Datasets
Models
Downloads: 1
[a0d6db]:
/
Unet_model.ipynb
25512 lines (25512 with data), 1.4 MB
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"gpuType": "T4"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "code",
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "U0WAuatqrUci",
"outputId": "8a9a5c3e-446d-4e84-a27a-30465aaaea7f"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Mounted at /content/drive\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"! kaggle datasets download -d nikhilroxtomar/ct-heart-segmentation"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "WhWHmPTJssU7",
"outputId": "119201b9-87ce-4374-da48-f64636b398ac"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Dataset URL: https://www.kaggle.com/datasets/nikhilroxtomar/ct-heart-segmentation\n",
"License(s): unknown\n",
"Downloading ct-heart-segmentation.zip to /content\n",
" 98% 530M/541M [00:05<00:00, 152MB/s]\n",
"100% 541M/541M [00:05<00:00, 105MB/s]\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"! unzip ct-heart-segmentation.zip"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ZHIsaok2s-Jl",
"outputId": "61ca5a46-67ef-44a8-bcb7-1b6c6dc3b4d8"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
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}
]
},
{
"cell_type": "code",
"source": [
"\n",
"import os\n",
"import numpy as np\n",
"import cv2\n",
"from glob import glob\n",
"from tqdm import tqdm\n",
"from sklearn.model_selection import train_test_split\n",
"import albumentations as A\n",
"#os.chdir(r\"E:\\UNET CT\")\n",
"\n",
"def create_dir(path):\n",
" \"\"\" Create a directory. \"\"\"\n",
" if not os.path.exists(path):\n",
" os.makedirs(path)\n",
"\n",
"def load_data(path, split=0.2):\n",
" \"\"\" Load the images and masks \"\"\"\n",
" images = sorted(glob(f\"{path}/*/image/*.png\"))\n",
" masks = sorted(glob(f\"{path}/*/mask/*.png\"))\n",
"\n",
" \"\"\" Split the data \"\"\"\n",
" split_size = int(len(images) * split)\n",
" train_x, valid_x = train_test_split(images, test_size=split_size, random_state=42)\n",
" train_y, valid_y = train_test_split(masks, test_size=split_size, random_state=42)\n",
"\n",
" return (train_x, train_y), (valid_x, valid_y)\n",
"\n",
"def augment_data(images, masks, save_path, augment=True):\n",
" \"\"\" Performing data augmentation. \"\"\"\n",
" H = 256\n",
" W = 256\n",
"\n",
" for idx, (x, y) in tqdm(enumerate(zip(images, masks)), total=len(images)):\n",
" \"\"\" Extracting the dir name and image name \"\"\"\n",
" dir_name = x.split(\"/\")[-3]\n",
" name = dir_name + \"_\" + x.split(\"/\")[-1].split(\".\")[0]\n",
"\n",
" \"\"\" Read the image and mask \"\"\"\n",
" x = cv2.imread(x, cv2.IMREAD_COLOR)\n",
" y = cv2.imread(y, cv2.IMREAD_COLOR)\n",
"\n",
" augmentation_pipeline = A.Compose([\n",
" A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2, p=0.7), # Adjust brightness and contrast\n",
" A.HueSaturationValue(hue_shift_limit=20, sat_shift_limit=30, val_shift_limit=20, p=0.7), # Adjust hue, saturation, and value\n",
" A.RGBShift(r_shift_limit=20, g_shift_limit=20, b_shift_limit=20, p=0.7), # Shift RGB channels\n",
" A.RandomGamma(gamma_limit=(80, 120), p=0.7), # Random gamma adjustment\n",
" A.CLAHE(clip_limit=4.0, tile_grid_size=(8, 8), p=0.7), # Contrast Limited Adaptive Histogram Equalization\n",
" A.ChannelShuffle(p=0.7), # Randomly shuffle channels\n",
" ])\n",
"\n",
" X = [x]\n",
" Y = [y]\n",
"\n",
" if augment == True:\n",
" for _ in range(9):\n",
" augmented = augmentation_pipeline(image=x,mask=y)\n",
" X.append(augmented[\"image\"])\n",
" Y.append(augmented[\"mask\"])\n",
"\n",
"\n",
"\n",
" idx = 0\n",
" for i, m in zip(X, Y):\n",
" i = cv2.resize(i, (W, H))\n",
" m = cv2.resize(m, (W, H))\n",
" m = m/255.0\n",
" m = (m > 0.5) * 255\n",
"\n",
" if len(X) == 1: # each loop it will take one image\n",
" tmp_image_name = f\"{name}.jpg\"\n",
" tmp_mask_name = f\"{name}.jpg\"\n",
" else:\n",
" tmp_image_name = f\"{name}_{idx}.jpg\"\n",
" tmp_mask_name = f\"{name}_{idx}.jpg\"\n",
"\n",
" image_path = os.path.join(save_path, \"image/\", tmp_image_name)\n",
" mask_path = os.path.join(save_path, \"mask/\", tmp_mask_name)\n",
"\n",
" cv2.imwrite(image_path, i)\n",
" cv2.imwrite(mask_path, m)\n",
"\n",
" idx = idx + 1\n",
" #break\n",
"\n",
"\n",
"if __name__ == \"__main__\":\n",
" \"\"\" Load the dataset \"\"\"\n",
" dataset_path = os.path.join(\"data\", \"train\")\n",
" (train_x, train_y), (valid_x, valid_y) = load_data(dataset_path, split=0.2)\n",
"\n",
" print(\"Train: \", len(train_x))\n",
" print(\"Valid: \", len(valid_x))\n",
"\n",
" create_dir(\"new_data/train/image/\")\n",
" create_dir(\"new_data/train/mask/\")\n",
" create_dir(\"new_data/valid/image/\")\n",
" create_dir(\"new_data/valid/mask/\")\n",
"\n",
" augment_data(train_x, train_y, \"new_data/train/\", augment=True)\n",
" augment_data(valid_x, valid_y, \"new_data/valid/\", augment=False)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "CWPohbyhtJ55",
"outputId": "5b9132eb-c3fb-4738-8140-63172b630c69"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.11/dist-packages/albumentations/__init__.py:24: UserWarning: A new version of Albumentations is available: 2.0.3 (you have 1.4.20). Upgrade using: pip install -U albumentations. To disable automatic update checks, set the environment variable NO_ALBUMENTATIONS_UPDATE to 1.\n",
" check_for_updates()\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Train: 2026\n",
"Valid: 506\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 2026/2026 [04:24<00:00, 7.67it/s]\n",
"100%|██████████| 506/506 [00:04<00:00, 120.24it/s]\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from tensorflow.keras.layers import Conv2D, BatchNormalization, Activation, MaxPool2D, Conv2DTranspose, Concatenate, Input, Dropout\n",
"from tensorflow.keras.models import Model\n",
"\n",
"def conv_block(input, num_filters, drop = 0.0):\n",
" x = Conv2D(num_filters, 3, padding=\"same\")(input)\n",
" x = BatchNormalization()(x)\n",
" x = Activation(\"relu\")(x)\n",
" x = Dropout(drop)(x)\n",
"\n",
" x = Conv2D(num_filters, 3, padding=\"same\")(x)\n",
" x = BatchNormalization()(x)\n",
" x = Activation(\"relu\")(x)\n",
" x = Dropout(drop)(x)\n",
"\n",
" return x\n",
"\n",
"\n",
"def encoder_block(input, num_filters, drop = 0.0):\n",
" x = conv_block(input, num_filters, drop)\n",
" p = MaxPool2D((2, 2))(x)\n",
" return x, p\n",
"\n",
"\n",
"def decoder_block(input, skip_features, num_filters, drop = 0.0):\n",
" x = Conv2DTranspose(num_filters, (2, 2), strides=2, padding=\"same\")(input)\n",
" x = Concatenate()([x, skip_features])\n",
" x = conv_block(x, num_filters, drop)\n",
" return x\n",
"\n",
"def build_unet(input_shape):\n",
" inputs = Input(input_shape)\n",
"\n",
" s1, p1 = encoder_block(inputs, 16)\n",
" s2, p2 = encoder_block(p1, 16)\n",
" s3, p3 = encoder_block(p2, 32)\n",
" s4, p4 = encoder_block(p3, 64)\n",
" s5, p5 = encoder_block(p4, 128, 0.3)\n",
"\n",
" b1 = conv_block(p5, 256, 0.3)\n",
"\n",
" d1 = decoder_block(b1, s5, 128, 0.3)\n",
" d2 = decoder_block(d1, s4, 64)\n",
" d3 = decoder_block(d2, s3, 32)\n",
" d4 = decoder_block(d3, s2, 16)\n",
" d5 = decoder_block(d4, s1, 16)\n",
"\n",
" outputs = Conv2D(1, 1, padding=\"same\", activation=\"sigmoid\")(d5)\n",
"\n",
" model = Model(inputs, outputs, name=\"U-Net\")\n",
" return model\n",
"\n",
"\n",
"if __name__ == \"__main__\":\n",
" input_shape = (256, 256, 3)\n",
" model = build_unet(input_shape)\n",
" model.summary()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "fh1fnlrmzQJR",
"outputId": "7197cd57-7c32-4a04-9850-c84b511650e9"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"\u001b[1mModel: \"U-Net\"\u001b[0m\n"
],
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"U-Net\"</span>\n",
"</pre>\n"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
"┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to \u001b[0m\u001b[1m \u001b[0m┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
"│ input_layer_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m3\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n",
"│ (\u001b[38;5;33mInputLayer\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_14 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m448\u001b[0m │ input_layer_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_14 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m64\u001b[0m │ conv2d_14[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_14 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalization_1… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ dropout_14 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_14[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_15 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m2,320\u001b[0m │ dropout_14[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_15 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m64\u001b[0m │ conv2d_15[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_15 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalization_1… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ dropout_15 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_15[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ max_pooling2d_5 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ dropout_15[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_16 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m2,320\u001b[0m │ max_pooling2d_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_16 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m64\u001b[0m │ conv2d_16[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_16 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalization_1… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ dropout_16 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_16[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_17 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m2,320\u001b[0m │ dropout_16[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_17 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m64\u001b[0m │ conv2d_17[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_17 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalization_1… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ dropout_17 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_17[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ max_pooling2d_6 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ dropout_17[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_18 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m4,640\u001b[0m │ max_pooling2d_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_18 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │ conv2d_18[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_18 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalization_1… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ dropout_18 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_18[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_19 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m9,248\u001b[0m │ dropout_18[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_19 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │ conv2d_19[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_19 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalization_1… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ dropout_19 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_19[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ max_pooling2d_7 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ dropout_19[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_20 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m18,496\u001b[0m │ max_pooling2d_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_20 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │ conv2d_20[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_20 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalization_2… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ dropout_20 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_20[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_21 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m36,928\u001b[0m │ dropout_20[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_21 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │ conv2d_21[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_21 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalization_2… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ dropout_21 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_21[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ max_pooling2d_8 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ dropout_21[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_22 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m73,856\u001b[0m │ max_pooling2d_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_22 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │ conv2d_22[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_22 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalization_2… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ dropout_22 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_22[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_23 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m147,584\u001b[0m │ dropout_22[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_23 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │ conv2d_23[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_23 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalization_2… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ dropout_23 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_23[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ max_pooling2d_9 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ dropout_23[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_24 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m295,168\u001b[0m │ max_pooling2d_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_24 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │ conv2d_24[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_24 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalization_2… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ dropout_24 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_24[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_25 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m590,080\u001b[0m │ dropout_24[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_25 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m1,024\u001b[0m │ conv2d_25[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_25 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalization_2… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ dropout_25 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_25[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_transpose_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m131,200\u001b[0m │ dropout_25[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mConv2DTranspose\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ concatenate_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m256\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv2d_transpose_2[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ (\u001b[38;5;33mConcatenate\u001b[0m) │ │ │ dropout_23[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_26 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m295,040\u001b[0m │ concatenate_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_26 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │ conv2d_26[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_26 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalization_2… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ dropout_26 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_26[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_27 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m147,584\u001b[0m │ dropout_26[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_27 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │ conv2d_27[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_27 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalization_2… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ dropout_27 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_27[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_transpose_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m32,832\u001b[0m │ dropout_27[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mConv2DTranspose\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ concatenate_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv2d_transpose_3[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ (\u001b[38;5;33mConcatenate\u001b[0m) │ │ │ dropout_21[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_28 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m73,792\u001b[0m │ concatenate_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_28 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │ conv2d_28[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_28 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalization_2… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ dropout_28 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_28[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_29 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m36,928\u001b[0m │ dropout_28[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_29 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │ conv2d_29[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_29 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalization_2… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ dropout_29 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_29[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_transpose_4 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m8,224\u001b[0m │ dropout_29[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mConv2DTranspose\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ concatenate_4 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv2d_transpose_4[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ (\u001b[38;5;33mConcatenate\u001b[0m) │ │ │ dropout_19[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_30 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m18,464\u001b[0m │ concatenate_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_30 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │ conv2d_30[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_30 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalization_3… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ dropout_30 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_30[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_31 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m9,248\u001b[0m │ dropout_30[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_31 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │ conv2d_31[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_31 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalization_3… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ dropout_31 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_31[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_transpose_5 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m2,064\u001b[0m │ dropout_31[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mConv2DTranspose\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;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv2d_transpose_5[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ (\u001b[38;5;33mConcatenate\u001b[0m) │ │ │ dropout_17[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_32 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m4,624\u001b[0m │ concatenate_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_32 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m64\u001b[0m │ conv2d_32[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_32 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalization_3… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ dropout_32 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_32[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_33 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m2,320\u001b[0m │ dropout_32[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_33 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m64\u001b[0m │ conv2d_33[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_33 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalization_3… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ dropout_33 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_33[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_transpose_6 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m1,040\u001b[0m │ dropout_33[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mConv2DTranspose\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;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ conv2d_transpose_6[\u001b[38;5;34m0\u001b[0m]… │\n",
"│ (\u001b[38;5;33mConcatenate\u001b[0m) │ │ │ dropout_15[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_34 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m4,624\u001b[0m │ concatenate_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_34 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m64\u001b[0m │ conv2d_34[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_34 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalization_3… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ dropout_34 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_34[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_35 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m2,320\u001b[0m │ dropout_34[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_35 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m64\u001b[0m │ conv2d_35[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_35 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ batch_normalization_3… │\n",
"│ (\u001b[38;5;33mActivation\u001b[0m) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ dropout_35 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m16\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_35[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_36 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m256\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m17\u001b[0m │ dropout_35[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
"└───────────────────────────┴────────────────────────┴────────────────┴────────────────────────┘\n"
],
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
"┃<span style=\"font-weight: bold\"> Layer (type) </span>┃<span style=\"font-weight: bold\"> Output Shape </span>┃<span style=\"font-weight: bold\"> Param # </span>┃<span style=\"font-weight: bold\"> Connected to </span>┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
"│ input_layer_1 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ - │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_14 (<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\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">448</span> │ input_layer_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_14 │ (<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\">16</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span> │ conv2d_14[<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\">BatchNormalization</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_14 │ (<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\">16</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_1… │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ dropout_14 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_14[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_15 (<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\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">2,320</span> │ dropout_14[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_15 │ (<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\">16</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span> │ conv2d_15[<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\">BatchNormalization</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_15 │ (<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\">16</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_1… │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ dropout_15 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_15[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ max_pooling2d_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\">16</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dropout_15[<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\">MaxPooling2D</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_16 (<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\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">2,320</span> │ max_pooling2d_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_16 │ (<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\">16</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span> │ conv2d_16[<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\">BatchNormalization</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_16 │ (<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\">16</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_1… │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ dropout_16 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_16[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_17 (<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\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">2,320</span> │ dropout_16[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_17 │ (<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\">16</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span> │ conv2d_17[<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\">BatchNormalization</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_17 │ (<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\">16</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_1… │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ dropout_17 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_17[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ max_pooling2d_6 │ (<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\">16</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dropout_17[<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\">MaxPooling2D</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_18 (<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\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">4,640</span> │ max_pooling2d_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_18 │ (<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\">32</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ conv2d_18[<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\">BatchNormalization</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_18 │ (<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\">32</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_1… │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ dropout_18 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_18[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_19 (<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\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │ dropout_18[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_19 │ (<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\">32</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ conv2d_19[<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\">BatchNormalization</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_19 │ (<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\">32</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_1… │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ dropout_19 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_19[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ max_pooling2d_7 │ (<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\">32</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dropout_19[<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\">MaxPooling2D</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_20 (<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\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │ max_pooling2d_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_20 │ (<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\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │ conv2d_20[<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\">BatchNormalization</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_20 │ (<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\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_2… │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ dropout_20 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_20[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_21 (<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\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │ dropout_20[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_21 │ (<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\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │ conv2d_21[<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\">BatchNormalization</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_21 │ (<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\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_2… │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ dropout_21 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_21[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ max_pooling2d_8 │ (<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\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dropout_21[<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\">MaxPooling2D</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_22 (<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\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │ max_pooling2d_8[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_22 │ (<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\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │ conv2d_22[<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\">BatchNormalization</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_22 │ (<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\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_2… │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ dropout_22 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_22[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_23 (<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\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │ dropout_22[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_23 │ (<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\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │ conv2d_23[<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\">BatchNormalization</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_23 │ (<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\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_2… │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ dropout_23 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_23[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ max_pooling2d_9 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dropout_23[<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\">MaxPooling2D</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_24 (<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\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">295,168</span> │ max_pooling2d_9[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_24 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │ conv2d_24[<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\">BatchNormalization</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_24 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_2… │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ dropout_24 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_24[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_25 (<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\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │ dropout_24[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_25 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │ conv2d_25[<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\">BatchNormalization</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_25 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_2… │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ dropout_25 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_25[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_transpose_2 │ (<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\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">131,200</span> │ dropout_25[<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\">Conv2DTranspose</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ concatenate_2 │ (<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\">256</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_transpose_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>) │ │ │ dropout_23[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_26 (<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\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">295,040</span> │ concatenate_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_26 │ (<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\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │ conv2d_26[<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\">BatchNormalization</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_26 │ (<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\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_2… │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ dropout_26 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_26[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_27 (<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\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │ dropout_26[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_27 │ (<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\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │ conv2d_27[<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\">BatchNormalization</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_27 │ (<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\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_2… │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ dropout_27 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_27[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_transpose_3 │ (<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\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32,832</span> │ dropout_27[<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\">Conv2DTranspose</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ concatenate_3 │ (<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\">128</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_transpose_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>) │ │ │ dropout_21[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_28 (<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\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">73,792</span> │ concatenate_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_28 │ (<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\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │ conv2d_28[<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\">BatchNormalization</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_28 │ (<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\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_2… │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ dropout_28 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_28[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_29 (<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\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │ dropout_28[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_29 │ (<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\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │ conv2d_29[<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\">BatchNormalization</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_29 │ (<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\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_2… │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ dropout_29 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_29[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_transpose_4 │ (<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\">32</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">8,224</span> │ dropout_29[<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\">Conv2DTranspose</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ concatenate_4 │ (<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\">64</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_transpose_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>) │ │ │ dropout_19[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</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\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">18,464</span> │ concatenate_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_30 │ (<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\">32</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ conv2d_30[<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\">BatchNormalization</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_30 │ (<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\">32</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_3… │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ dropout_30 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_30[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_31 (<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\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │ dropout_30[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_31 │ (<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\">32</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ conv2d_31[<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\">BatchNormalization</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_31 │ (<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\">32</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_3… │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ dropout_31 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_31[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_transpose_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\">16</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">2,064</span> │ dropout_31[<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\">Conv2DTranspose</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\">32</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_transpose_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>) │ │ │ dropout_17[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_32 (<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\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">4,624</span> │ concatenate_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_32 │ (<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\">16</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span> │ conv2d_32[<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\">BatchNormalization</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_32 │ (<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\">16</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_3… │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ dropout_32 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_32[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_33 (<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\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">2,320</span> │ dropout_32[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_33 │ (<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\">16</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span> │ conv2d_33[<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\">BatchNormalization</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_33 │ (<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\">16</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_3… │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ dropout_33 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_33[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_transpose_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\">16</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">1,040</span> │ dropout_33[<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\">Conv2DTranspose</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\">32</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv2d_transpose_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>) │ │ │ dropout_15[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_34 (<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\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">4,624</span> │ concatenate_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_34 │ (<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\">16</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span> │ conv2d_34[<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\">BatchNormalization</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_34 │ (<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\">16</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_3… │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ dropout_34 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_34[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_35 (<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\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">2,320</span> │ dropout_34[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ batch_normalization_35 │ (<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\">16</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span> │ conv2d_35[<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\">BatchNormalization</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ activation_35 │ (<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\">16</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalization_3… │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ │ │ │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ dropout_35 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_35[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
"│ conv2d_36 (<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\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">17</span> │ dropout_35[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
"└───────────────────────────┴────────────────────────┴────────────────┴────────────────────────┘\n",
"</pre>\n"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,959,873\u001b[0m (7.48 MB)\n"
],
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,959,873</span> (7.48 MB)\n",
"</pre>\n"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,956,801\u001b[0m (7.46 MB)\n"
],
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,956,801</span> (7.46 MB)\n",
"</pre>\n"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m3,072\u001b[0m (12.00 KB)\n"
],
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">3,072</span> (12.00 KB)\n",
"</pre>\n"
]
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"import numpy as np\n",
"import tensorflow as tf\n",
"from tensorflow.keras import backend as K\n",
"\n",
"def iou(y_true, y_pred):\n",
" \"\"\"Calculate IOU using pure TensorFlow operations\"\"\"\n",
" y_true = tf.cast(y_true, tf.float32)\n",
" y_pred = tf.cast(y_pred, tf.float32)\n",
"\n",
" # Flatten the tensors\n",
" y_true = tf.reshape(y_true, [-1])\n",
" y_pred = tf.reshape(y_pred, [-1])\n",
"\n",
" intersection = tf.reduce_sum(y_true * y_pred)\n",
" union = tf.reduce_sum(y_true) + tf.reduce_sum(y_pred) - intersection\n",
"\n",
" # Add smooth factor to avoid division by zero\n",
" iou_score = (intersection + 1e-15) / (union + 1e-15)\n",
" return iou_score\n",
"\n",
"\n",
"smooth = 1e-15\n",
"def dice_coef(y_true, y_pred):\n",
" y_true = tf.cast(y_true, tf.float32)\n",
" y_pred = tf.cast(y_pred, tf.float32)\n",
"\n",
" y_true = tf.keras.layers.Flatten()(y_true)\n",
" y_pred = tf.keras.layers.Flatten()(y_pred)\n",
" intersection = tf.reduce_sum(y_true * y_pred)\n",
" return (2. * intersection + smooth) / (tf.reduce_sum(y_true) + tf.reduce_sum(y_pred) + smooth)\n",
"\n",
"\n",
"def dice_loss(y_true, y_pred):\n",
" return 1.0 - dice_coef(y_true, y_pred)"
],
"metadata": {
"id": "zo4DZ3e508T5"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import os\n",
"os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\"\n",
"import numpy as np\n",
"import cv2\n",
"from glob import glob\n",
"from sklearn.utils import shuffle\n",
"import tensorflow as tf\n",
"from tensorflow.keras.callbacks import ModelCheckpoint, CSVLogger, ReduceLROnPlateau, EarlyStopping, TensorBoard, LearningRateScheduler\n",
"from tensorflow.keras.optimizers import Adam\n",
"from tensorflow.keras.metrics import Recall, Precision\n",
"#from model import unet\n",
"#from metrics import dice_loss, dice_coef, iou\n",
"\n",
"H = 256\n",
"W = 256\n",
"\n",
"def create_dir(path):\n",
" \"\"\" Create a directory. \"\"\"\n",
" if not os.path.exists(path):\n",
" os.makedirs(path)\n",
"\n",
"def shuffling(x, y):\n",
" x, y = shuffle(x, y, random_state=42)\n",
" return x, y\n",
"\n",
"def load_data(path):\n",
" x = sorted(glob(os.path.join(path, \"image\", \"*.jpg\")))\n",
" y = sorted(glob(os.path.join(path, \"mask\", \"*.jpg\")))\n",
" return x, y\n",
"\n",
"def read_image(path):\n",
" path = path.decode()\n",
" x = cv2.imread(path, cv2.IMREAD_COLOR)\n",
" x = x/255.0\n",
" x = x.astype(np.float32)\n",
" return x\n",
"\n",
"def read_mask(path):\n",
" path = path.decode()\n",
" x = cv2.imread(path, cv2.IMREAD_GRAYSCALE)\n",
" x = x/255.0\n",
" x = x > 0.5\n",
" x = x.astype(np.float32)\n",
" x = np.expand_dims(x, axis=-1)\n",
" return x\n",
"\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.float32, tf.float32])\n",
" x.set_shape([H, W, 3])\n",
" y.set_shape([H, W, 1])\n",
" return x, y\n",
"\n",
"def tf_dataset(x, y, batch=8):\n",
" dataset = tf.data.Dataset.from_tensor_slices((x, y))\n",
" dataset = dataset.map(tf_parse)\n",
" dataset = dataset.batch(batch)\n",
" dataset = dataset.prefetch(10)\n",
" return dataset\n",
"\n",
"\n",
"if __name__ == \"__main__\":\n",
" \"\"\" Seeding \"\"\"\n",
" np.random.seed(42)\n",
" tf.random.set_seed(42)\n",
"\n",
" \"\"\" Directory for storing files \"\"\"\n",
" create_dir(\"files\")\n",
"\n",
" \"\"\" Hyperparameters \"\"\"\n",
" batch_size = 16\n",
" lr = 1e-4\n",
" num_epochs = 20\n",
" model_path = os.path.join(\"files\", \"model.keras\")\n",
" csv_path = os.path.join(\"files\", \"data.csv\")\n",
"\n",
" \"\"\" Dataset \"\"\"\n",
" dataset_path = os.path.join(\"new_data\")\n",
" train_path = os.path.join(dataset_path, \"train\")\n",
" valid_path = os.path.join(dataset_path, \"valid\")\n",
"\n",
" train_x, train_y = load_data(train_path)\n",
" train_x, train_y = shuffling(train_x, train_y)\n",
" valid_x, valid_y = load_data(valid_path)\n",
"\n",
" print(f\"Train: {len(train_x)} - {len(train_y)}\")\n",
" print(f\"Valid: {len(valid_x)} - {len(valid_y)}\")\n",
"\n",
" train_dataset = tf_dataset(train_x, train_y, batch=batch_size)\n",
" valid_dataset = tf_dataset(valid_x, valid_y, batch=batch_size)\n",
"\n",
"\n",
" \"\"\" Model \"\"\"\n",
" model = build_unet((H, W, 3))\n",
" metrics = [dice_coef, iou, Recall(), Precision()]\n",
" model.compile(loss=dice_loss, optimizer=Adam(lr), metrics=metrics)\n",
"\n",
" #def schedule(epoch, lr):\n",
" # return lr * 0.85 # Reduce learning rate by 10% after each epoch\n",
"\n",
" # Create the LearningRateScheduler callback\n",
" #lr_scheduler = tf.keras.callbacks.LearningRateScheduler(schedule)\n",
"\n",
" callbacks = [\n",
" ModelCheckpoint(model_path, verbose=1, save_best_only=True),\n",
" ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=10, min_lr=1e-7, verbose=1),\n",
" #lr_scheduler,\n",
" CSVLogger(csv_path),\n",
" TensorBoard(),\n",
" EarlyStopping(monitor='val_loss', patience=50, restore_best_weights=True),\n",
" ]\n",
"\n",
" history = model.fit(\n",
" train_dataset,\n",
" epochs=num_epochs,\n",
" validation_data=valid_dataset,\n",
" callbacks=callbacks,\n",
" shuffle=False\n",
" )"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "IPANRiXl1M63",
"outputId": "124ebfaf-fdad-41b6-9c4a-0fb89c8b4fec"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Train: 20260 - 20260\n",
"Valid: 506 - 506\n",
"Epoch 1/20\n",
"\u001b[1m1267/1267\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - dice_coef: 0.1516 - iou: 0.0839 - loss: 0.8484 - precision: 0.1687 - recall: 0.9645\n",
"Epoch 1: val_loss improved from inf to 0.70630, saving model to files/model.keras\n",
"\u001b[1m1267/1267\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m212s\u001b[0m 130ms/step - dice_coef: 0.1517 - iou: 0.0839 - loss: 0.8483 - precision: 0.1687 - recall: 0.9645 - val_dice_coef: 0.2924 - val_iou: 0.1854 - val_loss: 0.7063 - val_precision: 0.8023 - val_recall: 0.9558 - learning_rate: 1.0000e-04\n",
"Epoch 2/20\n",
"\u001b[1m1266/1267\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - dice_coef: 0.4252 - iou: 0.2785 - loss: 0.5748 - precision: 0.7811 - recall: 0.9684\n",
"Epoch 2: val_loss improved from 0.70630 to 0.41991, saving model to files/model.keras\n",
"\u001b[1m1267/1267\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m144s\u001b[0m 114ms/step - dice_coef: 0.4254 - iou: 0.2786 - loss: 0.5746 - precision: 0.7811 - recall: 0.9683 - val_dice_coef: 0.5795 - val_iou: 0.4493 - val_loss: 0.4199 - val_precision: 0.8979 - val_recall: 0.9431 - learning_rate: 1.0000e-04\n",
"Epoch 3/20\n",
"\u001b[1m1266/1267\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - dice_coef: 0.7362 - iou: 0.5911 - loss: 0.2638 - precision: 0.9005 - recall: 0.9454\n",
"Epoch 3: val_loss improved from 0.41991 to 0.26843, saving model to files/model.keras\n",
"\u001b[1m1267/1267\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m201s\u001b[0m 113ms/step - dice_coef: 0.7363 - iou: 0.5912 - loss: 0.2637 - precision: 0.9005 - recall: 0.9454 - val_dice_coef: 0.7319 - val_iou: 0.6261 - val_loss: 0.2684 - val_precision: 0.9274 - val_recall: 0.9376 - learning_rate: 1.0000e-04\n",
"Epoch 4/20\n",
"\u001b[1m1266/1267\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - dice_coef: 0.8661 - iou: 0.7672 - loss: 0.1339 - precision: 0.9328 - recall: 0.9404\n",
"Epoch 4: val_loss improved from 0.26843 to 0.20438, saving model to files/model.keras\n",
"\u001b[1m1267/1267\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m201s\u001b[0m 112ms/step - dice_coef: 0.8661 - iou: 0.7672 - loss: 0.1339 - precision: 0.9328 - recall: 0.9404 - val_dice_coef: 0.7963 - val_iou: 0.7158 - val_loss: 0.2044 - val_precision: 0.9347 - val_recall: 0.9446 - learning_rate: 1.0000e-04\n",
"Epoch 5/20\n",
"\u001b[1m1266/1267\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - dice_coef: 0.9117 - iou: 0.8395 - loss: 0.0883 - precision: 0.9436 - recall: 0.9412\n",
"Epoch 5: val_loss improved from 0.20438 to 0.15726, saving model to files/model.keras\n",
"\u001b[1m1267/1267\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m201s\u001b[0m 112ms/step - dice_coef: 0.9117 - iou: 0.8395 - loss: 0.0883 - precision: 0.9436 - recall: 0.9412 - val_dice_coef: 0.8435 - val_iou: 0.7714 - val_loss: 0.1573 - val_precision: 0.9280 - val_recall: 0.9545 - learning_rate: 1.0000e-04\n",
"Epoch 6/20\n",
"\u001b[1m1266/1267\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - dice_coef: 0.9328 - iou: 0.8749 - loss: 0.0672 - precision: 0.9487 - recall: 0.9461\n",
"Epoch 6: val_loss improved from 0.15726 to 0.14235, saving model to files/model.keras\n",
"\u001b[1m1267/1267\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m142s\u001b[0m 112ms/step - dice_coef: 0.9328 - iou: 0.8749 - loss: 0.0672 - precision: 0.9487 - recall: 0.9461 - val_dice_coef: 0.8585 - val_iou: 0.7943 - val_loss: 0.1423 - val_precision: 0.9308 - val_recall: 0.9430 - learning_rate: 1.0000e-04\n",
"Epoch 7/20\n",
"\u001b[1m1266/1267\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - dice_coef: 0.9411 - iou: 0.8896 - loss: 0.0589 - precision: 0.9509 - recall: 0.9469\n",
"Epoch 7: val_loss improved from 0.14235 to 0.13087, saving model to files/model.keras\n",
"\u001b[1m1267/1267\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m142s\u001b[0m 112ms/step - dice_coef: 0.9411 - iou: 0.8896 - loss: 0.0589 - precision: 0.9509 - recall: 0.9469 - val_dice_coef: 0.8699 - val_iou: 0.8129 - val_loss: 0.1309 - val_precision: 0.9243 - val_recall: 0.9621 - learning_rate: 1.0000e-04\n",
"Epoch 8/20\n",
"\u001b[1m1266/1267\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - dice_coef: 0.9494 - iou: 0.9041 - loss: 0.0506 - precision: 0.9544 - recall: 0.9524\n",
"Epoch 8: val_loss did not improve from 0.13087\n",
"\u001b[1m1267/1267\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m142s\u001b[0m 112ms/step - dice_coef: 0.9494 - iou: 0.9041 - loss: 0.0506 - precision: 0.9544 - recall: 0.9524 - val_dice_coef: 0.8532 - val_iou: 0.7925 - val_loss: 0.1476 - val_precision: 0.9356 - val_recall: 0.9388 - learning_rate: 1.0000e-04\n",
"Epoch 9/20\n",
"\u001b[1m1266/1267\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - dice_coef: 0.9523 - iou: 0.9093 - loss: 0.0477 - precision: 0.9566 - recall: 0.9531\n",
"Epoch 9: val_loss did not improve from 0.13087\n",
"\u001b[1m1267/1267\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m203s\u001b[0m 113ms/step - dice_coef: 0.9523 - iou: 0.9093 - loss: 0.0477 - precision: 0.9566 - recall: 0.9531 - val_dice_coef: 0.8589 - val_iou: 0.8062 - val_loss: 0.1422 - val_precision: 0.9292 - val_recall: 0.9534 - learning_rate: 1.0000e-04\n",
"Epoch 10/20\n",
"\u001b[1m1266/1267\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - dice_coef: 0.9556 - iou: 0.9152 - loss: 0.0444 - precision: 0.9575 - recall: 0.9569\n",
"Epoch 10: val_loss improved from 0.13087 to 0.12350, saving model to files/model.keras\n",
"\u001b[1m1267/1267\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m201s\u001b[0m 112ms/step - dice_coef: 0.9556 - iou: 0.9152 - loss: 0.0444 - precision: 0.9575 - recall: 0.9569 - val_dice_coef: 0.8772 - val_iou: 0.8257 - val_loss: 0.1235 - val_precision: 0.9298 - val_recall: 0.9606 - learning_rate: 1.0000e-04\n",
"Epoch 11/20\n",
"\u001b[1m1266/1267\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - dice_coef: 0.9602 - iou: 0.9236 - loss: 0.0398 - precision: 0.9621 - recall: 0.9601\n",
"Epoch 11: val_loss did not improve from 0.12350\n",
"\u001b[1m1267/1267\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m142s\u001b[0m 112ms/step - dice_coef: 0.9602 - iou: 0.9236 - loss: 0.0398 - precision: 0.9621 - recall: 0.9601 - val_dice_coef: 0.8740 - val_iou: 0.8225 - val_loss: 0.1271 - val_precision: 0.9368 - val_recall: 0.9538 - learning_rate: 1.0000e-04\n",
"Epoch 12/20\n",
"\u001b[1m1266/1267\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - dice_coef: 0.9595 - iou: 0.9226 - loss: 0.0405 - precision: 0.9618 - recall: 0.9597\n",
"Epoch 12: val_loss improved from 0.12350 to 0.11628, saving model to files/model.keras\n",
"\u001b[1m1267/1267\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m143s\u001b[0m 113ms/step - dice_coef: 0.9595 - iou: 0.9226 - loss: 0.0405 - precision: 0.9618 - recall: 0.9597 - val_dice_coef: 0.8845 - val_iou: 0.8383 - val_loss: 0.1163 - val_precision: 0.9454 - val_recall: 0.9489 - learning_rate: 1.0000e-04\n",
"Epoch 13/20\n",
"\u001b[1m1266/1267\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - dice_coef: 0.9635 - iou: 0.9298 - loss: 0.0365 - precision: 0.9649 - recall: 0.9632\n",
"Epoch 13: val_loss did not improve from 0.11628\n",
"\u001b[1m1267/1267\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m142s\u001b[0m 112ms/step - dice_coef: 0.9635 - iou: 0.9298 - loss: 0.0365 - precision: 0.9649 - recall: 0.9632 - val_dice_coef: 0.8643 - val_iou: 0.8086 - val_loss: 0.1366 - val_precision: 0.9237 - val_recall: 0.9576 - learning_rate: 1.0000e-04\n",
"Epoch 14/20\n",
"\u001b[1m1266/1267\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - dice_coef: 0.9632 - iou: 0.9291 - loss: 0.0368 - precision: 0.9641 - recall: 0.9634\n",
"Epoch 14: val_loss did not improve from 0.11628\n",
"\u001b[1m1267/1267\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m142s\u001b[0m 112ms/step - dice_coef: 0.9632 - iou: 0.9291 - loss: 0.0368 - precision: 0.9641 - recall: 0.9634 - val_dice_coef: 0.8682 - val_iou: 0.8201 - val_loss: 0.1329 - val_precision: 0.9401 - val_recall: 0.9560 - learning_rate: 1.0000e-04\n",
"Epoch 15/20\n",
"\u001b[1m1266/1267\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - dice_coef: 0.9650 - iou: 0.9326 - loss: 0.0350 - precision: 0.9665 - recall: 0.9642\n",
"Epoch 15: val_loss did not improve from 0.11628\n",
"\u001b[1m1267/1267\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m143s\u001b[0m 113ms/step - dice_coef: 0.9650 - iou: 0.9326 - loss: 0.0350 - precision: 0.9665 - recall: 0.9642 - val_dice_coef: 0.8746 - val_iou: 0.8218 - val_loss: 0.1262 - val_precision: 0.9249 - val_recall: 0.9575 - learning_rate: 1.0000e-04\n",
"Epoch 16/20\n",
"\u001b[1m1266/1267\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - dice_coef: 0.9668 - iou: 0.9359 - loss: 0.0332 - precision: 0.9676 - recall: 0.9668\n",
"Epoch 16: val_loss did not improve from 0.11628\n",
"\u001b[1m1267/1267\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m200s\u001b[0m 112ms/step - dice_coef: 0.9668 - iou: 0.9359 - loss: 0.0332 - precision: 0.9676 - recall: 0.9668 - val_dice_coef: 0.8740 - val_iou: 0.8221 - val_loss: 0.1270 - val_precision: 0.9354 - val_recall: 0.9545 - learning_rate: 1.0000e-04\n",
"Epoch 17/20\n",
"\u001b[1m1266/1267\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - dice_coef: 0.9683 - iou: 0.9387 - loss: 0.0317 - precision: 0.9688 - recall: 0.9685\n",
"Epoch 17: val_loss did not improve from 0.11628\n",
"\u001b[1m1267/1267\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m142s\u001b[0m 111ms/step - dice_coef: 0.9683 - iou: 0.9387 - loss: 0.0317 - precision: 0.9688 - recall: 0.9685 - val_dice_coef: 0.8755 - val_iou: 0.8242 - val_loss: 0.1255 - val_precision: 0.9480 - val_recall: 0.9419 - learning_rate: 1.0000e-04\n",
"Epoch 18/20\n",
"\u001b[1m1266/1267\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - dice_coef: 0.9693 - iou: 0.9407 - loss: 0.0307 - precision: 0.9699 - recall: 0.9691\n",
"Epoch 18: val_loss improved from 0.11628 to 0.11568, saving model to files/model.keras\n",
"\u001b[1m1267/1267\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m142s\u001b[0m 112ms/step - dice_coef: 0.9693 - iou: 0.9407 - loss: 0.0307 - precision: 0.9699 - recall: 0.9691 - val_dice_coef: 0.8852 - val_iou: 0.8396 - val_loss: 0.1157 - val_precision: 0.9481 - val_recall: 0.9510 - learning_rate: 1.0000e-04\n",
"Epoch 19/20\n",
"\u001b[1m1266/1267\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - dice_coef: 0.9698 - iou: 0.9414 - loss: 0.0302 - precision: 0.9704 - recall: 0.9690\n",
"Epoch 19: val_loss did not improve from 0.11568\n",
"\u001b[1m1267/1267\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m141s\u001b[0m 111ms/step - dice_coef: 0.9698 - iou: 0.9414 - loss: 0.0302 - precision: 0.9704 - recall: 0.9690 - val_dice_coef: 0.8772 - val_iou: 0.8268 - val_loss: 0.1237 - val_precision: 0.9396 - val_recall: 0.9515 - learning_rate: 1.0000e-04\n",
"Epoch 20/20\n",
"\u001b[1m1266/1267\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - dice_coef: 0.9725 - iou: 0.9465 - loss: 0.0275 - precision: 0.9730 - recall: 0.9720\n",
"Epoch 20: val_loss did not improve from 0.11568\n",
"\u001b[1m1267/1267\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m141s\u001b[0m 111ms/step - dice_coef: 0.9725 - iou: 0.9465 - loss: 0.0275 - precision: 0.9730 - recall: 0.9720 - val_dice_coef: 0.8521 - val_iou: 0.8059 - val_loss: 0.1492 - val_precision: 0.9447 - val_recall: 0.9475 - learning_rate: 1.0000e-04\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"# Define a figure with an appropriate size\n",
"fig, axes = plt.subplots(2, 2, figsize=(12, 10))\n",
"\n",
"# Plot IoU\n",
"axes[0, 0].plot(history.history['iou'], label='Train IoU', marker='o')\n",
"axes[0, 0].plot(history.history['val_iou'], label='Val IoU', marker='o')\n",
"axes[0, 0].set_title('Model IoU')\n",
"axes[0, 0].set_xlabel('Epoch')\n",
"axes[0, 0].set_ylabel('IoU')\n",
"axes[0, 0].legend(loc='upper left')\n",
"axes[0, 0].grid(True)\n",
"\n",
"# Plot Loss\n",
"axes[0, 1].plot(history.history['loss'], label='Train Loss', marker='o')\n",
"axes[0, 1].plot(history.history['val_loss'], label='Val Loss', marker='o')\n",
"axes[0, 1].set_title('Model Loss')\n",
"axes[0, 1].set_xlabel('Epoch')\n",
"axes[0, 1].set_ylabel('Loss')\n",
"axes[0, 1].legend(loc='upper right')\n",
"axes[0, 1].grid(True)\n",
"\n",
"# Plot Precision\n",
"axes[1, 0].plot(history.history['precision'], label='Train Precision', marker='o')\n",
"axes[1, 0].plot(history.history['val_precision'], label='Val Precision', marker='o')\n",
"axes[1, 0].set_title('Model Precision')\n",
"axes[1, 0].set_xlabel('Epoch')\n",
"axes[1, 0].set_ylabel('Precision')\n",
"axes[1, 0].legend(loc='upper left')\n",
"axes[1, 0].grid(True)\n",
"\n",
"# Plot Recall\n",
"axes[1, 1].plot(history.history['recall'], label='Train Recall', marker='o')\n",
"axes[1, 1].plot(history.history['val_recall'], label='Val Recall', marker='o')\n",
"axes[1, 1].set_title('Model Recall')\n",
"axes[1, 1].set_xlabel('Epoch')\n",
"axes[1, 1].set_ylabel('Recall')\n",
"axes[1, 1].legend(loc='upper left')\n",
"axes[1, 1].grid(True)\n",
"\n",
"# Adjust layout to prevent overlap\n",
"plt.tight_layout()\n",
"\n",
"# Show the plots\n",
"plt.show()\n"
],
"metadata": {
"id": "wpA7JOhd-57B",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"outputId": "79d384ad-efe6-4559-b741-44ff4dcfc42d"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1200x1000 with 4 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"model.save('/content/drive/MyDrive/CT_Heart/best_model.keras')"
],
"metadata": {
"id": "4iMKJCiu_8cl"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import os\n",
"os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\"\n",
"\n",
"import numpy as np\n",
"import cv2\n",
"import pandas as pd\n",
"from glob import glob\n",
"from tqdm import tqdm\n",
"import tensorflow as tf\n",
"from tensorflow.keras.utils import CustomObjectScope\n",
"from sklearn.metrics import accuracy_score, f1_score, jaccard_score, precision_score, recall_score\n",
"#from train import load_data\n",
"\n",
"H = 256\n",
"W = 256\n",
"\n",
"\"\"\" Creating a directory \"\"\"\n",
"def create_dir(path):\n",
" if not os.path.exists(path):\n",
" os.makedirs(path)\n",
"\n",
"def save_results(image, mask, y_pred, save_image_path):\n",
" ## i - m - y\n",
" line = np.ones((H, 10, 3)) * 128\n",
"\n",
" \"\"\" Mask \"\"\"\n",
" mask = np.expand_dims(mask, axis=-1)\n",
" mask = np.concatenate([mask, mask, mask], axis=-1)\n",
"\n",
" \"\"\" Predicted Mask \"\"\"\n",
" y_pred = np.expand_dims(y_pred, axis=-1)\n",
" y_pred = np.concatenate([y_pred, y_pred, y_pred], axis=-1)\n",
" y_pred = y_pred * 255\n",
"\n",
" cat_images = np.concatenate([image, line, mask, line, y_pred], axis=1)\n",
" cv2.imwrite(save_image_path, cat_images)\n",
"\n",
"if __name__ == \"__main__\":\n",
" \"\"\" Seeding \"\"\"\n",
" np.random.seed(42)\n",
" tf.random.set_seed(42)\n",
"\n",
" \"\"\" Directory for storing files \"\"\"\n",
" create_dir(\"results\")\n",
"\n",
" \"\"\" Loading model \"\"\"\n",
" with CustomObjectScope({'iou': iou, 'dice_coef': dice_coef, 'dice_loss': dice_loss}):\n",
" model = tf.keras.models.load_model(\"files/model.keras\")\n",
"\n",
" \"\"\" Load the dataset \"\"\"\n",
" test_x = sorted(glob(os.path.join(\"new_data\", \"valid\", \"image\", \"*\")))\n",
" test_y = sorted(glob(os.path.join(\"new_data\", \"valid\", \"mask\", \"*\")))\n",
" print(f\"Test: {len(test_x)} - {len(test_y)}\")\n",
"\n",
" \"\"\" Evaluation and Prediction \"\"\"\n",
" SCORE = []\n",
" for x, y in tqdm(zip(test_x, test_y), total=len(test_x)):\n",
" \"\"\" Extract the name \"\"\"\n",
" name = x.split(\"/\")[-1].split(\".\")[0]\n",
"\n",
" \"\"\" Reading the image \"\"\"\n",
" image = cv2.imread(x, cv2.IMREAD_COLOR)\n",
" x = image/255.0\n",
" x = np.expand_dims(x, axis=0)\n",
"\n",
" \"\"\" Reading the mask \"\"\"\n",
" mask = cv2.imread(y, cv2.IMREAD_GRAYSCALE)\n",
" y = mask/255.0\n",
" y = y > 0.5\n",
" y = y.astype(np.int32)\n",
"\n",
" \"\"\" Prediction \"\"\"\n",
" y_pred = model.predict(x)[0]\n",
" y_pred = np.squeeze(y_pred, axis=-1)\n",
" y_pred = y_pred > 0.5\n",
" y_pred = y_pred.astype(np.int32)\n",
"\n",
" \"\"\" Saving the prediction \"\"\"\n",
" save_image_path = f\"results/{name}.png\"\n",
" save_results(image, mask, y_pred, save_image_path)\n",
"\n",
" \"\"\" Flatten the array \"\"\"\n",
" y = y.flatten()\n",
" y_pred = y_pred.flatten()\n",
"\n",
" \"\"\" Calculating the metrics values \"\"\"\n",
" acc_value = accuracy_score(y, y_pred)\n",
" f1_value = f1_score(y, y_pred, labels=[0, 1], average=\"binary\", zero_division=1)\n",
" jac_value = jaccard_score(y, y_pred, labels=[0, 1], average=\"binary\", zero_division=1)\n",
" recall_value = recall_score(y, y_pred, labels=[0, 1], average=\"binary\", zero_division=1)\n",
" precision_value = precision_score(y, y_pred, labels=[0, 1], average=\"binary\", zero_division=1)\n",
" SCORE.append([name, acc_value, f1_value, jac_value, recall_value, precision_value])\n",
"\n",
" \"\"\" Metrics values \"\"\"\n",
" score = [s[1:]for s in SCORE]\n",
" score = np.mean(score, axis=0)\n",
" print(f\"Accuracy: {score[0]:0.5f}\")\n",
" print(f\"F1: {score[1]:0.5f}\")\n",
" print(f\"Jaccard: {score[2]:0.5f}\")\n",
" print(f\"Recall: {score[3]:0.5f}\")\n",
" print(f\"Precision: {score[4]:0.5f}\")\n",
"\n",
" df = pd.DataFrame(SCORE, columns=[\"Image\", \"Accuracy\", \"F1\", \"Jaccard\", \"Recall\", \"Precision\"])\n",
" df.to_csv(\"files/score.csv\")"
],
"metadata": {
"id": "aiXYWRsTJbxg",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "d7bbd7b6-0b6d-4bb7-f767-4be34fb1c754"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Test: 506 - 506\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\r 0%| | 0/506 [00:00<?, ?it/s]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3s/step\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\r 0%| | 1/506 [00:02<22:29, 2.67s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\r 0%| | 2/506 [00:02<09:53, 1.18s/it]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\r 1%| | 3/506 [00:03<06:07, 1.37it/s]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n"
]
},
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"text": [
"Accuracy: 0.99675\n",
"F1: 0.96950\n",
"Jaccard: 0.95323\n",
"Recall: 0.97991\n",
"Precision: 0.97219\n"
]
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"\n"
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]
},
{
"cell_type": "code",
"source": [
"! pip install pydicom"
],
"metadata": {
"id": "tOPanEMppBr-",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "33daf516-025a-4815-9c06-fe3d0a7a500b"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting pydicom\n",
" Downloading pydicom-3.0.1-py3-none-any.whl.metadata (9.4 kB)\n",
"Downloading pydicom-3.0.1-py3-none-any.whl (2.4 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.4/2.4 MB\u001b[0m \u001b[31m23.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hInstalling collected packages: pydicom\n",
"Successfully installed pydicom-3.0.1\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import os\n",
"os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\"\n",
"import numpy as np\n",
"import cv2\n",
"import pydicom as dicom\n",
"import pandas as pd\n",
"from glob import glob\n",
"from tqdm import tqdm\n",
"import tensorflow as tf\n",
"from tensorflow.keras.utils import CustomObjectScope\n",
"from sklearn.metrics import accuracy_score, f1_score, jaccard_score, precision_score, recall_score\n",
"\n",
"\"\"\" Creating a directory \"\"\"\n",
"def create_dir(path):\n",
" if not os.path.exists(path):\n",
" os.makedirs(path)\n",
"\n",
"if __name__ == \"__main__\":\n",
" \"\"\" Seeding \"\"\"\n",
" np.random.seed(42)\n",
" tf.random.set_seed(42)\n",
"\n",
" \"\"\" Directory for storing files \"\"\"\n",
" create_dir(\"test\")\n",
"\n",
" \"\"\" Loading model \"\"\"\n",
" with CustomObjectScope({'iou': iou, 'dice_coef': dice_coef, 'dice_loss': dice_loss}):\n",
" model = tf.keras.models.load_model(\"files/model.keras\")\n",
"\n",
" \"\"\" Load the dataset \"\"\"\n",
" test_x = glob(\"data/test/*/*/*.dcm\")\n",
" print(f\"Test: {len(test_x)}\")\n",
"\n",
" \"\"\" Loop over the data \"\"\"\n",
" for x in tqdm(test_x):\n",
" \"\"\" Extract the names \"\"\"\n",
" dir_name = x.split(\"/\")[-3]\n",
" name = dir_name + \"_\" + x.split(\"/\")[-1].split(\".\")[0]\n",
"\n",
" \"\"\" Read the image \"\"\"\n",
" image = dicom.dcmread(x).pixel_array\n",
" image = cv2.resize(image, (W, H))\n",
" print(image.shape)\n",
" image = np.expand_dims(image, axis=-1)\n",
" print(image.shape)\n",
" image = image/np.max(image) * 255.0\n",
" x = image/255.0\n",
" x = np.concatenate([x, x, x], axis=-1)\n",
" x = np.expand_dims(x, axis=0)\n",
"\n",
" \"\"\" Prediction \"\"\"\n",
" mask = model.predict(x)[0]\n",
" mask = mask > 0.5\n",
" mask = mask.astype(np.int32)\n",
" mask = mask * 255\n",
"\n",
" cat_images = np.concatenate([image, mask], axis=1)\n",
" cv2.imwrite(f\"test/{name}.png\", cat_images)"
],
"metadata": {
"id": "yKopdmWCbim5",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "900be35e-d0f2-4181-ecd1-9b2e687b0b4f"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Test: 832\n"
]
},
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{
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"name": "stderr",
"text": [
"\r100%|██████████| 832/832 [02:00<00:00, 6.90it/s]\n"
]
}
]
}
]
}