{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preparing Data and Library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RpmSdGJEHX3t"
   },
   "source": [
    "## Import Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_files\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from PIL import Image\n",
    "from PIL import ImageEnhance\n",
    "from skimage.io import imread\n",
    "import matplotlib.pyplot as plt\n",
    "import os, random, pathlib, warnings, itertools, math\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D,MaxPooling2D, Activation, Dense, Flatten, Dropout, GlobalAveragePooling2D, BatchNormalization\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.applications import Xception\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras import backend as K\n",
    "from IPython.display import Image\n",
    "from tensorflow.keras.utils import plot_model\n",
    "from tensorflow.keras.callbacks import ReduceLROnPlateau\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "import wandb\n",
    "from wandb.integration.keras import WandbCallback, WandbMetricsLogger"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "J-AVtaXHHlqy"
   },
   "source": [
    "## Import Data Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "A6bLbR-gn3Xm",
    "outputId": "c31b182b-8e64-4e51-ec34-e7128f432170"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading complete!\n",
      "Training set size :  2527\n"
     ]
    }
   ],
   "source": [
    "ds = 'dataset'\n",
    "\n",
    "def load_dataset(path):\n",
    "    data = load_files(path)\n",
    "    files = np.array(data['filenames'])\n",
    "    targets = np.array(data['target'])\n",
    "    target_labels = np.array(data['target_names'])\n",
    "    return files,targets,target_labels\n",
    "\n",
    "x_train, y_train,target_labels = load_dataset(ds)\n",
    "print('Loading complete!')\n",
    "\n",
    "print('Training set size : ' , x_train.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "R5DvR_IYxjSm",
    "outputId": "96b04d25-f606-4ca7-9417-256744cf1fe1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cardboard folder contains 403 images.\n",
      "glass folder contains 501 images.\n",
      "metal folder contains 410 images.\n",
      "paper folder contains 594 images.\n",
      "plastic folder contains 482 images.\n",
      "trash folder contains 137 images.\n"
     ]
    }
   ],
   "source": [
    "garbage_types = os.listdir(ds)\n",
    "\n",
    "# Iterate over each trash type (folder) to process images\n",
    "for garbage_type in garbage_types:\n",
    "    folder_path = os.path.join(ds, garbage_type)\n",
    "\n",
    "    # Verify that the current item is a directory\n",
    "    if os.path.isdir(folder_path):\n",
    "        image_files = [f for f in os.listdir(folder_path) if f.endswith(('jpg', 'jpeg'))]\n",
    "\n",
    "        # Display the count of images in the current folder\n",
    "        num_images = len(image_files)\n",
    "        print(f\"{garbage_type} folder contains {num_images} images.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Processing Image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CtCtDzq9HskX"
   },
   "source": [
    "## Augmentation Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YeTx7UWFsSHW",
    "outputId": "c9833c28-2d68-4c0f-e1a7-a971c0814cd1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2024 images belonging to 6 classes.\n",
      "Found 503 images belonging to 6 classes.\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing\n",
    "train_datagen = ImageDataGenerator(\n",
    "    rescale=1./255,                     # Normalize pixel values to [0,1]\n",
    "    rotation_range=20,                  # Rotate image by up to 20 degrees\n",
    "    width_shift_range=0.2,              # shift images horizontally by up to 20% of the width\n",
    "    height_shift_range=0.2,             # shift images vertically by up to 20% of the height\n",
    "    horizontal_flip=True,               # flip images horizontally\n",
    "    vertical_flip=True,                 # flip images vertically\n",
    "    brightness_range=[0.8, 1.2],        # Vary brightness between 80% to 120% of original\n",
    "    zoom_range=0.2,                     # Zoom in or out by up to 20%\n",
    "    validation_split=0.2                # Split data into training and validation sets\n",
    ")\n",
    "\n",
    "# Generator validasi\n",
    "val_datagen = ImageDataGenerator(\n",
    "    rescale=1./255,                     # Normalize\n",
    "    validation_split=0.2                # Split data into training and validation sets\n",
    ")\n",
    "\n",
    "# Load data\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "    ds,\n",
    "    target_size=(224, 224),\n",
    "    batch_size=32,\n",
    "    seed=42,\n",
    "    class_mode='categorical',\n",
    "    shuffle=True,\n",
    "    subset='training'\n",
    ")\n",
    "\n",
    "validation_generator = val_datagen.flow_from_directory(\n",
    "    ds,\n",
    "    target_size=(224, 224),\n",
    "    batch_size=32,\n",
    "    seed=42,\n",
    "    class_mode='categorical',\n",
    "    shuffle=False,\n",
    "    subset='validation'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cxoEKye47OB3",
    "outputId": "dafd8539-ab42-4ac7-dcc3-8122baa37102"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cardboard': 0, 'glass': 1, 'metal': 2, 'paper': 3, 'plastic': 4, 'trash': 5}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_generator.class_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 452
    },
    "id": "HRsOcFe9Pwyj",
    "outputId": "526f4899-c8e8-4726-c53b-18d8d57c1446"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Mendapatkan satu batch gambar dan label\n",
    "images, labels = next(train_generator)\n",
    "# Menampilkan gambar pertama\n",
    "plt.imshow(images[0])\n",
    "plt.title(f\"Label: {labels[0]}\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XVns_tntI7aP"
   },
   "source": [
    "# Make a Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scratch a Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 691
    },
    "id": "vgYSHRDn7yDZ",
    "outputId": "e8438d34-5b65-4815-ea1b-c89ed3da232b"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\iqbal\\AppData\\Roaming\\Python\\Python312\\site-packages\\keras\\src\\layers\\convolutional\\base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)   │           <span style=\"color: #00af00; text-decoration-color: #00af00\">208</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,080</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │         <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">32,896</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25088</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">150</span>)            │     <span style=\"color: #00af00; text-decoration-color: #00af00\">3,763,350</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">150</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">150</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">906</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m16\u001b[0m)   │           \u001b[38;5;34m208\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation (\u001b[38;5;33mActivation\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m16\u001b[0m)   │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m16\u001b[0m)   │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │         \u001b[38;5;34m2,080\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │         \u001b[38;5;34m8,256\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │        \u001b[38;5;34m32,896\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_3 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout (\u001b[38;5;33mDropout\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25088\u001b[0m)          │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m150\u001b[0m)            │     \u001b[38;5;34m3,763,350\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_1 (\u001b[38;5;33mActivation\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m150\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m150\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m)              │           \u001b[38;5;34m906\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">3,807,696</span> (14.53 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m3,807,696\u001b[0m (14.53 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">3,807,696</span> (14.53 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m3,807,696\u001b[0m (14.53 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = Sequential()\n",
    "#Input Layer\n",
    "model.add(Conv2D(filters = 16, kernel_size = 2,input_shape=(224, 224, 3),padding='same'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "\n",
    "#Hidden Layer 1\n",
    "model.add(Conv2D(filters = 32,kernel_size = 2,activation= 'relu',padding='same'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "\n",
    "#Hidden Layer 2\n",
    "model.add(Conv2D(filters = 64,kernel_size = 2,activation= 'relu',padding='same'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "\n",
    "#Hidden Layer 3\n",
    "model.add(Conv2D(filters = 128,kernel_size = 2,activation= 'relu',padding='same'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "\n",
    "#Output Layer\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(150))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.4))\n",
    "model.add(Dense(6,activation = 'softmax'))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "Gs6mrSZd7Ym9",
    "outputId": "1412dae2-49c6-4d76-b9fb-fc6ea95ac12f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot model\n",
    "plot_model(\n",
    "    model, \n",
    "    to_file=\"model_plot.png\",  # Save Plot\n",
    "    show_shapes=True,          # Show Tensor Shape\n",
    "    show_layer_names=True      # Show Layer Name\n",
    ")\n",
    "\n",
    "# Tampilkan plot di notebook\n",
    "Image('model_plot.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compile Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yQKIga1kR1gh",
    "outputId": "bcc52f49-7fb6-4507-e932-9d9e9334fe01"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compiled!\n"
     ]
    }
   ],
   "source": [
    "model.compile(\n",
    "  loss='categorical_crossentropy',\n",
    "  optimizer='adam',\n",
    "  run_eagerly=True,\n",
    "  metrics=['accuracy']\n",
    ")\n",
    "print('Compiled!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Callback for Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "owpeqyNJH_aZ"
   },
   "outputs": [],
   "source": [
    "# Callback ReduceLROnPlateau\n",
    "reduce_lr = ReduceLROnPlateau(\n",
    "    monitor='val_loss',       # Monitor Validation Loss Metric\n",
    "    factor=0.5,               # Reduce learning rate around 50%\n",
    "    patience=5,               # Reduce Learning Rate if didn't improvement while 5 epoch\n",
    "    verbose=1,                # Showing Log\n",
    "    min_lr=1e-6               # Learning rate minimum\n",
    ")\n",
    "\n",
    "# Callback ModelCHeckpoint\n",
    "checkpointer = ModelCheckpoint(\n",
    "    filepath = 'cnn_item.keras',\n",
    "    verbose = 1,\n",
    "    save_best_only = True)\n",
    "\n",
    "# Callback EarlyStopping\n",
    "early_stopping = EarlyStopping(\n",
    "    monitor='val_loss',            # Monitor Validation Loss Metric\n",
    "    patience=8,                    # Wait 8 Epoch\n",
    "    restore_best_weights=True,     # Return to best weight\n",
    "    verbose=1                      # Show Log\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "U1tMTrDJR-DT",
    "outputId": "fd874b17-a4fa-4800-ace4-1fbfdfaa36ec"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\iqbal\\AppData\\Roaming\\Python\\Python312\\site-packages\\keras\\src\\trainers\\data_adapters\\py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
      "  self._warn_if_super_not_called()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "\n",
      "Epoch 1: val_loss improved from inf to 1.57070, saving model to cnn_item.keras\n",
      "64/64 - 14s - 219ms/step - accuracy: 0.2831 - loss: 1.6442 - val_accuracy: 0.3300 - val_loss: 1.5707 - learning_rate: 1.0000e-03\n",
      "Epoch 2/50\n",
      "\n",
      "Epoch 2: val_loss improved from 1.57070 to 1.41816, saving model to cnn_item.keras\n",
      "64/64 - 14s - 224ms/step - accuracy: 0.4091 - loss: 1.4332 - val_accuracy: 0.4175 - val_loss: 1.4182 - learning_rate: 1.0000e-03\n",
      "Epoch 3/50\n",
      "\n",
      "Epoch 3: val_loss improved from 1.41816 to 1.40651, saving model to cnn_item.keras\n",
      "64/64 - 14s - 218ms/step - accuracy: 0.4625 - loss: 1.3456 - val_accuracy: 0.4374 - val_loss: 1.4065 - learning_rate: 1.0000e-03\n",
      "Epoch 4/50\n",
      "\n",
      "Epoch 4: val_loss improved from 1.40651 to 1.38490, saving model to cnn_item.keras\n",
      "64/64 - 14s - 218ms/step - accuracy: 0.4946 - loss: 1.2539 - val_accuracy: 0.4632 - val_loss: 1.3849 - learning_rate: 1.0000e-03\n",
      "Epoch 5/50\n",
      "\n",
      "Epoch 5: val_loss improved from 1.38490 to 1.30539, saving model to cnn_item.keras\n",
      "64/64 - 14s - 214ms/step - accuracy: 0.5321 - loss: 1.2032 - val_accuracy: 0.4692 - val_loss: 1.3054 - learning_rate: 1.0000e-03\n",
      "Epoch 6/50\n",
      "\n",
      "Epoch 6: val_loss improved from 1.30539 to 1.23812, saving model to cnn_item.keras\n",
      "64/64 - 14s - 213ms/step - accuracy: 0.5627 - loss: 1.1579 - val_accuracy: 0.5129 - val_loss: 1.2381 - learning_rate: 1.0000e-03\n",
      "Epoch 7/50\n",
      "\n",
      "Epoch 7: val_loss did not improve from 1.23812\n",
      "64/64 - 14s - 217ms/step - accuracy: 0.5652 - loss: 1.1301 - val_accuracy: 0.4970 - val_loss: 1.2552 - learning_rate: 1.0000e-03\n",
      "Epoch 8/50\n",
      "\n",
      "Epoch 8: val_loss improved from 1.23812 to 1.23065, saving model to cnn_item.keras\n",
      "64/64 - 14s - 221ms/step - accuracy: 0.5993 - loss: 1.0808 - val_accuracy: 0.5348 - val_loss: 1.2306 - learning_rate: 1.0000e-03\n",
      "Epoch 9/50\n",
      "\n",
      "Epoch 9: val_loss improved from 1.23065 to 1.20878, saving model to cnn_item.keras\n",
      "64/64 - 14s - 216ms/step - accuracy: 0.6052 - loss: 1.0890 - val_accuracy: 0.5726 - val_loss: 1.2088 - learning_rate: 1.0000e-03\n",
      "Epoch 10/50\n",
      "\n",
      "Epoch 10: val_loss did not improve from 1.20878\n",
      "64/64 - 19s - 291ms/step - accuracy: 0.6062 - loss: 1.0568 - val_accuracy: 0.5328 - val_loss: 1.2363 - learning_rate: 1.0000e-03\n",
      "Epoch 11/50\n",
      "\n",
      "Epoch 11: val_loss improved from 1.20878 to 1.14342, saving model to cnn_item.keras\n",
      "64/64 - 22s - 347ms/step - accuracy: 0.6191 - loss: 1.0254 - val_accuracy: 0.5805 - val_loss: 1.1434 - learning_rate: 1.0000e-03\n",
      "Epoch 12/50\n",
      "\n",
      "Epoch 12: val_loss did not improve from 1.14342\n",
      "64/64 - 22s - 347ms/step - accuracy: 0.6107 - loss: 1.0341 - val_accuracy: 0.5746 - val_loss: 1.1743 - learning_rate: 1.0000e-03\n",
      "Epoch 13/50\n",
      "\n",
      "Epoch 13: val_loss did not improve from 1.14342\n",
      "64/64 - 22s - 343ms/step - accuracy: 0.6556 - loss: 0.9526 - val_accuracy: 0.5825 - val_loss: 1.1448 - learning_rate: 1.0000e-03\n",
      "Epoch 14/50\n",
      "\n",
      "Epoch 14: val_loss did not improve from 1.14342\n",
      "64/64 - 22s - 344ms/step - accuracy: 0.6462 - loss: 0.9665 - val_accuracy: 0.5447 - val_loss: 1.2278 - learning_rate: 1.0000e-03\n",
      "Epoch 15/50\n",
      "\n",
      "Epoch 15: val_loss did not improve from 1.14342\n",
      "64/64 - 22s - 349ms/step - accuracy: 0.6492 - loss: 0.9483 - val_accuracy: 0.5726 - val_loss: 1.2046 - learning_rate: 1.0000e-03\n",
      "Epoch 16/50\n",
      "\n",
      "Epoch 16: val_loss improved from 1.14342 to 1.10867, saving model to cnn_item.keras\n",
      "64/64 - 22s - 352ms/step - accuracy: 0.6551 - loss: 0.9343 - val_accuracy: 0.5825 - val_loss: 1.1087 - learning_rate: 1.0000e-03\n",
      "Epoch 17/50\n",
      "\n",
      "Epoch 17: val_loss did not improve from 1.10867\n",
      "64/64 - 23s - 352ms/step - accuracy: 0.6487 - loss: 0.9626 - val_accuracy: 0.5706 - val_loss: 1.1510 - learning_rate: 1.0000e-03\n",
      "Epoch 18/50\n",
      "\n",
      "Epoch 18: val_loss did not improve from 1.10867\n",
      "64/64 - 22s - 344ms/step - accuracy: 0.6655 - loss: 0.9152 - val_accuracy: 0.5984 - val_loss: 1.1122 - learning_rate: 1.0000e-03\n",
      "Epoch 19/50\n",
      "\n",
      "Epoch 19: val_loss did not improve from 1.10867\n",
      "64/64 - 22s - 345ms/step - accuracy: 0.6660 - loss: 0.8904 - val_accuracy: 0.5905 - val_loss: 1.1396 - learning_rate: 1.0000e-03\n",
      "Epoch 20/50\n",
      "\n",
      "Epoch 20: val_loss improved from 1.10867 to 1.07271, saving model to cnn_item.keras\n",
      "64/64 - 23s - 352ms/step - accuracy: 0.6670 - loss: 0.8836 - val_accuracy: 0.5964 - val_loss: 1.0727 - learning_rate: 1.0000e-03\n",
      "Epoch 21/50\n",
      "\n",
      "Epoch 21: val_loss did not improve from 1.07271\n",
      "64/64 - 22s - 350ms/step - accuracy: 0.6789 - loss: 0.8747 - val_accuracy: 0.6064 - val_loss: 1.1234 - learning_rate: 1.0000e-03\n",
      "Epoch 22/50\n",
      "\n",
      "Epoch 22: val_loss did not improve from 1.07271\n",
      "64/64 - 22s - 345ms/step - accuracy: 0.6887 - loss: 0.8675 - val_accuracy: 0.5785 - val_loss: 1.2263 - learning_rate: 1.0000e-03\n",
      "Epoch 23/50\n",
      "\n",
      "Epoch 23: val_loss improved from 1.07271 to 1.06663, saving model to cnn_item.keras\n",
      "64/64 - 22s - 347ms/step - accuracy: 0.6724 - loss: 0.8965 - val_accuracy: 0.6163 - val_loss: 1.0666 - learning_rate: 1.0000e-03\n",
      "Epoch 24/50\n",
      "\n",
      "Epoch 24: val_loss improved from 1.06663 to 1.02116, saving model to cnn_item.keras\n",
      "64/64 - 17s - 260ms/step - accuracy: 0.6873 - loss: 0.8614 - val_accuracy: 0.6243 - val_loss: 1.0212 - learning_rate: 1.0000e-03\n",
      "Epoch 25/50\n",
      "\n",
      "Epoch 25: val_loss did not improve from 1.02116\n",
      "64/64 - 14s - 216ms/step - accuracy: 0.6952 - loss: 0.8379 - val_accuracy: 0.5765 - val_loss: 1.1141 - learning_rate: 1.0000e-03\n",
      "Epoch 26/50\n",
      "\n",
      "Epoch 26: val_loss did not improve from 1.02116\n",
      "64/64 - 14s - 219ms/step - accuracy: 0.7095 - loss: 0.8018 - val_accuracy: 0.6203 - val_loss: 1.0263 - learning_rate: 1.0000e-03\n",
      "Epoch 27/50\n",
      "\n",
      "Epoch 27: val_loss did not improve from 1.02116\n",
      "64/64 - 14s - 219ms/step - accuracy: 0.7110 - loss: 0.8197 - val_accuracy: 0.6064 - val_loss: 1.0684 - learning_rate: 1.0000e-03\n",
      "Epoch 28/50\n",
      "\n",
      "Epoch 28: val_loss did not improve from 1.02116\n",
      "64/64 - 14s - 222ms/step - accuracy: 0.6887 - loss: 0.8444 - val_accuracy: 0.6143 - val_loss: 1.1009 - learning_rate: 1.0000e-03\n",
      "Epoch 29/50\n",
      "\n",
      "Epoch 29: val_loss improved from 1.02116 to 1.01769, saving model to cnn_item.keras\n",
      "64/64 - 14s - 217ms/step - accuracy: 0.7194 - loss: 0.7889 - val_accuracy: 0.6262 - val_loss: 1.0177 - learning_rate: 1.0000e-03\n",
      "Epoch 30/50\n",
      "\n",
      "Epoch 30: val_loss improved from 1.01769 to 0.98448, saving model to cnn_item.keras\n",
      "64/64 - 14s - 216ms/step - accuracy: 0.7273 - loss: 0.7512 - val_accuracy: 0.6342 - val_loss: 0.9845 - learning_rate: 1.0000e-03\n",
      "Epoch 31/50\n",
      "\n",
      "Epoch 31: val_loss did not improve from 0.98448\n",
      "64/64 - 13s - 211ms/step - accuracy: 0.7297 - loss: 0.7571 - val_accuracy: 0.6441 - val_loss: 1.0491 - learning_rate: 1.0000e-03\n",
      "Epoch 32/50\n",
      "\n",
      "Epoch 32: val_loss did not improve from 0.98448\n",
      "64/64 - 13s - 209ms/step - accuracy: 0.7302 - loss: 0.7383 - val_accuracy: 0.6163 - val_loss: 1.0345 - learning_rate: 1.0000e-03\n",
      "Epoch 33/50\n",
      "\n",
      "Epoch 33: val_loss did not improve from 0.98448\n",
      "64/64 - 13s - 210ms/step - accuracy: 0.7263 - loss: 0.7709 - val_accuracy: 0.6123 - val_loss: 1.0775 - learning_rate: 1.0000e-03\n",
      "Epoch 34/50\n",
      "\n",
      "Epoch 34: val_loss improved from 0.98448 to 0.97360, saving model to cnn_item.keras\n",
      "64/64 - 20s - 305ms/step - accuracy: 0.7421 - loss: 0.7311 - val_accuracy: 0.6461 - val_loss: 0.9736 - learning_rate: 1.0000e-03\n",
      "Epoch 35/50\n",
      "\n",
      "Epoch 35: val_loss did not improve from 0.97360\n",
      "64/64 - 22s - 346ms/step - accuracy: 0.7416 - loss: 0.7314 - val_accuracy: 0.6620 - val_loss: 1.0160 - learning_rate: 1.0000e-03\n",
      "Epoch 36/50\n",
      "\n",
      "Epoch 36: val_loss did not improve from 0.97360\n",
      "64/64 - 22s - 342ms/step - accuracy: 0.7441 - loss: 0.7015 - val_accuracy: 0.6660 - val_loss: 0.9952 - learning_rate: 1.0000e-03\n",
      "Epoch 37/50\n",
      "\n",
      "Epoch 37: val_loss did not improve from 0.97360\n",
      "64/64 - 15s - 241ms/step - accuracy: 0.7302 - loss: 0.7645 - val_accuracy: 0.6362 - val_loss: 1.1333 - learning_rate: 1.0000e-03\n",
      "Epoch 38/50\n",
      "\n",
      "Epoch 38: val_loss did not improve from 0.97360\n",
      "64/64 - 13s - 209ms/step - accuracy: 0.7470 - loss: 0.7151 - val_accuracy: 0.6302 - val_loss: 1.0251 - learning_rate: 1.0000e-03\n",
      "Epoch 39/50\n",
      "\n",
      "Epoch 39: val_loss did not improve from 0.97360\n",
      "\n",
      "Epoch 39: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
      "64/64 - 14s - 212ms/step - accuracy: 0.7307 - loss: 0.7584 - val_accuracy: 0.5924 - val_loss: 1.1165 - learning_rate: 1.0000e-03\n",
      "Epoch 40/50\n",
      "\n",
      "Epoch 40: val_loss improved from 0.97360 to 0.95296, saving model to cnn_item.keras\n",
      "64/64 - 13s - 209ms/step - accuracy: 0.7619 - loss: 0.6666 - val_accuracy: 0.6640 - val_loss: 0.9530 - learning_rate: 5.0000e-04\n",
      "Epoch 41/50\n",
      "\n",
      "Epoch 41: val_loss improved from 0.95296 to 0.91792, saving model to cnn_item.keras\n",
      "64/64 - 14s - 216ms/step - accuracy: 0.7683 - loss: 0.6419 - val_accuracy: 0.6779 - val_loss: 0.9179 - learning_rate: 5.0000e-04\n",
      "Epoch 42/50\n",
      "\n",
      "Epoch 42: val_loss improved from 0.91792 to 0.89763, saving model to cnn_item.keras\n",
      "64/64 - 14s - 215ms/step - accuracy: 0.7816 - loss: 0.6069 - val_accuracy: 0.6859 - val_loss: 0.8976 - learning_rate: 5.0000e-04\n",
      "Epoch 43/50\n",
      "\n",
      "Epoch 43: val_loss did not improve from 0.89763\n",
      "64/64 - 14s - 219ms/step - accuracy: 0.7816 - loss: 0.6332 - val_accuracy: 0.6839 - val_loss: 0.9081 - learning_rate: 5.0000e-04\n",
      "Epoch 44/50\n",
      "\n",
      "Epoch 44: val_loss did not improve from 0.89763\n",
      "64/64 - 14s - 215ms/step - accuracy: 0.7792 - loss: 0.5957 - val_accuracy: 0.6620 - val_loss: 0.9444 - learning_rate: 5.0000e-04\n",
      "Epoch 45/50\n",
      "\n",
      "Epoch 45: val_loss did not improve from 0.89763\n",
      "64/64 - 14s - 211ms/step - accuracy: 0.7871 - loss: 0.6084 - val_accuracy: 0.6799 - val_loss: 0.9718 - learning_rate: 5.0000e-04\n",
      "Epoch 46/50\n",
      "\n",
      "Epoch 46: val_loss did not improve from 0.89763\n",
      "64/64 - 13s - 207ms/step - accuracy: 0.7880 - loss: 0.6043 - val_accuracy: 0.6740 - val_loss: 0.9379 - learning_rate: 5.0000e-04\n",
      "Epoch 47/50\n",
      "\n",
      "Epoch 47: val_loss improved from 0.89763 to 0.86591, saving model to cnn_item.keras\n",
      "64/64 - 13s - 208ms/step - accuracy: 0.7698 - loss: 0.6165 - val_accuracy: 0.7018 - val_loss: 0.8659 - learning_rate: 5.0000e-04\n",
      "Epoch 48/50\n",
      "\n",
      "Epoch 48: val_loss did not improve from 0.86591\n",
      "64/64 - 13s - 207ms/step - accuracy: 0.7871 - loss: 0.5865 - val_accuracy: 0.7018 - val_loss: 0.8730 - learning_rate: 5.0000e-04\n",
      "Epoch 49/50\n",
      "\n",
      "Epoch 49: val_loss did not improve from 0.86591\n",
      "64/64 - 13s - 204ms/step - accuracy: 0.7806 - loss: 0.5961 - val_accuracy: 0.6879 - val_loss: 0.8978 - learning_rate: 5.0000e-04\n",
      "Epoch 50/50\n",
      "\n",
      "Epoch 50: val_loss did not improve from 0.86591\n",
      "64/64 - 13s - 205ms/step - accuracy: 0.7826 - loss: 0.5806 - val_accuracy: 0.6561 - val_loss: 0.9540 - learning_rate: 5.0000e-04\n",
      "Restoring model weights from the end of the best epoch: 47.\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(train_generator,\n",
    "        batch_size = 32,\n",
    "        steps_per_epoch=len(train_generator),\n",
    "        epochs=15,\n",
    "        validation_data=(validation_generator),\n",
    "        validation_steps=len(validation_generator),\n",
    "        callbacks = [checkpointer, reduce_lr, early_stopping, WandbCallback()],\n",
    "        verbose=2, \n",
    "        shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Learning Rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "b0n5b96i-qES"
   },
   "outputs": [],
   "source": [
    "def plot_learning_curves(history):\n",
    "    # Convert the history.history dict to a pandas DataFrame\n",
    "    df = pd.DataFrame(history.history)\n",
    "\n",
    "    # Add Epoch Column to dataframe\n",
    "    df['epoch'] = range(1, len(df) + 1)\n",
    "\n",
    "    # Set the style of seaborn for better visualization\n",
    "    sns.set(rc={'axes.facecolor': '#f0f0fc'}, style='darkgrid')\n",
    "\n",
    "    # Plotting the learning curves\n",
    "    plt.figure(figsize=(15, 6))\n",
    "\n",
    "    # Plotting the training and validation loss\n",
    "    plt.subplot(1, 2, 1)\n",
    "    sns.lineplot(x=df['epoch'], y=df['loss'], color='royalblue', label='Train Loss')\n",
    "    sns.lineplot(x=df['epoch'], y=df['val_loss'], color='orangered', linestyle='--', label='Validation Loss')\n",
    "    plt.title('Loss Evolution')\n",
    "    plt.xlabel('Epoch')\n",
    "\n",
    "    # Plotting the training and validation accuracy\n",
    "    plt.subplot(1, 2, 2)\n",
    "    sns.lineplot(x=df['epoch'], y=df['accuracy'], color='royalblue', label='Train Accuracy')\n",
    "    sns.lineplot(x=df['epoch'], y=df['val_accuracy'], color='orangered', linestyle='--', label='Validation Accuracy')\n",
    "    plt.title('Accuracy Evolution')\n",
    "    plt.xlabel('Epoch')\n",
    "\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 573
    },
    "id": "ZK4QansW-vk1",
    "outputId": "a363e7ec-ce93-4570-b4b1-fe91c2fcbbae"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_learning_curves(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Model Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "7hGqgUjIAkQC"
   },
   "outputs": [],
   "source": [
    "def evaluate_model_performance(model, val_generator, class_labels):\n",
    "    # Getting all the true labels for the validation set\n",
    "    true_labels = validation_generator.classes\n",
    "\n",
    "    # Get the class labels (names) from the generator\n",
    "    class_labels = list(validation_generator.class_indices.keys())\n",
    "\n",
    "    # To get the predicted labels, we predict using the model\n",
    "    predictions = model.predict(validation_generator, steps=len(validation_generator))\n",
    "\n",
    "    # Take the argmax to get the predicted class indices.\n",
    "    predicted_labels = np.argmax(predictions, axis=1)\n",
    "\n",
    "    # Extracting true labels from the validation generator\n",
    "    true_labels = validation_generator.classes\n",
    "\n",
    "    # Classification report\n",
    "    report = classification_report(true_labels, predicted_labels, target_names=class_labels)\n",
    "    print(report)\n",
    "    print('\\n')\n",
    "\n",
    "    # Define a custom colormap\n",
    "    colors = [\"white\", \"royalblue\"]\n",
    "    cmap_cm = LinearSegmentedColormap.from_list(\"cmap_cm\", colors)\n",
    "\n",
    "    # Confusion Matrix\n",
    "    cm = confusion_matrix(true_labels, predicted_labels)\n",
    "\n",
    "    # Plotting confusion matrix using seaborn\n",
    "    plt.figure(figsize=(8,6))\n",
    "    sns.heatmap(cm, annot=True, cmap=cmap_cm, fmt='d', xticklabels=class_labels, yticklabels=class_labels)\n",
    "    plt.xlabel('Predicted Labels')\n",
    "    plt.ylabel('True Labels')\n",
    "    plt.title('Confusion Matrix')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "tIAuhFSqA9lx"
   },
   "outputs": [],
   "source": [
    "class_labels = list(validation_generator.class_indices.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 868
    },
    "id": "aoB1kTvDAl0D",
    "outputId": "e0a72d52-e580-439a-98d5-78c18d250098"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 46ms/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "   cardboard       0.90      0.70      0.79        80\n",
      "       glass       0.59      0.81      0.68       100\n",
      "       metal       0.64      0.76      0.69        82\n",
      "       paper       0.80      0.91      0.85       118\n",
      "     plastic       0.77      0.35      0.49        96\n",
      "       trash       0.45      0.48      0.46        27\n",
      "\n",
      "    accuracy                           0.70       503\n",
      "   macro avg       0.69      0.67      0.66       503\n",
      "weighted avg       0.72      0.70      0.69       503\n",
      "\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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s2AFdXV3Y2tpi1apVWLNmDfz8/DB48GCxwyMiIqI8SFN7ml++fIlJkybh4sWLKFGihNyy7du3Q19fH1OnToWenh4cHR0RFRWFFStWoHXr1khOTsbq1asxYsQI1K5dGwAwf/581KhRA0eOHEHTpk2zFINGVCABwNXVFS4uLnj79i1cXFywdOlSTJw4ERYWFmKHRkRERKQx7ty5A319fezduxcVKlSQW3blyhVUqVIFenr/rxFWrVoVjx8/xuvXrxEWFobPnz/D29tbttzc3Bxly5bF5cuXsxyDRlQgAWD9+vVYsWIF3rx5AwAoWLAgAgIC0K1bN3EDIyIiojxJnQVIPz+/by4/fvy4wmW+vr7w9fXNdFlMTAycnJzk2goXLgwAePHihez6Eltb2wzrKHPtiUYkkFu3bkVQUBA6duwIT09PCIKAy5cvY968eTA1NUWbNm3EDpGIiIhI4yUmJsLAwECuzdDQEACQlJSEhIQEAMh0nQ8fPmT5dTQigVy7di1Gjx6Nzp07y9rq1asHe3t7rFu3LlclkKkpSVgwvg6k0jS5dn2DfBg64yQA4E3sY5zcvwhPI65CR0cPxUt6oE6zIbCwKipCxNpDKpVi+/Zt2LplM55GR8OqQAH4+vph4KDBMDU1FTs8rRH7Mgadf2qB2UHBqFipSpaXUUZ8T6oOj6VqXLl8CX16/axweZ++A9G774AcjEizqXMM5LcqjD/CyMgow8UwSUlJAABjY2MYGRkBAJKTk2U/f1knX758WX4djUggnz9/jpo1a2Zor1GjBgIDA0WISH1exTyCVJqGJj9NgeV/EkKJji4A4OP7l9i0uBcKFLJHs07TkJKchDOHl2H7ysHoPnwT9PWNFG06zwsJWYWFvy9A9x4BqFrVG1GPI7Fw4UI8ePAAq0JW54p5t9Tt5csXGDq4Fz59ilNqGWWO70nV4bFUDZcyZbF2/ZYM7UsW/447d26jQaPGIkRFqmRjY4PY2Fi5ti+/W1tbIzU1VdZmZ2cnt46zs3OWX0cjEsgiRYrg9u3bcjsCAKGhoShYsKBIUalH7PNw6OjowtnNF3p6BhmWnzuyEoZGpmjfexH0DdKTxfwFimDX2hGIeXoPxUt65HTIWkEqlSJk1Uq0a9cew4YNBwBUq1YNFhaWGD58KO7cuY1y5cqLHKXmkkqlOHhgDxb9HgRBELK8jBTje1J1eCxVx9TUFOXd3OXaTp08gUsXLyAwaAHs7R3ECUxTaeHfJZ6enti6dSvS0tKgq5tenLpw4QIcHBxgZWUFMzMzmJqa4uLFi7K86+PHj7h7965cT/D3aMRV2B06dMCUKVOwefNmhIWFISwsDJs2bcLUqVPRunVrscNTqdjn4ShQuESmyaMgCAgP/RvlPZvJkkcAsC1eBgMm7Gfy+A2fPn1Cs+Yt0OSr6QccSpYEADx9otwEqXnNw4f3ETR7Cho2boGJU2ZneRkpxvek6vBYqk9iYiLmBE5H9Rq1ULdeA7HDIRVo3bo1Pn36hHHjxuHhw4fYtWsX1q5diz59+gBIH/vYuXNnzJ07F8ePH0dYWBiGDh0KGxsb1K9fP8uvoxEVyK5du+LZs2eYOXMm0tLSIAgC9PT00KFDB/Tr10/s8FTqSwVy+4pBePb4FnT1DODs5ovaTQcjIf4DkhI/Ib+lDY7umoN7N44iJSURDk5eqOc/EmYW1mKHr7HMzc0xbtz4DO3Hjx8DAJQqVSqnQ9IqNta22L7zEApb2+Da1UtZXkaK8T2pOjyW6rNl8wa8io3FsuVrxA5FI2lhARJWVlZYtWoVZsyYAX9/fxQqVAijRo2Cv7+/bJ3BgwcjNTUV48ePR2JiIjw9PRESEgJ9ff0sv45GJJA6OjoYN24chgwZgkePHgFIv6VhbhsYLQgCXr14CAGAW5Xm8K7bAy+e3sU/R1fhTWwk6jQdAgA4eWAxbIuXRbNO0xD/6R1OH1yCLcv7o9vQjTAwyPoA17zu5s2bWLVyBerUqYPSX01pQPLM81vAPL/yy0g5fE+qDo/lj0tJScaWzetRv0FjFLezFzscjaQNQ2tnz87YM+Tm5oZt27YpfI6uri5GjhyJkSNHZvt1NSKBBNKv/jl06BAePHgAAwMDODk5oVGjRnITYWo9QUCr7nNhbGKJgjbpXS/FS3rAxMwK+7dMQuT9CwAAE9MC8O8aCIlO+ggDy4LFsHFRT9y9dgjuVf0Vbp7+79q1q+jfry+KFiuGGTNmiR0OEd+TKsRjqRrHjh7Bm9ev0fXnHmKHQlpII8ZAhoWFwc/PDzNmzMDly5dx+vRpjB8/Hs2aNUN0dLTY4amMREcHdo6VZMnjF45lfABAdoFCSRdvWfIIAEXsy8PQyBSxz8JzLlgtdvDAAQT06A5bW1usWb0WFpaWYodEeRzfk6rDY6k6x48dhqNjKTg5u4gdisaSqPGh7TQigZwxYwbKlSuH06dPY9euXdizZw/+/vtvFC5cGNOnTxc7PJWJ+/AKNy/uxsd38jO9p6akz89kmM8UkEiQmpqS4blSaRr09A1zJE5ttnp1CEaMGAZ3d3es37AJhf6dfZ9ILHxPqg6PpeqkpKTg/D/nULd+Q7FDIS2lEQnkzZs3MWLECJiZmcnaChQogDFjxuDChQsiRqZagjQNh/+YhRsX/pRrD7t5FBIdXZR0qYbiDh54EPo3UlP/Pwlo1IPLSElOQDEH9xyOWLts27YVc4PmoGHDRli+YpXc+4lIDHxPqg6PpWo9fBiOxMQEuLtXFDsUzSaRqO+h5TRigKG1tTViY2MzXEn34cMHWOai7glzSxuU92yKS6c2Qk/fEEXtyyM68iYunFiLitXaoEAhO9Rs1B9bl/XDHyFDUaVWJ3yOe4tTBxbB1s4VpVxriL0LGuvVq1cInD0LRYsWRcdOnXDv7l255cXt7FCgQAGRoqO8iO9J1eGxVL2HDx4AABxKOoocCWkr0RLI58+fy37u2rUrxo8fj4kTJ6JixYrQ0dHBnTt3MGnSJAwZMkSsENWiXqvRyF+gKO5cO4jzx9fALH9hVG/QG1VqpU/eWbREeXTouwSnDy3F7vVjoK9vhFLlaqFO08HQ+fduNZTR6dOnkJiYiGfPnqFL504Zls+YOQv+/q1EiIzyKr4nVYfHUvXevnkNADDnFAvflAsKhWojEUS6tYSLi4vcrae+hPF1m0Qiwb1793749UL2vv/hbRDQramF2CHkGu/j0r6/EmWJhRn/uCLNkpAkFTuEXMM0n3ij7VqPeaa2be+cXfT7K2kw0SqQ69at471LiYiISGMxS1FMtATSy8tLrJcmIiIi+i7WuRQTLYEcO3ZsltedNYsTxRIRERFpCtESyP9OEC4IAq5cuYKCBQuibNmy0NPTQ1hYGF6+fAk/Pz+xQiQiIiKiTIiWQG7YsEH289y5c2FtbY1Zs2bBwMAAAJCWloaJEydynCQRERGRhtGIicS3bduG/v37y5JHIP1G3wEBAThw4ICIkREREVFexXnEFdOIBFJfX19uXsgvIiIiYGxsLEJERERERKSIRtyJpmnTphg3bhx++eUXlCtXDlKpFNeuXUNwcDA6duwodnhERESUB+WCQqHaaEQCOWLECCQmJmLSpElITU2FIAgwNDRE586dMXDgQLHDIyIiIqL/0IgE8tatWxg/fjxGjx6NyMhISCQSODg4sPuaiIiIRJMbxiqqi0aMgRw0aBAePHgAExMTlCtXDq6urkweiYiISFy8ikYhjUggCxQogLi4OLHDICIiIqIs0Igu7Jo1a6JPnz6oVasW7O3tYWhoKLec4yCJiIgop2l/nVB9NCKBPHz4MKysrHD79m3cvn1bbplEImECSURERKRBNCKBPHHihNghEBEREcnJBUMV1UYjxkAqkpycjKtXr4odBhERERH9h0ZUIG/fvo0JEyYgPDwcUqk0w/J79+6JEBURERHlZSxAKqYRFchZs2ZBV1cX48ePh76+PiZMmICff/4Zenp6mDdvntjhEREREdF/aEQF8u7du1i3bh3c3Nywa9cuODk5oWPHjrCxscH27dvRqFEjsUMkIiKivIYlSIU0ogIplUpRqFAhAIC9vT3Cw8MBAH5+fggLCxMzNCIiIsqjOI+4YhqRQNrb28sulilZsiRCQ0MBAHFxcUhOThYzNCIiIiL6ikZ0YXfp0gW//vorAKBBgwZo0aIFjIyMcO3aNbi7u4sbHBEREeVJuaBQqDYakUC2bdsWlpaWsLCwgKOjI2bNmoVly5ahePHimDBhgtjhEREREdF/aEQX9p07dzBu3DgcP34cANCsWTMkJCTg9u3biI+PFzk6IiIiyos4BlIxjUggZ8+eDV9fXwwdOlTWdvToUdSoUQOzZs0SMTIiIiIi+ppGJJC3b99G//79YWBgIGvT1dVF7969cfPmTREjIyIiIqKvaUQCaWJigqdPn2Zoj42NlUsqiYiIiEh8GpFANmjQAFOmTMH58+fx+fNnfP78GRcuXMCUKVNQr149scMjIiKiPEgikajtoe004irs4cOH48mTJ+jevbvcQa1Xrx5GjRolYmRERESUV+WCPE9tNCKBNDY2xsqVKxEZGYnw8HDo6enB0dERJUqUEDs0IiIiIvqKRiSQXzg4OMDBwUHsMIiIiIg4kfg3aMQYSCIiIiLSHhpVgSQiIiLSGCxBKpRnEsiujfOLHUKuMHzBS7FDyDUm9y4kdgi5hg4/5FUiKUUQO4Rcw8SIHXyUu+WZBJKIiIhIGfzbVDH+iURERERESmEFkoiIiCgTnAdSMSaQRERERJlg/qgYu7CJiIiISCmsQBIRERFlhiVIhViBJCIiIiKlsAJJRERElAkJr6JRiBVIIiIiIlIKK5BEREREmWD9UTFWIImIiIhIKaxAEhEREWWCQyAVYwWSiIiIiJTCBJKIiIiIlMIubCIiIqJMsAtbMVYgiYiIiEgprEASERERZYIVSMVYgSQiIiIipTCBJCIiIiKlMIEkIiIiIqVwDCQRERFRJjgGUjFWIImIiIhIKRqZQL59+xaHDh3C06dPxQ6FiIiI8iiJGh/aTiMSyPDwcDRo0ACXL1/Gx48f0bx5c/zyyy9o0qQJLly4IHZ4RERElAdJJBK1PbSdRiSQgYGBsLe3R8mSJfHXX38hNTUVp06dQkBAABYsWCB2eERERET0HxqRQF6/fh2jR4+GlZUVzpw5g1q1asHa2hqtWrVCWFiY2OERERFRXsQ+bIU0IoHU0dGBgYEBUlNTcenSJXh7ewMAPn/+DCMjI5GjIyIiIqL/0ohpfNzd3bF8+XIUKFAASUlJqFmzJl6+fIl58+bB3d1d7PCIiIgoD8oFhUK10YgK5IQJE3D37l1s2bIFv/76KwoUKIAVK1YgIiICo0aNEjs8IiIiIvoPjahA2tvbY9euXXJtAwYMwK+//gpdXV2RoiIiIqK8LBdcLK02GlGBBIDnz5/j06dPAIALFy5g0aJFOHjwoMhRqZ9UKsWaNSFo3KgBKlWsgFb+LfDXX/vEDksrVC2fD6O7WiFwUGGM/dkK1Svky3Q9HQnwy0+WaOhtksMRaiepVIqN61ejdYsGqOFdAe1bN8GObZvEDkvrxcTEwNOzMi5evCh2KFopKSkJlTzKoUJ5F7lH1SoVxQ5Nq/F9SdmlERXIo0ePYujQoVi+fDmKFy+Onj17onjx4ti1axc+fPiATp06iR2i2iwKXojVq0MwcOAglCtfHmdOn8KY0SOhI5GgcZOmYoensaqWy4cO9cxx+no8Qv9OgmMxfbTyNYOengQnr8bL1tPTBTo1zI8StgYIe5wsYsTa4/f5gdi6eT1atW6PWnXq4Vn0EyxfuhDPn0VjyLDRYoenlV68eIGePQMQFxcndiha6+HDcKSmpmLmrCAUL15c1q7DXqps4/vy+1iAVEwjEsglS5YgICAA3t7eWLp0KYoUKYL9+/fj0KFDCA4OzrUJZEJCAjZsWI/OXbqgZ6/eAICqVb1x5+4dbNq0gQnkN3iVM0JEdDJ2/Z3+wffgaTIKW+qhhruxLIEsWVQfrX3NYGHKL5isev/uHXZs24QWLdtg9K+T/231gbW1DUYOH4gW/m1RwqGkmCFqFalUit27d2POnEAIgtjRaLf7YWHQ09NDvfoNYGBgIHY4Wo3vSyUwg1RII7qwIyIi0K5dO+jo6ODcuXOoVasWdHR04O7ujmfPnokdntoYGBhg46Yt6PZzd7l2fX19JCWxWvYteroSJCbLf/J9TpTCxOj/Z3vPFhZ491GKuRvf5HR4WuvJk8dIS0tD9Zp15NoreXpBKpXi/PkzIkWmne7fv4/JkyehRYuWmDNnjtjhaLWwsHsoUcKByaMK8H2p/VJTU/H777+jTp068PDwQKdOnXDjxg3Z8nv37qFz585wd3eHr68v1q9fr/IYNCKBNDc3R1xcHOLi4nDr1i1Uq1YNAPDkyRNYWFiIG5wa6erqwtnZGQULFYIgCHj9+jVWrVyBC+fPo0OHn8QOT6Odvh4PlxIGqFTGCEYGErjYG8CzrBEu30uUrRO8/R1W7XmPd3FSESPVLl/Ot5gXz+Xao6PT70v/PDo6p0PSara2tjhy5CjGjh3LOW1/0P37YdDV00Of3j3gVcUDNXy8MHXKRHz+/Ens0LQO35dZp6nziC9duhQ7duzAtGnTsHv3bjg4OKBnz56IjY3Fu3fv0L17d9jZ2WHnzp0YMGAA5s6di507d/7gq8rTiC7sWrVqYeLEiTAxMYGZmRl8fHzwzz//YPLkyahdu7bY4eWIgwf2Y9SoEQCAmrVqoWmz5iJHpNmuhSWiVDEDdGmUX9Z273ES/jz5/7E8L16nihGaVrOzd0AF90pYuXwRChe2RmXPqnj27ClmzZgEAwMDJCTGf38jJJOb/wDOSYIg4EH4fQiCgFat2qB37364fScUy5cuxqNHEVi9ZgN0dDSiHqIV+L7UfseOHUPTpk1RvXp1AMCYMWOwY8cO3LhxA5GRkdDX18fUqVOhp6cHR0dHREVFYcWKFWjdurXKYtCIM27ChAmoWLEijI2NsXTpUhgYGODq1atwd3fPM/NAlivvhrXrNuDXX8fjxvXr6NunJwQOTlGoZwsLVHAyxJ7TcQje/hZ/nPiI4tb66N40//efTN80a84CuHtUxuiRg+FXuwoG9O2Olv5tkT+/BYyMMr/SnUidBEHA78FLsGHTNnT4qRMqVfbEzz/3wLjxk3H92lX8c+6s2CFSLiWRqO/xI6ysrPD3338jOjoaaWlp2LZtGwwMDODi4oIrV66gSpUq0NP7f42watWqePz4MV6/fv2DR+T/NKICaWRkhDFjxsi1DRo0SKRoxGFnZwc7OztUruwJE1NTjPt1DK5evYLKlT3FDk3jlLDVRxkHQ2w98hEXbicAACKiU/DmQxr6+FuirEMC7kZyDGl2WVkVRNC8RYiL+4hXr2JRrJgddHR0EDhrCszNmaBTztPR0YGnp1eG9ho1awFI796uXqNmTodFJJpx48ZhyJAh8PPzg66uLnR0dBAcHAw7OzvExMTAyclJbv3ChQsDSL/yvmDBgiqJQSMSSAAICwtDeHg4pNL08WqCICA5ORmhoaGYPn26yNGpx9u3b3HmzGlUr14DVlZWsvayZcsCAGJjY8UKTaMVME+/qjryuXySGBGdAgCwLajHBPIHHDm8Hw4lS6F0aWeYmZkDAO7dvQ2pVApnl7IiR0d5UWzsS5w5fQrVfKrD1raIrD0pKX3Ms2WBAmKFRrmcRI0zifv5+X1z+fHjxxUue/jwIczMzLB48WJYW1tjx44dGDFiBDZu3IjExMQMF5sZGhoCSJ9PVVU0IoFcs2YNAgMDAaT/Z33pupVIJKhcubKYoalVUmIixv06BkN+GYpevfrI2v85dw4A4OzkLFZoGu3l2/SxjSWLGuDl2wRZe8ki+gCA1+/TRIkrt1gTsgyOpZwwfeZvsrYtm9bB1NQMlSpVETEyyqvS0tIwdcpE9OzVB4MGD5W1Hz50ELq6uqhYsZKI0RHlrBcvXmD48OFYu3atLEcqX748Hj58iODgYBgZGSE5Wb6I8iVxNDY2VlkcGpFAbtq0Cb169cLAgQNRp04d/Pnnn3j//j2GDx/+3Qxdm9kWKQL/Vq2xbOkS6OnpoUyZsrh69QpCVq1Eq9Zt4FiqlNghaqRnr1JxIzwRLWuZwthIgqgXKbCx0kNDb1M8iUlB6EPV/YWVF7Xr0AWBMyfD0bE0yrt54OiRAzh86C+MHjsJpmZmYodHeZCtbRG0aNkKa9eshqGhESpUcMf161exauVydPipE0qUcBA7RCKlfavC+C03b95ESkoKypcvL9deoUIFnD59GkWKFMnQg/nld2tr6+wFmwmNSCBjYmLQtm1bGBoawsXFBaGhoahbty7GjBmD2bNno1u3bmKHqDYTJ0xCsWLF8ceO7Xj+/DlsbGwxcOBgdOveQ+zQNNqGAx9Qv6oJqrkZo5G3Dt7FpeHinQQcvvAZUl579EP8W7VDUmIidmzbhLWrV8DevgSmzpiLBg2biB0a5WHjJ0xGsWLF8ddfe7ByxVJYW9ug/4DB6NY9QOzQKBfTxHth29jYAEifz9PNzU3WHh4ejhIlSqBChQrYunUr0tLSoPvvnZouXLgABwcHueFyP0oiaMClvlWqVMG2bdvg4OCAyZMnw8bGBn379sWLFy/QuHFjXL9+/YdfIyVV9N3MFUYu5LhMVZncu5DYIeQa+U00YkIJrZeUws9JVTHU18DMQ0uJmcQN/11933m/DSmcredJpVJ07twZ7969w6RJk2BjY4Pdu3djxYoV2LJlC4oVK4ZGjRrB19cXPXv2xK1btzB58mRMmTIF/v7+KotfIz51K1asiBUrViAhIQFly5bFiRMnIJVKcfXqVZiYmIgdHhEREeVBmjiRuI6ODpYuXYqqVati7NixaNWqFS5cuIC1a9eiQoUKsLKywqpVqxAZGQl/f38sWrQIo0aNUmnyCGhIBTI8PBw9evRAt27d8NNPP6FZs2b4+PEjEhISEBAQgGHDhv3wa7ACqRqsQKoOK5CqwwqkarACqTqsQKqOmBXIEWqsQM7NZgVSU2jEGEgnJyccO3YM8fHxMDExwfbt27Fv3z7Y2tqiYcOGYodHREREeZAmjoHUFBqRQALpk4l/uSdnwYIF0b17d5EjIiIiIqLMiJZA+vr6ZnmCzuxe6k5ERESUbaxAKiRaAunv76/WGd6JiIiISD1ESyD/e6/rRYsWZbqORCKBvr4+9u7di5o1a8LCwiKHoiMiIqK8jmUuxTRiDOTly5dx+fJl6Ovrw8Eh/Y4CUVFRSExMhK2tLd6/fw9DQ0OsX78epUuXFjlaIiIiygvYUaqYRsx94ebmhkqVKuHEiRPYvXs3du/ejRMnTqBatWrw9/fHxYsXUbt2bcydO1fsUImIiIjyPI1IIP/44w/8+uuvcrfYsbS0xMiRI7F582bo6+sjICAA165dEzFKIiIiyks0cSJxTaERCWRqaipSUlIytCclJSExMREAYGBgAKlUmtOhEREREdFXNCKBrF69OqZMmYKoqChZW2RkJKZPn47q1asjLS0NW7ZsgbOzs4hREhERUZ4ikajvoeU04iKaCRMmoE+fPmjYsCHMzc0hCALi4uJQoUIFTJw4EWfOnMHWrVuxfPlysUMlIiIiyvM0IoEsUKAAtm/fjosXL+LevXvQ1dWFi4sLqlSpAgCoUKECTp8+DTMzM5EjJSIiorwiFxQK1UYjEkggfc7HqlWromrVqhmWWVpaihAREREREWVGYxJIIiIiIk3CAqRiTCCJiIiIMsEubMU04ipsIiIiItIeTCCJiIiISClMIImIiIhIKRwDSURERJQJjoFUjBVIIiIiIlIKK5BEREREmWABUjFWIImIiIhIKaxAEhEREWWCYyAVYwJJRERElBlmkAqxC5uIiIiIlMIKJBEREVEmWH9UjBVIIiIiIlIKK5BEREREmeAQSMVYgSQiIiIipbACSURERJQJFiAVYwWSiIiIiJSSZyqQ+nr8O0IVJgQUFDuEXGP9/g9ih5Br9GieX+wQcgUTI9YUiOQwdVAozySQRERERMrgRTSK8c9NIiIiIlIKK5BEREREmWABUjFWIImIiIhIKaxAEhEREWWCYyAVYwWSiIiIiJTCBJKIiIiIlMIEkoiIiIiUwjGQRERERJmQcBCkQkwgiYiIiDLB/FExdmETERERkVJYgSQiIiLKBAuQirECSURERERKYQWSiIiIKDMsQSrECiQRERERKYUVSCIiIqJMsACpGCuQRERERKQUViCJiIiIMsF5IBVjBZKIiIiIlMIKJBEREVEmWIBUjAkkERERUWaYQSokagL5/PnzLK9bpEgRNUZCRERERFklagLp6+sLyXdGqAqCAIlEgnv37uVQVERERET4bo6Sl4maQK5fv17MlyciIiKibBA1gaxSpYqYL09ERESkEOuPimnMRTRJSUnYtm0bwsPDkZaWJmtPTk7G7du3cfjwYRGjIyIiIqIvNCaBnD59Onbv3o2yZcsiNDQUHh4eiIqKwps3b9CtWzexwyMiIqI8hkMgFdOYicSPHz+OWbNmYdu2bShatCimTZuGv//+G35+fkhJSRE7PCIiIiL6l8YkkB8/fkTFihUBAKVKlcLdu3ehr6+PPn364O+//xY5OiIiIiL6QmMSyAIFCuDNmzcAgBIlSiA8PBwAYGlpidevX4sZGhEREeVBEon6HtpOYxLImjVrYsqUKXjw4AEqVaqEv/76C6Ghodi0aRNsbGzEDk+tzp49i9atW6NChQrw9fVFSEgIBEEQOyytEvsyBvV9vXDt6iWlllE6GytdtKptin6tLdCzRX7Uq2KMfIb//4QrVlgPreuYord/fgQ0z4/G1UyQ30RjPj400pXLl1DJvYzCx4pli8UOUWvFxMTA07MyLl68KHYoWo/HkrJLYy6iGTVqFMaMGYNLly6hY8eO2LZtG9q2bQs9PT0EBgaKHZ7a3LhxA3379kWjRo0wZMgQXL16FUFBQUhLS0Pv3r3FDk8rvHz5AkMH98KnT3FKLaN0hSx10aq2GZ6+TMH+s59gkk8H1dzyoamZLnYcj4NtQV20rGWKR89ScPjCZ+jrSlDF1Qht/Myw6dBHJCbzj53MuJQpi7Xrt2RoX7L4d9y5cxsNGjUWISrt9+LFC/TsGYC4OJ7TP4rH8vtyQ6VQXTQmgTQ3N8eSJUtkv69YsQL37t1DwYIFc/VM8MHBwShTpgyCgoIApFdiU1NTsWzZMnTt2hVGRkYiR6i5pFIpDh7Yg0W/B2Wo2H5rGcmrXiEfXr1Pw76zn2VtySkCalY0hrmJDiq5GOHtxzQc+Of/y1+8TkX3ZvlRxsEA1+8niRG2xjM1NUV5N3e5tlMnT+DSxQsIDFoAe3sHcQLTUlKpFLt378acOYHgKf1jeCxJFTSmD6pMmTJ4+/at7HeJRIKyZcsiOTkZ9evXFzEy9UlOTsbFixdRr149ufYGDRrg8+fPuHr1qkiRaYeHD+8jaPYUNGzcAhOnzM7yMvo/IwMJihbSw62H8klgxLMUrNn3AR8/SxHzJg03wuWXf04UkJwiIL+pbk6Gq9USExMxJ3A6qteohbr1Gogdjta5f/8+Jk+ehBYtWmLOnDlih6PVeCxJFbJdgYyPj4exsTEA4PDhw3j+/Dnq1KmDEiVKZHkbf/zxB/bu3Qsg/Z7XAwYMgL6+vtw6sbGxMDc3z26YGu3p06dISUnJcMzs7e0BAJGRkfDx8REhMu1gY22L7TsPobC1TYbxjd9aRv9X0EIXOjoSJCRJUb+qMUoWMQAARDxLxqlrCUhOEXDlXmKG5xUtpAcjQx28/ZCWYRllbsvmDXgVG4tly9eIHYpWsrW1xZEjR2FjY8Pxej+Ix5JUQekE8tGjR+jTpw+aNGmCX375BQsWLMDy5cshCAIWLFiA1atXo1KlSlnaVt26deWqbDY2Nhm6bJ2cnNCyZUtlw9QKX8admJqayrWbmJgAAD59+pTjMWkT8/wWMM+v/DL6vy8XytT1NEFUTAr+OvsJFmbpYyDzm+jijxMZx0YZGUjg62mMT/FS3HvM7uusSElJxpbN61G/QWMUt7MXOxytZGFhIXYIuQaPZdbl4hF0P0zpBHLu3LnQ09ODn58fkpOTsXnzZjRq1AhTp07FmDFjsGDBAmzYsCFL27KwsMCsWbNkv48bNy5DMpWbSaXSby7X0dGYEQaUS+nopH86xr5LxfHL8QCA6FggKVlAo2qmsLPWw5OXqbL1jY0kaFnLFCZGOvjzZBxSUjPdLH3l2NEjePP6Nbr+3EPsUIiIVELpDOXKlSsYPnw4ypcvj0uXLiEuLg7t27eHqakpOnTogNu3b2crkFmzZsHU1BSXL1/G1q1b8enTJzx8+BCpqbn3G8rMzAwA8PnzZ7n2L5XHvJRMkzhSUtJH0Ec+l7/bU1RM+nlXyPL/Yxyt8uugXV0zmObTwZ7TcXj5lt3XWXX82GE4OpaCk7OL2KEQkRIkanxoO6UrkCkpKbIxiadPn0a+fPlkXdZpaWnQ08vesMpPnz6hZ8+euHHjBiQSCXx8fDB37lw8efIEa9asgbW1dba2q8ns7Oygq6uLqKgoufYnT54AABwdHcUIi/KQ95/Sk0BdXfmPM91//7RM/TdHLFZYD018TJGcIuCPE3F4+/Hb1XP6v5SUFJz/5xx+7h4gdihEpKTcPAvMj1K6Aunk5IQjR47g1atXOHToEKpXrw49PT2kpKRg06ZNcHJyylYg8+bNAwAcPXpUNg5y5MiRMDQ0zLVXiRkaGqJy5co4evSo3FQzhw8fhpmZGdzc3ESMjvKCtx+l+PApDU52BnLtDkXSL2Z7/ioVhSx00ayGKT4lSLH92Ecmj0p6+DAciYkJcHevKHYoREQqo3QCOXjwYPzxxx+oWbMmPnz4gF69egFIn3rmwoULGDBgQLYC+fvvvzFq1CgUL15c1ubo6IiJEyfi/Pnz2dqmNujXrx9u3ryJIUOG4NSpU1iwYAFCQkLQp08f5MuXT+zwKA84dzMBtla6aOhtguLWeqhQ2hA1PYzx8GkyXr1Pg18VY+hIgAu3E2BmogMbK13Zg3ej+b6HDx4AABxKskeBSOuwD1shpfubfXx8sG/fPoSGhqJChQooWrQoAODnn39G1apV4ezsnK1A3r59i0KFCmVoNzc3R3x8fLa2qQ28vb0RHByMhQsXYsCAAbC2tsaoUaPQowcH21POeBidgn1nPqOKqxGa1TBFYrKA0IgkXAhNgLmJDgpbpn9MNPHJOCb3bmQSjl3KveenKrx98xoAYM5pAYgoF5EIGnKbjs6dO6NmzZro3bs3PDw8sHfvXhQvXhxTp05FWFgYNm/eLHaIBOAN5/1TmU2HPoodQq7RozmTM1UwMWJFmTSPmMMQg7e/U9u2B7WzVNu2c0KWKpBdu3bN8gYlEgnWrVundCDDhg1Djx49cOvWLaSmpmLp0qWIiIjAnTt3EBISovT2iIiIiHKr3bt3Y8WKFXj69Cns7OwwcOBANGrUCAAQHR2NadOm4fLlyzA2NkabNm0waNAg6Oqq7u5hWfpzUxCELD++N7ehIhUrVsTWrVthbGwMe3t73LhxAzY2Nti0aRO8vLyytU0iIiKi7JJI1Pf4EXv27MG4cePQqVMn7N+/H02bNsWwYcNw/fp1pKSkICAgfdaHrVu3YvLkydiyZQsWL16sgiPyfxrThU3agV3YqsMubNVhF7ZqsAubNJGYXdiLdqivC3tg2+x1YQuCAD8/PzRo0ACjR4+WtQcEBKBKlSooWrQoxo4di7NnzyJ//vTPxm3btmHOnDk4f/48DAwMFG1aKdm+F3ZERATOnTuHV69eoXPnznj69ClcXFyyPfl1cnIyduzYgfDwcCQnJ2dY/t871hARERGpmyZeLB0ZGYlnz56hWbNmcu1fhvtNnjwZrq6usuQRAKpWrYpPnz7h3r17qFChgkriUDqBlEqlmDhxInbu3AlBECCRSNCwYUMsWbIEUVFR2LRpE2xsbJQOZPTo0Th27BjKlCkDQ0NDpZ9PREREpFJqzCD9/Py+ufz48eOZtkdGRgIA4uPjERAQgLt376JYsWLo168ffH19ERMTkyEPK1y4MADgxYsX4iWQS5Yswb59+zB9+nTUrl0bPj4+ANIn/R4wYADmz5+PwMBApQM5ffo05s2bh3r16in9XCIiIqK84MvtjkePHo2BAwdixIgROHz4MPr37481a9YgMTFRdsfAL74U5pKSklQWh9IJ5M6dOzF48GC0bt0aaWn/Hw9XpkwZDB48GHPnzs1WIObm5nBwcMjWc4mIiIhUTZ1d2IoqjN+jr59+p7CAgAD4+/sDSM/B7t69izVr1sDIyCjDUMAviaOxsfEPRCxP6RHTr1+/RpkyZTJdZm1tjY8fs3dhQN++fTFr1iw8ffo0W88nIiIiyu2sra0BIMOto0uVKoXo6GjY2NggNjZWbtmX3788VxWUTiDt7e1x6tSpTJddunQJ9vb22QrEyckJt2/fRv369VGmTJkMDyIiIqKcpInT+Li6usLExAQ3b96Uaw8PD4ednR08PT1x9+5dWVc3AFy4cAEmJiZwcXHJ/gt/Reku7J9//hkTJ05ESkoK6tSpA4lEgqioKFy8eBGrV6/GmDFjshXIuHHjUKJECTRv3lylJVYiIiKi3MLIyAg9e/bE4sWLYW1tDTc3N+zfvx/nzp3D2rVr4e7ujgULFuCXX37BiBEjEB0djXnz5qFHjx4qm8IHyOY8kMuXL8fSpUuRlJSEL0/X19dHz549MWTIkGwF4ubmhr1796JEiRLZej7lDM4DqTqcB1J1OA+kanAeSNJEYs4DuWzXe7Vtu28rix96/po1a7Bx40a8fPkSjo6OGDRoEOrWrQsAiIqKwpQpU3DlyhXkz59fdicaHR3VnePZnkj806dPuH79Ot6/fw9zc3NUqFABFhYW2Q6kU6dO6N27N2rVqpXtbZD6MYFUHSaQqsMEUjWYQJImYgKpmbI9kbhUKpXNA2lgYCC7Kii7WrRogbFjx6JNmzYoXrx4hu21bNnyh7ZPRERERKqRrYnE58yZg82bNyMlJUXWhZ0vXz7069cPvXv3zlYgEydOBACsWLEiwzKJRMIEkoiIiHKUmNVPTad0Arl48WJs2LABnTt3Rr169WBlZYXXr1/jr7/+woIFC2BiYoJOnTopHUhYWJjSzyEiIiKinJeticT79euHgQMHytocHBzg6ekJU1NTrFmzJlsJJBEREZEmYQFSMaVHTL979w4eHh6ZLqtRowZevXr1w0ERERERkeZSOoH09vbGwYMHM132zz//oGLFij8cFBEREZHYNHEicU2RpS7s3bt3y352d3fHokWL8ObNGzRq1AiFChXC+/fvcerUKRw+fBjjxo1TV6xEREREpAGyNA+kMre+kUgkuHfv3g8FRZqL80CqDueBVB3OA6kanAeSNJGY1bqVe96rbdu9Wliobds5IUsVyOPHj6s7DiIiIiLSEllKIIsWLZrlDWbzxjZEREREGiUXDFVUm2zdiebAgQO4dOkSkpOTZQmjIAiIj4/HjRs3cPr0aZUGSURERJTTcsPFLuqidAK5aNEiLFq0CGZmZkhNTYW+vj709PTw9u1b6OjooG3btuqIk4iIiIg0hNIjpv/880+0bNkSly5dQrdu3VCnTh38888/+OOPP2BhYYHSpUurI04iIiKiHCVR40PbKZ1Avnz5Es2aNYNEIkGZMmVw/fp1AEC5cuXQt29f7NixQ+VBEhEREZHmULoL29jYGJJ/BwXY29sjOjoaiYmJMDIyQpkyZRAdHa3yIImIiIhyHAdBKqR0BbJ8+fKyicUdHBygq6uL8+fPAwAiIiJgYGCg0gCJiIiISLMoXYHs27cvunfvjo8fP2LZsmVo3rw5Ro8eDS8vL5w9exZ169ZVR5xEREREOYoFSMWUTiA9PT3xxx9/4P79+wCAiRMnQkdHB9euXUPDhg0xZswYlQdJRERERJojW/NAuri4yG5vaGhoiGnTpqk0KCIiIiKxsQCpmEpvfPrnn3+iQYMGqtwkERERkSgkEvU9tJ1KE8iPHz/iyZMnqtwkEREREWmYbHVha6M0qdgR5A4m+VT6N0eeNqCtpdgh5Br1BvIPV1U4vthO7BCISEswGyAiIiIipeSZCiQRERGRMnLDWEV1YQWSiIiIiJSSpQqki4uL7PaF3yIIQpbWIyIiItJ0zGgUy1ICOWDAACaGRERERAQgiwnkoEGD1B0HERERkUZh7UwxXkRDRERElBlmkArxIhoiIiIiUgorkERERESZYP1RMVYgiYiIiEgprEASERERZYJDIBXLVgL59u1bhISE4J9//sGrV6+watUqHDt2DC4uLqhbt66qYyQiIiIiDaJ0F/bTp0/RvHlzbN++HdbW1njz5g3S0tIQGRmJwYMH4+TJk2oIk4iIiChnSdT40HZKVyADAwNhZWWFDRs2wNjYGOXKlQMA/Pbbb0hKSsKyZctQu3ZtVcdJRERERBpC6Qrk+fPn0b9/f5ibm2e4O0379u3x4MEDlQVHREREJBqWIBXK1hhIPb3Mn5acnMxbHhIREVGuwJRGMaUrkJUrV8by5csRHx8va5NIJJBKpdiyZQsqVqyo0gCJiIiISLMoXYEcPnw4fvrpJ9SvXx9eXl6QSCQICQlBREQEoqKisHnzZnXESURERJSjWIBUTOkKpJOTE3bu3AkvLy9cvHgRurq6+Oeff2BnZ4etW7eiTJky6oiTiIiIiDREtsZAlihRAr/99ptKA3ny5Ans7OxUuk0iIiKi7OIYSMWUTiCfP3/+3XWKFCmidCCdOnXC4sWL4ebmpvRziYiIiCjnKJ1A+vr6fvdK63v37ikdiL6+vsKru4mIiIhIcyidsc2cOTNDAhkfH48rV67g4sWLmDlzZrYC8ff3R8+ePdGiRQvY29vDyMhIbnnLli2ztV0iIiIiUi2JIAiCqjY2a9YsvH79OlvjI11cXBQuk0gk2apq/lea9IeeTv9KSVXZ2yXP09fj4BpVqTfwidgh5ArHF3McOmkeMcch7jgep7Ztt/UzU9u2c4JK+4x9fX3Rv3//bD03LCxMlaEQERERkZooPY3Pt9y8efOHxzE+f/4cZ86cQWJiIt68eaOiyIiIiIiUI5Go76HtlM72xo4dm6FNKpUiJiYGly9fRps2bbIVSHJyMkaPHo2DBw9CR0cHhw8fRmBgID5//ozg4GCYmppma7tERERE2ZEL8jy1UboCefHixQyPK1eu4P379+jVq1emCWZWLF26FGFhYVi3bh0MDQ0BAF26dEFUVBTmzp2brW0SERERkeopXYFcuXIlHB0dVR7I/v37MXnyZHh5ecnavLy8MGPGDIwaNQqTJ09W+WsSERERKcQSpEJKVyA7duyI3bt3qzyQly9fZnonGltbW3z48EHlr0dERERE2aN0BVJfXx+WlpYqD8TR0RHnz59H27Zt5dr379+PUqVKqfz1NIVUKsX27duwdctmPI2OhlWBAvD19cPAQYM57lNJSUlJqFa1ElJTU+Xa8+UzxoVL10SKSvvwPam8gha6WD3eFhOWv8LNB0my9iKF9DCgjSXKOxoiTSrg1LV4rNj9HvGJ6dNhzfulMNydjBRtFr79OT1RZmJiYtCsWVMsWrRYrteKlMdj+W0sQCqmdAI5ZMgQzJkzB3FxcXBxcYGxsXGGdbJzK8NBgwZh6NChePjwIdLS0vDnn38iMjIShw8fxvz585XenrYICVmFhb8vQPceAaha1RtRjyOxcOFCPHjwAKtCVn/3rj/0fw8fhiM1NRUzZwWhePHisnYdXV0Ro9I+fE8qp5ClLgIHFoapsXyHjkk+CX4bUhjvPqZh9vo3sDTTQW9/S9hY6WHM4lcAgN+3voWxkfzzihTSw5ifrbD/7Kcc2wdt8uLFC/TsGYC4OPXNz5dX8FjSj1A6gZw8eTLS0tIwcuRIhetkZ9LvOnXqYOHChVi+fDl0dXUREhKC0qVLY/78+WjQoIHS29MGUqkUIatWol279hg2bDgAoFq1arCwsMTw4UNx585tlCtXXuQotcf9sDDo6emhXv0GMDAwEDscrcT3ZNZJJEB9LxP0bWWR6fLmNc1gbqKDPrNi8PFz+p0MXr1Lw+yBheFa0gB3HiUjKka+Wq4jAQa1s8Sj6BQs2vFO3bugVaRSKXbv3o05cwKhuttf5E08llnHv5cVUzqBnD59ujriAADUrFkTNWvWVNv2Nc2nT5/QrHkLNGrUSK7doWRJAMDTJ0/5Za2EsLB7KFHCgcnjD+B7MutKFtXH0J8KYM/pOFwLS8SsAYXllnuWMULowyRZ8ggAV+4l4nOCFF7l8uHOo+QM22xawxSl7QwwKOglUtPUvgta5f79+5g8eRJ++qkjqlWrhj59eosdktbisSRVyFIC2bVrV0yaNAmOjo7w9/dXWzA3btzAhg0bEB4eDl1dXbi6uqJbt24oXbq02l5TTObm5hg3bnyG9uPHjwFArh77qQ7374dBV08PfXr3wI0b12Ggb4B69Rtg+IhRMDHh2L2s4Hsy62LfpqHzpOd4/T4NFUobZlhuZ6OPk1fj5dqkAhDzJhXFC+tnWN/IUILuTfPj6MXPCIvKmFzmdba2tjhy5ChsbGxw8eJFscPRajyWWccCpGJZugr70qVL+Pz5s1oDOXHiBDp27Ijo6Gj4+PjA09MT9+/fR6tWrXDlyhW1vrYmuXnzJlatXIE6deqgtJOT2OFoDUEQ8CD8Pp4+iUKdOn5YsmQFevbug0MH92NA/z6QSnkz9OziezJzcfFSvH6vuExomk8H8YkZ33fxiVKY5Mv4tdTI2wSmxjrYfPijSuPMLSwsLGBjYyN2GLkCj6USJGp8aDmV3gv7R8yfPx8BAQEYPny4XHtgYCCCgoKwbds2kSLLOdeuXUX/fn1RtFgxzJgxS+xwtIogCPg9eAksLQugVKn0inWlyp4oaFUIv44diX/OnUX1GnlneISq8D2Zfd8aO5XZ3zMta5nh/K0ERMemZlxIRKRhVHov7B8RFRWF1q1bZ2hv3749wsLCRIgoZx08cAABPbrD1tYWa1avhYUapkrKzXR0dODp6SVLHr+oUbMWgPTubVIO35M/5nOCFPmMMn7EGufTweevKpMli+qjuLU+jl1Wb08PESlHIpGo7aHtslyBHDBgQJYuTpBIJDh27JjSgZQpUwbnz59HiRIl5Npv376da8dAfrF6dQh+mxuEKlWqYGHwYpiZmYkdktaJjX2JM6dPoZpPddja/n8aqaSkRACAZYECYoWmlfie/HFPX6agaCH5j1gdCWBrpYezNxLk2quWy4eEJCku3E7MyRCJiLItywlk2bJlUUCNX8LNmzfH3Llz8ejRI3h5eUFPTw+hoaFYt24dOnToIHf3m5YtW6otjpy2bdtWzA2ag0aNGmPW7EBeQZxNaWlpmDplInr26oNBg4fK2g8fOghdXV1UrFhJxOi0C9+TqnHlXiI61DNHflMdfPiUXnGsXMYIxkY6uHJPPoEs62CAB0+TkZzCOVWINIn21wnVR6kKpJubm9oCmTZtGgBgw4YN2LBhg9yyVatWyX6WSCS5JoF89eoVAmfPQtGiRdGxUyfcu3tXbnlxOzu1Ju25ia1tEbRo2Qpr16yGoaERKlRwx/XrV7Fq5XJ0+KkTSpRwEDtErcD3pOrsPfMJ/rXNEDSoMNYf+ABzEx309rfAxdsJGabwcShikCGpJCLSZBpzEU1eGOf4tdOnTyExMRHPnj1Dl86dMiyfMXMW/P1biRCZdho/YTKKFSuOv/7ag5UrlsLa2gb9BwxGt+4BYoemNfieVJ0Pn6QYtiAWA9pa4tfuVohPTL+V4bJd7zOsa2mug0/xnCmASNPkgqGKaiMRhO/PQ+/i4oLt27ertQL5LTExMT885UAaP5tVIiWVXWyqoq/HTyZVqTeQ94xWheOL7cQOgSgDMZO4fWfUd0vRZjW0e37iLFUg/f39YanmKzCfPn2KwMBAhIeHIy0tfW41QRCQnJyMt2/f4u5XXWlEREREJI4sTeMza9YsFC9eXK2BTJ06Fffv30eDBg3w8uVLNGnSBK6urnj9+jUmT56s1tcmIiIi+ppEor6HttOYMZDXrl3DkiVL4OXlhTNnzqBu3bpwc3PD/PnzcerUKbRr107sEImIiIgIGjSReHJyMuzs0sffODg44P79+wDSp+y5efOmmKERERFRHsQKpGIak0AWLVoU4eHhANITyHv37gEApFKp2u/DTURERERZpzFd2P7+/hg1ahTmzJmD2rVro2vXrihSpAjOnTsHZ2dnscMjIiIion9pTALZu3dvGBoaQhAEuLm5oX///li6dClsbW0RFBQkdnhERERE9K8szQOZG3AeSNXgPJCqw3kgVYfzQKoG54EkTSTmeMED/6hvHsjG1fLAPJA55caNG9iwYQPCw8Ohq6sLV1dXdOvWDaVLlxY7NCIiIiL6l8ZcRHPixAl07NgR0dHR8PHxgaenJ+7fv49WrVrhypUrYodHREREeYxEjQ9ViIyMhIeHB3bt2iVru3fvHjp37gx3d3f4+vpi/fr1Kno1eRpTgZw/fz4CAgIwfPhwufbAwEAEBQVh27ZtIkVGREREeZFEg+fbSUlJwYgRIxAfHy9re/fuHbp37w5fX19MmTIFN27cwJQpU2BiYoLWrVur9PU1JoGMiorKdOfat2+PzZs3ixARERERkWYKDg6Gqan8OMrt27dDX18fU6dOhZ6eHhwdHREVFYUVK1aoPIHUmC7sMmXK4Pz58xnab9++zTGQRERElPM0tA/78uXL2LZtG2bPni3XfuXKFVSpUgV6ev+vD1atWhWPHz/G69evf+xFv6IxFcjmzZtj7ty5ePToEby8vKCnp4fQ0FCsW7cOHTp0wO7du2XrtmzZUrQ4iYiIiMTy8eNHjBo1CuPHj4etra3cspiYGDg5Ocm1FS5cGADw4sULFCxYUGVxaEwCOW3aNADAhg0bsGHDBrllq1atkv0skUiYQBIREZHaqXMEpJ+f3zeXHz9+PNP2yZMnw8PDA82aNcuwLDExEQYGBnJthoaGAICkpKRsRpo5jUkgw8LCxA6BiIiISGPt3r0bV65cwb59+zJdbmRkhOTkZLm2L4mjsbGxSmPRmASSiIiISJOo8yJsRRXGb9m5cyfevHmD2rVry7VPmjQJBw4cgI2NDWJjY+WWffnd2to627FmhgkkERERkRaYO3cuEhMT5drq16+PwYMHo3nz5tizZw+2bt2KtLQ06OrqAgAuXLgABwcHWFlZqTQWjbkKm4iIiEiTaNpF2NbW1rC3t5d7AICVlRWsra3RunVrfPr0CePGjcPDhw+xa9curF27Fn369MnmKyrGCiQRERFRZjR3HvFMWVlZYdWqVZgxYwb8/f1RqFAhjBo1Cv7+/ip/LSaQRERERFrq/v37cr+7ubnlyN37mEASERERZULLCpA5imMgiYiIiEgprEASERERZUKd0/hoO1YgiYiIiEgprEASERERZULCEqRCrEASERERkVKYQBIRERGRUtiFTURERJQJ9mArxgokERERESmFFUgiIiKiTLAAqRgrkERERESkFFYgiYiIiDLBMZCKSQRBEMQOIifkjb1Uv/OhCWKHkGu4OxmKHUKu8fajVOwQcoXHz5PFDiHXqOhiJHYIuYaxkXidpaeuxatt27UqGqtt2zmBFUgiIiKizLACqRDHQBIRERGRUliBJCIiIsoEC5CKMYEkIiIiygQvolGMXdhEREREpBRWIImIiIgywQKkYqxAEhEREZFSWIEkIiIiygwHQSrECiQRERERKYUVSCIiIqJMsACpGCuQRERERKQUViCJiIiIMsECpGJMIImIiIgywS5sxdiFTURERERKYQJJREREREphAklERERESuEYSCIiIqJMcAykYqxAEhEREZFSNC6BTElJkf388uVLESMhIiKivEyixoe205gE8u3bt+jatSsWLVoka/P390ePHj3w4cMHESMjIiIiov/SmARyxowZSEhIQJMmTWRtK1euRFxcHAIDA0WMjIiIiPIiiUR9D22nMQnk2bNnMW3aNDg5OcnaXF1dMWnSJJw8eVK8wIiIiIhIjsZchZ2WlgZBEDK06+vrIyEhQYSIiIiIKE/LDaVCNdGYCqSnpyfmzZuHT58+ydo+ffqE33//HZ6eniJGRkRERHkRL6JRTGMqkGPHjkWnTp1Qs2ZNlChRAgDw+PFjWFhYYNWqVeIGR0REREQyGpNA2tnZ4cCBA9i/fz8ePHgAPT09/PTTT2jWrBmMjIzEDo+IiIjyGPZgK6YxCSQAmJmZoUOHDmKHQURERETfIGoC+WXeR3Nzc3Tp0gWSb6T669evz8HIiIiIKK9jAVIxURPIokWLQkdHR/bztxJIIiIiItIMoiaQs2bNkv08ePBg2NjYyBLKL1JTU3H37t2cDo2IiIjyOta1FNKYaXz8/Pzw/v37DO3R0dHo0qVLzgckgpiYGHh6VsbFixfFDkXjSaVSHNy7DqMHNUPvjl6YOKIdzp/ZL7fO2b/3YPyw1ujd0QtjB7fA0QObM51rlDK6desGegX8DG+vivCrUx0Txo/B2zdvxA5Lq7yKjUHzRlVx4/ol+fZXLzFz6ii0bFINzRpUwchfAvAg/J5IUWomqVSKQ/vWY+yQ5ujbuSomjWyHC2cOKFx/67q5CGjvkYMRajee36QKolYgN23ahNWrVwMABEFA69atM1QgP378iCJFiogRXo568eIFevYMQFxcnNihaIU/ty3BwT1r4d++PxxKueLWtbNYsXAcJBIdVK3eCKeO78LaZVPRqEU3lKvgjUcPQrF13W9ISoxH01Y9xQ5fo929ewe9e3aDl5c35s0PxqtXsQheOA9Dn0Rh3fotYoenFWJfvsDo4b3x+ZP8+Rwf/xlDB/4MfX19DB0xCQYGhti4bhlGDeuJVWt3w6pgIZEi1iy7ty/Fob1r0bJdP5RwdEXo9bNYuWgcJDoSePk0klv3/t2rOHZws0iRah+e38rhyDrFRE0gW7VqhXfv3kEQBCxevBgNGzaEiYmJ3DomJiaoX7++SBGqn1Qqxe7duzFnTiBYHMuapKQEHN2/CfUad0QT/x4AgLLlvfD40V0cO7AFVas3wv5dIahctS7adf5FtjzmeRSOHdzKBPI7fp8/F84uZTD/98WyP+hMTEwQNGcWnkVHo2ixYiJHqLmkUimOHNqD5UvmZlrt3rl9PT5+fI81G/bJkkVnF1f07dkON29cgm/dJjkdssZJSkrAsQObULdxRzRu+f/zOyryHo4d3CKXQCYmxmPNssmwKFAY7968FCtkrcLzWznMHxUTNYHMly8fBg4cCACQSCQICAhAvnz5xAwpx92/fx+TJ0/CTz91RLVq1dCnT2+xQ9J4+noGGDdjHczNC8i16+npIyE+/U5Gv4wNhoGBYYblKSlJORanNnr//h2uXLmEqdNmyfUG+NWtD7+6ufcPOVV5FHEfC36biuYtO6BSZW/8Oqqf3PLTJ4+gZq36cpXGAlaFsP3Pv3M6VI2lr2+AsdPWwjy//Pmtq6eP+PhPcm07Ns5H/vxWKFO+CvbtXJmTYWolnt+kShozBnLgwIF49+6d7FaGFy5cwNSpU/HXX3+JHJl62dra4siRoxg7diwnTM8iHV1dFLd3Qn7LghAEAR/ev8H+P1fjbuhF+DZoBwAoUqwkChYuCkEQ8CnuA04d34Vzp/+Cb/12Ikev2R6Eh0MqlcLSsgB+HTsSPt6VUK1qJYwfNxpxHz+KHZ7GK2xtiw1bDqL/oNEw/Op8Tk1NQdTjRyhuVwJrVi1E2xa1UL92BQwb3A2PIx+KFLHm0dH59/y2+P/5fWD3atwLvYg6/zl/79y6gH9O/4Xu/aZAItGYrzKNxvNbeRKJ+h7aTmMmEj969CiGDh2K5cuXo3jx4ujZsyeKFy+OXbt24cOHD+jUqZPYIaqFhYWF2CFotYvnDmH572MBABUq1oB3DfkuwIjwW5gx/mcAQAnHsmjQrGuOx6hN3r17CwCYPGkcfKrXwLwFi/AkKgrBC+fjWXQ0Vq/dyOm2vsHc3AIwz3xZXNxHpKWlYuf29bAtUgzDR09FSkoy1oYswtBBP2Pl2j9RsGDhHI1X01365xBWLPwVAOBWsQa8azQGAMTHx2Htsilo2bYfbIrYixmiVuH5TaqkMX+2LVmyBAEBAfD29sa+fftQpEgR7N+/HzNnzsTGjRvFDo80VMlS5TBmSgg69RiDB/dvYN6MAXJjz6wK2WL05FUI6D8FH969xozxPyMpKUHEiDVbSkoKAKBMWVdMmjwdXl7eaNuuA34dNxE3blzDhfP/iByh9kr999gCwOy5y1G1Wi3UqFUPM4OWIiH+M3bv5IUgX3NwLIdRk1ahY/fReHj/BubPTD+/t66diwJW1qjXpLPYIWoVnt+kShqTQEZERKBdu3bQ0dHBuXPnUKtWLejo6MDd3R3Pnj0TOzzSUIVtisO5bCXUbdQBHbuPwv17VxF+75psuWWBwnBxrYzqdVqgz5BZiHn+GFcuHBMxYs1m/O9FbDVr1pZr9/GpAQAIC+OcrNmVzzj92Fbw8JT9DADW1kVgZ18SDx9wKp+vfTm//Rp2wE8/j0T4vWvYt3MFLv1zGF17T4AgSJGWlgpBkAIA0tJSIZVKRY5ac/H8JlXSmC5sc3NzxMXFIS4uDrdu3UKvXr0AAE+ePGE3L8n5+OEtQm+cQ3l3H7mB9vYOZQAAb17H4PyZAyhZqhysbe0yLH//9lXOBqxF7OzSuwOTk5Pl2lNSUwEAhoYcp5tdpqZmsLAogJSU5AzLUlNTeWz/FffxLUKvn0O5r85vu3/P3/27VyM1JRkTR7TJ8NzeHT1RrVYzBPSfmmPxahOe38pjl75iGlOBrFWrFiZOnIjBgwfDzMwMPj4++OeffzBx4kTUrl1b7PBIg6QkJ2HVogk4feJPufY7N88DAOwdXLB22VQc3LtObvntW+nLi9mXzplAtVDJko4oUqQoDh8+IDcU4NTJEwCAihUriRVarlClag1cu3IBH96/k7U9fRKJp08fo7xbRREj0xzJyUkIWTIRZ74+v/89fyfN3owJMzfKPWr6tQIATJi5ES3a9M3xmLUFz29SJY2pQE6YMAELFizA06dPsXTpUhgYGODq1atwd3fHqFGjxA6PNIhVIVvUqNMSe3esgK6uHuwdXBB+7xr2716DGr7+KFrcEU38e2D39qUwz18ALq6eeBp1H3t2LEfZ8l5w86gu9i5oLIlEgl+GjcTokUMxZtQw+Ldui0cREVi8aAH86taHS5myYoeo1bp074dzZ09g1LBe6NKtH1JTU7B65e8oXNgGjZtlrKjlRVYFbVG9Tgvs27kSunr6sCvhjAdh13FgzxrUqNMSRYo5ZnjOzWtnAAAlHF1zOlytwvNbeSxAKqYxCaSRkRHGjBkj1zZo0CCRoiFN17XXOBSyLopTx3bizasXKGBlA//2/dCwWfoV181a94KZuSWOH9qKQ3vXw8zcEnXqtUXLdn3ZJfEd9eo1gOHvi7Fi+VIMGdQP5vnzo03bDhgwcIjYoWm9IkWKY+GSjVi5bB5mTx8DHV1dVKrsjf6DRsPY2OT7G8gjuvQch0KFi+H08f+f3y3b9uMsCirA81s5/LZQTCJo0M2Bw8LCEP7vPFVA+u0Nk5OTERoaiunTp//QtjVnL7Xb+VBewawq7k6G31+JsuTtR144oQqPn2ccn0nZU9GF4wlVxdhIvNF2oQ/Vd/OJ8qW0+ztAYyqQa9asQWBgIID0MvuXvFYikaBy5cpihkZERER5EUuQCmnMRTSbNm1Cr169cPPmTVhaWuLUqVPYs2cPHB0d4efnJ3Z4RERERPQvjUkgY2Ji0LZtWxgaGsLFxQWhoaFwdnbGmDFj8Mcff4gdHhEREeUxEjU+tJ3GJJDGxsZIS0sDANjZ2eHhw/R7wzo6OnIicSIiIiINojEJZMWKFbFixQokJCSgbNmyOHHiBKRSKa5evQoTE16dSERERDlLIlHfQ9tpTAI5bNgwnDlzBps2bUKTJk3w+vVrVKlSBaNHj0arVq3EDo+IiIiI/qUxV2E7OTnh2LFjiI+Ph4mJCbZv3459+/bB1tYWDRs2FDs8IiIiymNyQaFQbTQmgQTSJxM3MkqfO6tgwYLo3r27yBERERFRnsUMUiFRE0hfX98s3xXk+PHjao6GiIiIiLJC1ATS39+ft5UjIiIijcQcRTFRE0je65qIiIhI+2jUGMgbN25gw4YNCA8Ph66uLlxdXdGtWzeULl1a7NCIiIgoj2H9UTGNmcbnxIkT6NixI6Kjo+Hj4wNPT0/cv38frVq1wpUrV8QOj4iIiIj+pTEVyPnz5yMgIADDhw+Xaw8MDERQUBC2bdsmUmRERESUF3EIpGIaU4GMiopC69atM7S3b98eYWFhIkRERERERJnRmASyTJkyOH/+fIb227dvcwwkERERkQbRmC7s5s2bY+7cuXj06BG8vLygp6eH0NBQrFu3Dh06dMDu3btl67Zs2VK0OImIiChvYBe2YhJBEASxgwAAFxeXLK0nkUhw7949pbevGXup/c6HJogdQq7h7mQodgi5xtuPUrFDyBUeP08WO4Rco6KLkdgh5BrGRuJ1loY/Ud854WRnoLZt5wSNqUBynCMRERFpEk2sQL5//x7z5s3DyZMn8enTJzg7O2P48OGoXLkyAOD8+fMICgpCREQEbG1tMWjQIDRp0kTlcWjMGEgiIiIi+rZhw4bh+vXrmDdvHnbu3IkyZcogICAAjx49QkREBPr06YMaNWpg165daNu2LUaNGpXpNSY/SmMqkERERESkWFRUFM6dO4fNmzejUqVKAIAJEybgzJkz2LdvH968eQNnZ2cMHToUAODo6Ii7d+9i1apV8Pb2VmksrEASERERaQFLS0usWLEC5cuXl7VJJBJIJBJ8/PgRV65cyZAoVq1aFVevXoWqL3lhAklERESUCYlEfY/sMDc3R61atWBg8P8LcA4fPoyoqCjUqFEDMTExsLGxkXtO4cKFkZCQgHfv3v3IociAXdhEREREOczPz++by48fP/7dbVy7dg1jx45F/fr1Ubt2bSQmJsollwBkvycnq/aKclYgiYiIiDIhUePjRx07dgw9evSAu7s75s6dCwAwNDTMkCh++T1fvnwqeNX/YwWSiIiIKBMSNc7jk5UKoyIbN27EjBkz0LBhQwQGBsqqjLa2toiNjZVbNzY2FsbGxjAzM/uheL/GCiQRERGRlti8eTOmTZuGTp06Yd68eXJd1pUrV8alS5fk1r9w4QIqVqwIHR3VpnysQBIRERFlRsMmEo+MjMTMmTNRr1499OnTB69fv5YtMzIyQpcuXeDv74+5c+fC398fp06dwqFDh7Bq1SqVx8IEkoiIiEgLHD58GCkpKTh69CiOHj0qt8zf3x+zZ8/GkiVLEBQUhHXr1qFYsWIICgpS+RyQgAbdC1vd8sZeqh/vha06vBe26vBe2KrBe2GrDu+FrTpi3gs78nmK2rbtUERfbdvOCRwDSURERERKYRc2ERERUSbUeBG21mMFkoiIiIiUkmfGQJJqxCdyrJmqSHnmqYxpPv4trAr8NlCd2HepYoeQa1gXEK+zNOqF+sZA2ttq9xhIdmETERERZYZd2Arxz3YiIiIiUgorkERERESZYAFSMVYgiYiIiEgprEASERERZYLT+CjGCiQRERERKYUVSCIiIqJMSFiCVIgVSCIiIiJSChNIIiIiIlIKu7CJiIiIMsEebMVYgSQiIiIipbACSURERJQJFiAVYwWSiIiIiJTCCiQRERFRJjgGUjFWIImIiIhIKaxAEhEREWWGFUiFWIEkIiIiIqWwAklERESUCRYgFWMFkoiIiIiUwgokERERUSZ4FbZiTCCJiIiIMsH8UTF2YRMRERGRUliBJCIiIsoM+7AVYgWSiIiIiJSiERXIqKgojB8/Hrdv30ZiYmKG5ffu3RMhKiIiIsrLWIBUTCMSyAkTJuD169cYMmQI8ufPL3Y4RERERPQNGpFA3rx5E5s2bUK5cuXEDoWIiIgIAK/C/haNGANpYWEBQ0NDscMgIiIioizQiASyc+fOmD9/PuLj48UOhYiIiAhA+hhIdT20nWhd2L6+vpD85wg+e/YMXl5eKFiwIHR05PPa48eP53R4RERERKSAaAmkv7+/XAKZl509exbz58/Hw4cPYWVlhU6dOqFHjx48Ptlw69YNBP8+H7dvh8LY2BjVfKpj6NCRKGBlJXZoWuPK5Uvo0+tnhcv79B2I3n0H5GBE2o3nt+rFxMSgWbOmWLRoMby8vMQOR2vExsagW6eWmBG4EB4Vq8jaz587hTUhi/E48hHyW1igUeOW6NKtN/T1DUSMljSdaAnkoEGDxHppjXLjxg307dsXjRo1wpAhQ3D16lUEBQUhLS0NvXv3Fjs8rXL37h307tkNXl7emDc/GK9exSJ44TwMfRKFdeu3iB2e1nApUxZrMzleSxb/jjt3bqNBo8YiRKWdeH6r3osXL9CzZwDi4uLEDkWrvHz5AiN+6Y1Pn+SP26WL5zB21EA0bNQCvfsORVTUI6xYtgBv3rzCyDFTRIpWc/DvPMU04ipsANi3bx88PT1hY2ODJUuW4MCBA6hYsSLGjRuXqy+wCQ4ORpkyZRAUFAQAqFmzJlJTU7Fs2TJ07doVRkZGIkeoPX6fPxfOLmUw//fFsmEQJiYmCJozC8+io1G0WDGRI9QOpqamKO/mLtd26uQJXLp4AYFBC2Bv7yBOYFqI57fqSKVS7N69G3PmBEIQxI5Ge0ilUhw+uAdLgudCyOTAbVq/Ek7OZTFm/HQAQOUq3vjw4T02rF2OgUNGI18+45wOmbSERlxEs2TJEowbNw7Pnz/H1atXsXDhQnh4eODixYuYO3eu2OGpTXJyMi5evIh69erJtTdo0ACfP3/G1atXRYpM+7x//w5XrlxCu3Y/yY2h9atbH4eO/M3k8QckJiZiTuB0VK9RC3XrNRA7HK3B81u17t+/j8mTJ6FFi5aYM2eO2OFojYiH9/HbnKlo0Kg5xk2anWH56F+nYfxX7fp6+pBKpUhNTc2pMDWWRI0PbacRCeTOnTsRGBiIihUr4vDhw3B3d8e0adMwY8YMHDp0SOzw1Obp06dISUlBiRIl5Nrt7e0BAJGRkSJEpZ0ehIdDKpXC0rIAfh07Ej7elVCtaiWMHzcacR8/ih2eVtuyeQNexcZixMixYoeiVXh+q5atrS2OHDmKsWPHsnKrBGtrW2zecRADh4zO9LgVKVocdv/2Knz+/AmnTh7F1s1r4FevMczMzHM6XNIiGtGFHRsbCw8PDwDAP//8g4YNGwJI/8D4mIu//L+M4TE1NZVrNzExAQB8+vQpx2PSVu/evQUATJ40Dj7Va2DegkV4EhWF4IXz8Sw6GqvXbuRFC9mQkpKMLZvXo36DxihuZy92OFqF57dqWVhYiB2CVjLPb4GspIGvX79Cq2a1AaQnlb36DlFrXNqCXxuKaUQCaWNjg8jISCQlJeHhw4fw8fEBAFy5cgU2NjYiR6c+Uqn0m8u/ns6IFEtJSQEAlCnrikmT08fyeHl5w8zMDGPHjMCF8//Au5qPmCFqpWNHj+DN69fo+nMPsUPROjy/SZsYGhpifnAIPn74gNWrFqFfz5+wcs0OFCpsLXZopKE0IoHs0KEDfvnlFxgYGMDZ2RkeHh7YtGkT5syZg8GDB4sdntqYmZkBAD5//izX/qUy8XXlghQz/reqU7Nmbbl2H58aAICwsLtMILPh+LHDcHQsBSdnF7FD0To8v0mbmJmZo1LlqgAAl7Ll0KF1A+zftxPdAvqLHJnIWIJUSCMSyICAADg4OODp06do3rw5AMDc3BwTJkxAmzZtRI5Ofezs7KCrq4uoqCi59idPngAAHB0dxQhLK9n9272anJws157y7yBwQ0OOmVJWSkoKzv9zDj93DxA7FK3E85s0XVpaGk79fRTFitvDybmMrN3WtijMzfPj9etXIkanGZg+KqYxfSi+vr74+eefYWlpCQBo1qxZrk4egfQug8qVK+Po0aNy0yscPnwYZmZmcHNzEzE67VKypCOKFCmKw4cPyB3LUydPAAAqVqwkVmha6+HDcCQmJsDdvaLYoWglnt+k6XR1dbF86XwsXzpPrv3+/bv48OE9HEs5iRQZaQONqEAmJSVh27ZtCA8PR1pamqw9OTkZt2/fxuHDh0WMTr369euH7t27Y8iQIWjdujWuX7+OkJAQDB8+HPny5RM7PK0hkUjwy7CRGD1yKMaMGgb/1m3xKCICixctgF/d+nApU1bsELXOwwcPAAAOJVkpyy6e36Tpugf0x8xpv+K3OVNRu059PH/+FKtXLYZDydJo3MRf7PBExx5sxTQigZw+fTp2796NsmXLIjQ0FB4eHoiKisKbN2/QrVs3scNTK29vbwQHB2PhwoUYMGAArK2tMWrUKPTowYsWlFWvXgMY/r4YK5YvxZBB/WCePz/atO2AAQN5NWF2vH3zGgBgbp5f5Ei0F89v0nQNG7eAkZERNm0IweGDe5EvnzFq1PJDn36/wJDTJdE3SITMpqbPYdWqVcOvv/6Kpk2bol69eli+fDmKFy+OoUOHwsbGBuPHjxc7RPpXfOK3ryylrJOKfublHqb5NGY0jlYT/9sg94h9x0m4VcW6gHi1rrjP6vvOMzPR7s8tjYj+48ePqFgxfZxVqVKlcPfuXejr66NPnz74+++/RY6OiIiIiP5LIxLIAgUK4M2bNwCAEiVKIDw8HABgaWmJ169fixkaERER5VW8l6FCGpFA1qxZE1OmTMGDBw9QqVIl/PXXXwgNDcWmTZty9UTiRERERNpIIxLIUaNGoXDhwrh06RL8/Pzg6OiItm3bYsOGDbl6InEiIiLSXBKJ+h7aTiMuorly5Qrc3NxgYGAAABAEAffu3UPBggVRuHBhkaOj/+JFNKrDi2hUhxfRqIb43wa5By+iUR0xL6L5nKC+7zwTLf/c0ojoBw0ahAf/zjkHpM/pV7ZsWSaPRERERBpII+aBLFCgAOLi4sQOg4iIiEgmN3Q1q4tGJJA1a9ZEnz59UKtWLdjb28PQ0FBu+cCBA0WKjIiIiIi+phFjIH19fRUuEwSBc0FqEI6BVB2OgVQdjoFUDfG/DXIPjoFUHTHHQKrzO8/YSLs/tzSiAgkAO3fuhKWlpVzby5cv0bx5c5EiIiIiIqLMiJZAHjhwAGfOnAEAvHjxAtOmTcvQdf3s2TPo6Gh3hk5ERETaScJBkAqJlkB6eHhg69atEAQBgiDg+fPn0NfXly2XSCQwNjbG7NmzxQqRiIiIiDKhEWMgu3TpgsWLF8Pc3FzsUOg7OAZSdTgGUnU4BlI1xP82yD04BlJ1xBwDmZisvpPCyEC7q5sakUCS9mACqTpMIFWHCaRq8NtAdZhAqo6YCWSSGhNIQy1PIPmpS0RERERK0ZirsImIiIg0inYXCdWKFUgiIiIiUgorkERERESZYAFSMVYgiYiIiEgprEASERERZYLziCvGCiQRERERKYUVSCIiIqJMsACpGBNIIiIioswwg1SIXdhEREREpBRWIImIiIgyIeFVNAqxAklERERESmEFkoiIiCgTrD8qxgokERERESmFCSQRERFRJiQS9T1+hFQqxcKFC1GjRg24u7ujV69eePr0qWp2OouYQBIRERFpkSVLlmDz5s2YNm0atm7dCqlUip49eyI5OTnHYmACSURERKQlkpOTsXr1agwePBi1a9eGi4sL5s+fj5iYGBw5ciTH4mACSURERJQJTezCDgsLw+fPn+Ht7S1rMzc3R9myZXH58mUV7HXW8CpsIiIiohzm5+f3zeXHjx/PtD0mJgYAYGtrK9deuHBh2bKcwASSlGJsxKI1UW7FOZNVx7oAv15JPRISEgAABgYGcu2Ghob48OFDjsXBdzgRERFRDlNUYfweIyMjAOljIb/8DABJSUnIly+fSmLLCpaTiIiIiLTEl67r2NhYufbY2FhYW1vnWBxMIImIiIi0hIuLC0xNTXHx4kVZ28ePH3H37l14enrmWBzswiYiIiLSEgYGBujcuTPmzp2LAgUKoGjRoggKCoKNjQ3q16+fY3EwgSQiIiLSIoMHD0ZqairGjx+PxMREeHp6IiQkBPr6+jkWg0QQBCHHXo2IiIiItB7HQBIRERGRUphAEhEREZFSmEASERERkVKYQBIRERGRUphAEhEREZFSmEASERERkVKYQBIRERGRUphA5qAuXbpgzJgxAICLFy/C2dkZ0dHRIkeVbsyYMejSpYvYYXxXcHAwfH19xQ4jz3r37h127NiR5fU17X1OmkfV53R8fDw2bdok+11bPtuy6/nz59i/f7/ats/PXFKECSQRZdmcOXOwd+9escMgUmj16tUICQmR/T5u3DgEBweLGJF6jR49GmfOnBE7DMqDeCtDIsoy3riKNN3X71EzMzORIiHK3ViB/Nfnz58xbdo0VK9eHR4eHujcuTNu374NANixYweaNWsGNzc3uLu7o2PHjggNDZU919fXF4GBgWjcuDG8vLxw6dIlJCcnY+bMmfD29kalSpUQFBQEqVSa4XVPnDiBunXronz58ujSpQvCwsJky9LS0rB27Vo0aNAA5cuXR4MGDbBlyxa55x87dgxt27aFu7s7ypcvj1atWsn9NdqlSxdMmDABbdu2ReXKlbF3714IgoAlS5agZs2acHd3x9ixY5GUlKTqQ5ptb9++xdChQ1G5cmV4eXlh7ty56Nq1a6ZVhPDwcPTp0weenp4oV64c/Pz8sHr1atnyhIQEjBs3Dj4+PihfvjxatmyJI0eOyJY/fvwYAQEBqFSpEjw8PBAQEID79+/nyH6qm7OzM7Zt24aOHTuifPnyaNSoEa5du4Zt27ahdu3aqFixIn755RckJibKnnPt2jV06tQJbm5uqF27NqZMmYJPnz4BSO8K/PPPP3Hp0iU4OzsDAD58+IDx48ejRo0acHV1hbe3N8aPH4+EhARR9lkdnJ2dsWnTJrRr1w7ly5dHs2bNcPz4cdlyqVSK5cuXo0GDBihXrhwqVqyInj174smTJ1neBgD8/fffaNWqFdzc3FCvXj0sWLAAycnJcttYuHAh6tSpg+rVq+Px48dq33dVyMq+/9ePnNPBwcFYtGgRnj17Jhs68XUXdlRUFPr164dKlSrBy8sLw4YNw5s3b9R3ANSoS5cuuHTpEv7880/4+vpm+l2UlXM0JCQEdevWRbly5eDr64vFixdnSMRXrFiBmjVrws3NDV26dNGa9x+pkUCCIAhCz549hbp16wqnT58WHj9+LIwZM0bw9PQUjhw5IpQrV07YvXu3EB0dLVy/fl1o1aqV0Lx5c9lz69SpI5QrV044d+6ccOvWLSEpKUmYMGGC4OPjI5w8eVIIDw8Xhg0bJjg5OQmjR48WBEEQLly4IDg5OQm1atUSTp8+Ldy/f1/o06eP4OPjI8THxwuCIAjTp08XPD09hb179wqRkZHCunXrBFdXV2HNmjWCIAhCaGio4OLiIqxZs0Z48uSJcPfuXSEgIECoWrWqkJSUJAiCIHTu3FlwdnYW9u7dK9y/f194+/atsGzZMsHDw0PYt2+fEBERIcycOVNwcnISOnfunLMHPRNpaWlCmzZtBH9/f+H69evC7du3hU6dOgnOzs7CwoULhYULFwp16tQRBEEQ4uPjBR8fH2HUqFHCw4cPhcjISGHOnDmCk5OTcPfuXUEQBGHWrFlC69athdu3bwtPnjwRfvvtN6Fs2bLC06dPBUEQBH9/f2Hs2LFCZGSk8ODBA9n7IDdwcnISvLy8hOPHjwsRERFC27ZtBU9PT6F79+7C/fv3hUOHDgmurq7C+vXrBUEQhHv37glubm7C0qVLhcjISOHy5ctC27ZthbZt2wpSqVT4+PGjMGTIEKF9+/ZCbGysIAiC0LdvX8Hf31+4ceOG8PTpU2HPnj1y79Ev7/Mvx1sbOTk5Ce7u7sLGjRuFiIgIISgoSHBxcRGuXr0qCIIgrFmzRvD09BROnDghREdHC//884/g5+cn9OvXL8vbOHXqlODm5iZs2bJFiIqKEs6cOSPUr19fGDx4sNw2vLy8hFu3bgnXr1/P0WPwI76376o8pz99+iTMnj1bqFmzphAbGyukpqYKo0ePln22ffjwQfDx8RF69OghhIaGCnfu3BFat26tEZ992fHu3Tuhffv2wpAhQ4Q3b95k+l30vXP0+PHjgqenp3D27Fnh2bNnwv79+wVXV1dh9+7dgiCk//84OTkJffr0Ee7duyfcvHlTaNCggdCxY0cR95w0ARNIQRAiIiIEJycn4cyZM7K2xMREYebMmcLFixeFPXv2yK2/efNmwcXFRfZ7nTp1hAEDBsh+j4uLE1xdXYXt27fLba9atWoZEshjx47J1vnw4YPg7u4ubN++XbaNDRs2yL32jBkzBG9vb0EqlQp3794VNm3aJLf81KlTgpOTk/D8+XNBENITyJYtW8qWS6VSwcfHR5g/f77c81q0aKERH6Lnz58XnJychIiICFnbq1evhPLly2dIIN+8eSMsX75c+PTpk2zdxMREwcnJSfjzzz8FQRCEfv36CV27dhU+fPggCIIgpKamCqdPnxY+fvwoCIIgVKpUSQgKChKSk5MFQRCE2NhY4cKFC0JaWlpO7K5aOTk5CXPmzJH9vnHjRsHJyUmIjIyUtbVp00aYMGGCIAiCMGLECLmkRxAE4cmTJ4KTk5Nw4cIFQRAEuS9jQRCEDRs2CGFhYXLPadu2rTB27FhBEHJPAjl16lS5trZt2wpDhw4VBCH9C/jEiRNyy4OCggQ/P78sb+Onn34Spk+fLrf8y7nw5dg5OTkJM2fOVM1O5aDv7buqz+n/bk8Q5N+zW7ZsEdzd3YX379/Llt+7d0+YO3eu7I9ubdO5c2fZ98rX30WC8P1zdM2aNYKPj4/c58Lly5eFZ8+eCYKQfjxdXV2FuLg42fI1a9YIbm5u6tgd0iIcA4n0LhMAcHd3l7UZGhpi7NixAICIiAgsXrwYjx49QlRUFO7fv5+hO9re3l72c2RkJFJSUlC+fHm57ZUtWzbDa1eqVEn2s7m5OUqUKIHw8HA4OzsjJSVFbjkAVKlSBevWrcObN29QpkwZ5M+fHytWrJDF9qULPC0tLdPY3r17h1evXsnF9mXfIyIivn2gcsDdu3eRP39+lCxZUtZWsGBBODg4ZFi3QIEC6NixI/766y/cvXsXT548ke3/l/+fXr16oW/fvvD29oabmxt8fHzQrFkz2biooUOHYubMmdi8eTOqVKmCGjVqoGnTptDRyR2jO/77f58vXz4AgJ2dnazNyMhI1k169+5dREVFwcPDI8N2IiIi4OXllaG9Y8eOOHHiBP788088fvwYDx8+RHR0tNz/X27w9b57eHjg3LlzANKHsNy8eRO///47IiMjERkZiYcPH8La2jrL27h79y5u3bqFP/74Q7Zc+LcLMSIiAsWKFQMg//+pTb617/+linP6W8LDw1GiRAnkz59f1ubi4gIXF5cf2T2N8vV75HvnaPPmzbFz5040aNAApUqVQrVq1dCgQQMUKVJEtg0rKyuYmprKfjc3N5cb+kJ5ExNIAHp6ig/Dvn37MGbMGDRr1gwVK1ZEhw4dEB4ejqlTp8qtZ2RkJPtZIpEAyDiYO7PX0dXVlfs9LS0NBgYGCi9W+PIhqqenh0uXLiEgIAC1a9dGpUqV0KxZMyQkJGDAgAEqiU0Murq6mY4VzcyrV6/Qvn17FChQAL6+vqhevTrKly+PWrVqydbx8PDAqVOncO7cOZw/fx67d+/G0qVLsWrVKnh7e6NTp05o2LAhTp06hfPnz2PhwoVYunQpdu/ejYIFC6prN3NMZv+vipJjqVSKZs2aoW/fvhmWFShQINP1+/TpgwcPHqBp06Zo3LgxXF1dMWHChB8PXMN8fRzT0tJkx3HFihVYvHgx/P394e3tjW7duuH48eMZplb51jakUil69uwJf3//DK9dqFAh2c//PZe1ybf2/b9UcU4rE0du9N/3SFbO0QIFCmDPnj24fv06zp07h7Nnz2L9+vUYNGgQBg4cCCDj9xQRwItoAACOjo4AIHdhTGpqKnx9fbF06VK0adMGs2fPRqdOneDp6YmnT58CUHxFqoODAwwNDXHt2jW57f33ApkvvlyoA6RfPPL48WOULl0ajo6O0NfXx9WrV+XWv3LlCgoVKoT8+fNj9erV8PLyQnBwMLp16wYfHx+8ePHim7FZWlrC1tY2w3b/G4eYXFxcEBcXJ1cNfffuHaKiojKs+9dff+H9+/fYsmUL+vfvj3r16uHDhw8A/r//CxcuxNWrV+Hn54fx48fj8OHDKF68OA4fPow3b95g6tSpSElJQatWrRAUFIS9e/fi1atXuHTpUs7ssAYpXbo0Hj58CHt7e9kjNTUVs2bNkr2vvvwBAgD37t3D6dOn8fvvv2PEiBFo3rw57Ozs8OTJk1x3tfZ/PxsA4Pr163B1dQUALFu2DAMGDMDkyZPRvn17uLu74/HjxxmOwbe2Ubp0aURGRsod+5iYGMyZMwefP39W457ljG/t+3/96DkNyL9Hv1aqVCk8fvwYcXFxsrY7d+7A29sbMTExP7yfmiYr5+jevXuxZcsWVKpUCYMHD8b27dvRtm1bHDhwQOToSdPl/j/HssDBwQH169fHlClTMHnyZFhbW2PFihVISkpCqVKlcO3aNdy5cwdmZmY4ceIENm7cCABITk6GoaFhhu2ZmJigc+fOWLhwIQoVKgRHR0esXr0aL1++zLDuxIkTMXXqVFhYWGD27NmwtbVF48aNYWBggPbt22PhwoWwsLBA+fLlcfbsWWzevBnDhg2DRCKBra0tjh07hitXrsDGxgYXL17E77//LotNkV69eiEwMBAlS5ZE5cqVsWfPHty6dStDd7kYvLy8UKFCBYwaNQoTJkyAkZERgoKCkJCQAIlEIvelbGNjg4SEBBw6dAiVKlXCo0ePMGvWLAD/3/+nT59i7969mDZtGuzs7HDz5k08f/4cHh4eyJ8/P06ePIknT55g+PDhMDU1xa5du6Cvr49y5cqJsv9i6tGjBzp16oQpU6agc+fO+PjxI6ZMmYLExESUKFECAGBsbIzY2Fg8ffoUBQsWhJ6eHg4ePIgCBQrg/fv3WLZsGV69evXN9582WrduHUqWLIly5cph+/btuH//PmbMmAEAsLW1xblz5+Dr6wsdHR3s2bMHR44cyVDB/tY2evXqhV9++QWLFi1CkyZNEBMTg3HjxqFYsWJyFUht9a19/68fPaeB9Pfohw8fEBkZKev6/6JZs2ZYsmQJRo4ciV9++QWpqamYPHkynJycYGNjo+ajoB4mJiZ49uxZpglwVs7RpKQkBAYGwsTEBJUrV0ZMTAwuX76MypUr5/SukJZhAvmvmTNnYs6cORgyZAiSk5NRoUIFhISEwMTEBBMnTkTnzp1hYGAAFxcXzJkzB0OHDkVoaKjCk2z48OEwNDTE1KlT8fnzZzRq1CjT2fz79++PsWPH4u3bt/Dy8sKqVatgYGAAABg7diwsLS0xd+5cvH79GiVKlMDEiRPRrl07AMDgwYPx+vVrWZdjqVKlMHPmTIwcORKhoaGyyurXOnXqBKlUiqVLl+L169eoUaMG2rRpg8jISFUcyh8WHByMqVOnolu3bjA0NETHjh3x6NEj6OvryyUmDRs2xJ07dzB79mx8+vQJRYsWRdu2bXH8+HGEhobip59+wqRJkxAYGIiRI0fi/fv3KFq0KEaMGIEWLVoAAFauXInAwEB069YNCQkJKFOmDFasWCE3TjCvcHd3x6pVq/D777/D398fxsbG8Pb2xujRo2XvyZYtW+Lo0aNo2rQpjhw5gtmzZyM4OBibNm1CoUKFULt2bXTr1g0nTpwQeW9Uq0OHDli7di3Cw8Ph4uKCkJAQ2bi5OXPmYOrUqWjdujVMTExQoUIF2R+jz58/l40l+9Y2GjZsiPnz52P58uVYtmwZLCws4OvrixEjRoi2z6r0rX3/L1Wc0/Xr18f27dvRvHlz2R/7X+TLlw8hISGYNWsWOnToACMjI9SuXRujR4/OkeOgDh06dMDo0aPRvHlz2TjnL6ytrb97jrZt2xbv37/HkiVL8OLFC+TPnx8NGjTINe89Uh+JkNv6mkirvX37Fjdv3kT16tWhr68PIL3y4OXlhUmTJqFly5biBkh5jrOzM2bNmoVWrVqJug1tlZf3nSg3YwWSNIqenh6GDh2KDh064KeffkJKSgpCQkJgYGCAmjVrih0eERERgRfRkIYxNzfHsmXLcOPGDbRs2RLt27fH69evsX79+kyvBCYiIqKcxy5sIiIiIlIKK5BEREREpBQmkERERESkFCaQRERERKQUJpBEpHE4NJuISLMxgSTKZbp06QJnZ2e5R7ly5VC7dm1MmTJFdms4ddi1axecnZ0RHR0NIH1SeGdn5yw/PyYmBr1798azZ89+OJbo6Gg4Oztj165dCtdRNr4fea2s6tKlC7p06fLD2yEiUifOA0mUC5UtWxaTJk2S/Z6SkoI7d+5g3rx5uHfvHrZs2fLNewarStu2bVGjRo0sr//PP//g1KlTaoyIiIhUgQkkUS5kamoKd3d3uTZPT098/vwZCxcuxM2bNzMsVwcbGxutvccwEREpxi5sojykXLlyAIDnz58DSO8uHTFiBAYPHgx3d3d0794dAJCUlIQ5c+agVq1aKFeuHJo1a4YDBw7IbUsqlWLJkiWoXbs2KlSogP79+2foHs+si3j37t3w9/dHhQoVULt2bfz2229ITk7Grl27MHbsWACAn58fxowZI3vOjh070KRJE1lXfHBwMNLS0uS2e+TIETRv3hxubm7w9/dHWFiYCo5YusuXLyMgIACenp4oV64cfH19ERwcDKlUKrfey5cv0adPH7i5uaFWrVpYuHBhhjizsi//de7cObRr1w4eHh7w9PREv379EBERobJ9IyLKDiaQRHlIZGQkAKB48eKytoMHD8LExARLly5Fz549IQgCBgwYgK1bt6J79+5YunQpPDw8MHToUOzevVv2vKCgICxevBht2rTBokWLYGFhgd9+++2br79p0yaMHj0arq6uWLRoEXr37o0NGzZg+vTpqF27Nvr16wcAWLRoEfr37w8AWL58OSZMmABvb28sW7YMnTp1wsqVKzFhwgTZdk+cOIHBgwfD2dkZixcvRqNGjTBy5EiVHLOwsDB069YNFhYWmD9/PpYuXYrKlStj0aJFOHjwoNy6wcHBsLKywuLFi9G6dWssW7YMgYGBsuVZ2Zf/evr0Kfr3749y5cph6dKlmDFjBiIjI9G7d+8MySsRUU5iFzZRLiQIAlJTU2W/f/jwAZcuXZIlg18qkQCgr6+PKVOmwMDAAEB6xevMmTOYP38+GjduDACoUaMGEhISMHfuXDRt2hTx8fHYsGEDunfvjoEDB8rWiY2NxZkzZzKNSSqVYvHixahbty6mT58ua09ISMD+/fthZmYGOzs7AECZMmVQrFgxxMXFYcmSJWjfvj3Gjx8PAKhevTosLCwwfvx4dO/eHaVLl8bixYvh5uaGoKAgWSwAvpvQZkVYWBiqVauGoKAg6Oik/83t4+ODEydO4OLFi2jSpIls3Ro1amDmzJmynz99+oTNmzejf//+0NXVzdK+/NetW7eQmJiIPn36wNraGkD6sIDjx48jPj4epqamP7x/RETZwQSSKBe6fPkyXF1d5dp0dHRQrVo1TJ06Ve4CmpIlS8qSRwA4f/48JBIJatWqJZeE+vr6Yu/evXjw4AFevXqFlJQU1KlTR+41GjVqpDCBjIyMxJs3b1CvXj259oCAAAQEBGT6nOvXryMxMRG+vr4ZYgHSk93ixYvjzp07GDJkSIZYVJFAtmzZEi1btkRSUhIiIyMRFRWFe/fuIS0tDSkpKRle87/q16+PdevW4ebNm5BIJN/dl68TyAoVKsDQ0BBt2rRBw4YNUbNmTXh5ecHNze2H94uI6EcwgSTKhVxdXTFlyhQAgEQigaGhIWxtbTOtWJmYmMj9/v79ewiCgIoVK2a67djYWHz8+BEAYGlpKbesUKFCCmN6//49AMDKyirL+/HlOb1791YYy4cPHyAIQoZYChcunOXX+ZbExERMmzYNe/bsQWpqKooVKwYPDw/o6ellmK/y6/0vUKAAAMiNDf3WvnytWLFi2LhxI1asWIE//vgD69evh7m5OTp27IhffvklR66kJyLKDBNIolzIxMQE5cuXz9ZzzczMYGxsjPXr12e63N7eHrdu3QIAvHnzBiVLlpQt+5LwZcbc3BwA8PbtW7n2d+/e4e7du/Dw8FD4nLlz56JEiRIZlhcsWBAWFhbQ0dHB69ev5ZZ9KxZlzJgxA4cPH8aCBQtQrVo1GBsbAwC8vb0zrPv1RURfYrKyspJVK7+1L5lxc3PDokWLkJycjKtXr2Lbtm1YtmwZXFxcMlQ8iYhyCi+iISI5VapUQXx8PARBQPny5WWP8PBwLF68GKmpqfDw8ICRkREOHTok99y///5b4XZLliwJS0vLDOvs2bMHvXv3RkpKimyM4RcVKlSAvr4+Xr58KReLnp4e5s2bh+joaBgaGsLDwwNHjhyRqwieOHFCBUcDuHr1Kry8vFC3bl1Z8nj79m28ffs2w4UsJ0+elPt9//79yJcvHypUqJClffna2rVrUadOHSQnJ8PAwADe3t6YNm0agP9fSU9EJAZWIIlITq1ateDp6Yn+/fujf//+cHR0xK1bt7Bw4ULUqFFD1i3bv39/LFiwAPny5UPVqlVx6tSpbyaQurq6GDRoEKZOnQorKyv4+voiMjISCxcuRKdOnfC/du6Wpbk4DMD4dRQMA10aMwzLyoIIgjBd8EOsGBRFcKhsoJyiaDFpUfEEcc4XtqLuA4igYBL8CgbBbLAJgiI+bbhHnoedYrp+8X9e4G4Xh5uTTCZbXxxvbm4YHx8nm80yNzdHFEW8vr6Sz+d5fn4miiKCICCXywEQhiEzMzNUKhUmJiZ4enqiWq12PHO9Xv9x1tfXR7FYZGhoiKurK87Pz8lmszw8PHBwcEAQBLy9vbU9c319TTqdplAocHd3R7PZZGlpqbU60Mks342OjrK9vU25XGZqaoru7m4uLi7o6en5sX8qSb/JgJTUpquri1qtRhRFHB4e8vLyQjqdZnZ2lnK53Lpvfn6eRCJBo9Gg0WgwPDzMysoKGxsb/3z35OQkiUSCk5MTms0m/f39lEolSqUSAPl8nkKhwM7ODvf399RqNZaXl0mlUpydnXF8fEwymWRsbIwwDOnt7QVgZGSEo6Mjdnd3qVQqZDIZNjc3WVhY6Gjmra2tH2cDAwMUi0VWV1f5+Phgb2+P9/d3MpkMi4uLPD4+cnt72/YPx/X1dS4vL6nX66RSKdbW1pienm5d72SW73K5HNVqlf39fcIw5PPzk8HBQU5PT9tWByTptwVff2+BS5IkSf/hDqQkSZJiMSAlSZIUiwEpSZKkWAxISZIkxWJASpIkKRYDUpIkSbEYkJIkSYrFgJQkSVIsBqQkSZJiMSAlSZIUiwEpSZKkWAxISZIkxfIHwGcNgvmNJSMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_model_performance(model, validation_generator, class_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 900
    },
    "id": "kO-n1ebw515Y",
    "outputId": "9c5bc8dd-7f3d-4743-a5c6-bca1fd31cdf7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x900 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Iterasi untuk semua batch di validation_generator\n",
    "all_images = []\n",
    "all_labels = []\n",
    "all_preds = []\n",
    "\n",
    "for images, labels in validation_generator:\n",
    "    # Prediksi untuk batch saat ini\n",
    "    preds = model.predict(images)\n",
    "    all_images.extend(images)\n",
    "    all_labels.extend(labels)\n",
    "    all_preds.extend(preds)\n",
    "\n",
    "    # Berhenti jika semua data sudah diproses\n",
    "    if len(all_images) >= validation_generator.n:\n",
    "        break\n",
    "\n",
    "# Konversi hasil ke array numpy\n",
    "all_images = np.array(all_images)\n",
    "all_labels = np.array(all_labels)\n",
    "all_preds = np.array(all_preds)\n",
    "\n",
    "# Visualisasi\n",
    "fig = plt.figure(figsize=(16, 9))\n",
    "for i, idx in enumerate(np.random.choice(len(all_images), size=16, replace=False)):\n",
    "    ax = fig.add_subplot(4, 4, i + 1, xticks=[], yticks=[])\n",
    "    ax.imshow(all_images[idx])\n",
    "    pred_idx = np.argmax(all_preds[idx])\n",
    "    true_idx = np.argmax(all_labels[idx])\n",
    "    ax.set_title(\"{} ({})\".format(target_labels[pred_idx], target_labels[true_idx]),\n",
    "                 color=(\"green\" if pred_idx == true_idx else \"red\"))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6dRDpvRnBQ47"
   },
   "source": [
    "# Transfer Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make Pretrained Model Xception"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "6GgloENQBSho"
   },
   "outputs": [],
   "source": [
    "# Load the Xception model with weights pre-trained on ImageNet\n",
    "base_model = Xception(weights='imagenet', include_top=False, input_shape=(224, 224, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "_gVJSRXCBdTl",
    "outputId": "88a491dd-fe92-41f5-e3c3-4e6c1438d224"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"xception\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"xception\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)        </span>┃<span style=\"font-weight: bold\"> Output Shape      </span>┃<span style=\"font-weight: bold\">    Param # </span>┃<span style=\"font-weight: bold\"> Connected to      </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
       "│ input_layer_1       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                 │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)                │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1_conv1        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">111</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">111</span>,  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">864</span> │ input_layer_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1_conv1_bn     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">111</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">111</span>,  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ block1_conv1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1_conv1_act    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">111</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">111</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block1_conv1_bn[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1_conv2        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>,  │     <span style=\"color: #00af00; text-decoration-color: #00af00\">18,432</span> │ block1_conv1_act… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1_conv2_bn     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>,  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │ block1_conv2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1_conv2_act    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block1_conv2_bn[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2_sepconv1     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>,  │      <span style=\"color: #00af00; text-decoration-color: #00af00\">8,768</span> │ block1_conv2_act… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2_sepconv1_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>,  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │ block2_sepconv1[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2_sepconv2_act │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block2_sepconv1_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2_sepconv2     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>,  │     <span style=\"color: #00af00; text-decoration-color: #00af00\">17,536</span> │ block2_sepconv2_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2_sepconv2_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>,  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │ block2_sepconv2[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv2d_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">8,192</span> │ block1_conv2_act… │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2_pool         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block2_sepconv2_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalization │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>,    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │ conv2d_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block2_pool[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │ batch_normalizat… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3_sepconv1_act │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3_sepconv1     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>,    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">33,920</span> │ block3_sepconv1_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3_sepconv1_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │ block3_sepconv1[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3_sepconv2_act │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block3_sepconv1_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3_sepconv2     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>,    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">67,840</span> │ block3_sepconv2_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3_sepconv2_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │ block3_sepconv2[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv2d_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>,    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">32,768</span> │ add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3_pool         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block3_sepconv2_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │ conv2d_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block3_pool[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │ batch_normalizat… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4_sepconv1_act │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4_sepconv1     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">188,672</span> │ block4_sepconv1_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4_sepconv1_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block4_sepconv1[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4_sepconv2_act │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block4_sepconv1_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4_sepconv2     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block4_sepconv2_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4_sepconv2_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block4_sepconv2[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv2d_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">186,368</span> │ add_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4_pool         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block4_sepconv2_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ conv2d_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block4_pool[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │ batch_normalizat… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_sepconv1_act │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_sepconv1     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block5_sepconv1_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_sepconv1_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block5_sepconv1[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_sepconv2_act │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block5_sepconv1_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_sepconv2     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block5_sepconv2_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_sepconv2_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block5_sepconv2[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_sepconv3_act │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block5_sepconv2_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_sepconv3     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block5_sepconv3_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_sepconv3_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block5_sepconv3[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block5_sepconv3_… │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │ add_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6_sepconv1_act │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6_sepconv1     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block6_sepconv1_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6_sepconv1_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block6_sepconv1[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6_sepconv2_act │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block6_sepconv1_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6_sepconv2     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block6_sepconv2_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6_sepconv2_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block6_sepconv2[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6_sepconv3_act │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block6_sepconv2_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6_sepconv3     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block6_sepconv3_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6_sepconv3_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block6_sepconv3[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block6_sepconv3_… │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │ add_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7_sepconv1_act │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7_sepconv1     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block7_sepconv1_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7_sepconv1_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block7_sepconv1[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7_sepconv2_act │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block7_sepconv1_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7_sepconv2     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block7_sepconv2_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7_sepconv2_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block7_sepconv2[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7_sepconv3_act │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block7_sepconv2_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7_sepconv3     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block7_sepconv3_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7_sepconv3_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block7_sepconv3[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block7_sepconv3_… │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │ add_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block8_sepconv1_act │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block8_sepconv1     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block8_sepconv1_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block8_sepconv1_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block8_sepconv1[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block8_sepconv2_act │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block8_sepconv1_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block8_sepconv2     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block8_sepconv2_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block8_sepconv2_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block8_sepconv2[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block8_sepconv3_act │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block8_sepconv2_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block8_sepconv3     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block8_sepconv3_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block8_sepconv3_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block8_sepconv3[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block8_sepconv3_… │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │ add_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block9_sepconv1_act │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block9_sepconv1     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block9_sepconv1_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block9_sepconv1_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block9_sepconv1[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block9_sepconv2_act │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block9_sepconv1_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block9_sepconv2     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block9_sepconv2_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block9_sepconv2_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block9_sepconv2[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block9_sepconv3_act │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block9_sepconv2_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block9_sepconv3     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block9_sepconv3_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block9_sepconv3_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block9_sepconv3[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block9_sepconv3_… │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │ add_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block10_sepconv1_a… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block10_sepconv1    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block10_sepconv1… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block10_sepconv1_bn │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block10_sepconv1… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block10_sepconv2_a… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block10_sepconv1… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block10_sepconv2    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block10_sepconv2… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block10_sepconv2_bn │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block10_sepconv2… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block10_sepconv3_a… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block10_sepconv2… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block10_sepconv3    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block10_sepconv3… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block10_sepconv3_bn │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block10_sepconv3… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block10_sepconv3… │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │ add_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block11_sepconv1_a… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_8[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block11_sepconv1    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block11_sepconv1… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block11_sepconv1_bn │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block11_sepconv1… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block11_sepconv2_a… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block11_sepconv1… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block11_sepconv2    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block11_sepconv2… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block11_sepconv2_bn │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block11_sepconv2… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block11_sepconv3_a… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block11_sepconv2… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block11_sepconv3    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block11_sepconv3… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block11_sepconv3_bn │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block11_sepconv3… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block11_sepconv3… │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │ add_8[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block12_sepconv1_a… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_9[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block12_sepconv1    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block12_sepconv1… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block12_sepconv1_bn │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block12_sepconv1… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block12_sepconv2_a… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block12_sepconv1… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block12_sepconv2    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block12_sepconv2… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block12_sepconv2_bn │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block12_sepconv2… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block12_sepconv3_a… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block12_sepconv2… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block12_sepconv3    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block12_sepconv3… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block12_sepconv3_bn │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block12_sepconv3… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block12_sepconv3… │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │ add_9[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block13_sepconv1_a… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_10[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block13_sepconv1    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block13_sepconv1… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block13_sepconv1_bn │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block13_sepconv1… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block13_sepconv2_a… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block13_sepconv1… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block13_sepconv2    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">752,024</span> │ block13_sepconv2… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block13_sepconv2_bn │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">4,096</span> │ block13_sepconv2… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv2d_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>,      │    <span style=\"color: #00af00; text-decoration-color: #00af00\">745,472</span> │ add_10[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block13_pool        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>,      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block13_sepconv2… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>,      │      <span style=\"color: #00af00; text-decoration-color: #00af00\">4,096</span> │ conv2d_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>,      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block13_pool[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)             │            │ batch_normalizat… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block14_sepconv1    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>,      │  <span style=\"color: #00af00; text-decoration-color: #00af00\">1,582,080</span> │ add_11[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1536</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block14_sepconv1_bn │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>,      │      <span style=\"color: #00af00; text-decoration-color: #00af00\">6,144</span> │ block14_sepconv1… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1536</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block14_sepconv1_a… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>,      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block14_sepconv1… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1536</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block14_sepconv2    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>,      │  <span style=\"color: #00af00; text-decoration-color: #00af00\">3,159,552</span> │ block14_sepconv1… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">2048</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block14_sepconv2_bn │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>,      │      <span style=\"color: #00af00; text-decoration-color: #00af00\">8,192</span> │ block14_sepconv2… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">2048</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block14_sepconv2_a… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>,      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block14_sepconv2… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">2048</span>)             │            │                   │\n",
       "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape     \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m   Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to     \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
       "│ input_layer_1       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ -                 │\n",
       "│ (\u001b[38;5;33mInputLayer\u001b[0m)        │ \u001b[38;5;34m3\u001b[0m)                │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1_conv1        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m111\u001b[0m, \u001b[38;5;34m111\u001b[0m,  │        \u001b[38;5;34m864\u001b[0m │ input_layer_1[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1_conv1_bn     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m111\u001b[0m, \u001b[38;5;34m111\u001b[0m,  │        \u001b[38;5;34m128\u001b[0m │ block1_conv1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1_conv1_act    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m111\u001b[0m, \u001b[38;5;34m111\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ block1_conv1_bn[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1_conv2        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m,  │     \u001b[38;5;34m18,432\u001b[0m │ block1_conv1_act… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1_conv2_bn     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m,  │        \u001b[38;5;34m256\u001b[0m │ block1_conv2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1_conv2_act    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ block1_conv2_bn[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2_sepconv1     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m,  │      \u001b[38;5;34m8,768\u001b[0m │ block1_conv2_act… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2_sepconv1_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m,  │        \u001b[38;5;34m512\u001b[0m │ block2_sepconv1[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2_sepconv2_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ block2_sepconv1_… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2_sepconv2     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m,  │     \u001b[38;5;34m17,536\u001b[0m │ block2_sepconv2_… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2_sepconv2_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m,  │        \u001b[38;5;34m512\u001b[0m │ block2_sepconv2[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv2d_4 (\u001b[38;5;33mConv2D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m55\u001b[0m, \u001b[38;5;34m55\u001b[0m,    │      \u001b[38;5;34m8,192\u001b[0m │ block1_conv2_act… │\n",
       "│                     │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2_pool         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m55\u001b[0m, \u001b[38;5;34m55\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block2_sepconv2_… │\n",
       "│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalization │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m55\u001b[0m, \u001b[38;5;34m55\u001b[0m,    │        \u001b[38;5;34m512\u001b[0m │ conv2d_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add (\u001b[38;5;33mAdd\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m55\u001b[0m, \u001b[38;5;34m55\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block2_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n",
       "│                     │ \u001b[38;5;34m128\u001b[0m)              │            │ batch_normalizat… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3_sepconv1_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m55\u001b[0m, \u001b[38;5;34m55\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3_sepconv1     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m55\u001b[0m, \u001b[38;5;34m55\u001b[0m,    │     \u001b[38;5;34m33,920\u001b[0m │ block3_sepconv1_… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m256\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3_sepconv1_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m55\u001b[0m, \u001b[38;5;34m55\u001b[0m,    │      \u001b[38;5;34m1,024\u001b[0m │ block3_sepconv1[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3_sepconv2_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m55\u001b[0m, \u001b[38;5;34m55\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block3_sepconv1_… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m256\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3_sepconv2     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m55\u001b[0m, \u001b[38;5;34m55\u001b[0m,    │     \u001b[38;5;34m67,840\u001b[0m │ block3_sepconv2_… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m256\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3_sepconv2_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m55\u001b[0m, \u001b[38;5;34m55\u001b[0m,    │      \u001b[38;5;34m1,024\u001b[0m │ block3_sepconv2[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv2d_5 (\u001b[38;5;33mConv2D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m,    │     \u001b[38;5;34m32,768\u001b[0m │ add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
       "│                     │ \u001b[38;5;34m256\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3_pool         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block3_sepconv2_… │\n",
       "│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ \u001b[38;5;34m256\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m,    │      \u001b[38;5;34m1,024\u001b[0m │ conv2d_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_1 (\u001b[38;5;33mAdd\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block3_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n",
       "│                     │ \u001b[38;5;34m256\u001b[0m)              │            │ batch_normalizat… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4_sepconv1_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ add_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m256\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4_sepconv1     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m,    │    \u001b[38;5;34m188,672\u001b[0m │ block4_sepconv1_… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4_sepconv1_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block4_sepconv1[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4_sepconv2_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block4_sepconv1_… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4_sepconv2     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block4_sepconv2_… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4_sepconv2_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block4_sepconv2[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv2d_6 (\u001b[38;5;33mConv2D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m186,368\u001b[0m │ add_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "│                     │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4_pool         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block4_sepconv2_… │\n",
       "│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ conv2d_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_2 (\u001b[38;5;33mAdd\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block4_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n",
       "│                     │ \u001b[38;5;34m728\u001b[0m)              │            │ batch_normalizat… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_sepconv1_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ add_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_sepconv1     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block5_sepconv1_… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_sepconv1_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block5_sepconv1[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_sepconv2_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block5_sepconv1_… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_sepconv2     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block5_sepconv2_… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_sepconv2_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block5_sepconv2[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_sepconv3_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block5_sepconv2_… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_sepconv3     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block5_sepconv3_… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_sepconv3_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block5_sepconv3[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_3 (\u001b[38;5;33mAdd\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block5_sepconv3_… │\n",
       "│                     │ \u001b[38;5;34m728\u001b[0m)              │            │ add_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6_sepconv1_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ add_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6_sepconv1     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block6_sepconv1_… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6_sepconv1_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block6_sepconv1[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6_sepconv2_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block6_sepconv1_… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6_sepconv2     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block6_sepconv2_… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6_sepconv2_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block6_sepconv2[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6_sepconv3_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block6_sepconv2_… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6_sepconv3     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block6_sepconv3_… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6_sepconv3_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block6_sepconv3[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_4 (\u001b[38;5;33mAdd\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block6_sepconv3_… │\n",
       "│                     │ \u001b[38;5;34m728\u001b[0m)              │            │ add_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7_sepconv1_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ add_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7_sepconv1     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block7_sepconv1_… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7_sepconv1_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block7_sepconv1[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7_sepconv2_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block7_sepconv1_… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7_sepconv2     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block7_sepconv2_… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7_sepconv2_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block7_sepconv2[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7_sepconv3_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block7_sepconv2_… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7_sepconv3     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block7_sepconv3_… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7_sepconv3_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block7_sepconv3[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_5 (\u001b[38;5;33mAdd\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block7_sepconv3_… │\n",
       "│                     │ \u001b[38;5;34m728\u001b[0m)              │            │ add_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block8_sepconv1_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ add_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block8_sepconv1     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block8_sepconv1_… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block8_sepconv1_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block8_sepconv1[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block8_sepconv2_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block8_sepconv1_… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block8_sepconv2     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block8_sepconv2_… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block8_sepconv2_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block8_sepconv2[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block8_sepconv3_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block8_sepconv2_… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block8_sepconv3     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block8_sepconv3_… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block8_sepconv3_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block8_sepconv3[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_6 (\u001b[38;5;33mAdd\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block8_sepconv3_… │\n",
       "│                     │ \u001b[38;5;34m728\u001b[0m)              │            │ add_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block9_sepconv1_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ add_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block9_sepconv1     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block9_sepconv1_… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block9_sepconv1_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block9_sepconv1[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block9_sepconv2_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block9_sepconv1_… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block9_sepconv2     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block9_sepconv2_… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block9_sepconv2_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block9_sepconv2[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block9_sepconv3_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block9_sepconv2_… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block9_sepconv3     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block9_sepconv3_… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block9_sepconv3_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block9_sepconv3[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_7 (\u001b[38;5;33mAdd\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block9_sepconv3_… │\n",
       "│                     │ \u001b[38;5;34m728\u001b[0m)              │            │ add_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block10_sepconv1_a… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ add_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block10_sepconv1    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block10_sepconv1… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block10_sepconv1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block10_sepconv1… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block10_sepconv2_a… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block10_sepconv1… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block10_sepconv2    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block10_sepconv2… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block10_sepconv2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block10_sepconv2… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block10_sepconv3_a… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block10_sepconv2… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block10_sepconv3    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block10_sepconv3… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block10_sepconv3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block10_sepconv3… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_8 (\u001b[38;5;33mAdd\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block10_sepconv3… │\n",
       "│                     │ \u001b[38;5;34m728\u001b[0m)              │            │ add_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block11_sepconv1_a… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ add_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block11_sepconv1    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block11_sepconv1… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block11_sepconv1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block11_sepconv1… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block11_sepconv2_a… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block11_sepconv1… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block11_sepconv2    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block11_sepconv2… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block11_sepconv2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block11_sepconv2… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block11_sepconv3_a… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block11_sepconv2… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block11_sepconv3    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block11_sepconv3… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block11_sepconv3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block11_sepconv3… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_9 (\u001b[38;5;33mAdd\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block11_sepconv3… │\n",
       "│                     │ \u001b[38;5;34m728\u001b[0m)              │            │ add_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block12_sepconv1_a… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ add_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block12_sepconv1    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block12_sepconv1… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block12_sepconv1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block12_sepconv1… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block12_sepconv2_a… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block12_sepconv1… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block12_sepconv2    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block12_sepconv2… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block12_sepconv2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block12_sepconv2… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block12_sepconv3_a… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block12_sepconv2… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block12_sepconv3    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block12_sepconv3… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block12_sepconv3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block12_sepconv3… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_10 (\u001b[38;5;33mAdd\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block12_sepconv3… │\n",
       "│                     │ \u001b[38;5;34m728\u001b[0m)              │            │ add_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block13_sepconv1_a… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ add_10[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block13_sepconv1    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block13_sepconv1… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block13_sepconv1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block13_sepconv1… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block13_sepconv2_a… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block13_sepconv1… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block13_sepconv2    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m752,024\u001b[0m │ block13_sepconv2… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m1024\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block13_sepconv2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m4,096\u001b[0m │ block13_sepconv2… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1024\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv2d_7 (\u001b[38;5;33mConv2D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m,      │    \u001b[38;5;34m745,472\u001b[0m │ add_10[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
       "│                     │ \u001b[38;5;34m1024\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block13_pool        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m,      │          \u001b[38;5;34m0\u001b[0m │ block13_sepconv2… │\n",
       "│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ \u001b[38;5;34m1024\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m,      │      \u001b[38;5;34m4,096\u001b[0m │ conv2d_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1024\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_11 (\u001b[38;5;33mAdd\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m,      │          \u001b[38;5;34m0\u001b[0m │ block13_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │ \u001b[38;5;34m1024\u001b[0m)             │            │ batch_normalizat… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block14_sepconv1    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m,      │  \u001b[38;5;34m1,582,080\u001b[0m │ add_11[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m1536\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block14_sepconv1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m,      │      \u001b[38;5;34m6,144\u001b[0m │ block14_sepconv1… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1536\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block14_sepconv1_a… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m,      │          \u001b[38;5;34m0\u001b[0m │ block14_sepconv1… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m1536\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block14_sepconv2    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m,      │  \u001b[38;5;34m3,159,552\u001b[0m │ block14_sepconv1… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m2048\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block14_sepconv2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m,      │      \u001b[38;5;34m8,192\u001b[0m │ block14_sepconv2… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2048\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block14_sepconv2_a… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m,      │          \u001b[38;5;34m0\u001b[0m │ block14_sepconv2… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m2048\u001b[0m)             │            │                   │\n",
       "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">20,861,480</span> (79.58 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m20,861,480\u001b[0m (79.58 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">20,806,952</span> (79.37 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m20,806,952\u001b[0m (79.37 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">54,528</span> (213.00 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m54,528\u001b[0m (213.00 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "base_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Freeze Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TtZwnb6VBvUa",
    "outputId": "b92f0538-b919-43da-c480-631b0d0ef766"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 input_layer_1\n",
      "1 block1_conv1\n",
      "2 block1_conv1_bn\n",
      "3 block1_conv1_act\n",
      "4 block1_conv2\n",
      "5 block1_conv2_bn\n",
      "6 block1_conv2_act\n",
      "7 block2_sepconv1\n",
      "8 block2_sepconv1_bn\n",
      "9 block2_sepconv2_act\n",
      "10 block2_sepconv2\n",
      "11 block2_sepconv2_bn\n",
      "12 conv2d_4\n",
      "13 block2_pool\n",
      "14 batch_normalization\n",
      "15 add\n",
      "16 block3_sepconv1_act\n",
      "17 block3_sepconv1\n",
      "18 block3_sepconv1_bn\n",
      "19 block3_sepconv2_act\n",
      "20 block3_sepconv2\n",
      "21 block3_sepconv2_bn\n",
      "22 conv2d_5\n",
      "23 block3_pool\n",
      "24 batch_normalization_1\n",
      "25 add_1\n",
      "26 block4_sepconv1_act\n",
      "27 block4_sepconv1\n",
      "28 block4_sepconv1_bn\n",
      "29 block4_sepconv2_act\n",
      "30 block4_sepconv2\n",
      "31 block4_sepconv2_bn\n",
      "32 conv2d_6\n",
      "33 block4_pool\n",
      "34 batch_normalization_2\n",
      "35 add_2\n",
      "36 block5_sepconv1_act\n",
      "37 block5_sepconv1\n",
      "38 block5_sepconv1_bn\n",
      "39 block5_sepconv2_act\n",
      "40 block5_sepconv2\n",
      "41 block5_sepconv2_bn\n",
      "42 block5_sepconv3_act\n",
      "43 block5_sepconv3\n",
      "44 block5_sepconv3_bn\n",
      "45 add_3\n",
      "46 block6_sepconv1_act\n",
      "47 block6_sepconv1\n",
      "48 block6_sepconv1_bn\n",
      "49 block6_sepconv2_act\n",
      "50 block6_sepconv2\n",
      "51 block6_sepconv2_bn\n",
      "52 block6_sepconv3_act\n",
      "53 block6_sepconv3\n",
      "54 block6_sepconv3_bn\n",
      "55 add_4\n",
      "56 block7_sepconv1_act\n",
      "57 block7_sepconv1\n",
      "58 block7_sepconv1_bn\n",
      "59 block7_sepconv2_act\n",
      "60 block7_sepconv2\n",
      "61 block7_sepconv2_bn\n",
      "62 block7_sepconv3_act\n",
      "63 block7_sepconv3\n",
      "64 block7_sepconv3_bn\n",
      "65 add_5\n",
      "66 block8_sepconv1_act\n",
      "67 block8_sepconv1\n",
      "68 block8_sepconv1_bn\n",
      "69 block8_sepconv2_act\n",
      "70 block8_sepconv2\n",
      "71 block8_sepconv2_bn\n",
      "72 block8_sepconv3_act\n",
      "73 block8_sepconv3\n",
      "74 block8_sepconv3_bn\n",
      "75 add_6\n",
      "76 block9_sepconv1_act\n",
      "77 block9_sepconv1\n",
      "78 block9_sepconv1_bn\n",
      "79 block9_sepconv2_act\n",
      "80 block9_sepconv2\n",
      "81 block9_sepconv2_bn\n",
      "82 block9_sepconv3_act\n",
      "83 block9_sepconv3\n",
      "84 block9_sepconv3_bn\n",
      "85 add_7\n",
      "86 block10_sepconv1_act\n",
      "87 block10_sepconv1\n",
      "88 block10_sepconv1_bn\n",
      "89 block10_sepconv2_act\n",
      "90 block10_sepconv2\n",
      "91 block10_sepconv2_bn\n",
      "92 block10_sepconv3_act\n",
      "93 block10_sepconv3\n",
      "94 block10_sepconv3_bn\n",
      "95 add_8\n",
      "96 block11_sepconv1_act\n",
      "97 block11_sepconv1\n",
      "98 block11_sepconv1_bn\n",
      "99 block11_sepconv2_act\n",
      "100 block11_sepconv2\n",
      "101 block11_sepconv2_bn\n",
      "102 block11_sepconv3_act\n",
      "103 block11_sepconv3\n",
      "104 block11_sepconv3_bn\n",
      "105 add_9\n",
      "106 block12_sepconv1_act\n",
      "107 block12_sepconv1\n",
      "108 block12_sepconv1_bn\n",
      "109 block12_sepconv2_act\n",
      "110 block12_sepconv2\n",
      "111 block12_sepconv2_bn\n",
      "112 block12_sepconv3_act\n",
      "113 block12_sepconv3\n",
      "114 block12_sepconv3_bn\n",
      "115 add_10\n",
      "116 block13_sepconv1_act\n",
      "117 block13_sepconv1\n",
      "118 block13_sepconv1_bn\n",
      "119 block13_sepconv2_act\n",
      "120 block13_sepconv2\n",
      "121 block13_sepconv2_bn\n",
      "122 conv2d_7\n",
      "123 block13_pool\n",
      "124 batch_normalization_3\n",
      "125 add_11\n",
      "126 block14_sepconv1\n",
      "127 block14_sepconv1_bn\n",
      "128 block14_sepconv1_act\n",
      "129 block14_sepconv2\n",
      "130 block14_sepconv2_bn\n",
      "131 block14_sepconv2_act\n"
     ]
    }
   ],
   "source": [
    "for i, layer in enumerate(base_model.layers):\n",
    "  print(i, layer.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "PPO9wL0hBw8a"
   },
   "outputs": [],
   "source": [
    "# Freeze the layers\n",
    "for layer in base_model.layers[:123]: # include the layer 122\n",
    "    layer.trainable = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "v4aHIqihCDUu"
   },
   "outputs": [],
   "source": [
    "# Create the new model\n",
    "Xception = base_model.output\n",
    "Xception = GlobalAveragePooling2D()(Xception)\n",
    "Xception = Dropout(0.5)(Xception)\n",
    "Xception = Dense(6, activation='softmax')(Xception)\n",
    "\n",
    "transfer_Xception_model = Model(inputs=base_model.input, outputs=Xception)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compile Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "NNlcG8yf-rI6"
   },
   "outputs": [],
   "source": [
    "# Compile Model\n",
    "transfer_Xception_model.compile(\n",
    "    optimizer=Adam(learning_rate=0.0001),  # Lower Learning Rate for Greate Transfer Learning\n",
    "    loss='categorical_crossentropy',      # Clasification Multiclass\n",
    "    metrics=['accuracy']                  # use Accuracy for Metrics\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "Tm5w5kOXDtWA",
    "outputId": "92ce7c33-60fa-4acb-8189-e53b027a13d3"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_15\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"functional_15\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)        </span>┃<span style=\"font-weight: bold\"> Output Shape      </span>┃<span style=\"font-weight: bold\">    Param # </span>┃<span style=\"font-weight: bold\"> Connected to      </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
       "│ input_layer_1       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                 │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)                │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1_conv1        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">111</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">111</span>,  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">864</span> │ input_layer_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1_conv1_bn     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">111</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">111</span>,  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ block1_conv1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1_conv1_act    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">111</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">111</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block1_conv1_bn[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1_conv2        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>,  │     <span style=\"color: #00af00; text-decoration-color: #00af00\">18,432</span> │ block1_conv1_act… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1_conv2_bn     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>,  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │ block1_conv2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1_conv2_act    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block1_conv2_bn[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2_sepconv1     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>,  │      <span style=\"color: #00af00; text-decoration-color: #00af00\">8,768</span> │ block1_conv2_act… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2_sepconv1_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>,  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │ block2_sepconv1[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2_sepconv2_act │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block2_sepconv1_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2_sepconv2     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>,  │     <span style=\"color: #00af00; text-decoration-color: #00af00\">17,536</span> │ block2_sepconv2_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2_sepconv2_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">109</span>,  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │ block2_sepconv2[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv2d_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">8,192</span> │ block1_conv2_act… │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2_pool         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block2_sepconv2_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalization │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>,    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │ conv2d_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block2_pool[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │ batch_normalizat… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3_sepconv1_act │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3_sepconv1     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>,    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">33,920</span> │ block3_sepconv1_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3_sepconv1_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │ block3_sepconv1[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3_sepconv2_act │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block3_sepconv1_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3_sepconv2     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>,    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">67,840</span> │ block3_sepconv2_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3_sepconv2_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">55</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │ block3_sepconv2[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv2d_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>,    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">32,768</span> │ add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3_pool         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block3_sepconv2_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │ conv2d_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block3_pool[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │ batch_normalizat… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4_sepconv1_act │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4_sepconv1     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">188,672</span> │ block4_sepconv1_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4_sepconv1_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block4_sepconv1[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4_sepconv2_act │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block4_sepconv1_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4_sepconv2     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block4_sepconv2_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4_sepconv2_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block4_sepconv2[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv2d_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">186,368</span> │ add_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4_pool         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block4_sepconv2_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ conv2d_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block4_pool[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │ batch_normalizat… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_sepconv1_act │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_sepconv1     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block5_sepconv1_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_sepconv1_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block5_sepconv1[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_sepconv2_act │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block5_sepconv1_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_sepconv2     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block5_sepconv2_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_sepconv2_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block5_sepconv2[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_sepconv3_act │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block5_sepconv2_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_sepconv3     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block5_sepconv3_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_sepconv3_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block5_sepconv3[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block5_sepconv3_… │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │ add_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6_sepconv1_act │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6_sepconv1     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block6_sepconv1_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6_sepconv1_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block6_sepconv1[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6_sepconv2_act │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block6_sepconv1_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6_sepconv2     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block6_sepconv2_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6_sepconv2_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block6_sepconv2[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6_sepconv3_act │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block6_sepconv2_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6_sepconv3     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block6_sepconv3_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6_sepconv3_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block6_sepconv3[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block6_sepconv3_… │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │ add_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7_sepconv1_act │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7_sepconv1     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block7_sepconv1_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7_sepconv1_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block7_sepconv1[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7_sepconv2_act │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block7_sepconv1_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7_sepconv2     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block7_sepconv2_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7_sepconv2_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block7_sepconv2[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7_sepconv3_act │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block7_sepconv2_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7_sepconv3     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block7_sepconv3_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7_sepconv3_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block7_sepconv3[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block7_sepconv3_… │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │ add_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block8_sepconv1_act │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block8_sepconv1     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block8_sepconv1_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block8_sepconv1_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block8_sepconv1[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block8_sepconv2_act │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block8_sepconv1_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block8_sepconv2     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block8_sepconv2_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block8_sepconv2_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block8_sepconv2[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block8_sepconv3_act │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block8_sepconv2_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block8_sepconv3     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block8_sepconv3_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block8_sepconv3_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block8_sepconv3[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block8_sepconv3_… │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │ add_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block9_sepconv1_act │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block9_sepconv1     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block9_sepconv1_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block9_sepconv1_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block9_sepconv1[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block9_sepconv2_act │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block9_sepconv1_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block9_sepconv2     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block9_sepconv2_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block9_sepconv2_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block9_sepconv2[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block9_sepconv3_act │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block9_sepconv2_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block9_sepconv3     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block9_sepconv3_… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block9_sepconv3_bn  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block9_sepconv3[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block9_sepconv3_… │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │ add_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block10_sepconv1_a… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block10_sepconv1    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block10_sepconv1… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block10_sepconv1_bn │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block10_sepconv1… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block10_sepconv2_a… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block10_sepconv1… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block10_sepconv2    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block10_sepconv2… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block10_sepconv2_bn │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block10_sepconv2… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block10_sepconv3_a… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block10_sepconv2… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block10_sepconv3    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block10_sepconv3… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block10_sepconv3_bn │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block10_sepconv3… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block10_sepconv3… │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │ add_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block11_sepconv1_a… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_8[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block11_sepconv1    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block11_sepconv1… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block11_sepconv1_bn │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block11_sepconv1… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block11_sepconv2_a… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block11_sepconv1… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block11_sepconv2    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block11_sepconv2… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block11_sepconv2_bn │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block11_sepconv2… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block11_sepconv3_a… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block11_sepconv2… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block11_sepconv3    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block11_sepconv3… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block11_sepconv3_bn │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block11_sepconv3… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block11_sepconv3… │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │ add_8[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block12_sepconv1_a… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_9[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block12_sepconv1    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block12_sepconv1… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block12_sepconv1_bn │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block12_sepconv1… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block12_sepconv2_a… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block12_sepconv1… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block12_sepconv2    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block12_sepconv2… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block12_sepconv2_bn │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block12_sepconv2… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block12_sepconv3_a… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block12_sepconv2… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block12_sepconv3    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block12_sepconv3… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block12_sepconv3_bn │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block12_sepconv3… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block12_sepconv3… │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │ add_9[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block13_sepconv1_a… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_10[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block13_sepconv1    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">536,536</span> │ block13_sepconv1… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block13_sepconv1_bn │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">2,912</span> │ block13_sepconv1… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block13_sepconv2_a… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block13_sepconv1… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">728</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block13_sepconv2    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">752,024</span> │ block13_sepconv2… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block13_sepconv2_bn │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">4,096</span> │ block13_sepconv2… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv2d_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>,      │    <span style=\"color: #00af00; text-decoration-color: #00af00\">745,472</span> │ add_10[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block13_pool        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>,      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block13_sepconv2… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>,      │      <span style=\"color: #00af00; text-decoration-color: #00af00\">4,096</span> │ conv2d_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>,      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block13_pool[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)             │            │ batch_normalizat… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block14_sepconv1    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>,      │  <span style=\"color: #00af00; text-decoration-color: #00af00\">1,582,080</span> │ add_11[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1536</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block14_sepconv1_bn │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>,      │      <span style=\"color: #00af00; text-decoration-color: #00af00\">6,144</span> │ block14_sepconv1… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1536</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block14_sepconv1_a… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>,      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block14_sepconv1… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1536</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block14_sepconv2    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>,      │  <span style=\"color: #00af00; text-decoration-color: #00af00\">3,159,552</span> │ block14_sepconv1… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)   │ <span style=\"color: #00af00; text-decoration-color: #00af00\">2048</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block14_sepconv2_bn │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>,      │      <span style=\"color: #00af00; text-decoration-color: #00af00\">8,192</span> │ block14_sepconv2… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">2048</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block14_sepconv2_a… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>,      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block14_sepconv2… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">2048</span>)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ global_average_poo… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2048</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block14_sepconv2… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePool…</span> │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dropout_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2048</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ global_average_p… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>)         │     <span style=\"color: #00af00; text-decoration-color: #00af00\">12,294</span> │ dropout_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]   │\n",
       "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape     \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m   Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to     \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
       "│ input_layer_1       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ -                 │\n",
       "│ (\u001b[38;5;33mInputLayer\u001b[0m)        │ \u001b[38;5;34m3\u001b[0m)                │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1_conv1        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m111\u001b[0m, \u001b[38;5;34m111\u001b[0m,  │        \u001b[38;5;34m864\u001b[0m │ input_layer_1[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1_conv1_bn     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m111\u001b[0m, \u001b[38;5;34m111\u001b[0m,  │        \u001b[38;5;34m128\u001b[0m │ block1_conv1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1_conv1_act    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m111\u001b[0m, \u001b[38;5;34m111\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ block1_conv1_bn[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1_conv2        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m,  │     \u001b[38;5;34m18,432\u001b[0m │ block1_conv1_act… │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1_conv2_bn     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m,  │        \u001b[38;5;34m256\u001b[0m │ block1_conv2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1_conv2_act    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ block1_conv2_bn[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2_sepconv1     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m,  │      \u001b[38;5;34m8,768\u001b[0m │ block1_conv2_act… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2_sepconv1_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m,  │        \u001b[38;5;34m512\u001b[0m │ block2_sepconv1[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2_sepconv2_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ block2_sepconv1_… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2_sepconv2     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m,  │     \u001b[38;5;34m17,536\u001b[0m │ block2_sepconv2_… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2_sepconv2_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m109\u001b[0m, \u001b[38;5;34m109\u001b[0m,  │        \u001b[38;5;34m512\u001b[0m │ block2_sepconv2[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv2d_4 (\u001b[38;5;33mConv2D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m55\u001b[0m, \u001b[38;5;34m55\u001b[0m,    │      \u001b[38;5;34m8,192\u001b[0m │ block1_conv2_act… │\n",
       "│                     │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2_pool         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m55\u001b[0m, \u001b[38;5;34m55\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block2_sepconv2_… │\n",
       "│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalization │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m55\u001b[0m, \u001b[38;5;34m55\u001b[0m,    │        \u001b[38;5;34m512\u001b[0m │ conv2d_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add (\u001b[38;5;33mAdd\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m55\u001b[0m, \u001b[38;5;34m55\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block2_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n",
       "│                     │ \u001b[38;5;34m128\u001b[0m)              │            │ batch_normalizat… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3_sepconv1_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m55\u001b[0m, \u001b[38;5;34m55\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3_sepconv1     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m55\u001b[0m, \u001b[38;5;34m55\u001b[0m,    │     \u001b[38;5;34m33,920\u001b[0m │ block3_sepconv1_… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m256\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3_sepconv1_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m55\u001b[0m, \u001b[38;5;34m55\u001b[0m,    │      \u001b[38;5;34m1,024\u001b[0m │ block3_sepconv1[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3_sepconv2_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m55\u001b[0m, \u001b[38;5;34m55\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block3_sepconv1_… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m256\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3_sepconv2     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m55\u001b[0m, \u001b[38;5;34m55\u001b[0m,    │     \u001b[38;5;34m67,840\u001b[0m │ block3_sepconv2_… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m256\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3_sepconv2_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m55\u001b[0m, \u001b[38;5;34m55\u001b[0m,    │      \u001b[38;5;34m1,024\u001b[0m │ block3_sepconv2[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv2d_5 (\u001b[38;5;33mConv2D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m,    │     \u001b[38;5;34m32,768\u001b[0m │ add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
       "│                     │ \u001b[38;5;34m256\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3_pool         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block3_sepconv2_… │\n",
       "│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ \u001b[38;5;34m256\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m,    │      \u001b[38;5;34m1,024\u001b[0m │ conv2d_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m256\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_1 (\u001b[38;5;33mAdd\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block3_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n",
       "│                     │ \u001b[38;5;34m256\u001b[0m)              │            │ batch_normalizat… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4_sepconv1_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ add_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m256\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4_sepconv1     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m,    │    \u001b[38;5;34m188,672\u001b[0m │ block4_sepconv1_… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4_sepconv1_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block4_sepconv1[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4_sepconv2_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block4_sepconv1_… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4_sepconv2     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block4_sepconv2_… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4_sepconv2_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block4_sepconv2[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv2d_6 (\u001b[38;5;33mConv2D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m186,368\u001b[0m │ add_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "│                     │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4_pool         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block4_sepconv2_… │\n",
       "│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ conv2d_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_2 (\u001b[38;5;33mAdd\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block4_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n",
       "│                     │ \u001b[38;5;34m728\u001b[0m)              │            │ batch_normalizat… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_sepconv1_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ add_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_sepconv1     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block5_sepconv1_… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_sepconv1_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block5_sepconv1[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_sepconv2_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block5_sepconv1_… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_sepconv2     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block5_sepconv2_… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_sepconv2_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block5_sepconv2[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_sepconv3_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block5_sepconv2_… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_sepconv3     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block5_sepconv3_… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_sepconv3_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block5_sepconv3[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_3 (\u001b[38;5;33mAdd\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block5_sepconv3_… │\n",
       "│                     │ \u001b[38;5;34m728\u001b[0m)              │            │ add_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6_sepconv1_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ add_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6_sepconv1     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block6_sepconv1_… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6_sepconv1_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block6_sepconv1[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6_sepconv2_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block6_sepconv1_… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6_sepconv2     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block6_sepconv2_… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6_sepconv2_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block6_sepconv2[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6_sepconv3_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block6_sepconv2_… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6_sepconv3     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block6_sepconv3_… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block6_sepconv3_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block6_sepconv3[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_4 (\u001b[38;5;33mAdd\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block6_sepconv3_… │\n",
       "│                     │ \u001b[38;5;34m728\u001b[0m)              │            │ add_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7_sepconv1_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ add_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7_sepconv1     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block7_sepconv1_… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7_sepconv1_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block7_sepconv1[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7_sepconv2_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block7_sepconv1_… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7_sepconv2     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block7_sepconv2_… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7_sepconv2_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block7_sepconv2[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7_sepconv3_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block7_sepconv2_… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7_sepconv3     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block7_sepconv3_… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block7_sepconv3_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block7_sepconv3[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_5 (\u001b[38;5;33mAdd\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block7_sepconv3_… │\n",
       "│                     │ \u001b[38;5;34m728\u001b[0m)              │            │ add_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block8_sepconv1_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ add_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block8_sepconv1     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block8_sepconv1_… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block8_sepconv1_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block8_sepconv1[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block8_sepconv2_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block8_sepconv1_… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block8_sepconv2     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block8_sepconv2_… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block8_sepconv2_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block8_sepconv2[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block8_sepconv3_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block8_sepconv2_… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block8_sepconv3     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block8_sepconv3_… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block8_sepconv3_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block8_sepconv3[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_6 (\u001b[38;5;33mAdd\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block8_sepconv3_… │\n",
       "│                     │ \u001b[38;5;34m728\u001b[0m)              │            │ add_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block9_sepconv1_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ add_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block9_sepconv1     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block9_sepconv1_… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block9_sepconv1_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block9_sepconv1[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block9_sepconv2_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block9_sepconv1_… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block9_sepconv2     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block9_sepconv2_… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block9_sepconv2_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block9_sepconv2[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block9_sepconv3_act │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block9_sepconv2_… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block9_sepconv3     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block9_sepconv3_… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block9_sepconv3_bn  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block9_sepconv3[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_7 (\u001b[38;5;33mAdd\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block9_sepconv3_… │\n",
       "│                     │ \u001b[38;5;34m728\u001b[0m)              │            │ add_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block10_sepconv1_a… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ add_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block10_sepconv1    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block10_sepconv1… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block10_sepconv1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block10_sepconv1… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block10_sepconv2_a… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block10_sepconv1… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block10_sepconv2    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block10_sepconv2… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block10_sepconv2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block10_sepconv2… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block10_sepconv3_a… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block10_sepconv2… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block10_sepconv3    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block10_sepconv3… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block10_sepconv3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block10_sepconv3… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_8 (\u001b[38;5;33mAdd\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block10_sepconv3… │\n",
       "│                     │ \u001b[38;5;34m728\u001b[0m)              │            │ add_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block11_sepconv1_a… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ add_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block11_sepconv1    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block11_sepconv1… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block11_sepconv1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block11_sepconv1… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block11_sepconv2_a… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block11_sepconv1… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block11_sepconv2    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block11_sepconv2… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block11_sepconv2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block11_sepconv2… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block11_sepconv3_a… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block11_sepconv2… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block11_sepconv3    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block11_sepconv3… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block11_sepconv3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block11_sepconv3… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_9 (\u001b[38;5;33mAdd\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block11_sepconv3… │\n",
       "│                     │ \u001b[38;5;34m728\u001b[0m)              │            │ add_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block12_sepconv1_a… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ add_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block12_sepconv1    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block12_sepconv1… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block12_sepconv1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block12_sepconv1… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block12_sepconv2_a… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block12_sepconv1… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block12_sepconv2    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block12_sepconv2… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block12_sepconv2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block12_sepconv2… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block12_sepconv3_a… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block12_sepconv2… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block12_sepconv3    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block12_sepconv3… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block12_sepconv3_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block12_sepconv3… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_10 (\u001b[38;5;33mAdd\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block12_sepconv3… │\n",
       "│                     │ \u001b[38;5;34m728\u001b[0m)              │            │ add_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block13_sepconv1_a… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ add_10[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block13_sepconv1    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m536,536\u001b[0m │ block13_sepconv1… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block13_sepconv1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m2,912\u001b[0m │ block13_sepconv1… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block13_sepconv2_a… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block13_sepconv1… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m728\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block13_sepconv2    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │    \u001b[38;5;34m752,024\u001b[0m │ block13_sepconv2… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m1024\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block13_sepconv2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m,    │      \u001b[38;5;34m4,096\u001b[0m │ block13_sepconv2… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1024\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv2d_7 (\u001b[38;5;33mConv2D\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m,      │    \u001b[38;5;34m745,472\u001b[0m │ add_10[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
       "│                     │ \u001b[38;5;34m1024\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block13_pool        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m,      │          \u001b[38;5;34m0\u001b[0m │ block13_sepconv2… │\n",
       "│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ \u001b[38;5;34m1024\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m,      │      \u001b[38;5;34m4,096\u001b[0m │ conv2d_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1024\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ add_11 (\u001b[38;5;33mAdd\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m,      │          \u001b[38;5;34m0\u001b[0m │ block13_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │ \u001b[38;5;34m1024\u001b[0m)             │            │ batch_normalizat… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block14_sepconv1    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m,      │  \u001b[38;5;34m1,582,080\u001b[0m │ add_11[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m1536\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block14_sepconv1_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m,      │      \u001b[38;5;34m6,144\u001b[0m │ block14_sepconv1… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m1536\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block14_sepconv1_a… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m,      │          \u001b[38;5;34m0\u001b[0m │ block14_sepconv1… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m1536\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block14_sepconv2    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m,      │  \u001b[38;5;34m3,159,552\u001b[0m │ block14_sepconv1… │\n",
       "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)   │ \u001b[38;5;34m2048\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block14_sepconv2_bn │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m,      │      \u001b[38;5;34m8,192\u001b[0m │ block14_sepconv2… │\n",
       "│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m2048\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block14_sepconv2_a… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m,      │          \u001b[38;5;34m0\u001b[0m │ block14_sepconv2… │\n",
       "│ (\u001b[38;5;33mActivation\u001b[0m)        │ \u001b[38;5;34m2048\u001b[0m)             │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ global_average_poo… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\u001b[0m)      │          \u001b[38;5;34m0\u001b[0m │ block14_sepconv2… │\n",
       "│ (\u001b[38;5;33mGlobalAveragePool…\u001b[0m │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\u001b[0m)      │          \u001b[38;5;34m0\u001b[0m │ global_average_p… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m)         │     \u001b[38;5;34m12,294\u001b[0m │ dropout_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]   │\n",
       "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">20,873,774</span> (79.63 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m20,873,774\u001b[0m (79.63 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">4,763,142</span> (18.17 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m4,763,142\u001b[0m (18.17 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">16,110,632</span> (61.46 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m16,110,632\u001b[0m (61.46 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "transfer_Xception_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make a Callback ModelCheckpoint for TL Xception"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "LsH6o-WeIWto"
   },
   "outputs": [],
   "source": [
    "# Callback ModelCheckpoint\n",
    "checkpointer_Xception = ModelCheckpoint(\n",
    "    filepath = 'Xception_item.keras',\n",
    "    verbose = 1,\n",
    "    save_best_only = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Augmentation Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2024 images belonging to 6 classes.\n",
      "Found 503 images belonging to 6 classes.\n"
     ]
    }
   ],
   "source": [
    "train_datagen_Xception = ImageDataGenerator(\n",
    "    rescale=1./255,\n",
    "    rotation_range=30,\n",
    "    width_shift_range=0.3,\n",
    "    height_shift_range=0.3,\n",
    "    brightness_range=[0.7, 1.3],\n",
    "    horizontal_flip=True,\n",
    "    vertical_flip=True,\n",
    "    zoom_range=0.3,\n",
    "    validation_split=0.2\n",
    ")\n",
    "\n",
    "# Generator validasi\n",
    "val_datagen_Xception = ImageDataGenerator(\n",
    "    rescale=1./255,                     \n",
    "    validation_split=0.2             \n",
    ")\n",
    "\n",
    "# Load data\n",
    "train_generator_Xception = train_datagen_Xception.flow_from_directory(\n",
    "    ds,\n",
    "    target_size=(224, 224),\n",
    "    batch_size=32,\n",
    "    seed=42,\n",
    "    class_mode='categorical',\n",
    "    shuffle=True,\n",
    "    subset='training'\n",
    ")\n",
    "\n",
    "validation_generator_Xception = val_datagen_Xception.flow_from_directory(\n",
    "    ds,\n",
    "    target_size=(224, 224),\n",
    "    batch_size=32,\n",
    "    seed=42,\n",
    "    class_mode='categorical',\n",
    "    shuffle=False,\n",
    "    subset='validation'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Xception Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "background_save": true,
     "base_uri": "https://localhost:8080/"
    },
    "id": "OzqQhp6-88FV",
    "outputId": "3bda116c-20cf-4535-de8e-b8d32ad5e46d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'Node' object has no attribute 'inbound_layers'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[39], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Train the model\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m history_xception \u001b[38;5;241m=\u001b[39m \u001b[43mtransfer_Xception_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_generator_Xception\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m      3\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m32\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m      4\u001b[0m \u001b[43m        \u001b[49m\u001b[43msteps_per_epoch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtrain_generator_Xception\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m      5\u001b[0m \u001b[43m        \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m50\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m      6\u001b[0m \u001b[43m        \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mvalidation_generator_Xception\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m      7\u001b[0m \u001b[43m        \u001b[49m\u001b[43mvalidation_steps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mvalidation_generator_Xception\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m      8\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mcheckpointer_Xception\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreduce_lr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mearly_stopping\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mWandbCallback\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m      9\u001b[0m \u001b[43m        \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m     10\u001b[0m \u001b[43m        \u001b[49m\u001b[43mshuffle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\keras\\src\\utils\\traceback_utils.py:122\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    119\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[0;32m    120\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[0;32m    121\u001b[0m     \u001b[38;5;66;03m# `keras.config.disable_traceback_filtering()`\u001b[39;00m\n\u001b[1;32m--> 122\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    123\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m    124\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\wandb\\integration\\keras\\keras.py:667\u001b[0m, in \u001b[0;36mWandbCallback.on_train_batch_end\u001b[1;34m(self, batch, logs)\u001b[0m\n\u001b[0;32m    664\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mon_train_batch_end\u001b[39m(\u001b[38;5;28mself\u001b[39m, batch, logs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m    665\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msave_graph \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_graph_rendered:\n\u001b[0;32m    666\u001b[0m         \u001b[38;5;66;03m# Couldn't do this in train_begin because keras may still not be built\u001b[39;00m\n\u001b[1;32m--> 667\u001b[0m         wandb\u001b[38;5;241m.\u001b[39mrun\u001b[38;5;241m.\u001b[39msummary[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgraph\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mwandb\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mGraph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_keras\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    668\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_graph_rendered \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m    670\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlog_batch_frequency \u001b[38;5;129;01mand\u001b[39;00m batch \u001b[38;5;241m%\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlog_batch_frequency \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\wandb\\sdk\\data_types\\graph.py:357\u001b[0m, in \u001b[0;36mGraph.from_keras\u001b[1;34m(cls, model)\u001b[0m\n\u001b[0;32m    354\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m relevant_nodes \u001b[38;5;129;01mand\u001b[39;00m in_node \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m relevant_nodes:\n\u001b[0;32m    355\u001b[0m     \u001b[38;5;66;03m# node is not part of the current network\u001b[39;00m\n\u001b[0;32m    356\u001b[0m     \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[1;32m--> 357\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m in_layer \u001b[38;5;129;01min\u001b[39;00m _nest(\u001b[43min_node\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minbound_layers\u001b[49m):\n\u001b[0;32m    358\u001b[0m     inbound_keras_node \u001b[38;5;241m=\u001b[39m Node\u001b[38;5;241m.\u001b[39mfrom_keras(in_layer)\n\u001b[0;32m    360\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m inbound_keras_node\u001b[38;5;241m.\u001b[39mid \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m graph\u001b[38;5;241m.\u001b[39mnodes_by_id:\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'Node' object has no attribute 'inbound_layers'"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "history_xception = transfer_Xception_model.fit(train_generator_Xception,\n",
    "        batch_size = 32,\n",
    "        steps_per_epoch=len(train_generator_Xception),\n",
    "        epochs=15,\n",
    "        validation_data=(validation_generator_Xception),\n",
    "        validation_steps=len(validation_generator_Xception),\n",
    "        callbacks = [checkpointer_Xception, reduce_lr, early_stopping],\n",
    "        verbose=2,\n",
    "        shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Learning Rate Xception"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "-Miy9bmlc8pA"
   },
   "outputs": [],
   "source": [
    "def plot_learning_curves_Xception(history):\n",
    "    # Convert the history.history dict to a pandas DataFrame\n",
    "    df = pd.DataFrame(history.history)\n",
    "\n",
    "    # Add Epoch Column to dataframe\n",
    "    df['epoch'] = range(1, len(df) + 1)\n",
    "\n",
    "    # Set the style of seaborn for better visualization\n",
    "    sns.set(rc={'axes.facecolor': '#f0f0fc'}, style='darkgrid')\n",
    "\n",
    "    # Plotting the learning curves\n",
    "    plt.figure(figsize=(15, 6))\n",
    "\n",
    "    # Plotting the training and validation loss\n",
    "    plt.subplot(1, 2, 1)\n",
    "    sns.lineplot(x=df['epoch'], y=df['loss'], color='royalblue', label='Train Loss')\n",
    "    sns.lineplot(x=df['epoch'], y=df['val_loss'], color='orangered', linestyle='--', label='Validation Loss')\n",
    "    plt.title('Loss Evolution')\n",
    "    plt.xlabel('Epoch')\n",
    "\n",
    "    # Plotting the training and validation accuracy\n",
    "    plt.subplot(1, 2, 2)\n",
    "    sns.lineplot(x=df['epoch'], y=df['accuracy'], color='royalblue', label='Train Accuracy')\n",
    "    sns.lineplot(x=df['epoch'], y=df['val_accuracy'], color='orangered', linestyle='--', label='Validation Accuracy')\n",
    "    plt.title('Accuracy Evolution')\n",
    "    plt.xlabel('Epoch')\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 301
    },
    "id": "4Nxv7LL8EIAq",
    "outputId": "e8b6b6fe-4d50-479a-867a-6aa3b597132f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_learning_curves_Xception(history_xception)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Xception Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "lXXBgdjLc4rL"
   },
   "outputs": [],
   "source": [
    "def evaluate_model_performance(model, val_generator, class_labels):\n",
    "    # Getting all the true labels for the validation set\n",
    "    true_labels = val_generator.classes\n",
    "\n",
    "    # Get the class labels (names) from the generator\n",
    "    class_labels = list(val_generator.class_indices.keys())\n",
    "\n",
    "    # To get the predicted labels, we predict using the model\n",
    "    predictions = model.predict(val_generator, steps=len(val_generator))\n",
    "\n",
    "    # Take the argmax to get the predicted class indices.\n",
    "    predicted_labels = np.argmax(predictions, axis=1)\n",
    "\n",
    "    # Extracting true labels from the validation generator\n",
    "    true_labels = val_generator.classes\n",
    "\n",
    "    # Classification report\n",
    "    report = classification_report(true_labels, predicted_labels, target_names=class_labels)\n",
    "    print(report)\n",
    "    print('\\n')\n",
    "\n",
    "    # Define a custom colormap\n",
    "    colors = [\"white\", \"royalblue\"]\n",
    "    cmap_cm = LinearSegmentedColormap.from_list(\"cmap_cm\", colors)\n",
    "\n",
    "    # Confusion Matrix\n",
    "    cm = confusion_matrix(true_labels, predicted_labels)\n",
    "\n",
    "    # Plotting confusion matrix using seaborn\n",
    "    plt.figure(figsize=(8,6))\n",
    "    sns.heatmap(cm, annot=True, cmap=cmap_cm, fmt='d', xticklabels=class_labels, yticklabels=class_labels)\n",
    "    plt.xlabel('Predicted Labels')\n",
    "    plt.ylabel('True Labels')\n",
    "    plt.title('Confusion Matrix')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 999
    },
    "id": "vCbMp-oKGzx6",
    "outputId": "c54a63f7-2f0a-4d69-eca9-d410d8992140"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 396ms/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "   cardboard       0.96      0.82      0.89        80\n",
      "       glass       0.88      0.91      0.89       100\n",
      "       metal       0.84      0.91      0.88        82\n",
      "       paper       0.84      0.94      0.89       118\n",
      "     plastic       0.88      0.79      0.84        96\n",
      "       trash       0.83      0.70      0.76        27\n",
      "\n",
      "    accuracy                           0.87       503\n",
      "   macro avg       0.87      0.85      0.86       503\n",
      "weighted avg       0.87      0.87      0.87       503\n",
      "\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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069YNnp6eEAQBly5dwpIlS2BqaopOnTqJHSIRERER/UcjEshNmzZh/Pjx6NGjh6ytadOmsLOzw+bNmwtUAmltpYu61YxhbaWH9EwBMU8zcPpqMlLSBACASREJ6rkbw95GHzo6EjyLz8Tpa8l48TpL5Mi1w9mzZ7F06VLcv38fVlZW6N69O/r27VsglkwQS1xcHNq2bYPly1fAy8tL7HC0ilQqxbZfNmHP7h14/jwOpUuXRadvu+HbLt3FDk2r8Zz8OlKpFJs3b8TOnTvx7Fkc7Ozs4e/fD23atBU7NI3Djw7FNGISzZMnT1C/fv0c7fXq1UNMTIwIEalHyWK66OxrjoxMAQfOJODstWTYWeujXT1TAIC+HvBtY3OULKaHE5eS8Oc/idDXk6BjIzOYGPEs/pxr165h0KBBqFChAoKCgtC2bVsEBARg3bp1YoemtZ4+fQp//75ISEgQOxSttGzpQgT+HIBaXt5YvGQlvu3aA+vWLMeyJQvFDk1r8Zz8ekFBgVi2bBk6dOiIlStXw9vbG+PH/4iDB/8QOzTSIhpRgSxdujRu3boFW1tbufabN2+iePHiIkWlevXcjfH8dSb2n06UtaVnCGhYwxjmJjqobG8AI0MJthx8i6TU7Irks1eZ6N68KMqW0sfdmHSxQtcKQUFBqFy5suzyl/Xr10dmZiZWr16NXr16wcjISOQItYdUKsW+ffuwaNFCCILY0WinN69fY9eOEHzTvhMmTJrxX6sPSpWyxo9jhqG9X2fYl68gZohaheekaqSkpGDr1i3o2bMn+vcfAACoXdsb4eG3ERKyFa1btxE5Qg3D2o1CGlGB7Nq1K2bOnIlt27YhIiICERERCAkJwaxZs9CxY0exw1MJIwMJypbUw/X7aXLt9x9lYP2Bt3iXJEWlcga4F5suSx4BIDlVwLr9b5g8fkZ6ejpCQ0PRtGlTufbmzZsjKSlJtjg95c3du3cxY8Z0fPNNeyxatEjscLTSw4cPkJWVhXr1G8m1e3h6QSqV4vz5MyJFpp14TqqGgYEBQkK24/vv+8i16+vrIy2NnzOUdxpRgezVqxceP36MefPmISsrC4IgQE9PD127dsXgwYPFDk8lilvoQkdHgpRUAS28TeBQxgAAcP9ROv4OS0ZGpgDLorq48yAd3q5F4OpgCCNDCZ68yMRfl5MR/45jID8lNjYWGRkZsLe3l2u3s7MDAERHR8PHx0eEyLSTjY0Njh49Bmtra4SGhoodjlYqamEBAHj69Ilc+6NHsQCAx48e5XdIWo3npGro6urCyckJACAIAuLj47F37x6cP38e06fPFDk6zcMCpGIakUDq6Ohg8uTJGDlyJP79918A2Zc0NDU1FTky1TE2zC72NvMyQfTTdBw4k4BiZrrwqVYERU1N8cfZROjqSFDDyQhvk7Jw7GISdHUAb9ci6NzEDFv/fIukFPbbKPJ+PNTH54yJiQkAIDExMcd9SDGL/5If+nJ2duVRzb0m1q1ZjpIlS8HTszYeP47FvLnTYWBggJTUZLFD1Co8J1Xv0KGDGDduLACgQYMGaNu2ncgRaR5OolFMI7qwASAtLQ2HDx/GwYMHcezYMfz111/IzMwUOyyV0dHN/vfZq0wcv5iM2GeZuHE/DScuJaNMCX3YWevL9t37dwKin2Tg/qMM7DuVPZHGvRLH732KVCr95HYdHY051akQWbBoGdyre2D8jyPg27AWhgzqAz+/ziha1AJGRkXEDo8KOVdXN2zevBWTJk3B1atXMXBgPwgcYEp5pBEVyIiICPTr1w9JSUkoX748srKysGXLFqxcuRLr1q1D2bJlxQ7xq6VnZL8po59kyLXHPM3+vahpdoLz6HkGMj7ImxOSpXj1LgsliunmT6BayszMDACQlJQk1/6+8liQqtmkPaysimPxkuVISHiHFy+eo2xZW+jo6GDB/Jkoal5U7PCokLO1tYWtrS08PDxhamqKSZMmICzsMjw8PMUOTWOwAKmYRpRl5s6dCxcXF5w+fRp79uzB/v378ddff6FkyZKYM2eO2OGpxJuE7DGMuh+94u8LY6npApJTpdDVzXm66uoAmRwC+Um2trbQ1dXNsezTw4cPAQAODg5ihEWF3NEjB3Hv3l2YmZmjQoWKMDAwQGRkBKRSKZycq4gdHhVCr169wv79+2RXfXuvSpXs8/H58+dihEVaSCMSyOvXr2Ps2LGyKhIAWFpaYsKECbhw4YKIkanOq3dSvE3MgpOdgVx7hTLZXdePX2Qi+kkGbEvpwcjg/0lkMTMdFDPTxeMX8pVLkmdoaAgPDw8cO3ZMrgvmyJEjMDMzg5ubm4jRUWG1IXg1Nm9cK9e2PWQzTE3NULNmLZGiosIsNTUVkyZNwJ49v8m1nzt3DgDg6OgkRliaSyJR303LaUQCWapUqVy/9bx9+xbFihUTISL1OHMtGTbF9dCqjglsS+nB3dEQDWuY4N7DdLx4nYXQWykQAHRoZAaHMvpwLGeAbxqYISFZiltRaZ89fmE3ePBgXL9+HSNHjsSpU6ewbNkyBAcHY+DAgShShOPNKP916doTx47+iQ3Bq3H5Uijmz52OI4f/wNDho2H6wRdmovxSunRpdOjQEatWrcTGjcG4cOE8VqwIws8/L0XHjp1QsWJFsUMkJa1ZswY9e/aUa7tz5w569OgBd3d3+Pr6YsuWLXLbpVIpAgMDUa9ePbi7u6N///6IjY1V6nFFGwP55Mn/l7bo1asXpkyZgmnTpqFGjRrQ0dHB7du3MX36dIwcOVKsEFXuXmwG9p9ORG2XIvimgRlS0wTcuJ+Kf26kAADeJkmx49g71HM3RgtvU0gFAQ/jMnHqSpLcuEjKnbe3N4KCghAYGIihQ4eiVKlSGDduHPr27St2aFRI+XX4Fmmpqdi5IwSbNqyFnZ09Zs9djOYtWosdGhViU6dOR9my5bBr1048efIE1tY2GDZsBPr04d/Kj2l6oTAkJATLli2Dh4eHrO3169fo06cPfH19MXPmTFy7dg0zZ86EiYmJbG3tlStXYtu2bViwYAGsra0REBCAfv364ffff4eBgYGih5MjEUSacuXs7Cx3feL3YXzcJpFIcOfOna9+vKXbX331MQgY9Z2l2CEUGJzsqDrvkj49C5/yxtxEIzqlCoSsLL7BVUVPT7wsruOEx2o79u4FZb74vs+ePcP06dMRGhoKa2trFC9eHFu3bgWQXZH85Zdf8Ndff0FPL7tOuGTJEhw5cgRHjhxBeno6ateujbFjx6Jbt24AgHfv3qFevXqYO3cu2rTJ29WIRKtAbt68WS5ZJCIiItIkmpql3L59G/r6+jhw4ABWrFiBx4//n+hevnwZtWrVkiWPAFC7dm2sWbMGL1++xJMnT5CUlARvb2/ZdnNzc1SpUgWXLl3S/ATSy8tLrIcmIiIi+ix11rkaN278ye0nTpxQuM3X1xe+vr65bouLi4Ojo6NcW8mSJQEAT58+RVxcHIDsqzt9vM/7bXkhWgI5ceLEPO87f/58NUZCREREVDCkpqbmGMdoaGgIIPuiLSkp2fMuctvn7du3eX4c0RLIRx9cB1YQBFy+fBnFixdHlSpVoKenh4iICDx79uyzGToRERGRtvlUhfFrGBkZIT09Xa4tLS17JRdjY2MYGWVf2S49PV328/t9lFmxRLQE8v1gTwBYvHgxSpUqhfnz58sy4qysLEybNo3jJImIiIjyyNraOsfSiO9/L1WqlOwy0c+fP4etra3cPk5OeV8HVCOm3O3YsQNDhgyRK6fq6urC398fhw4dEjEyIiIiKqy0cR1xT09PhIWFISvr/5ewu3DhAsqXLw8rKys4OzvD1NQUoaGhsu3v3r1DeHg4PD3zfhlLjUgg9fX15daFfC8qKgrGxsYiRERERESkfTp27IjExERMnjwZ9+/fx549e7Bp0yYMHDgQQPbYxx49emDx4sU4ceIEIiIiMGrUKFhbW6NZs2Z5fhzRurA/1KZNG0yePBk//PADXFxcIJVKceXKFQQFBcnWKCIiIiLKT9o4iM7Kygrr16/H3Llz4efnhxIlSmDcuHHw8/OT7TNixAhkZmZiypQpSE1NhaenJ4KDg6Gvr5/nxxFtIfEPpaenY86cOdi7dy8yMzMhCAIMDQ3Ro0cPjB07ViXjILmQuGpwIXHVEf+dV3BwIXHV4ELiqsOFxFVHzIXEu0xS30LiO+Z9+ULimkAjKpA3btzAlClTMH78eERHR0MikaB8+fLsviYiIiLRcB6vYhrxdXP48OG4d+8eTExM4OLigqpVqzJ5JCIiInFp4yyafKIRCaSlpSUSEhLEDoOIiIiI8kAjurDr16+PgQMHokGDBrCzs5OtmP7esGHDRIqMiIiICivtrxOqj0YkkEeOHIGVlRVu3bqFW7duyW2TSCRMIImIiIg0iEYkkCdPnhQ7BCIiIiI5BWCootpoxBhIRdLT0xEWFiZ2GERERET0AY2oQN66dQtTp05FZGQkpNKc67nduXNHhKiIiIioMGMBUjGNqEDOnz8furq6mDJlCvT19TF16lR8//330NPTw5IlS8QOj4iIiIg+oBEVyPDwcGzevBlubm7Ys2cPHB0d0a1bN1hbW2Pnzp1o2bKl2CESERFRYcMSpEIaUYGUSqUoUaIEAMDOzg6RkZEAgMaNGyMiIkLM0IiIiKiQ4jriimlEAmlnZyebLFOhQgXcvHkTAJCQkID09HQxQyMiIiKij2hEF3bPnj0xadIkAEDz5s3xzTffwMjICFeuXIG7u7u4wREREVGhVAAKhWqjEQlk586dUaxYMVhYWMDBwQHz58/H6tWrUa5cOUydOlXs8IiIiIjoAxrRhX379m1MnjwZJ06cAAC0bdsWKSkpuHXrFpKTk0WOjoiIiAojjoFUTCMSyAULFsDX1xejRo2StR07dgz16tXD/PnzRYyMiIiIiD6mEQnkrVu3MGTIEBgYGMjadHV1MWDAAFy/fl3EyIiIiIjoYxqRQJqYmCA2NjZH+/Pnz+WSSiIiIiISn0YkkM2bN8fMmTNx/vx5JCUlISkpCRcuXMDMmTPRtGlTscMjIiKiQkgikajtpu00Yhb2mDFj8PDhQ/Tp00fuRW3atCnGjRsnYmRERERUWBWAPE9tNCKBNDY2xrp16xAdHY3IyEjo6enBwcEB9vb2YodGRERERB/RiATyvfLly6N8+fJih0FERETEhcQ/QSPGQBIRERGR9tCoCiQRERGRxmAJUqFCk0CO+s5S7BAKhH5zn4odQoGxYpy12CEUGOYm7ExRhfRMQewQCgxDfWYeVLAVmgSSiIiISBn8GqAYv7YTERERkVJYgSQiIiLKBdeBVIwJJBEREVEumD8qxi5sIiIiIlIKK5BEREREuWEJUiFWIImIiIhIKaxAEhEREeVCwlk0CrECSURERERKYQWSiIiIKBesPyrGCiQRERERKYUVSCIiIqJccAikYqxAEhEREZFSmEASERERkVLYhU1ERESUC3ZhK8YKJBEREREphRVIIiIiolywAqkYK5BEREREpBQmkERERESkFCaQRERERKQUjoEkIiIiygXHQCrGCiQRERERKUUjE8hXr17h8OHDiI2NFTsUIiIiKqQkarxpO41IICMjI9G8eXNcunQJ7969Q7t27fDDDz+gdevWuHDhgtjhERERUSEkkUjUdtN2GpFALly4EHZ2dqhQoQL++OMPZGZm4tSpU/D398eyZcvEDo+IiIiIPqARCeTVq1cxfvx4WFlZ4cyZM2jQoAFKlSqFDh06ICIiQuzwiIiIqDBiH7ZCGpFA6ujowMDAAJmZmbh48SK8vb0BAElJSTAyMhI5OiIiIiL6kEYs4+Pu7o41a9bA0tISaWlpqF+/Pp49e4YlS5bA3d1d7PCIiIioECoAhUK10YgK5NSpUxEeHo7t27dj0qRJsLS0xNq1axEVFYVx48aJHR4RERERfUAjKpB2dnbYs2ePXNvQoUMxadIk6OrqihQVERERFWYFYLK02mhEBRIAnjx5gsTERADAhQsXsHz5cvz5558iR5U/zp49i44dO6JatWrw9fVFcHAwBEEQOyyNJgHQzMsE8waXwKrx1pg9sAQaeRgr3H9QBwv0aVM0/wIsQEaNHI4WzXzFDkPrxcXFwdPTA6GhoWKHovV4Tn49fu7Q19KIBPLYsWNo1qwZrl+/jocPH6Jfv344f/48pkyZgpCQELHDU6tr165h0KBBqFChAoKCgtC2bVsEBARg3bp1Yoem0b5tYoZvm5gjPDoNQTtf4cSlJLSrZ4ZvG5vJ7ScB0KWpOTwqFxEnUC33x+8HcOLEMbHD0HpPnz6Fv39fJCQkiB2K1uM5+fX4uZN3nIStmEZ0Ya9cuRL+/v7w9vbGqlWrULp0aRw8eBCHDx9GUFAQunfvLnaIahMUFITKlSsjICAAAFC/fn1kZmZi9erV6NWrF2eh58K0iAS+niY4fTUZvxx+BwAIj07Hq3dZGNa5GE5fS0ZcfBbKltTDd83MYV/aAGkZ/GatrOfPn2HB/LkoVcpa7FC0llQqxb59+7Bo0UKwuPP1eE6qBj93lFAQMj010YgKZFRUFL799lvo6Ojg3LlzaNCgAXR0dODu7o7Hjx+LHZ7apKenIzQ0FE2bNpVrb968OZKSkhAWFiZSZJqtlKUedHUkuH4vVa49IiYdOjoSuFQwBAD0bWcBHR0J5m96iYSkLDFC1Wozpk9FnTo+8KpdW+xQtNbdu3cxY8Z0fPNNeyxatEjscLQez8mvx88dUhWNSCDNzc2RkJCAhIQE3LhxA3Xq1AEAPHz4EBYWFuIGp0axsbHIyMiAvb29XLudnR0AIDo6WoSoNF9iihQAYFVUfoJVyWLZv5coll1YD97/Bgu3xOPR88z8DbAA2P3bLoTfvo2Jk6eKHYpWs7GxwdGjxzBx4kRWdb4Sz0nV4OeOctiFrZhGdGE3aNAA06ZNg4mJCczMzODj44N//vkHM2bMQMOGDcUOT23ej4cyNTWVazcxMQEA2aQikvfsVRYiH6bjm/pmeJ2QhYgH6ShuoYterYoiI1OAgX72W/PxCyaOX+LJk8dYHLAAs+bMQ7FixcQOR6sV5C/A+YnnpOrwc4dURSMqkFOnTkWNGjVgbGyMVatWwcDAAGFhYXB3dy/Q60BKpdJPbtfR0Yj/Ho20es9rRD5Mx9BOlggaa42x3a1w+moyElOkSOd4xy8mCAKmTZ2MevUaoGnT5mKHQ8RzUsX4uaMciUR9N22nERVIIyMjTJgwQa5t+PDhIkWTf8zMsmcMJyUlybW//wb48TdE+r93SVKs+O01ihhKYGGmixevMyGVAj1bFkVS6qf/QJJiv24Pwb3Iu9i99wAyM7MruO+X9sjMzISOjg4/YChf8ZxULX7ukKpoRAIJABEREYiMjJR9OxIEAenp6bh58ybmzJkjcnTqYWtrC11dXcTExMi1P3z4EADg4OAgRlhawbOKEZ6+zMSj55lIScv+ULGz0YeOjgQP4zJEjk57HTt6BK9fv4Zvw3o5ttVwd8GgwUMxZGjB/3JHmoPnpGrxc0c5koJQKlQTjUggN27ciIULFwLI/s96/+1SIpHAw8NDzNDUytDQEB4eHjh27Bj8/f1lJ+qRI0dgZmYGNzc3kSPUXG18TPHoRSbW7Xsja2taywTJqVLcjUkXLzAtN3X6TCR/VJlYtWoF7oTfRmDQSpQoWVKkyKiw4jmpWvzcIVXRiAQyJCQE/fv3x7Bhw9CoUSPs3bsXb968wZgxY9C4cWOxw1OrwYMHo0+fPhg5ciQ6duyIq1evIjg4GGPGjEGRIlz8WpETl5PQo2VRPHlhivuP0lGrihFquxTB1j/fIiWNYyC/VPnyFXK0WVhYQF9fH1VdXEWIiAo7npOqx88dUgWNGDgSFxeHzp07w9DQEM7Ozrh58yacnJwwYcIE/Pbbb2KHp1be3t4ICgpCdHQ0hg4dit9//x3jxo1D//79xQ5No52+moIdx97Bx60IRnxbDPalDbB272ucupIsdmhERBqNnzt5x0k0ikkEDbj4Za1atbBjxw6UL18eM2bMgLW1NQYNGoSnT5+iVatWuHr1qtgh0n/6zX0qdggFxopxvJqGqhjoFYC/xhogPVP0j4MCw1Cf52RBMObn52o79k8jtXv4hUZUIGvUqIG1a9ciJSUFVapUwcmTJyGVShEWFiZbm4qIiIgoP3EhccU0IoEcPXo0zpw5g5CQELRu3RovX75ErVq1MH78eHTo0EHs8IiIiIjoAxoxicbR0RHHjx9HcnIyTExMsHPnTvz++++wsbFBixYtxA6PiIiICqGCMFZRXTSiAglkLyZuaWkJAChevDj69OnD5JGIiIjoI5mZmfj555/RqFEjVK9eHd27d8e1a9dk2+/cuYMePXrA3d0dvr6+2LJli8pjEK0C6evrm+cFOk+cOKHmaIiIiIg+oqEVyFWrVmHXrl1YsGABypUrh3Xr1qFfv344dOgQ9PX10adPH/j6+mLmzJm4du0aZs6cCRMTE3Ts2FFlMYiWQPr5+XGFdyIiIiIlHT9+HG3atEHdunUBABMmTMCuXbtw7do1REdHQ19fH7NmzYKenh4cHBwQExODtWvXqjSBFK0Le/jw4Rg2bBiGDRumcB+JRAIDAwMcOHAAb968yb/giIiIqNDT1FnYVlZW+Ouvv/Do0SNkZWVhx44dMDAwgLOzMy5fvoxatWpBT+//NcLatWvjwYMHePny5Vc+8v9pxCSaS5cu4dKlS9DX10f58uUBADExMUhNTYWNjQ3evHkDQ0NDbNmyBZUqVRI5WiIiIioM1NlR+rkr7X1q+N7kyZMxcuRING7cGLq6utDR0UFQUBBsbW0RFxcHR0dHuf1L/nfJz6dPn6J48eJfHzw0ZBKNm5sbatasiZMnT2Lfvn3Yt28fTp48iTp16sDPzw+hoaFo2LAhFi9eLHaoRERERKK6f/8+zMzMsGLFCuzYsQMdOnTA2LFjcefOHaSmpsLAwEBuf0NDQwBAWlqaymLQiArkb7/9hg0bNsDKykrWVqxYMfz444/o06cPhg8fDn9/f3z33XciRklERESFiTpnanzpBOGnT59izJgx2LRpEzw8PAAArq6uuH//PoKCgmBkZIT09HS5+7xPHI2Njb8u6A9oRAUyMzMTGRkZOdrT0tKQmpoKADAwMIBUKs3v0IiIiIg0xvXr15GRkQFXV1e59mrVqiEmJgbW1tZ4/lz+Eozvfy9VqpTK4tCIBLJu3bqYOXMmYmJiZG3R0dGYM2cO6tati6ysLGzfvh1OTk4iRklERESFikSivtsXsra2BgDcvXtXrj0yMhL29vbw9PREWFgYsrKyZNsuXLiA8uXLy/X0fi2NSCCnTp0KHR0dtGjRAl5eXqhVqxZatWoFXV1dTJs2DWfOnMGvv/6KH374QexQiYiIiETzft7I+PHjceHCBTx48ADLli3D+fPnMWDAAHTs2BGJiYmYPHky7t+/jz179mDTpk0YOHCgSuOQCIIgqPSIX0gQBISGhuLOnTvQ1dWFs7MzatWqBQB4/fo19PT0YGZmJnKU1G/uU7FDKDBWjLMWO4QCw0CPa8qqQnqmRnwcFAiG+jwnC4KJK1+o7djzh5T44vu+ffsWy5Ytw99//423b9/C0dERo0ePluVNN27cwNy5cxEeHo4SJUqgb9++6NGjh6pCB6BBCSRpByaQqsMEUnWYQKoGE0jVYQJZMGhqAqkJNGIWNhEREZGm4dcAxZhAEhEREeWCV1xWTCMm0RARERGR9mACSURERERKYQJJRERERErhGEgiIiKiXHAMpGKsQBIRERGRUliBJCIiIsoFC5CKsQJJREREREphBZKIiIgoFxwDqRgTSCIiIqLcMINUiF3YRERERKQUViCJiIiIcsH6o2KsQBIRERGRUliBJCIiIsoFh0AqxgokERERESmFFUgiIiKiXLAAqRgrkERERESkFFYgSSmrx1uLHUKBMWdjvNghFBhT+liJHUKBYKDHeguRHL4lFGICSURERJQLTqJRjF3YRERERKQUViCJiIiIcsECpGKsQBIRERGRUliBJCIiIsoFx0AqxgokERERESmFCSQRERERKYUJJBEREREphWMgiYiIiHIh4SBIhZhAEhEREeWC+aNi7MImIiIiIqWwAklERESUCxYgFWMFkoiIiIiUwgokERERUW5YglSIFUgiIiIiUgorkERERES5YAFSMVYgiYiIiEgprEASERER5YLrQCrGCiQRERERKYUVSCIiIqJcsACpGBNIIiIiotwwg1RI1ATyyZMned63dOnSaoyEiIiIiPJK1ATS19cXks+MUBUEARKJBHfu3MmnqIiIiIjw2RylMBM1gdyyZYuYD09EREREX0DUBLJWrVpiPjwRERGRQqw/KqYxk2jS0tKwY8cOREZGIisrS9aenp6OW7du4ciRIyJGR0RERETvaUwCOWfOHOzbtw9VqlTBzZs3Ub16dcTExCA+Ph69e/cWOzwiIiIqZDgEUjGNWUj8xIkTmD9/Pnbs2IEyZcpg9uzZ+Ouvv9C4cWNkZGSIHR4RERER/UdjEsh3796hRo0aAICKFSsiPDwc+vr6GDhwIP766y+RoyMiIiKi9zQmgbS0tER8fDwAwN7eHpGRkQCAYsWK4eXLl2KGRkRERIWQRKK+m7bTmDGQ9evXx8yZMzF//nzUrFkT8+bNQ9OmTXHo0CFYW1uLHZ5anT17FkuXLsX9+/dhZWWF7t27o2/fvlx/SklSqRSbN2/Ezp078exZHOzs7OHv3w9t2rQVOzSNZW+jj95tiirc/ldYEk5dSUHftkVha62fY/vavW/w5GWmOkPUajwn1SMuLg5t27bB8uUr4OXlJXY4WomfO/S1NCaBHDduHCZMmICLFy+iW7du2LFjBzp37gw9PT0sXLhQ7PDU5tq1axg0aBBatmyJkSNHIiwsDAEBAcjKysKAAQPEDk+rBAUFYsOGYAwbNhyurq44ffoUxo//ERKJBK1btxE7PI309GUm1u9/k6Pd18MYpUvo4VZUGgCglKUu/rmRgvDoNLn9Xrxh8vgpPCdV7+nTp+jXzx8JCQlih6K1+LmTd8ynFdOYBNLc3BwrV66U/b527VrcuXMHxYsXL9DfiIKCglC5cmUEBAQAyK7EZmZmYvXq1ejVqxeMjIxEjlA7pKSkYOvWLejZsyf698/+A1i7tjfCw28jJGQrP6wVSMsQ8Oi5fBLoZGuACmUMsPP4O8S/lcLSXAeGBjq4F5ueY19SjOekakmlUuzbtw+LFi2EIIgdjXbj5w6pgsaMgaxcuTJevXol+10ikaBKlSpIT09Hs2bNRIxMfdLT0xEaGoqmTZvKtTdv3hxJSUkICwsTKTLtY2BggJCQ7fj++z5y7fr6+khLSxcpKu2jpwu0rGOCyIfpCI/Oft2srbK/Z8bFM3lUBs9J1bp79y5mzJiOb75pj0WLFokdjtbi5w6pyhdXIJOTk2FsbAwAOHLkCJ48eYJGjRrB3t4+z8f47bffcODAAQDZ17weOnQo9PXlx1k9f/4c5ubmXxqmRouNjUVGRkaO18zOzg4AEB0dDR8fHxEi0z66urpwcnICkH0uxcfHY+/ePTh//jymT58pcnTao7ZLEZib6GDLobeyNmsrPaSlS9HMywROdgYw0JMg+kkGDl9IQvzbrE8crXDjOalaNjY2OHr0GKytrREaGip2OFqLnzukKkonkP/++y8GDhyI1q1b44cffsCyZcuwZs0aCIKAZcuWYcOGDahZs2aejtWkSRO5bzvW1tY5SueOjo5o3769smFqhfdjeExNTeXaTUxMAACJiYn5HlNBcOjQQYwbNxYA0KBBA7Rt207kiLSDrg7g5VIEt6LS8OqdVNZubaUHQwMdpKYL+PXYO1iY6qJBDWP0bVsUq/e8QUKy9BNHJYDnpCpYWFiIHUKBwM8d5RTgEXRfTekEcvHixdDT00Pjxo2Rnp6Obdu2oWXLlpg1axYmTJiAZcuWYevWrXk6loWFBebPny/7ffLkyTlO6oJMKv30B6+OjsaMMNAqrq5u2Lx5K+7evYvlywMxcGA/bNq0tUCPpVWFKuUNYWasg3M3UuTaT1xKwrnryYiJy+7CfohMxD7LwNDOxeDlYoTjF5PFCFer8JwkTcHPHVIVpRPIy5cvY968eXB1dcXZs2eRkJCALl26wNTUFF27dsXw4cO/KJD3ieSlS5cQFRWFNm3aIC4uDvb29tDT05i5PiplZmYGAEhKSpJrf/8NsDAl06pka2sLW1tbeHh4wtTUFJMmTUBY2GV4eHiKHZpGq1LeAM9fZeLZK/lu6Y9/B4DXCVK8fJMFa8uC+d5UNZ6TpCn4uaMcfsVTTOmvGhkZGbIxiadPn0aRIkVkXdZZWVlfnOwlJiaia9eu6NmzJ2bOnInXr19j8eLFaNeuHZ49e/ZFx9R0tra20NXVRUxMjFz7w4cPAQAODg5ihKWVXr16hf3798kWo3+vSpUqALLH0pJiOhLAoawBbv+blqO9WiVDlC2Z832tpwskpbL7WhGek6SJ+LmjHIlEorabtlM6gXR0dMTRo0fx4sULHD58GHXr1oWenh4yMjIQEhICR0fHLwpkyZIlAIBjx47JxkH++OOPMDQ0LLAz7gwNDeHh4YFjx45B+GBdiiNHjsDMzAxubm4iRqddUlNTMWnSBOzZ85tc+7lz5wAAjo5OYoSlNUpZ6sJAX4KHz+RnWksFoGENYzTzMpFrt7HShaW5Lh484XXqFeE5SZqInzukKkqXC0eMGIGhQ4ciJCQEBgYG6N+/P4DsJQBevnyJ1atXf1Egf/31F3766SeUK1dO1ubg4IBp06Zh6NChX3RMbTB48GD06dMHI0eORMeOHXH16lUEBwdjzJgxKFKkiNjhaY3SpUujQ4eOWLVqJfT09FC5chWEhV3G+vXr0LFjJ1SsWFHsEDVayf+6ol+8zrlUz99XkuHX0Ax+DU1x/V4aLEx10KimMeLis3DtXlqO/Skbz0nSVPzcUYL2FwrVRukE0sfHB7///jtu3ryJatWqoUyZMgCA77//HrVr15YtW6GsV69eoUSJEjnazc3NkZxccAfpe3t7IygoCIGBgRg6dChKlSqFcePGoW/fvmKHpnWmTp2OsmXLYdeunXjy5AmsrW0wbNgI9OnD1/JzTItkd0akpudcofn6vTRkZgnwcSuCrk3NkZEp4M6DNJy4lMwFnT+D5yRpIn7ukCpIBEEzPgJ69OiB+vXrY8CAAahevToOHDiAcuXKYdasWYiIiMC2bdvEDpEAZGZqxOlSIMzZGP/5nShPpvSxEjuEAkFXl+UWVSkAQ9wIQNDO12o79vBvi6nt2PkhTxXIXr165fmAEokEmzdvVjqQ0aNHo2/fvrhx4wYyMzOxatUqREVF4fbt2wgODlb6eERERESkHnmaRCMIQp5vn1tjSpEaNWrg119/hbGxMezs7HDt2jVYW1sjJCQEXl5eX3RMIiIioi8lkajvpu3yVIHM68LgX8vZ2bnAzrgmIiIiKii+eBXgqKgonDt3Di9evECPHj0QGxsLZ2fnL16END09Hbt27UJkZCTS09NzbP/wijVERERE6lYACoVqo3QCKZVKMW3aNOzevRuCIEAikaBFixZYuXIlYmJiEBISAmtra6UDGT9+PI4fP47KlSvD0NBQ6fsTERERqRQzSIWUTiBXrlyJ33//HXPmzEHDhg3h4+MDIHvR76FDh2Lp0qVYuHCh0oGcPn0aS5YsQdOmTZW+LxERERHlH6WvRLN7926MGDECHTt2hIWFhay9cuXKGDFihOwqC8oyNzdH+fLlv+i+RERERKomUeNN2ymdQL58+RKVK1fOdVupUqXw7t27Lwpk0KBBmD9/PmJjY7/o/kRERESUP5Tuwrazs8OpU6dQp06dHNsuXrwIOzu7LwrE0dERS5YsQbNmzXLdfufOnS86LhEREdGXKAjL7aiL0gnk999/j2nTpiEjIwONGjWCRCJBTEwMQkNDsWHDBkyYMOGLApk8eTLs7e3Rrl07GBsbf9ExiIiIiAqDffv2Ye3atYiNjYWtrS2GDRuGli1bAgAePXqE2bNn49KlSzA2NkanTp0wfPhw6OrqquzxlU4gO3fujFevXmHVqlXYvn07BEHA6NGjoa+vj379+uG77777okAePXqEAwcOwN7e/ovuT0RERKRKEg0tQe7fvx+TJ0/GpEmTUK9ePRw8eBCjR4+GtbU1XFxc4O/vD3t7e/z66694+PAhJk+eDB0dHYwYMUJlMXzROpADBw5E9+7dcfXqVbx58wbm5uaoVq2a3KQaZbm6uiImJoYJJBEREZECgiDg559/Rq9evdC9e3cAwODBg3H58mVcvHgRjx8/xpMnT7Bz504ULVoUjo6OiI+Px6JFizBo0CAYGBioJI4vXkhcKpXK1oE0MDCAvr7+VwXyzTffYOLEiejUqRPKlSuX43jt27f/quMTERERabvo6Gg8fvwYbdu2lWsPDg4GAMyYMQNVq1ZF0aJFZdtq166NxMRE3LlzB9WqVVNJHF+0kPiiRYuwbds2ZGRkQBAEAECRIkUwePBgDBgw4IsCmTZtGgBg7dq1ObZJJBImkERERJSvNLEHOzo6GgCQnJwMf39/hIeHo2zZshg8eDB8fX0RFxeX44IuJUuWBAA8ffpUvARyxYoV2Lp1K3r06IGmTZvCysoKL1++xB9//IFly5bBxMREVlJVRkREhNL3ISIiItJGjRs3/uT2EydO5NqemJgIIPsKfsOGDcPYsWNx5MgRDBkyBBs3bkRqairMzc3l7vP+Cn9paWkqiDyb0gnk7t27MXjwYAwbNkzWVr58eXh6esLU1BQbN278ogSSiIiISJNoYAFSNsTP398ffn5+ALIv5hIeHo6NGzfCyMgI6enpcvd5nziqcpUbpRPI169fo3r16rluq1evHn755ZevDoqIiIioIFNUYfycUqVKAcheP/tDFStWxN9//41atWohMjJSbtvz58/l7qsKSl+JxtvbG3/++Weu2/755x/UqFHjq4MiIiIiEptEor7bl6patSpMTExw/fp1ufbIyEjY2trC09MT4eHhsq5uALhw4QJMTEzg7Oz85Q/8kTxVIPft2yf72d3dHcuXL0d8fDxatmyJEiVK4M2bNzh16hSOHDmCyZMnqyw4IiIiIvo/IyMj9OvXDytWrECpUqXg5uaGgwcP4ty5c9i0aRPc3d2xbNky/PDDDxg7diwePXqEJUuWoG/fvipbwgcAJML7adSfoEzGKpFIeNnBAiwz87OnC+XRnI3xYodQYEzpYyV2CAWCrq4mjvjSTpo4e5eUt27/G7Udu/83Fl91/40bN+KXX37Bs2fP4ODggOHDh6NJkyYAgJiYGMycOROXL19G0aJFZVei0dFRuuNZoTwlkI8fP1bqoGXKlPnigEizMYFUHSaQqsMEUjWYQKoOE8iCQZMTSLHlqQtbmYQwD/koERERkcbj9wDFvuhKNIcOHcLFixeRnp4uSxgFQUBycjKuXbuG06dPqzRIIiIiovzGSrJiSieQy5cvx/Lly2FmZobMzEzo6+tDT08Pr169go6ODjp37qyOOImIiIhIQyg9mnLv3r1o3749Ll68iN69e6NRo0b4559/8Ntvv8HCwgKVKlVSR5xERERE+Uqixpu2UzqBfPbsGdq2bQuJRILKlSvj6tWrAAAXFxcMGjQIu3btUnmQRERERKQ5lO7CNjY2huS/QQF2dnZ49OgRUlNTYWRkhMqVK+PRo0cqD5KIiIgo33EQpEJKVyBdXV1lC4uXL18eurq6OH/+PAAgKipKpYtUEhEREZHmUboCOWjQIPTp0wfv3r3D6tWr0a5dO4wfPx5eXl44e/asbBFLIiIiIm3GAqRiSieQnp6e+O2333D37l0AwLRp06Cjo4MrV66gRYsWmDBhgsqDJCIiIiLN8UXrQDo7O8sub2hoaIjZs2erNCgiIiIisbEAqZjqLoqI7CV+mjdvrspDEhEREYlCIlHfTdupNIF89+4dHj58qMpDEhEREZGG+aIubG2UmclrdKuCrm4B+NqkIab3Ky52CAVG46H84qoKx5bbih1CgcE/lVTQqbQCSUREREQFX6GpQBIREREpoyCMVVQXViCJiIiISCl5qkA6OzvLLl/4KYIg5Gk/IiIiIk3HjEaxPCWQQ4cOZWJIRERERADymEAOHz5c3XEQERERaRTWzhTjJBoiIiKi3DCDVIiTaIiIiIhIKaxAEhEREeWC9UfFWIEkIiIiIqWwAklERESUCw6BVOyLEshXr14hODgY//zzD168eIH169fj+PHjcHZ2RpMmTVQdIxERERFpEKW7sGNjY9GuXTvs3LkTpUqVQnx8PLKyshAdHY0RI0bg77//VkOYRERERPlLosabtlO6Arlw4UJYWVlh69atMDY2houLCwDgp59+QlpaGlavXo2GDRuqOk4iIiIi0hBKVyDPnz+PIUOGwNzcPMfVabp06YJ79+6pLDgiIiIi0bAEqdAXjYHU08v9bunp6bzkIRERERUITGkUU7oC6eHhgTVr1iA5OVnWJpFIIJVKsX37dtSoUUOlARIRERGRZlG6AjlmzBh89913aNasGby8vCCRSBAcHIyoqCjExMRg27Zt6oiTiIiIKF+xAKmY0hVIR0dH7N69G15eXggNDYWuri7++ecf2Nra4tdff0XlypXVEScRERERaYgvGgNpb2+Pn376SaWBPHz4ELa2tio9JhEREdGX4hhIxZROIJ88efLZfUqXLq10IN27d8eKFSvg5uam9H2JiIiIKP8onUD6+vp+dqb1nTt3lA5EX19f4exuIiIiItIcSmds8+bNy5FAJicn4/LlywgNDcW8efO+KBA/Pz/069cP33zzDezs7GBkZCS3vX379l90XCIiIiJSLYkgCIKqDjZ//ny8fPnyi8ZHOjs7K9wmkUi+qKr5ocxMlT3NQk1XlwNCSPM0HvpQ7BAKhGPLOQ5dVXSVnqJKmmjXiQS1HbtzYzO1HTs/qLTP2NfXF0OGDPmi+0ZERKgyFCIiIiJSE5V+R7p+/fpXj2N88uQJzpw5g9TUVMTHx6soMiIiIiLlSCTqu2k7pbO9iRMn5miTSqWIi4vDpUuX0KlTpy8KJD09HePHj8eff/4JHR0dHDlyBAsXLkRSUhKCgoJgamr6RcclIiIi+hIFIM9TG6UrkKGhoTluly9fxps3b9C/f/9cE8y8WLVqFSIiIrB582YYGhoCAHr27ImYmBgsXrz4i45JRERERKqndAVy3bp1cHBwUHkgBw8exIwZM+Dl5SVr8/Lywty5czFu3DjMmDFD5Y9JREREpBBLkAopXYHs1q0b9u3bp/JAnj17luuVaGxsbPD27VuVPx4RERERfRmlE0h9fX0UK1ZM5YE4ODjg/PnzOdoPHjyIihUrqvzxNIVUKsXGjcFo2bI5atSoBj+/b/DHH7+LHZbWi4uLg6enB0JDQ8UORevxtcyb4ha6OLC4LKpVMlRqW162U/bfyl9/3Y7237RFzZrV0axpYyyYPw+JiYlih6aVzp49i44dO6JatWrw9fVFcHAwVLiqX4EhUeNN2yndhT1y5EgsWrQICQkJcHZ2hrGxcY59vuRShsOHD8eoUaNw//59ZGVlYe/evYiOjsaRI0ewdOlSpY+nLYKCArFhQzCGDRsOV1dXnD59CuPH/wiJRILWrduIHZ5Wevr0Kfr180dCgvrW7yos+FrmTYliulg4rCRMjXN+J//Utrxsp2zBwesR+PMy9Onrj9q1vRHzIBqBgYG4d+8e1gdv+OwV0uj/rl27hkGDBqFly5YYOXIkwsLCEBAQgKysLAwYMEDs8EhLKJ1AzpgxA1lZWfjxxx8V7vMli343atQIgYGBWLNmDXR1dREcHIxKlSph6dKlaN68udLH0wYpKSnYunULevbsif79s9+0tWt7Izz8NkJCtjKBVJJUKsW+ffuwaNFC8Iv01+FrmTcSCdDMywSDOlgotS0v2+n/pFIpgtevw7ffdsHo0WMAAHXq1IGFRTGMGTMKt2/fgouLq8hRao+goCBUrlwZAQEBAID69esjMzMTq1evRq9evXJcCa4w4/cSxZROIOfMmaOOOABkn8T169dX2/E1jYGBAUJCtsPS0lKuXV9fHwkJ7JZR1t27dzFjxnR891031KlTBwMH8pv0l+JrmTcVyuhj1HeW2H86AVciUjF/aMk8bcvLdvq/xMREtG33DVq2bCnXXr5CBQBA7MNYJpB5lJ6ejtDQUIwYMUKuvXnz5li/fj3CwsLg4+MjUnSkTfKUQPbq1QvTp0+Hg4MD/Pz81BbMtWvXsHXrVkRGRkJXVxdVq1ZF7969UalSJbU9pph0dXXh5OQEABAEAfHx8di7dw/Onz+P6dNnihyd9rGxscHRo8dgbW3N8Xpfia9l3jx/lYUe05/g5ZusHOMXP7UtL9vp/8zNzTF58pQc7SdOHAeAAj1OXtViY2ORkZEBe3t7uXY7OzsAQHR0NBPID7AAqVieEsiLFy8iKSlJrYGcPHkSw4YNg6urK3x8fJCVlYWrV6+iQ4cO2LhxIzw8PNT6+GI7dOggxo0bCwBo0KAB2rZtJ3JE2sfCwkLsEAoMvpZ5k5AsRUKy8tvysp0+7fr161i/bi0aNWqESo6OYoejNd6PZ/744hwmJiYAwElJH2MGqZBKr4X9NZYuXQp/f3+MGTNGrn3hwoUICAjAjh07RIosf7i6umHz5q24e/culi8PxMCB/bBp01YODCci+siVK2EYMngQypQti7lz54sdjlaRSqWf3K6jw8lclDcac6bExMSgY8eOOdq7dOmCiIgIESLKX7a2tvDw8ET37j0wYcIkXL58GWFhl8UOi4hIo/x56BD8+/aBjY0NNm7YBAs1LCtXkJmZmQFAjl7F95VHXjZYnkQiUdtN2+W5Ajl06FAYGBh8dj+JRILjx48rHUjlypVx/vz5HOMybt26VWDHQL569QpnzpxG3br1YGVlJWuvUqUKAOD58+dihUZEpHE2bAjGT4sDUKtWLQQGrZAlQ5R3tra20NXVRUxMjFz7w4cPAUAtV5qjginPCWSVKlVyzBZWpXbt2mHx4sX4999/4eXlBT09Pdy8eRObN29G165d5a5+0759e7XFkZ9SU1MxadIE/PDDKPTvP1DWfu7cOQCAo6OTWKEREWmUHTt+xeKARWjZshXmL1iYp4IG5WRoaAgPDw8cO3YM/v7+skrYkSNHYGZmBjc3N5Ej1CzaXydUH6UqkOo8sWbPng0A2Lp1K7Zu3Sq3bf369bKfJRJJgUkgS5cujQ4dOmLVqpXQ09ND5cpVEBZ2GevXr0PHjp04s5CICMCLFy+wcMF8lClTBt26d8ed8HC57eVsbdVa4ChoBg8ejD59+mDkyJHo2LEjrl69iuDgYIwZMwZFihQROzzSEhoziaYwjHPMzdSp01G2bDns2rUTT548gbW1DYYNG4E+ffqKHRoRkUY4ffoUUlNT8fjxY/Ts0T3H9rnz5sPPr4MIkWknb29vBAUFITAwEEOHDkWpUqUwbtw49O3Lz52PFYChimojEfJw8UtnZ2fs3LlTtNJ2XFwcrK2tv+oYmZm8nIYq6Ory3USap/HQh2KHUCAcW24rdggFhq7GTFGlr/H7GfUta9S2nnZPWMpTBdLPzw/F1DzTLTY2FgsXLkRkZCSysrIAZC+unZ6ejlevXiH8oy4LIiIiIhJHnr4jzZ8/H+XKlVNrILNmzcLdu3fRvHlzPHv2DK1bt0bVqlXx8uVLzJgxQ62PTURERPQxiUR9N22nMWMgr1y5gpUrV8LLywtnzpxBkyZN4ObmhqVLl+LUqVP49ttvxQ6RiIiIiKBBC4mnp6fD1jZ7/E358uVx9+5dANlL9ly/fl3M0IiIiKgQYgVSMY1JIMuUKYPIyEgA2QnknTt3AGRfdknd1+EmIiIiorzTmC5sPz8/jBs3DosWLULDhg3Rq1cvlC5dGufOnYOTExfUJiIiItIUGpNADhgwAIaGhhAEAW5ubhgyZAhWrVoFGxsbBAQEiB0eEREREf0nT+tAFgRcB1I1uA4kaSKuA6kaXAdSdbgOZMFw6B/1rQPZqk4hWAcyv1y7dg1bt25FZGQkdHV1UbVqVfTu3RuVKlUSOzQiIiIi+o/GfEc6efIkunXrhkePHsHHxweenp64e/cuOnTogMuXL4sdHhERERUyEjXetJ3GVCCXLl0Kf39/jBkzRq594cKFCAgIwI4dO0SKjIiIiAojSUFYb0dNNKYCGRMTg44dO+Zo79KlCyIiIkSIiIiIiIhyozEJZOXKlXH+/Pkc7bdu3eIYSCIiIsp/7MNWSGO6sNu1a4fFixfj33//hZeXF/T09HDz5k1s3rwZXbt2xb59+2T7tm/fXrQ4iYiIiAo7jVnGx9nZOU/7SSQS2VVqlMFlfFSDy/iQJuIyPqrBZXxUh8v4FAxHLqjvSnjNa5uo7dj5QWMqkBznSERERJR30dHR6NChA6ZOnYoOHToAAO7cuYO5c+fi1q1bsLS0RO/evdGrVy+VPza/IxERERHlQiJR3+1rZWRkYOzYsUhOTpa1vX79Gn369IGtrS12796NoUOHYvHixdi9e/fXP+BHNKYCSURERER5ExQUBFNT+avZ7Ny5E/r6+pg1axb09PTg4OCAmJgYrF27NteVbr4GK5BEREREudDUSdiXLl3Cjh07sGDBArn2y5cvo1atWtDT+399sHbt2njw4AFevnz5lY8qjwkkERERUW40MIN89+4dxo0bhylTpsDGxkZuW1xcHKytreXaSpYsCQB4+vTplz9oLtiFTURERJTPGjdu/MntJ06cyLV9xowZqF69Otq2bZtjW2pqKgwMDOTaDA0NAQBpaWlfGGnumEASERER5ULTFq7bt28fLl++jN9//z3X7UZGRkhPT5dre584GhsbqzQWJpBERERE+UxRhfFTdu/ejfj4eDRs2FCuffr06Th06BCsra3x/PlzuW3vfy9VqtQXx5obJpBEREREuVDFcjuqtHjxYqSmpsq1NWvWDCNGjEC7du2wf/9+/Prrr8jKyoKuri4A4MKFCyhfvjysrKxUGgsn0RARERFpgVKlSsHOzk7uBgBWVlYoVaoUOnbsiMTEREyePBn379/Hnj17sGnTJgwcOFDlsbACSURERJQLiaaVID/DysoK69evx9y5c+Hn54cSJUpg3Lhx8PPzU/ljMYEkIiIi0lJ3796V+93NzQ07duxQ++OyC5uIiIiIlMIKJBEREVEutKwHO1+xAklERERESmEFkoiIiCgXLEAqxgokERERESmFFUgiIiKiXHAMpGKFJoHU0+NZoAoJSVKxQygw9PV5TqrKH0vKih1CgTBz3QuxQygwZg4oIXYIBQaTOM1UaBJIIiIiIqUweVWIYyCJiIiISCmsQBIRERHlggVIxZhAEhEREeWC4y8VYxc2ERERESmFFUgiIiKiXLAAqRgrkERERESkFFYgiYiIiHLDQZAKsQJJREREREphBZKIiIgoFyxAKsYKJBEREREphRVIIiIiolywAKkYE0giIiKiXLALWzF2YRMRERGRUphAEhEREZFSmEASERERkVI4BpKIiIgoFxwDqRgrkERERESkFI1LIDMyMmQ/P3v2TMRIiIiIqDCTqPGm7TQmgXz16hV69eqF5cuXy9r8/PzQt29fvH37VsTIiIiIiOhDGpNAzp07FykpKWjdurWsbd26dUhISMDChQtFjIyIiIgKI4lEfTdtpzEJ5NmzZzF79mw4OjrK2qpWrYrp06fj77//Fi8wIiIiIpKjMbOws7KyIAhCjnZ9fX2kpKSIEBEREREVagWhVKgmGlOB9PT0xJIlS5CYmChrS0xMxM8//wxPT08RIyMiIqLCiJNoFNOYCuTEiRPRvXt31K9fH/b29gCABw8ewMLCAuvXrxc3OCIiIiKS0ZgE0tbWFocOHcLBgwdx79496Onp4bvvvkPbtm1hZGQkdnhERERUyLAHWzGNSSABwMzMDF27dhU7DCIiIiL6BFETyPfrPpqbm6Nnz56QfCLV37JlSz5GRkRERIUdC5CKiZpAlilTBjo6OrKfP5VAEhEREZFmEDWBnD9/vuznESNGwNraWpZQvpeZmYnw8PD8Do2IiIgKO9a1FNKYMZCNGzfGuXPnYGlpKdf+6NEj9OzZE9evXxcpMvU7e/Ysli5divv378PKygrdu3dH3759WZH9Anv37MT2bVvw5MkTWFvb4Nsu3dD52258LZWUlpaGOrVrIjMzU669SBFjXLh4RaSotNONG9cQ9PNS3Lp1E8bGxqjjUxejRv0ISysrsUPTSPY2+ujbzkLh9pOXk/B3WDLMjHXQrLYJKpUzgI4O8Ph5Jo5cSEJcfKbC+1JOcXFxaNu2DZYvXwEvLy+xwyEtImoCGRISgg0bNgAABEFAx44dc1Qg3717h9KlS4sRXr64du0aBg0ahJYtW2LkyJEICwtDQEAAsrKyMGDAALHD0yr79u7C3DnT0aVrDzRo4IurV8MQsGgu0tPT0aNnH7HD0yr370ciMzMT8+YHoFy5crJ2HV1dEaPSPuHhtzGgX294eXljydIgvHjxHEGBSzDqYQw2b9kudnga6enLTKzd+zpHe2NPE5QpoYeb99NgoC+BfzsLZEoFHDidgMwsoGENY3zfuihW/PYaiclSESLXPk+fPkW/fv5ISEgQOxSNxdqDYqImkB06dMDr168hCAJWrFiBFi1awMTERG4fExMTNGvWTKQI1S8oKAiVK1dGQEAAAKB+/frIzMzE6tWr0atXLy5hpIQD+/fA3b0mfhw3GQBQy8sbMTHR2LkjhAmkku5GREBPTw9NmzWHgYGB2OForZ+XLoaTc2Us/XmF7MuxiYkJAhbNx+NHj1CmbFmRI9Q8aRkCHj2XryI62RnAoawBfj36FvFvs9CghjGKGEkQtPONLFl88iIDgzoWQ3kbfdyMShMjdK0hlUqxb98+LFq0ELlcAI4+wPxRMVETyCJFimDYsGEAAIlEAn9/fxQpUkTMkPJVeno6QkNDMWLECLn25s2bY/369QgLC4OPj49I0WmftPQ0FC9eQq6taFELvH37RpyAtFhExB3Y25dn8vgV3rx5jcuXL2LW7PlyPSuNmzRD4yYF90uxqunpAq19THE3Jg3h0ekAgKoVDBH+b5pcpTExRcDiX16JFaZWuXv3LmbMmI7vvuuGOnXqYOBA9naR8jTmUobDhg3D69evZZcyvHDhAmbNmoU//vhD5MjUJzY2FhkZGbIr77xnZ2cHAIiOjhYhKu313Xe9cOH8ORw6eACJCQk4/89ZHPxjH1q1aid2aFrn7t0I6OrpYeCAvvCqVR31fLwwa+Y0JCUlfv7OBAC4FxkJqVSKYsUsMWnij/Dxrok6tWtiyuTxSHj3TuzwtIa3axGYmejgz3+yzz0dHaCEhS5evs2Cr4cxfuxhien9iqNPm6IoUYxDLPLCxsYGR48ew8SJE9nL9RkSifpu2k5jJtEcO3YMo0aNwpo1a1CuXDn069cP5cqVw549e/D27Vt0795d7BBV7v24E1NTU7n29934H14XnD6veYtWCLt8EdOmjpe1eXvXxZixE0WMSvsIgoB7kXchCAI6dOiEAQMG49btm1izagX+/TcKGzZuzTFWmXJ6/Tq7GjZj+mT41K2HJcuW42FMDIICl+Lxo0fYsOkXTu76DF0doLZLEdyKSsOrd9nVxiIGEujqSuDtWgSv30mx/3QidHUBXw8T9G1rgZW/vUYCx0B+koWFhdghUAGgMQnkypUr4e/vD29vb6xatQqlS5fGwYMHcfjwYQQFBRXIBFIq/fQfOX5IK2fMqGG4di0MI0aORVUXV9y/dw9r1y7H+PGjsPinIH5Y55EgCPg5aCWKFbNExYqVAAA1PTxR3KoEJk38Ef+cO4u69eqLHKXmy8jIAABUrlIV02fMAQB4eXnDzMwMEyeMxYXz/8C7DoeofEqVCoYwM9HF2WtvZW26uv9/H2899Abp/w2XfPIiEyO7WsLLpQiOX0zK71CJCh2NyVCioqLw7bffQkdHB+fOnUODBg2go6MDd3d3PH78WOzw1MLMzAwAkJQk/8fufeXx48okKXb9+lX8888ZjB4zAb2+90fNmrXQpWt3zJy1AKf+PoGzZ/4WO0StoaOjA09PL1ny+F69+g0AZHdv0+cZ/9eTUL9+Q7l2H596AICICK5v+zlVKxji2atMPHuVJWtLy8ie9fHgSYYseQSAt4lSvHidBRsrjamLEBVoGpNAmpubIyEhAQkJCbhx4wbq1KkDAHj48GGBLbfb2tpCV1cXMTExcu0PHz4EADg4OIgRllZ6+vQJAKBatRpy7TVqeAAAov69n+8xaavnz59h9287Za/pe2lpqQCAYh+t1Uq5s7XNHsucnp4u157x39qahoYce/YpOjpAxbIGuP3RjOq0dAGJKVK5SuR7ujpARhanFZPqSCQStd20ncYkkA0aNMC0adMwYsQImJmZwcfHB//88w+mTZuGhg0bih2eWhgaGsLDwwPHjh2D8MFaCkeOHIGZmRnc3NxEjE672NuXBwBcvRom13792lUAQNky5XLch3KXlZWFWTOn4bddO+Tajxz+E7q6uqhRo6ZIkWmXChUcULp0GRw5ckju/X3q75MAwNfxM0pZ6sFAX4KHzzJybLv3MB0OZfRhbPT/D2GrorqwstBFzNOc+xOR6mlMrX/q1KlYtmwZYmNjsWrVKhgYGCAsLAzu7u4YN26c2OGpzeDBg9GnTx+MHDkSHTt2xNWrVxEcHIwxY8YUqiWNvpazcxX4Nm6GpUsW4l3CW7i4VMO/Ufewds0KVK5cFQ0bNRE7RK1hY1Ma37TvgE0bN8DQ0AjVqrnj6tUwrF+3Bl2/6y5L1unTJBIJfhj9I8b/OAoTxo2GX8fO+DcqCiuWL0PjJs3gXLmK2CFqtFKW2TOqn7/OyrHt77AkONsboFerovj7SjJ0dSRoUssY7xKlCItIze9QqQArAIVCtZEIApcRFduxY8cQGBiI6OholCpVSnYpQ02UkKS5sxszMtIRvH41Dh08gBcvnsPa2gYNGzVB/wFDYGxs8vkD5DN9fc39y5Seno5NG4Pxxx/78fTJE5QqZY0OHTujdx9/jZzcJZVq7p+x06f+wto1q3Dv3l2YFy2KVq3aYuiwkRq5xuaCzfFihyBTt1oRNKttilnrXyAzZw6JEha6aFbbBPY2+pAKQNSjDBw+n4h3GvI3auaAEp/fSQOEhobi++97YfPmLRp7KUMxk7ib99W3KL1rRUO1HTs/aFQCGRERgcj/1k4DsmeDpqen4+bNm5gzZ47I0RGg2QmkttHkBFLbaHICqU00KYHUdtqSQGoDJpCaSWO6sDdu3IiFCxcCyO76eZ/XSiQSeHh4iBkaERERFUb8nq+QxvRFhYSEoH///rh+/TqKFSuGU6dOYf/+/XBwcEDjxo3FDo+IiIiI/qMxCWRcXBw6d+4MQ0NDODs74+bNm3BycsKECRPw22+/iR0eERERFTISNd60ncYkkMbGxsjKyh4pbWtri/v3s9ftc3BwKLALiRMRERFpI41JIGvUqIG1a9ciJSUFVapUwcmTJyGVShEWFia7NjQRERFRfpFI1HfTdhqTQI4ePRpnzpxBSEgIWrdujZcvX6JWrVoYP348OnToIHZ4RERERPQfjZmF7ejoiOPHjyM5ORkmJibYuXMnfv/9d9jY2KBFixZih0dERESFTAEoFKqNxiSQAGBkZAQjo+zrwxYvXhx9+vQROSIiIiIqtJhBKiRqAunr65vnC4qfOHFCzdEQERERUV6ImkD6+fnlOYEkIiIiyk/MURQTNYEcPny4mA9PRERERF9Ao8ZAXrt2DVu3bkVkZCR0dXVRtWpV9O7dG5UqVRI7NCIiIipkWH9UTGOW8Tl58iS6deuGR48ewcfHB56enrh79y46dOiAy5cvix0eEREREf1HYyqQS5cuhb+/P8aMGSPXvnDhQgQEBGDHjh0iRUZERESFEYdAKqYxFciYmBh07NgxR3uXLl0QEREhQkRERERElBuNSSArV66M8+fP52i/desWx0ASERERaRCN6cJu164dFi9ejH///RdeXl7Q09PDzZs3sXnzZnTt2hX79u2T7du+fXvR4iQiIqLCgV3YikkEQRDEDgIAnJ2d87SfRCLBnTt31BwNKZKQJBU7hAJDX59/mVRFKtWIP2Nab8HmeLFDKDBmDighdggFhphJXOTDdLUd29HWQG3Hzg8aU4HkOEciIiLSJKxAKqYxYyCJiIiISDswgSQiIiIipTCBJCIiIiKlMIEkIiIiyoVEor7bl3rz5g2mTZuG+vXro0aNGvjuu+/krth3/vx5dOjQAdWqVUOLFi1w8OBBFbwSOTGBJCIiItISo0ePxtWrV7FkyRLs3r0blStXhr+/P/79919ERUVh4MCBqFevHvbs2YPOnTtj3Lhxua6z/bU0ZhY2ERERkSbRtEnYMTExOHfuHLZt24aaNWsCAKZOnYozZ87g999/R3x8PJycnDBq1CgAgIODA8LDw7F+/Xp4e3urNBZWIImIiIhyIZFI1Hb7EsWKFcPatWvh6uqaI8Z3797h8uXLORLF2rVrIywsDKpe9psVSCIiIqJ81rhx409uP3HiRI42c3NzNGjQQK7tyJEjiImJwaRJk7B3715YW1vLbS9ZsiRSUlLw+vVrWFpafn3g/2ECSURERJQbTevD/siVK1cwceJENGvWDA0bNkRqaioMDOSvcPP+9/R01V5VhwkkERERUT7LrcKojOPHj2Ps2LGoUaMGFi9eDAAwNDTMkSi+/71IkSJf9Xgf4xhIIiIiolxI1Hj7Gr/88guGDx+ORo0aYfXq1TA0NAQA2NjY4Pnz53L7Pn/+HMbGxjAzM/vKR5XHBJKIiIhIS2zbtg2zZ89G9+7dsWTJErkuaw8PD1y8eFFu/wsXLqBGjRrQ0VFtyscubCIiIqJcfM2C3+oQHR2NefPmoWnTphg4cCBevnwp22ZkZISePXvCz88Pixcvhp+fH06dOoXDhw9j/fr1Ko+FCSQRERGRFjhy5AgyMjJw7NgxHDt2TG6bn58fFixYgJUrVyIgIACbN29G2bJlERAQoPI1IAFAIqh6YSAq0Hi2qE5WFl9MVZHoaFiZgAq92GcZYodQYNjb6Iv22DFP1ff/aCfi81IFViCJiIiIcsPvpgpxEg0RERERKYUVSCIiIqJcsACpGCuQRERERKQUViCJiIiIcqFpy/hoElYgiYiIiEgprEASERER5ULCEqRCrEASERERkVKYQBIRERGRUtiFTURERJQL9mArxgokERERESmFFUgiIiKiXLAAqRgrkERERESkFFYgiYiIiHLBMZCKsQJJREREREphBZKIiIgoN6xAKsQKJBEREREphRVIIiIiolywAKkYK5BEREREpBRWIImIiIhywVnYijGBJCIiIsoF80fF2IVNREREREphBZKIiIgoN+zDVogVSCIiIiJSikZUIGNiYjBlyhTcunULqampObbfuXNHhKiIiIioMGMBUjGNSCCnTp2Kly9fYuTIkShatKjY4RARERHRJ2hEAnn9+nWEhITAxcVF7FCIiIiIAHAW9qdoxBhICwsLGBoaih0GEREREeWBRiSQPXr0wNKlS5GcnCx2KEREREQAssdAquum7UTrwvb19YXkg1fw8ePH8PLyQvHixaGjI5/XnjhxIr/DIyIiIiIFREsg/fz85BLIwuzs2bNYunQp7t+/DysrK3Tv3h19+/bl6/MV4uLi0LZtGyxfvgJeXl5ih6N1pFIpNm/eiJ07d+LZszjY2dnD378f2rRpK3ZoWkUqlWLnzh34dfs2xD56BCtLS/j6Nsaw4SNgamoqdnhaha/l13vxPA4D+/hh+pyfUa16LVn7hX/+RsiW1YiOikTRosVQr2EzfN93OIoYG4sYLWk60RLI4cOHi/XQGuXatWsYNGgQWrZsiZEjRyIsLAwBAQHIysrCgAEDxA5PKz19+hT9+vkjISFB7FC0VlBQIDZsCMawYcPh6uqK06dPYfz4HyGRSNC6dRuxw9MawcHrEfjzMvTp64/atb0R8yAagYGBuHfvHtYHb+CXRCXwtfw6z58/xeQfByIpSf7v4rkzxzF72ii4uXti8oyfkJmRgZCtqxE+5hqWBm2Frp5GzLUVDU8rxTTmzPj999/h6ekJa2trrFy5EocOHUKNGjUwefLkAj3BJigoCJUrV0ZAQAAAoH79+sjMzMTq1avRq1cvGBkZiRyh9pBKpdi3bx8WLVoIQRA7Gu2VkpKCrVu3oGfPnujfP/tLTO3a3ggPv42QkK1MIPNIKpUieP06fPttF4wePQYAUKdOHVhYFMOYMaNw+/YtuLi4ihylduBr+eWkUimOHzmAdasWQ0DOP4xbN61EObsKmLtoDfT19QEALm410LtbSxw5vA+t2nTK75BJS2jEJJqVK1di8uTJePLkCcLCwhAYGIjq1asjNDQUixcvFjs8tUlPT0doaCiaNm0q1968eXMkJSUhLCxMpMi00927dzFjxnR88017LFq0SOxwtJaBgQFCQrbj++/7yLXr6+sjLS1dpKi0T2JiItq2+wat28gn3OUrVAAAxD6MFSMsrcTX8stFR0UicMksNGneDuMmzc+xPTbmX3h4+siSRwAoZlkctnYVcPH86fwMVSNJ1HjTdhpRgdy9ezcWLlyIGjVqYN68eXB3d8fs2bNx+fJljBo1CpMnTxY7RLWIjY1FRkYG7O3t5drt7OwAANHR0fDx8REhMu1kY2ODo0ePwdraGqGhoWKHo7V0dXXh5OQEABAEAfHx8di7dw/Onz+P6dNnihyd9jA3N8fkyVNytJ84cRwAULFixfwOSWvxtfxyJUrZYGPIIZQoaY3rVy/m2G5etBiePXsi15aZmYHnz58iI4NfGEkxjUggnz9/jurVqwMA/vnnH7Ro0QJAdkLw7t07MUNTq/dj9D4eAG5iYgIg+1s35Z2FhYXYIRQ4hw4dxLhxYwEADRo0QNu27USOSLtdv34d69etRaNGjVDJ0VHscLQaX8u8MTcvCpgrvsJb85Z+2P7LWuzYFozmrfyQnpaGTcGBSEpMRJEinETDMZCKaUQXtrW1NaKjoxETE4P79+/Lqm6XL1+GtbW1yNGpj1Qq/eT2j5czIspvrq5u2Lx5KyZNmoKrV69i4MB+EDjA9ItcuRKGgQP6oUzZspg7N2dXIuUdX0vV6dl7CL79zh9bNgShS/v66NO9JYyNTeBdtxEMDYuIHR5pMI2oQHbt2hU//PADDAwM4OTkhOrVqyMkJASLFi3CiBEjxA5PbczMzAAASUlJcu3vK49cmoLEZmtrC1tbW3h4eMLU1BSTJk1AWNhleHh4ih2aVvnz0CFMmjQB9vb2WLt2PSyKFRM7JK3F11K1dPX04D9wFHr2HoKnTx/ByqoETM3MMWbE9zD7ROWy0GAJUiGNSCD9/f1Rvnx5xMbGol277C4yc3NzTJ06FZ06FdwZYLa2ttDV1UVMTIxc+8OHDwEADg4OYoRFhdyrV69w5sxp1K1bD1ZWVrL2KlWqAMgeckJ5t2FDMH5aHIBatWohMGiF7IsjKY+vpepdv3oRGRkZ8KjlAzv77M+crMxMPPj3Hpq2+Ebk6MTH9FExjekj9fX1xffff49i/32bbNu2bYFOHgHA0NAQHh4eOHbsmFy34JEjR2BmZgY3NzcRo6PCKjU1FZMmTcCePb/JtZ87dw4A4OjoJEZYWmnHjl+xOGARWrRoiTVr1zPh+Qp8LdXjzKljWLZ4OjIzM2RtR/7ci8TEd6hT11fEyEjTaUQFMi0tDTt27EBkZCSysrJk7enp6bh16xaOHDkiYnTqNXjwYPTp0wcjR45Ex44dcfXqVQQHB2PMmDEoUoTjTyj/lS5dGh06dMSqVSuhp6eHypWrICzsMtavX4eOHTtxxmsevXjxAgsXzEeZMmXQrXt33AkPl9teztYWlpaWIkWnXfhaqk+bdt/i8MHfsHjBFDRv6Yd/o+5iw9qlaNCoBdzcOVSFPdiKaUQCOWfOHOzbtw9VqlTBzZs3Ub16dcTExCA+Ph69e/cWOzy18vb2RlBQEAIDAzF06FCUKlUK48aNQ9++fcUOjQqxqVOno2zZcti1ayeePHkCa2sbDBs2An368LzMq9OnTyE1NRWPHz9Gzx7dc2yfO28+/Pw6iBCZ9uFrqT72FSph5vwV2Lh2GaZPGoZilsXxXY8B6Nqjv9ihkYaTCBowpbJOnTqYNGkS2rRpg6ZNm2LNmjUoV64cRo0aBWtra0yZknP9LxKH+GdLwZGVxRdTVSQ6LBOQZol9lvH5nShP7G30P7+TmiQkfXq1lK9hZqIxowi/iEZE/+7dO9SoUQNA9oKw4eHh0NfXx8CBA/HXX3+JHB0RERERfUgjEkhLS0vEx8cDAOzt7REZGQkAKFasGF6+fClmaERERFRY8VqGCmlEAlm/fn3MnDkT9+7dQ82aNfHHH3/g5s2bCAkJKdALiRMRERFpI41IIMeNG4eSJUvi4sWLaNy4MRwcHNC5c2ds3bq1QC8kTkRERJpLIlHfTdtpxCSay5cvw83NDQYGBgAAQRBw584dFC9eHCVLlhQ5OvqQ+GdLwcFJNKrDSTSkaTiJRnXEnESTlKK+STQmRTSihvfFNCL64cOH4969e7LfJRIJqlSpwuSRiIiISANpxDqQlpaWSEhIEDsMIiIiIpmC0NWsLhqRQNavXx8DBw5EgwYNYGdnB0NDQ7ntw4YNEykyIiIiIvqYRoyB9PVVfL1NQRC4FqQGEf9sKTg4BlJ1OAaSNA3HQKqOmGMgk1PVNwbS2EgjRhF+MY2oQALA7t27UaxYMbm2Z8+eoV27diJFRERERES5ES2BPHToEM6cOQMAePr0KWbPnp2j6/rx48fQ0dHuDJ2IiIi0k4SDIBUSLYGsXr06fv31VwiCAEEQ8OTJE+jr/79MLZFIYGxsjAULFogVIhERERHlQiPGQPbs2RMrVqyAubm52KHQZ4h/thQcHAOpOhwDSZqGYyBVR8wxkKnp6vs7bWSg3X+3NCKBJO3Bs0V1mECqDhNI0jRMIFVHzAQyTY0JpKGWJ5AcYEhEREREStGYWdhEREREGkW7i4RqxQokERERESmFFUgiIiKiXLAAqRgrkERERESkFFYgiYiIiHLBdcQVYwWSiIiIiJTCCiQRERFRLliAVIwJJBEREVFumEEqxC5sIiIiIlIKK5BEREREuZBwFo1CrEASERERkVJYgSQiIiLKBeuPirECSURERERKYQJJRERElAuJRH23ryGVShEYGIh69erB3d0d/fv3R2xsrGqedB4xgSQiIiLSIitXrsS2bdswe/Zs/Prrr5BKpejXrx/S09PzLQYmkERERERaIj09HRs2bMCIESPQsGFDODs7Y+nSpYiLi8PRo0fzLQ4mkERERES50MQu7IiICCQlJcHb21vWZm5ujipVquDSpUsqeNZ5w1nYRERERPmscePGn9x+4sSJXNvj4uIAADY2NnLtJUuWlG3LD0wgSSlcU1V19PT4YhIVVPY2+mKHQAVUSkoKAMDAwECu3dDQEG/fvs23OJhAEhEREeUzRRXGzzEyMgKQPRby/c8AkJaWhiJFiqgktrzgGEgiIiIiLfG+6/r58+dy7c+fP0epUqXyLQ4mkERERERawtnZGaampggNDZW1vXv3DuHh4fD09My3ONiFTURERKQlDAwM0KNHDyxevBiWlpYoU6YMAgICYG1tjWbNmuVbHEwgiYiIiLTIiBEjkJmZiSlTpiA1NRWenp4IDg6Gvn7+Td6SCIIg5NujEREREZHW4xhIIiIiIlIKE0giIiIiUgoTSCIiIiJSChNIIiIiIlIKE0giIiIiUgoTSCIiIiJSChNIIiIiIlIKE8h81LNnT0yYMAEAEBoaCicnJzx69EjkqLJNmDABPXv2FDuMzwoKCoKvr6/YYRRar1+/xq5du/K8v6ad56R5VP2eTk5ORkhIiOx3bfnb9qWePHmCgwcPqu34/JtLijCBJKI8W7RoEQ4cOCB2GEQKbdiwAcHBwbLfJ0+ejKCgIBEjUq/x48fjzJkzYodBhRAvZUhEecYLV5Gm+/gcNTMzEykSooKNFcj/JCUlYfbs2ahbty6qV6+OHj164NatWwCAXbt2oW3btnBzc4O7uzu6deuGmzdvyu7r6+uLhQsXolWrVvDy8sLFixeRnp6OefPmwdvbGzVr1kRAQACkUmmOxz158iSaNGkCV1dX9OzZExEREbJtWVlZ2LRpE5o3bw5XV1c0b94c27dvl7v/8ePH0blzZ7i7u8PV1RUdOnSQ+zbas2dPTJ06FZ07d4aHhwcOHDgAQRCwcuVK1K9fH+7u7pg4cSLS0tJU/ZJ+sVevXmHUqFHw8PCAl5cXFi9ejF69euVaRYiMjMTAgQPh6ekJFxcXNG7cGBs2bJBtT0lJweTJk+Hj4wNXV1e0b98eR48elW1/8OAB/P39UbNmTVSvXh3+/v64e/duvjxPdXNycsKOHTvQrVs3uLq6omXLlrhy5Qp27NiBhg0bokaNGvjhhx+Qmpoqu8+VK1fQvXt3uLm5oWHDhpg5cyYSExMBZHcF7t27FxcvXoSTkxMA4O3bt5gyZQrq1auHqlWrwtvbG1OmTEFKSoooz1kdnJycEBISgm+//Raurq5o27YtTpw4IdsulUqxZs0aNG/eHC4uLqhRowb69euHhw8f5vkYAPDXX3+hQ4cOcHNzQ9OmTbFs2TKkp6fLHSMwMBCNGjVC3bp18eDBA7U/d1XIy3P/0Ne8p4OCgrB8+XI8fvxYNnTi4y7smJgYDB48GDVr1oSXlxdGjx6N+Ph49b0AatSzZ09cvHgRe/fuha+vb66fRXl5jwYHB6NJkyZwcXGBr68vVqxYkSMRX7t2LerXrw83Nzf07NlTa84/UiOBBEEQhH79+glNmjQRTp8+LTx48ECYMGGC4OnpKRw9elRwcXER9u3bJzx69Ei4evWq0KFDB6Fdu3ay+zZq1EhwcXERzp07J9y4cUNIS0sTpk6dKvj4+Ah///23EBkZKYwePVpwdHQUxo8fLwiCIFy4cEFwdHQUGjRoIJw+fVq4e/euMHDgQMHHx0dITk4WBEEQ5syZI3h6egoHDhwQoqOjhc2bNwtVq1YVNm7cKAiCINy8eVNwdnYWNm7cKDx8+FAIDw8X/P39hdq1awtpaWmCIAhCjx49BCcnJ+HAgQPC3bt3hVevXgmrV68WqlevLvz+++9CVFSUMG/ePMHR0VHo0aNH/r7oucjKyhI6deok+Pn5CVevXhVu3boldO/eXXBychICAwOFwMBAoVGjRoIgCEJycrLg4+MjjBs3Trh//74QHR0tLFq0SHB0dBTCw8MFQRCE+fPnCx07dhRu3bolPHz4UPjpp5+EKlWqCLGxsYIgCIKfn58wceJEITo6Wrh3757sPCgIHB0dBS8vL+HEiRNCVFSU0LlzZ8HT01Po06ePcPfuXeHw4cNC1apVhS1btgiCIAh37twR3NzchFWrVgnR0dHCpUuXhM6dOwudO3cWpFKp8O7dO2HkyJFCly5dhOfPnwuCIAiDBg0S/Pz8hGvXrgmxsbHC/v375c7R9+f5+9dbGzk6Ogru7u7CL7/8IkRFRQkBAQGCs7OzEBYWJgiCIGzcuFHw9PQUTp48KTx69Ej4559/hMaNGwuDBw/O8zFOnToluLm5Cdu3bxdiYmKEM2fOCM2aNRNGjBghdwwvLy/hxo0bwtWrV/P1Nfgan3vuqnxPJyYmCgsWLBDq168vPH/+XMjMzBTGjx8v+9v29u1bwcfHR+jbt69w8+ZN4fbt20LHjh014m/fl3j9+rXQpUsXYeTIkUJ8fHyun0Wfe4+eOHFC8PT0FM6ePSs8fvxYOHjwoFC1alVh3759giBk//84OjoKAwcOFO7cuSNcv35daN68udCtWzcRnzlpAiaQgiBERUUJjo6OwpkzZ2Rtqampwrx584TQ0FBh//79cvtv27ZNcHZ2lv3eqFEjYejQobLfExIShKpVqwo7d+6UO16dOnVyJJDHjx+X7fP27VvB3d1d2Llzp+wYW7dulXvsuXPnCt7e3oJUKhXCw8OFkJAQue2nTp0SHB0dhSdPngiCkJ1Atm/fXrZdKpUKPj4+wtKlS+Xu980332jEH9Hz588Ljo6OQlRUlKztxYsXgqura44EMj4+XlizZo2QmJgo2zc1NVVwdHQU9u7dKwiCIAwePFjo1auX8PbtW0EQBCEzM1M4ffq08O7dO0EQBKFmzZpCQECAkJ6eLgiCIDx//ly4cOGCkJWVlR9PV60cHR2FRYsWyX7/5ZdfBEdHRyE6OlrW1qlTJ2Hq1KmCIAjC2LFj5ZIeQRCEhw8fCo6OjsKFCxcEQRDkPowFQRC2bt0qREREyN2nc+fOwsSJEwVBKDgJ5KxZs+TaOnfuLIwaNUoQhOwP4JMnT8ptDwgIEBo3bpznY3z33XfCnDlz5La/fy+8f+0cHR2FefPmqeZJ5aPPPXdVv6c/PJ4gyJ+z27dvF9zd3YU3b97Itt+5c0dYvHix7Eu3tunRo4fsc+XjzyJB+Px7dOPGjYKPj4/c34VLly4Jjx8/FgQh+/WsWrWqkJCQINu+ceNGwc3NTR1Ph7QIx0Aiu8sEANzd3WVthoaGmDhxIgAgKioKK1aswL///ouYmBjcvXs3R3e0nZ2d7Ofo6GhkZGTA1dVV7nhVqlTJ8dg1a9aU/Wxubg57e3tERkbCyckJGRkZctsBoFatWti8eTPi4+NRuXJlFC1aFGvXrpXF9r4LPCsrK9fYXr9+jRcvXsjF9v65R0VFffqFygfh4eEoWrQoKlSoIGsrXrw4ypcvn2NfS0tLdOvWDX/88QfCw8Px8OFD2fN////Tv39/DBo0CN7e3nBzc4OPjw/atm0rGxc1atQozJs3D9u2bUOtWrVQr149tGnTBjo6BWN0x4f/90WKFAEA2NraytqMjIxk3aTh4eGIiYlB9erVcxwnKioKXl5eOdq7deuGkydPYu/evXjw4AHu37+PR48eyf3/FQQfP/fq1avj3LlzALKHsFy/fh0///wzoqOjER0djfv376NUqVJ5PkZ4eDhu3LiB3377TbZd+K8LMSoqCmXLlgUg//+pTT713D+kivf0p0RGRsLe3h5FixaVtTk7O8PZ2flrnp5G+fgc+dx7tF27dti9ezeaN2+OihUrok6dOmjevDlKly4tO4aVlRVMTU1lv5ubm8sNfaHCiQkkAD09xS/D77//jgkTJqBt27aoUaMGunbtisjISMyaNUtuPyMjI9nPEokEQM7B3Lk9jq6urtzvWVlZMDAwUDhZ4f0fUT09PVy8eBH+/v5o2LAhatasibZt2yIlJQVDhw5VSWxi0NXVzXWsaG5evHiBLl26wNLSEr6+vqhbty5cXV3RoEED2T7Vq1fHqVOncO7cOZw/fx779u3DqlWrsH79enh7e6N79+5o0aIFTp06hfPnzyMwMBCrVq3Cvn37ULx4cXU9zXyT2/+rouRYKpWibdu2GDRoUI5tlpaWue4/cOBA3Lt3D23atEGrVq1QtWpVTJ069esD1zAfv45ZWVmy13Ht2rVYsWIF/Pz84O3tjd69e+PEiRM5llb51DGkUin69esHPz+/HI9dokQJ2c8fvpe1yaee+4dU8Z5WJo6C6MNzJC/vUUtLS+zfvx9Xr17FuXPncPbsWWzZsgXDhw/HsGHDAOT8nCICOIkGAODg4AAAchNjMjMz4evri1WrVqFTp05YsGABunfvDk9PT8TGxgJQPCO1fPnyMDQ0xJUrV+SO9+EEmffeT9QBsiePPHjwAJUqVYKDgwP09fURFhYmt//ly5dRokQJFC1aFBs2bICXlxeCgoLQu3dv+Pj44OnTp5+MrVixYrCxsclx3A/jEJOzszMSEhLkqqGvX79GTExMjn3/+OMPvHnzBtu3b8eQIUPQtGlTvH37FsD/n39gYCDCwsLQuHFjTJkyBUeOHEG5cuVw5MgRxMfHY9asWcjIyECHDh0QEBCAAwcO4MWLF7h48WL+PGENUqlSJdy/fx92dnayW2ZmJubPny87r95/AQGAO3fu4PTp0/j5558xduxYtGvXDra2tnj48GGBm6394d8GALh69SqqVq0KAFi9ejWGDh2KGTNmoEuXLnB3d8eDBw9yvAafOkalSpUQHR0t99rHxcVh0aJFSEpKUuMzyx+feu4f+tr3NCB/jn6sYsWKePDgARISEmRtt2/fhre3N+Li4r76eWqavLxHDxw4gO3bt6NmzZoYMWIEdu7cic6dO+PQoUMiR0+aruB/HcuD8uXLo1mzZpg5cyZmzJiBUqVKYe3atUhLS0PFihVx5coV3L59G2ZmZjh58iR++eUXAEB6ejoMDQ1zHM/ExAQ9evRAYGAgSpQoAQcHB2zYsAHPnj3Lse+0adMwa9YsWFhYYMGCBbCxsUGrVq1gYGCALl26IDAwEBYWFnB1dcXZs2exbds2jB49GhKJBDY2Njh+/DguX74Ma2trhIaG4ueff5bFpkj//v2xcOFCVKhQAR4eHti/fz9u3LiRo7tcDF5eXqhWrRrGjRuHqVOnwsjICAEBAUhJSYFEIpH7ULa2tkZKSgoOHz6MmjVr4t9//8X8+fMB/P/5x8bG4sCBA5g9ezZsbW1x/fp1PHnyBNWrV0fRokXx999/4+HDhxgzZgxMTU2xZ88e6Ovrw8XFRZTnL6a+ffuie/fumDlzJnr06IF3795h5syZSE1Nhb29PQDA2NgYz58/R2xsLIoXLw49PT38+eefsLS0xJs3b7B69Wq8ePHik+efNtq8eTMqVKgAFxcX7Ny5E3fv3sXcuXMBADY2Njh37hx8fX2ho6OD/fv34+jRozkq2J86Rv/+/fHDDz9g+fLlaN26NeLi4jB58mSULVtWrgKprT713D/0te9pIPscffv2LaKjo2Vd/++1bdsWK1euxI8//ogffvgBmZmZmDFjBhwdHWFtba3mV0E9TExM8Pjx41wT4Ly8R9PS0rBw4UKYmJjAw8MDcXFxuHTpEjw8PPL7qZCWYQL5n3nz5mHRokUYOXIk0tPTUa1aNQQHB8PExATTpk1Djx49YGBgAGdnZyxatAijRo3CzZs3Fb7JxowZA0NDQ8yaNQtJSUlo2bJlrqv5DxkyBBMnTsSrV6/g5eWF9evXw8DAAAAwceJEFCtWDIsXL8bLly9hb2+PadOm4dtvvwUAjBgxAi9fvpR1OVasWBHz5s3Djz/+iJs3b8oqqx/r3r07pFIpVq1ahZcvX6JevXro1KkToqOjVfFSfrWgoCDMmjULvXv3hqGhIbp164Z///0X+vr6colJixYtcPv2bSxYsACJiYkoU6YMOnfujBMnTuDmzZv47rvvMH36dCxcuBA//vgj3rx5gzJlymDs2LH45ptvAADr1q3DwoUL0bt3b6SkpKBy5cpYu3at3DjBwsLd3R3r16/Hzz//DD8/PxgbG8Pb2xvjx4+XnZPt27fHsWPH0KZNGxw9ehQLFixAUFAQQkJCUKJECTRs2BC9e/fGyZMnRX42qtW1a1ds2rQJkZGRcHZ2RnBwsGzc3KJFizBr1ix07NgRJiYmqFatmuzL6JMnT2RjyT51jBYtWmDp0qVYs2YNVq9eDQsLC/j6+mLs2LGiPWdV+tRz/5Aq3tPNmjXDzp070a5dO9mX/feKFCmC4OBgzJ8/H127doWRkREaNmyI8ePH58vroA5du3bF+PHj0a5dO9k45/dKlSr12fdo586d8ebNG6xcuRJPnz5F0aJF0bx58wJz7pH6SISC1tdEWu3Vq1e4fv066tatC319fQDZlQcvLy9Mnz4d7du3FzdAKnScnJwwf/58dOjQQdRjaKvC/NyJCjJWIEmj6OnpYdSoUejatSu+++47ZGRkIDg4GAYGBqhfv77Y4RERERE4iYY0jLm5OVavXo1r166hffv26NKlC16+fIktW7bkOhOYiIiI8h+7sImIiIhIKaxAEhEREZFSmEASERERkVKYQBIRERGRUphAEpHG4dBsIiLNxgSSqIDp2bMnnJyc5G4uLi5o2LAhZs6cKbs0nDrs2bMHTk5OePToEYDsReGdnJzyfP+4uDgMGDAAjx8//upYHj16BCcnJ+zZs0fhPsrG9zWPlVc9e/ZEz549v/o4RETqxHUgiQqgKlWqYPr06bLfMzIycPv2bSxZsgR37tzB9u3bP3nNYFXp3Lkz6tWrl+f9//nnH5w6dUqNERERkSowgSQqgExNTeHu7i7X5unpiaSkJAQGBuL69es5tquDtbW11l5jmIiIFGMXNlEh4uLiAgB48uQJgOzu0rFjx2LEiBFwd3dHnz59AABpaWlYtGgRGjRoABcXF7Rt2xaHDh2SO5ZUKsXKlSvRsGFDVKtWDUOGDMnRPZ5bF/G+ffvg5+eHatWqoWHDhvjpp5+Qnp6OPXv2YOLEiQCAxo0bY8KECbL77Nq1C61bt5Z1xQcFBSErK0vuuEePHkW7du3g5uYGPz8/REREqOAVy3bp0iX4+/vD09MTLi4u8PX1RVBQEKRSqdx+z549w8CBA+Hm5oYGDRogMDAwR5x5eS4fOnfuHL799ltUr14dnp6eGDx4MKKiolT23IiIvgQTSKJCJDo6GgBQrlw5Wduff/4JExMTrFq1Cv369YMgCBg6dCh+/fVX9OnTB6tWrUL16tUxatQo7Nu3T3a/gIAArFixAp06dcLy5cthYWGBn3766ZOPHxISgvHjx6Nq1apYvnw5BgwYgK1bt2LOnDlo2LAhBg8eDABYvnw5hgwZAgBYs2YNpk6dCm9vb6xevRrdu3fHunXrMHXqVNlxT548iREjRsDJyQkrVqxAy5Yt8eOPP6rkNYuIiEDv3r1hYWGBpUuXYtWqVfDw8MDy5cvx559/yu0bFBQEKysrrFixAh07dsTq1auxcOFC2fa8PJcPxcbGYsiQIXBxccGqVaswd+5cREdHY8CAATmSVyKi/MQubKICSBAEZGZmyn5/+/YtLl68KEsG31ciAUBfXx8zZ86EgYEBgOyK15kzZ7B06VK0atUKAFCvXj2kpKRg8eLFaNOmDZKTk7F161b06dMHw4YNk+3z/PlznDlzJteYpFIpVqxYgSZNmmDOnDmy9pSUFBw8eBBmZmawtbUFAFSuXBlly5ZFQkICVq5ciS5dumDKlCkAgLp168LCwgJTpkxBnz59UKlSJaxYsQJubm4ICAiQxQLgswltXkRERKBOnToICAiAjk72d24fHx+cPHkSoaGhaN26tWzfevXqYd68ebKfExMTsW3bNgwZMgS6urp5ei4funHjBlJTUzFw4ECUKlUKQPawgBMnTiA5ORmmpqZf/fyIiL4EE0iiAujSpUuoWrWqXJuOjg7q1KmDWbNmyU2gqVChgix5BIDz589DIpGgQYMGckmor68vDhw4gHv37uHFixfIyMhAo0aN5B6jZcuWChPI6OhoxMfHo2nTpnLt/v7+8Pf3z/U+V69eRWpqKnx9fXPEAmQnu+XKlcPt27cxcuTIHLGoIoFs37492rdvj7S0NERHRyMmJgZ37txBVlYWMjIycjzmh5o1a4bNmzfj+vXrkEgkn30uHyeQ1apVg6GhITp16oQWLVqgfv368PLygpub21c/LyKir8EEkqgAqlq1KmbOnAkAkEgkMDQ0hI2NTa4VKxMTE7nf37x5A0EQUKNGjVyP/fz5c7x79w4AUKxYMbltJUqUUBjTmzdvAABWVlZ5fh7v7zNgwACFsbx9+xaCIOSIpWTJknl+nE9JTU3F7NmzsX//fmRmZqJs2bKoXr069PT0cqxX+fHzt7S0BAC5saGfei4fK1u2LH755ResXbsWv/32G7Zs2QJzc3N069YNP/zwQ77MpCciyg0TSKICyMTEBK6url90XzMzMxgbG2PLli25brezs8ONGzcAAPHx8ahQoYJs2/uELzfm5uYAgFevXsm1v379GuHh4ahevbrC+yxevBj29vY5thcvXhwWFhbQ0dHBy5cv5bZ9KhZlzJ07F0eOHMGyZctQp04dGBsbAwC8vb1z7PvxJKL3MVlZWcmqlZ96Lrlxc3PD8uXLkZ6ejrCwMOzYsQOrV6+Gs7NzjoonEVF+4SQaIpJTq1YtJCcnQxAEuLq6ym6RkZFYsWIFMjMzUb16dRgZGeHw4cNy9/3rr78UHrdChQooVqxYjn3279+PAQMGICMjQzbG8L1q1apBX18fz549k4tFT08PS5YswaNHj2BoaIjq1avj6NGjchXBkydPquDVAMLCwuDl5YUmTZrIksdbt27h1atXOSay/P3333K/Hzx4EEWKFEG1atXy9Fw+tmnTJjRq1Ajp6ekwMDCAt7c3Zs+eDeD/M+mJiMTACiQRyWnQoAE8PT0xZMgQDBkyBA4ODrhx4wYCAwNRr149WbfskCFDsGzZMhQpUgS1a9fGqVOnPplA6urqYvjw4Zg1axasrKzg6+uL6OhoBAYGonv37ihatKis4njs2DHUr18fDg4O6NevH37++WckJibCy8sLz549w88//wyJRAJnZ2cAwOjRo/H9999j2LBh6NKlC6Kjo7F69eo8P+dNmzblaDM3N0eHDh3g5uaGP//8E9u3b4eDgwMiIiKwatUqSCQSpKSkyN3n6NGjKFWqFOrUqYOzZ89ix44dGDlypGzoQF6ey4dq166NxYsXY+jQoejRowd0dXXxv3bul0WBII7D+PcMhgUxLRoWs8EiCKIvwyoIhkVFg2xRtJg0KRoEXf/gFtEXYLX6JnwLvgFF7pqcJ3c45dLziTPDwLQn/JjdbqdwOPwyfwoA/4mABPAkFArJ931NJhPN53NdLhfFYjGVy2XV6/XHuUqlIsuyFASBgiBQOp1Wq9VSr9f79e5isSjLsrRarbTf7xWPx+W6rlzXlSRls1nl83kNh0OdTif5vq9msynbtrXdbrVcLhWNRpXL5eR5niKRiCQpk8losVhoNBqp0WjIcRz1+31Vq9W33jwYDF7WEomECoWC2u22brebxuOxrterHMdRrVbT+XzW8Xh8+sOx2+3qcDhos9nItm11Oh2VSqXH/jtv+S6ZTGo2m2k6ncrzPN3vd6VSKa3X66fRAQD4bx+fP6fAAQAAgD8wAwkAAAAjBCQAAACMEJAAAAAwQkACAADACAEJAAAAIwQkAAAAjBCQAAAAMEJAAgAAwAgBCQAAACMEJAAAAIwQkAAAADBCQAIAAMDIF4UClkrmfVe0AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_model_performance(transfer_Xception_model, validation_generator_Xception, class_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# WANDB"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Login"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Login to wandb account\n",
    "wandb.login()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initiate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Tracking run with wandb version 0.18.7"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Run data is saved locally in <code>c:\\Users\\iqbal\\Documents\\ML\\wandb\\run-20241202_190313-iywmsvx5</code>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Syncing run <strong><a href='https://wandb.ai/naufaldewanto37-institute-of-technology-sumatra/Garbage%20Classification/runs/iywmsvx5' target=\"_blank\">breezy-haze-1</a></strong> to <a href='https://wandb.ai/naufaldewanto37-institute-of-technology-sumatra/Garbage%20Classification' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/developer-guide' target=\"_blank\">docs</a>)<br/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View project at <a href='https://wandb.ai/naufaldewanto37-institute-of-technology-sumatra/Garbage%20Classification' target=\"_blank\">https://wandb.ai/naufaldewanto37-institute-of-technology-sumatra/Garbage%20Classification</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View run at <a href='https://wandb.ai/naufaldewanto37-institute-of-technology-sumatra/Garbage%20Classification/runs/iywmsvx5' target=\"_blank\">https://wandb.ai/naufaldewanto37-institute-of-technology-sumatra/Garbage%20Classification/runs/iywmsvx5</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run = wandb.init(\n",
    "    # Set the project where this run will be logged\n",
    "    project=\"Garbage Classification\",\n",
    "    # Track hyperparameters and run metadata\n",
    "    config={\n",
    "        \"learning_rate\": 0.0001,\n",
    "        \"epochs\": 50,\n",
    "        \"batch_size\": 32\n",
    "    },\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001b[1m64/64\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 224ms/step - accuracy: 0.7867 - loss: 0.6128 - val_accuracy: 0.6819 - val_loss: 0.9961\n",
      "Epoch 2/10\n",
      "\u001b[1m64/64\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 217ms/step - accuracy: 0.7969 - loss: 0.5782 - val_accuracy: 0.6660 - val_loss: 0.9216\n",
      "Epoch 3/10\n",
      "\u001b[1m64/64\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 213ms/step - accuracy: 0.7851 - loss: 0.5770 - val_accuracy: 0.6839 - val_loss: 0.9289\n",
      "Epoch 4/10\n",
      "\u001b[1m64/64\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 224ms/step - accuracy: 0.7866 - loss: 0.6059 - val_accuracy: 0.6660 - val_loss: 0.9307\n",
      "Epoch 5/10\n",
      "\u001b[1m64/64\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 225ms/step - accuracy: 0.7897 - loss: 0.5615 - val_accuracy: 0.6740 - val_loss: 0.9436\n",
      "Epoch 6/10\n",
      "\u001b[1m64/64\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 218ms/step - accuracy: 0.7765 - loss: 0.5789 - val_accuracy: 0.6700 - val_loss: 0.8938\n",
      "Epoch 7/10\n",
      "\u001b[1m64/64\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 215ms/step - accuracy: 0.7885 - loss: 0.5926 - val_accuracy: 0.6918 - val_loss: 0.9237\n",
      "Epoch 8/10\n",
      "\u001b[1m64/64\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 231ms/step - accuracy: 0.8076 - loss: 0.5526 - val_accuracy: 0.6998 - val_loss: 0.8850\n",
      "Epoch 9/10\n",
      "\u001b[1m64/64\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 219ms/step - accuracy: 0.8103 - loss: 0.5434 - val_accuracy: 0.7097 - val_loss: 0.8960\n",
      "Epoch 10/10\n",
      "\u001b[1m64/64\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 218ms/step - accuracy: 0.8104 - loss: 0.5305 - val_accuracy: 0.6501 - val_loss: 0.9936\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x1c40340cd70>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(\n",
    "    train_generator,\n",
    "    epochs=10,\n",
    "    validation_data=validation_generator,\n",
    "    callbacks=[\n",
    "        WandbMetricsLogger()\n",
    "    ]\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <style>\n",
       "        .wandb-row {\n",
       "            display: flex;\n",
       "            flex-direction: row;\n",
       "            flex-wrap: wrap;\n",
       "            justify-content: flex-start;\n",
       "            width: 100%;\n",
       "        }\n",
       "        .wandb-col {\n",
       "            display: flex;\n",
       "            flex-direction: column;\n",
       "            flex-basis: 100%;\n",
       "            flex: 1;\n",
       "            padding: 10px;\n",
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