X_Brain.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MrSQxJQ0hQfJ"
      },
      "source": [
        "### **Init**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "viLD0dtChQfL"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.optimizers import Adam, SGD, RMSprop\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.regularizers import L2\n",
        "\n",
        "import time\n",
        "import math\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import confusion_matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xZqmfsC_hQfL",
        "outputId": "e793a298-881b-4d84-93cb-b30928cfbc62"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Fri Apr 19 12:04:34 2024       \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 536.23                 Driver Version: 536.23       CUDA Version: 12.2     |\n",
            "|-----------------------------------------+----------------------+----------------------+\n",
            "| GPU  Name                     TCC/WDDM  | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                                         |                      |               MIG M. |\n",
            "|=========================================+======================+======================|\n",
            "|   0  NVIDIA GeForce RTX 4080      WDDM  | 00000000:01:00.0  On |                  N/A |\n",
            "|  0%   39C    P8              10W / 340W |  15470MiB / 16376MiB |      7%      Default |\n",
            "|                                         |                      |                  N/A |\n",
            "+-----------------------------------------+----------------------+----------------------+\n",
            "                                                                                         \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                            |\n",
            "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
            "|        ID   ID                                                             Usage      |\n",
            "|=======================================================================================|\n",
            "|    0   N/A  N/A       620      C   ...ER\\anaconda3\\envs\\tonmoy\\python.exe    N/A      |\n",
            "|    0   N/A  N/A      4300    C+G   ...1.0_x64__8wekyb3d8bbwe\\Video.UI.exe    N/A      |\n",
            "|    0   N/A  N/A      4320    C+G   ...siveControlPanel\\SystemSettings.exe    N/A      |\n",
            "|    0   N/A  N/A      6508    C+G   ...crosoft\\Edge\\Application\\msedge.exe    N/A      |\n",
            "|    0   N/A  N/A     10112    C+G   C:\\Windows\\explorer.exe                   N/A      |\n",
            "|    0   N/A  N/A     10524    C+G   ...__8wekyb3d8bbwe\\WindowsTerminal.exe    N/A      |\n",
            "|    0   N/A  N/A     10620    C+G   ... Files (x86)\\AnyViewer\\RCClient.exe    N/A      |\n",
            "|    0   N/A  N/A     11440    C+G   ...inaries\\Win64\\EpicGamesLauncher.exe    N/A      |\n",
            "|    0   N/A  N/A     12076    C+G   ...2txyewy\\StartMenuExperienceHost.exe    N/A      |\n",
            "|    0   N/A  N/A     12504    C+G   ....Search_cw5n1h2txyewy\\SearchApp.exe    N/A      |\n",
            "|    0   N/A  N/A     13692    C+G   ...5n1h2txyewy\\ShellExperienceHost.exe    N/A      |\n",
            "|    0   N/A  N/A     14624    C+G   ...b3d8bbwe\\Microsoft.Media.Player.exe    N/A      |\n",
            "|    0   N/A  N/A     14972    C+G   ...CBS_cw5n1h2txyewy\\TextInputHost.exe    N/A      |\n",
            "|    0   N/A  N/A     15704    C+G   ...ne\\Binaries\\Win64\\EpicWebHelper.exe    N/A      |\n",
            "|    0   N/A  N/A     15720    C+G   ...m Files\\Mozilla Firefox\\firefox.exe    N/A      |\n",
            "|    0   N/A  N/A     18572    C+G   ...ekyb3d8bbwe\\PhoneExperienceHost.exe    N/A      |\n",
            "|    0   N/A  N/A     18616    C+G   ...cal\\Microsoft\\OneDrive\\OneDrive.exe    N/A      |\n",
            "+---------------------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "gpu_info = !nvidia-smi\n",
        "gpu_info = '\\n'.join(gpu_info)\n",
        "if gpu_info.find('failed') >= 0:\n",
        "  print('Not connected to a GPU')\n",
        "else:\n",
        "  print(gpu_info)\n",
        "\n",
        "gpus = tf.config.experimental.list_physical_devices('GPU')\n",
        "for gpu in gpus:\n",
        "    tf.config.experimental.set_memory_growth(gpu, True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wSvgAXHVhQfM"
      },
      "outputs": [],
      "source": [
        "IMG_SIZE = (224, 224)\n",
        "BATCH_SIZE = 32\n",
        "lr = 1e-3\n",
        "EPOCH = 500\n",
        "lambda_ = 1e-4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "voeEKGjUhQfM",
        "outputId": "4d5b958d-91d2-43b7-831a-315e2e2806bd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 14919 images belonging to 4 classes.\n",
            "Found 4261 images belonging to 4 classes.\n",
            "Found 2135 images belonging to 4 classes.\n"
          ]
        }
      ],
      "source": [
        "train_dir = 'G:/X-Brain/Data/train'\n",
        "val_dir = 'G:/X-Brain/Data/val'\n",
        "test_dir = 'G:/X-Brain/Data/test'\n",
        "\n",
        "train_datagen = tf.keras.preprocessing.image.ImageDataGenerator(\n",
        "    rotation_range=45,\n",
        "    zoom_range=0.2,\n",
        "    width_shift_range=0.2,\n",
        "    height_shift_range=0.2,\n",
        "    shear_range=0.2,\n",
        "    fill_mode='nearest',\n",
        "    horizontal_flip=True,\n",
        "    vertical_flip=True,\n",
        "    rescale=1./255,\n",
        ")\n",
        "train_set = train_datagen.flow_from_directory(\n",
        "    train_dir,\n",
        "    target_size=IMG_SIZE,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    class_mode='categorical'\n",
        ")\n",
        "\n",
        "val_datagen = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1./255)\n",
        "val_set = val_datagen.flow_from_directory(\n",
        "    val_dir,\n",
        "    target_size=IMG_SIZE,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    class_mode='categorical',\n",
        ")\n",
        "\n",
        "\n",
        "test_datagen = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1./255)\n",
        "test_set = test_datagen.flow_from_directory(\n",
        "    test_dir,\n",
        "    target_size=IMG_SIZE,\n",
        "    class_mode='categorical',\n",
        "    shuffle=False\n",
        ")\n",
        "\n",
        "num_classes = len(train_set.class_indices)\n",
        "class_labels = list(train_set.class_indices.keys())\n",
        "class_indices = list(train_set.class_indices.values())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2zxkyTRfhQfN",
        "outputId": "ab2e64f8-c26a-493a-cfff-78c7618b44a1"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x500 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, axes = plt.subplots(1, num_classes, figsize=(12, 5))\n",
        "\n",
        "for i, class_idx in enumerate(class_indices):\n",
        "    image_batch, label_batch = next(val_set)\n",
        "    index = np.where(label_batch[:, class_idx] == 1)[0][0]\n",
        "    image = image_batch[index]\n",
        "\n",
        "    axes[i].imshow(image)\n",
        "    axes[i].set_title(class_labels[i])\n",
        "    axes[i].axis('off')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s9kYI-AXhQfN"
      },
      "source": [
        "### **Model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PqPVcSXOhQfO"
      },
      "outputs": [],
      "source": [
        "def ConvBlock(x, units=32, kernel_size=3, strides=1, padding='same'):\n",
        "    x = layers.Conv2D(units, kernel_size, strides, padding, kernel_regularizer=L2(lambda_))(x)\n",
        "    x = layers.BatchNormalization()(x)\n",
        "    x = layers.ReLU()(x)\n",
        "\n",
        "    return x\n",
        "\n",
        "def DepthwiseConvBlock(x, units=32, kernel_size=3, strides=1, padding='same'):\n",
        "    x = layers.DepthwiseConv2D(kernel_size, strides=strides, padding=padding)(x)\n",
        "    x = layers.BatchNormalization()(x)\n",
        "    x = layers.ReLU()(x)\n",
        "    x = layers.SeparableConv2D(units, 1)(x)\n",
        "    x = layers.BatchNormalization()(x)\n",
        "    x = layers.ReLU()(x)\n",
        "\n",
        "    return x\n",
        "\n",
        "def ResidualAttentionBlock(x, units):\n",
        "    res = x\n",
        "\n",
        "    a = ConvBlock(x, units)\n",
        "    a = ConvBlock(a, units)\n",
        "    a = layers.MultiHeadAttention(num_heads=1, key_dim=units, value_dim=units)(a, a)\n",
        "\n",
        "    b = ConvBlock(x, units)\n",
        "    b = ConvBlock(b, units)\n",
        "    b = layers.MultiHeadAttention(num_heads=1, key_dim=units, value_dim=units)(b, b)\n",
        "\n",
        "    x = layers.Concatenate()([a, b])\n",
        "    x = DepthwiseConvBlock(x, units)\n",
        "    x = ConvBlock(x, units)\n",
        "    x = layers.Add()([x, res])\n",
        "\n",
        "    return x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wUil2aKrhQfO",
        "outputId": "98e35019-3a2e-4644-e010-591d81bac73b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"x_brain\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_3 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " conv2d_28 (Conv2D)             (None, 112, 112, 16  448         ['input_3[0][0]']                \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv2d_29 (Conv2D)             (None, 112, 112, 16  448         ['input_3[0][0]']                \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " batch_normalization_48 (BatchN  (None, 112, 112, 16  64         ['conv2d_28[0][0]']              \n",
            " ormalization)                  )                                                                 \n",
            "                                                                                                  \n",
            " batch_normalization_51 (BatchN  (None, 112, 112, 16  64         ['conv2d_29[0][0]']              \n",
            " ormalization)                  )                                                                 \n",
            "                                                                                                  \n",
            " re_lu_48 (ReLU)                (None, 112, 112, 16  0           ['batch_normalization_48[0][0]'] \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " re_lu_51 (ReLU)                (None, 112, 112, 16  0           ['batch_normalization_51[0][0]'] \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " depthwise_conv2d_10 (Depthwise  (None, 112, 112, 16  160        ['re_lu_48[0][0]']               \n",
            " Conv2D)                        )                                                                 \n",
            "                                                                                                  \n",
            " depthwise_conv2d_11 (Depthwise  (None, 112, 112, 16  160        ['re_lu_51[0][0]']               \n",
            " Conv2D)                        )                                                                 \n",
            "                                                                                                  \n",
            " batch_normalization_49 (BatchN  (None, 112, 112, 16  64         ['depthwise_conv2d_10[0][0]']    \n",
            " ormalization)                  )                                                                 \n",
            "                                                                                                  \n",
            " batch_normalization_52 (BatchN  (None, 112, 112, 16  64         ['depthwise_conv2d_11[0][0]']    \n",
            " ormalization)                  )                                                                 \n",
            "                                                                                                  \n",
            " re_lu_49 (ReLU)                (None, 112, 112, 16  0           ['batch_normalization_49[0][0]'] \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " re_lu_52 (ReLU)                (None, 112, 112, 16  0           ['batch_normalization_52[0][0]'] \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " separable_conv2d_10 (Separable  (None, 112, 112, 32  560        ['re_lu_49[0][0]']               \n",
            " Conv2D)                        )                                                                 \n",
            "                                                                                                  \n",
            " separable_conv2d_11 (Separable  (None, 112, 112, 32  560        ['re_lu_52[0][0]']               \n",
            " Conv2D)                        )                                                                 \n",
            "                                                                                                  \n",
            " batch_normalization_50 (BatchN  (None, 112, 112, 32  128        ['separable_conv2d_10[0][0]']    \n",
            " ormalization)                  )                                                                 \n",
            "                                                                                                  \n",
            " batch_normalization_53 (BatchN  (None, 112, 112, 32  128        ['separable_conv2d_11[0][0]']    \n",
            " ormalization)                  )                                                                 \n",
            "                                                                                                  \n",
            " re_lu_50 (ReLU)                (None, 112, 112, 32  0           ['batch_normalization_50[0][0]'] \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " re_lu_53 (ReLU)                (None, 112, 112, 32  0           ['batch_normalization_53[0][0]'] \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " concatenate_4 (Concatenate)    (None, 112, 112, 64  0           ['re_lu_50[0][0]',               \n",
            "                                )                                 're_lu_53[0][0]']               \n",
            "                                                                                                  \n",
            " conv2d_30 (Conv2D)             (None, 56, 56, 64)   36928       ['concatenate_4[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_54 (BatchN  (None, 56, 56, 64)  256         ['conv2d_30[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " re_lu_54 (ReLU)                (None, 56, 56, 64)   0           ['batch_normalization_54[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_31 (Conv2D)             (None, 56, 56, 64)   36928       ['re_lu_54[0][0]']               \n",
            "                                                                                                  \n",
            " conv2d_33 (Conv2D)             (None, 56, 56, 64)   36928       ['re_lu_54[0][0]']               \n",
            "                                                                                                  \n",
            " batch_normalization_55 (BatchN  (None, 56, 56, 64)  256         ['conv2d_31[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_57 (BatchN  (None, 56, 56, 64)  256         ['conv2d_33[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " re_lu_55 (ReLU)                (None, 56, 56, 64)   0           ['batch_normalization_55[0][0]'] \n",
            "                                                                                                  \n",
            " re_lu_57 (ReLU)                (None, 56, 56, 64)   0           ['batch_normalization_57[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_32 (Conv2D)             (None, 56, 56, 64)   36928       ['re_lu_55[0][0]']               \n",
            "                                                                                                  \n",
            " conv2d_34 (Conv2D)             (None, 56, 56, 64)   36928       ['re_lu_57[0][0]']               \n",
            "                                                                                                  \n",
            " batch_normalization_56 (BatchN  (None, 56, 56, 64)  256         ['conv2d_32[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_58 (BatchN  (None, 56, 56, 64)  256         ['conv2d_34[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " re_lu_56 (ReLU)                (None, 56, 56, 64)   0           ['batch_normalization_56[0][0]'] \n",
            "                                                                                                  \n",
            " re_lu_58 (ReLU)                (None, 56, 56, 64)   0           ['batch_normalization_58[0][0]'] \n",
            "                                                                                                  \n",
            " multi_head_attention_4 (MultiH  (None, 56, 56, 64)  16640       ['re_lu_56[0][0]',               \n",
            " eadAttention)                                                    're_lu_56[0][0]']               \n",
            "                                                                                                  \n",
            " multi_head_attention_5 (MultiH  (None, 56, 56, 64)  16640       ['re_lu_58[0][0]',               \n",
            " eadAttention)                                                    're_lu_58[0][0]']               \n",
            "                                                                                                  \n",
            " concatenate_5 (Concatenate)    (None, 56, 56, 128)  0           ['multi_head_attention_4[0][0]', \n",
            "                                                                  'multi_head_attention_5[0][0]'] \n",
            "                                                                                                  \n",
            " depthwise_conv2d_12 (Depthwise  (None, 56, 56, 128)  1280       ['concatenate_5[0][0]']          \n",
            " Conv2D)                                                                                          \n",
            "                                                                                                  \n",
            " batch_normalization_59 (BatchN  (None, 56, 56, 128)  512        ['depthwise_conv2d_12[0][0]']    \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " re_lu_59 (ReLU)                (None, 56, 56, 128)  0           ['batch_normalization_59[0][0]'] \n",
            "                                                                                                  \n",
            " separable_conv2d_12 (Separable  (None, 56, 56, 64)  8384        ['re_lu_59[0][0]']               \n",
            " Conv2D)                                                                                          \n",
            "                                                                                                  \n",
            " batch_normalization_60 (BatchN  (None, 56, 56, 64)  256         ['separable_conv2d_12[0][0]']    \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " re_lu_60 (ReLU)                (None, 56, 56, 64)   0           ['batch_normalization_60[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_35 (Conv2D)             (None, 56, 56, 64)   36928       ['re_lu_60[0][0]']               \n",
            "                                                                                                  \n",
            " batch_normalization_61 (BatchN  (None, 56, 56, 64)  256         ['conv2d_35[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " re_lu_61 (ReLU)                (None, 56, 56, 64)   0           ['batch_normalization_61[0][0]'] \n",
            "                                                                                                  \n",
            " add_2 (Add)                    (None, 56, 56, 64)   0           ['re_lu_61[0][0]',               \n",
            "                                                                  're_lu_54[0][0]']               \n",
            "                                                                                                  \n",
            " conv2d_36 (Conv2D)             (None, 28, 28, 64)   36928       ['add_2[0][0]']                  \n",
            "                                                                                                  \n",
            " batch_normalization_62 (BatchN  (None, 28, 28, 64)  256         ['conv2d_36[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " re_lu_62 (ReLU)                (None, 28, 28, 64)   0           ['batch_normalization_62[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_37 (Conv2D)             (None, 28, 28, 64)   36928       ['re_lu_62[0][0]']               \n",
            "                                                                                                  \n",
            " batch_normalization_63 (BatchN  (None, 28, 28, 64)  256         ['conv2d_37[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " re_lu_63 (ReLU)                (None, 28, 28, 64)   0           ['batch_normalization_63[0][0]'] \n",
            "                                                                                                  \n",
            " depthwise_conv2d_13 (Depthwise  (None, 28, 28, 64)  640         ['re_lu_63[0][0]']               \n",
            " Conv2D)                                                                                          \n",
            "                                                                                                  \n",
            " batch_normalization_64 (BatchN  (None, 28, 28, 64)  256         ['depthwise_conv2d_13[0][0]']    \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " re_lu_64 (ReLU)                (None, 28, 28, 64)   0           ['batch_normalization_64[0][0]'] \n",
            "                                                                                                  \n",
            " separable_conv2d_13 (Separable  (None, 28, 28, 128)  8384       ['re_lu_64[0][0]']               \n",
            " Conv2D)                                                                                          \n",
            "                                                                                                  \n",
            " batch_normalization_65 (BatchN  (None, 28, 28, 128)  512        ['separable_conv2d_13[0][0]']    \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " re_lu_65 (ReLU)                (None, 28, 28, 128)  0           ['batch_normalization_65[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_38 (Conv2D)             (None, 14, 14, 128)  147584      ['re_lu_65[0][0]']               \n",
            "                                                                                                  \n",
            " batch_normalization_66 (BatchN  (None, 14, 14, 128)  512        ['conv2d_38[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " re_lu_66 (ReLU)                (None, 14, 14, 128)  0           ['batch_normalization_66[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_39 (Conv2D)             (None, 14, 14, 128)  147584      ['re_lu_66[0][0]']               \n",
            "                                                                                                  \n",
            " batch_normalization_67 (BatchN  (None, 14, 14, 128)  512        ['conv2d_39[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " re_lu_67 (ReLU)                (None, 14, 14, 128)  0           ['batch_normalization_67[0][0]'] \n",
            "                                                                                                  \n",
            " depthwise_conv2d_14 (Depthwise  (None, 14, 14, 128)  1280       ['re_lu_67[0][0]']               \n",
            " Conv2D)                                                                                          \n",
            "                                                                                                  \n",
            " batch_normalization_68 (BatchN  (None, 14, 14, 128)  512        ['depthwise_conv2d_14[0][0]']    \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " re_lu_68 (ReLU)                (None, 14, 14, 128)  0           ['batch_normalization_68[0][0]'] \n",
            "                                                                                                  \n",
            " separable_conv2d_14 (Separable  (None, 14, 14, 256)  33152      ['re_lu_68[0][0]']               \n",
            " Conv2D)                                                                                          \n",
            "                                                                                                  \n",
            " batch_normalization_69 (BatchN  (None, 14, 14, 256)  1024       ['separable_conv2d_14[0][0]']    \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " re_lu_69 (ReLU)                (None, 14, 14, 256)  0           ['batch_normalization_69[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_40 (Conv2D)             (None, 7, 7, 256)    590080      ['re_lu_69[0][0]']               \n",
            "                                                                                                  \n",
            " batch_normalization_70 (BatchN  (None, 7, 7, 256)   1024        ['conv2d_40[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " re_lu_70 (ReLU)                (None, 7, 7, 256)    0           ['batch_normalization_70[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_41 (Conv2D)             (None, 7, 7, 256)    590080      ['re_lu_70[0][0]']               \n",
            "                                                                                                  \n",
            " batch_normalization_71 (BatchN  (None, 7, 7, 256)   1024        ['conv2d_41[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " re_lu_71 (ReLU)                (None, 7, 7, 256)    0           ['batch_normalization_71[0][0]'] \n",
            "                                                                                                  \n",
            " global_average_pooling2d_2 (Gl  (None, 256)         0           ['re_lu_71[0][0]']               \n",
            " obalAveragePooling2D)                                                                            \n",
            "                                                                                                  \n",
            " dense_2 (Dense)                (None, 4)            1028        ['global_average_pooling2d_2[0][0\n",
            "                                                                 ]']                              \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 1,869,220\n",
            "Trainable params: 1,864,868\n",
            "Non-trainable params: 4,352\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "def model_architecture():\n",
        "    inputs = layers.Input(shape=(*IMG_SIZE, 3))\n",
        "    p = ConvBlock(inputs, 16, strides=2)\n",
        "    p = DepthwiseConvBlock(p, 32)\n",
        "\n",
        "    q = ConvBlock(inputs, 16, strides=2)\n",
        "    q = DepthwiseConvBlock(q, 32)\n",
        "\n",
        "    x = layers.Concatenate()([p, q])\n",
        "    x = ConvBlock(x, 64, strides=2)\n",
        "    x = ResidualAttentionBlock(x, 64)\n",
        "\n",
        "    x = ConvBlock(x, 64, strides=2)\n",
        "    x = ConvBlock(x, 64)\n",
        "\n",
        "    x = DepthwiseConvBlock(x, 128)\n",
        "    x = ConvBlock(x, 128, strides=2)\n",
        "    x = ConvBlock(x, 128)\n",
        "\n",
        "    x = DepthwiseConvBlock(x, 256)\n",
        "    x = ConvBlock(x, 256, strides=2)\n",
        "    x = ConvBlock(x, 256)\n",
        "\n",
        "    x = layers.GlobalAveragePooling2D()(x)\n",
        "\n",
        "    outputs = layers.Dense(num_classes, activation='softmax')(x)\n",
        "    model = Model(inputs, outputs, name=\"x_brain\")\n",
        "    model.summary()\n",
        "    return model\n",
        "\n",
        "model = model_architecture()\n",
        "model.compile(optimizer=Adam(learning_rate=lr), loss='categorical_crossentropy', metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OrkExncphQfO"
      },
      "outputs": [],
      "source": [
        "checkpoint_filepath = 'G:/X-Brain/Checkpoints/X-Brain.weights.h5'\n",
        "model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n",
        "    filepath=checkpoint_filepath,\n",
        "    save_weights_only=True,\n",
        "    monitor='val_accuracy',\n",
        "    mode='max',\n",
        "    save_best_only=True,\n",
        "    verbose=1\n",
        ")\n",
        "plateau_lr_reduction = tf.keras.callbacks.ReduceLROnPlateau(\n",
        "    monitor='val_accuracy',\n",
        "    factor=0.5,\n",
        "    patience=5,\n",
        "    verbose=1,\n",
        "    mode='auto',\n",
        "    min_delta=0.0001,\n",
        "    cooldown=0,\n",
        "    min_lr=0.0000001,\n",
        ")\n",
        "bas_kar_bhai = tf.keras.callbacks.EarlyStopping(\n",
        "    monitor='val_accuracy',\n",
        "    min_delta=0.0001,\n",
        "    patience=20,\n",
        "    verbose=1,\n",
        "    mode='auto'\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pejREufNhQfO",
        "outputId": "b89719fc-63b4-4bf5-cd57-1f6a455ac242"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.9867 - accuracy: 0.6573\n",
            "Epoch 1: val_accuracy improved from -inf to 0.30040, saving model to G:/X-Brain/Checkpoints\\X-Brain.weights.h5\n",
            "467/467 [==============================] - 140s 294ms/step - loss: 0.9867 - accuracy: 0.6573 - val_loss: 2.4538 - val_accuracy: 0.3004 - lr: 0.0010\n",
            "Epoch 2/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.6494 - accuracy: 0.8056\n",
            "Epoch 2: val_accuracy improved from 0.30040 to 0.76531, saving model to G:/X-Brain/Checkpoints\\X-Brain.weights.h5\n",
            "467/467 [==============================] - 137s 293ms/step - loss: 0.6494 - accuracy: 0.8056 - val_loss: 0.7468 - val_accuracy: 0.7653 - lr: 0.0010\n",
            "Epoch 3/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.5338 - accuracy: 0.8528\n",
            "Epoch 3: val_accuracy did not improve from 0.76531\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.5338 - accuracy: 0.8528 - val_loss: 0.7778 - val_accuracy: 0.7423 - lr: 0.0010\n",
            "Epoch 4/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.4833 - accuracy: 0.8696\n",
            "Epoch 4: val_accuracy did not improve from 0.76531\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.4833 - accuracy: 0.8696 - val_loss: 1.0232 - val_accuracy: 0.7212 - lr: 0.0010\n",
            "Epoch 5/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.4275 - accuracy: 0.8892\n",
            "Epoch 5: val_accuracy improved from 0.76531 to 0.78174, saving model to G:/X-Brain/Checkpoints\\X-Brain.weights.h5\n",
            "467/467 [==============================] - 137s 293ms/step - loss: 0.4275 - accuracy: 0.8892 - val_loss: 0.7631 - val_accuracy: 0.7817 - lr: 0.0010\n",
            "Epoch 6/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.4029 - accuracy: 0.8962\n",
            "Epoch 6: val_accuracy improved from 0.78174 to 0.89134, saving model to G:/X-Brain/Checkpoints\\X-Brain.weights.h5\n",
            "467/467 [==============================] - 137s 293ms/step - loss: 0.4029 - accuracy: 0.8962 - val_loss: 0.4056 - val_accuracy: 0.8913 - lr: 0.0010\n",
            "Epoch 7/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.3867 - accuracy: 0.9041\n",
            "Epoch 7: val_accuracy did not improve from 0.89134\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.3867 - accuracy: 0.9041 - val_loss: 0.7312 - val_accuracy: 0.7672 - lr: 0.0010\n",
            "Epoch 8/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.3696 - accuracy: 0.9087\n",
            "Epoch 8: val_accuracy did not improve from 0.89134\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.3696 - accuracy: 0.9087 - val_loss: 0.4136 - val_accuracy: 0.8913 - lr: 0.0010\n",
            "Epoch 9/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.3617 - accuracy: 0.9141\n",
            "Epoch 9: val_accuracy did not improve from 0.89134\n",
            "467/467 [==============================] - 137s 293ms/step - loss: 0.3617 - accuracy: 0.9141 - val_loss: 0.5246 - val_accuracy: 0.8435 - lr: 0.0010\n",
            "Epoch 10/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.3459 - accuracy: 0.9196\n",
            "Epoch 10: val_accuracy did not improve from 0.89134\n",
            "467/467 [==============================] - 137s 293ms/step - loss: 0.3459 - accuracy: 0.9196 - val_loss: 0.4455 - val_accuracy: 0.8848 - lr: 0.0010\n",
            "Epoch 11/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.3444 - accuracy: 0.9186\n",
            "Epoch 11: val_accuracy did not improve from 0.89134\n",
            "\n",
            "Epoch 11: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
            "467/467 [==============================] - 137s 293ms/step - loss: 0.3444 - accuracy: 0.9186 - val_loss: 1.2509 - val_accuracy: 0.6592 - lr: 0.0010\n",
            "Epoch 12/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.2746 - accuracy: 0.9428\n",
            "Epoch 12: val_accuracy did not improve from 0.89134\n",
            "467/467 [==============================] - 137s 293ms/step - loss: 0.2746 - accuracy: 0.9428 - val_loss: 0.4731 - val_accuracy: 0.8838 - lr: 5.0000e-04\n",
            "Epoch 13/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.2591 - accuracy: 0.9458\n",
            "Epoch 13: val_accuracy improved from 0.89134 to 0.96315, saving model to G:/X-Brain/Checkpoints\\X-Brain.weights.h5\n",
            "467/467 [==============================] - 138s 294ms/step - loss: 0.2591 - accuracy: 0.9458 - val_loss: 0.2077 - val_accuracy: 0.9632 - lr: 5.0000e-04\n",
            "Epoch 14/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.2531 - accuracy: 0.9464\n",
            "Epoch 14: val_accuracy did not improve from 0.96315\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.2531 - accuracy: 0.9464 - val_loss: 0.6120 - val_accuracy: 0.8315 - lr: 5.0000e-04\n",
            "Epoch 15/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.2539 - accuracy: 0.9470\n",
            "Epoch 15: val_accuracy did not improve from 0.96315\n",
            "467/467 [==============================] - 137s 293ms/step - loss: 0.2539 - accuracy: 0.9470 - val_loss: 0.2326 - val_accuracy: 0.9521 - lr: 5.0000e-04\n",
            "Epoch 16/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.2407 - accuracy: 0.9493\n",
            "Epoch 16: val_accuracy did not improve from 0.96315\n",
            "467/467 [==============================] - 137s 293ms/step - loss: 0.2407 - accuracy: 0.9493 - val_loss: 0.2580 - val_accuracy: 0.9458 - lr: 5.0000e-04\n",
            "Epoch 17/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.2309 - accuracy: 0.9529\n",
            "Epoch 17: val_accuracy did not improve from 0.96315\n",
            "467/467 [==============================] - 137s 293ms/step - loss: 0.2309 - accuracy: 0.9529 - val_loss: 1.0804 - val_accuracy: 0.7390 - lr: 5.0000e-04\n",
            "Epoch 18/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.2297 - accuracy: 0.9509\n",
            "Epoch 18: val_accuracy did not improve from 0.96315\n",
            "\n",
            "Epoch 18: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
            "467/467 [==============================] - 137s 293ms/step - loss: 0.2297 - accuracy: 0.9509 - val_loss: 0.2080 - val_accuracy: 0.9632 - lr: 5.0000e-04\n",
            "Epoch 19/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.1967 - accuracy: 0.9625\n",
            "Epoch 19: val_accuracy did not improve from 0.96315\n",
            "467/467 [==============================] - 137s 293ms/step - loss: 0.1967 - accuracy: 0.9625 - val_loss: 0.2581 - val_accuracy: 0.9425 - lr: 2.5000e-04\n",
            "Epoch 20/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.1807 - accuracy: 0.9668\n",
            "Epoch 20: val_accuracy did not improve from 0.96315\n",
            "467/467 [==============================] - 137s 293ms/step - loss: 0.1807 - accuracy: 0.9668 - val_loss: 0.1958 - val_accuracy: 0.9601 - lr: 2.5000e-04\n",
            "Epoch 21/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.1738 - accuracy: 0.9672\n",
            "Epoch 21: val_accuracy did not improve from 0.96315\n",
            "467/467 [==============================] - 137s 293ms/step - loss: 0.1738 - accuracy: 0.9672 - val_loss: 0.2027 - val_accuracy: 0.9563 - lr: 2.5000e-04\n",
            "Epoch 22/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.1749 - accuracy: 0.9667\n",
            "Epoch 22: val_accuracy improved from 0.96315 to 0.97066, saving model to G:/X-Brain/Checkpoints\\X-Brain.weights.h5\n",
            "467/467 [==============================] - 137s 293ms/step - loss: 0.1749 - accuracy: 0.9667 - val_loss: 0.1669 - val_accuracy: 0.9707 - lr: 2.5000e-04\n",
            "Epoch 23/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.1713 - accuracy: 0.9682\n",
            "Epoch 23: val_accuracy improved from 0.97066 to 0.97841, saving model to G:/X-Brain/Checkpoints\\X-Brain.weights.h5\n",
            "467/467 [==============================] - 137s 293ms/step - loss: 0.1713 - accuracy: 0.9682 - val_loss: 0.1455 - val_accuracy: 0.9784 - lr: 2.5000e-04\n",
            "Epoch 24/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.1675 - accuracy: 0.9694\n",
            "Epoch 24: val_accuracy did not improve from 0.97841\n",
            "467/467 [==============================] - 137s 293ms/step - loss: 0.1675 - accuracy: 0.9694 - val_loss: 0.2631 - val_accuracy: 0.9308 - lr: 2.5000e-04\n",
            "Epoch 25/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.1647 - accuracy: 0.9683\n",
            "Epoch 25: val_accuracy did not improve from 0.97841\n",
            "467/467 [==============================] - 137s 293ms/step - loss: 0.1647 - accuracy: 0.9683 - val_loss: 0.1979 - val_accuracy: 0.9580 - lr: 2.5000e-04\n",
            "Epoch 26/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.1585 - accuracy: 0.9716\n",
            "Epoch 26: val_accuracy did not improve from 0.97841\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.1585 - accuracy: 0.9716 - val_loss: 0.2549 - val_accuracy: 0.9383 - lr: 2.5000e-04\n",
            "Epoch 27/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.1523 - accuracy: 0.9725\n",
            "Epoch 27: val_accuracy did not improve from 0.97841\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.1523 - accuracy: 0.9725 - val_loss: 0.1696 - val_accuracy: 0.9648 - lr: 2.5000e-04\n",
            "Epoch 28/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.1590 - accuracy: 0.9710\n",
            "Epoch 28: val_accuracy improved from 0.97841 to 0.97982, saving model to G:/X-Brain/Checkpoints\\X-Brain.weights.h5\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.1590 - accuracy: 0.9710 - val_loss: 0.1323 - val_accuracy: 0.9798 - lr: 2.5000e-04\n",
            "Epoch 29/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.1472 - accuracy: 0.9751\n",
            "Epoch 29: val_accuracy improved from 0.97982 to 0.98310, saving model to G:/X-Brain/Checkpoints\\X-Brain.weights.h5\n",
            "467/467 [==============================] - 137s 293ms/step - loss: 0.1472 - accuracy: 0.9751 - val_loss: 0.1265 - val_accuracy: 0.9831 - lr: 2.5000e-04\n",
            "Epoch 30/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.1488 - accuracy: 0.9737\n",
            "Epoch 30: val_accuracy did not improve from 0.98310\n",
            "467/467 [==============================] - 137s 293ms/step - loss: 0.1488 - accuracy: 0.9737 - val_loss: 0.1273 - val_accuracy: 0.9803 - lr: 2.5000e-04\n",
            "Epoch 31/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.1407 - accuracy: 0.9747\n",
            "Epoch 31: val_accuracy did not improve from 0.98310\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.1407 - accuracy: 0.9747 - val_loss: 0.1276 - val_accuracy: 0.9798 - lr: 2.5000e-04\n",
            "Epoch 32/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.1399 - accuracy: 0.9766\n",
            "Epoch 32: val_accuracy did not improve from 0.98310\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.1399 - accuracy: 0.9766 - val_loss: 0.2191 - val_accuracy: 0.9510 - lr: 2.5000e-04\n",
            "Epoch 33/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.1471 - accuracy: 0.9730\n",
            "Epoch 33: val_accuracy did not improve from 0.98310\n",
            "467/467 [==============================] - 137s 293ms/step - loss: 0.1471 - accuracy: 0.9730 - val_loss: 0.1521 - val_accuracy: 0.9716 - lr: 2.5000e-04\n",
            "Epoch 34/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.1426 - accuracy: 0.9761\n",
            "Epoch 34: val_accuracy improved from 0.98310 to 0.98592, saving model to G:/X-Brain/Checkpoints\\X-Brain.weights.h5\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.1426 - accuracy: 0.9761 - val_loss: 0.1155 - val_accuracy: 0.9859 - lr: 2.5000e-04\n",
            "Epoch 35/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.1351 - accuracy: 0.9756\n",
            "Epoch 35: val_accuracy did not improve from 0.98592\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.1351 - accuracy: 0.9756 - val_loss: 0.1419 - val_accuracy: 0.9742 - lr: 2.5000e-04\n",
            "Epoch 36/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.1353 - accuracy: 0.9751\n",
            "Epoch 36: val_accuracy did not improve from 0.98592\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.1353 - accuracy: 0.9751 - val_loss: 0.1317 - val_accuracy: 0.9796 - lr: 2.5000e-04\n",
            "Epoch 37/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.1372 - accuracy: 0.9743\n",
            "Epoch 37: val_accuracy did not improve from 0.98592\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.1372 - accuracy: 0.9743 - val_loss: 0.1776 - val_accuracy: 0.9643 - lr: 2.5000e-04\n",
            "Epoch 38/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.1319 - accuracy: 0.9779\n",
            "Epoch 38: val_accuracy did not improve from 0.98592\n",
            "467/467 [==============================] - 137s 293ms/step - loss: 0.1319 - accuracy: 0.9779 - val_loss: 0.1214 - val_accuracy: 0.9817 - lr: 2.5000e-04\n",
            "Epoch 39/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.1255 - accuracy: 0.9802\n",
            "Epoch 39: val_accuracy did not improve from 0.98592\n",
            "\n",
            "Epoch 39: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.1255 - accuracy: 0.9802 - val_loss: 0.1271 - val_accuracy: 0.9810 - lr: 2.5000e-04\n",
            "Epoch 40/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.1129 - accuracy: 0.9845\n",
            "Epoch 40: val_accuracy improved from 0.98592 to 0.98709, saving model to G:/X-Brain/Checkpoints\\X-Brain.weights.h5\n",
            "467/467 [==============================] - 137s 294ms/step - loss: 0.1129 - accuracy: 0.9845 - val_loss: 0.1048 - val_accuracy: 0.9871 - lr: 1.2500e-04\n",
            "Epoch 41/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.1092 - accuracy: 0.9832\n",
            "Epoch 41: val_accuracy did not improve from 0.98709\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.1092 - accuracy: 0.9832 - val_loss: 0.1065 - val_accuracy: 0.9850 - lr: 1.2500e-04\n",
            "Epoch 42/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.1048 - accuracy: 0.9862\n",
            "Epoch 42: val_accuracy improved from 0.98709 to 0.99085, saving model to G:/X-Brain/Checkpoints\\X-Brain.weights.h5\n",
            "467/467 [==============================] - 137s 293ms/step - loss: 0.1048 - accuracy: 0.9862 - val_loss: 0.0951 - val_accuracy: 0.9908 - lr: 1.2500e-04\n",
            "Epoch 43/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.1059 - accuracy: 0.9855\n",
            "Epoch 43: val_accuracy did not improve from 0.99085\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.1059 - accuracy: 0.9855 - val_loss: 0.0939 - val_accuracy: 0.9906 - lr: 1.2500e-04\n",
            "Epoch 44/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.1042 - accuracy: 0.9851\n",
            "Epoch 44: val_accuracy did not improve from 0.99085\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.1042 - accuracy: 0.9851 - val_loss: 0.0980 - val_accuracy: 0.9883 - lr: 1.2500e-04\n",
            "Epoch 45/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.1023 - accuracy: 0.9860\n",
            "Epoch 45: val_accuracy improved from 0.99085 to 0.99132, saving model to G:/X-Brain/Checkpoints\\X-Brain.weights.h5\n",
            "467/467 [==============================] - 137s 294ms/step - loss: 0.1023 - accuracy: 0.9860 - val_loss: 0.0894 - val_accuracy: 0.9913 - lr: 1.2500e-04\n",
            "Epoch 46/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.1018 - accuracy: 0.9853\n",
            "Epoch 46: val_accuracy did not improve from 0.99132\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.1018 - accuracy: 0.9853 - val_loss: 0.1138 - val_accuracy: 0.9836 - lr: 1.2500e-04\n",
            "Epoch 47/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0997 - accuracy: 0.9867\n",
            "Epoch 47: val_accuracy did not improve from 0.99132\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.0997 - accuracy: 0.9867 - val_loss: 0.1050 - val_accuracy: 0.9852 - lr: 1.2500e-04\n",
            "Epoch 48/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.1016 - accuracy: 0.9853\n",
            "Epoch 48: val_accuracy did not improve from 0.99132\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.1016 - accuracy: 0.9853 - val_loss: 0.0985 - val_accuracy: 0.9873 - lr: 1.2500e-04\n",
            "Epoch 49/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0934 - accuracy: 0.9888\n",
            "Epoch 49: val_accuracy did not improve from 0.99132\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.0934 - accuracy: 0.9888 - val_loss: 0.1455 - val_accuracy: 0.9702 - lr: 1.2500e-04\n",
            "Epoch 50/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0927 - accuracy: 0.9875\n",
            "Epoch 50: val_accuracy did not improve from 0.99132\n",
            "\n",
            "Epoch 50: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05.\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.0927 - accuracy: 0.9875 - val_loss: 0.0977 - val_accuracy: 0.9857 - lr: 1.2500e-04\n",
            "Epoch 51/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0918 - accuracy: 0.9878\n",
            "Epoch 51: val_accuracy improved from 0.99132 to 0.99319, saving model to G:/X-Brain/Checkpoints\\X-Brain.weights.h5\n",
            "467/467 [==============================] - 137s 293ms/step - loss: 0.0918 - accuracy: 0.9878 - val_loss: 0.0866 - val_accuracy: 0.9932 - lr: 6.2500e-05\n",
            "Epoch 52/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0867 - accuracy: 0.9893\n",
            "Epoch 52: val_accuracy did not improve from 0.99319\n",
            "467/467 [==============================] - 151s 322ms/step - loss: 0.0867 - accuracy: 0.9893 - val_loss: 0.0944 - val_accuracy: 0.9904 - lr: 6.2500e-05\n",
            "Epoch 53/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0872 - accuracy: 0.9895\n",
            "Epoch 53: val_accuracy did not improve from 0.99319\n",
            "467/467 [==============================] - 159s 340ms/step - loss: 0.0872 - accuracy: 0.9895 - val_loss: 0.0858 - val_accuracy: 0.9918 - lr: 6.2500e-05\n",
            "Epoch 54/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0901 - accuracy: 0.9885\n",
            "Epoch 54: val_accuracy did not improve from 0.99319\n",
            "467/467 [==============================] - 136s 292ms/step - loss: 0.0901 - accuracy: 0.9885 - val_loss: 0.0913 - val_accuracy: 0.9885 - lr: 6.2500e-05\n",
            "Epoch 55/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0847 - accuracy: 0.9907\n",
            "Epoch 55: val_accuracy did not improve from 0.99319\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.0847 - accuracy: 0.9907 - val_loss: 0.0921 - val_accuracy: 0.9892 - lr: 6.2500e-05\n",
            "Epoch 56/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0788 - accuracy: 0.9923\n",
            "Epoch 56: val_accuracy did not improve from 0.99319\n",
            "\n",
            "Epoch 56: ReduceLROnPlateau reducing learning rate to 3.125000148429535e-05.\n",
            "467/467 [==============================] - 136s 292ms/step - loss: 0.0788 - accuracy: 0.9923 - val_loss: 0.0830 - val_accuracy: 0.9916 - lr: 6.2500e-05\n",
            "Epoch 57/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0779 - accuracy: 0.9919\n",
            "Epoch 57: val_accuracy improved from 0.99319 to 0.99390, saving model to G:/X-Brain/Checkpoints\\X-Brain.weights.h5\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.0779 - accuracy: 0.9919 - val_loss: 0.0789 - val_accuracy: 0.9939 - lr: 3.1250e-05\n",
            "Epoch 58/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0758 - accuracy: 0.9926\n",
            "Epoch 58: val_accuracy did not improve from 0.99390\n",
            "467/467 [==============================] - 136s 292ms/step - loss: 0.0758 - accuracy: 0.9926 - val_loss: 0.0815 - val_accuracy: 0.9939 - lr: 3.1250e-05\n",
            "Epoch 59/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0769 - accuracy: 0.9929\n",
            "Epoch 59: val_accuracy did not improve from 0.99390\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.0769 - accuracy: 0.9929 - val_loss: 0.0811 - val_accuracy: 0.9937 - lr: 3.1250e-05\n",
            "Epoch 60/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0752 - accuracy: 0.9928\n",
            "Epoch 60: val_accuracy improved from 0.99390 to 0.99413, saving model to G:/X-Brain/Checkpoints\\X-Brain.weights.h5\n",
            "467/467 [==============================] - 137s 293ms/step - loss: 0.0752 - accuracy: 0.9928 - val_loss: 0.0795 - val_accuracy: 0.9941 - lr: 3.1250e-05\n",
            "Epoch 61/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0756 - accuracy: 0.9922\n",
            "Epoch 61: val_accuracy did not improve from 0.99413\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.0756 - accuracy: 0.9922 - val_loss: 0.0817 - val_accuracy: 0.9908 - lr: 3.1250e-05\n",
            "Epoch 62/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0763 - accuracy: 0.9917\n",
            "Epoch 62: val_accuracy did not improve from 0.99413\n",
            "467/467 [==============================] - 137s 293ms/step - loss: 0.0763 - accuracy: 0.9917 - val_loss: 0.0822 - val_accuracy: 0.9913 - lr: 3.1250e-05\n",
            "Epoch 63/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0727 - accuracy: 0.9936\n",
            "Epoch 63: val_accuracy did not improve from 0.99413\n",
            "467/467 [==============================] - 137s 293ms/step - loss: 0.0727 - accuracy: 0.9936 - val_loss: 0.0786 - val_accuracy: 0.9937 - lr: 3.1250e-05\n",
            "Epoch 64/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0727 - accuracy: 0.9934\n",
            "Epoch 64: val_accuracy did not improve from 0.99413\n",
            "467/467 [==============================] - 137s 294ms/step - loss: 0.0727 - accuracy: 0.9934 - val_loss: 0.0744 - val_accuracy: 0.9939 - lr: 3.1250e-05\n",
            "Epoch 65/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0751 - accuracy: 0.9925\n",
            "Epoch 65: val_accuracy did not improve from 0.99413\n",
            "\n",
            "Epoch 65: ReduceLROnPlateau reducing learning rate to 1.5625000742147677e-05.\n",
            "467/467 [==============================] - 137s 293ms/step - loss: 0.0751 - accuracy: 0.9925 - val_loss: 0.0811 - val_accuracy: 0.9916 - lr: 3.1250e-05\n",
            "Epoch 66/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0708 - accuracy: 0.9937\n",
            "Epoch 66: val_accuracy did not improve from 0.99413\n",
            "467/467 [==============================] - 137s 293ms/step - loss: 0.0708 - accuracy: 0.9937 - val_loss: 0.0776 - val_accuracy: 0.9927 - lr: 1.5625e-05\n",
            "Epoch 67/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0719 - accuracy: 0.9932\n",
            "Epoch 67: val_accuracy did not improve from 0.99413\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.0719 - accuracy: 0.9932 - val_loss: 0.0747 - val_accuracy: 0.9939 - lr: 1.5625e-05\n",
            "Epoch 68/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0713 - accuracy: 0.9934\n",
            "Epoch 68: val_accuracy did not improve from 0.99413\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.0713 - accuracy: 0.9934 - val_loss: 0.0781 - val_accuracy: 0.9932 - lr: 1.5625e-05\n",
            "Epoch 69/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0715 - accuracy: 0.9930\n",
            "Epoch 69: val_accuracy did not improve from 0.99413\n",
            "467/467 [==============================] - 137s 293ms/step - loss: 0.0715 - accuracy: 0.9930 - val_loss: 0.0741 - val_accuracy: 0.9939 - lr: 1.5625e-05\n",
            "Epoch 70/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0687 - accuracy: 0.9942\n",
            "Epoch 70: val_accuracy did not improve from 0.99413\n",
            "\n",
            "Epoch 70: ReduceLROnPlateau reducing learning rate to 7.812500371073838e-06.\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.0687 - accuracy: 0.9942 - val_loss: 0.0748 - val_accuracy: 0.9937 - lr: 1.5625e-05\n",
            "Epoch 71/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0684 - accuracy: 0.9944\n",
            "Epoch 71: val_accuracy did not improve from 0.99413\n",
            "467/467 [==============================] - 137s 293ms/step - loss: 0.0684 - accuracy: 0.9944 - val_loss: 0.0742 - val_accuracy: 0.9934 - lr: 7.8125e-06\n",
            "Epoch 72/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0704 - accuracy: 0.9936\n",
            "Epoch 72: val_accuracy improved from 0.99413 to 0.99484, saving model to G:/X-Brain/Checkpoints\\X-Brain.weights.h5\n",
            "467/467 [==============================] - 137s 294ms/step - loss: 0.0704 - accuracy: 0.9936 - val_loss: 0.0728 - val_accuracy: 0.9948 - lr: 7.8125e-06\n",
            "Epoch 73/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0683 - accuracy: 0.9942\n",
            "Epoch 73: val_accuracy did not improve from 0.99484\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.0683 - accuracy: 0.9942 - val_loss: 0.0753 - val_accuracy: 0.9946 - lr: 7.8125e-06\n",
            "Epoch 74/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0691 - accuracy: 0.9940\n",
            "Epoch 74: val_accuracy improved from 0.99484 to 0.99554, saving model to G:/X-Brain/Checkpoints\\X-Brain.weights.h5\n",
            "467/467 [==============================] - 137s 293ms/step - loss: 0.0691 - accuracy: 0.9940 - val_loss: 0.0739 - val_accuracy: 0.9955 - lr: 7.8125e-06\n",
            "Epoch 75/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0671 - accuracy: 0.9943\n",
            "Epoch 75: val_accuracy did not improve from 0.99554\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.0671 - accuracy: 0.9943 - val_loss: 0.0750 - val_accuracy: 0.9932 - lr: 7.8125e-06\n",
            "Epoch 76/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0687 - accuracy: 0.9939\n",
            "Epoch 76: val_accuracy did not improve from 0.99554\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.0687 - accuracy: 0.9939 - val_loss: 0.0738 - val_accuracy: 0.9951 - lr: 7.8125e-06\n",
            "Epoch 77/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0694 - accuracy: 0.9940\n",
            "Epoch 77: val_accuracy did not improve from 0.99554\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.0694 - accuracy: 0.9940 - val_loss: 0.0723 - val_accuracy: 0.9948 - lr: 7.8125e-06\n",
            "Epoch 78/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0678 - accuracy: 0.9948\n",
            "Epoch 78: val_accuracy did not improve from 0.99554\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.0678 - accuracy: 0.9948 - val_loss: 0.0732 - val_accuracy: 0.9955 - lr: 7.8125e-06\n",
            "Epoch 79/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0658 - accuracy: 0.9955\n",
            "Epoch 79: val_accuracy did not improve from 0.99554\n",
            "\n",
            "Epoch 79: ReduceLROnPlateau reducing learning rate to 3.906250185536919e-06.\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.0658 - accuracy: 0.9955 - val_loss: 0.0751 - val_accuracy: 0.9932 - lr: 7.8125e-06\n",
            "Epoch 80/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0668 - accuracy: 0.9944\n",
            "Epoch 80: val_accuracy did not improve from 0.99554\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.0668 - accuracy: 0.9944 - val_loss: 0.0739 - val_accuracy: 0.9944 - lr: 3.9063e-06\n",
            "Epoch 81/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0680 - accuracy: 0.9940\n",
            "Epoch 81: val_accuracy did not improve from 0.99554\n",
            "467/467 [==============================] - 137s 293ms/step - loss: 0.0680 - accuracy: 0.9940 - val_loss: 0.0734 - val_accuracy: 0.9955 - lr: 3.9063e-06\n",
            "Epoch 82/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0668 - accuracy: 0.9946\n",
            "Epoch 82: val_accuracy did not improve from 0.99554\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.0668 - accuracy: 0.9946 - val_loss: 0.0751 - val_accuracy: 0.9937 - lr: 3.9063e-06\n",
            "Epoch 83/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0674 - accuracy: 0.9939\n",
            "Epoch 83: val_accuracy did not improve from 0.99554\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.0674 - accuracy: 0.9939 - val_loss: 0.0753 - val_accuracy: 0.9925 - lr: 3.9063e-06\n",
            "Epoch 84/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0666 - accuracy: 0.9942\n",
            "Epoch 84: val_accuracy did not improve from 0.99554\n",
            "\n",
            "Epoch 84: ReduceLROnPlateau reducing learning rate to 1.9531250927684596e-06.\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.0666 - accuracy: 0.9942 - val_loss: 0.0761 - val_accuracy: 0.9927 - lr: 3.9063e-06\n",
            "Epoch 85/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0667 - accuracy: 0.9945\n",
            "Epoch 85: val_accuracy did not improve from 0.99554\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.0667 - accuracy: 0.9945 - val_loss: 0.0751 - val_accuracy: 0.9944 - lr: 1.9531e-06\n",
            "Epoch 86/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0653 - accuracy: 0.9949\n",
            "Epoch 86: val_accuracy did not improve from 0.99554\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.0653 - accuracy: 0.9949 - val_loss: 0.0746 - val_accuracy: 0.9946 - lr: 1.9531e-06\n",
            "Epoch 87/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0654 - accuracy: 0.9949\n",
            "Epoch 87: val_accuracy did not improve from 0.99554\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.0654 - accuracy: 0.9949 - val_loss: 0.0739 - val_accuracy: 0.9946 - lr: 1.9531e-06\n",
            "Epoch 88/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0680 - accuracy: 0.9942\n",
            "Epoch 88: val_accuracy did not improve from 0.99554\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.0680 - accuracy: 0.9942 - val_loss: 0.0757 - val_accuracy: 0.9932 - lr: 1.9531e-06\n",
            "Epoch 89/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0663 - accuracy: 0.9942\n",
            "Epoch 89: val_accuracy did not improve from 0.99554\n",
            "\n",
            "Epoch 89: ReduceLROnPlateau reducing learning rate to 9.765625463842298e-07.\n",
            "467/467 [==============================] - 137s 293ms/step - loss: 0.0663 - accuracy: 0.9942 - val_loss: 0.0747 - val_accuracy: 0.9939 - lr: 1.9531e-06\n",
            "Epoch 90/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0666 - accuracy: 0.9942\n",
            "Epoch 90: val_accuracy did not improve from 0.99554\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.0666 - accuracy: 0.9942 - val_loss: 0.0746 - val_accuracy: 0.9939 - lr: 9.7656e-07\n",
            "Epoch 91/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0655 - accuracy: 0.9948\n",
            "Epoch 91: val_accuracy did not improve from 0.99554\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.0655 - accuracy: 0.9948 - val_loss: 0.0741 - val_accuracy: 0.9941 - lr: 9.7656e-07\n",
            "Epoch 92/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0662 - accuracy: 0.9944\n",
            "Epoch 92: val_accuracy did not improve from 0.99554\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.0662 - accuracy: 0.9944 - val_loss: 0.0745 - val_accuracy: 0.9939 - lr: 9.7656e-07\n",
            "Epoch 93/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0687 - accuracy: 0.9937\n",
            "Epoch 93: val_accuracy did not improve from 0.99554\n",
            "467/467 [==============================] - 136s 292ms/step - loss: 0.0687 - accuracy: 0.9937 - val_loss: 0.0758 - val_accuracy: 0.9934 - lr: 9.7656e-07\n",
            "Epoch 94/500\n",
            "467/467 [==============================] - ETA: 0s - loss: 0.0656 - accuracy: 0.9946\n",
            "Epoch 94: val_accuracy did not improve from 0.99554\n",
            "\n",
            "Epoch 94: ReduceLROnPlateau reducing learning rate to 4.882812731921149e-07.\n",
            "467/467 [==============================] - 137s 292ms/step - loss: 0.0656 - accuracy: 0.9946 - val_loss: 0.0754 - val_accuracy: 0.9937 - lr: 9.7656e-07\n",
            "Epoch 94: early stopping\n",
            "Training Time : 215minutes\n",
            "Best weights restored!!\n"
          ]
        }
      ],
      "source": [
        "t1=time.time()\n",
        "\n",
        "history = model.fit(\n",
        "    train_set,\n",
        "    validation_data=val_set,\n",
        "    epochs=EPOCH,\n",
        "    callbacks=[model_checkpoint_callback, plateau_lr_reduction, bas_kar_bhai]\n",
        ")\n",
        "\n",
        "# Training time\n",
        "t2=time.time()\n",
        "print(f'Training Time : {round((t2-t1)/60)}minutes')\n",
        "\n",
        "# selecting the best model\n",
        "model.load_weights(checkpoint_filepath)\n",
        "print(\"Best weights restored!!\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QW7ueNJ7hQfP",
        "outputId": "19c65c76-9038-47bb-8f94-5cf5b2fa0e12"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training Loss:\n",
            " [0.986665666103363, 0.6493767499923706, 0.5338176488876343, 0.4833284914493561, 0.42754775285720825, 0.4029292166233063, 0.38674768805503845, 0.3696100115776062, 0.3617073595523834, 0.34594786167144775, 0.34444552659988403, 0.27464842796325684, 0.25906088948249817, 0.2531387507915497, 0.253924697637558, 0.24071259796619415, 0.23088207840919495, 0.22971217334270477, 0.19670158624649048, 0.1807401180267334, 0.17383931577205658, 0.17489418387413025, 0.17131154239177704, 0.16745424270629883, 0.16469790041446686, 0.15846370160579681, 0.15230035781860352, 0.15900596976280212, 0.14715299010276794, 0.1488419622182846, 0.14070625603199005, 0.13985837996006012, 0.14714409410953522, 0.1425940990447998, 0.13511735200881958, 0.1352671980857849, 0.13719335198402405, 0.13192865252494812, 0.12551507353782654, 0.11293085664510727, 0.10922478139400482, 0.1048230230808258, 0.10590831190347672, 0.10419126600027084, 0.10229778289794922, 0.10176055878400803, 0.09966199845075607, 0.10161692649126053, 0.09344212710857391, 0.09265851229429245, 0.09175848960876465, 0.08673255145549774, 0.08721718937158585, 0.09005947411060333, 0.08469972014427185, 0.07876425981521606, 0.07794196903705597, 0.0758400559425354, 0.07685065269470215, 0.07523249089717865, 0.07564027607440948, 0.07626677304506302, 0.07269865274429321, 0.07268449664115906, 0.07510679960250854, 0.07080445438623428, 0.07189919054508209, 0.07130686193704605, 0.07146567851305008, 0.06865441799163818, 0.06837011128664017, 0.07041443139314651, 0.06827522814273834, 0.06909139454364777, 0.06708261370658875, 0.06874917447566986, 0.06944366544485092, 0.06780426949262619, 0.06580206006765366, 0.06679388880729675, 0.06800836324691772, 0.06680069863796234, 0.06736236810684204, 0.06656859815120697, 0.06668604165315628, 0.06534445285797119, 0.06539459526538849, 0.067988321185112, 0.0662764236330986, 0.06658460199832916, 0.06549479067325592, 0.06623954325914383, 0.06869390606880188, 0.06560035049915314]\n",
            "Validation Loss:\n",
            " [2.45383620262146, 0.746819257736206, 0.7777563333511353, 1.023217797279358, 0.7630971670150757, 0.4056112766265869, 0.7312425971031189, 0.41359612345695496, 0.5245785117149353, 0.44549378752708435, 1.2508957386016846, 0.47312065958976746, 0.20770102739334106, 0.6120400428771973, 0.2326379269361496, 0.25796788930892944, 1.0803663730621338, 0.20801298320293427, 0.25808441638946533, 0.19584187865257263, 0.20266085863113403, 0.16693425178527832, 0.14554981887340546, 0.26311010122299194, 0.1979130059480667, 0.2549285590648651, 0.16959001123905182, 0.13234546780586243, 0.1264902800321579, 0.12726569175720215, 0.12756307423114777, 0.21908949315547943, 0.15208612382411957, 0.11552389711141586, 0.1418873369693756, 0.13168032467365265, 0.17762936651706696, 0.1213899552822113, 0.12711641192436218, 0.10478952527046204, 0.10654008388519287, 0.09508241713047028, 0.09387845546007156, 0.09804007411003113, 0.08940081298351288, 0.11377542465925217, 0.10498671233654022, 0.098479725420475, 0.14548836648464203, 0.0976845771074295, 0.08655161410570145, 0.09436170011758804, 0.08584156632423401, 0.09126176685094833, 0.09211751073598862, 0.08297592401504517, 0.07894919067621231, 0.08149449527263641, 0.08111055940389633, 0.07953222095966339, 0.0817088782787323, 0.08224385231733322, 0.07859982550144196, 0.07440820336341858, 0.08109256625175476, 0.07759997993707657, 0.07470370084047318, 0.07807152718305588, 0.07413728535175323, 0.0748133584856987, 0.0742240771651268, 0.07284720242023468, 0.07532192021608353, 0.07389738410711288, 0.07500730454921722, 0.07380294054746628, 0.0722866803407669, 0.07315050065517426, 0.07508184760808945, 0.07385249435901642, 0.07336722314357758, 0.07506319880485535, 0.07534510642290115, 0.07605777680873871, 0.07511627674102783, 0.0745730847120285, 0.07391658425331116, 0.07571779936552048, 0.0747443288564682, 0.07457204908132553, 0.07409639656543732, 0.07449455559253693, 0.07582810521125793, 0.07539848983287811]\n",
            "Training Accuracy:\n",
            " [0.6572826504707336, 0.8056169748306274, 0.8528051376342773, 0.8696293234825134, 0.8892017006874084, 0.896239697933197, 0.9040820598602295, 0.9087070226669312, 0.9140692949295044, 0.9196326732635498, 0.9185602068901062, 0.9427575469017029, 0.9457738399505615, 0.9464441537857056, 0.946980357170105, 0.9493263363838196, 0.95294588804245, 0.9509350657463074, 0.9625310301780701, 0.9668208360671997, 0.9672229886054993, 0.9666867852210999, 0.9682284593582153, 0.9693679213523865, 0.9682954549789429, 0.9716469049453735, 0.972518265247345, 0.9710436463356018, 0.975132405757904, 0.9737247824668884, 0.9747301936149597, 0.9766070246696472, 0.9729874730110168, 0.9761378169059753, 0.9756015539169312, 0.975132405757904, 0.9742609858512878, 0.9778805375099182, 0.9802265763282776, 0.9845163822174072, 0.9831758141517639, 0.9861921072006226, 0.9855217933654785, 0.9850526452064514, 0.9859910011291504, 0.9853207468986511, 0.986728310585022, 0.9853207468986511, 0.9888062477111816, 0.9875326752662659, 0.9878007769584656, 0.989342451095581, 0.9894765019416809, 0.9884710907936096, 0.9906830191612244, 0.9922916889190674, 0.9918895363807678, 0.9925598502159119, 0.9928949475288391, 0.9928279519081116, 0.9922246932983398, 0.9916884303092957, 0.9935652613639832, 0.993364155292511, 0.9924927949905396, 0.993699312210083, 0.9932301044464111, 0.9934312105178833, 0.993028998374939, 0.9941685199737549, 0.9944366216659546, 0.9935652613639832, 0.9942355155944824, 0.994034469127655, 0.9943025708198547, 0.9939004182815552, 0.9939674139022827, 0.9947717785835266, 0.9955090880393982, 0.9944366216659546, 0.994034469127655, 0.9946377277374268, 0.9939004182815552, 0.9942355155944824, 0.9945036768913269, 0.9949058294296265, 0.9949058294296265, 0.9942355155944824, 0.9942355155944824, 0.9941685199737549, 0.9948387742042542, 0.9943695664405823, 0.993699312210083, 0.9946377277374268]\n",
            "Validation Accuracy:\n",
            " [0.30039897561073303, 0.7653133273124695, 0.7423139810562134, 0.7211921811103821, 0.7817413806915283, 0.8913400769233704, 0.7671908140182495, 0.8913400769233704, 0.8434639573097229, 0.8847688436508179, 0.6592349410057068, 0.8838300704956055, 0.9631541967391968, 0.831494927406311, 0.9521239399909973, 0.9457873702049255, 0.7390283942222595, 0.9631541967391968, 0.9425017833709717, 0.9601032733917236, 0.9563482999801636, 0.9706641435623169, 0.9784088134765625, 0.9307674169540405, 0.9579910635948181, 0.9382774233818054, 0.9647970199584961, 0.9798169732093811, 0.983102560043335, 0.9802863001823425, 0.9798169732093811, 0.9509505033493042, 0.9716029167175293, 0.9859188199043274, 0.9741844534873962, 0.9795822501182556, 0.9643276333808899, 0.9816944599151611, 0.9809903502464294, 0.9870922565460205, 0.984980046749115, 0.9908472299575806, 0.9906125068664551, 0.9882656931877136, 0.9913166165351868, 0.9835719466209412, 0.9852147102355957, 0.9873269200325012, 0.9701948165893555, 0.9856840968132019, 0.9931941032409668, 0.9903778433799744, 0.9917859435081482, 0.9885003566741943, 0.9892044067382812, 0.9915512800216675, 0.9938981533050537, 0.9938981533050537, 0.9936634302139282, 0.9941328167915344, 0.9908472299575806, 0.9913166165351868, 0.9936634302139282, 0.9938981533050537, 0.9915512800216675, 0.9927247166633606, 0.9938981533050537, 0.9931941032409668, 0.9938981533050537, 0.9936634302139282, 0.9934287667274475, 0.9948368668556213, 0.9946022033691406, 0.995540976524353, 0.9931941032409668, 0.9950715899467468, 0.9948368668556213, 0.995540976524353, 0.9931941032409668, 0.9943675398826599, 0.995540976524353, 0.9936634302139282, 0.9924900531768799, 0.9927247166633606, 0.9943675398826599, 0.9946022033691406, 0.9946022033691406, 0.9931941032409668, 0.9938981533050537, 0.9938981533050537, 0.9941328167915344, 0.9938981533050537, 0.9934287667274475, 0.9936634302139282]\n"
          ]
        }
      ],
      "source": [
        "training_loss = history.history['loss']\n",
        "validation_loss = history.history['val_loss']\n",
        "training_accuracy = history.history['accuracy']\n",
        "validation_accuracy = history.history['val_accuracy']\n",
        "print(\"Training Loss:\\n\", training_loss)\n",
        "print(\"Validation Loss:\\n\", validation_loss)\n",
        "print(\"Training Accuracy:\\n\", training_accuracy)\n",
        "print(\"Validation Accuracy:\\n\", validation_accuracy)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vx-I7HOzhQfP"
      },
      "outputs": [],
      "source": [
        "plt.rcdefaults()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1O7-9si-hQfP",
        "outputId": "b169e2c8-0087-4063-ba5f-2f1d3ac15a8e"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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AnU5X7x8EiIhaEytORERt6Oyzz0ZcXBymTJmCv/zlLxAEAe+++26bTkc72cSJEzFr1iyYzWbceuutAc0CKioq0KlTJ1xzzTUYOHAgIiMj8d1332Hjxo147rnn1PNefvllzJkzB6tXrw7Yu6chgiDgxhtvxNNPPw0AmDt3bsDXx48fj/nz5+Oiiy7CjTfeiIKCArzyyivo3r07tm7dGtLrMxgMePLJJ3HHHXfg/PPPx8SJE5GTk4M333yzTkXk0ksvxYoVK3DllVdi/PjxyMnJwcKFC9G3b9+AaW4WiwV9+/bFsmXL0LNnT8THx6N///71rgcaOHAgpkyZgtdff12dmrlhwwa8/fbbmDBhgtqWviVcLhc+/vhjXHDBBQ1u/nv55ZfjhRdeQEFBAbp3745HHnkETzzxBM4991xcddVVMJlM2LhxI9LT0zFv3jxER0fj+eefx2233YZhw4bhxhtvRFxcHLZs2YKqqip1D7DbbrsNH330ES666CJcd911OHDgAJYsWaJWrIJx6aWXYu7cuZg2bRrOPvtsbNu2De+9916d78/kyZPxzjvvYMaMGdiwYQPOPfdc2O12fPfdd7jrrrsC9m8aP348EhISsHz5clx88cVITk5uxjtLRBSCMHXzIyLqsBpqR96vX796z//pp5+Us846S7FYLEp6erry97//Xfn2228DWmorSsPtyJ955pk690QDrbLrs2/fPgWAAkD58ccfA77mcrmUBx54QBk4cKASFRWlREREKAMHDlReffXVgPP8Lbhrj7cpO3bsUAAoJpNJKS0trfP1RYsWKT169FBMJpPSu3dv5c0336zT6ltRmm5H7vfqq68qXbt2VUwmkzJ06FBl7dq1dVpmy7KsPP3000pmZqZiMpmUwYMHK1988UWd915RFOXnn39WhgwZohiNxoD3u74xejweZc6cOUrXrl0Vg8GgdO7cWZk5c2ZAu3n/axk/fnyd9+LkcZ7s448/VgAoixYtavCcNWvWKACUF154QT22ePFiZfDgwYrJZFLi4uKU0aNHKytXrgy47rPPPlPOPvtsxWKxKNHR0crw4cOV999/P+Cc5557TsnIyFBMJpNyzjnnKL/++muD7ciXL19eZ2xOp1P529/+pqSlpSkWi0U555xzlHXr1tX7uquqqpRHHnlEfS9TU1OVa665Rjlw4ECd+951110KAGXp0qUNvi9ERK1FUJR2/LMnERERUSu57777sGjRIuTl5anTK4mI2grXOBEREVGH43Q6sWTJElx99dUMTUTULrjGiYiIiDqMgoICfPfdd/joo49QXFyMe++9N9xDIqLTBIMTERERdRg7d+7EpEmTkJycjBdffLHBdutERK2Na5yIiIiIiIiawDVORERERERETWBwIiIiIiIiasJpt8ZJlmWcOHECUVFREAQh3MMhIiIiIqIwURQFFRUVSE9PD9ggvj6nXXA6ceIEOnfuHO5hEBERERGRRhw9ehSdOnVq9JzTLjhFRUUB8L050dHRYR4NERERERGFi81mQ+fOndWM0JjTLjj5p+dFR0czOBERERERUVBLeNgcgoiIiIiIqAkMTkRERERERE1gcCIiIiIiImoCgxMREREREVETGJyIiIiIiIiawOBERERERETUBAYnIiIiIiKiJjA4ERERERERNSGswWnt2rW47LLLkJ6eDkEQ8MknnzR5zZo1a3DmmWfCZDKhe/fueOutt9p8nEREREREdHoLa3Cy2+0YOHAgXnnllaDOz8nJwfjx43Heeedh8+bN+Otf/4rbbrsN3377bRuPlIiIiIiITmf6cD75xRdfjIsvvjjo8xcuXIiuXbviueeeAwD06dMHP/74I55//nmMGzeurYZJRERERESnuQ61xmndunUYO3ZswLFx48Zh3bp1DV7jcrlgs9kCHkRERERERKHoUMEpLy8PKSkpAcdSUlJgs9ngcDjqvWbevHmIiYlRH507d26PoRIRERER0SkkrFP12sPMmTMxY8YM9XObzcbwRERE1I4URYEiSZDdHigeNxRFgSEmBoIghHtomibLMio9VajyOCDJku+hyNX/1nxuEA2wGsywiAYYqtwQKxzwlpXDXVoGb2UlBFGEoBMhiDooogAJCjyCDC9kSG434HBBcTigVDmhVDkgVzkgVVVBcbphSIyHIT0VhrQU6NKSoUtJBKxmSLIEANDr9NAJOugcLihFZZCKS+EtKoG3uASQFUAnVj+/DhBF9XNZkeGqqoKnqhIehwNeRxW8DidklxOy0wXJYoQrIRKO+AhUxplRHmNEhQVweF3wyF5YDRZEGiMQabQiyhSBSBgR6RFhdQEGhwdilROi3QHYHVAqq6ofdkhVDuiio6BLjAPiY6HER0OKi4I3LhJuix4KALPeBLPeBIvBDIveBLPeDLPBBLPOBADwyl54FQleWfJ9XP298cpeSLJc63sjwev1wGurhFRaCq/TBcnrhtfrgeT1QPJ4IUkeSF4vZElq9GdBMBqgj42BITYWprhYmC0RMOlNMOmMMOtNEIUmaiECIECAAACC7yNB8H3u8rjgcrvgdDvgdDvg9jjhcvsektcDnd4Ag8EEg94IvdGofmw0GGGqfq9MemP1vyYYRH3Af9teyQu7pwp2jwNVbgeqPA7f524Hzus6EjpR1/z/SNpZhwpOqampyM/PDziWn5+P6OhoWCyWeq8xmUwwmUztMTwiIqIAiiShcO0PqNizD5aMNER07YqIrCzoIyPCOi6v5EWpsxwOjxOiR4JSUAIprwDeE/lw5+bBdTwXzhO5kD2eRu6iQBFF3y/DogiIAhRRhCIKgCgAigLBI0HwSoBXgqAoAVfLERYgKx1i104wdMuEpVtXmCOjYNIZYdQbYdQZqh/GOr+ISbKEoooi5B/ah9JDB2E/dgxSbgF0hWUwVDohyoCgKBBkJeBjQVHgjjDBnRAFb1IMkBIPXWoyjOmpMCclwaAzoNJdBZvThsryUjjKSuAqL4fXZoNcaYfB4YHFDVhcMswuGSanBKPTC6PDA53LC0UnQjLqIBl8D69BhLf6XwiAwatA75Gh98jQeSTo3BJEjxeiWwIEQBYFKAIgC4BU/a8sovqYAFn0HVNO+tjokRHhlGFxKSd/k1rMkXOozjG7WURptA5uvYDoKglRlTJM3tZ5bgGArvph3A9EAUiu/ppbL6A0WoeKCB0MHt/r9X8vDNW5w1P9aA6PDqi06uAwCXCaRDiqH06TUP2vCAWAqPh+pkQZEBX/z5jv+xvpkBFZJSGyqvpfhwydHPg8erT8F/Byg4Aqiwi7WYTDLEKUFRi9CgweBQZvzcPoVaBrJJM19KcLU/WjPv73uAqAJAJeUYCkAySdAK9OgKQTIOtEKDoRiiwBsgJR8b1Xgux7/8TqY+UvdkN8UlpL3op21aGC08iRI/HVV18FHFu5ciVGjhwZphEREVFtiiSh9PfNcBw9BkGng6ATAVGnfizo9BD1OhgTE2HJSIchKqrJe3qrHLDnHETl/gOoOnQYlowMpF5yMfTW+v9gpgWKJKHwfz/g6IfL4czNq/N1U3IyIrpmISK7KyKyMiEaDHCXlsFTVgZ3aWmtj8sgOaoQkZmJyJ49ENWrJ6J69oQhuuH3TVEUVLgqkVtZgNyKApQ4ylBiL4WtKB/evAKIBaUwF9sRZ/Mi3uZFtF1u8F5NESQZkGquD7Z+pAC+asCOA8COA/AAcAM4GKNDXqIBtkgddJLvFz6dpEAvA0YZMMgijF4FETYXoisk6BTfLzIxIYzZVOmCqdIFHC4CcEA97tYLKLWIMHoUxLtkJDUnB8gSdJ7GKwcNUgCd3PLwIQmAw+z7hbrKIsJpFAFU/+Iq1/6FH9ArAmS9CJdRgMsgwmkAXAYBDgPgMvp+AY6ulBBvkxBfISGuXEKEQ0KE0xfUTmY3i7BFiLBF6lBh1UES/SGj5hdlUYEvxALwGETAaIBiMkA0mSCaTdCZzdCZzLC6ZESUOGApscNYVAFdiQ1Gr4KUEi9SSrz1vnZZJ8Bt1sNp0sFlFuEw+UJQlQmoMgCVRsBtEBDhVBBfBcRUKYiu9CKi0guL3QODBMRVSIiraPG3IYACwGXRw2vSAzrfHxnUPzjofP8KYuMVI8Htha7SAYPdBVFSYPYoMHt835u2ogi+IC8oCoQGfjR1cvXPrRfwvdLQSN7mxtzwCGtwqqysxP79+9XPc3JysHnzZsTHx6NLly6YOXMmjh8/jnfeeQcAcOedd+Lll1/G3//+d9xyyy34/vvv8eGHH+LLL78M10sgIiIAjtw8FHy3CgXfr4G7pCTo6wwx0bBkZMDSqfqRkQGdxQL7wRxU7j+Ayv0H4Dh+HDipWnH8k8/Q6eorkXrJRdC18awCm7MCu4sOoNxZgWhzJGJMUYg2RSLaHIUIgzWgEuILTGtx9MOP1MCkj45G4rnnwFFQgMqcHEhFJXAVFMBVUICS9RuCGkNZaRnKNm9RP5cSYyBkZcDYLRPeGCtKy4tgs5XAXlGOqspywOWBwavA5JYRWykh2ybB5Gn4lxqnUUBJjB4l0TqURutRGq1DaZQOboPvlzlREGDWm6orQUaYdUYYdAYYBR30EKFXBN+/EKGHAL0sADodZL3vL8+y+rHvX8XrhTG3FOZjRYg4UYboXBusFW4klktILA/+F0GPXoQj3go5OQ76tGREdOqEiJQ0KHoRMmpVcKqrM5Iiw1NcAk9uPuS8IogFJTAU2WAstcPoVWCsCHxu2WiAEGGBGBUBfVQUdFERUCKtUKxmyFYzpAgjvBYTPBYDvCY9BK8vOIluL0S3F4L6rweQZUhGvVqJkowiPHoRHr0Ar16E2WBChM6MSL0FEToTrDozrKIRFp0JoqxAkWUokuR7qB/LUCQv9FYrDHFxMMTGQLGY4JBcqPI4UOVxwiW51elcjU2nOpmsyJAVBaIgBEwD81ZVwXH8BBzHjkF2uWFKToIpORmmpEQIRoNv+prkhUf2/UIsQAAEAaJvrlj1dDEBoijCpDMGPV1T9njgzMuH49hxuIqLoY+wwhAdDX10NAzRUdBHRUNnMTd6P0VRICsyREGsc57s8cBVVAx3STG8tkp4Kmzw2irgsdngsdngLrfBXWHzhQhRB1Hv+4OQqNOrfyASjUaYEuJhTEyAKSEBxoQEmBITYIiLg6hvnV+5FUWBZLfDXer7I4unrAyechtEgwGi2QydxewLn9Ufi2YzRIMBaOR9EUTfNE7fH7d06rTKgP9tk+WAnzvIEmSvBMXrhexxQ3Z74HU54XJUweWsgstVBY/LCZPRArPRArPRDFGvV+/vn75pTew41SYAEBRFaf26bpDWrFmD8847r87xKVOm4K233sLUqVNx6NAhrFmzJuCa++67Dzt37kSnTp3w2GOPYerUqUE/p81mQ0xMDMrLyxEdHd0Kr4KI6NTiLitH0dof4LXbYU5NgTktDZa0VOijowP+j1R2u1H8y3rkr1yF8q3b1OP66ChY+/eBTtRDVBRAlqt/wfP90id7PHDmF8BdVBT0mIyJiYjs3g3Wzp1Q9NM6OE+cAAAY4mLR6ZqrkTruAt8vB7UoioIqjwNlTlv1oxwAEGuOQawxEtZKL1BUCmduHpx5efCUliHtsktQkRKF3UUHsbtoP/YUHUBuRUGD49IJIqJNUYgxRKDLvjL0+PUEIm1uAIDTrMOOAbHY1SsalaIXLq8LAGByy0gs9SKp+pFY5vvreVV1hcBuFlFlFmG3+P716gQkl3qRWuRBarGn2X9hVgQBYmIcjOlpiOzcCTGZXWHNyIC1cycYqv//UJIleGSvb92G5IVep4dZZ4Je1/Z/Z3WXlqJi7z5U7NkLT3k5RKMR0Ouh6HWQdQIUvQhJJ0I26BCT3gmJXXvAnJjYKuukZK8Xzrx8uEtKYIiKgj46CoaoKN8YiOiUFko2CGtwCgcGJyJqT7lff4tjH61A2sXjkHHVhCanY4SLoiiwbd+BvG/+i+Jf1kPx1p0Ko7NaYU5LhTk1FTqLGSW/bIC3stJ3vQBUdk3Gru5WbIx3wCvW/F+LQWfwLbbW1SwkNuoMMEsComweRJU61Sk55pJKiE4PqpKjYUuOQFGiCQVxepQYPL6FxR4HBFnBgCNeDNliQ1Slb5yOSCOOnJWFwgEZsHnssFWWwVtaBnOl27fWwCEhyi4j2i4hptL3OHndAQA4TSKWXRCLsujAoNA5Og3JkYmodNlR7qpAuasCDo8TABBX7sVla8sRV12tqDIJ+K2PFdt6WHxTkWqxGiyIs8Qg3hKDOEss4i2xiDVHw2qwqIu1/X/hFwR1KTfs7irYPVWodFfBUV4G4XAuTEcLYT1RCpNLgmixwGiJgDkiEtbIGERGxcJojYBoNsOcmgJrpwyY09LqhEsiotMdg1MjGJyITi2KosBdVISKPXthP3QYxvh439qRrEzoGmga016OfbQCh999T/08bthQ9PzrX8LeGKA2T0UFClf/Dye+/gauE7nq8bwEPYpi9YiplBBbISGqqv51MBVWETuyzdjZzYKKiPbtjCRKCvoddGLYdjuiHL7xVZkFQAGsQSyS94qALVKHskgdyqN0SC/wIKXUi/JIHX6/fjC6ZvZB78Ru6JmQjUhT3e+ZW/Kg+NABHJrzD8jlFUCkFaYLz4XlvJHQWazQizroRB10ggijzoBYSwzMejYrIiLSklCyQYdqDkFEJDmdqNx/ABV79voee/fCU1pW90RBgDkttTpE+RbhWzLSIRpNEI0GiEajOt+6tSmKgiNLluLYRysAAAkjz0LJr7+hdOOv2PK3v6P3zAcQkZXV6s9bm7ukFAde+z848/OhM1vUue7+ee+CyYSy/BOo2vi7r+sZfIvj92SZsK27BeVJVsSYo2FzVsAluaHzKoixS4ip8AUpq1PGsRQDjqQaEWWJQre4LsiO74LsuExkx3dBgiUOHskDp+SG0+uCy+uCy+uG0+uE0+uGR/bAI3nhljzwSB54ZA/ckhceyQNZkRFhtMJqsCDCaEGEwf+x718FSvU9q+/nsMOx5hfI3/4Ea2WV+h4IRgOMCQkwJyaqaw2MiYmwpKfBnJYKxETD5qlUp/JVFhdBfGkZYgpLcOmaUvR/6iLozOYG32O5qATHnpoPubwC1swu6P/k3EabNhARUcfGihMR1SG5XFA8HugjI8M9FJXkdGLfgpdQvH4DIAdWPwSdzheQunaFu6QE9pxDQTcoEPR636JaoxGpl1yELtdf16JxKrKMnEVvIvcLXwfQzCk3o9NVE1C5/wB2//MZuAoKIRqN6Hb3nUgeM7rB+7hLS1H0w08o3fQ7ks8/D0mj/hD0GCSnE9sengX7gQNNnwygMFaPbT0ssPXvgv6ZZ2BwWj/0TuoOo843rcvpdaHcaUO5swJlThtsrgpUuquQHpWC7LguSLDGaWI/Hn+o1kdGwpSYAF1ERMjjcpw4ga1/fxjeigrEDRuCPjMfrDdcuwoLse3hx+AqKISlUyf0f2oujLGh9HYjIiIt4FS9RjA4ETVMcjqR++XXOP6fT6B4JQx6YT7MKclNX9gO49r5xNOwbd8BADAmJPjaMlc/IrK71ums5ikvhz3nUK1HDpz5Bb59aeQG2i+LIga/9DysnTqph+zuKhTai1HlcSArtjOsxoan/ymShP2vLkTBd98DALLvmI60Sy6CW/JAFEQolVXYO38Byn7fDABIvfgidL11qrruRHI4ULx+AwrXrEXZlq014xQE9PjLPUg+f0yj75OsyDhWdgI5/1oA/c4cOM06rBzmC7/+/TwMXgUGjwyDV4FO1EMY1AvdzxyJQWn9kRyZ2Oj9Txe23Xuw47HHIbvdSL3oQmTfeXtAAHMVl2D7w4/BmZcHc3oaBjz1BIzxcWEcMRERNReDUyMYnIjqkt1u5H3zXxz7aAU85eXq8cybJ6HTNVeFcWSBoUlnsaDPYw8jpl/fgHNcXjd2Fe7D3uKDMOqMiDVHBzyiTVEQazVlkLwe2O0VsFfZUGm3ocpRgcr3PwN27EdF387YekkvFNqLUVBVrDYAAABBEJAV2wl9ErujT3IP9Ensjmizb2qW7PVi3/MvoujHnwBBgP3aMdjZ1YwDJYeRb/d1jxMFESZBj2HbKzF4cxkAoCQlAofPTEfawTIkHSiGzlsT6qoy4iFHWRG5+xgUAfBOuhjms86E1WCpfphR5XFid9F+7Crcj90F+3DmunwM2uuAVwQ+/mMc8pIMiDVHIyM6FelRKdX/piIjOgUJ1rimd5s/TRWvW4/d/3wGUJSA/w7cZWXY/sgsOI4dhyklGQOefhKmxIQwj5aIiJqLwakRDE5ENWSPB/nffY9jyz+Cu9g3tc2cmoLInj1QtPZHRPbogYHP/qNtxyDLcHidqPI44KjecyTKFIk4cwz0XjkgNPWbMwtRvXpCVmQcLjuOLXk7sTVvF3YXHYBXrn9DRMAXeKKNkTDpjWpntpP/py+hzItJX5VAALD0ojgUxtd0H4sxRcGgM6Coqu70v4yoVPSN64pOH69H9L48SCLwzdnR2N+l4bUxAJB13IVxP9tgPmlvnbJIHXZ1NWNPlgnlUXpAUXDexkqcsd8BWQC+HRmNvVn133vQ7iqM3uTrcld8w3nIPm8susZ1QYTR2uhYqH4nPv8SOf9eDADoOeOviB08ENsfnY2qw0dgTEzEgKef0ERFloiImo/BqREMTkS+dTgFq9fg6AfL4Srw7VFjTExE54nXIPn88+CtqMDGadMBRcHQxa/DlNCyv6hXuu3YV5yDvUU52F+Sg+KqMjg81WHJ66z3Gr1XwZX/syE93wWvUYd9E4fD2C0Lpc5ybMvfDZurMuD8BGsc+if3ggABZc5ydcF/uauiTkjyM+gMiDRYEWH0PYatPISEXbnw9slC9L3TkBSRgCRrAkx6314uJVVl2FW0D7sKfRWeo+UnIMoKLl9Thsw8D7w64Ms/xKC8ezK6x2ehW3wmusdnISuuM0QIcEseuCV39b8eOPLyUPl/SyGXlAFn9ob3zN5wZSTAI1c3TZCrz3M7kPT5RiRtOQpZADZekIX9Xcyo8jggCiJ6JnTFwAI9opesBBQFWVMnI+PKK1r0PSOfnMVv4cSnn0PQ62FOSYbj+AkY4+PR/+m5sKR1rI0biYioLganRjA40elO9nqx74WXUbT2BwC+DUQ7X3s1Ui6s2UBUkiVsefBhOPbuh+XGy+E+ewAq3XbYXJWodNlR6a6CXqdHpNGKSGMEoowRiDRFINJoRZQxEpIiYV/xIewtPoh9RTk4XpHX5Lj0oh5WgxlGnRFVleW4+PsidCrwwGUQ8Ml5schLDNx/xqw3oV9yT5yR0gcDU/sgLSql3kYAsizD5q5EmcMGl+RCRK2g5G9+4Oc4cQKb7r4XkGUM+MdTiO7Tu9ExV7gqsePfr0P670+QjXoY/nQDep41BvHW2CZfr5+iKEE1MFBkGftffg0Fq74HRBG9HpiBxLNH+saxdx+2PzKrwTU51HyKLGPPM/NR/PM6AIAhNhb9n5oTsA6OiIg6LrYjJzoN5FUWYn9xDlxeNzyyF17ZC4/kVT/2Sr6pa0a9EUadAUadEUZFgOXdbyFu3w+IIjwXn43cYdnY7C1FyS9voNRRhlJHOcqdFRgcYce5APas+hr/wS8tHm9qZBJ6JmSjZ2JXpEWlqOt0LAYzrAaLGmIkh8M3Pa8gF4LFjPh7p+HatGiUOstR5iiH2WBC/+Te6JnQFXpd0/8TJoqiutapKZb0dKT88Xzkr/wOh5csRf8n5zQaQKRdByCt/BkA0Oe+v6pBJhTBBhxBFNH97juhyDIKV6/B3mefh/B3ERFds7DryXmQ3W7EDRmM7NtvY2hqRYIooud9f8FujweOo8fQ++EHGZqIiE5TDE5EHYTb68bOwn34PXcHNufuQG5lQUjX670KLl1bjsw8N7wi8NW5UciJ2Qvs3Vvv+Qc7m3Du75XoVOBBv8hMWKJjEGWKRJQpApHGCHgkDyrcvuqT3W1Hpcuufi4pErLjuqBHQldfWEroqjZRaIwamnbshM5qRb/HH0NUr54hvc6W6jzxWhSsXgPb9h0o37IVsYMG1nueu6wcexe8CCgKUsZd2KzQFCpBp0OPP98FyDIK/7cWe56ZD2N8HDzl5Yjo2hU97/9bm+xLdboTjUb0fXRm0NVBIiI6NTE40WnBVVyCkl/WI/n8MdBZGm4n3d4cJ07AtnMXkkaPUqfJ1VZoL8avx7dic94O7CjYC7fkUb+mE0R0j89CpCkCBtEAvU4PvaiDQdTDIOqh1xmgKDLckgfeqipkfvAzovLckAwiNl/aB/qMCJxpikScJRbxlpiAf+MsMYg2RWLLlr+h6vAR/Ml6FpLPHdPm70fOorfCGpoAwJSUiNSLxyH38y9xeMlSxAw8o84vy4osY/+LL8FTWgZrl87oeuvUdhufoNOhx733QJElFP3wE1wFhTAmJKDPYzOht2rnZ/tUxNBERHR6Y3Ci08KRJUtR8P1qKIqC9EsvCfdwVDn/fhOlv22C/dBhZN92i3pcURR8u/9/eHvzR5BkST0eb4nFoLR+GJzWDwNSesNqaPoXZY+tAjvnPIHKY6XQRVgx4LFHMKqJtTvq840YjqrDR1D8ywYknzcm1JcXksqDB5H/3SoAQJ+HHwxLaPLrdM1VyP/vd6jctx8lGzYiYcTwgK+f+PxLlP72O0SjET3vn1FnD6m2Juh06HnfvdBZrbDt2IleD8xocQMPIiIiahyDE50WKvbtAwA4T5wI80gCuYp8+/vkfv4lYgeegfhhQ+H0OLHw1/fw85FfAQA9E7IxLGMgBqf1Q+eYdPWv3rlffo1t7y+DNbMLYgb0R0y/vojq1ROi0aje311aih2z56Lq8BHoo6LQb84sRHbLDnp8CSNH4NiHH6Fs0++QXK42CwiKoiBn0VuAoiDx3HMQM6B/mzxPsIyxsUi/bDyOfbQCR957H/FDh6hT4Cr3H8Dhd5YAALJumYKIzC5hGaOg06H7XXeG5bmJiIhORwxOdMqTXC44jvsCkz+oaIW30q5+vO/FV5Dy5N/xws4PcdyWB50g4qaBV+GSnufXmSJU/Mt6HHxjEaAosG3fAdv2HTgKQDAYENWrJ2L690Nkt2zkvPk2nCdyYYiLQ/+5s2Ht0jmk8UV07QpTchJcBYUo+30zEs4a0eQ1uV9+jfyV36HHvX9GRNesoJ6nZP0G2LbvgGg0InPyTSGNsa1kXHkFcr/+BlWHj6Dox5+RNPpceKsc2PPsfCheL+LPGoHUi8aFe5hERETUTrhlPJ3yqg4fAWQZAOAqKg7zaAJ57b7gZExIgNdmw4ann8CJslzEWWIw+7wZGN/rj3VCU+X+A9j73AJAUZA89o/odtcdSBz1Bxji4qB4PL4Q9cGH2PXUP+A8kQtTchIGzHsy5NAE+NZ0xI/whaXiXzY0eb6rsAiH3noH9pxD2P2PZ9TX1xjZ48GhN98BAKRffinMydrYUFQfGYmMCb69kI68/wFkrxc5b/wbztw8GBMT0f2eP3HNCxER0WmEFSc65dkP5qgfuwrbt+JUVFWC/cWHYNabkBKZhCRrvNpCW/Z4IDt9m7/umzAIaW9/j4w8Fy45YsUV9z5cb/tsV2ERdla3no49czC633UHBJ0OqeMuhKIocJ7IRfn27SjfvgO2HTthiI5Bn0cegikpsdmvIeGs4cj9/AuUbvwVstcLUd/w/2wcfu99yG43AMCZl4d9L76C3g890GjAyP3yazjz8mCIi0XG1Vc1e5xtIf2y8cj94ks4c/Ow55/PomTDRkAU0XPGvTBENd0lkIiIiE4dDE50yrPn1AQnr80W8lqdyoMHseeZ+YjMzkba+IsR1ad3vUFAURTkVuRjV+F+36NoPwrtgRUuAQISrHFIiUxEuhKJvtXHP7VvRe+hkbjwlwp0/+UoxCtygd6Bwclb5cDOJ5+Gp7QU1swu6PXAjIDW04IgwJKRDktGOlLHXRj062tKdJ/e0EdHw2uzwbZjJ2IHnlHveZUHD6Jwzf8AANl3TkfOv99EyS/rceKzz5FxxeX1XuMpL8fRD5cDADJvulFzXeF0Fgs6XXM1cha96QtN8LUrj+nXt4kriYiI6FTD4ESnvMpaFScAcBcXw5KeHvT1RWt/hPNELpwnclH040+wZmUi5eJxkM/sjVxPGY7b8nCw5Ah2Fe5Duasi4FpREJEZmwGvLKGgsgguyY2iqhIUVZXgRLkXfQG4DAIsJisuvflPiDZ+j6K1P2Lvc89j0PPPQR8ZAQBQJAl7n30OVYcOwxAbiz6PzoTeam3xexMMQadD/PChKPjuexT/sr7e4KQoim+6naIgcdQfkHbxRYACHPy/N3D47SWI6tkT0fV08jvy/oeQ7FWI6Nq1zbv2NVfqRRfi+CefwV1cjOh+fdH52qvDPSQiIiIKAwYnOqUpkoSqQ4cB+DaxlN1uuIpqgpOiKPBIHji9LrgkN9ySB27JA5fXDXf1565DewAA7pRY6IptqDp0GDmvvQ6nQcDObmZs7WFBeZTvPyWDqEf3hK7ok9QdfZK6o2dCNiwGs/pc5a4KFFQWIa+yEKW7dgL4Aoiw4J8XzkRKZBK8f+qBij174covwP5XX0OvB/4GADj478Vq++s+j85s93VACWeNQMF336Nk/QZk335bnYpb2abfUb51GwS9Hpk3TQIApF48DradO1H0w0/Y88xzGPT8szDExKjXVB05grxv/wsA6HrrVM1u3Coajeh5319QsPp/6DLpBs2Ok4iIiNoWgxOdsjySBwd3/Q7Z7YZs0KEoxYLEo24s/u517M6x+sKS1w0FSqP3mXi4BKkAvu2p4HhyPPoedOKMfVWIrZRx5m4HBu92wNW/K9LvmY5uydkw6upuZAv4ptLFmqMRa45Gz8RslBTpsQtfID4hFSmRSQAAvdWKXvfPwLaHHkHxT+uQP2gVZJcTeV99AwgCes64F1E9urf2W9Wk2IFnQDSb4S4uQeX+AwFjUCQJh97yNXdIu/QSmFOS1dfb7a4/wX4wB47jJ7B3/gvoO+sRNXjkLH4bkGXEnzUi7O3HmxIzoL/mx0hERERti8GJThn5lYXYlr8bB0qO4GDpYRwpP4HuB+24CEBejIASowuJAOSScpRleOtcbxD1MOoMMOqNMOqMvo91BsQ7SgEA/foMw+gevZFxYSrSIpMg7jqE/K+/Qelvv8O8PQedqwwNhqb6+FuR6yMiAo5H9eyBLjfdiMNvv4ucNxZB9ngAAJmTb0LCyLOa9+a0kGg0Im7IYBT/tA4lv6wPCE75q1aj6shR6CMj60xj01st6PXgA9h6/4Mo27wFR5d/jC7XX4fS3zah7PfNEPR6ZE29ub1fDhEREVHIGJzolLCvOAePfz8fHjkwEKXbfFPKorKzkZ6QDBz4EWPi+uGaCyfBrDfCpDfBrDfBpDNCFOt255ecTvzysm/q2Q2jJ6trjgAAwxKRMGwofr93BqoOHYbHZgtpzJK9EoCv7fXJMiZcjvItW1G2eQsAIOWCsci48oqQ7t/aEs4ageKf1qH4l/XIvNn3nkgOB44sfR+Ar2lCfa8lIrMLut11B/YteAlHP/gQkd274dCbbwPwVagsaWnt9yKIiIiImonBiTo8SZbw+q9L4ZG9yIzthMFp/ZAd1wXZ8Zko2PEyyrEVg4adD0EUsR8/wlLpQVZcp6Du7SooBADoIqyBoakWf1jwVlTU+/WGeCr8wanufQVRRI+//hm7nv4nLOnpyL5zetj3DIobciYEvR6OY8dRdewYrJ064finn8NTWgZzagpSL254M9jk88bAtnMX8v/7HXY99Q9AlqGPjkbn665pvxdARERE1AIMTtThfbV3NQ6XHUOkMQKPjf4Los2+/XUURUFOziEAQETXrpCqN2N1FRYGfW9nQQEANNqMwb+fj7c6CAVLnapXT5UGAIxxcRj4zD9Cumdb0kdEIGZAf5T9vhklv2yA/vwIHP/PpwCAzJsnQTQ0Pk0xe/qtqNx3QG0P3+XGiXWmKRIRERFpVd25SUQdSJG9BB9u/xwAcNPAq9TQBADu4hJ4bTZAFBGR2QXGRN8msO6i4nrvVR9/yDIlJzV4jj7a95yeECtOjU3V06qEs0YAAIp/WY8j738A2elEZM8eSDjn7CavFY1G9HrwfhjiYhHVqydSL7ygrYdLRERE1GpYcaIOS1EULNr0AVySG32SuuO8riMDvu6vbFg7d4JoNMKU5AtOksMBr90eVLXDP1XP1EjFqblT9WoqTh2n6hI/YhgOLHwdlfv2o/LAQQBA11umBj2N0JKWiqH//j8IogihnjVlRERERFrF31yow9p4fAt+O7ENOlGH6UNurPPLu71649uIrlkAAJ3JBH2UL+S4gqw6OfN9U/VMSQ1XnNSpepWhTtXreBUnY1wconr19H0iy0gYOaLejW0bI+r1DE1ERETU4fC3F+qQHB4nFm9aBgC4vNcF6BRTtzNbpT84ZXdVj5mqp+sFu87JX3FqbI2TXl3jFGrFqTo4dbB1Pv7peoJOh8zJN4V5NERERETtg1P1qENatv1zlDjKkBKRiKv7XlzvOf6pehFda4KTMTER9pxDQa9zclU3hzClNLLGqbqK5Wnl5hBalfzH81D62ybEjxgOS3p6uIdDRERE1C4YnKjDOVhyBF/vWw0AuHXIDTDqjXXO8Vba4aqeZuefqgfUqjgVFTX5PJLLBU95OYBgu+o1s+IU1bGCkyE6Gv2fnBPuYRARERG1K07Vow5FlmW88etSKIqCs7sMxaC0vvWeZz90CICvG54/2ACAKTEBQHBrnNQ9nKxW6BqZTqdvRjty2eOB7Hb7ro/oWMGJiIiI6HTE4EQdyn8PrMWB0sOwGiyYOqjhzVNPbgzhZwxhjZM6TS85qdGucf6KkdduhyJJTd4XqNVIQhCgs1qCuoaIiIiIwodT9UgTFEXBnqID2Hh8CxQAkUYrIo0RiDJFINLoewgA3t/q23D1xjOuQKwlpsH7qeubsrMDjvtbkgezximYzW+BWmuUFAVeexUM0VGNng/UWt8UEcEOc0REREQdAIMThVVBZRH+d+gXrD20Hvn2ptcdAUCP+CyM7XZuo+dUNlBxUtc4FRdDUZRGK0k1ezg13BgC8LXX1lksvv2hKiqCDE7+VuQdq6MeERER0emKwYnaXZXHgV+ObsL/Dv2CXYX71eNmvQnDOw1CjCkKle4qVLrtvn9dlah0V6HCbYdZb8LtwyZBFBqu0sgeDxxHjwEAImu1IgcAY0I8IAhQPB54ym0wxjZctVL3cGqi4gT41jlJDkfQezn5z9NxfRMRERFRh8DgRO3CK0vYmrcTaw9vwMbjW+CRPAAAAQL6p/TC6KyzMLzTIJj1pgbvoSiK75pGqkQAUHXkKBRJgj4yUl3T5Cfq9TDExsJTWgpXYWGjwSmYPZz89FFRcBUUwBNkZz1/cDJ0sI56RERERKcrBidqM4qiYG/xQfxweAPWHd2ECldNNSYjKhWju56FczOHI8EaF9T9mgpMfvaDBwH4Nr6t7xpTYiI8paW+dU49ujd4n2D2cPLzByCvLdjg5Fvj1Fi3PiIiIiLSDgYnanXHbXn44fAG/HR4Y8C6pRhTFM7uMhTnZg5Ht/jMoINQqOw5hwD4glN9TEmJqNy3r9G9nILdw8lP7awX7FQ9e8fc/JaIiIjodMXgRC0mKzL2Fx/Crye24tfjW3HMlqt+zaQ3YUTGIPwhczgGpPSCTtS1+Xgaagyhjkndy6nh4ORvV97UHk5+/r2cgp6qV30em0MQERERdQxhD06vvPIKnnnmGeTl5WHgwIF46aWXMHz48HrP9Xg8mDdvHt5++20cP34cvXr1wj//+U9cdNFF7Txqcnvd2FawB78e34rfTmxFmdOmfk0niBiY2hd/yByOoRlnNLpuqbUpsqxWnE5uDOFXs5dTI8GpVke9YCpj/spRsJvgqu3IWXEiIiIi6hDCGpyWLVuGGTNmYOHChRgxYgQWLFiAcePGYc+ePUiuZ3rUo48+iiVLluCNN95A79698e233+LKK6/Ezz//jMGDB4fhFZx+jtlysWLH19h4fAtckls9bjGYMTi1H4ZmDMTgtH6IMFrDMj5nXh5kpxOi0QhLRka95/hbkje2l5O/o14w0/QAqC3IvcFWnOz+duQMTkREREQdQViD0/z58zF9+nRMmzYNALBw4UJ8+eWXWLx4MR566KE657/77rt45JFHcMkllwAA/vSnP+G7777Dc889hyVLlrTr2E83BZVFWL7jS6w9vF7tbpdgjcPQ9DMwLGMg+ib1gF4X9gKmWm2yZnaBoKt/WqB/E9xGp+r5G0MkNd0YAqgJQMF31fNXnDhVj4iIiKgjCNtvum63G7/99htmzpypHhNFEWPHjsW6devqvcblcsFsNgccs1gs+PHHHxt8HpfLBZfLpX5us9kaPJfqKnGUYcXOr7Hq4E+QZAkAMCxjIK7sc1GbNnhoLnsT65uAWhWn0lIoklRvwFKn6gXRUQ+oWeMU6j5OrDgRERERdQxhC05FRUWQJAkpKSkBx1NSUrB79+56rxk3bhzmz5+PUaNGoVu3bli1ahVWrFgBSZIafJ558+Zhzpw5rTr200GFqxKf7v4vvt63Rt1zaWBqH0zsfzm6J2SFd3CNsOdUB6cG1jcBgCE2BoJeD8XrhbukpN6qkrMgxKl6USFO1VODEytORERERB1B+OdWheCFF17A9OnT0bt3bwiCgG7dumHatGlYvHhxg9fMnDkTM2bMUD+32Wzo3Llzewy3Qypz2vD13tX4Zt8aOLxOAECvxG64YcDl6JvcM8yja1pNR72Gg5MgijDGx8NVUABXUXG9wammOURwwcnfjtwT4j5O+ghWnIiIiIg6grAFp8TEROh0OuTn5wccz8/PR2pqar3XJCUl4ZNPPoHT6URxcTHS09Px0EMPITs7u8HnMZlMMJnar6tbR5VfWYjPd3+H1Tk/wyN7AQBdYztj4oDLMTitn+am5NXHXVoKT2kZIAiIyMps9FxTUqIvOBUWAX0Cvya5XPCUlfnOSw5tqp7sdEL2eCAaDA2eK7lcUDy+Kh4rTkREREQdQ9iCk9FoxJAhQ7Bq1SpMmDABACDLMlatWoV77rmn0WvNZjMyMjLg8Xjw8ccf47rrrmuHEZ+aDpUew6e7v8XPR39Tmz70juyMS8x9MGz0pdDpGw4A9VEkCbaduxDVpzdEffv+ePkbQ1gy0qE7aS3cyfzrnOprEKHu4WSxBL0GSR8RAQgCoCjwVlbCGBfX4Ln+ahNEETpreLoPEhEREVFowjpVb8aMGZgyZQqGDh2K4cOHY8GCBbDb7WqXvcmTJyMjIwPz5s0DAKxfvx7Hjx/HoEGDcPz4cTz++OOQZRl///vfw/kyOqTdhQfwn11f4/fcHeqxQal9MaHPOBg/XoP8b5agxJCIpFHnhnTfvP+uxMGFb6DTNVch8+ZJrT3sRgXTGMLPWL0Jrru+4BTiHk6Ab/qfPjIC3opKeCsqGg1Okr8VeUREh6jkEREREVGYg9PEiRNRWFiIWbNmIS8vD4MGDcI333yjNow4cuQIRFFUz3c6nXj00Udx8OBBREZG4pJLLsG7776L2NjYML2CjmnDsc149qf/AwAIgoCzOw/BFb0vRFacb+3XloO+NWP2nEMhB6fKfQcAAIX/W4suN93YrsFAXd/UyNRNv8YqTv49nIJd3+Snj4qCt6ISniY2wWUrciIiIqKOJ+zNIe65554Gp+atWbMm4PPRo0dj586d7TCqU1deRQFe2fA2AOCsTmdi0sAJSIkMXMfjzM0DUDNlLRT+a1yFRbAfzEFkt6ZDTGtRO+oFUXGq2cup7ia4rhA76vkZoqLgRG6TnfX8wYqtyImIiIg6DrHpU+hU4fa68dzPb8DhcaJXQjb+MvKWOqHJa7erv/i7ChreILYh/mluAFD8y/qQrrUfPoINU27FsRWfhPy83iqHGvga66jnp+7l1NhUvSD3cPLzB6Gm9nKqPVWPiIiIiDoGBqfTyKJNy3C47BiiTZG47+zp0It1N3515tV0OQy14qTIcsDUt5L1G0K6/sRnX8BTVoaSDRtDug4AHMePA4oCQ2wsjLExTZ7vX+PkKbdBqrVBMhD6Hk5+/s56TbUkV6fqRbHiRERERNRRMDidJr4/+DNW5/wMQRBw78hbEW+Nrfc8Z16e+rG7tBRyddvsYLhLy6B4vYAoQtDpUHX4CBy5uUFdK7lcKP55HYCmKzb18VfJDEGEJsBXHRKr29S7iwOn6/kDY+hrnCIDxtLgWCs5VY+IiIioo2FwOg0cKj2KRZs+AABM7H8ZBqT0bvBc/3Q3AICi1LsGqCFq4EiIR3S/vgCAkl+CqzqVbvwVUlUVgFrtukNQ03AhuDAiCEK965wkl8u3FxSC38PJz1BdcWoq+NVsfsupekREREQdBYPTKc7ursJzP78Bj+TB4LT+mNBnXKPn156qB4Q2XU9dG5SUhISzRgAAioOcrlew+n/qx5K9GcHJHnoVR+2sV+s1ugp9Uw1D2cPJzz9VjxUnIiIiolMPg9MpTFEUvLrhHeRXFiLJGo8/j5gKUWj8W157qh4Q2OyhKTVT3JIQP2I4AKBi9x64y8oavc5dVo7STb+rn8tuN2S3O+jnBZrX4rtmL6eaipO/o14oezj5+YNQ0+3I/cGJFSciIiKijoLB6RT2+Z6V2Hh8C/SiHjPOuR2RpqZ/UXdUT9WzZnYBEGLFqbCm4mRKTEBkj+6AojTZ7KHohx8BWUZk925A9b5doU7Xa04Vp769nGpvfhsqQ3SwFSd/yIsK+TmIiIiIKDwYnE5ROwv2YenWTwEAUwdfi27xmU1eI3s8aqOEmAH9AYRYcTopdPirTk2tcypc45uml3TeGOgjrABCbxChBqcQ1g3Vt8apuR31gNpT9ZqoONlZcSIiIiLqaBicTkEnKvLx3M+vQ1ZknJs5HBd0Ozeo65z5+YCiQDSbfdUiNL/iBEBd51S2ZSu81Y0fTlZ19Bgq9x+AoNMh6dxzoI+o7kwX4jqnUJtDAA2scVKn6jUjOAW5j1NzxkpERERE4cXgdIopc5Tj6f+9hApXJbLjumD60BuDXqvjbwxhSUtVw0+wwUlRFDgLAoOTpVMGzOnpULxelNVaw1Sbv9oUe+ZgGGJi1CpMsytOIYSR+tc4NX+qnr/iJLvddfaG8lMUhWuciIiIiDogBqdTiMPjxLwfXkGBvRgpkUl4aNTdMOtNQV/vb0VuTk2BuTo4uIqKochyk9d6KyshO50AaqbACYKAhLN80/WKf1lf5xpFllH4v7UAgOTzRgMIvmpzMn8nvlDCiL/iJDkcaoWrJVP1dBYzBL0eAOBtYBNc2eXy7XUFQBfBihMRERFRR8HgdIrwSl4899PryCk9imhTJB4ZdQ9izdEh3cPfUc+cmgpjfDwgilC8XrhLS5u81l+ZMsTEQGeqCWv+6Xqlv26qs5mubcdOuAqLoIuwIn7YUACALqL9Kk46s1k931VUfNIeTqEHJ0EQmgx+atMLUYTOYg75OYiIiIgoPBicTgGyIuO1je9ia/4umHRGPHTu3UiNCv0Xf/9UPXNqKgSdDqbqqWzBNIhoaIpbZI/uMMTFQXI4UL5te8DX/Hs3JZ59NkSjEUDtilNz1ziFNv1NbRBRWKju4SSazdBHNa8a5L/O00BnPX+gMkRFhtzunIiIiIjCh8HpFLB066f44fAGiIKIGedMR/eErGbdp6bilAKgpuoSVHA6qTGEnyCKSBgxDABQXKu7nuRyofjndQCApOppegCatcZJ9nohORzV14cWeGqvc/I3hjCnJDc71Bia2ATX31GP0/SIiIiIOhYGpw7uq73f47Pd/wUA3DnsJgxO69+s+yiSVFNxSksFgJAaRDTWVEFtS75hg7peqmT9RkgOB0zJyYju01s9tzkVJ6lWB75Q2pEDgXs5taQxhPr81RWnhlqSN7cyRkREREThxeDUgf185De8/ftHAIDrB1yOMV1HNvte7pISKF4vBL1eDRP+aWzOFlScAN+eUDqrFZ7SMlTs3Qeg1t5NY0ZBEGt+DNWKkz34ipM/jOisVgg6XdDXAbWDU3GLGkP4+TvrNThVryL0tVhEREREFH4MTh3U4bJjeHn9W1Cg4MLuo3Bln4tadD9HdUc9U3KSGj7UznpBVJycBb71QfUFJ9FgQNzQMwEAJb+sh7usDKW/bwYAJI8ZHXBucypOLWnvbUysvcbJX3FqQXDyj7+JqXqsOBERERF1LAxOHdSqgz/BK3sxMLUvbhk8scWNBmo3hvBTp+qFUHEyNzDNzd9dr/iX9Sha+yMgy4js2QOWjPSA85rTjtzfSlzfjHVDpqRaa5zyWz5Vr2aNU1NT9VhxIiIiIupI9OEeAIVOURRsPLYFADCu+2iIYsvz78mNIYCaAOEqLISiKA2GM8nphNdm811TT8UJAGIHD4ZgMMCZm4djKz4BULfaBNRuDtE+FSd1ql5xMSSXbx+qFk3Vi25iql4z2qYTERERUfix4tQBHSg5jGJHKUx6E85I7dMq96y9h5OfP1TILleDU88AqG28dVZrg+FFb7UgduAZAABPaSkEnQ6J555T97zqqlHthg9NaUnDBWN8PCAIUDyeFu3h5BfsPk6cqkdERETUsTA4dUAbjm8GAJyZ1h9GnaFV7lnfVD3RaIQhLhZA49P1ahpDJDb6HP7uegAQN+RMGKLrbtDrDxSy2w3Z7Q5q7P6Q0pwW36LBAENsbM3nLdjDCQiiHbm/4sR25EREREQdCoNTB6MoCtYf+x0AMLzTwFa7p7O6OYQlLSXga8G0JA+2jXf88GFA9XS/2ns31aazWIDqqYfBTtdryVQ9oKayBrRsDyegpqtek8GJFSciIiKiDoXBqYM5bstDbkUB9KK+2Xs2ncxbUQGpqgoAYEqpPzg11pK8sVbktRljY9Bl0g1IPn8M4ocNrfccQRShj7D6xhVkgwh/wPJXe0Jlqt4EF2j6NTTFX63yVFRCUZQ6X2dzCCIiIqKOic0hOhh/temMlN6wGiytck9/tcmYEA+dyRTwtWBakvtDVTCho/O1Vzd5jj4iEt6KyhCCk3+qXvOqOMZaFaeWdNQDagUiWYZUVVVnQ17JzuYQRERERB0RK04djH990/BOg1rtnvWtb/ILpiV5U63IQ1XTWS/I4GRvWRWn9tqslnTUAwCdyQTRaPSN66TpeoqisOJERERE1EExOHUgBfZi5JQehSAIGJp+Rqvdt76Oen6mICpOrhAqTsEIdRPc1lzj1JKOen7+dU6ek/Zykp1OKJLkO4drnIiIiIg6FAanDmTDsc0AgD6J3RFtbt56nvrUt4eTX1PNIWSvF+7SUt+5KS0PHUDNlDuvPbQ1Ts2uONVe49QKVTNDdP0NIvwBT9DrIZ40JZKIiIiItI3BqQPZ2AbT9IAmpupVBwlvRSUkh6PO193FxYAs+1qXx8S0ynjaveJUq1LWGtMN1fGfVHFSA15ERIs69xERERFR+2NziA6izGnD7sIDAFo/ODlycwEA5rS6wUlvtUIXEQHJboersBDWLl0Cvu6fpmdMTGy1MBDKGifZ64XsdFZf17yKkyEuFslj/whRr2uV8Ke2JK+sv+LUkn2iiIiIiCg8GJw6iF+Pb4UCBd3iMpFojW+1+0pOJzylZQAASz3BCfBVYew5djgL6glOrdwYAgit4lT7HL3V2qznEwQBPf58V7OurU9Da5xqKk4MTkREREQdDafqdRAb1E1vB7Xqff3T9PSRkQ1WbBpb5xRKK/JgqcEpiDVONa3IrRB0ulYbQ0sYovxT9U6qONm5+S0RERFRR8Xg1AFUuR3YVrAHADCi1YNTw40h/BprSa521GvVipN/ql7TFSfJrr0qjjpV7+SKUwX3cCIiIiLqqBicOoBNudsgyRIyolORHl3/dLrmaqwxhF9jLcn9x2rvhdRSNVP1gq84aamK4x+/p4GueloaKxEREREFh8GpA1hf3Ya8tatNQKgVp6I6Xwt3xcmjwSpOgxWn6uqYv906EREREXUcDE4a5/a6sTl3BwBgeMbgVr+/M7c6ODXQGAJouOKkyDJcRb4w1aprnKqn3fmn4TVG8q8b0lAYaWofJ0NU6+3BRURERETtg8FJ47bk74JLciPJGo+ucZ1b/f5qxamx4FQditylpZA9HvW4u7QMitcLiCJMCQkNXR4yf8VJdrshuVyNnqt2qtNQi++Gp+r5N+rVTsgjIiIiouAwOGnc+upuesM6DWr1TVNlr1ftitfYGidDTDREoxFQFLiKitXj6vqmhPhW7Wins1gA0fej2dR0vZp1QxoKTtUVJcluhyJJ6vGaDoDaGSsRERERBYfBScO8soTfTmwDAAzPGNTq93cVFgGyDNFohDEursHzBEFQmz/Unq7naoNW5AAgiCL0Eb49maQmWpLX7I2knSqOoVb1y1truiGbQxARERF1XAxOGrazYC/s7ipEmyLRO7Fbq9/fP03PlJIMQWz8R6G+luRqxakVG0P4+dc5NVlxsmuv4iTodNBVb8Zbu0FEzVQ97YyViIiIiILD4KRhG6q76Q3LGASxiWDTHDUd9ZpucV5fg4iaVuRtEJzUznpBVpw0VsXxr7nyr3NSFEWtPjE4EREREXU8YQ9Or7zyCrKysmA2mzFixAhs2LCh0fMXLFiAXr16wWKxoHPnzrjvvvvgdDrbabTtS52m12lgm9w/mD2c/OqtOLVBK3K/mr2cOt4aJ6Cmc56/s57kcACyDEB7IY+IiIiImhbW4LRs2TLMmDEDs2fPxqZNmzBw4ECMGzcOBQUF9Z6/dOlSPPTQQ5g9ezZ27dqFRYsWYdmyZXj44YfbeeRtz+V1o9hRCgDomZDdJs/hb0VuSWt4Dye/9q44+fc68ja5xkmbwUkNftVT9fzjFAwG6EymsI2LiIiIiJonrMFp/vz5mD59OqZNm4a+ffti4cKFsFqtWLx4cb3n//zzzzjnnHNw4403IisrCxdeeCFuuOGGJqtUHVGpsxwAYNIZYTVY2uQ5alqRpzV5rlpxqg5LiqKoHfnaIjj5Gyw0XXHS6FS96r2c/FP12BiCiIiIqGMLW3Byu9347bffMHbs2JrBiCLGjh2LdevW1XvN2Wefjd9++00NSgcPHsRXX32FSy65pMHncblcsNlsAY+OoNRRBgCIs8S0ehtyoDr4qFP1mq44mdWKUxEUSYK3shJy9RRJf8e91lQzVa/hipPs8UCu3udJuxUnf3Dyd//T1jiJiIiIKDj6cD1xUVERJElCSkrgL+0pKSnYvXt3vdfceOONKCoqwh/+8AffYnuvF3feeWejU/XmzZuHOXPmtOrY20Opw1dxirPEtsn9PaVlvtAhikFVjIzx8YAoQpEkuEvL4LH5xmeIiWmTqWfqVL1GKk5qq29BULvYaYW6xqkycKqeljbqJSIiIqLghb05RCjWrFmDp59+Gq+++io2bdqEFStW4Msvv8QTTzzR4DUzZ85EeXm5+jh69Gg7jrj5StTgFNMm91dbkScmQjQYmjxf0OlgSkwA4Juu15aNIYBaFZtG1jipG8parU22U29v/k1wPbaTKk6cqkdERETUIYWt4pSYmAidTof8/PyA4/n5+UhtoMvbY489hptvvhm33XYbAGDAgAGw2+24/fbb8cgjj9TbsttkMsHUARfj+6fqxZvbNjgFM03Pz5SUBFeBLzT5K05tsb4JqN2OvJGKU4V21w35K0vek9c4caoeERERUYcUtj/TG41GDBkyBKtWrVKPybKMVatWYeTIkfVeU1VVVScc6XQ6AL41O6eStp6q58j1N4ZouhW5X+0GEe1WcWpkjVPNvkhRbTKGlmhwqp4GQx4RERERNS1sFScAmDFjBqZMmYKhQ4di+PDhWLBgAex2O6ZNmwYAmDx5MjIyMjBv3jwAwGWXXYb58+dj8ODBGDFiBPbv34/HHnsMl112mRqgThX+rnptN1Uv+D2c/PwhyVlQCG+7VZyanqqnxTCiP2kfp5qpeqw4EREREXVEYQ1OEydORGFhIWbNmoW8vDwMGjQI33zzjdow4siRIwEVpkcffRSCIODRRx/F8ePHkZSUhMsuuwxPPfVUuF5CmynxT9Vr4zVOlmZWnDzltoBjrc0/pa3RqXoaXjfkn6rnqWDFiYiIiOhUENbgBAD33HMP7rnnnnq/tmbNmoDP9Xo9Zs+ejdmzZ7fDyMLLP1Uvtq2Ck3+qXggVJ7UleUEhPNVt3c1tNlXPFzAUjweSy1Vv5z6tbn4L1EzVk51OyB4PJDsrTkREREQdmbZakREAwOFxwun17U8U1wbNIbx2uzqFzJQSWnMIAHDm58Nra9uKk85iAaqrjQ1VnWr2RtJeFUdntdaMv6JS0yGPiIiIiJrG4KRB/o56Fr0ZFoO51e/vrzYZYmKgt1qCvs6/0a3i8QDwhYO2mnomiCL0Eb69maQGWpL7j2sxjPjG71+nVcE1TkREREQdHIOTBrX1Hk4Ve/YAACK6ZoV0nWg0whAbq37uD1Jtpal1Th4NtyMHau3lVFFRqx25NsdKRERERI1jcNKg0jYOTuXbdgAAovv3C/na2lPz2qoVuV9TnfUkDbcjBwCDfy8nW0Wt1umsOBERERF1RAxOGlTqLAPQNns4KYqC8h07AQAxzQlOyTVVprZa3+RXs5dTQ2ucOkbFyZlfAFTvM6bVsRIRERFR4xicNMg/Va8tWpE7jh6F12aDaDQisnu3kK8PqDi1V3BqYI2T1tcNqcGpuvW7aDRCNBrDOSQiIiIiaiYGJw1Sp+q1QUc9/zS9qD69IRoMIV9vTk6u9XF7TdXroBWn6kDnb8ah1YBHRERERE1jcNIgf1e9tpiqV77dF5yaM00PCFzXZKoVotpCzVS9uhUn2e2G7Hb7zovQZiAxRPsqTo7cXADaDXhERERE1DQGJw0qbaOpeoqiwLajhcGpHZtD6CIarjipxwQBuhBaqrcnfXVzCFdhke9zVpyIiIiIOiwGJ41RFAWlTl9wklasxM4n50FyOlvl3o6jx+Apr17f1KN7s+5hTkuFPioSpuQkGGLapuufX2MVp9rT9ARRmz/Garc/WQZQEwSJiIiIqOPRh3sAFKjK44Bb8m0wW/7fNVC8Xhxe8j6yb5vW4nv7p+lF9e7VrPVNAKAzmTD45Rch6nUQBKHFY2pMY+3I1fbeGp2mB9S0I2/ocyIiIiLqOLT5p/rTmH+aXqTeAsXrBQDkfvElbLt2t/jeLV3f5GeMjWmXaWeNtSPXemMIANBHB+4vpdNwyCMiIiKixjE4aUxJdWOIREN0zUFFwf6XX1WbITSHoiiwbW/+xrfhoFac6mlHXhOctBtGTh6blkMeERERETWOwUlj1MYQhppqhSE2Fo5jx3F02fJm39dx/Dg85eUQjUZE9ezR4nG2B/80vMaaQ2h53ZAhKrDipOWQR0RERESNY3DSGH9jiHiDLxAIOh263Xk7AODYik9QeeBgs+6r7t/Uq2ez1ze1N3+FRvF4ILlcAV9T1zhpOIyIZjMEfc0yQlaciIiIiDouBieN8U/Vi9VXByeDAQkjRyDhnLMBWcb+l16BXL32KRQdbZoeAOgsFqC6Y97JVSdvRQUAbYcRQRDUluSAtkMeERERETWOwUlj/FP1YkUrAEA0GgEA2bffBn1UFOw5h3B8xSch3VNRFJS3cP+mcBBEEfoI3/sgnbTOyR+ktB5Gak/X0/pYiYiIiKhhDE4a4w9O0TozgJrgZIyNQfb0WwEAR5ctR9WRI0Hf03H8BDylZRAMhg6zvsmvoXVO/oYRWg8jtcen5eoYERERETWOwUljSqun6kWJ/uBUsx4pcdQfEDdsCBSvF/teehWKJAV1T/80vahePdUg1lH4p7qdvJdTTcVJ22FEz4oTERER0SmBwUlDFEVBSXVziEjBBAABjRwEQUC3P90BndWKyr37cOKLL4O6b0ecpuenj/BvgntSxakDtCMHTgpOGu4ASERERESNY3DSkAq3HZLsqyJZ4QtMotEUcI4pIQFZ06YAAI4seR+O3NxG76koitpRr0MGJ/8muA2ucdJ2GDFUV8xEk6nDdDMkIiIioroYnDTEP00v2hQJoXoaXu2pen4pF/wRMWcMgOx2Y98LLzc6Zc95Ihee0lLf+qZePdtk3G1J3QS3oYpTRMeoOGm9MkZEREREjWNw0hB/Y4g4SyxktxsA6q1SCIKA7vfcBZ3Viopdu3HsoxUN3tM/TS+qZ48Ot74JqFVxqqipOEkuFxSPx/f1KG0HEv/4tF4ZIyIiIqLGMThpSEl1cIq3xNQEpwbCjjklGdl3TAcAHPngQ1Ts2VvveR15mh4A6PxrnGpN1VOrT6Lo2+tJw8ypqb5/U1LCPBIiIiIiagkGJw3xT9WLM8dAdvsqKo1ViZLHjELiqHMBWcbe+QvgraoK+LqiKB1y49va1IpTral6/j2d9BEREAQhLOMKVsyA/ujzyEPIvvP2cA+FiIiIiFqAwUlD1M1vLTGQPf7g1HhDgW53TIcpOQnOvHzkvLEo4GvOvDy4S0og6PUdcn0TUHuNU92KU0eY/iYIAuKHD4MpIT7cQyEiIiKiFmBw0hB/K/LaU/UEQ+PrkvSREeh5372AKKLg+zUo/OEn9Wv+aXpRPXtAZzI1dAtNq6/i1FFakRMRERHRqYPBSUPUqXq1m0ME0dAhum8fdLrmKgDAgdcWwlVYCAAdfpoeUKviVM8aJ+6LRERERETthcFJQ9SuegFrnILb+6fzxGsR2bMHJHsV9j7/IhRJQvn2jt0YAqhpN15vxUnjHfWIiIiI6NTB4KQRsiyjzGkDAMRbYqF4gq84AYCo16PnjL9CNJth27ETB157He7iYt/6pt692mzcbc1fcVI8HkguFwBO1SMiIiKi9sfgpBE2VwVkRYYAATHmqEb3cWqIJS0V3e64DQCQv/I7AEBkj+4ddn0TAF+7cdH3Y+qvOnGqHhERERG1NwYnjfDv4RRjjoJO1AXVjrw+SeeNQcI5Z6ufd+RpegAgiKIakPxtyP3rnVhxIiIiIqL2wuCkEaXVHfXiLDEAAFmdqhd8xQnwtb/uftcdMCYm+u535uBWHGV41LQkP6ni1AHakRMRERHRqUEf7gGQT+2OegBqKk5NtCOvjz4yEmf8ax4cR48ium+f1hpi2NS0JK8M+JcVJyIiIiJqLwxOGuGfqhdvrq44+dc4mUIPTgBgSog/ZTZd9U/VqwlOvoqTjmuciIiIiKidcKqeRqityP1T9dSKU2hT9U5FJ2+C6w9QhqiosI2JiIiIiE4vDE4aUbPGKRZA7TVOzas4nUpq1jhVQlGUWlP1WHEiIiIiovbB4KQR/jVO8ZbAqXoCK04BFSfZ7Ybi9QIAdBFc40RERERE7YPBSSNqpurFAkCz25Gfivxrmbz2SrXaBFGEzmIO46iIiIiI6HSiieD0yiuvICsrC2azGSNGjMCGDRsaPHfMmDEQBKHOY/z48e044tYlyRLKnRUAatY4KZyqp6pdcappRR4JQRDCOSwiIiIiOo2EPTgtW7YMM2bMwOzZs7Fp0yYMHDgQ48aNQ0FBQb3nr1ixArm5uepj+/bt0Ol0uPbaa9t55K2n3FkBBQpEQUS0yRcSaipOnKpXe40T1zcRERERUTiEHJyysrIwd+5cHDlypFUGMH/+fEyfPh3Tpk1D3759sXDhQlitVixevLje8+Pj45Gamqo+Vq5cCavV2qGDU4l/DydzDETB9y1R25Gz4nRSxYl7OBERERFR+ws5OP31r3/FihUrkJ2djQsuuAAffPABXC5Xs57c7Xbjt99+w9ixY2sGJIoYO3Ys1q1bF9Q9Fi1ahOuvvx4RDezp43K5YLPZAh5aU9NRr3qaniRBkSQAzdsA91SjVpzslQxORERERBQWzQpOmzdvxoYNG9CnTx/8+c9/RlpaGu655x5s2rQppHsVFRVBkiSkpKQEHE9JSUFeXl6T12/YsAHbt2/Hbbfd1uA58+bNQ0xMjPro3LlzSGNsD/6OeuoeTh6P+jVO1QP0EfWtceJUPSIiIiJqP81e43TmmWfixRdfxIkTJzB79mz8+9//xrBhwzBo0CAsXrwYiqK05jjrtWjRIgwYMADDhw9v8JyZM2eivLxcfRw9erTNxxWqkjqb37rVr3EDXEAf5QtOiscDT2mp7xhbkRMRERFRO9I390KPx4P//Oc/ePPNN7Fy5UqcddZZuPXWW3Hs2DE8/PDD+O6777B06dJG75GYmAidTof8/PyA4/n5+UhNTW30Wrvdjg8++ABz585t9DyTyQSTyRTciwoTfyvy+JNakQt6PQSdLlzD0gydxQKIIiDLcOb5flZYcSIiIiKi9hRycNq0aRPefPNNvP/++xBFEZMnT8bzzz+P3r17q+dceeWVGDZsWJP3MhqNGDJkCFatWoUJEyYAAGRZxqpVq3DPPfc0eu3y5cvhcrlw0003hfoSNKe0VnMIAJD9rchZbQIACIIAfUQEvBUVtYITK05ERERE1H5CDk7Dhg3DBRdcgNdeew0TJkyAoZ5f7rt27Yrrr78+qPvNmDEDU6ZMwdChQzF8+HAsWLAAdrsd06ZNAwBMnjwZGRkZmDdvXsB1ixYtwoQJE5CQkBDqS9Cchje/ZXDy00f6g1Oe+jkRERERUXsJOTgdPHgQmZmZjZ4TERGBN998M6j7TZw4EYWFhZg1axby8vIwaNAgfPPNN2rDiCNHjkAUA5di7dmzBz/++CP++9//hjp8TSpx+qfqBa5xYivyGv4Kk+RwBHxORERERNQeQg5OBQUFyMvLw4gRIwKOr1+/HjqdDkOHDg15EPfcc0+DU/PWrFlT51ivXr3apflEe/BIHlS4fC22T24OIbAVuUp/Urt5BiciIiIiak8hd9W7++676+1Md/z4cdx9992tMqjTSZnTt6+UXtQj0ugLB4qHU/VOdnJQ4lQ9IiIiImpPIQennTt34swzz6xzfPDgwdi5c2erDOp0oq5vMkdDEAQAgOTyN4dgxcnv5KDEduRERERE1J5CDk4mk6lO+3AAyM3NhV7f7O7mp60SdfPbWPWY4u+qZ2Jw8mPFiYiIiIjCKeTgdOGFF6qbyvqVlZXh4YcfxgUXXNCqgzsdlJ60+S1Qq6se25GrdLXWOAk6HUSzOYyjISIiIqLTTcglomeffRajRo1CZmYmBg8eDADYvHkzUlJS8O6777b6AE91pc56gpO/4sQ1TqraFSd9ZKQ6rZGIiIiIqD2EHJwyMjKwdetWvPfee9iyZQssFgumTZuGG264od49nahx/ql68bWm6rEdeV21p+Zxmh4RERERtbdmLUqKiIjA7bff3tpjOS3VNIeob6oeg5PfyRUnIiIiIqL21OxuDjt37sSRI0fgrq6O+F1++eUtHtTppN41TmxHXkdgcGLFiYiIiIjaV8jB6eDBg7jyyiuxbds2CIKgbkSrttKWpNYd4SmutL6pei4XAG6AW1vtsKRjK3IiIiIiamchd9W799570bVrVxQUFMBqtWLHjh1Yu3Ythg4dijVr1rTBEE9dLq8bdo8DQP0VJx3bkatYcSIiIiKicAq54rRu3Tp8//33SExMhCiKEEURf/jDHzBv3jz85S9/we+//94W4zwl+TvqGXUGWA0W9bh/jZPAZhsqncUCiCIgy1zjRERERETtLuSKkyRJiIqKAgAkJibixIkTAIDMzEzs2bOndUd3iiuttflt7fba6ga47KqnEgQB+uq9nBiciIiIiKi9hVxx6t+/P7Zs2YKuXbtixIgR+Ne//gWj0YjXX38d2dnZbTHGU5a/MUR8rWl6ACC5GJzqo4+MgLeiglP1iIiIiKjdhRycHn30UdjtdgDA3Llzcemll+Lcc89FQkICli1b1uoDPJWV1NOKHAAUdtWrlzEuDs7cPBjj4sI9FCIiIiI6zYQcnMaNG6d+3L17d+zevRslJSWIi4sLmG5GTStz+luRxwYcVzfA5RqnAFm3TEX5lq2IHXhGuIdCRERERKeZkIKTx+OBxWLB5s2b0b9/f/V4fHx8qw/sdNArsRvGdXejb3KPgONqcOJUvQBRPbojqkf3cA+DiIiIiE5DIQUng8GALl26cK+mVjIsYyCGZQysc7xmA1wGJyIiIiIiLQi5q94jjzyChx9+GCUlJW0xHgKn6hERERERaU3Ia5xefvll7N+/H+np6cjMzERERGCHs02bNrXa4E5XrDgREREREWlLyMFpwoQJbTAMqk1mO3IiIiIiIk0JOTjNnj27LcZBtchsR05EREREpCkhr3Gitudf4yQYWHEiIiIiItKCkCtOoig2ul8TO+61XE07claciIiIiIi0IOTg9J///Cfgc4/Hg99//x1vv/025syZ02oDO10pkgTIMgCucSIiIiIi0oqQg9MVV1xR59g111yDfv36YdmyZbj11ltbZWCnK3+1CWBwIiIiIiLSilZb43TWWWdh1apVrXW705a/MQTAfZyIiIiIiLSiVYKTw+HAiy++iIyMjNa43WnN34pc0OshiOzdQURERESkBSFP1YuLiwtoDqEoCioqKmC1WrFkyZJWHdzpSPZwDyciIiIiIq0JOTg9//zzAcFJFEUkJSVhxIgRiIuLa9XBnY5kd/UeTpymR0RERESkGSEHp6lTp7bBMMiPrciJiIiIiLQn5EU0b775JpYvX17n+PLly/H222+3yqBOZ5yqR0RERESkPSEHp3nz5iExMbHO8eTkZDz99NOtMqjTmTpVj8GJiIiIiEgzQg5OR44cQdeuXescz8zMxJEjR1plUKczf3ASuMaJiIiIiEgzQg5OycnJ2Lp1a53jW7ZsQUJCQqsM6nRWs8aJFSciIiIiIq0IOTjdcMMN+Mtf/oLVq1dDkiRIkoTvv/8e9957L66//vq2GONpReEaJyIiIiIizQm5q94TTzyBQ4cO4Y9//CP0et/lsixj8uTJXOPUCtiOnIiIiIhIe0IOTkajEcuWLcOTTz6JzZs3w2KxYMCAAcjMzGyL8Z122I6ciIiIiEh7Qg5Ofj169ECPHj1acyyE2sHJFOaREBERERGRX8hrnK6++mr885//rHP8X//6F6699tpWGdTpTPb425Gz4kREREREpBUhB6e1a9fikksuqXP84osvxtq1a0MewCuvvIKsrCyYzWaMGDECGzZsaPT8srIy3H333UhLS4PJZELPnj3x1Vdfhfy8WqVWnLjGiYiIiIhIM0KeqldZWQljPR3fDAYDbDZbSPdatmwZZsyYgYULF2LEiBFYsGABxo0bhz179iA5ObnO+W63GxdccAGSk5Px0UcfISMjA4cPH0ZsbGyoL0Oz2I6ciIiIiEh7Qq44DRgwAMuWLatz/IMPPkDfvn1Dutf8+fMxffp0TJs2DX379sXChQthtVqxePHies9fvHgxSkpK8Mknn+Ccc85BVlYWRo8ejYEDB4b6MjSrZqoegxMRERERkVaEXHF67LHHcNVVV+HAgQM4//zzAQCrVq3C0qVL8dFHHwV9H7fbjd9++w0zZ85Uj4miiLFjx2LdunX1XvPZZ59h5MiRuPvuu/Hpp58iKSkJN954Ix588EHodLp6r3G5XHC5XOrnoVbF2pu/HbnAqXpERERERJoRcsXpsssuwyeffIL9+/fjrrvuwt/+9jccP34c33//Pbp37x70fYqKiiBJElJSUgKOp6SkIC8vr95rDh48iI8++giSJOGrr77CY489hueeew5PPvlkg88zb948xMTEqI/OnTsHPcZwkN2+kMeKExERERGRdoQcnABg/Pjx+Omnn2C323Hw4EFcd911uP/++9t8ypwsy0hOTsbrr7+OIUOGYOLEiXjkkUewcOHCBq+ZOXMmysvL1cfRo0fbdIwtpW6Ay+BERERERKQZzd7Hae3atVi0aBE+/vhjpKen46qrrsIrr7wS9PWJiYnQ6XTIz88POJ6fn4/U1NR6r0lLS4PBYAiYltenTx/k5eXB7XbX27TCZDLBZOo4eyIpbEdORERERKQ5IVWc8vLy8I9//AM9evTAtddei+joaLhcLnzyySf4xz/+gWHDhgV9L6PRiCFDhmDVqlXqMVmWsWrVKowcObLea8455xzs378fsiyrx/bu3Yu0tLR6Q1NHVNOO/NR4PUREREREp4Kgg9Nll12GXr16YevWrViwYAFOnDiBl156qUVPPmPGDLzxxht4++23sWvXLvzpT3+C3W7HtGnTAACTJ08OaB7xpz/9CSUlJbj33nuxd+9efPnll3j66adx9913t2gcWqIGJxODExERERGRVgQ9Ve/rr7/GX/7yF/zpT39Cjx49WuXJJ06ciMLCQsyaNQt5eXkYNGgQvvnmG7VhxJEjRyCKNdmuc+fO+Pbbb3HffffhjDPOQEZGBu699148+OCDrTIeLVDbkbOrHhERERGRZgQdnH788UcsWrQIQ4YMQZ8+fXDzzTfj+uuvb/EA7rnnHtxzzz31fm3NmjV1jo0cORK//PJLi59Xq7gBLhERERGR9gQ9Ve+ss87CG2+8gdzcXNxxxx344IMPkJ6eDlmWsXLlSlRUVLTlOE8bDE5ERERERNoTcjvyiIgI3HLLLfjxxx+xbds2/O1vf8M//vEPJCcn4/LLL2+LMZ5W1HbknKpHRERERKQZzdrHya9Xr17417/+hWPHjuH9999vrTGd1mS2IyciIiIi0pwWBSc/nU6HCRMm4LPPPmuN253W/FP1BLYjJyIiIiLSjFYJTtQ6ZK8XqN6jSsd25EREREREmsHgpCH+9U0AIHCNExERERGRZjA4aYjicasfszkEEREREZF2MDhpSM36JgMEkd8aIiIiIiKt4G/nGqK2ImdHPSIiIiIiTWFw0hC5eqqeyI56RERERESawuCkIaw4ERERERFpE4OThvjXOIlGVpyIiIiIiLSEwUlD1ODEqXpERERERJrC4KQhsodT9YiIiIiItIjBSUNkF6fqERERERFpEYOThqhd9VhxIiIiIiLSFAYnDfF31RO4xomIiIiISFMYnDRE4RonIiIiIiJNYnDSELYjJyIiIiLSJgYnDWE7ciIiIiIibWJw0hC2IyciIiIi0iYGJw1hO3IiIiIiIm1icNKQmnbkDE5ERERERFrC4KQh/nbkooFT9YiIiIiItITBSUO4AS4RERERkTYxOGkI25ETEREREWkTg5OG+KfqCWxHTkRERESkKQxOGqKwHTkRERERkSYxOGmIxHbkRERERESaxOCkIQrbkRMRERERaRKDk4awHTkRERERkTYxOGkIN8AlIiIiItImBicNYTtyIiIiIiJtYnDSEHWqHrvqERERERFpCoOTRiiKAtnfjpz7OBERERERaQqDk0YoXi8gywA4VY+IiIiISGsYnDTCX20COFWPiIiIiEhrGJw0wr++CQAEtiMnIiIiItIUBieN8G9+KxgMEAQhzKMhIiIiIqLaGJw0QnKxFTkRERERkVZpIji98soryMrKgtlsxogRI7Bhw4YGz33rrbcgCELAw2w2t+No24biYStyIiIiIiKtCntwWrZsGWbMmIHZs2dj06ZNGDhwIMaNG4eCgoIGr4mOjkZubq76OHz4cDuOuG2om9+yFTkRERERkeaEPTjNnz8f06dPx7Rp09C3b18sXLgQVqsVixcvbvAaQRCQmpqqPlJSUtpxxG1DDU6cqkdEREREpDlhDU5utxu//fYbxo4dqx4TRRFjx47FunXrGryusrISmZmZ6Ny5M6644grs2LGjwXNdLhdsNlvAQ4tkTtUjIiIiItKssAanoqIiSJJUp2KUkpKCvLy8eq/p1asXFi9ejE8//RRLliyBLMs4++yzcezYsXrPnzdvHmJiYtRH586dW/11tAZO1SMiIiIi0q6wT9UL1ciRIzF58mQMGjQIo0ePxooVK5CUlIT/+7//q/f8mTNnory8XH0cPXq0nUccHP8+Tqw4ERERERFpjz6cT56YmAidTof8/PyA4/n5+UhNTQ3qHgaDAYMHD8b+/fvr/brJZILJZGrxWNsa1zgREREREWlXWCtORqMRQ4YMwapVq9Rjsixj1apVGDlyZFD3kCQJ27ZtQ1paWlsNs13IHn9wYsWJiIiIiEhrwlpxAoAZM2ZgypQpGDp0KIYPH44FCxbAbrdj2rRpAIDJkycjIyMD8+bNAwDMnTsXZ511Frp3746ysjI888wzOHz4MG677bZwvowW80/VE7jGiYiIiIhIc8IenCZOnIjCwkLMmjULeXl5GDRoEL755hu1YcSRI0cgijWFsdLSUkyfPh15eXmIi4vDkCFD8PPPP6Nv377hegmtglP1iIiIiIi0S1AURQn3INqTzWZDTEwMysvLER0dHe7hqA6/9z6OffgRUi+5CN3umB7u4RARERERnfJCyQYdrqveqUpR93FixYmIiIiISGsYnDRCbUduYHMIIiIiIiKtYXDSCK5xIiIiIiLSLgYnjahpR87gRERERESkNQxOGsGpekRERERE2sXgpBHqVD0TK05ERERERFrD4KQRanDiBrhERERERJrD4KQRstqOnFP1iIiIiIi0hsFJI/xrnASucSIiIiIi0hwGJ42Q3S4A7KpHRERERKRFDE4aoahT9RiciIiIiIi0hsFJI9R25FzjRERERESkOQxOGqF21WPFiYiIiIhIcxicNILtyImIiIiItIvBSQMURWE7ciIiIiIiDWNw0gDF6wUUBQArTkREREREWsTgpAH+aXoAIJoYnIiIiIiItIbBSQP80/QAQNDrwzgSIiIiIiKqD4OTBtTuqCcIQphHQ0REREREJ2Nw0gC2IiciIiIi0jYGJw3wb34rGNhRj4iIiIhIixicNEBhK3IiIiIiIk1jcNIAbn5LRERERKRtDE4aoAYntiInIiIiItIkBicN8K9xErnGiYiIiIhIkxicNED2sKseEREREZGWMThpgFpxYnAiIiIiItIkBicNqGkOwal6RERERERaxOCkAZyqR0RERESkbQxOGsANcImIiIiItI3BSQP8U/V0bEdORERERKRJDE4a4A9OrDgREREREWkTg5MGKB521SMiIiIi0jIGJw1gO3IiIiIiIm1jcNIAtR25kVP1iIiIiIi0iMFJA2r2cWLFiYiIiIhIixicNEBW1zix4kREREREpEUMThpQM1WPFSciIiIiIi1icNIATtUjIiIiItI2TQSnV155BVlZWTCbzRgxYgQ2bNgQ1HUffPABBEHAhAkT2naAbYxT9YiIiIiItC3swWnZsmWYMWMGZs+ejU2bNmHgwIEYN24cCgoKGr3u0KFDuP/++3Huuee200jbDqfqERERERFpW9iD0/z58zF9+nRMmzYNffv2xcKFC2G1WrF48eIGr5EkCZMmTcKcOXOQnZ3djqNtGwxORERERETaFtbg5Ha78dtvv2Hs2LHqMVEUMXbsWKxbt67B6+bOnYvk5GTceuutTT6Hy+WCzWYLeGiNfwNcwcCpekREREREWhTW4FRUVARJkpCSkhJwPCUlBXl5efVe8+OPP2LRokV44403gnqOefPmISYmRn107ty5xeNubQrXOBERERERaVrYp+qFoqKiAjfffDPeeOMNJCYmBnXNzJkzUV5erj6OHj3axqMMHafqERERERFpmz6cT56YmAidTof8/PyA4/n5+UhNTa1z/oEDB3Do0CFcdtll6jFZlgEAer0ee/bsQbdu3QKuMZlMMJlMbTD61qEoCoMTEREREZHGhbXiZDQaMWTIEKxatUo9JssyVq1ahZEjR9Y5v3fv3ti2bRs2b96sPi6//HKcd9552Lx5syan4TVF8XrVj0WucSIiIiIi0qSwVpwAYMaMGZgyZQqGDh2K4cOHY8GCBbDb7Zg2bRoAYPLkycjIyMC8efNgNpvRv3//gOtjY2MBoM7xjsJfbQJYcSIiIiIi0qqwB6eJEyeisLAQs2bNQl5eHgYNGoRvvvlGbRhx5MgRiGKHWooVEjU4CQIEfdi/HUREREREVA9BURQl3INoTzabDTExMSgvL0d0dHS4hwNnfgF+u/1PEI1GjFz+friHQ0RERER02gglG5y6pZwOQvawMQQRERERkdYxOIUZO+oREREREWkfg1OYyW5ufktEREREpHUMTmHmrzgJbEVORERERKRZDE5hpnj8FSftbtJLRERERHS6Y3AKs5o1Tqw4ERERERFpFYNTmKlrnDhVj4iIiIhIs7jjapixHTkRERGRtkiSBE/1cgrq+IxGI0Sx5fUiBqcwk10MTkRERERaoCgK8vLyUFZWFu6hUCsSRRFdu3aFsYW/bzM4hZnsYTtyIiIiIi3wh6bk5GRYrVYIghDuIVELybKMEydOIDc3F126dGnR95TBKczU5hAGVpyIiIiIwkWSJDU0JSQkhHs41IqSkpJw4sQJeL1eGFrQV4DNIcKspuLE4EREREQULv41TVarNcwjodbmn6InSVKL7sPgFGZsR05ERESkHZyed+ppre8pg1OY+YOTwHbkRERERESaxeAUZuo+TpyqR0REREQakZWVhQULFoR7GJrC4BRmNVP1GJyIiIiIKDSCIDT6ePzxx5t1340bN+L2229v3cF2cOyqF2aKh2uciIiIiKh5cnNz1Y+XLVuGWbNmYc+ePeqxyMhI9WNFUSBJEvT6piNAUlJS6w70FMCKU5ipU/XYjpyIiIhIUxRFgdPrCstDUZSgxpiamqo+YmJiIAiC+vnu3bsRFRWFr7/+GkOGDIHJZMKPP/6IAwcO4IorrkBKSgoiIyMxbNgwfPfddwH3PXmqniAI+Pe//40rr7wSVqsVPXr0wGeffdaab7fmseIUZmxHTkRERKRNLsmNyR//NSzP/c7VC2DWm1rlXg899BCeffZZZGdnIy4uDkePHsUll1yCp556CiaTCe+88w4uu+wy7NmzB126dGnwPnPmzMG//vUvPPPMM3jppZcwadIkHD58GPHx8a0yTq1jxSnMZBen6hERERFR25k7dy4uuOACdOvWDfHx8Rg4cCDuuOMO9O/fHz169MATTzyBbt26NVlBmjp1Km644QZ0794dTz/9NCorK7Fhw4Z2ehXhx4pTmMkeNocgIiIi0iKTzoh3rl4QtuduLUOHDg34vLKyEo8//ji+/PJL5Obmwuv1wuFw4MiRI43e54wzzlA/joiIQHR0NAoKClptnFrH4BRmNWucWHEiIiIi0hJBEFptulw4RUREBHx+//33Y+XKlXj22WfRvXt3WCwWXHPNNXBXd3tuiOGk31cFQYAsy60+Xq1icAoztiMnIiIiovb0008/YerUqbjyyisB+CpQhw4dCu+gOgCucQozTtUjIiIiovbUo0cPrFixAps3b8aWLVtw4403nlaVo+ZicAoz/1Q9gVP1iIiIiKgdzJ8/H3FxcTj77LNx2WWXYdy4cTjzzDPDPSzNE5Rgm8SfImw2G2JiYlBeXo7o6OhwDwfrrr0BstuNof9eCBM3GiMiIiIKC6fTiZycHHTt2hVmszncw6FW1Nj3NpRswIpTGCmKoq5xErgBLhERERGRZjE4hZFSvfktwH2ciIiIiIi0jMEpjPzrmwC2IyciIiIi0jIGpzDyT9ODKELQszM8EREREZFWMTiFkdqK3GCAIAhhHg0RERERETWEwSmM/FP1uL6JiIiIiEjbGJzCiJvfEhERERF1DAxOYSS7/FP1GJyIiIiIiLSMwSmMZA+n6hERERERdQQMTmHEzW+JiIiIKNzGjBmDv/71r+rnWVlZWLBgQaPXCIKATz75pMXP3Vr3aQ8MTmHkD046E4MTEREREYXusssuw0UXXVTv13744QcIgoCtW7eGdM+NGzfi9ttvb43hqR5//HEMGjSozvHc3FxcfPHFrfpcbYXBKYz8XfUEbn5LRERERM1w6623YuXKlTh27Fidr7355psYOnQozjjjjJDumZSUBKvV2lpDbFRqaipMJlO7PFdLMTiFkaJ21WNwIiIiItIaRVEgOZ1heSiKEtQYL730UiQlJeGtt94KOF5ZWYnly5djwoQJuOGGG5CRkQGr1YoBAwbg/fffb/SeJ0/V27dvH0aNGgWz2Yy+ffti5cqVda558MEH0bNnT1itVmRnZ+Oxxx6Dp3o9/1tvvYU5c+Zgy5YtEAQBgiCo4z15qt62bdtw/vnnw2KxICEhAbfffjsqKyvVr0+dOhUTJkzAs88+i7S0NCQkJODuu+9Wn6st6dv8GahBNfs4caoeERERkdbILhd+mTgpLM991rL3oDObmzxPr9dj8uTJeOutt/DII49AEAQAwPLlyyFJEm666SYsX74cDz74IKKjo/Hll1/i5ptvRrdu3TB8+PAm7y/LMq666iqkpKRg/fr1KC8vD1gP5RcVFYW33noL6enp2LZtG6ZPn46oqCj8/e9/x8SJE7F9+3Z88803+O677wAAMTExde5ht9sxbtw4jBw5Ehs3bkRBQQFuu+023HPPPQHBcPXq1UhLS8Pq1auxf/9+TJw4EYMGDcL06dObfD0toYmK0yuvvIKsrCyYzWaMGDECGzZsaPDcFStWYOjQoYiNjUVERAQGDRqEd999tx1H23r8a5zYjpyIiIiImuuWW27BgQMH8L///U899uabb+Lqq69GZmYm7r//fgwaNAjZ2dn485//jIsuuggffvhhUPf+7rvvsHv3brzzzjsYOHAgRo0ahaeffrrOeY8++ijOPvtsZGVl4bLLLsP999+vPofFYkFkZCT0ej1SU1ORmpoKi8VS5x5Lly6F0+nEO++8g/79++P888/Hyy+/jHfffRf5+fnqeXFxcXj55ZfRu3dvXHrppRg/fjxWrVoV6tsWsrBXnJYtW4YZM2Zg4cKFGDFiBBYsWIBx48Zhz549SE5OrnN+fHw8HnnkEfTu3RtGoxFffPEFpk2bhuTkZIwbNy4Mr6D52I6ciIiISLtEkwlnLXsvbM8drN69e+Pss8/G4sWLMWbMGOzfvx8//PAD5s6dC0mS8PTTT+PDDz/E8ePH4Xa74XK5gl7DtGvXLnTu3Bnp6enqsZEjR9Y5b9myZXjxxRdx4MABVFZWwuv1Ijo6OujX4H+ugQMHIiIiQj12zjnnQJZl7NmzBykpKQCAfv36QafTqeekpaVh27ZtIT1Xc4S94jR//nxMnz4d06ZNQ9++fbFw4UJYrVYsXry43vPHjBmDK6+8En369EG3bt1w77334owzzsCPP/5Y7/kulws2my3goRWsOBERERFplyAI0JnNYXn4p9wF69Zbb8XHH3+MiooKvPnmm+jWrRtGjx6NZ555Bi+88AIefPBBrF69Gps3b8a4cePgrv49tDWsW7cOkyZNwiWXXIIvvvgCv//+Ox555JFWfY7aDCc1VhMEAbIst8lz1RbW4OR2u/Hbb79h7Nix6jFRFDF27FisW7euyesVRcGqVauwZ88ejBo1qt5z5s2bh5iYGPXRuXPnVht/S6nBie3IiYiIiKgFrrvuOoiiiKVLl+Kdd97BLbfcAkEQ8NNPP+GKK67ATTfdhIEDByI7Oxt79+4N+r59+vTB0aNHkZubqx775ZdfAs75+eefkZmZiUceeQRDhw5Fjx49cPjw4YBzjEYjJElq8rm2bNkCu92uHvvpp58giiJ69eoV9JjbSliDU1FRESRJUstufikpKcjLy2vwuvLyckRGRsJoNGL8+PF46aWXcMEFF9R77syZM1FeXq4+jh492qqvoSVSL74I/ebORsrYP4Z7KERERETUgUVGRmLixImYOXMmcnNzMXXqVABAjx49sHLlSvz888/YtWsX7rjjjoD1Qk0ZO3YsevbsiSlTpmDLli344Ycf8MgjjwSc06NHDxw5cgQffPABDhw4gBdffBH/+c9/As7JyspCTk4ONm/ejKKiIrhcrjrPNWnSJJjNZkyZMgXbt2/H6tWr8ec//xk333xznbwQDmGfqtccUVFR2Lx5MzZu3IinnnoKM2bMwJo1a+o912QyITo6OuChFZa0VMQOPAOWjPSmTyYiIiIiasStt96K0tJSjBs3Tl2T9Oijj+LMM8/EuHHjMGbMGKSmpmLChAlB31MURfznP/+Bw+HA8OHDcdttt+Gpp54KOOfyyy/Hfffdh3vuuQeDBg3Czz//jMceeyzgnKuvvhoXXXQRzjvvPCQlJdXbEt1qteLbb79FSUkJhg0bhmuuuQZ//OMf8fLLL4f+ZrQBQQm2SXwbcLvdsFqt+OijjwK+gVOmTEFZWRk+/fTToO5z22234ejRo/j222+bPNdmsyEmJgbl5eWaClFEREREFD5OpxM5OTno2rUrzEG0AaeOo7HvbSjZIKwVJ6PRiCFDhgS0D5RlGatWraq3W0dDZFmut9xHRERERETUGsLejnzGjBmYMmUKhg4diuHDh2PBggWw2+2YNm0aAGDy5MnIyMjAvHnzAPiaPQwdOhTdunWDy+XCV199hXfffRevvfZaOF8GERERERGdwsIenCZOnIjCwkLMmjULeXl5GDRoEL755ht1AdiRI0cgijWFMbvdjrvuugvHjh2DxWJB7969sWTJEkycODFcL4GIiIiIiE5xYV3jFA5c40REREREJ+Map1PXKbHGiYiIiIhIS06zmsJpobW+pwxORERERHTaMxgMAICqqqowj4Ram9vtBgDodLoW3Sfsa5yIiIiIiMJNp9MhNjYWBQUFAHx7CgmCEOZRUUvJsozCwkJYrVbo9S2LPgxOREREREQAUlNTAUANT3RqEEURXbp0aXEQZnAiIiIiIgIgCALS0tKQnJwMj8cT7uFQKzEajQFdupuLwYmIiIiIqBadTtfi9TB06mFzCCIiIiIioiYwOBERERERETWBwYmIiIiIiKgJp90aJ/8GWDabLcwjISIiIiKicPJngmA2yT3tglNFRQUAoHPnzmEeCRERERERaUFFRQViYmIaPUdQgolXpxBZlnHixAlERUVpYlMzm82Gzp074+jRo4iOjg73cOgUwp8takv8+aK2wp8takv8+aKTKYqCiooKpKenN9my/LSrOImiiE6dOoV7GHVER0fzP2BqE/zZorbEny9qK/zZorbEny+qralKkx+bQxARERERETWBwYmIiIiIiKgJDE5hZjKZMHv2bJhMpnAPhU4x/NmitsSfL2or/NmitsSfL2qJ0645BBERERERUahYcSIiIqL/b+/uY6os/ziOfw5PhwMi8TAOUCOxmCA+TEUUcWsFC8m5oZRzOznUNuc8EA+r6SjS5gNpy5pWGK78Jx+KNgtb1ogam06EMAgXYlttugjJFWEU6jjX74/2O+uMfp3CX94eer+2ezvnui7gc2/f7fDdfV/3AQD4QeMEAAAAAH7QOAEAAACAHzROAAAAAOAHjZOFXnnlFU2ZMkXh4eFasGCB2trarI6EAFNbW6v58+crKipKCQkJKioqUm9vr8+akZERud1uxcXFadKkSSouLtbly5ctSoxA9dxzz8lms6miosI7Rm3hZnz77bd69NFHFRcXJ4fDoZkzZ+qzzz7zzhtj9MwzzygpKUkOh0P5+fn66quvLEyMQDE6OqqamhqlpqbK4XDonnvu0bZt2/T756FRXxgPGieLvPXWW6qqqtKWLVt09uxZzZ49WwUFBRoYGLA6GgJIS0uL3G63Wltb1dTUpBs3bujBBx/U8PCwd01lZaWOHz+uhoYGtbS0qK+vTytWrLAwNQJNe3u7XnvtNc2aNctnnNrCeP3444/Kzc1VaGioTpw4oS+//FIvvPCCYmJivGt2796tvXv3av/+/Tpz5owiIyNVUFCgkZERC5MjEOzatUt1dXV6+eWX1dPTo127dmn37t3at2+fdw31hXExsER2drZxu93e96OjoyY5OdnU1tZamAqBbmBgwEgyLS0txhhjBgcHTWhoqGloaPCu6enpMZLM6dOnrYqJAHL16lWTlpZmmpqazH333WfKy8uNMdQWbs6mTZvM4sWL/+e8x+MxiYmJ5vnnn/eODQ4OGrvdbo4cOXIrIiKALV261Kxbt85nbMWKFcblchljqC+MH1ecLHD9+nV1dHQoPz/fOxYUFKT8/HydPn3awmQIdD/99JMkKTY2VpLU0dGhGzdu+NRaenq6UlJSqDX8JW63W0uXLvWpIYnaws1pbGxUVlaWHnnkESUkJGjOnDk6cOCAd/6bb75Rf3+/T31FR0drwYIF1Bf8WrRokZqbm3XhwgVJUldXl06ePKnCwkJJ1BfGL8TqAP9GV65c0ejoqJxOp8+40+nU+fPnLUqFQOfxeFRRUaHc3FzNmDFDktTf36+wsDDdcccdPmudTqf6+/stSIlAcvToUZ09e1bt7e1j5qgt3Iyvv/5adXV1qqqqUnV1tdrb2/X4448rLCxMJSUl3hr6o89J6gv+bN68WUNDQ0pPT1dwcLBGR0e1Y8cOuVwuSaK+MG40TsAE4Xa7de7cOZ08edLqKJgALl26pPLycjU1NSk8PNzqOJhgPB6PsrKytHPnTknSnDlzdO7cOe3fv18lJSUWp0Oge/vtt3Xo0CEdPnxYmZmZ6uzsVEVFhZKTk6kv3BRu1bNAfHy8goODxzx96vLly0pMTLQoFQJZaWmp3n//fX366ae66667vOOJiYm6fv26BgcHfdZTa/Cno6NDAwMDmjt3rkJCQhQSEqKWlhbt3btXISEhcjqd1BbGLSkpSdOnT/cZy8jI0MWLFyXJW0N8TmI8nnzySW3evFmrVq3SzJkztXr1alVWVqq2tlYS9YXxo3GyQFhYmObNm6fm5mbvmMfjUXNzs3JycixMhkBjjFFpaamOHTumTz75RKmpqT7z8+bNU2hoqE+t9fb26uLFi9Qa/lReXp66u7vV2dnpPbKysuRyubyvqS2MV25u7pivTrhw4YLuvvtuSVJqaqoSExN96mtoaEhnzpyhvuDXL7/8oqAg339xg4OD5fF4JFFfGD9u1bNIVVWVSkpKlJWVpezsbL300ksaHh7W2rVrrY6GAOJ2u3X48GG99957ioqK8t6bHR0dLYfDoejoaD322GOqqqpSbGysJk+erLKyMuXk5GjhwoUWp8ftLCoqyrtX7r8iIyMVFxfnHae2MF6VlZVatGiRdu7cqZUrV6qtrU319fWqr6+XJO93hm3fvl1paWlKTU1VTU2NkpOTVVRUZG143PaWLVumHTt2KCUlRZmZmfr888+1Z88erVu3ThL1hZtg9WP9/s327dtnUlJSTFhYmMnOzjatra1WR0KAkfSHx8GDB71rfv31V7Nx40YTExNjIiIizPLly813331nXWgErN8/jtwYags35/jx42bGjBnGbreb9PR0U19f7zPv8XhMTU2NcTqdxm63m7y8PNPb22tRWgSSoaEhU15eblJSUkx4eLiZOnWqeeqpp8y1a9e8a6gvjIfNmN99jTIAAAAAYAz2OAEAAACAHzROAAAAAOAHjRMAAAAA+EHjBAAAAAB+0DgBAAAAgB80TgAAAADgB40TAAAAAPhB4wQAAAAAftA4AQDwN9hsNr377rtWxwAA3GI0TgCAgLFmzRrZbLYxx5IlS6yOBgCY4EKsDgAAwN+xZMkSHTx40GfMbrdblAYA8G/BFScAQECx2+1KTEz0OWJiYiT9dhtdXV2dCgsL5XA4NHXqVL3zzjs+P9/d3a0HHnhADodDcXFxWr9+vX7++WefNW+88YYyMzNlt9uVlJSk0tJSn/krV65o+fLlioiIUFpamhobG//ZkwYAWI7GCQAwodTU1Ki4uFhdXV1yuVxatWqVenp6JEnDw8MqKChQTEyM2tvb1dDQoI8//tinMaqrq5Pb7db69evV3d2txsZG3XvvvT5/49lnn9XKlSv1xRdf6KGHHpLL5dIPP/xwS88TAHBr2YwxxuoQAAD8FWvWrNGbb76p8PBwn/Hq6mpVV1fLZrNpw4YNqqur884tXLhQc+fO1auvvqoDBw5o06ZNunTpkiIjIyVJH3zwgZYtW6a+vj45nU7deeedWrt2rbZv3/6HGWw2m55++mlt27ZN0m/N2KRJk3TixAn2WgHABMYeJwBAQLn//vt9GiNJio2N9b7OycnxmcvJyVFnZ6ckqaenR7Nnz/Y2TZKUm5srj8ej3t5e2Ww29fX1KS8v708zzJo1y/s6MjJSkydP1sDAwHhPCQAQAGicAAABJTIycsytc/8vDofjL60LDQ31eW+z2eTxeP6JSACA2wR7nAAAE0pra+uY9xkZGZKkjIwMdXV1aXh42Dt/6tQpBQUFadq0aYqKitKUKVPU3Nx8SzMDAG5/XHECAASUa9euqb+/32csJCRE8fHxkqSGhgZlZWVp8eLFOnTokNra2vT6669Lklwul7Zs2aKSkhJt3bpV33//vcrKyrR69Wo5nU5J0tatW7VhwwYlJCSosLBQV69e1alTp1RWVnZrTxQAcFuhcQIABJQPP/xQSUlJPmPTpk3T+fPnJf32xLujR49q48aNSkpK0pEjRzR9+nRJUkREhD766COVl5dr/vz5ioiIUHFxsfbs2eP9XSUlJRoZGdGLL76oJ554QvHx8Xr44Ydv3QkCAG5LPFUPADBh2Gw2HTt2TEVFRVZHAQBMMOxxAgAAAAA/aJwAAAAAwA/2OAEAJgzuPgcA/FO44gQAAAAAftA4AQAAAIAfNE4AAAAA4AeNEwAAAAD4QeMEAAAAAH7QOAEAAACAHzROAAAAAOAHjRMAAAAA+PEfa511v/NBi0UAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 5))\n",
        "plt.plot(training_accuracy,'g', label='Train')\n",
        "plt.plot(validation_accuracy,'r', label='Validation')\n",
        "plt.title('Train vs. Validation Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "\n",
        "plt.savefig('x-brain_train-val_acc_plot.pdf', format='pdf', dpi=300, bbox_inches='tight')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "I2USd1yghQfP",
        "outputId": "357fd212-31ca-40ec-da23-c1650fe69f7e"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 5))\n",
        "plt.plot(training_loss,'g', label='Train')\n",
        "plt.plot(validation_loss,'r', label='Validation')\n",
        "plt.title('Train vs. Validation Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.savefig('x-brain_train-val_loss_plot.pdf', format='pdf', dpi=300, bbox_inches='tight')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7y1Spm7-hQfQ",
        "outputId": "73d39e20-9b4f-42ac-de6f-48da56bbb736"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "467/467 [==============================] - 126s 269ms/step - loss: 0.0589 - accuracy: 0.9981\n",
            "134/134 [==============================] - 11s 80ms/step - loss: 0.0739 - accuracy: 0.9955\n",
            "67/67 [==============================] - 8s 117ms/step - loss: 0.0763 - accuracy: 0.9930\n",
            "Train Accuracy : 0.998056173324585\n",
            "Train Loss : 0.0589049756526947\n",
            "-------------\n",
            "Val Accuracy : 0.995540976524353\n",
            "Val Loss : 0.0738973617553711\n",
            "-------------\n",
            "Test Accuracy : 0.9929742217063904\n",
            "Test Loss : 0.07626553624868393\n"
          ]
        }
      ],
      "source": [
        "train_loss, train_acc = model.evaluate(train_set)\n",
        "val_loss, val_acc = model.evaluate(val_set)\n",
        "test_loss, test_acc = model.evaluate(test_set)\n",
        "\n",
        "print(f\"Train Accuracy : {train_acc}\")\n",
        "print(f\"Train Loss : {train_loss}\")\n",
        "print(\"-------------\")\n",
        "print(f\"Val Accuracy : {val_acc}\")\n",
        "print(f\"Val Loss : {val_loss}\")\n",
        "print(\"-------------\")\n",
        "print(f\"Test Accuracy : {test_acc}\")\n",
        "print(f\"Test Loss : {test_loss}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JrGE41mQhQfQ",
        "outputId": "ea2a775b-ff4e-49af-b1c1-02c2d8416659"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "67/67 [==============================] - 6s 85ms/step\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "      glioma   0.994141  0.984526  0.989310       517\n",
            "  meningioma   1.000000  0.989031  0.994485       547\n",
            "    no_tumor   0.992352  1.000000  0.996161       519\n",
            "   pituitary   0.985689  0.998188  0.991899       552\n",
            "\n",
            "    accuracy                       0.992974      2135\n",
            "   macro avg   0.993045  0.992936  0.992964      2135\n",
            "weighted avg   0.993022  0.992974  0.992971      2135\n",
            "\n"
          ]
        }
      ],
      "source": [
        "predictions = model.predict(test_set)\n",
        "predicted_labels = tf.argmax(predictions, axis=1)\n",
        "\n",
        "true_labels = test_set.classes\n",
        "\n",
        "class_report = classification_report(true_labels, predicted_labels, target_names=class_labels, digits=6)\n",
        "print(class_report)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6qHUdSa5hQfQ",
        "outputId": "bf65d757-245e-4a7c-ad34-6f798df7e20f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "cm = confusion_matrix(true_labels, predicted_labels)\n",
        "class_names = class_labels\n",
        "\n",
        "plt.figure(figsize=(10, 5))\n",
        "sns.set(font_scale=1.5)\n",
        "ax = sns.heatmap(cm, annot=True, fmt='d', cmap='Purples', cbar=True, xticklabels=class_names, yticklabels=class_names)\n",
        "ax.set_xticklabels(class_names, rotation=45)\n",
        "ax.set_yticklabels(class_names, rotation=0)\n",
        "ax.set_xlabel('Predicted Values')\n",
        "ax.set_ylabel('Actual Values ')\n",
        "\n",
        "plt.savefig('x-brain_cm_test.pdf', format='pdf', dpi=300, bbox_inches='tight')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tEHnw40dhQfQ"
      },
      "outputs": [],
      "source": [
        "model.save('X-Brain.keras')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PfLJBqmmhQfQ"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kaggle": {
      "accelerator": "gpu",
      "dataSources": [
        {
          "datasetId": 3926788,
          "sourceId": 6829154,
          "sourceType": "datasetVersion"
        }
      ],
      "dockerImageVersionId": 30674,
      "isGpuEnabled": true,
      "isInternetEnabled": true,
      "language": "python",
      "sourceType": "notebook"
    },
    "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": "3.9.19"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}