n",
    "    tf.keras.layers.BatchNormalization(axis = 3),\n",
    "    tf.keras.layers.ReLU(),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(32, (3, 3)),\n",
    "    tf.keras.layers.BatchNormalization(axis = 3),\n",
    "    tf.keras.layers.ReLU(),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(64, (3, 3)),\n",
    "    tf.keras.layers.BatchNormalization(axis = -1),\n",
    "    tf.keras.layers.ReLU(),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(128, (3, 3)),\n",
    "    tf.keras.layers.BatchNormalization(axis = -1),\n",
    "    tf.keras.layers.ReLU(),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Dropout(0.3),\n",
    "    tf.keras.layers.Flatten(),\n",
    "    tf.keras.layers.Dense(n_classes, activation = \"softmax\")\n",
    "])\n",
    "callback = tf.keras.callbacks.ReduceLROnPlateau(monitor = \"val_accuracy\", factor = 0.8, patience = 1)\n",
    "model.compile(loss = \"categorical_crossentropy\", optimizer = tf.keras.optimizers.legacy.Adam(learning_rate = 0.001), metrics = [\"accuracy\"])\n",
    "history = model.fit(train_generator, epochs = 20, validation_data = vali_generator, callbacks = [callback])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7155aed6",
   "metadata": {},
   "source": [
    "<H1>Model Building (10 Epochs)</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b0da44ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metal device set to: Apple M2 Max\n",
      "Epoch 1/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-05-04 12:55:30.856225: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1120/1120 [==============================] - 95s 84ms/step - loss: 1.8223 - accuracy: 0.3972 - val_loss: 1.4003 - val_accuracy: 0.5344 - lr: 0.0010\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 96s 86ms/step - loss: 1.2951 - accuracy: 0.5781 - val_loss: 1.2239 - val_accuracy: 0.5946 - lr: 0.0010\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 95s 85ms/step - loss: 1.0796 - accuracy: 0.6478 - val_loss: 1.1566 - val_accuracy: 0.6183 - lr: 0.0010\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 96s 86ms/step - loss: 0.9329 - accuracy: 0.6933 - val_loss: 1.0952 - val_accuracy: 0.6355 - lr: 0.0010\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 96s 85ms/step - loss: 0.8216 - accuracy: 0.7266 - val_loss: 1.0273 - val_accuracy: 0.6621 - lr: 0.0010\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 95s 85ms/step - loss: 0.7305 - accuracy: 0.7557 - val_loss: 1.0899 - val_accuracy: 0.6594 - lr: 0.0010\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 95s 85ms/step - loss: 0.6241 - accuracy: 0.7891 - val_loss: 1.0187 - val_accuracy: 0.6710 - lr: 8.0000e-04\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 96s 86ms/step - loss: 0.5599 - accuracy: 0.8099 - val_loss: 1.0309 - val_accuracy: 0.6810 - lr: 8.0000e-04\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 96s 86ms/step - loss: 0.5202 - accuracy: 0.8197 - val_loss: 1.0225 - val_accuracy: 0.6888 - lr: 8.0000e-04\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 95s 85ms/step - loss: 0.4738 - accuracy: 0.8359 - val_loss: 1.1015 - val_accuracy: 0.6891 - lr: 8.0000e-04\n"
     ]
    }
   ],
   "source": [
    "#Main model - GTZAN\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Conv2D(8, (3, 3), activation = \"relu\", input_shape = (515, 389, 3)),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(16, (3, 3), activation = \"relu\"),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(32, (3, 3), activation = \"relu\"),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(64, (3, 3), activation = \"relu\"),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(128, (3, 3), activation = \"relu\"),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Dropout(0.3),\n",
    "    tf.keras.layers.Flatten(),\n",
    "    tf.keras.layers.Dense(n_classes, activation = \"softmax\")\n",
    "])\n",
    "callback = tf.keras.callbacks.ReduceLROnPlateau(monitor = \"val_accuracy\", factor = 0.8, patience = 1)\n",
    "model.compile(loss = \"categorical_crossentropy\", optimizer = tf.keras.optimizers.legacy.Adam(learning_rate = 0.001), metrics = [\"accuracy\"])\n",
    "history = model.fit(train_generator, epochs = 10, validation_data = vali_generator, callbacks = [callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d766aa58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 95s 85ms/step - loss: 1.7958 - accuracy: 0.4080 - val_loss: 1.4013 - val_accuracy: 0.5449 - lr: 0.0010\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 94s 84ms/step - loss: 1.3051 - accuracy: 0.5735 - val_loss: 1.2592 - val_accuracy: 0.5846 - lr: 0.0010\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 96s 86ms/step - loss: 1.1265 - accuracy: 0.6327 - val_loss: 1.1179 - val_accuracy: 0.6292 - lr: 0.0010\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 94s 84ms/step - loss: 1.0009 - accuracy: 0.6717 - val_loss: 1.1082 - val_accuracy: 0.6337 - lr: 0.0010\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 94s 84ms/step - loss: 0.9123 - accuracy: 0.7006 - val_loss: 1.0483 - val_accuracy: 0.6551 - lr: 0.0010\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 95s 85ms/step - loss: 0.8393 - accuracy: 0.7220 - val_loss: 1.0520 - val_accuracy: 0.6571 - lr: 0.0010\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 95s 85ms/step - loss: 0.7820 - accuracy: 0.7377 - val_loss: 1.0198 - val_accuracy: 0.6647 - lr: 0.0010\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 96s 86ms/step - loss: 0.7283 - accuracy: 0.7586 - val_loss: 1.0466 - val_accuracy: 0.6560 - lr: 0.0010\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 96s 85ms/step - loss: 0.6488 - accuracy: 0.7835 - val_loss: 1.0191 - val_accuracy: 0.6750 - lr: 8.0000e-04\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 95s 85ms/step - loss: 0.6092 - accuracy: 0.7955 - val_loss: 1.0308 - val_accuracy: 0.6781 - lr: 8.0000e-04\n"
     ]
    }
   ],
   "source": [
    "#Main model - Less filters\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Conv2D(4, (3, 3), activation = \"relu\", input_shape = (515, 389, 3)),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(8, (3, 3), activation = \"relu\"),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(16, (3, 3), activation = \"relu\"),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(32, (3, 3), activation = \"relu\"),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(64, (3, 3), activation = \"relu\"),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Dropout(0.3),\n",
    "    tf.keras.layers.Flatten(),\n",
    "    tf.keras.layers.Dense(n_classes, activation = \"softmax\")\n",
    "])\n",
    "callback = tf.keras.callbacks.ReduceLROnPlateau(monitor = \"val_accuracy\", factor = 0.8, patience = 1)\n",
    "model.compile(loss = \"categorical_crossentropy\", optimizer = tf.keras.optimizers.legacy.Adam(learning_rate = 0.001), metrics = [\"accuracy\"])\n",
    "history = model.fit(train_generator, epochs = 10, validation_data = vali_generator, callbacks = [callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "59d7b881",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 95s 85ms/step - loss: 1.7592 - accuracy: 0.4241 - val_loss: 1.3549 - val_accuracy: 0.5560 - lr: 0.0010\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 94s 84ms/step - loss: 1.1796 - accuracy: 0.6209 - val_loss: 1.1728 - val_accuracy: 0.6225 - lr: 0.0010\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 94s 84ms/step - loss: 0.9031 - accuracy: 0.7071 - val_loss: 1.1031 - val_accuracy: 0.6402 - lr: 0.0010\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 95s 85ms/step - loss: 0.7029 - accuracy: 0.7675 - val_loss: 1.1581 - val_accuracy: 0.6386 - lr: 0.0010\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 95s 85ms/step - loss: 0.4906 - accuracy: 0.8382 - val_loss: 1.1374 - val_accuracy: 0.6527 - lr: 8.0000e-04\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 95s 85ms/step - loss: 0.3766 - accuracy: 0.8746 - val_loss: 1.2949 - val_accuracy: 0.6507 - lr: 8.0000e-04\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 96s 85ms/step - loss: 0.2715 - accuracy: 0.9095 - val_loss: 1.4098 - val_accuracy: 0.6560 - lr: 6.4000e-04\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 98s 87ms/step - loss: 0.2186 - accuracy: 0.9252 - val_loss: 1.5238 - val_accuracy: 0.6491 - lr: 6.4000e-04\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 97s 87ms/step - loss: 0.1634 - accuracy: 0.9457 - val_loss: 1.6064 - val_accuracy: 0.6596 - lr: 5.1200e-04\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 97s 86ms/step - loss: 0.1495 - accuracy: 0.9513 - val_loss: 1.6548 - val_accuracy: 0.6574 - lr: 5.1200e-04\n"
     ]
    }
   ],
   "source": [
    "#Main model - Less Layers\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Conv2D(8, (3, 3), activation = \"relu\", input_shape = (515, 389, 3)),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(16, (3, 3), activation = \"relu\"),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(32, (3, 3), activation = \"relu\"),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(64, (3, 3), activation = \"relu\"),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Dropout(0.3),\n",
    "    tf.keras.layers.Flatten(),\n",
    "    tf.keras.layers.Dense(n_classes, activation = \"softmax\")\n",
    "])\n",
    "callback = tf.keras.callbacks.ReduceLROnPlateau(monitor = \"val_accuracy\", factor = 0.8, patience = 1)\n",
    "model.compile(loss = \"categorical_crossentropy\", optimizer = tf.keras.optimizers.legacy.Adam(learning_rate = 0.001), metrics = [\"accuracy\"])\n",
    "history = model.fit(train_generator, epochs = 10, validation_data = vali_generator, callbacks = [callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bfc1698a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 597s 532ms/step - loss: 2.1482 - accuracy: 0.2846 - val_loss: 1.7933 - val_accuracy: 0.4163 - lr: 0.0010\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 462s 412ms/step - loss: 1.6794 - accuracy: 0.4508 - val_loss: 1.5986 - val_accuracy: 0.4940 - lr: 0.0010\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 457s 408ms/step - loss: 1.5415 - accuracy: 0.4976 - val_loss: 1.5204 - val_accuracy: 0.5228 - lr: 0.0010\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 454s 405ms/step - loss: 1.4460 - accuracy: 0.5312 - val_loss: 1.4052 - val_accuracy: 0.5453 - lr: 0.0010\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 454s 405ms/step - loss: 1.3654 - accuracy: 0.5549 - val_loss: 1.3862 - val_accuracy: 0.5471 - lr: 0.0010\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 454s 406ms/step - loss: 1.3160 - accuracy: 0.5696 - val_loss: 1.3246 - val_accuracy: 0.5656 - lr: 0.0010\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 459s 410ms/step - loss: 1.2726 - accuracy: 0.5819 - val_loss: 1.2502 - val_accuracy: 0.5967 - lr: 0.0010\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 455s 406ms/step - loss: 1.2344 - accuracy: 0.5934 - val_loss: 1.2393 - val_accuracy: 0.5940 - lr: 0.0010\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 456s 407ms/step - loss: 1.1926 - accuracy: 0.6077 - val_loss: 1.2136 - val_accuracy: 0.6013 - lr: 8.0000e-04\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 453s 404ms/step - loss: 1.1774 - accuracy: 0.6108 - val_loss: 1.2200 - val_accuracy: 0.5955 - lr: 8.0000e-04\n"
     ]
    }
   ],
   "source": [
    "#Main model - Inverted pyramid\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Conv2D(128, (3, 3), activation = \"relu\", input_shape = (515, 389, 3)),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(64, (3, 3), activation = \"relu\"),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(32, (3, 3), activation = \"relu\"),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(16, (3, 3), activation = \"relu\"),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(8, (3, 3), activation = \"relu\"),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Dropout(0.3),\n",
    "    tf.keras.layers.Flatten(),\n",
    "    tf.keras.layers.Dense(n_classes, activation = \"softmax\")\n",
    "])\n",
    "callback = tf.keras.callbacks.ReduceLROnPlateau(monitor = \"val_accuracy\", factor = 0.8, patience = 1)\n",
    "model.compile(loss = \"categorical_crossentropy\", optimizer = tf.keras.optimizers.legacy.Adam(learning_rate = 0.001), metrics = [\"accuracy\"])\n",
    "history = model.fit(train_generator, epochs = 10, validation_data = vali_generator, callbacks = [callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c4a29aa4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 102s 91ms/step - loss: 1.9088 - accuracy: 0.3666 - val_loss: 1.5677 - val_accuracy: 0.4837 - lr: 0.0010\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 96s 86ms/step - loss: 1.3316 - accuracy: 0.5619 - val_loss: 1.1853 - val_accuracy: 0.6138 - lr: 0.0010\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 95s 85ms/step - loss: 1.1066 - accuracy: 0.6325 - val_loss: 1.1475 - val_accuracy: 0.6257 - lr: 0.0010\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 97s 86ms/step - loss: 0.9561 - accuracy: 0.6833 - val_loss: 1.0566 - val_accuracy: 0.6507 - lr: 0.0010\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 96s 86ms/step - loss: 0.8461 - accuracy: 0.7162 - val_loss: 1.0285 - val_accuracy: 0.6567 - lr: 0.0010\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 96s 86ms/step - loss: 0.7515 - accuracy: 0.7478 - val_loss: 0.9999 - val_accuracy: 0.6817 - lr: 0.0010\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 96s 86ms/step - loss: 0.6734 - accuracy: 0.7736 - val_loss: 1.0375 - val_accuracy: 0.6647 - lr: 0.0010\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 96s 86ms/step - loss: 0.5527 - accuracy: 0.8149 - val_loss: 1.0820 - val_accuracy: 0.6699 - lr: 8.0000e-04\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 96s 86ms/step - loss: 0.4505 - accuracy: 0.8459 - val_loss: 1.1167 - val_accuracy: 0.6708 - lr: 6.4000e-04\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 96s 86ms/step - loss: 0.3606 - accuracy: 0.8767 - val_loss: 1.0895 - val_accuracy: 0.6866 - lr: 5.1200e-04\n"
     ]
    }
   ],
   "source": [
    "#Main model - 5x5 Kernel\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Conv2D(8, (5, 5), activation = \"relu\", input_shape = (515, 389, 3)),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(16, (5, 5), activation = \"relu\"),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(32, (5, 5), activation = \"relu\"),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(64, (5, 5), activation = \"relu\"),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(128, (5, 5), activation = \"relu\"),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Dropout(0.3),\n",
    "    tf.keras.layers.Flatten(),\n",
    "    tf.keras.layers.Dense(n_classes, activation = \"softmax\")\n",
    "])\n",
    "callback = tf.keras.callbacks.ReduceLROnPlateau(monitor = \"val_accuracy\", factor = 0.8, patience = 1)\n",
    "model.compile(loss = \"categorical_crossentropy\", optimizer = tf.keras.optimizers.legacy.Adam(learning_rate = 0.001), metrics = [\"accuracy\"])\n",
    "history = model.fit(train_generator, epochs = 10, validation_data = vali_generator, callbacks = [callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d043dd02",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 98s 87ms/step - loss: 2.1542 - accuracy: 0.3197 - val_loss: 1.8883 - val_accuracy: 0.4080 - lr: 0.0010\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 94s 84ms/step - loss: 1.7111 - accuracy: 0.4745 - val_loss: 1.6932 - val_accuracy: 0.4837 - lr: 0.0010\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 95s 85ms/step - loss: 1.5330 - accuracy: 0.5378 - val_loss: 1.5018 - val_accuracy: 0.5321 - lr: 0.0010\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 94s 84ms/step - loss: 1.4209 - accuracy: 0.5749 - val_loss: 1.4178 - val_accuracy: 0.5723 - lr: 0.0010\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 95s 85ms/step - loss: 1.3437 - accuracy: 0.6011 - val_loss: 1.3859 - val_accuracy: 0.5850 - lr: 0.0010\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 106s 94ms/step - loss: 1.2797 - accuracy: 0.6231 - val_loss: 1.3665 - val_accuracy: 0.5913 - lr: 0.0010\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 106s 95ms/step - loss: 1.2352 - accuracy: 0.6367 - val_loss: 1.2960 - val_accuracy: 0.6109 - lr: 0.0010\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 1.2013 - accuracy: 0.6500 - val_loss: 1.3298 - val_accuracy: 0.6085 - lr: 0.0010\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 1.1346 - accuracy: 0.6702 - val_loss: 1.2548 - val_accuracy: 0.6250 - lr: 8.0000e-04\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 1.1082 - accuracy: 0.6759 - val_loss: 1.2620 - val_accuracy: 0.6196 - lr: 8.0000e-04\n"
     ]
    }
   ],
   "source": [
    "#Main model - L2 Regularization\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Conv2D(8, (3, 3), activation = \"relu\", input_shape = (515, 389, 3)),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(16, (3, 3), activation = \"relu\", kernel_regularizer = \"l2\"),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(32, (3, 3), activation = \"relu\", kernel_regularizer = \"l2\"),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(64, (3, 3), activation = \"relu\", kernel_regularizer = \"l2\"),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(128, (3, 3), activation = \"relu\", kernel_regularizer = \"l2\"),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Dropout(0.3),\n",
    "    tf.keras.layers.Flatten(),\n",
    "    tf.keras.layers.Dense(n_classes, activation = \"softmax\")\n",
    "])\n",
    "\n",
    "callback = tf.keras.callbacks.ReduceLROnPlateau(monitor = \"val_accuracy\", factor = 0.8, patience = 1)\n",
    "model.compile(loss = \"categorical_crossentropy\", optimizer = tf.keras.optimizers.legacy.Adam(learning_rate = 0.001), metrics = [\"accuracy\"])\n",
    "history = model.fit(train_generator, epochs = 10, validation_data = vali_generator, callbacks = [callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2a7336ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 2.0783 - accuracy: 0.4069 - val_loss: 1.5758 - val_accuracy: 0.4842 - lr: 0.0010\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 1.2930 - accuracy: 0.5765 - val_loss: 1.2930 - val_accuracy: 0.5817 - lr: 0.0010\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 1.0683 - accuracy: 0.6501 - val_loss: 1.0562 - val_accuracy: 0.6509 - lr: 0.0010\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.8964 - accuracy: 0.7088 - val_loss: 1.0023 - val_accuracy: 0.6656 - lr: 0.0010\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.7384 - accuracy: 0.7587 - val_loss: 1.0213 - val_accuracy: 0.6587 - lr: 0.0010\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 104s 93ms/step - loss: 0.5693 - accuracy: 0.8152 - val_loss: 0.9551 - val_accuracy: 0.6846 - lr: 8.0000e-04\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 103s 92ms/step - loss: 0.4725 - accuracy: 0.8453 - val_loss: 0.9088 - val_accuracy: 0.7069 - lr: 8.0000e-04\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 104s 92ms/step - loss: 0.3881 - accuracy: 0.8708 - val_loss: 0.8551 - val_accuracy: 0.7261 - lr: 8.0000e-04\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 101s 90ms/step - loss: 0.3304 - accuracy: 0.8915 - val_loss: 0.9042 - val_accuracy: 0.7158 - lr: 8.0000e-04\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 106s 95ms/step - loss: 0.2430 - accuracy: 0.9211 - val_loss: 0.9483 - val_accuracy: 0.7272 - lr: 6.4000e-04\n"
     ]
    }
   ],
   "source": [
    "#Main model - BatchNormalization\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Conv2D(8, (3, 3), input_shape = (515, 389, 3)),\n",
    "    tf.keras.layers.BatchNormalization(axis = 3),\n",
    "    tf.keras.layers.ReLU(),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(16, (3, 3)),\n",
    "    tf.keras.layers.BatchNormalization(axis = 3),\n",
    "    tf.keras.layers.ReLU(),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(32, (3, 3)),\n",
    "    tf.keras.layers.BatchNormalization(axis = 3),\n",
    "    tf.keras.layers.ReLU(),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(64, (3, 3)),\n",
    "    tf.keras.layers.BatchNormalization(axis = -1),\n",
    "    tf.keras.layers.ReLU(),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(128, (3, 3)),\n",
    "    tf.keras.layers.BatchNormalization(axis = -1),\n",
    "    tf.keras.layers.ReLU(),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Dropout(0.3),\n",
    "    tf.keras.layers.Flatten(),\n",
    "    tf.keras.layers.Dense(n_classes, activation = \"softmax\")\n",
    "])\n",
    "callback = tf.keras.callbacks.ReduceLROnPlateau(monitor = \"val_accuracy\", factor = 0.8, patience = 1)\n",
    "model.compile(loss = \"categorical_crossentropy\", optimizer = tf.keras.optimizers.legacy.Adam(learning_rate = 0.001), metrics = [\"accuracy\"])\n",
    "history = model.fit(train_generator, epochs = 10, validation_data = vali_generator, callbacks = [callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "21fb7907",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1120/1120 [==============================] - 303s 270ms/step - loss: 1.9783 - accuracy: 0.3912 - val_loss: 1.7956 - val_accuracy: 0.4250 - lr: 0.0010\n",
      "Epoch 2/10\n",
      "1120/1120 [==============================] - 1367s 1s/step - loss: 1.3361 - accuracy: 0.5651 - val_loss: 1.6813 - val_accuracy: 0.4563 - lr: 0.0010\n",
      "Epoch 3/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 1.1057 - accuracy: 0.6411 - val_loss: 1.1247 - val_accuracy: 0.6379 - lr: 0.0010\n",
      "Epoch 4/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.9256 - accuracy: 0.6964 - val_loss: 1.1611 - val_accuracy: 0.6187 - lr: 0.0010\n",
      "Epoch 5/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.7374 - accuracy: 0.7600 - val_loss: 1.2025 - val_accuracy: 0.6049 - lr: 8.0000e-04\n",
      "Epoch 6/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.5891 - accuracy: 0.8073 - val_loss: 0.8063 - val_accuracy: 0.7317 - lr: 6.4000e-04\n",
      "Epoch 7/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.4957 - accuracy: 0.8393 - val_loss: 0.8944 - val_accuracy: 0.7092 - lr: 6.4000e-04\n",
      "Epoch 8/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.3806 - accuracy: 0.8762 - val_loss: 0.8283 - val_accuracy: 0.7335 - lr: 5.1200e-04\n",
      "Epoch 9/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.3203 - accuracy: 0.8940 - val_loss: 0.9461 - val_accuracy: 0.7071 - lr: 5.1200e-04\n",
      "Epoch 10/10\n",
      "1120/1120 [==============================] - 100s 89ms/step - loss: 0.2432 - accuracy: 0.9190 - val_loss: 0.8536 - val_accuracy: 0.7496 - lr: 4.0960e-04\n"
     ]
    }
   ],
   "source": [
    "#Main model - BatchNormalization + 5x5\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Conv2D(8, (5, 5), input_shape = (515, 389, 3)),\n",
    "    tf.keras.layers.BatchNormalization(axis = 3),\n",
    "    tf.keras.layers.ReLU(),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(16, (5, 5)),\n",
    "    tf.keras.layers.BatchNormalization(axis = 3),\n",
    "    tf.keras.layers.ReLU(),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(32, (5, 5)),\n",
    "    tf.keras.layers.BatchNormalization(axis = 3),\n",
    "    tf.keras.layers.ReLU(),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(64, (5, 5)),\n",
    "    tf.keras.layers.BatchNormalization(axis = -1),\n",
    "    tf.keras.layers.ReLU(),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Conv2D(128, (5, 5)),\n",
    "    tf.keras.layers.BatchNormalization(axis = -1),\n",
    "    tf.keras.layers.ReLU(),\n",
    "    tf.keras.layers.MaxPooling2D(),\n",
    "    tf.keras.layers.Dropout(0.3),\n",
    "    tf.keras.layers.Flatten(),\n",
    "    tf.keras.layers.Dense(n_classes, activation = \"softmax\")\n",
    "])\n",
    "callback = tf.keras.callbacks.ReduceLROnPlateau(monitor = \"val_accuracy\", factor = 0.8, patience = 1)\n",
    "model.compile(loss = \"categorical_crossentropy\", optimizer = tf.keras.optimizers.legacy.Adam(learning_rate = 0.001), metrics = [\"accuracy\"])\n",
    "history = model.fit(train_generator, epochs = 10, validation_data = vali_generator, callbacks = [callback])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c414323",
   "metadata": {},
   "source": [
    "<H1>Results</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "9440cf18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "140/140 [==============================] - 11s 79ms/step - loss: 1.2412 - accuracy: 0.7824\n"
     ]
    }
   ],
   "source": [
    "#Evaluate the Model\n",
    "score = model.evaluate(testing_generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "905abfd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot loss of training vs validation\n",
    "plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
    "plt.plot(history.history[\"loss\"])\n",
    "plt.plot(history.history[\"val_loss\"])\n",
    "plt.title(\"Model Loss\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.legend([\"Training\", \"Validation\"], loc = \"upper left\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "7ab3a74c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot Training vs Validation Accuracy\n",
    "plt.plot(history.history[\"accuracy\"])\n",
    "plt.plot(history.history[\"val_accuracy\"])\n",
    "plt.title(\"Model Accuracy\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylim([0, 1])\n",
    "plt.legend([\"Training\", \"Validation\"], loc = \"upper left\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "8687f4f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 511, 385, 8)       608       \n",
      "                                                                 \n",
      " batch_normalization (BatchN  (None, 511, 385, 8)      32        \n",
      " ormalization)                                                   \n",
      "                                                                 \n",
      " re_lu (ReLU)                (None, 511, 385, 8)       0         \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 255, 192, 8)      0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 251, 188, 16)      3216      \n",
      "                                                                 \n",
      " batch_normalization_1 (Batc  (None, 251, 188, 16)     64        \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " re_lu_1 (ReLU)              (None, 251, 188, 16)      0         \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 125, 94, 16)      0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 121, 90, 32)       12832     \n",
      "                                                                 \n",
      " batch_normalization_2 (Batc  (None, 121, 90, 32)      128       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " re_lu_2 (ReLU)              (None, 121, 90, 32)       0         \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 60, 45, 32)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_3 (Conv2D)           (None, 56, 41, 64)        51264     \n",
      "                                                                 \n",
      " batch_normalization_3 (Batc  (None, 56, 41, 64)       256       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " re_lu_3 (ReLU)              (None, 56, 41, 64)        0         \n",
      "                                                                 \n",
      " max_pooling2d_3 (MaxPooling  (None, 28, 20, 64)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_4 (Conv2D)           (None, 24, 16, 128)       204928    \n",
      "                                                                 \n",
      " batch_normalization_4 (Batc  (None, 24, 16, 128)      512       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " re_lu_4 (ReLU)              (None, 24, 16, 128)       0         \n",
      "                                                                 \n",
      " max_pooling2d_4 (MaxPooling  (None, 12, 8, 128)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 12, 8, 128)        0         \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 12288)             0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 14)                172046    \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 445,886\n",
      "Trainable params: 445,390\n",
      "Non-trainable params: 496\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#Main Model Summary\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6315a21f",
   "metadata": {},
   "source": [
    "<H1>Precision per Class</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "96480d2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "140/140 [==============================] - 11s 75ms/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "     classic       0.93      0.95      0.94       320\n",
      "     country       0.72      0.77      0.74       320\n",
      "         edm       0.83      0.84      0.83       320\n",
      "    flamenco       0.91      0.85      0.88       320\n",
      "      hiphop       0.76      0.82      0.79       320\n",
      "       indie       0.73      0.78      0.75       320\n",
      "        jazz       0.90      0.94      0.92       320\n",
      "        kpop       0.73      0.60      0.66       320\n",
      "       latin       0.83      0.85      0.84       320\n",
      "       metal       0.72      0.73      0.73       320\n",
      "         pop       0.52      0.53      0.52       320\n",
      "         rap       0.83      0.77      0.80       320\n",
      "      reggae       0.93      0.94      0.93       320\n",
      "        rock       0.62      0.58      0.60       320\n",
      "\n",
      "    accuracy                           0.78      4480\n",
      "   macro avg       0.78      0.78      0.78      4480\n",
      "weighted avg       0.78      0.78      0.78      4480\n",
      "\n"
     ]
    }
   ],
   "source": [
    "precision_per_class(model, testing_generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "c22bfeb3",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "140/140 [==============================] - 11s 75ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "multiclass_confusion_matrix(model, testing_generator)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdf36260",
   "metadata": {},
   "source": [
    "<H1>Testing Popular Songs</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "4d6db4b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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etm7dqpCQEBUpUiRfO9etW6dnnnlGDz30UL46BwwYoHLlyikwMLDQdp98b0NsbKzKlCnjMb179+6nbPvJdZxue1avXl1FihTxuE+hVKlSkuRx2eTpfP/998rKyvJYV4UKFRQeHq6ffvpJ0v+7F+Lk9rh1YhtLlizp3Fd35MgRbdu2TY899pgqVap02nr+85//aMaMGWrQoIEeeOABjRkzRgEBAVq7dq327dvn0b6AgADVqVMnXx9O3l4nbqvQ0FDnck/p+MAeCQkJhbansP3tu+++0/79+109h35+fh7LR0ZG6sorrzyrnwbIM2TIEO3YsUMjR4506s67TOxsn8tevXpp4cKFqlWrlrp166aRI0dKOrP9p7BtX7JkSV1xxRWFbvuQkBD17dtXzz77rGbPnq3Ro0d7/MzC9u3bPY4rZ9PH7777Tqmpqac9PhV2nDhbrVu3drblihUr9I9//MNj/g033KCXXnpJGzduVIMGDXTPPffoyiuv1Msvv6yJEydq/vz5evjhh12vr7BtU9j9WYW9diV3+9nJz/n+/fs96j7V8atHjx7av3+/xo0bp9zcXOcPuJRxDxRwCShTpoz+85//aNGiRVq6dKnCw8NVpkyZAsuamfbu3esxLe9Dsb+/v3OvyqFDhwpdX0ZGhn755RePabm5uUpKSjrrPlSoUEHS8Q/UZ7Je6fg1+GlpaUpJSTmjN+6AgADt37/f4/4a6fg9Nm6dbnt6i/3/91cUtC5vriePn5+fs86MjAxJyrft09PTC7yfo2LFivr11181cuRIffrpp/rnP/+p3377zWt9ePHFF/Xhhx/q2LFjys3N1TfffKMuXbqcSfc89rezfQ4rVKhwyv31dObPn6+oqCj17du3wPt4zkatWrW0ceNG9e7dW7Nnz9Y///lP7d692yvbvkSJEpo8ebLmzZun3NxcHTt2TKtXr/bY9lOnTtWXX36pokWLqkOHDho6dKgkae3atV75baWzPT5J8vhJgzNVunRpJzi42V+KFi2qJ598Um+88YbS09P19ddf68477zzr9Z+JE1+70rnZz0709ddfq0OHDnr00Uc1ePBgXXHFFV5fB3ChIUABl4gnn3xSWVlZevDBB9W7d+9CyzVs2FA7d+7Utm3bnGkHDx6Uv7+/GjdurJo1a0o6/gONJ8sLJ7Vr19YLL7zgETTee+89j4EC3DhxgIM9e/aobNmypxwCvXbt2poxY4bi4+OdaT/88IM2btyomjVrKisrSytXriy03QXVZ2YeI+1lZmY6Z+/yzlycyum2p7dcf/31Klq0aL4fCz148KCaN29+RnW56deJypYtq8qVK2v06NEeAwrMnDmzwLo+/PBDlShRQkOGDNG6detUunRpzZ49WzVr1lRwcPDf7sNVV12lqlWravz48XrxxRc1YMAAPf3006dd7uT9rU6dOgoKCnL9HJ48IMeePXvUqFEjSVLx4sUlyQmUeftcYfteWlqaunXrpqioqEK/7Dgb//d//6cyZcpoypQpWrVqlVJSUvT+++97bf+pVauWAgMDNW7cOE2dOlVvv/227r33XklSUlKSVq5cqZYtW2rNmjXq1KmTpk2bJunMRt8rXry4RzA/8YyG2+NTYceJs7VlyxYnADVs2FA///yzx/Hu4MGDqlChgurUqeNM6969u7KystS/f3/VqVPHJ6Plnav97ESdO3fWgw8+qCpVqpyT+oELEQEKuEgdPXrU40NG3bp11bhxY9WuXVv//Oc/Cy3Xq1cvlS9f3vkhXklauHCh+vXrp5CQELVu3Vo1a9bUoEGDtGrVKmVmZmrJkiWSpB9//FFJSUkaOHCgtm/frptvvlnTpk3T0KFDtXLlSuc3i3JycvJ92CzIiUNpz58/XwMHDlSxYsevLM7NzXXOfOR57rnnlJaWpltuuUWTJk3SuHHjNHnyZN12223q1KmTypUrp6ioKG3atElHjhxxPmStXLlS6enpTpvy/m3evLluvPFGDRs2TCNGjNDcuXN177336vbbb5f0/y5V2bRpkz755BPl5ubmq+N021M6PgJgQR+kT7WN8p6zI0eOSJLCw8PVrVs3zZw50znT9/vvvys7O1vdunVztpmkfNvtZHmX6Ozbt0+ff/6505ZTtXHgwIHOaHOvvvqq+vXrp8OHDxf4G2ObNm1yRk2sVq2aGjZsqMjISJUoUUL//e9/tWjRIufStz179mj9+vXq37+/s61O7EtB26pnz56qXr26atas6fweV2EjAp4ob3/Lzs7W+++/75wdcfMcStKGDRucb/a//fZb5eTkOJd85v1I9YwZM7RmzRqNHj1a0vHnKO+D/Imvi9zcXGVmZmrhwoX666+/NGnSJPn5+Wnnzp365ptvXD+XJ/vqq6/07bffSpLq16+vq6++WpGRka72Hzfb/pFHHtENN9ygq666SldddZWSk5OdEdsyMzM1ZswYmZmKFSumf//734qMjJQkxcXFqXr16h71FtbHq666Sp9//rnWrVunOXPmaOXKldq/f7/WrFmj2rVrq3nz5poyZYo+//xzZWdn67PPPpMk/fzzz9q7d+8pjxNupaWlOWesEhIS9OmnnzrP6XPPPScz8xhhcNGiRRoxYoTHJXZlypRRly5d9Prrr6tz586u132qbXPy8Sfv/4W9dk+3n7l5zk93/Dp27Jg++ugjbd26Va+88ooOHz6shIQErVix4oz6DFxUfHLnFYC/5YcffrCHH37YypUrZ7Nnz3Zuwn7vvfecG/RTUlJs1qxZFhISYpJs9OjRzihU69ats6ZNm1qbNm3siSeesNGjR1tOTo5T/59//mlNmjSx4sWL2y233GLTpk2za665xl599VVLT083M7Pp06db1apVLTg42Lp06eLcPP3zzz/bddddZ0WLFrVXXnnFMjIy8rU/b3CGLl262PDhw61Tp042YsQIZ0S3d955xwIDA61MmTIeN0ubmX3wwQdWs2ZNCwwMtHvuucf27dvnzFu9erXVr1/fihcvbnfddZeNHDnSbrzxRps3b56lpKTYqFGjTJLdcccdtnHjRjMz27Vrl7Vu3dpKlChh9erVc25ENzPLysqyu+++28qXL2+LFy+2AwcO2JNPPmmSrGPHjs4N4qfanl999ZWFhYVZ6dKlbcGCBbZt2zZnwIw+ffp4tD/PsmXL7J///KczUtyqVavM7PiAC3369LH69etbz549rXv37rZjxw4zOz64xf3332+S7P7777c///yz0P1nx44dFhkZabVq1bKNGzfanDlzrHTp0hYREWGff/65rV271hnBcMyYMXb06FHLzc21UaNGWYUKFSw0NNT69+9vWVlZBdY/btw4K1asmD3yyCM2YMAAe+aZZ5yBOnJzc23kyJF23XXXWY8ePaxz587OSHb79++33r17O9t3y5YttnDhQgsODrbw8HBbtmyZmZmNGTPGQkNDrVSpUlakSBGTdMoBF/IGeOjbt6+NGDHCHnjgAY9BKE73HJodv1G/fv361qdPHxs4cKDde++9tn79eo86HnvsMStZsqS1bdvWDhw4YKGhoRYdHW27du2yFStWWJUqVax06dI2b948MzMbMWKElS5d2ho1amQbNmywhg0b2o033mjbt2+34cOHm6R8++TpPPHEE3bFFVdYjx49rF+/fs7IcGan3n927Njh7D99+vSxXbt22euvv27FihWzevXqOfvg448/bhUrVrSSJUs6275o0aK2aNEiS0xMNEl244032qBBg+zRRx+1TZs22dq1a53RMfP89ttvdvvtt5sk69WrlyUkJDjzYmNjrWrVqlapUiV75513bMSIEdaiRQv74IMPLCcnx/bs2WP/+te/LCAgwOrUqWOzZ8+28PBwGz9+vCUlJZnZqY8Tp7Nv3z7r06ePNWjQwDp16mTdunWzTZs2eZRZuXKl3XjjjXbfffdZ9+7d7bXXXiuwrk2bNtnNN9/set1mxwd6KOj5P3r0aL5jmJvXbmH72e7du23EiBFOfb/99pstXbrUqlWrZqVLl7aFCxe6On7NnDnTypQpY9ddd51999139sADD1hkZGS+1wdwKfEzO+FCWQA4D2JiYtSsWTPFxcV55Yd+cfnIzc3V448/rpdfftm51yIrK0vbt2/XU0895ZwtPdFbb72lrl276u+83d1+++2qVq3aKX/X6VKXnp6uPn366I033nAuR8vMzNT69es1fvx4vf/++z5u4YVnxYoV+vPPPwv9TT4AFycu4QMAXDRmzZqlIkWKeNyo7u/vr2rVqqlWrVo+bNmlb+zYsapRo4bHvTzFixdXjRo1nEv14Gn+/Pl68MEHfd0MAF7GMOYAzrvs7GxJ8hiQAHAjMTFRCxcuVIMGDXT33XerbNmy2rZtm6ZNm6bo6OgClzlxfzvbEQuzs7Mv+/01MTFRy5YtU40aNXT77bcrMDBQf/zxh1577TVNmTLF1827YHz++ed65513dOWVV6pChQrnbPAGAL5DgAJwXv3+++969dVXJUkvvPCChg4dqiuvvNLHrcLFIjo6WqVKldJLL72kp59+WqVLl9Zdd92lUaNG5RukQDr+u0Fz586VJI0aNUrPPPOMypYt63p9OTk5mjlzpn7//Xdt3bpVH3/8sdq1a+e1/pyJTp06efw+z8luuukm57eKzoWXXnpJEyZM0LBhw7Rr1y6VK1dO99xzjyZNmuQx2MaF6rffftPAgQNPWWbIkCFnNNhEQXbt2qWPP/5YDz30kF577bV88339PAL4+7gHCgAAAABc4h4oAAAAAHCJAAUAAAAALl3W90Dl5uYqISFBpUuXlp+fn6+bAwAAAMBHzEypqakKCwvzGHH0ZJd1gEpISHB+xR4AAAAAdu3adcoBri7rAFW6dGlJxzdSUFCQj1sDAAAAwFdSUlIUHh7uZITCXNYBKu+yvaCgIAIUAAAAgNPe2sMgEgAAAADgEgEKAAAAAFwiQAEAAACASwQoAAAAAHCJAAUAAAAALhGgAAAAAMAlAhQAAAAAuESAAgAAAACXCFAAAAAA4BIBCgAAAABcIkABAAAAgEsEKAAAAABwiQAFAAAAAC4RoAAAAADAJQIUAAAAALhUzNcNAAAAAHDhS/91qNfrLFV/tNfrPNc4AwUAAAAALhGgAAAAAMAlnwao9PR0RUVFKTo6Wn369FFGRkaBZe677z4FBgbq1ltv1fbt2z3mv/HGG+rfv7+6deum33777fw0HAAAAMBlyacBqlevXrrzzjs1btw4NWjQQNHR0fnKvP322xo1apQ2btyozMxMDRkyxJn39ddf67PPPtPkyZM1bdo0PfLII0pPTz+fXQAAAABwGfFZgEpISNDChQvVunVrSVLr1q01ffp0paamepTr2rWrrr32WoWHh6tbt24qWrSoM2/SpElq166dJKl06dKKiIjQggULzl8nAAAAAFxWfBagYmJiFBoaqhIlSkiSypcvr4CAAMXGxnqUK1mypPP/hIQE5wxUTk6OVq5cqYiICGd+ZGSkVq5cWeg6MzIylJKS4vEHAAAAAG75LEDFx8crJCTEY1pgYKASEhLylU1MTNSgQYP0/vvvKykpSZKUlJSkY8eOedRR2PJ5xo0bp+DgYOcvPDzcS70BAAAAcDnwWYDy8/Nzzj7lyczMlL+/f76yZcqUUevWrVWlShW1bdtWR44ckZ+fnyR51FHY8nmio6OVnJzs/O3atctLvQEAAABwOfBZgAoLC1NycrLHtLS0NIWFheUrW7JkSTVu3FiffPKJjh07pg0bNqhcuXIKCAjwqCM1NbXA5fMEBAQoKCjI4w8AAAAA3PJZgGrWrJl2796tzMxMSXIuvWvYsGGhywQGBqpmzZoKCwuTn5+fmjVrpi1btjjz//rrLzVr1uzcNhwAAADAZctnAapy5cpq1aqVM+jD0qVLFRUVpYCAAA0aNEiJiYmSpF9//VVHjhyRJMXFxalOnTqqUqWKJKl3795asmSJJCklJUXx8fG6//77fdAbAAAAAJeDYr5c+fTp0zVw4ECtXr1aSUlJGj9+vI4dO6YFCxaoXbt2qly5sp577jlt2rRJ7dq1U6VKlfTqq686y7dt21br16/XkCFDlJSUpAULFuS7rwoAAAAAvMXPzMzXjfCVlJQUBQcHKzk5mfuhAAAAgFNI/3Wo1+ssVX+01+s8W26zgc8u4QMAAACAiw0BCgAAAABcIkABAAAAgEsEKAAAAABwiQAFAAAAAC4RoAAAAADAJQIUAAAAALhEgAIAAAAAlwhQAAAAAOASAQoAAAAAXCJAAQAAAIBLBCgAAAAAcIkABQAAAAAuEaAAAAAAwCUCFAAAAAC4RIACAAAAAJcIUAAAAADgEgEKAAAAAFwiQAEAAACASwQoAAAAAHCJAAUAAAAALhGgAAAAAMAlAhQAAAAAuESAAgAAAACXCFAAAAAA4BIBCgAAAABcIkABAAAAgEsEKAAAAABwiQAFAAAAAC4RoAAAAADAJQIUAAAAALhEgAIAAAAAlwhQAAAAAOASAQoAAAAAXCJAAQAAAIBLBCgAAAAAcIkABQAAAAAuEaAAAAAAwCUCFAAAAAC4RIACAAAAAJcIUAAAAADgEgEKAAAAAFwiQAEAAACASwQoAAAAAHCJAAUAAAAALhGgAAAAAMAlAhQAAAAAuESAAgAAAACXCFAAAAAA4JJPA1R6erqioqIUHR2tPn36KCMjI1+ZvXv3qk2bNipdurQaN26szZs3e8z//vvv5efnJz8/PxUvXlx79uw5X80HAAAAcJnxaYDq1auX7rzzTo0bN04NGjRQdHR0vjLjx4/X448/rq+++krZ2dnq0KGDx/yFCxdq2bJlWrZsmb777jtVqlTpfDUfAAAAwGXGz8zMFytOSEjQVVddpUOHDqlEiRLav3+/IiIitHfvXpUuXVqSZGZauXKlbr/9dknSxo0bde2112rfvn0qX768Nm/erOHDh+ull15ShQoVzrgNKSkpCg4OVnJysoKCgrzZPQAAAOCSkv7rUK/XWar+aK/XebbcZgOfnYGKiYlRaGioSpQoIUkqX768AgICFBsb65Tx8/NzwpMkValSRYGBgSpTpowkad68efroo49UuXJl9e/fX1lZWadcZ0ZGhlJSUjz+AAAAAMAtnwWo+Ph4hYSEeEwLDAxUQkJCocusXr1a3bp1k7+/vyRp9OjRSk5O1ty5c/Xmm29q0KBBp1znuHHjFBwc7PyFh4f//Y4AAAAAuGz4LED5+fk5Z5/yZGZmOuGoIPPmzdOwYcM8phUvXlwPP/ywFi1apFdeeUU5OTmFLh8dHa3k5GTnb9euXX+vEwAAAAAuK8V8teKwsDAlJyd7TEtLS1NYWFiB5d999109/vjjKleuXIHzmzdvroiICB04cEAVK1YssExAQIACAgL+XsMBAAAAXLZ8FqCaNWumHj16KDMzU8WLF3cu3WvYsGG+sqtXr1bRokV12223nbLOqlWrntVgEgAA4NK2q9WdXq8zfMkyr9cJ4MLns0v4KleurFatWmnlypWSpKVLlyoqKkoBAQEaNGiQEhMTJUnr1q3Txx9/rBtvvFHbt2/X6tWrNWfOHEnSrFmznLNYixYt0mOPPSY/Pz/fdAgAAADAJc+nvwM1ffp0vffee3r++ee1du1ajRkzRseOHdOCBQu0Y8cObd26VS1atNDYsWNVvXp1Va9eXTfffLNq1qyp3NxczZs3T9dcc406deokf39/3X///b7sDgAAAIBLnM8u4ZOk0NBQzZw5M9/0uLg45//79u0rdPkVK1ack3YBAAAAQEF8egYKAAAAAC4mBCgAAAAAcIkABQAAAAAuEaAAAAAAwCUCFAAAAAC4RIACAAAAAJcIUAAAAADgEgEKAAAAAFwiQAEAAACASwQoAAAAAHCJAAUAAAAALhGgAAAAAMAlAhQAAAAAuESAAgAAAACXCFAAAAAA4BIBCgAAAABcIkABAAAAgEsEKAAAAABwiQAFAAAAAC4RoAAAAADAJQIUAAAAALhEgAIAAAAAlwhQAAAAAOASAQoAAAAAXCJAAQAAAIBLBCgAAAAAcIkABQAAAAAuEaAAAAAAwCUCFAAAAAC4RIACAAAAAJcIUAAAAADgEgEKAAAAAFwiQAEAAACASwQoAAAAAHCJAAUAAAAALhGgAAAAAMAlAhQAAAAAuESAAgAAAACXCFAAAAAA4BIBCgAAAABcIkABAAAAgEsEKAAAAABwiQAFAAAAAC4RoAAAAADAJQIUAAAAALhEgAIAAAAAlwhQAAAAAOASAQoAAAAAXCJAAQAAAIBLPg1Q6enpioqKUnR0tPr06aOMjIx8Zfbu3as2bdqodOnSaty4sTZv3uwxf+zYsRowYIC6deumHTt2nK+mAwAAALgM+TRA9erVS3feeafGjRunBg0aKDo6Ol+Z8ePH6/HHH9dXX32l7OxsdejQwZk3e/Zs7d27VxMmTNDQoUP1wAMPKDc393x2AQAAAMBlxGcBKiEhQQsXLlTr1q0lSa1bt9b06dOVmprqlDEz3XPPPWrfvr1uuukmzZ49Wxs2bND+/fslSRMnTtQ999wjSapevbrS0tK0fPny898ZAAAAAJcFnwWomJgYhYaGqkSJEpKk8uXLKyAgQLGxsU4ZPz8/3X777c7jKlWqKDAwUGXKlFFCQoI2b96siIgIZ35kZKRWrlxZ6DozMjKUkpLi8QcAAAAAbvksQMXHxyskJMRjWmBgoBISEgpdZvXq1erWrZv8/f0VHx8vSR51nG75cePGKTg42PkLDw//m70AAAAAcDnxWYDy8/Nzzj7lyczMlL+/f6HLzJs3T8OGDXOWl+RRx+mWj46OVnJysvO3a9euv9MFAAAAAJeZYr5acVhYmJKTkz2mpaWlKSwsrMDy7777rh5//HGVK1fOWV6SkpOTVbJkSUlSamqqateuXeg6AwICFBAQ4I3mAwAAALgM+ewMVLNmzbR7925lZmZKknPpXcOGDfOVXb16tYoWLarbbrvNmRYWFqZatWppy5YtzrS//vpLzZo1O8ctBwAAAHC58lmAqly5slq1auUM+rB06VJFRUUpICBAgwYNUmJioiRp3bp1+vjjj3XjjTdq+/btWr16tebMmSNJioqK0pIlSyRJ27ZtU0hIiBo3buybDgEAAAC45PmZmflq5QcOHNDAgQNVrVo1JSUlafz48crJydG1116rBQsWqHz58rrlllucYcvzrFq1SjfddJNyc3M1cOBAZ/CIwYMHn9HAECkpKQoODlZycrKCgoK83T0AAHCB2NXqTq/XGb5kmdfrBC5k6b8O9XqdpeqP9nqdZ8ttNvDZPVCSFBoaqpkzZ+abHhcX5/x/3759hS5fpEgRTZw48Zy0DQAAAABO5rNL+AAAAADgYkOAAgAAAACXCFAAAAAA4BIBCgAAAABcIkABAAAAgEsEKAAAAABwiQAFAAAAAC4RoAAAAADAJQIUAAAAALhEgAIAAAAAlwhQAAAAAOASAQoAAAAAXDqrALVq1SpvtwMAAAAALnhnFaAeffRRPfvss/r555+93R4AAAAAuGAVO5uFvvzyS4WGhur999/XzJkzdfXVV+uRRx5RxYoVvd0+AAAAFCQn3Pt1Ft3l/TqBS8xZnYGqXr26Spcure7du2vcuHH6448/FB4erocffliLFy9WTk6Ot9sJAAAAAD53VgFq5cqV2rhxo5588klFREQoPj5eH3/8sd555x2VK1dO//nPf/TLL794u60AAAAA4FNndQlf8+bNVaJECf3nP//RqlWrdO211zrzbr/9diUkJOihhx7Sli1bvNZQAAAAAPC1swpQrVq10ltvvaXy5csXOD8gIMAjVAEAAADApeCsLuF74okn8oWnP//8Uz/99JMkqUOHDvroo4/+fusAAAAA4AJyRmegdu7cKen4KHz169eXmTnzjhw5okceeUSbN2/2bgsBAAAA4AJxRgFq7dq1ioqK0u7du/Xaa695zCtevLgeeeQRrzYOAAAAAC4kZxSg2rZtq3r16mnBggV67rnnzlWbAAAAAOCCdMb3QF155ZWFhicu3wMAAABwKXN1Buqpp55Sx44ddeutt0qSevXqpYyMDI8yZqaffvpJ69ev934rAQAAAOAC4CpAVa1aVWXKlHEeZ2RkqHjx4qpUqZL8/PwkHQ9QW7duPSeNBAAAAIALgasAdfIle4MHD1bVqlXl7+/vMb1bt27eaxkAAAAAXGDO6negrrrqqnzhSZJ27979txsEAAAAABcqV2eg2rVrp7S0tFOWMTNt2LBB+/bt80rDAAAAAOBC4ypA1axZU2XKlFGVKlWce55OZmb69NNPvdo4AAAAALiQuApQAwYMUOnSpRUQEHDKcu3atfNKowAAAADgQuTqHqjQ0NDThidJ+uWXX/52gwAAAADgQuXqDFT9+vU1ePBg3XfffZKkG2+8UQcPHvQok5ubq7179+ro0aPebyUAAAAAXABcBahXXnlFtWrVch63adNGNWvWVFhYmHNPVG5urhYvXnxuWgkAAAAAFwBXAerWW2/1ePzss8+qVKlSKlLE8wrAG2+80XstAwAAAIALjKsAdbLSpUvrnXfe0RdffKGkpCTVrFlTvXv31j/+8Q9vtw8AAAAALhhn9UO6ffv21RNPPKGQkBDdc889qlSpkh577DHFxsZ6u30AAAAAcME4qzNQb7/9tt5//321adPGmfbss89q0KBBatiwodcaBwAAAAAXkrM6A9WqVStFRER4TCtatKgOHz7sjTYBAAAAwAXJ1RmoMWPGKCcnx3lco0YN9e3bV02aNHGmHTlyRNu2bfN+CwEAAADgAuEqQP3xxx+Kj49XRESEM/JeeHi44uLiPMqNGzfO+y0EAAAAgAuEqwA1fPhwBQUFqVKlSoWWSUhIUGhoqNcaBgAAAAAXGlcBKjIyMt+0X3/9VWlpaTIzSVJycrJmzJihTz75xLstBAAAAIALxFmNwvfII4/ok08+kb+/v0qXLi3peIC65ZZbvNo4AAAAALiQnFWAKlmypA4dOqRly5apWrVqqlmzpmJjY7Vx40Zvtw8AAAAALhhnNYx5+fLlVaRIEd1111165513JEnXXHONhg0b5tXGAQAAAMCF5KzOQF155ZUqUaKEvvzySzVr1kw1a9ZUenr6KQeZAAAAAICL3VkFqKioKHXq1Mm5/+mTTz7R+vXrdeedd3q1cQAAAABwITmrACVJa9eu1RdffKGkpCTVrFlTnTt3dgIVAAAAAFyKzuoeqHHjxqlp06Zau3atgoODtWnTJt1xxx36888/z6ie9PR0RUVFKTo6Wn369FFGRkaB5eLj49WrVy+NHTs237wdO3bI399ffn5+8vPz05o1a86mSwAAAABwWmd1Bmry5MmaNWuWOnfu7Ew7cuSIRowYoYkTJ7qup1evXmrfvr3at2+vOXPmKDo6WlOmTMlXbvv27fr5559VsWLFfPNmzpypTz75RMWKFZO/v7+uv/76s+kSAAAAAJzWWZ2Bqlevnho1auQx7YorrlBmZqbrOhISErRw4UK1bt1aktS6dWtNnz5dqamp+co2atRItWrVyjf90KFD+uWXX3TttdfqjjvuUNOmTc+wJwAAAADgnqszUHPnzpWZOY/vvvtuDR061Ak/0vEzUL/99pvrFcfExCg0NFQlSpSQdHxo9ICAAMXGxqpFixb5yhcpkj/rLVq0SN98840iIiL0n//8R9OnT1dgYGCh68zIyPC4TDAlJcV1ewEAAADAVYB699139eeffyosLMwjyLz55pse5QYNGuR6xfHx8QoJCfGYFhgYqISEBNd1PP744+rWrZuWLl2qnj17qmvXrlq4cGGh5ceNG6eRI0e6rh8AAAAATuQqQI0cOVIVK1ZUeHi411bs5+fnnH3Kk5mZKX9//zOqp2jRomrdurWWLl2qunXrKiEhQWFhYQWWjY6O1jPPPOM8TklJ8WqfAAAAAFzaXAWoBg0a5Jv2008/aebMmdq9e7dq1qypHj166JprrnG94rCwMCUnJ3tMS0tLKzT8nE7NmjXVokUL7dq1q9A6AgICFBAQcFb1AwAAAMBZDSIxd+5cNWnSRHv27FFkZKRyc3PVo0cPffzxx67raNasmXbv3u0MPJF36V7Dhg3PpkmSpFKlSp1RiAMAAACAM3HWAWrr1q366KOP9OKLL2rq1Kn65ptv9N1337muo3LlymrVqpVWrlwpSVq6dKmioqIUEBCgQYMGKTEx0aO8mXkMZCFJ8+fPd8r98MMPaty4sYKDg8+mSwAAAABwWmcVoG6++eYCL5M7dOjQGdUzffp0vffee3r++ee1du1ajRkzRseOHdOCBQu0Y8cOp9w333yj2NhYLV++3OOHcr/44gvVqVNHDz74oDZv3qy+ffueTXcAAAAAwJWz+iHdY8eO6euvv9Ytt9yirKwsbdq0SbNnz9a+ffvOqJ7Q0FDNnDkz3/S4uDiPx02aNNHGjRvzlZs7d+6ZNRwAAAAA/oazOgM1dOhQzZw5U0FBQQoJCdGtt96qvXv3atasWd5uHwAAAABcMM7qDNTOnTv12muv6aWXXtK2bdtUtWpVVa5c2dttAwAAAIALylmdgWrcuLE+/PBDlS9fXjfddBPhCQAAAMBl4awC1Pjx41WtWrV80996662/2RwAAAAAuHCd1SV8M2fO1J9//qmyZcvKz89PkpSdna3ExER16dLFm+0DAAAAgAvGGQeow4cPq1mzZurTp4+uvPJKJ0CZmebPn+/1BgIAAADAhcJ1gDpw4IA6duyo5cuXS5I6duyoN998U/7+/k6Z+vXre7+FAAAAAHCBcH0PVL9+/ZSSkqLJkyfrhRde0MaNGzVlyhSPMsHBwV5vIAAAAABcKFyfgYqPj9f333+vYsWOL/LEE0/oueee8yiTnZ3tzAcAAACAS43rM1BXXnmlRzi64oorFBkZ6VHm//7v/7zXMgAAAAC4wLg+XfTpp5+qSZMmHtMSExO1ePFiSVJOTo42bNigBx54wLstBAAAAIALhOsAFRERoaZNmxZ6iV52draOHDnitYYBAAAAwIXGdYCaNm1avjNQJ7vjjjv+doMAAAAA4ELl+h6o04UnSWratOnfagwAAAAAXMhcBygAAAAAuNwRoAAAAADAJQIUAAAAALhEgAIAAAAAlwhQAAAAAOASAQoAAAAAXCJAAQAAAIBLBCgAAAAAcIkABQAAAAAuEaAAAAAAwCUCFAAAAAC4RIACAAAAAJcIUAAAAADgEgEKAAAAAFwiQAEAAACASwQoAAAAAHCJAAUAAAAALhGgAAAAAMAlAhQAAAAAuESAAgAAAACXCFAAAAAA4BIBCgAAAABcIkABAAAAgEsEKAAAAABwiQAFAAAAAC4RoAAAAADAJQIUAAAAALhEgAIAAAAAlwhQAAAAAOASAQoAAAAAXCrm6wYAAC4vFa8e4fU6927xfp0AABSEM1AAAAAA4BIBCgAAAABcIkABAAAAgEsEKAAAAABwyaeDSKSnp+u5555TcHCw0tPTNWnSJAUEBOQrFx8fr+eff17h4eEaNGiQx7w33nhDmzZt0qFDh9SnTx/Vq1fvPLUeAAAAwOXGp2egevXqpTvvvFPjxo1TgwYNFB0dXWC57du36+eff1ZmZqbH9K+//lqfffaZJk+erGnTpumRRx5Renr6+Wg6AAAAgMuQzwJUQkKCFi5cqNatW0uSWrdurenTpys1NTVf2UaNGqlWrVr5pk+aNEnt2rWTJJUuXVoRERFasGDBuW04AAAAgMuWzwJUTEyMQkNDVaJECUlS+fLlFRAQoNjY2ALLFyni2dScnBytXLlSERERzrTIyEitXLmy0HVmZGQoJSXF4w8AAAAA3PJZgIqPj1dISIjHtMDAQCUkJLhaPikpSceOHfOo43TLjxs3TsHBwc5feHj42TUeAAAAwGXJZwHKz8/POfuUJzMzU/7+/q6Xl+RRx+mWj46OVnJysvO3a9eus2g5AAAAgMuVz0bhCwsLU3Jysse0tLQ0hYWFuVq+XLlyCggI8KgjNTX1lMsHBAQUOMofAAAAALjhszNQzZo10+7du52R9fIuvWvYsKGr5f38/NSsWTNt2bLFmfbXX3+pWbNm3m8sAAAAAMiHAapy5cpq1aqVM+jD0qVLFRUVpYCAAA0aNEiJiYke5c1MZuYxrXfv3lqyZIkkKSUlRfHx8br//vvPTwcAAAAAXHZ8+kO606dP18CBA7V69WolJSVp/PjxOnbsmBYsWKB27dqpcuXKkqRvvvlGsbGx2rZtm+655x5df/31kqS2bdtq/fr1GjJkiJKSkrRgwYJ891UBAAAAgLf4NECFhoZq5syZ+abHxcV5PG7SpIk2btxYYB0DBw48J20DAAAAgJP57BI+AAAAALjYEKAAAAAAwCUCFAAAAAC4RIACAAAAAJcIUAAAAADgEgEKAAAAAFwiQAEAAACASwQoAAAAAHCJAAUAAAAALhGgAAAAAMAlAhQAAAAAuESAAgAAAACXCFAAAAAA4BIBCgAAAABcIkABAAAAgEsEKAAAAABwiQAFAAAAAC4RoAAAAADAJQIUAAAAALhEgAIAAAAAlwhQAAAAAOASAQoAAAAAXCJAAQAAAIBLBCgAAAAAcIkABQAAAAAuEaAAAAAAwCUCFAAAAAC4RIACAAAAAJcIUAAAAADgEgEKAAAAAFwiQAEAAACASwQoAAAAAHCJAAUAAAAALhGgAAAAAMAlAhQAAAAAuESAAgAAAACXCFAAAAAA4BIBCgAAAABcIkABAAAAgEsEKAAAAABwiQAFAAAAAC4RoAAAAADAJQIUAAAAALhEgAIAAAAAlwhQAAAAAOASAQoAAAAAXCJAAQAAAIBLBCgAAAAAcIkABQAAAAAuFfPlytPT0/Xcc88pODhY6enpmjRpkgICAvKVe+ONN7Rp0yYdOnRIffr0Ub169Zx533//vW677TZJkr+/v3bu3KlKlSqdry4AAAAAuIz4NED16tVL7du3V/v27TVnzhxFR0drypQpHmW+/vprffbZZ/rwww+VmpqqW265RatXr1apUqUkSQsXLtSyZcskSUFBQYQnAAAAAOeMzy7hS0hI0MKFC9W6dWtJUuvWrTV9+nSlpqZ6lJs0aZLatWsnSSpdurQiIiK0YMECSdLmzZu1Z88eXXfddbrjjjvUsGHD89sJAAAAAJcVnwWomJgYhYaGqkSJEpKk8uXLKyAgQLGxsU6ZnJwcrVy5UhEREc60yMhIrVy5UpI0b948ffTRR6pcubL69++vrKysU64zIyNDKSkpHn8AAAAA4JbPAlR8fLxCQkI8pgUGBiohIcF5nJSUpGPHjnmUO7HM6NGjlZycrLlz5+rNN9/UoEGDTrnOcePGKTg42PkLDw/3Yo8AAAAAXOp8FqD8/Pycs095MjMz5e/v71FGkke5k8sUL15cDz/8sBYtWqRXXnlFOTk5ha4zOjpaycnJzt+uXbu81R0AAAAAlwGfDSIRFham5ORkj2lpaWkKCwtzHpcrV04BAQEe5VJTUz3K5GnevLkiIiJ04MABVaxYscB1BgQEFDjKHwAAAAC44bMzUM2aNdPu3buVmZkpSc5leScOBOHn56dmzZppy5YtzrS//vpLzZo1K7DOqlWrqkKFCuew1QAAAAAuZz4LUJUrV1arVq2cASGWLl2qqKgoBQQEaNCgQUpMTJQk9e7dW0uWLJEkpaSkKD4+Xvfff78kadasWc7ZqUWLFumxxx5zLvsDAAAAAG/zWYCSpOnTp+u9997T888/r7Vr12rMmDE6duyYFixYoB07dkiS2rZtq7p162rIkCEaOHCgFixYoBIlSig3N1fz5s3TNddco06dOsnf398JVgAAAABwLvj0h3RDQ0M1c+bMfNPj4uI8Hg8cODBfmSJFimjFihXnrG0AAAAAcDKfnoECAAAAgIuJT89AARebmrff6/U6N8d86PU6AQAAcG5wBgoAAAAAXCJAAQAAAIBLBCgAAAAAcIkABQAAAAAuEaAAAAAAwCUCFAAAAAC4RIACAAAAAJf4HSgAAADgHKj1ayOv17mx/vderxNnhjNQAAAAAOASAQoAAAAAXCJAAQAAAIBLBCgAAAAAcIkABQAAAAAuEaAAAAAAwCUCFAAAAAC4RIACAAAAAJcIUAAAAADgEgEKAAAAAFwiQAEAAACASwQoAAAAAHCpmK8bAMC3av3ayOt1bqz/vdfrBAAAuBBwBgoAAAAAXCJAAQAAAIBLBCgAAAAAcIkABQAAAAAuMYgEgEtK+q9DvV5nqfqjvV4nAAC4OHEGCgAAAABc4gwUzp2ccO/XWXSX9+sEAAAAXOIMFAAAAAC4RIACAAAAAJcIUAAAAADgEgEKAAAAAFwiQAEAAACASwQoAAAAAHCJAAUAAAAALhGgAAAAAMAlfkgXAIC/KXRARa/XeWDCXq/XCQD4+zgDBQAAAAAuEaAAAAAAwCUCFAAAAAC4RIACAAAAAJcIUAAAAADgEgEKAAAAAFwiQAEAAACAS/wOFAAAyKfztT94vc63/7jV63UCwPnGGSgAAAAAcIkABQAAAAAu+fQSvvT0dD333HMKDg5Wenq6Jk2apICAgHzl3njjDW3atEmHDh1Snz59VK9ePWfe2LFjlZycrP3792v48OGKiIg4jz0AAAAAcDnxaYDq1auX2rdvr/bt22vOnDmKjo7WlClTPMp8/fXX+uyzz/Thhx8qNTVVt9xyi1avXq1SpUpp9uzZ2rt3r6ZNm6a4uDg98MAD+vHHH1WkCCfWAFw6Fr34tdfrvK9fC6/XCQDA5cBnSSMhIUELFy5U69atJUmtW7fW9OnTlZqa6lFu0qRJateunSSpdOnSioiI0IIFCyRJEydO1D333CNJql69utLS0rR8+fLz2AsAAAAAlxOfnYGKiYlRaGioSpQoIUkqX768AgICFBsbqxYtjn8zmpOTo5UrV+q5555zlouMjNTKlSvVpk0bbd682eOSvbx5d9xxR4HrzMjIUEZGhvM4OTlZkpSSkuL1/kFSTq736yzq2+cqJzvL63X6ev/LScv2ep2+7FN6WsbpC52hHB8/R0eOpXu9Tl8+R7m53n+OfP06ys3w/vHO133KzLm09rvU7EvrWCfpknufvb5NR6/XuebzBV6v80zwHnt6vn6PPVHetjWzU5bzWYCKj49XSEiIx7TAwEAlJCQ4j5OSknTs2DGPcoGBgVq7dq3i4+MlKd+8E5c/2bhx4zRy5Mh808PDw8+6Hzjfgn3dAK8LDr4E+3TJPU+TfN0A7xvk6wZ4V3DweF83weuCp11qryPp3UutS5fg8ftSe5/lPfZicOG9x6ampp5y3/FZgPLz83POPuXJzMyUv7+/RxlJHuXyyhQ2r1SpUoWuMzo6Ws8884zzODc3V0lJSSpXrpxT34UuJSVF4eHh2rVrl4KCgnzdHK+gTxe+S60/0qXXp0utP9Kl16dLrT8SfboYXGr9kS69Pl1q/ZEu3j6ZmVJTUxUWFnbKcj4LUGFhYc4ldHnS0tI8GlyuXDkFBAR4lMvrVF655ORklSxZ0plXu3btQtcZEBCQb5S/MmXK/N2u+ERQUNBFtUO6QZ8ufJdaf6RLr0+XWn+kS69Pl1p/JPp0MbjU+iNden261PojXZx9cnPW0meDSDRr1ky7d+9WZmamJDmX3jVs2NAp4+fnp2bNmmnLli3OtL/++kvNmjVTWFiYatWqVeA8AAAAADgXfBagKleurFatWmnlypWSpKVLlyoqKkoBAQEaNGiQEhMTJUm9e/fWkiVLJB0/HRgfH6/7779fkhQVFeXM27Ztm0JCQtS4cWMf9AYAAADA5cCnvwM1ffp0DRw4UKtXr1ZSUpLGjx+vY8eOacGCBWrXrp0qV66stm3bav369RoyZIiSkpK0YMEC576nqKgoDRw4UKNGjXKGRb/UBQQEaPjw4QX+4PDFij5d+C61/kiXXp8utf5Il16fLrX+SPTpYnCp9Ue69Pp0qfVHujT7dCI/O904fQAAAAAAST68hA8AAAAALjYEKAAAAABwiQAFAAAAAC4RoHwsLS1No0eP1o033ujVeuPi4lStWjUdOXLEq/Xi1BYuXKg6depo+/btPln/pk2bNGrUKN1www2qX7++YmJifNKOc+Wrr77SrbfeWmC/oqKiNHHixFMun52drRkzZigiIuIctdC7Jk6cqN69e7suf/jwYQ0ePFj/+te/nGlt27bVu+++ey6a97edaf8uNJ999pluuummS+51FhcXp8cee0xPPPHEGS13sT+fZ+NstxVwuZo3b56uvfZan31O8hafjsKH46OUVK1aVfv37/dqvWFhYRo+fLiuuOIKr9aLUwsLC9OGDRt8tv4uXbro22+/1V133aXbb79dl9oYMVWrVtXatWsLnPfwww+f9oexc3NzFRISop07d56D1nlfmzZtdPjwYdflzUzZ2dlKTU11pkVFRalOnTrnoHV/X6tWrc6ofxeaBg0aaOPGjb5uhtdVqlRJfn5+ysjIOKPlznR/vRSc7bYCLlfXX3/9JXHc5AyUj/n7+6tq1aperzcgIEBdu3b1er3n2759+/Tee+/5uhmu3XLLLT5b95YtW5SQkCB/f3/ddNNNqlChgvz8/HzWnnMhMjJSISEhBc677bbbThsUihcvruuvv/5cNO2cqFOnjm677TbX5cuWLauaNWt6TGvTps05OcZ4w3XXXacmTZr4uhlnrWLFiqcN7RejkiVLKiws7LTlTj4+n+n+eqH75JNPTvstudttBeC4a665xtdN8AoC1AXgUvuQ6y0ZGRl69NFHdfToUV83xbUiRXz3ktqzZ4/H+i/V/ervbmNfPkfnw6XevwvNpbq9T3f8uBiPz2di9+7dioqKclX2Uj3WAufCpXLMvDR6cRHZtGmTBg4cqKefflp33HFHgZfuvfrqq4qOjtZjjz2m//znP8rNzZUkxcTEaMKECerdu7eqVKkiScrJydGoUaP0wgsvqHbt2po+fboyMzP1wgsvKDw83KkzOTlZgwcP1vDhw9WoUSOtXr3a6/1ISUnRc889p6FDh+rOO+/UnDlzJEmxsbGKjIzUiBEjJEmvv/66/Pz8tH37dv3+++/q0KGDRo4cqf79+6tMmTIaMGCApOPf/v3xxx9auHChpk6dqkWLFqlhw4aaPXu2atSooa5du6phw4YKCQnRr7/+Kkn6/fffVb169XN2ejg2NlbDhw9Xhw4d1LFjR6Wnp+vgwYPq0aOHnn/+eT322GNO2WXLlql58+Z64403FBUVpXLlyunZZ5/Vli1b1KRJE4WEhOjTTz/1Sru+/fZbvfbaazp06JAGDhyolStXeszfsGGDunfvrrFjx6pRo0Zat26dMjIyNGHCBIWHh2v9+vVq3LixKlasqO+++04zZ85URESEbrjhBueSnKysLI0fP16DBw9Ww4YN9dlnnyk5OVkjR45UgwYNtHTpUl111VWqW7eukpKSJB2/ZG7y5MkaO3asbr/9ds2aNctp04cffqj//ve/ioqKUvv27c/oMtaDBw+qbdu2KlOmjD755BOtWbNG//73vzV69GgdPnxYQ4cOVcOGDfXxxx+rUqVKqlWrln7++WePOtasWaO6deuqevXqHt8yv/HGGxoyZIgeffRRdenSRUeOHFFCQoKefvpptWvXTjNmzFBISIhuvPFGbd269QyfKff++usvde3aVU888YRmz56ta665RjExMWrcuLHKly+vH3/80Sm7dOlS9ezZU0OHDtUbb7zhTF+xYoXuuOMOvf3225KOX+L38ssva+TIkbr55ps1e/bsc9b+09myZYs6d+6sJ554QtnZ2XryySc1YcIEtW3bVlOmTJEk/fbbb7rttts0YMAADRw4UNdcc41q1Kih/fv367rrrnOm33TTTSpbtqwOHDjgs/6YmVq2bKmOHTtq9OjRqlatmlatWqUaNWqoatWq+uKLL5yy3333nfr166dnn31WLVu21NatW5WRkaFJkyadcjlfKOy5Ofn4fOL+Kum0++y59t5776lBgwb66KOP1LFjR5UpU0bTpk3T6tWrVb9+fVWqVEk//fSTpOP3ZIwePVpNmjTR888/L0maP3++du/erQkTJujjjz9WWlqaunfvrokTJ+qOO+7QggULzltfTuf1119X7dq1tWzZMl133XWqWLGi89reuHGj+vTpo+joaDVr1sw5Dp5qmQvBye/33bp1U9euXTVx4kQ1btxYy5cvv2BeMye3dfDgwYUeZ2fOnKlXXnlFUVFRuuaaa9ShQwctW7bslPNOte8V9JnkfPZ1yJAhBb5n5pk1a5bGjh2rVq1aOa+tEx09elT16tXTM888o/Xr15/Ttnud4bxJS0uzBg0a2JEjR8zMrH79+vb888/bihUrLCIiwszMkpOTzd/f33Jyciw7O9tKly5tP//8s5mZNW/e3LKzs83MbNCgQWZm9umnn9qECRPMzCwuLs5mzJhhOTk5tnLlSjvx6W3fvr39+uuvZmbWr18/u+OOO7zejwcffNCWLl1qZma7d++2gIAAW7VqlZmZPfroozZ8+HCnDkkWFxdnWVlZ1q5dO2vWrJnFxcVZbGysFS1a1NLS0szMrGnTpvbmm2+amdnBgwctODjYnn76afvuu+9s5cqV9vvvv1vx4sXt0KFDZmb2559/OtvD2w4fPmz333+/mZnl5OTYP//5Txs1apTde++99tVXX5mZ2U8//eT07ciRI3bNNdfYY489ZocOHbJVq1ZZkSJF7I033rDs7GybPHmy3XTTTV5r34n7kZlZRESErVixwszMOnToYG+//baZmUVFRdmTTz7psZ98/vnnlpubaz179rT69evb6tWrLTs72+rVq2evvfaamZmNHj3avvnmGzMzW7x4sQUHB1tqaqr93//9n5UtW9Y++ugjy8rKsuuuu86mT59uZmbTpk2zF1980czMPvroIwsICLDs7Gxbt26dNWnSxGlrVFSUtWnTxlU/IyIi7Nlnn7WjR4/auHHjrHHjxnbs2DFr2bKlDR8+3LKzs+3999+3smXL2qJFi+zw4cN21113Wc2aNS07O9vi4uJMki1evNiys7OtVatWNnToUDMz++KLL+zhhx921tWmTRuLioqy7OxsmzRpklWvXt2+/PJL27t3r9WrV89atGhx5k+USxkZGdajRw/r3Lmz7du3zyTZ+PHj7dixYxYVFWUPPfSQmZnFx8db7dq1LTMz08zMevfubU2bNjUzc/bBvNfQ3Llzbc6cOWZm9ssvv1ixYsVs27Zt56wPp5KdnW29e/e2zp072yeffGLNmjUzM7PY2FgLDAw0M7O1a9fa5s2bzczs999/t4CAAIuJibHDhw/bsmXLzMwsISHBypYt6/TrfMt7nX377bc2cuRIy83NtdWrV5ske/vtty0lJcW6detmISEhdvjwYduzZ4/VrVvXsrKyzMxs4sSJVqdOHcvOzj7lcufb8OHDT/ncmHken0/cX83slPvs+ZCcnGxBQUE2dOhQO3LkiL377rsWFBRkixcvttzcXHvqqafswQcftG+//dZGjx5tZmZ79+61YsWKOce5vGO5mdnLL79sXbt2NTOz999/3+rUqeOsK29b+UpiYqJJsjFjxlhycrINGzbM/P39bcOGDXbttdc6+8/7779vFSpUsNTU1EKX2bp1q8/6caKT3++bNWtmI0eONLPjr5m2bdue9rXmq7aOHDmywOPsH3/8Ydddd52ZHX+9hISEOOVONa+wfa+wzyTnu68FvWeaHX/P79u3r5kdP35Lst27d5vZ/3ttLVy40F5//fVz2uZzhTNQ59Enn3yiiIgIlSxZUpL05Zdf6umnn/YoExQUpC+//FJ+fn5atmyZihUrprS0NEnHv81/4IEHFBcXp759+0qSAgMDNWHCBM2ZM0fh4eG65557VKRIEY+zT3v27NE333yjevXqSZLGjBmjefPmebUfjzzyiBYuXOjcA1SlShW1bNlSM2fOlFT4JQ7FihVTmTJl1KRJE1WrVk3169dXTk6ODh48mK9sSEiIgoOD1a5dOzVq1EhNmjRx7qGYMWOGJOmdd95Rp06dzrpvp/LZZ5/p0KFDmjp1ql566SXVq1dPubm5WrJkiZo3by7p+E3leUqWLKkKFSqoUaNGKlOmjBo0aKDc3FzdcccdKlq0qOrVq6d9+/adk7aebPDgwWrXrp22bt2q7du3Ky0tzWM/ad26tfz8/NSgQQMFBQWpYcOGKlq0qOrWreu0cc6cOYqNjdXUqVP1xx9/6Oabb9bevXtVpkwZBQUFqV27dipWrJjq1q2rvXv3Sjp+NvWOO+6QJLVr106bNm1S0aJF9frrr3uMPNm9e3d9/vnnio+Pd9Wfu+++WyVKlFCDBg2UmJiogIAAVapUSZJUtGhRlS9fXkFBQerQoYOCg4M1cuRIbd68WVu2bHHq+Pe//62iRYvqhhtuUGJioiTplVde8biPrXv37s5Zs9DQUFWtWlUtW7ZUhQoVNHDgQC1fvvycXcJUvHhxp0+hoaGSpAcffFABAQGqX7++s41feeUVNWrUSP7+/pIK3gfzzJkzR5s2bdLUqVO1YsUKtWjRwun7+Va0aFGVK1dOktS4cWO98MILOnLkiH788UfnmFe3bl1FRkYqOzvbObvRtGlTBQcHO/vVE088ocaNG5+z170bsbGxevfddzVs2DD5+fk52/zRRx9V6dKlNXbsWCUlJenHH3/UvHnzFBkZqWLFjo/h1L17d61fv16rVq065XK+Uthzc7IT91fp1Pvs+RAUFKQyZcqoefPmKlmypBo0aKCUlBT9+9//lp+fn3P8nTNnjuLj4zV16lTNnz9frVu3LvBM5r///W8NGDBAhw4d0po1awrdDr6Qt90ffvhhBQUFadCgQSpVqpQ++OADlSxZUsHBwZKO9yEzM1MfffRRocssXbrUZ/040cnv99OnT1f37t2VmJioP/74Q2lpaad9rfmqrd99912Bx9l169apePHiko6/XmrUqOHsa6eaV9i+V9hnkvPZ159++qnA98ycnByP9//rrrtOcXFxztVTkvTxxx9r48aN6tGjxzlt87nCKHzn0Y4dOzxG6ilfvnyB5cxMQ4cO1SOPPKKgoCBnJLW3335bXbt2Vc2aNTVw4ECNGjVKTZs21ahRo/TUU09p4sSJWrhwYb7BA05eb8mSJZ3w461+rFq1Srm5ucrKynKm16hRQ5s3bz5tfSe2Ne9DRWEHAT8/v3xh7Omnn1bPnj319NNPa+/evapcufIZ9cetXbt2qUaNGh6hd/HixbriiisKDYgnTi9atKjHvCJFipzzg12e8PBwTZgwQTfddJOuv/567dq1K1/7TtfGXbt26eGHH863ffPqylOsWDFnmZP3lWrVqkk6fvnWiYMd1KhRQ9Lx+w5OPMCeTrFixZSdnV1gX05Uu3ZtSceH+T7xQ97JdWzZssUJw3ntysjIKPADVe3atWVmSk5O/luvp1PJ69PJfTtxG69du1aRkZGnrUM6/lyNHDnSecPr37+/t5t8RvLaFhwcrNWrV2vlypUFDioxfvx4paamaty4cR7T582bpx9++MGnI19K0vLly7V161alp6erVKlS+eZXrFhR5cqV0+HDh7VlyxaP42TeB5KC9v0Tl/OV0z03JzpxXzvVPnu+uDn+7tq1S48++qg6duwoSfm+1MxToUIFzZ07V2XKlFGjRo0uqEv4ThYQEKCrrrpKRYsW9djXihYtqoiICO3evbvQZS6kURRPfL+/8sorNWHCBF199dW6+eabFRcXV+AyvnrNnNjWwo6zO3fu1LZt27R3716FhIRo//79uvvuuyVJN998c6HzCtv3CvpMcr77eqr3zMLe//N8/vnnSktL06BBg/K9Pi8GnIE6j8LCwvTdd995XKP6/fffe5TZvHmzevbsqdGjR+cbqSQ3N1dff/215s6dqxdeeEHLly/X9u3b1bt3b/35558KDw8v8LcowsLClJaW5rGuk9f7d/uRk5Mj6fi9UXnMzPmQXKRIEaeMt7Vp00ZXXHGFevXqpWbNmp2TdUhS5cqV9dlnn+nYsWPOtL/++ktJSUnn9ZvVs/Hvf/9bLVu21L333nvWB6rKlStr8eLFzuPExMR84elkYWFhWrJkifM4Li5OiYmJqlq1ar59pWjRorrqqqvOuF3mYqj2zMxMSdLVV199yjoKaldISEiBX3ZkZmYqKCgoXyA734KCglzf83fyc3js2LFCh4U/n2bPnq1Vq1apX79+zlmpPBs2bNDYsWP11ltv6YorrnDekPfu3au+ffvqf//7nypWrOjTYaSfe+45lS9fXv369Su0TFZWliIjI/PtY5LnsbKw5XzlVM/NpeDk14Qk596oE40aNUpHjhzRE088ocDAwPPVvLOWmZmpqlWrKi4uzjn+Safe1zIzM326r51Kz549Va1aNT3yyCMKCAg4ZVlfv2YKO85WrVpVQ4cO1ZtvvqnZs2dr0aJFTjtPNa+wfa+gzySxsbHnqZdy2l3Ye+bJ7/8pKSke9zm98MILSkxM1IQJE85rm72FAHUe3X333crNzdXDDz+sH3/8UZMnT1Z6errMzPkAt379eqWlpSklJUWxsbFKTk5WcnKy4uPj9cILL0g6fklE69atZWZauXKlfv75Z1WsWFETJ0506jnx3/DwcN12223q1q2bvv76ay1evFirVq3yej/+9a9/edwsuWbNGj3++OOSjn8r9NNPP+no0aOaP3++pOMfgHJzc5Wbm5vvQ3De4+LFi+vQoUMeZ7JODmJ+fn56+umn9cEHH+iee+45636dTps2bZSamqp27dpp6dKleuWVV1S5cmWFhYUpOjpaZuZ8K7Znzx6nX6f6gO/mw79b2dnZHt82nrjuX3/9Vfv379ehQ4f0yy+/6OjRo4qLi8u3v5yqjR07dtSAAQP00ksvafny5Ro5cqSqVKlyyuevY8eOGjt2rObOnatvvvlGkydPVuXKldWjRw/FxMQ4gzfExsbq/vvvdy77OZXC1nXytk5KSnI+OMTExOjBBx9UuXLlCuxr3rSePXtq8eLFSk5OdtrVo0cPZ9SgPXv2OMvExMSoV69ep23v35HXp7xv7k9se97/77//fn355Zdavny5pOMh9cCBA86+cOJ26dixo6ZOnapRo0YpJiZG/fr1y/etoC/8+uuvOnjwoLKzs/X1119LOv7lREZGhrp06aKoqCjdeuutkuTM79Wrl5o3b66HHnrIY/r5ZmYqUqSI5s6dq/nz5+vDDz905uVdHrlmzRpde+21uv7669W5c2ft2rXL+RJr8+bNql27tnOJdWHLnW95+01hz01mZma+4/OJ+9qp9tnz3YdTze/YsaMWL16svn37KiYmRs8884xzHPL393f6l3cMzcrKUkxMjHMMdbOe8yVvv9mzZ48yMjJ09913q1SpUlq4cKGk42fgs7Ky1Lp161MucyHJe7/P2/5HjhzR999/77H9pQvjNZPX1sKOszt27NAXX3yhVq1aqXHjxgoJCfG4WqOweYXtewV9Jjlfv3GY19dTvWd27NhRb775pqZNm6ZVq1bpueeeU2RkpPNaKVWqlObMmaPRo0cX+KXFBc+L91PBheXLl1vNmjUtNDTUJk+ebOnp6darVy/z9/e3jz76yJKTk+3666+3K6+80mbPnm2tWrWyNm3a2NGjRy0iIsLatWtnkyZNsmHDhpmZ2ZtvvmkVKlSw4cOH27PPPmtr1qyxrKwsGzlypElybvDdsWOH3X777RYUFGSdO3e2o0ePerUfZsdvGm7btq1169bN+vfvbx988IFTfufOnRYREWG1a9e2X375xcLCwmzcuHH222+/2dVXX21NmjSxrVu32ttvv22SbNSoUZaZmWkzZ8608uXL26xZs2zRokVWvHhxe+ihhywxMdGjPXv37rXevXv/rT65sWLFCrv22mutbNmyzkAeq1evtjp16ljdunVtypQpVqVKFZswYYKtWrXKypUrZ/fff7/Fx8fb66+/bpJs9OjRtnv3buvRo4cVL17cPv3007/drl27dtmDDz5ofn5+9tZbb9l7771nxYoVs549e1pSUpINGzbMee5ff/11CwsLs9WrV9uIESNMks2YMcPi4+OtQ4cOVqFCBfvmm2/s559/tpo1a9ptt91mW7dutaNHj9rjjz9uZcqUsRtuuMHWr19vqamp1rNnT6cf69evtzp16liTJk0sLi7Ojh49at27d7fg4GBnWp633nrLGjdubIMHD7Z+/fpZcnLyafv54YcfWvHixe3JJ5+07du329NPP23Fixe3l156ySIjI61Jkya2fft2W7FihZUtW9aGDh1qU6ZMsV69etmBAwcsMzPThgwZYpLsjTfesG3btlnz5s2tVq1atnHjRjMzGz9+vLVo0cKGDBli0dHRlpGRYWbHX2vVqlWzESNG2KRJk6xv377OQCrnwvbt2+22226zWrVq2UsvveTsO9u3b7eOHTtaaGioM0jL888/bxUqVLC2bdta79697fbbb7evvvrKVq9e7eyDe/futZycHIuOjrbQ0FCrWbOmc7O8rwwfPty6du1qP/30k1WqVMnq169vy5cvtwoVKtiwYcNs8uTJVqRIERswYIANHz7coqOj7eabb7bFixebJIuKirLhw4fb4MGDLTIy8ry3/4MPPjB/f3/r06ePHTx40Jo0aWJBQUHO8XfIkCH24osv2hNPPGHbt293lvviiy/s1ltvtYEDB9pTTz1l8fHxZmbOACeFLXe+7Ny50xo1amSRkZEWExNT4HNjZh7H5xP31z/++MPeeOONU+6z59rnn39u/v7+9uSTT1p8fLzznMyYMcN27dpl9957r4WGhtr3339v06ZNs8qVK1vVqlVt8eLFTh3dunWzf/zjH7Zq1Sr79NNPrWzZstakSRNbsmSJlSlTxmbMmOGxrX777bfz0reC5L0epk6daj179nTa8tNPP1mjRo2sf//+9uSTT9qGDRtOu8yF4OT3+xkzZlhQUJC1a9fOFi5caCEhIfbJJ59cEK+Zk9ta2HF27969VqNGDQsKCrIiRYqYJIuIiLCEhIRTzits3zMr+DPJ+eyrWeHvmTk5OTZgwAArV66c1atXz3799VfLycmx1157zRnA5ODBg1azZk2rUqWKVz4LnU9+ZhfA1ybA3/Tmm2+qTp06HgMT4PIVExOjLl26nPZHMM/EW2+9pbfeeksxMTFeq/NyN3z4cGVlZWns2LG+bopXbd++XdWrVz/jsxJnuxzg5+enuLi4MzqjfDbLXGguptfMxx9/LD8/P/3rX/+SdPyqkc8//1w5OTkqWrRoofPat2/vy2ajEFzCh4varFmztGDBAi1fvpzwBIedg0tqzkWdl6ulS5cqMzNTaWlp5/S+RV/J20/OdH852+VweTub/eZS2dcupn4MHjxYCQkJzqBFR48e1W+//abbb7/9lPNwYSJA4aL20ksvacKECc6P9AJ79uzRvHnztGfPHr3//vteqXPr1q364IMPtHHjRn311VdeqfNy9t///lft2rVTeHi47rzzTl83x6tSUlKcn1V49dVXnQ9E52o5XN6ysrI0bdo0ScfPkqempp6TZS5EF9trZurUqZo7d64qVKigyMhI9ejRQ48++qjKli17ynm4MHEJHwAAAAC4xBkoAAAAAHCJAAUAAAAALhGgAAAAAMAlAhQAAAAAuESAAgAAAACXCFAAAAAA4FIxXzcAAIC/IyEhQWPGjFGxYsUUGBioo0ePqnTp0goJCVHfvn193TwAwCWGAAUAuGht3bpVbdq00bx583TjjTc60xcvXqwvv/zShy0DAFyqCFAAgItW165d1aFDB4/wJEkdOnRQZmamj1oFALiUcQ8UAOCi9NNPP+nbb7/Vww8/XOD8jh07SpJiY2M1fPhwdejQQR07dlR6erq++eYbtWjRQtOnT1fnzp0VFBSk//3vf5KkRYsWqWHDhpo9e7Zq1KihIUOGyMz08ssva+TIkbr55ps1e/ZsSVJaWpqGDRumcePGKTw8XEuWLDk/nQcA+IyfmZmvGwEAwJl6+eWX1adPH6WlpalUqVLO9B9++EFvv/22jhw5ovvuu0/vvPOO3n//feXm5ur6669Xhw4dFB0drRtuuEE1a9bUq6++qhUrVqhPnz5KTExUUlKSatSooa5du+q+++5TTk6Odu7cKTNTp06dtGbNGt100036888/9fnnn6t8+fJ64IEH9O233yo9PV2tWrXy4VYBAJxrXMIHALgopaenS5KysrI8pt966636v//7P8XGxqp169Y6dOiQpk6dKkmqV6+ecnNzVaxYMZUpU0Zt2rRRaGio6tevr71790qSQkJCFBwcrHbt2qlRo0aSpJYtW+rGG2/U1KlTlZOToxYtWigxMVGBgYH673//q5IlS6pt27bav3//+dsAAACfIEABAC5K1157rSTp999/V9OmTT3mBQYGqlSpUtq1a5dq1Kihp59+Ot/yfn5+zv+LFSumEy/I8PPz85i/a9cujRw5UrfccoskqX///pKkm2++Wdu2bdP999+vBg0aaOHChV7rHwDgwsQ9UACAi1KrVq1UtWpVzZo1q9AylStX1meffaZjx44502JjY894XZUrV9bixYudx8eOHdPatWu1c+dOjRw5Un/88YcyMjI0YMCAM64bAHBxIUABAC5KxYsX1/z58/XBBx9o3rx5HvPyzia1adNGqampateunZYuXapXXnlFO3fulCTl5ubq5NuAT3yck5Pj/L9jx46aOnWqRo0apZiYGPXr10/VqlXTe++9p927d6tGjRoaNmxYvvoAAJceLuEDAFy0GjVqpDVr1uj555/X559/rmrVqikzM1MHDhzQlClTFBoaqo8++ki9e/fWQw89pF69eql3795avXq1/vjjD33++edq3ry53n33XUnSm2++qeDgYCUmJmrmzJmqXbu2KlWqpO7duysuLk4vv/yy5s+frzfeeENBQUE6evSomjZtqu7du2v//v16/vnnfbxFAADnGqPwAQAAAIBLXMIHAAAAAC4RoAAAAADAJQIUAAAAALhEgAIAAAAAlwhQAAAAAOASAQoAAAAAXCJAAQAAAIBLBCgAAAAAcIkABQAAAAAuEaAAAAAAwCUCFAAAAAC49P8BW9SXDx/z/HkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([0.00000305, 0.01991448, 0.04291478, 0.02524541, 0.02163384,\n",
       "       0.0021003 , 0.10765203, 0.01246524, 0.00598185, 0.2473582 ,\n",
       "       0.18191696, 0.00097692, 0.03678963, 0.30546396])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_song(\"popular_songs/rude_boy_rihanna.wav\", model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "ebaac212",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([0.94105476, 0.00200079, 0.00284359, 0.00001949, 0.0003985 ,\n",
       "       0.03951033, 0.00056308, 0.00008584, 0.00007422, 0.0074202 ,\n",
       "       0.00372683, 0.00006291, 0.00001437, 0.00646239])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_song(\"popular_songs/beethoven.wav\", model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "17869e37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([0.04363568, 0.0383183 , 0.09443429, 0.00002368, 0.00004113,\n",
       "       0.00272173, 0.01360832, 0.00045767, 0.00020547, 0.15542716,\n",
       "       0.0852769 , 0.00000025, 0.00002702, 0.57832243])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_song(\"popular_songs/coldplay-viva-la-vida.wav\", model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "27d00af4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([0.02517022, 0.00411352, 0.61491535, 0.0004086 , 0.00002263,\n",
       "       0.01884582, 0.00031594, 0.00293218, 0.00199342, 0.18989808,\n",
       "       0.03962786, 0.00097025, 0.00000278, 0.11506907])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_song(\"popular_songs/avicii_silhouettes.wav\", model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "06e9156c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([0.00025909, 0.00164271, 0.36154521, 0.02135456, 0.00196274,\n",
       "       0.00769483, 0.00021082, 0.29755172, 0.05905501, 0.16966912,\n",
       "       0.03472218, 0.00336334, 0.02510383, 0.02994935])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_song(\"popular_songs/blackpink_kpop.wav\", model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "9f9b4263",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([0.02396224, 0.18475441, 0.07180672, 0.00034083, 0.00590399,\n",
       "       0.02932675, 0.0112257 , 0.00234132, 0.00204975, 0.16231864,\n",
       "       0.011417  , 0.00007006, 0.00000161, 0.50229349])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_song(\"popular_songs/nothing_else_matters_metallica.wav\", model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bec192f4",
   "metadata": {},
   "source": [
    "<H1>Results 9-Seconds Dataset</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "3a80032e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44/44 [==============================] - 3s 70ms/step - loss: 1.3618 - accuracy: 0.7507\n"
     ]
    }
   ],
   "source": [
    "#Evaluate the Model\n",
    "score = model.evaluate(testing_generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "74947030",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot loss of training vs validation\n",
    "plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
    "plt.plot(history.history[\"loss\"])\n",
    "plt.plot(history.history[\"val_loss\"])\n",
    "plt.title(\"Model Loss\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.legend([\"Training\", \"Validation\"], loc = \"upper left\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "dd94a43d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot Training vs Validation Accuracy\n",
    "plt.plot(history.history[\"accuracy\"])\n",
    "plt.plot(history.history[\"val_accuracy\"])\n",
    "plt.title(\"Model Accuracy\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylim([0, 1])\n",
    "plt.legend([\"Training\", \"Validation\"], loc = \"upper left\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "6c9fb63b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_10 (Conv2D)          (None, 511, 385, 8)       608       \n",
      "                                                                 \n",
      " batch_normalization_10 (Bat  (None, 511, 385, 8)      32        \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " re_lu_10 (ReLU)             (None, 511, 385, 8)       0         \n",
      "                                                                 \n",
      " max_pooling2d_10 (MaxPoolin  (None, 255, 192, 8)      0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_11 (Conv2D)          (None, 251, 188, 16)      3216      \n",
      "                                                                 \n",
      " batch_normalization_11 (Bat  (None, 251, 188, 16)     64        \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " re_lu_11 (ReLU)             (None, 251, 188, 16)      0         \n",
      "                                                                 \n",
      " max_pooling2d_11 (MaxPoolin  (None, 125, 94, 16)      0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_12 (Conv2D)          (None, 121, 90, 32)       12832     \n",
      "                                                                 \n",
      " batch_normalization_12 (Bat  (None, 121, 90, 32)      128       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " re_lu_12 (ReLU)             (None, 121, 90, 32)       0         \n",
      "                                                                 \n",
      " max_pooling2d_12 (MaxPoolin  (None, 60, 45, 32)       0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_13 (Conv2D)          (None, 56, 41, 64)        51264     \n",
      "                                                                 \n",
      " batch_normalization_13 (Bat  (None, 56, 41, 64)       256       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " re_lu_13 (ReLU)             (None, 56, 41, 64)        0         \n",
      "                                                                 \n",
      " max_pooling2d_13 (MaxPoolin  (None, 28, 20, 64)       0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_14 (Conv2D)          (None, 24, 16, 128)       204928    \n",
      "                                                                 \n",
      " batch_normalization_14 (Bat  (None, 24, 16, 128)      512       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " re_lu_14 (ReLU)             (None, 24, 16, 128)       0         \n",
      "                                                                 \n",
      " max_pooling2d_14 (MaxPoolin  (None, 12, 8, 128)       0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 12, 8, 128)        0         \n",
      "                                                                 \n",
      " flatten_2 (Flatten)         (None, 12288)             0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 14)                172046    \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 445,886\n",
      "Trainable params: 445,390\n",
      "Non-trainable params: 496\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#Main Model Summary\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "9f9d6fa7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44/44 [==============================] - 3s 71ms/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "     classic       0.89      0.96      0.92       100\n",
      "     country       0.68      0.75      0.71       100\n",
      "         edm       0.82      0.84      0.83       100\n",
      "    flamenco       0.84      0.82      0.83       100\n",
      "      hiphop       0.76      0.79      0.77       100\n",
      "       indie       0.71      0.67      0.69       100\n",
      "        jazz       0.90      0.89      0.89       100\n",
      "        kpop       0.72      0.64      0.68       100\n",
      "       latin       0.81      0.82      0.82       100\n",
      "       metal       0.71      0.79      0.75       100\n",
      "         pop       0.36      0.26      0.30       100\n",
      "         rap       0.74      0.73      0.74       100\n",
      "      reggae       0.89      0.94      0.91       100\n",
      "        rock       0.58      0.61      0.60       100\n",
      "\n",
      "    accuracy                           0.75      1400\n",
      "   macro avg       0.74      0.75      0.75      1400\n",
      "weighted avg       0.74      0.75      0.75      1400\n",
      "\n"
     ]
    }
   ],
   "source": [
    "precision_per_class(model, testing_generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "ae9ff6d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44/44 [==============================] - 3s 70ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "multiclass_confusion_matrix(model, testing_generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "efe0eada",
   "metadata": {},
   "outputs": [],
   "source": [
    "#We have to update this function process_wav to split into 9-second clips instead of 3\n",
    "def process_wav(song):\n",
    "    name, ext = os.path.splitext(song)\n",
    "    sound = AudioSegment.from_wav(song)\n",
    "    \n",
    "    #Stereo to Mono like original dataset\n",
    "    sound = sound.set_channels(1)\n",
    "    \n",
    "    #Change Hz to 22050Hz like original dataset\n",
    "    sound = sound.set_frame_rate(22050)\n",
    "    \n",
    "    sound.export(\"{0}_resampled.wav\".format(name), format = \"wav\")\n",
    "    \n",
    "    song = \"{0}_resampled.wav\".format(name)\n",
    "    \n",
    "    i = 0\n",
    "    to_return = []\n",
    "    \n",
    "    total_duration = len(AudioSegment.from_wav(song))\n",
    "    \n",
    "    intervals =  int(total_duration / 9000)\n",
    "    \n",
    "    for w in range(0, intervals):\n",
    "        i = i + 1\n",
    "        t1 = 9 * (w) * 1000\n",
    "        t2 = 9 * (w + 1) * 1000\n",
    "        newAudio = AudioSegment.from_wav(song)\n",
    "        new = newAudio[t1:t2]\n",
    "        new.export(str(w) + \".wav\", format = \"wav\")\n",
    "        to_return.append(str(w) + \".wav\")\n",
    "        \n",
    "    return to_return, intervals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "a9974bbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([0.06909454, 0.00019963, 0.42148998, 0.00002619, 0.00009937,\n",
       "       0.0031039 , 0.00000035, 0.02222914, 0.06503968, 0.03740867,\n",
       "       0.36871632, 0.00002538, 0.03981941, 0.00399744])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_song(\"popular_songs/rude_boy_rihanna.wav\", model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "90bfe829",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([0.95997998, 0.00000027, 0.00002102, 0.01398438, 0.00000005,\n",
       "       0.02095852, 0.00761808, 0.00000189, 0.01019101, 0.00005052,\n",
       "       0.00000989, 0.00000217, 0.0000009 , 0.00000184])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_song(\"popular_songs/beethoven.wav\", model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "08dd759d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([0.06369057, 0.00235346, 0.02507421, 0.07893343, 0.00000047,\n",
       "       0.04014043, 0.02020788, 0.00000041, 0.03323747, 0.0361662 ,\n",
       "       0.0138501 , 0.00039906, 0.40373674, 0.32067112])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_song(\"popular_songs/coldplay-viva-la-vida.wav\", model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "44623936",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([0.00202826, 0.00002604, 0.44639473, 0.03252135, 0.00000087,\n",
       "       0.22879496, 0.00000006, 0.0000078 , 0.04313774, 0.16711441,\n",
       "       0.03623007, 0.00000908, 0.00037808, 0.08683481])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_song(\"popular_songs/avicii_silhouettes.wav\", model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "91b01329",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([0.04337722, 0.00000365, 0.78781723, 0.00002944, 0.00184814,\n",
       "       0.00177351, 0.00002207, 0.04562086, 0.03423405, 0.12357358,\n",
       "       0.0018678 , 0.00007786, 0.00000078, 0.00323207])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_song(\"popular_songs/blackpink_kpop.wav\", model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "e27d21dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([0.03873717, 0.01243699, 0.09931514, 0.03660226, 0.01871479,\n",
       "       0.06402845, 0.02355923, 0.00122972, 0.06106302, 0.06459674,\n",
       "       0.04232564, 0.00304028, 0.02471872, 0.53344138])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_song(\"popular_songs/nothing_else_matters_metallica.wav\", model)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}