unet++.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CiWhcC7cbNNj",
    "outputId": "5f6fc990-0721-41ec-b43a-05d25b97f304"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-02 19:00:53.418757: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-06-02 19:00:54.031402: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensorflow ver. 2.16.1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sukrit/.local/lib/python3.10/site-packages/matplotlib/projections/__init__.py:63: UserWarning: Unable to import Axes3D. This may be due to multiple versions of Matplotlib being installed (e.g. as a system package and as a pip package). As a result, the 3D projection is not available.\n",
      "  warnings.warn(\"Unable to import Axes3D. This may be due to multiple versions of \"\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import cv2\n",
    "import keras\n",
    "import numpy as np\n",
    "from glob import glob\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.layers import Input, Conv2D, Conv2DTranspose, concatenate, BatchNormalization, Activation, Dropout, MaxPooling2D, UpSampling2D,LeakyReLU,ZeroPadding2D,multiply\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.applications import Xception\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n",
    "from tensorflow.keras.metrics import Recall, Precision\n",
    "from tensorflow.keras import backend as K\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.applications import VGG16\n",
    "\n",
    "from tensorflow.keras.layers import LeakyReLU\n",
    "\n",
    "from keras.layers import Activation, SpatialDropout2D\n",
    "from keras.layers import Input,Dropout,BatchNormalization,Activation,Add\n",
    "from keras.layers import Dense, Lambda\n",
    "from keras.layers import concatenate, add\n",
    "from keras.layers import GlobalAveragePooling2D, Reshape, Dense, multiply, Permute\n",
    "from keras.optimizers import SGD\n",
    "# from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "\n",
    "AUTOTUNE = tf.data.experimental.AUTOTUNE\n",
    "print(f\"Tensorflow ver. {tf.__version__}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "L07bXoGWaWY-"
   },
   "outputs": [],
   "source": [
    "# Input image size that we are going to use\n",
    "IMAGE_SIZE = 512\n",
    "num_classes = 4\n",
    "# Set batch size\n",
    "batch = 6\n",
    "# Set learning rate\n",
    "LR = 1e-4\n",
    "# Set epoch\n",
    "EPOCHS = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "kHdbK03TJUDx"
   },
   "outputs": [],
   "source": [
    "# Set image and mask paths\n",
    "image_path = \"../../Datasets/IE-dataset-All-tts-augmented/images/\"\n",
    "mask_path = \"../../Datasets/IE-dataset-All-tts-augmented/masks/\"\n",
    "training_data = \"train/\"\n",
    "val_data = \"validation/\"\n",
    "test_data = \"test/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "DNWla2B0JO7W"
   },
   "outputs": [],
   "source": [
    "# Function to create lists of paths for training, validation and testing data\n",
    "def load_data():\n",
    "  extensions = [\"*.jpg\", \"*.tif\", \"*.tiff\"]\n",
    "  file_patterns = [os.path.join(image_path, training_data, ext) for ext in extensions]\n",
    "\n",
    "  TRAIN_X = sorted(sum((glob(pattern) for pattern in file_patterns), []))\n",
    "  # TRAIN_X = sorted(glob(os.path.join(image_path + training_data, \"*.jpg\")))\n",
    "  train_x = TRAIN_X\n",
    "# image_path\n",
    "  TRAIN_Y = sorted(glob(os.path.join(mask_path + training_data, \"*.png\")))\n",
    "  train_y = TRAIN_Y\n",
    "\n",
    "  file_patterns = [os.path.join(image_path, val_data, ext) for ext in extensions]\n",
    "  VALID_TEST_X = sorted(sum((glob(pattern) for pattern in file_patterns), []))\n",
    "  # VALID_TEST_X = sorted(glob(os.path.join(image_path + val_data, \"*.jpg\")))\n",
    "  valid_x = VALID_TEST_X\n",
    "\n",
    "  VALID_TEST_Y = sorted(glob(os.path.join(mask_path + val_data, \"*.png\")))\n",
    "  valid_y = VALID_TEST_Y\n",
    "\n",
    "  file_patterns = [os.path.join(image_path, test_data, ext) for ext in extensions]\n",
    "  TEST_X = sorted(sum((glob(pattern) for pattern in file_patterns), []))\n",
    "  test_x = TEST_X\n",
    "  TEST_Y = sorted(glob(os.path.join(mask_path + test_data, \"*.png\")))\n",
    "  test_y = TEST_Y\n",
    "\n",
    "  return (train_x, train_y), (valid_x, valid_y), (test_x, test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zUmFBsULJPFO",
    "outputId": "bc04c49f-f96c-412e-f0f4-7257fdbe0719"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data:  308\n",
      "Validation data:  16\n",
      "Test data:  15\n"
     ]
    }
   ],
   "source": [
    "(train_x, train_y), (valid_x, valid_y), (test_x, test_y) = load_data()\n",
    "\n",
    "print(\"Training data: \", len(train_x))\n",
    "print(\"Validation data: \", len(valid_x))\n",
    "print(\"Test data: \", len(test_x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "SEHemrCvN1dr"
   },
   "outputs": [],
   "source": [
    "# Set image augmentation (to be called by tensorflow dataset later)\n",
    "data_augmentation = tf.keras.Sequential([\n",
    "  # layers.RandomFlip(\"horizontal_and_vertical\"),\n",
    "  # layers.RandomRotation(0.2),\n",
    "  # layers.RandomZoom(.5, .2),\n",
    "  # layers.RandomContrast(0.1)\n",
    "  ],\n",
    "  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "ju3FstcUJPJk"
   },
   "outputs": [],
   "source": [
    "# Functions to read image and mask paths to output numpy arrays\n",
    "def read_image(path):\n",
    "    path = path.decode()\n",
    "    x = cv2.imread(path, cv2.IMREAD_COLOR)\n",
    "    x = cv2.resize(x, (IMAGE_SIZE, IMAGE_SIZE))\n",
    "    x = x/255.0\n",
    "    return x\n",
    "\n",
    "def read_mask(path):\n",
    "    path = path.decode()\n",
    "    x = cv2.imread(path, cv2.IMREAD_GRAYSCALE)\n",
    "    x = cv2.resize(x, (IMAGE_SIZE, IMAGE_SIZE))\n",
    "    x = np.expand_dims(x, axis=-1)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "DgGbSZBlJPMB"
   },
   "outputs": [],
   "source": [
    "# Function to generate tensorflow dataset \n",
    "def tf_parse(x, y):\n",
    "    def _parse(x, y):\n",
    "        x = read_image(x)\n",
    "        y = read_mask(y)\n",
    "        return x, y\n",
    "\n",
    "    x, y = tf.numpy_function(_parse, [x, y], [tf.float64, tf.uint8])\n",
    "    x.set_shape([IMAGE_SIZE, IMAGE_SIZE, 3])\n",
    "    y.set_shape([IMAGE_SIZE, IMAGE_SIZE, 1])\n",
    "    return x, y\n",
    "\n",
    "num_threads = 4\n",
    "\n",
    "def tf_dataset_train(x, y, batch=batch):\n",
    "    dataset = tf.data.Dataset.from_tensor_slices((x, y))\n",
    "    dataset = dataset.shuffle(buffer_size=5000)\n",
    "    dataset = dataset.repeat()\n",
    "    dataset = dataset.map(tf_parse, num_parallel_calls=num_threads)\n",
    "    dataset = dataset.batch(batch)\n",
    "    # newdataset = dataset.map(lambda x, y: (data_augmentation(x, training=True), y), num_parallel_calls=tf.data.AUTOTUNE)\n",
    "    dataset = dataset.prefetch(1)\n",
    "    return dataset\n",
    "    \n",
    "\n",
    "\n",
    "def tf_dataset_valid(x, y, batch=batch):\n",
    "    dataset = tf.data.Dataset.from_tensor_slices((x, y))\n",
    "    dataset = dataset.shuffle(buffer_size=500)\n",
    "    dataset = dataset.repeat()\n",
    "    dataset = dataset.map(tf_parse, num_parallel_calls=num_threads)\n",
    "    dataset = dataset.batch(batch)\n",
    "    dataset = dataset.prefetch(1)\n",
    "    return dataset\n",
    "\n",
    "def tf_dataset_test(x, y):\n",
    "    dataset = tf.data.Dataset.from_tensor_slices((x, y))\n",
    "    dataset = dataset.map(tf_parse, num_parallel_calls=tf.data.experimental.AUTOTUNE)\n",
    "    # dataset = dataset.batch(batch)\n",
    "    dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)\n",
    "    return dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "qz53bnMXJPOY"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-02 19:00:55.751756: E external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:282] failed call to cuInit: CUDA_ERROR_COMPAT_NOT_SUPPORTED_ON_DEVICE: forward compatibility was attempted on non supported HW\n",
      "2024-06-02 19:00:55.751778: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:134] retrieving CUDA diagnostic information for host: ipsa-MS-7B98\n",
      "2024-06-02 19:00:55.751784: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:141] hostname: ipsa-MS-7B98\n",
      "2024-06-02 19:00:55.751848: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:165] libcuda reported version is: 535.171.4\n",
      "2024-06-02 19:00:55.751869: I external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:169] kernel reported version is: 470.239.6\n",
      "2024-06-02 19:00:55.751874: E external/local_xla/xla/stream_executor/cuda/cuda_diagnostics.cc:251] kernel version 470.239.6 does not match DSO version 535.171.4 -- cannot find working devices in this configuration\n"
     ]
    }
   ],
   "source": [
    "train_dataset = tf_dataset_train(train_x, train_y)\n",
    "valid_dataset = tf_dataset_valid(valid_x, valid_y)\n",
    "test_dataset = tf_dataset_test(test_x, test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "RiRabir-PqKd"
   },
   "outputs": [],
   "source": [
    "# Function to load image/mask and convert colour space from BGR to RGB so that image/mask can be displayed correctly by matplotlib\n",
    "def read_and_rgb(x):\n",
    "    x = cv2.imread(x)\n",
    "    x = cv2.cvtColor(x, cv2.COLOR_BGR2RGB)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining the Convolutional Block\n",
    "def conv_block(inputs, num_filters):\n",
    "\t# Applying the sequence of Convolutional, Batch Normalization\n",
    "\t# and Activation Layers to the input tensor\n",
    "\tx = tf.keras.Sequential([\n",
    "\t\t# Convolutional Layer\n",
    "\t\ttf.keras.layers.Conv2D(num_filters, 3, padding='same'),\n",
    "\t\t# Batch Normalization Layer\n",
    "\t\ttf.keras.layers.BatchNormalization(),\n",
    "\t\t# Activation Layer\n",
    "\t\ttf.keras.layers.Activation('relu'),\n",
    "\t\t# Convolutional Layer\n",
    "\t\ttf.keras.layers.Conv2D(num_filters, 3, padding='same'),\n",
    "\t\t# Batch Normalization Layer\n",
    "\t\ttf.keras.layers.BatchNormalization(),\n",
    "\t\t# Activation Layer\n",
    "\t\ttf.keras.layers.Activation('relu')\n",
    "\t])(inputs)\n",
    "\n",
    "\t# Returning the output of the Convolutional Block\n",
    "\treturn x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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: \"Unet_plus_plus\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"Unet_plus_plus\"\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_image         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</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\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>,  │      <span style=\"color: #00af00; text-decoration-color: #00af00\">1,792</span> │ input_image[<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\">Conv2D</span>)            │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1_conv2        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>,  │     <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</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\">Conv2D</span>)            │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1_pool         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</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\">MaxPooling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2_conv1        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>,  │     <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │ block1_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: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2_conv2        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>,  │    <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │ block2_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\">Conv2D</span>)            │ <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\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block2_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\">MaxPooling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3_conv1        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │    <span style=\"color: #00af00; text-decoration-color: #00af00\">295,168</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: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3_conv2        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │    <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │ block3_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\">Conv2D</span>)            │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3_conv3        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │    <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │ block3_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\">Conv2D</span>)            │ <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\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block3_conv3[<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\">MaxPooling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4_conv1        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │  <span style=\"color: #00af00; text-decoration-color: #00af00\">1,180,160</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: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │ <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4_conv2        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │  <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │ block4_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\">Conv2D</span>)            │ <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4_conv3        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │  <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │ block4_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\">Conv2D</span>)            │ <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4_pool         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block4_conv3[<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\">MaxPooling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_conv1        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │  <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</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: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │ <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_conv2        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │  <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │ block5_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\">Conv2D</span>)            │ <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_conv3        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │  <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │ block5_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\">Conv2D</span>)            │ <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_pool         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block5_conv3[<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\">MaxPooling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block5_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: #0087ff; text-decoration-color: #0087ff\">UpSampling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_1     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block5_conv3[<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\">UpSampling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block5_conv3[<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\">Concatenate</span>)       │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)             │            │ up_sampling2d[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_3     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block4_conv3[<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\">UpSampling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_1       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block4_conv3[<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\">Concatenate</span>)       │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)             │            │ up_sampling2d_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_1        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │  <span style=\"color: #00af00; text-decoration-color: #00af00\">7,083,008</span> │ concatenate[<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\">Sequential</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_6     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block3_conv3[<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\">UpSampling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_3       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block3_conv3[<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\">Concatenate</span>)       │ <span style=\"color: #00af00; text-decoration-color: #00af00\">768</span>)              │            │ up_sampling2d_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_2        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │  <span style=\"color: #00af00; text-decoration-color: #00af00\">2,951,680</span> │ concatenate_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Sequential</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_2     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ sequential_1[<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\">UpSampling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_10    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block2_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\">UpSampling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_6       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block2_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\">Concatenate</span>)       │ <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)              │            │ up_sampling2d_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_4        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │  <span style=\"color: #00af00; text-decoration-color: #00af00\">1,033,472</span> │ concatenate_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Sequential</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_4     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ sequential_2[<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\">UpSampling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_2       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ sequential_2[<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\">Concatenate</span>)       │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1280</span>)             │            │ block4_conv3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │                   │            │ up_sampling2d_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_10      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</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\">Concatenate</span>)       │ <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)              │            │ up_sampling2d_10… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_7        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>,  │    <span style=\"color: #00af00; text-decoration-color: #00af00\">258,688</span> │ concatenate_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Sequential</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_7     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ sequential_4[<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\">UpSampling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_4       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ sequential_4[<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\">Concatenate</span>)       │ <span style=\"color: #00af00; text-decoration-color: #00af00\">640</span>)              │            │ block3_conv3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │                   │            │ up_sampling2d_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_3        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │  <span style=\"color: #00af00; text-decoration-color: #00af00\">3,541,504</span> │ concatenate_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Sequential</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_11       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>,  │     <span style=\"color: #00af00; text-decoration-color: #00af00\">64,832</span> │ concatenate_10[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Sequential</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_11    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ sequential_7[<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\">UpSampling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_7       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ sequential_7[<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\">Concatenate</span>)       │ <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span>)              │            │ block2_conv2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │                   │            │ up_sampling2d_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_5        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │    <span style=\"color: #00af00; text-decoration-color: #00af00\">886,016</span> │ concatenate_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Sequential</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_5     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ sequential_3[<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\">UpSampling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_11      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ sequential_11[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)       │ <span style=\"color: #00af00; text-decoration-color: #00af00\">160</span>)              │            │ block1_conv2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │                   │            │ up_sampling2d_11… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_8        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>,  │    <span style=\"color: #00af00; text-decoration-color: #00af00\">221,824</span> │ concatenate_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Sequential</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_8     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ sequential_5[<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\">UpSampling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_5       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ sequential_5[<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\">Concatenate</span>)       │ <span style=\"color: #00af00; text-decoration-color: #00af00\">768</span>)              │            │ sequential_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │                   │            │ block3_conv3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │                   │            │ up_sampling2d_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_12       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>,  │     <span style=\"color: #00af00; text-decoration-color: #00af00\">55,616</span> │ concatenate_11[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Sequential</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_12    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ sequential_8[<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\">UpSampling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_8       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ sequential_8[<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\">Concatenate</span>)       │ <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)              │            │ sequential_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │                   │            │ block2_conv2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │                   │            │ up_sampling2d_8[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_6        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │  <span style=\"color: #00af00; text-decoration-color: #00af00\">1,033,472</span> │ concatenate_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Sequential</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_12      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ sequential_12[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)       │ <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)              │            │ sequential_11[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│                     │                   │            │ block1_conv2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │                   │            │ up_sampling2d_12… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_9        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>,  │    <span style=\"color: #00af00; text-decoration-color: #00af00\">258,688</span> │ concatenate_8[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Sequential</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_9     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ sequential_6[<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\">UpSampling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_13       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>,  │     <span style=\"color: #00af00; text-decoration-color: #00af00\">64,832</span> │ concatenate_12[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Sequential</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_13    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ sequential_9[<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\">UpSampling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_9       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ sequential_9[<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\">Concatenate</span>)       │ <span style=\"color: #00af00; text-decoration-color: #00af00\">448</span>)              │            │ sequential_8[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │                   │            │ sequential_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │                   │            │ block2_conv2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │                   │            │ up_sampling2d_9[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_13      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ sequential_13[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)       │ <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>)              │            │ sequential_12[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│                     │                   │            │ sequential_11[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│                     │                   │            │ block1_conv2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │                   │            │ up_sampling2d_13… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_10       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>,  │    <span style=\"color: #00af00; text-decoration-color: #00af00\">295,552</span> │ concatenate_9[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Sequential</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_14       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>,  │     <span style=\"color: #00af00; text-decoration-color: #00af00\">74,048</span> │ concatenate_13[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Sequential</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_14    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ sequential_10[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">UpSampling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_14      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ sequential_14[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)       │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │ sequential_13[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│                     │                   │            │ sequential_12[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│                     │                   │            │ sequential_11[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│                     │                   │            │ block1_conv2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │                   │            │ up_sampling2d_14… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_15       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>,  │     <span style=\"color: #00af00; text-decoration-color: #00af00\">83,264</span> │ concatenate_14[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Sequential</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv2d_30 (<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\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>,  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">132</span> │ sequential_15[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">4</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_image         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\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;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m,  │      \u001b[38;5;34m1,792\u001b[0m │ input_image[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1_conv2        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m,  │     \u001b[38;5;34m36,928\u001b[0m │ block1_conv1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1_pool         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ block1_conv2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2_conv1        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m,  │     \u001b[38;5;34m73,856\u001b[0m │ block1_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2_conv2        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m,  │    \u001b[38;5;34m147,584\u001b[0m │ block2_conv1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2_pool         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ block2_conv2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3_conv1        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │    \u001b[38;5;34m295,168\u001b[0m │ block2_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m256\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3_conv2        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │    \u001b[38;5;34m590,080\u001b[0m │ block3_conv1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m256\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3_conv3        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │    \u001b[38;5;34m590,080\u001b[0m │ block3_conv2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m256\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3_pool         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block3_conv3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ \u001b[38;5;34m256\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4_conv1        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │  \u001b[38;5;34m1,180,160\u001b[0m │ block3_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m512\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4_conv2        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │  \u001b[38;5;34m2,359,808\u001b[0m │ block4_conv1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m512\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4_conv3        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │  \u001b[38;5;34m2,359,808\u001b[0m │ block4_conv2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m512\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4_pool         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block4_conv3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ \u001b[38;5;34m512\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_conv1        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │  \u001b[38;5;34m2,359,808\u001b[0m │ block4_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m512\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_conv2        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │  \u001b[38;5;34m2,359,808\u001b[0m │ block5_conv1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m512\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_conv3        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │  \u001b[38;5;34m2,359,808\u001b[0m │ block5_conv2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m512\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_pool         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block5_conv3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ \u001b[38;5;34m512\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block5_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
       "│ (\u001b[38;5;33mUpSampling2D\u001b[0m)      │ \u001b[38;5;34m512\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_1     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block5_conv3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mUpSampling2D\u001b[0m)      │ \u001b[38;5;34m512\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block5_conv3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mConcatenate\u001b[0m)       │ \u001b[38;5;34m1024\u001b[0m)             │            │ up_sampling2d[\u001b[38;5;34m0\u001b[0m]… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_3     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ block4_conv3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mUpSampling2D\u001b[0m)      │ \u001b[38;5;34m512\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_1       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block4_conv3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mConcatenate\u001b[0m)       │ \u001b[38;5;34m1024\u001b[0m)             │            │ up_sampling2d_1[\u001b[38;5;34m…\u001b[0m │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_1        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │  \u001b[38;5;34m7,083,008\u001b[0m │ concatenate[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
       "│ (\u001b[38;5;33mSequential\u001b[0m)        │ \u001b[38;5;34m512\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_6     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ block3_conv3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mUpSampling2D\u001b[0m)      │ \u001b[38;5;34m256\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_3       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ block3_conv3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mConcatenate\u001b[0m)       │ \u001b[38;5;34m768\u001b[0m)              │            │ up_sampling2d_3[\u001b[38;5;34m…\u001b[0m │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_2        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │  \u001b[38;5;34m2,951,680\u001b[0m │ concatenate_1[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mSequential\u001b[0m)        │ \u001b[38;5;34m256\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_2     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ sequential_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mUpSampling2D\u001b[0m)      │ \u001b[38;5;34m512\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_10    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ block2_conv2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mUpSampling2D\u001b[0m)      │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_6       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ block2_conv2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mConcatenate\u001b[0m)       │ \u001b[38;5;34m384\u001b[0m)              │            │ up_sampling2d_6[\u001b[38;5;34m…\u001b[0m │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_4        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │  \u001b[38;5;34m1,033,472\u001b[0m │ concatenate_3[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mSequential\u001b[0m)        │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_4     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ sequential_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mUpSampling2D\u001b[0m)      │ \u001b[38;5;34m256\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_2       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ sequential_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mConcatenate\u001b[0m)       │ \u001b[38;5;34m1280\u001b[0m)             │            │ block4_conv3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │                   │            │ up_sampling2d_2[\u001b[38;5;34m…\u001b[0m │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_10      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ block1_conv2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mConcatenate\u001b[0m)       │ \u001b[38;5;34m192\u001b[0m)              │            │ up_sampling2d_10… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_7        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m,  │    \u001b[38;5;34m258,688\u001b[0m │ concatenate_6[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mSequential\u001b[0m)        │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_7     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ sequential_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mUpSampling2D\u001b[0m)      │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_4       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ sequential_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mConcatenate\u001b[0m)       │ \u001b[38;5;34m640\u001b[0m)              │            │ block3_conv3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │                   │            │ up_sampling2d_4[\u001b[38;5;34m…\u001b[0m │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_3        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │  \u001b[38;5;34m3,541,504\u001b[0m │ concatenate_2[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mSequential\u001b[0m)        │ \u001b[38;5;34m256\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_11       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m,  │     \u001b[38;5;34m64,832\u001b[0m │ concatenate_10[\u001b[38;5;34m0\u001b[0m… │\n",
       "│ (\u001b[38;5;33mSequential\u001b[0m)        │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_11    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ sequential_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mUpSampling2D\u001b[0m)      │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_7       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ sequential_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mConcatenate\u001b[0m)       │ \u001b[38;5;34m320\u001b[0m)              │            │ block2_conv2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │                   │            │ up_sampling2d_7[\u001b[38;5;34m…\u001b[0m │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_5        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │    \u001b[38;5;34m886,016\u001b[0m │ concatenate_4[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mSequential\u001b[0m)        │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_5     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ sequential_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mUpSampling2D\u001b[0m)      │ \u001b[38;5;34m256\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_11      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ sequential_11[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mConcatenate\u001b[0m)       │ \u001b[38;5;34m160\u001b[0m)              │            │ block1_conv2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │                   │            │ up_sampling2d_11… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_8        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m,  │    \u001b[38;5;34m221,824\u001b[0m │ concatenate_7[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mSequential\u001b[0m)        │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_8     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ sequential_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mUpSampling2D\u001b[0m)      │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_5       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ sequential_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mConcatenate\u001b[0m)       │ \u001b[38;5;34m768\u001b[0m)              │            │ sequential_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │                   │            │ block3_conv3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │                   │            │ up_sampling2d_5[\u001b[38;5;34m…\u001b[0m │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_12       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m,  │     \u001b[38;5;34m55,616\u001b[0m │ concatenate_11[\u001b[38;5;34m0\u001b[0m… │\n",
       "│ (\u001b[38;5;33mSequential\u001b[0m)        │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_12    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ sequential_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mUpSampling2D\u001b[0m)      │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_8       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ sequential_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mConcatenate\u001b[0m)       │ \u001b[38;5;34m384\u001b[0m)              │            │ sequential_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │                   │            │ block2_conv2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │                   │            │ up_sampling2d_8[\u001b[38;5;34m…\u001b[0m │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_6        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │  \u001b[38;5;34m1,033,472\u001b[0m │ concatenate_5[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mSequential\u001b[0m)        │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_12      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ sequential_12[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mConcatenate\u001b[0m)       │ \u001b[38;5;34m192\u001b[0m)              │            │ sequential_11[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│                     │                   │            │ block1_conv2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │                   │            │ up_sampling2d_12… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_9        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m,  │    \u001b[38;5;34m258,688\u001b[0m │ concatenate_8[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mSequential\u001b[0m)        │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_9     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ sequential_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mUpSampling2D\u001b[0m)      │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_13       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m,  │     \u001b[38;5;34m64,832\u001b[0m │ concatenate_12[\u001b[38;5;34m0\u001b[0m… │\n",
       "│ (\u001b[38;5;33mSequential\u001b[0m)        │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_13    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ sequential_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mUpSampling2D\u001b[0m)      │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_9       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ sequential_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mConcatenate\u001b[0m)       │ \u001b[38;5;34m448\u001b[0m)              │            │ sequential_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │                   │            │ sequential_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │                   │            │ block2_conv2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │                   │            │ up_sampling2d_9[\u001b[38;5;34m…\u001b[0m │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_13      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ sequential_13[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mConcatenate\u001b[0m)       │ \u001b[38;5;34m224\u001b[0m)              │            │ sequential_12[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│                     │                   │            │ sequential_11[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│                     │                   │            │ block1_conv2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │                   │            │ up_sampling2d_13… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_10       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m,  │    \u001b[38;5;34m295,552\u001b[0m │ concatenate_9[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mSequential\u001b[0m)        │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_14       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m,  │     \u001b[38;5;34m74,048\u001b[0m │ concatenate_13[\u001b[38;5;34m0\u001b[0m… │\n",
       "│ (\u001b[38;5;33mSequential\u001b[0m)        │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_14    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ sequential_10[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mUpSampling2D\u001b[0m)      │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_14      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ sequential_14[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mConcatenate\u001b[0m)       │ \u001b[38;5;34m256\u001b[0m)              │            │ sequential_13[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│                     │                   │            │ sequential_12[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│                     │                   │            │ sequential_11[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│                     │                   │            │ block1_conv2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │                   │            │ up_sampling2d_14… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_15       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m,  │     \u001b[38;5;34m83,264\u001b[0m │ concatenate_14[\u001b[38;5;34m0\u001b[0m… │\n",
       "│ (\u001b[38;5;33mSequential\u001b[0m)        │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv2d_30 (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m,  │        \u001b[38;5;34m132\u001b[0m │ sequential_15[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│                     │ \u001b[38;5;34m4\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\">32,621,316</span> (124.44 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m32,621,316\u001b[0m (124.44 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\">32,614,020</span> (124.41 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m32,614,020\u001b[0m (124.41 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\">7,296</span> (28.50 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m7,296\u001b[0m (28.50 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def unet_plus_plus_model(IMAGE_SIZE, num_classes=4, deep_supervision=False):\n",
    "    # Encoding Path\n",
    "    inputs = Input(shape=(IMAGE_SIZE, IMAGE_SIZE, 3), name=\"input_image\")\n",
    "    encoder = VGG16(input_tensor=inputs, include_top=False,weights=\"imagenet\")\n",
    "    \n",
    "    skip_connection_names = [\"block1_conv2\", \"block2_conv2\", \"block3_conv3\", \"block4_conv3\",\"block5_conv3\"]\n",
    "    encoder_output = encoder.get_layer(\"block5_pool\").output\n",
    "\n",
    "    \n",
    "    x = encoder_output #8\n",
    "    x_skip_1 = encoder.get_layer(skip_connection_names[-1]).output # 16\n",
    "    x_skip_2 = encoder.get_layer(skip_connection_names[-2]).output # 32\n",
    "    x_skip_3 = encoder.get_layer(skip_connection_names[-3]).output # 64\n",
    "    x_skip_4 = encoder.get_layer(skip_connection_names[-4]).output # 128\n",
    "    x_skip_5 = encoder.get_layer(skip_connection_names[-5]).output # 256\n",
    "\n",
    "    f4=conv_block(tf.keras.layers.concatenate(\n",
    "        [x_skip_1, tf.keras.layers.UpSampling2D()(x)]), 512)\n",
    "    \n",
    "    x_31= conv_block(tf.keras.layers.concatenate(\n",
    "        [x_skip_2, tf.keras.layers.UpSampling2D()(x_skip_1)]), 256)\n",
    "    f3=conv_block(tf.keras.layers.concatenate(\n",
    "        [x_31,x_skip_2, tf.keras.layers.UpSampling2D()(f4)]), 256)\n",
    "\n",
    "    x_21= conv_block(tf.keras.layers.concatenate(\n",
    "        [x_skip_3, tf.keras.layers.UpSampling2D()(x_skip_2)]), 128)\n",
    "    x_22= conv_block(tf.keras.layers.concatenate(\n",
    "        [x_21,x_skip_3, tf.keras.layers.UpSampling2D()(x_31)]), 128)\n",
    "    f2=conv_block(tf.keras.layers.concatenate(\n",
    "        [x_22,x_21,x_skip_3, tf.keras.layers.UpSampling2D()(f3)]), 128)\n",
    "\n",
    "    x_11= conv_block(tf.keras.layers.concatenate(\n",
    "        [x_skip_4, tf.keras.layers.UpSampling2D()(x_skip_3)]), 64)\n",
    "    x_12= conv_block(tf.keras.layers.concatenate(\n",
    "        [x_11,x_skip_4, tf.keras.layers.UpSampling2D()(x_21)]), 64)\n",
    "    x_13= conv_block(tf.keras.layers.concatenate(\n",
    "        [x_12,x_11,x_skip_4, tf.keras.layers.UpSampling2D()(x_22)]), 64)\n",
    "    f1=conv_block(tf.keras.layers.concatenate(\n",
    "        [x_13,x_12,x_11,x_skip_4, tf.keras.layers.UpSampling2D()(f2)]), 64)\n",
    "\n",
    "    x_01= conv_block(tf.keras.layers.concatenate(\n",
    "        [x_skip_5, tf.keras.layers.UpSampling2D()(x_skip_4)]), 32)\n",
    "    x_02= conv_block(tf.keras.layers.concatenate(\n",
    "        [x_01,x_skip_5, tf.keras.layers.UpSampling2D()(x_11)]), 32)\n",
    "    x_03= conv_block(tf.keras.layers.concatenate(\n",
    "        [x_02,x_01,x_skip_5, tf.keras.layers.UpSampling2D()(x_12)]), 32)\n",
    "    x_04= conv_block(tf.keras.layers.concatenate(\n",
    "        [x_03,x_02,x_01,x_skip_5, tf.keras.layers.UpSampling2D()(x_13)]), 32)\n",
    "    f0=conv_block(tf.keras.layers.concatenate(\n",
    "        [x_04,x_03,x_02,x_01,x_skip_5, tf.keras.layers.UpSampling2D()(f1)]), 32)\n",
    "    \n",
    "    \n",
    "    if deep_supervision:\n",
    "        outputs = [\n",
    "            tf.keras.layers.Conv2D(num_classes, 1)(x_01),\n",
    "            tf.keras.layers.Conv2D(num_classes, 1)(x_02),\n",
    "            tf.keras.layers.Conv2D(num_classes, 1)(x_03),\n",
    "            tf.keras.layers.Conv2D(num_classes, 1)(x_04),\n",
    "            tf.keras.layers.Conv2D(num_classes, 1)(f0)\n",
    "        ]\n",
    "        # Concatenating the segmentation maps\n",
    "        outputs = tf.keras.layers.concatenate(outputs, axis=0)\n",
    " \n",
    "    # If deep supervision is disabled, then the model will output the final segmentation map\n",
    "    # which is the segmentation map at the end of the decoding path\n",
    "    else:\n",
    "        outputs = tf.keras.layers.Conv2D(num_classes, 1)(f0)\n",
    " \n",
    "    # Creating the model\n",
    "    model = tf.keras.Model(inputs=inputs, outputs=outputs, name='Unet_plus_plus')\n",
    " \n",
    "    # Returning the model\n",
    "    return model\n",
    " \n",
    " \n",
    "# Testing the model\n",
    "    # Creating the model\n",
    "model = unet_plus_plus_model(IMAGE_SIZE, num_classes, deep_supervision=False)\n",
    "\n",
    "# Printing the model summary\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "dNp91mYW6n40"
   },
   "outputs": [],
   "source": [
    "# Specify all the encoder layers in a list\n",
    "encoder_layers = model.layers[0:-46]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-JldRPIq6IUb",
    "outputId": "7dc7e570-9233-48ce-9ef4-64baa7320c8b"
   },
   "outputs": [],
   "source": [
    "# Freeze the encoder layers for transfer learning (so that weights are only Augmentedchanged for the decoder layers druing training)\n",
    "for layer in encoder_layers:\n",
    "    layer.trainable = False\n",
    "    # print(layer, layer.trainable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "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: \"Unet_plus_plus\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"Unet_plus_plus\"\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_image         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</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\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>,  │      <span style=\"color: #00af00; text-decoration-color: #00af00\">1,792</span> │ input_image[<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\">Conv2D</span>)            │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1_conv2        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>,  │     <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</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\">Conv2D</span>)            │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1_pool         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</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\">MaxPooling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2_conv1        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>,  │     <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │ block1_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: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2_conv2        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>,  │    <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │ block2_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\">Conv2D</span>)            │ <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\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block2_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\">MaxPooling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3_conv1        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │    <span style=\"color: #00af00; text-decoration-color: #00af00\">295,168</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: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3_conv2        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │    <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │ block3_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\">Conv2D</span>)            │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3_conv3        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │    <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │ block3_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\">Conv2D</span>)            │ <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\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block3_conv3[<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\">MaxPooling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4_conv1        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │  <span style=\"color: #00af00; text-decoration-color: #00af00\">1,180,160</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: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │ <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4_conv2        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │  <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │ block4_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\">Conv2D</span>)            │ <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4_conv3        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │  <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │ block4_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\">Conv2D</span>)            │ <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4_pool         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block4_conv3[<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\">MaxPooling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_conv1        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │  <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</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: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)            │ <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_conv2        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │  <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │ block5_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\">Conv2D</span>)            │ <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_conv3        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │  <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │ block5_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\">Conv2D</span>)            │ <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_pool         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block5_conv3[<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\">MaxPooling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block5_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: #0087ff; text-decoration-color: #0087ff\">UpSampling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_1     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block5_conv3[<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\">UpSampling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block5_conv3[<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\">Concatenate</span>)       │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)             │            │ up_sampling2d[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_3     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block4_conv3[<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\">UpSampling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_1       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block4_conv3[<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\">Concatenate</span>)       │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)             │            │ up_sampling2d_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_1        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,    │  <span style=\"color: #00af00; text-decoration-color: #00af00\">7,083,008</span> │ concatenate[<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\">Sequential</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_6     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block3_conv3[<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\">UpSampling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_3       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block3_conv3[<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\">Concatenate</span>)       │ <span style=\"color: #00af00; text-decoration-color: #00af00\">768</span>)              │            │ up_sampling2d_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_2        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │  <span style=\"color: #00af00; text-decoration-color: #00af00\">2,951,680</span> │ concatenate_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Sequential</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_2     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ sequential_1[<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\">UpSampling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_10    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block2_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\">UpSampling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_6       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ block2_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\">Concatenate</span>)       │ <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)              │            │ up_sampling2d_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_4        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │  <span style=\"color: #00af00; text-decoration-color: #00af00\">1,033,472</span> │ concatenate_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Sequential</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_4     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ sequential_2[<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\">UpSampling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_2       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ sequential_2[<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\">Concatenate</span>)       │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1280</span>)             │            │ block4_conv3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │                   │            │ up_sampling2d_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_10      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</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\">Concatenate</span>)       │ <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)              │            │ up_sampling2d_10… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_7        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>,  │    <span style=\"color: #00af00; text-decoration-color: #00af00\">258,688</span> │ concatenate_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Sequential</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_7     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ sequential_4[<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\">UpSampling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_4       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ sequential_4[<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\">Concatenate</span>)       │ <span style=\"color: #00af00; text-decoration-color: #00af00\">640</span>)              │            │ block3_conv3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │                   │            │ up_sampling2d_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_3        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,    │  <span style=\"color: #00af00; text-decoration-color: #00af00\">3,541,504</span> │ concatenate_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Sequential</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_11       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>,  │     <span style=\"color: #00af00; text-decoration-color: #00af00\">64,832</span> │ concatenate_10[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Sequential</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_11    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ sequential_7[<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\">UpSampling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_7       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ sequential_7[<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\">Concatenate</span>)       │ <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span>)              │            │ block2_conv2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │                   │            │ up_sampling2d_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_5        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │    <span style=\"color: #00af00; text-decoration-color: #00af00\">886,016</span> │ concatenate_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Sequential</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_5     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ sequential_3[<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\">UpSampling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_11      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ sequential_11[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)       │ <span style=\"color: #00af00; text-decoration-color: #00af00\">160</span>)              │            │ block1_conv2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │                   │            │ up_sampling2d_11… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_8        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>,  │    <span style=\"color: #00af00; text-decoration-color: #00af00\">221,824</span> │ concatenate_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Sequential</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_8     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ sequential_5[<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\">UpSampling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_5       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ sequential_5[<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\">Concatenate</span>)       │ <span style=\"color: #00af00; text-decoration-color: #00af00\">768</span>)              │            │ sequential_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │                   │            │ block3_conv3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │                   │            │ up_sampling2d_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_12       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>,  │     <span style=\"color: #00af00; text-decoration-color: #00af00\">55,616</span> │ concatenate_11[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Sequential</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_12    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ sequential_8[<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\">UpSampling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_8       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ sequential_8[<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\">Concatenate</span>)       │ <span style=\"color: #00af00; text-decoration-color: #00af00\">384</span>)              │            │ sequential_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │                   │            │ block2_conv2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │                   │            │ up_sampling2d_8[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_6        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │  <span style=\"color: #00af00; text-decoration-color: #00af00\">1,033,472</span> │ concatenate_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Sequential</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_12      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ sequential_12[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)       │ <span style=\"color: #00af00; text-decoration-color: #00af00\">192</span>)              │            │ sequential_11[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│                     │                   │            │ block1_conv2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │                   │            │ up_sampling2d_12… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_9        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>,  │    <span style=\"color: #00af00; text-decoration-color: #00af00\">258,688</span> │ concatenate_8[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Sequential</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_9     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ sequential_6[<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\">UpSampling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_13       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>,  │     <span style=\"color: #00af00; text-decoration-color: #00af00\">64,832</span> │ concatenate_12[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Sequential</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_13    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ sequential_9[<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\">UpSampling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_9       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ sequential_9[<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\">Concatenate</span>)       │ <span style=\"color: #00af00; text-decoration-color: #00af00\">448</span>)              │            │ sequential_8[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │                   │            │ sequential_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │                   │            │ block2_conv2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │                   │            │ up_sampling2d_9[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_13      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ sequential_13[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)       │ <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>)              │            │ sequential_12[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│                     │                   │            │ sequential_11[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│                     │                   │            │ block1_conv2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │                   │            │ up_sampling2d_13… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_10       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>,  │    <span style=\"color: #00af00; text-decoration-color: #00af00\">295,552</span> │ concatenate_9[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Sequential</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_14       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>,  │     <span style=\"color: #00af00; text-decoration-color: #00af00\">74,048</span> │ concatenate_13[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Sequential</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_14    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ sequential_10[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">UpSampling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_14      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ sequential_14[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)       │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)              │            │ sequential_13[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│                     │                   │            │ sequential_12[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│                     │                   │            │ sequential_11[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│                     │                   │            │ block1_conv2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                     │                   │            │ up_sampling2d_14… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_15       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>,  │     <span style=\"color: #00af00; text-decoration-color: #00af00\">83,264</span> │ concatenate_14[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Sequential</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv2d_30 (<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\">512</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>,  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">132</span> │ sequential_15[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">4</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_image         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\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;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m,  │      \u001b[38;5;34m1,792\u001b[0m │ input_image[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1_conv2        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m,  │     \u001b[38;5;34m36,928\u001b[0m │ block1_conv1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block1_pool         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ block1_conv2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2_conv1        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m,  │     \u001b[38;5;34m73,856\u001b[0m │ block1_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2_conv2        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m,  │    \u001b[38;5;34m147,584\u001b[0m │ block2_conv1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block2_pool         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ block2_conv2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3_conv1        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │    \u001b[38;5;34m295,168\u001b[0m │ block2_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m256\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3_conv2        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │    \u001b[38;5;34m590,080\u001b[0m │ block3_conv1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m256\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3_conv3        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │    \u001b[38;5;34m590,080\u001b[0m │ block3_conv2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m256\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block3_pool         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block3_conv3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ \u001b[38;5;34m256\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4_conv1        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │  \u001b[38;5;34m1,180,160\u001b[0m │ block3_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m512\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4_conv2        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │  \u001b[38;5;34m2,359,808\u001b[0m │ block4_conv1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m512\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4_conv3        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │  \u001b[38;5;34m2,359,808\u001b[0m │ block4_conv2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m512\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block4_pool         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block4_conv3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ \u001b[38;5;34m512\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_conv1        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │  \u001b[38;5;34m2,359,808\u001b[0m │ block4_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m512\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_conv2        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │  \u001b[38;5;34m2,359,808\u001b[0m │ block5_conv1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m512\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_conv3        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │  \u001b[38;5;34m2,359,808\u001b[0m │ block5_conv2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mConv2D\u001b[0m)            │ \u001b[38;5;34m512\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ block5_pool         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block5_conv3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ \u001b[38;5;34m512\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block5_pool[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
       "│ (\u001b[38;5;33mUpSampling2D\u001b[0m)      │ \u001b[38;5;34m512\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_1     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block5_conv3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mUpSampling2D\u001b[0m)      │ \u001b[38;5;34m512\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block5_conv3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mConcatenate\u001b[0m)       │ \u001b[38;5;34m1024\u001b[0m)             │            │ up_sampling2d[\u001b[38;5;34m0\u001b[0m]… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_3     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ block4_conv3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mUpSampling2D\u001b[0m)      │ \u001b[38;5;34m512\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_1       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ block4_conv3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mConcatenate\u001b[0m)       │ \u001b[38;5;34m1024\u001b[0m)             │            │ up_sampling2d_1[\u001b[38;5;34m…\u001b[0m │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_1        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,    │  \u001b[38;5;34m7,083,008\u001b[0m │ concatenate[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
       "│ (\u001b[38;5;33mSequential\u001b[0m)        │ \u001b[38;5;34m512\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_6     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ block3_conv3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mUpSampling2D\u001b[0m)      │ \u001b[38;5;34m256\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_3       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ block3_conv3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mConcatenate\u001b[0m)       │ \u001b[38;5;34m768\u001b[0m)              │            │ up_sampling2d_3[\u001b[38;5;34m…\u001b[0m │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_2        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │  \u001b[38;5;34m2,951,680\u001b[0m │ concatenate_1[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mSequential\u001b[0m)        │ \u001b[38;5;34m256\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_2     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ sequential_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mUpSampling2D\u001b[0m)      │ \u001b[38;5;34m512\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_10    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ block2_conv2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mUpSampling2D\u001b[0m)      │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_6       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ block2_conv2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mConcatenate\u001b[0m)       │ \u001b[38;5;34m384\u001b[0m)              │            │ up_sampling2d_6[\u001b[38;5;34m…\u001b[0m │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_4        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │  \u001b[38;5;34m1,033,472\u001b[0m │ concatenate_3[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mSequential\u001b[0m)        │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_4     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ sequential_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mUpSampling2D\u001b[0m)      │ \u001b[38;5;34m256\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_2       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ sequential_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mConcatenate\u001b[0m)       │ \u001b[38;5;34m1280\u001b[0m)             │            │ block4_conv3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │                   │            │ up_sampling2d_2[\u001b[38;5;34m…\u001b[0m │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_10      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ block1_conv2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mConcatenate\u001b[0m)       │ \u001b[38;5;34m192\u001b[0m)              │            │ up_sampling2d_10… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_7        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m,  │    \u001b[38;5;34m258,688\u001b[0m │ concatenate_6[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mSequential\u001b[0m)        │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_7     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ sequential_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mUpSampling2D\u001b[0m)      │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_4       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ sequential_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mConcatenate\u001b[0m)       │ \u001b[38;5;34m640\u001b[0m)              │            │ block3_conv3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │                   │            │ up_sampling2d_4[\u001b[38;5;34m…\u001b[0m │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_3        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,    │  \u001b[38;5;34m3,541,504\u001b[0m │ concatenate_2[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mSequential\u001b[0m)        │ \u001b[38;5;34m256\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_11       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m,  │     \u001b[38;5;34m64,832\u001b[0m │ concatenate_10[\u001b[38;5;34m0\u001b[0m… │\n",
       "│ (\u001b[38;5;33mSequential\u001b[0m)        │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_11    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ sequential_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mUpSampling2D\u001b[0m)      │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_7       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ sequential_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mConcatenate\u001b[0m)       │ \u001b[38;5;34m320\u001b[0m)              │            │ block2_conv2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │                   │            │ up_sampling2d_7[\u001b[38;5;34m…\u001b[0m │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_5        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │    \u001b[38;5;34m886,016\u001b[0m │ concatenate_4[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mSequential\u001b[0m)        │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_5     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ sequential_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mUpSampling2D\u001b[0m)      │ \u001b[38;5;34m256\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_11      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ sequential_11[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mConcatenate\u001b[0m)       │ \u001b[38;5;34m160\u001b[0m)              │            │ block1_conv2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │                   │            │ up_sampling2d_11… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_8        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m,  │    \u001b[38;5;34m221,824\u001b[0m │ concatenate_7[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mSequential\u001b[0m)        │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_8     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ sequential_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mUpSampling2D\u001b[0m)      │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_5       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ sequential_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mConcatenate\u001b[0m)       │ \u001b[38;5;34m768\u001b[0m)              │            │ sequential_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │                   │            │ block3_conv3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │                   │            │ up_sampling2d_5[\u001b[38;5;34m…\u001b[0m │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_12       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m,  │     \u001b[38;5;34m55,616\u001b[0m │ concatenate_11[\u001b[38;5;34m0\u001b[0m… │\n",
       "│ (\u001b[38;5;33mSequential\u001b[0m)        │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_12    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ sequential_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mUpSampling2D\u001b[0m)      │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_8       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ sequential_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mConcatenate\u001b[0m)       │ \u001b[38;5;34m384\u001b[0m)              │            │ sequential_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │                   │            │ block2_conv2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │                   │            │ up_sampling2d_8[\u001b[38;5;34m…\u001b[0m │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_6        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │  \u001b[38;5;34m1,033,472\u001b[0m │ concatenate_5[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mSequential\u001b[0m)        │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_12      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ sequential_12[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mConcatenate\u001b[0m)       │ \u001b[38;5;34m192\u001b[0m)              │            │ sequential_11[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│                     │                   │            │ block1_conv2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │                   │            │ up_sampling2d_12… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_9        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m,  │    \u001b[38;5;34m258,688\u001b[0m │ concatenate_8[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mSequential\u001b[0m)        │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_9     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ sequential_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mUpSampling2D\u001b[0m)      │ \u001b[38;5;34m128\u001b[0m)              │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_13       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m,  │     \u001b[38;5;34m64,832\u001b[0m │ concatenate_12[\u001b[38;5;34m0\u001b[0m… │\n",
       "│ (\u001b[38;5;33mSequential\u001b[0m)        │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_13    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ sequential_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mUpSampling2D\u001b[0m)      │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_9       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ sequential_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mConcatenate\u001b[0m)       │ \u001b[38;5;34m448\u001b[0m)              │            │ sequential_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │                   │            │ sequential_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │                   │            │ block2_conv2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │                   │            │ up_sampling2d_9[\u001b[38;5;34m…\u001b[0m │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_13      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ sequential_13[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mConcatenate\u001b[0m)       │ \u001b[38;5;34m224\u001b[0m)              │            │ sequential_12[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│                     │                   │            │ sequential_11[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│                     │                   │            │ block1_conv2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │                   │            │ up_sampling2d_13… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_10       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m,  │    \u001b[38;5;34m295,552\u001b[0m │ concatenate_9[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mSequential\u001b[0m)        │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_14       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m,  │     \u001b[38;5;34m74,048\u001b[0m │ concatenate_13[\u001b[38;5;34m0\u001b[0m… │\n",
       "│ (\u001b[38;5;33mSequential\u001b[0m)        │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ up_sampling2d_14    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ sequential_10[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mUpSampling2D\u001b[0m)      │ \u001b[38;5;34m64\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ concatenate_14      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ sequential_14[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mConcatenate\u001b[0m)       │ \u001b[38;5;34m256\u001b[0m)              │            │ sequential_13[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│                     │                   │            │ sequential_12[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│                     │                   │            │ sequential_11[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│                     │                   │            │ block1_conv2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│                     │                   │            │ up_sampling2d_14… │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ sequential_15       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m,  │     \u001b[38;5;34m83,264\u001b[0m │ concatenate_14[\u001b[38;5;34m0\u001b[0m… │\n",
       "│ (\u001b[38;5;33mSequential\u001b[0m)        │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ conv2d_30 (\u001b[38;5;33mConv2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m, \u001b[38;5;34m512\u001b[0m,  │        \u001b[38;5;34m132\u001b[0m │ sequential_15[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│                     │ \u001b[38;5;34m4\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\">32,621,316</span> (124.44 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m32,621,316\u001b[0m (124.44 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\">17,899,332</span> (68.28 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m17,899,332\u001b[0m (68.28 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\">14,721,984</span> (56.16 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m14,721,984\u001b[0m (56.16 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "3Z6CSlVthZSu"
   },
   "outputs": [],
   "source": [
    "# Functions for dice coefficient and dice loss \n",
    "\n",
    "import tensorflow.keras.backend as K\n",
    "\n",
    "def dice_coef(y_true, y_pred, smooth=1e-7):\n",
    "    y_true_f = K.flatten(K.one_hot(K.cast(y_true, 'int32'), num_classes=4)[Ellipsis,1:])\n",
    "    y_pred_f = K.flatten(y_pred[...,1:])\n",
    "    intersect = K.sum(y_true_f * y_pred_f, axis=-1)\n",
    "    true_sum = K.sum(y_true_f , axis=-1)\n",
    "    pred_sum = K.sum(y_pred_f, axis=-1)\n",
    "    return K.mean((2. * intersect / (true_sum+pred_sum + smooth)))\n",
    "\n",
    "def dice_loss(y_true, y_pred):\n",
    "    return 1 - dice_coef(y_true, y_pred)\n",
    "\n",
    "def mean_iou(y_true, y_pred, num_classes=4):\n",
    "  smooth = 1e-7  \n",
    "  y_true_f = K.flatten(K.one_hot(K.cast(y_true, 'int32'), num_classes=num_classes)[..., 1:])\n",
    "  y_pred_f = K.flatten(y_pred[..., 1:])\n",
    "  intersection = K.sum(K.cast(y_true_f * y_pred_f, dtype='float32'), axis=-1)\n",
    "  set_sum = K.sum(K.cast(y_true_f + y_pred_f, dtype='float32'), axis=-1)\n",
    "  iou = (intersection + smooth) / (set_sum -intersection+ smooth)\n",
    "  mIoU = K.mean(iou)\n",
    "  return mIoU\n",
    "\n",
    "def mIoU_loss(y_true, y_pred):\n",
    "    return 1 - mean_iou(y_true, y_pred)\n",
    "   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "ZJXLUf_PHVB-"
   },
   "outputs": [],
   "source": [
    "# Set up loss function, optimiser and complile the model \n",
    "opt = tf.keras.optimizers.AdamW(LR)\n",
    "metrics = [dice_coef,mean_iou]\n",
    "model.compile(loss=dice_loss, optimizer=opt, metrics=metrics)\n",
    "#model.compile(optimizer=opt, loss = tf.keras.losses.SparseCategoricalCrossentropy(),metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "vG6v0riTtGoi"
   },
   "outputs": [],
   "source": [
    "# Set up early stopping and reduce learning rate on plateau\n",
    "early_stopping = EarlyStopping(\n",
    "    monitor='mean_iou', \n",
    "    patience=4, \n",
    "    mode='max'\n",
    ")\n",
    "\n",
    "reduce_lr = ReduceLROnPlateau(\n",
    "    monitor='mean_iou', \n",
    "    factor=0.8,   \n",
    "    patience=3, \n",
    "    min_lr=0.0001,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "GwDoSxG_xPWn"
   },
   "outputs": [],
   "source": [
    "# Set up checkpoint for saving model architecture and weights\n",
    "checkpoint_filepath = 'checkpoint.model.keras'\n",
    "model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n",
    "    filepath=checkpoint_filepath,\n",
    "    save_weights_only=False,\n",
    "    monitor='mean_iou',\n",
    "    mode='max',\n",
    "    save_best_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Xs1czLFzP8yi",
    "outputId": "197147f6-a333-4941-ac33-40d8be74f066"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "\u001b[1m 4/52\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m33:02\u001b[0m 41s/step - dice_coef: 0.7081 - loss: 0.2919 - mean_iou: 0.5622"
     ]
    }
   ],
   "source": [
    "# Commencement of training\n",
    "train_steps = len(train_x)//batch\n",
    "valid_steps = len(valid_x)//batch\n",
    "\n",
    "if len(train_x) % batch != 0: \n",
    "    train_steps += 1\n",
    "if len(valid_x) % batch != 0: \n",
    "    valid_steps += 1\n",
    "\n",
    "history=model.fit(\n",
    "    train_dataset,\n",
    "    validation_data = valid_dataset,\n",
    "    epochs=EPOCHS,\n",
    "    steps_per_epoch=train_steps,\n",
    "    validation_steps=valid_steps,\n",
    "    callbacks=[model_checkpoint_callback,reduce_lr]\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(history.history[\"loss\"])\n",
    "plt.plot(history.history[\"val_loss\"])\n",
    "plt.title(\"Training Loss\")\n",
    "plt.ylabel(\"loss\")\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.show()\n",
    "plt.plot(history.history[\"mean_iou\"])\n",
    "plt.plot(history.history[\"val_mean_iou\"])\n",
    "plt.title(\"Training MIOU\")\n",
    "plt.ylabel(\"MIOU\")\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean_iouTEST(y_true, y_pred, num_classes=4):\n",
    "  smooth = 1e-7  \n",
    "  y_true_f = K.flatten(K.one_hot(K.cast(y_true, 'int32'), num_classes=num_classes)[..., 1:])\n",
    "  y_pred_f = K.flatten(K.one_hot(K.cast(y_pred, 'int32'), num_classes=num_classes)[..., 1:])\n",
    "  # y_pred_f = K.flatten(y_pred[..., 1:])\n",
    "  intersection = K.sum(K.cast(y_true_f * y_pred_f, dtype='float32'), axis=-1)\n",
    "  set_sum = K.sum(K.cast(y_true_f + y_pred_f, dtype='float32'), axis=-1)\n",
    "  iou = (intersection + smooth) / (set_sum -intersection+ smooth)\n",
    "  mIoU = K.mean(iou)\n",
    "  return mIoU.numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "aM7BkUisDlh2"
   },
   "outputs": [],
   "source": [
    "def display_sample(sample_image,sample_mask_seperated,pred_mask_seperated):\n",
    "    # plt.figure(figsize=(16, 16))\n",
    "    title = ['bg','fat','cell','bone']\n",
    "    # for i in range(len(display_list)):\n",
    "    #     plt.subplot(1, len(display_list), i+1)\n",
    "    #     plt.title(title[i])\n",
    "    #     plt.imshow(tf.keras.preprocessing.image.array_to_img(display_list[i]),cmap='gray')\n",
    "    #     plt.axis('off')\n",
    "    # plt.show()\n",
    "\n",
    "    fig,axs=plt.subplots(2,5,figsize=(30,12))\n",
    "    axs[0][0].imshow(sample_image)\n",
    "    axs[0][0].axis('off')\n",
    "    axs[0][0].set_title('Original Image')\n",
    "    for i in range(4):\n",
    "        axs[0][i+1].imshow(sample_mask_seperated[i],cmap='gray')\n",
    "        axs[0][i+1].axis('off')\n",
    "        axs[0][i+1].set_title(f'Original {title[i]}')\n",
    "    for i in range(4):\n",
    "        axs[1][i+1].imshow(pred_mask_seperated[i],cmap='gray')\n",
    "        axs[1][i+1].axis('off')\n",
    "        axs[1][i+1].set_title(f'Predicted {title[i]}')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "bhpYkNq1Bhwk"
   },
   "outputs": [],
   "source": [
    "def create_mask(pred_mask):\n",
    "    pred_mask = tf.argmax(pred_mask, axis=-1) \n",
    "    pred_mask = tf.expand_dims(pred_mask, axis=-1)\n",
    "    return pred_mask\n",
    "    \n",
    "def show_predictions(sample_image,sample_mask):\n",
    "    one_img = sample_image[0][tf.newaxis, ...] \n",
    "    prediction = model.predict(one_img)\n",
    "    pred_mask = create_mask(prediction)\n",
    "    miou=mean_iouTEST(sample_mask[0],pred_mask)\n",
    "\n",
    "    sample_mask_seperated=[(sample_mask[0]==i) for i in [0,1,2,3]]\n",
    "    pred_mask_seperated=[(pred_mask[0]==i) for i in [0,1,2,3]]\n",
    "\n",
    "    display_sample(sample_image[0], sample_mask_seperated,pred_mask_seperated)#sample_mask_seperated, pred_mask_seperated)\n",
    "    \n",
    "    fat_true=np.sum(sample_mask_seperated[1])\n",
    "    cell_true=np.sum(sample_mask_seperated[2])\n",
    "    fat_pred=np.sum(pred_mask_seperated[1])\n",
    "    cell_pred=np.sum(pred_mask_seperated[2])\n",
    "    cellularity_true=(cell_true*100)/(cell_true+fat_true)\n",
    "    cellularity_pred=(cell_pred*100)/(cell_pred+fat_pred)\n",
    "    cellularity_accuracy=(1-abs(cellularity_pred-cellularity_true))\n",
    "    print(f\"mIoU={miou*100:.3f}%\\tcellulu true={cellularity_true:.3f}%\\tcellulu pred={cellularity_pred:.3f}%\\tDifference={cellularity_accuracy:.3f}%\")\n",
    "    return cellularity_accuracy,miou*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for i in range(15):\n",
    "def tf_dataset_test(x, y):\n",
    "    dataset = tf.data.Dataset.from_tensor_slices((x, y))\n",
    "    dataset = dataset.map(tf_parse, num_parallel_calls=tf.data.experimental.AUTOTUNE)\n",
    "    dataset=dataset.repeat()\n",
    "    dataset = dataset.batch(batch)\n",
    "    dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)\n",
    "    return dataset\n",
    "    \n",
    "test_dataset = tf_dataset_test(test_x, test_y)\n",
    "\n",
    "net_cellulu=[]\n",
    "net_miou=[]\n",
    "totalMasks=15\n",
    "\n",
    "for image, mask in test_dataset.take(15):\n",
    "        sample_image, sample_mask = image, mask\n",
    "        cellulu,miou=show_predictions(sample_image, sample_mask)\n",
    "        net_cellulu.append(cellulu)\n",
    "        net_miou.append(miou)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HrTJdIDsB9p1",
    "outputId": "31ebccfa-42a8-4420-d2e6-ed791012a55e"
   },
   "outputs": [],
   "source": [
    "test_steps = len(test_x)//batch\n",
    "if len(test_x) % batch != 0: \n",
    "    test_steps += 1\n",
    "\n",
    "result = model.evaluate(test_dataset, steps=test_steps)\n",
    "dict(zip(model.metrics_names, result))\n",
    "print(f\"Testing Cellularity Accuracy={np.mean(net_cellulu):.3f}%\\tStandard Deviation: {np.std(net_cellulu):.3f}\")\n",
    "print(f\"Testing mean_iou Standard Deviation: {np.std(net_miou):.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model.save('model_saves/VGG16_256.h5')  # Saves in the older HDF5 format\n",
    "model.save(f\"../../kerasModels/model_saves/Unet++{IMAGE_SIZE}.keras\")  # Recommended format (uses ZIP archive)\n",
    "model.save(f\"../../kerasModels/model_saves/Unet++{IMAGE_SIZE}.h5\")  # Recommended format (uses ZIP archive)"
   ]
  }
 ],
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